Technical Paper Series

Congressional Budget Office

Washington, DC

PROJECTING LONGITUDINAL MARRIAGE PATTERNS

FOR LONG-RUN POLICY ANALYSIS

Josh O’Harra (e-mail: josho@cbo.gov)

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

 Congressional Budget Office

Washington, DC

October 2002

2002-2

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

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

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

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

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

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

Lieberman, Noah Meyerson, Kevin Perese, Michael Simpson, and Joel Smith of CBO’s LongTerm Modeling Group for their helpful comments. 

1

I. Introduction

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

demographic variables such as age, sex, education, marital status, and spouse’s characteristics for

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

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

issues, because the longitudinal data file would have all the information needed to tabulate taxes

paid and benefits received under any set of program rules. The goal of dynamic micro-simulation

modeling is to produce a longitudinal data set like the one described above. This paper describes

one set of building blocks in dynamic micro-simulation: marital transition equations that predict

first marriage, divorce, and remarriage for individuals over time. 

A dynamic micro-simulation model starts with economic and demographic data for a

current sample of the population and then stochastically “ages” that sample forward through time,

ultimately generating a longitudinal data file, which is useful for studying Social Security and

other programs under various policy rules. The stochastic simulation process involves applying a

sequence of “transition equations” to each person in the sample. Those transition equations are,

in effect, probability distributions for demographic events (such as marital transitions, educational

attainment, or even death) and economic events (such as changes in earnings or pension

coverage). Random numbers are compared with estimated transition probabilities to determine

actual outcomes and thus preserve underlying heterogeneity in the population.

Projecting marital transitions (first marriage, divorce, and remarriage) is an integral part of

dynamic micro-simulation, because marriage histories directly affect beneficiary status and benefit

amounts under many government programs and because marital status influences labor supply,
2

saving, and other types of relevant behaviors. However, there are several ways to interpret the

existing data on marital transitions, and the interpretation chosen may affect one’s long-run

projections of marital outcomes. 

The first half of this paper considers historical patterns of marriage and divorce using the

retrospective marital history module in the 1996 Survey of Income and Program Participation

(SIPP). The SIPP data show significant effects on transition probabilities from some expected

variables, such as age, education, and income (for all marital transitions), and nonlinear duration

effects (for divorce and remarriage). The data also indicate some dramatic differences across

cohorts even after controlling for age, education, income, and duration; thus, dynamic microprojections require choosing out-of-sample values for those cohort effects. For example, people

born in the 1960s and 1970s are less likely to get married at younger ages (even after controlling

for education and other variables) than people born in the 1950s or earlier. Therefore, making

projections for future generations requires speculating whether first marriage probabilities for

people born in the 1980s and beyond will be the same, lower, or possibly even higher than those

for the 1960s and 1970s cohorts. 

The second half of this paper uses those estimated marital transition equations to show

how longitudinal marriage patterns might be projected through 2075 for a sample of the U.S.

population. Given this technique and the underlying assumptions it implies, aggregate results from

the dynamic micro-simulation modeling are fundamentally consistent with projections generated

by standard actuarial techniques. These results generally suggest that fewer people are ever

marrying, divorce rates are relatively flat, and remarriage rates are declining. Furthermore, they

imply that the mean age of first marriage will creep up over the period, leveling off around age 28. 
1. At Social Security’s inception, divorced spouses were not eligible for benefits on their ex-spouses’

earnings records. In 1965, divorced spouses with 20 or more years of marriage to a fully insured worker

became eligible for benefits; the threshold was lowered to 10 years in 1977.

3

The effect of those trends on future cohorts suggests that the percentage of nonwidowed women

eligible to collect benefits on a spouse's earnings record will fall. If the past relationship between

income and marital behavior continues to hold, this trend will probably affect low-earning women

more adversely than it will affect high-earning women. These projections illustrate the capabilities

of one set of modeling techniques. As with any forecast, the results depend on assumptions about

future trends in behavior; undoubtedly, other assumptions would produce different results. 

Because Social Security’s Old-Age and Survivors Insurance benefits represent the single

largest government transfer program, with future obligations expected to increase substantially as

the baby-boom generation begins to retire, detailed longitudinal earnings and marital projections

represent a valuable tool for long-range policy analysis. There are two ways to collect OAI

benefits. Most OAI beneficiaries draw an award based on their own earnings record (so-called

fully insured workers). Individuals who are not fully insured workers but who are or were

married to a fully insured worker can receive OAI benefits based on the earnings records of their

spouse. Those people must either be currently married to a fully insured worker, a widow(er) of a

fully insured worker, or an ex-spouse of a fully insured worker with a marital duration of 10 or

more years.1 It is also possible for an individual to be dually entitled--that is, to receive a

combination of own-worker benefits and some type of spousal benefits. 

In addition to their significance with respect to explicit benefit rules, marital histories

affect other behavior relevant to forecasting Social Security obligations. For example, marital

status affects labor supply. The theory is that the presence of a second income may dictate
2. See Frees (1999) and Bell (1997).

4

whether both partners need to work. That decision, in turn, affects the number of retirees

claiming benefits based on their own earnings history. Retirement timing decisions are also partly

determined by marital status. Workers who do not have a spouse or family reliant upon their

wages could be more likely to exit the labor force earlier than those with family obligations.

Married couples are also likely to have higher personal savings. Finally, the well-documented

relationship between marriage and mortality suggests that changes in marital patterns may operate

on Social Security’s finances by changing the annuitized value of OAI benefits at retirement. That

is, if more people enter retirement unmarried, their life expectancy will be shorter, on average, and

the annuitized value of their benefits will be smaller. 

The multiple interactions between marital status and other determinants of Social

Security’s finances make it necessary to model each process explicitly. Actuarial methods treat

the age/sex/marital status group as the relevant unit of analysis. Those estimates generate

reasonable cross-sectional marital distributions but fail to account for the timing of transitions and

the characteristics of people experiencing them.2 The alternative method--dynamic microsimulation--allows for interaction between covariates and reflects true population heterogeneity. 
3. The NCHS data is the main data set used by the Social Security Administration in its actuarial analysis of

marital transitions. See, for example, Bell (1997). The data were compiled annually from state Vital

Statistics offices and issued in numerous public-use formats. However, as a result of budget cuts in 1990,

those data are no longer collected or reported.

5

II. Marital Transitions Across Age and Cohort Groups

Marriage patterns in the United States have changed in fundamental ways during the past

several decades, and the interpretation of those changes will dominate future projections. For

example, rates of first marriage at the youngest age groups have fallen significantly for cohorts

born after 1950, but has that trend stopped? Also, has the observed increase in divorce

probabilities for cohorts born after 1950 slowed or even reversed? This section of the paper uses

data from the 1996 Survey of Income and Program Participation retrospective marital history

module to analyze marital transitions across age and cohort groups. The SIPP data confirm

observations about marital transitions that are evident in aggregate data.

Although the SIPP is a large and nationally representative sample of the population, there

are still reasons for caution when using its data to analyze marital transitions, especially over time. 

Table 1 compares SIPP marital transition rates with aggregate data from the Vital Statistics

database collected by the National Center for Health Statistics (NCHS).3 In general, the

aggregate transition rates in the data sets are similar; noticeable differences between the 1990 and 

1995 measures include lower divorce rates for both sexes and a lower remarriage rate for men. 

Those differences can be explained by the slightly different time periods, the fact that the SIPP

data exclude the institutional population, and reporting problems associated with distinctions

between (for example) divorce and separation. The table does not compare SIPP and NCHS

marital transitions for earlier time periods because the SIPP, by construction, only questioned
4. Although the 1996 SIPP has over 69,000 observations eligible for the marital history module, straight

tabulation by age, sex, and cohort would still generate noisy graphs because of the low rates of occurrence

for marital events.

6

people alive in 1996 about their marital history. Therefore, any comparison of earlier time periods

would not be meaningful, but it should be acknowledged that recall bias might affect the analysis

that follows.

Figures 1 through 8 show estimated marital transition probabilities from the SIPP across

age and cohort groups. The graphs are produced using a kernel-smoothing technique, where any

point on the graph is a weighted average of all observations within a fixed “band” around that

age.4 For example, the value for age 25 in a given cohort is actually a weighted average of people

ages 23, 24, 25, 26, and 27 in that cohort, with people age 25 having the highest weight, people

ages 24 and 26 having smaller weights, and people ages 23 and 27 having even smaller weights. 

All of the vertical axes are expressed as rates per 100 eligible, where eligibility varies with the

transition--first marriage eligibles have never married, divorce eligibles are currently married, and

remarriage eligibles are currently divorced or widowed. 

First marriage rates are shown in Figures 1 through 4. Figures 1 and 2 show annual rates

of first marriage, while Figures 3 and 4 show cumulative rates. (One-hundred minus the

cumulative rate at any given age is the fraction of people who never marry.) The two sets of

figures tell a similar story in slightly different ways. There have been significant declines in rates

of first marriage at young ages (through the mid-20s) for all cohorts since the 1950s. However,

the rate of decline (the gap between the sequentially ordered cohort probabilities at a given age)

for both men and women seems to have slowed. Also, there is some evidence that rates of first

marriage may actually be slightly higher at older ages (late 20s and beyond) for more recent
7

cohorts. Thus, as noted, there is no single statement about time/cohort effects that describes what

is happening at all ages, which suggests flexibility in the econometric specification for the

transition equations. The one distinguishing feature for both men and women is the existence of

the hump-shaped annual probability distribution by age, but the exact shape has evolved over

time.

Figures 3 and 4 show rates of first marriage in a slightly different way, as a cumulative

probability from age 16 onward. Men and women born in more recent cohorts are less likely to

have ever been married by that age. However, the fact that the gaps between cohorts shrink with

age is consistent with the slight increases in marriage rates at older ages. More important, this

fact implies that applying the same marriage rates to a larger (as yet unmarried) population will

eventually asymptote to similar cumulative levels. 

Figures 5 and 6 show divorce rates by age and cohort for females and males, respectively. 

For both sexes, divorce rates for cohorts born after 1950 are noticeably higher at younger ages,

but those rates are approaching (or even below) the pre-1950 cohort levels in the latest years for

which the SIPP data are available. There is also some evidence that divorce rates for some age

groups in the 1960s cohort (25- to 35-year-olds) are actually lower than those of the 1950s

cohort, signifying a reversal back toward the pre-1950 cohort rates. 

The remarriage rates in Figures 7 and 8 indicate less change across cohorts than the rates

for first marriage and divorce. There seems to be little, if any, difference in the rates of remarriage

for women over time, and only a slight decline (but proportional across age groups for the

observed cohorts) for men. As with the other transitions, there is a smooth declining pattern in

remarriage probabilities by age. 
8

The important forecasting question with respect to all three sets of marital transitions is

whether the observed differences across cohort groups can be explained empirically. For

example, as will be shown, the micro data indicate that whether or not a person attends college

affects his or her probability of first marriage, and it is well known that college attendance rates

have risen over time. But, is the increase in college attendance (or any other reasonable

explanatory variable one might enter into a transition equation) enough to explain the changes in

first marriage rates across cohorts as shown in Figures 1 through 4? That issue is addressed in the

next section.

III. Modeling Marital Transitions

The 1996 SIPP data used to generate Figures 1 through 8 indicate key aspects about

marriage and divorce rates that should be considered when estimating marital transition equations:

there are smooth but generally nonlinear patterns of transition probabilities across age groups

within any given cohort, and there seem to have been changes in transition rates at certain ages

across cohorts. It remains to be shown in this section whether those observations can be

explained by underlying determinants of marital transition, such as education and income (for all

transitions) or duration in state (for divorce and remarriage). But it is clear that flexibility in the

econometric analysis across both age and cohort dimensions is crucial--one would not, for

example, want to impose a cohort or time effect that is proportional across all age groups,

because some transition probabilities have actually moved in different directions along the age

dimension. 

It is useful to begin this section by considering the possible determinants of marital
5. Widowhood is a function of spouse mortality probabilities and is considered separately in the dynamic

micro-simulation model.

6. This list is compiled based on Lillard and Panis (1999), Perese (1999), Peters (1988), and Lillard and

Waite (2000).

7. Caldwell and others (1998).

9

transitions and existing empirical evidence about their effects. The three transitions considered

here are first marriage, divorce, and remarriage.5 Theory suggests that first marriage is influenced

by factors such as age, sex, race, educational attainment, income, the presence or absence of

children, urbanicity, possibly some parental characteristics, and some version of a time or cohort

effect.6 Divorce determinants are basically the same but also include the number of previous

marriages and marital duration. Finally, the factors affecting remarriage are similar to those

affecting divorce, except the duration variable is time since divorce and an additional control can

be added to account for the method of marital termination (divorce or death). Also, those

processes can be modeled at the couple level so that the effect of a spouse’s characteristics could

also be estimated (for example, the effect of a husband’s and wife’s income differential on the

probability of marital dissolution).

There is some variation in the actual variables used and methods employed in dynamic

micro-simulation models because of differences in underlying data sources and differences in the

list of control variables available in the model. The Social Security Administration’s POLISIM (a

spin-off of the CORSIM model) considers first marriage as a logistic process estimated separately

on 20 demographic groups.7 The sample breaks are by age, sex, race, education, and weeks

worked. Divorce is modeled for four groups as a function of marital duration, race and, since

POLISIM uses linked data, spouse wage differentials. Finally, POLISIM models remarriage as a
8. Perese (1999).

9. Lillard and Panis (1999).

10

two-step process. First, logits are used to determine who from different age/sex/racial groups

reenters the marriage market and, from there, actual spouses are assigned. POLISIM does not

control for time trends or cohort effects in any of the transition equations.

The Urban Institute’s DYNASIM model uses a similar approach. Its sample relies on

different data but employs similar sample breaks.8 There are eight logits estimating first marriage

probabilities for various age/sex groups that rely on standard social and economic characteristics

of individuals. Divorce probabilities are generated in a similar fashion, but they rely on the

estimation of a single equation and the use of couples as the relevant unit of observation.

Remarriage is randomly assigned on the basis of age-specific remarriage rates from the 1990 Vital

Statistics. Again, while the recent trends in marital phenomena are considered in determining the

relevant sample, in no case does DYNASIM explicitly account for time or cohort effects.

A final example is the micro-simulation model used by the Social Security Administration

to project policy outcomes for the next 20 years: the Modeling Income in the Near Term (MINT)

project. The MINT model uses only two hazard models, one for divorce and one for any

marriage.9 Although that model employs a different functional form (a hazard model), the

determinants of marital transitions are standard: age, sex, race, educational attainment, number of

previous marriages, and income. Finally, unlike the previous two models, MINT explicitly

controls for time. With respect to marriage, calendar time (1901-1994) is used; for divorce,

calendar time, with a node at 1980, is employed. For both transitions, that time trend is extended

by multiplying the coefficient estimate by the calendar year. Consequently, the effect of time
11

continues to grow. 

The marital transitions considered here, as in the models above, are limited by both the

data set used for estimation and by the list of control variables available in the micro-simulation

model itself. The SIPP data set has detailed demographics such that any variable listed above is a

potential candidate for use in the equations, but the fact that (for example) race is not available on

the data set used for the Congressional Budget Office’s micro-simulation model implies that it

would not be meaningful to control for race in the SIPP-based estimation. In general, when using

the SIPP one only has access to current income information. But the estimation here relied on a

special version of the SIPP that has been linked to SSA’s administrative earnings data, so it can

use earnings for the entire longitudinal history of sample members. Furthermore, the marital

history topical module allowed for the creation of longitudinal marital histories. When combined,

those data allowed for person-year units of observation and a final set of controls that considered

age, sex, education, lagged income, and time/cohort effects (for all three transitions) and a

nonlinear function of duration (for divorce and remarriage). 

Given the list of control variables, the next decision involves choosing an estimation

strategy. Most of the exercises above involve splitting the sample by demographics and

estimating either logit or hazard models. Although it is clear that one should estimate different

equations across sex groups, the impression of the data one gets from Figures 1 through 8 is that 

broad age groups will not capture the interesting curvature of the underlying transition

probabilities across age groups. Also, it is apparent that cohort/time effects will vary with age,

and it is also likely that the effect of some control variables (education or income) will vary across

age groups. So, even if one used a polynomial in age to capture the curvature of the transition
10. The “bands” actually vary in size at the youngest ages; the equation for 17-year-olds includes ages 16

through 19, for 18-year-olds includes ages 16 through 20, etc; for most ages the bands are set to plus or

minus four years.

12

probabilities by age, the equation would still be imposing the same effect from the other control

variables by age. As indicated in the last section, these observations motivate a very flexible

approach to measuring determinants of transition probabilities.

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

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

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

used in the estimation actually includes every observation within a fixed age band around that

point. For example, the estimation for males age 25 actually includes all males ages 21 though 29,

though, as in the development of the smoothed Figures 1 through 8, the observations farther from

the center are weighted less heavily. As a further control, age itself is actually one of the variables

used in the estimation. This approach has the desirable aspect that the effect of every other

control variable (income, education, duration, and cohort/time effects) is allowed to vary with age

(which would be true with separate equations for each age group) but does not suffer from small

sample size problems (which would be the case if one ran single equations for each individual

age). 

The concerns about using an overly restrictive estimation strategy for the three marital

transitions are borne out in the results, which are shown in Tables 2 through 7. Each table shows

a series of logits estimated for a given sex group; for example, Table 2 has the results of

estimating separate first marriage logits for females ages 17 through 60.10 Each set of estimated

coefficients is reported on a separate row, with significant (90 percent level) variables indicated by
13

BOLD numbering. As indicated, there is both a constant and age coefficient for each single year

of the age group, which at first blush seems odd but makes sense in the context of the agecentered approach. The age coefficient is an estimate of the slope of the probability function at

that age, but it may make more sense to think of the actual simulation “constant” that applies to

everyone of a given age as the estimated constant term plus the age coefficient times the value of

age. 

The predominance of BOLD numbering in Tables 2 and 3, especially at the youngest ages,

implies that the chosen correlates do in fact significantly affect first marriage probabilities. For

both men and women, education (a dummy variable indicating 14 or more years of schooling) has

a significant negative effect on first marriage through the early 20s but then becomes positive and

significant for age groups through the mid- to late 30s. Income (which is lagged, not

contemporaneous) has a significant positive effect for both women and men at most ages, though

the effect turns negative (and eventually becomes significant) for women past their late 40s. 

Tables 2 and 3 also provide an answer to one of the questions posed above: do the other

control variables (education and income) explain the trends in first marriage rates across cohorts

as shown in Figures 1 and 2? The answer is clearly no, as the residual cohort effects are all

significantly negative for the youngest age groups. But, as expected, that effect diminishes with

age, as the first marriage probabilities converge for the older age groups (and, as noted, might

even be higher for the more recent female cohorts at older ages). The obvious question for

forward-looking micro-simulation is how to fill in cohort effects after the historical data ends--

that is, for ages 46 and above for the 1950s cohort, ages 36 and above for the 1960s cohort, and
11. The SIPP data did not suggest any basis for extending trends to the 1980s cohort and beyond, so the last

set of residual cohort effects derived in all cases is for the 1970s group. Those residual terms are then

applied to the 1980s cohort and all future cohorts “born” into the model. 

14

ages 26 and above for the 1970s cohort.11 This paper explores one of several possible

approaches.

When considering how to extend the cohort effects, it helps to show the patterns in a

graphical form. The solid lines in Figures 9 and 10 show the residual cohort effects for first

marriage (from Tables 2 and 3) by age for the 1950s, 1960s, and 1970s cohorts. The dotted lines

show the extensions applied to the cohort residuals in order to produce forecasts. Notice first

that the last observed cohort residual for both males and females (the age 45 value for the 1950s

cohort) is assigned to all older age groups for that cohort. This is consistent with the following

interpretation: women (men) in the 1950s cohort were slightly more (slightly less) likely to get

married at age 45 than previous cohorts, and thus it seems reasonable to expect that the same will

be true at ages 46 and above. 

The second set of observations applies to the values for the 1960s and 1970s cohorts

between the ages of 26 and 46. The “s-shaped” pattern of estimated residuals is consistent with

the shifting of the peak age of first marriage rates to the right over time at the same time the

height of the peak is falling (see Figures 1 and 2). By assigning a pattern of residual cohort terms 

that is proportionally shifted from the previous cohort, the extensions shown in Figures 9 and 10 

keep the shape of the probability distribution constant but continue the shift. Thus, there is a

smooth transition of first marriage probabilities at all ages across the three cohorts. The

convergence of cohort effects at older ages is consistent with the underlying convergence of the

first marriage rates, again, as indicated by the available data in Figures 1 and 2. 
12. This is consistent with SSA’s assumption of an unchanging central (age-adjusted) divorce rate during

recent years and for the 75-year forecast horizon.

15

Tables 4 and 5 show age-centered logit estimates for divorce rates of women and men,

respectively; again, the importance of flexibility is underscored by the results. Education has

mixed (but generally negative) effects on divorce, and income actually has different signs for the

two sexes: lower lagged income is more likely to lead to divorce for men but less likely to lead to

divorce for women. The divorce equations also show a nonlinear specification in marital duration;

the positive or insignificant linear term and negative squared term for many age groups are

consistent with underlying divorce patterns: the probability of divorce initially rises after couples

are married but eventually starts to fall after they have been married a certain number of years

(though again, the data indicate that this nonlinear effect itself varies with age). 

Strong residual cohort effects are as evident for divorce as they are for first marriage, but

here the data may actually offer a little guidance about how to extend the residuals. The residual

cohort-dummy coefficient estimates in Tables 4 and 5 (also shown in Figures 11 and 12) clearly

reflect the patterns in the overall divorce probabilities by age and cohort (Figures 5 and 6). 

Divorce rates are higher for both sexes at younger ages in the 1950s and 1960s cohorts, but that

effect disappears with age--there are no significant residual differences in divorce probabilities for

the two younger cohorts at the latest ages that can be observed. Thus, one could speculate that

divorce rates will match those for the pre-1950 cohorts at older ages.12

The two sets of remarriage equations also confirm the importance of education, income,

and duration as underlying determinants, but residual cohort effects seem smaller (again, as

suggested by Figures 7 and 8). Tables 6 and 7 show the estimated coefficients; education has

mixed effects across age and sex groups, while income is generally a positive predictor of
16

remarriage. One also observes the same stable nonlinear duration effects as probability of

remarriage initially rises after a breakup, then begins to fall. The residual cohort terms are

generally smaller and often insignificant, reflecting the similarity of the remarriage rates by cohort

and age shown in Figures 13 and 14. To the extent that there are patterns (slightly lower

remarriage rates at all ages), it seems reasonable to extend those forward in time. 

The dramatic changes in marital transition rates indicated by the SIPP data across age, sex,

and cohort groups cannot be explained away by underlying economic variables or changes in

educational attainment. Further, the changes are not easily captured by time trends, because the

changes in transition rates often go in different directions at different ages, and even the trends

that are in a given direction (for example, the drop in first marriage rates at young ages) seem to

have changed slope, slowing in recent years. To make projections, one can do little more than

speculate how those patterns will continue to unfurl for existing cohorts as they age and for future

cohorts at all ages. The implications of the choices made for extending the residual terms are

drawn out in the next section, which uses the estimated transition equations in a dynamic microsimulation setting.
13. The labor force participation and earnings equations used in conjunction with these marital transition

equations are discussed in Harris and Sabelhaus (2002).

14. For a more thorough discussion, see CBO (2001).

17

IV. Projecting Longitudinal Marriage Patterns: 2000-2075

The goal of this paper is to show how to generate a set of longitudinal marriage histories

for a future sample of the population in a dynamic micro-simulation context. Given the structure

described above, the inputs to projecting micro marriage patterns include a base micro data file

with economic and demographic information for a current sample of the population; a set of

marital transition equations for first marriage, divorce, and remarriage; and another set of

transition equations that predicts the independent variables that affect marital transitions (in this

case, income and education). This section describes how those pieces are brought together in the

Congressional Budget Office Long Term (CBOLT) policy simulation model and provides some

basic results of the marital projection over the 2000 to 2075 period.13 The results depend on a

variety of assumptions, including how transition probabilities will evolve in the future. The

illustrative calculations presented here show the implication of just one set of assumptions. 

The CBOLT project began as an actuarial (“cell based”) model intended to mimic the

Office of the Chief Actuary’s (OCACT’s) methods for predicting the finances of Old-Age,

Survivors, and Disability Insurance (OASDI). The second version of the model built upon the

standard actuarial capabilities and introduced stochastic (Monte Carlo) simulation as well as

dynamic macro-feedback effects.14 CBOLT is currently entering its third stage, in which a

dynamic micro-simulation model embedded in the stochastic macro growth framework will

replace the standard actuarial projection modules for OASDI tax and benefit outcomes. The

marital equations developed in this paper are an integral part of that third phase of CBOLT
15. The simulations presented here do not use the entirety of the CBOLT projection machinery. For

example, some of the micro/macro linkages that will exist in the final model (marriage affects labor

supply, which affects aggregate real wage growth; that, in turn, affects individual income, which feeds

back into marital transitions) are ignored. 

16. See Smith (1989).

17. An exception is the SIPP data file used by the SSA in its MINT model, which has linked earnings and

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

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

18

development.15

The base data set used for all CBOLT projections is the Continuous Work History Sample

(CWHS). This data set, administered by SSA, contains longitudinal earnings information on a 1

percent stratified cluster sample of all people ever issued Social Security Numbers, which

translates into a sample size of roughly 3 million individuals with earnings reported for 1951 to

1998.16 Each year, the entire sample is followed, recording taxable earnings, total compensation,

self-employment status, OASDI benefit entitlement, or death. In addition, new individuals are

introduced to reflect the issuance of new Social Security Numbers. This sampling structure

makes for a truly unique longitudinal data set, in which each annual cross-section represents 1

percent of the population with Social Security Numbers. 

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

with the available cross-section or public-use longitudinal files such as the Panel Survey of Income

Dynamics (PSID). Available cross-section data sets do not have the requisite longitudinal

histories needed to project forward using dynamic micro-simulation.17 Publicly available

longitudinal data sets like the PSID are much smaller than the CWHS, and those data also suffer

from response problems for the highest-earning individuals as well as recall bias. The CWHS is

an administrative sample and therefore has much better reporting of income for the entire earnings

distribution. The downside is that the administrative nature of the CWHS limits the demographic
18. Throughout the imputations and projections, race is not used because the information on the CWHS

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

19. A discussion of the methods and data used in the imputations is available upon request.

19

data to the information that is available on the initial Social Security Number application: year of

birth, sex, and race.18 

Given the lack of detailed demographics, it is necessary to impute several key variables--

such as educational attainment, marital status, and marital history--when using the CWHS to

project future Social Security taxes and benefits. Those variables are imputed on the basis of

measured correlations between CWHS-observable and the imputed variables in the PSID, the

Survey of Income and Program Participation, and the Current Population Survey.19 Additionally,

part of the imputation strategy relies on historical simulation using CBOLT’s own simulation

modules over a period for which there are no actual micro data, but there are aggregate control

totals with which simulation results can be compared. 

The historical simulation exercise is a useful gauge of whether the estimated transition

equations are doing a good job of predicting marital outcomes in recent history. The simulations

start in 1984, at which point everyone alive in the CWHS has been assigned a marital status,

education, and duration in the relevant marital state. The assignments are made such that the

starting (1983) population matches the characteristics of the (SSA Area) population that CBOLT

uses for its aggregate reference point. The simulation then proceeds forward for 1984 through

1998 using the actual CWHS earnings and estimated marital transition equations. The results of

the exercise are a set of “calibration factors” that are, in effect, average errors in targeting the

observed aggregate marital distributions (again, as reported in the SSA Area population

estimates). Figures 15 and 16 illustrate the nature of the marital transition calibration factors (by
20. The only noticeable errors from the perspective of simulating forward are for divorce; the SIPP data are

too thin to completely capture the drop in divorce rates for younger cohorts that occurred in the 1980s and

1990s. These “calibration factors” for divorce effectively act as another set of cohort terms when

projecting into the future.

21. The similarities are even more striking when one considers that some of the processes used to project

forward are very different. For example, OCACT uses the distribution of “available” mates by age and

sex to predict how many marriages will occur, while CBOLT does not. For a thorough discussion of the

OCACT actuarial projections, see Bell (1997).

20

age) produced by the historical comparison. As hoped, there is little systematic divergence

between the actual trends in marital status and the results of the micro marriage modules.20

The fact that the marital transition equations estimated here are able to capture the trends

in aggregate marital distributions during the 1984 to 1998 period suggests that (setting aside

uncertainty about how to extend the residual cohort terms) the model is capable of generating

reasonable cross-sectional marital distributions. That conclusion is borne out in Table 8 for the

entire population and in Figures 17 and 18 for people ages 62 to 67 for whom Social Security

projections are particularly relevant. 

Table 8 shows cross-sectional marital distributions over time in both the OCACT and

CBOLT base-case projections. The most striking result is the similarity between the crosssectional marital distributions over the 75-year period; closer inspection shows a few small but

systematic differences.21 For example, CBO’s approach consistently produces fewer divorcees as

a proportion of the population. Also, by the end of the projection period, CBO’s estimates yield

about the same percentage of never married females but a slightly higher fraction of never married

males. The proportion widowed is slightly different, but that result stems from different mortality

projections. 

At first glance the fact that the proportion of the population that never married is

decreasing while the proportion married is increasing might appear to contradict the earlier story
21

suggesting that fewer people are marrying. However, because the age composition of the

population is changing, measures of overall cross-sectional distribution by marital status do not

accurately reflect the trends in either the OCACT or CBOLT projections. Figures 17 and 18

isolate the trends in the cross-sectional marriage distribution for people ages 62 through 67. That

age group is interesting because, by this point in their lives, most of the cumulative effects of all

three transitions modeled in this paper will probably be evident. These graphs suggest that the

age-centered transition equations, combined with the minor adjustment factors derived from the

historical simulations, generate individual behaviors that (when added up) approximately match

the aggregate projections used by the Social Security actuaries. Furthermore, these pictures

support our hypothesis that the transition equations should generate a smooth transition to a new

distribution of marital status outcomes. 

The CBOLT marital transition equations seem capable of generating reasonable crosssectional patterns, but what about longitudinal outcomes? In addition to classifying the right

number of people as marrying, divorcing, and remarrying in any given year, it is important to

ensure that these transitions occur at the appropriate points in an individual’s life-cycle and that

the distributions of the number of marriages and marital durations across cohorts are reasonable. 

The first bit of evidence that the longitudinal properties are satisfactory comes from

looking at mean age at first marriage, shown in Figure 19. The solid lines represent Vital Statistics

data covering the period between 1964 and 1990. The CBOLT projections start in 1984 and

extend through 2075, thus there is a six-year overlap in the graphs. Historically, the mean age at

first marriage has been drifting upward, which is consistent with the cohort analysis presented in

Figures 1 and 2. Between 1984 and 1990, the CBOLT historical simulation produced mean ages

of first marriage very near those registered in the Vital Statistics. That similarity suggests that the
22. See Bumpass and Sweet (1989).

22

model accurately predicts the timing of transitions into first marriage. While the trend of delaying

first marriage is predicted to continue for some time, it eventually stabilizes at about age 28 for

both sexes, because once the 1980s cohort is through the relevant ages there are (by assumption)

no further cohort effects. Note that the average age differential between the sexes shrinks over

time, which is consistent with the extended positive residual cohort term for female first marriage

probabilities (and negative term for males).

The second observation on longitudinal outcomes comes from looking at the trend in

marital duration at the time of divorce, as shown in Table 9. This statistic is particularly

interesting to Social Security analysts because of the duration requirement that governs benefit

eligibility for divorced spouses. The CBOLT transition equations suggest that the typical duration

of those marriages ending is divorce is not necessarily expected to fall. Again, at first glance, this

result may seem counterintuitive, because of increased divorce rates. This trend is, however,

supported both by actual data and theoretical determinants of divorce. Vital Statistics reports a

steady, albeit slight, increase in the duration of marriages ending in divorce. This increase

suggests a smaller proportion of very short marriages. Divorce researchers have posited that as

marriage ages drift upward and cohabitation rates increase, the number of weak, official marriages

will decline.22

There are ultimately many ways to tabulate longitudinal marriage outcomes in a dynamic

micro-simulation context and thus draw conclusions about the model’s properties. When trying

to understand a model it sometimes helps to ask a policy question and see what the model offers. 

For example, one interesting question about marriage in the Social Security context is, What will
23. Social Security Administration (2000).

24. Labor force participation is also forecast; currently it is modeled as a nested logit where participation is a

function of age, sex, marital status, birth cohort, and beneficiary status.

23

happen to the number of women eligible for spousal benefits? That question is policy relevant

insofar as elderly women have historically been vulnerable to high poverty rates, but recent trends

in female labor force participation are poised to ameliorate the condition of future cohorts.23,24

The longitudinal marital patterns presented in this paper and implemented within the context of a

larger micro simulation permit an illustration of how the distribution of marital outcomes varies

across women with different lifetime income.

Table 10 addresses the above question. It presents the percentage of nonwidowed women

ages 62 to 67 who are eligible to draw OAI spouse benefits. The relevance of controlling for

widowhood warrants a brief explanation. As male mortality rates decline, the number of husbands

increases, thus increasing overall spouse eligibility. This “mortality improvement” confounds the

effect of the retreat from marriage described above. Once the marital status composition is

controlled for, two trends stand out. The first, going down a given column, suggests that women

with lower lifetime earnings are more likely to be eligible for spouse benefits than are their richer

counterparts. In 2005, 95 percent of women in the lowest lifetime earnings decile will be covered

by their spouse’s earnings while only 77 percent of women in the highest decile will be

comparably covered. This paper does not address whether these women stay married because

they are likely low earners or if they are low earners because they know they are likely to stay

married. 

The second trend, reading the rows across, suggests that the probability of a low-earning

woman losing OAI spouse coverage is greater than the probability of a high-earning woman 
24

losing that coverage. The estimated change in coverage among the lowest decile of nonwidowed

women is eight percentage points, from 95 percent in 2005 to 87 percent by 2075. The

comparable decline for women in the highest decile is only five percentage points. 
25

V. References

Bell, Felicitie C. Social Security Area Population Projections: 1997. Actuarial Study #112,

Social Security Administration, Office of the Chief Actuary. SSA Pub. No. 11-11553.

(August 1997).

Bumpass, Larry L. and John A. Sweet. “National Estimates of Cohabitation,” Demography, vol.

26, no. 4, pp. 615-625 (November 1989).

Caldwell, Steven, Melissa Favreault, and others. “Social Security’s Treatment of Postwar

Americans.” NBER Working Paper #6603 (June 1998).

Congressional Budget Office. Uncertainty in Social Security’s Long-Term Finances: A

Stochastic Analysis. CBO Paper (December 2001).

Frees, Edward W. (Jed). “Summary of Social Security Administration Projections of the OASDI

System,” Working Paper for the 1999 Technical Panel on Assumptions and Methods,

Social Security Advisory Board (December 1999).

Goldstein, Joshua R. “The Leveling of Divorce in the United States,” Demography, vol. 36, no.

3, pp. 409-414 (August 1999).

Harris, Amy Rehder, and John Sabelhaus. “Projecting Longitudinal Earnings for Long-Run

Policy Analysis,” Congressional Budget Office, Washington, D.C. (Forthcoming).

Kreider, Rose M. and Jason M. Fields. Number, Timing, and Duration of Marriages and

Divorces: Fall 1996. Current Population Reports, P70-80. U.S. Census Bureau,

Washington, D.C. (2001).

Lillard, Lee, and Stan Panis. “Demographic Projections.” Paper presented at the First Annual

Joint Conference for the Retirement Research Consortium, “New Developments in

Retirement Research” (May 20-21, 1999).

Lillard, Lee, and Linda J. Waite. “Marriage and the Work and Earnings Careers of Spouses.” 

Paper presented at the Second Annual Joint Conference for the Retirement Research

Consortium, “The Outlook for Retirement Income” (May 17-18, 2000).

Panis, Constantijn, and Lee Lillard. “Final Report: Near Term Model Development, Part II.” Rand

Corporation, Santa Monica, Calif. (August 1999).

Perese, Kevin. “Documentation for the Divorce Module.” The Urban Institute, Washington,

D.C. (1999).

Peters, H. Elizabeth. “Retrospective Versus Panel Data in Analyzing Lifecycle Events,” The

Journal of Human Resources, vol. 23, no. 4, pp. 488-513 (Fall 1988).
26

Preston, Samuel H. and John McDonald. “The Incidence of Divorce Within Cohorts of American

Marriages Contracted Since the Civil War.” Demography, vol. 16, no. 1, pp. 1-25

(February 1979).

Raley, R. Kelly. “Recent Trends and Differentials in Marriage and Cohabitation: The United

States.” The Ties that Bind, ed., Linda Waite. New York: Aldine de Gruyter (2000): 19-

39.

Ruggles, Steven. “The Rise of Divorce and Separation in the United States, 1880-1990.” 

Demography, vol. 34, no. 4, pp. 455-466 (November 1997).

Schoen, Robert, and Robin M. Weinick. “The Slowing Metabolism of Marriage: Figures from

1988 U.S. Marital Status Life Tables.” Demography, vol. 30, no. 4, pp. 737-746

(November 1993). 

Schoen, Robert, and Nicola Standish. Footprints of Cohabitation: Results from Marital Status

Life Tables for the U.S., 1995. Working Paper 00-12, Population Research Institute,

University Park, Penn. (September 2000). 

Smith, Creston M. “The Social Security Administration’s Continuous Work History Sample.” 

Social Security Bulletin 52, 10 (October 1989): 20-28.

Social Security Administration. Income of the Population 55 or Older, 2000. Washington, D.C.
Table 1.

Comparison of Marital Transition Rates in SIPP 

 and Vital Statistics (NCHS) Data

Men Women

First Marriage Rate (Per 1,000 People)

SIPP (1995) 48.6 58.3

NCHS (1990) 47.0 57.7

Divorce Rate (Per 1,000 People)

SIPP (1995) 14.5 17.4

NCHS (1990) 19.2 18.7

Remarriage Rate (Per 1,000 People)

SIPP (1995) 73.2 38.9

NCHS (1990) 84.5 35.8

Note: SIPP = Survey of Income and Program Participation

 NCHS = National Center for Health Statistics
Figure 1: Female First Marriage Rates by Cohort and Age

0

5

10

15

20

25

16182022242628303234363840424446485052545658606264666870

Age

Percent Marrying

Before 1950

1950s

1960s

1970s

Source: Congressional Budget Office based on data from the 1996 Survey of Income and Program Participation.
Figure 2: Male First Marriage Rates by Cohort and Age

0

2

4

6

8

10

12

14

16

18

16182022242628303234363840424446485052545658606264666870

Age

Percent Marrying

Before 1950

1950s

1960s

1970s

Source: Congressional Budget Office based on data from the 1996 Survey of Income and Program Participation.
Figure 3: Female Cumulative First Marriage Probabilities by Cohort and Age

0

102030405060708090

100

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

Age

Percent Ever Marrying

Before 1950

1950s

1960s

1970s

Source: Congressional Budget Office based on data from the 1996 Survey of Income and Program Participation.
Figure 4: Male Cumulative First Marriage Probabilities by Cohort and Age

0

102030405060708090

100

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

Age

Percent Ever Marrying

Before 1950

1950s

1960s

1970s

Source: Congressional Budget Office based on data from the 1996 Survey of Income and Program Participation.
Table 2.

Age-Centered Regression Coefficients, Female First Marriage

Age Coefficient on: 

Group Age Educ. Income Cohort50 Cohort60 Cohort70 Constant

17 0.4078 -1.2836 0.0505 -0.1849 -0.7488 -1.3069 -8.8695

18 0.1984 -0.9851 0.0646 -0.2654 -0.7750 -1.2554 -5.3802

19 0.1180 -0.6605 0.0687 -0.3493 -0.7948 -1.1919 -4.0094

20 0.0659 -0.4325 0.0692 -0.4082 -0.7761 -1.1156 -3.1019

21 0.0132 -0.2548 0.0652 -0.4492 -0.7397 -1.0336 -2.0682

22 -0.0232 -0.0900 0.0618 -0.4828 -0.6770 -0.9533 -1.3510

23 -0.0532 0.0513 0.0564 -0.4876 -0.5919 -0.8607 -0.7396

24 -0.0727 0.1578 0.0496 -0.4645 -0.4916 -0.7593 -0.3318

25 -0.0838 0.2281 0.0432 -0.4024 -0.3601 -0.6494 -0.1167

26 -0.0840 0.2624 0.0390 -0.3151 -0.2245 -0.1754

27 -0.0793 0.2945 0.0362 -0.2254 -0.1026 -0.3651

28 -0.0835 0.3008 0.0346 -0.1457 -0.0056 -0.3108

29 -0.0940 0.3058 0.0337 -0.0877 0.0742 -0.0644

30 -0.1042 0.2852 0.0340 -0.0342 0.1274 0.2044

31 -0.1142 0.2674 0.0309 -0.0095 0.1480 0.5204

32 -0.1114 0.2229 0.0295 0.0093 0.1486 0.4556

33 -0.1080 0.1985 0.0257 0.0166 0.1408 0.3846

34 -0.1009 0.1816 0.0224 0.0332 0.1040 0.1813

35 -0.0910 0.1917 0.0157 0.0290 0.0497 -0.1097

36 -0.0733 0.1938 0.0135 0.0284 -0.7145

37 -0.0724 0.2457 0.0068 0.0432 -0.7342

38 -0.0822 0.2901 0.0034 0.0897 -0.3866

39 -0.0928 0.2675 0.0022 0.1479 0.0042

40 -0.1096 0.2058 0.0061 0.2174 0.6326

41 -0.0974 0.1301 0.0085 0.3348 0.1209

42 -0.0704 -0.0180 0.0147 0.4543 -1.0136

43 -0.0275 -0.2326 0.0202 0.5487 -2.8201

44 -0.0171 -0.4013 0.0197 0.6266 -3.2628

45 -0.0426 -0.5414 0.0165 0.6478 -2.0977

46 -0.1045 -0.7567 -0.0020 0.8374

47 -0.1456 -0.7762 -0.0145 2.8212

48 -0.1245 -0.6992 -0.0240 1.8959

49 -0.1060 -0.4757 -0.0248 0.9858

50 -0.0741 -0.4092 -0.0412 -0.5410

51 -0.0463 -0.3358 -0.0530 -1.8714

52 0.0067 -0.2539 -0.0525 -4.5669

53 0.0246 -0.2141 -0.0578 -5.5385

54 0.0383 -0.2991 -0.0756 -6.1978

55 -0.0063 -0.2090 -0.0763 -3.7633

56 -0.0514 -0.0590 -0.0762 -1.2904

57 -0.0860 -0.1108 -0.0827 0.6373

58 -0.1177 -0.2401 -0.0986 2.5872

59 -0.1025 -0.3964 -0.0975 1.7546

60 -0.0212 -0.6622 -0.0912 -3.0412

Note: Coefficients in BOLD are significant at the 90 percent level. 
Table 3.

Age-Centered Regression Coefficients, Male First Marriage

Age Coefficient on: 

Group Age Educ. Income Cohort50 Cohort60 Cohort70 Constant

17 0.5626 -1.0578 0.0778 0.0000 -0.4407 -1.2186 -12.8694

18 0.3540 -0.8410 0.0891 -0.1243 -0.6510 -1.1558 -9.2769

19 0.2512 -0.5673 0.0890 -0.2672 -0.7204 -1.0829 -7.4000

20 0.1761 -0.3767 0.0859 -0.3591 -0.7265 -1.0163 -5.9724

21 0.1187 -0.2460 0.0793 -0.4191 -0.7196 -0.9445 -4.7703

22 0.0705 -0.1339 0.0740 -0.4628 -0.6910 -0.8824 -3.7333

23 0.0277 -0.0372 0.0680 -0.4944 -0.6512 -0.8263 -2.7730

24 -0.0062 0.0453 0.0626 -0.4967 -0.5944 -0.7504 -1.9937

25 -0.0316 0.1084 0.0585 -0.4842 -0.5204 -0.6805 -1.3986

26 -0.0497 0.1596 0.0550 -0.4417 -0.4370 -0.9735

27 -0.0590 0.2023 0.0534 -0.3964 -0.3607 -0.7759

28 -0.0639 0.2371 0.0537 -0.3281 -0.2780 -0.7048

29 -0.0717 0.2678 0.0535 -0.2575 -0.2134 -0.5385

30 -0.0765 0.2896 0.0522 -0.1731 -0.1607 -0.4452

31 -0.0782 0.3218 0.0486 -0.1033 -0.1307 -0.4086

32 -0.0866 0.3404 0.0429 -0.0179 -0.1105 -0.1495

33 -0.0899 0.3644 0.0379 0.0345 -0.1166 -0.0369

34 -0.0802 0.3993 0.0366 0.0468 -0.1225 -0.3564

35 -0.0741 0.4259 0.0359 0.0410 -0.1457 -0.5621

36 -0.0725 0.4104 0.0361 0.0257 -0.6189

37 -0.0762 0.4474 0.0416 -0.0314 -0.5162

38 -0.0905 0.4715 0.0453 -0.0809 0.0098

39 -0.0990 0.4521 0.0444 -0.0867 0.3427

40 -0.1062 0.4707 0.0418 -0.1204 0.6351

41 -0.1299 0.4741 0.0416 -0.1419 1.6004

42 -0.1231 0.3462 0.0424 -0.1272 1.3771

43 -0.0757 0.1881 0.0495 -0.0826 -0.6233

44 -0.0359 0.1113 0.0599 -0.0970 -2.4015

45 -0.0104 -0.0477 0.0806 -0.0547 -3.6654

46 0.0142 -0.1178 0.0967 -4.8912

47 -0.0149 -0.1106 0.1082 -3.6658

48 -0.0432 -0.0940 0.1066 -2.3195

49 -0.0738 -0.1186 0.0996 -0.7745

50 -0.0903 -0.0423 0.0837 0.1637

51 -0.0863 -0.0259 0.0619 0.0935

52 -0.0860 0.0766 0.0359 0.2574

53 -0.1133 0.1095 0.0175 1.7711

54 -0.1305 0.2696 0.0056 2.7245

55 -0.1141 0.2572 -0.0050 1.9071

56 -0.1018 0.2967 0.0001 1.2352

57 -0.0114 0.3009 0.0059 -3.9150

58 0.0047 0.4180 0.0114 -4.9085

59 -0.0534 0.3186 0.0047 -1.5017

60 -0.1370 0.4306 0.0080 3.3958

Note: Coefficients in BOLD are significant at the 90 percent level. 
 Table 4.

 Age-Centered Regression Coefficients, Female Divorce

Age Coefficient on: 

Group Age Educ Duration Durat Sq Income Prev Marr Cohort50 Cohort60 Constant

17 0.0543 0.2758 0.0000 0.0161 0.0604 0.4342 0.9722 -5.1091

18 -0.0097 0.1493 -0.1871 0.1004 0.0704 0.6378 0.9239 -3.9443

19 -0.1305 0.1641 0.2359 0.0066 0.0897 0.7421 1.0766 -2.2157

20 -0.1777 0.1270 0.3546 -0.0145 0.0897 0.7757 1.1460 -1.4277

21 -0.1821 0.1042 0.3838 -0.0198 0.0899 0.7881 1.1581 -1.3668

22 -0.1757 0.0708 0.3707 -0.0198 0.0874 0.7996 1.1335 -1.4377

23 -0.1648 0.0306 0.3462 -0.0186 0.0848 0.7830 1.0674 -1.5608

24 -0.1498 0.0016 0.3161 -0.0172 0.0837 0.7604 0.9687 -1.7622

25 -0.1491 0.0201 0.3012 -0.0154 0.0859 0.6304 0.7155 0.8218 -1.7486

26 -0.1334 0.0049 0.2592 -0.0127 0.0863 0.6340 0.6721 0.6813 -1.9579

27 -0.1215 -0.0114 0.2108 -0.0096 0.0868 0.6064 0.6191 0.5448 -2.0599

28 -0.1048 -0.0300 0.1719 -0.0075 0.0882 0.6035 0.5699 0.4142 -2.3213

29 -0.0861 -0.0505 0.1336 -0.0055 0.0874 0.6136 0.5154 0.3073 -2.6473

30 -0.0694 -0.0846 0.1041 -0.0041 0.0871 0.6158 0.4697 0.2288 -2.9740

31 -0.0523 -0.1223 0.0840 -0.0032 0.0861 0.6164 0.4390 0.1573 -3.3570

32 -0.0443 -0.1410 0.0744 -0.0030 0.0865 0.6207 0.3988 0.0999 -3.5204

33 -0.0398 -0.1331 0.0554 -0.0021 0.0857 0.6185 0.3712 0.0637 -3.5659

34 -0.0390 -0.1116 0.0522 -0.0020 0.0863 0.6229 0.3536 0.0199 -3.5703

35 -0.0485 -0.0593 0.0420 -0.0016 0.0840 0.6180 0.3309 -0.0434 -3.1774

36 -0.0574 0.0327 0.0408 -0.0016 0.0830 0.5967 0.2954 -2.8440

37 -0.0650 0.0796 0.0338 -0.0014 0.0767 0.6298 0.2938 -2.5039

38 -0.0726 0.1074 0.0254 -0.0011 0.0725 0.6581 0.2656 -2.1389

39 -0.0761 0.1306 0.0132 -0.0007 0.0674 0.6709 0.2280 -1.8909

40 -0.0713 0.1437 0.0075 -0.0006 0.0637 0.6954 0.2096 -2.0240

41 -0.0629 0.1395 -0.0004 -0.0003 0.0621 0.6993 0.1824 -2.2789

42 -0.0495 0.1493 -0.0070 0.0000 0.0638 0.7239 0.1190 -2.8274

43 -0.0432 0.1678 -0.0042 -0.0001 0.0627 0.7327 0.1014 -3.1162

44 -0.0409 0.2085 -0.0008 -0.0003 0.0621 0.7434 0.0841 -3.2389

45 -0.0482 0.2429 0.0002 -0.0004 0.0637 0.7490 -0.0144 -2.9070

46 -0.0444 0.2635 0.0116 -0.0008 0.0657 0.7489 -3.1332

47 -0.0434 0.2572 0.0120 -0.0009 0.0605 0.7521 -3.0950

48 -0.0532 0.2318 0.0109 -0.0010 0.0593 0.7411 -2.5465

49 -0.0532 0.1314 0.0173 -0.0013 0.0618 0.7368 -2.5271

50 -0.0380 0.0346 0.0281 -0.0017 0.0614 0.7013 -3.2604

51 -0.0361 -0.0345 0.0346 -0.0020 0.0619 0.6379 -3.3134

52 -0.0378 -0.0838 0.0491 -0.0024 0.0676 0.5849 -3.2983

53 -0.0458 -0.1341 0.0651 -0.0028 0.0701 0.5816 -3.0124

54 -0.0452 -0.0628 0.0671 -0.0028 0.0663 0.5461 -3.0347

55 -0.0561 0.0207 0.0552 -0.0025 0.0600 0.5374 -2.3724

56 -0.0573 0.1384 0.0383 -0.0020 0.0581 0.5230 -2.1941

57 -0.0578 0.2248 0.0137 -0.0014 0.0482 0.4981 -1.9207

58 -0.0377 0.3774 -0.0189 -0.0008 0.0353 0.4333 -2.6780

59 -0.0024 0.4647 -0.0297 -0.0006 0.0341 0.4387 -4.6634

60 0.0097 0.4895 -0.0185 -0.0009 0.0422 0.4158 -5.4251

61 0.0035 0.5604 0.0014 -0.0014 0.0405 0.4452 -5.2106

62 -0.0120 0.6306 0.0363 -0.0023 0.0461 0.4314 -4.5039

63 -0.0098 0.7281 0.0748 -0.0031 0.0631 0.4435 -5.0705

64 -0.0727 0.7160 0.0832 -0.0033 0.0611 0.3757 -1.0626

65 -0.0958 0.6852 0.0845 -0.0033 0.0575 0.4366 0.3086

66 -0.0994 0.5004 0.0579 -0.0026 0.0657 0.4600 0.7356

67 -0.0379 0.2292 0.0042 -0.0012 0.0912 0.6709 -3.2858

68 0.0464 -0.1925 -0.0345 -0.0002 0.1144 0.8795 -9.0234

69 0.0862 0.1137 -0.0368 0.0000 0.1551 1.0658 -12.2533

70 0.0605 0.3973 -0.0539 0.0004 0.2015 1.0900 -10.5904

Note: Coefficients in BOLD are significant at the 90 percent level. 
 Table 5.

 Age-Centered Regression Coefficients, Male Divorce

Age Coefficient on: 

Group Age Educ. Duration Durat Sq Income Prev Marr Cohort50 Cohort60 Constant

17 -1.1723 -1.6886 0.1394 0.1394 0.1472 0.8718 1.1750 15.5299

18 -0.1229 0.1031 -1.0627 0.3932 0.0781 0.6675 1.1775 -1.6450

19 -0.1372 0.2257 -0.0149 0.0898 0.0383 0.6617 1.1010 -1.6426

20 -0.2106 0.1728 0.2063 0.0271 0.0201 0.7039 1.0119 -0.0718

21 -0.2233 0.1190 0.2824 0.0090 0.0154 0.7164 0.9764 0.2337

22 -0.2221 0.0498 0.3217 -0.0028 0.0111 0.7339 0.9544 0.2774

23 -0.2164 -0.0224 0.3470 -0.0103 0.0065 0.7311 0.8917 0.2459

24 -0.2004 -0.1067 0.3411 -0.0131 0.0022 0.7155 0.8311 0.0256

25 -0.1886 -0.1449 0.3399 -0.0146 -0.0017 0.5890 0.6988 0.7637 -0.1720

26 -0.1657 -0.1796 0.3101 -0.0136 -0.0082 0.6695 0.6650 0.6845 -0.5560

27 -0.1473 -0.1910 0.2883 -0.0128 -0.0142 0.7271 0.6302 0.5915 -0.8715

28 -0.1324 -0.1951 0.2612 -0.0113 -0.0181 0.7615 0.6056 0.5070 -1.1242

29 -0.1174 -0.1814 0.2322 -0.0098 -0.0210 0.7695 0.5883 0.4096 -1.3952

30 -0.1074 -0.1824 0.2012 -0.0082 -0.0231 0.7497 0.5443 0.2995 -1.5148

31 -0.0967 -0.1944 0.1688 -0.0067 -0.0222 0.6919 0.5297 0.2096 -1.6719

32 -0.0838 -0.2185 0.1399 -0.0055 -0.0201 0.6283 0.5052 0.1259 -1.9149

33 -0.0705 -0.2334 0.1071 -0.0041 -0.0191 0.5853 0.4727 0.0527 -2.1666

34 -0.0611 -0.2592 0.0861 -0.0034 -0.0172 0.5722 0.4298 -0.0044 -2.3554

35 -0.0523 -0.2633 0.0700 -0.0028 -0.0150 0.5757 0.4151 -0.0512 -2.5707

36 -0.0492 -0.2357 0.0658 -0.0027 -0.0145 0.5836 0.3743 -2.6462

37 -0.0453 -0.2078 0.0551 -0.0023 -0.0169 0.6282 0.3196 -2.7123

38 -0.0426 -0.1799 0.0476 -0.0020 -0.0189 0.6404 0.2534 -2.7369

39 -0.0454 -0.1269 0.0386 -0.0017 -0.0227 0.6350 0.1962 -2.5360

40 -0.0442 -0.0915 0.0383 -0.0017 -0.0262 0.6566 0.0997 -2.5428

41 -0.0414 -0.0352 0.0314 -0.0015 -0.0296 0.6637 0.0051 -2.5746

42 -0.0394 0.0052 0.0244 -0.0013 -0.0314 0.6574 -0.0674 -2.5969

43 -0.0326 0.0604 0.0175 -0.0011 -0.0326 0.6647 -0.1432 -2.8334

44 -0.0204 0.0874 0.0202 -0.0013 -0.0314 0.6681 -0.2542 -3.3595

45 -0.0264 0.1461 0.0139 -0.0012 -0.0307 0.6392 -0.3389 -3.0313

46 -0.0346 0.1865 0.0074 -0.0010 -0.0275 0.5919 -2.6187

47 -0.0566 0.1652 0.0037 -0.0011 -0.0265 0.5580 -1.5193

48 -0.0852 0.0948 0.0039 -0.0012 -0.0285 0.5429 -0.0944

49 -0.1000 0.0356 -0.0045 -0.0010 -0.0324 0.5155 0.7462

50 -0.0899 -0.0500 -0.0066 -0.0010 -0.0383 0.4857 0.3825

51 -0.0648 -0.1122 -0.0007 -0.0012 -0.0467 0.4545 -0.7630

52 -0.0390 -0.1313 0.0036 -0.0014 -0.0527 0.4397 -2.0632

53 -0.0032 -0.0422 0.0063 -0.0015 -0.0524 0.3654 -3.8923

54 0.0109 0.0588 0.0148 -0.0018 -0.0516 0.3155 -4.6891

55 0.0163 0.0940 0.0277 -0.0021 -0.0499 0.2724 -5.0848

56 0.0067 0.1473 0.0347 -0.0022 -0.0448 0.2560 -4.6894

57 -0.0215 0.1545 0.0482 -0.0025 -0.0413 0.2417 -3.2256

58 -0.0655 0.0909 0.0625 -0.0027 -0.0395 0.2495 -0.9003

59 -0.0745 -0.0350 0.0861 -0.0033 -0.0368 0.2233 -0.5297

60 -0.0760 -0.0742 0.0876 -0.0032 -0.0302 0.2632 -0.5605

61 -0.0587 -0.1367 0.0876 -0.0031 -0.0269 0.2939 -1.6836

62 -0.0252 -0.0746 0.0789 -0.0028 -0.0233 0.3129 -3.8189

63 -0.0451 -0.0093 0.0630 -0.0024 -0.0257 0.3749 -2.5349

64 -0.0660 0.1021 0.0286 -0.0014 -0.0245 0.5238 -1.2080

65 -0.1109 0.1424 0.0196 -0.0012 -0.0334 0.5892 1.6943

66 -0.1260 0.2046 0.0298 -0.0014 -0.0319 0.6737 2.4686

67 -0.1419 0.1018 0.0324 -0.0014 -0.0249 0.8168 3.3959

68 -0.0786 0.1695 0.0278 -0.0013 -0.0053 0.9273 -0.9322

69 -0.0690 0.1962 0.0364 -0.0015 0.0262 1.0116 -1.8784

70 -0.0489 0.2294 0.0282 -0.0013 0.0802 1.0093 -3.4575

Note: Coefficients in BOLD are significant at the 90 percent level. 
 Table 6.

 Age-Centered Regression Coefficients, Female Remarriage

Age Coefficient on: 

Group Age Educ. Duration Durat Sq Income Prev Marr Cohort50 Cohort60 Constant

17 0.0143 0.7561 0.5913 -0.9115 0.0663 -0.9232 -0.8718 -0.5290

18 -0.1778 -0.2285 0.2924 -0.2813 0.0252 -0.7318 -0.9164 3.0791

19 -0.1949 -0.4663 0.2486 -0.1067 0.0255 -0.5442 -0.5765 3.1503

20 -0.1363 -0.4542 0.3237 -0.0924 0.0237 -0.4023 -0.3827 1.7917

21 -0.1245 -0.4015 0.3420 -0.0834 0.0253 -0.3065 -0.2613 1.4120

22 -0.1189 -0.3028 0.3244 -0.0666 0.0278 -0.2261 -0.1769 1.1702

23 -0.1086 -0.1968 0.2946 -0.0542 0.0301 -0.1678 -0.1223 0.8531

24 -0.0937 -0.1032 0.2800 -0.0478 0.0323 -0.1101 -0.0683 0.4174

25 -0.0800 -0.0329 0.2556 -0.0401 0.0321 0.0101 -0.0941 -0.0245 0.0307

26 -0.0646 0.0161 0.2298 -0.0341 0.0299 0.0060 -0.0978 -0.0245 -0.3522

27 -0.0535 0.0381 0.2096 -0.0296 0.0269 -0.0099 -0.0981 -0.0208 -0.6136

28 -0.0515 0.0288 0.1959 -0.0272 0.0241 -0.0191 -0.0954 -0.0185 -0.6352

29 -0.0560 0.0135 0.1728 -0.0234 0.0214 -0.0168 -0.0858 -0.0352 -0.4736

30 -0.0670 -0.0018 0.1441 -0.0195 0.0201 -0.0360 -0.0615 -0.0811 -0.0918

31 -0.0741 -0.0255 0.1160 -0.0160 0.0200 -0.0527 -0.0232 -0.0924 0.1658

32 -0.0823 -0.0506 0.0883 -0.0130 0.0214 -0.0353 -0.0119 -0.1402 0.4306

33 -0.0866 -0.0522 0.0665 -0.0105 0.0226 -0.0413 -0.0109 -0.2112 0.6017

34 -0.0822 -0.0589 0.0438 -0.0081 0.0234 -0.0487 -0.0391 -0.2625 0.5014

35 -0.0787 -0.0783 0.0292 -0.0066 0.0243 -0.0573 -0.0794 -0.2736 0.4214

36 -0.0697 -0.0837 0.0205 -0.0056 0.0238 -0.0795 -0.1346 0.1608

37 -0.0634 -0.0538 0.0109 -0.0046 0.0216 -0.1237 -0.1599 0.0157

38 -0.0583 -0.0610 0.0016 -0.0038 0.0205 -0.1380 -0.1726 -0.1354

39 -0.0647 -0.0691 -0.0001 -0.0035 0.0211 -0.1492 -0.1754 0.1119

40 -0.0680 -0.0590 0.0004 -0.0034 0.0192 -0.1545 -0.1700 0.2549

41 -0.0754 -0.0925 0.0007 -0.0032 0.0181 -0.1539 -0.1605 0.5579

42 -0.0683 -0.1399 0.0026 -0.0032 0.0204 -0.1511 -0.1762 0.2533

43 -0.0553 -0.1707 -0.0022 -0.0027 0.0211 -0.1659 -0.2206 -0.2760

44 -0.0531 -0.1818 -0.0145 -0.0019 0.0189 -0.1659 -0.2771 -0.3157

45 -0.0541 -0.2315 -0.0229 -0.0014 0.0217 -0.1512 -0.3605 -0.2905

46 -0.0690 -0.2418 -0.0277 -0.0012 0.0258 -0.1464 0.3692

47 -0.0825 -0.2581 -0.0352 -0.0007 0.0268 -0.0960 0.9463

48 -0.0947 -0.2619 -0.0290 -0.0010 0.0284 -0.0517 1.4377

49 -0.0806 -0.2762 -0.0204 -0.0012 0.0320 -0.0486 0.7034

50 -0.0718 -0.2290 -0.0143 -0.0014 0.0307 -0.0552 0.2514

51 -0.0750 -0.1921 -0.0076 -0.0014 0.0293 -0.0660 0.3776

52 -0.0891 -0.0952 -0.0021 -0.0015 0.0227 -0.1118 1.1404

53 -0.1046 -0.0196 -0.0014 -0.0013 0.0206 -0.1434 1.9855

54 -0.1061 0.0350 0.0034 -0.0014 0.0217 -0.1448 2.0307

55 -0.0927 0.0531 0.0040 -0.0013 0.0242 -0.1196 1.2403

56 -0.0781 0.0505 0.0031 -0.0013 0.0317 -0.1030 0.3614

57 -0.0816 -0.0005 0.0007 -0.0013 0.0449 -0.0394 0.4282

58 -0.0845 -0.0311 -0.0047 -0.0012 0.0508 0.0143 0.5169

59 -0.1220 -0.0472 -0.0172 -0.0008 0.0496 0.1123 2.6162

60 -0.1643 -0.0373 -0.0233 -0.0007 0.0484 0.1548 5.1012

61 -0.1800 0.0209 -0.0296 -0.0004 0.0411 0.2158 6.0408

62 -0.1545 0.0586 -0.0474 0.0004 0.0308 0.2349 4.5726

63 -0.1104 0.0284 -0.0576 0.0009 0.0293 0.2098 1.8694

64 -0.0794 -0.0566 -0.0528 0.0009 0.0349 0.1243 -0.0556

65 -0.0724 -0.1079 -0.0489 0.0009 0.0486 0.0160 -0.4435

66 -0.0493 -0.2360 -0.0557 0.0013 0.0645 -0.1172 -1.8111

67 -0.0522 -0.2406 -0.0505 0.0011 0.0816 -0.1832 -1.6502

68 -0.0664 -0.2615 -0.0538 0.0011 0.0880 -0.1715 -0.6940

69 -0.0957 -0.2773 -0.0707 0.0015 0.0857 -0.2510 1.4962

70 -0.1433 -0.4711 -0.0859 0.0019 0.0645 -0.1665 4.8139

Note: Coefficients in BOLD are significant at the 90 percent level. 
 Table 7.

 Age-Centered Regression Coefficients, Male Remarriage

Age Coefficient on: 

Group Age Educ. Duration Durat Sq Income Prev Marr Cohort50 Cohort60 Constant

17 -1.4174 -0.8372 9.4046 -8.7273 0.0154 -1.8340 -1.2695 24.1328

18 0.0623 0.4297 0.9700 -0.4327 0.0440 -0.2670 0.0398 -3.0280

19 -0.0380 0.0427 0.8746 -0.3446 0.0440 -0.2095 0.0268 -1.0730

20 -0.0096 0.0792 0.7054 -0.2206 0.0479 -0.1635 -0.1636 -1.6572

21 -0.0103 -0.0055 0.6160 -0.1747 0.0420 -0.1761 -0.2398 -1.5588

22 -0.0179 -0.0214 0.5247 -0.1316 0.0410 -0.2928 -1.3694 -1.3694

23 -0.0343 -0.0173 0.4708 -0.1081 0.0418 -0.1809 -1.2951 -0.9823

24 -0.0413 -0.0365 0.3922 -0.0800 0.0375 -0.1716 -0.3066 -0.7804

25 -0.0473 -0.0471 0.3385 -0.0601 0.0359 0.5153 -0.1694 -0.2761 -1.1587

26 -0.0434 -0.0270 0.3026 -0.0489 0.0350 0.4289 -0.1629 -0.2484 -1.1786

27 -0.0335 -0.0093 0.2760 -0.0421 0.0336 0.3246 -0.1457 -0.2217 -1.3375

28 -0.0261 -0.0115 0.2608 -0.0380 0.0317 0.2056 -0.1065 -0.1855 -1.4333

29 -0.0276 0.0019 0.2404 -0.0340 0.0316 0.1406 -0.0971 -0.1831 -1.3233

30 -0.0314 0.0155 0.2136 -0.0297 0.0313 0.0850 -0.0789 -0.2016 -1.1374

31 -0.0388 0.0177 0.1886 -0.0261 0.0315 0.0224 -0.0563 -0.2196 -0.8351

32 -0.0411 0.0190 0.1695 -0.0234 0.0311 -0.0282 -0.0516 -0.2207 -0.6909

33 -0.0443 0.0512 0.1495 -0.0207 0.0316 -0.0493 -0.0797 -0.2659 -0.5392

34 -0.0452 0.0774 0.1303 -0.0186 0.0319 -0.0953 -0.1006 -0.2891 -0.4384

35 -0.0503 0.1087 0.1119 -0.0167 0.0317 -0.1139 -0.1378 -0.3318 -0.2148

36 -0.0566 0.1659 0.1040 -0.0154 0.0314 -0.0853 -0.1798 -0.0158

37 -0.0682 0.2144 0.0809 -0.0130 0.0337 -0.0324 -0.2395 0.3622

38 -0.0613 0.2402 0.0590 -0.0106 0.0369 -0.0107 -0.2735 0.0840

39 -0.0424 0.2603 0.0447 -0.0087 0.0380 0.0156 -0.2926 -0.6705

40 -0.0257 0.2886 0.0330 -0.0070 0.0394 0.0333 -0.3042 -1.3725

41 -0.0168 0.2974 0.0172 -0.0053 0.0397 0.0224 -0.3185 -1.7217

42 -0.0233 0.2778 0.0080 -0.0045 0.0378 -0.0094 -0.2831 -1.3922

43 -0.0375 0.2724 0.0021 -0.0040 0.0331 -0.0267 -0.2666 -0.7198

44 -0.0451 0.2701 -0.0074 -0.0034 0.0327 -0.0252 -0.2594 -0.3722

45 -0.0423 0.2235 -0.0142 -0.0034 0.0324 -0.0424 -0.2172 -0.4302

46 -0.0321 0.1831 -0.0103 -0.0039 0.0358 -0.0595 -0.8846

47 -0.0239 0.1664 -0.0121 -0.0038 0.0356 -0.0804 -1.2291

48 -0.0154 0.1304 -0.0197 -0.0033 0.0417 -0.1106 -1.6165

49 -0.0250 0.0921 -0.0246 -0.0031 0.0443 -0.1653 -1.0912

50 -0.0371 0.1086 -0.0292 -0.0025 0.0464 -0.2087 -0.4639

51 -0.0317 0.1132 -0.0396 -0.0018 0.0457 -0.2538 -0.6494

52 -0.0258 0.1063 -0.0499 -0.0012 0.0461 -0.3079 -0.8603

53 -0.0134 0.1654 -0.0503 -0.0010 0.0416 -0.3504 -1.4577

54 -0.0076 0.2310 -0.0500 -0.0009 0.0392 -0.3711 -1.7423

55 -0.0056 0.2626 -0.0497 -0.0010 0.0361 -0.3988 -1.7909

56 -0.0042 0.3318 -0.0385 -0.0015 0.0305 -0.3993 -1.8824

57 -0.0145 0.4630 -0.0334 -0.0017 0.0226 -0.3753 -1.3327

58 -0.0513 0.5173 -0.0365 -0.0018 0.0148 -0.3350 0.7883

59 -0.0818 0.5534 -0.0432 -0.0016 0.0056 -0.2960 2.6038

60 -0.0893 0.6313 -0.0481 -0.0015 -0.0032 -0.2192 3.0104

61 -0.0995 0.6743 -0.0649 -0.0007 -0.0036 -0.1438 3.5539

62 -0.0943 0.6556 -0.0800 0.0001 -0.0009 -0.0613 3.1516

63 -0.0988 0.6960 -0.0858 0.0009 0.0004 -0.0093 3.3217

64 -0.1326 0.7335 -0.0904 0.0013 0.0005 0.0628 5.3414

65 -0.1239 0.6982 -0.0995 0.0018 0.0036 0.1046 4.7363

66 -0.1160 0.7150 -0.1071 0.0021 0.0022 0.1165 4.2463

67 -0.0760 0.6866 -0.1205 0.0023 -0.0034 0.1005 1.7064

68 -0.0223 0.4795 -0.1446 0.0026 -0.0105 0.1066 -1.7748

69 -0.0030 0.2762 -0.1515 0.0024 -0.0116 0.0291 -2.9089

70 -0.0152 0.1493 -0.1464 0.0018 -0.0084 -0.0882 -1.9173

Note: Coefficients in BOLD are significant at the 90 percent level. 
Figure 5: Female Divorce Rates by Cohort and Age

01234567

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

6

Age

Percent Divorcing

Before 1950

1950s

1960s

Source: Congressional Budget Office based on data from the 1996 Survey of Income and Program Participation.
Figure 6: Male Divorce Rates by Cohort and Age

012345678

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

Age

Percent Divorcing

Before 1950

1950s

1960s

Source: Congressional Budget Office based on the data from the 1996 Survey of Income and Program Participation.
Figure 7: Female Remarriage Rates by Cohort and Age

0

5

10

15

20

25

30

35

16182022242628303234363840424446485052545658606264666870

Age

Percent Remarrying

Before 1950

1950s

1960s

Source: Congressional Budget Office based on data from the 1996 Survey of Income and Program Participation.
Figure 8: Male Remarriage Rates by Cohort and Age

05

10152025

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

Age

Percent Remarrying

Before 1950

1950s

1960s

Source: Congressional Budget Office based on data from the 1996 Survey of Income and Program Participation.
Figure 9: Residual Cohort Effects, Female First Marriage

-1.5000-1.0000-0.5000

0.00000.50001.0000

17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 

Age

Parameter Estimate

1950s

1960s

1970s
Figure 10: Residual Cohort Effects, Male First Marriage

-1.4000-1.2000-1.0000-0.8000-0.6000-0.4000-0.2000

0.00000.2000

17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 

Age

Parameter Estimate

1970s

1950s

1960s
Figure 11: Residual Cohort Effects, Female Divorce

-0.2000

0.00000.20000.40000.60000.80001.00001.20001.4000

17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 

Age

Parameter Estimate

1950s

1960s
Figure 12: Residual Cohort Effects, Male Divorce

-0.6000-0.4000-0.2000

0.00000.20000.40000.60000.80001.00001.20001.4000

17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 

Age

Parameter Estimate

1950s

1960s
Figure 13: Residual Cohort Effects, Female Remarriage

-1.0000-0.9000-0.8000-0.7000-0.6000-0.5000-0.4000-0.3000-0.2000-0.1000

0.0000

17

19

21

23

25

27

29

31

33

35

37

39

41

43

45

47

49

51

53

55

57

59

61

Age

Parameter Estimate

1950s

1960s
Figure 14: Residual Cohort Effects, Male Remarriage

-2.0000-1.5000-1.0000-0.5000

0.00000.5000

17

19

21

23

25

27

29

31

33

35

37

39

41

43

45

47

49

51

53

55

57

59

61

Age

Parameter Estimate

1950s

1960s
 Table 8: Marital Status Distributions Over Time, by Sex and Year

 Social Security Administration (OCACT) Congressional Budget Office Long-

Never Married Married Divorced Widowed Never Married Married Divo

Overall Population 

2001 43% 44% 8% 5% 44% 44% 72025 41% 45% 9% 5% 42% 46% 62050 40% 45% 9% 6% 41% 46% 62075 40% 46% 9% 6% 40% 47% 6

Female Population

2001 40% 43% 8% 8% 41% 43% 82025 38% 45% 10% 8% 38% 46% 72050 37% 45% 10% 9% 36% 47% 72075 36% 46% 10% 8% 35% 48% 7

Male Population

2001 47% 45% 7% 2% 47% 45% 62025 45% 46% 7% 2% 46% 46% 52050 44% 46% 7% 3% 46% 45% 52075 43% 46% 7% 3% 45% 46% 5

Source: Authors' calculations based on SSA Area Population Projections 1941 to 2080, Office of the Chief Actuary. 
Figure 15: Historical Simulation Calibration Factors, Females

-0.4-0.3-0.2-0.1

0

0.10.20.30.4

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

62

64

66

68

70

72

74

76

Age

Percentage Point Calibration

Never Married Married Divorced Widowed
 Figure 16: Historical Simulation Calibration Factors, Males 

-0.4-0.3-0.2-0.1

0

0.10.20.30.4

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

62

64

66

68

70

72

74

76

Age

Percentage Point Calibration

Never Married Married Divorced Widowed
Figure 17: Marital Status for Females Ages 62-67, by Year

0

102030405060708090

100

1983

1986

1989

1992

1995

1998

2001

2004

2007

2010

2013

2016

2019

2022

2025

2028

2031

2034

2037

2040

2043

2046

2049

2052

2055

2058

20

Year

Percent

CBOLT - Never Married OCACT 

CBOL

OCACT - Divorced

CBOLT - Married or Widowed

OCACT - Married or Widowed
Figure 18: Marital Status for Males Ages 62-67, by Year

0

102030405060708090

100

1983

1986

1989

1992

1995

1998

2001

2004

2007

2010

2013

2016

2019

2022

2025

2028

2031

2034

2037

2040

2043

2046

2049

2052

2055

2058

Year

Percent

OCACT - Married or Wido

CBOLT - Marri

CBOLT - Never Married

OCACT 

CBOLT - Divorced

OC
Figure 19: Mean Age At First Marriage

05

101520253035

196419681972197619801984198819921996200020042008201220162020202420282032203620402044204820522056

Year

Age

Males

Females
Table 9.

Distribution of Marital Durations at Time of Divorce,

 by Number of Years Married

0 1 to 4 5 to 9 10 to14 15 or More

NCHS - Actual

1990 3% 32% 28% 15% 22%

Projected

2005 9% 30% 24% 16% 23%

2015 8% 28% 24% 15% 27%

2025 6% 31% 27% 14% 23%

2035 6% 30% 25% 15% 24%

2045 7% 28% 28% 13% 25%

2055 6% 30% 25% 14% 23%

2065 8% 31% 22% 14% 26%

2075 7% 29% 26% 13% 26%
Table 10.

 Percentage of Nonwidowed Women Ages 62-67 Eligible

 for OAI Spouse Benefits, by Lifetime Earnings Deciles

Year

2005 2015 2025 2035 2045 2055 2065 2075

Earnings Decile

1 95% 88% 88% 86% 83% 86% 85% 87%

2 95% 89% 87% 87% 82% 87% 88% 89%

3 90% 89% 85% 89% 84% 88% 89% 87%

4 90% 83% 83% 86% 83% 85% 85% 87%

5 86% 80% 83% 85% 79% 84% 85% 85%

6 88% 83% 83% 82% 80% 82% 84% 83%

7 80% 81% 82% 79% 74% 80% 83% 81%

8 84% 80% 77% 78% 74% 77% 78% 77%

9 81% 77% 75% 71% 69% 76% 77% 78%

10 77% 75% 72% 64% 65% 68% 70% 72%

All 87% 83% 81% 81% 77% 81% 82% 83%