THE LABOR FORCE 

PARTICIPATION RATE 

SINCE 2007:

CAUSES AND POLICY IMPLICATIONS

 

July 2014
2

Introduction and Executive Summary

In 2008, the U.S. economy collided with two historic forces. The first force was the Great 

Recession, the most severe economic crisis in a generation. While the economy has recovered 

considerably over the last five years, there is little doubt that more work remains to address some 

of the challenges left in the wake of the Great Recession. The turmoil of 2008 inflicted 

tremendous pain on millions of families, overshadowing the fact that 2008 also marked a unique 

milestone in U.S. economic history. That year, the first baby boomers (those born in 1946) turned 

62 and became eligible for Social Security early retirement benefits. This second force—the 

demographic inflection point stemming from the retirement of the baby boomers—was felt far 

less acutely than the Great Recession, but will continue to have a profound influence on the 

economy for years to come, well after the business cycle recovery from the Great Recession is 

considered complete.

In addition to these inflection points in 2008, a number of longer-term trends had been playing 

out in the U.S. labor force prior to 2008—and have continued since then. These include the nearly 

continuous decline in labor force participation rates for prime-age males (i.e., age 25-54) since 

the mid-1950s and the dramatic rise in labor force participation rates for prime-age females in 

the 1970s and 1980s followed by a stalling and slight trend decline after the late 1990s.

Many dimensions of the economy’s performance over the last several years can only be properly 

evaluated when the effects of the Great Recession, the retirement boom, and the longer-term 

labor force trends are taken into account. One of the clearest illustrations of this point is the labor 

force participation rate, which represents the fraction of the adult population either working or 

looking for work. Changes in labor force participation reflect not just current economic conditions 

like job availability and workers’ assessments of job-finding prospects, but also more structural 

factors like the age distribution of the population and other aspects of society that impact 

people’s decisions to participate in the labor force.

This report analyzes the evolution of the labor force participation rate since late 2007 and 

attempts to quantify the effects of these various forces. We examine the period since 2007 to 

focus on how each of the two largest forces, the Great Recession and the retirement of the baby 

boomers, has impacted labor force participation in recent years. We find that the combination 

of demographic changes and the drop in labor force participation that would have been expected 

based on historical business cycle patterns explain most but not all of the recent drop in labor 

force participation. This implies that other factors, likely including both a continuation of preexisting trends in labor force participation by certain groups and the unique effects of the Great 

Recession have also been important. This report also discusses the labor force participation rates 

for different groups, discusses potential future scenarios for the participation rate, and lays out 

policies that would help to boost participation in the years to come.
3

Summary of the Main Results

Since the final quarter of 2007, the labor force participation rate has fallen from 65.9 percent to 

62.8 percent in the second quarter of 2014, a decline of 3.1 percentage points. In this report, the 

Council of Economic Advisers estimates that this 3.1 percentage point decline can be attributed 

to three main sources:

About half of the decline (1.6 percentage point) is due to the aging of the population.

Because older individuals participate in the labor force at lower rates than younger 

workers, the aging of the population exerts downward pressure on the overall labor force 

participation rate. While older workers today are participating in the labor force at higher 

rates than older workers of previous generations, there is still a very large drop-off in 

participation when workers enter their early 60s. 

About a sixth of the decline in the overall participation rate (0.5 percentage point) is a cyclical 

decline in line with historical patterns in previous recessions.

While the unemployment rate has come down from a peak of 10.0 percent in October 

2009 to 6.1 percent in June 2014, it has remained elevated for the last several years. 

Historically, elevation in the unemployment rate is associated with a decline in labor force 

participation, as potential workers may decide to defer looking for a job until the economy 

improves. A portion of the most recent decline in the participation rate reflects this 

historical pattern, which this report refers to as the “cyclical” effect. 

About a third of the decline (1.0 percentage point) arises from other factors, which may include

trends that pre-date the Great Recession and consequences of the unique severity of the Great 

Recession. 

In particular the two elements of this “residual” not explained by the standard factors are:

 The fact that participation rates conditional on age were declining for many groups in 

the run-up to 2008, including for prime-age men from the 1950s and for prime-age 

women from the late 1990s, may also have contributed to the decline in participation. 

This would have been expected to result in a decline in the participation rate above 

and beyond the pure aging effect even in the absence of a recession. Note, these 

effects were partly offset by other pre-existing trends, like a rise in the participation 

rate for older workers.

 The severity of the Great Recession, which hasresulted in an unusual pattern of a very 

large share of long-term unemployed relative to total unemployment, may have 

lowered the participation rate more than would have been expected through normal 

channels. In fact, we find that a declining participation rate is historically correlated 

with the elevation of long-term unemployment, suggesting that both issues have a 

common cause or cause each other.
4

In the last few years the aging of the population has been an increasingly important source of 

the decline in the participation rate. From the beginning of 2011 to the second quarter of 2014,

the participation rate fell by 1.4 percentage points. Around 70 percent of that decline (1.0 

percentage point) can be directly attributed to the aging of the population and increased 

retirements.

Overview of the Report

Section I of this report provides historical context with a broad look at the participation rate since 

1948. Then Section II surveys the literature on the participation rate in the Great Recession and 

subsequent recovery. Most researchers have found that long-term trends including the aging of 

the population explain a majority of the recent decline in participation, and that cyclical effects 

explain about a quarter of the decline. Section III explains CEA’s decomposition of movements in 

the participation rate into the aging of the population, the business cycle, and other factors. 

Section IV takes a deeper look at the trends in participation for several demographic groups: 

young men and women, older workers, prime-age women, prime-age men, and black men. 

Participation among young men and women has decreased steadily over the past decade, largely 

reflecting decreased participation among students and increased enrollment. Meanwhile, older 

workers have been participating at increasing rates over the last few decades. Black male 

participation has been on a troubling downward trend since the mid-1970s, falling about 10 

percentage points over that time, while a similar, albeit slightly more muted decline has been 

seen among all men. 

Section V lays out potential scenarios for the participation rate going forward assuming no policy 

changes are made. The aging of the population will likely continue to exert downward pressure 

on the overall participation rate. At the same time, continued recovery of the economy from the 

Great Recession would put upward pressure on participation. Assuming no change in policy we 

estimate that the recovery in the participation rate from cyclical factors will be roughly offset by

the aging effect, leading to a mostly unchanged participation rate on net. However, it is difficult 

to know how much recovery in the residual will occur as the economy improves, and participation 

may rise as those additional workers are pulled back into the labor force. Once the cyclical 

rebound fades, however, assuming no policy or other structural changes, the participation rate 

will resume its downward trend driven mainly by aging. This discussion lays out a baseline for 

what might happen in the absence of policy steps to promote participation in the workforce. 

Section VI lays out policy measures that can boost participation and in so doing, expand the 

economy’s long-run growth potential. First, immigration reform would raise the size of the labor 

force as well as boost participation rates and could largely offset further declines in participation 

due to aging. Second, adopting family-friendly workplace policies can boost female labor force 

participation. For instance, many states are currently considering adopting paid leave policies,

and evidence suggests that the availability of paid leave and other family-friendly policies could 

increase the participation of women by about 7 percentage points (Blau and Kahn, 2013). Third, 

the long-run decline in participation among men, especially minority men, is unsustainable, and
5

the Obama Administration is aggressively pursuing policies aimed at stemming these declines.

Finally, in the near-term, the recovery from the Great Recession remains incomplete, and steps 

to strengthen the economy and encourage best practices for hiring the long-term unemployed 

can still make a major difference. These types of steps are critical for combating the portion of 

the decline in the participation rate that reflects the unique severity of the Great Recession and 

the major challenges left in its wake. 
6

I. The Labor Force Participation Rate Over the Past 50 Years

The labor force participation rate is the share of the civilian population over age 15 that is either 

working or searching for work (i.e., unemployed). Increases in the participation rate reflect 

individuals joining the workforce, either by looking for a job or taking one, while decreases in the 

participation rate can reflect an increase in retirements, people taking time out of the labor force 

for a variety of reasons (e.g., to go to school, care for a young child, etc.), or unemployed 

individuals who become discouraged and stop looking for a job. Decreased labor force 

participation may or may not be desirable, depending on the reason. Achieving a labor force 

participation rate of 100 percent is not the goal, since many people who leave the labor force are 

engaged in important nonmarket activities, such as schooling, raising children, or retirement. 

However, discouraged workers dropping out of the labor force would be a cause for concern and 

would motivate enacting policies to return these workers to the labor force. In the long run, the 

growth rate of the labor force underpins the growth rate of employment, which, along with 

productivity growth, is a key determinant of the growth rate of potential GDP.

The labor force participation rate in the United States since 1948 has three key features, as shown 

in Figure 1. The first is the striking inverted U-shaped trend in the participation rate: while just 

under 59 percent of civilian adults were in the labor force in 1963, this rose to approximately 66

percent in 1990 and peaked at 67 percent in 2000, but since 2000 the participation rate has 

generally fallen. As discussed in more detail below, this inverted U-shaped pattern can be more 

fully understood by looking at the separate trends for men and women. In general, the left (rising) 

side of the inverted U-shape reflects a historic surge of women into the labor force in the second 

half of the twentieth century, as well as the baby boomers reaching their prime working years. 

The right (falling) side reflects the plateauing of female participation, combined with the 

retirement of the baby-boom generation and the massive shock of the Great Recession.

58

60

62

64

66

68

70

1948 1958 1968 1978 1988 1998 2008

Figure 1: Total Civilian Labor Force Participation Rate

Percent

2014:Q2
7

The second key feature of the participation rate visible in Figure 1 is the procyclical pattern. In 

other words, participation rises during an expansion and falls during a recession. For example, 

these cyclical patterns, which occur with a lag, are clearly visible in the 1990 recession. During 

the strong labor force growth of the 1970s and 1980s, the procyclical movements do not produce 

distinct declines in the participation rate, but instead produce temporary plateaus, as the cyclical 

decline was mitigated by an upward underlying trend that resumed after the recovery was under 

way. Similarly, because the participation rate was on a downward trajectory during the first half 

of the 2000s, the cyclical decline during and after the 2007 recession combined with the 

underlying demographic trend to produce a particularly sharp drop. 

The third feature of the participation rate visible in Figure 1 is that the series exhibits considerable 

volatility around the long-term trends and cyclical movements. For example, during the recovery 

from the 1990 recession, the participation rate rose more than half a percentage point in the first 

half of 1992, only to subsequently fall by nearly the same amount. Much of this volatility stems 

from factors like the normal statistical noise that exists in any data series based on a limited 

sample and seasonally adjusted. 

As noted earlier, the inverted U-shaped trend in the overall participation rate can be better 

understood by considering the separate trends for male and female labor force participation. 

The male labor force participation rate has been falling since data collection began in 1948, as 

shown in Figure 2. Some of this decline reflects the increasing longevity of men over time and the 

resulting lengthening of their retirement. However, the rate of participation among prime-age 

men has also fallen over this time period, although to a lesser extent. 

In contrast, women’s labor force participation rose through much of that period, with a surge

from the mid-1960s through the 1990s. This profound shift in America’s economy and society 

reflected changes in social norms and access to education, including the legalization of oral 

contraceptives in the early 1960s, reductions in discrimination through legislation like Title VII of 

the Equal Rights Act and Title IX of the Educational Amendments to the Equal Rights Act, and 

developments in labor-saving household technology. The rise in women’s labor force 

participation exceeds the decline in men’s participation so that overall adult participation

increased during this interval.
8

Since 2000, the participation rates of men and women have both declined, although the rate of 

decline has been slower for women than for men. Participation rates have long been influenced 

by an aging population, however its impact became more pronounced starting in 2008 when the 

first cohort of baby boomers (those born in 1946) turned 62 and became eligible for early Social 

Security benefits. Starting in 2008, the share of the working age population over age 55 began to 

rise rapidly (Figure 3). Also, while the share of the population over age 65 has risen gradually 

since 1960 due to increased longevity, the increase accelerated in 2008 and is now rising faster 

than at any point since 1960. This sharp and predictable retirement of a large cohort coincided 

with the start of the Great Recession, making it difficult to separate the effects of the economy 

and normal demographic patterns on participation. Even though these baby boomers are more 

likely to work at older ages than their predecessors, the employment rates at older ages are still 

much lower than for younger workers.

30

40

50

60

70

80

90

1948 1958 1968 1978 1988 1998 2008

Figure 2: Male and Female Labor Force 

Percent Participation Rates

2014:Q2

Men

Women

10

15

20

25

30

35

40

1960 1970 1980 1990 2000 2010

Figure 3: Share of the Adult Population Over 

Percent Age 55

2013

55+

65+
9

II. Literature on the Labor Force Participation Rate in the Great 

Recession and Subsequent Recovery

There has been considerable research on the decline in the participation rate in recent years.

Table 1 lists the findings of several recent studies of the participation rate, showing how much of 

the decline each study attributes to long-term trends and cyclical factors. The time periods 

covered by each of these analyses vary, so the results are not always directly comparable—

although we provide CEA’s estimates to match the time periods of each of the studies. While 

some studies estimate either the trend or cycle component and then infer the other component 

indirectly, this table only shows direct estimates of these components. 

Despite differences in data and methods, several broad conclusions are common across the 

literature. Taken together, the aging of the population and the business cycle account for the 

vast majority of the decline in the participation rate since 2007 in all of the studies. While there 

are some differences in the share attributed to the aging of the population, the median estimate 

is that half of the decline is due to aging.

Trend Cycle Trend Cycle

CEA (2014) 2007:Q4 - 2014:Q2 52% 16% -- --

Beginning in 2007

Erceg & Levin (2013) 2007 - 2012 25% 60% 55% 35%

Shierholz (2012) 2007 - 2011 30% -- 50% 52%

Van Zandweghe (2012) 2007 - 2011 42% 58% 50% 52%

CBO (2014) 2007:Q4 - 2013:Q4 50% 33% 49% 20%

Fallick & Pingle (2013) 2007:Q4 - 2013:Q2 75% 16% 55% 29%

Aaronson et al. (2006) 2007 - 2013 80% -- 49% 20%

Kudlyak (2013) 2007 - 2012 80% 20% 55% 35%

Other Time Periods

Aaronson, Davis & Hu (2012) 2000 - 2011 40% -- 46% 38%

Fujita (2014) 2000:Q1 - 2013:Q4 65% 30% 42% 16%

CEA (2014) 2011:Q1 - 2014:Q2 69% -34% -- --

Table 1: Comparison of Participation Rate Estimates

Time Period

Shares of the Total Decline

Note: This table only includes direct estimates of the trend and cyclical components. CEA-equivalent decompositions 

for time periods listed on an annual basis are computed from Q4 to Q4 of the corresponding years. 

CEA Estimated Shares of Total 

Decline Over Same Time Period

CEA estimates that the long-term trend of an aging population explains just over half of the 

decline in participation since the end of 2007, putting CEA’s estimate in the middle of the range 

of estimates. Across most of these studies, the effects of an aging population typically account 

for a third to half of the decline, and the other longer-term trends account for a varying 

proportion of the decline. The estimates of the trend component from Aaronson et al. (2006), 

Kudlyak (2013), and Fallick and Pingle (2013) also include notable effects from other long-term 

trends, and so are correspondingly about 25 to 30 percentage points larger than estimates of the 

aging effects alone. 
10

In analyzing the effects of the business cycle on participation, the literature is split between a 

group of analyses that attribute about 25 percent of the decline to the Great Recession and its 

aftermath, and a few studies that estimate the cyclical component to be at least a majority of the 

decline. Each paper takes a different approach to estimating the cyclical portion. Both CEA’s 

estimate and Van Zandweghe (2012) are based on time series evidence but arrive at different 

conclusions. 

While some studies recognize an unexplained or residual component of the decline in 

participation, others hold that the full decline in participation can be explained by a combination 

of longer-term trends and cyclical effects. CEA estimates that about one quarter of the decline is 

not explained by either trend or historical estimates of cyclical movements, but there is some 

evidence that this residual may be related to the current recession through its unusually high 

long-term unemployment rate. The Congressional Budget Office (CBO) estimates a slightly 

smaller residual component and suggests that it may be due to elevated Social Security Disability 

Insurance (SSDI) rolls. Other possible explanations for the residual component include increased 

enrollment in school (Fujita 2014).
11

III. Explaining the Decline in the Participation Rate Since 2007

CEA’s analysis of the decline in the labor force participation rate since the end of 2007 is based 

on a variety of methodologies, including direct measurement of the aging trend, panel data, and 

time series analysis—all of which produce generally similar results in disentangling three effects: 

the pure aging effect, the standard cyclical effect, and a residual which picks up a combination of 

pre-existing trends unrelated to aging and factors unique to the Great Recession.

The Aging of the Workforce

The largest single factor in the decline of the participation rate since the end of 2007 is the aging 

of the workforce—something that was predicted well before the Great Recession. Every year 

since 2000, the Economic Report of the President has mentioned the post-2008 decline in the 

labor force participation rate as a factor slowing growth of potential real GDP. In 2004, the first 

year in which their ten-year forecast included the 2007-2013 period, CBO predicted that 

participation would fall by 1 percentage point between 2007 and 2013 due to aging. Also, in 2006 

researchers from the Federal Reserve predicted, based solely on aging and other trends, that the 

participation rate would fall to 63.3 percent in 2013—the exact value to which it fell (Aaronson 

et al. 2006).

Labor Force Participation over the Life Cycle

Labor force participation varies considerably over the course of a person’s life, rising rapidly at 

early ages before flattening out through the working years and falling as workers retire. In order 

to understand how an aging population will affect overall labor force participation, it is helpful to 

look at how the participation rates of different age groups have changed over time. Figures 4 and 

5 show the participation rates for men and women over the course of their lives for various birth 

cohorts. For both men and women, participation rises rapidly throughout the 20s as individuals 

leave full-time schooling and enter the workforce, then flattens out at a high level during prime 

working-age years, before falling to low levels of participation again as individuals enter their 60s. 

In the decade leading up to age 65, participation for both men and women falls by at least 40 

percentage points, and continues to fall by nearly 20 percentage points over the subsequent 

decade of the life cycle. 
12

The pattern of participation among prime-age women has shifted dramatically over the past half 

century. Women born in 1933 decreased their participation over the course of their 20s—the 

ages of maternity and childrearing--before slowly increasing their participation in their 30s and 

40s. The cohort of women born ten years later, in 1943, had much the same pattern of decreasing 

and increasing participation, but over the course of their lifetime participated at a rate about five 

to ten percentage points higher. In subsequent cohorts, women participated at much higher rates 

in their 20s and 30s, while participation rose only slightly in older years. Most of this increase is 

likely due to women in more recent cohorts returning to the labor force more quickly after 

bearing children than previous generations.

0

10

20

30

40

50

60

70

80

90

100

21 26 31 36 41 46 51 56 61 66 71 76

1933

1943

1953

1963

Figure 4: Male Labor Force Participation by Age & 

Birth Year Percent

Birth Year

0

10

20

30

40

50

60

70

80

90

100

21 26 31 36 41 46 51 56 61 66 71 76

1933

1943

1953

1963

Figure 5: Female Labor Force Participation

by Age & Birth Year Percent

Birth Year
13

Male participation has shown less variation from cohort to cohort, but there are two notable 

changes. The first is that among prime working-age men, each successive cohort has participated 

slightly less on average, demonstrating that the long-run decline in male participation has been 

partially driven by the behavior of prime-age men. The second is that more recent cohorts of 

older men have increased their participation. Men born in 1943 participated at age 65 at a rate 

about five to ten percentage points higher than men born in 1933. It is also worth noting that 

men born in 1943 had a life expectancy roughly 5 years longer than those born in 1933. Despite 

this increase in participation relative to previous cohorts, there is still a very large drop off in 

participation at older ages, and so an aging population will, all else equal, reduce the economywide labor force participation rate. The substantial aging of the population that has begun to 

occur and will continue to unfold for years to come is discussed in the next subsection.

Aging and the Retirement of the Baby Boomers

In the immediate aftermath of World War II, the U.S. birth rate spiked, as couples who were 

reunited following the war were able to settle down and have children, while others took 

advantage of the relative sense of peace and prosperity that emerged in the 1950s and 1960s to 

expand their families (Figure 6). The birth rate remained elevated into the mid-1960s before 

dropping sharply, and it has not since returned to anywhere close to what it was during the baby 

boom. Consequently, the baby boomers, who are currently between the ages of 49 and 67, are 

more numerous than any previous or subsequent cohort of Americans.

As Figure 7 shows, the distribution of the population by age becomes more heavily tilted toward

older ages as the baby boomers age. The first baby boomers reached retirement age in 2008, and 

over the next decade and a half, increasingly-large cohorts of baby boomers will follow suit. In 

2009, 16 percent of the adult population was at or above the retirement age; the Social Security 

Administration projects this fraction will grow to 25 percent by 2029, increasing by half over two 

decades. 

12

16

20

24

28

32

1910 1930 1950 1970 1990 2010

Figure 6: U.S. Birth Rate

2013

Births per thousand people
14

The retirement of the baby boom generation is particularly consequential for the participation 

rate, because the labor force participation rate falls sharply as workers grow older. Both men and 

women decrease their participation by at least 40 percentage points in the decade leading up to 

age 65, and participate at even lower rates afterwards. Because older individuals participate in 

the labor force at lower rates than other age groups, the shift in the age structure from aging 

baby boomers is responsible for a substantial portion of the decline in the participation rate. 

Although the participation rate of those over age 60 has risen in recent years and the 

participation of older baby boomers is continuing to rise relative to previous cohorts, this effect 

is relatively small compared to the typical decline as people age and so does little to mitigate the 

substantial negative effect that aging of the population has on the overall participation rate. The

participation rate for the male cohort born in 1943 fell more than 40 percentage points as they 

aged from 55 to 65, even though their participation was about 10 percentage points higher than 

those in the previous cohort at age 65. The pattern for women is largely similar, as the increase 

in participation at older ages from cohort-to-cohort is much smaller than the drop in participation 

over the lifecycle. 

Modeling the Effect of Aging on the Participation Rate

Two longer-term trends contribute to movements in the participation rate: first, the effect of an 

aging population, and second, ongoing trends in participation within age groups that are 

unrelated to aging. The magnitude of the trend can be estimated in several ways, each of which 

captures long-term movements in participation differently, with some capturing only the effects 

of aging and others including both aging and non-aging-related trends. We employ three 

different methods of modeling the participation rate trend and find that they provide similar 

estimates, with approximately half of the decline in participation since 2007 attributable to aging 

and a negligible fraction of the decline attributable to trends within age groups. 

0

1

2

3

4

5

20 25 30 35 40 45 50 55 60 65 70

2019 Population Projection 2013 Population

Estimates

Baby Boomers

Figure 7: U.S. Population Distribution By Age

Millions
15

Projecting Forward the 2007 Participation Rate Profile With Changing Age Structure

A straightforward way to estimate the effect of the aging of the population on labor force 

participation is to hold constant the participation rates by age in a given year, and observe how 

the participation rate evolves if only the age distribution of the population is allowed to vary. For 

example, this allows computing the effect of an increasing share of 65 year olds, under the 

assumption that 65 year olds retire in the future at the same rate that they did in 2007. This 

approach is implemented using the age-based participation profile as of 2007. The advantage of 

fixing participation rates in 2007 is that it predated the recession, and thus avoids confounding

the analysis by introducing the further effect of a sharp cyclical movement. However, the results 

are essentially the same if we instead hold the current (2014) participation rates by age group 

constant, as discussed later.

To estimate the effect of aging, we constructed data on participation rates for single-year age 

categories by gender, combining unpublished participation rates for those over 55 with

participation for single-year age categories below age 55 obtained by interpolating from the 

published rates for ten-year age categories. For individuals above age 55, data on annual 

participation by single-year age category were provided to us by the Bureau of Labor Statistics 

(BLS), using unperturbed data from the Current Population Survey (CPS). These data are less noisy 

than estimates from the CPS microdata but are not very different on average (Appendix Figures 

1 and 2). 

Figure 8 shows the counterfactual participation rate between 2007 and 2014, under the 

assumption that only the age distribution of the population changed over this time. Over this 

period, the counterfactual participation rate fell by 1.6 percentage points, accounting for slightly 

more than half of the actual observed decline. 1 Thus, the aging of the population alone can 

explain about half the decline in the participation rate since late 2007. 

 

1

Fixing the participation rate in 2013 and projecting it backwards using the actual demographic data yields a very 

similar estimate that 1.4 percentage point of the decline in the participation rate was due to aging. This estimate is 

slightly lower than the estimate using 2007 data because the Great Recession narrowed the gap in participation 

between prime-age and older workers. 
16

The aging portion of the trend has been more rapidly declining since 2011. Between the end of 

2007 and the end of 2010, the counterfactual participation rate fell only 0.6 percentage point, 

but it subsequently fell 1.0 percentage points from the beginning of 2011 through the first 

quarter of 2014—explaining the majority of the reduction in participation over that period. In 

2011, the first baby boomers reached age 65, which is the point of steepest decline in 

participation over the lifecycle. Baby boomers will continue to turn 65 until 2029 and the aging 

portion of the trend in the participation rate will correspondingly continue to decline, possibly at 

an even more rapid pace in the coming years. 

Casselman (2014) conducts a similar calculation using the publicly available data on participation 

published by BLS and concludes that aging is responsible for about a third of the decline. 

However, the seasonally adjusted statistics published by BLS categorize all adults over 55 into the 

same group, which obscures significant differences in participation at older ages. Participation 

falls by about 40 percentage points between age 55 and 65, so compositional shifts within the 

55+ age group can have a large effect on the overall participation rate; using the coarse agebracket data will not capture this effect. In contrast, the analysis here enables an estimate of a 

more detailed, year-by-year aging effect. 

Time Series Trend

Econometric analysis of time series data can also be used to estimate the long-run trend in the 

participation rate. Unlike the previous approach that was designed to isolate the effect of 

population aging, this approach identifies a single unified trend in the participation time series 

and so does not separate aging and non-aging forces driving the trend. Although the theory of 

time series trend estimation is well-developed, this application is challenging because the deep 

recession and prolonged recovery serve to conflate the cyclical and trend behavior beginning in 

late 2007. For this reason, the time series trend used here is estimated using a standard time 

series smoother (biweighted kernel), modified to be cyclically adjusted using a partially linear

semiparametric model; more detail is given in Appendix A. The resulting estimate shows that the 

62

63

64

65

66

67

2007 2008 2009 2010 2011 2012 2013 2014

Figure 8: Effect of Aging on Labor Force Participation

Percent

Aging Effects

Actual
17

trend component of the participation rate peaked in the late 1990s, and has been falling since, 

declining at a more rapid rate in recent years (Appendix Figure 3). According to the time series 

estimate of the trend, the long-run trend in the participation time series fell by 1.6 percentage 

points between the end of 2007 and the second quarter of 2014.

The 1.6 percentage point decline in the time series trend happens to equal the estimated decline 

in the previous section that focused on the aging-based trend. Since the time series model

includes trends within age groups while the aging-based trend does not, by itself this estimate 

would suggest that non-aging trends within age groups have had little net effect on participation. 

However, in the presence of a break in the series—which we know occurred around 2008 with 

the retirement boom—time series methods will tend to understate the trend. As a result, the 

residual between the time series and aging methods will tend to understate the consequences 

of pre-2008 trends unrelated to aging.

Structural Micro-Data Models of Participation

Another approach to estimating the trend in participation involves estimating a structural model 

of participation decisions using individual-level data. This approach was originally developed by 

Aaronson et al. (2006), who extend the model with age and cohort effects to include covariates 

representing human capital, the ability to finance nonparticipation spells, and family structure. 

By including a measure of life expectancy, for example, the authors control for longer lives

requiring greater retirement savings, which in turn would increase participation. In contrast to 

the previous two methods, this approach attempts to ascertain the separate effect of various 

factors driving differencesin participation across individuals and across cohorts. Aaronson et al.’s 

projections, which assumed full employment, were consistent with a decline in the participation 

rate from 2007:Q4 through 2014:Q2 of 2.5 percentage points. If this is correct, then most of the 

decline in participation since the end of 2007 would have been foreseeable—and in fact was 

almost precisely foreseen—based on pre-existing trendsin aging and other demographic factors.

Fallick and Pingle (2007) propose a more detailed structural model of participation. They use 

several covariates to estimate the trend in participation, including educational attainment, life 

expectancy, marriage and fertility variables for women, minimum wages and school enrollment 

measures for teenagers, and Social Security rule changes. In a recent update of their model using

data collected in the years following publication, they estimate that the participation rate trend 

decreased 2.0 percentage points between the end of 2007 and the second quarter of 2014. Fallick 

and Pingle’s model includes some portion of what we estimate as cyclical rather than trend

components, however, because several of the covariates they include exhibit cyclical variation

(in particular the return to education and the level of private wealth). To the extent that their 

estimated trend component is based on variables that fluctuate cyclically, their estimate arguably 

overstates the portion of the decline attributable to long-term trends.
18

Standard Business Cycle Effects

The decline in the labor force participation rate since 2007 is partially due to the standard effects 

associated with an economy operating below its full potential. However, because the shock of 

the Great Recession was so large, the standard cyclical effects on participation were also quite 

large relative to previous cycles. When the economy is booming, the labor force participation 

rate rises and often exceeds the predicted long-term trend, and during downturns the 

participation rate falls below trend. Economic contractions historically result in both greater 

unemployment and lowerlabor force participation, as nonparticipants become less likely to enter 

the labor force and the unemployed (who always exhibit a higher tendency to exit the labor force) 

become more numerous relative to the employed (Elsby, Hobijn, and Sahin 2013). While 

movements in the participation rate over decades are driven largely by the long-term trends, in 

the short-and medium-term the variation in the participation rate is driven by these cyclical 

factors.

Figure 9 shows the cyclicality of the participation rate by comparing the detrended participation 

rate and the detrended unemployment gap, defined as the difference between the actual 

unemployment rate and the natural unemployment rate (to simplify comparison, the 

unemployment gap is plotted inverted)2. For example, in the expansion of the 1990s, the 

detrended participation rate rose, and during the Great Recession, the detrended participation 

rate declined. Visual inspection suggests that movements in the participation rate lag, by perhaps 

a year or so, movements in the unemployment rate, a phenomenon discussed in more detail 

below. The cyclical pattern of the participation rate is also evident when looking at state-level 

data, where the states with the largest increases in unemployment tended to have larger declines 

in labor force participation (Figure 10). Roughly speaking, for every percentage point increase in 

 

2 The trend component of the participation rate was constructed using a semiparametric procedure described in 

Appendix A. Quarterly averages of monthly participation were used to reduce the noise in the series. 

-6

-4

-2

0

2

4

6

1960 1970 1980 1990 2000 2010

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Figure 9: Detrended Participation Rate and 

(Inverted) Unemployment Gap

Participation Rate

(left axis)

Unemployment Gap

(right axis)

Percentage Points Percentage Points
19

the unemployment rate, the participation rate falls by about 0.2 percentage point, a pattern that 

has roughly held over the last four recessions (Figure 11). 

While it is possible to estimate a cyclical effect on participation using microdata, the studies in 

the literature that have tried this approach have produced cyclical estimates that do not comport 

with the dynamics evident in the total participation time series (Bengali, Daly, and Valletta 2013; 

Erceg and Levin 2013). The cyclicality of participation varies significantly across the population, 

with participation among younger workers being highly cyclical and participation among primeage workers hardly responding to the business cycle. Alternatively, one can estimate the cyclical 

component indirectly by subtracting the trend component from the observed participation rate 

and treating this entire residual as the cyclical component (Van Zandweghe 2012). However, this 

-6

-5

-4

-3

-2

-1

0

0 2 4 6 8 10

Percentage Point Change in Unemployment (2007-2010)

NV CA

FL

IL

NY

TX

Figure 10: Labor Force Participation and 

Unemployment by State

Percentage Point Change in LFPR (2007-2012)

Trend Line

4.7

2.1 1.6

4.5

-0.6 -0.6 -0.4

-1.0

-1.0

-0.8

-0.6

-0.4

-0.2

0 0.0

1

2

3

4

5

1979-1983 1990-1993 2001-2003 2007-2010

Rise in Unemployment

Cyclical Effect on Participation

Figure 11: Cyclical Declines In Labor Force Participation

Percentage Points Percentage Points
20

approach does not distinguish between the typical cyclical response from a severe recession and 

the decline in participation coming from challenges unique to the Great Recession and its 

aftermath.

In this report, we estimate the cyclical component using econometric analysis of time series data, 

where the cycle is defined by the unemployment gap. We consider two alternative distributed 

lag specifications, one in which the detrended overall participation rate is regressed on the level 

of the detrended unemployment gap and its lags, and a second in which the quarterly first 

difference of the detrended overall participation rate is regressed on the four-quarter difference 

of the detrended unemployment gap and its lags. The regression results for the two methods are 

summarized in Tables 2 and 3, respectively.

VARIABLES (1) (2) (3) (4)

Unemp. Gap -0.0142 -0.120** 0.165 0.140

(0.0615) (0.0530) (0.121) (0.126)

Unemp. Gap (t-1) -0.216** -0.191*

(0.0957) (0.102)

Unemp. Gap (t-2) 0.0184 0.0298

(0.0607) (0.0649)

Unemp. Gap (t-3) -3.71e-05 -0.110

(0.0889) (0.0798)

Unemp. Gap (t-4) -0.140*** -0.138 0.0451

(0.0428) (0.0853) (0.0686)

Unemp. Gap (t-5) -0.0773

(0.0729)

Unemp. Gap (t-6) 0.0652

(0.0622)

Unemp. Gap (t-7) -0.0858

(0.0884)

Unemp. Gap (t-8) -0.0462 -0.00831

(0.0378) (0.0854)

Constant 0.0840 0.0619 0.0761 0.0821

(0.0870) (0.0902) (0.0863) (0.0878)

Observations 210 218 214 210

F-test of Included Coefficients 8.530 5.143 6.075 4.006

Prob > F 2.29e-05 0.0243 2.84e-05 9.99e-05

F-test of Restricted Coefficients 1.919 3.551 2.248

Prob > F 0.0793 0.000719 0.0652

Predicted Cyclical Decline (2007:Q4 - 

2014:Q2) -0.477 -0.113 -0.465 -0.486

Note: Regressions are estimated using data from 1960:Q1 to 2014:Q2. Newey-West 

standard errors using a maximum lag of 12 are reported in parentheses. 

Participation rate and unemployment gap are detrended using the procedure 

described in Appendix A. F-tests of restricted coefficients are the joint significance 

test of the omitted coefficients all equaling zero in a regression with all eight lags 

(i.e. in regression (3),it tests the hypothesis that the coefficients on the 

unemployment gap in t-5 through t-8 are all equal to zero). 

*** p<0.01, ** p<0.05, * p<0.1

Detrended Participation Rate, 16+

Table 2: Levels on Levels Regressions using the 

Unemployment Gap
21

VARIABLES (5) (6) (7) (8)

Year-over-Year Difference of Unemp. Gap -0.0340*** -0.0355*** 0.0105 0.0259

(0.00891) (0.00783) (0.0239) (0.0292)

Year-over-Year Difference of Unemp. Gap (t-1) -0.0689 -0.107*

(0.0500) (0.0551)

Year-over-Year Difference of Unemp. Gap (t-2) -0.00142 0.0459

(0.0578) (0.0573)

Year-over-Year Difference of Unemp. Gap (t-3) 0.0545 -0.00622

(0.0468) (0.0536)

Year-over-Year Difference of Unemp. Gap (t-4) -0.0187** -0.0473** 0.0630

(0.00740) (0.0204) (0.0514)

Year-over-Year Difference of Unemp. Gap (t-5) -0.131**

(0.0637)

Year-over-Year Difference of Unemp. Gap (t-6) 0.0899*

(0.0531)

Year-over-Year Difference of Unemp. Gap (t-7) -0.0491

(0.0508)

Year-over-Year Difference of Unemp. Gap (t-8) -0.00563 0.0179

(0.00751) (0.0393)

Constant -0.00141 -0.00289 -0.00371 -0.00162

(0.00731) (0.00836) (0.00807) (0.00754)

Observations 210 218 214 210

F-test of Included Coefficients 11.74 20.52 9.623 6.686

Prob > F 3.90e-07 9.74e-06 2.81e-08 2.34e-08

F-test of Restricted Coefficients 1.872 2.590 2.422

Prob > F 0.0873 0.0103 0.0496

Predicted Cyclical Decline (2007:Q4 - 2014:Q2) -0.507 -0.303 -0.544 -0.474

Note: Regressions are estimated using data from 1960:Q1 to 2014:Q2. Newey-West standard errors 

using a maximum lag of 12 are reported in parentheses. Participation rate and unemployment gap 

are detrended using the procedure described in Appendix A. F-tests of restricted coefficients are the 

joint significance test of the omitted coefficients all equaling zero in a regression with all eight lags 

(i.e. in regression (3),it tests the hypothesis that the coefficients on the unemployment gap in t-5 

through t-8 are all equal to zero). 

*** p<0.01, ** p<0.05, * p<0.1

Quarter-to-Quarter Difference of Detrended 

Participation Rate, 16+

Table 3: Differences on Differences Regressions using the 

Unemployment Gap

Several results are evident in Tables 2 and 3. First, in the specifications with the full set of eight 

lags, many of the coefficients are individually statistically insignificant. However, restricting the 

lag structure to allow only the contemporaneous unemployment rate to enter (regressions (2) 

and (6)) is rejected at the 5 percent significance level. But once lags are introduced, some 

restrictions are consistent with the data: one of the specifications with shorter lags (3) and both 

specifications with the contemporaneous, fourth, and eighth lag ((1) and (5)) are not rejected at 

the 5 percent level. Moreover, the predicted values from regressions (1), (4), (5), and (8) are 

largely the same (Appendix Figure 4). Thus, the predicted results are largely robust across the 

non-rejected specifications.
22

An important distinction between the first-difference and levels specification is that the levels 

specification imposes the restriction that the long-term forecast of the labor force participation 

rate reverts to its long-term trend, whereas the differences specification permits a persistent gap 

between the long-term trend line and the participation rate (so that in effect the trend shifts up 

or down to match permanent shifts in the level of the participation rate). In the current situation, 

this distinction corresponds to whether the gap between the labor force participation rate and 

its cyclical component (the residual) will entirely disappear, or whether that gap might persist 

because of the labor market disruptions of the Great Recession. We view this as an empirical 

question, and to avoid imposing the additional mean-reverting structure on the cyclical 

component, we adopt the first-difference specification. Further, to reduce estimation noise we 

adopt the restricted-lag specification; this reasoning leads us to choose (5) as our preferred 

specification. Note, however, that the decomposition results are not particularly sensitive to 

these choices because varying them yields similar estimates of the cyclical components.

The predicted path for the participation rate, combining our estimate of the time series trend 

with our preferred specification (regression (5) in Table 3), is shown below in Figure 12. We 

estimate that between 2007 and 2014:Q2, business cycle effects alone explain 0.5 percentage 

point (about one sixth) of the total fall in the participation rate. 

With the improvement of the labor market, the usual cyclical drag on participation due to 

elevated unemployment is declining. We estimate that, as of 2011, the participation rate was 0.9 

percentage point lower because of cyclical factors. By 2014:Q2, however, the cyclical portion had 

shrunk to 0.5 percentage point because the unemployment rate has fallen. The drop in the 

cyclical contribution corresponds to nearly 1 million workers returning to the labor force because 

of cyclical reasons since 2011.

3

 

3 Due to other simultaneous flows of workers into and out of the labor force, this return of workers to the labor 

force can be difficult to identify in the aggregated data.

62

63

64

65

66

67

68

1980 1990 2000 2010

Figure 12: Estimated Trend Plus Cyclical Component of 

the Labor Force Participation Rate, 1980 - 2014

2014:Q2

Percent

Actual

Predicted Cyclical Path 

(Including Trend)
23

Other Effects on Participation: Non-Aging Trends and Factors Unique to the 

Great Recession

Most of the decline in participation since the end of 2007 is accounted for by the aging of the 

population and the standard cyclical effects associated with the elevated unemployment rate. 

The remaining reduction in the participation rate—a 1.0 percentage point reduction in the 

participation rate since the end of 2007—is not explained by either of these two previously 

discussed factors.

This residual is a relatively recent phenomenon in this recovery. Up until the beginning of 2012 

the participation rate was generally slightly higher than would have been predicted based on the 

aging trend and the standard business cycle effects. But in the last two years, the participation 

rate has continued to fall at about the same rate even though the unemployment rate has been 

declining rapidly. This means that the standard cyclical effect has been giving way to a growing 

unexplained residual (Figure 13).

The literature has put forward several possible explanations for this residual component which 

fall mainly into two categories: long-term trends within age groups and factors unique to the 

Great Recession. We cannot precisely break down these two factors. At least some of the residual 

appears to be these pre-existing trends, many of which are discussed in the next section about 

different demographic groups, although some of these pre-existing trends are partially offsetting 

(e.g., prime-age male labor force participation was already falling while older workers were 

seeing a rising participation rate).

The fact that the residual only emerged later in the recovery suggests that much of it might be 

the result of factors specific to the Great Recession and its aftermath. We focus here on testing 

three unique factors of the Great Recession that may be linked to the residual component, but 

other forces may also explain the residual decline. 
24

Significantly Elevated Long-Term Unemployment

The Great Recession has led to an unprecedented increase in long-term unemployment, with the 

number of individuals unemployed for 27 weeks or more rising from 1.3 million at the end of 

2007 to more than 6 million at the end of 2010. Even though the rate of short-term 

unemployment (26 weeks or less) has since returned to its 2001-07 average, the long-term 

unemployment rate remains more than double what it averaged during the previous expansion.

The emergence of a large residual in our participation rate model coincides with a sustained 

elevation of the long-term unemployment rate, suggesting that these two phenomena could be 

different manifestations of the same underlying challenge, or that one is causing the other. To 

test the plausibility of this hypothesis, we look at the historical relationship between the residual 

(unexplained component) and the extent of long-term unemployment. Specifically, we ran 

several regressions using the detrended mean duration of unemployment as a regressor in 

addition to the detrended unemployment gap that was used in the standard cyclical estimates 

above.

4 As Tables 4 (levels on levels) and 5 (differences on differences) show, the mean duration 

of unemployment was highly statistically significant in the differences specification but only 

marginally significant in the levels specification. These tables also show the effects of disability 

insurance and school enrollments, which will be discussed below.

 

4 The Current Population Survey was changed at the beginning of 2011 to permit respondents to report longer 

durations of unemployment. This change may have led to a break in the level of the series, which might influence 

our regressions in levels. The change is not likely to affect our regression in differences, which is where we find 

that mean duration is significant. Using the median duration of unemployment (which was not affected by the 

change), we find that unemployment duration is significant in the levels specifications, but not the differences 

specifications, providing further evidence that long-term unemployment accounts for the residual component. 
25

VARIABLES (1) (2) (3) (4) (5) (6)

Unemployment Gap -0.0142 -0.0712 -0.0130 -0.0154 -0.00373 0.0153

(0.0723) (0.0805) (0.0574) (0.0597) (0.0619) (0.0640)

Unemp. Gap (t-4) -0.140*** -0.169** -0.142*** -0.137*** -0.137*** -0.135***

(0.0525) (0.0654) (0.0417) (0.0455) (0.0431) (0.0404)

Unemp. Gap (t-8) -0.0462 0.0249 -0.0512 -0.0499 -0.0332 -0.0453

(0.0609) (0.0569) (0.0425) (0.0453) (0.0402) (0.0468)

Mean Duration of Unemp. -8.35e-06 0.0400

(0.0224) (0.0279)

Mean Duration of Unemp. (t-4) -0.0356

(0.0296)

Mean Duration of Unemp. (t-8) -0.0119

(0.0242)

SSDI Claims 0.000933** 0.000615

(0.000468) (0.00115)

SSDI Claims (t-4) 0.000272

(0.00188)

SSDI Claims (t-8) 0.000121

(0.00124)

Enrollment Rate -22.92 -41.04

(19.18) (32.51)

Enrollment Rate (t-4) 5.962

(27.62)

Enrollment Rate (t-8) 26.26

(39.03)

Constant 0.0840 0.0842 0.0860 0.0859 0.0997 0.0892

(0.0892) (0.0900) (0.0881) (0.0881) (0.0799) (0.0792)

Observations 210 210 204 204 210 210

F-test 1.38e-07 2.624 3.974 0.157 1.429 0.379

Prob > F 1.000 0.0750 0.0476 0.855 0.233 0.685

Table 4: Levels on Levels Regressions using Disability Claims, Duration of Unemployment, and 

Enrollment Rate

Detrended Participation Rate, 16+

*** p<0.01, ** p<0.05, * p<0.1

Note: Regressions are estimated using data from 1960:Q1 to 2014:Q2. Newey-West standard errors using a maximum lag of 12 are reported 

in parentheses. Participation rate and unemployment gap are detrended using the procedure described in Appendix A. F-tests are joint 

significance tests of the disability insurance, mean duration, and schooling variables. 
26

VARIABLES (7) (8) (9) (10) (11) (12)

Year-over-Year Difference of Unemp. Gap -0.0297*** -0.0330*** -0.0322*** -0.0321*** -0.0330*** -0.0328***

(0.00890) (0.00914) (0.00989) (0.00992) (0.00884) (0.00877)

Year-over-Year Difference of Unemp. Gap (t-4) -0.0130 0.00429 -0.0228** -0.0149 -0.0181** -0.0190**

(0.0103) (0.0146) (0.00943) (0.0111) (0.00724) (0.00737)

Year-over-Year Difference of Unemp. Gap (t-8) -0.00350 0.0151 -0.0117 -0.0170* -0.00481 -0.00569

(0.00776) (0.0114) (0.00916) (0.00901) (0.00786) (0.00744)

Year-over-Year Difference of Mean Duration -0.00387 -0.00406

(0.00436) (0.00534)

Year-over-Year Difference of Mean Duration (t-4) -0.0142***

(0.00524)

Year-over-Year Difference of Mean Duration (t-8) 0.00222

(0.00527)

Year-over-Year Difference of SSDI Claims 0.000546 -0.000610

(0.000614) (0.000874)

Year-over-Year Difference of SSDI Claims (t-4) 0.00120

(0.00117)

Year-over-Year Difference of SSDI Claims (t-8) -0.000719

(0.000756)

Year-over-Year Difference of Enrollment Rate -2.510 -2.910

(4.275) (4.702)

Year-over-Year Difference of Enrollment Rate (t-4) 2.876

(4.415)

Year-over-Year Difference of Enrollment Rate (t-8) 0.444

(3.391)

Constant -0.000523 0.00247 0.00154 -0.00225 -0.00124 -0.00147

(0.00743) (0.00676) (0.00658) (0.00698) (0.00732) (0.00719)

Observations 210 210 145 137 210 210

F-test 0.789 3.529 0.788 0.373 0.345 0.161

Prob > F 0.376 0.0159 0.376 0.773 0.558 0.923

Note: Regressions are estimated using data from 1960:Q1 to 2014:Q2. Newey-West standard errors using a maximum lag of 12 are reported in parentheses. 

Participation rate and unemployment gap are detrended using the procedure described in Appendix A. F-tests are joint significance tests of the disability 

insurance, mean duration, and schooling variables. 

*** p<0.01, ** p<0.05, * p<0.1

Quarter-to-Quarter Difference of Detrended Participation Rate, 16+

Table 5: Differences on Differences Regressions using Disability Claims, Duration of Unemployment, and 

Enrollment Rate

Using these regression results, we can predict the path of the participation rate given the large 

increase in the mean duration of unemployment since 2007. Estimating this new set of additional 

factors explains much of the previously unexplained decline in participation since 2009, as seen 

in Figure 14. This suggests that the unusually high rate of long-term unemployment may be 

exerting downward pressure on the participation rate—or that these two developments are 

manifestations of a common underlying cause.
27

One theory put forward to explain the rise in long-term unemployment during the most recent 

recession is that the long-term unemployed are less productive workers on average and will take 

longer to find new jobs (Cowen and Lemke 2011). However, the long-term unemployed appear 

similar to the short-term unemployed in many dimensions, including gender, educational 

attainment, and previous industry and occupation (Mitchell 2013; Krueger, Cramer, and Cho 

2014). Lower productivity among the long-term unemployed could be related to unobservable 

characteristics, but it is not clear whether the differences among unobservable traits are large 

enough to explain the rise in long-term unemployment. 

A more plausible explanation for the rise in long-term unemployment is that firms are screening 

out applicants based on duration of unemployment. Long-term unemployed workers are about 

half as likely to get a callback for an interview as equally-qualified short-term unemployed 

workers (Kroft, Lange, and Notowidigdo 2013). Short-term unemployed applicants with no 

relevant work experience for a particular job are more likely to be interviewed for the job than 

long-term applicants with relevant work experience (Ghayad 2013). Workers who are not lucky 

enough to find a job within the first few months of unemployment will face disadvantages in the 

hiring process, further lengthening their unemployment spells on average and raising the rate of 

long-term unemployment. 

Recent research finds that the period of elevated long-term unemployment that we are 

experiencing is consistent with historical patterns after adjusting for the deeper decline and 

slower recovery from the latest recession (Kroft, Lange, Notowidigdo, and Katz 2014). Consistent 

with the fact that the long-term unemployed are observably similar to the short-term 

unemployed, Kroft et al. find that demographics, occupations, industries, regions or reasons for 

unemployment do little to explain the variation in unemployment spell length. Instead, they 

attribute a large share of the rise in long-term unemployment to negative duration dependence, 

meaning that as a spell of unemployment persists, it becomes more likely to persist even longer. 

They further find that changes the flows between unemployment and non-participation in the 

62.5

63.0

63.5

64.0

64.5

65.0

65.5

66.0

66.5

67.0

2009 2010 2011 2012 2013 2014

Figure 14: Labor Force Participation Decomposition

Including Mean Duration of Unemployment

Percent

Remaining Residual

Other Effects

Mean Duration

Actual
28

labor force account for another large share. They allow that the long-term unemployed may be 

unobservably less productive, but their two favored explanations leave little unexplained.

Disability Insurance

Since the end of 2007, the number of disabled workers receiving benefits through the Social 

Security Disability Insurance (SSDI) program has increased by nearly 2 million. Some have argued 

that changes in Disability Insurance are causing an unusual decline in the participation rate. 

However, we find little evidence that the increase in Disability Insurance beneficiaries explains 

the residual component of the decline in participation. We estimated a series of regressions using 

the detrended time series of Disability Insurance disabled worker beneficiaries as an additional 

explanatory variable. Tests of the coefficients were mostly statistically insignificant and only 

barely significant in one specification (regression (3) in Table 4). Looking at the predicted values 

from these regressions, the estimated path of participation accounting for Disability Insurance 

did not fit the historical series substantially better than our preferred specification using only the 

unemployment gap.

Disability Insurance generally increases when the unemployment rate increases. As such, the 

increase in Disability Insurance beneficiaries is captured in our estimate of the cyclical effect and 

does not appear to have had a disproportionate effect in recent years. In terms of this 

decomposition, Disability Insurance does not appear to explain the residual component of the 

decline in the participation rate.5

In fact, if anything, the increase in disability rolls since 2009 have been somewhat lower than one 

would have predicted given the predicted cyclical and demographic effects, although it is still 

within a 95 percent confidence interval (Figure 15).

 

5

Figure 15 shows the predicted level of SSDI beneficiaries using the same approach for doing time series 

decomposition as our analysis of the participation rate. We regressed first differences in the detrended log of SSDI 

beneficiaries at a quarterly level on four quarter differences in the detrended unemployment gap. Using the actual 

number of beneficiaries at the end of 2007 as an initial point, we then cumulated the differences and the 

estimated confidence interval using Newey-West standard errors to produce an estimated range for the number of 

disabled worker beneficiaries.
29

Schooling

Young men and women are increasingly enrolling in school full-time instead of entering the labor 

force. The effects of the Great Recession have further encouraged young workers to enroll in 

school and delay entering the labor force. However, school enrollment tends to be countercyclical and therefore any effect of a typical increase in schooling during a recession is already 

included in our estimate of the cyclical component. If the increase in school enrollment between 

2007 and 2014 is larger than the typical increase during a recession, though, it may explain some 

of the residual decline in participation. 

We estimate several regression specifications in both levels and differences using the percent of 

the population enrolled in high school or college as an additional explanatory variable. As Tables 

4 and 5 show, the enrollment rate is not statistically significant in any of the specifications we 

estimate. Analyzing the cyclical patterns in the enrollment rate itself reveals that the dynamics 

of enrollment between 2007 and 2014 are largely in line with the expected path given the 

counter-cyclical nature of enrollment in the past.

It is important to note that this analysis only captures the effect of changes in enrollment 

decisions, and not changing participation conditional on enrollment. Students enrolling in school 

participate at lower rates than their non-enrolled peers, so a higher enrollment rate leads to 

lower participation on average. However, the participation of students enrolled in school has 

been on a downward trend since 2000, which accounts for a portion of the decline in 

participation among younger workers. This implies that the participation decisions of young 

workers may have changed in ways that are not fully captured in the enrollment rate. However, 

when looking at the overall participation rate, this change is part of the other offsetting trends 

discussed in greater detail below.

6

7

8

9

10

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 15: Predicted SSDI Beneficiaries

Actual

Beneficiaries (Millions)

Predicted

Upper/Lower

95% Bounds
30

IV. Changes in Labor Force Participation for Different Demographic 

Groups

Overall, the major aggregate trend affecting the participation rate in the recession and recovery 

is the aging of the population. But there are a number of important trends and developments for 

different subgroups of the population. For example, we have seen declining participation by 

younger Americans as they stay in school longer, increasing participation by older Americans, a 

plateauing of, and subsequent slight decline in, female labor force participation in the last fifteen 

years following earlier increases, and the continuation of a 65-year trend of declining male labor 

force participation (which is especially stark for young minority men).

Table 6 shows the trends for a wide range of groups and the remainder of this section discusses 

trends for some of the specific groups.

1948-1990 1990-2007 2007-2014

All 62.8 0.2 0.0 -0.5

Men 69.1 -0.2 -0.2 -0.6

Women 56.9 0.6 0.1 -0.3

16-24 54.7 0.2 -0.4 -0.8

25-54 80.8 0.5 0.0 -0.3

55+ 40.0 -0.3 0.5 0.2

Men 25-54 88.0 -0.1 -0.1 -0.5

Women 25-54 73.9 1.0 0.1 -0.2

White* 63.1 0.2 0.0 -0.5

Black** 60.9 0.3 0.0 -0.5

Hispanic*** 65.8 0.5 0.1 -0.5

Black Men** 63.3 -0.1 -0.2 -0.5

Black Women** 58.9 0.6 0.1 -0.4

* Series begins in 1954

** Series begins in 1972

*** Series begins in 1973

Table 6: Participation Rate by Selected Groups 

2014-Q2

Change (per year in p.p.)

Young Men and Women

Labor force participation among young workers (16-24 year olds) is at its lowest level in more 

than 50 years, having fallen steadily since its peak in the late 1980s, and is now 15 percentage 

points lower than it was 30 years ago, as seen in Figure 16. However, given the relatively small 

size of this group, this trend has had a much smaller impact on the overall labor force 

participation rate. Since 2007, participation among young people has fallen by about 0.8 

percentage point per year, while school enrollment has increased. Enrollment has risen since the 

1980s with larger increases in enrollment among lower-income households, as shown in Figure 
31

17. As a result, the share of young people who are either in the labor force or in school has largely 

been unchanged. 

Aaronson, Park, and Sullivan (2006) examine the secular decline in teen (16-19 year olds) 

employment dating back to the mid-1980s and hypothesize that teens are responding to the rise 

in returns to college by enrolling in school. They find that most of the decline in employment 

between 1987 and 1997 was due to a rise in school enrollment, but since then most of the decline 

is due to reduced participation among young people enrolled in school. Over the last two 

decades, the share of 16-24 year olds neither enrolled in school nor participating in the labor 

force has not changed significantly, ticking down slightly from 11.9 percent in 1993 to 11.7 

percent in 2013. The lack of any increase in the share neither enrolled nor participating implies

50

60

70

80

90

100

1948 1958 1968 1978 1988 1998 2008

Figure 16: Labor Force Participation Rate of 

16-24 Year Olds

2014:Q2

Percent

Participation Rate

Share Either Enrolled in School or 

Participating in the Labor Force 

(4-Qtr. Moving Average)
32

that the decline in the 16-24 year old participation rate since 1993 has come from a reduction in 

participation among 16-24 year olds enrolled in school. Indeed, the participation rate of 16-24 

year olds enrolled in school has declined more than 10 percentage points over the past two 

decades. 

Two main factors have likely played a role in the increase in enrollment since 1980, which 

accounted for much of the decrease in participation among the young between 1980 and the 

mid-1990s. First, increased subsidies for college combined with higher returns to staying in school 

have changed the economic calculus around schooling decisions. Second, a reduction in middleskilled jobs (which economists have referred to as job “polarization”) has led less-skilled adult 

workers to push teenage workers out of the labor force. 

Smith (2011) examines the decline in the teen (16-19 year olds) employment-population ratio 

which has been trending down since the mid-1980s, steepening in the recent period since early 

2000s. Using cross-state analysis, the author finds a significant negative relationship between 

changes in teen employment and the fraction of adults in jobs that often employ teens such as

low-wage service sector jobs. Job polarization—declining middle-skilled jobs and growing lowwage jobs—may have resulted in increased competition between less-skilled adult workers and 

teenage workers. The author finds that such explanations can account for over half of the 

cyclically-adjusted decline in teen employment since the mid-1980s. It is plausible that these 

forces have grown over the recent recession as youth, particularly teens, faced greater 

competition for jobs from more experienced adults who were laid-off (Fogg and Harrington 

2011).

Older Workers

After many decades of decline, labor force participation rates among older workers have 

stabilized since the 1980s and have started to increase in recent years (Figure 18). Participation 

among older women was largely flat for several decades before experiencing an unprecedented 

increase over the last two decades, rising more than 10 percentage points. This rise in the labor 

force participation for older workers has been attributed to several factors. Maestas and 

Zissimopoulos (2010) and Blau and Goodstein (2010) identify rising education levels, labor force 

participation of wives, and changes in Social Security as the most important factors leading to the 

reversal in the trend. While there is less direct evidence available, it seems likely that 

improvements in health and increases in life expectancy have also played a role.
33

The increase in participation by older workers is partially due to differences in the skills that they 

acquired when they were younger. Between 1876 and 1950, the average years of schooling for 

each birth-year cohort increased steadily every year, according to Goldin and Katz (2007). The 

increased education among each successive cohort meant that retirees in more recent years have 

had more income and better quality, less physically demanding jobs over the course of their lives

(Maestas and Zissimopoulos 2010). Blau and Goodstein (2010) estimate that at least 15 percent

of the increase in labor force participation since 1988 can be attributed to the rising education 

levels in each successive cohort, but their robustness checks indicate that this may be an 

underestimate and rising education might in fact explain the entire increase in participation

among older workers since 1988. 

Another portion of the recent rise in participation among married men has come from the 

increased labor force participation of their wives. The rise in female labor force participation 

since the late 1980s explains about a quarter of the rise among older men (Blau and Goodstein 

2010, Schirle 2008). This effect is likely due to complementarity of leisure time between husbands 

and wives, where retirement is more appealing if one’s spouse is also retired. Couples tend to 

retire at similar times and men’s retirement decisions are very responsive to the incentives faced 

by their wives (Coile 2004). 

Additionally, there have been a number of policy changes to the Social Security program since 

1980, and these policies may have had an effect on the retirement decisions of older workers. In 

particular, research in this area has centered around two types of policies: changing the level of 

full benefits and changing the timing of when full benefits are paid out. Changes in the generosity 

of benefits appear to have had little effect on the labor supply of older workers, while changes in 

the timing of full benefits account for much of the increase in participation at older ages (Blau 

and Goodstein 2010, Gustman and Steinmeier 2008). In particular, the increase in the Normal 

Retirement Age and the creation of the Delayed Retirement Credit (that is, the increase in 

benefits from working past the normal retirement age) together can explain between a quarter 

25

30

35

40

45

1948 1958 1968 1978 1988 1998 2008

Figure 18: Labor Force Participation Rate of 

55+ Year Olds

2014:Q2

Percent
34

and half of the increase in labor force participation among older workers since 1988 (Blau and 

Goodstein 2010). 

There appears to be, as yet, little in the way of rigorous empirical studies examining two factors 

which likely contributed to the recent rise in participation among older workers: improvements 

in health and technology. Improvements in health can enable workers to continue to work later 

in life and the corresponding increase in longevity will raise the amount of saving needed for 

retirement. Additionally, technological improvements can also help accommodate older workers 

in the workplace. 

As older workers have delayed retirement and increased their participation in the labor force, 

there has been a rise in partial retirement as a means of shifting gradually from the lifestyle of 

prime working years to retirement (Maestas 2007). According to Maestas, most of these retirees 

anticipated working in retirement, and retirees who unexpectedly return to the labor force 

typically do so because they find leisure less enjoyable than they expected. Additionally, older 

workers are increasingly likely to be self-employed (Maestas and Zissimopoulos 2004). About 25

percent of all 65 year old workers are self-employed, and of these workers more than 40 percent

have employees working for them (Maestas and Zissimopoulos 2010). 

Between 1990 and 2007, participation among workers over 55 grew by an average of 0.5 

percentage point per year, but this slowed to 0.2 percentage point per year since 2007 (Table 6). 

Our analysis of the over 55 participation rate suggests that the increase since 2007 was not driven 

by the Great Recession, and in fact we estimate that the cyclical downturn actually slightly 

decreased the participation of older workers relative to what it would have otherwise been. 

Research on the effects of the Great Recession is largely in line with this result, finding that wealth 

shocks were concentrated in wealthier households, and as a result did not substantially alter 

retirement decisions in recent cohorts (Gustman, Steinmeier, and Tabatabai 2011). 

Prime-Age Women

The labor force participation rate for prime-age women rose rapidly from around 1970 through 

2000. This rapid rise was driven by many factors coming together: reductions in discrimination 

facilitated by Title VII of the Civil Rights Act and Title IX of the Educational Amendments to the 

Civil Rights Act, the availability of more effective birth control through the invention and 

subsequent legalization of the birth control pill, and changes in economic forces stemming from 

the greater educational gains of women, the advent of labor-saving household technology, and 

increased international trade. While these changes drove participation in the 1970s and 1980s, 

further increases in the 1990s stemmed from the incentives provided by the expansion of the 

Earned Income Tax Credit (EITC), the replacement of Aid to Families with Dependent Children 

(AFDC) with the work-centered Temporary Assistance for Needy Families (TANF), and reforms to 

divorce laws. But the participation rate has since levelled out, as seen in Figure 19. As a result, 

the United States has gone from leading many other European countries in prime-age female 

labor force participation to lagging them (Figure 20). 
35

Research has found that family-friendly policies are partially responsible for the rise in 

participation in other advanced countries, and the lack of these policies explains why the United 

States has lost ground. Policies such as paid parental and sick leave, high quality available 

daycare, and support for part-time work have been enacted in other advanced countries, 

enabling mothers to more easily balance work and family obligations. Blau and Kahn (2013) 

investigated data on participation across developed countries and found that the lack of familyfriendly policies in the United States can explain roughly a quarter of the relative decline in 

women’s participation between 1990 and 2010. 

Access to paid family leave is a particularly important factor for mothers considering whether or 

not to remain in the labor force, since it enables mothers to continue working for an employer 

after giving birth. In 2004, California became the first State to implement a paid family leave 

35

45

55

65

75

85

1948 1958 1968 1978 1988 1998 2008

Figure 19: Labor Force Participation Rate of 

Percent 25-54 Year Old Women

2014:Q2
36

program. Rossin-Slater, Ruhm, and Waldfogel (2013) examined the decisions of mothers before 

and after the program’s implementation, finding that it significantly boosted the number of hours 

that mothers worked two to three years after giving birth. They also find that the program 

reduced the number of mothers without jobs, although this effect was not quite statistically 

significant. Other research on paid leave programs has shown that access to paid leave increases 

the likelihood that a recent mother will return to her employer in the United States, the United 

Kingdom, and Japan (Waldfogel, Higuchi, and Abe 1999), although a study of paid leave in Norway 

found no effect on labor force participation, perhaps reflecting stronger pre-existing work 

supports (Dahl et al. 2013). 

At the same time as the participation rate for women has stagnated, the role of women in the 

workforce has continued to grow in importance. More and more women are working in 

traditionally male-dominated occupations and becoming breadwinners for their families:

married women now contribute almost half of family earnings. This is in part because young 

women are now more likely than young men to have graduated from college, and the share of 

female students enrolling in MD, JD, and MBA programs has grown to nearly 50 percent for each 

of these degrees.

Prime-Age Men

There has been a steady decline in the labor force participation of prime-age men since the 1950s

(Figure 21). In 1950, 92 percent of prime-age men were employed, while the most recent data

show that the percentage has fallen to 83 percent, in other words a decline of nearly 10 percent. 

This decline in part reflectsthe fact that prime-age men have historically been more concentrated 

in jobs that can be replaced by technology or sent overseas. As described in Goldin and Katz 

(1998) and Autor, Katz, and Kearney (2008), skill-biased technological change has long exerted 

an influence on the wage structure by depressing wages for occupations with manual or routine 

labor. As Autor, Dorn, and Hanson (2013) note, these effects are concentrated among industries 

exposed to foreign competition and international trade. 
37

There is some disagreement in the literature on whether SSDI accounts for a part of the 

participation decline, particularly for lower-skilled male workers. Wage stagnation for lesseducated workers have made Social Security benefits increasingly valuable compared to lowwage workers’ earnings, and reforms passed in 1984 increased SSDI receipt, although this 

increase may have reflected the fact that the level of SSDI receipt was temporarily depressed 

before 1983 and subsequently returned to its normal level after reforms were enacted (Autor 

and Duggan 2003, Pattison and Waldron 2013). As discussed above, more recent growth in the 

program has been driven mostly by demographics and the recession. 

Regardless of the source, the prolonged decline in participation among prime-age men is

worrying because this group has historically comprised the largest share of the workforce. Even 

seemingly modest declines in their participation cumulate over long periods of time and result in 

large changes in the numbers of Americans available for work, and this in turn has predictable 

negative effects on growth.

Black Men 

Black male labor force participation has fallen steadily since the mid-1970s, dropping more than 

10 percentage points over that time. In part, this reflects lower education levels among black 

men, only 47 percent of whom today have at least some college education (compared with 57

percent for white men). For both black and white men, labor force participation has fallen more 

for those with lower education levels. At the same time, however, the decline in participation 

has been greater for black men across the education spectrum. Participation among black 

workers with just a high school degree has declined nearly 10 percentage points over the last 

decade, while the participation rate among black college graduates has declined by 3.5 

percentage points; this compares with 4.6 percentage points and 2.7 percentage points declines 

for white men, respectively. This troubling trend of reduced participation has continued in the 

85

87

89

91

93

95

97

99

1948 1958 1968 1978 1988 1998 2008

Figure 21: Labor Force Participation Rate of 

Percent 25-54 Year Old Men

2014:Q2
38

latest business cycle, with the participation rate for black men down 0.5 percentage point per 

year since the end of 2007 (Figure 22).

The decline in black male participation, which has persisted through both recessions and 

recoveries, has long been the subject of research (Moynihan 1965, Bound and Freeman 1992). 

The decline has also been particularly pronounced among the young: while the participation rate 

of older black men is also below average, theirs is higher than that of young black men, who have 

a lower labor force participation rate than young white and Hispanic men. This persistent gap 

between black men and their peers in participation and employment opportunities more 

generally is likely due to a range of factors such as disparities in family life and education, as well 

as employer attitudes and the structure of the labor market (Danziger and Gottschalk 1995,

Bound et al 1995, Juhn 1992, 2003, Chandra 2003, Bertrand and Mullainathan 2003). 

Over the last decade or so, labor force participation among black men has deteriorated even 

further. One of the leading explanations for this development is the steep rise in incarceration of 

young black males. Freeman (2003) estimated that roughly 20 percent of the black male 

population is incarcerated. In addition to the obvious incapacitating effect of current 

incarceration, even previous incarceration can have a negative effect on labor force activity. For 

one, incarceration tends to weaken employment networks (Travis, Solomon, and Waul 2001). 

Moreover, an audit study of employers in Milwaukee by Pager (2003) suggests that employers 

seem much more averse to hiring black males with criminal records than comparable white men, 

and that this deterrence extends even to those without criminal records (Holzer, Raphael, and

Stoll 2002, 2006). These demand-side factors serve to reinforce supply-side factors that keep 

black men out of the labor market.

Holzer, Offner, and Sorenson (2005) test this hypothesis in their analysis of black males ages 16-

34 with a high school education or less in the 1980s and 1990s. They find that previous 

incarceration can account for a quarter or more of the decline in participation among black males 

62

66

70

74

78

82

1972 1980 1988 1996 2004 2012

Figure 22: Male Labor Force Participation 

Rates by Race

2014:Q2

Percent

White

Black
39

ages 16-24 and at least half of the decline among black males ages 25-34. These estimates of the 

effects of incarceration are corroborated by work by Western and Pettit (2000).
40

V. The Outlook for the Participation Rate

Assuming a continuation of current policies, over the longer term the participation rate will 

continue to trend down as the aging of the population continues. Over the next few years, 

however, it is likely that the continued recovery of the economy will tend to push in the other 

direction from the aging trend. The evolution of the residual component of participation is harder 

to predict given the uncertainty about its exact cause. All told, over the next few years, the 

participation rate could rise or fall slightly—followed by a resumption of its longer-term decline

due to the continued aging of the population. Policy has the ability to affect these trends as we 

discuss in Section VI.

While the first baby boomers became eligible for early Social Security benefits in 2008, the last 

baby boomers will not reach this point of their lives until 2026. The share of the population over 

the age of 55 will continue to grow rapidly during the coming decade, and most of this growth 

will be in the share of the population over the age of 65 (Figure 23). While the share of the 

population over age 65 has risen gradually in the past due to increased longevity, the retirement 

of baby boomers accelerated this growth and it is now rising faster than at any point since 1960.

If the participation profile by age were fixed at 2013 levels, the changing population share would 

subtract about 0.2 percentage point per year from the participation rate through 2024.

As the economy has recovered from the Great Recession, the cyclical drag on the participation 

rate has lessened to about 0.5 percentage point in the second quarter of 2014. As the economy 

continues to return to full employment, this cyclical factor will continue to dissipate. If this gap 

were to be closed over about two years, then the dissolution of the cyclical drag on participation 

would add about 0.2 percentage point per year to the participation rate for the next two years—

roughly offsetting the aging trend.
41

The residual component of the decline in participation represents the greatest source of 

uncertainty about the participation rate going forward, in part reflecting the uncertainty about 

its causes. This component may shrink to zero, reflecting temporary factors dissipating as the 

economy recovers. On the other hand, to the degree it represents trends that pre-date the Great 

Recession, then absent policy shifts or other unexpected developments it could persist or even 

grow larger—Aaronson et. al (2006) projected that it would reduce the participation rate by 0.2 

percentage point per year above and beyond the normal aging trend. Moreover, while long-term 

unemployment and other indicators of labor market challenges have been steadily improving, 

there is always a danger that without policy changes some individuals would permanently drop 

out of the labor force, resulting in a participation rate that would be permanently lower as a 

result. It also remains possible that the residual could grow over the next few years, as it has 

grown considerably over the past few years and may continue to do so in the near future.

Ultimately, absent changes in policies, a meaningful increase in the participation rate from 

current rates appears unlikely. Figure 24 shows several scenarios for the participation rate under 

current policies, with a common assumption about the aging trend but varying assumptions 

about the evolution of the cyclical and residual components discussed above.These scenarios are 

not projections and do not represent the full range of possibilities, but they do indicate that the 

participation rate is likely to be roughly stable in the near term and will decrease more rapidly 

after the economy returns to full employment and the aging effects once again dominate. 
42

VI. The President’s Agenda and the Labor Force Participation Rate

The outlook for the participation rate, however, ultimately depends, at least in part, on policy 

choices that we make and the practices adopted by companies and individuals. We discuss some 

of the major policies the Administration is pursuing to improve the trajectory of the participation 

rate here.

Aging of the Population

It is important to put the aging trend in perspective. That older workers are able to retire is in 

many ways a positive development. But it also creates challenges, especially for overall fiscal 

policy and in particular for programs like Social Security and Medicare. 

Part of the answer to these challenges is enabling older workers who want to continue to work 

to do so. Workplace flexibility policies that allow employees to work part-time or flexible hours 

can help older workers tailor their jobs to their specific needs and can create roles that are less 

demanding than full-time employment. In this year’s State of the Union, the President called on 

employers to expand access to flexible workplace policies. For similar reasons, many older 

workers choose self-employment, opting to retire partially or set their own hours. Many other 

older workers would like to be self-employed, but have medical conditions that make health 

insurance a necessity. Although in the past these workers would have had to seek work at a large 

employer offering health insurance coverage, the Affordable Care Act enables these workers to 

become self-employed by providing access to quality, affordable health insurance (Fairlie, Kapur,

and Gates 2011) until they are eligible for Medicare.

Probably the most significant policy response to falling labor force participation rates is 

immigration reform, which would counteract the labor force effects of an aging population and 

spur economic growth. On average, immigrants are younger and participate in the labor force at 

higher rates than native-born Americans. The Congressional Budget Office (2013) estimates that 

the immigration reform bill passed by the Senate would increase the labor force by 6 million 

people, or five percent, by 2023, and raise the labor force participation rate by 0.7 percentage 

point. This trend would continue over time, providing a range of other economic benefits such 

as increasing GDP, lowering budget deficits, and improving Social Security solvency. 

Trends Within Demographic Groups

Policies can also help address the long-term trends in participation within age groups. The 

participation rates for prime-age men and women have declined steadily over the past decade, 

continuing a decades-long trend among men and reversing a decades-long trend among women. 

Among younger workers, participation rates have fallen more rapidly. The fall in participation 

among young black men has been particularly drastic. The President’s agenda addresses these 

trends and lays out a series of policies that could help encourage individuals to remain in the 

labor force. 
43

To encourage more women to participate in the labor force, the Administration has a robust 

policy agenda around working families, which was further developed at the White House 

Working Families Summit in June 2014. The summit brought together government, privatesector, labor, and academic leaders to discuss how to help workers balance their personal and 

professional lives. Policies such as paid family leave, flexible work schedules, and affordable 

childcare are vital for families, and mothers in particular. Such policies not only encourage

participation in the labor force, but they also help people find the jobs best suited to their skills 

and excel in those positions. Given that women often take on well over half of family obligations, 

such policies are particularly helpful in raising women’s labor force participation (Blau and Kahn 

2013). As women now make up nearly half of the labor force and earn 59 percent of all higher 

education degrees, policies that facilitate their participation in the labor force can have a large 

impact on the labor force participation rate and overall economic growth.

Another very successful policy tool to encourage labor force participation among low-income 

workers has been to raise the returns to work through the EITC, with the 1980s and 1990s EITC 

expansions pulling more than half a million people into the labor force. But the EITC available to 

workers without children and non-custodial parents (the “childless worker” EITC) is very small, 

and the current rules prevent workers younger than 25 and older than 64 from claiming the credit 

at all. The President has proposed to double the EITC available to workers without children and 

non-custodial parents and to make it available to younger and older workers. This policy would 

target a number of groups with falling or low labor force participation rates, including minority 

men, young adults not enrolled in school, workers with disabilities, and older workers.

In addition, to specifically support young men of color, the President launched the My Brother’s 

Keeper Initiative in February 2014. This initiative created a Federal Task Force charged with 

assessing which public and private efforts are most effective at helping young men of color

succeed academically, in their communities, and in the workplace. So far, eleven leading 

philanthropic foundations have committed to investing $200 million to study which programs 

keep these young men on track and bring these programs to scale. 

Finally, building labor force attachment among young people can raise their labor force 

participation throughout life and offset the long-term declining trend. Earlier this year, the 

President announced $100 million in funding for American Apprenticeship Grants, which will use 

existing funds to expand apprenticeships, training young people for high-demand jobs, and 

helping them build relationships with employers. Additionally, the Administration will award 

$500 million to community colleges to implement job training programs based on the skills and 

credentials in demand among businesses. The President has similarly committed to redesigning 

high school curricula to focus more on industry-relevant skills. All of these initiatives aim to give 

young people labor market skills and connect them with employment opportunities, raising their 

labor force participation over the long-term.
44

The Cyclical Component of Participation

While the sections above highlight policies that can alter long-term labor force participation 

trends, a number of policies could noticeably reduce the cyclical drag on the participation rate

by strengthening our economic recovery and increasing aggregate demand. For example, 

increasing investment in infrastructure would provide an immediate boost to employment and 

demand, as well as the long-term benefits of improved transportation systems. The President 

recently proposed a $302 billion four-year transportation reauthorization plan that would 

support hundreds of thousands of jobs. 

Likewise, the President’s proposal to increase investments in research, education, infrastructure, 

security, and other critical areas through the Budget’s Opportunity, Growth, and Security 

Initiative would both speed the return to full employment and help lay the groundwork for 

stronger long-run growth. By strengthening the economy, this proposal could raise the cyclical 

portion of the labor force participation rate. 

Extending Emergency Unemployment Compensation (EUC) would also boost economic activity 

and aggregate demand by supporting job-seekers’ incomes while they are unemployed. 

Moreover, research shows that EUC helps long-term unemployed workers stay connected to the 

labor force and keep looking for work. 

Other steps would also help, like reauthorizing the Export-Import Bank. In the absence of action 

from Congress, President Obama will press forward by using his executive authority to speed 

the permitting of infrastructure projects, launch new hubs of manufacturing innovation, and 

attract foreign investment.

The Administration is also taking action on its own to strengthen the recovery of the housing 

market. In May, the Federal Housing Administration (FHA) embarked on a program to address 

concerns about accessibility of mortgage credit, and acting independently, Federal Housing 

Finance Agency (FHFA) Director Mel Watt has also undertaken steps to provide clarity on socalled put-back risk and thereby ease credit constraints in the mortgage market. Also, the 

Administration has extended the Making Home Affordable program to continue to help 

struggling homeowners facing foreclosure or whose mortgages are underwater.

Factors Unique to the Great Recession: Long-term Unemployment

A complete recovery from the Great Recession will also require addressing the problem of longterm unemployment. Policies are needed to help the long-term unemployed find jobs and 

prevent them from leaving the labor force; the same policies may also help encourage some of 

those who have left the labor force over the last few years to re-enter it. One issue that 

researchers have identified is discrimination against the long-term unemployed by employers 

who often use computer programs or policies to screen out the long-term unemployed from job 

applicants. Long-term unemployed job seekers have to send out 3.5 times as many applications 

as short-term unemployed seekers to get an interview (Ghayad 2013). To address this and other 

issues facing the long-term unemployed, the President convened private sector leaders to discuss 
45

how the government and private industry can work together on this challenge. More than 300 

CEOs signed on to best practices for recruiting and hiring the long-term unemployed, including 

80 of the nation’s largest businesses and over 20 from the Fortune 50. The Administration is 

continuing to work with employers to learn and build on what works in helping the long-term 

unemployed return to work.

To further this cause, the Administration will use $150 million in existing funds to scale up 

partnerships between employers and non-profits that help get the long-term unemployed back 

to work through work-based training, job placement assistance, and employer outreach. The 

Administration also recognizes the value of extending EUC benefits in keeping the long-term 

unemployed workers attached to the labor force. Bringing down the rate of long-term 

unemployment through policies such as these, and strengthening the recovery overall, may help 

resolve the residual effect on labor force participation.
46

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Sources

Figure 1: Bureau of Labor Statistics. 

Figure 2: Bureau of Labor Statistics. 

Figure 3: Census Bureau; CEA calculations.

Figure 4: Bureau of Labor Statistics; CEA calculations.

Figure 5: Bureau of Labor Statistics; CEA calculations. 

Figure 6: National Center for Health Statistics. 

Figure 7: Social Security Administration. 

Figure 8: Bureau of Labor Statistics; Social Security Administration; CEA calculations. 

Figure 9: Bureau of Labor Statistics; CEA calculations.

Figure 10: Bureau of Labor Statistics; CEA calculations.

Figure 11: Bureau of Labor Statistics; CEA calculations. 

Figure 12: Bureau of Labor Statistics; CEA calculations. 

Figure 13: Bureau of Labor Statistics; CEA calculations. 

Figure 14: Bureau of Labor Statistics; CEA calculations.

Figure 15: Social Security Administration; CEA calculations. 

Figure 16: Bureau of Labor Statistics. 

Figure 17: National Center for Education Statistics.

Figure 18: Bureau of Labor Statistics.

Figure 19: Bureau of Labor Statistics.

Figure 20: International Labour Organization. 

Figure 21: Bureau of Labor Statistics.

Figure 22: Bureau of Labor Statistics.

Figure 23: Census Bureau; CEA calculations. 

Figure 24: Bureau of Labor Statistics; Social Security Administration; CEA calculations.

Table 1: Various Sources; CEA calculations. 

Table 2: Bureau of Labor Statistics; CEA calculations.

Table 3: Bureau of Labor Statistics; CEA calculations.

Table 4: Bureau of Labor Statistics; Social Security Administration; CEA calculations.

Table 5: Bureau of Labor Statistics; Social Security Administration; CEA calculations. 

Table 6: Bureau of Labor Statistics; CEA calculations.

Appendix Figure 1: Bureau of Labor Statistics; CEA calculations.

Appendix Figure 2: Bureau of Labor Statistics; CEA calculations. 

Appendix Figure 3: Bureau of Labor Statistics; CEA calculations.

Appendix Figure 4: Bureau of Labor Statistics; CEA calculations. 
51

Appendix A – Time Series Analysis Specifications

Construction of a Cyclically-Adjusted Trend Component

A cyclically-adjusted trend component of the participation rate was estimated using a 

semiparametric procedure for a partially linear model (Robinson 1988, Stock 1989), combining a 

nonparametric biweight filter with a parametric cyclical adjustment step using leads and lags of 

the unemployment gap. The biweight filter (using a window width of 40 quarters) was applied to 

the first difference of the participation rate and the trend component was constructed by 

cumulating the trend in first differences. First differences in the detrended participation were 

then regressed on two leads, two lags, and the present value of the unemployment gap. 

The predicted values from this regression were cumulated to produce the estimated cyclical 

component, which was subtracted from the participation rate to adjust for the business cycle. 

The cyclically adjusted series was then run through the biweight filter in first differences again 

and the estimates were cumulated to produce the estimated trend component of the 

participation rate (Appendix Figure 3). 

Looking at the period since the end of 2007, the estimated time series trend was very similar to 

the estimated aging effect, indicating that changes in participation within age groups may have 

offset and produced only a small reduction in total participation.
52
53

62

63

64

65

66

67

68

1980 1990 2000 2010

(1) (5)

(4) (8)

Appendix Figure 4: Estimated Cyclical Components 

from Regressions 1, 4, 5, and 8, 1980-2014

2014:Q2

Percent

Actual