Changes in labor market policies and practices have played a key role in the rise of inequality and the wage stagnation the vast majority of workers have seen since the 1970s. One example is the erosion of workers’ right to earn overtime pay for working excessive hours.

To ensure the right to a limited workweek, the Fair Labor Standards Act (FLSA) requires that workers covered by FLSA overtime provisions must be paid at least “time-and-a-half,” or 1.5 times their regular pay rate, for each hour of work per week beyond 40 hours. The rule is meant to protect workers who lack control over their time and tasks and do not receive high pay. The rule aims to exclude professional and managerial employees from overtime pay requirements by exempting workers who pass certain “duties tests” or who make over the salary threshold under which all salaried workers, regardless of their work duties, are covered by the overtime provisions. However, this salary threshold has been changed only once since 1975 and it is not indexed to inflation.

The figure below shows the share of salaried workers under the salary threshold in 1975 and 2013 by education level. In 2013, in every education category, the share under (and protected by) the salary threshold was much lower than it was in 1975. For example, fifty-one percent of salaried workers with a college degree were covered by the salary threshold in 1975, compared with just six percent in 2013. As detailed in this recent EPI report, the salary threshold would need to be $1,122 per week to cover similar shares of workers as were covered in 1975.

Last year marked the 75th anniversary of the Fair Labor Standards Act (FLSA), the legislation that established many of the basic labor protections workers enjoy today, such as a 40-hour workweek, overtime protection, and a national minimum wage. There have been periodic amendments to the FLSA over the years, but the 1966 amendments were especially significant. They extended protections to hotel, restaurant, and other service workers who had previously been excluded from the FLSA, but also introduced a new “subminimum wage” for workers who customarily and regularly receive tips.¹ Unlike temporary subminimum wages (such as those for students, youths, and workers in training), the “tip credit” provision afforded to employers uniquely established a permanent sub-wage for tipped workers, under the assumption that these workers’ tips, when added to the sub-wage, would ensure that these workers’ hourly earnings were at least equal to the regular minimum wage. The creation of the tip credit—the difference, paid for by customers’ tips, between the regular minimum wage and the sub-wage for tipped workers—fundamentally changed the practice of tipping. Whereas tips had once been simply a token of gratitude from the served to the server, they became, at least in part, a subsidy from consumers to the employers of tipped workers. In other words, part of the employer wage bill is now paid by customers via their tips.

Today, this two-tiered wage system continues to exist, yet the subsidy to employers provided by customers in restaurants, salons, casinos, and other businesses that employ tipped workers is larger than it has ever been. At the federal level, it currently stands at $5.12 per hour, as employers are required to pay their tipped staff a “tipped minimum wage” of only $2.13 per hour, and the federal regular minimum wage is currently $7.25.² Remarkably, the federal tipped minimum wage has been stuck at $2.13 since 1991—a 23-year stretch, over which time inflation has lowered the purchasing power of the federal tipped minimum wage to its lowest point ever.

Proposed federal minimum-wage legislation, the Fair Minimum Wage Act of 2014—also known as the Harkin–Miller bill—would not only increase the federal regular minimum wage to $10.10, but for the first time in decades would also reconnect the subminimum wage for tipped workers back to the regular minimum wage by requiring the former be equal to 70 percent of the latter. This would be a strong step in the right direction; however, we present evidence that tipped workers would be better off still if we simply eliminated the tipped minimum wage, and paid these workers the full regular minimum wage.

Raising the wage floor for tipped workers is crucial for a number of reasons. Rising income inequality and the accompanying slowdown in improving American living standards over the past four decades has been driven by weak hourly wage growth, a problem that has been particularly acute for low-wage workers (Bivens et al. 2014). Tipped workers—whose wages typically fall in the bottom quartile of all U.S. wage earners, even after accounting for tips³—are a growing portion of the U.S. workforce. Employment in the full-service restaurant industry has grown over 85 percent since 1990, while overall private-sector employment grew by only 24 percent.⁴ In fact, today more than one in 10 U.S. workers is employed in the leisure and hospitality sector, making labor policies for these industries all the more central to defining typical American work life.⁵ Ensuring fair pay for tipped workers is also a women’s issue. Women comprise two out of every three tipped workers; of the food servers and bartenders who make up over half of the tipped workforce, roughly 70 percent are women.

In their 2011 paper, Allegretto and Filion gave a historical account of the tipped-minimum-wage policy and brought much-needed attention to how the two-tiered wage system results in significantly different living standards for tipped versus non-tipped workers. For instance, tipped workers experience a poverty rate nearly twice that of other workers. The 2011 report, coupled with more recent publications from the White House (2014) and the Congressional Budget Office (2014), contradicts the notion that these workers’ tips provide adequate levels of income and reasonable economic security.

Given recent policy interest in the minimum wage and greater attention to the lesser-known subminimum wage for tipped workers, this paper updates the 2011 report to reflect recent changes to state wage policies, and includes updated demographic and earnings profiles of tipped workers. We extend the 2011 analysis especially with regard to the family structure of tipped workers, noting important differences between men and women. We also provide new data on family income levels and participation in federal assistance programs among tipped workers, as well as measures of job quality in the food service industry.

Key findings include:

• The subminimum wage for tipped workers has remained at $2.13 since 1991. In 1996, it was decoupled from the regular minimum wage, such that the tipped wage remained at $2.13 even as the regular minimum wage was increased. At that time, the tipped minimum wage was equal to 50 percent of the regular minimum wage; today it is only equal to a record low 29.4 percent of the regular federal minimum wage of $7.25.
• Customers’ tips pay the $5.12 difference between the federal tipped minimum wage and the federal regular minimum wage. Thus, customers provide a subsidy to employers of tipped workers worth more than twice the wage these employers are required to pay their tipped staff.
• The restaurant industry is an intense user of both minimum-wage and tipped-wage workers, with more than 60 percent of tipped workers employed in food service. The full-service restaurant sector has grown about 86 percent from 1990 to 2013, while overall growth in the private sector was up 24 percent—illustrating why it is increasingly important to raise wages for these workers.
• Tipped workers are predominantly women (66.6 percent) and disproportionately young; however, the majority are at least 25, and over one in four are at least 40 years of age.
• Tipped workers have a median wage (including tips) of $10.22, compared with $16.48 for all workers.
• While the poverty rate of non-tipped workers is 6.5 percent, tipped workers have a poverty rate of 12.8 percent. Tipped workers are thus nearly twice as likely to live in poverty as are non-tipped workers. Yet poverty rates are significantly lower for tipped workers in states where they receive the full regular minimum wage.
• Due to their low wages and higher poverty levels, about 46.0 percent of tipped workers and their families rely on public benefits, compared with 35.5 percent of non-tipped workers and their families. While it is a good thing that workers faced with challenging circumstances can turn to these programs for assistance, these programs were not designed to serve as a permanent wage subsidy or part of the business strategy for low-wage employers.
• Job quality, as measured by access to benefits, is far worse for tipped workers. Workers in the accommodation and food service industry—an industry with a high concentration of tipped workers—are offered paid leave (sick, holiday, and vacation leave), health insurance, and retirement benefits at rates far below those of private-sector workers overall.
• Paying tipped workers the regular minimum wage has had no discernable effect on leisure and hospitality employment growth in the seven states where tipped workers receive the full regular minimum wage. In fact, sector growth in these states has been stronger since 1995 than in the states where tipped workers are paid a subminimum wage.

History of the two-tiered wage floor system

The 1966 amendments to the Fair Labor Standards Act (FLSA) provided for a 50 percent “tip credit” for employers of tipped workers, allowing tipped workers’ income from tips to count toward half the regular minimum hourly wage guaranteed to workers by the FLSA, with the newly established subminimum wage comprising the other half. The real (inflation-adjusted) value of the two wages is illustrated in Figure A. In real terms, both wages are lower today than in 1966. Over time, the federal tip credit provision—the difference, made up for by customers’ tips, between the regular minimum wage and the tipped minimum wage—dropped to as low as 40 percent (1980–1989) while never exceeding half of the regular minimum wage prior to 1996. The roughly proportional relationship between the two wages changed when President Clinton signed into law the Minimum Wage Increase Act of 1996. The act eliminated the FLSA provision that required the tipped minimum wage remain a certain percentage of the full minimum wage, instead locking in the tipped minimum wage at $2.13 per hour. At the time of the bill’s passage, the tip credit stood at 50 percent. In October of that year, as the bill’s regular minimum-wage increase from $4.25 to $4.75 took effect, the $2.13 tipped minimum wage remained frozen—bringing the tip credit for employers above 50 percent (Whittaker 2006).

Figure A

Figure A (continued)

Real value of the federal minimum wage and subminimum wage for tipped workers, 1966–2014 * The difference, paid for by customers' tips, between the regular minimum wage and the subminimum wage for tipped workers Note: Real 2014 dollars adjusted using the CPI-RS.  Minimum- or tipped-wage changes that occurred in mid-year are averaged. Source: Authors' analysis of Fair Labor Standards Act and amendments

In fact, the 1996 law set the stage for an ever-increasing tip credit. When the federal minimum wage was raised in 2007, 2008, and 2009, the tipped minimum wage was left unchanged. Today, the tipped wage remains at $2.13, meaning it is equal to a record low 29.4 percent of the regular minimum wage of $7.25, while the employer tip credit ($5.12) is equal to 70.6 percent of the regular minimum wage. As such, the customer-provided subsidy afforded to employers ($5.12) is now more than twice the base wage ($2.13) employers are required to pay to workers.⁶

The change to the tip credit in 1996 effectively shifted responsibility for an increasing portion of tipped workers’ wages from employers to customers; it greatly reduced employers’ future wage bill by locking in a low base wage for tipped workers that would remain fixed, even as prices rose or the regular minimum wage was increased. Legally, employers of tipped workers are still required to ensure that the sum of tipped workers’ base wages plus their tips is equal to at least the full regular minimum wage; however, as is discussed later in detail, enforcement of this requirement is fraught with problems, and evidence suggests that tipped workers are subject to high rates of wage theft.

State policies

The two-tiered wage system established under the 1966 amendments to the FLSA did not remain uniform across the country, as states have implemented an array of mixed rules for both the regular minimum wage and the subminimum wage for tipped workers that differ from federal policy.⁷ The map in Figure B depicts state regular minimum-wage levels and tipped-minimum-wage levels as of January 1, 2014 (a summary of these data is also presented in the top panel of Table 1, which is introduced in the following section). Of the 50 states plus the District of Columbia, 29 follow the federal regular minimum wage level of $7.25. The 21 states (plus the District of Columbia) with regular minimum wages above the federal $7.25 are denoted in the figure with hash marks.

We next examine states’ tipped wage policies. As noted previously, the difference between the tipped minimum wage and the regular minimum wage determines the allowable tip credit for employers in each state. In broad terms we refer to the level of tip credit in each state as full, partial, or no tip credit. A full tip credit state (these 19 states are depicted in red on the map) follows the federal $2.13 subminimum wage for tipped workers and thus takes advantage of the maximum allowable tip credit.

There are 31 states (plus the District of Columbia) with tipped minimum wages above the federal $2.13. These include partial tip credit and no tip credit states. A partial tip credit state sets a subminimum wage above $2.13 but below the binding regular minimum wage for that state. These 24 states, plus the District of Columbia, are depicted in blue. Of these, 11 follow the federal regular minimum wage rate, while 14 have higher regular minimum wages. The range of tipped wages (tip credits) varies considerably across the partial tip credit states, from a low of $2.23 ($5.02) in Delaware to a high of $7.00 (25 cents) in Hawaii.

Lastly, states that do not allow for a subminimum wage are referred to as no tip credit states, or “equal treatment” states, meaning that tipped workers are paid the same minimum wage paid to non-tipped workers (these seven states are depicted in green on the map). In these states, tipped workers are paid regular minimum wages ranging from Minnesota’s $7.25 to Washington State’s $9.32.⁸

Seventeen states follow federal policy on both counts, while Minnesota is the only one that has a $7.25 regular minimum but does not allow for a subminimum wage. A complete list of state policies is included in Appendix Table A1.

State minimum-wage scenarios are often changing given decades of federal inaction on the tipped wage and five years of federal inaction on the regular minimum wage. Thus far in 2014, 34 states have considered raising their wage floor, and eight have enacted increases (National Conference of State Legislatures 2014). Yet even as states adopt wage floors above the federal level, they often overlook tipped-wage policy. Indeed, recent minimum-wage increases in seven states plus the District of Columbia were mixed on tipped-minimum-wage policy. For instance, as just noted, Minnesota does not allow for a subminimum wage; thus, tipped workers there will receive the full regular minimum wage when it increases to $9.50 in 2016. Hawaii’s regular minimum wage will increase to $10.10 by 2018, and the state also eliminated the previous 25-cent tip credit for employers, although a 75-cent tip credit exception will be allowable in instances where tipped workers earn at least $17.10 per hour after tips. In contrast, Maryland increased its state minimum to $10.10 earlier this year, but kept its subminimum wage frozen at $3.63, where it has remained since 2009. Similarly, the minimum wage in the District of Columbia is set to rise to $11.50 by 2017, but the District’s tipped minimum wage will remain fixed at $2.77.

Even if the federal government does not move on the tipped minimum wage, the increasing potential for state action makes it important to understand more about this policy, the population it affects, and the potential effects any policy changes would have. Interestingly, the variation in minimum-wage and tipped-wage policies depicted in the map constitutes a natural experiment of sorts. The restaurant industry is an intense user of both minimum-wage and tipped-wage workers, with more than 60 percent of tipped workers employed in food service. The industry has fought hard to keep the federal $2.13 floor in place over the last two decades, arguing that raising the tipped wage would be severely damaging to the industry and its workers. But restaurants exist in every state and under all the various minimum- and subminimum-wage policies depicted in the map. Moreover, Allegretto (2013) finds that increases in the tipped minimum wage over the last several decades had little to no effect on employment—specifically, employment effects are “small and not distinguished from zero.” Allegretto (2013) finds that implementing a policy similar to the Harkin-Miller proposal would have very little effect on restaurant employment while boosting overall earnings for both tipped and minimum-wage workers in full-service restaurants.

Demographic characteristics of tipped workers

To examine the demographic characteristics of workers who would be affected by increasing the tipped minimum wage, we use the 2011–2013 Current Population Surveys (CPS) to identify workers and tipped workers, as well as a subset of wait staff and bartenders. The three survey years are combined to produce a sufficient sampling of tipped workers. While there is no data set that explicitly identifies tipped workers, we define our sample as best we can by using several variables available in the CPS.⁹ Workers are included in the sample if they are at least 16 years old, employed but not self-employed, and report positive wage income. Tipped workers in our sample include those workers in occupations that are predominantly tipped, such as waiters and waitresses, bartenders, gaming service workers, barbers, hairdressers, and other personal appearance workers (see Appendix Table A2 for details). This section first examines the number of tipped workers and their distribution among the state tipped-wage categories. It then examines the gender, age, education, and family characteristics of these workers.

Number and distribution of tipped workers

The middle part of Table 1 provides the distribution of our sample of all workers, tipped workers, and a subsample of waiters and bartenders. We single out waiters and bartenders because most are tipped workers and because they comprise the bulk (58 percent) of the tipped workforce. There are approximately 4.3 million tipped workers in the United States, and roughly 2.5 million are waiters and bartenders.¹⁰ A plurality of workers, including tipped workers, reside in partial tip credit states that set tipped wages above $2.13 but below the binding state regular minimum wage—46.4 percent of the overall workforce and 49.4 percent of tipped workers reside in these states. Approximately one-third of all tipped workers reside in states with a $2.13 tipped wage rate, and less than one-fifth (18.4 percent) are in “equal treatment” states that do not allow for a tipped or subminimum wage.

The bottom panel of Table 1 shows the tipped worker and waiter/bartender shares of employment, overall and in each of the state tipped wage categories. Tipped workers and waiters/bartenders represent 3.4 percent and 2.0 percent, respectively, of the overall U.S. workforce. Looking across the state tipped wage categories, tipped workers comprise 3.1 percent of the workforce in states with a $2.13 subminimum wage, 3.6 percent of the workforce in partial tip credit states, and 3.5 percent of the workforce in states with no subminimum wage. There are no significant differences in waiters’ and bartenders’ share of overall employment across the tipped wage scenarios. These results suggest that tipped work opportunities are not diminished in states that do not allow for a subminimum wage.

Who are tipped workers?

We interact with tipped workers on a regular basis, from our favorite bartender at the local pub to those who serve us at the restaurants we frequent. But, by and large, tipped workers are not representative of the overall workforce. As Table 2 shows, tipped workers are overwhelmingly (two-thirds) female, younger, and tend to have lower levels of education than the overall workforce. Still, although tipped workers are younger than the overall workforce, they are not mainly teenagers, as is often thought. In fact, as shown in Figure C, only a small share of tipped workers are teens (12.6 percent). Furthermore, about a quarter of tipped workers are young adults, ages 20 to 24, while the vast majority (62.8 percent) are at least 25 years old. Nearly 30 percent are at least 40 years old.

Tipped workers have lower levels of education than the overall workforce, as 36.5 percent of all workers lack more than a high school education, compared with around 48.2 percent of tipped workers and 45.4 percent of waiters/bartenders. However, more than half of all tipped workers and waiters/bartenders have at least some college experience.

Tipped workers, especially waiters/bartenders, are less likely to be black, as they are underrepresented in this line of work as compared with their representation in the overall workforce. Tipped workers are also less likely to work full time; while this may be at the behest of some of these workers, it is also the case that many workers desire more hours and are unable to receive them.

The bottom of Table 2 details workers’ family dynamics. Approximately 35 percent of all U.S. workers are parents. Among tipped workers the corresponding figure is 25.6 percent, yet that figure rises to 30.4 percent if we only look at women, who are the majority of the tipped workforce. Similarly, 29.2 percent of women wait staff and bartenders are parents, and more than half of these parents are single parents.

Tipped workers’ wages

There are two common misconceptions of tipped workers’ pay. First, many people are simply unaware of the low base wage that employers actually pay tipped workers, and second, they believe these service workers make a large amount of “extra” money in tips. As already discussed, for most tipped workers, a significant portion of their tip income compensates for their receiving a low subminimum base wage from their employer. More importantly, the data belie the notion that most of these workers make substantial tipped income.

Table 3 reports median wages—that is, the wage of the worker right in the middle of the distribution—for all workers, tipped workers, and waiters/bartenders. The wage of a typical U.S. worker is about $16.48; females make less than males ($15.09 versus $18.13); workers under 20 earn less than older workers ($8.27 versus $16.98); and the least-educated make less than those with the most education ($9.98 for those with less than a high school degree, versus $25.91 for those with at least a bachelor’s degree).

Regardless of demographic category, the median tipped worker and waiter/bartender earns less than the median U.S. worker. Compared with the $16.48 median wage of all workers, tipped workers have a median wage (including tips¹¹) of $10.22; waiters/bartenders make slightly less ($10.11). Even as women represent two-thirds of tipped workers, they still make less than their male counterparts ($10.07 for women at the median versus $10.63 for men). The respective figures are $9.89 and $10.71 for waiters and bartenders.

For the most part, tipped work is low-wage work. Hourly pay across all the demographic categories in Table 3 varies from a low of $8.11 to a high of $12.88. The median wages of tipped workers who are also parents are $10.11 and $11.41 for single and married parents, respectively. The wages of the nearly one-in-three female tipped workers who are also parents are even lower, at $10.00 and $10.83 for single and married parents, respectively.

The low wages of tipped workers derive, in part, from the lower base wage or tipped wage provided by employers. As the data show in Table 4, the tip credit allowance—the amount of tips an employer can use as credit toward a worker’s wage—matters. Across the three broad tip credit policies, there are measurable differences in wages: There is a progression of increasing median wages from the low to higher tipped-wage states both for tipped workers and all workers. This likely also reflects other policies besides tipped-wage floors that would affect wages more broadly. For example, low tipped-wage states are more likely to have low regular state minimum wages, while high tipped-wage states are more likely to have high regular state minimum wages.

Still, the relative gains from low to high tipped-wage states are greater for tipped workers, especially waiters/bartenders, compared with the gains of the general workforce—even though tipped workers earn less in each case. For example, tipped workers in equal treatment states earn 14.2 percent more than tipped workers in low tipped minimum states, compared with an 11.4 percent gain for all workers (the relative difference is 20.6 percent for waiters/bartenders). Looking more closely at the gains for waiters and bartenders, we see that total wages of women are 22.5 percent higher, and of men 19.1 percent higher, in states that do not allow for a subminimum wage compared with the states that follow the $2.13 policy. Restaurant industry advocates often argue that raising or eliminating the tipped minimum wage would leave tipped workers worse off because customers would disproportionately reduce tips if they knew employers were paying higher base wages. These data show that the evidence for this claim is weak, and even if it were true, tipped workers in equal treatment states still earn higher total wages (tips plus base wage) than workers in states with lower tipped minimum wages.

While these relative differences are important, tipped workers in equal treatment states are still low-wage workers, earning around $11.00 to $12.00 per hour—still in the bottom third of all U.S. wage earners, yet better than the $9.00 to $10.00 per hour for workers in the lowest tipped-wage states.¹²

Family income and poverty

The family structure data reported in Table 2 showed that while tipped workers are less likely to be married and have children compared with the overall workforce, they are more likely to be single parents—especially female workers. As that table shows, of tipped workers who are women, 30.4 percent are parents, and 14.1 percent are single parents. Among wait staff and bartenders who are women, 16.4 percent are single parents.

Low wages combined with lower marriage rates translate into low family incomes for most tipped workers. The distribution of tipped workers, waiters and bartenders, and the total workforce across family income levels is reported in Figure D. About 30.5 percent of all U.S. workers are in families that earn less than $40,000. That share jumps to 47.2 percent for tipped workers and 49.9 percent for waiters and bartenders. Looking only at women (gender-specific breakdowns are not displayed in the figure), the trends are similar, if not slightly more pronounced: 31.7 percent of all working women are in families with total incomes below $40,000, compared with 47.6 percent of tipped workers who are women. Among female wait staff and bartenders, over half (51.8 percent) fall into this low-income group.

The relatively low family incomes of tipped workers mean they also experience poverty at much higher rates than other workers. As shown in Table 5, the poverty rate of non-tipped workers is 6.5 percent, while it is 12.8 percent for tipped workers in general and 14.9 percent for waiters and bartenders. While the magnitude of this difference by itself is startling, it is important to note how poverty rates for tipped workers vary significantly based on states’ tipped-minimum-wage policies.

As depicted in Figure E, poverty rates for non-tipped workers do not vary much by state tipped-wage policies. Yet for tipped workers, and particularly for waiters and bartenders, the correlation between low tipped wages and high poverty rates is dramatic. Among wait staff and bartenders, 18.0 percent are in poverty in states that follow the $2.13 subminimum wage, compared with 14.4 percent in medium-tipped-wage states and 10.2 percent in equal treatment states that do not allow for a lesser tipped minimum wage. This pattern strongly suggests that higher tipped wages mitigate poverty to some extent, yet it is still the case that poverty among tipped workers is far too high even in states that do not allow for a subminimum wage.

Public-support programs

When workers do not earn enough in wages to make ends meet, they are often forced to rely on publicly provided support programs. While it is a good thing that workers faced with challenging circumstances can turn to these programs for assistance, these programs were not designed to serve as a permanent wage subsidy or part of the business strategy for low-wage employers. Using the Current Population Survey Annual Social and Economic Supplement, we calculated the share of the workforce, non-tipped workers, and tipped workers who receive some publicly funded benefits—such as federal housing or energy subsidies, the earned income tax credit (EITC), school lunch subsidies, Supplemental Nutrition Assistance Program (SNAP) benefits (i.e., “food stamps”), or Supplemental Nutrition Program for Women, Infants, and Children (WIC) benefits—to meet their family needs. As reported in Table 6, 35.5 percent of non-tipped workers and their families rely on public benefits, compared with 46.0 percent and 46.2 percent, respectively, of tipped workers in general and waiters/bartenders in particular. Tipped workers receiving some public assistance get about $475 more, on average, in benefits than non-tipped workers who receive aid; waiters and bartenders receive over $600 more, on average, than non-tipped workers receiving aid.¹³

Job quality

We have shown thus far that tipped workers are subject to low pay, low levels of total family income, and a greater likelihood of being in poverty. It is also the case that tipped workers are much less likely to have workplace benefits, as shown in Figure F. The Bureau of Labor Statistics National Compensation Survey (NCS) reports benefits for all private-sector workers and benefits by various establishment and worker characteristics, including a breakout for “accommodation and food services (AFS)”—a sector with the majority of tipped workers.¹⁴ The NCS reports that two of the most common benefits offered to workers are paid vacations and paid holidays—each offered to 77.0 percent of private-sector workers. But among workers in accommodation and food services, just 45.0 percent and 36.0 percent are offered paid vacations and paid holidays, respectively—and these figures are for all workers in that industry, including managers and supervisors.

Health care and retirement benefits are crucial to most workers, but they are offered to just a fraction of AFS workers—30.0 percent and 27.0 percent, respectively, compared with 70.0 percent and 64.0 percent, respectively, of other private-sector workers. Given that the majority of tipped workers are wait staff and bartenders who handle food and drink, it is particularly unfortunate—and potentially dangerous—that their industry does not provide widespread access to paid sick leave; just 23.0 percent of AFS workers receive paid sick leave, compared with 61.0 percent of the total private-sector workforce.

As we’ve shown, many tipped workers are parents—many single parents—with low family incomes; thus, life insurance and short-term disability protection would be particularly important to these workers and their families, especially given the physical nature of their work. Life insurance and short-term disability protection are offered to 57.0 percent and 40.0 percent of private-sector workers, yet only 17.0 percent and 19.0 percent, respectively, of AFS workers.

Again, while the shares of workers in the broad category of accommodation and food services (which includes managers, supervisors, etc.) who have access to these benefits are very low, we would likely find that the shares of tipped workers and waiters/bartenders with access are even lower, if such data were available.¹⁵

Other challenges for tipped workers

Tipped workers face a number of other unique challenges in the workplace. As mentioned earlier, in states that allow for a subminimum wage, a tipped worker’s tips plus her base or tipped wage must equal at least the regular state minimum wage—if not, her employer must make up the difference. However, tipped workers are often unaware that their tips and base wage must sum to at least the regular minimum wage. This regulation is hard to implement in practice, both because it is logistically difficult to do so and because it is up to the worker to request that her employer make up the difference. To determine compliance with the FLSA’s wage requirements, the total of tips plus the subminimum base wage is to be assessed on a workweek basis.¹⁶ A workweek is defined as any fixed and regularly recurring 168-hour period. But many tipped workers work irregular schedules, and the practical implementation of this regulation is unclear; at what point does an employer stop the clock to tally up hours, tips, and base wages? Compliance is difficult to assess even if a good-faith employer would like to do so.

Moreover, a tipped employee seeking to monitor her employer’s compliance with the law would need to record her hours for the determined workweek—erratic schedules notwithstanding—record all tips she receives (how employees should treat tips shared with other staff, such as restaurant bussers or bar-backs, in this calculation is also unclear), record the base wage she was paid from her employer, and calculate if the effective hourly rate equaled the required state or federal minimum wage. If it did not, this employee would have to approach her employer seeking the missing pay. Of course, this is the same employer who determines whether this employee will be given the most lucrative shifts, the best restaurant sections (in the case of waiters and waitresses), or if the employee will retain her job at all. It is unrealistic to think that a tipped employee dealing with an unscrupulous employer would be able to reclaim her lost wages, let alone confront someone with such power over her near-term financial health.

Indeed, the restaurant industry is fraught with violations. In the most recent (2010–2012) compliance sweep of nearly 9,000 full-service restaurants by the U.S. Department of Labor’s Wage and Hour Division (WHD), 83.8 percent of investigated restaurants had some type of violation. In total, WHD recovered $56.8 million in back wages for nearly 82,000 workers and assessed $2.5 million in civil money penalties. Violations included 1,170 tip credit infractions that resulted in nearly $5.5 million in back wages.¹⁷

Research has also shown that the practice of tipping is often discriminatory, with white service workers receiving larger tips than black service workers for the same quality of service (Lynn et al. 2008). The worker advocacy group Restaurant Opportunities Centers (ROC) United has published numerous testimonies from both tipped and non-tipped workers in the restaurant industry that anecdotally describe these problems. ROCs “Behind the Kitchen Door” worker survey reports echo what is found in much of the data presented here: Workers report an array of problems, from low earnings and low to no benefits, to overtime violations, working off the clock, and issues of safety (ROC various years; Jayaraman 2013).

Should we have a tipped minimum wage?

The results obtained here are informative for policy. The Harkin–Miller minimum-wage bill proposes to increase the regular federal minimum wage from $7.25 to $10.10 in three 95-cent steps. The bill would also reconnect the severed historical link between the tipped and the regular minimum wages by raising the former to 70 percent of the latter over six years. This would certainly improve the situation for tipped workers, adding greater stability to their income and boosting their total pay. Contrary to claims from industry advocates, research has shown that increasing the tipped minimum wage would boost earnings for tipped-wage workers without unduly harming employment, particularly full-service restaurant employment (Allegretto 2013).

Yet given all the problems that the current two-tiered wage system creates, it appears prudent to simply do away with the tipped minimum wage and have tipped workers nationwide be paid the regular minimum wage. Industry advocates may claim that this would be damaging for businesses that employ tipped workers, but the data do not support this claim. From January 1995 to May 2014, employment in the leisure and hospitality sector—a sector with a high concentration of tipped workers—grew by 41 percent nationwide, far faster than total U.S. nonfarm employment growth of roughly 19 percent. Over that period, leisure and hospitality employment grew 43.2 percent in the seven “equal treatment” states without subminimum wages, compared with 39.2 percent growth in the 43 states (plus the District of Columbia) that have lower minimum wages for tipped workers.¹⁸ In other words, the sector that comprises the bulk of tipped employment actually grew faster in states where tipped workers received the regular minimum wage than it did in states with a lower tipped minimum wage.¹⁹ Moreover, the restaurant industry itself projects higher growth rates in these “equal treatment” states than in those with subminimum wages (National Restaurant Association 2014).²⁰

Conclusion

This report explored the history and rationale for the subminimum wage earned by workers who receive tips for their services. The federal tipped minimum wage was originally set at 50 percent of the regular minimum wage. Today, the subminimum wage for tipped workers is a mere $2.13 an hour, where it has been for 23 years. Over that time, its value has eroded to just 29.4 percent of the regular minimum wage that applies to non-tipped workers. Still, many states have enacted tipped minimum wages above $2.13. This report shows that tipped workers receive higher total wages and have lower levels of poverty in states where the tipped minimum wage is relatively high.

Often, discussion and action surrounding the minimum wage ignores or excludes tipped workers and the subminimum wage they receive. Yet this is a growing occupational sector, and effective policy could transform the low-wage, high-poverty jobs in the sector into better quality jobs. Much of tipped employment is the epitome of “just-in-time” employment—adjusting staffing levels on an immediate basis in response to customer flows. While this may be good for the employer, it is far less beneficial for workers because it can produce highly unpredictable work hours, and thus highly unpredictable pay. Wage volatility is further exacerbated by workers’ reliance on tips from customers, which also vary considerably. A tipped worker’s paycheck can vary wildly depending on the fluctuations of customer tips and assigned shifts, making it difficult for tipped workers to budget, or make investments that require more stable and predictable income levels—such as buying a home or a car, or seeking further education.

In real terms, the U.S. minimum and subminimum wage floors have long been in decline, exacerbating the general stagnation or decline of wages for the vast majority of American workers, particularly low-wage workers. Even the broader world is taking notice, as the International Monetary Fund (2014) recently recommended that the U.S. minimum wage be increased, given its current low level (compared both with its historical values and international standards). The Organization for Economic Cooperation and Development (2014) has also recommended an increase in the U.S. minimum wage as a measure to improve job quality and workers’ well-being. Reports from the Congressional Budget Office (2014) as well as research from academia (Dube 2013) conclude that raising the wage floor would lift hundreds of thousands, if not millions, out of poverty.

For all these reasons, it is perhaps no surprise that polling consistently shows that most Americans (73 percent) would like to see a minimum-wage hike (Pew Research Center 2014). To the best of our knowledge, there has not been a poll conducted specifically on changing the $2.13 tipped wage, but in all likelihood, this is simply another indication of the lack of public awareness on this issue. We cannot help but wonder whether, if more Americans knew the exceptionally low base wages being paid to tipped workers, they might prefer these employers pay tipped workers a higher base wage, and let tips once again be simply an expression of gratitude for good service. We suspect most would agree that the consumer subsidy to these employers has grown for too long.

It is certainly time to raise both wage floors, but given the dramatic differences in living standards for tipped versus non-tipped workers, we question whether there should be a two-tiered wage system at all. Tipped workers in the seven “equal treatment” states appear to be noticeably better off than their counterparts in the rest of the country, receiving higher total wages and experiencing poverty at significantly lower rates. At the same time, industries that employ tipped workers in these states are thriving. Raising the tipped minimum wage up to a higher percentage of the regular minimum wage would be a step in the right direction, but perhaps we should simply eliminate the tipped minimum wage altogether, and give tipped workers the same basic protection afforded to other workers.

— The Economic Policy Institute gratefully acknowledges the generous support it received from the Ford Foundation for this project.

About the authors

Sylvia A. Allegretto, Ph.D. is an economist and co-director of the Center on Wage and Employment Dynamics at the Institute for Research on Labor and Employment at the University of California, Berkeley. She is also a research associate of the Economic Policy Institute and is co-author of many EPI publications, including past editions of The State of Working America, How Does Teacher Pay Compare?, and The Teaching Penalty: Teacher Pay Losing Ground.

The Center on Wage and Employment Dynamics is a center at the Institute for Research on Labor and Employment at the University of California, Berkeley. CWED was established in 2007 to provide a focus for research projects on wage and employment dynamics in contemporary labor markets. Current research topics include minimum wage impacts, tipped worker labor markets, transformation of retail labor markets, health insurance and health policy, immigration, worker turnover and job training, labor relations and productivity, and models of low-wage labor markets.

David Cooper is an economic analyst with the Economic Policy Institute. He conducts national and state-level research on a variety of issues, including the minimum wage, employment and unemployment, poverty, and wage and income trends. He also provides support to the Economic Analysis and Research Network (EARN) on data-related inquiries and quantitative analyses. David has been interviewed and cited by local and national media for his research on the minimum wage, poverty, and U.S. economic trends. His graduate research focused on international development policy and intergenerational social mobility. He holds a Master of Public Policy degree from Georgetown University.

The Economic Policy Institute is a nonprofit, nonpartisan think tank that seeks to broaden the public debate about strategies to achieve a prosperous and fair economy. EPI stresses real-world analysis and a concern for the living standards of working people, and it makes its findings accessible to the general public, the media, and policymakers through books, studies, and popular education materials.

Appendices

Appendix Table A1 shows each state’s minimum-wage and tipped-minimum-wage level, as of January 1, 2014.

The Current Population Survey does not explicitly identify tipped workers. Rather, there is a variable that indicates workers who regularly receive tips, overtime, or commissions. In order to identify workers who are likely to be tipped, we first looked at all occupations that had a high share receiving tips, overtime, or commissions. From this group, we selected those occupations that were most likely to receive tips, as shown in Appendix Table A2.

Endnotes

1. Legislation originally enacted in 1966 required that a worker had to customarily and regularly earn at least $20 a month in tips for their employers to qualify for the tip credit. This was raised to $30 a month in 1978. Adjusted for inflation, that $20 minimum would now be about $146.34 per month.

2. If a tipped employee’s tips plus her base wage do not sum to at least the full minimum wage in any two-week period, the tipped worker’s employer is required to make up the difference through a higher base wage; however, as explained in later sections, this intended safeguard is fraught with problems.

3. Authors’ analysis of Current Population Survey Outgoing Rotation Group microdata, 2013

4. Bureau of Labor Statistics, Quarterly Census of Employment and Wages data, 1990Q1 to 2013Q1

5. Authors’ analysis of Local Area Unemployment Statistics data from the Bureau of Labor Statistics

6. For more historical information, see Allegretto and Filion (2011).

7. The scenarios are always changing as federal and/or state wage polices are adopted; the map depicts the wage policies as of January 1, 2014. Some states have already legislated both regular and tipped-minimum-wage increases that will occur over the next several years.

8. There are variations within state policy. For example, Nevada, a no tip credit state, has a wage floor of $7.25 for workers with or $8.25 without employer-provided health insurance.

9. It is important to keep in mind that not all tipped workers have been identified because the Current Population Survey does not have a unique identifier for such a distinction. The CPS includes a variable that identifies persons who usually receive “overtime pay, tips or commissions.” Likely tipped workers were identified using this variable along with additional analysis. There are other workers, especially in the restaurant industry or food service occupations (such as bussers, delivery workers, and runners) who may also be tipped workers—even though their tips may come indirectly from wait staff, instead of directly from customers.

10. In Allegretto and Filion (2011), we included slightly fewer occupational categories as predominantly tipped occupations. White House (2014) uses this slightly smaller set of predominantly tipped occupations as well; however, CBO (2014) employs the broader set of tipped occupations listed in Appendix Table A2. We analyzed demographic characteristics, wage rates, poverty rates, and public transfer receipt rates between the two sets of predominantly tipped occupations and found that they were not substantially different, and thus opted to use the set of occupations consistent with the CBO’s definition of tipped workers.

11. All wage values in this report reflect total wages, i.e., base wages plus tips.

12. In 2013, the 30th percentile wage for U.S. workers was $12.00, according to data from the Current Population Survey.

13. These findings are similar to those in Fast Food, Poverty Wages by Allegretto et al. (2013a). In that paper, it was estimated that 25 percent of working families rely on public benefits (Medicaid, Children’s Health Insurance Program [CHIP], federal EITC, SNAP, and Temporary Assistance for Needy Families [TANF]), while the share rises considerably to 52 percent among fast-food workers and their families. That study was restricted to workers who worked at least 27 weeks per year and at least 10 hours per week. Using the same data and methodology from the fast-food worker study, we estimate that 40 percent of tipped workers and their families, and 42 percent of waiters/bartenders and their families, receive public benefits to make ends meet—again compared with 24 percent of working families in general.

14. The industry sector of accommodation and food services captures many tipped workers but also non-tipped workers and staff such as managers who are likely to have higher wages and benefits. These data are from an unpublished NCS report on 2013 benefits (forthcoming in 2014).

15. We requested figures from the BLS for tipped workers and/or wait staff from the NCS, but sample sizes were too small for these populations by themselves.

16. See, e.g., 29 U.S.C. 206(a) in the FLSA (http://bit.ly/1qoLKwH).

17. Email correspondence with U.S. Department of Labor program analysts from the Wage and Hour Division

18. Authors’ analysis of Local Area Unemployment Statistics (LAUS) data from the Bureau of Labor Statistics

19. We do not mean to imply any sort of causal relationship here, and looking at changes in specific industries within the leisure and hospitality sector or over different timeframes might yield different results. There are undoubtedly other factors correlated with state tipped-wage policies influencing employment growth between these groups of states. A more precise inquiry into the relationship between tipped-wage policies and employment that controls for these factors is beyond the scope of this paper. However, Allegretto (2014) examines this specific question with an appropriate set of controls and finds no significant effect on employment from higher tipped minimum wages. The relevant point here is that if paying the regular minimum wage to tipped workers were significantly harmful to these industries, we would expect these harmful effects to be readily apparent—and they are not.

20. For more information, see ROC United (2014).

References

Allegretto, Sylvia A., and Kai Filion. 2011. Waiting for Change: The $2.13 Federal Subminimum Wage. Economic Policy Institute and Center for Wage and Employment Dynamics, Briefing Paper #297. http://www.epi.org/publication/waiting_for_change_the_213_federal_subminimum_wage/

Allegretto, Sylvia A. 2013. Waiting for Change: Is It Time to Increase the $2.13 Subminimum Wage? Institute for Research on Labor and Employment, Working Paper No. 155-13. http://irle.berkeley.edu/workingpapers/155-13.pdf

Allegretto, Sylvia A., Marc Doussard, Dave Graham-Squire, Ken Jacobs, Dan Thompson, and Jeremy Thompson. 2013a. Fast Food, Poverty Wages: The Public Cost of Low-Wage Jobs in the Fast-Food Industry. Center for Labor Research and Education. http://laborcenter.berkeley.edu/publiccosts/fastfoodpovertywages.shtml

Allegretto, Sylvia, Arindrajit Dube, Michael Reich, and Ben Zipperer. 2013b. Credible Research Designs for Minimum Wage Studies. IRLE Working Paper No. 148-13. http://irle.berkeley.edu/workingpapers/148-13.pdf

Bivens, Josh, Elise Gould, Lawrence Mishel, and Heidi Shierholz. 2014. Raising America’s Pay: Why It’s Our Central Economic Policy Challenge. Economic Policy Institute, Briefing Paper #378. http://www.epi.org/publication/raising-americas-pay/

Bureau of Labor Statistics. Various years. Local Area Unemployment Statistics. http://www.bls.gov/lau/ststdsadata.txt

Bureau of Labor Statistics. Various years. Quarterly Census of Employment and Wages. http://www.bls.gov/cew/

Bureau of Labor Statistics. 2014 [forthcoming]. National Compensation Survey: Employee Benefits in the United States. Unpublished data forthcoming summer 2014.

Congressional Budget Office (CBO). 2014. The Effects of a Minimum-Wage Increase on Employment and Family Income. http://www.cbo.gov/publication/44995

Current Population Survey Annual Social and Economic Supplement microdata. Various years. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [machine-readable microdata file]. Washington, D.C.: U.S. Census Bureau. http://www.bls.census.gov/cps_ftp.html#cpsmarch

Current Population Survey Outgoing Rotation Group microdata. Various years. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [machine-readable microdata file]. Washington, D.C.: U.S. Census Bureau. http://www.bls.census.gov/cps_ftp.html#cpsbasic

Dube, Arindrajit. 2013. Minimum Wages and the Distribution of Family Incomes. University of Massachusetts Amherst, Working Paper. https://dl.dropboxusercontent.com/u/15038936/Dube_MinimumWagesFamilyIncomes.pdf

International Monetary Fund (IMF). 2014. “Article IV Consultation with the United States of America Concluding Statement of the IMF Mission.” Accessed June 19, 2014. http://www.imf.org/external/np/ms/2014/061614.htm

Jayaraman, Saru, and Eric Schlosser. 2013. Behind the Kitchen Door. Ithaca, N.Y.: ILR Press.

Lynn, M., M. Sturman, C. Ganley, E. Adams, M. Douglas, and J. McNeil. 2008. “Consumer Racial Discrimination in Tipping: A Replication and Extension.” Journal of Applied Social Psychology, vol. 38, 1045–1060. http://onlinelibrary.wiley.com/doi/10.1111/j.1559-1816.2008.00338.x/full

National Conference of State Legislatures. 2014. “State Minimum Wages” [data table accessed June 18, 2014]. http://www.ncsl.org/research/labor-and-employment/state-minimum-wage-chart.aspx

National Restaurant Association. 2014. 2014 Restaurant Industry Forecast.

Organization for Economic Cooperation and Development (OECD). 2014. OECD Economic Surveys: United States 2014. http://dx.doi.org/1.1781/econ_surveys-usa-2014-en

Pew Research Center. 2014. “Most See Inequality Growing, but Partisans Differ over Solutions.” January 23. http://www.people-press.org/2014/01/23/most-see-inequality-growing-but-partisans-differ-over-solutions/

Restaurant Opportunities Centers (ROC) United. Various years. http://rocunited.org/research-resources/our-reports/.

Restaurant Opportunities Centers (ROC) United. 2014. “Recipe for Success: Abolish the Subminimum Wage to Strengthen the Restaurant Industry.” http://rocunited.org/recipeforsuccess/

United States Department of Labor, Wage and Hour Division. Various years. “Fair Labor Standards Act.” http://www.dol.gov/whd/flsa/

United States Department of Labor, Wage and Hour Division. 2014. “Minimum Wages for Tipped Employees” [web page], accessed June 1. http://www.dol.gov/whd/state/tipped.htm

White House. 2014. The Impact of Raising the Minimum Wage on Women and the Importance of Ensuring a Robust Tipped Minimum Wage. http://www.whitehouse.gov/sites/default/files/docs/20140325minimumwageandwomenreportfinal.pdf

Whittaker, William G. 2006. The Tip Credit Provisions of the Fair Labor Standards Act. Congressional Research Service Report for Congress.

Changes in labor market policies and practices have played an important role in the dynamic of rising inequality and wage stagnation for the vast majority. One example of a change in labor market policy that has eroded the standing of typical workers vis-à-vis their employers and contributed to this dynamic is the right of workers to earn overtime pay premiums for working excessive hours.

To ensure the right to a limited workweek, the Fair Labor Standards Act (FLSA) requires that workers covered by FLSA overtime provisions must be paid at least “time-and-a-half,” or 1.5 times their regular pay rate, for each hour of work per week beyond 40 hours. The rule is meant to protect workers who lack control over their time and tasks and do not receive high pay. The rule aims to exclude professional and managerial employees from overtime pay requirements by exempting workers who pass certain “duties tests” or who make over the salary threshold under which all salaried workers, regardless of their work duties, are covered by the overtime provisions. However, this salary threshold has rarely been updated—it has been changed only eight times in 75 years and only once since 1975—nor is it indexed to inflation. The salary threshold for the exemption to the overtime rules of the Fair Labor Standards Act has been allowed to fall to a level—$455 per week—that is currently less than the poverty threshold for a family of four for someone who works year-round.

Fortunately, the Obama administration can remedy this through executive action. In their paper New Inflation-adjusted Salary Test Would Bring Needed Clarity to FLSA Overtime Rules, Ross Eisenbrey and Jared Bernstein propose increasing the salary threshold to $984, which is simply the 1975 threshold adjusted for inflation. In that paper, they outline a number of justifications for their threshold. These include (but are not limited to) the following:

• The threshold should be well above the median wage. “To be commensurate with the status and prestige expected of exempt managers and executives, the salary level should be well above the median wage, the wage paid to the typical production, nonsupervisory employee. When the Ford administration raised the salary threshold in 1975, it was 1.57 times the median wage. The median wage today is $16.70 per hour. Were we to update that same ratio—1.57 times the median wage—the threshold would be … around $1,050 on a weekly basis.”
• The threshold should be well above the entry-level college wage. “The salary level for exemption must also be, according to the 1949 report, ‘considerably higher’ than the level of newly hired ‘college graduates just starting on their working careers.’ … Entry level wages and salaries for college graduates in 2011 were $21.68 per hour for men and $18.80 per hour for women. Using the Department of Labor’s reasoning in 1949, we determine that the salary level for exemption must be ‘considerably higher’ than $800 a week or $41,600 a year, a view that is again consistent with our updated 1975 threshold.”¹
• The threshold should be several times the minimum wage. “In 1975, before the 29-year period when the department failed to increase the salary levels, the … salary level was set at a ratio [to the minimum wage] of approximately 3-to-1, close to our choice of the 1975 test adjusted for inflation.” (Bernstein and Eisenbrey 2014)

In this issue brief, I discuss another important benchmark which strongly suggests that the proposed threshold of $984 is modest, and that a threshold of $1,122 is more consistent with the 1975 benchmark, even after accounting for the shift toward higher-level white collar professional and managerial jobs that has occurred since that time.

The share of salaried workers covered by the threshold

In 1975, the share of salaried workers under the salary threshold was 65 percent (note, a salaried worker is a worker who is paid on a regular schedule and receives a guaranteed minimum amount on each pay date regardless of hours worked).² In other words, in 1975, nearly two-thirds of salaried workers were covered by overtime protections in the Fair Labor Standards Act based on their earnings alone. The real value of the threshold has dropped so dramatically that in 2013, just 11 percent of salaried workers were under the threshold. These results are displayed in Table 1.

Table 1 also shows that if the threshold were raised to $984—the 1975 threshold in inflation-adjusted terms—47 percent of salaried workers would be below the threshold and therefore automatically covered by overtime protections, a much smaller share than was covered in 1975. To cover the same share of workers as was covered in 1975, the threshold would have to be $1,327. This suggests that $984 may be far too modest relative to the 1975 benchmark.

However, the labor market has changed since 1975, with a larger share of the workforce now in higher-level white-collar professional or managerial roles. This means it is arguably appropriate that the share of workers covered by overtime protections is somewhat lower than in 1975. Since these higher-level jobs require higher levels of education, a reasonable way to “control” for these changes in the labor market is to look at what has happened within educational categories instead of at the workforce as a whole. Another way to see the importance of controlling for changes in the labor market is to note, first, that the share of workers falling under a given salary threshold is lower for workers with higher levels of education, and, second, that the workforce today is significantly more educated now than it was in 1975. (In 1975, 30 percent of salaried workers had a four-year college degree or more, while in 2013, the share with this level of education was 57 percent). Together these facts mean that some of the overall decline in the share covered by overtime protections since 1975 can be explained by the fact that more salaried workers today are in higher-level jobs that require higher levels of education. This underscores the usefulness of accounting for such changes by looking within educational categories instead of at the labor market as a whole.

Breakdowns by education

Figure A shows the share of salaried workers under the salary threshold in 1975 and 2013 by education. (These data are also provided in the first two columns of Table 2.) In 2013, for every education category, the share under the salary threshold was much lower than it was in 1975. Fifty-one percent of salaried workers with a college degree were covered by the salary threshold in 1975, compared with just six percent in 2013. For salaried workers with an advanced degree, 36 percent fell under the salary threshold in 1975, compared with just 4 percent in 2013. These education breakdowns show that the shift toward higher-level jobs requiring higher levels of education does not come close to fully explaining the post-1975 drop in the share of salaried workers falling under the threshold for automatic access to overtime protections.

Column (3) of Table 2 shows that if the salary threshold were raised to $984, the share of workers under the threshold in 2013 would be substantially lower than in 1975 within every education category except for those with less than a high school degree, where it would be modestly higher (88 percent compared with 85 percent). It should be noted that this is a small group—in 2013, only 3 percent of salaried workers did not have a high school degree.

If the salary threshold were raised to $984, just 39 percent of college-educated workers would fall under the salary threshold, substantially lower than the 1975 share, 51 percent. The share of workers with an advanced degree under the salary threshold would be 27 percent, also substantially lower than the 1975 share, 36 percent. In other words, even after controlling for the shift toward higher-level jobs since 1975 by looking at what would happen within educational categories instead of at the workforce as a whole, $984 is still a very modest threshold. As shown in column (4) of Table 2 and in Figure B, to cover the same share of college-educated workers as was covered in 1975, the threshold would have to be $1,177, and to cover the same share of workers with an advanced degree as was covered in 1975, the threshold would have to be $1,153.

A weighted average of the thresholds in Figure B—the thresholds that would cover the same share of workers as were covered in 1975 for each education category—is $1,122. The last column of Table 2 shows the share of salaried workers that would fall under a salary threshold of $1,122 in 2013. Overall, just 54 percent of salaried workers would fall under the threshold, a far smaller share than in 1975 (65 percent), which at first glance suggests that $1,122 is too modest a threshold. However, when looking by education category, the 2013 shares are similar to the 1975 shares, with college-educated and advanced-degreed workers having slightly lower coverage rates and those with less education having the same or slightly higher coverage rates. What these columns show is that $1,122 is the threshold level that is most consistent with the 1975 benchmark, after accounting for the shift toward higher-level jobs that has occurred since that time.

About the author

Heidi Shierholz joined the Economic Policy Institute as an economist in 2007. She has researched and spoken widely on the economy and economic policy as it affects middle- and low-income families, especially in regards to employment, unemployment, labor force participation, compensation, income and wealth inequality, young workers, unemployment insurance, and the minimum wage. Shierholz is a coauthor of The State of Working America, 12th Edition, is a frequent contributor to broadcast and radio news outlets, is regularly quoted in print and online media outlets, and has repeatedly been called to testify in Congress on labor market issues. Prior to joining EPI, Shierholz worked as an assistant professor of economics at the University of Toronto. She holds a Ph.D. in economics from the University of Michigan at Ann Arbor.

Acknowledgments

The Economic Policy Institute gratefully acknowledges the support it received from the Ford Foundation for this project.

References

Bernstein, Jared, and Ross Eisenbrey. 2014. New Inflation-adjusted Salary Test Would Bring Needed Clarity to FLSA Overtime Rules. Washington, D.C.: Economic Policy Institute. http://www.epi.org/publication/inflation-adjusted-salary-test-bring-needed/

Bivens, Josh, Elise Gould, Lawrence Mishel, and Heidi Shierholz. 2014. Raising America’s Pay. Economic Policy Institute, Briefing Paper No. 378. http://www.epi.org/publication/raising-americas-pay/

Current Population Survey Outgoing Rotation Group microdata. Various years. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [machine-readable microdata file]. Washington, D.C.: U.S. Census Bureau. http://www.bls.census.gov/cps_ftp.html#cpsbasic

U. S. Department of Labor (DOL) Wage and Hour and Public Contracts Divisions. 1949. Report and Recommendations on Proposed Revisions of Regulations, Part 541.

Endnotes

1. The 1949 report referred to by the authors is a report by the U.S. Department of Labor outlining recommended regulatory changes to the Fair Labor Standards Act of 1938. The report (U.S. DOL 1949) led in 1950 to the first major change in FLSA regulations since amending regulations in 1940 (Bernstein and Eisenbrey 2014).

2.  In 1975, 45.9 percent of workers were salaried; that dropped slightly to 41.3 percent by 2013.

Figure A shows the hires rate, the quits rate, and the layoff rate. The first thing to note is that layoffs, which shot up during the recession, recovered quickly once the recession officially ended. Layoffs have been at prerecession levels for more than three years. This makes sense; the economy is in a recovery and businesses are no longer shedding workers at an elevated rate.

But for a full recovery in the labor market to occur, two key things need to happen: Layoffs need to come down, and hiring needs to pick up. Hiring is the side of that equation that, while generally improving, has not yet come close to a full recovery. There are more than 10 percent fewer hires each month than there were before the recession began, and hires actually dropped by 52,000 in May. The rate of hires has seen no sustained improvement since last August.

The final piece of the puzzle is voluntary quits. A larger number of people voluntarily quitting their job indicates a labor market in which hiring is prevalent and workers are able to leave jobs that aren’t right for them and find new ones. Voluntary quits, while slowly improving, are also nowhere near a full recovery. There are more than 15 percent fewer voluntary quits each month than there were before the recession began, and the quit rate has seen no improvement since last October. Low voluntary quits indicate that there are a huge number of workers who are locked into jobs that they would leave if they could.

No job openings for more than half of job seekers

The total number of job openings in May was 4.6 million, up from 4.5 million in April. In May, there were 9.8 million job seekers (unemployment data are from the Current Population Survey), meaning that there were 2.1 times as many job seekers as job openings. Put another way: Job seekers so outnumbered job openings that more than half of job seekers were not going to find a job in May no matter what they did. In a labor market with strong job opportunities, there would be roughly as many job openings as job seekers.

Further, the 9.8 million unemployed workers understates how many job openings will be needed when a robust jobs recovery finally begins, due to the existence of 6.0 million would-be workers who are currently not in the labor market, but who would be if job opportunities were strong. Many of these “missing workers” will become job seekers when we enter a robust jobs recovery, so job openings will be needed for them, too.

Even further, a job opening when the labor market is weak often does not mean the same thing as a job opening when the labor market is strong. There is a wide range of “recruitment intensity” with which a company can deal with a job opening. For example, if a company is trying hard to fill an opening, it may increase the compensation package and/or scale back the required qualifications. Conversely, if it is not trying very hard, it may hike up the required qualifications and/or offer a meager compensation package. Perhaps unsurprisingly, research shows that recruitment intensity is cyclical; it tends to be stronger when the labor market is strong, and weaker when the labor market is weak. This means that when a job opening goes unfilled when the labor market is weak, as it is today, companies may very well be holding out for an overly qualified candidate at a cheap price.

Labor market weakness is not due to workers lacking the right skills

Figure B shows the number of unemployed workers and the number of job openings by industry. This figure is useful for diagnosing what’s behind our sustained high unemployment. If our current elevated unemployment were due to skills shortages or mismatches, we would expect to find some sectors where there are more unemployed workers than job openings, and some where there are more job openings than unemployed workers. What we find, however, is that unemployed workers dramatically outnumber job openings across the board. There are between 1.2 and 7.0 times as many unemployed workers as job openings in every industry. In other words, even in the industry with the most favorable ratio of unemployed workers to job openings (health care and social assistance), there are still 20 percent more unemployed workers than job openings. In no industry does the number of job openings even come close to the number of people looking for work. This demonstrates that the main problem in the labor market is a broad-based lack of demand for workers—not, as is often claimed, available workers lacking the skills needed for the sectors with job openings.

All-in-all, this is a strong report. But it’s important to keep in mind that we still face a huge hole in the labor market, and even if we saw June’s rate of job growth every month from here on out, we still wouldn’t get back to health in the labor market for another two and a half years.

Methodology

How do we estimate the number of missing workers?

Labor force participation rate projections published by the Bureau of Labor Statistics in November 2007—before the start of the Great Recession—are available in Table 3 of Mitra Toossi, “Labor Force Projections to 2016: More Workers in Their Golden Years,” Bureau of Labor Statistics Monthly Labor Review, November 2007. The projections assumed a healthy labor market over the period in question, 2006–2016, so the projected participation rate changes reflect purely non-cyclical factors (e.g., the impact of retiring baby boomers). The difference between these projections and the actual labor force participation rate is thus a good measure of the cyclical change in the labor force participation rate, i.e., the change that is a direct result of the weak labor market in the Great Recession and its aftermath. It does not count, for example, those retiring baby boomers who would have left the labor force whether or not the Great Recession happened.

Based on this logic, missing workers are estimated in the following way: The labor force participation rate projections for 2016 by gender and age group (age groups 16–19, 20–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75+) available in Table 3 of Toossi (2007) are assumed to be structural rates. The current month’s structural rates (by gender and age group) are calculated by linearly interpolating between 2006 and 2016. The size of the potential labor force is calculated by multiplying the current month’s structural rates by actual population numbers (available by gender and age group from the Current Population Survey public data series). The difference between the size of the potential labor force and size of the actual labor force (also available by gender and age group from the Current Population Survey public data series) is the number of missing workers.

The 1963 March on Washington for Jobs and Freedom was one of the most powerful, yet peaceful, social demonstrations in American history. Belying all initial fears of violence, rioting, or disorderliness, the multitude of faces and voices present on that August day were unified in their demands for equal access to public accommodations, freedom from employment discrimination, voting rights, access to decent housing, adequate and integrated education, full employment, and a living wage. There’s no doubt the imagery and articulation of this unity were essential to creating the political momentum that led to the passage of the Civil Rights Act of 1964. This watershed piece of civil rights legislation, signed into law by President Lyndon B. Johnson, dealt a decisive final blow to legally segregated public accommodations, in addition to prohibiting employment discrimination and creating the Equal Employment Opportunity Commission.

The Civil Rights Act of 1964 effectively met the marchers’ demand for equal access to public accommodations—although it would take several years before public establishments, particularly those in the South, fully complied with the act’s requirements. However, more than 50 years after the March on Washington, the hard economic goals of the march, critical to transforming the life opportunities of African Americans, have not been fully achieved. As documented in other papers in EPI’s Unfinished March series, these include the demands for decent housing, adequate and integrated education, a federal jobs program for full employment, and a national minimum wage of over $13 an hour in today’s dollars.

As we continue to march toward these goals, it is instructive to recognize that the Civil Rights Act of 1964 and its guarantee of equal access to public accommodations was the culmination of a movement spanning generations and propelled by countless victories and defeats along the way.

In fact, prior to the 1960s, the fight for equal access to public accommodations had been characterized by a long history of temporary advancements precipitated by protest, followed by legal retrenchments at the hands of lawmakers and the courts. The Civil Rights Bill of 1875 guaranteed all American citizens “full and equal enjoyment of public accommodations,” but was declared unconstitutional by the U.S. Supreme Court in 1883. During the 1880s and 1890s a series of local ordinances and state statutes, known as Jim Crow laws, were issued to further restrict the freedoms of blacks in the South. As the 19th century came to a close, the Supreme Court set the course of Southern race relations for the next 58 years as the 1896 ruling in Plessy v. Ferguson sanctioned the policy of “separate but equal.” Notwithstanding, direct action and legal challenges persisted until the 1954 Supreme Court ruling in Brown v. Board of Education ended legal segregation of public schools, building momentum to continue the fight against the unflinching racist policies of the South.

In the decades leading up to the 1963 March on Washington, the spirit of the movement characterized by the march had been growing and taking shape in communities across the country through various local demonstrations and protests. In many instances, demonstrators targeted local businesses—either leveraging buying power through boycotts, or, in the case of sit-ins, using their physical presence to defy segregation as an acceptable business practice. While these demonstrations were predicated on the principle of nonviolence, demonstrators often faced opposition and brutality at the hands of local police as well as ordinary citizens. Ultimately however, these boycotts and sit-ins imposed a sort of economic sanction that served to gradually dismantle discriminatory policies at the local level, setting the stage for the larger national response.

This report presents a timeline of some of the most pivotal demonstrations for the right to equal access to public accommodations preceding the 1963 March on Washington. It begins by examining the pre-1960 history of the fight for equal access. The paper then analyzes the various demonstrations and tactics pursued throughout the South in 1960 and beyond, with an emphasis on the sit-in movement and economic boycotts. Next, the paper examines a crucial facet of this fight: the transportation boycotts that began in the late 1940s and continued through the 1960s. The paper concludes by briefly analyzing the confluence of events that culminated in the passage of the Civil Rights Act of 1964, which served to open public accommodations and transportation to all races everywhere in the country. The paper finds:

• In 1960, the impulses released by the Brown v. Board of Education decision ending legal segregation in public schools, the bus boycotts in Montgomery, Alabama, and other antidiscrimination demonstrations took a new turn, as African American direct action against segregationist Jim Crow laws was seized by a new generation.
  • This is evidenced by the “sit-in movement” that began in Greensboro, North Carolina, in 1961, when four college students sat down at an all-white lunch counter and requested service. The movement soon spread throughout the South.

• While African American civil rights demonstrators were generally nonviolent, they often attracted a violent backlash from police officials as well as ordinary persons.
• The movement was marked by an extraordinary degree of unity among both genders and all classes, but it rarely had women in leadership roles. There were also differences in philosophies and tactics—sometimes separating young from old, advocates of violence from those espousing nonviolence, and integrationists from separatists.
• One of the more significant results of the struggle to gain equal access to public accommodations was the founding of two new civil rights organizations, the Southern Christian Leadership Conference (SCLC) and the Student Non-Violent Coordinating Committee (SNCC). These direct-action groups differed in philosophy and tactics from the National Association for the Advancement of Colored People (NAACP), which generally eschewed direct action in favor of legal challenges to segregation and discrimination.
• The fight for equal access to public accommodations shows that the foundation for large paradigm-shifting national movements is often built upon smaller community and grassroots demonstrations.
• More than 50 years after the March on Washington, the hard economic goals of the march, critical to transforming the life opportunities of African Americans, have not been fully achieved. These include the demands for decent housing, adequate and integrated education, a federal jobs program for full employment, and a national minimum wage of over $13 an hour in today’s dollars.
• As we continue to press for achievement of these goals as well, there are important lessons to be learned from places such as Greensboro, North Carolina, and Birmingham, Alabama, about how individuals and communities can leverage their collective power to set new standards and effect change.

Pre-1960 history of the fight for equal access

As early as the colonial period, African Americans who were not enslaved faced restrictions on their freedom. Although not legally slaves, the restrictions and prohibitions they endured have led scholars to refer to them as quasi-free blacks (Litwack 1961; Hornsby and Salvatore 2004).

The legal and extralegal restraints on black freedom were similar in both North and South. In the area of public accommodations, most colonies excluded blacks. The practice of reserving these facilities or places for whites continued into the birth of the republic and beyond. But by the mid-1800s, public opinion and African American protest led to loosening of some of these restrictions in the North (Litwack 1961; Hornsby and Salvatore 2004).

Up to the Civil War, the situation in the South ranged from limited desegregation to separation to exclusion; most free blacks there were restricted to their own restaurants, hotels, theaters, and other public accommodations.

With Reconstruction after the war came the first national legislative action to provide equal access to public accommodations for all Americans, the Civil Rights Act of 1866. This act, which promoted African American citizenship and foreshadowed the 14th Amendment to the U.S. Constitution, faced considerable opposition in various parts of the country, but particularly in the former Confederate states. The act was, in many places, poorly enforced or ignored. Then, once the Southern legislatures were restored to white Democratic control, public accommodations were segregated or forbidden to blacks by statutes (Hornsby and Salvatore 2004).

Blacks in much of the South witnessed streetcar boycotts; between 1900 and 1906, for example, boycotts occurred in 25 Southern cities. These boycotts sometimes led to limited or brief desegregation of the streetcars, but backlash in the form of white boycotts and violence was common. And in many instances, African Americans ignored the boycotts or failed to take advantage of opportunities for desegregated transportation.

Cognizant of the discrimination still confronting African Americans in both the North and South, the Radical Republicans in Congress pushed through a new law, the Civil Rights Act of 1875. While the act specifically guaranteed all American citizens “full and equal enjoyment of public accommodations,” white America remained largely opposed to open access to public accommodations. In 1883, the U.S. Supreme Court responded to some of the legal challenges and declared the Civil Rights Act of 1875 unconstitutional (Miller 1966; Hornsby and Salvatore 2004).

In response to the Supreme Court’s ruling, several Northern states passed their own statutes forbidding discrimination in public accommodations. Concurrently, in the South laws were strengthened or new ones were enacted to maintain or require segregation and discrimination. This spate of local ordinances and state statutes, mostly passed in the 1880s and early 1890s, became known as Jim Crow laws (Miller 1966; Hornsby and Salvatore 2004).

Black protest, however, was not stifled. Direct action and legal challenges were most prominent with respect to seating in railroad cars. Unfortunately, these legal challenges were rebuffed by the U.S. Supreme Court in 1896. In Plessy v. Ferguson, a case involving railway car seating, the court set the pattern for Southern race relations for more than half a century (Woodward 1981; Hornsby and Salvatore 2004).

By sanctioning “separate but equal” facilities, places, and institutions for blacks, the court left open only direct protests for blacks and the possibility of success with future legal challenges. Fifty-eight years after Plessy v. Ferguson, the great black victory over Jim Crow came in 1954, when the U.S. Supreme Court in Brown v. Board of Education declared public school segregation unconstitutional (Kluger 2004; Woodward 1981).

The decision in the Brown case reinvigorated a growing civil rights consciousness among African Americans that had been gaining strength at least since World War II. A major breakthrough in the area of transportation occurred with the Montgomery, Alabama, bus boycott of 1955 and the Supreme Court’s ruling the next year declaring bus segregation unconstitutional. Over the next several years, as a result of black boycotts or legal challenges, bus segregation fell in several Southern cities, including Baton Rouge and Tallahassee (King 1958; Morris 1984). The final section of the paper will examine in greater detail the effects of transportation boycotts in the pre- and post-1960 periods.

The fight for equal access: 1960 and beyond

In 1960, the impulses released by the Brown decision, the Montgomery protests, and other antidiscrimination demonstrations took a new turn; African American direct action against Jim Crow was seized by a new generation. This is evidenced by the “sit-in movement” that began at Greensboro, North Carolina, in 1961 when four college students sat down at an all-white lunch counter and requested service. The movement soon spread throughout the South. While the participants were mostly black college students, they were often joined by white collegians, black high school students, and adults of both races.

The pattern that was established involved an initial sit-in at a lunch counter, restaurant, theater, or other place of public accommodation. Often additional sit-ins followed until the participants were arrested or the facilities were closed. Simultaneously, boycotts were called against local white merchants in the area. Sometimes, particularly after mass jailings or appalling acts of white violence, large numbers of African Americans (and occasionally their white allies) would march to the city center, to the jail, or to a government building. In all of the demonstrations the participants included a cross section of the African American community—ministers, church members, and the nonreligious; professionals, domestics, and laborers; men, women, and children. But there were also divisions. Although women were a large part of the movement, they were rarely afforded any of the top leadership roles. And there were differences in philosophies and tactics. Eventually younger, more militant protestors, many of them associated with SNCC, broke with the nonviolent creed and tactics of Martin Luther King Jr. and the SCLC and embraced “Black Power.”

To be sure, sit-ins were not new. For example, the Congress of Racial Equality, or CORE (a biracial group founded by 100 men and women in 1942), launched sit-ins at two Chicago restaurants in 1942. Although Illinois laws prohibited discrimination, the two targeted restaurants, Jack Spratt Coffee House and Stoner’s restaurant, continued to exclude blacks. The two restaurants grudgingly agreed to serve all customers after local police refused to arrest the demonstrators. In that same year, three students from Howard University began protests at Washington, D.C., restaurants. The protests continued into the next year. The John R. Thompson Restaurant in downtown Washington was targeted. The restaurant’s owners at first seemed to relent, but the offer to serve blacks lasted only a few days. Their hardened attitude was partially aided by Howard University president Mordecai Wyatt Johnson who, fearing that a Southern-dominated Congress might cut off funds to Howard, ordered protesting students to desist. But using a long-overlooked Reconstruction-era civil rights law, African Americans, led by one of the original sit-inners, Pauli Murray, successfully petitioned the Supreme Court, which barred discrimination at John R. Thompson in 1953 (District of Columbia v. John R. Thompson Co.). In 1958 Clara Luper, a white leader in the local NAACP, led a sit-in at the Katz Drugstore in Oklahoma City. Eventually other department store lunch counters were also visited by sit-inners. Results were mixed, but within a few years after the initial protests, more than 100 restaurants and lunch counters began serving African Americans. Pre-1960 protests were also held in Wichita, Kansas, in 1958 and in Tampa, Florida, and Louisville, Kentucky, in 1959 (Graves 1989; Raines 1977; Lawson 2004).

While many Southern cities, large and small, as well as a few rural Southern areas experienced direct action protests, several sites stand out for their overall significance in the civil rights movement. These include Greensboro, North Carolina; Nashville, Tennessee; St. Augustine, Florida; Louisville, Kentucky; Atlanta; Albany, Georgia; Baltimore; Danville, Virginia; Orangeburg, South Carolina; Cambridge, Maryland; Birmingham, Alabama; and Jackson, Mississippi (Zinn 1965; Oppenheimer 1989).

Greensboro, North Carolina

Following the initial sit-ins and limited desegregation of lunch counters and restaurants in Greensboro, North Carolina, in 1960, demonstrations continued over the next several years, aimed at desegregating remaining eating establishments, theaters, and other public accommodations. As of May 1963, Greensboro prisons were overcrowded with more than 700 protestors, mainly students. Then on May 22, 1963, 2,000 demonstrators, youth as well as adult, staged a silent march into downtown Greensboro. At this time the city’s mayor, David Schenck, appointed a negotiating committee to seek solutions to the city’s problems. African American negotiators demanded full desegregation of public accommodations as well as school desegregation and an end to employment discrimination. In return they would end the marches. Once negotiations proceeded apace, the marches were indeed temporarily suspended. But when it became clear that the negotiations were not producing the desired results, demonstrations resumed. Among the leaders of the new marches was Jesse Jackson, then a student at North Carolina A&T University. When Jackson and his fellow protestors were arrested and charged with “inciting a riot,” other demonstrators blocked major streets in the downtown area. Mayor Schenck then called for full desegregation of public accommodations and a halt to the marches. By the fall of 1963 desegregation of Greensboro eating establishments had gone from less than a dozen to more than 25 percent (Chafe 1980; Wolff 1970).

Nashville, Tennessee

African American students in Nashville, Tennessee, began preparing for nonviolent protests as early as 1959, and starting on February 13, 1960, students from Fisk University, Baptist Theological Seminary, and Tennessee State University—mainly led by James Lawson, Diane Nash, and John Lewis—began sit-ins in various stores with the goal of desegregation at lunch counters. After about two weeks, the owners closed the counters without serving any of the students. During the next three months the sit-ins continued, not only at the stores but also at the Greyhound and Trailways bus terminals (Williams 1987; Lawson 2004; Hornsby 2011).

The first violent responses from the opposition occurred on February 27, “Big Saturday,” as James Lawson dubbed it. A group of whites attacked the sit-in, resulting in the arrest of 81 protesters, but none of the whites. The demonstrators were found guilty of disorderly conduct. They chose jail rather than paying the fines levied against them.

In an attempt to resolve the conflict between the storeowners and the protesters, Mayor Ben West appointed a biracial committee to investigate segregation in the city. However, despite numerous attempts at a compromise, the students declared that they would accept nothing less than the desegregation of public accommodations. The negotiating committee recommended that the lunch counters be divided into black and white sections, but the protestors rejected the proposal (Williams 1987).

On April 19, 1960, the home of one of the adult leaders of the movement, attorney Z. Alexander Looby, was bombed. Later that day thousands of black and white protestors marched silently to the courthouse, where they confronted city officials. They prayed and demanded responses from Mayor West. The mayor, who had previously opposed the student leaders because of the economic losses suffered by business owners in Nashville, did on this occasion listen to such leaders as C.T. Vivian and Diane Nash. He was especially moved by Nash’s eloquent oration on the immorality of blacks buying in one part of a store, but not being able to eat in another. He was also very concerned about the intensification of white violence. Thus at the end of the meeting, he declared that the lunch counters should be desegregated. On May 10, 1960, three months after the first sit-ins, “Nashville became the first major city in the Deep South to begin desegregating” its public accommodations (Hornsby 2011; Lovett 2005; Williams 1987; Wynne 2011).

St. Augustine, Florida

The NAACP chapter in St. Augustine, Florida, began demonstrations in 1963 that resulted in the desegregation of several lunch counters. But much segregation and discrimination remained; demonstrations thus continued, and white racist attitudes remained virulent. In 1964, when Martin Luther King Jr. and other members of the SCLC led marches in the city, they were accompanied by Northern college students and adults, including Mrs. Mary Parkman Peabody, the 72-year-old mother of the governor of Massachusetts. They were assaulted by angry whites. On June 11, 1964, Martin Luther King Jr. was arrested at the Monson Hotel. While incarcerated he wrote a letter to Rabbi Israel Desner of New Jersey asking him to recruit other clergy to participate in the movement. A week later several Jewish rabbi were arrested during a “pray in” at the Monson Hotel. During this time St. Augustine was also the scene of the first major “wade-in” of the civil rights movement. The wade-ins occurred at the St. Augustine beach between June 18 and July 1, 1964. On June 19 the wade-inners were viciously attacked by a white mob, and some were nearly drowned. Whites continued to verbally and physically oppose the demonstrators until the protests ended on July 1, 1964 (Garrow 1989; Colburn 1985; Warren 2008).

Louisville, Kentucky

Starting in the mid-1950s members of the NAACP, CORE, and others conducted sporadic demonstrations aimed at desegregating public accommodations in Louisville, Kentucky. At the same time they petitioned the mayor and city council to adopt legislation requiring desegregation. The city refused. Thus on February 9, 1961, the activists launched a full-scale campaign, including sit-ins and stand-ins at downtown lunch counters and restaurants. Arrests, which eventually totaled 700, followed. At the same time the protest leaders called for a boycott of downtown department and variety stores. The boycott was particularly effective during the Easter season of 1961. When early negotiations to achieve desegregation and end the boycott failed, the demonstrations were intensified. In April 1961 the mayor announced a plan to desegregate all eating places by May 1. However, the city council declined to pass the proposed ordinance. Large protests resumed. This time, however, there was violence perpetrated by whites. Twenty-nine persons, including seven whites, were arrested during the melee. The protests continued for the next two years. On May 14, 1963, the Louisville Board of Aldermen passed an ordinance granting equal access to public accommodations for all of its citizens (Ford and Morgan 2009).

Atlanta

One of the longest, most dramatic, and nationally influential sit-in movements occurred in Atlanta. Atlanta had long achieved a reputation of handling racial disputes peacefully through negotiated settlements among white and black leaders. Hence, as the sit-in movement erupted in early 1960 and black college students prepared for protest, adult black leaders, including college administrators, counseled caution. Hoping to achieve restraint and delay, they supported full-page ads in Atlanta newspapers documenting the pervasiveness of segregation and discrimination in Atlanta and calling for a redress of the grievances. The manifesto was called “An Appeal for Human Rights.” While state segregationist leaders denounced the appeal—with some calling it communist-inspired—others, such as Atlanta’s moderate mayor, William B. Hartsfield, called it reasonable and worthy of a response. The appeal even caught the eye of national political leaders and journalists who saw it as a possible alternative to disruptive sit-ins. However, their hopes and those of local leaders were dashed when on March 15, black college students from the six schools comprising the Atlanta University Center conducted a sit-in blitz in downtown Atlanta. Reflecting the moderation of civil rights activities in Atlanta, they initially targeted only lunch counters and restaurants in government buildings and in train stations. Soon, however, the sit-ins spread to downtown department and variety stores and were accompanied by massive arrests and some violence (Hornsby 2009; Lefever 2005).

Because many of Atlanta’s top leaders, black and white, were either opposed to or skeptical of direct-action protests, a negotiated settlement seemed elusive. In this stalemate, the sit-in leaders turned to a reluctant Martin Luther King Jr., an Atlanta native, for help. King joined a demonstration on October 19 at Rich’s department store, the largest in the South. His arrest brought national and international attention to the Atlanta sit-in movement. But more importantly, it may have determined the outcome of the 1960 presidential election (Lawson 2004; Hornsby 2009; Grady-Willis 2006).

Immediately after the arrest, King was placed with the other black demonstrators arrested with him in the Fulton County jail in Atlanta. But soon local and state segregationists saw an opportunity to further prosecute and persecute the national civil rights leader. Discovering that he was on probation for a minor traffic offense in neighboring DeKalb County, a local judge there revoked his probation and ordered him sent immediately to the notorious state prison in Reidsville, Georgia. These developments sent shockwaves across black America, as well as much of white America and the world. The two leading presidential candidates, Republican Richard M. Nixon and Democrat John F. Kennedy, were engaged in a very close race for the White House. Both candidates acknowledged the concern that many Americans, especially King’s family and friends, had for King’s safety at Reidsville. However, candidate Kennedy went further than Nixon when he personally expressed his sympathy in a telephone call to Mrs. King. The candidate’s brother, Robert M. Kennedy, used his influence with Georgia state Democratic leaders to win King’s release on bail. These actions caused jubilation in much of black America and persuaded enough additional black voters, especially in the North, to give their votes to Kennedy and to give him the election—albeit by a narrow margin of over 2 percent.

Interestingly and ironically, all of this did not persuade Atlanta’s leaders to begin desegregating lunch counters and restaurants. Instead, after reluctant and painstaking negotiations, they only agreed, in early 1961, to begin limited desegregation after Atlanta’s public schools were desegregated in the fall. The black leaders, old and young, were roundly scolded for assenting to this agreement. Were it not for the calming influence of Martin Luther King Jr., the agreement might have been broken and the leadership thoroughly discredited. The first desegregation came to Atlanta’s restaurants and lunch counters in late September 1961—one-and-a-half years after the sit-in movement began (Lawson 2004; Hornsby 2009).

The Atlanta sit-in movement was remarkable in three other aspects. First, it used modern technology, including two-way radios, to assign and move demonstrators. Second, the masses of black Atlantans broke with their more timid older leaders and supported the students in a very effective boycott of downtown merchants. And third, it produced a “poster boy” of the movement, Julian Bond.

The efforts to desegregate Atlanta theaters involved much smaller numbers of African American demonstrators, but their goals were still accomplished largely through the Atlanta style of negotiated settlement. The first target was Atlanta’s large and luxurious Fox Theater. While the Metropolitan Opera Company (Met) was on tour at the Fox, two blacks, with tickets in hand, attempted to enter the dress circle to witness a performance. Later four blacks with balcony tickets also attempted to claim their seats but were turned away. However, within three weeks of these incidents, Rudolph Bing, general manager of the Met, advised his “friends” in Atlanta that the Met would no longer perform before segregated audiences. By the end of 1961, with the decision of the Met fresh in mind, African American students threatened movie “stand-ins” at all downtown theaters if racial barriers were not lifted. Then in March 1962 former Atlanta mayor William B. Hartsfield, who had become known as a racial moderate, brokered a deal that led to the limited desegregation of most downtown theaters by June 1 (Finkleman and Harmon 1996; LeFever 2005; Hornsby 2009).

Albany, Georgia

The Albany, Georgia, direct action movement was launched in 1961 with sit-ins at lunch counters, bus stations, and libraries. They involved students from Albany State College and members of SNCC. The sit-ins were accompanied by boycotts and marches. Hundreds were jailed. However, local police chief Laurie Pritchett was able to disperse the prisoners among jails in several surrounding counties, thus preventing the jails from filling up (Branch 1988; Williams 1987).

When little progress was made toward desegregation, the local leadership invited Martin Luther King Jr. and his SCLC to join the protests. King himself was arrested during a large demonstration on December 1, 1961. King declared that he would refuse bail until the city negotiated a settlement. The city agreed to some concessions, but the agreement broke down once King left the city. King returned to Albany in July 1962 and was again arrested for demonstrating. He was later sentenced to 45 days in jail or to pay a fine of $176. King vowed to remain in jail. But after only serving three days of his sentence, the civil rights leader was released. Albany police chief Pritchett had arranged for his fine to be paid and ordered him out of jail. King’s release highlighted growing divisions among the Albany demonstrators as to philosophies and tactics. With the entrance of SCLC into the protests, divisions among the local black leadership were now coupled with disagreements between SNCC and SCLC. Thus when young blacks threw toys and paper balls at police, King called for “A Day of Penance” to encourage nonviolence and remain on “the moral high ground.” In July King was again arrested in Albany, remained in jail for two weeks, and then left town for good (Williams 1987; Branch 1988; Lawson 2004).

Some observers and scholars have called the Albany movement a major failure for the civil rights movement and for Martin Luther King Jr. Pritchett’s tactics contributed to the lack of success. Pritchett decided to try to avoid adverse national publicity by preventing overcrowding in the jails and avoiding extreme acts of police brutality. He thus robbed the movement of the later sensationalism it achieved at Birmingham. Secondly, there were divisions among young and old, and between traditional or moderate black leaders and more radical ones. Thus, the movement did not seem to have a guiding philosophy. Yet others reject the thesis, pointing to the large numbers of ordinary blacks who participated in the movement, risking jobs, limb, and life; as Martin Luther King Jr. himself said, “They straightened their backs up” (Williams 1987; Tuck 2003; Branch 1988; Carson 1981).

Baltimore

In March 1960 African American college students and a few white allies began picketing at a Baltimore, Maryland, department store, theater, and ice cream parlor. There were several arrests. Shortly thereafter, several downtown department stores desegregated their lunch counters. However, other restaurants and theaters remained segregated. In June several students, including 16-year-old Robert Mack Bell, were arrested during a sit-in at a Baltimore restaurant and convicted of trespassing. Led by Juanita Jackson Mitchell and Thurgood Marshall of the NAACP, the convictions were appealed to the Maryland Supreme Court, which upheld the convictions. When the case reached the U.S. Supreme Court, the court at first refused to hear it and sent it back to the Maryland Supreme Court. Meanwhile the state of Maryland passed a public accommodations law, and the U.S. Congress passed the Civil Rights Act of 1964. Then on April 9, 1965, in the case of Maryland v. Robert M. Bell et al., the U.S. Supreme Court reversed the 1960 convictions.

In February 1963, students from predominately black Morgan State University began a demonstration at the all-white Northwood Theatre in Baltimore. Twenty-five of them entered the lobby of the movie house while others picketed outside. When the protestors refused orders to leave the theatre, they were arrested and charged with trespassing.

Danville, Virginia

Outside of Birmingham, perhaps the most violent responses to direct-action protests came in Danville, Virginia, during the summer of 1963. On May 31, African Americans, led mainly by ministers in the Danville Christian Progressive Association, marched downtown to the municipal building. They demanded, among other things, desegregated public accommodations.

The city promptly rejected the demand and instead, using a pre–Civil War statute, sought injunctions against “any person conspiring to incite the colored population to insurrection.” On June 10, 60 high school students marched to the municipal building. Their leaders were arrested. Many of the other protesters ran away, but were chased into a blind alley where high-pressure hoses were turned on them. Many were knocked down, and some had their clothes blown off. The police then pounced on the protesters with night sticks and arrested them. When their parents came to the jail to look after them they, too, were arrested for contributing to the delinquency of a minor. But demonstrations continued through the summer of 1963. Meanwhile, responding to a plea from local leaders, members of SNCC and CORE joined the protests. Several of them were arrested. In the end there were more than 600 arrests in Danville. On July 11, Martin Luther King Jr. arrived in Danville, but did not lead or participate in a demonstration because the group that showed up to march was too small; the size of the group was indicative of the waning enthusiasm for demonstrations. The Danville protesters’ demands were never met by local authorities; desegregation of public accommodations came only after the passage of the Civil Rights Act of 1964 (Garrow 1989).

Orangeburg, South Carolina

On February 25, 1960, several students from predominately black South Carolina State University and Claflin University sat in at a variety store lunch counter in downtown Orangeburg, South Carolina. The lunch counter was immediately closed and its stools were removed. The students, however, continued their sit-ins and picketing over the next few weeks. On March 15 more than 1,000 students marched downtown in a peaceful protest. They were attacked by police with billy clubs and tear gas. Firemen also turned high-powered water hoses on them in freezing weather. About 400 of them were arrested and convicted of “breach of peace.” But in 1963 the U.S. Supreme Court declared that the arrest violated the First Amendment guarantee of the right to petition for a redress of grievances.

In February 1968, black students in Orangeburg attempted to desegregate the town’s All Star Bowling Lane. The students were denied entrance into the facility. Over the next two days, 20 mostly student protesters gathered on the campus of South Carolina State University to demonstrate against the continued segregation at the bowling alley. That night, the students threw firebombs, bricks, and bottles, and started a bonfire. As police attempted to put out the fire, an officer was injured by an object thrown at him. The police later claimed that they believed they were under attack by small arms fire. Police then fired into the crowd, killing three male students. Twenty-eight others were injured by police action. The protesters, however, consistently maintained that they did not fire at police officers, but rather threw objects and insulted the policemen.

The federal government later brought charges of excessive force against the state patrolmen. But in the federal trial all nine defendants were acquitted. In a state trial in 1970, the activist Cleveland Sellers, one of the black protesters, was convicted of a charge of rioting. He served seven months in state prison, after getting time off for good behavior. Twenty-five years later, Sellers was officially pardoned by the governor of South Carolina (Sellers and Terrell 1990; Nelson and Bass 1999; Shuler 2012).

Cambridge, Maryland

Cambridge, Maryland, became the scene of some of the most sensational and dramatic events during the civil rights movement. On March 29, 1963, African Americans and their white allies, mostly students, marched to downtown theaters with the intention of sitting in at theaters and a skating rink. A group of hostile whites, yelling epithets, blocked their way. Several protesters, including the principal leader, Gloria Richardson, were arrested and charged with trespassing. Once protesters were released after a local judge assessed fines of one cent, the protests resumed in May. Restaurants, theaters, and skating rinks were targeted, and more arrests ensued. Then on June 12 more than 500 protesters again marched in downtown Cambridge. Again they faced a white mob, but on this occasion some of the blacks were carrying weapons. Two days later several white-owned stores in an African American community were burned. At the same time, in a gun battle between whites and blacks, two whites were killed. When police entered the area they were pelted with rocks. As this violence subsided, Maryland Governor J. Millard Tawes called for a one-year moratorium on demonstrations. African American leaders rejected the request. The governor then declared martial law in Cambridge and ordered the National Guard to patrol the city. Interestingly enough, many African Americans welcomed the presence of the Guard as a better alternative to the racist local police force, while many other blacks resented the presence of the army “of occupation.”

In July 1963, as tensions seemed to cool a bit, the Guard was withdrawn. But almost immediately demonstrations resumed, and so did white attacks on the protesters. Some blacks again reacted violently. White-owned stores were set on fire, and at least a dozen whites were shot. The governor recalled the National Guard. The soldiers were to remain in the city for almost one year—the longest deployment of a military force in an American city since the reconstruction era. On July 23, U.S. Attorney General Robert Kennedy entered into the biracial discussions aimed at resolving the crisis. He helped forge a “Treaty of Cambridge” which, among other things, called for the desegregation of public accommodations. The treaty divided both black and white Cambridge. Some in both communities supported it; others vigorously opposed it. For example, a group of whites were successful in calling for a referendum to overturn the accord. Some black leaders urged African Americans to boycott the vote, arguing that their group should not “beg” for freedom. Thus no more than 50 percent of black voters participated. The referendum was approved 53 percent to 47 percent. After more than two years of protest, Jim Crow’s back could not be broken in Cambridge; it would take the Civil Rights Act of 1964 to desegregate public accommodations in the town (Levy 2003).

Birmingham, Alabama

As significant as other demonstrations were, the protests in Birmingham, Alabama, in the first three months of 1963 had such a profound impact nationally and internationally that they have been called a turning point in the civil rights movement.

Known as “Bombingham” because of the numerous explosions ignited by white supremacists to repel black advancement, the city remained completely segregated. For years Reverend Fred Shuttlesworth, a leader of SCLC, and the Alabama Christian Movement for Human Rights (ACMHR, an affiliate of the SCLC) had led demonstrations to integrate schools and public accommodations with no success. Shuttlesworth had been beaten and his home bombed. In 1962, after black students at the city’s Miles College had initiated an effective boycott of downtown businesses to protest segregation and discrimination, merchants agreed to desegregate lunch counters, toilets, and drinking fountains. But Public Safety Commissioner Eugene “Bull” Connor instead arrested Shuttlesworth and sent municipal inspectors to the establishments, threatening to close them down for building code violations if they went ahead with the desegregation. The merchants then called off their plans to desegregate.

On April 3, 1963, Martin Luther King Jr. and the SCLC launched a new round of demonstrations in the city. King rejected calls for further delay pending additional negotiations, contending that African Americans had waited long enough (Williams 1987; Lawson 2004).

Shortly thereafter the police started to arrest downtown marchers, and an Alabama judge enjoined King and more than 130 civil rights activists from participating in demonstrations. King decided to violate the state court order and staged a march on Good Friday, April 12. The civil rights leader was arrested and spent the next week incarcerated. From his cell he wrote the famous “Letter from a Birmingham Jail,” which was smuggled outside and published. In it King explained to moderate white clergy why he did not call off the demonstrations to allow time for new negotiations to succeed. “For years now I have heard the word ‘Wait!’” King complained. “It rings in the ear of every Negro with piercing familiarity. This ‘Wait’ has almost always meant ‘Never!’ We must come to see, with one of our distinguished jurists, that ‘justice too long delayed is justice denied’” (Williams 1987; Lawson 2004).

However, with King and others in jail, the demonstrations lost some of their momentum. As a result, on April 20, King chose to post bail. He then made one of the most controversial decisions since Albany: He approved of using children as demonstrators. On May 2, children ranging in age from six to 18 left the Sixteenth Street Baptist Church, adjacent to downtown, and marched into the streets of Birmingham. Bull Connor, policemen, and firemen greeted them with snarling, biting police dogs and high-pressure water hoses. The youngsters as well as adults in the march were knocked to the ground and against buildings and trees by the force of the water. Several were also struck by police billy clubs. Those who escaped ran back to the church. Hundreds were arrested, adding to those already incarcerated. As the jails overflowed, some protesters were imprisoned at the city’s state fairground (Williams 1987; Branch 1988; Lawson 2004).

The scenes of the violent repression at Birmingham were seen across the country and around the world. The outpouring of public opinion against Birmingham forced the White House to take urgent notice. President Kennedy sent the assistant attorney general for civil rights, Burke Marshall, to Birmingham to mediate between civil rights leaders and the city’s businessmen. Secret negotiations began on May 5, while demonstrations continued. On May 8, a “Senior Citizens’ Committee” of white businessmen and King and his allies agreed to a deal desegregating lunch counters, restrooms, fitting rooms, and drinking fountains in large downtown department and variety stores, as well as the hiring of an unspecified number of black sales clerks. By the end of July, five department stores had integrated their lunch counters, a few black clerks were hired, the city council removed its segregation laws from the books, and the municipal golf course, which Connor had closed, opened to blacks (Williams 1987; Branch 1988; Lawson 2004).

Schools, theatres, hotels, and restaurants remained segregated, and white violence continued. On May 11, a bomb exploded at the Gaston Motel, where Martin Luther King Jr. had been staying, though King was not there at the time. That same evening, white racists planted sticks of dynamite that blew away the front portion of the home of the Reverend Alfred Daniel Williams (A.D.) King, Martin’s brother. In response, a crowd of blacks left the Gaston Motel and retaliated by throwing rocks and bottles at the police who came to investigate the bombing. The blacks also attacked white pedestrians and burned stores in the surrounding area. The violence ended the next day as King and other African American leaders helped restore order. A month later, on Sunday, September 12, white racists struck again. A bomb blast ripped through the basement of the Sixteenth Street Baptist Church, killing four young girls attending Sunday school in the church basement and injuring other worshippers attending services upstairs. Once again, rioting erupted, and before the day was over two more black teenagers had been killed (Eskew 1996; McWhorter 2001; Williams 1987).

Jackson, Mississippi

On May 12, 1963, African American leaders in Jackson, Mississippi, including members of the NAACP, sent a letter to white political and business leaders demanding, among other things, desegregation of the city’s public accommodations. Jackson’s white leaders rejected the demands. Instead, Jackson Mayor Allen Thompson appointed his own “Negro Committee,” which was “composed of conservative, pro-segregation” blacks (Moody 2004). On May 28, 1963, sit-ins began at a lunch counter in a Jackson department store. The demonstrators, mostly students from nearby Tougaloo College, were attacked by a white mob which, among other things, poured coffee, salt, and syrup all over the students. Police eventually rescued the demonstrators from the mob. Two weeks later one of the leading forces of the civil rights movement in Jackson, Medgar Evers, was murdered in the driveway of his home. It was later determined that a Ku Klux Klansman, Byron De La Beckwith, was responsible for the crime. This tragedy inspired the Kennedy administration to intervene in the Jackson protests. As a result, a few African Americans were employed by the city, most notably policemen to patrol black communities. Local black leaders then called off the protests without achieving desegregation of lunch counters (Salter 1987; Dittmer 1995; Andrews 2004; Marshall 2013).

Equal access to transportation

In addition to fighting for equal access to public establishments such as lunch counters, stores, restaurants, and theaters, civil rights advocates also sought equal access to transportation. Prior to the 1960s, segregation on streetcars, buses, and railroad cars was consistently challenged by blacks and their allies, both in courts and through direct action including boycotts, such as the famous one in Montgomery, Alabama, in 1955. Other pre-1960 protests occurred in Baton Rouge, Louisiana; Tallahassee, Florida; and Atlanta.

The Montgomery boycott began on December 1, 1955, when Rosa Parks, a local seamstress and civil rights activist, refused to give up her seat to a white person. Her arrest enraged much of Montgomery’s African American community. Spurred by the eloquent and fiery oratory of a newly arrived minister, Martin Luther King Jr., the community, in a crowded mass meeting, unanimously agreed to boycott the buses.

In subsequent mass rallies in Montgomery and elsewhere, the meetings oft-times included fervent preaching and spirited singing. Despite economic intimidation, arrests, beatings, and bombings, Montgomery’s blacks refused to ride the buses until the U.S. Supreme Court ruled segregation on them unconstitutional on December 20, 1956 (Branch 1988; Robinson 1987).

The bus boycott that began in Baton Rouge, Louisiana, on February 11, 1953, was actually the first successful black boycott in the modern era. The African American community was angered by a recent increase in bus fares. Blacks, most of them domestic workers and laborers, made up at least 80 percent of the transit company’s passengers. This fare increase seemed especially unreasonable to many in light of the fact that blacks faced daily segregation and discrimination on the buses. The Reverend T.J. Jemison, a recent arrival in the city, led an appeal to the city council to amend the segregation laws to permit blacks to sit in the front seat of buses as long as they did not sit in front of a white passenger. The city council passed the amendment unanimously, and it was to take effect on March 29, 1953. However, during the first three months of its existence, the law was not enforced. In June 1953, reacting to the rough treatment of a black woman who tried to sit in “the white section” of a bus, blacks demanded enforcement of the recently passed amendment. Bus company officials complied with the request; however, some white bus drivers refused to accept the order and, instead, went on strike in protest. On June 19, 1953, Louisiana’s attorney general ruled the new amendment unconstitutional. African American leaders immediately called for a bus boycott. The highly successful boycott crippled the bus company and had other negative economic impacts in the city. Thus, some white leaders were encouraged to enter into negotiations with African American leaders. The agreement restored the essence of the amended bus seating law, and on June 24, 1953, the boycott ended (Fairclough 1995; Lawson 2004).

The incident that provoked the Tallahassee, Florida, boycott occurred on May 27, 1956, when two Florida A&M University students defied segregated seating laws on buses and were arrested for “trying to incite a riot.” The next day Florida A&M student leaders called for a student boycott of the buses. Two days later adult black leaders, under the leadership of the Rev. C.K. Steele (head of the Tallahassee NAACP) and James Hudson (president of the Tallahassee Ministerial Alliance), called a mass meeting of local citizens, which endorsed a community-wide boycott. By July 1, 1956, the bus company was insolvent and was forced to shut down service throughout the city. In August, the bus company hired several black bus drivers for routes in the African American communities. This move encouraged some blacks to return to the buses. But the Florida A&M student leaders and the leaders of the adult coordinating committee urged continuation of the protests. The city responded by arresting several of the blacks who were operating carpools; they were convicted and assessed fines of more than $10,000. Among the African Americans’ response was an attempt by two ministers to ride “whites only” buses on Christmas Eve 1956. White racists, including members of the Ku Klux Klan, simultaneously stepped up their acts of intimidation and violence. Meanwhile, aided by some white students from Florida State University, black leaders continued their attempts to desegregate the buses. Amid these activities and increased national publicity, Florida’s “moderate” governor, Leroy Collins, ordered bus service suspended. Meanwhile, behind-the-scenes negotiations continued in an effort to break the stalemate. In late January 1957, these efforts led to some bus seating desegregation, particularly on predominantly black routes, and the boycott faded (Rabby 1999; Branch 1988).

Freedom Rides

In 1941 the Interstate Commerce Commission (ICC) issued a ruling in the case of Sarah Keys v. Carolina Coach Company declaring segregation in interstate bus travel illegal, but the ICC did not enforce its ruling. In two cases in 1946, Boynton v. Virginia and Morgan v. Virginia, the U.S. Supreme Court ruled that segregation on interstate buses was unconstitutional. The next year the first notable “Freedom Rides” testing the ability to ride desegregated on interstate buses occurred. Led by biracial groups, including members of CORE, the riders selected the Upper South states of Kentucky, North Carolina, Tennessee, and Virginia. Characterizing their foray as a “journey of reconciliation,” they entered these states from Washington, D.C. They first encountered trouble in Virginia and North Carolina, where at Chapel Hill the mob turned its fury particularly on one of the white riders, James Peck. Although no further serious outbreaks of violence occurred as the riders continued their journey, there were many arrests, and when they ended their demonstration on April 23, segregation on buses and in terminal waiting rooms continued (Peck 1962; Meier and Rudwick 1973).

The next major demonstrations seeking to ride desegregated interstate buses were the more-publicized Freedom Rides that began in 1961. Based on the earlier model provided by CORE in 1947, a biracial group of riders started out from Washington, D.C., on May 4. They planned to ride through the Deep South into New Orleans, hoping to arrive on May 17, the seventh anniversary of Brown v. Board of Education.

One of the first major acts of violence occurred in Rock Hill, South Carolina, where now-Congressman John Lewis was attacked. In other towns and cities, including Charlotte, North Carolina, and Jackson, Mississippi, several riders were arrested, either for sitting desegregated or attempting to use all-white waiting rooms or cafeterias. Then on May 14, a mob, composed largely of Ku Klux Klansmen, firebombed a bus carrying Freedom Riders. As the riders escaped from the burning vehicle they were viciously beaten by the mob. Their lives were probably saved by warning shots fired over the heads of the Klansmen by Alabama highway patrolmen. However, another bus with riders aboard reached Anniston, Alabama, shortly after this incident. Klansmen boarded the bus and beat the riders into semiconsciousness. Then, at Birmingham, Alabama, another mob of Klansmen, aided by local police under direction of police commissioner Eugene “Bull” Connor, severely injured several riders, beating them with baseball bats, bicycle chains, and iron pipes. White riders especially were targets of the mob’s fury. For example, CORE member James Peck, who as noted above also participated in the 1947 Freedom Rides, required more than 50 stitches for the wounds to his head (Branch 1988).

In view of these incidents and the dangers ahead, as well as the refusal of some operators to drive buses with Freedom Riders as passengers, some Freedom Riders wanted to abandon their journey and fly to New Orleans. But others, like SNCC leader Diane Nash, argued successfully that violence should not be allowed to halt the movement. Thus, on May 17, a new group of riders left Nashville for Birmingham. They were arrested, but later released and driven by police to the Tennessee state line. Within short order, however, they returned to Birmingham (Branch 1988; Arsenault 2006; Niven 2003).

As new riders joined the group in Birmingham, the plan was to go on to the Alabama state capital at Montgomery. But bus drivers again refused to move their buses. It took pressure from Attorney General Robert Kennedy to force Greyhound Bus company officials to order drivers to take the riders from Birmingham to Montgomery. The attorney general’s office also persuaded a reluctant Alabama Governor John Patterson to offer protection for the riders during their trek from Birmingham to Montgomery. But when the bus carrying the riders reached the Montgomery city limits on May 20, the Alabama highway patrolmen withdrew their protection. As the bus reached the downtown bus station, a mob lay in waiting and savagely beat the riders, causing several of them to be hospitalized. Also injured was John Seigenthaler, a justice department official, who was beaten into unconsciousness (Branch 1988; Niven 2003).

Appalled by this new round of violence and concerned by the unfavorable publicity generated throughout the world by this bloody incident, the Kennedy administration sent some 400 U.S. marshals to Montgomery and worked behind the scenes to negotiate a settlement. Meanwhile, on May 21, Martin Luther King Jr., who had not been involved in the planning or direction of the Freedom Rides, arrived in Montgomery and spoke before a crowd packed into Ralph Abernathy’s First Baptist Church. Outside, white mobs formed, assaulted black onlookers, torched parked cars, and flung rocks and Molotov cocktails at the church. Meanwhile, King kept in telephone communication with Attorney General Kennedy, who monitored the crisis. U.S. marshals fought to repel the siege and fired tear gas into the crowd, but were outnumbered. As gas fumes sifted inside the church, King counseled calmness and peace. Finally, Governor John Patterson, under intense pressure from the federal government, declared martial law and sent in the National Guard to restore order and free the churchgoers (Branch 1988; Niven 2003).

The attorney general finally worked out an agreement for Alabama state troopers to protect the bus riders on the next leg of their trip and then have Mississippi authorities escort them to Jackson. Once safely there, city officials would have them peacefully arrested, tried, and convicted for violating the state’s segregation laws. All went according to plan as Freedom Riders continued to pour into Jackson throughout the summer and fill the cells at the state penitentiary. On May 29, Attorney General Kennedy petitioned the ICC to promulgate regulations banning interstate bus segregation. The Freedom Rides maintained pressure on the administration and the commission, and finally in late September the ICC issued a decree declaring that by November 1, 1961, interstate as well as intrastate bus carriers and terminals must abandon segregation. By the end of 1961, CORE reported that it had surveyed 200 bus stations in the South and discovered that most obeyed the ICC regulation. The majority of recalcitrant operators were located in Mississippi and northern Louisiana, but by the end of 1962, legal action had dismantled much of the remaining segregated terminal facilities (Williams 1987; Branch 1988; Niven 2003).

The Civil Rights Act of 1964

Because of the need for Southern congressmen’s votes for his legislative initiatives, President John F. Kennedy had tread lightly during the civil rights movement, intervening only to make sure that fundamental law was adhered to and to prevent serious violence. But the repressive acts at Birmingham, viewed nightly on television around the world, and the pressures of African American leaders finally forced him into public action. On June 11, 1963, following the desegregation of the University of Alabama under the protection of federal marshals, President Kennedy adopted some of the spirit of Martin Luther King Jr.’s “Letter from a Birmingham Jail” and went on television and embraced the goals of the movement on legal and moral grounds. Shortly thereafter his allies in Congress introduced the Civil Rights Act of 1963. However, it was stalled in Congress by a combination of Southern Democrats and Northern Republicans. It was only after Kennedy’s assassination on November 22, 1963, and the ascendancy of Lyndon Johnson to the presidency that the bill was passed. Johnson, using the mood of the country after Kennedy’s death and his skills as a longtime Southern senator, was able to secure passage of the measure and signed it on July 2, 1964 (Risen 2014).

The 1964 Civil Rights Act guaranteed to all persons “the full and equal enjoyment of the goods, services, facilities, privileges, advantages, and accommodations of any place of public accommodation . . . without discrimination or segregation on the ground of race, color, religion, or national origin.”

Conclusion

History is often marked by major shifts or turning points in society. One such turning point in American history and for African American history in particular was when President Lyndon Johnson signed into law the Civil Rights Act of 1964. This watershed piece of legislation forever changed the quality of life for African Americans in this country by dealing a decisive final blow to legally segregated public accommodations and prohibiting employment discrimination.

The 1963 March on Washington for Jobs and Freedom—still held as a pinnacle of the civil rights movement—is often credited with creating the political momentum necessary for passing this law. However, this paper shows that the foundation for large paradigm-shifting national movements is often built upon smaller community and grassroots demonstrations, such as those documented in this report. Still, more than 50 years after the March on Washington, the hard economic goals of the march, critical to transforming the life opportunities of African Americans, have not been fully achieved. As documented in other papers in EPI’s Unfinished March series, these include the demands for decent housing, adequate and integrated education, a federal jobs program for full employment, and a national minimum wage of over $13 an hour in today’s dollars. As we continue to press for achievement of these goals as well, there are important lessons to be learned from places such as Greensboro, North Carolina, and Birmingham, Alabama, about how individuals and communities can leverage their collective power to set new standards and effect change.

About the author

Alton Hornsby Jr. is Fuller E. Callaway Professor of History Emeritus at Morehouse College. His latest publications include African Americans in the Post-Emancipation South: The Outsiders View and Black Power in Dixie: A Political History of African Americans in Atlanta.

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The trade association that represents these CEOs, the National Restaurant Association, has vehemently opposed any increase in the minimum wage.  But as the figure makes clear, since 2007, when Congress passed the last minimum wage increase, restaurant CEO pay has increased so fast that the ratio of CEO pay to the minimum rose from 609-to-1 to 721-to-1.

For a broader context, this recent EPI analysis examines overall CEO compensation relative to average worker pay in the United States.

In U.S. policymaking circles in recent years there have been recurrent calls to increase infrastructure investments. This is hardly a surprise, as increased infrastructure investments could go a long way to solving several pressing challenges that the American economy faces.

In the near term, the most pressing economic challenge for the U.S. economy remains the depressed labor market. As of May 2014, the share of prime-age adults (age 25–54) currently employed is just 0.5 percentage points higher than it was at the official end of the Great Recession in June 2009. And it is more than 3.9 percentage points lower than during the labor market peak of the mid-2000s, and 5.4 percentage points lower than its 1999 peak.

In the longer term, the most pressing economic challenges for the U.S. economy concern how to provide satisfactory living standards growth for the vast majority of people. Such growth requires two components: rapid overall productivity growth, and a stabilization (or even reversal) of the large rise in income inequality that occurred in the three decades before the Great Recession, a rise in inequality that kept overall productivity growth from translating into living standards growth for most Americans.

This report examines the short- and long-term economic and employment impacts of infrastructure investment. It examines three possible scenarios for infrastructure investment and estimates their likely impact on overall economic activity, productivity, and the number and types of jobs, depending on how the investments are financed. The data show that by far the biggest near-term boost to gross domestic product and jobs comes from financing the new investment through new federal government debt rather than a progressive increase in taxation, a regressive increase in taxation, or cuts to government transfer programs. Our research also shows that this debt-financed impact is greater than that deriving from increases in infrastructure investment that are driven not by direct public investments but through other actions, such as regulatory mandates.

Key findings of the report are:

Three potential infrastructure packages would yield from $18 billion to $250 billion annually for infrastructure investment.

• Scenario one cancels all of the scheduled cuts stemming from the budget “sequester” (automatic, across-the-board cuts to discretionary spending called for in the Budget Control Act (BCA) of 2011), yielding an average of $30 billion annually over the next decade for infrastructure investments. (As of January 2014, a third of the scheduled sequester cuts were cancelled for the next two years only.)
• Scenario two implements a package of green investments that includes a large increase in investments in the energy efficiency of residential and commercial buildings and upfront investments to construct a national “smart grid,” yielding $92 billion annually in infrastructure investments over the next decade.
• Scenario three makes an ambitious investment in largely traditional infrastructure projects in transportation and utilities (particularly water treatment, distribution, and sewage systems) to nearly close the U.S. “infrastructure deficit” identified by the American Society of Civil Engineers (ASCE) and yield $250 billion annually in infrastructure investment between now and 2020.

In the near term, increases in infrastructure spending would significantly boost economic activity and employment.

• Under scenario one, a debt-financed $18 billion annual investment in infrastructure yields a $29 billion increase in GDP and 216,000 net new jobs by the end of the first year, with the increased levels then sustained over the next decade.
• Under scenario two, a debt-financed package of green investments totaling $92 billion annually boosts GDP by $147 billion and generates 1.1 million net new jobs by the end of the first year, with the increased levels then sustained over the next decade.
• Under scenario three, a debt-financed $250 billion annual investment boosts GDP by $400 billion and overall employment by 3 million net new jobs by the end of the first year, with the increased levels then sustained over the seven-year life of the investment.
• Any method of making these infrastructure investments deficit-neutral reduces their impact on near-term activity and employment, but every method except cuts to government transfers still leaves a net positive impact.

Over the long term, we can reliably predict only the impact of infrastructure investments on the composition, not the overall level, of labor demand.

Because the impact of infrastructure investments on the overall level of economic activity depends on the degree of productive slack in the economy, the stance of monetary policy, and how the investments are financed, it is impossible to reliably forecast the long-term (further than five years out) effects of such investments on the overall level of economic activity. However, we can reliably project the impact of infrastructure investments on the composition of labor demand. Even if these investments crowd out other forms of spending and do not affect the overall level of activity and employment, it remains the case that composition of employment supported by additional spending on infrastructure would be different than that of the economic activity it potentially displaces.

• Under all scenarios, jobs created are disproportionately male, Latino, and skewed away from younger workers.
• Under scenario one, male employment accounts for 77 percent of all jobs created, while under scenario two it accounts for 80.4 percent of all jobs created, and under scenario three it accounts for 74.1 percent, compared with an economy-wide average of 50.2 percent of all jobs being held by men.
• Under scenario one Latino employment accounts for 15.4 percent of all jobs created, while under scenario two it accounts for 16.2 percent of all jobs created, and under scenario three it accounts for 14.3 percent, compared with an economy-wide average of 12.3 percent.
• Under scenario one, employment of young adults (under 25 years old) accounts for 9.3 percent of all jobs created, while under scenario two it accounts for 9.5 percent of all jobs created, and under scenario three it accounts for 7.8 percent, compared with an economy-wide average of 13.2 percent.

• Under all scenarios, jobs created are disproportionately filled by workers without a four-year university degree. Under scenario one, workers with a bachelor’s degree or more education fill 23 percent of all jobs created, while under scenario two college-educated employment accounts for 19.6 percent of all jobs created, and under scenario three it accounts for 21.4 percent, compared with an economy-wide average of 32.6 percent.
• Under all scenarios, jobs created are disproportionately middle- and/or high-wage. Under scenario one employment in the bottom wage quintile accounts for just 9.5 percent of all jobs created, while under scenario two it accounts for 9.4 percent of all jobs created, and under scenario three it accounts for 11.2 percent of all jobs created, compared with an economy-wide average of 18.9 percent.

Infrastructure investments provide the potential to boost economy-wide productivity growth.

Productivity growth has slowed significantly in the U.S. economy, beginning even before the onset of the Great Recession. Our analysis conforms with a large and growing body of research persuasively arguing that infrastructure investments can boost even private-sector productivity growth.

• An ambitious effort to increase infrastructure investment by $250 billion annually over seven years would likely increase productivity growth by 0.3 percent annually—a boost more than half as large as the productivity acceleration in the U.S. economy between 1995 and 2005, one that was attributed to information and communications technology (ICT) advances.
• A productivity acceleration of 0.3 percent would have measurable impacts on the estimated Non-Accelerating Inflation Rate of Unemployment (NAIRU) and could allow macroeconomic policymakers to target significantly lower rates of unemployment. Extrapolating from the experience of the late 1990s, the NAIRU could be lowered by as much as 1 full percentage point by a sustained $250 billion annual increase in infrastructure investment. This could mean that more than 1 million additional workers each year find employment.

Scenarios for infrastructure investments

Simple political realism about the current state-of-play of American fiscal policy argues that large-scale infrastructure investments financed by the federal government are unlikely in coming years. However, economic analysis stands apart from current politics, and the economic case for boosting these investments is strong—and perhaps made even stronger by the growing threat of global climate change (GCC) caused by greenhouse gas (GHG) emissions.

Given these conflicting imperatives—political realism versus economic necessity—this report examines three different scenarios for infrastructure investments. The first looks at the implications for infrastructure investment if sharp cuts to federal discretionary spending called for in the Budget Control Act of 2011 are cancelled.¹ A 2013 budget deal signed into law does indeed cancel a portion (but just a portion) of the automatic sequester cuts for the next two years. Given this, the paper examines what reversing these cuts completely would mean for boosting future infrastructure investments.

The second scenario is more ambitious, and addresses the need for the United States to transition to an economy that emits fewer greenhouse gases. It packages a mix of investments in energy efficiency in the building sector with start-up investments in a national “smart grid.” The dollar figure for building efficiency investments is taken from a (now-famous) McKinsey report detailing the benefits of energy efficiency. For this scenario, we identify all building efficiency investments that were identified by McKinsey as having a net negative cost over the useful lives of the projects. For the smart-grid investments, we relied on assessments from the Electric Power Research Institute (EPRI) for the upfront costs. After estimating these two components of this “green” package of energy, we checked to ensure that it would constitute a genuinely ambitious step toward mitigating GHG emissions, using work by Pacala and Socolow (2004).

Pacala and Socolow (2004) introduced the concept of the “stabilization wedge” of GHG abatement. They essentially look at the decline in carbon emissions needed to stabilize atmospheric concentrations of carbon at 450 parts per million (PPM) by 2054, a benchmark that they identify as the minimally ambitious goal for reducing the future costs imposed by climate change.²

They next divided the entire wedge between carbon emissions projected to occur under a “business as usual” (BAU) scenario and the emissions that would be allowed under a scenario that kept carbon at less than 450 parts per million into seven smaller “sub-wedges”. Next they identified a number of actions that would be sufficient to “fill” one of these smaller sub-wedges. So, for example, increasing fuel efficiency of 2 billion cars from 30 mpg to 60 mpg would constitute one sub-wedge, while boosting coal-fired power plant efficiency by 50 percent would constitute another sub-wedge.

The package of green investments that constitutes our second infrastructure investment scenario would seem to be ambitious indeed—probably coming close to accounting for well over half of a stabilization sub-wedge, meaning that this package would be moving the U.S. economy more than 10 percent of the way toward stabilizing GHG emissions at 450 PPM by itself.

The building investments alone likely account for well over half of a stabilization wedge. Pacala and Socolow (2004) identify “cut[ing] carbon emissions by one-fourth in buildings and appliances” as a wedge. The McKinsey report estimates that the efficiency investments called for in it would lead to almost exactly a one-quarter reduction in carbon emissions (23 percent), with more than 60 percent of this efficiency effect coming from the buildings channel alone.

Further, EPRI has identified a number of ways a smart grid could facilitate the achievement of other stabilization wedges. For example, they note that a smart grid could accommodate an increase in electric and hybrid automobiles, which could help achieve the stabilization wedge possible through increased fuel efficiency of cars. And they note that household efficiencies would be much easier to achieve if energy savings stemming from them were more salient to households, and that this salience would be much easier to achieve through “smart metering” and other mechanisms that more closely tie household energy bills to their consumption patterns.

The last scenario examines an across-the-board increase in infrastructure spending concentrated in traditional transportation and utilities investments. The magnitude of this spending—$250 billion annually—is chosen to close the “infrastructure deficit” identified by the American Society of Civil Engineers (ASCE) in recent reports about the state of the nation’s infrastructure, including its most recent such report (ASCE 2013).

Scenario One: Undoing the discretionary spending caps imposed by the 2011 Budget Control Act

In 2011, the U.S. Congress passed the Budget Control Act (BCA), which significantly reduced 10-year discretionary spending growth. Most of the American social insurance system (Social Security, Medicare, Medicaid, and the Affordable Care Act) was unaffected. However, because the vast bulk of public investments (including infrastructure investments) are financed by the discretionary side of the federal budget, these spending caps—including the now-famous budget “sequester”—have steep consequences for infrastructure spending. Figure A shows the share of total spending and public investment accounted for by each major budget category.

The bars on the left of each two-bar set show the share of total federal spending accounted for by each of the three major spending categories. Nondefense discretionary spending accounts for 21.0 percent of total federal spending, defense spending accounts for 20.4 percent, while mandatory spending (dominated by the retirement security programs Medicare and Social Security as well as other health spending) accounts for 58.7 percent.

The bars on the right of each two-bar set show the share of total federal public investment accounted for by each of the three major spending categories. Nondefense discretionary spending, despite accounting for just a fifth of total spending, accounts for well over half of total public investment, 55.4 percent. Defense spending accounts for 39.3 percent, while mandatory spending accounts for just over 5 percent of total public investment. What this figure demonstrates is that any policy change that leads to large changes in the trajectory of discretionary spending will almost inevitably have large impacts on the future course of public investment.

In estimating the impacts on economic activity and employment from undoing these spending caps, we assume that the composition of discretionary spending is essentially unchanged by shifts in the level of spending. It is theoretically true that cuts to infrastructure spending could be less or more steep than overall spending cuts, but this is nearly impossible to forecast. Further, the discretionary spending cuts currently constituting the policy baseline in the United States (i.e., the budget “sequester”) are across-the-board cuts to every category of discretionary spending, making the assumption that the composition of discretionary spending cuts will be unaffected by the level in fact consistent with current budget law.

While economic analysis of the nation’s infrastructure needs would drive public investment policy in a more rational world, it is important to emphasize what an uphill struggle it will be in coming years to overcome the political barriers to increasing public investment. Figure B shows the implications for overall public investment if various 2014 budget proposals were passed. The budget proposals include, on the Democratic side, those from the White House (the Obama plan), the Senate Budget Committee (Murray plan), Democrats in the House of Representatives (House Democratic budget alternative), and the Congressional Progressive Caucus, and, on the Republican side, that of the House Committee on the Budget (Ryan plan). It demonstrates clearly the downward pressure of austerity on public investment possibilities, with nearly all budget proposals coming from the U.S. Congress, except the Congressional Progressive Caucus (CPC) budget, calling for steep cuts in coming years. (See the text box titled “U.S. budget politics” for a broad overview of the differences between the budgets and an explanation of why there are so many competing budget proposals in U.S. politics today.) On the one hand, these projections show the widespread acceptance of large cuts in discretionary investment (and hence public investment) in coming years, which just highlights the political hurdles to increased infrastructure spending. On the other hand, these scenarios show just how much room there is in coming years to boost infrastructure spending just by returning the public commitment to it to recent historic norms.

Figure B

Figure B (continued)

Actual and implied public investment as a share of GDP under various 2013 U.S. budget proposals, 2000–2023 Source: Author's analysis based on Bivens (2013)

Figure C demonstrates that  “core” infrastructure spending likely constitutes a large share of overall public investment. The Figure examines the portion of public investment classified as structures and does not cover equipment, as only data on structures are broken down in detail that allows us to differentiate core infrastructure investment from other forms of public investment.  What the figure shows is that most of what is classified as public investment in official data sources on public investment in structures is indeed infrastructure spending (highways, transportation projects, water and sewer projects, utilities, etc.).

A similar breakdown between core infrastructure and other public investments for the equipment component of public investments is not available. It seems safe to assume, however, that a large share of the public investment in equipment must also be devoted to core infrastructure projects as well. Airports, water treatment plants, and rail systems, for example, have investment needs that go far beyond the buildings sitting on them.

The message is clear: Unless the proposed cuts in discretionary spending in coming years are done with surgical precision to avoid cutting infrastructure investments, they will deeply impact annual investments. And it should be noted that the budget “sequester” explicitly disallows such discrimination among different discretionary spending priorities and demands across-the-board cuts.

Economic and employment impact

In order to assess the economic and employment impact of the extra infrastructure investment made possible by undoing the BCA spending caps, we need to know just what kinds of infrastructure investments are currently financed by the federal government.

First, we translate proposed cuts in discretionary spending into cuts in overall public investments, using the same data sources we used to construct Figure A (data sources which show the share of each type of federal government spending that is public investment instead of public consumption). These data (available upon request) are non-public data supplied by the Office of Management and Budget (OMB) to EPI. The main contribution of the data is to identify how many dollars in each detailed budget function (of which there are over 6,000) are dedicated to investment, as opposed to current consumption.

Using these data we can estimate the share of these cuts to public investment that will take the form of cuts to infrastructure spending specifically. We do this by using data from the National Income and Product Accounts (NIPA) from the Bureau of Economic Analysis (BEA) to allocate the cuts to infrastructure spending (simply based on our definition that infrastructure investments cover highway, transportation, sewer and water-treatment industries) to the various industries in our input-output model. Table 1 presents these allocations as gains: how much additional infrastructure spending could flow into each of these industries if these proposed cuts were reversed. Overall it shows that a reversal of the proposed cuts would yield an average of $18 billion annually over the next decade for infrastructure investments.

Unsurprisingly, the cuts—and thus gains—are heavily concentrated in the construction sector, as federally financed infrastructure projects are heavily concentrated in road building and other forms of transit construction. Federally financed infrastructure projects also lean heavily toward the manufacturing sector, with transportation equipment (aerospace, ships and boats, and motor vehicles manufacturing) dominating. The high shares of aerospace and ship building could be driven in part by the fact that defense spending accounts for a non-trivial share of total public investment. As defense spending is also cut by the provisions of the BCA, one could in theory include these in our overall measures. However, for the purposes of this examination we exclude the aerospace and ship-building sectors, as they are probably less associated with traditional infrastructure projects. 

Scenario Two: Combining investments in building energy efficiency and the smart grid for carbon mitigation

A second, more ambitious policy proposal combines large increases in infrastructure spending to improve the energy efficiency of buildings, along with start-up investments for a national “smart grid.” The impetus for examining this scenario is to emphasize that the cost of out-of-date infrastructure is likely rising fast given the enormous threat posed by global climate change driven by greenhouse gas emissions.

Building efficiency

For example, a key part of American infrastructure is simply buildings—both residential and commercial, private and publicly owned. We now know that inefficiency of buildings is a huge contributor to excess carbon emissions, and that investments in energy-efficient buildings represent an enormous low-cost opportunity to reduce GHG emissions. While publicly owned buildings are the obvious first place to start an infrastructure investment effort, it should be noted that the economics of GHG emissions imply that a substantial public investment even in boosting efficiency of privately owned buildings would yield high economic returns. Without externalities, inefficient buildings represent a cost only to their owners, and the case for including improvements to these inefficient buildings in an infrastructure effort is weak. But with unpriced externalities, inefficient buildings—even those privately owned—inflict a cost on everybody, and the case for using public resources to improve them is much stronger.

McKinsey and Company (2009) have famously identified more than $500 billion in energy efficiency opportunities that would have negative net economic cost and offer present-value returns of nearly 100 percent over their useful lives. Pacala and Socolow (2004) have identified improving energy efficiency of buildings as a large enough opportunity to by itself account for more than 10 percent of the total possibility for moving carbon emissions to an economically sustainable level. Rogers (2007) has noted that many market failures even besides the unpriced externality of GHG emissions exist in markets for energy efficiency investments, and that these market failures argue for a key public role in fostering this investment. All of this argues strongly that investing in energy efficiency of buildings would be an extraordinarily high-return activity.

Smart-grid investments

Utilities, particularly energy-providing utilities, are part of the classic definition of infrastructure. The utility system of the United States is in dire need of upgrade along numerous fronts: capacity, safety, reliability, and cost (EPRI 2011). Further, the need to foster a smooth transition to an economy where production is less carbon-intensive in the future will require a much different, and much better, national system of electrical power generation. For example, the benefits of putting a price on carbon emissions will only come to pass if electricity consumers reliably see a price signal from changes in electricity use. Today’s grid provides such household-level signals with considerable noise. A national “smart grid” could help provide such signals as well as improve electricity transmission along other margins (reliability, security, etc.).

The Electric Power Research Institute (EPRI 2011) has undertaken a rigorous estimate of the costs and benefits of investing in a state-of-the-art smart grid for the United States. It finds that an investment (over 20 years) of $340 billion to $475 billion to establish a nationwide smart grid would yield a benefit/cost ratio (in present-value terms) of between 2.8 to 6.0. Part of this benefit includes the enabling role that a smart grid would play in implementing some of the stabilization “wedges” identified by Pacala and Socolow (2004). For example, EPRI demonstrates that a key benefit of a state-of-the-art smart grid would be the capacity to provide charging stations for electrical vehicles. The switch from carbon-fuel-based vehicles to electric vehicles accounts for a stabilization wedge by itself in the Pacala and Socolow analysis.

The portfolio of infrastructure investments packaged in this second scenario is obviously politically ambitious for the present moment in the United States. However, it does serve as a useful reminder that potentially high-return investments are numerous, in large part because of the relative decline in public investment in recent decades (a decline reversed for a few years by the American Recovery and Reinvestment Act (ARRA)). Further, the economic returns to “green” investments in particular are likely rising quickly, as the costs of global climate change begin to manifest, and while policy initiatives aiming to put a price on GHG emissions have so far failed. A key benefit of pricing GHG emissions is to make investments in their mitigation profitable, and hence likely to be undertaken by private actors. But the failure to put a price on these emissions in the United States does not have to mean that no action is taken on this front. Instead, public investments, including infrastructure investments, can be begun even before price signals to drive private investments begin.

Model inputs for scenario two

The model inputs for investments in the smart grid were helped greatly by two previous reports. The first, a 2011 report by the Electric Power Research Institute (EPRI), provided the overall cost, as well as providing a breakdown of this cost between transmission and distribution. Further, a report by Pollin, Heintz, and Garrett-Peltier (2009) for the Political Economy Research Institute (PERI) provides an estimate of the industrial inputs needed for investments to update the smart grid. We use the EPRI (2011) number for overall investment effort needed along with the PERI data on the allocation of investment flows into the industries in our model. This package of green investments—a large increase in efficiency investments in the residential and commercial building sectors, along with upfront investments to construct a national smart grid—would lead to $92 billion annually in infrastructure investments over the next decade. In the case of the smart grid, the industrial allocation of these investments is described in Table 2.

Again, given the emphasis on improving the efficiency of buildings, this package of infrastructure spending predictably leans heavily toward construction. However, there are also direct inflows into a number of manufacturing and utility industries.

Scenario Three: An ambitious across-the-board increase in infrastructure spending

Our last scenario looks at a truly ambitious across-the-board increase in infrastructure spending, on a scale sufficient, for example, to close the accumulated “infrastructure deficit” identified by experts such as the American Society of Civil Engineers (ASCE). This scenario was recommended, for example, by the Congressional Progressive Caucus (CPC) in its budget proposals in recent years.

ASCE issues an annual “report card” on the nation’s infrastructure, and in recent years it has given the U.S. investment effort a failing grade (ASCE 2013). It identifies the needed investment to erase the nation’s substantial “infrastructure deficit” between now and 2020 as $3.6 trillion. Further, it estimates that only about half of this investment amount is likely to be provided under the current trajectory of public investment. Given this, for our third scenario we target $250 billion in new infrastructure spending per year until 2020, an amount that would close the remaining half of the needed investments over that time.

Because transportation systems, water distribution, water treatment, and sewage systems figure prominently in the ASCE report’s documentation of the nation’s infrastructure deficit, we allocate roughly three-quarters of the entire $250 billion additional investments to these sectors (which includes construction activities). The remainder is allocated to other utility sectors and to industries associated with efforts needed to expand high-speed Internet access throughout the country.

Model inputs for Scenario Three

The specific receiving industries for infrastructure spending under the third scenario were picked to correspond with these priorities. Table 3 shows the receiving industries, identified by BLS industry code.

Near-term effects of infrastructure spending

In terms of projecting the near-term, net employment impacts of increased infrastructure spending under the scenarios presented in the previous sections, a number of pieces of economic context must be specified. First, how much economic slack exists—particularly in labor markets. Second, and related but not identical to the question of economic slack, how will monetary policy authorities likely respond to a macroeconomically significant increase in infrastructure investments? Third, how will the infrastructure investments be financed? Through public debt? Through increased revenues or user fees? Or through private borrowing or retained earnings?

In the case of near-term increases in infrastructure investments in the U.S. economy, the answers to the first two of these questions are unfortunately quite simple: There is a very large amount of overall economic slack in the U.S. economy today, and monetary policymakers are highly unlikely to try to neutralize demand increases stemming from near-term infrastructure investments. These answers simply reflect that the United States is very far from having fully recovered from the Great Recession of 2008–2009. Figure D shows the amount of slack in two ways, the ratio of actual to potential gross domestic product (GDP) and the share of prime-age adults (25–54) who are employed.³ During the Great Recession (shaded in grey), both of these measures declined precipitously. Since the official end of the Great Recession, in contrast, the reversal has been quite slow—and over the past year steady progress in improving each has nearly stopped completely. This argues strongly that the U.S. economy has large amounts of productive slack.

Figure D

Figure D (continued)

Two measures of economic slack: Ratio of actual to potential GDP, and employment-to-population ratio (EPOP) for workers age 25–54, 2000–2013 Note: Shaded areas denote recessions. Source: Author's analysis of Bureau of Economic Analysis National Income and Product Accounts (Table 1.1.6),  Congressional Budget Office (2012), and Current Population Survey (CPS) public data series

Further, the Federal Reserve has continually committed to keeping monetary policy extraordinarily accommodative in coming years and has indeed noted that increased fiscal stimulus (like an increase in infrastructure spending) would be useful for macroeconomic stabilization (see, for example, Yellen (2013).

Given this degree of economic slack and promise of monetary policy accommodation, it seems that the most natural assumption for how a near-term increase in infrastructure investments would be financed is simply through new federal government debt. This would allow the investments to have the largest impact on near-term economic activity and employment.

However, we also calculate near-term impacts stemming from infrastructure investments that are financed by a progressive increase in taxation, a regressive increase in taxation, and cuts to government transfer programs. Lastly, we discuss the likely macroeconomic impact of increases in infrastructure investment that are driven not by direct public investments, but through other actions, such as regulatory mandates.

It should be noted that most of the analysis in this section will rely on a set of macroeconomic “multipliers” culled from various data sources. While such multipliers were considered slightly controversial as recently as the immediate aftermath of the Great Recession, there has been a clear and decisive intellectual shift in recent years in favor of the view that public spending can indeed help stabilize an economy with large amounts of productive slack when the monetary authority is accommodative (see the appendix for a long discussion of the debate over multipliers and discretionary fiscal policy as a macroeconomic stabilization tool).

Multipliers

Table 4 reproduces a table from Bivens (2011), showing multipliers for various fiscal policy changes from the Congressional Budget Office (CBO), Council of Economic Advisers (CEA), and Mark Zandi from Moody’s Analytics Economy.com (MAEC). We will use these and the method described in Bivens (2011) to construct our estimates of economic activity and employment growth spurred by increases in infrastructure investment of various kinds. In brief, this method uses estimates of the total “fiscal impulse” created by a policy change (the increase in infrastructure investment, in this case) and then applies macroeconomic multipliers from various sources to measure the impact of the fiscal impulse on economic output (GDP). Next, we translate the incremental gain or loss in GDP into the number of jobs supported by this increased activity.

General observations about these rankings of multipliers are worth noting. First, infrastructure investments have some of the highest multipliers in the table. Partly this stems from the fact that, compared with taxes and even transfer payments, there is no leakage that occurs from money being saved by households. By definition, infrastructure spending is spending, not savings. Further, infrastructure investments tend to be less import-intensive than overall spending, so there is less scope for leakages from imports as well.

Second, multipliers on transfer payments (i.e., food stamps, unemployment insurance, and income support payments) rival or even exceed those from infrastructure investments, so paying for stepped-up public investments in infrastructure by cutting government transfers is likely to be a poor strategy for boosting near-term economic activity and employment.

Third, progressive tax cuts (i.e., providing the bulk of the increase in after-tax income growth to households in the low and moderate end of the income distribution) have higher multipliers than regressive tax cuts, and business tax cuts have the lowest multipliers of all. This means that if one is determined to pay for increased public investment in infrastructure with tax increases, raising revenue progressively by taxing high-income households and businesses will provide the smallest countervailing drag on near-term activity and employment.

Lastly, it should be noted that there has been some recent marking down of multipliers on infrastructure investments in CBO reports. However, the rationale for this marking down is clearly not an assessment of the economic effectiveness of infrastructure investment in spurring activity and employment. Instead, it reflects the political judgment of CBO that grants to state and local governments to finance infrastructure investments may result in these governments just substituting the federal finance for their own revenue without increasing overall infrastructure spending. If this is true, this argues for a change in design in infrastructure grants to subnational governments (say, by including maintenance-of-effort requirements in these grants), not an abandonment of infrastructure spending as a method of macroeconomic stabilization.

Estimates on near-term impacts

Generally, such multipliers (and the job estimates of net new employment generated by them) are only significant and relevant during times of elevated unemployment. During other times, countervailing forces—both the monetary authority’s response as well as potential “crowding out” from higher interest rates—will neutralize much of the increased activity spurred by the infrastructure spending. However, given that unemployment rates have been historically high for years and threaten to be high for years to come, these types of job estimates will likely be useful to inform policy debates for quite some time.

These estimates of economic activity and employment creation stemming from infrastructure spending financed by federal government debt need no further steps. For spending financed by either tax increases or cuts in government transfers, we can just apply “reverse multipliers” to obtain the countervailing impact on activity and jobs.

It gets a bit trickier to assess any countervailing impact on economic activity and jobs stemming from infrastructure spending increases financed by private actors that are driven by, for example, regulatory change. But one could imagine a large increase in infrastructure spending driven by mandates on efficiency levels on new (or even existing) buildings or in utility transmission and generation (say, with a clean energy standard applied to all existing electrical utilities). Bivens (2012c) makes the case that increases in private-sector investments driven by regulatory changes will, in today’s economy characterized by a large output gap, largely go through with very little countervailing effects in the form of reduced spending elsewhere in the economy.

This is due to a number of factors. For one, there remains a huge excess of desired savings over planned investment, epitomized by the large (and growing) stash of liquid assets piling up on corporate-sector balance sheets. Unless the regulatory changes were extensive enough that they could not be easily financed (on net) from this excess accumulation of liquid assets, it is very hard to see reasons why the resulting investments should drive up interest rates and crowd out other corporate investments or private consumption.

Further, historically high profit margins (themselves a function of an economy too slack to provide workers with the bargaining power necessary to generate wage growth in line with productivity) will act as a strong buffer against increased spending translating into higher prices that can choke off demand through this channel. Producers have traditionally allowed profit margins to fall to keep the full amount of aggregate demand increases from translating into higher prices (and hence choking off demand for their output). Bivens (2012c) estimates that as other nonlabor costs rise (say, costs imposed by regulatory burdens), unit profits tend to buffer about 20 percent of the increase on average in recent decades. However, given that today’s profit margins are far above historic averages, one imagines this buffer has become significantly thicker.

Lastly, many researchers have noted that inflation arising from increased spending would actually be helpful in spurring economic recovery, and could well lead to faster growth in other sectors of the economy, given the very extraordinary circumstances in the current U.S. economy. A longstanding macroeconomic argument maintains that during normal economic times a higher price level will reduce the real purchasing power of fixed nominal wealth and hence reduce aggregate demand. However, another longstanding argument maintains that a higher price level also decreases the real burden of debt, not just wealth, and if the propensity to consume out of current debt is higher than the propensity to consume out of current wealth, then a higher price level, by effectively redistributing purchasing power from lenders to debtors, can actually raise aggregate demand. Eggerston and Krugman (2012) argue that this “debt-deflation” effect is much more likely to occur in economies that have a large overhang of private debt, like the U.S. economy today. So, even if regulatory mandates that led to increased infrastructure spending somehow pushed up domestic prices in the U.S. economy, it is highly unlikely that this would reduce spending growth in other sectors of the economy.

With all of these considerations in mind, tables 5, 6, and 7 lay out the near-term impacts from an increase in infrastructure spending in each of the different financing options, for each of the three infrastructure investment scenarios.

Near-term impacts on activity and employment from Scenario One

Scenario One, again, tries to project the boost to infrastructure investment that would result from undoing caps to discretionary spending imposed by the Budget Control Act of 2011. Our estimate (detailed in the previous section on our investment scenarios) is that over the next 10 years reversing these discretionary caps would free up roughly $18 billion annually for (non-defense) infrastructure investment.

The first column of Table 5 reports the near-term impact on GDP and employment stemming from this $18 billion boost in federally financed infrastructure investment if it is debt-financed. It uses multipliers based on data from the CBO, CEA, and MAEC, which are summarized in Table 4. For infrastructure spending we use a multiplier of 1.6—firmly in the middle range of estimated multipliers for this type of spending. This implies that the $18 billion (annual) increase in infrastructure spending yields $29 billion in additional GDP (primarily by the end of the first year, with the new increased level essentially sustained over the course of the investment period).

Unfortunately, this multiplier on infrastructure spending is unable to capture differences in economic activity spurred depending on the type of spending. The reason for this is that macroeconometric estimates of multipliers just are not that precise. The key barrier to estimating them is that there is very little truly exogenous variation in such spending. What would be needed for clean empirical identification of the effect of different types of experiments would be random assignment of different infrastructure projects across geographic spaces and economic contexts, along with a commitment from other macroeconomic policymakers (particularly the Federal Reserve) that no action would be taken to boost or restrain economic activity across these experiments. This obviously cannot (and should not) happen.

Further, the multiplier on economic activity in times when the Federal Reserve is not trying to counteract any stimulus from spending (as in the current U.S. economy) is essentially a function of three parameters: the marginal propensity to spend income generated by the spending, the marginal effective tax rate on income flows generated by the spending, and the import content of demand generated by the spending. As each of these parameters rise, the estimated multiplier falls. Any kind of direct government spending scores well on the first count; by definition the first round of spending is entirely not saved. This contrasts strongly with tax cuts. The marginal effective tax rate on income generated by any kind of direct spending is just not going to vary that much across infrastructure projects, as there is just not that much variation in income and payroll tax rates across the bottom 90 percent of the income distribution. This leaves the import content of final demand generated by infrastructure spending. And again, because the U.S. economy is quite closed relative to many of its international peers, it is hard to imagine very large differences in the import content of infrastructure spending varying enough across types of infrastructure projects to make a large difference in the final amount of (domestic) economic activity spurred by the spending.

To estimate the employment impacts stemming from increased economic activity, we start with the evidence reviewed in Bivens (2011) estimating each 1 percent of generic GDP increase in economic activity will generate 1.2 million additional jobs. So, the $29 billion in additional annual spending spurred by infrastructure investment leads to 216,000 net new jobs created (primarily by the end of the first year, with the new increased level essentially sustained over the course of the investment period).

Columns (2) through (4) then examine the net impact of financing this increase in spending with progressive revenue increases, regressive revenue increases, and cuts to government transfer payments, each in an amount equal to the $30 billion boost to infrastructure spending. For each, we use multipliers based on the evidence examined in Bivens (2011). For regressive tax increases (by which we mean revenue raised disproportionately from lower and moderate-income taxpayers), we average the multipliers estimated for across-the-board payroll tax cuts and a refundable tax credit, yielding a multiplier of 0.9. For progressive tax increases (revenue raised disproportionately from higher-income taxpayers), we average the multipliers that were estimated based on the overall extension of the 2001 and 2003 tax cuts and a corporate tax cut (specifically, allowing accelerated depreciation of plant and equipment for tax purposes), yielding a multiplier of 0.3. Finally, for government transfers, we average the multipliers for food stamps (officially the Supplemental Nutrition Assistance Program, or SNAP), unemployment insurance, and one-time lump-sum payments to retirees, yielding a multiplier of 1.6.

The bottom line is simple enough: Any offset to the impact of infrastructure spending on the federal budget deficit blunts the GDP and employment impact of such spending. But, the biggest drag stems from trying to pay for infrastructure spending by cutting government transfers, which essentially neutralizes any near-term boost to activity or employment. Financing the boost to infrastructure investment through a progressive increase in taxes provides the smallest countervailing drag on activity and employment, with GDP increasing by $22.5 billion and employment rising by 169,000 jobs even after the financing drag is factored in.

The middle column in the table shows that financing the infrastructure spending boost through a regressive increase in taxes still results in a $12.6 billion boost to GDP and an employment boost of 94,500, but regressive tax increases do neutralize more than half of the near-term stimulus.

In the last column, we draw on the analysis of Bivens (2012c) to get a less-precise estimate of the impact of implicitly financing the increase in infrastructure investment through regulatory mandates. Bivens (2012c) examined the likely macroeconomic impact of a major environmental regulation that would have forced significant investment by owners of power plants to restrict emissions of toxic pollutants. It surveyed the literature on how an exogenous increase in privately funded investment was likely to crowd out other private spending during conditions that currently hold in the U.S. economy. It found that because such investment is very unlikely to place appreciable upward pressure on economy-wide interest rates, it was unlikely that it could be substantially “crowded out” by reduced spending elsewhere. Bivens (2012c) provides a high-end estimate that 20 percent of economic activity and employment generated by the regulatory mandate would be neutralized through reduced spending elsewhere in the economy. Column (5) hence reflects this 20 percent crowding out if this amount of infrastructure investment were financed by regulatory mandates on private-sector actors rather than directly financed by the federal government.

Near-term impacts on activity and employment from Scenario Two

Table 6 generates the same numbers for the more ambitious second scenario of infrastructure investment. The starting amount of annual spending increases is $92 billion—more than triple the amount in Scenario One. Column (1) indicates that the resulting GDP boost if this amount of spending were financed by an increase in federal debt is $147 billion, with 1.1 million jobs generated (primarily by the end of the first year, with the new increased level essentially sustained over the course of the investment period).

Column (2) indicates that financing this increase in infrastructure investment with progressive revenue sources leads to a net increase in GDP of $115 billion, with employment increased by 862,500. Column (3) indicates that financing this increase in infrastructure investment with regressive revenue sources leads to a net increase in GDP of $64 billion with employment increased by 483,000. Column (4) confirms that financing infrastructure investments with cuts to government transfers completely neutralizes any near-term boost to activity or employment. Column (5) again applies the (high-end) 20 percent crowd-out estimate from Bivens (2012c) to the gross increase in activity and employment spurred by the increase in infrastructure investment if it is financed through regulatory mandates rather than through direct public spending. Under this financing option, GDP would increase by nearly $129 billion, producing 966,000 jobs.

Near-term impacts on activity and employment from Scenario Three

Table 7 generates the same numbers for the much more ambitious third scenario of infrastructure investment. The starting amount of annual spending increases is $250 billion—more than eight times the amount in scenario one. Column (1) indicates that the resulting GDP boost if this amount of spending were financed by an increase in federal debt is $400 billion, with 3 million jobs generated (by the end of the first year, with the level essentially sustained over the course of the investment period).

Column (2) indicates that financing this increase in infrastructure investment with progressive revenue sources leads to a net increase in GDP of $313 billion, with employment increased by over 2.3 million. Column (3) indicates that financing this increase in infrastructure investment with regressive revenue sources leads to a net increase in GDP of $175 billion with employment increased by 1.3 million. Column (4) confirms that financing infrastructure investments with cuts to government transfers completely neutralizes any near-term boost to activity or employment. Column (5) again applies the (high-end) 20 percent crowd-out estimate from Bivens (2012c) to the gross increase in activity and employment spurred by the increase in infrastructure investment if it is financed through regulatory mandates rather than through direct public spending. Under the regulatory mandates option, GDP increases by $350 billion, and employment increases by 2.6 million.

Caveats about near-term employment impacts: labor intensity of infrastructure investment

We cautioned above that these estimates of the near-term boost provided by infrastructure investment are highly context-dependent. So, these estimates would be totally uninformative about a program of infrastructure investments that, say, began in 2020, a year in which the overall state of the economy is impossible to predict with any certainty. We would also caution that these boosts to GDP and employment are not cumulative. Under an infrastructure investment program that boosts this spending by, say, $30 billion annually for 10 years, these boosts to GDP and employment would manifest in the first year (roughly, some of the increase could take a bit longer to manifest), but would not continue to rise thereafter. These estimates to the near-term boost to GDP and employment are increases in the level, not the growth rate, of these variables. This is because spurring increases in the growth of GDP or employment from public investment would require a steadily increasing contribution from year to year. So, once policymakers assign, say, $500 billion in total public investment in 2014, the only way public investment can boost the level of GDP in 2015 is to increase that year’s public investment flow. Making the same public investment effort each year for a number of years only increases the level of GDP in the initial year, and then provides no further boost thereafter.

A further caution is that there is one possible way that these macroeconomic estimates of the employment impacts of infrastructure investments could be slightly biased: if the labor intensity of such investments differed markedly from the economy-wide labor intensity of production that these estimates are implicitly based on. In Figure E, we quickly check if such issues are severely biasing our results by comparing the average labor intensity (jobs created directly, and overall, including through supplier effects) of our three scenarios of infrastructure investments with overall measures of labor intensity. We find that infrastructure investments are indeed less labor-intensive than economy-wide averages; each $1 million in infrastructure spending generates roughly 20–25 percent fewer jobs than each $1 million in general economic output.

This decreased labor intensity is driven by a couple of factors.

First, manufacturing activity in the United States is far less labor-intensive than economy-wide averages. There has been extraordinarily rapid automation and capital-deepening in this sector for decades. Globalization has surely contributed to this in the United States, as standard trade theory argues that increased opportunities for trade should (and almost surely did) lead capital-abundant countries like the United States to focus tradable-goods production in capital-abundant industries and shed production in labor-intensive sectors. One, however, should be careful to not assume this move toward capital-intensive production holds generally. The same logic of globalization that pushes U.S. production toward capital-intensive sectors works in reverse for many other countries. Labor-abundant countries (like those in much of the global South) should actually increase production in labor-intensive sectors as a result of global integration.

Second, the construction sector is a very input-intensive sector. But the problem from the perspective of raw job creation is that many of these inputs come from the very capital-intensive manufacturing sector, and this leads to total labor intensity of construction spending that is even a shade below economy-wide averages as well.

This argues that a strategy aimed at maximizing the number of jobs generated through infrastructure investments needs to carefully pick sectors that receive direct spending flows. Of course, since construction and manufacturing industries both are notably less labor-intensive than economy-wide averages (and utilities even more so), it may be quite hard to find traditional infrastructure projects that will generate a greater-than-average number of jobs through direct and supplier channels.

However, the lower number of jobs generated through direct and supplier channels could well be counterbalanced, at least in part, by the higher wages and capital incomes generated through such spending. This is true essentially by definition: If $1 million in final demand for industry X supports fewer jobs than $1 million in final demand for industry Y, then industry X must either have higher wages or see more of the income generated through final demand flow to capital owners. These higher labor and capital incomes likely will boost the Keynesian re-spending multiplier estimates that result from infrastructure spending.

Additionally, we should note here an important distinction, that between the implicit employment multiplier of a given amount of spending versus the implicit employment multiplier of a specific job. Because manufacturing and construction activity in the U.S. economy are capital and intermediate-input intensive, this means that the direct and supplier jobs supported by a given spending flow are lower than if that spending flow went into other industries. But, the high capital and intermediate input intensity means that each job in manufacturing and construction is associated with the support of many more jobs in other sectors. To say it another way, it might cost a bit more to generate a job in construction and manufacturing, but this job will support more jobs in ancillary sectors than a job created more cheaply in other sectors. Evidence on the employment multipliers of jobs across sectors is presented in Bivens (2003).

It is important to note again that the estimates of near-term economic activity and employment in this section are highly context-dependent and will not be valid during periods when there is substantially less economic slack. The next section will look at the types of jobs likely to be created through these scenarios of infrastructure investment, and these estimates of the structural employment composition of infrastructure investments are much less context-dependent and should hold for investments undertaken over at least the next decade and do not depend on the extent of economic slack.

Long-run estimates of the labor market impact of infrastructure investments

Assessing the composition (as opposed to net new level) of employment generated through infrastructure investments takes a much different set of tools than assessing the near-term impacts on net economic activity and employment. Further, such compositional impacts will hold regardless of the state of the larger macroeconomy.

To assess the composition effects of infrastructure investments, we primarily rely on two datasets. The first is the employment requirements matrix (ERM) generated by the Bureau of Labor Statistics (BLS) as part of its employment projections program. The ERM, based on extensive input-output relationships between industries and occupations, provides data on direct employment and employment in supplier industries generated by a given amount of spending on final output of 195 separate industries. So, $1 million spent on the final output of the automobile manufacturing sector generates X number of jobs directly in this industry, but also X jobs in the steel manufacturing industry, Y jobs in the glass manufacturing industry, Z jobs in the accounting services industry, etc.

This information on supplier industries is particularly important in assessments of infrastructure investments. Spending on the final output of construction and manufacturing (the two most prominent input sectors in infrastructure-investment packages) generates many more supplier jobs than other sectors, so a full accounting of jobs (number and composition) generated through increased activity in them demands analysis of supplier industries.

The second core dataset used in this analysis is the Current Population Survey (CPS), a monthly survey of roughly 60,000 households that is used to estimate the national unemployment rate (among other things). Workers surveyed in the CPS are asked their industry of employment. While the industry coding scheme used in the CPS differs from that used by the BLS ERM, we developed a crosswalk between them that allowed a near-perfect match. The key advantage to merging the ERM with the CPS is that the latter (as a household survey) contains rich information on demographics and labor-force characteristics of a given industry’s workforce.

For this study, we have used information on gender, race, union status, educational attainment, and wage levels to assess the composition of employment. To gain sufficiently large sample sizes in each of the 195 industries to make reliable estimates, we pooled four years of CPS data (2008 to 2012). Data on the demographic and labor-force characteristics of each of the 195 industries’ workforces are available from the author upon request.

Methodology

In the language of matrix algebra, the total number of jobs created through a given vector of spending can be represented as follows. Let i be the 195×1 vector with 195 rows (one for each industry) and only one column, which indicates how much new infrastructure spending has been earmarked for each industry. Obviously, many (most, in fact) of the entries in this vector will be zeroes—as very few industries will receive money directly (retail trade, for example, is not generally a sector that people think of supporting directly in the name of improving the nation’s infrastructure).

Let e be the 195×195 ERM. Each of the 195 columns and rows corresponds to a single industry. A given column represents $1 million in final demand. Each of the 195 rows in this column displays how many jobs are supported in every industry by this $1 million in final demand for spending in the industries that directly receive infrastructure investments. While the single-largest share of total jobs supported by $1 million in construction spending is always in the directly receiving industry itself (and generally on the diagonal of the matrix), nearly all industries see at least some share of the total jobs supported through infrastructure investments.

To estimate this number of jobs supported by infrastructure investment, J, simply perform the following matrix operation:

J=i*e

This operation yields a 195×1 vector, with 195 rows again corresponding to each industry in the model. The single column summarizes how many jobs in each industry are supported by the given spending on infrastructure.

Perhaps counterintuitively, even though direct spending may occur in a small number of rows (sometimes just one) of our initial 195×1 spending vector, there will be very few zeros in the rows of the 195×1 jobs vector output. Almost all kinds of production require a huge array of inputs from nearly every other industry.

Most of the jobs created in supplier industries through this amount of construction spending will be very small relative to the jobs directly created in construction, but non-zero job support will be widespread.

It is important to note that the number of jobs supported by infrastructure spending output from the jobs model is a measure of gross, not net, job creation. That is, if a given amount of infrastructure spending supports 1 million jobs in total, this does not mean that the economy as a whole will see a net increase in employment of 1 million. Rather, a portion of these 1 million jobs may be pulled from currently employed sectors of the economy.

Again, the macroeconomic multipliers identified in the previous section are far superior in assessing the net job creation impacts of infrastructure spending. That said, the gross jobs numbers identified in our model do convey important information. For one, they give a good relative ranking of the labor intensity of different kinds of spending and can, by themselves, allow judgments to be made about the best place to engage in investment spending if the goal is to increase the greatest number of job opportunities in the economy. And, even more importantly, it is the gross number of jobs created that must be combined with the types of jobs created that will allow researchers to judge how relative labor demand for different subpopulations in the labor market will fare. This point will be made plain in the section below where we examine how the number and type of jobs created through infrastructure spending result in changing demands for workers with different levels of educational attainment.

Next, we simply multiply the number of jobs created in each industry (either through direct spending or through supplier effects) by the industry demographic shares and then sum these up across industries to get the total number of jobs in each category (both direct and supplier jobs) that are created through a given amount of infrastructure spending.

Again, in the language of matrix algebra, this can be expressed as follows. Let d be the 195×22 vector of demographic characteristics by industry (these 22 demographic categories are those listed in Tables 8–10 of this paper). Define f as the 1×22 vector of jobs supported in each demographic category through a package of infrastructure investment and compute it as:

F=j*d

Tables 8, 9, and 10 present the outcomes, showing the total number of jobs, broken down into direct and supplier jobs, generated by scenarios one, two, and three, respectively. Additionally, the composition of these jobs by demographic and labor-force characteristics is also presented.

Outputs from infrastructure investment Scenario One

In the first scenario, the $18 billion annual increase in infrastructure spending from lifting BCA spending caps is sufficient to support roughly 139,000 jobs, with 83,000 direct jobs in industries receiving the spending flows and 56,000 jobs in industries that supply intermediate goods to the final industries.

Induced jobs

This job count does not include jobs “induced” by Keynesian effects. There are essentially three methods one could take for estimating the number of induced jobs created.

The first would assume that the net employment impacts consistent with the top-down approach to estimating economic activity and jobs created through infrastructure spending used in the previous section are preserved regardless of the labor intensity of jobs created through the direct and supplier channels estimated here. In this case, one could just take the difference between the overall macroeconomic estimates of employment creation and the numbers supported in direct and supplying industries as the “induced” job creation; this numbers roughly 76,000 jobs supported through Keynesian multiplier effects. This method implicitly assumes that the extra labor and capital incomes associated with each job generated through the direct and supplier channels makes up one-for-one for the lower-than-average job counts per $1 million in final demand through infrastructure spending. However, there is very little direct evidence to support an assumption this strong.

The second approach would assume that the ratio of jobs generated through the direct and supplier channels to total jobs created is the same as the ratio of economic activity generated through the direct flow of spending and induced effects, i.e., the macroeconomic GDP multiplier. For the infrastructure investments assessed in this report, this ratio is 1/1.4. For this first scenario of infrastructure investments, this would lead to an estimate of just under 56,000 induced jobs. This method, however, yields a much-reduced overall number of jobs generated through infrastructure spending, and implicitly assumes that even the jobs induced through infrastructure investment are less labor-intensive per $1 million in final demand than economy-wide averages.

The last approach would be to simply average these two approaches. This would allow the total amount of employment spurred by infrastructure investment to be lowered (correctly) by the fact that the direct and supplier industries are less labor-intensive on average, yet would not carry through the strong and not obviously correct assumption that even jobs induced through infrastructure investments are less labor-intensive than the economy-wide average. In the first scenario, the average of these two approaches is 66,000 induced jobs.

Finally, we should note again that long-run estimates of induced jobs are essentially impossible to forecast. During most economic times, the level of overall employment in the U.S. economy is primarily driven by decisions made by the Federal Reserve, which has generally succeeded in setting the overall unemployment rate for the economic generation before the Great Recession struck. To be clear, the Fed’s unemployment target has often been too conservative, and they likely have used monetary policy to keep unemployment rates higher than they needed to be to foster inflation stability (the second prong of the Fed’s “dual mandate”). But, they have hit the targets they aimed for in reasonable amounts of time, at least until the Great Recession.

Characteristics of direct and supplier jobs

As the receiving industries are heavily tilted toward construction and manufacturing (which is naturally going to be the case for infrastructure investments), the characteristics of jobs created through this spending skews heavily toward these sectors, which are quite different from the rest of the economy.

For example, 77  percent of the total jobs created through these investments are held by men, including 85.4 percent of the direct jobs. Even the supplier jobs, however, are 64.9 percent male, compared with an economy-wide average male share of employment of just 50.2 percent.

The share of jobs supported through this scenario of infrastructure investments accounted for by non-Hispanic whites is actually quite close to the economy-wide averages: 72.9 percent of these jobs relative to non-Hispanic whites’ 71.9 percent share of overall employment. The share of non-Hispanic whites in jobs supported in direct versus supplier industries is essentially identical.

Non-Hispanic blacks are significantly under-represented in jobs generated through this infrastructure spending scenario, with 5.9 percent of jobs, compared with an overall non-Hispanic black employment share of 8.7 percent. Much of this is driven by the low share of non-Hispanic blacks in direct jobs generated through infrastructure spending, 4.7 percent.

Conversely, Hispanics are over-represented in jobs supported by infrastructure spending, with a share of 15.4 percent, compared with the 12.3 percent share of Hispanics in overall employment. This over-representation is entirely driven by a relatively high Hispanic share in direct jobs generated through infrastructure spending, 17.6 percent.

In regards to age, infrastructure investments in this scenario skew heavily toward the employment of prime-age workers (25 to 54 years old), with this group accounting for 72.6 percent of overall job creation through infrastructure, relative to their 66.9 percent share of overall employment. Older workers (55 and over) are roughly proportionately represented relative to economy-wide averages, so it is younger workers (younger than 25) who are disproportionately under-represented in jobs supported by infrastructure investments. Again, these trends are largely driven by jobs supported through direct spending in infrastructure, not jobs supported in supplier industries.

Educationally, jobs supported by infrastructure investments in this scenario skew toward fewer credentials: 12.7 percent of jobs supported are held by those who do not have high school degrees, compared with 9.4 percent of overall employment. This is largely driven by the 14.9 percent share of direct jobs holders that lack high school degrees. On the higher end, 23.0 percent of jobs supported through infrastructure spending in this scenario are filled by someone with a bachelor’s degree or more education, compared with 32.6 percent of overall employment. Only 18.5 percent of direct jobs created through infrastructure spending in this scenario include workers with a bachelor’s degree or more education.

However, despite this relative lack of formal educational credentials, the jobs generated through infrastructure spending in this scenario are much less likely to pay low wages than the economy-wide average. Only 9.5 percent of jobs generated through infrastructure investments in this scenario, and only 7.0 percent of jobs directly generated, are in the overall bottom quintile of the wage distribution. And only 18.0 percent of jobs generated through infrastructure spending in this scenario, and only 17.5 percent of jobs directly generated, are in the second-lowest wage quintile. Conversely, 23 percent of jobs generated through infrastructure spending in this scenario, and 23.3 percent of jobs directly generated, are in the highest wage quintile.

Outputs for Scenario Two

In the second scenario, the $92 billion annual increase in infrastructure spending derived from investing in building energy efficiency and the smart grid would support nearly 888,000 jobs, with over 599,000 directly in industries receiving the spending flows and 288,000 in industries that supply intermediate goods to the final industries.

Induced jobs

Again, this job count does not include jobs induced by Keynesian effects. Using the three methods described in the section on Scenario One outputs for job creation, one finds that the induced jobs created in Scenario Two number 210,000 jobs, 355,000 jobs, and 282,500 jobs, respectively.

Characteristics of direct and supplier jobs

Overall, the construction sector accounts for an especially significant weight in the overall investment package in this case, and its job characteristics are even more different from the rest of the economy than are jobs in the manufacturing sector.

This reveals itself perhaps most starkly in the breakdown of jobs allocated between male and female workers: 80.4 percent of the total jobs created through investments in this second scenario are held by men, including 87.8 percent of the direct jobs. Even the supplier jobs, however, are 65.2 percent male, compared with an economy-wide average male share in employment of just 50.2 percent.

The share of jobs supported through this scenario of infrastructure investments accounted for by non-Hispanic whites is actually quite close to the economy-wide averages: 73.4 percent of these jobs, compared with a 71.9 percent share of overall employment. The share of non-Hispanic whites in jobs supported in direct versus supplier industries is, just like in the first scenario, essentially identical.

Non-Hispanic blacks are even more significantly under-represented in jobs generated through this infrastructure spending scenario than under the first scenario, with just 5.4 percent of jobs, compared with an overall non-Hispanic black employment share of 8.7 percent. Much of this is driven by the low share of non-Hispanic blacks in direct jobs generated through infrastructure spending, 4.4 percent.

Conversely, Hispanics are even more over-represented in jobs supported by infrastructure spending in this scenario than in the first, with a share of 16.2 percent, compared with the 12.3 percent share of Hispanics in overall employment. This over-representation is again predominantly driven by a relatively high Hispanic share in direct jobs generated through infrastructure spending, 17.9 percent.

In regards to age, infrastructure investments in this scenario skew even more heavily toward the employment of prime-age workers (25 to 54 years old) than in the first scenario, with this group accounting for 72.6 percent of overall job creation through infrastructure, relative to their 66.9 percent share in overall employment. Further, infrastructure investments in this scenario see significant under-representation of both older workers and younger workers (younger than 25). Younger and older workers account for 9.5 and 17.8 percent of jobs, respectively, supported by infrastructure investments in this scenario, compared with economy-wide averages of 13.2 and 19.9 percent. Again, these trends are largely driven by jobs supported through direct spending in infrastructure, not jobs supported in supplier industries.

Educationally, jobs supported by infrastructure investments in this scenario skew even more heavily toward fewer credentials than in the first scenario: 13.7 percent of jobs supported are held by those who do not have high school degrees, compared with 9.4 percent of overall employment. This is largely driven by the 15.5 percent share of job holders supported by direct spending who lack high school degrees. On the higher end, only 19.6 percent of job holders supported through infrastructure spending in this scenario have a bachelor’s degree or higher, compared with 32.6 percent of the overall population. Only 15.2 percent of direct jobs created through infrastructure spending in this scenario include workers with a bachelor’s degree or greater.

Again, however, despite this relative lack of formal educational credentials, the jobs generated through infrastructure spending in this scenario are much less likely to pay low wages than the economy-wide average. Only 9.4 percent of jobs generated through infrastructure investments in this scenario, and only 7.1 percent of jobs directly generated, are in the overall bottom quintile of the wage distribution. In this scenario, however, the large under-representation of jobs in the lowest wage quintile is not matched by over-representation in the highest quintile. Instead, it is the middle and upper-middle quintiles that see a large over-representation of jobs supported by infrastructure investments in this scenario: 24.6 percent and 26.2 percent of jobs generated through this scenario’s infrastructure investments are accounted for by the third and fourth wage quintiles. In both cases this is driven more by wages in direct industries, although jobs supported by supplier industries are also mildly over-represented in these wage quintiles relative to the economy-wide average.

Outputs for scenario three

In the third scenario, the $250 billion annual increase in infrastructure spending made possible through an across-the-board increase would support more than 2.2 million jobs, with nearly 1.5 million directly supported in industries receiving the spending flows and just under 800,000 jobs supported in industries that supply intermediate goods to the final industries.

Induced jobs

Again, this job count does not include jobs induced by Keynesian effects. Using the three methods described in the section on scenario one, outputs for job creation, one finds that the induced jobs created in scenario three number 1.8 million jobs, 900,000 jobs, and 1.4 million jobs, respectively.

Characteristics of direct and supplier jobs

The receiving industries in this scenario are again heavily tilted toward construction and manufacturing (which is naturally going to be the case for infrastructure investments).

Largely as a result, 74.1 percent of the total jobs created through investments in this third scenario are held by men, including 79.1 percent of the direct jobs. Even the supplier jobs, however, are 64.5 percent male, compared with an economy-wide average male share in employment of just 50.2 percent.

The share of jobs supported through this scenario of infrastructure investments accounted for by non-Hispanic whites is close to the economy-wide averages: 69.1 percent of these jobs relative to a 71.9 percent share of non-Hispanic whites in overall employment. The share of non-Hispanic whites in jobs supported in direct versus supplier industries is very close.

Non-Hispanic blacks account for 10.0 percent of jobs in this scenario, compared with an overall non-Hispanic black employment share of 8.7 percent. Much of this is driven by the relatively high share of non-Hispanic blacks in direct jobs generated through infrastructure spending, 11.2 percent.

Hispanics are again slightly over-represented in jobs supported by infrastructure spending in this scenario, with a share of 14.3 percent, compared with the 12.3 percent share of Hispanics in overall employment. This over-representation is again predominantly driven by a relatively high Hispanic share of direct jobs generated through infrastructure spending, 15.1 percent.

In regards to age, the employment of prime-age workers (25 to 54 years old) accounts for 70.4 percent of overall job creation through this infrastructure scenario, relative to their 66.9 percent share of overall employment. Further, infrastructure investments in this scenario see significant under-representation of younger workers (younger than 25), with this group accounting for 7.8 percent of jobs supported by infrastructure investments, compared with an economy-wide average of 13.2 percent. Older workers are slightly over-represented, accounting for 21.9 percent of jobs supported in this infrastructure investment scenario, compared with an economy-wide average of 19.9 percent. Again, these trends are largely driven by jobs supported through direct spending in infrastructure, not jobs supported in supplier industries.

Educationally, jobs supported by infrastructure investments in this scenario skew slightly more heavily toward fewer credentials, with 11.2 percent of jobs supported held by those without high school degrees, compared with 9.4 percent of overall employment. On the higher end, only 21.4 percent of those in jobs supported through infrastructure spending in this scenario have a bachelor’s degree or higher, compared with 32.6 percent of employees overall.

Again, however, despite this relative lack of formal educational credentials, the jobs generated through infrastructure spending in this scenario are much less likely to pay low wages than the economy-wide average. Only 11.2 percent of jobs generated through infrastructure investments in this scenario, and only 10.0 percent of jobs directly generated, are in the overall bottom quintile of the wage distribution.

Degree and treatment of residential construction bias in our results

The construction sector is hugely important in infrastructure investment, accounting for a disproportionate share of such spending relative to its economy-wide importance. Further, because construction is relatively labor-intensive compared with many other forms of infrastructure spending (if not compared with economy-wide averages), it has large impact on employment estimates spurred by such spending.

However, the construction activity undertaken in infrastructure spending projects is overwhelmingly nonresidential construction. Yet neither of the core datasets used in this analysis—the BLS ERM and the CPS—disaggregate the overall construction sector into residential versus nonresidential construction. If the demographic and/or labor force characteristics of the residential construction sector are notably different than the rest of the construction sector, then the estimates above may be biased. More specifically, one could imagine that the residential sector of construction is more Hispanic and less likely to be unionized than the nonresidential sector. We have been unable to find any previous attempt to assess the extent of this bias. Therefore we propose a test to examine how much this issue biases our results.

Our approach is to use variation provided by state-level differences in the share of the overall construction workforce that is in the residential sector, as well as state-level differences in the demographic and labor-force characteristics of the overall construction workforce, to see if residential-heavy state construction workforces are also disproportionately Latino and/or characterized by high union density.

Figure F1 is a scatterplot showing the bivariate relationship between the share of construction employment that is residential and the share of a state’s construction sector that is Latino, averaged across all years in our sample. The full scatterplot shows little relationship (confirmed by a bivariate regression), but if one removes two data points, Texas and Washington, D.C. (Figure F2), a positive relationship between the residential share of construction employment and share of the construction workforce that is Latino does appear (and is also confirmed by a bivariate regression).

Figures F3 and F4 show similar scatterplots, but this time examining union density as the relevant labor force characteristic. F3 shows, for all states, the relationship between the average unionization rate of construction in the state and the residential share of construction employment in the state; there is no apparent relationship in the scatterplot. Scatterplot F4 controls for overall union density in each state by looking at the difference between union density in construction relative to union density in the state overall. This still shows little obvious relationship to the residential share of construction employment in a state.

Figure F

Figure F (continued)

Source: Author’s analysis of Current Population Survey microdata

Next, we test to see if the relationships (or lack thereof) in the simple bivariate scatterplots hold up in multivariate regressions. We run regressions that examine the correlation between the Latino share of overall construction employment and the share of the construction workforce that is unionized in a given state and the residential share of construction employment.

Table 11 shows the results from multivariate regression testing the relationships in the scatterplots for robustness. It adds a number of controls to examine whether or not the bivariate relationships examined in Figure F continue to hold.

Results on Latino share of population

Specifically, the regression in column (1) relates the Latino share of construction employment to the residential share of construction employment and includes state unemployment rates, state and year fixed effects, and a state-specific time trend.

The coefficient on the residential share of construction employment is economically significant, but does not pass conventional thresholds of statistical significance (significant only at the 25 percent threshold). Moreover, we should account for the possibility that high shares of residential construction may be associated with overall Latino population in a state, not just construction workers. This check is particularly important if rising Latino population shares actually cause higher rates of residential investment.

Including this state Latino population share reduces the size of the coefficient on the residential share of construction employment, as shown in Column (2).

It is certainly possible, of course, that this examination can miss ways in which a larger residential share in overall construction could have a higher share of Latino employment than the nonresidential sector. We control for a state’s overall population that is Latino in our regressions. But if, for example, Latino workers are disproportionately mobile across state lines and actively seek work in residential employment, then both the Latino share of state population and the Latino share in residential employment would rise as workers moved to find residential construction jobs.

We assessed this by looking at the share of young Latino men in construction as our dependent variable, and use only the overall Latino share in the state’s population in our list of controls. If it is young Latino men searching for residential construction who are more mobile than other Latino groups, then by controlling only for overall Latino population in a state, one can still allow this “job chasing” to boost Latino shares in construction employment when residential shares of construction are larger. However, our results essentially mirrored our earlier regressions (shown in Columns (3) and (4)).

Results on unionization

The results are different, however, for testing whether or not high shares of residential employment in the overall construction sector might bias estimates of unionization rates of nonresidential construction. Column (5) shows the results of a regression that has the state-level unionization rate of the construction sector as the dependent variable. Independent variables include the overall state unionization rate, the state unemployment rate, a state and year fixed effect, and a state-specific time trend. The coefficient on the residential share of construction employment is negative and statistically significant. Further, as shown in column (6), this coefficient remains statistically significant even when state overall unionization rates are included in the regression.

Because the effect of residential shares of construction employment on Latino shares of construction employment did not pass standard statistical thresholds for significance, we do not make any allowance for how the types of jobs created by nonresidential construction (i.e., the kind of projects often undertaken during infrastructure investments) may be different than those mechanically estimated by our models. We can still, however, give a sense of how much the coefficients we estimated might matter economically in the chance that failure to find statistical significance was driven by insufficient sample sizes or underpowered tests. The relative stability of the coefficient on residential construction through various specifications makes us think this is worth doing.

The coefficient estimates from column (2) suggest that each 1 percent increase in the residential share of construction employment leads to roughly a 0.1 percent (.084 in the table) increase in the Latino share of overall construction. Nationally, the residential share of construction employment is almost exactly 50 percent. If one wanted to know what would happen to the Latino share of construction employment in projects that were 100 percent nonresidential, one would simply reduce the Latino share of employment by 10 percent. Given the overall national average Latino share in construction employment of 16.2 percent in 2012, this translates into a 1.6 percentage-point reduction in the share of employment generated through infrastructure projects that is accounted for by Latinos, relative to what is generated by our input-output model in this paper.

More relevantly (because it was statistically significant), the coefficient relating the residential share of construction employment to unionization can also be used to provide an adjustment to the previous estimates in this paper regarding the share of jobs created through infrastructure investments that are unionized. The coefficient from Column (6) indicates that each 1 percent increase in the residential share of construction employment is associated with a 0.73 percent decrease in the unionization rate. Again, taking the fact that today’s residential/nonresidential employment share is 50/50, a series of infrastructure projects that were entirely nonresidential would boost the unionization rate by 73 percent relative to what would be estimated through our input-output model. Given the current unionization rate in construction of 15 percent, this would imply roughly a 10 percentage-point increase in unionization rates relative to our input-output model results. This is clearly an economically significant difference.

Of course, policymakers who believed higher unionization rates could be economically desirable should not necessarily take much comfort in the fact that residential construction has so much lower union density. The degree of unionization—both nationwide and by sector—is strongly influenced by national policy (see Schmitt and Mitukiewicz 2012 on this). But infrastructure investment is not particularly well-suited to affecting the degree of unionization.

Issues regarding the optimal financing of infrastructure investments

As noted previously, if the goal is to increase economic activity and employment in a slack economy, the optimal mode of financing infrastructure investments in the near term clearly is with increased public-sector debt. However, it is generally thought that other forms of government spending that serve as effective economic stimulus when deficit-financed during times of economic slack (transfer payments directed to lower-income households, for example) should be made deficit-neutral if they are to be continued during times of normal economic functioning.

In the United States, the clearest example of this rule regarding “pay-fors” can be seen in the construction of the Affordable Care Act (ACA), often known simply as “health reform.” In this case, even though the ACA was legislated during times of extraordinary economic weakness in 2010, the architects of it ensured that the entire cost of the ACA was more than paid for in the 10-year budget window.

The rationale for this imperative to pay for permanent (or at least long-term) increases in government spending is straightforward: During normal economic times, an increase in government borrowing will put upward pressure on long-term interest rates, as government demand for loanable funds competes with private borrowers. This increase in long-term interest rates can threaten to “crowd out” a range of private investment projects, lowering the potential size of the private capital stock and reducing productivity growth.

However, this logic does not apply so forcefully to permanent (or long-term) increases in public investments (including infrastructure). Even if these are deficit-financed and do indeed lead to some crowding out of private capital investments, as long as the marginal public investments are as productive as the marginal private investments that were crowded out, overall productivity growth is unaffected. Given the falling ratio of public to private capital stocks in recent decades, it seems quite possible that marginal public investments will have rates of return that are competitive with (if not exceeding) marginal private investments. Further, if public capital formation is complementary with private capital formation (as is found in much research), then a boost in the level of infrastructure investments might lead to a “crowding in” of private investments.

Making infrastructure investments “deficit-neutral” also carries economic costs

This recognition of the deep flaws in the conventional wisdom insisting that all increases in public spending be fully paid for in deficit terms informs such actions as past calls for separate capital and current accounts in the U.S. federal budget. Further, it is explicitly acknowledged in the “golden rules of budgeting” released by the Treasury of the United Kingdom in the late 1990s, which stated that government consumption spending should be balanced with revenue over the business cycle, but that borrowing for public investments may be deficit-financed.

The recognition that public investments can raise productivity growth even if deficit-financed becomes even more salient when one considers that the alternative to debt financing often carries economic costs of its own. Taxation is the most commonly considered alternative to debt financing, and taxation is clearly not “free” in terms of economic costs. Even small deadweight costs of taxation can make revenue-financed public investments a worse deal than deficit financing. Of course, taxation of harmful externalities is clearly good policy in and of itself, regardless of what these taxes finance.

While this report has focused on “core” infrastructure investments, it is important to note that often the dividing line between what is a public “investment” versus “consumption” can be blurry, and erring too much on the side of classifying public spending as consumption can lead to suboptimal policy responses. Take the biggest category of public spending: transfers to individuals. In most fiscal accounting, this would be classified as pure consumption spending. As such, if one focused mechanically on boosting the rate of measured productivity growth, it would seem to make theoretical economic sense to finance increases in public investments (infrastructure) with cutbacks to government transfers.

However, a growing body of research has pointed out the substantial economic gains that result from wide swathes of transfer spending (see Bivens 2012b for an extended analysis of the rates of return from “non-core” public investments). Spending on nutrition assistance, for example, can be both economically and fiscally beneficial in the long run because it is an investment in children’s healthy physical and mental development. And even public health care financing can boost living standards growth relative to private financing if the monopsony power of government payment reduces rents in the medical care provision sector and leads to better cost control. So, even besides the normative implications of cutting transfer payments to finance public investment, one should examine very carefully the implicit rate of return even of spending classified as pure transfers before assuming that this method of infrastructure finance is clearly better than either deficit- or revenue-financed spending.

So far, the discussion in this section has involved a number of cautions about financing stepped-up infrastructure investments with instruments (increased revenue or cuts to other government spending, particularly transfers) that may reduce living standards of households. However, it should also be noted that because infrastructure investments have the potential to provide benefits progressively, even investments that are financed directly by user fees may well be a net plus for such households. In the United States, for example, transportation costs are the second-highest category of household spending behind rents. And the share of household budgets accounted for by transportation costs are much higher for the bottom 90 percent of households than for the top 10 percent (see Walsh et al. 2011). Given this, infrastructure investments that can reduce the cost of transportation significantly—say, by providing public transit options or by repairing highways so that automobiles do not require as frequent repairs—will provide benefits that are progressively distributed.

Even from the perspective of aggregate economic efficiency, the optimal mode of financing infrastructure investments is far from clear. Conventional wisdom about government spending (that any long-term increase always needs to be made deficit neutral) is clearly wrong, and even some propositions that are firmly accepted by professional economists (that financing public investments by cutting government transfers will boost prospects for aggregate living standards growth) may well be wrong. But given the extraordinarily large rise in income inequality in recent decades in the United States, we would argue that focusing just on aggregate economic efficiency is far too narrow. The link between aggregate productivity growth and living standards growth for the vast majority has weakened enormously in recent decades. Given the nature of public investments and infrastructure spending, they seem to us a prime opportunity to ensure that some of the benefits of economic growth can be enjoyed by a wider swath of American households than have benefited from trends in market income growth in recent decades. Policymakers should not shy away from analyzing this redistributive effect of infrastructure spending and its optimal financing and making normative judgments about this spending and financing.

Climate economics argues strongly for deficit-financing public investments

There is, moreover, one issue regarding infrastructure investments and redistribution that pure positive economics can prove useful in analyzing: the degree of “sacrifice” required of present generations to begin mitigation of carbon emissions to slow global climate change and bequeath future generations a much-reduced likelihood of climate catastrophes. In much of the economics literature regarding this issue, the degree of current “sacrifice” is assumed to depend upon the discount rate. The larger the discount rate, the less current generations should sacrifice.

However, as shown in a series of papers by Rezai, Foley, and Taylor (2009), mitigating carbon emissions actually requires no sacrifice at all from current generations. Carbon emissions are an unpriced externality, so correcting for this externality only increases the economy’s intertemporal production possibilities frontier (PPF), making any requirement of generational sacrifice unnecessary. Intuitively, what this means is that today’s generation can invest more in carbon mitigation while keeping current consumption unchanged simply by investing less in conventional capital stock.

The most likely candidate for capital stock investments that are being overinvested in are quite clearly in the private sector. Private capital investments are driven strongly by assessments of profitability and hence relative prices. But it is exactly these relative prices that are “wrong” because of the unpriced externality of carbon emissions. On the other hand, public capital investment decisions are much less directly connected to issues of profitability and relative prices, and so are much less likely to have been overinvested in due to the unpriced externality of GHG emissions.

There are many ways theoretically to engineer this expenditure-switching from conventional capital to capital that mitigates carbon emissions, but in practice one way seems obvious: Finance public investments—including infrastructure investments—that mitigate carbon emissions by increasing budget deficits that will crowd out some conventional capital stock investments. It’s important to note that this is not a normative result: The expenditure switch from conventional capital to carbon-mitigating investments is clearly efficient in the positive sense. And, this method of accommodating this expenditure switch is also efficient; by deficit-financing the investments in carbon mitigation and placing upward pressure on interest rates, the conventional capital investments that will be forgone are those with the lowest rates of return.

The impact of infrastructure investments on growth and macroeconomic stabilization

Besides boosting the potential for broad-based living standards growth, an acceleration of productivity carries other potential benefits as well. A number of researchers have identified the acceleration of productivity growth in the late 1990s as a key reason why estimates of the non-accelerating inflation rate of unemployment (NAIRU) fell over this period. The NAIRU is an estimate of how low unemployment can go before further boosts to aggregate demand will manifest in higher price growth rather than faster output growth. In the years leading up to the late-1990s boom, estimates of the NAIRU for the United States had risen to well over 5 percent, and sometimes close to 6 percent, meaning that policymakers (particularly the Federal Reserve) were prepared to slow economic growth through contractionary macroeconomic policies if the unemployment rate threatened to go below this threshold.

Further, this was not an idle threat. Between 1979 and 1995, the actual unemployment rate exceeded the estimated NAIRU by more than 30 percentage points cumulatively—and not just during official recessions.

Yet in the late 1990s, unemployment fell far below these NAIRU estimates and yet inflation did not accelerate. Instead, millions of American workers were employed who would not have been had policymakers put on the brakes when unemployment passed below ex ante estimates of the NAIRU, and American wages saw their first across-the-board period of growth in a generation. This episode highlights two things.

First, the idea that a well-estimated NAIRU can ever be a good guidepost for policymakers should be reexamined and likely abandoned. Besides the U.S. episode between 1979 and 1995, it is quite likely that many large Western European economies also suffered through a decade or more of excess unemployment because the monetary authorities in these countries similarly strove too hard to not allow the overall unemployment rate reach too-conservative NAIRU targets.

Second, however important it is to do away with the concept of a well-estimated NAIRU as a reliable ex ante guide to policy, it remains the case that in the near future official estimates of the NAIRU will likely be vitally important to what policymakers do. If these estimates are too high, then millions of potential work years and hundreds of billions of potential wage earnings for low- and moderate-income workers could be sacrificed.

One must stress that it is the estimated value of the NAIRU that is important, not whether a hard and fast NAIRU actually exists or what its actual (as opposed to estimated) value is. This is because even if there is no firm NAIRU, as long as policymakers think that there is and aim to keep the unemployment rate from breaching it, then great economic gains can be had by lowering the estimated value of the NAIRU. To put it bluntly, as long as the U.S. Federal Reserve thinks it knows the value of the NAIRU, this makes it extraordinarily unlikely that unemployment will be allowed to drift beneath it. Given this, what the U.S. Federal Reserve estimates the NAIRU to be becomes extraordinarily important.

An acceleration of productivity, particularly when preceded by a period of sluggish wage growth, has the potential to significantly reduce the estimated NAIRU. In perfectly flexible labor markets, an acceleration of productivity growth would be accompanied by an equal acceleration of wage demands. However, as a long line of research, summarized by Ball and Mankiw (2002) has pointed out, workers’ wage aspirations are likely inertial. So, when hourly wage growth averaged far less than 1 percent per year for the period between 1979 and 1995, this became the accustomed pace of wage growth for these workers. When productivity began accelerating in 1995, however, this opened up a large and growing wedge between wage aspirations and productivity growth, providing more room for unemployment to fall without sparking wage-push inflation.

It should be noted that the conditions for an acceleration of productivity to push down the estimated NAIRU clearly exist today. Productivity has slowed dramatically in recent years, with average productivity growth between the beginning of the Great Recession through the first half of 2013 essentially matching the 1979 to 1995 pace that has often been dubbed the “Great Productivity Slowdown.” Further, wage aspirations for American workers are clearly at rock-bottom levels. The bottom 80 percent of American workers saw inflation-adjusted declines in wages in each of 2010, 2011, 2012, and the first half of 2013.

Further, the potential for an ambitious investment effort in infrastructure to boost measured productivity levels is real. Bivens (2012a) estimated that an effort that boosted infrastructure investments by $250 billion per year for an extended period would boost measured productivity growth by roughly 0.3 percent per year—more than half the acceleration seen in the late 1990s that was associated with the information and communications technology (ICT) investment boom.

Given the very large downward adjustments to estimates of the NAIRU during the late 1990s ICT boom, as well as the conditions prevailing in the U.S. economy today (specifically, low rates of productivity growth and very low wage aspirations on the part of most American workers), it does not seem unreasonable to think that an ambitious public investment agenda focused on infrastructure spending over the next decade could lower the estimated NAIRU by a percentage point over that decade. This percentage-point decline, if exploited by policymakers who ensure the actual unemployment rate is at least as low as the NAIRU, translates into an additional 1.4 million employed Americans each year and to significantly higher wage growth even for those workers who would have been employed over the decade anyway.

Conclusion

This report has analyzed the potential effectiveness of increasing infrastructure investments as a means to alleviate large economic challenges facing the U.S. economy in the short and long run.

In the short run, this pressing challenge is the failure to make significant progress in spurring a full recovery from the Great Recession. As of the end of 2013, key measures of labor market recovery, such as the employment-to-population ratio of prime-age adults, had recovered just a fifth of the decline experienced during the Great Recession. Further, overwhelming evidence exists that the wedge between actual economic activity and employment levels and levels that would prevail in a healthy economy is nearly entirely a function of deficient aggregate demand.

Infrastructure spending, particularly if deficit-financed, is routinely found by macroeconomic modelers to be among the most effective tools in pushing the economy back toward full employment. Any policy that aims to blunt the impact of infrastructure investment on federal budget deficits will also blunt its impacts in spurring recovery, but infrastructure investments financed by nearly any means besides cuts to transfer spending (i.e., unemployment insurance, safety net programs, and social insurance programs such as Social Security and Medicare) will still provide a substantial boost to economic activity and employment.

In the longer term, some of the U.S. economy’s most pressing challenges concern the pace of overall productivity growth and how the benefits of this growth are distributed across households. After an acceleration of productivity growth beginning in 1995, the years before and since the Great Recession have seen a relatively steady decline in the pace of growth. Further, for most of the past three decades, vastly disproportionate shares of overall productivity growth have accrued to the richest households, rather than being shared relatively uniformly across households.

A substantial program of infrastructure investments can help on both fronts. A long literature (some of it quite recent) has identified strong impacts of infrastructure investments on spurring overall productivity growth. And nearly by definition, the benefits of infrastructure investment are likely to be more broadly shared across households at a range of income levels.

Another (related) long-term challenge the U.S. economy faces is ensuring access to high-quality jobs for traditionally disadvantaged segments of the labor market: women, minorities, workers without a four-year college degree, and young workers. A key question for policymakers is whether or not infrastructure investments could be expected to provide high-quality jobs for such groups without any other policy action to ensure that it does. This report has examined three scenarios of infrastructure investments in the U.S. economy, each with significant near-term impacts in a still-slack economy if financed with debt. The first scenario examines the potential boost to infrastructure spending made possible by cancelling the budget “sequester” that would otherwise automatically reduce spending levels over the next decade. It would generate $30 billion annually in additional infrastructure over the next decade and create 360,000 jobs at the end of the first year. The second scenario examines infrastructure investments that combine substantial upgrades to residential and commercial building efficiency along with an upfront investment in constructing a national smart grid. It would generate $92 billion annually over the next decade, creating 1.1 million jobs. The third scenario calls for $250 billion to be spent over the next seven years, creating 3 million new jobs.

Making these stepped-up infrastructure investments deficit-neutral reduces their boost to near-term activity and employment, though they are still net boosts to activity and employment under any method of financing except for cutting government transfers.

The estimates of near-term impacts are admittedly imprecise. For example, macroeconomic multipliers used by private-sector forecasters and official government agencies are not fine-grained enough to vary significantly across different types of infrastructure spending. This is simply because there is not enough exogenous variation in the data regarding these different types of projects to allow fine-grained differences in economic multipliers to be estimated.

This report noted that the labor intensity of infrastructure projects in the United States tends to be lower than economy-wide averages. This is due mostly (directly or indirectly) to the very low labor intensity of U.S. manufacturing. A key driver of this low labor intensity in U.S. manufacturing, however, is specialization driven by globalization. This specialization, however, will work in reverse for many countries in the global South, so one should be very careful indeed in assuming that what holds in data regarding the labor intensity of U.S. infrastructure spending will also hold for other countries.

While there are not enough data to provide precise estimates, a number of careful researchers have suggested that infrastructure maintenance projects (or, an emphasis on “fix it first”) may well be more labor-intensive than new construction. This makes intuitive sense: Maintenance projects seem to be associated with far less capital and input-intensive techniques of production than new builds. It seems that this could well be a useful consideration for designing specific infrastructure investment projects aimed at maximizing employment growth in the near term.

In the longer term, even if such investments do not lead to net new jobs created because of countervailing macroeconomic influences, they will still significantly change the composition of labor demand in the U.S. economy. Specifically, the jobs generated through such investments are disproportionately male, disproportionately Latino, disproportionately require less than a four-year college degree, disproportionately middle- and high-wage, and skew away from younger workers.

In terms of many employment and social goals that might plausibly be met through infrastructure spending, this is a mixed bag of results. Spurring employment opportunities for non-white workers in the U.S. economy is a laudable goal, and infrastructure projects do indeed skew toward Hispanic, non-white workers. However, employment generated through these projects skews away from black workers, and overall infrastructure investments do not generate employment that skews toward non-white workers generally.

Similarly, infrastructure investments tend to generate employment that skews very heavily male. For those concerned generally about securing equal access to occupations for women, this could seem like a strike against such investments as employment policy.

Finally, on the downside, infrastructure investments generate jobs disproportionately for workers older than 25. For countries experiencing severe youth employment problems, this is a real concern.

However, these genuine concerns could argue more strongly for creating complementary policies to infrastructure investments, rather than arguing simply for not undertaking these investments, the latter of which would, of course, do damage well beyond employment outcomes. For example, regulatory, policy, and legal levers should be used to ensure that jobs in construction and manufacturing are indeed open to workers of all genders, races, and ethnicities. And in the United States, the lack of young workers in construction and manufacturing could well argue that the country’s apprenticeship programs are sorely lacking and need modernization and support.

But some of the news about the employment outcomes that would be expected from infrastructure investments even without complementary policies can be seen as hopeful. For one, the jobs generated would boost demand for workers without a four-year university degree. This is a group that in the United States in recent decades has seen the worst wage outcomes, so anything boosting demand for their labor would be a positive. Importantly, this group remains the large majority of American workers.

Further, the jobs generated by infrastructure investment are predominantly middle-wage jobs—and the share of jobs generated in the bottom quintile is very small. This is most welcome in an economy that has had extraordinary difficulty in generating decent jobs for most of the labor force in the past decade.

Besides their direct impacts on the labor market, an increase in infrastructure investments has been shown by a large and growing research literature to yield large economic returns and carry the potential to boost productivity growth. Given the sharp deceleration in U.S. productivity growth since the beginning of the Great Recession, this effect alone could justify additional infrastructure investments over the next decade.

Even more importantly, if this boost in productivity led (as it did in the late 1990s) to a drifting down of policymakers’ estimate of the NAIRU, and if this lower estimated NAIRU led to more expansionary macroeconomic policy, this would be a huge win for employment generation across the board. Further, because traditionally disadvantaged workers (non-white minorities, workers without a four-year college degree, and young workers particularly) benefit the most from any reduction in overall unemployment, infrastructure investments that boost overall productivity carry the potential to also hit many social and employment goals.

All in all, if policymakers were determined to ensure that any spending flow directly employed as much labor as possible in the U.S. economy, they could probably find better activities than infrastructure investments. But given the large potential benefits of infrastructure investments stemming from its boost to productivity growth, macroeconomic stabilization, and job quality, and given as well that any direct (and supplier) employment generation disadvantage is quite mild, concerns about employment generation should certainly not preclude infrastructure investments in the United States. Further, developing countries assessing the impacts of infrastructure spending should take heart that much of the labor-intensity disadvantage of infrastructure investment may be particular to the United States (and maybe its advanced-country peers).

About the author

Josh Bivens joined the Economic Policy Institute in 2002 and is currently the director of research and policy. His primary areas of research include mac­roeconomics, social insurance, and globalization. He has authored or co-authored three books (including The State of Working America, 12th Edition) while working at EPI, edited another, and has written numerous research papers, including for academic journals. He appears often in media outlets to offer eco­nomic commentary and has testified several times before the U.S. Congress. He earned his Ph.D. from The New School for Social Research.

Acknowledgments

This work was prepared for a project undertaken by the International Labour Organization (ILO) to study the employment impacts of infrastructure spending. Financial support from the ILO is gratefully acknowledged.

Appendix: Macroeconomic multipliers

Since the Great Recession of 2008 and the attendant brief resurgence of fiscal policy as a macroeconomic stabilization tool, there has been an ongoing debate about the size of fiscal multipliers: how much economic activity (GDP) is spurred by an increase in government spending.

The broadest case that public spending can boost economic activity comes directly from the accounting identity for GDP (identified as national output, or Y, in the identity below):

(1) Y = Consumption Spending (or, C) + Investment (I) + Government spending (G) + Net exports (X-M)

Increasing government spending directly increases gross domestic product, per (1). Further, it is theoretically possible that each dollar of increased government spending (or tax cuts) can lead to more than a dollar of increased economic output. The intuition is simply that if, say, $100 is spent by the government to employ new street cleaners, these cleaners will spend this income buying, say, food and clothing. This boosts the income of food and clothing retailers, who can then go out and increase their spending on other items. This iterative process is often referred to in macroeconomics textbooks as the “multiplier effect” of fiscal support, and it is driven simply by the fact that consumption spending is both a component of and is itself a function of overall income.

We can express this by having consumption spending be composed of an autonomous component (C0) and a component that depends on disposable (that is, after-tax) income (c(1-t)Y). This allows us to rewrite our identity for GDP as:

(2) Y = A + MPC*Y, where A is simply C0 + I + G + (X-M), and the MPC (or marginal propensity to consume) is simply c(1-t).

Rearranging terms gives us the following expression for Y:

(3) Y = A/(1-MPC)

From here, changes in autonomous expenditures (including G) will boost GDP by an amount equal to their change multiplied by 1/(1-MPC). The higher is the MPC, the larger is the multiplier. It is largely differences in the MPC that lead to differences in estimated multipliers for different sorts of fiscal support. Support aimed at low-income households and direct government spending for infrastructure projects, for example, are often thought to have higher multipliers, as less money “leaks” out of aggregate demand because savings rates are either zero (infrastructure spending) or quite low (low-income households tend to spend a much larger share of any incremental gain to income than higher-income households).⁴

Textbook macroeconomics clearly teaches that the most effective way to use discretionary fiscal policy to boost economic activity is to finance this support with increased debt. If increased government spending (which adds to GDP directly through the accounting identity) is instead financed with increased taxes (which subtract from GDP by reducing households’ disposable personal income and hence reduce consumption spending), then it is much less effective. Because of this, the size of the “fiscal impulse” stemming from discretionary fiscal stabilizations is often measured simply as the increase in the federal budget deficit engendered by a fiscal policy intervention.

However, textbook macroeconomics also clearly teaches that even deficit-financed fiscal policy support may not boost overall GDP in many economic circumstances.

Crowding out

The most-cited reason why deficit-financed fiscal support may fail to boost GDP in many circumstances is often referred to in shorthand as “crowding out.” By increasing its borrowing, the federal government is competing with private-sector borrowers for loanable funds. This increased competition may well raise overall interest rates, and some private-sector borrowers may decide at these higher rates to not engage in the investment or consumption project they would have engaged in at lower rates. Hence, the extra activity spurred by fiscal policy crowds out some degree of private-sector activity by pushing up interest rates. In the extreme, this crowding-out can be complete, leading to no increase at all in economic activity stemming from large increases in fiscal support.⁵

These simple mechanics of crowding out, however, assume that interest rates move sharply enough, and assume as well that economic activity is responsive enough to these interest rate movements to materially negate the impact of increased fiscal support. However, when overall weakness in the demand for loanable funds (say, in the aftermath of the burst housing bubble) has pushed interest rates all the way down to zero lower-bound (or ZLB), it dims the prospects for fiscal support to completely overwhelm this intense downward private pressure on rates and push interest rates up high enough to begin choking off more privately supported activity than the fiscal support is supporting itself. Yet many arguments expressing skepticism about the efficacy of ARRA leaned clearly on the role of crowding out in rendering it ineffective.⁶

The importance of the ZLB on interest rates in contemporary debates should be stressed. The primary reason why there was much stronger and more widespread support among macroeconomists for discretionary fiscal support for the economy in 2009 than in any other recession in recent memory is entirely explained by the fact that interest rates were at the zero bound. This bound both constrains the ability of the Federal Reserve to fight recessions with its own conventional tools (and hence adds to the desirability of expanding the portfolio of countercyclical policies), and substantially allays the fear that increased fiscal support will lead to crowding out. If fundamental economic forces have pushed interest rates down to zero (or as close to zero as they can effectively go, in the case of longer-term rates), that should allay fears that increased government borrowing will lead to upward pressure on these rates so intense that it leads to great withdrawal of investment spending in the economy.

Ricardian equivalence

Another argument against the efficacy of discretionary fiscal support concerns the notion of Ricardian equivalence: the notion that an increase in deficits will be recognized by households as a future tax increase, and hence will spur them to increase their own savings to build up wealth to pay these higher future taxes. There are a couple of reasons to doubt that the full Ricardian effect of rising private savings sterilizes increased public dissaving. For one, some of the increased future taxes that will pay back today’s deficits will fall on future generations, so the current generation will indeed see a fall in its lifetime tax burden as a result of the public dissaving. Second, many households (particularly in a downturn associated with financial market distress) may be liquidity-constrained, preferring a marginal dollar of consumer spending over a marginal dollar of savings, but currently unable to borrow. To the extent that public dissaving relieves this constraint, it can increase current spending. Lastly, if the fiscal boost from dissaving comes in the form of spending (say, on infrastructure projects), then there is no reason why private saving should rise to pay off this extra public debt one-for-one in the current year. For example, if the federal government borrows and spends $1,000 per person to build highways this year, households will only have to reduce their spending by (the net present value of) $1,000 over the rest of their lives to pay the higher future taxes that result. So, the Ricardian equivalence mechanisms do not mean that it is impossible for any kind of public dissaving to boost overall spending in a given year.

Timing lags

Besides the mechanics of crowding out and Ricardian offsets, however, the case against discretionary fiscal policy stabilizations has also rested on issues of timing. Because fiscal policy support is often associated with lags both in deliberation (the inside lag) as well as implementation (the outside lag), many macroeconomists have argued that fiscal policy support may arrive too late, that is, after an economic recovery had already spontaneously begun. These arguments went so far as to claim that the fiscal support could arrive late enough to push an economy directly into overheating, leading to inflation and interest rate spikes. Because monetary policy tends to operate with a much-shorter inside lag, recent decades have seen a growing (but not universal) agreement among policymakers and macroeconomists that most recession-fighting responsibilities should be borne by central banks, and not by Congress and the president.⁷

Ironically, the case against discretionary fiscal stabilizations seems to have achieved its greatest foothold among policymakers and economists just as this crucial timing argument was clearly losing much of its force. While recessions between 1947 and 1990 were indeed quite short and recoveries tended to follow rapidly after business cycle troughs, recessions since 1981 have taken progressively longer time before economic resources were again fully utilized. Given this record, it seems very hard to give credence to worries that fiscal support legislated during a recessionary period will come so late that it will push an already-recovered economy directly into overheating.

Automatic stabilizers versus discretionary policy

We will end this discussion by noting a glaring disconnect between the amount of political controversy surrounding the increase in budget deficits associated with automatic stabilizers and those associated with discretionary fiscal support (say, for example, the ARRA passed in the United States in 2009). From an economic point of view, except for the issues raised by timing lags, deficits are deficits, and if they are desirable or undesirable, one’s analysis should not change based on whether they occur mechanically or through policy changes.

Yet ARRA was a much larger political controversy than the much larger increases in deficits associated with the role of automatic stabilizers during the Great Recession. For example, many criticisms of ARRA leveled by economists opposed to it invoked the problem of crowding out as the reason why it would not work to stabilize economic activity.⁸

But very few economists (none, in fact, that this author could find) argued in 2008 that the rising budget deficits driven mechanically by the slowing of economic growth should be closed rapidly through policy action. If concerns over crowding out were not thought to apply to these large increases in deficits stemming from automatic stabilizers, then it is far from obvious why they would apply to increases stemming from discretionary fiscal measures either. After all, the market for loanable funds does not know which increment of increased federal government demand for borrowing is discretionary (i.e., legislated specifically to fight an ongoing recession) versus which increment is nondiscretionary (i.e., responses in means- and circumstances-tested programs and progressive marginal tax rates) and cannot respond differently to each. Further, the types of public dissaving associated with rising deficits driven by automatic stabilizers (specifically, falling taxes and increased government transfers) are much more likely to engender a full Ricardian offset from rising private savings in theoretical models (tax cuts in particular are fully sterilized in the most rigid Ricardian models).

The U.S. federal budget deficit rose by more than 9 percent of GDP between 2007 and 2009, but ARRA could only account for a bit over 2 percentage points of this increase; the large bulk of the increase between those years was the automatic outcome of tax revenues falling as economic activity collapsed, and needs-based safety-net spending rising.

Given this near-completely sanguine acceptance among policymakers and applied macroeconomists of the large increases in budget deficits stemming from automatic stabilizers, timing lags associated with discretionary fiscal policy interventions are the only source of worry about the potential effectiveness of ARRA that make much analytical sense. Given the track record of long recoveries from recessions in the early 1990s and early 2000s, and given that as of December 2011—four years after the previous business cycle peak and two-and-a-half years after the official end of the recession—employment remained 5.8 million below the prerecession peak, the timing-lags objections to ARRA are not in retrospect particularly compelling.⁹

Of course, there is a more cynical reason why many policy analysts had no problem at all with the much-larger increases in budget deficits that predated ARRA: they took place under a different presidential administration.

Multiplier estimates

In recent years, the debate about the size of economic multipliers in the U.S. economy centered almost entirely around the impact of ARRA. Contemporaneous estimates of the effect of infrastructure projects undertaken in a slack economy when the monetary authority was highly likely to fully accommodate the increase in federal debt in the United States relied, understandably enough, on models and estimates of prior fiscal policy interventions. So, for example, when the CBO released its quarterly report on the effect of ARRA on economic activity and employment, it relied on multipliers estimated in this previous literature. This led to some misunderstanding; several critics of the ARRA and increased public spending during recessions claimed that estimates of ARRA’s effect simply reflected “assumptions” made about multipliers by the CBO (or other private-sector forecasters who had quite-similar estimates of its impact). This was not the case: Multipliers (upon which the CBO estimates were based) are not simple assumptions, they are the result of estimations, and these “model-based” estimates of ARRA’s impacts were perfectly valid and, as subsequent econometric work has shown (to be taken up later in this section) actually quite good predictors of the effectiveness of ARRA spending in supporting economic activity. Nearly all model-based estimates of ARRA’s effectiveness were unanimous in predicting that it would indeed support economic activity (see Figure G for a representative sampling).

Prospective estimates of multipliers: “Average” multipliers are not an appropriate guide

However, it is clearly true that a good estimation of macroeconomic multipliers is challenging. By far the most important first step in obtaining estimates of multipliers that are applicable to the effect of ARRA on the U.S. economy in 2009 and 2010 is to restrict one’s estimates of prior episodes of fiscal policy interventions to those undertaken in similar economic contexts. Importantly, this means situations during which unemployment rates were high and capacity utilization rates were low, as well as situations where interest rates were quite unlikely to rise in response to increased fiscal support, either because the central bank had moved to lean against the fiscal impulse or because private-sector demand was so high that the increase in marginal borrowing done by the federal government rapidly pushed up interest rates.

Many studies did not follow this rule of looking only at fiscal support in these specific conditions. Many studies instead looked at fiscal support undertaken over very long stretches, and unsurprisingly found small average multipliers. But these small averages could well be the result of very large multipliers during times of high unemployment and low interest rates, combined with low (or even negative) multipliers during times of increased fiscal support when unemployment was already low and interest rates high (see Romer 2011 for more on this point).

In short, average multipliers are not useful for answering the questions about ARRA’s effectiveness; instead, only studies of the effect of fiscal support on high-unemployment economies near the ZLB on interest rates are useful.

This obviously rules out some of the more prominent claims about the effectiveness of fiscal policy, such as those of Barro and Redlick (2011). This study looked at increases in deficit-financed government spending undertaken since World War II to obtain an estimate of multipliers. They use military spending as the measure of government spending. Their empirical results are dominated by World War II and Korean War periods, when very large increases in military spending were not accompanied by large increases in economic activity. However, by the onset of World War II the economy enjoyed more-than-full employment; the unemployment rate by 1941 was 4.7 percent, and between 1942 and 1945 unemployment averaged less than 1.5 percent. Further, wage and price controls were passed (in part) to contain inflation that would have resulted from excess aggregate demand.¹⁰ Similarly, during the Korean War (1950–1953), the unemployment rate averaged just 3.3 percent.

The simple prescription that multipliers cannot be reliably estimated during times when the economy was close to full employment and when interest rates would rise sharply in response to increased fiscal support is violated in a surprising number of studies invoked in the debate over ARRA’s effectiveness in its first two years. Some notable studies that did not suffer from this problem, however, were Eggertsson (2009); Hall (2009); Ilzetzki, Mendoza, and Vegh (2010); and Woodford (2011). Unsurprisingly, given that they are actually all estimating the central parameter of interest—the effect of fiscal support in depressed economies near the ZLB on interest rates—all these studies find that the multiplier on particular forms of fiscal support (direct government spending, in particular) is well over 1 and often close to 2.

It is also worth noting that the Council of Economic Advisers (CEA) did not just use previously estimated values of economic multipliers to predict the effect of ARRA. They also estimated a vector autoregression (VAR) model to predict what GDP would have been with no fiscal support based on its pre-ARRA trajectory (and historical relationships between key variables) and then compared that prediction with its actual (post-ARRA) performance. This VAR approach predicted effects of ARRA that were in line with those predicted by economic multipliers.

The estimates in this report on the impact of infrastructure investment rely heavily on the CBO and CEA multiplier estimates. As the next section will note, however, these prospective estimates have been largely vindicated by retrospective, more direct empirical estimates of the effect of the components of ARRA.

Retrospective estimates of economic multipliers of the components of ARRA

Now that ARRA has largely run its course, there is now actual data to try to test to see if one can directly estimate its impact on economic activity. Again, however, this is much harder to do than is often recognized. The problem, in the jargon of econometrics, is how to gain “clean identification” of ARRA’s impact. Take, for example, one obvious (but naïve) way to glean its effects: Compare states that have gained more ARRA funding than others to see if economic conditions improved more significantly in those high-ARRA-aid states. The problem with this approach is that much ARRA funding (expanded unemployment insurance and food stamps, for example) was contingent on economic circumstances, with more money mechanically going to states that have a bigger contraction of economic activity. Simply examining correlations between state-level spending and economic activity could well find a negative correlation, but one that is driven by a chain of causality that runs from depressed economic activity to more ARRA funds.

The chief challenge in many attempts to estimate the impact of ARRA is to precisely find ways around this problem of reverse causality, and this is the problem of “clean identification.”

A series of attempts to do this came from John B. Taylor (2011), who used aggregate time series evidence on personal income and consumption to estimate the impact of the tax rebates and some social transfers (particularly the increase in unemployment benefits) contained in ARRA, along with tax rebates that were passed in 2001 and 2008 in the name of providing fiscal support to the economy. Taylor used these results to argue that one could not reject the hypothesis that the tax rebates and transfers had no impact on personal consumption spending.

Taylor (2011) runs a time-series regression of personal consumption expenditures (PCE) as the dependent variable. As explanatory variables he includes “stimulus payments” and disposable personal income minus stimulus payments, with controls for oil prices and net worth lagged two quarters. His sample period runs from the first quarter of 2001 to the first quarter of 2011. As the coefficient on stimulus payments does not register as statistically significant at the 95 percent confidence level, Taylor takes this as evidence stimulus payments did not work, i.e., that they did not boost consumption spending and hence failed to provide support to overall economic activity.¹¹

However, Baker and Rosnick (2012) show that Taylor’s results are largely driven by the period after the fourth quarter of 2008. More specifically, the ARRA tax rebates and transfers took place in the midst of a collapse in overall spending and against the backdrop of a financial crisis. When they add a dummy variable for the post-2007 period to the same time-series regression run by Taylor (2011), they find that stimulus payments are both statistically and economically significant determinants of consumption spending.

A number of papers try to exploit the state-level variation in ARRA payments to assess ARRA’s economic impact. Again, the key challenge in doing this is to avoid the problem of endogeneity of state receipts—that is, the problem of states with the most depressed economic activity receiving more ARRA funds by design. What is good stabilization policy (focusing more money on more depressed areas) makes for quite difficult evaluation.

Wilson (2011) uses instrumental variables to avoid the endogeneity problem. All of them seek to isolate the purely exogenous portion of ARRA fiscal relief allocated to state governments. For example, the increased aid ARRA provided states for Medicaid payments during the recession was allocated based on a formula that made this aid a function of the prerecession Medicaid share paid by a state, a “hold harmless” component of funding that is based on the three prior years of state per capita income growth, and the change in the state unemployment rate. Through controls in his regression (say, by including the change in state unemployment), Wilson is able to isolate that component of increased state aid that is orthogonal to its economic performance. Beside the Medicaid formula, Wilson also isolates the exogenous components of Departments of Transportation and Education aid to states associated with ARRA. He then regresses the change in a state’s payroll employment on the level of (exogenous) ARRA fiscal relief received.

Wilson (2011) finds that the ARRA fiscal relief is positively correlated to state employment growth, and the result is both economically and statistically significantly (as well as robust to different specifications). He finds that ARRA’s peak impact on employment (he does not use state-level measures of GDP) occurred in the first quarter of 2010, when it was associated with employment levels in that quarter that were roughly 2 to 2.9 million jobs higher than would have been the case absent ARRA’s support. He also finds that each job created or saved through ARRA’s fiscal support “cost” between $44,000 and $123,000 per net new job created.

Feyrer and Sacerdote (2011) also use state-level variation in ARRA spending flows to estimate its impact. They, like Wilson (2011), also utilize instrumental variables to control for endogeneity of ARRA state spending. The instruments Feyrer and Sacerdote (2011) utilize are mean seniority of the state’s delegation in the U.S. House of Representatives and the size of the state’s population. The first instrument, the mean seniority of the state’s House delegation, is assumed to be positively correlated with state-level ARRA spending because a more senior delegation is thought to be able to steer more resources toward its state. The second instrument, state population, is assumed to be negatively correlated with ARRA spending flows. One rationale for this is that the structure of the Senate (with each state, regardless of size, having the same representation) tends to favor small states. Another rationale put forward by Feyrer and Sacerdote (2011) is that smaller states have more miles of roads and highways per capita; because much of ARRA’s direct spending was directed toward highway funds, this results in more funds being directed toward the smaller states. Whatever the precise mechanism of the inverse relationship between state population and per capita ARRA spending, it holds in the data. Further, because both instruments—mean seniority of the House delegation and state population—are clearly driven by historical trends that predate the Great Recession, they are unlikely to be systematically correlated with macroeconomic performance in the state during 2009 and 2010.

The Feyrer and Sacerdote (2011) results are not precisely estimated (the overall multiplier for the stimulus package in various specifications runs from 0.5 to 2), but across a wide range of specifications the results are positive and statistically significantly different from zero.

Chodorow-Reich et al. (2012) also use state-level variation in ARRA spending to test ARRA’s impact. They also surmount the endogeneity of state ARRA spending by utilizing instrumental variables. The instrument they choose is the component of increased federal Medicaid spending directed toward states that is unrelated to changes in economic circumstances. The formula that determines the amount of federal aid directed toward states for Medicaid is driven by a number of factors that are related to the state of the economy (for example, the change in beneficiaries over the previous period, the change in the unemployment rate in the previous period, and the change in average spending per beneficiary). But the formula is also influenced by the amount of Medicaid spending in the state in the period before the recession began, and this is not plausibly related to subsequent developments in state economies.

The Chodorow-Reich et al. (2012) findings provide the largest positive effects of ARRA on employment outcomes. They find that each $100,000 in Medicaid aid provided through ARRA to the states leads to 3.5 job years created or saved. Given that the Medicaid aid alone in ARRA was nearly $90 billion, this means that this portion of ARRA alone (less than one-eighth the total) could have created more than 3 million job years by itself.

Conley and Dupor (2011) is the last study to use state-level variation in ARRA spending to assess its impacts. Like the others, Conley and Dupor (2011) utilize instrumental variables to assess ARRA’s impact. The instruments they choose are the highway funding components in ARRA, the prerecession ratio of a state’s federal taxes and federal receipts, and the political affiliation of the state’s governor.

Conley and Dupor (2011) find no statistical evidence that ARRA created (or destroyed) private-sector jobs in the aggregate. They instead parse private-sector jobs into three rather unconventional categories: goods-producing; a bundle of service-sector industries that includes health, education, leisure, hospitality, business, and professional services (a bundle they call HELP); and all other non-HELP service industries. Not enough justification is given for this unique parsing of service-sector industries. In their benchmark finding, no industry groupings—none of the private industry groups nor the public sector—show any statistically significant relationship to ARRA spending. In a second specification, which they label “fungibility imposed,” two of the three private-sector groupings (goods-producing industries and HELP services) and the public sector show no statistically significant relationship to ARRA spending, while employment in HELP services is shown to be negatively correlated with ARRA spending flows.

Further, the specific regression estimated by Conley and Dupor (2011) makes their results not directly comparable to the other state-based econometric estimates of the specific impact of ARRA. Instead, their preferred econometric specification uses the difference between state aid received by ARRA and negative state revenue shocks as the key independent variable. This specification seems more appropriate for answering a general question as to how state employment is affected by (net) negative revenue shocks, but, given the state-specific shock, it does not then tell us how ARRA aid specifically impacted employment growth. The simplest way to state the inability to directly compare the Conley and Dupor (2011) studies and those of others in this vein is that the estimated multipliers cannot be compared because the multiplicands are different.

This issue of the size of the multiplicand is also the key issue in regards to Cogan and Taylor (2012). This study does not exploit cross-state variation in ARRA spending to assess its impacts, but instead tries to account for the drag imposed by state and local government spending cutbacks in blunting the overall support to the economy provided by the government sector as a whole. Cogan and Taylor (2012) find that states cut back their own purchases by more than the ARRA provided to them in aid. Noting that state and local spending is fungible with respect to the ARRA flows transferred to state and local governments, Cogan and Taylor (2012) interpret this finding as demonstrating that ARRA did not boost government purchases materially and hence could not have had an effect on economic activity. But again, their paper is about the size of the multiplicand, not the multiplier, of ARRA spending.

Given the failure to find statistically significant results from their measures of ARRA spending net of state and local contraction, neither Cogan and Taylor (2012) nor Conley and Dupor (2011) can actually reject the argument that the simple size of ARRA was insufficient to measurably impact state-level trends in economic activity and employment, and not that the marginal effectiveness of a dollar spent by ARRA was low. This is an important point. Cogan and Taylor (2012) and Conley and Dupor (2011) are essentially assuming that states would not have cut back their own spending as much had ARRA funds not been allocated to them; this is the heart of their argument about fungibility. But, as shown by McNichol (2012), even with the ARRA state aid, state and local governments had very large budget shortfalls in 2009 and 2010 (and indeed are expected to see shortfalls for years to come). Given that most states have balanced budget requirements, this means that one cannot plausibly say that state spending would have been higher in the absence of the ARRA funds. In fact, relative to any plausible counterfactual, state spending must have been higher following the receipt of Recovery Act funds.

To argue that the Cogan and Taylor (2012) and Conley and Dupor (2011) papers are not estimating comparable multipliers to other state-based studies is not to say that their findings are of no note to applied macroeconomists and policymakers. The CBO (2012), for example, has actually reduced its estimate of the likely impact of infrastructure spending increases that are managed through grants to state and local governments precisely because of the worry that these governments will reduce their own spending in response to the grants. However, this does not mean that assessments of the all-else-equal impact of infrastructure spending have been reduced because of economic evidence. Rather, it means that policymakers should strive to ensure (perhaps through maintenance of effort requirements for the receipt of federal grants-in-aid) that state and local governments do not sterilize any of the stimulative effect of grants by reducing their own spending.

The studies of ARRA’s impacts that exploit state-level variation to estimate employment multipliers of ARRA spending all come to the conclusion that the effects are statistically and economically significant. The range of estimated multipliers is fully in line with model-based estimates of ARRA’s impact that are based on past estimation of the effect of fiscal policy, and in fact contain many estimates that are above the high range of these model-based estimates. It is worth noting that this state-based evidence is extraordinarily strong evidence of ARRA’s effectiveness, given the many limitations to measuring ARRA’s impact in this way.

For one, the state-level regression may miss some of ARRA’s impact because while money may have been directed to a specific state and created economic activity, some of the employees hired may well live in other states. If, for example, ARRA funds road improvements in Manhattan, many of the employees working on that project will surely come from New Jersey and Connecticut.

Second, much of the economic activity spurred by the direct spending components of ARRA (infrastructure investments in particular) is quite input-intensive; bulldozers and concrete for building roads, for example. Given this, money spent paving roads in Florida may well have spurred economic activity in bulldozer factories in Ohio and concrete plants in Alabama.

Lastly, much of the re-spending effects of the Recovery Act are also likely to leak across state borders. If highway projects in Arizona provide the purchasing power to construction workers to buy new cars, this second-round spending effect will be felt in automobile-producing states like Michigan, not directly in the receiving state of Arizona.

Summing up: Can we still rely on prospective multipliers included in ARRA?

In the end, what is striking about the actual econometric estimates of the specific effect of the Recovery Act and its components—including infrastructure investments—is how cautious model-based estimates like those of the CBO, CEA, and private-sector forecasters were relative to what was actually estimated. This actually should not be a huge surprise. Economic theory teaches clearly that fiscal support has much larger multiplier effects when economies are deeply depressed, when interest rates are pinned at the ZLB, and when central banks are committed to forestalling any countervailing monetary contraction in the face of the fiscal expansion. For the first time since the Great Depression, all of these conditions held in the U.S. economy of 2009 and 2010.

Given this track record, we remain firmly confident that the multipliers used to estimate near-term impact of infrastructure spending on economic activity and employment are solid.

Endnotes

1. These cuts include not only the well-known budget “sequestration” but also include the discretionary spending caps imposed by the Budget Control Act (BCA) of 2011.

2. The “stabilization wedge” concept was first introduced by Pacala and Socolow and is described in S. Pacala and R. Socolow, “Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies,” Science, August 2004, http://www.sciencemag.org/content/305/5686/968.abstract#aff-1.

3. For our measure of potential GDP, we use the series estimated by the Congressional Budget Office. It is an estimate of what GDP would be if the economy were at the Non-Accelerating Inflation Rate of Unemployment (NAIRU) as estimated by the CBO.

4. For the United States, the simple value of the multiplier is also limited in practice by the fact that a significant portion of marginal expenditures is actually satisfied by imports, which do not add to GDP.

5. This presentation is the closed-economy version of crowding out. It should also be noted that in models with a fixed global interest rate, fiscal support can be crowded out by a one-for-one decrease in net exports stemming from a strengthening of the national currency’s value that follows the increased fiscal support.

6. Cogan et al. (2010), for example, find small multipliers in part because they assume a countervailing response from the central bank, which is an endogenous form of crowding out.

7. Blinder (2006) outlines the timing arguments in some detail. Probably the most famous statement of how countercyclical interventions have the potential to increase economic instability comes from Friedman (1953).

8. For example, in the run-up to ARRA’s passage, John Cochrane (2009) wrote, “If the government borrows a dollar from you, that is a dollar that you do not spend, or that you do not lend to a company to spend on new investment. Every dollar of increased government spending must correspond to one less dollar of private spending. Jobs created by stimulus spending are offset by jobs lost from the decline in private spending.”

9. The experience of Japan—which has seen output gaps nearly continuously for nearly 15 years—is another reason to think that the timing argument against discretionary fiscal stabilizations is much less compelling in the context of severely depressed economies facing the aftermath of burst asset market bubbles.

10. Wage and price controls were also put into place to make sure that key wartime industries had the resources they needed.

11. In a related article, Lewis and Seidman (2012) note that an earlier paper by Taylor (2009) used the same methodology and came to the same conclusion regarding the 2008 tax rebates. Yet Lewis and Seidman (2012) make a good point about the limits of arbitrary thresholds of statistical significance: The Taylor (2009) results on the 2008 stimulus payments are indeed statistically insignificant measured at the 95 percent confidence threshold, but are statistically significant measured at the 94 percent confidence threshold.

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In many respects, the beginning of the Great Recession in the United States should have ushered in a Golden Age of infrastructure investment. The nation entered the Great Recession having underinvested in public investments across the board, and infrastructure specifically, for decades. In its annual reports, the American Society of Civil Engineers (ASCE) has consistently given failing grades to the nation’s infrastructure in a bid to attract policymakers’ attention. And this slowdown in infrastructure investment, which began in the 1970s, has been convincingly linked to the slowdown in overall productivity growth that began in the same period. In short, the case for expanded infrastructure investments was strong even before the Great Recession hit.

The case was made much stronger in 2008 as the U.S. economy entered a long and steep recession driven by a severe negative shock to private spending by households and businesses. This reduced spending led to a large increase in private savings and sharp cutbacks in private investment, which together drove interest rates to historic lows. A large increase in infrastructure investment would have filled the hole in aggregate demand caused by the pullback in private-sector spending, and the extraordinarily low interest rates, which made deficit financing so attractive, could have enabled temporary tabling of the politically difficult choices about how to finance the increase.

The American Recovery and Reinvestment Act of 2009 (ARRA) was a promising beginning, providing for substantial short-term increases in public investment. But it was also essentially the end of such investment: By the end of 2011 ARRA’s boost to the U.S. economy had passed, yet there remained a large gap between aggregate demand and potential supply, and a substantial chunk of the prerecession infrastructure deficit. Since then, political developments in the United States have led to extreme downward pressure on public spending of all kinds, and growth of spending on public investments across the board slowed to historic lows.

Yet six years after the beginning of the Great Recession, the case for a significant increase in infrastructure investments remains extraordinarily strong, as evident in recent calls for more infrastructure investments by former economic advisors to presidents Obama and Reagan. This report assesses the likely short- and long-term economic impacts under three different scenarios for expanded infrastructure investments.

It finds that these three potential infrastructure packages—rescinding the cuts scheduled under the budget “sequester,” investing in the energy efficiency of buildings and a “smart grid,” and undertaking an ambitious program of transportation and utilities investments— would yield from $18 billion to $250 billion for infrastructure investment. In the near term, these increases in infrastructure spending would boost gross domestic product (GDP) by between $29 billion and $400 billion and create between 216,000 and 3 million net new jobs (if financed with government debt). Any method of making these infrastructure investments deficit-neutral reduces their impact on near-term activity and employment, but every method of financing them except cuts to government transfers still leaves a net positive impact. And because we can predict the effect of these investments on the composition of labor demand (creating a disproportionate share of jobs that skew towards men and Latinos, and away from younger workers) we can recommend workforce policies to ensure that traditionally underserved populations benefit from these investments. Finally our analysis conforms with a large and growing body of research persuasively arguing that infrastructure investments can boost even private-sector productivity growth—by as much as 0.3 percent annually, allowing macroeconomic policymakers to target significantly lower rates of unemployment. Extrapolating from the experience of the late 1990s, the NAIRU could be lowered by as much as 1 full percentage point by a sustained $250 billion annual increase in infrastructure investment. This could mean that more than 1 million additional workers each year find employment.

Three scenarios for infrastructure investment examined in the report

The first scenario examines the $18 billion in annual increased public investment (mostly infrastructure investment) that could be financed over the next decade by completely canceling the automatic spending cuts to discretionary programs in the U.S. federal budget in 2014 and 2015. These cuts (often known as the budget “sequester”) were enacted in 2011 and have since reliably driven down domestic spending in the federal budget. As this report shows, because the majority of public investment in the federal budget is actually funded through this discretionary spending, anything that cuts this spending will be extraordinarily likely to reduce infrastructure investment as well. This is admittedly a parochial scenario to examine, as it relies on the minutiae of U.S. budget policy to interpret the dollar amounts. But it could well illustrate how broad and unfocused efforts to simply rein in public spending across large categories in the name of fiscal responsibility are extraordinarily likely to lead to steep cuts in public investment. This is a warning applicable to policymakers in other countries, given the sharp cuts in public spending across the developed world.

The rush to cut spending that sweeps up public investments and infrastructure in its wake is particularly irrational: Those who make an economic case for reducing budget deficits claim to be concerned about public investment “crowding out” productive private capital formation. But preserving productive private capital formation by cutting productive public capital formation makes little sense.

The second scenario stresses a relatively new rationale for investing in infrastructure: making green investments that help the transition to an economy that emits fewer greenhouse gases (GHGs). Over 10 years an infrastructure investment package that combines the construction of a national “smart grid” with investments in the energy-efficiency of the nation’s building stock would yield $92 billion in additional annual infrastructure investments, with roughly half stemming from the smart-grid construction and half stemming from energy- efficiency investments. While the rationale for this type of infrastructure package is relatively new, the activities undertaken are really quite traditional infrastructure investments: building new capacity for utilities and maintaining, repairing, and constructing new buildings and structures.

The third and most ambitious scenario aims to fully close the “infrastructure deficit” identified by the ASCE in a seven-year window by increasing infrastructure investments by $250 billion annually over that period. While high-reaching, this level of increased annual spending is in line with budgets proposed by the Congressional Progressive Caucus (CPC) in the U.S. House of Representatives, so it is hardly at the fringe of U.S. political debate. This package would essentially be spread proportionately over all components of traditional infrastructure: highways, other transportation projects, utilities, and water treatment and sewage projects.

Short-term challenges and the role of infrastructure investment in meeting them

The need to boost aggregate demand is a bit less pressing than it was in 2009, but still remains acute. The U.S. economy continues to suffer from underappreciated degrees of economic “slack”—unused resources, with idle potential labor being the most damaging. This slack is still the result of deficient aggregate demand. A significant boost to demand would absorb some of this slack and would put idle resources and unemployed adults back to work in large numbers, with little danger at all of running into near-term capacity constraints. Further, unlike in many economic contexts, any boost to demand stemming from infrastructure investments in the near term is quite unlikely to spur a countervailing contractionary response from other macroeconomic policymakers—particularly those at the Federal Reserve.

Given these considerations, this report estimates the near-term effects of our three infrastructure investment scenarios on net new economic activity and employment as well as how much this net boost to economic activity and employment would change depending on how the infrastructure investments were financed.

On average, each $1 billion in infrastructure investments yields $1.6 billion in additional economic activity and roughly 11,000 net new jobs, if these investments are deficit-financed. Deficit-financed investments under the three different scenarios translate into increases in economic activity of $29 billion, $147 billion, and $400 billion respectively. The associated employment increases are 216,000, 1.1 million, and 3 million jobs.

If the increase in infrastructure investment is financed through means other than issuing government debt, the boost to economic activity and employment is attenuated, although it remains positive in every mode of finance except for cuts to government transfer spending. If infrastructure investments are paired with equivalent cuts to transfers, the entire short-term stimulative effect will essentially be blunted. The smallest countervailing drag on infrastructure investment financing comes from progressive tax increases (i.e., tax increases that fall most heavily on higher-income households) and from regulatory mandates that compel infrastructure investments from private-sector households. Besides government transfers, the largest countervailing drag on infrastructure investment financing comes from regressive tax increases (i.e., tax increases that fall heavily on low- and moderate-income households).

While it is important to stress that these boosts to economic activity and employment are context-specific—they would significantly lessen were such investments undertaken when the U.S. economy is much closer to full employment, it is equally important to not underweight their importance because of their context-dependent nature. Put simply, the United States is stuck in what is known as a “liquidity trap” wherein the traditional tools of macroeconomic stabilization cannot easily remedy a shortfall of aggregate demand. This is inflicting enormous costs on households and there is little guarantee that these conditions will change reasonably soon absent a strong policy response. Further, while macroeconomists of the past generation often assumed that generating sufficient aggregate demand is a relatively simple task, recent analyses (for example by Summers (2013) and Krugman (2013)) have raised the possibility that much of the advanced world is entering a period of chronic demand shortfalls. Given this, the macroeconomic boost from infrastructure spending can, even in the long run, provide a positive “aggregate demand externality” that will serve as a buffer against falling into future liquidity traps.

Long-term challenges and the role of infrastructure investment in meeting them

In the longer term, two of the most pressing challenges faced by the U.S. economy are generating acceptable rates of productivity growth and ensuring that the benefits of this growth are broadly shared across U.S. households.

A now-extensive literature strongly suggests that a slowdown in the rate of public investment can largely explain the slowdown in overall productivity growth that began in the early 1970s. While this productivity growth temporarily reaccelerated in the late 1990s and early 2000s despite no increase in public investment, productivity growth has since slowed markedly, a slowdown that occurred even before the Great Recession. Greater commitments to public investments, including infrastructure, would help reverse the slowdown.

Increasing investments in infrastructure would also help address the significant rise in income inequality in the United States over the last three decades. Due to this rise in inequality, living standards of low- and moderate-income households have badly lagged both historic growth rates (i.e., those prevailing in the three decades before 1979) and overall average growth rates. Almost by definition, the benefits of infrastructure investments are more broadly shared than benefits generated by private investments, and so would provide some assurance that future productivity growth will boost living standards of low- and moderate-income households.

Additionally, infrastructure investments would tackle a related, decades-long problem—how to generate high-quality jobs, particularly for groups traditionally disadvantaged in the labor market: women, minorities, young workers, and workers without a four-year university degree. This report assesses the degree to which investments in infrastructure would mechanically affect the creation of high-quality jobs for these groups, and assesses the impact of these investments on job quality more generally.

Overall, it finds that infrastructure investments raise overall job quality by creating jobs that skew heavily away from the bottom fifth of wages, and disproportionately fall in the top four-fifths of the wage distribution. Less hopefully, among these traditionally disadvantaged groups, only Hispanics and workers without a four-year college degree would see disproportionate employment growth from infrastructure investments. Of course, while infrastructure investments in the U.S. economy do not mechanically create high-quality jobs for traditionally disadvantaged groups, they can be part of an overall economic strategy to support this kind of employment growth; the investments just may need to be paired with complementary policies to ensure that jobs in sectors heavily concentrated in infrastructure spending (construction and manufacturing, broadly) are equally available to all qualified groups of workers. And regarding younger workers, the relatively small share of young workers in construction and manufacturing could argue for substantial investment in apprenticeships and training in these sectors.

Policy recommendations

This report finds that expanded infrastructure investments would help address a wide range of challenges faced by the U.S. economy and yield large economic returns. In short, the case for a campaign to significantly increase infrastructure investments could hardly be stronger.

In the near term, increased infrastructure investment should be financed with government debt. Interest rates remain at historic lows and the short-term stimulative benefits of infrastructure investments are maximized if they are financed with debt rather than with tax cuts or cuts to other forms of public spending. If politics demands that increased infrastructure investments are paid for, progressive tax increases (i.e., those that fall heavily on higher-income households) would provide the smallest countervailing drag to near-term economic activity and employment.

For infrastructure investments aimed at mitigating the emission of greenhouse gases (GHGs), the optimal financing is government debt even over the long run. The key problem of GHGs is that they inflict an unpriced externality (the threat of global climate change) on the economy, making their emissions “too cheap.” This relative cheapness leads economic actors to invest too much in traditional (GHG-emitting) economic production and too little in GHG mitigation. One way to shift this (absent putting the correct price on GHG emissions) is through public investments in GHG mitigation that are deficit-financed; the deficit finance will, in a full-employment economy, lead to higher interest rates and less private investment in traditional (GHG-emitting) capital.

In the longer term, deficit-financed infrastructure investment is unlikely to do economic harm. As long as the social rate of return to infrastructure matches the social rate of return to private investment, deficit financing infrastructure investments will just produce the substitution of public infrastructure capital for private-sector capital. However, there may be other reasons to want infrastructure investments to be deficit-neutral in the longer run. In this case, issues of distributional equity should be considered. In particular, the decades-long rise in income inequality argues strongly that the burden of financing infrastructure investments may appropriately fall most heavily on higher-income households, which have seen their incomes grow much faster than average over this period.

To ensure that progress in providing quality employment to traditionally disadvantaged groups is not compromised, infrastructure investments should be paired with robust efforts to ensure equal opportunity for all qualified workers in jobs in infrastructure-heavy sectors such as construction and manufacturing, and with better apprenticeship programs. Besides government regulation and oversight, one key way to ensure this equal representation is through private labor-market institutions such as unions and worker centers for immigrant workers. Barriers to forming these labor market institutions should be removed. One key example of such barriers is the failure of labor law to keep pace with employer hostility to collective bargaining. Reforms that ensure that the ability of willing workers to form unions can help both in private-sector monitoring of access to good jobs as well as in the creation of apprenticeship programs that are beneficial to both employers and workers.

The last policy recommendation is to not generalize the results of this study to other countries, particularly in the global South. Infrastructure investments in the United States tend to be significantly less labor-intensive than other forms of spending because U.S. construction and manufacturing sectors are very capital-intensive. However, a key driver of this capital-intensity is the logic of globalization: Capital-abundant countries like the United States will focus production in capital-intensive sectors. But this logic works in reverse for poorer countries in the global South: Growing globalization will pressure labor-abundant countries to specialize in labor-intensive sectors. In short, the relative labor-intensity of infrastructure investments in other countries is likely very different than in the United States.

About the author

Josh Bivens joined the Economic Policy Institute in 2002 and is currently the director of research and policy. His primary areas of research include mac­roeconomics, social insurance, and globalization. He has authored or coauthored three books (including The State of Working America, 12th Edition) while working at EPI, edited another, and has written numerous research papers, including for academic journals. He appears often in media outlets to offer eco­nomic commentary and has testified several times before the U.S. Congress. He earned his Ph.D. from The New School for Social Research.

Acknowledgments

This work was prepared for a project undertaken by the International Labour Organization (ILO) to study the employment impacts of infrastructure spending. Financial support from the ILO is gratefully acknowledged.

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