Copyright © 2005-2014 by Chmura Economics & Analytics. All Rights Reserved. http://chmuraecon.com Tue, 04 September 2018 08:54:05 Tue, 04 September 2018 08:54:05 Chris Chmura http://chmuraecon.com/blog/2018/september/04/economic-impact-consumer-confidence-is-running-high-no-apparent-bubbles-to-precipitate-a-recession-anytime-soon/ Consumer confidence was higher in August than it has been since October 2000. The Conference Board’s index, which is published monthly, reflects consumers’ perception of business health, employment and income for the current month as well as six months into the future. Since it is indexed to 1985, the August 2018 value of 133.4 means consumers feel about 33 percent better about today’s economic conditions than they did in 1985. As a point of reference, the unemployment rate averaged 7.2 percent in 1985 and was decreasing over the year. Inflation, which started the decade of the 1980s with double digits, retreated to 3.5 percent in 1985 and slowed further in 1986. In 1985, President Ronald Reagan started his second term in office, the Food and Drug Administration approved a blood test for AIDS, 21-year-old Whitney Houston released her debut album, and Microsoft Corp. released its first version of Windows 1.0. The previous recession ended in November 1982 and the economy continued to grow until entering another recession in July 1990. The Consumer Confidence index is widely watched because it is one of the first economic indicators available each month. It is also important because consumer spending makes up about 70 percent of gross domestic product. Historically, however, consumer spending and confidence only show a tight correlation around recessions — confidence drops off rapidly as the economy deteriorates and the unemployment rate rises. In contrast, when economic performance starts to improve, confidence shoots up and consumer spending increases, especially on high-dollar items that consumers delayed purchasing for fear that their jobs might get cut. Now, back to the present. Consumer optimism also improved in August with an increase in the percentage of consumers anticipating improved business conditions. With the unemployment rate continuing to decline and many consumers benefiting from the federal tax cuts, consumer optimism is yet one more signal of favorable conditions for continued growth. There are no apparent imbalances or bubbles that will precipitate a recession anytime soon, which points toward increasing consumer confidence in the months ahead. http://chmuraecon.com/blog/2018/september/04/economic-impact-consumer-confidence-is-running-high-no-apparent-bubbles-to-precipitate-a-recession-anytime-soon/ http://chmuraecon.com/blog/2018/september/04/economic-impact-consumer-confidence-is-running-high-no-apparent-bubbles-to-precipitate-a-recession-anytime-soon/ Tue, 04 September 2018 08:54:05 Doug Rice http://chmuraecon.com/blog/2018/august/24/why-statistics/ Over the last few decades, the number of statistics programs in undergraduate higher education has continued to increase [1] —shifting away from the earlier mindset that statistics courses are merely necessities for graduate studies pursuing other fields. Additionally, since its inception in the late 1990s, enrollment in AP (advanced placement) statistics courses has been increasing [2] more rapidly than for any other subject. That being said, statistics remains undervalued relative to other subject areas. The National Council of Teachers of Mathematics reports [3] that in 2010, precalculus, calculus, and statistics courses in two-year and four-year colleges enrolled a total of 1.2 million, 900 thousand, and 400 thousand students, respectively. In other words, enrollment is twice as high for calculus courses and three times as high for precalculus courses as it is for statistics courses. Even among the students taking AP courses, statistics remains overshadowed by calculus. The College Board reports [4] that while there were a little over 200 thousand students taking the AP Statistics exam, there were well over 400 thousand taking an AP Calculus exam. In this article, we seek to make a case for greater emphasis on statistics in education at all levels, given the current market demand [5] for statistics-related skills. Statistics has come a long way, but it nevertheless still has a long way to go. Why does statistics education matter for the development of a more robust workforce?  Statistics in Education --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('container', { title: { text: 'Growth of Statistics-Related Degrees Compared to All Others' }, /*subtitle: { text: 'Source: thesolarfoundation.com' },*/ yAxis: [{ // Primary yAxis min:20000, max:40000, labels: { // format: '{value}%', format: '{value:,.0f}' }, title: { text: 'Instructional Programs Related to Statistics', } }, { // Secondary yAxis min:3000000, max:6000000, title: { text: 'Instructional Programs NOT Related to Statistics', }, labels: { // format: '{value}%', format: '{value:,.0f}' }, opposite: true }], tooltip: { pointFormat: "{point.y:,0.f}" }, legend: { //layout: 'vertical', //align: 'right', //verticalAlign: 'middle' }, credits: { //enabled: false text: 'Source: National Center for Education Statistics', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, plotOptions: { series: { label: { connectorAllowed: false }, pointStart: 2010 } }, series: [{ name: 'Statistics Programs', yAxis: 0, data: [26111,28665,31078,33872,35217,37527,39930] }, { name: 'Non-Statistics Programs', yAxis: 1, data: [4435192,4742945,4878194,4880772,4916709,4953445,4982371] }, ], responsive: { rules: [{ condition: { maxWidth: 500 }, chartOptions: { legend: { layout: 'horizontal', align: 'center', verticalAlign: 'bottom' } } }] } });   Perhaps the first item to consider in terms of the viability of investing in more statistics education is how statistics-related programs are doing in academic institutions. The number of statistics-related [6] degrees grew by 52.9% between 2010 and 2016 compared with all other programs which grew by only 12.3% during the same period.  As of 2016, however, 69% of the awards for statistics-related degrees were in General Mathematics—a program that encompasses a broad range of mathematical subjects and, therefore, leads to a variety of occupations beyond that of a statistician. So, how are we to know which areas of expertise employers value most under the large umbrella of “mathematics?” Skills: Statistics vs Calculus --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('container2', { chart: { type: 'column' }, title: { text: 'Top 10 Jobs Listing "Calculus" as a Skill, 2017' }, xAxis: { categories: [ 'Civil Engineers','Tutors','Electronics Engineers, Except Computer','Biomedical Engineers','Physical Scientists, All Other','Materials Engineers','Mathematical Science Teachers, Postsecondary','Agricultural Engineers','Mechanical Engineers','Statisticians' ] }, yAxis: { min: 0, max: 900, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { pointFormat: "{point.y:,.0f} Job Openings" }, plotOptions: { series: { dataLabels:{ enabled:true, align: 'center', color: '#fff', rotation: 0, /*formatter: function() { return this.y +'%'; },*/ x: 0, y: 30, style: { textOutline: 0, fontSize: 11 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ Real-Time Intelligence; data reflect online job postings for 2017.', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '6pt' // you can style it! }, href: '' }, series: [{ name: 'Job Openings', showInLegend: false, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [869,812,612,535,430,342,259,230,225,215], }, ], }); Highcharts.chart('container3', { chart: { type: 'column' }, title: { text: 'Top 10 Jobs Listing "Statistics" as a Skill, 2017' }, xAxis: { categories: [ 'Management Analysts','Computer and Information Research Scientists','Business Intelligence Analysts','Education Administrators, Postsecondary','Human Resources Specialists','Operations Research Analysts','Computer User Support Specialists','Medical and Health Services Managers','Sales Representatives, Services, All Other','Software Developers, Applications' ] }, yAxis: { min: 0, max: 8000, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { pointFormat: "{point.y:,.0f} Job Openings" }, plotOptions: { series: { dataLabels:{ enabled:true, align: 'center', color: '#fff', rotation: 0, /*formatter: function() { return this.y +'%'; },*/ x: 0, y: 30, style: { textOutline: 0, fontSize: 11 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ Real-Time Intelligence; data reflect online job postings for 2017.', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '6pt' // you can style it! }, href: '' }, series: [{ name: 'Job Openings', showInLegend: false, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [6990,4909,4873,4054,3792,3579,3359,3263,3256,3119], }, ], });   One way to gauge the value of statistics versus other areas of competency is to consider what skills employers are requesting in job postings. As an example, we used our RTI [7] data to compare the total number of online job postings from 2017 mentioning “statistics” as a skill with those mentioning “calculus” as a skill. While “calculus” was mentioned in roughly 7,500 job postings, “statistics” was mentioned much more frequently, in about 90,000 job postings. Curiously, while there were 812 job postings for tutors mentioning “calculus,” there were only 423 mentioning “statistics.” In other words, while there are about twelve times as many jobs specifying the need for statistics skills compared to calculus skills, there are twice as many tutors with calculus skills being sought than there are tutors with statistics skills.   Statisticians: A Growing Occupation Statisticians as a profession [8] has experienced significant growth in recent years and is projected to continue on a similar trajectory. Employment in the statisticians occupation is projected to grow at an average annual rate of 3.0%, over four times the average of 0.7% for all occupations, making it the seventh-fastest growing occupation of the 800+ standard occupation classifications provided by the BLS. [9] That being said, relatively few workers are employed directly as statisticians. Despite the rapid growth in the statisticians profession, the current employment level of 38.5 thousand falls far below the average of 189 thousand across all occupations. Does this mean, then, that being trained in statistics is not quite as meaningful as it first appears? The answer to this question lies in our previous comparison of calculus and statistics. As can be seen, the vast majority of job advertisements requiring training in statistics are not seeking to hire statisticians. Rather than merely considering this single profession, we might consider statistics as a useful skill that can be applied in many different professions.   --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('occ3', { chart: { type: 'bar', }, title: { text: 'Occupations with Highest Percentage of Job Ads Requesting Statistics Skills' }, xAxis: { reversed: true, categories: [ 'Statisticians','Statistical Assistants','Computer and Information Research Scientists','Economists','Epidemiologists','Actuaries','Industrial-Organizational Psychologists','Operations Research Analysts','Social Science Research Assistants','Mathematicians','Industrial Engineers','Financial Specialists, All Other','Agricultural Engineers','Cartographers and Photogrammetrists','Social Scientists and Related Workers, All Other','Computer Programmers','Survey Researchers','Management Analysts','Education Administrators, Postsecondary','Market Research Analysts and Marketing Specialists' ], }, yAxis: { min: 0, max: 40, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false }, //Turns label into percent labels: { formatter: function() { return this.value+"%"; } }, tickInterval: 5, //Not really needed if below is hidden. gridLineWidth: 1, //Hides verticle gridlines }, tooltip: { pointFormat: " {point.y:,.2f}% Statistics and Related Skill Job Postings as a Percentage of All Job Postings, 2017" }, plotOptions: { series: { dataLabels:{ enabled:false, align: 'center', color: '#fff', rotation: 0, formatter: function() { return this.y +'%'; }, x: -20, y: -1, style: { textOutline: 0, fontSize: 8 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, RTI', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '6pt' // you can style it! }, href: '' }, series: [{ name: 'Statistics and Related Skill Job Postings as a Percentage of All Job Postings, 2017', showInLegend: true, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [39.26,24.65,20.86,20.36,18.89,16.79,15.65,13.92,13.01,10.85,7.19,7.06,6.70,5.67,5.66,5.43,4.78,4.33,4.10,3.57], }, ], });   First, we consider which occupations have a high concentration of workers with statistics skills. In other words, these are the occupations with the highest percentage of job advertisements listing “statistics” as a required skill out of all the job ads for those occupations. Not surprisingly, statisticians and statistical assistants are at the top. [10] Beyond these explicit statistical fields, though, there is also a good amount of variety among the top occupations—with skills in statistics being sought out in fields including mathematics, information technology, engineering, and finance. Looking at the concentration of statistics skills within each occupation gives us an idea of how likely the occupations are to require statistics, but it doesn’t tell us the occupations into which most workers with statistics skills are being recruited. For that, we look at the job counts data.   --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('occ4', { chart: { type: 'bar', }, title: { text: 'Occupations with the Most Job Ads Requesting Statistics Skills' }, xAxis: { reversed: true, categories: [ 'Computer Occupations, All Other','Management Analysts','Computer and Information Research Scientists','Education Administrators, Postsecondary','Human Resources Specialists','Statisticians','Operations Research Analysts','Computer User Support Specialists','Medical and Health Services Managers','Sales Representatives, Services, All Other','Software Developers, Applications','Financial Managers','Financial Analysts','Marketing Managers','Sales Managers','Business Operations Specialists, All Other','First-Line Supervisors of Retail Sales Workers','Network and Computer Systems Administrators','Managers, All Other','Market Research Analysts and Marketing Specialists' ], }, yAxis: { min: 0, max: 8000, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: true }, //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } },*/ tickInterval: 1000, //Not really needed if below is hidden. gridLineWidth: 1, //Hides verticle gridlines }, tooltip: { pointFormat: " {point.y:,.0f} Job Postings Listing Statistics as a Skill, 2017" }, plotOptions: { series: { dataLabels:{ enabled:false, align: 'center', color: '#fff', rotation: 0, /*formatter: function() { return this.y +'%'; },*/ x: -15, y: -1, style: { textOutline: 0, fontSize: 8 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, RTI', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '6pt' // you can style it! }, href: '' }, series: [{ name: 'Job Postings Listing Statistics as a Skill, 2017', showInLegend: true, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [7678,6990,4909,4054,3792,3687,3579,3359,3263,3258,3119,3079,2853,2572,2561,2201,2144,2039,1777,1734], }, ], });   When we consider the count of workers with statistics skills, we are looking at the total job ads listing statistics as a skill for each occupation. In other words, how many job postings for each occupation list statistics as a required skill? Looking at statistics skills from this angle, we see a somewhat different array of occupations. The top occupation for which workers trained in statistics are being pursued is “computer occupations, all other”—a category including business intelligence analysts and search marketing strategists. The top 20 occupations hiring workers with statistics skills tend to fall into three general categories—business roles such as management analysts and human resource specialists, managerial roles such as education administrators and medical and health services managers, and information technology roles such as computer and information resource specialists and software developers for applications. As can be seen, there are five occupations seeking more workers with skills in statistics than the occupation of statisticians, itself.   Adding Complementary Skills One other piece of information that could be useful in gauging the demand for statistics skills is to think about complementary or dependent skills in job postings. In other words, which other required skills are most likely to show up in job postings along with statistics? For example, a job posting may not list “statistics,” but it may list “statistical analysis software.” In this case, it is assumed that statistics is also important. Below, we take the most relevant complementary skills [11] into account and look at the breakdown of job postings by occupation just like before.   --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('bubbleC', { chart: { type: 'bubble', }, title: { text: 'Job Postings Requiring Statistics and Related Skills, 2017' }, xAxis: { gridLineWidth: 1, title: { text: 'Total Number of Job Ads Requiring Statistics' }, labels: { format: '{value:,.0f}' }, type: 'logarithmic', minorTickInterval: 0.1, min: 100, max: 7000, }, yAxis: { min:8, max:25, startOnTick: false, endOnTick: false, title: { text: 'Job Ads Requiring Statistics as a Percentage of All Ads for Related Skill' }, labels: { format: '{value}%' }, maxPadding: 0.2, }, tooltip: { /*useHTML: true, headerFormat: ' ', pointFormat: ' {point.country} ' + ' Total Number of Job Ads Requiring Statistics: {point.x} ' + ' Job Ads Requiring Statistics as a Percentage of All Ads for Related Skill: {point.y}% ', footerFormat: ' ', followPointer: true*/ headerFormat: '', pointFormat: " {point.country} " + " " +"Total Number of Job Ads Requiring Statistics {point.x:,.0f} " + ' ' + "Job Ads Requiring Statistics as a Percentage of All Ads for Related Skill {point.y:,.0f}% " }, plotOptions: { series: { dataLabels: { enabled: true, format: '{point.name}' } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, RTI, data reflect online job postings for 2017', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ color: 'rgba(128,151,188,.9)', showInLegend: false, data: [ { x: 386, y: 22, z: 13, name: 'Oracle Hyperion', country: 'Oracle Hyperion' }, { x: 2018, y: 21, z: 13, name: 'R', country: 'R' }, { x: 422, y: 16, z: 13, name: 'Stata', country: 'Stata' }, { x: 1189, y: 14, z: 13, name: 'SPSS', country: 'SPSS' }, { x: 284, y: 12, z: 13, name: 'Statistical' + ' ' + 'Analysis' + ' ' + 'Software', country: 'Statistical Analysis Software' }, { x: 215, y: 12, z: 13, name: 'Workforce' + ' ' + 'Management' + ' ' + 'Software', country: 'Workforce Management Software' }, { x: 5624, y: 12, z: 13, name: 'SAS', country: 'SAS' }, { x: 148, y: 11, z: 13, name: 'Data' + ' ' + 'Visualization' + ' ' + 'Software', country: 'Data Visualization Software' }, { x: 533, y: 10, z: 13, name: 'Minitab', country: 'Minitab' } ] }], });   The skills most highly associated with statistics are shown here. As far as the total volume of job postings, SAS (Statistical Analysis System) is included in job postings also listing statistics as a skill more so than any other skill. However, when it comes to the percentage of all a skill’s job postings that also lists statistics, R (programming language) and Oracle Hyperion are the most complementary skills (that is, most often found together). If we take all of these complementary skills into account, how does it influence the top occupations for statistics? --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('occ8', { chart: { type: 'bar', }, title: { text: 'Occupations with Highest Percentage of Job Ads Requesting Statistics and Related Skills' }, xAxis: { categories: [ 'Statisticians','Statistical Assistants','Computer and Information Research Scientists','Economists','Epidemiologists','Actuaries','Industrial-Organizational Psychologists','Operations Research Analysts','Social Science Research Assistants','Mathematicians','Industrial Engineers','Financial Specialists, All Other','Agricultural Engineers','Cartographers and Photogrammetrists','Social Scientists and Related Workers, All Other','Computer Programmers','Survey Researchers','Management Analysts','Education Administrators, Postsecondary','Market Research Analysts and Marketing Specialists' ], }, yAxis: { min: 0, max: 40, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: true }, //Turns label into percent labels: { formatter: function() { return this.value+"%"; } }, tickInterval: 5, //Not really needed if below is hidden. gridLineWidth: 1, //Hides verticle gridlines }, tooltip: { pointFormat: " {point.y:,.0f}% Statistics and Related Skill Job Postings as a Percentage of All Job Postings, 2017" }, plotOptions: { series: { dataLabels:{ enabled:false, align: 'center', color: '#fff', rotation: 0, /*formatter: function() { return this.y +'%'; },*/ x: 0, y: 25, style: { textOutline: 0, fontSize: 11 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, RTI', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '6pt' // you can style it! }, href: '' }, series: [{ name: 'Statistics and Related Skill Job Postings as a Percentage of All Job Postings, 2017', showInLegend: true, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [39.26,24.65,20.86,20.36,18.89,16.79,15.65,13.92,13.01,10.85,7.19,7.06,6.70,5.67,5.66,5.43,4.78,4.33,4.10,3.57], }, ], });   When adding in complementary skills, the value of statistics in the statisticians and statistical assistants occupations becomes more pronounced. Moreover, one clear trend that emerges is the sudden presence of job postings for occupations in the social sciences—such as epidemiologists and social science research assistants. The inclusion of these occupations makes perfect sense when we consider the requirements for social scientists to learn statistical software packages such as SPSS and SAS—which have been included as complementary skills in this new query. --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('container10', { chart: { type: 'bar', }, title: { text: 'Occupations with the Most Job Ads Requesting Statistics and Related Skills' }, xAxis: { categories: [ 'Computer Occupations, All Other','Management Analysts','Computer and Information Research Scientists','Statisticians','Operations Research Analysts','Software Developers, Applications','Financial Managers','Computer User Support Specialists','Education Administrators, Postsecondary','Financial Analysts','Human Resources Specialists','Marketing Managers','Medical and Health Services Managers','Sales Representatives, Services, All Other','Network and Computer Systems Administrators','Business Operations Specialists, All Other','Market Research Analysts and Marketing Specialists','First-Line Supervisors of Retail Sales Workers','Sales Managers','Financial Specialists, All Other' ], }, yAxis: { min: 0, max: 18000, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: true }, //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } },*/ tickInterval: 2000, //Not really needed if below is hidden. gridLineWidth: 1, //Hides verticle gridlines }, tooltip: { pointFormat: " {point.y:,.0f} Job Postings Mentioning Statistics and Related Skills, 2017" }, plotOptions: { series: { dataLabels:{ enabled:false, align: 'center', color: '#fff', rotation: 0, /*formatter: function() { return this.y +'%'; },*/ x: 0, y: 25, style: { textOutline: 0, fontSize: 11 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, RTI', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '6pt' // you can style it! }, href: '' }, series: [{ name: 'Job Postings Mentioning Statistics and Related Skills, 2017', showInLegend: true, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [16260,13779,8499,7801,7047,6453,5499,5010,4715,4591,4527,4145,3766,3637,3596,3225,2968,2875,2664,2346], }, ], });   Adding complementary skills does not dramatically change the overall composition of top occupations in terms of the frequency of job postings requiring statistics skills. However, the volume of job postings increases significantly. The volume of the job ads among the top few occupation doubles and, overall, the total number of job postings increases by about 70%. Top MSAs for Statistics and Related Skills --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('adsA', { chart: { type: 'column', }, title: { text: 'Job Postings Requring Statistics and Related Skills, 2017' }, xAxis: { categories: [ 'New York-Newark-Jersey City, NY-NJ-PA MSA','Washington-Arlington-Alexandria, DC-VA-MD-WV MSA','Chicago-Naperville-Elgin, IL-IN-WI MSA','Los Angeles-Long Beach-Anaheim, CA MSA','San Francisco-Oakland-Hayward, CA MSA','Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA','Boston-Cambridge-Newton, MA-NH MSA','Dallas-Fort Worth-Arlington, TX MSA','Atlanta-Sandy Springs-Roswell, GA MSA','Seattle-Tacoma-Bellevue, WA MSA','Minneapolis-St. Paul-Bloomington, MN-WI MSA','San Jose-Sunnyvale-Santa Clara, CA MSA','Detroit-Warren-Dearborn, MI MSA','Phoenix-Mesa-Scottsdale, AZ MSA','Charlotte-Concord-Gastonia, NC-SC MSA' ], }, yAxis: { min: 0, max: 13000, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false }, //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } },*/ tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { pointFormat: "{point.y:,.0f} Total Job Postings, 2017" }, plotOptions: { series: { dataLabels:{ enabled:true, align: 'center', color: '#fff', rotation: 0, /*formatter: function() { return this.y +'%'; },*/ x: 0, y: 25, style: { textOutline: 0, fontSize: 11 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: RTI, JobsEQ', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '6pt' // you can style it! }, href: '' }, series: [{ name: 'Total Job Postings, 2017', showInLegend: false, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [12519,7606,5996,5573,5225,4707,4611,4266,3647,3215,3033,2701,2543,2305,2292], }, ], }); Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('adsB', { chart: { type: 'column', }, title: { text: 'Job Postings Requring Statistics and Related Skills Per 100K Workers, 2017' }, xAxis: { categories: [ 'Burlington, NC MSA','Raleigh, NC MSA','Ann Arbor, MI MSA','Madison, WI MSA','Des Moines-West Des Moines, IA MSA','San Jose-Sunnyvale-Santa Clara, CA MSA','Flagstaff, AZ MSA','Washington-Arlington-Alexandria, DC-VA-MD-WV MSA','San Francisco-Oakland-Hayward, CA MSA','State College, PA MSA','Grand Forks, ND-MN MSA','Charlotte-Concord-Gastonia, NC-SC MSA','Boulder, CO MSA','Iowa City, IA MSA','Olympia-Tumwater, WA MSA' ], }, yAxis: { min: 0, max: 500, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false }, //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } },*/ tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { pointFormat: "{point.y:,.0f} Job Postings Per 100k People, 2017" }, plotOptions: { series: { dataLabels:{ enabled:true, align: 'center', color: '#fff', rotation: 0, /*formatter: function() { return this.y +'%'; },*/ x: 0, y: 25, style: { textOutline: 0, fontSize: 11 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: RTI, JobsEQ', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '6pt' // you can style it! }, href: '' }, series: [{ name: 'Job Postings Per 100k People, 2017', showInLegend: false, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [360,282,255,250,237,236,235,228,206,196,186,183,176,170,169], }, ], });   One final way of looking at the importance of statistics as a skill is to consider its demand on a geographic basis. Of the job postings from 2017 listing statistics as a skill, where are most of the advertisements coming from? To put it another way, where are people trained in statistics most likely to be working? As far as the total job ads, the top MSAs [12] listed are not all that surprising, with some of the largest metros—such as New York, Washington DC, Chicago, Los Angeles, and San Francisco—being the locations with the most job ads. However, when the total employment [13] for each MSA is taken into account, only Washington DC, San Francisco, San Jose, and Charlotte remain among the top 15. Adjusted for employment, the number one MSA for statistics skills is Burlington, North Carolina. In addition to opportunities available from the University of North Carolina, one of the nation’s largest university systems, Burlington is also the national headquarters of medical diagnostic laboratory firm The Laboratory Corporation of America. Coming in second is Raleigh, North Carolina, which includes Cary, North Carolina—home of one of the largest and most well-known organizations in the world of statistics—the statistical analytics software firm SAS. Ann Arbor, Michigan—home of the University of Michigan—comes in at the third best MSA for statistics. In addition to Ann Arbor being a relatively small MSA, the University of Michigan is also ranked among the top 10 schools nationwide for both its engineering and business programs—two fields in which statistics is heavily involved. Each of these remaining cities tells a story about why statistics matters to its local economy. Some are the homes of large academic institutions (i.e. Pennsylvania State University in State College, PA), while others are the headquarters of large financial institutions (i.e. Bank of America in Charlotte, NC). Ultimately, the variety of reasons that statistics remains valuable in these cities further drives home the point—statistics education matters. Whether or not students will grow up to enter into work specifically as statisticians, it is becoming increasingly more likely that they’ll end up in an occupation that relies on statistics in some fashion. Whatever they do and wherever they go, statistics will be there waiting for them. The only question is, will they be ready for it?     [1] (1 Sept. 2012). “A Major Trend: The Rise of Undergraduate Programs in Statistics.” Amstat News . http://magazine.amstat.org/blog/2012/08/01/prescornerundergradstats [2] Boslaugh, S. (2016). Statistics in a Nutshell: A Desktop Reference . [3] Doessey, J., McCrone, S.S., & Halvorsen, K. (2016). “Mathematics Education in the United States: 2016,” pp. 39-40. https://www.nctm.org/uploadedFiles/About/MathEdInUS2016.pdf [4] The College Board. (2016). “Student Score Distributions: AP Exams – May 2016.” https://secure-media.collegeboard.org/digitalServices/pdf/research/2016/Student-Score-Distributions-2016.pdf [5] Litton, S. (1 Oct. 2015). “More Students Earning Statistics Degrees, But Not Enough to Meet Surging Demand for Statisticians.” This Is Statistics . http://thisisstatistics.org/more-students-earning-statistics-degrees-but-not-enough-to-meet-surging-demand-for-statisticians [6] ”Statistics-related” programs are defined as the programs that map to the statistician occupation per the NCES CIP 2010 – SOC 2010 Crosswalk . These programs are: General Mathematics (27.0101), General Statistics (27.0501), Applied Mathematics (27.0301), Business Statistics (52.1302), Biostatistics (26.1102), Mathematical Statistics and Probability (27.0502), Computational and Applied Mathematics (27.0304), Other Statistics (27.0599), Mathematics and Statistics (27.0503), and Research Methodology and Quantitative Methods (45.0102). [7]   Chmura’s RTI (Real-Time Intelligence) is a dataset comprising online job postings data in the United States, updated daily, to provide insight on potential hiring. A “job posting” in this dataset is a unique (deduped) job posted from one of more than 20,000 sources which has been analyzed to be classified by SOC and location, along with other information. [8] Statisticians profession as discussed here corresponds to the BLS SOC (Standard Occupation Classification) 15-2041. [9]   https://www.bls.gov/emp/tables/occupational-projections-and-characteristics.htm [10] It is certainly reasonable to assume that employers would expect candidates for statisticians and statistical assistants to be skilled in statistics even if it isn’t specifically mentioned in the job ads; nevertheless, these data only reflect when the skill is overtly requested in the ad text. [11] Complementary skills included are: R programming , Oracle Hyperion , Stata , general statistical software, SPSS , general workforce management software, SAS , general statistical analysis software, general data visualization software, and Minitab . [12] [1] In RTI, Job posting locations are retrieved and assigned to a ZCTA. For this analysis, job postings within each ZCTA were assigned to the corresponding county with the highest employment and then aggregated from the county up to the MSA. [13] Source: JobsEQ. Average employment for 2017. http://chmuraecon.com/blog/2018/august/24/why-statistics/ http://chmuraecon.com/blog/2018/august/24/why-statistics/ Fri, 24 August 2018 15:26:23 James Stinchcomb http://chmuraecon.com/blog/2018/august/20/introducing-regional-underemployment/ The unemployment rate in the United States is at historically low levels, coming in at 3.9% for July 2018, with many observers suggesting the nation is now at full employment. Despite the low unemployment rate, some questions remain about the state of the workforce. For example, one question is “how is the employment being utilized?” Or to put it another way, just because people have jobs right now, does that mean they have good jobs? One way to measure employment utilization is to look at the under employment rate. In the broadest sense, underemployment is a gauge of workforce utilization defined as the condition when people are overqualified for the job they are currently working in, typically as judged by the worker’s educational attainment or experience level compared to that required by their job. The Federal Reserve Bank of New York publishes a monthly series on the national underemployment rate, with data going back to 1990 and a current underemployment rate of 34.2%. [1] The New York Fed uses a sample of the workforce aged 22-65 and measures underemployment as the number of college graduates (bachelor’s degree or higher) working in a “non-college” job (that is, a job that does not typically require a college degree); the underemployment rate is the percentage of all college educated workers in a non-college job. Interestingly, while the unemployment rate has fallen consistently since the end of the last recession, from a high of 9.6% in September 2010 to 3.9% last month (and similarly the unemployment rate for college graduates has fallen from 5.0% in August 2010 to 2.3% in June of this year), the national underemployment rate has remained relatively stable—ranging between 34.1% and 34.7% from August 2010 through June of this year. There was a noticeable increase immediately coming out of the last recession, jumping from 33.7% in May 2009 to 34.7% in December 2010, followed by a fall to a low of 34.2% in October 2011, but since then the movement has been slight. From here, the next logical step would be to explore underemployment at a regional level. Working from the New York Fed’s methodology and measure of underemployment, Chmura has developed a model for estimating regional underemployment down to the county level along with estimates for detailed occupations, [2] which can now be found in JobsEQ . Similar to the New York Fed, we use the workforce aged 22-64 and measure underemployment as a worker with a bachelor’s degree or higher working in a non-college occupation, using a similar “non-college” definition as the Fed. [3] Below we have the rank-ordered underemployment rates for the 50 largest MSAs [4] . Underemployment – Fifty Largest US Metropolitan Areas Rank Region Underemployment Rate USA 34.4% 1 San Jose-Sunnyvale-Santa Clara, CA MSA 27.7% 2 Sacramento--Roseville--Arden-Arcade, CA MSA 31.3% 3 Hartford-West Hartford-East Hartford, CT MSA 31.5% 4 Cleveland-Elyria, OH MSA 31.5% 5 Riverside-San Bernardino-Ontario, CA MSA 31.7% 6 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 32.1% 7 Oklahoma City, OK MSA 32.1% 8 Boston-Cambridge-Newton, MA-NH MSA 32.1% 9 Salt Lake City, UT MSA 32.9% 10 San Diego-Carlsbad, CA MSA 33.2% 11 Providence-Warwick, RI-MA MSA 33.3% 12 Detroit-Warren-Dearborn, MI MSA 33.4% 13 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 33.8% 14 Houston-The Woodlands-Sugar Land, TX MSA 34.0% 15 Cincinnati, OH-KY-IN MSA 34.1% 16 San Antonio-New Braunfels, TX MSA 34.1% 17 Baltimore-Columbia-Towson, MD MSA 34.3% 18 Virginia Beach-Norfolk-Newport News, VA-NC MSA 34.3% 19 Columbus, OH MSA 34.5% 20 Seattle-Tacoma-Bellevue, WA MSA 34.9% 21 Portland-Vancouver-Hillsboro, OR-WA MSA 35.1% 22 Tampa-St. Petersburg-Clearwater, FL MSA 35.1% 23 New Orleans-Metairie, LA MSA 35.2% 24 Austin-Round Rock, TX MSA 35.2% 25 Los Angeles-Long Beach-Anaheim, CA MSA 35.2% 26 Pittsburgh, PA MSA 35.4% 27 Phoenix-Mesa-Scottsdale, AZ MSA 35.4% 28 Minneapolis-St. Paul-Bloomington, MN-WI MSA 35.5% 29 Dallas-Fort Worth-Arlington, TX MSA 35.6% 30 Buffalo-Cheektowaga-Niagara Falls, NY MSA 35.7% 31 San Francisco-Oakland-Hayward, CA MSA 35.7% 32 Denver-Aurora-Lakewood, CO MSA 36.0% 33 Milwaukee-Waukesha-West Allis, WI MSA 36.0% 34 Indianapolis-Carmel-Anderson, IN MSA 36.0% 35 Richmond, VA MSA 36.5% 36 Chicago-Naperville-Elgin, IL-IN-WI MSA 36.8% 37 Jacksonville, FL MSA 36.9% 38 Memphis, TN-MS-AR MSA 36.9% 39 Louisville/Jefferson County, KY-IN MSA 37.1% 40 St. Louis, MO-IL MSA 37.3% 41 Miami-Fort Lauderdale-West Palm Beach, FL MSA 37.4% 42 Las Vegas-Henderson-Paradise, NV MSA 37.4% 43 Kansas City, MO-KS MSA 37.8% 44 Atlanta-Sandy Springs-Roswell, GA MSA 38.1% 45 Grand Rapids-Wyoming, MI MSA 38.3% 46 Raleigh, NC MSA 38.3% 47 New York-Newark-Jersey City, NY-NJ-PA MSA 38.3% 48 Charlotte-Concord-Gastonia, NC-SC MSA 38.3% 49 Nashville-Davidson--Murfreesboro--Franklin, TN MSA 39.1% 50 Orlando-Kissimmee-Sanford, FL MSA 39.9% Source: JobsEQ , data as of 2018Q1 Using this regional-based method and aggregating results to the nation, the Chmura model tracks closely to the NY Fed’s national estimate with a 34.4% underemployment rate. At the MSA level, the underemployment rates range from a high of 39.9% in Orlando to a low of 27.7% in San Jose. The differences at the regional level are driven primarily by two factors: (1) the portion of jobs that require a college degree and (2) the portion of the workforce that has a college degree. Together these two factors make up the supply and demand for college educated labor in a region. On the demand side, the fewer the college jobs available in a region, the lower the demand for workers with a college education, making it more likely some of these workers will need to take jobs with lower requirements, and thus a higher likelihood of underemployment. On the supply side, the more workers with a college degree, the higher the labor supply, meaning workers are facing more competition for college-requirement jobs, again making it more likely these workers will need to take jobs with lower requirements and a higher likelihood of underemployment. As examples of the demand-side effects, consider San Jose, Washington, and Boston, which all have very high proportions of college jobs and correspondingly low underemployment rates. The large volume of college jobs in these regions keeps the demand for college-educated labor high, resulting in low underemployment. Inversely, regions such as Orlando, Grand Rapids, and Las Vegas have low proportions of college jobs and higher-than-average underemployment rates. The low demand for college-educated labor in these regions help produce the higher underemployment in these regions. Going a little further, one can see how the supply side, the percentage of the workforce with a college degree, plays into underemployment as well. For example, the Riverside metro area has one of the lowest rates of college jobs in this comparison, so one might expect its underemployment rate to be correspondingly high—however, it is near the bottom of the list. This can be explained largely by the fact that Riverside has the lowest percentage of its workforce with a college degree of all the metros in this comparison. This low rate of a college-educated workforce means the supply of these workers is very low, so the college-educated workers there face little competition, and thus the low underemployment rate. Of course, these two labor supply and demand metrics don’t tell the full story of regional underemployment as there are other factors that also contribute. [5] These factors include how well the supply and demand of skills and education match at the detailed level—meaning, specific occupation requirements versus specific educational attainment and fields of study. Given these issues, underemployment can be alleviated by various approaches. If institutions of higher education and local businesses are in alignment, the skills of graduates will more closely correspond, lessening the chance of underemployment. Workforce developers play a role as well, with programs to help align skilled workers with appropriate careers. Getting on the same page is key and understanding the dynamics of the local labor market underlies these relationships. Using the local measures of underemployment rate as described here is a piece of that—communicating one aspect of local workforce utilization that can otherwise be overlooked. Finally, the measurement of local underemployment is also important to economic development and the site selection process. Underemployed workers can be an important source of labor supply for expanding firms. From the economic development side, attracting and encouraging the growth of certain industries can also help alleviate existing underemployment.   [1] https://www.newyorkfed.org/research/college-labor-market/college-labor-market_underemployment_rates.html [2] “Detailed occupations” being the 6-digit SOC level. [3] Chmura’s college job definitions match very closely to the Fed’s definitions, however, because the Fed uses CPS Occupation codes (about 500 unique occupations) rather than 6-digit SOC codes (over 800 unique occupations) in their analysis, there are some small differences, particularly when an OCC code maps to multiple SOC codes. [4] Metropolitan Statistical Areas.  Size is based on total employment in the MSAs. [5] Some of these factors would be difficult or even impossible to measure given the methodology described here—a robust, local-level survey would be a more ideal approach to measuring underemployment, though of course such approaches are often costly and not practical. http://chmuraecon.com/blog/2018/august/20/introducing-regional-underemployment/ http://chmuraecon.com/blog/2018/august/20/introducing-regional-underemployment/ Mon, 20 August 2018 09:27:26 Chris Chmura http://chmuraecon.com/blog/2018/august/07/economic-impact-even-with-brighter-outlook-ahead-for-defense-spending-can-virginia-achieve-economic-diversity/ Diversity is an important notion when thinking about investments. Yet it seems that we only really appreciate diversity when the stock market is down. As the market is rising, we often wish our portfolio were more heavily weighted in stocks. That’s where financial advisers earn their keep. The same seems to be true of economies. The Virginia economy is heavily dependent on Department of Defense spending. In fact, firms in Virginia performed more defense contracts in the federal fiscal year that ended Sept. 30, 2017 than any other state except California. The state’s increasing dependence on defense spending propelled employment to grow faster than most states during the first decade of 2000, when defense spending also was on the increase. I don’t recall much talk about diversity during that period. Then came the Budget Control Act of 2011 that enforced federal government spending cuts. On top of that, the federal government shutdown in the first half of October 2013 hampered employment growth in Virginia. Defense contracts performed in the state, for example, fell from $40 billion in the fiscal year ending in September 2013 to a low of $34.4 billion in the fiscal year ending in September 2016. Since the federal government shutdown in October 2013, Virginia’s year-over-year employment growth has only outpaced that of the nation for 10 months. Calls for diversification away from Defense Department were heard around the state during this period. With the new administration, defense contracts performed in the state inched up to $34.6 billion in the last fiscal year. Based on the President Trump’s budget and our calculations, Virginia firms look to gain $4.4 billion in Defense Department business over the next two years — a 12.6 percent increase through the fiscal year that ends Sept. 30, 2019. So it should not be too surprising that employment in Virginia grew 1.5 percent over the 12 months ending with June 2018 — that’s almost back up to the national growth rate of 1.6 percent — after 27 straight months of Virginia lagging the nation. The Northern Virginia metro area, which performed 63.6 percent of the Defense Department contracts last fiscal year, is back to its historical norm of creating 40 percent of the jobs in the state. Employment in Virginia rose 57,500 over the 12 months ending in June and Northern Virginia added 23,372 of those jobs. Hampton Roads, after shedding jobs each month from October 2017 to January 2018, added 4,144 over the 12 months ending in June and will see more gains as Huntington Ingalls Industries’ Newport News Shipbuilding division announced it will be hiring 7,000 shipbuilders over the next five years. The Richmond metro area has little dependence on defense spending — it performed only 3 percent of all the defense work in the state during the last fiscal year. The region added 4,114 jobs over the 12 months ending with June. Richmond area’s jobs growth outperformed Northern Virginia and Hampton Roads from 2012 to 2015, largely due to its diverse economy and less reliance on defense spending. The Richmond region’s diversity reflects that of the nation, with major industry sectors making up a similar proportion of employment. The two exceptions are manufacturing (5 percent in Richmond versus 9 percent in the nation) and professional business services (17 percent in Richmond versus 14 percent in the nation). In contrast, Northern Virginia is heavily weighted in professional business services (27 percent) — the sector that receives most of the defense spending. Hampton Roads is more heavily weighted in manufacturing (7 percent) largely because of shipbuilding. A diverse economy, similar to that of the nation, implies that the regional economy will not be hurt disproportionally when an economic shock occurs, whether it be defense cuts or a trade war with China. With a brighter outlook ahead for defense spending, it will be interesting to see if economic diversity remains a concern. http://chmuraecon.com/blog/2018/august/07/economic-impact-even-with-brighter-outlook-ahead-for-defense-spending-can-virginia-achieve-economic-diversity/ http://chmuraecon.com/blog/2018/august/07/economic-impact-even-with-brighter-outlook-ahead-for-defense-spending-can-virginia-achieve-economic-diversity/ Tue, 07 August 2018 08:55:43 Alex Doherty http://chmuraecon.com/blog/2018/july/25/popular-travel-destinations-poised-for-continued-growth/ Commercial air travel across the nation has increased by over 124 million passengers from 2012 through 2017, [1] but some regions are benefitting more than others. The Los Angeles and New York City metro areas have each seen over 11 million more passengers board planes at their airports over the past five years. Following close behind, the Chicago, Dallas, San Francisco, Seattle, and Miami markets have also added more than 5 million air passengers from 2012 through 2017. The following map highlights these changes, with labels for all metro areas that gained over 2.5 million additional airplane passengers.   Changes in Commercial Air Passenger Enplanement by Metro Area, 2012-2017   While the gains in passengers are, unsurprisingly, predominantly at the nation’s largest airports, popular tourist destinations such as Las Vegas and Orlando also appear. When the air travel data are adjusted for the population of each metro area, we see which additional popular tourist destinations saw increases in air travel over the past five years. Smaller metro areas that have recently gained popularity in tourism appear, such as Kahului, Hawaii (+2.68 passengers per capita); New Orleans, Louisiana (+1.22); Nashville, Tennessee (+0.85); Portland, Oregon (+0.72); Austin, Texas (+0.71); and Charleston, South Carolina (+0.67). Increased commercial air travel coincides with increased employment in travel industries. Hotels, car rental companies, restaurants, and other retail and transportation businesses reap the rewards of more air travel. By looking at locations of job postings, we can see what areas in the nation are hiring, likely in anticipation of increased travel and tourism to the area.  The following table shows the top 12 regions with job postings for hotel, motel, and resort clerks over the past 90 days. Texas, in particular, is expanding in the hotel industry as four of the top 12 regions are within the Lone Star State. As most of the top regions seeking hotel clerks have also experienced some of the largest increases in air travel, these areas are poised for continued growth in the travel industries.   Top Job Posting Locations for Hotel Clerks Location Total Ads Orlando, Florida 222 New York, New York 219 Denver, Colorado 189 Chicago, Illinois 154 Houston, Texas 150 Austin, Texas 136 Atlanta, Georgia 130 Nashville, Tennessee 125 San Antonio, Texas 117 Dallas, Texas 107 Las Vegas, Nevada 107 Source: Chmura's JobsEQ   [1] Source: Federal Aviation Administration and Chmura http://chmuraecon.com/blog/2018/july/25/popular-travel-destinations-poised-for-continued-growth/ http://chmuraecon.com/blog/2018/july/25/popular-travel-destinations-poised-for-continued-growth/ Wed, 25 July 2018 15:08:14 Greg Chmura http://chmuraecon.com/blog/2018/july/20/how-much-education-is-really-needed/ The amount of education someone needs for specific career is not necessarily simple to determine. As an example of this, in this blog we attempt to answer the question: “How much education do you need to be a registered nurse?” According to the Bureau of Labor Statistics (BLS), the "typical entry-level education" for registered nursing is a bachelor's degree. [1] It isn't as simple as just that, though. The BLS also publishes information about the actual educational attainment of workers by occupation. [2] According to these data, while about half of registered nurses have a bachelor's degree as their highest level of education, a third of nurses have highest attainment of an associate's degree. --> Highcharts.chart('container', { chart: { //type: 'column' }, title: { text: 'Educational Attainment: Registered Nurses' }, xAxis: { categories: [ 'Less than high school diploma', 'High school diploma or equivalent', 'Some college, no degree',"Associate's degree", "Bachelor's degree", "Master's degree", 'Doctoral or professional degree', ], crosshair: true }, yAxis: { min:0, max:50, tickInterval: 10, labels: { format: '{value}%', }, title: { text: '', } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, //tickInterval: 1, //Not really needed if below is hidden. //gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { shared: true, //valueSuffix: '%', pointFormat: "{point.y: .1f}%" }, plotOptions: { series: { pointPadding: 0, groupPadding: 0.1, borderWidth: 5, dataLabels:{ enabled:true, align: 'center', color: '#000', format: '{point.y: .1f}%',// puts the label to one decimal place /*formatter: function() { return this.y +'%'; //return Highcharts.numberFormat(this.percentage, 1) +' %'; },*/ x: -2, style: { textOutline: 0, fontSize: 13, fontWeight: '100', }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: BLS, 2015-16; attainment for workers 25 years and older', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Nurses', type:'column', showInLegend: false, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [0.3,1.0,5.0,33.0,49.6,9.2,1.8], }, ] });   How is this possible? Isn't a bachelor's the minimum needed to be a nurse? No—BLS is careful to characterize a bachelor's degree in this case as the “ typical entry-level education.” Furthermore, what is typical today may not have been typical yesterday. So some workers who are nurse today may have entered the occupation when the requirements were different. [3] Now the picture is not quite as clear. What education level do you need today to get a job as a registered nurse? How about we ask the employers who are actively hiring registered nurses? We can do this very easily with RTI [4] data, which captures and parses out information from hundreds of thousands of job ads every day. var paths = [ "/Scripts/blogData/us.json", "/media/1469/state-percent.tsv" ]; #map-cont { position: relative; } .map-overlay { position: absolute; top: 0; left: 0; pointer-events: none; opacity: 0; } #map { border: none; margin-top: 20px; } #legend { position: absolute; bottom: 35px; padding: 10px; width: 126px; right: 10px; background: #fff; } #legend ul { list-style-type: none; margin: 0; padding: 0; overflow: hidden; } #legend li { margin: 0; padding: 0; line-height: 0px; } #legend .legend-title { font-size: 14px; line-height: 18px; margin-bottom: 10px; } #legend .color-block { position: relative; width: 15px; height: 22px; } #legend .color-label { top: 10px; left: 25px; width: 85px; position: absolute; text-align: left; font-size: 11px; } .foot { font-size: 0.8rem; } .states { fill: none; stroke: #fff; pointer-events: none; } .counties { fill: none; stroke: none; } .counties--border:hover { stroke: #444; } .tooltip { position: absolute; z-index: 10; visibility: hidden; background: rgba(255,255,255,0.925); border: solid 1px #ddd; padding: 12px; width: 200px; opacity: 1; } .post-body .tooltip h3 { text-align: left; font-size: 14px; padding-bottom: 5px; border-bottom: 1px solid #ddd; font-weight: bold; margin-bottom: 5px; margin-top: 0; } .post-body .tooltip h4 { font-size: 14px; color: #000; margin-bottom: 0; } .viz-footer { margin-bottom: 20px; clear: both; } .mainTitle { color: #333333; font-size: 18px; font-family: "Lucida Grande", "Lucida Sans Unicode", Arial, Helvetica, sans-serif; text-align: center; font-weight: bold; } .subTitle { color: #333333; font-size: 13px; font-family: "Lucida Grande", "Lucida Sans Unicode", Arial, Helvetica, sans-serif; text-align: center; } Registered Nurses: Percent Needing a Bachelor's or Higher Online Job Ads, Six Months Ending July 15, 2018 ) < 53.0% 53.0% to 58.0% 58.0% to 62.0% 62.0% to 65.0% 65.0% to 68.0% ≥ 68.0% --> --> Source: Chmura Economics & Analytics , JobsEQ var width = 960, height = 600, baseYear = 2010, dataHash, countyHash, tooltipCountyId; var color = d3.scale.threshold() .domain([0, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) .range(['#fff', 'rgb(239,243,255)','rgb(201,220,242)','rgb(163,198,229)','rgb(125,175,215)','rgb(87,153,202)','rgb(49,130,189)',]); var projection = d3.geo.albersUsa() .scale(1280) .translate([0, 0]); var path = d3.geo.path() .projection(projection); var svg = d3.select("#map").append("svg") .attr("width", width) .attr("height", height) d3.selectAll('div.map-overlay').style({ "width": width + 'px', "height": height + 'px'}); //make sure these are the same size as the map var gZ = svg.append('g') .attr("transform", "translate(" + width / 2 + "," + height / 2 + ")"); var g = gZ.append('g'); //tooltip div for mouseovers var tooltip = d3.select('#tooltip'); //round numbers to one decimal place var fmt = d3.format('0,.1f'); //setup the tooltip chart var subWidth = 200, subHeight = 30, subTopMargin = 5, subYPad = 10, subXPad = 16, subBoxWidth = (subWidth - subYPad) / (2030 - baseYear); var svgSub = d3.select('#tooltip-chart').append('svg') .attr('class', 'barchart') .attr('width', subWidth) .attr('height', subHeight); //x scale & axis for sub chart var subScaleX = d3.scale.linear().domain([baseYear, 2030]).range([subYPad, subWidth - subYPad * 2]); var subAxisX = d3.svg.axis() .ticks(3) .tickFormat(d3.format('.0f')) .scale(subScaleX); //var subColorScale = d3.scale.linear().domain([baseYear, 2030]).range(['blue', 'green']); ////draw axis var subX = svgSub.append('g') .attr('class', 'x axis') .attr('transform', 'translate(' + (subBoxWidth / 2) + ', ' + (subHeight - subXPad - subTopMargin) + ')') //offset it a little bit so that the tick marks align w/ center of boxes .call(subAxisX); //draw initial lines & points var lines = svgSub.append('g') .attr('class', 'lines'); //create an array of years that we'll use to draw boxes var yearArray = []; for (var yr = baseYear; yr " + fmt(row.pct * 100.0) + "% "); }; }   According to these data, when a U.S. employer describes education requirements in the job post, 32% of the time at least an associate's degree was required for a job as a registered nurse. The other 68% of the time a bachelor's degree or higher was needed. [5] Furthermore, these data show variation in these requirements by state. For example, California employers, on average, are more demanding with their registered nursing requirement—76% require a bachelor's or higher. In Florida, on the other hand, an associate's is more likely to be sufficient, described as acceptable in 43% of the registered nursing ads there. [6] The answer to our initial question, therefore, is that the education needed to work as a registered nurse varies case by case. Some occupations certainly have norms that may hold true more often than not. But variation in the labor market is quite typical.   [1] According to the Occupational Outlook Handbook . [2] https://www.bls.gov/emp/tables/educational-attainment.htm . [3] There is also the question of those extreme data in the first graphic—is it possible, for example, that some registered nurses haven't even graduated from high school? In this data set, some extreme points may actually be accurate. On the other hand, this is information from survey data, and the nature of survey data is to be not 100% clean. According to this same data set, for example, a half percent of surgeons have no more education than a high school diploma. Such outlier data can be viewed as likely errata. [4] Real Time Intelligence, a data set within JobsEQ . [5] These data exclude internships where a finished degree isn’t required. [6] A number of factors could play into regional differences, such as law, workforce availability, and industry mix (for an example of the latter: hospital employers and home health care employers may have different requirements; states with different mixes of these industries will thus be effected by that). http://chmuraecon.com/blog/2018/july/20/how-much-education-is-really-needed/ http://chmuraecon.com/blog/2018/july/20/how-much-education-is-really-needed/ Fri, 20 July 2018 15:23:31 Mike Kyffin http://chmuraecon.com/blog/2018/july/06/jobseq-new-feature-round-up/ The  JobsEQ  team never stops optimizing and expanding the platform. The latest set of updates includes two exciting workflow personalization features within the  JobsEQ platform, the addition of union breakout data to our Demographic Profile Analytic, and some expanded analytic outputs for clients who have access to our Real-Time Intelligence (RTI) job posting data.     Report Section Customization   Report-based Analytics are some of the most popular components in  JobsEQ , and many users take advantage of these time-saving reports to quickly output a curated set of concise data for easy distribution to clients, partner organizations, and other stakeholders.    Each report is comprised of several sections, and users can now select only the sections they need – providing the functionality for more specific and targeted reports.     Personalized Analytic Dashboard   Everyone's workflow is unique. Now you can keep  your  most important and commonly used JobsEQ Analytics close at hand in your own custom dashboard. Every  JobsEQ  user has been assigned a location for a personalized dashboard. This dashboard can contain selected JobsEQ Analytics for ease of access, recall, and organization.     Dashboard personalization is a helpful way to stay efficient in  JobsEQ .         Union Membership Breakout in Demographic Profile JobsEQ now expands union data to detail private sector, public sector, and manufacturing membership in the Demographic Profile Analytic.       Addition of Job Posting Data (RTI) to the Maps Analytic   Our clients’ users now have the ability to visually represent job posting data geographically by selecting the 'Online Job Ads' dataset in our Maps Analytic. Users can now easily view and explore the spatial distribution of job postings by occupation or custom occupation group.     Job Posting Data (RTI) for US Territories   The latest updates to  JobsEQ  include an important addition to our RTI   job posting data module. It is now possible to view job posting information within the U.S. Territories of Puerto Rico, Guam, and the Virgin Islands. Furthermore, in the case of Puerto Rico, we can examine job postings in each  municipo , allowing us to perform county-level style job posting queries within the territory.   Like other federally designated geographic entities, territories and  municipos  are discoverable by text search and FIPS-matching. http://chmuraecon.com/blog/2018/july/06/jobseq-new-feature-round-up/ http://chmuraecon.com/blog/2018/july/06/jobseq-new-feature-round-up/ Fri, 06 July 2018 14:30:18 Chris Chmura http://chmuraecon.com/blog/2018/july/02/economic-impact-statistics-not-calculus-more-in-demand-by-employers/ When I was much younger and learning to cook, I wanted to fix a ham in the pan that my mother gave to me. She always made the best hams. So I proceeded to cut both ends off the ham, prepare it with her special spices and put it into the oven. I wondered to myself, “Why am I cutting off both ends? Mom always did.” So I asked my mom about it. Her answer was, “Mom always made it that way.” So I called up my grandmother and asked this question of her. Her answer upended my thinking. She said, “The ham was too big for the pan.” I wonder if the same is true for calculus. Do we take calculus in high school because we need this skill in our future career, or is it what we historically needed to get into prestigious colleges? Last week, I spoke to a group of about 130 businesspeople and educators. I asked, “How many of you took calculus in school?” About 90 percent raised their hand. And “how many of you use calculus in your job today?” I didn’t see any hands raised. Based on job postings from Chmura’s JobsEQ over the past 180 days ending with June 25, nearly 6,000 job ads in the nation listed “calculus” as a preferred skill. That’s 0.03 percent of all the jobs posted during that period. The top job posted was for engineers with 1,707 job ads. The next highest job postings were for tutors with 820 ads. Presumably, those tutors are needed to prepare high school students to pass courses needed for college. In the Richmond metro area, there were 11 job postings that required calculus (about 0.01 percent of all openings); most were tutors. On the other hand, it appears that statistics is not taught as often in high school or sought after by college students. Based on the 2016 National Council of Teachers of Mathematics report, students are more likely to take AP calculus tests than statistics tests in high school. But there were nearly 60,000 jobs requiring statistics in the nation for the past 180 days ending with June 25. That’s about 0.3 percent of the job openings over the same period. The job ads covered a number of occupations. There were 4,431 jobs ads in the nation for management analysts that included the need for statistics. It was followed by 3,950 openings for computer and information research scientists with statistics skills and 2,916 business intelligence analysts. In the Richmond area, 464 job ads required statistics — about 0.4 percent of total openings. Based on our insights on calculus and statistics, the latter appears to be the skill most in demand by employers today. Sometimes we just need to ask “Why?” http://chmuraecon.com/blog/2018/july/02/economic-impact-statistics-not-calculus-more-in-demand-by-employers/ http://chmuraecon.com/blog/2018/july/02/economic-impact-statistics-not-calculus-more-in-demand-by-employers/ Mon, 02 July 2018 13:57:45 Doug Rice http://chmuraecon.com/blog/2018/june/29/where-are-occupational-health-and-safety-professionals-needed-most/ Since June is celebrated as National Safety Month , we thought it would be interesting to consider safety in the context of the workforce. When we first think of occupations related to safety, we may consider workers such as law enforcement professionals, fire fighters, and EMTs—people whose job it is to respond to a specific and often anticipated threat. However, in addition to fields that concentrate on responding to narrowly-defined emergency situations, there are also fields that focus on prevention of a broad array of emergency situations. Namely, occupational health and safety specialists and technicians [1] evaluate the environment in a variety of workplaces in order to design and implement policies to improve safety on the job.   Currently, there are roughly 100,000 employees working as occupational health and safety specialists and technicians, and that number is projected to grow at an average annual rate of 0.8% over the next five years. For workers who have an inclination toward safety and possess strong organizational and communication skills, this career might be a good fit. Using our JobsEQ  Real-Time Intelligence [2] platform, we found that, within the last 180 days ending June 26, 2018, the skills most requested by employers include expertise in Microsoft Office, presentation, teaching/training, and language. That being said, the availability of such work depends somewhat on where you live. Opportunities to work in occupational health and safety are concentrated more heavily in some areas than others. --> Highcharts.chart('container', { chart: { //type: 'column' }, title: { text: 'Location Quotients by MSA* for Occupational Health and Safety Specialists and Technicians, Four Quaters Ending with 2018, Quarter 1' }, xAxis: { categories: [ 'Midland,TX MSA (3326)','Kennewick-Richland, WA MSA (2842)','Trenton, NJ MSA (4594)','Odessa, TX MSA (3622)','Baton Rouge, LA MSA (1294)','Beaumont-Port Arthur, TX MSA (1314)','Lake Charles, LA MSA (2934)','Houston-The Woodlands-Sugar Land, TX MSA (2642)','Corpus Christi, TX MSA (1858)','Anchorage, AK MSA (1126)','Columbia, SC MSA (1790)','Oklahoma City, OK MSA (3642)','Lafayette, LA MSA (2918)','Augusta-Richmond County, GA-SC MSA (1226)','San Antonio-New Braunfels, TX MSA (4170)','Gulfport-Biloxi-Pascagoula, MS MSA (2506)','Knoxville, TN MSA (2894)','Bakersfield, CA MSA (1254)','Albany-Schenectady-Troy, NY MSA (1058)','Virginia Beach-Norfolk-Newport News, VA-NC MSA (4726)', ], crosshair: true }, yAxis: { min:0, max:5, labels: { format: '{value}%', }, title: { text: '', } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, //tickInterval: 1, //Not really needed if below is hidden. //gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { shared: true, valueSuffix: '%' }, plotOptions: { series: { pointPadding: 0, groupPadding: 0.1, borderWidth: 5, dataLabels:{ enabled:false, align: 'right', color: '#fff', /*formatter: function() { return this.y +'%'; },*/ x: -2, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQTM, BLS, Notes:*MSAs include the 100 MSAs with the most occupational health and safety employees.', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Location Quotient', type:'column', showInLegend: false, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [5.10,4.75,3.38,3.37,2.88,2.85,2.68,2.43,2.23,1.93,1.93,1.62,1.61,1.60,1.57,1.56,1.53,1.51,1.43,1.41,], }, ] });   Looking at the top MSAs for occupational health and safety workers by location quotient, [3] we can see that the geographic distribution of employees in this field is relatively wide—stretching from New Jersey to South Carolina to Alaska. Nevertheless, the strongest concentrations tend to fall within Texas and—to a lesser extent—Louisiana. Why does one region employ more occupational health and safety workers than another? Well, there are likely many reasons, but one important factor to account for is the assortment of industries that are prevalent within those areas. Some industries will naturally need more guidance on matters of workplace safety than others. When we consider the number of occupational health and safety workers employed in each industry as a percentage of all employees, [4] we gain some insight as to why some regions may be more reliant on occupational safety professionals.  --> Highcharts.chart('container2', { chart: { //type: 'column' }, title: { text: 'Concentration of Occupational Health and Safety Workers by Industry, Four Quarters Ending with Quarter 1, 2018 ' }, xAxis: { categories: [ 'Waste Treatment and Disposal (5622)','Other Heavy and Civil Engineering Construction (2379)','Support Activities for Mining (2131)','Petroleum and Coal Products Manufacturing (3241)','Oil and Gas Extraction (2111)','Basic Chemical Manufacturing (3251)','Electric Power Generation',' Transmission and Distribution (2211)','Management',' Scientific',' and Technical Consulting Services (5416)','Nonresidential Building Construction (2362)','Remediation and Other Waste Management Services (5629)', ], crosshair: true }, yAxis: { min:0, max:1.3, labels: { format: '{value}%', }, title: { text: '', } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, //tickInterval: 1, //Not really needed if below is hidden. //gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { shared: true, valueSuffix: '%' }, plotOptions: { series: { pointPadding: 0, groupPadding: 0.1, borderWidth: 5, dataLabels:{ enabled:false, align: 'right', color: '#fff', /*formatter: function() { return this.y +'%'; },*/ x: -2, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQTM, BLS', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Occupational Health and Safety Workers as a Percentage of Total Industry Employment', type:'column', showInLegend: false, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [1.30,1.14,1.03,1.01,0.88,0.69,0.51,0.51,0.45,0.39,0.06], }, ] });   While the number of occupational health and safety workers across all industries [5] averages out to about one in every 1,500 employees, the top ten industries range from one per 250 to one per 75 employees. Four industries in particular employ at least one occupational health and safety professional per 100 employees: water treatment and disposal, other heavy and civil engineering construction, support activities for mining, and petroleum and coal products manufacturing. In looking at the location quotients by MSA for each industry, we found that half of the top 20 MSAs for occupational health and safety workers listed above fall within the top ten MSAs of one of these four industries. The industry with the highest concentration of occupational health and safety workers is waste treatment and disposal. The MSA with the 3 rd highest concentration of workers in this industry is Kennewick-Richland, WA—the region that also contains the 2 nd highest concentration of occupational health and safety workers. Considering that this region is the home of the most contaminated nuclear site in the US, it’s no surprise that it has become an ongoing cleanup and remediation project. The number one MSA for occupational health and safety professionals, Midland, TX, is also the number one MSA in support activities for mining. And, finally, the Lake Charles, LA MSA, ranked 7 th in terms of occupational health and safety employment concentration, is also the number one region in the other heavy and civil engineering construction industry and number two in the petroleum and coal products manufacturing industry. While generalized knowledge in workplace standards and procedures is helpful, workers interested in pursuing a career in occupational health and safety will also benefit from gaining subject matter expertise in the industries most reliant on the field. At the same time, workforce developers in regions driven by these industries will also benefit from investing in training and development for occupational health and safety professionals needed to keep their workers safe and healthy enough for their local economies to flourish.   [1] Occupational health and safety specialists and technicians is an occupation measured at the five-digit SOC level (29-9010) and includes both occupational health and safety specialists (29-9011) and occupational health and safety technicians (29-9012). [2] Real-Time Intelligence extracts online job postings on a daily basis, categorizing them by criteria such as occupation, location, employer, title, and required skills, to identify real-time job demand. [3] Derived from the BLS, the location quotient (LQ) is the ratio of the regional employment for a given occupation or industry to the national average for that occupation or industry. For example, an LQ of 2 means that the regional employment is twice the national average, an LQ of 0.5 means that it’s half the national average, and an LQ of 1 means that it’s equal to the national average.  [4] Derived using staffing patterns in conjunction with occupational and industry data provided by the BLS. [5] Industries include all four-digit NAICS codes in which there are at least 100,000 employees. http://chmuraecon.com/blog/2018/june/29/where-are-occupational-health-and-safety-professionals-needed-most/ http://chmuraecon.com/blog/2018/june/29/where-are-occupational-health-and-safety-professionals-needed-most/ Fri, 29 June 2018 08:36:32 Alex Doherty http://chmuraecon.com/blog/2018/june/28/more-trouble-ahead-for-dairy-farmers/ June is national dairy month, but not all dairy farmers are milking it up. Dairy farms in the nation have declined every year over the past decade from 59,130 in 2007 to 40,219 in 2017 according to the USDA, and reports of struggling dairy farmers are more and more prevalent . Just over half of all dairy farms are in the Midwest, and these states have been hit the hardest over the past decade, suffering a 34.5% decline from 32,245 farms to 21,120 farms. Wisconsin is home to nearly one-quarter of all dairy farms in the nation. In that state, dairy farms declined by more than 5,000 from 2007 to 2017. As all states have lost at least 12% of their dairy farms since 2007— no state has escaped the dairy decline. A number of factors impacted the declines in dairy farms: dairy substitutes gaining market share milk consumption declining dairy production rising production costs increasing major retailers consolidating their supply chains Taken together, these conditions have created a perfect storm for the dairy industry. In recent years the consumer price index (CPI) for dairy and related products has decreased, diverging from the overall consumer price index and the producer price index (PPI) for dairy manufacturing. As the costs for dairy production have risen in line with inflation, shown in the chart below by an average annual increase in the CPI of 1.2% from 2014 to 2017, the CPI for dairy products has decreased at an average annual rate of 1.2% over the same period. Unless something changes in the dairy market, this divergence in price indexes could spell more trouble for dairy farmers. http://chmuraecon.com/blog/2018/june/28/more-trouble-ahead-for-dairy-farmers/ http://chmuraecon.com/blog/2018/june/28/more-trouble-ahead-for-dairy-farmers/ Thu, 28 June 2018 16:41:06 Chris Chmura http://chmuraecon.com/blog/2018/june/04/economic-impact-virginia-receives-a-grant-to-create-a-defense-department-contract-spending-impact-tool/ Virginia’s dependence on defense contracts puts it in both an enviable and awkward position. It’s enviable because firms in the state received more revenue from defense contracts performed than any other state, except California, in the federal fiscal year that ended Sept. 30, 2017. It’s awkward because with 4.4 percent of Virginia’s gross domestic product dependent on defense contracts, the state’s economy can take a hit during defense downsizing — as it did in 2013. During that year, employment stalled in Virginia and grew only 0.7 percent, compared with 1.6 percent growth nationally. In light of the state’s dependence on defense contracts, the Defense Department’s Office of Economic Adjustment awarded Virginia a grant in 2012 to create a tool that gave the state and localities an understanding of the regions and industries that would be adversely affected by cuts in Pentagon spending. The tool would allow users to estimate the impact of spending reductions on localities so that mitigating strategies could be employed. The fiscal year that ended September 2012 turned out to be the peak year of DoD spending for contracts performed in the state at $43.2 billion. The latest data available for fiscal 2017 puts contracts performed at $34.6 billion, or a drop of 19.9 percent from the peak. Moreover, in 2012, Virginia received more Defense Department contract spending for work performed than any other state. That changed in 2016 when California, with $35.5 billion in defense contracts, slipped ahead of Virginia’s $34.4 billion. In the most recent fiscal year, California performed $34.9 billion in contracts. Gov. Ralph Northam recently announced that the Office of Economic Adjustment provided Virginia with another grant to release the next generation of the Defense Department contract spending impact tool on Virginia. The tool, which can be accessed at   http://dod-va.chmura   econ.com , shows that 5.5 percent of the state’s workers are directly or indirectly dependent on defense contracts. Many of those employees work in professional, scientific and technical services firms and computer-related skills. In fact, the Northern Virginia portion of the Washington metropolitan area performed $22 billion in Defense Department contracts in the most recent fiscal year, the most of any region in the state. Three-fifths of these contracts were in professional, scientific and technical services firms. Northrop Grumman Systems had $1.4 billion in contracts, followed by Booz Allen Hamilton at $1.2 billion and Leidos Holdings (formerly Science Applications International Corp.) at $800 million. The Virginia portion of the Hampton Roads metro area received $9.7 billion in contracts — the second-largest amount of contract spending for work performed in 2017. As one might expect, shipbuilding firms received a large amount in contracts ($4.2 billion) with Huntington Ingalls Industries performing $3.3 billion of contracts last fiscal year. Looking ahead, President Donald Trump’s defense budget calls for a 15 percent increase in defense spending from the current fiscal year through the fiscal year that will end Sept. 30, 2023. That bodes well for Defense Department contractors in the state, where we forecast contracts to rise 12.6 percent from the last fiscal year to the 2019 fiscal year. Virginia’s dependence on defense contracts puts it in both an enviable and awkward position. It’s enviable because firms in the state received more revenue from defense contracts performed than any other state, except California, in the federal fiscal year that ended Sept. 30, 2017. It’s awkward because with 4.4 percent of Virginia’s gross domestic product dependent on defense contracts, the state’s economy can take a hit during defense downsizing — as it did in 2013. During that year, employment stalled in Virginia and grew only 0.7 percent, compared with 1.6 percent growth nationally. In light of the state’s dependence on defense contracts, the Defense Department’s Office of Economic Adjustment awarded Virginia a grant in 2012 to create a tool that gave the state and localities an understanding of the regions and industries that would be adversely affected by cuts in Pentagon spending. The tool would allow users to estimate the impact of spending reductions on localities so that mitigating strategies could be employed. The fiscal year that ended September 2012 turned out to be the peak year of DoD spending for contracts performed in the state at $43.2 billion. The latest data available for fiscal 2017 puts contracts performed at $34.6 billion, or a drop of 19.9 percent from the peak. Moreover, in 2012, Virginia received more Defense Department contract spending for work performed than any other state. That changed in 2016 when California, with $35.5 billion in defense contracts, slipped ahead of Virginia’s $34.4 billion. In the most recent fiscal year, California performed $34.9 billion in contracts. Gov. Ralph Northam recently announced that the Office of Economic Adjustment provided Virginia with another grant to release the next generation of the Defense Department contract spending impact tool on Virginia. The tool, which can be accessed at   http://dod-va.chmura   econ.com , shows that 5.5 percent of the state’s workers are directly or indirectly dependent on defense contracts. Many of those employees work in professional, scientific and technical services firms and computer-related skills. In fact, the Northern Virginia portion of the Washington metropolitan area performed $22 billion in Defense Department contracts in the most recent fiscal year, the most of any region in the state. Three-fifths of these contracts were in professional, scientific and technical services firms. Northrop Grumman Systems had $1.4 billion in contracts, followed by Booz Allen Hamilton at $1.2 billion and Leidos Holdings (formerly Science Applications International Corp.) at $800 million. The Virginia portion of the Hampton Roads metro area received $9.7 billion in contracts — the second-largest amount of contract spending for work performed in 2017. As one might expect, shipbuilding firms received a large amount in contracts ($4.2 billion) with Huntington Ingalls Industries performing $3.3 billion of contracts last fiscal year. Looking ahead, President Donald Trump’s defense budget calls for a 15 percent increase in defense spending from the current fiscal year through the fiscal year that will end Sept. 30, 2023. That bodes well for Defense Department contractors in the state, where we forecast contracts to rise 12.6 percent from the last fiscal year to the 2019 fiscal year. http://chmuraecon.com/blog/2018/june/04/economic-impact-virginia-receives-a-grant-to-create-a-defense-department-contract-spending-impact-tool/ http://chmuraecon.com/blog/2018/june/04/economic-impact-virginia-receives-a-grant-to-create-a-defense-department-contract-spending-impact-tool/ Mon, 04 June 2018 11:26:20 Chris Chmura http://chmuraecon.com/blog/2018/june/01/governor-northam-announces-the-virginia-department-of-defense-contract-spending-impact-tool/ RICHMOND —Governor Ralph Northam today announced the release of the latest update of the Virginia Department of Defense (DoD) Contract Spending Impact Tool , featuring supply-chain mapping of DoD contract awards. The website is designed to provide clear and meaningful state, county, and metropolitan statistical area details about the current and projected economic impacts of DoD contract spending, including spending and employment impacts. “Department of Defense contract spending is a key underpinning of Virginia’s economy and we have a responsibility to help compete and win for these important projects,” said Governor Northam . “The Virginia DoD Contract Spending Impact Tool will be available for all to use—from government leaders, to workforce partners, to the general public—and will not only equip us to better understand where exactly Virginia’s defense contract spending is, but also help us understand long-term effects and the ripple impacts on our local economies as we see changes in defense-related contract spending.” In Federal Fiscal Year (FFY) 2017, defense contract spending in Virginia was $34.6 billion. Based on President Trump’s budget and historical spending trends, defense contracts performed in the Commonwealth are estimated to increase by 12.6 percent to $38.9 billion in FFY 2019. The impact model provides clear and meaningful details about the current and projected economic impacts of federal contract spending in the Commonwealth across counties, metropolitan statistical areas, and workforce development areas. The model is intended to be user-friendly, equipped with links that outline navigating the tool and uses of the model . “We are thankful for our partnership with Chmura Economics & Analytics,” said Virginia Secretary of Veterans and Defense Affairs Carlos Hopkins . “The grant my office received from the federal Office of Economic Adjustment (OEA) for the evaluation model will make this tool available at no cost to the Commonwealth through 2020. The information provided by this tool will prove invaluable as we continue to work with our federal, state, and local partners to protect Virginia’s defense assets and support local economies.” “Even though defense spending is expected to increase over at least the next two years, regions that are dependent on defense spending need to be considering ways to diversify their economies for an eventual downturn in defense spending and this tool is intended to do just that,” said Dr. Christine Chmura, Chief Executive Officer and Chief Economist, Chmura Economics & Analytics . http://chmuraecon.com/blog/2018/june/01/governor-northam-announces-the-virginia-department-of-defense-contract-spending-impact-tool/ http://chmuraecon.com/blog/2018/june/01/governor-northam-announces-the-virginia-department-of-defense-contract-spending-impact-tool/ Fri, 01 June 2018 11:09:32 Patrick Clapp http://chmuraecon.com/blog/2018/may/24/real-time-demand-for-stem/ Jobs in the fields of science, technology, engineering, and math (STEM [1] ) are expected to grow faster than the average occupation over the next 10 years, but what does demand for STEM vs. non-STEM look like today?  Real-time intelligence from online job ads can help compare these occupation groups. In 2017, STEM ads closed at about the same rate as non-STEM ads but took a slightly longer time to fill, suggesting it may be harder for employers to fill openings for STEM occupations. Out of the nearly 27 million deduplicated online ads collected in 2017, more than 2 million were for STEM-related occupations. Over this period, 85% of both STEM and non-STEM ads were closed (a proxy for hiring). However, on average, STEM jobs were active online for a median duration of 29.5 days, nearly two days longer than the average median duration for non-STEM ads (27.8 days). The table below shows the STEM occupations with the longest median durations. Online Job Ads for STEM Occupations, 2017 SOC Occupations Median Duration (days) Total Ads 17-3025 Environmental Engineering Technicians 42 2,303 25-1054 Physics Teachers, Postsecondary 42 0,839 25-1052 Chemistry Teachers, Postsecondary 41 1,539 25-1022 Mathematical Science Teachers, Postsecondary 40 2,568 25-1061 Anthropology and Archeology Teachers, Postsecondary 37 0,706 15-2021 Mathematicians 34 0,130 19-1029 Biological Scientists, All Other 34 3,906 19-1032 Foresters 34 1,851 19-1099 Life Scientists, All Other 34 1,940 25-1032 Engineering Teachers, Postsecondary 34 1,820 Source: JobsEQ Though STEM ads as a whole were generally online longer, many individual non-STEM occupations had longer median durations, also suggesting difficulty filling these positions. The table below shows duration and total ads for non-STEM occupations that were active online the longest in 2017. Note that some of these occupations are healthcare related, and often included in definitions of STEM-H occupations. Online Job Ads for Non STEM Occupations, 2017 SOC Occupations Median Duration (days) Total Ads 49-9043 Maintenance Workers, Machinery 129 0,929 51-9194 Etchers and Engravers 56 1,127 29-1131 Veterinarians 51 8,448 29-1041 Optometrists 49 4,264 19-3041 Sociologists 48 0,073 29-1127 Speech-Language Pathologists 47 81,381 25-1064 Geography Teachers, Postsecondary 46 0,147 47-2071 Paving, Surfacing, and Tamping Equipment Operators 45 5,596 25-1062 Area, Ethnic, and Cultural Studies Teachers, Postsecondary 43 1,283 33-9092 Lifeguards, Ski Patrol, and Other Recreational Protective Service Workers 41 18,294 Source: JobsEQ From this analysis, it seems STEM ads may generally take somewhat longer to fill than non-STEM jobs, but some detailed occupations within both groups appear especially difficult to fill. Further analysis of online job ads may yield additional insights. For example, differences in hard and soft skills most often required or differences in advertised wages may be the subject of future blogs.   [1] STEM occupations were identified based on the SOC Policy Committee recommendations to the Office of Management and Budget; health occupations were excluded. For more on defining STEM, see our related blog http://chmuraecon.com/blog/2018/may/24/real-time-demand-for-stem/ http://chmuraecon.com/blog/2018/may/24/real-time-demand-for-stem/ Thu, 24 May 2018 09:42:00 Doug Rice http://chmuraecon.com/blog/2018/may/23/pedaling-power-parity-economics-and-demographics-of-bike-friendly-states/ Happy National Bike Month, [1] everyone! May is the month set aside to celebrate the joys of cycling!  Consequently, it is a good time to explore the relationships between the nation’s most bicycle-friendly regions and their economic and demographic characteristics. The League of American Bicyclists [2] publishes an annual ranking of states based on the following criteria: Number of bicycle-friendly actions taken Infrastructure and funding Education and encouragement Legislation and enforcement Policies and programs Evaluation and planning In this article, we consider the Bicycle Friendly State 2017 Rankings in conjunction with other characteristics often found in the more highly ranked states.   Percentage of Total Population in Urban Areas Based on the “infrastructure and funding” criterion used for the state rankings, one would think that urban areas might be more bike-friendly. With shorter distances to travel between destinations, urban areas would also intuitively seem to be more conducive to bicycle transit. When we compared the percentage of the total population found in urban counties [3] for each state to the Bicycle Friendly State rankings, we indeed found a strong relationship. [4] --> Highcharts.chart('container2', { chart: { //type: 'column' }, title: { text: 'Urban Population and Bike-Friendly States' }, xAxis: { categories: [ '#1) Washington','#2) Minnesota','#3) California','#4) Massachusetts','#5) Oregon','#6) Colorado','#7) Delaware','#8) Utah','#9) New Jersey','#10) Virginia','#11) Maryland','#12) Pennsylvania','#13) Michigan','#14) Vermont','#15) Florida','#16) Illinois','#17) Maine','#18) Ohio','#19) Georgia','#20) North Carolina','#21) Arizona','#22) Rhode Island','#23) New York','#24) Connecticut','#25) Texas','#26) Wisconsin','#27) Tennessee','#28) Idaho','#29) Louisiana','#30) Iowa','#31) Nevada','#32) Missouri','#33) South Dakota','#34) New Hampshire','#35) Arkansas','#36) Alaska','#37) West Virginia','#38) Indiana','#39) Alabama','#40) Mississippi','#41) South Carolina','#42) Wyoming','#43) Kentucky','#44) New Mexico','#45) Montana','#46) Oklahoma','#47) Kansas','#48) North Dakota','#49) Hawaii','#50) Nebraska', ], crosshair: true }, yAxis: { min:0, max:100, labels: { format: '{value}%', }, title: { text: '', } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, //tickInterval: 1, //Not really needed if below is hidden. //gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { shared: true, valueSuffix: '%' }, plotOptions: { series: { pointPadding: 0, groupPadding: 0.1, borderWidth: 5, dataLabels:{ enabled:false, align: 'right', color: '#fff', /*formatter: function() { return this.y +'%'; },*/ x: -2, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, US Census, USDA, League of American Bicyclists', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Percentage of Population in Urban Counties', type:'column', showInLegend: true, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [77.4,66.0,94.8,93.4,66.7,81.3,81.7,80.1,97.2,78.0,94.4,79.1,70.2,0.0,90.9,80.0,39.4,75.2,67.2,68.6,81.6,100.0,89.8,94.8,82.3,49.7,68.5,40.6,64.7,37.6,88.5,62.7,0.0,62.6,48.7,54.6,20.7,60.4,50.2,40.7,73.8,0.0,48.5,43.4,0.0,61.9,51.6,0.0,69.8,58.5], }, { type: 'line', name: 'Trendline', data: [[0, 87.55], [49, 37.05]], marker: { enabled: false }, color: 'rgba(119,127,128,.9)', dashStyle: 'shortdot', states: { hover: { lineWidth: 0 } }, enableMouseTracking: false }, ] });   While there is a great deal of variability, states that are ranked as more bike-friendly tend to have greater percentages of their populations concentrated in urban spaces. Six of the top ten states have over 80% of their populations located in urban counties, and seven of the bottom ten states have less than 60% of their populations located in urban counties. Several other characteristics were related to bike-friendliness; but when we took all our variables together, these attributes were linked more directly to the urban variable. Two examples are: Percentage of workforce commuting via public transportation : the more workers commuting via public transit in each state, the greater the concentration tends to be in urban counties. [5] This relationship is intuitive as we would expect greater availability of public transportation services in areas with a population density high enough to support them. Percentage of population foreign-born : the greater the percentage of foreign-born residents living in each state, the greater the concentration tends to be in urban counties. [6] There could be many reasons for this relationship. For example, it’s possible that immigrants are drawn to more urban areas due to the expectations of greater work opportunities. To sum up, bike-friendly states are more likely to have a greater concentration of its population located in urban areas and, indirectly, a greater percentage of public transit commuters and foreign-born residents.   Average Annual Wages Per Worker One additional variable that showed a strong relationship [7] with the Bike-Friendly State rankings is the average annual wages per worker for each state. That is, states that were more bike-friendly tended to have higher average wages.   --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('container1', { chart: { //type: 'column' }, title: { text: 'Wages and Bike-Friendly States' }, xAxis: { categories: [ '#1) Washington','#2) Minnesota','#3) California','#4) Massachusetts','#5) Oregon','#6) Colorado','#7) Delaware','#8) Utah','#9) New Jersey','#10) Virginia','#11) Maryland','#12) Pennsylvania','#13) Michigan','#14) Vermont','#15) Florida','#16) Illinois','#17) Maine','#18) Ohio','#19) Georgia','#20) North Carolina','#21) Arizona','#22) Rhode Island','#23) New York','#24) Connecticut','#25) Texas','#26) Wisconsin','#27) Tennessee','#28) Idaho','#29) Louisiana','#30) Iowa','#31) Nevada','#32) Missouri','#33) South Dakota','#34) New Hampshire','#35) Arkansas','#36) Alaska','#37) West Virginia','#38) Indiana','#39) Alabama','#40) Mississippi','#41) South Carolina','#42) Wyoming','#43) Kentucky','#44) New Mexico','#45) Montana','#46) Oklahoma','#47) Kansas','#48) North Dakota','#49) Hawaii','#50) Nebraska', ], crosshair: true }, yAxis: { min:30000, max:70000, labels: { formatter: function () { return '$' + this.axis.defaultLabelFormatter.call(this); } }, title: { text: '', } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, //tickInterval: 1, //Not really needed if below is hidden. //gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { shared: true, valuePrefix: '$', //pointFormat: "${point.y:0,.0f}" }, plotOptions: { series: { pointPadding: 0, groupPadding: 0.1, borderWidth: 5, dataLabels:{ enabled:false, align: 'right', color: '#fff', /*formatter: function() { return this.y +'%'; },*/ x: -2, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, BLS, League of American Bicyclists', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Avg Ann Wages per Worker', type:'column', showInLegend: true, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [61133,55517,64572,68770,50601,55690,56426,46218,63494,55565,58396,53185,51509,45329,47810,56627,43225,48378,50982,48172,49854,52456,68090,65656,54433,46600,47893,40882,45725,46010,48865,46446,42625,54289,42663,52757,42372,45552,44979,38307,43741,45234,44537,42894,41979,43957,45038,50085,49620,45606], }, { type: 'line', name: 'Trendline', data: [[0, 58122], [49, 42169]], marker: { enabled: false }, color: 'rgba(119,127,128,.9)', dashStyle: 'shortdot', states: { hover: { lineWidth: 0 } }, enableMouseTracking: false }, ] });   The state with the highest average annual wage is the fourth-most bike-friendly, Massachusetts at $68,770, and the state with the lowest average annual wage is fortieth-most bike friendly, Mississippi at $38,307. There could be a host of factors to explain this relationship. Some research [8] has shown that cyclists tend to have higher-than-usual income levels—with 24% of the cyclists in a recent survey reporting a housing income of $100,000 or more. It could also be that the states that are bike-friendly also tend to have more robust economies in general. Perhaps high-earning workers are attracted to areas that are bike-friendly, or perhaps high-earning workers care more about making their communities bike-friendly. Whatever the explanation, there seems to be a relatively strong relationship. Four of the top ten states have an average annual wage per worker over $60,000, and five of the bottom ten states have an average annual wage per worker under $45,000. One important piece of information to keep in mind while making these comparisons is that there will likely be a great deal of variation within most states. MSAs, counties, and places within each state will differ in their emphasis on making their communities bike-friendly. Sufficient data don’t exist to make comparisons across the nation on more detailed regional levels. So, we would caution you to use these comparisons as prompts for further research rather than hard-and-fast relationships. Having said that, some states and municipalities provide information on bike paths and cycling activity within their regions. For example, consider Washington—the #1 state in the 2017 rankings. When we add the layer of bike lanes [9] from the Washington State Department of Transportation to our JobsEQ™ Maps [10] analytic, we can see visually how different variables might correspond to the common cycling areas.     Consider again the relationship between the urban/rural classifications for each of Washington’s counties and the concentration of bike paths currently in operation throughout the state. In the image above, the bike lanes throughout the state are shown in red, with the rural counties shaded darker and the urban counties shaded lighter. Consistent with the trend involving state-level urban population and bike-friendly state rankings, the Washington counties classified as more urban generally tend to also be those with the highest concentration of bike lanes.   [1] See here for more information on National Bike Month: http://bikeleague.org/bikemonth   [2] See here for more information on the League of American Bicyclists, as well as the Bicycle Friendly State 2017 Rankings used in the article: http://bikeleague.org/content/ranking [3] To estimate the percentage of population in urban counties, we used the ACS 2012-2016 population in counties with a Rural-Urban Continuum Code of “1” as a percentage of the total population for each state. [4] Pearson correlation coefficient of -0.65 between the state numeric ranking and the percentage of total population found in urban counties. [5] Person correlation coefficient of 0.52 between the percentage of the workforce commuting via public transportation and the percentage of total population found in urban counties. [6] Pearson correlation coefficient of 0.68 between the percentage of population foreign-born and the percentage of total population found in urban counties. [7] Pearson correlation coefficient of -0.62 between the state numeric ranking and the average annual wages (using wage data as of 2017Q4).  [8] Damant-Sirois, G., Grimsrud, M. & El-Geneidy, A.M. What’s your type: a multidimensional cyclist typology . Transportation (2014) 41: 1153. See Table 2: Demographic characteristics.  [9] See here for the WSDOT special use lanes date set, filtered for bike lanes: http://geo.wa.gov/datasets/WSDOT::wsdot-roadway-data-special-use-lanes/data?selectedAttribute=SUType&where=SUType%20%3D%20%27BL%27 [10] Our Maps analytic in JobsEQ™ allows users to upload custom shape files as layers to place on top of the many regional data sets available within the program. http://chmuraecon.com/blog/2018/may/23/pedaling-power-parity-economics-and-demographics-of-bike-friendly-states/ http://chmuraecon.com/blog/2018/may/23/pedaling-power-parity-economics-and-demographics-of-bike-friendly-states/ Wed, 23 May 2018 08:25:27 Doug Rice http://chmuraecon.com/blog/2018/may/17/occupational-demand-pennsylvania-versus-the-nation/ For workforce developers, knowing which occupations are most in demand is essential for developing programs that meet the needs of their local labor markets. Notably, a particular occupation growing nationally doesn’t necessarily mean it is growing in all geographical markets. In this article, we consider the projected occupational growth demand [1] in Pennsylvania compared with growth demand in the nation. To compare the two regions, we standardized [2] the distribution of growth demand values for each and then converted them to percentiles. Now let’s compare the growth demand for Pennsylvania with that of the nation.   --> Highcharts.chart('container2', { chart: { //type: 'column' }, title: { text: '20 Occupations with the Highest Growth Demand in Pennsylvania, Compared to the US' }, xAxis: { categories: [ 'Personal Care Aides','Home Health Aides','Registered Nurses','"Software Developers, Applications"','Medical Assistants','Nursing Assistants','Market Research Analysts and Marketing Specialists','Medical Secretaries','General and Operations Managers','Social and Human Service Assistants','Accountants and Auditors','Licensed Practical and Licensed Vocational Nurses','Financial Managers','Management Analysts','"Child, Family, and School Social Workers"','Physical Therapists','"Sales Representatives, Services, All Other"','Medical and Health Services Managers','Receptionists and Information Clerks','Billing and Posting Clerks', ], crosshair: true }, yAxis: { min: 80, max: 100, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { shared: true, valueSuffix: '%' }, plotOptions: { series: { pointPadding: 0, groupPadding: 0.1, borderWidth: 5, dataLabels:{ enabled:false, align: 'right', color: '#fff', /*formatter: function() { return this.y +'%'; },*/ x: -2, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, BLS', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'US Growth Demand', type:'column', showInLegend: true, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [100.00,100.00,100.00,100.00,99.99,99.95,99.82,99.62,100.00,91.97,99.95,95.23,98.88,98.83,83.46,85.26,99.28,89.27,98.37,92.46], }, { name: 'PA Growth Demand', type: 'spline', showInLegend: true, color: 'rgba(104,113,114,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [100.00,100.00,100.00,100.00,99.96,99.89,99.28,98.83,97.05,96.76,95.38,94.40,93.71,91.06,90.08,89.13,88.35,87.52,87.28,86.41], }, ] });   It turns out that the six occupations with the highest growth demand in Pennsylvania are also among the fastest growth occupations in the entire nation. Among these occupations, five are related in some way to healthcare: personal care aides, home health aides, registered nurses, medical assistants, and nursing assistants. There are a few occupations, however, which display noticeable differences in how fast demand is growing in Pennsylvania compared to the nation. Several occupations—including sales representatives, receptionists and information clerks, and management analysts—show relatively higher growth in the nation than they do in Pennsylvania. On the other hand, a few occupations are relatively higher in Pennsylvania than they are in the nation as a whole: social and human service assistants; child, family, and school social workers; and physical therapists. All 20 of the fastest growing occupations in Pennsylvania are over the 80 th percentile of the fastest growing occupations in the nation. So the differences for these occupations between the two regions aren’t necessarily the largest. The greatest differences in demand growth between Pennsylvania and the nation are shown in the chart below.   --> Highcharts.chart('container1', { chart: { //type: 'column' spacingLeft: 0, }, title: { text: '10 Occupations with the Greatest Difference in the Demand Growth Percentile in PA versus the US' }, xAxis: { categories: ['Mental Health Counselors','Mental Health & Subs. Abuse Social Workers','Subs. Abuse & Behav. Dis. Counselors','"Business Teachers, Postsecondary"','Residential Advisors','"Ambul. Drivers & Attendants, Except EMTs"','Statisticians','Rehabilitation Counselors','Psychiatric Aides','Actuaries',], crosshair: true }, yAxis: [{ // Primary yAxis min:35, max:90, labels: { format: '{value}%', }, title: { text: 'Standardized Growth Demand Percentile', } }, { // Secondary yAxis min:7, max:18, title: { text: 'Percentile Difference, PA - US', }, labels: { format: '{value}%', }, opposite: true }], /*yAxis: { min: 35, max: 90, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: true } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/ /*tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines },*/ tooltip: { shared: true }, plotOptions: { series: { pointPadding: 0, groupPadding: 0.1, borderWidth: 5, dataLabels:{ enabled:false, align: 'right', color: '#fff', /*formatter: function() { return this.y +'%'; },*/ x: -2, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, BLS', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'PA Growth Demand', type: 'column', yAxis: 0, color: 'rgba(246,138,73,.9)', data: [85.23,74.56,70.27,62.40,62.40,51.66,58.58,59.49,50.49,52.59], tooltip: { valueSuffix: '%' } }, { name: 'US Growth Demand', type: 'column', yAxis: 0, color: 'rgba(160,174,212,.9)', data: [69.41,60.35,58.18,50.56,52.52,41.94,49.43,50.55,41.56,43.73], tooltip: { valueSuffix: '%' } },{ name: 'Growth Demand Difference, PA - US', type: 'spline', yAxis: 1, color: 'rgba(104,113,114,.9)', data: [15.83,14.22,12.09,11.84,9.88,9.72,9.15,8.94,8.93,8.86], tooltip: { valueSuffix: '%' } }] });   Six of the top ten occupations with the largest relative growth demand differences are in healthcare-related fields. So although healthcare occupations are also projected to grow rapidly in the nation, at least a few in Pennsylvania have relatively higher expectations compared to the remaining set of regional occupations. Also noteworthy in this list of occupations is that the top three all fall within the social services space: mental health counselors; mental health and substance abuse social workers; and substance abuse and behavioral disorder counselors. There are many other avenues that can be explored in terms of the growth demand by occupation. As the labor markets examined get smaller and more specialized, we can expect to find even larger differences in expectations. This underscores the importance for workforce developers to drill down into regional-specific data rather than simply relying on national trends.     [1] For the purposes of this article, we looked at all 6-digit SOC occupations excluding 106 occupations that typically require no education for entry. Growth demand refers to the demand due to growth or contraction of total employment of an occupation. [2] We converted each group of occupation growth demand values to z-scores, such that the mean is 0 and the standard deviation is 1. Then, we converted the z-scores to percentiles, such that 0 is 50%, 1 standard deviation is about 84%, 2 is about 98%, and 3 is about 100%--with the higher percentiles representing the higher demand relative to other occupations. The purpose of this conversion is to facilitate comparison of the nation and Pennsylvania. http://chmuraecon.com/blog/2018/may/17/occupational-demand-pennsylvania-versus-the-nation/ http://chmuraecon.com/blog/2018/may/17/occupational-demand-pennsylvania-versus-the-nation/ Thu, 17 May 2018 09:05:14 Chris Chmura http://chmuraecon.com/blog/2018/may/07/economic-impact-is-a-recession-lurking-or-will-the-us-economy-continue-growing/ The U.S. economy has expanded for 107 months as of this month, making it the second longest expansion on record. The big question is whether economic growth will last 15 more months to hit record territory in July 2019 when it would surpass the longest expansion on record at 121 months. The pessimists point out that interest rates are rising, the yield curve is flattening, consumer debt is increasing, inflation is inching up, and the stock market is down — all pointing toward a recession, not an expansion. Yes, interest rates are rising, but they still remain at historic low levels. The benchmark 10-year Treasury yield, for example, has risen sharply from an all-time low in 2016 to end Wednesday at 2.97 percent, but still that is far from the 5 percent yield that hit before the Great Recession began. An inverted yield curve often proceeds a recession as the Federal Reserve is pushing the target federal funds rate higher, which causes 3-month bills and short-maturity Treasuries to rise above the longer-term 10-year Treasury. As of last Wednesday, the yield spread, or difference between the 10-year and 3-month yields, was 113 basis points which provides plenty of room for the Fed to raise short-term interest rates during the process they say will be one of “gradual tightening.” Consumer spending makes up about two-thirds of gross domestic product, so their health is critical to national growth. Debt has been rising slightly but the broad household debt service ratio from the Federal Reserve was at a fairly low 10.3 percent in the fourth quarter of 2017, the latest figures available. That ratio was much lower than the 13.2 percent at the start of the Great Recession. On the other hand, the recent federal tax cuts, low unemployment and rising wages point to more spending power to fuel economic growth. Rising wages do suggest the potential of a pickup in inflation, which could lead to more Fed tightening than currently expected, but such a scenario does not seem likely to occur over the next 15 months. Increased investment by businesses in new plants and equipment resulting from corporate tax cuts and reductions in regulations also will support future economic growth. Aside from the increased sales to firms that construct plants and manufacture equipment, business investment will ultimately increase productivity, which enables the economy to growth faster. But what about the 10 percent decline in the Dow Jones Industrial Average since its all-time high on Jan. 26? Even with the recent drop, the Dow remains about 30 percent higher since President Trump was elected. The stock market tends to be pessimistic as Paul Samuelson, a well-known economist, said in 1966: “The stock market has called nine of the last five recessions.” Based on these trends, and many others not noted in this column, I’m betting on this expansion going down in the record books as the longest in history. http://chmuraecon.com/blog/2018/may/07/economic-impact-is-a-recession-lurking-or-will-the-us-economy-continue-growing/ http://chmuraecon.com/blog/2018/may/07/economic-impact-is-a-recession-lurking-or-will-the-us-economy-continue-growing/ Mon, 07 May 2018 11:56:15 Xiaobing Shuai http://chmuraecon.com/blog/2018/april/20/tax-season-help-wanted/ April 17, 2018 is the deadline for Americans to file their individual tax returns for 2017. With the passage of the Tax Cut and Jobs Act of 2017, there are significant changes in the federal individual income tax rates and deductions allowed. While those changes will mostly impact American taxpayers when they file tax returns in 2019, it is never too early to start understanding and planning for changes. Working on tax returns is stressful for many individuals under normal circumstances, more so this year with major tax changes taking effect. In addition to gathering documents for 2017, taxpayers are wondering whether they need to change their withholdings, family budgets, or record keeping under the new tax law. Due to the complexity of tax laws and time involved in filing tax returns, most Americans do not like doing taxes. A 2013 national survey by the Pew Research Center found that 56% of Americans either hate or dislike doing their own taxes. [1] As a result, more people request extensions. In 2016, 7.9% of tax returns were received after April, which increased to 9.4% in 2017. [2] With changes in the tax code, that percentage is expected to rise next year. Many Americans are seeking help to prepare their tax returns. In a 2014 statement, the Internal Revenue Service (IRS) commissioners reported that 56% of total tax returns filed for 2013 were done by paid preparers, and another 34% of taxpayers used tax preparation software, making a total of 90% of taxpayers seeking assistance. [3] One can only imagine that those percentages will be higher after major changes in the tax law. With an increasing number of Americans using paid tax preparers, one might think there will be a spike of “help-wanted” advertisements for tax related positions during tax season, and it will be easy for people with tax preparation skills to switch jobs at that time. Data from Chmura’s JobsEQ© Real Time Intelligence (RTI) database provides some insights into those questions. To look closely at labor market dynamics during the tax season, we first define tax-related occupations. While the occupation of tax preparers (SOC code 13-2082) is the quintessential tax professional, many other jobs are involved in filing tax returns, including accountants and auditors; bookkeeping, accounting and auditing clerks; and personal financial advisors.  We also include positions such as bill and account collectors; payroll and timekeeping clerks; and billing, cost, and rate clerks. While those individuals may not directly prepare tax returns, they may be called upon to prepare various tax forms, locate receipts and invoices, and provide other help. Finally, tax examiners and collectors and revenue agents are also included as tax-related professionals, even though most of them will not help taxpayers prepare returns, they typically work for government agencies that will receive tax filing. In the first quarter of 2018, there were 277,018 job openings for tax-related professionals. Among those, 91,748 openings were for bookkeeping, accounting and auditing clerks, 59,182 for accountants and auditors, and 21,841 for tax preparers. Compared with the open positions for the previous three quarters, there is no anticipated spike in job openings for tax-related professionals in the first quarter. In fact, the total job openings in the first quarter is almost the same as the fourth and third quarter of 2017. But there was a sharp drop in the second quarter of 2017, as there were only 206,407 total jobs openings, about 75% of jobs openings in other quarters. [4]   --> Highcharts.setOptions({ lang: { thousandsSep: ',' } }) Highcharts.chart('container2', { chart: { type: 'column' }, title: { text: 'Tax-related Job Openings' }, xAxis: { categories: [ '2017 Q2', '2017 Q3', '2017 Q4', '2018 Q1' ] }, yAxis: { min: 0, max: 300000, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { pointFormat: "{point.y:,.0f} Job Openings" }, plotOptions: { series: { dataLabels:{ enabled:true, align: 'center', color: '#fff', /*formatter: function() { return this.y +'%'; },*/ x: 0, y: 30, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ RTI', position: { align: 'left', y: -2, // position of credits x: 10 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Job Openings', showInLegend: false, color: 'rgba(128,151,188,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [206407,274086,278629,277018], }, ], });   The seasonal pattern of tax-related job openings implies that while we typically associate the first four months of the year with the tax season, companies do not recruit a disproportionally large number of tax-related professionals in that period. The recruitment starts well before tax season. The data indicate that in the third and fourth quarter of the year, accounting and tax preparation firms increase their recruitment in anticipation of the upcoming tax season, and hiring continues through the first quarter.  However, the demand of such positions drops sharply in the second quarter after the tax season is over. This pattern makes sense for the accounting and tax industry. A well-managed business can predict increased work load for tax-related professionals and plan their staffing accordingly. Chmura’s RTI data imply that it takes 20 to 30 days for a tax-related position to be filled in 2017.  Hiring for the tax season should start at least one month before the new year. But with holidays in November and December, it is not surprising that the recruitment for such positions start in October. Businesses also understand that good job candidates may not be available when tax season starts, giving them more incentive to start recruitment early and lock in talent needed. The most job openings for tax-related occupations are with large staffing agencies for accounting and finance professionals, such as Accountemps, Robert Half, and Accounting Principles. H&R Block, and Intuit, two firms associated with tax preparation, also have one of the largest job openings in this field. For job seekers, it appears that the second quarter is the worst time to be looking for tax-related positions. Unfortunately, the second quarter is the time when upcoming college graduates intensively look for work. Students in accounting and related fields may encounter a lack of openings in the job market. It is wise for students to start their job search in the fall semester. For the tax-related professionals who are currently working and are considering changing jobs, a good strategy is to polish their resumes in the third or fourth quarter of each year.   [1] Source: http://www.people-press.org/2013/04/11/a-third-of-americans-say-they-like-doing-their-income-taxes/ [2] Source: https://www.irs.gov/newsroom/2018-and-prior-year-filing-season-statistics/ [3] Source: https://www.cnsnews.com/news/article/susan-jones/irs-90-taxpayers-seek-help-preparing-their-returns [4] Source: JobsEQ. http://chmuraecon.com/blog/2018/april/20/tax-season-help-wanted/ http://chmuraecon.com/blog/2018/april/20/tax-season-help-wanted/ Fri, 20 April 2018 13:45:49 James Stinchcomb http://chmuraecon.com/blog/2018/april/19/exploring-the-labor-market-in-amazon-hq2-finalist-cities/ A lot has already been said regarding Amazon and its search for a second headquarters (HQ2). Some argue HQ2 will bring a great benefit to the selected region, with Amazon saying they will invest over $5 billion and hire up to 50,000 employees for HQ2. Others have been more critical , arguing that the cost in the form of tax subsidies may not be worth the benefit gained. Additionally, there has been a lot of comparing the cities competing for HQ2, with front runners including Austin , Washington DC , and Denver , depending on which analysis you read. Here, we don’t intend to “pick a winner” for which finalist city will be chosen for Amazon’s HQ2. Rather, the purpose of this analysis is to explore the existing labor market for the occupations that Amazon HQ2 will likely demand in the finalist cities, using job postings data to measure current labor supply and demand to compare the cities. The first thing we need to do is determine the occupations that Amazon will likely be hiring for at its HQ2. To do this we use JobsEQ RTI job postings data to look at the job openings for which Amazon has advertised at its current Seattle headquarters. Looking at every posting from Amazon for its headquarters in the past year, [1] we see that there are four broad occupation groups that make up the large majority of Amazon postings: computer occupations ( SOC 15-1000), advertising, marketing, promotions, public relations, and sales managers (11-2000), operations specialties managers (11-3000), and business operations specialists (13-1000). Over half of all postings are in the first occupation group, computer occupations, while these four occupation groups combined make up 84% of the total job postings that Amazon placed for its Seattle headquarters in the past year. Amazon HQ2 will likely be filling jobs in the same occupation groups, so these are the occupations we examine below in the finalist cities. Percent of Job Postings on Amazon’s Job Board Occupation Percent of Postings                 Computer Occupations 52%                 Advertising, Marketing, Promotions, Public Relations, and Sales Managers 17%                 Operations Specialties Managers 7%                 Business Operations Specialists 7% Source: JobsEQ RTI , Amazon While there are twenty finalist “cities” selected by Amazon, here we are looking at the larger metro regions associated with each finalist, and only those in the United States. Because Toronto is not in the United States and five other finalists are within two metro areas (Newark and New York City in the NYC Metro area; Washington DC, Montgomery County, MD, and Northern Virginia in the Washington DC Metro area), we are left with sixteen distinct metro areas for our analysis:  Atlanta-Sandy Springs-Roswell, GA MSA Austin-Round Rock, TX MSA Boston-Cambridge-Newton, MA-NH MSA Chicago-Naperville-Elgin, IL-IN-WI MSA Columbus, OH MSA  Dallas-Fort Worth-Arlington, TX MSA Denver-Aurora-Lakewood, CO MSA Indianapolis-Carmel-Anderson, IN MSA Los Angeles-Long Beach-Anaheim, CA MSA Miami-Fort Lauderdale-West Palm Beach, FL MSA Nashville-Davidson--Murfreesboro--Franklin, TN MSA New York-Newark-Jersey City, NY-NJ-PA MSA Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA Pittsburgh, PA MSA Raleigh, NC MSA Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  Employment in the Amazon HQ2 Finalist Metros Computer Occupations Advertising, Marketing, Promotions, Public Relations, and Sales Managers Operations Specialties Managers Business Operations Specialists Atlanta 107,025 18,712 39,419 108,312 Austin 54,984 5,320 11,572 38,550 Boston 132,913 26,317 61,330 111,745 Chicago 139,228 36,631 71,999 170,300 Columbus 39,933 4,905 15,021 42,662 Dallas 131,241 15,205 39,829 119,637 Denver 65,671 7,364 14,935 73,476 Indianapolis 30,297 5,060 12,089 37,220 Los Angeles 168,910 42,782 81,967 235,840 Miami 59,486 10,475 22,824 93,288 Nashville 24,252 5,641 16,173 32,410 New York 287,100 60,423 130,852 330,229 Philadelphia 87,154 12,919 33,955 102,474 Pittsburgh 33,025 4,047 11,734 34,695 Raleigh 32,867 3,086 8,157 26,211 Washington DC 206,973 17,828 52,854 226,152 Source: JobsEQ To first get a sense of the existing labor market in each region, we look at the current employment and forecast growth rate [2] for the four occupation groups, seen in the charts above. Notably, Austin is currently projected to be the fastest growing region in each of these four occupation groups, while Pittsburgh currently has the slowest expected growth rate. These figures are what we might expect, as they follow larger growth trends in these regions. In order to get a deeper look at the labor market for these occupation groups, we next turn to job postings data in the finalist regions. JobsEQ RTI [3] data provides the following additional information about the job market in the finalist regions: the median duration, which measures the length of time a job posting typically stays open, and the JPLQ [4] (Job Posting Location Quotient) which is a measure of relative volume for job postings within a region. The median duration can be useful in exploring labor supply by showing how difficult it is to fill a position within an occupation, with a longer median duration suggesting it is more difficult for firms to fill openings. There are a number of reasons why a firm may have difficulty filling a job opening, ranging from overall labor market conditions (example: as unemployment falls, there are less workers looking for a job, making it more difficult to fill open positions) to regional and occupation-specific reasons (example: if there is a relatively low volume of qualified applicants within a region, it is more difficult to fill openings). While the broader market conditions will be represented similarly across regions, the differences between regions provides insight on the region-specific conditions and a region’s labor supply. Below is a graphic of the median duration for each occupation group and each metro area. Computer occupation openings appear to be relatively easier to fill than the other occupations, with every metro area having a median duration below 30 days, followed by business operation specialists, with the two management occupation groups taking relatively longer to fill. Of particular interest is the variation we see in the computer occupations group. At the low end, Columbus has a median duration of only 21 days and Atlanta has a median duration of 22 days, suggesting openings in these occupations tend to be filled relatively quickly in those regions. On the other end, Boston and Pittsburgh each show a median duration of 29 days. From a labor supply perspective, this suggests there may be a stronger relative supply of workers in these occupations in Columbus and Atlanta compared to Boston and Pittsburgh.  --> Highcharts.chart('container2', { chart: { type: 'column' }, title: { text: 'Median Duration of Job Postings' }, xAxis: { categories: [ 'Computer Occupations', 'Advertising, Marketing, Promotions, Public Relations, and Sales Managers', 'Operations Specialties Managers', 'Business Operations Specialists' ] }, yAxis: { min: 20, max: 35, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { //valueSuffix: '%' }, plotOptions: { series: { dataLabels:{ enabled:false, align: 'right', color: '#fff', /*formatter: function() { return this.y +'%'; },*/ x: -2, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, BLS.', position: { align: 'left', y: -2, // position of credits x: 45 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Atlanta', showInLegend: true, color: '#f7955d', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [22,27,30,25], }, { name: 'Austin', showInLegend: true, color: '#faba96', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [26,31,27,26], }, { name: 'Boston', showInLegend: true, color: '#fecd7f', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [29,31,31,30], }, { name: 'Chicago', showInLegend: true, color: '#fedeac', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [25,30,31,29], }, { name: 'Columbus', showInLegend: true, color: '#57b4a8', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [21,31,29,23], }, { name: 'Dallas', showInLegend: true, color: '#92cec6', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [23,30,30,28], }, { name: 'Denver', showInLegend: true, color: '#b3ddd8', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [27,26,29,23], }, { name: 'Indianapolis', showInLegend: true, color: '#6482ba', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [24,25,30,23], }, { name: 'Los Angeles', showInLegend: true, color: '#9aaed2', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [23,31,30,28], }, { name: 'Miami', showInLegend: true, color: '#9aaed2', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [24,30,28,27], }, { name: 'Nashville', showInLegend: true, color: '#8e4bba', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [28,23,28,25], }, { name: 'New York', showInLegend: true, color: '#b68ad2', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [24,32,31,30], }, { name: 'Philadelphia', showInLegend: true, color: '#ccaee0', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [27,30,31,28], }, { name: 'Pittsburgh', showInLegend: true, color: '#777f80', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [29,30,31,29], }, { name: 'Raleigh', showInLegend: true, color: '#b0b2b2', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [23,28,30,25], }, { name: 'Washington DC', showInLegend: true, color: '#dedfdf', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [24,30,27,23], }, ] });   Moving to the other occupation groups, Nashville has the shortest duration for the advertising managers group at only 23 days, 9 days lower than New York at 32 days. For the business operations specialists group, there are four regions with a median duration of 23 days at the low end—Columbus, Denver, Indianapolis, and Washington DC—while there are two regions at the high end with a median duration of 30 days—Boston and New York. Lastly, in the operations managers group there is relatively little variation with only 4 days separating the lows in Austin and Washington DC at 27 days and the highs in Boston, Chicago, New York, Philadelphia, and Pittsburgh at 31 days. This smaller variation we see with the operations managers group suggests there is relatively little difference in the labor supply for these occupations across the different regions compared to the other occupation groups. While the duration of job postings provides insight on the labor supply, we can use the JPLQ to explore labor demand in an occupation and region. The JPLQ is a measure of relative job posting volume, and for a given occupation is defined as the ratio of job postings to employment in a region divided by the same ratio in the nation. A JPLQ equal to one means that job postings per employment in a region matches the national baseline, while a JPLQ greater than one means that there is a larger volume of job postings in the region relative to the nation, implying that labor demand for that particular occupation is higher than average.  The above graphic shows the JPLQ for the four occupation groups in each metro area. First looking at computer occupations, in contrast to what we saw with the median durations, there is relatively little variation in the JPLQs, with only one region, Raleigh, having a JPLQ greater than 1.5 and the lowest being 0.88 in Miami. This tells us that labor demand in this occupation group is relatively consistent across these regions, which may speak to a larger consistent growth in demand for these occupations, as firms across the country are seeking to fill positions in these occupations. We see a similar pattern in the business operations specialists group, where excepting two lower end exceptions in Los Angeles and Miami—with JPLQs of 0.75 and 0.77, respectively—all other regions have a JPLQ above 1.0 and none greater than 1.5. Again, this suggest demand is relatively consistent in these occupations. The two manager groups tell a more interesting story. In these we see much more variation with Denver leading in each group, with an JPLQ over 2.0 in each; while on the low end we see Los Angeles with a JPLQ of 0.74 and 0.86 in advertising managers and operations managers, respectively. Overall, comparing all four occupation groups, there appears to be a larger trend in a few of the regions. Denver and Raleigh have relatively high JPLQs in each group while Los Angeles, and to a lesser extent Miami, have relatively lower JPLQs. This suggests that demand is relatively high for these occupations in Denver and Raleigh as firms try to grow relative to the current employment levels. In Los Angeles and Miami, however, the growth and demand is much lower relative to current employment levels.  So, what can this analysis tell us about Amazon and it’s HQ2 location choice? From a recruitment perspective, Columbus or Atlanta look appealing because their low median durations for computer occupations suggests a strong labor supply for those occupations. At the same time, Denver and Raleigh may be less ideal as the high JPLQs in those regions suggest higher labor demand, meaning Amazon will be facing more competition for employees. Of course, Amazon is a unique case and this type of analysis cannot tell the whole story. For one thing, with Amazon being such a massive company, they will extend recruitment beyond the region in which they choose and may try to recruit the “best of the best” globally, not just locally. Additionally, Amazon will likely be able to pay more for workers than most of their competitors and that may result in them poaching top talent from existing employers, rather than recruiting workers specifically looking for work. More broadly, though, this type of analysis using real-time job postings can provide insight for both firms and local governments in seeing what the current local labor market looks like and how it compares to other regions.      [1] This is every posting on Amazon’s job board from April 2017 through March 2018 and located in Seattle, WA. [2] Employment and growth rates as of 2017Q4. Source: JobsEQ [3] RTI job posting data used in this analysis are jobs that were posted in 2017Q4 [4] For a given occupation, the JPLQ = (# of job ads locally / employment locally) / (# of ads in USA / employment USA) http://chmuraecon.com/blog/2018/april/19/exploring-the-labor-market-in-amazon-hq2-finalist-cities/ http://chmuraecon.com/blog/2018/april/19/exploring-the-labor-market-in-amazon-hq2-finalist-cities/ Thu, 19 April 2018 13:53:16 Chris Chmura http://chmuraecon.com/blog/2018/april/09/economic-impact-what-occupations-should-have-the-smallest-student-loan-default-rate/ Colleges and universities offering programs that lead to careers in occupations that are relatively high-paying and have low unemployment tend to have lower student loan default rates. Some of the top “low-default occupations” for jobs that typically require an associate’s degree theoretically would be dental hygienists and respiratory therapists. For jobs needing a bachelor’s degree, some careers with the best combination of high wages and low unemployment rates are aerospace engineers and software developers. For master’s degrees, nurse practitioners and physician assistants are career choices where there are low default rates. The professional and technical services industry, for example, provides higher-than-average wages with lower-than-average unemployment as does the health care industry. Finding the right occupation with higher-than average wages and lower-than-average jobless rates is important because those jobs tend to have lower student loan default rates. Student loan default rates have been generally rising in the past decade, according to the U.S. Department of Education. Default rates reached a historical low of 4.5 percent in 2003 — prior to the Great Recession. But by 2014, the latest data available, the average student loan default rate in the nation had risen to 11.5 percent. Finding what programs offered by a college or university is important in determining whether its students are likely to default. In addition to occupational characteristics of graduates, Chmura Economics & Analytics created a student loan default model that includes institutional characteristics directly related to the school and its student body, as well as the economic climate of the state in which the school is located. Those factors were important predictors as well. For example, institutions with a higher percentage of students who did not graduate with a degree had higher default rates. This finding is expected, since a student who takes on debt for a four-year degree and drops out after two years is unlikely to obtain a job that will pay a salary sufficient to repay the loan. It is not easy to answer the question whether students who graduate with a degree in an occupation that is not in demand are more likely to default on their loans than students with degrees that are needed by employers. Student loan default is affected by many factors other than occupation, including overall economic conditions and students’ family finances. Another challenge in determining what occupations will have the smallest default rate is the availability of data availability. Although higher education institutions know the degree choice of the students who default on their loans, the Department of Education only releases the overall loan default rate of institutions. We do know the number of students who graduate from each institution and the degree they were awarded, such as engineering, economics or registered nurse. Some degrees, such as registered nurse, sync up easily to a specific occupation. Others, such as economics, could end up in a variety of occupations from financial adviser to risk assessment. http://chmuraecon.com/blog/2018/april/09/economic-impact-what-occupations-should-have-the-smallest-student-loan-default-rate/ http://chmuraecon.com/blog/2018/april/09/economic-impact-what-occupations-should-have-the-smallest-student-loan-default-rate/ Mon, 09 April 2018 14:27:01 Doug Rice, Greg Chmura, Guest Author: Jim Fong http://chmuraecon.com/blog/2018/april/02/upceachmura-study-exploring-the-determinants-of-student-loan-default-rates/ Student loan defaults in the United States have been a growing problem. After reaching a historic low of 4.5% in 2003, student loan default rates have been trending upward, rising to 10.0% for the 2011 cohort. Total student debt has also been increasing in the nation, rising above $1 trillion in 2010 and approaching $1.5 trillion in Fall 2017. As student loan debt increases for an individual student, default rates actually tend to decrease—the assumption being that students who borrow more also end up earning more with professional degrees, such as in law or medicine. Increasing total debt and increasing default rates together, however, is certainly a concerning trend. What can the student loan default situation tell us about the future of higher education? While the majority of loan defaults come from traditional college graduates or students who do not finish their degree, professional, continuing, and online education units may be able to play a part in adding value to credits earned through degree completion or alternative credentialing. The latter may also play a role in helping to reduce loan defaults by increasing an employee’s value in the workplace. Other factors that could also increase value are more convenient delivery of programming through online delivery and more modular learning.     Download the study here http://chmuraecon.com/blog/2018/april/02/upceachmura-study-exploring-the-determinants-of-student-loan-default-rates/ http://chmuraecon.com/blog/2018/april/02/upceachmura-study-exploring-the-determinants-of-student-loan-default-rates/ Mon, 02 April 2018 10:45:32 Doug Rice http://chmuraecon.com/blog/2018/march/23/workers-per-establishment-structural-shifts-in-manufacturing/ Recently, we wrote about employment growth in manufacturing . Here, we look at manufacturing growth [1] in terms of workers per establishment. [2] --> Highcharts.chart('container2', { chart: { type: 'column' }, title: { text: 'Workers Per Establishment in Manufacturing and Select Sub-Sectors, 2006 - 2016' }, xAxis: { categories: [ { name: "Manufacturing", //categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] },{ name: "Beverage and Tobacco Product Manufacturing", //categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] },{ name: "Computer and Electronic Product Manufacturing", //categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] },{ name: "Food Manufacturing", //categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] },{ name: "Plastics and Rubber Products Manufacturing", //categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] },{ name: "Transportation Equipment Manufacturing", //categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] },{ name: "Wood Product Manufacturing", //categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] }, /*'Manufacturing', 'Beverage and Tobacco Product Manufacturing', 'Computer and Electronic Product Manufacturing', 'Food Manufacturing', 'Plastics and Rubber Products Manufacturing', 'Transportation Equipment Manufacturing', 'Wood Product Manufacturing',*/ ] }, yAxis: { min: 0, max: 140, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { //valueSuffix: '%' }, plotOptions: { series: { dataLabels:{ enabled:false, align: 'right', color: '#fff', /*formatter: function() { return this.y +'%'; },*/ x: -2, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: JobsEQ, BLS.', position: { align: 'left', y: -2, // position of credits x: 45 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: '2006', showInLegend: true, color: 'rgba(246,138,73,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [39.0,42.1,67.3,52.0,54.3,115.7,31.4], }, { name: '2007', showInLegend: true, color: 'rgba(252,193,153,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [38.4,40.8,66.7,51.9,54.6,112.0,29.1], }, { name: '2008', showInLegend: true, color: 'rgba(254,199,108,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [37.4,38.8,65.1,51.9,52.3,105.6,26.5], }, { name: '2009', showInLegend: true, color: 'rgba(255,225,177,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [34.0,36.4,60.1,51.1,45.9,92.5,22.1], }, { name: '2010', showInLegend: true, color: 'rgba(71,172,158,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [33.7,33.8,58.1,50.1,46.5,93.9,22.0], }, { name: '2011', showInLegend: true, color: 'rgba(166,207,200,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [34.9,32.3,58.9,49.9,48.1,99.1,22.5], }, { name: '2012', showInLegend: true, color: 'rgba(82,118,180,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [35.6,30.9,57.9,49.0,49.4,105.0,23.2], }, { name: '2013', showInLegend: true, color: 'rgba(160,174,212,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [35.8,28.8,56.2,48.1,50.3,108.0,24.5], }, { name: '2014', showInLegend: true, color: 'rgba(104,113,114,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [35.9,26.5,54.6,47.6,51.6,110.5,25.5], }, { name: '2015', showInLegend: true, color: 'rgba(167,170,170,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [36.0,24.9,54.1,47.4,52.1,111.6,26.1], }, { name: '2016', showInLegend: true, color: 'rgba(218,219,220,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [35.8,24.2,53.0,47.0,53.1,112.7,26.6], }, ] });   Prior to the recession, the average number of workers per manufacturing establishment had been declining. During the recession, the decline accelerated before recovering somewhat and then plateauing at an average of about 36 workers per establishment in 2012. Most manufacturing sub-sectors (three-digit NAICS [3] codes) followed the same general trend. Some sub-sectors, however, departed from the norm. For example, the number of workers per establishment at beverage and tobacco product, computer and electronic product, and food manufacturing have been declining since 2006—and the recession appears to have had little effect on that trend. Conversely, the average establishment size in the sub-sectors of plastics and rubber products, transportation equipment, and wood product manufacturing has continued to grow steadily since the recession—this trend is not too surprising, since these industries typically rebound after a recession ends. So, what accounts for these differences? A contributing factor is the aggregate effects of companies expanding or contracting in size due the economic cycle and their own business fortunes.  A second factor is productivity—as manufacturers buy new equipment and implement new processes, they can often produce more product with less workers. Beyond these effects, however, some manufacturing industries may have also shifted structurally in their mix of smaller and larger establishments. For example, let’s look at the number of establishments across different establishment-size groups in the beverage and tobacco manufacturing industry compared to those in the transportation equipment manufacturing industry.  --> --> Highcharts.chart('container3', { chart: { type: 'column' }, title: { text: 'Percentage of Total Establishments by Establishment-Size Group, 2007 - 2017' }, xAxis: { categories: [{ name: "Beverage and tobacco product manufacturing", categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] },{ name: "Transportation equipment manufacturing", categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] },{ name: "Manufacturing", categories: ["2007", "2008", "2009","2010","2011","2012","2013","2014","2015","2016","2017"] }], labels: { rotation: 0 } }, yAxis: { min: 0, max: 140, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { valueSuffix: '%' }, plotOptions: { column: { stacking: 'normal', dataLabels: { enabled: false, color: (Highcharts.theme && Highcharts.theme.dataLabelsColor) || 'white' } }, series: { /*dataLabels:{ enabled:false, align: 'right', color: '#fff', formatter: function() { return this.y +'%'; }, x: -2, style: { textOutline: 0, fontSize: 14 }, }*/ } }, legend: { show: false }, credits: { //enabled: false text: 'Source: QCEW. https://www.bls.gov/cew/cewsize.htm', position: { align: 'left', y: -2, // position of credits x: 45 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: '100 or more Employees', showInLegend: true, color: 'rgba(167,170,170,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [9.0,8.9,8.2,7.8,7.4,7.1,6.4,5.9,5.5,5.1,4.7,19.4,18.6,15.8,15.6,16.4,17.4,17.6,18.0,18.2,18.4,18.2,8.0,7.9,7.0,6.8,7.1,7.3,7.4,7.5,7.5,7.5,7.4], },{ name: '10 to 99 Employees', showInLegend: true, color: 'rgba(82,118,180,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [35.9,36.0,34.8,34.9,35.1,34.9,34.6,33.6,32.9,34.1,34.6,36.3,35.6,35.3,35.1,34.9,34.9,35.2,34.9,34.3,33.9,34.2,37.0,36.5,34.9,34.3,34.8,35.3,35.4,35.4,35.4,35.2,35.0], },{ name: 'Fewer than 10 Employees', showInLegend: true, color: 'rgba(71,172,158,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [55.1,55.1,57.1,57.3,57.4,58.0,58.9,60.4,61.6,60.8,60.6,44.4,45.7,48.9,49.4,48.7,47.7,47.2,47.2,47.4,47.8,47.6,55.0,55.6,58.1,59.0,58.1,57.4,57.2,57.2,57.1,57.4,57.6], }, ], });   The percentage of establishments in beverage and tobacco manufacturing with fewer than 10 employees increased from 55.1% in 2007 to 60.6% in 2017. During the same period, the percentage of establishments with 100 or more employees decreased from 9.0% to 4.7%. While not quite as dramatic, transportation equipment manufacturing experienced the opposite effect—likely, in large part, due to the rebound after the recession when previously laid-off workers are called back to work. Between 2009 and 2017, the percentage of establishments in this industry with fewer than 10 employees decreased from 48.9% to 47.6%, while the percentage of establishments with 100 or more employees increased from 15.8% to 18.2%. For manufacturing as a whole, the percentage of establishments with fewer than 10 employees increased from 55.0% to 59.0% between 2007 and 2010 before decreasing to 57.4% in 2012 and flattening thereafter. At the same time, the percentage of establishments with 100 or more employees decreased between 2007 and 2010 from 8.0% to 6.8.0% and then increased to plateau at 7.3% in 2012.   [1] Industry data in this analysis are compiled in JobsEQ and based on covered employment from the QCEW , published by the BLS . [2] See the BLS’s definition of establishments: https://www.bls.gov/cew/cewfaq.htm#Q22 . [3] NAICS stands for the North American Industrial Classification System. NAICS codes begin at the two-digit level (referred to as “sectors”) and subdivide into industries down to six digits (three-digit codes shown in the table). http://chmuraecon.com/blog/2018/march/23/workers-per-establishment-structural-shifts-in-manufacturing/ http://chmuraecon.com/blog/2018/march/23/workers-per-establishment-structural-shifts-in-manufacturing/ Fri, 23 March 2018 15:19:58 Greg Chmura http://chmuraecon.com/blog/2018/march/21/most-secure-jobs-that-need-little-training/ For jobs that typically don’t need a college degree, which are the most secure? Here we take a look at five popular ones (see chart below). Each of these are “secure” in the sense that they are jobs with very low unemployment rates. In addition, each of these jobs typically do not require a college award, plus they usually don’t require extensive training. [1] --> Highcharts.chart('container2', { chart: { type: 'bar' }, title: { text: 'Jobs Needing Little Training with Low Unemployment Rates' }, xAxis: { categories: [ 'Postal Service Mail Carriers', 'Secretaries and Administrative Assistants', 'Tellers', 'Teacher Assistants', 'Taxi Drivers and Chauffeurs', ] }, yAxis: { min: 0, max: 4, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { valueSuffix: '%' }, plotOptions: { series: { dataLabels:{ enabled:true, align: 'right', color: '#fff', formatter: function() { return this.y +'%'; }, x: -2, style: { textOutline: 0, fontSize: 14 }, } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: Bureau of Labor Statistics; unemployment rates for 2017.', position: { align: 'left', y: -2, // position of credits x: 45 }, style: { fontSize: '8pt' // you can style it! } }, series: [{ name: 'Unemployment Rate', showInLegend: false, color: 'rgba(128,151,188,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [0.9,2.5,2.6,3.6,3.9], }, ] });   There are over 330,000 [2] postal service mail carriers in the nation. Though overall employment in the Postal Service is projected to contract over the long-term, [3] mail carriers currently have a very low unemployment rate. When these jobs open, there also tends to be strong competition, with the number of applicants typically exceeding the number of open positions. [4] The secretaries and administrative assistants occupation group also has a low unemployment rate. There are several detailed occupations within this group, each with different requirements. Legal and medical secretaries typically require a moderate amount of on-the-job training. Executive secretaries and administrative assistants typically require prior work experience. The remainder of secretary and administrative assistant jobs—which is the majority—are positions which usually need short-term on-the-job training. There are roughly a half million tellers jobs in the nation. Another occupation with low unemployment, these positions are primarily at banks. The majority of tellers do not have a college degree and undergo about one month of on-the-job training; roughly 30% of these jobs are part-time. [5] For teacher assistant positions, at least some college background is a typical requirement. However, close to 60% of teacher assistants do not have a college degree and about a third have no college at all. [6] There are over 1.2 million of these jobs and, in addition to having a low unemployment rate, this occupation is expected to grow at a slightly faster-than-average pace over the next ten years. [7] Taxi drivers and chauffeurs is the last of our highlighted occupations. This job may be a surprising inclusion—Uber, after all, has been reported to have a high turnover rate. [8] Regardless, this occupation had a 3.9% unemployment rate in the nation in 2017, lower than the 4.4% overall unemployment rate   [1] Training here is defined per the Bureau of Labor Statistics education and training assignments where—for the jobs examined here—the “typical education needed for entry” is less than a college award and the “typical on-the-job training needed to attain competency in the occupation” is none or short-term on-the-job training. [2] All employment figures are per JobsEQ and represent the estimated average for 2017. [3] https://www.bls.gov/emp/ep_table_207.htm [4] https://www.bls.gov/ooh/office-and-administrative-support/postal-service-workers.htm [5] https://www.bls.gov/ooh/office-and-administrative-support/tellers.htm [6] https://www.bls.gov/emp/ep_table_111.htm [7] https://www.bls.gov/emp/ep_table_102.htm [8] https://www.cnbc.com/2017/04/20/only-4-percent-of-uber-drivers-remain-after-a-year-says-report.html http://chmuraecon.com/blog/2018/march/21/most-secure-jobs-that-need-little-training/ http://chmuraecon.com/blog/2018/march/21/most-secure-jobs-that-need-little-training/ Wed, 21 March 2018 15:52:44 Chris Chmura http://chmuraecon.com/blog/2018/march/12/economic-impact-understanding-the-size-of-the-gig-economy-is-challenging/ Performing gigs at different nightclubs and to various audiences is common for musicians. The proliferation of smartphones and online platforms have lowered the barrier for suppliers to provide on-demand services, making it easier for more individuals to complete some small jobs and participate in the new gig economy. It is also called the sharing economy because it has its origin as individuals giving or sharing goods and services. Uber, Airbnb and Task Rabbit are examples where peer-to-peer internet platforms connect passengers with drivers, travelers with places to stay, and homeowners with local handymen. Individuals enjoy the flexibility of choosing their hours or supplementing full-time employment with additional income. Users of the gig economy generally enjoy lower prices than the traditional alternatives. As a result, the gig economy is growing rapidly and is reported to threaten such well-established industries as taxi services or hotels. But understanding the size of the gig economy is more challenging. Neither the Census Bureau nor the Bureau of Labor Statistics publishes estimates of gig employment. In a recent report, the Virginia Employment Commission used nonemployer statistics from the Internal Revenue Service that track businesses with no employees as a proxy for the gig economy. Nonemployer growth in Virginia from 2010 through 2015 was faster than overall employment growth, and the gains were largest in such gig-inclined industries as ground transportation. The census also used the nonemployer statistics to measure passenger transportation establishments without employees in all states. California topped the list of states with 111,486 passenger transportation establishments in 2015, up 92 percent from 57,990 in 2014. New York was second with 95,201 establishments, but it grew only 16 percent over the prior year. Virginia ranked eighth with 20,931 passenger transportation establishments, up 46 percent from the prior year. While the data indicate the reach of ride sharing, it remains to be seen how the gig economy has penetrated other industries. Academic studies show that the gig economy is impacting traditional sectors. A 2017 study by Georgios Vervas, Davide Proserpio and John W. Byers in the Journal of Marketing Research found that hotel revenue from January 2003 through August 2014 was down 8 to 10 percent in Austin, Texas, where Airbnb supply was the highest. Lower-end hotels and those not catering to business travelers were most vulnerable. Jonathan Hall and Alan Krueger used data from Uber to study the characteristics of their drivers in a 2016 study released by the National Bureau of Economic Research. They found that the number of active Uber drivers about doubled every six months from the middle of 2012 to the end of 2015. That rate, they admitted, would undoubtedly slow down, or every American would be an Uber driver by 2020. Measuring the gig economy is important because without that information, the growth rate of employment in the nation is understated and productivity is overstated. In light of this need, the Bureau of Labor Statistics added four questions to its May 2017 Contingent Worker survey that measures employees who are hired on-demand by organizations. Once the results to these four questions about the gig economy are published, we’ll have a better sense about its size in the nation. http://chmuraecon.com/blog/2018/march/12/economic-impact-understanding-the-size-of-the-gig-economy-is-challenging/ http://chmuraecon.com/blog/2018/march/12/economic-impact-understanding-the-size-of-the-gig-economy-is-challenging/ Mon, 12 March 2018 10:09:03 James Stinchcomb http://chmuraecon.com/blog/2018/march/06/california-wildfires-and-day-to-day-job-impacts/ Last year, we posted a blog examining the effects of Hurricane Harvey on job postings and, by extension, employment. Below, we take the same approach to look at the impacts of a different natural disaster prevalent in late 2017 and early this year: the wildfires in California. Specifically, we are focusing on the Tubbs Fire , which affected parts of Napa, Sonoma, and Lake counties in Northern California during October 2017; and the Thomas Fire , which affected Ventura and Santa Barbara counties in Southern California in December 2017. While the financial cost of these wildfires will not reach the extreme of Hurricane Harvey, they have both had large impacts on their respective regions. The Tubbs Fire was the most destructive wildfire in California history, having destroyed over 5,600 structures, with cost estimates of $1 billion in lost property. Additionally, the wildfire burned over 36,000 acres and was associated with 22 deaths. The Thomas Fire was the largest wildfire in California history, having burned over 280,000 acres, and the 7 th most destructive in history having destroyed over 1,000 structures, along with being associated with one death. [1] The purpose of this article is to see what impact, if any, each wildfire had on job postings and, by extension, employment in their respective regions. Similar to the Hurricane Harvey analysis, we are using JobsEQ RTI job postings data to look at new job openings during the times before, during, and after the wildfires. For the Tubbs Fire we are looking at job postings in Napa, Sonoma, and Lake counties while for the Thomas Fire we focus on job postings in Ventura and Santa Barbara counties. To account for fluctuations in the data beyond the regional impacts of the wildfires (such as weekly volume patterns), the data below are seven-day moving averages [2] of new job postings. Furthermore, data are presented as job posting volume relative to the nation in order to account for seasonal patterns and other national fluctuations. --> Highcharts.chart('container', { chart: { type: 'area', }, title: { text: 'New Online Jobs Ads per 10,000 New Ads in the Nation' }, xAxis: { tickColor: '#000', //tickWidth: 0, //lineWidth: 0, categories: [ '9-1-2017','9-2-2017','9-3-2017','9-4-2017','9-5-2017','9-6-2017','9-7-2017','9-8-2017','9-9-2017','9-10-2017','9-11-2017','9-12-2017','9-13-2017','9-14-2017','9-15-2017','9-16-2017','9-17-2017','9-18-2017','9-19-2017','9-20-2017','9-21-2017','9-22-2017','9-23-2017','9-24-2017','9-25-2017','9-26-2017','9-27-2017','9-28-2017','9-29-2017','9-30-2017','10-1-2017','10-2-2017','10-3-2017','10-4-2017','10-5-2017','10-6-2017','10-7-2017','10-8-2017','10-9-2017','10-10-2017','10-11-2017','10-12-2017','10-13-2017','10-14-2017','10-15-2017','10-16-2017','10-17-2017','10-18-2017','10-19-2017','10-20-2017','10-21-2017','10-22-2017','10-23-2017','10-24-2017','10-25-2017','10-26-2017','10-27-2017','10-28-2017','10-29-2017','10-30-2017','10-31-2017','11-1-2017','11-2-2017','11-3-2017','11-4-2017','11-5-2017','11-6-2017','11-7-2017','11-8-2017','11-9-2017','11-10-2017','11-11-2017','11-12-2017','11-13-2017','11-14-2017','11-15-2017','11-16-2017','11-17-2017','11-18-2017','11-19-2017','11-20-2017','11-21-2017','11-22-2017','11-23-2017','11-24-2017','11-25-2017','11-26-2017','11-27-2017','11-28-2017','11-29-2017','11-30-2017','12-1-2017','12-2-2017','12-3-2017','12-4-2017','12-5-2017','12-6-2017','12-7-2017','12-8-2017','12-9-2017','12-10-2017','12-11-2017','12-12-2017','12-13-2017','12-14-2017','12-15-2017','12-16-2017','12-17-2017','12-18-2017','12-19-2017','12-20-2017','12-21-2017','12-22-2017','12-23-2017','12-24-2017','12-25-2017','12-26-2017','12-27-2017','12-28-2017','12-29-2017','12-30-2017','12-31-2017','1-1-2018','1-2-2018','1-3-2018','1-4-2018','1-5-2018','1-6-2018','1-7-2018','1-8-2018','1-9-2018','1-10-2018','1-11-2018','1-12-2018','1-13-2018','1-14-2018','1-15-2018','1-16-2018','1-17-2018','1-18-2018','1-19-2018','1-20-2018','1-21-2018','1-22-2018','1-23-2018','1-24-2018','1-25-2018','1-26-2018','1-27-2018','1-28-2018','1-29-2018','1-30-2018','1-31-2018','2-1-2018','2-2-2018','2-3-2018','2-4-2018','2-5-2018','2-6-2018','2-7-2018','2-8-2018','2-9-2018', ], tickInterval: 12, plotBands: [{ color: "#f9f6c1", from: 37, to: 44, label: { text: 'Tubbs Fire', align: 'center', //x: +10 } },{ color: "#f9f6c1", from: 95, to: 113, label: { text: 'Thomas Fire', align: 'center', //x: +10 } }] }, yAxis: { min: 10, max: 45, title: true/*{ text: 'Forecast Average Annual Growth Percent', align: 'middle' },*/, labels: { /*formatter: function() { return this.value+"%"; }*/ enabled: true }, tickInterval: 5,gridLineWidth: 0.3, }, tooltip: { formatter: function () { var s = ' ' + this.x + ' '; $.each(this.points, function(i, point) { s += ' ' + point.series.name + ' ' + ': ' + ' ' + point.y.toFixed(2) + ' ' + ' jobs per 10,000 USA jobs'; }); return s; }, shared: true, crosshairs: true, }, plotOptions: { /*bar: { dataLabels: { enabled: false }, pointWidth: 20, },*/ series: { fillOpacity: 0.4 }, }, legend: { show: false }, credits: { //enabled: false text: 'Note: This is the average of the seven-day period encompassing three days prior and three days following. Source: Chmura'+"'"+'s JobsEQ RTI', position: { align: 'left', y: -3, // position of credits x: 4 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Thomas Fire Region (Ventura and Santa Barbara counties)', showInLegend: true, color: '#fc9d59', data: 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marker: { enabled: false } }, ] }); This first chart shows the number of new jobs posted in each region relative to 10,000 new jobs in the nation, with the period of each wildfire shaded in yellow. The first thing you can see in this chart is that while the Tubbs wildfire does appear to correspond with a significant decline in job postings in its affected region, the Thomas wildfire seems to have had very little impact on job postings in its region. Indeed, in the 30 days prior to the Tubbs Fire there were an average of 20.37 new job postings per 10,000 nationwide, and that average was quite stable. During the period of the wildfire, job postings dropped to a low of 12.47 per 10,000 on 10/11 – a decline of 39% from the pre-fire average. Similar to what we saw with Hurricane Harvey in Houston, post-disaster job postings volume increased to levels above the pre-disaster average and actually stayed above average for 28 consecutive days, from October 22 to November 18. (Online job ads in Houston, for comparison, remained above the pre-hurricane average for 12 days during that region’s recovery). One thing to note here, however, is that some of this increase may not be due entirely to the fire recovery. The timing for the fire and recovery also fell in-line with the timing when some employers are ramping up hiring to prepare for the holiday season; and given that wine-making and wineries are such a large industry for the region, the Tubbs Fire region may be much more sensitive to holiday season hiring fluctuations. This may also account for the relatively large dip seen in the final days of 2017 in the chart above as well. Moving on to the region affected by the Thomas Fire, we see that there doesn’t appear to have been any significant effect on job postings during the fire. [4] Before the fire in the month of November, the Thomas Fire region had a daily average of 33.88 new job postings per 10,000 in the nation. During the period of the fire, the average number of daily new postings was 34.69 per 10,000, a slight increase, but not a statistically significant change. Similarly, there are slight changes in the period following the fire— an increase after the fire reached 85% containment, followed by a small decline—however, these changes are not statistically significant. To better see the difference in the effect on job postings in the times during and immediately following the fires, below is a chart of per 10,000 U.S. postings starting seven days prior to the fire and through fourteen days after the fire, for each region. --> Highcharts.chart('containerfire1', { chart: { type: 'area', }, title: { text: 'New Online Jobs Ads per 10,000 New Ads in the Nation' }, xAxis: { tickColor: '#000', //tickWidth: 0, //lineWidth: 0, categories: [ '10-1-2017','10-2-2017','10-3-2017','10-4-2017','10-5-2017','10-6-2017','10-7-2017','10-8-2017','10-9-2017','10-10-2017','10-11-2017','10-12-2017','10-13-2017','10-14-2017','10-15-2017','10-16-2017','10-17-2017','10-18-2017','10-19-2017','10-20-2017','10-21-2017','10-22-2017','10-23-2017','10-24-2017','10-25-2017','10-26-2017','10-27-2017','10-28-2017','10-29-2017' ], tickInterval: 5, plotBands: [{ color: "#f9f6c1", from: 7, to: 14, label: { text: 'Tubbs Fire', align: 'center', //x: +10 } }] }, yAxis: { min: 10, max: 24, title: true/*{ text: 'Forecast Average Annual Growth Percent', align: 'middle' },*/, labels: { /*formatter: function() { return this.value+"%"; }*/ enabled: true }, tickInterval: 5,gridLineWidth: 0.3, }, tooltip: { formatter: function () { var s = ' ' + this.x + ' '; $.each(this.points, function(i, point) { s += ' ' + point.series.name + ' ' + ': ' + ' ' + point.y.toFixed(2) + ' ' + ' jobs per 10,000 USA jobs'; }); return s; }, shared: true, crosshairs: true, }, plotOptions: { /*bar: { dataLabels: { enabled: false }, pointWidth: 20, },*/ series: { fillOpacity: 0.4 }, }, legend: { show: false }, credits: { //enabled: false text: 'Note: This is the average of the seven-day period encompassing three days prior and three days following. Source: Chmura'+"'"+'s JobsEQ RTI', position: { align: 'left', y: -3, // position of credits x: 4 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Napa, Sonoma, and Lake counties', showInLegend: true, color: '#7790b8', data: [18.6228781039435,19.8907693345021,19.7443528845962,19.7757719861184,19.7260534425528,20.3626979566738,18.2840538453087,16.8832752523474,14.1914524325471,13.0338099108952,12.4703527697446,12.7636121022249,12.9929923041507,14.2563070835193,15.1911150903896,15.9180131410389,16.900016687335,17.8791234844966,18.3991587086898,19.2118329470084,19.5831200931207,20.6496369781796,21.0153968175857,21.5966582321508,21.4993172248799,20.7526804346261,22.6389682655715,22.7759061123246,22.3903315667667], marker: { enabled: false } } ] }); --> Highcharts.chart('containerfire2', { chart: { type: 'area', }, title: { text: 'New Online Jobs Ads per 10,000 New Ads in the Nation' }, xAxis: { tickColor: '#000', //tickWidth: 0, //lineWidth: 0, categories: [ '11-27-2017','11-28-2017','11-29-2017','11-30-2017','12-1-2017','12-2-2017','12-3-2017','12-4-2017','12-5-2017','12-6-2017','12-7-2017','12-8-2017','12-9-2017','12-10-2017','12-11-2017','12-12-2017','12-13-2017','12-14-2017','12-15-2017','12-16-2017','12-17-2017','12-18-2017','12-19-2017','12-20-2017','12-21-2017','12-22-2017','12-23-2017','12-24-2017','12-25-2017','12-26-2017','12-27-2017','12-28-2017','12-29-2017','12-30-2017','12-31-2017','1-1-2018','1-2-2018','1-3-2018','1-4-2018','1-5-2018','1-6-2018' ], tickInterval: 5, plotBands: [{ color: "#f9f6c1", from: 8, to: 26, label: { text: 'Thomas Fire', align: 'center', //x: +10 } }] }, yAxis: { min: 26, max: 40, title: true/*{ text: 'Forecast Average Annual Growth Percent', align: 'middle' },*/, labels: { /*formatter: function() { return this.value+"%"; }*/ enabled: true }, tickInterval: 5,gridLineWidth: 0.3, }, tooltip: { formatter: function () { var s = ' ' + this.x + ' '; $.each(this.points, function(i, point) { s += ' ' + point.series.name + ' ' + ': ' + ' ' + point.y.toFixed(2) + ' ' + ' jobs per 10,000 USA jobs'; }); return s; }, shared: true, crosshairs: true, }, plotOptions: { /*bar: { dataLabels: { enabled: false }, pointWidth: 20, },*/ series: { fillOpacity: 0.4 }, }, legend: { show: false }, credits: { //enabled: false text: 'Note: This is the average of the seven-day period encompassing three days prior and three days following. Source: Chmura'+"'"+'s JobsEQ RTI', position: { align: 'left', y: -3, // position of credits x: 4 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Ventura and Santa Barbara counties', showInLegend: true, color: '#fc9d59', data: [32.0735735448317,33.761180289494,33.5337796099493,33.2285697443169,32.4244159536724,32.3871879201258,32.4244588240626,34.44717373294,33.569907983262,34.1115215427661,34.8820443428977,34.2671077609185,34.9989644142036,34.5516633746609,33.7918361384273,34.0123783411098,34.0646395505846,33.1880588463978,34.4028545839671,33.7888760488368,34.2434892313237,34.7517835375837,36.1618294123179,36.4222219766002,36.1890772849677,36.1180234624955,35.7415224653326,34.9959358604892,38.3691393097313,36.4223963803829,37.5937450135631,37.1442308598795,36.481617602757,35.6750225860173,35.8406267213415,32.8274493161696,31.6847188838906,32.0346337064592,32.7870274396194,32.5167776449335,32.1020257205519], marker: { enabled: false } } ] }); These charts show the stark difference in the impact on job postings. The next logical question is: why do we see such differences when both fires had devastating impacts on their regions? The answer appears to lie in where exactly these fires hit within the regions and the industries that make up the local economies of these regions. Tubbs Fire Region (Napa, Sonoma, and Lake counties) NAICS Industry Employment LQ                 312130                 Wineries 18,235 132.18                 611110                 Elementary and Secondary Schools 15,369 0.91                 722511                 Full-Service Restaurants 14,697 1.29                 624120                 Services for the Elderly and Persons with Disabilities 11,157 2.96                 622110                 General Medical and Surgical Hospitals 9,280 0.75                 445110                 Supermarkets and Other Grocery (except Convenience) Stores 8,735 1.62                 721110                 Hotels (except Casino Hotels) and Motels 8,142 2.37                 722513                 Limited-Service Restaurants 7,108 0.77                 111332                 Grape Vineyards 4,960 77.06                 561320                 Temporary Help Services 4,527 0.74 Notes: Four Quarters Ending with 2017q4, LQ is a measure of relative industry size within a region. See: https://jobseq.eqsuite.com/help/3.2/Content/Miscellaneous Pages/Location_Quotient.htm , Source: JobsEQ Thomas Fire Region (Ventura and Santa Barbara counties) NAICS Industry Employment LQ                 611110                 Elementary and Secondary Schools 29,341 1.01                 722511                 Full-Service Restaurants 21,553 1.10                 722513                 Limited-Service Restaurants 19,234 1.22                 622110                 General Medical and Surgical Hospitals 14,574 0.68                 111333                 Strawberry Farming 13,944 122.81                 445110                 Supermarkets and Other Grocery (except Convenience) Stores 11,474 1.24                 115115                 Farm Labor Contractors and Crew Leaders 10,913 16.38                 624120                 Services for the Elderly and Persons with Disabilities 10,440 1.61                 611310                 Colleges, Universities, and Professional Schools 9,693 0.89                 621111                 Offices of Physicians (except Mental Health Specialists) 9,506 1.00 Notes: Four Quarters Ending with 2017q4, LQ is a measure of relative industry size within a region. See: https://jobseq.eqsuite.com/help/3.2/Content/Miscellaneous Pages/Location_Quotient.htm , Source: JobsEQ The regions impacted by the Tubbs Fire—Napa, Sonoma, and Lake counties—are part of an area known as “wine country.” Indeed, wineries and grape vineyards are two of the largest industries in the region, with the wineries industry (NAICS [5] 312130) having the highest employment of any six-digit NAICS industry in the region. These wineries and vineyards that the region is so heavily dependent on were heavily impacted by the Tubbs Fire, with many of them greatly damaged or destroyed. [6] The damage to these industries not only affected them, but every other industry in the region that is dependent on them; and that is consistent with what we see with the fall in job postings during the fire. In contrast, while the Thomas Fire was absolutely devastating for its region, much of the damage appears to have occurred in residential areas [7] with relatively less employment. Additionally, employment in the region is more diverse with less dependence on any single industry, so the damage that did occur to businesses didn’t have the same impact on the overall economy. Seeing the vastly different reactions in job postings volume to these similar disasters, what take away can we gain from this analysis? Obviously, every event and every region is different; and the impacts an event has on a particular region will vary. When it comes to unplanned events, such as a natural disaster, it can be especially hard to predict the economic and employment impacts and it is usually not until well after the event that the full impact is known. Using JobsEQ RTI job postings data, one can gain some insight in real-time, even as the event is occurring, on what kind of impact the event may have on a local economy by seeing the immediate impact on job postings volume.   [1] http://www.fire.ca.gov/communications/downloads/fact_sheets/Top20_Destruction.pdf [2] This is the average of the seven-day period encompassing three days prior and three days following. [3] Officially the Tubbs wildfire began on October 8 and lasted until October 31, when it reached 100% containment. For our analysis, however, when referring to the period of the Tubbs Fire, we use a time period of October 8 to October 16, when the fire reached 75% containment, the majority of the damage had already been done, and recovery had begun for some areas. [4] The Thomas Fire began on December 5 and lasted until January 12, when it reached 100% containment. For our analysis, the period of the Thomas Fire is from December 5 to December 24, when the fire reached 86% containment. [5] North American Industrial Classification System . [6] https://www.mercurynews.com/2017/10/16/a-closer-look-at-the-22-wineries-damaged-by-wine-country-fires/ [7] http://ktla.com/2017/12/17/the-massive-numbers-of-the-thomas-fire-116-million-in-costs-18000-structures-threatened-and-hundreds-of-homes-destroyed/ http://chmuraecon.com/blog/2018/march/06/california-wildfires-and-day-to-day-job-impacts/ http://chmuraecon.com/blog/2018/march/06/california-wildfires-and-day-to-day-job-impacts/ Tue, 06 March 2018 09:12:14 Greg Chmura http://chmuraecon.com/blog/2018/february/28/occupational-separations-components-and-applications/ For many years, the Bureau of Labor Statistics (BLS) used replacement rates to measure occupational demand due to individuals leaving their occupation. When the BLS switched from replacement rates to separation rates , it was quite a shock for many of us. On average, these rates measuring this type of labor demand more than quadrupled! For workforce planning professionals who have used replacement rates for years and who have developed strategic plans based upon these data, making the transition is not at all trivial. What do these separation rates mean? How can we use them? Let’s take a look. Components Occupational separations are a count of the number of workers who are projected to leave an occupation. Separations are not meant to capture all movement in and out of occupations, but rather provide an estimate of workers who permanently leave an occupation. [1] There are two components to separations: occupational transfers and labor force exits. Occupational transfers measure the number of workers expected to leave an occupation and move into a different one. [2] Labor force exits are the projected number of workers leaving the labor force; exits include workers who retire as well as those who leave the labor force for some other reason, such as pursuing an education. [3] It is important to note that occupational separations are not the same thing as all job separations, such as those measured by JOLTS (the Job Openings and Labor Turnover Survey ). For example, in 2016 there were 60.4 million separations per JOLTS  which compares to an estimated 17.6 million occupational separations for the same year. The JOLTS job separations include workers leaving one job for another with a different employer, regardless if it represents a different occupation. Similar differences apply to measurements of openings. In 2016, there were 62.7 million hires per JOLTS, comparable to an estimated 18.7 million occupational openings (“occupational openings” equals occupational separations plus occupational growth demand). [4] These national numbers suggest a good rule of thumb to keep in mind: overall job openings are roughly three times larger than occupational openings. (A corollary of this, however, is that job openings overstate occupational demand. For example, at one point in time, three firms in a region may be advertising to replace a welder. It could well be that one of those ads is to replace a worker who retired, but the other two ads can be simply due to churn—openings created as welders move from one company to another. The true labor pool of welders in a region is not just the employed welders, but includes the unemployed as well.) Applications In JobsEQ , we show both the occupational transfers as well as the labor force exits. This is done so our clients have flexibility in applying these data. “Occupational demand” can be examined in total as well as broken into its three components: labor force exits, occupational transfers, and growth. For an example, let’s take a look at two occupations of similar size in one community, Olmsted County, Minnesota. [5] Projected Annual Occupational Demand; Olmsted County, MN Separations Growth SOC Title 2017 Empl Total New Demand Exits Transfers Empl Avg Ann Rate 29-1171 Nurse Practitioners 854 74 18 28 28 3.3% 39-9011 Childcare Workers 846 125 70 53 2 0.3% Source: JobsEQ   The training requirements for these two occupations are very different. Childcare workers typically do not need education beyond the high-school level. Nurse practitioner jobs, on the other hand, typically require a master’s degree. [6] Having the demand numbers for these occupations broken into their details allow us to have a better picture of what is happening and how we—for example, as workforce planners—may need to react. Of these two occupations, the highest overall occupational demand is for childcare workers: 125 in projected new occupation demand per year. Using our rule of thumb, we can estimate that overall job openings for this occupation could be on the order of 375 per year. Roughly 250 of those openings, however, are likely to be filled by childcare workers moving from one employer to another. Of the 125 occupational openings expected for childcare workers in Olmsted County, about 70 will be due to labor force exits. It is unlikely that all these 70 will be due to retirements. The average age of childcare workers in the nation is 36.5 years. [7] Given the age mix in this occupation, we can expect about 13 retirements per year in Olmsted County. [8] This means the other 57 labor force exits are people who may be going back to school or perhaps dropping out of the labor force for some reason such as raising a family. Since our other occupation, nurse practitioners, typically requires longer, formalized training, we may ask ourselves how many newly trained workers for this occupation are needed per year in Olmsted County. The nurse practitioner occupation has a quick expected growth rate putting its growth demand at about 28 jobs per year. At minimum, we’ll need at least 28 new labor force entrants to support this projected growth. Given the typical age mix of the nurse practitioner occupation, nearly all the 18 labor force exits may be due to retirements. [9] That brings our training demand to at least 46 annually. There are projected to be another 28 annual separations among nurse practitioners due to occupational transfers. Will all these openings need to be filled with newly trained workers as well? The answer depends on the particular occupation under consideration. First, note that these 28 occupational transfers describe workers moving out of the nurse practitioner occupation, not into it. These workers who transfer out are likely primarily moving up career pathways, such as into management (e.g. SOC 11-9111, medical and health services managers). If we ask ourselves, “how likely is it that a worker who has long had the proper credentials will make an occupational transfer into the nurse practitioner occupation?” The answer is: “not likely.” If someone has nurse-practitioner credentials, they are probably: (1) already in the occupation, (2) have moved up a career ladder after having been in that position, or (3) if they are moving from another occupation into a nurse practitioner job, it is probably because they have recently attained the needed credentials. Thus, in this case, it is reasonable to add all the 28 transfers to the total training need in Olmsted County, bringing it to 74 per year. This means we should expect to need about 74 new entrants to our workforce with the training credentials to be nurse practitioners. It is likely all will either be newly trained or need to be recruited from outside the region. (Measuring this supply is of course the next step, but how we can do that is beyond the scope of this article.) Conclusion Occupational demand data help us break down the supply of new workers needed to support job growth in our regions. Especially, these data help us identify the demand for trained, skilled workers. This puts us into the position of being able to take the next step of comparing this demand to the training output and capacity that supports our regional economies. Knowing the components of occupational demand allows us to further refine our view of this demand and develop appropriate strategic plans in reaction.   [1] See: Employment Projections, Occupational Separations Methodology FAQs . [2] In their methodology , the BLS actually measures when a worker moves from one occupation to an occupation in a different major occupational group (that is, a different 2-digit SOC group). [3] In their methodology , the BLS captures when a worker leaves the labor force for at least four months. [4] See: Employment Projections, Occupational Separations Methodology FAQs . Growth demand is the positive or negative change in the overall employment level of an occupation. For example, if welder employment in a certain city is 120 in 2018 and forecast to be 140 in 2028, growth demand from 2018-28 would be 20. [5] Olmsted County is the location of the city of Rochester , home to the Mayo Clinic. [6] Typical entry-level education is defined here: https://www.bls.gov/emp/ep_table_112.htm . Note that for childcare workers this is the “typical education needed for entry”—some positions may certainly require higher-levels of education. [7] See https://www.bls.gov/cps/cpsaat11b.htm . [8] Using national age mix data , about 182 thousand childcare workers are in the age 55-64 cohort. As a proxy for retirement, we can use the estimate of how many people exit that age cohort per year. Roughly, that would be about one-tenth of that group: 18.2 thousand workers which is 1.49% of all childcare workers. Applying that percentage to the current employment in Olmsted County (846) yields an estimate of 13 retirements per year. [9] Per the same calculation method used above for childcare workers, we estimate about 18 retirements per year in Olmsted County for nurse practitioners, which accounts for all of the estimated labor force exits. http://chmuraecon.com/blog/2018/february/28/occupational-separations-components-and-applications/ http://chmuraecon.com/blog/2018/february/28/occupational-separations-components-and-applications/ Wed, 28 February 2018 16:00:58 Patrick Clapp http://chmuraecon.com/blog/2018/february/21/hurricane-recovery-assistance-in-hendry-county-florida/ At first glance, Hendry County’s statistics show an area with significant challenges. Every mention of the County in the news is immediately followed with the unemployment rate – 6.5% in December 2017, the highest in the state of Florida by a full percentage point. The population is just above 38,000, with 26.3% below the poverty level and 37.5% without a high school diploma. [1] Although the local economy is dependent on crop and animal production, especially citrus and sugar cane, the County has no fresh food markets . To make matters worse, Hurricane Irma last year hit the County hard . As part of its disaster recovery program, IEDC deploys economic development experts to support local economic recovery needs. Over the week of February 12-16, I represented Chmura Economics & Analytics in Hendry County to offer assistance with identified needs for data to guide workforce development efforts through an economic development lens. On the ground, Hendry County has a feeling of being poised for growth . In my discussions with residents I was shown some of the strengths and possibilities within the County, as well as the hard work being done to position Hendry County to best capitalize on them. FEMA’s Community Planning and Capacity Building team has spent months building connections between key local, state, and federal partners to provide a structure for locally driven, coordinated hurricane recovery that should build a strong foundation for future collaboration and growth. New possibilities such as perishable cargo delivery at the Airglades Airport , growing local companies, a wealth of available land, strong support from key employers, and funding sources available from hurricane relief and the County’s Promise Zone designation position Hendry County strongly for future economic and workforce development. Data provided to the County from JobsEQ ® can help tell this story. Since lack of available and affordable housing in the County means many of its workers commute in, examining the labor shed by drive-time reveals a significantly larger population and more realistic picture of the County’s available workforce. With a larger labor pool to analyze, I was able to get past the typical refrain of simply “not enough workers” to review potential firm expansions within target industries, identify occupations where the County has ample supply, and show where training might be needed. Chmura’s real-time intelligence from online job postings shows potential short-term supply gaps and sparked rich discussion at the high school’s newly constructed building for the expanding CTE programs. These data formed part of a brief report delivered to the County, along with findings from interviews with local employers and identification of potential grant opportunities. This was a rewarding experience for me, especially in learning about the many different aspects of disaster recovery; ultimately my hope is that the analysis I provided will assist the County in moving from “poised for growth” to growing strong.   [1] Source: JobsEQ®, ACS 2012-2016 http://chmuraecon.com/blog/2018/february/21/hurricane-recovery-assistance-in-hendry-county-florida/ http://chmuraecon.com/blog/2018/february/21/hurricane-recovery-assistance-in-hendry-county-florida/ Wed, 21 February 2018 13:04:34 Doug Rice http://chmuraecon.com/blog/2018/february/21/occupational-olympics-careers-in-physical-therapy-are-growing-faster-higher-and-stronger/ Caralyn Baxter [1] grew up filled with dreams of someday competing in the Winter Olympics. As a competitive figure skater, she was heartbroken when her teenage years brought about an unexpected series of injuries—but she didn’t let that deter her… This week, Caralyn is finally realizing her dream as a participant in the 2018 Pyeongchang Winter Olympic Games. But she won’t be participating as she had originally planned. Instead of figure skating, she will be a participant in freestyle skiing. And she actually won’t be the one skiing. Instead, she is one of the people who have played a critical role in getting US freestyle skier David Wise prepared for the games. No, Caralyn Baxter isn’t a professional athlete; she’s a physical therapist. Physical therapists “assess, plan, organize, and participate in rehabilitative programs that improve mobility, relieve pain, increase strength, and improve or correct disabling conditions resulting from disease or injury.” [2] Becoming a physical therapist typically requires attaining a doctorate or professional degree, [3] but there are also careers available as physical therapist assistants and physical therapist aides, which typically only require an associate’s degree and high school diploma, respectively. While few physical therapists get the opportunity to work with Olympians, the general field of physical therapy is on the rise. In all three occupations centered on physical therapy, employment [4] is expected to increase significantly relative to other occupations requiring the same level of education. Physical Therapists Employment for physical therapists is forecast to grow at an average annualized pace of 2.5% compared to 1.3% for all occupations requiring a doctorate or professional degree. It is projected to be the second-fastest growing career of all occupations requiring a doctorate or professional degree, a bit slower than postsecondary health teachers but quicker than postsecondary nursing instructors, postsecondary business teachers, veterinarians, and dentists. --> Highcharts.chart('container', { chart: { type: 'bar', width: 900 }, title: { text: 'Top 10 Occupations Requiring a Doctorate or Professional Degree According to Forecast Growth Rate, 2016-2026' }, xAxis: { categories: [ 'All Occupations Requiring a Doctorate or Professional Degree', 'Health specialties teachers, postsecondary', 'Physical therapists', 'Nursing instructors and teachers, postsecondary', 'Business teachers, postsecondary', 'Veterinarians', 'Dentists, general', 'Family and general practitioners', 'Biological science teachers, postsecondary', 'Clinical, counseling, and school psychologists', 'Physicians and surgeons, all other' ], labels: { align: 'right', reserveSpace: true } }, yAxis: { min: 0, max: 3, title: { text: 'Forecast Average Annual Growth Percent', align: 'high' }, labels: { formatter: function() { return this.value+"%"; } }, tickInterval: .5 }, tooltip: { valueSuffix: '%' }, plotOptions: { bar: { dataLabels: { enabled: false } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: BLS Employment Projections. Occupations with fewer than 50,000 employees are omitted.', position: { align: 'left', y: -2, // position of credits x: 45 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Forecast Average Annual Growth Percent', showInLegend: false, color: 'rgba(128,151,188,.9)', data: [1.3, 2.6, { y: 2.5, color: '#fa8a40' }, 2.4, 1.8, 1.8, 1.7, 1.6, 1.5, 1.4, 1.3], }, ] }); Physical Therapist Assistants Employment for physical therapist assistants is expected to grow at an average annualized pace of 3.1% compared to 1.1% for all occupations requiring an associate’s degree. Physical therapist assistants are projected to be the fastest growing career of all occupations requiring an associate’s degree, with the next closest being respiratory therapists, diagnostic medical sonographers, dental hygienists, and veterinary technologists/technicians. --> Highcharts.chart('container2', { chart: { type: 'bar', width: 900 }, title: { text: 'Top 10 Occupations Requiring an Associate' +"'" +'s Degree According to Forecast Growth Rate, 2016-2026' }, xAxis: { categories: [ 'All Occupations Requiring an Associate' + "'" +'s Degree', 'Physical therapist assistants', 'Respiratory therapists', 'Diagnostic medical sonographers', 'Veterinary technologists and technicians', 'Dental hygienists', 'Paralegals and legal assistants', 'Medical and clinical laboratory technicians', 'Web developers', 'Radiologic technologists', 'Preschool teachers, except special education' ], labels: { align: 'right', reserveSpace: true } }, yAxis: { min: 0, max: 3.5, title: { text: 'Forecast Average Annual Growth Percent', align: 'high' }, labels: { formatter: function() { return this.value+"%"; } }, tickInterval: .5 }, tooltip: { valueSuffix: '%' }, plotOptions: { bar: { dataLabels: { enabled: false } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: BLS Employment Projections. Occupations with fewer than 50,000 employees are omitted.', position: { align: 'left', y: -2, // position of credits x: 45 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Forecast Average Annual Growth Percent', showInLegend: false, color: 'rgba(128,151,188,.9)', data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0], }, ] }); Physical Therapist Aides Employment for physical therapist aides is projected to grow at an average annualized pace of 2.9% compared to 0.5% for all occupations requiring a high school diploma (or equivalent). Physical therapist aides are projected to be the third-fastest growing job of all occupations requiring an associate’s degree—not as fast as home health aides and personal care aides, but quicker than nonfarm animal caretakers and medical secretaries. --> Highcharts.chart('container3', { chart: { type: 'bar', width: 900 }, title: { text: 'Top 10 Occupations Requiring a High School Diploma According to Forecast Growth Rate, 2016-2026' }, xAxis: { categories: [ 'All Occupations Requring a High School Diploma', 'Home health aides', 'Personal care aides', 'Physical therapist aides', 'Medical secretaries', 'Nonfarm animal caretakers', 'Veterinary assistants and laboratory animal caretakers', 'Helpers--pipelayers, plumbers, pipefitters, and steamfitters', 'Community health workers', 'Social and human service assistants', 'Plumbers, pipefitters, and steamfitters' ], labels: { reserveSpace: true } }, yAxis: { min: 0, max: 5, title: { text: 'Forecast Average Annual Growth Percent', align: 'high' }, labels: { formatter: function() { return this.value+"%"; } }, tickInterval: .5 }, tooltip: { valueSuffix: '%' }, plotOptions: { bar: { dataLabels: { enabled: false } } }, legend: { show: false }, credits: { //enabled: false text: 'Source: BLS Employment Projections. Occupations with fewer than 50,000 employees are omitted.', position: { align: 'left', y: -2, // position of credits x: 45 }, style: { fontSize: '8pt' // you can style it! }, href: '' }, series: [{ name: 'Forecast Average Annual Growth Percent', showInLegend: false, color: 'rgba(128,151,188,.9)', data: [0.5,4.7,3.7, { y: 2.9, color: '#fa8a40' }, 2.2,2.2,1.9,1.9,1.8,1.6,1.6], }, ] }); Not every athlete opts to hang up the skates due to injury. For most competitors, it’s a matter of practicality. There simply aren’t enough opportunities to compete professionally. According to the NCAA, [5] there are a total of 492 thousand student-athletes competing in NCAA institutions and 7.3 million in high school sports programs. This compares to roughly 12,600 jobs as athletes and sports competitors in the nation according to the most recent data from JobsEQ . [6] There are many reasons people go into physical therapy. The field is certainly not exclusive to athletes. Nevertheless, jobs in physical therapy may provide rewarding careers specifically for workers who show an affinity for competitive sports.       [1] Bowers, Rachel. (2018, February 3). “ There’s more than one way to make an Olympic dream come true. ” The Boston Globe . [2] https://www.onetonline.org/link/summary/29-1123.00 [3] Education levels determined by the BLS classification of “typical education needed for entry.” https://www.bls.gov/emp/ep_table_112.htm [4] Employment Projections provided by the BLS: https://www.bls.gov/emp/ep_table_102.htm [5] NCAA Recruiting Facts. https://www.ncaa.org/sites/default/files/Recruiting%20Fact%20Sheet%20WEB.pdf [6] Figure represents employment as of 2017Q3 for SOC 27-2021, “athletes and sports competitors.” http://chmuraecon.com/blog/2018/february/21/occupational-olympics-careers-in-physical-therapy-are-growing-faster-higher-and-stronger/ http://chmuraecon.com/blog/2018/february/21/occupational-olympics-careers-in-physical-therapy-are-growing-faster-higher-and-stronger/ Wed, 21 February 2018 09:09:33 Chris Chmura http://chmuraecon.com/blog/2018/february/07/economic-impact-certain-occupations-dependent-on-rd-show-an-impressive-relationship-between-innovation-and-growth/ Businesses and nations that spend more on research and development grow at a faster pace, according to many studies. More than half of all R&D expenses on average are attributable to labor, according to an analysis of countries that belong to the Organization for Economic Co-operation and Development. Do occupations and industries performing R&D grow faster and continue growing faster than non-research-intensive occupations and industries? This certainly would be good information for policymakers as they encourage economic growth. This is not an easy question to answer because R&D is performed by many different occupations, and the Bureau of Labor Statistics does not have one code that classifies R&D as an occupation. But using job postings data to identify the occupations that most-often referenced R&D in titles serves as a proxy. Computer and information research scientists and mechanical engineers were among the most-often identified R&D occupations. Over the past five years, R&D-related occupations grew at a faster annual average pace — 2.2 percent — than all occupations (at 1.7 percent) in the nation, but rates of growth in engineering and chemical-related occupations were much slower than average. As a group, employment of R&D occupations (average 2 percent per year) are forecast by the BLS to outpace all occupations (average 0.7 percent per year) over the next decade. To consider the impact of R&D on industries, we calculated the number and percentage of R&D occupations in various industries. The five industries that have the highest percentage of their workforce with R&D skills employ 46 percent of all R&D-related occupations in the nation. They are: software publishers (27 percent R&D occupations); management, scientific and technical consulting services (20 percent R&D occupations); computer systems design and related services (18 percent R&D occupations); scientific research and development services (12 percent R&D occupations); and architectural, engineering and related services (8 percent R&D occupations). By comparison, only 1.5 percent of the workforce in the average industry in the nation are R&D occupations. Not surprisingly, employment in R&D-intensive industries grew at a faster pace (3.1 percent) than all industries (1.7 percent) over the past year and are forecast to continue growing at a faster pace (1.7 percent) than all industries (0.7 percent). The two industries employing the smallest percentage of R&D workers (scientific research and development and architectural, engineering and related services) experienced slower employment growth than the national average in the past five years but are forecast to overtake that average over the next 10 years. These industries and occupations dependent on R&D show an impressive relationship between innovation and growth. The industries seeking to hire a relatively high concentration of R&D workers are generally industries that have grown faster than average and are expected to continue expanding more quickly. Likewise, the occupations most associated with research and development show the same pattern of fast job growth. Given this association, fostering a climate conducive to innovation is a strategy worth considering by economic developers and policymakers to promote a growing, healthy economy. Christine Chmura is CEO and chief economist at Chmura Economics & Analytics. She can be reached at (804) 649-3640 or   chris.chmura@chmuraecon.com . http://chmuraecon.com/blog/2018/february/07/economic-impact-certain-occupations-dependent-on-rd-show-an-impressive-relationship-between-innovation-and-growth/ http://chmuraecon.com/blog/2018/february/07/economic-impact-certain-occupations-dependent-on-rd-show-an-impressive-relationship-between-innovation-and-growth/ Wed, 07 February 2018 08:51:04 Doug Rice http://chmuraecon.com/blog/2018/january/22/rd-employment-innovation-and-growth/ Studies show a positive relationship between innovation and growth. Companies with a  higher degree of R&D orientation [1] grow faster than other firms. Further,  nations with greater expenditures on R&D as a percentage of GDP [2] also experience faster economic growth. On average, half of all R&D expenditures [3] are attributed to labor costs, underscoring the key role of a skilled workforce in driving innovation. Indeed, examining the employment in occupations and industries most heavily associated with research and development reveals a strong relationship with faster employment growth. Since no single SOC code [4] encompasses all positions related to research and development, we begin by identifying the occupations that most-often reference R&D in job posting titles. This serves as our proxy for “R&D-related occupations”--it represents job ads containing either “Research and Development” or “R&D” in the job titles. Using JobsEQ ’s [5] Real-Time Intelligence (RTI) online job postings, the table below shows the most common occupations associated with R&D in their titles. Job Postings: Top Ten Occupations with “Research and Development” or “R&D” in the Job Titles, 180 Days Ending 01/11/2018 SOC Code Occupation Title Total Ads 15-1111.00 Computer and Information Research Scientists 1,017 17-2141.00 Mechanical Engineers 692 19-2031.00 Chemists 625 13-1111.00 Management Analysts 529 19-4031.00 Chemical Technicians 503 15-1122.00 Information Security Analysts 464 19-1012.00 Food Scientists and Technologists 406 15-1132.00 Software Developers, Applications 402 17-2161.00 Nuclear Engineers 397 19-4011.02 Food Science Technicians 383 Source: JobsEQ. Data reflect online job postings active during the 180-day period ending 01/11/2018. There is quite a bit of diversity among these R&D-related occupations. It includes computer occupations such as information research scientists and security analysts, engineering occupations such as mechanical and nuclear engineers, science occupations such as chemists and food scientists, and management analysts from the business occupations group. Next, we consider the relationship R&D-related occupations have to the industries that employ these workers. Specifically, we looked at the employment [6] for this group of occupations within each 4-digit NAICS [7] code. The top six industries that most-often employ these types of workers account for roughly half [8] of the employment of the R&D-related occupation group. These “R&D-related industries” are: NAICS 5415 - Computer Systems Design and Related Services NAICS 5416 - Management, Scientific, and Technical Consulting Services NAICS 5413 - Architectural, Engineering, and Related Services NAICS 5112 - Software Publishers NAICS 5511 - Management of Companies and Enterprises NAICS 5417 - Scientific Research and Development Services Now that we have these groups defined, we can compare the growth rates of the R&D-related occupations and industries with the economy as a whole. R&D-related occupations have grown [9] over the last four years at a faster pace (5.1%) than all occupations (1.9%), and are expected to continue growing [10] at a faster pace (2.0%) than all occupations (0.7%). Likewise, R&D-related industries have grown at a faster pace (3.0%) than all industries (1.7%), and are projected to continue growing at a faster pace (1.4%) than all industries (0.7%). Employment in five of the six R&D-related industries are projected to grow at a faster pace than the average for all industries.    --> Highcharts.chart('container', { chart: { type: 'column' }, title: { text: 'Annual Employment Growth Rates for Industries Related to Top R&D Occupations, 2017Q3-2027Q3' }, credits: { //enabled: false text: 'Source: JobsEQ. 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Annual Growth Percent', color: 'rgba(48,107,159,1)', data: [1.4, 0.7, 2.1, 1.9, 1.8, 1.2, 1.0, 0.6], tooltip: { valueSuffix: '%' }, pointPadding: 0, //pointPlacement: -0.2 }, { name: '% of Employment in Top 10 R&D Occupations', color: 'rgba(128,151,188,.9)', data: [13.11, 1.46, 20.21, 18.47, 27.21, 11.55, 7.84, 4.72], tooltip: { valueSuffix: '%' }, pointPadding: 0.22, //pointPlacement: -0.2, yAxis: 1 }] });   One industry, management of companies and enterprises, is projected to grow at a slightly slower pace than average. However, this industry also contains the lowest percentage of its total employment within the R&D-related occupations group. Indeed, with a slight deviation for software publishers, we can see that the projected annual growth rate increases in tandem with the percentage of the total industry employment attributed to R&D-related occupations. These industry and occupation employment data are consistent with a strong, positive relationship between innovation and growth. The industries seeking to hire a relatively high concentration of R&D workers are also industries that have grown faster than average and are expected to continue expanding more quickly. Likewise, the occupations most associated with research and development show the same pattern of fast job growth. Given this association, fostering a climate conducive to innovation is a strategy worth considering by economic developers and policy makers to promote a growing, healthy economy.   [1] Rosli, M.M., & Sidek. S. (2013). Innovation and firm performance: evidence from Malaysian small and medium enterprises. Entrepreneurship Vision 2020: Innovation, Development Sustainability, and Economic Growth , pp. 794-809. Retrieved from: https://pdfs.semanticscholar.org/5602/2deca5e07c9d9f94c555fcda4c3173169869.pdf [2] Akcali, B.Y., & Sismanoglu, E. (2015). Innovation and the effect of research and development (R&D) expenditure on growth in some developing and developed countries. Procedia - Social and Behavioral Sciences, 195 , pp. 768-775. Retrieved from: https://www.sciencedirect.com/science/article/pii/S1877042815039531 [3] OECD (2016), OECD Factbook 2015-2016: Economic, Environmental and Social Statistics , OECD Publishing, Paris. http://dx.doi.org/10.1787/factbook-2015-en [4] SOC (Standard Occupational Classification) codes begin at the two-digit level and subdivide into occupations down to six digits (and in some cases further subdivide to eight digits ). [5] Chmura’s JobsEQ platform offers Real-Time Intelligence, or RTI —a dataset comprising online job postings data in the United States, updated daily, to provide insight on potential hiring. A “job posting” in this dataset is a unique (deduped) job posted from one of more than 15,000 sources which has been classified by SOC and location as well as analyzed for additional attributes such as skills and certifications. [6] Industry data compiled in JobsEQ are based on covered employment from the QCEW , published by the BLS, with self-employment calculations imputed by Chmura Economics and Analytics. Data as of 2017 Quarter 3. [7] NAICS stands for the North American Industrial Classification System. NAICS codes begin at the two-digit level and subdivide into industries down to six digits. NAICS codes at the four-digit level are used in this analysis. [8] Employment in R&D-related occupations group is approximately 2.2 million, and among these approximately 1.1 million workers are attributed to the six industries included in the R&D-related industries group. [9] Occupation and industry historic employment are provided in the Occupational Employment Statistics from the BLS. Note: occupation data are based on an average annual growth rate 2012-2016, and industry data are based on an average annual growth rate 2012Q3-2017Q3. [10] Occupation and industry 10-year employment projections are per JobsEQ, with national projections derived from the 2016-26 Employment Projections from the BLS . http://chmuraecon.com/blog/2018/january/22/rd-employment-innovation-and-growth/ http://chmuraecon.com/blog/2018/january/22/rd-employment-innovation-and-growth/ Mon, 22 January 2018 10:22:35 Chris Chmura http://chmuraecon.com/blog/2018/january/02/economic-impact-tax-cuts-reform-likely-to-set-new-records-for-us-recovery/ Sometimes it’s hard to believe that this expansion is already more than 8 years long--only 18 months short of the record 120-month expansion that occurred from March 1991 to March 2001. A few analysts are concerned about a recession occurring over the next year or two.  From our view, the tax cuts and reform just passed by Congress and signed into law by President Trump increases the probability that this expansion will enter the record books. Real gross domestic product (GDP), the broadest indicator of economic activity, is expected to expand by 2.3 percent in 2017 according to our forecasts after growing just 1.5 percent in the prior year. Next year is stacking up to produce the fastest growth of this recovery as we see it.  We expect real GDP to advance 3.0 percent in 2018 and 3.3 percent in 2019. Most economists have increased their outlook for growth over the next two years, in large part, due to the tax cuts and reform, but the expectations vary based on their assumptions about how businesses and consumers will react to the tax cuts.  We expect the reduction in the corporate tax rate will enable businesses to invest more in computers and equipment which will enhance productivity.  This will initially increase employment for businesses making these goods.  It will also eventually lead to wage gains for workers who are now more productive. The increased wages, employment, and individual tax cuts will all lead to more consumer spending.  With consumer spending making up about two-thirds of GDP, consumers are an important factor in economic growth. http://chmuraecon.com/blog/2018/january/02/economic-impact-tax-cuts-reform-likely-to-set-new-records-for-us-recovery/ http://chmuraecon.com/blog/2018/january/02/economic-impact-tax-cuts-reform-likely-to-set-new-records-for-us-recovery/ Tue, 02 January 2018 11:20:04 Patrick Clapp http://chmuraecon.com/blog/2017/december/29/our-most-popular-blogs-of-2017/ This year on our blog we dug into applications of real-time intelligence data, explained changes to classification systems and forecasts, peeked behind how JobsEQ handles certain calculations and data, and a lot more. As we look forward to all the new content to come in 2018, here are our most popular blog posts from this year. In honor of counting down the new year, the top ten posts are sorted from least to most read.    10: Math Majors Solve Employers Problems With students packing up to go back to college in August with declared or undeclared majors, it was a good time to consider the fields that graduating students are going into and whether they match up with the skills businesses need. We do the math on computer science degrees and quantitative skills. Top 10 Metros with Job Postings for Computer Occupation Location Post Count New York-Newark-Jersey City, NY-NJ-PA MSA 47,417 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 46,526 San Francisco-Oakland-Hayward, CA MSA 25,394 Los Angeles-Long Beach-Anaheim, CA MSA 25,082 Chicago-Naperville-Elgin, IL-IN-WI MSA 23,777 Dallas-Fort Worth-Arlington, TX MSA 22,337 San Jose-Sunnyvale-Santa Clara, CA MSA 20,602 Boston-Cambridge-Newton, MA-NH MSA 20,513 Seattle-Tacoma-Bellevue, WA MSA 18,505 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 16,450 Source: JobsEQ ® Online postings for the 30-day period preceding 08/07/2017   9: Trends in Postsecondary Completions: The 2016 NCES Data Release   In July, the National Center for Education Statistics released preliminary completions data for U.S. postsecondary schools for the 2015-16 academic year. In this blog, we highlight some of the trends in these data.   8: Using Job Postings to Visualize the Employment Impact of Hurricane Harvey 2017 has been a very active hurricane season. Some analysts argue Hurricane Harvey will be one of the most costly natural disasters in history. In this article, we examine the potential impact of Hurricane Harvey on employment in the Houston, Texas, region by using daily job postings data. 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That’s probably because the sector has declined by more than 4.9 million jobs since January 2000.The industry also has perception issues. Who wants to work in a dirty old factory with oil on the floor and dust in the air? But the manufacturing industry has changed - it is more high tech and offers better wages, making for a good career choice.   6: Underemployment in the United States According to a 2015 Federal Reserve Bank of New York Staff Report, almost one-half (46%) of recent college graduates were underemployed in 2014. The so-called underemployed workers are employed in an occupation below their level of qualification—for example, a graduate with a Bachelor’s Degree in economics who is waiting tables or working at a retail store is considered underemployed. Chmura calculates a proxy for underemployment by comparing educational attainment supply and demand in a given labor market at various skill levels.    5: A Case Study in Labor Market Analytics How would you handle a request from your boss asking whether you’ll be able to continue to attract the right talent needed for your current business operations? With information from JobsEQ in hand, an expansion decision can be made with more confidence.   4: Changes in Labor Statistics Signal Major Occupational Shifts and Impact to Higher Education Credentials A joint research report by UPCEA and Chmura Economics & Analytics reveals a number of major occupational shifts in technology, healthcare, and other industries and their impact on higher education. The study,  Occupational Shifts and Higher Education Credentials  found a rise in alternative credentialing in the form of certificates, short-term courses, licensing, badges and micro-credentials among adult learners as well as traditional students.    3: Help Wanted: Applications Software Developers, Anywhere, USA   Odds are, many of us live in a region where it is difficult to find qualified developers. Using our JobsEQ® technology platform alongside our newly unveiled Real Time Intelligence (RTI) tool, Chmura made several “top ten lists” pertaining to application software developers in regional labor markets across the United States.   2: Job Growth in Manufacturing   While the U.S. manufacturing sector employed about 1.8 million fewer workers in 2016 compared to ten years prior, many industries within the sector have posted solid to robust job gains.     1: Regional Occupation Employment   A good estimate of occupation employment at the local geographic level is a critical piece of labor data. How is such an estimate made in JobsEQ, and what are the advantages of using these data in comparison to retrieving an occupation estimate straight from the Bureau of Labor Statistics? http://chmuraecon.com/blog/2017/december/29/our-most-popular-blogs-of-2017/ http://chmuraecon.com/blog/2017/december/29/our-most-popular-blogs-of-2017/ Fri, 29 December 2017 09:57:01 Chris Chmura http://chmuraecon.com/blog/2017/december/07/economic-impact-holiday-retail-sales-should-be-stronger-this-season/ The holiday selling season got off to a strong start on Black Friday but the results were mixed when comparing online versus in-store sales. Online spending for Black Friday, the traditional start of the holiday selling season, was up 16.9 percent compared with the same day in 2016, according to Adobe Digital Insights, which tracks online spending in the nation’s 100 largest retail websites. On the other hand, data from research firm ShopperTrak shows Thanksgiving Day and Black Friday foot traffic at brick-and-mortar stores fell when compared to the same days in 2016. Shopper traffic at stores just on Black Friday has essentially been unchanged for the last two years, but flat foot traffic doesn’t necessarily translate into flat revenue. So how will overall holiday sales fare during this time of year that represents about 20 percent of the retail industry’s total sales? One method for predicting holiday spending is comparing back-to-school spending during the same year. For instance, back-to-school spending in August 2012 increased 2.9 percent, while holiday spending — defined as sales in November and December of the same year — also rose 3.1 percent. The retail spending figures exclude food and auto sales. The relationship between back-to-school and holiday sales is not perfect, and sometimes back-to-school sales are a bit higher than holiday sales (as what happened in 2011, 2013 and 2014) or a bit lower (as in 2015 and 2016). But back-to-school spending generally is a reliable gauge of holiday spending. This year’s August back-to-school sales rose 4.3 percent over 2016 sales, suggesting strong growth in holiday sales this November and December. And that’s great news for retailers. Recent announcements by some national retailers and online sellers to hire more seasonal workers than last year supports an optimistic outlook for the upcoming holiday selling season. Results from Cyber Monday also point to a good year. According to Adobe Analytics, Cyber Monday was the biggest U.S. online shopping day ever, with a record $6.59 billion spent, up 17 percent from last year. The National Retail Federation, the nation's largest retail trade group, predicts an increase in holiday sales between 3.6 to 4 percent, including online business, to $678.8 billion compared with $655.8 billion in 2016. Global financial services firm Deloitte is more optimistic, looking for this year's holiday sales to rise between 4 percent and 4.5 percent from November through January compared with the same period a year ago. Economic reports also point to higher sales this year. Employment in the nation is picking up and the jobless rate is declining. Personal income is up 3.4 percent for the 12 months that ended in October, or by $539 billion. American consumers like to spend, so much of that increase in income will translate into purchases. Christine Chmura is CEO and Chief Economist at Chmura Economics & Analytics. She can be reached at (804) 649-3640 or receive e-mail at chris.chmura@chmuraecon.com . December 2017 http://chmuraecon.com/blog/2017/december/07/economic-impact-holiday-retail-sales-should-be-stronger-this-season/ http://chmuraecon.com/blog/2017/december/07/economic-impact-holiday-retail-sales-should-be-stronger-this-season/ Thu, 07 December 2017 11:02:38 Doug Rice http://chmuraecon.com/blog/2017/december/01/2016-employment-projections-by-educational-requirements/ The Bureau of Labor Statistics recently released its employment projections for 2016, [1] forecasting the change of employment by occupation ten years into the future. Included in this analysis is the attribution of each occupation to a specific category of “typical education for entry.” Grouping the employment numbers and estimates by these categories, we are able to see how employment is likely to change across various levels of educational attainment. 2016-2026 Employment Projections by Educational Requirements Typical education needed for entry Employment, 2016 (millions) Projected Employment, 2026 (millions) Projected Total Change (millions) Projected Percentage Change No formal educational credential 37.2 39.6 2.4 6.4% High school diploma or equivalent 61.5 64.7 3.2 5.2% Some college, no degree 3.9 4.0 0.2 4.2% Postsecondary nondegree award 9.6 10.6 1.0 10.8% Associate's degree 3.6 4.0 0.4 10.9% Bachelor's degree 33.4 36.7 3.3 10.0% Master's degree 2.7 3.1 0.4 15.8% Doctoral or professional degree 4.2 4.8 0.6 13.5% Total, all occupations 156.1 167.6 11.5 7.4% Source: BLS Jobs requiring postgraduate degrees are projected to grow at the quickest rate over the next ten years—with those requiring master’s degrees projected to grow by 15.8% and those requiring doctoral or professional degrees expected to grow by 13.5%. Occupations in this group include lawyers, physicians, physician assistants, and nurse practitioners. By sheer number, the largest projected increase in employment belongs to occupations requiring a bachelor’s degree. Despite only representing about half the amount of total employment, these occupations are expected to add even more jobs than occupations requiring only a high school diploma. Among the 3.3 million jobs expected to be added for jobs requiring a bachelor’s degree, the occupations with the highest projected growth rates are software developers, applications; information security analysts; and operations research analysts. While occupations requiring bachelor’s degrees are expected to add slightly more jobs between 2016 and 2026, the largest amount of total employment still belongs to occupations requiring a high school diploma or equivalent. Among these, the occupations with the highest levels of employment are general office clerks and customer service representatives. Home health aides and personal care aides are two occupations requiring high school diplomas that are expected to grow significantly between 2016 and 2026, projected to expand employment 46.7% and 37.4%, respectively. The story is clear: jobs requiring higher education are generally expected to have better job growth. As we’ve written previously, there are other benefits to higher education as well, benefits for individuals as well as communities .   [1] BLS Employment Projections: Occupational projections and worker characteristics. https://www.bls.gov/emp/ep_table_107.htm http://chmuraecon.com/blog/2017/december/01/2016-employment-projections-by-educational-requirements/ http://chmuraecon.com/blog/2017/december/01/2016-employment-projections-by-educational-requirements/ Fri, 01 December 2017 14:29:09 Greg Chmura http://chmuraecon.com/blog/2017/november/28/2017-naics-code-changes/ The North American Industry Classification System ( NAICS ), used to classify businesses for statistical economic data, is typically updated every five years to reflect the changing economy. There were relatively few changes for the set of new codes just released for use with 2017 data.  However, there certainly were some interesting as well as notable changes. Highlights of the changes are detailed in the table below. Also shown in this table are the employment figures for 2016 and the change over the preceding ten years. The NAICS code changes include industries splitting and merging as well as changes in code while the descriptions remain the same. Some of the industries involved in these changes have experienced extreme employment contractions or expansions. Video tape and disc rental, for example, has seen employment plummet from over 150,000 in 2004 to under 12,000 in 2016. [1] On the other extreme, employment in electronic shopping soared over the same period from a little over 50,000 to over 230,000. #chart2{ max-height: 500px; } Video Tape and Disc Rental (NAICS 532230) Covered Employment, United States Source: Bureau of Labor Statistics .c3 path.domain, .c3 line { stroke: #999; } .c3-line { stroke-dasharray: 13px; stroke-width: 3px; } path.c3-shape.c3-shape.c3-line.c3-line-Construction-and-Extraction{ stroke-width: 6px; } .chartSource{ font-size: .7em; } .chartTitle{ font-weight: bold; padding-top: 1%; } var chart2 = c3.generate({ size: { height: 480 }, data: { x: 'x', // xFormat: '%Y%m%d', // 'xFormat' can be used as custom format of 'x' columns: [ ['x', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016'], ['Annual',157654,153444,153143,152268,143799,127687,116408,102472,86049,60246,38385,27507,21602,16150,14042,11921] ], type: 'bar', colors: { Total: '#7790b8' } }, legend: { show: false }, axis: { x: { type: 'category', }, y:{ tick:{ format: d3.format("000,") // outer: false, // count: 7, // values: [1.30, 1.40, 1.50, 1.60, 1.70, 1.80, 1.90], // max: 1.90, // min: 1.30 } } }, /*regions: [ {axis: 'x', start: '2017-08-25', end: '2017-08-30', class: 'regionX'}, ],*/ point: { show: false } }); /*var rectOffset = function () { return +d3.select(this.parentNode).select("rect").attr("x"); }; d3.selectAll(".c3-region-0") .append("text") .text("Hurricane Harvey") .attr("dy","15") .attr("dx", "33%") .style("fill-opacity", 1) .attr("text-anchor", "start") ;*/   #chart2{ max-height: 500px; } Electronic Shopping (NAICS 454111) Covered Employment, United States Source: Bureau of Labor Statistics and JobsEQ .c3 path.domain, .c3 line { stroke: #999; } .c3-line { stroke-dasharray: 13px; stroke-width: 3px; } path.c3-shape.c3-shape.c3-line.c3-line-Construction-and-Extraction{ stroke-width: 6px; } .chartSource{ font-size: .7em; } .chartTitle{ font-weight: bold; padding-top: 1%; } var chart2 = c3.generate({ size: { height: 480 }, data: { x: 'x', // xFormat: '%Y%m%d', // 'xFormat' can be used as custom format of 'x' columns: [ ['x', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016'], ['Annual',53136,47228,49345,54292,62329,69039,75939,83234,87681,91870,105879,124663,142703,172952,196365,230662] ], type: 'bar', colors: { Total: '#7790b8' }, /*labels: { format: d3.format("0,000") }*/ }, legend: { show: false }, axis: { x: { type: 'category', }, y:{ tick:{ format: d3.format("0,000"), // outer: false, count: 6, values: [0, 50000, 100000, 150000, 200000, 250000], //max: 250000, //min: 0 } } }, /*regions: [ {axis: 'x', start: '2017-08-25', end: '2017-08-30', class: 'regionX'}, ],*/ point: { show: false } }); /*var rectOffset = function () { return +d3.select(this.parentNode).select("rect").attr("x"); }; d3.selectAll(".c3-region-0") .append("text") .text("Hurricane Harvey") .attr("dy","15") .attr("dx", "33%") .style("fill-opacity", 1) .attr("text-anchor", "start") ;*/   2012-2017 NAICS Change Industries and Employment 2017 NAICS Code 2017 NAICS Title 2012 NAICS Code 2012 NAICS Title (and specific piece of the 2012 industry that is contained in the 2017 industry) 2016 Empl Change in Empl, 2006-16 Percent Change in Empl, 2006-16 211120 Crude Petroleum Extraction 211111 Crude Petroleum and Natural Gas Extraction - crude petroleum extraction 167,722 37,530 29% 211130 Natural Gas Extraction 211111 Crude Petroleum and Natural Gas Extraction - natural gas extraction (above) (above) (above) 211130 Natural Gas Extraction 211112 Natural Gas Liquid Extraction 7,254 2,584 55% 212230 Copper, Nickel, Lead, and Zinc Mining 212231 Lead Ore and Zinc Ore Mining 2,336 618 36% 212230 Copper, Nickel, Lead, and Zinc Mining 212234 Copper Ore and Nickel Ore Mining 13,021 3,009 30% 333914 Measuring, Dispensing, and Other Pumping Equipment Manufacturing 333911 Pump and Pumping Equipment Manufacturing 25,623 -2,990 -10% 333914 Measuring, Dispensing, and Other Pumping Equipment Manufacturing 333913 Measuring and Dispensing Pump Manufacturing 3,232 363 13% 335220 Major Household Appliance Manufacturing 335221 Household Cooking Appliance Manufacturing 13,718 -4,247 -24% 335220 Major Household Appliance Manufacturing 335222 Household Refrigerator and Home Freezer Manufacturing 15,716 -2,270 -13% 335220 Major Household Appliance Manufacturing 335224 Household Laundry Equipment Manufacturing 804 -13,653 -94% 335220 Major Household Appliance Manufacturing 335228 Other Major Household Appliance Manufacturing 20,544 8,141 66% 452210 Department Stores 452111 Department Stores (except Discount Department Stores) 489,924 -189,027 -28% 452210 Department Stores 452112 Discount Department Stores - insignificant perishable grocery sales 848,373 -75,612 -8% 452311 Warehouse Clubs and Supercenters 452112 Discount Department Stores - significant perishable grocery sales (above) (above) (above) 452311 Warehouse Clubs and Supercenters 452910 Warehouse Clubs and Supercenters 1,492,718 434,592 41% 452319 All Other General Merchandise Stores 452990 All Other General Merchandise Stores 415,715 88,325 27% 454110 Electronic Shopping and Mail-Order Houses 454111 Electronic Shopping 230,662 161,623 234% 454110 Electronic Shopping and Mail-Order Houses 454112 Electronic Auctions 10,916 6,319 137% 454110 Electronic Shopping and Mail-Order Houses 454113 Mail-Order Houses 125,782 -34,615 -22% 512250 Record Production and Distribution 512210 Record Production 1,887 -709 -27% 512250 Record Production and Distribution 512220 Integrated Record Production/Distribution 3,867 177 5% 541713 Research and Development in Nanotechnology 541711 Research and Development in Biotechnology - nanobiotechnologies research and experimental development laboratories 170,841 30,644 22% 541713 Research and Development in Nanotechnology 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology) - nanotechnology research and experimental development laboratories 469,811 46,291 11% 541714 Research and Development in Biotechnology (except Nanobiotechnology) 541711 Research and Development in Biotechnology - except nanobiotechnologies research and experimental development laboratories (above) (above) (above) 541715 Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology) 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology) - except nanotechnology research and experimental development laboratories (above) (above) (above) 517311 Wired Telecommunications Carriers 517110 Wired Telecommunications Carriers 590,299 -105,104 -15% 517312 Wireless Telecommunications Carriers (except Satellite) 517210 Wireless Telecommunications Carriers (except Satellite) 123,076 -78,446 -39% 532281 Formal Wear and Costume Rental 532220 Formal Wear and Costume Rental 7,051 -6,834 -49% 532282 Video Tape and Disc Rental 532230 Video Tape and Disc Rental 11,901 -115,784 -91% 532283 Home Health Equipment Rental 532291 Home Health Equipment Rental 38,641 -609 -2% 532284 Recreational Goods Rental 532292 Recreational Goods Rental 14,428 3,347 30% 532289 All Other Consumer Goods Rental 532299 All Other Consumer Goods Rental 63,032 17,520 38% Source: Bureau of Labor Statistics and JobsEQ The next scheduled review of NAICS codes is for a possible 2022 revision .   [1] All employment references in this article refer to Covered Employment as reported in the Quarterly Census of Employment and Wages . http://chmuraecon.com/blog/2017/november/28/2017-naics-code-changes/ http://chmuraecon.com/blog/2017/november/28/2017-naics-code-changes/ Tue, 28 November 2017 14:05:11 Patrick Clapp http://chmuraecon.com/blog/2017/november/20/predicting-holiday-spending/ While consumers may look forward to lower prices on Thanksgiving turkeys this year, signs are also good for retailers more concerned with the day after Thanksgiving. One method for predicting the outlook for holiday spending is comparing the back-to-school spending during the same year. For instance, the back-to-school spending in August 2012 increased 2.9 percent, while early holiday spending—defined here as sales in November of the same year—also rose 2.9 percent. The retail spending figures exclude food and auto sales. The relationship between back-to-school and holiday sales is not perfect, and sometimes back-to-school sales are a bit higher than holiday sales (as happened in 2013, 2014, and 2015) or a bit lower (as in 2016). But back-to-school spending generally is a reliable gauge of early holiday spending, as seen in the chart below. This year’s August back-to-school sales rose 4.3 percent over 2016 sales, suggesting strong growth in early holiday sales this November. And that’s great news for retailers. #chart2{ max-height: 500px; } Year Over Year % Change in Retail Sales, August and November Source: Census, Chmura Economics .c3 path.domain, .c3 line { stroke: #999; } .c3-line { stroke-dasharray: 13px; stroke-width: 3px; } path.c3-shape.c3-shape.c3-line.c3-line-Construction-and-Extraction{ stroke-width: 6px; } .chartSource{ font-size: .7em; } .chartTitle{ font-weight: bold; padding-top: 1%; } var chart2 = c3.generate({ size: { height: 480 }, data: { x: 'x', // xFormat: '%Y%m%d', // 'xFormat' can be used as custom format of 'x' columns: [ ['x', '1993','1994','1995','1996','1997','1998','1999','2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017'], ["August (Back to School)",0.047,0.083,0.038,0.056,0.051,0.043,0.077,0.067,0.033,0.024,0.063,0.056,0.090,0.059,0.029,0.045,-0.082,0.032,0.085,0.029,0.024,0.045,0.003,0.015,0.043], ["November (Black Friday)",0.058,0.072,0.043,0.060,0.042,0.050,0.080,0.065,0.013,0.041,0.056,0.078,0.078,0.031,0.071,-0.069,0.011,0.051,0.065,0.029,0.018,0.043,-0.003,0.033] ], //type: 'area-spline', colors: { Total: '#7790b8' } }, legend: { show: true }, axis: { x: { type: 'category', }, y:{ tick:{ format: d3.format(",.1%") // outer: false, // count: 7, // values: [1.30, 1.40, 1.50, 1.60, 1.70, 1.80, 1.90], // max: 1.90, // min: 1.30 } } }, /*regions: [ {axis: 'x', start: '2017-08-25', end: '2017-08-30', class: 'regionX'}, ],*/ point: { show: false } }); /*var rectOffset = function () { return +d3.select(this.parentNode).select("rect").attr("x"); }; d3.selectAll(".c3-region-0") .append("text") .text("Hurricane Harvey") .attr("dy","15") .attr("dx", "33%") .style("fill-opacity", 1) .attr("text-anchor", "start") ;*/ http://chmuraecon.com/blog/2017/november/20/predicting-holiday-spending/ http://chmuraecon.com/blog/2017/november/20/predicting-holiday-spending/ Mon, 20 November 2017 10:28:53 Patrick Clapp http://chmuraecon.com/blog/2017/november/20/early-signs-of-cheaper-turkeys/ Good news for Thanksgiving procrastinators—turkey prices peak in October before dropping in November, and prices are generally lower this year. The Consumer Price Index (CPI) measures the changing cost of goods and services over time. While the CPI for all goods is commonly known as a measure of inflation, we can use subsets of CPI data to track the price changes of specific goods, such as candy or frozen turkeys. Turkey prices are lower in October 2017 than in five of the last seven years, [1] which suggests lower prices per pound this November. #chart2{ max-height: 500px; } Average Price of Turkey, Frozen, Whole, per Pound Source: BLS .c3 path.domain, .c3 line { stroke: #999; } .c3-line { stroke-dasharray: 13px; stroke-width: 3px; } path.c3-shape.c3-shape.c3-line.c3-line-Construction-and-Extraction{ stroke-width: 3px; } .chartSource{ font-size: .7em; } .chartTitle{ font-weight: bold; padding-top: 1%; } var chart2 = c3.generate({ size: { height: 480 }, data: { x: 'x', // xFormat: '%Y%m%d', // 'xFormat' can be used as custom format of 'x' columns: [ ['x', 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'], ['2007',1.103,1.136,1.079,1.081,1.146,1.223,1.222,1.229,1.216,1.241,1.113,1.01], ['2008',1.207,1.23,1.151,1.17,1.258,1.238,1.27,1.288,1.32,1.222,1.309,1.332], ['2009',1.365,1.369,1.347,1.354,1.366,1.41,1.445,1.461,1.454,1.48,1.336,1.365], ['2010',1.398,1.375,1.425,1.479,1.464,1.474,1.554,1.521,1.566,1.677,1.407,1.38], ['2011',1.459,1.526,1.572,1.562,1.596,1.581,1.603,1.641,1.676,1.673,1.541,1.574], ['2012',1.671,1.671,1.812,1.791,1.608,1.557,1.561,1.586,1.621,1.661,1.488,1.433], //['2013',1.579,1.591,1.593,1.649,1.654,1.595,1.624,1.663,1.819, 0,1.721,1.65], ['2014',1.713,1.699,1.733,1.61,1.602,1.606,1.641,1.604,1.584,1.667,1.425,1.331], ['2015',1.445,1.48,1.503,1.487,1.527,1.541,1.568,1.546,1.538,1.558,1.424,1.448], ['2016',1.528,1.494,1.509,1.488,1.527,1.515,1.586,1.622,1.649,1.692,1.527,1.495], ['2017',1.581,1.57,1.585,1.567,1.532,1.576,1.622,1.615,1.623,1.656] ], //type: 'area-spline', colors: { Total: '#7790b8' } }, legend: { show: true }, axis: { x: { type: 'category', }, y:{ tick:{ format: d3.format("$,") // outer: false, // count: 7, // values: [1.30, 1.40, 1.50, 1.60, 1.70, 1.80, 1.90], // max: 1.90, // min: 1.30 } } }, /*regions: [ {axis: 'x', start: '2017-08-25', end: '2017-08-30', class: 'regionX'}, ],*/ point: { show: false } }); [1] 2013 data are excluded as data are not reported for October of that year http://chmuraecon.com/blog/2017/november/20/early-signs-of-cheaper-turkeys/ http://chmuraecon.com/blog/2017/november/20/early-signs-of-cheaper-turkeys/ Mon, 20 November 2017 10:14:35 Chris Chmura http://chmuraecon.com/blog/2017/november/10/economic-impact-job-openings-in-region-consistent-with-nation/ Every once in a while, I hear someone say tens of thousands of jobs are open in the Richmond region and candidates can’t be found for those positions. There might be only some truth to that. From August through October, for instance, more than 65,000 jobs were posted online for positions in the Richmond region. But just because a job is posted on the internet does not mean that candidates can’t be found. The median number of days of 65,167 job postings in Richmond was 27 days, meaning that 32,583 get filled in less than 27 days and the remainder take longer. In fact, 52 percent of the job postings in the Richmond area were filled within a month, and about 70 percent were filled by the 45-day mark. But about 13 percent of jobs remain unfilled after three months. It seems reasonable to interpret these unfilled jobs after being open for a long while as those difficult to fill due to supply-demand imbalances. Of the 65,167 total job postings from August through October, more than 8,400 may be hard to fill. The amount of time a job stays open in the Richmond region varies by occupation. Highly skilled jobs and those needing specific licenses or certifications, such as education, arts and design, engineers and health care practitioners, stay open longer. For low-skilled jobs such as office, retail sales and personal services, it takes a shorter time to fill open positions. There is one exception — food service, which shows a high percentage of open positions. In this case, rather than advertising job postings for specific vacancies, companies hiring in these occupations often keep a posting open perpetually in order to collect an applicant pool, which they then draw from when a vacancy arises. A few firms, including Chmura Economics & Analytics, collect online job postings to identify current job demand and trends for occupations and regions. These job postings can provide some insight into how many jobs go unfilled, but there are some caveats. For example, some openings are not posted on the internet. This is particularly true in the construction industry, where openings are often communicated by word of mouth. On the other hand, some job openings may be posted on the internet even though a candidate is already identified for the job. This is more prevalent in large businesses where the human resources department requires that jobs be posted for a period of time or for firms that are constantly recruiting for the same positions due to high turnover. Despite those caveats, job posting data collected by Chmura Economics generally are consistent with national reports and can yield useful insights on what jobs get filled and how soon. The data show that there were 25,765 active open positions to be filled in the Richmond metro area as of last week. These numbers appear to be in line with the U.S. Bureau Labor Statistics’ Job Openings and Labor Turnover survey which reports 6.1 million open positions in August across the nation. With the Richmond area having about 0.4 percent of the national population, proportionally, it is likely that about 24,400 of the nation’s job openings are in the region. How does Richmond stand relative to the nation on job postings? Using the nation as a benchmark, the Richmond metro area, with 65,167 total postings from August through October, has 28 percent more job openings than the average region based on the number of people employed here. That ranks Richmond 93rd among the 381 metropolitan areas in the country. The Charlottesville and Washington metro areas have higher job openings — 29 percent and 33 percent, respectively. http://chmuraecon.com/blog/2017/november/10/economic-impact-job-openings-in-region-consistent-with-nation/ http://chmuraecon.com/blog/2017/november/10/economic-impact-job-openings-in-region-consistent-with-nation/ Fri, 10 November 2017 11:24:15 Sharon Simmons http://chmuraecon.com/blog/2017/november/06/examining-industry-mobility-for-occupations/ Industry mobility indicates the potential ease of switching industries based on an occupation’s employment share across industries. A mobile occupation is one in which workers can move from one industry to another with relative ease. This is important in designing training programs with skills needed across multiple industries as well as in the event of industry decline or a dislocation event. For example, almost 80% of electricians are employed in the construction sector. A downturn in construction is therefore likely to be a challenge for electricians because they may not be able to find a new job in another industry since such a large majority of employment opportunities for electricians are concentrated in the construction sector. To measure an occupation’s industry mobility, Chmura calculated an index score based on occupation staffing data by industry. [1] A higher index score indicates an occupation is needed by many industries while a lower score suggests an occupation is employed by fewer industries and workers therefore have limited flexibility to change industries. The occupations with the highest industry mobility are shown in the table below. [2] Occupations with the Highest Industry Mobility Labor Force Participation Rate --> Occupation Index Score Employment as of 2017:Q2 Average Annual Wages as of 2016 General and Operations Managers 98.4 2,259,553 $122,100 Production, Planning, and Expediting Clerks 84.2 329,289 $49,100 Maintenance Workers, Machinery 75.5 91,138 $46,000 First-Line Supervisors of Production and Operating Workers 71 618,628 $61,500 Office Clerks, General 67.6 3,065,477 $33,000 Bookkeeping, Accounting, and Auditing Clerks 66.9 1,725,464 $40,200 Purchasing Agents, Except Wholesale, Retail, and Farm Products 61.5 303,628 $67,400 Industrial Production Managers 60.1 172,970 $107,100 Shipping, Receiving, and Traffic Clerks 52.7 691,606 $33,200 Administrative Services Managers 49.5 273,864 $98,900 Source: Chmura Economics & Analytics and JobsEQ. General and operations managers; production, planning, and expediting clerks; and machinery maintenance workers topped the list of most mobile occupations in the United States. General and operations managers plan, direct, or coordinate public or private-sector organizations’ operations. Almost every 4-digit NAICS level industry employs general and operations managers and at $122,100, average annual wages are well above the national average. [3] Production, planning, and expediting clerks coordinate and expedite the flow of work within or between departments based on the production schedule. About 92% of 4-digit NAICS level industries employ production, planning, and expediting clerks. Wages for production, planning, and expediting clerks are very close to the national average annual wage. The third most mobile occupation in the nation is machinery maintenance workers; they perform routine machinery maintenance including lubricating machinery and changing parts and are employed in almost three-quarters (74%) of industries at the 4-digit NAICS level. Average annual wages for machinery maintenance workers are slightly below the national average. An occupation’s industry mobility depends in part on the location of employment. More specifically, an occupation in a rural area may be more or less mobile when compared with the same occupation in a non-rural area. [4] While there is significant overlap between the national top-10 list for most mobile occupations and the list for most-mobile occupations in rural areas, there are occupations where there is a considerable difference compared with the national mobility. Laborers and freight, stock, and material movers, hand, for example, is the 21 st most mobile occupation in rural areas while being only the 107 th most mobile occupation nationally. Security guards also enjoy relatively more mobility in rural areas; it is the 100 th most mobile occupation in rural areas compared with the 544 th most mobile occupation overall. In contrast, first-line supervisors of office and administrative support workers are more mobile at the national level than in rural areas (12 th most mobile occupation in the nation compared with the 48 th most mobile occupation in rural areas). Occupations with Largest Increases in Mobility in Rural Areas vs. Nationally Labor Force Participation Rate --> Occupation USA Rural Purchasing Managers 26.2 55.1 Production Workers, All Other 7.3 33.0 Laborers and Freight, Stock, and Material Movers, Hand 15.8 34.6 Human Resources Specialists 22.1 39.5 Advertising and Promotions Managers 6.7 23.7 Technical Writers 15.9 30.0 Drafters, All Other 9.9 23.8 Materials Scientists 8.8 22.1 Marketing Managers 23.7 36.9 Security Guards 2.8 15.7 Source: Chmura Economics & Analytics, JobsEQ, and the U.S. Department of Agriculture Economic Research Service   [1] Data as of 2017 Q2. Source: JobsEQ ® [2] Occupations are at the detailed (i.e., 6-digit Standard Occupational Classification) level. [3] The North American Industry Classification System (NAICS) is the standard used by federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy. [4] Rural areas were identified using the United States Department of Agriculture, Economic Research Service’s Rural-Urban Continuum Codes. http://chmuraecon.com/blog/2017/november/06/examining-industry-mobility-for-occupations/ http://chmuraecon.com/blog/2017/november/06/examining-industry-mobility-for-occupations/ Mon, 06 November 2017 15:22:52 Xiaobing Shuai, Greg Chmura http://chmuraecon.com/blog/2017/november/06/introducing-chmura-s-economic-impact-model/ Chmura ’s economic impact model is an integral component of its proprietary JobsEQ technology platform. It gives practitioners in economic development, workforce development, education, and other areas a tool to evaluate the economic impact of a potential project such as a business expansion or relocation. It allows for a seamless transition from JobsEQ’s industry and occupation data to economic impact analysis, thus ensuring data integrity and consistency. Chmura’s economic impact model is based on economic theory related to the input-output (I-O) accounting of the U.S. economy, while incorporating other data sources and industry best practices. It starts with the national I-O matrix published by Bureau of Economic Analysis (BEA). With that, there remain three questions of practical importance and that is where various models differ. The BEA I-O matrix enables the estimation of multipliers at the national How can state- and county-level multipliers be estimated? The BEA I-O matrix enables the estimation of economic output multipliers directly. Many economic development professionals are also interested in understanding the employment multipliers or labor income multipliers. How can these additional multipliers be estimated? BEA publishes an I-O matrix for close to 400 industries every five years, and an I-O matrix for about 100 major industries annually. How can multipliers for 4-digit (more than 300 industries) and 6-digit (more than 1,000 industries) North America Industry Classification System (NAICS) industries be estimated on a more up-to-date basis? There are several economic impact models. They all use the same fundamental economic I-O theory, and are based on the same national I-O matrix from BEA. However, each model uses its own proprietary method to address the three challenges noted above. Chmura’s methodology takes into consideration the regional industry mix, supply capacity, and economic diversity, among other factors, to estimate the I-O matrix for each county and state in the country. Using the latest wage and salary data, Chmura’s model converts the output multipliers into employment and labor income multipliers of a region. Finally, Chmura imputes the input-output matrix at the 6-digit NAICS industry level, utilizing the employment and wages of those industries. When comparing models from various providers, the indirect output multipliers [1] for the United States should be very similar in all models because these multipliers are almost the direct results of the inversion of the national I-O matrix which most models use (Figure 1). Other multipliers will vary based on customization by the provider. At the national level, the first customization occurs for the induced output multipliers , [2] as different models use a variety of additional data to estimate the percentage of wages and salaries devoted to household spending. The next customization occurs for employment multipliers . Differences in employment multipliers occur depending on the productivity and wage data used. Chmura takes a best-practice approach by using the latest productivity and wage data, which are updated quarterly. Other models may use annual wage data which are updated less frequently and thus result in larger employment multipliers. That is because, starting with the same amount of revenue, more jobs can be supported when wages are lower. Assuming wages increase over time, using the latest—thus typically higher—wages will yield lower estimates of jobs supported and smaller employment multipliers. --> Highcharts.chart('container1', { chart: { type: 'column' }, title: { text: 'Comparison of National Multipliers' }, xAxis: { categories: [{ name: "Output Multiplier", categories: ["Indirect", "Induced"] },{ name: "Employment Multiplier", categories: ["Indirect", "Induced"] }] }, yAxis: { min: 0, max: 1.15, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { //valueSuffix: '%' // adds percent sign to tooltip }, plotOptions: { column: { //stacking: 'normal', dataLabels: { enabled: true, color: (Highcharts.theme && Highcharts.theme.dataLabelsColor) || 'black' } }, series: { pointPadding: 0, //Controls spacing between bars groupPadding: 0.1 //Controls spacing between bar groups /*dataLabels:{ enabled:false, align: 'right', color: '#fff', formatter: function() { return this.y +'%'; }, x: -2, style: { textOutline: 0, fontSize: 14 }, }*/ } }, legend: { show: false }, credits: { //enabled: false text: 'Source: Chmura and undisclosed impact software', position: { align: 'left', y: -2, // position of credits x: 45 }, style: { fontSize: '8pt' // you can style it! } }, series: [{ name: 'JobsEQ', showInLegend: true, color: 'rgba(82,118,180,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [0.68, 0.65,0.76,0.79], },{ name: 'Popular Industry Model', showInLegend: true, color: 'rgba(167,170,170,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [0.67,0.82,0.86,1.15], } ], });   Different providers of economic impact models also have their own proprietary methodologies to estimate state and local impact multipliers. The essence of this process is to construct state and local input-output matrices based on the national matrix. Academic research indicates that many factors affect how much each industry purchases from local areas, including the availability and the size of supplying industries, industry mix, as well as trade flows. After regional input-output matrices are customized, indirect and induced output and employment multipliers can be calculated as was done for national multipliers. Using Virginia as an example as shown in Figure 2, JobsEQ’s regional multipliers tend to be more conservative than some other popular impact software multipliers. The reasons lie in the customization method used by Chmura. As previously noted, Chmura uses the most recent quarterly wages in the model, which tends to result in lower employment multipliers. Furthermore, Chmura uses additional methods to constrain economic multipliers. For example, in other impact models it is not uncommon to find county multipliers being larger than state multipliers for certain industries. Chmura’s model has geographic consistency checks so that multipliers are logically consistent when comparing a smaller region to a larger region in which it is contained (e.g. comparing a county to its state). --> Highcharts.chart('container2', { chart: { type: 'column' }, title: { text: 'Comparison of Virginia Multipliers' }, xAxis: { categories: [{ name: "Output Multiplier", categories: ["Indirect", "Induced"] },{ name: "Employment Multiplier", categories: ["Indirect", "Induced"] }] }, yAxis: { min: 0, max: 0.57, title: { //text: 'Forecast Average Annual Growth Percent', text: null, align: 'high' }, labels: { enabled: false } //Turns label into percent /*labels: { formatter: function() { return this.value+"%"; } }*/, tickInterval: 1, //Not really needed if below is hidden. gridLineWidth: 0, //Hides verticle gridlines }, tooltip: { //valueSuffix: '%' // adds percent sign to tooltip }, plotOptions: { column: { //stacking: 'normal', dataLabels: { enabled: true, color: (Highcharts.theme && Highcharts.theme.dataLabelsColor) || 'black' } }, series: { pointPadding: 0, //Controls spacing between bars groupPadding: 0.1 //Controls spacing between bar groups /*dataLabels:{ enabled:false, align: 'right', color: '#fff', formatter: function() { return this.y +'%'; }, x: -2, style: { textOutline: 0, fontSize: 14 }, }*/ } }, legend: { show: false }, credits: { //enabled: false text: 'Source: Chmura and undisclosed impact software', position: { align: 'left', y: -2, // position of credits x: 45 }, style: { fontSize: '8pt' // you can style it! } }, series: [{ name: 'JobsEQ', showInLegend: true, color: 'rgba(82,118,180,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [0.33, 0.29,0.39,0.35], },{ name: 'Popular Industry Model', showInLegend: true, color: 'rgba(167,170,170,.9)', //Data with a bar called out and highlighted a different color /*data: [1.1, { y: 3.1, color: '#fa8a40' }, 2.3,2.3,2.0,2.0,1.5,1.4,1.3,1.2,1.0],*/ data: [0.34,0.39,0.46,0.57], } ], });   The multipliers in Chmura’s model are further refined with consistency checks via modeling of supply chain, regional gross domestic product (GDP), and productivity. It is verified in aggregate at the national level that the multipliers are consistent with national productivity data. Chmura computes county-level GDP data (based on employment and wages and BEA state- and industry-level GDP data ) and the multipliers are checked for consistency with this data set. Furthermore, multipliers are used within regional supply chain modeling; feedback from this model is used to restrain the multipliers to ensure the model is logically consistent with local supply chain effects—for example, multipliers that are too high might imply manifest ripple effects that are inconsistent with the actual size and mix of industries within a local economy. Finally, while economic multipliers from various models provide good estimates of the economic impact, they are still model estimates based on industry averages. The estimated multipliers may deviate from an actual event where specific suppliers are known. When supplier data are available, it is a good practice to incorporate business-specific data to generate more accurate multipliers. For that reason, Chmura’s economists routinely conduct business surveys and use that survey data to improve the estimation of economic multipliers.   [1] The indirect multipliers estimate ripple effects due to the supply chain, the economic activity generated by suppliers of goods and services to the industry in question. [2] The induced multipliers are used to estimate ripple effects due to household spending, the economic activity generated by employees spending wages as consumers. http://chmuraecon.com/blog/2017/november/06/introducing-chmura-s-economic-impact-model/ http://chmuraecon.com/blog/2017/november/06/introducing-chmura-s-economic-impact-model/ Mon, 06 November 2017 14:22:33 Greg Chmura http://chmuraecon.com/blog/2017/november/02/site-selection-cost-of-living-or-payroll-analysis/ Although cost-of-living is important in estimating expenses for potential new locations for a firm expansion, and though cost-of-living is generally correlated with payroll costs, it is not a safe substitute for a full payroll analysis.  A payroll analysis consists of defining staffing needs by occupation, capturing wage estimates specific to each occupation in the geographies under consideration, and then comparing the total payroll costs across those locations. As an example, suppose an expanding firm is a manufacturer of lawn and garden tractors. [1] The staffing needs for the expansion should be known by the site selector, and these will be unique to the individual company. In this case, however, we'll use the typical staffing pattern for this industry. [2] The top occupations used in lawn and garden tractor manufacturing are team assemblers, welders, and machinists. In addition to production occupations, examples of other prominent occupations in this industry are mechanical engineers, sales representatives, and industrial truck and tractor operators.   Lowest and Highest Projected Payrolls for a Potential Lawn & Garden Tractor Manufacturer with 250 Employees among the 50 Largest MSAs Labor Force Participation Rate --> Payroll Rank MSA Payroll(M) 1 Tampa-St. Petersburg-Clearwater, FL $11.9 2 Oklahoma City, OK $12.0 3 Nashville-Davidson--Murfreesboro--Franklin, TN $12.1 4 Orlando-Kissimmee-Sanford, FL $12.2 5 Indianapolis-Carmel-Anderson, IN $12.3 6 Memphis, TN-MS-AR $12.3 7 Miami-Fort Lauderdale-West Palm Beach, FL $12.4 8 Birmingham-Hoover, AL $12.4 9 Louisville/Jefferson County, KY-IN $12.5 10 Salt Lake City, UT $12.6 41 San Diego-Carlsbad, CA $14.0 42 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $14.3 43 Hartford-West Hartford-East Hartford, CT $14.4 44 Houston-The Woodlands-Sugar Land, TX $14.8 45 New York-Newark-Jersey City, NY-NJ-PA $14.8 46 Washington-Arlington-Alexandria, DC-VA-MD-WV $15.1 47 Boston-Cambridge-Newton, MA-NH $15.1 48 Seattle-Tacoma-Bellevue, WA $15.3 49 San Francisco-Oakland-Hayward, CA $15.8 50 San Jose-Sunnyvale-Santa Clara, CA $16.3 Source: JobsEQ. Occupation wages and staffing patterns based on source data from the BLS OES program. Taking the mix of occupations needed along with average wages [3] for each, we can estimate the total payroll a typical firm would have. The above table contains payroll estimates at a potential lawn and garden tractor manufacturing with 250 employees based on locations within the largest 50 metropolitan statistical areas (MSAs) in the nation.  Estimating relative payroll costs can be performed another way, by figuring the average payroll cost nationwide and then adjusting that by a cost-of-living index (COLI) for each MSA. That approach has some merit, as payrolls and cost-of-living are generally related, but that approach can also fall flat. A good example of this is the Houston MSA, a region with a below-average cost-of-living, meaning the cost of living is cheaper on average compared to the rest of the nation. The COLI for Houston is 98.1, [4] which is slightly below the national average of 100. For a potential lawn and garden tractor manufacturer, however, the Houston MSA is well above average in terms of payroll costs, ranking 44 th among the largest 50 metros. While not all of the occupations typically found in this type of manufacturer have higher wages in Houston compared to the nation, most do. For example, the average annual wage of welders is $49,900 in the Houston MSA compared to $42,400 in the nation. For mechanical engineers, the average wage in Houston is $109,800 compared to $89,800 in the nation. A region with higher-than-average wages and a lower-than-average cost-of-living is beneficial for workers—as not only are wages high, but the costs of goods and services is low. From the employer standpoint, however, employee wages are a cost of doing business. As the Houston example points out, measuring those wages directly can give very different results compared to using cost-of-living as a proxy.     [1] Specifically, defined by NAICS 333112, lawn and garden tractor and home lawn and garden equipment manufacturing. [2] Staffing patterns can be obtained from the BLS OES program , or through a system such as JobsEQ which incorporates the BLS staffing patterns, but has advantages such as ease of access and completeness. [3] Average occupation wages for this analysis were obtained through JobsEQ which uses a source data the Bureau of Labor Statistics Occupation Employment Statistics . [4] Cost-of-living for Houston is per C2ER 2017Q3 data. C2ER provides COLI estimates for three urban areas within the Houston MSA: the Houston area, shown in the text, as well as Conroe and Brazoria County. All three areas have a lower-than-average COLI. http://chmuraecon.com/blog/2017/november/02/site-selection-cost-of-living-or-payroll-analysis/ http://chmuraecon.com/blog/2017/november/02/site-selection-cost-of-living-or-payroll-analysis/ Thu, 02 November 2017 16:46:10 Patrick Clapp http://chmuraecon.com/blog/2017/october/27/carve-your-pumpkin-with-labor-market-data/ What can a jack-o’-lantern tell you about your state’s labor market? Chernoff faces are designed to display multiple variables as facial expressions, but in honor of Halloween we’ve created Chernoff jack-o’-lanterns. Each facial feature on the pumpkin displays data on employment or wages in the state. Eyes : the size of the eyes is mapped to the percent change in average annual wages per worker over the past year, with larger eyes showing faster growth. The direction of the eyes up or down indicates positive or negative change in wages, respectively.   Nose: The larger the nose, the higher the unemployment rate as of August, 2017. The highest unemployment rates are in Florida (6.6%) and New Mexico (6.4%), while North Dakota (2.1%) and Colorado (2.2%) have the lowest.  Mouth: mouth width corresponds to the percent change in the four quarter moving average of total employment in the state between 2016Q2 – 2017Q2. A wider mouth indicates greater change, while the direction of the mouth (smiling or frowning) indicates positive or negative change in employment. The large grins on Nevada (+3.0%),  Utah (+2.9%), and Washington DC (+2.8%) indicate the fastest growth over this period, while the nine states with stagnant or declining employment, led by Wyoming (-3.2%) and North Dakota (-2.1%), should be easy to pick out.  One note of caution: don’t read too much into any given combination of features—your state should not be interpreted as “shocked,” “dismayed,” or “ecstatic”. One reason Chernoff faces are not used more often is our brains can pick out facial features from almost anything, and we tend to interpret some features more strongly than others. Each feature is to scale with the corresponding features on other pumpkins (nose to nose, for example), but the scale is not consistent across different features.  Happy Halloween! http://chmuraecon.com/blog/2017/october/27/carve-your-pumpkin-with-labor-market-data/ http://chmuraecon.com/blog/2017/october/27/carve-your-pumpkin-with-labor-market-data/ Fri, 27 October 2017 11:24:55 Doug Rice http://chmuraecon.com/blog/2017/october/24/shorter-certifications-on-the-rise/ For programs less than two years in length, more students have been opting for awards that take less than a year to complete. The NCES tracks two levels of awards lower than an associate’s degree: (1) certificates less than one year and (2) certificates more than one year but less than two years. From 2012 to 2016, [1] completions [2] for certificates between one and two years in length declined 12.6% while completions for certificates less than one year in length expanded 4.9%.  This trend is especially prevalent in programs that are technical or mathematics based. For example, Computer and Information Sciences and Support Services (CIP 11) [3] expanded 57.8% for certificates less than a year while declining 5.3% for certificates of one but less than two years. Engineering Technologies and Engineering-Related Fields (CIP 15) expanded 27.8% for certificates less than a year and decreased 22.3% for certificates of one but less than two years. Business, Management, Marketing and Related Support Services (CIP 52) expanded 45.7% for certificates less than a year, but decreased 5.7% for certificates of one but less than two years   There are several award programs, however, for which this trend did not hold. Largely, these tend to be programs that are less focused on mathematics and more focused on the humanities. For example, Education (CIP 13), Family and Consumer Sciences/Human Sciences (CIP 19), and Theology and Religious Vocations (CIP 39) expanded at the same rate for certificates less than a year as they did for certificates of one but less than two years. In other humanities-oriented programs, the trend was completely reversed and certificates of one but less than two years grew more rapidly than certificates less than a year. For example, English Language and Literature/Letters (CIP 23) expanded only 21.8% in certificates less than a year but 72.7% in certificates of one but less than two years. Even more dramatically, Liberal Arts and Sciences, General Studies and Humanities (CIP 24) decreased 34.3% in certificates less than a year but expanded 88.4% in certificates of one but less than two years. Among programs related to skilled trades, the trends seem to be consistent with the overall trend, with certificates less than a year increasing at a faster pace. For example, Construction Trades (CIP 46) expanded 16.4% in certificates less than a year but decreased 12.7% in certificates of one but less than two years; Mechanic and Repair Technologies/Technicians (CIP 47) expanded 5.1% in certificates less than a year and decreased 11.2% in certificates of one but less than two years; and Precision Production (CIP 48) expanded 80.7% in certificates less than a year but only expanded 36.9% in certificates of one but less than two years.  [1] Completions data are provided by the National Center for Education Statistics ( NCES ). For simplicity of reference, single years are used to refer to school years with the year referenced being the end year; for example, “2016” designates the 2015-16 academic year. [2] “Completions” refers to degrees and other awards granted by postsecondary schools. The NCES completions data covers all schools participating in any federal financial assistance program authorized by Title IV of the Higher Education Act of 1965, as amended; institutions not participating may not be represented in this data set. [3] “CIP” stands for the Classification of Instructional Programs . http://chmuraecon.com/blog/2017/october/24/shorter-certifications-on-the-rise/ http://chmuraecon.com/blog/2017/october/24/shorter-certifications-on-the-rise/ Tue, 24 October 2017 14:32:02 Alex Doherty http://chmuraecon.com/blog/2017/october/18/sweet-halloween-savings/ As Halloween approaches, U.S. consumers are seeing some savings as they purchase candy for October 31 st . The Consumer Price Index (CPI) measures the changing cost of goods and services over time. While the CPI for all goods is commonly known as a measure of inflation, we can use subsets of CPI data to track the price changes of specific goods, such as candy and chewing gum. The price of candy this time of year is the lowest it’s been since 2014. If consumers spend the same amount on candy as the past two years, this may translate to more sweets for trick-or-treaters! http://chmuraecon.com/blog/2017/october/18/sweet-halloween-savings/ http://chmuraecon.com/blog/2017/october/18/sweet-halloween-savings/ Wed, 18 October 2017 11:16:09 James Stinchcomb http://chmuraecon.com/blog/2017/october/16/using-job-postings-to-visualize-the-employment-impact-of-hurricane-harvey/ 2017 has been a very active hurricane season. Some analysts argue Hurricane Harvey will be one of the most costly natural disasters in history [1] .   As a region is preparing for a category 4  hurricane such as Harvey, businesses close down and most people evacuate. When some businesses are destroyed during the hurricane, a rise in unemployment follows.  One indicator of this effect is an increase in initial jobless claims. This is particularly apparent in Texas following Hurricane Harvey, where initial jobless claims jumped from 12,105 the week ending with August 26 to 63,788 the week ending with September 2, immediately following Harvey’s August 25 landfall. In this article, we examine the potential impact of Hurricane Harvey on employment in the Houston, Texas, region by using daily job postings data. We explore the immediate impact when Harvey first made landfall in Texas along with the impacts, including recovery, in the weeks following. While not an exact proxy for employment, job postings are a leading indicator—when job postings fall, we can expect new hires and employment to fall as well. Chmura’s JobsEQ ® RTI [2] dataset is used to measure real-time job postings. To account for fluctuations in the data beyond the regional impacts of Hurricane Harvey (such as weekly volume patterns), the data shown below are presented as a five-day moving average [3] of new job postings. Furthermore, data are presented as job posting volume relative to the nation, in order to account for seasonal patterns and other national fluctuations; specifically, as the number of new jobs posted in the Houston metro area per 10,000 new jobs posted in the nation. Volume of New Job Postings in Houston per 10,000 U.S. Job Postings Source: Chmura's JobsEQ ® .c3 path.domain, .c3 line { stroke: #999; } line { stroke-dasharray: 5; } path.c3-shape.c3-shape.c3-line.c3-line-Total { stroke-dasharray: 0; } .chartSource{ font-size: .7em; padding-bottom: 1%; } .chartTitle{ font-weight: bold; padding-top: 1%; } #chart .c3-grid text { fill: #555; } var chart = c3.generate({ size: { height: 480 },padding: { top: 20 }, data: { x: 'x', // xFormat: '%Y%m%d', // 'xFormat' can be used as custom format of 'x' columns: [ ['x', '2017-08-03', '2017-08-04', '2017-08-05', '2017-08-06', '2017-08-07', '2017-08-08', '2017-08-09', '2017-08-10', '2017-08-11', '2017-08-12', '2017-08-13', '2017-08-14', '2017-08-15', '2017-08-16', '2017-08-17', '2017-08-18', '2017-08-19', '2017-08-20', '2017-08-21', '2017-08-22', '2017-08-23', '2017-08-24', '2017-08-25', '2017-08-26', '2017-08-27', '2017-08-28', '2017-08-29', '2017-08-30', '2017-08-31', '2017-09-01', '2017-09-02', '2017-09-03', '2017-09-04', '2017-09-05', '2017-09-06', '2017-09-07', '2017-09-08', '2017-09-09', '2017-09-10', '2017-09-11', '2017-09-12', '2017-09-13', '2017-09-14', '2017-09-15', '2017-09-16', '2017-09-17', '2017-09-18', '2017-09-19', '2017-09-20', '2017-09-21', '2017-09-22', '2017-09-23', '2017-09-24', '2017-09-25', '2017-09-26', '2017-09-27', '2017-09-28', '2017-09-29', '2017-09-30', '2017-10-01', '2017-10-02', '2017-10-03', '2017-10-04', '2017-10-05', '2017-10-06', '2017-10-07'], // ['x', '20130101', '20130102', '20130103', '20130104', '20130105', '20130106'], ['Total', 156,155,150,154,152,157,159,162,159,161,158,158,158,160,160,160,154,153,155,153,147,141,135,111,90,75,78,83,88,92,98,103,122,125,139,145,150,150,157,159,162,170,174,180,178,173,172,169,166,164,165,151,145,143,147,153,151,151,154,154,150,159,163,160,160,162] ], type: 'area-spline', colors: { Total: '#7790b8' } }, legend: { show: false }, axis: { x: { type: 'timeseries', tick: { format: '%m/%d/%Y', outer: false } }, y:{ tick:{ outer: false, count: 5, values: [0, 50, 100, 150, 200], } } }, regions: [ {axis: 'x', start: '2017-08-25', end: '2017-08-30', class: 'regionX'}, ], tooltip: { format: { name: function (name, ratio, id, index) { return 'Houston jobs per 10,000 USA jobs' + '\xa0\xa0\xa0\xa0'; } } }, grid:{ y: { lines: [ {value: 157, text: 'Pre-hurrricane Average', position: 'middle'}, ] } } }); //var rectOffset = function () { return +d3.select(this.parentNode).select("rect").attr("x"); }; d3.selectAll(".c3-region-1") .append("text") .text("Hurricane Harvey") .attr("dy","15") .attr("dx","33%") .style("fill-opacity", 1) .attr("text-anchor", "start") ; To interpret the graph, a value of 156 on August 3 means that for every 10,000 new jobs posted in the nation on that day, 156 of them were posted in the Houston MSA. A quick scan of this chart shows that leading up to Hurricane Harvey, Houston had a consistent volume of new job openings relative to the USA as a whole, averaging about 157 per 10,000 for the 20 days between August 3 and August 22. On August 23, volume started to decrease slightly and dropped to 135 on August 25, the day Harvey made landfall in Texas. As the hurricane continued to rain down large amounts of water over the next three days, job posting volume dropped significantly, falling by more than 20% each day. It hit a low of 75 on August 28, only 48% of the daily average prior to the hurricane. Job advertisements began to increase at this point, including a rapid rise between September 4 and September 6, signaling that recovery efforts had begun. In total, the relative volume of job advertisements in the Houston area stayed below the pre-hurricane average for eighteen days, from August 23 through September 9. As the recovery efforts further picked up, the relative volume increased slightly above the pre-hurricane average for twelve days, between September 11 and September 22, peaking at 180 (14% above average) on September 15, before settling closer to the pre-hurricane average. One would expect Hurricane Harvey’s impact to vary across occupations, depending on the degree to which places of employment were disrupted.  So, we explore how Hurricane Harvey impacted: education, training, and library occupations; healthcare support occupations; and construction and extraction occupations. Volume of New Job Postings per 10,000 U.S. Job Postings by Occupation Category Source: Chmura's JobsEQ ® .c3 path.domain, .c3 line { stroke: #999; } .c3-line { stroke-dasharray: 5; } path.c3-shape.c3-shape.c3-line.c3-line-Construction-and-Extraction{ stroke-width: 2px; } .chartSource{ font-size: .7em; padding-bottom: 1%; } .chartTitle{ font-weight: bold; padding-top: 1%; } var chart2 = c3.generate({ size: { height: 480 },padding: { top: 20 }, data: { x: 'x', // xFormat: '%Y%m%d', // 'xFormat' can be used as custom format of 'x' columns: [ ['x', '2017-08-03', '2017-08-04', '2017-08-05', '2017-08-06', '2017-08-07', '2017-08-08', '2017-08-09', '2017-08-10', '2017-08-11', '2017-08-12', '2017-08-13', '2017-08-14', '2017-08-15', '2017-08-16', '2017-08-17', '2017-08-18', '2017-08-19', '2017-08-20', '2017-08-21', '2017-08-22', '2017-08-23', '2017-08-24', '2017-08-25', '2017-08-26', '2017-08-27', '2017-08-28', '2017-08-29', '2017-08-30', '2017-08-31', '2017-09-01', '2017-09-02', '2017-09-03', '2017-09-04', '2017-09-05', '2017-09-06', '2017-09-07', '2017-09-08', '2017-09-09', '2017-09-10', '2017-09-11', '2017-09-12', '2017-09-13', '2017-09-14', '2017-09-15', '2017-09-16', '2017-09-17', '2017-09-18', '2017-09-19', '2017-09-20', '2017-09-21', '2017-09-22', '2017-09-23', '2017-09-24', '2017-09-25', '2017-09-26', '2017-09-27', '2017-09-28', '2017-09-29', '2017-09-30', '2017-10-01', '2017-10-02', '2017-10-03', '2017-10-04', '2017-10-05', '2017-10-06', '2017-10-07'], ['Education, Training, and Library',164,151,126,135,138,162,170,206,205,213,184,164,181,170,159,171,188,181,190,199,174,157,139,107,65,52,39,51,59,68,75,98,101,126,132,145,153,168,172,167,151,157,154,153,165,158,144,139,145,142,150,147,149,137,150,155,175,198,228,222,206,208,188,162,150,154], ['Healthcare Support',185,182,173,182,175,171,164,160,151,129,124,135,140,177,191,194,182,190,170,183,187,193,193,146,94,55,51,56,60,62,70,85,118,118,132,137,136,136,152,150,152,153,158,159,167,168,170,157,161,156,158,145,145,129,136,138,143,143,149,163,154,158,150,154,139,145], ['Construction and Extraction',177,169,173,188,182,199,214,221,209,211,194,184,184,191,218,225,200,207,239,205,192,208,190,129,130,122,140,166,173,182,210,230,270,247,283,281,274,261,288,250,256,254,261,242,235,208,222,218,216,215,204,162,170,178,181,212,217,217,187,219,200,201,213,237,225,218] ], //type: 'area-spline', colors: { Total: '#7790b8' } }, legend: { show: true }, axis: { x: { type: 'timeseries', tick: { format: '%m/%d/%Y', outer: false } }, y:{ tick:{ outer: false, count: 7, values: [0, 50, 100, 150, 200, 250, 300], } } }, regions: [ {axis: 'x', start: '2017-08-25', end: '2017-08-30', class: 'regionX'}, ], point: { show: false } }); var rectOffset = function () { return +d3.select(this.parentNode).select("rect").attr("x"); }; d3.selectAll(".c3-region-0") .append("text") .text("Hurricane Harvey") .attr("dy","15") .attr("dx","33%") .style("fill-opacity", 1) .attr("text-anchor", "start") ; The above chart of the relative volume of the three occupation categories is based on the number of new jobs posted in a particular occupation group per 10,000 new U.S. job postings in that same occupation group, using a five-day average as before. Not surprisingly, we see that Hurricane Harvey caused a decline in job postings in each of these occupation groups; however, there is a noticeable contrast in the overall impact. Healthcare support as well as education, training, and library occupations had some of the largest declines in relative job postings, decreasing from pre-hurricane averages of 173 and 168 respectively, to lows of 39 and 51 on August 29, only 23% and 30% of the pre-hurricane averages. After hitting these lows, these two occupation groups followed a similar trend to the overall relative job postings, recovering and eventually settling back near their pre-hurricane averages. In contrast, construction and extraction occupations was one of the least affected occupation groups. Here, relative job postings declined less, falling from a pre-hurricane average of 200 to a low of 122 of August 28, 61% of the pre-hurricane average. Following the low, we see not only rapid recovery in construction job postings but growth above pre-hurricane levels. This is something we might expect, given the rebuilding effort that is required after a major hurricane. While the peak for total relative volume was achieved on September 15, eighteen days after the low seen during the hurricane, for construction and extraction occupations there is an initial spike to 283 on September 6, just nine days after the low; and it remained well above average for five days before starting to decline and settling back near the pre-hurricane average. As the data for the Houston region shows, there was a sharp decline in job postings when the hurricane hit, followed by a recovery to return to normal. This decline in job postings doesn’t necessarily mean that employment will fall by the same amount. Some hiring may simply be postponed by a few days or weeks, which could explain some of the increase above the pre-hurricane average seen during the recovery. Regardless, the job postings decline does imply that we can expect to see an overall short-term decline in new hires, and in turn employment, following a major hurricane. Looking at specific employment categories, it is clear that the employment impact varies by occupation. In this case, we see that a hurricane may lead to overall growth in new hires for construction occupations, where the growth above the average during the recovery far exceeds the decline seen during the hurricane.   [1] Hurricane Harvey and Hurricane Irma have had devastating production impacts on major industries in the regions hit, with oil refineries in Texas and citrus crops in Florida being greatly impacted [2] Chmura’s RTI (Real-Time Intelligence) is a dataset comprising online job postings data in the United States, updated daily, to provide insight on potential hiring. A “job posting” in this dataset is a unique (deduped) job posted from one of more than 15,000 sources which has been analyzed to be classified by SOC and location, along with other information. http://www.chmuraecon.com/jobseq/real-time-intelligence/ [3] Specifically, the average of the five-day period from two days prior to two days following. http://chmuraecon.com/blog/2017/october/16/using-job-postings-to-visualize-the-employment-impact-of-hurricane-harvey/ http://chmuraecon.com/blog/2017/october/16/using-job-postings-to-visualize-the-employment-impact-of-hurricane-harvey/ Mon, 16 October 2017 11:23:34 Chris Chmura, Patrick Clapp http://chmuraecon.com/blog/2017/october/12/changes-in-labor-statistics-signal-major-occupational-shifts-and-impact-to-higher-education-credentials/ WASHINGTON, D.C. – October 12, 2017   – A joint research report released today by UPCEA and Chmura Economics & Analytics reveals a number of major occupational shifts in technology, healthcare, and other industries and their impact on higher education. The study,   Occupational Shifts and Higher Education Credentials   found a rise in alternative credentialing in the form of certificates, short-term courses, licensing, badges and micro-credentials among adult learners as well as traditional students. The study, conducted by Jim Fong, director of UPCEA’s Center for Research and Strategy; Christine Chmura, founder of Chmura Economics & Analytics; and Patrick Clapp, economist at Chmura Economics & Analytics; explored how alternative credentials are an attractive substitute to a four-year degree in the changing economic landscape. The study also examined the generational and socioeconomic factors that have led to increased demand for alternative credentials. “This labor analysis shows that our market is changing much quicker than many of us anticipated and that we have to acknowledge a stronger role for alternative credentialing,  said Jim Fong, Director of the UPCEA Center for Research and Strategy. “The degree is not dead, but colleges and universities need to develop educational programs that are in alignment with rapidly changing business needs and societal demographics.” Among the study’s key findings: The economy is   shifting faster than the current educational model . Alternative credentialing is an effective way to develop   “middle skills”   for today’s blue-collar jobs. Two of the fastest growing fields in the next decade are predicted to be   healthcare and professional services . Today’s adult learners have different priorities and preferences than past generations—they want   greater flexibility, shorter commitments, more options, and studies that bring value immediately . For-profit/private sector entities staked out alternative credentialing early on, but   the market is large enough to accommodate colleges and universities that wish to supply non-degree programming . They have the brand recognition, size, and resources to reach larger populations. “With the national unemployment rate dipping below 4.5 percent, firms will seek creative ways to fill positions,” said Christine Chmura, founder of Chmura Economics & Analytics. “Credentials offered by higher education institutions can help fill these skill gaps.” The study indicates the importance of alternative credentialing in the years to come. Adult learners are interested in more focused learning and limiting expenses. Jobs in healthcare, social assistance, virtual reality, drones and interpretation services, among others, are expected to rise over the next ten years. Higher education must be ready to adapt to the changing market of professional development. The complete study,   Occupational Shifts and Higher Education Credentials , is   available here . ###   About Chmura Economics & Analytics Chmura Economics and Analytics is a leader in providing the education, business and government sectors with economic and analytical consulting.  It also offers JobsEQ, an industry leading platform delivering labor market data with a priority on data governance. Chmura brings a research team approach to client assignments and products, providing a broad spectrum of expertise to solve problems and answer questions. Chmura’s economists conduct primary research, investigate prior art, and create custom forecasting and economic models. Its mathematicians and statisticians analyze primary and secondary data and ensure data integrity while data scientists develop technology solutions to deliver data and create visualizations to help tell the story of the data. Chmura’s strategic planners develop implementation plans to move from data-driven analysis to action plans.  For more, visit   chmuraecon.com   About UPCEA UPCEA is the association for leaders in professional, continuing, and online education. Founded in 1915, UPCEA membership includes the leading public and private colleges and universities in North America. For more than 100 years, the association has served its members with innovative conferences and specialty seminars, research and benchmarking information, professional networking opportunities and timely publications. Based in Washington, D.C., UPCEA also builds greater awareness of the vital link between adult and nontraditional learners and public policy issues. Learn more at   upcea.edu . http://chmuraecon.com/blog/2017/october/12/changes-in-labor-statistics-signal-major-occupational-shifts-and-impact-to-higher-education-credentials/ http://chmuraecon.com/blog/2017/october/12/changes-in-labor-statistics-signal-major-occupational-shifts-and-impact-to-higher-education-credentials/ Thu, 12 October 2017 15:37:17 Kyle West http://chmuraecon.com/blog/2017/october/10/heartbeats-and-eats-of-an-economy/ In honor of the National Association of State Workforce Agencies (NASWA) Workforce Summit/81 st Annual Meeting being held October 2017 in Coeur d’Alene, Idaho, we want to provide attendees and other potential visitors with some essential local knowledge and a few insights related to Coeur d’Alene’s economy.   The city is named for its region’s natives, the Coeur d’Alene Tribe , whom French traders in the 18 th century dubbed the Coeur d’Alene people, which translates to “heart of an awl.” Apparently, the Coeur d’Alenes were perceived as tough negotiators by their French counterparts. [1]   Coeur d’Alene (CDA) is the seat of Kootenai County and the namesake for the Gem State’s second largest metropolitan statistical area (MSA) behind Boise , with 154,311 residents versus 691,423, respectively. [2] In 2013, CDA became officially recognized by the US Census Bureau as part of the Spokane – Spokane Valley – Coeur d’Alene combined statistical area, a move that was not fully embraced by locals . As for the economy in CDA, employment in CDA’s economy used to be led by retail trade, thanks in large part to tourism and CDA’s position as a services hub to neighboring rural counties. While the retail sector’s share of employment remains robust at 14.2% in 2016 (compared to 10.8% for the U.S.), the largest share of employment is now held by the health care sector. In fact, since surpassing retail to become CDA’s largest employer in 2010, health care’s share of employment has continued to rise from 14.6% to 17.0% (it’s risen from 13.6% to 14.2% in the U.S.). According to JobsEQ ®, from 2010q2 – 2017q2, the predominant driver of this growth has been hospitals (+1,302), but gains have been widespread across most health care industries. Total gains for the sector (+2,775) have accounted for nearly 40% of all new jobs (+6,984) in CDA during this period. With Spokane located 30 miles west of CDA, one might wonder how CDA has experienced such rapid growth in Spokane’s shadow, where the health care sector is also the largest employer. Well, for decades Spokane was the main place in the Inland Northwest for patients to seek hospital care and specialized health care services. Spokane became, and remains , a major destination for consuming health care services in the Inland Northwest, with little interregional competition. Now, judging from the employment growth in many of its health care industries, it appears that CDA is intercepting some of this traffic. For example, see the highlighted industries and their side-by-side average annual growth comparisons in Table X above. Clearly, CDA’s hospitals have been expanding rapidly as have specialized services and some supporting industries. So, CDA is not just a destination for tourists, but a destination for patients too! Both Spokane and CDA are forecast to continue growing employment in health care for the foreseeable future. Further, both regions currently face potential shortfalls for key health care occupations such as registered nurses, physical therapists, nursing assistants, massage therapists, medical secretaries, and doctors. Below is JobsEQ’s occupation gaps forecast for the Spokane-CDA combined statistical area. Four of these potential shortfalls – RNs, PTs, nursing assistants, and medical secretaries – match gaps forecast for the nation. So, this challenge in key healthcare occupations is not unique to CDA or the Spokane-CDA combined statistical area. However, there is a stark contrast between these two places relative to wages, which seems rather out of the ordinary given these two places are within 30 miles of one another. See below for a comparison of mean wages by occupation. Occupation Wages in Coeur d'Alene, ID MSA, 2016 Labor Force Participation Rate --> Average Annual Wages Comparison Regions Percentiles Spokane-Spokane Valley, WA MSA USA SOC Title Mean 50% (Median) Mean Mean 29-1123 Physical Therapists $67,200 $66,700 $93,700 $87,200 29-1141 Registered Nurses $67,700 $70,700 $80,600 $72,200 31-1014 Nursing Assistants $27,000 $27,300 $28,700 $27,700 31-9011 Massage Therapists $43,100 $40,200 $51,400 $44,500 31-9092 Medical Assistants $32,000 $32,900 $35,500 $32,900 43-6013 Medical Secretaries $30,400 $29,800 $39,700 $35,100 3751 Spokane CDA HC Gaps $49,500 $51,000 $58,000 $53,300 00-0000 Total - All Occupations $40,000 $37,700 $46,900 $49,300 Source: JobsEQ® Data as of 2017Q2 Note: Figures may not sum due to rounding. Exported on: Tuesday, October 3, 2017 3:53 PM Could these labor markets really be this distinct despite their close geographic proximity? After all, their costs of living are virtually identical. Cost of Living Labor Force Participation Rate --> Annual Average Salary Cost of Living Index (Base US) US Purchasing Power Cost of Living Index (Base Coeur d'Alene, ID MSA) Coeur d'Alene, ID MSA Purchasing Power Coeur d'Alene, ID MSA $37,149 98.3 $37,799 100.0 $37,149 Spokane-Spokane Valley, WA MSA $44,196 97.9 $45,144 99.6 $44,368 USA $53,284 100.0 $53,284 101.8 $52,367 Source: JobsEQ® Cost of Living per C2ER, data as of 2017q1, imputed by Chmura where necessary. Exported on: Tuesday, October 3, 2017 6:50 PM Over dessert (at one of the restaurants I’ve suggested below), you might want to ponder what could be driving these wage gaps between neighboring regions. While the Gem State pays notoriously low wages, CDA could be an awfully nice place to call home. With its quaint size, vibrant main street, and idyllic scenery, very few of its most in-demand workers choose to work outside the region where they live (see below), especially on the higher end of the skills spectrum. Cost of Living --> Labor Force Participation Rate --> Coeur d'Alene, ID MSA (1766) Spokane-Spokane Valley, WA MSA (4406) Occupation Empl (Place of Residence) Empl (Place of Work) Ann 50th %ile Wage (Median) Ann 50th %ile Wage (Median) Registered Nurses (29-1141) 1,496 1,384 $70,700 $78,700 Physical Therapists (29-1123) 126 120 $66,700 $79,700 Nursing Assistants (31-1014) 754 690 $27,300 $27,900 Massage Therapists (31-9011) 105 97 $40,200 $50,200 Medical Secretaries (43-6013) 222 190 $29,800 $38,500 Anesthesiologists (29-1061) 18 18 $283,500 $115,000 Family and General Practitioners (29-1062) 74 75 $190,700 $169,900 Internists, General (29-1063) 25 25 $262,600 $206,600 Obstetricians and Gynecologists (29-1064) 12 12 $233,900 $207,500 Pediatricians, General (29-1065) 17 16 $160,800 $198,800 Psychiatrists (29-1066) 12 11 $103,000 $195,700 Surgeons (29-1067) 24 23 $332,400 $316,800 Physicians and Surgeons, All Other (29-1069) 207 205 $232,300 $168,900 Exported on: Tuesday, October 3, 2017 7:07 PM Source: JobsEQ® Note: Figures may not sum due to rounding. Employment data as of 2017Q2. Wage data are as of 2016 and represent the average for all Covered Employment. Interestingly, the wage gap moves in the other direction for a few very specialized doctor types, many of whom were likely recruited away from Spokane. If you get a chance, feel free to seek me out at NASWA’s conference, and we can exchange some ideas on where these gaps may be coming from. Now, for the most important piece of local knowledge: top spots to dine! Should you opt to skip a conference meal or find a break in the event’s agenda for time “on your own,” the firm recommendations below are backed up by years of this author’s first-hand experience. Breakfast : The Garnet Cafe is always fresh and its menu is vintage northwest. A long walk from the conference hotel , a shuttle, an Uber ride, or a short jog will get you there. Lunch : Open midday only, don’t miss Café Carambola . Get there a bit early or risk eating outside, which might not be bad if the sun is shining; also, a long walk or a quick shuttle/Uber ride. For a delicious burger backed by a century of practice, try Hudsons , just two blocks north of the conference hotel.  Dinner : Two “gastropubs” (both on Sherman Avenue) earn a “strong buy” rating. For authenticity, charm, and more vintage northwest, check out Moon Time ; a long walk could land you there, but plan for a ride back to the hotel (due to darkness, weather, and the threat of strong ales). Crafted is a bit more walkable (round-trip) and offers cozy, fireside drinks/dining outside with a more contemporary gastropub menu in a renovated, former auto repair shop.    [1] See https://en.wikipedia.org/wiki/Coeur_d%27Alene,_Idaho , retrieved 10/03/2017. [2] Census 2016. http://chmuraecon.com/blog/2017/october/10/heartbeats-and-eats-of-an-economy/ http://chmuraecon.com/blog/2017/october/10/heartbeats-and-eats-of-an-economy/ Tue, 10 October 2017 09:25:25 Patrick Clapp http://chmuraecon.com/blog/2017/october/05/future-jobs-of-the-past/ “High-skill job groups are projected to continue pacing occupational growth as groups requiring the most education and training are estimated to grow faster than average” The prediction above comes from a 1987 article in the Monthly Labor Review from the Bureau of Labor Statistics.   Many of the predicted trends in occupations of thirty years ago still ring true today. In the article, the importance of computers and robotics across industries was clearly evident. The easiest-to-automate jobs were predicted to have flat or negative growth: “Office automation and other technological changes are expected to cause employment to decline in several [administrative support] occupations, such as typists and word processors.” “The drop in manufacturing employment and increasing factory automation are largely responsible for the lack of employment growth in [the operators, fabricators, and laborers group]” “The increasing use of industrial robots, for example, is expected to cause electrical and electronic assemblers to be the fastest declining occupation…and to cause a more modest…decline for welders and cutters” Meanwhile, jobs more closely aligned with computer use and higher-order thinking were projected to be among the fastest growing: “…the computer and peripheral equipment operators group [is] expected to grow rapidly due to the ever-increasing use of computers throughout the economy.” “The number of operations and systems researchers is projected to grow very rapidly due to the increased importance of quantitative analysis throughout industries” “Employment of executive, administrative and managerial workers is expected to increase…due to the ever-increasing complexity of business operations…” “Computer-assisted design equipment will allow architects to provide more flexible services by producing variations in design more easily” Of course, some specific job predictions reflect the latest developments of the time, such as the promise of genetics (before the sequencing of the human genome in 2003) and other popular topics in science: “The number of life scientists is expected to grow…as genetic research expands into such areas as new medicines, plant and animal variations, and diagnostic techniques for genetic defects.” “Employment opportunities are expected to open up in laser research, high energy physics, and other areas of advanced science” So what can we expect over the next 30 years? The takeaway from the 1987 article was that more educational attainment and training was going to be key to meeting the demand for the fastest growing jobs. Numerous articles in recent years repeat this prediction. Automation and robotics continue, with even lawyers now at risk of eventually being replaced. And computer science jobs are certainly still on the rise . If you’re interested in reading more on this topic, Chmura has written a number of blog posts that touch on predictive trends in demographics , productivity , labor force participation, and automation . http://chmuraecon.com/blog/2017/october/05/future-jobs-of-the-past/ http://chmuraecon.com/blog/2017/october/05/future-jobs-of-the-past/ Thu, 05 October 2017 15:40:27 , Alex Doherty http://chmuraecon.com/blog/2017/october/04/prime-expansion-locations-for-automobile-manufacturers/ Over the past five years, transportation equipment manufacturers have experienced some of the fastest employment growth within the manufacturing sector. Employment for these manufacturers in the United States grew an annual average 2.6% over the five years ending with the second quarter of 2017 compared with an annual average 1.7% for the total manufacturing sector. [1] Within transportation equipment, employment grew the fastest for manufacturers that produced motor vehicles (+4.8%), motor vehicle bodies and trailers (+4.8%), and motor vehicle parts (+4.7%). At $72,720, transportation equipment manufacturers pay a high annual average wage. These wages are higher than the manufacturing average of $65,047 and much higher than the national average for all industries, $53,284. [2] The fast growth rate and above-average wages of transportation equipment manufacturing make it an attractive recruitment target for economic developers. Within transportation equipment, automobile manufacturers are one of the largest industries (national employment of 119,838 for the four quarters ending in 2017Q2), offer high average annual wages ($84,778), and have grown relatively quickly over the past five years (3.3% per year). What locations are best suited to meet auto manufacturers’ workforce needs?  To determine potential expansion locations for an automobile manufacturer, a chief consideration is a region’s ability to supply the company with the skilled workers for its operations. Using staffing patterns in Chmura’s JobsEQ software, a hypothetical automobile manufacturer with 500 workers will need at least 10 people in each of the occupations shown below.​  Sample Expansion Staffing Demands of an Automobile Manufacturing Firm SOC Code Title New Employer Demand 51-2092 Team Assemblers 279 51-1011 First-Line Supervisors of Production and Operating Workers 21 51-9199 Production Workers, All Other 17 17-2112 Industrial Engineers 13 51-9061 Inspectors, Testers, Sorters, Samplers, and Weighers 11 51-2099 Assemblers and Fabricators, All Other 10 47-2111 Electricians 10 Source: ACS 2011-2015, ACS 2006-2010 --> The first consideration of the automobile manufacturer may be the payroll costs associated with these 500 new employees. Chmura’s LaborEQ platform ranks all metro areas in the country by the payroll associated with this hypothetical expansion. Shown in the map below, many areas in Florida jump out as having a relatively low-cost labor supply. Other pockets in the South and West also appear attractive to the manufacturer for their low labor costs. MSA Payroll Rankings for a 500-Employee Expansion Automobile Manufacturing While labor cost is an important consideration for expanding firms, there are many other factors to consider. For example, regional attributes such as labor availability, cost of living, the industry’s regional location quotient, and supply chain contribute to the firm’s decision on where to locate. Chmura’s LaborEQ platform also ranks metro areas by their ability to meet all of these demands for expanding firms. The weight of each metric measuring labor supply, labor cost, and economic climate can be adjusted based on its importance to the expanding firm. Taking this holistic view of many contributing factors and using Chmura’s recommended default weights for each attribute, Florida’s attractiveness fades and the Midwest emerges as having many favorable locations. However, Dallas-Fort Worth, Texas ranks first for the automobile manufacturing industry, followed by Ann Arbor, Michigan, and Nashville, Tennessee.  Overall MSA Rankings for a 500-Employee Expansion in Automobile Manufacturing   [1] Based upon a four-quarter moving average. Calculated using JobsEQ employment estimates, derived from the Quarterly Census of Employment and Wages (QCEW) through 2016Q4 and preliminary estimates for the first half of 2017. [2] All wage estimates reflect the year ending 2017Q2. Source: JobsEQ, using QCEW through 2016Q4 and preliminary estimates in 2017. http://chmuraecon.com/blog/2017/october/04/prime-expansion-locations-for-automobile-manufacturers/ http://chmuraecon.com/blog/2017/october/04/prime-expansion-locations-for-automobile-manufacturers/ Wed, 04 October 2017 13:40:12 Chris Chmura http://chmuraecon.com/blog/2017/october/02/economic-impact-some-manufacturing-employment-growing-at-such-a-fast-pace-that-it-might-raise-some-eyebrows/ Most people probably wouldn’t be too surprised to learn that employment at online retailers or internet publishing and web search portal firms grew at a double-digit annual average pace over the past 10 years ending with 2016. On the other hand, manufacturing employment growing at such a fast pace might raise some eyebrows. The U.S. manufacturing sector employed about 1.8 million fewer workers in 2016 compared with 10 years earlier, but many industries within the sector have posted solid to robust job gains. For instance, breweries and wineries top the list of manufacturing industries with the largest number of job gains over the last decade, with 33,185 and 24,785 jobs, respectively, in the nation, according to data from the North American Industrial Classification System. Employment in these industries plateaued during the Great Recession and did not decline like manufacturing overall. Employment at wineries has been growing at a steady annual average rate that translates into 5.3 percent growth in the past 10 years, while breweries saw a 8.6 percent increase, based on data from the Bureau of Labor Statistics that are enhanced with self-employment calculations imputed by Chmura Economics & Analytics. In the Richmond area, employment at breweries jumped from 176 to 268 in the past decade, while employment at area wineries increased to 38 people from four workers a decade ago. In addition to breweries and wineries, other food and beverage manufacturing industries also are among those with the most job growth during the last decade. Within the past 10 years, retail bakeries nationally expanded by 21,074 jobs — a 2.6 percent annual average rate of growth — while perishable prepared food manufacturing increased by 17,884 jobs, or an annual average growth of 4.4 percent. Employment at retail bakeries in the Richmond metro area outpaced the nation with 3.6 percent annual average growth, but prepared food manufacturing declined by an annual average 0.6 percent in the region over the same period. All food and beverage manufacturing industries combined added 149,716 jobs to the U.S. economy over the last decade and 629 jobs in the Richmond metro area. Manufacturing employment in the nation reached the highest level in August that it has seen since January 2009. This is good news because manufacturing jobs pay a much better wage than the average industry sector and provide opportunities for workers at all education levels. http://chmuraecon.com/blog/2017/october/02/economic-impact-some-manufacturing-employment-growing-at-such-a-fast-pace-that-it-might-raise-some-eyebrows/ http://chmuraecon.com/blog/2017/october/02/economic-impact-some-manufacturing-employment-growing-at-such-a-fast-pace-that-it-might-raise-some-eyebrows/ Mon, 02 October 2017 14:05:46 Patrick Clapp http://chmuraecon.com/blog/2017/september/29/marine-jobs-in-the-ocean-state/ Rhode Island may be nicknamed the “Ocean State,” but “Marine” appears more often in online job ads than “Ocean” does. Though there are a number of jobs available for someone who loves the ocean, many are not designated specifically in the standard occupation codes. For example, there is no Standard Occupational Classification (SOC) code specific to coastal engineers or oceanographers. A review of online job postings can help identify these niche careers and tie them back to standard codes for further analysis  Among online job postings [1] from a recent 30-day period in Rhode Island, five ads included “ocean” in the job title, including Professor of Oceanography and Ocean Lifeguard. However, 25 jobs included the keyword “marine” in the title, including Test Engineer – Submarine Combat System; Marine Aluminum Welder; and Marine Diesel Mechanic. These SOC designations for these 25 jobs are shown in the table below. 8-Digit SOC Occupation Categorizations of Online Job Postings for “Marine” Jobs in Rhode Island 8-Digit SOC Occupation Categorizations of Online Job Postings for “Marine” Jobs in Rhode Island --> Motorboat Mechanics and Service Technicians Pipe Fitters and Steamfitters Computer Systems Engineers/ Architects Painters, Construction and Maintenance Upholsterers Sales Representatives, Services, All Other Layout Workers, Metal and Plastic Environmental Science Teachers, Postsecondary Welders, Cutters, and Welder Fitters Environmental Restoration Planners Riggers Medical Scientists, Except Epidemiologists Maintenance and Repair Workers, General Mechanical Engineering Technologists Computer User Support Specialists Aerospace Engineering and Operations Technicians Information Security Analysts Mechanical Drafters Electrical and Electronics Installers and Repairers, Transportation Equipment Source: JobsEQ®  Employers posting the most online ads overall in Rhode Island over this period include the Graduate School of Oceanography at the University of Rhode Island; General Dynamics; and General Electric. The state is home to a number of defense related employers—defense contract spending totaled almost $1.5 billion in Rhode Island in fiscal year 2015, including $732.5 million in contracts for ships, small craft, pontoon, and docks products and services. [2] Getting back to the “marine” related jobs, further information can be gained by considering the above list of occupation categorizations as a group of “possible marine” occupations. For example, more than 16,000 people are employed across the state in these “possible marine” occupations, though they are certainly not all tied to marine or ocean related industries. Some of the industries employing these occupations include ship and boat building; national security and international affairs; computer systems design and related services; and colleges, universities, and professional schools. Additionally, though it is more concentrated in zip codes bordering the water, employment in these occupations is not strictly limited to the coast. As a whole, this group of occupations have grown faster than the state average over the last five years, and many are projected to continue growing over the next ten years. [3] The “possible marine” occupations grew at an annual average rate of 1.3% between 2007 and 2017, compared to 1.0% across all occupations. The occupations in this group with the fastest growth forecasts for the next 10 years are information security analysts; computer user support specialists; plumbers, pipefitters, and steamfitters; and medical scientists, except epidemiologists.   [1] The RTI (Real-Time Intelligence) data set is solely constructed and maintained by Chmura Economics & Analytics . The data set consists of online job postings, updated daily and categorized to the 8-digit SOC level . Job counts represent ads for the United States that have been deduplicated and were active at any point in the 30 days prior to September 20, 2017. RTI is available through the JobsEQ online platform. [2] Source: Regional Aerospace & Defense Exchange (RADE) Economic Modeling Platform , powered by the FedSpendTOP data set . [3] Source: JobsEQ ®. These region-specific forecasts within JobsEQ are consistent with the national level forecasts developed in the BLS Employment Projections Program . http://chmuraecon.com/blog/2017/september/29/marine-jobs-in-the-ocean-state/ http://chmuraecon.com/blog/2017/september/29/marine-jobs-in-the-ocean-state/ Fri, 29 September 2017 15:37:20 Doug Rice http://chmuraecon.com/blog/2017/september/22/how-colleges-can-attract-workers/ In 2008, PEW Research published a study [1] investigating the reasons people either remain in or move away from their hometown. The study found that—among characteristics measured such as gender, age, race, and family income—the largest difference between those who moved and those who stayed centered around educational attainment. Among the people surveyed, 77% of the college graduates had moved at least once, compared to only 56% of those without college degrees.   This trend tells an interesting story. Many students who attend college may not return home when they are finished, possibly finding long-term work in the area where they went to school. Therefore, the availability of academic programs in your area can play a key role in bringing in more workers. So, how might you go about measuring how well your area compares in this respect? One way to assess the strength of your locality’s academic institutions is to look at the number of awards that are being earned per capita. Accessing population and completions data in JobsEQ , [2] we can look at how the postsecondary awards per 1,000 people compare across the four major regions of the United States. [3] In addition to the four major U.S. regions, we’ve included the awards per capita for the entire United States and for the Chicago-Naperville-Elgin, IL-IN-WI MSA. Across the nation, the two-year [4] degrees and bachelor’s degrees per capita are relatively equal, with the postgraduate degrees being about half as common. In the Midwest and Northeast regions, the bachelor’s and postgraduate degrees tend to skew higher while, in the West and South, the two-year degrees run above average. Taking Chicago as an example of how one metropolitan area compares, we can see where it may have an advantage and opportunities for improvement. In Chicago, both the two-year degrees and the bachelor’s degrees per capita fall below the national average, but only by a modest amount. Compared to the rest of the Midwest, however, the bachelor’s degrees are significantly lower in Chicago. On the other hand, the Chicago MSA exceeds all major regions in postgraduate awards per capita. Schools in the Chicago MSA offer 4.5 postgraduate awards per thousand people, compared to the Midwest region’s average of 3.5 and the national average of 3.1. This attribute can translate into an advantage for Chicago, giving the area access to a highly-educated workforce that could be enticed to stay in the area, bringing economic benefits such as higher wages and greater innovation. [5]     [1] “American Mobility: Who Moves? Who Stays Put? Where’s Home?” Paul Taylor et al, Pew Research Center , December 2008, Available at: http://www.pewsocialtrends.org/files/2010/10/Movers-and-Stayers.pdf [2] JobsEQ is economic data analysis software provided by Chmura Economics and Analytics. Awards (completions) data is retrieved from the NCES and population data is retrieved from the US Census . [3] For a definition or map of the US Census Regions used in this analysis, visit this webpage: https://www.census.gov/geo/reference/gtc/gtc_census_divreg.html [4] For the purposes of this analysis, ”Two Year Degrees” include all award levels less than a bachelor’s degree, “Bachelor’s Degrees” include bachelor’s degrees and post-baccalaureate certificates, and ”Postgraduate Degrees” include all award levels at the master’s level and higher.  [5] See, for example, http://www.chmuraecon.com/blog/2015/august/28/as-employment-grows-when-will-we-see-wage-growth , as well as http://eprints.lse.ac.uk/67680/ http://chmuraecon.com/blog/2017/september/22/how-colleges-can-attract-workers/ http://chmuraecon.com/blog/2017/september/22/how-colleges-can-attract-workers/ Fri, 22 September 2017 09:48:38 Greg Chmura http://chmuraecon.com/blog/2017/september/21/what-are-the-hottest-it-certifications/ With the JobsEQ RTI data set, [1] one can easily stay up-to-date with which certifications are being requested most by employers in their job ads. The below graphic shows the top four IT certifications for the thirty days ending September 15, 2017; the counts indicate how often these certifications were mentioned by U.S. employers in online ads for computer occupations. [2]   The top credential for this period is Certified Information Systems Security Professional (CISSP) . This certification is especially important for the information security analysts occupation. [3] About 18% of the ads for this occupation included a reference to the CISSP certification. Next in line is the Project Management Professional (PMP) credential. This certification was mentioned in 17% of the ads for information technology project managers. This credential is also used outside IT—for example, in the fields of engineering, finance, and health care. The third-most mentioned IT certification for the thirty days ending September 15 is Information Technology Infrastructure Library Certification (ITIL) . Mentions for this credential were dispersed across several different IT occupations, the most common being computer user support specialists and the next-most prevalent being network and computer systems administrators. Coming in fourth place, the final top certification in our survey, is Cisco Certified Network Associate (CCNA) . This certification was mentioned most often in ads for network and computer systems administrators—about 5% of the ads for this occupation referenced the CCNA certification.    [1] The RTI (Real-Time Intelligence) data set is solely constructed and maintained by Chmura Economics & Analytics . The data set consists of online job postings, updated daily and categorized to the 8-digit SOC level . Job counts represent ads for the United States that have been deduplicated and were active at any point during the reference period. RTI is available through the JobsEQ online platform. [2] "Computer occupations" are defined here as SOC 15-1000. [3] All occupation titles referenced here are based upon the 8-digit SOC codes. http://chmuraecon.com/blog/2017/september/21/what-are-the-hottest-it-certifications/ http://chmuraecon.com/blog/2017/september/21/what-are-the-hottest-it-certifications/ Thu, 21 September 2017 14:16:19 Patrick Clapp http://chmuraecon.com/blog/2017/september/21/where-participation-points-matter-labor-force-participation-rates-in-us-counties/ var paths = [ "/Scripts/blogData/us.json", "/media/1317/county-labor-force.tsv" ]; #map-cont { position: relative; } .map-overlay { position: absolute; top: 0; left: 0; pointer-events: none; opacity: 0; } #map { border: none; margin-top: 20px; } #legend { position: absolute; bottom: 35px; padding: 10px; width: 126px; right: 10px; background: #fff; } #legend ul { list-style-type: none; margin: 0; padding: 0; overflow: hidden; } #legend li { margin: 0; padding: 0; line-height: 0px; } #legend .legend-title { font-size: 14px; line-height: 18px; margin-bottom: 10px; } #legend .color-block { position: relative; width: 15px; height: 22px; } #legend .color-label { top: 10px; left: 25px; width: 85px; position: absolute; text-align: left; font-size: 11px; } .foot { font-size: 0.8rem; } .states { fill: none; stroke: #fff; pointer-events: none; } .counties { fill: none; stroke: none; } .counties--border:hover { stroke: #444; } .tooltip { position: absolute; z-index: 10; visibility: hidden; background: rgba(255,255,255,0.925); border: solid 1px #ddd; padding: 12px; width: 200px; opacity: 1; } .post-body .tooltip h3 { text-align: left; font-size: 14px; padding-bottom: 5px; border-bottom: 1px solid #ddd; font-weight: bold; margin-bottom: 5px; margin-top: 0; } .post-body .tooltip h4 { font-size: 14px; color: #000; margin-bottom: 0; } .viz-footer { margin-bottom: 20px; clear: both; } Labor Force Participation Rate and Size (civilian population 16 years and over) < 53.0% 53.0% to 58.0% 58.0% to 62.0% 62.0% to 65.0% 65.0% to 68.0% ≥ 68.0% Source: Chmura Economics & Analytics , JobsEQ var width = 960, height = 600, baseYear = 2010, dataHash, countyHash, tooltipCountyId; var color = d3.scale.threshold() .domain([0, 0.53, 0.58, 0.62, 0.65, 0.68, 1.0]) .range(['#fff', 'rgb(239,243,255)', 'rgb(201,220,242)', 'rgb(163,198,229)', 'rgb(125,175,215)', 'rgb(87,153,202)', 'rgb(49,130,189)']); var projection = d3.geo.albersUsa() .scale(1280) .translate([0, 0]); var path = d3.geo.path() .projection(projection); var svg = d3.select("#map").append("svg") .attr("width", width) .attr("height", height) d3.selectAll('div.map-overlay').style({ "width": width + 'px', "height": height + 'px'}); //make sure these are the same size as the map var gZ = svg.append('g') .attr("transform", "translate(" + width / 2 + "," + height / 2 + ")"); var g = gZ.append('g'); //tooltip div for mouseovers var tooltip = d3.select('#tooltip'); //round numbers to one decimal place var fmt = d3.format('0,.1f'); //setup the tooltip chart var subWidth = 200, subHeight = 30, subTopMargin = 5, subYPad = 10, subXPad = 16, subBoxWidth = (subWidth - subYPad) / (2030 - baseYear); var svgSub = d3.select('#tooltip-chart').append('svg') .attr('class', 'barchart') .attr('width', subWidth) .attr('height', subHeight); //x scale & axis for sub chart var subScaleX = d3.scale.linear().domain([baseYear, 2030]).range([subYPad, subWidth - subYPad * 2]); var subAxisX = d3.svg.axis() .ticks(3) .tickFormat(d3.format('.0f')) .scale(subScaleX); //var subColorScale = d3.scale.linear().domain([baseYear, 2030]).range(['blue', 'green']); ////draw axis var subX = svgSub.append('g') .attr('class', 'x axis') .attr('transform', 'translate(' + (subBoxWidth / 2) + ', ' + (subHeight - subXPad - subTopMargin) + ')') //offset it a little bit so that the tick marks align w/ center of boxes .call(subAxisX); //draw initial lines & points var lines = svgSub.append('g') .attr('class', 'lines'); //create an array of years that we'll use to draw boxes var yearArray = []; for (var yr = baseYear; yr ' + fmt(row.laborForce * 100.0) + '% '); }; } In Eagle County, Colorado, 80% of the civilian population 16 or older were either working or looking for work in 2015. In Sumpter County, Florida, that figure was below 25%. An active working-age population is a critical component to support economic growth, and the labor force participation rate—calculated as the percentage of the civilian population 16 years and older who have a job or are looking for one—provides a helpful measure of this activity and the health of a region’s economy. The national labor force participation rate was 63.3% in 2015, but participation rates vary widely by county. The top 10 counties [1] with the highest labor force participation rates (and populations greater than 10,000) are detailed in the table below. Many of the top counties benefitted from growth in bordering counties due to the oil and gas boom occurring during the survey years (2011-2015) or are bedroom communities for commuters working in nearby metro centers experiencing growth (such as Washington, D.C.) and tourist attractions. Manufacturing Industries Adding the Most Jobs, 2006-2016 --> Labor Force Participation Rate --> County with Population Greater than 10,000 Labor Force Participation Rate 2015 Eagle County, Colorado 80.0% Teton County, Wyoming 78.6% Summit County, Colorado 77.9% Alexandria City, Virginia 77.7% Lincoln County, South Dakota 77.4% Arlington County, Virginia 77.3% Scott County, Minnesota 76.7% Campbell County, Wyoming 76.6% Teton County, Idaho 76.5% Carver County, Minnesota 76.1% Source: JobsEQ® , ACS 2011-2015 The national unemployment rate of 4.4% in September 2017  suggests full employment . At the same time, some regions continue to struggle to recover from the Great Recession based on their low participation rates. The participation rate provides a measure of the population willing to work as well as groups that have been discouraged from participating or are unwilling to enter the labor force. Increasing participation in the labor market can boost production and economic growth in a region if firms are not producing at full capacity because of labor shortages. People may not participate in the labor force for a number of reasons, including retirement, disabilities, taking care of a family member, and attending school. In a previous blog post we discussed the population facing barriers to employment which may discourage them from participating in the labor force. We estimated integrating this group into the labor market could increase the national labor force participation rate by 1.2 percentage points. Nationally, the labor force participation rate has been declining since 2000, and there is some confusion about why. Some of the change can be explained by the baby boomer population entering retirement as well as more young adults staying in school longer and pursuing higher education. Numerous other explanations exist for the remaining decline, including recently that opioids account for a large share of the drop in labor force participation among men. However, participation rates are not declining everywhere. Between the 2010 and 2015 estimates, labor force participation rates rose in about 20% of counties.  The ten counties with the largest percentage point change in labor force participation between 2010 and 2015 are shown in the table below. Areas at the top of this list tended to have particularly low participation rates in 2010, and often also saw improved educational attainment of the population. Manufacturing Industries Adding the Most Jobs, 2006-2016 --> Labor Force Participation Rate County with Population Greater than 10,000 2010 2015 Change (Percentage Points) Buckingham County, Virginia 27.4% 51.4% 24 Goochland County, Virginia 45.1% 60.3% 15.2 Powhatan County, Virginia 47.8% 59.6% 11.8 Karnes County, Texas 35.5% 45.8% 10.3 Summers County, West Virginia 38.5% 46.9% 8.3 Limestone County, Texas 48.1% 54.8% 6.6 Harrisonburg City, Virginia 51.8% 58.3% 6.5 Iron County, Missouri 46.8% 52.6% 5.8 Clarke County, Alabama 44.4% 50.2% 5.8 Rio Grande County, Colorado 54.0% 59.8% 5.7 Source: ACS 2011-2015, ACS 2006-2010 [1] Though data in the map are shown for every county, data in the tables are limited to regions with a population of at least 10,000 people to minimize potentially misleading estimates in small counties. http://chmuraecon.com/blog/2017/september/21/where-participation-points-matter-labor-force-participation-rates-in-us-counties/ http://chmuraecon.com/blog/2017/september/21/where-participation-points-matter-labor-force-participation-rates-in-us-counties/ Thu, 21 September 2017 09:49:59 Doug Rice http://chmuraecon.com/blog/2017/september/14/job-growth-in-manufacturing/ While the U.S. manufacturing sector employed about 1.8 million fewer workers in 2016 compared to ten years prior, many industries within the sector have posted solid to robust job gains. At the six-digit NAICS [1] code levels, 18 manufacturing industries expanded by at least 8,000 jobs over the last ten years (2006 – 2016). While there is some diversity within these industries, there are several clusters where most of the employment growth has occurred.   Manufacturing Industries Adding the Most Jobs, 2006-2016 NAICS Industry Empl, 2016 Avg Ann Wages, 2016 Total Empl Change, 06-16 Avg Ann % Chg in Empl, 06-16 312,120 Breweries 59,194 $50,516 33,185 8.6% 312,130 Wineries 61,518 $44,231 24,785 5.3% 311,811 Retail Bakeries 93,919 $22,287 21,074 2.6% 311,991 Perishable Prepared Food Manufacturing 50,839 $35,217 17,884 4.4% 336,413 Other Aircraft Parts and Auxiliary Equipment Manufacturing 107,850 $70,941 16,391 1.7% 336,611 Ship Building and Repairing 132,982 $70,853 13,103 1.0% 339,112 Surgical and Medical Instrument Manufacturing 120,583 $81,806 12,270 1.1% 336,360 Motor Vehicle Seating and Interior Trim Manufacturing 72,658 $51,459 11,762 1.8% 311,612 Meat Processed from Carcasses 122,722 $44,514 11,213 1.0% 325,413 In-Vitro Diagnostic Substance Manufacturing 25,298 $100,275 9,376 4.7% 326,112 Plastics Packaging Film and Sheet (including Laminated) Manufacturing 19,923 $59,786 8,927 6.1% 311,920 Coffee and Tea Manufacturing 23,478 $50,064 8,758 4.8% 333,611 Turbine and Turbine Generator Set Units Manufacturing 28,584 $85,184 8,718 3.7% 311,919 Other Snack Food Manufacturing 41,402 $49,961 8,429 2.3% 336,411 Aircraft Manufacturing 232,964 $104,124 8,274 0.4% 323,113 Commercial Screen Printing 79,213 $35,964 8,264 1.1% 311,513 Cheese Manufacturing 46,335 $53,875 8,168 2.0% 335,228 Other Major Household Appliance Manufacturing 20,557 $62,744 8,116 5.2% Source: JobsEQ®   [2]   Alcoholic Beverage Manufacturing Breweries and wineries top the list of manufacturing industries with the largest number of job gains over the last ten years, with 33,185 and 24,785, respectively. These industries plateaued during the Great Recession, not declining like manufacturing overall. Following the recession and its residual effects, wineries and breweries have been growing at a steady rate—and establishment counts of breweries have been expanding nearly exponentially.     Food and Other Beverage Manufacturing In addition to breweries and wineries, other food and beverage manufacturing industries also number among those with the most job growth during the last decade. Within this time frame, retail bakeries and perishable prepared food manufacturing expanded by 21,074 and 17,884 jobs, respectively. Meat processed from carcasses grew by 11,213 jobs. Other snack food manufacturing, coffee and tea manufacturing, and cheese manufacturing each expanded by between 8,000 and 9,000 jobs over the last ten years. All food and beverage manufacturing industries [3] combined added 149,716 jobs to the economy over the last decade.   Transportation Equipment Manufacturing Another significant contributor to manufacturing employment growth over the last decade is transportation equipment manufacturing. Other aircraft parts and auxiliary equipment manufacturing grew the most within this group, adding 16,391 jobs. Ship building and repairing and motor vehicle seating and interior trim manufacturing were close behind, expanding by 13,103 and 11,762 jobs, respectively. Finally, aircraft manufacturing also contributed to growth in this group, expanding by 8,274 jobs over the last decade. Other Industries A few other noteworthy industries that contributed significantly to recent manufacturing employment growth include surgical and medical instrument manufacturing and in-vitro diagnostic substance manufacturing, expanding by 12,270 and 9,376 jobs, respectively. Both of these are related to healthcare: the former is a subset of the medical equipment industry and the latter a subset of the pharmaceutical industry. Other industries rounding out the most significant contributors to manufacturing job growth over the last decade are plastics packaging film and sheet manufacturing, turbine and turbine generator set units manufacturing, commercial screen printing manufacturing, and other major household appliance manufacturing , each expanding by between 8,000 and 9,000 jobs.      [1] NAICS stands for the North American Industrial Classification System. NAICS codes begin at the two-digit level (referred to as “sectors”) and subdivide into industries down to six digits (as shown in the table). [2]  Industry data compiled in JobsEQ are based on covered employment from the QCEW , published by the BLS , with self-employment calculations imputed by Chmura Economics and Analytics .  [3] For the purposes of this piece, “all food and beverage manufacturing industries” is defined as the combination of NAICS codes 311 and 3121. http://chmuraecon.com/blog/2017/september/14/job-growth-in-manufacturing/ http://chmuraecon.com/blog/2017/september/14/job-growth-in-manufacturing/ Thu, 14 September 2017 14:10:35 Patrick Clapp http://chmuraecon.com/blog/2017/september/08/vulnerable-communities-in-coastal-florida/ More than 15.5 million people live in coastal counties in Florida at risk of at least storm surge from Hurricane Irma. Some populations may be more at risk than others. Inspired by the Social Vulnerabilities maps by Direct Relief , the maps below from JobsEQ ® show concentration by county of communities that may be especially vulnerable when Hurricane Irma lands. Individuals with a disability may have difficulty moving or special medical needs that impair evacuation. The largest concentration is in the north west counties of Florida, especially Dixie (22.4%), Franklin (19.9%), and Taylor (19.7%). In total, there are nearly 868 thousand people age 18-64 with a disability living in Florida’s coastal counties.     The map of poverty levels looks fairly similar, led by Levy (22.0%), Dixie (21.1%), and Miami-Dade (20.4%). Communities with higher poverty rates may face difficulties with access to transportation and basic necessities, as well as more difficulty rebuilding in the aftermath. http://chmuraecon.com/blog/2017/september/08/vulnerable-communities-in-coastal-florida/ http://chmuraecon.com/blog/2017/september/08/vulnerable-communities-in-coastal-florida/ Fri, 08 September 2017 16:37:02 Chris Chmura http://chmuraecon.com/blog/2017/september/08/a-case-study-in-labor-market-analytics/ How would you handle a request from your boss asking whether you’ll be able to continue to attract the right talent needed for your current business operations? In today’s economy, that conversation might go something like this: The unemployment rate in Northern Virginia is 3.1 percent as of April 2017 and the company’s growth plans are contingent on adding at least 50 more cyber security jobs a year in the Stafford County office over the next four years.  I need you to provide me with an assessment of whether we can acquire the talent in the region as well as a contingency plan for expanding to an alternative location where the labor is available. That is a tall order, but one that Human Resource professionals will continue to receive as the labor market tightens further. Access to a labor market tool such as Chmura Economics & Analytics’ JobsEQ along with its Real Time Intelligence (RTI) job postings makes the task achievable, especially since “cyber security” is not yet an occupation that the U.S. Bureau of Labor Statistics tracks under their Standard Occupation Classification (SOC) system. This is where job postings data are necessary. RTI, which is based on scraping and analyzing millions of job postings, allows key word searches. There were 1,613 job openings related to “cyber” in Northern Virginia over the 30 days ending with June 8, 2017. Main competitors in the region are Booz Allen Hamilton with 178 cyber-related job posts, Northrop Grumman with 175, and ManTech International with 64. The main SOC code associated with these cyber job postings is “information security analysts.” In fact, as shown in the graphic below, that occupation makes up 80 percent of the cyber job postings in Northern Virginia.  As of the fourth quarter of 2016, there were 5,529 people employed in the region as information security analysts.   A rule-of-thumb to assess the ability of the local market to meet your workforce needs is to have at least 50 people currently employed in the region with the skills needed for every worker you need to add.  From that perspective, Northern Virginia is a good location.  There are 111 information security analysts for every one you need (5,529/50), which puts Northern Virginia in a good position to provide the needed workforce. With all the higher education institutions in Northern Virginia, the pipeline of graduates may also provide additional candidates.  The National Center for Education Statistics (NCES) indicates that 1,672 students received a degree for the 2015 academic year in Northern Virginia that would position them well to work as an information security analyst.  Your firm tends to hire people with experience or with a master’s degree or higher.  From that perspective, the local higher education institutions graduated 286 students at that degree level. A potential issue with Northern Virginia is the cost of living.  Based on the C2ER cost of living index, Northern Virginia is 49.6 percent more expensive than the average for the nation. Based on these findings it seems prudent to also consider other locations around the nation.  You begin by focusing only those locations where there are at least 50 information security analyst jobs in the region per opening to fill.  In other words, there needs to be at least 2,500 Information Security Analysts.  The accompanying map of metropolitan areas around the country shows that only seven MSAs meet that qualification.   Although the Washington-Arlington-Alexandria, DC-VA-MD-WV MSA is among the metro areas, your firm has historically used only the Northern Virginia portion of the area because of your location in the southern part of the region. Not surprisingly, all the candidate regions for expansion are large metropolitan areas.  The Washington D.C. metro area tops the list followed by New York City and Chicago. Potential Information Security Analysts Region Number of Employed Information Security Analysts Potential Candidate to Opening Ratio Total Awards Certs and 2-Year Degrees 4-Year Degrees Postgrad Degrees RTI Job Openings Related to Cyber Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 9,192 184 4,382 1,009 1,636 1,737 2,388 New York-Newark-Jersey City, NY-NJ-PA MSA 6,408 128 3,325 1,115 1,165 1,045 624 Chicago-Naperville-Elgin, IL-IN-WI MSA 3,476 70 3,817 1,277 1,136 1,404 241 Dallas-Fort Worth-Arlington, TX MSA 3,393 68 1,025 679 285 61 282 Los Angeles-Long Beach-Anaheim, CA MSA 3,143 63 4,047 1,091 1,649 1,307 223 Boston-Cambridge-Newton, MA-NH MSA 3,088 62 2,062 395 1,045 622 214 San Francisco-Oakland-Hayward, CA MSA 2,777 56 926 244 501 181 151 Minneapolis-St. Paul-Bloomington, MN-WI MSA 2,511 50 1,653 538 625 490 110 Northern Virginia Portion of Washington MSA 5,529 111 1,672 466 620 286 1,437 Source: JobsEQ® Employment represent fourth quarter of 2016; Job openings are for the 30 days ending with June 8, 2017 and awards represent the 2015 academic year.  All the potential regions for expansion provide an ample supply of graduates. Washington, Los Angeles, and Chicago graduated the most potential cyber security candidates in the latest academic year.  Individuals with postgraduate degrees are relatively mobile so any region around the country can benefit from those graduates if they are attractive to the students.  A natural amenities index created by the U.S. Department of Agriculture rates Los Angeles and San Francisco the highest in terms of amenities. Of course, payroll costs are a consideration for any firm and particularly professional business firms where payroll makes up the majority of costs.  Based on the average wage for each metro area, total annual payroll for 50 information security analysts can vary from $6.0 million in New York to $4.5 million in Minneapolis—a difference of $1.5 million. Region Natural Amenities Index (1 to 7) * Cost of Living Index (Base US=100) Average Wage for Information Security Analysts Total Payroll for 50 People Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 3.0 144 $108,200 $5,410,000 New York-Newark-Jersey City, NY-NJ-PA MSA 3.6 155 $120,800 $6,040,000 Chicago-Naperville-Elgin, IL-IN-WI MSA 2.7 115 $97,500 $4,875,000 Dallas-Fort Worth-Arlington, TX MSA 4.0 101 $91,600 $4,580,000 Los Angeles-Long Beach-Anaheim, CA MSA 7.0 145 $104,200 $5,210,000 Boston-Cambridge-Newton, MA-NH MSA 3.5 145 $99,600 $4,980,000 San Francisco-Oakland-Hayward, CA MSA 6.7 160 $111,700 $5,585,000 Minneapolis-St. Paul-Bloomington, MN-WI MSA 2.6 105 $89,900 $4,495,000 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 3.0 125 $97,000 $4,850,000 Northern Virginia Portion of Washington MSA 2.9 150 $107,600 $5,380,000 Source: JobsEQ® * U.S. Department of Agriculture. With this information in hand, an expansion decision can be made with more confidence. http://chmuraecon.com/blog/2017/september/08/a-case-study-in-labor-market-analytics/ http://chmuraecon.com/blog/2017/september/08/a-case-study-in-labor-market-analytics/ Fri, 08 September 2017 10:22:40 Chris Chmura http://chmuraecon.com/blog/2017/september/04/who-is-missing-from-the-labor-force/ As we celebrate the achievement of American workers on this Labor Day, we shouldn’t forget those who are left out of the labor market. The labor force participation rate, which is the percent of the civilian population 16 years and over who have a job or are looking for one, hasn’t been lower since 1978. In July 2017, the labor force participation rate was 62.9 percent — down from a peak of 67.3 in March 2000. The Federal Reserve Bank of Atlanta, which analyzes the changes in labor force at its Center for Human Capital Studies, concluded that the retiring population explains about half of the 3-percentage point decline in the labor force participation rate since 2008. The Atlanta Fed also consider the reasons that people are not in the labor force, which varies by age, education, race and gender. Not surprisingly, a higher percentage of people younger than 25 are not in the labor force because they are in school full-time, while many people (mainly women) 25 to 40 years old are taking care of the family. Disability or illness increases as a reason for not being in the labor force for the 45 to 60 age group. After age 60, retirement is the leading reason why individuals are not in the labor force. One particular group not in the labor force includes those “hard-to-employ” people with barriers to work. Kevin Corinth wrote an article in July when he was a research fellow at the American Enterprise Institute that digs deeper into why those individuals are not participating in the labor force and estimates the number of people not working because they face barriers. He estimated that in 2015, 14.5 million adults between 25 and 54 had incomes below 200 percent of the poverty line and at least one of the following barriers to work: Homelessness; Serious mental illness; Substance abuse; or Arrested and booked for breaking the law. Not all people with barriers would be willing to work if offered a job, so Corinth used information on peers with similar demographic characteristics and education but who do not face barriers to tease out the percentage with barriers who would work. This method resulted in an estimate of 2 million people who potentially would be willing to work. If those people are successfully integrated into the labor market, they can increase the national labor force participation by 1.2 percentage points. Corinth points to social enterprises as a solution to help this population, particularly those with recent experiences of homelessness, serious mental illness, and those with multiple barriers, which makes up 1.6 million adults where the gap between the actual and predicted work rate is largest. These enterprises offer on-the-job training to people that are hard-to-employ with the goal of transitioning them into conventional employment. When we look at macro-level statistics, it’s easy to lose sight of the human side of those numbers. This Labor Day might be a good time to think differently about those who are not participating in the labor force as well as the social enterprises that help bring these individuals back into the labor force. http://chmuraecon.com/blog/2017/september/04/who-is-missing-from-the-labor-force/ http://chmuraecon.com/blog/2017/september/04/who-is-missing-from-the-labor-force/ Mon, 04 September 2017 13:24:48 Xiaobing Shuai http://chmuraecon.com/blog/2017/august/18/are-american-workers-becoming-fickler/ With the millennial generation entering the labor force, there is much talk that this new generation of workers is more mobile and will work for dozens of companies over their careers. Some observers also say that American workers, in general, are not loyal to the firms that hire them. Are those claims supported by facts, or are they just fueled by anecdotal stories from Silicon Valley or other high-tech hotspots where young tech workers jump from job to job for ever-growing pay packages or stock options?  A Bureau of Labor Statistics (BLS) report provides some answers to that question. Since 1996, BLS has published a biennial report on employee tenure which measures how long workers have been with their current employers. The latest data for 2016 indicate that median employee tenure for all American workers was 4.2 years. That means half of workers stayed with their employers for more than 4.2 years, and half stayed for less. In addition, the median employee tenure for men was 4.3 years, slightly higher than that for women at 4.0 years. BLS also reported that the median employee tenure for 2016 was down from 2014, which was 4.6 years. This decline suggests that the average worker stayed with their employers for a shorter time. Is this the evidence that employees are becoming more fickle and less loyal? The answer is “no.”   From a historic perspective, this decline is not unexpected. It represents employee tenure reversing to its historic norm. It does not signal a fundamental change in employee loyalty. While employee tenure declined in 2016, it was not low by historic standards. As shown in the following chart that goes back 20 years, employee tenure was the lowest in 2000 (it dropped to 3.5 years) when millennials were still in elementary school. In fact, the median employee tenure was below 4.0 years throughout the second half of the 1990s and the first half of 2000s.  We cannot attribute the recent decline in employee tenure as evidence that today’s workers are less loyal. If that is the case, why was employee tenure even lower at the turn of the century? It is likely that the high employee tenure from 2010 to 2014 is the result of the Great Recession of 2007-2009 and the slow recovery thereafter. The U.S. economy lost 8.7 million jobs due to the recession and the unemployment rate reached as high as 10.0% in October 2009. Jobs did not start to grow until June 2010. Facing high unemployment and limited job opportunities, it is not surprising that American workers held onto their jobs and were reluctant to change. The slow economic recovery reinforced the perception that the job market remained weak and people continued to safeguard their jobs well into the recovery.  The pace of job creation picked up in the last couple of years and we are now in the 8 th year of continuous economic expansion.  As expected, employee tenure declines as workers become more optimistic about the job market and are not afraid of leaving their employers to pursue other opportunities. Even for younger workers (between the age of 25 and 34), the current employee tenure of 2.8 years is not different from the tenure of younger workers before the Great Recession, or younger workers of the late 1990s. The data do not provide evidence that today’s younger workers change jobs more frequently than their counterparts of the past. The news stories of young millennials job-hopping may well be true, but that might be just for high-tech industries with fast-paced innovations and ever-present new opportunities. Tech-jobs are growing rapidly, but they still account for only a fraction of the American labor force. Many millennials will be working at retail shops, healthcare facilities, manufacturing plants, banks, or business offices. Changes in those industries can be modest and workers may settle in their careers for a long period of time.  Job-hopping can also occur in the regions where rapid growths occur.  There is little evidence that American workers are more fickle. Instead, the driving force for employee tenure is more likely to be the fundamental economic conditions of the country, a region, or an industry. http://chmuraecon.com/blog/2017/august/18/are-american-workers-becoming-fickler/ http://chmuraecon.com/blog/2017/august/18/are-american-workers-becoming-fickler/ Fri, 18 August 2017 11:20:30 Chris Chmura http://chmuraecon.com/blog/2017/august/08/math-majors-solve-employers-problems/ With students packing up to go back to college this month with declared or undeclared majors, it’s a good time to consider the fields that graduating students are going into and whether they match up with the skills businesses need. Do the math. From 2010 to 2016, students with a degree in computer and information sciences and support services grew at a strong 6.9 percent annualized pace — more than three times as fast as overall graduates (2 percent). This is based on the National Center for Education Statistics completions data for U.S. postsecondary schools.  Growth in the computer field was especially robust in master’s degrees, which more than doubled from 18,114 degrees to 40,316 from 2010 through 2016. Bachelor’s degrees in computer programs also experienced strong growth, moving from 40,446 degrees to 66,559 over the same period. Those graduates should have no problem finding a job. There were more than 620,000 job openings in the nation for computer occupations, according to Chmura’s Real Time Intelligence . The top four metro areas for those jobs are New York (with 47,417 postings), Washington D.C. (46,526) San Francisco (25,394), and Los Angeles (25,082). Top 10 Metros with Job Postings for Computer Occupation Location Post Count New York-Newark-Jersey City, NY-NJ-PA MSA 47,417 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 46,526 San Francisco-Oakland-Hayward, CA MSA 25,394 Los Angeles-Long Beach-Anaheim, CA MSA 25,082 Chicago-Naperville-Elgin, IL-IN-WI MSA 23,777 Dallas-Fort Worth-Arlington, TX MSA 22,337 San Jose-Sunnyvale-Santa Clara, CA MSA 20,602 Boston-Cambridge-Newton, MA-NH MSA 20,513 Seattle-Tacoma-Bellevue, WA MSA 18,505 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 16,450 Source: JobsEQ ® Online postings for the 30-day period preceding 08/07/2017 Among the fastest growing individual programs over the past several years are those found where the fields of business and mathematics intersect. Graduates in programs in management sciences and quantitative methods saw impressive growth over the last six years. For example, graduates in business statistics surged from 70 completions to 963 in the nation from 2010 to 2016 and other management sciences and quantitative degrees almost tripled from 638 to 1,908 over the same period. Large increases in the number of graduates also occurred for students with degrees in statistics and applied mathematics, including financial mathematics where the number of degrees surged from 351 in 2010 to 1,366 six years later. These graduates shouldn’t have trouble finding jobs either with more than 48,000 job openings in the nation for people with strong quantitative skills. [1] These positions range from financial analysts to fraud examiners and clinical data managers. New York, Washington, and Chicago are the top metro areas with job openings. Top 10 Metros with Job Postings for Quantitative Skills Location Post Count New York-Newark-Jersey City, NY-NJ-PA MSA 5,764 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 2,856 Chicago-Naperville-Elgin, IL-IN-WI MSA 2,209 Los Angeles-Long Beach-Anaheim, CA MSA 2,061 San Francisco-Oakland-Hayward, CA MSA 1,771 Boston-Cambridge-Newton, MA-NH MSA 1,548 Dallas-Fort Worth-Arlington, TX MSA 1,533 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 1,481 Atlanta-Sandy Springs-Roswell, GA MSA 1,078 Minneapolis-St. Paul-Bloomington, MN-WI MSA 1,057 Source: JobsEQ ® Online postings for the 30-day period preceding 08/07/2017       [1] This group of occupations is defined by eight digit O*NET Standard Occupational Classification codes for financial analysts; risk management specialists; operations research analysts; fraud examiners, investigators and analysts; statisticians; investment underwriters; financial quantitative analysts; clinical data managers; biostatisticians; economists; financial specialists, all other; environmental economists; mathematicians; and mathematical science occupations, all other. http://chmuraecon.com/blog/2017/august/08/math-majors-solve-employers-problems/ http://chmuraecon.com/blog/2017/august/08/math-majors-solve-employers-problems/ Tue, 08 August 2017 11:33:06 Greg Chmura http://chmuraecon.com/blog/2017/july/26/trends-in-postsecondary-completions-the-2016-nces-data-release/ The National Center for Education Statistics recently released preliminary completions [1] data for U.S. postsecondary schools for the 2015-16 academic year. Some of the trends in these data are highlighted below. [2] From 2010 to 2016, [3] completions in Computer and Information Sciences and Support Services (CIP [4] 11) grew impressively, expanding at a 6.9% annualized pace, over three times as fast as the overall growth rate in completions (2.0%). Growth in this computer field was especially robust in master’s degrees, which more than doubled during this period from 18,114 to 40,316. Bachelor’s degrees in computer programs also experienced strong growth, moving from 40,446 to 66,559 over the same period.   Among the fastest growing individual programs over the past several years are those found where the fields of business and mathematics intersect. Programs in Management Sciences and Quantitative Methods (CIP 52.13) saw impressive growth over the last six years; for example, Business Statistics (CIP 52.1302) surged from 70 completions to 963 during this time, and Management Sciences and Quantitative Methods, Other (CIP 52.1399) approximately tripled from 638 to 1,908 over the same period. Awards in Economics (CIP 45.06) also grew strongly over this period, including Econometrics and Quantitative Economics (CIP 45.0603) which increased from 242 to 1,241. Large gains were also posted in Statistics (CIP 27.05) and Applied Mathematics (CIP 27.03), including Financial Mathematics (CIP 27.0305) which surged from 351 awards in 2010 to 1,366 six years later.   Not everything has been growing, however. From 2010 to 2016, completions in Health Professions and Related Programs (CIP 51) expanded for bachelor’s degrees and all higher levels, but declined in all levels lower than a bachelor’s (associate’s and certificates of less than four years). Some of the big losses in programs that are primarily at those levels are illustrated below. The biggest drop was for Medical/Clinical Assistant (CIP 51.0801) which fell from 121,618 to 77,794 awards over the six years ending 2016. Licensed Practical/Vocational Nurse Training (CIP 51.3901) slipped the same period from 58,813 to 44,315. Some smaller health programs also recorded steep declines from 2010 to 2016: Massage Therapy/Therapeutic Massage (CIP 51.3501) dropped from 27,495 to 17,680 and Pharmacy Technician/Assistant (CIP 51.0805) saw its completions fall from 20,212 to 11,795.   Certificate-level programs, though generally declining in the health professions, have grown elsewhere. Certificates that take less than a year to complete grew strongly in Precision Production (CIP 48) with most of that growth driven by an increase in Welding Technology/Welder (CIP 48.0508), expanding from 13,131 completions in 2010 to 22,261 in 2016. [5] Certificates requiring less than a year to complete also have been growing among programs in Business, Management, Marketing, And Related Support Services (CIP 52) which expanded from 39,509 to 64,901 awards over this same period.       [1] “Completions” refers to degrees and other awards granted by postsecondary schools. The NCES completions data covers all schools participating in any federal financial assistance program authorized by Title IV of the Higher Education Act of 1965, as amended; institutions not participating may not be represented in this data set. [2] Note that some of the trends shown here may be influenced by individual programs being reclassified. For example, a school’s program may be classified under Curriculum and Instruction (13.0301) in the NCES data one year, and the next year the same program may be classified under Education, Other (13.9999). Such changes are not necessarily typical or haphazard, but they do occur. [3] For simplicity, single years are used to refer to school years with the year referenced being the end year; for example, “2016” designates the 2015-16 academic year. [4] “CIP” stands for the Classification of Instructional Programs . [5] Completions data cited in the text for 48.0508 is at the “less than one year” certificate level only. Overall completions in this program in 2016 numbered 38,660 awards. http://chmuraecon.com/blog/2017/july/26/trends-in-postsecondary-completions-the-2016-nces-data-release/ http://chmuraecon.com/blog/2017/july/26/trends-in-postsecondary-completions-the-2016-nces-data-release/ Wed, 26 July 2017 16:18:56 Chmura Staff http://chmuraecon.com/blog/2017/july/10/computer-science-jobs-on-the-rise/ Many recent high school graduates are likely considering a postsecondary education in computer science because those jobs are growing quickly today. But what will demand be when those graduates enter the job market? Chmura’s JobsEQ, which projects the growth of jobs requiring information technology skills, shows that many but not all of these occupations will grow faster than the national average. Current Forecast (Next 10 Years) Title Employment Avg. Annual Wages 1 Total Replacement Demand Total Growth Demand Avg. Annual Growth Percent Web Developers 161,915 $72,200 29,483 45,820 2.5% Computer Systems Analysts 607,868 $91,600 87,805 133,962 2.0% Software Developers, Applications 853,826 $104,300 144,195 176,596 1.9% Information Security Analysts 103,591 $96,000 13,571 19,057 1.7% Software Developers, Systems Software 448,891 $110,600 61,784 61,759 1.3% Computer User Support Specialists 623,223 $53,100 83,780 84,578 1.3% Database Administrators 121,991 $87,100 26,597 13,820 1.1% Computer and Information Research Scientists 29,450 $116,300 3,583 2,947 1.0% Computer Network Architects 169,180 $104,200 21,924 16,048 0.9% Network and Computer Systems Administrators 393,327 $84,500 52,469 34,480 0.8% Computer Network Support Specialists 194,859 $67,800 26,311 14,288 0.7% Computer Occupations, All Other 274,708 $88,900 42,359 18,754 0.7% Computer Programmers 298,839 $85,200 68,255 -28,096 -1.0% Total - All Occupations 153,412,155 $49,300 40,749,478 10,380,143 0.7% Source: JobsEQ® On average, information technology occupations are forecast to grow 1.3% per year, nearly twice the average for all occupations. For college freshmen deciding majors and college graduates looking for their first job, identifying occupations with increasing employer demand is extremely important. At first glance, it’s easy to get caught up in the high average annual growth rates. However, the average annual growth rate can be a misleading statistic without considering the number of jobs to be added as well as replacement demand, which reflects the number of people retiring or changing to a different occupation. For instance, computer and information research scientists can expect demand for their expertise to grow an annual average 1.0% over the next ten years, which is faster than the national average. However, less than 3,000 new jobs result from that growth. An additional 3,583 research scientist jobs will need to be filled based on replacement demand. In contrast, computer systems analysts and software applications developers are expected to see both high annual growth rates and a large number of openings over the next ten years. When new growth and replacement demand is combined, over 218,000 positions will need to be filled. Replacement demand is an important component of future demand for occupations. Without considering replacement demand, one could easily dismiss computer programming as a viable career option due to the negative growth rate over the next ten years. Despite the negative growth rate, the replacement demand for computer programmers is projected at almost 70,000 jobs over the next ten years, more than twice the forecasted contraction over the same period.   Wages also play a crucial role with many students as they are deciding on a career. Three standout occupations, software systems developers, software applications developers, and computer systems analysts, show high growth rates, demand, and wages. While these occupations do not garner wages as high as computer and information research scientists, the required education is typically a bachelor’s degree compared to a doctorate degree for the research scientists. There are many factors to be considered while choosing a career path, but making sure your skills will be in demand in the future is critical.    This blog was completed with research support from intern Lydia Boswell. http://chmuraecon.com/blog/2017/july/10/computer-science-jobs-on-the-rise/ http://chmuraecon.com/blog/2017/july/10/computer-science-jobs-on-the-rise/ Mon, 10 July 2017 13:15:40 Chris Chmura http://chmuraecon.com/blog/2017/july/06/economic-impact-student-loan-debt-is-rising/ Household debt surpassed its 2008 recession peak in the first quarter of 2017, according to a recent report from the Federal Reserve Bank of New York. As of March 31, household debt stood at $12.73 trillion, a $149 billion, or 1.2 percent, increase above the previous quarter. Mortgage balances, which make up the largest amount of debt, rose 1.7 percent to $8.63 trillion. But outstanding student loan debt is growing at a faster pace — rising 2.6 percent in the first quarter to $1.34 trillion. Student loan debt is the second-largest component of household debt since exceeding the amount of credit card debt in the second quarter of 2010. There is no question that with a growing number of people in college and rising tuition costs, more students are taking on loans and face years of paying down that debt — whether they successfully graduate or not. In 2016, about 36 percent of student debt holders owed less than $10,000, according to the New York Fed, while 65 percent owed less than $25,000. Just 5 percent owed more than $100,000. Eleven percent of student loans were delinquent by 90 days or more in the first quarter of 2017, compared with a 3.4 percent delinquent rate for all household loans. One leading explanation for the high student loan default rates has been the growth of “nontraditional” borrowers attending community colleges and for-profit institutions. These borrowers, who tend to be older and from less wealthy families, complete programs at lower rates and have weaker labor market outcomes with jobs and earnings. All of those factors contribute to higher default rates. Another potential explanation for part of the increase in student loan defaults relates to the drop in home prices during the Great Recession. The falling values of home prices accounts for about 24 to 32 percent of the increase in student loan defaults, according to a recent working paper from the National Bureau of Economic Research. When home prices collapsed in 2007, it triggered a drop in household consumer spending, which in turn led to large employment losses. The layoffs reduced the ability of individuals with student loans to make loan repayments, especially if they had lower earnings to begin with. The study authors used individual-level student loan data linked to tax records and ZIP code level data on home price changes to estimate the effects. They found a strong relationship between home prices, employment losses and student loan defaults, especially among low-wage jobs, which accounts for the increase in student loan defaults. Even though a college degree often comes with student loans, an analysis by mortgage giant Fannie Mae of its National Housing Survey data concluded that the higher income associated with obtaining at least a bachelor’s degree outweighs the burden of student loans in terms of likelihood to own a home even though it may delay homeownership. In contrast, those who started college, accumulated debt and did not earn a degree are 32 percent less likely to own a home than high school graduates who have no student loans. http://chmuraecon.com/blog/2017/july/06/economic-impact-student-loan-debt-is-rising/ http://chmuraecon.com/blog/2017/july/06/economic-impact-student-loan-debt-is-rising/ Thu, 06 July 2017 16:44:56 Greg Chmura http://chmuraecon.com/blog/2017/june/12/regional-occupation-employment/ A good estimate of occupation employment at the local geographic level is a critical piece of labor data. How is such an estimate made in JobsEQ, and what are the advantages of using these data in comparison to retrieving an occupation estimate straight from the Bureau of Labor Statistics ( BLS )? OES Data The BLS provides local occupation employment estimates through the OES program ( Occupation Employment Statistics ) for metropolitan and nonmetropolitan areas. These data are an outstanding resource; indeed, they are the standard to which any other estimates should be compared. Nevertheless, there are several issues that users of these data need to be aware of. First, the OES data are not disclosed for all occupations. In the May 2016 Knoxville metro area data, for example, over a third of the detailed-level occupations (6-digit SOC codes ) have no employment information available. So, while most occupations—especially the larger ones—have estimates provided, you cannot find employment counts in Knoxville for occupations such as Pharmacy Aides, Materials Engineers, and Electronic Equipment Assemblers. Next, the data can be a bit outdated. The May 2016 estimates were released in March 2017. These 2016 data will continue to be the latest available until around March 2018. The occupation data will thus range from ten to twenty-one months out-of-date. While this may not be a big issue in some regions, the occupation employment will likely change substantially if there is significant job growth, contractions, or shifts in the mix of industries. Additionally, OES data do not cover all types of employment. The self-employed are excluded as well as private households and most of agriculture. By missing the self-employed piece alone, estimates for many occupations are significantly understated. For example, Dentists and Lawyers will be short--on average--by about 20%; Carpenters and Food Service Managers could be understated by more than 30%; and over half of the Exercise Physiologists and Real Estate Sales Agents will likely be unaccounted for. Further, the OES data are not available for geographies smaller than metropolitan and nonmetropolitan regions. If you need estimates for a single county or a commuting distance from a zip code, the metropolitan or nonmetropolitan data may not be nearly specific enough. Finally, while the OES data are gathered via surveys of regional employers—indisputably a great primary resource—the sample sizes may not be large enough to get precise and firm results. For example, the Southwest Alabama Nonmetropolitan Area is a collection of twelve counties near the metro of Mobile. This region’s OES estimate of employment for Elementary School Teachers (SOC 25-2021)—an occupation with typically little job variance year to year—bounced from 1,650 in 2013 to 1,940 in 2014 to 1,180 in 2015. The industry employment available for Elementary and Secondary Schools ( NAICS 6111) in this region doesn’t indicate any unusual events that would cause such extreme swings in employment to actually occur. One reason for erratic estimates is that, when working with a small sample size, survey-driven results can veer off from time to time. Indeed, the BLS is transparent about disclosing the uncertainty of these estimates. For example, the value of 1,940 teachers in Southwest Alabama in 2014 was accompanied by a published Relative Standard Error of 26%. In other words, this particular employment estimate would have a 95% confidence interval of about ± 1,000 jobs. [1] Now, the above [2] was just one example cherry-picked from the data. While there are certainly others like it, a greater amount are likely to be more stable over time with tighter windows of uncertainty. [3] Nevertheless, even just a few examples of imprecise data raise the issue of needing a way to produce more consistently reliable estimates. The JobsEQ Approach Regional planners need both good and stable estimates of local occupation employment. As detailed above, the OES estimates may not fit this need for a variety of reasons. The occupation employment estimates in JobsEQ try to address these issues while still using the OES information as a guide and standard. One piece of this approach is leveraging industry staffing patterns. [4] Using the staffing mix of industries to estimate employment allows for use of the very latest industry data, which builds on the highly regarded QCEW data set, [5] and which in JobsEQ is typically only three to five months behind the present date. Furthermore, this approach allows JobsEQ to make estimates at the county or zip code level. And, finally, it allows for estimates of all occupations, circumventing the non-disclosure problem. That being said, the regional OES estimates are not simply ignored. JobsEQ does incorporate these data, but only to the extent indicated by the confidence intervals. That way, variations due to year-to-year sampling changes will not cause erratic changes in employment estimates. In addition to everything mentioned above, JobsEQ also brings in regional estimates for the industries missing from OES (agriculture, etc.), as well as the mix of regional self-employed occupations. As it is important for these additional estimates to be grounded in solid source data, JobsEQ creates estimates consistent with the data available in another BLS data set--the Employment Projections program. Overall, JobsEQ’s approach allows occupation employment estimates to be consistent with the published OES data while also avoiding the associated problems of relying only upon that source. If you are interested in a demo of the JobsEQ system, or simply want to learn more about the data, please contact us and we’d be more than happy to speak with you.     [1] To be precise, the 95% confidence interval is ±1.96*RSE, which is 989 jobs. Thus, in this case, the BLS is indicating that--accounting for the uncertainty from sampling errors--it can say with 95% confidence that the actual employment of teachers falls somewhere between 951 and 2,929. [2] While the BLS cautions , “The Bureau of Labor Statistics at present does not use or encourage the use of OES data for time-series analysis…,” note that with this graphic we are not performing a traditional time-series analysis, looking at the year-to-year change for the purpose of trying to measure the change over time, but rather we are considering the data sequentially for the purpose of examining the reliability of these point-in-time estimates. [3] Furthermore, this isn’t the only example of uncertainty and changes in these data. The categorization of occupations has inherent uncertainty. Many occupations are similar to one another and classifying a job into one occupation versus another can be subjective. Indeed, the entire categorization system is constantly changing—the current 2010 system will be soon giving way to the 2018 SOC revision which is currently in process. In can be expected with this update that some codes will be added, others deleted, and the definitions of still others revised. [4] The origin of complete set of staffing patterns really deserves a discussion unto itself—which is beyond the scope of this piece. Suffice it to say, the BLS is again a great resource in laying out most of these staffing patterns. [5] The Quarterly Census of Employment and Wages (QCEW) is derived from quarterly reports filed by almost every employer in the nation. This data set has issues as well—non-disclosures, timeliness, and lack of published sub-county level estimates—but how those points are addressed in JobsEQ data requires a separate article. http://chmuraecon.com/blog/2017/june/12/regional-occupation-employment/ http://chmuraecon.com/blog/2017/june/12/regional-occupation-employment/ Mon, 12 June 2017 16:11:15 Chris Chmura http://chmuraecon.com/blog/2017/may/02/economic-impact-health-care-and-retail-jobs-will-be-in-demand-during-the-next-10-years/ What jobs will be in hot demand in the Richmond region during the next decade? Jobs in the health care and social assistance sector and in the retail industry top the list. For instance, the health care and social assistance sector is expected to need nearly 40,000 workers in the Richmond metro area over the next decade, according to Chmura Economics & Analytics’ JobsEQ technology platform. About 19,000 of those jobs are needed because of growth in the health care sector, while the remainder represent positions that will be open because health care workers are retiring or moving to different occupations. Within the health care sector, registered nurses top the list of growing occupations with nearly 5,000 more nurses needed in the region over the 10 years. About 2,000 of those nurses represent growth in the health care industry, while the remainder are a result of employees retiring or moving to different occupations. Personal care aides, nursing assistants and home health aides round out the top four occupations in demand in the health care sector — and each of those occupations is expected to increase by more than 2,000 workers over the next decade. The retail trade sector is second on the list of expected job openings over the next 10 years with 26,000 openings expected. Of the expected openings in the region, about 4,000 are because the retail sector is growing. The remaining 22,000 jobs are a result of those retiring or moving to different occupations. Retail sales positions top the list with nearly 8,000 positions to be filled, followed by cashiers at about 6,000 and stock clerks and order fillers with nearly 2,500 jobs. And for students looking for jobs, more than 37,000 positions were available in the Richmond metro area during the past 30 days, according to the JobsEQ analytics. More than 2,000 openings in the region were for retail sales jobs. Registered and critical care nurses also were at the top of the list with nearly 1,500 job postings by employers in the area. Demand also is strong for those with an information technology skill set. More than 800 job postings in the region over the past 30 days were for computer-user support specialists, and nearly 500 were for application software developers. http://chmuraecon.com/blog/2017/may/02/economic-impact-health-care-and-retail-jobs-will-be-in-demand-during-the-next-10-years/ http://chmuraecon.com/blog/2017/may/02/economic-impact-health-care-and-retail-jobs-will-be-in-demand-during-the-next-10-years/ Tue, 02 May 2017 08:45:30 Patrick Clapp http://chmuraecon.com/blog/2017/may/01/students-in-distress/ Student loan debt ballooned during the Great Recession, and student loan default rates have nearly doubled since 2007.  One leading explanation for the rising default rates has been the growth of “non-traditional” borrowers attending community colleges and for-profit institutions. The tendency of these borrowers to be older, from less-wealthy families, complete programs at lower rates, and have weaker labor market outcomes (employment and earnings) all contribute to higher default rates. A recent working paper from the NBER suggests another potential avenue to explain part of the increase in student loan defaults. In Students in Destress: Labor Market Shocks, Student Loan Default, and Federal Insurance Programs (NBER Working Paper No. 23284), the study authors find the drop in home prices during the Great Recession accounts for approximately 24% to 32% of the increase in student loan defaults. The mechanism for this effect follows a familiar narrative of the Great Recession: collapsing home prices triggered a sharp drop in household consumer spending, which in turn led to massive employment losses. Layoffs and earnings declines then weakened the ability of individuals with student loans to make loan repayments, especially if they had lower earnings to begin with. Previous studies have shown that student loan borrowers spend less time on their job search, needing to accept a job quicker to have income sooner. As a result, they earn less annually in the first ten years after graduation. The study authors used individual-level student loan data linked to tax records and zip-code level data on home price changes to estimate these effects. They found a strong relationship between home prices, employment losses, and student loan defaults, especially among low income jobs, which accounts for about 24% - 34% of the increase in student loan defaults. With regards to potential policy considerations, they find that the Income Based Repayment (IBR) program made it easier for borrowers to mitigate the effects of home price changes. Under the program, student loan repayments don’t exceed 15% of their discretionary income, and terms can be extended to up to 25 years. While the program provided valuable insurance for those who participated, student borrowers who were eligible for the IBR repayment option but did not actually take it up continued to default on student loans at high rates. http://chmuraecon.com/blog/2017/may/01/students-in-distress/ http://chmuraecon.com/blog/2017/may/01/students-in-distress/ Mon, 01 May 2017 08:09:11 Chris Chmura http://chmuraecon.com/blog/2017/april/10/economic-impact-manufacturing-industry-still-challenged-but-the-sector-has-changed/ Manufacturing often gets a bad rap. That’s probably because the sector has declined by more than 4.9 million jobs since January 2000. The industry also has perception issues. Who wants to work in a dirty old factory with oil on the floor and dust in the air? But the manufacturing industry has changed - it is more high tech and offers better wages, making for a good career choice. The manufacturing industry employs more than 12.6 million people in the nation. The Bureau of Labor Statistics forecasts that it will decline by about 900,000 jobs in the next 10 years. However, the sector will add over 2.8 million workers over that same period because employees in the manufacturing industry are either retiring or moving into new occupations. And even though the industry is shedding jobs, output continues to grow. Since 2000, manufacturing production increased by 37 percent or by $1.555 trillion in 2016, according to the Bureau of Economic Analysis. Productivity gains are part of the reason for the historical decline in jobs as well as the expected future decline. Productivity in the manufacturing industry grew an annual average 1.7 percent from 2007 through 2016 compared with 1.2 percent for nonfarm businesses. Although high productivity means doing more with fewer workers, it also means the remaining workers are paid well. Average annual wages for manufacturing workers in the nation was $63,903 during the 12 months that ended December 31, 2016 compared with $52,285 for all industries, based on data computed by Chmura Economics & Analytics. And what about the perception issues? Consider touring the Rolls-Royce North America plant in Prince George County, Virginia, where the company makes components for aircraft engines. As you drive to the plant in the Crosspointe development, you pass through a well-manicured manufacturing campus that would convince you you were entering an upscale office park. Entering the nearly 300,000-square-foot plant, you see spotless floors, large machines suspended off the floors, and workers with computer skills operating the facility. On the same campus is the Commonwealth Center for Advanced Manufacturing, a 60,000-square-foot research center jointly operated by private companies and several Virginia universities. The environment at both facilities is far from your grandfather’s plant. http://chmuraecon.com/blog/2017/april/10/economic-impact-manufacturing-industry-still-challenged-but-the-sector-has-changed/ http://chmuraecon.com/blog/2017/april/10/economic-impact-manufacturing-industry-still-challenged-but-the-sector-has-changed/ Mon, 10 April 2017 13:40:29 Chris Chmura http://chmuraecon.com/blog/2017/march/31/chmura-economics-fedspendtop-database-provides-a-more-accurate-picture-of-federal-contract-spending/ Many local economies are dependent on federal contracts, but the contract spending data available from government sources are often incomplete and inaccurate. Chmura Economics & Analytics created the Federal Spending by Time of Performance ( FedSpendTOP ) database to correct many of these issues. Understanding trends in federal contract spending is important for many regions where federal contracts have significant impacts. USASpending.gov is the publicly available official government source for data on grants, contracts, etc. but these data may be incomplete or inaccurate due to data quality problems. [1] To resolve these problems, Chmura created the FedSpendTOP database. FedSpendTOP improves upon the USASpending.gov data by: Adjusting for the length of the contract Basing regional spending on the place of performance Including purchases by non-Department of Defense (DoD) agencies which end up in DoD products as DoD spending Identifying and correcting errors through quality control processes Manually adjusting based on research The following table shows the 10 metropolitan statistical areas (MSAs) with the highest DoD contract spending in fiscal year (FY) 2015 based on FedSpendTOP data and their corresponding rank based on unadjusted data from USASpending.gov. Top 10 DoD Contract Spending by MSA, FY 2015 Unadjusted FedSpendTOP Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 1 1 Dallas-Fort Worth-Arlington, TX MSA 2 2 Virginia Beach-Norfolk-Newport News, VA-NC MSA 7 3 Boston-Cambridge-Newton, MA-NH MSA 4 4 Los Angeles-Long Beach-Anaheim, CA MSA 6 5 San Diego-Carlsbad, CA MSA 3 6 St. Louis, MO-IL MSA 9 7 Baltimore-Columbia-Towson, MD MSA 5 8 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 8 9 New York-Newark-Jersey City, NY-NJ-PA MSA 10 10 Source: Chmura FedSpendTOP & USASpending.gov The unadjusted data put the Virginia Beach MSA 7 th in the nation when it should actually be 3 rd according to FedSpendTOP data. Adjustments based on company research and adjustments based on the length of contracts (shipbuilding contracts are typically multi-year) account for much of this difference. This is one of many examples of how FedSpendTOP data more accurately reflect where and when work supported by federal contracts is being performed. [1] In 2014, the GAO estimated “with 95% confidence that between 2% and 7% of the awards contained information that was fully consistent with agencies' records for all 21 data elements examined.” http://www.gao.gov/products/D07755 http://chmuraecon.com/blog/2017/march/31/chmura-economics-fedspendtop-database-provides-a-more-accurate-picture-of-federal-contract-spending/ http://chmuraecon.com/blog/2017/march/31/chmura-economics-fedspendtop-database-provides-a-more-accurate-picture-of-federal-contract-spending/ Fri, 31 March 2017 13:06:16 Xiaobing Shuai http://chmuraecon.com/blog/2017/march/21/putting-corporate-tax-reform-in-perspective/ Since assuming office in January, the Trump Administration has taken steps to enact policies with a wide range of impacts in the areas of healthcare, environment, immigration, and the economy. Economic policies have not been at the front and center of the media or public discourse lately, but understanding what may come is still extremely important.  Potential changes in economic policies may include personal income tax cuts, corporate tax reform, and federal budget shifts, each of which can be quite complex.  In this blog, we take a closer look at the different proposals for corporate tax reform. During the 2016 campaign, then presidential candidate Trump promised to enact a straightforward cut on the corporate income tax rate, from the current 35% to 15%. Corporate income tax is applied to the profit portion of business income. Profit is defined as the net income of a business, equaling total revenue minus total cost. Examples of costs are state and local taxes, intermediate goods and services, wages and benefits for labor, and interest payments.   The Trump plan also attempts to address the issue of profit offshoring, where companies leave overseas profit abroad to avoid paying U.S. corporate income taxes. His plan will allow companies to move profits to the United States by taxing them at a much lower 10% rate.  A straightforward reduction in the corporate tax rate will reduce tax revenue in the short term.  Consequently, the federal government either needs to cut spending or allow the deficit to increase. A different proposal circulated by House Speaker Paul Ryan and House Ways and Means Committee Chairman Kevin Brady attempts to address some of the same issues with the current corporate tax. Their plan would change the corporate income tax in five significant ways: Lower the tax rate to 20%. Businesses will be able to fully write off capital investments in the year they purchased them instead of depreciating them. Profits earned overseas by businesses would no longer be taxed. Interest would no longer be deductible as a business expense. The corporate tax would be “border adjusted.” Together, these changes turn the corporate income tax into a “destination-based cash flow tax (DBCFT).” As shown in the red border in the above diagram, the tax base of DBCFT is broader than the current corporate income tax because it will tax interest expenses which are exempt from the current corporate income tax which only applies to profit.  By lowering the tax rate but broadening the tax base, the Ryan-Brady plan attempts to avoid a sharp decline in tax revenue that could result from a simple rate cut. To address the issue of profit offshoring, the Ryan-Brady plan proposes to tax the cash flow differentiated by destination. Only cash flows generated domestically will be subject to DBCFT, which means companies can move their overseas profits back to the United States without being hit with a huge tax bill.  Similarly, exporters will benefit from the DBCFT as their revenues from abroad will be exempt. Though the proposed cash-flow tax has a broader base than the current corporate income tax, it is not a value-added-tax (VAT). As the above diagram shows, VAT applies to the value-added portion of the business revenue. Since a large portion of value-add is labor income , a VAT has a much broader tax base than DBCFT.  Finally, the broadest business tax is a sales or gross receipt tax, which is a tax on total business revenue without allowing a deduction for intermediary inputs. The much-discussed border-adjustment-tax (BAT) can fit into the DBCFT framework. For importers, the Ryan-Brady plan proposes that the value of imported goods will not be exempt from this tax. Thus, corporate tax is adjusted at the border to become the border-adjustment-tax for importers. At this time, it is not clear which type of corporate tax will be enacted into law. The DBCFT is a major tax change in the U.S. tax system that would affect all businesses in the country.  Policymakers may choose to implement a narrowly focused DBCFT, or the border-adjustment-tax, applying only to importers. Or they may combine BAT with a traditional cut of corporate income tax rates, like the Trump plan, using BAT to offset revenue lost due to the tax cut on profits.  Regardless, policymakers have a wide range of options to choose from in terms of corporate tax reform, from a broad-based gross receipt tax, to a narrowly focused tax on profits, or something in between. http://chmuraecon.com/blog/2017/march/21/putting-corporate-tax-reform-in-perspective/ http://chmuraecon.com/blog/2017/march/21/putting-corporate-tax-reform-in-perspective/ Tue, 21 March 2017 12:25:37 Leslie Peterson http://chmuraecon.com/blog/2017/march/16/what-does-your-degree-do-for-you/ If your accounting degree landed you a job as a financial analyst in the Richmond metro area, Virginia, do you know what that gets you? Well, for starters, your entry-level wages are somewhere around $55,000 per year. The average worker earns $83,000 per year and experienced workers earn $111,000 per year. About 45% of all accountants in the Richmond metro area have a bachelor’s degree while 38% have their masters. Some employers require additional certifications for accountants to work as financial analysts.  Examples of certifications identified in the Real-Time Intelligence (RTI) for accountants in the Richmond metro area include CPA (certified public accountant), CMA (certified management accountant), CFA (certified financial analyst), and PMP (project management professional). What does the future look like for a financial analyst in Richmond?  According to the Career Concourse, the growth rate appears to be about average for that occupation in that marketplace. Source: JobsEQ & Career Concourse http://chmuraecon.com/blog/2017/march/16/what-does-your-degree-do-for-you/ http://chmuraecon.com/blog/2017/march/16/what-does-your-degree-do-for-you/ Thu, 16 March 2017 16:52:00 Chris Chmura http://chmuraecon.com/blog/2017/march/06/economic-impact-increased-military-spending-could-benefit-virginias-economy/ Virginia stands to receive more benefit than any other state in the country under the president’s plan to increase military spending. In President Trump’s address to Congress last week, his plan “calls for one of the largest increases in national defense spending in American history.” Defense spending contributes to the Virginia economy in multi-facet ways. The Department of Defense operates in Virginia as well as military bases such as Quantico, Fort A.P. Hill, Norfolk Naval Base and Fort Lee. Those operations and bases create thousands of military and civilian jobs in the state. Virginia’s firms also benefit from defense spending through contracts. Some of the nation’s largest defense contractors, such as Huntington Ingalls Industries, General Dynamics and Northrop Grumman, are based in the state. In fact, Virginia ranked first in the nation in defense spending with $53 billion in the fiscal year that ended Sept. 30, 2015, according to the defense department’s spending by state report. That’s 13 percent of all the defense spending in the nation. California ranked second with $49.3 billion during the same time period, or 12.1 percent of the defense spending in the nation. If proportions hold, Virginia and California stand to receive a quarter of the benefit of the increase in spending. But the increased spending will have a bigger impact on the Virginia economy given its relative size. Defense spending in Virginia represented 11.2 percent of the total gross regional product of $473.2 billion during the fiscal year that ended Sept. 30, 2015. That ranks Virginia No. 1 in the nation for its dependence on defense. By comparison, only 2.1 percent of California’s gross regional product came from defense during the same time period. That ranked California the 24th most dependent state on defense spending. The buildup in defense spending helped fuel Virginia’s growth in the first decade of this century. The state’s dependence on defense contributed to the relatively smaller decline in employment during the Great Recession when compared to the nation. Going further back in history to the Reagan-era Cold War build up in defense, the state saw similar benefits largely because of spending in Northern Virginia. Northern Virginia was once considered recession proof — that was until the Cold War draw bore down on the region. The draw down from the war in Afghanistan and sequestration related to the Budget Control Act of 2011 also dampened economic activity in Virginia. Employment growth in the state slowed from a year-over-year pace of 1.4 percent in December 2012 to a contraction of 0.2 percent in February 2014. During the same time, national employment growth continued to recover from the Great Recession and hovered around 2 percent on a year-over-year basis. The year-over-year pace of employment growth has picked back up to 1.3 percent in Virginia based on the latest data for December 2016. However, cutbacks in defense spending contributed to a slower-growing state economy that lead to budget shortfalls in Virginia and calls for diversifying the economy. With the prospect of an acceleration in defense spending, Virginia is poised to benefit more than other states. However, the state should take this opportunity and continue its diversification effort to achieve sustained long-term growth, with an understanding that defense spending inevitably runs cycles as well. http://chmuraecon.com/blog/2017/march/06/economic-impact-increased-military-spending-could-benefit-virginias-economy/ http://chmuraecon.com/blog/2017/march/06/economic-impact-increased-military-spending-could-benefit-virginias-economy/ Mon, 06 March 2017 10:15:53 Sharon Simmons http://chmuraecon.com/blog/2017/january/24/underemployment-in-the-united-states/ According to a 2015 Federal Reserve Bank of New York Staff Report, almost one-half (46%) of recent college graduates were underemployed in 2014. [1] The so-called underemployed workers are employed in an occupation below their level of qualification. For example, a graduate with a Bachelor’s Degree in economics who is waiting tables or working at a retail store is considered underemployed. Chmura calculates a proxy for underemployment by comparing educational attainment supply and demand in a given labor market at various skill levels. Some metropolitan statistical areas (MSAs) around the country have a higher percentage of underemployed than others.  MSAs in Massachusetts, the District of Columbia, Colorado, and California top the list of regions that possess a surplus of high-skilled workers in the latest update to Chmura Economics & Analytics’ underemployment dataset. Underemployment is a useful supplement to other indicators of labor market health. The traditional measure of unemployment from the Bureau of Labor Statistics does not distinguish between workers who are employed in a position aligned with their skills and education. Workers who are underemployed and not necessarily contributing as much as they could to the labor market, represent potential lost productivity, wages, and tax revenue for the region. High underemployment in a region may also be a positive measure, reflecting the desire of workers to live in a particular area (like the scenic Cape Cod waterfront of Barnstable Town, Massachusetts) and/or higher standards for occupations in certain regions (such as for computer occupations in San Francisco). Chmura’s underemployment proxies for MSAs, along with more detailed methodology and definitions, are available on  our website  and at the county, MSA, and state levels within  JobsEQ ®. [1] Abel, Jaison R. & Deitz, Richard, 2015. " Underemployment in the early careers of college graduates following the Great Recession ,"   Staff Reports   749, Federal Reserve Bank of New York, revised 01 Sep 2016. http://chmuraecon.com/blog/2017/january/24/underemployment-in-the-united-states/ http://chmuraecon.com/blog/2017/january/24/underemployment-in-the-united-states/ Tue, 24 January 2017 09:18:01 Kyle West http://chmuraecon.com/blog/2017/january/23/help-wanted-applications-software-developers-anywhere-usa/ Over the past ten years, aside from registered nurses, employment gains for applications software developers have outperformed those for all other highly skilled occupations, adding more than 150,000 workers in the nation over the ten years ending with the third quarter of 2016. More than 775,000 workers were classified as applications software developers in the third quarter of 2016. There are now more software developers employed in the United States than lawyers (747,559), bartenders (618,136), or bank tellers (520,398); and industry demand for more seems insatiable. Over the next ten years, average annual employment growth for software developers is forecast to advance at a pace more than 2.7 times the rate of growth for all occupations (1.9% per year versus 0.7% per year, respectively). [1] Odds are, many of us live in a region where it is difficult to find qualified developers. With a median annual salary of $98,300 in the United States, applications software developers can be an attractive career choice for those with the right mix of skills. Based on current demand, an influx of developers would be a welcome addition to most regions. While these workers are predominantly employed by computer systems design firms, and to a lesser extent, software publishers, their distribution across industries is increasingly widespread as their skills and expertise become more valuable to a broader range of businesses. Most people would probably guess that the majority of developers work in some of our nation’s established technology hubs and/or largest metropolitan areas, such as Seattle, San Francisco, San Jose, Los Angeles, Dallas, New York, Chicago, or Atlanta. And, as shown in the table below, each of these metropolitan areas appears in the top ten list for employment of developers. http://chmuraecon.com/blog/2017/january/23/help-wanted-applications-software-developers-anywhere-usa/ http://chmuraecon.com/blog/2017/january/23/help-wanted-applications-software-developers-anywhere-usa/ Mon, 23 January 2017 11:41:23 Chris Chmura http://chmuraecon.com/blog/2017/january/03/economic-impact-president-elect-trump-s-plans-should-support-economic-growth/ Based on the increase in stock prices since the election, investors believe the Trump administration is going to usher in faster economic growth. Are they being too optimistic? Tax cuts for individuals and corporations, which is a key component of President-elect Trump’s campaign promises, should support economic growth. Since consumer spending makes up about 70 percent of U.S. gross domestic product, reductions in individual income taxes will support growth. Not all of the tax cut will translate into spending, however, because some households likely will use the tax savings to pay down debt or increase savings. Even so, the proposed Trump policies call for tax cuts in various degrees for all income groups. Based on an estimate by the Urban Institute and the Brookings Institution, the plan would cut taxes by an average $2,940 in 2017 for households. The plan also calls for the elimination of the alternative minimum tax, the estate tax, and the gift tax. Increasing productivity growth is critical to boosting the potential growth rate of the U.S. economy and improving living standards. This is where Trump’s promises of lower corporate tax rates and reduced regulation can make a big difference in GDP growth. Cutting the corporate tax rate from the existing 35-percent rate to a 15-percent rate and repatriating overseas profits should boost profits to domestic firms. These changes, combined with other policies allowing businesses to expense investments rather than depreciate them over a long period, should increase business investment in plant and equipment that will lead to productivity gains. Promised reductions in business regulations, including key environmental and financial ones, also can increase productivity growth as increased regulations are generally associated with lower productivity growth. Many analysts expect changes to the Dodd-Frank law regulating financial institutions and the Clean Power Plan that affects energy companies. Relaxing environmental regulations also will allow for more domestic energy exploration and production. This will increase the domestic energy supply and keep oil prices down. Lower oil prices can dampen future inflation, while increasing business investment and personal consumption at the same time. On government spending, the new administration has promised to increase spending on military and infrastructure, which should stimulate economic growth. On the other hand, Trump also vows to decrease the size of government agencies, which tends to reduce overall government spending. The net effect of these policies will depend on whether some or all the plans are implemented. Incorporating these policy changes into Chmura Economics & Analytics’ macroeconomic model indicates the proposed economic policies of the new administration can boost 2017 GDP growth by 0.6 percentage points, from 2.4 percent under the pre-election forecast to 3.0 percent. For 2018, the new policies can increase GDP growth to 3.9 percent, up from 2.7 percent under the pre-election forecast. In the new forecast, we assume that Trump’s trade policies impact both exports and imports, with a larger reduction in the growth rate of imports that reduces the U.S. trade deficit thereby increasing GDP growth. With the presidency and both chambers of Congress under the control of the Republican Party, big shifts are likely regarding future economic policy. The longer-term effects of a Trump administration depend on whether economic activity increases enough to offset tax cuts and new spending as well as how other countries react to Trump’s trade policies. And, of course, the midterm election in two years may impact the Trump administration’s ability to put in place new policies in the second-half of his term. http://chmuraecon.com/blog/2017/january/03/economic-impact-president-elect-trump-s-plans-should-support-economic-growth/ http://chmuraecon.com/blog/2017/january/03/economic-impact-president-elect-trump-s-plans-should-support-economic-growth/ Tue, 03 January 2017 11:55:37 Chris Chmura http://chmuraecon.com/blog/2016/december/05/economic-impact-optimistic-outlook-should-mean-strong-holiday-sales-for-retailers/ The holiday selling season is in full force at stores in malls and shopping centers. It is the time of year that represents about 20 percent of the retail industry's total sales. And based on recent announcements by some national retailers and online sellers that are looking to hire more seasonal workers than last year, it looks like the chains are optimistic about the upcoming holiday selling season. There is good reason for that bright outlook. Employment in the nation is picking up and the unemployment rate is declining. Personal income is up 3.9 percent for the 12 months that ended in October compared with the previous 12-month period, or by $612 billion. American consumers like to spend, so much of that increase in income will translate into purchases. But will that result in more holiday sales than last year? Back-to-school sales are often a good barometer of holiday sales. This year, back-to-school sales were pretty good, according to department stores such as Macy’s, Kohl’s and J.C. Penney. The National Retail Federation, the nation's largest retail trade group, predicts a 3.6 percent increase in holiday sales, including online business, to $655.9 billion compared with $632.8 billion in 2015. In September, global financial services firm Deloitte forecast this year's holiday sales to rise between 3.6 percent and 4 percent from November through January compared with the same period a year ago. That sales increase sounds about right for the nation. Based on the most recent economic figures, Virginia and Richmond sales are not as easy to forecast. Nonfarm employment grew 1.8 percent in Virginia during September compared with a year ago. During the same period, employment increased 2.9 percent in Richmond and 1.8 percent in the nation. The unemployment rate in Virginia remains below that of the nation but has inched up recently. After dropping to a low point of 3.8 percent on a seasonally adjusted basis in May, 2016, the jobless rate in Virginia has inched upward to 4.4 percent in October. For the Richmond region, the seasonally adjusted rate rose from 3.7 percent in May to 4.4 percent in October. The local rate also was up from 4.1 percent in September and 4.3 percent in October 2015. For the U.S., the jobless rate held steady at 4.9 percent during the same period. Even though economic growth in the state and Richmond is on par or better than the nation, the latest retail sales figures don’t reflect that strength. A seasonally adjusted six-month moving average of retail sales in Virginia shows 1.1 percent growth from a year ago in September. The Richmond area has seen an average retail sales decline of 0.6 percent, perhaps because of some store closings including the two Macy’s stores in Regency Square mall and a Macy's store in Virginia Center Commons in March. A lot has happened in the U.S. economy since the September and October holiday sales predictions were released. A new president was elected, who has ushered in optimism about increased economic growth based on the reaction of the major U.S. stock indexes. This might be a good year to throw out the recent historical trends and look for sales in the nation to be higher than the National Retail Federation and Deloitte forecast. With the more optimistic outlook for national growth and the strength of the labor market, 3 percent growth in retail sales in both Virginia and the Richmond region this holiday selling season compared with last year is possible. http://chmuraecon.com/blog/2016/december/05/economic-impact-optimistic-outlook-should-mean-strong-holiday-sales-for-retailers/ http://chmuraecon.com/blog/2016/december/05/economic-impact-optimistic-outlook-should-mean-strong-holiday-sales-for-retailers/ Mon, 05 December 2016 16:07:33 Xiaobing Shuai http://chmuraecon.com/blog/2016/december/01/the-economic-effect-of-the-proposed-economic-policy-of-the-trump-administration/ With the election over, many people are asking, “What are the possible changes to the economy under the Trump Administration?” With the presidency and both houses of Congress under the control of the Republican Party, big shifts are likely regarding future economic policy. To understand the effect of the new administration’s policy on the economy, Chmura prepared two forecasts for the nation for the years 2017, 2018, and 2019. One is the “status quo” forecast that assumes no change in economic policies, and the other is the forecast assuming the new administration will implement many policy changes promised by the Trump campaign. Chmura did not prepare a long-term forecast because the long-term economic effects of these policies are much less certain.  If economic activity does not increase enough to offset tax cuts and new spending, it is not clear whether the new administration will pay for the proposed tax cuts and infrastructure spending by borrowing or reducing the federal budget. The approach taken will have distinct impacts on the long-term economy. In addition, there will be a midterm election in two years and another presidential election in four years, which may result in further changes to the economic policies of the Trump administration.  Thus, any economic projection beyond two years is more uncertain and therefore less meaningful. Under the status quo scenario, Chmura projects that the U.S. economy would continue its current trajectory of modest economic growth, with gross domestic product (GDP) forecast to expand 2.4% in 2017 and 2.7% in 2018. These are improvements over the slow GDP growth of 2016, which is expected to be 1.6%. The Trump economic policies will have widespread impacts on the economy, affecting all major components of GDP, i.e., personal consumption, business investment, government spending, as well as imports and exports. The key component of the Trump economic policy package is the tax cut plan that will reduce both corporate and individual income taxes.  For example, the Trump proposal plans to cut the corporate income tax rate from the existing 35% to 15%. This measure, combined with other policies on lowering taxes on repatriated overseas profits and allowing businesses to expense investment rather than depreciate it, can boost corporate profits and business investment. Due to the sheer size of personal consumption expenditures in the U.S. economy, any changes in individual income tax can potentially have significant effects on the economy. The proposed Trump policy plans to cut taxes for individuals in all income groups, even though the degree of reduction varies. For example, the marginal tax rate for the top income bracket will be reduced from 39.6% to 33.0%.  The plan also calls for the elimination of the alternative minimum tax (AMT), the estate tax, and the gift tax. Based on an estimate by the Urban Institute & Brookings Institution, the plan would cut taxes by $2,940 in 2017 for an average household. [1] The tax cut is expected to stimulate consumer spending and boost GDP, yet households may also use some of the tax savings to pay down debt or increase their personal savings. The new administration also plans to reduce business regulations, including some key environmental and financial ones. Potential changes can be implemented to the Dodd-Frank Act regulating financial institutions or the Clean Power Plan that affects energy companies. [2] Reduced regulations tend to stimulate business investment, which could accelerate productivity and GDP growth.  In addition, it is likely that the new administration will relax environmental regulations and allow more domestic energy exploration and production. This will increase the domestic energy supply and keep oil prices down. Lower oil prices can dampen future inflation, while increasing investment and personal consumption at the same time. On government spending, the new administration has promised to increase spending on military and infrastructure. This spending can provide a stimulus to the economy. On the other hand, the Trump plan also vows to decrease the size of government agencies, which tends to reduce overall government spending. The net effect of these policies will depend on whether some or all of the plans are implemented. Finally, the possible changes to trade polices by the new administration may include the withdrawal from the Trans-Pacific Partnership (TPP) and a renegotiation of the North American Free Trade Agreement (NAFTA). The new administration also intends to pressure China to stop manipulating its currency, which may cause the Chinese currency to appreciate thereby reducing imports from China.  The general effect of these trade policies is to impose some type of restrictions on international trade. While both exports and imports can be affected, it is expected that a larger reduction may occur to imports, thus cutting the U.S. trade deficit and increasing GDP. If all of these policies are implemented, Chmura’s model indicates that they will start to impact the U.S.  economy in the second half of 2017.  It is projected that the proposed economic policies of the new administration can boost 2017 GDP growth by 0.6 percentage points, from 2.4% under the status quo scenario to 3.0%.  For 2018, the new policies can increase GDP growth to 3.9%, up from 2.7% under the status quo scenario. [1] Source: An Analysis of Donald Trump’s Revised Tax Plan, by Jim Nunns, Len Burman, Ben Page, Jeff Rohaly, and Joe Rosenberg, Tax Policy Center, Urban Institute and Brookings Institution. October 18, 2016. [2] Source: http://www.foxnews.com/politics/2016/11/15/fight-looms-between-fired-up-obama-and-trump-over-reg-roll-back.html http://chmuraecon.com/blog/2016/december/01/the-economic-effect-of-the-proposed-economic-policy-of-the-trump-administration/ http://chmuraecon.com/blog/2016/december/01/the-economic-effect-of-the-proposed-economic-policy-of-the-trump-administration/ Thu, 01 December 2016 11:29:10 Chris Chmura http://chmuraecon.com/blog/2016/november/07/economic-impact-be-cautious-in-reading-too-much-into-the-data-from-political-pollsters/ Not only will Tuesday’s election give us a new president, it also will tell us which, if any, pollsters were correct in predicting the winner. What most people don’t realize is that the results of political polls vary greatly depending on the assumptions the pollsters make. Ideally, poll respondents reflect the demographic makeup of the population. That almost never happens. So pollsters should weigh the sample results of their survey to reflect reality. For example, 16.3 percent of the national population said they were Hispanic or Latino, according to the 2010 census. If only 9 percent of the people who answer the survey are Hispanic or Latino, then the pollster gives their responses a higher weight to determine the predicted winner. Some other demographics that are typically included are age and gender. Other demographics that are more debated in terms of the ability to predict the next president are party registration and educational attainment. Polls are typically conducted at the state level and then combined for national results. Polls conducted at a sub-state level may provide different results than those conducted at a state level. And, of course, getting statistically significant results becomes very expensive as more regions are polled. There are two general approaches to the second consideration of who will show up to vote. The pollster can ask the person answering the survey about their likelihood to vote. Or the pollster can use historical voter turnout on the assumption that past behavior is a good predictor of the future. With all the data from the survey gathered, the weighting decisions of the pollster can have a large influence on the predicted election results. Do we assume the same percentage of African-Americans will vote in 2016 as they did in 2012 or 2008? Will female and Hispanic turnout be higher? How does the likelihood of a person voting change based on two unpopular candidates? And how will a third candidate impact the overall results? Based on the assumptions that are made, well-regarded pollsters are predicting different outcomes to the presidential election. On top of the question of whether we trust the pollsters’ assumptions is the 3.1 percent margin of error that is typically associated with surveying about 1,000 people. As Election Day progresses, exit polls will provide better predictions of the announcement that will come later in the evening. In the meantime, caution is warranted in reading too much into poll headlines. http://chmuraecon.com/blog/2016/november/07/economic-impact-be-cautious-in-reading-too-much-into-the-data-from-political-pollsters/ http://chmuraecon.com/blog/2016/november/07/economic-impact-be-cautious-in-reading-too-much-into-the-data-from-political-pollsters/ Mon, 07 November 2016 07:33:06 Leslie Peterson http://chmuraecon.com/blog/2016/october/26/midwest-s-manufacturing-mojo-chemical-mfg/ The Midwest is where a typical 250-worker chemical manufacturer should locate if it is in an expansion mode, per LaborEQ. The Indianapolis-Carmel-Anderson, Indiana metropolitan statistical area has a slight advantage over the next three sites shown in the table below due to its lower cost of living and an impressive pipeline of new graduates to fill jobs in that industry. MSA Labor Availability Pipeline of New Workers Payroll (M) Cost of Living Indianapolis-Carmel-Anderson, IN MSA 97% 132% $13.22 89.0 Cleveland-Elyria, OH MSA 100% 71% $13.54 95.8 Akron, OH MSA 99% 70% $13.17 92.2 Cincinnati, OH-KY-IN MSA 98% 93% $13.88 91.9 Source: LaborEQ Of course, chemists are important to this industry. An expanding plant may want to consider its location in the Indianapolis MSA based on where chemists currently live or work. From that perspective Marion County ranks first by far. Chemists: Where They Work and Live Marion County Work: 993 Live: 629 Hancock County Work: 44 Live: 49 Hamilton County Work: 38 Live: 49 Hendricks County Work: 20 Live: 75 Source: JobsEQ http://chmuraecon.com/blog/2016/october/26/midwest-s-manufacturing-mojo-chemical-mfg/ http://chmuraecon.com/blog/2016/october/26/midwest-s-manufacturing-mojo-chemical-mfg/ Wed, 26 October 2016 09:01:10 Sharon Simmons http://chmuraecon.com/blog/2016/strong-gains-in-median-household-income-last-year/ According to data released in the U.S. Census Bureau’s 2015 Income and Poverty in the United States report, median household income jumped 5.2% in real terms last year, the largest increase since the Census Bureau began recording such data in 1967. [1] Despite the healthy jump in median household income last year, however, it still remains slightly below where it stood in 2007, the beginning of the Great Recession.   Source: U.S. Census Bureau Note: Income questions were redesigned in 2014. 2013 data consisted of two subsamples—one which received the redesigned income questions and one which did not. The change in 2014 is calculated using the 2013 subsample which received the redesigned questions while the change in 2013 is calculated using the 2013 subsample which did not receive the redesigned questions. The 5.2% gain in real median household income last year was driven by increases in both median income and the number of workers. The number of full-time, year-round male and female workers climbed by 1.4 million and 1.0 million, respectively, in 2015; real median earnings rose 1.5% and 2.7%, respectively, in 2015 for male and female full-time, year-round workers. Hispanic-origin households experienced the largest increase in real median income last year at 6.1% compared with increases of 4.4% for non-Hispanic White households, 4.1% for Black households, and 3.7% for Asian households. [2] 2015 median income was highest for Asian households ($77,166) followed by non-Hispanic White households ($62,950), Hispanic households ($45,148), and finally Black households ($36,898). Source: U.S. Census Bureau Median income for households living in Metropolitan Statistical Areas (MSAs) climbed 6.0% from 2014 to 2015 compared with a 2.0% drop in median income for households living outside MSAs. [3] , [4] Median income for households living inside MSAs was $59,258 in 2015 compared with $44,657 for households living outside MSAs.   Source: U.S. Census Bureau By age cohort, households maintained by householders aged 35 to 44 experienced the largest gain in median income last year at 7.0%. Householders aged 25 to 34 experienced the next largest increase in median income at 5.6% followed by those 65 and older at 4.3%. Median household income for householders aged 45 to 54 and 15 to 24 increased 4.2% last year while householders 55 to 64 saw the smallest increase in median income from 2014 to 2015 at 3.5%. All changes in median household income except for householders aged 15 to 24 were statistically significant. Households maintained by householders aged 45 to 54 ($73,857) and 35 to 44 ($71,417) had the highest median income last year while those maintained by householders age 15 to 24 ($36,108) and 65 years and older ($38,515) had the lowest median income. Source: U.S. Census Bureau Median income rose 6.4% for households in the West, 5.1% in the Midwest, 4.9% in the Northeast, and 2.9% in the South from 2014 to 2015. [5] In 2015, median household income was highest in the Northeast ($62,182) followed by the West ($61,442), the Midwest ($57,082), and finally the South ($51,174). Source: U.S. Census Bureau [1] Household income includes the income of the householder and all other individuals in the house who are at least 15 years of age, regardless of whether or not they are related to the householder. A household can consist of only one person. [2] The 2014 to 2015 percentage change was statistically significant for all groups but Asian households. [3] The change in median income was statistically significant for households within MSAs but was not for households outside MSAs. [4] Approximately 86% of the U.S. population lives in metropolitan areas. [5] The percentage change was statistically significant for all regions. http://chmuraecon.com/blog/2016/strong-gains-in-median-household-income-last-year/ http://chmuraecon.com/blog/2016/strong-gains-in-median-household-income-last-year/ Thu, 20 October 2016 11:24:16 Chris Chmura http://chmuraecon.com/blog/2016/october/10/economic-impact-signs-in-the-economy-show-presidential-race-is-too-close-to-call/ With the election less than a month away, pundits are providing a variety of reasons why either presidential candidate will win in November. What does the economy tell us about a potential winner? It seems to be telling us that the race is too close to call. One key indicator gives Donald Trump the advantage while two other indicators tend to give Hillary Clinton the edge. The analysis is based on three economic indicators that give us a sense of the mood of voters looking at whether the incumbent party retained or lost control of the White House over the past 16 presidential elections - from 1952-2012. The three economic indicators are: • Stock market performance (three months leading up to the election); • Change in the unemployment rate (year leading up to the election); and • Change in employment (six months leading up to the election). For the stock market indicator, if the S&P 500 is up in the three months leading up to the election, then the incumbent party won 78 percent of the time. But the incumbent party lost 86 percent of the time when the S&P 500 is down over the same period. The S&P 500 is currently down 0.8 percent from the beginning of August which predicts that Trump has a slight advantage over Clinton. But we still have another month of stock market results to consider. If the unemployment rate increases in the year leading up to the election, then the incumbent party lost 100 percent of the time. Yet the incumbent party won 67 percent of the time when the jobless rate decreases. The jobless rate edged up to 5 percent in September after three months at 4.9 percent. But September’s rate is down from the 5.1 percent rate in September 2015 but it is unchanged from October and November 2015 figures. That gives Clinton a slight edge over Trump. But one more jobless report is slated to be released before the election. On the employment indicator, the incumbent party lost 100 percent of the time when the number of jobs created fell over the six months leading up to the election. But the party’s candidate won 68 percent of the time when employment rose over the same period. Employers have added about 850,000 jobs since May 2016, which gives the advantage to Clinton. Using historical view of changes in the stock market, unemployment rate, and employment as a predictor of the election is a simple approach. Even using more complex models such as the Vote-Share Model created by Ray Fair at Yale University or the Political Economy model by Michael Lewis-Beck of the University of Iowa and Charles Tien of Hunter College, the presidential outcomes differ by narrow margins.  The Fair Model—which is based on broad measures of growth captured in GDP—predicts a Trump victory, while the Political Economy Model—which is based on economic growth as well as Gallup’s Presidential Approval—suggests a Clinton victory. With no clear winner based on the economy or the most recent polls, it looks like it will be a long election night. http://chmuraecon.com/blog/2016/october/10/economic-impact-signs-in-the-economy-show-presidential-race-is-too-close-to-call/ http://chmuraecon.com/blog/2016/october/10/economic-impact-signs-in-the-economy-show-presidential-race-is-too-close-to-call/ Mon, 10 October 2016 12:38:47 Sharon Simmons http://chmuraecon.com/blog/2016/august/16/labor-day-time-for-leisure/ Labor Day is approaching, signaling the end of summer is near. In the United States, Labor Day—a federal holiday which celebrates and honors the accomplishments of American workers past and present—is the first Monday in September. For most Americans, Labor Day means a day off from work and a three-day weekend. Just what do Americans do for fun when they aren’t working? The average American (age 15 years and older) spends 6.43 hours in leisure and sports activities on weekends and holidays according to the 2015 results from the Bureau of Labor Statistics’ American Time Use Survey. How much time Americans spend on leisure and sports activities and what specifically they are doing with their leisure time varies significantly with employment status, gender, age, and educational attainment. Those who are employed, for example, spend an average of 5.81 hours on weekends and holidays in leisure and sports activities compared with 7.46 hours for individuals who are not employed. Both groups spend about half of their leisure time watching television. Employed individuals spend more time (0.40 hours) and a larger share of their leisure time (7%) participating in sports, exercise and recreation while individuals who are not employed spend only 0.26 hours or 3% of their leisure time. Men spend more time in leisure and sports activities on the weekend and holidays than women. On average, men spend 7.02 hours compared with 5.88 hours for women. Men spend 3.75 hours on average watching television on weekends and holidays, accounting for 53% of their leisure time while women spend 2.86 hours or 49% of their leisure time. Women, however, spend an average of 1.13 hours socializing and communicating on weekends and holidays (19% of their leisure time) while men spend 1.00 hours (14% of their leisure time). By age group, 35- to 44-year olds spend the least amount of time on leisure activities on weekends and holidays at 5.43 hours while those 65 to 74 years old (7.42 hours) and 75 years and older (8.21 hours) spend the most. Younger individuals spend less of their leisure time reading and watching television and more of their leisure time using the computer and playing games and participating in sports, exercise, and recreation when compared with older individuals.   Note: Relaxing/thinking time for 20- to 24-year olds was estimated. Individuals with more education spend less time on average in leisure and sports activities on weekends and holidays. Individuals with at least a bachelor’s degree spend an average of 6.03 hours in leisure and sports activities on weekends and holidays compared with 6.83 hours for those with only a high school diploma. College graduates spend less of their leisure time watching television and more of it reading and socializing compared with those with less than a college degree. http://chmuraecon.com/blog/2016/august/16/labor-day-time-for-leisure/ http://chmuraecon.com/blog/2016/august/16/labor-day-time-for-leisure/ Tue, 16 August 2016 10:18:41 Chris Chmura http://chmuraecon.com/blog/2016/august/15/pokénomics-sees-regional-differences-among-players/ If you’ve seen people walking around looking down at their phone, you’ve likely just witnessed someone playing Pokémon. Since its release on July 6, 2016, Pokémon Go! has exploded as a phone game application, becoming the most active mobile game ever in the United States. In the original video game version of Pokémon games, players were placed in a fictional region with a mix of characters that helped Pokémon trainers become a Pokémon master. Advances in GPS and augmented reality software enable Pokémon Go! players to now catch Pokémon in their own neighborhoods!   Pokémon Go! trainers (the players) advance in the game through a number of ways including catching Pokémon, battling at gyms, and hatching eggs that turn into Pokémon. If Pokémon were real, trainers would be most successful in regions with the greatest concentration of people with the skills to help them become masters. As a preferred provider of labor market data, Chmura Economics & Analytics set out to answer this question: “If Pokémon were real, which U.S. metropolitan areas would be best for Pokémon Trainers?” We created the following five industries that have employees best equipped to support players of Pokémon: Trainers, Gyms,   Veterinarians (Healers or Pokémon Centers), Selected retail shops (or Poke Mart), and Teachers (Professors). Each of these industries employ people with unique skills that the Bureau of Labor Statistics has identified as occupations. We used our JobsEQ ® technology platform to identify the number of people employed in each industry, in each of the 381 metropolitan statistical areas in the nation.  (Click here if you are interested in the occupations that are grouped in each industry.) Finally, we used the location quotient (LQ) , a measure of the concentration of the occupations in each metro area relative to the nation, to put large and small metro areas on an even playing field. If Pokémon were real, then trainers would be best served in Ithaca, New York! It had the highest LQ Score of all the metro areas that take into account the five Pokémon industries. Dalton, Georgia ranked the lowest with a score of 56.5, primarily attributable to having the fewest concentration of gym leaders and Pokémon professors relative to the nation. Click on the map below to see how your metro area fares. For those readers who don’t know much about Pokémon Go!, players advance partly by catching as many Pokémon as possible. Once you get to level 5 , you can join a gym where you train the Pokémon and they battle each other. And, importantly, you can join a team with your friends to battle other teams thereby moving closer to “master” status. The teams are Instinct (yellow), Mystic (blue), and Valor (red). Based on the team a particular trainer chooses, a metro area other than Ithaca may be preferred. Team Instinct doesn’t just go with their gut; they also put a lot of trust in their Pokémon’s instincts. To be recognized as a formidable Pokémon trainer, they need to be prepared for the consequences of following their instincts. The Santa Maria-Santa Barbara, California metro area has the highest concentration of occupations best suited for Pokémon Centers and items to help in those crucial (and instinctive) moments of battle. Team Instinct Metropolitan Statistical Area Score Santa Maria-Santa Barbara, CA 169.3 Boulder, CO 157.5 Sherman-Denison, TX 138.9 Columbus, IN 138.2 Austin-Round Rock, TX 136.1 Team Mystic is known for its calculation and focus on Pokémon evolution. For these lovers of wisdom, we focused on areas that have a significant concentration of occupations that would be perfect for Pokémon professors. And the professionals in California-Lexington Park, Maryland metro area can help Team Mystic learn about the world and the Pokémon within it, which would be crucial to crafting their mysterious strategies. Team Mystic Metropolitan Statistical Area Score California-Lexington Park, MD 296.7 Ann Arbor, MI 282.4 State College, PA 266.3 Ames, IA 257.0 College Station-Bryan, TX 246.2 Team Valor is focused on researching ways to enhance Pokémon’s natural power. They go by the original Pokémon theme song: “… to be the very best, like no one ever was .” And, since training Pokémon is their cause, the best way to do that is by training and pitting them against fellow Pokémon trainers and gym leaders. Which, from our research, is best done in the Hanford-Corcoran, California metro area. Since Team Valor is filled with those who “… will travel across the land…” If Pokémon were real, a lot of Team Valor members would probably move to Hanford-Corcoran. Team Valor Metropolitan Statistical Area Score Hanford-Corcoran, CA 325.0 Merced, CA 306.2 Visalia-Porterville, CA 278.6 Madera, CA 222.9 Ithaca, NY 211.9 Conclusions Pokémon Go! is a very popular game. It’s an innovative application of GPS and augmented reality software.  But what it also reveals is that regions matter when it comes to leveraging skills and occupations to achieve master status.  Applying this to the real world we live in, some regions stand out as the best investments for success by businesses and workers based on their concentration of real (or imagined) human capital. Research support for this post was provided by Brent Keath. Definitions Location Quotient The location quotient (LQ) is a measure of the relative size of an occupation in a region compared to the average size in the nation. An LQ of 1.0 indicates an occupation is the same size in the region as is average in the nation; an LQ of 2.0 means the occupation is twice as large in the region compared to average; and an LQ of 1/2 indicates the occupation is half as large regionally as average in the nation. The  location quotient  for an occupation identifies the degree to which the occupation specializes in or is concentrated in a region. With an LQ of 1.25 or higher, a region is considered to possess a  competitive advantage  in that occupation. Firms in a specific occupation often aggregate because of some competitive advantage found in an area such as geographic location, natural resources, and human resources. (A region can have a competitive advantage in a growing or declining occupation.) By formula, the location quotient is the ratio of an occupation’s share of total employment within the region to the same occupation’s share of employment in the nation: To get the final scores, we multiplied the location quotient by 100. Occupations by Industry Pokemon Centers Veterinarians (29-1131) Veterinary Technologists and Technicians (29-2056) Veterinary Assistants and Laboratory Animal Caretakers (31-9096) Pokemon Gyms Coaches and Scouts (27-2022) Lifeguards, Ski Patrol, and Other Recreational Protective Service (33-9092) Fitness Trainers and Aerobics Instructors (39-9031) Exercise Physiologists (29-1128) Athletic Trainers (29-9091) Pokemon Professors Animal Scientists (19-1011) Food Scientists and Technologists (19-1012) Soil and Plant Scientists (19-1013) Biochemists and Biophysicists (19-1021) Microbiologists (19-1022) Zoologists and Wildlife Biologists (19-1023) Biological Scientists, All Other (19-1029) Conservation Scientists (19-1031) Foresters (19-1032) Astronomers (19-2011) Physicists (19-2012) Atmospheric and Space Scientists (19-2021) Environmental Scientists and Specialists, Including Health (19-2041) Geoscientists, Except Hydrologists and Geographers (19-2042) Hydrologists (19-2043) Anthropologists and Archeologists (19-3091) Geographers (19-3092) Historians (19-3093) Agricultural and Food Science Technicians (19-4011) Biological Technicians (19-4021) Environmental Science and Protection Technicians, Including Health (19-4091) Forest and Conservation Technicians (19-4093) Life, Physical, and Social Science Technicians, All Other (19-4099) Agricultural Sciences Teachers, Postsecondary (25-1041) Biological Science Teachers, Postsecondary (25-1042) Forestry and Conservation Science Teachers, Postsecondary (25-1043) Atmospheric, Earth, Marine, and Space Sciences Teachers, Postsecondary (25-1051) Environmental Science Teachers, Postsecondary (25-1053) Anthropology and Archeology Teachers, Postsecondary (25-1061) Geography Teachers, Postsecondary (25-1064) Self-Enrichment Education Teachers (25-3021) Curators (25-4012) Museum Technicians and Conservators (25-4013) Pokemon Shops/Marts Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products (41-4011) Sales Engineers (41-9031) Production, Planning, and Expediting Clerks (43-5061) Buyers and Purchasing Agents, Farm Products (13-1021) Bicycle Repairers (49-3091) Pokemon Trainers Hunter and Trappers (45-3021) Animal Trainers (39-2011) Nonfarm Animal Caretakers (39-2021) Tour Guides and Escorts (39-7011) Animal Breeders (45-2021) Farmworkers, Farm, Ranch, and Aquacultural Animals (45-2093) Fishers and Related Fishing Workers (45-3011) Forest and Conservation Workers (45-4011) Occupations by Teams Team Instinct Veterinarians (29-1131) Veterinary Technologists and Technicians (29-2056) Veterinary Assistants and Laboratory Animal Caretakers (31-9096) Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products (41-4011) Sales Engineers (41-9031) Production, Planning, and Expediting Clerks (43-5061) Buyers and Purchasing Agents, Farm Products (13-1021) Bicycle Repairers (49-3091) Team Mystic Animal Scientists (19-1011) Food Scientists and Technologists (19-1012) Soil and Plant Scientists (19-1013) Biochemists and Biophysicists (19-1021) Microbiologists (19-1022) Zoologists and Wildlife Biologists (19-1023) Biological Scientists, All Other (19-1029) Conservation Scientists (19-1031) Foresters (19-1032) Astronomers (19-2011) Physicists (19-2012) Atmospheric and Space Scientists (19-2021) Environmental Scientists and Specialists, Including Health (19-2041) Geoscientists, Except Hydrologists and Geographers (19-2042) Hydrologists (19-2043) Anthropologists and Archeologists (19-3091) Geographers (19-3092) Historians (19-3093) Agricultural and Food Science Technicians (19-4011) Biological Technicians (19-4021) Environmental Science and Protection Technicians, Including Health (19-4091) Forest and Conservation Technicians (19-4093) Life, Physical, and Social Science Technicians, All Other (19-4099) Agricultural Sciences Teachers, Postsecondary (25-1041) Biological Science Teachers, Postsecondary (25-1042) Forestry and Conservation Science Teachers, Postsecondary (25-1043) Atmospheric, Earth, Marine, and Space Sciences Teachers, Postsecondary (25-1051) Environmental Science Teachers, Postsecondary (25-1053) Anthropology and Archeology Teachers, Postsecondary (25-1061) Geography Teachers, Postsecondary (25-1064) Self-Enrichment Education Teachers (25-3021) Curators (25-4012) Museum Technicians and Conservators (25-4013) Team Valor Coaches and Scouts (27-2022) Lifeguards, Ski Patrol, and Other Recreational Protective Service (33-9092) Fitness Trainers and Aerobics Instructors (39-9031) Exercise Physiologists (29-1128) Athletic Trainers (29-9091) Hunter and Trappers (45-3021) Animal Trainers (39-2011) Nonfarm Animal Caretakers (39-2021) Tour Guides and Escorts (39-7011) Animal Breeders (45-2021) Farmworkers, Farm, Ranch, and Aquacultural Animals (45-2093) Fishers and Related Fishing Workers (45-3011) Forest and Conservation Workers (45-4011) http://chmuraecon.com/blog/2016/august/15/pokénomics-sees-regional-differences-among-players/ http://chmuraecon.com/blog/2016/august/15/pokénomics-sees-regional-differences-among-players/ Mon, 15 August 2016 08:45:58 Kyle West http://chmuraecon.com/blog/2016/august/15/what-to-do-and-where-to-live-intersecting-education-earnings-purchasing-power-and-return-on-investment-part-i/ Few would debate that there will be an abundance of attractive employment opportunities in health-related industries for the foreseeable future. Chmura Economics & Analytics forecasts an average annual employment growth rate of 1.9% through 2026 for the healthcare and social assistance sector, which is more than three times the average annual growth rate of all industries (0.6%) in the nation. The ambulatory healthcare services industry alone is forecast to grow at an annual rate of 3.1%! [1] However, from the perspective of an aspiring healthcare worker, there is plenty to consider (and debate) when evaluating which healthcare careers to embark upon. Aside from the qualitative aspects that draw workers into healthcare fields, there are of course, quantitative aspects to consider. Chief among these might be questions like “how much will I earn?” Or “what will the return on my investment in the required training be?” Let us imagine that Marty is our career explorer in this case and that Marty wants to examine three different careers in healthcare, each typically requiring a different level of education: diagnostic medical sonographer (associate’s degree); registered nurse (bachelor’s degree); physical therapist (postgraduate degree). [2] Through basic research, [3] Marty has decided to pursue training in one of three regions: Omaha-Council Bluffs, NE-IA MSA; Pittsburgh, PA MSA; Seattle-Tacoma-Bellevue, WA MSA. Moreover, Marty is committed to remaining in the region to work after completing training. To help guide this decision, Marty first wants to discover what career earnings in each of these occupations might be and Marty assumes these earnings will be amassed over a span of 40 years. Naturally, Marty reached out to Chmura for this data request and received the worksheet below in response: Earnings and Growth for Select Healthcare Occupations by Region Occupation Mean Cost of Living (Base US) Adjusted Mean COLA Real Career Earnings Avg. Annual Growth (10 years) Omaha-Council Bluffs, NE-IA MSA Physical Therapists $73,800 91.4% $80,744 $3,229,759 3.1% Registered Nurses $60,900 91.4% $66,630 $2,665,208 1.5% Diagnostic Medical Sonographers $64,700 91.4% $70,788 $2,831,510 2.5% Total - All Occupations $44,800 91.4% $49,015 $1,960,613 0.8% Pittsburgh, PA MSA Registered Nurses $63,100 95.5% $66,073 $2,642,932 1.0% Diagnostic Medical Sonographers $56,300 95.5% $58,953 $2,358,115 1.8% Total - All Occupations $47,100 95.5% $49,319 $1,972,775 0.1% Seattle-Tacoma-Bellevue, WA MSA Registered Nurses $81,600 127.8% $63,850 $2,553,991 1.9% Diagnostic Medical Sonographers $86,300 127.8% $67,527 $2,701,095 2.8% Total - All Occupations $56,900 127.8% $44,523 $1,780,908 1.2% Source: JobsEQ® Wage data as of 2015; forecast data as of 2016Q1 Marty was immediately reassured that a career in healthcare was a prudent move based upon the mean (average) wages and annual growth forecast for each occupation compared to the average values for all occupations. Next, Marty’s eyes were drawn down the page to the Seattle region because the mean wages were so much higher than Omaha or Pittsburgh (plus, Marty could finally justify rooting for the Seahawks after spending two decades as a closet fan – bonus points to Seattle!). But wait… “what’s this cost of living stuff?” Marty wondered. So Marty wrote back to Chmura seeking an explanation. And sure enough, Chmura had one: “ The Cost of Living Index estimates the relative price levels for consumer goods and services. When applied to wages and salaries, the result is a measure of relative purchasing power. ” Based on Chmura’s cost-of-living index, it turned out that adjusted mean wages for physical therapists were highest in the Pittsburgh area and actually lowest in the Seattle region. Head scratch. And adjusted mean wages for registered nurses and diagnostic medical sonographers were highest in the Omaha region. This was quite the revelation for Marty… what at first seemed like a simple and clear-cut decision was suddenly begging to become more precise and intentional. Based on cost-of-living-adjusted (COLA) real career earnings, Marty stands to potentially earn the most by moving to the Pittsburgh region and pursuing a career as a physical therapist. But of course, it’s not this simple. What’s it going to cost Marty to earn a postgraduate degree in the Pittsburgh region? And how will this cost measure up against the potential earnings to be reaped over a forty-year career? How does this cost/benefit ratio compare to the ratios of the other careers that Marty is considering? What other factors must be considered? These questions and more will be addressed over the next few weeks as Marty discovers reliable new approaches to further informing this decision. Stay tuned. [1] JobsEQ® [2] JobsEQ ® [3] Namely that Marty has extended family in each of these regions who have basement apartments available and each of these regions has reputable postsecondary institutions that confer the relevant academic credentials. http://chmuraecon.com/blog/2016/august/15/what-to-do-and-where-to-live-intersecting-education-earnings-purchasing-power-and-return-on-investment-part-i/ http://chmuraecon.com/blog/2016/august/15/what-to-do-and-where-to-live-intersecting-education-earnings-purchasing-power-and-return-on-investment-part-i/ Mon, 15 August 2016 06:00:00 Chris Chmura http://chmuraecon.com/blog/2016/august/01/economic-impact-component-of-career-choice-should-be-balanced-with-job-opportunities/ It is that time of year when high school graduates and rising seniors are making decisions on which courses to take that will impact their work-related opportunities when they graduate from college. While interest is an important component of career choice, it should be balanced with job opportunities. High school students often say that they would like to be a photographer or an actor, but job opportunities are scarce in those fields. Based on estimates made by Chmura Economics & Analytics, 445 photographers worked in the Richmond metro area in the first quarter of 2016 and 276 of them are sole proprietors. On the other hand, an average 502 registered nurses will be needed each year over the next 10 years in the metro area to fill new jobs or those vacated by retirees or people moving to another occupation. Software developers and computer systems analysts also are on the list of top 10 occupations requiring a bachelor’s degree that businesses in the Richmond area will need over the next decade. Another factor that might help students narrow their career choice is potential earnings, which varies significantly by occupation. And it also is important to consider the entry-level wage that a new graduate is likely to receive rather than the annual average wage that includes individuals with experience. All of the top 10 occupations requiring a bachelor’s degree in the Richmond area receive a higher entry-level wage than the average of $31,500 for all occupations in the region. The highest paying entry-level occupation is financial managers at $75,700, which requires previous experience working in finance before managing other workers. At $58,200, software developers earn the highest starting wage without previous experience. Students who have done their homework to find out if they will be able to find a job in the career of their choice when they graduate along with how much they can expect to make in that job will be in better shape to pay off their student loans when they are handed their degrees. http://chmuraecon.com/blog/2016/august/01/economic-impact-component-of-career-choice-should-be-balanced-with-job-opportunities/ http://chmuraecon.com/blog/2016/august/01/economic-impact-component-of-career-choice-should-be-balanced-with-job-opportunities/ Mon, 01 August 2016 13:51:17 Xiaobing Shuai http://chmuraecon.com/blog/2016/july/internal-rate-of-return-irr-tends-to-overstate-education-return-on-investment-roi/ With dwindling government appropriations for higher education and elevated student loan default rates, more colleges and universities are conducting Return on Investment (ROI) analysis to demonstrate that higher education is a sound investment for students, taxpayers, and society at large. Those institutions include for-profit colleges, community colleges, and public and private not-for-profit four-year colleges. Some of these studies borrowed a measure from finance, called Internal Rate of Return (IRR), as a measurement of return on investment for students and taxpayers. The origin of IRR can be traced to investment analysis, where the goal is to achieve a positive net present value (NPV) for an investment. Net present value depends on the discount rate chosen in the analysis and there is a need to see the break-even discount rate, which gives rise to IRR. IRR is defined as an annual percentage rate where the net present value of an investment (benefit minus the cost) is zero. So an IRR higher than the commonly used discount rate means the investment has a positive NPV, and vice versa.  In a sense, a higher IRR points to a better return on the initial investment. However, IRR is not the same as return on investment (ROI), which is defined as the benefit of an investment with respect to its cost. ROI can be expressed as benefit/cost ratio—the total benefit of an investment divided by the total cost. If an investment spans over multiple years, an average annual rate of return can be calculated based on the investment benefit and cost. While IRR and ROI are related, and tend to move in the same direction, caution is needed when using IRR to represent the ROI of an investment. A simple example can illustrate the difference. Let us assume that Al and Bob each place $10,000 in a savings account for 20 years with a 5% annual interest rate that is compounded once a year. Al and Bob have different investment strategies. At the end of each year, Al takes that year’s interest income out ($500) and puts it under his mattress, while Bob keeps the interest in his account, which will generate interest for future years. [1] Bob’s strategy will generate a higher return over the 20 years. At the end of 20 years, the total amount of money for Al is $20,000 (including the initial $10,000), while that for Bob is $26,533—much higher than Al’s total. The surprising part is that both Al and Bob’s investment strategies yield the same IRR at 5%. So, based only on IRR, the decision makers will think those two investments have the same return. But we have just seen that Bob’s investment results in an amount about 30% more than Al’s; and thus, has a higher ROI.  More specifically, the benefit and cost ratio for Al is 2.0, which is equivalent to a 3.5% annual rate of return. Meanwhile, the benefit and cost ratio for Bob is 2.6, which is equivalent to a 5.0% annual rate of return. Investing $10,000 at 5% Annual Interest   IRR Annual Average ROI Savings at Year 20 Al (Spends Annual Interest) 5% 3.5% $20,000 Bob (Leaves Annual Interest in Bank Account) 5% 5% $26,533   In this case, using IRR will overestimate the rate of return for Al’s investment strategy. In fact, the IRR is equivalent to the annual rate of return on investment only if the annual benefit is reinvested at the same rate of the IRR. [2] That is the case in Bob’s strategy, where he invests interest income in the savings account that earns 5% interest.  However, when the annual benefit is not reinvested (like Al), or is reinvested in something with less than a 5% annual interest rate, the actual annual rate of ROI is smaller than the IRR. In ROI studies for higher education, the benefits are typically measured as the incremental income due to more education, and those are not universally reinvested. We rarely see college graduates save or invest all of that incremental income and maintain a lifestyle based on the income level of high school graduates. Some of the graduates may save a little more, but that amount is still only a small fraction of the incremental income.  As a result, using IRR will overstate the actual rate of return for both students and taxpayers in ROI studies for higher education institutions. Moreover, the longer the time horizon, the higher the degree of distortion with IRR.  For example, in Al’s investment strategy, when the investment horizon is 20 years, IRR overstates Al’s ROI by 40% (5.0% IRR and 3.5% annual rate of ROI). If the investment horizon is 40 years or more (typical for education ROI studies because students typically will work more than 40 years after college), the actual annual rate of ROI is only about half of IRR. When some reports claim that the IRR for higher education is 15% per year for community college students, the actual return on investment might only be half of that. [1] This is called interest compounding—the interest income of prior years is treated as principal in future years and generates interest of its own. [2] Source: Internal Rate of Return: A Cautionary Tale, The McKinsey Quarterly, 2004. http://chmuraecon.com/blog/2016/july/internal-rate-of-return-irr-tends-to-overstate-education-return-on-investment-roi/ http://chmuraecon.com/blog/2016/july/internal-rate-of-return-irr-tends-to-overstate-education-return-on-investment-roi/ Fri, 29 July 2016 09:09:19 Leslie Peterson http://chmuraecon.com/blog/2016/july/18/hot-jobs-and-cool-moves/ Are you wondering what area of study your recent college-bound freshmen will choose? Maybe you can help by giving them something to aim for! And let them know where they can find a job once they’ve earned that degree, as some people may like warmer weather while others prefer cooler climates. In fact, providing information about the wages they could earn with that potential degree might be the best advise you can give them as they prepare for their upcoming campus experience! Let’s consider the Austin-Round Rock, Texas metropolitan statistical area (MSA), as an example of a potential scenario for the freshman that loves technology. Austin-Round Rock MSA, Texas Employment in Computer Systems Design and Related Services Industry NAICS Industry Empl Avg. Ann Wages Avg. Ann Growth % 541511 Custom Computer Programming Services 18,951 $102,648 3.5% 541512 Computer Systems Design Services 12,333 $104,833 3.5% 541513 Computer Facilities Management Services 252 $82,476 3.6% 541519 Other Computer Related Services 1,219 $99,321 3.5% 5415 Computer Systems Design and Related Services 32,755 $103,246 3.5% Source: JobsEQ - Note Employment is a 4-quarter average for the four quarters ending with 2016 Q1 If this college-bound student is interested in living in Austin—a trendy location—the computer systems design services industry is booming! And with industry employment expected to grow at an average annual rate of 3.5% over the next decade, job prospects in this industry look promising in four years at graduation. Some well-paying jobs in the computer systems design services industry include the following for the Austin region: SOC Title Current Empl Regional Avg Wage 15-1143 Computer Network Architects 250 $120,200 17-2061 Computer Hardware Engineers 115 $110,100 17-2199 Engineers, All Other 17 $102,900 15-1122 Information Security Analysts 146 $102,200 15-1133 Software Developers, Systems Software 864 $100,700 Source: JobsEQ - note Employment is a 4-quarter average for the four quarters ending with 2016 Q1. Regional average wage is as of 2015. In terms of the number of people employed as computer hardware engineers, Austin is ranked as having the 9 th most jobs of all metro areas in the nation for this occupation. There are 1,985 computer hardware engineers working in the Austin-Round Rock, MSA. With a cost of living index of 107.8 (where the U.S. is 100), Austin is about 8% more expensive than the national average. In the San Jose-Sunnyvale-Santa Clara, CA MSA, the top MSA for employment of computer hardware engineers, the cost of living is a whopping 206.4—or about twice the national average. The wages for computer hardware engineers in San Jose region are much higher at $141,000 but housing in the San Jose MSA is expensive with a median house value of $654,800; the median house value in the Austin MSA is significantly lower at  $196,500. And speaking of hot jobs, Austin’s average July temperature is 96 degrees (F) while the San Jose region is a bit cooler with an average July around 82 degrees (F). http://chmuraecon.com/blog/2016/july/18/hot-jobs-and-cool-moves/ http://chmuraecon.com/blog/2016/july/18/hot-jobs-and-cool-moves/ Mon, 18 July 2016 13:39:12 Chris Chmura http://chmuraecon.com/blog/2016/july/13/federal-budget-cuts-could-hamper-growth-in-some-metro-areas/ The map below shows non-defense contract spending by MSA from FY 2000 to 2015. The circles are proportional (i.e., they are scaled to the value of defense contracts in that MSA). Beginning in FY 2001, the color of each circle indicates whether non-defense contract spending in the MSA declined (red) or increased (green) from a year earlier. The map can be manually advanced or will advance automatically when you click on “Play.” The table below the map shows DoD contract spending in the top 100 MSAs and updates each time a new fiscal year is selected. To learn more about Chmura’s expertise and research regarding defense spending and supply chain mapping, contact us here . The federal government spends billions of dollars buying goods and services from private sector firms each year. Just as the fortunes of businesses dependent on federal spending ebb and flow with federal budgets, so do the budgets of the metropolitan statistical areas (MSAs) where those employers are concentrated. Although non-defense contract spending has supported economic growth in many MSAs over the past decade, contract spending by federal agencies besides the Department of Defense will decline when budget reform becomes a priority. When that happens, MSAs whose budgets are more dependent on that spending may experience shortfalls. In the fiscal year (FY) that ended on September 30, 2015, [1] federal contracts decreased by $5 billion or 1.2%. Sixty-four percent of the purchases made in the most recent fiscal year were driven by the Defense Department. [2] While the majority of federal contract awards support the Defense Department, numerous other federal agencies enter contracts to purchase goods and services. For example, the Department of Health and Human Services contracts with pharmaceutical companies to produce vaccines and the Department of Energy awards contracts for, among other things, research and development. Non-Defense Department contract spending advanced at a modest 32% from FY 2005 to 2010 compared with a 49% gain in Defense Department contract spending over the same period. The non-defense spending was particularly strong, jumping by $30 billion from FY 2008 through 2010, as fiscal policy expanded in response to the recession. After peaking at $152.9 billion in FY 2010, annual non-defense contract spending has declined by only 1%. That compares with a 19.4% drop in Defense Department contract spending after it peaked at $336.7 billion in FY 2009. Annual non-defense contract spending, which peaked at $152.9 billion in FY 2010, hovered around $145 billion from FY 2011 to 2014 before inching up to $151.2 billion in FY 2015. Over the entire ten-year period from FY 2005 to 2015, non-defense contract spending increased $35.2 billion or 30.3%. This represents a 2.7% average annual increase compared with a 1.9% increase in defense contract spending over the same period. The five largest increases in spending from FY 2005 to 2015 occurred in metropolitan statistical areas (MSAs) with populations of at least 2.5 million. On a per capita basis, however, nine of the ten largest increases in non-defense contract spending were in MSAs with populations of less than 1,000,000. Non-DoD contract spending increased $3,554 per capita from FY 2005 to 2015 in the Idaho Falls MSA. Battelle Energy Alliance operates Idaho National Laboratory, a Government-owned, contractor-operated facility conducting nuclear energy research for the Department of Energy in the Idaho Falls MSA. On a per-capita basis, non-DoD contract spending increased $3,214 from FY 2005 to 2015 in the Gulfport-Biloxi-Pascagoula MSA. This region is home to the John C. Stennis Space Center, a NASA rocket testing facility. Contractors including Computer Sciences Corporation and HP Enterprise Services perform work at Stennis Space Center. From FY 2005 to 2015, non-DoD contract spending in the East Stroudsburg, Pennsylvania MSA rose $3,118 on a per capita basis. Global pharmaceutical company Sanofi Pasteur received large contracts from the Department of Health and Human Services to produce vaccines in its Swiftwater, Pennsylvania manufacturing facility. Non-Defense Contract Gains by MSA, FY 2005 to 2015 MSA Total Non-Defense Contract Gains FY 2005 to 2015 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA $16,814,165,419 Baltimore-Columbia-Towson, MD MSA $2,718,937,331 Boston-Cambridge-Newton, MA-NH MSA $2,561,989,563 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA $1,793,736,449 Los Angeles-Long Beach-Anaheim, CA MSA $1,526,046,564 Durham-Chapel Hill, NC MSA $1,475,297,394 Gulfport-Biloxi-Pascagoula, MS MSA $1,251,186,212 Denver-Aurora-Lakewood, CO MSA $734,444,088 Augusta-Richmond County, GA-SC MSA $719,503,522 East Stroudsburg, PA MSA $518,907,310 Idaho Falls, ID MSA $496,698,414 Huntsville, AL MSA $485,735,682 Palm Bay-Melbourne-Titusville, FL MSA $479,553,829 Austin-Round Rock, TX MSA $478,263,087 Indianapolis-Carmel-Anderson, IN MSA $467,981,002 New York-Newark-Jersey City, NY-NJ-PA MSA $459,152,692 Tampa-St. Petersburg-Clearwater, FL MSA $442,855,923 Harrisburg-Carlisle, PA MSA $402,595,975 Pittsburgh, PA MSA $390,683,897 Virginia Beach-Norfolk-Newport News, VA-NC MSA $383,699,861   Non-Defense Contract Gains per Capita by MSA, FY 2005 to 2015 MSA Total Non-Defense Contract Gains FY 2005 to 2015 $ Gain per Capita Idaho Falls, ID MSA $496,698,414 $3,554 Gulfport-Biloxi-Pascagoula, MS MSA $1,251,186,212 $3,214 East Stroudsburg, PA MSA $518,907,310 $3,118 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA $16,814,165,419 $2,757 Durham-Chapel Hill, NC MSA $1,475,297,394 $2,670 Coeur d'Alene, ID MSA $356,418,728 $2,371 Kennewick-Richland, WA MSA $340,687,406 $1,221 Augusta-Richmond County, GA-SC MSA $719,503,522 $1,219 Houma-Thibodaux, LA MSA $243,455,945 $1,147 Huntsville, AL MSA $485,735,682 $1,092 Baltimore-Columbia-Towson, MD MSA $2,718,937,331 $972 Manhattan, KS MSA $85,662,766 $869 Palm Bay-Melbourne-Titusville, FL MSA $479,553,829 $844 Missoula, MT MSA $94,157,102 $825 Hagerstown-Martinsburg, MD-WV MSA $203,291,497 $777 Morgantown, WV MSA $100,274,121 $726 Harrisburg-Carlisle, PA MSA $402,595,975 $713 Hanford-Corcoran, CA MSA $101,700,075 $674 Fargo, ND-MN MSA $149,126,315 $638 Lansing-East Lansing, MI MSA $277,034,010 $587   Non-Defense Contract Cuts by MSA, FY 2005 to 2015 MSA Total Non-Defense Contract Cuts FY 2005 to 2015 Albuquerque, NM MSA -$1,172,664,844 Las Vegas-Henderson-Paradise, NV MSA -$864,425,494 Amarillo, TX MSA -$856,455,560 Knoxville, TN MSA -$734,016,405 Albany-Schenectady-Troy, NY MSA -$641,739,567 Kansas City, MO-KS MSA -$527,221,645 San Francisco-Oakland-Hayward, CA MSA -$383,575,742 Cincinnati, OH-KY-IN MSA -$363,333,301 Nashville-Davidson--Murfreesboro--Franklin, TN MSA -$307,156,592 Charleston-North Charleston, SC MSA -$290,371,099   Non-Defense Contract Cuts per Capita by MSA, FY 2005 to 2015 MSA Non-Defense Contract Cuts 2005 to 2015 $ Cuts Per Capita Amarillo, TX MSA -$856,455,560 -$3,268 Albuquerque, NM MSA -$1,172,664,844 -$1,292 Knoxville, TN MSA -$734,016,405 -$852 Iowa City, IA MSA -$137,893,317 -$828 Albany, GA MSA -$113,479,615 -$739 Albany-Schenectady-Troy, NY MSA -$641,739,567 -$728 Canton-Massillon, OH MSA -$190,377,222 -$472 Brunswick, GA MSA -$48,652,325 -$419 Las Vegas-Henderson-Paradise, NV MSA -$864,425,494 -$409 Jackson, MS MSA -$231,060,733 -$399 [1] The U.S. federal government’s fiscal year begins on October 1 of the previous calendar year and ends on September 30. [2] Based on Chmura’s FedSpendTOP data that are derived from USASpending.gov data but provide a more accurate picture of federal spending based on the time and place of performance when compared with published federal awards data. The data are adjusted for the length of the contract as well as for an associated subcontract’s place of performance (i.e., regional spending is based on place of performance with out-of-region awards subcontracted into the area added in and in region awards subcontracted out of the region subtracted out); FedSpendTOP data also include purchases by non-DoD agencies which end up in DoD products and have been corrected for errors identified during Chmura’s quality control process. http://chmuraecon.com/blog/2016/july/13/federal-budget-cuts-could-hamper-growth-in-some-metro-areas/ http://chmuraecon.com/blog/2016/july/13/federal-budget-cuts-could-hamper-growth-in-some-metro-areas/ Wed, 13 July 2016 16:22:05 Chris Chmura http://chmuraecon.com/blog/2016/july/11/economic-impact-federal-budget-reform-could-have-impact-on-virginias-fortunes/ The federal government spends billions of dollars buying goods and services from private sector firms each year. Just as the fortunes of businesses dependent on federal spending ebb and flow with federal budgets, so do the budgets of the states where those employers are concentrated. And while non-defense contract spending has supported economic growth in many states over the past decade, it will contract when budget reform becomes a priority. When that happens, states like Virginia whose budgets are more dependent on that spending may experience shortfalls. In the fiscal year that ended Sept. 30, federal contracts decreased by $5 billion, or 1.2 percent. And 64 percent of the purchases in the most recent fiscal year were driven by the Defense Department. In Virginia, federal government contracts decreased more sharply, falling 4.4 percent — or by $2.4 billion — based on data from  USASpending.com  that Chmura Economics & Analytics adjusted for place and time of performance. Virginia is more dependent on DoD spending than the nation. Sixty-five percent of the purchases in Virginia during the most recent fiscal year are from Defense Department spending — and those purchases made up 7.1 percent of the state’s gross domestic product, compared with 1.5 percent in the nation. While the majority of these awards support the Defense Department, numerous other federal agencies enter contracts to purchase goods and services. For example, the Department of Health and Human Services contracts with pharmaceutical companies to produce vacc-ines, and the Department of Energy awards contracts for, among other things, research and development. The state also is more dependent on non-Defense Department federal spending, which makes up 3.8 percent of its GDP compared with 0.8 percent in the nation. Since non-Defense Department contract spending has been less volatile than the department’s spending over the past 10 years, it has contributed to more stable growth in some states — like Virginia — that are dependent on defense spending. In Virginia for the fiscal year that ended Sept. 30 compared with the prior 12 month-period, a $1.4 billion increase in non-Defense Department contract spending partially offset a $3.8 billion decrease in the department’s contract spending. In other words, Virginia would have been impacted more severely without the expansion in non-Defense Department contract spending. Non-Defense Department contract spending advanced at a modest 32 percent from fiscal year 2005 to fiscal year 2010 compared with a 49 percent gain in Defense Department spending over the same period. The non-defense spending was particularly strong, jumping by $30 billion from fiscal year 2008 through fiscal year 2010 as fiscal policy expanded in response to the recession. After peaking at $152.9 billion in fiscal year 2010, annual non-defense contract spending has declined by only 1 percent. That compares with a 19.4 percent drop in defense contract spending after it peaked at $336.7 billion in fiscal year 2009. Non-defense contract gains by state Here are the non-defense contract gains by state from fiscal year 2005 (12-month ended Sept. 30, 2005) to fiscal year 2015. (The per capital gains are in parentheses.): Virginia:  $8,542,008,667 ($1,019) District of Columbia: $5,839,744,820 ($8,687) Maryland:  $5,593,916,049 ($931) Pennsylvania:  $3,285,748,606 ($257) Massachusetts:  $2,857,615,828 ($421) California:  $2,039,365,210 ($52) North Carolina: $1,173,863,398 ($117) Florida:  $1,173,274,726 ($58) Colorado:  $939,922,764 ($172) Mississippi:  $929,920,035 ($311) Source:  Chmura Economics & Analytics http://chmuraecon.com/blog/2016/july/11/economic-impact-federal-budget-reform-could-have-impact-on-virginias-fortunes/ http://chmuraecon.com/blog/2016/july/11/economic-impact-federal-budget-reform-could-have-impact-on-virginias-fortunes/ Mon, 11 July 2016 11:26:05 Xiaobing Shuai http://chmuraecon.com/blog/2016/june/20/demystifying-gdp/ Gross Domestic Product (GDP) is one of the most widely used and cited economic indicators.  One cannot discuss the economy of a country or a region without talking about the national or regional GDP. However, many often misunderstand GDP. Specially, some may confuse GDP with the value of the total output (revenue) of an economy. The reason GDP is misunderstood can be traced to its intended use. The origin of GDP is the need for an economic indicator to measure the overall size of a national or regional economy, so that people can compare which country has the largest or second largest economy in the world. When we think of a business such as a retail shop or a manufacturing plant, we typically use their total sales or revenue as an indicator of the size of their business. For example, Fortune magazine routinely publishes America’s 500 largest companies based on their total revenue. Similarly, when we need a measure of the size of a national or regional economy, it is natural for people to think of this measure as the sum of the revenue (output) of all businesses in a country or region. While that thinking has its merit, total output (revenue) it is not a good indicator of a true size of the national or regional economy because it allows the possibility of double counting, which could inflate the size of an economy. Consider a business, which buys cotton from famers, and make shirts for the consumer. It has two plants—one turns cotton into fabric, and the other plant has sewing machines to stitch fabrics into shirts. Each shirt sells for $50 dollars. If the business makes only one shirt, the total revenue for the company is $50.  What if the owner decides to split the two plants into two separate businesses: one produces fabric and the other purchases the fabric and produces shirts?  If the price of the fabric is $20, the total revenue of those two separate companies are $70, while the total sale of the company prior to split is $50. Even though only one shirt is produced in the process, the total output jumps from $50 to $70. One would think the economy represented by two companies is larger, even though only one shirt is produced. The difference is that the value of the fabric is included in both the revenue of the fabric and shirt companies.  This example shows that summing up total output (revenue) of all businesses in a country or region is not a good measure of the true size of the economy due to the fact that the value of the intermediary goods (in this case the fabric) is counted twice or multiple times. Thus, the concept of GDP was born. GDP is the sum of all companies’ total sales minus the value of the intermediary inputs.  The difference between the total sales and the intermediary input is also defined as the value added of a business. Another commonly used definition of GDP is that it is the sum of consumer expenditures, business investment, government spending, and net exports. This definition is equivalent to the total value added of a country or region, because this definition counts only the value of final products and services; and not the value of intermediary products. What types of values are added to the intermediary inputs and turned into another product? As the following diagram shows, the main components of value added are the labor income, business tax, and gross surplus.  In addition, gross surplus is made up of the consumption of capital (or depreciation), corporate profits, and other income such as rents, interest, and proprietors’ income.  In 2014, U.S. GDP (or value added) was 54% of the total output of the country. Within GDP, more than half (53%) of it is labor income, with the rest making up gross surplus and business tax. http://chmuraecon.com/blog/2016/june/20/demystifying-gdp/ http://chmuraecon.com/blog/2016/june/20/demystifying-gdp/ Mon, 20 June 2016 11:33:35 Leslie Peterson http://chmuraecon.com/blog/2016/june/20/how-competitive-is-your-msa/ A tortilla-manufacturing firm is in a high-growth phase of their business cycle. They are looking for an MSA to locate their expanding operations with an expected 250 employees. Where should they begin their search? What are the key attributes around their geolocation decision? Let’s start with labor availability. According to LaborEQ ™ , they should give the following regions a serious look: Region Labor Availability Atlanta-Sandy Springs-Roswell, GA MSA 99% Dallas-Fort Worth-Arlington, TX MSA 100% Chicago-Naperville-Elgin, IL-IN-WI MSA 100% Phoenix-Mesa-Scottsdale, AZ MSA 97% Houston-The Woodlands-Sugar Land, TX MSA 99% Source: LaborEQ The top 3 occupations needed for a firm in this industry are:        Bakers        Food Batchmakers        Packers and Packagers, Hand Source: JobsEQ ® Wages are another important consideration.  They are affected by the cost of living in a region. So how competitive are the MSAs where the most bakers live and work today? If we consider the wages and regional cost of living for the top 10 regions in the nation that employ the most bakers, Houston has the lowest annual average wage for bakers, $22,100 with a related cost of living adjustment (COLA) of 92.9 where the U.S. COLA equals 100. In other words, the COLA in Houston is 8% lower than the average of the nation. Home values are an important component of COLA. And for regions employing the most bakers, Houston’s home values are certainly more affordable for employees that work in tortilla manufacturing. US Rank Region Employment Avg. Annual Wages Cost of Living Index Median House Value 1 New York-Newark-Jersey City, NY-NJ-PA MSA 12,810 $28,000 154.5 $400,000 2 Los Angeles-Long Beach-Anaheim, CA MSA 8,707 $25,100 153.6 $454,200 3 Chicago-Naperville-Elgin, IL-IN-WI MSA 6,982 $26,300 102.9 $217,300 4 Dallas-Fort Worth-Arlington, TX MSA 4,083 $23,800 97.4 $151,900 5 Boston-Cambridge-Newton, MA-NH MSA 3,946 $29,200 136.9 $363,600 6 Houston-The Woodlands-Sugar Land, TX MSA 3,751 $22,100 92.9 $144,000 7 Atlanta-Sandy Springs-Roswell, GA MSA 3,740 $23,200 99.9 $167,400 8 Miami-Fort Lauderdale-West Palm Beach, FL MSA 3,581 $24,300 112.3 $188,700 9 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 3,489 $28,300 108.3 $237,600 10 San Francisco-Oakland-Hayward, CA MSA 3,229 $30,200 204.5 $592,000 To request a demo of LaborEQ, click here . http://chmuraecon.com/blog/2016/june/20/how-competitive-is-your-msa/ http://chmuraecon.com/blog/2016/june/20/how-competitive-is-your-msa/ Mon, 20 June 2016 10:36:01 Leslie Peterson http://chmuraecon.com/blog/2016/may/25/may-is-graduation-month/ Employment is brighter for some graduating students than for others, depending on the degree earned. Registered nurses (RNs) top the list for most in demand in the nation over the next decade with an expected 1,110,841 openings. About 60% of the job openings are expected because RNs are either retiring or changing occupations (called replacement demand in the table). A bachelor’s degree is typically required to be an RN and it pays an entry wage of around $48,500. According to the latest data (fourth quarter of 2015), there are 2.9 million employed RNs in the nation. The annual average wage for all RNs is $69,800. Other occupations in high demand over the next decade include general and operations managers, accountants and auditors, and computer systems analysts. Top 10 Occupations in the Nation with Most Openings that Typically Require a Bachelor's Degree, 2015-2025 Current Forecast Four Quarters Ending with 2015q4 Over the Next 10 Years Occupation Employment Avg. Annual Wages(1) Total Replacement Demand Total Growth Demand Total Openings Entry-Level Wages(1) Registered Nurses 2,855,420 $69,800 674,656 436,185 1,110,841 $48,500 General and Operations Managers 2,153,362 $117,200 578,853 160,090 738,943 $51,900 Managers, All Other 956,533 $110,200 581,923 69,310 651,233 $61,100 Accountants and Auditors 1,329,500 $73,700 395,893 157,410 553,303 $43,900 Elementary School Teachers, Except Special Education 1,304,861 $56,800 278,301 68,513 346,814 $38,200 Software Developers, Applications 761,466 $99,500 128,545 156,726 285,271 $61,700 Secondary School Teachers, Except Special and Career/Technical Education 915,675 $59,300 218,530 48,529 267,059 $39,700 Management Analysts 737,144 $90,900 136,669 108,926 245,595 $49,800 Computer Systems Analysts 572,220 $87,300 82,653 125,941 208,594 $54,900 Financial Managers 548,319 $130,200 141,656 39,332 180,988 $68,600 1. Occupation wages are as of 2014. Source: JobsEQ Preschool teachers top the list of in-demand occupations for jobs that typically require an associate’s degree. Openings in the nation are expected to total 151,181 over the next decade with entry-level wages of $20,000. Entry-level wages for some jobs that require an associate’s degree pay better than those requiring a bachelor’s degree.  For example, the entry-level wage for the average dental hygienist in the nation is $52,600 and the education required is an associate’s degree compared to a registered nurse requiring a bachelor’s degree who has an entry wage of $48,500. When determining a major for post-secondary degrees, the forecasted number of openings and entry-level wages is important to both the employer and the new hires coming out of the education pipeline. Top 10 Occupations in the Nation with Most Openings that Typically Require an Associate's Degree, 2015-2025 Current Forecast Four Quarters Ending with 2015q4 Over the Next 10 Years Occupation Employment Avg. Annual Wages(1) Total Replace-ment Demand Total Growth Demand Total Openings Entry-Level Wages(1) Preschool Teachers, Except Special Education 427,081 $32,000 121,808 29,373 151,181 $20,000 Paralegals and Legal Assistants 268,735 $51,800 58,260 21,671 79,931 $32,500 Web Developers 154,910 $68,700 28,230 44,016 72,246 $37,500 Dental Hygienists 202,937 $72,000 32,322 37,769 70,091 $52,600 Medical and Clinical Laboratory Technicians 169,257 $40,800 38,609 29,661 68,270 $27,200 Physical Therapist Assistants 81,908 $54,300 26,249 33,001 59,250 $35,800 Radiologic Technologists 205,569 $57,500 36,532 17,181 53,713 $39,900 Respiratory Therapists 127,245 $58,500 29,213 14,499 43,712 $43,300 Life, Physical, and Social Science Technicians, All Other 69,799 $47,900 35,992 5,391 41,383 $28,300 Computer Network Support Specialists 180,776 $66,100 24,555 15,431 39,986 $39,000 1. Occupation wages are as of 2014. Source: JobsEQ http://chmuraecon.com/blog/2016/may/25/may-is-graduation-month/ http://chmuraecon.com/blog/2016/may/25/may-is-graduation-month/ Wed, 25 May 2016 13:42:34 Chris Chmura http://chmuraecon.com/blog/2016/may/25/defense-contract-spending-declines-66-billion-from-fiscal-year-2009-to-2015/ The map below shows defense contract spending by MSA from FY 2000 to 2015. The circles are proportional (i.e., they are scaled to the value of defense contracts in that MSA). Beginning in FY 2001, the color of each circle indicates whether defense contract spending in the MSA declined (red) or increased (green) from a year earlier. The map can be manually advanced or will advance automatically when you click on “Play.” The table below the map shows DoD contract spending in the top 100 MSAs and updates each time a new fiscal year is selected. The map informs the following question categories:         Which MSAs are the largest recipients of DoD contract spending?         In which regions is DoD contract spending concentrated?         In which MSAs is DoD contract spending increasing/decreasing from the prior year? For example, the map shows DoD contract spending surges in the Washington, D.C. MSA from FY 2000 to FY 2010 when spending climbed from $8.0 billion to $39.5 billion before declining beginning in FY 2011. To learn more about Chmura’s expertise and research regarding defense spending and supply chain mapping, contact us here . Department of Defense (DoD) contract spending grew at a strong pace in the first decade of the 21 st century, but has been in decline since fiscal year (FY) 2010 based on Chmura’s proprietary FedSpendTOP data. [1] , [2] Spending peaked at $336.7 billion in FY 2009 and has since fallen by 19.6% to $270.7 billion in FY 2015. [3] Contributing to the decline was the Budget Control Act of 2011 (sequestration took effect in 2013) and the drawdown of U.S. forces in both Iraq and Afghanistan. The impact of defense contract spending cuts has been and will continue to be uneven across regions and states. This analysis shows which metropolitan statistical areas (MSAs) have been most impacted by defense contract spending cuts. At the aggregate level, some of the largest MSAs have seen the most dramatic cuts from fiscal year 2009—the peak nationally of DoD contract spending—to fiscal year 2015, which is the most recent fiscal year of data available. On a per capita basis, however, the 10 largest declines in defense contract spending were experienced in MSAs with populations of less than 500,000. While the Washington, D.C. MSA—home to the headquarters of several large defense contractors including Lockheed Martin, General Dynamics, and Northrop Grumman—experienced the largest drop in defense contract spending ($6.5 billion) from FY 2009 to 2015, on a per capita basis the decline was only $1,072; this was the 31 st largest per capita decline in defense contract spending among the MSAs. Defense contract spending fell $2.3 billion in the New York metropolitan area from FY 2009 to 2015; this, however, only represented a loss of $116 on a per capita basis. In contrast, a $2.3 billion decline in the Oshkosh MSA represented a loss of $13,378 per person, the largest per capita decline among all MSAs over this period. The Oshkosh MSA is home to the Oshkosh Corporation, a military vehicle manufacturer, which has reduced its workforce in response to declining defense contract spending. From FY 2009 to 2015, defense contract spending decreased by $301.9 million or $2,891 per capita in the Lima, Ohio MSA; the Joint Systems Manufacturing Center, a government-owned, contractor-operated tank production facility in Lima operated by General Dynamics Land Systems, has experienced a production hiatus as the Army shifts production from the M1A2 Abrams fleet to the M1A3.       The labor market impact of defense contract spending cuts can vary widely depending on the type and nature of the defense contract spending. Every industry in the area will have a different economic impact based on the size of its local supply chain and the spending spillover from its directly employed workers. It stands to reason, however, that these spending cuts, as steep as they are, can be a driving force to upset labor markets in many of the nation’s MSAs, both large and small. Defense Contract Cuts by MSA, FY 2009 to 2015 MSA Total Defense Contract Cuts FY 2009 to 2015 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA -$6,534,964,135 Phoenix-Mesa-Scottsdale, AZ MSA -$4,540,569,745 Los Angeles-Long Beach-Anaheim, CA MSA -$4,334,692,207 Detroit-Warren-Dearborn, MI MSA -$2,930,431,025 New York-Newark-Jersey City, NY-NJ-PA MSA -$2,338,177,154 Oshkosh-Neenah, WI MSA -$2,268,233,215 Chicago-Naperville-Elgin, IL-IN-WI MSA -$2,238,919,430 New Orleans-Metairie, LA MSA -$2,159,563,955 York-Hanover, PA MSA -$2,016,694,563 Boston-Cambridge-Newton, MA-NH MSA -$1,779,815,870 Louisville/Jefferson County, KY-IN MSA -$1,717,377,395 Cincinnati, OH-KY-IN MSA -$1,664,995,187 Memphis, TN-MS-AR MSA -$1,620,314,173 Houston-The Woodlands-Sugar Land, TX MSA -$1,488,712,685 San Antonio-New Braunfels, TX MSA -$1,391,688,781 Hartford-West Hartford-East Hartford, CT MSA -$1,359,347,332 South Bend-Mishawaka, IN-MI MSA -$1,140,970,964 El Paso, TX MSA -$1,112,974,753 Bellingham, WA MSA -$966,597,640 Charleston-North Charleston, SC MSA -$910,442,557   Defense Contract Cuts per Capita by MSA, FY 2009 to 2015 MSA Total Defense Contract Cuts FY 2009 to 2015 $ Cut per Capita Oshkosh-Neenah, WI MSA -$2,268,233,215 -$13,378 York-Hanover, PA MSA -$2,016,694,563 -$4,554 Bellingham, WA MSA -$966,597,640 -$4,553 South Bend-Mishawaka, IN-MI MSA -$1,140,970,964 -$3,564 Lima, OH MSA -$301,908,063 -$2,891 Crestview-Fort Walton Beach-Destin, FL MSA -$725,869,109 -$2,769 Watertown-Fort Drum, NY MSA -$300,052,222 -$2,551 Anniston-Oxford-Jacksonville, AL MSA -$287,665,595 -$2,488 Fort Wayne, IN MSA -$852,247,912 -$1,983 California-Lexington Park, MD MSA -$213,631,915 -$1,917 New Orleans-Metairie, LA MSA -$2,159,563,955 -$1,710 Anchorage, AK MSA -$591,040,245 -$1,478 Sioux City, IA-NE-SD MSA -$248,450,379 -$1,470 Roanoke, VA MSA -$450,132,483 -$1,431 Louisville/Jefferson County, KY-IN MSA -$1,717,377,395 -$1,343 El Paso, TX MSA -$1,112,974,753 -$1,327 Binghamton, NY MSA -$323,305,058 -$1,314 Columbus, GA-AL MSA -$410,919,789 -$1,310 Bangor, ME MSA -$192,447,838 -$1,260 Palm Bay-Melbourne-Titusville, FL MSA -$713,521,119 -$1,256   Defense Contract Gains by MSA, FY 2009 to 2015 MSA Total Defense Contract Gains FY 2009 to 2015 Minneapolis-St. Paul-Bloomington, MN-WI MSA $2,003,699,292 Seattle-Tacoma-Bellevue, WA MSA $1,713,789,488 Baltimore-Columbia-Towson, MD MSA $1,054,626,982 Bridgeport-Stamford-Norwalk, CT MSA $803,582,514 Pittsburgh, PA MSA $803,508,009 Norwich-New London, CT MSA $777,377,637 Mobile, AL MSA $679,625,713 Portland-South Portland, ME MSA $575,384,923 Amarillo, TX MSA $513,600,860 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA $415,749,550   Defense Contract Gains per Capita by MSA, FY 2009 to 2015 MSA Federal Contract Gains 2009 to 2015 $ Gains Per Capita Norwich-New London, CT MSA $777,377,637 $2,859 Pittsfield, MA MSA $301,455,515 $2,358 Amarillo, TX MSA $513,600,860 $1,960 Mobile, AL MSA $679,625,713 $1,636 Lynchburg, VA MSA $326,223,707 $1,255 Portland-South Portland, ME MSA $575,384,923 $1,093 Pine Bluff, AR MSA $85,602,563 $914 Bridgeport-Stamford-Norwalk, CT MSA $803,582,514 $848 Idaho Falls, ID MSA $91,296,319 $653 Minneapolis-St. Paul-Bloomington, MN-WI MSA $2,003,699,292 $568 Source: Chmura Economics & Analytics and U.S. Census Bureau Note: July 2015 population estimates used to calculate cuts and gains per capita.   [1] The U.S. federal government’s fiscal year begins on October 1 of the previous calendar year and ends on September 30. For example, FY 2001 began October 1, 2000 and ended September 30, 2001. [2] FedSpendTOP data are derived from USASpending.gov data but provide a more accurate picture of federal spending based on the time and place of performance when compared with published federal awards data. The data are adjusted for the length of the contract as well as for an associated subcontract’s place of performance (i.e., regional spending is based on place of performance with out-of-region awards subcontracted into the area added in and in region awards subcontracted out of the region subtracted out); FedSpendTOP data also include purchases by non-DoD agencies which end up in DoD products and have been corrected for errors identified during Chmura’s quality control process. [3] In nominal dollars (i.e., not adjusted for inflation). http://chmuraecon.com/blog/2016/may/25/defense-contract-spending-declines-66-billion-from-fiscal-year-2009-to-2015/ http://chmuraecon.com/blog/2016/may/25/defense-contract-spending-declines-66-billion-from-fiscal-year-2009-to-2015/ Wed, 25 May 2016 10:07:34 Leslie Peterson http://chmuraecon.com/blog/2016/may/25/creating-strategic-plans-for-workforce-development/ Identifying occupations that are needed by businesses in a region is a precursor to the creation of a workforce development strategic plan. Moreover, identifying occupations that are currently in demand and those that will be needed over the next decade enables institutions in a region to better align their resources in workforce and education to meet the needs of businesses. Knowing which occupations are now in-demand and expected to remain in-demand over the next decade also empowers residents or communities to obtain the skills to compete for those jobs and to obtain living wages. Click here to read Virginia’s Combined State Plan for WIOA (Chmura Economics & Analytics supported the development of the plan): http://www.elevatevirginia.org/wp-content/uploads/2014/04/VBWD-Strategic-Plan_final.pdf http://chmuraecon.com/blog/2016/may/25/creating-strategic-plans-for-workforce-development/ http://chmuraecon.com/blog/2016/may/25/creating-strategic-plans-for-workforce-development/ Wed, 25 May 2016 10:00:00 Leslie Peterson http://chmuraecon.com/blog/2016/april/22/wioa-data-driven-directions/ Now that the states' WIOA plans are completed, local Workforce Development Areas' key staff are in the throes of developing their local strategic plans. As we heard while attending the NAWB Forum 2016 conference, local areas need actionable labor market data and information. To this end, Chmura is working with several WDAs to provide labor data through our technology platform, JobsEQ. With JobsEQ, you have the data you need, when you need it. Investment outcomes are more manageable and predictable with reliable labor force data. There are 19,703,061 residents in the nation that are living with some sort of disability according to the latest data available. This is about 10% of the population. An example of how this impacts local regions can be seen in the Southern Ohio nonmetropolitan area, which has 17% of its population living with disabilities compared to 11.6% for the State of Ohio. http://chmuraecon.com/blog/2016/april/22/wioa-data-driven-directions/ http://chmuraecon.com/blog/2016/april/22/wioa-data-driven-directions/ Fri, 22 April 2016 15:09:14 Chris Chmura http://chmuraecon.com/blog/2016/april/22/why-railroads-religious-organizations-and-self-employed-are-not-included-in-most-employment-estimates/ The evolution of employment data collection in the United States has led to a few exceptions that can have an impact on your view of employment in a region. To understand these missing data, a brief overview of a few employment data sources is helpful. The Quarterly Census of Employment and Wages (QCEW), conducted by the Bureau of Labor Statistics with the cooperation of State Employment Security Agencies, collects employment data from nearly every employer in the nation—specifically, any employers that report to U.S. Unemployment Insurance programs. The program began in the 1930s as the ES-202 program, only adopting the QCEW name relatively recently in 2003. Due to the QCEW’s nearly comprehensive sample (about 97% of all employment), the quarterly data serve as a benchmark for other employment estimates such as the Current Employment Statistics (used in the monthly job reports often covered in the media). This near census of employment information collected in the QCEW program is called “covered workers” and is subdivided into private, local government, state government, and federal government jobs. However, some groups are not covered: Members of the armed forces The self-employed Proprietors Domestic workers Unpaid family workers Railroad workers Some religious organizations Understandably, some of these non-covered workers would be difficult to track. But why are railroad workers and religious organizations not covered? Why not proprietors? It has to do with the fact that QCEW data are collected with the cooperation of state unemployment insurance agencies. In the 1920s, the railroad industry’s private pension plans already faced a number of problems, which were then compounded by the Great Depression in the 1930s. There was a push for establishing a separate federal retirement program for railroad workers around the same time as the new Social Security system was being created. The initiatives continued under separate unemployment insurance laws, and today the Railroad Retirement Board administers the separate unemployment insurance for railroad workers. Due to this separate program, railroad workers are not “covered” in the QCEW. Religious organizations have a more recent reason for not being covered. Under a 1981 Supreme Court ruling , schools affiliated with a religion are not required to be covered by Unemployment Insurance programs. Due to differences between state UI laws, certain types of nonprofit employers, including religious organizations, are given a choice of coverage or noncoverage. While there is an industry (NAICS) code for religious organizations, some employment is covered in NAICS 8131 but most is non-covered. Finally, proprietors are generally not included in the QCEW data because, as sole proprietors or partners in a firm, they are not required to pay unemployment insurance for themselves, no matter how large the firm is. Recognizing the need for a more complete view of local labor force, Chmura includes estimates of employment for railroads, religious organizations, and the self-employed to its JobsEQ ® technology platform. “Total employment” is the default employment type in JobsEQ, representing the sum of these three data sets and covered employment. We are excited about these break-outs within JobsEQ and the insights they provide our users about their regional economies. If you are interested in seeing a demonstration, please contact us . Research support provided by Patrick Clapp. http://chmuraecon.com/blog/2016/april/22/why-railroads-religious-organizations-and-self-employed-are-not-included-in-most-employment-estimates/ http://chmuraecon.com/blog/2016/april/22/why-railroads-religious-organizations-and-self-employed-are-not-included-in-most-employment-estimates/ Fri, 22 April 2016 14:29:13 http://chmuraecon.com/blog/2016/march/18/recession-on-the-horizon/ Due in part to recent stock market volatility, fears of another recession have been growing. This despite a relatively healthy U.S. labor market and few indications that the economy is slowing with the exception of weakness in the manufacturing sector. Nobel laureate Paul Samuelson famously said, “The stock market has called nine of the last five recessions.” That is, stock market declines often give us false positives. The yield curve is one of the few predictors of recessions which does not send out false positives. An inverted yield curve (i.e., when short-term interest rates are higher than long-term interest rates) has preceded each of the last seven recessions. The chart below shows the difference in basis points (i.e., one hundredth of a percentage point) between the yield on the 10-Year Treasury and the 3-Month Treasury going back to 1987. The spread turned negative before all three recessions over this period. Currently, the yield spread is 163 basis points. Chmura’s recession model uses the yield spread among other variables to forecast the probability of recession in the next six months. Based on January 2016 data, the model estimated the probability of recession at 10%, up from just 1% in December 2015; the increase was mainly due to the drop in the stock market. According to the most recent February data, the probability of recession is 6% through August 2016—suggesting the U.S economy will continue to expand. To learn more about or subscribe to Chmura’s monthly Recession Monitor or Chmura’s other publications including the Weekly Economic Update , please visit http://www.chmuraecon.com/publications/ . http://chmuraecon.com/blog/2016/march/18/recession-on-the-horizon/ http://chmuraecon.com/blog/2016/march/18/recession-on-the-horizon/ Fri, 18 March 2016 08:59:53 Chris Chmura http://chmuraecon.com/blog/2016/march/11/sleepbetter-lost-hour-economic-index/ These are the findings of Chmura Economics & Analytics in a study entitled “Estimating the Economic Loss of Daylight Saving Time for U.S. Metropolitan Statistical Areas” commissioned by the Carpenter Co. The study focused on only the aspects of economic losses where solid evidence from peer-reviewed academic journals could be obtained, showing how the DST change can lead to an increase in heart attacks, workplace injuries in the mining and construction sectors, and increased cyberloafing that reduces productivity for people who typically work in offices. A reasonable economic cost was then developed from the economic costs of heart attacks, workplace accidents and cyberloafing and applied to the more than 300 Metropolitan Statistical Areas (MSA) in the U.S. MSA Total Cost of Lost Hour Population in 2010 Index Rank Per Capita Cost % Difference From Nat'l Avg. MSA Rank National $433,982,548.00 260,392,304 $1.65449 Morgantown, WV $445,685.00 129,709 1 $3.37947 104.26% 293 Huntington-Ashland, WV-KY-OH $930,759.00 287,702 2 $3.18188 92.32% 162 Parkersburg-Marietta, WV-OH $519,472.00 162,056 3 $3.15273 90.56% 245 Charleston, WV $973,594.00 304,284 4 $3.14694 90.21% 156 Kingsport-Bristol-Bristol, TN-VA $925,487.00 309,544 5 $2.94061 77.74% 151 Lakeland, FL $1,582,213.00 602,095 6 $2.58458 56.22% 87 Tampa-St. Petersburg-Clearwater, FL $7,283,123.00 2,783,243 7 $2.57369 55.56% 18 Ocala, FL $863,182.00 331,298 8 $2.56256 54.89% 147 North Port-Bradenton-Sarasota, FL $1,822,027.00 702,281 9 $2.55172 54.23% 73 Punta Gorda, FL $404,984.00 159,978 10 $2.48982 50.49% 247 Scranton--Wilkes-Barre, PA $1,412,054.00 563,631 11 $2.46403 48.93% 90 Myrtle Beach-Conway-North Myrtle Beach, SC $662,576.00 269,291 12 $2.41994 46.27% 168 Pittsburgh, PA $5,794,723.00 2,356,285 13 $2.41877 46.19% 22 Palm Bay-Melbourne-Titusville, FL $1,336,302.00 543,376 14 $2.41877 46.19% 96 Evansville, IN-KY $873,111.00 358,676 15 $2.39418 44.71% 142 Tulsa, OK $2,277,053.00 937,478 16 $2.38892 44.39% 54 Sebastian-Vero Beach, FL $334,825.00 138,028 17 $2.38584 44.20% 283 Bloomington, IN $464,931.00 192,714 18 $2.37282 43.42% 218 Chattanooga, TN-GA $1,268,241.00 528,143 19 $2.36178 42.75% 98 Deltona-Daytona Beach-Ormond Beach, FL $1,187,424.00 494,593 20 $2.36128 42.72% 103 Bangor, ME $368,824.00 153,923 21 $2.35671 42.44% 259 Alexandria, LA $367,169.00 153,922 22 $2.34615 41.81% 257 Palm Coast, FL   $227,338.00 95,696 23 $2.33651 41.22% 347 Muncie, IN $279,430.00 117,671 24 $2.33557 41.17% 317 Crestview-Fort Walton Beach-Destin, FL $428,270.00 180,822 25 $2.32946 40.80% 224 Cape Coral-Fort Myers, FL $1,455,015.00 618,754 26 $2.31281 39.79% 84 Mobile, AL $967,710.00 412,992 27 $2.30459 39.29% 126 Jacksonville, FL $3,146,585.00 1,345,596 28 $2.29993 39.01% 40 Toledo, OH $1,523,209.00 651,429 29 $2.29976 39.00% 82 Lafayette, IN $471,375.00 201,789 30 $2.29752 38.87% 210 Canton-Massillon, OH $943,763.00 404,422 31 $2.29519 38.72% 128 Johnson City, TN $461,978.00 198,716 32 $2.28654 38.20% 214 Dayton, OH $1,952,542.00 841,502 33 $2.28210 37.93% 62 Knoxville, TN $1,617,373.00 698,030 34 $2.27890 37.74% 74 Pensacola-Ferry Pass-Brent, FL $1,037,154.00 448,991 35 $2.27193 37.32% 110 Terre Haute, IN $394,960.00 172,425 36 $2.25291 36.17% 233 Miami-Fort Lauderdale-Miami Beach, FL $12,725,469.00 5,564,635 37 $2.24919 35.95% 8 Port St. Lucie-Fort Pierce, FL $969,275.00 424,107 38 $2.24782 35.86% 117 State College, PA $351,910.00 153,990 39 $2.24765 35.85% 256 Asheville, NC $969,459.00 424,858 40 $2.24427 35.65% 116 Lewiston-Auburn, ME $243,661.00 107,702 41 $2.22512 34.49% 334 Lexington-Fayette, KY $1,066,009.00 472,099 42 $2.22084 34.23% 106 Jackson, TN $260,264.00 115,425 43 $2.21771 34.04% 321 Michigan City-La Porte, IN $251,036.00 111,467 44 $2.21503 33.88% 329 Cheyenne, WY $206,604.00 91,738 45 $2.21503 33.88% 354 Naples-Marco Island, FL $723,778.00 321,520 46 $2.21405 33.82% 149 Bowling Green, KY $282,675.00 125,953 47 $2.20733 33.42% 302 Kokomo, IN $221,462.00 98,688 48 $2.20711 33.40% 345 South Bend-Mishawaka, IN-MI $715,354.00 319,224 49 $2.20402 33.21% 150 Hickory-Lenoir-Morganton, NC $818,612.00 365,497 50 $2.20285 33.14% 141 Anderson, IN $294,790.00 131,636 51 $2.20256 33.13% 295 Atlantic City, NJ $612,213.00 274,549 52 $2.19317 32.56% 169 Columbus, IN $170,968.00 76,794 53 $2.18967 32.35% 362 Johnstown, PA $318,637.00 143,679 54 $2.18119 31.83% 275 Harrisburg-Carlisle, PA $1,215,731.00 549,475 55 $2.17610 31.53% 94 Lawton, OK $274,358.00 124,098 56 $2.17442 31.43% 307 Louisville, KY-IN $2,826,250.00 1,283,566 57 $2.16562 30.89% 42 Morristown, TN $300,143.00 136,608 58 $2.16094 30.61% 286 Wheeling, WV-OH $323,960.00 147,950 59 $2.15361 30.17% 268 Wichita Falls, TX $331,130.00 151,306 60 $2.15244 30.10% 266 Youngstown-Warren-Boardman, OH-PA $1,236,309.00 565,773 61 $2.14919 29.90% 91 Owensboro, KY $249,981.00 114,752 62 $2.14258 29.50% 322 Shreveport-Bossier City, LA $868,062.00 398,604 63 $2.14190 29.46% 129 Gainesville, FL $574,037.00 264,275 64 $2.13636 29.13% 173 Altoona, PA $275,914.00 127,089 65 $2.13528 29.06% 305 Williamsport, PA $251,895.00 116,111 66 $2.13372 28.97% 319 Panama City-Lynn Haven, FL $364,847.00 168,852 67 $2.12517 28.45% 236 Detroit-Warren-Livonia, MI $9,268,747.00 4,296,250 68 $2.12188 28.25% 13 Birmingham, AL $2,431,810.00 1,128,047 69 $2.12028 28.15% 50 Elizabethtown, KY $257,362.00 119,736 70 $2.11403 27.78% 313 Buffalo-Niagara Falls, NY $2,440,468.00 1,135,509 71 $2.11384 27.76% 49 Ocean City, NJ $207,878.00 97,265 72 $2.10205 27.05% 351 Fort Wayne, IN $888,743.00 416,257 73 $2.09993 26.92% 122 Steubenville-Weirton, OH-WV $265,544.00 124,454 74 $2.09854 26.84% 311 Huntsville, AL $890,913.00 417,593 75 $2.09832 26.83% 119 Erie, PA $598,252.00 280,566 76 $2.09720 26.76% 164 Montgomery, AL $798,346.00 374,536 77 $2.09646 26.71% 136 Gulfport-Biloxi, MS $528,298.00 248,820 78 $2.08825 26.22% 185 Carson City, NV $116,608.00 55,274 79 $2.07490 25.41% 366 Anniston, AL $250,003.00 118,572 80 $2.07373 25.34% 316 Mansfield, OH $261,914.00 124,475 81 $2.06951 25.08% 310 Sandusky, OH $161,894.00 77,079 82 $2.06578 24.86% 363 Elkhart-Goshen, IN $414,780.00 197,559 83 $2.06496 24.81% 216 Florence, AL $308,807.00 147,137 84 $2.06422 24.76% 206 Auburn-Opelika, AL $293,975.00 140,247 85 $2.06161 24.61% 276 Lafayette, LA $573,540.00 273,738 86 $2.06072 24.55% 167 Lima, OH $221,940.00 106,331 87 $2.05289 24.08% 336 Clarksville, TN-KY $571,732.00 273,949 88 $2.05264 24.07% 166 Lebanon, PA $278,565.00 133,568 89 $2.05123 23.98% 292 Reading, PA $857,038.00 411,442 90 $2.04871 23.83% 125 Gadsden, AL $217,236.00 104,430 91 $2.04596 23.66% 338 York-Hanover, PA $904,776.00 434,972 92 $2.04583 23.65% 113 Barnstable Town, MA $447,310.00 215,888 93 $2.03784 23.17% 198 Ann Arbor, MI $713,184.00 344,791 94 $2.03440 22.96% 146 Springfield, OH $285,322.00 138,333 95 $2.02862 22.61% 285 Wilmington, NC $747,299.00 362,315 96 $2.02861 22.61% 139 Dothan, AL $299,824.00 145,639 97 $2.02478 22.38% 270 Monroe, LA $362,816.00 176,441 98 $2.02244 22.24% 229 Hattiesburg, MS $293,083.00 142,842 99 $2.01802 21.97% 272 Lancaster, PA $1,064,016.00 519,445 100 $2.01465 21.77% 99 Decatur, AL $313,503.00 153,829 101 $2.00444 21.15% 258 Oklahoma City, OK $2,550,073.00 1,252,987 102 $2.00169 20.99% 43 Pascagoula, MS $330,101.00 162,246 103 $2.00107 20.95% 242 Hagerstown-Martinsburg, MD-WV $546,398.00 269,140 104 $1.99674 20.69% 172 Casper, WY $153,138.00 75,450 105 $1.99624 20.66% 364 Greenville, SC $1,289,377.00 636,986 106 $1.99086 20.33% 83 Greensboro-High Point, NC $1,464,797.00 723,801 107 $1.99044 20.31% 71 New Orleans-Metairie-Kenner, LA $2,360,696.00 1,167,764 108 $1.98827 20.17% 46 Las Vegas-Paradise, NV $3,941,238.00 1,951,269 109 $1.98658 20.07% 30 Indianapolis, IN $3,537,009.00 1,756,241 110 $1.98081 19.72% 35 Missoula, MT $219,657.00 109,299 111 $1.97660 19.47% 332 Ithaca, NY $203,399.00 101,564 112 $1.96970 19.05% 342 Dover, DE $324,756.00 162,310 113 $1.96789 18.94% 241 Cleveland-Elyria-Mentor, OH $4,151,800.00 2,077,240 114 $1.96580 18.82% 28 Allentown-Bethlehem-Easton, PA-NJ $1,614,902.00 821,173 115 $1.93420 16.91% 64 Prescott, AZ $414,603.00 211,033 116 $1.93229 16.79% 203 Providence-New Bedford-Fall River, RI-MA $3,141,889.00 1,600,852 117 $1.93032 16.67% 38 Worcester, MA $1,565,876.00 798,552 118 $1.92861 16.57% 67 Portland-South Portland-Biddeford, ME $1,005,605.00 514,098 119 $1.92385 16.28% 101 Billings, MT $308,857.00 158,050 120 $1.92200 16.17% 249 Springfield, MA $1,353,410.00 692,942 121 $1.92098 16.11% 77 Ames, IA $174,611.00 89,542 122 $1.91794 15.92% 356 Fayetteville, NC $713,043.00 366,383 123 $1.91413 15.69% 137 Fort Smith, AR-OK $580,963.00 298,592 124 $1.91364 15.66% 158 Houma-Bayou Cane-Thibodaux, LA $403,825.00 208,178 125 $1.90787 15.31% 205 Rapid City, SD $245,126.00 126,382 126 $1.90763 15.30% 300 Lewiston, ID-WA $118,048.00 60,888 127 $1.90686 15.25% 365 Lake Charles, LA $386,936.00 199,607 128 $1.90657 15.24% 213 Akron, OH $1,359,432.00 703,200 129 $1.90138 14.92% 75 Baton Rouge, LA $1,550,929.00 802,484 130 $1.90084 14.89% 66 Jackson, MS $1,040,664.00 539,057 131 $1.89874 14.76% 95 Cleveland, TN $223,348.00 115,788 132 $1.89718 14.67% 318 Iowa City, IA $293,659.00 152,586 133 $1.89286 14.41% 255 Richmond, VA $2,419,992.00 1,258,251 134 $1.89163 14.33% 44 Baltimore-Towson, MD $5,209,507.00 2,710,489 135 $1.89034 14.26% 20 Blacksburg, VA $312,735.00 162,958 136 $1.88752 14.08% 244 Orlando, FL $4,092,620.00 2,134,411 137 $1.88588 13.99% 26 Great Falls, MT $155,815.00 81,327 138 $1.88437 13.89% 359 Charlottesville, VA $384,484.00 201,559 139 $1.87614 13.40% 208 Farmington, NM $247,716.00 130,044 140 $1.87350 13.24% 301 Winston-Salem, NC $909,101.00 477,717 141 $1.87168 13.13% 105 Greenville, NC $359,766.00 189,510 142 $1.86714 12.85% 220 Pittsfield, MA $248,995.00 131,219 143 $1.86631 12.80% 296 Sioux City, IA-NE-SD $271,947.00 143,577 144 $1.86290 12.60% 273 Reno-Sparks, NV $805,515.00 425,417 145 $1.86230 12.56% 115 Columbia, MO $327,147.00 172,786 146 $1.86219 12.55% 232 Saginaw-Saginaw Township North, MI $378,070.00 200,169 147 $1.85766 12.28% 215 Davenport-Moline-Rock Island, IA-IL $716,738.00 379,690 148 $1.85661 12.22% 134 Kalamazoo-Portage, MI $614,919.00 326,589 149 $1.85185 11.93% 148 St. Louis, MO-IL $5,295,893.00 2,812,896 150 $1.85172 11.92% 19 Jackson, MI $300,340.00 160,248 151 $1.84336 11.42% 251 Columbus, OH $3,441,849.00 1,836,536 152 $1.84324 11.41% 32 Coeur d'Alene, ID $259,303.00 138,494 153 $1.84148 11.30% 280 Cincinnati, OH-KY-IN $3,984,972.00 2,130,151 154 $1.83994 11.21% 27 Battle Creek, MI $254,031.00 136,146 155 $1.83515 10.92% 288 Bay City, MI $201,047.00 107,771 156 $1.83479 10.90% 335 Niles-Benton Harbor, MI $292,506.00 156,813 157 $1.83460 10.89% 254 Albany-Schenectady-Troy, NY $1,621,964.00 870,716 158 $1.83212 10.74% 59 Amarillo, TX $462,864.00 249,881 159 $1.82184 10.11% 184 Roanoke, VA $570,732.00 308,707 160 $1.81834 9.90% 153 Harrisonburg, VA $230,573.00 125,228 161 $1.81091 9.45% 306 Burlington, NC $277,822.00 151,131 162 $1.80802 9.28% 261 Trenton-Ewing, NJ $673,675.00 366,513 163 $1.80780 9.27% 140 Jefferson City, MO $275,316.00 149,807 164 $1.80754 9.25% 265 Waterloo-Cedar Falls, IA $307,311.00 167,819 165 $1.80106 8.86% 237 Vineland-Millville-Bridgeton, NJ $287,125.00 156,898 166 $1.79988 8.79% 253 Hot Springs, AR $175,556.00 96,024 167 $1.79815 8.68% 349 Cumberland, MD-WV $188,630.00 103,299 168 $1.79599 8.55% 339 Binghamton, NY $458,868.00 251,725 169 $1.79288 8.36% 186 Augusta-Richmond County, GA-SC $1,015,110.00 556,877 170 $1.79285 8.36% 92 Dubuque, IA $170,614.00 93,653 171 $1.79177 8.30% 353 Rocky Mount, NC $277,596.00 152,392 172 $1.79160 8.29% 263 Flint, MI $775,183.00 425,790 173 $1.79060 8.23% 120 Springfield, IL $382,338.00 210,170 174 $1.78923 8.14% 204 Lynchburg, VA $459,487.00 252,634 175 $1.78884 8.12% 183 Glens Falls, NY $234,236.00 128,923 176 $1.78696 8.01% 299 Utica-Rome, NY $543,419.00 299,397 177 $1.78516 7.90% 160 Nashville-Davidson--Murfreesboro, TN $2,880,135.00 1,589,934 178 $1.78166 7.69% 37 Muskegon-Norton Shores, MI $311,558.00 172,188 179 $1.77962 7.56% 234 Danville, VA $192,681.00 106,561 180 $1.77840 7.49% 337 Goldsboro, NC $221,405.00 122,623 181 $1.77585 7.34% 309 Florence, SC $370,844.00 205,566 182 $1.77431 7.24% 267 Kingston, NY $329,107.00 182,493 183 $1.77370 7.21% 226 Monroe, MI $273,894.00 152,021 184 $1.77202 7.10% 264 Lansing-East Lansing, MI $833,169.00 464,036 185 $1.76592 6.74% 109 Holland-Grand Haven, MI $473,601.00 263,801 186 $1.76574 6.72% 174 Pine Bluff, AR $179,721.00 100,258 187 $1.76307 6.56% 344 Texarkana, TX-Texarkana, AR $243,750.00 136,027 188 $1.76242 6.52% 287 Springfield, MO $782,413.00 436,712 189 $1.76210 6.50% 112 Salisbury, MD $224,297.00 125,203 190 $1.76197 6.50% 308 Spartanburg, SC $509,124.00 284,307 191 $1.76127 6.45% 163 College Station-Bryan, TX $408,296.00 228,660 192 $1.75620 6.15% 193 St. Joseph, MO-KS $227,058.00 127,329 193 $1.75388 6.01% 303 Syracuse, NY $1,180,655.00 662,577 194 $1.75257 5.93% 80 Rochester, NY $1,877,514.00 1,054,323 195 $1.75146 5.86% 51 Elmira, NY $157,948.00 88,830 196 $1.74881 5.70% 357 Memphis, TN-MS-AR $2,337,491.00 1,316,100 197 $1.74683 5.58% 41 Winchester, VA-WV $227,756.00 128,472 198 $1.74361 5.39% 298 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $10,561,043.00 5,965,343 199 $1.74125 5.24% 6 Anderson, SC $331,261.00 187,126 200 $1.74111 5.24% 222 Jacksonville, NC $314,639.00 177,772 201 $1.74076 5.21% 227 Jonesboro, AR $213,245.00 121,026 202 $1.73297 4.74% 312 Sumter, SC $188,886.00 107,456 203 $1.72886 4.49% 333 Bloomington-Normal, IL $297,450.00 169,572 204 $1.72524 4.28% 235 Bismarck, ND $190,424.00 108,779 205 $1.72174 4.06% 330 Champaign-Urbana, IL $404,833.00 231,891 206 $1.71704 3.78% 192 Bremerton-Silverdale, WA $438,118.00 251,133 207 $1.71584 3.71% 182 Lawrence, KS $192,383.00 110,826 208 $1.70732 3.19% 328 San Antonio, TX $3,716,198.00 2,142,508 209 $1.70595 3.11% 24 Des Moines, IA $986,927.00 569,633 210 $1.70404 3.00% 88 Spokane, WA $815,061.00 471,221 211 $1.70120 2.82% 108 Tyler, TX $362,634.00 209,714 212 $1.70071 2.79% 200 Athens-Clarke County, GA $332,237.00 192,541 213 $1.69713 2.58% 219 Wichita, KS $1,073,694.00 623,061 214 $1.69488 2.44% 86 Poughkeepsie-Newburgh-Middletown, NY $1,154,051.00 670,301 215 $1.69334 2.35% 79 Joplin, MO $301,371.00 175,518 216 $1.68877 2.07% 231 Abilene, TX $282,987.00 165,252 217 $1.68426 1.80% 240 Boston-Cambridge-Quincy, MA-NH $7,793,647.00 4,552,402 218 $1.68380 1.77% 10 San Angelo, TX $191,352.00 111,823 219 $1.68303 1.73% 325 Topeka, KS $400,150.00 233,870 220 $1.68282 1.71% 189 Pocatello, ID $155,071.00 90,656 221 $1.68238 1.69% 355 Madison, WI $971,417.00 568,593 222 $1.68033 1.56% 89 Virginia Beach-Norfolk-Newport News, VA-NC $2,848,285.00 1,671,683 223 $1.67579 1.29% 36 Lubbock, TX $483,463.00 284,890 224 $1.66908 0.88% 161 Tuscaloosa, AL $371,157.00 219,461 225 $1.66337 0.54% 195 Sherman-Denison, TX $203,597.00 120,877 226 $1.65660 0.13% 314 La Crosse, WI-MN $224,725.00 133,665 227 $1.65357 -0.06% 290 Grand Forks, ND-MN $165,417.00 98,461 228 $1.65236 -0.13% 346 Waco, TX $394,549.00 234,906 229 $1.65195 -0.15% 188 Longview, TX $359,095.00 214,369 230 $1.64754 -0.42% 197 Beaumont-Port Arthur, TX $650,601.00 388,745 231 $1.64604 -0.51% 132 Cape Girardeau-Jackson, MO-IL   $160,928.00 96,275 232 $1.64403 -0.63% 350 Kansas City, MO-KS $3,396,584.00 2,035,334 233 $1.64133 -0.80% 29 Fayetteville-Springdale-Rogers, AR-MO $772,340.00 463,204 234 $1.63993 -0.88% 107 Peoria, IL $631,004.00 379,186 235 $1.63670 -1.07% 135 Corpus Christi, TX $710,406.00 428,185 236 $1.63179 -1.37% 114 Sioux Falls, SD $378,499.00 228,261 237 $1.63088 -1.43% 191 Decatur, IL $183,380.00 110,768 238 $1.62828 -1.58% 331 Manchester-Nashua, NH $663,068.00 400,721 239 $1.62744 -1.63% 130 Boise City-Nampa, ID $1,017,199.00 616,561 240 $1.62263 -1.93% 85 Chicago-Naperville-Joliet, IL-IN-WI $15,568,610.00 9,461,105 241 $1.61844 -2.18% 3 Eau Claire, WI $265,021.00 161,151 242 $1.61748 -2.24% 243 Oshkosh-Neenah, WI $274,532.00 166,994 243 $1.61690 -2.27% 238 Savannah, GA $570,207.00 347,611 244 $1.61335 -2.49% 143 Little Rock-North Little Rock, AR $1,147,493.00 699,757 245 $1.61285 -2.52% 72 Rome, GA $157,878.00 96,317 246 $1.61216 -2.56% 352 Burlington-South Burlington, VT $346,073.00 211,261 247 $1.61116 -2.62% 201 Warner Robins, GA $229,125.00 139,900 248 $1.61081 -2.64% 274 Victoria, TX $188,788.00 115,384 249 $1.60923 -2.74% 320 Macon, GA $378,813.00 232,293 250 $1.60391 -3.06% 190 Columbus, GA-AL $480,773.00 294,865 251 $1.60364 -3.07% 157 Midland, TX $221,613.00 136,872 252 $1.59247 -3.75% 281 Charlotte-Gastonia-Concord, NC-SC $2,845,880.00 1,758,038 253 $1.59213 -3.77% 33 Valdosta, GA $225,264.00 139,588 254 $1.58721 -4.07% 278 Kankakee-Bradley, IL $182,897.00 113,449 255 $1.58561 -4.16% 324 Brunswick, GA $181,130.00 112,370 256 $1.58537 -4.18% 326 Milwaukee-Waukesha-West Allis, WI $2,506,228.00 1,555,908 257 $1.58426 -4.24% 39 Danville, IL $131,406.00 81,625 258 $1.58337 -4.30% 360 Albany, GA $253,037.00 157,308 259 $1.58206 -4.38% 252 Logan, UT-ID $201,329.00 125,442 260 $1.57853 -4.59% 304 New Haven-Milford, CT $1,384,050.00 862,477 261 $1.57832 -4.60% 60 Killeen-Temple-Fort Hood, TX $649,274.00 405,300 262 $1.57558 -4.77% 127 Green Bay, WI $490,506.00 306,241 263 $1.57533 -4.78% 152 Rockford, IL $559,542.00 349,431 264 $1.57493 -4.81% 145 Fond du Lac, WI $161,165.00 101,633 265 $1.55964 -5.73% 341 Manhattan, KS   $201,274.00 127,081 266 $1.55775 -5.85% 297 Cedar Rapids, IA $408,023.00 257,940 267 $1.55581 -5.96% 177 Odessa, TX $216,475.00 137,130 268 $1.55262 -6.16% 282 Albuquerque, NM $1,399,881.00 887,077 269 $1.55210 -6.19% 57 Wenatchee, WA $174,873.00 110,884 270 $1.55112 -6.25% 327 New York-Northern New Jersey-Long Island,NY-NJ-PA $29,682,674.00 18,897,109 271 $1.54489 -6.62% 1 Sheboygan, WI $181,234.00 115,507 272 $1.54320 -6.73% 323 Norwich-New London, CT $429,665.00 274,055 273 $1.54199 -6.80% 170 Wausau, WI $210,056.00 134,063 274 $1.54105 -6.86% 291 Gainesville, GA $280,222.00 179,684 275 $1.53385 -7.29% 225 Appleton, WI $350,752.00 225,666 276 $1.52871 -7.60% 194 Hartford-West Hartford-East Hartford, CT $1,877,815.00 1,212,381 277 $1.52336 -7.93% 45 Omaha-Council Bluffs, NE-IA $1,333,208.00 865,350 278 $1.51529 -8.41% 58 Racine, WI $301,037.00 195,408 279 $1.51519 -8.42% 217 Janesville, WI $246,458.00 160,331 280 $1.51187 -8.62% 250 Las Cruces, NM $321,250.00 209,233 281 $1.51009 -8.73% 199 Mankato-North Mankato, MN $147,728.00 96,740 282 $1.50192 -9.22% 348 Grand Rapids-Wyoming, MI $1,178,645.00 774,160 283 $1.49741 -9.49% 69 McAllen-Edinburg-Pharr, TX $1,179,244.00 774,769 284 $1.49700 -9.52% 68 Duluth, MN-WI $425,512.00 279,771 285 $1.49589 -9.59% 165 Yakima, WA $369,819.00 243,231 286 $1.49541 -9.61% 187 Mount Vernon-Anacortes, WA $177,569.00 116,901 287 $1.49396 -9.70% 315 Dalton, GA $215,804.00 142,227 288 $1.49234 -9.80% 277 Los Angeles-Long Beach-Santa Ana, CA $19,390,317.00 12,828,837 289 $1.48658 -10.15% 2 Brownsville-Harlingen, TX $612,204.00 406,220 290 $1.48226 -10.41% 124 Fairbanks, AK $147,010.00 97,581 291 $1.48174 -10.44% 343 Riverside-San Bernardino-Ontario, CA $6,352,361.00 4,224,851 292 $1.47881 -10.62% 12 Longview, WA $153,768.00 102,410 293 $1.47677 -10.74% 340 Bellingham, WA $301,501.00 201,140 294 $1.47428 -10.89% 209 Rochester, MN $278,633.00 186,011 295 $1.47328 -10.95% 223 Columbia, SC $1,148,180.00 767,598 296 $1.47118 -11.08% 70 El Paso, TX $1,192,338.00 800,647 297 $1.46470 -11.47% 65 Minneapolis-St. Paul-Bloomington, MN-WI $4,842,583.00 3,279,833 298 $1.45216 -12.23% 16 Hinesville-Fort Stewart, GA $114,891.00 77,917 299 $1.45025 -12.34% 361 Raleigh-Cary, NC $1,663,591.00 1,130,490 300 $1.44734 -12.52% 47 St. Cloud, MN $277,151.00 189,093 301 $1.44155 -12.87% 221 Laredo, TX $366,846.00 250,304 302 $1.44147 -12.88% 181 San Luis Obispo-Paso Robles, CA $393,949.00 269,637 303 $1.43698 -13.15% 171 Sacramento--Arden-Arcade--Roseville, CA $3,125,517.00 2,149,127 304 $1.43037 -13.55% 25 Hanford-Corcoran, CA $221,963.00 152,982 305 $1.42702 -13.75% 260 Houston-Baytown-Sugar Land, TX $8,591,542.00 5,946,800 306 $1.42095 -14.12% 5 Lincoln, NE $435,900.00 302,157 307 $1.41888 -14.24% 154 Charleston-North Charleston, SC $958,180.00 664,607 308 $1.41799 -14.29% 78 Olympia, WA $361,151.00 252,264 309 $1.40807 -14.89% 180 Corvallis, OR $122,493.00 85,579 310 $1.40778 -14.91% 358 Fargo, ND-MN $298,526.00 208,777 311 $1.40634 -15.00% 202 Atlanta-Sandy Springs-Marietta, GA $7,462,139.00 5,268,860 312 $1.39295 -15.81% 9 Santa Cruz-Watsonville, CA $371,038.00 262,382 313 $1.39083 -15.94% 175 Chico, CA $310,836.00 220,000 314 $1.38963 -16.01% 196 Eugene-Springfield, OR $496,260.00 351,715 315 $1.38774 -16.12% 144 Santa Barbara-Santa Maria-Goleta, CA $595,677.00 423,895 316 $1.38211 -16.46% 118 Redding, CA $249,004.00 177,223 317 $1.38190 -16.48% 228 Napa, CA $191,518.00 136,484 318 $1.38012 -16.58% 284 Santa Rosa-Petaluma, CA $677,960.00 483,878 319 $1.37803 -16.71% 104 Colorado Springs, CO $897,143.00 645,613 320 $1.36672 -17.39% 81 San Diego-Carlsbad-San Marcos, CA $4,279,163.00 3,095,313 321 $1.35970 -17.82% 17 Idaho Falls, ID $177,313.00 130,374 322 $1.33764 -19.15% 294 San Francisco-Oakland-Fremont, CA $5,859,991.00 4,335,391 323 $1.32941 -19.65% 11 Ogden-Clearfield, UT $737,490.00 547,184 324 $1.32560 -19.88% 93 San Jose-Sunnyvale-Santa Clara, CA $2,472,499.00 1,836,911 325 $1.32385 -19.98% 31 Vallejo-Fairfield, CA $554,586.00 413,344 326 $1.31961 -20.24% 123 Oxnard-Thousand Oaks-Ventura, CA $1,102,236.00 823,318 327 $1.31673 -20.41% 63 Denver-Aurora, CO $3,395,406.00 2,543,482 328 $1.31296 -20.64% 21 Medford, OR $268,465.00 203,206 329 $1.29939 -21.46% 207 Durham, NC $666,098.00 504,357 330 $1.29894 -21.49% 102 Salinas, CA $544,618.00 415,057 331 $1.29055 -22.00% 121 Dallas-Fort Worth-Arlington, TX $8,355,402.00 6,371,773 332 $1.28972 -22.05% 4 Anchorage, AK $499,298.00 380,821 333 $1.28952 -22.06% 133 Austin-Round Rock, TX $2,235,782.00 1,716,289 334 $1.28124 -22.56% 34 Bend, OR $205,348.00 157,733 335 $1.28044 -22.61% 248 Kennewick-Richland-Pasco, WA $325,878.00 253,340 336 $1.26515 -23.53% 176 Portland-Vancouver-Beaverton, OR-WA $2,855,767.00 2,226,009 337 $1.26179 -23.74% 23 Salem, OR $500,670.00 390,738 338 $1.26025 -23.83% 131 Madera, CA $191,973.00 150,865 339 $1.25153 -24.36% 262 Boulder, CO $374,413.00 294,567 340 $1.25013 -24.44% 159 Modesto, CA $653,370.00 514,453 341 $1.24912 -24.50% 100 Grand Junction, CO $186,143.00 146,723 342 $1.24778 -24.58% 269 Fresno, CA $1,179,885.00 930,450 343 $1.24720 -24.62% 55 Pueblo, CO $200,950.00 159,063 344 $1.24254 -24.90% 246 El Centro, CA $220,389.00 174,528 345 $1.24198 -24.93% 230 Yuba City, CA $210,683.00 166,892 346 $1.24160 -24.96% 239 Stockton, CA $862,343.00 685,306 347 $1.23761 -25.20% 76 Bakersfield, CA $1,051,318.00 839,631 348 $1.23150 -25.57% 61 Washington-Arlington-Alexandria, DC-VA-MD-WV $6,970,911.00 5,582,170 349 $1.22822 -25.76% 7 Salt Lake City, UT $1,401,163.00 1,124,197 350 $1.22585 -25.91% 48 Greeley, CO $314,296.00 252,825 351 $1.22267 -26.10% 179 Tallahassee, FL $453,660.00 367,413 352 $1.21441 -26.60% 138 Seattle-Tacoma-Bellevue, WA $4,226,240.00 3,439,809 353 $1.20840 -26.96% 15 Merced, CA $308,988.00 255,793 354 $1.18807 -28.19% 178 Bridgeport-Stamford-Norwalk, CT $1,105,338.00 916,829 355 $1.18576 -28.33% 56 Visalia-Porterville, CA $531,838.00 442,179 356 $1.18296 -28.50% 111 St. George, UT $165,901.00 138,115 357 $1.18140 -28.59% 279 Santa Fe, NM $164,210.00 144,170 358 $1.12025 -32.29% 271 Fort Collins-Loveland, CO $340,812.00 299,630 359 $1.11871 -32.38% 155 Provo-Orem, UT $517,472.00 526,810 360 $0.96610 -41.61% 97 http://chmuraecon.com/blog/2016/march/11/sleepbetter-lost-hour-economic-index/ http://chmuraecon.com/blog/2016/march/11/sleepbetter-lost-hour-economic-index/ Fri, 11 March 2016 07:58:40 Chris Chmura http://chmuraecon.com/blog/2016/march/today-is-international-women-s-day/ March 8th is International Women’s Day , and as a women-owned business, Chmura is pleased to highlight some results from a recent report that we completed on the State of Women Owned Business in Virginia. The inaugural report was undertaken by the National Association of Women Business Owners (NAWBO) Richmond Chapter , and included a survey of Women Owned Businesses (WOBs) in Virginia, in order to better support women entrepreneurs in Virginia and understand their business characteristics. Some of the key findings of the study include: Women-owned businesses in the U.S., including those in Virginia, have experienced robust growth in recent years. Nationally, women-owned businesses grew an annual average of 5.0% per year from 2007 to 2012. In Virginia the number of WOBs grew an average 4.3% per year over this period. In 2012, there were over 9.9 million women-owned businesses in the nation, accounting for 36.0% of all firms. In Virginia, there were 237,371 WOBs, which accounted for 36.3% of total firms. Total revenue of all WOBs in Virginia was $45.0 billion in 2012, and $1,616.3 billion in the U.S. WOBs responding to the survey in Virginia tend to rely on personal finances for funding their businesses. Only a small percentage use structured borrowing such as commercial or government loans. In general, a credit card is the most popular tool for respondent WOBs to fund their business needs. 43% of responding WOBs expressed a need for mentorship. Of responding WOBs that have been in existence for 1 year or less, 68.4% said they need mentors. For more detail and other findings, an executive summary of the report is available online at http://nawborichmond.org/state-of-women-owned-business-in-virginia/ . http://chmuraecon.com/blog/2016/march/today-is-international-women-s-day/ http://chmuraecon.com/blog/2016/march/today-is-international-women-s-day/ Tue, 08 March 2016 16:55:40 Sharon Simmons http://chmuraecon.com/blog/2016/february/29/stem-job-growth-expected-to-exceed-growth-in-non-stem-jobs-over-the-next-decade/ Growth in Science, Technology, Engineering, and Mathematics (STEM) jobs is expected to continue to outpace growth in non-STEM jobs over the next decade. The Bureau of Labor Statistics released new 10-year employment projections in December for 2014 to 2024. At the detailed occupation level , 603 out of 821 occupations are expected to grow over this period while employment in 198 occupations is projected to decline; 20 occupations are expected to see no growth or decline in employment over the next ten years. For STEM occupations, 8% are expected to decline over the next decade compared with 27% of non-STEM occupations. [1] On average, STEM jobs pay a large premium over non-STEM jobs. Based on 2014 wages, STEM occupations paid an average of $85,200 compared with $45,100 for non-STEM occupations. While part of this wage gap is due to differences in educational attainment, [2] STEM employees with a bachelor’s degree or higher command higher average annual wages ($92,800) than non-STEM employees with at least a bachelor’s degree (average annual wages of $83,900). In addition to paying high wages, STEM jobs have grown faster than non-STEM jobs over the past decade and are expected to continue that trend over the next ten years. As of the third quarter of 2015, there were 9.2 million workers in STEM occupations in the United States, making up 6.1% of total employment in the nation. [3] From the third quarter of 2005 to the third quarter of 2015, STEM employment increased 9.8%, twice as fast as the 4.5% increase experienced in non-STEM employment. Employment in STEM occupations is projected to increase 10.1% from the third quarter of 2015 through the third quarter of 2025 while non-STEM employment is forecast to rise 6.5%.   All STEM jobs are not expected to grow equally, however. At the major occupation group level, projected employment growth for STEM occupations over the next decade ranges from 14.3% for computer and mathematical occupations down to 4.0% for architecture and engineering occupations (below the average expected growth in Non-STEM jobs). Not surprisingly, within major occupation groups projected employment growth for STEM occupations also varies by detailed occupation. In the computer and mathematical occupations group, for example, employment for mathematical technicians is expected to grow at a 0.3% annual average rate compared with 2.5% projected growth for web developers. Employment Growth for STEM Occupations by Major Group, 2015:Q3-2025:Q3 SOC Major Group Projected Growth Rate Computer and Mathematical Occupations 14.3% Education, Training, and Library Occupations 12.6% Management Occupations 10.9% Life, Physical, and Social Science Occupations 8.5% Sales and Related Occupations 8.2% Architecture and Engineering Occupations 4.0% Source: BLS and JobsEQ® [1] STEM occupations were identified based on the SOC Policy Committee recommendations to the Office of Management and Budget; health occupations were excluded. For more on defining STEM, see our related blog . [2] Eighty-one percent of those employed in STEM occupations hold at least a bachelor’s degree compared with 25% of those employed in non-STEM occupations. [3] Employment includes estimates for proprietors as well as railroad and religious employees that are not covered by unemployment insurance. http://chmuraecon.com/blog/2016/february/29/stem-job-growth-expected-to-exceed-growth-in-non-stem-jobs-over-the-next-decade/ http://chmuraecon.com/blog/2016/february/29/stem-job-growth-expected-to-exceed-growth-in-non-stem-jobs-over-the-next-decade/ Mon, 29 February 2016 09:47:12 Chris Chmura http://chmuraecon.com/blog/2016/january/21/searching-for-commonality-in-stem-occupation-definitions/ The media often reports that STEM (Science, Technology, Engineering, and Mathematics) jobs are in-demand.  They have been growing, are expected to continue growing, and generally pay a good wage. But when it comes to the occupations that should count as“STEM jobs,” there is far less agreement. Forecasts and reports of STEM growth are often cited in the media, but different definitions make comparisons between studies extremely difficult, if not impossible. To explore this problem, we compared four often-used definitions of STEM occupations: A widely cited study from the Department of Commerce Economics and Statistics Administration; The Occupational Information Network O*NET OnLine sponsored by the Department of Labor; The National Science Foundation Science and Engineering Indicators 2014 ; and The SOC Policy Committee recommendation . Among the different definitions, 314 unique occupation titles were identified as STEM. Those 314 titles represent over a third of the 840 detailed Standard Occupational Classification (SOC) codes. [1] The Department of Commerce study had the narrowest definition, with 54 occupations. The SOC Policy Committee recommended 184 STEM occupation codes and was the only definition to include healthcare occupations (64 health occupations). Only 17 titles were included in all four definitions of STEM occupations. About half of these 17 occupations are engineers, while another 4 are related to computer science. Math is represented by mathematicians and statisticians, while science is limited to astronomers, atmospheric and space scientists, and natural sciences managers. Based on the 4 STEM definitions reviewed here, the following 17 occupations should be included in any analysis of STEM jobs, though they are clearly not comprehensive (the complete list is shown in alphabetical order): STEM Occupations Defined in All Four Sources Aerospace Engineers Computer Programmers Agricultural Engineers Database Administrators Astronomers Environmental Engineers Atmospheric and Space Scientists Mathematicians Biomedical Engineers Mechanical Engineers Chemical Engineers Natural Sciences Managers Computer and Information Systems Managers Nuclear Engineers Computer Hardware Engineers Petroleum Engineers Statisticians Source: Chmura Economics & Analytics Similarly, 22 occupations were included in three definitions. Often in this category, the incongruence between sources is due to SOC code changes over time and/or degree of detail in the occupation code chosen (the full list is presented in the table below in alphabetical order): STEM Occupations Defined in Three Sources Biochemists and Biophysicists Marine Engineers and Naval Architects Biological Technicians Materials Engineers Chemical Technicians Materials Scientists Chemists Mining and Geological Engineers, Including Mining Safety Engineers Civil Engineers Network and Computer Systems Administrators Computer and Information Research Scientists Operations Research Analysts Computer Network Architects Physicists Electrical Engineers Sales Engineers Electronics Engineers, Except Computer Software Developers, Applications Industrial Engineers Software Developers, Systems Software Information Security Analysts Surveying and Mapping Technicians Source: Chmura Economics & Analytics Although the definition of STEM is somewhat dependent on how the information is used, there is some common ground in defining STEM occupations. Thirty-nine detailed occupation codes match across at least three sources, and 64 of the 215 codes with only one source can be explained by their relation to healthcare—usually differentiated in STEM-H definitions. As we read and conduct studies about STEM jobs, these common occupations should be kept in mind as the types of jobs typically considered “STEM.” The full list of occupations and number of sources is show here: STEM Occupations . Research support provided by Patrick Clapp. [1] Some of the occupations are from older SOC definitions, cross two or more codes, or are otherwise not included in the standard detailed codes (hence much of the problem with pinning down a definition). http://chmuraecon.com/blog/2016/january/21/searching-for-commonality-in-stem-occupation-definitions/ http://chmuraecon.com/blog/2016/january/21/searching-for-commonality-in-stem-occupation-definitions/ Thu, 21 January 2016 13:25:56 Greg Chmura http://chmuraecon.com/blog/2016/january/20/highlights-from-the-new-occupation-employment-projections/ Every two years, the Bureau of Labor Statistics (BLS) updates their employment growth projections for both occupations and industries. We looked at highlights of the industry changes last month. Here we look at highlights and implications of the occupation forecasts. Every one of the major occupation groups has slower job growth forecast compared to the previous. Healthcare occupations are still expected to grow the fastest—this includes healthcare support (+2.1% average annualized rate, 2014-2024) and healthcare practitioners and technical occupations (+1.5% AAR). The computer and mathematical group as well as personal care and service occupations have the next-highest growth projections at 1.2% per year. Construction and extraction occupations are still expected to see above-average job growth (+1.0% AAR), though this is twice as slow compared to the prior forecast. Two groups are expected to see declines from 2014-2024: production and farming, fishing, and forestry occupations. Among the healthcare occupations, occupational therapy and physical therapist assistants and aides are expected to continue to be among the fastest growing (+3.4% AAR 2014-2024). Home health aides are also expected to see employment expand quickly (+3.3% AAR 2014-2024), though a bit slower than in the prior estimates (+4.0% AAR 2012-2022). Among notable changes, job growth expectations for pharmacy occupations have been lowered. Growth for pharmacists is now expected to be a below-average 0.3% AAR in 2014-2024 compared to prior expectations of 1.4% AAR growth in 2012-2022. Pharmacy technician jobs are projected to expand 0.9% AAR in 2014-2024 compared to 1.8% AAR expectations in 2012-2022. Registered nurses (RNs) and licensed practical and licensed vocational nurses (LPNs) are both projected to see 1.5% AAR growth 2014-2024—this represents just a slight slowing for RNs (+1.8% AAR in 2012-2022), but more of a lowered expectation for LPNs (+2.2% AAR in 2012-2022). Within the computer occupation group, employment growth for web developers is projected to be the fastest (+2.4% AAR 2014-2024) in addition to being upgraded from prior expectations (+1.9% AAR 2012-2022). Information security analyst jobs are still expected to grow briskly (+1.7% AAR 2014-2024) though not quite as hot as previously forecast (+3.2% AAR 2012-2022). Computer programmer jobs were previously expected to grow at a slower-than-average pace (+0.8% AAR 2012-2022), but that occupation is now expected to contract (-0.8% AAR 2014-2024). While production occupation employment overall is expected to decline, some production occupations are projected to expand. Food processing workers, for example, are expected to see modest growth (+0.3% AAR 2014-2024), the same expectation as in the prior forecast. Woodworkers were expected to expand in the 2012-2022 forecast, but are projected to see a slight annualized 0.1% decline in 2014-2024. Printing workers are expected to see employment declines (-1.5% AAR 2014-2024) at a quicker pace compared to the last projection (-0.5% 2012-2022). Previously on this site we’ve talked about the hollowing out of the middle class and illustrated what that looked like over the past ten years. With the new BLS projections, we look at the next ten years and see no relief from this trend. The twenty percent of jobs with the highest wages are forecast to expand faster than average (+9.9% over the next ten years) and the twenty percent of jobs with the lowest wages are expected to grow faster than average (+6.8% over the same period). That leaves the middle sixty percent getting pinched—growing at a below-average 5.2%. Bottom line: ten years from now, if these forecasts play out, the middle class will be relatively smaller than it is today. For job-seekers using the new BLS forecasts to gauge career prospects, it is important to note that job growth expectations should be considered along with replacement needs —openings resulting from retirements and movement between occupations—to get a full picture of expected employment opportunities. Regional factors also play a large role in job opportunities, and regional demand can be gauged with the help of a local labor market tool such as JobsEQ ® . http://chmuraecon.com/blog/2016/january/20/highlights-from-the-new-occupation-employment-projections/ http://chmuraecon.com/blog/2016/january/20/highlights-from-the-new-occupation-employment-projections/ Wed, 20 January 2016 07:26:36 Greg Chmura http://chmuraecon.com/blog/2015/december/21/highlights-from-the-new-industry-employment-projections/ Every two years, the Bureau of Labor Statistics updates their long-term growth projections for industry and occupation employment. Their latest was published earlier this month and includes many notable changes. The biggest change, perhaps, is in the forecast for overall employment growth. For the 2012-2022 period, job growth overall was expected to average 1.0% per year—equivalent to about 1.6 million jobs per year. For 2014-2024, however, job growth is expected to be an average annualized 0.6%, or little under 1.0 million per year. Contributing to this slower projection is the aging baby boom generation which is moving into retirement and out of the labor force. In the industry projections, nearly all of the sectors that had been projected to expand in 2012-2022 are expected to continue to grow but more slowly. The health care and social assistance sector is forecast to grow the quickest, at a 1.9% average annualized rate (AAR). Construction is projected to expand at a 1.2% pace, much slower than the 2.6% rate projected for 2012-2022. Professional and business services as well as educational services are projected to have above-average 0.9% annualized growth for 2014-2024. Nonagriculture self-employment is the only group to have an increase in its rate of expected growth, from 0.6% AAR in 2012-2022 to 0.7% AAR for 2014-2024. This is another projection influenced by shifting demographics as older workers, in general, are more likely to be self-employed . While health care industries are expected to see quick job growth in 2014-2024—especially home health care services (+4.8% AAR) and outpatient care centers (+4.1% AAR)—projections for social assistance job growth were muted. Child day care services (+0.7% AAR) and individual and family services (+1.3% AAR) are both projected to expand employment, but at significantly slower paces compared with 2012-2022 expectations. Within the education sector, the private postsecondary schools are forecast to have slower employment growth. Private junior colleges, colleges, universities, and professional schools were projected to have 2.2% annualized average job growth in 2012-2022, but that forecast was cut to 1.2% per year for 2014-2024. This slowdown shouldn’t be a surprise in the light of the decline in college-aged population . Within manufacturing, wood product is expected to contract employment at an annualized pace of 0.2% in 2014-2024, much slower than prior expectations of an average pace of 1.4% job growth for 2012-2022. Manufacturing industries that are expected to expand employment in 2014-2024 include architectural and structural metals (+0.3% AAR); agriculture, construction, and mining machinery (+0.5% AAR); medical equipment and supplies (+0.1% AAR); and beverage manufacturing (+0.3% AAR). The professional, scientific, and technical services sector is expected to continue to grow at an above-average pace. Fast-growing industries in this sector are projected to include computer systems design and related services (+2.1% AAR 2014-2024); management, scientific, and technical consulting services (+2.4% AAR); and architectural, engineering, and related services (+0.8% AAR). As mentioned in the opening, the BLS also updated their occupation projections. We’ll go into highlights from those in my next blog post. http://chmuraecon.com/blog/2015/december/21/highlights-from-the-new-industry-employment-projections/ http://chmuraecon.com/blog/2015/december/21/highlights-from-the-new-industry-employment-projections/ Mon, 21 December 2015 07:43:52 Chris Chmura http://chmuraecon.com/blog/2015/december/18/tracking-liftoff-liftoff/ In a highly anticipated move, the Federal Reserve finally raised the target range for the federal funds rate, hiking it by a quarter-percentage point. This was the first rate hike in nearly a decade and signals the central bank’s confidence in the U.S. economy. The course of interest rate normalization is expected to be gradual but will ultimately be determined by incoming economic data. Based on the Fed’s “dot plot,” which shows Federal Open Market Committee members’ expectations for interest rates in the future, officials are expecting the federal funds rate target to increase one percentage point by this time next year. View the evolution of this decision in our interactive graphic below. http://chmuraecon.com/blog/2015/december/18/tracking-liftoff-liftoff/ http://chmuraecon.com/blog/2015/december/18/tracking-liftoff-liftoff/ Fri, 18 December 2015 15:40:37 Chris Chmura http://chmuraecon.com/blog/2015/december/18/the-economic-impact-of-richmond-2015-in-richmond-msa-and-virginia/ In September 2015, Richmond hosted the Union Cycliste Internationale (UCI) World Road Cycling Championships. Based on the number of spectators and spending in the region, the event was a success! This international sporting event attracted an estimated 645,000 spectators from around the world. In addition to the spectators, there were 5,284 credentialed participants at Richmond 2015, including athletes and their supporting staff, UCI and Richmond 2015 organizers, race officials and staff, and media representatives and journalists. An intercept survey we performed during the event found that spending by visitors to Richmond 2015 generated an estimated $138.4 million in economic impact (direct, indirect, and induced) in the Richmond metropolitan statistical area (MSA), and $145.9 million in Virginia. Combining event organization and visitor spending, the total economic impact of Richmond 2015 was estimated at $161.5 million (direct, indirect, and induced) in the Richmond MSA and $169.8 million in Virginia. See the full economic impact and survey results here:  The Economic Impact of Richmond 2015 in Richmond MSA and Virginia  (PDF). http://chmuraecon.com/blog/2015/december/18/the-economic-impact-of-richmond-2015-in-richmond-msa-and-virginia/ http://chmuraecon.com/blog/2015/december/18/the-economic-impact-of-richmond-2015-in-richmond-msa-and-virginia/ Fri, 18 December 2015 11:04:56 Chris Chmura http://chmuraecon.com/blog/2015/december/08/economic-impact-manufacturing-sector-is-changing/ The manufacturing sector often gets a bad rap. After all, who wants to do physical work for a declining industry in a dirty factory for relatively low wages? Each one of those impressions has a historical root of truth, but times, they are a-changin’. The manufacturing sector is typically more cyclical than other industries, such as health care and professional business services. That is, it tends to lay off a larger percentage of its workforce during recessions because of reduced demands for goods produced. This is especially true of industries that produce expensive durable goods, such as cars, refrigerators and furniture, that consumers delay purchasing during downturns for fear that they won’t be able to pay off the credit used to buy the items if they lose their jobs. Long-term trends show manufacturing employment peaked in the nation at 19.5 million jobs in mid-1979 and hit a low of 11.5 million in early 2010 as the nation recovered from the last recession. Since then, manufacturers have added 864,000 jobs. Looking to the next 10 years, the U.S. Bureau of Labor Statistics conservatively estimates that at least 2.8 million positions will open up in manufacturing as workers retire or move on to new occupations. In Virginia and the Richmond area, manufacturing employment has increased from its post-recession trough. According to the Virginia Economic Development Partnership, expansion announcements in 2014 and 2015 by manufacturing firms eventually will add 12,118 jobs to the state — 3,416 of those jobs are expected in the Richmond area. Movies such as “Rocky” remind us of hard, labor-intensive work at factories, even though the dirty old factory is from a bygone era. What was once made from brute labor is now created with programmable robotics and simulation models. And if you still think factory floors are dirty, look at the images of the Rolls-Royce airplane engine plant in Prince George County. You’ll see clean and sheen flooring with suspended equipment, looking more like a laboratory than a factory floor. Manufacturing jobs also pay very well. In the nation, they average an annual $63,154 in the first quarter of 2015, compared with $51,656 for all industries in total. The wage for an average manufacturing job stood at $61,659 in the Richmond area during the same time period compared with $50,082 for all jobs in our region. http://chmuraecon.com/blog/2015/december/08/economic-impact-manufacturing-sector-is-changing/ http://chmuraecon.com/blog/2015/december/08/economic-impact-manufacturing-sector-is-changing/ Tue, 08 December 2015 08:13:36 Chris Chmura http://chmuraecon.com/blog/2015/november/24/turkey-by-the-numbers/ As the most common main dish of Thanksgiving dinner, turkey prices have a large impact on the overall price of the holiday meal. The average price per pound of a frozen whole turkey rose 1.3% last month from September to $1.56 according to data from the U.S. Bureau of Labor Statistics. But turkey prices have been coming down over the past two years. From October 2014, the price was down 6.5% and was 14.3% lower than its September 2013 peak of $1.82 per pound. For six straight years beginning in 2009, turkey prices have declined from October to November suggesting that the average price paid this month may actually be less than $1.56 per pound. Turkey prices have risen faster than overall inflation over the past decade, increasing 37% compared with a 19% increase in overall consumer prices. And where are all those turkeys produced around the nation?  Based on the number of people employed, Minnesota and North Carolina topped the list in the first quarter of 2015 with 890 and 870 employees, respectively.  Missouri (527 workers), California (513), Ohio (292), and Indiana (286) make up the other states that employ at least 200 people in turkey production. http://chmuraecon.com/blog/2015/november/24/turkey-by-the-numbers/ http://chmuraecon.com/blog/2015/november/24/turkey-by-the-numbers/ Tue, 24 November 2015 15:50:41 Greg Chmura http://chmuraecon.com/blog/2015/november/23/major-change-coming-to-replacement-rates/ A heads-up if you use occupation replacement rates in your work: new rates are being developed and are planned to be released in two years…and it looks like they will be very, very different. On average, over four times larger. Right—four times larger; that is not a misprint. Occupation replacement rates are developed by the Bureau of Labor Statistics (BLS) to describe the number of workers who leave their occupation and need to be replaced by new entrants into the occupation. The rates describe demand due to workers leaving the workforce (such as retiring) plus those moving from one occupation to another. Replacement demand is important in the fields of workforce development and education because—along with growth demand—it helps gauge the future training needs for specific occupations. When publishing replacement estimates, the BLS includes the caution that the replacement needs are “underestimated” due to limitations in the methodology used for calculating these rates. In turn, in our JobsEQ system which uses these BLS rates, we reference the same caveat. The Bureau of Labor Statistics has developed a new method that they believe is a more accurate measure for this type of occupation demand. Accompanying this change, the BLS plans to change terminology from “replacement rate” to “separation rate,” partially to help highlight the change in methodology. Regardless of the name change, the BLS emphasizes that the “new method is designed to measure the same concept as the old methodology: workers who leave their occupation and need to be replaced by new entrants into the occupation.” [1] While the new data have not yet been officially released, the Bureau of Labor Statistics has posted some “experimental” results for comparison purposes. Even though the rates for some occupations change only slightly—dentists, for example—the new rates for most occupations are substantially larger. As one example, the estimated replacement demand for machinists (SOC 51-4041) increases roughly by a factor of four when put in terms of “occupational separations.” Using the old method, replacement needs for machinists during the period from 2012 through 2022 were estimated at about 91,000 workers; using the new method, separations for machinists in the same period are estimated to be over 392,000. The reason for the large increase from the current replacement rates to the new separation rates, to quote the BLS, is that “ the current method undercounts openings because it only accurately measures workers who follow a traditional career path—entering an occupation at a young age, working in the same occupation for many years, then retiring—which is not the case for many workers in most occupations.” [2] The new method is generally described as being “more robust and more statistically sound,” [3] the details of which can be found on the BLS website . To be complete, there is another technical difference between replacement and separation rates in how each deals with declining occupations, but that change is not as impactful on the change in magnitude in the rates compared to the other methodological changes. The bottom line is that even though the new official rates may not be available for another two years, this impending change highlights the need to keep in mind the caution about replacement rates: the current rates should be considered only as a minimum measure of training needs due to replacements. [4] It is a caveat of particular importance, given that the degree of underestimation may be severalfold. [1] http://www.bls.gov/emp/ep_separations_faqs.htm [2] Ibid. [3] Ibid. [4] For example, see the BLS description of that here: http://www.bls.gov/emp/ep_replacements.htm . http://chmuraecon.com/blog/2015/november/23/major-change-coming-to-replacement-rates/ http://chmuraecon.com/blog/2015/november/23/major-change-coming-to-replacement-rates/ Mon, 23 November 2015 16:24:59 Chris Chmura http://chmuraecon.com/blog/2015/november/20/the-highest-paying-jobs-that-don-t-require-a-college-degree-or-significant-training/ There are plenty of lists identifying the top 10 high-paying jobs that don’t require a college degree, but it is misleading to suggest a recent high school graduate can easily step into most of those occupations. Many of the jobs that top these lists are supervisory roles that require years of experience in the industry, while others such as elevator installer and repairer may require a lengthy apprenticeship. The graphic below (based on data from the BLS ) shows that lower education requirements for an occupation are often offset by on-the-job training. Seventy-seven percent of occupations that typically need an associate’s degree or higher don’t require on-the-job training, and the same is true for 55% of those that require some college but not a 2-year degree. Only 8% of occupations that typically need a high school diploma or less also don’t require on-the-job training. Instead, 37% require some short-term training, and 41% require moderate-term training. There are high-paying jobs for workers without a college degree, but most of them require experience or other training. Postal service mail carriers top the list of occupations requiring short-term on the job training along with a high school diploma or less.  First-line supervisors of police and detectives is the highest paid occupation with moderate-term on-the-job training. Top 10 Occupations That Require a High School Diploma or Less and Short-Term On-The-Job Training SOC code Occupation Title Median Annual Wage, 2012 43-5052 Postal service mail carriers $56,490 33-3052 Transit and railroad police $55,210 43-5051 Postal service clerks $53,090 43-5053 Postal service mail sorters, processors, and processing machine operators $53,090 53-7111 Mine shuttle car operators $52,110 53-7033 Loading machine operators, underground mining $48,420 33-3031 Fish and game wardens $48,070 47-5011 Derrick operators, oil and gas $46,900 53-6011 Bridge and lock tenders $45,940 53-7121 Tank car, truck, and ship loaders $44,100 Source: BLS   Top 10 Occupations That Require a High School Diploma or Less and Moderate-Term On-The-Job Training SOC code Occupation Title Median Annual Wage, 2012 33-1012 First-line supervisors of police and detectives $78,270 33-3021 Detectives and criminal investigators $74,300 53-2012 Commercial pilots $73,280 53-6051 Transportation inspectors $63,680 11-9131 Postmasters and mail superintendents $63,050 53-4041 Subway and streetcar operators $62,730 33-1011 First-line supervisors of correctional officers $57,840 49-9097 Signal and track switch repairers $55,450 33-3051 Police and sheriff's patrol officers $55,270 53-4031 Railroad conductors and yardmasters $54,700 Source: BLS When it comes to jobs that require no college degree and no on-the-job training, BLS has identified only 35 jobs (out of 820 detailed occupations) that fall in that category. However, recent high school graduates cannot easily step into most of those jobs, as they typically require a few years of related work experience in a different occupation. The list is even smaller for occupations that require no college degree, no on-the-job training, and no related work experience—only eight occupations fit those criteria. Of those eight, five fall under an “all other” title, a bucket for occupations that don’t easily fit into one of the Standard Occupational Classification codes.  The highest paid of those occupations, business operations specialists, all other, earned a median annual wage of $65,120 in 2012—much higher than the $34,750 national median wage in 2012. Occupations That Don’t Require a College Degree or On-The-Job Training SOC code Occupation Title Median Annual Wage, 2012 13-1199 Business operations specialists, all other $65,120 29-2092 Hearing aid specialists $41,430 29-2099 Health technologists and technicians, all other $40,700 31-9099 Healthcare support workers, all other $32,800 41-9099 Sales and related workers, all other $25,800 41-9012 Models $18,750 35-9031 Hosts and hostesses, restaurant, lounge, and coffee shop $18,580 27-2099 Entertainers and performers, sports and related workers, all other — Source: BLS Research support provided by Patrick Clapp. http://chmuraecon.com/blog/2015/november/20/the-highest-paying-jobs-that-don-t-require-a-college-degree-or-significant-training/ http://chmuraecon.com/blog/2015/november/20/the-highest-paying-jobs-that-don-t-require-a-college-degree-or-significant-training/ Fri, 20 November 2015 14:09:41 Chris Chmura http://chmuraecon.com/blog/2015/november/03/economic-impact-college-degrees-provide-resiliency-amid-change/ Apparently a college degree does make a difference — at least when it comes to a region’s ability to recover from recession. Northern Virginia is a driver of growth in our state. This was the case in the period between the last two recessions. From 2002 through 2007, employment in the Northern Virginia portion of the Washington metro area expanded at an annual average rate of 2.7 percent, compared with a 1.5 percent rate statewide. Employment in Northern Virginia also recovered from the most recent recession more quickly than the state and nation. It reached the former peak in employment early in 2011, compared with 2014 in both the state and the nation. Then came federal budget cuts and a government shutdown. During this period, the lack of growth in Northern Virginia caused the economy in Virginia to stall. Employment growth in Northern Virginia contracted 0.7 percent on a year-over-year basis in February 2014 and drove the overall state growth down 0.3 percent during the same period. In contrast, employment growth in the nation was accelerating and stood at 1.6 percent. One would expect such a sharp slowdown in employment that is caused by one industry sector — federal spending — to generate a prolonged slowdown in economic activity as displaced workers try to find other employment. Similar to the 1990s, when a cut in military spending slowed growth in Northern Virginia for a short time, the latest employment report shows the region growing at the same rate as the nation. For the 12 months ending with September 2015, employment grew 2 percent in Northern Virginia compared with 2 percent in the nation and 0.9 percent in the state. A highly educated population is a major reason for the quick rebound in Northern Virginia. Based on census data from 2013, 54 percent of residents in the region have a bachelor’s degree or higher, compared with 30.5 percent in the nation. The unemployment rate for people in the labor force with a bachelor’s degree was 2.5 percent in September, compared with 5.2 percent for those who have only a high school diploma. Skills that come with a bachelor’s degree are more easily transferable from one industry to another. It’s not quite as easy as changing a consultant’s letterhead from Defense Inc. to Cyber Security LLC, but the transferable skills possessed by workers in Northern Virginia clearly give the region resiliency during times of economic change. http://chmuraecon.com/blog/2015/november/03/economic-impact-college-degrees-provide-resiliency-amid-change/ http://chmuraecon.com/blog/2015/november/03/economic-impact-college-degrees-provide-resiliency-amid-change/ Tue, 03 November 2015 07:30:58 Chris Chmura http://chmuraecon.com/blog/2015/october/20/tracking-liftoff-will-it-be-october/ The Federal Open Market Committee (FOMC) is getting ready for it October 27-28 two-day meeting, where it will decide whether it is time to raise the federal funds rate target. Based on comments by some Fed officials, it appears that a rate hike might come before year-end.  As shown in the right-hand column of the graphic below, however, monthly employment gains have slowed, as has capacity utilization.  The deceleration in growth is causing some analysts to predict that the Fed won’t raise rates until its December meeting or even delay until 2016. The graphic below allows you to track how FOMC members are thinking about when that liftoff in rates should occur. Click on the photo of an FOMC member to see that person’s view about the timing of liftoff, how their view may have evolved since last December, and key quotes that are hyperlinked to full speeches. The photos of voting members are shown in circles with nonvoting members in squares. Key economic indicators are presented on the right (where the data shown represent the original estimates that were available at the time of the meeting rather than more recent revisions). http://chmuraecon.com/blog/2015/october/20/tracking-liftoff-will-it-be-october/ http://chmuraecon.com/blog/2015/october/20/tracking-liftoff-will-it-be-october/ Tue, 20 October 2015 10:52:47 Chris Chmura http://chmuraecon.com/blog/2015/october/08/the-number-of-people-in-college-age-range-is-declining/ Colleges and universities may want to take note: The number of people in the college age range of 18 to 24 is falling. The nation's population is expected to grow by more than 13 million, or an annual average of 0.8 percent a year, from this year through 2020. Virginia will see 403,585 new residents, or 0.9 percent a year, in the next five years. And the Richmond metropolitan statistical area will gain 67,346 people, or 1 percent a year. However, not all age groups will grow over that period. While the number of echo boomers and retirees will increase, the number of college attendees over this next five-year period will decline, according to data based on the U.S. Census projections. The “echo boom” births in the United States peaked in 1990. The children of that peak became college-freshman around 2008. Since then, the population of 18 to 19 year olds in the nation has trailed off. Children born at the peak of the echo boom are now about age 25, and most are out of college. As a result, the size of the prime college-aged population is on the downswing. The prime college-attending ages of 18 to 24 makes up about 58 percent of the college student population according to fall 2013 enrollment data from the 2014 Digest of Education Statistics. The U.S. population of people in that age range peaked in 2013 at 31,535,000. As of 2015, this segment of the population has slipped 1 percent to about 31,214,000. This downturn is expected to continue until 2020 when the number of people 18 to 24 hits a trough of about 30,555,000 - a drop of 2.1 percent from 2015 levels.  Some areas of the country will see more drastic declines, while other areas can expect to see no drop at all. Nine states are projected to grow in the 18 to 24 segment in the next five years, including Utah (up 3.9 percent) and Texas (up 3.4 percent). States forecast to see steeper-than-average declines include Michigan (down 6.9 percent) and New Mexico (down 6.8 percent). By comparison, Virginia is expected to see a 0.9 percent drop and the Richmond metro area is projected to decline by 1.0 percent. There is some good news for those wanting to see an increase of population in the college-aged segment. The number of U.S. births hit a trough in 1997. Many children born in that year are beginning their freshman years in college. Following 1997, the number of births began trending upward and peaked in 2007 at a height surpassing that of the echo boom. So while post-secondary schools are facing unfavorable demographics in the short run, another swell is on its way. On another end of the pendulum, retirees - aged 69 and older - are growing by double digits.  Nationwide, the population in that age group is expected to increase by 18 percent from 2015 through 2020.  The growth of this segment in Virginia (up 19 percent) and the Richmond metro area (up 21 percent) are both faster than the nation. The fastest growing states are expected to be Alaska (27 percent) and District of Columbia (26.1 percent), while the slowest growth is expected in Connecticut (14.5 percent) and Rhode Island (14.9 percent). This demographic group will put increased demand on the health care system for many years to come. http://chmuraecon.com/blog/2015/october/08/the-number-of-people-in-college-age-range-is-declining/ http://chmuraecon.com/blog/2015/october/08/the-number-of-people-in-college-age-range-is-declining/ Thu, 08 October 2015 13:20:07 Greg Chmura http://chmuraecon.com/blog/2015/october/06/the-graying-of-america/ Most people know that the percentage of older Americans is increasing dramatically. What’s less known to the average person is how that graying will impact different areas of the country. The overall demographic shift is illustrated in the chart below that shows four age cohorts. The first three are each based on equal twenty-year spreads: people age 0 to 19, those who are age 20 to 39, and the age group 40 to 59. The fourth cohort is defined as those age 60 and older. The age group 60 and up was roughly 40% smaller than the other cohorts in 2000. By 2010 it had begun to close the gap, but it was still about a third smaller than the other cohorts. By 2030, however, this 60+ age group will be nearly the largest cohort—surging from 57 million in 2010 to over 93 million in 2030. The reason for this increase is the aging of the baby boom generation along with the overall increase in life expectancy. The ramifications are many. The shift affects consumer spending patterns, health care needs, labor force mix, and so on. The demographic shift will be manifest differently throughout the nation. The map below shows the age mix for every county in the nation as it transforms from 2010 through 2030. Each county is colored according to the cohort that is the largest during that given year (using the same cohorts from above). A word about these data: the age mix data at the county level for 2010 are derived from the decennial census. The projections by age through 2030 at the national level follow forecasts from the Census Bureau. The county-level projections are produced by Chmura and can be accessed through the JobsEQ labor market system. What do the data show? In 2010, fewer than 10% of the counties in the nation had demographic mixes where the 60+ age cohort was largest. By 2019, however, the 60+ cohort should be largest in over half of the counties. By 2027, this cohort is expected to be largest in three-quarters of the nation’s counties. Areas of the country not expected to see the 60+ cohort become the largest during this timeframe include regions in Texas and California, various metropolitan areas, and counties with large college student populations—the latter of which stick out on the map among the light blue areas, as the age 20 to 39 cohort will remain the largest in these places. The interaction of a shifting age mix and overall population growth also can create surprising effects. For example, the population of the Cincinnati metropolitan statistical area (MSA) is projected to grow an average annualized 0.5% from 2015 through 2030. This population growth, though, is being driven by expansion in the age 60+ cohort (+2.2% per year). The population age 0 to 59 in Cincinnati is expected to actually decline overall during this same period. And Cincinnati is not alone in this boat; other MSAs expected to see overall population growth but declines in population under age 60 are Philadelphia, Chicago, Memphis, Green Bay, Mobile, and many others. The Social Security “full benefit retirement age” is currently 66 for people reaching that age today, and that will rise to 67 for people reaching that age in 2030. Despite this forestalling in retirement age, the number of people in the nation at full retirement age will increase substantially. From 12% at full retirement age in 2010, by 2030 in the United States approximately 18% will be at full retirement age. This shift is shown among the largest metropolitan areas below. Despite variety in current mix and growth rates, a dramatic increase in the percentage of population at full retirement age is coming across the board.  Top 50 MSAs by % Population at Full-Retirement Age MSA 2010 (% age 66+) 2030 (% age 67+) New York-Newark-Jersey City, NY-NJ-PA MSA 12% 18% Los Angeles-Long Beach-Anaheim, CA MSA 10% 16% Chicago-Naperville-Elgin, IL-IN-WI MSA 11% 17% Dallas-Fort Worth-Arlington, TX MSA 8% 13% Houston-The Woodlands-Sugar Land, TX MSA 8% 13% Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 12% 19% Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 9% 14% Miami-Fort Lauderdale-West Palm Beach, FL MSA 15% 20% Atlanta-Sandy Springs-Roswell, GA MSA 8% 14% Boston-Cambridge-Newton, MA-NH MSA 12% 18% San Francisco-Oakland-Hayward, CA MSA 12% 17% Phoenix-Mesa-Scottsdale, AZ MSA 12% 17% Riverside-San Bernardino-Ontario, CA MSA 10% 15% Detroit-Warren-Dearborn, MI MSA 12% 19% Seattle-Tacoma-Bellevue, WA MSA 10% 16% Minneapolis-St. Paul-Bloomington, MN-WI MSA 10% 16% San Diego-Carlsbad, CA MSA 11% 16% Tampa-St. Petersburg-Clearwater, FL MSA 16% 22% St. Louis, MO-IL MSA 13% 19% Baltimore-Columbia-Towson, MD MSA 12% 18% Denver-Aurora-Lakewood, CO MSA 9% 15% Charlotte-Concord-Gastonia, NC-SC MSA 10% 16% Pittsburgh, PA MSA 16% 24% Portland-Vancouver-Hillsboro, OR-WA MSA 11% 17% San Antonio-New Braunfels, TX MSA 10% 15% Orlando-Kissimmee-Sanford, FL MSA 12% 17% Sacramento--Roseville--Arden-Arcade, CA MSA 11% 17% Cincinnati, OH-KY-IN MSA 11% 18% Kansas City, MO-KS MSA 11% 17% Las Vegas-Henderson-Paradise, NV MSA 11% 15% Cleveland-Elyria, OH MSA 14% 22% Columbus, OH MSA 10% 16% Indianapolis-Carmel-Anderson, IN MSA 10% 16% San Jose-Sunnyvale-Santa Clara, CA MSA 10% 15% Austin-Round Rock, TX MSA 8% 13% Nashville-Davidson--Murfreesboro--Franklin, TN MSA 10% 16% Virginia Beach-Norfolk-Newport News, VA-NC MSA 11% 17% Providence-Warwick, RI-MA MSA 13% 21% Milwaukee-Waukesha-West Allis, WI MSA 12% 18% Jacksonville, FL MSA 11% 18% Memphis, TN-MS-AR MSA 10% 16% Oklahoma City, OK MSA 11% 16% Louisville/Jefferson County, KY-IN MSA 12% 19% Richmond, VA MSA 11% 18% New Orleans-Metairie, LA MSA 11% 18% Raleigh, NC MSA 8% 14% Hartford-West Hartford-East Hartford, CT MSA 13% 20% Salt Lake City, UT MSA 8% 13% Birmingham-Hoover, AL MSA 12% 19% Buffalo-Cheektowaga-Niagara Falls, NY MSA 15% 22% Source: Chmura Economics & Analytics , JobsEQ Research support was provided by Allison Magee and Asim Timalsina http://chmuraecon.com/blog/2015/october/06/the-graying-of-america/ http://chmuraecon.com/blog/2015/october/06/the-graying-of-america/ Tue, 06 October 2015 07:40:33 Leslie Peterson http://chmuraecon.com/blog/2015/october/02/winning-the-next-prospect-with-labor-data/ How do you make big decisions? If you’re involved in a site selection, you know that reliable labor data are critical to the process. Chmura has been helping communities for twenty years and we’ve seen fantastic results! For insights into why we do what we do, please view the attached brief video! http://chmuraecon.com/blog/2015/october/02/winning-the-next-prospect-with-labor-data/ http://chmuraecon.com/blog/2015/october/02/winning-the-next-prospect-with-labor-data/ Fri, 02 October 2015 16:15:24 Greg Chmura http://chmuraecon.com/blog/2015/september/15/the-decline-in-college-aged-students/ The “echo boom” of births in the United States peaked in 1990. The children of that peak became college-freshman aged around 2008. Since then, the population of 18 to 19 year olds in the nation has been trailing off. Children born at the peak of the echo boom are now about age 25, and most from that peak are out of college. As a result, the size of the prime college-aged population is on the downswing. The prime college-attending ages are 18 to 24. This cohort makes up about 58% of the college student population. [1] The population in the United States age 18 to 24 peaked in 2013 at 31,535,000. As of 2015, this population cohort is estimated to have slipped to approximately 31,214,000, a drop of one percent. This downturn is expected to continue till 2020 when the age 18 to 24 cohort population hits a trough of roughly 30,555,000 —a drop of 2.1% from 2015 levels.  Some areas of the country will be seeing more drastic declines than this, while other areas can expect to see no drop at all. Nine states are projected to see growth in the 18 to 24 cohort over the coming five years. From 2015 to 2020, states expected to see this population cohort expand include Utah (+3.9%) and Texas (+3.4%).On the other hand, states forecast to see steeper-than-average declines include Michigan (-6.9%) and New Mexico (-6.8%).  There is some good news, however, for those wanting to see an increase of population in the college-aged cohort. The number of births in the United States hit a trough in 1997—many children born in that year are just beginning their freshman year in college right now. Following 1997, the number of births began trending upward and peaked in 2007 at a height surpassing that of the echo boom. So while postsecondary schools are facing some unfavorable demographics in the short run, another swell is on its way.  Research support was provided by Allison Magee and Asim Timalsina. [1] Based on Fall 2013 enrollment data from the 2014 Digest of Education Statistics . http://chmuraecon.com/blog/2015/september/15/the-decline-in-college-aged-students/ http://chmuraecon.com/blog/2015/september/15/the-decline-in-college-aged-students/ Tue, 15 September 2015 10:46:22 Chris Chmura http://chmuraecon.com/blog/2015/september/15/defense-budgets-and-actual-funding-presidents-don-t-typically-get-what-they-ask-for/ Another budget showdown this fall seems inevitable. The President’s Budget for Fiscal Year 2016 calls for $561 billion in defense spending (excluding overseas contingency operations).  That’s $38 billion above sequestration levels. Ultimately, however, budgeting is decided in Congress, and a look back at previous budget proposals shows that the president never gets exactly what he asks for. The chart below shows a five-year projection of Department of Defense (DoD) funding in each president’s budget proposal (the dashed line) compared with the actual funding levels passed by Congress (the solid black line). [1] Differences between proposed and actual budgets have varied by president—especially during the last drawdown in defense spending in the late ‘80s and early ‘90s.  As in the past, we should expect changes to this year’s proposed budget. Research support was provided by Patrick Clapp. [1] This chart is a reproduction of Figure 21 in the Center for Strategic and Budgetary Assessments’ Analysis of the FY2015 Defense Budget , recalculated and updated with the FY2016 Budget. The numbers are shown in 2015 dollars. http://chmuraecon.com/blog/2015/september/15/defense-budgets-and-actual-funding-presidents-don-t-typically-get-what-they-ask-for/ http://chmuraecon.com/blog/2015/september/15/defense-budgets-and-actual-funding-presidents-don-t-typically-get-what-they-ask-for/ Tue, 15 September 2015 10:39:06 Chris Chmura http://chmuraecon.com/blog/2015/august/28/as-employment-grows-when-will-we-see-wage-growth/ Wage growth remains relatively flat despite indicators of economic recovery. As the unemployment rate falls and employment grows, the increasingly smaller supply of workers is expected to lead to wage growth. As Loretta Mester, president of the Federal Reserve Bank of Cleveland, put it recently in the Wall Street Journal , “basic economics hasn’t gone out the window […]when employment grows, wages will start to grow.” Over the past two years, however, most workers have not seen much wage growth. In fact, there is a somewhat weak but negative relationship between employment growth and wage growth for over 800 occupations from 2012 through 2014 (each occupation is weighted by the number of people employed in that occupation in 2014). There are some outliers, including occupations in the arts with an especially wide range of wages (such as models and makeup artists), but the majority of occupations are clumped approximately equally around low employment growth and low wage growth. Using the Bureau of Labor Statistics’ occupation profiles and typical entry-level education requirements for each occupation, the relationship between employment growth and wage growth from 2012 through 2014 differs depending on the education typically required for an occupation. Based on a review of the charts by education required, much of the negative relationship for all occupations is being driven by lower-skilled occupations, those that typically require a high school diploma or equivalent or less. Low-skill occupations with employment growth have seen a decline in real wages over this period, while many of the higher paying occupations have seen declining or stagnant employment. This is likely an indication that there is a surplus of available workers at the low-skill level. In fact, unemployment rates for workers without a college degree were well above the national average, as shown in the chart below from the Bureau of Labor Statistics . For higher-skilled occupations such as those requiring at least a bachelor’s degree, the expected positive relationship between employment growth and wage growth is evident, indicating that labor market slack for these positions has been eliminated or nearly eliminated. Meanwhile, for middle-skill occupations (typically requiring some college or an associate’s degree) the relationship has been flat, which may suggest a tipping point in the near future as the remaining slack diminishes. Even so, the wage disparity between high and low-skilled jobs has been increasing for decades . These charts align well with other reports indicating that employment is growing for jobs with higher wages and benefits packages, and most of these jobs are going to people with a bachelor’s degree or higher. This is good news for college graduates, but only part of the story—occupations requiring at least a bachelor’s degree made up less than a quarter of total employment in 2014, while  66% of employment in 2014 was in low-skill occupations. Until the negative or flat relationship between wage growth and employment growth in lower- and middle-skill occupations reverses, we will likely continue to see little real wage growth in the economy at large. Research support was provided by Patrick Clapp. http://chmuraecon.com/blog/2015/august/28/as-employment-grows-when-will-we-see-wage-growth/ http://chmuraecon.com/blog/2015/august/28/as-employment-grows-when-will-we-see-wage-growth/ Fri, 28 August 2015 13:54:25 Xiaobing Shuai http://chmuraecon.com/blog/2015/august/27/estimating-spectators-for-economic-impacts-is-tricky/ The UCI World Championships will soon be held in Richmond, Virginia from September 19th through the 27 th . In 2011, when the city was preparing the bid for hosting the cycling championship, Chmura was asked to estimate the event’s potential economic impact. One of the most formidable challenges in that process was to estimate the number of spectators. We estimated the number of spectators would be 450,000.  As we clarified in a previous blog, 450,000 spectators is not the same as 450,000 visitors.  Despite having thought we explained this difference , we continue to field questions like “how did that number come about?”  The purpose of this blog is to de-mystify the process of estimating this figure and to provide guidance for others who are estimating the economic impact of events in their region. Four years ago, faced with the challenge of estimating the number of spectators for a major event, the prudent approach was to look at past, similar events.  This mirrors the typical process of any economic projection— utilize data from the past to provide valuable information that helps predict the future.  The number of spectators did not come out of a magic “black box.” Rather, the process was guided by academic research in the tourism industry. Tourism literature consistently indicates that a region’s population base is one of the key determining factors for the number of visitors/attendants to tourism attractions such as historic sites, festivals, concerts, parks, and museums. Other key factors are the population’s interests, economic conditions such as travel costs, and the existence of a tourism infrastructure such as roads and airports.  Since we know the population base of Richmond, its surrounding counties, and other major cities within a few hours’ drive, the missing piece is how many of the nearby residents are interested enough to attend the race.  For that information, we examined the number of spectators who attended past UCI World Championship racing events relative to the population base of the host region.  Right away, we faced challenges. The majority of past races have been held in Europe, which has a long history of public support for cycling. This being said, the public interest in these races in Europe would be higher than can be reasonably expected in the United States, therefore using European races as examples would likely over-estimate the attendance in Richmond.  Over the past decade, the only two non-European championship races were held in 2003 in Hamilton, Canada, and in 2010 in Melbourne, Australia. The 2003 Hamilton race reported 230,000 spectators while the 2010 Melbourne race reported 300,000 spectators. Among those two races, Australia is very far from cycling centers in Europe or North America while Hamilton has more similarity to Richmond. For that reason, we used a survey from the Hamilton race to derive our estimate.  Hamilton is a mid-sized city (over 700,000 persons in the metro area) not too far from Toronto with a gateway international airport. Richmond is also a mid-sized city (over 1,000,000 population in the metro area) and not too far from several major U.S. cities.  Both Hamilton and Richmond are on the eastern part of the North American continent and are relatively easy to reach. Hamilton’s survey of over 1,000 race attendants identified the spectators based on the distance of their home to Hamilton. Using that information as a proxy, we calculated the percentage of the regional population base that would travel to see the race based on their distance to Richmond.  The Hamilton survey also contained information on the number of races each visitor attended. Adjusting for the fact that the Richmond race is longer (9 days as opposed to 6 days for Hamilton), we estimated that the total spectators to the Richmond race would be 450,000. Four years ago, we used the past events to make a future projection, just like any economist would do.  Like any economic projections, there are many unforeseeable events that can affect the actual number of spectators. For example, the global economy and exchange rates can play a role in attendance. With the European economy struggling and the high value of the dollar, European visitors may find it too expensive to travel to America.  Marketing and outreach efforts will also affect the number of spectators. Locally, traffic and parking can affect the number of spectators from the region, and even weather can play a role in attendance. With the race just about to begin in 22 days, we look forward to measuring the true number of spectators. http://chmuraecon.com/blog/2015/august/27/estimating-spectators-for-economic-impacts-is-tricky/ http://chmuraecon.com/blog/2015/august/27/estimating-spectators-for-economic-impacts-is-tricky/ Thu, 27 August 2015 16:53:06 Xiaobing Shuai http://chmuraecon.com/blog/2015/august/10/how-many-visitors-for-richmond-2015/ The residents of the city of Richmond and surrounding communities are eagerly awaiting the Union Cycliste Internationale (UCI) Road World Championships to be held in Richmond in September - referred to locally as “Richmond 2015”.  Likewise, cycling enthusiasts around the country are excited about the championships returning to the United States for the first time in almost 30 years. Businesses in the region, including hotels, restaurants, shops, and taxi drivers are hoping that the influx of athletes, race officials, and visitors to Richmond 2015 will mean a boon for businesses.  According to a study completed by Chmura Economics & Analytics, “T he World Championships is a 9-day event from which Richmond can expect to draw more than 450,000 on-site spectators over the course of the nine-day event from around the United States and the world.” [1] The number “450,000” has been quoted frequently by many media outlets. While some quoted it properly as 450,000 spectators, unfortunately, there are some mischaracterizations that warrant clarification. First, the number 450,000 refers to the total number of spectators, not visitors. Some social media and general public comments have included phrases such as “450,000 visitors are expected in Richmond,” [2] giving the impression that there will be 450,000 individuals visiting the Richmond region during the event. However, “spectators” are defined as people watching races on-site (and it includes some double counting). According to the Richmond 2015 website, there are a dozen races over 9 days. If an individual attends multiple races, he or she would be counted multiple times.  In fact, the Chmura report assumed the average individual will watch over 4 races during the 9-day event. Obviously, visitors travelling to Richmond for the race may attend more races, while casual fans from the region may only watch a couple of them. Another misconception by the general public is to assume that all spectators are visitors from outside the region. For example, a report from Airbnb.com in Richmond quoted one local real estate agent as saying, “450,000 visitors are expected in Richmond. There’s not enough hotels to house those people.” [3]   If hotel operators expect that there will be 450,000 people needing lodging, they will be disappointed. That is because the 450,000 spectators include local residents in the Richmond region, as well as a large number of people living outside Richmond that will make a day trip to the region. The overnight visitors are estimated to total over 50,000. Adding athletes and their support staff, journalists, and officials, the overnight visitors are estimated to be more than 60,000. With about 18,000 hotel rooms in the Richmond region, [4] even the estimated fifty to sixty thousand overnight visitors seem to be exceeding the region’s capacity. Is it really? The answer is no. That is because many visitors will share rooms, reducing the demand for hotel rooms. Furthermore, many visitors will not stay for all nine days. Some will come for the entire event and some will come for a few days while many may only come for a weekend.  Additionally, some overnight visitors may choose to stay with families and friends in the region or rent rooms or houses directly from area residents. In the end, the regional hotels rooms may be sufficient after all.     [1] Source: http://richmond2015.com/about/economic-impact/ [2] For an example, please see http://www.nbc12.com/story/20979198/richmond-2015-helping-businesses-prepare-for-450000-race-fans [3] For an example, please see http://wtvr.com/2015/05/18/airbnb-not-legal-in-city-of-richmond/ [4] Source: http://www.visitrichmondva.com/where-to-stay/ http://chmuraecon.com/blog/2015/august/10/how-many-visitors-for-richmond-2015/ http://chmuraecon.com/blog/2015/august/10/how-many-visitors-for-richmond-2015/ Mon, 10 August 2015 13:09:55 Chris Chmura http://chmuraecon.com/blog/2015/july/29/federal-spending-back-on-the-increase/ Federal spending is back on the upswing. Or, at least it is according to the President’s budget which shows an increase of 7% percent in Fiscal Year (FY) 2015 (October 2014 through September 30, 2015) compared with the prior year. [1]   The President’s Budget has federal spending growing on average 5.4 percent a year from FY2015 through FY2020 with national defense increasing essentially 0 percent. [2]  Although slower than the 6.6 percent annual average growth from FY2000 through the peak in FY2011, the return to some growth in spending is good news for states and metropolitan statistical areas (MSAs) that are dependent on federal contract spending. We’re not out of the woods yet. A January 2015 Congressional Budget Office report indicates that defense and nondefense funding are equal to or below the FY 2015 budget caps, [3] but the President’s Budget for FY 2016  ignores the caps put in place by the Budget Control Act of 2011 and modified by the Bipartisan Budget Act of 2013.  If the caps are exceeded, a sequestration will reduce federal discretionary spending by approximately $139 billion in FY2016 below the President’s Budget request. [4]  About 64 percent of the reduction will occur in defense spending based on current laws. So which metro area economies are most dependent on revenues derived from contract work for the federal government? Which ones are most at-risk if we see another round of sequestration? Chmura Economics & Analytics took a look at federal contract spending data and assigned it a metro geography based on where the awarded firm performed the work and adjusted it for time of performance since some contracts are awarded for work that is performed over a number of years. Out of 381 MSAs in the country, the Washington-Arlington-Alexandria, DC-VA-MD-WV MSA topped the list of federal spending with $71 billion in FY 2014. Dallas-Fort Worth-Arlington, TX was a far second with $19.3 billion, and Los Angeles-Long Beach-Anaheim, CA was third at $14.8 billion in FY 2014. A better way to assess the risk of a region to potential cuts in federal spending is to consider the concentration relative to employment. From that perspective, Idaho Falls, ID ranked the highest ($47,691 per employee); followed by California-Lexington Park, Maryland ($39,940); Amarillo, Texas ($31,407); and Huntsville, Alabama ($30,799). The interactive map and table below show the dependence of all MSAs in the nation on federal spending. Research support was provided by Patrick Clapp.   [1] President’s Budget FY 2016, Table S-1 [2] President’s Budget FY 2016, Table 28-1 Net Outlays by Function, Category, and Program [3] https://www.cbo.gov/sites/default/files/cbofiles/attachments/49889-sequestration.pdf [4] President’s Budget FY 2016, Table 5.6—Budget Authority for Discretionary Programs: 1976–2020 http://chmuraecon.com/blog/2015/july/29/federal-spending-back-on-the-increase/ http://chmuraecon.com/blog/2015/july/29/federal-spending-back-on-the-increase/ Wed, 29 July 2015 12:00:56 Chris Chmura http://chmuraecon.com/blog/2015/july/28/hollowing-out-of-the-middle-class/ We’ve heard a lot about the hollowing out of the middle class.  That is, a trend of solid job growth for middle-class Americans which turned into contraction during the Great Recession. This contraction persisted after the recession ended as more of the jobs held by the middle class moved offshore, were consolidated into fewer jobs, or were lost to productivity gains resulting from technology and innovation. Was this hollowing out driven by the recession or is it indicative of a new norm? Chmura economists looked at this issue in 2013 and verified a clear hollowing out of the middle class, as shown in the chart below. [1] During the period from 2001 through 2011 , wages were mostly stagnant, with gains exceeding inflation for only those occupations on the high end of the wage scale. Also, job growth was mainly isolated to those jobs paying the least and those paying the most (see our related post Where the Jobs Are for more) . Now, with data available through 2014 and three additional years’ distance from the end of the recession, are the jobs in the middle faring any better? Using the same methodology as in 2013, the results demonstrate a continuation of the trends identified in the 2011 data with modest improvement. Wages grew slower than the pace of inflation for all but the 7 th , 9 th and 10 th deciles, with deciles 2, 3, and 6 showing the slowest growth. Specifically, inflation-adjusted wages declined 3.5%, 5.0%, and 3.3% in deciles 2, 3, and 6, respectively. Similar to our findings in 2013, job growth has been the fastest in the 10 th , 9 th , and 1 st deciles. Also, the middle class—represented by deciles 4, 6, and 7—showed negative job growth between 2004 and 2014, albeit relatively smaller declines than reported in the previous blog.  On a more positive note, employment grew in the 5 th decile, which includes occupations such as dental assistants, medical secretaries, and customer service representatives. In addition, employment grew in a number of middle class occupations between 2004 and 2014, and many are expected to continue to grow, as demonstrated in the sample selection of occupations in the table below.       Avg Ann Empl Growth 2004-2014 Forecast Avg Ann Empl Growth 2014-2024 Decile Interpreters and Translators 7% 4% 7 Health Technologists and Technicians, All Other 7% 2% 7 Medical Equipment Repairers 6% 3% 7 Insulation Workers, Mechanical 5% 4% 7 Occupational Therapy Aides 5% 3% 4 Medical Assistants 4% 3% 4 Industrial Machinery Mechanics 4% 2% 7 Computer User Support Specialists 4% 1% 7 Pharmacy Technicians 4% 2% 4 Physical Therapist Assistants 3% 3% 7 Medical Appliance Technicians 3% 1% 6 Machinists 1% 1% 6 Source: Chmura Economics & Analytics and JobsEQ ® Growth in high-demand occupations such as these may lead to more competitive wages for the middle-deciles that could reverse current trends. For now, the data help explain the frustration that many have felt towards slow job recovery and wage growth post-recession. Stagnant wages have not been limited to the middle; spending power declined for seven of the ten deciles. Meanwhile, the middle-wage deciles in this analysis accounted for more than 40% of all jobs where job growth has been slow, at best, for those workers over the past decade. Research assistance for this post was provided by Johnny Constable, Claire Brunner, and Leah Deskins. [1] This analysis was created by using over 800 occupations identified by the Bureau of Labor Statistics. We broke the occupations into ten groups based on employment and wages earned and analyzed each deciles based on job and wage growth. http://chmuraecon.com/blog/2015/july/28/hollowing-out-of-the-middle-class/ http://chmuraecon.com/blog/2015/july/28/hollowing-out-of-the-middle-class/ Tue, 28 July 2015 12:22:40 Chris Chmura http://chmuraecon.com/blog/2015/july/13/economic-impact-jobs-are-being-created-but-workers-are-underemployed/ The unemployment rate is dropping and jobs are becoming more plentiful, but that doesn’t mean workers are in their ideal jobs. Some people who are working are “ underemployed .” The Bureau of Labor Statistics officially defines underemployment as someone who wants to work full-time and can only find part-time work. Arizona and California had the highest number of underemployment among states for the four quarters that ended in March, BLS data shows. Underemployment can be measured at the state level by looking at the difference between two different jobless rates that the BLS compiles. Arizona and California had the largest gap between the two rates, having a difference of 6.2 percentage points each. Nevada followed with 6.1 percentage point difference. The state with the least amount of underemployment, because workers are employed part time for economic reasons, was North Dakota with a 1.8 percentage point gap. For the U.S., the difference between the two rates that the BLS compiles was 4.4 percentage points for the four quarters that ended in March. Virginia ranked with the 32nd gap in the nation at 3.8 percentage points. However, BLS’s definition does not capture those who are working in an occupation below their level of qualifications. For example, it doesn't count someone with a master’s degree who is working as a retail salesperson. There is no official measure of such underemployment by occupation. But it can be estimated by comparing the educational skills  of residents in a region to the education achievements required by occupations employed by industries in the same region. Looking at all 381 metropolitan statistical areas, the three MSAs with the largest surpluses of high-skilled workers are Barnstable, Mass., with a 13.2 percentage point surplus; Washington, D.C. with a 12.5 percentage point surplus; and San Francisco, with an 11.5 percentage point surplus. Some of those are desirable areas to live that attract many high-skilled residents. Some of those are college towns where a lot of graduates choose to stay. The three MSAs with the largest deficits of high-skilled workers are Hanford-Corcoran, Calif., with a 16.3 percentage point deficit; Hinesville, Ga., with a 14.7 percentage point deficit; and Cumberland, Md., with a 14.6 percentage point deficit. In Virginia, the Richmond MSA is ranked 78th nationally with a 1.3 percentage point surplus of high-skilled workers, indicating that underemployment here is not as much of an issue as it is in other regions. The Hampton Roads MSA has a 3.4 percentage point deficit of high-skilled workers (ranked 177th), indicating a less severe issue of underemployment by occupation. Viewing underemployment across the nation shows that even as the unemployment rate continues to drop, underemployment will vary by metropolitan area. http://chmuraecon.com/blog/2015/july/13/economic-impact-jobs-are-being-created-but-workers-are-underemployed/ http://chmuraecon.com/blog/2015/july/13/economic-impact-jobs-are-being-created-but-workers-are-underemployed/ Mon, 13 July 2015 13:47:43 Chris Chmura http://chmuraecon.com/blog/2015/june/16/underemployment-in-the-united-states/ Many students who graduated during the Great Recession and over the last few years were unable to find jobs for which they were trained. The so-called underemployed workers are employed in an occupation below their level of qualification. For example, a graduate with a Bachelor’s Degree in economics who is waiting tables or working at a retail store is considered underemployed. Chmura calculates a proxy for underemployment by comparing educational attainment supply and demand in a given labor market at various skill levels. Some metropolitan statistical areas (MSAs) around the country have a higher percentage of underemployed than others.  MSAs in Massachusetts, the District of Columbia, and California top the list of regions that possess a surplus of high-skilled workers in the latest update to Chmura Economics & Analytics’ underemployment dataset. Underemployment is a useful supplement to other indicators of labor market health. The traditional measure of unemployment from the Bureau of Labor Statistics does not distinguish between workers who are employed in a position aligned with their skills and education. Workers who are underemployed and not necessarily contributing as much as they could to the labor market, represent potential lost productivity, wages, and tax revenue for the region. High underemployment in a region may also be a positive measure, reflecting the desire of workers to live in a particular area (like the scenic Cape Cod waterfront of Barnstable Town, Massachusetts) and/or higher standards for occupations in certain regions (such as for computer occupations in San Francisco). Chmura’s underemployment proxies for MSAs, along with more detailed methodology and definitions, are available on our website and at the county, MSA, and state levels within JobsEQ ®. Research assistance for this post was provided by Patrick Clapp. http://chmuraecon.com/blog/2015/june/16/underemployment-in-the-united-states/ http://chmuraecon.com/blog/2015/june/16/underemployment-in-the-united-states/ Tue, 16 June 2015 09:41:18 Chris Chmura http://chmuraecon.com/blog/2015/june/16/using-job-postings-to-measure-employment-demand/ An article in the Harvard Business Review recently touched on Why Job Postings Don’t Equal Jobs , explaining that these data should be considered unreliable when trying to estimate job demand under various circumstances. Specifically: Professional-type jobs are more likely to be posted online Companies often advertise the same job multiple times, and For job boards that require payment to post openings, firms may post more openings when there is a discount offered, whether or not they currently need those workers. A few additional concerns were not mentioned in that article: Some jobs are posted for legal reasons, such as firms sponsoring foreign workers for permanent residence (green card). Firms have to “test” the labor market by advertising those jobs even though they have hired a foreign worker already and have no intention of hiring someone else. Most of these cases are professional jobs as well. The methods used to collect and clean online postings and estimate trends over time can be problematic. The methods used to obtain and clean job postings data are varied and are typically closely guarded. For an objective review of several providers of these data, see the Vendor Product Review:  A Consumer’s Guide to Real-time Labor Market Information . Vendors scrape and spider job boards automatically and manually, code results into anywhere from 5 to 70 data elements, and deduplicate 60 to 90 percent of job ads. Based on around 4 million job postings daily, and assuming ads are only duplicates (not triplicates, etc.), that could mean anywhere from 1.2 to 1.8 million job postings are thrown out as duplicates every day. Methods for analyzing the postings range from keyword searches to natural language processing and text analytics, but small details in methodology can have outsized effects on what gets counted. Take, for example, the difference between searching job postings on Indeed.com for registered nurses using different keywords such as “rn,” “registered nurse,” or “registered nurses.” Source: Indeed.com This simple search raises a few questions: Which keyword or collection of search terms best represents job postings for registered nurses? Do the keyword results change by region?  In another field, how might different data providers distinguish between R, the statistical programming language , and H.R. (Human Resources) or R&D (Research & Development)? The answers to these types of questions will likely vary by data provider and should be considered before relying on the data for analysis. Many providers make a concerted effort to improve collection, parsing, and deduplication methods; however, significant changes in methodologies can cause additional confusion and inconsistency in job advertisement data if used in analysis over time. Changes to the deduplication methodology used by The Conference Board, for example, resulted in revisions lowering estimates by about 460,000 jobs for every month in the series. The overall curves were fairly consistent, showing similar shape and trends, but anyone relying on the actual levels for measuring or forecasting employment demand could find old estimates too high by hundreds of thousands of jobs. In summary, the use of online job postings data to glean labor market information is promising, but there are a number of concerns that suggest these data are not sufficient replacements for traditional labor market data. Research assistance for this post was provided by Patrick Clapp. http://chmuraecon.com/blog/2015/june/16/using-job-postings-to-measure-employment-demand/ http://chmuraecon.com/blog/2015/june/16/using-job-postings-to-measure-employment-demand/ Tue, 16 June 2015 07:00:00 Chris Chmura http://chmuraecon.com/blog/2015/may/31/economic-impact-when-will-the-fed-raise-rates/ After six years of an essentially zero percent federal funds rate target, it looks like rates will begin increasing soon. The timing of that rate increase is based on the current and future strength of the economy. However, we get clues about when the rate increase will occur from speeches and interviews of voting members of the Federal Open Market Committee, which is the Federal Reserve’s policy-making committee. Federal Reserve Chair Janet Yellen reaffirmed in a speech about 10 days ago that she believes it will be appropriate to raise rates this year. “If the economy continues to improve as I expect, I think it will be appropriate at some point this year to take the initial step to raise the federal funds rate target and begin the process of normalizing monetary policy,” she said in her speech. But she is not the only voting member. For instance, Fed Governor Daniel Tarullo takes an opposite view of when to raise interest rates.  “In my view, it likely will not be appropriate to begin raising the federal funds rate until sometime in early 2016,” he said in a presentation May 4. Richmond Fed President Jeffrey M. Lacker, a voting member this year of the FOMC, was quoted by Reuters last week saying, “What I’ve said is that a case might be strong in June. I still think that’s possible. But as I said . . .I haven’t made up my mind yet about June.” The upcoming rate increase is good news. It means that the FOMC members believe the economy is strong enough to continue growing with higher interest rates. Yet, policy makers also are concerned that a premature rate increase could dampen the recovery if it is still not strong enough. The rate hike also is great news for savers. When the Fed raises the federal funds rate target, which is the rate that banks use when they borrow from each other on an overnight basis, banks then increase the rate they pay depositors. The iMoneyNet money fund average, the seven-day average yield over all taxable money market funds, is currently  0.02 percent in the nation. Late in 2008, when the federal funds rate target was 0.50 percent, the money fund average was 1.22 percent. Some analysts believe that the federal funds rate target will eventually get back to a more normal rate of 3 percent over the next two years. If historical relationships hold true, savers will see a money fund average around 3 percent as well. This would certainly help retirees who are on a fixed budget. On the other hand, borrowers will find that it costs more to get a home mortgage or to use a credit card. The interest rate on many loans is tied to either the prime lending rate or LIBOR. The prime rate is currently at 3.25 percent and the one-month LIBOR is 0.18 percent. In 2008, we saw how quickly the prime and LIBOR rates fell when the Fed dropped the federal funds rate. While the federal funds rate target stood at 3 percent in February 2008, the prime rate was 6 percent and the one-month LIBOR rate was 3.14 percent. It dramatically changed by November, when the federal funds rate target was 0.50 percent and the prime rate was 4 percent and the 1-month LIBOR rate was 1.44 percent. Longer-term interest rates also typically rise with increases in the federal funds rate, but are more dependent on inflation expectations. The average rate for a 30-year fixed mortgage was 3.87 percent as of Thursday, up from 3.84 percent a week earlier and matching the level at the end of 2014, mortgage lender Freddie Mac said. The average 15-year rate increased to 3.11 percent from 3.05 percent. A forecast from Chmura Economics & Analytics expects the 30-year fixed mortgage rate to rise to 6 percent by the end of 2016. For now, it looks like higher interest rates are still possible by year’s end. As many Fed officials say, the exact time of liftoff is data dependent. But it’s important to track FOMC member comments because not everyone interprets the data the same way. http://chmuraecon.com/blog/2015/may/31/economic-impact-when-will-the-fed-raise-rates/ http://chmuraecon.com/blog/2015/may/31/economic-impact-when-will-the-fed-raise-rates/ Sun, 31 May 2015 07:52:24 Greg Chmura http://chmuraecon.com/blog/2015/may/21/why-did-our-unemployment-rate-change/ You may have noticed that your recent historical unemployment rate numbers look different—very different—compared to how you remember them looking just a few months ago. If you’ve noticed this, you’re not alone. With the publication of 2015 unemployment rate data, the Bureau of Labor Statistics implemented its “2015 LAUS Redesign,” a set of methodological changes in their Local Area Unemployment Statistics program (LAUS). In conjunction with this, local unemployment rate estimates for the period 2010 to 2014 were revised and rereleased. At the county level, the impact on the numbers ranged from slight to eye-popping. And while smaller regions tended to have more dramatic adjustments, even some large areas saw fairly significant revisions. For example, the monthly unemployment rate estimates for 2010 to 2014 in Marion County, Indiana (Indianapolis) were revised upward an average of 0.6 percentage points. Miami-Dade County, Florida, on the other hand, saw an average 1.1 percentage point decrease for its unemployment rates over the same period. Other examples of increased estimates included Sumter County, Florida (+3.1 percentage points on average) and Clarke County, Alabama (+3.6 percentage points). At the county level, the largest average increase in monthly unemployment rates happened in Lake and Peninsula Borough, Alaska, with a whopping 7.0 average percentage point revision. Places with downward revisions from 2010 to 2014 included York County, South Carolina (-1.8 percentage points) and Santa Cruz County, Arizona (-2.0 percentage points). The largest overall drop happened in Chattahoochee County, Georgia, where the monthly unemployment rates were revised downward an average of 5.6 percentage points for the period. You can use the tools and charts above to select your region, see how your unemployment rate estimates were changed, and compare the scale of these changes with revisions in other regions. The data used in the above charts—as well as in all the comparisons made in the above text—are based upon the LAUS release of April 29, 2015 compared with data from February 4, 2015, the date of the last release before the revisions were implemented. http://chmuraecon.com/blog/2015/may/21/why-did-our-unemployment-rate-change/ http://chmuraecon.com/blog/2015/may/21/why-did-our-unemployment-rate-change/ Thu, 21 May 2015 10:27:30 Chris Chmura http://chmuraecon.com/blog/2015/may/04/economic-impact-worker-productivity-growth-might-influence-fed-s-interest-rate-policy/ Taking a look at worker productivity growth could shed some light on what the Federal Reserve might do with interest rates. The faster the rate of productivity growth, the faster the Fed can let the economy grow without inflation picking up. But productivity is slow right now compared to historical benchmarks. By definition, growth in productivity means the same amount of output can be produced with fewer hours, which sometimes translates into fewer workers. Over the long run, this often means the workers who remain in the industry are paid more. Productivity growth leads to rising living standards. Yet, it also is a double-edged sword as workers who lose their jobs due to productivity improvements need to find employment elsewhere. Productivity growth also is an important contributor to how fast the overall economy can grow. It is an important driver of what the Federal Reserve has referred to as the maximum sustainable growth rate or potential growth rate of real gross domestic product. Productivity in manufacturing grew 2.4 percent in the fourth quarter of 2014 compared with the same quarter in the previous year while mining industries productivity grew 3.9 percent in 2014, according to the U.S. Bureau of Labor Statistics. About 75 percent of manufacturing and mining industries posted gains in 2014 compared with about 60 percent in 2013. Productivity grew the fastest in 2014 in oil and gas extraction category followed by textile and fabric finishing and coating mills. The potential growth rate varies over time and is dependent on productivity and labor force expansion. A simple way to estimate the potential growth rate is to add the annual productivity growth rate, which was 0.9 percent from 2008 through 2014, with the labor force growth rate of 0.5 percent during that same period. As a result, the potential growth rate of real gross domestic product was 1.4 percent during that period. That is much slower than the 3.3 percent average potential GDP growth rate from 1950 through 2014. During that time, productivity averaged a faster 1.8 percent a year and the labor force grew an average annual 1.5 percent. Looking ahead, the Congressional Budget Office is estimating productivity to grow 1.6 percent and the labor force to advance 0.5 percent a year from 2015 through 2025. That would equate to a potential growth rate of 2.1 percent a year. In other words, growth in living standards could be much slower over the next decade than it was over the last half century. In addition to predicting changes in living standards, the difference between the potential growth rate and projected GDP growth is considered by the Federal Reserve when it decides whether to try to speed up or slow down economic growth to meet its long-term goals. The Federal Reserve tends to increase the overnight interest rate that banks charge each other, known as the Federal Funds Rate target, when the economy is consistently growing faster than it potentially should and to lower this rate when the economy is growing too slow. Despite an anemic 0.2% real GDP growth in the first quarter of 2015, many economists are looking for about 3 percent GDP growth in the second half of 2015 and in 2016, which provides another reason why the Federal Reserve will likely be increasing the federal funds rate target later this year. http://chmuraecon.com/blog/2015/may/04/economic-impact-worker-productivity-growth-might-influence-fed-s-interest-rate-policy/ http://chmuraecon.com/blog/2015/may/04/economic-impact-worker-productivity-growth-might-influence-fed-s-interest-rate-policy/ Mon, 04 May 2015 13:59:30 Xiaobing Shuai http://chmuraecon.com/blog/2015/april/30/shift-in-mix-of-industries-contributed-to-the-slow-wage-growth-in-2014/ After adjusting for inflation, annual average wages for workers in the United States grew a meager 0.3% between 2013 and 2014. In contrast, annual average wages grew at an annualized pace of 3.5% between 2001 and 2007 (also inflation adjusted). Is recent slow wage growth a result of firms being stingy with raises, or is it due to other factors? Chmura’s analysis shows that a shift in industry mix towards lower-paying sectors was a major driver of the relatively slow wage growth. To examine the recent slow wage growth, we will split that growth into two contributing components: Changes in pay within individual industries, and Shifts in employment share represented by the percentage of workers in that industry. For example, if employment in an industry expands, but grows more slowly than other industries, its share of regional employment may decline. Looking at 2014 annual wages in a state as if the 2013 industry mix was constant and comparing that to actual 2014 wages illustrates how much of the actual change in wages was due to the shifting mix of employment by industry. Virginia and North Dakota provide contrasting examples of the changes in industry mix and their effect on wage trends. In Virginia, major sectors paying below-average wages per worker—such as accommodation and food services, health care and social assistance, and retail trade—gained a greater share of employment. Meanwhile, employment in many sectors that tend to pay above-average wages—especially professional, scientific, and technical services as well as public administration—declined relative to their share of employment in 2013. Overall, the change in industry mix reduced wages by an average $202 per worker in Virginia.   In North Dakota, however, industries like construction; mining, quarrying, and oil and gas extraction; and transportation and warehousing—which pay an above-average wage in the state—gained employment relatively faster than lower-paying industries such as health care and social assistance and accommodation and food services. As a result, the change in industry mix boosted wages by an average $446 per worker in the state. The map and chart below detail how the shifting industry mix has contributed to the change in average annual wage growth in each state. A negative value (shown in red on the map) indicates that below-average wage industries have gained a larger share of employment in the state in 2014, while a positive value (in green) indicates that higher-wage industries gained relatively more employment. Data on industry sectors are also included in the chart to illustrate which industries gained or lost employment share in the nation or state of interest over the past year, and which of these pay above-average or below-average wages. http://chmuraecon.com/blog/2015/april/30/shift-in-mix-of-industries-contributed-to-the-slow-wage-growth-in-2014/ http://chmuraecon.com/blog/2015/april/30/shift-in-mix-of-industries-contributed-to-the-slow-wage-growth-in-2014/ Thu, 30 April 2015 07:17:00 Chris Chmura http://chmuraecon.com/blog/2015/april/27/economic-impact-wage-gains-remain-elusive/ Slow wage growth is one of the side effects of a weak labor market. Even though the U.S. unemployment rate has fallen to 5.5 percent in February, the rate that includes people working part time who would rather work full time and the marginally attached is 11.0 percent. With plenty of jobseekers to choose from, firms have been stingy with wage increases. From 2009 (the year the recession ended) through 2014, annual average wages in the Richmond metro area rose 1.7 percent. That’s slightly better than the 1.6 percent in Virginia but not as good as the 2.2 percent growth in the nation. Over that same period, inflation rose an average 2.0 percent a year. From that perspective, the purchasing power of consumers in Virginia fell over that period and barely rose in the nation. That may partially explain why retail sales have not been as strong as many observers expected with the declines in gasoline prices. Wage gains in some industries have kept pace with inflation. Annual average wages in the finance and insurance industry rose a respectable 5.4 percent over the last five years in the metro area followed by an average 4.6 percent for mining and quarrying and 3.3 percent for real estate. In contrast, wages in the arts and entertainment industries grew an annual average 0.7 percent in the region and the state to make it one of the slowest growing industries over the period. Wage growth was much stronger in the five years before the recession. From 2002 through 2007, annual average wages rose 4 percent in the Richmond region and 4.3 percent in the state. The wage increase is better than the average 2.9 percent rise in inflation during the same period. Labor markets were tight just before the recession started. The unemployment rate in the nation hit a low of 4.4 percent in 2007. The metro area and state saw an even lower 2.9 percent during that same year. Slack in the labor market is not the only factor that impacts wage growth in a region. The changing mix of industries also is important. During the five years before the beginning of the last recession, professional business services firms added more than 76,000 jobs to the state. That was nearly 30 percent of all new jobs. That sector, which is dependent on federal contracting, paid an average annual wage of $82,790 in 2007 — 180 percent higher than the average of all jobs in the state. In contrast, professional business services added a little less than 9,000 jobs since the recession ended or 8 percent of all jobs created in the state. In both the Richmond metro area and the state, the health care and social services sector added the most jobs over the last five years. Although those jobs made up 35 percent of total employment gained in the metro area and state during that period, the average wage in the sector is 7 percent below the metro average and 12 percent below the average in the state. The next three largest employment gains provided a little more than half of the jobs in the metro area and state. Each of these three sectors — retail, arts and entertainment, and accommodation and food services — paid less than half the annual salary of all jobs in 2014. As the labor market continues to improve, annual average wage gains will start to accelerate. That trend has begun to emerge in the Richmond metro area. State wage growth remains slow, partially because of the slow employment growth in the federally-dependent professional business services sector. http://chmuraecon.com/blog/2015/april/27/economic-impact-wage-gains-remain-elusive/ http://chmuraecon.com/blog/2015/april/27/economic-impact-wage-gains-remain-elusive/ Mon, 27 April 2015 15:34:18 http://chmuraecon.com/blog/2015/march/25/much-of-the-nation-still-waiting-to-grow-beyond-recovery/ As of April 2014, employment in the U.S. economy exceeded the 138.35 million jobs that existed when the recession began in December 2007. Employment in more than a third of the U.S. metropolitan areas, however, has yet to reach the same levels of employment they experienced in December 2007. While national employment describes the recovery in aggregate, not all metropolitan statistical areas (MSAs) are recovering at the same pace. In Texas, for example, many MSAs dipped briefly below pre-recession levels before recovering and expanding, likely buoyed by oil production. The Washington, D.C. area also recovered fairly quickly, supported by federal stimulus money going to a relatively higher concentration of federal contractors in the region. In contrast, no MSAs in Arizona have recovered to December 2007 employment levels, and conditions vary considerably in the state. The Phoenix area has added an average of 4,475 jobs each month (about 0.2% of total employment) over the last twelve months of available data; at that rate, the region could recover to December 2007 levels of employment in about seven months. The Tucson area has added an average of 317 jobs (about 0.9% of employment) monthly over the past year—but at that rate, it would take the region another four years or more to fully recover recession job losses. While most of the largest metro areas have reached pre-recession levels of employment and continue to expand, the map shown below indicates that many MSAs are still waiting to get back to the employment levels that existed over seven years ago. http://chmuraecon.com/blog/2015/march/25/much-of-the-nation-still-waiting-to-grow-beyond-recovery/ http://chmuraecon.com/blog/2015/march/25/much-of-the-nation-still-waiting-to-grow-beyond-recovery/ Wed, 25 March 2015 09:00:30 Chris Chmura http://chmuraecon.com/blog/2015/march/25/the-right-data/ The internet has become a widely used source of data for regional labor market information, but that doesn't always mean it provides the level of detail needed to make reliable decisions. Finding the required workers is at the top of the list of most growing businesses. For example, an expanding firm may require production workers. If this firm were to rely on an overall count of production workers to gauge the supply of skilled labor, it could miss badly since there are over 100 different types of production workers. Just a few examples of production workers are electromechanical equipment assembles, food batch makers, and computer-controlled machine tool operators—all with vastly different skills. Wages vary as well for production occupations, from a high of $78,400 for nuclear power reactor operators to a low of $20,900 for pressers, textile, garment, and related materials workers ( National average wages as of 2013). Labor costs are also critical to ensuring that an expanding firm meets profitability goals. From that perspective, using the mean wage for an occupation to estimate overall labor costs may be misleading. For example, the mean wage for a tool and die maker in San Bernardino, California, is $50,500. However, entry-level workers in this occupation are typically paid $30,600 and experienced workers earn $60,500. Consequently, a firm could overestimate or underestimate labor costs if it assumes the mean wage instead of the RIGHT wage for the required workers. Chmura Economic & Analytics has the RIGHT data . Call us for the RIGHT data for labor market analysis . http://chmuraecon.com/blog/2015/march/25/the-right-data/ http://chmuraecon.com/blog/2015/march/25/the-right-data/ Wed, 25 March 2015 07:33:22 Chris Chmura http://chmuraecon.com/blog/2015/march/sleepbetter-lost-hour-economic-index/ These are the findings of Chmura Economics & Analytics in a study entitled “Estimating the Economic Loss of Daylight Saving Time for U.S. Metropolitan Statistical Areas” commissioned by the Carpenter Co. The study focused on only the aspects of economic losses where solid evidence from peer-reviewed academic journals could be obtained, showing how the DST change can lead to an increase in heart attacks, workplace injuries in the mining and construction sectors, and increased cyberloafing that reduces productivity for people who typically work in offices. A reasonable economic cost was then developed from the economic costs of heart attacks, workplace accidents and cyberloafing and applied to the more than 300 Metropolitan Statistical Areas (MSA) in the U.S. MSA Total Cost of Lost Hour Population in 2010 Index Rank Per Capita Cost % Difference From Nat'l Avg. MSA Rank National $433,982,548.00 260,392,304   $1.65449     Morgantown, WV $445,685.00 129,709 1 $3.37947 104.26% 293 Huntington-Ashland, WV-KY-OH $930,759.00 287,702 2 $3.18188 92.32% 162 Parkersburg-Marietta, WV-OH $519,472.00 162,056 3 $3.15273 90.56% 245 Charleston, WV $973,594.00 304,284 4 $3.14694 90.21% 156 Kingsport-Bristol-Bristol, TN-VA $925,487.00 309,544 5 $2.94061 77.74% 151 Lakeland, FL $1,582,213.00 602,095 6 $2.58458 56.22% 87 Tampa-St. Petersburg-Clearwater, FL $7,283,123.00 2,783,243 7 $2.57369 55.56% 18 Ocala, FL $863,182.00 331,298 8 $2.56256 54.89% 147 North Port-Bradenton-Sarasota, FL $1,822,027.00 702,281 9 $2.55172 54.23% 73 Punta Gorda, FL $404,984.00 159,978 10 $2.48982 50.49% 247 Scranton--Wilkes-Barre, PA $1,412,054.00 563,631 11 $2.46403 48.93% 90 Myrtle Beach-Conway-North Myrtle Beach, SC $662,576.00 269,291 12 $2.41994 46.27% 168 Pittsburgh, PA $5,794,723.00 2,356,285 13 $2.41877 46.19% 22 Palm Bay-Melbourne-Titusville, FL $1,336,302.00 543,376 14 $2.41877 46.19% 96 Evansville, IN-KY $873,111.00 358,676 15 $2.39418 44.71% 142 Tulsa, OK $2,277,053.00 937,478 16 $2.38892 44.39% 54 Sebastian-Vero Beach, FL $334,825.00 138,028 17 $2.38584 44.20% 283 Bloomington, IN $464,931.00 192,714 18 $2.37282 43.42% 218 Chattanooga, TN-GA $1,268,241.00 528,143 19 $2.36178 42.75% 98 Deltona-Daytona Beach-Ormond Beach, FL $1,187,424.00 494,593 20 $2.36128 42.72% 103 Bangor, ME $368,824.00 153,923 21 $2.35671 42.44% 259 Alexandria, LA $367,169.00 153,922 22 $2.34615 41.81% 257 Palm Coast, FL   $227,338.00 95,696 23 $2.33651 41.22% 347 Muncie, IN $279,430.00 117,671 24 $2.33557 41.17% 317 Crestview-Fort Walton Beach-Destin, FL $428,270.00 180,822 25 $2.32946 40.80% 224 Cape Coral-Fort Myers, FL $1,455,015.00 618,754 26 $2.31281 39.79% 84 Mobile, AL $967,710.00 412,992 27 $2.30459 39.29% 126 Jacksonville, FL $3,146,585.00 1,345,596 28 $2.29993 39.01% 40 Toledo, OH $1,523,209.00 651,429 29 $2.29976 39.00% 82 Lafayette, IN $471,375.00 201,789 30 $2.29752 38.87% 210 Canton-Massillon, OH $943,763.00 404,422 31 $2.29519 38.72% 128 Johnson City, TN $461,978.00 198,716 32 $2.28654 38.20% 214 Dayton, OH $1,952,542.00 841,502 33 $2.28210 37.93% 62 Knoxville, TN $1,617,373.00 698,030 34 $2.27890 37.74% 74 Pensacola-Ferry Pass-Brent, FL $1,037,154.00 448,991 35 $2.27193 37.32% 110 Terre Haute, IN $394,960.00 172,425 36 $2.25291 36.17% 233 Miami-Fort Lauderdale-Miami Beach, FL $12,725,469.00 5,564,635 37 $2.24919 35.95% 8 Port St. Lucie-Fort Pierce, FL $969,275.00 424,107 38 $2.24782 35.86% 117 State College, PA $351,910.00 153,990 39 $2.24765 35.85% 256 Asheville, NC $969,459.00 424,858 40 $2.24427 35.65% 116 Lewiston-Auburn, ME $243,661.00 107,702 41 $2.22512 34.49% 334 Lexington-Fayette, KY $1,066,009.00 472,099 42 $2.22084 34.23% 106 Jackson, TN $260,264.00 115,425 43 $2.21771 34.04% 321 Michigan City-La Porte, IN $251,036.00 111,467 44 $2.21503 33.88% 329 Cheyenne, WY $206,604.00 91,738 45 $2.21503 33.88% 354 Naples-Marco Island, FL $723,778.00 321,520 46 $2.21405 33.82% 149 Bowling Green, KY $282,675.00 125,953 47 $2.20733 33.42% 302 Kokomo, IN $221,462.00 98,688 48 $2.20711 33.40% 345 South Bend-Mishawaka, IN-MI $715,354.00 319,224 49 $2.20402 33.21% 150 Hickory-Lenoir-Morganton, NC $818,612.00 365,497 50 $2.20285 33.14% 141 Anderson, IN $294,790.00 131,636 51 $2.20256 33.13% 295 Atlantic City, NJ $612,213.00 274,549 52 $2.19317 32.56% 169 Columbus, IN $170,968.00 76,794 53 $2.18967 32.35% 362 Johnstown, PA $318,637.00 143,679 54 $2.18119 31.83% 275 Harrisburg-Carlisle, PA $1,215,731.00 549,475 55 $2.17610 31.53% 94 Lawton, OK $274,358.00 124,098 56 $2.17442 31.43% 307 Louisville, KY-IN $2,826,250.00 1,283,566 57 $2.16562 30.89% 42 Morristown, TN $300,143.00 136,608 58 $2.16094 30.61% 286 Wheeling, WV-OH $323,960.00 147,950 59 $2.15361 30.17% 268 Wichita Falls, TX $331,130.00 151,306 60 $2.15244 30.10% 266 Youngstown-Warren-Boardman, OH-PA $1,236,309.00 565,773 61 $2.14919 29.90% 91 Owensboro, KY $249,981.00 114,752 62 $2.14258 29.50% 322 Shreveport-Bossier City, LA $868,062.00 398,604 63 $2.14190 29.46% 129 Gainesville, FL $574,037.00 264,275 64 $2.13636 29.13% 173 Altoona, PA $275,914.00 127,089 65 $2.13528 29.06% 305 Williamsport, PA $251,895.00 116,111 66 $2.13372 28.97% 319 Panama City-Lynn Haven, FL $364,847.00 168,852 67 $2.12517 28.45% 236 Detroit-Warren-Livonia, MI $9,268,747.00 4,296,250 68 $2.12188 28.25% 13 Birmingham, AL $2,431,810.00 1,128,047 69 $2.12028 28.15% 50 Elizabethtown, KY $257,362.00 119,736 70 $2.11403 27.78% 313 Buffalo-Niagara Falls, NY $2,440,468.00 1,135,509 71 $2.11384 27.76% 49 Ocean City, NJ $207,878.00 97,265 72 $2.10205 27.05% 351 Fort Wayne, IN $888,743.00 416,257 73 $2.09993 26.92% 122 Steubenville-Weirton, OH-WV $265,544.00 124,454 74 $2.09854 26.84% 311 Huntsville, AL $890,913.00 417,593 75 $2.09832 26.83% 119 Erie, PA $598,252.00 280,566 76 $2.09720 26.76% 164 Montgomery, AL $798,346.00 374,536 77 $2.09646 26.71% 136 Gulfport-Biloxi, MS $528,298.00 248,820 78 $2.08825 26.22% 185 Carson City, NV $116,608.00 55,274 79 $2.07490 25.41% 366 Anniston, AL $250,003.00 118,572 80 $2.07373 25.34% 316 Mansfield, OH $261,914.00 124,475 81 $2.06951 25.08% 310 Sandusky, OH $161,894.00 77,079 82 $2.06578 24.86% 363 Elkhart-Goshen, IN $414,780.00 197,559 83 $2.06496 24.81% 216 Florence, AL $308,807.00 147,137 84 $2.06422 24.76% 206 Auburn-Opelika, AL $293,975.00 140,247 85 $2.06161 24.61% 276 Lafayette, LA $573,540.00 273,738 86 $2.06072 24.55% 167 Lima, OH $221,940.00 106,331 87 $2.05289 24.08% 336 Clarksville, TN-KY $571,732.00 273,949 88 $2.05264 24.07% 166 Lebanon, PA $278,565.00 133,568 89 $2.05123 23.98% 292 Reading, PA $857,038.00 411,442 90 $2.04871 23.83% 125 Gadsden, AL $217,236.00 104,430 91 $2.04596 23.66% 338 York-Hanover, PA $904,776.00 434,972 92 $2.04583 23.65% 113 Barnstable Town, MA $447,310.00 215,888 93 $2.03784 23.17% 198 Ann Arbor, MI $713,184.00 344,791 94 $2.03440 22.96% 146 Springfield, OH $285,322.00 138,333 95 $2.02862 22.61% 285 Wilmington, NC $747,299.00 362,315 96 $2.02861 22.61% 139 Dothan, AL $299,824.00 145,639 97 $2.02478 22.38% 270 Monroe, LA $362,816.00 176,441 98 $2.02244 22.24% 229 Hattiesburg, MS $293,083.00 142,842 99 $2.01802 21.97% 272 Lancaster, PA $1,064,016.00 519,445 100 $2.01465 21.77% 99 Decatur, AL $313,503.00 153,829 101 $2.00444 21.15% 258 Oklahoma City, OK $2,550,073.00 1,252,987 102 $2.00169 20.99% 43 Pascagoula, MS $330,101.00 162,246 103 $2.00107 20.95% 242 Hagerstown-Martinsburg, MD-WV $546,398.00 269,140 104 $1.99674 20.69% 172 Casper, WY $153,138.00 75,450 105 $1.99624 20.66% 364 Greenville, SC $1,289,377.00 636,986 106 $1.99086 20.33% 83 Greensboro-High Point, NC $1,464,797.00 723,801 107 $1.99044 20.31% 71 New Orleans-Metairie-Kenner, LA $2,360,696.00 1,167,764 108 $1.98827 20.17% 46 Las Vegas-Paradise, NV $3,941,238.00 1,951,269 109 $1.98658 20.07% 30 Indianapolis, IN $3,537,009.00 1,756,241 110 $1.98081 19.72% 35 Missoula, MT $219,657.00 109,299 111 $1.97660 19.47% 332 Ithaca, NY $203,399.00 101,564 112 $1.96970 19.05% 342 Dover, DE $324,756.00 162,310 113 $1.96789 18.94% 241 Cleveland-Elyria-Mentor, OH $4,151,800.00 2,077,240 114 $1.96580 18.82% 28 Allentown-Bethlehem-Easton, PA-NJ $1,614,902.00 821,173 115 $1.93420 16.91% 64 Prescott, AZ $414,603.00 211,033 116 $1.93229 16.79% 203 Providence-New Bedford-Fall River, RI-MA $3,141,889.00 1,600,852 117 $1.93032 16.67% 38 Worcester, MA $1,565,876.00 798,552 118 $1.92861 16.57% 67 Portland-South Portland-Biddeford, ME $1,005,605.00 514,098 119 $1.92385 16.28% 101 Billings, MT $308,857.00 158,050 120 $1.92200 16.17% 249 Springfield, MA $1,353,410.00 692,942 121 $1.92098 16.11% 77 Ames, IA $174,611.00 89,542 122 $1.91794 15.92% 356 Fayetteville, NC $713,043.00 366,383 123 $1.91413 15.69% 137 Fort Smith, AR-OK $580,963.00 298,592 124 $1.91364 15.66% 158 Houma-Bayou Cane-Thibodaux, LA $403,825.00 208,178 125 $1.90787 15.31% 205 Rapid City, SD $245,126.00 126,382 126 $1.90763 15.30% 300 Lewiston, ID-WA $118,048.00 60,888 127 $1.90686 15.25% 365 Lake Charles, LA $386,936.00 199,607 128 $1.90657 15.24% 213 Akron, OH $1,359,432.00 703,200 129 $1.90138 14.92% 75 Baton Rouge, LA $1,550,929.00 802,484 130 $1.90084 14.89% 66 Jackson, MS $1,040,664.00 539,057 131 $1.89874 14.76% 95 Cleveland, TN $223,348.00 115,788 132 $1.89718 14.67% 318 Iowa City, IA $293,659.00 152,586 133 $1.89286 14.41% 255 Richmond, VA $2,419,992.00 1,258,251 134 $1.89163 14.33% 44 Baltimore-Towson, MD $5,209,507.00 2,710,489 135 $1.89034 14.26% 20 Blacksburg, VA $312,735.00 162,958 136 $1.88752 14.08% 244 Orlando, FL $4,092,620.00 2,134,411 137 $1.88588 13.99% 26 Great Falls, MT $155,815.00 81,327 138 $1.88437 13.89% 359 Charlottesville, VA $384,484.00 201,559 139 $1.87614 13.40% 208 Farmington, NM $247,716.00 130,044 140 $1.87350 13.24% 301 Winston-Salem, NC $909,101.00 477,717 141 $1.87168 13.13% 105 Greenville, NC $359,766.00 189,510 142 $1.86714 12.85% 220 Pittsfield, MA $248,995.00 131,219 143 $1.86631 12.80% 296 Sioux City, IA-NE-SD $271,947.00 143,577 144 $1.86290 12.60% 273 Reno-Sparks, NV $805,515.00 425,417 145 $1.86230 12.56% 115 Columbia, MO $327,147.00 172,786 146 $1.86219 12.55% 232 Saginaw-Saginaw Township North, MI $378,070.00 200,169 147 $1.85766 12.28% 215 Davenport-Moline-Rock Island, IA-IL $716,738.00 379,690 148 $1.85661 12.22% 134 Kalamazoo-Portage, MI $614,919.00 326,589 149 $1.85185 11.93% 148 St. Louis, MO-IL $5,295,893.00 2,812,896 150 $1.85172 11.92% 19 Jackson, MI $300,340.00 160,248 151 $1.84336 11.42% 251 Columbus, OH $3,441,849.00 1,836,536 152 $1.84324 11.41% 32 Coeur d'Alene, ID $259,303.00 138,494 153 $1.84148 11.30% 280 Cincinnati, OH-KY-IN $3,984,972.00 2,130,151 154 $1.83994 11.21% 27 Battle Creek, MI $254,031.00 136,146 155 $1.83515 10.92% 288 Bay City, MI $201,047.00 107,771 156 $1.83479 10.90% 335 Niles-Benton Harbor, MI $292,506.00 156,813 157 $1.83460 10.89% 254 Albany-Schenectady-Troy, NY $1,621,964.00 870,716 158 $1.83212 10.74% 59 Amarillo, TX $462,864.00 249,881 159 $1.82184 10.11% 184 Roanoke, VA $570,732.00 308,707 160 $1.81834 9.90% 153 Harrisonburg, VA $230,573.00 125,228 161 $1.81091 9.45% 306 Burlington, NC $277,822.00 151,131 162 $1.80802 9.28% 261 Trenton-Ewing, NJ $673,675.00 366,513 163 $1.80780 9.27% 140 Jefferson City, MO $275,316.00 149,807 164 $1.80754 9.25% 265 Waterloo-Cedar Falls, IA $307,311.00 167,819 165 $1.80106 8.86% 237 Vineland-Millville-Bridgeton, NJ $287,125.00 156,898 166 $1.79988 8.79% 253 Hot Springs, AR $175,556.00 96,024 167 $1.79815 8.68% 349 Cumberland, MD-WV $188,630.00 103,299 168 $1.79599 8.55% 339 Binghamton, NY $458,868.00 251,725 169 $1.79288 8.36% 186 Augusta-Richmond County, GA-SC $1,015,110.00 556,877 170 $1.79285 8.36% 92 Dubuque, IA $170,614.00 93,653 171 $1.79177 8.30% 353 Rocky Mount, NC $277,596.00 152,392 172 $1.79160 8.29% 263 Flint, MI $775,183.00 425,790 173 $1.79060 8.23% 120 Springfield, IL $382,338.00 210,170 174 $1.78923 8.14% 204 Lynchburg, VA $459,487.00 252,634 175 $1.78884 8.12% 183 Glens Falls, NY $234,236.00 128,923 176 $1.78696 8.01% 299 Utica-Rome, NY $543,419.00 299,397 177 $1.78516 7.90% 160 Nashville-Davidson--Murfreesboro, TN $2,880,135.00 1,589,934 178 $1.78166 7.69% 37 Muskegon-Norton Shores, MI $311,558.00 172,188 179 $1.77962 7.56% 234 Danville, VA $192,681.00 106,561 180 $1.77840 7.49% 337 Goldsboro, NC $221,405.00 122,623 181 $1.77585 7.34% 309 Florence, SC $370,844.00 205,566 182 $1.77431 7.24% 267 Kingston, NY $329,107.00 182,493 183 $1.77370 7.21% 226 Monroe, MI $273,894.00 152,021 184 $1.77202 7.10% 264 Lansing-East Lansing, MI $833,169.00 464,036 185 $1.76592 6.74% 109 Holland-Grand Haven, MI $473,601.00 263,801 186 $1.76574 6.72% 174 Pine Bluff, AR $179,721.00 100,258 187 $1.76307 6.56% 344 Texarkana, TX-Texarkana, AR $243,750.00 136,027 188 $1.76242 6.52% 287 Springfield, MO $782,413.00 436,712 189 $1.76210 6.50% 112 Salisbury, MD $224,297.00 125,203 190 $1.76197 6.50% 308 Spartanburg, SC $509,124.00 284,307 191 $1.76127 6.45% 163 College Station-Bryan, TX $408,296.00 228,660 192 $1.75620 6.15% 193 St. Joseph, MO-KS $227,058.00 127,329 193 $1.75388 6.01% 303 Syracuse, NY $1,180,655.00 662,577 194 $1.75257 5.93% 80 Rochester, NY $1,877,514.00 1,054,323 195 $1.75146 5.86% 51 Elmira, NY $157,948.00 88,830 196 $1.74881 5.70% 357 Memphis, TN-MS-AR $2,337,491.00 1,316,100 197 $1.74683 5.58% 41 Winchester, VA-WV $227,756.00 128,472 198 $1.74361 5.39% 298 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $10,561,043.00 5,965,343 199 $1.74125 5.24% 6 Anderson, SC $331,261.00 187,126 200 $1.74111 5.24% 222 Jacksonville, NC $314,639.00 177,772 201 $1.74076 5.21% 227 Jonesboro, AR $213,245.00 121,026 202 $1.73297 4.74% 312 Sumter, SC $188,886.00 107,456 203 $1.72886 4.49% 333 Bloomington-Normal, IL $297,450.00 169,572 204 $1.72524 4.28% 235 Bismarck, ND $190,424.00 108,779 205 $1.72174 4.06% 330 Champaign-Urbana, IL $404,833.00 231,891 206 $1.71704 3.78% 192 Bremerton-Silverdale, WA $438,118.00 251,133 207 $1.71584 3.71% 182 Lawrence, KS $192,383.00 110,826 208 $1.70732 3.19% 328 San Antonio, TX $3,716,198.00 2,142,508 209 $1.70595 3.11% 24 Des Moines, IA $986,927.00 569,633 210 $1.70404 3.00% 88 Spokane, WA $815,061.00 471,221 211 $1.70120 2.82% 108 Tyler, TX $362,634.00 209,714 212 $1.70071 2.79% 200 Athens-Clarke County, GA $332,237.00 192,541 213 $1.69713 2.58% 219 Wichita, KS $1,073,694.00 623,061 214 $1.69488 2.44% 86 Poughkeepsie-Newburgh-Middletown, NY $1,154,051.00 670,301 215 $1.69334 2.35% 79 Joplin, MO $301,371.00 175,518 216 $1.68877 2.07% 231 Abilene, TX $282,987.00 165,252 217 $1.68426 1.80% 240 Boston-Cambridge-Quincy, MA-NH $7,793,647.00 4,552,402 218 $1.68380 1.77% 10 San Angelo, TX $191,352.00 111,823 219 $1.68303 1.73% 325 Topeka, KS $400,150.00 233,870 220 $1.68282 1.71% 189 Pocatello, ID $155,071.00 90,656 221 $1.68238 1.69% 355 Madison, WI $971,417.00 568,593 222 $1.68033 1.56% 89 Virginia Beach-Norfolk-Newport News, VA-NC $2,848,285.00 1,671,683 223 $1.67579 1.29% 36 Lubbock, TX $483,463.00 284,890 224 $1.66908 0.88% 161 Tuscaloosa, AL $371,157.00 219,461 225 $1.66337 0.54% 195 Sherman-Denison, TX $203,597.00 120,877 226 $1.65660 0.13% 314 La Crosse, WI-MN $224,725.00 133,665 227 $1.65357 -0.06% 290 Grand Forks, ND-MN $165,417.00 98,461 228 $1.65236 -0.13% 346 Waco, TX $394,549.00 234,906 229 $1.65195 -0.15% 188 Longview, TX $359,095.00 214,369 230 $1.64754 -0.42% 197 Beaumont-Port Arthur, TX $650,601.00 388,745 231 $1.64604 -0.51% 132 Cape Girardeau-Jackson, MO-IL   $160,928.00 96,275 232 $1.64403 -0.63% 350 Kansas City, MO-KS $3,396,584.00 2,035,334 233 $1.64133 -0.80% 29 Fayetteville-Springdale-Rogers, AR-MO $772,340.00 463,204 234 $1.63993 -0.88% 107 Peoria, IL $631,004.00 379,186 235 $1.63670 -1.07% 135 Corpus Christi, TX $710,406.00 428,185 236 $1.63179 -1.37% 114 Sioux Falls, SD $378,499.00 228,261 237 $1.63088 -1.43% 191 Decatur, IL $183,380.00 110,768 238 $1.62828 -1.58% 331 Manchester-Nashua, NH $663,068.00 400,721 239 $1.62744 -1.63% 130 Boise City-Nampa, ID $1,017,199.00 616,561 240 $1.62263 -1.93% 85 Chicago-Naperville-Joliet, IL-IN-WI $15,568,610.00 9,461,105 241 $1.61844 -2.18% 3 Eau Claire, WI $265,021.00 161,151 242 $1.61748 -2.24% 243 Oshkosh-Neenah, WI $274,532.00 166,994 243 $1.61690 -2.27% 238 Savannah, GA $570,207.00 347,611 244 $1.61335 -2.49% 143 Little Rock-North Little Rock, AR $1,147,493.00 699,757 245 $1.61285 -2.52% 72 Rome, GA $157,878.00 96,317 246 $1.61216 -2.56% 352 Burlington-South Burlington, VT $346,073.00 211,261 247 $1.61116 -2.62% 201 Warner Robins, GA $229,125.00 139,900 248 $1.61081 -2.64% 274 Victoria, TX $188,788.00 115,384 249 $1.60923 -2.74% 320 Macon, GA $378,813.00 232,293 250 $1.60391 -3.06% 190 Columbus, GA-AL $480,773.00 294,865 251 $1.60364 -3.07% 157 Midland, TX $221,613.00 136,872 252 $1.59247 -3.75% 281 Charlotte-Gastonia-Concord, NC-SC $2,845,880.00 1,758,038 253 $1.59213 -3.77% 33 Valdosta, GA $225,264.00 139,588 254 $1.58721 -4.07% 278 Kankakee-Bradley, IL $182,897.00 113,449 255 $1.58561 -4.16% 324 Brunswick, GA $181,130.00 112,370 256 $1.58537 -4.18% 326 Milwaukee-Waukesha-West Allis, WI $2,506,228.00 1,555,908 257 $1.58426 -4.24% 39 Danville, IL $131,406.00 81,625 258 $1.58337 -4.30% 360 Albany, GA $253,037.00 157,308 259 $1.58206 -4.38% 252 Logan, UT-ID $201,329.00 125,442 260 $1.57853 -4.59% 304 New Haven-Milford, CT $1,384,050.00 862,477 261 $1.57832 -4.60% 60 Killeen-Temple-Fort Hood, TX $649,274.00 405,300 262 $1.57558 -4.77% 127 Green Bay, WI $490,506.00 306,241 263 $1.57533 -4.78% 152 Rockford, IL $559,542.00 349,431 264 $1.57493 -4.81% 145 Fond du Lac, WI $161,165.00 101,633 265 $1.55964 -5.73% 341 Manhattan, KS   $201,274.00 127,081 266 $1.55775 -5.85% 297 Cedar Rapids, IA $408,023.00 257,940 267 $1.55581 -5.96% 177 Odessa, TX $216,475.00 137,130 268 $1.55262 -6.16% 282 Albuquerque, NM $1,399,881.00 887,077 269 $1.55210 -6.19% 57 Wenatchee, WA $174,873.00 110,884 270 $1.55112 -6.25% 327 New York-Northern New Jersey-Long Island,NY-NJ-PA $29,682,674.00 18,897,109 271 $1.54489 -6.62% 1 Sheboygan, WI $181,234.00 115,507 272 $1.54320 -6.73% 323 Norwich-New London, CT $429,665.00 274,055 273 $1.54199 -6.80% 170 Wausau, WI $210,056.00 134,063 274 $1.54105 -6.86% 291 Gainesville, GA $280,222.00 179,684 275 $1.53385 -7.29% 225 Appleton, WI $350,752.00 225,666 276 $1.52871 -7.60% 194 Hartford-West Hartford-East Hartford, CT $1,877,815.00 1,212,381 277 $1.52336 -7.93% 45 Omaha-Council Bluffs, NE-IA $1,333,208.00 865,350 278 $1.51529 -8.41% 58 Racine, WI $301,037.00 195,408 279 $1.51519 -8.42% 217 Janesville, WI $246,458.00 160,331 280 $1.51187 -8.62% 250 Las Cruces, NM $321,250.00 209,233 281 $1.51009 -8.73% 199 Mankato-North Mankato, MN $147,728.00 96,740 282 $1.50192 -9.22% 348 Grand Rapids-Wyoming, MI $1,178,645.00 774,160 283 $1.49741 -9.49% 69 McAllen-Edinburg-Pharr, TX $1,179,244.00 774,769 284 $1.49700 -9.52% 68 Duluth, MN-WI $425,512.00 279,771 285 $1.49589 -9.59% 165 Yakima, WA $369,819.00 243,231 286 $1.49541 -9.61% 187 Mount Vernon-Anacortes, WA $177,569.00 116,901 287 $1.49396 -9.70% 315 Dalton, GA $215,804.00 142,227 288 $1.49234 -9.80% 277 Los Angeles-Long Beach-Santa Ana, CA $19,390,317.00 12,828,837 289 $1.48658 -10.15% 2 Brownsville-Harlingen, TX $612,204.00 406,220 290 $1.48226 -10.41% 124 Fairbanks, AK $147,010.00 97,581 291 $1.48174 -10.44% 343 Riverside-San Bernardino-Ontario, CA $6,352,361.00 4,224,851 292 $1.47881 -10.62% 12 Longview, WA $153,768.00 102,410 293 $1.47677 -10.74% 340 Bellingham, WA $301,501.00 201,140 294 $1.47428 -10.89% 209 Rochester, MN $278,633.00 186,011 295 $1.47328 -10.95% 223 Columbia, SC $1,148,180.00 767,598 296 $1.47118 -11.08% 70 El Paso, TX $1,192,338.00 800,647 297 $1.46470 -11.47% 65 Minneapolis-St. Paul-Bloomington, MN-WI $4,842,583.00 3,279,833 298 $1.45216 -12.23% 16 Hinesville-Fort Stewart, GA $114,891.00 77,917 299 $1.45025 -12.34% 361 Raleigh-Cary, NC $1,663,591.00 1,130,490 300 $1.44734 -12.52% 47 St. Cloud, MN $277,151.00 189,093 301 $1.44155 -12.87% 221 Laredo, TX $366,846.00 250,304 302 $1.44147 -12.88% 181 San Luis Obispo-Paso Robles, CA $393,949.00 269,637 303 $1.43698 -13.15% 171 Sacramento--Arden-Arcade--Roseville, CA $3,125,517.00 2,149,127 304 $1.43037 -13.55% 25 Hanford-Corcoran, CA $221,963.00 152,982 305 $1.42702 -13.75% 260 Houston-Baytown-Sugar Land, TX $8,591,542.00 5,946,800 306 $1.42095 -14.12% 5 Lincoln, NE $435,900.00 302,157 307 $1.41888 -14.24% 154 Charleston-North Charleston, SC $958,180.00 664,607 308 $1.41799 -14.29% 78 Olympia, WA $361,151.00 252,264 309 $1.40807 -14.89% 180 Corvallis, OR $122,493.00 85,579 310 $1.40778 -14.91% 358 Fargo, ND-MN $298,526.00 208,777 311 $1.40634 -15.00% 202 Atlanta-Sandy Springs-Marietta, GA $7,462,139.00 5,268,860 312 $1.39295 -15.81% 9 Santa Cruz-Watsonville, CA $371,038.00 262,382 313 $1.39083 -15.94% 175 Chico, CA $310,836.00 220,000 314 $1.38963 -16.01% 196 Eugene-Springfield, OR $496,260.00 351,715 315 $1.38774 -16.12% 144 Santa Barbara-Santa Maria-Goleta, CA $595,677.00 423,895 316 $1.38211 -16.46% 118 Redding, CA $249,004.00 177,223 317 $1.38190 -16.48% 228 Napa, CA $191,518.00 136,484 318 $1.38012 -16.58% 284 Santa Rosa-Petaluma, CA $677,960.00 483,878 319 $1.37803 -16.71% 104 Colorado Springs, CO $897,143.00 645,613 320 $1.36672 -17.39% 81 San Diego-Carlsbad-San Marcos, CA $4,279,163.00 3,095,313 321 $1.35970 -17.82% 17 Idaho Falls, ID $177,313.00 130,374 322 $1.33764 -19.15% 294 San Francisco-Oakland-Fremont, CA $5,859,991.00 4,335,391 323 $1.32941 -19.65% 11 Ogden-Clearfield, UT $737,490.00 547,184 324 $1.32560 -19.88% 93 San Jose-Sunnyvale-Santa Clara, CA $2,472,499.00 1,836,911 325 $1.32385 -19.98% 31 Vallejo-Fairfield, CA $554,586.00 413,344 326 $1.31961 -20.24% 123 Oxnard-Thousand Oaks-Ventura, CA $1,102,236.00 823,318 327 $1.31673 -20.41% 63 Denver-Aurora, CO $3,395,406.00 2,543,482 328 $1.31296 -20.64% 21 Medford, OR $268,465.00 203,206 329 $1.29939 -21.46% 207 Durham, NC $666,098.00 504,357 330 $1.29894 -21.49% 102 Salinas, CA $544,618.00 415,057 331 $1.29055 -22.00% 121 Dallas-Fort Worth-Arlington, TX $8,355,402.00 6,371,773 332 $1.28972 -22.05% 4 Anchorage, AK $499,298.00 380,821 333 $1.28952 -22.06% 133 Austin-Round Rock, TX $2,235,782.00 1,716,289 334 $1.28124 -22.56% 34 Bend, OR $205,348.00 157,733 335 $1.28044 -22.61% 248 Kennewick-Richland-Pasco, WA $325,878.00 253,340 336 $1.26515 -23.53% 176 Portland-Vancouver-Beaverton, OR-WA $2,855,767.00 2,226,009 337 $1.26179 -23.74% 23 Salem, OR $500,670.00 390,738 338 $1.26025 -23.83% 131 Madera, CA $191,973.00 150,865 339 $1.25153 -24.36% 262 Boulder, CO $374,413.00 294,567 340 $1.25013 -24.44% 159 Modesto, CA $653,370.00 514,453 341 $1.24912 -24.50% 100 Grand Junction, CO $186,143.00 146,723 342 $1.24778 -24.58% 269 Fresno, CA $1,179,885.00 930,450 343 $1.24720 -24.62% 55 Pueblo, CO $200,950.00 159,063 344 $1.24254 -24.90% 246 El Centro, CA $220,389.00 174,528 345 $1.24198 -24.93% 230 Yuba City, CA $210,683.00 166,892 346 $1.24160 -24.96% 239 Stockton, CA $862,343.00 685,306 347 $1.23761 -25.20% 76 Bakersfield, CA $1,051,318.00 839,631 348 $1.23150 -25.57% 61 Washington-Arlington-Alexandria, DC-VA-MD-WV $6,970,911.00 5,582,170 349 $1.22822 -25.76% 7 Salt Lake City, UT $1,401,163.00 1,124,197 350 $1.22585 -25.91% 48 Greeley, CO $314,296.00 252,825 351 $1.22267 -26.10% 179 Tallahassee, FL $453,660.00 367,413 352 $1.21441 -26.60% 138 Seattle-Tacoma-Bellevue, WA $4,226,240.00 3,439,809 353 $1.20840 -26.96% 15 Merced, CA $308,988.00 255,793 354 $1.18807 -28.19% 178 Bridgeport-Stamford-Norwalk, CT $1,105,338.00 916,829 355 $1.18576 -28.33% 56 Visalia-Porterville, CA $531,838.00 442,179 356 $1.18296 -28.50% 111 St. George, UT $165,901.00 138,115 357 $1.18140 -28.59% 279 Santa Fe, NM $164,210.00 144,170 358 $1.12025 -32.29% 271 Fort Collins-Loveland, CO $340,812.00 299,630 359 $1.11871 -32.38% 155 Provo-Orem, UT $517,472.00 526,810 360 $0.96610 -41.61% 97 http://chmuraecon.com/blog/2015/march/sleepbetter-lost-hour-economic-index/ http://chmuraecon.com/blog/2015/march/sleepbetter-lost-hour-economic-index/ Mon, 09 March 2015 12:54:51 Chris Chmura http://chmuraecon.com/blog/2015/march/economic-impact-the-jobless-rate-doesnt-tell-the-full-story/ The national unemployment rate continues to improve, but don’t tell that to people who are jobless and looking for work. Many of today’s unemployed, particularly those who are younger, have a more negative view of the labor market. Who’s right? The official jobless rate in the nation peaked at 10 percent in October 2009, three months after the recovery began. Over the past five years, it dropped considerably to 5.7 percent in January 2015. Despite the large drop in the rate, there still is debate about the health of the U.S. labor market as some argue much of the decline is a result of people leaving the labor force rather than labor market improving. The Labor Department publishes an alternative jobless rate - called the U6 unemployment rate - that includes people without work looking for full-time employment as well as those marginally attached to the labor force and those working part-time who would prefer to work full time. The U6 unemployment rate remains high by historical standards. It stood at 11.3 percent in January 2015 — down from an all-time high of 17.1 percent in April 2010. The Labor Department started collecting these statistics in 1994 and before the last national recession, the previous high of 11.8 percent was recorded in January 1994. Based on an unemployment rate that includes the underemployed and discouraged workers, the labor market still has much room for improvement. In addition to not including them, the official U.S. unemployment rate overstates the improvement in labor market conditions when the drop has been caused by those leaving the workforce. The labor force participation rate, which represent the share of the civilian noninstitutional population that is in the labor force, stood at 66 percent in December 2007, the first month of the national recession. As of January 2015, the labor force participation rate fell to 62.9 percent, hitting a 36-year low in the prior month. While some have argued that the drop in labor force participation has been driven by demographic factors (such as baby boomers retiring), non-participation due to disability and increased school enrollment, among other factors, also have contributed to this decline. Partly driving the decline are those in the 16-to-24-year age category whose labor force participation rate has decreased 7.3 percentage points from January 2003 to January 2015. The labor force participation rate for those 55 years old and older has increased 4.5 percentage points over the same period, dispelling the argument that the drop is driven by retirees. The bottom line is that five years into the current expansion, the labor market remains weaker than the official unemployment rate suggests. Christine Chmura is CEO and Chief Economist at Chmura Economics & Analytics. She can be reached at (804) 649-3640 or receive e-mail at chris.chmura@chmuraecon.com . http://chmuraecon.com/blog/2015/march/economic-impact-the-jobless-rate-doesnt-tell-the-full-story/ http://chmuraecon.com/blog/2015/march/economic-impact-the-jobless-rate-doesnt-tell-the-full-story/ Mon, 02 March 2015 09:02:21 Chris Chmura http://chmuraecon.com/blog/2015/february/16/chmura-welcomes-laura-leigh-savage/ Chmura is pleased to welcome Laura Leigh Savage as Director of Operations and Economic Development Specialist. Laura Leigh has worked with the Virginia Economic Development Partnership (VEDP) since 2004 in project management and business development roles for business attraction as well as business retention and expansion . In January 2015, she was recognized as one of North America’s top 50 economic developers by Consultant Connect . Prior to working with the VEDP, Laura Leigh spent 20 years in banking and commercial real estate financing followed by experience in a marketing consultant firm. Laura Leigh brings a seasoned, real-world, and proven background as an economic development practitioner to Chmura’s seventeen years of experience working with economic developers through our consulting practice and technology solutions — JobsEQ and LaborEQ. You can contact Laura Leigh at LauraLeigh.Savage [at] chmuraecon.com. With Laura Leigh’s addition to the firm, Leslie Peterson’s new role will be President, Chief Strategy Officer and Dr. Christine Chmura will be the Chief Executive Officer while remaining the firm’s Chief Economist.  Leslie’s new position will allow her to more fully utilize her 16 years of strategic planning and sales skills from the chemical industry as Chmura Economics & Analytics takes on a more prominent role in commercial real estate and site selection markets. As CEO, Dr. Chmura will continue to lead the company in its vision to be the nation’s preferred provider of economic research, software, and data solutions. To this end, she will focus on researching important economic issues surrounding labor and regional growth, forecasting, and Department of Defense economic modeling. http://chmuraecon.com/blog/2015/february/16/chmura-welcomes-laura-leigh-savage/ http://chmuraecon.com/blog/2015/february/16/chmura-welcomes-laura-leigh-savage/ Mon, 16 February 2015 09:44:21 Chris Chmura http://chmuraecon.com/blog/2015/february/upcoming-events-march-30-ieds-presentation/ Event Date:  March 30, 2015 Event Time: 2:15pm - 3:45pm Location: Renaissance Arlinton Capital View 2800 S Potomac Ave Arlington, VA 22202   -Breakout: Data and Information: Fueling Your Economy Data provide the foundation for myriad decisions made by economic developers and elected officials. Available resources for data have increased over the past few years, from Census, Bureau of Economic Analysis, Bureau of Labor Statistics, and many private sources. This workshop will focus on a combination of hard skills associated with data research and utilization, discussion on effective uses of data, and what are that ‘data holes' that need to be filled.  What you will learn:  How can you leverage all of the options out there into useful information to better inform your next big decision?  What information proves the most influential when communicating your needs to your local-elected officials?  What data do you need that you're NOT getting and how can economic developers communicate that to data collectors?  Moderator: George Harben, CEcD, CCR, Director , Existing Business, Prince William County Department of Economic Development, Manassas, VA Speakers: Chris Chmura, PhD , President & Chief Economist, Chmura Economics & Analytics, Richmond, VA  The Honorable Dr. Erica Groshen , Commissioner, Bureau of Labor Statistics, U.S. Department of Labor, Washington, DC  Lynn Overman , Deputy Chief Data Officer, U.S. Department of Commerce, Washington, DC  Conference Website:  International Economic Development Council http://chmuraecon.com/blog/2015/february/upcoming-events-march-30-ieds-presentation/ http://chmuraecon.com/blog/2015/february/upcoming-events-march-30-ieds-presentation/ Fri, 13 February 2015 14:36:16 Chris Chmura http://chmuraecon.com/blog/2015/february/job-growth-in-virginia-wasnt-very-stellar-last-year/ Job growth in Virginia wasn't very stellar last year. Virginia gained only 12,900 jobs in 2014, or a 0.3 percent increase from the previous year. That increase was the worst year-over-year performance the state has had since 2010, when it shrunk by 4,800 jobs in the aftermath of the Great Recession. The meager employment growth in Virginia last year is in stark contrast to the 1.9 percent growth in the nation over the same period. The Northern Virginia metro area, which typically undergirds economic growth in the state, grew 0.4 percent in 2014 as federal spending cuts continue to dampen growth. Richmond, on the other hand, had a stellar performance of 1.9 percent employment growth in 2014, making it the fastest growing metro area in the state. Employment in the state is expected to pick up to 0.6 percent growth in 2015, according to the forecast in the annual Thomas Jefferson Institute “Virginia Economic Forecast” produced by Chmura Economics & Analytics. By comparison, national employment is forecast to expand 1.7 percent this year. Across-the-board cuts in federal spending are cited as the main factor contributing to the sub-par growth in the state. Three months after our original forecast, we still expect national employment to grow 1.7 percent in 2015. However, based on the latest data, 0.9 percent seems more realistic for the state. Why the upward revision? Forecasting is always difficult, but regional forecasts are even more difficult during the final months of the year, particularly when the economy is shifting to a much faster or slower pace of growth. The monthly employment numbers for Virginia and its metro areas are based on a sample of firms that represent about 30 percent of the employees in the state. It is called the current employment statistics data. In March of every year, the Virginia Employment Commission revises previously released employment estimates with more reliable quarterly census of employment and wages data through an annual process known as benchmarking. The data collected through the employment and wage program represent almost a complete count of employment. More than 96 percent of civilian jobs are counted through this wage and salary program because employers are required by law to provide the employment commission with a quarterly count of the number of employees covered under unemployment insurance. So, the further away from March, the greater the potential for error in the employment data. And if a forecast is created based on employment growth that is too high or too low, it will contribute to an incorrect forecast. To minimize the potential error of the revised data, we used data with a 6 month to 9 month lag as well as other data such as retail sales and payroll withholding figures that are not subject to revisions to inform the forecast. Based on those data, it’s looking like Virginia’s employment growth will be faster than our original forecast. The potential of further sequestration in October would once again dampen Virginia’s growth. Newly benchmarked data to be released in March will provide a more accurate base for our forecast.   Christine Chmura is CEO and Chief Economist at Chmura Economics & Analytics. She can be reached at (804) 649-3640 or receive e-mail at chris.chmura@chmuraecon.com . http://chmuraecon.com/blog/2015/february/job-growth-in-virginia-wasnt-very-stellar-last-year/ http://chmuraecon.com/blog/2015/february/job-growth-in-virginia-wasnt-very-stellar-last-year/ Mon, 02 February 2015 14:14:44 Chris Chmura http://chmuraecon.com/blog/2015/january/14/have-population-and-commuting-patterns-changed-in-your-region/ Metropolitan Statistical Areas (MSAs) and Micropolitan Statistical Areas (µSAs) are collections of counties where there is so much interconnectivity between the counties that they should be measured as one economy rather than separate counties.  For example, in the Dallas-Fort Worth-Arlington, Texas MSA many individuals live in Tarrant County but work in Dallas County. The Office of Management and Budget (OMB) periodically updates the definitions of MSAs and µSAs based on Census commuting and population data. MSAs “have at least one urbanized area of 50,000 or more population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties.” Similarly, µSAs “have at least one urban cluster of at least 10,000 but less than 50,000 population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties.” Though the OMB provides historical delineations  and current definitions , it is difficult to find a comprehensive list of changes made to the statistical areas. The current definitions were released in February 2013, and the previous definitions were released in December 2009. The dashboard below allows users to view changes to MSAs and µSAs definitions between the 2009 and 2013. Learn About Tableau Using Texas as an example, click through the tabs to see the filters that can be applied to your statistical area. Learn About Tableau To summarize the changes in Texas: Hudspeth County was added to the El Paso, TX MSA Oldham County was added to the Amarillo, TX MSA Lynn County was added to the Lubbock, TX MSA Martin County was added to the Midland, TX MSA Falls County was added to the Waco, TX MSA Newton County was added to the Beaumont-Port Arthur, TX MSA Little River County, AR was added to the Texarkana, TX-AR MSA Glasscock County was added to the Big Spring, TX µSA Trinity County was added to the Huntsville, TX µSA Zapata County was added to the Zapata, TX µSA Roberts County was removed from the Pampa, TX µSA Fannin County was removed from the Bonham, TX µSA Burnet County was removed from the Marble Falls, TX µSA Delta County was removed from the Dallas-Fort Worth-Arlington, TX MSA San Jacinto County was removed from the Houston-Sugar Land-Baytown, TX MSA Hood County and Somerville County were removed from the Granbury, TX µSA and absorbed into the Dallas-Fort Worth-Arlington, TX MSA Calhoun County was removed from the Victoria, TX MSA and added to the Port Lavaca, TX µSA The Killeen-Temple-Fort Hood, TX MSA was renamed Killeen-Temple, TX MSA The Austin-Round Rock-San Marcos, TX MSA was renamed Austin-Round Rock, TX MSA The Houston-Sugar Land Baytown, TX MSA was renamed Houston-The Woodlands-Sugar Land, TX MSA http://chmuraecon.com/blog/2015/january/14/have-population-and-commuting-patterns-changed-in-your-region/ http://chmuraecon.com/blog/2015/january/14/have-population-and-commuting-patterns-changed-in-your-region/ Wed, 14 January 2015 07:50:48 Chris Chmura http://chmuraecon.com/blog/2015/january/13/economic-impact-as-economy-gains-risk-of-recession-seems-slight/ The current economic expansion that began in June 2009 is now in its 68th month. That makes it nine months longer than the average of the 11 expansions that occurred between 1945 and 2009, although the last three expansions have lasted an average of 95 months. Does the current expansion have the strength to continue or will the U.S. economy be heading into a recession? The continued drop in the price of oil to less than $50 a barrel is occurring, in part, because of the increase in production in the United States and may reflect a drop in demand. That drop in demand, some point out, is occurring because of weakness in Eurozone economies and a slowdown in growth in China. Some observers point out that the global weakness will lead to less demand for U.S.-made goods and services, which will slow our growth. With this scenario, it’s important to recognize that about 13 percent of U.S. gross domestic product came from exports compared with the prior year. Deflation, which remains a real concern in the Eurozone, could contribute to weakness in their banking system. The most pessimistic analysts also see this impacting our economy, which could translate into a recession. Those arguing for a recession might also point out that each of the three major stock indices fell about 3 percent early last week, which may signal a recession. However, we’re not even close to a bear market, which is defined as a 20 percent drop in one of the major U.S. stock market indices. Even if we were, bear markets aren’t always accompanied by recessions. Over the past 30 years, there have been five bear markets and three recessions. Pointing toward continued expansion is real gross domestic product growth, which was revised up to a whopping 5 percent annualized rate in the third quarter of 2014. That’s the fastest pace since the third quarter of 2003 when GDP grew at a 6.9 percent annualized rate. Third quarter growth comes on the heels of a 4.6 percent annualized pace in the second quarter that was partially a rebound from a contraction in the first quarter. The first quarter contraction was mostly attributable to severe winter weather. Early results on holiday spending reflect a consumer that is loosening up their pocket books. Of course, lower gas prices are supporting some of that spending. The increase in job hiring also is putting more money into the U.S. economy and creating the momentum to fuel further growth. Increased demand causes businesses to hire more people and purchase more supplies and equipment which leads to more hiring and more economic growth. From this perspective, we don’t expect the nation to dip into recession anytime soon.  In fact, we are looking for continued growth in the national economy to the tune of an increase of 3.4 percent in real gross domestic product in 2015 and 3.7 percent in 2016. http://chmuraecon.com/blog/2015/january/13/economic-impact-as-economy-gains-risk-of-recession-seems-slight/ http://chmuraecon.com/blog/2015/january/13/economic-impact-as-economy-gains-risk-of-recession-seems-slight/ Tue, 13 January 2015 15:11:37 Greg Chmura http://chmuraecon.com/blog/2015/january/12/cost-of-living/ The cost of living is one of many metrics we examine when assisting site selectors in identifying attractive labor markets for relocations or expansions. The below map displays the Cost of Living Index for each U.S. metropolitan area. The average cost of living for the nation is set to 100; areas with higher cost of living are shown in red (where the index is greater than 100) and areas with lower cost of living are shown in green (where the index is less than 100). Learn About Tableau A quick look at the map shows that higher costs of living are generally found on the east and west coasts. Lower cost of living is not uniform in the interior of the country, however. Pockets of above-average cost of living exist in non-coast metros such as Denver, Minneapolis, and Chicago. For firms looking to move into new regions, cost of living can serve as a quick proxy for the relative wages of workers in a region, but the relationship between wages and the Cost of Living Index is rarely if ever simple. For site selection purposes, occupation wages should be examined in addition to the cost of living, and the relative difference of wages typically varies depending on the nature of the industry and the mix of occupations employed. http://chmuraecon.com/blog/2015/january/12/cost-of-living/ http://chmuraecon.com/blog/2015/january/12/cost-of-living/ Mon, 12 January 2015 11:00:56 Chris Chmura http://chmuraecon.com/blog/2015/january/05/msas-dont-tell-the-whole-story/ Expanding firms often say skilled labor is one of the most important factors determining where they locate their next operations.  Although most firms know they need accurate labor data, they often don’t consider the importance of using an accurate labor shed that defines where they will be drawing workers. Both metropolitan statistical areas (MSA) and a specified mile radius around a location can be inaccurate, leading to costly worker shortages or excluding a location that may be ideal. A drive-time analysis, such as that available in Chmura Economics’ JobsEQ ® tool, is needed to prevent such problems.   Two examples illustrate the need to use drive times to measure a labor shed. A firm located in the center of Isle of Wight County, Virginia would overstate the talent it could draw if they used the Virginia Beach-Norfolk-Newport News MSA or a 50-mile radius to define its labor pool. Almost 2,100 machinists work in the MSA and nearly 2,300 work within a 50-mile radius of the firm location. However, the presence of the Chesapeake Bay and its tunnels, and James River limit the number of workers available within a 45-minute commute—only 620 machinists work within that drive time. Similar shortages are found with most occupations. About 900 structural metal fabricators and fitters work in the MSA and nearly 960 are within a 50-mile radius but less than 300 work within a 45-minute drive time. The opposite problem occurs for a firm located in the center of the Akron MSA in Northeast Ohio. Almost 2,000 machinists work in the Akron MSA. Because this MSA is close to the Cleveland MSA, a 60-minute drive time for a firm located in the center of the MSA identifies a much larger pool of nearly 11,000 machinists.  The larger labor pool is true for all occupations. About 1,400 application software developers work in the Akron MSA but nearly 7,000 work within a 60-minute drive time. Nearly 4,000 bookkeeping and audit clerks work in the Akron MSA and over 21,000 work within a 60-minute drive time. The bottom line is that drive-time analysis is crucial for expanding firms to ensure that their labor force needs will be met. http://chmuraecon.com/blog/2015/january/05/msas-dont-tell-the-whole-story/ http://chmuraecon.com/blog/2015/january/05/msas-dont-tell-the-whole-story/ Mon, 05 January 2015 11:14:48 John Chmura http://chmuraecon.com/blog/2015/january/05/announcing-jobseq-31/ We are pleased to announce the release of JobsEQ 3.1!  This is a very exciting release, bringing thorough and detailed labor data to our users’ fingertips. Here are the major components of today’s release: Labor Data at the Zip-Code Level Chmura has computed and rigorously tested a detailed, unsuppressed dataset at the zip-code level that is now available in nearly all JobsEQ analytics. On the industry side, zip level data are available down to 6-digit NAICS codes, and for occupations, down to 6-digit SOC codes. And if zip-codes aren’t detailed enough for your needs, contact us for information on how Chmura’s team of expert economists can do a custom analysis of your labor market using our exclusive block-level labor data . Custom Regions by Drive Time Metropolitan areas (MSAs) and simple n-mile radius commonly used in desktop analysis don’t tell the whole story , and can often misrepresent your labor market. JobsEQ now allows users to run analytics based on custom drivetime regions. This powerful, yet easy to use tool, allows our users to gain an accurate picture of their labor market, regional conditions, and demographics.  Occupation Profile Report JobsEQ 3.1 adds another powerful report to your toolkit, the Occupation Profile Report ( download a sample ). With the click of a button, this report provides an overview of employment, wages, geographic distribution, and educational pipeline. Combined with the zip-level data and the drive time feature, JobsEQ reports pack an unprecedented amount of information into an easy-to-read download.  Also related: view a sample of the JobsEQ Economic Overview Report . Access Today JobsEQ 3.1 is available to existing users immediately through your normal login. New users can contact sales to schedule a demo or start your subscription today. http://chmuraecon.com/blog/2015/january/05/announcing-jobseq-31/ http://chmuraecon.com/blog/2015/january/05/announcing-jobseq-31/ Mon, 05 January 2015 08:38:28 Chris Chmura http://chmuraecon.com/blog/2014/october/upcoming-events-october-26-29-2014-corenet-global-north-american-summit/ Event Date: October 26-29, 2014 Location: Gaylord National Resort & Convention Center Exhibition Booth Number: 1934 CoreNet Global’s “One Summit a Year” Model in North America comes to Washington D.C!   More than 2000 corporate real estate professionals are expected to gather for top notch peer-to-peer education, networking and the opportunity to meet with the service providers and community leaders who make their lives easier every day. Get solutions to your biggest problems, build relationships that will stick with you throughout your career and impart your own lessons-learned over three days of high-impact education and networking. Conference Website: CoreNet Global North American Summit http://chmuraecon.com/blog/2014/october/upcoming-events-october-26-29-2014-corenet-global-north-american-summit/ http://chmuraecon.com/blog/2014/october/upcoming-events-october-26-29-2014-corenet-global-north-american-summit/ Fri, 24 October 2014 10:09:35 http://chmuraecon.com#err-1839 http://chmuraecon.com#err-1839 http://chmuraecon.com#err-1839 Mon, 20 October 2014 09:26:01 Chris Chmura http://chmuraecon.com/blog/2014/october/upcoming-events-october-19-22-iedc-annual-conference-steering-towards-the-future-convergence-connectivity-and-creativity/ Event Date: October 19-22, 2014 Location: Fort Worth Convention Center Exhibition Booth Number:  315 Big Data: Raising the Bar in Site Selection Since the recession there has been an unprecedented convergence of inter-disciplinary research experts such as economists, geo-spatial experts, data scientists and sociologists who are coming together to understand shifting labor supply and demand and how labor trends impact communities. As a result, site selectors, economic developers and prospecting companies are designing innovative approaches that uncover hidden geographies of opportunity at the hyper-local level, from sub markets within urban areas to rural gems. In this session, industry experts and economic developers will dive into real-world examples and share how they are helping companies from multiple sectors identify the right community for their organization based on labor cost, availability and sustainability. What you will learn: • How companies are using new and expanded data sources to influence site selection decisions • Tips for improving the positioning of your community within the new site selection landscape • Strategies for working with local partners to access important data requested in the site selection process Moderator: Amy Fobes , Principal and Founder, geoCommunica, Dallas, TX Speakers: • Josh Bays , Principal, Site Selection Group, LLC, Dallas, TX • Christine Chmura, PhD , President & Chief Economist, Chmura Economics & Analytics, Richmond, VA  Conference Website: IEDC 2014 Annual Conference http://chmuraecon.com/blog/2014/october/upcoming-events-october-19-22-iedc-annual-conference-steering-towards-the-future-convergence-connectivity-and-creativity/ http://chmuraecon.com/blog/2014/october/upcoming-events-october-19-22-iedc-annual-conference-steering-towards-the-future-convergence-connectivity-and-creativity/ Mon, 20 October 2014 09:26:01 Chris Chmura http://chmuraecon.com/blog/2014/september/upcoming-events-october-7-9-2014-veda-fall-conference-governors-conference-on-economic-development/ Event Date: October 7-9, 2014 Location: Hilton Richmond Hotel and Spa   Conference Website: 2014 VEDA Fall Conference Session I on Wednesday, October 8th, 2014 at 8:30 AM: Dr. Chris Chmura How Labor Availability is Changing the Conversation Around Deal Flow Chris Chmura is the President, Chief Economist, and Principal of Chmura. Chris Chmura is the President and Chief Economist for Chmura Economics & Analytics, a quantitative research and economic development and workforce consulting firm located in Richmond, Virginia, that she founded in December 1999. She is a quoted source on regional and national trends in the media throughout the Mid-Atlantic and Southeast, as well as national publications such as the Wall Street Journal . She writes a monthly column on the economy for the Richmond Times Dispatch . http://chmuraecon.com/blog/2014/september/upcoming-events-october-7-9-2014-veda-fall-conference-governors-conference-on-economic-development/ http://chmuraecon.com/blog/2014/september/upcoming-events-october-7-9-2014-veda-fall-conference-governors-conference-on-economic-development/ Mon, 15 September 2014 16:44:20 http://chmuraecon.com/blog/2014/september/02/economic-impact-health-care-and-construction-jobs-will-be-fastest-growing-in-region/ If you are looking for work during the next decade, consider health care and construction jobs. Those two industries should see the fastest-growing employment in the Richmond area. The health care and social assistance sector should grow an average 2.5 percent a year in the Richmond area during the next 10 years compared with an average 1.2 percent for all industries, according to analysis by Chmura Economics & Analytics. That translates into a need for 24,480 more health care workers over the next 10 years. In addition to that, 19,168 people will be needed in that sector to take the place of people who retire or leave the industry. Skill sets that will be needed in the Richmond health care sector include: • personal care aides (2,884 new positions expected with an additional 469 positions replaced); • registered nurses (2,043 new positions expected with an additional 2,248 positions replaced); • home health aides (1,843 new positions expected with an additional 786 positions replaced); and • nursing assistants (1,173 new positions expected with an additional 1,140 positions replaced). Employment at health care firms consistently outpaced the overall economy during the recession and since it ended. In contrast to health care, employment in construction saw a larger percentage contraction during the recession than any other major sector. Looking ahead, Chmura Economics & Analytics expects employment in that sector to grow at the second-fastest pace in the Richmond area as recovery in the construction industry builds momentum. Employment at area construction firms is expected to grow an average 2.7 percent a year in the next decade, adding 20,568 jobs with an additional 7,632 positions for retirements or transfers to new positions. The largest openings related to construction are expected to be: • construction laborers (1,470 new positions expected with an additional 1,268 positions replaced); • carpenters (927 new positions expected with an additional 560 positions replaced); and • electricians (753 new positions expected with an additional 666 positions replaced). Aside from growth in health care and construction-related occupations, bookkeepers, accountants, first-line supervisors, and sales representatives are expected to expand by the largest number of jobs in the metro area. On the flip side, the largest number of area job losses are expected at the U.S. Post Office as it continues to grapple with the fast pace of communication over the Internet. Job opportunities currently are better in the Richmond area compared with the state, which is still struggling with federal spending cuts. Nonfarm employment rose 2.1 percent in July in the Richmond area from the same month a year ago, while it rose 0.6 percent in the state during the same time period. National employment grew 1.9 percent over the same period. As we celebrate achievements of workers this Labor Day, we should also look at what jobs will be here in the future. Students, take note. Christine Chmura is president and chief economist at Chmura Economics & Analytics. She can be reached at (804) 649-3640 or at chris@chmuraecon.com. http://chmuraecon.com/blog/2014/september/02/economic-impact-health-care-and-construction-jobs-will-be-fastest-growing-in-region/ http://chmuraecon.com/blog/2014/september/02/economic-impact-health-care-and-construction-jobs-will-be-fastest-growing-in-region/ Tue, 02 September 2014 12:23:46 http://chmuraecon.com#err-1790 http://chmuraecon.com#err-1790 http://chmuraecon.com#err-1790 Tue, 02 September 2014 12:17:57 Chmura Staff http://chmuraecon.com/blog/2014/september/02/state-level-gross-domestic-product-gdp-now-available-by-quarter/ State-level gross domestic product (GDP) is now available by quarter. Previously, the U.S. Bureau of Economic Analysis (BEA) only provided state-level GDP on an annual basis. The agency’s news release explains “These new statistics provide a more complete picture of economic growth across states that can be used with other regional data to gain a better understanding of regional economies as they evolve from quarter to quarter.” Below is a dashboard showing the quarterly annualized percent change in GDP by state from 2005Q2 through 2013Q4. Use the bar in the middle of the page to slide through time or select a specific quarter from the dropdown menu. You can hover over any state for more detailed information, and the percentage change for the United States is summarized in the bar chart below the map for comparison purposes. One note on interpretation: U.S. GDP by state excludes federal military and civilian activity located overseas (it cannot be attributed to a particular state), so a summation of quarterly GDP by state for the nation will differ somewhat from the GDP in the national income and product accounts (NIPAs). Learn About Tableau This format in the link above makes it easy to see, for example, that while GDP in the fourth quarter of 2013 increased or was unchanged over the quarter for the nation as well as in 49 states, it decreased 3.0% in Mississippi. More data are available from the BEA, including 21 industry sectors—check it out and let us know what you think. http://chmuraecon.com/blog/2014/september/02/state-level-gross-domestic-product-gdp-now-available-by-quarter/ http://chmuraecon.com/blog/2014/september/02/state-level-gross-domestic-product-gdp-now-available-by-quarter/ Tue, 02 September 2014 08:56:53 Chris Chmura http://chmuraecon.com/blog/2014/august/upcoming-events-october-15-17-2014-cradle-to-career-network-convening/ Event Date: October 15-17, 2014 Location: Paradise Point Chris Chmura will be speaking at this workshop: Labor Market Analytics as a "Flashlight": Using Data to Drive Post-Secondary Attainment Action Friday, October 17 11:00-12:15 p.m. Additional Event Details 2014 CRADLE TO CAREER NETWORK CONVENING MAKING WAVES: ACTION THAT MOVES OUTCOMES OCTOBER 15-17, 2014 IN SAN DIEGO, CA What: Join Cradle to Career Network members from communities throughout the country who are working to improve student outcomes through building cradle to career civic infrastructure.  Come learn, share and network with others doing like work and facing similar challenges.  Leave with concrete ideas on how to move this work further faster in your community to get to Proof Point. Where: Paradise Point, 1404 Vacation Road, San Diego, CA 92109 When: October 15-17, 2014; Time: 5:30 pm PT on Oct 15.-2:30 pm PT on Oct. 17 Who: Cradle to Career Network members who have completed the Civic Infrastructure Assessment and met the Exploring quality benchmarks in the Theory of Action are eligible to attend.  For more information on Network membership, contact robinsonk@strivetogether.org . How: Registration is open until October 1, 2014. Interested in being a sponsor or exhibtor?  Learn more about the opportunities available here. http://chmuraecon.com/blog/2014/august/upcoming-events-october-15-17-2014-cradle-to-career-network-convening/ http://chmuraecon.com/blog/2014/august/upcoming-events-october-15-17-2014-cradle-to-career-network-convening/ Thu, 21 August 2014 13:47:33 Chris Chmura http://chmuraecon.com/blog/2014/august/economic-impact-some-signs-emerging-for-growth-in-midwage-jobs/ A lot has been made about the hollowing out of America, also called “job polarization” by economists. It happened during the recession, when employment in midwage occupations declined by a larger number than lower-wage or higher-wage occupations dropped. Since the recovery began, job growth has been lagging in midwage occupations. The greatest increase has been in lower-wage occupations, such as jobs in retail and food services. Growing income inequality is a concern if the reduction in midwage jobs is part of a long-term trend as opposed to the temporary effects of recessions. In fact, the last three recessions were associated with a “jobless recovery,” where output grew for several quarters while employment growth remained lethargic. A 2012 study by Nir Jaimovich and Henry Siu for the National Bureau of Economic Research, using data since 1970, showed that job polarization mainly occurs during economic downturns and is not a gradual phenomenon that takes place over the long run. Moreover, the jobs that are lost to technological advancements and more liberal trading policies are often middle-wage jobs that focus on routine tasks. Are midwage jobs growing now that we are in the fifth year of the expansion? Twenty-two percent of U.S. jobs created in 2012-13 were midwage jobs, according to the latest data from 2013 figures by the Bureau of Labor Statistics. (Midwage jobs are occupations with median hourly wages from $13.84 to $21.13, or the equivalent of annual income of $28,510 to $43,950.) A much higher percentage of jobs created during the same period came from lower-wage positions (42 percent) and from higher-wage occupations (35 percent). Overall, midwage jobs accounted for 27 percent of the workforce, compared with 38 percent for lower-wage jobs and 35 percent for higher-wage jobs. The mix was even worse in Virginia. The state also had 22 percent of jobs created over the same period in midwage positions, but 58 percent were lower-wage occupations and 21 percent were higher-wage ones. The substandard trend in Virginia is probably a result of federal government cuts that are impacting the economy. In this sense, Virginia’s economy is undergoing a restructuring similar to a business cycle downturn that is dampening growth in middle-wage jobs in industry sectors such as construction and professional services. A different trend is emerging in the Richmond area, where employment in midwage jobs is growing faster than the nation. Midwage jobs made up 41 percent of all jobs created in 2012-13, compared with 49 percent for lower-wage positions and 10 percent for higher-wage ones. Although the latest occupation data through 2013 is not showing a lot of support in the nation or state that midwage jobs are returning, national industry employment from the Bureau of Labor Statistics through this June is showing some promising signs. Employment in construction, which has many midwage occupations, is up 3 percent from a year ago, and residential builders have expanded their payroll by 8 percent over the same period. Employment also has grown 1 percent over the same period in manufacturing, which also has many midwage occupations. The more recent industry data show signs of hope for midwage jobs. http://chmuraecon.com/blog/2014/august/economic-impact-some-signs-emerging-for-growth-in-midwage-jobs/ http://chmuraecon.com/blog/2014/august/economic-impact-some-signs-emerging-for-growth-in-midwage-jobs/ Mon, 11 August 2014 10:05:27 http://chmuraecon.com#err-1774 http://chmuraecon.com#err-1774 http://chmuraecon.com#err-1774 Mon, 04 August 2014 09:19:12 Chris Chmura http://chmuraecon.com/blog/2014/august/upcoming-events-october-19-22-iedc-2014-annual-conference/ Event Date: October 19-22, 2014 Location: Fort Worth Convention Center Creating Jobs and Saving Farms with Ag-Tech It's a familiar story: fast-growing community seeks jobs and tax base, builds residential and commercial infrastructure over formerly active, prime farmland. Becomes suburbanized in the short term but loses potential economic asset in the long run. In areas caught between an agricultural past and a suburban present, formulating a coherent development strategy amidst the frenzied pace of economic growth can be a challenge. This session will demonstrate how communities with an agricultural heritage can redefine their role in a regional economy and transform themselves from an aspirational "bedroom community" to a vibrant, diverse economic hub by stimulating the growth of agriculture and technology-based industries.  What you will learn:  • How to capitalize on regional and state assets across existing agriculture and technology clusters • Strategies for integrating active farmland with mixed-use development within a traditional suburban community • Tips for using ag-tech to support new workforce development, entrepreneurship, and agri-tourism initiatives that yield additional benefits for your community  Speakers:  • Sean Garretson, AICP , President, Pegasus Planning and Development • Carlos Gutierrez , Chief Strategy Officer, Larta Institute, Los Angeles, CA • Brenda Sherwood , Economic Development, City of Meridian, ID  Big Data: Raising the Bar in Site Selection Since the recession there has been an unprecedented convergence of inter-disciplinary research experts such as economists, geo-spatial experts, data scientists and sociologists who are coming together to understand shifting labor supply and demand and how labor trends impact communities. As a result, site selectors, economic developers and prospecting companies are designing innovative approaches that uncover hidden geographies of opportunity at the hyper-local level, from sub markets within urban areas to rural gems. In this session, industry experts and economic developers will dive into real-world examples and share how they are helping companies from multiple sectors identify the right community for their organization based on labor cost, availability and sustainability. What you will learn:  • How companies are using new and expanded data sources to influence site selection decisions • Tips for improving the positioning of your community within the new site selection landscape • Strategies for working with local partners to access important data requested in the site selection process  Moderator: Amy Fobes , Principal and Founder, geoCommunica, Dallas, TX  Speakers: • Josh Bays , Principal, Site Selection Group, LLC, Dallas, TX • Christine Chmura, PhD , President & Chief Economist, Chmura Economics & Analytics, Richmond, VA • Wayne Gearey, PhD , Global Chief GIS Officer, Jones Lang LaSalle, Dallas, TX  Conference Website: IEDC 2014 Annual Conference http://chmuraecon.com/blog/2014/august/upcoming-events-october-19-22-iedc-2014-annual-conference/ http://chmuraecon.com/blog/2014/august/upcoming-events-october-19-22-iedc-2014-annual-conference/ Mon, 04 August 2014 09:19:12 http://chmuraecon.com#err-1553 http://chmuraecon.com#err-1553 http://chmuraecon.com#err-1553 Wed, 23 July 2014 14:15:30 Chris Chmura http://chmuraecon.com/blog/2014/july/upcoming-events-august-5-8-logistics-development-forum/ Event Date: August 5-8, 2014 Location: Stein Eriksen Lodge 7700 Stein Way Park City, UT 84060 The Logistics Development Forum addresses the importance of logistics in a corporation’s decision for a new manufacturing distribution and/or warehousing location. Through educational sessions and one-on-one meetings with Industry experts and extensive networking activities, attendees will have the opportunity to share ideas and learn how to become more competitive in the logistics supply chain market. Conference Website: Logistics Development Forum http://chmuraecon.com/blog/2014/july/upcoming-events-august-5-8-logistics-development-forum/ http://chmuraecon.com/blog/2014/july/upcoming-events-august-5-8-logistics-development-forum/ Wed, 23 July 2014 14:15:30 http://chmuraecon.com#err-1671 http://chmuraecon.com#err-1671 http://chmuraecon.com#err-1671 Wed, 09 July 2014 09:08:29 Chmura Staff http://chmuraecon.com/blog/2014/july/09/dont-mess-with-texas-federal-contract-spending-cuts-and-the-texas-economy/ Today Chmura released the second in a series of white papers examining the reliance of states on federal contract spending. Download the full report . Firms in Texas received $39.0 billion in federal contract awards in fiscal year (FY) 2013 in the United States [1] —more than all other states except Virginia ($51.2 billion) and California ($47.6 billion). [2] The Lone Star State boasts plentiful natural resources and an advanced industrial sector which are two of the reasons it is a large recipient of federal spending. Major metropolitan statistical areas such as Dallas, Houston, and San Antonio rely on this spending to support economic growth, particularly since the slow recovery from the Great Recession of 2007 to 2009. Based on the latest data from the General Service Administration (GSA), $404.4 billion of federal contract spending went to firms in Texas from fiscal year FY 2003 through FY 2013. In FY 2013, the state received $39.0 billion, with over 80% of all federal contract awards coming from the Department of Defense (DoD). Given the dependence of the Texas economy on federal government spending, it is important for federal, state, and local Texas representatives and citizens to understand the potential consequence for the economy during this period of defense downsizing. To learn more about the role federal procurement spending plays in the state of Texas as well as its impact by region and industry sector, download our full white paper here . [1] Source: USASpending website, available at http://www.usaspending.gov/state-summary-tabular?tab=By+Location&contracts=Y&tabletype=statesummary. [2] Federal government contracts are payments for goods and services rendered by the private sector. In this report, “federal contract spending,” “federal procurement spending,” and “federal contract awards” are interchangeable. http://chmuraecon.com/blog/2014/july/09/dont-mess-with-texas-federal-contract-spending-cuts-and-the-texas-economy/ http://chmuraecon.com/blog/2014/july/09/dont-mess-with-texas-federal-contract-spending-cuts-and-the-texas-economy/ Wed, 09 July 2014 07:47:37 http://chmuraecon.com#err-1675 http://chmuraecon.com#err-1675 http://chmuraecon.com#err-1675 Tue, 08 July 2014 14:14:59 Chris Chmura http://chmuraecon.com/blog/2014/july/08/economic-impact-college-students-carrying-high-student-loan-debt-and-not-buying-homes/ The national labor market continues to slowly improve, but so many people with work experience are still seeking jobs. This is making it another tough summer for high school and college students who want to work as well as new college graduates entering the workforce. Making matters worse, the mounting debt that college graduates are carrying is likely playing a role in the sluggish housing market. Even though the national unemployment rate dipped to 6.1 percent last month compared with 7.5 percent for the same month a year ago, the Labor Department’s rate that accounts for people who have stopped looking for work or cannot find full-time jobs is almost double at 12.1 percent. And while the number of people jobless for 27 weeks or longer fell to 3.1 million in June, it still made up 32.8 percent of the unemployed. With so many experienced workers to choose from, it’s not surprising that the unemployment rate for 16- and 17-year-olds stood at 23.3 percent in June and was 19.3 percent for 18- and 19-year-olds, according to the Labor Department. A quick review of openings on job posting sites for the Richmond area show students have plenty of job possibilities as receptionists, office clerks, cashiers and customer service representatives. One problem they may face, however, is that employers need someone year round rather than just during the summer. College graduates who are looking for full-time employment might be having an easier time finding jobs based on the jobless rate for 20- to 24-year-olds that stood at 10.5 percent in June. As usual, employability varies greatly based on skill sets. There are hundreds of job openings for registered nurses in the Richmond metro area, but only three postings for photographers. The sluggish job market along with the debt college students have incurred is one factor contributing to slow growth in the housing market. Based on data from the Federal Reserve Bank of New York, 43 percent of 25-year-olds had student debt in 2012 compared with 25 percent in 2003. The average debt carried by 25-year-olds was $20,326 in 2012, nearly double from the $10,649 in debt in 2003. The average student loan per borrower for all ages in Virginia was $26,310 in 2012, compared with an average of $24,810 across the nation, according to the New York Fed. Some troubling trends become apparent when we consider the percentage of borrowers with student loan debt who have home mortgage debt at age 30, which historically has been about the median age for first-time home buyers, according to the National Association of Realtors. In 2003, for instance, a higher percentage of 30-year-olds with college debt were able to have a home mortgage as well a student loan, largely because the average student loan debt was much smaller than it is today. This is not surprising because those who were college-educated in 2003 earned higher incomes on average than those without a college education, and student loan debt was low enough so that it did not deter a home purchase then. But this past recession was a tipping point for the traditional 30-year-old first-time home buyer. Educated consumers with student loan debt as well as buyers without student loans saw home ownership rates fall, but they fell more dramatically for those with a student loan. In 2013, 30-year-olds without student loans — presumably not college educated — were more likely to have purchased a home with a mortgage. These trends suggest that for some individuals who are about 30 years old, the single biggest lifetime investment is not a home but an education. http://chmuraecon.com/blog/2014/july/08/economic-impact-college-students-carrying-high-student-loan-debt-and-not-buying-homes/ http://chmuraecon.com/blog/2014/july/08/economic-impact-college-students-carrying-high-student-loan-debt-and-not-buying-homes/ Tue, 08 July 2014 12:26:06 Chris Chmura http://chmuraecon.com/blog/2014/july/02/growing-student-loan-debt-and-its-impact-on-housing/ A recent report by the Brookings Institution has stirred up the debate about whether there is a looming student loan crisis. But there is no question that with a growing number of people in college and rising tuition costs , more and more students are taking on loans and face years of paying down that debt— whether they successfully graduate or not. To illustrate a simple measure of the growing debt from student loans, Chmura built the maps below from Federal Reserve Bank of New York (“FRBNY”) Consumer Credit Panel data to show the balance of student loan debt per capita in each state between 1999 and 2012.  To use Virginia as an example, student loans stood at $580 per capita in 1999; by 2012 that had increased 597% to $4,040 (note that the data are not adjusted for inflation). The average student loan per borrower in Virginia was $26,310 in 2012, compared with an average of $24,810 across the United States. As for fears about graduates not being able to pay off their debt, the percentage of student loan debt balance 90+ days delinquent in Virginia rose 2.79 percentage points between 2011 and 2012, but was still comparable to historic levels.  It is important to note in interpreting this data, though, that the percentage of delinquent loans may be similar to that of a decade ago, but with increasing enrollments a lot more people in the state are having difficulty paying off their student loans. In addition, many loans have six-month grace periods before requiring payments, and there are a variety of options to postpone or decrease payments that graduates who are unemployed or low-income can use to prevent default.   So is this additional debt impacting any other financial decisions, especially among young households? Analysis released by the FRBNY using the same data series suggests a shift in homeownership among student borrowers age 30 and younger: “Prior to the most recent recession, homeownership rates were substantially higher for thirty-year-olds with a history of student debt than for those without. This pre-recession pattern is typically explained by the fact that student debt holders have higher levels of education on average, and hence, higher income potential. Simply put, these more educated, often higher-earning, consumers were more likely to buy homes by the age of thirty.  However, the recession brought a sudden reversal in this relationship. As house prices fell, homeownership rates declined for all types of borrowers, and declined most for those thirty-year-olds with histories of student loan debt. In last year’s blog, we reported that 2012 was the first time in at least ten years that thirty-year-olds with no history of student loans were actually more likely to have home-secured debt than those with a history of student loans … student loan holders were still less likely to invest in houses than nonholders in 2013, despite the marked improvements in the aggregate housing market.” (Emphasis added)       The data from FRBNY and Brookings Institute suggest that a crisis of young borrowers unable to pay off their student loans may not be a concern in the near future, but with over 40% of young adults (25 years old) carrying student debt , the rise in student debt over the past decade is likely preventing those households from contributing to a recovery in the housing market today. http://chmuraecon.com/blog/2014/july/02/growing-student-loan-debt-and-its-impact-on-housing/ http://chmuraecon.com/blog/2014/july/02/growing-student-loan-debt-and-its-impact-on-housing/ Wed, 02 July 2014 14:01:52 http://chmuraecon.com#err-1649 http://chmuraecon.com#err-1649 http://chmuraecon.com#err-1649 Mon, 02 June 2014 09:35:13 Chris Chmura http://chmuraecon.com/blog/2014/june/02/virginia-s-employment-growth-continues-to-lag/ Virginia’s employment growth continues to lag the nation and it should remain subpar at least through next year. The reason: federal spending cuts. Stephen Fuller, an economist at George Mason University, recently pointed to a potential 4.5 percent decline in federal spending across the nation during the fiscal year that will end Sept. 30 and a further 9.2 percent decrease in the following fiscal year. If Virginia businesses experience the same percentage decline in federal contract spending, Chmura Economics & Analytics estimates that could translate into a direct loss of 11,300 jobs in Virginia during calendar year 2014 and 21,600 jobs in 2015. Based on those forecasts that would mean employment would grow 0.5 percent in Virginia in 2014 and 0.7 percent in 2015 — much lower than the national growth rate of 1.6 percent forecasted for 2014 and 1.1 percent in 2015. The Richmond region should fare better. Employment in the area is forecast to grow at a faster rate of 1.4 percent in 2014 and 1.6 percent in 2015. In the Richmond metro area, only 1.1 percent of its employment is dependent on defense contract awards compared to 11.3 percent in Northern Virginia, according to a model created by Chmura Economics & Analytics ( www.chmuraecon.com/dodimpact ). Northern Virginia is bearing most of the brunt of the cuts in federal spending. Employment grew 0.1 percent during the 12 months that ended in April and the region is forecast to see a 0.3 percent growth for all of 2014 and 0.5 percent in 2015. Employment growth in the nation has been hovering around 1.7 percent on a year-over-year basis since January 2012 through April 2014, the latest data available. By contrast, employment growth in Virginia decelerated over the same period. It grew 1.3 percent for much of 2011 but fell 0.1 percent two months ago April 2014 compared with April 2013. The professional and business services sector, which is dependent on federal contracting, continues to show the largest losses with a contraction of 18,300 jobs during the 12-months that ended in April. To put that into context, Virginia expanded by 29,000 jobs in 2012 and 43,000 in 2013. But before the Great Recession, Virginia added an average 67,000 jobs a year from 2004 through 2007. http://chmuraecon.com/blog/2014/june/02/virginia-s-employment-growth-continues-to-lag/ http://chmuraecon.com/blog/2014/june/02/virginia-s-employment-growth-continues-to-lag/ Mon, 02 June 2014 09:35:13 http://chmuraecon.com#err-1650 http://chmuraecon.com#err-1650 http://chmuraecon.com#err-1650 Mon, 02 June 2014 08:57:20 Chris Chmura http://chmuraecon.com/blog/2014/may/27/upcoming-events-jun-4-strategic-diversification-in-the-wake-of-defense-cuts/ Event Date: June 4, 2014 Location: Crystal Gateway Marriott, 1700 Jefferson Davis Highway Arlington, VA 22202 From this session you will learn how to use data about your labor market for workforce and economic development planning.  The panel will discuss the types of defense impacts occurring in the Oshkosh region and how the local communities are organizing, responding, and positioning for strategic transformation by leverage their assets. From the education pipeline to the dynamic labor shed, learn how to think about, act upon, and plan effectively in the intersection of labor and industry diversification.  Moderator: Dr. Chris Chmura, President and Chief Economist, Chmura Economics & Analytics   Speakers: Katherine Ahlquist, Economic Development Planner, East Central Wisconsin Regional Planning Commission Leslie Peterson, Chief Operations Officer, Chmura Economics and Analytics  Conference Website: Association of Defense Communities http://chmuraecon.com/blog/2014/may/27/upcoming-events-jun-4-strategic-diversification-in-the-wake-of-defense-cuts/ http://chmuraecon.com/blog/2014/may/27/upcoming-events-jun-4-strategic-diversification-in-the-wake-of-defense-cuts/ Tue, 27 May 2014 16:59:16 http://chmuraecon.com#err-1601 http://chmuraecon.com#err-1601 http://chmuraecon.com#err-1601 Thu, 22 May 2014 15:21:15 http://chmuraecon.com/blog/2014/may/virginia-department-of-defense-dod-procurement-economic-impact-evaluation-model/ Dr. Christine Chmura, Chmura Economics & Analytics, and Dr. Stephen Fuller, George Mason University, demonstrated the Virginia Department of Defense (DoD) Procurement Economic Impact Evaluation Model to the Virginia Deputy Secretary of Veterans and Defense Affairs and other public officials on May 20, 2014.  The evaluation model is made up of a supply-chain mapping of DoD contract awards, including sales and employment impacts from product service codes to industries and occupations.  The model, available for public use at www.chmuraecon.com/DoDimpact , is designed to provide clear and meaningful state, county, and metropolitan statistical area (MSA) level details about the current and projected economic impacts of DoD contract spending. The Virginia DoD Procurement Economic Impact Evaluation Model was developed by Chmura Economics & Analytics under the direction of the George Mason University Center for Regional Analysis.   Community and economic development leaders can use insights from the model to: Focus economic development resources on at-risk industries and occupations. Direct business retention efforts to firms likely to be affected by changes in spending patterns. Provide valuable inputs to workforce organizations to identify or create programs to help unemployed workers. Tabulate impact data to support applications for federal or state assistance programs. To view the website, go to www.chmuraecon.com/DoDimpact . Please note you will be asked to provide your name, email address, and company affiliation (optional) the first time you access the site. http://chmuraecon.com/blog/2014/may/virginia-department-of-defense-dod-procurement-economic-impact-evaluation-model/ http://chmuraecon.com/blog/2014/may/virginia-department-of-defense-dod-procurement-economic-impact-evaluation-model/ Thu, 22 May 2014 15:21:15 Chris Chmura http://chmuraecon.com/blog/2014/may/14/the-reliance-of-the-california-economy-on-federal-contract-spending/ California, the largest state economy in the nation, benefits from billions of dollars of federal contract spending each year. Chmura Economics & Analytics estimates that 1.8% or 280,364 of California’s jobs were directly supported by federal contract spending in fiscal year (FY) 2013. Metropolitan areas such as San Diego and industries such as manufacturing and professional and business services have a higher level of dependency on federal contract spending. Leaders in those regions and industries need to be prepared for future reductions in the federal budget. Based on the latest data from the General Service Administration, total federal contract spending in California was $632.2 billion from FY 2000 to FY 2014—the largest amount for all states. 1 Among all federal agencies, the Department of Defense (DOD) plays an oversized role in federal contract spending in California—in FY 2013, DOD contract awards amounted to $34.0 billion or 71.4% of all federal contract spending in the state. State and community leaders of California need to understand the role of federal procurement spending in the state economy to prepare for future budget cuts. Since 2011, the threat of drastic cuts in the federal budget has surfaced several times, from the debt ceiling crisis in mid-2011 to the Budget Control Act of 2011, which gave rise to the “sequestration” cuts in 2013. The government shutdown in October 2013 further exacerbated the risk of budget uncertainty. To learn more about the role federal procurement spending plays in the state of California as well as its impact by region and industry sector, download our full white paper here: Whitepaper: The Reliance of the California Economy on Federal Contract Spending .   1 Federal government contract awards represent awards granted to the private sector. Here we treat the phrases “federal contract spending,” “federal procurement spending,” and “federal contract awards” as interchangeable. http://chmuraecon.com/blog/2014/may/14/the-reliance-of-the-california-economy-on-federal-contract-spending/ http://chmuraecon.com/blog/2014/may/14/the-reliance-of-the-california-economy-on-federal-contract-spending/ Wed, 14 May 2014 16:29:00 http://chmuraecon.com#err-1627 http://chmuraecon.com#err-1627 http://chmuraecon.com#err-1627 Wed, 14 May 2014 16:29:00 http://chmuraecon.com#err-1602 http://chmuraecon.com#err-1602 http://chmuraecon.com#err-1602 Thu, 08 May 2014 13:10:53 Chris Chmura http://chmuraecon.com/blog/2014/may/08/nvtc-big-data-symposium-may-9-2014/ Chris Chmura will be speaking at the NVTC Big Data Symposium NTSB Training Center The GWU Virginia Science and Technology Campus - 45065 Riverside Parkway, Ashburn, VA http://chmuraecon.com/blog/2014/may/08/nvtc-big-data-symposium-may-9-2014/ http://chmuraecon.com/blog/2014/may/08/nvtc-big-data-symposium-may-9-2014/ Thu, 08 May 2014 13:10:53 Chris Chmura http://chmuraecon.com/blog/2014/may/04/economic-impact-richmond-region-s-job-growth-is-better-than-state-and-nation/ The Metro Richmond’s economy was hit harder during the Great Recession than the state and nation, in part, because of its dependence on finance and insurance industries. Employment growth in Richmond is now outpacing all of the metropolitan areas in the state and is growing slightly faster than the nation on a year-over-year basis. The latest data for March shows Richmond employment is 1.8% above a year ago compared with 0.0% in Virginia and 1.7% in the nation. As of November 2013, employment in Richmond passed the peak employment level of 635,541 that was reached prior to the last recession.  Since then, it has exceeded that peak by 8,599 jobs. The nation will likely reach its previous peak this summer.  With Virginia job growth stalling mainly in Northern Virginia under the weight of Department of Defense cuts in spending; it may not reach its previous peak until 2015. Of the ten major industry sectors that economists classify firms into, the information sector grew the fastest in the Richmond metro area.  Employment is up 8.8% from a year ago, but it is a small sector of only 8,700 workers. That growth translates into an additional 700 jobs over the last year.  This sector includes firms that provide information such as newspapers, software publishers, sound recording studios, and satellite communications. According to the latest information from the fourth quarter of 2013, the average wages of this sector are $60,034.  That’s better than the average $46,962 for all industries in the metro area.  The higher the wages of new jobs, the more it creates a ripple effect in the economy.  That is because those workers have more money to spend on goods and services in the area. The retail sector is undoubtedly benefitting from the faster employment growth in Richmond.  In fact, it experienced the largest increase in jobs over the last twelve months ending with March by adding 3,900 jobs.  Health and education services sector followed with 2,900 additional jobs. And, with the third largest increase of 1,400 jobs in the last twelve months, the finance, insurance, and real estate sector continues to recover. The 11,600 gain in employment in Richmond over the last year occurred across most major industry sectors.  Such broad-based growth points to a healthy economy.  Clearly a lack of dependence on federal government spending is now favoring the RVA economy. Christine Chmura is president and chief economist at Chmura Economics & Analytics. She can be reached at (804) 649-3640 or receive e-mail at chris@chmuraecon.com . http://chmuraecon.com/blog/2014/may/04/economic-impact-richmond-region-s-job-growth-is-better-than-state-and-nation/ http://chmuraecon.com/blog/2014/may/04/economic-impact-richmond-region-s-job-growth-is-better-than-state-and-nation/ Sun, 04 May 2014 12:10:07 http://chmuraecon.com#err-1584 http://chmuraecon.com#err-1584 http://chmuraecon.com#err-1584 Mon, 07 April 2014 11:59:09 Chris Chmura http://chmuraecon.com/blog/2014/april/07/virginia-employment-growth-stuck-in-neutral/ This article was originally published in the Richmond TImes-Dispatch on April 2, 2014 . Employment in the nation is finally starting to pick up. Nonfarm payrolls rose by 192,000 in March, which is 1.7 percent higher than a year ago. The Richmond metropolitan area is seeing similar growth. Employment rose 1.5 percent over the year ending in February, which is the latest data available at the regional level. In contrast, employment growth in Virginia has stalled. Employment declined in seven of the past 12 months. In fact, Virginia’s employment in February 2014 was 3.767 million — slightly below the February 2013 level of 3.769 million. This is unusual. Over the past two decades, Virginia’s employment growth typically outpaced that of the nation. Even more unusual is the sector that is leading the decline. The professional and business services sector, which grew at a 2.2 percent annual average rate in the 10 years ending in 2013, contracted 3 percent in the year ending in February 2014. The Northern Virginia metro area, which has been the engine of growth in the commonwealth, appears to have run out of steam. Employment fell 0.2 percent for the year ending in February; and similar to the state, the professional and business services sector contracted 2.5 percent over the same period. Why is this happening? Chalk it up to cuts in defense spending. There is no industry sector called “defense” because businesses that receive defense contracts range from manufacturers to retailers and service providers. However, almost half of the defense contracts awarded to firms in Virginia during the past two fiscal years were for professional and business services. Defense contracts awarded to firms in Virginia more than doubled from $20 billion in fiscal year 2003 to a peak of $43.1 billion in fiscal year 2011. Over that period, Virginia’s economy became more dependent on defense contract awards as they made up 6.5 percent of gross state product in 2003 and rose to 9.9 percent of GSP in 2011. Contract awards to Virginia firms have since dropped to $33.5 billion in fiscal year 2013. It stands to reason that just as Virginia benefited from the surge in defense spending, the drawdown will now dampen its growth. http://chmuraecon.com/blog/2014/april/07/virginia-employment-growth-stuck-in-neutral/ http://chmuraecon.com/blog/2014/april/07/virginia-employment-growth-stuck-in-neutral/ Mon, 07 April 2014 11:59:09 http://chmuraecon.com/blog/2014/march/06/sleepbetter-lost-hour-economic-index/ These are the findings of Chmura Economics & Analytics in a study entitled “Estimating the Economic Loss of Daylight Saving Time for U.S. Metropolitan Statistical Areas” commissioned by the Carpenter Co. The study focused on only the aspects of economic losses where solid evidence from peer-reviewed academic journals could be obtained, showing how the DST change can lead to an increase in heart attacks, workplace injuries in the mining and construction sectors, and increased cyberloafing that reduces productivity for people who typically work in offices. A reasonable economic cost was then developed from the economic costs of heart attacks, workplace accidents and cyberloafing and applied to the more than 300 Metropolitan Statistical Areas (MSA) in the U.S. MSA Total Cost of Lost Hour Population in 2010 Index Rank Per Capita Cost % Difference From Nat'l Avg. MSA Rank National $433,982,548.00 260,392,304   $1.65449     Morgantown, WV $445,685.00 129,709 1 $3.37947 104.26% 293 Huntington-Ashland, WV-KY-OH $930,759.00 287,702 2 $3.18188 92.32% 162 Parkersburg-Marietta, WV-OH $519,472.00 162,056 3 $3.15273 90.56% 245 Charleston, WV $973,594.00 304,284 4 $3.14694 90.21% 156 Kingsport-Bristol-Bristol, TN-VA $925,487.00 309,544 5 $2.94061 77.74% 151 Lakeland, FL $1,582,213.00 602,095 6 $2.58458 56.22% 87 Tampa-St. Petersburg-Clearwater, FL $7,283,123.00 2,783,243 7 $2.57369 55.56% 18 Ocala, FL $863,182.00 331,298 8 $2.56256 54.89% 147 North Port-Bradenton-Sarasota, FL $1,822,027.00 702,281 9 $2.55172 54.23% 73 Punta Gorda, FL $404,984.00 159,978 10 $2.48982 50.49% 247 Scranton--Wilkes-Barre, PA $1,412,054.00 563,631 11 $2.46403 48.93% 90 Myrtle Beach-Conway-North Myrtle Beach, SC $662,576.00 269,291 12 $2.41994 46.27% 168 Pittsburgh, PA $5,794,723.00 2,356,285 13 $2.41877 46.19% 22 Palm Bay-Melbourne-Titusville, FL $1,336,302.00 543,376 14 $2.41877 46.19% 96 Evansville, IN-KY $873,111.00 358,676 15 $2.39418 44.71% 142 Tulsa, OK $2,277,053.00 937,478 16 $2.38892 44.39% 54 Sebastian-Vero Beach, FL $334,825.00 138,028 17 $2.38584 44.20% 283 Bloomington, IN $464,931.00 192,714 18 $2.37282 43.42% 218 Chattanooga, TN-GA $1,268,241.00 528,143 19 $2.36178 42.75% 98 Deltona-Daytona Beach-Ormond Beach, FL $1,187,424.00 494,593 20 $2.36128 42.72% 103 Bangor, ME $368,824.00 153,923 21 $2.35671 42.44% 259 Alexandria, LA $367,169.00 153,922 22 $2.34615 41.81% 257 Palm Coast, FL   $227,338.00 95,696 23 $2.33651 41.22% 347 Muncie, IN $279,430.00 117,671 24 $2.33557 41.17% 317 Crestview-Fort Walton Beach-Destin, FL $428,270.00 180,822 25 $2.32946 40.80% 224 Cape Coral-Fort Myers, FL $1,455,015.00 618,754 26 $2.31281 39.79% 84 Mobile, AL $967,710.00 412,992 27 $2.30459 39.29% 126 Jacksonville, FL $3,146,585.00 1,345,596 28 $2.29993 39.01% 40 Toledo, OH $1,523,209.00 651,429 29 $2.29976 39.00% 82 Lafayette, IN $471,375.00 201,789 30 $2.29752 38.87% 210 Canton-Massillon, OH $943,763.00 404,422 31 $2.29519 38.72% 128 Johnson City, TN $461,978.00 198,716 32 $2.28654 38.20% 214 Dayton, OH $1,952,542.00 841,502 33 $2.28210 37.93% 62 Knoxville, TN $1,617,373.00 698,030 34 $2.27890 37.74% 74 Pensacola-Ferry Pass-Brent, FL $1,037,154.00 448,991 35 $2.27193 37.32% 110 Terre Haute, IN $394,960.00 172,425 36 $2.25291 36.17% 233 Miami-Fort Lauderdale-Miami Beach, FL $12,725,469.00 5,564,635 37 $2.24919 35.95% 8 Port St. Lucie-Fort Pierce, FL $969,275.00 424,107 38 $2.24782 35.86% 117 State College, PA $351,910.00 153,990 39 $2.24765 35.85% 256 Asheville, NC $969,459.00 424,858 40 $2.24427 35.65% 116 Lewiston-Auburn, ME $243,661.00 107,702 41 $2.22512 34.49% 334 Lexington-Fayette, KY $1,066,009.00 472,099 42 $2.22084 34.23% 106 Jackson, TN $260,264.00 115,425 43 $2.21771 34.04% 321 Michigan City-La Porte, IN $251,036.00 111,467 44 $2.21503 33.88% 329 Cheyenne, WY $206,604.00 91,738 45 $2.21503 33.88% 354 Naples-Marco Island, FL $723,778.00 321,520 46 $2.21405 33.82% 149 Bowling Green, KY $282,675.00 125,953 47 $2.20733 33.42% 302 Kokomo, IN $221,462.00 98,688 48 $2.20711 33.40% 345 South Bend-Mishawaka, IN-MI $715,354.00 319,224 49 $2.20402 33.21% 150 Hickory-Lenoir-Morganton, NC $818,612.00 365,497 50 $2.20285 33.14% 141 Anderson, IN $294,790.00 131,636 51 $2.20256 33.13% 295 Atlantic City, NJ $612,213.00 274,549 52 $2.19317 32.56% 169 Columbus, IN $170,968.00 76,794 53 $2.18967 32.35% 362 Johnstown, PA $318,637.00 143,679 54 $2.18119 31.83% 275 Harrisburg-Carlisle, PA $1,215,731.00 549,475 55 $2.17610 31.53% 94 Lawton, OK $274,358.00 124,098 56 $2.17442 31.43% 307 Louisville, KY-IN $2,826,250.00 1,283,566 57 $2.16562 30.89% 42 Morristown, TN $300,143.00 136,608 58 $2.16094 30.61% 286 Wheeling, WV-OH $323,960.00 147,950 59 $2.15361 30.17% 268 Wichita Falls, TX $331,130.00 151,306 60 $2.15244 30.10% 266 Youngstown-Warren-Boardman, OH-PA $1,236,309.00 565,773 61 $2.14919 29.90% 91 Owensboro, KY $249,981.00 114,752 62 $2.14258 29.50% 322 Shreveport-Bossier City, LA $868,062.00 398,604 63 $2.14190 29.46% 129 Gainesville, FL $574,037.00 264,275 64 $2.13636 29.13% 173 Altoona, PA $275,914.00 127,089 65 $2.13528 29.06% 305 Williamsport, PA $251,895.00 116,111 66 $2.13372 28.97% 319 Panama City-Lynn Haven, FL $364,847.00 168,852 67 $2.12517 28.45% 236 Detroit-Warren-Livonia, MI $9,268,747.00 4,296,250 68 $2.12188 28.25% 13 Birmingham, AL $2,431,810.00 1,128,047 69 $2.12028 28.15% 50 Elizabethtown, KY $257,362.00 119,736 70 $2.11403 27.78% 313 Buffalo-Niagara Falls, NY $2,440,468.00 1,135,509 71 $2.11384 27.76% 49 Ocean City, NJ $207,878.00 97,265 72 $2.10205 27.05% 351 Fort Wayne, IN $888,743.00 416,257 73 $2.09993 26.92% 122 Steubenville-Weirton, OH-WV $265,544.00 124,454 74 $2.09854 26.84% 311 Huntsville, AL $890,913.00 417,593 75 $2.09832 26.83% 119 Erie, PA $598,252.00 280,566 76 $2.09720 26.76% 164 Montgomery, AL $798,346.00 374,536 77 $2.09646 26.71% 136 Gulfport-Biloxi, MS $528,298.00 248,820 78 $2.08825 26.22% 185 Carson City, NV $116,608.00 55,274 79 $2.07490 25.41% 366 Anniston, AL $250,003.00 118,572 80 $2.07373 25.34% 316 Mansfield, OH $261,914.00 124,475 81 $2.06951 25.08% 310 Sandusky, OH $161,894.00 77,079 82 $2.06578 24.86% 363 Elkhart-Goshen, IN $414,780.00 197,559 83 $2.06496 24.81% 216 Florence, AL $308,807.00 147,137 84 $2.06422 24.76% 206 Auburn-Opelika, AL $293,975.00 140,247 85 $2.06161 24.61% 276 Lafayette, LA $573,540.00 273,738 86 $2.06072 24.55% 167 Lima, OH $221,940.00 106,331 87 $2.05289 24.08% 336 Clarksville, TN-KY $571,732.00 273,949 88 $2.05264 24.07% 166 Lebanon, PA $278,565.00 133,568 89 $2.05123 23.98% 292 Reading, PA $857,038.00 411,442 90 $2.04871 23.83% 125 Gadsden, AL $217,236.00 104,430 91 $2.04596 23.66% 338 York-Hanover, PA $904,776.00 434,972 92 $2.04583 23.65% 113 Barnstable Town, MA $447,310.00 215,888 93 $2.03784 23.17% 198 Ann Arbor, MI $713,184.00 344,791 94 $2.03440 22.96% 146 Springfield, OH $285,322.00 138,333 95 $2.02862 22.61% 285 Wilmington, NC $747,299.00 362,315 96 $2.02861 22.61% 139 Dothan, AL $299,824.00 145,639 97 $2.02478 22.38% 270 Monroe, LA $362,816.00 176,441 98 $2.02244 22.24% 229 Hattiesburg, MS $293,083.00 142,842 99 $2.01802 21.97% 272 Lancaster, PA $1,064,016.00 519,445 100 $2.01465 21.77% 99 Decatur, AL $313,503.00 153,829 101 $2.00444 21.15% 258 Oklahoma City, OK $2,550,073.00 1,252,987 102 $2.00169 20.99% 43 Pascagoula, MS $330,101.00 162,246 103 $2.00107 20.95% 242 Hagerstown-Martinsburg, MD-WV $546,398.00 269,140 104 $1.99674 20.69% 172 Casper, WY $153,138.00 75,450 105 $1.99624 20.66% 364 Greenville, SC $1,289,377.00 636,986 106 $1.99086 20.33% 83 Greensboro-High Point, NC $1,464,797.00 723,801 107 $1.99044 20.31% 71 New Orleans-Metairie-Kenner, LA $2,360,696.00 1,167,764 108 $1.98827 20.17% 46 Las Vegas-Paradise, NV $3,941,238.00 1,951,269 109 $1.98658 20.07% 30 Indianapolis, IN $3,537,009.00 1,756,241 110 $1.98081 19.72% 35 Missoula, MT $219,657.00 109,299 111 $1.97660 19.47% 332 Ithaca, NY $203,399.00 101,564 112 $1.96970 19.05% 342 Dover, DE $324,756.00 162,310 113 $1.96789 18.94% 241 Cleveland-Elyria-Mentor, OH $4,151,800.00 2,077,240 114 $1.96580 18.82% 28 Allentown-Bethlehem-Easton, PA-NJ $1,614,902.00 821,173 115 $1.93420 16.91% 64 Prescott, AZ $414,603.00 211,033 116 $1.93229 16.79% 203 Providence-New Bedford-Fall River, RI-MA $3,141,889.00 1,600,852 117 $1.93032 16.67% 38 Worcester, MA $1,565,876.00 798,552 118 $1.92861 16.57% 67 Portland-South Portland-Biddeford, ME $1,005,605.00 514,098 119 $1.92385 16.28% 101 Billings, MT $308,857.00 158,050 120 $1.92200 16.17% 249 Springfield, MA $1,353,410.00 692,942 121 $1.92098 16.11% 77 Ames, IA $174,611.00 89,542 122 $1.91794 15.92% 356 Fayetteville, NC $713,043.00 366,383 123 $1.91413 15.69% 137 Fort Smith, AR-OK $580,963.00 298,592 124 $1.91364 15.66% 158 Houma-Bayou Cane-Thibodaux, LA $403,825.00 208,178 125 $1.90787 15.31% 205 Rapid City, SD $245,126.00 126,382 126 $1.90763 15.30% 300 Lewiston, ID-WA $118,048.00 60,888 127 $1.90686 15.25% 365 Lake Charles, LA $386,936.00 199,607 128 $1.90657 15.24% 213 Akron, OH $1,359,432.00 703,200 129 $1.90138 14.92% 75 Baton Rouge, LA $1,550,929.00 802,484 130 $1.90084 14.89% 66 Jackson, MS $1,040,664.00 539,057 131 $1.89874 14.76% 95 Cleveland, TN $223,348.00 115,788 132 $1.89718 14.67% 318 Iowa City, IA $293,659.00 152,586 133 $1.89286 14.41% 255 Richmond, VA $2,419,992.00 1,258,251 134 $1.89163 14.33% 44 Baltimore-Towson, MD $5,209,507.00 2,710,489 135 $1.89034 14.26% 20 Blacksburg, VA $312,735.00 162,958 136 $1.88752 14.08% 244 Orlando, FL $4,092,620.00 2,134,411 137 $1.88588 13.99% 26 Great Falls, MT $155,815.00 81,327 138 $1.88437 13.89% 359 Charlottesville, VA $384,484.00 201,559 139 $1.87614 13.40% 208 Farmington, NM $247,716.00 130,044 140 $1.87350 13.24% 301 Winston-Salem, NC $909,101.00 477,717 141 $1.87168 13.13% 105 Greenville, NC $359,766.00 189,510 142 $1.86714 12.85% 220 Pittsfield, MA $248,995.00 131,219 143 $1.86631 12.80% 296 Sioux City, IA-NE-SD $271,947.00 143,577 144 $1.86290 12.60% 273 Reno-Sparks, NV $805,515.00 425,417 145 $1.86230 12.56% 115 Columbia, MO $327,147.00 172,786 146 $1.86219 12.55% 232 Saginaw-Saginaw Township North, MI $378,070.00 200,169 147 $1.85766 12.28% 215 Davenport-Moline-Rock Island, IA-IL $716,738.00 379,690 148 $1.85661 12.22% 134 Kalamazoo-Portage, MI $614,919.00 326,589 149 $1.85185 11.93% 148 St. Louis, MO-IL $5,295,893.00 2,812,896 150 $1.85172 11.92% 19 Jackson, MI $300,340.00 160,248 151 $1.84336 11.42% 251 Columbus, OH $3,441,849.00 1,836,536 152 $1.84324 11.41% 32 Coeur d'Alene, ID $259,303.00 138,494 153 $1.84148 11.30% 280 Cincinnati, OH-KY-IN $3,984,972.00 2,130,151 154 $1.83994 11.21% 27 Battle Creek, MI $254,031.00 136,146 155 $1.83515 10.92% 288 Bay City, MI $201,047.00 107,771 156 $1.83479 10.90% 335 Niles-Benton Harbor, MI $292,506.00 156,813 157 $1.83460 10.89% 254 Albany-Schenectady-Troy, NY $1,621,964.00 870,716 158 $1.83212 10.74% 59 Amarillo, TX $462,864.00 249,881 159 $1.82184 10.11% 184 Roanoke, VA $570,732.00 308,707 160 $1.81834 9.90% 153 Harrisonburg, VA $230,573.00 125,228 161 $1.81091 9.45% 306 Burlington, NC $277,822.00 151,131 162 $1.80802 9.28% 261 Trenton-Ewing, NJ $673,675.00 366,513 163 $1.80780 9.27% 140 Jefferson City, MO $275,316.00 149,807 164 $1.80754 9.25% 265 Waterloo-Cedar Falls, IA $307,311.00 167,819 165 $1.80106 8.86% 237 Vineland-Millville-Bridgeton, NJ $287,125.00 156,898 166 $1.79988 8.79% 253 Hot Springs, AR $175,556.00 96,024 167 $1.79815 8.68% 349 Cumberland, MD-WV $188,630.00 103,299 168 $1.79599 8.55% 339 Binghamton, NY $458,868.00 251,725 169 $1.79288 8.36% 186 Augusta-Richmond County, GA-SC $1,015,110.00 556,877 170 $1.79285 8.36% 92 Dubuque, IA $170,614.00 93,653 171 $1.79177 8.30% 353 Rocky Mount, NC $277,596.00 152,392 172 $1.79160 8.29% 263 Flint, MI $775,183.00 425,790 173 $1.79060 8.23% 120 Springfield, IL $382,338.00 210,170 174 $1.78923 8.14% 204 Lynchburg, VA $459,487.00 252,634 175 $1.78884 8.12% 183 Glens Falls, NY $234,236.00 128,923 176 $1.78696 8.01% 299 Utica-Rome, NY $543,419.00 299,397 177 $1.78516 7.90% 160 Nashville-Davidson--Murfreesboro, TN $2,880,135.00 1,589,934 178 $1.78166 7.69% 37 Muskegon-Norton Shores, MI $311,558.00 172,188 179 $1.77962 7.56% 234 Danville, VA $192,681.00 106,561 180 $1.77840 7.49% 337 Goldsboro, NC $221,405.00 122,623 181 $1.77585 7.34% 309 Florence, SC $370,844.00 205,566 182 $1.77431 7.24% 267 Kingston, NY $329,107.00 182,493 183 $1.77370 7.21% 226 Monroe, MI $273,894.00 152,021 184 $1.77202 7.10% 264 Lansing-East Lansing, MI $833,169.00 464,036 185 $1.76592 6.74% 109 Holland-Grand Haven, MI $473,601.00 263,801 186 $1.76574 6.72% 174 Pine Bluff, AR $179,721.00 100,258 187 $1.76307 6.56% 344 Texarkana, TX-Texarkana, AR $243,750.00 136,027 188 $1.76242 6.52% 287 Springfield, MO $782,413.00 436,712 189 $1.76210 6.50% 112 Salisbury, MD $224,297.00 125,203 190 $1.76197 6.50% 308 Spartanburg, SC $509,124.00 284,307 191 $1.76127 6.45% 163 College Station-Bryan, TX $408,296.00 228,660 192 $1.75620 6.15% 193 St. Joseph, MO-KS $227,058.00 127,329 193 $1.75388 6.01% 303 Syracuse, NY $1,180,655.00 662,577 194 $1.75257 5.93% 80 Rochester, NY $1,877,514.00 1,054,323 195 $1.75146 5.86% 51 Elmira, NY $157,948.00 88,830 196 $1.74881 5.70% 357 Memphis, TN-MS-AR $2,337,491.00 1,316,100 197 $1.74683 5.58% 41 Winchester, VA-WV $227,756.00 128,472 198 $1.74361 5.39% 298 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $10,561,043.00 5,965,343 199 $1.74125 5.24% 6 Anderson, SC $331,261.00 187,126 200 $1.74111 5.24% 222 Jacksonville, NC $314,639.00 177,772 201 $1.74076 5.21% 227 Jonesboro, AR $213,245.00 121,026 202 $1.73297 4.74% 312 Sumter, SC $188,886.00 107,456 203 $1.72886 4.49% 333 Bloomington-Normal, IL $297,450.00 169,572 204 $1.72524 4.28% 235 Bismarck, ND $190,424.00 108,779 205 $1.72174 4.06% 330 Champaign-Urbana, IL $404,833.00 231,891 206 $1.71704 3.78% 192 Bremerton-Silverdale, WA $438,118.00 251,133 207 $1.71584 3.71% 182 Lawrence, KS $192,383.00 110,826 208 $1.70732 3.19% 328 San Antonio, TX $3,716,198.00 2,142,508 209 $1.70595 3.11% 24 Des Moines, IA $986,927.00 569,633 210 $1.70404 3.00% 88 Spokane, WA $815,061.00 471,221 211 $1.70120 2.82% 108 Tyler, TX $362,634.00 209,714 212 $1.70071 2.79% 200 Athens-Clarke County, GA $332,237.00 192,541 213 $1.69713 2.58% 219 Wichita, KS $1,073,694.00 623,061 214 $1.69488 2.44% 86 Poughkeepsie-Newburgh-Middletown, NY $1,154,051.00 670,301 215 $1.69334 2.35% 79 Joplin, MO $301,371.00 175,518 216 $1.68877 2.07% 231 Abilene, TX $282,987.00 165,252 217 $1.68426 1.80% 240 Boston-Cambridge-Quincy, MA-NH $7,793,647.00 4,552,402 218 $1.68380 1.77% 10 San Angelo, TX $191,352.00 111,823 219 $1.68303 1.73% 325 Topeka, KS $400,150.00 233,870 220 $1.68282 1.71% 189 Pocatello, ID $155,071.00 90,656 221 $1.68238 1.69% 355 Madison, WI $971,417.00 568,593 222 $1.68033 1.56% 89 Virginia Beach-Norfolk-Newport News, VA-NC $2,848,285.00 1,671,683 223 $1.67579 1.29% 36 Lubbock, TX $483,463.00 284,890 224 $1.66908 0.88% 161 Tuscaloosa, AL $371,157.00 219,461 225 $1.66337 0.54% 195 Sherman-Denison, TX $203,597.00 120,877 226 $1.65660 0.13% 314 La Crosse, WI-MN $224,725.00 133,665 227 $1.65357 -0.06% 290 Grand Forks, ND-MN $165,417.00 98,461 228 $1.65236 -0.13% 346 Waco, TX $394,549.00 234,906 229 $1.65195 -0.15% 188 Longview, TX $359,095.00 214,369 230 $1.64754 -0.42% 197 Beaumont-Port Arthur, TX $650,601.00 388,745 231 $1.64604 -0.51% 132 Cape Girardeau-Jackson, MO-IL   $160,928.00 96,275 232 $1.64403 -0.63% 350 Kansas City, MO-KS $3,396,584.00 2,035,334 233 $1.64133 -0.80% 29 Fayetteville-Springdale-Rogers, AR-MO $772,340.00 463,204 234 $1.63993 -0.88% 107 Peoria, IL $631,004.00 379,186 235 $1.63670 -1.07% 135 Corpus Christi, TX $710,406.00 428,185 236 $1.63179 -1.37% 114 Sioux Falls, SD $378,499.00 228,261 237 $1.63088 -1.43% 191 Decatur, IL $183,380.00 110,768 238 $1.62828 -1.58% 331 Manchester-Nashua, NH $663,068.00 400,721 239 $1.62744 -1.63% 130 Boise City-Nampa, ID $1,017,199.00 616,561 240 $1.62263 -1.93% 85 Chicago-Naperville-Joliet, IL-IN-WI $15,568,610.00 9,461,105 241 $1.61844 -2.18% 3 Eau Claire, WI $265,021.00 161,151 242 $1.61748 -2.24% 243 Oshkosh-Neenah, WI $274,532.00 166,994 243 $1.61690 -2.27% 238 Savannah, GA $570,207.00 347,611 244 $1.61335 -2.49% 143 Little Rock-North Little Rock, AR $1,147,493.00 699,757 245 $1.61285 -2.52% 72 Rome, GA $157,878.00 96,317 246 $1.61216 -2.56% 352 Burlington-South Burlington, VT $346,073.00 211,261 247 $1.61116 -2.62% 201 Warner Robins, GA $229,125.00 139,900 248 $1.61081 -2.64% 274 Victoria, TX $188,788.00 115,384 249 $1.60923 -2.74% 320 Macon, GA $378,813.00 232,293 250 $1.60391 -3.06% 190 Columbus, GA-AL $480,773.00 294,865 251 $1.60364 -3.07% 157 Midland, TX $221,613.00 136,872 252 $1.59247 -3.75% 281 Charlotte-Gastonia-Concord, NC-SC $2,845,880.00 1,758,038 253 $1.59213 -3.77% 33 Valdosta, GA $225,264.00 139,588 254 $1.58721 -4.07% 278 Kankakee-Bradley, IL $182,897.00 113,449 255 $1.58561 -4.16% 324 Brunswick, GA $181,130.00 112,370 256 $1.58537 -4.18% 326 Milwaukee-Waukesha-West Allis, WI $2,506,228.00 1,555,908 257 $1.58426 -4.24% 39 Danville, IL $131,406.00 81,625 258 $1.58337 -4.30% 360 Albany, GA $253,037.00 157,308 259 $1.58206 -4.38% 252 Logan, UT-ID $201,329.00 125,442 260 $1.57853 -4.59% 304 New Haven-Milford, CT $1,384,050.00 862,477 261 $1.57832 -4.60% 60 Killeen-Temple-Fort Hood, TX $649,274.00 405,300 262 $1.57558 -4.77% 127 Green Bay, WI $490,506.00 306,241 263 $1.57533 -4.78% 152 Rockford, IL $559,542.00 349,431 264 $1.57493 -4.81% 145 Fond du Lac, WI $161,165.00 101,633 265 $1.55964 -5.73% 341 Manhattan, KS   $201,274.00 127,081 266 $1.55775 -5.85% 297 Cedar Rapids, IA $408,023.00 257,940 267 $1.55581 -5.96% 177 Odessa, TX $216,475.00 137,130 268 $1.55262 -6.16% 282 Albuquerque, NM $1,399,881.00 887,077 269 $1.55210 -6.19% 57 Wenatchee, WA $174,873.00 110,884 270 $1.55112 -6.25% 327 New York-Northern New Jersey-Long Island,NY-NJ-PA $29,682,674.00 18,897,109 271 $1.54489 -6.62% 1 Sheboygan, WI $181,234.00 115,507 272 $1.54320 -6.73% 323 Norwich-New London, CT $429,665.00 274,055 273 $1.54199 -6.80% 170 Wausau, WI $210,056.00 134,063 274 $1.54105 -6.86% 291 Gainesville, GA $280,222.00 179,684 275 $1.53385 -7.29% 225 Appleton, WI $350,752.00 225,666 276 $1.52871 -7.60% 194 Hartford-West Hartford-East Hartford, CT $1,877,815.00 1,212,381 277 $1.52336 -7.93% 45 Omaha-Council Bluffs, NE-IA $1,333,208.00 865,350 278 $1.51529 -8.41% 58 Racine, WI $301,037.00 195,408 279 $1.51519 -8.42% 217 Janesville, WI $246,458.00 160,331 280 $1.51187 -8.62% 250 Las Cruces, NM $321,250.00 209,233 281 $1.51009 -8.73% 199 Mankato-North Mankato, MN $147,728.00 96,740 282 $1.50192 -9.22% 348 Grand Rapids-Wyoming, MI $1,178,645.00 774,160 283 $1.49741 -9.49% 69 McAllen-Edinburg-Pharr, TX $1,179,244.00 774,769 284 $1.49700 -9.52% 68 Duluth, MN-WI $425,512.00 279,771 285 $1.49589 -9.59% 165 Yakima, WA $369,819.00 243,231 286 $1.49541 -9.61% 187 Mount Vernon-Anacortes, WA $177,569.00 116,901 287 $1.49396 -9.70% 315 Dalton, GA $215,804.00 142,227 288 $1.49234 -9.80% 277 Los Angeles-Long Beach-Santa Ana, CA $19,390,317.00 12,828,837 289 $1.48658 -10.15% 2 Brownsville-Harlingen, TX $612,204.00 406,220 290 $1.48226 -10.41% 124 Fairbanks, AK $147,010.00 97,581 291 $1.48174 -10.44% 343 Riverside-San Bernardino-Ontario, CA $6,352,361.00 4,224,851 292 $1.47881 -10.62% 12 Longview, WA $153,768.00 102,410 293 $1.47677 -10.74% 340 Bellingham, WA $301,501.00 201,140 294 $1.47428 -10.89% 209 Rochester, MN $278,633.00 186,011 295 $1.47328 -10.95% 223 Columbia, SC $1,148,180.00 767,598 296 $1.47118 -11.08% 70 El Paso, TX $1,192,338.00 800,647 297 $1.46470 -11.47% 65 Minneapolis-St. Paul-Bloomington, MN-WI $4,842,583.00 3,279,833 298 $1.45216 -12.23% 16 Hinesville-Fort Stewart, GA $114,891.00 77,917 299 $1.45025 -12.34% 361 Raleigh-Cary, NC $1,663,591.00 1,130,490 300 $1.44734 -12.52% 47 St. Cloud, MN $277,151.00 189,093 301 $1.44155 -12.87% 221 Laredo, TX $366,846.00 250,304 302 $1.44147 -12.88% 181 San Luis Obispo-Paso Robles, CA $393,949.00 269,637 303 $1.43698 -13.15% 171 Sacramento--Arden-Arcade--Roseville, CA $3,125,517.00 2,149,127 304 $1.43037 -13.55% 25 Hanford-Corcoran, CA $221,963.00 152,982 305 $1.42702 -13.75% 260 Houston-Baytown-Sugar Land, TX $8,591,542.00 5,946,800 306 $1.42095 -14.12% 5 Lincoln, NE $435,900.00 302,157 307 $1.41888 -14.24% 154 Charleston-North Charleston, SC $958,180.00 664,607 308 $1.41799 -14.29% 78 Olympia, WA $361,151.00 252,264 309 $1.40807 -14.89% 180 Corvallis, OR $122,493.00 85,579 310 $1.40778 -14.91% 358 Fargo, ND-MN $298,526.00 208,777 311 $1.40634 -15.00% 202 Atlanta-Sandy Springs-Marietta, GA $7,462,139.00 5,268,860 312 $1.39295 -15.81% 9 Santa Cruz-Watsonville, CA $371,038.00 262,382 313 $1.39083 -15.94% 175 Chico, CA $310,836.00 220,000 314 $1.38963 -16.01% 196 Eugene-Springfield, OR $496,260.00 351,715 315 $1.38774 -16.12% 144 Santa Barbara-Santa Maria-Goleta, CA $595,677.00 423,895 316 $1.38211 -16.46% 118 Redding, CA $249,004.00 177,223 317 $1.38190 -16.48% 228 Napa, CA $191,518.00 136,484 318 $1.38012 -16.58% 284 Santa Rosa-Petaluma, CA $677,960.00 483,878 319 $1.37803 -16.71% 104 Colorado Springs, CO $897,143.00 645,613 320 $1.36672 -17.39% 81 San Diego-Carlsbad-San Marcos, CA $4,279,163.00 3,095,313 321 $1.35970 -17.82% 17 Idaho Falls, ID $177,313.00 130,374 322 $1.33764 -19.15% 294 San Francisco-Oakland-Fremont, CA $5,859,991.00 4,335,391 323 $1.32941 -19.65% 11 Ogden-Clearfield, UT $737,490.00 547,184 324 $1.32560 -19.88% 93 San Jose-Sunnyvale-Santa Clara, CA $2,472,499.00 1,836,911 325 $1.32385 -19.98% 31 Vallejo-Fairfield, CA $554,586.00 413,344 326 $1.31961 -20.24% 123 Oxnard-Thousand Oaks-Ventura, CA $1,102,236.00 823,318 327 $1.31673 -20.41% 63 Denver-Aurora, CO $3,395,406.00 2,543,482 328 $1.31296 -20.64% 21 Medford, OR $268,465.00 203,206 329 $1.29939 -21.46% 207 Durham, NC $666,098.00 504,357 330 $1.29894 -21.49% 102 Salinas, CA $544,618.00 415,057 331 $1.29055 -22.00% 121 Dallas-Fort Worth-Arlington, TX $8,355,402.00 6,371,773 332 $1.28972 -22.05% 4 Anchorage, AK $499,298.00 380,821 333 $1.28952 -22.06% 133 Austin-Round Rock, TX $2,235,782.00 1,716,289 334 $1.28124 -22.56% 34 Bend, OR $205,348.00 157,733 335 $1.28044 -22.61% 248 Kennewick-Richland-Pasco, WA $325,878.00 253,340 336 $1.26515 -23.53% 176 Portland-Vancouver-Beaverton, OR-WA $2,855,767.00 2,226,009 337 $1.26179 -23.74% 23 Salem, OR $500,670.00 390,738 338 $1.26025 -23.83% 131 Madera, CA $191,973.00 150,865 339 $1.25153 -24.36% 262 Boulder, CO $374,413.00 294,567 340 $1.25013 -24.44% 159 Modesto, CA $653,370.00 514,453 341 $1.24912 -24.50% 100 Grand Junction, CO $186,143.00 146,723 342 $1.24778 -24.58% 269 Fresno, CA $1,179,885.00 930,450 343 $1.24720 -24.62% 55 Pueblo, CO $200,950.00 159,063 344 $1.24254 -24.90% 246 El Centro, CA $220,389.00 174,528 345 $1.24198 -24.93% 230 Yuba City, CA $210,683.00 166,892 346 $1.24160 -24.96% 239 Stockton, CA $862,343.00 685,306 347 $1.23761 -25.20% 76 Bakersfield, CA $1,051,318.00 839,631 348 $1.23150 -25.57% 61 Washington-Arlington-Alexandria, DC-VA-MD-WV $6,970,911.00 5,582,170 349 $1.22822 -25.76% 7 Salt Lake City, UT $1,401,163.00 1,124,197 350 $1.22585 -25.91% 48 Greeley, CO $314,296.00 252,825 351 $1.22267 -26.10% 179 Tallahassee, FL $453,660.00 367,413 352 $1.21441 -26.60% 138 Seattle-Tacoma-Bellevue, WA $4,226,240.00 3,439,809 353 $1.20840 -26.96% 15 Merced, CA $308,988.00 255,793 354 $1.18807 -28.19% 178 Bridgeport-Stamford-Norwalk, CT $1,105,338.00 916,829 355 $1.18576 -28.33% 56 Visalia-Porterville, CA $531,838.00 442,179 356 $1.18296 -28.50% 111 St. George, UT $165,901.00 138,115 357 $1.18140 -28.59% 279 Santa Fe, NM $164,210.00 144,170 358 $1.12025 -32.29% 271 Fort Collins-Loveland, CO $340,812.00 299,630 359 $1.11871 -32.38% 155 Provo-Orem, UT $517,472.00 526,810 360 $0.96610 -41.61% 97 http://chmuraecon.com/blog/2014/march/06/sleepbetter-lost-hour-economic-index/ http://chmuraecon.com/blog/2014/march/06/sleepbetter-lost-hour-economic-index/ Thu, 06 March 2014 12:54:51 http://chmuraecon.com#err-1565 http://chmuraecon.com#err-1565 http://chmuraecon.com#err-1565 Tue, 04 March 2014 13:41:15 Chris Chmura http://chmuraecon.com/blog/2014/march/04/economic-impact-jobless-numbers-are-key-in-the-economy/ The jobless rate is falling, and that’s good news. But it is declining for the wrong reasons, and that’s not good for a variety of reasons. The rate has fallen significantly since the recession ended. It stood at 6.6 percent in January. The government will release the February figures Friday. The Federal Open Market Committee set a 6.5 percent jobless rate as its target to start raising the federal funds rate. But recently, the Fed said it also will consider “additional measures of labor market conditions” before raising the funds rate. The committee is downplaying the unemployment rate because it is falling for the wrong reasons. The jobless rate is declining, in part, because people who can’t find jobs have stopped looking for work. These “discouraged workers” are no longer counted in the official unemployment rate. An alternative measure of unemployment would count people currently not looking for work but would take a job if offered and they have looked for a job in the past 12 months. The alternative measure also would take into account those who are working part time but would prefer full-time work. Add those into the definition, and unemployment soared to 12.7 percent in January. The participation rate, which has been falling nationally, also points toward an increase in discouraged workers. The percentage of people 16 years and older who are working fell from 66 percent when the recession started in December 2007 to 63 percent in January — a level not seen since 1978. In fact, if people were not discouraged and the participation rate still stood at 66 percent today, the official unemployment rate would be 10.9 percent. Economists debate whether the downward trend in the participation rate is a lingering result of the recession that might change when growth picks up. Besides, there has been a larger percentage point drop in the participation rate among the relatively young population rather than the baby boomers. The drop in participation among people ages 15 to 24 appears to be happening, in part, because a larger percentage of that age group is staying in school than occurred before the recession. Furthermore, more than 3.6 million people have been unemployed for 27 weeks or more – another statistic that points to weakness in the labor market. That figure represented 52.3 percent of the unemployment in January compared with 17.4 percent before the recession started. Two groups that remain especially hard hit are those 16- to 19-year olds where the jobless rate is 20.7 percent. The Fed’s shift away from the unemployment rate as a target for lifting its accommodative policy reflects what the man on the street still feels: The economy is not growing fast enough to generate strong job growth. http://chmuraecon.com/blog/2014/march/04/economic-impact-jobless-numbers-are-key-in-the-economy/ http://chmuraecon.com/blog/2014/march/04/economic-impact-jobless-numbers-are-key-in-the-economy/ Tue, 04 March 2014 13:41:15 Greg Chmura http://chmuraecon.com/blog/2014/january/27/recent-industry-reclassifications-have-major-impact-on-analysis-in-health-care-finance-sectors/ In the first quarter of 2013, many establishments that provide home care for the elderly were reclassified from NAICS 814110 (private households) to 624120 (services for the elderly). This reclassification, while appropriate according to the BLS, may cause problems for anyone analyzing the health care industry, especially in the regions that were most affected. Nationwide, this reclassification greatly contributed to the large jump in health care and social assistance (NAICS 62) employment over the quarter, from a post-recession average year-over-year growth rate of 1.7% to 4.3% growth in the first quarter of 2013. The effect is even more pronounced in sub-industries of health care, as illustrated in the table below.   Industry Employment growth 2012 Q4 Employment growth 2013 Q1 Health Care and Social Assistance (NAICS 62) 1.8% 4.3% Social Assistance (NAICS 624) 2.4% 19.6% Individual and Family Services (NAICS 6241) 4.6% 36.8% Individual states most affected by this reclassification are California, Massachusetts, Missouri, Nebraska, and Washington, with California and Washington as the top two. California recorded 21.4% growth in the health care and social assistance sector; Washington recorded 11.5% growth. The map below illustrates the regions with the largest percentage change in employment in the health care sector for the first quarter of 2013. This change also has a corresponding deflationary effect in the “other services” sector (NAICS 81). Nationally, year-over-year employment growth in that sector went from a positive 3.4% in the fourth quarter of 2012 to a negative 8.5% in the first quarter of 2013. There is a similar magnification of the effect in sub-industries of other services as well. Again, California and Washington experienced the greatest percentage change in this sector, with Massachusetts, Missouri, and Nebraska rounding out the top five. California recorded 41.4% loss and Washington 35.3% loss. Industry Employment growth 2012 Q4 Employment growth 2013 Q1 Other Services (except Public Administration) (NAICS 81) 3.4% -8.5% Private Households (NAICS 814 & 8141) 15.9% -61.7% The BLS published a note on their Business Employment Dynamics (BED) website regarding the change: First quarter 2013 data were affected by an administrative change to the count of establishments in the education and health services industry. A review of the administrative data from which the BED data are derived revealed that certain establishments that provide non-medical, home-based services for the elderly and persons with disabilities had been misclassified in the private households industry (NAICS 814110)…. These establishments are now…classified in services for the elderly and persons with disabilities (NAICS 624120.) This non-economic industry code change artificially inflates the data for gross job gains, openings, births, and the net employment change for the following data series: national total private, state total private, the education and health services sector, and firm size class. The note makes it clear that the change is a result of a misclassification in prior periods, rather than a fundamental change in the industry. That does not diminish the inflationary effect for the first quarter of 2013, however, and this effect is important to take into consideration when performing any analysis on employment in the health care or other services industries. Another important change in the first quarter of 2013 is that to the funds, trusts, and other financial vehicles sub-sector (NAICS 525). This is a sector that should typically have little to no employment, according to the census definition : … These entities earn interest, dividends, and other property income, but have little or no employment and no revenue from the sale of services. The adjustment that occurred in this period reassigned many employees from these types of funds to other, more appropriate industries in the finance sector. Typically, employees who manage financial vehicles of these types are recorded as part of the other financial investment activities industry group (NAICS 5239). Much of the employment loss from the funds, trusts, and other financial vehicles sub-industry was moved to that industry in this quarter, but some also went to other industries in the finance sector (NAICS 52), depending on firm and region. The nationwide adjustment in the first quarter of 2013 caused a reduction in employment in the funds, trusts, and other financial vehicles sub-industry in the majority of regions in the United States. The reduction varies, however, depending on the level of employment prior to the adjustment. Connecticut, Nevada, and Virginia all recorded more than 98% employment loss in this sub-industry. In Virginia, 88% of employment affected by this change moved from other insurance funds (NAICS 525190) to direct property and casualty insurance carriers (NAICS 524126). Other states experienced other movements, depending on the employment landscape prior to the reclassification. See the map below for more information on how individual states were affected. Again, this is an adjustment in reporting practice, not a fundamental change in the classification of the industry. However, as with the changes to the health care and other services sectors, the deflationary effect of reclassifying a large percentage of employees of the sub-sector must be taken into consideration when performing any analysis of the finance sector. http://chmuraecon.com/blog/2014/january/27/recent-industry-reclassifications-have-major-impact-on-analysis-in-health-care-finance-sectors/ http://chmuraecon.com/blog/2014/january/27/recent-industry-reclassifications-have-major-impact-on-analysis-in-health-care-finance-sectors/ Mon, 27 January 2014 15:21:52 Chmura Staff http://chmuraecon.com/blog/2013/october/24/improving-unemployment-claims-data-masks-decades-high-variance-between-the-states/ Over the past several months the labor market has been sending out some mixed signals in terms of its relative strength. For instance, initial unemployment claims data—typically a reasonable signal of the overall labor market—has been trending downward and is not far off from a new 35-year low. Once adjusted for the size of the current population, the September figure of the seasonally adjusted 4-week moving average of 305,000 initial claims would “traditionally” be associated with a very strong labor market. In October the number of claims have edged upwards, but still remain fairly low. Recently several economists & bloggers— Marginal Revolution ,  The Money Allusion ,  Calculated Risk , and  others —have noted the disconnect between the recent unemployment claim numbers and the rest of the labor market—job creation, the unemployment rate, and wage growth.  Some  have taken it as a sign that the labor market is not really as weak as the headline unemployment rate suggests, while others see it as a symptom of the decline of manufacturing, which frequently relies on mass layoffs to adjust output. At its lowest in early 2000 there were 0.94 claims per 1,000 people in the country. The only other periods to approach this—1989 and in 2006—the same metric was 1.16 and .96 respectively. As of the end of September, the current rate was .96 claims per 1,000 citizens—given a U.S. population estimate of 316.4 million in 2013. However, masked in these national numbers is the marked difference in claims data between the states. Similar to our analysis that showed great disparity in job  gains by state , the claims data shows a very different picture when examined at the state level. While some states are at or near record lows in terms of the level of unemployment claims per 1,000 residents, other states are still well above the number of monthly unemployment claims that would signal a return to a healthy labor market. In contrast to the past two recoveries where a vast majority of states had also seen their claim data nadir in close correlation with the national job market, this recovery has seen several states lag well behind the leaders in terms of layoff declines. One simple measure is to see how the average state was doing in terms of its claims per 1,000 residents when the national economy experienced a new low in initial claims. The elevated figure for this simple average among the 50 states and the District of Columbia in 2013 also features a higher weighted variance for these claims per 1,000 people currently among the states as compared to the corresponding periods in 2000 and 2006.   2000 2006 2013 National Low in 52-Week Moving Average of Initial Claims per 1,000 People 0.94 0.96 0.96 Average of All States 52-Week Moving Average of Initial Claims per 1,000 People (same week as national low) 1.01 1.04 1.11 Source: Federal Reserve Bank of St. Louis and Chmura Economics & Analytics Another way to examine this is look at the 52-week moving average of claims by state to see in each of these periods—1989, 2000, 2006, and 2013—how close each state was to its lowest ever ratio of claims per 1,000 residents. For instance in 2000 and 2006, on average when the nation claims rate bottomed out in terms of initial claims the average state was within 14 and 17 percentage points respectively above its historic low of number of claims per 1,000 residents. In contrast in 2013, as the nation approaches its historic low for initial claims per 1,000 residents, on average the states are 26 percentage points above their previous historic low. Essentially many states approached their historic lows in 2000 and in 2006, but much fewer states are close to their historic lows currently despite the healthy national numbers in terms of initial claims. Another way to think about it would be to posit that the economic expansions in the late 1990s and mid 2000s were rising tides that spread business activity relatively broadly across most states and MSAs. Whereas today’s expansion is a more of story of states that have and have not, where several states are beginning to  forge new records  for employment and many other states lag significantly behind in terms of their labor market. While it is possible that the average of initial claims per 1,000 will narrow across many more states as the nation actually reaches new 35-year low of claims per 1,000 citizens, it is unlikely that the states which have the highest current claims rate will make enough progress in the next several months to fundamentally change the shape of this distribution. This would require large drop of in claims in Alaska, Wisconsin, Pennsylvania, Oregon, and New Jersey all of which have more been averaging more than 1.5 claims per 1,000 residents for the past year. Furthermore, we find little preliminary evidence lately that the level of initial unemployment claims are associated with the size of the manufacturing sector—as gauged by the location quotient, but we will explore this relationship more fully in subsequent blogs. http://chmuraecon.com/blog/2013/october/24/improving-unemployment-claims-data-masks-decades-high-variance-between-the-states/ http://chmuraecon.com/blog/2013/october/24/improving-unemployment-claims-data-masks-decades-high-variance-between-the-states/ Thu, 24 October 2013 11:18:35 http://chmuraecon.com/blog/2013/october/11/regional-effects-of-government-shutdown-on-private-sector-industries-examples-from-1995-1996/ Written by Patrick Clapp and Chris Chmura. The impact of shutting down the U.S. government is much larger than the 800,000 federal workers who are staying at home and not spending money on commuting and lunch. Even though Congress has agreed to pay these workers, the reduction and shutdown of certain services is rippling through the economy. The Wall Street Journal cites an estimate from J.P Morgan Chase economists that “each week of a shutdown would reduce the annualized pace of fourth-quarter economic growth by 0.12 percentage point.” However, the forecast only looks at effects on government workers, not the private sector or consumer confidence. One question missing from the various lists of FAQs about the shutdown is whether any private sector industries could be affected. Fewer people are making retail purchases, going out to eat, enjoying arts or entertainment, and/or traveling and staying at hotels when tourist attractions like the Grand Canyon or Smithsonian museums are closed. They might also delay big purchases, like a new house, even if they could get a loan from an understaffed Federal Housing Administration. That gives us four North American Industrial Classification System ( NAICS ) sectors for closer inspection: retail trade (NAICS code 44), real estate and rental and leasing (53), arts, entertainment and recreation (71), and accommodation and food services (72). Next, we need to look at a comparable period. The last time the government shut down was between December 16, 1995 and January 6, 1996, a total of 21 days. We consider the effects on our selected sectors around that period for metropolitan statistical areas (MSAs). We also consider the percentage of federal employees in the area during that time period—areas with a higher percentage of federal workers implies those areas would have more furloughed workers, and likely larger impacts on local industries. Data are obtained from Chmura’s database powering the EQSuite ®. For each of the four sector categories in every MSA, we first calculated the year-over-year percent change in employment by month and wages by quarter to filter out some seasonal fluctuations. The graphs later in this post shows the percentage point changes in growth rates month to month or quarter to quarter relative to the number of people employed by the federal government in each region as a percent of total regional employment. With less demand during the shutdown, we would expect firms in these industries to cut back employees’ hours, which would show as a decrease in the growth rate of wages. To a lesser extent, they might cut their staff size, which would appear as a negative growth rate in employment. Now for some results: The clearest story is in the retail sector, where there was a strong negative relationship between the percentage of federal employees in a region and changes in the growth rate of wages quarter to quarter in the time periods surrounding the last government shutdown. Employment in the retail sector also declined, but not as dramatically as wages, implying that businesses cut hours, rather than laying off workers to cope with the government shutdown. The Washington, D.C. metro area ranked fifth of 353 MSAs in the percentage of federal employees in the workforce. The year-over-year growth rate in wages in the retail sector slowed 0.6 percentage points between the last quarter of 1995 and the first quarter of 1996. In the metro area with the largest percentage of federal workers, Warner Robins, Georgia, the year-over-year growth rate of retail wages fell 4.5 percentage points over that time. Tourist areas such as national parks and museums also suffered. The year-over-year growth rate of retail wages in the Flagstaff, Arizona MSA, near the Grand Canyon, fell 1.7 percentage points between the last quarter of 1995 and the first quarter of 1996. The effects of furloughed workers rippled through the arts, entertainment, and recreation sector as well. As federal employees were unsure if they would get paid, metro areas with a large percentage of federal employees experienced sharper drops in wages for this sector over the shutdown period and a similar drop in employment. In the Jacksonville, North Carolina MSA, home of Marine Corps Base Camp Lejeune, the year-over-year employment growth rate in the arts, entertainment, and recreation sector fell over 16 percentage points between December and January. The year-over-year growth rate of employment and wages in the real estate sector and accommodation and food services sector were not negatively affected, suggesting they are more resistant to uncertainty caused by a government shutdown. This makes intuitive sense for the real estate sector in particular—if the only thing keeping someone from purchasing a house is delayed paperwork at the FHA, they are still going to buy a house, just somewhat later than expected. Percentage Point Change in Year-Over-Year Growth Rate of Wages in Select Sectors, Fourth Quarter of 1995 to First Quarter of 1996   % Federal Employees Retail Trade Real Estate Arts Ent & Rec Accom & Food Svcs Warner Robins, GA MSA 28.1% -4.5 4.9 -14.0 -0.4 Bremerton-Silverdale, WA MSA 24.0% -6.5 10.0 -6.1 3.1 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 14.9% -0.6 * -3.2 -1.2 Jacksonville, NC MSA 13.3% -2.5 -10.8 -6.3 -3.9 Lawton, OK MSA 10.8% -1.0 -1.6 8.9 1.1 Fairbanks, AK MSA 10.5% 2.0 27.1 2.0 -5.7 Fort Walton Beach-Crestview-Destin, FL MSA 10.1% -4.2 3.5 16.6 12.8 Huntsville, AL MSA 9.9% -0.5 -1.4 -10.5 -4.4 Anniston-Oxford, AL MSA 9.8% 3.3 3.6 1.1 -6.1 Clarksville, TN-KY MSA 8.7% 1.6 -2.9 2.0 7.8 Anchorage, AK MSA 8.2% -3.2 -3.4 6.7 -2.1 Cheyenne, WY MSA 7.5% 0.8 -14.6 -17.9 -0.7 Honolulu, HI MSA 7.3% 1.9 4.3 -1.9 1.4 Las Cruces, NM MSA 7.2% 6.0 -44.7 -8.3 8.2 Lebanon, PA MSA 6.8% -5.8 -4.9 -0.3 -0.5 Bakersfield, CA MSA 6.7% -1.3 -8.0 0.2 2.6 Texarkana, TX-Texarkana, AR MSA 6.7% -4.7 2.9 -1.0 -4.3 * Data for the real estate sector of the Washington, DC metro area is unavailable prior to 2001 Similar to the last shutdown, Congress said it will pay the furloughed employees even though they did not work, and employment and wages in these industries quickly bounced back—and we would expect the same when the current shutdown ends. Continuing to look at the retail sector, year-over-year growth in wages in the quarter following the shutdown was positively correlated with the percentage of federal workers in a region, implying renewed demand from now-working government employees rippled out to more hours in the retail sector. However, that quick return to growth took place in a strong mid-1990s economy. In the midst of our current slow recovery, a similarly long shutdown could be a much larger and longer setback for workers in both the public and private sectors. The above chart shows the MSAs most likely to be impacted by the federal shutdown based on the most recent data available. The size of each bubble represents total MSA employment, and the color of a bubble indicates the political party affiliations of the senators representing that state. MSAs closer to the top right of the chart have higher unemployment rates and a larger percentage of federal workers in their local workforce, and are expected to be hit harder by the shutdown. http://chmuraecon.com/blog/2013/october/11/regional-effects-of-government-shutdown-on-private-sector-industries-examples-from-1995-1996/ http://chmuraecon.com/blog/2013/october/11/regional-effects-of-government-shutdown-on-private-sector-industries-examples-from-1995-1996/ Fri, 11 October 2013 12:57:13 http://chmuraecon.com/blog/2013/october/10/reflections-on-the-2013-workforce-boardroom-conference/ Chmura Economics & Analytics recently presented its second annual Workforce Boardroom Conference. The conference theme was Illuminate to Innovate, and it brought thought leaders from a variety of disciplines together to discuss smart solutions to some big challenges facing our regions, states, and nation. The event kicked off with a presentation from Marge Connelly, who brought her unique insight and private sector perspective on customer-centered innovation from her career in financial services and higher education. Following are some other highlights from the event. Federal Issues, Sequestration and Your Economy Chmura has been researching defense spending, the sequestration, and their impacts on state and regional economies. The team has developed one of the most complete datasets on federal defense spending ever constructed, and Chris Chmura’s remarks were designed to identify, inform, and inspire defense-dependent communities to action. Data-Driven Decision Making Jay Dougherty, partner at Mercer and co-founder of the Workforce Sciences Institute , shared his candid experience with companies making location decisions today. He emphasized the use of quantitative data analysis to optimize the location, labor, cost of doing business, and business climate of a corporate decision. Chmura team member Dan Meges joined Bruce Stephen of Monster Government Solutions to talk about workforce planning and the use of Real-Time Labor Intelligence. Monster, which started the online job posting revolution, is now manipulating its massive database of candidates and openings to help create a clearer picture of labor supply and demand. This perspective is invaluable for communities and higher education working to better align their program offerings with market trends. Chmura’s chief statistician, Greg Chmura, took the stage to share some interesting analysis on the connection between education, innovation, and job growth. His research built on findings of the Fund for Our Economic Future in northeastern Ohio. The research is surprising - innovation and job growth don’t necessarily go hand in hand. Measuring What Matters – New Paradigms for Economic Development One of the goals of the conference was to challenge traditional thinking about economic development and Ed Burghard of the Burghard Group ’s talk on the American Dream Composite Index (ADCI) did not disappoint. Ed suggested the audience push away from traditional outcome measures such as job creation and capital investment and instead focus on the well-being and satisfaction of the populace. Ed has partnered with Xavier University to advance the ADCI, which uses household survey data to measure the pulse of the American sentiment. It’s a holistic measure that includes five components, including economic, a well-being, societal, diversity, and environmental indexes. Four Business Perspectives The conference brought four unique and valuable business perspectives to the forefront. The first was from a return presentation by David B. Trebing, General Manager, State and Local Relations for Daimler . David shared the new reality for manufacturers operating in a competitive, dynamic, global marketplace. This reality includes the need to locate production facilities in growing and emerging markets, a push towards cost reduction and efficiency, and the need for a qualified workforce. The second business topic was presented by Carlos Solari, VP at Wilkitech . Carlos’s remarks enlightened the group on emerging trends in the cyber security industry and what communities need to know to attract and retain businesses in that sector. Jeff Harris with Xerox Services spoke on the relationship between innovation, people, and the environment, and what that means for business and communities today. His futuristic remarks about the shifting trends in the definitions of work and the workplace prompted healthy discussions over lunch about how economic developers will adapt. The conference closed with some practical public relations strategies from Hope Katz Bibbs, founder and president of The Inkandescent Group, LLC . Hope has helped many entrepreneurs and early-stage companies transform their innovative ideas into growth, and she shared her ideas about what it takes to help a new enterprise launch successfully and get the attention it deserves. What’s Next? It was an exciting two days in Richmond and Chmura thanks the engaged attendees and speakers. Now, we’re busy planning for 2014. If you would like to learn more about the 2014 conference, contact Maggie Bishop at mbishop@chmuraecon.com . http://chmuraecon.com/blog/2013/october/10/reflections-on-the-2013-workforce-boardroom-conference/ http://chmuraecon.com/blog/2013/october/10/reflections-on-the-2013-workforce-boardroom-conference/ Thu, 10 October 2013 13:40:40 Greg Chmura http://chmuraecon.com/blog/2013/september/05/advanced-education-beneficial-to-job-growth-or-no/ The benefit of higher education for an individual is well established : higher wages and lower unemployment rates among other things. But what about the benefits for communities? A recent study suggested a surprising answer. What Matters to Metros , published by the Fund for Our Economic Future, is an intriguing look at the factors that influence the economies of U.S. metropolitan areas. It found that higher education is linked positively with income, output, and productivity growth, but its data did not support a positive link with employment growth. Furthermore, the data show that metropolitan areas which have a large percentage of workers with low educational attainment (no high school diploma or equivalent) more often have faster job growth. This latter fact, actually, should not be so surprising. After all, since businesses want to minimize costs it is reasonable that high-labor-intensive industries would relocate or expand in areas with a large number of low-skilled workers for filling low wage jobs. Furthermore, as we pointed out in a previous blog , middle-skilled jobs in aggregate have been declining over the past decade while lower-skilled jobs have been on the upswing. But the same blog also showed growth among the jobs requiring the most skills. Because of this, shouldn’t we see a positive link between higher educational attainment and regional job growth? For this answer, we must return to the Fund for Our Economic Future data. A first look shows that, indeed, there appears to be virtually no relationship between employment growth (1990-2011) and the percentage of population with educational attainment of a four-year degree or higher (which we’ll call “advanced education”). Each dot in the below chart represents a metropolitan area (encompassing 115 “mid-sized” regions). The dotted line represents the trend line best fitting the data. Poking around in the details, however, turns up some curiosities. In certain subsets (such as metro areas with a small percentage of foreign born or metro areas in the Northeast and Midwest), we find a positive correlation between advanced education and job growth. Could the variables be interacting in complex ways so that the positive job growth effects of advanced education in communities are obscured by other factors? Apparently so. If we adjust job growth against expectations based upon some other variables, the effect of advanced education is clearer to see. For this purpose, I’m not going to attempt to adjust job growth for all possible variables, but will just look at two that have big impacts: (1) the industry mix—that is, the share of jobs in manufacturing, government, retail, and other sectors—and (2) the business climate. This latter factor was explored and defined nicely in the Fund for Our Economic Future work and comprises three pieces: tax costs, the rate of unionization, and energy costs. The below chart illustrates the strong correlation between job growth and a composite index of industry mix and business climate. Using this relationship to set expectations, we can now examine how metros perform against this benchmark. Do metros with higher rates of advanced education more often perform above expectations? The answer is yes. The next and final chart shows job growth beyond expectations on the vertical axis versus population with advanced education along the horizontal. The data show a significant, positive link between advanced education and job growth. It looks like higher education is, indeed, correlated with job growth! There are, however, a couple of items that need to be highlighted. First, you may notice the horizontal scale on the last chart is irregular. This is because a stronger link between advanced education and job growth was found according to an exponential relationship rather than linear. Essentially, incremental changes in advanced education were found to have a more pronounced effect on job growth where education was higher to begin with. While certainly interesting, the exact nature of this relationship should be explored further before concluding that this non-linear relationship is the best, most accurate model. Also, it should be noted that while rates of advanced education do positively influence job growth, the data show that other factors are more closely related. For example, industry mix, business climate, and immigration each appear to be more strongly linked with job growth than the rate of advanced education. Furthermore, having a high percentage of population with advanced education in addition to a high percentage with low education still tests out as having a stronger connection with job growth than only having a high percentage of population with advanced education. Again, this is not terribly surprising given the occupation trends we referenced earlier. This last point does, however, raise some questions about the community benefits of increasing education and skill levels from the lowest skill levels to mid-skill levels. It stands to reason that we should see benefits in terms of income and productivity. Perhaps we can find job growth benefits as well at this level, but that will have to wait for another blog. http://chmuraecon.com/blog/2013/september/05/advanced-education-beneficial-to-job-growth-or-no/ http://chmuraecon.com/blog/2013/september/05/advanced-education-beneficial-to-job-growth-or-no/ Thu, 05 September 2013 10:17:39 Chris Chmura http://chmuraecon.com/blog/2013/august/19/research-finds-17-billion-in-business-lost-due-to-defense-cuts/ Reveals Top Ten Most Impacted Metros in the Nation Nearly three years of continuous budget wrangling in Congress has left the fragile U.S. economic recovery limping along, in many respects, instead of galloping. Sequestration and the Budget Control Act have put the squeeze on both defense and non-defense spending. In this environment, Chmura has spent a great deal of time helping state lawmakers, city officials, and other civic stakeholders understand the economic impact of these defense cuts at the community level. What does it mean to your community if a specific defense contract gets cut back or cancelled altogether? To answer these questions, Chmura mapped the supply chain of defense contractors in the nation and analyzed defense spending contract data over the past thirteen years. First, defense spending is by design a bit opaque as credible and specific national security considerations oftentimes keep U.S. policy makers from telegraphing openly the true nature or extent of specific programs. Second, many defense contracts are multiyear projects, but public documents, such as contract award notices, make it difficult to see how payments to contractors are set to be dispersed or spent in detail. Third, many government contractors, who competitively bid and win large contracts, subcontract many aspects of the work to other firms. Thus, a single contract can impact several disparate communities at different times with different intensities over the life of the contract. Chmura has dealt with these issues by analyzing typical contract payouts and by adjusting these figures to more accurately model the flow of funds to contractors and subcontractors. To begin answering questions regarding the previous and looming defense cuts, one must first determine how much of the budget is being cut by region. The defense industry is big, and as President Eisenhower famously noted in 1961, the military industrial complex has political momentum all its own that can alter spending based upon the peculiarities of power and influence. The Department of Defense’s spending has declined substantially from its peak in 2010. By 2012, defense spending was already cut by close to 6% (without adjusting for inflation). While these cuts are large and further cuts are expected in 2013 and 2014, they are not unprecedented. In a recent study, the Center for Strategic and International Studies examined real (adjusted for inflation) defense spending cuts since World War II and found that in the aftermath of the Korean and Vietnam wars, and at the end of the Cold War, defense spending cuts were more severe in each case than in the current environment. (For more see the full CSIS report here: http://csis.org/publication/defense-budgets-double-whammy-drawing-down-while-hollowing-out-within ) While the cuts in defense spending are real, they vary greatly across the military by branch and function. For instance, from 2013 to 2014, Army procurement is being cut 3% and Marine procurement will be down 14% while Navy procurement spending is set to rise to $39 billion—a 13% increase from the year before—and Air Force procurement spending is set to increase by 1%. Moreover, the Army’s and the Navy’s Operation and Maintenance budgets will both be cut by 4%, while the Marine’s Operation and Maintenance budget is set to expand by 4% and the Air Force’s by 5%. A public advocacy infographic shop, Timeplots, assembled an impressive infographic depicting the size and scale of the changes in government spending from 2013 through 2014, including defense spending by spending category. See the full infographic here: http://visual.ly/death-and-taxes-2014-us-federal-budget In order to help make sense of the community impact of the recent pending defense cuts, Chmura created the following analysis to see which metropolitan statistical areas (MSAs) have been most impacted by the recent defense spending cuts. At the aggregate level, some of the largest MSAs have seen the most dramatic cuts in the period from fiscal year 2010 to fiscal year 2012. However, after adjusting for the size of the MSA, several much smaller areas stand out for the level of cuts they have experienced over this period. Similarly, by aggregate dollar figure, a few of the largest MSAs have gained the largest increases in government contracts over this period, but after adjusting for the size of the labor market in these metro areas, several much smaller U.S. metros stand out in terms of the contractual gains. MSA Total Defense Contract Cuts 2010 to 2012 New Orleans-Metairie-Kenner, LA MSA -$2,205,619,764 Oshkosh-Neenah, WI MSA -$1,894,064,198 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA -$1,486,275,593 St. Louis, MO-IL MSA -$1,452,512,297 Tucson, AZ MSA -$1,383,208,070 Memphis, TN-MS-AR MSA -$1,375,595,134 San Antonio, TX MSA -$1,207,102,665 New York-Northern New Jersey-Long Island,NY-NJ-PA MSA -$1,099,061,559 Riverside-San Bernardino-Ontario, CA MSA -$1,074,130,554 Hartford-West Hartford-East Hartford, CT MSA -$1,073,509,712 MSA Total Defense Contract Cuts 2010 to 2012 $ Cut per Capita Oshkosh-Neenah, WI MSA -$1,894,064,198 -$11,342 Johnstown, PA MSA -$782,445,252 -$5,446 Hinesville-Fort Stewart, GA MSA -$187,835,293 -$2,411 Manhattan, KS MSA -$289,552,359 -$2,278 Crestview-Fort Walton Beach-Destin, FL MSA -$367,857,608 -$2,034 New Orleans-Metairie-Kenner, LA MSA -$2,205,619,764 -$1,889 Columbus, GA-AL MSA -$438,654,422 -$1,488 Tucson, AZ MSA -$1,383,208,070 -$1,411 York-Hanover, PA MSA -$572,827,209 -$1,317 Binghamton, NY MSA -$308,778,050 -$1,227 MSA Total Defense Contract Gains 2010 to 2012 Seattle-Tacoma-Bellevue, WA MSA $2,040,368,382 Portland-South Portland-Biddeford, ME MSA $1,610,361,678 Phoenix-Mesa-Scottsdale, AZ MSA $1,581,407,851 Amarillo, TX MSA $1,311,645,313 Norwich-New London, CT MSA $1,244,943,132 MSA Federal Contract Gains 2010 to 2012 $ Gains Per Capita Amarillo, TX MSA $1,311,645,313 $5,249 Norwich-New London, CT MSA $1,244,943,132 $4,543 Portland-South Portland-Biddeford, ME MSA $1,610,361,678 $3,132 Bellingham, WA MSA $476,416,342 $2,369 Huntsville, AL MSA $584,875,448 $1,401 The labor market impact of these spending cuts can vary widely depending on the type and nature of the defense spending. Every industry in the area will have a different economic impact based on the size of its local supply chain and the spending spillover from its directly employed workers. However, it stands to reason that these spending cuts, as steep as they are, can be a driving force to upset labor markets in many of the nation’s MSAs, both big and small. To learn more about Chmura’s expertise and research regarding defense spending and supply chain mapping, contact us here . Contract Dollars Gain/Loss per Capita by MSA, 2010 to 2012 Gain/Loss per Capita All Gain/Loss per Capita Defense Gain/Loss per Capita Non-Defense .esriAttribution { display: none; } http://chmuraecon.com/blog/2013/august/19/research-finds-17-billion-in-business-lost-due-to-defense-cuts/ http://chmuraecon.com/blog/2013/august/19/research-finds-17-billion-in-business-lost-due-to-defense-cuts/ Mon, 19 August 2013 14:27:37 Chmura Staff http://chmuraecon.com/blog/2013/july/25/the-jobs-are-coming-back-just-not-always-where-they-were-lost/ The latest U.S. jobs report from early July indicates the national economy continues to add jobs at a slow but steady pace. The latest data indicates the economy has added approximately 195,000 jobs each month for the past three months. If we go back to January 2011, the nation added on average close to 185,000 jobs per month. The U.S. economy is gradually recovering and now the labor market may even be gaining some momentum. In February 2010, the U.S. job market had hit rock bottom. From the pre-recession high of 138 million people employed, the economy had shed some 8.7 million jobs—more than 6% of total nonfarm employment had vanished. Since then, total nonfarm employment has grown to almost 136 million, which is only 1.5% below the former peak. It is likely—assuming the current pace of about 180,000 new jobs created per month continues—that the U.S economy will reach its previous peak of employment in mid-2014. At that point, the U.S. economy will be deemed to have fully “recovered” from the previous recession’s job losses. However, while the jobs are coming back, they are not where we left them. As of the first quarter of 2013, in fact, a small number of states have already fully recovered the number of jobs lost during the last recession and have begun to set new records of employment. These states are Texas, North Dakota, Alaska, South Dakota, West Virginia, and Utah as well as the District of Columbia. Close behind these areas are another 14 states that have recovered between 98% and 99% of the jobs they lost during the previous recession—states like Massachusetts, New York, and Nebraska. These states, along with the U.S. economy as a whole, will likely regain their previous employment peak within the next 12 months. For the remainder of the country, however, the recovery of lost jobs has been much less robust and many states remain well below their pre-recession employment levels. For instance, California’s economy was hit much harder in the previous recession than the nation; at its nadir, California had lost nearly 9% of nonfarm employment. California’s labor market is rebounding, but it still remains as of roughly 4% below its previous employment peak. It is likely that it will take California well into 2015 to recover its previous peak employment, whereas by then many other states will be setting new employment records. States like Arizona and Florida are more than 7% below their previous employment peaks, while Nevada and Michigan are more 10% below their peak nonfarm employment. In many previous recessions, employment bounced back quickly in the nation, typically within two years of the recession starting and many times in the very same places that lost employment in first place. This recession has seen the slowest employment recovery since the Great Depression, and the jobs that are being created today are in different sectors of the economy and in different localities. The map below depicts by county how employment levels have recovered from the previous pre-recession peaks across the nation. While some areas of the country enjoy new employment records, many others are still years away from recovering all the jobs that were lost, and in some locations it is likely that the job market may never recover back to previous peak. The jobs are coming back, just not where we left them. Source: JobsEQ® http://chmuraecon.com/blog/2013/july/25/the-jobs-are-coming-back-just-not-always-where-they-were-lost/ http://chmuraecon.com/blog/2013/july/25/the-jobs-are-coming-back-just-not-always-where-they-were-lost/ Thu, 25 July 2013 13:25:44 http://chmuraecon.com/blog/2013/july/22/2013-workforce-boardroom-conference/ We know it. Workforce planning and workforce training are topics being discussed in boardrooms all over the country. Whether you believe the skills gap is real or imagined, it’s a big challenge facing businesses of all sizes as well as communities and educators. It’s also the reason Chmura started a plan in 2012 to bring smart people together to talk about new and innovative ways to change the conversation. On September 16 th and 17 th , Chmura is hosting its second annual Workforce Boardroom Conference in Richmond, VA, and the list of speakers is impressive. This year’s theme, Illuminate to Innovate!, focuses on how data (the bigger the better) can help improve the way we develop and manage talent forces across the country. The schedule includes some important names from economic development, workforce development, business and industry, the site selection community, and education. These five perspectives promise to provide new insight and a true 360-degree view of the workforce imperative. The full schedule can be downloaded here , but here are a few highlights. Monday kicks off with a few words from Dr. Christine Chmura, President and Chief Economistof Chmura Economics and Analytics. Chris will be discussing the impact of sequestration on industries and regions, including the impact on supply chains. Just prior to lunch, Ed Burghard, CEO of the Burghard Group, LLC and former Executive Director of the Ohio Business Development Coalition will be sharing new research and perspective on new ways to measure community success. Ed is a place marketing and branding expert working to advance the field of economic development. The lunch keynote speaker on Day One will be David Trebing, General Manager, Sales and Local Relations of Daimler. Daimler is a global automotive company with headquarters in Stuttgardt, Bade-Wuttemberg, Germany. In 2012, Daimler sold 2.2 million vehicles and employed a workforce of 275,000 people. Day One also features two site selection experts who will share their perspectives on how communities are evaluated as potential locations with a particular focus on the evaluation process for regional labor pools. September 17 th kicks off with breakfast and two special presentations on how data can measure and drive innovation as well as how new and better tools can facilitate better decision making and planning in workforce development. This is just a snapshot of the complete agenda of this not-to-miss event, and registration is now open. For more information and to register, visit the conference page of the Chmura website . http://chmuraecon.com/blog/2013/july/22/2013-workforce-boardroom-conference/ http://chmuraecon.com/blog/2013/july/22/2013-workforce-boardroom-conference/ Mon, 22 July 2013 08:00:39 Greg Chmura http://chmuraecon.com/blog/2013/july/11/pipeline-assessment-engineering-awards-by-state-2011-2012/ In the region-to-region battle for jobs and investments, having a postsecondary training pipeline aligned with industry needs is a nice competitive advantage. When examining postsecondary awards data for engineers, for example, it is easy to see inequalities in the system that can contribute to shortages or surpluses in some regions of the nation. In the 2011-12 academic year, an estimated 113,742 awards were granted in the United States that flow into careers in engineering occupations. (An aside: Chmura Economics & Analytics models the complex interaction between graduate supply and occupation demand. Through this model, every award is linked to an occupation for which it provides training. The occupation awards data cited here are the product of this modeling.) The map below shows the production by state for all engineers as well as five specific types of engineers—use the dropdown box to update the map. http://chmuraecon.com/blog/2013/july/11/pipeline-assessment-engineering-awards-by-state-2011-2012/ http://chmuraecon.com/blog/2013/july/11/pipeline-assessment-engineering-awards-by-state-2011-2012/ Thu, 11 July 2013 15:53:15 Chris Chmura http://chmuraecon.com/blog/2013/july/09/economic-impact-look-for-the-careers-with-high-job-opportunities/ Originally published on July 8, 2013 in the Richmond Times Dispatch . Many high school graduates and rising high school seniors are making plans that will impact their work-related opportunities when they graduate from college. While interest is certainly an important component of career choice, it should be balanced with job opportunities. Photographers and actors are two careers that high school students think about, but job opportunities are scarce in those fields. Based on estimates from Chmura Economics & Analytics, 215 photographers work for firms in the Richmond area along with another 363 who are sole proprietors. On the other hand, an average 538 registered nurses are needed each year in the metro area to fill new jobs or those vacated by retirees or people moving to another occupation. Along with nurses, accountants and bookkeepers are among the top 10 occupations needed by businesses in the Richmond metropolitan area over the next decade. Another factor that might help students narrow their career choice is potential earnings. The State Council of Higher Education for Virginia recently started providing the average first-year earnings (based on the last five years) by degree level along with the earnings by institution where the degree was awarded. This database consists of graduates employed in Virginia and is not adjusted for the regional cost of living. There is significant variation by degree and even for the same degree awarded at different institutions. Registered nurses with a bachelor’s degree earned an average $48,959 for their first year working in the state. Graduates from the University of Virginia at Wise earned $37,492 – at the low end of the scale – compared with $54,765 for Jefferson College of Health Sciences graduates. In some cases, the skills acquired with a two-year degree earned more than those with a four-year degree. A graduate with an associate accounting degree made $30,964 for the first year and an electrician with two years of education commanded $36,734. In contrast, graduates with a photography bachelor’s degree earned $23,035 in the first year while general English majors earned $23,423. Virginia is only one of a handful of states that compiles earnings by degree and institution. CollegeMeasures.org has packaged the information and made it available in an easy-to-navigate website for students who are trying to decide on a career. Later this summer, State Council of Higher Education for Virginia plans to make even more information available to the public: average student debts of graduates by program and institution. With this information in hand, students can consider the debt-to-earnings ratio they may face when they are handed their degree. http://chmuraecon.com/blog/2013/july/09/economic-impact-look-for-the-careers-with-high-job-opportunities/ http://chmuraecon.com/blog/2013/july/09/economic-impact-look-for-the-careers-with-high-job-opportunities/ Tue, 09 July 2013 07:25:27 Chris Chmura http://chmuraecon.com/blog/2013/may/14/economic-impact-job-market-is-improving/ Originally published in the Richmond Times-Dispatch on May 13, 2013. The unemployment rate in the Richmond metro area drifted down to 5.6 percent in March. While that rate is better than the national average of 7.6 percent, the local economy has a way to go before it is fully recovered from the recession. Employment in the Richmond metro area stood at 628,800 in March — still about 6,600 jobs below the peak of 635,400 jobs in August 2007. Based on the pace of employment growth in the region during the past year, we should exceed that peak before the end of the year. That job recovery is better than the for nation, which isn’t expected to reach the previous employment peak until July 2014 based on recent growth. The prolonged recovery from the recession means new college graduates will again face a tight labor market. Their ability to find a job, however, will vary greatly based on the knowledge and skills they acquired in high school or college. New graduates hoping to work for professional business services firms might have a hard time finding jobs because those industries are still contracting in the Richmond metro area. For instance, two of the region’s largest law firms reduced employment over the past year, according to the Top 50 list of the area’s largest private employers. Prospects are much better for new graduates with skills needed in the health care sector. A little more than 1,600 new health care jobs were added to the metro area during the year ending in March, according to estimates from Chmura Economics & Analytics. Eight of the employers on the Top 50 list are health care related. Looking ahead, Chmura Economics & Analytics is forecasting a need for an average 2,600 health care workers a year during the next decade in the Richmond metro area. Of that amount, about 1,000 health care workers are needed annually to fill positions from which people have retired or moved to new occupations. Even construction is looking promising again. This sector added 1,645 jobs in the Richmond region in the 12 months ending in March, according to the Virginia Employment Commission. Along with a moderate amount of on-the-job training, cabinetmakers or drywall installers can make an average of about $30,000 in the region, according to the Bureau of Labor Statistics. The average wage of electricians in the Richmond area, which typically requires an apprenticeship, is about $46,000. Based on our forecasts, next year’s college graduates will see further improvements in the jobs market. And those students who choose a career path linked to jobs that are in demand by regional businesses will clearly have better prospects upon graduation. http://chmuraecon.com/blog/2013/may/14/economic-impact-job-market-is-improving/ http://chmuraecon.com/blog/2013/may/14/economic-impact-job-market-is-improving/ Tue, 14 May 2013 08:41:14 http://chmuraecon.com/blog/2013/may/13/governor-announces-new-chmura-economics-report-finding-new-spending-on-construction-will-annually-sustain-13-058-jobs-and-have-95-billion-in-economic-impact/ Governor McDonnell Ceremonially Signs Virginia's Historic Bi-Partisan Transportation Funding Bill – Legislation Provides Over $3.4 Billion in Statewide Funding, $1.5 Billion for Northern Virginia, and $1 Billion for Hampton Roads Over Next 5 Years Alone – ~Reduces Prices at the Pump While Providing Virginia's First New Dedicated, Sustainable Revenue for Transportation in 27 Years~ ~At Signing, Governor Announces New Chmura Economics Report Finding New Spending on Construction Will Annually Sustain 13,058 Jobs and Have $9.5 Billion in Economic Impact~ ***McDonnell: "This may technically be transportation legislation, but at the end of the day, it's a jobs bill."*** RICHMOND - Today, surrounded by legislators and community and transportation leaders from across the Commonwealth, Governor Bob McDonnell ceremonially signed Virginia's Road to the Future (HB 2313), the state's first comprehensive transportation funding plan approved in 27 years. Following on the heels of nearly three decades of inaction on the critical challenges facing transportation funding in Virginia, this historic bi-partisan legislation supported by Republicans and Democrats from each chamber will provide more than $3.4 billion in additional statewide transportation funding, more than $1.5 billion in additional funding for Northern Virginia, and more than $1 billion in additional funding for Hampton Roads, over the next five years alone. "For 27 years our citizens have sat in traffic as congestion has increased and our bridges and roadways have deteriorated," said Governor McDonnell. "For 27 years our citizens and businesses have demanded solutions. For 27 years, Democrats and Republicans in the General Assembly failed to reach an agreement on this critical issue. However, we stand here today, thanks to the leadership and support of Speaker Howell and a broad bi-partisan coalition of legislators, business leaders and citizens, to celebrate this historic achievement. Not only will this legislation address both the short and long-term funding needs of our transportation system, it will also annually sustain 13,058 new jobs, have more than $9.5 billion in economic impact, and improve Virginians' quality of life. Instead of sitting in traffic, our citizens will be able to spend more time with their families enjoying the many benefits this great Commonwealth has to offer. Most importantly, this legislation will ensure that Virginia's economy can grow in the years ahead, and that businesses will have the infrastructure they need to create the good-paying jobs Virginians deserve. This may technically be transportation legislation, but at the end of the day, it's a jobs bill. "When we put forward our comprehensive transportation funding plan this year, we called for three fundamental changes to how Virginia funds transportation, all based on conservative fiscal policy. First, we called for a reduction or elimination of our dependence on an archaic, outdated gasoline tax in the decades ahead. Second, we called for tying future transportation funding to the far more sustainable sales tax. Third, we called for treating transportation like the true core function of government that it is, one essential to economic growth and job creation in our state. While the final bill was in some ways different than our original proposal, it met all three of the goals we established. The final product is the very essence of compromise in that this legislation has components some will like, and components others may dislike. That's the nature of any true compromise. Our success demonstrates that both parties - be it here in the Commonwealth or up in Washington - can still achieve a great deal when partisan differences are put to the side and we work collaboratively toward the solutions our citizens demand. This bill is crucial to the future growth of Virginia's economy, and this is a great day for job-creation in the Commonwealth." Over the first five years, HB 2313 will: Generate more than $1.8 billion in additional funding for maintenance, thereby eliminating maintenance crossover transfers Provide $660 million in dedicated new construction funding, which, when combined with the elimination of maintenance crossover, will grow construction spending by more than $2.4 billion Increase funding for Virginia's transit providers by $509 million Provide more than $256 million in funding for intercity passenger rail, the first dedicated state funding for this vital service Generate additional revenue for Virginia's airports and seaports Generate annually between $272 million to $335 million in Northern Virginia and $172 million to $226 million in Hampton Roads for regional transportation priorities Additionally, in his remarks, the governor today announced the findings of recently completed economic impact analyses conducted by nationally renowned firm Chmura Economics. The first analysis, which focuses on new roadway construction spending as a result of HB 2313, determined that the additional funds in the Commonwealth Transportation Board's Six-Year Improvement Program will have an economic impact of $8.1 billion and annually sustain 10,133 jobs from FY 2014 through FY 2019. The second analysis, which focuses on new transit and rail spending, determined that the additional funding provided to the Department of Rail and Public Transportation will have an economic impact of more than $1.4 billion support 14,625 jobs, or 2,925 jobs per year, between FY 2014 and FY 2018. The new dedicated intercity passenger rail funding will enable Virginia to extend passenger rail service to Roanoke within the next four years. "This year, Republicans and Democrats put their differences aside, sat down at the table and demonstrated to our citizens that unlike Washington we are able to work together to achieve the results they demand," said Speaker of the House Bill Howell. "I applaud Governor McDonnell for his leadership and willingness to tackle the difficult challenges facing Virginia, and I thank the broad bi-partisan group of legislators and stakeholders who made today possible." Speaking about this historic legislation, Marty Nohe, Prince William County supervisor and chairman of the Northern Virginia Transportation Authority said, "Years of inaction in the General Assembly have resulted in Northern Virginia now being ranked as part of the most congested region in the United States. No other singular issue has as great an impact on our ability to attract and retain economic development opportunities and jobs. Now, with the passage of this historic compromise, the local governments in Northern Virginia will be able to work collaboratively to address our critical regional needs so that we can remain economically competitive and improve our citizens' quality of life." "As home to one of the largest naval installations in the U.S. and the economically crucial Port of Virginia, the Hampton Roads' region has for years struggled with our unique transportation challenges," said Hampton Mayor Molly Ward, chair of the Hampton Roads Transportation Planning Organization. "Our region's infrastructure needs are tremendous and with the inclusion of the Hampton Roads regional component, HB 2313 will finally provide us with the foundation to begin tackling these difficult challenges." "Following on the heels of the 2011 bond package, Governor McDonnell made a promise to continue his successful efforts to address Virginia's transportation funding needs by putting in place dedicated, sustainable revenues for the long-term," said Virginia Transportation Construction Alliance Executive Vice President Jeff Southard. "This year the governor delivered. Not only will this legislation improve mobility, reduce congestion and promote further economic activity, but it will put in place the sustainability necessary to build upon and continue the progress Governor McDonnell has made over the past three years." Following General Assembly approval of the governor's recommended amendments, HB 2313: Eliminates the 17.5 cents per gallon excise tax on gasoline and diesel fuel Replaces the motor fuels tax with a 3.5 percent sales tax on the wholesale price of gasoline and a 6 percent sales tax on the wholesale price of diesel fuel Increases the state and local sales and use tax from 5 percent to 5.3 percent Partially eliminates the 2 percent motor vehicle titling tax exemption by increasing the rate from 3 percent to 4.15 percent Creates a $64 Alternative Fuel Vehicle fee to ensure that all drivers are contributing to Virginia's roadways Levies an additional 0.7 percent local sales tax, a $0.15/$100 Grantor's Tax, and a 2 percent Transient Occupancy Tax in Planning District 8 Levies an additional 0.7 percent local sales tax and a 2.1 percent fuel sales tax in Planning District 23 A final summary of the HB 2313 and the Chmura economic impact analyses can be found at: http://www.varoadtothefuture.com/ . http://chmuraecon.com/blog/2013/may/13/governor-announces-new-chmura-economics-report-finding-new-spending-on-construction-will-annually-sustain-13-058-jobs-and-have-95-billion-in-economic-impact/ http://chmuraecon.com/blog/2013/may/13/governor-announces-new-chmura-economics-report-finding-new-spending-on-construction-will-annually-sustain-13-058-jobs-and-have-95-billion-in-economic-impact/ Mon, 13 May 2013 15:38:57 Chris Chmura http://chmuraecon.com/blog/2013/april/22/where-the-jobs-are/ The golden ticket for most economic development programs is job creation, and the more the better. For folks involved in workforce development, the perfect job would provide a sustaining wage for individuals and families. This ticket has been hard to come by for many communities as the nation’s economy limps forward, offering little in terms of opportunity for a large portion of job seekers in the middle. Chmura’s economists took a look at more than 800 occupations, analyzed employment growth and wage gains that occurred between 2001 and 2011, and identified a few trends that help us understand where the jobs are (and where they aren’t). We broke occupations into ten groups based on employment and wages earned and analyzed each decile based on job and wage growth. The results demonstrate two troubling facts. First, wages have been mostly stagnant during the ten-year study period, with gains exceeding inflation for only occupations on the high end of the scale. Second, job growth has been mostly isolated to those jobs paying the least and those that pay the most. Over the past ten years, employment growth has been negative for occupations in the middle and it’s a substantial middle, representing more than 60% of the total jobs and wages. The data suggest it’s one part of the explanation for the skills gap – those who lost jobs in the middle may be unwilling to accept a position that pays less than their previous job and unable (because of skills and experience shortfalls) to successfully compete for jobs at the higher end of the scale. http://chmuraecon.com/blog/2013/april/22/where-the-jobs-are/ http://chmuraecon.com/blog/2013/april/22/where-the-jobs-are/ Mon, 22 April 2013 14:11:11 Chris Chmura http://chmuraecon.com/blog/2013/april/17/if-you-train-them-will-they-come/ Chris Chmura joined a distinguished panel in Washington DC on Sunday, April 14th to kick off the International Economic Development Council’s Federal Economic Development Forum. The plenary session, entitled “Workforce Development: If You Train Them, Will They Come” set the stage for a lively discussion around the importance of workforce development, the ever-changing needs of business and industries, and how the federal agenda may help shape the intersection between the two. Big on almost everyone’s mind was the topic of the nation’s skills gaps, broadly defined as the mismatch between the skills and qualifications of the labor force and employers workforce needs. Chris’ remarks offered a unique perspective and practical advice on how economic developers can better understand and harness the power of their regional workforce (and workforce systems) to improve their economic development outcomes. Chris shared specific examples and data supporting the need to better align economic development and workforce development strategies. She also underscored the importance of developing a deeper understanding of regional labor markets and external market forces to point to new, unexplored opportunities or mitigate risks for communities. Other notable panelists included Jane Oates, Assistant Secretary of Employment & Training Administration, who shared her view of economic development and workforce development as synonymous—the public workforce system role is to help people connect to jobs. Mary Jo Waits, Director at the National Governor’s Association Center for Best Practices pointed out three best-practice models where businesses are successfully working together to drive change: the Commonwealth Center for Advanced Manufacturing in Virginia, Clemson University’s International Center for Automotive Research , and Kentucky’s Automotive Technical Education Collaborative . Karin Norington-Reaves, CEA of Chicago Cook Workforce Partnership , spoke of the need for workforce investment boards to make a paradigm shift from social service delivery to business service delivery. She now has 7 employees in her new business relations and economic development group that are focusing on 40 occupations with the greatest needs. Also on the panel was Peter Cappelli, with the Center for Human Resources at the Wharton School. Mr Cappelli was not as confident a skills gap exists at all and called on employers to renew investment in training and skills transfer in their organizations. The IEDC Federal Forum is an annual event providing opportunity for the economic development community to get educated and advocate for federal policies that will encourage and support economic growth. You can view Chris’s entire presentation here . http://chmuraecon.com/blog/2013/april/17/if-you-train-them-will-they-come/ http://chmuraecon.com/blog/2013/april/17/if-you-train-them-will-they-come/ Wed, 17 April 2013 09:05:39 Leslie Peterson http://chmuraecon.com/blog/2013/march/21/thoughts-from-the-nawb-forum-2013-connect-discover-elevate/ The Chmura team recently attended the 2013 National Association of Workforce Boards Forum held in Washington DC.  The event was well-attended by both public and private sector organizations including workforce development agencies, their board leadership, key staff members and community partners such as economic developers, educators, businesses, and elected officials. This year’s big buzz was around businesses-critical and ever-changing need for talent. Workforce Investment Boards and others are talking, now more than ever, about their need to become more relevant to the business community.  To accomplish that, thought leaders are placing a lot of emphasis on the importance of quality, insightful labor market data to shape the strategic direction for WIBs.  Even more exciting was the conversation around the need to connect workforce planning with the larger regional goals of the communities.  This year’s theme “Dialogue for Workforce Excellence” got to the heart of the matter and the need for workforce development agencies to innovate and deliver relevant programs to support the needs of business and industry.  One speaker built upon that by giving the audience a cautious reminder that “innovation has no value until a customer stands beside it.” The lunch speakers were characteristically amazing and included the inspirational Bill Strickland, President and CEO of Manchester Bidwell Corporation and the insightful Jim Clifton, Chairman and CEO of Gallup . Jim remarked that customers create jobs and not vice versa.  We met hundreds of passionate professionals, learned a lot and relished the opportunity to share our own labor marketing information solution, JobsEQ. Thanks to those who stopped and chatted with Chris Chmura and Leslie Peterson at our booth.  We hope you’ll take an opportunity to check out the NAWB Forum 2013 program site (post-conference material is available now) and leave you with the following ruminations. Collaboration is no longer an option—it’s the new normal. Leadership is critical and the only way to affect real change in systems. Passion without planning is like nailing JELLO to a brick wall. Technology and big data has the potential to transform the way WIBs operate and affect change in their communities.  We’ve got an incredible opportunity to get this one right and we’re committed to contributing to the solution.  http://chmuraecon.com/blog/2013/march/21/thoughts-from-the-nawb-forum-2013-connect-discover-elevate/ http://chmuraecon.com/blog/2013/march/21/thoughts-from-the-nawb-forum-2013-connect-discover-elevate/ Thu, 21 March 2013 14:15:07 http://chmuraecon.com/blog/2013/february/28/is-your-metro-area-on-the-edge-of-a-fiscal-cliff/ The topic of sequestration is on everyone’s mind today and the nation’s legislators are faced with some tough decisions. Sequestration, by definition, refers to automatic across-the-board spending cuts that will take place if a budget deal isn’t cut by tomorrow. While many state and local officials are putting pencil to paper to see how the cuts will affect them directly, it’s important to remember that the federal government spends more than $500 billion each year through contracts with private industry in the United States. These funds make their way into regional economies as the federal government procures goods and services, supporting businesses and jobs. So which metro area economies are most dependent on revenues derived from contract work for the federal government? Which ones are most at-risk due to the impending budget cuts? We took a look at federal contract spending data and assigned it a metro geography based on where the awarded firm performed the work. Then we divided that amount by the total number of jobs in each region. The interactive map below shows how federal contract dollars are concentrated in the nation’s 369 metro areas. At the top of the list is the Pascagoula, MS, a small metro area with businesses employing about 55,000 people. Pascagoula is home to naval shipbuilding giant Ingalls Shipbuilding, which was founded there on the banks of the Pascagoula River in 1938. Ingalls is one of Mississippi’s largest employers. Over the past three years, federal contract spending has averaged $3 billion a year in Pascagoula. That’s more than $54,000 federal dollars spent locally for each job in the metro area. Will sequestration happen? That remains to be seen. It’s safe to say federal spending will be reduced in the future, perhaps with a bit more surgical precision than sequestration mandates. Regions should understand their exposure as it relates to these issues and be prepared to support businesses (and employees) who could be impacted. You can download the full list here . The Top 10 Metros Area by Federal Contract Concentration Metro Area Federal Contract Spending Federal Defense Contract Spending Federal Contract Spending per Job Pascagoula, MS MSA $2,922,177,953 $2,505,380,719 $54,539 Oshkosh-Neenah, WI MSA $4,474,326,706 $4,469,966,458 $49,012 Huntsville, AL MSA $6,670,436,889 $5,683,705,823 $33,593 Norwich-New London, CT MSA $4,084,966,755 $4,044,240,317 $33,326 Amarillo, TX MSA $3,214,086,928 $2,568,993,190 $29,166 Kennewick-Richland-Pasco, WA MSA $3,081,472,369 $28,242,914 $28,263 Idaho Falls, ID MSA $1,332,962,380 $38,597,418 $27,285 Washington-Arlington-Alexandria, DC-VA-MD-WV MSA $79,366,339,680 $38,328,580,911 $27,137 Jacksonville, NC MSA $1,029,940,406 $1,012,934,451 $21,845 Hinesville-Fort Stewart, GA MSA $365,082,105 $364,647,044 $18,919 Source: JobsEQ® and FPDS. Employment data as of 2012q4, Spending data FY2010-12 as of 1/15/2013.   You can download the full list here . http://chmuraecon.com/blog/2013/february/28/is-your-metro-area-on-the-edge-of-a-fiscal-cliff/ http://chmuraecon.com/blog/2013/february/28/is-your-metro-area-on-the-edge-of-a-fiscal-cliff/ Thu, 28 February 2013 15:31:58 Chmura Staff http://chmuraecon.com/blog/2013/february/14/ohio-s-target-industries-post-strong-job-gains-in-2012/ Each of Ohio’s nine target industries added employment 1 in 2012 and five of the nine sectors posted job growth far stronger than Ohio’s overall job growth of 1.8% during the year. Target industry job gains were led by Ohio’s BioHealth sector, which mostly comprises pharmaceutical and medical equipment manufacturing, posting 14.1% year-over-year growth in the fourth quarter of 2012. The Energy sector also saw strong gains, due in large part to the development of Ohio’s shale gas reserves. What is especially impressive about the job gains in the BioHealth and Energy sectors is that these are two target industries where Ohio is only beginning to develop a competitive cluster—as gauged by the state’s location quotient. Sector Employment Employment Change Q4-2011 - Q4-2012 Average Annual Wages Location Quotient Ohio - BioHealth 17,306 14.1% $56,381 0.65 Ohio - Energy 44,403 7.8% $76,895 0.64 Ohio - Food Processing 62,455 5.2% $47,921 0.99 Ohio - Information Technology and services 84,306 4.5% $71,307 0.95 Ohio - Automotive 90,616 4.3% $62,138 2.72 Ohio - Financial Services 174,692 1.6% $66,487 0.98 Ohio - Advanced Manufacturing 121,232 0.8% $62,938 2.43 Ohio - Polymers and Chemicals 90,437 0.7% $58,874 2.04 Ohio - Aviation & Aerospace 38,802 0.3% $79,650 0.72 Ohio - Total All Target Industries 724,249 2.8% $64,460 1.53 Ohio - Total All Industries 5,036,462 1.8% $44,059 1.00 Source: JobsEQ® The location quotient is measure of how big these industries are given the overall size of Ohio’s economy. In this case, a location quotient less than one indicates an industry that is underweight in terms of what one might expect given the overall size of Ohio’s economy. In this context, given Ohio’s assets and labor market strengths, there can be optimism that these two industries will continue to expand employment over the next three to five years and Ohio will see the location quotient in these two sectors move from below to above one. If the location quotient of these two sectors approached the average for location quotient all of Ohio’s target industries (1.53), this would translate into the creation of more than 80,000 new jobs. In addition to the strong growth in Ohio’s target industries in 2012 was the fact that these job gains were spread out across the state. Seventy two of Ohio’s 88 counties experienced at least some job growth in these nine target industries. In fact, some of the strongest year-over-year job gains were in rural communities which saw Ohio’s target industries collectively growing in excess of 5%. Only about 11% of Ohio’s counties saw job declines in aggregate employment in these target industries. Similarly, in the BioHealth, Energy, Financial Services, Food Processing, and Information Technology sectors, more than 75% of all Ohio counties with these industries experienced job growth in 2012. The BioHealth sector expanded last year in 95% of the counties where it was present. In the more traditional manufacturing groups—Advance Manufacturing, Automotive, Aviation and Aerospace, and Chemicals and Polymers—the employment gains were slightly more concentrated with only about two-thirds of Ohio’s counties registering employment gains in these sectors. Employment changes were the most mixed in the advanced manufacturing sector which registered job gains in 52 counties, but job losses in 34 counties. Top 10 Counties in Job Gains for Ohio’s Target Industries Average Employment Q4 2011 Target Industries Average Employment Q4 2012 Target Industries Year-over- Year % Change Wyandot County, Ohio 2,441 2,687 10% Lawrence County, Ohio 602 657 9% Scioto County, Ohio 1,560 1,692 8% Huron County, Ohio 3,363 3,624 8% Erie County, Ohio 5,749 6,154 7% Brown County, Ohio 1,011 1,082 7% Van Wert County, Ohio 2,493 2,649 6% Fayette County, Ohio 1,727 1,832 6% Wayne County, Ohio 8,892 9,395 6% Tuscarawas County, Ohio 4,264 4,497 5% Source: JobsEQ Ohio Target Industries Counties with Expanding Employment in 2012 Counties with Contracting Employment 2012 Number of Counties w/ Industry Presence Ohio - Advanced Manufacturing 60% 40% 86 Ohio - Automotive 68% 33% 80 Ohio - Aviation & Aerospace 68% 32% 57 Ohio - BioHealth 96% 4% 54 Ohio - Energy 85% 15% 88 Ohio - Financial Services 75% 25% 88 Ohio - Food Processing 83% 17% 84 Ohio - Information Technology and services 97% 3% 88 Ohio - Polymers and Chemicals 67% 33% 81 Source: JobsEQ Employment figures in this article reflect average Quarterly Census of Employment and Wages (QCEW) employment in Q4 2011 compared to Chmura’s preliminary estimate for average employment in Q4 2012, which is subject to revision. QCEW employment data at the county level was not available beyond Q2 2012 at the time of this publication. http://chmuraecon.com/blog/2013/february/14/ohio-s-target-industries-post-strong-job-gains-in-2012/ http://chmuraecon.com/blog/2013/february/14/ohio-s-target-industries-post-strong-job-gains-in-2012/ Thu, 14 February 2013 10:53:13 Chmura Staff http://chmuraecon.com/blog/2013/january/04/supply-chain-mapping-can-highlight-opportunities-to-grow-an-economy/ Chmura’s economists have been working with local communities for nearly a decade to understand the sources of their local competitiveness and strengths of their key industrial clusters. Several techniques exist for this type of analysis and one commonly utilized component is to examine an area’s key clusters for missing links in the supply chain. Many times this helps for marketing campaigns or economic gardening efforts in order to attract firms or spur entrepreneurial activity to address these gaps and augment and strengthen an existing industry cluster. In many cases, the search for supply chain gaps utilizes national models of what an industry and its many sub-industries have in common. Where additional data are available or can be collected, an alternative methodology can be used for much greater specificity, particularly when the local industry cluster has very specific suppliers or itself is part of a very complex value-chain from start to finish. Always looking to innovate, Chmura has produced a unique way to map the actual supply chain of industry clusters utilizing cutting edge network analysis. The results speak for themselves in an unprecedented, granular look at economic leakages, supply chain gaps, and cluster synergies.  http://chmuraecon.com/blog/2013/january/04/supply-chain-mapping-can-highlight-opportunities-to-grow-an-economy/ http://chmuraecon.com/blog/2013/january/04/supply-chain-mapping-can-highlight-opportunities-to-grow-an-economy/ Fri, 04 January 2013 13:06:08 http://chmuraecon.com/blog/2012/december/20/governor-mcdonnell-announces-that-commonwealth-signs-comprehensive-agreement-and-reaches-financial-close-to-build-the-new-route-460-in-southeast-virginia/ Governor McDonnell Announces That Commonwealth Signs Comprehensive Agreement and Reaches Financial Close to Build the New Route 460 in Southeast Virginia – Project to greatly improve transportation, create thousands of jobs and have a multi-billion dollar economic impact – RICHMOND - Governor Bob McDonnell announced today that the Commonwealth has reached a commercial and financial close with US 460 Mobility Partners (a partnership of Ferrovial Agroman, S.A. and American Infrastructure) and the Route 460 Funding Corporation of Virginia to finance, design and build a new 55-mile section of U.S. Route 460 in southeastern Virginia. Project development begins immediately for the new $1.4 billion roadway, which has been a top transportation priority locally, regionally and statewide for nearly a decade. The project was developed to address roadway deficiencies, improve safety, accommodate increasing freight shipments and reduce travel delays among many other needs. "As recognized by local officials and the General Assembly years ago, there is a clear and critical need for the new U.S. 460," said Governor McDonnell. "In 2000, the Virginia Transportation Act designated U.S. 460 as a high priority in southeastern Virginia. In 2003, the General Assembly passed a law requiring the Virginia Department of Transportation (VDOT) to build a new stretch of U.S. 460 under the Public-Private Transportation Act of 1995. Legislative leaders supported the project because it would improve safety for motorists and connectivity for freight and military traffic among other benefits. Today, the Commonwealth is finally delivering on that need and building a project that will not only make transportation better for the southeastern region and the state, it will also generate jobs and economic development opportunities, bringing extensive long-term benefits in so many ways." The key benefits of the new U.S. 460 include: Safety - Improve travel safety and efficiency along the corridor, including expanding westbound hurricane-evacuation routes Jobs - Generate approximately 4,000 jobs during construction and 14,000 jobs over the long-term, according to Chmura Economics Economic development - Attract new business opportunities, boost tourism and accommodate greater freight traffic from the growth in demand at the Port of Virginia Connectivity - Enhance connectivity among the region's military installations Choice and time savings - Provide a reliable alternative to I-64 between Richmond and Norfolk, saving 20 minutes compared to taking the existing U.S. 460 Economic impact - Chmura Economics estimates that the new highway will have an annual economic impact of $7.3 billion by 2020 The new U.S. 460 will be a four-lane divided highway from Prince George County to Suffolk. The toll road will be parallel to the existing U.S. 460. The existing Route 460 will remain a free alternative. Secretary of Transportation Sean T. Connaughton explained, "The Commonwealth has worked extensively with localities, the region and the public to complete environmental work, establish a corridor and then go through a lengthy evaluation process to select a private-sector partner and develop a financial plan to design and build the new highway. Today marks a major milestone with a signed contract to begin work on a transportation project that will increase safety and provide a critical link to jobs, commerce and the military." VDOT, in coordination with the Office of Transportation Public-Private Partnerships, procured the project under Virginia's Public-Private Transportation Act, which allows the Commonwealth to partner with the private sector to finance, design and build transportation improvements. The comprehensive agreement was signed today between VDOT Commissioner Greg Whirley, US 460 Mobility Partners and the Route 460 Funding Corporation of Virginia. Financial close was also reached, which releases funding to launch project work. Bonds issued by the Route 460 Funding Corporation of Virginia to finance the project were oversubscribed, meaning there was demand for more bonds than were available. The bonds were also sold at a lower than planned interest rate, which benefits the Commonwealth. "VDOT will work with the Route 460 Funding Corporation of Virginia to lead this project and oversee the work performed by US 460 Mobility Partners during construction," said VDOT Commissioner Whirley. "The private-sector team will design and build the project at a fixed cost by a fixed date and will take significant risks associated with delivering the project. The Commonwealth will continue to involve the community and public, seeking their input and addressing their concerns throughout project development and construction." "We are proud to have worked with the Commonwealth, a visionary state, and regional officials to achieve financial close on the new U.S. 460 project," said Ignacio Vivancos, president of Ferrovial Agroman US, which leads the US 460 Mobility Partners. "Achieving financial close allows us to get down to the real business of delivering this important project to the citizens of Virginia." William Fralin who chairs the Virginia Port Authority (VPA) Board of Commissioners added, "The VPA is investing in the new U.S. 460 project because it will be an economic engine for the Commonwealth over the long-term, creating opportunities for distribution centers and light manufacturing that will drive cargo through the Port of Virginia. This creates jobs and grows our economy." "The new U.S. 460 will bring greatly needed job and business benefits to the citizens and residents I represent along the corridor," said Delegate Rick Morris, R-Carrollton. "I support this project because it is an investment in the future of southeastern Virginia. This project comes at a perfect time as the Commonwealth looks for ways to assist the smaller communities to take advantage of economic opportunities." "The new U.S. 460 will support economic development and private industry development at a time when many in our community are unemployed and under-employed," said Al Casteen, chairman of the Isle of Wight County Board of Supervisors. "The jobs generated during construction and long term will be sought after by both local business and jobseekers. Without a doubt, the new U.S. 460 will bring economic prosperity that will benefit the region and the state well into the future." Suffolk Mayor Linda T. Johnson said, "The City of Suffolk was recently named one of America's best places to live for job growth. The benefits that the new U.S. 460 will bring including job opportunities and economic development will further enhance this mark of distinction. I welcome this project to our community." Key business terms and costs: VDOT will oversee the work performed by US 460 Mobility Partners during construction, and operate and maintain the facility after the construction is completed. VDOT will retain ownership and all potential excess revenues of the project as well as set the initial toll rates. US 460 Mobility Partners will design and build the project. The Route 460 Funding Corporation of Virginia is a non-profit corporation that has sold tax-exempt bonds to finance part of the project. The debt will be non-recourse to VDOT, the Commonwealth and US 460 Mobility Partners. The funding corporation will collect the tolls, adjust the toll rates and manage the toll collection system over the course of 40 years. The project cost is $1.396 billion including design, construction and toll collection set-up. Funding sources are as follows: Public funding from VDOT - $903 million, which is lower than originally forecasted due to reduced interest rates in the bond market. A lower amount is anticipated should the Commonwealth secure a low-interest federal loan from the Transportation Infrastructure Finance and Innovation Act (TIFIA) program. Public funding from the Virginia Port Authority - $250 million, a lower amount is possible if a TIFIA loan is secured. Private sector tax-exempt bonds sold this month by the Route 460 Funding Corporation of Virginia - $243 million (net amount). Project highlights: The new U.S. 460 will be a 55-mile four-lane divided, limited-access highway from Suffolk to Prince George County. It will parallel the existing U.S. 460. There will be seven interchanges at routes 156, 625, 602, 40, 620, 616 and 258. Design and right of way work is expected to begin in 2013, which will include public meetings. Construction is anticipated to start in 2014. When the road opens in 2018, tolls will begin at approximately 7 cents per mile ($0.067) for cars and 21 cents per mile ($0.213) for trucks. This equates to $3.69 for cars and $11.72 for trucks for the entire 55 miles. Tolls will be collected electronically using E-ZPass and license plate video tolling. There will be no manual toll collection. The existing U.S. 460 will have no tolls and remain a free alternative. http://chmuraecon.com/blog/2012/december/20/governor-mcdonnell-announces-that-commonwealth-signs-comprehensive-agreement-and-reaches-financial-close-to-build-the-new-route-460-in-southeast-virginia/ http://chmuraecon.com/blog/2012/december/20/governor-mcdonnell-announces-that-commonwealth-signs-comprehensive-agreement-and-reaches-financial-close-to-build-the-new-route-460-in-southeast-virginia/ Thu, 20 December 2012 11:47:05 Chris Chmura http://chmuraecon.com/blog/2012/november/05/agritourism-a-growth-market/ Roger Gonzalez of The News Virginian writes ( original article ): More than $22 million was spent on agritourism in the Shenandoah Valley last year, according to a recent study, and that number could rise in the next decade. “There is potential to grow the number of agritourism businesses, room to grow jobs and capture some more revenue,” Bonnie Riedesel, executive director of Central Shenandoah Planning District Commission, said Thursday. “We knew that there was potential for it. We just didn’t have the numbers to back it up. Now we do.” The planning district commission announced the news at the Fields of Gold Harvest Jubilee at Barren Ridge Vineyards. The event was named for the award-winning Fields of Gold program, which involves six Valley counties and five cities. It promotes the region as an agritourism destination, whether for visits to a working farm, a winery, a corn maze or a horse farm. It also seeks to create jobs on the farm and tourism jobs off. In 2011, 226 businesses employed 704 people in agritourism locally, noted the study, done by Richmond-based consulting firm Chmura Economics & Analytics. And including multipliers, the $22.4 million spending figure rises to $34.8 million, and the employment number to 811 jobs. Chmura estimates that about 6.7 million visitors traveled more than 50 miles to come to the region in 2010, and that that total tourism number could grow 6.2 percent in the next 10 years. And, given that 15 percent of potential visitors surveyed said they would be very interested in agri- or ecotourism, the study said, such sales here could expand at a rate of 9.3 percent per year. That delighted many in the crowd at Barren Ridge. “Agriculture is Virginia’s No.1 industry,” said Matt Lohr, commissioner of the state Department of Agriculture and Consumer Services, keynote speaker at the event. “I think what we are seeing is that agritourism and opportunities to have more direct marketing between the producers and consumers, it really is big business. As agriculture changes, and we have more of a society that wants to be connected, it certainly gives more and more opportunities for farmers to take advantage of. It’s exciting.” Moving forward, the plan is to take advantage of the Fields of Gold statistics and implement a plan to try to realize the potential growth. Complete results of the study will be available at the planning district commission’s website, cspdc.org, early next week. “We have some other grants out there that are pending,” Riedesel said. “Hopefully we can begin to market and carry this program forward.”   http://chmuraecon.com/blog/2012/november/05/agritourism-a-growth-market/ http://chmuraecon.com/blog/2012/november/05/agritourism-a-growth-market/ Mon, 05 November 2012 08:12:45 http://chmuraecon.com/blog/2012/october/30/mumford-s-gotr-bristol-stopover-economic-impact-released/ Bristolrhythm.com mentioned a recent Chmura study in their blog post Mumford’s GOTR Bristol Stopover Economic Impact Released : Today representatives from Birthplace of Country Music, Bristol Rhythm & Roots Reunion, Believe in Bristol, and Pick Bristol gathered media and members of city and local governments to the City of Bristol, VA School Board Office Conference Room to reveal details of the Virginia Tourism Corporation‘s economic impact study researched during the GOTR Bristol Stopover. The research company Chmura Economics & Analytics was contracted by VTC to perform the study over the course of the August 11, 2012 weekend, combining the impacts of event staging and visitor spending. “Gentlemen of the Road generated $5.1 million in tax revenue for the cities of Bristol alone,” says Believe in Bristol Executive Director Christina Blevins, “in the state of Virginia visitors spent $6.7 million. It’s amazing that one event in our cities can generate that much income.” Read the rest of the article or the full GOTR Economic Impact Study . http://chmuraecon.com/blog/2012/october/30/mumford-s-gotr-bristol-stopover-economic-impact-released/ http://chmuraecon.com/blog/2012/october/30/mumford-s-gotr-bristol-stopover-economic-impact-released/ Tue, 30 October 2012 08:12:53 http://chmuraecon.com/blog/2012/october/24/nba-site-would-bring-virginia-503m-a-year/ A recent study conducted by Chmura Economics & Analytics was featured in the Virginian-Pilot . The $31,240 study, completed by Richmond-based Chmura Economics & Analytics, was commissioned by the city's economic development authority. It follows an economic impact study on Virginia Beach done for the authority by Old Dominion University economics professor James Koch. Read the full article . http://chmuraecon.com/blog/2012/october/24/nba-site-would-bring-virginia-503m-a-year/ http://chmuraecon.com/blog/2012/october/24/nba-site-would-bring-virginia-503m-a-year/ Wed, 24 October 2012 10:02:59 Chris Chmura http://chmuraecon.com/blog/2012/october/10/virginia-florida-and-pennsylvania-among-states-to-be-hardest-hit-by-sequestration/ Forbes.com writes about a research report published by George Mason University & Chmura Economics and Analytic : With sequestration looming, the Obama administration told contractors to not warn employees that they may be laid off due to massive cuts to defense spending. The political calculus here is obvious, if contractors comply with the Obama administration’s directive, workers across the country —including those in the swing states of Virginia, Florida and Pennsylvania— will not receive notifications under the WARN Act. The WARN Act is designed to protect “workers, their families, and communities by requiring most employers with 100 or more employees to provide notification 60 calendar days in advance of plant closings and mass layoffs.” Many commentators believe that such notifications, if sent, might impact the outcome of the presidential election. Of course, sequestration will have an impact beyond politics, with economists predicting a devastating impact on the national economy and the economy of Virginia, Florida, and Pennsylvania. According to a research report published by George Mason University & Chmura Economics and Analytics, total job losses across those three swing states would total 365,484 people. On a national level, the report states that implementing the cuts in the Budget Control Act of 2011 “would severely impact the economy in 2013 with these losses reflected in reduced Gross Domestic Product (GDP) and a broad based loss of jobs that could add an estimated 1.5 percentage points to the current U.S. unemployment rate.” The report continued, “[a]s currently formulated, the automatic spending cuts affecting DOD and non-DOD agencies’ discretionary spending authorities beginning January 2, 2013 will: Reduce the nation’s GDP by $215 billion; Decrease personal earnings of the workforce by $109.4 billion; and, Cost the U.S. economy 2.14 million jobs.” Read more http://chmuraecon.com/blog/2012/october/10/virginia-florida-and-pennsylvania-among-states-to-be-hardest-hit-by-sequestration/ http://chmuraecon.com/blog/2012/october/10/virginia-florida-and-pennsylvania-among-states-to-be-hardest-hit-by-sequestration/ Wed, 10 October 2012 09:16:31 Chmura Staff http://chmuraecon.com/blog/2012/october/03/pipeline-of-highly-educated-workers-is-misaligned-in-localities-big-and-small/ Ever wonder if your colleges and universities are aligned with the needs of your industries?  Sure, it is easy to pinpoint the number of engineers or biochemists in a given market, but what about the pipeline and how many employers are chasing these graduates? Put another way, given the size of the industries in your region that employ highly educated workers, is your area graduating enough postsecondary students to adequately meet demand? The answer can surprise you. In big and small markets, most areas do a good job of graduating students with some work-relevant degrees, but fall below average in some segments. A case in point, the fast growing cities of Texas are struggling to align their higher education programs with the ever evolving needs of industry in a few areas.  Chmura created its training concentration index to answer precisely this question. Is this area graduating a sufficient number of postsecondary—associate’s, bachelor’s, master’s, and doctoral degrees—students given the demands of the area’s industry mix? If the answer turns out to be a deficit in certain skills areas, companies in these sectors are either importing talent from outside the region or hiring employees without the work-related skills and background they need, and thus committing themselves longer training periods and probably more turnover. In the table below, Chmura’s economist calculated the training concentration at the 2-digit SOC aggregate level for several MSAs of different sizes. What this table showcases is that it is neither geography nor size that predicts if an area is producing a sufficient number of college graduates given the industry size and mix. In the table below, the red arrows indicate an occupational grouping where the MSA is not graduating enough degrees that track into these occupations given the size of the industries that use these employees. Green cross-arrows indicate approximate equilibrium between supply and demand for degree awards that feed into these jobs, whereas the blue arrows indicate a regional oversupply of students earning degrees that track into these positions. This analysis helps JobsEQ® users demonstrate their area’s relative strengths in terms of the education pipeline as well as understand areas where there may be opportunity to work with local education providers to expand programs that feed into occupations important to an area’s industry clusters. JobsEQ® enables this analysis at a more granular level—for an individual occupation—and for any kind of geography—county, group of counties, or at the state or MSA level. However, when using this analytic, it is important to define your labor shed appropriately to take into account natural commuting patterns into and out of your region. http://chmuraecon.com/blog/2012/october/03/pipeline-of-highly-educated-workers-is-misaligned-in-localities-big-and-small/ http://chmuraecon.com/blog/2012/october/03/pipeline-of-highly-educated-workers-is-misaligned-in-localities-big-and-small/ Wed, 03 October 2012 09:29:48 Chris Chmura http://chmuraecon.com/blog/2012/september/20/closer-alignment-with-labor-demands-justifies-higher-education-investment/ An article written by Chris Chmura was recently published on evolllution.com. Read the full article here . Now, perhaps more than ever, education providers of all types can reap the rewards of adjusting their curriculum and programs to the needs of their region’s fastest growing industries. Two trends have come together to provide a once-in-a-generation chance for institutions of higher learning, career and vocational schools, and workforce training centers to boost their market share and improve student outcomes. First, the Great Recession has completely up-ended the status quo in the U.S. labor market. We have the largest number of unemployed and under-employed individuals in a generation, and there is a tremendous amount of training money available from Washington to help retrain these folks. From community colleges to career schools, from traditional four-year universities to workforce training centers, funding can be obtained if you can demonstrate that your program will result in a “positive student outcome.” In other words, you need to build a skill-set in your students that leads them to a job. However, many schools, colleges, and workforce training centers struggle to understand which fields are growing the fastest, which industries have the best long-term prospects, and what competencies are needed for today’s “in demand” occupations. Read more . http://chmuraecon.com/blog/2012/september/20/closer-alignment-with-labor-demands-justifies-higher-education-investment/ http://chmuraecon.com/blog/2012/september/20/closer-alignment-with-labor-demands-justifies-higher-education-investment/ Thu, 20 September 2012 11:05:02 Chmura Staff http://chmuraecon.com/blog/2012/september/13/unemployment-by-occupation-can-vary-significantly-by-region/ Finding data on the number of unemployed by various occupations and skill levels for most municipalities has been very difficult or even impossible—until now. Even unemployment rates for detailed occupations at the national level are rare to find in published materials. Nothing has been readily and consistently available to analyze unemployment by occupation at the state, MSA, or county level. With this in mind, Chmura’s economists have developed a new model to impute the number of unemployed at the county, MSA, or state level, which can dramatically increase one’s awareness of the occupations and specific types of training programs on which regions need to focus. It also gives economic development professionals and chamber of commerce officials added insight as to where they have sufficient excess labor to meet the demands for industry expansion or contraction. The results of this analysis speak for themselves. Different occupations exhibit wide variation in their unemployment rates by geographic location. In different areas, the same occupation can either be in high demand or in low demand—shifting the balance of power in wage negotiations and employee retention significantly. The maps below depict the unemployment rate at the county level for three occupations—middle school teachers (25-2022), registered nurses (29-1111) and carpenters (47-2031). Each of these positions requires specialized skills, but each are in very different industries and their employment prospects vary considerably by region. Geography, industry mix, and regional economic outlook all come in to play when examining regional unemployment by occupation. Middle School Teachers, Except Special and Career/Technical EducationRegistered NursesCarpentersComputer Support SpecialistsAccountants and Auditors   http://chmuraecon.com/blog/2012/september/13/unemployment-by-occupation-can-vary-significantly-by-region/ http://chmuraecon.com/blog/2012/september/13/unemployment-by-occupation-can-vary-significantly-by-region/ Thu, 13 September 2012 10:12:34 Chmura Staff http://chmuraecon.com/blog/2012/august/01/wage-pressure-index-spotlights-hot-job-markets/ More and more over the past year, as companies have begun researching expansion initiatives or re-shoring opportunities, Chmura has been asked to provide guidance as to the availability and cost effectiveness of workers in a given area. Many times these industry executives or their site selection consultants have asked Chmura not only to assess the current supply and cost of qualified workers—a task readily accomplished via several analytics in JobsEQ®—but also to compare regions across the country in terms of potential wage pressure by industry or occupation. Companies want to know that they are expanding or relocating into an area with ample supply of qualified workers, but are wary of markets where they could face rapidly increasing labor costs. Chmura’s economists responded to these requests by utilizing several proprietary features of JobsEQ® to create a wage pressure index that can provide an indicator between two cities or regions, indicating which is likely to experience faster wage growth in a given occupation over the next 2-4 years. Chmura’s wage pressure index combines actual 5-year (historic) wage growth in an occupation or industry with an imputation of the unemployment rate for an occupation (or group of occupations if we are estimating the wage pressure for an industry). These two factors are combined with long-term projections regarding the expected surplus or deficit of a given occupation (or a group of occupations for an industry analysis) and the 10-year employment growth forecast for the city or region. Chmura’s economists then compared these four factors to a national level norm to provide a composite index that is a valid predictor of latent wage pressure in a given city or region for a specific occupation or industry. Currently, Chmura’s economists are computing the index on an ad-hoc basis for our clients while preparing the method for implementation into JobsEQ® by the end of 2012. To help illustrate the value of this analytic, we have calculated the wage pressure index for software application developers (SOC 15-1132) across the 50 largest US metropolitan statistical areas. The results are quite interesting. Several midwestern and southern MSAs score as having very low wage pressure while many western and southwestern metros, despite having relatively low wages for this position, are likely to see continued high wage appreciation over the next several years. Interestingly, most of the traditional IT hotspots in California and Boston score in the middle of the pack in terms of wage pressure. Texas metros run the gamut with Dallas being one of the MSAs with the lowest wage pressure and San Antonio being one of the highest, with Austin and Houston ranking in the middle. This is analysis is simply an example of the analytic and Chmura strongly feels that this tool is best used at the industry level or for a group of key occupations analyzed together—such as all IT jobs—in order to inform an actual expansion or relocation decision.   MSA Wage Change Gap Growth Unempl Phoenix-Mesa-Glendale, AZ MSA Oklahoma City, OK MSA Salt Lake City, UT MSA Orlando-Kissimmee-Sanford, FL MSA San Antonio-New Braunfels, TX MSA Washington-Arlington-Alexandria, DC-VA-MD-WV MSA ... Los Angeles-Long Beach-Santa Ana, CA MSA ... Cleveland-Elyria-Mentor, OH MSA Memphis, TN-MS-AR MSA Chicago-Joliet-Naperville, IL-IN-WI MSA St. Louis, MO-IL MSA Milwaukee-Waukesha-West Allis, WI MSA Detroit-Warren-Livonia, MI MSA http://chmuraecon.com/blog/2012/august/01/wage-pressure-index-spotlights-hot-job-markets/ http://chmuraecon.com/blog/2012/august/01/wage-pressure-index-spotlights-hot-job-markets/ Wed, 01 August 2012 08:22:59 http://chmuraecon.com/blog/2012/july/17/more-than-2-million-jobs-will-be-lost-if-automatic-spending-cuts-kick-in-report-says/ Fox News recently mentioned a study performed by Chmura Economics & Analytics: Automatic cuts in federal spending will cost the economy more than 2 million jobs, from defense contracting to border security to education, if Congress fails to resolve the looming budget crisis, according to an analysis released Tuesday. The study, obtained by The Associated Press, was conducted for the Aerospace Industries Association, but it examined the shared pain for defense and domestic programs from the across-the-board reductions slated to kick in Jan. 2. The cuts would reduce the nation's gross domestic product by $215 billion next year while consumer confidence would plummet, said the report by Dr. Stephen Fuller of George Mason University and Chmura Economics and Analytics. "If they are allowed to occur as currently scheduled, the long-term consequences will permanently alter the course of the U.S. economy's performance, changing its competitive position in the global economy," said the report. Read more: http://www.foxnews.com/politics/2012/07/17/more-than-2-million-jobs-will-be-lost-if-automatic-spending-cuts-kick-in-report/#ixzz20uSAqZrm http://chmuraecon.com/blog/2012/july/17/more-than-2-million-jobs-will-be-lost-if-automatic-spending-cuts-kick-in-report-says/ http://chmuraecon.com/blog/2012/july/17/more-than-2-million-jobs-will-be-lost-if-automatic-spending-cuts-kick-in-report-says/ Tue, 17 July 2012 15:24:30 Chmura Staff http://chmuraecon.com/blog/2012/july/06/using-forecasts-to-further-policy-insight/ Chmura's models and analytics, which form the backbone of our flagship product, JobsEQ, were recently utilized by a leading public policy think-tank, America's Edge, to highlight the skills gap that exists in California and Illinois. In part, through the use of forecasts of occupational supply and demand contained in JobsEQ, America's Edge was able to empirically demonstrate the skills-gap that exists in these states, particularly at the mezzanine level (jobs requiring some post-secondary training but less than a full bachelor's degree) of education. There was also a pronounced shortage of workers with Science, Technology, Engineering, and Math (STEM) skill-sets, which will increasingly be important to these economies as STEM-related jobs are growing faster than overall job growth. For instance, utilizing Chmura's JobsEQ America's Edge found that in Los Angeles high-skilled jobs are projected to grow at roughly twice the rate of low-skilled jobs between 2010 and 2020, and that roughly 60 percent of the fastest growing occupations over the next 10 years will be jobs that require an education foundation in STEM. Based on this, they found that LA County has an undersupply of at least 81,000 high and middle-skilled workers. Similarly Illinois' job growth is highly skewed toward jobs that are either highly skilled (bachelor's degree or above) or middle-skilled (associate's degree, vocational degree or professional accreditation.) In Illinois the highest-skilled jobs are expected to grow at more than four times the rate of the lowest-skilled jobs. The Chicago Metropolitan Area has a projected deficit of over 90,000 middle-skilled workers alone. The full reports for California (June) and Illinois (May) can be found at America's Edge website: http://www.americasedge.org/research/americas-edge-research/ America's Edges research conforms to independent research that Chmura conducted with National Association of Manufacturers, which found a pronounced skill bias in the manufacturing sector. Changes in technology relating to automation of processes and utilizing advance composite materials over the past decade have greatly increased the need for high skilled technicians and tradesmen while simultaneously reducing the need for low-skilled assembly and material moving positions. In almost every sector of the economy jobs requiring more skills, particularly STEM skills, command the higher wages and provide greater job security. Even though this last recession has hit all workers, regardless of education, very hard, education is still the best avenue to securing higher wages. STEM-related occupations also command a premium across the country as well as in the states of Illinois and California. http://chmuraecon.com/blog/2012/july/06/using-forecasts-to-further-policy-insight/ http://chmuraecon.com/blog/2012/july/06/using-forecasts-to-further-policy-insight/ Fri, 06 July 2012 09:44:11 Chris Chmura http://chmuraecon.com/blog/2012/june/28/chris-chmuras-talk-from-the-virginia-leadership-summit-2012/ http://chmuraecon.com/blog/2012/june/28/chris-chmuras-talk-from-the-virginia-leadership-summit-2012/ http://chmuraecon.com/blog/2012/june/28/chris-chmuras-talk-from-the-virginia-leadership-summit-2012/ Thu, 28 June 2012 14:23:27 http://chmuraecon.com/blog/2012/june/08/economic-development-of-northwest-arkansas/ Mike Harvey from the Northwest Arkansas Council giving a wonderful talk about the Arkansas economy utilizing JobsEQ. http://chmuraecon.com/blog/2012/june/08/economic-development-of-northwest-arkansas/ http://chmuraecon.com/blog/2012/june/08/economic-development-of-northwest-arkansas/ Fri, 08 June 2012 15:22:25