I have been using Feedly for almost a year and can recommend it. Check out their blog for some tips on their service.

Or if you have just a few subscriptions and need a very simple blog reader try Bloglovin. You can start your import here.

One more thing...

For many years blog, my subscription service (which manages my VisualizingEconomics subscribers) was Feedburner and I currently have 3,500 RSS subscribers using feeds.feedburner.com/visualizingeconomics.

However, I am very nervous that the Feedburner service will one day be shutdown by Google, so over the next few months I want to move these subscribers to a new RSS feed address.

If you subscribed to my blog using email or you subscribed to my RSS feed AFTER May 15th you are already using my new subscription service, no worries for you (unless you are still using Google Reader!).

Please take a moment and resubscribe to my blog RSS feed using this address below. I would appreciated it: 

http://feeds.visualizingeconomics.com/visualizingeconomics

You will also want to delete your old feed for VisualingEconomics so you don't see duplicate posts :)

Thanks... Catherine

     

 

 

How risky is gold compared to other assets? For most of the of the United States' history  gold's price was set by the government (given that the value of the US dollar was often pegged to gold). In this graphic, I have taken the annual price change of gold back to 1928 and compared it to the returns on stock, bonds and t-bills.

Since the early 1970s when the US completely abandoned the gold standard, gold's price changes has made it very volatile and if we look at the annualized compound return from 1976-2012 (gold 6.6%;  stocks 10.9%;  10-year bonds 8.1%;  3-month t-bills 5.1%). Gold returns was just little better than 3-month t-bills. However if we look at the annualized return for 1928-2012: gold's return of 5.3% beats t-bills at 3.6% and bonds at 5.1% but still underpreforms stock's return of 9.3%.

Gold Data from MeasuringWorth.org. Stock and treasury bonds data from Damodaran Online | Updated Data | Historical Returns on Stock Bonds and Bills - United States. CPI from Measuring Worth. 

Graphs created in OmniGraphSketcher then pasted into Illustrator. 

     

 

 

In 2008, I graphed the annual US price of gold. In this post, I revisit gold but this time I graphed the market price along with the real price (adjusted for inflation using 2012 dollars). When you take the inflation into account, the annual price of gold has spiked over $1,700 twice since the US left the gold standard, once in 1980 and again in 2012.

In this graphic, I briefly touch upon the history of gold standard. Beginning in 1792, the US Mint pegged the dollar to gold and silver. In 1900 the US went on the gold standard (i.e. the dollar was pegged just to gold). During this time, except for monetary crisises, the market price for gold matched the "official" price set by the US government. In the 1970s, the US finally left the gold standard and allowed the dollar to float freely on international currency markets.

Data from MeasuringWorth.org . Graphs created in OmniGraphSketcher then pasted into Illustrator. 

     

 

 

Revisited the Stock, Bonds and Bills data from my previous post, I created a set of real (inflation adjusted) average annual compound returns for rolling time periods (e.g. rolling 5-year returns: 1928-1933, 1929-1934, 1930-1935 etc...) I also included the  average annual compound real return for 1928-2012: stocks 6.0% 10-year bonds 2.0% and 3-month bills 0.5%

Some of the outliers in these returns are due to bubbles. For example stocks in the late 1920s, mid-1930s and late-1990s had unusual large real returns with a corresponding drop. See Long-term Growth Rate of US Stocks)

Return Data from Damodaran Online | Updated Data | Historical Returns on Stock Bonds and Bills - United States CPI from Measuring Worth. 

Chart created using OmniGraphSketcher; labels added with Adobe Illustrator. 

     

 

 

Here is a simple graph comparing the variation of annual returns for US stocks, US 10-year bonds and US 3-month t-bills. I have included both nominal returns (not adjusted for inflation) and real returns. 

Stocks are of course the most risky of the three with both the highest and the lowest  returns then comes 10-year bonds and finally t-bills with the smallest but the most consistant returns. However, after 3-month t-bills are adjusted for inflation there are many years you will "lose" money. And in years with deflation, your real return will be larger than the nominal return (i.e. you did better holding stocks in 1933 when you take into account the effects of deflation in that year while 1954 had the highest nominal stock return).

Return Data from Damodaran Online | Updated Data | Historical Returns on Stock Bonds and Bills - United States CPI form Measuring Worth. 

Chart created using OmniGraphSketcher; labels added with Adobe Illustrator. 

     

 

 

I am defining middle-income households as $30,000-$80,000. One of the stories these graph tell is that for 20+ years after 1945 more households entered the "middle class". However, over the next 40 years, the percent of middle-income households shrank in part because the percent of households with more than $80,000 a year grew. 

Graphs created in OmniGraphSketcher and annotated in Illustrator. Data from the US Census. You take a look at some of the older reports they have online here:

US Census Bureau. “Families and Individual Money Income in the United States: 1945. Table 2.” September 2011. http://www2.census.gov/prod2/popscan/p60-002.pdf.

———. “Income of Families and Persons in the United States: 1950. Table 1.” September 2011.  http://www2.census.gov/prod2/popscan/p60-009.pdf.

———. “Income of Families and Persons in the United States: 1960. Table 5.” September 2011.  http://www2.census.gov/prod2/popscan/p60-037.pdf.

———. “Income, Poverty, and Health Insurance Coverage in the United States: 2010. Table A-2.” September 2011. http://www.census.gov/prod/2011pubs/p60-239.pdf.

     

 

 

Copies of this graphic can be purchased at Zazzle

Inflation is when prices go up. However, inflation's relationship to stock prices can be a little more complex. In this graph, I am revisiting historical data I used several years ago in a series of historical graphs looking at the stock market (see GDP per Capita vs US Stock Prices and Real Growth in Stock Returns Dividends Reinvested).

I graphed the price/earnings ratio back to 1880 and highlighted the years that inflation was high and when there was deflation. The P/E is calculated from trailing 10-year earnings. The low inflation years (5% or less) had the highest P/E while the high inflation years has the lowest P/Es. Deflation years had P/Es near the average.

S&P data from Robert Shiller and CPI data from MeasuringWorth. Graphic was created using OmniGraphSketcher.

     

 

 

Information Industry's share of GDP 1947-2010 

Starting with the Information industry's share of GDP (from page 119 of my book), while it has grown over the last 50 years it is still around 4% of GDP as of 2010. 

However, the Information industry represents only 2% of the 170 million jobs in the US economy. 

In this treemap of all occupations in the US economy, each occupation is represented by a rectangle, the bigger it is the more jobs it has. Look at the upper right corner to see Information's share of jobs. The dark red represents the percentage of jobs loss and the only area more red than the Information sector is Manufacturing.

Now drilling into just the Information industry, some of the bigger occupations are: editors, computer software engineers, customer service representatives, telecommunications equipment installers, reporters and finally producers & directors. Out of that list only producers & directors had job growth between 2001-2011. 

If you are interested in the income of these occupations or want to explore additional industries take a look at my book. 

Data used in these graphics was based on BLS Occupational Handbook (provided by 
EMSI) and value-added GDP from the BEA. The area graph was created in Omnigraphsketcher and the treemaps by R. Label were later added using Illustrator.

     

 

 

I start with graphing the distribution of all public employee salaries below $250,000, 99.7% of all state employees, listing the most common job titles I found: teachers and professors, police officers, clerks and administrative assistants, bus operators, child protective services specialist and mental retardation assistants.

However, to graph the long tail of the income distribution I have to graph individual salaries. Many are heads of surgery departments and head coaches. The highest salary goes to the head football coach at the University of Texas at Austin. (Salary of $2.5 million plus a bonuses of $2.7 million for a total of $5.2 million)

Another way to show how much inequality exists with these salaries is to plot the cumulative share of Texas public employees vs cumulative share of their salaries. But how does this compare to the Untied States as a whole? I found in a report from the CBO, a graph plotting the 2007 cumulative share of all US households against household income which I used as a stand in for everyone in the US. You can see that the US household income distribution is more unequal than salaries of Texas state employees.

Data sources: The Texas Tribune and the Congressional Budget Office.

Design notes: Graphs were created using OmniGraphSketcher, copied into Adobe Illustrator where annotations were added. The illustrator file was then placed into an InDesign document for the book. View all the graphics from the book online.

     

 

 

I also looked at the households with more than $200,000 a year and mapped where they are clustered. (BTW, if you want to see the voting patterns of high-income counties  and find out if the wealthy counties voted for Obama or Romney check out a map I created for Design & Geography). 

Data is from the US Census Bureau, American Community Survey.

     

 

 

From heteconomist: Why so many jobs are crappy

Satisfying jobs – let's call them 'good jobs' – will generally be ones where learning occurs at a steady pace more or less indefinitely, probably as part of a defined career path. [...] Once you gain experience in a good job, you will soon become much more efficient in the role than an inexperienced replacement would be.

Summarizing this post, MarginalRevolution comments

The first key point is that if you learn more on the job on a regular basis (i.e., your job is interesting), you become harder to replace from the point of view of your boss.  Over time you win more of the bargaining surplus. 

So looking at my treemaps of occupations from An Illustrated Guide to Income in the United States, I have a category called "Long-term on-the-job training" defined as:

More than 12 months of on-the-job training or, alternatively, combined work experience and formal classroom instruction, are needed for workers to develop the skills to attain competency. Training is occupation specific rather than job specific; therefore, skills learned can be transferred to another job in the same occupation. This on-the-job training category also includes employer-sponsored training programs.

This sound like some of the "good jobs" people are looking for that don't require a college degree.

Click on images to enlarge.

WIth this data graphic, I wanted to give an overview of all occupations grouped by the education/training required. The size of each rectangle represents the number of jobs.

Occupations like: electricians, carpenters, farmers & ranchers, restaurant cooks, photographers, police & sheriff's patrol offices can be found in  "Long-term on-the-job training".

The last three occupations (restaurants cooks, photographers, police & sheriff's patrol offices) increased in number of jobs from 2001-2011.

To learn more about which industries have a high number of  "long-term on-the-job training" jobs, first check out pages 128-148 in my book and then goto the BLS's Occupational Handbook to search for more jobs with long-term on-the-job training but no college requirement.

Data for the occupation treemaps was provide by EMSI based on data from Bureau of Labor Statistics (BLS).
.

     

 

 

In my previous post, I looked at wages, showing that for many occupations, they have not kept up with the overall growth of the economy. But what about the growing number households with incomes well above the national average of $67,500? (Focus on the orange lines/area in the 3 graphs below)

Click on the images for a closer look. This will open a lightbox the same size as the browser window.

In 1945, less than 10% of family households had incomes above $80,000 (adjusted for inflation) steadily increasing to 30% in the late 1990s. They continued to increase up until 2000 even while wages flatten or dropped.

Looking at the bottom graph, the number of family households with two earners grew and surpassed number of single earner household in the late 1960s. This growth mirrors the growth of families with $80,000 or more a year. One can easily assume that second earner in these households were women as women entered the labor force in larger numbers boosting their family's income. 

Note: Single people living alone are not included in "Family Households," bottom graph, but are included in "All Households," top graph.

In 2010, 72% of the households with income above $80,000 a year have two or more earners compared to 41% of households with income between $30,000 and $80,000. It is this increase in the number of two-earner households that accounts for household incomes increasing when wages have not.

To learn more about Income in the US buy or read the book.

Data for households came from the US Census

US Census Bureau. “Historical Income Tables: Households.” June 2011. http://www.census.gov/hhes/www/income/data/historical/household/index.html.

US Census Bureau. “Table HINC-01. Selected Characteristics of Households, by Total Money Income in 2010.” 2012. http://www.census.gov/hhes/www/cpstables/032011/hhinc/new01_001.htm

 (See bibliography for additional data)

     

 

 

Click on the images for a closer look. This will open a lightbox the same size as the browser window.

Over the last couple of centuries there has been a steady increase in wages for both unskilled workers...

....and production workers. A lot of this growth is a result of the increases in worker productivity due the industrial revolution of the late 1770s and 1800s. However, over the last 40 years, this long-term growth has stopped or slowed down...

Updated 3/25/13: This Production Workers series includes benefits all the way back to when benefits became measurable in the early 1900s. More detailed definition can be found here.

...even though the GDP per person continues to grow. At the same time, the growth rate of GDP per worker has slowed compared to the overall growth of the economy.

Looking at just goods-producing industries, wages dropped for manufacturing, construction, and mining & logging since their a peak in the 1970s.

But among so-called service industries over the same time period there has been either a dip in wages or no real growth. Exceptions include jobs in the financial industry, education & health services and "other" services (which is mashup of occupations like auto mechanics, pet care, promoting political causes or religious activities). 

Data Sources for Wages (See bibliography for more references)

Officer, Lawrence H., and Samuel H. Williamson. “Annual Wages in the United States, 1774–Present.” MeasuringWorth.com, 2011. http://www.measuringworth.com/uswage/. 

US Bureau of Labor Statistics. “Table B-8. Average hourly and weekly earnings of production and nonsupervisory employees on private nonfarm payrolls by industry sector, seasonally adjusted.” November 2012. http://www.bls.gov/webapps/legacy/cesbtab8.htm. 

Designer's Notes: Some of my thoughts on the design and the approaches I used. 

One of the many data series that have exponential growth. They are a challenge  since I want to make the graph accessible to a wide (non-finance) audience but log graphs are very helpful when you need to see the relative changes across the entire time series.

Through out the book you will see me stacking two or more graphs when they have same timeline on the x-axies. EIther I want to facility comparisons of different time series or in this case show the same data on a "normal" y-axis and a log scale. You may also notice the absence of the GDP per person on this graph. SInce I was working with average hourly wages over 200-year period I didn't feel comfortable converting it to an annual salary for production workers who can often work irregular hours.

Unlike the previous two pages, values of the salaries was not the focus of this graphic. In this case I wanted to compare the change over time of GDP per person vs GDP per worker so I converted the series so they both started from the same point in 1947 (the first year I had the data for the civilian labor force). 

When looking at the historical wages for the major industries, I tried several ways to group the series hoping I could show some interesting patterns. I finally went with the simplest approach and created one graph for goods-producing industries... 

...and a second graph for service-providing industries. While this worked well for the first graph, 3 time series with similar behavior, with the service graph I was left with a less optimal design, a "spaghetti graph", which I normally try to avoid. I decided to keep scale the same on both graphs to show the overall pattern for the goods and service industries rather than splitting each series into separate smaller line charts.  

In each case, the original graph was created in OmniGraphSketcher. Additional annotations were added in Illustrator and when multiple graphs are need they are laid out in Illustrator. Finally, I link to the Illustrator file from within an InDesign document where i add page titles, a footer for data sources and pagination.

     

 

 

I wanted to let you that after 2 years of work I have just published An Illustrated Guide to Income in the United States. You can peek inside the book here. 

Over the next month, I will be posting highlights from the book and describing some of the challenges of making it.  Enjoy!  Catherine

     

 

 

An infographic I worked on for the NYC Comptroller report on eduction called Beyond High School. I created this in Adobe Illustrator.

     

 

 

Data from EMSI