Monthly Labor Review • May 2011 25

Labor Force Participation 

A behavioral model for projecting 

the labor force participation rate

Various factors, including economic cycles, wages, school

enrollment, and marital status, affect the participation

of different groups of workers in the labor force;

a behavioral model that accounts for these variables

yields results similar to those obtained from the current

BLS model used to project the labor force participation rate

Mitra Toossi

Mitra Toossi is an economist in 

the Office of Occupational Statistics and Employment Projections, Bureau of Labor Statistics. 

Email: Toossi.Mitra@bls.gov

Economic growth depends primarily on changes in two factors: the 

growth of the labor force and 

changes in labor force productivity. The 

entry of large numbers of baby boomers 

into the U.S. labor market, coupled with 

the rapid increase in women’s labor force 

participation rates during the 1970s and 

1980s, resulted in a sizable increase in the 

supply of the labor force and contributed 

considerably to the economic growth of 

that period. Consequently, of the 3.2-percent annual rate of growth of gross domestic product (GDP) over that period, 

2.5 percent was attributable to labor force 

growth and 0.7 percent resulted from 

changes in labor productivity.1

 Growth in 

labor productivity, however, has been considerably greater since then. During the 

1991–2001 period, out of the 3.1-percent 

annual growth of GDP, 1.2 percent was 

the result of labor force growth and the 

remaining 1.9 percent was attributable to 

rising productivity growth. More recently, 

out of the 2.7-percent growth of GDP

over the 2002–09 timeframe, the labor 

force grew at a rate of 1.0 percent while 

productivity growth was 1.7 percent.2

Because the growth of the labor supply 

has such a significant impact on economic 

growth, projecting the size and composition 

of the labor force is a major task in macroeconomic forecasting.

The Bureau of Labor Statistics (BLS, the 

Bureau) publishes medium-term, or 10-year, 

labor force projections every 2 years. The Bureau takes a long-term view by assuming a 

long-run full-employment economy in which 

unemployment is frictional and not a consequence of deficient demand.3

 The projected 

labor supply in the BLS model is a product of 

two factors: the size and growth of the population, by age, gender, race, and ethnicity; and 

the future trend of labor force participation 

rates—that is, the percentages of the civilian 

noninstitutional population in various age, 

gender, race, and ethnic groups that are in the 

labor force.

By definition, labor force participation is a 

binary variable: an individual is either in the 

labor force or not in the labor force. That definition does not require a minimum number of 

hours of work for someone to be a participant 

in the labor force.

The BLS labor force projections are based 

on the U.S. Census Bureau’s projections of 

the U.S. resident population. The population 

projections use alternative assumptions about 
Labor Force Participation

26 Monthly Labor Review • May 2011

three main factors that affect population growth: fertility, life expectancy, and net international migration. In 

the past, the Bureau of Labor Statistics has used the 

“middle series” population projection, which assumes 

mid-level values for the three factors and is considered by 

the Census Bureau to be the most likely path of future 

population change.4

In the first stage of the labor force projection process, 

the concept of the resident population is converted to that 

of the civilian noninstitutional population. The conversion 

takes place in four steps. First, children under 16 years 

are taken out of the total resident population. Second, the 

Armed Forces, broken down into different age, gender, 

race, and ethnic categories, are eliminated from totals in 

order to estimate the civilian population. Third, the institutional population is subtracted from the civilian population to estimate the civilian noninstitutional population.5

Finally, the civilian noninstitutional population is benchmarked to the most recent annual average data for that 

population from the Current Population Survey (CPS).6

The 2006–16 BLS labor force projection model has utilized the CPS participation rate series from 1970 to 2006 

for 136 age, gender, race, and ethnic groups.

In the second stage of the projections, a nonlinear filter is used to smooth historical labor force participation 

rates for all age, gender, race, and ethnicity groups. The 

filter smooths the trend line by using, first, a running median of length 5, then one of length 3, and then a centerweighed moving average of length 3.7

 The smoothed data 

are then transformed into logits, also called the natural 

logarithm of the odds ratio or flogs (folded logs). Once 

the data are thus transformed, they are extrapolated linearly by regressing the flog of the rate against time and 

then extending the fitted series to or beyond the target 

year. When the series are transformed back into participation rates, the projected path is nonlinear. Participation rates that have been changing slowly will continue 

to change slowly, and the pattern will be linear. Participation rates that have been changing rapidly will continue 

to increase rapidly in the short run and then gradually 

decrease their rate of change.8

Next, projected labor force participation rates are reviewed for consistency. The time path, cross section in the 

target year, and cohort patterns of participation are all reviewed and, if necessary, modified. Finally, projected labor 

force participation rates are multiplied by the projected 

civilian noninstitutional population, yielding labor force 

projections for each age, gender, race, and ethnic group.9 

In addition, the Bureau carries out periodic evaluations 

of its labor force projections. Comparisons of past BLS 

labor force projections with actual data enable the Bureau to identify the strengths and weaknesses in its projections process. The purpose of any evaluation is to find 

the sources of errors in past projections and to improve 

the accuracy of future projections.10 On the basis of the 

2005 independent study of BLS labor force projections, 

it was concluded that BLS estimates were more accurate 

than those obtained from a naïve model that was used 

as the standard of comparison for the 2000 labor force 

estimates.11 The BLS projections also accurately predicted 

the structural changes that occurred in the labor force between 1988 and 2000.12 In the most recent evaluation, the 

BLS 1996–2006 projection again outperformed the alternative, naïve model.13

In addition to the Bureau, several other public agencies, 

including the Congressional Budget Office and the Social 

Security Administration, project labor force participation 

rates and the labor force. Also, a number of private firms 

project participation rates and the labor force, either alone 

or in conjunction with their macroeconomic projections. 

In accordance with their priorities and their access to 

data sources, all of these organizations take into account 

different demographic, social, and economic factors and 

select different assumptions, methodologies, and models 

in their projections. In addition, their projection horizons 

vary from the short term to the very long term; the Social 

Security Administration, for example, projects 75 years 

into the future.

BLS labor force projections face uncertainties with regard to the two primary factors that are important to labor force change: population and participation. Although 

population growth generates most of the change in the 

labor supply, gradual changes in participation rates also 

make significant differences in the long run. Thus, the relevant questions become (1) Which participation trends 

are likely to continue into the future, and which may 

change? and (2) How do demographic changes, as well 

as structural and cyclical features of the economy, affect 

participation rates?

Labor force participation reflects the labor market behavior of different groups in the population. Changes in 

the overall labor force participation rate and in the rates of 

the various population groups are the result of a combination of the three factors mentioned in question (2) in the 

previous paragraph. Each of these factors—demographic 

changes in the population and structural and cyclical features of the economy—affects the participation rates of 

the different groups in various ways. A prime example of 

a demographic change affecting the labor force participation rate is the aging of the baby-boom generation. In the 
Monthly Labor Review • May 2011 27

year 2000, baby boomers were ages 36 to 54 years and 

were in the group with the highest participation rates. 

With the passage of every year thereafter, a segment of 

the baby-boomer population enters the 55-years-andolder age group and thus moves from a group with a high 

participation rate in the labor force into another age category with a much lower participation rate (a phenomenon called a negative demographic composition effect), 

causing the overall participation rate to decrease.

The U.S. labor market is currently experiencing a 

negative demographic composition effect. In contrast, 

the 1970s saw a positive demographic composition effect 

when baby boomers were increasingly joining the primeage workforce, causing an increase in the labor force participation rate.

Among the structural changes to the U.S. economy are 

long-term changes in tastes, preferences, educational attainment, and technology. The rise in school attendance 

in the past two decades is a structural change that has left 

a permanent mark on the labor market.

Cyclical changes in participation rates happen in response to business cycles and are generally short-term 

phenomena. Labor force participation rates usually increase during economic expansions and decline in economic downturns. Historically, cyclical factors have had 

the largest impact on the labor force participation of 

youths. Interestingly, a structural change has the potential 

to exaggerate or ease a cyclical effect. For example, the 

rising school attendance of youths, a structural change, 

strengthened the impact of the recession of 2001, a cyclical effect. This combination of structural and cyclical 

changes depressed youth participation rates to a new low 

at the time. At one time or another, a combination of demographic, structural, and cyclical changes has affected 

the overall participation rate, as well as the participation 

rates of different age, race, gender, and ethnic groups in 

the past.

The standard BLS labor force projection model is based 

on an extrapolation of past participation rates after a process 

of numerical smoothing and filtering. Such a model incorporates demographic factors, but does not directly take into 

account the behavioral aspects, economic factors, structural 

changes, and dynamic conditions of the labor market.

The desirability of a model-based approach in which 

economic and social factors determine participation in the 

labor force has been raised frequently as a topic of interest. 

To pursue that interest, this article develops a behavioral 

model that uses and tests variables other than trends in 

labor force participation rates to project such rates for 

selected age groups over the 2006–16 timeframe.14 The 

projected participation rates obtained are then compared 

with their counterparts in the standard BLS projection 

model for the 2006–16 timeframe in order to highlight 

differences between the two approaches. Finally, for a 

comparative evaluation of the accuracy of the projections 

produced by both models, projected participation rates 

obtained from each model are compared with actual CPS

data for 2007–09. (Note that 2006 data are historical data 

in both models. In addition, actual data for 2007, 2008, 

and 2009 were used only for purposes of comparison and 

not as input data in the projection model.) 

Specifications of the behavioral model

The behavioral model discussed next uses a set of economic and demographic variables to explain why participation rates for specific groups of the population change 

over time.15 These explanatory variables were used for all 

or some of the selected age groups and gender groups. 

The final specification for each group was decided on the 

basis of a model that was empirically consistent with both 

economic theories and current empirical studies on labor 

force participation and that also resulted in coefficients 

which were statistically significant.

General level of economic activity. The state of the job 

market is a key variable that affects the decisions of individuals to participate in the labor force. During economic 

expansions, there is a higher demand for labor; thus, participation rates generally increase for all groups. During 

economic contractions, by contrast, there is less hiring and 

less demand for labor; consequently, the labor force participation rate decreases.

To capture the impact of the general level of economic 

activity and the stage of the economic cycle on participation rates, the change in the ratio of total employment 

to total population (the employment-population ratio) 

is used as an independent variable.16 This variable, which 

tends to move in a direction opposite that of the unemployment rate, closely reflects the ups and downs of aggregate demand and expansionary or recessionary phases of 

the business cycle and is thus a good proxy for aggregate 

demand in measuring the economic cycle.17

Note that the general level of economic activity also 

affects participation rates in part through changes in the 

number of discouraged workers. During recessions, the 

number of discouraged workers increases. By definition, 

discouraged workers are persons who are not in the labor 

force, but who want and are available for a job and who 

have looked for work sometime in the 12 months prior 
Labor Force Participation

28 Monthly Labor Review • May 2011

to their being interviewed for the CPS (or since the end 

of their last job if they held one within those 12 months), 

but who are not currently looking because they believe 

that there are no jobs available or that there are none for 

which they would qualify. In 2009, the number of discouraged workers was more than 3 times the number in 2000. 

As economic conditions improve and discouraged workers find jobs and reenter the job market, the labor force 

participation rate begins to show some cyclical recovery 

and increases.

The wage effect. In addition to the general level of economic activity, wages are a determining factor in changes 

in the labor force participation rate.18 The wage variable 

used in the model presented here is total wages paid at the 

macrolevel, not wages paid to the various age and gender 

groups. Nor does it include reservation wages and the expenses associated with joining the labor force or the cost 

of forgone home production.19 Economic theories suggest 

that the decision to participate in the labor market is an 

economic choice whereby individuals rationally decide 

how to allocate time between work and leisure, with the 

objective of optimizing their total resources. An individual participates in the labor market on the basis of the expected return from working compared with the expected 

satisfaction acquired from not working.20

When individuals are confronted with a wage increase, 

two factors affect their decisions about allocating time 

between work and leisure: the income effect and the substitution effect. The decision depends on which effect is 

dominant. The income effect of a wage increase results 

in an increase in the demand for all goods and services, 

including leisure. A higher demand for leisure will lead 

to lower participation in the labor market. The substitution effect of an increase in wages has the opposite effect, 

by increasing the opportunity cost for leisure, resulting in 

reduced demand for leisure and higher participation in 

the paid labor market. However, in many instances, income and substitution effects may lead to reduced work 

hours and not necessarily increase or decrease participation. In general, the net impact of income and substitution effects will determine the effects of wages and other 

wage-induced factors on the individual’s participation in 

the labor market.

The income and substitution effects of a wage increase 

for women are discussed in Claudia Goldin’s numerous 

research publications on the role of women in the labor 

market.21 Goldin states that the increased labor force participation rate for women was the most significant change 

in labor markets during the 20th century. She divided the 

period from the late 19th century to the present into three 

“evolutionary” and one “revolutionary” period, with the 

following different substitution and income effects: 

1. In the first period, from the late 19th century to the 

1920s, the negative income effect from an increase in 

the husband’s income greatly exceeded the positive 

substitution effect from an increase in the wife’s 

earnings.

2. The second phase, from 1930 to the 1950s, was a 

transition era. As work for women became more 

accepted by both society and their husbands, and 

as opportunities for part-time work increased, 

the income effect declined. At the same time, the 

substitution effect rose substantially. As the real 

wage for women rose, the margin of change was 

participation, not hours.

3. The third phase, which Goldin calls the “roots of the 

revolution,” was from 1950 to the mid- or late 1970s. 

In this phase, the female labor supply was rather 

elastic. The large increase in final demand with the 

expansion of part-time work led to a considerable 

increase in the participation rate of married women. 

The income effect continued its decline and the 

substitution effect increased. 

4. The final phase, which Goldin calls the “quiet revolution,” started in the 1970s and is continuing to the 

present. In this phase, women born in the late 1940s 

and who were teenagers in the mid-1960s began to 

perceive that their adult lives would differ substantially 

from those of their mothers’ generation. The income 

and substitution effects of the labor supply changed 

once again, and no longer was women’s labor supply 

highly elastic. Indeed, it was influenced even less than 

before by the husband’s earnings.

School enrollment. School attendance dampens youth 

participation rates. The current downward trend in the 

labor force participation of teenagers ages 16 to 19 years 

and young adults ages 20 to 24 years has been attributed 

primarily to increasing school attendance among these age 

groups.22 One work on this subject found that school enrollments have increased by roughly 25 percent since 1985, 

mostly from an increase in summer school enrollments.23

The increase in the number of students enrolled at different education levels is considered a structural change with 

a long-term impact on the participation rate of teenagers, 

young adults, and, ultimately, the overall labor force. 
Monthly Labor Review • May 2011 29

Women’s marital status. Since the 1950s, the labor force 

participation rate of women has increased steadily, reaching a peak of 60.0 percent in 1999. This strong growth 

was caused by rising participation of different groups of 

women in the labor force, including single women, a group 

that consists of widowed, divorced, separated, and nevermarried women. Historical data show that, as the share of 

single women in the population increases, the labor force 

participation rate of all women increases. Single women, 

including divorced women, spend more time in the labor 

market than married women do because of the absence of 

the husband’s income.24 In this model, the share of single 

women in the total female population has been used as 

a variable explaining the long-term change in women’s 

participation rates.

Lagged participation rates and time trend. These two variables are respectively used to capture the effects of the 

short- and long-term patterns of change in participation 

rates. The lagged participation rate emphasizes the aggregate cyclical effects and picks up any trend effects not 

captured by other explanatory variables. The time trend, 

by contrast, reflects more of the long-term structural 

movements.

The model. The general specification of the behavioral 

model is 

LFPRt= f(LEPt, Wt, Mt, Et, T),

where 

LFPRt = Labor force participation rate at time t, 

LEPt = Logarithm of change in employmentpopulation ratio

 = Logarithm of employment-population ratio 

at time t minus logarithm of employment-population 

ratio at time t – 1

 = log(EMPt/POPt) – log(EMPt–1/POPt–1),

in which

EMPt = Employment at time t,

POPt = Total population at time t,

EMPt–1 = Employment at time t – 1,

POPt–1 = Total population at time t – 1,

Wt = Total wages,

Mt = Marital status,

Et = Education and school attendance,

T = Time trend, reflecting the long-term trend

Data Sources

The time-series data used to estimate the model’s coefficients range from 1970 to 2006 and constitute 36 observations in total. This timeframe was selected, first, to 

correspond with the 2006–16 BLS labor force projections 

published in the November 2007 Monthly Labor Review 

and, second, to provide projected values for 2007, 2008, 

and 2009, for which actual data are currently available. 

Historical labor force participation rates are from CPS annual averages from 1970 to 2005. The population projections utilized in the behavioral model are based on the 

Census Bureau’s projection of the resident population, the 

same data series used in the computation of the civilian 

noninstitutional population in the BLS labor force projections for 2006–16.25

Employment data used in the estimation of the logarithm of future employment-population ratios (variable 

LEP in the model) were obtained from the 2006–16 BLS 

macroeconomic projections of the U.S. economy.26 Historical data on the employment-population ratio were derived 

from the CPS. The projected values for the model’s wage 

variable Wt

 were estimated from the wage variable used in 

the 2006–16 projections from the BLS macromodel.27

The historical share of single women in the total female 

population was estimated from the CPS data. Future values of the share of single women were extrapolated on the 

basis of past trends. Future values for men’s and women’s 

school enrollment data were based on projection data 

from the National Center for Education Statistics for the 

2006–16 period.

Estimation

The method of ordinary least squares was used to estimate 

the model’s equations. The model was run repeatedly, using various independent variables and a lagged dependent 

variable for different age and gender groups. The final 

specification for each group was decided on the basis of 

four factors: the availability of data, compatibility with 

economic theory, the statistical significance of the coefficients, and the goodness of fit of the regressions. All of 

the variables were transformed into natural logarithms so 

that the growth in the variance over time would not overwhelm the model. The log-linear nature of the model affords a comparison of the magnitudes of the coefficients, 

which represent the elasticity of labor force participation 

with respect to changes in the explanatory variables. Using the estimated equations, BLS analysts projected labor 
Labor Force Participation

30 Monthly Labor Review • May 2011

force participation rates to 2016 on the basis of projected 

values, or assumptions about the future values, of the independent variables. 

Projections were made for 10 population groups consisting of the following five age groups of men and five 

age groups of women: 

• Teenagers: 16- to 19-year-olds 

• Young adults: 20- to 24-year-olds 

• Those in the prime ages: 25- to 54 year-olds 

• The older age group: 55- to 64-year-olds 

• The oldest age group: 65 years and older

Regression results for the model are given in table 1 and 

discussed in more detail next. 

Teenagers (16- to 19-year-olds). Changes in the level of 

economic activity represented by the overall employmentpopulation ratio had the largest impact on the participation rates of both male and female teenagers. In general, 

the participation rates of teenagers increase during economic expansions, decline in economic downturns, and 

are extremely dependent on economic cycles. It is teenagers’ lack of experience and skills, as well as the fact that a 

large proportion of them work part time, that makes this 

group vulnerable and more at risk of being laid off during 

recessions. As was expected, the estimates produced by 

the model showed positive, statistically significant coefficients for the changes in (the logarithm of ) the teenage 

employment-population ratio (LEP).

In addition to the rise and fall in the level of economic 

activity, increases in school enrollment lower the participation rate of teenagers. In recent years, increases in school 

attendance and enrollment at the secondary and college 

levels—especially increasing rates of enrollment during 

the summer months—have had a large impact on the 

declining teen participation rate.28 The rising enrollment 

rates led to an increase in the share of students in the total population of 16- to 19-year-olds, another reason the 

participation rate of teenagers has been decreasing. Economic theory suggests that teenagers turn to schooling 

when the labor market is weak and, at the same time, the 

opportunity cost of school enrollment is low while the return from investment in education is high.29 Consistent 

with other research on this subject,30 the behavioral model 

showed a negative impact of school enrollment on youth 

participation rates. However, the estimates obtained point 

to a lower elasticity for this variable, and the coefficient is 

not statistically as significant as the effect of changes in 

the employment-population ratio. By contrast, the coefficient of the trend factor, reflected in the lagged value of 

the labor force participation rate, was positive and statistically significant.

Young adults (20- to 24-year-olds). For this age group, the 

change in (the logarithm of ) the employment-population 

ratio (LEP), along with wages and the lagged dependent 

variable, yielded the best fit and produced statistically 

significant coefficients. As with teens, the labor force 

participation rates of young adults who were enrolled in 

school were lower than those of their counterparts who 

were not in school. Although school enrollment rates for 

both teens and young adults have increased substantially 

over the past several decades, enrollment rates for 20- to 

24-year-olds, not surprisingly, are lower than those for 16- 

to 19-year-olds, because many in the former group have 

already completed their formal education.31 School attendance for this age group turned out not to be statistically 

significant and was omitted from the final specifications. 

It appears that, although schooling is a significant factor 

in delaying the entry of young adults into the workforce, 

once they do enter the workforce, higher wages play a 

stronger role, both in absorbing these individuals into the 

labor market and in keeping them there. The coefficient 

of the wage variable was negative for 20- to 24-year-olds. 

There are two views on the sign of this variable. On one 

view, the curve designating the supply of labor, like most 

other supply curves, should rise in relation to wages. That 

is, an increase in wages results in both higher income and 

increases in the consumption of all goods and services, 

including leisure, which is time not spent in the labor 

market. Therefore, the income effect of a wage increase 

can lead to less work and more leisure time, resulting in a 

lower labor force participation rate. On the other view, the 

substitution effect plays a role and suggests an opposite 

outcome, namely, that an increase in wages increases the 

opportunity cost of leisure time, leading to less demand 

for leisure and more time spent working.32 This outcome 

would yield a higher participation rate. The net result of 

these two factors—income and substitution effects—will 

decide the sign of the wage variable. For young adults, it 

seems that the income effect is greater than the substitution effect, leading to a negative coefficient for wages as an 

explanatory variable. 

Prime ages (25- to 54-year-olds). Of all age groups, 25- 

to 54-year-olds have the strongest ties to the labor market. The participation rate of men of these ages was 93.4 

percent in 1990, 91.6 percent in 2000, and 89.4 percent, 
Monthly Labor Review • May 2011 31

the lowest ever since records were kept, in 2009. The labor force participation rate of women was 74.0 percent in 

1990, 76.7 percent in 2000, and 75.6 percent in 2009. The 

overall participation rate for this age group has trended 

downward over the past decade, falling from a peak of 

84.1 percent in 1998 to 82.6 percent in 2009. The primeage workforce is the least sensitive to economic downturns, compared with other age groups.

For men in the prime-age group, wages turned out to be 

a major determinant of their decisions to participate in the 

labor force. The coefficient of the wage variable was positive 

and statistically significant, indicating a positive correlation 

between wages and the participation rate of 25- to 54-yearold men. A trend variable was added to the model to include the impact of all other factors besides wages affecting 

the long-term decline in the participation rate of men in 

the prime-age group. The trend variable could identify factors such as the shift seen over the past couple of decades 

from workers’ participation in so-called defined benefit 

pension plans, which encourage early retirement, to defined 

contribution plans, which might prolong the working years. 

Also included in this variable are factors such as increases 

in Social Security disability benefits. A study by David Autor and Mark Duggan reviewed changes in the labor force 

participation rate of the less skilled labor force between 

1984 and 2000 and concluded that the liberalization of the 

disability program during that timeframe could explain the 

role of disability benefits in lowering the participation rate 

of the nonelderly at the time.33

Because men in this age group have the highest participation rates and the strongest ties to the labor market, 

the employment-population ratio did not yield satisfactory results and was omitted from the final equation for 

the group.

Women in the 25- to 54-year age group increased their 

labor market participation significantly during the latter 

 Regression results from the behavioral model

Age 

group

Constant

Logarithm

of 

employmentpopulation 

ratio

Logarithm

of wage

Logarithm

of school 

enrollment

Logarithm

of lagged 

dependent 

variable

Trend

Logarithm 

of share of 

single 

women

R2

Autoregressive

AR(

1

)

result

16 to 19

years

Men......... 0.316590 2.166528 (4.72) – –0.026908 (0.81) 0.960388 (13.97) – – 0.968188 –

Women.. .424009 1.8889721 (4.41) – –.024202 (–1.08) .928673 (11.22) – – .947721 –

20 to 24

years

Men......... .597093 .385658 (6.12) –.006399 (–4.2) – .875761 (19.02) – – .967237 –

Women.. .316898 .39202 (3.18) –.007332 (–1.76) – .939467 (21.83) – – .9793 –

25 to 54

 years 

Men......... 4.201151 – .054081 (3.62) – – –.004532 (6.24) – .98962 .98962

Women.. 2.086541 – .135303 (2.82) – – – .37347 (2.09) .94889 –

55 to 64

years

Men......... .379050 – .024435 (2.50) – .879639 (13.28) –.021799 (1.74) – .98313 –

Women.. .067075 – .020965 (3.92) – .943514 (28.80) – – .991415 –

65 years

 and

 older

Men......... –.208864 – .031327 (3.21) – .986187 (20.64) – – .956004 –

Women.. –.148524 – .031044 (3.46) – .960363 (16.63) – – .991415 –

NOTE: t-statistics are shown in parentheses after the value. All results displayed are statistically significant at the 95-percent level of confidence. Dash 

indicates variable was omitted from regression because of statistical insignificance.

Table 1.
Labor Force Participation

32 Monthly Labor Review • May 2011

half of the 20th century. In 1950, the women’s participation 

rate was 35.0 percent. After reaching an all-time peak of 

76.8 percent in 1997, the rate dropped to 75.6 percent in 

2009. Even with the drop, the rate posted an increase of 

40.6 percentage points over 60 years. A large part of this 

increase reflects a generational shift, as women of the babyboom generation participated in the labor force at a rate 

significantly higher than their predecessors did.34

The increase in the women’s participation rate, specifically for the 25- to 54-year age group, applies to all 

subgroups of women: women who have never been married, married women, and married women with children 

less than 6 years of age. However, single women, a group 

that includes divorced, widowed, and separated women, as 

well as women who have never married, contributed significantly to the rapid expansion of women’s participation 

rates. Single women have high labor force participation 

rates compared with those of other groups of women. In 

fact, the participation rate of single women in the labor 

market is as strong as the participation rate of their male 

counterparts.

As the share of single women in the female civilian 

noninstitutional population has increased (from 35 percent in 1950 to 50.1 percent in 2009), their participation rate also has increased substantially.35 Several factors 

have been responsible for the increase in the percentage 

of single women. First, women remain single more often, and marry later in life, than they used to, which is 

why the median age at first marriage has increased by 4.3 

years since 1970, to 25.3 in 2003.36 In addition, collegeeducated women marry 2 years later, on average, than the 

rest of the female population, so, given that the number 

of college-educated women has risen over the past several decades, the median age at first marriage has risen as 

well. In addition, among those women ages 25 to 44 years, 

single mothers with children increased their participation 

rate in the labor force, especially after the passage of the 

Federal Welfare Reform Act in 1996.

Moreover, divorce rates rose sharply, doubling between 

the mid-1960s and the mid-1970s. Afterward, the divorce 

rate peaked in the late 1970s and has been on a decline 

since then, from a high of 22.8 divorces per 1,000 married 

couples in 1979 to 16.7 in 2005.37 In addition, women 

have higher life expectancies than men, a factor that, over 

time, increases the number of single women in the older 

age groups in the labor force. Finally, between 1994 and 

2005, the participation rate of unmarried mothers who 

were high school dropouts rose by 13.3 percentage points. 

It is possible that this rate of increase in the labor force 

participation rate of single mothers with low levels of educational attainment was due in large part to the stringent 

work requirements of welfare reform legislation enacted 

during the 1990s.38

In the behavioral model, the regression on the share of 

single women in the total civilian noninstitutional population of women resulted in a strong positive, statistically 

significant coefficient for the female prime-age group. In 

addition, their rise in wages has been a factor encouraging higher participation among women: according to one 

study, as wages rise, women tend to delay marriage and 

have fewer children, thus increasing their participation in 

the labor force.39 The substitution effect affects the participation rate of women in a positive fashion, whereas 

the income effect does so in a negative way. Most research 

points to the dominance of the substitution effect of a 

wage increase for women, leading to an increase in the 

participation rate.40

In the model, the wage variable was positively correlated with the participation rate of women in the prime-age 

group whereas the trend factor had a negative coefficient.

Older workers (55- to 64-year-olds). The wage variable 

for men in this age group had a positive coefficient, indicating that higher wages encourage more participation 

from older men. The long-term trend had a negative effect 

on participation rates of the group, while the short-term 

trend, reflected in the lagged participation rate, had a positive effect. The results are consistent with the historical 

data. The labor force participation rate of men in the 55- 

to 64-year age group was 83.0 percent in 1970, declined to 

66.0 percent in 1995, and then increased to 70.2 percent 

in 2009. All coefficients in the equation of this group were 

statistically significant, and the regression had a correlation coefficient of 98.0 percent.

The labor force participation rate of women in the 

55- to 64-year age group has increased substantially since 

1970, when it stood at 43.0 percent. The group posted a 

49.2-percent rate in 1995, after which it saw its participation rate accelerate, reaching an all-time high of 60.0 

percent in 2009. As with men in the same age group, 

total wages and the lagged dependent variable showed 

the best results, with a positive effect on the participation 

rate. However, the effect of the long-term trend was not 

statistically significant for women and was consequently 

omitted from the equation for women in the 55- to 64-

year age group.

Oldest age group (65 years and older). The labor force participation rate of the 65-years-and-older age group is the 

lowest of all age groups, for both men and women. How-
Monthly Labor Review • May 2011 33

ever, because of reasons such as (1) a scheduled increase 

in the Social Security retirement age to 67 years, (2) the 

effect of various policies meant to discourage retirement 

at earlier ages and before the full retirement age, (3) the 

trend of opting out of defined benefit pension plans and 

toward defined contribution pension plans, and (4) the 

long-established incentive to keep employer-based health 

insurance, the labor force participation rates of older men 

and women have been on the rise since the late 1990s. The 

participation rate of men in this age group rose from 16.3 

percent in 1990 to 21.9 percent in 2009. The participation 

rate of women in the group was 8.6 percent in 1990 and 

increased to 13.6 percent in 2009.

The regressions for both men and women in the 

65-years-and-older group had the best fit with statistically significant coefficients when wages and the lagged 

participation rates were used as explanatory variables. The 

regression had a correlation coefficient of 97.0 percent for 

men and 92.0 percent for women.

Comparing the two models 

The behavioral model’s equations were used to project labor force participation rates for each of the selected age 

groups and for men and women over the period 2006–16. 

Projected values for the explanatory variables were obtained from various sources, as described in the previous 

section. The behavioral model timeframe of 2006–16 is 

the same as the BLS timeframe for its labor force participation rate projections. In what follows, the participation 

rates projected by the behavioral model are compared 

with the BLS projections, and both models’ projections are 

then compared against the actual 2007, 2008, and 2009 

participation rate annual averages from the CPS. 

Overall and age- and gender-grouped labor force participation rates. The overall participation rate projected by the 

behavioral model shows a decline of 0.7 percentage point, 

from 66.1 percent in 2007 to 65.4 percent in 2016. (See 

chart 1 and table 2.) This result is consistent with most 

projections, including those from the BLS model, which 

projects slow growth for both the overall participation 

rate and the total labor force in the coming years. In the 

Congressional Budget Office’s projection for the 2010–20 

timeframe, for example, shifts in the age composition of 

the population and the aging of the labor force are expected to dampen overall participation rates, causing the 

labor force to grow by just 0.7 percent over the next decade.41 The most recent BLS projections for the 2008–18 

timeframe also point to a declining overall participation 

rate, as well as a labor force growth of 0.8 percent.42 Other 

research confirms the slowdown in labor force growth, but 

projects little or no decline in the aggregate labor force 

participation rate over the next decade.43

According to the 2006–16 BLS model, the overall labor 

force participation rate was projected to be 66.2 percent in 

2007 and 65.5 percent in 2016. The same 0.7-percentagepoint drop in the overall labor force participation rate as is 

projected by the behavioral model suggests that the difference in the magnitude of the projected overall labor force 

participation rates between the two models is negligible, 

remaining relatively flat over the projection period. The 

participation rates of men and of women show slightly 

wider gaps between the two models.

The participation rate of men in the behavioral model 

is projected to be 73.2 percent in 2007, decreasing gradually to 71.8 percent in 2016. (See chart 2 and table 2.)

The men’s rate in the BLS model is 73.5 percent in 2007, 

declining to 72.3 percent in 2016. The behavioral model 

projects the women’s labor force participation rate to be 

59.5 percent in 2007 and remain relatively flat thereafter, 

dipping slightly to 59.4 percent in 2016. (See chart 3 and 

table 2.) The labor force participation rate for women in 

the BLS model shows a similar pattern, falling from 59.4 

percent in 2007 to 59.2 percent in 2016. 

Although overall participation rates from both models 

are almost identical and the gaps in the rates for all men 

and for all women in the two models are negligible (see 

chart 4), the projected values for men and women of some 

age groups differ between the models:

• The two models project identical or very close values for both male and female teenagers and young 

adults, but are farther apart as regards the labor force 

participation rates of both men and women of prime 

and older age groups.

• For women in the prime-age group, the projected 

values of both models are close and the differences 

are small. However, the behavioral model projected 

a 0.2-percent decline in the labor force participation 

rate of these women over the 2007–16 timeframe, 

whereas the BLS model projected a 0.5-percent 

increase. For men in the prime-age group, the BLS 

model projected increasing participation rates from 

2007 to 2016, whereas the behavioral model showed 

a decreasing trend.

• The greatest differences between the two models are 

for women in older age groups. Both models pro-
Labor Force Participation

34 Monthly Labor Review • May 2011

jected significantly higher labor force participation 

rates in 2016 than in 2007; however, the behavioral 

model projected a significantly higher participation 

rate for women 55 to 64 years, and a significantly 

lower rate for women 65 years and older, compared 

with the BLS model over the 2007–16 timeframe.

• For older men, the gap between the two models is 

high for those in the 55- to 64-year age group and 

widens over time, from 0.2 percent in 2007 to 3.1 

percent in 2016.

Comparison of the two models with actual data. The projected values for the selected age and gender groups in 

the behavioral model over the 2006–16 timeframe are 

quite comparable to the BLS labor force participation rate 

projections over the same timeframe. However, as just discussed, there are some variations in the projected participation rates for some age groups between the two models. 

The best way to evaluate the resulting projections from 

the two models is to compare both with the actual annual average participation rates in 2007, 2008, and 2009, 

for which data are currently available from the CPS. Note 

that the actual labor force participation rates for 2007–09 

were not used in the estimates of the behavioral model; 

therefore, comparing actual CPS data from 2007, 2008, 

and 2009 with the projections obtained from that model 

shows how accurately the model projects labor force participation rates and how it compares with the current BLS

model in respect of accuracy. (See chart 5.) 

A comparison of projected labor force participation 

rates for 2006–16 from the behavioral model, on the one 

hand, and the current BLS model, on the other, with actual 

participation rates from the CPS for 2007, 2008, and 2009 

is presented in table 3. In addition, table 4 shows, for each 

of the two models, the absolute value of the difference of 

the overall, men’s, and women’s participation rates, for the 

different age groups, and the actual data for 2007, 2008, 

and 2009. For each age group and each year, the boldface 

number denotes the more accurate projection between the 

two models (the smaller of the two values). 

The actual overall labor force participation rate for both 

2007 and 2008 was 66.0 percent. In 2009, the rate declined by 0.6 percentage point, to 65.4 percent. The BLS 

projection for 2007 was 66.2 percent, whereas the behavioral model projected 66.1 percent. (See table 3.) Both 

Percent Percent

 Chart 1. Overall labor force participation rates, by age group, behavioral model, 1970–2006 and projected 

2007–16

100

90

80

70

60

50

40

30

20

10

0

1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014

100

90

80

70

60

50

40

30

20

10

0

Projections

65 years and older

20–24 years

16–19 years

55–64 years

25–54 years

All ages
Monthly Labor Review • May 2011 35

projections were extremely close to the actual rate, with 

the behavioral model projections closer by 0.1 percent.The 

behavioral model projected exactly the actual 66.0-percent rate for 2008, while the BLS model overestimated 

the rate by 0.2 percent. In 2009, with all the recessionary 

forces at work in the labor market, the actual participation 

rate stood at 65.4 percent; the BLS projection was 66.2 

percent, the behavioral model 66.0 percent. 

The actual participation rate for men was 73.2 percent 

in 2007, 73.0 percent in 2008, and 72.0 percent in 2009. 

The BLS model projected 73.5 percent in 2007, 73.5 percent in 2008, and 73.4 percent in 2009. The behavioral 

Labor force participation rates, behavioral model and BLS projections, by age group and gender, 2007–16

Year All ages 16 to 19 years 20 to 24 years 25 to 54 years 55 to 64 years 65 years and older

BLS Behavioral BLS Behavioral BLS Behavioral BLS Behavioral BLS Behavioral BLS Behavioral

Total

2007........... 66.2 66.1 43.0 43.2 74.3 74.5 83.1 82.9 63.9 64.1 16.3 15.7

2008........... 66.2 66.0 42.5 42.6 74.0 74.2 83.2 82.9 64.3 64.8 17.0 16.1

2009........... 66.2 66.0 42.0 41.8 73.7 73.8 83.3 82.9 64.4 65.5 17.7 16.6

2010........... 66.2 65.9 41.3 41.0 73.3 73.5 83.4 82.8 64.6 66.3 18.2 17.1

2011........... 66.1 65.9 40.7 40.4 73.0 73.2 83.4 82.8 64.8 67.0 18.8 17.6

2012........... 66.0 65.7 40.0 39.4 72.8 72.8 83.5 82.6 65.3 67.8 19.5 18.2

2013........... 66.0 65.6 39.5 39.0 72.7 72.5 83.5 82.5 65.7 68.6 20.1 18.8

2014........... 65.8 65.5 38.9 38.5 72.5 72.2 83.6 82.4 66.1 69.5 20.7 19.5

2015........... 65.7 65.4 38.2 37.9 72.2 71.9 83.6 82.3 66.4 70.3 21.2 20.1

2016........... 65.5 65.4 37.5 37.6 71.8 71.7 83.6 82.2 66.7 71.2 21.7 20.9

Men

2007........... 73.5 73.2 42.9 43.0 79.1 79.5 90.8 90.4 69.7 69.5 21.7 20.7

2008........... 73.5 73.0 42.3 42.4 78.7 79.3 91.0 90.3 69.7 69.9 22.5 21.3

2009........... 73.4 72.8 41.7 41.3 78.4 78.9 91.1 90.2 69.6 70.2 23.2 21.9

2010........... 73.4 72.7 41.0 40.5 78.0 78.6 91.2 90.0 69.5 70.6 23.8 22.5

2011........... 73.3 72.6 40.3 39.7 77.7 78.4 91.3 89.9 69.5 71.0 24.4 23.2

2012........... 73.1 72.4 39.6 38.7 77.5 78.0 91.3 89.8 69.7 71.4 25.1 24.0

2013........... 72.9 72.2 38.9 38.1 77.2 77.8 91.3 89.6 69.9 71.9 25.7 24.8

2014........... 72.7 72.0 38.3 37.5 77.0 77.6 91.4 89.5 70.0 72.3 26.2 25.7

2015........... 72.5 71.9 37.5 36.8 76.7 77.3 91.4 89.3 70.1 72.7 26.7 26.6

2016........... 72.3 71.8 36.8 36.4 76.4 77.2 91.3 89.2 70.1 73.2 27.1 27.6

Women 

2007........... 59.4 59.5 43.1 43.1 69.4 69.4 75.5 75.6 58.5 59.0 12.2 11.9

2008........... 59.4 59.5 42.7 42.7 69.2 69.1 75.6 75.7 59.2 60.1 12.8 12.2

2009........... 59.4 59.5 42.2 42.2 68.9 68.7 75.6 75.7 59.6 61.1 13.4 12.6

2010........... 59.4 59.6 41.7 41.7 68.5 68.3 75.7 75.8 60.0 62.2 14.0 12.9

2011........... 59.5 59.6 41.1 41.1 68.2 67.9 75.7 75.7 60.5 63.3 14.6 13.3

2012........... 59.4 59.5 40.5 40.5 68.1 67.4 75.8 75.7 61.2 64.5 15.2 13.7

2013........... 59.4 59.5 40.0 40.0 68.1 67.1 75.8 75.6 61.8 65.6 15.8 14.2

2014........... 59.4 59.4 39.5 39.5 67.9 66.8 75.9 75.5 62.4 66.8 16.4 14.6

2015........... 59.3 59.4 38.9 38.9 67.6 66.4 75.9 75.4 63.0 68.0 17.0 15.1

2016........... 59.2 59.4 38.3 38.3 67.2 66.1 76.0 75.4 63.5 69.3 17.5 15.6

Table 2.
Labor Force Participation

36 Monthly Labor Review • May 2011

 Chart 2 Men’s labor force participation rates, by age group, behavioral model, 1970–2006 and projected 

2007–16

20–24 years

65 years and older

Percent Percent

100

90

80

70

60

50

40

30

20

10

0

1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014

100

90

80

70

60

50

40

30

20

10

0

16–19 years Projections

55–64 years

25–54 years

 Chart 3. Women’s labor force participation rates, by age group, behavioral model, 1970–2006 and projected 

2007–16

20–24 years

65 years and older

Percent Percent

100

90

80

70

60

50

40

30

20

10

0

1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014

100

90

80

70

60

50

40

30

20

10

0

16–19 years

55–64 years

25–54 years

Projections

All ages

All ages
Monthly Labor Review • May 2011 37

model correctly projected both the 73.2-percent rate in 

2007 and the 73.0-percent rate in 2008. However, the behavioral model overprojected the 2009 rate by 0.8 percent. 

(See table 3.) 

The actual participation rate for women was 59.3 percent in 2007, 59.5 percent in 2008, and 59.2 percent in 

2009. The BLS projection was 59.4 percent for the 3 consecutive years, while the behavioral model projected 59.5 

percent for the same 3 years. (See table 3.) 

For all the age groups from 20 to 54 years, both models’ projections were very close to the actual participation 

rates in 2007, 2008, and 2009. However, larger differences 

appear both between each model’s projections and the 

actual rates and between the projected values of the two 

models, for the teenage group of 16- to 19-year-olds and 

the 65-years-and-older age group. Because the prime-age 

attachment to the labor market has always been high and 

relatively stable, both the BLS model and the behavioral 

model were successful in projecting the trend of that age 

group’s participation rate. But projections for the younger 

and older cohorts have missed the actual values mainly 

because of significant changes that have occurred in recent 

years in the participation rates of the two groups. Between 

the two models, the BLS model projected the participation 

rates of the older labor force more accurately. (See table 

3.)

The participation rate of the teenage group has been 

declining significantly over the past several decades. The 

Bureau projected a participation rate of 43.0 percent for 

this age group in 2007, and the behavioral model projected 

a rate of 43.2 percent; both models overprojected the actual 

participation rate, which was 41.3 percent. Both models 

also overprojected the actual participation rates of 40.2 

percent and 37.5 percent in 2008 and 2009, respectively. 

The actual participation rate of the 55- to 64-year age 

group was 63.8 percent in 2007, 64.5 percent in 2008, and 

64.9 percent in 2009. The BLS projection for 2007 was 63.9 

percent, only slightly higher than the actual rate. The behavioral model’s estimation of 64.1 percent overestimated 

the actual rate by 0.3 percent. The BLS projection for 55- 

to 64-year-olds for 2008 was 64.3 percent, a 0.2-percent 

underprojection, whereas the behavioral model overprojected the rate at 64.8 percent. Again in 2009, the BLS

projection of the participation rate for the 55- to 64-year 

age group was more on target than the behavioral model’s 

projection, albeit only slightly. (See table 3.) 

 Chart 4. Labor force participation rates, behavioral model and BLS model, projected 2007–16

Women

Percent

80

78

76

74

72

70

68

66

64

62

60

58

56

54

52

50

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Men

Overall

Percent

80

78

76

74

72

70

68

66

64

62

60

58

56

54

52

50

NOTE: Behavioral model, solid lines; BLS model, dashed lines.
Labor Force Participation

38 Monthly Labor Review • May 2011

 Chart 5. 

Percent

100

90

80

70

60

50

40

30

20

10

0

Percent

100

90

80

70

60

50

40

30

20

10

0

2008

100

90

80

70

60

50

40

30

20

10

0

16–19 years 20–24 years 25–54 years 55–64 years 65 years and older

Percent

Age group

100

90

80

70

60

50

40

30

20

10

0

Percent 2009

Age group

16–19 years 20–24 years 25–54 years 55–64 years 65 years and older

BLS and behavioral model labor force participation rate projections compared with actual rates, by 

age group, 2007, 2008, and 2009

Percent

100

90

80

70

60

50

40

30

20

10

0

Percent

Actual 2007 Behavioral model projection BLS projection

Actual 2007 Behavioral model projection BLS projection

Actual 2007 Behavioral model projection BLS projection

100

90

80

70

60

50

40

30

20

10

0

2007

Age group

16–19 years 20–24 years 25–54 years 55–64 years 65 years and older
Monthly Labor Review • May 2011 39

Overall, it seems that, at this level of aggregation of 

age and gender, the behavioral model provides projections that are comparable to, and consistent with, those 

obtained from the standard BLS model of participation 

rate projections and that also are very close to actual labor 

force participation rates. The behavioral model can be extended to include age, gender, race, and ethnic groups at 

levels of disaggregation that are similar to those used in 

the current BLS projections model.

THIS ARTICLE HAS PRESENTED A BEHAVIORAL MODEL

of the U.S. economy that measures the impact of a selected number of economic and behavioral variables on 

labor force participation rates. The variables selected include, besides a measure of change in the overall level 

of economic activity, the wage rate, school enrollment, 

past trends of participation rates, and the share of single 

women in the total female population. The projections of 

participation rates generated by this model turned out 

similar, and in some cases identical, to the projections 

produced by the current BLS model. Further, comparisons 

of the projections obtained from the behavioral model 

with actual data showed that the projections were, by and 

large, as accurate as those obtained from the current BLS

model in comparison to actual data. This similarity in results may be explained by the fact that, although the two 

models differ in their method of estimation, both depend 

to some extent on the extrapolation of past trends. In 

the current BLS model, the smoothed trend of historical 

participation rates is regressed on a time variable and the 

time is extended to project future participation rates. In 

the behavioral model, the coefficients that are generated 

are based on the past correlation of participation rates 

with explanatory variables. If future values of independent 

explanatory variables are assumed to be the continuation 

of past trends, then the behavioral model could result in 

projections similar to those of the current BLS model. The 

difference between the two models, however, is that in a 

Labor force participation rates, behavioral model and current BLS model, by age group and gender, 2007, 2008 

and 2009

Age group

2007 2008 2009

Actual Behavioral 

model 

Current

BLS model Actual Behavioral 

model

Current

BLS model Actual Behavioral model

Current

BLS model

Total

16 years and older....... 66.0 66.1 66.2 66.0 66.0 66.2 65.4 66.0 66.2

16–19 years................ 41.3 43.2 43.0 40.2 42.6 42.5 37.5 41.8 42.0

20–24 years................ 74.4 74.5 74.3 74.4 74.2 74.0 72.9 73.8 73.7

25–54 years................ 83.0 82.9 83.1 83.1 82.9 83.2 82.6 82.9 83.3

55–64 years................ 63.8 64.1 63.9 64.5 64.8 64.3 64.9 65.5 64.4

65 years and older... 16.0 15.7 16.3 16.8 16.1 17.0 17.2 16.6 17.7

Men

16 years and older....... 73.2 73.2 73.5 73.0 73.0 73.5 72.0 72.8 73.4

16–19 years................ 41.1 43.0 42.9 40.1 42.4 42.3 37.3 41.3 41.7

20–24 years................ 78.7 79.5 79.1 78.7 79.3 78.7 76.2 78.9 78.4

25–54 years................ 90.9 90.4 90.8 90.5 90.3 91.0 89.7 90.2 91.1

55–64 years................ 69.6 69.5 69.7 70.4 69.9 69.7 70.2 70.2 69.6

65 years and older... 20.5 20.7 21.7 21.5 21.3 22.5 21.9 21.9 23.2

Women

16 years and older....... 59.3 59.5 59.4 59.5 59.5 59.4 59.2 59.5 59.4

16–19 years................ 41.5 43.4 43.1 40.2 42.9 42.7 37.7 42.2 42.2

20–24 years................ 70.1 69.4 69.4 70.0 69.1 69.2 69.6 68.7 68.9

25–54 years................ 75.4 75.6 75.5 75.8 75.7 75.6 75.6 75.7 75.6

55–64 years................ 56.6 59.0 58.5 59.1 60.1 59.2 60.0 61.1 59.6

65 years and older... 12.6 11.9 12.2 13.3 12.2 12.8 13.6 12.6 13.4

Table 3.
Labor Force Participation

40  Monthly Labor Review • May 2011

Absolute value of difference of actual labor force participation rate and projected value, behavioral model and 

current BLS model, by age group and gender, 2007, 2008, and 2009

Age group

2007 difference of— 2008 difference of— 2009 difference of—

Actual and 

behavioral 

Actual and 

current BLS

Actual and 

behavioral 

Actual and 

current BLS

Actual and 

behavioral 

Actual and 

current BLS

Total

16 years and older.. 0.1 0.2 0.0 0.2 0.6 0.8

16–19 years........... 1.9 1.7 2.4 2.3 4.3 4.5

20–24 years........... .1 .1 .2 .4 .9 .8

25–54 years........... .1 .1 .2 .1 .3 .7

55–64 years........... .3 .1 .3 .2 .6 .5

65 years and older. .3 .3 .7 .2 .6 .5

Men

16 years and older.. .0 .0 .0 .5 .8 1.4

16–19 years........... 1.9 1.8 2.3 2.2 4.0 4.4

20–24 years........... .8 .4 .6 .0 2.7 2.2

25–54 years........... .5 .1 .2 .5 .5 1.4

55–64 years........... .1 .1 .5 .7 .0 .6

65 years and older. .2 1.2 .2 1.0 .0 1.3

Women

16 years and older.. .2 .1 .0 .1 .3 .2

16–19 years........... 1.9 1.6 2.7 2.5 4.5 4.5

20–24 years........... .7 .7 .9 .8 .9 .7

25–54 years........... .2 .1 .1 .2 .1 .0

55–64 years........... 2.4 1.9 1.0 .1 1.1 .4

65 years and older. .7 .4 1.1 .5 1.0 .2

NOTE:  Boldface numbers denote the more accurate of the two models, where accuracy is defined by the smallest absolute difference between

the actual and projected values. When the two models yield the same number, neither number is in boldface.

Table 4.

change assumptions about the future values of independent variables, such as the unemployment rate, and obtain 

different projected values, whereas in a purely extrapolation-based model the past always dictates the future.

The model presented here is an exercise to test the effects 

of behavioral variables on projections of the labor force participation rate. The model could be extended to include not 

only other possible explanatory variables, but also age, race, 

gender, and ethnic groups at more detailed levels of aggregation.

Notes

 1 The Budget and Economic Outlook: Fiscal Years 2010 to 2020 (Congressional Budget Office, January 2010) (see especially p. 39), http://

www.cbo.gov/ftpdocs/108xx/doc10871/01-26-Outlook.pdf (visited 

Mar. 7, 2010).

2 Ibid., p. 39. 

3 For further information, see “Employment Projections,” chapter 

13 in BLS Handbook of Methods (U.S. Bureau of Labor Statistics, 1999), 

http://www.bls.gov/opub/hom/homch13_a.htm (visited Feb. 7, 2010).

4 All of the information on the Census Bureau’s interim population projections presented in this article is from “U.S. Population Projections: 2008 

National Population Projections” (U.S. Census Bureau, no date), http://

www.census.gov/population/www/projections/2008projections.html 

(visited Jan. 7, 2010). The article uses the 2008 National Population 

Projections released on August 14, 2008. The 2009 National Population 

Projections are a supplemental series to the 2008 series and provide various results based on different assumptions regarding international migration; all other methodological considerations and assumptions, including 

assumptions about mortality and fertility, are the same as those used in 

the 2008 National Population Projections. The 2009 series is useful for 

analyzing possible outcomes due to different levels of net international 

migration. The 2008 series, however, remains the Census Bureau’s recommended series for data users.

5 The projections of the Armed Forces and institutional population 
Monthly Labor Review • May 2011  41

according to age, gender, race, and ethnicity for 2008–18 are based on 

the Census Bureau’s estimations. 

6 The CPS is a personal-interview survey conducted monthly by the 

Census Bureau for the Bureau of Labor Statistics. The sample consists 

of about 60,000 households selected to represent the U.S. civilian noninstitutional population 16 years and older.

7

 For more information, see Paul F. Velleman, “Definition and comparison of robust nonlinear data smoothing algorithms,” Journal of the 

American Statistical Association, September 1980, pp. 609–15. 

8

 See Howard N Fullerton, Jr., “Notes on BLS labor force projections 

model,” unpublished manuscript (U.S. Bureau of Labor Statistics, 2000). 

9

 For further information, see “Employment Projections” in BLS 

Handbook of Methods; H. O. Stekler and Rupin Thomas, “Evaluating 

BLS labor force, employment, and occupation projections for 2000,” 

Monthly Labor Review, July 2005, pp. 46–56, http://www.bls.gov/

opub/mlr/2005/07/art5full.pdf (visited Aug. 9, 2010); and Howard 

N Fullerton, Jr., “Evaluating the BLS labor force projections to 2000,” 

Monthly Labor Review, October 2003, pp. 3–12, http://www.bls.gov/

opub/mlr/2003/10/art1full.pdf (visited May 7, 2010).

10 See Stekler and Thomas, “Evaluating BLS labor force.” 

11 A naïve model assumes that the growth of the labor force in the 

next 10 years will equal that of the previous 10 years.

12 See Fullerton, “Evaluating the BLS labor force projections”; and 

Ian D. Wyatt, “Evaluating the 1996–2006 employment projections,” 

Monthly Labor Review, September 2010, pp. 33–69, http://www.bls.

gov/opub/mlr/2010/09/art3full.pdf (visited Oct. 8, 2010).

13 See Wyatt, “Evaluating the 1996–2006 employment projections.” 

14 The behavioral model presented here has benefited from a similar 

model set forth by Dan Schrier in “British Columbia labor force participation rate model” (BC Stats, Ministry of Finance and Corporate 

Relations, Government of British Columbia, June 2000). 

15 Ibid. 

16 See Julius Shiskin, “Employment and unemployment: the doughnut or the hole?” Monthly Labor Review, February 1976, pp. 3–10.

17 See Carol Boyd Leon, “The employment-population ratio: its 

value in labor force analysis, Monthly Labor Review, February 1981, pp. 

36–45, http://www.bls.gov/opub/mlr/1981/02/art4full.pdf (visited 

Oct. 10, 2010).

18 See Gary Becker, The Economic Approach to Human Behavior 

(Chicago, University of Chicago Press, 1976).

19 By definition, the reservation wage is the lowest wage at which a 

worker would be willing to accept a particular type of job. A job offer 

involving the same type of work and the same working conditions, but 

at a lower wage, would be rejected by the worker.

An individual’s reservation wage may change over time, depending on a number of factors, such as changes in the individual’s overall 

wealth, marital status, or living arrangements; the length of time the 

person is unemployed; and health and disability issues. 

20 Becker, The Economic Approach; see also Gary Becker, A Treatise on 

the Family (Cambridge, MA, Harvard University Press, 1980).

21 See, for example, Claudia Goldin, “The Quiet Revolution That 

Transformed Women’s Employment, Education, and Family,” Richard 

T. Ely Lecture, published in American Economic Review, May 2006, 

pp. 1–21, http://www.economics.harvard.edu/faculty/goldin/files/

GoldinEly.pdf (visited Sept. 3, 2010).

22 See Abraham Mosisa and Steven Hipple, “Trends in labor force 

participation in the United States,” Monthly Labor Review, October 

2006, pp. 35–57, http://www.bls.gov/opub/mlr/2006/10/art3full.

pdf (visited Aug. 10, 2010). Besides their own finding regarding the 

connection between falling teenage labor force participation and increasing school attendance, Mosisa and Hipple cite the work of other 

researchers who have come to the same conclusion.

23 David Aaronson, Kyung-Hong Park, and Daniel Sullivan, “Explaining the Decline in Teen Labor Force Participation,” Chicago Fed Letter, no. 234 (Chicago, Federal Reserve Bank of Chicago, January 2007). 

24 Reuben Gronau, “Leisure, Home Production, and Work—the 

theory of allocation of time revisited,” Journal of Political Economy, December 1977, 1099–1123.

25 See Mitra Toossi, “Labor force projections to 2016: more workers in their golden years,” Monthly Labor Review, November 2007, pp. 

33–52, http://www.bls.gov/opub/mlr/2007/11/art3full.pdf (visited 

June 3, 2010).

26 For a detailed discussion of the projection of the U.S. economy, 

see Betty W. Su, “The U.S. economy to 2016: slower growth as boomers 

begin to retire,” Monthly Labor Review, November 2007, pp. 13–32, 

http://www.bls.gov/opub/mlr/2007/11/art2full.pdf (visited Apr. 9, 

2010).

27 Ibid. 

28 Katie Kirkland, “Declining teen labor force participation,” Issues 

in Labor Statistics, Summary 02–06 (U.S. Bureau of Labor Statistics, 

September 2002). (See also Steven Hipple, “Labor force participation 

during recent labor market downturns,” Issues in Labor Statistics, Summary 03–03 (U.S. Bureau of Labor Statistics, September 2003).)

29 See Aaronson, Park, and Sullivan, “Explaining the Decline.” 

30 See Mosisa and Hipple, “Trends in labor force participation.” 

31 Ibid. 

32 See Melody Sheehan, The Effect of Real Wage Rates on Female 

LFPR, vol. V (La Crosse, WI, University of Wisconsin, 2002).

33 See David H. Autor and Mark G. Duggan, “The rise in disability recipiency and the decline in unemployment,” NBER working 

paper 8336 (Cambridge, MA, National Bureau of Economic Research, 

2001).

34 CBO’s projection of the Labor Force (Congressional Budget Office, 

September 2004). 

35 See “Table A1. Marital Status of People 15 Years and Over, by 

Age, Sex, Personal Earnings, Race, and Hispanic Origin, 2009” (U.S. 

Census Bureau, January 2010), http://www.census.gov/population/

socdemo/hh-fam/cps2009/tabA1-all.xls (visited July 7, 2010).

36 See “Table MS-2. Estimated Median Age at First Marriage, by 

Sex: 1890 to Present” (U.S. Census Bureau, Sept. 15, 2004), http://

www.census.gov/population/socdemo/hh-fam/tabMS-2.pdf (visited 

May 17, 2011).

37 See Betsey Stevenson and Justin Wolfers, “Marriage and Divorce: Changes and Their Driving Forces,”Journal of Economic Perspectives, spring 2007, pp. 27–57. 

38 Mosisa and Hipple, “Trends in labor force participation.” The 

Personal Responsibility and Work Opportunity Reconciliation Act of 

1996 changed the Nation’s welfare system into one that requires work 

in exchange for time-limited assistance. The bill contains strong work 

requirements, a performance bonus to reward States for moving welfare 

recipients into jobs, State maintenance-of-effort requirements, comprehensive child support enforcement, and supports for families moving 

from welfare to work, including increased funding for childcare and 

guaranteed health care coverage. These changes encourage single mothers to enter the labor force. (For more information on the impact of 

welfare reform, see The National Evaluation of the Welfare-to-Work Grants 

Program: Final Report (Princeton, NJ, Washington, DC, and Cambridge, 

MA, Mathematica Policy Research, Inc., September 2004); and Robert 
Labor Force Participation

42  Monthly Labor Review • May 2011

F. Schoeni and Rebecca M. Blank, “What Has Welfare Reform Accomplished? Impacts on Welfare Participation, Employment, Income, 

Poverty, and Family Structure,” Working Paper No. 7627 (Cambridge, 

MA, National Bureau of Economic Research, March 2000).)

39 See Becker, A Treatise on the Family. 

40 Jacob Mincer, “Labor Force Participation of Married Women,” 

in National Bureau of Economic Research, Aspects of Labor Economics

(Princeton, NJ, Princeton University Press, 1962), pp. 63–106.

41 The Budget and Economic Outlook, p. 39. 

42 See Mitra Toossi, “Labor force projections to 2018: older workers staying more active,” Monthly Labor Review, November 2009, pp. 

30–51, http://www.bls.gov/opub/mlr/2009/11/home.htm (visited 

Apr. 3, 2010).

43 See Kenneth J. Matheny, “Trends in the Aggregate Labor Force,” 

Federal Reserve Bank of St. Louis Review, July/August 2009, pp. 297–309.