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Federal Reserve Bank of Chicago
Mortality, Mass-Layoffs, and Career
Outcomes: An Analysis using
Administrative Data
Daniel Sullivan and Till von Wachter
REVISED
November 2007
WP 2006-21
Mortality, Mass-Layoffs, and Career Outcomes:
An Analysis using Administrative Data1
Daniel Sullivan
Federal Reserve Bank of Chicago
Till von Wachter
Columbia University, NBER, and CEPR
Abstract
This paper uses administrative data on quarterly employment and earnings matched to death records
to estimate the effects of job displacement on mortality. We find that job displacement leads to a
15-20% increase in death rates during the following 20 years. If such increases were sustained
beyond this period, they would imply a loss in life expectancy of about 1.5 years for a worker
displaced at age 40. These results are robust to extensive controls for sorting and selection, and are
consistent with estimates of the effects of job loss on mortality pooling displaced workers and
stayers that are not affected by selective job displacement. To examine the channels through which
mass layoffs raise mortality, we exploit the panel nature of our data – covering over 15 years of
earnings – to analyze the correlation of long-run career outcomes, such as the mean and standard
deviation of earnings, with mortality at the individual and group level, something not possible with
typical data sets. Our findings suggest that factors correlated with a decrease in mean earnings and a
rise in standard deviation of earnings have the potential to explain an important fraction of the
effect of a job displacement on mortality.
1 Contact Daniel.Sullivan@chi.frb.org or vw2112@columbia.edu. We would like to thank Marianne Bertrand, David
Card, Janet Currie, Ed Glaeser, Michael Greenstone, Larry Katz, David Lee, Adriana Lleras-Muney, Claudio Lucifora,
Chris Paxson, Chris Ruhm, Jon Skinner, and seminar participants at the SOLE 2006 Meetings, the NBER 2006 Summer
Institute, the Milan Mills Workshop, London School of Economics, University College London (UCL), Pompeu Fabra
Barcelona, Boston University, University of Illinois Urbana Champaign, Harris School, Harvard University, Columbia
University Social Work, UC San Diego, UC Santa Barbara, UC Berkeley, Tufts University, Oxford University, UCL
Epidemiology, Princeton University, Prague CERGEI, Catholic University of Milan, University of North Carolina
Greensboro, University of Texas Houston, Texas A&M, RWI Essen, and Columbia University for helpful comments.
We would like to thank Elizabeth Weber Handwerker for sharing her programs to load the mortality data. Alice
Henriques and Phil Doctor provided excellent research assistance.
1
1. Introduction
A growing literature shows that what should be short-term career shocks – such as
involuntary job losses or cyclical downturns – can have large and persistent effects on workers’ lives.
For example, displaced workers with high-seniority on the lost job tend to experience long-term
earnings losses, reduced employment rates, early retirement, increased job instability, lower
consumption and loss of health insurance coverage.2 Similarly, cyclical shocks can lead to persistent
earnings declines and long-term increases in the variance of career outcomes, especially for younger
and lower skilled workers.3 These effects have likely played a role in recent increases in earnings
instability, declines in long-term employment relationships, and increases in displacement rates for
high-tenured workers.4
Existing evidence suggests it is plausible that job loss would also raise mortality. A large
literature documents a strong correlation of socio-economic status with health. Moreover, an
increasing amount of research links stress from economic uncertainty and unemployment to
unhappiness and mental health problems and a growing literature in epidemiology shows that job
loss is associated with strokes and heart attacks and other detailed health measures, especially for
older workers. However, while highly suggestive of an important relationship between career shocks
and health outcomes, the existing evidence linking economic status and job loss to health suffers
from a variety of measurement and identification problems associated with omitted variable bias and
reverse causality. At present, no comprehensive study of the effect of exogenous labor market
shocks on health exists, partly due to a lack of data on detailed longitudinal career and health
information for a large sample of workers.
We use a large administrative longitudinal data set of individual earnings and employment
histories matched to employer characteristics and individual mortality outcomes to study the effect
See for example Ruhm (1991), Jacobson, Lalonde, and Sullivan (1993), Chan and Stevens (2001), Stevens (1997),
Gruber (1997), Olson (1992).
3 E.g., Oreopoulos, von Wachter, and Heisz (2006), Oyer (2006), Kahn (2005), Hines, Hoynes, and Krueger (2002),
Okun (1973).
4 See for example Gottschalk and Moffitt (1994, 1998), Farber (2003), Stevens (2001), Aaronson and Sullivan (2005),
Farber (2007).
2
2
of workers losing their jobs as part of a mass layoff by their employer, or simply “job displacement,”
on their mortality rates. Following Jacobson, Lalonde, and Sullivan (1993), we use our data, spanning
over 15 years of quarterly earnings and employer records from the unemployment insurance system
in Pennsylvania, to identify mass-layoffs. We then follow the incidence of mortality for these
workers up until 20 years after job loss and compare their mortality to that of similar workers who
did not lose their job. This allows us to estimate both the short and long-term response of mortality
rates to job displacement.
Our large samples allow us to compare the effect of job displacement on mortality across
age and industry groups, and to engage in a thorough sensitivity analysis. Our estimates are robust to
the inclusion of industry or firm fixed effects, and to controls for detailed pre-displacement career
experiences. In addition, we show that our results are consistent with estimates of job loss that pool
displaced workers with those not leaving the firm. These intent-to-treat type estimates are unaffected
by residual selection into job loss or misclassification of job losers.
To examine the multiple channels through which job loss can affect mortality, we first study
the correlation of several long-term career outcomes – such as average earnings, employment
mobility, and the standard deviation of earnings – with death. We then use these results combined
with additional estimates of the impact of job displacement on our measures long-term career
outcomes to interpret the alternative channels of the ‘reduced form’ effect of mass-layoffs on
mortality. In addition, we document an important correlation between displaced workers’ earnings
losses and their long-run subsequent mortality rates. To further gauge the importance of the
earnings channel, we can also exploit predictable differences in earnings losses across groups of
workers as an additional source of variation.
Our data have several advantages for our purposes. First, longitudinal information on firm
size enables us to identify large changes in employment at the firm level that are plausibly exogenous
to workers’ own health developments. This allows us to overcome some of the problems of reverse
causality and omitted variable bias in existing studies of the effect of job loss on health. Second,
reliable information on death from administrative sources for a long follow-up period allows us to
3
estimate the effect of job loss on an objective health measure with sufficient precision over both
short and long horizons. Third, the longitudinal information on individual workers’ earnings and
employment allows us to construct detailed measures of long-term career outcomes and examine
their correlation with mortality; we are unaware of any other study of the socio-economic
determinants of mortality using such detailed career information.
We find that job displacement substantially raises mortality rates. The mortality increase is
particularly high in the years immediately following job loss and then converges to a persistent
positive effect of 15-20%. If sustained beyond the 20 year window we can follow workers, these
increases imply substantial loss of life expectancy for workers displaced in middle age or earlier. In
contrast, we find little effect of job loss on mortality for workers displaced near retirement age.
These results are consistent with an initial increase in mortality from acute stress and a longterm increase in mortality from chronic stress, partly resulting from permanently lower average
earnings and initial increases in the instability of earnings and employment. Given our estimates of
the correlation of the mean and standard deviation of earnings with mortality, the effects of job
displacement on these career outcomes could explain about 50-75% and 20% of the mass-layoff
effects on long-term mortality, respectively. A comparison of groups of workers with different
earnings losses confirms that a majority of the long-term increase we find could be driven by
persistent earnings losses. The stress or depression caused by job displacement is likely to have
additional direct effects on mortality not captured by our measures of career outcomes.
Our paper contributes to the existing literature in several ways. First, it is one of the first
studies estimating the long-term effect of a plausibly exogenous labor market event on an objective
measure of health for a large group of workers. It thereby helps to establish a causal link between
labor market shocks and health outcomes. Second, we use a novel approach to obtain estimates of
the effect of job loss that are not affected by any bias from firms selectively displacing their least
healthy workers. These and an extensive sensitivity analysis confirm our results. Third, it shows how
persistent earnings losses are likely to be an important channel through which layoffs permanently
raise mortality rates. Last, it is the first study to provide a comprehensive analysis of the correlation
4
of permanent earnings and long-term earnings and employment instability with mortality. It also
provides estimates of the long-term effects of mass-layoffs on a range of career outcomes that are a
contribution to the literature on the effects job displacements in their own right.
The next section gives a brief overview of the existing literature. The third section describes
our approach and our data. The fourth section presents our main results, discusses the sensitivity
analysis, and summarizes the implications of our estimates for losses in life expectancy. The fifth
section assesses the role of alternative channels of the effect we find. The last section concludes.
2. Literature Review: Why Should Job Displacement Affect Mortality?
Job displacements and other negative economic events can have both direct and indirect
impacts on health outcomes. First, job loss has strong effects on several economic outcomes that
may influence health. A large literature has shown that job losers can experience substantial and
long-lasting declines in earnings (e.g., Ruhm 1991, Jacobson, Lalonde, and Sullivan 1993, Schoeni
and Dardia 2003, Couch 2006). The decline in earnings is larger for older and high tenured workers
(e.g., Kletzer 1998), for workers in manufacturing, and for workers living in economically depressed
areas. Job losses can also lead to substantial increases in earnings instability, by raising the propensity
of non-employment and further job losses (e.g., Stevens 1997, Farber 2003). In addition, it has been
shown that job losses affect consumption (Gruber 1997, Browning and Crossley 2001), and access
to health insurance (Olson 1992). The incidence of job loss and the variance of transitory and
permanent earnings shocks have been increasing since the mid-1980s, especially among older and
high-tenured workers (e.g., Aaronson and Sullivan 1998, Farber 2003, Gottschalk and Moffitt 1994,
2002).5
A separate strand of literature documents a significant correlation of income with health and
mortality. Indeed, at face value, the existing estimates of the impact of income on health would
imply a strong effect of job loss on mortality (e.g., Deaton and Paxson 1999). However, such
Similarly, a growing literature shows that economic conditions in the local labor market and the firm have long term
effects on workers’ earnings and career instability. Cyclical swings have highly persistent effect on earnings (e.g.,
Oreopoulos, von Wachter, and Heisz 2006, Kahn 2006, Oyer 2006), and have lasting effects on job mobility and job
quality, especially for less-advantaged workers (e.g., Oreopoulos et al. 2006).
5
5
estimates may be biased due to reverse causality running from health to income, omitted worker
characteristics, and measurement error.6 These difficulties have led recent observers of the literature
to deemphasize the effect of income on health in favor of more lasting individual traits such as
education (e.g., Cutler, Deaton, and Lleras-Muney 2006). There are indeed few studies of the effect
of income on health with a research design that allow identification of a causal relationship.7 Yet,
even if there is no direct effect of earnings on health, earnings losses due to job displacement are still
likely to capture the worsened life conditions of job losers through reduced health insurance, less
consumption, or increased earnings instability.
In addition to indirect effects running through income, earnings instability, or access to
health insurance, job displacement may also have direct effects on well-being. A large literature in
economics and sociology has shown that unemployment correlates strongly with the incidence of
depression, low self-esteem, unhappiness, and even suicide.8 While some of these outcomes are
conceivably due to lower income, some of them may derive from factors independent of the
earnings situation. Similarly, several authors in sociology, social work, and epidemiology have
analyzed the effect of job loss on mental and physical health. These studies use either longitudinal
data with health and career information, such as the Health and Retirement Survey (HRS), or study
the effect of single plant closures (see Burgard, Brand, and House 2005 for an excellent survey).
Most prominently, a series of studies using the HRS has found increased incidence of adverse health
outcomes such as heart attacks or strokes among older job losers (e.g., Gallo et al. 2000, 2006). A
smaller set of studies has analyzed randomized trials to study the effect of alternative forms of
assistance to job losers on job search, employment, and health outcomes (e.g., Price, Choi, and
Vinokur 2002).
Another important question is regarding the source of any true income effects of health; these might arise from lack of
access to health improving investments, or from effects through social-status and relative deprivation (e.g., Deaton 1999,
Miller and Paxson 2001).
7 An exception is a recent experimental study analyzing lottery winners in Sweden that confirms a significant causal
relationship running from income to health (Lindahl 2005).
8 E.g., see papers summarized in Darity and Goldsmith (1996) and Burgard, Brand, and House (2005).
6
6
Several recent studies based on European data have analyzed the effect of job loss on health
using administrative data on earnings, firm sizes, and health outcomes. These studies find
contrasting results on mortality and outcomes such as mental health, with some indicating significant
negative effects of job loss on longevity.9 Important methodological differences aside, it is difficult
to compare the results from these studies to ours, since the underlying experience of job losers is so
different. Most European countries have generous unemployment insurance and welfare programs
and have universal health insurance coverage. As a consequence, studies of job displacement in
Europe often find much smaller and short lived effects on earnings losses than is the case for our
sample of job losers in Pennsylvania from the early to mid 1980s.10
Although the current estimates of job loss on health outcomes indicate that there are
potentially significant effects on health even after controlling for pre-job loss characteristics such as
health and socio-economic status, they suffer from several potential drawbacks. Most importantly,
since an important body of literature shows that job loss is in itself affected by poor health (e.g.,
Smith 2003) special care has to be taken to avoid selection bias (Gibbons and Katz 1991). While
several studies focus on workers displaced by plant closing and try to control for background
characteristics, selection problems remain even for workers displaced in plant closings.11 Another
concern is that health environments differ across sectors and firms, possibly attracting different
types of workers. Thus, it is important to analyze the effect of job loss within sectors, and to control
for the sorting of workers between firms (von Wachter and Bender 2006). Finally, recent studies
typically focus on older workers, possibly missing the effects of more long-lasting exposure to low
income and chronic stress from earnings instability due to job loss earlier in workers’ careers.
Eliason and Storrie (2004) find male workers losing their jobs in establishment closures in Sweden experience excess
mortality for up to ten years after job loss. Martikainen, Maki, and Jantti (2007) find no such effects in Finland. Similarly,
while for example Kuhn, Lalive, and Zweimueller (2007) find that job loss reduces mental health of men in Austria,
Browning, Dano, and Heinesen (2007) find no such effects in Denmark.
10 Not many studies analyze the effect of job loss on earnings in Europe, the focus being more often employment
effects. Of those analyses that study wages, there is considerable heterogeneity in approaches and results. For example,
the effects of job loss on earnings in Austria are very small (Card, Chetty, and Weber 2006). Earnings losses in Sweden
have been found to be short lived (Ohlsson and Storrie 2006) or persistent (Eliason and Storrie 2007), depending on the
sample used. See von Wachter (2007) for a survey of literature on the cost of job displacement in the U.S. and Europe.
11 This is because plant closings are more likely to occur in smaller firms that pay less and attract different employees
(Krashinsky 2002).
9
7
An important part of the difficulty in studying the effects of exogenous career shocks such
as a job displacement on health is the lack of proper data. Studies based on survey data sets have
difficulty isolating events at the firm level that are a plausibly exogenous source of job loss.12 In
addition, small samples prevent the inclusion of industry or firm fixed effects and make it difficult to
deal with remaining selection. Small samples and short follow-up periods also preclude the separate
study of short- and long-run impacts of job loss on health or the analysis of differences in the effect
for alternative groups of workers. Similarly, previous studies do not have good measures of
permanent income and other long-term career outcomes such as earnings instability. This makes it
difficult to assess the effect of alternative channels of the job loss effect on health.13
Obtaining information on the effect of individual level career shocks such as job
displacement is also important since influential recent studies have shown that reduced economic
activity during recessions actually reduces mortality. In a seminal study, looking at outcomes across
U.S. states, Ruhm (2000) reports that mortality declines during economic downturns, perhaps
because workers have more time to invest in their health, reduce risky health behaviors such as
smoking, and face fewer car and work-related accidents.14 This suggests that job loss could have
some beneficial effect by reducing accidents and possibly risky health behavior.15 We will explore the
role of non-employment directly in Section 5. However, while our study of mass-layoffs can provide
additional information on possible channels operating at the macro level, care has to be taken when
Instead they have to rely on workers’ self-reports that have been shown to be affected by measurement error. See
Hildreth, von Wachter, and Weber (2005) and studies cited therein.
13 The best income data currently available for studies of health in the U.S. comes from the Current Population Surveys,
which provide information only on income in the year prior to the survey with little longitudinal information on careers
or employers. This can lead to a downward bias in estimates of the impact of permanent income on mortality due to
classical measurement error arising from respondents’ inaccurate reporting (Bound and Krueger 1991) as well as an error
in approximating permanent income by current income (e.g., Haider and Solon 2006).
14 Ruhm (2000) regresses mortality rates by causes at the state-year level on state unemployment rates controlling for
state and year fixed effects. The largest responses are obtained for vehicle deaths, other accidents and suicide, and for
‘preventable causes’ (such as heart or liver disease), with the effect of vehicle deaths five times larger than that of
‘preventable causes.’ Due to the large share of preventable causes in overall death rates, these causes explain about 40%
of predicted mortality, whereas all accidents explain about 25% of predicted mortality.
15 On the other hand, comparing cohorts before and after the Social Security ‘Notch’, Evans and Snyder (2002) suggest
that reduced economic activity may negatively affect health outcomes for older workers, perhaps due to a loss in social
status or purpose. However, Weber-Handwerker (2007) shows these results may partly arise from overall cohortdifferences in mortality.
12
8
comparing the two approaches. First, the event we analyze is of different nature than the aggregate
variation used in Ruhm (2000). Only a fraction of reallocation of employment in recessions occurs
through job separations and mass-layoffs, and these are often concentrated in particular regions,
sectors, or groups of workers. Second, we analyze affects associated with earnings losses exceeding
what is typically observed in an economic downturn or in the career of an average worker. Thus, the
beneficial effects from a lower rate accidents or improved health behavior in a recession may be
more than offset by the large negative effects of job loss on workers’ careers in our sample.16
3. Empirical Approach: Mortality, Career Outcomes, and Mass Layoffs
In the present paper, we merge two large administrative data sets to study the effects of
career outcomes and exogenous labor market shocks on mortality. Specifically, panel data on
individual earnings and employment information is merged with administrative data on the date of
death. The former is derived from the unemployment insurance records of the state of
Pennsylvania and covers male workers from the period from 1974 to 1991. The latter is derived
from a database compiled by the Social Security Administration and covers deaths between 1974
and 2002.17 The combined data set has several attractive features. First, it allows us to identify largescale layoffs at the firm level that constitute plausibly exogenous shocks to workers’ career
development. Second, an extended period of follow-up using administrative data provides us with
reliable and rich information on mortality patterns. In particular, since we examine displacements
occurring in the early 1980s we can trace both the short and long-term effects of job loss on
mortality. Third, our large samples allow us to implement estimation strategies dealing explicitly with
sorting and selection. Fourth, the long time period covered by the data allow us to better examine
the effect of career outcomes such as permanent earnings or earnings stability on mortality.
We would like to thank Chris Ruhm for helpful feedback on these questions.
The Social Security Administration’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 with complete mortality data from the National Center for Health Statistics (cited in Weber-Handwerker 2006)
suggests coverage for men is between 80-90% before age 65, and above 95% after age 65. We replicated these
tabulations for deaths in Pennsylvania from 1980-2002 and find similar results (in the main sample, we also include
deaths occurring in other states).
16
17
9
Our empirical analysis proceeds in four steps. First, we analyze the effect of job
displacement on short and long-run mortality rates for different samples and workers, including a
thorough sensitivity analysis dealing with selection, sorting, and misclassification. In a second step,
we analyze differences in the long-term effect of job loss on mortality by age and industry, and use
these estimates to provide an assessment of the losses in life-expectancies arising from mass-layoffs.
Third, we use our data to provide estimates of the correlation of mortality with detailed measures of
permanent earnings and other long-term career outcomes, factors not previously analyzed in the
literature on mortality and socio-economic status. Together with new estimates of the long-term
effects of job loss on a range of career outcomes, these results to give a first assessment of
alternative channels underlying the effect of job loss on death. Fourth, we document the relationship
between earnings changes due to job displacement and mortality, and use predictable differences in
earnings losses across groups in the population to gain further insight into the channel of the effect
of job displacement on mortality.
3.1 Structure of Sample
To analyze the effect of job loss and other career outcomes on mortality, we divide our
sample into a baseline period (1974-1979), a mass-layoff period (1980-1986), and a follow-up period.
The follow up period ranges from 1980-2002 or 1987-2002, depending on the sample we use. We
then analyze the probability of dying each year in the follow-up period as a function of job loss in
the mass-layoff period and of career outcomes in the baseline period. This structure has several
advantages. Separating the baseline period and the job loss event from the follow up period reduces
the bias from reverse causality running from health to income or job loss. It also replicates
important features of the well-established research design of the paper on the effects of mass-layoff
by Jacobson, Lalonde, and Sullivan (1993) further discussed below. In addition, it allows us to
replicate existing studies of the effect of income on mortality (e.g., Deaton and Paxson 1999, see our
Appendix Table 5).
10
We define job displacement exactly as in Jacobson, Lalonde, and Sullivan (1993) (henceforth
JLS). Specifically, we identify the workers who left their employer at the same time that the employer
experienced a 30% or larger decline in employment. We work with three main samples that each
satisfy a common set of restrictions, but differ in the degree of labor force attachment they require.
Following JLS, both samples focus on workers born between 1930 and 1959 who lost their job
between 1980 and 1986 after having remained with the same employer in the period from 1974 to
1979. The first sample makes no restrictions on employment after 1979 or the control group, and
thus simply compares mortality of workers who lost their job in the displacement period to all other
workers. For these workers, we analyze the incidence of death from 1980 to 2002.18
Our second sample is the sample used by JLS in their analysis of long-term earnings losses.
It differs from the first by focusing on workers who remained highly attached to the Pennsylvania
labor force as evidenced by positive reported earnings in each year 1980 to 1986. Since we only
observe earnings information for workers who remain in PA, this allows us to consider various
career outcomes such as earnings losses as potential channels of the displacement effect. By focusing
on high-attachment male employees this sample also limits the underlying heterogeneity of workers
in our main samples. In addition, as in the earlier paper, when working with this sample we also
drop non-mass layoff separators for whom displacement status is not as well measured in our data.
We also consider an intermediate sample with weaker restrictions on labor force attachment after
job loss. Since the labor force attachment requirement obviously implies workers are alive through
1986, for these samples the follow up period starts in 1987. In addition, to explore the role of age at
job loss, we replicate our analysis including workers born between 1920 and 1929.
3.2 Main Estimation Strategy
Using these samples, we estimate several logistic models of the annual probability of dying as
a function of an indicator for whether a worker left his 1974-79 employer during a mass-layoff
occurring from 1980-1986 ( MLFi 80−86 ), prior career information Ci74 − 79 measured over the horizon
18
Mortality data comes from the Social Security Administration and is not limited to deaths that occur in Pennsylvania.
11
1974 to 1979 (the baseline period), as well as controls for age and calendar year. The model we
estimate can be represented as
⎛ p
ln⎜⎜ it
⎝ 1 − pit
⎞
⎟⎟ = πC i74−79 + γMLFi 80−86 + χ a + φ t
⎠
(1)
where the annual probability of dying in year t given survival until year t-1 (the hazard function),
pit ≡ Pr{Deathit = 1 | Deathit −1 = 0} = Λ ( x' β ) , is measured over the follow-up period.19 All
specifications include year dummies, and we alternatively model the role of age ( χ a ) as a 4th order
polynomial or as separate age dummies (with little difference in results). The coefficient on the
MLF-dummy measures the overall increase in the log odds of dying in a year due to job
displacement.
Given that the probability of death is typically quite small, the log-odds ratio approximates
the log of the death rate itself, and the coefficients have an approximate interpretation as percentage
changes of the probability of death. In addition, we report the proper marginal effects on death
probabilities that control for the curvature of the logistic distribution. The marginal effects will be
higher for groups of workers who tend to have higher mortality, such as older or low-income
workers. We have also made sure that the logistic functional form did not drive our estimates; all
results are evident in straightforward tabulations of average mortality rates and in linear probability
models.20
Since the shock that triggers job loss in this analysis occurs at the firm level, it should be
exogenous to workers’ own health problems. However, it is possible that the firm lays off its least
productive workers, who may in turn be in poor health. To address this potential problem we
control for the mean and standard deviation of earnings in the baseline period, with little difference
in results. To further control for differences among job losers and stayers we also included other
The cumulative distribution function of the extreme value distribution is Λ ( x' β ) = exp{x' β } (1 − exp{x' β }) .
Workers contribute one observation for each year that they are alive from either 1980 or 1987 to 2002.
20 Note that once we include interactions of MLF-dummy with dummies for years since job loss, the logit specification
estimates the hazard function (see Efron 1988).
19
12
career characteristics such as earnings growth and non-employment spells.21 Similarly, to account for
underlying differences in average mortality rates and mass-layoff rates across sectors our baseline
specification controls for one-digit industry effects. Thus, our flexible baseline specification is
⎛ p
ln⎜⎜ it
⎝ 1 − pit
)
(
74 − 79
⎞
74 − 79
⎟⎟ = δMLFi 8−86 + π ln qearnit
+ γSD(ln (qearnit ))
+ χ a + φt + ψ j
⎠
(2)
where qearn stands for quarterly earnings as measured by unemployment insurance records, and ψ j
represent six industry dummies. To account for the possibility that workers of different ability and
health outcomes might be sorted into firms with different rates of mass-layoff, we also replicate all
our results including fixed effects for workers’ employers.22 This compares the short and long-term
mortality for workers from the same firm with similar age, average career earnings, and variability
and, should be robust to selection within and between employers. We address the potential for
remaining selection directly below.
To examine the source of the effect we estimate, we also estimate interactions of the MLFdummy with age at layoff and initial industry. In particular, we are interested in how the mortality
effects of job loss vary with age at displacement. Older workers face potentially higher immediate
losses due to difficulties of re-integrating in the labor market, but the reduced earnings may occur
over a shorter period, limiting the life-time earnings reduction. Similarly, we are interested in
whether the results are driven by layoffs from particular sectors, or whether workers losing jobs in
traditionally ‘unhealthy’ sectors actually benefit from the layoff.
We also consider the evolution of the mortality effect with years since layoff to distinguish
between initial and long-term effects. This is of particular interest since in our analysis of the effect
of job loss on career outcomes we find that there are important short term responses in a wide range
This also helps to control for the fact that the logarithm of earnings is not defined for quarters of zero earnings.
A long literature suggests that more able workers are sorted into larger higher-paying firms. That such sorting behavior
can bias estimates of the effects of layoffs has been shown by von Wachter and Bender (2006). Since in PA in the early
1980s downsizing occurred in traditionally high-wage manufacturing sectors, this may mean that workers in mass-layoff
firms may be of above average earnings potential and health.
21
22
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of career outcomes. However, the only effect remaining past four years after a job loss is a sizeable
reduction in earnings.
3.3 Sensitivity: Selection and Misclassification
Most estimators of the effects of job loss face the problem that certain types of workers may
be sorted into firms with higher layoff risk and that the firm selectively displaces certain kinds of
workers (von Wachter and Bender 2006). While in the analysis of earnings the potential biases from
sorting and selection can be controlled for by the inclusion of worker fixed effects (e.g., Jacobson,
Lalonde, Sullivan 1993), this is not possible in the case of mortality. Our basic estimates control for
sorting by including industry or firm fixed effects and for selection with an extensive set of longterm pre-layoff career outcomes that are related to productivity (and thereby to the firm’s selection
criteria), such as the average, trend, and variance of earnings and employment. Moreover, the scope
for selective displacement was limited in the period we study because many firms – especially in
manufacturing where our layoffs are concentrated – were constrained by seniority rules. This held
whether the firm was unionized or not (Abraham and Meadoff 1984).
Nevertheless, we develop two strategies to directly address the concern that firms may still
have information on workers’ underlying health status and selectively dismiss the least healthy. First,
we use a novel strategy to estimate the costs of job loss that directly avoids the problem of selective
job displacement by pooling the sample of displaced workers with those not displaced. The pooled
estimator compares the short and long-term mortality rate of all workers present in firms that
experience a mass-layoff with similar workers at non-mass layoff firms in the same industry. If we
were to treat a mass-layoff at the firm level as an instrument for individual job loss, this would be
the reduced form estimate, also known as the intent-to-treat estimator.23 Thus, to compare the
resulting estimates to those in the main tables, we have to rescale it by dividing by the effect of
mass-layoff on job loss.
23 Von Wachter and Bender (2006) discuss the problems of sorting and selection in job loss estimates when fixed effects
cannot be used and discuss alternative instrumental variable strategies using layoffs at the firm-occupation level as
instruments for job loss.
14
Second, if there is selection, the degree of bias should be related to the size of the layoff; i.e.,
the larger the layoff, the less selective should be the sample of job losers. We thus analyze the effect
of job loss at mass-layoffs by size of layoffs. The caveat in such an analysis is the concern that larger
layoffs and closures are typically experienced by smaller firms that also attract less able workers and
provide less desirable career environments. Similarly, layoff rules such as a seniority rule are less
likely to bind in larger layoffs, suggesting caution when interpreting this sensitivity check.
Another potential concern is that workers dying around the layoff date are automatically
classified as job losers. A straightforward calculation shows that the approximate misclassification
bias is expected to be small because the average mortality rate is small.24 Another advantage of the
pooled estimator is that it is robust to such mechanical misclassification. An additional concern that
has found little attention in the literature on job loss is that no true measure of involuntary job loss
is available, and displacement is approximated using firm level events. This may lead to some degree
to misclassification of voluntary movers as job losers (Hildreth, von Wachter and Weber 2005). The
pooled estimates do not suffer from this ambiguity in interpretation.
3.4 Sources of the Mass-Layoff Effect on Mortality
As discussed in Section 2, job loss is likely to affect mortality through a variety of channels.
We take three approaches to gauging the effect of those channels running through career outcomes.
In the first approach, we show that job loss has strong effects on average earnings and career
instability. We then combine such estimates with estimates of the association of these long-term
career outcomes with mortality to see how much of the effect of job displacement on mortality can
be explained by its effects on the mean and standard deviation of earning. In a second approach, we
address the potential correlation of the response to job loss with underlying health status by
24
If
β
is the true difference in mortality rates of job losers and stayers, then one can show that the actual comparison is
a weighted function of the true mortality gap and the difference between the actual mortality of stayers ( δ 2 ) and the
mortality of misclassified stayers (which is equal to one). The weight is the misclassification probability
{
}
q = Pr D * = 0 | D = 1 ; i.e., we have that the probability limit of the simple difference in mortality rates of job losers
and stayers is b = β (1 − q ) + (1 − δ 2 )q . The misclassification rate q can itself be approximated as a function of the
overall mortality rate and the fraction of job losers, and turns out to be very small under reasonable scenarios.
15
exploiting differences in predicted earnings impacts of displacement at the cell-level. Third, to learn
more about the cumulative effect of lower earnings due to mass-layoff on health, we assess the
potential of life-time losses in earnings in explaining the loss in total life expectancy due job
displacement.
In the first approach, we exploit the large sample sizes and limited degree of top-coding of
quarterly earnings information in the unemployment insurance records to examine the correlation of
long-term earnings and mortality; we also use our results to gauge the role of two important sources
of measurement error afflicting estimates of the income-death correlation based only on survey
measures of a single year of income – classical measurement error affecting surveyed earnings, and
the mis-measurement of permanent earnings by current earnings. We then analyze the effect of
various measures of instability over worker’s careers, among others the standard deviation of
earnings, number of quarters worked, number of transitions between jobs and into nonemployment, or the number of large earnings declines. The correlations we obtain should not be
afflicted by short term reverse causality or strong measurement error. However, we do not interpret
the results as a causal link between career earnings outcomes and mortality since they may still be
affected by omitted variable bias.
In a second step, we estimate the effect dynamic effects of losing a job displacement on
annual earnings, the variability of earnings, non-employment, job changes, industry changes, and
regional mobility.25 The results can be combined with the correlations obtained from models as in
equation (2) to interpret the sources of the effects estimated from the effects of mass-layoff on
We do this by re-estimating the distributed lag model used by JLS with different career outcomes as the
dependent variable. The model we estimate at the annual level is
25
y it = α i + γ t + βX it +
∑δ
k ≥− m,
k
Ditk + u it
where the outcome variable yit represents a measure of annual earnings or another career outcome, the time dummies
γ t are identified by the presence of ‘stayers’ (i.e., workers not losing their job), and the error u it represents truly
random components affecting the outcome. The coefficients δ k on the dummies indicating the k-th period before,
during, or after job loss ( Ditk ) measure the time path of earnings changes of job losers before and after a displacement
relative to the baseline and the control group. The displacement effect is identified by the inclusion of workers staying at
their employers throughout the period under study (the comparison group).
16
mortality (estimated in model (3)). Clearly, this does not allow an exact decomposition of the effect,
since the correlations may not represent causal estimates, but it will allow us to obtain important
insights regarding the potential magnitudes of the likely importance of alternative channels.
To go beyond the basic approximation in assessing how much of the mass-layoff effect can
be explained by variation in earnings, ideally we would like to control directly for individual earnings
losses in our model of mortality. We thereby face the common problem that although a mass-layoff
(MLF) provides a valid quasi-experimental design for the overall effect of a job loss on mortality, it
may not in itself provide sufficient identifying variation to estimate the effect running through
alternative channels such as earnings losses.
Specifically, we would like to directly estimate the following augmented logit model for the
annual hazard of death after a MLF
⎛ p
ln⎜⎜ it
⎝ 1 − pit
⎞
⎟⎟ = χ a + φ t + βMLFi + π ln y i74−79 + γ∆ ln y i ,
⎠
(
)
(3)
where ∆ ln y i ≡ ln( y i87 −91 ) − ln( y i74−79 ) is the earnings change during the mass-layoff period.26 This
model yields consistent estimates if the change in earnings is uncorrelated with other factors
resulting in higher mortality rates. If this were true, the resulting estimates would tell us how much
earnings changes affect mortality, and to what extent any of the MLF effect is left conditioning on
the earnings change. However, we may be concerned that part of the response to MLF in earnings
may itself be driven by underlying health conditions that also affect mortality. For example, even
though job losers are on average as healthy as stayers, physically frail job losers may both have high
earnings losses and die earlier.
An alternative approach is to find an additional source of variation that helps to determine
the response of alternative channels to MLF. For example, if different observable labor force groups
experience predictably different earnings losses due to job displacement, we can use this source of
This model is a special case of a more general model where current and past earnings have flexible dynamic effects on
mortality. Since little is known about the true relationship between life-time earnings and mortality, after experimenting
with alternative specifications, we have settled on (implicitly) age-adjusted medium-term averages of earnings as a
sensible representation of the underlying functional form.
26
17
variation to re-estimate equation (3). As further explained in Section 5, since existing theory and
evidence suggest there are permanent wage differentials along these dimensions, we will use groups
based on alternative combinations of age at job loss, average earnings prior to job loss, region, and
industry of displacement. Note that were we to interpret these as causal estimates of earnings on
mortality, we have to be willing to assume that there are no other responses to MLF at the group
level correlated with both earnings and mortality.27 However, there may still be group-level
responses that drive both earnings and mortality. For example, differential rates of depression across
groups may affect both earnings capacity as well as long-term mortality outcomes. Instead, we
interpret the resulting estimates as an additional descriptive measure of the correlation of earnings
losses at MLF and increases in mortality among job losers. Note, however, that a general effect of
mass-layoff on stress or depression does not affect group level estimates.
To implement the group level model, we first include group-level dummies ( Di (i ∈ c) ) and
their interaction with the MLF dummy in a model for earnings after a job loss (modeling the
changes in average earnings is equivalent)
(
)
C
C
c =1
c =1
∆ ln y i = δ a + π t + π ln y i74−79 + ∑θ c Di (i ∈ c) + ∑ψ c Di (i ∈ c) MLFi + u i
(4)
To implement our strategy using a linear probability model of mortality we then use the fitted value
from this regression to replace the individual earnings change in equation in model (4). Equivalently,
we estimate the linear reduced form equation for mortality
(
)
C
C
c =1
c =1
pit = χ a + φ t + βMLFi + π ln y i74−79 + ∑ γ c Di (i ∈ c) + ∑ λ c Di (i ∈ c) MLFi ,
and run a linear regression of the dummies on the interaction in the reduced form and the first stage,
properly weighted. The resulting slope coefficient would be the IV estimator for the earnings loss if
the interactions of mass-layoff and group dummies were to be used as instrument for earnings. The
sum of squared errors of the group level model divided by the number of groups is the test statistic
27 The intuition for identification in the cell-level model is parallel to that at the individual level model. I.e., we obtain
consistent estimates if there are no group level error components correlated with both group level earnings losses and
mortality.
18
of a Chi-Squared test for the validity of the group-level dummies as instruments. Although we do
not treat our group-level estimates as two stage least squares estimates since we cannot exclude other
group level changes affecting both earnings and mortality, we will exploit this interpretation below.28
The cell-level analysis misses potential cumulative effect of reduced career opportunities and
earnings on mortality. The long-term increase in the hazard of death we find suggest the loss in lifeexpectancy might be a useful alternative summary measure of the effect of job loss on mortality.
Thus, we use our estimates of the decline in life-expectancy by age- and industry-groups combined
with estimates of the present-discounted value of life-time earnings estimates to provide an
additional group-level estimate of the potential role of career losses in earnings on long-term health
outcomes.
4. Mass Layoffs, Career Outcomes, and Mortality
4.1. Mass-Layoff, Job Loss, and Mortality
Table 1 shows the workers’ average characteristics in the pre-period (1974-79) by layoffstatus for our least and most restrictive samples. As discussed by Jacobson, Lalonde, and Sullivan
(1993) [JLS], job losers are somewhat younger and have slightly lower average earnings. They also
have higher earnings growth and lower variance; they tend to come from larger establishments and
from the manufacturing sector. These patterns suggest it is important to control for potential
differences across sectors and firms, but also for pre-job loss differences in career outcomes among
movers and stayers. Note that the two samples are very similar from 1974-79, but differ in the
aftermath; the sample of job losers without work restriction have much larger average earnings
losses and about double as many quarters of non-employment.
Appendix Table 1 shows additional characteristics of our two main samples of male workers
for every two years of the follow up period. The typical worker is in middle age at the time of layoff,
and the sample ages over time. We capture these differences by controlling for age in our log-odds
28
See Angrist (1991) for a detailed exposition.
19
models.29 Our sample sizes are substantial, but given the overall mortality rate is low the number of
deaths every year is sufficient for the main analysis, but not large. This limits the number of detailed
interactions we can estimate. There is some attrition as the sample ages, but the pattern of average
quarterly earnings suggests it is not selective. The last column shows the layoff rate, which at 3035% is higher than in the U.S. population because our focus on Pennsylvania in the early 1980s. The
high-employment sample, shown in Panel B, has slightly higher average earnings and lower mortality
rates, but otherwise looks similar to the broad sample in Panel A.
Our sample extends the original sample used by JLS by three years. Since we are particularly
interested in the long-term effect of mass-layoffs, we replicated the main result of the earlier paper
using the high attachment sample. The results are shown in Figure 1, which displays estimates of
the percentage difference in annual earnings relative to the baseline period and to the comparison
group of workers remaining at their employer in 1980 to 1986 controlling for year, age, and worker
fixed effects. The estimates confirm the result in JLS that job displacements in the course of a masslayoff lead to large immediate effects and a pattern of incomplete reversion in the first five years
after layoff. In addition, they show that there is no sign of catching up even eight to eleven years
after a job loss. The long-run decline in earnings we estimate is on the order of 20%.
We next analyze the effect of the same mass-layoff events on mortality. As a preliminary step,
Table 2 shows various averages of raw and adjusted mortality rate by job loss status and their
difference. These straightforward tabulations give a preview of our main results.
•
First, we find an increase in average mortality rates from 1987-2002 for those losing jobs at
mass-layoffs during the period from 1980-86 (Column 4). For the sample without work
restriction, we find that the initial effect occurring during the layoff period is particularly
large; the effect is stable for the remainder of the period (Panel B).
We replicated estimates of the age-gradient in mortality in the appendix (Appendix Figure 1), and find it to be quite
similar to the typical pattern for representative U.S. samples.
29
20
•
Second, these differences are robust to controls for age, prior earnings, and initial firm size
(Column 6). Below we also show that they are also robust to adding industry and firm fixed
effects, adding further controls, or pooling the samples of movers and stayers.
•
Third, once we control for the changes in average earnings at job loss, the difference in
mortality rates becomes small and insignificant (Column 7).
The comparison of average mortality rates suggest that job displacement can lead to substantial
increases in mortality in the short and long run, and thereby significantly reduce job losers’ overall
life-expectancy. In what follows, we substantially deepen our analyses of these results.
To fully exploit the structure of our data, we estimated several logistic regression models for
death in a year given survival until the previous year and include controls for age, year of the sample,
and baseline career outcomes shown in equation (2). The first group of estimates, based on the
broad sample with no employment restriction past 1979, is shown in the first column of Table 3.
The entries in the table are parameter estimates on the MLF-dummy from separate logit models;
parameter estimates for other covariates for our most extended model are shown in Table 4. Recall
that since average mortality rates are small, these coefficients can be interpreted as approximate
percentage changes.
The coefficient shown in the first column of Table 3 indicates that displacement between
1980 and 1986 is associated with an increase in the log odds of death each year of about 0.28.
Because the probability of death each year is low, this implies about a 28% increase in the
probability of dying each year given survival to the previous year. The inclusion of log average
earnings and the log standard deviation of earnings in 1974-79 affected the estimate only marginally.
To further assess whether our estimates might overstate the effect of job loss on mortality
because firms selectively fired the least healthy workers, rows (A) to (D) add additional
characteristics of workers’ careers from 1974 to 1979. Including dummies for 1-digit industry during
the baseline reduces the coefficient slightly. While added career outcomes – such as time worked or
the growth of earnings – are significantly correlated with long-term mortality (Table 4), and partly
21
differ by job loss status (Table 1), their inclusion matters only slightly. In results not reported here,
the same holds when we also add number of job changes or number of large earnings shocks.
Similarly, interacting average quarterly earnings and the standard deviation with dummies for agecategories at baseline to allow for a more flexible specification does not affect our results (row D).
We interpret these results to mean that there are large and robust effects of job
displacements on long-term mortality outcomes. In particular, our results are not affected by
displacement workers being selected on pre-job loss career outcomes. The robustness to industry
fixed effects also implies that our results are not driven by differences in the rate of downsizing and
mortality (e.g., because of job safety) across industries.
The bottom panel of Table 3 re-estimates all specifications using fixed effects for the firm in
1979 instead of baseline industry. Due to the difficulty in estimating fixed effects in the logit model,
we use a linear probability model. To make the estimates comparable, we also show the percentage
effect relative to mean mortality in the sample.30 Including firm fixed effects, the estimates are
typically the same or somewhat larger (although the difference is never statistically significant). This
may be consistent with the fact that Pennsylvania experienced downsizing in some traditionally highwage sectors with large firms, such as steel manufacturing, and it appears that these firms also
tended to employ healthier workers.
The second column of Table 3 shows the results when we use our high-attachment sample
(the original JLS sample). The effects are somewhat smaller but still large and significant. As shown
in column 4, the difference in the estimates arises primarily because the later start of the follow-up
period for this sample excludes deaths occurring right after the job loss. In addition, by focusing on
workers who continue to work for a certain number of years after a layoff the high-attachment
sample is likely to exclude the frailest workers most likely to die as a result of a layoff. As for the
broad sample, including industry or firm effects and the standard deviation of pre-job loss earnings
When we replicated the main results with a linear probability model, the estimates are very similar to the results in
sTables 2 and 3.
30
22
reduces the effect of job loss on mortality by a bit less than a standard error. Again, including further
career characteristics has hardly any bearing on the results.
Overall, for cohorts born after 1930, the effects range from 10% to 28% depending on the
sample and follow up period. In particular, once the follow up period is limited to start in 1987, the
estimates are of similar order of magnitude for alternative degrees of labor force attachment and
range between 10% and 17%. The right hand side of Table 3 shows estimates including workers
born from 1920 to 1929, who would be on average 55 to 60 at layoff. Including older workers the
effect of layoff on mortality is somewhat smaller, albeit still large and highly significant. These
differences are further addressed in Tables 5 and 7.
Although the coefficients in Table 3 give an approximate effect of MLF on the hazard of
death, the curvature of the logit-function implies that the true marginal effect varies by age and
earnings. In Figure 3 we show differences in the fitted probabilities of dying due to mass-layoffs by
age and average earnings at layoff. Since the probability of dying increases faster for older and
poorer individuals, the effect of mass-layoff rises with age and declines with average earnings. As
shown in Appendix Table 2, the percentage change relative to the baseline is similar across ageearnings cells, with exception of the youngest workers due to their low baseline hazards.
Table 5 breaks up the effect of job loss on death by year since layoff. This is shown
graphically in Figure 2, which plots the point estimates and two standard error bands. For our main
sample in columns 2 and 3, we see large percentage increases immediately after job displacement
when the baseline hazard is low. The effect remains high for the first five years after job loss.
However, the effect gradually declines with time since layoff and bottoms out at a long run average
of about 15%, a result more similar to the other estimates in Table 3. These estimates suggest strong
immediate responses to when the impact of a layoff on career outcomes is most severe. The effects
then stabilize at a permanent difference as workers continue to suffer negative consequences of
layoffs in terms of reduced earnings.
Columns 5 and 6 show the same estimates including the earlier cohorts. The effect of job
loss on the long-term mortality rate is similar in the two samples. This is expected, because ten years
23
after job loss the results in the second part of the table are again driven by a younger group of
workers. However, the results indicate an important difference in the initial response to job loss
when workers age 55 to 65 at job loss have most weight. Older workers’ mortality appears to
respond much less in the first five years after job loss than that of workers in middle age. This is
perhaps not surprising, since workers in that age range are likely to have either access to Social
Security Benefits at age 62 or to company pension plans. Even for workers not yet at retirement age,
access to disability insurance improves substantially at age 55, when workers can claim loss of
vocational qualifications to qualify for disability insurance (e.g., Chan and Van der Klaauw 2005,
Black, Daniel, and Sanders 2002). Middle aged workers do not have access to similar mechanisms to
smooth long-term earnings losses.
One can see that in both samples the effect actually increases somewhat 15 years after job
loss, amounting to a U-shape, albeit a weak one.31 A similar pattern has been observed for the event
of losing a spouse that leads to an initial increase in mortality from stress, and a 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
impact of increased chronic stress due to lower earnings.
4.2. Sensitivity Analysis: Selection and Misclassification
We have shown that our estimates are very robust to the inclusion of an extensive set of
alternative control variables and industry and firm fixed effects. We have argued that this suggests
the remaining bias from selective displacement or from sorting of workers into firms is small,
especially in an environment of Pennsylvania in the early to mid 1980s, when firms were either
unionized or followed seniority rules in dismissals, and mass-layoffs were large. Both of these factors
may have reduced the ability to selectively displace workers. Yet, as in any non-experimental study,
31 Appendix Figure 1 explores the role of the time-pattern of the effects of job further. The upper panel shows the raw
mortality rate by mass-layoff status over the period under study. The aging of the sample is apparent for both lines. In
addition, it appears the difference in mortality rates is greater at the beginning and at the end of the sample period. To
eliminate age and year effects, the lower panel of Appendix Figure 1 plots the coefficients of an interaction of a job loss
dummy with annual dummies for years since 1987, the beginning of our follow up period.
24
we cannot fully exclude that firms use information on workers’ health development not captured by
workers’ earnings level, growth, or patterns of non-employment when deciding which workers to
displace.
To address the question of a remaining bias from selective displacement directly, we present
results obtained from estimates that pool movers and stayers. These estimates replicate the main
specification shown in equation (2), but define the main variable of interest at the firm level, not the
worker level. Since the mass-layoff dummy now varies only at the firm, not at the worker level, the
estimates compare the mortality of all workers present at a firm experiencing a mass-layoff before
and after the layoff with similar workers at stable firms. The resulting intent-to-treat estimates are
not affected by selection at the time of job loss. However, they may still be affected by sorting of
workers into firms prior to layoff. To control for any remaining bias from sorting, we also include
industry fixed effects and our core career characteristics prior to layoff.32
The resulting estimates in Table 6 confirm our main findings. The top panel shows the
results for mortality as an outcome, the bottom panel replicates the estimates for earnings. The table
shows estimates for two sets of mass-layoff definitions. The first defines mass-layoff as in JLS and in
our main estimates. Since this includes gradually declining firms, it makes less sense when working at
the firm level. Thus, Table 6 also includes specifications where mass-layoff is defined as a sudden
large drop in employment at the firm. We focus on the results based on this cleaner layoff definition
(Columns 3 and 4). The estimates for the average effect on mortality during the period from 1980 or
1987 to 2002 in Panel A is imprecisely estimated once industry dummies are included. However, the
magnitudes, when scaled by about a factor of three (the inverse of the effect of mass-layoff on
32 While the pooled estimates should be free of bias from selective job displacement, they may still be affected by a bias
from sorting of less healthy workers into sectors or firms with higher displacement rates. In our main analysis, we
showed that the results are unaffected by the inclusion of industry or firm fixed effects. Here, we cannot include firm
fixed effects without losing precision, but the results are robust for inclusion of industry controls.
25
mobility, which is about 0.3), 33 are comparable to the main results, especially for the 1987-2002
period.
The dynamic estimates in Panel B (columns 3 and 4) show an increase in death rates in the
two years prior to closure (parallel to the decline in earnings), as well as a large positive spike at
mass-layoff. Rescaled by a factor of three, the magnitude of the increase is remarkably similar to the
size of the effects shown in Table 5 (when the mass-layoff dummy was defined as an individual job
loss at a firm experiencing a mass-layoff). The effect then drops in the years following the layoff and
increase in the long run. The long run effect is again significant and of similar magnitude as for the
main estimates that distinguish between movers and stayers. The results are similar but noisier in the
case of the gradual mass layoff definition in columns (1) and (2). We see a spike at layoff and a
positive long-run effect, but also a rise a mortality difference four years prior to layoff. This suggests
that the timing of the shock affecting workers at the firm level is better captured by a sudden in
employment.34
The definition of mass-layoffs also makes a difference for the analysis of earnings shown in
Panels C and D of Table 6. In particular, the sudden drop leads to sharper losses in earnings around
the mass-layoff date and larger earnings losses in the first ten years after layoff. The effect of the
definition of mass-layoff based on administrative earnings estimates on the cost of job loss in terms
of earnings is discussed in detail in Hildreth, von Wachter, and Weber (2005).
These results confirm that our main estimates are unlikely to be driven by selection. The
same conclusion is borne out when we split the sample by the size of layoff. The results in Appendix
Table 3 suggest that at best, workers at firms with higher layoff rates have higher mortality (columns
If we estimate the same regression models with an individual dummy for the event of job displacement as dependent
variable, the coefficient is 0.299 and 0.289 for the gradual and sudden drop definitions of mass-layoff, respectively. This
number also results from the fraction displaced in Appendix Table 1.
34 This is less of a problem with job loss definition at the individual level used in our main estimates. There, the shock
was an individual job change from a firm experiencing strong gradual decline in employment as in JLS. When the main
shock is defined at the firm level, the gradual nature of the decline leads some workers to be affected prior to the date at
which we assign them the mass-layoff. This is confirmed by the results based on a sudden drop in employment.
33
26
4 to 6).35 The table also shows that other separators (those leaving their employers but not during a
mass-layoff) do not have higher mortality rates (column 3). Consistent with results in Tables 3 and 6,
these numbers imply that selection into job loss appears not to be a pervasive problem in our
application.
Finally, note that the fact that the estimate of the effect at the time of layoff in Table 6
(properly rescaled) coincides with that in Table 5 suggests that the initial spike we find is not driven
by a mechanic misclassification of dying stayers as job movers. This confirms the prediction of the
bias calculations discussed in section 3. This does not imply that there is no misclassification just
that it is unlikely to play an important role in determining our results.
4.3. Mass-Layoff Effects on Mortality by Age and Industry
Given the role of age at layoff apparent in Tables 3 and 5, Table 7 shows the results for
specifications allowing a more flexible impact of mass-layoffs by age at job loss. To cover a broader
age range, these models are estimated only including the earlier cohorts. Clearly, the largest effect of
MLF on mortality is for younger and middle-aged workers. The lowest effect is for workers age 60
and above, the difference being largest for the broad sample. This is consistent with the fact that
younger workers experience the negative consequences of mass-layoffs on their careers for much
longer periods of time. Being laid off before retirement appears to have no health consequences.
This will make an important difference when calculating the effects on life expectancy below.
Table 7 also shows the results by industry, mean earnings prior to job loss, and region. To
maximize sample size, we again show the results for workers born after 1920. The effects are among
the highest in steel and other durable goods manufacturing. This is to be expected, since there are
sectors with traditionally high paying jobs that were over-represented in Pennsylvania and
experienced large reductions in economic activity. However, the effects of job loss at mass-layoff are
also large in construction and transportation, and in services.
This result, robust to controls for age, average earnings, and other pre-job loss outcomes, is consistent with the
hypothesis that smaller firms are more likely to close and provide less favorable career environments and possibly attract
less able workers.
35
27
When we break up the sample by quartile of average earnings in 1974-79, the results differ
somewhat across samples. When we require no or some work attachment during the layoff-period,
the effect is lower for those from the highest quartile of the pre-layoff distribution; when we
consider the sample with high work attachment, the effect is reversed. This may indicate that the
high attachment sample excludes low earners with unstable work attachment and higher mortality.
The bottom of the table also shows that the effects tend to be larger in eastern Pennsylvania. This is
perhaps somewhat surprising, since most of the steel manufacturing and heavy industry is in the
western part of the state. However, we also see strong effects of job displacement on mortality in
transportation, construction, and services, sectors relatively more important in eastern Pennsylvania.
4.4. The Reduction in Life-Expectancy due to Job Loss
Since increased mortality affects workers over a long time horizon, our estimates imply
substantial reductions in life expectancy. The average loss in life-expectancy can also be used as a
summary measure of the cost of job loss at mass-layoff in terms of life-years, much as the present
discounted value of life-time earnings losses provide summary measures in conventional analyses of
the cost of job loss. In Table 8, we present a range of estimates of losses in life expectancies for
alternative samples and ages. Since 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 among laid off workers and the control
group past our observation window. Specifically, we maintain the assumption of constant
proportional differences in the odds of death. Given that the typical profile of the log odds of dying
is stable through older age ranges, and is so within the groups of our sample, this is a plausible
assumption. To take into account that the effect of mass-layoff on mortality differs by age at
displacement, the losses in life expectancy in Table 8 are based on estimates in Table 7. All life-
28
expectancies are calculated for workers with average annual earnings in 1974-79 and working in the
steel industry in 1979. 36
The first set of estimates shows the loss in life expectancy for our most general sample.
Since there are large spikes in mortality at layoff, for this sample we also take into account the
dynamic response in mortality estimated in Table 5. The last column shows large losses in life
expectancy of more than two years for workers losing their job in their 30s and 40s. Life-expectancy
of workers displaced in their 50s still declines, but about half as much. This difference arises first,
because younger workers are exposed to higher mortality rates over a longer period of time. Second,
because the increase in mortality rates are greater for younger workers, possibly because earnings
losses accrue over a longer period of time.
The remaining sets of estimates show results for samples restricting workers to have some
employment in the period of job loss. Since for these samples follow up starts in 1987, we first
replicate the calculation for our broad sample using the shorter follow up period. The results show
smaller losses, since we take out the initial period with strong mortality effects and reduce the range
of time over which losses can accrue. The next two sets of estimates are for a sample requiring three
year employment, and the JLS sample requiring some work every year from 1980 to 1986. The
estimates decline further but remain in the range of one to one and a half years in particular for
younger and middle aged workers.
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 typical
measure of the cost of displacements is based on the loss in present discounted value of earnings.
For the average worker in our high attachment sample this amounts to about $200,000 at a real
interest rate of four percent. To make the losses in life expectancy comparable, we can monetize
them by choosing an estimate of the statistical value of life. At about five million dollars per
statistical life, a loss in one and a half years would amount to a monetary loss of $100,000. Although
Life expectancy can be calculated as the sum of survivor probabilities over the remaining age-years an individual is
alive. The difference in life-expectancies then is the sum of the differences in survivor probabilities.
36
29
they can be no more than broadly indicative, values of this order of magnitude implies that the
willingness to pay of a worker to avoid a job loss may be substantially underestimated by traditional
summary measures such as the loss in life-time earnings.37
5. Potential Channels of Mass-Layoff Effect on Death
The most frequently studied consequence of job displacement is the long-term decline in
earnings. The long-term earnings decline observed in Figure 1 is a likely channel through which
layoffs raise mortality in the long run. Lower earnings may affect necessary health investments or
lead to unhealthy life styles. Alternatively, it may raise mortality by increasing social stress. However,
layoff can affect other career outcomes such as variability of earnings, job changes, or regional
mobility. Such adjustments are likely to be closely correlated with the earnings loss. They may also
directly affect mortality by increasing stress from adjustment, loss in familiar environments, or put
strain on social relationships. A common difficulty in accounting for the role of each separate
channel is that workers’ responses to a job loss may be itself correlated with their underlying health
status. An added difficulty is that very little is known about the correlation of permanent earnings
and other long-term career outcomes with mortality. Similarly, remarkably little systematic and
representative evidence exists about the effect of mass-layoff on outcomes other than earnings.
Despite the difficulty of the problem, the exceptional nature of our data allows us to gain
important insights on the potential role of alternative channels not possible with existing sources of
information. First, we can gauge the potential magnitude of the sources of the mortality effect we
find by providing new the first estimates of the association of long-term career outcomes with death
To think about how these two numbers should be treated, consider that in the typical neo-classical model of health
investments in which long-term health is an outcome of optimally chosen inputs given a life-time budget constraint. In
this case, the loss in the PDV of earnings is a sufficient statistic for the cost of job loss, since the reduction of life time
would be the optimal response to the reduced resources. However, only a small fraction of health expenditures are out
of pocket. Moreover, health is likely to be affected by factors other than consumption or health inputs. For example, it
may be affected by losses in social status related to earnings declines at layoff. In that case, the PDV of earnings losses is
not a sufficient statistic anymore, and to obtain the total costs of job displacements the monetary value of the direct
effect of layoffs on mortality should be added to the PDV of earnings losses. In particular, we should have that the Total
cost of job loss = PDV of Earnings Losses + α × Monetary Value of Lost Life, where α is between zero and one. In the
neoclassical model, α = 0; in a model of social status, α = 1. The right value probably lies in between, but given the
limited role of individual health expenditures it is likely to be closer to one than to zero. This implies that increases in
mortality due to layoffs substantially raise the costs of job loss and workers’ willingness to pay to avoid displacement.
37
30
and job loss. Second, we directly estimate the effect of earnings losses and changes in other
outcomes on the effect of job loss on mortality. Third, we address the fact that earnings losses may
be correlated with underlying health status by exploiting predictable variation in earnings loss at cell
level. Using this strategy, we obtain four results:
•
First, while all career outcomes are likely to have some impact in the short run, the predicted
effect of earnings is largest and the only effect lasting more than five years after mass-layoff; in
the individual level models, earnings changes can explain the entire mass-layoff effect on
mortality.
•
Second, it does not appear job losers’ earnings changes are more strongly correlated with
mortality than that of stayers, suggesting that the effect of earnings losses at job displacement on
mortality is not primarily due to ill health of job losers.
•
Third, the potential role of earnings changes is confirmed when we exploit variation in
predictable earnings losses by groups of age, industry, or region groups.
•
Fourth, those groups of workers with the highest loss in present discounted value of lifetime
earnings are those with the largest losses in life-expectancy.
Overall, we interpret these results as suggesting that at least for job losses involving such dramatic
earnings declines as in Pennsylvania in the early to mid 1980s, the sources of the increase in
mortality are likely to be strongly correlated with earnings losses. This may include direct effects of
earnings, but also stress and adjustment costs correlated with long-term earnings declines. Clearly,
although suggestive, our results do not exclude the possibility that earnings losses and mortality
increases may also be driven by a common underlying variable such as depression as a result from
job loss.
5.1. Long-Term Career Outcomes and Mortality
To help us gauge the magnitude of the effect of potential channels, we exploit the
longitudinal nature of our data to provide estimates of the correlation of career outcomes in the
baseline period with mortality in the follow up period. Table 4 shows the estimates from our most
31
complete model for the various alternative samples, with more detail contained in Appendix Table 6.
Although these estimates of career outcomes on long-term mortality are of substantial independent
interest, we limit ourselves to summarizing the main points. The Appendix discusses the results in
more detail. First, average earnings have a large and robust effect on long-term mortality; only
average earnings matter in the long run, not earnings in any given year. The effect we estimate is
about a third larger than typical estimates in the literature, and we show that this is due both to
classical measurement error from the use of earnings from survey data as well as measurement error
from approximating permanent earnings by current earnings.
Second, the standard deviation of log quarterly earnings in 1974 to 1979 has robust and
sizeable effects the long term mortality rate.38 Figure 4 suggests that the correlations for both
measures are well captured by a log-specification. These results suggest workers with lower average
earnings and higher earnings variability die younger. Of course, while reverse causality and
measurement error are less of a concern here, omitted variable bias still is. Last, other career
measures we tried have little discernible effect on long-term mortality outcomes. The exception is
time worked which enters significantly negatively for some samples, consistent with Ruhm’s (2002)
analysis of the effects of recession on mortality.
5.2. Effects of Mass Layoffs on Career Outcomes
To better understand how displacement impacts mortality, we also studied the impact of
displacement on a broad range of workers’ career outcomes. As noted above, our estimates are
based on an annual distributed lag model that controls for worker fixed effects, year effects, and a
quadratic in age. Because in our Pennsylvania administrative data earnings may appear to be zero
when workers have moved out of state, we focus on a sample of workers who had positive earnings
in every year through 1991.
As Table 9 shows, we estimate that displacement some time during the 1980-1986 time
period reduces workers’ earnings by approximately 22% in the 1987 to 1991 period. These estimates
We use the standard deviation of the log of quarterly earnings to obtain a measure of earnings variability based on
percentage changes that is comparable across wage levels.
38
32
are quite similar to those reported in Jacobson, Lalonde, and Sullivan (1993) for a somewhat shorter
follow up period. Thus the earnings losses associated with job displacement for workers with
substantial job tenure appear to be extremely persistent.
Table 9 also shows that many other career outcomes are strongly affected by a layoff in the
short run but return to normal at about the fifth year after a job loss. In particular, we find strong
temporary increases in absolute value of earnings growth (our measure of annual career variability)
and job and regional mobility. These results suggest that after an initial turbulent phase, job losers
settle into a stable career at a permanently lower level of earnings.
5.3 Gauging the Effects of Alternative Channels
Using the results in Table 9, one can gauge the potential order of magnitude of different
channels through which mass-layoffs can affect mortality. To do so, we combine the mass-layoff
results with the correlations shown in Table 4. Clearly, this does not yield a true decomposition of
the effect as if these were causal parameters. Below, we will try to gauge our interpretation using
predictable differences in earnings losses across groups of workers.
The two main career outcomes that were most important in our analysis of correlations are
mean and standard deviation of log quarterly earnings. Taken at face value, the estimated correlation
of average earnings with mortality of -0.5 would imply that we expect an increase in mortality of
about 12.5% for workers with high attachment (0.25*0.5). Thus about two-thirds of the long-run
effect of mass-layoff on mortality, about 0.15 to 0.2, could be explained by the observed declines in
average earnings. Clearly, if frail workers die younger and have lower average earnings, this
prediction overstates the potential effect of job loss through earnings. 39
In his study of Swedish lottery winners, Lindahl (2005, Appendix Table 2) shows that the effect of controlling for
initial health conditions tends to reduce the correlation between mortality and earnings by about a third. (The
magnitudes of the OLS effect that Lindahl estimates are somewhat smaller but in the same ball-park as ours. He does
not report logit estimates, so the comparison is based on approximate percentage effects derived from the results of
linear probability models.) Were this to be the case, the predicted role of earnings in explaining the mass layoff effects is
reduced to about half of the effect, which is still substantial. Note however that our high-attachment sample is likely to
be of better health than the older sample used in Lindahl (2005).
39
33
The remaining effects could partially be driven by increases in the instability of earnings and
other career outcomes. From a replication of Table 8 in with a difference-in-difference specification,
we find the standard deviation of log quarterly earnings increases on average by about 16% at a
mass-layoff. At a coefficient of -0.2 (Table 7), this implies an increase in the probability of dying of
about 3.2%. While the order of magnitude of this effect is much lower than the potential impact of
earnings, it can still account for about 20% of the mass-layoff effect. Although the fact that they also
attain for workers with high job attachment and are robust to controls for baseline average income
lends credibility to the results, if frail people have higher earnings instability and higher death rates,
this prediction is likely to be over stated. Moreover, as shown in Table 8, this effect is relevant
mostly in the short term and is unlikely to be able to explain persistent increases in mortality rates.
These straightforward calibrations suggest a potentially important role for long term earnings
losses in explaining the effect of layoff on mortality we find. To further explore the role of longterm earnings losses in accounting for the effect of job displacement on mortality, we also directly
included the long-term earnings change into our logit model (equation 3). The result is shown in the
first panel of Table 9 for alternative samples. The results suggest a strong correlation between
earnings changes and long term mortality. Moreover, once we condition on earnings changes, the
effect of the mass-layoff dummy becomes numerically small and insignificant. Figure 5 confirms
these patterns by showing the non-parametric relationship between long-term earnings losses and
mortality, controlling for age, year, and past average earnings.
These results further suggest that the mechanism explaining the mass-layoff effect on
mortality strongly correlates with long-term earnings losses. However, as discussed in Section 3, we
cannot interpret these results as indicating a causal channel from earnings losses to mortality, unless
we are willing to assume that the earnings response to layoff is not correlated with workers’ health.
Note that if there were a strong correlation, then we would expect the correlation of earnings losses
and mortality to rise for workers experiencing a job displacement. However, if we replicate our
estimates focusing on the sample of job losers, the coefficients on the earnings change becomes
smaller (row two of Table 9).
34
To further examine whether earnings losses really capture the effect of increasing stress in
the labor market due to uncertainty, we also included the standard deviation of quarterly earnings
from 1987 to 1991 (not shown). If earnings losses just index the degree of stress due to adjustments
in the labor market, we might expect that this would reduce the effect of earnings changes on
mortality. However, although as expected the standard deviation has a positive significant effect, the
impact of earnings changes on long-term mortality outcomes is unaffected.
The results in this section are suggestive of an important role of earnings losses in explaining
the effect of layoffs on mortality. However, here may be omitted variables that drive both earnings
losses and mortality increases, such as a differential increases in depression in response to layoffs. To
explore the role of earnings losses further, we next try to exploit cell-level variation in earnings
losses.
5.4. Cell-Level Analysis
A long literature suggests that there are differential effects of job loss in the population (e.g.,
Farber 2003, JLS 1993, Kletzer 1989, Neal 1995). Part of these differences arise from differences in
rent workers receive on the job that are permanently lost as workers move employers (von Wachter
and Bender 2007). We would like to isolate groups of workers that have different rents and thereby
predictable differences in earnings losses. If these groups do not have other differential responses to
layoffs, we can study the effect of earnings changes at job loss on long-term mortality at the group
level.
We use various cell definitions based on age, average earnings, industry, and region, all
measured at the end of our baseline period. Wage differences among age and average earnings
groups partially reflect differences in skills. However, they also reflect differences in wage levels due
to contractual premiums (Beaudry and DiNardo 1991, Oreopoulos, von Wachter, and Heisz 2006),
job search (Topel and Ward 1992), and firm and industry wage premiums (e.g., Krueger and
Summers 1988, Abowd, Creecy, and Kramarz 2002). Similarly, differences in age lead to differential
cumulated long-term earnings losses due to layoffs. Thus, differences in earnings losses among cells
35
partly arise from labor market factors unrelated to productivity and health. Clearly, health responses
to layoff may also differ across cells.
To implement our approach we regress mortality increases on average earnings losses at the
cell-level. As explained in Section 3, this is equivalent to using cell-level dummies as instruments for
earnings losses in a linear probability model of mortality. The lower panel of Table 10 shows the
coefficient estimates on actual earnings changes in a linear probability model and the slope
coefficient in the cell level model. The model also controls for cell-level dummies. Thus, the
variation of earnings changes at the cell-level arises from the interaction of cell-dummies with the
mass-layoff dummy. 40
We find that the effect of earnings losses on mortality is similar at the individual and cell
level for the samples with no or low work requirement. The cell-level effect is smaller for the highattachment sample, but standard errors increase substantially as well.41 As before, if we include the
mass-layoff dummy, its coefficient is small and insignificant. However, now the estimates lose
precision. The bottom panel in Figure 5 shows the corresponding cells and a linear regression line
for the high attachment sample. There is a clear negative and relatively precise association, albeit
there is a high degree of variation left. The results are very similar for different definition of cells.
Unfortunately, we could not include changes in the standard deviation at the cell level without
further losing precision.
The resulting value for the mean-squared error of the group-level regression is the test
statistics for of a test of over-identification with a limiting Chi-Squared distribution with degrees of
freedom equal to the number of cells. This test is equivalent to the standard test of overidentification in 2SLS and amounts to a joint test of the equality of the different Wald-estimates
corresponding to each group (Angrist 1991). In results not reported here, for neither model can we
The cell-level model is implemented in two steps. First, we regressed individual 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 the mass-layoff dummy. The coefficients
on the interaction are used in the second step. Second, we regress cell-level changes in mortality on cell level earnings
losses weighting by the inverse sampling variance of the cell-specific mortality increases.
41 Note that the standard errors are from the group-level model and are thus corrected for group-level error
components.
40
36
reject the over-identifying restrictions at any reasonable degree of significance. This is particularly
helpful in our case, since failure to reject the null-hypothesis supports an underlying model of a
constant proportional effect of earnings on mortality.
Although the results discussed so far refer to changes in average earnings, standard models
of health investment refer to life-time resources as the relevant earnings concept. That this could
make an important difference is suggested in Table 7. Although older workers typically have the
largest losses in earnings (e.g., Farber 1997, JLS 1993), they experience the lowest increase in average
mortality rates after a job loss. This may be because older workers are typically exposed to reduced
earnings for a shorter period of time.
To address this question, we calculated the present-discounted value (PDV) of life-time
earnings losses following a job loss at a mass-layoff by age-group.42 The results, shown in Figure 6,
show that the PDV of earnings losses after a job loss is a declining function of age. The figure also
displays losses in life-expectancy taken from Table 8 by age-group. Young workers not only have
higher average mortality increases after a job loss (Table 7), but also the largest life-time losses in
earnings and remaining life-time. Thus, it is not the oldest workers that are most affected, but those
in prime working age that are exposed to the negative consequences of job loss over a longer period
of time.
Given the robustness of our results across specifications, and the similarity of results at the
individual level and cell level, we interpret the foregoing analysis as confirming an important channel
for earnings changes on mortality. Clearly there are other explanations consistent with our results.
For example, changes in depression or stress at the group level may lead to both greater earnings
losses and larger increases in mortality. In either case, our results suggest that any channel explaining
the effect of job displacement at mass-layoffs on mortality is likely to be at least correlated with the
degree of earnings losses.
42 To do so, we allowed the short- and long-term effects of job loss on earnings to vary by either 10 year or 5 year agegroups. Since we do not have complete earnings histories after job loss for all workers, we assume that the earnings loss
decays at the same speed of reversion observed between years 6 to 11 after a job loss eventually staying fixed at zero.
37
As discussed in Section 2, our results are not in conflict with recent studies suggesting that
mortality declines in recessions (e.g., Ruhm 2000), since the shock we analyze is of different nature
than the impact of aggregate variation in economic activity. In addition, the role of employment in
our regressions suggests that stress and work-accidents may play an important role in our sample as
well. Some non-employment in the years prior to job loss turns out to reduce mortality in the long
run (Table 4); conditional on earnings losses, job losers with some non-employment live longer on
average (Appendix Table 4); similarly, conditional on earnings losses, those job losers with largest
earnings increases have higher mortality as well.
Thus, while the strong earnings losses play a particularly important role in our sample of job
losers in the recessions of the early 1980s in Pennsylvania, this does not exclude the influence of
other channels. In particular, it does not preclude the dominance of other channels in situations
where earnings losses are not as large and sustained as in our case.
6. Conclusion
This paper uses administrative data covering over 15 years of quarterly earnings and
employer records matched to information on date of death to study the effects of job displacement
on mortality. To measure an event plausibly exogenous to workers’ own health outcomes, we
analyze job losses occurring when employers experience mass layoffs affecting at least 30% of their
work force. 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-free estimates pooling
movers and stayers. The results suggest a particularly pronounced increase in mortality during the
period immediately following job loss and long-run increase of 15-20% in the annual probability of
dying persisting for at least the next 20 years. These effects, robust across alternative samples and
specifications, are consistent with strong responses to both acute and chronic stress.
To analyze the channels underlying the mass-layoff effect we analyze the correlation of longrun career outcomes with mortality. We show that the mean and standard deviation of earnings
during a baseline period have large and significant correlations with mortality in a later follow up
38
period. The results on the role of earnings and employment instability are novel with respect to the
existing literature on mortality that has had not access to long-run career outcomes. 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 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.
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 results shows
these effects can be large. A worker displaced in mid-career can expect to live about two years less
than a luckier counterpart. The reduction in life expectancy is smaller for older workers who
experience lower life-time earnings losses and are exposed to increased mortality for a shorter period
of time. Our results do not speak to the role of non-economic factors such as stress, self-worth, and
happiness. Yet, they suggest 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.
39
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43
Appendix 1: Average Earnings and Mortality
This is one of the few studies of earnings and mortality that approximate permanent
earnings with more than a single year of earnings data. Figure 4shows various estimates of the
correlation of average quarterly earnings in the period of 1974-1979 on mortality in 1986-2002. Since
the natural logarithm turns out to be a good approximation to the declining correlation of average
earnings and mortality, all our econometric specifications include the log of average quarterly
baseline earnings.43 The first three columns of Appendix Table 5 show various estimates of Model
(1) for our main mass-layoff sample (without a mass-layoff dummy).44 The effect of log average
earnings is about -0.5 for the group of workers with high job attachment in the baseline period.
Given that the probability of death is very small, the coefficient approximately implies a 10%
decrease in mortality for a 20% increase in average quarterly earnings. For example, a two standard
deviation increase in average quarterly earnings would lead to about a 4% decline in mortality. To
properly assess the magnitudes of the estimated effects, the second panel in Appendix Table 2 and
Figure 3 show the predicted values of the probability of dying by age- and income-group based on
the estimates shown in Appendix Table 5 (1st column and 1st row). As expected, the earnings
gradient rises with age – a comparison of differences in mortality rates at 25th and 75th earnings
percentiles shows substantial differences across age-groups. Equivalently, the role of age decreases
for high-income workers.
These results are robust for cohort controls, are similar across age groups, are unaffected by
controls for other career measures such as quarters worked, and are not changed if the baseline
period is extended to cover 13 instead of 6 years (1974-1986). Interestingly, the effect of income is
significantly higher for workers who worked in manufacturing during the baseline period (see
Appendix Table 6, Models 6 and 7). To replicate results in the existing literature, we also estimated
models of 5 and 10 year follow-up (e.g., see Deaton and Paxson 1999). The results, shown in
columns 4 to 9 in Appendix Table 5, essentially confirm our main estimates.45 These estimates are
bigger in absolute terms than the effects estimated by Deaton and Paxson (1999, Table 5) using CPS
43 For high and low earnings, a fourth order polynomial does a better job at approximating the ‘non-parametric’ dummyvariable estimates. We chose the more parsimonious specifications, but double-checked our results by including
earnings-dummies. This is particularly important when non-linearities can matter, such as in the analysis of earnings
ranks. The more fully non-parametric results in the lower panel of Figure 1 confirm that a smooth concave function is
appropriate to capturing the relationship between earnings and mortality.
44 Appendix Table 5 compares results across alternative samples, further discussed below.
45 Changes in the fraction of older workers in our panel sample over time are likely to explain variation in the results
across age groups for these models.
44
earnings data matched to mortality information. Even when we follow the literature by using a single
year of earnings at beginning of the follow up period (row 3 of Appendix Table 5), our estimates are
slightly larger, albeit weaker than the effects of average earnings. As shown in Bound and Krueger
(1991, Table 6), the reliability ratio for men at single employers is about .8 in the case of classical
measurement error, and .95-1 for mean-reverting measurement error. Thus classical measurement
error could explain part of the difference of using annual earnings measures from survey data (CPS)
and annual earnings information from administrative data (row 3).
The interpretation of the difference between the estimates in row 1 and row 3 is only slightly
more involved. Consider a typical model of current earnings yit as a function of persistent ( θ it ) and
transitory ( u it ) components
yit = θ it + u it , where θ it = θ it −1 + vit , u it ~ ARMA, vit ~ WN .
(A1)
If both components affect health and longevity, when included as the sole explanatory variable
current earnings captures the joint effect of the permanent and transitory earnings components. On
the other hand, if only permanent earnings matter for long-term health outcomes, then current
earnings is a noisy measure of permanent earnings. If so, then row 3 is downward biased by either
classical (if u it has short memory) or mean-reverting measurement error. Since we have measures of
permanent earnings available in our data, we can check this directly by including them jointly into
the model. If current earnings are simply a noisy measure of permanent earnings, its coefficient
should drop to zero. This is shown in the last two rows of Appendix Table 5 (and Appendix Table 6
for other samples). In most cases, including a measure of average earnings indeed drives the effect
of current earnings to zero.46 This suggests that other studies having at disposition only measures of
annual earnings should scale their coefficients up by a factor taking into account measurement error
from survey responses as well as from transitory earnings disturbances. In separate calculations, we
show that the combined reliability ratio can be as low as 0.6.
The current results are obtained from workers with a stable job from 1974 to 1979 who
either continue to hold that job through the end of 1986 or who leave their baseline job at the time
of a mass layoff. In both cases workers are required to have positive earnings in each year from 1979
to 1986. To assess to what extent our results are driven by the focus on high-attachment workers,
Appendix Table 6 replicates our main specifications for a series of different samples. The second
46 Since the relationship between permanent earnings and current earnings also differs by age, the measurement error
might be more complicated (Haider and Solon 2006). This should be partially taken into account by the age controls
included in our models.
45
column shows results based on all workers who have a stable job in 1974 to 79 (i.e., with respect to
column 1 it includes non-mass layoff job changers), and confirms the results from the mass-layoff
sample for all specifications. The third column substantially weakens the restriction on labor force
attachment by including all workers who where employed or received unemployment insurance at
least half of the time in 1974-79. The effect of log average earnings is somewhat lower, but partly
due to correlation with quarters worked, which has a negative coefficient. 47 This somewhat
surprising result is consistent with Ruhm (2000)’s analysis suggesting lower economic activity in
recessions may be beneficial to health. However, it turns out that the beneficial effect of nonemployment is not a very robust feature of our data.48 The last two columns show the results for
high and low attachment samples of older workers for whom we calculate average quarterly earnings
with all quarters available from 1974 to 1986 (spanning the last 10 to 15 years of workers’ earnings).
The results confirm those in corresponding columns 2 and 3, and suggest that our main estimates
are not driven by the choice of a particular time period or reference period within a workers’ career
as baseline.
Appendix 2: Career Instability and Mortality
In addition to average quarterly earnings, other career outcomes such as the degree of
employment or earnings stability may also be related to health and mortality. We have considered
alternative measures of career instability, including the standard deviation of log earnings, the
number of quarters worked, the number of large declines in quarterly earnings (drops in earnings of
more than two standard deviations), the number of transitions into non-employment, the number of
job changes, and the number of long non-employment spells. Workers in the main mass-layoff
sample experienced important variation in earnings over the baseline period, as shown by the
standard deviation and the high fraction of workers with large drops in earnings. Although by
construction this sample has very few transitions to non-employment or job changes, both earnings
and employment instability become very relevant for the broader samples in the remaining
The fourth column shows the results for a very broad sample that includes all workers who were ever present in the
UI system between 1974 and 1979. The effects of log average earnings are even lower for this sample. These results
partly derive from the fact that the standard deviation of log average earnings is much higher in the broader samples (see
Table 4); i.e., a two standard deviation increase in log average earnings is of much more similar magnitude across
samples. This also hints at the fact that for the broader sample, on average fewer observations are used to calculate
average quarterly earnings. Thus, part of the difference will be due to attenuation bias from classical measurement error,
too. In addition, it may be that for low-income workers, average earnings are a worse approximation of the actual
amount of resources due to greater importance of second earners, family ties, and government subsidy programs.
48 The effects of quarters worked are positive for the sample of older high-attachment workers in column 5 and
insignificant for more workers with some labor force attachment in column 6.
47
46
columns.49 For these samples, the standard deviation of log earnings is substantially higher and a
larger fraction of workers experience job changes, transitions to non-employment, and longer nonemployment spells.
To estimate the correlation of these measures with mortality, we present results from logitmodels as shown in equation (1). These are shown for different samples and specifications in Table
4 and Appendix Table 6. All models include age dummies, year dummies, and log average earnings
as controls, and all results are robust to inclusion of measures of the number of quarters worked.
The main result of Table 4 is that the effect of the standard deviation of log earnings on mortality is
positive, non-linear (the effect is positive and decreasing and well-captured by a log-specification, see
Figure 4), and substantial. The results in column 1 of the first panel in Table 4 suggest that a 20%
increase in the standard deviation of earnings leads to an increase in the risk of death of about 4%.
In the pooled model, this corresponds to the effect of a 10% decline in average earnings. The
estimates are also robust to inclusions of other measures career shocks such as quarters worked,
non-employment transitions, or the presence and frequency of large earnings drops.
Given it is calculated over a relatively short period of time (six years), we interpret the
standard deviation of log-earnings as a measure of the transitory component ( u it ) of the earnings
process in equation (A1). We believe this to be a reasonable interpretation of the short-term
earnings risk an individual faces. This interpretation is helped by the fact that the effect of the
standard deviation is robust to the inclusion of indicators for the presence of large earnings shocks
and non-employment transitions. Thus, we interpret the results in Table 4 to imply that the
variability a worker faces in the labor market is negatively correlated with long-term health
outcomes. Clearly, less healthy workers are likely to have higher standard deviations in earnings.
However, the fact that the result also attains for very stable workers and is largely unaffected by the
inclusion of log average earnings as additional control suggests it may not be fully explained by
omitted variable bias. Reverse causation is not an issue given that the follow up period begins seven
years after the baseline period at which the standard deviation is calculated.
Table 4 also shows results for other measures of career instability and more broadly defined
samples of workers. The effect of the standard deviation is present across all samples; it is smaller
for the broader sample, partly due to the fact that the range of values the standard deviation takes is
much higher (such that a change of two standard deviations would have a more similar effect).
Note that some mobility is possible even for those workers due to temporary layoffs and short breaks in the
employment spell.
49
47
Similarly, its effect is essentially unaffected if it is calculated over a much longer time period. Not
surprisingly, for the main high-attachment sample, the only other significant effect is the presence of
large earnings declines. However, this effect fades for the sample with the long baseline in the
bottom of the table. For the broader samples, transitions to non-employment tend to raise mortality
even when controlling for the incidence of large earnings declines. This result stands in contrast to
the sometimes beneficial effect of the amount of time spend in non-employment. Once we control
for the number of times individuals transit to non-employment, the incidence of job changes has no
effect for either sample.
Overall, we interpret these results to signify that the interaction between individual health
and career outcomes is potentially complex, and goes beyond a simple correlation between earnings
and mortality. In particular, it appears that various measures capturing the degree of earnings and
employment stability have robust effect on mortality, too. It appears that workers with unstable
careers die younger. To address part of the concerns of omitted variable bias afflicting these
correlations, in the next section we study the short and long-term mortality effect of an explicit
shock to workers careers – an involuntary job loss in the course of mass-layoffs at the plant level.
48
Table 1: Sample Characteristics in Baseline Period (1974-1979) by Displacement Status
Work Every Year 1980-86
Workers
Displaced
NonAll
at Mass- Displaced
Workers
Layoff
Workers
No Work Restriction 1980-86
Workers
Displaced
NonAll
at Mass- Displaced
Workers
Layoff
Workers
Sample Size
15377
4802
10575
21323
7190
14133
Age in 1979
37.54
(7.014)
37.02
(7.295)
37.77
(6.870)
37.36
(7.114)
37.09
(7.406)
37.49
(6.956)
Log Average Quarterly
Earnings in 1974-79
8.75
(0.334)
8.70
(0.337)
8.77
(0.330)
8.74
(0.358)
8.70
(0.346)
8.76
(0.362)
Log Std. Dev. of Log Quarterly
Earnings 1974-79
-1.678
(0.711)
-1.545
(0.749)
-1.738
(0.685)
-1.638
(0.731)
-1.484
(0.768)
-1.716
(0.699)
Percent Change in Quarterly
Earnings 1974-79
0.468
(5.645)
0.580
(7.274)
0.418
(4.729)
0.517
(5.769)
0.682
(7.733)
0.434
(4.453)
Number of Quarters in NonEmployment 1974-79
0.45
(0.913)
0.54
(1.031)
0.40
(0.851)
0.48
(0.978)
0.58
(1.102)
0.43
(0.905)
Establishment Employment
Size in 1979
8608
(13649)
9040
(15001)
8412
(12984)
8564
(13950)
10459
(16272)
7599
(12496)
Fraction Durable Goods
Manufacturing (Non Steel)
0.304
(0.460)
0.177
(0.382)
0.365
(0.481)
0.259
(0.438)
0.277
(0.448)
0.140
(0.347)
0.298
(0.457)
0.180
(0.384)
0.350
(0.477)
0.291
(0.454)
0.271
(0.445)
0.123
(0.328)
Fraction Other Manufacturing
0.195
(0.396)
0.184
(0.388)
0.200
(0.400)
0.191
(0.393)
0.165
(0.371)
0.204
(0.403)
Fraction East PA
0.565
(0.496)
0.522
(0.500)
0.584
(0.493)
0.561
(0.496)
0.472
(0.499)
0.607
(0.489)
Log Average Quarterly
Earnings in 1987-91
8.735
(0.876)
8.421
(1.064)
8.871
(0.740)
8.607
(1.067)
8.187
(1.307)
8.792
(0.880)
Log Std. Dev. of Log Quarterly
Earnings in 1987-91
-1.396
(0.737)
-1.199
(0.758)
-1.481
(0.712)
-1.344
(0.763)
-1.120
(0.792)
-1.440
(0.730)
Fraction Steel Industries
Number of Quarters in NonEmployment in 1987-91
2.09
3.32
1.53
4.12
6.50
2.91
(4.568)
(5.900)
(3.678)
(6.904)
(8.126)
(5.829)
Notes: Standard deviations in parentheses. The samples only include male workers in stable employment 1974-1979
at an employer of size 50 in 1979.
Table 2: Mortality Rates in Different Periods by Displacement Status
Panel A: Work Every Year 1980-86
Time
Period
87-02
87-91
92-96
97-02
Displaced
All Workers Workers
5.720
(0.165)
3.641
(0.218)
6.562
(0.270)
8.047
(0.334)
6.204
(0.308)
4.287
(0.424)
7.063
(0.503)
8.753
(0.626)
Difference Adjusted For
NonDisplaced
Workers
Simple
Difference
5.502
(0.195)
3.348
(0.252)
6.336
(0.320)
7.729
(0.394)
0.702
(0.365)
0.939
(0.493)
0.727
(0.596)
1.024
(0.740)
Age
Age and Base
Period
Earnings and
Firm Size
Age and Base
Period and
Change in
Earnings and
Firm Size
0.974
(0.364)
1.095
(0.493)
0.996
(0.595)
1.541
(0.740)
0.757
(0.364)
0.891
(0.493)
0.775
(0.595)
1.209
(0.738)
-0.055
(0.369)
0.076
(0.498)
-0.011
(0.603)
0.537
(0.747)
Notes: Deaths per 1000 per year. Standard errors in parentheses. Displaced Workers left jobs in a year in which their
former firms' employment was 30% or more below its 1974-1979 peak. Nondisplaced workers remain at their 1979
firm through 1986.
Panel B: No Work Restriction
Difference Adjusted For
Time
Period
87-02
80-86
87-91
92-96
97-02
Displaced
All Workers Workers
6.185
(0.146)
2.465
(0.128)
4.008
(0.194)
6.981
(0.237)
8.699
(0.296)
7.031
(0.269)
3.996
(0.279)
4.946
(0.372)
7.776
(0.425)
9.527
(0.536)
NonDisplaced
Workers
Simple
Difference
Age
Age and Base
Period
Earnings and
Firm Size
5.758
(0.173)
1.677
(0.130)
3.533
(0.224)
6.562
(0.284)
8.284
(0.354)
1.273
(0.319)
2.320
(0.307)
1.412
(0.434)
1.249
(0.517)
1.243
(0.642)
1.384
(0.319)
2.344
(0.307)
1.468
(0.434)
1.348
(0.516)
1.481
(0.640)
1.004
(0.319)
1.978
(0.307)
1.091
(0.434)
0.966
(0.515)
1.096
(0.640)
Age and Base
Period and
Change in
Earnings and
Firm Size
0.294
(0.341)
-0.147
(0.152)
0.323
(0.457)
0.416
(0.558)
0.543
(0.694)
Notes: Deaths per 1000 per year. Standard errors in parentheses. Displaced Workers left jobs in a year in which their
former firms' employment was 30% or more below its 1974-1979 peak.
Table 3: Impact of Layoff During Mass-Layoff on Log-Odds of Death for Various Samples, Follow-Up Periods, and Specifications, Workers in Stable
Employment 1974-1979, Employment Size 50 in 1979
Mass-Layoff Sample:
Follow-Up Period:
No Work Work Every
Restriction
Year
1980-02
Born
Cohort Range:
Specification:
1987-02
Work At
Least 3
Years
No Work
Restriction
1987-02
1987-02
No Work Work Every
Restriction
Year
1980-02
1930-59
1987-02
Born
Work At
Least 3
Years
No Work
Restriction
1987-02
1987-02
1920-59
(1)
(3)
(5)
(7)
(2)
(4)
(6)
(8)
0.275
(0.041)
0.145
(0.056)
0.166
(0.046)
0.170
(0.045)
0.154
(0.025)
0.089
(0.039)
0.122
(0.028)
0.111
(0.027)
0.262
(0.043)
0.129
(0.058)
0.145
(0.048)
0.149
(0.047)
0.139
(0.026)
0.076
(0.040)
0.104
(0.029)
0.092
(0.028)
(C) Model in Row (A) with 1-Digit
Industry Effects and Added
Career Variables
0.247
(0.044)
0.102
(0.060)
0.117
(0.050)
0.129
(0.049)
0.134
(0.026)
0.066
(0.041)
0.100
(0.030)
0.089
(0.029)
(D) Model in Row (A) with Industry
Effects, Career*Age
Interactions
0.262
(0.043)
0.127
(0.058)
0.144
(0.048)
0.149
(0.047)
0.143
(0.026)
0.077
(0.040)
0.105
(0.029)
0.094
(0.028)
0.0014
(0.00027)
0.0008
(0.00035)
0.0010
(0.00032)
0.0010
(0.00033)
0.0015
(0.00031)
0.0008
(0.00042)
0.0014
(0.00039)
0.0013
(0.00039)
0.0019
(0.00051)
0.0008
(0.00054)
0.0006
(0.00051)
0.0006
(0.00054)
0.0015
(0.00053)
0.0010
(0.00075)
0.0013
(0.00059)
0.0008
(0.00059)
0.274
0.135
0.150
0.155
0.148
0.085
0.114
0.101
0.369
0.131
0.091
0.090
0.147
0.102
0.106
0.066
478,664
237,122
319,358
327,775
681,614
301,531
442,643
458,910
(A) Baseline Model with Average
and Std. Dev. of Earnings in
1974-79
(B) Model in Row (A) with 1-Digit
Industry Fixed Effects
(E) Linear Probability Model
(Specification Row B)
(F) Linear Probability Model
(Spedificattion Row A) with
Firm Effects
Percentage Effect For Linear
Probability Model in Row (E)
Percentage Effect For Linear
(H)
Model in Row (F)
(G)
Observations
Notes: Dependent variable is the log odds of death in a year between 1980 or 1987 and 2002. The entries in the tables are the coefficient on a dummy for job loss
during mass-layoff. See Figure 3 and the Appendix for marginal effects. All models include year effects and a quartic in age as well as the indicated variables in the
column "Specification." In all models, the average of quarterly earnings from 1974-79 is entered in logs; the standard deviation is of the log quarterly earnings, and
also entered in logs. The additional "Career Variables" in Rows (C) to (F) are growth in quarterly earnings from 1974-79 and the total time spent in nonemployment in 1974-79. Row (D) interacts the log of average earnings and the log of the standard deviation of log earnings with five dummies for age at layoff.
Table 4: Coefficients on Career Variables in Extended Log-Odds of Death Model, Various Samples, Workers in Stable Employment 19741979, Employment Size 50 in 1979
Mass-Layoff Sample:
Follow-Up Period:
No Work Work Every
Restriction
Year
1980-02
Born
Cohort Range:
Displacement-Dummy
Log(Average Quarterly
Earnings 1974-79)
Log(Std. Dev. of Log Quarterly
Earnings 1974-79)
Number of Quarters in NonEmployment 1974-79
Percent Change in Quarterly
Earnings 1974-79
1-Digit Dummies for 1979
Industry
Observations
1987-02
Work At
Least 3
Years
No Work
Restriction
1987-02
1987-02
No Work Work Every
Restriction
Year
1980-02
1930-59
1987-02
Born
Work At
Least 3
Years
No Work
Restriction
1987-02
1987-02
1920-59
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.247
(0.044)
0.102
(0.060)
0.117
(0.050)
0.129
(0.049)
0.135
(0.026)
0.066
(0.041)
0.100
(0.030)
0.089
(0.029)
-0.536
(0.064)
-0.491
(0.091)
-0.532
(0.072)
-0.543
(0.070)
-0.474
(0.036)
-0.431
(0.058)
-0.465
(0.041)
-0.472
(0.039)
0.130
(0.035)
0.144
(0.046)
0.109
(0.039)
0.104
(0.038)
0.121
(0.020)
0.107
(0.032)
0.097
(0.023)
0.103
(0.022)
-0.123
(0.034)
-0.122
(0.047)
-0.097
(0.037)
-0.094
(0.036)
-0.091
(0.020)
-0.077
(0.033)
-0.070
(0.023)
-0.067
(0.022)
-0.044
(0.062)
-0.021
(0.084)
-0.026
(0.069)
-0.033
(0.067)
-0.128
(0.038)
0.044
(0.062)
-0.064
(0.044)
-0.101
(0.042)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
437,420
219,284
291,923
299,450
631,411
280,872
409,692
424,765
Notes: These are coefficients on covariates included in Model C of Table 4. Please refer to Notes of Table 4 for further explanations.
Table 5: Effect of Job Loss at Mass-Layoff By Time Since Displacement, No Work Restsriction 1980-1986, Alternative AgeGroups
(1)
(2)
(3)
(4)
Born 1930-59
(5)
(6)
Born 1920-59
Log(Average Quarterly Earnings)
-0.458
(0.057)
-0.463
(0.057)
-0.463
(0.057)
-0.413
(0.032)
-0.413
(0.032)
-0.414
(0.032)
Log(Standard Deviation of Log
Quarterly Earnings)
0.143
(0.029)
0.146
(0.029)
0.146
(0.029)
0.135
(0.017)
0.139
(0.017)
0.139
(0.017)
Displacement-Dummy
0.250
(0.043)
0.135
(0.026)
Displacement Year
0.983
(0.119)
0.982
(0.119)
0.297
(0.072)
0.297
(0.071)
Displacement Year + 1
0.688
(0.189)
0.685
(0.189)
0.490
(0.099)
0.489
(0.099)
Displacement Year + 2-3
0.555
(0.137)
0.549
(0.137)
0.243
(0.077)
0.240
(0.077)
Displacement Year + 4-5
0.249
(0.139)
0.240
(0.138)
0.053
(0.076)
0.048
(0.075)
Displacement Year + 6-10
0.179
(0.081)
0.119
(0.045)
Displacement Year + 11-15
0.065
(0.07)
0.047
(0.041)
Displacement Year + 16+
0.153
(0.077)
0.142
(0.047)
Displacement Year + 6+
Observations
0.127
(0.048)
478,664
478,664
478,664
0.098
(0.029)
681,614
681,614
681,614
Notes: Samples are workers in stable jobs from 1974-79 with an employer of over 50 workers. Dependent variable is the log odds of
death. Columns (1)-(3) include workers born 1930-59. Columns (4)-(6) inclue workers born 1920-59. All models include industry
fixed effects.
Table 6: Effect of Mass-Layoff at Establishment Level on Mortality and Earnings Pooling
Movers and Stayers, No Work Restriction, Born 1920+ (Intent-To-Treat Estimates)
Mass Layoff Definition:
30% Drop Relative to
Employment in 1974-79
Model 1
30% Drop Relative to
Employment in Past Year
Model 2
Model 1
Model 2
Panel A: Average Effect on Hazard of Death
1987-2002
0.046
(0.025)
0.007
(0.027)
0.066
(0.029)
0.027
(0.031)
1980-2002
0.021
(0.028)
-0.019
(0.030)
0.067
(0.032)
0.029
(0.034)
Panel B: Dynamic Effect on Hazard of Death, 1980-2002
t-4 to t-3
0.197
(0.133)
0.192
(0.133)
0.066
(0.174)
0.027
(0.174)
t-2 to t-1
0.162
(0.098)
0.142
(0.099)
0.166
(0.117)
0.139
(0.118)
Year of Mass Layoff (= t)
0.386
(0.104)
0.367
(0.105)
0.397
(0.133)
0.372
(0.133)
t+1 to t+10
0.056
(0.038)
0.035
(0.040)
0.033
(0.044)
0.007
(0.045)
t+11 to t+22
0.047
(0.033)
0.021
(0.035)
0.106
(0.037)
0.078
(0.038)
Panel C: Average Effect on Annual Earnings
1987-2002
-0.181
(0.003)
-0.103
(0.003)
-0.256
(0.004)
-0.180
(0.004)
1980-2002
-0.241
(0.006)
-0.140
(0.006)
-0.329
(0.007)
-0.230
(0.007)
Panel D: Dynamic Effect on Annual Earnings, 1980-2002
t-4 to t-3
0.005
(0.007)
0.043
(0.007)
0.009
(0.008)
0.073
(0.008)
t-2 to t-1
-0.119
(0.006)
-0.052
(0.006)
-0.216
(0.007)
-0.143
(0.007)
Year of Mass Layoff (= t)
-0.272
(0.007)
-0.214
(0.007)
-0.432
(0.010)
-0.366
(0.010)
t+1 to t+10
-0.290
(0.004)
-0.230
(0.004)
-0.364
(0.004)
-0.301
(0.004)
t+11 to t+22
-0.141
(0.026)
-0.137
(0.026)
-0.120
(0.055)
-0.108
(0.054)
Notes: Entries are coefficients on establishment-level mass-layoff dummy in logit model of the event of
dying in a given year. All regressions include a quartic in age, year effects, and the log of the average and
standard deviation of annual earnings in 1974-79 as control. In addition, Model 2 includes six dummies
for 1979 industry. Standard errors are in parentheses.
Table 7: Mortality Impact of Job Displacment by Age, Industry, Earnings, Region of Job Loss,
Workers Born 1920-1959
Work Restriction in 1980-1986
Period of Death Follow-Up
Displacement-Dummy
Displacement aged 30-39
Displacement aged 40-49
Displacement aged 50-59
Displacement aged 60-69
Displaced in Non-Manufacturing
Goods Sector
Displaced in Non-Durables
Manufacturing
Displaced in Steel Manufacturing
Displaced in Other Durables
Manufacturing
Displaced in Transportation,
Construction, Public Utilities
Displaced in Trade
Displaced in Services
Displaced and Mean Quarterly
Earnings 1974-79 < 25th Percentile
Displaced and Mean Qrt. Earnings
1974-79 >25th Perc. and < Median
Displaced and Mean Qrt. Earnings
1974-79 >Median and < 75th Perc.
Displaced and Mean Qrt. Earnings
1974-79 >75th Perc.
Displaced in West Pennsylvania
Displaced in East Pennsylvania
No Work
Restriction
1980-2002
0.139
(0.026)
0.313
(0.090)
0.266
(0.058)
0.219
(0.034)
-0.005
(0.036)
0.068
(0.124)
0.079
(0.056)
0.136
(0.057)
0.191
(0.044)
0.248
(0.086)
-0.074
(0.097)
0.220
(0.105)
0.132
(0.043)
0.153
(0.040)
0.172
(0.040)
0.101
(0.043)
0.095
(0.037)
0.180
(0.034)
1987-2002
Work At Least 3
Years
1987-2002
0.076
(0.040)
0.181
(0.130)
0.094
(0.082)
0.071
(0.055)
0.046
(0.066)
-0.071
(0.213)
-0.036
(0.086)
0.137
(0.088)
0.173
(0.068)
0.143
(0.137)
-0.239
(0.152)
0.108
(0.185)
0.027
(0.070)
0.028
(0.070)
0.100
(0.068)
0.144
(0.070)
0.027
(0.060)
0.115
(0.053)
0.197
(0.031)
0.172
(0.103)
0.144
(0.066)
0.141
(0.040)
0.044
(0.041)
0.255
(0.151)
0.071
(0.064)
0.202
(0.072)
0.288
(0.054)
0.243
(0.090)
0.040
(0.096)
0.285
(0.114)
0.225
(0.049)
0.198
(0.044)
0.245
(0.042)
0.128
(0.047)
0.191
(0.045)
0.202
(0.040)
Work Every Year
Notes: Samples are workers born 1920-59 in stable jobs from 1974-79 with an employer of over 50
workers. Dependent variable is the log odds of death. Entries are coefficient estimates from the logitmodel. All models include year fixed effects, industry fixed effects, a quartic in age, the log of average
quarterly earnings in 1974-79, and the log of the standard deviation of quarterly earnings in 1974-79.
Table 8: Impact of Job Displacement at Mass-Layoff on Life Expectancy, Alternative Samples (May Upd
Data
(1) Stable job 1974-79
No restrictions on
earnings 1980-86
non mass layoff
separators included
Estimated 1980-2002
(2) Stable job 1974-79
No restrictions on
earnings 1980-86
non mass layoff
separators included
Model
Displacement by age
at displacement in 10
year increments plus
five time since
displacment categories
Displacement by age
at displacement in 10
year increments
Estimated 1987-2002
(3) Stable job 1974-79
earnings three years
1980-86
non mass layoff
separators included
Displacement by age
at displacement in 10
year increments
Estimated 1987-2002
(4) Stable job 1974-79
earnings every year
1980-86
no non mass layoff
separators
Estimated 1987-2002
Displacement by age
at displacement in 10
year increments
Age
Life
Expectancy
given not
Displaced
Life
Expectancy
given
Displaced
Lost Years of
Life due
to Job
Displacement
30
35
40
45
50
75.86
75.47
75.64
75.90
76.28
73.79
73.38
74.21
74.40
75.30
2.06
2.09
1.43
1.49
0.98
55
76.85
75.74
1.11
30
35
40
45
50
75.96
76.07
76.25
76.51
76.90
74.21
74.34
75.23
75.51
75.74
1.75
1.74
1.02
1.00
1.16
55
77.46
76.36
1.10
30
35
40
45
50
76.21
76.32
76.50
76.76
77.15
74.60
74.73
75.42
75.71
76.05
1.61
1.59
1.07
1.05
1.10
55
77.71
76.67
1.04
30
35
40
45
50
76.47
76.57
76.73
76.98
77.34
74.75
74.87
75.87
76.13
76.41
1.72
1.70
0.86
0.84
0.93
55
77.87
76.99
0.89
Notes: All models include log of mean earnings, log of standard deviation of log quarterly earnings, 1-digit
industry dummies, and a linear age effect. The numbers are based on a linear extrapolation in age for cohorts
still alive.
Table 9: Impact of Mass-Layoff on Career Outcomes
Panel A: stable job 1974-79; born 1930-59; earnings every year through 1991 (17,576 workers)
Years since displacement
Outcome Measure
-1 to -2
0 to 2
3 to 6
7 to 11
ln average earnings
-0.047
(0.004)
-0.002
(0.001)
0.008
(0.003)
0.003
(0.001)
-0.459
(0.004)
-0.069
(0.001)
0.309
(0.003)
0.036
(0.001)
0.404
(0.002)
0.253
(0.003)
0.195
(0.003)
-0.266
(0.004)
-0.013
(0.001)
0.065
(0.003)
0.007
(0.001)
0.124
(0.002)
0.026
(0.003)
0.028
(0.002)
-0.195
(0.005)
0.002
(0.001)
-0.008
(0.004)
0.000
(0.001)
0.057
(0.003)
-0.024
(0.004)
-0.010
(0.003)
Quarterly employment probability
Absolute value of earnings growth
Transitions to nonemployment per year
Probability of job change in year
Probability of industry change in year
Probability of county of employer change in year
Panel B: stable job 1974-79; born 1920-59; earnings every year 1974-79 and 1987-91 (24,520 workers)
Years since displacement
Outcome Measure
-1 to -2
0 to 2
3 to 6
7 to 11
ln average earnings
-0.015
(0.003)
0.011
(0.001)
-0.006
(0.003)
0.003
(0.000)
-0.454
(0.004)
-0.123
(0.001)
0.305
(0.003)
0.040
(0.001)
0.403
(0.002)
0.272
(0.002)
0.221
(0.002)
-0.325
(0.003)
-0.043
(0.001)
0.097
(0.003)
0.010
(0.001)
0.141
(0.002)
0.057
(0.002)
0.053
(0.002)
-0.240
(0.004)
0.009
(0.001)
0.013
(0.004)
0.004
(0.001)
0.067
(0.002)
-0.022
(0.003)
-0.020
(0.003)
Quarterly employment probability
Absolute value of earnings growth
Transitions to nonemployment per year
Probability of job change in year
Probability of industry change in year
Probability of county of employer change in year
Table 10: Mortality Increases by Career Outcomes after Job Loss at Individual and Cell Level
Coefficient on Covariate in Logit
Model of Annual Mortality
Model Hazard:
(1)
(2)
No Work Restriction,
Cohort>1930
Work Every Year 1980-1986,
Cohort>1930
All Workers
Only
Displaced
All Workers
Only
Displaced
Dummy for Job Loss During
Mass-Layoff
-0.013
(0.046)
---
0.010
(0.063)
---
Percent Change in Long-Term
Average Earnings
-1.169
(0.069)
-0.997
(0.111)
-0.706
(0.142)
-0.567
(0.199)
Dummy for Job Loss During
Mass-Layoff
-0.073
(0.059)
---
-0.079
(0.066)
---
Change in Individual Long-Term
Average Earnings
-0.345
(0.111)
-0.421
(0.188)
-0.479
(0.152)
-0.422
(0.231)
At Least One Transition to NonEmployment
0.065
(0.059)
-0.095
(0.109)
0.067
(0.065)
-0.076
(0.112)
Linear Probability Models: Average
Earnings Change Predicted by
Interaction of MLF and Cell-Effects
No Work Restriction,
Cohort>1930
Work Every Year 1980-1986,
Cohort>1930
Individual
Level
Cell Level
Individual
Level
Cell Level
(3)
Cells of Age in 1979 and Avg.
Earnings in 1974-79
-0.0028
(0.0001)
-0.0030
(0.0007)
-0.0033
(0.0003)
-0.0021
(0.0011)
(4)
Cells of Age in 1979 and Industry
in 1979
-0.0028
(0.0001)
-0.0027
(0.0007)
-0.0033
(0.0002)
-0.0021
(0.0012)
(5)
Cells of Avg. Earnings in 1974-79
and Industry in 1979
-0.0028
(0.0001)
-0.0025
(0.0005)
-0.0033
(0.0003)
-0.0013
(0.0009)
(6)
Cells of Avg. Age in 1979 and
Local Unemployment in 1979
-0.0028
(0.0001)
-0.0026
(0.0005)
-0.0033
(0.0003)
-0.0016
(0.0010)
Notes: The models in the first half of the table show coefficients for logit models of the annual hazard of death contr
dummies, a quartic in age, pre-mass layoff career outcomes (log average quarterly earnings, log of standard deviation o
number of quarters in non-employment, average quarterly growth in earnings, all measured from 1974-79), as well as
from 1974-79 to 1987-91 (percent change in the standard deviation of log quarterly earnings, change in the average gr
The models in the second half of the table report coefficients on linear probability models of the hazard of death con
dummies, a quartic in age, pre-mass layoff career outcomes.
Figure 1: Estimate of the Long-term Earnings Decline Due to Job Displacement at Mass-Layoffs
Using Jacobson,Lalonde, and Sullivan (1993)'s Mass-Layoff Sample
0.1
0
Log-Difference
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
Years Since Displacement
Notes: Solid line represent coefficient estimates of the interaction of year effects and displacement dummies
in a regression model of log quarterly earnings including year fixed effects, person fixed effects, and a quartic
for age. Two standard error bands are drawn around main effects.
Figure 2: Percentage Effect of Job Displacement at Mass-Layoff on Mortality Rate by Time Since
Layoff (Sample of Men in Stable Employment 1974-1979, Born 1930-1959)
1.5
1.25
1
0.75
0.5
0.25
0
-0.25
-0.5
0
2
4
6
8
10
12
14
Years since Displacement
16
18
20
Notes: Solid line represents coefficients of log-odds model shown in Table 4, column (3). Dashed lines
represent two-standard errors bands.
22
Figure 3: Predicted Effects of Job Loss at Mass-Layoff on Mortality and Average Earnings
on Mortality by Age
Panel A: Mortality by Mass-Layoff Status [Average Mean 1974-1979 Quarterly Earnings]
0.07
Movers (Mass-Layoff)
0.06
Stayers
0.05
0.04
0.03
0.02
0.01
0
40
45
50
55
Age
60
65
70
Panel B: Mortality by Average, 25th, 75th Percentile of Mean 1974-19 79 Quarterly Earnings
0.06
0.05
Average
10th Percentile
90th Percentile
0.04
0.03
0.02
0.01
0
40
45
50
55
Age
60
65
70
Figure 4: Mortality Gradient in Average 1974-1979 Earnings and Standard Deviation: Men
in Stable Employment 1974-1986
Panel A: Correlation between Average Quarterly Earnings and Mortality, Alternative
Specifications
0.0025
0.0023
Dummies for Earnings Categories
0.0021
Log-Earnings
Mortality Rate
0.0019
Quartic in Earnings
0.0017
0.0015
0.0013
0.0011
0.0009
0.0007
0.0005
3000
4000
5000 6000 7000 8000 9000 10000
Average Quarterly Earnings in 1974-1979
11000
12000
Panel B: Correlation between Standar Deviation of Log Quarterly Earnings and Mortality,
Alternative Specifications
-2.2
-2.3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Mortality Rate
-2.4
-2.5
-2.6
-2.7
-2.8
-2.9
Dummy-Specification
Log-Specification
-3
Standard Deviation of Log Earnings
0.8
0.9
Figure 5: The Effect of Earnings Changes at Mass Layoff on Mortality at Individual Level
and at Cell Level
Panel A: Differences in Mortality by Deciles of Changes in Average Earnings, Workers Born in 1920,
Alternative Samples
Marginal Effect on Death Rate
0.1
0
-0.1
-0.2
-0.3
No Work Restriction
Work 1980-86
Some Work 1980-86
-0.4
-0.5
1
2
3
4
5
6
7
8
Decile of Earnings Change (First Decile Omitted)
9
10
Notes: Coefficients on dummies for deciles of changes in average earnings from 1974-79 to 1980-86 in a logit model
of death. Other variables include year effects, a quartic in age, and the average and standard deviation of earnings
1974-79.
Panel B: Mortality and Earnings Effects of Mass-Layoff By Cells
Differences in Death Rate/Regression Line
-.01 -.005
0
.005
.01
By Industry and Local UR at Job Loss (35 Cells)
-.8
-.6
-.4
-.2
-5.55e-17
Average Percent Differences in Annual Earnings
Differences in Death Rate
Notes: Birth Year>=1930, Stable Workers
Regression Line
.2
Figure 6: PDV Earnings Losses and Life-Expectancy By Five Year Age-Group, Alternative PDV
Estimates
12
2.5
2
8
1.5
6
1
PDV1
4
PDV2
0.5
2
Life Expectancy
0
0
25
30
35
40
45
50
55
60
Age-Group
Notes: PDV 1 is based on 10-year age-interactions with job loss dummy, PDV 2 is based on 5-year
interactions with job loss dummy.
Loss in Years of Life-Expectancy
Loss in PDV Earnings ($1000)
10
Appendix Table 1: Sample Statistics by Alternate Years and Samples for Workers in Stable Job 19741979 at Employer with at least 50 Employees
Panel A: Broad Sample (No Work Restriction 1980-1986)
Average
Year of
Fraction
Average
Fraction Sample Mortality Number Quart.
FollowMin. Age Max. Age
MassAge
Age >=60
Size
Rate
Dead Earnings
Up
Layoff
1974-79
1981
39
22
51
0.00
21655
0.0021
45
6663
0.352
1983
41
24
53
0.00
21567
0.0026
57
6663
0.351
1985
43
26
55
0.00
21456
0.0028
60
6665
0.350
1987
45
28
57
0.00
21323
0.0040
86
6664
0.348
1989
47
30
59
0.00
21158
0.0036
76
6664
0.347
1991
49
32
61
0.09
20986
0.0041
87
6663
0.347
nts from the
51
34
63
0.16
20788
0.0059
122
6664
0.346
1995
53
36
65
0.24
20534
0.0075
155
6664
0.346
1997
55
38
67
0.31
20206
0.0084
169
6664
0.345
1999
57
40
69
0.39
19894
0.0082
164
6667
0.345
2001
59
42
71
0.46
19536
0.0111
216
6668
0.344
Panel B: Original Mass-Layoff Sample (Some Earnings each Year 1980-86)
Average
Fraction
Year of
Average
Fraction Sample Mortality Number Quart.
MassFollowMin. Age Max. Age
Age
Age >=60
Size
Rate
Dead Earnings
Layoff
Up
1974-79
1987
46
28
57
0.00
15532
0.003
53
6707
0.319
1989
47
30
59
0.00
15430
0.003
53
6705
0.318
1991
49
32
61
0.08
15309
0.004
57
6703
0.318
1993
51
34
63
0.16
15182
0.006
87
6704
0.318
1995
53
36
65
0.24
15009
0.007
111
6703
0.317
1997
55
38
67
0.32
14777
0.008
116
6704
0.317
1999
57
40
69
0.40
14570
0.008
119
6707
0.318
2001
59
42
71
0.47
14323
0.010
147
6705
0.317
Notes: Both sample include only male workers working at a stable job in 1974-1979 at firms with at least 50
employees in 1979. The mass-layoff sample in Panel B further requires some employment every year 1980-86
and only includes workers that either experience a mass layoff in the period 1980-86 or who remain with the
present employer for that period. Both samples limit workers to birth cohorts 1930 to 1959. Further details on
sample specifications in the text.
Appendix Table 2: Sample Statistics by Alternate Years and Samples for Workers in
Stable Job 1974-1979 at Employer with at least 50 Employees
Panel A: Broad Sample (No Work Restriction 1980-1986)
Absolute Difference
Percent Change to Baseline
Average
25th
50th
75th
Age
Average
25th
50th
75th
40
0.0002
0.0002
0.0002
0.0002
0.13
0.12
0.13
0.11
50
0.0004
0.0005
0.0004
0.0004
0.12
0.14
0.13
0.11
60
0.0013
0.0014
0.0013
0.0013
0.13
0.13
0.13
0.11
70
0.0036
0.0038
0.0036
0.0034
0.13
0.13
0.14
0.11
Notes: Probabilities predicted using coefficients from the logit model of row A, column 1 in Table 3.
Panel B: Fitted Probability of Death by Age and Earnings Groups
Mean Quarterly 1974-79 Earnings
Age
25th
minus
75th
Average
10th
25th
50th
75th
90th
$6,416
$4,314
$5,257
$6,451
$7,828
$9,370
40
0.0016
0.0016
0.0017
0.0015
0.0018
0.0014
-0.0001
50
0.0034
0.0034
0.0036
0.0032
0.0038
0.003
-0.0002
60
0.0104
0.0104
0.0111
0.0098
0.0117
0.0093
-0.0006
70
0.0282
0.0281
0.0299
0.0264
0.0317
0.0252
-0.0018
70 minus
40
0.0266
0.0265
0.0282
0.0249
0.0299
0.0238
Notes: Probabilities predicted using coefficients from the logit model of row 1 and column 1 in Appendix Table
5.
Appendix Table 3: Mortality Rates in Different Periods by Displacement Status
Panel A: Work Every Year 1980-86
87-02
87-91
92-96
97-02
All Workers
Same Firm
1974-86
Non-MassLayoff
Separators
Displaced
30%-60%
Below Peak
5.716
(0.154)
3.607
(0.203)
6.539
(0.252)
8.221
(0.315)
5.502
(0.195)
3.348
(0.252)
6.336
(0.320)
7.729
(0.394)
5.688
(0.429)
3.381
(0.548)
6.386
(0.695)
9.405
(0.941)
5.727
(0.453)
3.743
(0.606)
6.749
(0.752)
7.354
(0.876)
Displaced
Displaced Greater Than
60%-90%
90% Below
Below Peak
Peak
6.402
(0.506)
4.723
(0.718)
7.255
(0.824)
9.346
(1.047)
6.875
(0.743)
4.630
(1.008)
7.381
(1.178)
10.735
(1.592)
Notes: Deaths per 1000 per year. Standard errors in parentheses. Displaced Workers left jobs in a year in
which their former firms' employment was 30% or more below its 1974-1979 peak. Nondisplaced workers
remain at their 1979 firm through 1986.
Panel B: No Work Restriction in 1980-86
All Workers
87-02
80-86
87-91
92-96
97-02
6.185
(0.146)
2.465
(0.128)
4.008
(0.194)
6.981
(0.237)
8.699
(0.296)
Same Firm
1974-86
5.502
(0.195)
0.000
3.348
(0.252)
6.336
(0.320)
7.729
(0.394)
Non-MassLayoff
Separators
Displaced
30%-60%
Below Peak
6.525
(0.367)
6.525
(0.367)
4.083
(0.480)
7.244
(0.591)
9.957
(0.776)
6.774
(0.396)
5.537
(0.492)
4.517
(0.535)
7.661
(0.645)
8.656
(0.768)
Displaced
Displaced Greater Than
90% Below
60%-90%
Peak
Below Peak
6.828
(0.434)
3.410
(0.422)
4.987
(0.613)
7.605
(0.700)
9.975
(0.899)
8.051
(0.666)
1.477
(0.395)
5.874
(0.938)
8.584
(1.052)
10.717
(1.322)
Notes: Deaths per 1000 per year. Standard errors in parentheses. Displaced Workers left jobs in a year in
which their former firms' employment was 30% or more below its 1974-1979 peak.
Appendix Table 4: Effect of Mass-Layoff on Mortality by Earnings Loss at Job Loss and Employment Status in Displacement Period
No Work Restriction, Birth Year>1930
Log Average Earnings 1974-79
% Change in Long-Term Average
Earnings 74-79 vs. 80-86 (∆E)
Dummy for Job Loss During MassLayoff (MLF)
Displaced at MLF and ∆E >25th
Perc. and < Median
Displaced at MLF and ∆E >Median
and < 75th Perc.
Displaced at MLF and ∆E >75th
Perc.
No Work Restriction, Birth Year>1920
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
-0.448
(0.032)
-0.491
(0.059)
-0.488
(0.059)
-0.471
(0.058)
-0.471
(0.058)
-0.505
(0.057)
-0.491
(0.059)
-0.488
(0.059)
-0.471
(0.058)
-0.471
(0.058)
--
-0.891
(0.156)
-0.749
(0.154)
-0.754
(0.155)
--
--
-1.200
(0.067)
--
-1.200
(0.067)
-0.891
(0.156)
-0.749
(0.154)
-0.754
(0.155)
0.140
(0.026)
-0.008
(0.045)
-0.143
(0.076)
0.105
(0.158)
0.119
(0.162)
0.262
(0.043)
-0.008
(0.045)
-0.143
(0.076)
0.105
(0.158)
0.119
(0.162)
0.034
(0.111)
-0.151
(0.134)
-0.153
(0.134)
--
--
--
--
0.034
(0.111)
-0.151
(0.134)
-0.153
(0.134)
0.270
(0.124)
0.055
(0.166)
0.054
(0.166)
--
--
--
--
0.270
(0.124)
0.055
(0.166)
0.054
(0.166)
0.147
(0.182)
0.151
(0.183)
--
--
--
--
0.368
(0.140)
0.147
(0.182)
0.151
(0.183)
0.260
(0.067)
0.259
(0.067)
--
--
--
--
--
--
0.260
(0.067)
0.259
(0.067)
0.539
(0.099)
0.536
(0.099)
--
--
--
--
--
--
0.539
(0.099)
0.536
(0.099)
-0.199
(0.127)
-0.197
(0.127)
--
--
--
--
--
--
-0.199
(0.127)
-0.197
(0.127)
-0.266
(0.146)
--
--
--
--
--
--
-0.272
(0.146)
-0.266
(0.146)
--
--
--
--
--
--
--
--
--
--
--
--
0.368
(0.140)
Dummy for 1-2 Quarters NonEmployment
--
--
--
--
--
--
Dummy for More Than 2 Quarters
Non-Employment
--
--
--
--
--
--
Displaced at MLF and NonEmployed for 1-2 Periods
--
--
--
--
--
--
Displaced at MLF and NonEmployed More Than 2 Periods
--
--
--
--
--
--
-0.272
(0.146)
Dummies for Quartiles of ∆E
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Controls for Age at Separation
--
--
--
--
Yes
--
--
--
--
Yes
681614
478664
478664
478664
478664
478664
478664
478664
478664
478664
Observations
Notes: Samples are workers born 1920-59 in stable jobs from 1974-79 with an employer of over 50 workers. Dependent variable is the log odds of death. Entries are the coefficient
estimates from the logit-model. All models include year fixed effects, industry fixed effects, a quartic in age, and the log of the standard deviation of quarterly earnings in 1974-79. All
models also include three dummies for whether percent change in average earnings (∆E) lies in between 25th and 50th percentile, 50th and 75th percentile, or above 75th percentile of the
distribution in the population. None of the three dummy variables has an effect that is statistically significant from zero.
Appendix Table 5: Correlation of Mortality and Average Earnings, Sample With Work Every Year 1980-86
Pooled Logit - Model
1986-2002
Basic Model [Average 1974 1979 Quarterly Earnings]
With Birth Cohort-Controls
Single Year Earnings (1979)
Average 1974-79 Earnings
With 1979 Earnings
Deaton and Paxson Models
Death 0-5 Years post 1986
Death 5-10 Years post 1986
All
Age < 60
Age > 60
All
Age < 60
Age > 60
All
Age < 60
Age > 60
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
-0.509
-0.514
-0.506
-0.309
-0.591
-0.309
-0.515
0.093
-0.936
(0.075)
(0.105)
(0.108)
(0.179)
(0.096)
(0.179)
(0.099)
(0.990)
(0.195)
-0.508
-0.511
-0.506
-0.309
-0.591
-0.309
-0.515
0.093
-0.936
(0.075)
(0.105)
(0.108)
(0.179)
(0.096)
(0.179)
(0.099)
(0.990)
(0.195)
-0.388
-0.347
-0.433
-0.326
-0.437
-0.326
-0.377
0.057
-0.746
(0.066)
(0.092)
(0.095)
(0.155)
(0.083)
(0.155)
(0.086)
(0.925)
(0.174)
-0.603
-0.806
-0.342
0.130
-0.752
0.130
-0.692
0.296
-1.090
(0.182)
(0.244)
(0.267)
(0.400)
(0.225)
(0.400)
(0.235)
(2.843)
(0.486)
1979 Earnings with Average
1974-79 Earnings
0.092
0.289
-0.159
-0.428
0.159
-0.428
0.173
-0.201
0.154
(0.163)
(0.219)
(0.237)
(0.346)
(0.201)
(0.346)
(0.209)
(2.639)
(0.446)
Observations
238672
193261
45411
17642
17326
727
17316
15879
1437
Notes: Columns 1 to 3 show the coefficients of a logit model of the annual probability of dying from 1987 to 2002 on the natural logarithms of average
quarterly income from 1974 to 1979. Columns 4-9 show the probability of dying within the indicated follow-up duration. All models include year and age
effects. Standard errors are in parentheses.
Appendix Table 6: Correlation of Other Career Outcomes and Mortality, Various Samples and Specifications
Alternative Samples
MassLayoff
Sample
Major
Some
Stable Job
Presence 74- Presence
1974-79
79
1974-79
Stable Job
1974-79
Major
Presence
1974-79
Average 1974-79, Born 1930-59
Average 1974-86, Born 1924-29
Log(Average Quarterly Earnings)
-0.380
(0.049)
-0.411
(0.034)
-0.091
(0.006)
-0.208
(0.011)
-0.555
(0.050)
-0.251
(0.021)
Log(Standard Deviation of Log
Quarterly Earnings)
0.167
(0.026)
0.134
(0.018)
0.081
(0.008)
0.066
(0.009)
0.097
(0.032)
0.120
(0.022)
Log(Average Quarterly Earnings)
-0.400
(0.050)
-0.426
(0.035)
-0.082
(0.006)
-0.199
(0.012)
-0.553
(0.053)
-0.235
(0.022)
Log(Standard Deviation of Log
Quarterly Earnings)
0.176
(0.027)
0.141
(0.019)
0.061
(0.008)
0.057
(0.010)
0.096
(0.035)
0.084
(0.025)
Number of Transitions to NonEmployment
-0.053
(0.031)
-0.040
(0.022)
0.046
(0.007)
0.023
(0.008)
0.004
(0.020)
0.035
(0.011)
One Drop in Earnings More Than 2
Std. Dev.
0.221
(0.086)
0.161
(0.061)
-0.021
(0.019)
0.014
(0.022)
0.046
(0.080)
-0.042
(0.052)
More Than One Drop in Earnings
More Than 2 Std. Dev.
0.140
(0.076)
0.103
(0.054)
-0.017
(0.017)
0.022
(0.019)
0.028
(0.063)
-0.041
(0.042)
Log(Average Quarterly Earnings)
-0.443
(0.051)
-0.467
(0.035)
-0.066
(0.005)
-0.215
(0.012)
-0.572
(0.052)
-0.265
(0.021)
Number of Employer Changes
-0.063
(0.040)
-0.036
(0.029)
0.012
(0.007)
-0.002
(0.008)
-0.003
(0.021)
-0.016
(0.010)
Number of Transitions to NonEmployment
-0.019
(0.031)
-0.008
(0.021)
0.071
(0.007)
0.041
(0.008)
0.024
(0.018)
0.056
(0.010)
Log(Average Quarterly Earnings)
-0.439
(0.051)
-0.465
(0.035)
-0.082
(0.006)
-0.225
(0.012)
-0.529
(0.054)
-0.256
(0.024)
Number of Non-Employment Spells
Lasting At Least A Year
-0.263
(0.414)
-0.253
(0.293)
-0.053
(0.015)
-0.045
(0.018)
0.187
(0.047)
0.001
(0.025)
(6)
Log(Average Quarterly Earnings)
Workers Non-Manufacturing
-0.362
(0.126)
-0.435
(0.088)
-0.100
(0.010)
-0.339
(0.023)
-0.406
(0.031)
-0.602
(0.055)
(7)
Log(Average Quarterly Earnings)
Workers Manufacturing
-0.594
(0.099)
-0.616
(0.080)
-0.234
(0.026)
-0.507
(0.041)
-0.500
(0.048)
-0.564
(0.100)
(8)
Log(Average Quarterly Earnings)
With 1979 or 1986 Qrt. Earnings
-0.616
(0.184)
-0.437
(0.134)
-0.075
(0.021)
-0.321
(0.034)
-0.364
(0.042)
-0.434
(0.074)
(9)
Log(1979 or 1986 Qrt. Earnings)
With Log Average Qrt. Earn.
0.099
(0.164)
-0.096
(0.115)
-0.125
(0.019)
-0.074
(0.023)
-0.071
(0.028)
-0.043
(0.029)
(Model) Baseline Period, Cohorts
(1)
(2)
(3)
(5)
Notes: Entries in table are coefficients from logit models of the annual probability of dying from 1987 to 2002. All models also
include dummies for age and year. Standard errors are in parentheses. Columns give details on sample specifications, the range of
birth cohorts, and the length of the base-line period over which career outcomes are calculated. Different rows correspond to
different models. Model 5 also controls for the number of non-employment spells.
Appendix Figure 1: Age-Gradient in Death Rates - Mass-Layoff Sample
Panel A: Log-Odds Ratio by Age
-2
-3
Age-Dummies
Quartic in Age
-4
-5
-6
-7
-8
40
45
50
55
60
65
70
Age
Panel B: Mortality Rate by Age
0.04
0.035
Age-Dummies
0.03
Quartic in Age
0.025
0.02
0.015
0.01
0.005
0
40
45
50
55
Age
60
65
70
Appendix Figure 2: Effect of Mass-Layoffs during 1980 to 1986 on Mortality by Time
Since Displacement
Panel A: Mortality Rate by Mass-Layoff Status
0.012
0.01
Stayers
Movers
Mortality Rate
0.008
0.006
0.004
0.002
0
1986
1988
1990
1992
1994
1996
1998
2000
2002
1998
2000
2002
Year
Panel B: Estimated Effect of Mass-Layoff on Mortality
1
0.8
Difference in Mortality Rate
0.6
0.4
0.2
0
1986
1988
1990
1992
1994
-0.2
-0.4
Year
1996
Working Paper Series
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Not Working: Demographic Changes, Policy Changes,
and the Distribution of Weeks (Not) Worked
Lisa Barrow and Kristin F. Butcher
WP-04-23
The Role of Collateralized Household Debt in Macroeconomic Stabilization
Jeffrey R. Campbell and Zvi Hercowitz
WP-04-24
Advertising and Pricing at Multiple-Output Firms: Evidence from U.S. Thrift Institutions
Robert DeYoung and Evren Örs
WP-04-25
Monetary Policy with State Contingent Interest Rates
Bernardino Adão, Isabel Correia and Pedro Teles
WP-04-26
Comparing location decisions of domestic and foreign auto supplier plants
Thomas Klier, Paul Ma and Daniel P. McMillen
WP-04-27
China’s export growth and US trade policy
Chad P. Bown and Meredith A. Crowley
WP-04-28
Where do manufacturing firms locate their Headquarters?
J. Vernon Henderson and Yukako Ono
WP-04-29
Monetary Policy with Single Instrument Feedback Rules
Bernardino Adão, Isabel Correia and Pedro Teles
WP-04-30
4
Working Paper Series (continued)
Firm-Specific Capital, Nominal Rigidities and the Business Cycle
David Altig, Lawrence J. Christiano, Martin Eichenbaum and Jesper Linde
WP-05-01
Do Returns to Schooling Differ by Race and Ethnicity?
Lisa Barrow and Cecilia Elena Rouse
WP-05-02
Derivatives and Systemic Risk: Netting, Collateral, and Closeout
Robert R. Bliss and George G. Kaufman
WP-05-03
Risk Overhang and Loan Portfolio Decisions
Robert DeYoung, Anne Gron and Andrew Winton
WP-05-04
Characterizations in a random record model with a non-identically distributed initial record
Gadi Barlevy and H. N. Nagaraja
WP-05-05
Price discovery in a market under stress: the U.S. Treasury market in fall 1998
Craig H. Furfine and Eli M. Remolona
WP-05-06
Politics and Efficiency of Separating Capital and Ordinary Government Budgets
Marco Bassetto with Thomas J. Sargent
WP-05-07
Rigid Prices: Evidence from U.S. Scanner Data
Jeffrey R. Campbell and Benjamin Eden
WP-05-08
Entrepreneurship, Frictions, and Wealth
Marco Cagetti and Mariacristina De Nardi
WP-05-09
Wealth inequality: data and models
Marco Cagetti and Mariacristina De Nardi
WP-05-10
What Determines Bilateral Trade Flows?
Marianne Baxter and Michael A. Kouparitsas
WP-05-11
Intergenerational Economic Mobility in the U.S., 1940 to 2000
Daniel Aaronson and Bhashkar Mazumder
WP-05-12
Differential Mortality, Uncertain Medical Expenses, and the Saving of Elderly Singles
Mariacristina De Nardi, Eric French, and John Bailey Jones
WP-05-13
Fixed Term Employment Contracts in an Equilibrium Search Model
Fernando Alvarez and Marcelo Veracierto
WP-05-14
Causality, Causality, Causality: The View of Education Inputs and Outputs from Economics
Lisa Barrow and Cecilia Elena Rouse
WP-05-15
5
Working Paper Series (continued)
Competition in Large Markets
Jeffrey R. Campbell
WP-05-16
Why Do Firms Go Public? Evidence from the Banking Industry
Richard J. Rosen, Scott B. Smart and Chad J. Zutter
WP-05-17
Clustering of Auto Supplier Plants in the U.S.: GMM Spatial Logit for Large Samples
Thomas Klier and Daniel P. McMillen
WP-05-18
Why are Immigrants’ Incarceration Rates So Low?
Evidence on Selective Immigration, Deterrence, and Deportation
Kristin F. Butcher and Anne Morrison Piehl
WP-05-19
Constructing the Chicago Fed Income Based Economic Index – Consumer Price Index:
Inflation Experiences by Demographic Group: 1983-2005
Leslie McGranahan and Anna Paulson
WP-05-20
Universal Access, Cost Recovery, and Payment Services
Sujit Chakravorti, Jeffery W. Gunther, and Robert R. Moore
WP-05-21
Supplier Switching and Outsourcing
Yukako Ono and Victor Stango
WP-05-22
Do Enclaves Matter in Immigrants’ Self-Employment Decision?
Maude Toussaint-Comeau
WP-05-23
The Changing Pattern of Wage Growth for Low Skilled Workers
Eric French, Bhashkar Mazumder and Christopher Taber
WP-05-24
U.S. Corporate and Bank Insolvency Regimes: An Economic Comparison and Evaluation
Robert R. Bliss and George G. Kaufman
WP-06-01
Redistribution, Taxes, and the Median Voter
Marco Bassetto and Jess Benhabib
WP-06-02
Identification of Search Models with Initial Condition Problems
Gadi Barlevy and H. N. Nagaraja
WP-06-03
Tax Riots
Marco Bassetto and Christopher Phelan
WP-06-04
The Tradeoff between Mortgage Prepayments and Tax-Deferred Retirement Savings
Gene Amromin, Jennifer Huang,and Clemens Sialm
WP-06-05
Why are safeguards needed in a trade agreement?
Meredith A. Crowley
WP-06-06
6
Working Paper Series (continued)
Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi
WP-06-07
A New Social Compact: How University Engagement Can Fuel Innovation
Laura Melle, Larry Isaak, and Richard Mattoon
WP-06-08
Mergers and Risk
Craig H. Furfine and Richard J. Rosen
WP-06-09
Two Flaws in Business Cycle Accounting
Lawrence J. Christiano and Joshua M. Davis
WP-06-10
Do Consumers Choose the Right Credit Contracts?
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles
WP-06-11
Chronicles of a Deflation Unforetold
François R. Velde
WP-06-12
Female Offenders Use of Social Welfare Programs Before and After Jail and Prison:
Does Prison Cause Welfare Dependency?
Kristin F. Butcher and Robert J. LaLonde
Eat or Be Eaten: A Theory of Mergers and Firm Size
Gary Gorton, Matthias Kahl, and Richard Rosen
Do Bonds Span Volatility Risk in the U.S. Treasury Market?
A Specification Test for Affine Term Structure Models
Torben G. Andersen and Luca Benzoni
WP-06-13
WP-06-14
WP-06-15
Transforming Payment Choices by Doubling Fees on the Illinois Tollway
Gene Amromin, Carrie Jankowski, and Richard D. Porter
WP-06-16
How Did the 2003 Dividend Tax Cut Affect Stock Prices?
Gene Amromin, Paul Harrison, and Steven Sharpe
WP-06-17
Will Writing and Bequest Motives: Early 20th Century Irish Evidence
Leslie McGranahan
WP-06-18
How Professional Forecasters View Shocks to GDP
Spencer D. Krane
WP-06-19
Evolving Agglomeration in the U.S. auto supplier industry
Thomas Klier and Daniel P. McMillen
WP-06-20
Mortality, Mass-Layoffs, and Career Outcomes: An Analysis using Administrative Data
Daniel Sullivan and Till von Wachter
WP-06-21
7
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