BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Productivity and Technology 

A Micro-level Analysis of Recent Increases in Labor Force 

Participation among Older Men 

Kevin E. Cahill, Analysis Group, Inc. 

Michael D. Giandrea, U.S. Bureau of Labor Statistics 

Joseph F. Quinn, Boston College 

Working Paper 400

October 2006

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
 

A Micro-level Analysis of Recent Increases 

in Labor Force Participation among Older Workers 

 

Kevin E. Cahill, Ph.D. 

(corresponding author) 

Analysis Group, Inc. 

111 Huntington Avenue, 10th Floor 

Boston, MA 02199 

Email: kcahill@analysisgroup.com 

Phone: (617) 425-8380 

Michael D. Giandrea, Ph.D. 

U.S. Bureau of Labor Statistics 

Office of Productivity and Technology 

Postal Square Building, Room 2180 

2 Massachusetts Ave., NE 

Washington, DC 20212-0001 

Email: giandrea.michael@bls.gov

Phone: (202) 691-5628 

Joseph F. Quinn, Ph.D. 

Department of Economics 

Gasson Hall 103 

Boston College 

Chestnut Hill, MA 02467-3803 

Email: joseph.quinn@bc.edu

Phone: (617) 552-2393 

September 29, 2006 

* All views expressed in this paper are those of the authors and do not necessarily reflect the views or 

policies of the U.S. Bureau of Labor Statistics. 
A Micro-level Analysis of Recent Increases 

in Labor Force Participation among Older Workers 

Kevin E. Cahill 

Michael D. Giandrea 

Joseph F. Quinn 

Abstract 

Aggregate data reveal a sizable increase in labor force participation rates since 

2000 among workers on the cusp of retirement, reverting back to levels for older men not 

seen since the 1970s. These aggregate numbers are useful in that they document overall 

trends, but they lack the ability to identify the reasons behind workers’ decisions. The 

Health and Retirement Study (HRS) spans the last dozen years from 1992 to 2004, 

includes two cohorts of retirees, and provides micro-level data regarding these recent 

trends. Moreover, the HRS contains information on older Americans and the types of 

jobs they are taking (full-time versus part-time, self-employed versus wage-and-salary, 

low-paying versus high-paying, blue collar versus white collar, etc.). This study 

capitalizes on the richness of the HRS data and explores labor force determinants and 

outcomes of older Americans, with an emphasis on retirees' choices in recent years. We 

present a cross-sectional and longitudinal description of the financial, health, and 

employment situation of older Americans. We then explore retirement determinants 

using a multinomial approach to model gradual retirement and a two-step approach to 

model the work-leisure and hours intensity decisions of older workers. Evidence 

suggests that the majority of older Americans retire gradually, in stages, and that younger 

retirees continue to respond to financial incentives just as their predecessors did. In 

addition, recent macro-level changes appear to have blurred the distinction between 

younger and middle-aged retirees.

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I. Introduction 

The 2001 recession was unique in that older workers experienced increases in 

labor force participation rates while other workers’ rates followed the typical pattern 

during a recession and declined. Older workers’ choices during this recessionary period 

are even more notable since their decisions reversed a broader trend of ever-earlier 

retirements that bottomed out in the mid-80s and held steady through the 90s. Another 

key feature of today’s retirees is the way they exit the labor force. Traditional one-time, 

permanent retirements appear to be the exception rather than the rule, as older workers 

increasingly change jobs later in life or reenter the labor force after “retiring.” Why are 

so many of today’s older Americans breaking from the traditional retirement pattern? 

The answer to the pro-work mindset of many of today’s older Americans is likely 

a reflection of many factors, on both the supply and the demand sides. People are living 

longer, healthier lives, and have higher levels of formal education compared to earlier 

generations. Jobs are also less physically demanding than in the past, as the economy 

shifts away from manufacturing occupations towards service ones. At the same time, a 

strong labor market, like that of the 1990s and mid-2000s, provides older workers with 

many job opportunities. These changes have allowed older workers to remain productive 

well beyond their late 50s and early 60s. 

Many of the financial incentives surrounding retirement have changed as well. 

Today’s retirees have experienced a general shift towards a “do-it-yourself” approach to 

retirement. Defined-benefit pension plans, which offer a set annuity payment upon 

retirement, are less common in today’s private sector and many existing defined-benefit 

plans are being converted to cash balance plans or replaced with defined-contribution 

plans managed by the worker. Social Security, the bedrock of financial security late in 

life, is facing financial strain and will likely provide lower replacement rates than in the 

past. Finally, private saving, the third pillar of retirement income, has always been 

directed by the individual. Thus, workers are now in charge of their retirement finances 

more so than at any time in the post-war era. 

While these long-term trends will undoubtedly impact retirement patterns in the 

long run, they do not, in and of themselves, explain why labor force participation rates 

have jumped so abruptly in recent years. For insight here we examine how long-term 

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changes have made retirees vulnerable to short-run market forces. Perhaps it took a 

shock in the financial markets, such as the 2001 recession, to uncover the impact of the 

“do-it-yourself” approach. Seen this way, the key to understanding workers’ retirement 

decisions in recent years is to understand the interaction between long-run incentives and 

short-term market fluctuations. This interaction may explain why the early retirement 

trend subsided in the mid-80s and 90s and why older workers returned to the labor force 

in the early part of this decade. The interaction may also explain why the trend subsided 

somewhat in recent years as the economy improved. 

Under this hypothesis, we expect the timing of retirement to be cyclical, as 

workers’ expectations and plans are continuously updated in response to the changing 

state of financial markets. This is a fundamental shift from the past. Before, the timing 

of retirement was largely immune to changes in market conditions, as investment risk 

was borne by the federal government and an individual’s employer. With the advent of 

401(k)s and with the extension of Social Security’s Normal Retirement Age (NRA) from 

65 to 66, and eventually to 67, a worker’s retirement benefit now depends on the current 

state of the market. One possible implication, going forward, is that more older workers 

can be expected to reenter the labor market during a recession, and then retreat from the 

labor force during a boom. 

Aggregate data on work force participation are consistent with this explanation; 

however, micro-level data are required to test this hypothesis. We analyze data from the 

Health and Retirement Study (HRS), a nationally-representative dataset of two cohorts of 

older Americans (Juster and Suzman, 1995). The first group of respondents, the “HRS 

Core,” was born between 1931 and 1941 and was 51 to 61 years old in 1992, the first 

year data were collected. The second group, the “HRS War Babies,” was born between 

1942 and 1946 and was 51 to 56 years old in 1998, the year of their first interview. Both 

cohorts were interviewed every other year, so by 2004 twelve years of data were 

available for the first group and six years for the War Babies. Detailed information on 

demographics, health status, work history, income, wealth, and more are available for 

each respondent, making the HRS an ideal data set for this study. 

 This paper is structured as follows. Section II provides some background on 

retirement trends and exit patterns, with a focus on recent developments. Sections III, IV, 

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and V outline the estimation strategy for how we plan to identify the key determinants of 

retirement outcomes, especially in recent years, and the data we use for our analyses. We 

present our results in Section VI and comment on the implications of our research. We 

conclude that future retirees are likely to be much more flexible with respect to their 

eventual labor force exit compared to those in the past. 

II. Background 

Labor force participation rates among older workers have risen in recent years, 

both among men, whose rates were relatively constant from the mid-1980s through much 

of the 1990s, and among women, whose rates have increased steadily since the mid1980s. Men 60 to 64 years old experienced an increase in labor force participation from 

53 percent in 1995 to 57 percent in 2004, a 7 percent increase over the decade. Men aged 

65 to 69 exhibited an even larger increase over the same time period, from 27 percent to 

33 percent. The story is similar for women, but begins at a younger age. Women aged 

55 to 59 had an increase in labor force participation from 60 percent to 65 percent, while 

those aged 60 to 64 and aged 65 to 69 experienced increases of 18 percent and 29 

percent, respectively (U.S. Bureau of Labor Statistics, 2005; Purcell, 2005). 

Several recent studies have noted this trend, as well as the diverse patterns by 

which older workers exit the labor force. Cahill, Giandrea, and Quinn (2006), for 

example, examined transitions from career jobs and found that more than one-half of 

older workers utilized bridge jobs prior to complete labor force withdrawal. Moreover, 

not only were bridge jobs common among older cohorts of workers, but diverse patterns 

were even seen among the youngest of retirees (i.e., those age 57-62 in 2004). Maestas 

(2004), Quinn (1999), and Ruhm (1990) have also examined the transition that workers 

take when exiting the labor force. They have described an increasingly “blurred” 

(Mutchler, Burr, Pienta, and Massagli, 1997) retirement process where employees 

gradually transition from career jobs to a retirement that sometimes includes reentry to 

the labor market before a permanent exit. 

Research and anecdotal evidence provide insight into the key determinants of 

these trends. The retirement literature has shown that demographic and socio-economic 

characteristics matter, as do financial incentives (e.g., Gruber and Madrian, 1995; 

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Mutchler, Burr, Pienta and Massagli, 1997; Quinn, 1999; Coile 2003). More recently, the 

importance of other factors has been examined. For example, the long-term shift from 

defined-benefit pension plans that provide a regular benefit amount in retirement to 

employee-controlled defined-contribution plans has been shown to influence the timing 

of retirement (Munnell, Cahill, and Jivan, 2003). 

Cyclical economy-wide factors, such as stock market performance, have recently 

been proposed as an important determinant of labor force participation. Eschtruth and 

Gemus (2002) outlined the way older workers increased participation in the labor force 

during the most recent recession. Using a micro-level approach to the same topic, Coile 

and Levine (2004) investigated the relationship between changes in stock market 

valuations and retirement activity using the HRS, the Current Population Survey (CPS), 

and the Survey of Consumer Finances (SCF). They estimated reduced form equations to 

determine whether the growth of stock market values in the late 1990s and the subsequent 

decrease in the early 2000s impacted retirement behavior of near-retirement workers. 

Coile and Levine found that rates of retirement are not significantly different between 

older workers with larger stock portfolios and those without. 

Gardner and Orszag (2003) also took a micro-level approach based on a survey of 

almost 4,500 individuals aged 50-64 in the U.K. and found that one-quarter planned to 

delay their expected retirement date because of the stock market decline of 2000. 

Coronado and Perozek (2003) used HRS data to investigate the effects of wealth on 

leisure decisions later in life. They found that the unexpected stock market gains led 

workers who held stocks to retire, on average, about seven months earlier than nonstockholders. 

 This paper follows the micro-level research and concentrates on the determinants 

of the labor market participation decisions of older workers using two cohorts of retirees 

spanning 1992 to 2004. 

III. Three Period Model of Retirement 

The decision to take on a bridge job can be presented graphically. Figure 1 

outlines a three period model of retirement that depicts the path from full-time career 

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(FTC) employment to complete labor force withdrawal as a function of the choice 

variables of older workers. 

The first period, from t0 to t1, represents the FTC job. At time t1 the worker leaves 

the FTC job and chooses between a bridge job and retirement. If the individual stays in 

the labor force, he or she chooses how long to remain working, t2-t1, and the number of 

hours to work per year, H. The bridge job may require fewer, the same, or even more 

hours than the FTC job. If a worker leaves a FTC job for complete retirement, then H = 0 

and t1= t2. From time t2 forward the individual is fully retired and does not work. The set 

of factors that affect the timing of the retirement decision are similar to those that 

influence a worker’s choice of when to leave his or her FTC job (Quinn, 1999). 

We use this model as a guide to examine the labor force transitions of older 

workers. 

IV. Methodology 

The purpose of this analysis is to assess how the shift towards a do-it-yourself 

approach to retirement has altered decisions regarding the timing of labor force 

withdrawal, bridge job behavior, and labor force reentry. We hypothesize that workers 

are now more sensitive to short-term market conditions, as the size of potential retirees’ 

non-annuitized retirement nest eggs rises and falls with financial markets. To test this 

hypothesis, we first make a distinction between two underlying causes for differences in 

outcomes, changes in determinants and changes in impacts. As retirement inputs change, 

such as the switch from defined-benefit to defined-contribution plans over the past two 

decades, retirement outcomes change. The underlying cause of the change (i.e., inputs) 

does not necessarily imply a change in retirement decision making. A more subtle 

change, one that is addressed in part here by examining cohort differences, is whether 

known determinants of retirement now influence retirement decisions differently from in 

the past. 

 Our sample consists of two cohorts of retirees, the HRS Core (born between 1931 

and 1941) and the HRS War Babies (born between 1942 and 1947), with multiple data 

points over time within each cohort. We model the retirement decision in three ways, 

each making use of the longitudinal nature of the HRS and each providing a different 

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insight into the choice between work and retirement later in life. The first model is based 

on the multinomial regression model presented in Cahill, Giandrea, and Quinn (2006) in 

which gradual retirement is examined among a set of respondents who were on a FTC job 

at the time of the first interview. Gradual retirement from full-time career employment is 

measured using a three-way indicator of labor force withdrawal, equal to zero (still on a 

full-time career job), one (transitioned to a bridge job), or two (direct exit from the labor 

force). The model is structured as follows: 

it it RET it X i X it R βββα +ε − + − ++= 13121

*

3 (1) 

* 3R it is the latent variable that determines the observed choice and indicates 

the actual outcome, equal to zero, one, or two, as noted above. Xi and Xit represent, 

respectively, time invariant and time-varying vectors of characteristics believed to be 

significant determinants of the retirement process, such as age, health status, marriage 

status, and other demographic characteristics. RETit is a set of retirement incentive 

variables associated with private pensions and health insurance plans. 

R3it

The model differs from the one presented in Cahill et al. mainly because of the 

time at which the independent variables are measured. Instead of using a wave-one 

baseline measure, time-varying determinants are defined as of the time period just prior 

to the transition. For example, if a respondent in the HRS Core sample moves from a 

FTC job to a bridge job in the fourth wave (1998), the independent variables are 

measured as of the third wave (1996). This prior-to-transition designation is an 

improvement over the existing model specification because changes in status since the 

first interview are incorporated into the model, which are particularly important for the 

Core sample given the potential for a 12-year gap between the initial interview and first 

transition. We estimate equation one separately for the Core and War Baby samples. 

This allows us to examine the extent to which differences exist by cohort. 

The multinomial logistic model provides an intuitive way of examining gradual 

retirement. Another way to model workforce decisions is to make use of the fact that 

each respondent contributes multiple observations, one from each survey. This approach 

allows us to control for individual-specific factors and time effects, both of which are not 

possible in the framework of the multinomial regression model. Fixed effects can now be 

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identified because respondents contribute multiple observations. And time effects can be 

identified because the date of transition is known in each wave (previously, the date of 

transition was not known for all respondents still on a FTC job in 2004). 

Using person-year observations, we model retirement as a series of decisions. 

The respondent first decides whether to stay working and, if so, whether to remain on a 

FTC job or to transition to a bridge job. He or she then decides how many hours to work. 

For the purposes of our analysis, we examine hours worked conditional on being on a 

FTC job or a bridge job at the time of the interview. That is, we do not model job type 

and hours worked as being jointly determined. 

We begin by pooling the data for each cohort and transforming the dataset into 

one with person-year observations. We then model the work-leisure decision later in life 

among those with work experience since age 49 as follows: 

it i

v

t

Y i WarBaby

t Y i WarBaby it RET it X i X it R

β ε

βββα β β

+ ++

+++= + +

( )* 6

321 4 5

*

 (2) 

Observations are person-year, with i indicating individual and t indicating time. 

The latent variable, R , determines the observed choice. it

* Rit indicates the actual outcome 

and is equal to 1 if individual i is working at time t, and is equal to zero otherwise. Xi, 

Xit, and RETit are as defined above for model one. WarBabyi is a binary variable equal 

to one if the individual respondent is among the War Babies cohort. Yt is a series of year 

indicator variables (or, alternatively, macro-economic measures, such as the 

unemployment rate) that are intended to account for economy-wide factors. The error 

term consists of two parts, an individual-specific component, v , assumed to be 

uncorrelated with the set of explanatory variables, and a white noise error component, εit. 

i

The equation includes interaction terms between the War Baby indicator and the 

time dummies, which is a key interest for this study. The interaction terms examine the 

extent to which the work decisions of the War Babies differed systematically by year 

from those of the Core. We hypothesize that the Core sample, those on the cusp of 

retirement, might have been more vulnerable to the macro events, such as the stock 

market decline (i.e., 0 β 6 ≠ implies cross-cohort differences). We estimate the model in 

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three ways, using a linear probability model with and without fixed effects, and using a 

logistic model. 

Given the decision to remain working, we then examine hours worked on the fulltime career job (i.e., prior to transition) and hours worked on the bridge job (i.e., post 

transition) among those who have had a FTC job since age 49. The hours equation is as 

follows: 

it i

v

t

Y i WarBaby

t Y i WarBaby it RET it X i X it H

β ε

βββα β β

+ ++

+++= + +

( )* 6

321 4 5 (3) 

The dependent variable, , is a measure of total hours worked, conditional on 

either working on an FTC job or a bridge job. Other variables are as defined above. 

Separate equations are estimated for FTC hours and for bridge job hours, with time 

indicators measured as of the current wave of HRS data; a respondent’s hours decision in 

any given wave is based on the incentives that exist at that time and the respondent’s 

choices made up to that point. The interaction terms, as in equation two above, allow us 

to test whether cohort and time differences exist with respect to the work and workintensity decision, respectively. 

Hit

V. Data 

The Health and Retirement Study (HRS) is a nationally-representative, 

longitudinal survey of older Americans that began in 1992. The survey now spans the 

dozen years from 1992 to 2004, includes two cohorts of retirees, those born between 

1931 and 1941 (“HRS Core”) and those born between 1942 and 1946 (“HRS War 

Babies”), among others, and provides micro-level information regarding labor force 

decisions. Moreover, the HRS provides detailed information on the demographic and 

economic characteristics of older Americans and the types of jobs they hold (full-time 

versus part-time, self-employed versus wage-and-salary, low-paying versus high-paying, 

blue collar versus white collar, etc.). 

 Table 1 shows that the original HRS Core set of respondents consisted of about 

12,600 persons from approximately 7,600 households with respondents 51 to 61 years old 

in 1992 (born between 1931 and 1941), and their spouses. Respondents were first 

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interviewed in 1992 and follow-up interviews have been conducted every two years. The 

Core was expanded dramatically in 1998 (wave 4) when a new cohort, the HRS War 

Babies, was added to the sample. The War Babies were born between 1942 and 1947 

(age 51 to 56 in 1998). Like the core HRS sample, the War Babies are interviewed every 

two years. 

This study analyzes the respondents from the Core, interviewed every two years 

from 1992 to 2004 (waves 1-7), and about 2,500 War Babies, interviewed every two 

years from 1998 to 2004 (waves 4-7). Attrition across waves ranged from 3 to 10 

percent, so that after six years, about 85 percent of the Core sample and 89 percent of the 

War Babies remained in the sample. After twelve years, about 75 percent of the Core 

sample remained. 

This paper focuses on labor force exit and retirement patterns, and we therefore 

exclude respondents with no work experience after age 49. As shown in Table 1, the 

large majority of both HRS men and women do indeed have work experience later in life. 

Just over 90% of men have worked since age 49 in both the Core and the War Babies 

samples, 5,344 and 1,122, respectively. Work experience is somewhat lower among 

women, although the gap is narrower among the War Babies with 5,196 and 1,159 

women with work experience after age 49 among the Core and War Babies, respectively. 

For the hours equation, we make an additional restriction based on whether these 

HRS respondents had a FTC job since age 49. The longitudinal nature of the HRS allows 

us to do this, since the questionnaire from the initial interview asks about a respondent’s 

current job and all previous jobs that have lasted five years or more. If a respondent is 

not working at the time of the wave one interview, he or she is asked about the most 

recent job held, if any. Information on short-term jobs in the past lasting less than five 

years is not available, unless the respondent is not working at the time of the first 

interview and tenure on the last job held is shorter than five years. For the purposes of 

this paper, we define a full-time career (FTC) job as one that requires at least 1600 hours 

per year (“full time”) and that lasts ten or more years (“career”). Therefore, a bridge job 

is employment following a FTC job that does not meet these two requirements. 

One concern with this methodology, especially among the War Babies, is that 

younger respondents might not have enough tenure in 2004 for a job to be considered a 

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career job, even though the respondent may continue working and increase tenure. In 

some instances, these jobs will in fact turn out to be career jobs if the individual remains 

on the job for ten years. When subsequent waves do not cover work status through age 

62, or when a respondent does not participate in subsequent waves, we assume that the 

respondent would have worked on the job until age 62. This assumption results in 

conservative estimates of the number of bridge jobs observed since a portion of these 

individuals may indeed leave their jobs before age 62. 

We find that over 70% of males in both cohorts had a FTC job since age 49. This 

yields a sample of 4,280 men from the Core and 890 men from the War Babies cohort. 

About 45 percent of the Core women had a FTC job since age 49, compared to about 

51% of the War Baby women. This yields a sample of 3,082 Core and 675 War Baby 

women. Some of our analyses require the sample to be restricted to those on FTC jobs at 

the time of the first interview. Among the Core, 3,057 men and 2,513 women were on a 

FTC job in 1992. For the War Babies, 843 men and 664 women were on a FTC job in 

1998. This represents more than half (two thirds) of the males and about one third (one 

half) of the females in the initial Core (War Babies) survey. 

VI. Results 

Retirement Outcomes 

 The two outcomes of interest are work status and the prevalence of bridge jobs. 

Each of these outcomes is examined over time, from 1992 through 2004, and by cohort, 

defined as the HRS Core and the HRS War Babies. For one of our analyses, we split the 

Core sample into two groups, those aged 51 to 56 in 1992 and those aged 57 to 61 in 

1992. This split allows us to make analogous comparisons between the War Babies and 

the younger group within the Core, because the two groups of retirees are the same age at 

the time of the first interview. 

 Work status in each survey year for each of these three groups of workers is 

shown in Figures 3a (men) and 3b (women). Not surprisingly, older retirees are less 

likely to be working each year. The relevant comparison, however, is how each of the 

cohorts compare over time. The work status of the younger Core group of men appears 

to resemble that of the War Babies in 1998, with a participation rate of nearly 90 percent 

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at ages 51 to 56. Six years later, about three quarters of each group is still working. 

Among men, it does not appear as though there are cohort differences with respect to 

work status. 

 The story is different among women, although not dramatically so. More women 

in the War Babies cohort were working at the time of the first interview compared to the 

younger Core cohort. The difference continued six years later, and has grown slightly. 

Perhaps more interestingly, the older Core women had a rapid decline in work status in 

the first six years of the survey, which then leveled off and remained fairly stable 

between 2002 and 2004. These descriptive statistics for the older Core women provide 

some evidence that cohort differences and year effects may be important when examining 

work status in the pooled sample. 

 The second outcome of interest focuses on the way older workers exit the labor 

force. We measure gradual exits from the labor force using the bridge job concept 

defined earlier. Table 2 describes bridge job prevalence as of 2004, stratified by work 

status and gender. By 2004 about 50 percent of Core men were either currently working 

on a bridge job or were currently not working, but had worked on a bridge job prior to 

exiting the labor force. A similar percentage is observed among the women. A nontrivial portion of both men and women were still working on a FTC job (13 and 17 

percent, respectively), so the eventual incidence of bridge job behavior will be even 

higher. If we assume those still on FTC jobs will leave their jobs in a way resembling 

those who have already left, then about 65 percent, or two-thirds of men and women with 

FTC jobs in their work history, will have taken on a bridge job prior to retiring. 

 The War Babies were much more likely to still be on an FTC job in 2004, not 

surprisingly since they are six years younger than the youngest of the Core sample. 

When we examine the bridge job behavior among the War Babies who left their FTC job, 

we see that a similar percentage as the Core, and even slightly more, take on bridge jobs 

(between 66 and 69 percent). 

 Taken together, a comparison of the HRS Core and the HRS War Babies shows 

that, for the most part, the two groups are alike with respect to their work status and 

bridge job behavior. Yet these two groups likely faced different retirement incentives as 

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they aged, and the way they responded (or did not respond) to these financial incentives 

is of interest. With this thought in mind we discuss the determinants of retirement. 

The Determinants of Retirement 

 The retirement literature has identified key demographic, socio-economic, and 

financial retirement incentives that influence the retirement decision. In this section, we 

explore how the two outcome variables of interest, work status and bridge job behavior, 

compare with respect to these predictors by cohort and by gender. We examine work 

status and hours worked among all respondents, and bridge job status among those who 

were on a full-time career job as of the first interview. 

 Tables 3a and 3b show (for Core and War Babies samples, respectively) that 38 

percent of the Core men and 76 percent of the male War Babies were still working in 

2004. Among those with FTC jobs in wave one, about 16 percent of Core men were still 

on that FTC job in 2004 and, among those who left, about 55 percent had taken a bridge 

job. More than half of the War Babies with FTC jobs at the time of the first interview 

were still on the FTC job, and about two-thirds of those who left took a bridge job. 

Not surprisingly, men were more likely to be working in 2004 if they were 

younger, reported being in better health, or had dependent children. Bridge job status 

was more common among younger retirees who moved off of a FTC job for both the 

Core group and War Babies. Bridge jobs were also more common among those who 

reported better health status, and those who had a college degree. So these determinants 

appear to be influencing the Core and War Babies in similar ways. 

 The general story appears to be the same for women, albeit at different levels, and 

with cross-cohort differences more pronounced than among the men. For example, Core 

women with dependent children were 16 percentage points more likely to be working 

than the overall average (55 percent vs. 39 percent). Among the War Babies, women 

with dependent children were six percentage points less likely to be working in 2004 (65 

percent vs. 71 percent). Core women with dependent children were also much more 

likely to take on a bridge job relative to the overall average than were those women with 

children in the War Babies cohort. 

- 14 - 
 Differences by cohort and gender also exist with respect to economic 

characteristics, such as health insurance status, pension status, wage, and occupation. We 

discuss a few of these variables here, and refer to Table 3c (men) and Table 3d (women) 

for complete details. The impact of a defined-benefit pension plan is pronounced among 

Core males, as men with defined-benefit plans are less likely to be working and less 

likely to be on an FTC job in 2004 compared to the overall average. These pension plans 

often have specific incentives incorporated into their benefit structures that induce 

individuals to leave their jobs at specific early retirement ages, so the result is plausible. 

Interestingly, these impacts are not seen among the War Babies, perhaps because this 

group has not yet reached the pivotal ages for early retirement with these plans. 

A second point that we highlight is a “u-shaped” relationship between both wage 

and bridge job status and occupational status and bridge job status. Those who are fairly 

well off (high wage or white collar, highly skilled) may take a bridge job to “try 

something new” or for a change – not out of necessity. Those who are struggling 

financially (low wage or blue collar, not highly skilled), however, may take on a bridge 

job because they have no other choice. And, finally, those in the middle might not take 

bridge jobs at all. The wage and occupational variables appear to support this 

relationship, as those with high wages or in white-collar, highly-skilled occupations and 

those with low wages or in blue-collar, not-highly-skilled occupations are the most likely 

to take on bridge jobs. The result holds for both cohorts, although the relationship is 

stronger among the Core sample. 

The relationships discussed above hold for Core women as well, although as 

before, cross-cohort differences exist. Core women with defined-benefit pension plans 

are less likely to be working in 2004 compared to other workers, and the “u-shaped” 

relationship for occupational status holds as well. Female War Babies with definedbenefit plans are not less likely to be working and the “u-shaped” occupational status 

relationship looks instead like the left-hand side of a “u-shaped” curve. Bridge job status 

is lowest among those in blue-collar, not-highly-skilled positions, and highest among 

those in white-collar, highly-skilled occupations. 

Multivariate Analysis 

- 15 - 
We now examine how the retirement determinants described above affect the 

outcome variables of interest in a multivariate setting. Overall, the multivariate analyses 

support the bivariate descriptive results, and provide some additional insights. We begin 

with the multinomial logistic regression model using those respondents who are on a fulltime career job at the time of the first survey. We then examine the retirement process as 

a sequence of decisions, under the assumption that the work-leisure decision (whether to 

work) and the work intensity decision (how much to work) are separable. 

Specification #1: Multinomial Regression Model of Gradual Retirement 

The multinomial regression model examines first transitions from FTC job 

employment. We estimate separate models for the HRS Core and HRS War Babies 

cohorts, by gender, primarily because each cohort has a different observation period, 

twelve years for the Core and six years for the War Babies. One benefit of this approach 

is that coefficient values can be compared for each retirement determinant included in the 

regression. 

We find that men are more likely to remain on their FTC job if they are younger, 

have a dependent child, have health insurance, have a higher wage, are self employed, or 

own a home (Table 4a). Men are less likely to remain on a FTC job if their spouse is in 

excellent or very good health and if they have a defined-benefit pension plan. 

Interestingly, being in excellent or very good health does not appear to be a statistically 

significant indicator of staying on a FTC job while spouse health status does matter. On 

the surface this result appears to run counter to some of our earlier findings, presented in 

Cahill et al. (2006). We suspect, however, that the discrepancy in findings is likely due 

to the difference in when the dependent variables are measured (i.e., prior to transition 

versus in wave one). 

Bridge job status depends strongly on the respondent’s health status, with those in 

excellent or very good health being more likely to take on a bridge job and those in fair or 

poor health being less likely to do so, compared to those who reported being in good 

health. Bridge job status is also more prevalent among those who are younger, married, 

college educated, and without health insurance, and less prevalent among those with a 

defined benefit plan or with lower wages. 

- 16 - 
The experience among the male War Babies is, overall, similar to their Core 

counterparts (shown in Table 4b), although some differences do exist. Health insurance 

portability, for example, is a significant predictor of leaving a FTC job among the male 

War Babies, and having any pension plan, either a defined-benefit or definedcontribution, has a positive influence on remaining at a FTC job. With respect to the 

bridge job transition, having a spouse in excellent or very good health increases the 

likelihood of taking on a bridge job among the male War Babies. 

The factors that influence FTC and bridge job behavior among men also have 

similar effects among the Core and War Baby women (Tables 4c and 4d, respectively), 

although cross-cohort differences appear stronger for the women. Unlike the War 

Babies, Core women who have a spouse in excellent or very good health are less likely to 

remain working on their FTC job. Core women who are themselves in excellent or very 

good health are more likely to take on bridge jobs, too, a finding not seen for the War 

Babies. Another notable cross-cohort difference is that having a dependent child has a 

negative impact on staying on the FTC job for the Core women but a positive impact for 

the War Babies. In terms of economic characteristics, Core women who had a pension 

on their FTC job or who had non-portable health insurance were much less likely to take 

on a bridge job than those who did not. These factors did not have a statisticallysignificant impact among female War Babies. 

Specification #2a: Logistic Regression Model of the Work-Leisure Decision 

As described in the methodology section, the three-way retirement specification 

presented above can also be viewed as a two-step decision under the assumption that the 

work-leisure decision and work intensity decision are made sequentially (as opposed to 

jointly). This separation provides the flexibility necessary to utilize the longitudinal 

nature of the HRS, by pooling the Core and War Babies data and constructing a dataset of 

person-year observations. We restrict our sample to those who have had work experience 

since age 49 to ensure that the work-leisure decisions are for those who have had work 

experience later in life. 

Tables 5a and 5b (men and women, respectively) present marginal effects from a 

logistic regression of work status in each wave within the pooled Core and War Babies 

- 17 - 
samples.1

 As expected, men are more likely to be working if they are younger and in 

excellent or very good health, if they have higher levels of formal education or dependent 

children, or if they are self employed or earn more per hour. Men are less likely to be 

working if they report being in fair or poor health. Men are also less likely to be working 

if their spouse is in excellent or very good health and more likely to be working if their 

spouse is in fair or poor health. 

Older workers respond to key retirement incentives as well, as expected. If health 

insurance is portable in retirement, that is, if a worker does not lose his health insurance if 

he stops working at that job, then he is more likely to stop working, all else equal. Men 

with defined-benefit pension plans on their jobs are also less likely to be working, a result 

that is consistent with the age-specific early retirement incentives incorporated in such 

plans. Defined-contribution plans also have a negative influence on work relative to 

those without pension plans, although the impact of defined-contribution plans is much 

weaker than that of defined-benefit plans. 

A focus of our analysis is the interaction between the War Babies indicator and 

macroeconomic factors. Overall, the War Babies are more likely to be working and, 

overall, the probably of remaining in the labor force declines consistently over the survey 

years. When these two variables are interacted, however, we find that the differences 

between the Core sample and the War Babies are insignificant in 2002 and in 2004. One 

explanation for this finding is that after the stock market collapse in 2000, Core 

respondents returned to the labor force and their work decisions started to resemble those 

of the War Babies. Said differently, the events that transpired since 2000 appear to have 

impacted the work-leisure decisions of the Core respondents differently than the War 

Babies. 

 The results of the analysis for the HRS female respondents are remarkably similar 

to those of the male sample. The fact that so many of the coefficients are similar across 

both equations implies that both the male and female samples could be combined. We 

keep them separate here, though, because of potential differences in work intensity on 

FTC jobs or bridge jobs, discussed below. We also highlight that the same time effects 

 

1

 We also perform this estimation two additional ways, using a linear probability model and a linear 

probability model with fixed effects. We obtain similar results with all three methods. 

- 18 - 
are seen among the women as with the men. The work decisions of the Core females no 

longer differed from those of the War Babies after 2000, while they did differ before. 

Again, like their male counterparts, the work-leisure decisions of the female Core 

respondents relative to the War Babies appear to have been influenced by 

macroeconomic factors. 

Specification #2b: OLS Model of Work Intensity 

 Given the decision to work, respondents then decide how much. This work 

intensity decision can be quite complicated, with decisions about job type and hours 

worked being jointly determined. For the purposes of our analysis, however, we simplify 

the decision and explore hours worked conditional on job type. Table 6a reports 

estimated coefficients from an OLS regression of hours worked per year conditional on 

being on a FTC job among the sample of male respondents who have had a FTC job 

since age 49. Like the work-leisure regressions, observations are person-year with timedependent variables measured as of the survey year. Table 6b then examines hours 

worked conditional on being on a bridge job. Therefore, the results presented in Table 6a 

represent hours worked pre-transition and the results presented in Table 6b represent 

hours worked post transition. Tables 6c and 6d present pre- and post-transition hours, 

respectively, among female respondents. 

 Hours worked on the FTC job are higher among younger men and those in 

excellent or very good health, as expected. The order of magnitude is nontrivial as well, 

as men over age 65 worked 133 fewer hours per year than those under age 58. Men who 

were in excellent or very good health worked about 25 more per year than those in good 

health, and those in fair or poor health worked about 18 hours less. Hours worked were 

also positively associated with those having pension plans and in white collar 

occupations. Men with defined-contribution plans worked about 72 hours more per year 

prior to making a transition compared to those with no pension plan, and those with 

defined-benefit plans worked about 14 hours more. College graduates and those with 

portable health insurance worked fewer hours, all else equal. And, finally, while self 

employment status was consistently a strong predictor of working later in life, being self 

- 19 - 
employed had no statically significant impact on the number of hours worked prior to 

transition. 

 Work intensity after a transition exhibits similar patterns as those prior to 

transition, albeit with higher magnitudes and some notable exceptions. Spouse’s health 

status impacts hours worked post transition. Men with a spouse in fair or poor health 

work on average about 60 more hours per year on their bridge jobs than otherwise similar 

males. Those with less than a high school degree also work more post transition, by 

about 85 hours per year. The largest sway in hours worked per year post transition, of 

150 hours or more, was associated with health insurance status, pension status, and self 

employment status. Men with portable health insurance or no health insurance worked 

much fewer hours than those with non-portable health insurance, while those with 

pensions worked much more. Interestingly, while self employment status had no 

significant impact on hours worked prior to transition, those in self employment jobs 

worked more than 200 hours less than wage-and-salary men in their post-transition job. 

This finding may be indicative of men using self-employment as a method of reducing 

the number of hours worked as they transition to full retirement. 

The War Baby indicator variable was not significantly different than zero either 

before or after transition. As before, we also interacted the War Baby indicator variable 

with the year dummies to determine whether any macroeconomic effects affected the 

War Babies differently than the Core respondents. Male War Babies still employed on 

for hours worked in post-transition employment resembled that for the work-leisure 

analysis – differences between the Core and War Babies vanished after the stock market 

decline. 

Many of the main determinants of hours worked prior to transition among the 

male sample did not hold for the female sample. Most notably, perhaps, was that age and 

health status did not have a statistically significant impact on hours worked prior to 

transition. Several retirement incentives did, however, such as health insurance status 

and pension status, with patterns that resembled those of the male sample. Self 

employment was also not statistically significant. One finding of note was that while 

being married and having dependent children had a positive influence on hours worked 

- 20 - 
among men, albeit with the latter effect not being statistically significant, these two 

factors have a negative impact on hours worked among women. 

The determinants of post-transition hours worked among women closely resemble 

those among the men, almost surprisingly so, especially with respect to age, college 

degree, health insurance and pension status, and self employment. A similar pattern with 

respect to HRS Core and HRS War Baby differences also holds for women. Some 

differences, however, warrant a mention. Statistical significance is not found for own 

health status and spouse health status or with not being a high school graduate. And, 

while being married is associated with more post-transition hours worked among men, 

the opposite is true for women. 

VII. Conclusion 

 The advent of 401(k)s in the 1980s and their explosive growth since then, 

combined with an increase in Social Security’s Normal Retirement Age and low savings 

rates, means that today’s retirees are more vulnerable to short-run market forces than at 

any point in the post-war era. This shift means that retirement income security of many 

individuals is dependent upon the existing state of financial markets. Older workers may 

therefore need to re-think their long-term retirement plans in light of short-run market 

conditions. Going forward, the timing of retirement may be influenced by 

macroeconomic factors to the extent that these affect pensions and other financial 

variables. 

In this paper, we examine retirement patterns from full-time career employment 

using a three-way outcome measure and we examine the work-leisure decision and work 

intensity later in life using data on two cohorts from the Health and Retirement Study. 

We find that work status across cohorts is consistent over time among men while some 

differences exist for women, with the younger cohorts being more likely to work longer. 

We also find that bridge job status continues to be common among younger retirees, as 

with older ones, with about two thirds of those making a transition from a full-time career 

job taking on a bridge job. While the descriptive findings suggest little that would imply 

stark time or cohort differences, the multivariate analysis shed some additional light on 

how the two cohorts compare. 

- 21 - 
Overall, cohort differences are more pronounced among women than men, 

although key determinants of retirement, such as age, health status, and health insurance 

and pension status, influence work decisions across all groups. We also find that crosscohort differences in terms of work-leisure decisions and hours worked per year seem to 

have vanished after 2000, all else equal. One explanation, consistent with aggregate 

findings, is that the older HRS Core respondents altered their work decisions after the 

stock market collapse to the point where they now resemble their younger counterparts. 

It will be interesting to see how this plays out in the years to come. Another finding of 

note is that self employment may be used as a mechanism by which retirees gain work 

flexibility later in life. Those who are self employed are much more likely to be working 

in general, yet their number of hours worked on the FTC job resemble those in wage-andsalary employment. That changes on the bridge job, as those who are self employed 

work significantly fewer hours. 

Placing these results in the context of the overarching theme of this study, we 

view the shift towards “do-it-yourself” retirement as a mixed bag. On the one hand, 

workers have more control of their retirement assets and, as shown in this paper and 

others, they respond to many of the financial incentives associated with retirement, by 

working longer and by taking on bridge jobs after FTC employment. This result implies 

that if retirement assets are less than expected upon retirement many older workers may 

remain active members of the labor force well into their late 60s and 70s. On the other 

hand, if work later in life is not an option, for health reasons or inflexible work options, 

some retirees’ long-run well being will be vulnerable to short-term fluctuations in market 

conditions. 

What is clear is that retirement incentives have changed and these changes will 

likely influence the retirement decisions of older workers for years to come. With preemptive action by today’s middle-aged and younger workers, in the form of increased 

saving or more realistic work expectations, the timing of retirement may be less 

susceptible to short term macro-level influences. 

- 22 - 
References 

Cahill, K. E., Giandrea, M. D., & Quinn, J. F. (2006). Retirement Patterns from Career 

Employment. .The Gerontologist, Vol. 46, No. 4, pp. 524-532. 

Coile, C. C. (2004). Retirement incentives and couples’ retirement decisions. Topics in 

Economic Analysis and Policy, Vol. 4, no. 1, pp 1-28. 

Coile, C. C., & Levine, P. B. (2004). Bulls, bears, and retirement behavior. National 

Bureau of Economic Research Working Paper 10779. 

Coronado, J. L., & Perozek, M. (2003). Wealth effects and the consumption of leisure: 

Retirement decisions during the stock market boom of the 1990s. Federal Reserve 

Board Finance and Economics Discussion Series Number 2003-20. Retrieved Feb. 

21, 2006 from 

http://www.federalreserve.gov/pubs/feds/2003/200320/200320abs.html

Eschtruth, A. D., & Gemus, J. (2002). Are older workers responding to the bear market? 

Center for Retirement Research at Boston College: Just the Facts on Retirement 

Issues, Number 5. Retrieved Feb. 21, 2006 from 

http://www.bc.edu/centers/crr/jtf_5.shtml

Gardner, J., & Orszag, M. (2003). How have older workers responded to scary markets? 

Watson Wyatt Technical paper 2003-LS04. Retrieved Feb. 21, 2006 from 

Gruber, J., & Madrian, B. C. (1995). Health-insurance availability and the retirement 

decision. The American Economic Review, Vol. 85, No. 4, pp. 938-948. 

Juster, F. T., & Suzman, R. (1995). An Overview of the Health and Retirement Study. 

Journal of Human Resources, 30 (Supplement), S7-S56. 

- 23 - 
Maestas, N. (2004). Back to work: Expectations and realizations of work after retirement. 

(Rand Working Paper WR-196). Retrieved July 29, 2005 from 

http://www.rand.org/publications/WR/WR196/ 

Munnell, A. H., & Cahill, K. E., & Jivan, N. A. (2003). How has the shift to 401(k)’s 

affected the retirement age? Issue in Brief 13 (September). Chestnut Hill, MA: 

Center for Retirement Research at Boston College. 

Mutchler, J. E., Burr, J. A., Pienta, A. M., & Massagli, M. P. (1997) Pathways to labor 

force Exit: Work transitions and work instability. Journal of Gerontology: Social 

Sciences, 52B(1), S4-S12. 

Purcell, P. J. (2005). Older workers: Employment and retirement trends. CRS Report 

 for Congress, Congressional Research Service. The Library of Congress. Washington, 

DC. 

Quinn, J. F. (1999). Retirement patterns and bridge jobs in the 1990s. EBRI Issue Brief 

 206 (February). Washington, DC: Employee Benefit Research Institute. 1-23. 

Ruhm, C. J. (1990). Bridge jobs and partial retirement. Journal of Labor Economics, 

8(4): 482-501. 

U.S. Department of Labor. Bureau of Labor Statistics. Employment and Earnings.

Washington, DC: U.S. Government Printing Office (January issues of many years). 

- 24 - 
Figure 1a

Labor Force Participation Rates

Actual and Fitted Values, 1964-2004

Males, Aged 60-64

35

40

45

50

55

60

65

70

75

80

85

1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003

Year

LFPR

Actual

Fitted

Males, Aged 65-69

0

5

10

15

20

25

30

35

40

45

50

1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003

Year

LFPR

Actual

Fitted
Figure 1b

Labor Force Participation Rates

Actual and Fitted Values, 1964-2004

Females, Aged 55-59

25

30

35

40

45

50

55

60

65

70

75

1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003

Year

LFPR

Actual

Fitted

Females, Aged 60-64

0

5

10

15

20

25

30

35

40

45

50

1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003

Year

LFPR

Actual

Fitted
Figure 2: A Model of Retirement in Three Stages

Hours of

Work

 R1

H

R2

t 0 t 1 t 2 

 Full-Time Career Job Bridge Job Complete Retireme

 Period 1 Period 2 Period 3

- 21 -
Figure 3a

Work Status, by Wave and Cohort

Men

1992 - 2004

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

1992 1994 1996 1998 2000 2002 2004

Year

Core 51-56

Core 57-61

WB 51-56
Figure 3b

Work Status, by Wave and Cohort

Women

1992 - 2004

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

1992 1994 1996 1998 2000 2002 2004

Year

Core 51-56

Core 57-61

WB 51-56
Table 1

Sample Size

by Gender, Survey Participation, and Work Status

HRS Core: Respondents Aged 51-61 in 1992

Men Women Total

Particpated in wave 1

n 5,869 6,783 12,652

Worked since age 49

n 5,344 5,196 10,540

% of HRS core 91.1% 76.6% 83.3%

Had FTC job after age 49

n 4,280 3,082 7,362

% of HRS core 72.9% 45.4% 58.2%

On FTC in 1992

n 3,057 2,513 5,570

% of HRS core 52.1% 37.0% 44.0%

HRS War Babies: Respondents Aged 51-56 in 1998

Men Women Total

Particpated in wave 4

n 1,200 1,329 2,529

Worked since age 49

n 1,122 1,159 2,281

% of HRS WB 93.5% 87.2% 90.2%

Had FTC job after age 49

n 890 675 1,565

% of HRS WB 74.2% 50.8% 61.9%

On FTC in 1998

n 843 664 1,507

% of HRS WB 70.3% 50.0% 59.6%

Source: Authors’ calculations based on the Health and Retirement Study.
Table 2

Current Employment Status in 2004, by Gender

Individuals with a Full-Time Career Job in their Work History

and a FTC Job Since Age 50

HRS Core: Respondents Aged 51-61 in 1992

Full Time Bridge Don't % with

n Career Job Job Know Bridge1

Men, Working 1,210 14.4% 24.2% 2.4%

Men, Nonworking, Last job was 1,736 30.3% 22.2% 6.3%

Total 2,946 44.8% 46.5% 8.8% 60.5%

Women, Working 1,105 20.4% 24.1% 1.7%

Women, Nonworking, Last job was 1,288 29.4% 19.6% 4.9%

Total 2,393 49.8% 43.7% 6.6% 59.8%

HRS War Babies: Respondents Aged 51-56 in 1998

Full Time Bridge Don't % with

n Career Job Job Know Bridge1

Men, Working 699 44.3% 29.1% 3.3%

Men, Nonworking, Last job was 212 13.1% 7.4% 2.9%

Total 911 57.4% 36.4% 6.1% 73.6%

Women, Working 653 35.6% 32.9% 2.4%

Women, Nonworking, Last job was 269 9.8% 16.3% 3.1%

Total 922 45.3% 49.1% 5.5% 83.4%

1: calculated as the ratio of those who moved to a bridge job among those who have made a transition.

Source: Authors’ calculations based on the Health and Retirement Study.
Table 3a

Employment Status of HRS Core in 2004, by Demographic Characteristics

Men Women

Percentage 

Percentage Who Moved to a Percentage 

Percentage Still on Full-Time Bridge Job Percentage Still on Full-Time 

Determinants Working1 Career Job2 in First Transition3 Working1 Career Job2 Age in 2004 Overall 38.1% 16.1% 55.2% 39.3% 20.8%  < 62 69.7% 31.6% 66.7% 66.6% 38.8%  62 - 64 54.0% 25.5% 63.0% 47.1% 21.7%  65 - 69 39.6% 13.7% 51.9% 33.5% 10.9%  70 + 24.8% 5.8% 51.6% 20.2% 4.0% Subjective Health Status excellent or very good 45.0% 18.4% 57.6% 45.2% 23.8%  good 33.2% 12.5% 54.4% 36.7% 16.9%  fair or poor 21.5% 12.2% 44.1% 21.6% 12.0%  College Degree 48.3% 19.4% 61.5% 46.8% 24.1%  Less than College Degree 34.9% 14.9% 53.3% 37.7% 19.9%  Married 38.7% 16.2% 55.8% 40.7% 23.0%  Not Married 32.7% 14.7% 50.3% 34.9% 14.1%  Dependent Children 48.7% 22.9% 57.7% 55.3% 35.4%  No Dependent Children 35.9% 14.3% 54.7% 36.6% 17.6%  Spouse Employed 41.8% 16.8% 56.9% 43.6% 23.4%  Spouse Not Employed 33.3% 14.9% 52.6% 34.0% 17.0% 1: among those who have worked since age 492: among those on a FTC job in the first wave of data3: among those on a FTC job in the first wave and who have moved off of that FTC jobSource: Authors’ calculations based on the Health and Retirement Study.
Table 3b

Employment Status of HRS War Babies in 2004, by Demographic Characteristic

Men Wome

Percentage 

Percentage Who Moved to a Percentag

Percentage Still on Full-Time Bridge Job Percentage Still on Full-T

Determinants Working1 Career Job2 in First Transition3 Working1 Career Job

Age in 2004 Overall 75.9% 53.7% 66.6% 70.9% 54.9%  < 60 80.9% 58.7% 70.1% 72.9% 58.4%  61 - 62 71.7% 48.4% 63.9% 63.8% 39.2%  > 62 59.4% 33.8% 57.1% 66.7% 53.3% Subjective Health Status excellent or very good 83.8% 47.7% 72.0% 78.4% 49.9%  good 73.6% 46.2% 58.2% 70.9% 47.6%  fair or poor 49.6% 38.7% 52.8% 46.5% 36.1%  College Degree 81.8% 43.7% 78.7% 74.2% 46.7%  Less than College Degree 74.8% 46.8% 62.8% 70.4% 47.6%  Married 76.6% 46.8% 65.4% 71.5% 50.1%  Not Married 72.2% 43.5% 67.2% 69.2% 40.9%  Dependent Children 76.7% 47.1% 64.8% 65.3% 38.8%  No Dependent Children 75.4% 45.8% 66.3% 72.5% 50.2%  Spouse Employed 78.7% 48.5% 70.0% 71.1% 53.2%  Spouse Not Employed 71.5% 43.1% 56.0% 71.4% 44.1% 1: among those who have worked since age 492: among those on a FTC job in the first wave of data3: among those on a FTC job in the first wave and who have moved off of that FTC jobSource: Authors’ calculations based on the Health and Retirement Study.
Table 3c

Employment Status of HRS Core in 2004, by FTC Job Characteristics

Men Wo

Percentage 

Percentage Who Moved to a Percen

Percentage Still on Full-Time Bridge Job Percentage Still on Fu

Determinants Working1 Career Job2 in First Transition3 Working1 Career 

Health Insurance Status Not covered on career job 38.7% 21.3% 73.0% 39.4% 23.1 "Covered and would maintain " coverage 35.4% 14.0% 53.2% 37.7% 19.1 "Covered and would lose" coverage 50.5% 22.6% 51.9% 46.5% 24.5Pension Status No Pension 42.4% 21.5% 65.6% 37.3% 19.9 Defined - Contribution only 43.9% 19.4% 59.1% 43.7% 23.5 Defined - Benefit only 31.2% 10.6% 46.3% 38.7% 18.4 Defined - Contribution and Defined - Benefit 48.8% 16.4% 63.4% 56.6% 39.4Self-Employed 58.3% 27.6% 77.2% 48.7% 20.6Wage and Salary 33.7% 13.1% 50.7% 38.2% 20.8Wage Rate < $6/hour 46.1% 20.3% 72.1% 42.1% 24.7 $6 - $10/hour 38.8% 17.1% 56.5% 43.5% 21.0 $10 - $20/hour 41.1% 14.4% 48.6% 46.2% 18.5 $20 - $50/hour 45.2% 14.0% 57.2% 44.2% 24.7 > $50/hour 54.7% 30.2% 68.6% 38.9% 0.0% Occupation Status White collar, highly skilled 49.0% 17.7% 60.6% 49.5% 22.8 White collar, other 48.7% 17.3% 58.4% 46.1% 22.2 Blue collar, highly skilled 38.1% 12.6% 49.2% 35.0% 13.7 Blue collar, other 39.2% 20.6% 54.9% 41.5% 18.71: among those who have worked since age 492: among those on a FTC job in the first wave of data3: among those on a FTC job in the first wave and who have moved off of that FTC jobSource: Authors’ calculations based on the Health and Retirement Study.
Table 3d

Employment Status of HRS War Babies in 2004, by FTC Job Characteristics

Men Wo

Percentage 

Percentage Who Moved to a Percen

Percentage Still on Full-Time Bridge Job Percentage Still on Fu

Determinants Working1 Career Job2 in First Transition3 Working1 Career 

Health Insurance Status Not covered on career job 65.5% 44.9% 79.2% 69.9% 41.2 "Covered and would maintain " coverage 73.4% 50.5% 65.1% 65.4% 50.3 "Covered and would lose" coverage 82.0% 59.9% 61.0% 83.1% 61.6Pension Status No Pension 70.6% 50.6% 83.0% 61.8% 46.3 Defined - Contribution only 82.5% 57.3% 59.7% 81.5% 59.1 Defined - Benefit only 75.3% 52.2% 52.9% 79.0% 55.5 Defined - Contribution and Defined - Benefit 75.3% 54.6% 47.4% 74.6% 49.6Self-Employed 86.4% 62.8% 87.8% 79.2% 50.9Wage and Salary 80.4% 51.9% 63.2% 77.5% 55.3Wage Rate < $6/hour 90.2% 64.3% 90.0% 64.5% 41.0 $6 - $10/hour 83.2% 50.6% 83.3% 76.0% 56.3 $10 - $20/hour 81.8% 53.2% 63.1% 81.2% 53.3 $20 - $50/hour 81.1% 56.6% 62.0% 83.3% 63.6 > $50/hour 89.7% 39.1% 85.7% 83.3% 100.0Occupation Status White collar, highly skilled 80.2% 52.1% 72.3% 78.7% 59.1 White collar, other 74.3% 52.8% 61.7% 68.1% 51.9 Blue collar, highly skilled 74.2% 56.8% 60.8% 71.8% 59.0 Blue collar, other 69.4% 54.0% 61.5% 57.9% 47.11: among those who have worked since age 492: among those on a FTC job in the first wave of data3: among those on a FTC job in the first wave and who have moved off of that FTC jobSource: Authors’ calculations based on the Health and Retirement Study.
Table 4a

Marginal Effects from Multinomial Logistic Regression

Dependent Variable: First Transition from Full-Time Career Job

HRS Core Men on a Full-Time Career Job in 1992

Full-Time Career Job Bridge Job

coef p-value coef p-value

Age -0.0174 0.000 -0.0085 0.002

Education

 Less than high school 0.0239 0.121 -0.0290 0.294

 High school graduate ------ ------ ------ ------

 College graduate 0.0075 0.618 0.0834 0.005

Respondent Health

 Excellent/very good -0.0022 0.860 0.0994 0.000

 Good ------ ------ ------ ------

 Fair/poor -0.0190 0.302 -0.0958 0.007

Spouse Health

 Excellent/very good -0.0228 0.094 0.0167 0.539

 Good ------ ------ ------ ------

 Fair/poor -0.0121 0.509 -0.0325 0.390

Married 0.0075 0.722 0.0955 0.016

Dependent Child 0.0328 0.018 0.0000 1.000

Occupational Status

 White collar - high skilled 0.0317 0.090 -0.0609 0.072

 White collar - other 0.0459 0.024 -0.0392 0.305

 Blue collar - high skilled 0.0080 0.643 -0.0983 0.001

 Blue collar - other ------ ------ ------ ------

Health Insurance Status

 Portable -0.0003 0.979 -0.0163 0.537

 Non-portable ------ ------ ------ ------

 None -0.0614 0.017 0.1127 0.024

Pension Status

 Defined-benefit -0.0723 0.000 -0.2348 0.000

 Defined-contribution 0.0135 0.319 -0.0550 0.051

 None ------ ------ ------ ------

 Both -0.0271 0.448 0.1004 0.095

Self-Employed 0.0913 0.000 0.0448 0.220

Wage 0.0017 0.022 -0.0059 0.000

Wage Squared 0.0000 0.440 0.0001 0.010

Wealth 0.0001 0.631 -0.0003 0.650

Wealth Squared 0.0000 0.973 0.0000 0.737

Own Home 0.0654 0.001 0.0062 0.846

Constant 0.7359 0.000 0.5464 0.000

Source: Authors’ calculations based on the Health and Retirement Study.

- 28 -
Table 4b

Marginal Effects from Multinomial Logistic Regression

Dependent Variable: First Transition from Full-Time Career Job

HRS War Babies Men on a Full-Time Career Job in 1998

Full-Time Career Job Bridge Job

coef p-value coef p-value

Age -0.0258 0.020 0.0192 0.051

Education

 Less than high school 0.1274 0.076 -0.0920 0.176

 High school graduate ------ ------ ------ ------

 College graduate -0.1161 0.029 0.1691 0.001

Respondent Health

 Excellent/very good -0.0617 0.168 0.1045 0.013

 Good ------ ------ ------ ------

 Fair/poor -0.1132 0.113 0.1160 0.070

Spouse Health

 Excellent/very good -0.1073 0.033 0.1496 0.002

 Good ------ ------ ------ ------

 Fair/poor 0.0451 0.548 -0.0301 0.686

Married 0.0490 0.507 -0.0796 0.243

Dependent Child 0.0050 0.906 0.0032 0.936

Occupational Status

 White collar - high skilled 0.0614 0.342 -0.0940 0.116

 White collar - other 0.0128 0.849 -0.0577 0.358

 Blue collar - high skilled 0.0687 0.245 -0.0905 0.097

 Blue collar - other ------ ------ ------ ------

Health Insurance Status

 Portable -0.1107 0.008 0.0212 0.591

 Non-portable ------ ------ ------ ------

 None -0.1945 0.039 0.0875 0.278

Pension Status

 Defined-benefit 0.1059 0.034 -0.1580 0.001

 Defined-contribution 0.1178 0.015 -0.0679 0.142

 None ------ ------ ------ ------

 Both -0.1254 0.162 0.0643 0.476

Self-Employed 0.2335 0.001 -0.0639 0.297

Wage 0.0064 0.012 -0.0063 0.005

Wage Squared 0.0000 0.194 0.00004 0.038

Wealth 0.0001 0.947 -0.0018 0.125

Wealth Squared 0.0000 0.383 0.0000 0.267

Own Home 0.0121 0.848 -0.0474 0.386

Constant 1.4893 0.013 -0.9596 0.071

Source: Authors’ calculations based on the Health and Retirement Study.

- 28 -
Table 4c

Marginal Effects from Multinomial Logistic Regression

Dependent Variable: First Transition from Full-Time Career Job

HRS Core Women on a Full-Time Career Job in 1992

Full-Time Career Job Bridge Job

coef p-value coef p-value

Age -0.0231 0.000 -0.0060 0.016

Education

 Less than high school -0.0060 0.809 -0.0918 0.006

 High school graduate ------ ------ ------ ------

 College graduate -0.0038 0.856 0.0863 0.010

Respondent Health

 Excellent/very good 0.0114 0.514 0.0850 0.001

 Good ------ ------ ------ ------

 Fair/poor -0.0431 0.126 -0.0279 0.462

Spouse Health

 Excellent/very good -0.0425 0.048 0.0233 0.467

 Good ------ ------ ------ ------

 Fair/poor 0.0168 0.516 -0.0510 0.218

Married -0.0166 0.494 0.0074 0.841

Dependent Child -0.0487 0.004 0.0384 0.125

Occupational Status

 White collar - high skilled 0.0599 0.032 -0.0982 0.009

 White collar - other 0.1020 0.000 -0.0867 0.008

 Blue collar - high skilled 0.1132 0.000 -0.0389 0.416

 Blue collar - other ------ ------ ------ ------

Health Insurance Status

 Portable -0.0217 0.191 0.0727 0.006

 Non-portable ------ ------ ------ ------

 None -0.0643 0.072 0.2292 0.000

Pension Status

 Defined-benefit -0.0362 0.063 -0.2521 0.000

 Defined-contribution 0.0271 0.133 -0.1922 0.000

 None ------ ------ ------ ------

 Both 0.0288 0.478 0.2040 0.004

Self-Employed 0.0543 0.055 -0.0919 0.042

Wage 0.0044 0.005 -0.0020 0.405

Wage Squared 0.0000 0.410 0.0000 0.298

Wealth 0.0006 0.273 -0.0020 0.011

Wealth Squared 0.0000 0.283 8.4E-06 0.010

Own Home 0.0474 0.051 0.0396 0.222

Constant 1.0118 0.000 0.4925 0.000

Source: Authors’ calculations based on the Health and Retirement Study.

- 29 -
Table 4d

Marginal Effects from Multinomial Logistic Regression

Dependent Variable: First Transition from Full-Time Career Job

HRS War Babies Women on a Full-Time Career Job in 1998

Full-Time Career Job Bridge Job

coef p-value coef p-value

Age -0.0087 0.464 0.0091 0.389

Education

 Less than high school 0.2434 0.030 -0.2469 0.021

 High school graduate ------ ------ ------ ------

 College graduate -0.0535 0.380 0.1002 0.068

Respondent Health

 Excellent/very good -0.0306 0.563 0.0774 0.121

 Good ------ ------ ------ ------

 Fair/poor -0.0755 0.358 -0.0596 0.431

Spouse Health

 Excellent/very good 0.0163 0.786 0.0383 0.498

 Good ------ ------ ------ ------

 Fair/poor 0.0784 0.385 0.0133 0.878

Married 0.0163 0.857 -0.1400 0.103

Dependent Child 0.1060 0.030 -0.0973 0.033

Occupational Status

 White collar - high skilled 0.1882 0.012 -0.1100 0.100

 White collar - other 0.2000 0.003 -0.1405 0.022

 Blue collar - high skilled 0.2933 0.002 -0.1361 0.117

 Blue collar - other ------ ------ ------ ------

Health Insurance Status

 Portable -0.0677 0.161 0.0680 0.135

 Non-portable ------ ------ ------ ------

 None -0.2052 0.090 0.1815 0.069

Pension Status

 Defined-benefit -0.0120 0.829 -0.0474 0.373

 Defined-contribution 0.0638 0.227 -0.0995 0.048

 None ------ ------ ------ ------

 Both 0.0369 0.731 -0.0072 0.947

Self-Employed 0.1925 0.042 -0.0275 0.738

Wage 0.0078 0.214 -0.0025 0.664

Wage Squared 0.0001 0.677 -0.0001 0.296

Wealth 0.0000 0.979 -0.0005 0.726

Wealth Squared 0.0000 0.917 0.0000 0.806

Own Home 0.0359 0.673 -0.1261 0.080

Constant 0.4049 0.536 -0.2447 0.671

Source: Authors’ calculations based on the Health and Retirement Study.

- 29 -
Table 5a

Marginal Effects from Logistic Regression

Dependent Variable: Working at time t (working = 1)

Men Who Have Worked Since Age 49

coef p-value

Age in 2004

 57 or younger ------ ------

 58-61 -0.0792 0.000

 62-64 -0.2659 0.000

 65-69 -0.3229 0.000

 70 or older -0.3257 0.000

Respondent Health

 Excellent/very good 0.0472 0.000

 Good ------ ------

 Fair/poor -0.2082 0.000

Spouse Health

 Excellent/very good -0.0506 0.000

 Good ------ ------

 Fair/poor 0.0464 0.004

Education

 Less than high school 0.0037 0.780

 High school graduate ------ ------

 College graduate 0.0506 0.000

Married -0.0050 0.876

Dependent Child 0.0427 0.001

Health Insurance Status

 Portable -0.5163 0.000

 Non-portable ------ ------

 None -0.5297 0.000

Pension Status

 Defined-benefit -0.3078 0.000

 Defined-contribution -0.0661 0.000

 Both 0.0910 0.001

 None ------ ------

Occupational Status

 White collar - high skilled 0.1038 0.000

 White collar - other 0.1478 0.000

 Blue collar - high skilled 0.0952 0.000

 Blue collar - other ------ ------

Self Employed 0.2096 0.000

Wage 0.0049 0.000

Wage Squared -5.1E-06 0.000

Wealth -0.0010 0.000

Wealth Squared 6.7E-07 0.000

Own Home -0.0079 0.606

Constant 0.6275 0.000
Regressors (continued) coef p-value

War Baby Indicator 0.1589 0.000

Year Indicators

 1992 1.1682 0.000

 1994 0.4175 0.000

 1996 0.2484 0.000

 1998 0.1955 0.000

 2000 0.1195 0.000

 2002 0.0661 0.000

 2004 ------ ------

War Baby Interaction Terms

 War Baby * 1998 0.8244 0.000

 War Baby * 2000 0.1682 0.002

 War Baby * 2002 0.0017 0.970

 War Baby * 2004 ------ ------

Source: Authors’ calculations based on the Health and Retirement Study.
Table 5b

Marginal Effects from Logistic Regression

Dependent Variable: Working at time t (working = 1)

Women Who Have Worked Since Age 49

coef p-value

Age in 2004

 57 or younger ------ ------

 58-61 -0.1021 0.000

 62-64 -0.2422 0.000

 65-69 -0.3106 0.000

 70 or older -0.3958 0.000

Respondent Health

 Excellent/very good 0.0353 0.004

 Good ------ ------

 Fair/poor -0.2202 0.000

Spouse Health

 Excellent/very good -0.0474 0.002

 Good ------ ------

 Fair/poor 0.0384 0.032

Education

 Less than high school -0.0093 0.541

 High school graduate ------ ------

 College graduate -0.0305 0.062

Married -0.0526 0.170

Dependent Child 0.0485 0.000

Health Insurance Status

 Portable -0.4896 0.000

 Non-portable ------ ------

 None -0.4643 0.000

Pension Status

 Defined-benefit -0.2532 0.000

 Defined-contribution -0.1423 0.000

 None ------ ------

 Both 0.2051 0.000

Occupational Status

 White collar - high skilled 0.0909 0.000

 White collar - other 0.1467 0.000

 Blue collar - high skilled 0.1116 0.000

 Blue collar - other ------ ------

Self Employed 0.1809 0.000

Wage 0.0107 0.000

Wage Squared -1.4E-05 0.000

Wealth -0.0010 0.000

Wealth Squared 2.3E-07 0.000

Own Home -0.0024 0.876

Constant 0.5910 0.000
Regressors (continued) coef p-value

War Baby Indicator 0.1354 0.001

Year Indicators

 1992 0.9709 0.000

 1994 0.4776 0.000

 1996 0.2637 0.000

 1998 0.2514 0.000

 2000 0.1780 0.000

 2002 0.1126 0.000

 2004 ------ ------

War Baby Interaction Terms

 War Baby * 1998 0.6714 0.000

 War Baby * 2000 0.1490 0.013

 War Baby * 2002 -0.0119 0.814

 War Baby * 2004 ------ ------

Source: Authors’ calculations based on the Health and Retirement Study.
Table 6a

Coefficients from OLS Regression

Dependent Variable: Hours Worked per Year 

Men with a FTC Job Since Age 49

Prior to Transition from FTC Job

coef p-value

Age in 2004

 57 or younger ------ ------

 58 - 61 27.1 0.002

 62 - 64 -72.3 0.000

 65 - 69 -133.2 0.000

 70 or older -207.3 0.000

Respondent Health

 Excellent/very good 24.7 0.002

 Good ------ ------

 Fair/poor -17.7 0.143

Spouse Health

 Excellent/very good 12.1 0.172

 Good ------ ------

 Fair/poor 0.6 0.961

Education

 Less than high school -8.7 0.386

 High school graduate ------ ------

 College graduate -31.0 0.004

Race

 White ------ ------

 Black -33.4 0.003

 Other -29.6 0.145

Married 47.1 0.049

Dependent Children 11.6 0.258

Health Insurance Status

 Portable -24.6 0.003

 Non-portable ------ ------

 None 1.8 0.927

Pension Status

 Defined-benefit 14.1 0.094

 Defined-contribution 72.3 0.000

 None ------ ------

 Both -24.2 0.100

Occupational Status

 White collar - high skilled 57.0 0.000

 White collar - other 54.8 0.000

 Blue collar - high skilled -2.7 0.778

 Blue collar - other ------ ------

Self-employed 1.9 0.896

Wage -1.7 0.000

Wage Squared 0.002 0.000

Wealth ($1,000) 0.1 0.169

Wealth Squared ($1,000) 0.000 0.290

Own Home 18.9 0.082

Constant 2230.8 0.000
Regressors (continued) coef p-value

War Baby Indicator -36.4 0.179

Year Indicators

 1992 -110.3 0.000

 1994 -72.2 0.002

 1996 -147.2 0.000

 1998 -75.5 0.002

 2000 -76.3 0.002

 2002 -113.2 0.000

 2004 ------ ------

War Baby Interaction Terms

 War Baby * 1998 18.1 0.574

 War Baby * 2000 4.6 0.892

 War Baby * 2002 75.7 0.034

 War Baby * 2004 ------ ------

Source: Authors’ calculations based on the Health and Retirement Study.
Table 6b

Coefficients from OLS Regression

Dependent Variable: Hours Worked per Year 

Men with a FTC Job Since Age 49

After Transition from FTC Job

coef p-value

Age in 2004

 57 or younger ------ ------

 58 - 61 -4.7 0.862

 62 - 64 -253.6 0.000

 65 - 69 -397.2 0.000

 70 or older -602.1 0.000

Respondent Health

 Excellent/very good 24.5 0.227

 Good ------ ------

 Fair/poor -77.1 0.006

Spouse Health

 Excellent/very good -14.6 0.511

 Good ------ ------

 Fair/poor 60.2 0.034

Education

 Less than high school 85.0 0.001

 High school graduate ------ ------

 College graduate -107.5 0.000

Race

 White ------ ------

 Black -73.5 0.010

 Other 56.0 0.312

Married 109.4 0.056

Dependent Children 90.0 0.000

Health Insurance Status

 Portable -234.6 0.000

 Non-portable ------ ------

 None -171.9 0.000

Pension Status

 Defined-benefit 175.7 0.000

 Defined-contribution 301.7 0.000

 None ------ ------

 Both -174.0 0.006

Occupational Status

 White collar - high skilled 61.3 0.037

 White collar - other 68.6 0.018

 Blue collar - high skilled 38.1 0.120

 Blue collar - other ------ ------

Self-employed -207.3 0.000

Wage -2.5 0.000

Wage Squared 0.001 0.040

Wealth ($1,000) -0.5 0.007

Wealth Squared ($1,000) 0.0003 0.000

Own Home -124.8 0.000

Constant 1695.8 0.000
Regressors (continued) coef p-value

War Baby Indicator 8.1 0.905

Year Indicators

 1992 91.2 0.056

 1994 129.1 0.005

 1996 95.8 0.025

 1998 103.7 0.010

 2000 123.8 0.002

 2002 175.6 0.000

 2004 ------ ------

War Baby Interaction Terms

 War Baby * 1998 274.7 0.001

 War Baby * 2000 181.7 0.045

 War Baby * 2002 1.2 0.989

 War Baby * 2004 ------ ------

Source: Authors’ calculations based on the Health and Retirement Study.
Table 6c

Coefficients from OLS Regression

Dependent Variable: Hours Worked per Year 

Women with a FTC Job Since Age 49

Prior to Transition from FTC Job

coef p-value

Age in 2004

 57 or younger ------ ------

 58 - 61 -18.2 0.039

 62 - 64 -15.7 0.318

 65 - 69 -0.4 0.989

 70 or older -59.2 0.365

Respondent Health

 Excellent/very good 10.1 0.204

 Good ------ ------

 Fair/poor -17.0 0.136

Spouse Health

 Excellent/very good -4.3 0.666

 Good ------ ------

 Fair/poor 18.8 0.124

Education

 Less than high school 9.6 0.441

 High school graduate ------ ------

 College graduate 25.4 0.027

Race

 White ------ ------

 Black -40.3 0.000

 Other 4.7 0.789

Married -77.7 0.007

Dependent Children -22.9 0.002

Health Insurance Status

 Portable -12.8 0.095

 Non-portable ------ ------

 None -47.9 0.026

Pension Status

 Defined-benefit 38.9 0.000

 Defined-contribution 79.3 0.000

 None ------ ------

 Both -33.5 0.055

Occupational Status

 White collar - high skilled 50.2 0.000

 White collar - other -4.0 0.721

 Blue collar - high skilled 15.2 0.265

 Blue collar - other ------ ------

Self-employed 26.5 0.196

Wage -4.1 0.000

Wage Squared 0.003 0.029

Wealth ($1,000) 0.4 0.003

Wealth Squared ($1,000) -0.0003 0.028

Own Home 8.2 0.457

Constant 2124.9 0.000
Regressors (continued) coef p-value

War Baby Indicator 62.6 0.010

Year Indicators

 1992 -53.5 0.004

 1994 -1.7 0.924

 1996 -80.7 0.000

 1998 -1.6 0.933

 2000 2.9 0.881

 2002 -18.6 0.390

 2004 ------ ------

War Baby Interaction Terms

 War Baby * 1998 -53.6 0.078

 War Baby * 2000 -53.1 0.086

 War Baby * 2002 -28.1 0.406

 War Baby * 2004 ------ ------

Source: Authors’ calculations based on the Health and Retirement Study.
Table 6d

Coefficients from OLS Regression

Dependent Variable: Hours Worked per Year 

Women with a FTC Job Since Age 49

After Transition from FTC Job

coef p-value

Age in 2004

 57 or younger ------ ------

 58 - 61 -22.8 0.316

 62 - 64 -223.2 0.000

 65 - 69 -360.2 0.000

 70 or older -532.7 0.000

Respondent Health

 Excellent/very good -6.2 0.762

 Good ------ ------

 Fair/poor -26.9 0.391

Spouse Health

 Excellent/very good -32.4 0.219

 Good ------ ------

 Fair/poor -4.0 0.903

Education

 Less than high school 16.5 0.567

 High school graduate ------ ------

 College graduate -130.5 0.000

Race

 White ------ ------

 Black -148.1 0.000

 Other -48.9 0.291

Married -113.4 0.059

Dependent Children -9.6 0.636

Health Insurance Status

 Portable -249.7 0.000

 Non-portable ------ ------

 None -190.1 0.000

Pension Status

 Defined-benefit 164.2 0.000

 Defined-contribution 271.4 0.000

 None ------ ------

 Both -140.4 0.017

Occupational Status

 White collar - high skilled 19.6 0.541

 White collar - other -26.0 0.309

 Blue collar - high skilled 12.5 0.759

 Blue collar - other ------ ------

Self-employed -55.9 0.082

Wage -4.5 0.000

Wage Squared 0.005 0.008

Wealth ($1,000) -1.7 0.000

Wealth Squared ($1,000) 0.001 0.000

Own Home -39.4 0.141

Constant 1842.5 0.000
Regressors (continued) coef p-value

War Baby Indicator 50.0 0.536

Year Indicators

 1992 35.4 0.489

 1994 100.4 0.034

 1996 227.4 0.000

 1998 165.4 0.000

 2000 144.7 0.001

 2002 169.6 0.000

 2004 ------ ------

War Baby Interaction Terms

 War Baby * 1998 240.2 0.014

 War Baby * 2000 106.4 0.286

 War Baby * 2002 -41.7 0.664

 War Baby * 2004 ------ ------

Source: Authors’ calculations based on the Health and Retirement Study.