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The Effects of Health Insurance and
Self-Insurance on Retirement Behavior
Eric French and John Bailey Jones
Federal Reserve Bank of Chicago
REVISED November 2007
WP 2001-19
The Effects of Health Insurance and
Self-Insurance on Retirement Behavior
Eric French
Federal Reserve Bank of Chicago
John Bailey Jones∗
SUNY-Albany
November 27, 2007
Abstract
This paper provides an empirical analysis of the effects of employer-provided health
insurance, Medicare, and Social Security on retirement behavior. Using data from the
Health and Retirement Study, we estimate the first dynamic programming model of re-
tirement that accounts for both saving and uncertain medical expenses. Our results
suggest that uncertainty and saving are both important for understanding the labor sup-
ply responses to Medicare. Furthermore, we find evidence that individuals with stronger
preferences for leisure self-select into jobs that provide post-retirement health insurance
coverage. Properly accounting for this self-selection reduces the estimated effect of Medi-
care on retirement behavior. Nevertheless, we find that health insurance is an important
determinant of retirement—the labor supply responses to the Medicare eligibility age are
as large as the responses to the Social Security normal retirement age.
∗Comments welcome at efrench@frbchi.org and jbjones@albany.edu. We thank Joe Altonji, Peter
Arcidiacono, Gadi Barlevy, David Blau, John Bound, Chris Carroll, Mariacristina De Nardi, Tim Erikson,
Hanming Fang, Donna Gilleskie, Lars Hansen, John Kennan, Spencer Krane, Hamp Lankford, Guy Laroque,
John Rust, Dan Sullivan, Chris Taber, the editor (Costas Meghir) and referees, students of Econ 751 at Wis-
consin, and participants at numerous seminars for helpful comments. We received advice on the HRS pension
data from Gary Englehardt and Tom Steinmeier, and excellent research assistance from Kate Anderson, Olesya
Baker, Diwakar Choubey, Phil Doctor, Ken Housinger, Kirti Kamboj, Tina Lam, and Santadarshan Sadhu.
The research reported herein was supported by the Center for Retirement Research at Boston College (CRR)
and the Michigan Retirement Research Center (MRRC) pursuant to grants from the U.S. Social Security Ad-
ministration (SSA) funded as part of the Retirement Research Consortium. The opinions and conclusions are
solely those of the authors, and should not be construed as representing the opinions or policy of the SSA or
any agency of the Federal Government, the CRR, the MRRC, or the Federal Reserve System. Recent versions
of the paper can be obtained at http://www.albany.edu/~jbjones/papers.htm.
1
1 Introduction
One of the most important social programs for the rapidly growing elderly population is
Medicare. In 2005, Medicare had 42.5 million beneficiaries and $330 billion of expenditures,
making it only slightly smaller than Social Security.1Prior to receiving Medicare at age 65,
many individuals receive health insurance only if they continue to work. At age 65, however,
Medicare provides health insurance to almost everyone. Thus an important work incentive
disappears at age 65. An important question, therefore, is whether Medicare significantly af-
fects the labor supply of the elderly. This question is particularly important when considering
changes to the Medicare or Social Security programs; the fiscal cost of changing the programs
depends critically on labor supply responses. Although there is a great deal of research on
the labor supply responses to Social Security, there is much less research on the responses to
Medicare.
This paper provides an empirical analysis of the effect of employer-provided health in-
surance and Medicare in determining retirement behavior. Using data from the Health and
Retirement Study, we estimate the first dynamic programming model of retirement that ac-
counts for both saving and uncertain medical expenses. Our results suggest that uncertainty
and saving are both important for understanding the labor supply responses to Medicare. Fur-
thermore, we find evidence that individuals with stronger preferences for leisure self-select
into jobs that provide post-retirement health insurance coverage. Properly accounting for
this self-selection reduces the estimated effect of Medicare on retirement behavior. Nonethe-
less, we find that health insurance is an important determinant of retirement—the Medicare
eligibility age is as important for understanding retirement as the Social Security normal re-
tirement age. For example, shifting forward the Medicare eligibility age from 65 to 67 would
delay the average age of retirement by 0.07 years, whereas shifting forward the Social Security
retirement age from 65 to 67 would delay retirement by 0.09 years.
Our work builds upon, and in part reconciles, several earlier studies. Assuming that in-
1Figures taken from 2006 Medicare Annual Report (The Boards of Trustees of the Hospital Insurance and
Supplementary Medical Insurance Trust Funds, 2006).
2
dividuals value health insurance at the cost paid by employers, Lumsdaine et al. (1994) and
Gustman and Steinmeier (1994) find that health insurance has a small effect on retirement
behavior. One possible reason for their results is that the average employer contribution to
health insurance is modest, and it declines by a relatively small amount after age 65.2If indi-
viduals are risk-averse, however, and if health insurance allows them to smooth consumption
in the face of volatile medical expenses, they could value employer-provided health insurance
well beyond the cost paid by employers. Medicare’s age-65 work disincentive thus comes not
only from the reduction in average medical costs paid by those without employer-provided
health insurance, but also from the reduction in the volatility of those costs.
Addressing this point, Rust and Phelan (1997) and Blau and Gilleskie (2006a, 2006b) esti-
mate dynamic programming models that account explicitly for risk aversion and uncertainty
about out-of-pocket medical expenses. Their estimated labor supply responses to health in-
surance are larger than those found in studies that omit medical expense risk. Rust and
Phelan and Blau and Gilleskie, however, assume that an individual’s consumption equals his
income net of out-of-pocket medical expenses. In other words, they ignore an individual’s
ability to smooth consumption through saving. If individuals can self-insure against medical
expense shocks by saving, prohibiting saving will overstate the consumption volatility caused
by medical cost volatility. It is therefore likely that Rust and Phelan and Blau and Gilleskie
overstate the value of health insurance, and thus the effect of health insurance on retirement.
The first major contribution of this paper is that we construct a life-cycle model of labor
supply that not only accounts for health cost uncertainty and health insurance, but also
has a saving decision. Moreover, we include the coverage provided by means-tested social
insurance to account for the fact that Medicaid provides a close substitute for other forms
of health insurance. To our knowledge, ours is the first study of its kind. While van der
Klaauw and Wolpin (2006) also estimate a retirement model that accounts for both savings
and uncertainty, they do not focus on the role of health insurance, and thus use a much
2Gustman and Steinmeier (1994) find that the average employer contribution to employee health insurance
is about $2,500 per year before age 65. (Data are from the 1977 NMES, adjusted to 1998 dollars with the
medical component of the CPI.)
3
simpler model of health costs.
The second major contribution of this paper is that it measures self-selection into jobs
with different health insurance plans, along both observable and unobservable dimensions.
Identifying the effect of Medicare on retirement is difficult because virtually everyone is
eligible for Medicare at age 65. Furthermore, the Social Security system and pensions also
provide retirement incentives at age 65. Thus it is difficult to distinguish whether the high
job exit rates at age 65 are due to Medicare, Social Security, or pensions. We circumvent
this problem by exploiting variation in employer-provided health insurance. Some individuals
receive employer-provided health insurance only while they work, so that their coverage is
tied to their job. Other individuals have retiree coverage, and receive employer-provided
health insurance even after they retire. We find that individuals with retiree coverage tend
to retire about a half year earlier than individuals with tied coverage. This suggests that
employer-provided health insurance is an important determinant of retirement. Because
employer-provided health insurance and Medicare are close substitutes, Medicare could be
an important determinant of retirement as well.
One concern with using employer-provided health insurance to identify Medicare’s effect
on retirement is that individuals potentially choose to work for a firm because of its post-
retirement benefits. Individuals with strong preferences for early retirement might choose
firms that provide generous retiree health insurance benefits. The fact that early retirement
is common for individuals with retiree coverage may not reflect the effect of health insurance
on retirement. Instead, individuals with preferences for early retirement may be self-selecting
into jobs that provide retiree coverage.
In order to better understand whether self-selection is important, we model preference
heterogeneity, using the approach found in Keane and Wolpin (1997). We allow the value of
leisure and the time discount factor to vary across individuals, and find evidence of preference
heterogeneity along both dimensions. Furthermore, we find that individuals with strong
preferences for leisure self-select into firms that provide retiree health insurance. We identify
the extent of self-selection in part by using a series of self-reports of preferences for work, such
4
as the response to the question, “Even if I didn’t need the money, I would keep on working.”
We find that these responses are highly correlated with future retirement decisions, even
after controlling for all of the other incentives in the model. Furthermore, these responses
are correlated with the type of health insurance the individual has. Individuals with retiree
coverage are more likely to self-report that they would like to stop working than individuals
whose health insurance is tied to their job. This is an important finding because many studies
that measure the effect of health insurance on retirement have assumed that preferences for
leisure are uncorrelated with the health insurance plan. Our findings on self-selection are
corroborated by the fact that those with tied coverage still have high labor force participation
rates after age 65, when Medicare severs the link between labor supply and health insurance.
If health insurance were the sole driving factor, differences across health insurance types
should vanish after age 65.
Estimating the model by the Method of Simulated Moments, we find that the model fits
the data well with reasonable parameter values. Furthermore, the model fits the data well out-
of-sample. Because the Social Security benefit rules vary with year of birth, the Health and
Retirement Survey (HRS) contains households that face different Social Security incentives.
This allows us to perform an out-of-sample validation exercise: we estimate the model on
households with earlier birth years, and then use it to predict the retirement behavior of
households with later birth years. We find that the model does a good job of predicting the
differences between the two groups observed to date.
Next, we simulate the labor supply response to raising the Medicare eligibility age to 67
and by raising the normal Social Security retirement age to 67. We find that shifting the
Medicare eligibility age to 67 will increase the labor force participation of workers aged 60-67
by 0.07 years. Failure to account for self-selection into health insurance plans results in a
larger estimated effect. Nevertheless, even after allowing for both savings and self-selection
into health insurance plan, the effect of the Medicare eligibility on labor supply is as large
as the effect of the Social Security normal retirement age on labor supply. An important
reason why we find that Medicare is important is that we find that medical expense risk is
5
important. We find that even when we allow individuals to save, they value the consumption
smoothing benefits of health insurance.
The rest of paper proceeds as follows. Section 2 develops our dynamic programming model
of retirement behavior. Section 3 describes how we estimate the model using the Method of
Simulated Moments. Section 4 describes the HRS data that we use in our analysis. Section 5
presents life cycle profiles drawn from these data. Section 6 contains preference parameter
estimates for the structural model, and an assessment of the model’s performance, both within
and outside of the estimation sample. In Section 7, we conduct several policy experiments.
In Section 8 we consider a few important robustness checks. Section 9 concludes.
2 The Model
In order to capture the richness of retirement incentives, our model is very complex and
has many parameters. Appendix A provides definitions for all the variables used in the main
text.
2.1 Preferences and Demographics
Consider a household head seeking to maximize his expected discounted (where the sub-
jective discount factor is β) lifetime utility at age t,t= 59,60, ..., 95. Each period that he
lives, the individual derives utility from consumption, Ct, and hours of leisure, Lt, so that
the within-period utility function is of the form
U(Ct, Lt) = 1
1−νCγ
tL1−γ
t1−ν.(1)
We allow both βand γto vary across individuals. Differences in βreflect differences in pa-
tience, while differences in γrepresent differences in preferences for leisure. Furthermore, we
allow these parameters to be correlated with differences in employer-provided health insur-
ance plans, and thus allow for the possibility that workers with strong preferences for leisure
select jobs that provide generous post-retirement health insurance.
6
The quantity of leisure is
Lt=L−Ht−φPPt−φMMt,(2)
where Lis the individual’s total annual time endowment. Participation in the labor force is
denoted by Pt, a 0-1 indicator equal to zero when hours worked, Ht, equal zero. The fixed
cost of work, φP, is treated as a loss of leisure. Including fixed costs helps us capture the
empirical regularity that annual hours of work are clustered around 2000 hours and 0 hours
(Cogan, 1981). We treat retirement as a form of the participation decision, and thus allow
retired workers to reenter the labor force; as stressed by Rust and Phelan (1997) and Ruhm
(1990), reverse retirement is a common phenomenon. Finally, the quantity of leisure depends
on an individual’s health status through the 0-1 indicator Mt= 1{healtht=bad}, which
equals one when his health is bad. A positive value of the parameter φMimplies that people
in bad health find it more painful to work.
Following De Nardi (2004), workers that die values bequests of assets, At, according to
the function b(At):
b(At) = θBAt+κ(1−ν)γ
1−ν.(3)
The parameter κdetermines the curvature of the bequest function. When κ > 0, the marginal
utility of a zero-dollar bequest is finite, and bequests are a luxury good.
Health status, Mt, and the probability of being alive at age tconditional on being alive
at age t−1, st, both depend on age and previous health status.
2.2 Budget Constraints
The individual holds three forms of wealth: assets (including housing); pensions; and
Social Security. He receives several sources of income: asset income, rAt, where rdenotes the
constant pre-tax interest rate; labor income, WtHt, where Wtdenotes wages; spousal income,
yst; pension benefits, pbt; Social Security benefits, sst; and government transfers, trt. The
7
asset accumulation equation is
At+1 =At+Y(rAt+WtHt+yst+pbt, τ ) + sst+trt−hct−Ct.(4)
hctdenotes medical expenses, and post-tax income, Y(rAt+WtHt+yst+pbt, τ ), is a function
of taxable income and the vector τ, described in Appendix B, that captures the tax structure.
Individuals face the borrowing constraint
At+Yt+sst+trt−Ct≥0.(5)
Because it is illegal to borrow against future Social Security benefits and difficult to borrow
against many forms of future pension benefits, individuals with low non-pension, non-Social
Security wealth may not be able to finance their retirement before their Social Security
benefits become available at age 62. This borrowing constraint excludes medical expenses,
which we assume are realized after labor decisions are made. We view this assumption as
more reasonable than the alternative, namely that the time-tmedical expense shocks are fully
known when workers decide whether to hold on to their employer-provided health insurance.3
Following Hubbard et al. (1994, 1995), government transfers provide a consumption floor:
trt=max{0, Cmin −(At+Yt+sst)}.(6)
Equation (6) implies that government transfers bridge the gap between an individual’s “liquid
resources” (the quantity in the inner parentheses) and the consumption floor. Equation (6)
also implies that if transfers are positive, Ct=Cmin. Our treatment of government transfers
implies that individuals can always consume at least Cmin, even if their out-of-pocket medical
expenses have exceeded their financial resources. With the government effectively providing
3Given the borrowing constraint and timing of medical expenses, an individual with extremely high med-
ical expenses this year could have negative net worth next year. Given that many people in our data still
have unresolved medical expenses, medical expense debt seems reasonable. Because debt cannot legally be
bequeathed in the US, we assume that all debts are erased at time of death when calculating the value of the
bequest in equation (3).
8
low-asset individuals with health insurance, these people may place a low value on employer-
provided insurance. This of course depends on the value of Cmin; if Cmin is low enough, it
will be the low-asset individuals who value health insurance most highly. Those with very
high asset levels should be able to self-insure.
2.3 Medical Expenses, Health Insurance, and Medicare
Medical expenses, hct, are defined as the sum of out-of-pocket costs (including those
covered by the consumption floor) and insurance premia. We assume that an individual’s
health costs depend upon five different components. First, medical expenses depend on the
individual’s employer-provided health insurance, HIt. Second, they depend on whether the
person is working, Pt, because workers who leave their job often pay a larger fraction of their
insurance premiums. Third, they depend on the individual’s self-reported health status, Mt.
Fourth, medical expenses depend on age. At age 65, individuals become eligible for Medicare,
which is a close substitute for employer-provided coverage.4Offsetting this, as people age
their health declines (in a way not captured by Mt), raising medical expenses. Finally, medical
expenses depend on person-specific effects, which we capture in the variable ψt, yielding:
ln hct=hc(Mt, HIt, t, Pt) + σ(Mt, HIt, t, Pt)×ψt.(7)
Note that health insurance affects both the expectation of medical expenses, through hc(.)
and the variance, through σ(.).5
Even after controlling for health status, French and Jones (2004a) find that medical
expenses are very volatile and persistent. Thus we model the idiosyncratic component of
4Individuals who have paid into the Medicare system for at least 10 years become eligible at age 65. A
more detailed description of the Medicare eligibility rules is available at http://www.medicare.gov/.
5We follow the existing literature and impose the simplifying assumption that medical expenditures are
exogenous. To our knowledge, Blau and Gilleskie (2006b) is the only structural retirement study to have
endogenous medical expenditures.
9
medical expenses, ψt, as
ψt=ζt+ξt, ξt∼N(0, σ2
ξ),(8)
ζt=ρhcζt−1+ǫt, ǫt∼N(0, σ2
ǫ),(9)
where ξtand ǫtare serially and mutually independent. ξtis the transitory component of
health cost uncertainty, while ζtis the persistent component, with autocorrelation ρhc.
Differences in labor supply behavior across health insurance categories, H It, are an im-
portant part of identifying our model. We assume that there are three mutually exclusive
categories of health insurance coverage. The first is retiree coverage, where workers keep
their health insurance even after leaving their jobs. The second category is tied health insur-
ance, where workers receive employer-provided coverage as long as they continue to work. If
a worker with tied health insurance leaves his job, he can keep his health insurance coverage
for that year. This is meant to proxy for the fact that most firms must provide “COBRA”
health insurance to workers after they leave their job. After one year of tied coverage and not
working, the individual’s insurance ceases.6The third category consists of individuals whose
potential employers provide no health insurance at all, or none. Workers move between these
insurance categories according to7
HIt=
retiree if H It−1=retiree
tied if HIt−1=tied and Ht−1>0
none if HIt−1=none or (HIt−1=tied and Ht−1= 0)
.(10)
This transition rule implies those with tied coverage work not only for labor income, but also
6Although there is some variability across states as to how long individuals are eligible for employer-provided
health insurance coverage, by Federal law most individuals are covered for 18 months (Gruber and Madrian,
1995). Given a model period of one year, we approximate the 18-month period as one year. We do not model
the option to take up COBRA, assuming that the take-up rate is 100%. Although the actual take-up rate
is around 2
3(Gruber and Madrian, 1996), we have simulated the model assuming that the rate was 0%, and
found very similar labor supply patterns.
7In imposing this transition rule, we are assuming that people out of the work force are never offered jobs
with insurance coverage, and that workers with tied coverage never upgrade to retiree coverage.
10
for health insurance. Thus, differences in labor supply patterns among those with tied and
retiree coverage are useful for inferring the effect of health insurance on retirement.
2.4 Wages and Spousal Income
We assume that the logarithm of wages at time t, ln Wt, is a function of health status
(Mt), age (t), hours worked (Ht) and an autoregressive component, ωt:
ln Wt=W(Mt, t) + αln Ht+ωt,(11)
The inclusion of hours, Ht, in the wage determination equation captures the empirical regu-
larity that, all else equal, part-time workers earn relatively lower wages than full time work-
ers. The autoregressive component ωthas the correlation coefficient ρWand the normally-
distributed innovation ηt:
ωt=ρWωt−1+ηt, ηt∼N(0, σ2
η).(12)
Because spousal income can serve as insurance against medical shocks, we include it in
the model. In the interest of computational simplicity, we assume that spousal income is a
deterministic function of an individual’s age and the exogenous component of his wages:
yst=ys(W(Mt, t) + ωt, t).(13)
These features allow us to capture assortive mating and the age-earnings profile.
2.5 Social Security and Pensions
Because pensions and Social Security both generate potentially important retirement
incentives, we model the two programs in detail.
Individuals receive no Social Security benefits until they apply. Individuals can first
apply for benefits at age 62. Upon applying the individual receives benefits until death.
11
The individual’s Social Security benefits depend on his Average Indexed Monthly Earnings
(AIME), which is roughly his average income during his 35 highest earnings years in the
labor market.
The Social Security System provides three major retirement incentives.8First, while
income earned by workers with less than 35 years of earnings automatically increases their
AIME, income earned by workers with more than 35 years of earnings increases their AIM E
only if it exceeds earnings in some previous year of work. Because Social Security benefits
increase in AIME, this causes work incentives to drop after 35 years in the labor market.
We describe the computation of AI M E in more detail in Appendix D.
Second, the age at which the individual applies for Social Security affects the level of
benefits. For every year before age 65 the individual applies for benefits, benefits are reduced
by 6.67% of the age-65 level. This is roughly actuarially fair. But for every year after age 65
that benefit application is delayed, benefits rise by 5.5% up until age 70. This is less than
actuarially fair, and encourages people to apply for benefits by age 65.
Third, the Social Security Earnings Test taxes labor income of beneficiaries at a high rate.
For individuals aged 62-64, each dollar of labor income above the “test” threshold of $9,120
leads to a 1/2 dollar decrease in Social Security benefits, until all benefits have been taxed
away. For individuals aged 65-69 before 2000, each dollar of labor income above a threshold
of $14,500 leads to a 1/3 dollar decrease in Social Security benefits, until all benefits have
been taxed away. Although benefits taxed away by the earnings test are credited to future
benefits, after age 64 the crediting rate is less than actuarially fair, so that the Social Security
Earnings Test effectively taxes the labor income of beneficiaries aged 65-69.9When combined
with the aforementioned incentives to draw Social Security benefits by age 65, the Earnings
Test discourages work after age 65. In 2000, the Social Security Earnings Test was abolished
8A description of the Social Security rules can be found in recent editions of the Green Book (Committee
on Ways and Means). Some of the rules, such as the benefit adjustment formula, depends on an individual’s
year of birth. Because we fit our model to a group of individuals that on average were born in 1933, we use
the benefit formula for that birth year.
9The credit rates are based on the benefit adjustment formula. If a year’s worth of benefits are taxed away
between ages 62 and 64, benefits in the future are increased by 6.67%. If a year’s worth of benefits are taxed
away between ages 65 and 66, benefits in the future are increased by 5.5%.
12
for those 65 and older. Because those born in 1933 (the average birth year in our sample)
turned 67 in 2000, we assume that the earnings test was repealed at age 67. These incentives
are incorporated in the calculation of sst, which is defined to be net of the earnings test.
Pension benefits, pbt, are a function of the worker’s age and pension wealth. Pension
wealth (the present value of pension benefits) in turn depends on pension accruals. We
assume that pension accruals are a function of a worker’s age, labor income, and health
insurance type, using a formula estimated from confidential HRS pension data. The data
show that pension accrual rates differ greatly across health insurance categories; accounting
for these differences is essential in isolating the effects of employer-provided health insurance.
When finding an individual’s decision rules, we assume further that the individual’s existing
pension wealth is a function of his Social Security wealth, age, and health insurance type.
Details of our pension model are described in Section 4.3 and Appendix C.
2.6 Recursive Formulation
In addition to choosing hours and consumption, eligible individuals decide whether to
apply for Social Security benefits; let the indicator variable Bt∈ {0,1}equal one if an
individual has applied. In recursive form, the individual’s problem can be written as
Vt(Xt) = max
Ct,Ht,Bt(1
1−νCγ
t(L−Ht−φPPt−φMMt)1−γ1−ν
+β(1 −st+1)b(At+1 )
+βst+1 ZVt+1 (Xt+1)dF (Xt+1 |Xt, t, Ct, Ht, Bt)),(14)
subject to equations (5) and (6). The vector Xt= (At, Bt−1, Mt, AIM Et, HIt, ωt, ζt−1) con-
tains the individual’s state variables, while the function F(·|·) gives the conditional distri-
bution of these state variables, using equations (4) and (7) - (13).10 The solution to the
individual’s problem consists of the consumption rules, work rules, and benefit application
rules that solve equation (14). Given that the model lacks a closed form solution, these de-
10Spousal income and pension benefits (see Appendix C) depend only on the other state variables and are
thus not state variables themselves.
13
cision rules are found numerically using value function iteration. Appendix E describes our
numerical methodology.
3 Estimation
To estimate the model, we adopt a two-step strategy, similar to the one used by Gourinchas
and Parker (2002) and French (2005). In the first step we estimate or calibrate parameters
that can be cleanly identified identified without explicitly using our model. For example,
we estimate mortality rates and health transitions straight from demographic data. In the
second step, we estimate the preference parameters of the model, as well as the consumption
floor, using the method of simulated moments (MSM).
3.1 Moment Conditions
The objective of MSM estimation is to find the preference vector that yields simulated
life-cycle decision profiles that “best match” (as measured by a GMM criterion function) the
profiles from the data. The moment conditions that comprise our estimator are:
1. Because an individual’s ability to self-insure against medical expense shocks depends
critically upon his asset level, we match 1/3rd and 2/3rd asset quantiles by age.11 We
match these quantiles in each of Tperiods (ages), for a total of 2Tmoment conditions.
2. We match exit rates by age for each health insurance category. With three health
insurance categories (none,retiree and tied), this generates 3Tmoment conditions.
3. Because the value a worker places on employer-provided health insurance may depend
on his wealth, we match labor force participation conditional on the combination of
asset grouping and health insurance status. With 2 quantiles (generating 3 quantile-
conditional means) and 3 health insurance types, this generates 9Tmoment conditions.
11Our approach to constructing quantile-related moment conditions follows Manski (1988), Powell (1994),
or Buchinsky (1998). Chernozhukov and Hansen (2002) provide additional, earlier, references. Related ap-
proaches appear in Epple and Seig (1999) and Cagetti (2003).
14
4. To help identify preference heterogeneity, we utilize a series of questions in the HRS
that ask workers about their preferences for work. We combine the answers to these
questions into a time-invariant index, pref ∈ {high, low, out}, which is described in
greater detail in Section 4.4. Matching participation conditional on each value of this
index generates another 3Tmoment conditions.
5. Finally, we match hours of work and participation conditional on our binary health
indicator. This generates 4Tmoment conditions.
Combined, the five preceding items result in 21Tmoment conditions. Appendix F pro-
vides a detailed description of the moment conditions, the mechanics of our MSM estimator,
the asymptotic distribution of our parameter estimates, and our choice of weighting matrix.
3.2 Initial Conditions and Preference Heterogeneity
A key part of our estimation strategy is to compare the behavior of individuals with
different forms of employer-provided health insurance. If access to health insurance is an
important factor in the retirement decision, we should find that individuals who receive health
insurance only while they work should retire later than individuals who receive employer-
provided health insurance even if they retire early. In making such a comparison, however,
we must account for the possibility that individuals with different health insurance options
differ systematically along other dimensions as well. For example, individuals with retiree
coverage tend to have higher wages and more generous pensions.
We control for this “initial conditions” problem in three ways. First, the initial distribu-
tion of simulated individuals is drawn directly from the data. Because wealthy households
are more likely to have retiree coverage in the data, wealthy households are more likely to
have retiree coverage in our initial distribution. Second, we model carefully the way in which
pension and Social Security accrual varies across individuals and groups.
Finally, we control for unobservable differences across health insurance groups by intro-
ducing permanent preference heterogeneity, using the approach introduced by Heckman and
15
Singer (1984) and adapted by (among others) Keane and Wolpin (1997) and van der Klaauw
and Wolpin (2006). Each individual is assumed to belong to one of a finite number of prefer-
ence “types”, with the probability of belonging to a particular type a logistic function of the
individual’s preference index, initial wealth, wages and health insurance type. Our approach
allows for the possibility that people with different preferences systematically self-select into
different types of health insurance coverage. We estimate the type probability parameters
jointly with the preference parameters and the consumption floor.
4 Data and Calibrations
4.1 HRS Data
We estimate the model using data from the Health and Retirement Survey (HRS). The
HRS is a sample of non-institutionalized individuals, aged 51-61 in 1992, and their spouses.
With the exception of assets and health costs, which are measured at the household level,
our data are for male household heads. The HRS surveys individuals every two years, so
that we have 7 waves of data covering the period 1992-2004. The HRS also asks respondents
retrospective questions about their work history that allow us to infer whether the individual
worked in non-survey years. Details of this, as well as variable definitions, selection criteria,
and a description of the initial joint distribution, are in Appendix G.
As noted above, the Social Security benefit adjustment formula depends on an individual’s
year of birth. To ensure that workers in our sample face a similar set of Social Security
retirement incentives, we fit our model to the decision profiles of the cohort of individuals
aged 57-61 in 1992. However, when estimating the stochastic processes that individuals face,
we often use the full sample in order to increase sample size.
With the exception of wages, we do not adjust the data for cohort effects. Because our
subsample of the HRS covers a fairly narrow age range, this omission should not generate
much bias.
16
4.2 Health Insurance and Health Costs
We assign individuals to one of three mutually exclusive health insurance groups: retiree,
tied, and none, as described in Section 2. Because of small sample problems, the none group
includes those with private health insurance as well as those with no insurance at all. Both
face high medical expenses because they lack employer-provided coverage. Private health
insurance is usually not a substitute for employer-provided coverage, as high administra-
tive costs and adverse selection problems can result in prohibitively expensive premiums.
Moreover, private coverage often does not cover pre-existing medical conditions, whereas
employer-provided coverage typically does. Because the model includes a consumption floor
to capture the insurance provided by Medicaid, the none group also includes those who re-
ceive health care through Medicaid. We assign those who have health insurance provided by
their spouse to the retiree group, along with those who report that they could keep their
health insurance if they left their jobs. Neither of these types has their health insurance tied
to their job. We assign individuals who would lose their employer-provided health insurance
after leaving their job to the tied group.
Although the HRS’s insurance-related data are detailed, they are never completely con-
sistent with our definitions of tied or retiree coverage. Appendix H shows, however, that the
health-insurance-specific job exit rates are not very sensitive to the assumptions we imposed
in interpreting the data.
The HRS has data on self-reported medical expenses. Medical expenses are the sum of
insurance premia paid by households, drug costs, and out-of-pocket costs for hospital, nursing
home care, doctor visits, dental visits, and outpatient care. We are interested in the medical
expenses that households face. Unfortunately, we observe only the medical expenses that
these households actually pay for. This means that the observed medical expense distribu-
tion for low-wealth households is censored, because programs such as Medicaid pay much of
their medical expenses. Because our model explicitly accounts for government transfers, the
appropriate measure of medical expenses includes medical expenses paid by the government.
Therefore, we assign Medicaid payments to households that received Medicaid benefits. The
17
2000 Green Book (Committee on Ways and Means, 2000, p. 923) reports that in 1998 the
average Medicaid payment was $10,242 per beneficiary aged 65 and older, and $9,097 per
blind or disabled beneficiary. Starting with this average, we then assume that Medicaid pay-
ments have the same volatility as the medical care payments made by uninsured households.
This allows us to generate a distribution of Medicaid payments.
We fit these data to the health cost model described in Section 2. Because of small
sample problems, we allow the mean, hc(.), and standard deviation, σ(.), to depend only
on the individual’s Medicare eligibility, health insurance type, health status, labor force
participation and age. Following the procedure described in French and Jones (2004a), hc(.)
and σ(.) are set so that the model replicates the mean and 95th percentile of the cross-
sectional distribution of medical expenses (in levels, not logs) in each of these categories. We
found that this procedure did an extremely good job of matching the top 20% of the medical
expense distribution. Details are in Appendix I.
Table 1 presents some summary statistics, conditional on health status. Table 1 shows
that for healthy individuals who are 64 years old, and thus not receiving Medicare, average
annual medical costs are $2,950 for those with tied coverage and $5,140 for those with no
employer-provided coverage, a difference of $2,190. With the onset of Medicare at age 65, the
difference shrinks to $410. For individuals in bad health, the difference shrinks from $2,810
at age 64 to $530 at age 65.12
As Rust and Phelan (1997) emphasize, it is not just differences in mean medical expenses
that determine the value of health insurance, but also differences in variance and skewness.
If health insurance reduces health cost volatility, risk-averse individuals may value health
insurance at well beyond the cost paid by employers. To give a sense of the volatility, Table 1
also presents the standard deviation and 99.5th percentile of the health cost distributions.
Table 1 shows that for healthy individuals who are 64 years old, average annual medical costs
have a standard deviation of $7,150 for those with tied coverage and $19,060 for those with no
12The pre-Medicare cost differences are roughly comparable to EBRI’s (1999) estimate that employers on
average contribute $3,288 to their employees’ health insurance.
18
Retiree - Retiree - Tied - Tied -
Working Not Working Working Not Working None
Age = 64, without Medicare, Good Health
Mean $2,930 $3,360 $2,950 $3,670 $5,140
Standard Deviation $6,100 $7,050 $7,150 $8,390 $19,060
99.5th Percentile $35,530 $41,020 $40,210 $47,890 $91,560
Age = 65, with Medicare, Good Health
Mean $2,590 $2,800 $3,420 $2,750 $3,830
Standard Deviation $4,700 $4,700 $5,370 $5,420 $8,090
99.5th Percentile $28,000 $28,240 $32,460 $31,880 $47,010
Age = 64, without Medicare, Bad Health
Mean $3,750 $4,300 $3,770 $4,690 $6,580
Standard Deviation $7,970 $9,220 $9,330 $10,960 $24,840
99.5th Percentile $46,240 $53,380 $52,210 $62,240 $118,400
Age = 65, with Medicare, Bad Health
Mean $3,310 $3,580 $4,380 $3,520 $4,910
Standard Deviation $6,150 $6,150 $7,040 $7,080 $10,570
99.5th Percentile $36,530 $36,890 $42,460 $41,520 $61,180
Table 1: Medical Expenses, by Medicare and Health Insurance Status
employer-provided coverage. With the onset of Medicare at age 65, average annual medical
costs have a standard deviation of $5,370 for those with tied coverage and $8,090 for those
with no employer-provided coverage. Therefore, Medicare not only reduces average health
costs for those without employer-provided health insurance. It reduces health cost volatility
as well.
Relative to other research on the cross sectional distribution of medical expenses, we find
higher medical expenses at the far right tail of the distribution. For example, Blau and
Gilleskie (2006a) use different data and methods to find average medical expenses that are
comparable to our estimates. However, they find that medical expenses are much less volatile
than our estimates suggest. For example, they find that for households in good health and
younger than 65, the maximum expense levels (which seem to be slightly less likely than
0.5% probability events) were $69,260 for those without coverage, $6,400 for those with
retiree coverage, and $6,400 for those with tied coverage. Table 1 shows that our estimates
of the 99.5th percentile (i.e., the top 0.5 percentile of the distribution) of the distributions
for healthy individuals are $91,560 for those with no coverage, $41,020 for those with retiree
19
coverage, and $40,210 for those with tied coverage.
Berk and Monheit (2001) use data from the MEPS, which arguably has the highest
quality medical expense data of all the surveys. Using a measure of medical expenses that
should be comparable to our estimates for the uninsured,13 Berk and Monheit find that
those in the top 1% of the medical expense distribution have average medical expenses of
$57,900 (in 1998 dollars). Again, this is below our estimate of $91,560 for the uninsured.
This discrepancy is not surprising. Berk and Monheit’s estimates are for all individuals in
the population, whereas our estimates are for older households (many of which include two
individuals). Furthermore, Berk and Monheit’s estimates exclude all nursing home expenses,
while the HRS, although initially consisting only of non-institutionalized households, captures
the nursing home expenses these households incur in later waves.
The parameters for the idiosyncratic process ψt, (σ2
ξ, σ2
ǫ, ρhc), are taken from French and
Jones (2004a). Table 2 presents the parameters, which have been normalized so that the
overall variance, σ2
ψ, is one. Table 2 reveals that at any point in time, the transitory compo-
nent generates almost 67% of the cross-sectional variance in medical expenses. The results in
French and Jones reveal, however, that most of the variance in cumulative lifetime medical
expenses is generated by innovations to the persistent component. Given the autocorrelation
coefficient ρhc of 0.925, this is not surprising.
Parameter Variable Estimate
σ2
ǫinnovation variance of persistent component 0.04811
ρhc autocorrelation of persistent component 0.925
σ2
ξinnovation variance of transitory component 0.6668
Table 2: Variance and Persistence of Innovations to Medical Expenses
4.3 Pension Accrual
Appendix C gives details on how we use the confidential HRS pension data to construct
an accrual rate formula. Figure 1 shows the average pension accrual rates generated by this
13Berk and Monheit use data on total billable expenses. The uninsured should pay all billable expenses, so
Berk and Monheit’s estimated distribution should be comparable to our distribution for the uninsured.
20
formula, conditional on having average income.
Figure 1: Average Pension Accrual Rates, by Age and Health Insurance Coverage
Workers with retiree coverage are the most likely to have defined benefit plans (which
often have sharp drops in pension accrual after age 60), workers with tied coverage are the
most likely to have a defined contribution plan, and workers with no coverage are the most
likely no have no pension plan. As a result, those with retiree coverage face the sharpest
drops in pension accrual after age 60.14 Furthermore, the confidential pension data show
that, conditional on having a defined benefit pension plan, those with retiree coverage face
the sharpest drops in pension accrual after age 60. In short, not only does retiree coverage
in and of itself provide an incentive for early retirement, but the pension plans associated
with retiree coverage provide the strongest incentives for early retirement. Modeling the
association between pension accrual and health insurance coverage is thus critical; failing to
capture this link will lead the econometrician to overstate the importance of retiree coverage
on retirement.
14Because Figure 1 is based on our estimation sample, it does not show accrual rates for earlier ages.
Estimates that include the validation show, however, that those with retiree coverage have the highest pension
accrual rates in their early and middle 50s.
21
4.4 Preference Index
In order to better measure preference heterogeneity in the population (and how it is
correlated with health insurance), we estimate a person’s “willingness” to work using three
questions from the first (1992) wave of the HRS. The first question asks the respondent the
extent to which he agrees with the statement, “Even if I didn’t need the money, I would
probably keep on working.” The second question asks the respondent, “When you think
about the time when you will retire, are you looking forward to it, are you uneasy about it,
or what?” The third question asks, “How much do you enjoy your job?”
To combine these three questions into a single index, we regress wave 5-7 (survey year
2000-2004) participation on the response to the three questions along with polynomials and
interactions of all the state variables in the model: age, health status, wages, wealth, and
AIME, medical expenses, and health insurance type. Multiplying the numerical responses to
the three questions by their respective estimated coefficients and summing yields an index.
We then discretize the index into three values: high, for the top 50% of the index for those
working in wave 1; low, for the bottom 50% of the index for those working in wave 1; and out
for those not working in wave 1. Appendix J provides additional details on the construction
of the index. Figure 7 below shows that the index has great predictive power: at age 65,
participation rates are 56% for those with an index of high, 39% for those with an index of
low, and 12% for those with an index of out.
The discretized preference index plays a major role in our strategy for estimating prefer-
ence heterogeneity. The preference index is included—and, as shown below, has significant
effects—in the preference type prediction equations. Furthermore, in our MSM estimation
procedure we require the model to match participation at each value of the preference index.
As a result, our estimated distribution of unobserved preference heterogeneity depends on
the distribution of observed preference heterogeneity, which should improve the precision of
our estimates.
22
4.5 Wages
Recall from equation (11) that ln Wt=αln(Ht) + W(Mt, t) + ωt.Following Aaronson and
French (2004), we set α= 0.415,which implies that a 50% drop in work hours leads to a 25%
drop in the offered hourly wage. This is in the middle of the range of estimates of the effect
of hours worked on the offered hourly wage. Because the wage information in the HRS varies
from wave to wave, we take the second term, W(Mt, t), from French (2005), who estimates a
fixed effects wage profile using data from the Panel Study of Income Dynamics. We rescale
the level of wages to match the average wages observed in the HRS at age 59.
Because fixed-effects estimators estimate the growth rates of wages of the same individuals,
the fixed-effects estimator accounts for cohort effects—the cohort effect is the average fixed
effect for all members of that cohort. However, if individuals leave the market because of a
sudden wage drop, such as from job loss, wage growth rates for workers will be greater than
wage growth rates for non-workers. This will bias estimated wage growth upward. To correct
for this problem, our baseline analysis uses the selection-adjusted wage profiles estimated by
French (2005).
The parameters for the idiosyncratic process ωt, (σ2
η, ρW) are also estimated by French
(2005). The results indicate that the autocorrelation coefficient ρWis 0.977; wages are almost
a random walk. The estimate of the innovation variance σ2
ηis 0.0141; one standard deviation
of an innovation in the wage is 12% of wages. These estimates imply a high degree of long-run
wage uncertainty.
4.6 Remaining Calibrations
We set the interest rate requal to 0.03. Spousal income depends upon an age polynomial
and the wage. Health status and mortality both depend on previous health status interacted
with an age polynomial. We estimate the Markov transition matrices using data from the
HRS and Assets and Health Dynamics of the Oldest Old.
23
5 Data Profiles and Initial Conditions
5.1 Data Profiles
Figure 2 shows the 1/3rd and 2/3rd asset quantiles at each age for the HRS sample. About
one third of the men sampled live in households with less than $80,000 in assets, and about
one third live in households with over $270,000 of assets. The asset profiles also show that
assets grow with age. This growth is higher than that reported in other studies (for example,
Cagetti, 2003, and French, 2005). Earlier drafts of this paper showed that the run-up in asset
prices during the sample period can explain some, although not all, of this run-up.
Figure 2: Asset Quantiles, Data
The first panel of Figure 3 shows empirical job exit rates by health insurance type. Recall
that Medicare should provide the largest labor market incentives for workers that have tied
health insurance. If these people place a high value on employer-provided health insurance,
they should either work until age 65, when they are eligible for Medicare, or they should
work until age 63.5 and use COBRA coverage as a bridge to Medicare. The job exit profiles
provide some evidence that those with tied coverage do tend to work until age 65. While the
age-65 job exit rate is similar for those whose health insurance type is tied (18.4%), retiree
(16.1%), or none (16.9%), those with retiree coverage have significantly higher exit rates at
24
62 (21.9%) than those with tied (14.2%) or none (14.5%).15 At almost every age other than
65, those with retiree coverage have higher job exit rates than those with tied or no coverage.
These differences across health insurance groups, while large, are smaller than the differences
in the empirical exit profiles reported in Rust and Phelan (1997).
The low job exit rates before age 65 and the relatively high job exit rates at age 65 for
those with tied coverage suggests that some people with tied coverage are working until age
65, when they become eligible for Medicare. The profiles therefore provide evidence that
there is a causal effect of health insurance on retirement. On the other hand, job exit rates
for those with tied coverage are lower than those with retiree coverage for every age other
than 65, and are not much higher at age 65. This suggests that differences in health insurance
coverage may not be the only reason for differences in job exit rates between those with tied
coverage and those with retiree coverage.
Although our health insurance classifications probably contain measurement error, Ap-
pendix H shows that the estimated job exit rates are not very sensitive to different coding
decisions for health insurance.
The bottom panel of Figure 3 presents observed labor force participation rates. In com-
paring participation rates across health insurance categories, it is useful to keep in mind the
transitions implied by equation (10): retiring workers in the tied insurance category transi-
tion into the none category. Because of this, the labor force participation rates for those with
tied insurance are calculated over a group of individuals that were all working in the previous
period. It is therefore unsurprising that the tied category has the highest participation rates.
Conversely, it is not surprising that the none category has the lowest participation rates,
given that category includes tied workers who retire.
15The differences across groups are not statistically different at the 5% level at age 62. However, when
we include our validation sample of younger individuals, the differences are statistically different at age 62.
Furthermore, F-tests reject the hypothesis that the three groups have identical exit rates at all ages at the
5% level.
25
Figure 3: Job Exit and Participation Rates, Data
26
5.2 Initial Conditions
Each artificial individual in our model begins its simulated life with the year-1992 state
vector of an individual, aged 57-61 in 1992, observed in the data. Table 3 summarizes this
initial distribution, the construction of which is described in Appendix G. Table 3 shows
that individuals with retiree coverage tend to have the most asset and pension wealth, while
individuals in the none category have the least—the median individual in the none category
has no pension wealth at all. Individuals in the none category are also more likely to be in
bad health, and not surprisingly, less likely to be working. In contrast, individuals with tied
coverage have high values of the preference index, suggesting that their delayed retirement
reflects differences in preferences as well as in incentives.
Retiree T ied N one
Age
Mean 58.7 58.6 58.7
Standard deviation 1.5 1.5 1.5
AIME (in thousands of 1998 dollars)
Mean 25.1 25.3 16.5
Median 27.2 26.9 16.4
Standard deviation 9.1 8.6 9.2
Assets (in thousands of 1998 dollars)
Mean 229.7 203.5 201.6
Median 146.0 112.3 55.6
Standard deviation 246.1 254.3 306.7
Pension Wealth (in thousands of 1998 dollars)
Mean 129.2 80.0 18.7
Median 65.1 14.5 0.0
Standard deviation 181.2 213.4 100.8
Wage (in 1998 dollars)
Mean 17.2 17.7 12.6
Median 14.7 14.6 8.5
Standard deviation 12.2 12.3 14.4
Preference index
Fraction out 0.27 0.04 0.48
Fraction low 0.42 0.44 0.18
Fraction high 0.32 0.52 0.33
Fraction in bad health 0.20 0.13 0.41
Fraction working 0.73 0.96 0.52
Number of observations 1,022 225 454
Table 3: Summary Statistics for the Initial Distribution
27
6 Baseline Results
6.1 Preference Parameter Estimates
The goal of our MSM estimation procedure is to match the life cycle profiles for assets,
hours and participation found in the HRS data. In order to use these profiles to identify pref-
erences, we make several identifying assumptions, the most important being that preferences
vary with age only as a result of changes in health status. Therefore, age can be thought of
as an “exclusion restriction”, which changes the incentives for work and savings but does not
change preferences.
Table 4 presents preference parameter estimates. The first 3 rows of Table 4 show the
parameters that vary across the preference types. We assume that there are three types
of individuals, and that the types differ in the utility weight on consumption, γ, and their
time discount factor, β. Individuals with high values of γhave stronger preferences for work.
Individuals with high values of βare more patient and thus more willing to defer consumption
and leisure.
Table 4 reveals significant differences in γand βacross preference types. To understand
these differences, it is useful to consider Table 5, which shows simulated summary statistics
for each of the preference types. Table 5 reveals that Type-0 individuals have the lowest value
of γ, i.e., they place the highest value on leisure. 93% of Type-0 individuals were out of the
labor force in wave 1. Type-2 individuals, in contrast, have the highest value of γ. 99.8%
of Type-2 individuals have a preference index of high, meaning that they were working in
wave 1 and self-reported having a low preference for leisure. Type-1 individuals fall in the
middle, valuing leisure less than Type-0 individuals, but more than Type-2 individuals. 59%
of Type-1 individuals have a preference index value of low. Figure 7 shows that low-index
individuals are also an intermediate case: although they initially work as much as high-index
individuals, low-index workers leave the labor force much more quickly.
Including preference heterogeneity allows us to control for the possibility that workers with
different preferences select jobs with different health insurance packages. Table 5 suggests
28
that some self-selection is occurring, as it reveals that workers with tied coverage are more
likely to be Type-2 agents, who have the strongest preference for work. This suggests that
workers with tied coverage might be more willing to retire at later dates simply because they
have a lower disutility of work.16
Parameters that vary across individuals
γ: consumption weight β: time discount factor
Type 0 0.438 Type 0 0.828
(0.080) (0.072)
Type 1 0.620 Type 1 1.115
(0.011) (0.016)
Type 2 0.907 Type 2 0.971
(0.028) (0.077)
Parameters that are common to all individuals
ν: coefficient of relative 7.49 θB: bequest weight†0.0320
risk aversion, utility (0.421) (0.0009)
L: leisure endowment, 3,863 κ: bequest shifter, 449
in hours (51.9) in thousands (31.7)
φP: fixed cost of work, 835 cmin: consumption floor 4,118
in hours (27.4) (159.5)
φM: hours of leisure lost, 445
bad health (38.8)
χ2statistic = 1,677; Degrees of freedom = 181
Method of simulated moments estimates.
Diagonal weighting matrix used in calculations. See Appendix F for details.
Standard errors in parentheses.
†Parameter expressed as marginal propensity to consume out of
final-period wealth.
Parameters estimated jointly with type probability prediction equation. See
Appendix K for estimated coefficients of the type probability prediction equation.
Table 4: Estimated Structural Parameters
The bottom line of Table 5 shows the fraction of each preference type. Averaging over the
three preference types reveals that the average value of βimplied by our model is 1.02, which
is slightly higher than most estimates. There are two reasons for this. The first reason is clear
upon inspection of the Euler Equation: ∂Ut
∂Ct≥βst+1(1 + r(1 −τt))Et∂ Ut+1
∂Ct+1 , where τtis the
16Interestingly, Type-2 agents also include wealthy individuals who have no health insurance coverage.
Given that many of these individuals are entrepreneurs, it is not surprising that they are often placed in the
“motivated” group.
29
Type 0 Type 1 Type 2
Key preference parameters
γ∗0.438 0.620 0.907
β∗0.828 1.115 0.971
Means by preference type
Assets ($000s) 164 236 239
Pension Wealth ($000s) 92 103 56
Wages ($/hour) 10.5 19.4 11.7
Probability of health insurance type, given preference type
Health insurance = none 0.405 0.232 0.184
Health insurance = retiree 0.578 0.642 0.461
Health insurance = tied 0.018 0.126 0.355
Probability of preference index value, given preference type
Preference Index = out 0.930 0.098 0.0002
Preference Index = low 0.004 0.591 0.0018
Preference Index = high 0.066 0.311 0.998
Fraction with preference type 0.250 0.600 0.150
∗Values of βand γare from Table 4.
Table 5: Mean Values by Preference Type, Simulations
marginal tax rate.17 Note that this equation identifies the product βst+1(1 + r(1 −τt)), but
not its individual elements. Therefore, a lower value of st+1 or (1+r(1−τt)) results in a higher
estimate of β. Given that many studies omit mortality risk and taxes—implicitly setting st+1
and 1 −τtto one—it is not surprising that they find lower values of β. The second reason is
that βis identified not only by the intertemporal substitution of consumption, as embodied
in the asset profiles, but also by the intertemporal substitution of leisure, as embodied in
the labor supply profiles.18 Models of labor supply and savings, such as MaCurdy (1981) or
French (2005), often suggest that agents are very patient.
Another important parameter is ν, the coefficient of relative risk aversion for flow utility.
A more familiar measure of risk aversion is the coefficient of relative risk aversion for con-
sumption. Assuming that labor supply is fixed, it can be approximated as −(∂2U/∂C 2)C
∂U/∂C =
−(γ(1−ν)−1). As we move across preference types, the coefficient increases from 3.8 to 5.0 to
17Note that this equation does not hold exactly when individuals value bequests.
18This restriction is often relaxed by adding a time trend to leisure- (or consumption-) related utility
parameters. See, e.g., Rust and Phelan, 1997, Blau and Gilleskie, 2006a and 2006b, and Gustman and
Steinmeier, 2005, Rust et al., 2003, van der Klaauw and Wolpin, 2006.
30
6.9. These values are within the range of estimates found in recent studies by Cagetti (2003)
and French (2005), but they are larger than the values of 1.1, 1.8, and 1.0 reported by Rust
and Phelan (1997), Blau and Gilleskie (2006a), and Blau and Gilleskie (2006b) respectively,
in their studies of retirement.
The consumption floor cmin and νare identified in large part by the asset quantiles, which
reflect precautionary motives. The bottom quantile in particular depends on the interaction of
precautionary motives and the consumption floor. If the consumption floor is sufficiently low,
the risk of a catastrophic health cost shock, which over a lifetime could equal over $100,000
(see French and Jones (2004a)), can generate strong precautionary incentives. Conversely,
as emphasized by Hubbard, Skinner and Zeldes (1995), a high consumption floor discourages
saving among the poor, since the consumption floor effectively imposes a 100% tax on the
saving of those with high medical expenses and low income and assets.
Our estimated consumption floor of $4,118 is similar to other estimates of social insurance
transfers for the indigent. For example, when we use Hubbard, Skinner and Zeldes’s (1994,
Appendix A) procedures and more recent data, we found that the average benefits available
to a childless household with no members aged 65 or older was $3,500.19 A value of $3,500
understates the benefits available to individuals over age 65; in 1998 the Federal SSI benefit
for elderly (65+) couples was nearly $9,000 (Committee on Ways and Means, 2000, p. 229).
On the other hand, about half of eligible households do not collect SSI benefits (Elder and
Powers, 2006, Table 2), possibly because transactions or “stigma” costs outweigh the value
of public assistance. Low take-up rates, along with the costs that probably underly them,
suggest that the effective consumption floor need not equal statutory benefits.
The bequest parameters θBand κare identified largely from the top asset quantile. It
follows from equation (3) that when the shift parameter κis large, the marginal utility of
bequests will be lower than the marginal utility of consumption unless the individual is rich.
19Our treatment of the consumption floor differs markedly from that of Rust and Phelan (1997) and Blau and
Gilleskie (2006a, 2006b), who simply impose a penalty when an individual’s implied consumption is negative.
Although Rust and Phelan’s estimates do not translate into a consumption floor, they find the penalty to be
large, implying a fairly low floor.
31
In other words, the bequest motive mainly affects the saving of the rich; for more on this
point, see De Nardi (2004). Our estimate of θBimplies that the marginal propensity to
consume out of wealth in the final period of life (which is a nonlinear function of θB,β,γ,ν
and κ) is 1 for low income individuals and 0.032 for high-income individuals.
Turning to labor supply, we find that individuals in our sample are willing to intertem-
porally substitute their work hours. In particular, simulating the effects of a 2% wage
change reveals that the wage elasticity of average hours is 0.535 at age 60. This relatively
high labor supply elasticity arises because the fixed cost of work generates volatility on the
participation margin. The participation elasticity is 0.404 at age 60, implying that wage
changes cause relatively small hours changes for workers. For example, the Frisch labor
supply elasticity of a type-1 individual working 2000 hours per year is approximated as
−L−Ht−φP
Ht×1
(1−γ)(1−ν)−1= 0.21.
The fixed cost of work, φP, is identified by the life cycle profile of hours worked by workers.
Average hours of work (available upon request) do not drop below 1,000 hours per year (or
20 hours per week, 50 weeks per year) even though labor force participation rates decline
to near zero. In the absence of a fixed cost of work, one would expect hours worked to
parallel the decline in labor force participation. The time endowment Lis identified by the
combination of the participation and hours profiles. The time cost of bad health, φM, is
identified by noting that unhealthy individuals work fewer hours than healthy individuals,
even after conditioning on wages.20
6.2 Simulated Profiles
The bottom of Table 4 displays the overidentification test statistic. Even though the
model is formally rejected, the life cycle profiles generated by the model for the most part
resemble the life cycle profiles generated by the data.
Figure 4 shows that the model fits both asset quantiles fairly well. The model is able to
20In the current specification, we have not imposed any re-entry costs. Adding previous employment as
a state variable doubles the computational burden, and the current specification matches observed re-entry
patterns very well.
32
Figure 4: Asset Quantiles, Data and Simulations
fit the lower quantile in large part because of the consumption floor of $4,118; the predicted
lower asset quantile rises dramatically when the consumption floor is lowered. This result
is consistent with Hubbard, Skinner, and Zeldes (1995). They show that if the government
guarantees a minimum consumption level, those with low income will tend not to save because
their savings will reduce the transfers they receive from the government. It is therefore not
surprising that within the model the consumption floor reduces saving by individuals with
low income and assets.
The three panels in the left hand column of Figure 5 show that the model is able to
replicate the two key features of how labor force participation varies with age and health
insurance. The first key feature is that participation declines with age, and the declines are
especially sharp between ages 62 and 65. The model is also able to match the aggregate
decline in participation at age 65 (a 5.3 percentage point decline in the data versus a 5.8
percentage point decline predicted by the model), although it underpredicts the decline in
participation at 62 (a 10.6 percentage point decline in the data versus a 3.5 percentage point
decline predicted by the model).
33
Figure 5: Participation and Job Exit Rates, Data and Simulations
34
Figure 6: Labor Force Participation Rates by Asset Grouping, Data and Simulations
35
The second key feature is that there are large differences in participation and job exit rates
across health insurance types. The model does a good job of replicating observed differences
in participation rates. For example, the model matches the low participation levels of the
uninsured. Turning to the lower left panel of Figure 6, the data show that the group with the
lowest participation rates are the uninsured with low assets. The model is able to replicate
this fact because of the consumption floor. Without a high consumption floor, the risk of
catastrophic medical expenses, in combination with risk aversion, would cause the uninsured
to remain in the labor force and accumulate a buffer stock of assets.
The panels in the right hand column of Figure 5 compare observed and simulated job
exit rates for each health insurance type. They show that the model correctly predicts that
workers with retiree coverage and no health insurance have fairly high exit rates after age 62.
In contrast, the model under-predicts exit for workers with tied health insurance.
Figure 7: Labor Force Participation Rates by Preference Index, Data and Simulations
Figure 7 shows how participation differs across the three values of our discretized prefer-
ence index. The model does a good job of replicating the observed differences in participation.
Recall that an index value of out implies that the individual was not working in 1992. Not
surprisingly, participation for this group is always low. Individuals with positive values of the
preference index differ primarily in the rate at which they leave the labor force, i.e., the slopes
of their participation profiles. As noted in our discussion of the preference parameters, the
36
model replicates these differences by allowing the taste for leisure (γ) and the discount rate
(β) to vary across preference types. When we do not allow for preference heterogeneity, the
model is unable to replicate the patterns observed in Figure 7. This highlights the importance
of the preference index in identifying preference heterogeneity in the population.
6.3 The Effects of Employer-Provided Health Insurance
The labor supply patterns shown in Figures 3 and 5 show a correlation between health
insurance and labor supply. However, they do not identify the effects of health insurance on
retirement, for three reasons. First, as shown in Table 3, the distributions of wages and wealth
in our sample differ across health insurance types. For example, those with retiree coverage
have greater pension wealth than other groups. Second, as shown in Figure 1, pension plans
for workers with retiree coverage provide stronger incentives for early retirement than the
pension plans held by other groups. Third, as shown in Table 5, preferences for leisure
vary by health insurance type. In short, retirement incentives differ across health insurance
categories for reasons unrelated to health insurance incentives.
To isolate the effects of employer-provided health insurance on labor supply, we conduct
some additional simulations. We fix pension accrual rates so that they are identical across
health insurance types. We then simulate the model twice, assuming first that all workers
retiree health insurance coverage at age 59, then tied coverage at age 59. Across the two sim-
ulations, households face different medical expense distributions, but in all other dimensions
the distribution of incentives faced by individuals is identical.
This exercise reveals that the job exit rate at age 60 would be 2.6 percentage points higher
if all workers had retiree coverage rather than tied coverage. The gap is 3.8 percentage points
at age 61 and 3.2 percentage points at age 62, then declines slightly to 1.0 percentage points at
age 65. These differences in exit rates across health insurance types are smaller than the raw
differences in exit rates observed in the data (see Section 5) and the raw differences predicted
by the model. Such results are consistent with Tables 3 and 5, which show that workers with
retiree coverage have more generous pension plans and stronger preferences for leisure than
37
those with tied coverage. Failing to account for these effects will lead the econometrician to
overstate the effect of health insurance on exit rates.
The effect of health insurance can also be measured by comparing participation rates.
We find that the labor force participation rate for ages 60-67 would be 6.0 percentage points
lower if workers had retiree, rather than tied, coverage at age 59. Yet another way to measure
the effect of health insurance is consider the retirement age, defined here as the oldest age
at which the individual worked. Moving from retiree to tied coverage increases the average
retirement age by 0.41 years.
A useful comparison appears in the reduced form model of Blau and Gilleskie (2001), who
study labor market behavior between ages 51 and 62 using waves 1 and 2 of the HRS data.
They find that having retiree coverage, as opposed to tied coverage, increases the job exit
rate around 1% at age 54 and 7.5% at age 61. They also find that accounting for selection
into health insurance plans modestly increases the estimated effect of health insurance on
exit rates. Other reduced form findings in the literature are qualitatively similar to Blau and
Gilleskie. For example, Madrian (1994) finds that retiree coverage reduces the retirement
age by 0.4-1.2 years, depending on the specification and the data employed. Karoly and
Rogowski (1994), who attempt to account for selection into health insurance plans, find that
retiree coverage increases the job exit rate 8 percentage points over a 21
2year period. Our
estimates, therefore, lie within the lower bound of the range established by previous reduced
form studies, giving us confidence that the model can be used for policy analysis.
Structural studies that omit medical expense risk usually find smaller health insurance
effects than we do. For example, Gustman and Steinmeier (1994) find that retiree coverage
reduces years in the labor force by 0.1 year. Lumsdaine et al. (1994) find even smaller effects.
In contrast, structural studies that include medical expense risk but omit self-insurance usu-
ally find effects that are at least as large as ours. Our estimated effects are larger than Blau
and Gilleskie’s (2006a, 2006b), who find that retiree coverage reduces average labor force
participation 1.7 and 1.6 percentage points, respectively,21 but are smaller than the effects
21Blau and Gilleskie (2006a) consider the retirement decision of couples, and allow husbands and wives to
38
found by Rust and Phelan (1997).
6.4 Model Validation
In order to better understand whether structural models produce accurate predictions,
it has become increasingly common to subject them to out-of-sample validation exercises
(see, e.g., Keane and Wolpin, 2006, and the references therein). Recall that we estimate
the model on a cohort of individuals aged 57-61 in 1992. We test our model by considering
the HRS cohort aged 51-55 in 1992; we refer to this cohort as our validation sample. These
individuals faced different Social Security incentives than did the estimation cohort. The
validation sample did not face the Social Security earnings test after age 65, had a slightly later
full retirement age, and faced a benefit adjustment formula that more strongly encouraged
delayed retirement. In addition to facing different Social Security rules, the validation sample
possessed different endowments of wages, wealth, and employer benefits. A valuable test of
our model, therefore, is to see if it can predict the behavior of the validation sample.
Data Model
1933 1939 Difference†1933 1939 Difference∗
Age (1) (2) (3) (4) (5) (6)
60 0.694 0.700 0.006 0.621 0.654 0.033
61 0.656 0.652 -0.003 0.589 0.619 0.030
62 0.551 0.549 -0.002 0.554 0.574 0.021
63 0.487 0.509 0.023 0.522 0.537 0.016
64 0.433 0.475 0.042 0.484 0.501 0.017
65 0.379 0.427 0.048 0.426 0.444 0.018
66 0.338 0.429 0.091 0.370 0.400 0.030
67 0.327 0.484 0.157 0.338 0.343 0.005
Total, 60-65 3.198 3.312 0.114 3.195 3.330 0.135
Total, 60-67 3.863 4.225 0.362 3.903 4.073 0.171
†Column (2) −Column (1) ∗Column (5) −Column (4)
Table 6: Participation Rates by Birth Year Cohort
retire at different dates. Blau and Gilleskie (2006b) allow workers to choose their medical expenses. Because
these modifications provide additional mechanisms for smoothing consumption over medical expense shocks,
they could reduce the effect of employer-provided health insurance. Furthermore, eliminating the ability to
save reduces a worker’s willingness to substitute labor across time; as current consumption becomes more
closely linked to current earnings, labor supply becomes less flexible. Domeij and Floden (2006) find that
borrowing constraints reduce the effective intertemporal elasticity of substitution by 50 percent.
39
Columns (1)-(3) of Table 6 show the participation rates observed in the data for each
cohort, and the difference. The data suggest that the change in the Social Security rules
coincides with increased labor force participation, especially at later ages. The estimated
increase in labor supply at ages 62-65 is similar, and the estimated increase at ages 66-67
larger than the increases in labor supply reported in Song and Manchester (2007).22
Columns (4)-(6) of Table 6 show the differences predicted by the model. The simulations
for the validation sample use the initial distribution observed for the validation cohort, but
use the decision rules estimated on the older estimation cohort.23 Comparing Columns (3)
and (6) shows that although the model does not always match the data year-by-year, it
predicts that total labor supply over ages 60-65 will increase by 0.135 years, compared to the
difference of 0.114 years years in the data. We conclude that the model does a good job of
fitting the data out of sample.
7 Policy Experiments
The preceding section showed that the model fits the data very well, given plausible
preference parameters. In this section, we use the model to predict how changing the Social
Security and Medicare rules would affect retirement behavior. In particular, we increase both
the normal Social Security retirement age and the Medicare eligibility age from 65 to 67, and
measure the resulting changes in simulated work hours and exit rates. The results of these
experiments are summarized in Table 7.
The first column of Table 7 shows model-predicted labor market participation at ages 60
through 67 under the 1998 Social Security rules. Under the 1998 rules, the average person
works a total of 3.90 years over this eight-year period. The fifth column of Table 7 shows
that this is close to the total of 3.86 years observed in the data.
The second column shows the average hours that result when the 1998 Social Security rules
22Our participation rates for ages 66 and 67 are imprecisely estimated because at later ages we observe a
decreasing fraction of the validation sample; at age 66, for example, we observe only the individuals born in
1937 and 1938—roughly two fifths of the sample—and at age 67 we observe only the individuals born in 1937.
23We do not adjust for business cycle conditions.
40
1998 rules: 2030 rules:
SS = 65 SS = 67 SS = 65 SS = 67
MC = 65 MC = 65 MC = 67 MC = 67 Data
Age (1) (2) (3) (4) (5)
60 0.621 0.628 0.624 0.633 0.694
61 0.589 0.597 0.594 0.602 0.656
62 0.554 0.559 0.558 0.566 0.550
63 0.522 0.527 0.527 0.534 0.487
64 0.484 0.492 0.491 0.499 0.432
65 0.426 0.435 0.445 0.454 0.379
66 0.370 0.401 0.389 0.416 0.338
67 0.338 0.355 0.342 0.358 0.327
Total 60-67 3.903 3.994 3.971 4.062 3.863
SS = Social Security normal retirement age
MC = Medicare eligibility age
Table 7: Effects of Changing the Social Security Retirement and Medicare Eligibil-
ity Ages
are replaced with the rules planned for the year 2030. Imposing the 2030 rules: (1) increases
the normal Social Security retirement age, the date at which the worker can receive “full
benefits”, from 65 to 67; (2) significantly increases the credit rates for deferring retirement
past the normal age; and (3) eliminates the earnings test for workers at the normal retirement
age or older. The second column shows that imposing the 2030 rules leads the average worker
to increase years worked between ages 60 and 67 from 3.90 years to 3.99 years, an increase
of 0.09 years.24
The third column of Table 7 shows participation when the Medicare eligibility age is
increased to 67.25 This change increases total years of work by 0.07 years. Averaged over an
8-year interval, a 0.07-year increase in total years of work translates into a 0.9-percentage-
point increase in annual participation rates. This amount is larger than the changes found by
24In addition to changing the benefit accrual rate, raising the normal retirement age from 65 to 67 effec-
tively eliminates two years of Social Security benefits. Therefore, raising the normal retirement age has both
substitution and wealth effects, both of which cause participation to increase. To measure the size of the
wealth effect, we raise the retirement age to 67 while increasing annual benefits at every age by 15.4%. The
net effect of these two changes is to alter the Social Security incentive structure while keeping the present
value of Social Security wealth (at any age) roughly equivalent to the age-65 level. Using this configuration
to eliminate wealth effects, we find that total years of work increase by 0.048 years, implying that 0.043 years
of the 0.091-year increase is due to wealth effects.
25By shifting forward the Medicare eligibility age to 67, we increase from 65 to 67 the age at which medical
expenses can follow the “with Medicare” distribution shown in Table 1.
41
Blau and Gilleskie (2006a), whose simulations show that increasing the Medicare age reduces
the average probability of non-employment by about 0.1 percentage points, but is smaller
than the effects suggested by Rust and Phelan’s (1997) analysis. The fourth column shows
the combined effect of raising both the Social Security retirement and the Medicare eligibility
age. The joint effect is an increase of 0.16 years, 0.07 more than that generated by raising
the Social Security normal retirement age in isolation.
In short, the model predicts that raising the normal Social Security retirement age will
have a slightly larger effect on retirement behavior than increasing the Medicare eligibility
age. One reason that Social Security has larger labor market effects than Medicare is that
most workers in our sample do not have tied coverage at age 59.26 Medicare provides smaller
retirement incentives to workers in the retiree or none categories. Simulations reveal that
for those with tied coverage at age 60, shifting forward the Social Security age to 67 increases
years in the labor force by 0.09 years, whereas shifting forward the Medicare eligibility age
to 67 would increase years in the labor force by 0.11 years.
To understand better the incentives generated by Medicare, we compute the value that
Type-1 individuals place on employer-provided health insurance, by finding the increase in as-
sets that would make an uninsured Type-1 individual as well off as a person with retiree cover-
age. In particular, we find the compensating variation λt=λ(At, Bt, Mt, AIM Et, ωt, ζt−1, t),
where
Vt(At, Bt, Mt, AIM Et, ωt, ζt−1, retiree) = Vt(At+λt, Bt, Mt, AIM Et, ωt, ζt−1, none).
Table 8 shows the compensating variation λ(At,0, good, $32000,0,0,60) at several different
26Only 13% of the workers in our sample had tied coverage at age 59. This figure, however, is probably too
low. For example, Kaiser/HRET (2006) estimates that about 50% of large firms offered tied coverage in the
mid-1990s. One potential reason that we may be understating the share with tied coverage is that, as shown
in the Kaiser/HRET (2006) study, the fraction of workers with tied (instead of retiree) coverage grew rapidly
in the 1990s, and our health insurance measure is based on wave-1 data collected 1992. In fact, the HRS data
indicate that later waves had a higher proportion of individuals with tied coverage than in wave 1. We may
also be understating the share with tied coverage because of changes in the wording of the HRS questionnaire;
see Appendix G for details.
42
asset (At) levels.27 The first column of Table 8 shows the valuations found under the baseline
specification. One of the most striking features is that the value of employer-provided health
insurance is fairly constant through much of the wealth distribution. Even though richer
individuals can better self-insure, they also receive less protection from the government-
provided consumption floor. In the baseline case, these effects more or less cancel each other
out over the asset range of -$2,300 to $149,000. However, individuals with asset levels of
$600,000 place less value on retiree coverage, because they can better self-insure against
medical expense shocks.
Compensating Assets Compensating Annuity
With Without With Without
Uncertainty Uncertainty Uncertainty Uncertainty
Asset Levels (1) (2) (3) (4)
Baseline Case
-$2,300 $51,150 $18,710 $4,580 $1,970
$54,400 $55,860 $18,700 $4,070 $1,960
$149,000 $57,170 $19,690 $3,980 $1,950
$600,000 $40,000 $19,500 $2,830 $1,780
No-Saving Case
-$2,150 463,700 $28,100 $11,100 $1,910
Compensating variation between retiree and none coverages
Calculations described in text
Baseline results are for agents with type-1 preferences
Table 8: Value of Employer-Provided Health Insurance
Part of the value of retiree coverage comes from a reduction in average medical expenses—
because retiree coverage is subsidized—and part comes from a reduction in the volatility of
medical expenses—because it is insurance. In order to separate the former from the latter,
we eliminate health cost uncertainty, by setting the variance shifter σ(Mt, HIt, t, Bt, Pt) to
zero, and recompute λt, using the same state variables and mean medical expenses as before.
Without health cost uncertainty, λtis approximately $19,000. Comparing the two values of
λtshows that for the typical worker (with $150,000 of assets) about one-third of the value
27In making these calculations, we remove health-insurance-specific differences in pensions, as described
in section 6.3. It is also worth noting that for the values of Mtand ζt−1considered here, the conditional
differences in expected health costs are smaller than the unconditional differences shown in Table 1.
43
of health insurance comes from the reduction of average medical expenses, and two-thirds is
due to the reduction of medical expense volatility.
The first two columns of Table 8 measure the lifetime value of health insurance as an asset
increment that can be consumed immediately. An alternative approach is to express the value
of health insurance as an illiquid annuity comparable to Social Security benefits. Columns (3)
and (4) show this “compensating annuity”.28 When the value of health insurance is expressed
as an annuity, the fraction of its value attributable to reduced medical expense volatility falls
from two-thirds to about one-half. In most other respects, however, the asset and annuity
valuations of health insurance have similar implications.
To sum, allowing for medical expense uncertainty greatly increases the value of health
insurance. It is therefore unsurprising that we find larger effects of health insurance on
retirement than do Gustman and Steinmeier (1994) and Lumsdaine et al. (1994), who assume
that workers value health insurance at its actuarial cost.
8 Alternative Specifications
To consider whether our findings are sensitive to our modelling assumptions, we re-
estimate the model under two alternate specifications.29 Table 9 shows model-predicted
participation rates under the different specifications, along with the data. Column (1) of
Table 9 presents our baseline case. Column (2) presents the case where individuals are not
allowed to save. Column (3) presents the case with no preference heterogeneity. Column (4)
presents the data. In general, the different specifications generate similar profiles.
28To do this, we first find compensating AIM E ,b
λt, where
Vt(At, Bt, Mt, AIM Et, ωt, ζt−1, retiree) = Vt(At, Bt, Mt, AI M Et+b
λt, ωt, ζt−1, none).
This change in AIM E in turn allows us to calculate the change in expected pension and Social Security
benefits that the individual would receive at age 65, the sum of which can be viewed as a compensating
annuity. Because these benefits depend on decisions made after age 60, the calculation is only approximate.
29We have also estimated a specification where housing wealth is illiquid. As described in Appendix L,
parameter estimates and model fit for this case were somewhat different than our baseline results, although
the policy simulations looked similar. In earlier drafts of this paper (French and Jones, 2004b) we estimated a
specification where we added measurement error to the simulated asset histories. Adding measurement error,
however, had little effect on either the preference parameter estimates or policy experiments, and we thus
dropped this case.
44
No Homogeneous
Baseline Saving Preferences Data
Age (1) (2) (3) (4)
60 0.621 0.605 0.676 0.695
61 0.589 0.581 0.639 0.656
62 0.554 0.536 0.570 0.550
63 0.522 0.512 0.513 0.486
64 0.484 0.480 0.464 0.433
65 0.426 0.449 0.440 0.379
66 0.370 0.406 0.372 0.339
67 0.338 0.322 0.367 0.327
Total 60-67 3.903 3.891 4.041 3.866
Table 9: Model Predicted Participation, by Age: Alternative Specifications
The parameter estimates behind these simulations can be found in Table 14 of Ap-
pendix K, which presents parameter estimates for all the specifications.
8.1 No Saving
We have argued that the ability to self-insure through saving significantly affects the value
of employer-provided health insurance. One test of this hypothesis is to modify the model so
that individuals cannot save, and examine how labor market decisions change. In particular,
we require workers to consume their income net of health costs, as in Rust and Phelan (1997)
and Blau and Gilleskie (2006a, 2006b).
The second column of Table 9 shows participation rates. The baseline case fits the labor
supply profiles slightly better, and obviously fits the asset profiles much better, than the
no-savings case.30
The compensating annuity calculations in Table 8 show that eliminating the ability to
save greatly increases the value of retiree coverage: when assets are -$2,000, the compensating
annuity increases from $4,600 in the baseline case (with savings) to $11,100 in the no-savings
case. When there is no health cost uncertainty, the comparable figures are $1,970 in the
30Because the baseline and no-savings cases are estimated with different moments, the overidentification
statistics shown in the first two columns of Table 4 are not comparable. However, inserting the decision
profiles generated by the baseline model into the moment conditions used to estimate the no-savings case
produces an overidentification statistic of 958, while the no-saving specification produces an overidentification
statistic of 1,211.
45
baseline case and $1,910 in the no-savings case. Thus, the ability to self-insure through
saving significantly reduces the value of employer-provided health insurance.
Simulating the responses to policy changes, we find that raising the Medicare eligibility
age to 67 leads to an additional 0.05 years of work, an amount close to that of the baseline
specification. Moving the Social Security normal retirement age to 67 generates an almost
identical response, which is also consistent with the baseline results.
8.2 No Preference Heterogeneity
To assess the importance of preference heterogeneity, we estimate and simulate a model
where individuals have identical preferences. Table 14 in the Appendix contains the revised
parameter estimates.
Comparing columns (1), (3) and (4) of Table 9 shows that the model without preference
heterogeneity matches aggregate participation rates as well as the baseline model. However,
the no-heterogeneity specification does much less well in replicating the way in which partic-
ipation varies across the asset distribution, and, not surprisingly, does not replicate the way
in which participation varies across our discretized preference index.31
When preferences are homogenous the simulated response to delaying the Medicare eligi-
bility age, 0.11 years, is larger than the response in the baseline specification, and it exceeds
the effect of increasing the Social Security normal retirement age. This provides further
evidence that failure to account for preference heterogeneity and self-selection into health
insurance plan likely lead us to overstate the effect of health insurance on retirement.
9 Conclusion
Prior to age 65, many individuals receive health insurance only if they continue to work.
At age 65, however, Medicare provides health insurance to almost everyone. Therefore, a
potentially important work incentive disappears at age 65. To see if Medicare benefits have
31We do not include the index-related moments in the revised GMM criterion function.
46
a large effect on retirement behavior, we construct a retirement model that includes health
insurance, uncertain medical costs, a savings decision, a non-negativity constraint on assets
and a government-provided consumption floor. Including all these features produces a general
model that can reconcile previous results.
Using data from the Health and Retirement Study, we estimate the structural parameters
of our model. The model fits the data well, with reasonable preference parameters. In addi-
tion, the model does a good job of predicting the behavior of individuals who, by belonging
to a younger cohort, faced different Social Security rules than the individuals upon which the
model was estimated.
We find that health care uncertainty significantly affects the value of employer-provided
health insurance. Our calculations suggest that about two thirds of the value workers place on
employer-provided health insurance comes from its ability to reduce medical expense risk. We
also find, however, that the ability to save significantly reduces the value of health insurance:
when saving is prohibited, the value of insurance doubles. Furthermore, we find evidence of
self-selection into employer-provided health insurance plans, which also reduces the estimated
effect of Medicare on retirement. Nevertheless, we find that the labor supply effects of raising
the Medicare eligibility age from 65 to 67 (0.07 years) are just as important as the effects of
raising the Social Security retirement age from 65 to 67.
47
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51
Appendix A: Cast of Characters
Preference Parameters Health-related Parameters
γconsumption weight Mthealth status
βtime discount factor hctout-of-pocket medical expenses
νcoefficient of RRA, utility H Ithealth insurance type
Lleisure endowment hc(·) mean shifter, logged medical expenses
φPfixed cost of work σ(·) volatility shifter, logged medical expenses
φMleisure cost of bad health ψtidiosyncratic medical expense shock
θBbequest weight ζtpersistent medical expense shock
κbequest shifter ǫtinnovation, persistent shock
Cmin consumption floor ρhc autocorrelation, persistent shock
Decision Variables σ2
ǫinnovation variance, persistent shock
Ctconsumption ξttransitory medical expense shock
Hthours of work σ2
ξvariance, transitory shock
Ltleisure Wage-related Parameters
Ptparticipation Wthourly wage
Atassets w(·) mean shifter, logged wages
BtSocial Security application αcoefficient on hours, logged wages
Financial Variables ωtidiosyncratic wage shock
Y(·) after-tax income ρWautocorrelation, wage shock
τtax parameter vector ηtinnovation, wage shock
rreal interest rate σ2
ηinnovation variance, wage shock
ystspousal income Miscellaneous
ys(·) mean shifter, spousal income stsurvival probability
sstSocial Security income pref discrete preference index
AIMEtSocial Security wealth Xtstate vector, worker’s problem
pbtpension benefits λ(·) compensating variation
Table 10: Variable Definitions, Main Text
Appendix B: Taxes
Individuals pay federal, state, and payroll taxes on income. We compute federal taxes on
income net of state income taxes using the Federal Income Tax tables for “Head of Household”
in 1998. We use the standard deduction, and thus do not allow individuals to defer medical
expenses as an itemized deduction. We also use income taxes for the fairly representative
state of Rhode Island (27.5% of the Federal Income Tax level). Payroll taxes are 7.65% up to
a maximum of $68,400, and are 1.45% thereafter. Adding up the three taxes generates the
following level of post tax income as a function of labor and asset income:
52
Pre-tax Income (Y) Post-Tax Income Marginal Tax Rate
0-6250 0.9235Y 0.0765
6250-40200 5771.88 + 0.7384(Y-6250) 0.2616
40200-68400 30840.56 + 0.5881(Y-40200) 0.4119
68400-93950 47424.98 + 0.6501(Y-68400) 0.3499
93950-148250 64035.03 + 0.6166(Y-93950) 0.3834
148250-284700 97515.41 + 0.5640(Y-148250) 0.4360
284700+ 174474.21 + 0.5239(Y-284700) 0.4761
Table 11: After Tax Income
Appendix C: Pensions
Although the HRS pension data and pension calculator allow one to estimate pension
wealth with a high degree of precision, Bellman’s curse of dimensionality prevents us from
including in our dynamic programming model the full range of pension heterogeneity found
in the data. Thus we thus use the HRS pension data and calculator to construct a simpler
model. The fundamental equation behind our model of pensions is the accumulation equation
for pension wealth, pwt:
pwt+1 =
(1/st+1)[(1 + r)pwt+pacct−pbt] if living at t+ 1
0 otherwise
(15)
where pacctis pension accrual and pbtis pension benefits. Two features of this equation
bear noting. First, a pension is worthless once an individual dies. Therefore, in order to
be actuarially fair, surviving workers must receive an above-market return on their pension
balances. Dividing through by the survival probability st+1 ensures that the expected value
of pensions E(pwt+1|pwt, pacct, pbt) equals (1 + r)pwt+pacct−pbt. Second, since pension
accrual and pension interest are not directly taxed, the appropriate rate of return on pension
wealth is the pre-tax one. Pension benefits, on the other hand, are included in the income
used to calculate an individual’s income tax liability.
Simulating equation (15) requires us to know pension benefits and pension accrual. We
calculate pension benefits by assuming that at age t, the worker receives the expected pension
53
benefit
pbt=pft×pbmax
t,(16)
where pbmax
tis the benefit received by individuals actually receiving pensions (given the
earnings history observed at time t) and pftthe probability that a person with a pension is
currently drawing pension benefits. We estimate pftas the fraction of respondents who are
covered by a pension that receive pension benefits at each age; the fraction increases fairly
smoothly, except for a 23-percentage-point jump at age 62. To find the annuity pbmax
tgiven
the earnings history at time t(and assuming no further pension accruals so that pacck= 0
for k=t, t + 1, ..., T ), note first that recursively substituting equation (15) and imposing
pwT+1 = 0 reveals that pension wealth is equal to the present discounted value of future
pension benefits:
pwt=1
1 + r
T
X
k=t
S(k, t)
(1 + r)k−tpfkpbmax
t,(17)
where S(k, t) = (1/st)Qk
j=tsjgives the probability of surviving to age k, conditional on
having survived to time t. If we assume further that the maximum pension benefit is constant
from time tforward, so that pbmax
k=pbmax
t,k=t, t + 1, ..., T , this equation reduces to
pwt= Γtpbmax
t,(18)
Γt≡1
1 + r
T
X
k=t
S(k, t)
(1 + r)k−tpfk.(19)
Using equations (16) and (18), pension benefits are thus given by
pbt=pftΓ−1
tpwt.(20)
Next, we assume pension accrual is given by
pacct=α0(HIt, WtHt, t)×WtHt,(21)
where α0(.) is the pension accrual rate as a function of health insurance type, labor income,
54
and age. We estimate α0(.) in two steps, estimating separately each component of:
α0=E(pacct|WtHt, HIt, t, pent= 1) Pr(pent= 1|HIt, WtHt) (22)
where pacctis the accrual rate for those with a pension, and pentis a 0-1 indicator equal to 1
if the individual has a pension.
We estimate the first component, E(pacct|WtHt, H It, t, pent= 1), from restricted HRS
pension data. To generate a pension accrual rate for each individual, we combine the pension
data with the HRS pension calculator to estimate the pension wealth that each individual
would have if he left his job at different ages. The increase in pension wealth gained by
working one more year is the accrual. Put differently, if pension benefits are 0 as long as the
worker continues working, it follows from equation (15) that
pacct=st+1pwt+1 −(1 + r)pwt.(23)
It bears noting that the HRS pension data have a high degree of employer- and worker-level
detail, allowing us to estimate pension accrual quite accurately. With accruals in hand, we
then estimate E(pacct|WtHt, HIt, t, pent= 1) on the subset of workers that have a pension
on their current job. We regress accrual rates on a fourth-order age polynomial, indicators
for age greater than 62 or 65, log income, log income interacted with the age variables, health
insurance indicators, and health insurance indicators interacted with the age variables.
Figure 8 shows estimated pension accrual, by health insurance type and earnings. It
shows that those with retiree coverage have the sharpest declines in pension accrual after age
60. It also shows that once health insurance and the probability of having a pension plan are
accounted for, the effect of income on pension accrual is relatively small. Our estimated age
(but not health insurance) pension accrual rates line up closely with Gustman et al. (1998),
who also use the restricted firm based HRS pension data.
In the second step, we estimate the probability of having a pension, Pr(pent= 1|HIt, WtHt, t),
using unrestricted self-reported data from individuals who are working and are ages 51-55.
55
−.05 0 .05 .1 .15 .2
Accrual Rate
50 55 60 65 70
age
retiree tied
none one s.d. increase in earnings
Pension Accrual Rates, by Age and Health Insurance Type
Figure 8: Pension Accrual Rates for Individuals with Pensions, by Age, Health Insur-
ance Coverage and Earnings
The function Pr(pent= 1|HIt, WtHt, t) is estimated as a logistic function of log income,
health insurance indicators, and interactions between log income and health insurance.
Table 12 shows the probability of having different types of pensions, conditional on health
insurance. The table shows that only 8% of men with no health insurance have a pension,
but 64% of men with tied coverage and 74% of men with retiree insurance have a pension.
Furthermore, it shows that those with retiree coverage are also the most likely to have defined
benefit (DB) pension plans, which provide the strongest retirement incentives at age 65.
Probability of Pension Type
Variable No Insurance Retiree Insurance Tied Insurance
Defined Benefit .026 .412 .260
Defined Contribution .050 .172 .270
Both DB and DC .006 .160 .106
Total .082 .744 .636
Number of
Observations 343 955 369
Table 12: Probability of having a pension on the current job, by health insurance
type, working men, age 51-55
Combining the restricted data with the HRS pension calculator also yields initial pension
balances as of 1992. Mean pension wealth in our estimation sample is $93,300. Disaggre-
56
gating by health insurance type, those with retiree coverage have $129,200, those with tied
coverage have $80,000, and those with none have $18,700. With these starting values, we
can then simulate pension wealth in our dynamic programming model with equation (15),
using equation (21) to estimate pension accrual, and using equation (20) to estimate pension
benefits. Using these equations, it is straightforward to track and record the pension balances
of each simulated individual.
But even though it is straightforward to use equation (15) when computing pension wealth
in the simulations, it is too computationally burdensome to include pension wealth as a
separate state variable when computing the decision rules. Our approach is to impute pension
wealth as a function of age and AIME. In particular, we impute a worker’s annual pension
benefits as a function of his Social Security benefits:
b
pbt(P I At, HIt−1, t) = X
k
γ0,k,t1{H It−1=k}+γ3P I At+ (24)
γ4,t max{0, P I At−9,999.6}+γ5,t max{0, P I At−14,359.9},
where P I Atis the Social Security benefit the worker would get if he were drawing benefits at
time t; as shown in Appendix D below, PIA is a simple monotonic function of AIME. Using
equations (18) and (24) yields imputed pension wealth, cpwt= Γtb
pbt. The coefficients of this
equation were estimated with regressions on simulated data generated by the model, with
age effects captured by interacting the health insurance and PIA variables with a quadratic
polynomial in age. Since these simulated data depend on the γ’s— cpwtaffects the decision
rules used in the simulations—the γ’s solve a fixed-point problem. Fortunately, estimates of
the γ’s converge after a few iterations.
This imputation process raises two complications. The first is that we use a different
pension wealth imputation formula when calculating decision rules than we do in the simula-
tions. If an individual’s time-tpension wealth is cpwt, his time-t+ 1 pension wealth (if living)
should be
c
cpwt+1 = (1/st+1)[(1 + r)cpwt+pacct−pbt].
57
This quantity, however, might differ from the pension wealth that would be imputed using
P I At+1,cpwt+1 = Γt+1 b
pbt+1 where b
pbt+1 is defined in equation (24). To correct for this, we
increase non-pension wealth, At+1, by st+1(1 −τt)( c
cpwt+1 −cpwt+1). The first term in this
expression reflects the fact that while non-pension assets can be bequeathed, pension wealth
cannot. The second term, 1 −τt, reflects the fact that pension wealth is a pre-tax quantity—
pension benefits are more or less wholly taxable—while non-pension wealth is post-tax—taxes
are levied only on interest income.
A second problem is that while an individual’s Social Security application decision affects
his annual Social Security benefits, it should not affect his pension benefits. (Recall that we
reduce PIA if an individual draws benefits before age 65.) The pension imputation procedure
we use, however, would imply that it does. We counter this problem by recalculating PIA
when the individual begins drawing Social Security benefits. In particular, suppose that a
decision to accelerate or defer application changes P IAtto remtP I At. Our approach is to
use equation (24) find a value P IA∗
tsuch that
(1 −τt)b
pbt(P I A∗
t) + P I A∗
t= (1 −τt)b
pbt(P I At) + remtP IAt,
so that the change in the sum of PIA and imputed after-tax pension income equals just the
change in PIA, i.e., (1 −remt)P I At.
Appendix D: Computation of AIME
We model several key aspects of Social Security benefits. First, Social Security benefits
are based on the individual’s 35 highest earnings years, relative to average wages in the
economy during those years. The average earnings over these 35 highest earnings years are
called Average Indexed Monthly Earnings, or AIME. It immediately follows that working
an additional year increases the AIME of an individual with less than 35 years of work.
If an individual has already worked 35 years, he can still increase his AIME by working an
additional year, but only if his current earnings are higher than the lowest earnings embedded
in his current AIME. To account for real wage growth, earnings in earlier years are inflated
58
by the growth rate of average earnings in the overall economy. For the period 1992-1999,
real wage growth, g, had an average value of 0.016 (Committee on Ways and Means, 2000,
p. 923). This indexing stops at the year the worker turns 60, however, and earnings accrued
after age 60 are not rescaled.32 Third, AIME is capped. In 1998, the base year for the
analysis, the maximum AIME level was $68,400.
Precisely modelling these mechanics would require us to keep track of a worker’s entire
earnings history, which is computationally infeasible. As an approximation, we assume that
(for workers beneath the maximum) annualized AIME is given by
AIMEt+1 = (1 + g×1{t≤60})AIM Et(25)
+1
35 max 0, WtHt−αt(1 + g×1{t≤60})AIMEt,
where the parameter αtapproximates the ratio of the lowest earnings year to AIM E. We
assume that 20% of the workers enter the labor force each year between ages 21 and 25, so that
αt= 0 for workers aged 55 and younger. For workers aged 60 and older, earnings only update
AIMEtif current earnings replace the lowest year of earnings, so we estimate αtby simulating
wage (not earnings) histories with the model developed in French (2003), calculating the
sequence 1{time-tearnings do not increase AIMEt}t≥60 for each simulated wage history,
and estimating αtas the average of this indicator at each age. Linear interpolation yields α56
through α59.
AIME is converted into a Primary Insurance Amount (PIA) using the formula
P I At=
0.9×AIMEtif AIM Et<$5,724
$5,151.6 + 0.32 ×(AIMEt−5,724) if $5,724 ≤AI M Et<$34,500
$14,359.9 + 0.15 ×(AIMEt−34,500) if AI M Et≥$34,500
.
(26)
Social Security benefits sstdepend both upon the age at which the individual first receives
Social Security benefits and the Primary Insurance Amount. For example, pre-Earnings Test
32After age 62, nominal benefits increase at the rate of inflation.
59
benefits for a Social Security beneficiary will be equal to PIA if the individual first receives
benefits at age 65. For every year before age 65 the individual first draws benefits, benefits
are reduced by 6.67% and for every year (up until age 70) that benefit receipt is delayed,
benefits increase by 5.0%. The effects of early or late application can be modelled as changes
in AIME rather than changes in PIA, eliminating the need to include age at application as a
state variable. For example, if an individual begins drawing benefits at age 62, his adjusted
AIME must result in a PIA that is only 80% of the PIA he would have received had he first
drawn benefits at age 65. Using equation (26), this is easy to find.
Appendix E: Numerical Methods
Because the model has no closed form solution, the decision rules it generates must be
found numerically. We find the decision rules using value function iteration, starting at time
Tand working backwards to time 1. We find the time-Tdecisions by maximizing equation
(14) at each value of XT, with VT+1 =b(AT+1).This yields decision rules for time Tand the
value function VT. We next find the decision rules at time T−1 by solving equation (14),
having solved for VTalready. Continuing this backwards induction yields decision rules for
times T−2, T −3, ..., 1. The value function is directly computed at a finite number of points
within a grid, {Xi}I
i=1;33 We use linear interpolation within the grid (i.e., we take a weighted
average of the value functions of the surrounding gridpoints) and linear extrapolation outside
of the grid to evaluate the value function at points that we do not directly compute. Because
changes in assets and AIME are likely to cause larger behavioral responses at low levels of
assets and AIME, the grid is more finely discretized in this region.
At time t, wages, medical expenses and assets at time t+ 1 will be random variables.
To capture uncertainty over the persistent components of medical expenses and wages, we
convert ζtand ωt+1 into discrete Markov chains, following the approach of Tauchen (1986);
33In practice, the grid consists of: 32 asset states, Ah∈[−$55,000,$1,200,000]; 5 wage residual states,
ωi∈[−0.99,0.99]; 16 AIME states, AI M Ej∈[$4,000,$68,400]; 3 states for the persistent component of
health costs, ζk, over a normalized (unit variance) interval of [−1.5,1.5]. There are also two application states
and two health states. This requires solving the value function at 30,720 different points for ages 62-69, when
the individual is eligible to apply for benefits, at 15,630 points before age 62 (when application is not an
option) or at ages 70-71 (when we impose application), and at 7,680 points after age 71 (when we impose
retirement as well).
60
using discretization rather than quadrature greatly reduces the number of times one has to
interpolate when calculating Et(V(Xt+1)). We integrate the value function with respect to
the transitory component of medical expenses, ξt, using 5-node Gauss-Hermite quadrature
(see Judd, 1999).
Because of the fixed time cost of work and the discrete benefit application decision, the
value function need not be globally concave. This means that we cannot find a worker’s opti-
mal consumption and hours with fast hill climbing algorithms. Our approach is to discretize
the consumption and labor supply decision space and to search over this grid. Experimenting
with the fineness of the grids suggested that the grids we used produced reasonable approxi-
mations.34 In particular, increasing the number of grid points seemed to have a small effect
on the computed decision rules.
We then use the decision rules to generate simulated time series. Given the realized state
vector Xi0, individual i’s realized decisions at time 0 are found by evaluating the time-0
decision functions at Xi0. Using the transition functions given by equations (4) through (13),
we combine Xi0, the time-0 decisions, and the individual i’s time-1 shocks to get the time-1
state vector, Xi1. Continuing this forward induction yields a life cycle history for individual
i. When Xit does not lie exactly on the state grid, we use interpolation or extrapolation
to calculate the decision rules. This is true for ζtand ωtas well. While these processes
are approximated as finite Markov chains when the decision rules are found, the simulated
sequences of ζtand ωtare generated from continuous processes. This makes the simulated life
cycle profiles less sensitive to the discretization of ζtand ωtthan when ζtand ωtare drawn
from Markov chains.
Finally, to reduce the computational burden, we assume that all workers apply for Social
34The consumption grid has 100 points, and the hours grid is broken into 500-hour intervals. When this grid
is used, the consumption search at a value of the state vector Xfor time tis centered around the consumption
gridpoint that was optimal for the same value of Xat time t+1. (Recall that we solve the model backwards in
time.) If the search yields a maximizing value near the edge of the search grid, the grid is reoriented and the
search continued. We begin our search for optimal hours at the level of hours that sets the marginal rate of
substitution between consumption and leisure equal to the wage. We then try 6 different hours choices in the
neighborhood of the initial hours guess. Because of the fixed cost of work, we also evaluate the value function
at Ht= 0, searching around the consumption choice that was optimal when Ht+1 = 0. Once these values are
found, we perform a quick, “second-pass” search in a neighborhood around them.
61
Security benefits by age 70, and retire by age 72: for t≥70, Bt= 1; and for t≥72, Ht= 0.
Appendix F: Moment Conditions, Estimation Mechanics, and the Asymp-
totic Distribution of Parameter Estimates
Following Gourinchas and Parker (2002) and French (2005), we estimate the parameters
of the model in two steps. In the first step we estimate or calibrate parameters that can be
cleanly identified without explicitly using our model. For example, we estimate mortality
rates and health transitions from demographic data. As a matter of notation, we call this set
of parameters χ. In the second step, we estimate the vector of “preference” parameters, θ=
γ0, γ1, γ2, β0, β1, β2, ν, L, φP, φM, θB, κ, Cmin,preference type prediction coefficients, using the
method of simulated moments (MSM).
We assume that the “true” preference vector θ0lies in the interior of the compact set
Θ⊂R29. Our estimate, ˆ
θ, is the value of θthat minimizes the (weighted) distance between
the estimated life cycle profiles for assets, hours, and participation found in the data and the
simulated profiles generated by the model. We match 21Tmoment conditions. They are,
for each age t∈ {1, ..., T }, two asset quantiles (forming 2Tmoment conditions), labor force
participation rates conditional on asset quantile and health insurance type (9T), labor market
exit rates for each health insurance type (3T), labor force participation rates conditional on
the preference indicator described in the main text (3T), and labor force participation rates
and mean hours worked conditional upon health status (4T).
Consider first the asset quantiles. As stated in the main text, let j∈ {1,2, ..., J }index
asset quantiles, where Jis the total number of asset quantiles. Assuming that the age-
conditional distribution of assets is continuous, the πj-th age-conditional quantile of measured
assets, Qπj(Ait, t), is defined as
Pr Ait ≤Qπj(Ait, t)|t=πj.
In other words, the fraction of age-tindividuals with less than Qπjin assets is πj.Therefore,
Qπj(Ait, t) is the data analog to gπj(t;θ0, χ0), the model-predicted quantile. As is well known
62
(see, e.g., Manski, 1988, Powell, 1994 or Buchinsky, 1998; or the review in Chernozhukov and
Hansen, 2002), the preceding equation can be rewritten as a moment condition. In particular,
one can use the indicator function to rewrite the definition of the πj-th conditional quantile
as
E1{Ait ≤Qπj(Ait, t)}|t=πj.(27)
If the model is true then the data quantile in equation (27) can be replaced by the model
quantile, and equation (27) can be rewritten as:
E1{Ait ≤gπj(t;θ0, χ0)} − πj|t= 0, j ∈ {1,2, ..., J}, t ∈ {1, ..., T }.(28)
Since J= 2, equation (28) generates 2Tmoment conditions. We compute gπj(t;θ, χ) by
finding the model’s decision rules for consumption, hours, and benefit application, using the
decision rules to generate artificial histories for many different simulated individuals, and
finding the quantiles of the collected histories.
Equation (28) is a departure from the usual practice of minimizing a sum of weighted
absolute errors in quantile estimation. The quantile restrictions just described, however, are
part of a larger set of moment conditions, which means that we can no longer estimate θby
minimizing weighted absolute errors. Our approach to handling multiple quantiles is similar
to the minimum distance framework used by Epple and Seig (1999).35
The next set of moment conditions uses the quantile-conditional means of labor force
participation. Let Pj(HI, t;θ0, χ0) denote the model’s prediction of labor force participation
given asset quantile interval j, health insurance type HI, and age t. If the model is true,
Pj(HI, t;θ0, χ0) should equal the conditional participation rates found in the data:
Pj(HI, t;θ0, χ0) = E[Pit |HI, t, gπj−1(t;θ0, χ0)≤Ait ≤gπj(t;θ0, χ0)],(29)
35Buchinsky (1998) shows that one could include the first-order conditions from multiple absolute value
minimization problems in the moment set. However, his approach involves finding the gradient of gπj(t;θ, χ)
at each step of the minimization search.
63
with π0= 0 and πJ+1 = 1. Using indicator function notation, we can convert this conditional
moment equation into an unconditional one:
E([Pit −Pj(HI, t;θ0, χ0)] ×1{HIit =HI}
×1{gπj−1(t;θ0, χ0)≤Ait ≤gπj(t;θ0, χ0)} | t) = 0,(30)
for j∈ {1,2, ..., J + 1}, HI ∈ {none, retiree, tied}, t ∈ {1, ..., T }. Note that gπ0(t)≡ −∞
and gπJ+1 (t)≡ ∞. With 2 quantiles (generating 3 quantile-conditional means) and 3 health
insurance types, equation (29) generates 9Tmoment conditions.
The HRS asks workers about their willingness to work and/or their expectations about
working in the future. We combine the answers to these questions into a time-invariant index,
pref ∈ {high, low, out}. Because labor force participation differs significantly across values
of pref , and because pref significantly improves reduced-form predictions of employment,
we interpret this index as a measure of otherwise unobserved preferences toward work. This
leads to the following moment condition:
EPit −P(pref, t;θ0, χ0)|prefi=pref , t= 0,(31)
for t∈ {1, ..., T }, pref ∈ {0,1,2}. Equation (31) yields 3Tmoment conditions, which are
converted into unconditional moment equations with indicator functions.
We also match exit rates for each health insurance category. Let E X (H I, t;θ0, χ0) denote
the fraction of time-t−1 workers predicted to leave the labor market at time t. The associated
moment condition is
E[1 −Pit]−EX(H I, t;θ0, χ0|HIi,60 =H I, Pi,t−1= 1, t= 0,(32)
[1 −Pit]6=P r ob(P= 0|Pt−1= 1)] for HI ∈ {none, retiree, tied}, t ∈ {1, ..., T }. Equation
(32) generates 3Tmoment conditions, which are converted into unconditional moments as
64
well.36
Finally, consider health-conditional hours and participation. Let ln H(M, t;θ0, χ0) and
P(M, t;θ0, χ0) denote the conditional expectation functions for hours (when working) and
participation generated by the model for workers with health status M; let ln Hit and Pit
denote measured hours and participation. The moment conditions are
Eln Hit −ln H(M, t;θ0, χ0)|Pit >0, Mit =M , t= 0,(33)
EPit −P(M, t;θ0, χ0)|Mit =M , t= 0,(34)
for t∈ {1, ..., T }, M ∈ {0,1}. Equations (33) and (34), once again converted into uncondi-
tional form, yield 4Tmoment conditions, for a grand total of 21Tmoment conditions.
Combining all the moment conditions described here is straightforward: we simply stack
the moment conditions and estimate jointly.
Suppose we have a data set of Iindependent individuals that are each observed for T
periods. Let ϕ(θ;χ0) denote the 21T-element vector of moment conditions that was described
in the main text and immediately above, and let ˆϕI(.) denote its sample analog. Note that
we can extend our results to an unbalanced panel, as we must do in the empirical work, by
simply allowing some of the individual’s contributions to ϕ(.) to be “missing”, as in French
and Jones (2004a). Letting c
WIdenote a 21T×21Tweighting matrix, the MSM estimator ˆ
θ
is given by
arg min
θ
I
1 + τˆϕI(θ, χ0)′c
WIˆϕI(θ, χ0),(35)
where τis the ratio of the number of observations to the number of simulated observations.
To find the solution to equation (35), we proceed as follows:
1. We aggregate the sample data into life cycle profiles for hours, participation, exit rates
36Because exit rates apply only to those working in the previous period, they normally do not contain the
same information as participation rates. However, this is not the case for workers with tied coverage, as
a worker stays in the tied category only as long as he continues to work. To remove this redundancy, the
exit rates in equation (32) are conditioned on the individual’s age-60 health insurance coverage, while the
participation rates in equation (29) are conditioned on the individual’s current coverage.
65
and assets.
2. Using the same data used to estimate the profiles, we generate an initial distribution
for health, health insurance status, wages, medical expenses, AIME, and assets. See
Appendix G for details. We also use these data to estimate many of the parameters
contained in the belief vector χ, although we calibrate other parameters. The initial
distribution also includes preference type, assigned using our type prediction equation.
3. Using χ, we generate matrices of random health, wage, mortality and medical expense
shocks. The matrices hold shocks for 40,000 simulated individuals.
4. We compute the decision rules for an initial guess of the parameter vector θ, using χ
and the numerical methods described in Appendix E.
5. We simulate profiles for the decision variables. Each simulated individual receives a
draw of preference type, assets, health, wages and medical expenses from the initial
distribution, and is assigned one of the simulated sequences of health, wage and health
cost shocks. With the initial distributions and the sequence of shocks, we then use
the decision rules to generate that person’s decisions over the life cycle. Each period’s
decisions determine the conditional distribution of the next period’s states, and the
simulated shocks pin the states down exactly.
6. We aggregate the simulated data into life cycle profiles.37
7. We compute moment conditions, i.e., we find the distance between the simulated and
true profiles, as described in equation (35).
8. We pick a new value of θ, update the simulated distribution of preference types, and
repeat steps 4-7 until we find the value of θthat minimize that minimizes the distance
37Because the moments we match include asset quantiles and asset-quantile-conditional participation rates,
measurement error could affect our results. In earlier drafts of this paper (French and Jones, 2004b) we added
measurement error to the simulated asset histories. Adding measurement error, however, had little effect on
either the preference parameter estimates or policy experiments. For the moments we fit, the measurement
error is largely averaged out.
66
between the true data and the simulated data. This vector of parameter values, ˆ
θ, is
our estimated value of θ0.38
Under the regularity conditions stated in Pakes and Pollard (1989) and Duffie and Single-
ton (1993), the MSM estimator ˆ
θis both consistent and asymptotically normally distributed:
√I(ˆ
θ−θ0) N(0,V),
with the variance-covariance matrix Vgiven by
V= (1 + τ)(D′WD)−1D′WSWD(D′WD)−1,
where: Sis the variance-covariance matrix of the data;
D=∂ϕ(θ, χ0)
∂θ′θ=θ0
(36)
is the 21T×29 Jacobian matrix of the population moment vector; and W= plim→∞{c
WI}.
Moreover, Newey (1985) shows that if the model is properly specified,
I
1 + τˆϕI(ˆ
θ, χ0)′R−1ˆϕI(ˆ
θ, χ0) χ2
21T−29,
where R−1is the generalized inverse of
R=PSP,
P=I−D(D′WD)−1D′W.
The asymptotically efficient weighting matrix arises when c
WIconverges to S−1, the
inverse of the variance-covariance matrix of the data. When W=S−1,Vsimplifies to
38Because the GMM criterion function is discontinuous, we search over the parameter space using a Simplex
algorithm written by Honore and Kyriazidou. It usually takes around 2 weeks to estimate the model on a
20-node cluster, with each iteration (of steps 4-7) taking around 30 minutes.
67
(1 + τ)(D′S−1D)−1, and Ris replaced with S. But even though the optimal weighting
matrix is asymptotically efficient, it can be severely biased in small samples. (See, for example,
Altonji and Segal, 1996.) We thus use a “diagonal” weighting matrix, as suggested by Pischke
(1995). The diagonal weighting scheme uses the inverse of the matrix that is the same as S
along the diagonal and has zeros off the diagonal of the matrix.
We estimate D,Sand Wwith their sample analogs. For example, our estimate of
Sis the 21T×21Testimated variance-covariance matrix of the sample data. That is, a
typical diagonal element of b
SIis the variance estimate 1
IPI
i=1[1{Ait ≤Qπj(Ait, t)} − πj]2,
while a typical off-diagonal element is a covariance. When estimating preferences, we use
sample statistics, so that Qπj(Ait, t) is replaced with the sample quantile b
Qπj(Ait, t). When
computing the chi-square statistic and the standard errors, we use model predictions, so
that Qπjis replaced with its simulated counterpart, gπj(t;ˆ
θ, ˆχ). Covariances between asset
quantiles and hours and labor force participation are also simple to compute.
The gradient in equation (36) is straightforward to estimate for hours worked and par-
ticipation conditional upon age and health status; we merely take numerical derivatives of
ˆϕI(.). However, in the case of the asset quantiles and labor force participation, discontinu-
ities make the function ˆϕI(.) non-differentiable at certain data points. Therefore, our results
do not follow from the standard GMM approach, but rather the approach for non-smooth
functions described in Pakes and Pollard (1989), Newey and McFadden (1994, section 7) and
Powell (1994). We find the asset quantile component of Dby rewriting equation (28) as
F(gπj(t;θ0, χ0)|t)−πj= 0,
where F(gπj(t;θ0, χ0)|t) is the c.d.f. of time-tassets evaluated at the πj-th quantile. Differ-
entiating this equation yields
Djt =f(gπj(t;θ0, χ0)|t)∂gπj(t;θ0, χ0)
∂θ′,(37)
where Djt is the row of Dcorresponding to the πj-th quantile at year t. In practice we find
68
f(gπj(t;θ0, χ0)|t), the p.d.f. of time-tassets evaluated at the πj-th quantile, with a kernel
density estimator. We use a kernel estimator for GAUSS written by Ruud Koning.
To find the component of the matrix Dfor the asset-conditional labor force participation
rates, it is helpful to write equation (30) as
Pr(HIt−1=HI)×Zgπj(t;θ0,χ0)
gπj−1(t;θ0,χ0)E(Pit|Ait, HI, t)−Pj(HI, t;θ0, χ0)f(Ait|H I, t)dAit = 0,
which implies that
Djt =−Pr(gπj−1(t;θ0, χ0)≤Ait ≤gπj(t;θ0, χ0)|H I, t)∂Pj(H I, t;θ0, χ0)
∂θ′
+ [E(Pit|gπj(t;θ0, χ0), H I, t)−Pj(H I, t;θ0, χ0)]f(gπj(t;θ0, χ0)|H I, t)∂gπj(t;θ0, χ0)
∂θ′
−[E(Pit|gπj−1(t;θ0, χ0), H I, t)−Pj(H I, t;θ0, χ0)]f(gπj−1(t;θ0, χ0)|H I, t)∂gπj−1(t;θ0, χ0)
∂θ′
×Pr(HIt−1=HI),(38)
with f(gπ0(t;θ0, χ0)|HI, t)∂gπ0(t;θ0,χ0)
∂θ′=f(gπJ+1 (t;θ0, χ0)|H I, t)∂ gπJ+1 (t;θ0,χ0)
∂θ′≡0.
Appendix G: Data and Initial Joint Distribution of the State Variables
Our data are drawn from the HRS, a sample of non-institutionalized individuals aged 51-
61 in 1992. The HRS surveys individuals every two years; we have 7 waves of data covering
the period 1992-2004. We use men in the analysis.
The variables used in our analysis are constructed as follows. Hours of work are the
product of usual hours per week and usual weeks per year. To compute hourly wages, the
respondent is asked about how they are paid, how often they are paid, and how much they
are paid. If the worker is salaried, for example, annual earnings are the product of pay per
period and the number of pay periods per year. The wage is then annual earnings divided by
annual hours. If the worker is hourly, we use his reported hourly wage. We treat a worker’s
hours for the non-survey (e.g. 1993) years as missing.
For survey years the individual is considered in the labor force if he reports working over
69
300 hours per year. The HRS also asks respondents retrospective questions about their work
history. Because we are particularly interested in labor force participation, we use the work
history to construct a measure of whether the individual worked in non-survey years. For
example, if an individual withdraws from the labor force between 1992 and 1994, we use the
1994 interview to infer whether the individual was working in 1993.
The HRS has a comprehensive asset measure. It includes the value of housing, other real
estate, autos, liquid assets (which includes money market accounts, savings accounts, T-bills,
etc.), IRAs, stocks, business wealth, bonds, and “other” assets, less the value of debts. For
non-survey years, we assume that assets take on the value reported in the preceding year.
This implies, for example, that we use the 1992 asset level as a proxy for the 1993 asset level.
Given that wealth changes rather slowly over time, these imputations should not severely
bias our results.
To measure health status we use responses to the question: “would you say that your
health is excellent, very good, good, fair, or poor?” We consider the individual in bad health
if he responds “fair” or “poor”, and consider him in good health otherwise.39 We treat the
health status for non-survey years as missing. Appendix H describes how we construct the
health insurance indicator.
We use Social Security Administration earnings histories to construct AIME. Approxi-
mately 74% of our sample released their Social Security Number to the HRS, which allowed
them to be linked to their Social Security earnings histories. For those who did not release
their histories, we use the procedure described below to impute AIME as a function of assets,
health status, health insurance type, labor force participation, and pension type.
The HRS collects pension data from both workers and employers. The HRS asks indi-
viduals about their earnings, tenure, contributions to defined contribution (DC) plans, and
their employers. HRS researchers then ask employers about the pension plans they offer
their employees. If the employer offers different plans to different employees, the employee is
matched to the plan based on other factors, such as union status. Given tenure, earnings, DC
39Bound et al. (2003) consider a more detailed measure of health status.
70
contributions, and pension plan descriptions, it is then possible to calculate pension wealth
for each individual who reports the firm he works for. Following Scholz et al. (2006), we use
firm reports of defined benefit (DB) pension wealth and individual reports of DC pension
wealth if they exist. If not, we use firm-reported DC wealth and impute DB wealth as a
function of wages, hours, tenure, health insurance type, whether the respondent also has a
DC plan, health status, age, assets, industry and occupation. We discuss the imputation
procedure below.
Workers are asked about two different jobs: (1) their current job if working or last job if
not working; (2) the job preceding the one listed in part 1, if the individual worked at that
job for over 5 years. Both of these jobs are included in our measure of pension wealth. Below
we give descriptives for our estimation sample (born 1931-1935) and validation sample (born
1936-1941). 41% of our estimation sample [and 52% of our validation sample] are currently
working and have a pension (of which 56% [57% for the validation sample] have firm-based
pension details), 6% [5%] are not working, and had a pension on their last job (of which 62%
[62%] have firm-based pension details), and 32% [32%] of all individuals had a pension on
another job (of which 35% [29%] have firm-based pension details).
We dropped respondents for the following reasons. First, we drop all individuals who
spent over 5 years working for an employer who did not contribute to Social Security. These
individuals usually work for state governments. We drop these people because they often
have very little in the way of Social Security wealth, but a great deal of pension wealth, a
type of heterogeneity our model is not well suited to handle. Second, we drop respondents
with missing information on health insurance, labor force participation, hours, and assets.
When estimating labor force participation by asset quantile and health insurance for those
born 1931-35 for the estimation sample [and 1936-41 for the validation sample], we begin
with 19,547 [30,890] person year observations. We lose 3,139 [5,227] observations because of
missing participation, 1,930 [2,162] observations who worked over 5 years for firms that did not
contribute to Social Security, 150 [384] observations due to missing wave 1 participation, and
1,967 [2,883] observations due to missing health insurance data observations due to missing
71
asset data. In the end, from a potential sample of 19,547 [30,890] person-year observations
for those between ages 51 and 69, we keep 11,773 [19,407] observations.
To generate the initial joint distribution of assets, wages, AIME, pensions, participation,
health insurance, health status and health costs, we draw random vectors (i.e., random draws
of individuals) from the empirical joint distribution of these variables for individuals aged 57-
61 in 1992, or 1,701 observations. We drop observations with missing data on labor force
participation, health status, insurance, assets, and age. We impute values for observations
with missing wages, health costs, pension wealth, and AIME.
To impute these missing variables, we follow David et al. (1986) and Little (1988) and use
the following predictive mean matching regression approach. First, we regress the variable of
interest yi(e.g., pension wealth) on a vector of observable variables xi,yi=xiβ+ǫi. Second,
we generate a predicted value ˆyi=xiˆ
βand generate a residual εi=yi−ˆyifor every member
of the sample. Third, we split the predicted value ˆyiinto deciles. Fourth, we impute a value
of yiby taking a residual for a random individual jwith a value of ˆyjthat is in the same
decile of the distribution as is ˆyi. Thus the imputed value of yiis ˆyi+εj.
As David et al. (1986) point out, our imputation approach is equivalent to hot-decking
when the “x” variables are discretized and include a full set of interactions. The advantages
of the above approach over hot-decking are two-fold. First, many of the “x” variables are
continuous, and it seems unwise to discretize them. Second, we have very few observations for
some variables (such as pension wealth on past jobs), and hot-decking is very data-intensive.
Only a small number of “x” variables are needed to generate a large number of hot-decking
cells, as hot-decking uses a full set of interactions. We found that the interaction terms
are relatively unimportant, but adding extra variables were very important for improving
goodness of fit when imputing pension wealth.
If someone is not working (and thus does not report a wage), we use the wage on their
last job as a proxy for their current wage if it exists, and otherwise impute the log wage
as a function of assets, health, health insurance type, labor force participation, AIME, and
quarters of covered work. We predict medical expenses using assets, health, health insurance
72
type, labor force participation, AIME, and quarters of covered earnings.
Lastly, we must infer the persistent component of the health cost residual from health
costs. Given an initial distribution of health costs, we construct ζt, the persistent health cost
component, by first finding the normalized log deviation ψt, as described in equations (7)
and (10), and then applying standard projection formulae to impute ζtfrom ψt.
Appendix H: Measurement of Health Insurance Type
Much of the identification in this paper comes from differences in medical expenses and job
exit rates between those with tied health insurance coverage and those with retiree coverage.
Unfortunately, identifying these health insurance types is not straightforward. The HRS has
rather detailed questions about health insurance, but the questions asked vary from wave to
wave. Moreover, in no wave are the questions asked consistent with our definitions of tied
or retiree coverage. Nevertheless, estimated health insurance specific job exit rates are not
very sensitive of our definition of health insurance, as we show below.
In all of the HRS waves (but not AHEAD waves 1 and 2), the respondent is asked whether
he has insurance provided by a current or past employer or union, or a spouse’s current or
past employer or union. If he responds yes to this question, we code him as having either
retiree or tied coverage. We assume that this question is answered accurately, so that there
is no measurement error when individual reports that his insurance category is none. All of
the measurement error problems arise when we allocate individuals with employer-provided
coverage between the retiree and tied categories.
If an individual has employer-provided coverage in waves 1 and 2 he is asked “Is this
health insurance available to people who retire?” In waves 3, 4, and 5 the analogous question
is “If you left your current employer now, could you continue this health insurance coverage
up to the age of 65?”. For individuals younger than 65, the question asked in waves 3
through 5 is a more accurate measure of whether the individual has retiree coverage. In
particular, a “yes” response in waves 1 and 2 might mean only that the individual could
acquire COBRA coverage if he left his job, as opposed to full, retiree coverage. Thus the
fraction of individuals younger than 65 who report that they have employer-provided health
73
insurance but who answer “no” to the follow-up question roughly doubles between waves 2
and 3. On the other hand, for those older than 65, the question used in waves 3, 4, and 5 is
meaningless.
Our preferred approach to the misreporting problem in waves 1 and 2 is to assume that a
“yes” response in these waves indicates retiree coverage. It is possible, however, to estimate
the probability of mismeasurement in these waves. Consider first the problem of distinguish-
ing the retiree and tied types for those younger than 65. As a matter of notation, let HI
denote an individual’s actual health insurance coverage, and let HI∗denote the measure of
coverage generated by the HRS questions. To simplify the notation, assume that the individ-
ual is known to have employer-provided coverage—HI =tied or H I =retiree—so that we
can drop the conditioning statement in the analysis below. Recall that many individuals who
report retiree coverage in waves 1 and 2 likely have tied coverage. We are therefore interested
in the misreporting probability Pr(HI =tied|HI∗=retiree, wv < 3, t < 65), where wv
denotes HRS wave and tdenotes age. To find this quantity, note first that by the law of total
probability:
Pr(HI =tied|wv < 3, t < 65) =
Pr(HI =tied|HI∗=tied, wv < 3, t < 65) ×Pr(HI∗=tied|wv < 3, t < 65) +
Pr(HI =tied|HI∗=retiree, wv < 3, t < 65) ×Pr(HI∗=retiree|wv < 3, t < 65).(39)
Now assume that all reports of tied coverage in waves 1 and 2 are true:
Pr(HI =tied|HI∗=tied, wv < 3, t < 65) = 1.
Assume further that for individuals younger than 65 there is no measurement error in waves
74
3-5, and that the share of individuals with tied coverage is constant across waves:
Pr(HI =tied|wv < 3, t < 65) = Pr(HI =tied|wv ≥3, t < 65)
= Pr(HI∗=tied|wv ≥3, t < 65).
Inserting these assumptions into equation (39) and rearranging yields the mismeasurement
probability:
Pr(HI =tied|HI∗=retiree, wv < 3, t < 65)
=Pr(HI∗=tied|wv ≥3, t < 65) −Pr(H I ∗=tied|wv < 3, t < 65)
Pr(HI∗=retiree|wv < 3, t < 65)
=Pr(HI∗=retiree|wv < 3, t < 65) −Pr(H I ∗=retiree|wv ≥3, t < 65)
Pr(HI∗=retiree|wv < 3, t < 65) .(40)
To estimate the mismeasurement in waves 1 and 2 for those aged 65 and older, we make
the same assumptions as for those who are younger than 65. We assume that all reports of
tied health insurance are true and the probability of having tied health insurance given a
report of retiree insurance is the same as for individuals in waves 1 and 2 who are younger
than 65. We can then use equation (40) to estimate this probability.
The second misreporting problem is that the “follow-up” question in waves 3 through 5
is completely uninformative for those older than 65. Our strategy for handling this problem
is to treat the first observed health insurance status for these individuals as their health
insurance status throughout their lives. Since we assume that reports of tied coverage are
accurate, older individuals reporting tied coverage in waves 1 and 2 are assumed to receive
tied coverage in waves 3 through 5. (Recall, however, that if an individual with tied coverage
drops out of the labor market, his health insurance is none for the rest of his life.) For older
individuals reporting retiree coverage in waves 1 and 2, we assume that the misreporting
probability—when we choose to account for it—is the same throughout all waves. (Recall
that our preferred assumption is to assume that a “yes” response to the follow-up question
in waves 1 and 2 indicates retiree coverage.)
75
A related problem is that individuals’ health insurance reports often change across waves,
in large part because of the misreporting problems just described. Our preferred approach
for handling this problem is classify individuals on the basis of their first observed health
insurance report. We also consider the approach of classifying individuals on the basis of
their report from the previous wave, analogous to the practice of using lagged observations
as instruments for mismeasured variables in an instrumental variables regression.
Figure 9 shows how our treatment of these measurement problems affects measured job
exit rates. The top two graphs in Figure 9 do not adjust for the measurement error problems
described immediately above. The bottom two graphs account for the measurement error
problems, using the approached described by equation 40. The two graphs in the left column
use the first observed health insurance report whereas the graphs in the right column use
the previous period’s health insurance report. Figure 9 shows that the profiles are not very
sensitive to these changes. Those with retiree coverage tend to exit the labor market at age
62, whereas those with tied and no coverage tend to exit the labor market at age 65.
Another, more conceptual, problem is that the HRS has information on health insurance
outcomes, not choices. This is an important problem for individuals out of the labor force with
no health insurance; it is unclear whether these individuals could have purchased COBRA
coverage but elected not to do so.40 To circumvent this problem we use health insurance in
the previous wave and the transitions implied by equation (10) to predict health insurance
options. For example, if an individual has health insurance that is tied to his job and was
working in the previous wave, that individual’s choice set is tied health insurance and working
or COBRA insurance and not working.41
40For example, the model predicts that all HRS respondents younger than 65 who report having tied health
insurance two years before the survey date, work one year before the survey date, and are not currently
working should report having COBRA coverage on the survey date. However, 19% of them report having no
health insurance.
41Note that this particular assumption implies that 100% of those eligible for COBRA take up coverage. In
practice, only about 2
3of those eligible take up coverage (Gruber and Madrian, 1996). In order to determine
whether our failure to model the COBRA decision is important, we shut down the COBRA option (imposed
a 0% take-up rate) and re-ran the model. Eliminating COBRA had virtually no effect on labor supply.
76
0 .05 .1 .15 .2
58 60 62 64 66 68
age
tied health insurance retiree health insurance coverage
no health insurance
exit rate
no measurement error corrections, use first observed health insurance
Robustness Check:
0 .05 .1 .15 .2
58 60 62 64 66 68
age
tied health insurance retiree health insurance coverage
no health insurance
exit rate
no measurement error corrections, last period’s health insurance
Robustness Check:
0 .05 .1 .15 .2 .25
58 60 62 64 66 68
age
tied health insurance retiree health insurance coverage
no health insurance
exit rate
measurement error corrections and first observed health insurance
Robustness Check:
0 .05 .1 .15 .2 .25
58 60 62 64 66 68
age
tied health insurance retiree health insurance coverage
no health insurance
exit rate
measurement error corrections, last period’s health insurance
Robustness Check:
Figure 9: Job Exit Rates Using Different Measures of Health Insurance Type
77
0 .05 .1 .15 .2
58 60 62 64 66 68
age
tied health insurance retiree health insurance coverage
no health insurance
exit rate
no measurement error corrections, use first observed health insurance
Robustness Check:
.05 .1 .15 .2 .25
58 60 62 64 66 68
age
tied health insurance retiree health insurance coverage
no health insurance
exit rate
no measurement error corrections, first observed health insurance
Robustness Check: exclude the self−employed
Figure 10: The Effect of Dropping the Self-Employed on Job Exit Rates
78
Another measurement issue is the treatment of the self-employed. Figure 10 shows the
importance of dropping the self-employed on job exit rates. The top panel treats the self-
employed as working, whereas the bottom panel excludes the self-employed. The main differ-
ence caused by dropping the self-employed is that those with no health insurance have much
higher job exit rates at age 65. Nevertheless, those with retiree coverage are still most likely
to exit at age 62 and those with tied and no health insurance are most likely to exit at age 65.
Our preferred specification, which we use in the analysis, is to include the self-employed,
to use the first observed health insurance report, and to not use the measurement error
corrections.
Because agents in our model are forward-looking, we need to know the health-insurance-
conditional process for health costs facing the very old. The data we use to estimate health
costs for those over age 70 comes from the Assets and Health Dynamics of the Oldest Old
survey. French and Jones (2004a) discuss some of the details of the survey, as well as some of
our coding decisions. The main problem with the AHEAD is that there is no question asked
of respondents about whether they would lose their health insurance if they left their job, so
it is not straightforward to distinguish those who have retiree coverage from those with tied
coverage. In order to distinguish these two groups, we do the following. If the individual exits
the labor market during our sample, and has employer-provided health insurance at least one
full year after exiting the labor market, we assume that individual has retiree coverage. All
individuals who have employer-provided coverage when first observed, but do not meet this
criteria for having retiree coverage, are assumed to have tied coverage.
Appendix I: The Health Cost Model
Recall from equation (7) that health status, health insurance type, labor force partici-
pation and age affect health costs through the mean shifter hc(.) and the variance shifter
σ(.). Health status enters hc(.) and σ(.) through 0-1 indicators for bad health, and age enters
through linear trends. On the other hand, the effects of Medicare eligibility, health insurance
and labor force participation are almost completely unrestricted, in that we allow for an
almost complete set of interactions between these variables. This implies, for example, that
79
mean health costs are given by
hc(Mt, HIt, t, Pt) = γ0Mt+γ1t+X
h∈HI X
P∈{0,1}X
a∈{t<65,t≥65}
γh,P,a.
The one restriction we impose is that γnone,0,a =γnone,1,a for both values of a, i.e., participa-
tion does not affect health care costs if the individual does not have insurance. This implies
that there are 10 γh,P,a parameters, for a total of 12 parameters apiece in the hc(.) and the
σ(.) functions.
To estimate this model, we group the data into 10-year-age (55-64, 65-74, 75-84) ×health
status ×health insurance ×participation cells. For each of these 60 cells, we calculate both
the mean and the 95th percentile of medical expenses. We estimate the model by finding
the parameter values that best fit this 120-moment collection. One complication is that the
medical expense model we estimate is an annual model, whereas our data are for medical
expenses over two-year intervals. To overcome this problem, we first simulate a panel of
medical expense data at the one-year frequency, using the dynamic parameters from French
and Jones (2004a) shown in Table 2 of this paper and the empirical age distribution. We
then aggregate the simulated data to the two-year frequency; the means and 95th percentiles
of this aggregated data are comparable to the means and 95th percentiles in the HRS. Our
approach is similar to the one used by French and Jones (2004a), who provide a detailed
description.
Appendix J: The Preference Index
We construct the preference index for each member of the sample using the wave 1 vari-
ables V3319, V5009, V9063. All three variables are self-reported responses to questions about
preferences for leisure and work. In V3319 respondents were asked if they agreed with the
statement (if they were working): “Even if I didn’t need the money, I would probably keep
on working.” In V5009 they were asked: “When you think about the time when you [and
your (husband/wife/partner)] will (completely) retire, are you looking forward to it, are you
80
uneasy about it, or what? In V9063 they were asked (if they were working): “On a scale
where 0 equals dislike a great deal, 10 equals enjoy a great deal, and 5 equals neither like nor
dislike, how much do you enjoy your job?”
Because it is computationally intensive to estimate the parameters of the type probability
equations in our method of simulated moments approach, we combine these three variables
into a single index that is simpler to use. To construct this index, we regress labor force
participation on current state variables (age, wages, assets, health, etc.), squares and interac-
tions of these terms, the wave 1 variables V3319, V5009, V9063, and indicators for whether
these variables are missing. We then partition the xˆ
βmatrix from this regression into: x1ˆ
β1,
where the x1matrix includes V3319, V5009, V9063, and indicators for these variables being
missing; and x2ˆ
β2, where the x2matrix includes all other variables. Our preference index is
x1ˆ
β1.
Individuals who were not working in 1992 were not asked any of the preference questions,
and are not included in the construction of our index. Because there is no variation in
participation in 1992, we estimate the regression models with participation data from 1998-
2004.
Finally, we discretize the index into three values: out, for those not employed in 1992; low,
for workers with an index in the bottom half of the distribution; and high for the remainder.
Appendix K: Additional Parameter Estimates
We assume that the probability of belonging to a particular type follows a multinomial
logit function. Table 13 shows the coefficients of the preference type prediction equation.
Table 14 shows the parameter estimates for the robustness checks. In the no-saving case,
shown in column (2), βand θBare both very weakly identified. We therefore follow Rust
and Phelan and Blau and Gilleskie by fixing β, in this case to its baseline values of 0.83,
1.12, and 0.97 (for types 0, 1 and 2, respectively). Similarly, we fix θBto zero. Since the
asset distribution is degenerate in this no-saving case, we no longer match asset quantiles or
quantile-conditional participation rates, matching instead participation rates for each health
insurance category.
81
Preference Type 1 Preference Type 2
Parameters Std. Errors Parameters Std. Errors
(1) (2) (3) (4)
Preference Index = out -4.51 0.69 -5.22 18.58
Preference Index = low 3.97 7.25 0.62 4.94
Preference Index = high -0.07 0.46 5.55 0.82
No HI Coverage 1.69 0.67 -4.17 1.28
Retiree Coverage 0.23 0.33 -2.48 0.60
Initial Wages†2.64 0.46 -0.85 0.45
Assets/Wages†-0.44 0.45 -0.52 0.23
Assets†×(No HI Coverage) 0.20 0.30 1.85 0.82
†Variables expressed as fraction of average
Table 13: Preference type prediction coefficients
Appendix L: Illiquid Housing
Although allowing for no savings seems extreme, it has often been argued (e.g., Rust and
Phelan, 1997, Gustman and Steinmeier, 2005) that housing equity is considerably less liquid
than financial assets. Since housing comprises a significant proportion of most individuals’
assets, its illiquidity would greatly weaken their ability to self-insure through saving.
To account for this possibility, we re-estimate the model using “liquid assets”, which ex-
cludes housing and business wealth.42 The third column of Table 14 contains the revised
parameter estimates. The most notable changes are: (1) the coefficient of relative risk aver-
sion, ν, drops from 7.5 to 6.5; (2) the type-1 value of β, the discount factor, drops from 1.115
to 0.858; (3) the consumption floor, cmin , increases from $4,100 to $6,300. All three changes—
lower risk aversion, lower patience and more government protection—help the model fit the
bottom third of liquid asset holdings, which averages less than $5,000.
Column (3) of Table 9 shows participation when housing assets are illiquid. The most no-
table result is that simulated participation drops markedly at age 62. Several authors (Kahn,
1988, Rust and Phelan, 1997, and Gustman and Steinmeier, 2005) have argued that, because
they cannot borrow against their Social Security benefits, many workers that would other-
42A complete analysis of illiquid housing would require us to treat housing as an additional state variable,
with its own accumulation dynamics, and to impute the consumption services provided by owner-occupied
housing. This is not computationally feasible. In this paper, we simply allow these effects to be captured in
the preference parameters.
82
No Homogeneous Illiquid
Baseline Saving Preferences Housing
Parameter and Definition (1) (2) (3) (4)
γ: consumption weight
Type 0 0.438 0.239 NA 0.403
(0.080) (9.760) (0.113)
Type 1 0.620 0.548 0.700 0.695
(0.011) (0.006) (0.007) (0.006)
Type 2 0.907 0.928 NA 0.911
(0.028) (0.031) (0.026)
β: time discount factor
Type 0 0.828 0.828 NA 0.821
(0.072) (NA) (0.074)
Type 1 1.115 1.115 0.971 0.858
(0.016) (NA) (0.010) (0.012)
Type 2 0.971 0.971 NA 0.957
(0.077) (NA) (0.067)
ν: coefficient of relative 7.49 6.61 3.93 6.46
risk aversion, utility (0.421) (0.166) (0.202) (0.196)
L: leisure endowment, 3,863 4,052 4,101 3,960
in hours (51.9) (26.2) (34.3) (47.6)
φP: fixed cost of work, 835 1,146 1,196 904
in hours (27.4) (30.5) (16.3) (20.4)
φM: hours of leisure lost, 445 432 432 412
bad health (38.8) (29.6) (28.1) (12.9)
θB: bequest weight†0.0320 0.00 0.0241 0.0338
(0.0009) (NA) (0.0005) (0.0022)
κ: bequest shifter, 449 0.00 509 460
in thousands (31.7) (NA) (12.6) (32.3)
cmin: consumption floor 4,118 3,517 5,386 6,275
(159.5) (159.1) (141.1) (215.7)
χ2statistic 1,677 1,211 1,009 3,081
Degrees of freedom 181 96 171 181
Diagonal weighting matrix used in calculations. See Appendix F for details.
Standard errors in parentheses.
†Parameter expressed as marginal propensity to consume out of
final-period wealth.
Table 14: Robustness Checks
83
wise retire earlier cannot fund their retirement before age 62. Making housing illiquid, along
with the large decrease in the estimated value of β, strengthens this effect. The underlying
asset-conditional profiles reveal that the participation drop is most pronounced for simulated
workers in the bottom 1/3rd of the asset distribution. This contrasts with the data, where
the age-62 exit rates vary across the asset quantiles to a much smaller extent.
We find that in this framework delaying the Medicare eligibility age has a bigger effect
than delaying the Social Security normal retirement age. Shifting forward the Medicare
eligibility age to 67 increases total years in the labor force by 0.11 years (versus the 0.07
years for the baseline specification that we presented in Table 7).
84
1
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