- Original article
- Open Access
What factors contributed to changes in employment during and after the Great Recession?
© Farooq and Kugler; licensee Springer. 2015
- Received: 21 October 2013
- Accepted: 29 December 2014
- Published: 28 February 2015
Unemployment increased drastically over the course of the Great Recession from 4.5 percent prior to the recession to 10 percent at its peak in October 2009. Since then, the unemployment rate has come down steadily, and it stood at 5.8 percent in November 2014. Based on existing analyses and some new evidence, this paper establishes that much of the change in unemployment during the Great Recession and during the recovery can be attributed to cyclical factors rather than structural factors. The paper then presents new suggestive evidence to quantify the employment impacts of various counter-cyclical policies introduced during this time. We conduct a counter-factual and find that employment would have been between 4.2 percent and 4.5 percent lower had it not been because of the spending in Medicaid injected in local economies by the Recovery Act. In addition, we conduct a differences-in-differences and triple difference analysis, which suggests that the Work Opportunity Tax Credits increased the likelihood of employment by about 4.7 percent for disconnected youth but had no effect on disabled and unemployed veterans. Finally, we also find evidence that suggests that the Hiring Incentive to Restore Employment (HIRE) Act increased employment of the unemployed by 2.6 percent and that the reemployment reforms introduced in 2012 as part of the UI extensions increased employment by 6 percent for the long-term unemployed.
JEL codes: JE24, J23, J63, J64, J65, J68
- Labor demand
- Unemployment incidence
- Job search
- Unemployment insurance
- Tax credits
- Hiring subsidies
Just as the goods, services, financial, credit, and housing markets were all affected by the Great Recession, so was the labor market. The sharp contraction in demand generated massive layoffs, a sharp drop in employment and a rise in unemployment. While total employment and private sector employment have been growing steadily by 178,000 jobs per month and 188,000 jobs per month on average, respectively, since February 2010,1 the loss of jobs during the Great Recession was so great that the economy only recently (in May 2014) recovered the 8.7 million jobs it lost. Unemployment reached a peak of 10 percent in October 2010 and has been declining steadily since. Indeed, the share of the short-term unemployed has returned to pre-recession levels, so the continued high unemployment reflects the larger share of long-term unemployed during the recovery. As explained below, the large share of long-term unemployed has important implications in terms of the speed at which the unemployment rate can continue to fall.
In Section 2, we discuss the severity of the labor market downturn during the Great Recession. In Section 3, we review past evidence and present some new evidence on the extent to which unemployment during the Great Recession and the recovery can be attributed to cyclical or structural factors. In Section 4, we explain the policy tools used during this period and present some evidence of their employment impacts during the recession and later during the recovery. We conclude in Section 5.
1.1 2 The labor market during the Great Recession and recovery
Changes in unemployment rates during each post-war recession and recovery
Lowest unemployment rate in the year before start of recession
Highest unemployment rate during or after the official end of recession
Difference from trough to peak
Highest unemployment rate during or after the official end of recession
Lowest unemployment rate in the recovery (up to 6 years after the recession)
Difference from peak to trough
Much of the rise and sustained unemployment can be explained by private sector job losses during the recession and public sector job losses during the recovery. In addition, a big part of the sustained unemployment can be explained by the inability of people to exit unemployment as private sector employers have not been creating enough jobs, and public sector employers have not created jobs on net during this period.
There are a number of reasons why the long-term unemployed may find it harder to find jobs than the short-term unemployed and contribute to keeping the share of the long-term unemployed and overall unemployment high. First, employers may simply take employment status or duration of unemployment as a signal of worker quality. Even though the extent of mass layoffs suggests that employment status and unemployment duration were probably relatively bad signals of quality during the Great Recession,4 in their audit study, Kroft et al. (2012) find evidence of substantial statistical discrimination against the long-term unemployed. Second, the unemployed, and in particular the long-term unemployed, may find it much more difficult to find a job because individuals may lose their skills and motivation as they remain longer in unemployment. This may be because they are either less skilled or less desirable to hire as their spells of unemployment prolong. Third, the unemployed, may have less access to information about jobs because those in their networks may also be unemployed, and many jobs are filled through informal channels. There is anecdotal evidence that many employers turned to using informal channels during the recovery as a way to save on recruiting costs given the abundance of potential applicants. In fact, the share of individuals searching for work through family and friends grew from 19.7 percent in 2006 to 26.4 percent in 2007, then to 28 percent in 2008, and has stayed at over 32 percent since 2009.5 This increased reliance on family and friends as a way to find jobs has occurred even though networks are likely less effective in generating job offers on average given the higher unemployment today.6 Finally, as the financial assets of the unemployed deplete the longer they have been in unemployment, it becomes harder to pay for transportation, to move to take a job and to pay for other costs associated with looking for jobs.
This section shows that the Great Recession was atypical in terms of the drastic impact it had on the labor market. All measures show large losses for workers, including extensive mass layoffs, widespread drops in employment, a steep rise in unemployment, and greater difficulty in finding employment. In addition, this section shows that the recovery after the Great Recession was different in that government jobs have delayed rather than sped up the recovery of the labor market, as in past recessions. Finally, even though the labor market is back to where it was in terms of most indicators, the Great Recession has been different in that it has had a long-lasting impact in the labor market because of its effect on long-term unemployment. The share of long-term unemployed is twice the pre-recession share, and the long-term unemployed face numerous difficulties finding employment even as the economy has continued to recover.
1.2 3 Cyclical vs. structural unemployment during the great recession and beyond
In this section, we explore how much of the unemployment during the Great Recession and beyond was cyclical and how much reflected mismatches and other structural challenges faced by the economy. Below, we review the large body of past evidence and present some new evidence pointing to the unemployment problem being largely driven by cyclical factors during this period. Yet, the evidence also indicates that some new problems have emerged in the labor market which could potentially turn into structural factors if they persist over the years to come.
1.2.1 3.1 Evidence from Okun’s law
A number of previous studies have re-estimated Okun’s Law during the Great Recession and recovery. These studies show that much of the change in unemployment is due to changes in GDP and that the persistent high unemployment is due to a slow recovery in GDP. Arteta et al. (2011) show that the majority of movements in unemployment since the Great Recession were due to changes in output. According to this study, the drop in GDP can explain 63 percent of the rise in unemployment during the recession. Similarly, about 57 percent of the drop in unemployment during the recovery can be explained by the rise in GDP. This indicates that both during the recession and during the recovery, cyclical factors were key in explaining changes in unemployment and that cyclical factors have remained important during the recovery. Importantly, Ball et al. (2012) find that the relation between the change in unemployment and the change in GDP has been stable over the periods 1990–91, 2001, and 2007–09. Rather, they conclude that the strength of economic growth relative to the trend is what has been different during these periods.
In Okun’s law, the rest of the changes in unemployment are considered as unexplained or explained by factors not included in the regression. These could be factors such as skill, sectoral, or regional mismatches.7 Thus, an extension of Okun’s Law would include measures of mismatches in the regression. Estevão and Tsounta (2011) estimate a relation of changes in unemployment at the state level on changes in gross state product (GSP), a measure of skill mismatches in the state, and a measure of geographical immobility. This study finds that much of the change in state unemployment can be explained by GSP and that only about 0.5 percentage points of the increase in the NAIRU can be explained by skill gaps.
1.2.2 3.2 Evidence from the Beveridge curve
The Beveridge Curve, which establishes the relation between the job openings rate and the unemployment rate, is yet another way to disentangle how much of the change in unemployment is due to cyclical factors and how much is due to other factors. The Beveridge Curve, estimated by the Bureau of Labor Statistics over the last decade,8 shows that during the early 2000s, unemployment increased and the job openings declined starting in November 2001 until around November 2007. These movements along the Beveridge Curve are consistent with cyclical factors driving the changes in unemployment during this period. In December 2007, the unemployment rate increased rapidly together with much slower drops in the job openings rate. These movements since 2007 are also consistent with cyclical factors driving these changes. In October 2009, the unemployment rate started to drop and the job openings rate started to rise. The backward movement to the Northwest points to a decline in cyclical unemployment. Yet, the fall in unemployment has not been fast enough to match the rise in the job openings rate. This indicates that for a given job openings rate, the unemployment rate is higher than it used to be, pointing potentially to the rise in the importance of structural factors.
A number of studies have examined and explained this shift in detail. Diamond (2013) explains that the Beveridge Curve may be using proxies rather than the correct measures of those searching for work and the right measure of job openings. Indeed, the unemployment rate may not capture everyone looking for work. Diamond (2013) does a thorough analysis of flows from and to employment, unemployment, and out of the labor force and finds transitions of similar magnitudes from non-employment to employment and back as from unemployment to employment and back. This is indicative that many of those classified as out of the labor force may be actively looking for work. In addition, Davis et al. (2012) have found that the speed of filling vacancies and the proportion of hiring varies by industry and over the business cycle. For instance, the industry composition of job openings has been changing over time, with many fewer job openings in construction, which have short durations, and many more job openings in health and education, which have longer durations. Given that long durations imply low closing rates of vacancies or high job opening rates, then one may worry that changes in the composition of vacancies may be accounting for the high opening rate associated with the same unemployment rate as before.
Ghayad and Dickens (2012) disaggregate the unemployment-vacancy relationship by industry, age, education, and duration of unemployment and by blue- and white-collar groups to understand if the shifts in the aggregate Beveridge Curve are driven by particular groups. They find that the outward shift in the Beveridge Curve was common among all major industries, all age groups, all education groups and among blue- and white-collar workers. What is noteworthy is that the outward shift in the Beveridge Curve is only evident for the long-term unemployed. Thus, whether this shift becomes a structural problem will depend on how permanent the obstacles faced by the long-term unemployed become and on how effective the policies to help the long-term unemployed, discussed in the following section, have been.
Finally, Daly et al. (2012) indicate that a longer series to estimate the Beveridge Curve, like the one they construct using the Help-Wanted Index to go back to the 1960s, shows that the shift in the Beveridge Curve observed in the current cycle is within the range of what occurred in past business cycles. Moreover, when they combine the Beveridge Curve with the Job Creation Curve, they estimate an increase in the natural rate of unemployment of between 0.4 and 1.4 percentage points, or between 7 percent and 25 percent of the rise in unemployment during the last recession.
1.2.3 3.3 Evidence of mismatches
Lazear and Spletzer (2012) conduct a rigorous quantification of the extent of mismatches and find that changes in industrial mismatch are cyclical. They construct an industrial mismatch index using JOLTS and CPS data and they find that the index was the same in 2011 as prior to the Great Recession. Instead, they find that industrial mismatches increased sharply because unemployment went up in every industry, exceeding the number of vacancies in every industry. Much of the increase in the gap between unemployment and vacancies during the recession and the subsequent decline during the recovery can be explained by four industries: health services, government, construction and manufacturing. Lazear and Spletzer (2012) also estimate an occupational mismatch index and find that this is much higher than the industrial mismatch index, but, like the industrial mismatch, it is pro-cyclical, and the occupational mismatch index has already returned to its pre-recession level. Importantly, they point out that mismatch indices were higher during this period because unemployment was higher in all industries and occupations and not because the skills desired by employers are less in line with what they desired in the past.
In fact, this is exactly what is borne out when employers are asked about their ability to find workers qualified to fill their vacancies. Only 6 percent of employers in 2012 and 5 percent of employers in 2013 reported the low quality of labor as a major concern for their businesses. Moreover, finding qualified applicants was less of a concern during the recovery than it was before the recession. In 2013, 36 percent of employers reported that there were too few or no qualified applicants, compared to 41 percent in 2012 and 48 percent in 2007.9 By contrast, employers continue to report poor sales as one of their top concerns. In 2012, about 21 percent of employers reported poor sales as their most important challenge, and around 17 percent of employers continue to report poor sales as a major challenge in 2013. This suggests that aggregate demand problems are more prominent in employers’ decisions than skill gaps problems. It also suggests that skill gaps were there before the recession and after the recession, but do not appear to have become more pronounced in the view of employers.
Another approach towards estimating the extent of mismatches is to directly estimate the efficiency of matching in the economy instead of estimating the relation between the unemployment rate and measures of mismatches. Estimating matching functions of employment on the number of unemployed and the number of vacancies allows for estimating the parameters of the matching function and the efficiency of matching parameter. Sahin et al. (2014) estimate matching functions using recent data from the Current Population Survey, the Job Openings and Labor Turnover Survey (JOLTS), and the Conference Board’s Help Wanted Online (HWOL) which covers the universe of online U.S. job advertisements. They find that mismatches across industries and 3-digit occupations can only explain a third of the total observed increase in the recent rise in the unemployment rate.
Barlevi (2011) also estimates matching functions using a Cobb-Douglas specification and using data on unemployment from Haver analytics and vacancy data from JOLTS for the period from 2000 to 2011. He finds that the reduced matching productivity from normal times (defined as 2000-August 2008) to the end of 2011 can explain an increase in unemployment to 7.1 percent. Given that the rise in unemployment from his defined normal times was 5.3 percent and 9.3 during the period after, then mismatches could explain 45 percent of the rise in unemployment, and 55 percent would be explained by other factors. However, Barlevi (2011) acknowledges that his assumption of a fixed ratio of the value of a job to the cost of filling a vacancy leads to an over-estimate of the effect of mismatches and, thus, provides an upper bound of the impact. Given these two studies, the impact of mismatch ranges between 33 percent and 45 percent (with the latter being an upper bound), while the impact of other factors (including slack demand) account for between 67 percent and 55 percent of the rise in unemployment during the Great Recession.
In contrast to industry and occupational mismatches, the study by Dickens (2010), which looks at geographic mismatch, finds no evidence at all of mismatches in this dimension. This study and others (Lazear and Spletzer, 2012; Elsby et al., 2011) provide support to the broadly accepted view that housing lock and the inability to move to look for jobs due to the lackluster housing market cannot account for any of the increase in unemployment.
Overall, this section shows that regardless of the method used to identify the relative importance of demand and structural factors in explaining the rise in unemployment, the answer is always the same – the majority of the increase in unemployment can be explained by cyclical factors rather than structural factors.10 Nonetheless, the evidence does provide a range of estimates of the importance of structural factors ranging from 25 percent for estimates from the Beveridge Curve, to 33 percent using the most credible mismatch function estimates, and to 40 percent when relying on Okun’s law.
1.3 4 Policies to address the unemployment problem
It is clear from the previous sections that there were two key factors contributing to unemployment that needed to be addressed during the recession and recovery. The most important challenge facing employers and workers during the Great Recession, and even through the recovery, was slack demand. In addition, labor markets are now facing new problems during the recovery which were not present prior to the Great Recession. In particular, the large rise in the fraction of long-term unemployed made it more difficult to bring down the unemployment rate during this time period. The majority of measures introduced to address unemployment during this period, thus, focused on different policies to stimulate labor demand. Later, during the recovery, policy measures focused on how to aid the long-term unemployed.
1.3.1 4.1 Impact of fiscal spending on employment: American recovery and reinvestment act
In 2009, the American Recovery and Reinvestment Act (ARRA) generated a large fiscal stimulus. The Recovery Act introduced $840 billion in government spending directed towards tax benefits; contracts, grants and loans; and entitlements. The initial spending was divided into $290.7 billion for tax benefits including individual tax credits, tax incentives for businesses, energy incentives, and manufacturing incentives. Another $261.2 billion was spent on contracts, grants, and loans for education, transportation, infrastructure, energy and the environment, research and development, housing, health, and job training. Finally, a total of $264.4 billion was spent in entitlements, including $105.7 billion, which was spent on Medicaid and Medicare, mostly for Medicaid Grants to States, another $61.3 billion was spent on unemployment insurance programs and the rest on family services and energy and housing subsidies.11 The economic rationale behind this spending was based on the evidence on fiscal multipliers and the idea that public spending would spur economic activity in the private sector. The implication for the labor market was that government spending would have an impact on direct job creation and, at the same time, induce hiring in the private sector. In December 2010, the government passed the Middle Class Tax Relief Act of 2010, introducing another large packet of fiscal spending of close to $700 billion that also financed tax cuts and income support programs.
The time-series evidence suggests that fiscal spending was effective. Employment losses quickly lessened after the passing for the Recovery Act, and employment growth started a year later. In fact, employment reignited again 7 months after GDP picked up. Indeed, this is in line with the usual lag between GDP and employment growth.12 Likewise, employment appears to have grown at a faster pace after the additional fiscal stimulus was introduced in December 2010. A problem with this evidence is that it is not possible to distinguish if that employment growth would have taken place even without the stimulus.
Thus, panel data evidence is more useful to disentangle the causal effect of the stimulus. Feyrer and Sacerdote (2011) indeed present evidence from panel data that exploits the fact that different states and localities received different amounts of stimulus funds over that time period. Their study finds a broad range of fiscal multipliers from 0, for expenditures on education, to 2, for support programs for low-income households and infrastructure. More importantly, Feyrer and Sacerdote (2011) find that regions of the country that received more recovery funds experienced faster employment growth during the recession and recovery. They found that a state’s receipt of $100,000 generated between half a job and one job. Another study by Chodorow-Reich et al. (2012) finds that recovery fund outlays in Medicaid expenditures had substantial impacts on job creation. By focusing on Medicaid outlays, they are able to address the endogeneity of state receipt by instrumenting the Recovery Act funds for Medicaid with previous expenditures in Medicaid in the state. They find that a state’s receipt of a marginal $100,000 in Medicaid outlays generates 3.8 additional job-years, with 84 percent of those new job-years created outside of government, health, and education.
We use the Feyrer and Sacerdote (2011) and the Chodorow-Reich et al. (2012) estimates to construct counter-factuals of the employment impact of the fiscal expenditures and the multiplier effects introduced by the Recovery Act. To conduct this counter-factual exercise we take the lower bound of the Feyrer and Sacerdote (2011) study and assume that the entire amount of $840 billion of expenditures from the Recovery Act was evenly distributed over the course of the Recovery Act. We then determine what employment would have been if the Recovery Act was not passed. Although this is not a perfect counter-factual exercise, it provides an estimate on the effectiveness of the various fiscal programs on employment creation. We find that employment would have been, on average, 72,000 lower per month without the Recovery Act or about 63% to 67% lower. However, since Recovery Act resources may have gone to those states which were ready to start investments and support contracts, or by contrast the federal government may have distributed entitlements to those places that needed them the most, the Feyrer and Sacerdote (2011) multiplier may be biased up or down.
The counter-factual employment using the Chodorow-Reich et al. (2012) multiplier is smaller. While Chodorow-Reich et al. (2012) found a bigger multiplier, the amounts spent in Medicaid were much smaller. This multiplier is credible since their estimation strategy takes into account the potential endogeneity of the distribution of resources to different states. Applying the $88 billion allocated to Medicaid funds in the original Recovery Act, the counter-factual shows much smaller reductions in employment of 5,000 jobs per month, on average, or between 4.2 percent and 4.5 percent had the Recovery Act not spent resources in Medicaid. While these results are smaller, these effects are more reliable.
1.3.2 4.2 Impacts of tax credits and subsidies for employers
4.2.1 Impact of the work opportunity tax credits (WOTC)
Work Opportunity Tax Credits (WOTC) were first introduced in 1996. These were tax credits to employers hiring workers in specific target groups. Work Opportunity Tax Credits were introduced to incentivize employers to hire people from groups generally considered to have low skill levels and, thus, less likely to find employment. The target groups covered by WOTC have been expanded or changed a few times since 1996. More recently, these credits were expanded during the period covering the recession. The passage of the U.S. Troops Readiness, Veteran’s Care, Katrina Recovery and Iraq Accountability Act of 2007 expanded the tax credits until August 21, 2011 to cover disabled veterans who were discharged from active duty in the past year. Before this, WOTC credits covered members of families receiving TANF, veterans who were members of families receiving food stamps, 18–39 year olds who were members of families receiving food stamps, ex-felons, SSI recipients and those in communities designated as empowerment zones, enterprise communities, and renewal communities.
The Recovery Act further included $32.6 billion in tax credits to provide incentives for employers to hire members of two additional groups until December 31, 2010. The two groups for which WOTC was expanded included: unemployed veterans who were discharged in the past 5 years and who had collected unemployment insurance payments for at least 4 weeks in the past 12 months, and disconnected youth aged 16–24 years old who had neither regularly worked nor attended school in the past 6 months. While these credits expired in December 31, 2010, on November 21, 2011, Congress passed the VOW to Hire Heroes Act, which extended tax credits for unemployed veterans who first benefited from the Recovery Act WOTCs. The VOW act expired on December 31, 2012. The timeline below describes the introduction and expiration of these credits and the reforms, which are discussed in more detailed in the next two sections (Figure 8).
4.2.2 Impact of the work opportunity tax credits (WOTC) on veterans
To examine the impact of these credits, we do a difference-in-difference analysis to compare the treatment groups to non-treated veterans. For veterans there are two treatment groups. The first is the group of disabled veterans who were discharged in the last year. The second is the group of unemployed veterans who have received UI for at least 4 weeks in the past year and who were discharged from active duty in the past 5 years. Since the Current Population Survey, which we use for our analysis, does not allow us to identify collection of UI in the past year, we can only identify those that were unemployed for at least 4 weeks in the past year. Given that not all of the unemployed qualify for UI benefits, this group will include some who would not have qualified for the credits, but this is the best that can be done with CPS data. Similarly, we cannot identify disabled veterans discharged in the past year or unemployed veterans discharged in the past 5 years. The best we can do with the CPS is identify those discharged from active duty since 2001. In this case too, we may be including some individuals in the treatment group who would not have qualified for WOTC. We then focus on the years 2008, 2009, 2010 and 2011 for disabled veterans and on the years 2009, 2010, and 2012 for the group of unemployed veterans. The analysis leaves out TANF and SNAP recipients to avoid including in the comparison group individuals who continued to benefit from the earlier tax credits introduced before the 2007 U.S. Troops Readiness Act and the 2009 Recovery Act. Unfortunately, we cannot identify ex-felons, those receiving SSI, or those in designated communities, so the comparison group will have some who may have still benefited from WOTCs. We also do a difference-in-difference-in-difference analysis in which we also use non-veterans (disabled non-veterans and unemployed non-veterans) as comparison groups.
Descriptive statistics for different WOTC eligible groups
Disabled Veterans Before WOTC Eligibility (Pre-2008)
Disabled veterans during WOTC eligibility (Post-2008)
Unemployed Veterans Before WOTC eligibility (Pre- 2010)
Unemployed veterans during WOTC eligibility (Post-2010)
Youths (16–24 year olds)
Disconnected youth before WOTC eligibility (Pre- 2010)
Disconnected youth after WOTC eligibility (Post- 2010)
Weeks looking for work
Less than H.S.
Bachelor’s and higher
Difference-in-difference (DD) effects of work opportunity tax credits (WOTC) on disabled veterans and unemployed veterans
Recent veterans only
Disabled veteran X WOTC eligibility period for disabled veterans
Disabled veteran dummy
WOTC eligibility period for disabled veterans
Unemployed veterans X WOTC eligibility period for unemployed veterans
Unemployed veterans dummy
WOTC eligibility period for unemployed veterans
State fixed effects
Time fixed effects
Difference-in-difference-in-difference (DDD) effects of work opportunity tax credits (WOTC) on disabled veterans and unemployed veterans
All veterans eligible for WOTC based on employment history
Only recent veterans eligible for WOTC based on employment history
Disabled X veteran X WOTC eligibility period for disabled veterans
Disabled X WOTC eligibility period for disabled veterans
Veteran X WOTC eligibility period for disabled veterans
Disabled X veteran
WOTC eligibility period for disabled veterans
Unemployed X veterans X WOTC eligibility period for unemployed veterans
Unemployed X veterans
Veterans X WOTC eligibility period for unemployed veterans
Unemployed X WOTC eligibility period for unemployed veterans
WOTC eligibility period for unemployed veterans
State fixed effects
Time fixed effects
Heaton (2012) has done similar analysis using the American Community Survey (ACS) and double and triple difference specifications. Our results are remarkably similar to those of Heaton (2012) when focusing on all veterans even though he uses a different source of data. However, Heaton (2012) fails to take into account that one of the requirements to qualify for WOTC was to have been recently discharged. While we cannot perfectly identify those discharged 1 and 5 years ago, we can at least identify those discharged since 2001. This makes a big difference – the impact of WOTC on veterans disappears.
4.2.3 Impact of the work opportunity tax credits (WOTC) on disconnected youth
Difference-in-difference (DD) effects of work opportunity tax credits (WOTC) on disconnected youth under the ARRA
Disconnected youth X WOTC eligibility period
WOTC eligibility period (Under ARRA)
State fixed effects
Time fixed effects
We find mixed results of targeted tax credits on employment. While we find a positive and non-trivial impact on the employment of disconnected youth, we do not find evidence of an impact on veterans. Previous evidence on tax credits is also mixed. Katz (1998) finds that the Targeted Jobs Tax Credit, a major wage subsidy program for the economically disadvantaged introduced between 1979 and 1991 had modest but positive employment effects. Hamersma (2008) argues that the WOTC had minimal effects on the employment of targeted groups because of low take-up of the credits. Burtless’ (1985) analysis of a randomized targeted wage subsidy program in Dayton, Ohio suggests that vouchers may have even hurt the targeted groups by stigmatizing them. Our results are a little smaller than the ones reported by Katz (1998) for disadvantaged youth of a reduction in employment of 7.7 percent due to the discontinuation of the Targeted Jobs Tax Credit, though the TJTC applied to an older age group of 23 to 24-year-olds, who Katz (1998) argues are more attached to the labor force and, thus, more likely to benefit from the credits13.
4.2.4 Impact of the Hiring Incentives to Restore Employment (HIRE) Act
An alternative to tax credits attached to individual groups are tax credits provided to employers hiring any workers, which would avoid the problem of stigmatization. In March 18, 2010 the Hiring Incentives to Restore Employment (HIRE) Act was passed, which instead gave a direct payroll tax exemption of 6.2 percent to employers hiring unemployed individuals who had been unemployed for at least 60 days or who worked less than 40 hours (part-time workers) in the last 60 days. The HIRE Act expired on December 31, 2010. While there has been no evaluation of this program, at the time, the Treasury Department indicated that there were 3.2 million jobs created which, in principle, qualified for these credits over the time period during which the credits were effective. Here we attempt to quantify the impact of the HIRE Act.
Descriptive statistics for different unemployed groups
HIRE act eligible
HIRE act eligible pre-2011
HIRE act eligible post-2012
Long term unemployed
Long term unemployed pre-2013
Long term unemployed 2013 & 2014
Weeks looking for work
Less than H.S.
Bachelor’s and higher
Difference-in-difference (DD) effects of unemployment assistance programs on the employment of the long-term unemployed
HIRE eligible X 2011
2011 Year dummy
LTU X LTU assistance period
Long Term Unemployed (LTU)
LTU assistance period (2013,2014)
State fixed effects
Time fixed effects
Grijalva and Neumark (2013) similarly find that state tax credits increased employment for the unemployed, but that the effects were not large. By contrast, evaluations of similar tax credits in other countries suggest that these credits have been effective in encouraging hiring. Kugler (2011) presents an extensive literature review with evidence on the effectiveness of payroll tax cuts for employers from a number of natural experiments around the world as well as from cross-country panel data studies. While there is no data on take-up of credits from the HIRE Act, there was a perception that take-up of the hiring credits was low, and this may be one reason why the impact was not bigger.
1.3.3 4.3 Employment impacts of policies to get the long-term unemployed back to work
As shown in Figure 7, those unemployed for more than six months are about half as likely to find a job as those who have been unemployed for less than six months. Yet, the share of long-term unemployed increased sharply during the Great Recession and remains twice as high as before the recession.
Yet, under most states’ unemployment systems, individuals are entitled to unemployment benefits for up to 26 weeks, and the replacement rate is close to 50%. Given that long-term unemployment rises during recessions, over the past several decades, emergency unemployment compensation has been extended 8 times to provide additional unemployment benefits to the long-term unemployed.
During this last recession, emergency unemployment compensation (EUC) was first introduced in June 2008, then extended in February and November of 2009, and again in December 2010, February 2012, and January 2013. The initial program introduced two ‘tiers’ of additional weeks of benefits. In November of 2009, the program was expanded to include two additional tiers. While the exact weeks and qualification for each tier has changed with each new extension, the current four tiers have been in place since 2009. The latest extension of EUC in January 2013 provided 14 additional weeks of benefits in the first tier to all states. The second tier provided 14 additional weeks for states with unemployment rates over 6 percent. Tier 3 provided 9 additional weeks if the unemployment rate is above 7 percent, and tier 4 provided 10 additional weeks if the unemployment rate is above 9 percent. The rationale in providing more weeks of benefits in those states with higher unemployment is that those are precisely the places where the long-term unemployed will be facing the biggest hurdles in getting jobs. In addition to emergency unemployment compensation, extended benefits (EB) trigger in for up to 20 weeks in states where the unemployment rate is above 6% and remains above what it was in the past three years.
These extensions have provided income support to close to 25 million workers and their families since the beginning of the recession and have helped many of these families from falling into poverty. However, one concern with unemployment benefits extensions is that they may generate a moral hazard and cause people to search less and to lower their acceptance of jobs. Yet, there are a number of reasons why extending unemployment benefits may be beneficial on economic grounds. First, unemployment benefits are an automatic stabilizer and avoid big consumption drops by households facing unemployment. Gruber (1997) finds that consumption drops by 22% for those without unemployment benefits while only dropping by 7% among those receiving unemployment benefits. Also, Vroman (2010) finds a multiplier of 2 for unemployment insurance. This means that the economies of entire regions and states where the long-term unemployed received benefits grew by twice as much as the benefits received in those states.14 Second, Krueger and Mueller (in progress) find that unemployment benefits help the unemployed stay attached to the labor force rather than going into disability insurance. Both Rothstein (2011) and Farber and Valletta (2013) find that the unemployment insurance extensions reduced exits from unemployment but that this was largely due to reductions in exits from the labor force rather than a decrease in exits to employment. This is particularly important given the decline in labor force participation which started in 2000 and which has continued during the recession and also given the rise in disability insurance enrollments since the 1980s. These studies suggest that while the UI extensions may prolong unemployment, this is not because the unemployed are turning down job offers but because they are staying attached to the labor force rather than going into disability or stopping their job searches.15 Also, Chetty (2008) shows evidence that unemployment benefits may also provide liquidity to individuals during periods of unemployment, which may improve the quality of the jobs they get. Finally, Farooq and Kugler (2013) find that public insurance programs increase labor mobility by increasing occupational and industry mobility but also mobility into self-employment and wage employment.
In addition to extending the period of time for which individuals can receive unemployment benefits, the Middle Class Tax Relief and Job Creation Act of 2012 introduced important reforms to help the long-term unemployed get back to work. First, the 2012 act introduced reemployment assistance and eligibility assessments (REAs) for those getting additional unemployment insurance. The REAs required in-person check-ins in UI offices, skill assessments, and job search counseling during those visits for the long-term unemployed. This proposal was based on a number of studies of randomized trials in Nevada, Minnesota, Illinois and Florida showing substantial reemployment effects. Michaelides et al. (2012) showed that UI recipients who were randomly assigned to REA’s were 15 percent less likely to exhaust benefits, reduced the period for which they received benefits by 3 weeks, and increased their earnings by 18% in the 6 quarters following participation in REAs. Second, the reform introduced a self-employment assistance (SEA) program which allows the long-term unemployed to continue using UI benefits while setting up their own business. Benus (2009) evaluated a similar program, the Growing America through Entrepreneurship (GATE) program introduced in Pennsylvania, Minnesota, and Maine, which randomly assigned half of the people to training and business counseling and provided assistance in applying for business financing, and found that those assigned to the program were 6 percent more likely to own a business, were likely to start their business sooner, and their businesses had greater longevity. They also found that the program was most effective among those receiving UI. Benus et al. (1994) and Benus et al. (1995) also show positive employment impacts from the Self Employment Enterprise Demonstration (SEED) and the Massachusetts Enterprise Program, which allowed the unemployed to continue claiming benefits while receiving entrepreneurial training and setting up a new business.
This paper documents the severity of the Great Recession on the labor market and presents evidence that the majority of the sharp rise in unemployment during the Great Recession was generated by cyclical as opposed to structural factors. This paper also presents new evidence on whether different policies introduced to help address the cyclical unemployment problem helped to increase employment. The tools included direct fiscal spending as well as broad and targeted hiring tax credits. The most reliable results from a counter-factual exercise show that employment would have been 4.5% lower had it not been for the spending on Medicaid from the Recovery Act. In addition, the results suggest that the targeted Work Opportunity Tax Credits increased employment for disconnected youth by 4.7 percent. However, the credits seemed to have been ineffective for disabled and unemployed veterans, which could be due to lack of take-up since employers appeared to have been increasing the hiring of all veteran groups during this time. Finally, measures targeted toward the long-term unemployed seemed to have been somewhat effective. The HIRE Act appears to have increased employment by 2.6 percent for those unemployed for more than 2 months; though, take up was low probably due to the complicated rules to qualify for the hiring credit. The Reemployment and Eligibility Assessments introduced in 2012 as part of the extension of unemployment benefits appear to have had greater impacts on those unemployed for more than six months. The REAs appear to have helped to boost employment for the long-term unemployed by 6 percent. All in all, the reduction of the unemployment rate from the high of 10 percent in October 2009 to the current rate of 5.8 percent probably was helped by the various measures taken to help boost labor demand and support the long-term unemployed get back to work.
2.1 6 Endnotes
1These average job growth numbers refer to growth during the period from February 2010 to November 2014.
2See Hyatt and McEntarfer (2012).
3See BLS’s The Employment Situation (November 2014). Also, Hyatt and McEntarfer (2012) report a sizable flow from employment to out of the labor force and back to employment.
5Shares of unemployed using friends as a search method come from the Bureau of Labor Statistics’ Labor Force statistics calculated from the Current Population Survey: http://www.bls.gov/cps/tables.html.
6One reason why employers may prefer using informal channels is to save on monitoring and screening costs. Kugler (2003) shows that while informal channels may generate fewer offers and applicants, they generate access to high paying jobs, and they save employers on monitoring costs.
7If mismatches were correlated with GDP growth, this would bias upwards the estimate in Okun’s Law. However, since sectoral and skill mismatches are most likely to be a drag on growth, this means that mismatches are likely negatively related to GDP growth and are, thus, likely to bias downwards the estimates in Okun’s Law relation.
8See the following link from BLS: http://www.bls.gov/opub/ted/2013/ted_20130612.htm.
9Various reports from the National Federation of Independent Businesses.
10Others instead argue that the increase in unemployment is due to a decrease in the incentives to work due to extended unemployment benefits and other programs introduced by the Recovery Act (Mulligan 2014). If this story was correct, this should reduce labor supply and push up wages, but Rothstein (2011) presents evidence showing that wages fell rather than increased during the Great Recession and recovery. Moreover, there is no evidence that the increased unemployment largely results from decreased desire to work. Instead, the increase in the number of people out of the labor force is driven by individuals who indicate they are available for work and would like to get a job but who are unable to. The number of marginally attached workers more than doubled during the Great Recession and recovery, increasing from 1.3 million in December of 2007 and hitting a high of 2.8 million as late as January 2011 (Employment Situation BLS releases: http://www.bls.gov/schedule/archives/empsit_nr.htm). Moreover, close to half of the marginally attached were discouraged workers who had given up looking for a job because they believed no jobs were available. Likewise, the number of part-timers for economic reasons (those who work part-time but would prefer to work full time) was 4.7 million at the time of the official start of the recession and almost doubled, hitting a high of 9.3 million, in October 2009 (Employment Situation BLS releases: http://www.bls.gov/schedule/archives/empsit_nr.htm).
11Information on the amounts spent on various programs by the ARRA comes from http://www.recovery.gov/arra/Transparency/RecoveryData/Pages/JobSummary.aspx.
12Hamermesh (1993) surveys the literature on GDP and employment and shows that employment typically lags GDP by about two quarters.
13Neumark (2011) provides a good summary of the research in this area.
14The fact that those who receive UI are less likely to reduce their consumption and that UI disbursements have big multiplier effects are inconsistent with Mulligan’s hypothesis that UI generates both a voluntary reduction in effort and consumption by households that causes both longer unemployment durations and lower propensity to consume.
15The fact that more generous UI receipts are not associated with lower exits out of unemployment due to greater attachment to the labor force is also inconsistent with the hypothesis that UI prolongs unemployment due to lower search effort.
We are grateful to George Akerlof, Bill Gale, Ed Montgomery, Casey Mulligan, David Neumark, Coen Teulings, and Robert Valletta for helpful comments and conversations as well as participants at the IZA Workshop on Labor Market Reforms during the Great Recession for helpful conversations and comments. The authors would like to thank the anonymous referee.
Responsible editor: David Neumark
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