JOB DISPLACEMENT AND MORTALITY: AN ANALYSISUSING ADMINISTRATIVE DATA∗DANIEL SULLIVAN AND TILL VON WACHTER

I. INTRODUCTION
A growing literature shows that displaced workers—
individuals who lose their jobs as part of plant closings, mass
layoffs, and other firm-level employment reductions—tend to experience significant long-term earnings losses as well as decreased
job stability, lower employment rates, earlier retirement, lower
consumption, and decreased health insurance coverage.1 In this∗ We would like to thank Doug Almond, Marianne Bertrand, David Card,
Janet Currie, Eric French, Ed Glaeser, Michael Greenstone, Larry Katz, Wojciech
Kopczuk, David Lee, Adriana Lleras-Muney, Claudio Lucifora, Chris Paxson, Jesse
Rothstein, Chris Ruhm, Jon Skinner, and two referees for helpful suggestions.
Seminar participants at the SOLE 2006 Meetings, the NBER 2006 Summer
Institute, the Milan Mills Workshop, London School of Economics, University
College London, Pompeu Fabra Barcelona, Boston University, the University of
Illinois Urbana–Champaign, Harris School, Harvard University, Columbia University Social Work, the University of California San Diego, the University ofCalifornia Santa Barbara, the University of California Berkeley, Tufts University,
Oxford University, UCL Epidemiology, Princeton University, Prague CERGEI,
the Catholic University of Milan, the University of North Carolina Greensboro, the University of Texas Houston, Texas A&M University, RWI Essen, theFederal Reserve Bank of Chicago, and Columbia University provided helpful comments. Alice Henriques and Phil Doctor provided excellent research assistance.
Daniel.Sullivan@chi.frb.org, vw2112@columbia.edu.
1. See, for example, Ruhm (1991); Olson (1992); Jacobson, LaLonde, andSullivan (1993); Gruber (1997); Stevens (1997); Chan and Stevens (2001); andFarber (2003). The Bureau of Labor Statistics defines displaced workers to be
individuals who lose their main jobs because of the operating decisions of theiremployers, where in the case of multiple jobs “main job” refers to the job held thelongest (see, e.g., Hildreth, von Wachter, and Handwerker [2008]).
C 2009 by the President and Fellows of Harvard College and the Massachusetts Institute ofTechnology.
The Quarterly Journal of Economics, August 2009

1265

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

We use administrative data on the quarterly employment and earnings ofPennsylvanian workers in the 1970s and 1980s matched to Social Security Administration death records covering 1980–2006 to estimate the effects of job displacement on mortality. We find that for high-seniority male workers, mortality
rates in the year after displacement are 50%–100% higher than would otherwise
have been expected. The effect on mortality hazards declines sharply over time, buteven twenty years after displacement, we estimate a 10%–15% increase in annual
death hazards. If such increases were sustained indefinitely, they would imply aloss in life expectancy of 1.0–1.5 years for a worker displaced at age forty. We showthat these results are not due to selective displacement of less healthy workers or
to unstable industries or firms offering less healthy work environments. We also
show that workers with larger losses in earnings tend to suffer greater increases inmortality. This correlation remains when we examine predicted earnings declines
based on losses in industry, firm, or firm-size wage premiums.

1266

QUARTERLY JOURNAL OF ECONOMICS

2. We analyze workers with at least six or at least three years of tenure at thetime of job displacement.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

paper, we provide evidence that displaced workers can also experience higher rates of mortality.
To study the link between displacement and mortality, we
use administrative data on earnings and employment histories formale workers from Pennsylvania in the 1970s and 1980s matched
to Social Security Administration (SSA) death records covering
the entire United States from 1980 to 2006. Following Jacobson,
LaLonde, and Sullivan (1993) (hereafter JLS), we identify instances of displacement as those in which high-tenure workers
leave firms experiencing large employment declines.2 We thencompare these displaced workers’ subsequent mortality rates withthose of similar workers who did not suffer job loss.
We find that high-tenure male workers displaced during theearly and mid-1980s experienced a significant increase in mortality. Indeed, our estimates suggest a 50%–100% increase in themortality hazard during the years immediately following job loss.
The estimated impact of displacement on annual mortality rates
declines substantially over time, but appears to converge to a
10%–15% increase in the hazard rate. If these increases lasted
beyond the 25-year window we follow, they would imply a loss inlife expectancy of 1.0–1.5 years for workers displaced in middle
age. In contrast, we find little effect of job loss on mortality forworkers displaced near retirement age.
Firm-level employment declines should be exogenous toindividual workers’ health developments. Moreover, our results
control for the mean and standard deviation of workers’ earnings
over a period of several years prior to job loss and are robust to
the inclusion of industry or firm effects. They should thus be little
affected if, for example, firms selectively lay off less productive
workers and less productive workers tend to be less healthy,
or if unstable industries or firms provide less healthy work
environments. In addition, we show that these worker-level
results are consistent with a firm-level analysis of the impact of
employment declines on mortality that pools displaced workers
with those remaining with affected firms. By construction, these“intent-to-treat” estimates are unaffected by the possibility offirms selecting the least healthy workers for layoffs or by misclassification of dying workers as job losers. Thus, our estimates
likely identify the causal effect of job loss on mortality.

JOB DISPLACEMENT AND MORTALITY

1267

3. For example, a shift in the lifetime budget constraint would reduce healthinvestments in a neoclassical model of health; alternatively, it could reduce social
status and may raise mortality through social stress (see Deaton [2001] for further
discussion of these and other approaches). Although some of these factors are likely
to operate in the short run as well, too many factors vary simultaneously for theeffect of any single channel to be separately assessed.
4. Another potential channel to which our data does not speak is the loss
in health insurance. Losses in health insurance may be correlated with earnings
reductions (Olson 1992) but may have independent effects as well.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Our estimates of the short- and long-run effects of displacement on the mortality hazard roughly parallel the short- and longrun effects of displacement on earnings and employment reported
in JLS and elsewhere. In the short run, displacement is associated with a sharp drop in mean earnings, increased unemployment, and high earnings instability. Our results are consistent
with these effects causing acute stress, which may substantiallyraise the mortality hazard in the short term. In the long run,displacement is associated with a substantial drop in mean earnings and modestly higher employment instability and earnings
variability. Several economic models of health determination predict that a decline in lifetime resources should raise mortality.3
Our empirical findings are consistent with a reduction in such
resources leading to reduced investments in health or chronic
stress, which, in turn, lead to a smaller, but longer term increasein the mortality hazard. Increased earnings instability may also
contribute to chronic stress and a long-run increase in mortality.4
To gain insight into the relative importance of some of thechannels through which job loss could affect the long-run mortality hazard, we compare our estimates of the “reduced form”
effect of displacement on mortality to what one would expect on
the basis of displacement’s long-run effect on the mean and variability of workers’ earnings and the correlation of those factors
with mortality. In our Pennsylvania data, displacement reduces
the mean of long-run earnings by 15%–20%. Given the correlation of mean earnings with mortality, this effect can explain an
increase in the death rate hazard equal to 50%–75% of our estimate of the reduced-form effect. Though displacement does nothave a significant long-run effect on employment rates, it does
raise the variability of earnings somewhat. Given the significant
correlation of earnings variability with mortality in our data, this
implies an additional effect of displacement on mortality on theorder of 20%–25% of what we estimate for the full reduced-form
effect of job loss on mortality. Thus, the impact of displacement

1268

QUARTERLY JOURNAL OF ECONOMICS

5. Typical estimates suggest a strong correlation between income and mortality (e.g., Deaton and Paxson [1999]). In addition, a growing literature in economics, sociology, and epidemiology has shown that unemployment and job loss
correlate with the incidence of depression, low self-esteem, heart attack, and evensuicide (see, e.g., Darity and Goldsmith [1996]; Burgard, Brand, and House [2005,2007]; Gallo et al. [2006]). However, cross-sectional estimates may not represent
causal effects of earnings on mortality because of reverse causality, omitted worker
characteristics, and measurement error (e.g., Smith [1999]; Cutler, Deaton, and
Lleras-Muney [2006]).
6. Most studies are not based on exogenous sources of variation in individuals’
labor market conditions, objective measures of health and job loss, large sample
sizes, a long follow-up period, or detailed pre–job loss career outcomes such as
used in our empirical work. This has made it difficult to study the effect of labormarket events on health free of measurement error, reverse causality, and omitted
variable bias.
7. Rege, Telle, and Votruba (forthcoming) find that workers (men and women)
losing their jobs in a plant downsizing during 1993–1998 are more likely to receive
disability insurance in 1999 and have a somewhat higher probability of death during 1999–2002. Eliason and Storrie (2007) find that male workers losing their jobs
in establishment closures in Sweden during 1987–1988 experience excess mortality for up to four years after job loss. Martikainen, Maki, and Jantti (2007) find nosuch effects in Finland. Results are similarly mixed for other measures of health.
For example, Kuhn, Lalive, and Zweimueller (2007) find that job loss reduces themental health of men in Austria, whereas Browning, Dano, and Heinesen (2006)
find no such effects in Denmark.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

on the mean and variability of earnings may explain an important fraction of the increase in the long-run mortality hazard that
we estimate. Our analysis of groups of workers who by their industry or their employer’s characteristics have greater predicted
earnings losses confirms that larger earnings reductions at job
displacement are associated with greater increases in long-term
mortality risk.
Our results are consistent with those of the large literature
documenting a strong correlation of socioeconomic status withhealth.5 However, our paper is one of the first studies to use
U.S. data to estimate the long-term effect of a plausibly exogenous labor market event on an objective measure of health fora large group of workers. It thereby establishes a much clearer
causal link between labor market and health outcomes than mostof the previous literature.6 Our study complements important recent studies based on European administrative data, which findmixed results on the effects of job loss on health.7 Our paper isthe only study to closely replicate and extend the approach used
in JLS’s well-known analysis of job displacements. In addition
to methodological differences, the European studies differ fromours in that they analyze the effect of displacement over shorter
horizons. In addition, U.S. health care and labor market institutions differ substantially from those in Europe, where workers
often have access to universal health insurance and where theJOB DISPLACEMENT AND MORTALITY

1269

8. There is considerable heterogeneity in approaches and results among studies analyzing the effect of job loss on earnings in Europe. For example, the effects of job loss on earnings in Austria are small (Card, Chetty, and Weber 2007).
Earnings losses in Sweden have been found to be more persistent (Eliason andStorrie 2006). Income losses in Norway fall somewhere in between (Rege, Telle,
and Votruba, forthcoming).
9. Earnings losses of duration and magnitude similar to those found by JLS
for Pennsylvania have been found in other states in the 1990s, such as California,
Connecticut, or Massachusetts (in Schoeni and Dardia [2003], Couch and Placzek[forthcoming], and Kodrzycki [2007]), respectively, and for the entire United States
during the early 1980s (von Wachter, Song, and Manchester 2009).

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

earnings consequences of job loss typically are less severe than in
the United States.8 Our results do not conflict with those of Ruhm
(2000), who finds that aggregate mortality rates tend to fall during recessions. As we discuss more fully in the conclusion, thesituation of an individual displaced worker differs qualitatively
from that of the average worker during a recession. Briefly, forthe average worker, short-term declines in economic activity may
increase time available for healthy activities without significantly
reducing lifetime resources. However, the high-tenure displaced
workers we study suffer significant long-term earnings reductions
without benefiting from an offsetting increase in leisure time.
A potential limitation of our data is that the experiences ofmale workers displaced from jobs in Pennsylvania during the earlyand mid-1980s may not be fully representative of those of the typical displaced worker. Indeed, given the severity of the early 1980s
recession in Pennsylvania, it is quite possible that our results
somewhat overstate the average impact of displacement on mortality. However, the qualitative effects of displacement on otheraspects of workers’ lives have been found to be reasonably robust
across time and place,9 so our results likely give a good indicationof the direction and at least the rough magnitude of the effects thatcan be expected for the typical displaced male high-tenure worker.
The next section discusses the properties of our data andintroduces our econometric framework. Section III contains our
main results; Section III.A presents the average effect of displacement on mortality; Section III.B distinguishes between the shortand long-run effects of displacement on mortality and breaks out
the effects by current age, age at displacement, and job tenure;
Section III.C discusses the implied reductions of life expectancy;and Section III.D summarizes our sensitivity analysis. Section IV
discusses our assessment of potential mechanisms through whichdisplacement raises mortality, and Section V concludes.

1270

QUARTERLY JOURNAL OF ECONOMICS

II. EMPIRICAL APPROACH: DATA AND ECONOMETRIC FRAMEWORKThis section details the construction of our data set, whichmerges quarterly wage records derived from the state of Pennsylvania’s unemployment insurance (UI) system with death records
maintained by the SSA. It also explains how we identify displaced
workers and contrasts their characteristics with those of workers
not affected by displacement. It then describes our basic empiricalstrategy, which is to compare the mortality experience of workers
identified as being displaced with that of otherwise similar workers who are not displaced.

Our data on workers’ employment and earnings histories arederived from the UI records of the state of Pennsylvania (PA) overthe period from 1974 to 1991. For a 5% sample of workers whoheld jobs covered by UI, we observe quarterly earnings from each
PA employer, as well as the employer’s industry.10 Our data on
mortality are derived from a database compiled by the SSA andcover deaths occurring anywhere in the United States between
1974 and 2006. The accuracy of the death information has beenfound to be good for the sample of mature and older male workers
we consider.11
We follow JLS in focusing on workers who had very stable
employment relationships in the 1970s. Specifically, we analyze
data on male workers who had the same principal employer from1974 to 1979, where the principal employer for a year was theemployer from which the worker received the most wage income.
We also replicate our results for men with at least three yearsof job tenure in 1979. In both cases the restriction isolates stableworkers separating from what they had reasons to expect to be
10. JLS (1993) used the same data for the period 1974 to 1986. For a detailed
description of the data and their advantages and shortcomings, please see theirpaper. An “employer” in our data refers to a firm, which may operate multiple
establishments, as long as they are in Pennsylvania.
11. The SSA’s Death Master File (DMF) is described and evaluated in
Hill and Rosenwaike (2002). Coverage of the death data is better in the
1990s, for older workers, and for men. Recent work comparing the DMF withcomplete mortality data from the National Center for Health Statistics suggests that coverage for men is between 80% and 90% before age 65 andabove 95% after age 65 (see extensive notes by Elizabeth Weber Handwerker,
http://socrates.berkeley.edu/∼eweber/DMFnotes.htm). We replicated these tabulations for deaths in Pennsylvania for 1980–2002 and found similar results (in ourempirical analysis, we also include deaths occurring in other states).

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

II.A. Data Construction and the Characteristics
of Displaced Workers

JOB DISPLACEMENT AND MORTALITY

1271

12. Our sample is not meant to capture all job losers but maintains the focus
on workers losing stable jobs that is common in the literature on job displacement (e.g., JLS [1993]; Schoeni and Dardia [2003]; Couch and Placzek [forthcoming]). Another reason to impose a minimal tenure restriction when working withadministrative data is to exclude voluntary movers. This is discussed in detailin Hildreth, von Wachter, and Handwerker (2008). Hildreth, von Wachter, andHandwerker (2008) and von Wachter, Song, and Manchester (2009) show using
administrative data that earnings losses from job displacements are substantial
and long-lasting even at shorter tenure durations.
13. Hildreth, von Wachter, and Handwerker (2008) explore the issues thatarise in the measurement of displacement using administrative data in detail andconclude that the results based on JLS’s way of identifying mass layoffs are robust
to alternative definitions of mass layoffs.
14. These workers were excluded in JLS because, due to their uncertain layoff
status, they may belong in the treatment group, in which case including them in thecomparison group would underestimate the effects of displacement. On the other
hand, if these workers are of worse underlying health, excluding them would bias
our results upward. Thus, to err on the conservative side, we included them in our
main sample as nondisplaced workers. We also show results based on the original
JLS sample restriction.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

long-term jobs in the absence of mass layoffs. For these workers,
displacement was likely to be unexpected and costly.12
A limitation of administrative data is that we do not have a
direct measure of whether a particular separation was voluntary
or involuntary. As in JLS, we deal with this limitation by defining
displaced workers to be those who leave their firms during theperiod 1980–1986 and for whom their former firms’ employment
in the following year was 30% or more below its peak since 1974.13
Other workers leaving their firms during this period are not considered displaced, and in most specifications are left in the comparison group. JLS found that such non-mass-layoff job separators
did not, on the average, experience long-term earnings losses.14
Because we use percentage changes in firm employment to
identify displaced workers and such changes are not very meaningful for small employers, we further limit our sample to those
whose firms employed at least 50 workers in 1979. In addition, we
restrict our analysis to male workers. During the period we study,
there were relatively few female workers with such stable employment relationships. As a result, sample sizes are too small formeaningful findings for women to be derived. Again following JLS,
we restrict some of our analysis to workers born between 1930 and
1959, a group for whom retirement before the 1990s is unlikely.
However, for some analyses, which are noted below, we expand
the age range to include workers born between 1920 and 1959.
A potential concern with our procedure for identifying displacement is that workers who just happen to die in a year inwhich their firms substantially reduce employment will appear

1272

QUARTERLY JOURNAL OF ECONOMICS

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

to be job losers and, thus, displaced workers, even if they would
have been able to retain their jobs had they lived. This misclassification of some dying workers as displaced rather than nondisplaced workers would tend to bias simple estimates of the effectof displacement on mortality upward. To address this problem,
following a suggestion from a referee, we drop from our samples
workers who died during the years their firms suffered mass layoffs. Because we find below that the effects of displacement tend
to be largest immediately after job loss, this likely leads us tounderestimate the average effect of displacement on mortality.
The first three columns of Table I show means for a number ofworker characteristics for the full sample just described, as well as
for displaced and nondisplaced workers separately. Both groups ofworkers were, on the average, in their late thirties, with earnings
in the middle of the income distribution for the period. Displaced
workers were about half a year younger, had earnings about 6%
lower, and experienced slightly more quarters without earnings in
the 1974–1979 base period than nondisplaced workers. Displaced
workers did, however, have somewhat faster earnings growth during the base period. In addition, during this period, displaced
workers were employed by larger firms and were more likely towork in the steel industry or other durable-goods-producing industries. These patterns suggest that it is important to control for
potential differences across sectors and firms, but also for pre-jobloss differences in career outcomes among movers and stayers.
Despite having relatively similar earnings during the 1974–
1979 base period, displaced workers had much lower average
earnings between 1987 and 1991. In part, however, this difference
may reflect some displaced workers leaving the state or taking
jobs in sectors not covered by UI. Such workers have zero reported earnings, but may, in fact, have income not covered in the
PA UI system. To mitigate such concerns, the last three columns ofTable I show results limited to workers who had positive reported
earnings in each calendar year from 1980 to 1986, a restriction
JLS imposed in their empirical analysis. Differences between displaced and nondisplaced workers in the period 1974–1979 arelittle affected by this restriction. However, the earnings differential for the period 1987–1991 is narrowed considerably, though it
remains quite large.
Figure I displays estimates of the percentage difference inannual earnings relative to the base period and to the comparisongroup of workers remaining at their employer from 1980 to 1986

Fraction eastern PA

Fraction other manufacturing

21,57330.42(7.124)8.74(0.358)−1.637(0.732)0.513(5.736)0.48(0.977)
8,556(13,944)
0.179(0.384)0.297(0.457)0.191(0.393)0.562(0.496)All
workers
(1)
7,256
30.14
(7.422)8.70(0.346)−1.483(0.767)0.677(7.699)0.58(1.100)
10,483(16,287)
0.292(0.455)0.349(0.477)0.164(0.370)0.475(0.499)Displaced
workers(2)
14,31730.55(6.964)8.76(0.362)−1.715(0.700)0.430(4.425)0.43(0.904)
7,579(12,479)
0.122(0.328)0.271(0.444)0.204(0.403)0.606(0.489)Nondisplaced
workers(3)

No work restriction

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Fraction other durable goods manufacturing (nonsteel)Fraction steel industries

1979 firm’s employment

Number of quarters in nonemployment 1974–1979

Percent change in quarterly earnings 1974–1979

Log(std. dev. of log quarterly earnings 1974–1979)

Log(average quarterly earnings in 1974–1979)

Sample size
Age in 1979

Work restriction in Pennsylvania
labor market during 1980–1986

TABLE ISAMPLE CHARACTERISTICS BY DISPLACEMENT STATUS

Displaced
workers
(5)
4,785
37.01
(7.295)8.70(0.338)−1.545(0.749)0.582(7.287)0.54(1.029)
9,065(15,018)
0.260(0.438)0.365(0.481)0.183(0.387)0.521(0.500)All
workers(4)
17,641
37.42(7.031)8.75(0.345)−1.680(0.709)0.459(5.343)0.45(0.919)8,087(13,267)
0.163(0.370)0.300(0.458)0.200(0.400)0.581(0.493)

12,85637.57(6.925)8.76(0.346)−1.731(0.687)0.413(4.410)0.42(0.873)
7,723(12,534)
0.128(0.334)0.275(0.447)0.206(0.405)0.603(0.489)Nondisplaced
workers(6)

Work every year

JOB DISPLACEMENT AND MORTALITY

1273

8.606(1.069)−1.344(0.764)4.31(7.070)6.764(0.143)4.167(0.181)7.407(0.227)10.815(0.427)All
workers(1)
8.184(1.310)−1.119(0.793)
6.66(8.207)7.639(0.263)5.151(0.347)8.114(0.411)11.909(0.777)

Displaced
workers(2)
8.791(0.883)−1.440(0.730)
3.11(6.079)6.325(0.170)3.670(0.208)7.053(0.272)
10.270(0.510)Nondisplaced
workers(3)

No work restriction

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Displaced
workers(5)
8.421(1.064)−1.197(0.757)3.32(5.900)6.913(0.306)4.400(0.393)7.451(0.481)11.033(0.911)All
workers(4)
8.728(0.891)−1.393(0.736)2.20(4.736)6.343(0.152)3.745(0.189)6.994(0.242)
10.347(0.458)8.838(0.792)−1.462(0.716)
1.79(4.145)6.132(0.175)3.502(0.214)6.826(0.280)10.094(0.529)Nondisplaced
workers(6)

Work every year

Notes. Standard deviations in parentheses (with exception for death rates, which show standard errors). The samples include only male workers born 1930–1959 in stable
employment 1974–1979 at an employer of size fifty in 1979. Displaced workers left jobs in firms whose employment the subsequent year was 30% or more below its post-1974 peak.
Information pertaining to employment and earnings is from Pennsylvania. Deaths can occur anywhere in the United States.

Deaths per 1,000 per year 2000–2006

Deaths per 1,000 per year 1994–1999

Deaths per 1,000 per year 1987–1993

Deaths per 1,000 per year 1987–2006

Number of quarters in nonemployment in 1987–1991

Log(std. dev. of log quarterly earnings in 1987–1991)

Log(average quarterly earnings in 1987–1991)

Work restriction in Pennsylvania
labor market during 1980–1986

TABLE I(CONTINUED)

1274
QUARTERLY JOURNAL OF ECONOMICSJOB DISPLACEMENT AND MORTALITY

1275

controlling for year, age, and worker fixed effects. As in the last
three columns of Table I, the model is estimated using a sample
that is restricted to workers who had positive earnings every year
from 1980 to 1986. In the year immediately after displacement,
earnings are over 50 log points below levels expected in the absence of displacement. Losses decline over time, but even elevenyears after displacement they are approximately 15%. Clearly, displacement is a major economic setback for the affected workers.
We have also analyzed in a similar manner the impact of displacement on several other career outcomes, finding that displacement
leads to modest long-run increases in earnings variability and thelikelihood of changing jobs or industries. The effect of displacement on other outcomes—such as incidence of nonemployment,
industry mobility, or mobility across counties—is significant in
the first two to four years after layoffs but not afterward.15
15. See Table 9 of our longer working paper for detailed results (Sullivan and
von Wachter 2007).

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

FIGURE I
Estimate of the Decline in Annual Earnings due to Job Displacement (Sampleof Men in Stable Employment 1974–1979, Firm 1979 Employment ≥50,
Born 1930–1959, Work in PA Labor Force Every Year 1980–1986)
Solid line represents coefficient estimates of the interaction of year effects anddisplacement dummies in a regression model of log quarterly earnings includingyear fixed effects, person fixed effects, and a quartic for age. Two standard error
bands are drawn around main effects.

1276

QUARTERLY JOURNAL OF ECONOMICS

II.B. Main Estimation Strategy
To control for differences in other variables that may affectmortality, we employ a standard logistic regression framework.
Specifically, we estimate a number of logistic regression models of
the formpit= xi β + δ Dit + χa(i,t) + φt ,(1)ln
1 − pitwhere pit ≡ Pr{Deathit = 1|Deathit−1 = 0} is the hazard of worker
i dying in year t given survival through year t − 1, and Dit is adummy variable equal to one if worker i has been displaced prior
to year t and zero otherwise.17 Thus, the coefficient on the indicator variable for displacement measures the increase in the log
odds of death in a given year, holding constant the other variables in the model. Because the probability of death is typically
16. Note that we also replicated standard estimates of the age-gradient inmortality for our sample (Appendix, Figure 1, Sullivan and von Wachter [2007])and found them to be quite similar to typical patterns for representative U.S.
samples.
17. This is a standard logistic regression model, and we obtain our parameter estimates by maximum likelihood. Workers contribute one observation foreach year that they are alive during the follow-up period. The risk set evolves
over time as workers die. Efron (1988) shows that the logistic model we estimate
approximates standard continuous parametric models of the survival hazard.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

The last several rows of Table I show mortality rates over anumber of time periods (deaths can occur anywhere in the UnitedStates). As explained above, to avoid misclassifying dying nonseparators as displaced, we drop workers dying in the year of displacement. Not surprisingly, rates for all workers rise over time, fromabout four per thousand between 1987 and 1993 to more than ten
per thousand between 2000 and 2006. The table also shows that
displaced workers experienced higher mortality rates than those
who were not displaced. The gap between the groups’ mortality
rates was especially high in the period 1987–1993, shortly after
displaced workers lost their jobs. Indeed, during this period, displaced workers were more than 40% more likely to die as nondisplaced workers (5.151 per 1,000 versus 3.670 per 1,000). However,even twenty years later, during the period 2000–2006, mortality
rates were more than 15% higher for the displaced workers. Of
course, these simple comparisons of mortality rates do not control
for the systematic differences between displaced and nondisplaced
workers that are illustrated in the upper portions of the table.16

JOB DISPLACEMENT AND MORTALITY

1277

III. DISPLACEMENT, MORTALITY, AND LIFE EXPECTANCYThis section presents our basic estimates of the effect of displacement on the mortality hazard. We first show results basedon models that assume a constant effect on the hazard. We thenshow how the effect varies with time since displacement and othervariables. Finally, we derive the implications of our estimates forlife expectancy and summarize our sensitivity analysis.
III.A. Displacement and the Mortality Hazard
The first column of Table II shows estimates of the coefficienton the displacement dummy of model (1) for various sets of control variables (xi ). Models are estimated using the full sample ofworkers over the entire period 1980–2006. Controlling only forthe mean and standard deviation of earnings during the period
1974–1979 as shown in row (1), we estimate that displacement isassociated with about a 17% increase in the mortality hazard. The

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011quite small, the increase in the log-odds ratio approximates thepercentage increase in the death rate itself. In some models, we
also include interactions of the displacement dummy with othervariables, which allows the effect of displacement on mortality tovary in a number of important ways.
All the specifications we report below include year dummies(φt ), which among other things may control for variation overtime in the completeness of the SSA’s death records. They also
include a fourth-order polynomial in age (χa(i,t) ). Results are very
similar if the age quartic is replaced by an unrestricted set of age
dummies, or even a simple linear time trend. None of our results
are sensitive to the logistic functional form; they are all evident
in straightforward tabulations of average mortality rates and in
linear probability models.
The firm-level shocks that lead to employment reductions
should be exogenous to workers’ individual health problems. However, it is possible that firms faced with the need to reduce employment may tend to lay off their least productive workers, who
may in turn be in poor health. To address this potential problem,
we consider a number of specifications that control for variables
likely to capture productivity differences in the period 1974–1979(xi ). In Section III.D, we summarize several additional robustness
checks confirming that our results are not affected by selective job
displacement.

Model in row (1) with one-digit industry effects andadded career variables
Model in row (1) with industry effects and career
variables*age interactions
Linear probability model (specification row (2))

Linear probability model (specification row (1))
with firm effects

(3)

(6)

(5)

(4)

(2)

Baseline model with average and std. dev. of
earnings in 1974–1979
Model in row (1) with one-digit industry fixed effects

0.170(0.036)0.170(0.037)0.163(0.038)0.169(0.037)0.0012(0.00026)
0.0013(0.00038)

0.147(0.037)0.137(0.038)0.129(0.039)0.136(0.038)0.0011(0.00032)
0.0008(0.00050)

No work
restriction(2)
1987–2006

Work every
year(4)
1987–2006
0.088(0.044)0.077(0.045)0.069(0.047)0.077(0.045)0.0006(0.00034)
0.0006(0.00054)

Work at least
three years(3)
1987–2006
0.148(0.038)0.139(0.039)0.128(0.040)0.138(0.039)0.0012(0.00031)
0.0010(0.00048)

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

(1)

Death follow-up period

No work
restriction(1)
1980–2006

0.104(0.046)0.098(0.047)0.088(0.048)0.098(0.047)0.0008(0.00034)
0.0009(0.00051)

Work every year,
exclude non-MLF
separators(JLS sample)(5)
1987–2006

TABLE IIEFFECT OF JOB DISPLACEMENT ON LOG-ODDS OF DEATH FOR VARIOUS SAMPLES, FOLLOW-UP PERIODS, AND SPECIFICATIONS (WORKERS IN
STABLE EMPLOYMENT 1974–1979, FIRM 1979 EMPLOYMENT ≥50, BORN 1930–1959)

1278
QUARTERLY JOURNAL OF ECONOMICS

0.194(0.041)
0.200(0.059)553,1670.140(0.027)0.104(0.042)402,844

0.143(0.041)0.120(0.063)
392,536

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

0.081
(0.029)0.075(0.046)
334,598

0.103(0.042)0.117(0.063)291,373

Notes. The dependent variable is the log odds of death in a year between 1980 (or 1987) and 2006. Deaths can occur anywhere in the United States. The entries in the table
are the coefficients on a dummy for job loss during mass layoff. See Sullivan and von Wachter (2007) for marginal effects. Columns represent different samples; rows represent
different model specifications. All models include year effects and a quartic in age as well as the indicated variables in the first column. In all models, the average of quarterly
earnings 1974–1979 is entered in logs; the standard deviation is of the log quarterly earnings, also entered in logs. Industry dummies are for nonmanufacturing goods, nondurables
manufacturing, other durables manufacturing, steel manufacturing, transportation–construction–public-utilities, trade, and services. The additional “career variables” in rows (3) to(6) are growth in quarterly earnings during 1974–1979 and the total time spent in non-employment in 1974–1979. Row (4) interacts the log of average earnings and the log of thestandard deviation of log earnings with five dummies for age at layoff. The firm in row (6) refers to the 1979 employer. The last row shows the number of person–year observations.
Column (5) corresponds to the sample used in JLS (1993), which excludes nonmass layoff separators from the control group. Standard errors are in parentheses (for rows (7) and (8),these are calculated by the delta-method).

Observations(8) Percentage effect for linear probabiliy model in row (6)(7) Percentage effect for linear probability model in row (5)Death follow-up period

Work every yeasr,
exclude non-MLF
No work
No work Work at least Work every
separators
restriction restriction
three years
year(JLS sample)
(1)
(2)
(3)
(4)
(5)
1980–2006 1987–2006
1987–2006
1987–2006
1987–2006

TABLE II(CONTINUED)JOB DISPLACEMENT AND MORTALITY

1279

1280

QUARTERLY JOURNAL OF ECONOMICS

18. If we include only year and age effects for our most general sample incolumn (1), we obtain a displacement effect of 0.227 (0.0354); if we include only
log average earnings as an additional control variable, we obtain 0.2005 (0.0355).
19. The fact that the within-firm estimate of the effect of displacement on
death is not smaller suggests that workers remaining at the firm experiencing
mass layoff do not have higher mortality that may have arisen, say, due to increased uncertainty. This is consistent with our finding that mortality increases
are correlated with large earnings losses of displaced workers.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

remaining rows probe the robustness of this result. Adding 1-digit
industry fixed effects, the growth in earnings and the number ofquarters of zero earnings during the base period, and interactions of the career variables with age as shown in rows (2)–(4)has very little effect on the estimate.18 Row (5) shows the estimate for a linear probability model version of row (2), whereasrow (6) shows results from a linear probability model that includes firm fixed effects. When expressed as percentages of thebaseline hazard, these latter two estimates are modestly higher
than but in the same ballpark as the estimates from the logit
models. Overall, there is no indication that our effects can be
explained by firms selectively displacing less productive workers who are also less healthy than their peers. Similarly, it does
not appear that firms or sectors with high average layoff rates
provide less healthy career environments or attract less healthy
workers.19
The remaining columns of Table II show the impact of changing the data set over which model (1) is estimated. In column (2)we continue to use the full set of workers, but restrict the time
period over which we track mortality to 1987–2006. Restricting
the time period in this way lowers the estimates to the 10%–15%range, which is consistent with the biggest effects being observed
immediately after displacement. In column (3), we restrict theset of workers to those who have positive reported earnings in
at least three years between 1980 and 1986. This has very little
effect on the estimates. However, requiring workers to have earnings in all years, as shown in column (4), lowers the estimates tothe 7%–9% range. This suggests that part of the effect estimatedon our more general sample in column (1) is due to workers permanently dropping out of the labor force or leaving PA. However,
the majority of the effect is still present for workers with stableattachment to the PA labor force after job loss. Finally, column (5)shows results using the original JLS sample, which, in addition
to requiring earnings in each year from 1980 to 1986, drops nonmass-layoff separators. These estimates again range from 9% toJOB DISPLACEMENT AND MORTALITY

1281

III.B. Mortality Effects by Year since Layoff, Age, and Job Tenure
The results in Table II suggest that the immediate impact
of displacement on mortality differs from the long-run effect. To
explore this pattern further, column (1) in Table IV breaks up theeffect of displacement on death by year since layoff for our mostgeneral sample (as in column (1) in Table II). Row (1) shows thelong-run effect in manufacturing industries. The effect is statistically significantly different from zero and substantial even at 16
or more years after layoff. The last row of the table shows that
this effect is not statistically significantly different in nonmanufacturing industries.
The remaining rows of column (1) show how the effect differs in the first 15 years after layoff. To obtain the full effect of
20. This suggests that non-mass-layoff separators experience mortality increases as well, which is confirmed in Appendix Table 3 of our longer working
paper (Sullivan and von Wachter 2007); this is not surprising because for highattachment workers in the difficult economic environment in the early to mid1980s, most job separations tended to lead to nontrivial earnings losses (see also
von Wachter, Song, and Manchester [2009]).
21. This estimate is somewhat higher than typical estimates of the correlationof mortality with a single year of income (e.g., Deaton and Paxson [1999]). Thisis because our data on average earnings over a six-year period do a better job
capturing a notion of permanent income and are less affected by measurement
error present in self-reported income measures in survey data. This is further
discussed in Sullivan and von Wachter (2009) and in Appendix 1 of our longer
working paper.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 201111%.20 Overall, we consider the estimates shown in Table II as
indicating a reasonable degree of robustness to the set of additional control variables and the sample of workers included in theestimation.
Table III displays the other coefficients in the models of row(3) of Table II, which, as we discuss in Section IV, are useful fortrying to understand the channels through which displacement affects mortality. The elasticity of the mortality hazard with respect
to average quarterly earnings in 1974 to 1979 is about −0.5.21
The elasticity of mortality with respect to the standard deviationof the logarithm of quarterly earnings is estimated to be around
0.17, indicating that higher earnings variability tends to increasemortality. Holding average earnings and earnings variability
constant, an additional quarter of nonemployment due to sickleave or temporary layoffs in the base period reduces mortality by
about 9%, an effect that is, perhaps, consistent with the findingsof Ruhm (2000). Conditional on the other variables, the earnings
growth trend from 1974 to 1979 has little effect on mortality.

0.163(0.038)−0.504(0.055)0.172(0.027)−0.090(0.025)−0.002(0.052)
Yes
505,316

No work
restriction(1)
1980–2006
0.129(0.040)−0.516(0.057)0.163(0.028)−0.090(0.026)0.008(0.054)Yes
367,890

No work
restriction(2)
1987–2006
0.128(0.040)−0.499(0.058)0.170(0.028)−0.087(0.026)0.016(0.055)
Yes
358,660

Work at least
three years(3)
1987–2006

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Work every year,
exclude non-MLF
separators(JLS sample)(5)
1987–2006
0.069(0.047)−0.472(0.066)0.174(0.032)−0.095(0.031)
0.015(0.062)
Yes
308,345

Work every
year(4)
1987–2006
0.069(0.047)−0.472(0.066)0.174(0.032)−0.095(0.031)
0.015(0.062)
Yes
308,345

Notes. These are coefficients on covariates included in model (3) of Table II. Please refer to notes to Table II for further explanations. Standard errors are in parentheses.

1-digit dummies for 1979 industry
Observations

Growth in quarterly earnings 1974–1979

Number of quarters in nonemployment 1974–1979

Log(std. dev. of log quarterly earnings 1974–1979)

Log(average quarterly earnings 1974–1979)

Displacement dummy

Death follow-up period

Work restriction in
Pennsylvania labor market
during 1980–1986

TABLE III
COEFFICIENTS ON CAREER VARIABLES IN EXTENDED LOG-ODDS OF DEATH MODEL (VARIOUS SAMPLES, WORKERS IN STABLE EMPLOYMENT
1974–1979, FIRM 1979 EMPLOYMENT ≥50, BORN 1930–1959)

1282
QUARTERLY JOURNAL OF ECONOMICS

0.131(0.054)0.716(0.199)0.559(0.147)0.198(0.147)0.057(0.094)−0.066(0.081)

Displacement effect 16+ years after displacement

0.108(0.034)0.619(0.105)0.307(0.084)0.040(0.082)0.045(0.054)−0.045(0.047)

1920–1959(2)
0.133(0.055)0.582(0.113)0.279(0.091)0.020(0.086)0.036(0.057)−0.046(0.048)0.383(0.131)0.136(0.075)1920–1959(3)

1920–1959(5)
0.123(0.032)0.606(0.099)0.318(0.078)0.033(0.077)0.053(0.051)−0.044(0.044)0.161(0.05)
0.782(0.176)0.525(0.136)0.204(0.135)0.027(0.087)−0.053(0.073)0.152(0.05)
0.585(0.106)0.303(0.084)0.024(0.081)
0.051(0.053)−0.042(0.045)0.220(0.116)0.117(0.066)1920–1959(6)

Tenure in 1979 at least three years
1930–1959(4)

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Displacement and current age between 46 and 55

Displacement and current age less than or equal to 45

Added effect for 11–15 years after displacement year

Added effect for 6–10 years after displacement year

Added effect for 4–5 years after displacement year

Added effect for 2–3 years after displacement year

Added effect for 1 year after displacement year

1930–1959(1)

Tenure in 1979 at least six years

Birth cohort

Restriction on job tenure

TABLE IVMORTALITY IMPACT OF JOB DISPLACEMENT BY TIME SINCE DISPLACEMENT, AGE-GROUP, INDUSTRY, AND TENURE AT JOB LOSS FOR DIFFERENTSAMPLES (WORKERS IN STABLE EMPLOYMENT 1974–1979, FIRM 1979 EMPLOYMENT ≥50, NO FURTHER PRESENCE RESTRICTION IN
PA LABOR MARKET)JOB DISPLACEMENT AND MORTALITY

1283

0.045(0.084)1930–1959(1)−0.065(0.051)

1920–1959(2)

1920–1959(5)−0.059(0.047)

1930–1959(4)−0.040(0.074)−0.003(0.054)−0.092(0.041)−0.070(0.051)−0.009(0.050)−0.099(0.039)−0.066(0.047)

1920–1959(6)

Tenure in 1979 at least three years

1920–1959(3)

Tenure in 1979 at least six years

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Notes. Samples are workers born 1920–1959 in stable jobs from 1974–1979 with an employer of over 50 workers. Dependent variable is the log odds of death. Death can occur
anywhere in the United States. Entries are coefficient estimates from the logit model. All models include year fixed effects, industry fixed effects, a quartic in age, the log of averagequarterly earnings in 1974–1979, and the log of the standard deviation of quarterly earnings in 1974–1979. The coefficient in the first row is the main effect; the excluded categories
are “16 or more years after displacement” and “displaced from manufacturing job.” In addition, in columns (2), (3), (5), and (6), the excluded group is “displacement and current age
between 56 and 64.” Standard errors are in parentheses.

Displaced from nonmanufacturing job

Displacement at age 60–69

Displacement and current age above age 65

Birth cohort

Restriction on job tenure

TABLE IV
(CONTINUED)

1284
QUARTERLY JOURNAL OF ECONOMICSJOB DISPLACEMENT AND MORTALITY

1285

displacement on mortality at different years since displacement,
the coefficients on the interactions have to be added to the main
effect in row (1).22 We see large percentage increases immediately
after job displacement. The effect remains high for the first five
years after job loss, then gradually declines with time since layoff, and bottoms out at a long-run average of about 13%. This isshown graphically in Panel A of Figure II, which plots the point
22. For example, for a displaced worker two to three years after layoff, theeffect of displacement on mortality would be 0.131 + 0.559 = 0.69.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

FIGURE II
The Effect of Displacement on Log-Odds of Death by Years since Displacement(Sample of Men in Stable Employment 1974–1979, Firm 1979 Employment ≥50,
No Further Presence Restriction in PA Labor Market)(A) Effect by years since displacement for workers born 1930–1959 (including
two standard error bands). Solid line represents coefficients of log-odds model ofmortality on years since displacement and basic other control variables. These are
the main effects corresponding to column (1), Table IV. Dashed lines represent twostandard-errors bands. (B) Simulated effect of displacement by current age andage at displacement for workers born 1920–1959. The lines represent coefficientsfrom a log-odds model of death on four dummies for current age interacted withdisplacement, to which dummies for years since displacement were added, as well
as a dummy for whether age at displacement was sixty or greater. Coefficients aretaken from column (3), Table IV. See text for details.

1286

QUARTERLY JOURNAL OF ECONOMICS

23. For men of the birth cohorts in our samples at ages forty to fifty, average life
expectancy is about 70–75 (National Center for Health Statistics 2006, Table 11).
24. Another possibility is that more frail displaced workers die first, reducing
the gap in mortality rates between displaced workers and nondisplaced workers.
Such dynamic selectivity would lead us to understate the effect of displacement on
mortality as workers age. However, because the number of deaths is small relative
to the overall population at risk of death, the average underlying health of the

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

estimates and two-standard-error bands. These estimates suggest strong immediate responses when the impact of a layoff onearnings, employment, job mobility, and other career outcomes ismost severe. The effects then stabilize at a permanent difference
as workers continue to suffer negative consequences of layoffs interms of reduced earnings.
As in Tables II and III, column (1) of Table IV includes workers
born after 1930 that we observe up to age 76. To further study thelong-run effect of displacement on death, column (2) shows thesame estimates when we include workers whom we observe closer
to the end of their lives (up to age 86).23 The short- and long-run
effects of job loss on the long-term mortality rate are similar but
somewhat smaller. This suggests that the proportional effect of
displacement on the mortality rate varies by age. Thus, column(3) includes interactions of displacement with age groups. The
excluded age group is 56–64. The specification also includes adummy for whether workers were displaced near retirement age(ages 60–69).
We find that workers younger than age 55 suffer significantly
higher percentage increases in mortality hazards in response to adisplacement than older workers. This difference is particularly
strong for workers under 45 but still present for workers aged 46
to 54. We also find that workers displaced near retirement age
appear to respond significantly less to job loss than workers displaced in middle age. This is perhaps not surprising, because older
workers are more likely to have access to Social Security benefits,
to company pension plans, or to Medicare. Even for workers not
yet at retirement age, access to federal disability insurance increases substantially at age 55, when workers can claim loss ofvocational qualifications to qualify for disability insurance (e.g.,
Chen and Van der Klaauw [2008]; Black, Daniel, and Sanders[2002]). Younger and middle-aged workers do not have access tosimilar mechanisms to smooth long-term earnings losses. Moreover, as further discussed in Section IV, for these workers the
reduction in lifetime earnings is larger because earnings losses
accrue over a longer period.24

JOB DISPLACEMENT AND MORTALITY

1287

population at risk is unlikely to be greatly affected by selection. Thus, the effect ofdynamic selection is likely to be small in the present case.
25. We obtain similar patterns with complete interactions of age-atdisplacement and years-since-layoff; however, with the increased number of parameters, the estimates become imprecise.
26. The figure also shows that for workers aged fifty and older at displacement, the effect actually increases somewhat 15 years after job loss, amounting toa U-shape, albeit a weak one. A similar pattern has been observed for the eventof losing a spouse, which leads to an initial increase in mortality from stress anda weak long-term rise of the mortality rate to the level of single individuals (e.g.,
Martikainen and Valkonen [1996]). The pattern is suggestive of an initial response
due to acute stress caused by the job loss, followed by a long-term cumulative impact of increased chronic stress due to lower earnings.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

As workers age, the total effect of displacement on mortality
is determined by the sum of the long-run mortality effect inrow (1) and the coefficients of the relevant interactions of displacement with year since displacement, current age, and age
at layoff; for example, for a worker under 45 and two to three
years after displacement, the effect of displacement on mortality
is 0.795 (=0.133 + 0.279 + 0.383). The resulting total effects ofdisplacement on mortality by current age for different ages ofseparation are shown in Panel B of Figure II. The entries in the
figure are obtained by summing up the relevant coefficients incolumn (3) of Table IV.25 In the first years after layoff, workers
at all ages experience large increases in the probability of deathin the range of 50%–100%. The effect declines as workers age
and settles at a positive long-run effect sixteen years after layoff
for all age groups (the effect in row (1) of Table IV). Only forworkers displaced after age sixty is the long-run effect close to
zero.26
To assess whether our results are robust to restricting oursample to those with at least six years tenure, columns (4) to(6) of Table IV replicate the estimates for a sample of workers
with at least three years of tenure. Column (4) shows that theresults are now larger both in the long and the short run. This
is not surprising, because the lower tenure requirement tends
to draw younger workers into the sample of displaced workers,
and we find that younger workers tend to show a larger mortality
effect. Once we consider a sample including older workers (column(5)) and control for age (column (6)), the results are quite similar
across tenure restrictions. Thus, reducing the tenure requirement
to at least three years of job tenure at displacement does notsubstantially affect our results, especially once we account fordifferences in the effect of job displacement on mortality by age.

1288

QUARTERLY JOURNAL OF ECONOMICS

III.C. The Reduction in Life Expectancy because of Job Loss

27. Life expectancy can be calculated as the sum of survivor probabilities over
the remaining potential age-years of an individual. The difference in life expectancies then is the sum of the differences in survivor probabilities. We experimented
with different windows of extrapolation and found that our results are not driven
by differences in extrapolated survival probabilities outside our sample period.
28. Because extrapolation of a quartic polynomial in age can be unstable, tocalculate life expectancies we worked with a linear age specification for calculating
differences in life expectancy. We find that the log-odds ratio is well approximatedby a linear function in age. Efron (1988) shows that with a linear age-component,
our logistic model would imply a Gompertz-distribution for lifetime in a continuous time setting, a distribution commonly used in the analysis of survival times.
We have also experimented with a quartic polynomial and found that it did not
significantly alter our results.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Because increased mortality affects workers over a long time
horizon, our estimates imply substantial reductions in life expectancy. The average loss in life expectancy can be used as asummary measure of the cost of job loss at mass layoff in terms oflife-years, much as the present discounted value of lifetime earnings losses provide summary measures in conventional analysesof the cost of job loss. In Table V, we present a range of estimatesof losses in life expectancies for alternative samples and ages.
Because some cohorts are still alive at the end of the sample period, to calculate the total cumulated effect of permanently
greater mortality hazards we have to make an assumption about
the development of mortality differences between laid-off workers
and the control group past our observation window.27 Specifically,
we assume that the proportional increase in the odds of deaththat we estimate for the highest observed age is maintained indefinitely. Given that the typical profile of the increase in the log
odds of dying is stable through older age ranges, and remains
so within the groups of our sample, this is a plausible assumption.28 All life expectancies are based on our most general sample
with no further employment restriction, include older workers,
and are calculated for workers with average annual earnings in
1974–1979 working in nonmanufacturing industries in 1979.
Because there are large increases in mortality right after layoff, Table V is based on estimates of survival curves that take
into account the dynamic response in mortality found in Table IV.
Because the effect of displacement status also differs by age,
we use our most general specification in Table IV to calculate
life expectancy. The last column of Table V shows that losses inlife expectancy are larger for workers losing their jobs in theirthirties and forties. Life expectancy of workers displaced in theirYears since displacement
categories
Current age categories
Displaced age ≥60
Nonmanufacturing(2) Stable job 1974–1979; norestrictions on earnings
1980–1986; 1920–1959 birth
year; tenure in 1979 at least
three years

74.85
74.99
75.22
75.58
76.01
76.64
74.97
75.10
75.29
75.58
76.00
76.62

76.45
76.56
76.73
76.99
77.37
77.92
76.56
76.67
76.85
77.11
77.49
78.05

30
35
40
45
50
55
30
35
40
45
50
55

Age atseparationLife
expectancy
given
displacedLife
expectancy
given notdisplaced

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

−1.57
−1.56
−1.53
−1.50
−1.43

−1.56
−1.51
−1.41
−1.36
−1.29
−1.59

−1.59

Lost yearsof life due todisplacement

Notes. All models include log of mean earnings, log of standard deviation of log quarterly earnings, one-digit industry dummies, and a linear age effect. The rows labeled (1) and(2) correspond to models equivalent to columns (3) and (6) of Table IV, respectively. The numbers are based on a linear extrapolation in age for cohorts still alive. See text for further
information.

Years since displacement
categories
Current age categories
Displaced age ≥60
Nonmanufacturing(1) Stable job 1974–1979; norestrictions on earnings
1980–1986; 1920–1959 birth
year; tenure in 1979 at least six
years

SampleDisplacement
interactions
included

TABLE VIMPACT OF JOB DISPLACEMENT ON LIFE EXPECTANCY BY AGE AT SEPARATION AND JOB TENURE

JOB DISPLACEMENT AND MORTALITY

1289

1290

QUARTERLY JOURNAL OF ECONOMICS

III.D. Sensitivity Analysis: Pooling Displaced
and Nondisplaced Workers
We have shown that our estimates are robust to the inclusion of an extensive set of alternative control variables as well as
industry and firm fixed effects. This suggests that any remaining bias from selective displacement or from sorting of workers
into firms is likely to be small, especially in the environment of
29. Because life expectancy is the sum of the survivor probabilities over theremaining potential lifetime, summing up reduced survivor probabilities over alonger period reduces predicted life expectancy. The effect of displacement onsurvivor probabilities varies with age because of the functional form of the logistic
function itself (as discussed in Sullivan and von Wachter [2007, Figure 3]), as well
as from explicit age-displacement interactions as included in Table IV.
30. The loss in the present discounted value (PDV) of earnings is a sufficient
statistic for the cost of job loss only if long-term health is exclusively an outcome of
optimally chosen inputs given a lifetime budget constraint. In this case, the observed reduction of lifetime would represent the optimal response to the decline
in resources following a job loss. However, only a small fraction of health expenditures are out of pocket. Moreover, health is likely to be affected by factors otherthan consumption or health inputs that are directly affected by job loss, such associal status. In that case, to obtain the total costs of job displacements, at least
part of the monetary value of the direct effect of layoffs on mortality should be
added to the PDV of earnings losses.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

fifties still declines, but by less. This difference arises both because younger workers are exposed to higher mortality rates overa longer period of time and because the increase in mortality rates
tends to be greater for younger workers.29 In our longer working
paper (Sullivan and von Wachter 2007), we confirm similar ordersof magnitude for samples restricted to workers who have some
employment in the period of job loss.
Given these substantial declines in life expectancy, an important question is how these losses should be treated with respect
to more conventional estimates of the cost of job loss. The typicalmeasure of the cost of displacements is based on the loss in present
discounted value of earnings. For the average worker in our hightenured sample, this amounts to about $200,000 at a real interest
rate of 4%. To make the losses in life expectancy comparable,
we can monetize them by choosing an estimate of the statisticalvalue of life. At about five million dollars per statistical life, aloss of one and one-half years would amount to a monetary loss of$100,000. Although they can be no more than broadly indicative,
values of this order of magnitude imply that the cost of job loss fordisplaced workers may be substantially underestimated by traditional summary measures such as the loss in lifetime earnings.30

JOB DISPLACEMENT AND MORTALITY

1291

31. The estimates may still be affected by sorting of workers into firms prior
to layoff. In our main analysis, we showed that the results are unaffected by
the inclusion of firm fixed effects, indicating that sorting plays no major role inour sample. Here, we cannot include firm fixed effects without losing precision.
Yet the results are robust to the inclusion of industry effects and of controls forcharacteristics of employers (average wage and average employment size from1974 to 1979 of a worker’s 1979 employer).
32. Our results confirm that the timing of the shock at the firm level appears
better captured by a sudden drop in employment. In the case of more gradual
employment reductions, it is generally difficult to assign the year of a distinct
shock occurring at the firm level. This is less of a problem at the individual levelin our main estimates, because an individual’s job separation always constitutes
a distinct treatment.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

PA in the early to mid-1980s, when employment reductions were
often severe. Moreover, in this period, most firms in the sectors
most prominent in our sample either were unionized or followed
seniority rules in dismissals (Abraham and Medoff 1984). Bothof these factors are likely to have reduced employers’ ability toselectively displace workers.
To address the question of a remaining bias from selective displacement directly, in this section we present estimates that pooldisplaced and nondisplaced workers. These are based on a specification similar to equation (1), but with the variable of interest
defined at the firm level, rather than the worker level. Because
the mass-layoff dummy now varies only at the firm level, the estimates compare the change over time in mortality of all workers
present at a firm experiencing a mass layoff with that of similar
workers at stable firms. By construction, these “intent-to-treat”
estimates are not affected by selection at the time of job loss or by
misclassification of dying workers as job losers.31
The results shown in Table VI are consistent with the findingsof our main analysis. Working at the firm level considerably reduces our degrees of freedom, so to maximize precision, samples
include workers born after 1920 and the specification includes
fewer interactions. In the first two columns, mass layoff is defined as in our main estimates—employment falling 30% below
its previous peak. In many cases, however, employment declines
are gradual, so firms may be laying off workers before they reachthe 30% threshold. Thus, in the third and fourth columns, we
present a specification in which mass layoff is defined as a sudden
large drop in employment at the firm.32
To compare the magnitudes of the estimates in Table VI tothose in earlier tables, columns (2) and (4) show estimates divided by 0.3, which is approximately the effect of mass layoff onRescaled forcomparison(2)

Coefficientestimate(3)

Panel A: Average effect of mass layoff at 1979 employer on hazard of death
0.066
0.221
0.042
(0.027)(0.023)

Coefficientestimate(1)

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Notes. Entries are coefficients on firm-level mass-layoff dummy (Panel A) and its interactions with year since layoff (Panel B) in a logit model of the event of dying in a given year.
All models include a quartic in age, year effects, the log of average quarterly earnings in 1974–1979, the log of the standard deviation of log quarterly earnings in 1974–1979, and theaverage of the 1979 employer’s employment and quarterly earnings from 1974 to 1979 as control variables. In addition, the models include six dummies for 1979 industry. Columns(2) and (4) divide the coefficients in columns (1) and (3) by 0.3, the effect of mass layoff at the firm level on job separations. Standard errors clustered at the level of the 1979 employer
are in parentheses.

0.158

0.092

0.916

0.139

Rescaled forcomparison(4)

Year in which firm
employment drops 30%
relative to employment in yeart−2

Panel B: Dynamic effect of mass layoff at 1979 employer on hazard of death, 1980–2006
Effect in year of mass layoff
0.252
0.841
0.275
(0.087)(0.101)
Effect 1–15 years after year of mass layoff
0.059
0.196
0.028
(0.036)(0.032)
Effect 16+ years after year of mass layoff
0.029
0.095
0.047
(0.035)(0.029)Includes mean earnings and employment of 1979 employer
Yes
Yes
Includes effects for 1979 industry
Yes
Yes

Average effect in 1980–2006

Mass layoff definition:

Year in which firm
employment drops 30%
relative to peak employment in
1974–1979

TABLE VI
INTENT-TO-TREAT ESTIMATES OF THE EFFECT OF MASS-LAYOFF AT FIRM LEVEL ON MORTALITY POOLING MOVERS AND STAYERS (WORKERSIN STABLE EMPLOYMENT 1974–1979, FIRM 1979 EMPLOYMENT ≥50, BORN 1920–1959, NO FURTHER PRESENCE RESTRICTION
IN PA LABOR MARKET)

1292
QUARTERLY JOURNAL OF ECONOMICSJOB DISPLACEMENT AND MORTALITY

1293

IV. POTENTIAL CHANNELS OF MASS-LAYOFF EFFECT ON MORTALITY
Our estimates of the short- and long-run effects of displacement on the mortality hazard in Figure II roughly parallel the
short- and long-run effects of displacement on earnings shown in
Figure I and reported in JLS and elsewhere. In the short run,displacement is associated not only with a sharp drop in meanearnings, but also with increased unemployment, job, region, and
33. If we estimate the same regression models with an individual dummy forthe event of job displacement as dependent variable, the coefficient is 0.299 and0.289 for the gradual and sudden drop definitions of mass layoff, respectively. This
number also results from the fraction displaced shown in Appendix Table 1 of ourlonger working paper.
34. Note that the identification strategy differs for the two sets of estimates;in Table VI, the effect of the firm level event on separators and nonseparators gets
added, whereas in the estimates in Table IV the estimates result from subtracting
the effect on nonseparators from that of separators.
35. In an additional indication that selective displacement of less healthy
workers mattered little in a similar context, Eliason and Storrie (2007) showthat using detailed information on predisplacement occupation, health status,and demographics does not affect their estimates of the impact of job loss during
establishment closures during 1986–1987 on health in Sweden (see the second andthird sets of estimates in their Table VI).

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

job mobility.33 For either definition of firm-level mass layoff, therescaled long-run effects of displacement on mortality shown inthe bottom of Panel B in Table VI are of the same order of magnitude as the estimates shown in row (1) of Table IV.34 Table VI alsosuggests a large effect of displacement on mortality in the yearof layoff. This suggests that dropping separators who die in theyear of layoff in our main analysis may lead us to underestimate
the full effect of displacement on mortality shown in Table II.
Correspondingly, row (1) of Table VI shows that the average effect of displacement on the mortality hazard, properly rescaled, issomewhat larger.
These findings suggest that our main results are unlikely tobe driven by selective displacement of less healthy workers. Twofurther results contained in the Appendix and further discussed
in our longer working paper indicate that firms with greater flexibility in selecting which workers to lay off did not systematicallydisplace their least healthy workers. First, we show that the effectof displacement on mortality does not decline with the fraction ofworkers involved in a mass layoff. Second, we also find that otherseparators (those permanently leaving their long-term employers
but not during a mass layoff) do not have higher mortality rates.35

1294

QUARTERLY JOURNAL OF ECONOMICS

36. An extensive discussion of these results is contained in our longer working
paper version (Table 9, Sullivan and von Wachter [2007]).
37. As noted previously, there are other potentially relevant channels to which
our data do not speak directly, such as the loss of health insurance or the role ofthe family environment. Some of these channels may be associated with earnings losses—for example, if lower-paying jobs are less likely to provide healthinsurance—but may have independent effects. However, it is currently not possible to link information on health insurance, family status, or other worker or firm
characteristics to our data.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011industry mobility, as well as high earnings instability.36 Our results suggest these effects may lead to acute stress that substantially raises the mortality hazard. After this initially turbulent
phase, in the medium to long run the majority of job losers settle into relatively stable employment at substantially lower meanearnings and modestly higher employment instability and earnings variability. Our empirical findings are consistent with a reduction in resources and increased instability leading to reduced
investments in health and chronic stress that lead to a smaller,
but longer term increase in the mortality hazard.37
To gain insight into the relative importance of some of thechannels through which job loss could affect the long-run mortality hazard, we compare our estimates of the “reduced form”
effect of displacement on mortality to what one would expect on
the basis of displacement’s long-run effect on the mean and variability of workers’ earnings and the correlation of those factors
with mortality shown in Table III. In our PA data, displacement
reduces the mean of long-run earnings by 15%–20%. Taken atface value, the estimated −.5 correlation of average earnings withmortality would imply that we expect an increase in mortalityof about 7.5%–10% for workers with high job attachment (0.15
or 0.2 times 0.5). Thus about 50%–75% of the long-run effect ofmass layoff on mortality reported in Table IV, about 0.1 to 0.15,
could be explained by the observed declines in average earnings.
Similarly, in the medium to long run, the standard deviation oflog quarterly earnings increases on average by about 16% after amass layoff (results not shown). At a coefficient of −0.2 (Table III),
this implies an increase in the probability of dying of about 3.2%.
Although the order of magnitude of this effect is much lower thanthe potential impact of earnings, it could still account for about
20% of the mass-layoff effect, at least in the short run.
In addition to these adverse effects, job loss may have beneficial effects on health. At least while not employed, displaced
workers may be able to spend more time investing in their health

JOB DISPLACEMENT AND MORTALITY

1295

IV.A. Evidence from Individual-Level Models
To further explore the role of long-term earnings losses using
the longitudinal data at our disposition, we also directly relate
38. Exploiting time-use data, Krueger and Mueller (2008) find some evidence
that unemployed workers sleep more, spend more time purchasing goods and services (which includes obtaining medical services), and spend more time in leisurely
activities (although the majority of the difference is explained by watching television). They do not find that the unemployed spend more time on personal care(which includes health-related self-care) or sports (their Table 3). They confirm
findings from the previous literature that the unemployed are more unhappy or
sad on the average (their Table 4).
39. In fact, we do find a beneficial effect of lower employment in 1974–1979 on
mortality thereafter (Table III). However, spending time in nonemployment after
displacement has no statistically significant effect on mortality (Table VII). Such
potential beneficial effects of job loss on health are suggested by, among others,
Ruhm (2000), who shows that mortality at the state level declines in recessions.
We relate our results to Ruhm’s (2000) findings in the conclusion.
40. Reverse causation is less of a problem, because average earnings andearnings variability are measured in 1974–1979, whereas death is measured from1987 to 2006 or from 1980 to 2006. In his study of Swedish lottery winners, Lindahl(2005, Appendix Table 2) shows that the effect of controlling for initial healthconditions tends to reduce the correlation between mortality and earnings by abouta third. Were this result to apply to our sample of high-attachment workers (whoare likely of better health than the sample of older workers studied by Lindahl),
the predicted role of earnings in explaining the mass-layoff effects is reduced byabout a third.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

and face reduced exposure to workplace and driving accidents.38
Although this channel may be present in our sample as well,39
on balance, the health of job losers we study did not benefit fromshort-term employment reduction. This may be because in the
short run, when employment reduction is more frequent, job loss
also involves very large earnings losses and a stressful adjustment period involving multiple job changes, including changes inindustry or location. In the long run, the majority of workers westudy did not benefit from reduced employment but instead suffered from continued employment at significantly lower earnings
with a higher degree of uncertainty.
Overall, these considerations suggest that the impact of displacement on the mean and variability of earnings may explaina large fraction of the increase in the long-run mortality hazard
that we estimate. Of course, if frail people have large earnings
losses, higher earnings instability, and higher death rates, the estimates underlying the above decomposition may not be causal
parameters, and the predicted impact of each single mechanism
is likely to be overstated.40 Nevertheless, among the channels we
can measure, these calculations suggest that long-term earnings
losses are likely to play a dominant role in explaining our results.

1296

QUARTERLY JOURNAL OF ECONOMICS

IV.B. Evidence from Group-Level Models
To mitigate the problem of omitted variable bias, we replicated our individual-level analysis at the group level. A long
41. Specifically, the figure shows coefficients on dummies for deciles of changes
in the log of average annual earnings from 1974–1979 to 1980–1986 in a logistic
model of death. The omitted category is that for earnings changes in the range[−0.05, 0.05]. Other variables include year effects, a fourth-order polynomial inage, and the average and standard deviation of earnings 1974–1979. To maximize
sample sizes, the figure shows results based on the sample that includes older
workers (see Table IV). The different lines correspond to different restrictions onpresence in the Pennsylvania labor market during the period 1980–1986. All but
the coefficients on the dummies [−0.2, −0.05], [0.05, 0.15], and [0.15, 0.3] arestatistically significantly different from zero.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

the size of earnings losses at job displacement to the long-term
increase in the hazard of death. This is shown in Figure III,
Panel A. The figure shows the increase in mortality by decilesof long-term changes in mean earnings, controlling for age, year,
and past average earnings.41 On average, those workers who havemore substantial earnings losses also experience larger long-term
increases in mortality hazard.
We also directly included the long-term earnings change forboth displaced workers and the comparison group as a control
variable in our main logistic model. The results suggest a strong
correlation between the change in average earnings and the longterm mortality hazard (model (1), Panel A, Table VII). Once
we condition on earnings changes, the effect of the mass-layoff
dummy becomes numerically small and insignificant. To directly
assess the potential role of the increase in the variability of earnings at job loss, we also estimated models that included the change
in the standard deviation of quarterly earnings from 1987 to 1991
as an additional control variable (model (2), Panel A, Table VII). As
expected, an increase in the standard deviation has a positive significant effect on death rates, and the impact of earnings changes
on long-term mortality declines somewhat. The results are robust
if we divide the sample by degree of labor force attachment or
displacement status.
Estimates from the individual-level models appear to confirm the results of our approximations based on the estimates inTable III. However, we do not interpret these results as necessarily indicating causal channels running from earnings losses or
earnings variability to mortality, because there may be omitted
variables driving both earnings losses and mortality increases,
such as differential increases in depression in response to layoffs.

JOB DISPLACEMENT AND MORTALITY

1297

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

FIGURE III
Mortality Rate by Size of Earnings Change from 1974–79 to 1980–1986 at
Individual Level and at Cell Level (Sample of Men in Stable Employment
1974–1979, Firm 1979 Employment ≥50)
(A) Differences in mortality by deciles of the change in average earnings (relative to workers with no change in earnings), alternative degrees of presence in PA
labor force in 1980–1986, workers born 1920–1959. Coefficients on dummies for
deciles of changes in the log of average annual earnings from 1974–1979 to 1980–1986 in a logit model of death. The omitted category are earnings changes in therange [−0.05, 0.05]. Other variables include year effects, a quartic in age, and theaverage and standard deviation of earnings 1974–1979. (B) Effect of displacement
on mortality and annual earnings by cells of industry and local unemployment
rate in 1979 (28 cells), work every year in PA labor force 1980–1986, workers
born 1930–1959. Cells corresponding to model (7) in Table VII. The slope of theregression line in the figure corresponds to the coefficient in the last column of thetable. The effect on annual earnings refers to the effect of displacement on changes
in the log of average annual earnings from 1974–1979 to 1980–1986 by cell. The
effect on mortality rate refers to the effect of displacement on mortality by cell.
Both models include cell-level dummies. The regression line is from a regressionof mortality effects on earnings effects by cell level weighted by cell size. See thetext for further information.

(2)

(1)

All
workers

Only
displacedAllworkers——−0.502(0.160)——−0.433(0.186)0.155(0.054)−0.054(0.090)Only
displaced

Work every year in
PA during 1980–1986

Panel A: Log-odds model of death as function of displacement and changes in earnings
Dummy for job loss during mass layoff−0.022—−0.011(0.040)—(0.048)Percent change in long-term average earnings−0.861−0.672−0.669(0.060)(0.096)(0.102)
Dummy for job loss during mass layoff−0.041—−0.060(0.048)—(0.051)
Percent change in long-term average earnings−0.391−0.432−0.524(0.089)(0.150)(0.110)Change in log standard deviation of log earnings
0.201
0.184
0.203
(0.026)(0.046)(0.028)At least one transition to nonemployment
0.012−0.0760.025(0.047)(0.088)(0.048)

Coefficient in logit model

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Model

No work restriction
during 1980–1986

TABLE VII
EFFECT OF JOB DISPLACEMENT ON DEATH CONTROLLING FOR CAREER OUTCOMES AFTER DISPLACEMENT (WORKERS IN STABLE EMPLOYMENT
1974–1979, FIRM 1979 EMPLOYMENT ≥50, BORN 1930–1959)

1298
QUARTERLY JOURNAL OF ECONOMICS

−0.0042(0.0006)−0.0043(0.0006)−0.0043(0.0006)−0.0042(0.0006)−0.0042(0.0006)−0.0027(0.0008)−0.0028(0.0008)−0.0024(0.0009)−0.0026(0.0003)−0.0025(0.0005)−0.0046(0.0003)−0.0046(0.0003)−0.0047(0.0003)−0.0046(0.0003)−0.0046(0.0003)

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

−0.0019(0.0014)−0.0022(0.0014)−0.0022(0.0005)−0.0018(0.0004)−0.0016(0.0009)

Predicted
earnings
changeNotes. The models in Panel A show coefficients for logit models of the annual hazard of death controlling for year and industry dummies, a quartic in age, pre–mass layoffcareer outcomes (log average quarterly earnings, log of standard deviation of log quarterly earnings, number of quarters in nonemployment, average quarterly growth in earnings,
all measured from 1974 to 1979), as well as changes in career outcomes from 1974–1979 to 1987–1991 (percent change in the standard deviation of log quarterly earnings, change in
the average growth rate of quarterly earnings). The models in Panel B report coefficients on linear probability models of the hazard of death on either the actual individual change inaverage quarterly earnings or the change in earnings predicted by interactions of cell-level dummies with mass-layoff dummy; all models control for cell level dummies, year dummies,
industry dummies, a quartic in age, and pre–mass layoff career outcomes. The coefficient on the individual level earnings change varies across specifications because different celllevel dummies are included. Standard errors are in parentheses.

(7) Seven industry groups in 1979 times four groupsof county unempl. in 1979

(6) Ten groups of average 1974–1979 employment of 1979 employer(5) Ten groups of average 1974–1979 earnings of 1979 employer(3) Four age groups in 1979 times four groups of avg.
earnings in 1974–1979(4) Four age groups in 1979 times seven industry groups in 1979

Cells used to predict change in average quarterly earnings

Individual
earnings
changePredicted
earnings
changeIndividual
earnings
changePanel B: Linear probability models of death on individual level change in average earnings and change
in average earnings predicted by interaction of displacement and cell-level dummies
No work restriction
Work every year induring 1980–1986
PA during 1980–1986

TABLE VII
(CONTINUED)JOB DISPLACEMENT AND MORTALITY

1299

1300

QUARTERLY JOURNAL OF ECONOMICS

42. See, for example, Kletzer (1989), JLS (1993), Neal (1995), and Farber(2003).
43. Such rents could arise due to contractual premiums (Beaudry and
DiNardo 1991), job search (Topel and Ward 1992), and firm, industry, and regional wage premiums (e.g., Krueger and Summers [1988]; Abowd, Creecy, andKramarz [2002]).
44. The cell-level model is implemented in two steps. First, we regressedindividual earnings and mortality on characteristics such as baseline earnings,
baseline standard deviation, a quartic in age, and year effects; in addition, we
included cell-level dummies and interactions between cell-level dummies and themass-layoff dummy. The coefficients on the interaction are used in the second
step. Second, we regressed cell-level changes in mortality on cell-level earnings
losses, weighting by the inverse sampling variance of the cell-specific mortality
increases. The resulting slope coefficient is equal to the two-stage least-squares
estimate from using the interactions of group dummies and mass-layoff dummy
as an instrument for earnings. See Section 3 of our longer working paper for the
relevant equations and further discussion (Sullivan and von Wachter 2007).

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

literature suggests that there are systematic differences in theeffects of job loss on earnings,42 some of which arise fromindustry-, firm-, or job-match-specific rents workers receive on
the job that are permanently lost as workers change employers(e.g., von Wachter and Bender [2006]).43 Because at least part ofthe loss in earnings for workers leaving high-paying industries orlarge employers is likely to be uncorrelated to health changes atthe cell level, we expect the omitted-variable bias of the cell-level
estimates to be lower than that of individual-level estimates.
To implement the group-level analysis, we use the detailed information available from our administrative data to divide workers into cells based on age at layoff, industry and local labormarket conditions before layoff, and average employment size or
average wage of the 1979 employer. We then run a regression ofincreases in the mortality hazard on losses in average earnings
at the cell level. The model controls for permanent differencesin average health and earnings across groups through cell-leveldummies. Similarly, it accounts for a common effect of displacement on all groups through a mass-layoff dummy.44
The results suggest that even when we use cell-level variation, the effect of earnings losses on mortality is economically andstatistically significant. Panel B of Table VII shows the coefficientestimates on actual earnings changes in a linear probability model
as a benchmark, as well as the slope coefficient in the cell-level
model. The effect of earnings losses on mortality at the cell levelis 40%–50% of the effect at the individual level for the samples
with no or a low work requirement. The cell-level effect is somewhat smaller but still statistically and economically significant
for the high-attachment sample, yet standard errors increase as

JOB DISPLACEMENT AND MORTALITY

1301

IV.C. Earnings Losses and Life Expectancy
The analysis thus far has concentrated on the impact of longterm declines in quarterly earnings, irrespective of their duration.
However, standard models of health investment refer to lifetime
resources as the relevant earnings concept. Thus, we calculated
the present discounted value (PDV) of lifetime earnings losses
following a job loss in a mass layoff by age group (Sullivan and
von Wachter, 2007).46 We then compared losses in life expectancy
taken from Table V with the corresponding PDV of earnings losses
by age group. The correlation is strong, monotonic, and numerically large; as the percentage loss in the PDV of earnings doubles
for thirty- to forty-year-olds relative to fifty- to sixty-year-olds, life
expectancy declines by about 25%. Young workers have not only
45. The mean squared error of the group-level regressions is a test statistic foroveridentification with a limiting chi-squared distribution with degrees of freedom
equal to the number of cells minus the number of parameters (e.g., Angrist [1991]).
In results not reported here, for none of our group-level models can we reject
the overidentifying restrictions at any reasonable level of statistical significance.
This supports an underlying model of a constant proportional effect of earnings on
mortality and gives no indication of endogeneity of cell-level earnings changes withrespect to mortality that differs across cells. The fact that our cell-level estimates
tend to be smaller than individual-level estimates suggests that the latter indeed
are affected by omitted-variable bias common across groups.
46. To do so, we allowed both the short- and long-term effects of job loss on
earnings to vary by either ten-year or five-year age groups. Because we do nothave complete earnings histories after job loss for all workers, we assume that theearnings loss decays at the same speed of reversion observed between years six
and eleven after a job loss, and eventually stays fixed at zero. The resulting values
are robust to alternative specifications. The percentage decreases in the present
discounted value of earnings of our preferred specification for the age groups in
Table V are, going from youngest to oldest, 12.3%, 10.8%, 9.2%, 7.4%, 5.6%, and3.8%, respectively.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

well. Our findings are quite similar for the different definitions ofcells shown in the table (models (3)–(7)).
Panel B in Figure III displays the corresponding cell-level averages and a linear regression line for the high-attachment sample for the results of model (7) in Table VII. The figure displays aclear negative and relatively precise association between earnings
losses and mortality increases at the cell level, although there is
an important degree of variation left. Overall, the results confirm
that earnings losses after job displacement appear to be strongly
correlated with increases in mortality rates.45 Because we cannot exclude that health and earnings responses to layoff may becorrelated across cells as well, we do not interpret the resulting
cell-level estimates as causal effects of earnings changes on thelong-term mortality hazard.

1302

QUARTERLY JOURNAL OF ECONOMICS

V. CONCLUSION
This paper uses administrative data covering over fifteen
years of quarterly earnings and employer records matched to information on date of death to study the effects of job displacement
on mortality of high-seniority male workers losing their jobs in
PA in the early to mid-1980s. To measure an event plausibly exogenous to workers’ own health outcomes, we analyze job losses
occurring when employers experience mass layoffs affecting atleast 30% of their work forces. To further control for selection, we
also control for workers’ average earnings and a range of career
outcomes in the period before job loss and present selection-freeestimates pooling movers and stayers. The results suggest a particularly pronounced increase in mortality during the period immediately following job loss and a long-run increase of 10%–15%in the annual probability of dying persisting for at least the next
twenty years. These effects, robust across alternative samples
and specifications, are consistent with strong responses to bothacute and chronic stress associated with worsened labor market
opportunities.
To assess the channels underlying the mass-layoff effect, weanalyze the correlation of long-run career outcomes with mortality. We show that the mean and standard deviation of earnings
during a baseline period have high and significant correlations
with mortality in a later follow-up period. Together with estimates of the effects of mass layoffs on long-run career outcomes,
these results suggest that an important fraction of the effect of job

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011higher average mortality increases after a job loss (Table IV), butalso the largest losses in remaining lifetime (Table V) and in lifetime earnings. Thus, it is not the oldest workers who are mostaffected, but those at prime working age who are exposed to thenegative consequences of job loss over a longer period of time.
Overall, we interpret the results in Section IV as suggestingthat at least for job losses involving earnings declines as dramatic as those in PA in the early to mid-1980s, the sources of
the increase in mortality are likely to be associated with longterm losses in average earnings and increases in the variability ofearnings. This may include direct effects of reduced earnings andincreased variability, but clearly also include stress, adjustment
costs, and other factors correlated with both long-term earnings
declines and mortality.

JOB DISPLACEMENT AND MORTALITY

1303

47. See, for example, Aaronson and Sullivan (1998). The gains in health during recessions measured by Ruhm (2000) may be due to changes in hours worked
by employed workers or to changes in employment rates of those with less strong
job attachment.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

loss on mortality can be attributed to persistent losses in earnings.
This is confirmed by a direct analysis of differences in mortality
responses by groups of workers with differential earnings losses at
job displacement associated with industry or employer affiliation
before displacement.
These results suggest that events in the labor market shaping workers’ careers also have long-run effects on health outcomes.
The losses in life expectancy implied by our estimates show thatthese effects can be large. A worker displaced in mid-career can
expect to live about one and one-half years less than a nondisplaced counterpart. The reduction in life expectancy is smaller for
older workers, who experience lower lifetime earnings losses andare exposed to increased mortality for a shorter period of time.
Our results do not speak to the role of noneconomic factors suchas stress, self-worth, and happiness. Yet they suggest that an important avenue for future research would be to examine whether
the negative health consequences of mass layoffs can be prevented
by providing assistance that stabilizes the level and variance of
earnings. Similarly, although the experience of displaced workers
has been found to be similar in other states and time periods, it isimportant to replicate our study of male workers displaced in PA
in the 1980s for other regions and time periods, and for women.
Finally, our results are not in conflict with recent worksuggesting that mortality declines during recessions, possibly
because of healthier lifestyles and a reduction in accidents related
to work or commuting (Ruhm 2000). First, although recessions
do increase the number of high-tenure displaced workers, whose
mortality we find to be elevated, such workers are a small fractionof those affected by economic downturns.47 Second, Ruhm (2000)
focuses on fluctuations in mortality that are contemporaneouswith cyclical fluctuations in economic activity, whereas the bulk of
the effects we observe take place many years after displacement.
Finally, from the perspective of the aggregate economy, a recession is a relatively minor event that only marginally reduces thepresent value of lifetime income for the representative workerconsumer and at the same time provides a modest increase inleisure. For an individual high-tenure worker, however, job loss
is a major economic setback that significantly reduces lifetime

1304

QUARTERLY JOURNAL OF ECONOMICS

income, without a corresponding reduction in work activity. Thus,
the workers we study, although having fewer lifetime resources,
did not enjoy the increases in leisure, healthier lifestyles, or
reductions in accidents that may explain Ruhm’s results.
APPENDIXMORTALITY RATES IN DIFFERENT PERIODS BY DISPLACEMENT STATUS AND BY SIZE
OF EMPLOYMENT DROP OF 1979 EMPLOYER
DisplacedNon–mass Displaced Displaced more than
Range
All
Same firmlayoff30%–60% 60%–90% 90% below
of years workers 1974 to 1986 separators below peak below peak
peak
6.764
(0.143)4.167(0.181)7.407(0.227)10.815(0.427)

Panel A: No work restriction in 1980–1986
6.013
7.253
7.191
7.764
(0.191)(0.362)(0.384)(0.435)
3.446
4.334
5.012
5.132
(0.234)(0.451)(0.516)(0.569)
6.790
7.837
7.520
8.033
(0.308)(0.571)(0.596)(0.671)
9.639
12.16311.12912.847(0.570)(1.108)(1.130)(1.324)8.449(0.640)5.518(0.830)9.677(1.039)11.892(1.803)

1987–
2006
1987–
1991
1992–
1996
1997–
2006

6.343
(0.152)3.745(0.189)6.994(0.242)
10.347(0.458)Panel B: Work every year 1980–1986
6.013
6.682
6.219
7.319
(0.191)(0.434)(0.444)(0.509)
3.446
3.762
4.134
4.798
(0.234)(0.526)(0.583)(0.664)
6.790
6.990
6.680
7.639
(0.308)(0.673)(0.698)(0.789)9.639
12.211
9.581
12.010(0.570)(1.383)(1.298)(1.541)7.645(0.733)4.202(0.875)8.790(1.191)12.351(2.205)

Notes. Deaths per thousand per year. Standard errors in parentheses. Panel A: Displaced workers left
jobs in a year in which their former firms’ employment was 30% or more below its 1974–1979 peak. Panel B:
Displaced workers left jobs in a year in which their former firms’ employment was 30% or more below its
1974–1979 peak; nondisplaced workers remained at their 1979 firms through 1986.

RESEARCH DEPARTMENT, FEDERAL RESERVE BANK OF CHICAGODEPARTMENT OF ECONOMICS, COLUMBIA UNIVERSITY, NBER, CEPR, AND IZA

REFERENCES
Aaronson, Daniel, and Daniel Sullivan, “The Decline of Job Security in the 1990s:
Displacement, Anxiety, and Their Effect on Wages,” Federal Reserve Bank ofChicago, Economic Perspectives, 22 (1998), 17–43.
Abowd, John, Robert Creecy, and Francis Kramarz, “Computing Person andFirm Effects Using Linked Longitudinal Employer–Employee Data,” mimeo,
Cornell University, 2002.

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

1987–
2006
1987–
1991
1992–
1996
1997–
2006

JOB DISPLACEMENT AND MORTALITY

1305

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Abraham, Katharine G., and James L. Medoff, “Length of Service and Layoffs inUnion and Nonunion Work Groups,” Industrial Labor Relations Review, 38(1984), 87–97.
Angrist, Joshua, “Grouped Data Estimation and Testing in Simple Labor Supply
Models,” Journal of Econometrics, 47 (1991), 243–266.
Beaudry, Paul, and John DiNardo, “The Effect of Implicit Contracts on the Movement of Wages over the Business Cycle: Evidence from Micro Data,” Journalof Political Economy, 99 (1991), 665–688.
Black, Dan, Kermit Daniel, and Seth Sanders, “The Impact of Economic Conditions
on Participation in Disability Programs: Evidence from the Coal Boom andBust,” American Economic Review, 92 (2002), 27–50.
Browning, Martin, Anne Moller Dano, and Eskil Heinesen, “Job Displacement andStress-Related Health Outcomes,” Health Economics, 15 (2006), 1061–1075.
Burgard, Sarah, Jennie Brand, and James House, “Causation and Selection in the
Relationship of Job Loss to Health in the United States,” mimeo, Universityof Michigan, 2005.
——, “Toward a Better Estimation of the Effect of Job Loss on Health,” Journal of
Health and Social Behavior, 48 (2007), 369–384.
Card, David, Raj Chetty, and Andrea Weber, “Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market,” Quarterly Journal of Economics, 122 (2007), 1511–1560.
Chan, Sewin, and Ann Huff Stevens, “Job Loss and Employment Patterns of Older
Workers,” Journal of Labor Economics, 19 (2001), 484–521.
Chen, Susan, and Wilbert van der Klaauw, “The Work Disincentive Effects of the
Disability Insurance Program in the 1990s,” Journal of Econometrics, 142(2008), 757–784.
Couch, Kenneth, and Dana Placzek, “The Earnings Impact of Job Displacement
Measured with Longitudinally Matched Individual and Firm Data,” American
Economic Review (forthcoming).
Cutler, David, Angus Deaton, and Adriana Lleras-Muney, “The Determinants ofMortality,” Journal of Economic Perspectives, 20 (2006), 97–120.
Darity, William, and Arthur Goldsmith, “Social Psychology, Unemployment, andMacroeconomics,” Journal of Economic Perspectives, 10 (1996), 121–140.
Deaton, Angus, “Inequalities in Income and Inequalities in Health,” in The Causes
and Consequences of Increasing Inequality, Finis Welch, ed. (Chicago: University of Chicago Press, 2001).
Deaton, Angus, and Christina Paxson, “Mortality, Education, Income and Inequality among American Cohorts,” NBER Working Paper No. 7140, 1999.
Efron, Bradley, “Logistic Regression, Survival Analysis, and the Kaplan–Meier
Curve,” Journal of the American Statistical Association, 83 (1988), 414–425.
Eliason, Marcus, and Donald Storrie, “Latent or Lasting Scars: Swedish Evidence
on the Long-Term Effects of Job Displacement,” Journal of Labor Economics,
24 (2006), 831–856.
——, “Does Job Loss Shorten Life?” Göteborg University Department of Economics
Working Paper Series No. 153, 2007.
Farber, Henry, “Job Loss in the United States, 1981–2001,” Princeton University
Industrial Relations Section Working Paper No. 471, 2003.
Gallo, W. T., H. M. Teng, T. A. Falba, S. V. Kasl, H. M. Krumholz, and E. H. Bradley,“The Impact of a Late-Career Job Loss on Myocardial Infarction and Stroke:
A 10-Year Follow-Up Using the Health and Retirement Survey,” Occupational
and Environmental Medicine, 63 (2006), 683–687.
Gruber, Jonathan, “The Consumption Smoothing Benefit of Unemployment Insurance,” American Economic Review, 87 (1997), 192–205.
Hildreth, Andrew, Till von Wachter, and Elizabeth Weber Handwerker, “Estimating the ‘True’ Cost of Job Loss: Evidence Using Matched Data from California
1991–2000,” mimeo, Columbia University, 2008.
Hill, Mark E., and Ira Rosenwaike, “The Social Security Administration’s Death
Master File: The Completeness of Death Reporting at Older Ages,” Social
Security Bulletin, 64 (2002), 45–51.
Jacobson, Louis, Robert LaLonde, and Daniel Sullivan, “Earnings Losses of Displaced Workers,” American Economic Review, 83 (1993), 685–709.

1306

QUARTERLY JOURNAL OF ECONOMICS

Downloaded from qje.oxfordjournals.org at Columbia University on September 21, 2011

Kletzer, Lori, “Returns to Seniority after a Permanent Job Loss,” American Economic Review, 79 (1989), 536–43.
Kodrzycki, Yolanda K., “Using Unexpected Recalls to Examine the Long-Term
Earnings Effects of Job Displacement,” Federal Reserve Bank of Boston Working Paper 07-2, 2007.
Krueger, Alan, and Andreas Mueller, “The Lot of the Unemployed: A Time Use
Perspective,” IZA Discussion Paper No. 3490, 2008.
Krueger, Alan, and Lawrence Summers, “Efficiency Wages and the Inter-industry
Wage Structure,” Econometrica, 56 (1988), 259–293.
Kuhn, Andreas, Rafael Lalive, and Josef Zweimüller, “The Public Health Costsof Unemployment,” Cahiers de Recherches Economiques du Départementd’Econométrie et d’Economie politique (DEEP) 07.08, Université de Lausanne,
Faculté des HEC, DEEP, 2007.
Lindahl, Mikael, “Estimating the Effect of Income on Health and Mortality Using
Lottery Prizes as Exogenous Source of Variation in Income,” Journal of Human
Resources, 40 (2005), 144–168.
Martikainen, Pekka, Netta Mäki, and Markus Jäntti, “The Effects of Unemployment on Mortality Following Workplace Downsizing and Workplace Closure:
A Register-Based Follow-Up Study of Finnish Men and Women during Economic Boom and Recession,” American Journal of Epidemiology, 165 (2007),
1070–1075.
Martikainen, Pekka, and Tapani Valkonen, “Mortality after Death of a Spouse in
Relation to Duration of Bereavement in Finland,” Journal of Epidemiology
and Community Health, 50 (1996), 264–268
National Center for Health Statistics, “United States Life Tables, 2003,” National
Vital Statistics Reports, 54 (2006), 1–40.
Neal, Derek, “Industry-Specific Human Capital: Evidence from Displaced Workers,” Journal of Labor Economics, 13 (1995), 653–677.
Olson, Craig, “The Impact of Permanent Job Loss on Health Insurance Benefits,”
Princeton University Industrial Relations Section Working Paper No. 684,
1992.
Rege, Mari, Kjetil Telle, and Mark Votruba, “The Effect of Plant Downsizing on
Disability Pension Utilization,” Journal of European Economic Association,
forthcoming.
Ruhm, Christopher, “Are Workers Permanently Scarred by Job Displacements?”
American Economic Review, 81 (1991), 319–24.
——, “Are Recessions Good for Your Health?” Quarterly Journal of Economics, 115(2000), 617–650.
Schoeni, Robert, and Michael Dardia, “Estimates of Earnings Losses of Displaced
Workers Using California Administrative Data,” PSC Research Report No.
03–543, 2003.
Smith, James, “Healthy Bodies and Thick Wallets: The Dual Relation between
Health and Economic Status,” Journal of Economic Perspectives, 13 (1999),
145–166.
Stevens, Ann Huff, “Persistent Effects of Job Displacement: The Importance ofMultiple Job Losses,” Journal of Labor Economics, 15 (1997), 165–188.
Sullivan, Daniel, and Till von Wachter, “Mortality, Mass-Layoffs, and Career Outcomes: An Analysis Using Administrative Data,” NBER Working Paper No.
13626, 2007.
——, “Average Earnings and Long-Term Mortality: Evidence from Administrative
Data,” American Economic Review: Papers and Proceedings, 99 (2009), 133–
138.
Topel, Robert, and Michael Ward, “Job Mobility and the Careers of Young Men,”
Quarterly Journal of Economics, 107 (1992), 439–479.
Von Wachter, Till, and Stefan Bender, “At the Right Place at the Wrong Time:The Role of Firms and Luck in Young Workers’ Careers,” American Economic
Review, 96 (2006), 1679–1705.
Von Wachter, Till, Jae Song, and Joyce Manchester, “Long-Term Earnings Losses
Due to Mass Layoffs during the 1982 Recession: An Analysis Using Longitudinal Administrative Data from 1974 to 2004,” mimeo, Columbia University,
2009.