Technical Paper Series

Congressional Budget Office

Washington, DC

PROJECTING LONGITUDINAL EARNINGS PATTERNS

FOR LONG-RUN POLICY ANALYSIS

Amy Rehder Harris (e-mail: amyh@cbo.gov)

John Sabelhaus (e-mail: johnsa@cbo.gov

Congressional Budget Office

Washington, DC

April 2003

2003-2

Technical papers in this series are preliminary and are circulated to stimulate discussion

and critical comment. These papers are not subject to CBO’s formal review and editing

processes. The analysis and conclusions expressed in them are those of the authors and

should not be interpreted as those of the Congressional Budget Office. References in

publications should be cleared with the authors. Papers in this series can be obtained by

sending an email to techpapers@cbo.gov. The authors would like to thank Steve Lieberman,

Josh O’Harra, Kevin Perese, Michael Simpson, and Joel Smith of CBO’s Long-Term Modeling

Group for their support and helpful comments. 

Abstract

This paper presents a method for projecting person-level labor force participation and earnings

for the U.S. population in a dynamic micro-simulation setting. A dynamic micro-simulation

model starts with economic and demographic data for a current sample of the population, then

stochastically “ages” that sample forward through time, ultimately generating a longitudinal

micro data file, which is useful for studying Social Security and other long-term issues. The

stochastic projections described here proceed in four steps: in each year, every person is

sequentially assigned labor force participation, hours worked, unemployment spells, and

earnings. The equations used to project the sequence of outcomes are designed to generate

realistic cross-sectional and longitudinal heterogeneity, to capture cohort-level trends, and to be

consistent with the underlying macro/policy environment in which the outcomes are projected. 

The projections suggest significant increases in the overall percentage of females in the labor

force and the share of females working full time. Also, the relative earnings of lower-educated

males are expected to continue declining, while the relative earnings of higher-educated females

are projected to rise disproportionately. 

1

1. Introduction

Imagine a longitudinal micro data file that contains individual earnings along with basic

demographic variables such as age, sex, education, marital status, and marital pairings for a

representative future sample of the population. Such a data set would be invaluable for analyzing 

solvency and distributional questions about Social Security and other long-run policy issues

because the data file would have all the information needed to tabulate taxes paid and benefits

received under any set of program rules. The goal of dynamic micro-simulation modeling is to

produce a longitudinal data set like the one defined above. The role of this paper is to describe

one set of building blocks for such a dynamic micro model: methods for predicting labor force

participation and earnings. 

A dynamic micro-simulation model typically starts with an actual micro sample of the

population as of some base year, then “ages” that sample forward through time to generate the

prospective longitudinal data set. The simulation process involves applying Monte Carlo

techniques to a sequence of transition equations for each person in the model in each year of the

simulation. These transition equations are effectively person-level probability distributions for

demographic events (such as marital transitions and mortality) and economic events (such as

labor force participation and earnings). Given demographic and economic outcomes for the

micro sample, one can solve for the budgetary and distributional implications of various policies. 

The methods presented here for projecting micro-level labor force participation and

earnings are designed to accomplish several goals. First, the equations must be able to reproduce

the observed cross-sectional and longitudinal earnings heterogeneity observed in historical data,

which involves extending the partial earnings histories in the base micro sample and generating

complete earnings profiles for people born during the simulation. Second, the equations must be

able to capture cohort-level trends in labor force participation and earnings distributions and

appropriately extend those trends forward through time. Last, the equations for labor force

participation and earnings operate on a micro-sample of individuals, but the overall modeling

context may require reconciliation between those individual outcomes and a specified

macro/policy environment in which they are embedded.

The goal of generating reasonable cross-sectional and longitudinal heterogeneity is

accomplished through a sequence of labor market transition equations. The first step determines

which individuals participate in the labor force, the second step determines whether these

individuals work full or part time, the third step assigns unemployment spells to those working,

and the last step generates earnings outcomes as a function of the previously projected labor force

outcomes. Each step is designed to capture both the cross-sectional and longitudinal variability

for the process actually observed in historical data. As will be shown, those goals are consistent

with introducing substantial random, or unexplained, variability in labor force participation and

earnings across people at any point in time along with strong correlations between actual labor

supply and earnings outcomes for any given person over time. 

The same equations that are used to capture cross-sectional and longitudinal

heterogeneity must also be capable of reflecting cohort-level trends observed in the data. For

example, labor force participation of women has risen dramatically by cohort, so it is

inappropriate to use the labor force participation of today’s older women to project participation

for future cohorts of older women. Similarly, there have been marked changes in the relationship
1

The 2000 CW HS file has recently been released, which extends the sample collection an additional two

years. The CWHS is not a public-use file but has been made available to the CBO through an agreement to carry out

work that advances the study of Social Security.

2

between men’s and women’s earnings, and changes in relative earnings across education groups

(especially for men). Earnings equations are estimated with cohort terms and cohort by

education interaction terms (in addition to the expected life-cycle-related age terms) in order to

capture these trends. 

A final design goal for the labor force and earnings equations described here is to

maintain consistency between the simulated longitudinal sample and the underlying macro/policy

environment in which the micro model may be embedded. One implication of micro/macro

consistency is that person-level earnings are specifed in relative terms; the dynamic micro

equations generate individual earnings outcomes, which are then multiplied by a wage index in

order to solve for total earnings. 

The discussion thus far has ignored the fact that there are no existing public-use

longitudinal data files suitable for use as the base sample in a dynamic micro-simulation model. 

The results presented here are based on an administrative data set maintained by the Social

Security Administration, the Continuous Wage History Sample (CWHS). The CWHS contains

high-quality earnings data and basic demographics for a 1 percent sample of the population for

1951 through 1998.1 Remaking that data set into the dynamic model base file requires imputing

various demographic characteristics, reconciling differences between the administrative data and

various survey data sets used in the estimation, and performing a “historical” simulation ,which

ages the sample through the current year for which aggregate and budgetary data are available

(currently, 2001). 

Applying the preferred specifications for the labor force participation and earnings

equations to this micro base sample leads to several interesting predictions. Because estimated

cohort differences at younger ages affect labor force participation of those groups as they get

older, the projections suggest significant increases in the overall percentage of females in the

labor force and the share of females working full time. Also, the relative earnings of lowereducated males are projected to continue declining, while the relative earnings of highereducated females are projected to rise disproportionately. Those earnings projections are also the

result of extending the identified cohort effects across age groups and time.

2. Projecting Individual Labor Force Participation and Hours Worked

The projection of individual earnings begins with the projection of individual labor

supply, modeled in three sequential steps. The first step models labor force participation, which

depends on demographic variables such as age, sex, birth cohort, and marital status, along with

lagged labor force participation (to preserve longitudinal heterogeneity) and Social Security

beneficiary status. The second step models part- versus full-time employment, which also

depends on basic demographics and lagged values. In both the participation and hours equations,

the approach is to estimate separate logit equations by age and sex in order to capture changes in

participation across time and cohorts while simultaneously allowing variables like marital status
3

to have effects that differ by age and sex. The final step in the labor supply projection is to

assign unemployment spells to people in the labor force. 

Labor Force Participation

Over the past 20 years, starkly different labor force participation trends have been

observed for males and females. Figures 1 and 2 plot labor force participation rates by five-year

cohorts for males and females based on pooled cross-sections of the March Current Population

Survey (CPS). These labor force participation rates are the share of the population in the CPS

who worked at least two months in the previous year. Older males, 50-70, are working at lower

rates and more-recent cohorts of male teenagers are also working less while middle-aged

females, 25-49, are working at higher rates. In particular, the dip during child-bearing ages is

less pronounced for more recent cohorts of females. Projecting how these cohort-level trends

will continue is a key hurdle for the labor force participation model.

The probability an individual of a given age and sex works is estimated using a logit

equation estimated on pooled cross-sections of 16- to 90-year-olds from the March CPS, 1966-

2001. Covariates include marital status, Social Security beneficiary status, lagged labor force

participation, cohort effects, and a time trend. Rather than fit one participation equation for all

age groups, a flexible econometric specification is used because there is no single statement

about time/cohort effects that describes what is happening at all ages. The one distinguishing

feature for both men and women is the existence of the hump-shaped age profiles, but the exact

shape has evolved over time. It is clear that flexibility across both age and cohort dimensions is

crucial. One would not, for example, want to impose a cohort or time effect that is proportional

across all age groups because labor force participation rates have actually moved in different

directions along the age dimension. Similarly, introducing broad age groups in the specification

will not capture the interesting curvature of the underlying rates across age groups. Using a

polynomial in age to capture the curvature of the transition probabilities by age would impose the

same effect from the other control variables by age. But it is apparent that cohort/time effects

will vary with age, and it is also likely that the effect of some control variables (marital status or

lagged labor force participation) will vary across age groups.

The econometric approach used here can be thought of as an “age-centered” estimation

technique that is an extension of the group-based approach used by other models. A separate

participation equation is estimated for each single year of age and sex group, but the sample used

for each equation actually includes every observation within a fixed age band around that point. 

For example, the estimation for males aged 25 includes all males ages 21 through 29, although

the observations farther from the center are weighted less. As a further control, age itself is

actually one of the variables used in the estimation. This approach has the desirable aspect that

the effect of every other control variable (marital status, Social Security beneficiary status, lagged

labor force participation, and cohort/time effects) is allowed to vary with age (which would be

true with separate equations for each age group) but does not suffer from small sample size

problems (which would occur if single equations were estimated for each individual age). 

Although the CPS is not a longitudinal data set, it can be used to model labor force

participation as a function of lagged labor force participation because the March supplement asks

questions about work in the week previous to the survey and in the previous year. Thus, the
4

dependent variable is an indicator for whether the individual worked in the previous week, while

the lagged variable is an indicator for whether the individual worked at least two months in the

previous year. As one may guess, estimated labor force participation rates differ using these two

measurements. “Working last week” in the CPS is similar to the Bureau of Labor Statistics’

labor force participation rate measure that is the average of monthly participation rates. 

“Working last year” in the CPS is similar to the covered worker labor force participation rate

measure in the CWHS that is the total of all workers divided by the population. In the CWHS,

an individual is designated as working if he or she earned $101 or more in 1993 dollars during

the year. The covered worker concept counts a teenager who worked 10 weeks in the summer

equivalent to a full-time, full-year worker, whereas the BLS concept would only capture the

teenager in three monthly rates, and thus counts him or her as only one-fourth of a worker

relative to a full-time employee. Thus, an equation estimating the probability of working last

week will project fewer workers than is suggested by the covered worker concept in the CWHS. 

To reconcile the conceptual differences between the CPS and the CWHS, calibration

factors are used. These calibration factors equal the average difference in the labor force

participation rates by age, sex, and marital status observed in the CWHS data and the rates

predicted in the CWHS using the CPS equation for the most recent three years of data in the

CWHS. As expected, the values of these calibration factors are, on average, positive, with the

largest magnitudes for the youngest ages. In the projection, the applicable calibration factor is

added to the predicted probability of working for each individual before the labor force

assignment is made.

Unfortunately, even when comparing similar labor force concepts--the CPS “worked last

year” to the CWHS “covered worker”--the two data sets historically produce different labor force

participation rates by age. As can be seen in Figures 3 and 4, the CWHS has higher labor force

participation rates for both male and female teenagers. The CPS has much higher rates for males

during the prime working years. Older males in the CWHS also have lower reported labor force

participation rates than in the CPS due to incomplete reporting of earnings for workers not

covered by the Social Security system in the earlier years of the CWHS. Because the model

employs lagged labor force participation in the projections, the underlying data differences cause

stark adjustments in the early years of the projections. As will be seen later, higher participation

rates are projected for ages 25 through 40 as the young cohorts observed in the CWHS reach

middle age.

Coefficients for the labor force participation projection equations are shown in Tables 1

and 2, with statistically significant coefficient estimates indicated by BOLD. As mentioned,

there is both a constant and age coefficient for each single year of the age group, which at first

blush seems odd, but makes sense in the context of the age-centered approach. The age

coefficient is an estimate of the slope of the probability function at that age, but it may make

more sense to think of the actual simulation “constant” that applies to everyone of a given age as

the estimated constant term plus the age coefficient times the value of the age. 

The time trend is turned off for the projections. As noted in the tables, the cohort terms

are carried forward at each age based on the most recent cohort observed in the March CPS. This

does not capture the possibility that cohort trends observed at younger ages could provide

information about how those same cohorts may react at older ages. However, with lagged labor
2

This part-time hours definition accords with the definition used by the Bureau of Labor Statistics. 

Hotchkiss (1991) empirically estimates the employer-perceived cut-off between part-time and full-time work on

separate samples of males and females as 38 hours. When she divides the sample by occupational category, the fulltime hour cut-offs range from 31 to 40.

3Note that the PSID equations cannot estimate 1980 cohort terms since the data used only extend to 1992.

5

force participation as a covariate, any higher or lower rate for working at the youngest ages gets

carried through the entire lifetime for the individual. As Tables 1 and 2 indicate, the explanatory

power of lagged labor force participation outweighs the time and cohort trends with coefficients

of much greater magnitude across all ages and ubiquitous statistical significance. Thus, lagged

labor force participation is key to capturing the changing participation rates over observed

cohorts as they age. 

Labor force participation is projected for each person in the base file by comparing a

random draw with the computed probability of working. In the dynamic micro-simulation, the

individual labor force participation decision is made after demographic transitions, including any

marriages, divorces, or spouse deaths, and after benefit claiming decisions, including Disability

Insurance incidence and Old-Age Insurance claiming. This sequencing is important, since

current marital status and Social Security benefit status are covariates in the labor force

participation equation. Once the fertility module is more sophisticated and new babies are

assigned to mothers, the presence of a young child in the household will also be a key covariate

driving labor force participation. 

Full-Time Versus Part-Time Hours

Similar to trends in labor force participation, full-time work has been increasing for

young and middle-aged women and decreasing for older men. Figures 5 and 6 present the share

of the population working full-time (conditional on working at all) by five-year cohorts from the

Panel Study of Income Dynamics (PSID). Overall, the pictures look similar to Figures 1 and 2,

suggesting the same “age-centered” modeling technique as was used for the labor force

participation decision can be used to model the full-time work decision. 

Since the CPS does not collect current and lagged annual hours worked, it is necessary to

switch to the PSID to estimate the hours model. The probability of working full time is

estimated using a sample of working individuals ages 16 to 90 in the 1968 through 1992 waves

of the PSID. The sample is limited to those observations with data on hours worked in the

previous year, including those with zero lagged hours. The dependent variable for the logit

estimation is an indicator for whether the individual worked full time in the current year. 

Individuals reporting 1,750 or more hours annually, a minimum of 35 hours for at least 50 weeks,

are considered full-time workers.2 Part-time workers include those working full-time hours for

only part of the year (e.g., 40 hours for 26 weeks) and those working part-time hours for the full

year (e.g., 20 hours for 52 weeks). Covariates in the full-time logit equation include age, martial

status, lagged labor force participation, lagged full-time status, and birth cohort.3 The two lagged

variables identify separate effects for people who did not work last year, worked part-time last

year, or worked full-time last year. Tables 3 and 4 show the coefficients by sex and age, with the

statistically significant coefficients in BOLD.
4

Each person in the CPS is recorded as having at most one unemployment spell during the year if he or she

reports one or more weeks looking for a job or on a layoff.

6

Similar to labor force participation, cohort terms are carried forward at each age based on

the most recent cohort observed in the PSID. This extension does not capture the possibility that

cohort trends observed at younger ages could provide information about how those same cohorts

may react at older ages; however, with lagged labor force participation and full-time status as

covariates, any higher or lower rate for working full time at the youngest ages is carried forward

as the cohort ages.

Full-time work is assigned in the projections by comparing a random draw with the

computed probability of working full time for each person assigned to be in the labor force. The

full-time/part-time assignment is important for two reasons, projecting total hours worked and

assigning earnings. For the former, it is not enough to assign whether individuals will work full

time or part time, hours worked must also be assigned. Currently, hours are assigned in a static

manner. All full-time workers are assigned 2,080 annual hours (40 hours a week for 52 weeks),

which is actually lower than the average hours reported by full-time workers in the March CPS.

All part-time workers are assigned the average part-time hours by age and sex reported in the

March CPS for the previous year, covering the period 1984 to 1998. As seen in Figure 7,

measurable differences exist between average part-time hours worked by age and sex. Both male

and female part-time workers report more hours during middle age, with males reporting more

hours than females at all ages. Average hours reported for age 71 is the average over ages 71 to

90 and is assigned to any part-time worker above age 70. 

After a slight rise in the first 10 years, the micro model projects average hours worked

will remain flat, at around 1,660 hours (see Figure 8). This is not surprising since annual hours

worked by age and sex are fixed throughout the projection, and thus average hours worked in the

micro-simulation will only change due to a shift in the distribution of full-time and part-time

workers or a shift in the age/sex composition of the population. The model includes the

capability to target a change in the growth of average hours worked, such as the 0.1 percent drop

in average hours projected by the OASDI Trustees in 2002. This targeting of average hours is

carried out by applying an adjustment factor to all predicted probabilities of working full time. In

the case of targeting a drop in average hours worked, the adjustment pushes marginal full-time

workers (those whose random draw falls just above the unadjusted probability) into part-time

status, which one would expect to be driving any drop in average annual hours worked in the

economy. 

Unemployment Spells

The final step in the labor force projections is the assignment of unemployment spells. 

Unemployment is modeled very simply; the aggregate unemployment rate (determined by the

underlying macro model) is targeted by assigning a corresponding number of unemployment

spells based on an estimated relationship between the aggregate unemployment rate and the

number of unemployment spells reported in the CPS.4

 The fraction of people experiencing spells

is almost twice as large as the aggregate unemployment rate; an unemployment rate of 5.5

percent suggests 10.5 percent of the working population will experience an unemployment spell. 
5

The complete array of labor force participation values in the micro model is 0=out of the labor force,

1=part time, unemployed, 2=part time, not unemployed, 3=full time, unemployed, and 4=full time, not unemployed. 

7

In addition, approximately two-thirds of unemployment spells are experienced by full-time

workers, according to the CPS. Thus, after the number of spells is determined, the spells are

randomly allocated over the workers based on full-time/part-time status.5 

It is assumed that unemployment occurs in increments of 13 weeks, or 1/4 of a year. This

assumption is slightly less than the 15-week mean unemployment duration found in the CPS over

1994-2000 (Abraham and Shimer, 2001). An individual assigned to experience an

unemployment spell has his or her hours, and thus earnings, reduced by 25 percent. In reality,

some individuals are unemployed for an entire year, while others are unemployed for only a few

weeks. Assuming that everyone experiences roughly the average length unemployment spell will

not affect total hours worked, a key variable for the macro model. However, this simplifying

assumption reduces the effects of heterogeneous labor force experiences on earnings. Future

work will change the unemployment model to a random assignment of unemployment spells for

all working individuals as a function of age, sex, education, marital status, part-time/full-time

employment status, lagged earnings, tenure, private pension plan coverage, and information on

previous unemployment spells. As a part of this new model, heterogeneous lengths for

unemployment spells will also be introduced.

3. Projecting Individual Earnings

Given projections of labor supply for the micro sample, the next step is to assign

earnings. The approach starts with standard equations that project full-time-equivalent earnings

based on control variables like age, sex, education, and cohort. These earnings equations are able

to capture trends and compositional effects, yet are still consistent with the underlying

macro/policy environment because the individual earnings measures are all relative to an overall

wage index. One key to using this approach for micro-simulation is to explicitly consider the

idiosyncratic component of earnings. Essentially, the significant unexplained variability in

earnings is decomposed into “permanent” and “transitory” deviations from predicted values that

evolve over time. In the simulation model, the two components are “shocked” in each year,

generating the sort of longitudinal and cross-sectional variability observed in historical data. 

Relative Earnings Across Cohort and Age Groups

Figures 9 and 10 present mean real earnings by five-year cohorts for males and females

from the PSID. Both sexes exhibit hump-shaped age-earnings profiles reflecting changing wages

and changing hours, with males peaking at much higher mean values. Similar to the rising labor

force participation and full-time employment status, mean earnings are also rising for more

recent cohorts of females.

As noted above, the micro model projects relative earnings or real, full-time-equivalent

(FTE) earnings. Four adjustments are applied to the nominal earnings available in the PSID to

derive the real, FTE person-level earnings used in the estimation. The first two adjustments

remove the effects of inflation and productivity. First, earnings are converted to 1993 dollars
6Alternatively, the CPI-X1 inflation index could have been used, giving different answers for the historical

inflation adjustments. The CPI-W is used because that is the inflation measure used by SSA.

7

Lettau (1994) discusses the theory behind the full-time wage premium. Possible explanations include

endogeneity in the choice of part-time work among the least productive or dual labor markets where the bad jobs are

disproportionately part time.

8

using the consumer price index for urban wage earners and clerical workers, the CPI-W.6

Second, earnings are adjusted to 1993 productivity levels using an index that accounts for

historical real wage growth. Removing the aggregate component of earnings growth for

individual earnings is consistent with an environment in which the macro sector generates overall

average earnings outcomes. In interpreting the estimates below, it helps to keep in mind that the

ultimate goal is to predict the distribution of earnings across a population (cross-sectional and

longitudinal) as opposed to trying to predict earnings levels of particular individuals at particular

points in time. Still, it is important to note that adjusting longitudinal earnings for aggregate

price and productivity does not necessarily mean that earnings distributions are stable over time. 

The third and fourth adjustments create FTE earnings values, and thus only affect

earnings of part-time workers. As noted above, anyone in the PSID reporting less than 1,750

annual hours of work is designated part time. The third adjustment multiplies earnings for parttime workers by the ratio of full-time hours (2,080) to hours worked in part time. In anticipation

of the method for assigning hours in the projections, PSID earnings are adjusted using the

average part-time hours by age and sex observed in pooled cross-sections of the March CPS (see

Figure 7). 

The fourth adjustment accounts for the full-time wage premium, i.e., higher wages paid to

those working full time.7 The literature has documented a range for the full-time wage premium

between 3 and 30 percent (Averett and Hotchkiss, 1996). Ehrenberg et al. (1988) estimate a

wage differential of 18 percent using individual data from the 1984 March CPS. They also find

negative and statistically significant probabilities of health insurance and pension coverage for

part-time workers, which suggest even lower total compensation for those workers. Lettau

(1994) estimates a 16 percent premium for full-time workers using a sample of earnings for

employees working in the same job at the same establishment. Based on these estimates from the

literature, a 15 percent discount is applied for those workers designated as part time.

Although not shown, the age-earnings profiles for FTE earnings from the PSID look

similar to Figures 9 and 10, but as expected all levels are higher, particularly at the youngest and

oldest ages, when part-time work is more prevalent.

Estimated Earnings Equations

Earnings equations are estimated for males and females ages 16 to 90 in the labor force

using data from the PSID, 1968-1992. Log, real, full-time-equivalent earnings are regressed on

age dummies and on age dummies interacted with an indicator for 14 or more years of education,
8When possible, high-educated status was limited to those who reported an associate’s degree or higher. 

Unfortunately, the PSID only reports years of education. It is possible that 14 years of education could reflect

someone who dropped out of college without completing any degree, potentially biasing down the effect of a college

degree in the earnings estimation.

9

the minimum for a college degree.

8

 Individuals attending college start full-time work at a later

age and have steeper personal earnings growth, particularly during the first years of full-time

work. Therefore, age-earnings profiles should differ markedly by education status. Murphy and

Welch (1990) recognize both these points in their analysis of age-earnings profiles. They use

different starting ages for accumulating labor market experience and document the different

slopes of age-earnings profiles by education status. The PSID data confirm that age-earnings

profiles differ by education status, where the coefficients on the age/education dummies are

jointly significant with F-statistics of 76 for males and 42 for females.

Rising wage inequality by educational status dampened wage growth for males with

lower education between the middle 1970s and early 1990s. Gohmann et al. (1998) compare

age-earnings profiles between 1979 and 1989 for a split into four education statuses. They found

that over the decade, real earnings actually dropped for workers with less than a college

education. Additional research has also documented this phenomenon (see Katz and Murphy,

1992; Juhn, Murphy, and Pierce, 1993; Bound and Johnson, 1992). To capture this observed

trend, 10-year cohort effects, and cohort effects interacted with high education status, are also

included in the earnings equations. Similar to the labor force and hours modules discussed

above, in the simulations the cohort terms are carried forward at each age based on the most

recent cohort observed in the PSID.

Table 5 presents the coefficients for the earnings equations with separate columns by

education status. The high-educated columns present the sum of the age (cohort) coefficient and

the coefficient on the interaction of age (cohort) and education. Coefficients in BOLD reflect

statistically significant interaction terms. 

As discussed above, in the case of labor force participation, using an equation estimated

on one data set to project future values on a second data set can be problematic. Although

earnings in the PSID and the CWHS can be adjusted for the same price and productivity

differences, because both data sets are samples of the population there is bound to be some

difference in the distribution of earnings. Figures 11 and 12 present mean real earnings by cohort

for males and females from the CWHS. Although similar to Figures 9 and 10, differences do

exist, particularly in the slope of the profile over the youngest ages and the level of peak

earnings. These differences are addressed in the projections through the use of calibrations, or

mean error adjustments computed by comparing actual with predicted earnings for the base file. 

The recorded earnings in the 1998 CWHS are adjusted to FTE values using the four adjustments

discussed above. Note that the CWHS historical data do not include information on hours

worked, thus the third and fourth adjustments are weighted by the predicted probability of parttime employment, with the probabilities based on the hours model described in Section 2, that is:
10

fte_earningsit = actual_earningsit*prob_full-time + actual_earningsit*

(1-prob_full-time)*(1/1.15)*(CPS_average_part-time_hours(age,sex)it/2080)

Using the equation coefficients estimated on the PSID, earnings are also predicted for individuals

with recorded earnings for 1998 in the CWHS base file. The actual FTE earnings from the

CWHS are then compared to the predicted FTE earnings. The measured differences, interpreted

as the error from using the PSID to estimate an equation used to predict on the CWHS, are

averaged by age and sex. These errors are relatively small, with log values ranging from 0.56 to 

-0.35 for prime working ages, with slightly larger magnitudes at ages above 70. The resulting

FTE earnings mean error adjustments by age and sex are added to all future projections of

earnings.

Modeling Idiosyncratic Earnings Differentials

In principle, the estimated earnings equations could be used to make individual earnings

projections by simply adding an error term randomly drawn from the probability distribution of

the equation residuals to the earnings prediction based on person i’s age, sex, birth year, and

education. However, equations designed to capture the average effect of controls like age and

education are not capable of generating individual earnings patterns with the desired amount of

cross-sectional and longitudinal heterogeneity. In particular, individuals with actual earnings on

either side of the predicted value tend to stay there in future periods. The desired heterogeneity

can be generated by separately modeling the equation residuals as a time series; effectively, the

idiosyncratic component of earnings is modeled after the predictable component is determined. 

In the approach here the idiosyncratic component evolves over time through a sequence of

uncorrelated “permanent” and “transitory” shocks. 

Specifically, log earnings are modeled as the sum of three pieces. The first is the

predictable component, which is the mean for person i’s age/sex/education/cohort group from the

estimated earnings equation. The second component is a “permanent” earnings differential for

person i that measures how the expected difference between actual and predicted values tends to

change slowly over time. Permanent earnings differentials evolve through a sequence of

symmetric shocks designed to capture longitudinal heterogeneity. The third component is a

“transitory” shock for person i that measures the difference between actual and predicted

earnings. Because transitory shocks are not auto-correlated, they capture any residual variation in

earnings that is not expected to persist over time. Both the permanent and transitory income

shocks are specified as independent, identically distributed (i.i.d.) processes with an expected

value of zero.

The decomposition of the difference between person i’s predicted and actual earnings into

transitory and permanent differences has proved useful in other contexts in which the goal was to

generate realistic earnings heterogeneity in a micro sample. The approach below follows closely

the work of Carroll (1992) that focused on uncertainty about future labor income while studying

consumption behavior in a stochastic setting. Alternative models of the earnings process can

lead to different predictions about earnings patterns (and thus consumption and saving) but the
9

 See, for example, Carroll (1997); Hubbard, Skinner, and Zeldes (1995); and Huggett and Ventura (2000). 

11

permanent/transitory decomposition seems most realistic.9

 Decomposing earnings changes into

permanent and transitory shocks is also consistent with the patterns of within-cohort earnings

variation by age, as documented by Deaton and Paxson (1994). 

In a micro-simulation, the approach of using permanent and transitory shocks involves an

initial permanent earnings differential assignment for each individual, then a random draw of

permanent and transitory shock values in each year. The value of the permanent shock

determines movement in the permanent earnings differential over time, and the value of the

transitory shock determines actual income. Because the earnings decomposition is modeled in

logs, one can think of this as a ratio decomposition--the permanent earnings differential indicates

the ratio of “potential” earnings to the mean for person i’s age/sex/education/cohort group, and

the transitory shock applies a further ratio to determine actual (relative to potential) earnings.

The first step in building this type of earnings projection model is to estimate the

variances for the permanent and transitory earnings shocks. Again, the approach that follows is

based on the work of Carroll (1992). Given longitudinal data and an earnings equation, the

estimation involves computing the actual residuals for every person at each age, then measuring

the changes in error terms over time for each individual. If the errors were all positive or

negative by the same amount at every age for every individual, there would be complete

persistence in the earnings differential and both the permanent and transitory shocks would have

zero variance. To the extent that equation residuals do vary over time, those changes can be

decomposed into permanent and transitory movements.

Computing and averaging the variance of residual earnings changes for every individual

in the longitudinal data file creates a vector of up to “m” period gaps, where m+1 is the number

of years in the panel. For example, if three years of data are available, for each person there are

two sets of one-year gaps (between periods one and two and periods two and three) and one set

of two-year gaps (between periods one and three) that can be used to compute the variance terms. 

Using CWHS data for 1984 to 1998 yields up to 14 periods for earnings errors for each person. 

The variance of changes in the estimated equation residuals in the CWHS are shown separately

for men and women in Table 6.

If movement in the idiosyncratic errors is consistent with both permanent and transitory

shocks, the variance of changes in the earnings equation residuals will rise with the length of the

gap because the changes further apart in time are more persistent. Indeed, for any two pairs of

variances measured at distinct gaps, there are two known sample variances and two unknowns

(the variances of permanent and transitory shocks) to solve for. The approach here is to follow

Carroll (1992) and use all of the information from the 14 periods to generate more efficient

estimates of the variance. The estimated variances are regressed on the length of the gap “m”

where the slope term is the variance of the permanent shocks and the intercept (divided by two)

is the variance of the transitory shocks. As Carroll (1992) points out, there is likely to be shortrun correlation that would distort the decomposition, so the preferred approach is to fit the

regression over gaps between periods three and 14.

Applying the Carroll (1992) methodology to the CWHS data yields estimates for the

standard deviation of permanent shocks that are about 0.1 for men and 0.06 for women. The
12

estimated standard deviations for the transitory shocks are roughly 0.55 for men and 0.60 for

women, but those are likely to be biased upward because of the unexplained widening of the

earnings distribution in the 1990s. Therefore (after some experimentation) the actual values used

in the simulations are 0.35 and 0.40. These differences between men and women are intuitive,

because men’s earnings disperse more as they move through the life cycle, and thus permanent

shocks are quantitatively more important.

Before these permanent and transitory shocks can be used to project earnings, it is

necessary to assign permanent earnings differentials to individuals in the micro base file (who

have some earnings history) and to new individuals “born” into the model (specifically, those

who have no actual earnings history). The key to understanding how initial permanent

differentials are assigned is to consider the following: at any point in time, the overall variance of

the earnings equation residuals is the sum of the variance of the permanent differentials and the

variance of the transitory shocks. Within any given cohort, there is some initial or “base”

dispersion of earnings differentials, which linearly grows as the cohort ages because of

permanent shocks. It is straightforward to show that the overall variance for a sample (whose

earnings have always been generated by these processes) is the sum of the base permanent

differential variance, the permanent shock variance times the average age, and the transitory

variance. Thus, the base variance is solved for by subtracting the other pieces.

The error variance decomposition described above is applied to the CWHS sample in

Table 7. The overall variance of the earnings equation error terms is 0.9706 for men and 0.8582

for women. The average age term is measured relative to age 22 (where base earnings

differentials are assigned to the sample to avoid the confounding effects of pre- and post-college

jobs; for individuals aged 16 to 21, the error is assumed to be all transitory shock). Subtracting

the estimated permanent and transitory components of earnings variation from the total yields

estimates for the base permanent differential variance of 0.6454 for men and 0.6264 for women. 

That implies a significant amount of initial, unexplained earnings heterogeneity within every

cohort. In a sense, that result is just a restatement of general conclusions about the sort of

earnings equations used here: the equations show statistically significant effects of age, sex,

education, and cohort on earnings, but the overall explanatory power of the equation (measured

by the R-squared) is generally quite low.

Given the estimated variance of the base values for the permanent differentials, it remains

to assign those differentials to actual observations. For new observations that have no earnings

history, one simply draws a value from the distribution of permanent differentials (in practice, the

model uses a bootstrap draw). For individuals with some earnings history as of the base period

(1998), an inference about how much of their error is transitory and how much is permanent

differential is needed. The approach is to assume (since transitory shocks are expected to be

zero) that the actual error is an unbiased estimate of the permanent earnings differential. 

However, the overall variance of the permanent differentials has to match the variance derived

above, so weights are applied to earnings equation errors to correct for differences and make the

overall variances match.

The actual projections of earnings conditioned on labor force participation can now be

summarized. In the first year that an individual is projected to work, an initial permanent

earnings differential is assigned (or derived in the first year of the simulation if the individual has
10An exception is the SIPP data file used by the SSA in its MINT model, which has linked earnings and

Social Security claims and benefits information. The matched SIPP data still suffer from some of the problems

mentioned with respect to the PSID–sample size and coverage of the earnings distribution. 

11Since the CWHS was originally set up for administering Social Security, recorded earnings were truncated

at the taxable maximum until the early 1980s. Imputations for earnings above the taxable maximum are made for the

1951-1983 period using data on the frequency each individual reports truncated earnings and targeting earnings

distributions from the CPS. More details are available on request.

12Throughout the imputations and projections, race is not used because the information on the CWHS

(collected when the Social Security Number was issued) is incomplete.

13

an actual earnings history). In that year and all future years, the predictable component of

earnings is projected using the earnings equation, the permanent earnings differential is

augmented by a randomly drawn permanent earnings shock, and the transitory shock is drawn for

the current year. These three components are added to give predicted real, FTE earnings. The

FTE earnings are then adjusted to actual earnings according to assigned labor force status (that is,

scaled down if the person is part time and/or unemployed), inflation, and real wage growth. 

4. Developing a Micro-Simulation Base File

The base longitudinal data file used for the micro-simulation described here is the

Continuous Work History Sample. This data set, administered by SSA, contains longitudinal

earnings information on a 1-percent stratified cluster sample of all people ever issued Social

Security numbers, which translates into a sample size of roughly three million individuals with

earnings reported for 1951 to 1998 (Smith, 1989). Each year, the entire sample is followed,

recording OASDI and HI taxable earnings, total wages plus employee defined contribution plan

deposits (starting in 1984), self-employment status, OAI and DI worker benefit entitlement, or

death. In addition, new individuals are introduced to reflect the issuance of new Social Security

numbers. This sampling structure makes for a truly unique longitudinal data set, where each

annual cross-section represents 1 percent of the population with Social Security numbers. 

The CWHS is preferable for use as a dynamic micro-simulation base file when compared

to the available public-use cross-sectional or longitudinal files such as the CPS or PSID.

Available cross-sectional data sets do not have the requisite longitudinal histories needed to

project forward using dynamic micro-simulation.10 The CWHS contains earnings records for

each individual that span more than 40 years. Publicly available longitudinal data sets like the

PSID are much smaller than the CWHS, and those data also suffer from recall bias and response

problems for the highest-earning individuals. As an administrative data set, the CWHS has much

better reporting of income for the entire earnings distribution.11 The downside is that the

administrative nature of the CWHS limits the demographic data to the information that is

available on the initial Social Security Number application: year of birth, sex, and race.12 

As noted in the discussion of labor force participation, one complication with using the

CWHS as a micro base file but estimating projection equations using other survey data like the

CPS is that the concept of labor supply differs. Labor force participation in the CWHS is a

“covered worker” concept, such that positive earnings above $100 indicate an individual is
13See O’Harra and Sabelhaus (2002) for a discussion of the marital imputations.

14

working. Many data surveys, such as the CPS, base labor force participation on reports of hours

worked during the survey month or week. Therefore, discrepancies emerge in labor force

participation rates, especially in age/sex groups for whom seasonal or part-time work is

important. Table 8 shows labor force participation rates by age and sex, and the effect is

pronounced for the youngest age groups. As discussed above, these discrepancies are controlled

for with the use of calibration factors that align the CPS-based estimates with observed CWHS

participation rates.

A second complication is the lack of information on hours worked in the CWHS. 

Therefore, part-time or full-time status is imputed in each year of the historical data as part of the

preparation of the micro base file. Before that imputation is carried out, marital histories and

education are imputed.13

 

Education, currently an indicator for 14 or more years of schooling, is imputed based on

the earnings projections equations. Using the earnings equation described in Section 3, earnings

are predicted under assumptions of both high and low education. These two predictions are

subtracted from actual earnings, giving two errors based on the education assumption. The

probability that an individual would have that observed error given each education status is

computed using a cumulative normal distribution, with variance taken from the PSID equation

results. These probabilities are averaged over all years the individual has earnings and

normalized to give a measure of the probability of being high educated relative to the probability

of being low educated. The average probabilities of being high educated are adjusted in order to

target observed educational distributions by birth cohort from the CPS. A random draw from the

uniform distribution over the unit [0,1] interval is compared to the individual’s adjusted

probability of being highly educated. High-educated status is assigned if the draw is less than the

computed probability. Individuals without any recorded earnings are randomly assigned an

education status based on the educational distribution observed in the CPS for the corresponding

birth cohort. 

Table 9 presents the share of high-educated males and females for various cohorts in the

CPS and the CWHS imputations. Both sexes have experienced an upward trend in the share with

a college degree, although the male share dipped for the 1955-1959 cohorts. Starting with the

1955-1959 cohorts, more females have high-educated status than do males. In the projections, all

future cohorts are randomly assigned an education status at birth corresponding with the

observed educational distribution for the 1965-1975 cohorts in the CPS. 

Figures 13 through 20 present age-earnings profiles split by education status from the

PSID and CWHS as a demonstration of how well the education imputation worked. Although it

appears that the low-education imputation captured lower earners and the high-education

imputation captured higher earners, the differences are not quite as stark as in the PSID data. 

Even though the education imputation relies on earnings, the ultimate assignment is still done

based on a random draw compared to the probability of being highly educated. Another possible

approach would be to assign the highest earners to high education status. This assignment would

lessen the bias reported above but would remove the important heterogeneity found in real data,

where some high-educated individuals earn less than some low-educated individuals.
15

Hours imputations are made using equations similar to the projections equations

described in Section 2; however, actual earnings are included as a covariate. A time trend is also

included to capture the rising share of both men and women who are working full time over the

past few decades, where the trend for women was much greater. For everyone with positive

labor force participation (i.e., reported real earnings of more than $100) the probability of having

worked full time that year is computed based on a logit estimated on the PSID. For the first year

of observed earnings for any individual (censored by data collection to 1951 for many in the

sample), it is not possible to use lagged labor force participation or full-time status in the

equation. Thus, the probability of working full time for the first observed year is based on age,

sex, marital status, time, birth cohort, and log real earnings. (Note that age, time, and birth cohort

effects can all be estimated since birth cohorts are 10-year dummies). Once an initial hours

imputation is made, all further imputations are based on a logit equation including lagged labor

force participation and lagged imputed full-time status. 

The last consideration when working with the CWHS data file is that there will always

exist a lag between the availability of the micro data and the first year of the integrated

micro/macro model projections, or the first year for which aggregate data are not available. 

Micro labor force and earnings outcomes for those interim years are generated using the

projection methods discussed above. Because aggregate data for labor force, hours, and earnings

are known for those interim years, micro assignments for earnings, labor force participation, and

hours are reconciled with those aggregates. This reconciliation is accomplished by solving for

“alignment” factors that actually provide useful information--those values indicate how well the

micro model is predicting “jump-off” values for labor force and earnings.

5. Properties of Simulated Earnings Trajectories

As noted in the introduction, the goal of this paper is to explain the methods used to

generate longitudinal earnings for a future sample of the population in a dynamic microsimulation context. Given the structure described above, the inputs to projecting micro earnings

patterns include a base micro data file with economic and demographic information for a current

sample of the population; a set of equations for labor force participation, hours worked, and

earnings; and another set of transition equations that predict the independent variables that affect

earnings (in this case, education, Social Security beneficiary status, and marital status). In the

case of earnings, it is also important to introduce reasonable time-series properties for the error

term so that the generated longitudinal heterogeneity is similar to that which exists in real data. 

All of those pieces together can be used to create long-term projections of earnings for a

representative sample of the population. 

The discussion below focuses on the micro labor force and earnings outcomes in history

and a baseline simulation of the integrated micro/macro model. A complete description of the

rest of the model is beyond the scope of this paper, but details can be found in Harris et al.

(2002). Briefly, the macro environment is a standard neoclassical growth model in which

average wage growth and interest rates are determined using simple first-order conditions and a

Cobb-Douglas production technology. Although the trajectory of GDP growth is sensitive to

choices about overall budget policy and private saving behavior (which together determine

capital accumulation), the scenario here is intended to be fairly neutral. That is, overall economic
16

growth is generally stable over the 75-year period, varying principally with changes in the labor

force that result from changing demographics and the micro model described here. 

The first set of results are labor force participation projections. Figures 21 and 22 present

historical and projected labor force participation rates by age and cohort for males and females in

the micro sample. For males, projections are quite steady over the cohorts. The estimates

suggest a slight increase in labor force participation projected for ages 16 to 49. As noted in

Section 2, this reflects a higher labor force participation rate observed for the 1976-1980 cohort

at ages 16 to 19 in the CWHS that feeds through to later ages for that same cohort due to the

lagged labor force participation rates, and all future cohorts due to the cohort effect. For the

females, the increase in labor force participation rates at nearly all ages continues through the

1986-1990 cohorts, then levels off for future cohorts. 

The shifts in labor force participation by sex and cohort have some important cumulative

effects in lifetime labor force participation patterns, especially for females. Figures 23 and 24

show the number of years the current age 62 cohort spent in the labor force. The cumulative

participation rates for males are not predicted to undergo much change. This is not the case for

females, however. Only about 24 percent of females who reached age 62 in 2002 had worked 36

or more years. That share is expected to rise to over 60 percent for the groups reaching age 62

after about 2040.These projections are the direct effect of extending identified cohort-level

differences (or lack of differences in the case of men) across age groups and time. 

The second set of results are full-time versus part-time employment projections. Figures

25 and 26 present historical and projected full-time shares for the CWHS micro base file by sex

and cohort. As with labor force participation, the projected share of males working full time is

fairly steady over the cohorts, with a slight increase over the age 25 to 45 range. For females, the

rising trend in the share working full time during child-bearing years is projected to continue

through the 1990 cohort, then stabilize.

The final set of results are the earnings projections. There are several questions to be

addressed when considering the earnings projections. First, how are average earnings within sex

and education groups projected to change over time? Second, how are the percentiles of the

annual earnings distribution projected to change? Finally, do the estimated equations and

permanent/transitory shocks to earnings work together to preserve the desired levels of

longitudinal heterogeneity? These questions are addressed in turn.

First, the models developed here suggest continued changes in average earnings across

sex and education groups. Figures 27 to 30 show average earnings in history and projection

periods by age, sex, cohort, and education status. It is important to keep in mind that the concept

of “real” earnings in these graphs involves removing the effects of both inflation and average real

wage growth; these are, in a sense, relative earnings normalized to 1993 dollars. 

Figures 27 and 28 show average earnings for males, first the less than college group, then

those with 14 or more years of education. The projections of relative average earnings for the

low-educated males shows deterioration for future cohorts in their 30s, 40s, and beyond. That is

the effect of carrying the cohort by education terms in the earnings equation forward through

time. It is consistent with assuming that the declines experienced by less than college-educated

males will continue in the future. Figure 28 shows that some of those relative losses are offset by

the expected relative gains of high-educated males.
17

Figures 29 and 30 show historical and projected average earnings for females, again split

by education status. Unlike males, the relative earnings of low-educated females are not

expected to change much for future cohorts. (Again, one need only look at the cohort by

education terms in the estimated equations (Table 5) to see why this is true.) Relative earnings of

high-educated females in middle age and beyond are expected to rise for future cohorts, however,

continuing the strong growth for recent cohorts at younger ages. 

The second set of outcomes to consider are various percentiles of cross-sectional earnings

distributions. Figures 31 and 32 show 1st, 5th, 25th, 50th, 75th, 95th, and 99th percentiles for men

and women between 1984 and 2076. The trends show that the approach of extending cohort

effects (but shutting off time trends) has modest effects on earnings cross-sections. Earnings for

men and women at various points in the distribution show some continuation of trends in the

forecast period, but generally the distributions stabilize near their current values. This outcome

is also affected by the specifications for the idiosyncratic component of earnings; if the variance

of the base permanent differentials or the permanent shocks increased over time, the percentiles

of the cross-sectional earnings distribution would widen.

Given the stability in annual cross-sections, the next question to address is whether the

model is able to capture the desired longitudinal heterogeneity in earnings patterns. Tables 10

and 11 show cross-tabulations of annual and lifetime earnings by sex for selected cohorts of 50-

60-year-olds at various points in time. If every person’s annual earnings decile always matched

his or her lifetime earnings decile, the diagonal elements would be exactly 10 percent. The

amount of variation in each person’s earnings over his or her life is reflected in the off-diagonal

terms. Starting in the top panel, which is based on actual CWHS earnings data from 1998, it is

clear that there is significant variation in individual earnings in historical data. The other panels

(for 2010, 2040, and 2070) show that the model is generally successful at replicating that

variation in the projections, although there is some increased concentration in the joint

distribution of annual and lifetime earnings at the lowest deciles. 

Figure 33 reinforces the conclusion that the permanent and transitory shocks are creating

the desired longitudinal heterogeneity. This figure plots the average variation in the difference

between projected annual earnings over ages 16 to 59 for each individual and average earnings in

the economy for the corresponding age, sex, and year, normalized by average lifetime earnings. 

If there were significant changes in the patterns of permanent earnings differentials in the

projections, the overall variance would deviate from history, which is not the case. There is an

unexplained spike in the amount of variation for the 60-year-olds in the early part of the

projection period, but the variation quickly settles down to historical levels. 

6. Conclusions

A key component in any dynamic micro-simulation model of the economy is the

projection of longitudinal earnings. This paper presents methods for projecting individual labor

force participation, hours, unemployment spells, and earnings, using the CWHS as the micro

base file. The equations used to project the sequence of outcomes are designed to generate

realistic cross-sectional and longitudinal heterogeneity, to capture cohort-level trends, and to be

consistent with the underlying macro/policy environment in which the outcomes are projected. 

The projections suggest significant increases in the overall percentage of females in the labor
18

force and the share of females working full time. Also, the relative earnings of lower-educated

males are expected to continue declining, while the relative earnings of higher-educated females

are projected to rise disproportionately. 

7. References

Abraham, Katharine G., and Robert Shimer (2001). “Changes in Unemployment Duration and

Labor Force Attachment,” NBER Working Paper 8513 (October).

Averett, Susan L., and Julie L. Hotchkiss (1996). “Discrimination in the Payment of Full-Time

Wage Premiums,” Industrial and Labor Relations Review, 49:2, 287-301.

Bound, John, and George Johnson (1992). “Changes in the Structure of Wages in the 1980's: An

Evaluation of Alternative Explanations,” The American Economic Review. 82(3), 371-

392.

Carroll, Christopher D. (1992) "The Buffer-Stock Theory of Saving: Some Macroeconomic

Evidence." Brookings Papers on Economic Activity, 2: 61-135.

Carroll, Christopher D. (1997). "Buffer-Stock Saving and the Life Cycle/Permanent Income

Hypothesis," The Quarterly Journal of Economics, 112(1), 1-56.

Deaton, Angus, and Christina Paxson (1994). “Intertemporal Choice and Inequality.” Journal of

Political Economy, 102:3, 437-467.

Ehrenberg, Ronald G., Pamela Rosenberg, and Jeanne Li (1988). “Part-time Employment in the

United States,” in Employment, Unemployment, and Labor Utilization, ed. Robert A.

Hart, Unwin Hyman: Boston, 256-287.

Gohmann, Stephan F., Myra J. McCrickard, and Frank Slesnick (1998). “Age-Earnings Profiles

Estimates: Do They Change Over Time?” Journal of Forensic Economics. 11(3), 173-

188.

Harris, Amy Rehder, Josh O’Harra, Ben Page, Kevin Perese, John Sabelhaus, Michael Simpson,

and Joel V. Smith (2002). “Overview of the Congressional Budget Office Long-Term

(CBOLT) Policy Simulation Model.” Unpublished paper, Washington, D.C.:

Congressional Budget Office (December).

Hotchkiss, Julie L. (1991). “The Definition of Part-Time Employment: A Switching Regression

Model with Unknown Sample Selection,” International Economic Review, 32:4, 899-917.
19

Hubbard, R. Glenn, Jonathan Skinner, and Stephen P. Zeldes (1995). "Precautionary Saving and

Social Insurance." Journal of Political Economy, 103: 360-399.

Huggett, Mark, and Gustavo Ventura (2000). "Understanding Why High Income Households

Save More Than Low Income Households." Journal of Monetary Economics, 45:2, 361-

397 (April).

Juhn, Chinhui, Kevin M. Murphy, and Brooks Pierce (1993). “Wage Inequality and the Rise in

Returns to Skill,” Journal of Political Economy, 101(3), 410-442.

Katz, Lawrence F., and Kevin M. Murphy (1992). “Changes in Relative Wages, 1963-1987:

Supply and Demand Factors,” The Quarterly Journal of Economics, 35-78.

Lettau, Michael K. (1994). “Compensation in Part-Time Jobs versus Full-Time Jobs: What if the

Job is the Same?” BLS Working Paper 260, U.S. Department of Labor, Bureau of Labor

Statistics.

Murphy, Kevin M., and Finis Welch (1990). “Empirical Age-Earnings Profiles,” Journal of

Labor Economics, 8(2), 202-229.

O’Harra, Josh, and John Sabelhaus (2002). “Projecting Longitudinal Marriage Patterns for

Long-Run Policy Analysis,” Technical Paper 2002-2, Congressional Budget Office,

Washington, D.C. 

Smith, Creston M. (1989). “The Social Security Administration’s Continuous Work History

Sample.” Social Security Bulletin 52, 10 (October): 20-28.
Figure 1. Male Labor Force Participation Rates by Age and Cohort

CPS 1965-2000

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working 1966-1970 

1926-1930

1946-1950

1916-1920

1936-1940

1956-1960

1976-1980
Figure 2. Female Labor Force Participation Rates by Age and Coho

CPS 1965-2000

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working

1966-1970

1926-1930 

1946-1950

1916-1920

1936-1940

1956-1960

1976-1980
Figure 3. Male Cohort Labor Force Participation,

CPS versus CWHS

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working CPS, 1976-1980 CWHS, 1926-1930

CWHS, 1956-1960

CPS, 19

CPS, 1956-1960

CWHS, 1976-1980
Figure 4. Female Cohort Labor Force Participation,

CPS versus CWHS

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working

CPS, 1956-1960

CWHS, 1926-1930

CWHS, 1976-1980

CPS, 1926-1930

CWHS, 1956-1960

CPS, 1976-1980
Table 1

Age Age-Centered Regression Coefficients, Male Labor Force Participation

Group Age OAI/DI Never Mar Married Lag LFP 1910 1920 1930 1940 1950 1960 1970 

16 0.0670 1.6714 0.1207 0.0066 0

17 0.3558 -0.1751 1.5254 1.9632 0.7882 0.6670 018 0.2597 -0.3537 0.8725 1.9694 0.4772 0.1965 019 0.1989 -0.3431 -0.5155 0.4680 2.0674 0.0379 0.4009 0.0514 0

20 0.1881 -0.4266 -0.5939 0.2500 2.2023 0.0830 0.3575 -0.0133 -0

21 0.1707 -0.5760 -0.6257 0.1438 2.3719 0.1279 0.2706 -0.1231 -0

22 0.1731 -0.7490 -0.6123 0.0670 2.5951 0.2001 0.2689 -0.0899 -0

23 0.1835 -0.9667 -0.5600 0.0213 2.8612 0.3145 0.3639 0.0946 0

24 0.1825 -1.1656 -0.4812 0.0062 3.1224 0.4615 0.5128 0.3611 0

25 0.1652 -1.3394 -0.3931 0.0050 3.3734 0.6266 0.6792 0.6552 0

26 0.1355 -1.4682 -0.3044 0.0049 3.6069 0.7512 0.7761 0.8457 0

27 0.1075 -1.5890 -0.2202 0.0189 3.8002 0.7687 0.7015 0.8101 0

28 0.0863 -1.6849 -0.1567 0.0406 3.9475 0.6972 0.5137 0.6075 0

29 0.0496 -1.7839 -0.1220 0.0527 4.0633 -0.3815 0.1342 -0.2832 -0.2832 -0

30 0.0317 -1.8694 -0.1004 0.0627 4.1376 -0.2476 0.1294 -0.3609 -0.3609 -0

31 0.0187 -1.9342 -0.0955 0.0738 4.1984 -0.0804 0.1878 -0.3175 -0.3175 -0

32 0.0107 -1.9786 -0.1066 0.0836 4.2469 0.1364 0.3361 -0.1103 -0.1103 -0

33 0.0076 -2.0221 -0.1342 0.0808 4.3045 0.3885 0.5501 0.1901 0.1901 0

34 0.0079 -2.0390 -0.1707 0.0775 4.3649 0.6364 0.7649 0.4910 0.4910 0

35 0.0145 -2.0529 -0.2082 0.0746 4.4297 0.8453 0.9442 0.7442 0.7442 0

36 0.0219 -2.0848 -0.2413 0.0804 4.4813 0.9775 1.0142 0.8642 0.8642 0

37 0.0301 -2.1426 -0.2742 0.0863 4.5170 1.0073 0.9631 0.8341 0.8341 0

38 0.0350 -2.2122 -0.2934 0.1127 4.5203 0.9344 0.7917 0.6710 0.6710 0

39 0.0258 -2.3146 -0.3182 0.1522 4.5160 -0.4264 0.3846 0.0396 0.0396 0.0396 0

40 0.0205 -2.4109 -0.3376 0.2064 4.5019 -0.3347 0.3051 -0.1193 -0.1193 -0.1193 -0

41 0.0136 -2.4964 -0.3607 0.2556 4.5001 -0.2035 0.2935 -0.1375 -0.1375 -0.1375 -0

42 0.0064 -2.5726 -0.3908 0.3096 4.5050 0.0028 0.3993 0.0253 0.0253 0.0253 0

43 0.0071 -2.6421 -0.4332 0.3565 4.5403 0.2608 0.6192 0.3592 0.3592 0.3592 0

44 0.0117 -2.7046 -0.4753 0.3889 4.5846 0.5216 0.8604 0.7149 0.7149 0.7149 0

45 0.0127 -2.7528 -0.5038 0.4129 4.6286 0.7244 1.0293 0.9901 0.9901 0.9901 0

46 0.0082 -2.8023 -0.5242 0.4355 4.6607 0.8619 1.0871 1.1055 1.1055 1.1055 1

47 -0.0081 -2.8667 -0.5129 0.4582 4.6802 0.8933 1.0239 1.0622 1.0622 1.0622 148 -0.0308 -2.9327 -0.4796 0.4877 4.6809 0.8388 0.8632 0.8842 0.8842 0.8842 0

49 -0.0736 -3.0215 -0.4546 0.5093 4.6816 -0.4576 0.1957 -0.0736 -0.0736 -0.0736 -0.0736 -0

50 -0.0833 -3.0894 -0.4380 0.5223 4.6666 -0.3463 0.2010 -0.1310 -0.1310 -0.1310 -0.1310 -0

51 -0.0912 -3.1412 -0.4161 0.5345 4.6461 -0.2159 0.2118 -0.1310 -0.1310 -0.1310 -0.1310 -0

52 -0.0909 -3.1669 -0.4095 0.5333 4.6306 -0.0518 0.2827 -0.0166 -0.0166 -0.0166 -0.0166 -053 -0.0875 -3.1802 -0.4043 0.5190 4.6268 0.1595 0.4459 0.2399 0.2399 0.2399 0.2399 0
Table 1 (continued)

Age Age-Centered Regression Coefficients, Male Labor Force Participation

Group Age OAI/DI Never Mar Married Lag LFP 1910 1920 1930 1940 1950 1960 1970 

54 -0.0851 -3.1717 -0.3817 0.5082 4.6339 0.3806 0.6452 0.5449 0.5449 0.5449 0.5449 0

55 -0.0829 -3.1471 -0.3549 0.4996 4.6548 0.5936 0.8471 0.8475 0.8475 0.8475 0.8475 056 -0.0873 -3.1254 -0.3309 0.4825 4.6922 0.7473 0.9943 1.0729 1.0729 1.0729 1.0729 157 -0.0997 -3.1036 -0.3124 0.4640 4.7207 0.7838 0.9911 1.0890 1.0890 1.0890 1.0890 158 -0.1148 -2.9719 -0.3171 0.4255 4.7171 0.7181 0.8700 0.9437 0.9437 0.9437 0.9437 059 -0.1343 -2.7301 -0.3176 0.3848 4.7030 -0.4290 0.0446 -0.0752 -0.0752 -0.0752 -0.0752 -0.0752 -0

60 -0.1208 -2.5079 -0.3114 0.3361 4.6613 -0.3279 -0.0182 -0.1970 -0.1970 -0.1970 -0.1970 -0.1970 -0

61 -0.0974 -2.2958 -0.2888 0.2855 4.5791 -0.2038 -0.0201 -0.2241 -0.2241 -0.2241 -0.2241 -0.2241 -0

62 -0.0576 -2.1457 -0.2494 0.2469 4.4797 -0.0233 0.1063 -0.0547 -0.0547 -0.0547 -0.0547 -0.0547 -0

63 -0.0166 -2.0409 -0.1900 0.2311 4.4265 0.1981 0.3196 0.2243 0.2243 0.2243 0.2243 0.2243 0

64 0.0101 -1.9247 -0.1318 0.2242 4.4190 0.4455 0.5930 0.5858 0.5858 0.5858 0.5858 0.5858 0

65 0.0224 -1.7897 -0.0882 0.2153 4.4517 0.7108 0.9032 0.9875 0.9875 0.9875 0.9875 0.9875 0

66 0.0248 -1.6834 -0.0564 0.2231 4.5589 0.9890 1.1932 1.3529 1.3529 1.3529 1.3529 1.3529 1

67 0.0090 -1.5771 -0.0284 0.2277 4.6944 1.1988 1.3552 1.5273 1.5273 1.5273 1.5273 1.5273 1

68 -0.0012 -1.4669 -0.0150 0.2198 4.8260 1.2996 1.3432 1.4528 1.4528 1.4528 1.4528 1.4528 1

69 -0.0156 -1.3720 -0.0080 0.2019 4.9534 1.2829 1.1659 1.1659 1.1659 1.1659 1.1659 1.1659 1

70 -0.0363 -1.2956 0.0008 0.1913 5.0766 1.1633 0.8506 0.8506 0.8506 0.8506 0.8506 0.8506 071 -0.0491 -1.1730 0.0088 0.1660 5.1646 0.8894 0.3752 0.3752 0.3752 0.3752 0.3752 0.3752 072 -0.0654 -1.0233 0.0040 0.1363 5.2469 0.5707 -0.1127 -0.1127 -0.1127 -0.1127 -0.1127 -0.1127 -073 -0.0841 -0.8454 -0.0043 0.1101 5.3256 0.2522 -0.5667 -0.5667 -0.5667 -0.5667 -0.5667 -0.5667 -074 -0.0998 -0.6799 -0.0062 0.0972 5.4082 -0.0509 -0.9747 -0.9747 -0.9747 -0.9747 -0.9747 -0.9747 -0

75 -0.1139 -0.5361 -0.0010 0.0892 5.4943 -0.3202 -1.3227 -1.3227 -1.3227 -1.3227 -1.3227 -1.3227 -1

76 -0.1236 -0.4347 0.0002 0.0875 5.5719 -0.5704 -1.6333 -1.6333 -1.6333 -1.6333 -1.6333 -1.6333 -1

77 -0.1250 -0.3800 0.0088 0.0908 5.6494 -0.7578 -1.8519 -1.8519 -1.8519 -1.8519 -1.8519 -1.8519 -1

78 -0.1287 -0.3818 0.0077 0.0921 5.7276 -0.9082 -2.0028 -2.0028 -2.0028 -2.0028 -2.0028 -2.0028 -2

79 -0.1149 -0.3566 -0.0090 0.0976 5.8373 -1.0453 -1.0453 -1.0453 -1.0453 -1.0453 -1.0453 -1.0453 -1

80 -0.1146 -0.3769 -0.0518 0.1025 5.9030 -1.1285 -1.1285 -1.1285 -1.1285 -1.1285 -1.1285 -1.1285 -181 -0.1166 -0.3880 -0.0861 0.1150 5.9734 -1.1514 -1.1514 -1.1514 -1.1514 -1.1514 -1.1514 -1.1514 -182 -0.1157 -0.4167 -0.1054 0.1310 6.0693 -1.1685 -1.1685 -1.1685 -1.1685 -1.1685 -1.1685 -1.1685 -183 -0.1272 -0.4320 -0.0875 0.1589 6.1528 -1.1646 -1.1646 -1.1646 -1.1646 -1.1646 -1.1646 -1.1646 -184 -0.1252 -0.4143 -0.0376 0.1868 6.2265 -1.1483 -1.1483 -1.1483 -1.1483 -1.1483 -1.1483 -1.1483 -185 -0.1142 -0.3988 0.0619 0.1871 6.3162 -1.1331 -1.1331 -1.1331 -1.1331 -1.1331 -1.1331 -1.1331 -1

86 -0.0892 -0.4599 0.1996 0.1764 6.4760 -1.1383 -1.1383 -1.1383 -1.1383 -1.1383 -1.1383 -1.1383 -1

87 -0.0622 -0.4761 0.3200 0.1948 6.5484 -1.0704 -1.0704 -1.0704 -1.0704 -1.0704 -1.0704 -1.0704 -188 0.0379 -0.6325 0.2345 0.2353 6.8503 -0.8848 -0.8848 -0.8848 -0.8848 -0.8848 -0.8848 -0.8848 -0

89 -0.1393 -0.8305 0.5303 0.1392 7.9466 -0.7732 -0.7732 -0.7732 -0.7732 -0.7732 -0.7732 -0.7732 -0

90 -1.8214 1.2545 0.2426 7.9211 

 Bold indicates statistical significance at the 10% level.
Table 2

Age Age-Centered Regression Coefficients, Female Labor Force Participation

Group Age OAI/DI Never Mar Married Lag LFP 1910 1920 1930 1940 1950 1960 1970 1

16 -0.0954 -0.4605 0.1934 0.0386 -0.

17 0.3558 0.0106 -0.3664 2.1133 1.0008 0.8085 0.818 0.2570 0.0374 -0.3332 2.0688 0.5964 0.2935 0.219 0.1847 -0.2671 0.0456 -0.3613 2.1842 -0.2435 0.1250 -0.3813 -0.

20 0.1602 -0.3329 0.0937 -0.4019 2.3370 -0.0732 0.1789 -0.3629 -0.

21 0.1278 -0.4395 0.1251 -0.4378 2.5020 0.0764 0.1802 -0.3991 -0.

22 0.1058 -0.5594 0.1795 -0.4782 2.6956 0.2817 0.3011 -0.2501 -0.

23 0.0849 -0.6855 0.2328 -0.5300 2.9142 0.5237 0.5055 0.0178 0.0

24 0.0631 -0.7745 0.2882 -0.5828 3.1198 0.7623 0.7147 0.2972 0.2

25 0.0436 -0.8347 0.3339 -0.6418 3.3089 0.9703 0.8692 0.5135 0.5

26 0.0293 -0.8491 0.3623 -0.6981 3.4631 1.1084 0.9248 0.6077 0.6

27 0.0232 -0.8755 0.3445 -0.7510 3.5756 1.1125 0.8007 0.4623 0.4

28 0.0210 -0.8956 0.3029 -0.7917 3.6463 1.0030 0.5392 0.1500 0.1

29 0.0154 -0.8973 0.2389 -0.8272 3.7015 -0.2399 0.6397 -0.0427 -0.0427 -0.30 0.0150 -0.8859 0.1944 -0.8395 3.7378 -0.1413 0.5125 -0.2899 -0.2899 -0.31 0.0161 -0.8931 0.1609 -0.8402 3.7793 -0.0031 0.4360 -0.4206 -0.4206 -0.32 0.0239 -0.8829 0.1375 -0.8315 3.8189 0.2201 0.5109 -0.2971 -0.2971 -0.33 0.0375 -0.8705 0.1177 -0.8184 3.8739 0.5086 0.7269 0.0483 0.0483 0.0

34 0.0518 -0.8600 0.1014 -0.8081 3.9428 0.8350 1.0260 0.5169 0.5169 0.5

35 0.0622 -0.8519 0.0887 -0.8008 4.0298 1.1584 1.3226 0.9708 0.9708 0.9

36 0.0665 -0.8439 0.0715 -0.7988 4.1210 1.4079 1.5181 1.2764 1.2764 1.2

37 0.0609 -0.8472 0.0564 -0.7962 4.2059 1.4847 1.4753 1.2528 1.2528 1.2

38 0.0487 -0.8593 0.0513 -0.7922 4.2685 1.4065 1.2371 0.9736 0.9736 0.9

39 0.0211 -0.8767 0.0341 -0.7876 4.3245 -0.3850 0.8256 0.3093 0.3093 0.3093 0.3

40 0.0095 -0.8963 0.0265 -0.7845 4.3555 -0.2964 0.6426 -0.0382 -0.0382 -0.0382 -0.

41 0.0024 -0.9057 0.0132 -0.7831 4.3795 -0.1707 0.5097 -0.2526 -0.2526 -0.2526 -0.

42 0.0033 -0.9198 -0.0004 -0.7858 4.3954 0.0458 0.5605 -0.1523 -0.1523 -0.1523 -0.43 0.0112 -0.9302 -0.0260 -0.7913 4.4177 0.3473 0.7897 0.2227 0.2227 0.2227 0.2

44 0.0230 -0.9400 -0.0428 -0.7988 4.4622 0.7040 1.1191 0.7544 0.7544 0.7544 0.7

45 0.0329 -0.9452 -0.0505 -0.8102 4.5324 1.0841 1.4857 1.3134 1.3134 1.3134 1.346 0.0357 -0.9554 -0.0409 -0.8198 4.6210 1.3844 1.7288 1.6879 1.6879 1.6879 1.6

47 0.0292 -0.9604 -0.0292 -0.8319 4.7081 1.5086 1.7192 1.7079 1.7079 1.7079 1.7

48 0.0182 -0.9784 0.0001 -0.8442 4.7721 1.4655 1.4847 1.4353 1.4353 1.4353 1.4

49 -0.0323 -0.9955 0.0084 -0.8495 4.8309 -0.5684 0.5548 0.0280 0.0280 0.0280 0.0280 0.0

50 -0.0452 -1.0287 0.0394 -0.8544 4.8571 -0.4800 0.3660 -0.3532 -0.3532 -0.3532 -0.3532 -0.

51 -0.0517 -1.0697 0.0592 -0.8660 4.8681 -0.3436 0.2534 -0.5518 -0.5518 -0.5518 -0.5518 -0.

52 -0.0464 -1.1269 0.0853 -0.8776 4.8657 -0.1052 0.3415 -0.3985 -0.3985 -0.3985 -0.3985 -0.

53 -0.0359 -1.1740 0.1033 -0.8910 4.8678 0.2246 0.5966 0.0300 0.0300 0.0300 0.0300 0.0

Table 2 (continued)
Age Age-Centered Regression Coefficients, Female Labor Force Participation

Group Age OAI/DI Never Mar Married Lag LFP 1910 1920 1930 1940 1950 1960 1970 1

54 -0.0246 -1.2168 0.1228 -0.9046 4.8923 0.6109 0.9423 0.5969 0.5969 0.5969 0.5969 0.5

55 -0.0143 -1.2439 0.1346 -0.9157 4.9443 1.0289 1.3458 1.2191 1.2191 1.2191 1.2191 1.2

56 -0.0127 -1.2799 0.1620 -0.9200 5.0160 1.3881 1.6419 1.6716 1.6716 1.6716 1.6716 1.6

57 -0.0204 -1.3134 0.1827 -0.9250 5.0826 1.5594 1.6783 1.7447 1.7447 1.7447 1.7447 1.7

58 -0.0350 -1.3623 0.1946 -0.9253 5.1277 1.5329 1.4691 1.5008 1.5008 1.5008 1.5008 1.5

59 -0.0842 -1.3578 0.1873 -0.9146 5.1566 -0.5648 0.6053 0.0383 0.0383 0.0383 0.0383 0.0383 0.060 -0.0898 -1.3323 0.1890 -0.9044 5.1447 -0.4589 0.4217 -0.3350 -0.3350 -0.3350 -0.3350 -0.3350 -0.61 -0.0923 -1.2844 0.1700 -0.8861 5.1110 -0.3348 0.2305 -0.6400 -0.6400 -0.6400 -0.6400 -0.6400 -0.62 -0.0872 -1.2292 0.1575 -0.8572 5.0700 -0.1336 0.1991 -0.6759 -0.6759 -0.6759 -0.6759 -0.6759 -0.63 -0.0748 -1.1615 0.1551 -0.8269 5.0361 0.1466 0.3449 -0.4355 -0.4355 -0.4355 -0.4355 -0.4355 -0.

64 -0.0633 -1.0817 0.1538 -0.7950 5.0292 0.4859 0.6164 -0.0112 -0.0112 -0.0112 -0.0112 -0.0112 -0.

65 -0.0530 -0.9821 0.1621 -0.7600 5.0423 0.8878 0.9930 0.5605 0.5605 0.5605 0.5605 0.5605 0.5

66 -0.0456 -0.8704 0.1899 -0.7338 5.1084 1.3435 1.4604 1.2311 1.2311 1.2311 1.2311 1.2311 1.2

67 -0.0493 -0.7401 0.2284 -0.7113 5.2235 1.7428 1.8433 1.7596 1.7596 1.7596 1.7596 1.7596 1.7

68 -0.0545 -0.5908 0.2737 -0.6831 5.3494 2.0030 2.0137 1.9776 1.9776 1.9776 1.9776 1.9776 1.9

69 -0.0644 -0.4350 0.3330 -0.6544 5.4774 2.0894 1.9366 1.9366 1.9366 1.9366 1.9366 1.9366 1.9

70 -0.0848 -0.2878 0.3856 -0.6284 5.6131 1.9385 1.5648 1.5648 1.5648 1.5648 1.5648 1.5648 1.5

71 -0.1037 -0.1512 0.4398 -0.5933 5.7417 1.5345 0.8664 0.8664 0.8664 0.8664 0.8664 0.8664 0.8

72 -0.1202 -0.0162 0.4815 -0.5518 5.8511 1.0714 0.1388 0.1388 0.1388 0.1388 0.1388 0.1388 0.1

73 -0.1355 0.0916 0.5140 -0.5250 5.9643 0.6214 -0.5458 -0.5458 -0.5458 -0.5458 -0.5458 -0.5458 -0.74 -0.1472 0.1723 0.5446 -0.5032 6.0863 0.1910 -1.1453 -1.1453 -1.1453 -1.1453 -1.1453 -1.1453 -1.75 -0.1568 0.2127 0.5806 -0.4879 6.2144 -0.2048 -1.6617 -1.6617 -1.6617 -1.6617 -1.6617 -1.6617 -1.76 -0.1579 0.2318 0.6006 -0.4874 6.3328 -0.5565 -2.0527 -2.0527 -2.0527 -2.0527 -2.0527 -2.0527 -2.77 -0.1611 0.2207 0.6328 -0.5036 6.4799 -0.8452 -2.3552 -2.3552 -2.3552 -2.3552 -2.3552 -2.3552 -2.78 -0.1676 0.2315 0.6794 -0.4901 6.6289 -1.0726 -2.4841 -2.4841 -2.4841 -2.4841 -2.4841 -2.4841 -2.79 -0.1536 0.2340 0.7061 -0.5027 6.8523 -1.2206 -1.2206 -1.2206 -1.2206 -1.2206 -1.2206 -1.2206 -1.

80 -0.1674 0.2123 0.7346 -0.5089 6.9214 -1.4192 -1.4192 -1.4192 -1.4192 -1.4192 -1.4192 -1.4192 -1.81 -0.1697 0.2186 0.7635 -0.4798 6.9235 -1.5631 -1.5631 -1.5631 -1.5631 -1.5631 -1.5631 -1.5631 -1.82 -0.1727 0.2159 0.7773 -0.4133 6.8575 -1.6555 -1.6555 -1.6555 -1.6555 -1.6555 -1.6555 -1.6555 -1.83 -0.1676 0.1462 0.7682 -0.3845 6.7630 -1.7207 -1.7207 -1.7207 -1.7207 -1.7207 -1.7207 -1.7207 -1.84 -0.1646 0.0810 0.7606 -0.3262 6.7186 -1.7816 -1.7816 -1.7816 -1.7816 -1.7816 -1.7816 -1.7816 -1.

85 -0.1683 -0.0192 0.7156 -0.2487 6.7171 -1.8493 -1.8493 -1.8493 -1.8493 -1.8493 -1.8493 -1.8493 -1.

86 -0.1731 -0.1932 0.6266 -0.1199 6.8677 -1.8383 -1.8383 -1.8383 -1.8383 -1.8383 -1.8383 -1.8383 -1.

87 -0.1761 -0.3885 0.4051 0.0345 7.1895 -1.8560 -1.8560 -1.8560 -1.8560 -1.8560 -1.8560 -1.8560 -1.

88 -0.1469 -0.3475 0.1143 0.4443 7.6419 -1.3854 -1.3854 -1.3854 -1.3854 -1.3854 -1.3854 -1.3854 -1.

89 -0.1493 -0.5246 0.5186 0.8816 7.4674 -0.8098 -0.8098 -0.8098 -0.8098 -0.8098 -0.8098 -0.8098 -0.

90 -0.9363 1.1464 1.1705 7.4942 

 Bold indicates statistical significance at the 10% level.
Figure 5. Male Cohort Full-Time Employment, PSID 1968-1992

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working Full Time 1956-1960 

1946-1950 1926-1930 1936-1940

1916-1920

1966-1970
Figure 6. Female Cohort Full-Time Employment, PSID 1968-1992

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working Full Time 1956-1960 

1926-1930 1946-1950 1936-1940 1916-1920 1966-1970
Table 3

Age Age-Centered Regression Coefficients, Male Full-Time Employment

Group Age Never Mar Married Lag LFP Lag FT 1910 1920 1930 1940 1950 19

16 -0.3

17 0.7096 -1.5936 0.7383 0.2548 2.6291 -0.6

18 0.8661 -0.7407 1.1593 0.5797 2.0440 -0.1

19 0.5362 -0.4174 1.0730 0.5095 2.0085 -0.0

20 0.3409 -0.4267 0.7884 0.4303 2.0551 0.0

21 0.2656 -0.3605 0.7209 0.3498 2.0858 -0.0172 0.0

22 0.1971 -0.3202 0.6494 0.2709 2.0977 -0.0949 -0.0

23 0.1423 -0.2492 0.6259 0.2089 2.0917 -0.1483 -0.0

24 0.1064 -0.1847 0.6060 0.2088 2.0654 -0.1925 -0.0

25 0.0842 -0.1228 0.5972 0.2455 2.0566 -0.2145 -0.0

26 0.0711 -0.0814 0.5964 0.2975 2.0432 -0.2154 -0.0

27 0.0562 -0.0475 0.6010 0.3594 2.0421 -0.2137 -0.0

28 0.0430 -0.0620 0.5935 0.4028 2.0513 -0.2032 0.0

29 0.0351 -0.0918 0.5871 0.3727 2.0868 -0.1909 -0.0

30 0.0260 -0.1347 0.5777 0.3232 2.1288 -0.1758 -0.0

31 0.0087 -0.1818 0.5627 0.2993 2.1796 -0.3713 -0.4857 -0.4

32 0.0050 -0.2498 0.5490 0.2984 2.2208 -0.3026 -0.3952 -0.3

33 0.0078 -0.2749 0.5477 0.3233 2.2671 -0.3155 -0.3707 -0.3

34 0.0070 -0.2926 0.5495 0.3796 2.3046 -0.3120 -0.3199 -0.3

35 0.0071 -0.3256 0.5265 0.4675 2.3276 -0.2713 -0.2518 -0.2

36 0.0008 -0.3413 0.5004 0.5304 2.3416 -0.2619 -0.2195 -0.2

37 0.0013 -0.3378 0.4803 0.5406 2.3677 -0.2568 -0.2184 -0.2

38 0.0011 -0.3577 0.4438 0.5103 2.3902 -0.2208 -0.2182 -0.2

39 -0.0006 -0.3594 0.4081 0.4831 2.4180 -0.1781 -0.2308 -0.2

40 -0.0037 -0.3198 0.4195 0.4176 2.4608 -0.1553 -0.2579 -0.2

41 -0.0082 -0.2805 0.4041 0.3366 2.4939 -0.2546 -0.3841 -0.3841 -0.3

42 -0.0162 -0.2296 0.3897 0.2757 2.5169 -0.2623 -0.3530 -0.3530 -0.3

43 -0.0180 -0.1467 0.3875 0.2314 2.5231 -0.2344 -0.2781 -0.2781 -0.2

44 -0.0168 -0.1150 0.3802 0.1880 2.5067 -0.2150 -0.2218 -0.2218 -0.2

45 -0.0154 -0.0803 0.3374 0.1536 2.4691 -0.2253 -0.2011 -0.2011 -0.2

46 -0.0127 -0.1187 0.3144 0.1660 2.4551 -0.2060 -0.1573 -0.1573 -0.1

47 -0.0240 -0.1623 0.2841 0.1826 2.4401 -0.1647 -0.1176 -0.1176 -0.1
Table 3 (continued)

Age Age-Centered Regression Coefficients, Male Full-Time Employment

Group Age Never Mar Married Lag LFP Lag FT 1910 1920 1930 1940 1950 19

48 -0.0405 -0.2877 0.2385 0.1843 2.4285 -0.1552 -0.1300 -0.1300 -0.1

49 -0.0492 -0.3671 0.1985 0.1930 2.4352 -0.1381 -0.1485 -0.1485 -0.1

50 -0.0528 -0.4163 0.1863 0.2042 2.4598 -0.1054 -0.1399 -0.1399 -0.1

51 -0.0484 -0.4294 0.2111 0.2115 2.4461 -0.1943 -0.2686 -0.2686 -0.2686 -0.2

52 -0.0363 -0.4043 0.2709 0.2309 2.4484 -0.1472 -0.2040 -0.2040 -0.2040 -0.253 -0.0376 -0.2865 0.3605 0.2203 2.4912 -0.1442 -0.1604 -0.1604 -0.1604 -0.1

54 -0.0416 -0.2197 0.4282 0.2022 2.5199 -0.1291 -0.1092 -0.1092 -0.1092 -0.1

55 -0.0491 -0.1850 0.5013 0.1997 2.5260 -0.1053 -0.0471 -0.0471 -0.0471 -0.0

56 -0.0522 -0.0582 0.5718 0.1850 2.5650 -0.1029 -0.0068 -0.0068 -0.0068 -0.0

57 -0.0488 0.0597 0.6015 0.1894 2.5819 -0.0732 0.0274 0.0274 0.0274 0.0

58 -0.0538 0.0989 0.5958 0.2370 2.5653 -0.0314 0.0582 0.0582 0.0582 0.0

59 -0.0723 0.1305 0.5948 0.2745 2.5533 -0.0223 0.0503 0.0503 0.0503 0.0

60 -0.1099 0.1429 0.5568 0.3029 2.5496 -0.0379 -0.0150 -0.0150 -0.0150 -0.0

61 -0.1539 0.0040 0.4544 0.3268 2.5256 -0.5902 -0.5862 -0.5862 -0.5862 -0.5862 -0.5

62 -0.1925 -0.1648 0.3443 0.2651 2.5124 -0.4953 -0.5069 -0.5069 -0.5069 -0.5069 -0.5

63 -0.2271 -0.2941 0.2570 0.1773 2.4844 -0.4270 -0.4716 -0.4716 -0.4716 -0.4716 -0.4

64 -0.2471 -0.3743 0.1780 0.0467 2.4756 -0.3471 -0.4146 -0.4146 -0.4146 -0.4146 -0.4

65 -0.2324 -0.4410 0.1461 -0.0467 2.4834 -0.2046 -0.2799 -0.2799 -0.2799 -0.2799 -0.2

66 -0.1994 -0.3742 0.1906 -0.1377 2.4853 -0.0452 -0.1155 -0.1155 -0.1155 -0.1155 -0.167 -0.1530 -0.2056 0.2889 -0.1377 2.5487 0.0665 0.0127 0.0127 0.0127 0.0127 0.0

68 -0.0787 -0.1308 0.3492 -0.1184 2.6841 0.2158 0.1747 0.1747 0.1747 0.1747 0.1

69 -0.0200 -0.0534 0.3584 0.0823 2.7898 0.3630 0.3254 0.3254 0.3254 0.3254 0.370 0.0227 -0.0013 0.2995 0.2765 2.8275 0.4864 0.4021 0.4021 0.4021 0.4021 0.4

71 0.0121 -0.0673 0.1986 0.4611 2.9011 0.4865 0.4865 0.4865 0.4865 0.4865 0.472 -0.0212 -0.3399 0.0710 0.4893 2.9540 0.5516 0.5516 0.5516 0.5516 0.5516 0.573 -0.0277 -0.3414 0.1091 0.4632 2.9540 0.5677 0.5677 0.5677 0.5677 0.5677 0.574 -0.0007 -0.3159 0.3044 0.2319 2.9581 0.6450 0.6450 0.6450 0.6450 0.6450 0.675 -0.0124 -0.0056 0.4563 -0.0905 3.0812 0.6153 0.6153 0.6153 0.6153 0.6153 0.676 -0.0735 0.7908 0.7049 -0.3217 3.2016 0.6925 0.6925 0.6925 0.6925 0.6925 0.677 -0.0767 1.8182 1.2226 -0.2659 3.2815 0.7886 0.7886 0.7886 0.7886 0.7886 0.7

78 -0.1401 2.6051 1.3994 -0.1533 3.4486 0.9523 0.9523 0.9523 0.9523 0.9523 0.9

79 -0.1811 3.0472 1.5790 0.0326 3.6040 0.8182 0.8182 0.8182 0.8182 0.8182 0.8

80-90 -0.1324 5.0667 

 Bold indicates statistical significance at the 10% level.
Table 4

Age Age-Centered Regression Coefficients, Female Full-Time Employment

Group Age Never Mar Married Lag LFP Lag FT 1910 1920 1930 1940 1950 19

16 

17 0.8133 -1.6570 -0.0195 0.6325 2.2852 -0.3

18 0.8694 -0.4101 0.1558 0.6564 1.9512 -0.2

19 0.4987 -0.1367 0.1264 0.6666 1.8383 -0.120 0.3003 -0.1002 -0.0147 0.7356 1.8568 -0.0

21 0.2321 -0.1027 -0.1058 0.7441 1.8663 0.0977 0.1

22 0.1705 -0.0815 -0.1660 0.7590 1.8660 0.1124 0.2

23 0.1226 -0.0428 -0.2074 0.8051 1.8469 0.1294 0.2

24 0.0835 -0.0262 -0.2642 0.8516 1.8280 0.1594 0.3

25 0.0628 0.0031 -0.2921 0.8840 1.8294 0.1683 0.3

26 0.0414 0.0253 -0.3079 0.9081 1.8440 0.1686 0.3

27 0.0296 0.0367 -0.3259 0.9198 1.8668 0.1590 0.3

28 0.0204 0.0335 -0.3369 0.8980 1.9171 0.1415 0.3

29 0.0175 0.0434 -0.3316 0.8583 1.9789 0.1243 0.3

30 0.0144 0.0235 -0.3396 0.8517 2.0420 0.1232 0.3

31 0.0049 0.0055 -0.3578 0.8440 2.1139 0.0748 0.2352 0.2

32 0.0053 -0.0292 -0.3761 0.8543 2.1881 0.0385 0.2096 0.2

33 0.0053 -0.0772 -0.4094 0.8954 2.2535 0.0505 0.2356 0.2

34 0.0129 -0.1244 -0.4253 0.9641 2.3260 0.0690 0.2444 0.2

35 0.0169 -0.1395 -0.4349 0.9961 2.3986 0.0916 0.2519 0.2

36 0.0220 -0.1460 -0.4307 1.0401 2.4617 0.1402 0.2724 0.2

37 0.0205 -0.1313 -0.4219 1.0422 2.5124 0.1871 0.2785 0.2

38 0.0175 -0.0887 -0.3915 0.9897 2.5547 0.2054 0.2638 0.2

39 0.0141 -0.0717 -0.3904 0.9422 2.5666 0.2080 0.2677 0.2

40 0.0153 -0.0507 -0.3887 0.9235 2.5645 0.2213 0.2718 0.2

41 0.0122 -0.0138 -0.3822 0.8579 2.5657 0.0961 0.3111 0.3111 0.3

42 0.0153 -0.0034 -0.3859 0.7901 2.5686 0.1513 0.3550 0.3550 0.3

43 0.0157 0.0079 -0.4029 0.7165 2.5788 0.1791 0.3787 0.3787 0.3

44 0.0131 0.0406 -0.4074 0.6174 2.6103 0.1723 0.3775 0.3775 0.3

45 0.0057 0.0711 -0.4154 0.5102 2.6514 0.1541 0.3698 0.3698 0.3

46 0.0014 0.0852 -0.4212 0.4283 2.6748 0.1331 0.3682 0.3682 0.3

47 -0.0025 0.0563 -0.4175 0.3803 2.6898 0.1089 0.3435 0.3435 0.3
Table 4 (continued)

Age Age-Centered Regression Coefficients, Female Full-Time Employment

Group Age Never Mar Married Lag LFP Lag FT 1910 1920 1930 1940 1950 19

48 0.0002 0.0016 -0.4123 0.3891 2.6894 0.1058 0.3314 0.3314 0.3

49 0.0001 -0.0170 -0.3897 0.3815 2.6776 0.1008 0.3071 0.3071 0.3

50 -0.0004 -0.0426 -0.3505 0.3740 2.6541 0.0999 0.2723 0.2723 0.2

51 -0.0082 -0.0991 -0.3316 0.3910 2.6487 -0.0245 0.1068 0.1068 0.1068 0.1

52 -0.0153 -0.0733 -0.3112 0.3774 2.6379 -0.0799 0.0552 0.0552 0.0552 0.0

53 -0.0170 -0.0451 -0.2845 0.3414 2.6362 -0.1205 0.0046 0.0046 0.0046 0.0

54 -0.0192 -0.0752 -0.2763 0.3983 2.6365 -0.1322 0.0043 0.0043 0.0043 0.0

55 -0.0183 -0.0768 -0.2745 0.4358 2.6427 -0.1401 0.0133 0.0133 0.0133 0.0

56 -0.0139 0.0273 -0.2628 0.4304 2.6335 -0.1377 0.0420 0.0420 0.0420 0.0

57 -0.0148 0.0904 -0.2510 0.4757 2.6259 -0.1060 0.0620 0.0620 0.0620 0.0

58 -0.0190 0.1894 -0.2212 0.5208 2.6199 -0.0849 0.0880 0.0880 0.0880 0.0

59 -0.0394 0.3026 -0.1986 0.4701 2.6065 -0.0566 0.0974 0.0974 0.0974 0.0

60 -0.0664 0.4089 -0.1827 0.4151 2.5928 -0.0278 0.1093 0.1093 0.1093 0.1

61 -0.0873 0.4763 -0.1556 0.4290 2.6003 0.2560 0.2553 0.2553 0.2553 0.2553 0.2

62 -0.1122 0.4952 -0.1275 0.3578 2.6272 0.2731 0.2667 0.2667 0.2667 0.2667 0.2

63 -0.1323 0.4777 -0.1488 0.2812 2.6481 0.2289 0.2442 0.2442 0.2442 0.2442 0.2

64 -0.1518 0.4682 -0.1500 0.2047 2.7012 0.2220 0.2534 0.2534 0.2534 0.2534 0.2

65 -0.1512 0.4161 -0.1498 0.2000 2.7796 0.1733 0.2161 0.2161 0.2161 0.2161 0.2

66 -0.1391 0.3562 -0.1504 0.0748 2.8876 0.1169 0.1768 0.1768 0.1768 0.1768 0.1

67 -0.1091 0.2999 -0.1882 0.0574 2.9984 0.1000 0.1692 0.1692 0.1692 0.1692 0.1

68 -0.0854 0.2171 -0.1746 0.0624 3.0863 0.1477 0.1694 0.1694 0.1694 0.1694 0.1

69 -0.0361 0.1369 -0.1815 0.1400 3.1447 0.1844 0.1222 0.1222 0.1222 0.1222 0.1

70 -0.0182 0.0867 -0.1492 0.1270 3.1267 0.1813 0.0869 0.0869 0.0869 0.0869 0.0

71 -0.0515 -0.0120 -0.0978 0.2428 2.9377 0.1581 0.1581 0.1581 0.1581 0.1581 0.1

72 -0.0680 -0.0896 0.0329 0.2697 2.6538 0.0978 0.0978 0.0978 0.0978 0.0978 0.0

73 -0.0678 -0.2878 0.1973 0.2643 2.5574 0.0936 0.0936 0.0936 0.0936 0.0936 0.0

74 0.0084 -0.4295 0.4256 0.3377 2.4492 0.1159 0.1159 0.1159 0.1159 0.1159 0.1

75 0.0419 -0.6286 0.5964 0.4996 2.3370 0.2488 0.2488 0.2488 0.2488 0.2488 0.2

76 0.0641 -0.4931 0.6000 0.5508 2.4080 0.3768 0.3768 0.3768 0.3768 0.3768 0.3

77 0.0635 -0.2593 0.6729 0.7069 2.5606 0.5600 0.5600 0.5600 0.5600 0.5600 0.5

78 0.0021 0.1850 0.6702 0.8128 2.4981 0.6806 0.6806 0.6806 0.6806 0.6806 0.6

79 -0.0572 -0.0657 0.4936 0.8152 2.2260 0.7999 0.7999 0.7999 0.7999 0.7999 0.7

80-90 -0.2909 1.7717 

 Bold indicates statistical significance at the 10% level.
Figure 7. Average Part-Time Hours by Age and Sex, CPS 1984-199

0

200400600800

1000120014001600

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

62

Age

Annual Hours Worked

Males

Females
Figure 8. Projected Average Annual Hours Worked, 2002-2076

1400145015001550160016501700

2002 2006 2010 2014 2018 2022 2026 2030 2034 2038 2042 2046 2050 2054 2058 2062 20

Year

Average Annual Hours Worked

Micro constrained -0.1 percent average hours growth 

Micro unconstrained average hours growth
Figure 9. Male Cohort Mean Earnings, PSID 1968-1992

0

1000020000300004000050000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960 

1926-1930

1946-1950

1936-1940 1916-1920

1966-1970
Figure 10. Female Cohort Mean Earnings, PSID 1968-1992

0

1000020000300004000050000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960

1906

1926-1930

1946-1950

1936-1940

1916-1920

1966-1970
Table 5

Log FTE Earnings Regression Coefficients

Males Females

Low Educated High Educated Low Educated High Educated

Constant 10.202 10.202 9.435 9.435

Age

16 -1.235 -1.203 -1.148 -1.15317 -1.244 -1.210 -1.167 -0.956

18 -1.086 -1.148 -0.963 -0.88119 -0.852 -1.016 -0.685 -0.67220 -0.623 -0.859 -0.507 -0.55121 -0.507 -0.739 -0.418 -0.46322 -0.434 -0.607 -0.350 -0.30923 -0.326 -0.399 -0.324 -0.12924 -0.255 -0.269 -0.316 0.05925 -0.247 -0.181 -0.345 0.143

26 -0.212 -0.039 -0.338 0.22727 -0.151 0.058 -0.259 0.24128 -0.141 0.097 -0.292 0.27929 -0.119 0.187 -0.227 0.29830 -0.134 0.253 -0.245 0.31731 -0.133 0.289 -0.244 0.33632 -0.079 0.331 -0.213 0.32833 -0.089 0.368 -0.210 0.33934 -0.059 0.392 -0.173 0.34935 -0.095 0.410 -0.126 0.37336 -0.080 0.436 -0.168 0.40237 -0.048 0.450 -0.118 0.39138 -0.050 0.445 -0.123 0.456

39 -0.010 0.497 -0.091 0.436

40 -0.024 0.526 -0.061 0.425

41 -0.023 0.547 -0.071 0.45942 -0.014 0.552 -0.083 0.488

43 -0.006 0.531 -0.037 0.519

44 0.032 0.514 -0.052 0.520

45 0.000 0.552 0.000 0.526
Table 5 (continued)

Log FTE Earnings Regression Coefficients

Males Females

Low Educated High Educated Low Educated High Educated

46 -0.011 0.609 -0.001 0.433

47 0.000 0.580 0.030 0.52848 -0.005 0.600 -0.005 0.520

49 -0.016 0.595 0.038 0.563

50 -0.023 0.591 0.033 0.628

51 -0.062 0.595 0.022 0.634

52 -0.041 0.565 0.028 0.603

53 -0.033 0.566 0.065 0.618

54 -0.058 0.564 0.091 0.59655 -0.080 0.568 0.125 0.70556 -0.069 0.555 0.098 0.64457 -0.058 0.525 0.032 0.692

58 -0.077 0.547 0.053 0.71259 -0.123 0.544 0.055 0.71360 -0.111 0.505 0.019 0.72761 -0.118 0.381 0.002 0.70662 -0.159 0.415 0.002 0.70263 -0.212 0.368 0.041 0.89264 -0.265 0.418 -0.153 0.60065 -0.325 0.109 -0.131 0.45166 -0.482 0.102 -0.196 0.61767 -0.723 -0.061 -0.430 0.45968 -0.736 -0.331 -0.302 0.47469 -0.839 -0.522 -0.402 0.15870 -0.934 -0.845 -0.633 0.036

Cohort

1940's -0.029 -0.049 0.228 0.203

1950's -0.137 -0.135 0.260 0.2011960's -0.385 -0.227 0.129 0.167

For low-educated columns, bold indicates statistical significance at the 10% level. For high-educated columns, bold indicates statistical difference from low-educatedat the 10% level.
Figure 11. Male Cohort Mean Earnings, CWHS 1984-1998

0

1000020000300004000050000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960 1

1946-1950 1936-1940 1966-1970

1916
Figure 12. Female Cohort Mean Earnings, CWHS 1984-1998

0

1000020000300004000050000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars

1956-1960

1946-1950 1926

1936-1940

1966-1970
Table 6

Variance of Unexplained Earnings Changes

(CWHS Data, Ages 25 to 55, 1984 to 1998)

Year 

Gap Men Women

1 0.329 0.406

2 0.446 0.562

3 0.502 0.626

4 0.541 0.664

5 0.571 0.691

6 0.592 0.706

7 0.603 0.710

8 0.615 0.712

9 0.623 0.711

10 0.626 0.702

11 0.632 0.698

12 0.625 0.687

13 0.617 0.662

14 0.605 0.634

Table 7

Solving for Base Variance of Permanent Earnings Differentials

(CWHS Data, 1998)

Men Women

Base-Year Residual Variance 0.9706 0.8582

Less: Transitory Variance 0.1225 0.1600

Less: Weighted Average Age 20.2715 19.9585

 *Permanent Variance *0.0100 *0.0036

0.2027 0.0719

Equals: Base Variance of 

Permanent Earnings Differentials 0.6454 0.6264
Table 8

1995 Labor Force Participation Rates, CWHS versus CPS

Males Females

Age CWHS CPS CWHS CPS

16-19 66.7 50.8 66.3 50.6

20-24 84.0 84.3 80.2 74.7

25-29 86.6 91.2 79.5 77.6

30-34 84.7 92.4 75.6 75.9

35-39 85.8 91.9 76.8 76.9

40-44 86.3 90.6 79.8 79.8

45-49 85.4 90.3 80.2 78.7

50-54 81.5 87.0 71.7 73.4

55-59 75.4 80.3 61.6 61.6

60-64 57.7 60.8 44.3 44.0

65-69 33.2 32.7 22.9 19.9

70-90 14.4 13.1 6.6 6.2

Table 9

Share with 14+ Years of Education, CWHS versus CPS

Males Females

Birth Cohort CWHS CPS CWHS CPS

1905-1909 15.53 14.23 13.19 12.30

1915-1919 19.02 17.69 14.26 13.58

1925-1929 25.44 24.49 16.18 16.02

1935-1939 29.85 29.46 21.85 21.57

1945-1949 40.94 41.27 33.71 33.95

1955-1959 39.26 39.01 40.37 39.98

1965-1969 41.61 42.01 45.02 44.50
Figure 13. Low-Educated Male Cohort Mean Earnings, PSID 1968-19

0

10000200003000040000500006000070000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960

1

1926-1930

1946-1950

1936-1940

1916-1920

1966-1970
Figure 14. High-Educated Male Cohort Mean Earnings, PSID 1968-19

0

10000200003000040000500006000070000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960 

19

1946-1950

1936-1940

1916-1920

1966-1970
Figure 15. Low-Educated Female Cohort Mean Earnings, PSID 1968-1

0

5000

10000150002000025000300003500040000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960

1906-

1926-1930 1946-1950

1936-1940

1916-1920

1966-1970
Figure 16. High-Educated Female Cohort Mean Earnings, PSID 1968-1

0

5000

10000150002000025000300003500040000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960 1906

1926-1930 1946-1950 1936-1940 

1966-1970
Figure 17. Low-Educated Male Cohort Mean Earnings, CWHS 1984-19

0

10000200003000040000500006000070000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 

Age

Earnings for Workers, 1993 Dollars 1956-1960 

1946-1950 1936-1940 1966-1970
Figure 18. High-Educated Male Cohort Mean Earnings, CWHS 1984-1

0

10000200003000040000500006000070000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 

Age

Earnings for Workers, 1993 Dollars 1956-1960 

1946-1950

1936-1940

1966-1970
Figure 19. Low-Educated Female Cohort Mean Earnings, CWHS 1984-

0

5000

10000150002000025000300003500040000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 

Age

Earnings for Workers, 1993 Dollars 1956-1960 1926-1

1946-1950 1936-1940 1966-1970
Figure 20. High-Educated Female Cohort Mean Earnings, CWHS 1984-

0

5000

10000150002000025000300003500040000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 

Age

Earnings for Workers, 1993 Dollars 1956-1960 

1946-1950 1936-1940 1966-1970
Figure 21. Male Cohort Labor Force Participation,

CWHS Historical and Projected

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working1966-1970 1926-1930

1946-1950

1986-1990 2006-2010

2026-2030

1936-1940

1956-1960

1976-1980
Figure 22. Female Cohort Labor Force Participation,

CWHS Historical and Projected

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working

1966-1970

1926-1930

1946-1950

1986-1990

2006-2010

2026-20

1936-1940

1956-1960

1976-1980
Figure 23. Projected Longitudinal Labor Force Participation

for 62-year-old Males 

0

102030405060708090

100

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

2035

2038

2041

2044

2047

2050

2053

2056

2059

2062

20

Year

Share by Number of Years Worked 36+ years 21-35 years

11-20 years

6-10 years

1-5 years

0 years
Figure 24. Projected Longitudinal Labor Force Participation

for 62-year-old Females 

0

102030405060708090

100

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

2035

2038

2041

2044

2047

2050

2053

2056

2059

2062

2

Year

Share by Number Years Worked 36+ years 21-35 years

11-20 years

6-10 years

1-5 years

0 years
Figure 25. Male Cohort Full-Time Employment

CWHS Historical and Projected

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working Full Time 1966-1970 1926-1930 1936-1940 1956-1960 1946-1950

1976-1980

2026-2030

2006-2010
Figure 26. Female Cohort Full-Time Employment

CWHS Historical and Projected

0%

10%20%30%40%50%60%70%80%90%

100%

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Percentage Working Full Time 1966-1970 1926-1930 1946-1950 1956-1960 1936-1940

1976-1980

19

2006-2010

2026-2030
Figure 27. Low-Educated Male Cohort Mean Earnings,

CWHS Historical and Projected

0

10000200003000040000500006000070000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960 1986-1990 1946-1950 1

1976-1980 1966-1970 2006-2010 2026-2030
Figure 28. High-Educated Male Cohort Mean Earnings,

CWHS Historical and Projected

0

10000200003000040000500006000070000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960

2006-2010

1946-1950

2026-2030 

1966-1970

1976-1980

1986-1990
Figure 29. Low-Educated Female Cohort Mean Earnings,

CWHS Historical and Projected

0

5000

10000150002000025000300003500040000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960 2006-2010 1946-1950 1936-1940 1976-1980

1966-1970

1986-1990

2026-2030
Figure 30. High-Educated Female Cohort Mean Earnings,

CWHS Historical and Projected

0

5000

10000150002000025000300003500040000

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

Age

Earnings for Workers, 1993 Dollars 1956-1960 2026-2030 1946-1950 1936-1940

1976-1980

1966-1970

1986-1990

2006-2010
Figure 31. Lower Half of the Annual Earnings Distribution, Males and Fe

0

5000

100001500020000250003000035000

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

2035

2038

2041

2044

2047

2050

2053

2056

2059

2

Year

Annual Earnings, 2002 Dollars

Males 1st Percentile

Males 5th Percentile

Females 25th Percentile

Females 50th Percentile

Males 25th Percentile

Males 50th Percentile

Females 5th Percentile

Fe
Figure 32. Top Half of the Annual Earnings Distribution, Males and Fem

0

50000

100000150000200000250000300000

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

2035

2038

2041

2044

2047

2050

2053

2056

2059

2

Year

Annual Earnings, 2002 Dollars

Females 75th Percentile

Females 95th Percentile

Females 99th Percentile

Males 75th P

Males 95th Perce

Males 99th Percentile
Table 10

Annual Earnings Distribution by Lifetime Earnings Decile, 50- to 60-Year-Old Males

Year Lifetime Earnings Decile

1 2 3 4 5 6 7 8 9 10

1998 1 3.47 1.61 1.12 0.83 0.74 0.53 0.52 0.51 0.47 0.19

2 2.86 2.24 1.40 0.93 0.74 0.50 0.42 0.47 0.31 0.15

3 1.77 2.32 1.94 1.34 0.93 0.56 0.45 0.29 0.23 0.17

Annual 4 1.06 1.56 1.81 1.74 1.46 0.99 0.57 0.40 0.25 0.16

Earnings 5 0.37 1.01 1.53 1.86 1.65 1.56 0.93 0.56 0.41 0.13

Decile 6 0.18 0.68 1.02 1.37 1.80 2.02 1.46 0.91 0.36 0.20

7 0.14 0.39 0.66 0.89 1.46 1.78 2.05 1.59 0.78 0.25

8 0.07 0.15 0.35 0.72 0.70 1.27 1.94 2.37 1.94 0.52

9 0.03 0.04 0.16 0.27 0.38 0.67 1.31 2.05 3.20 1.87

10 0.02 0.02 0.03 0.05 0.14 0.14 0.35 0.85 2.06 6.36

1 2 3 4 5 6 7 8 9 10

2010 1 4.70 2.36 1.20 0.88 0.40 0.25 0.12 0.06 0.01 0.00

2 1.90 2.26 2.00 1.57 1.05 0.61 0.30 0.24 0.06 0.02

3 1.32 1.62 1.67 1.73 1.35 1.00 0.63 0.47 0.17 0.04

Annual 4 0.77 1.26 1.60 1.46 1.59 1.36 0.99 0.59 0.31 0.08

Earnings 5 0.52 0.88 1.19 1.31 1.41 1.52 1.46 0.95 0.61 0.14

Decile 6 0.32 0.59 0.89 1.02 1.55 1.51 1.57 1.37 0.91 0.27

7 0.25 0.48 0.59 0.80 1.03 1.58 1.64 1.64 1.46 0.54

8 0.12 0.35 0.44 0.64 0.92 1.09 1.61 1.85 1.94 1.03

9 0.06 0.12 0.34 0.45 0.55 0.77 1.21 1.90 2.51 2.09

10 0.03 0.06 0.08 0.15 0.15 0.30 0.47 0.94 2.01 5.79

1 2 3 4 5 6 7 8 9 10

2040 1 7.07 1.97 0.63 0.16 0.10 0.05 0.01 0.01 0.00 0.00

2 1.70 3.76 2.17 1.24 0.60 0.30 0.14 0.06 0.01 0.01

3 0.52 2.19 2.49 1.96 1.30 0.85 0.38 0.24 0.07 0.01

Annual 4 0.32 1.07 2.10 2.24 1.75 1.19 0.72 0.38 0.19 0.04

Earnings 5 0.13 0.49 1.25 1.81 2.05 1.79 1.22 0.88 0.33 0.04

Decile 6 0.10 0.26 0.77 1.31 1.81 1.83 1.76 1.29 0.73 0.15

7 0.07 0.12 0.39 0.76 1.33 1.90 2.03 1.76 1.25 0.38

8 0.05 0.07 0.10 0.36 0.71 1.32 2.03 2.33 2.08 0.95

9 0.02 0.04 0.10 0.16 0.29 0.64 1.33 2.16 3.06 2.20

10 0.01 0.01 0.02 0.02 0.05 0.13 0.36 0.88 2.30 6.23

1 2 3 4 5 6 7 8 9 10

2070 1 7.06 1.99 0.61 0.16 0.10 0.04 0.02 0.00 0.00 0.00

2 1.69 3.65 2.38 1.06 0.63 0.36 0.18 0.03 0.04 0.00

3 0.47 2.14 2.46 2.09 1.37 0.77 0.49 0.15 0.06 0.00

Annual 4 0.24 1.13 1.95 2.24 1.83 1.23 0.83 0.35 0.16 0.03

Earnings 5 0.21 0.56 1.31 1.89 1.86 1.64 1.26 0.93 0.26 0.08

Decile 6 0.12 0.29 0.65 1.29 1.78 1.96 1.72 1.33 0.65 0.18

7 0.06 0.16 0.40 0.79 1.29 1.78 1.99 1.90 1.29 0.36

8 0.08 0.04 0.16 0.32 0.81 1.39 1.94 2.24 2.16 0.86

9 0.05 0.03 0.06 0.12 0.29 0.67 1.25 2.26 3.06 2.18

10 0.00 0.01 0.03 0.03 0.04 0.14 0.31 0.80 2.32 6.31
Table 11

Annual Earnings Distribution by Lifetime Earnings Decile, 50- to 60-Year-Old Females

Year Lifetime Earnings Decile

1 2 3 4 5 6 7 8 9 10

1998 1 3.46 1.96 1.16 1.02 0.67 0.52 0.35 0.26 0.24 0.35

2 2.99 2.20 1.53 0.85 0.86 0.50 0.40 0.20 0.25 0.22

3 1.81 2.21 1.87 1.35 0.85 0.72 0.45 0.31 0.21 0.22

Annual 4 1.04 1.71 1.82 1.69 1.35 0.93 0.65 0.43 0.24 0.12

Earnings 5 0.40 0.97 1.60 1.75 1.75 1.41 1.05 0.57 0.37 0.17

Decile 6 0.17 0.47 1.07 1.52 1.65 1.68 1.50 1.12 0.57 0.24

7 0.06 0.27 0.46 1.01 1.49 1.83 1.76 1.66 1.07 0.36

8 0.06 0.10 0.32 0.51 0.88 1.32 1.96 2.20 1.98 0.71

9 0.00 0.06 0.07 0.19 0.37 0.92 1.31 2.00 2.94 2.11

10 0.02 0.02 0.11 0.11 0.13 0.16 0.59 1.22 2.15 5.51

1 2 3 4 5 6 7 8 9 10

2010 1 4.59 1.94 1.29 0.83 0.67 0.32 0.22 0.05 0.04 0.03

2 1.86 2.06 1.64 1.29 1.12 0.74 0.67 0.36 0.21 0.07

3 1.08 1.46 1.61 1.34 1.16 1.17 0.94 0.68 0.42 0.16

Annual 4 0.73 1.37 1.31 1.33 1.26 1.10 1.06 0.82 0.67 0.34

Earnings 5 0.54 1.04 1.17 1.28 1.20 1.25 1.14 1.04 0.87 0.48

Decile 6 0.45 0.78 0.88 1.13 1.20 1.33 1.24 1.34 1.00 0.61

7 0.30 0.51 0.76 0.96 1.07 1.38 1.42 1.41 1.37 0.84

8 0.25 0.44 0.58 0.86 1.07 1.18 1.30 1.57 1.67 1.09

9 0.15 0.26 0.49 0.61 0.83 0.93 1.29 1.59 1.84 1.99

10 0.03 0.14 0.26 0.36 0.42 0.61 0.73 1.13 1.91 4.39

1 2 3 4 5 6 7 8 9 10

2040 1 6.57 2.23 0.69 0.27 0.13 0.05 0.05 0.01 0.01 0.00

2 1.70 3.00 2.00 1.32 0.88 0.53 0.34 0.16 0.06 0.01

3 0.77 1.87 1.98 1.43 1.40 0.98 0.71 0.51 0.26 0.09

Annual 4 0.40 1.11 1.55 1.70 1.62 1.11 0.97 0.84 0.51 0.19

Earnings 5 0.12 0.85 1.29 1.63 1.40 1.45 1.12 1.02 0.82 0.30

Decile 6 0.13 0.38 1.03 1.37 1.42 1.52 1.52 1.20 0.89 0.52

7 0.14 0.26 0.68 1.00 1.23 1.49 1.47 1.53 1.40 0.84

8 0.11 0.11 0.47 0.77 0.98 1.40 1.63 1.67 1.64 1.23

9 0.04 0.13 0.26 0.39 0.66 1.10 1.47 1.74 2.16 2.05

10 0.03 0.05 0.06 0.14 0.27 0.36 0.73 1.33 2.25 4.78

1 2 3 4 5 6 7 8 9 10

2070 1 6.46 2.33 0.71 0.28 0.12 0.02 0.02 0.00 0.00 0.00

2 1.83 2.85 2.00 1.31 0.96 0.47 0.35 0.19 0.06 0.00

3 0.65 2.04 1.93 1.63 1.25 0.99 0.66 0.52 0.30 0.04

Annual 4 0.33 1.08 1.81 1.54 1.37 1.29 0.98 0.90 0.52 0.16

Earnings 5 0.20 0.73 1.18 1.63 1.58 1.42 1.31 0.97 0.66 0.31

Decile 6 0.19 0.42 0.94 1.38 1.48 1.45 1.26 1.24 0.99 0.64

7 0.15 0.21 0.66 1.02 1.24 1.54 1.66 1.44 1.29 0.78

8 0.09 0.17 0.42 0.62 0.98 1.37 1.60 1.69 1.74 1.33

9 0.05 0.10 0.27 0.43 0.78 0.96 1.46 1.81 2.16 1.96

10 0.02 0.07 0.08 0.15 0.23 0.48 0.69 1.24 2.26 4.78
Figure 33. Average Earnings Variation over Ages 16-59 for 60-Year-O

0.00.20.40.60.81.01.2

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 

Year

Males

Females