Congressional Budget Offi ce

Background Paper

THE CONGRESS OF THE UNITED STATES

Projecting Labor Force

Particiption and Earnings in 

CBO’s Long-Term

Microsimulation Model

October 2006
Pub. No. 2795
The Congress of the United States O Congressional Budget Office

CBO

Projecting Labor Force Participation and 

Earnings in CBO’s Long-Term 

Microsimulation Model

October 2006

Preface

The Congressional Budget Office (CBO) uses a microsimulation approach to analyze 

the budgetary and distributional impact of Social Security and other age-related policy 

issues. This background paper provides a detailed description of one important component in the microsimulation model: the equations used to project labor force participation, hours worked, unemployment, and earnings outcomes. The paper describes 

the estimated relationships in CBO’s microsimulation model, the data sets used, the 

principles underlying causal effects, and the properties of the projections over time. In 

keeping with CBO’s mandate to provide objective, nonpartisan analysis, the paper 

makes no policy recommendations.

Amy Rehder Harris, John Sabelhaus, and Jonathan A. Schwabish wrote the paper. 

Paul Cullinan and David Brauer provided helpful comments.

Allan Keaton edited the paper, and Christine Bogusz and Loretta Lettner proofread it. 

Maureen Costantino prepared the paper for publication, Lenny Skutnik produced the 

printed copies, and Simone Thomas prepared the electronic version for CBO’s Web 

site (www.cbo.gov).

Donald B. Marron

Acting Director

October 2006

v

Contents

Introduction 1

Development of the Model 1

Evaluating Projected Labor Market Outcomes 5

Projecting Labor Market Participation and Hours Worked 6

Labor Force Participation 6

Full-Time Versus Part-Time Employment 11

Hours for Part-Time Workers 17

Unemployment Spells 17

Projecting Individual Earnings Outcomes 20

Predictable Earnings Differences Across Groups and Time 22

Idiosyncratic Earnings Differentials 28

Appendix A: March Current Population Survey Data Used in the

Analysis 77

Appendix B: CBOLT Representative Sample Data 81

Appendix C: Labor Force Equations Used to Model Persistence 85

Appendix D: Permanent and Transitory Earnings Shocks 111

Tables

1. Labor Force Participation Logit Coefficients for Men 34

2. Labor Force Participation Logit Coefficients for 

Women 38

3. Full-Time Employment Logit Coefficients for Men 42

4. Full-Time Employment Logit Coefficients for 

Women 46

5. Full-Time or Part-Time Employment Persistence for 

Men, by Age Group 50

6. Full-Time or Part-Time Employment Persistence for 

Women, by Age Group 51
vi

7. Part-Time Hours Ordered Logit Coefficients for

Men 52

8. Part-Time Hours Ordered Logit Coefficients for 

Women 56

9. Unemployment Logit Coefficients for Men 60

10. Unemployment Logit Coefficients for Women 64

11. Earnings Coefficients for Men 68

12. Earnings Coefficients for Women 70

13. Annual Earnings Distribution for 50- to 60-Year-Old

Men, by Lifetime Earnings Decile 72

14. Annual Earnings Distribution for 50- to 60-Year-Old 

Women, by Lifetime Earnings Decile 74

C-1. Historical Full-Time Employment Logit 

Coefficients for Men 86

C-2. Historical Full-Time Employment Logit 

Coefficients for Women 90

C-3. Historical Part-Time Hours Ordered Logit 

Coefficients for Men 94

C-4. Historical Part-Time Hours Ordered Logit

Coefficients for Women 98

C-5. Historical Unemployment Logit Coefficients for 

Men 102

C-6. Historical Unemployment Logit Coefficients for

Women 106

Tables (Continued)
vii

Figures

1. Steps to Project Earnings in CBO’s Microsimulation 

Model 2

2. Actual and Projected Labor Force Participation for

Men, by Birth Cohort 7

3. Actual and Projected Labor Force Participation for 

Women, by Birth Cohort 8

4. Projected Longitudinal Labor Force Participation for 

62-Year-Old Men 12

5. Projected Longitudinal Labor Force Participation for 

62-Year-Old Women 13

6. Actual and Projected Full-Time Employment for 

Men, by Birth Cohort 14

7. Actual and Projected Full-Time Employment for

Women, by Birth Cohort 15

8. Number of Part-Time Hours That Men Worked 18

9. Number of Part-Time Hours That Women Worked 19

10. Mean Unemployment Spell for Men, by Age 20

11. Mean Unemployment Spell for Women, by Age 21

12. Predicted Log Full-Time-Equivalent Earnings for

Men, by Education Level, Age, and Birth

Cohort 25

13. Predicted Log Full-Time-Equivalent Earnings for 

Women, by Education Level, Age, and Birth 

Cohort 26

14. Lower Half of the Projected Annual Earnings

Distribution 31

15. Upper Half of the Projected Annual Earnings

Distribution 32

1

Introduction

This background paper describes the methods underlying labor force and earnings 

projections in the Congressional Budget Office’s (CBO’s) long-term microsimulation 

model (CBOLT).1 CBO developed a microsimulation approach for analyzing Social 

Security and other long-term policy issues in order to provide the Congress with comprehensive analyses of the budgetary, distributional, and aggregate economic aspects 

of various policy choices. The microsimulation approach makes it possible to examine 

how policy affects individuals’ benefits under current law and proposed alternatives, 

including individual accounts.2

The methodological strategy in microsimulation is to generate realistic demographic 

and economic outcomes for a representative sample of the population and then apply 

tax and benefit rules to that sample in order to draw inferences about the effects of 

various policy alternatives. Labor market outcomes are fundamental when analyzing 

Social Security and other age-related policy issues because the payroll taxes collected 

from individuals and benefits paid to individuals both depend on earnings. Projected 

individual earnings determine aggregate revenues and outlays and play a key role in 

the distribution of taxes and benefits across the population. 

Development of the Model

A microsimulation model starts with individual data from a representative sample of 

the population and projects demographic and economic outcomes for that sample 

through time. In CBOLT, the basic demographic processes include fertility, educational attainment, marital transitions, marital partner assignments, and eventual 

death.3

 This background paper describes the methods for projecting labor-related 

economic outcomes for the representative sample in CBOLT; those are the modules 

for labor force participation, hours worked, unemployment, and annual earnings. The 

equations are designed to generate realistic patterns of behavior in the representative 

sample and to operate as part of an overall framework that includes macroeconomic 

and aggregate budgetary outcomes. 

1. These modules have been updated from earlier work; see Amy Rehder Harris and John Sabelhaus, 

“Projecting Longitudinal Earnings Patterns for Long-Run Policy Analysis,” Technical Paper 2003-

02 (April 2003), available at www.cbo.gov.

2. See, for example, Congressional Budget Office, Long-Term Analysis of S. 2427, the Sustainable 

Solvency First for Social Security Act of 2006 (April 2006); Long-Term Analysis of the LiebmanMacGuineas-Samwick Social Security Proposal (February 2006); and H.R. 3304, Growing Real 

Ownership for Workers Act of 2005 (September 2005). 

3. See Josh O’Harra, John Sabelhaus, and Michael Simpson, “Overview of the Congressional Budget 

Office Long-Term (CBOLT) Policy Simulation Model,” Technical Paper 2004-01 (January 2004); 

Kevin Perese, “Mate Matching for Microsimulation Models,” Technical Paper 2002-03 (November 

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

Long-Run Policy Analysis,” Technical Paper 2002-02 (October 2002). 
2

Figure 1.

Steps to Project Earnings in CBO’s Microsimulation Model

Source: Congressional Budget Office. 

Note: LFP = labor force participation; OASDI = Old-Age, Survivors, and Disability Insurance. 

This discussion of labor market and earnings projections starts with the basic demographic outcomes for the representative sample. Then, given an individual’s age, sex, 

education, marital status, fertility history, and Social Security beneficiary status, how 

should current-year labor force activity and earnings best be projected? The CBOLT 

approach is to break the process of projecting earnings into several logically separate 

steps (see Figure 1). The steps are sequential, and involve solving for each person’s 

labor force participation, full-time or part-time employment status, number of hours 

Labor Force Participation =

f (age, education, lagged LFP,

marital status, trend, birth cohort,

in school, number of children under

age 6, OASDI status)

Full Time or Part Time =

f (age, education, marital status,

birth cohort, in school, number of

children under age 6, OASDI status)

Part-Time Hours =

f (age, education, marital status,

trend, birth cohort, in school, number

of children under age 6, OASDI status)

Not in Labor

Force

Transitory

Shock

Previous

Year

Earnings

Differential

Permanent

Shock

Permanent

Earnings

Differential

No

Earnings

In Labor

Force

Part Time

Full Time Hours =

2,080 per year

Unemployment Spell =

f (age, education, marital status,

trend, birth cohort, in school, number

of children under age 6, OASDI

status, full-time status, aggregate

unemployment rate, full-time

equivalent earnings)

Earnings =

f (age, education, cohort, OASDI

status, interactions)
3

worked, presence of an unemployment spell, and an idiosyncratic earnings component that determines final earnings. 

Each step of the labor market outcomes involves a deterministic, equation-based component but also involves some degree of randomness. For example, a given equation 

may generate a 60 percent chance that some outcome (for example, entering the labor 

market) will occur for a given individual. A model-generated random number 

(between 0 and 100 percent) for that individual is then compared with that probability to determine the actual outcome (in this example, if the random number is less 

than 60 percent, the individual enters the labor market). Given a large enough sample, the fraction of the population assigned a particular outcome will match the average probability of that outcome. 

Each simulation step uses a different set of predictive variables, and relationships 

between the labor market outcomes and the underlying determinants are estimated 

using March Current Population Survey (CPS) data. (See Appendix A for a description of the CPS data.) The modeled processes often exhibit a high degree of persistence—for example, after controlling for observable characteristics, people who 

worked in a given year are more likely to work the following year. That persistence is 

captured explicitly in some equations using lagged variables or implicitly in other 

equations using an error-correction approach applied to the CBOLT sample data. 

(See Appendix B for a description of the CBOLT micro data file, the Continuous 

Work History Sample, CWHS.)

Although the methodological steps for determining earnings have some common 

features, looking at the steps in sequence helps explain the aspects of behavior that 

each equation or process attempts to capture:

B Labor Force Participation. In the first step, the decision to enter the paid labor 

force is predicted for each person using binomial logit regressions with the following set of covariates: age, education level, in-school status, marital status, Social 

Security beneficiary status (shown in the figures as Old-Age, Survivors, and Disability Insurance, or OASDI, status), number of children under age 6 (for 

women), and lagged labor force participation, along with effects of time (trend) 

and birth cohort.4 Separate equations for men and women are estimated using 

CPS data and are based on an age-centering approach that allows flexibility in the 

relationship between labor force participation and the underlying determinants 

across age groups. Observed strong persistence in labor force participation behavior is captured directly through the lagged labor force term, an effect that grows 

with age and is uniformly larger for women than for men.

4. Individuals are grouped into birth cohort by decade of birth—for example, people born between 

1940 and 1949 constitute one birth cohort.
4

B Full-Time Versus Part-Time Employment. For those individuals in the representative sample predicted to be in the labor force, the second step is to assign full-time 

or part-time employment status. Like labor force participation, full- versus parttime work is predicted using separate age-centered regressions for men and 

women, where the explanatory variables include age, education level, in-school 

status, marital status, Social Security beneficiary status, number of children under 

age 6 (for women), and birth cohort. The effect of education on full-time status is 

large and statistically significant for both men and women but has a slightly stronger impact for men. The notable difference between the equation for labor force 

participation and the equation for full-time or part-time work is the method for 

capturing persistence in individual behavior. Given the lack of information about 

lagged work hours in the CPS data, the method used for the full-time versus parttime equation is an error-correction approach, in which the estimated equations 

are adjusted to match actual longitudinal patterns in the historical CBOLT sample 

data. (See Appendix C for details about the equations used in the error correction.)

B Hours Worked. The third step in estimating a labor force is to assign hours to all 

workers. Full-time workers by definition work 2,080 hours per year (40 hours a 

week for 52 weeks, including paid vacation and sick leave). The number of hours 

worked for part-time workers is estimated using an ordered logit model with six 

categories of hours worked. The portion of part-time workers with less than 1,000 

hours per year has fallen since the mid-1970s, driven almost entirely by the dramatic fall in the number of women working fewer part-time hours. The ordered 

logits attempt to capture this secular trend by using the following regressors: age, 

education level, in-school status, marital status, Social Security beneficiary status, 

and the number of children under age 6 (for women), along with trend and birth 

cohort. The resulting projections show a stable trend in the share of workers in 

each of the six categories of hours worked, with a greater portion of women than 

men in the categories for fewer hours worked. As with the full-time versus parttime work equation, the part-time hours equations are calibrated to match longitudinal outcomes from the CWHS. 

B Unemployment. Given hours projections, all individuals designated as being in the 

labor force are probabilistically assigned an unemployment spell, again using CPS 

data. The regressors in these age-centered logit regressions include age, education 

level, in-school status, marital status, Social Security beneficiary status, the number 

of children under age 6 (for women), an indicator for full-time status, projected 

full-time equivalent (FTE) earnings, and the aggregate unemployment rate, along 

with trend and birth cohort. As expected, the aggregate unemployment rate, used 

to account for fluctuations in the macroeconomy, has a positive effect on an individual worker’s likelihood of experiencing an unemployment spell and is larger for 

men than for women. Full-time status enters the model with a negative coefficient, 

also as expected.
5

B Earnings. The first four processes project labor force participation, hours worked, 

and unemployment spells for a representative microsample. Moving from effective 

labor supply to actual earnings requires two more steps. The first is an estimated 

relationship between potential earnings (in the CBOLT microsimulation model, 

this is FTE earnings) and demographic variables such as age, sex, education, and 

birth cohort. The second is a method for projecting the idiosyncratic earnings 

piece—the so-called earnings differential that distinguishes a given individual from 

other people in the same demographic group. Earnings differentials are generally 

persistent, but there is a great deal of variance in a person’s earnings over his or her 

lifetime, so CBOLT uses an approach that allows for both permanent and transitory earnings components. 

The relationship between demographics and potential earnings is designed to capture 

the predictable differences in earnings across groups. After controlling for changes in 

average wage rates over time that are attributable to productivity and inflation, the 

goal of the earnings equations is to isolate a stable historical relationship between 

earnings and observable demographics. One complicating factor is the increase in the 

relative earnings of college graduates over the last few decades, but this can be statistically reconciled using a simple interaction between birth cohort and education. The 

results of the estimated equations are age-earnings profiles that vary by sex, education, 

and birth cohort; those profiles reflect predictable differences across groups. 

The predictable component of differences in earnings across individuals represents 

only a small fraction of the overall differences, which means the estimated earnings 

equations are merely a starting point for predicting earnings outcomes. The more 

important task is to project how a given individual’s actual earnings will differ from 

the value predicted by the earnings equation. This idiosyncratic difference in CBOLT 

has two components (see Figure 1 and Appendix D). The first component of the error 

term is a permanent differential, which evolves slowly over time through a series of 

permanent shocks. The second is a transitory shock, which has a much larger standard 

deviation but lasts only one year. The probability distributions for the permanent and 

transitory shocks are chosen to generate an actual earnings distribution (around the 

predicted earnings) that matches the recent historical period. The ultimate realized 

earnings distribution in a given year depends on the equations that determine potential earnings, the idiosyncratic error terms, and the projections of actual hours 

worked. 

Evaluating Projected Labor Market Outcomes 

The objective of the labor force and earnings modules is to predict realistic outcomes 

across the population through time. Those projected outcomes are evaluated in this 

background paper using tabulations that compare the projections with historical 

experience. Some behaviors, such as the persistence in labor force attachment, are projected to look much as they did in history. Other features, such as the generally rising 

labor force participation of women, are projected to evolve and eventually reach a new 

equilibrium level as birth cohort effects are fully phased in. Some of the divergent 
6

trends, such as the increase in women’s earnings, have direct implications for features 

of Social Security such as spousal and auxiliary benefits. 

Projecting Labor Market Participation and Hours Worked

As indicated earlier, the CBOLT microsimulation approach for projecting annual 

hours worked proceeds in four steps. The first predicts labor force participation, 

which is the decision about whether to enter the paid labor force. The second step, 

conditional on entering the labor force, assigns full-time versus part-time employment 

status. The third step assigns total annual hours, conditional on full- or part-time 

employment status. (Individuals designated as full time are assigned 2,080 work 

hours. Individuals designated as part time are assigned annual hours from one of six 

possible values.) Finally, every individual in the labor force is at risk for an unemployment spell that reduces actual hours worked and thus earnings. 

Labor Force Participation

The portion of the U.S. population that participates in the labor market in a given 

year is very high (see Figure 2 for men and Figure 3 for women). Beyond that simple 

observation, the most noticeable patterns of labor force participation are a distinct 

inverted U-shape (indicating that middle-aged men and women are much more likely 

to work than the young and the old) and trends across birth cohorts (indicating generally rising participation for women and generally falling participation for men). The 

equations used to predict labor force participation in CBOLT are designed to reflect 

those general patterns across age and cohort groups and to capture differences across 

individuals that are correlated with Social Security benefit receipt, marital status, educational attainment, in-school status, and number of children under age 6 (for 

women). The equations also include terms for whether the person was in the labor 

force in the previous year because of the persistence in labor force attachment that 

remains even after controlling for other determining variables. 

The approach used to estimate the labor force participation equations for CBOLT is 

chosen to allow maximum flexibility in the relationships between the explanatory 

variables and the predicted outcome. The approach used here, age-centered regressions, estimates the equation—in this case, a logit equation for the probability of 

working—separately for each age and sex group. Given the limited available data, 

however, it is impossible to estimate the equations using data for just a single year of 

age, so the age-centered approach uses every observation for ages within a preset band 

around the specific age group being analyzed. The band on each side of a specific age 

group for the CBOLT labor force equations is generally set to five years, so (for example) the sample used to estimate the labor force participation equation for 25-year-
7

Figure 2.

Actual and Projected Labor Force Participation for Men, by 

Birth Cohort

(Percentage working)

Source: Congressional Budget Office. 

Note: All values for the 1990-1999 and 2000-2009 birth cohorts are projections. 

olds actually includes everyone between 21 and 29 years old. The sample for estimating the 26-year-old equation includes everyone between 22 and 30, and so on.5

5. The exceptions to the five-year age bands are at either end of the age distribution, where the data 

are not symmetric, and at ages 61 through 66 where single-year age groups are used to isolate the 

effects of Social Security retirement benefit eligibility at those ages. Also, in addition to restricting 

the sample used to within five years of the age group being considered, the estimation procedure 

applies declining weights to observations on the basis of differences in age from the group being 

considered. Each person in the reference group (age 25 in the example) gets a weight of 1.0, each in 

the group on either side (24- and 26-year-olds) gets a weight of 0.8, each in the next two groups 

(23- and 27-year-olds) gets 0.6, and so forth, and each person in the last groups considered (21- 

and 29-year-olds) gets a weight of 0.2. The declining weights are a version of kernel smoothing 

often used in data analysis to try to get maximum statistical precision out of small samples. 

16–25 26–35 36–45 46–55 56–65 66–75 76+

0

10

20

30

40

50

60

70

80

90

100

1940–1949

1950–1959

1960–1969

1970–1979

1980–1989

1990–1999

2000–2009

Age
8

Figure 3.

Actual and Projected Labor Force Participation for Women, 

by Birth Cohort

(Percentage working) 

Source: Congressional Budget Office. 

Note: All values for the 1990-1999 and 2000-2009 birth cohorts are projections. 

The age-centering technique for estimating statistical relationships leads to separate 

sets of coefficients for each age group (see Table 1 on page 34 for men and Table 2 on 

page 38 for women).6 This is the sense in which age-centering introduces maximum 

flexibility into the relationship between explanatory and dependent variables. Notice, 

for example, that the negative effect of Social Security benefit receipt on labor force 

participation rises with age for both men and women up to retirement age, and thereafter falls off. If the model used just one equation for men and one for women to predict labor force participation, the coefficient on Social Security benefit receipt would 

be equal for all age groups.7

 The age-centering approach allows the coefficients on 

6. In the coefficient tables in this document, coefficient values shown in bold indicate statistical significance at the 5 percent level. Statistical significance of the threshold values of the ordered logit 

regressions is not evaluated.

7. An alternative specification could include interaction of age and beneficiary status terms. Such a 

specification builds additional structure into a regression that is already categorized by separate age 

groups.

16–25 26–35 36–45 46–55 56–65 66–75 76+

0

10

20

30

40

50

60

70

80

90

100

1940–1949

1950–1959

1960–1969

1970–1979

1980–1989

1990–1999

2000–2009

Age
9

Social Security benefit receipt and on the other independent variables to vary with 

age. The results show that the values vary dramatically.8

The basic relationships reflected in the coefficient patterns are generally those that 

intuition would suggest. Social Security benefit receipt has a negative effect on labor 

force participation for both men and women, and the effect is largest in middle age, 

where disability insurance is the likely source of benefit receipt. Disability beneficiaries are inherently less likely to work; even those able to work in some capacity are discouraged from doing so by program restrictions. At older ages the effect of benefit 

receipt is not as strong because many older worker or spousal beneficiaries still participate in the labor force to some degree even though they are getting benefits. 

The correlation between marital status and labor force participation is captured by 

using two dummy variables that reflect whether the person is currently married or 

never married (alternatives to those possibilities are that they are widowed or 

divorced). The strongest correlation is the negative relationship between marriage and 

women’s participation; for men the correlation is weaker and varies somewhat by age. 

There is a strong positive relationship of marriage for men in middle age; middle-aged 

married men are more likely to work than middle-aged widowed or divorced men. 

Another key demographic determinant of labor force participation for women is the 

number of children under 6 years of age. Again, the effect is expected because the 

presence of young children lowers labor force participation, and the effect is strongest 

during the most likely childbearing years. The importance of age-centering comes 

through again in these estimates; the coefficient on children under age 6 for a woman 

in her 50s is less than half of that for a woman in her 20s. That occurs because, if a 

woman in her 50s has children 6 years of age or younger, she is likely to have started 

her family much later and therefore is probably more attached to the labor market. 

The effect of educational attainment on labor force participation is captured through 

a variable for whether the individual is currently enrolled in school and through a 

series of dummy variables for ultimate education level. The in-school variable is used 

in the equations for ages 16 through 24, and the education-level dummies are used for 

all age groups 25 and older. For the labor force participation regressions, the omitted 

group is those with less than high school education; the three reported coefficients are 

for high school graduates, those with some college, or college graduates. 

Coefficients on the in-school variables are all negative, as expected. If individuals are 

in school they are less likely to be in the labor force. The effect rises with age between 

8. The age-centering technique includes age itself as an independent variable but only to capture any 

residual differences not reflected in the other variables. In the tables, the column “Coefficient for 

Age + Constant” includes the estimated constant term, plus the age coefficient multiplied by the 

age for that group. Because of the multicollinearity introduced by the combination of the constant 

and age terms, statistical significance is determined by either of the two variables. 
10

16 and 24 for both men and women for the following reason: Most 16-year-olds are 

in school, so that is not a clearly delimiting factor for predicting who among them will 

be in the labor force. At age 24, the relationship is stronger because most people who 

are not in college are working to some extent. 

The education coefficients for both men and women are generally positive, increasing 

with the level of educational attainment, which suggests stronger labor force attachment as education rises. There are some notable exceptions—for example, men 

through age 40 with some college education work less than the control group (those 

with less than a high school education). The effects are also stronger and more systematic for women: college-educated women are always more likely to work than women 

in the other education groups. 

The magnitudes of the coefficients on the demographic variables are all modest when 

compared with the effect of lagged labor force participation. The coefficient on the 

variable that measures whether the individual worked the previous year is positive and 

is an order of magnitude greater than the coefficients on marital status, education, or 

(for women) the number of children under age 6. As noted before, the portion of the 

population participating in the labor force at most ages is quite high, so one might 

think of the labor force participation equation as really predicting those who will not 

participate in a given year. That makes it easier to interpret the coefficient on lagged 

participation: the individuals most likely not to work this year are the ones who did 

not work last year. This variable captures the unobserved heterogeneity in behavior 

that is crucial to getting the sample to reflect the real-world population. 

Even after controlling for demographic effects and lagged labor force participation, 

there has been a noticeable increase in labor force participation for women at most 

ages and a less noticeable but statistically significant decrease for men. Some of those 

trends are captured by the demographic variables and lagged effects: women in their 

early 20s are increasingly less likely to have children and thus are more likely to work 

at those ages. That increase in labor force participation also leads to higher predicted 

participation at older ages through the lagged labor force participation coefficient. 

Still, there are unexplained changes in participation rates across time and birth cohort. 

For example, married women with children today are more likely to work than were 

married women with children in the past. Those unexplained trends are captured 

using trend terms at each age, along with birth cohort effects. The differences between 

men and women come through clearly in those terms, with strong positive trend coefficients for women and weak, often negative, trend coefficients for men. In the projections, trend terms are not extended, and the cohort terms (for future cohorts) are all 

set equal to those of the most recently observed birth cohort that has attained that 

age. 

The equations for predicting labor force participation in CBOLT can be characterized 

as simply describing patterns of labor force participation across demographic groups 

and time, as opposed to generating predictions based on economic behavior. Econo-
11

mists often focus on how policy affects labor supply behavior; many models predict 

behavior on the basis of utility-maximizing principles. The labor force participation 

equations in CBOLT are not derived from an explicit optimizing model that would 

include, for example, the effect that tax policy has on the trade-off between leisure and 

consumption. This is in large part because the primary goal of the microsimulation is 

to generate a rich data set with realistic demographic and economic heterogeneity. 

Utility-maximizing models place significant constraints on the extent to which that 

richness can be generated within the model. 

Although CBOLT does not start with a utility-maximizing framework, there are 

important policy effects built into the labor force modules, albeit indirectly. For example, the decision to claim Social Security benefits in CBOLT is based on factors such 

as the level of benefits compared with lifetime earnings, actuarial adjustment factors 

for early claiming, and Medicare eligibility age. Those policy variables have an effect 

on labor supply through the coefficient on Social Security beneficiary status. Once an 

individual makes the decision to claim benefits, he or she is less likely to work. Also, 

as shown later in the descriptions of the labor market equations, beneficiaries who 

enter the paid labor force are less likely to work many hours. 

The predicted lifetime labor force participation rates generated by the model for current and future cohorts of workers make sense in the context of historical trends (see 

Figure 4 for men and Figure 5 for women).9 For each birth cohort, one of the important measures of labor force participation outcomes for Social Security purposes is the 

number of years worked as of retirement age, fixed here at age 62. For men, there is 

great stability in the predicted distribution of years worked, with fewer than 40 percent working less than 36 years. The only significant variability for men is for those 

who will retire in the next few years; they are less likely to be covered by Social Security for most of their lives than future cohorts. For women, there are noticeable 

increases in lifetime participation for all retiring cohorts through about 2030, at 

which point the portion of lifetime that is worked stabilizes to less than that for men 

but remains significantly above that for women retiring today. Those trends show how 

the trend and cohort terms interact with forecast demographics in the CBOLT projections; the equations capture changes in behavior that have occurred through the end 

of the historical period but, beyond letting those effects work their way through the 

future on a cohort basis, there are no further projected changes in behavior. 

Full-Time Versus Part-Time Employment

Conditional on participating in the paid labor market, the portion of people working 

full time has an inverted U-shaped pattern across age groups (see Figure 6 on page 14 

for men and Figure 7 on page 15 for women) similar to the pattern observed for the 

9. These projections come from a CBOLT baseline simulation in which all the demographic inputs 

for the labor force participation equations—marital status and education—are generated by other 

parts of the model. 
12

Figure 4.

Projected Longitudinal Labor Force Participation for 

62-Year-Old Men

(Share, by number of years worked)

Source: Congressional Budget Office. 

decision to enter the labor market.10 There are sharp differences between men and 

women, with more than 90 percent of middle-aged men in the labor force working 

full time but only about 70 percent of middle-aged women working full time. The 

equations used to predict full-time participation are structured much like the labor 

force participation equations described in the previous section: they are age-centered 

logits with the same basic control variables, including Social Security benefit receipt, 

marital status, education, number of children under age 6 (for women), and birth 

cohort.

Estimated coefficients for the age-centered full-time work equations show patterns 

that are quite similar to those for labor force participation (see Table 3 on page 42 for 

men and Table 4 on page 46 for women). The coefficients on Social Security benefit 

receipt are all negative and are quite large compared with any of the demographic variables. It is also interesting to distinguish those estimates from estimates for labor force

10. In the CPS data used to estimate the labor force modules, the cutoff for full-time status is set to 

1,750 annual hours, which is about 35 hours per week. That definition concurs with the definition 

used by the Bureau of Labor Statistics and with the definition provided (for example) by Julie L. 

Hotchkiss, “The Definition of Part-Time Unemployment: A Switching Regression Model,” International Economic Review, vol. 32, no. 4 (1991), pp. 899–917.

2006 2016 2026 2036 2046 2056 2066 2076 2086 2096 2106

0

20

40

60

80

100

36+ Years

21–35 Years

0 Years 6–10 Years

1–5 Years

11–20 Years
13

Figure 5.

Projected Longitudinal Labor Force Participation for 62-Year-Old Women

(Share, by number of years worked)

Source: Congressional Budget Office. 

participation: in the labor force participation equations, the magnitudes are lower for 

groups around retirement age, but that is not the case in the equations for full-time 

work. The probability of working full time is conditional on working in the first 

place: if a person is both working and collecting benefits, he or she is more likely to be 

working part time. 

The estimated effects of marital status on full-time work are of opposite directions for 

men and women over most prime-age working groups. For men, marriage has a 

strong positive effect on full-time work, and never having been married (compared 

with the omitted reference group of the widowed and divorced) has a negative impact. 

For women, the effects are just as strong, but in the opposite direction. Being married 

has a negative impact on the probability of working full time. In addition, for women, 

the effect of having children under age 6 further depresses the likelihood of full-time 

work. 

Educational attainment also plays an important role in the probability of working full 

time. For those 24 and younger, being enrolled in school lowers the probability of 

full-time work for both men and women. Although there are a few mixed coefficients 

for people in their mid-20s, the effect of educational attainment for prime-age workers is strong for both men and women, generally rising as education level increases. 

2006 2016 2026 2036 2046 2056 2066 2076 2086 2096 2106

0

20

40

60

80

100

36+ Years

21–35 Years

11–20 Years

0 Years

1–5 Years 6–10 Years
14

Figure 6.

Actual and Projected Full-Time Employment for Men, by 

Birth Cohort

(Percentage working)

Source: Congressional Budget Office. 

Note: All values for the 1990-1999 and 2000-2009 birth cohorts are projections. 

The equations for full-time work use a simpler approach than do the labor force participation equations to control for the effects of differences across time. The equations 

for full-time work include only 10-year birth cohort dummies, which capture changes 

in behavior over time that are not associated with other control variables. Much like 

the equations for labor force participation, time effects are generally small for middleaged men but are strong and positive for middle-aged women. 

One other important difference between the full-time work equations and the labor 

force participation equations is the method to control for person-level heterogeneity. 

There is significant persistence in the probability of working full time, just as there is 

in labor force participation, but there are no lagged variables used in the full-time 

equation. That difference in specification is because of data limitations not because 

the principles are inherently different. The CPS data used to estimate both sets of 

equations have a measure of lagged labor force participation but have no measure of 

lagged employment status. (See Appendix A for details.)

16–25 26–35 36–45 46–55 56–65 66–75 76+

0

10

20

30

40

50

60

70

80

90

100

1940–1949

1950–1959

1960–1969

1970–1979

1980–1989

1990–1999

2000–2009

Age
15

Figure 7.

Actual and Projected Full-Time Employment for Women, by 

Birth Cohort

(Percentage working)

Source: Congressional Budget Office. 

Note: All values for the 1990-1999 and 2000-2009 birth cohorts are projections. 

Because of those data limitations, CBOLT uses an alternative error-correction 

approach for capturing persistence in individuals’ employment status over time.11

That alternative approach derives from the method used to impute hours worked in 

the historical administrative data files that are inputs to CBOLT. Those historical 

CWHS data have several demographic variables and total annual earnings, but there 

are no measures of hours worked. Therefore, CBOLT uses modified versions of the 

hours-worked equations to infer the total annual hours that people in the sample 

worked in the historical period. Those predicted historical hours worked are then 

used as a benchmark against CPS data to assign hours worked in the CBOLT micro 

data file.

11. Another alternative is to use a longitudinal data set such as the Panel Survey on Income Dynamics 

(PSID), which would allow a specification with lagged employment status similar to the equation 

for labor force participation. Indeed, earlier versions of CBOLT relied on that approach, but significant problems arose because of the size of the PSID data sample and because there are some conceptual differences between CPS and PSID measures. 

16–25 26–35 36–45 46–55 56–65 66–75 76+

0

10

20

30

40

50

60

70

80

90

100

1940–1949

1950–1959

1960–1969

1970–1979

1980–1989

1990–1999

2000–2009

Age
16

The equations used to impute historical hours worked are the same as the ones used 

to project earnings in the future, except that they add one variable: actual earnings 

during the year (see Table C-1 on page 86 for men and Table C-2 on page 90 for 

women). For example, the equations consider how much a person earned during the 

year (along with the other demographic variables and time effects) to infer whether 

the person worked full or part time. Those equations have much stronger statistical 

capabilities than do the equations described above for predicting employment status 

in the CBOLT projections because the amount a person earned generally indicates 

whether he or she is likely to have worked full or part time. 

The historical versions of the employment status equations are not useful for making 

projections. Such equations are inappropriate for the CBOLT projection modules 

because, looking forward, CBOLT solves for labor supply before it solves for earnings. 

Yet, there is a role for the historical employment status equations in the projections. In 

a given year, CBOLT makes three calculations for each person to predict employment 

status. The first is the baseline probability from the equations described in this section, in which the probability of working full time is a function of age, sex, Social 

Security beneficiary status, marital status, education, number of children under age 6 

(for women), and birth cohort. The second uses the same calculation for the same 

person but in the prior year, when that person’s demographic variables were slightly 

different. The third calculation is also for the prior year but uses the historical version 

of the equation, which is possible because the person’s earnings for the prior year are 

known. 

It is the difference between the second and third calculations that is used to introduce 

persistence into the employment status patterns. Consider a person who has a 50 percent chance of working full time this year in the baseline equation. Then, using that 

person’s demographics from the previous year, consider that the same person had a 45 

percent chance of working full time the previous year. Finally, given knowledge of the 

previous year’s earnings, consider that this person had an 80 percent chance of working full time the pervious year (because he or she had higher earnings than other people in the same demographic situation). The one-year lagged gap between the two 

predictions (in the example, 80 percent minus 45 percent, equaling 35 percent) is 

added to the current year projection (50 percent) to yield the final probability of 

working full time in the current year (85 percent). 

That method of error correction is reasonably accurate for introducing the degree of 

persistence in employment status over time observed in longitudinal data (see Table 5 

on page 50 for men and Table 6 on page 51 for women). For any age group at any 

point in time, it is possible to compute one-year transitions across employment status 

groups of not working, working full time, and working part time. Those one-year 

transitions vary across age and sex groups but, as shown in the tables, the transition 

rates do not vary significantly over time. The persistence introduced through the 

error-correction approach is crucial for simultaneously generating realistic crosssectional and longitudinal earnings patterns in the CBOLT microsimulation.
17

Hours for Part-Time Workers

The large majority of middle-aged people in the labor force work full time and are 

assigned a standard full-time measure of annual hours: 2,080. The equations for parttime hours apply to the rest of the labor force (those who do not work full time). The 

goal is to generate realistic heterogeneity in annual hours in the part-time population 

by putting those workers into one of six groups. The approach uses ordered logit 

equations where the possible outcomes for annual hours range from very low (125 

hours per year) to nearly full time (1,625 hours per year).12

The factors used to predict part-time hours worked are the same factors used to predict labor force participation and full- versus part-time work (see Table 7 on page 52 

for men and Table 8 on page 56 for women). The list of control variables includes 

Social Security benefit receipt, marital status, education, number of children under 

age 6 (for women), trend and birth cohort. As with the labor force participation and 

full- versus part-time equations, the approach uses an age-centered estimation. 

Most observations about coefficient values mirror those made for the equation for 

full- versus part-time work. However, most of those observations are somewhat 

muted. For example, a woman who is married and has a child under age 6 is less likely 

to work full time than a woman who is unmarried and without children. She is also 

less likely to work many hours conditional on being part time. However, the magnitude of the second effect is smaller; the key is that she works part time, and the annual 

hours worked conditional on being part time are harder to predict. 

As in the case for the full- versus part-time equation, the CPS has no lagged information about hours worked that can be used to directly incorporate individual persistence over time. Therefore, CBOLT uses the same error-correction mechanism 

described above for full- versus part-time work: it makes three calculations for each 

part-time person in each year. The first is the prediction from the equations discussed 

here (see Tables 7 and 8), the second is from the same equations but lagged one year, 

and the third is from the imputations of the lagged equations that include actual 

earnings as a regressor (see Table C-3 on page 94 for men and Table C-4 on page 98 

for women). As before, the gap between the two lagged probabilities is used to errorcorrect the equations going forward and to maintain longitudinal correlations. 

CBOLT projects that the portion of the population assigned to each possible outcome 

for part-time hours will remain stable going forward (see Figure 8 for men and 

Figure 9 for women). 

Unemployment Spells

The probability that a person will experience unemployment in a given year depends 

on his or her demographic characteristics, as in the other labor force equations, but 

12. The full set of discrete values for annual hours worked is 125, 375, 625, 875, 1,125, 1,375, and 

1,625. Those are the midpoints for the ranges used in the ordered logit estimation.
18

Figure 8.

Number of Part-Time Hours That Men Worked

(Percent)

Source: Congressional Budget Office. 

there is also a cyclical macroeconomic effect. As the overall unemployment rate (a 

variable determined in the CBOLT macroeconomic model) rises or falls, the unemployment spells across age and sex groups also rise or fall, maintaining consistency 

between the macroeconomic and microeconomic models. The equation for unemployment also includes a dummy variable for whether the person works full time 

because middle-aged people who work full time are less likely to have an unemployment spell than those who work part time. For a person with an unemployment spell, 

the number of annual hours worked is adjusted downward (using the results from 

equations for full- versus part-time work and part-time hours worked). In the model, 

the reduction in annual hours for a person who experiences an unemployment spell is 

based on whether he or she works full time or part time.13

The equations for unemployment spells are age-centered logits (see Table 9 on 

page 60 for men and Table 10 on page 64 for women). The coefficients on the independent variables are generally as expected: some coefficients are small and insignificant, suggesting weak correlations. For example, being married, having a higher level 

of education, or being in school all make a person less likely to experience unemployment, but receiving Social Security benefits has only small and variable effects. 

13. The fraction of hours lost to unemployment spells has been fairly stable over time—between 30 

percent and 40 percent of potential hours worked. 

2006 2016 2026 2036 2046 2056 2066 2076 2086 2096 2106

0

8

10

12

14

16

18

20

1,500–1,749

1,000–1,249

250–499

1,250–1,499

500–749

750–999

1–249
19

Figure 9.

Number of Part-Time Hours That Women Worked

(Percent)

Source: Congressional Budget Office.

The two sets of coefficients that show up in the unemployment equations, but in 

none of the other labor force equations are for the aggregate unemployment rate and 

for the dummy variable for working full time. The combination of constant terms 

and coefficients on the aggregate unemployment rate across age and sex groups reveals 

significant information about average and cyclical unemployment across groups: the 

young always have higher unemployment rates than do the middle-aged, but the 

unemployment rates for the middle-aged (especially men) are affected more by the 

state of the macroeconomy, as reflected in larger coefficients on the aggregate unemployment rate. The coefficients on full-time work are used to capture an important 

correlation in the data: middle-aged people who work full time are much less likely to 

experience unemployment than middle-aged people who work part time. The effect 

actually reverses sign for the youngest and oldest age groups, reflecting attachment to 

the labor force: only middle-aged full-time workers are likely to be so attached to the 

labor force that they would report themselves as unemployed if they did not currently 

have a job. 

Finally, the equations for unemployment use the same error-correction mechanism as 

do the equations for full-time and part-time hours. The equations are identical to 

those described above but include actual earnings as an extra independent variable 

(see Table C-5 on page 102 for men and Table C-6 on page 106 for women).

2006 2016 2026 2036 2046 2056 2066 2076 2086 2096 2106

0

8

10

12

14

16

18

20

1,500–1,749

1,000–1,249 250–499

750–999 500–749

1,250–1,499

1–249
20

Figure 10.

Mean Unemployment Spell for Men, by Age

(Percentage of workers) 

Source: Congressional Budget Office. 

Projected unemployment incidence in CBOLT shows the differences mentioned 

before across age groups but shows no cyclical fluctuations (see Figure 10 for men and 

Figure 11 for women). As age (shown in groups) increases, unemployment rates are 

uniformly lower for both men and women. However, the baseline CBOLT projections are flatter than those seen historically, and other than a few small secular trends 

for a couple of age groups that are tied to the demographic variables, there is little 

movement other than random sampling variability. That observation is attributable to 

the way in which the model has been solved to produce these graphs. The CBOLT 

macroeconomic model does not attempt to introduce business-cycle fluctuations into 

the baseline simulations, although fluctuations are a feature of the stochastic simulations.14

Projecting Individual Earnings Outcomes

Many years of data are required to properly isolate the effects of age, sex, education 

level, and birth cohort on earnings.15 The method for projecting the number of 

annual work hours, described in the previous section, is the first step in solving for 

individual earnings. Realized annual earnings for each person in the microsimulation

14. See Congressional Budget Office, Quantifying Uncertainty in the Analysis of Long-Term Social Security Projections, Background Paper (November 2005).

15. The CBOLT equations are based on about 30 years of data.

1984 1994 2004 2014 2024 2034 2044 2054 2064 2074 2084 2094 2104

0

5

10

15

20

25

16–24

25–34

35–44 45–54

55–64

65+

Actual Projected
21

Figure 11.

Mean Unemployment Spell for Women, by Age

(Percentage of workers) 

Source: Congressional Budget Office. 

depend on hours worked, earnings ability, and other factors. The CBOLT strategy of 

separating the effects of annual hours worked from earnings ability makes it possible 

to evaluate how a change in behavior (such as an increase in the portion of people 

working full time) affects individual outcomes. 

One direct approach for introducing earnings ability would be to project an hourly 

wage rate for each person and then to multiply annual hours worked by the hourly 

wage to solve for total earnings. However, projecting hourly wage rates is problematic 

because the earnings realized by many individuals are not directly tied to total hours 

worked. Many individuals are full-time salaried employees or are self-employed, and 

variability in their earnings is not directly related to changes in hours worked. Therefore, the approach used in CBOLT is to solve for potential or full-time equivalent 

earnings—the amount that the individual could earn if fully employed throughout 

the year. In the model, projected FTE earnings are then adjusted for actual hours 

worked to solve for annual earnings. 

The FTE earnings approach used in CBOLT makes it possible to distinguish several 

factors that independently affect earnings. There are several well-known predictable 

determinants of earnings associated with observable characteristics, including age, sex, 

education level, and birth cohort. The data also suggest that earnings patterns change 

(by more than predicted by the change in hours worked) when people start collecting 

1984 1994 2004 2014 2024 2034 2044 2054 2064 2074 2084 2094 2104

0

5

10

15

20

25

16–24

25–34

35–44 45–54

55–64

65+

Actual Projected
22

Social Security benefits; this effect is generally associated with movement from career 

jobs to the types of lower-paying jobs that people tend to hold after retirement. 

In addition to those observable factors, there are also heterogeneous or unobservable 

determinants of earnings that are crucial for modeling earnings outcomes. That is, 

within any given age, sex, education, and cohort group there is a wide range of earnings, and those differences across individuals tend to persist. The statistical procedure 

used in CBOLT to model those idiosyncratic earnings differentials relies on a combination of permanent and transitory shocks to earnings over time. The variances on 

those shocks are chosen to generate realistic longitudinal and cross-sectional earnings 

patterns, the ultimate goal of the earnings microsimulation. 

Predictable Earnings Differences Across Groups and Time

CBOLT uses a regression equation to isolate predictable differences in full-time equivalent earnings across age, sex, education, beneficiary status, and birth cohort groups. 

Before estimating the earnings equations, two important modifications are applied to 

the data. First, FTE earnings for each person in the data are computed by starting 

with reported earnings and then adjusting for the hours a person actually worked during the year.16 That adjustment eliminates much of the age-related earnings variance 

in the data; people at all ages are put on equivalent footing in terms of hours worked. 

Second, an average wage adjustment indexes the derived FTE earnings in each year to 

a common base year. (Simply indexing by price inflation would leave the effect of productivity increases in the data; the goal is to isolate changes associated with age, sex, 

education, beneficiary status, and birth cohort.) 

The algorithm used to measure FTE earnings (which is, in turn, used to estimate the 

earnings equations) is based on a few simple principles. If a person reported working 

full time (2,080 hours or more) and experienced no unemployment spell, then his or 

her FTE earnings are simply set equal to actual earnings. If a person reported working 

part time, his or her FTE earnings will be greater than actual earnings, because he or 

she could have earned more if he or she had worked full time. (See Appendix A for 

more details.) However, the adjustment is not a simple rescaling based on hours 

worked, because part-time workers receive disproportionately lower earnings than 

full-time workers. This is often referred to as a full-time wage premium; based on 

empirical studies, the premium is assumed to be 15 percent for people working fewer

16. This is the reverse of the approach in the model, where given FTE earnings are used to solve for 

actual earnings and are adjusted for hours worked.
23

than 1,500 hours per year.17 There is also an adjustment for unemployment which is 

quite simple: for example, a person who works full time for half the year and is unemployed the other half of the year is assumed to have FTE earnings exactly double the 

earnings actually observed. 

As noted above, the algorithm for measuring FTE earnings to estimate the earnings 

equations is basically the reverse of the approach in CBOLT. The model solves the 

amount a person could earn if he or she worked full time with no unemployment 

shock (that is, his or her FTE earnings) and then adjusts that potential earnings 

amount down if annual hours are below full time. Thus, a policy-induced change in 

annual hours worked affects earnings, but the magnitude of the earnings change 

depends on whose hours are adjusted, as discussed below. 

The groups with the largest differences between actual and FTE earnings are the 

young (who tend to work part time and have higher unemployment rates) and the old 

(who tend to work part time). If one simply graphed total earnings by age (without 

constructing FTE earnings to eliminate the effect of hours worked) then the pattern 

would exhibit a distinct inverted U-shape where earnings rise rapidly through middle 

age and then fall off as people pass through usual retirement age. But much of the ageearnings pattern—especially the drop-off for older workers—arises because of systematic variation in hours worked across age groups. Failure to properly control for differences in hours worked could lead to biased projections. For example, if an older 

worker is projected to delay claiming Social Security benefits for one year, it is reasonable to assume that he or she will continue to work at his or her full-time job, earning 

a full-time salary, rather than experience the typical reduction in earnings associated 

with leaving a career job. 

In addition to adjusting earnings for hours worked and unemployment, the values in 

the data file are also adjusted for wage growth over time. But this introduces a new set 

of consistency problems: average earnings rise over time because of economywide 

inflation and productivity. The goal of the earnings equations—to isolate stable earnings patterns across groups—requires that the effects of inflation and productivity in 

the data be eliminated before estimation. Those effects are then added back in during 

a CBOLT simulation, varying appropriately with the state of the macroeconomy. 

Rather than simply adjusting earnings by means of a standard price index, such as the 

consumer price index for urban wage earners and clerical workers (CPI-W), earnings 

are adjusted by using overall average earnings growth, computed by using Bureau of 

17. The academic literature on the full-time wage premium has identified a range of outcomes that 

center on the 15 percent used in CBOLT. See, for example, Susan L. Averett and Julie L. Hotchkiss, “Discrimination in the Payment of Full-Time Wage Premiums,” Industrial and Labor Relations 

Review, vol. 49, no. 2 (1996), pp. 287–301; and Ronald G. Ehrenberg, Pamela Rosenberg, and 

Jeanne Li, “Part-Time Employment in the United States,” Employment, Unemployment, and Labor 

Utilization, Robert A. Hart, ed. (Boston: Unwin Hyman, 1988), pp. 256–287. 
24

Economic Analysis total wages divided by Bureau of Labor Statistics total number of 

workers.18 The resulting wage index is highly correlated with the CPI-W because 

much of the growth in wages over time is attributable to inflation. However, merely 

adjusting for CPI-W suggests that $25,000 of earnings in 1993 was worth $32,500 in 

2004, but the wage index constructed for the microsimulation model suggests that 

$25,000 in 1993 was worth $37,000 in 2004. That difference in projected value is 

consistent with the concept that productivity is also important for comparing earnings over long periods of time. 

A relatively simple regression analysis is applied to the wage-indexed FTE earnings to 

isolate the observable component of earnings differences. Men and women are analyzed separately. For each group, the regressions map out the statistical relationship 

between average earnings and age for each of the four education groups (less than high 

school, high school, some college, and college graduate) and then investigates (by 

birth cohort) how and why those patterns are shifting over time in order to gain 

insights about the best way to project earnings. The regression equations are estimated 

using CPS data for 1975 through 2003 for everyone ages 16 to 70 with earnings. The 

dependent variable is the log of FTE earnings, and the independent variables are a 

series of interacted age and education dummies, interacted birth cohort and education 

dummies, and interacted OASDI beneficiary status and education dummies (see 

Table 11 on page 68 for men and Table 12 on page 70 for women). 

Figure 12 for men and Figure 13 for women show how the functional form captures 

differences across groups. These figures illustrate the earnings patterns that the coefficients in Tables 11 and 12 imply. The four panels in each figure show log age-earnings 

profiles for an education group; the multiple lines within each education group capture differences across birth cohorts. For each birth cohort, the values of predicted log 

earnings are shown only for the period during which actual earnings data are 

observed. For example, the oldest member of the 1970s birth cohort was 33 in 2003, 

when the historical data end, so that is the last data point shown for that cohort. Similarly, the youngest member of the pre-1940 cohort was 36 in 1975, when the data 

begin, so that is the first data point shown for that cohort.

The first notable observation is that lifetime earnings rise markedly with education for 

every cohort and for both sexes. The education ranking from less than high school to 

college graduate is as expected: higher education leads to higher earnings over a lifetime. The second observation is that there are marked differences in the slope of the 

age-earnings profiles across education groups. Average earnings grow significantly

18. This is consistent with a view of economywide productivity growth. The CPI-W is chosen over 

other price indexes because it is used to determine the annual increases in OASDI monthly benefits. See Social Security Administration, The 2006 Annual Report of the Board of Trustees of the 

Federal Old-Age and Survivors Insurance and Disability Insurance Trust Funds (May 1, 2006), 

Appendix 3, p. 176. 
25

Figure 12.

Predicted Log Full-Time-Equivalent Earnings for Men, 

by Education Level, Age, and Birth Cohort

(Indexed to 1993 wage levels)

Source: Congressional Budget Office.

Note: Predicted full-time-equivalent earnings are indexed by overall average earnings over time, 

which makes the comparison across cohorts relative, not absolute. A drop in earnings for 

younger cohorts at a particular age does not mean that earnings fell in absolute terms; 

instead, it means that average earnings for that particular age and education group did not 

keep pace with overall earnings growth over time.

16 20 24 28 32 36 40 44 48 52 56 60 64 68

0

9

10

11

Before 1940 1940

1950 1970 1960

16 20 24 28 32 36 40 44 48 52 56 60 64 68

0

9

10

11

Less Than High School

High School Graduates

Before 1940

1940

1960 1950

1970

16 20 24 28 32 36 40 44 48 52 56 60 64 68

0

9

10

11

Some College

Before 1940

1940

1960 1950

1970

16 20 24 28 32 36 40 44 48 52 56 60 64 68

0

9

10

11

College Graduates

Before 1940

1940

1950

1960

1970

Age
26

Figure 13.

Predicted Log Full-Time-Equivalent Earnings for Women, 

by Education Level, Age, and Birth Cohort

(Indexed to 1993 wage levels)

Source: Congressional Budget Office.

Note: Predicted full-time-equivalent earnings are indexed by overall average earnings over time, 

which makes the comparison across cohorts relative, not absolute. A drop in earnings for 

younger cohorts at a particular age does not mean that earnings fell in absolute terms; 

instead, it means that average earnings for that particular age and education group did not 

keep pace with overall earnings growth over time.

16 20 24 28 32 36 40 44 48 52 56 60 64 68

0

9

10

11

Before 1940 1940 1950

1970 1960

16 20 24 28 32 36 40 44 48 52 56 60 64 68

0

9

10

11

Less Than High School

High School Graduates

Before 1940

1940

1950

1960 1970

16 20 24 28 32 36 40 44 48 52 56 60 64 68

0

9

10

11

Some College

Before 1940 1940

1950

1960

1970

16 20 24 28 32 36 40 44 48 52 56 60 64 68

0

9

10

11

College Graduates

1940 Before 1940

1950

1960

1970

Age
27

with age for college graduates but much less so for the other groups, especially those 

who do not finish high school.

The differences in earnings by age and education are straightforward; the shifts across 

birth cohorts are a bit more subtle. Before explaining those patterns, it is worth 

remembering that each earnings observation in the data has been indexed by overall 

average earnings, so they are effectively relative earnings profiles by birth cohort. 

Thus, when one looks at (for example) men with less than a high school education 

(top left corner of Figure 12), the drop in earnings by birth cohort is not absolute; it is 

relative to overall average earnings. Given that framework, the patterns in the figures 

reveal significant information about historical earnings patterns and have important 

implications for earnings projections. 

One of the most noted features about earnings distributions over the last several 

decades is a marked increase in the economic benefit of a college education. That is 

reflected in Figure 12 by stable age-earnings profiles for men and in Figure 13 by rising age-earnings profiles for women for the college-educated across all birth cohorts. 

For the other education groups, average log earnings are falling compared with the 

overall average wage, which means that, in effect, the college-educated are gaining.19

Although economists are not in total agreement, several explanations have been suggested for the shift in relative earnings, including the shift from traditional manufacturing to technology-based employment.20

The simple decomposition of earnings differences by age, sex, education, and birth 

cohort is a useful way to describe historical earnings. It is also a good starting point for 

projecting earnings differences across groups. One interpretation of the historical data 

is that the changing demand for education-related skills and the increased participation of women in the labor force have caused a shift in relative earnings over the last 

few decades. Although there is no reason to expect that pattern to reverse, there is also 

no reason to expect it to cause any more divergence in relative earnings. Therefore, the 

CBOLT assumption is that relative earnings across education groups will stabilize at 

the values for the most recent cohort (in this case, the 1970s). Thus, in the distant 

future, the only equation-based differences in FTE earnings will be associated with 

age, sex, and education. In the actual simulations, CBOLT’s macroeconomic modules 

will solve for additional differences in age-earnings profiles across cohorts that are 

19. The magnitude of the shift for any education group depends on the wage index used to adjust the 

data—for example, one could use an index that attempts to explicitly net out age-related wage differences and could get different absolute shifts for any education group. However, any wage index 

that ultimately varies only by year will lead to the same historical relative shifts across birth and 

education groups. The key is that, when projecting forward, individual earnings are rescaled by the 

same index so that the overall average wage is the same as the weighted average across groups. 

20. See, for example, Lawrence F. Katz and Kevin M. Murphy, “Changes in Relative Wages, 1963–87: 

Supply and Demand Factors,” Quarterly Journal of Economics (1992), pp. 35–78; and David Autor, 

Lawrence Katz, and Melissa Kearney, “Rising Wage Inequality: The Role of Composition and 

Prices,” National Bureau of Economic Research, Working Paper No. 11628 (2005).
28

properly associated with differences in inflation and productivity, but those are not 

attributable to systematic cohort-level shifts. 

Idiosyncratic Earnings Differentials

The CBOLT earnings equations described above are closely related to the equations 

used to study many issues in labor economics, and they share a common drawback in 

predictive ability. Equations such as these explain only a small fraction of the overall 

earnings differences across the population. That is, the range of earnings within the 

identified groups (by age, sex, education, and birth cohort) is larger than differences 

in average earnings between the identified groups. Those unexplained differences 

within groups (referred to below as individual heterogeneity) are also persistent; for 

example, high earners within a particular group in one year are very likely to be high 

earners in that group in subsequent years. 

Incorporating that unobserved but persistent individual heterogeneity into the projections is of first-order importance for microsimulation models. If one ignored the heterogeneity and assigned each individual the group-level expected value from the earnings equation, the range of earnings within each group would be too narrow. This 

would show up in the projections as compressed cross-sectional earnings distributions. At the other extreme, if each person received the expected value of earnings 

from the group-level equation, plus some simple annual random error term chosen to 

capture the range of earnings within the group, there would be too much longitudinal 

variability in earnings, as people would randomly move (within a group) up and down 

the earnings distribution. That would show up in the projections as compressed longitudinal earnings distributions, which would not generate enough differences 

between high and low earners over entire lifetimes. 

For each individual in the CBOLT microsimulation, the earnings differential is 

defined as the gap between actual earnings and the predicted value from the earnings 

equation; it is the component described as individual heterogeneity. By definition, 

there are no observable characteristics that help explain those differentials statistically, 

so the model uses an ad hoc approach to separate the earnings differentials into persistent and transitory components. The ultimate test of any given specification is 

whether it can generate realistic earnings profiles across the population at any point in 

time and over time—that is, whether the simulated cross-sectional and longitudinal 

earnings patterns are consistent with historical patterns. 

There are several statistical approaches for modeling the idiosyncratic earnings differentials. One approach often used in empirical labor economics is the fixed-effects 

model, which relies on longitudinal data to separate an individual error component 

for each person while the earnings equations are estimated. That is, one would estimate an earnings equation in a way similar to the approach used in CBOLT (with 

explanatory variables such as age, sex, education, and birth cohort) but would also 

include an individual error term for each person in the sample. The individual terms 

can be recovered statistically because the longitudinal data have multiple observations 
29

on the same person. The results of this approach are instructive: the portion of overall 

earnings variability explained by the earnings equation generally increases from less 

than 30 percent to more than 70 percent when one adds the individual fixed effect; 

this increase shows the statistical importance of considering persistence. 

The method for modeling idiosyncratic earnings differentials in CBOLT takes the 

logic of the fixed-effects approach one step further. (See Appendix D for more details 

about how earnings differentials are modeled in CBOLT.) The starting point for the 

CBOLT approach is that, under closer inspection, fixed effects seem better characterized as slowly changing rather than as absolutely fixed.21 That is, a person with high 

or low earnings within a group at some point in time will tend to keep that earnings 

position within the group over time, but that position is not absolute. The data suggest it is more appropriate to allow some small permanent shocks to the earnings differentials across individuals in each year rather than to hold those differentials fixed 

over time for each person. 

These slowly evolving permanent differentials capture fundamental differences in 

earnings ability within age, sex, education, and birth cohort groups but are not sufficient to generate the overall desired amount of earnings variability. As stated above, 

even the fixed-effects earnings equations explain only 70 percent of earnings variation; 

30 percent of the earnings variation is still random. In order to capture this additional 

variability, each person in the microsimulation also receives an annual transitory earnings shock that captures temporary fluctuations associated with negative factors such 

as temporary job displacement or positive factors such as annual bonuses. There is 

also an important overall adding-up constraint that comes into play when simulating 

the two types of shocks to earnings: at any point in time, the variance of the permanent differentials plus the variance of the transitory shocks must sum to the overall 

unexplained variance from the underlying earnings equation. 

To summarize, in any year, each individual’s earnings can be characterized as the sum 

of three components. The first is the predicted value from the earnings equation; this 

is the average value for that person’s age, sex, education, and birth cohort group. The 

second is the value of the individual’s permanent differential, which measures the 

long-run gap between that person’s earnings and the overall average for the group. 

The third is a transitory differential, which measures any additional (but temporary) 

variation in that person’s earnings compared with predicted values, using the grouplevel average plus permanent differential. This characterization of earnings differences 

21. This important insight about simulating earnings is indirectly derived from Angus Deaton and 

Christina Paxson, “Intertemporal Choice and Inequality,” Journal of Political Economy, vol. 102, no. 

3 (1994), pp. 437–467. Those authors observed an increasing range for earnings within any given 

birth cohort as its members age, and that widening of the earnings distribution is not consistent 

with constant fixed effects. The pattern is consistent with continuous low-variance shocks to permanent differentials, which is the specification in CBOLT. 
30

across people is sufficient to generate statistically robust patterns of individual earnings through time. 

The following explains how each of the three earnings components is assigned in a 

CBOLT simulation. First, at the beginning of the simulation, consider the individuals 

in the micro sample who have been working and therefore have some actual earnings 

history. The equation-based component of earnings can be predicted using the estimated earnings equation, so the difference between those people’s actual (reported) 

earnings and the predicted value is, by definition, the sum of the permanent and transitory shocks. Because, by construction, the expected value of transitory shocks is 

zero, the gap between actual and predicted earnings is used to estimate the permanent 

differentials in the year before the beginning of the simulation. After the simulation 

begins, those individuals receive two annual updates using random numbers. The first 

is the low-variance shock to their permanent earnings differentials, and the second is 

the higher-variance transitory shock. 

The same annual permanent and transitory shocks are applied to people who have no 

earnings history at the beginning of the simulation; this includes everyone who has 

not completed his or her education when the simulation starts.22 The only modification for those individuals is how the initial permanent earnings differentials are 

assigned. When earnings differentials for the group with earnings histories are computed, they are stored by the model and used to bootstrap differentials for people 

entering the work force in later years. That bootstrap approach guarantees that the 

distribution of earnings differentials for the young population in any given year will 

always look like the distribution of earnings differentials for the young population at 

the beginning of the simulation. Thus, other than the predictable dispersion of 

within-cohort earnings that will occur as a cohort ages, the only shifts in earnings distributions that will occur are associated with predictable group-level movements. 

The ultimate tests of whether a particular method for simulating individual earnings 

is successful involve looking at the properties of the projections. The first set of outcomes to consider is cross-sectional earnings distributions, looking at the behavior of 

various percentiles of the earnings distribution in the projections (see Figure 14 for 

the lower half of the earnings distribution and Figure 15 for the top half). The outcomes for every percentile graphed (1st, 5th, 25th, 50th, 75th, 95th, and 99th) evolve 

smoothly over time. The values are indexed by overall average wage growth, so there is 

no productivity-related growth built into the percentiles over time, and the lines are 

generally flat. 

The second set of results that suggest the CBOLT approach generates the appropriate 

amount of individual heterogeneity comes from cross-tabulations of lifetime versus

22. Individuals do not receive an initial permanent differential until their schooling is completed 

because the data suggest that using data from before graduation would be inappropriate. Predicted 

earnings before graduation are from a separate earnings equation with a simple transitory shock. 
31

Figure 14.

Lower Half of the Projected Annual Earnings Distribution

(Annual indexed earnings, in dollars)

Source: Congressional Budget Office. 

annual earnings (see Table 13 on page 72 for men and Table 14 on page 74 for 

women). To construct these tables, each person in the microsimulation between ages 

50 and 60 is classified first by total lifetime earnings (which determines the column 

decile) and then by annual earnings (which determines the row deciles). Theoretically, 

if every person in the 1st decile of lifetime earnings had annual earnings in the 1st 

decile for every year worked, each person in the 2nd lifetime decile had annual earnings in the 2nd decile for each year worked, and so on, then every diagonal entry 

would be 10 percent. 

In general, the entries on the diagonals are larger than any other, as expected. People 

who have low lifetime earnings tend to have low earnings in the years they worked, 

and vice versa. The real question is whether the CBOLT simulation strategy preserves 

the cross-tabulation through time. The first matrix for both men and women is a tabulation done in 2000. All the data in that matrix constitute history; nothing has been 

simulated. Moving down the matrices (the calculations are shown for 2010, 2040, 

and 2070), the portion of earnings that is simulated increases. In 2010, there are 

about five years of simulated data, by 2040 there are 35 years’ worth, and by 2070 all 

the data are simulated. Other than a slight increase in concentration in the 1st decile, 

the historical relationship between longitudinal and cross-sectional earnings shows up 

in the projected data as well. 

2006 2014 2022 2030 2038 2046 2054 2062 2070 2078 2086 2094 2102

0

5,000

10,000

15,000

20,000

25,000

30,000

1st Percentile for

Men and Women 5th Percentile for Men 5th Percentile for Women

25th Percentile for Men

25th Percentile for Women

50th Percentile for Men

50th Percentile for Women
32

Figure 15.

Upper Half of the Projected Annual Earnings Distribution

(Annual indexed earnings, in dollars)

Source: Congressional Budget Office. 

2006 2014 2022 2030 2038 2046 2054 2062 2070 2078 2086 2094 2102

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

75th Percentile for Women

75th Percentile for Men

95th Percentile for Men

95th Percentile for Women

99th Percentile for Women

99th Percentile for Men

34

Table 1.

Labor Force Participation Logit Coefficients for Men

Age

Group

16 0.3868 0.0506 0.5287

17 -2.4436 0.1881 0.8676 1.9992

18 -0.6991 -0.0970 0.2803 2.0191

19 -0.1404 -0.3290 -0.2751 -0.0748 2.0708

20 -0.5482 -0.4149 -0.3328 -0.2000 2.1318

21 -1.1605 -0.5682 -0.3447 -0.2560 2.2344

22 -1.7496 -0.7656 -0.3101 -0.2930 2.3856

23 -2.0271 -1.0174 -0.2510 -0.2904 2.5627

24 -2.0120 -1.2166 -0.2077 -0.2742 2.7343

25 -3.1034 -1.3564 -0.1289 -0.2137 2.9833 -0.1812 -0.8039 -0.4663

26 -2.5541 -1.4617 -0.0967 -0.1949 3.1267 -0.1806 -0.6602 -0.3897

27 -2.1304 -1.5558 -0.0644 -0.1623 3.2451 -0.1689 -0.5532 -0.3091

28 -1.9183 -1.6095 -0.0589 -0.1392 3.3307 -0.1498 -0.4836 -0.2172

29 -1.7245 -1.6557 -0.0488 -0.1067 3.3982 -0.1221 -0.4238 -0.1286

30 -1.4662 -1.6869 -0.0551 -0.0860 3.4484 -0.0876 -0.3634 -0.0397

31 -1.1955 -1.7152 -0.0534 -0.0563 3.5010 -0.0506 -0.3263 0.0308

32 -0.9594 -1.7249 -0.0598 -0.0337 3.5440 -0.0154 -0.2916 0.0875

33 -0.7384 -1.7511 -0.0731 -0.0138 3.5867 0.0111 -0.2745 0.1258

34 -0.5711 -1.7555 -0.0995 -0.0037 3.6228 0.0301 -0.2627 0.1568

35 -0.4824 -1.7645 -0.1220 0.0018 3.6616 0.0466 -0.2488 0.1813

36 -0.5317 -1.7707 -0.1589 -0.0069 3.6988 0.0611 -0.2191 0.2046

37 -0.7849 -1.7953 -0.1860 -0.0114 3.7469 0.0795 -0.1910 0.2186

38 -1.0816 -1.8223 -0.2021 -0.0001 3.7893 0.1104 -0.1425 0.2372

39 -1.4016 -1.8778 -0.2172 0.0186 3.8445 0.1520 -0.0844 0.2576

40 -1.6604 -1.9570 -0.2252 0.0546 3.9047 0.2010 -0.0121 0.2878

41 -1.9885 -2.0485 -0.2228 0.1008 3.9786 0.2455 0.0559 0.3258

42 -1.8689 -2.1355 -0.2242 0.1505 4.0510 0.2860 0.1210 0.3826

43 -1.6993 -2.2192 -0.2325 0.1925 4.1374 0.3065 0.1700 0.4350

44 -1.4717 -2.2961 -0.2376 0.2328 4.2174 0.3166 0.2180 0.4957

45 -1.0485 -2.3481 -0.2461 0.2712 4.2887 0.3176 0.2521 0.5530

46 -0.5051 -2.3847 -0.2536 0.3044 4.3549 0.3168 0.2811 0.5912

47 0.1940 -2.4344 -0.2472 0.3335 4.4176 0.3087 0.3158 0.6106

48 0.9899 -2.4807 -0.2221 0.3627 4.4673 0.3052 0.3508 0.6381

49 1.6722 -2.5136 -0.2053 0.3718 4.5079 0.2928 0.3532 0.6467

50 2.0126 -2.5350 -0.1825 0.3656 4.5436 0.2707 0.3278 0.6421

51 2.2266 -2.5257 -0.1658 0.3557 4.5607 0.2420 0.2932 0.6329

52 2.6546 -2.4798 -0.1683 0.3366 4.5704 0.2226 0.2441 0.6199

53 3.1588 -2.4193 -0.1786 0.3107 4.5794 0.1981 0.1846 0.5766

54 4.0216 -2.3520 -0.1656 0.2979 4.5876 0.1747 0.1470 0.5296

55 4.6451 -2.2923 -0.1636 0.2836 4.5915 0.1512 0.1240 0.4682

56 5.3027 -2.2616 -0.1504 0.2663 4.6056 0.1252 0.0993 0.4009

57 6.0619 -2.2355 -0.1226 0.2505 4.6125 0.0878 0.0854 0.3400

58 6.4266 -2.2286 -0.0990 0.2307 4.6184 0.0520 0.0821 0.2766

Lagged

Labor High

College

Graduate

Marital Statusa

Never School Some

Education Levelb

Force

Constant Receipt Married Married Participation Graduate College

Social

Security

Age + Benefit

Coefficient

for
35

Continued

1910 1920 1930 1940 1950 1960 1970 1980

-0.8492 -0.0269 0.0318 0.1484

-0.7664 -0.0053 -0.0120 0.0344

-1.0256 0.0063 0.0256 -0.0152

-1.2168 0.0167 0.0047 -0.1361

-1.2837 0.0221 -0.0237 -0.2059

-1.3392 0.0144 0.0899 0.1547

-1.4132 0.0189 0.0354 0.0767

-1.4628 0.0181 0.0241 0.0545

-1.4331 0.0153 0.0043 0.0258

0.0032 -0.0425 0.0006

0.0027 -0.0438 -0.0147

0.0023 -0.0484 -0.0231

0.0023 -0.0503 -0.0333

0.0007 -0.0383 -0.0126

-0.0009 -0.0222 0.0099

-0.0013 0.0238 0.0053

-0.0025 0.0330 0.0206

-0.0020 0.0340 0.0009

-0.0001 0.0273 -0.0405

0.0021 0.0183 -0.0907

0.0035 -0.0007 -0.1340

0.0029 -0.0075 -0.1454

0.0012 -0.0148 -0.1387

-0.0019 -0.0129 -0.1107

-0.0053 -0.0152 -0.0753

-0.0111 0.0731 0.0760

-0.0112 0.0817 0.0628

-0.0105 0.0770 0.0390

-0.0080 0.0447 -0.0228

-0.0050 -0.0059 -0.0874

-0.0023 -0.0811 -0.1668

-0.0025 -0.1168 -0.1797

-0.0044 -0.1204 -0.1596

-0.0070 -0.1011 -0.1156

-0.0093 -0.0704 -0.0801

-0.0140 0.0852 0.0647

-0.0147 0.0832 0.0808

-0.0136 0.0702 0.0611

-0.0105 0.0332 -0.0101

-0.0078 0.0019 -0.0666

-0.0074 -0.0016 -0.0586

-0.0087 0.0147 -0.0114

-0.0097 0.0239 0.0363

Birth Cohort (By first year of cohort)

Trend

In

School
36

Table 1.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level.

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

Age

Group

59 8.0803 -2.2193 -0.1098 0.1901 4.6114 0.0184 0.0685 0.2342

60 10.3859 -2.1222 -0.1778 0.1097 4.5638 0.0115 0.0675 0.2298

61 -2.1910 -2.0667 -0.0191 0.0988 4.4960 0.0071 0.1453 0.3357

62 -2.4327 -1.8866 -0.1567 -0.0175 4.1583 0.1339 0.3705 0.6057

63 -2.1126 -1.8534 0.0910 0.0069 4.1248 0.0627 0.3005 0.4086

64 -2.3710 -1.5620 0.2693 0.0689 4.2919 0.1167 0.1616 0.3119

65 -3.0146 -0.9181 0.1224 -0.0282 4.1361 -0.0208 0.1654 0.4631

66 -2.9498 -1.0086 0.1709 0.0916 4.4131 0.0963 0.2740 0.4356

67 -1.0472 -0.9192 0.1076 0.0469 4.6233 0.1337 0.2906 0.3598

68 -1.2480 -0.8801 0.0804 -0.0049 4.7275 0.1273 0.2620 0.3626

69 -2.2449 -0.8140 0.1321 -0.0114 4.7989 0.1089 0.2778 0.4100

70 -0.6508 -0.4580 0.1630 -0.0770 5.2289 0.0977 0.2798 0.4649

Lagged

Labor

Coefficient

for

Age + Benefit Some College

Constant Receipt Married Married Participation Graduate College Graduate

Never Force School

Social Education Levelb

Security Marital Statusa High
37

1910 1920 1930 1940 1950 1960 1970 1980

-0.0130 0.0637 0.1569

-0.0187 0.1691 0.3611

0.0020 -0.2374 -0.2593

0.0016 -0.1079 -0.0595

0.0011 -0.0690 0.0927

0.0013 -0.0844 0.0938

0.0231 -0.0198 0.0184

0.0058 0.1286 0.3200

0.0044 0.0194 0.2478

0.0157 -0.1262 -0.0133

0.0148 -0.1068 -0.0224

0.0104 -0.0897 -0.0705

School Trend

In Birth Cohort (By first year of cohort)
38

Table 2.

Labor Force Participation Logit Coefficients for Women

Age

Group

16 -0.5744 0.2264 -0.0652

17 -2.8524 0.0272 -0.3723 2.1646

18 -1.2497 0.0253 -0.3720 2.1728

19 -1.1164 -0.2270 -0.0720 -0.4057 2.2570

20 -1.4851 -0.2792 -0.0952 -0.4198 2.3820

21 -1.9782 -0.3639 -0.1026 -0.4268 2.5347

22 -2.4001 -0.4773 -0.0641 -0.4212 2.7289

23 -2.3654 -0.6071 -0.0209 -0.4165 2.9349

24 -2.0286 -0.7132 0.0211 -0.4116 3.1207

25 -2.5743 -0.7161 -0.0788 -0.4816 3.2064 0.4476 0.3373 0.6889

26 -2.0627 -0.7555 -0.0270 -0.4931 3.3402 0.4408 0.4419 0.6905

27 -1.7486 -0.8016 0.0021 -0.5069 3.4568 0.4374 0.4966 0.6958

28 -1.6406 -0.8450 0.0101 -0.5212 3.5531 0.4321 0.5126 0.6961

29 -1.5497 -0.8682 0.0046 -0.5298 3.6406 0.4331 0.5171 0.6967

30 -1.5676 -0.8816 -0.0023 -0.5316 3.7173 0.4387 0.5232 0.6973

31 -2.2292 -0.8994 -0.0086 -0.5266 3.7984 0.4420 0.5216 0.6943

32 -2.1872 -0.8932 -0.0219 -0.5201 3.8740 0.4495 0.5184 0.6949

33 -2.1355 -0.8880 -0.0343 -0.5148 3.9478 0.4544 0.5186 0.6985

34 -2.0291 -0.8825 -0.0455 -0.5150 4.0191 0.4518 0.5188 0.6963

35 -1.8033 -0.8743 -0.0489 -0.5166 4.0909 0.4450 0.5143 0.6915

36 -1.6244 -0.8718 -0.0495 -0.5207 4.1597 0.4396 0.5144 0.6801

37 -1.4958 -0.8859 -0.0466 -0.5239 4.2296 0.4362 0.5163 0.6710

38 -1.3666 -0.8999 -0.0444 -0.5271 4.2987 0.4373 0.5189 0.6666

39 -1.5013 -0.9213 -0.0357 -0.5248 4.3622 0.4457 0.5283 0.6777

40 -1.6290 -0.9561 -0.0331 -0.5238 4.4222 0.4576 0.5414 0.6976

41 -2.2630 -0.9797 -0.0377 -0.5228 4.4797 0.4675 0.5536 0.7258

42 -2.2005 -1.0073 -0.0463 -0.5176 4.5323 0.4701 0.5673 0.7577

43 -1.9504 -1.0262 -0.0569 -0.5068 4.5812 0.4709 0.5803 0.7856

44 -1.6513 -1.0414 -0.0750 -0.5080 4.6315 0.4699 0.5907 0.8025

45 -1.3123 -1.0425 -0.0922 -0.5091 4.6791 0.4729 0.6039 0.8204

46 -0.9224 -1.0552 -0.1040 -0.5162 4.7221 0.4746 0.6136 0.8301

47 -0.6196 -1.0541 -0.1134 -0.5280 4.7636 0.4777 0.6153 0.8260

48 -0.4184 -1.0691 -0.1158 -0.5432 4.8011 0.4729 0.6079 0.8139

49 -0.2496 -1.0789 -0.1146 -0.5440 4.8349 0.4626 0.5945 0.8031

50 -0.1002 -1.0825 -0.1127 -0.5429 4.8675 0.4457 0.5748 0.7836

51 -0.3063 -1.0740 -0.1130 -0.5349 4.9023 0.4286 0.5497 0.7640

52 -0.0434 -1.0840 -0.1142 -0.5312 4.9326 0.4081 0.5174 0.7375

53 0.3137 -1.0855 -0.1249 -0.5322 4.9632 0.3948 0.4876 0.7057

54 0.8432 -1.0897 -0.1374 -0.5427 4.9942 0.3811 0.4531 0.6555

55 1.2630 -1.1149 -0.1542 -0.5592 5.0267 0.3698 0.4178 0.5956

56 1.7236 -1.1601 -0.1614 -0.5670 5.0535 0.3512 0.3868 0.5223

57 2.1024 -1.1816 -0.1608 -0.5764 5.0807 0.3302 0.3538 0.4550

58 2.4058 -1.2105 -0.1560 -0.5866 5.1115 0.3025 0.3140 0.3693

College

Some

Married

Marital Statusa

Education Levelb Social

Security High

Lagged

Labor

Benefit

Constant Receipt Participation Graduate

Never Force School

Married Graduate

College

Coefficient

for

Age +
39

Continued

1910 1920 1930 1940 1950 1960 1970 1980

-0.3025 -0.1307 -0.0164 0.1052 0.2018

-0.3721 -0.1454 -0.0014 -0.0144 0.0490

-0.6273 -0.2657 0.0088 -0.0310 -0.0572

-0.7594 -0.3797 0.0173 -0.0700 -0.1650

-0.8255 -0.4566 0.0214 -0.0914 -0.2250

-0.8913 -0.4969 0.0141 0.0910 0.0720

-0.9759 -0.5238 0.0136 0.1044 0.1020

-1.0470 -0.5270 0.0121 0.1283 0.1300

-1.0652 -0.5153 0.0113 0.1343 0.1171

-0.4370 0.0091 0.1070 0.0114

-0.4387 0.0144 0.0504 -0.1059

-0.4372 0.0215 -0.0282 -0.2518

-0.4367 0.0266 -0.0910 -0.3544

-0.4369 0.0295 -0.1377 -0.4136

-0.4348 0.0300 -0.1558 -0.4225

-0.4357 0.0085 0.2441 0.2290

-0.4373 0.0086 0.2363 0.2100

-0.4385 0.0094 0.2226 0.1798

-0.4398 0.0109 0.1945 0.1341

-0.4424 0.0145 0.1428 0.0425

-0.4415 0.0190 0.0773 -0.0662

-0.4405 0.0224 0.0252 -0.1459

-0.4320 0.0248 -0.0150 -0.2060

-0.4182 0.0255 -0.0401 -0.2380

-0.4051 0.0246 -0.0473 -0.2301

-0.3870 0.0101 0.1479 0.1912

-0.3664 0.0106 0.1394 0.1616

-0.3531 0.0119 0.1299 0.1190

-0.3446 0.0148 0.1045 0.0570

-0.3234 0.0187 0.0628 -0.0277

-0.3053 0.0223 0.0200 -0.0995

-0.2908 0.0258 -0.0240 -0.1652

-0.2764 0.0286 -0.0712 -0.2213

-0.2564 0.0282 -0.0875 -0.2289

-0.2538 0.0258 -0.0768 -0.1914

-0.2592 0.0153 0.0856 0.0860

-0.2405 0.0133 0.0897 0.1016

-0.2313 0.0117 0.0842 0.1017

-0.2251 0.0122 0.0555 0.0660

-0.2141 0.0122 0.0332 0.0426

0.0121 0.0020 0.0154

0.0114 -0.0131 0.0111

0.0099 -0.0157 0.0313

Birth Cohort (By first year of cohort)

Trend

Number of

Under

School

Children

Age 6

In
40

Table 2.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level.

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

Age

Group

59 3.3000 -1.2014 -0.1257 -0.5755 5.1565 0.2814 0.2772 0.3212

60 4.0249 -1.2188 -0.0964 -0.5498 5.1679 0.2703 0.2903 0.2983

61 -2.9748 -1.1576 -0.0462 -0.6244 5.1191 0.2685 0.2221 0.3324

62 -2.8245 -1.3052 -0.3809 -0.6206 4.8496 0.1254 0.2260 0.1901

63 -2.7286 -1.2539 0.0309 -0.5292 4.8552 0.0301 0.2221 -0.0371

64 -3.1729 -1.0665 -0.1495 -0.3908 5.1340 0.1571 0.2323 0.0702

65 -3.4676 -0.6440 -0.1251 -0.2738 4.7672 0.1960 0.3370 0.2022

66 -3.3812 -0.8147 -0.0748 -0.4126 4.9536 0.1365 0.1896 0.1179

67 -3.3176 -0.6579 0.0617 -0.3422 5.1845 0.1109 0.2066 0.2288

68 -0.6947 -0.5913 0.0469 -0.3054 5.2627 0.1075 0.2054 0.2606

69 -0.1994 -0.5446 0.0710 -0.2843 5.3515 0.0949 0.1761 0.2510

70 0.6105 -0.4191 0.3538 -0.1277 5.9402 0.1393 0.2544 0.2825

Constant Receipt Married Married Participation Graduate College Graduate

Age + Benefit Never Force School Some College

Security Marital Statusa Labor High

Social Education Levelb Coefficient Lagged

for
41

1910 1920 1930 1940 1950 1960 1970 1980

0.0064 0.0118 0.1121

0.0026 0.0132 0.1654

0.0138 -0.0566 -0.0456

0.0221 -0.1667 -0.2928

0.0211 -0.0916 -0.1256

0.0087 0.0954 0.2323

0.0211 -0.0642 -0.0839

0.0376 -0.2482 -0.3191

0.0249 -0.1095 -0.0915

0.0234 -0.0747 -0.0526

0.0228 -0.0703 -0.0327

0.0270 -0.0256 -0.1704

School Age 6 Trend

In Under Birth Cohort (By first year of cohort)

Children

Number of
42

Table 3.

Full-Time Employment Logit Coefficients for Men

Age

Group

16 0.8361 -0.5055 -1.2518 1.4020

17 -9.4944 -0.5162 -1.3899 0.4878

18 -9.5507 -0.8498 -0.9485 0.4530

19 -6.1952 -1.1394 -0.8213 0.4078

20 -4.5609 -1.3267 -0.7583 0.3658

21 -4.0055 -1.4807 -0.7408 0.3607

22 -3.4304 -1.6213 -0.6975 0.3616

23 -2.8853 -1.7608 -0.6413 0.3769

24 -2.5591 -1.8653 -0.5677 0.4072

25 -4.3279 -1.9555 -0.3773 0.5238 0.4700 -0.4178 -0.3713

26 -3.4478 -1.9675 -0.3377 0.5358 0.5160 -0.1583 -0.1265

27 -2.3402 -1.9609 -0.3033 0.5521 0.5556 0.0650 0.1006

28 -1.3383 -1.9400 -0.2771 0.5700 0.5839 0.2373 0.2964

29 -0.6626 -1.9291 -0.2618 0.5867 0.5994 0.3696 0.4423

30 -0.3702 -1.9040 -0.2616 0.6000 0.6146 0.4788 0.5577

31 0.0839 -1.9407 -0.2566 0.6222 0.6330 0.5657 0.6570

32 0.2587 -1.9613 -0.2650 0.6368 0.6436 0.6290 0.7351

33 0.4718 -1.9843 -0.2760 0.6469 0.6533 0.6763 0.7917

34 0.7500 -1.9979 -0.2848 0.6585 0.6598 0.7139 0.8365

35 1.0103 -1.9951 -0.2899 0.6649 0.6613 0.7368 0.8714

36 1.2379 -1.9486 -0.3068 0.6534 0.6560 0.7524 0.8985

37 1.4378 -1.9133 -0.3200 0.6486 0.6551 0.7637 0.9260

38 1.5635 -1.8733 -0.3293 0.6465 0.6533 0.7649 0.9509

39 1.6044 -1.8554 -0.3415 0.6369 0.6580 0.7571 0.9746

40 1.5836 -1.8832 -0.3572 0.6330 0.6647 0.7531 0.9990

41 2.0026 -1.9463 -0.3546 0.6335 0.6840 0.7649 1.0257

42 2.0907 -2.0114 -0.3519 0.6378 0.6879 0.7612 1.0250

43 2.0519 -2.1046 -0.3556 0.6320 0.6863 0.7685 1.0283

44 1.9815 -2.1903 -0.3526 0.6376 0.6687 0.7731 1.0174

45 1.9114 -2.2384 -0.3375 0.6365 0.6484 0.7689 0.9976

46 2.0342 -2.2770 -0.3345 0.6320 0.6255 0.7463 0.9720

47 2.3397 -2.3260 -0.3308 0.6246 0.6033 0.7217 0.9559

48 2.7010 -2.3660 -0.3203 0.6304 0.5817 0.6820 0.9315

49 3.1416 -2.4132 -0.3226 0.6202 0.5598 0.6354 0.9019

50 3.6060 -2.4779 -0.3337 0.6138 0.5326 0.5857 0.8652

51 4.5203 -2.5226 -0.3345 0.5930 0.5117 0.5626 0.8497

52 5.0292 -2.5427 -0.3257 0.5758 0.4743 0.5128 0.7968

53 5.5408 -2.5494 -0.3154 0.5466 0.4341 0.4624 0.7257

54 6.0328 -2.5492 -0.2800 0.5233 0.3964 0.4160 0.6570

55 6.4746 -2.5529 -0.2403 0.4914 0.3584 0.3697 0.5824

56 6.8068 -2.5860 -0.1971 0.4587 0.3232 0.3259 0.4964

57 7.2041 -2.6165 -0.1698 0.4177 0.2834 0.2847 0.4111

58 7.1648 -2.6331 -0.1384 0.3721 0.2456 0.2488 0.3387

Coefficient

for Marital Statusa Education Levelb

Social

Security

Constant Receipt Married Married Graduate College Graduate

Age + Benefit High School Never Some College
43

Continued

1910 1920 1930 1940 1950 1960 1970 1980

-3.1369 -0.6746 -0.1528

-1.4402 -0.1302 0.0443

-1.6019 0.0766 0.1764

-1.8451 0.1829 0.2719

-1.9011 0.2243 0.3058

-1.9108 0.1439 0.3841

-1.8944 0.1530 0.3880

-1.8908 0.1658 0.3845

-1.8547 0.1625 0.3511

0.0399 0.1405

0.0498 0.1440

0.0543 0.1483

0.0538 0.1451

0.0538 0.1405

0.0513 0.1253

-0.1360 -0.0313

-0.1557 -0.0431

-0.1654 -0.0474

-0.1705 -0.0511

-0.1706 -0.0486

-0.1670 -0.0406

-0.1590 -0.0316

-0.1547 -0.0238

-0.1421 -0.0115

-0.1309 -0.0037

-0.1852 -0.2128

-0.1673 -0.1799

-0.1486 -0.1456

-0.1218 -0.1125

-0.1014 -0.0794

-0.1026 -0.0604

-0.1130 -0.0518

-0.1214 -0.0469

-0.1320 -0.0387

-0.1410 -0.0320

-0.1683 -0.2158

-0.1813 -0.2104

-0.1929 -0.2045

-0.2031 -0.2056

-0.2121 -0.2084

-0.2170 -0.2066

-0.2220 -0.2108

-0.2289 -0.2130

Birth Cohort (By first year of cohort)

In

School
44

Table 3.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level.

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

Age

Group

59 6.5310 -2.6888 -0.1195 0.3218 0.2156 0.2211 0.2686

60 5.5311 -2.7513 -0.0942 0.2779 0.1861 0.1971 0.2039

61 1.6522 -2.8598 -0.0668 0.2376 0.1666 0.1793 0.1696

62 1.6510 -2.7980 -0.0377 0.2063 0.1510 0.1644 0.1502

63 1.6318 -2.7519 0.0079 0.1821 0.1428 0.1546 0.1436

64 1.5835 -2.6924 0.0677 0.1596 0.1287 0.1411 0.1540

65 1.5199 -2.6299 0.1274 0.1222 0.1221 0.1297 0.1844

66 1.4541 -2.5891 0.1777 0.0737 0.1279 0.1404 0.2156

67 3.0651 -2.4657 0.2335 0.0154 0.1380 0.1570 0.2577

68 3.1336 -2.3337 0.2419 -0.0483 0.1442 0.1710 0.3008

69 2.5228 -2.2057 0.2355 -0.0975 0.1606 0.1928 0.3358

70 0.5933 -1.7113 -0.0017 -0.1726 0.2620 0.3001 0.4679

Coefficient

for

Social

Security Marital Statusa Education Levelb

Age + Benefit Never High School Some College

Constant Receipt Married Married Graduate College Graduate
45

1910 1920 1930 1940 1950 1960 1970 1980

-0.2365 -0.2053

-0.2390 -0.1916

-0.1551 -0.2929

-0.1650 -0.2641

-0.1606 -0.1967

-0.1369 -0.0785

-0.0899 0.0877

-0.0267 0.2592

0.0258 0.3781

0.0700 0.4710

0.1110 0.5257

0.0941 0.2856

In Birth Cohort (By first year of cohort)

School
46

Table 4.

Full-Time Employment Logit Coefficients for Women

Age

Group

16 -0.3538 -0.3074 -0.4023 -2.0725

17 -15.9217 -0.2636 -0.2992 -1.1484

18 -11.4091 -0.7018 -0.1178 -0.4702

19 -7.1667 -1.1017 -0.0657 -0.3348

20 -5.6061 -1.2455 -0.1142 -0.3032

21 -4.9146 -1.3307 -0.1456 -0.2955

22 -4.1933 -1.3371 -0.1802 -0.2709

23 -3.4201 -1.3302 -0.2093 -0.2266

24 -2.6357 -1.2723 -0.2271 -0.1655

25 -3.1840 -1.1437 -0.2571 -0.1716 0.4802 0.1765 0.3771

26 -1.9403 -1.0182 -0.3025 -0.1117 0.4714 0.3331 0.5169

27 -0.7692 -0.9373 -0.3455 -0.0438 0.4614 0.4220 0.6334

28 0.0734 -0.8862 -0.3967 0.0135 0.4465 0.4624 0.7052

29 0.5929 -0.8338 -0.4524 0.0524 0.4313 0.4754 0.7390

30 0.8236 -0.7850 -0.5087 0.0782 0.4065 0.4664 0.7377

31 0.6060 -0.7694 -0.5620 0.0950 0.3652 0.4285 0.6967

32 0.6155 -0.7383 -0.6189 0.1098 0.3324 0.3953 0.6525

33 0.6037 -0.7129 -0.6622 0.1287 0.2953 0.3488 0.5921

34 0.6014 -0.7145 -0.7006 0.1535 0.2580 0.2991 0.5212

35 0.5689 -0.7304 -0.7308 0.1772 0.2304 0.2584 0.4592

36 0.5143 -0.7448 -0.7593 0.1920 0.2098 0.2267 0.4072

37 0.4548 -0.7816 -0.7818 0.2020 0.1999 0.2049 0.3673

38 0.3886 -0.8240 -0.8052 0.2076 0.2057 0.2005 0.3432

39 0.2645 -0.8618 -0.8215 0.2116 0.2209 0.2084 0.3363

40 0.1454 -0.9022 -0.8357 0.2133 0.2320 0.2156 0.3310

41 -0.2053 -0.9459 -0.8397 0.2163 0.2300 0.2087 0.3096

42 -0.2653 -0.9861 -0.8391 0.2158 0.2354 0.2171 0.3059

43 -0.3071 -1.0274 -0.8360 0.2121 0.2385 0.2257 0.3074

44 -0.3064 -1.0786 -0.8319 0.1939 0.2377 0.2343 0.3105

45 -0.2512 -1.1161 -0.8235 0.1798 0.2494 0.2486 0.3278

46 -0.1377 -1.1433 -0.8157 0.1667 0.2651 0.2653 0.3491

47 0.0437 -1.1677 -0.8090 0.1567 0.2807 0.2787 0.3692

48 0.2653 -1.1902 -0.7963 0.1461 0.2934 0.2857 0.3907

49 0.5069 -1.2071 -0.7866 0.1527 0.3081 0.2896 0.4106

50 0.7216 -1.2360 -0.7815 0.1612 0.3122 0.2941 0.4182

51 1.0089 -1.2556 -0.7744 0.1751 0.3069 0.2948 0.4312

52 1.2776 -1.2836 -0.7679 0.1880 0.3048 0.2949 0.4380

53 1.5865 -1.3172 -0.7678 0.1948 0.2987 0.2991 0.4342

54 1.8926 -1.3430 -0.7621 0.1916 0.2846 0.2984 0.4185

55 2.1394 -1.3623 -0.7519 0.1819 0.2714 0.2907 0.3988

56 2.4551 -1.4283 -0.7407 0.1777 0.2606 0.2868 0.3684

57 2.7855 -1.5343 -0.7407 0.1559 0.2522 0.2849 0.3386

58 2.8005 -1.7122 -0.7430 0.1347 0.2502 0.2784 0.3078

Receipt Married Married Graduate College Graduate

Social

Security

Never

Marital Statusa

College

Education Levelb

High 

Age + Benefit School Some

Coefficient

for

Constant
47

Continued

1910 1920 1930 1940 1950 1960 1970 1980

-2.1415 0.1250 -0.0003 0.4279

-1.0545 -0.0183 -0.1403 0.1386

-1.4249 -0.3499 -0.0618 0.1619

-1.6314 -0.4899 0.0295 0.2112

-1.6370 -0.5523 0.0610 0.2033

-1.6170 -0.5830 0.0919 0.1840

-1.5787 -0.6060 0.1189 0.1940

-1.5615 -0.6286 0.1512 0.2103

-1.5344 -0.6458 0.1768 0.2174

-0.6227 0.1534 0.1161

-0.6218 0.1827 0.1561

-0.6193 0.2056 0.1955

-0.6102 0.2242 0.2360

-0.5974 0.2392 0.2720

-0.5773 0.2497 0.2948

-0.5565 0.2640 0.4680

-0.5307 0.2715 0.4717

-0.5066 0.2733 0.4667

-0.4790 0.2681 0.4521

-0.4528 0.2579 0.4322

-0.4302 0.2469 0.4076

-0.4121 0.2383 0.3822

-0.3942 0.2302 0.3571

-0.3812 0.2265 0.3369

-0.3682 0.2260 0.3216

-0.3539 0.2058 0.4111

-0.3374 0.2093 0.3983

-0.3209 0.2149 0.3893

-0.3013 0.2198 0.3804

-0.2858 0.2205 0.3707

-0.2595 0.2230 0.3628

-0.2428 0.2276 0.3594

-0.2232 0.2278 0.3565

-0.2091 0.2276 0.3530

-0.2025 0.2283 0.3486

-0.2008 0.0938 0.3330

-0.2102 0.0734 0.3064

-0.2200 0.0465 0.2767

-0.2197 0.0188 0.2523

-0.1966 -0.0043 0.2372

-0.0224 0.2214

-0.0280 0.2110

-0.0288 0.2083

Number of

Children

School Age 6

In Birth Cohort (By first year of cohort) Under
48

Table 4.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level.

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

Age

Group

59 2.3461 -1.9225 -0.7517 0.1045 0.2548 0.2741 0.2770

60 1.4716 -2.1005 -0.7657 0.0706 0.2566 0.2749 0.2481

61 0.9991 -2.1958 -0.7741 0.0417 0.2635 0.2873 0.2406

62 1.0228 -2.2273 -0.7606 0.0302 0.2567 0.2761 0.2078

63 1.0364 -2.2501 -0.7334 0.0282 0.2392 0.2646 0.1856

64 1.0116 -2.2495 -0.6865 0.0393 0.2222 0.2648 0.1942

65 0.9524 -2.2288 -0.6213 0.0567 0.2147 0.2631 0.2147

66 0.8680 -2.2154 -0.5513 0.0715 0.2064 0.2678 0.2524

67 2.4817 -2.1367 -0.4916 0.0989 0.2046 0.2780 0.3057

68 3.1728 -2.0339 -0.4251 0.1391 0.2102 0.2983 0.3429

69 3.0272 -1.9287 -0.3677 0.1866 0.2072 0.3001 0.3458

70 -1.2909 -1.7590 -0.1242 0.3972 0.2062 0.2451 0.2368

Social Education Levelb

Security Marital Statusa High 

Age + Benefit Never School Some College

Constant Receipt Married Married Graduate College Graduate

Coefficient

for
49

1910 1920 1930 1940 1950 1960 1970 1980

-0.0253 0.2017

-0.0227 0.1848

-0.0422 0.0159

-0.0409 0.0063

-0.0224 0.0197

0.0165 0.0659

0.0582 0.1472

0.1131 0.2516

0.1596 0.3490

0.1974 0.4388

0.2254 0.5126

0.2494 0.5337

Number of

Children

In Under Birth Cohort (By first year of cohort)

School Age 6
50

Table 5.

Full-Time or Part-Time Employment Persistence for 

Men, by Age Group

(Percent)

Source: Congressional Budget Office.

Work Status

at (t -1)

Not Working at (t -1) 19.9 9.0 5.5 10.6 0.3 5.7

Part Time at (t -1) 5.0 22.4 10.9 0.8 4.3 5.5

Full Time at (t -1) 2.6 4.7 20.2 3.9 5.4 63.5

Not Working at (t -1) 21.3 8.9 5.3 13.4 0.3 5.4

Part Time at (t -1) 4.8 19.3 9.7 0.8 3.7 4.7

Full Time at (t -1) 3.0 3.4 24.3 4.1 4.7 62.9

t =2100

Not Working at (t -1) 21.7 8.9 5.2 13.7 0.3 5.3

Part Time at (t -1) 4.7 19.4 9.6 0.7 3.7 4.7

Full Time at (t -1) 2.9 3.3 24.5 4.1 4.7 62.8

Not Working at (t -1) 7.9 0.1 3.2 11.9 0.2 2.6

Part Time at (t -1) 0.3 1.6 2.7 0.4 1.7 2.4

Full Time at (t -1) 2.6 3.1 78.4 1.7 3.1 76.0

Not Working at (t -1) 11.0 0.2 2.8 14.5 0.2 2.0

Part Time at (t -1) 0.5 2.0 3.0 0.6 2.2 2.8

Full Time at (t -1) 2.4 3.3 74.9 1.6 3.4 72.6

Not Working at (t -1) 11.6 0.2 2.8 14.4 0.3 1.9

Part Time at (t -1) 0.5 2.0 3.0 0.6 2.4 2.8

Full Time at (t -1) 2.5 3.3 74.2 1.7 3.5 72.5

Not Working at (t -1) 22.1 0.8 2.1 71.2 1.2 1.1

Part Time at (t -1) 1.8 5.0 3.5 2.7 7.5 3.0

Full Time at (t -1) 2.8 6.5 55.3 2.1 3.9 7.2

Not Working at (t -1) 25.0 0.9 1.5 71.5 1.2 0.9

Part Time at (t -1) 2.3 5.8 4.1 2.5 8.1 2.9

Full Time at (t -1) 3.2 6.7 50.5 2.0 3.6 7.3

Not Working at (t -1) 25.6 0.9 1.5 73.4 1.2 0.9

Part Time at (t -1) 2.2 6.1 4.2 2.4 7.8 2.8

Full Time at (t -1) 3.0 6.8 49.7 1.8 3.3 6.4

Part Time Full Time

Ages 25 to 34

Not Working 

at (t)

Not Working 

at (t) at (t) at (t)

Ages 16 to 24

Part Time Full Time

t =2050

t =2000

Ages 65 and Older

Ages 45 to 54

Ages 55 to 64

Ages 35 to 44

Work Status at (t) Work Status at (t)

t =2100

at (t) at (t)

t =2050

t =2100

t =2000

t =2050

t =2000
51

Table 6.

Full-Time or Part-Time Employment Persistence for 

Women, by Age Group

(Percent)

Source: Congressional Budget Office.

Work Status

at (t -1)

Not Working at (t -1) 19.6 9.4 3.0 16.7 1.5 4.1

Part Time at (t -1) 4.4 32.9 10.2 1.9 15.7 7.9

Full Time at (t -1) 1.4 5.0 14.1 3.2 8.7 40.3

Not Working at (t -1) 21.4 8.9 2.7 18.4 1.4 3.9

Part Time at (t -1) 3.9 29.0 10.2 1.6 12.9 6.9

Full Time at (t -1) 1.6 4.0 18.5 3.4 7.6 43.8

t =2100

Not Working at (t -1) 21.4 8.9 2.7 18.4 1.4 3.9

Part Time at (t -1) 3.9 29.1 10.3 1.6 12.8 6.9

Full Time at (t -1) 1.6 3.9 18.2 3.5 7.5 43.9

Not Working at (t -1) 18.4 1.0 3.6 20.1 0.7 2.7

Part Time at (t -1) 1.2 13.5 7.6 0.9 11.9 7.0

Full Time at (t -1) 2.6 8.3 43.8 2.4 7.7 46.6

Not Working at (t -1) 20.0 0.9 3.3 21.7 0.7 2.5

Part Time at (t -1) 1.3 13.6 7.3 1.2 12.5 6.8

Full Time at (t -1) 2.7 7.5 43.5 2.5 7.3 44.9

t =2100

Not Working at (t -1) 19.7 1.0 3.3 21.2 0.7 2.4

Part Time at (t -1) 1.3 13.7 7.3 1.1 13.0 6.8

Full Time at (t -1) 2.7 7.5 43.5 2.4 7.2 45.1

Not Working at (t -1) 32.1 1.0 1.7 82.6 0.5 0.3

Part Time at (t -1) 1.9 13.4 5.2 1.7 6.8 1.5

Full Time at (t -1) 2.7 7.3 34.7 1.0 1.9 3.7

Not Working at (t -1) 34.8 1.0 1.6 81.3 0.5 0.2

Part Time at (t -1) 2.3 13.9 4.9 1.7 7.2 1.6

Full Time at (t -1) 2.7 6.7 32.0 1.1 1.9 4.5

t =2100

Not Working at (t -1) 33.6 1.1 1.5 82.5 0.5 0.2

Part Time at (t -1) 2.4 14.5 4.9 1.6 6.9 1.5

Full Time at (t -1) 2.8 6.6 32.7 1.1 1.6 4.1

at (t)

Work Status at (t) Work Status at (t)

Not Working

at (t) at (t) at (t)

Not Working

at (t)

Full Time Part Time

t =2050

t =2000

t =2050

t =2000

t =2050

Full Time

Ages 65 and Older

Part Time

t =2000

Ages 45 to 54

Ages 25 to 34

at (t)

Ages 55 to 64

Ages 35 to 44

Ages 16 to 24
52

Table 7.

Part-Time Hours Ordered Logit Coefficients for Men

Age

Group

16 -0.1376 0.6166 0.7352 -0.7623 -0.0139

17 0.4898 -0.1691 -0.1496 0.7636 -0.3245 -0.0151

18 0.3213 -0.2197 -0.4439 0.6621 -0.5763 -0.0066

19 0.2037 -0.2989 -0.4319 0.6801 -0.7184 0.0026

20 0.1524 -0.3651 -0.4296 0.5609 -0.7449 0.0085

21 0.1192 -0.4508 -0.4523 0.4882 -0.7341 0.0038

22 0.0917 -0.5555 -0.4591 0.3919 -0.7225 0.0067

23 0.0680 -0.6882 -0.4595 0.2865 -0.7302 0.0096

24 0.0572 -0.8179 -0.4368 0.2092 -0.6892 0.0115

25 0.0912 -0.9659 -0.2304 0.2449 0.2926 -0.4008 -0.4919 0.0046

26 0.0652 -1.1098 -0.2129 0.1766 0.2724 -0.2987 -0.4599 0.0058

27 0.0446 -1.2022 -0.1711 0.1491 0.2555 -0.2118 -0.4216 0.0060

28 0.0301 -1.2143 -0.1355 0.1404 0.2462 -0.1496 -0.3709 0.0048

29 0.0230 -1.2059 -0.1015 0.1424 0.2428 -0.0961 -0.3121 0.0021

30 0.0271 -1.2056 -0.0867 0.1583 0.2302 -0.0562 -0.2405 -0.0003

31 0.0195 -1.2283 -0.0774 0.1685 0.2127 -0.0340 -0.1797 0.0022

32 0.0087 -1.2276 -0.0856 0.1684 0.1953 -0.0204 -0.1218 0.0040

33 -0.0020 -1.2337 -0.0893 0.1696 0.1832 -0.0205 -0.0719 0.0071

34 -0.0123 -1.2052 -0.1035 0.1724 0.1720 -0.0233 -0.0231 0.0073

35 -0.0137 -1.1578 -0.1048 0.1765 0.1636 -0.0221 0.0152 0.0057

36 -0.0109 -1.1104 -0.1012 0.1843 0.1677 0.0014 0.0649 0.0028

37 -0.0087 -1.0834 -0.0908 0.1940 0.1680 0.0305 0.1068 -0.0001

38 -0.0028 -1.1012 -0.0810 0.1997 0.1646 0.0653 0.1411 -0.0036

39 -0.0048 -1.1602 -0.0692 0.2025 0.1480 0.0945 0.1620 -0.0022

40 -0.0083 -1.2310 -0.0696 0.2004 0.1288 0.0953 0.1712 0.0011

41 -0.0034 -1.2911 -0.0801 0.1972 0.0928 0.0581 0.1541 0.0004

42 -0.0034 -1.3466 -0.0874 0.1997 0.0701 0.0189 0.1431 0.0021

43 -0.0086 -1.3411 -0.0971 0.1985 0.0623 -0.0237 0.1599 0.0039

44 -0.0148 -1.3289 -0.1002 0.1940 0.0713 -0.0713 0.1866 0.0051

45 -0.0169 -1.3125 -0.1039 0.1917 0.0716 -0.1022 0.2078 0.0048

46 -0.0178 -1.3132 -0.1024 0.1896 0.0778 -0.1041 0.2332 0.0040

47 -0.0155 -1.2898 -0.1045 0.1811 0.0812 -0.1032 0.2552 0.0026

48 -0.0158 -1.2954 -0.1204 0.1812 0.0576 -0.0989 0.2475 0.0013

49 -0.0169 -1.2835 -0.1428 0.1795 0.0318 -0.0825 0.2354 -0.0008

50 -0.0163 -1.2833 -0.1679 0.1666 0.0225 -0.0668 0.2286 -0.0041

51 -0.0309 -1.2273 -0.1844 0.1525 0.0174 -0.0612 0.2172 0.0014

52 -0.0402 -1.2383 -0.1991 0.1277 0.0102 -0.0632 0.1946 0.0000

53 -0.0480 -1.2449 -0.1785 0.0997 0.0196 -0.0636 0.1555 -0.0004

54 -0.0563 -1.2544 -0.1351 0.0789 0.0228 -0.0676 0.1079 -0.0003

55 -0.0627 -1.2442 -0.0839 0.0644 0.0102 -0.0802 0.0465 0.0006

56 -0.0637 -1.2575 -0.0431 0.0487 -0.0042 -0.0967 -0.0239 0.0010

57 -0.0639 -1.1969 -0.0135 0.0485 -0.0222 -0.1035 -0.0868 0.0001

58 -0.0445 -0.9137 -0.0223 0.0424 -0.0405 -0.1078 -0.1390 -0.0020

College

College Graduate School Trend

High

Graduate

Education Levelb Social

Security Marital Statusa

Age Benefit Never School Some In

Coefficient Receipt Married Married
53

Continued

1910 1920 1930 1940 1950 1960 1970 1980

-0.0113 0.1725 -1.2580 0.3056 1.1911 1.8890 3.0190 3.9287

0.0433 0.2246 6.2598 7.7319 8.5322 9.1870 10.2075 11.0979

0.0685 0.1522 2.9811 4.4644 5.2403 5.8400 6.8042 7.6382

0.0625 0.0508 0.8790 2.3816 3.1551 3.7281 4.6568 5.4454

0.0354 -0.0331 -0.0279 1.4776 2.2483 2.8087 3.7178 4.4848

0.1020 0.1927 -0.6529 0.8555 1.6216 2.1706 3.0641 3.8179

0.0719 0.1455 -1.2195 0.2816 1.0403 1.5808 2.4636 3.2123

0.0456 0.0878 -1.7272 -0.2469 0.5045 1.0445 1.9189 2.6643

0.0047 0.0099 -1.9178 -0.4829 0.2558 0.7985 1.6654 2.4060

-0.0533 -0.0462 -1.0891 0.2841 1.0083 1.5647 2.4401 3.1907

-0.0735 -0.0950 -1.6601 -0.3369 0.3724 0.9392 1.8143 2.5604

-0.0765 -0.1147 -2.1202 -0.8316 -0.1329 0.4431 1.3184 2.0541

-0.0650 -0.1090 -2.4459 -1.1851 -0.4959 0.0834 0.9602 1.6831

-0.0417 -0.0672 -2.6188 -1.3680 -0.6831 -0.1001 0.7789 1.4893

-0.0298 -0.0172 -2.4820 -1.2220 -0.5400 0.0372 0.9169 1.6152

-0.0854 -0.1297 -2.7265 -1.4660 -0.7847 -0.2101 0.6695 1.3581

-0.1382 -0.1995 -3.0661 -1.8122 -1.1284 -0.5577 0.3183 0.9998

-0.1887 -0.2875 -3.4043 -2.1577 -1.4718 -0.9038 -0.0379 0.6401

-0.1947 -0.3056 -3.7232 -2.4939 -1.8081 -1.2482 -0.3875 0.2928

-0.1674 -0.2703 -3.7322 -2.5341 -1.8461 -1.2884 -0.4299 0.2559

-0.1091 -0.1891 -3.5729 -2.4033 -1.7168 -1.1644 -0.3083 0.3813

-0.0593 -0.1143 -3.4471 -2.2928 -1.6153 -1.0659 -0.2122 0.4791

-0.0109 -0.0340 -3.2118 -2.0659 -1.3900 -0.8413 0.0168 0.7092

-0.0257 -0.0482 -3.2618 -2.1207 -1.4454 -0.8918 -0.0391 0.6550

-0.0691 -0.0981 -3.3852 -2.2411 -1.5650 -1.0063 -0.1639 0.5277

0.0579 -0.0318 -3.1923 -2.0375 -1.3614 -0.7963 0.0363 0.7287

0.0451 -0.0682 -3.2075 -2.0590 -1.3720 -0.8038 0.0188 0.7158

0.0279 -0.1026 -3.4052 -2.2768 -1.5865 -1.0172 -0.2062 0.4934

-0.0092 -0.1375 -3.6661 -2.5571 -1.8705 -1.3035 -0.4933 0.2051

-0.0383 -0.1397 -3.7554 -2.6581 -1.9778 -1.4185 -0.6113 0.0902

-0.0556 -0.1232 -3.7813 -2.6994 -2.0202 -1.4657 -0.6594 0.0432

-0.0683 -0.0925 -3.6832 -2.5972 -1.9309 -1.3794 -0.5680 0.1336

-0.0786 -0.0604 -3.7259 -2.6257 -1.9753 -1.4226 -0.6064 0.0964

-0.0518 -0.0026 -3.7936 -2.6928 -2.0512 -1.4925 -0.6791 0.0261

-0.0083 0.0759 -3.7864 -2.6962 -2.0617 -1.4948 -0.6765 0.0271

-0.0733 -0.1094 -4.5286 -3.4501 -2.8271 -2.2603 -1.4313 -0.7288

-0.0750 -0.0824 -5.0173 -3.9638 -3.3419 -2.7732 -1.9406 -1.2422

-0.0887 -0.0827 -5.4321 -4.4123 -3.7845 -3.2191 -2.3831 -1.6881

-0.1090 -0.1091 -5.9012 -4.8969 -4.2649 -3.7082 -2.8666 -2.1759

-0.1338 -0.1377 -6.2703 -5.2720 -4.6428 -4.0923 -3.2487 -2.5618

-0.1444 -0.1488 -6.3495 -5.3547 -4.7245 -4.1724 -3.3357 -2.6538

-0.1357 -0.1272 -6.3682 -5.3801 -4.7564 -4.2055 -3.3717 -2.6930

-0.1045 -0.0587 -5.2642 -4.2721 -3.6576 -3.1067 -2.2716 -1.5943

1

Part-Time Category Threshold

2

Birth Cohort (By first year of cohort)

3 4 5 6
54

Table 7.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level. Statistical significance of the

threshold values is not evaluated. 

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

c. Terms do not appear for ages 61 to 66 in order to isolate the effects of eligibility for Social Security retirement benefits at 

those ages.

Age

Group

59 -0.0299 -0.7985 -0.0335 0.0396 -0.0590 -0.1222 -0.1843 -0.0025

60 -0.0212 -0.7799 -0.0370 0.0360 -0.0680 -0.1356 -0.2170 -0.0026

61 c -0.8117 -0.0267 0.0315 -0.0765 -0.1420 -0.2337 0.0018

62 c -0.8187 0.0151 0.0230 -0.0772 -0.1444 -0.2415 0.0036

63 c -0.8577 0.0631 0.0089 -0.0753 -0.1443 -0.2356 0.0071

64 c -0.8795 0.0948 -0.0157 -0.0663 -0.1326 -0.2299 0.0106

65 c -0.8793 0.1202 -0.0320 -0.0548 -0.1129 -0.2129 0.0145

66 c -0.8828 0.1300 -0.0471 -0.0412 -0.0978 -0.1974 0.0171

67 -0.0216 -0.8447 0.1023 -0.0591 -0.0240 -0.0787 -0.1736 0.0246

68 -0.0220 -0.7958 0.0909 -0.0518 0.0003 -0.0494 -0.1429 0.0226

69 -0.0200 -0.7414 0.0801 -0.0399 0.0177 -0.0260 -0.1198 0.0196

70 -0.0140 -0.5012 0.1251 -0.0067 0.0894 -0.0260 -0.0862 0.0116

Coefficient Receipt Married Married Graduate College Graduate School Trend

Benefit Never School Some College In

Social Education Levelb

Security Marital Statusa High

Age
55

1910 1920 1930 1940 1950 1960 1970 1980

-0.0637 0.0151 -4.3995 -3.4062 -2.7934 -2.2386 -1.3964 -0.7199

-0.0150 0.0808 -3.8692 -2.8818 -2.2655 -1.7038 -0.8488 -0.1698

-0.0213 -0.0061 -2.5460 -1.5663 -0.9472 -0.3810 0.4899 1.1744

-0.0275 0.0167 -2.5254 -1.5492 -0.9226 -0.3523 0.5387 1.2351

-0.0502 0.0062 -2.4969 -1.5339 -0.9027 -0.3286 0.5823 1.2826

-0.0745 -0.0189 -2.4742 -1.5240 -0.8933 -0.3158 0.6133 1.3178

-0.1034 -0.0544 -2.4260 -1.4851 -0.8542 -0.2786 0.6628 1.3712

-0.1071 -0.0534 -2.3714 -1.4408 -0.8106 -0.2375 0.7180 1.4297

-0.1846 -0.1878 -3.7030 -2.7835 -2.1571 -1.5847 -0.6203 0.0865

-0.1590 -0.1321 -3.6619 -2.7468 -2.1213 -1.5534 -0.5882 0.1220

-0.1185 -0.0565 -3.4626 -2.5507 -1.9262 -1.3643 -0.4020 0.3149

-0.0727 -0.0122 -2.8073 -1.9126 -1.2951 -0.7519 0.2021 0.9362

123 456

Birth Cohort (By first year of cohort) Part-Time Category Threshold
56

Table 8.

Part-Time Hours Ordered Logit Coefficients for Women

Age

Group

16 -0.2109 -0.7481 -0.3690 -0.8626 -0.0050 -0.0018

17 0.5345 -0.1252 -0.4888 -0.0185 -0.3530 -0.0729 -0.0049

18 0.3252 -0.1816 -0.4320 -0.1435 -0.5469 -0.1750 0.0037

19 0.2097 -0.2441 -0.3593 -0.1077 -0.6331 -0.2639 0.0121

20 0.1622 -0.2772 -0.3376 -0.1273 -0.6369 -0.3099 0.0161

21 0.1307 -0.3297 -0.3338 -0.1407 -0.6185 -0.3218 0.0105

22 0.0991 -0.3817 -0.3069 -0.1616 -0.5992 -0.3265 0.0139

23 0.0722 -0.4427 -0.2527 -0.1683 -0.6055 -0.3263 0.0168

24 0.0481 -0.4862 -0.1930 -0.1709 -0.5889 -0.3219 0.0187

25 0.0509 -0.4866 -0.1675 -0.1903 0.2219 0.0226 0.1029 -0.2996 0.0167

26 0.0217 -0.5047 -0.1176 -0.2120 0.1959 0.1065 0.1366 -0.2998 0.0184

27 -0.0005 -0.5149 -0.0736 -0.2298 0.1726 0.1506 0.1764 -0.3063 0.0205

28 -0.0129 -0.5277 -0.0502 -0.2602 0.1515 0.1573 0.1977 -0.3097 0.0230

29 -0.0189 -0.5369 -0.0371 -0.2929 0.1272 0.1449 0.1911 -0.3090 0.0257

30 -0.0207 -0.5161 -0.0369 -0.3211 0.1090 0.1309 0.1688 -0.3102 0.0281

31 -0.0045 -0.4990 -0.0387 -0.3535 0.0812 0.1041 0.1286 -0.3073 0.0149

32 -0.0054 -0.4870 -0.0365 -0.3867 0.0673 0.0808 0.0950 -0.3030 0.0174

33 -0.0056 -0.4672 -0.0290 -0.4109 0.0593 0.0655 0.0718 -0.3020 0.0205

34 -0.0061 -0.4652 -0.0312 -0.4319 0.0590 0.0605 0.0596 -0.3057 0.0233

35 -0.0073 -0.4818 -0.0283 -0.4511 0.0617 0.0508 0.0479 -0.3038 0.0261

36 -0.0048 -0.4982 -0.0182 -0.4619 0.0708 0.0435 0.0399 -0.3019 0.0275

37 -0.0032 -0.5180 -0.0108 -0.4694 0.0799 0.0400 0.0357 -0.3002 0.0277

38 0.0000 -0.5423 -0.0030 -0.4687 0.0864 0.0354 0.0256 -0.2954 0.0260

39 0.0052 -0.5440 0.0044 -0.4625 0.1005 0.0315 0.0175 -0.2854 0.0237

40 0.0097 -0.5501 0.0089 -0.4503 0.1162 0.0335 0.0197 -0.2843 0.0216

41 0.0201 -0.5637 -0.0068 -0.4402 0.1226 0.0397 0.0224 -0.2883 0.0128

42 0.0215 -0.5667 -0.0305 -0.4313 0.1216 0.0489 0.0204 -0.2884 0.0121

43 0.0190 -0.5804 -0.0593 -0.4337 0.1229 0.0599 0.0288 -0.2950 0.0123

44 0.0145 -0.6050 -0.0637 -0.4309 0.1123 0.0659 0.0401 -0.3085 0.0132

45 0.0103 -0.6170 -0.0553 -0.4249 0.1056 0.0796 0.0579 -0.3178 0.0140

46 0.0062 -0.6187 -0.0231 -0.4179 0.1086 0.0926 0.0834 -0.3287 0.0139

47 0.0024 -0.6187 0.0192 -0.4060 0.1156 0.0943 0.1122 -0.3457 0.0131

48 -0.0010 -0.6161 0.0628 -0.3886 0.1188 0.0973 0.1367 -0.3576 0.0121

49 -0.0020 -0.6206 0.0737 -0.3829 0.1248 0.1010 0.1520 -0.3605 0.0115

50 -0.0018 -0.6259 0.0653 -0.3866 0.1201 0.0968 0.1538 -0.3561 0.0113

51 -0.0006 -0.6305 0.0539 -0.3905 0.1069 0.0882 0.1415 -0.3274 0.0095

52 -0.0050 -0.6575 0.0353 -0.3957 0.0955 0.0858 0.1261 -0.3052 0.0100

53 -0.0106 -0.6736 0.0081 -0.4008 0.0882 0.0739 0.1021 -0.2629 0.0098

54 -0.0188 -0.6878 -0.0075 -0.4067 0.0812 0.0609 0.0830 -0.2360 0.0106

55 -0.0259 -0.7177 -0.0168 -0.4138 0.0733 0.0415 0.0593 -0.2016 0.0104

56 -0.0295 -0.7365 -0.0557 -0.4165 0.0688 0.0214 0.0390 0.0092

57 -0.0290 -0.7260 -0.0953 -0.4282 0.0612 -0.0043 0.0096 0.0075

58 -0.0185 -0.6707 -0.1174 -0.4356 0.0535 -0.0206 -0.0217 0.0059

Marital Statusa

Education Levelb Social

Security

Coefficient Receipt

Age Benefit

Married Married College Graduate Graduate School

Never School

Children

Some College In

High

Trend

Number of

Under 

Age 6
57

Continued

1910 1920 1930 1940 1950 1960 1970 1980

0.0733 0.0868 -2.4075 -0.7868 0.1149 0.8994 2.0455 2.8533

0.1251 0.2103 6.9442 8.3977 9.2188 9.9253 11.0076 11.8380

0.1015 0.0998 3.3391 4.7739 5.5599 6.1951 7.1821 7.9709

0.0790 0.0008 1.3609 2.8023 3.5648 4.1723 5.1009 5.8709

0.0511 -0.0525 0.5391 1.9714 2.7183 3.3173 4.2137 4.9739

0.0830 0.1774 -0.0347 1.3818 2.1155 2.7071 3.5807 4.3315

0.0485 0.1007 -0.6438 0.7468 1.4668 2.0518 2.9053 3.6484

0.0235 0.0288 -1.1540 0.1996 0.9064 1.4876 2.3230 3.0567

0.0011 -0.0457 -1.6117 -0.3149 0.3780 0.9548 1.7756 2.4999

-0.0140 -0.1052 -1.3146 -0.0775 0.6013 1.1714 1.9766 2.6896

-0.0195 -0.1520 -1.9634 -0.7712 -0.1029 0.4629 1.2617 1.9703

-0.0264 -0.1837 -2.4860 -1.3253 -0.6695 -0.1063 0.6910 1.3980

-0.0397 -0.2094 -2.7955 -1.6581 -1.0117 -0.4506 0.3490 1.0554

-0.0610 -0.2363 -2.9615 -1.8408 -1.2018 -0.6414 0.1661 0.8698

-0.0844 -0.2631 -3.0129 -1.9058 -1.2723 -0.7115 0.1063 0.8059

0.1953 0.1909 -2.5243 -1.4269 -0.7963 -0.2343 0.5946 1.2897

0.1747 0.1344 -2.5771 -1.4946 -0.8640 -0.3013 0.5357 1.2236

0.1434 0.0623 -2.6000 -1.5308 -0.9020 -0.3377 0.5048 1.1884

0.1021 -0.0092 -2.6351 -1.5724 -0.9438 -0.3791 0.4668 1.1495

0.0603 -0.0818 -2.6911 -1.6324 -1.0038 -0.4402 0.4093 1.0939

0.0265 -0.1268 -2.6106 -1.5559 -0.9290 -0.3653 0.4880 1.1735

0.0066 -0.1434 -2.5695 -1.5123 -0.8860 -0.3234 0.5343 1.2216

0.0047 -0.1268 -2.4779 -1.4163 -0.7893 -0.2288 0.6335 1.3202

0.0154 -0.0969 -2.2928 -1.2303 -0.6024 -0.0414 0.8230 1.5084

0.0209 -0.0703 -2.1261 -1.0639 -0.4366 0.1268 0.9942 1.6774

0.1044 0.1757 -1.7014 -0.6408 -0.0115 0.5528 1.4212 2.1045

0.1148 0.1684 -1.6505 -0.5884 0.0410 0.6063 1.4756 2.1621

0.1219 0.1479 -1.7607 -0.6975 -0.0671 0.4982 1.3692 2.0599

0.1261 0.1167 -1.9574 -0.8936 -0.2620 0.3016 1.1756 1.8692

0.1219 0.0858 -2.1403 -1.0746 -0.4389 0.1220 0.9956 1.6892

0.1327 0.0833 -2.3162 -1.2452 -0.6067 -0.0475 0.8259 1.5180

0.1417 0.0953 -2.4732 -1.4038 -0.7635 -0.2066 0.6668 1.3533

0.1472 0.1157 -2.6105 -1.5470 -0.9046 -0.3484 0.5241 1.2046

0.1397 0.1195 -2.6494 -1.5944 -0.9541 -0.3979 0.4732 1.1487

0.1348 0.1223 -2.6370 -1.5937 -0.9573 -0.4017 0.4689 1.1413

0.0424 0.1706 -2.5592 -1.5303 -0.8998 -0.3450 0.5270 1.1972

0.0429 0.1400 -2.7903 -1.7735 -1.1445 -0.5890 0.2843 0.9538

0.0425 0.1198 -3.1021 -2.0964 -1.4718 -0.9157 -0.0381 0.6307

0.0265 0.0819 -3.5594 -2.5609 -1.9349 -1.3786 -0.4969 0.1714

0.0199 0.0601 -3.9760 -2.9850 -2.3559 -1.7989 -0.9096 -0.2379

0.0162 0.0595 -4.2079 -3.2226 -2.5858 -2.0296 -1.1366 -0.4625

0.0219 0.0809 -4.2198 -3.2450 -2.6058 -2.0469 -1.1497 -0.4712

0.0323 0.1098 -3.6472 -2.6821 -2.0392 -1.4760 -0.5748 0.1097

Birth Cohort (By first year of cohort) Part-Time Category Threshold

1 234 5 6
58

Table 8.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level. Statistical significance of the

threshold values is not evaluated. 

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

c. Terms do not appear for ages 61 to 66 in order to isolate the effects of eligibility for Social Security retirement benefits at 

those ages.

Age

Group

59 -0.0065 -0.6694 -0.1354 -0.4400 0.0460 -0.0341 -0.0672 0.0038

60 0.0038 -0.6948 -0.1619 -0.4396 0.0494 -0.0430 -0.1112 0.0026

61 c -0.7109 -0.1405 -0.4284 0.0359 -0.0634 -0.1612 0.0109

62 c -0.7283 -0.1199 -0.4047 0.0216 -0.0861 -0.2084 0.0117

63 c -0.7659 -0.0993 -0.3783 0.0057 -0.1105 -0.2511 0.0120

64 c -0.7894 -0.0794 -0.3423 -0.0142 -0.1381 -0.2817 0.0118

65 c -0.8012 -0.0403 -0.3075 -0.0447 -0.1664 -0.3036 0.0127

66 c -0.8057 -0.0368 -0.2865 -0.0477 -0.1695 -0.3069 0.0124

67 -0.0528 -0.7214 -0.0277 -0.3022 -0.0422 -0.1577 -0.3078 0.0261

68 -0.0540 -0.6695 -0.0192 -0.2972 -0.0319 -0.1463 -0.3214 0.0259

69 -0.0507 -0.6249 0.0113 -0.2962 -0.0056 -0.1232 -0.3551 0.0236

70 -0.0189 -0.5518 0.2503 -0.2442 0.0802 -0.0903 -0.5588 0.0107

Age College In Under 

Social Education Levelb

Security Marital Statusa High

Benefit Never School Some

Coefficient Receipt Married Married Age 6 Trend Graduate College Graduate School

Number of

Children
59

1910 1920 1930 1940 1950 1960 1970 1980

0.0438 0.1417 -2.9858 -2.0279 -1.3835 -0.8133 0.0974 0.7909

0.0478 0.1586 -2.4056 -1.4527 -0.8079 -0.2263 0.6926 1.3892

-0.0752 -0.0909 -2.6435 -1.6963 -1.0540 -0.4563 0.4743 1.1792

-0.0681 -0.0990 -2.6594 -1.7089 -1.0644 -0.4530 0.4894 1.2000

-0.0443 -0.0853 -2.6782 -1.7181 -1.0687 -0.4447 0.5088 1.2247

-0.0127 -0.0486 -2.6756 -1.7055 -1.0539 -0.4190 0.5417 1.2573

0.0118 -0.0258 -2.6639 -1.6796 -1.0242 -0.3843 0.5867 1.3077

0.0417 0.0153 -2.6384 -1.6348 -0.9756 -0.3344 0.6510 1.3695

-0.0942 -0.2569 -5.9847 -4.9667 -4.3039 -3.6621 -2.6565 -1.9375

-0.0843 -0.2389 -6.0189 -4.9915 -4.3302 -3.6908 -2.6650 -1.9495

-0.0615 -0.1863 -5.7705 -4.7409 -4.0766 -3.4408 -2.3971 -1.6764

0.0050 0.0208 -3.4857 -2.4792 -1.8018 -1.1945 -0.1504 0.5504

1 234 5 6

Birth Cohort (By first year of cohort) Part-Time Category Threshold
60

Table 9.

Unemployment Logit Coefficients for Men

Age

Group

16 -1.4723 0.2059 -0.2823 -0.6714 -0.1495

17 -2.8300 0.1662 -0.1822 -0.3126 -0.3999

18 -0.6538 0.1703 -0.3078 -0.4811 -0.5716

19 0.1193 0.1450 -0.2809 -0.5196 -0.6690

20 0.2073 0.1079 -0.2595 -0.5224 -0.7037

21 0.3048 0.0508 -0.2712 -0.5208 -0.7044

22 0.4828 -0.0030 -0.2734 -0.5144 -0.7114

23 0.6468 -0.0582 -0.2801 -0.5111 -0.7293

24 0.7149 -0.0970 -0.2860 -0.5092 -0.7169

25 0.3155 -0.0902 -0.1228 -0.4497 -0.3922 -0.8568 -1.0768

26 0.4611 -0.0464 -0.1249 -0.4554 -0.3668 -0.7749 -1.0977

27 0.4589 -0.0025 -0.1269 -0.4691 -0.3497 -0.7214 -1.1179

28 0.3503 0.0346 -0.1254 -0.4804 -0.3411 -0.6875 -1.1379

29 0.2827 0.0659 -0.1221 -0.4863 -0.3394 -0.6690 -1.1549

30 0.2184 0.0387 -0.1101 -0.4920 -0.3454 -0.6617 -1.1675

31 -0.1625 -0.0049 -0.0949 -0.4838 -0.3578 -0.6715 -1.1806

32 -0.1565 -0.0423 -0.0758 -0.4646 -0.3707 -0.6772 -1.1929

33 -0.1385 -0.0842 -0.0606 -0.4501 -0.3774 -0.6781 -1.1928

34 -0.1493 -0.1389 -0.0575 -0.4482 -0.3821 -0.6754 -1.1864

35 -0.1060 -0.1269 -0.0664 -0.4494 -0.3837 -0.6674 -1.1747

36 -0.1288 -0.1178 -0.0790 -0.4551 -0.3826 -0.6518 -1.1630

37 -0.1725 -0.1011 -0.0971 -0.4701 -0.3783 -0.6394 -1.1433

38 -0.1600 -0.0509 -0.1095 -0.4803 -0.3766 -0.6349 -1.1368

39 -0.1176 -0.0051 -0.1027 -0.4783 -0.3762 -0.6295 -1.1331

40 -0.0028 -0.0155 -0.0932 -0.4719 -0.3850 -0.6260 -1.1308

41 -0.4650 0.0047 -0.0764 -0.4670 -0.4002 -0.6218 -1.1260

42 -0.2695 -0.0025 -0.0674 -0.4617 -0.4183 -0.6147 -1.1277

43 -0.1543 -0.0515 -0.0710 -0.4644 -0.4366 -0.6029 -1.1243

44 -0.1421 -0.1076 -0.0773 -0.4710 -0.4564 -0.5926 -1.1198

45 -0.3264 -0.1096 -0.0783 -0.4772 -0.4689 -0.5853 -1.1181

46 -0.5133 -0.1540 -0.0860 -0.4829 -0.4764 -0.5810 -1.1090

47 -0.6942 -0.1658 -0.0801 -0.4810 -0.4803 -0.5812 -1.0944

48 -0.7313 -0.1501 -0.0630 -0.4652 -0.4747 -0.5771 -1.0710

49 -0.6572 -0.1293 -0.0643 -0.4489 -0.4645 -0.5773 -1.0503

50 -0.4452 -0.1447 -0.0716 -0.4394 -0.4465 -0.5739 -1.0203

51 -0.6351 -0.1368 -0.0742 -0.4307 -0.4311 -0.5652 -1.0003

52 -0.4326 -0.1697 -0.0858 -0.4319 -0.4173 -0.5603 -0.9932

53 -0.3253 -0.1773 -0.0885 -0.4397 -0.4193 -0.5626 -0.9987

54 -0.2734 -0.1569 -0.0870 -0.4515 -0.4273 -0.5607 -1.0047

55 -0.4332 -0.1069 -0.0928 -0.4633 -0.4436 -0.5581 -1.0251

56 -0.7843 -0.0642 -0.1065 -0.4748 -0.4568 -0.5633 -1.0482

57 -1.1473 0.0179 -0.1061 -0.4704 -0.4715 -0.5612 -1.0584

58 -1.1373 0.0686 -0.1184 -0.4668 -0.4748 -0.5544 -1.0575

In

Constant Receipt Married Married Graduate College Graduate School

Social

Security

Age + Benefit

Coefficient

for Marital Statusa

Education Levelb

Never School Some

High

College
61

Continued

1910 1920 1930 1940 1950 1960 1970 1980

0.0430 0.0009 1.3588 -0.0145 -0.1120 -0.1746

0.0699 0.0008 0.8002 -0.0068 -0.1322 -0.4354

0.0733 0.0000 0.4611 -0.0103 -0.0210 -0.2732

0.0811 -0.0013 0.2930 -0.0153 0.0647 -0.1317

0.0871 -0.0028 0.1523 -0.0177 0.0751 -0.0763

0.0833 -0.0041 0.0511 -0.0278 0.0827 0.2153

0.0882 -0.0057 -0.0701 -0.0246 0.0371 0.1177

0.0948 -0.0078 -0.2037 -0.0201 -0.0100 -0.0044

0.1000 -0.0100 -0.3288 -0.0165 -0.0430 -0.1139

0.1082 -0.0075 -0.4849 -0.0178 -0.0615 -0.1584

0.1128 -0.0089 -0.5928 -0.0164 -0.0591 -0.1884

0.1223 -0.0101 -0.6787 -0.0166 -0.0320 -0.1604

0.1339 -0.0109 -0.7447 -0.0170 -0.0024 -0.1099

0.1437 -0.0113 -0.8005 -0.0149 -0.0099 -0.1049

0.1507 -0.0113 -0.8423 -0.0107 -0.0533 -0.1461

0.1447 -0.0112 -0.8788 -0.0177 0.1674 0.1310

0.1465 -0.0109 -0.9117 -0.0119 0.1514 0.0367

0.1506 -0.0108 -0.9397 -0.0061 0.1352 -0.0553

0.1523 -0.0109 -0.9565 -0.0021 0.1181 -0.1275

0.1513 -0.0109 -0.9750 -0.0001 0.1059 -0.1732

0.1504 -0.0109 -0.9847 0.0004 0.0927 -0.1995

0.1497 -0.0108 -0.9929 -0.0004 0.0857 -0.2046

0.1489 -0.0104 -0.9953 -0.0010 0.0685 -0.2085

0.1483 -0.0098 -0.9950 -0.0003 0.0364 -0.2265

0.1470 -0.0093 -0.9832 0.0019 -0.0171 -0.2666

0.1310 -0.0089 -0.9657 -0.0165 0.2294 0.2927

0.1238 -0.0085 -0.9459 -0.0138 0.2363 0.2323

0.1211 -0.0081 -0.9256 -0.0109 0.2329 0.1728

0.1218 -0.0078 -0.8990 -0.0085 0.2297 0.1266

0.1258 -0.0074 -0.8781 -0.0064 0.2179 0.0975

0.1281 -0.0071 -0.8563 -0.0037 0.1945 0.0578

0.1303 -0.0070 -0.8388 -0.0007 0.1571 0.0125

0.1319 -0.0069 -0.8180 0.0018 0.1218 -0.0239

0.1331 -0.0068 -0.8160 0.0047 0.0718 -0.0718

0.1346 -0.0069 -0.8063 0.0068 0.0274 -0.1161

0.1278 -0.0066 -0.8010 -0.0040 0.1428 0.2043

0.1281 -0.0063 -0.7820 -0.0019 0.1474 0.1480

0.1307 -0.0059 -0.7583 0.0025 0.1410 0.0597

0.1349 -0.0056 -0.7085 0.0071 0.1216 -0.0312

0.1391 -0.0050 -0.6566 0.0110 0.1056 -0.1006

0.1449 -0.0047 -0.5972 0.0129 0.1030 -0.1262

0.1518 -0.0042 -0.5268 0.0122 0.1138 -0.0965

0.1559 -0.0038 -0.4412 0.0104 0.1230 -0.0663

Unemployment FTE Full Birth Cohort (By first year of cohort)

Rate Earnings Time Trend
62

Table 9.

Continued

Source: Congressional Budget Office.

Notes: Coefficient values shown in bold indicate statistical significance at the 5 percent level. 

FTE = full-time equivalent. 

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

Age

Group

59 -0.5469 0.0734 -0.1162 -0.4554 -0.4678 -0.5456 -1.0474

60 0.3616 0.1509 -0.0966 -0.4391 -0.4595 -0.5444 -1.0173

61 -2.6413 0.1448 -0.0560 -0.4139 -0.4468 -0.5238 -0.9555

62 -2.6971 0.2558 -0.0440 -0.4085 -0.4355 -0.5099 -0.8999

63 -2.8013 0.3686 -0.0349 -0.4092 -0.4285 -0.4998 -0.8483

64 -2.9840 0.4893 -0.0391 -0.4136 -0.4188 -0.4872 -0.7935

65 -3.1829 0.6020 -0.0335 -0.4067 -0.4198 -0.4624 -0.7408

66 -3.2884 0.6913 -0.0618 -0.4270 -0.4251 -0.4622 -0.7213

67 4.8393 0.8759 -0.0930 -0.4613 -0.4020 -0.4380 -0.6381

68 3.1791 0.8280 -0.1445 -0.4718 -0.3659 -0.4073 -0.5824

69 0.7337 0.7661 -0.2214 -0.4982 -0.3459 -0.3752 -0.5366

70 -1.8989 -0.1713 -0.2507 -0.4850 -0.1310 -0.0548 -0.4556

Coefficient

for

Social Education Levelb

Security Marital Statusa High

Age + Benefit Never School Some College In

Constant Receipt Married Married Graduate College Graduate School
63

1910 1920 1930 1940 1950 1960 1970 1980

0.1567 -0.0033 -0.3488 0.0084 0.1254 -0.0575

0.1546 -0.0029 -0.2328 0.0055 0.1323 -0.0328

0.1386 -0.0024 -0.0715 -0.0176 0.2179 0.5362

0.1196 -0.0020 0.1146 -0.0256 0.2615 0.6178

0.1060 -0.0017 0.3035 -0.0329 0.3015 0.6889

0.1010 -0.0013 0.4782 -0.0368 0.3127 0.7054

0.0961 -0.0013 0.6202 -0.0390 0.3271 0.6879

0.0888 -0.0013 0.6866 -0.0417 0.3701 0.6926

0.0697 -0.0015 0.6742 -0.0189 0.1239 0.1297

0.0686 -0.0020 0.6247 -0.0241 0.2284 0.2896

0.0633 -0.0027 0.5787 -0.0295 0.3573 0.4602

0.1266 -0.0050 0.4860 0.0054 -0.3046 -0.1741

Unemployment FTE Full Birth Cohort (By first year of cohort)

Rate Earnings Time Trend
64

Table 10.

Unemployment Logit Coefficients for Women

Age

Group

16 -1.8968 0.0601 0.6272 0.6060 -0.4712 0.1949 0.0384

17 -2.0872 0.0989 -0.3728 -0.2313 -0.3647 0.1088 0.0432

18 0.0807 0.1129 -0.4406 -0.3526 -0.5211 0.0678 0.0511

19 0.7332 0.1194 -0.4104 -0.4019 -0.5894 0.0766 0.0484

20 0.6486 0.1151 -0.3625 -0.4176 -0.5896 0.0958 0.0489

21 0.6985 0.1182 -0.3406 -0.4289 -0.5788 0.1053 0.0485

22 0.7408 0.1366 -0.3125 -0.4385 -0.5703 0.1039 0.0532

23 0.7930 0.1468 -0.2966 -0.4543 -0.5760 0.0933 0.0584

24 0.7976 0.1661 -0.2854 -0.4677 -0.5341 0.0752 0.0610

25 0.7594 0.1751 -0.2122 -0.4224 -0.4163 -0.6730 -0.5839 0.0141 0.0646

26 0.8040 0.1487 -0.2017 -0.4338 -0.4288 -0.6366 -0.6326 -0.0160 0.0638

27 0.6827 0.1046 -0.1921 -0.4390 -0.4296 -0.6087 -0.6717 -0.0416 0.0638

28 0.4462 0.0604 -0.1870 -0.4455 -0.4219 -0.5895 -0.6992 -0.0644 0.0674

29 0.2339 0.0309 -0.1842 -0.4544 -0.4188 -0.5754 -0.7193 -0.0836 0.0725

30 0.0801 0.0154 -0.1794 -0.4585 -0.4131 -0.5654 -0.7326 -0.0948 0.0788

31 -0.2524 0.0139 -0.1690 -0.4591 -0.4070 -0.5531 -0.7378 -0.1002 0.0755

32 -0.2340 0.0078 -0.1594 -0.4633 -0.4061 -0.5438 -0.7408 -0.1049 0.0807

33 -0.1811 0.0412 -0.1454 -0.4634 -0.4100 -0.5398 -0.7481 -0.1017 0.0851

34 -0.1245 0.0814 -0.1344 -0.4660 -0.4133 -0.5452 -0.7635 -0.0883 0.0899

35 -0.0793 0.1115 -0.1245 -0.4724 -0.4229 -0.5550 -0.7913 -0.0778 0.0894

36 -0.0060 0.1481 -0.1215 -0.4835 -0.4314 -0.5657 -0.8149 -0.0694 0.0845

37 0.0048 0.1932 -0.1230 -0.4915 -0.4374 -0.5796 -0.8376 -0.0558 0.0792

38 0.0475 0.2167 -0.1233 -0.4965 -0.4423 -0.5916 -0.8523 -0.0473 0.0766

39 0.0166 0.2252 -0.1266 -0.4916 -0.4514 -0.6032 -0.8678 -0.0443 0.0748

40 -0.0483 0.2323 -0.1305 -0.4821 -0.4561 -0.6053 -0.8607 -0.0325 0.0766

41 -0.6225 0.2211 -0.1298 -0.4653 -0.4643 -0.6109 -0.8538 -0.0166 0.0706

42 -0.7140 0.1945 -0.1297 -0.4513 -0.4733 -0.6137 -0.8445 -0.0102 0.0729

43 -0.7485 0.1721 -0.1350 -0.4393 -0.4823 -0.6196 -0.8449 0.0012 0.0802

44 -0.7778 0.1597 -0.1304 -0.4348 -0.4799 -0.6119 -0.8199 0.0031 0.0898

45 -0.6724 0.1556 -0.1282 -0.4351 -0.4819 -0.6131 -0.8184 -0.0021 0.0931

46 -0.5732 0.1595 -0.1280 -0.4350 -0.4800 -0.6017 -0.8187 -0.0010 0.0932

47 -0.4378 0.1751 -0.1322 -0.4343 -0.4794 -0.5926 -0.8229 0.0319 0.0921

48 -0.5390 0.1700 -0.1360 -0.4342 -0.4811 -0.5806 -0.8157 0.0711 0.0912

49 -0.4375 0.1458 -0.1460 -0.4340 -0.4931 -0.5781 -0.8269 0.1264 0.0851

50 -0.3641 0.1021 -0.1494 -0.4276 -0.5091 -0.5817 -0.8293 0.1797 0.0820

51 -0.1839 0.0711 -0.1500 -0.4215 -0.5322 -0.6009 -0.8380 0.2407 0.0791

52 -0.1264 0.0386 -0.1430 -0.4113 -0.5488 -0.6159 -0.8429 0.2573 0.0768

53 -0.1292 0.0429 -0.1435 -0.4050 -0.5641 -0.6275 -0.8515 0.2591 0.0730

54 -0.2975 0.0871 -0.1448 -0.3839 -0.5789 -0.6402 -0.8612 0.2375 0.0757

55 -0.4488 0.1438 -0.1532 -0.3703 -0.5799 -0.6371 -0.8631 0.2177 0.0754

56 -0.3347 0.1715 -0.1724 -0.3638 -0.5712 -0.6203 -0.8600 0.0745

57 -0.1256 0.1858 -0.1972 -0.3548 -0.5678 -0.6148 -0.8582 0.0751

58 0.3124 0.1777 -0.2182 -0.3389 -0.5595 -0.6143 -0.8607 0.0812

Coefficient

for Marital Statusa

Education Levelb

Graduate Rate Age 6

Unemployment

Constant Receipt Married Married College

Age + Benefit In Under Never School Some College

High Children

Social Number of

Security

Graduate School
65

Continued

1910 1920 1930 1940 1950 1960 1970 1980

-0.0028 1.1932 -0.0147 -0.0946 -0.2713

0.0014 0.6268 -0.0275 -0.0014 -0.0150

0.0004 0.1551 -0.0255 0.0328 0.0187

-0.0014 -0.0346 -0.0230 0.0372 0.0029

-0.0031 -0.1576 -0.0226 0.0352 0.0111

-0.0045 -0.2330 -0.0240 0.0154 0.0671

-0.0060 -0.3144 -0.0216 -0.0136 0.0152

-0.0079 -0.3937 -0.0191 -0.0407 -0.0367

-0.0097 -0.4579 -0.0173 -0.0627 -0.0878

-0.0088 -0.4783 -0.0173 -0.0683 -0.1089

-0.0097 -0.5224 -0.0155 -0.0733 -0.1420

-0.0105 -0.5411 -0.0141 -0.0775 -0.1613

-0.0106 -0.5496 -0.0133 -0.0662 -0.1529

-0.0104 -0.5483 -0.0133 -0.0505 -0.1339

-0.0099 -0.5390 -0.0126 -0.0421 -0.1292

-0.0095 -0.5316 -0.0220 0.1388 0.1609

-0.0091 -0.5245 -0.0199 0.1065 0.1138

-0.0088 -0.5119 -0.0165 0.0680 0.0376

-0.0086 -0.5066 -0.0127 0.0314 -0.0352

-0.0081 -0.5032 -0.0091 -0.0018 -0.1072

-0.0075 -0.5064 -0.0063 -0.0334 -0.1737

-0.0069 -0.5155 -0.0050 -0.0458 -0.2107

-0.0065 -0.5219 -0.0048 -0.0427 -0.2147

-0.0060 -0.5176 -0.0054 -0.0311 -0.2028

-0.0063 -0.5114 -0.0072 -0.0084 -0.1721

-0.0065 -0.4954 -0.0223 0.1556 0.2565

-0.0068 -0.4775 -0.0207 0.1310 0.2176

-0.0068 -0.4611 -0.0175 0.0906 0.1517

-0.0070 -0.4561 -0.0137 0.0543 0.0876

-0.0064 -0.4585 -0.0106 0.0261 0.0428

-0.0061 -0.4635 -0.0089 0.0109 0.0208

-0.0057 -0.4630 -0.0094 0.0177 0.0390

-0.0056 -0.4678 -0.0114 0.0444 0.0930

-0.0056 -0.4616 -0.0137 0.0699 0.1385

-0.0060 -0.4437 -0.0153 0.0916 0.1699

-0.0060 -0.4317 -0.0116 -0.0264 0.0522

-0.0062 -0.4197 -0.0129 -0.0043 0.0931

-0.0062 -0.4010 -0.0141 0.0191 0.1292

-0.0061 -0.3798 -0.0122 0.0056 0.1051

-0.0057 -0.3609 -0.0098 -0.0163 0.0630

-0.0058 -0.3280 -0.0064 -0.0530 -0.0127

-0.0059 -0.2947 -0.0034 -0.0881 -0.0911

-0.0058 -0.2486 -0.0009 -0.1171 -0.1496

Trend

Birth Cohort (By first year of cohort)

Earnings Time

FTE Full
66

Table 10.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level. 

FTE = full-time equivalent. 

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

Age

Group

59 0.9654 0.1609 -0.2301 -0.3412 -0.5429 -0.6038 -0.8592 0.0862

60 1.9182 0.2040 -0.2224 -0.3359 -0.5363 -0.6111 -0.8744 0.0946

61 -2.5056 0.1093 -0.2139 -0.3178 -0.5264 -0.6285 -0.8678 0.0894

62 -2.5841 0.1821 -0.1852 -0.2988 -0.5134 -0.6372 -0.8617 0.0771

63 -2.6696 0.2784 -0.1174 -0.2706 -0.4945 -0.6530 -0.8449 0.0610

64 -2.8709 0.3995 -0.0616 -0.2382 -0.4695 -0.6778 -0.8225 0.0541

65 -3.0383 0.5170 -0.0167 -0.2108 -0.4296 -0.6599 -0.7657 0.0403

66 -3.1754 0.6130 0.0215 -0.2027 -0.3876 -0.6251 -0.7279 0.0258

67 0.7527 0.7142 0.0523 -0.2253 -0.3370 -0.5993 -0.6889 0.0109

68 0.3844 0.6797 -0.0018 -0.2769 -0.3001 -0.5276 -0.6703 0.0109

69 0.5671 0.6330 -0.0252 -0.3231 -0.2788 -0.4532 -0.6672 0.0114

70 -3.6074 0.0035 -0.1546 -0.4972 -0.2494 -0.5345 -0.4797 0.0528

for

Coefficient Social Education Levelb Number of

Security Marital Statusa High Children

Age + Benefit Never School Some College In Under Unemployment

Constant Receipt Married Married Graduate College Graduate School Age 6 Rate
67

1910 1920 1930 1940 1950 1960 1970 1980

-0.0053 -0.1987 -0.0015 -0.1095 -0.1526

-0.0049 -0.1350 -0.0021 -0.0949 -0.1482

-0.0041 -0.0556 -0.0218 0.2017 0.3350

-0.0028 0.0617 -0.0257 0.2190 0.3798

-0.0017 0.2029 -0.0301 0.2405 0.4252

-0.0008 0.3589 -0.0302 0.2455 0.4120

0.0000 0.5133 -0.0288 0.2286 0.3455

0.0007 0.6380 -0.0271 0.2202 0.2605

0.0008 0.6884 -0.0093 0.0349 -0.1848

0.0006 0.6837 -0.0110 0.0963 -0.1507

0.0004 0.6897 -0.0129 0.1719 -0.1204

-0.0003 0.5265 -0.0119 0.0133 0.2696

Trend

FTE Full Birth Cohort (By first year of cohort)

Earnings Time
68

Table 11.

Earnings Coefficients for Men

Continued

Age

16 -0.3538 0.2820

17 -0.3640 0.2989

18 -0.3037 0.2473

19 -0.2880 0.2484 -0.2196

20 -0.2394 0.2352 -0.2290

21 -0.1717 0.2071 -0.2632

22 -0.1245 0.1992 -0.2663 -0.8048

23 -0.0694 0.1836 -0.2515 -0.8113

24 -0.0318 0.1594 -0.2325 -0.7516

25 0.0490 0.1405 -0.2036 -0.7174

26 0.0708 0.1460 -0.1744 -0.6677 Pre-1940s Cohort 10.2672

27 0.1050 0.1150 -0.1670 -0.6470 1940s Cohort 10.1515

28 0.1215 0.0937 -0.1585 -0.6028 1950s Cohort 10.0114

29 0.1415 0.1075 -0.1355 -0.5519 1960s Cohort 9.8756

30 0.1495 0.0973 -0.1197 -0.5342 1970s Cohort 9.7764

31 0.1583 0.0839 -0.1091 -0.5018 Pre-1940s×College Graduate 0.6328

32 0.1604 0.0905 -0.0923 -0.4526 1940s×College Graduate 0.7156

33 0.1698 0.0902 -0.1001 -0.4419 1950s×College Graduate 0.8312

34 0.1784 0.0839 -0.0826 -0.4144 1960s×College Graduate 0.9744

35 0.1900 0.0823 -0.0802 -0.3622 1970s×College Graduate 1.0479

36 0.2061 0.0624 -0.0830 -0.3595 Pre-1940s×Some College 0.1605

37 0.2025 0.0348 -0.0683 -0.3413 1940s×Some College 0.1878

38 0.2035 0.0690 -0.0629 -0.3195 1950s×Some College 0.2254

39 0.2068 0.0573 -0.0668 -0.3044 1960s×Some College 0.2650

40 0.2024 0.0448 -0.0563 -0.2888 1970s×Some College 0.2868

41 0.1874 0.0395 -0.0508 -0.2556 Pre-1940s×LSHS -0.3063

42 0.1778 0.0719 -0.0398 -0.2528 1940s×LSHS -0.3596

43 0.1882 0.0195 -0.0741 -0.2521 1950s×LSHS -0.3778

44 0.1918 0.0111 -0.0546 -0.2538 1960s×LSHS -0.3805

45 0.1620 0.0435 -0.0386 -0.2187 1970s×LSHS -0.3629

46 0.1676 0.0444 -0.0545 -0.1985 OASDI Status -0.2294

47 0.1792 0.0199 -0.0556 -0.2318 OASDI×College Graduate -0.1060

48 0.1772 0.0228 -0.0448 -0.2141 OASDI×Some College 0.0123

49 0.1625 0.0198 -0.0236 -0.2045 OASDI×LSHS 0.0713

50 0.1348 0.0398 -0.0111 -0.1858

51 0.1333 0.0468 -0.0221 -0.1611

52 0.1367 0.0383 -0.0267 -0.1903

53 0.1366 0.0306 -0.0278 -0.1796

54 0.1156 0.0482 -0.0314 -0.1597

55 0.0998 0.0558 -0.0368 -0.1538

56 0.1028 0.0590 -0.0493 -0.1624

57 0.0824 0.0722 -0.0215 -0.1354

58 0.0560 0.0961 -0.0149 -0.1268

Coefficients on 

Additional Control Variables

Education Levela

Coefficient

Age 

Some College,

Interacted with

Age

Less Than

High School,

Interacted with

College

Graduate,

Interacted with

Age Age
69

Table 11.

Continued

Source: Congressional Budget Office.

Notes: Coefficient values shown in bold indicate statistical significance at the 5 percent level. 

LSHS = less than high school; OASDI = Old-Age, Survivors, and Disability Insurance. 

a. The omitted category includes high school graduates.

Age

59 0.0732 0.0654 -0.0304 -0.1448

60 0.0622 0.0599 -0.0435 -0.1390

61 0.0315 0.0877 -0.0119 -0.1413

62 0.0615 0.0899 -0.0441 -0.1413

63 0.0806 0.0441 -0.0950 -0.1260

64 0.0612 0.0726 -0.0656 -0.1151

65 0.0843 0.0204 -0.0786 -0.1393

66 0.0456 0.0341 -0.0221 -0.0478

67 -0.0172 0.0507 -0.0451 0.0375

68 -0.0186 0.0675 -0.0029 0.0112

69 -0.0472 0.0755 0.0150 -0.0125

70 0.0061 -0.0399 0.0691 -0.0371

Education Levela

Less Than College

High School, Some College, Graduate,

Age Interacted with Interacted with Interacted with

Coefficient Age Age Age
70

Table 12.

Earnings Coefficients for Women

Continued

Age

16 -0.3573 0.3348

17 -0.4022 0.3998

18 -0.3495 0.3290

19 -0.3615 0.3068 -0.1278

20 -0.3281 0.3057 -0.1250

21 -0.2894 0.3046 -0.1109

22 -0.2434 0.2424 -0.1376 -0.4161

23 -0.2181 0.2505 -0.0979 -0.3808

24 -0.1840 0.2353 -0.0820 -0.3421

25 -0.1394 0.1978 -0.0567 -0.2816 Pre-1940s Cohort 9.9085

26 -0.1162 0.1949 -0.0462 -0.2560 1940s Cohort 9.8982

27 -0.1076 0.1981 -0.0193 -0.2012 1950s Cohort 9.8797

28 -0.0993 0.1912 -0.0209 -0.1586 1960s Cohort 9.8253

29 -0.0795 0.1577 -0.0147 -0.1378 1970s Cohort 9.7467

30 -0.0752 0.1749 0.0031 -0.1127 Pre-1940s×College Graduate 0.3760

31 -0.0894 0.1935 0.0102 -0.0681 1940s×College Graduate 0.4824

32 -0.0818 0.1558 0.0092 -0.0496 1950s×College Graduate 0.5520

33 -0.0848 0.1535 0.0077 -0.0251 1960s×College Graduate 0.6543

34 -0.0815 0.1657 0.0096 -0.0191 1970s×College Graduate 0.7269

35 -0.0816 0.1706 0.0294 0.0097 Pre-1940s×Some College 0.1231

36 -0.0694 0.1534 0.0137 0.0027 1940s×Some College 0.1746

37 -0.0574 0.1489 0.0165 -0.0010 1950s×Some College 0.1857

38 -0.0615 0.1530 0.0259 0.0027 1960s×Some College 0.1877

39 -0.0649 0.1643 0.0321 0.0079 1970s×Some College 0.1964

40 -0.0637 0.1369 0.0199 0.0058 Pre-1940s×LSHS -0.3510

41 -0.0575 0.1289 0.0215 0.0172 1940s×LSHS -0.3830

42 -0.0585 0.1211 0.0269 0.0218 1950s×LSHS -0.4246

43 -0.0588 0.1376 0.0336 0.0211 1960s×LSHS -0.4378

44 -0.0668 0.1508 0.0328 0.0387 1970s×LSHS -0.4096

45 -0.0468 0.1324 0.0197 0.0370 OASDI Status -0.1526

46 -0.0588 0.1147 0.0152 0.0458 OASDI×College Graduate 0.0135

47 -0.0528 0.1205 0.0136 0.0473 OASDI×Some College 0.0091

48 -0.0407 0.1171 0.0057 0.0271 OASDI×LSHS 0.0808

49 -0.0447 0.1156 0.0142 0.0550

50 -0.0446 0.1153 0.0251 0.0461

51 -0.0378 0.0922 0.0328 0.0487

52 -0.0588 0.1127 0.0407 0.0898

53 -0.0547 0.1219 0.0505 0.1036

54 -0.0516 0.1242 0.0391 0.0943

55 -0.0738 0.1201 0.0429 0.1098

56 -0.0580 0.1097 0.0471 0.0940

57 -0.0650 0.1246 0.0356 0.0988

58 -0.0746 0.1300 0.0502 0.1033

Coefficients on 

Additional Control Variables

Age

Some College,

Interacted with

Age

Education Levela

Coefficient

Age 

Less Than

High School,

Interacted with

Age

College

Graduate,

Interacted with
71

Table 12.

Continued

Source: Congressional Budget Office.

Notes: Coefficient values shown in bold indicate statistical significance at the 5 percent level.

LSHS = less than high school; OASDI = Old-Age, Survivors, and Disability Insurance. 

a. The omitted category includes high school graduates.

Age

59 -0.0781 0.1398 0.0448 0.1348

60 -0.0459 0.0958 0.0659 0.0687

61 -0.0513 0.0983 0.0560 0.1011

62 -0.0111 0.0860 0.0202 0.0466

63 0.0419 0.0524 -0.0209 0.0073

64 -0.0258 0.0912 0.0736 0.1195

65 0.0241 0.0593 0.0027 0.0576

66 -0.0148 0.1087 0.0637 0.1157

67 0.0161 0.0755 0.0568 0.1085

68 0.0118 0.0664 -0.0035 0.0503

69 -0.0722 0.0605 0.1427 0.0308

70 -0.0405 0.0676 0.0478 -0.0325

Education Levela

Less Than College

High School, Some College, Graduate,

Age Interacted with Interacted with Interacted with

Coefficient Age Age Age
72

Table 13.

Annual Earnings Distribution for 50- to 60-Year-Old Men, 

by Lifetime Earnings Decile

(Percent)

Continued

1 2 3 4 5 6 7 8 9 10

1 3.38 1.84 1.03 0.88 0.51 0.52 0.60 0.53 0.42 0.29

2 3.11 2.32 1.49 0.83 0.60 0.49 0.36 0.44 0.23 0.12

3 1.90 2.16 1.90 1.32 0.86 0.68 0.48 0.30 0.23 0.16

4 0.81 1.56 2.03 1.72 1.49 0.91 0.60 0.45 0.30 0.12

5 0.31 1.01 1.44 1.81 1.95 1.50 0.95 0.60 0.27 0.16

6 0.17 0.50 1.10 1.49 1.96 1.90 1.44 0.88 0.41 0.15

7 0.19 0.30 0.58 1.01 1.33 1.82 2.04 1.60 0.84 0.29

8 0.05 0.14 0.29 0.60 0.93 1.43 1.97 2.23 1.85 0.51

9 0.04 0.09 0.15 0.30 0.26 0.65 1.12 2.23 3.37 1.79

10 0.03 0.06 0.02 0.04 0.10 0.11 0.42 0.75 2.08 6.41

1 2.72 2.12 1.73 1.17 0.86 0.43 0.42 0.32 0.16 0.05

2 1.97 1.92 1.78 1.59 0.99 0.80 0.48 0.27 0.17 0.06

3 1.49 1.40 1.76 1.68 1.20 1.03 0.74 0.46 0.19 0.08

4 1.19 1.28 1.35 1.47 1.46 1.25 0.97 0.61 0.31 0.08

5 0.87 1.03 1.04 1.26 1.58 1.43 1.18 0.99 0.49 0.15

6 0.71 0.75 0.89 1.01 1.33 1.42 1.48 1.26 0.90 0.23

7 0.47 0.57 0.65 0.93 1.19 1.40 1.58 1.52 1.29 0.43

8 0.32 0.53 0.45 0.53 0.76 1.18 1.50 1.86 1.79 1.09

9 0.21 0.33 0.25 0.29 0.47 0.82 1.20 1.86 2.57 2.00

10 0.05 0.07 0.12 0.07 0.18 0.25 0.45 0.85 2.14 5.82

1 5.41 1.87 1.07 0.69 0.38 0.24 0.14 0.11 0.04 0.03

2 2.55 3.05 1.87 1.18 0.68 0.38 0.19 0.06 0.06 0.01

3 0.87 2.20 2.20 1.77 1.25 0.85 0.47 0.27 0.09 0.02

4 0.45 1.34 1.85 1.76 1.69 1.27 0.83 0.52 0.24 0.05

5 0.25 0.74 1.32 1.66 1.70 1.73 1.23 0.87 0.43 0.07

6 0.22 0.32 0.80 1.27 1.67 1.70 1.75 1.32 0.78 0.18

7 0.11 0.21 0.50 0.97 1.32 1.65 1.88 1.62 1.35 0.40

8 0.07 0.15 0.26 0.44 0.87 1.35 1.73 2.14 2.08 0.92

9 0.07 0.09 0.12 0.21 0.35 0.69 1.37 2.13 2.74 2.25

10 0.02 0.04 0.02 0.05 0.09 0.13 0.41 0.98 2.20 6.09

Annual 

Earnings 

Decile

2040

Lifetime Earnings Decile

2000

2010
73

Table 13.

Continued

(Percent)

Source: Congressional Budget Office.

1 2 3 4 5 6 7 8 9 10

1 5.25 2.14 1.13 0.61 0.31 0.24 0.17 0.05 0.07 0.02

2 2.43 2.64 1.98 1.36 0.71 0.38 0.30 0.12 0.06 0.02

3 0.95 2.12 2.20 1.89 1.32 0.79 0.39 0.24 0.08 0.01

4 0.49 1.27 1.69 1.77 1.73 1.25 1.02 0.55 0.20 0.04

5 0.33 0.82 1.38 1.51 1.78 1.44 1.33 0.90 0.39 0.10

6 0.22 0.47 0.78 1.23 1.50 1.77 1.74 1.31 0.76 0.25

7 0.19 0.29 0.47 0.77 1.22 1.68 1.66 1.78 1.45 0.48

8 0.08 0.14 0.24 0.53 0.85 1.38 1.67 2.09 1.98 1.03

9 0.05 0.08 0.10 0.26 0.48 0.83 1.25 1.95 2.78 2.23

10 0.00 0.04 0.03 0.06 0.10 0.23 0.49 1.00 2.24 5.82

2070

Lifetime Earnings Decile Annual 

Earnings 

Decile
74

Table 14.

Annual Earnings Distribution for 50- to 60-Year-Old 

Women, by Lifetime Earnings Decile

(Percent)

Continued

1 2 3 4 5 6 7 8 9 10

1 3.75 1.77 1.10 0.74 0.65 0.54 0.41 0.34 0.30 0.37

2 2.83 2.39 1.63 0.98 0.71 0.58 0.28 0.26 0.17 0.20

3 1.67 2.48 2.10 1.45 0.78 0.48 0.48 0.22 0.23 0.11

4 0.89 1.58 1.64 1.65 1.44 1.06 0.80 0.47 0.28 0.16

5 0.46 1.01 1.65 1.77 1.68 1.34 1.02 0.55 0.31 0.21

6 0.23 0.37 0.97 1.62 1.79 1.90 1.61 0.88 0.46 0.16

7 0.11 0.26 0.50 0.98 1.58 1.49 1.85 1.85 1.13 0.25

8 0.04 0.09 0.26 0.51 0.84 1.50 1.74 2.20 2.13 0.70

9 0.02 0.04 0.11 0.26 0.36 0.81 1.38 2.03 2.86 2.13

10 0.00 0.01 0.02 0.04 0.19 0.28 0.44 1.19 2.14 5.71

1 2.79 1.99 1.42 1.21 1.02 0.60 0.41 0.34 0.12 0.07

2 2.01 1.59 1.62 1.53 1.17 0.94 0.60 0.32 0.18 0.07

3 1.45 1.40 1.61 1.55 1.30 1.04 0.74 0.60 0.22 0.10

4 1.09 1.21 1.35 1.33 1.45 1.26 1.11 0.79 0.28 0.10

5 0.91 1.07 1.18 1.46 1.35 1.34 1.17 0.80 0.58 0.17

6 0.73 1.02 0.96 0.93 1.14 1.30 1.41 1.18 0.99 0.32

7 0.49 0.79 0.79 0.78 1.18 1.29 1.40 1.42 1.31 0.56

8 0.33 0.51 0.52 0.71 0.84 1.04 1.46 1.69 1.83 1.07

9 0.14 0.34 0.37 0.37 0.41 0.90 1.17 1.82 2.38 2.12

10 0.05 0.09 0.15 0.14 0.15 0.29 0.54 1.06 2.11 5.43

1 4.87 2.22 1.14 0.68 0.45 0.31 0.16 0.11 0.03 0.02

2 2.38 2.52 1.86 1.23 0.98 0.54 0.27 0.13 0.09 0.01

3 1.15 1.98 2.07 1.70 1.36 0.82 0.46 0.32 0.10 0.03

4 0.63 1.19 1.69 1.76 1.60 1.48 0.95 0.50 0.17 0.03

5 0.42 0.82 1.25 1.49 1.68 1.52 1.27 0.92 0.52 0.09

6 0.20 0.54 0.88 1.21 1.48 1.74 1.49 1.27 0.88 0.32

7 0.17 0.39 0.63 1.01 1.01 1.40 1.82 1.70 1.37 0.51

8 0.13 0.17 0.31 0.63 0.78 1.23 1.74 2.06 1.93 1.04

9 0.02 0.14 0.14 0.22 0.50 0.74 1.33 1.94 2.60 2.36

10 0.02 0.04 0.04 0.05 0.13 0.26 0.51 1.05 2.31 5.58

Annual 

Earnings 

Decile

Lifetime Earnings Decile

2000

2010

2040
75

Table 14.

Continued

(Percent)

Source: Congressional Budget Office.

1 2 3 4 5 6 7 8 9 10

1 4.71 2.16 1.22 0.76 0.52 0.24 0.18 0.11 0.05 0.03

2 2.44 2.63 1.77 1.21 0.78 0.64 0.30 0.15 0.05 0.02

3 1.16 1.91 2.02 1.78 1.36 0.89 0.48 0.27 0.11 0.02

4 0.61 1.19 1.77 1.72 1.55 1.29 1.14 0.54 0.17 0.03

5 0.47 0.86 1.20 1.49 1.60 1.51 1.31 0.98 0.49 0.09

6 0.24 0.54 0.86 1.14 1.54 1.66 1.55 1.35 0.85 0.26

7 0.18 0.35 0.55 0.97 1.29 1.51 1.69 1.75 1.31 0.41

8 0.08 0.21 0.30 0.63 0.69 1.26 1.62 1.92 2.17 1.11

9 0.08 0.10 0.27 0.20 0.50 0.72 1.31 1.85 2.64 2.33

10 0.02 0.04 0.06 0.08 0.16 0.27 0.43 1.09 2.14 5.71

Annual 

Earnings 

Decile

Lifetime Earnings Decile

2070
76
77

Appendix A: Appendix A: March Current Population Survey Data 

Used in the Analysis

The March Current Population Survey (CPS) collects information on about 60,000 

households each year. The data set contains a wide variety of economic and demographic information on the individual, family, and household. The complete data set 

used to create the inputs and estimate the equations described in the text combines 

annual March CPS data sets from 1976 to 2004 (or calendar years 1975 to 2003) for 

people ages 16 to 90 with annual earnings greater than $590 in real (inflationadjusted) 1993 dollars. This full data set comprises more than 2 million observations 

for the 29-year period. 

Although there are some measurement concerns associated with survey data (nonresponse rates, multiple job holding reporting, topcoded or imputed earnings) as well 

as conceptual concerns (respondent’s report of overtime work, number of hours 

worked, or other sources of income), the CPS is widely used in the economic literature and policy spheres and is regarded as one of the more reliable surveys for labor 

force data. In order to create the demographic, wage, and labor force variables used in 

the equations and inputs, the data were adjusted or categorized in several ways. 

Demographics

The CPS generally collects ultimate educational attainment by number of years in 

school or degree earned; the specific categories have changed over time. For purposes 

of the microsimulation model, ultimate educational attainment is grouped into four 

categories (less than high school, high school graduate, some college, and college graduate), using the more specific CPS definitions. People with missing educational 

attainment are dropped from the sample.

A respondent is categorized as in school if he or she reports current attendance at high 

school or college. This variable is available starting in 1986. Prior to 1986, the model 

assumes the following groups are in school: everyone under age 18; high school graduates 18 and younger; people 20 and younger with some college education; and college graduates 22 and younger. 

For the full sample period, the CPS provides two variables on the number of children 

in the family: children under age 6 or children under age 18. The model uses the 

number of children under age 6 living in the family, because that variable is expected 

to have the greater impact on women’s labor force and earnings behavior. 
78

Wages

Hourly wages are constructed by dividing total earnings by the total number of work 

hours. People with zero work hours are assumed to have an hourly wage of zero. The 

hourly wage is recomputed for respondents with very high or very low hourly wages, 

as follows:

Minimum Wage Restriction

For respondents reporting an hourly wage under $2, the hourly wage is set equal to 

the larger of $2 or total earnings divided by 2,080 hours.

Maximum Wage Restriction

For respondents reporting an hourly wage that exceeds $100, the model first assumes 

that a person’s maximum wage is a function of age. Thus, the maximum wage equals 

75 minus 16 plus the person’s age, capped at 100. The model then assumes people 

work the minimum of 2,080 hours or the number of hours derived by dividing total 

earnings by this maximum wage. The person’s hourly wage is then recomputed.

Unemployment

The number of weeks unemployed and hours unemployed are both used in the calculation of full-time equivalent (FTE) earnings (see below); for consistency, outlying 

values are adjusted.

Weeks Unemployed

The number of weeks unemployed is assumed to be the smaller of reported weeks 

unemployed or 52 minus the number of weeks worked the previous year. 

Hours Unemployed

If the number of work hours and unemployment hours exceed 2,080 and the person 

reports positive unemployment hours, unemployment hours are capped at 2,080 

minus the number of work hours. A floor of one hour of unemployment for these 

respondents is also assumed.

Work Hours

For people reporting positive earnings but no work hours, work hours are assumed to 

equal the average number of work hours by year, age, sex, education, and earnings.1

The hourly wage is then recomputed for those workers based on the imputed number 

of work hours. Respondents for whom average hours do not exist and who fall into a 

demographic cell with average work hours of zero are dropped from the sample. 

1. Earnings are grouped into six categories: $0 to $4,999; $5,000 to $14,999; $15,000 to $29,999; 

$30,000 to $49,999; $50,000 to $99,999; and $100,000 and over.
79

Full-Time Equivalent Earnings

For respondents whose reported earnings are below FTE earnings because of an 

unemployment spell, FTE earnings are constructed as a function of a respondent’s 

reported (or adjusted) work hours, unemployment hours, and total earnings. For simplicity, call the sum of work hours and unemployment hours total hours and the ratio 

of unemployment hours to work hours ratio hours. 

For people with positive unemployment hours: 

FTE earnings = total earnings × (ratio hours + 1).

For people with less than 2,080 but at least 1,750 total hours:

FTE earnings = total earnings × (ratio hours +1) × (2,080/total hours).

For people with total hours less than 1,500:

FTE earnings = total earnings × (ratio hours +1) × (2,080/total hours) × 1.15.

As discussed earlier, full-time workers receive a premium of about 15 percent over 

those who work a higher number of hours but remain part time.

For people with total hours between 1,500 and 1,750, the full-time premium 

adjustment is phased out as total hours worked approaches full time. This adjustment is the product of three components. The first component is ratio hours plus 

one; the second component divides 2,080 by total hours (these two components 

are the same as for the second group). The third component subtracts 1,500 hours 

from total hours and divides by 250; this term is subtracted from one and is multiplied by 0.15 (the hours premium for this group). The resulting term is added to 

one and multiplied by the first two components:

FTE earnings = total earnings × (ratio hours + 1) × (2,080/total hours) × 

(1+0.15×[1-(total hours-1,500)/250]).

81

Appendix B: Appendi x B: CBOLT Representative Sample Data

The Social Security Administration’s Continuous Work History Sample (CWHS) is 

a 1 percent sample of all Social Security numbers ever issued. In addition to taxable 

Old-Age, Survivors, and Disability Insurance (OASDI) wages and total compensation, the CWHS contains information on an individual’s sex, race, and year of birth. 

Data are collected for people with wage and salary earnings subject to Social Security 

taxes, those with earnings not subject to Social Security taxes, and those with both.1

The CWHS is a restricted-use data set; it may not necessarily be a representative sample of the population because it includes a sample of all Social Security numbers ever 

issued, and as a consequence it can include people who have duplicate Social Security 

numbers (for example, if someone replaced his or her original number). 

The CBOLT input from the CWHS contains approximately 300,000 observations in 

each year between 1983 and 2004, the most recent data available. 

The CWHS contains five variables with information about earnings:

B Federal Insurance Contributions Act (FICA) earnings up to the taxable maximum, 

for the years 1951 through 2003. This variable corresponds to total earnings, 

including earnings from self-employment, covered by Social Security.

B Self-Employment Contributions Act portion of earnings, up to the taxable maximum, for the years 1980 through 2003.

B A measure of total compensation for the years 1980 through 2003, including total 

wages and tips. It does not include deferred compensation or self-employment 

earnings but does include earnings not covered by the Social Security system. 

B Medicare earnings collected for the years 1983 through 2003, including Medicare 

earnings from self-employment.

B Medicare earnings from self-employment, collected separately for the years

1991 through 2003. 

Both measures of Medicare earnings (items 4 and 5) are subject to the taxable maximum. When Medicare payroll taxes were first levied in 1966, the same taxable maxi1. Creston Smith, “The Social Security Administration’s Continuous Work History Sample,” Social 

Security Bulletin, vol. 52, no. 10 (1989), pp. 20–28.
82

mum as FICA was applied until 1990. However, beginning in 1991, the Medicare 

taxable maximum became larger than the Social Security maximum. In 1991, the 

Medicare taxable maximum was $125,000, in 1992 it was $130,200, and in 1993

it was $135,000. Since 1994, there has been no limit on the taxable earnings of

Medicare. 

Because the model is concerned with earnings records back to 1984, there is a series of 

imputation procedures to create earnings not recorded because they exceeded the taxable maximum. 

Deferred Compensation

Deferred compensation is defined as the maximum of zero or the difference between 

the sum of Medicare earnings and Medicare self-employment earnings minus total 

compensation. Individuals with total wages between 1.5 and 2 times the average wage 

are assumed to be unaffected by the taxable maximum. For this group of workers, the 

distribution of the ratio of deferred compensation to total wages is computed. 

Each person with positive total compensation and with a level of deferred compensation that exceeds the taxable maximum is then randomly assigned to a percentile 

within the deferred compensation/total wage share distribution. Total wages for those 

workers are then incremented on the basis of the randomly assigned percentile; those 

incremented total wages are used to compute the level of deferred compensation 

above the taxable maximum.

Late Postings

Individuals 62 or younger whose earnings dropped by more than half between the last 

year reported in the CWHS (year t) and the previous year (t-1) are assumed to be late 

postings—people whose earnings were recorded after the CWHS data were collected. 

For those workers, the distribution of wage growth is calculated between years t-2 and 

t-1.2 Workers with late postings are then randomly assigned a percentile within that 

distribution, and an associated level of positive wage growth is then applied to their 

earnings. 

Self-Employment Earnings

Prior to 1993, Medicare earnings from self-employment were subject to the taxable 

maximum; hence, earnings above that level are imputed for self-employed individuals 

in those years. For each individual with earnings at the taxable maximum in any of 

those years, the first step is to check how many of the two previous and two following 

years the worker also had earnings at the taxable maximum. Individuals who hit the 

2. In computing this distribution, individuals for whom income more than doubled from year t-2 to 

year t-1 and individuals without income in year t-2 are not included.
83

taxable maximum in a higher number of surrounding years should have higher 

imputed self-employment earnings. 

For individuals with taxable maximum earnings in four surrounding years, the ratio of 

earnings to average earnings is computed in year t+1 and is applied to the current year 

(t) to impute earnings above the taxable maximum. The calculation proceeds recursively from 1994 to 1984. 

For individuals with fewer than four observations of taxable maximum earnings in 

surrounding years, the strategy is slightly different. Using March CPS data, the portion of individuals with earnings at the taxable maximum is computed in each year. In 

the CWHS, this same statistic is computed for individuals whose earnings reach the 

taxable maximum in the year of observation and in one, two, and three years surrounding the year of observation. 

Next, using the CPS distribution, average earnings for a similar worker are imputed to 

the CWHS. For example, suppose that, in 1960, 60 percent of males earned below 

the taxable maximum, 0.7 percent earned above the taxable maximum but did not do 

so in surrounding years, 1.4 percent earned above the taxable maximum and also did 

so in one surrounding year, 4.6 percent did so in 1960 and in two surrounding years, 

6.3 did so percent in 1960 and in three surrounding years, and 27 percent earned 

above the taxable maximum in 1960 and in four surrounding years. Then an individual with earnings at the taxable maximum in 1960 and in two surrounding years is 

assigned the average earnings for the 62nd-67th percentiles of the CPS distribution of 

workers ages 16 to 90 in that year.3

Earnings Above the Taxable Maximum

The earnings data available between 1951 and 1983 are from the individual’s FICA 

record and, because Social Security taxes were capped at the taxable maximum, earnings above this level must be imputed. The strategy to impute earnings above the taxable maximum is the same as the one used to impute self-employment earnings from 

1984 to 1993.

3. The 62nd percentile is used because 60 percent plus 0.7 percent plus 1.4 percent equals 62.1 percent. Then take the average earnings over the next 5 percentile points.

85

Appendix C:

Labor Force Equations

Used to Model Persistence

This appendix contains estimated coefficients in equations used to implement the 

error-correction mechanism for labor force outcomes as described in the text. Each set 

of results included here has a counterpart in the basic projection modules described in 

the main text. In each case, the only difference in each case is the addition of actual 

earnings as an independent variable, which makes it possible to introduce persistence 

through time. The method used to introduce persistence is described in detail in the 

body of the paper. See the tables that follow for the list of estimated coefficients.
86

Table C-1.

Historical Full-Time Employment Logit Coefficients for Men

Age

Group

16 -3.5081 -0.5115 1.2027 3.4278 -2.7214

17 -7.0808 -0.2746 -0.5601 0.6464 -1.2179

18 -6.6855 -0.5091 -0.4348 0.3100 -1.2825

19 -4.8507 -0.6699 -0.4888 0.1165 -1.4601

20 -3.6736 -0.7699 -0.5123 0.0450 -1.5241

21 -3.5459 -0.8310 -0.5514 0.0287 -1.5612

22 -3.0818 -0.9068 -0.5464 0.0297 -1.5698

23 -2.6572 -0.9859 -0.5289 0.0441 -1.5769

24 -2.4151 -1.0235 -0.4856 0.0738 -1.5547

25 -3.6634 -1.0799 -0.1646 0.2312 -0.0962 -0.9898 -1.4675

26 -3.0461 -1.1095 -0.1323 0.2435 -0.0624 -0.8202 -1.3113

27 -2.3144 -1.1243 -0.1029 0.2542 -0.0329 -0.6696 -1.1673

28 -1.6967 -1.1115 -0.0749 0.2668 -0.0084 -0.5451 -1.0389

29 -1.3175 -1.1276 -0.0617 0.2696 0.0078 -0.4441 -0.9401

30 -1.1454 -1.1212 -0.0625 0.2649 0.0245 -0.3574 -0.8595

31 -1.0842 -1.1738 -0.0597 0.2776 0.0395 -0.2903 -0.7849

32 -0.9464 -1.2127 -0.0623 0.2823 0.0555 -0.2325 -0.7219

33 -0.7177 -1.2690 -0.0652 0.2851 0.0683 -0.1864 -0.6755

34 -0.3751 -1.3085 -0.0641 0.2923 0.0794 -0.1479 -0.6388

35 -0.0774 -1.3406 -0.0617 0.2946 0.0856 -0.1213 -0.6114

36 0.1093 -1.3322 -0.0732 0.2800 0.0824 -0.1013 -0.5902

37 0.2136 -1.3179 -0.0863 0.2717 0.0816 -0.0896 -0.5690

38 0.2176 -1.2759 -0.0964 0.2641 0.0823 -0.0881 -0.5521

39 0.1421 -1.2578 -0.1068 0.2531 0.0893 -0.0902 -0.5317

40 0.0646 -1.2792 -0.1173 0.2522 0.0976 -0.0861 -0.5050

41 0.2318 -1.3331 -0.1068 0.2630 0.1136 -0.0757 -0.4885

42 0.2929 -1.4015 -0.0961 0.2739 0.1224 -0.0672 -0.4855

43 0.2634 -1.5095 -0.0937 0.2762 0.1229 -0.0486 -0.4744

44 0.1924 -1.5997 -0.0846 0.2888 0.1081 -0.0345 -0.4738

45 0.0828 -1.6542 -0.0628 0.2916 0.0944 -0.0264 -0.4773

46 0.0952 -1.6826 -0.0510 0.2913 0.0780 -0.0378 -0.4815

47 0.3129 -1.7198 -0.0445 0.2834 0.0639 -0.0504 -0.4700

48 0.5705 -1.7472 -0.0338 0.2862 0.0563 -0.0755 -0.4661

49 0.9454 -1.7891 -0.0400 0.2725 0.0501 -0.1071 -0.4692

50 1.3505 -1.8399 -0.0589 0.2666 0.0407 -0.1401 -0.4816

51 2.1097 -1.8786 -0.0622 0.2510 0.0293 -0.1449 -0.4822

52 2.5267 -1.8860 -0.0564 0.2432 0.0049 -0.1802 -0.5195

53 2.9702 -1.8723 -0.0437 0.2283 -0.0224 -0.2124 -0.5689

54 3.3269 -1.8565 -0.0043 0.2190 -0.0439 -0.2363 -0.6157

55 3.6342 -1.8451 0.0409 0.1992 -0.0665 -0.2609 -0.6685

56 3.8682 -1.8841 0.0931 0.1796 -0.0848 -0.2853 -0.7322

57 4.0948 -1.9345 0.1226 0.1500 -0.1059 -0.3064 -0.7933

58 4.0254 -2.0174 0.1506 0.1132 -0.1243 -0.3239 -0.8395

Coefficient

for

Constant

Age +

Married Married

Never

College Graduate

College

School

Social

Security Marital Statusa

Graduate

Education Levelb

High

Receipt

Benefit School Some In
87

Continued

1910 1920 1930 1940 1950 1960 1970 1980

1.7884 0.2722 -0.7135 -0.3357

1.3620 0.2802 -0.0398 0.1196

1.2354 0.2702 0.2678 0.3886

1.1638 0.2548 0.4099 0.5233

1.0579 0.2367 0.4708 0.5734

0.9891 0.2258 0.4373 0.8456

0.9003 0.2124 0.4574 0.8524

0.8113 0.1994 0.4700 0.8369

0.7296 0.1878 0.4629 0.7821

0.6015 0.1882 0.3218 0.5475

0.5269 0.1771 0.3072 0.5066

0.4647 0.1667 0.2924 0.4731

0.4040 0.1566 0.2772 0.4388

0.3390 0.1477 0.2662 0.4116

0.2782 0.1403 0.2588 0.3829

0.2188 0.1339 0.0968 0.3596

0.1661 0.1277 0.0946 0.3429

0.1212 0.1223 0.0957 0.3296

0.0933 0.1174 0.0913 0.3120

0.0672 0.1132 0.0869 0.2965

0.0513 0.1097 0.0856 0.2898

0.0384 0.1067 0.0892 0.2841

0.0328 0.1047 0.0914 0.2820

0.0254 0.1028 0.1033 0.2845

0.0280 0.1008 0.1143 0.2863

0.0254 0.0986 0.0125 0.1906

0.0356 0.0970 0.0384 0.2195

0.0437 0.0953 0.0645 0.2510

0.0623 0.0939 0.0926 0.2800

0.0741 0.0926 0.1163 0.3105

0.0887 0.0915 0.1184 0.3281

0.0973 0.0903 0.1092 0.3319

0.1110 0.0892 0.1024 0.3300

0.1009 0.0877 0.0937 0.3319

0.0977 0.0863 0.0860 0.3324

0.0822 0.0845 -0.0066 0.1665

0.0933 0.0833 -0.0032 0.1653

0.1035 0.0818 -0.0087 0.1574

0.1406 0.0807 -0.0140 0.1434

0.1808 0.0801 -0.0193 0.1280

0.2274 0.0797 -0.0208 0.1192

0.2798 0.0791 -0.0260 0.1052

0.3483 0.0785 -0.0279 0.0964

Earnings Birth Cohort (By first year of cohort)

(*1,000)

Unemployment 

Spell
88

Table C-1.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level.

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

Age

Group

59 3.4877 -2.0836 0.1625 0.0748 -0.1374 -0.3339 -0.8808

60 2.7005 -2.1304 0.1769 0.0445 -0.1464 -0.3364 -0.9104

61 0.2230 -2.1880 0.1920 0.0161 -0.1442 -0.3255 -0.9028

62 0.2608 -2.1350 0.2098 0.0043 -0.1375 -0.3086 -0.8822

63 0.2776 -2.0839 0.2411 0.0018 -0.1249 -0.2851 -0.8573

64 0.2568 -2.0204 0.2825 -0.0021 -0.1188 -0.2680 -0.8150

65 0.2035 -1.9465 0.3270 -0.0204 -0.1099 -0.2546 -0.7582

66 0.1452 -1.8795 0.3557 -0.0507 -0.0942 -0.2260 -0.7051

67 1.3416 -1.7343 0.3986 -0.0977 -0.0828 -0.2133 -0.6603

68 1.5318 -1.5903 0.4045 -0.1563 -0.0791 -0.2150 -0.6179

69 1.2020 -1.4667 0.4042 -0.2011 -0.0716 -0.2203 -0.6042

70 -0.0925 -1.2679 0.1586 -0.3033 -0.0137 -0.1563 -0.5210

Coefficient

for

Constant Receipt Married Married Graduate College Graduate School

Age + Benefit Never School Some College In

Social Education Levelb

Security Marital Statusa High
89

1910 1920 1930 1940 1950 1960 1970 1980

0.4146 0.0772 -0.0347 0.0948

0.4927 0.0751 -0.0412 0.0949

0.6056 0.0729 -0.1327 -0.0714

0.7197 0.0697 -0.1476 -0.0672

0.8225 0.0667 -0.1478 -0.0363

0.9191 0.0642 -0.1282 0.0376

1.0007 0.0626 -0.0860 0.1497

1.0261 0.0612 -0.0319 0.2626

1.0033 0.0609 0.0165 0.3376

0.9659 0.0609 0.0533 0.3978

0.9406 0.0614 0.0849 0.4342

0.9579 0.0614 0.0595 0.2274

Spell (*1,000)

Unemployment Earnings Birth Cohort (By first year of cohort)
90

Table C-2.

Historical Full-Time Employment Logit Coefficients for Women

Age

Group

16 -2.4692 0.0873 -1.4552 -0.3229 -2.0984

17 -12.0925 -0.0911 -0.8113 -0.2711 -0.9996

18 -7.6531 -0.3526 -0.3854 -0.2097 -1.1879

19 -4.7548 -0.5829 -0.2632 -0.1910 -1.3390

20 -3.5650 -0.6692 -0.2688 -0.2617 -1.3482

21 -3.1438 -0.7063 -0.2950 -0.3078 -1.3360

22 -2.6316 -0.6943 -0.3087 -0.3591 -1.3045

23 -2.1770 -0.6712 -0.3115 -0.4102 -1.2789

24 -1.8496 -0.6061 -0.2945 -0.4486 -1.2366

25 -2.5568 -0.5451 -0.0892 -0.3390 -0.1288 -0.6844 -1.2580

26 -1.7559 -0.4644 -0.0535 -0.3754 -0.1393 -0.6044 -1.2059

27 -1.0582 -0.4278 -0.0093 -0.4060 -0.1439 -0.5620 -1.1748

28 -0.5858 -0.3952 0.0271 -0.4398 -0.1513 -0.5421 -1.1721

29 -0.3318 -0.3555 0.0541 -0.4748 -0.1577 -0.5353 -1.1853

30 -0.2646 -0.3394 0.0738 -0.5085 -0.1648 -0.5347 -1.2050

31 -0.4377 -0.3527 0.0950 -0.5367 -0.1802 -0.5438 -1.2422

32 -0.5038 -0.3452 0.1098 -0.5671 -0.1961 -0.5534 -1.2801

33 -0.5360 -0.3441 0.1279 -0.5875 -0.2170 -0.5770 -1.3286

34 -0.5249 -0.3634 0.1506 -0.6059 -0.2396 -0.6058 -1.3852

35 -0.5499 -0.3867 0.1690 -0.6218 -0.2574 -0.6306 -1.4315

36 -0.6303 -0.4062 0.1734 -0.6415 -0.2744 -0.6513 -1.4660

37 -0.7520 -0.4430 0.1754 -0.6564 -0.2814 -0.6668 -1.4897

38 -0.8792 -0.4764 0.1759 -0.6721 -0.2756 -0.6677 -1.5010

39 -1.0392 -0.5052 0.1747 -0.6840 -0.2645 -0.6613 -1.5021

40 -1.1779 -0.5335 0.1785 -0.6939 -0.2586 -0.6574 -1.5059

41 -1.3542 -0.5648 0.1929 -0.6950 -0.2564 -0.6577 -1.5168

42 -1.4076 -0.5911 0.1991 -0.6950 -0.2493 -0.6521 -1.5206

43 -1.4916 -0.6267 0.2016 -0.6944 -0.2430 -0.6463 -1.5200

44 -1.5640 -0.6721 0.1903 -0.6932 -0.2380 -0.6331 -1.5128

45 -1.6191 -0.7064 0.1732 -0.6890 -0.2233 -0.6149 -1.4939

46 -1.6023 -0.7304 0.1452 -0.6867 -0.2096 -0.5952 -1.4760

47 -1.4961 -0.7479 0.1181 -0.6823 -0.1986 -0.5780 -1.4578

48 -1.3454 -0.7501 0.0876 -0.6723 -0.1902 -0.5664 -1.4367

49 -1.1676 -0.7438 0.0760 -0.6628 -0.1798 -0.5648 -1.4256

50 -0.9726 -0.7457 0.0760 -0.6538 -0.1761 -0.5629 -1.4264

51 -0.7267 -0.7394 0.0904 -0.6422 -0.1764 -0.5591 -1.4128

52 -0.6514 -0.7413 0.1060 -0.6297 -0.1760 -0.5647 -1.4192

53 -0.5597 -0.7586 0.1221 -0.6237 -0.1803 -0.5644 -1.4325

54 -0.5079 -0.7732 0.1278 -0.6114 -0.1922 -0.5660 -1.4518

55 -0.4627 -0.7893 0.1164 -0.5993 -0.2040 -0.5720 -1.4720

56 -0.2186 -0.8509 0.1165 -0.5878 -0.2139 -0.5725 -1.4946

57 0.0885 -0.9367 0.1016 -0.5846 -0.2180 -0.5662 -1.5020

58 0.3087 -1.0984 0.0868 -0.5820 -0.2157 -0.5664 -1.5089

Coefficient

for

Age +

Education Levelb

School

In

High

Social

Security

Constant Receipt Married Married Graduate College Graduate

Marital Statusa

Benefit Some College Never School
91

Continued

1910 1920 1930 1940 1950 1960 1970 1980

0.2280 1.6949 0.2925 -0.0696 0.2961

0.1546 1.2535 0.3517 -0.1496 0.1971

-0.0434 0.9969 0.3499 0.0478 0.2785

-0.1312 0.9083 0.3317 0.1679 0.3685

-0.1610 0.8293 0.3079 0.2089 0.3827

-0.1810 0.7795 0.2916 0.2173 0.4281

-0.1966 0.7225 0.2737 0.2427 0.4314

-0.2187 0.6740 0.2561 0.2636 0.4279

-0.2438 0.6392 0.2396 0.2723 0.4047

-0.3666 0.6656 0.2426 0.2550 0.3585

-0.3884 0.6328 0.2286 0.2562 0.3618

-0.4105 0.6129 0.2165 0.2522 0.3646

-0.4291 0.5924 0.2065 0.2497 0.3696

-0.4467 0.5720 0.1979 0.2505 0.3770

-0.4555 0.5560 0.1904 0.2520 0.3814

-0.4618 0.5374 0.1840 0.1679 0.4169

-0.4639 0.5243 0.1785 0.1829 0.4233

-0.4643 0.5236 0.1738 0.1952 0.4249

-0.4585 0.5244 0.1702 0.2010 0.4177

-0.4551 0.5241 0.1673 0.1990 0.4044

-0.4514 0.5161 0.1646 0.1931 0.3867

-0.4489 0.5023 0.1625 0.1848 0.3632

-0.4446 0.4884 0.1608 0.1758 0.3403

-0.4430 0.4828 0.1595 0.1719 0.3228

-0.4330 0.4844 0.1587 0.1729 0.3105

-0.4161 0.4932 0.1583 0.1244 0.3154

-0.3918 0.5143 0.1584 0.1294 0.3144

-0.3609 0.5364 0.1590 0.1345 0.3154

-0.3185 0.5495 0.1593 0.1353 0.3147

-0.2824 0.5530 0.1594 0.1340 0.3148

-0.2339 0.5547 0.1599 0.1347 0.3137

-0.1989 0.5594 0.1600 0.1425 0.3183

-0.1569 0.5582 0.1601 0.1435 0.3198

-0.1192 0.5675 0.1609 0.1487 0.3218

-0.0871 0.5859 0.1616 0.1552 0.3234

-0.0664 0.5955 0.1625 0.0708 0.2665

-0.0615 0.6080 0.1639 0.0605 0.2498

-0.0795 0.6236 0.1649 0.0450 0.2350

-0.1010 0.6404 0.1658 0.0299 0.2264

-0.0928 0.6577 0.1670 0.0187 0.2257

0.6836 0.1674 0.0080 0.2222

0.7076 0.1671 0.0032 0.2205

0.7410 0.1667 0.0018 0.2213

Birth Cohort (By first year of cohort)

Spell (*1,000)

Children

Under Unemployment Earnings

Number of

Age 6
92

Table C-2.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level.

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

1910 1920 1930 1940 1950 1960 1970 1980

0.7680 0.1651 0.0040 0.2145

0.8030 0.1626 0.0030 0.1958

0.8434 0.1601 -0.0557 0.0313

0.9037 0.1572 -0.0528 0.0261

0.9760 0.1546 -0.0363 0.0369

1.0592 0.1527 -0.0038 0.0682

1.1335 0.1510 0.0282 0.1226

1.1964 0.1500 0.0677 0.1855

1.2318 0.1497 0.0988 0.2472

1.2250 0.1499 0.1170 0.3040

1.2273 0.1498 0.1199 0.3496

0.9712 0.1349 0.1227 0.2884

Children

Birth Cohort (By first year of cohort)

Spell (*1,000)

Unemployment Earnings

Number of

Age 6

Under
93

1910 1920 1930 1940 1950 1960 1970 1980

0.7680 0.1651 0.0040 0.2145

0.8030 0.1626 0.0030 0.1958

0.8434 0.1601 -0.0557 0.0313

0.9037 0.1572 -0.0528 0.0261

0.9760 0.1546 -0.0363 0.0369

1.0592 0.1527 -0.0038 0.0682

1.1335 0.1510 0.0282 0.1226

1.1964 0.1500 0.0677 0.1855

1.2318 0.1497 0.0988 0.2472

1.2250 0.1499 0.1170 0.3040

1.2273 0.1498 0.1199 0.3496

0.9712 0.1349 0.1227 0.2884

Children

Birth Cohort (By first year of cohort)

Spell (*1,000)

Unemployment Earnings

Number of

Age 6

Under
94

Table C-3.

Historical Part-Time Hours Ordered Logit Coefficients for Men

Age

Group

16 -0.0606 1.2002 0.9672 -0.4733 1.2596 0.5604 -0.0110

17 0.2637 -0.1111 0.2689 0.8374 -0.2259 1.1633 0.3886 -0.0014

18 0.0909 -0.1195 -0.1658 0.3352 -0.3989 1.2193 0.3884 0.0154

19 0.0367 -0.1750 -0.2697 0.3202 -0.5112 1.1812 0.3239 0.0232

20 0.0158 -0.2200 -0.3130 0.2477 -0.5478 1.1333 0.2737 0.0277

21 0.0114 -0.2740 -0.3542 0.2170 -0.5472 1.0925 0.2368 0.0143

22 0.0007 -0.3356 -0.3651 0.1797 -0.5486 1.0464 0.2018 0.0165

23 -0.0069 -0.4189 -0.3727 0.1230 -0.5580 0.9990 0.1723 0.0181

24 -0.0055 -0.5018 -0.3491 0.0901 -0.5292 0.9638 0.1477 0.0193

25 0.0342 -0.6469 -0.0921 0.1832 0.0697 -0.5104 -0.8128 0.8781 0.1339 0.0133

26 0.0165 -0.7643 -0.0759 0.1364 0.0540 -0.4341 -0.7767 0.8493 0.1170 0.0153

27 0.0024 -0.8474 -0.0422 0.1155 0.0396 -0.3713 -0.7439 0.8322 0.1038 0.0173

28 -0.0063 -0.8590 -0.0107 0.1100 0.0386 -0.3224 -0.6999 0.8212 0.0928 0.0187

29 -0.0084 -0.8604 0.0209 0.1091 0.0412 -0.2817 -0.6565 0.8092 0.0854 0.0181

30 -0.0019 -0.8618 0.0333 0.1183 0.0364 -0.2531 -0.6044 0.8044 0.0804 0.0166

31 0.0037 -0.8967 0.0428 0.1214 0.0278 -0.2369 -0.5649 0.7881 0.0759 0.0090

32 -0.0038 -0.9077 0.0350 0.1124 0.0203 -0.2171 -0.5257 0.7689 0.0718 0.0102

33 -0.0137 -0.9205 0.0309 0.1031 0.0120 -0.2154 -0.4968 0.7437 0.0686 0.0134

34 -0.0232 -0.8878 0.0124 0.0959 0.0111 -0.2127 -0.4616 0.7249 0.0649 0.0140

35 -0.0227 -0.8460 0.0040 0.0901 0.0083 -0.2084 -0.4372 0.7030 0.0611 0.0132

36 -0.0186 -0.7961 -0.0022 0.0875 0.0138 -0.1892 -0.4026 0.6903 0.0580 0.0117

37 -0.0154 -0.7683 -0.0029 0.0926 0.0173 -0.1695 -0.3699 0.6717 0.0549 0.0100

38 -0.0092 -0.7922 -0.0029 0.0976 0.0167 -0.1405 -0.3473 0.6624 0.0521 0.0075

39 -0.0116 -0.8622 0.0042 0.1001 0.0006 -0.1153 -0.3403 0.6482 0.0496 0.0095

40 -0.0159 -0.9433 0.0020 0.0992 -0.0147 -0.1153 -0.3408 0.6328 0.0475 0.0129

41 -0.0040 -1.0214 -0.0094 0.0952 -0.0463 -0.1480 -0.3693 0.6170 0.0460 0.0051

42 -0.0025 -1.0853 -0.0172 0.0948 -0.0675 -0.1828 -0.3931 0.6168 0.0454 0.0058

43 -0.0060 -1.0847 -0.0287 0.0860 -0.0724 -0.2235 -0.3870 0.6219 0.0455 0.0067

44 -0.0110 -1.0738 -0.0352 0.0758 -0.0665 -0.2719 -0.3712 0.6284 0.0461 0.0079

45 -0.0141 -1.0619 -0.0374 0.0677 -0.0745 -0.3077 -0.3619 0.6462 0.0469 0.0089

46 -0.0157 -1.0509 -0.0274 0.0677 -0.0756 -0.3204 -0.3445 0.6705 0.0471 0.0091

47 -0.0160 -1.0286 -0.0230 0.0591 -0.0745 -0.3298 -0.3228 0.6888 0.0469 0.0087

48 -0.0183 -1.0354 -0.0320 0.0636 -0.0981 -0.3299 -0.3244 0.7066 0.0464 0.0089

49 -0.0215 -1.0245 -0.0493 0.0652 -0.1186 -0.3181 -0.3298 0.7349 0.0456 0.0089

50 -0.0217 -1.0152 -0.0755 0.0592 -0.1197 -0.2998 -0.3304 0.7544 0.0448 0.0075

51 -0.0331 -0.9632 -0.0962 0.0476 -0.1171 -0.2889 -0.3421 0.7611 0.0443 0.0112

52 -0.0388 -0.9661 -0.1070 0.0311 -0.1236 -0.2866 -0.3703 0.7723 0.0441 0.0087

53 -0.0435 -0.9757 -0.0824 0.0104 -0.1141 -0.2902 -0.4140 0.7843 0.0437 0.0077

54 -0.0500 -0.9960 -0.0279 -0.0017 -0.1071 -0.2920 -0.4609 0.7908 0.0432 0.0064

55 -0.0543 -0.9999 0.0388 -0.0060 -0.1151 -0.3020 -0.5206 0.8135 0.0428 0.0063

56 -0.0570 -1.0350 0.0910 -0.0103 -0.1238 -0.3132 -0.5783 0.8485 0.0424 0.0070

57 -0.0579 -0.9942 0.1265 -0.0079 -0.1342 -0.3149 -0.6289 0.8742 0.0422 0.0078

58 -0.0390 -0.7388 0.1272 -0.0114 -0.1473 -0.3121 -0.6723 0.8979 0.0426 0.0057

Age

Education Levelb

Unemployment Earnings

Coefficient Receipt Rate (*1,000) Trend Married Married Graduate School

Social

Security

Benefit Never

Marital Statusa High

School Some College In

College Graduate
95

Continued

1910 1920 1930 1940 1950 1960 1970 1980

0.1093 0.2384 0.8352 2.6865 3.8015 4.6772 6.0286 7.0324

0.1001 0.1535 4.1766 5.8423 6.8128 7.6112 8.8083 9.8014

0.0575 -0.0207 0.7552 2.4833 3.4881 4.2908 5.5588 6.5919

0.0217 -0.1226 -0.5168 1.2003 2.1795 2.9334 4.1404 5.1141

-0.0163 -0.1994 -1.0928 0.5990 1.5525 2.2739 3.4346 4.3715

0.1945 0.2806 -1.3185 0.3571 1.2901 1.9860 3.1100 4.0200

0.1776 0.2381 -1.6976 -0.0487 0.8596 1.5323 2.6238 3.5154

0.1650 0.1920 -2.0100 -0.4008 0.4826 1.1416 2.2045 3.0821

0.1276 0.1137 -2.0658 -0.5185 0.3359 0.9856 2.0225 2.8855

0.0571 0.0319 -1.3665 0.1119 0.9438 1.6039 2.6434 3.5153

0.0148 -0.0528 -1.8485 -0.4328 0.3705 1.0321 2.0578 2.9174

-0.0241 -0.1287 -2.2428 -0.8720 -0.0900 0.5733 1.5888 2.4313

-0.0562 -0.1941 -2.4929 -1.1583 -0.3943 0.2653 1.2735 2.0964

-0.0636 -0.2076 -2.5793 -1.2603 -0.5062 0.1528 1.1578 1.9639

-0.0617 -0.1869 -2.4223 -1.0979 -0.3499 0.2996 1.3024 2.0942

0.0141 0.0073 -2.3243 -1.0031 -0.2589 0.3848 1.3844 2.1644

-0.0268 -0.0436 -2.6016 -1.2908 -0.5465 0.0907 1.0839 1.8547

-0.0733 -0.1295 -2.9521 -1.6512 -0.9068 -0.2745 0.7048 1.4706

-0.0829 -0.1565 -3.2884 -2.0076 -1.2655 -0.6444 0.3252 1.0908

-0.0679 -0.1475 -3.2830 -2.0364 -1.2949 -0.6792 0.2826 1.0507

-0.0326 -0.1066 -3.1203 -1.9045 -1.1667 -0.5592 0.3955 1.1641

-0.0071 -0.0759 -3.0036 -1.8050 -1.0797 -0.4787 0.4666 1.2326

0.0193 -0.0315 -2.7922 -1.6044 -0.8833 -0.2861 0.6587 1.4211

-0.0155 -0.0824 -2.8946 -1.7140 -0.9960 -0.3960 0.5386 1.2991

-0.0728 -0.1596 -3.0801 -1.8983 -1.1813 -0.5777 0.3420 1.0965

0.1536 0.1039 -2.6219 -1.4306 -0.7147 -0.1051 0.8019 1.5557

0.1426 0.0758 -2.5982 -1.4137 -0.6859 -0.0722 0.8246 1.5832

0.1262 0.0486 -2.7473 -1.5826 -0.8503 -0.2343 0.6514 1.4145

0.0823 0.0066 -2.9599 -1.8138 -1.0838 -0.4689 0.4183 1.1826

0.0403 -0.0200 -3.0897 -1.9536 -1.2280 -0.6191 0.2683 1.0395

0.0100 -0.0267 -3.1465 -2.0247 -1.2992 -0.6939 0.1951 0.9697

-0.0192 -0.0228 -3.1701 -2.0431 -1.3307 -0.7278 0.1687 0.9436

-0.0562 -0.0358 -3.3129 -2.1700 -1.4739 -0.8692 0.0339 0.8098

-0.0569 -0.0227 -3.4731 -2.3286 -1.6413 -1.0294 -0.1289 0.6491

-0.0425 0.0093 -3.4970 -2.3629 -1.6834 -1.0628 -0.1567 0.6186

-0.0535 -0.1178 -4.0804 -2.9570 -2.2891 -1.6683 -0.7496 0.0245

-0.0276 -0.0573 -4.3763 -3.2774 -2.6096 -1.9860 -1.0625 -0.2928

-0.0312 -0.0366 -4.6262 -3.5604 -2.8851 -2.2638 -1.3357 -0.5694

-0.0394 -0.0444 -5.0090 -3.9574 -3.2762 -2.6632 -1.7284 -0.9675

-0.0615 -0.0672 -5.2683 -4.2206 -3.5401 -2.9320 -1.9939 -1.2380

-0.0825 -0.0980 -5.4487 -4.4017 -3.7180 -3.1062 -2.1749 -1.4256

-0.1029 -0.1301 -5.4978 -4.4547 -3.7759 -3.1641 -2.2350 -1.4894

-0.0769 -0.0781 -4.4178 -3.3681 -2.6979 -2.0850 -1.1535 -0.4093

Birth Cohort (By first year of cohort)

1 6 2 3 4 5

Part-Time Category Threshold
96

Table C-3.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level. Statistical significance of the

threshold values is not evaluated. 

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

c. Terms do not appear for ages 61 through 66 in order to isolate the effects of eligibility for Social Security retirement benefits at those ages.

Age

Group

59 -0.0232 -0.6235 0.1144 -0.0119 -0.1651 -0.3196 -0.7110 0.9294 0.0431 0.0042

60 -0.0147 -0.5884 0.0972 -0.0224 -0.1724 -0.3281 -0.7352 0.9476 0.0436 0.0031

61 c -0.5905 0.0937 -0.0330 -0.1808 -0.3376 -0.7461 0.9672 0.0448 0.0077

62 c -0.5759 0.1222 -0.0426 -0.1808 -0.3355 -0.7507 0.9973 0.0460 0.0085

63 c -0.5831 0.1535 -0.0552 -0.1752 -0.3279 -0.7423 1.0304 0.0465 0.0111

64 c -0.5694 0.1662 -0.0806 -0.1610 -0.3135 -0.7274 1.0557 0.0473 0.0136

65 c -0.5351 0.1796 -0.0925 -0.1436 -0.2916 -0.7037 1.0835 0.0483 0.0167

66 c -0.4930 0.1776 -0.1013 -0.1225 -0.2672 -0.6817 1.1180 0.0488 0.0191

67 -0.0078 -0.4246 0.1407 -0.1098 -0.0984 -0.2502 -0.6480 1.1203 0.0491 0.0232

68 -0.0102 -0.3524 0.1229 -0.1016 -0.0704 -0.2292 -0.6093 1.1153 0.0498 0.0210

69 -0.0116 -0.2960 0.1082 -0.0851 -0.0519 -0.2112 -0.5834 1.0935 0.0497 0.0179

70 -0.0159 -0.2989 0.1209 -0.0482 -0.0076 -0.2260 -0.5462 1.0553 0.0470 0.0105

Age

Social Education Levelb

Security Marital Statusa High

Benefit Never School Some College In Unemployment Earnings

Coefficient Receipt Married Married Graduate College Graduate School Rate (*1,000) Trend
97

1910 1920 1930 1940 1950 1960 1970 1980

-0.0301 0.0107 -3.4846 -2.4318 -1.7624 -1.1450 -0.2055 0.5383

0.0241 0.0900 -2.9668 -1.9192 -1.2464 -0.6224 0.3303 1.0768

-0.0630 -0.0145 -2.0501 -1.0095 -0.3336 0.2945 1.2650 2.0180

-0.0693 0.0236 -2.0146 -0.9772 -0.2937 0.3379 1.3300 2.0961

-0.0875 0.0222 -1.9671 -0.9434 -0.2562 0.3775 1.3901 2.1599

-0.1046 0.0039 -1.9192 -0.9092 -0.2236 0.4121 1.4432 2.2174

-0.1299 -0.0310 -1.8412 -0.8410 -0.1559 0.4767 1.5204 2.2991

-0.1398 -0.0486 -1.7466 -0.7581 -0.0749 0.5534 1.6104 2.3922

-0.1793 -0.1287 -2.1486 -1.1731 -0.4958 0.1295 1.1925 1.9674

-0.1570 -0.0913 -2.2183 -1.2482 -0.5726 0.0473 1.1090 1.8868

-0.1214 -0.0321 -2.2513 -1.2855 -0.6117 0.0005 1.0551 1.8365

-0.0909 -0.0340 -2.5881 -1.6390 -0.9706 -0.3773 0.6629 1.4523

Birth Cohort (By first year of cohort) Part-Time Category Threshold

1234 5 6
98

Table C-4.

Historical Part-Time Hours Ordered Logit Coefficients for Women

Age

Group

16 -0.1290 -0.4792 -0.3328 -0.7377 0.0065 1.1441 0.7168 -0.0009

17 0.1791 -0.0498 -0.3769 -0.1214 -0.2341 -0.0075 0.8045 0.4266 0.0074

18 0.1237 -0.1091 -0.4113 -0.1863 -0.4568 -0.0877 1.0524 0.4186 0.0185

19 0.0356 -0.1455 -0.2981 -0.1426 -0.4928 -0.1238 1.1004 0.4066 0.0267

20 0.0123 -0.1651 -0.2934 -0.1844 -0.4919 -0.1363 1.0788 0.3494 0.0288

21 -0.0036 -0.1919 -0.3108 -0.2241 -0.4719 -0.1288 1.0826 0.3255 0.0185

22 -0.0195 -0.2175 -0.3176 -0.2643 -0.4515 -0.1294 1.0545 0.2849 0.0204

23 -0.0298 -0.2350 -0.3025 -0.2969 -0.4479 -0.1318 1.0312 0.2538 0.0216

24 -0.0358 -0.2374 -0.2778 -0.3233 -0.4287 -0.1383 1.0077 0.2295 0.0214

25 -0.0125 -0.2553 -0.0998 -0.2597 -0.0143 -0.3721 -0.8030 -0.2107 1.0149 0.2253 0.0171

26 -0.0291 -0.2624 -0.0643 -0.2831 -0.0422 -0.3426 -0.8255 -0.2229 0.9928 0.2115 0.0167

27 -0.0373 -0.2601 -0.0355 -0.3006 -0.0649 -0.3394 -0.8464 -0.2405 0.9794 0.2004 0.0176

28 -0.0374 -0.2530 -0.0239 -0.3217 -0.0882 -0.3542 -0.8748 -0.2569 0.9588 0.1905 0.0186

29 -0.0346 -0.2437 -0.0155 -0.3400 -0.1112 -0.3778 -0.9084 -0.2716 0.9396 0.1817 0.0201

30 -0.0317 -0.2273 -0.0100 -0.3504 -0.1198 -0.3884 -0.9344 -0.2880 0.9161 0.1736 0.0219

31 -0.0178 -0.2161 -0.0017 -0.3597 -0.1328 -0.4001 -0.9660 -0.3005 0.8971 0.1663 0.0139

32 -0.0153 -0.2138 0.0192 -0.3640 -0.1363 -0.4032 -0.9871 -0.3111 0.8799 0.1600 0.0154

33 -0.0141 -0.2152 0.0399 -0.3648 -0.1358 -0.3999 -0.9957 -0.3246 0.8666 0.1547 0.0178

34 -0.0136 -0.2378 0.0502 -0.3663 -0.1292 -0.3891 -0.9919 -0.3405 0.8524 0.1506 0.0201

35 -0.0141 -0.2708 0.0601 -0.3714 -0.1215 -0.3841 -0.9836 -0.3522 0.8345 0.1469 0.0216

36 -0.0108 -0.3057 0.0674 -0.3781 -0.1122 -0.3811 -0.9729 -0.3612 0.8042 0.1434 0.0217

37 -0.0087 -0.3412 0.0600 -0.3873 -0.1011 -0.3776 -0.9581 -0.3701 0.7719 0.1404 0.0211

38 -0.0055 -0.3714 0.0514 -0.3871 -0.0921 -0.3791 -0.9525 -0.3742 0.7496 0.1377 0.0193

39 -0.0001 -0.3729 0.0391 -0.3813 -0.0768 -0.3807 -0.9529 -0.3718 0.7318 0.1356 0.0174

40 0.0047 -0.3669 0.0243 -0.3705 -0.0620 -0.3800 -0.9513 -0.3743 0.7278 0.1341 0.0166

41 0.0136 -0.3665 -0.0035 -0.3577 -0.0551 -0.3766 -0.9534 -0.3819 0.7355 0.1331 0.0098

42 0.0148 -0.3540 -0.0323 -0.3501 -0.0566 -0.3707 -0.9622 -0.3799 0.7474 0.1323 0.0097

43 0.0125 -0.3575 -0.0534 -0.3544 -0.0541 -0.3596 -0.9586 -0.3799 0.7575 0.1316 0.0102

44 0.0080 -0.3777 -0.0511 -0.3577 -0.0632 -0.3496 -0.9455 -0.3826 0.7656 0.1302 0.0114

45 0.0049 -0.3922 -0.0433 -0.3571 -0.0692 -0.3306 -0.9265 -0.3801 0.7634 0.1291 0.0120

46 0.0025 -0.3937 -0.0226 -0.3570 -0.0648 -0.3104 -0.8980 -0.3693 0.7565 0.1284 0.0112

47 0.0000 -0.3929 -0.0053 -0.3469 -0.0568 -0.3003 -0.8686 -0.3629 0.7452 0.1277 0.0102

48 -0.0015 -0.3834 0.0020 -0.3315 -0.0533 -0.2887 -0.8427 -0.3448 0.7351 0.1273 0.0093

49 -0.0021 -0.3757 -0.0166 -0.3221 -0.0476 -0.2844 -0.8334 -0.3170 0.7365 0.1283 0.0091

50 -0.0028 -0.3733 -0.0392 -0.3185 -0.0514 -0.2898 -0.8356 -0.2793 0.7512 0.1289 0.0090

51 -0.0015 -0.3689 -0.0579 -0.3130 -0.0667 -0.3046 -0.8518 -0.2441 0.7697 0.1294 0.0084

52 -0.0050 -0.3944 -0.0625 -0.3096 -0.0783 -0.3160 -0.8663 -0.2229 0.8043 0.1298 0.0092

53 -0.0105 -0.4225 -0.0815 -0.3082 -0.0883 -0.3395 -0.8924 -0.1989 0.8347 0.1294 0.0095

54 -0.0163 -0.4533 -0.0839 -0.3106 -0.0943 -0.3555 -0.9065 -0.1920 0.8521 0.1287 0.0100

55 -0.0222 -0.4983 -0.0862 -0.3196 -0.1011 -0.3728 -0.9187 -0.1608 0.8562 0.1275 0.0096

56 -0.0261 -0.5393 -0.1108 -0.3254 -0.1046 -0.3870 -0.9216 0.8564 0.1254 0.0079

57 -0.0259 -0.5387 -0.1451 -0.3423 -0.1104 -0.4013 -0.9290 0.8427 0.1234 0.0055

58 -0.0194 -0.4993 -0.1560 -0.3583 -0.1172 -0.4099 -0.9359 0.8283 0.1225 0.0029

Number of

Graduate Age 6

Never Under

Rate

College Unemployment Earnings

(*1,000) Trend

In

School

Children

Married Married

Marital Statusa

College

School Some

High

Graduate

Age

Social

Security

Benefit

Coefficient Receipt

Education Levelb
99

Continued

1910 1920 1930 1940 1950 1960 1970 1980

0.1541 0.1560 -0.6828 1.2734 2.4326 3.4310 4.8273 5.7666

0.0966 0.0789 2.2538 3.8797 4.8590 5.7096 6.9741 7.9056

0.0726 -0.0368 1.2385 2.8618 3.8257 4.6195 5.8152 6.7268

0.0381 -0.1410 -0.2659 1.4049 2.3865 3.1971 4.4164 5.3771

0.0084 -0.1672 -0.8368 0.8020 1.7485 2.5370 3.7054 4.6527

0.1192 0.2002 -1.2462 0.3812 1.3209 2.1120 3.2745 4.2285

0.0981 0.1419 -1.7247 -0.1391 0.7716 1.5435 2.6642 3.5989

0.0807 0.0855 -2.0647 -0.5275 0.3592 1.1195 2.2090 3.1277

0.0638 0.0299 -2.2864 -0.8144 0.0508 0.8019 1.8681 2.7729

0.0709 0.0381 -1.9165 -0.4936 0.3663 1.1213 2.1863 3.0918

0.0631 0.0087 -2.4099 -1.0323 -0.1848 0.5643 1.6198 2.5185

0.0428 -0.0298 -2.7035 -1.3570 -0.5246 0.2209 1.2728 2.1674

0.0224 -0.0603 -2.7908 -1.4688 -0.6494 0.0912 1.1417 2.0311

-0.0010 -0.0886 -2.7954 -1.4915 -0.6828 0.0541 1.1100 1.9913

-0.0255 -0.1209 -2.7691 -1.4813 -0.6822 0.0517 1.1143 1.9849

0.1361 0.1521 -2.3871 -1.1126 -0.3207 0.4106 1.4807 2.3409

0.1316 0.1201 -2.3472 -1.0921 -0.3037 0.4241 1.4981 2.3452

0.1135 0.0638 -2.3318 -1.0947 -0.3121 0.4139 1.4891 2.3277

0.0874 0.0066 -2.3381 -1.1125 -0.3339 0.3888 1.4628 2.2980

0.0640 -0.0423 -2.3781 -1.1622 -0.3878 0.3294 1.4028 2.2380

0.0452 -0.0684 -2.2942 -1.0883 -0.3210 0.3921 1.4646 2.2980

0.0303 -0.0805 -2.2597 -1.0572 -0.2954 0.4120 1.4846 2.3167

0.0276 -0.0679 -2.1785 -0.9771 -0.2188 0.4825 1.5568 2.3860

0.0287 -0.0554 -1.9999 -0.8029 -0.0475 0.6517 1.7254 2.5507

0.0178 -0.0586 -1.8191 -0.6269 0.1247 0.8243 1.8991 2.7203

0.0898 0.1404 -1.4476 -0.2612 0.4902 1.1888 2.2633 3.0840

0.1004 0.1302 -1.3958 -0.2118 0.5373 1.2353 2.3096 3.1340

0.1066 0.1076 -1.4948 -0.3132 0.4342 1.1298 2.2038 3.0324

0.1041 0.0722 -1.6949 -0.5169 0.2286 0.9187 1.9931 2.8237

0.0977 0.0459 -1.8323 -0.6561 0.0910 0.7750 1.8461 2.6757

0.1084 0.0541 -1.9370 -0.7584 -0.0109 0.6687 1.7375 2.5649

0.1137 0.0687 -2.0377 -0.8633 -0.1164 0.5581 1.6250 2.4452

0.1111 0.0847 -2.0888 -0.9231 -0.1760 0.4959 1.5596 2.3725

0.0935 0.0803 -2.0965 -0.9402 -0.1962 0.4752 1.5377 2.3458

0.0809 0.0801 -2.1124 -0.9686 -0.2294 0.4409 1.5023 2.3077

0.0403 0.1005 -2.0157 -0.8870 -0.1544 0.5144 1.5767 2.3794

0.0394 0.0678 -2.1835 -1.0672 -0.3361 0.3332 1.3959 2.1974

0.0355 0.0453 -2.4784 -1.3734 -0.6473 0.0218 1.0882 1.8876

0.0245 0.0242 -2.8051 -1.7080 -0.9809 -0.3127 0.7560 1.5526

0.0212 0.0161 -3.1574 -2.0689 -1.3394 -0.6723 0.4017 1.1991

0.0218 0.0325 -3.4157 -2.3339 -1.5972 -0.9335 0.1397 0.9357

0.0326 0.0661 -3.4602 -2.3903 -1.6524 -0.9880 0.0844 0.8811

0.0521 0.1079 -3.1302 -2.0704 -1.3288 -0.6612 0.4113 1.2113

Birth Cohort (By first year of cohort) Part-Time Category Threshold

12 5 3 4 6
100

Table C-4.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level. Statistical significance of the threshold 

values is not evaluated. 

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

c. Terms do not appear for ages 61 through 66 in order to isolate the effects of eligibility for Social Security retirement benefits at those 

ages.

Age

Group

59 -0.0113 -0.4869 -0.1737 -0.3715 -0.1299 -0.4199 -0.9576 0.8235 0.1213 0.0007

60 -0.0034 -0.4881 -0.1911 -0.3723 -0.1324 -0.4293 -0.9812 0.8219 0.1215 -0.0006

61 c -0.4984 -0.1756 -0.3675 -0.1453 -0.4454 -1.0241 0.8363 0.1247 0.0049

62 c -0.4941 -0.1538 -0.3491 -0.1610 -0.4673 -1.0732 0.8575 0.1293 0.0070

63 c -0.4956 -0.1204 -0.3211 -0.1751 -0.4819 -1.1162 0.8903 0.1345 0.0091

64 c -0.4816 -0.0919 -0.2818 -0.1857 -0.5004 -1.1418 0.9192 0.1410 0.0103

65 c -0.4593 -0.0496 -0.2528 -0.2055 -0.5189 -1.1576 0.9465 0.1479 0.0120

66 c -0.4259 -0.0260 -0.2333 -0.2042 -0.5193 -1.1399 0.9452 0.1528 0.0132

67 -0.0305 -0.3471 0.0042 -0.2426 -0.1944 -0.5066 -1.0967 0.9202 0.1565 0.0214

68 -0.0242 -0.3117 0.0225 -0.2429 -0.1846 -0.5062 -1.0551 0.8936 0.1606 0.0198

69 -0.0195 -0.3050 0.0679 -0.2552 -0.1659 -0.4970 -1.0267 0.8671 0.1633 0.0175

70 -0.0217 -0.4480 0.2608 -0.2654 -0.0926 -0.3609 -1.0000 0.8915 0.1375 0.0082

Trend

Under 

Coefficient Receipt Married Married Graduate College Graduate School Age 6 Rate (*1,000)

Unemployment Earnings

Marital Statusa High

Number of

Benefit Never School Some College In

Children

Social Education Levelb

Age

Security
101

1910 1920 1930 1940 1950 1960 1970 1980

0.0668 0.1362 -2.7083 -1.6561 -0.9137 -0.2403 0.8378 1.6439

0.0699 0.1443 -2.2640 -1.2165 -0.4741 0.2104 1.2933 2.0998

-0.0578 -0.0262 -2.0535 -1.0094 -0.2686 0.4348 1.5309 2.3475

-0.0566 -0.0497 -2.0208 -0.9698 -0.2245 0.4956 1.6060 2.4303

-0.0462 -0.0649 -1.9708 -0.9068 -0.1547 0.5804 1.7042 2.5363

-0.0291 -0.0567 -1.8900 -0.8115 -0.0544 0.6951 1.8295 2.6632

-0.0102 -0.0499 -1.8064 -0.7088 0.0561 0.8147 1.9657 2.8081

0.0009 -0.0484 -1.7195 -0.5989 0.1722 0.9337 2.1016 2.9391

-0.0806 -0.2146 -3.6045 -2.4685 -1.6933 -0.9310 0.2592 1.0943

-0.0619 -0.1835 -3.1349 -1.9882 -1.2133 -0.4526 0.7609 1.5897

-0.0436 -0.1407 -2.8142 -1.6656 -0.8870 -0.1301 1.1031 1.9351

-0.0359 -0.0499 -3.2266 -2.1282 -1.3581 -0.6570 0.5371 1.3232

1234 5 6

Birth Cohort (By first year of cohort) Part-Time Category Threshold
102

Table C-5.

Historical Unemployment Logit Coefficients for Men

Age

Group

16 -0.5124 0.1787 -0.5408 -0.3858 -0.6819

17 -3.2765 0.1011 -0.5271 -0.2000 -0.5999

18 -1.8493 0.0319 -0.5907 -0.3324 -0.8097

19 -0.7219 -0.0558 -0.5213 -0.3573 -0.9650

20 -0.4472 -0.1334 -0.4631 -0.3612 -1.0065

21 -0.2707 -0.2374 -0.4478 -0.3568 -1.0039

22 -0.1172 -0.3418 -0.4184 -0.3486 -0.9986

23 0.0466 -0.4455 -0.3916 -0.3430 -0.9962

24 0.1123 -0.5231 -0.3694 -0.3407 -0.9490

25 -0.3399 -0.4922 -0.2356 -0.2998 -0.1034 -0.5969 -0.6348

26 -0.0972 -0.4396 -0.2326 -0.3083 -0.0679 -0.4507 -0.5856

27 0.0036 -0.3786 -0.2304 -0.3216 -0.0423 -0.3512 -0.5451

28 -0.0128 -0.3301 -0.2293 -0.3326 -0.0279 -0.2857 -0.5142

29 -0.0145 -0.2783 -0.2252 -0.3351 -0.0233 -0.2454 -0.4921

30 -0.0622 -0.2862 -0.2135 -0.3358 -0.0285 -0.2227 -0.4740

31 -0.3020 -0.3144 -0.1996 -0.3223 -0.0411 -0.2212 -0.4609

32 -0.3003 -0.3347 -0.1839 -0.2992 -0.0550 -0.2199 -0.4514

33 -0.2787 -0.3555 -0.1734 -0.2806 -0.0597 -0.2137 -0.4304

34 -0.2974 -0.3911 -0.1760 -0.2753 -0.0625 -0.2043 -0.4063

35 -0.2482 -0.3538 -0.1893 -0.2741 -0.0636 -0.1927 -0.3801

36 -0.2372 -0.3183 -0.2052 -0.2779 -0.0611 -0.1740 -0.3565

37 -0.2285 -0.2817 -0.2234 -0.2914 -0.0551 -0.1580 -0.3258

38 -0.1641 -0.2267 -0.2345 -0.3004 -0.0558 -0.1537 -0.3115

39 -0.0661 -0.1829 -0.2276 -0.2984 -0.0579 -0.1508 -0.3011

40 0.0848 -0.2035 -0.2206 -0.2921 -0.0663 -0.1473 -0.2894

41 -0.2837 -0.1997 -0.2070 -0.2879 -0.0841 -0.1468 -0.2801

42 -0.0637 -0.2188 -0.2033 -0.2844 -0.1053 -0.1440 -0.2778

43 0.0584 -0.2702 -0.2114 -0.2900 -0.1242 -0.1357 -0.2722

44 0.0670 -0.3331 -0.2219 -0.2988 -0.1441 -0.1274 -0.2675

45 -0.1004 -0.3426 -0.2277 -0.3063 -0.1591 -0.1244 -0.2695

46 -0.2286 -0.3967 -0.2407 -0.3128 -0.1683 -0.1235 -0.2665

47 -0.3400 -0.4190 -0.2378 -0.3086 -0.1729 -0.1268 -0.2594

48 -0.2949 -0.4204 -0.2226 -0.2880 -0.1708 -0.1271 -0.2445

49 -0.1439 -0.4073 -0.2237 -0.2671 -0.1651 -0.1320 -0.2303

50 0.1261 -0.4331 -0.2271 -0.2552 -0.1531 -0.1351 -0.2097

51 -0.0511 -0.4268 -0.2251 -0.2452 -0.1424 -0.1321 -0.1912

52 0.2350 -0.4717 -0.2352 -0.2479 -0.1344 -0.1331 -0.1879

53 0.4159 -0.4924 -0.2386 -0.2613 -0.1425 -0.1422 -0.2004

54 0.6008 -0.4935 -0.2384 -0.2785 -0.1592 -0.1527 -0.2197

55 0.5839 -0.4630 -0.2472 -0.2955 -0.1838 -0.1628 -0.2541

56 0.3850 -0.4435 -0.2640 -0.3117 -0.2068 -0.1817 -0.2995

57 0.2058 -0.3806 -0.2628 -0.3109 -0.2321 -0.1942 -0.3320

58 0.3427 -0.3300 -0.2729 -0.3077 -0.2447 -0.1997 -0.3522

Marital Status High a

Constant Receipt Married Married

Education Levelb

Graduate College Graduate School

Never School Some College In

Social

Security

Age + Benefit

Coefficient

for
103

Continued

1910 1920 1930 1940 1950 1960 1970 1980

0.0341 -0.1666 -0.0135 -0.1658 -0.2078

0.0528 -0.4613 -0.0089 -0.1540 -0.4248

0.0502 -0.6386 -0.0159 -0.0109 -0.2149

0.0535 -0.7254 -0.0224 0.0780 -0.0650

0.0582 -0.7666 -0.0263 0.0905 0.0030

0.0574 -0.7882 -0.0320 0.0254 0.1407

0.0644 -0.8106 -0.0295 -0.0313 0.0348

0.0730 -0.8302 -0.0260 -0.0841 -0.0897

0.0799 -0.8424 -0.0233 -0.1218 -0.1974

0.0888 -0.8202 -0.0267 -0.1488 -0.2397

0.0935 -0.8246 -0.0265 -0.1371 -0.2508

0.1040 -0.8255 -0.0280 -0.0938 -0.1929

0.1170 -0.8194 -0.0300 -0.0442 -0.1062

0.1276 -0.8070 -0.0295 -0.0304 -0.0650

0.1340 -0.7906 -0.0263 -0.0591 -0.0835

0.1298 -0.7732 -0.0288 0.1152 0.0513

0.1320 -0.7561 -0.0233 0.0934 -0.0386

0.1356 -0.7445 -0.0178 0.0731 -0.1261

0.1358 -0.7355 -0.0144 0.0563 -0.1900

0.1337 -0.7245 -0.0128 0.0461 -0.2264

0.1329 -0.7138 -0.0124 0.0337 -0.2475

0.1327 -0.7018 -0.0132 0.0302 -0.2429

0.1335 -0.6856 -0.0139 0.0176 -0.2367

0.1345 -0.6691 -0.0130 -0.0109 -0.2456

0.1343 -0.6566 -0.0106 -0.0629 -0.2819

0.1201 -0.6441 -0.0265 0.2064 0.2096

0.1150 -0.6315 -0.0234 0.2055 0.1446

0.1128 -0.6200 -0.0204 0.1967 0.0841

0.1135 -0.6083 -0.0185 0.1961 0.0486

0.1170 -0.5963 -0.0171 0.1864 0.0294

0.1190 -0.5859 -0.0150 0.1676 0.0012

0.1205 -0.5807 -0.0122 0.1326 -0.0388

0.1223 -0.5772 -0.0102 0.1029 -0.0634

0.1234 -0.5760 -0.0073 0.0532 -0.1095

0.1256 -0.5748 -0.0055 0.0147 -0.1433

0.1193 -0.5722 -0.0175 0.1531 0.2142

0.1209 -0.5668 -0.0149 0.1496 0.1523

0.1246 -0.5592 -0.0100 0.1383 0.0588

0.1304 -0.5499 -0.0044 0.1105 -0.0423

0.1352 -0.5374 0.0005 0.0854 -0.1243

0.1409 -0.5280 0.0035 0.0737 -0.1620

0.1475 -0.5146 0.0034 0.0817 -0.1365

0.1522 -0.5039 0.0021 0.0856 -0.1096

Birth Cohort (By first year of cohort)

Trend

Earnings

Rate

Unemployment

(*1,000)
104

Table C-5.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level.

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

Age

Group

59 0.9980 -0.3565 -0.2679 -0.2995 -0.2452 -0.2002 -0.3634

60 1.9146 -0.3401 -0.2442 -0.2868 -0.2450 -0.2081 -0.3561

61 -1.8152 -0.4513 -0.1965 -0.2676 -0.2449 -0.2028 -0.3281

62 -1.7470 -0.4371 -0.1774 -0.2689 -0.2449 -0.2071 -0.3045

63 -1.7370 -0.4181 -0.1547 -0.2806 -0.2518 -0.2213 -0.2897

64 -1.8328 -0.3689 -0.1366 -0.2971 -0.2612 -0.2389 -0.2775

65 -1.9748 -0.2936 -0.1038 -0.3077 -0.2840 -0.2507 -0.2783

66 -2.0802 -0.2081 -0.1010 -0.3480 -0.3076 -0.2804 -0.3098

67 6.5030 0.0152 -0.1065 -0.4000 -0.3041 -0.2823 -0.2791

68 4.9293 0.0301 -0.1436 -0.4302 -0.2832 -0.2701 -0.2644

69 2.3259 0.0247 -0.2155 -0.4751 -0.2711 -0.2492 -0.2534

70 -0.9556 -0.7001 -0.2887 -0.4913 -0.0303 0.0808 -0.1957

Coefficient

for

School

Some College In

Social

Constant Receipt Married Married Graduate College Graduate

Education Levelb

Security Marital Statusa High

Age + Benefit Never School
105

1910 1920 1930 1940 1950 1960 1970 1980

0.1529 -0.4909 0.0006 0.0827 -0.1033

0.1516 -0.4769 -0.0018 0.0865 -0.0773

0.1343 -0.4550 -0.0261 0.2475 0.5362

0.1171 -0.4367 -0.0317 0.2748 0.5821

0.1053 -0.4098 -0.0359 0.2924 0.6150

0.1026 -0.3795 -0.0364 0.2809 0.6052

0.0983 -0.3452 -0.0361 0.2864 0.5994

0.0911 -0.3151 -0.0377 0.3364 0.6485

0.0708 -0.2956 -0.0121 0.0660 0.0677

0.0699 -0.2831 -0.0161 0.1691 0.2458

0.0659 -0.2784 -0.0220 0.3119 0.4507

0.1287 -0.3239 0.0080 -0.3019 -0.1516

Rate (*1,000)

Unemployment

Trend

Earnings Birth Cohort (By first year of cohort)
106

Table C-6.

Historical Unemployment Logit Coefficients for Women

Age

Group

16 -0.5744 0.0397 0.2364 0.4052 -0.6937

17 -2.8524 0.0696 -0.5093 -0.2517 -0.4547

18 -1.2497 0.0344 -0.5125 -0.3504 -0.6530

19 -1.1164 -0.0046 -0.4748 -0.3922 -0.7601

20 -1.4851 -0.0367 -0.4146 -0.4055 -0.7710

21 -1.9782 -0.0658 -0.3819 -0.4132 -0.7664

22 -2.4001 -0.0831 -0.3350 -0.4185 -0.7640

23 -2.3654 -0.1118 -0.2960 -0.4283 -0.7793

24 -2.0286 -0.1192 -0.2614 -0.4365 -0.7436

25 -2.5743 -0.0951 -0.2526 -0.4426 -0.2009 -0.4203 -0.0676

26 -2.0627 -0.1105 -0.2286 -0.4618 -0.2072 -0.3352 -0.0625

27 -1.7486 -0.1398 -0.2045 -0.4765 -0.2042 -0.2750 -0.0530

28 -1.6406 -0.1740 -0.1856 -0.4934 -0.1953 -0.2375 -0.0437

29 -1.5497 -0.1933 -0.1733 -0.5132 -0.1934 -0.2145 -0.0425

30 -1.5676 -0.1942 -0.1617 -0.5282 -0.1922 -0.2016 -0.0466

31 -2.2292 -0.1860 -0.1460 -0.5412 -0.1924 -0.1905 -0.0503

32 -2.1872 -0.1843 -0.1329 -0.5582 -0.1957 -0.1842 -0.0551

33 -2.1355 -0.1456 -0.1160 -0.5713 -0.2021 -0.1824 -0.0646

34 -2.0291 -0.1050 -0.1021 -0.5857 -0.2067 -0.1889 -0.0822

35 -1.8033 -0.0781 -0.0891 -0.5998 -0.2160 -0.1988 -0.1098

36 -1.6244 -0.0437 -0.0821 -0.6154 -0.2210 -0.2062 -0.1282

37 -1.4958 -0.0013 -0.0815 -0.6260 -0.2247 -0.2170 -0.1464

38 -1.3666 0.0167 -0.0811 -0.6331 -0.2273 -0.2273 -0.1589

39 -1.5013 0.0205 -0.0831 -0.6295 -0.2326 -0.2358 -0.1694

40 -1.6290 0.0196 -0.0888 -0.6233 -0.2337 -0.2365 -0.1630

41 -2.2630 -0.0044 -0.0933 -0.6104 -0.2406 -0.2413 -0.1588

42 -2.2005 -0.0433 -0.0961 -0.5981 -0.2482 -0.2411 -0.1479

43 -1.9504 -0.0760 -0.1043 -0.5862 -0.2560 -0.2430 -0.1420

44 -1.6513 -0.0997 -0.1033 -0.5814 -0.2544 -0.2342 -0.1123

45 -1.3123 -0.1112 -0.0999 -0.5785 -0.2543 -0.2305 -0.0944

46 -0.9224 -0.1119 -0.0939 -0.5746 -0.2488 -0.2153 -0.0808

47 -0.6196 -0.1013 -0.0915 -0.5718 -0.2444 -0.2048 -0.0739

48 -0.4184 -0.1123 -0.0875 -0.5691 -0.2439 -0.1939 -0.0624

49 -0.2496 -0.1421 -0.0885 -0.5678 -0.2540 -0.1919 -0.0710

50 -0.1002 -0.1954 -0.0869 -0.5641 -0.2713 -0.1978 -0.0796

51 -0.3063 -0.2348 -0.0841 -0.5607 -0.2967 -0.2174 -0.0884

52 -0.0434 -0.2760 -0.0752 -0.5532 -0.3169 -0.2337 -0.0971

53 0.3137 -0.2815 -0.0765 -0.5513 -0.3332 -0.2435 -0.1056

54 0.8432 -0.2472 -0.0813 -0.5350 -0.3504 -0.2564 -0.1218

55 1.2630 -0.1971 -0.0897 -0.5233 -0.3533 -0.2558 -0.1312

56 1.7236 -0.1849 -0.1113 -0.5205 -0.3461 -0.2431 -0.1471

57 2.1024 -0.1943 -0.1433 -0.5197 -0.3438 -0.2426 -0.1670

58 2.4058 -0.2260 -0.1699 -0.5121 -0.3372 -0.2486 -0.1943

Age + Benefit

High

School

Never School Some

Graduate College Graduate

College In

Constant Receipt

Coefficient

for

Education Levelb Social

Security

Married Married

Marital Statusa
107

Continued

1910 1920 1930 1940 1950 1960 1970 1980

0.2001 0.0325 -0.6237 -0.0116 -0.1316 -0.3254

0.0934 0.0359 -0.6004 -0.0296 -0.0004 0.0188

-0.0018 0.0387 -0.8024 -0.0290 0.0355 0.0600

-0.0396 0.0347 -0.9094 -0.0266 0.0407 0.0376

-0.0517 0.0359 -0.9583 -0.0259 0.0337 0.0310

-0.0593 0.0368 -0.9775 -0.0260 0.0084 0.0492

-0.0729 0.0436 -0.9941 -0.0230 -0.0249 -0.0141

-0.0909 0.0512 -1.0080 -0.0196 -0.0573 -0.0838

-0.1119 0.0559 -1.0121 -0.0165 -0.0861 -0.1555

-0.1241 0.0620 -0.9874 -0.0163 -0.0995 -0.2092

-0.1540 0.0611 -0.9906 -0.0137 -0.1064 -0.2559

-0.1805 0.0617 -0.9853 -0.0118 -0.1088 -0.2767

-0.2007 0.0668 -0.9678 -0.0106 -0.0953 -0.2625

-0.2145 0.0739 -0.9407 -0.0101 -0.0803 -0.2381

-0.2194 0.0820 -0.9137 -0.0089 -0.0746 -0.2325

-0.2169 0.0763 -0.8925 -0.0236 0.2008 0.2221

-0.2136 0.0819 -0.8730 -0.0209 0.1602 0.1586

-0.2046 0.0874 -0.8607 -0.0166 0.1107 0.0627

-0.1861 0.0933 -0.8568 -0.0119 0.0616 -0.0307

-0.1685 0.0934 -0.8487 -0.0076 0.0157 -0.1240

-0.1547 0.0879 -0.8438 -0.0041 -0.0280 -0.2116

-0.1356 0.0814 -0.8393 -0.0021 -0.0516 -0.2684

-0.1217 0.0777 -0.8320 -0.0017 -0.0557 -0.2839

-0.1133 0.0752 -0.8233 -0.0023 -0.0461 -0.2741

-0.1007 0.0766 -0.8239 -0.0043 -0.0231 -0.2398

-0.0848 0.0669 -0.8211 -0.0240 0.2034 0.3188

-0.0794 0.0675 -0.8226 -0.0226 0.1796 0.2764

-0.0702 0.0746 -0.8250 -0.0191 0.1384 0.2027

-0.0723 0.0847 -0.8309 -0.0147 0.0997 0.1258

-0.0810 0.0890 -0.8335 -0.0111 0.0680 0.0680

-0.0831 0.0902 -0.8395 -0.0090 0.0507 0.0370

-0.0525 0.0903 -0.8420 -0.0092 0.0552 0.0496

-0.0165 0.0902 -0.8484 -0.0110 0.0802 0.1010

0.0348 0.0852 -0.8508 -0.0131 0.1031 0.1457

0.0858 0.0831 -0.8532 -0.0144 0.1214 0.1754

0.1462 0.0800 -0.8553 -0.0126 -0.0070 0.1133

0.1610 0.0782 -0.8590 -0.0136 0.0074 0.1429

0.1688 0.0758 -0.8614 -0.0145 0.0227 0.1692

0.1569 0.0796 -0.8644 -0.0123 0.0003 0.1358

0.1431 0.0799 -0.8651 -0.0098 -0.0282 0.0895

0.0787 -0.8675 -0.0063 -0.0711 0.0073

0.0793 -0.8755 -0.0031 -0.1106 -0.0770

0.0846 -0.8747 -0.0001 -0.1462 -0.1437

Under Birth Cohort (By first year of cohort)

(*1,000) Trend

Unemployment Earnings

Age 6 Rate

Children

Number of
108

Table C-6.

Continued

Source: Congressional Budget Office.

Note: Coefficient values shown in bold indicate statistical significance at the 5 percent level.

a. The omitted category includes divorced and widowed individuals.

b. The omitted category includes individuals with less than a high school education.

Age

Group

59 3.3000 -0.2814 -0.1866 -0.5214 -0.3191 -0.2413 -0.2125

60 4.0249 -0.2974 -0.1929 -0.5284 -0.3095 -0.2483 -0.2479

61 -2.9748 -0.4446 -0.2008 -0.5227 -0.3016 -0.2728 -0.2673

62 -2.8245 -0.4228 -0.1792 -0.5024 -0.2958 -0.2953 -0.2858

63 -2.7286 -0.3924 -0.1171 -0.4690 -0.2904 -0.3326 -0.3022

64 -3.1729 -0.3376 -0.0640 -0.4248 -0.2865 -0.3857 -0.3180

65 -3.4676 -0.2568 -0.0098 -0.3720 -0.2746 -0.4089 -0.3142

66 -3.3812 -0.1903 0.0369 -0.3410 -0.2531 -0.4011 -0.3180

67 -3.3176 -0.0920 0.0778 -0.3523 -0.2161 -0.3912 -0.3089

68 -0.6947 -0.0939 0.0343 -0.3860 -0.1884 -0.3295 -0.3196

69 -0.1994 -0.1035 0.0220 -0.4170 -0.1730 -0.2651 -0.3509

70 0.6105 -0.4308 -0.0981 -0.5140 -0.1668 -0.4180 -0.3136

Benefit

Coefficient

for

School

College

Graduate College Graduate

Age +

Constant Receipt Married Married

In

Security Marital Statusa High

Never School Some

Social Education Levelb
109

1910 1920 1930 1940 1950 1960 1970 1980

0.0890 -0.8694 -0.0004 -0.1428 -0.1508

0.0973 -0.8709 -0.0008 -0.1314 -0.1492

0.0937 -0.8586 -0.0168 0.1745 0.2375

0.0840 -0.8166 -0.0190 0.1770 0.2513

0.0692 -0.7687 -0.0223 0.1921 0.2828

0.0617 -0.7142 -0.0222 0.2035 0.2840

0.0473 -0.6248 -0.0211 0.1967 0.2523

0.0325 -0.5538 -0.0199 0.2053 0.2176

0.0159 -0.5356 0.0008 -0.0072 -0.2645

0.0156 -0.5279 0.0002 0.0484 -0.2257

0.0162 -0.5179 -0.0015 0.1250 -0.1779

0.0532 -0.3598 -0.0059 0.0120 0.2668

Number of

Trend

Under 

Rate (*1,000)

Unemployment Earnings Birth Cohort (By first year of cohort)

Children

Age 6

111

Appendix D:

Appendi x D: Permanent and 

Transitory Earnings Shocks

The approach used to model the idiosyncratic component of earnings in the Congressional Budget Office’s long-term microsimulation model (CBOLT) was actually 

first proven useful in another context: generating a synthetic microsample for studying consumption and wealth accumulation behavior.1 Decomposing earnings shocks 

into distinct permanent and transitory components is particularly useful for researchers studying consumption behavior, because economic theory predicts that spending 

will respond differently to the two types of shocks. Indeed, one of the reasons for 

choosing the permanent or transitory shock approach is the ultimate goal of projecting micro-level saving and wealth along with the other projections in the microsimulation. For this paper, however, the key insight is that the result of generating realistic 

heterogeneity in a microsample in CBOLT is the same as in the consumption literature, and thus the technique lends itself to the demands of CBOLT. 

Every person (subscripted i = 1,...,n) in an actual or simulated data set has an actual 

log earnings observation at time t, denoted here using yit. Each person also has a predicted value for log earnings, which depends on observable characteristics (age, sex, 

education, and birth cohort) collectively denoted as Xit. That predicted value is 

denoted using the generic functional reference f(Xit); in the case of CBOLT, f(Xit) is 

the linear regression for log earnings. The gap between the actual and predicted values 

(denoted eit) is derived from the identity eit = yit - f(Xit). As described in the text, the 

component of interest here is the eit; the observable component is taken as given from 

the earnings equations. 

The CBOLT approach to simulating the idiosyncratic error terms is to separate the 

overall individual error into a permanent component (e

p

it) and a transitory component (e

t

it), such that the two pieces sum to the total error for every individual in every 

year: 

eit = e

p

it + e

t

it. 

The transitory component is random in each year, drawn from a log-normal distribution with variance s

2

t

. The permanent component moves slowly over time, according 

to e

p

it = e

p

it-1 + nit. The permanent shocks (the nit) are drawn from a log-normal distri1. See Christopher D. Carroll, “The Buffer-Stock Theory of Saving: Some Macroeconomic Evidence,” Brookings Papers on Economic Activity, vol. 2 (1992), pp. 61–135. 
112

bution with variance s

2

p. Any given person will start his or her earnings career with 

some initial value for the permanent differential (e

p

i0), which then evolves over time 

through the sequence of permanent shocks. 

The permanent and transitory decompositions of the error terms lead to useful adding-up conditions that make it possible to estimate the variances of the permanent 

and transitory shocks using longitudinal data. First, note that the total variance for 

any group of people in any year obeys the condition VAR(eit) = VAR(e

p

it) + VAR(e

t

it). 

Second, note that the variance of the permanent differentials for any group will 

increase over time, by exactly s

2

p per year because VAR(e

p

it) = VAR(e

p

it-1) + VAR(nit). 

Thus, if a given cohort starts at age 0 with an initial distribution of permanent differentials (VAR(e

p

i0)), then by the time the cohort reaches age a, the variance of the permanent differentials will be VAR(e

p

i0) + a × s2p. 

Those relationships make it possible to estimate underlying variances using longitudinal data. The idea is to measure the earnings equation error terms for each person one 

year apart, two years apart, three years apart, and so forth, and to observe how the 

variance of the error terms in the sample grows as the time gap increases. For example, 

the variance for the errors might be 6 percent at a one-year gap, 7 percent at a twoyear gap, 8 percent at a three-year gap, and 9 percent at a four-year gap. The growth in 

the variance of the differentials represents the permanent component; in the example, 

this would be 1 percent. The same analysis (through the adding-up condition) also 

produces an estimate of the transitory variance; in the example, that would be 3 percent. Indeed, these values (s

2

p = 1 percent, s

2

t

 = 3 percent) are roughly consistent with 

direct empirical estimates for the variances of the shocks.2

The actual values for the transitory variances in CBOLT are set higher than these earlier estimates in order to be consistent with patterns of earnings in the overall U.S. 

economy. (The earlier research focused on a subset of families whose earnings are, 

through sample selection criteria, much more stable.) In CBOLT, the variance of the 

permanent shocks for men is set to 1 percent, and the variance of the transitory 

shocks for men is set to 20.25 percent. The variance of the permanent shocks for 

women is set to 0.36 percent, and the variance of the transitory shocks for women is 

set to 16 percent. 

2. Ibid.



INSIDE MAIL

CONGRESS OF THE UNITED STATES

CONGRESSIONAL BUDGET OFFICE

WASHINGTON, DC 20515