Long-Term Earnings Losses due to Mass Layoffs During the 1982 Recession:
An Analysis Using U.S. Administrative Data from 1974 to 2004

Till von Wachter
Columbia University, NBER, and CEPR
Jae Song
Social Security Administration
Joyce Manchester
Congressional Budget Office
April 2009
Preliminary – Please do not cite without permission.
The findings and views expressed here are those of the authors and should not beinterpreted as those of the Social Security Administration or the Congressional Budget
Office.

Abstract
This paper uses longitudinal data from Social Security records covering up to 30 years ofearnings to present the first national estimates of the long-term cost of job displacements
during the 1982 recession. We use a new longitudinal data set containing firm size to isolate
workers who separate from their stable job during a sudden mass-layoff. When we compare
the workers displaced from their jobs to similar non-displaced workers, we find large
immediate losses in annual earnings of 30%. After 15 to 20 years, these losses are still 20%
and thus represent a significant setback in workers’ life-time resources. Our estimates arerobust to alternative specifications including industry-year or firm-year effects, also hold forworkers with weak prior job attachment, and are strong and long-lasting for all age- andindustry-groups we study. They are still large and permanent, albeit somewhat smaller, forworkers displaced at the peak of the late 1980s recovery. Our estimates confirm the larger
range of estimates from previous studies based on single U.S. states and selected samples ofworkers.

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1.

Introduction
Job displacements through layoffs or plant closures are a common feature of the

U.S. labor market. Conservative estimates suggest each year about 10% of workers leave
their job involuntarily (e.g., Farber 2003), although the actual fraction may be much higher,
in particular during recessions (e.g., Hildreth, von Wachter and Weber 2005, Farber 2007).
Studies based on large administrative earnings data sets from several U.S. states suggest
earnings losses after job displacements may be large and long lasting (e.g., Jacobson,
Lalonde, and Sullivan 1993 [JLS], Schoeni and Dardia 2002, Couch 2006, Kodrzycki 2007).1
A difficulty with the existing large estimates of the cost of job loss is that they arebased on administrative data covering only certain time periods and regions. This reduces
the external validity of these studies for the cost of job loss for the U.S. labor market as awhole. In fact, work based on the Displaced Worker Survey (DWS) covering the entire U.S.
labor market suggests the short-run cost of job loss in terms of reduced earnings iscomparatively small. At present, there are no representative estimates of the long-term
effects of job loss on earnings for the United States.2
In addition, the nature of state-specific data prevents fully including workers withzero reported earnings, since those workers may have moved out of state in response to a
job loss. Thus, existing studies based on administrative data are based on selected samples.
In addition, an important criticism is that often the focus is on high-seniority workers laid
off in manufacturing, further reducing external validity. Moreover, both survey andadministrative data sources are typically too short to allow an evaluation of the truly longterm consequences of job loss.
We exploit access to a new data source that contains longitudinal information on
earnings and employment histories for a large random sample of the U.S. population
spanning over 30 years. We use this data to evaluate the long-term effect of a job
displacement on the development of earnings and employment for up to 20 years after a job
loss during the early 1980s. In addition to allowing us to provide estimates covering almost
the entire U.S. labor market, a particular advantage of our data is that we do not lose workers

1 In addition, it has been shown that job losses reduce job stability, consumption and access to health insurance(Gruber 1997, Browning and Crossley 2001, Olson 1992).
2 An exception is Ruhm (1991) who reports persistent earnings losses lasting up to five years after a job loss
based on the PSID.

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as they move between states. Thus, we are able to estimate the cost of job loss on arepresentative sample of workers.
As is typically the case with administrative data sources, it is difficult to clearly
establish the cause of job separations with the information in our data. Thus, to isolate job
displacements, we have merged the worker data with longitudinal information on the size ofworkers’ employers created for the purpose of this project. Using the merged data, we
identify job displacements as an instance when a worker permanently leaves his stable job in
the course of a large and sudden reduction in the work force of his employer. To make themeasures of mass layoff we construct meaningful, we follow JLS and restrict ourselves tofirms that had initially at least 50 employees.
We strive to maintain as much comparability with previous studies as possible anduse the study by JLS as a benchmark. Where possible, we follow JLS closely in the way werestrict our sample and date worker separations. Thus, we analyze separations occurring
during mass layoffs from 1980 to 1986 for workers approximately in middle age. Similarly, as
in JLS we choose workers not switching employers during this period as our control group.
We analyze the effect for non-mass layoff separators separately. We also follow the literature
in dealing with non-random selection of displaced workers by including worker fixed effects(e.g., Ruhm 1991, Jacobson, Lalonde, and Sullivan 1993). However, besides our moregeneral sample, we depart from JLS in two ways. First, to maintain reasonable dataprocessing, we focus on men and only briefly discuss how women compare. Second, we
report results for broader definitions of ‘stable’ jobs that include workers with shorter tenureat the time of job loss than is typically analyzed in the literature.
We find job displacements during the early 1980s led to large and persistent earnings
losses that last over 20 years. Those workers in stable employment from 1974 to 1979 who
left their jobs in a mass-layoff during 1980 to 1986 had – compared to workers with nopermanent job separation during that period – initial earnings losses of up to 30%. These
losses decline to 20% after ten years but don’t recover much more even 20 years after a job
loss. Such estimates confirm the larger estimates from the previous literature based on more
limited data sets. These findings suggest that earnings losses extend well beyond the five-year
period typically studied and last up to 15-20 years after a displacement. We also find that
ignoring workers with zero earnings has biased the results of previous studies downward.

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Earnings losses are also substantial and last up to 20 years for displaced workers with preseparation tenure of only three instead of six years.
The rest of the paper proceeds as follows. Section 2 presents the worker and firm
data and discusses sample characteristics. Section 3 presents our main results on the longterm earnings trends of workers displaced during a mass-layoff (MLF), non-MLF separators,
and non-separators. Section 4 summarizes the statistical analysis of earnings losses at job
separation; there we compare the earnings trends of job separators and stable workers while
controlling for permanent heterogeneity in earnings potential using worker fixed effects.
Section 5 examines earnings losses by age at displacement, briefly compares earnings losses
for women, and contrasts earnings losses following displacement during the peak years of
the economic recovery of the late 190s. Throughout the discussion, we highlight the role ofzero earnings and of weaker restrictions on pre-separation job tenure. The last section
summarizes and presents directions of future research on this project.
2.

Data and Sample

2.1. Longitudinal Worker Data
The sample of workers used for our analysis of the long-term consequences of job
separations is based on three data sets. The three sources are the 2004 Continuous Work
History Sample (CWHS) active file, a 1% extract from the Master Earnings File (MEF), anda 1% extract from the Longitudinal Employee-Employer Data (LEED). The 2004 CWHS
gives us the baseline sample universe and basic demographic information for 1% of
individuals covered by Social Security from 1957 to 2004. In a first step, we merge thisbaseline sample with information on workers’ total uncapped annual earnings for each job
held in a given year beginning in 1978 through 2004 obtained from the MEF. Besides annual
earnings and an identification number for each employer (EIN), the MEF also contains
information on industry for each job. In a second step, we complement this data on
uncapped earnings with information on annual earnings for each job from 1974 to 1977
from the LEED. See the Data Appendix for more detailed information.
Our sample was explicitly chosen to be comparable to the seminal work of Jacobson,
Lalonde, and Sullivan (1993) (henceforth JLS), who studied the effect of job loss during theearly 1980s in Pennsylvania on workers in stable employment from 1974 to 1979. JLS havebecome the benchmark in the literature that examines the effect of job loss using4

administrative data, and we will compare our estimates explicitly to theirs below. To becomparable to JLS, however, we have to ensure that information from the LEED is
comparable to that of the MEF. This is achieved in two steps. First, earnings in the LEED
are capped at the Social Security taxable maximum. We follow a simple imputation
procedure described in Kopczuk, Saez, and Song (2007) to make the earnings levels from the
LEED comparable to those in the MEF.
Second, coverage of Social Security was extended in the late 1970s and early 1980s toencompass public administration and other sectors. To maintain consistency of our sample
over time, we exclude job separations from public administration and several social services(such as health and legal services) from our sample. Following Kopczuk, Saez, and Song(2007) we also exclude job separations from agriculture. To avoid censoring of our earnings
observations, these sectors remain as sources of post-separation employment. Excluding
those sectors also helps to avoid changes in employer identification numbers (EIN)
occurring due to administrative reasons. The exclusions also help to smooth the incidence ofjob separation considerably in the period from 1980 to 1987, when public administration was
gradually added to the sample, and in 1978, the year we change data sources for uncapped
earnings.
From this sample, we extract two groups of male workers with high attachment totheir employer. First, our main sample contains workers in stable employment from 1974 to
1979. Second, we also keep a sample of workers in stable employment from 1977 to 1979. A
criticism of JLS has been that by focusing on workers with six or more years of tenure at job
loss, they isolate those workers bound to experience the largest losses. Thus, the comparisonof the effect of job separation for workers with high job tenure (six years) with workers withshorter job tenure (three years) is of particular interest.
In our final restriction we follow JLS and require that workers are born in or after
1930. This implies that the average age at the time of job separation is roughly 40 years, andthat the majority of the sample is in their prime working years during the follow-up period.
Thereby, we avoid having to explicitly model retirement for our immediate follow-up. To
avoid an increasing fraction of workers dropping out of the labor force over time, we restrict
workers in our sample to be no older than 55. Clearly, our data series is so long that wecannot avoid the fact that an increasing fraction of workers drops out of the labor force.
This will be apparent in some of the results we discuss below.] In the future, we will be able5

to directly examine the receipt of public pension and disability insurance using administrative
information on benefit receipt from the Master Beneficiary Record (MBR).
A crucial step in our analysis is the dating of job separations. We have experimented
with alternative definitions of separation from the stable job held in the late 1970s. The moststraightforward definition is simply a change in EIN from one year to the next. However,there are many cases in which longer employment spells at the same EIN are interrupted fora single year. This occurs either due to a transition to non-employment – such as in the caseof temporary layoffs – or because a worker receives more earnings from another employer.
Since we are interested in permanent separations from the long-term job, an alternative
definition is to consider the first separation that is permanent. After some experimentation
we have settled on defining job separations as occurring when the first separation is also
permanent. This is the cleanest definition, and avoids possible intermediate cases, such as
when a worker leaves the firm and then receives severance pay a year later. The last two
definitions yield almost identical results; the first definition also yields similar results (see von
Wachter, Song, and Manchester 2007).
2.2. Longitudinal Firm Data
A key innovation of this study is to use firm-level employment data covering any
employer operating in the U.S. from 1978 to 2004 that reports earnings covered by Social
Security. For each employer in each calendar year, we have obtained the total number ofworkers with positive earnings at that employer in the given year.3 In the case of multiple
employers in a single year, we only count a worker at the employer at which the worker
received the highest earnings. This avoids double counting of workers switching employers
within a year or workers holding multiple jobs.4
We define mass layoffs as instances where the employment of a firm drops by atleast 30%.5 Since such a measure makes less sense for smaller firms with high variance of
employment, we follow JLS by restricting ourselves to firms that had at least 50 employees
3 Note that the unit of analysis is the EIN, which may contain multiple establishments.
4 Given substantial worker mobility within a year, there is some inherent ambiguity in measuring firm size.

Figures reported by the Census Bureau often refer to firm size at a given calendar date. This double counts
workers with multiple jobs, but does not count each job for workers switching jobs. We have experimented
with various ways in defining a worker-firm pair for generating estimates of firm size. This is discussed in moredetailed in the Appendix of our companion paper.
5 At present, this measure does not include plant closings because of a non-negligible number of reappearing
EINs. However, few EINs disappear suddenly, but instead have large employment declines prior to closing.

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in 1979. Since our employment measures are based on annual earnings, if workers receive
earnings for part of the year at the old employer, even a sudden drop in firm size may notappear immediately as a drop in employment. Thus, we consider changes in firm size over
one and over two years. To make sure we capture a permanent decline and not temporary
fluctuations in a firm’s work force, we require firms’ employment to have a minimum
amount of stability before and after a sudden drop in employment.6
The resulting incidence of mass layoffs at the firm level and job destruction at theworker level suggests a large spike of layoffs in the early 1980s. This is the subject of ourcompanion paper exploring the effect of the incidence of layoff on the labor market as awhole (von Wachter, Song, and Manchester 2008). In von Wachter, Song, and Manchester(2007) we have shown that the transition rate to non-employment was especially high in theearly 1980s, consistent with a high rate of layoffs. Here, we analyze the effect on earnings ofpermanently separating from a stable job during a mass-layoff.
2.3 Sample Characteristics
We call a worker displaced if he permanently leaves his stable job in the year the firm
experiences a sudden drop in employment. The majority of the workers leaving the firm
during a mass-layoff should in fact quit their jobs involuntarily, especially in the course of astrong recession. However, some workers may have left the firm voluntarily as well. If
voluntary movers change to higher paying jobs, this may lead us to understate the effect oflayoffs. On the other hand, an important concern is that employers may selectively lay off
their least productive workers. Again, this may be less of a concern during large layoffs, since
employers may not be able to be as selective regarding which workers to lay off. This is true
especially during the 1980s, when most firms – whether unionized or not – were bound byseniority rules (Abraham and Medoff 1984). We address the concern of selective
displacement directly in our regression analysis.
Table 1 displays basic characteristics of our sample by mobility status during theyears 1980-1985. We show sample statistics for workers not separating during that period,
those permanently leaving their long-term employer during a mass-layoff, as well as non-

6 Some of the permanent declines in employment we observe may be due to takeovers. To assess thispossibility, we constructed the complete matrix of worker flows between firms, and flagged employment
declines in which a high fraction of workers moved between just two EINs. Our results are robust to thisrestriction.

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mass layoff separators. The entries in the table confirm that there are important differencesin characteristics of workers permanently leaving their long-term employer in 1980-1985
relative to other workers not experiencing a permanent separation during that period. Job
separators – whether with six or with three years of job tenure – are on average younger andhave 15-20% lower annual earnings in 1979. Table 1 also shows the industry distribution forjob separators and non-separators. Job displacements are more likely in construction andmanufacturing and less likely in transportation. On the other hand, non-MLF separations are
more likely in trade and finance, insurance and real estate (FIRE). Interestingly, for workers
with at least three years of tenure, manufacturing and construction do not emerge as highlayoff industries. Given these significant pre-separation differences, it is important to control
for the potential of selective job displacement when comparing the earnings developmentsof job separators with a control group, something we will address in our regression analysis.
3.

Long-Term Earnings Trends of Displaced Workers
We begin with a descriptive analysis of earnings of workers displaced in 1981. Thisshould capture layoffs during the first year of the 1980-1982 double-dip recession. Given
our definition of job separation, 1980 is the last year these workers receive the majority ofearnings from their current employer. For example, if a worker left their employer in, say,March of 1980 and had no other job that year, we would assign 1981 as date of separation. If
he had earnings from another employer in 1980 that exceeded the earnings in that year fromhis long-term job, we would assign 1980 as separation date. Alternatively, many workers may
leave their long-term employer during 1980 but still have the majority of earnings from that
employer during that year. In that case, we assign 1981 as displacement date.
Trends in total annual average earnings for workers separating from their long-term
employer during a mass-layoff in 1980 appear in Figure 1. Figure 1A includes workers withzero earnings. To be consistent with the restrictions imposed by JLS and others working
with state-level administrative data, we include only workers with positive annual earnings in
Figure 1B. Several features of the data stand out. First, one can see a rise and decline inearnings prior to 1981 in tune with the business cycle. Second, the figures show a decline inearnings from 1979 to 1980, followed by another drop in 1981. The fact that earnings drop
gradually is a by-product of our definition of job separations. Third, one can see somerecovery through the year 2000. However, in the long run a clear gap in earnings relative to8the pre-separation level remains. Note that to avoid counting transitions into retirement asearnings loss, we have set earnings to missing for workers who are older than 55. This maylead us to undercount earnings losses if job separation leads to earlier retirement in the longrun.7
To put these earnings losses in perspective, Figure 1 also displays the developmentof annual earnings for workers not separating during 1980. It is not a priori clear whichgroup of workers yields the appropriate counterfactual earnings trend. Each different control
group will yield another answer to the question of the cost of job loss. The control group we
use here is relatively unrestricted, since we do not impose any further restriction on
employment or earnings. Generally, the more stable is the job attachment of the control
group, the higher are the predicted earnings losses of displaced workers.
Three results are apparent from the consideration of non-separators. First, as notedin Table 1, non-separators’ earnings are higher prior to separation. Second, had separators
not left their jobs, their earnings would have remained stable throughout the early 1980s andtrended slightly upwards. Third, it is apparent from the figure including zeros that even 1520 years after a job separation affected workers have somewhat lower annual earnings thanworkers who stayed with their employer in the early 1980s. These results are independent of
which control group is chosen as counterfactual (see von Wachter, Song, and Manchester2007). Once we restrict the sample to workers with positive earnings, we see that non-MLF
separators have somewhat higher annual earnings on average after 10 years.
Our results also suggest persistent losses in earnings for workers who lost their job
during a mass-layoff even 20 years later. The loss is especially noticeable when we include
workers with zero earnings. Restricting the sample to workers with positive earnings showsthat MLF separators almost have returned to their pre-layoff earnings level by 2000. (seeAppendix Figure 1A ??gone??).8
In contrast to the permanent decline in earnings experienced by displaced workers,
workers separating from their long-term job but not during mass layoffs do not experiencelong-run losses in earnings. In fact, after about five to eight years they have caught up with
7 Note that there is little difference in the effects whether we measure MLF over one or two years. The latter
series is smoother, since the 2-year measure captures a larger number of layoffs.
8
The fraction of workers with positive earnings among non-separators exhibits a monotonic downward trend.
For separators, there is a large decline in the fraction employed at job separation (over 15 percentage points).
From there, employment declines, albeit at a slower rate than for non-separators (note that as for earnings,
non-employment at age 60 or above is not included in the figures). Still true??

9their previous level and with the control group. Non-MLF separators even appear to havehigher earnings than the control group in the long run when we include workers with zeroearnings. However, some of these workers may experience a transition to disability or out of
employment covered by Social Security that is unrelated to layoff. Thus, we interpret theresults for non-MLF separators with caution.
These descriptive results can be compared to those in JLS’ Figure 1, which shows
quarterly earnings losses for all workers separating from their long-term job in the firstquarter of 1982 (a more detailed comparison follows below). The initial loss is about $3,000
in 1987 prices; inflating this to 2000 prices at a factor of 1.52 and converting to total annual
earnings, this would suggest annual earnings losses of about $18,000.9 The loss relative to the
pre-recession peak in earnings suggested by Figure 1B is about $10,000 when we exclude
zero earnings as in JLS. In Figure 1A, which includes zero annual earnings and is discussed
next, the loss is closer to $18,000. Both numbers have some validity as a point ofcomparison, since quarterly earnings in JLS can be zero (though annual earnings are
restricted to be positive). Thus, as perhaps expected, the earnings loss at job displacement on
a strictly comparable basis is smaller in the U.S. as a whole than in Pennsylvania, where steel
and other heavy industry was particularly hard hit during the 1982 recession. However, ourfindings point to earnings losses at job displacements that are similar in overall magnitude
and duration as found in JLS for Pennsylvania and by others for Massachusetts, California,
and Connecticut in the early 1990s.10
3.1 Including Zero Annual Earnings
An important constraint of analyses based on state-level administrative data such as
JLS for Pennsylvania is that nothing is known about workers moving out of state. To
counter this problem, JLS require minimal positive earning each year in the Pennsylvania
labor market of all workers, whether separators or not. By ignoring workers with zeroearnings, this may lead to an underestimation of the cost of job loss. Moreover, displaced
workers employed after job separation may be a positively selected sample with regard toearnings potential, further biasing the results towards finding smaller earnings losses. On the

9 With 2000 as a base, the 1987 CPI was 65.95.
10

See Schoeni and Dardia (2003), Couch (2006), Kodrzycki (2007), and Hildreth, von Wachter, andHandwerker (2008).

10other hand, if the most successful workers move out of state, excluding these workers maylead to an overestimate of the cost of job displacement.
Since we do have information on workers’ earnings no matter what state they residein, we do not need to impose such a condition. To see the impact of relaxing this restriction,
Figure 1A examines the trends in average annual earnings at the main job for separators but
including wage observations with zero earnings.11 The patterns in Figure 1A are overall quite
similar to those in Figure 1B. Thus, the main results in JLS and other state-specific analyses
are upheld in our more general sample.
However, some expected differences arise when zero earnings are included thatshould be addressed in a more complete analysis of the role of participation. First, theearnings loss is larger than in Figure 1B. [??This is not surprising, since Appendix Figure 1
still there?? indicates that an important fraction of displaced workers stop working after
leaving their stable job.] Second, the pattern of recovery after separation is somewhat
weaker. However, the earnings gap relative to the control group in the long run appears
similar,[?? in part due to a faster rise in non-employment among the latter??]. Third, thebiggest apparent difference is for non-MLF separators; once we include zeros, we see larger
and more persistent losses. As suggested above, this may partly be because some of theseworkers may not be laid off, but experience a voluntary transition out of the covered labor
force. However, some of these workers may indeed lose their attachment to the labor force
because of the separation.12
3.2 Including Shorter Job Durations
Another concern with the analysis of administrative data of JLS and others is thatthey impose six years of pre-separation job tenure to isolate workers in stable employment.
This may be an overly restrictive sample, in particular since earnings and job losses havebeen shown to increase with job tenure (e.g., Kletzer 1989).13 This implies that workers
leaving their employer after a shorter employment spell may fare better in terms of post11 Note that zero annual earnings here means no earnings from employment covered by Social Security. Thus,
it may be that workers with zero earnings are self-employed, or work for some levels of government (though
the majority of public employment is included in Social Security by the mid-1980s).
12 Note that the recovery itself is unlikely to be driven by selective changes in participation, [??since Appendix
Figure 1A suggests that employment for non-MLF separators stays relatively constant at the lower level after
separation??].
13
It has long been speculated that part of the persistent earnings losses after a job separation may be due to aloss in job-specific investments that workers and firms incur during a longer employment spell. Since bydefinition these investments are not productive in the outside market, workers’ earnings decline at a job loss.

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separation earnings. Another reason to contrast the earnings losses at job separation is thatjob separations at three years of tenure are more common than at six years.
We replicated our main results for workers in stable employment for three years,
from 1977 to 1979. The resulting numbers are shown in Figure 2A (including zero earnings)
and Figure 2B (positive earnings). Overall, the figures show quite similar patterns as forworkers with six years of job tenure in 1979. As expected, independently of separation statusmean earnings of low-tenured workers in the late 1970s is lower (see also Table 1). Similarly,
earnings declines at displacement are somewhat smaller and earnings appear to recoversomewhat faster. However, the difference is one of degree rather than in kind. The
magnitude of the losses will be easier to compare in our regression analysis. Nevertheless, it
is clear from these figures that the losses in annual earnings at displacement are large evenfor a broader group of workers with lower pre-displacement job attachment.
4.

The Long-Term Earnings Losses from Job Separation

4.1 Regression Approach
As documented in Table 1 and apparent from Figures 1 and 2, systematic differencesin average earnings and age exist between workers who separate from their long-term
employment in the early 1980s and workers who keep their jobs. It is also apparent thatstrong cyclical swings and trends in earnings may confound the effects of job loss. To get acomplete picture of the long-term earnings losses of job separators, we need to make acomparison to a control group but at the same time explicitly account for possible systematicdifferences among workers in a regression framework.
We will estimate various specifications of the following distributed lag modely it = α i + γ t + βX it + ∑ δ k Ditk + u it

(1)

k ≥− m,where the outcome variable yit represents a measure of annual earnings, the year dummies
γ t are identified by the presence of workers not separating from their job (the control

group), 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
separation ( Ditk ) measure the time path of earnings changes of job separators before andafter a displacement relative to the baseline and the control group. The ability to estimate the12dynamic effect of job separation is of particular interest since it will allow us to obtainsummary measures of the overall lifetime cost of job separation.
The displacement effect is identified by the inclusion of workers staying at theiremployers throughout the period under study (the control group). To interpret the estimatedeffects δ k as the causal impact of job separation on earnings, however, we have to assume
that conditional on worker fixed effects and included observable baseline characteristics,
displaced workers are observationally equal to those workers in the control group. This is thestrategy chosen by most classic studies of the effect of job loss (e.g., Ruhm 1990, Jacobson,
Lalonde, and Sullivan 1993). If workers are on average remunerated according to theirproductivity, then the long-run average of earnings should be a good index of their overall
earnings potential. In this case, comparing a job separator and a non-separator with similar
worker fixed effects yields a valid estimate of the effect of job loss.
Given the large change in earnings for job separators, this approach is mostpersuasive in the presence of a long window of observation prior to the job separation.
Similarly, it is most appropriate for mature workers whose earnings represent theirproductivity. For example, in the case of younger workers, wages often do not yet reflect
their long-term earnings potential and fixed effect strategies are not viable (e.g., von Wachterand Bender 2006). Since our observation window covers a long time period prior to job
separation and the average age of workers in our sample is close to 40, we believe our fixed
effect estimation strategy will uncover estimates that yield good first approximations of thecausal effect of job separations on earnings.
A potential concern with estimates obtained from the model in equation (1) is that it
may attribute negative trends in earnings in industries experiencing high rates of layoff to theevent of job displacement. To make sure our estimates do not simply reflect differential
industry trends we augmented model (1) by interacting the year effects with the industry inwhich workers held a job in 1979. If j denotes the 2-digit industry in 1979, then we estimate
y ijt = α i + γ tj + βX it + ∑ δ k Ditk + u ijt(2)k ≥− m,This effectively uses as a counterfactual earnings measure in the absence of displacement theevolution of earnings of workers who are not displaced belonging to the same 1979 industry.
(Below, we will also estimate a fully interacted model where we allow the effect of
displacement itself to differ by broad industry). We will also present estimates where we use
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workers employed at the same employer in 1979 who were not displaced as a control; there, jrepresents the identity of the employer in 1979, and model (2) includes employer-year fixed
effects. This can account for the fact that firms engaging in mass layoffs may have been in
economic difficulties before the layoff; we would not want to attribute such a trend to theeffect of displacement. A drawback of this estimate is that it effectively subtracts any effectof the mass-layoff on workers staying at the firm (‘stayers’) from the effect on displaced
workers. We return to this point below when we discuss an alternative strategy to estimate
the effects of mass-layoff within an intent-to-treat framework.
In our sensitivity analysis, we also estimate a range of other variations on the basic
regression model. Among others, we present estimates for groups of workers with different
degrees of labor force attachment. While the high tenured workers we focus on in our main
analysis constitute a small fraction of the overall labor market, these estimates are based onbroad groups of workers more representative of the U.S. labor market as a whole. A related
concern is that the industry and age decomposition of the U.S. labor market has changed
since the early 1980s. To show directly that our estimates are not driven by particularindustries or age-groups, below we analyze these groups separately. To summarize, we alsopresent estimates based on samples that have been re-weighted to reflect the characteristicsof the labor market in 2003.
4.2 Main Results
To display our results, we graph the coefficients δ k capturing the effect of
separation on annual earnings before and after the year of employment at the lost job (i.e.,
k=0 during the last year we record the old employer to be the main employer). We follow
JLS in pooling all job separations from 1980 to 1986 to maximize our sample size. All figures
show the results from our preferred specification, which includes year effects (and thereby
effectively introduces the control group); a fourth-order polynomial in current age (and thus
accounts for differential age of the treatment and control groups); and worker fixed effects
eliminating permanent differences in earnings potential between separators and nonseparators. A detailed discussion of the role of each of these specification choices can befound in von Wachter, Song, and Manchester (2007).
The results for losses in annual earnings among displaced workers in covered
employment are shown in Figure 3. The figure strongly confirms that job separation during

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the early 1980s recession and its immediate aftermath had very long-lasting effects on annual
earnings, and significantly reduced workers’ lifetime earnings. For workers displaced at mass
layoffs during 1980-1986, the losses are about $10,000 ten years after job loss when we lookat workers with positive earnings, or about 21% relative to average 1979 earnings shown in
Table 1. Twenty years after job separations, earnings remain about $10,000 below thebaseline – maintaining a long run loss of about 21%. We find very large persistent earnings
losses following job separation that decline ever so gradually and never recover fully.
Controlling for year fixed effects alone increases the persistence of earnings losses
compared to average losses shown in Figure 1. This result implies that part of the apparent
recovery in average earnings observed in Figure 1 is due to cyclical recovery in the mid1980s.
If we include observations with zero earnings in the analysis, the losses become evenlarger and initially more persistent (Figure 3). The immediate losses are $16,000 vs. $13,000,
and the 10 year loss stands at $13,000 vs. $10,000 if we include zero earnings instead of
excluding it. Those losses persist even after 20 years.
The magnitude of these estimates is on the same order as what Jacobson, Lalonde,
and Sullivan (1993) found for workers laid off in Pennsylvania during the same time period.
JLS report earnings losses up to six years after job loss. We show losses up to 20 years. The
loss JLS estimate for the sixth year, rescaled to represent annual earnings and converted to2000 dollars using the CPI, is $9,000 (the specification comparable to ours is in their Figure
4, model 2). This is of similar order of magnitude of the ten year loss we report, ranging
between $10,000 and $13,000, depending on whether zero earnings are excluded or not (aswe discussed above, both estimates bear some validity when compared to the quarterly
estimates in JLS restricted to workers with positive annual earnings).
The long-run results we report suggest that the medium-run effects reported by JLS
and others persist into the future, with lingering effects 15-20 years after a job displacement.
Last, a note of caution is appropriate. The sample used in this paper does not containthe same range of industries as JLS does, and so far we have only considered men, whereas
they consider both men and women. [??omit: Similarly, the final JLS estimate includes
worker-specific earnings trends, which will be included in the next version of this paper.]
4.3 Losses in Present-Discounted Value of Earnings

15

The annual losses we find accumulate to represent a substantial loss in life-time
earnings. This is shown in Table 2, where we present the implications for loss in the presentdiscounted value of earnings losses assuming that losses are zero after 20 years for different
interest rates. This can be thought of the earnings loss for a worker retiring twenty years
after a job displacement. The substantial losses range from $110,000 to $140,000. Had aworker earned about $50,000 each year in the absence of the loss for twenty years, at an 8%
real interest rate his remaining PDV for twenty years would have been about $530,000. A
loss of $110,000 would thus represent a decline in life-time earnings of about 20%.
The table has two additional messages. First, the number of high-tenured workers
potentially affected by these large earnings losses is substantial. Second, the number ofworkers affected by displacement during mass layoffs quickly grows if we consider workers
with shorter tenure durations or even weaker employment histories prior to job loss. Thus,
the number of potentially affected workers rises from about one million to three million if
we require six consecutive years of positive earnings at any employer instead of six years oftenure at the same employer. As further discussed in the next section, the remaining columnsof Table 2 reveal that albeit estimated earnings losses for these added groups of workers islower, it is still substantial.
4.4 Shorter Tenure Durations
Results from the regression analysis also reveal that relative to the control group of
workers not separating between 1980 and 1986, earnings losses among the sample that
includes shorter tenure durations at job loss are of the same order of magnitude andpersistence as for our main estimates. This is shown in Figure 5. There is no indication in the
figure that the cost of job loss is concentrated among displaced workers with six or more
years of tenure. In fact, relative to the control group of workers with three or more years oftenure, the losses even appear to last longer than 15 years beyond the layoff. Similarly, if we
include zero earnings, we find that workers displaced during a mass-layoff still faresignificantly worse than non-MLF separators with at least three years of job tenure in 1979
(not shown).
Figure 5 also shows estimates for two additional groups of workers with even weaker
restrictions on their prior labor force participation. In our broadest sample, we only requirethat workers have a significant attachment to the labor force, represented by three years of16

consecutive positive earnings in 1979. Table 3 shows that these groups represent a muchbroader fraction of all individuals in our sample with positive earnings in 1979. While thismay represent an advantage over the more narrowly defined high-tenure samples, it isimportant to realize that these groups are more likely to change jobs voluntarily. Since
voluntary movers typically gain from job mobility (e.g., Topel and Ward 1992), thisintroduces measurement error in our displacement indicator that is likely to bias ourestimates of the effect of layoff on earnings towards zero. Figure 5 shows that the broader
groups of workers indeed experience somewhat lower earnings losses than the high-tenuregroups. While this is expected given that higher job duration prior to displacement istypically associated with larger earnings losses, part of the difference might also be due tomeasurement error. In either case, the estimated reduction in earnings is still substantial,
persistent, and large after 15-20 years even for these broad groups of workers.
4.5 Industry- and Firm-Specific Fixed Effects
To estimate our augmented model (2), we first included year-effects for the 2-digit
industry of a worker’s main employer in 1979. If we compare the change in displaced
workers’ earnings with the evolution of earnings of non-displaced workers in the same 2digit industry, Figure 4 shows that earnings losses are somewhat lower, albeit still
significantly negative, long-lasting, and substantial. The same applies when we include firmspecific year effects, although now the reduction is somewhat larger, representing roughly
25-30% of the original effect obtained from model (1). Thus, even within 2-digit industries,
all workers in firms experiencing mass-layoff tend to fare somewhat worse. Note that theseestimates control for both a pre-existing trend at the firm level as well as the effect of mass
layoffs on workers staying at the firm. Whether this is desirable depends on our notion of
the appropriate counterfactual. Clearly, from the point of view of the single worker, had hestayed at the firm instead of being laid off, he would have suffered the effect of the masslayoff for those staying. On the other hand, to evaluate the overall cost of layoffs, we wantto compare the outcomes of both displaced and non-displaced workers at the affected firms
with the outcome of workers at comparable but stable employers. This will be discussed
below.
4.6 Estimates Based on Re-Weighted Samples

17

To directly address the concern that changes in the industry and age distribution in
the U.S. labor market would affect the relevance of our findings, we also estimated model (1)for various samples that have been reweighted to reflect the characteristics of our sample inlater years. Figure 6 shows the estimates for weights that are chosen to reflect the industryage distribution for high-tenured workers satisfying our main sample restrictions in the years
1986 and 2003. Panel A of Appendix Table 1 shows characteristics of the original samples in1979 and 2003, as well as of the reweighted sample.14 The findings, shown in Figure 6,
indicate that the reweighting makes little difference. This foreshadows the results in Section
5 demonstrating that the effects of displacement are pervasive and negative for all broad ageand industry-groups we study. Thus, our findings are neither driven by any particular groupof workers, nor would changes in the industry or age distribution imply radically different
qualitative results. This is not to say that the effect of layoff would not be different in, say,
2003, only that we would not predict it to be different based on our data.
5.

Age at Displacement, Earnings Losses for Women, and Non-recession

Displacement
The analysis thus far applies to men who were displaced during the recession of theearly 1980s. In this section, we explore how the effects of mass layoffs differ based on theage of workers when the layoffs occur, effects on earnings of women who are displaced
during mass layoffs in the early 1980s, and effects on men displaced during the peak of theeconomic recovery of the late 1980s.
We expect that older workers might suffer larger losses in earnings following mass
layoffs than younger workers. Older workers are likely to have more job-specific and firmspecific human capital and thus cannot replace their pre-layoff earnings as easily. Ourregression analysis supports our expectation, as shown in Figure 7. Including workers withzero earnings, workers who were displaced at ages 50-55 see an initial fall of about $23,000
in earnings, compared to a loss of about $19,000 for workers ages 40-50 at displacement(Figure 7A). Workers in their 20s and 30s at displacement suffer a loss in earnings of about$16,000. Recovery in earnings following the initial loss is very slow for all age groups.
Perhaps surprisingly, even the earnings of younger workers who were displaced at ages 20-40
14

Panel B shows similar numbers for characteristics of a sample with only some work restriction in 2003. The
resulting estimates based on the reweighted sample are very similar to what is shown in Figure 6.

18

do not recover fully. Earnings losses of about $12,000 persist 20 years after the mass layoffs.
The time horizon is shorter for the older workers because we drop workers above age 55,
but again the recovery in earnings is slow.
Excluding workers with zero earnings yields smaller initial losses in earnings and aless clear-cut age pattern (Figure 7B). Workers displaced at ages 20-55 experience losses ofabout $12,000 to $14,000 initially. The differences between workers in their 20s, 30s, 40s,
and early 50s are small. However, recovery is more pronounced for workers in their 20s and
30s than for workers in their 40s.
Women show smaller dollar losses in earnings following mass layoffs in the early
1980s, but larger percentage losses (Figure 8). Looking at women with positive earnings
shows initial losses of about $8000, or about 32% of their average earnings of $25,000 in
1979. Comparable results for men showed losses of $13,000, or about 27%. After 10 years,
the earnings loss for women is about $6000 and becomes about $5000 after 20 years. The
long-term loss of 20% is similar to the percentage loss for men. Including women with zeroearnings yields initial losses of about $10,000, or about 40%. Men showed losses of about$16,000, or about 33%. After 10 years, the earnings loss for women is about $7000 andbecomes about $4000 after 20 years. The long-term loss of 16% for women is somewhat
smaller than men’s loss of 25%.
To examine whether effects of job loss might differ if the layoffs occur at the peakof the late 1980s recovery, we compare earnings losses following job loss in 1987-1989 toour main results for the 1980-86 period (see Figure 9). Including workers with zero earnings
who had been in a stable job for six years, the initial loss in earnings following job loss in the
1987-89 period is a bit smaller, but the persistence of the loss is roughly similar (Figure 9A).
The initial earnings loss for the later period is $12,000, again expressed in 2000 dollars,compared with $16,000 for the early 1980s recession period. After 10 years, losses for thelater period are about $8000 compared with about $13,000 for the earlier period. Patterns formen in stable jobs for three years in the two periods show similar results (Figure 9B).
6.

Preliminary Summary and Suggestions for Further Research
Every year a large fraction of workers lose their job in the U.S. labor market. Yet,despite a long literature attempting to quantify the costs of job loss, no representative andcomprehensive estimate of the long-term consequences of job displacement exists. This is19particularly worrisome because existing estimates based on limited samples or time periods
suggest that the costs could be large and persistent at least for some groups of workers.
However, existing estimates suffer from important limitations since they are often based on
single U.S. states, have no information on earnings of workers moving between states, andcan follow workers only for a limited amount of time.
In this paper, we show that the results based on more limited samples are upheld in a
representative and large sample of the U.S. work force covering over 25 years. We merge
longitudinal administrative earnings data with data on employment of firms to study thelong-term effects of job separation on earnings for male high-seniority workers displaced
from their jobs during the 1982 recession. In doing so, we follow the literature in isolating
displaced workers as those permanently leaving their stable job when their employer
experiences a sudden large drop in employment.
Our sample allows us to follow workers who left a stable job for over 20 years after
job loss, and compare their earnings developments to the outcomes of workers of similar
age and earnings potential who did not separate from their employers. To control for the
potential of negative selection of job separators, the long time period of our data allows us tocontrol for worker fixed effects.
Our preliminary results suggest that displacements in mass layoffs occurring in theearly 1980s led to large and persistent earnings losses that last over 20 years. Those workers
in stable employment from 1974 to 1979 who were displaced from 1980 to 1986 – compared
to workers with no permanent job separation during that period – had initial earnings lossesof up to 33%. These losses decline to 21-27% after ten years and remain roughly similar 20
years after a job loss. We also show that even for workers with lower pre-separation tenure,losses are still substantial and last up to 20 years.
This has potentially important implications for the costs of economic adjustment in
the U.S. economy. In particular, while the ability to fire ‘at will’ may benefit adjustment in
the labor market as a whole, the costs in terms of lost productivity and earnings of individual
workers may be much higher than typical replacement rates of unemployment insurance orother programs designed to smooth temporary earnings fluctuations.
We will work on several extensions of our analysis. On a substantial level, we will
analyze how the speed of recovery depends on the number of workers affected. We will also
engage in further important sensitivity checks. First, we will make sure that the losses we
20

find are not driven by a correlation of the incidence of job separations with average earnings
in affected firms or industries. Second, our long time period allows us to follow JLS andcontrol for potential differences in individual long-term earnings trends of separators andnon-separators. Last, our large sample size allows us to analyze the effects of job separation
by age, gender, race, region, industry, and socio-economic status.

21

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23

Data Appendix
The sample of workers used for our analysis of the long-term consequences of job
separations is based on three data sets. The three sources are the 2004 Continuous Work
History Sample (CWHS) active file, a 1% extract from the Master Earnings File (MEF), anda 1% extract from the Longitudinal Employee-Employer Data (LEED).
SSA 1% samples are selected by a “stratified cluster design” based on certain serial
digits of the Social Security Number (SSN). They are generally considered to be random
samples and contain a large number of observations that represent the general population.
Individuals are followed through their lives, thereby giving us longitudinal data.
The 2004 CWHS is a 1% sample that gives us the baseline sample universe and thematching longitudinal earnings and demographic information. It contains information on
Social Security covered (“capped”) earnings from 1951 through 2004, uncapped total
earnings from 1978 through 2004, and basic demographic characteristics of persons who
have any report of covered earnings in 1951 through 2004 as well as those who have any
report of uncovered earnings in 1978 through 2004.
The MEF contains information on a worker’s total uncapped annual earnings foreach job held in a given year beginning in 1978 through 2004 and an identification number
for each employer (EIN). It also has information on industry for each job.
The LEED is a longitudinal employee-employer file that starts in 1957. The sampling
approach is the same as in the 1% CWHS, but individual earnings are reported at theemployer level. A record exists for each employer that employed a worker in the sample ineach year. The dataset includes basic demographic characteristics as well as compensation
information subject to top-coding at the employer-employee record level up to 1978 butwith no top-coding after 1978. The dataset also contains information about the employer
such as geographic information and a three-digit industry code. The LEED also includes
imputed wages above the taxable maximum from 1957 to 1977. The imputation procedure isbased on the quarter in which a person reached the taxable maximum and is known as
Method II. The idea is to use earnings for quarters when they are observed to imputeearnings in quarters that are not observed because the annual taxable maximum has beenreached. When the taxable maximum is reached in the first quarter, imputations rely on a
Pareto interpolation.
24

Table 1: Average Characteristics of High Attachment Workers Working at Firms with Employment>=50 in
1979 Before Permanent Job Separation
by Mobility Status in 1980-1985, All Separators vs. MLF SeparatorsoStable Employment 1974-1979
Separations During
NonMLF
MLF
Non(1 Yr)
(2 Yr)
Separators MLF

Stable Employment 1977-1979
Separations During
NonMLF
MLF
Non(1 Yr)
(2 Yr)
Separators MLF

Number of Workers

19084

7579

3696

4826

26893

15409

6602

8450

Average Age

37.7

36.5

37.3

37.1

35.9

33.4

34.6

34.5

Fraction Employed in
Mining in 1979

0.02

0.02

0.04

0.03

0.02

0.02

0.04

0.04

Fraction Employed inConstruction in 1979

0.03

0.04

0.05

0.04

0.04

0.04

0.07

0.06

Fraction Employed in
Manufacturing in 1979

0.56

0.50

0.60

0.62

0.55

0.47

0.55

0.57

Fraction Employed inTransportation in 1979

0.18

0.12

0.09

0.10

0.16

0.10

0.08

0.08

Fraction Employed in
Trade in 1979

0.10

0.18

0.09

0.10

0.12

0.21

0.12

0.13

Fraction Employed in
FIRE in 1979

0.07

0.10

0.06

0.05

0.07

0.10

0.06

0.06

Fraction Employed inServices in 1979

0.03

0.04

0.07

0.06

0.03

0.06

0.07

0.06

Average Earnings At
Main Employer in 1979

55.65848.73747.87047.84552.23842.05742.99343.108Average Earnings at All
Employers in 1979

56.07449.88848.85548.77952.74243.45344.21444.257

Median Earnings at Main
Employer in 1979

51.24043.73444.28344.72948.35338.05040.04140.471

Median Earnings at All
Employers in 1979

51.64544.72744.98945.44048.83139.11141.00141.470Notes:1% Sample Social Security Administrative Data. Earnings are in 1000s of dollars, deflated by 2000 CPI .

Table 2: PDV of Annual Earnings Losses for Workers Displaced at Mass-Layoffs 1980-1986, Individual and Total
Fraction
Displaced
Number of Workers
during
Displaced During MassMassLayoff in 1980-1986
Layoff in
1980-1986
Sample Population
(1)
(2)
(3)

PDV ofAnnualEarnings
Loss

Total PDV
of
Earnings
Loss(Billion)

PDV ofAnnualEarnings
Loss

Interest Rate = 4%
(4)
(5)

Total PDV
of
Earnings
Loss(Billion)Interest Rate = 8%
(6)(7)

Panel A: Including Zero Earnings in Calculation of Losses

Employment Situation in 1979
Six Years Job Tenure

0.15

4,826

965,200-$163,897-$158.2-$127,988-$123.5

Three Years Job Tenure

0.17

8,450

1,690,000-$141,190-$238.6-$110,818-$187.3

Six Years Employment

0.19

14,041

2,808,200-$106,672-$299.6-$82,035-$230.4

Three Years Employment

0.20

18,321

3,664,200-$102,897-$377.0-$79,632-$291.8

Panel B: Losses Based on Positive Earnings
Six Years Job Tenure

0.15

4,826

965,200-$131,661-$127.1-$103,976-$100.4

Three Years Job Tenure

0.17

8,450

1,690,000-$110,259-$186.3-$87,843-$148.5

Six Years Employment

0.19

14,041

2,808,200-$83,269-$233.8-$65,027-$182.6

Three Years Employment

0.20

18,321

3,664,200-$79,099-$289.8-$62,170-$227.8

Notes: Earnings figures are in 2000 Dollars. Discounted values are calculated at five years before displacement. Earnings losses are assumed
to have returned to zero 20 years after displacement. Calculations based on 0.5% sample, such that column 3 is 200 times column 2. The
total is the product of column 5 (7) is the product of columns 3 and 4 (6).

Table 3: Effect of Different Restrictions on Employment, Firm, and Industry in 1979 on Sample Size and Composition

Sample Restriction

A. Effect of Alternative Restrictions
Total Number of Individuals
Fraction of Total Number in Column (1)B. Drop Firm Size Restriction
Total Number of Individuals
Fraction of Total Number in Column (1)C. Drop Industry Restriction
Total Number of Individuals
Fraction of Total Number in Column (1)Notes: Calculations from Social Security Data

Works in
1979, Age
<=50

(1)+
Industry
Restr.

(3)+(3)+(2)+ Firm
Worked 3 Worked 6
Size >=50
Years
Years

(3)+ Job
Tenure 3Years(3)+ Job
Tenure 6
Years

(1)

(2)

(3)

(4)

(5)

(6)

(7)

1935241612090.83114673
0.59

93002
0.48

74232
0.38

50752
0.26

31489
0.16

127743
0.66

98875
0.51

64837
0.34

37670
0.19

111665
0.58

88348
0.46

55698
0.29

34023
0.18

Table 4: Effect of Displacement on Earnings in 1980-1986 by Major Industry in 1979, Men in Stable Employment 1974-1979, Earnings Including
Zeros (in $1000)

MiningConstr.

Manuf.

Transp.

Wholes.
Trade

RetailTrade

FIRE

Services

Industry Share of Employment

0.02

0.04

0.56

0.15

0.04

0.08

0.08

0.04

Ind. Layoff Rate (No Non-MLF Sep.)

0.29

0.24

0.21

0.12

0.24

0.19

0.16

0.33

Ind. Layoff Rate (All Workers)

0.22

0.18

0.17

0.09

0.16

0.12

0.11

0.23

Average Earnings in 1979 ($1000)

59.3

57.6

52.9

57.4

54.0

49.5

60.1

51.9

5 Years Before Displacement

-0.8

5.7

0.2

3.8

-0.5

-1.9

7.8

-5.6

1 Year After Displacement

-23.9

-14.8

-15.8

-20.9

-17.9

-19.4

-5.8

-24.6

5 Years After Displacement

-16.7

-10.4

-14.1

-18.2

-19.8

-15.7

-10.8

-22.6

10 Years After Displacement

-18.9

-4.0

-11.6

-15.2

-17.4

-11.1

-12.5

-19.2

15 Years After Displacement

-13.8

-5.2

-10.5

-11.8

-16.2

-7.0

-22.5

-19.2

Percent Relative to Baseline (15 Years)

-0.23

-0.09

-0.20

-0.20

-0.30

-0.14

-0.37

-0.37

Industry

Effect on Annual Earnings ($1000)

Notes: Industry refers to industry of main employer in 1979. Earnings in 2000 Prices. Industry layoff-rate not including non-mass layoff separators excludes
non-mass layoff separators from denominator. It represents the fraction in the actual estimation sample that is used to estimate the effect of displacement onearnings. The industry layoff-rate for all workers includes non-mass layoff separators in the denominator.

Appendix Table 1: Average Sample Characteristics before and after Reweighting
Panel A: Reweighted using Distribution of High-Tenured Sample in 2003
2003 Sample
1979 High- with Some
TenuredWork
SampleRestriction
Mean Age

1979 Sample Difference
Difference
Means at2003-1979
2003-1979 2003 Weights Reweighted

38.3

40.7

2.3

40.8

-0.1

Mining

2.1

1.0

-1.1

0.9

0.0

Construction

3.6

6.9

3.3

7.0

-0.1

Manufacturing

55.6

37.8

-17.8

37.5

0.4

Transportation

15.5

16.7

1.2

16.2

0.5

Trade

4.4

8.0

3.6

7.9

0.0

FIRE

7.6

12.4

4.8

12.7

-0.3

Services

7.7

7.9

0.1

7.9

0.0

Share in Industry:

Panel B: Reweighted using Distribution of Sample with Some Work in 2003
2003 Sample
1979 High- with SomeWork
TenuredSampleRestriction
Mean Age

1979 Sample Difference
2003-1979
Difference
Means at2003-1979 2003 Weights Reweighted

38.3

34.8

-3.6

37.0

-2.2

Mining

2.1

0.8

-1.3

0.8

0.0

Construction

3.6

12.8

9.2

13.7

-0.9

Manufacturing

55.6

21.1

-34.5

22.7

-1.5

Transportation

15.5

9.1

-6.3

9.5

-0.4

Trade

4.4

8.1

3.7

8.9

-0.8

FIRE

7.6

20.3

12.8

17.8

2.5

Services

7.7

8.0

0.3

8.2

-0.1

Share in Industry:

Notes: Tabulation from SSA Data. Weights calculated by a probit regression. High-tenured
sample refers to workers at same job from 1974-1979 or 1998-2003. Sample with some work
restriction refers to workers with three consecutive years of employment in 2003.

25

35

45

55

Figure 1A: Annual Earnings for Workers Separating and Not Separating in 1981
Earnings at All Jobs, Including Zeros, Men in Stable Job 1974−1979 (in $1000)

1975

1980

1985

1990

1995

2000

year
No Job Separation
Non−MLF Separation

Sep. at MLF (2 Yr)

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

25

35

45

55

Figure 1B: Annual Earnings for Workers Separating and Not Separating in 1981
Earnings at All Jobs, Excluding Zeros, Men in Stable Job 1974−1979 (in $1000)

1975

1980

1985

1990

1995

year
No Job Separation
Non−MLF Separation

Sep. at MLF (2 Yr)

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

2000

25

35

45

55

Figure 2A: Annual Earnings for Workers Separating and Not Separating in 1981
Earnings at All Jobs, Including Zeros, Men in Stable Job 1977−1979 (in $1000)

1975

1980

1985

1990

1995

2000

year
No Job Separation
Non−MLF Separation

Sep. at MLF (2 Yr)

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

25

35

45

55

Figure 2B: Annual Earnings for Workers Separating and Not Separating in 1981
Earnings at All Jobs, Excluding Zeros, Men in Stable Job 1977−1979 (in $1000)

1975

1980

1985

1990

1995

year
No Job Separation
Non−MLF Separation

Sep. at MLF (2 Yr)

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

2000

−15

−10

−5

0

Figure 3: Earnings Losses at Job Separation 1980−1986 vs. Non−Separators
Earnings at All Jobs Including Zeros, Men in Stable Job 1974−1979 (in $1000)

−10

−5

0
5
10
Year Before/After Job Displacement
Positive Earnings

15

Including Zero Earnings

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

20

−15

−10

−5

0

Figure 4: Earnings Losses at Job Separation 1980−1986 vs. Non−Separators
Earnings Including Zeros, Men in Stable Job 1974−1979 (in $1000)

−10

−5

0
5
10
Year Before/After Job Displacement

Basic Model
Firm Effects (1979 EIN)

15

Ind−Year Effects (2−Dig.)Firm−Year Effects

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

20

−15

−10

−5

0

Figure 5: Earnings Losses at Job Separation 1980−1986 vs. Non−Separators
Earnings Including Zeros (in $1000), Men, Various Work Histories in 1979

−10

−5

0
5
10
Year Before/After Job Separation
3 Years Tenure
6 Years Tenure

15

3 Years Work
6 Years Work

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

20

−15

−10

−5

0

Figure 6: Earnings Losses at Job Separation − Original and Reweighted
Earnings All Jobs Without Zeros, Men in Stable Job 6 Years (in $1000)

−10

−5

0
5
10
Year Before/After Job Displacement
Main Estimate
2003 Ind−Age−Distr.

15

1986 Ind−Age−Distr.

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

20

−15 −10−50Figure 7A: Earnings Losses at Job Separation By Age at Displacement
Earnings All Jobs Including Zeros, Men in Stable Job 1974−1979 (in $1000)

−10

−5

0
5
10
Year Before/After Job Displacement
Disp. Age 20−30
Disp. Age 40−50

1520Disp. Age 30−40
Disp. Age 50−55

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

−15

−10

−5

0

Figure 7B: Earnings Losses at Job Separation By Age at Displacement
Earnings All Jobs Without Zeros, Men in Stable Job 1974−1979 (in $1000)

−10

−5

0
5
10
Year Before/After Job Displacement
Disp. Age 20−30
Disp. Age 40−50

15Disp. Age 30−40
Disp. Age 50−55

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

20

−15

−10

−5

0

Figure 8: Earnings Losses at Job Separation 1980−1986 vs. Non−Separators
Earnings All Jobs Including Zeros, Women in Stable Job 1974−1979 (in $1000)

−10

−5

0
5
10
Year Before/After Job Displacement
Positive Earnings

15

Including Zero Earnings

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

20

−15

−10

−5

0

Figure 9A: Earnings Losses at Job Separation in Different Periods
Earnings All Jobs Including Zeros, Men in Stable Job 6 Years (in $1000)

−10

−5

0
5
10
Year Before/After Job Displacement
Job Loss 1987−89

15

20

Job Loss 1980−86

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

−15

−10

−5

0

Figure 9B: Earnings Losses at Job Separation in Different Periods
Earnings All Jobs Including Zeros, Men in Stable Job 3 Years (in $1000)

−10

−5

0
5
10
Year Before/After Job Displacement
Job Loss 1987−89

15

Job Loss 1980−86

Source: 1% Files of Social Security administrative data (see text). Earnings in 2000 Dollars.

20

.7

.8

.9

1

Appendix Figure 1A: Fraction Any Positive Earnings Among Workers Separating
and Workers Not Separating in 1981−83, Men in Stable Job 1974−1979

1975

1980

1985

1990

1995

2000

year
No Job Separation
Sep. at MLF (1 Yr)

Non−MLF Separation
Sep. at MLF (2 Yr)

Source: 1% Files of Social Security administrative data (see text).

.7

.8

.9

1

Appendix Figure 1B: Fraction Any Positive Earnings Among Workers Separating
and Workers Not Separating in 1981−83, Men in Stable Job 1977−1979

1975

1980

1985

1990

1995

year
No Job Separation
Sep. at MLF (1 Yr)

Non−MLF Separation
Sep. at MLF (2 Yr)

Source: 1% Files of Social Security administrative data (see text).

2000