enduring effects of job displacement on career outcomes

8/12/2019 ENDURING EFFECTS OF JOB DISPLACEMENT ON CAREER OUTCOMES

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ENDURING EFFECTS OF JOB DISPLACEMENT ON

CAREER OUTCOMES*

Jennie E. Brand

Department of Sociology

University of Wisconsin Madison

This draft: September, 2003

Running head: Job Displacement, Difference-in-differences, Career effects

Total word count: 13,083

* Please direct correspondence to Jennie E. Brand, Department of Sociology, University ofWisconsin - Madison, 1180 Observatory Dr., Madison, WI 53706 USA, (952) 926-1007, or email

[email protected]. Paper prepared for presentation at the 2003 New York meeting of theInternational Sociological Association Research Committee on Social Stratification and Mobility(RC28). I thank Charles N. Halaby for numerous discussions regarding this topic and usefulcomments and suggestions on an earlier version of this paper. I also thank Robert M. Hauser,Lincoln Quillian, and John Robert Warren for providing helpful comments and suggestions. TheWisconsin Longitudinal Study (WLS) data are publicly available on line athttp://dpls.dacc.wisc.edu/WLS/wlsarch.htm.


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ENDURING EFFECTS OF JOB DISPLACEMENT ON CAREER OUTCOMES

ABSTRACT

Job displacement is increasingly affecting the security of long-term steady employment

in America breeding an uneasy concern over the ability of workers to sustain a successful

career characterized by upward mobility. This study evaluates the enduring economic

and occupational consequences a worker suffers as a result of being displaced from a job.

Utilizing the richness of the Wisconsin Longitudinal Study (WLS), I construct a set of

time-invariant and time-varying covariates predicting the probability for job

displacement. I estimate the effects of job displacement on a range of career outcomes

using regression and matching estimators, including conditional difference-in-differences

matching, for six 3-year time intervals differing with respect to the amount of time a

worker has been displaced in reference to the 1992/3 interview date. Results indicate that

displaced workers suffer long-term non-employment rates and highly significant wage

and earnings losses as much as more than a decade after a worker was displaced.

Displaced workers also have less job autonomy, job authority and lower occupational

income on reemployed jobs.


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ENDURING EFFECTS OF JOB DISPLACEMENT ON CAREER OUTCOMES

The United States economy prospered from the World War II era until the early 1970s; it

saw steady economic growth, low inflation rates, moderate interest rates, and low

unemployment. The modern American middle class burgeoned during this period. The

1970s marked significant change in economic trends. The 1980s American economy

sustained manufacturing restructuring and periods of high unemployment.

Manufacturing processes changed such that many blue-collar and supervisory jobs were

eliminated. In the last 3 decades, there has been a widening gap between the rich and

poor and a financially and psychologically insecure middle class. Job displacement is a

significant component to the ability of workers to maintain a successful career

characterized by employment security, wage growth, and occupational advancement. A

worker is defined as being displaced if he or she has lost a job, without being recalled,

due to downsizing, restructuring, plant closing or relocation. Job displacement is a form

of job loss that is the result of conditions for the most part beyond the control of an

individual worker. It is not the result of a worker quitting or of a worker being fired.

Numerous studies, mostly the work of economists, have evaluated the economic

impact of job displacement for workers. In addition, numerous studies, mostly the work

of sociologists, have evaluated unemployment, bad jobs, career attainment and

economic mobility. This study expands the literature on job displacement by providing a

long-term assessment of the economic impact of job displacement as well as a range of

job quality outcomes, and contributes to the sociological literature by isolating job

displacement as an important component for the ability of workers to successfully shape


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a career characterized by upward mobility. This study utilizes the richness of the

Wisconsin Longitudinal Study (WLS), a panel study that allows a thorough causal

analysis of multifarious career consequences of job displacement. Despite the

attractiveness of its use for this topic, the WLS has never been used to study job

displacement.

I first examine the effect of job displacement for long-term employment status. I

then examine the effects of job displacement for ten career outcomes including hourly

wages, yearly earnings, pension, health insurance, occupational education, occupational

income, job autonomy, job authority, cleanliness, and job satisfaction. I do this for six 3-

year time intervals, such that I estimate job outcomes for workers that have been

displaced for 15-17 years, 12-14 years, 9-11 years, 6-8 years, 3-5 years, and 0-2 years.

This allows an analysis of the enduring effects of job displacement across the prime

working years. This classification also allows the propensity for displacement to be

constructed fluidly, such that for each 3 year period I calculate the probability of

displacement based upon both time-invariant covariates and the most recent

pretreatment time-varying job characteristics.

This study is also unique in the range of estimators I utilize to assess the effects of

displacement. First, I use both parametric regression methods and semi-parametric and

non-parametric matching estimators. I also examine several distinct matching

algorithms. Second, I estimate both average treatment effects and average treatment

effects on the treated. And finally, to assess the heterogeneity of treatment effects, I

estimate quantile treatment effects of displacement on wages and earnings.


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BACKGROUND

Socioeconomic Trends: The Risk of Displacement

The United States economy prospered from the time that the defense industry was built-

up in preparation for World War II until the early 1970s. American manufacturing

dominated world markets. All economic indicators showed steadily increasing economic

prosperity and growth of the American middle class (Farley 1996). Several factors

account for the favorable post-World War II economic times. The shift from agriculture

to manufacturing in the 1940s and 50s provided a unique opportunity for men with little

education. Skills that had been required on farms were well-suited for many industrial

jobs. Other nations who now sell us manufactured products had virtually no ability to

compete in U.S. markets during this period, allowing a consistently favorable balance of

trade for the American domestic economy (Farley 1996). The GDP increased at about a

healthy 4% and the unemployment rate remained around 3-4% throughout the 1950s and

60s. Real earnings increased at more than 2% per year from 1945-1973. This meant that

by the end of a 10 year span, the average person had about 25% more purchasing power

than a decade earlier.

Economic trends since the 1973 oil price shock are very different than the post-

World War II era. The United States has seen a shift from rising earnings to stagnation to

decline. While by most social indicators the United States is in a better position than it

was, there is a larger gap between those at the bottom and those at the top of the

economic ladder. As a result of new occupational demands, the skills that had allowed


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men to maintain middle class status prior to 1973 were not as rewarded in recent decades.

Throughout the 1970s, while the economy resumed growth, interest rates and inflation

remained high, such that firms did not introduce labor saving equipment, and kept wage

increases below inflation. The most severe recession since the Great Depression resulted

in 1982, with a 10% unemployment rate. During 1982, a record 26.5 million workers

experienced at least one spell of unemployment (Wetzel 1995). More importantly, 5.1

million workers with at least 3 years job tenure suffered permanent job displacement

during 1979-1984. Almost half of these displacements were blue collar, but high wage,

manufacturing jobs.

The years between 1982 and 1989 signified the largest and longest peacetime

economic expansion in American history (Farley 1996). Despite the U.S. being a

healthier, better educated, richer nation than a quarter-century ago and a nation that

provides more nearly equal opportunities to a larger share of the population, there is a

persistent American anxiety rooted in the fact that many people still seem to be falling

behind (Farley 1996). The economic recovery of the 1980s was unlike previous

economic expansions because the benefits were not equally distributed across the income

distribution: unemployment persisted at a relatively high rate, average earnings of men

stagnated and increased moderately for women, there was no growth in the middle class,

no change in the proportion of people in poverty, and there was an increase in income

inequality. Over the 1980s increased income inequality took the form of a moderate

hollowing out of the middle of the income distribution, with growing numbers at both

tails of the distribution (Levy 1995).


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Changes in income inequality were mirrored by changes in working conditions

(Fligstein and Shin N.d., p. 8). The most recent economic era rewarded some individuals

with great prosperity while others were threatened with displacement, unemployment,

and downward mobility. Technological innovation, foreign competition (including

international trade and the pressure that low-wage labor abroad puts on U.S. labor

markets), shift in consumer spending from goods to services, and the breakdown of the

social contract between labor and capital are all implicated as causes for the wave of

downsizing, restructuring, corporate mergers and takeovers, and plant closings. Many

firms that claim to downsize may have really altered the compositionof their workforces

more than their size (Baumol et al. 2003). In fact while the manufacturing industry has

experienced significant downsizing, such that manufacturing firms are regressing toward

the mean in terms of firm size, non-manufacturing industries have experienced significant

upsizing. Still, industrial restructuring can, and does, have devastating effects on

displaced workers. Tenure is consistently found to have a strong positive relation to

wages. Baumol et al. (1993) find that labor market churning, both by occupation and by

industry, has occurred more frequently from 1981 to 1992 than in earlier periods.

Between 1979 and 1983, it is estimated that approximately 14 million workers were

displaced. Between 1981 and 1988, 10.8 million people were involuntarily unemployed

(Latack, Kinicki, and Prussia 1995). Workers have been displaced from long-term jobs

where men had high wages relative to their educational attainment. Wetzel (1995)

writes:

The structural transformation of manufacturing led to permanent displacement of hundreds ofthousands of blue collar workers. Industrial firms that had prided themselves on lifetime


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paternalistic commitments to their production workers largely men with average or below-average educational attainment slashed employment The abrupt contraction struck at theheart of the middle class by drastically impacting mature family men with strong labor forceattachment, good work histories, and long job tenure (p. 101).

This statement exemplifies the possible change in the social contract; whereas in earlier

years workers may have enjoyed considerable job security and benefited from excess

profits, today shareholder value takes precedence over stakeholder rights and labor is

seen as just another commodity. Baumol et al (2003) find that downsizing does not

increase productivity, but it is profitable. It is profitable in part because it holds down

wages, transferring income from workers to owners.

Why cant workers displaced in the 1980s find jobs comparable to the ones they

lost? One possible reason is that specific jobs cease to exist as a result of industrial

restructuring or globalization. A shortage of the numberof jobs may not really be the

problem, however, as employment has grown rapidly in recent years. In fact, Baumol et

al. (2003) argues that nearly half of the large firms that announced major layoffs

subsequently increased their workforce by more than 10% within 2 to 3 years.

Employment growth, however, has been concentrated at the tail ends of the skill

distribution. Moreover, foreign localities are not the only locations that pull jobs away

from cities. Large, central cities in the U.S. have increasingly transformed from centers

of manufacturing and trade to centers of information-processing, finance, and

administration, such that higher education jobs have replaced lower-education jobs. In

contrast, city residential populations have increasingly become dominated by low-

educated workers creating a spatial and/or skill mismatch (Holzer 1996; Wilson

1987). Semi-skilled workers suffered not only from demand side forces; on the supply


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side, workers were affected by increased competition and the weakening of labor unions.

Additionally, federal policies in the last few decades have weakened government benefits

and protection for workers: there has been a steadily declining minimum wage (in real

dollars) and lack of regulation governing worker benefits and job security. Again,

although difficult to validate, it appears that there has been a move toward shareholder

value over stakeholder rights (Baumol et al. 2003; Fligstein and Shin N.d.).

While job displacement has been disproportionately concentrated in

manufacturing, there is a growing public perception that the structure of job displacement

qualitatively changed in recent years, such that highly skilled white-collar workers with

more tenure are increasingly vulnerable to job loss, reductions in earnings, and prolonged

unemployment (Farber 1993). This perception has aroused public concern, as long-term

employment relationships among highly skilled workers, inverse seniority rules for

layoffs, and younger less-skilled workers with less seniority suffering the consequences

of economic downturn have historically been the norm. The early 1990s recession was

unlike the recession of the early 1980s. It reflected a new philosophy of creating flat

organization and eliminating middle management positions. Downsizing, delayering

and right-sizing became common corporate terms in the early 1990s to designate the

laying-off of white collar workers (Kasarda 1995). While the evidence suggests that job

loss in the more recent recession was of a different character than in the past, the white

collar recession is only white collar relative to previous recessions.1

1Historically, a recession meant that the blue collar unemployment rate was about 3 times the white collar

rate. In the early 1990s recession, the blue collar rate was only twice the white collar rate (Levy 1995).


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The post-1993 economic boom left the highest employed adult proportion of the

population in history. Real hourly earnings fell in the 1980s recovery and from 1992 to

1996, and then rose in 1997, 1998, and 1999, as the unemployment rate dropped below

5% (Freeman and Rogers 2000). Still, the low rates of unemployment characteristic of

the 1990s boom have generated smaller increases in wages than previous economic

booms. Firms continue to report workforce reductions and increasingly employ a

blended workforce, comprised of regular full-time, temporary, outsourced, and part-time

workers. Farber (1997) has found that job loss has increased during the mid-1990s and

its costs are substantial for workers. According to the Mass Layoff Statistics, layoff

events increased from 1996 to 2002 from 14,000 to 20,000 and claimants for

unemployment insurance increased from 1.4 to 2.2 million. Job displacement in the

2000s jobless recovery continues to generate insecurity and anxiety. Intense media

attention lavished on downsizing provokes unease for many workers over the lingering

question, Is my job next?

Consequences of Job Displacement:

Non-employment, Bad Jobs, and Downward Mobility

Job loss is defined as a life event that removes paid employment from an individual

involuntarily. Unemployment is a potential outcome of job loss. Displaced workers are

more likely to be non-employed (Farber 1993).2

There are several different kinds of

unemployment with varying economic consequences for workers. For instance, half of

total joblessness in 1989 was the result of frictional unemployment, i.e. unemployment

2Non-employment refers to both unemployment and not in the labor force.


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arising from initial entry, reentry, and quits. A major source of temporary unemployment

is seasonal unemployment or cyclical unemployment, i.e. unemployment resulting from

workers being laid off, usually temporarily, from jobs because of seasonal conditions or a

shortage in demand. All 3 types of unemployment are not the result of job displacement.

Structural unemployment is considered by economists to be more serious for the

economy and the economic fate of workers. Some displaced workers are able to replace

jobs relatively quickly; those that are permanently displaced are more often considered

the structurally unemployed.

Early research on unemployment was heavily influenced by research conducted

during the Great Depression. More recent sociological studies of unemployment or job

loss have focused on crime (Cantor and Land 1985; DAlessio and Stolsenberg 1995;

Sampson 1987; Thornberry and Christenson 1984) or emotional functioning (Cohn 1978;

Kessler, Turner, and House 1989) or racial differences in unemployment and

underemployment (Bonacich 1976; DiPrete 1981; Lichter 1988; Petterson 1997). There

is consistent and convincing evidence that unemployment has a substantial negative

impact on the psychological health of workers. People who have lost their jobs have

been found to be more anxious, depressed, unhappy, and dissatisfied with life in general.

Some psychological effects of job loss can be minimized if financial strain is minimized,

particularly if opportunities exist for reemployment (Kessler et al. 1989; Turner 1995).

While some economic studies of job displacement have found that non-

employment differences appear to fade within about 4 years following displacement

(Ruhm 1991), others have found substantial non-employment 5 years post-displacement


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(Seitchik 1991). The length of non-employment has a high degree of variance. Seitchik

(1991) finds that while about 1/3 of all displaced workers are reemployed within 5 weeks,

about 1/3 are not reemployed until after more than 6 months. Individuals with more

education, more job tenure, and more pre-displacement earnings are more likely to be

working (Farber 1993). Workers more firmly attached to their industry experience longer

spells of non-employment, presumably because these workers wait for employment in

their sector (Fallick 1993; Fallick 1996). Men are more likely to be both reemployed and

unemployed, while women are more likely to exit the labor force following displacement.

The impact of job loss has been found to be considerable, even without long-term

or chronic unemployment (Latack and Dozier 1986). Displaced workers not only

undergo non-employment, but those reemployed may have jobs of inferior quality in

comparison to the jobs that were lost. Numerous studies have compared the

characteristics and availability of good jobs versus bad jobs (Blank 1998; Doeringer

and Piore 1971; Kalleberg, Reskin, and Hudson 2000; Kalleberg and Sorensen 1979).

Good jobs are those in the primary labor market with entry positions that via internal

promotion yield high wages, year round work, fringe benefits, on-the-job training and

opportunities for advancement, retirement benefits, and job security. Bad jobs are those

at the bottom end of the labor market or in the secondary market characterized by low

wages, episodic work, few or no benefits, poor working conditions, no training and no

real chance for advancement, and lack of job security. Several scholars have argued that

the deindustrialization of America has meant that high-paying, desirable jobs in

manufacturing industries, jobs that thousands of workers have been displaced from, have


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been replaced by bad jobs that do not yield middle class lifestyles (Bluestone and

Harrison 1982; Wetzel 1995). There is data to support these contentions. America lost

600,000 good manufacturing jobs in the 1980s, i.e. jobs that had allowed a (primarily

white) man with a family of four to support his family at twice the poverty level. A

coinciding trend is the rapid job growth of low-wage jobs. There is also evidence that

bad jobs are worse than they used to be, including lower wages, involuntary part-time

work, and fewer fringe benefits (Wetzel 1995). In general, most recent job growth has

been at the extremes of the wage spectrum, contributing to the increasing economic

polarization of America.

Quality of jobs in economic studies of displacement has been defined by hours

worked and wages. Displaced workers are more likely to be employed part-time (Farber

1993). While unemployment effects of worker displacement purportedly wane, the

effects of earnings losses are substantial and persistent. Farber (1993) finds that workers

who lost a full-time job and found a new full-time job experienced an 8% earnings loss,

or an 11% loss using a difference-in-differences estimator. Ruhm (1991) finds a

difference of 16% in the first year and a difference of still 14% 4 years after

displacement.3 Jacobson, Lalonde, and Sullivan (1993) find a 25% quarterly loss 6 years

post-displacement, and little evidence that displaced workers earnings will ever return

to their expected level (p. 697). However, Jacobson et al. were not able to test this

presumption with the data they use. Of course, workers that went from full- to part-time

3Some studies have found that moderate reductions in relative earnings begin before actual job

displacement occurs (Jacobson, Lalonde, and Sullivan, 1993; Ruhm 1992).


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and asks what effects workers that are displaced from jobs endure in their careers. At age

53, what economic and job conditions do workers that have been displaced from jobs

hold compared to what they would have held had they not been displaced? In addition,

how enduring are the effects of job displacement? If a worker has been displaced for 5

years, is his or her career back on track? After 10 years? After 15 years? This study

provides an important long-term quantification to the underlying anxiety concerning the

fate of workers who are displaced from jobs. While economists have examined the

effects of job displacement, they have typically been limited to relatively short-term

employment and economic status outcomes. Sociologists, conversely, tend to

characterize careers by a broad range of outcomes, but limited studies have isolated job

displacement as an important cause of the ability of workers to shape a successful career

characterized by upward mobility. This study integrates these consequential issues by

isolating the effects of job displacement across workers careers on a range of

employment, economic, and job quality outcomes.

DATA

The most frequently used data to study job displacement has been the Displaced Worker

Surveys (DWS) supplements to the Current Population Survey (CPS). The DWS has the

advantage of clearly indicating which workers are displaced, includes data on labor force

and demographic characteristics, and most significantly, benefits from a large sample of

displaced workers. Data on wage losses for displaced workers using the DWS has

several limitations. First, the DWS is cross-sectional, making it difficult to study


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in the sample graduated from high school. Also, the WLS includes predominately white

men and women; minorities are not well represented.4 That said, the WLS also has

unique strengths. The data provide a full record of social origins, cognitive ability,

educational attainment and performance, employment history, and job characteristics for

a large sample of respondents throughout their life course. The WLS has had remarkably

high rates of response and sample retention. The 1992/3 wave of the WLS includes a

detailed job history record that allows isolation of job displacement at an identified point

in time for 4 employment spells spanning almost 20 years.5 The WLS solves many of the

problems encountered by previous studies of job displacement using aforementioned

datasets. First, the WLS is longitudinal. In contrast to the PSID, however, the WLS

tracks individuals, and not households, and in contrast to the NLS, the WLS does not

combine temporary and seasonal job loss with other displacements. Second, the WLS

has data on hourly wages, as well as annual earnings. Third, for all variables gathered

with respect to displaced workers, the WLS has corresponding information for non-

displaced workers, allowing the construction of a comparable control group. Once a

control group is constructed, the WLS also allows difference-in-differences estimation of

4WLS respondents include workers predominantly living in Wisconsin. Displacement has historically

been viewed as a rust belt phenomenon, i.e. confined to east north central states. Setichik (1991) has

argued that while the Midwest states have a higher percentage of all displaced workers (19%), they have a

corresponding fraction of the labor force (18%). The WLS, hence, allows for a larger, but proportionate,

sample of displaced workers than might otherwise be obtained in a sample originating from a different

particular geographic region.

5WLS interviews occurred in both 1992 and 1993.


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long-term effects of job displacement: from 1975 to 1992 job outcomes, where a job

displacement occurs between 1975 and 1992. The WLS also allows study of a broad

range of potential sociological consequences of displacement.

I use survey data that was collected from the original respondents or their parents

in 1957, 1964, 1975, and 1992/3 and Wisconsin state records. I include a worker as

displaced if that worker reported the termination of an employment spell as a result of

downsizing/restructuring or business closing or relocating. Downsizing is broadly

defined as the reduction in the workforce or manpower within a company. Industrial

restructuring, i.e. job churning rather than net job reductions, is really what is occurring

in many circumstances broadly termed downsizing (Baumol et al. 2003). I also include

workers whose employment spell terminated due to business changed owners / bought

out / relocated / sold as this is another cause of lay-offs. Businesses often merge to

realize economies of scale, which involves laying-off workers. I do not include

temporary or seasonal lay-offs. I also do not include other involuntary termination

(help no longer needed). This last category likely includes workers who were fired for

cause as well as, perhaps, laid-off. Mass lay-offs will likely be included in the categories

of downsizing or business mergers. However, lay-offs that are the result of slack work

or the abolition of a position or shift not included in these categories may not be

captured.6 I restrict cases to those who responded to the 1992 survey (8,327 cases) and

6Studies have shown that when employers have more discretion about laying-off workers, workers with

lower ability will be included as displaced (Gibbons and Katz 1991). Hence, not including these workers

will likely lead to more conservative estimates of the effects of displacement.


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had a least one job spell in the years 1975-1992 (7,972 cases) and had no missing data on

reason for employment spell termination (7,878 cases). A total of 1,136 out of 7,878

workers experienced one or more displacement between 1975 and 1992. Some

respondents had multiple displacements between 1975 and 1992. Table 1 describes the

incidence of multiple displacements.

TABLE 1 ABOUT HERE

Table 2 describes characteristics of WLS workers by sex and by job displacement.

Displaced workers do not tend to have significantly different social background profiles.

They do, however, tend to be less educated than non-displaced workers. Displaced

workers are more likely to be private sector workers and to be displaced from jobs in

manufacturing and trade and less likely to be displaced from jobs in professional services.

Professional and managerial workers are less likely to be displaced than their labor force

representation would suggest, and sales/clerical and crafts/operators/laborers are more

likely to be displaced. Still, for men, professional/managerial workers are displaced in

the largest number, followed by crafts/operators/laborers and then sales/clerical workers;

for women, 60% of displaced workers are sales/clerical workers, followed by

professional/managerial and then crafts/operators/laborers. Glancing down Table 2 to

economic status, displaced workers have slightly less wages and earnings on average.

TABLE 2 ABOUT HERE

Data quality is a crucial component to any reliable estimation strategy (Heckman,

Ichimura, and Todd 1998; Smith and Todd 2003). In particular, matched control group

data are only found to perform well in replicating the results of an experiment when the

data satisfy the following conditions: (1) the same data sources are used for both treated


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and controls, so that earnings and other characteristics are measured in an analogous way;

(2) treated and controls reside in the same local labor market; and (3) the data contain a

rich set of variables relevant to modeling the probability of treatment. In contrast to

studies using the DWS, the WLS data satisfy condition 1 and condition 3 well. Condition

2 is somewhat more problematic, however there is a high geographic correspondence,

especially relative to other data used for displacement studies; all WLS respondents

originally resided in Wisconsin, and the majority of respondents continue to do so

throughout their lives. Moreover, the analytical approach used herein effectively deals

with these sources of potential bias.

ANALYTICAL APPROACH

The Evaluation Problem

The term event is generally used to refer to a discontinuity in the history of an

individual, which may be complex with multifarious consequences, such as job

displacement. Events benefit from the fact that they can be precisely situated in time. An

event such as job displacement can be thought of as a treatment for which we wish to

establish effects. The estimation of a treatment effect (i.e. an effect of job displacement,

such as earnings loss) hinges on a counterfactual; that is, inferences must be made about

outcomes that would have been observed for displaced workers had they not been

displaced (Rosenbaum and Rubin 1983; Rubin 1974). For any single point in time, a

person may be in either one of two potential states, but not in both (Heckman, Ichimura,

and Todd 1997). Let w= 1 indicate a treated unit, i.e. a WLS worker displaced from a


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job, and w= 0 indicate a control unit, i.e. a WLS worker that was not displaced from a

job.7 Two potential outcomes are indicated by Y1 and Y0, with Y1 the value of the

outcome, or for example, earnings, that would be observed if a person was displaced

from a job and Y0 the outcome value observed on the same person if he or she was not

displaced from a job. The treatment effect is defined as:

= Y1 Y0. (1)

The fundamental problem of causal inference is that it is impossible to observe the value

of Y1 and Y0 on the same person; i.e. we only observe Y = wY1+ (1-w)Y0. Determining

causal effects is essentially a problem of missing data.

Average Treatment Effects

An average treatment effect (ATE) is an average partial effect for a binary explanatory

variable on a randomly drawn person from the population (Woolridge 2000):

ATE () E(Y1 Y0). (2)

The assumption is that the effect of the treatment is the same for all persons. Neither

component of this mean has a sample analogue unless there is universal treatment or

treatment is randomly determined (Heckman 1997). This parameter is often confused

with the mean effect of the treatment on the treated.

To address the fundamental problem of causal inference, we must use a

comparison group. Randomization implies that the treatment effect for the treated group

7To reduce notation, the individual argument iwill be dropped throughout this section.


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is identical to the treatment effect for the untreated group, often labeled the

unconfoundedness assumption(Imbens 2003; Rosenbaum and Rubin 1983):

(Y1, Y0) w. (3)

In observational studies, units are not assigned to treatments at random, so treated and

control groups will not be directly comparable. The estimation of a causal effect

obtained by comparing a group of units exposed to a treatment with a nonexperimental

comparison group that is not exposed to the treatment is likely influenced by evaluation

bias, or the difference between the outcomes of the nontreated and the desired

counterfactual mean. While we do not have experimental control units, we do have the

same set of pretreatment covariates. LetXdenote a vector of observed covariates. The

assumption required to estimate an average treatment effect is that

(Y1, Y0) w|X. (4)

A second assumption is made concerning the joint distribution of treatments and

covariates, often labeled the overlap assumption (Imbens 2003):

0


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ATT (| w= 1,X) E(Y1 Y0| w= 1,X)

=E(Y1| w= 1,X) -E(Y0| w= 1,X). (6)

We can reliably estimateE(Y1| w= 1,X). This is the outcome for the treated. We do not

know E(Y0 | w = 1, X). The ATT can be consistently estimated under a weaker

assumption than the ATE, i.e., w is independent of Y0 conditional on pretreatment

covariates, without placing any restriction on the relationship between wand Y1:

Y0w|X.8 (7)

If the stronger conditional statement [(Y1,Y0) w| X]is true, then the ATE should equal

the ATT; generally, the ATE and ATT differ. Differences between ATE and ATT are

evidence that the stronger assumption does not hold, i.e. that the effect of treatment on

the treated and the effect of treatment on persons selected at random from the population

differ for persons with the same X characteristics.9 To evaluate the ATT, it is further

assumed that

P(w= 1|X)< 1; (8)

8Heckman, Ichimura, and Todd (1998) show that a conditional mean independence assumption suffices;

that is, E(Y0|X, w= 1) = E(Y0|X, w= 0) = E(Y0|X).

9The ATE is generally equal toE(|X) = 1(X) - 0(X) and the ATT equal toE(|X, w= 1) = 1(X) -

0(X) +E(U1 U0 |X, w= 1). This allows us to more easily see that ATE = ATT whenE(U1 U0|X, w=

1) = 0. This term added to the difference in population means is the gain of the treated over the average

gain that would be experienced by the entire population of interest with characteristicsX. In other words,

while the ATE assumes that there is no idiosyncratic difference between the treated and untreated in terms

of the effect that treatment has upon them, the ATT does not make this assumption.


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i.e., there is the possibility of a non-treated analogue for each treated unit.10

Based on these assumptions, the conditional average treatment effect on the

treated can be estimated by the following equation:

(| w= 1,X) =E(Y1| w= 1,X) -E(Y0| w= 0,X). (9)

There has been some controversy over the plausibility of the above assumptions in the

econometrics literature. One of the main concerns is over self-selection into treatment,

i.e. if individual i predicts his or her expected outcomes and chooses treatment status

based upon the largest expected utility. This is a potential source of unobservable (to the

researcher) bias. This source of bias is less problematic in treatments that are closer to

random shocks, such as (involuntary) job displacement, than in treatments such as

(voluntary) job training programs.

Propensity Score Matching

Matching involves pairing displaced and non-displaced workers that are similar in terms

of their observable characteristics in an attempt to answer, for example, What is the

effect on earnings for workers displaced from a job compared to what would be the

outcome had they not been displaced? Matching methods are useful for estimating

treatment effects as such estimators make no functional form assumptions. Matched

control units serve as counterfactuals; the use of observation-specific counterfactuals for

10For the average treatment effect, we require that 0


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each treated observation avoids potential bias due to misspecification of the functional

form in a linear model. If the treatment effect is not constant across all individuals,

unconfoundedness does not imply a linear functional relation with (mean) independent

errors (Imbens 2003). Matching also highlights the problem of common support in a way

that linear regression does not (Black and Smith 2003). Nonoverlapping support means

that for some treated/control units there are no comparable control/treated units. If

support is not common to treated and control group members, different parameters are

(often implicitly) defined and estimated. Regression analysis is not concerned with how

similar treated and control groups are in the distribution of covariates. The implied

counterfactual for workers outside the region of common support in a linear regression

analysis would be the product of the linear functional form assumption (Black and Smith

2003). By using propensity score matching methods, we uncover how many comparison

units are in fact comparable and hence, how much smoothing our estimator is expected to

perform. Heckman et al. (1997) find that comparing the incomparable, i.e. violating the

common support condition, is a major source of evaluation bias.

How can we condition on X in order to perform matching estimation? One

method by which to condition onXwould be stratify the data into bins each defined by a

particular value ofX. However, as the number of variables increases, the number of bins

increases exponentially creating a dimensionality problem. Rosenbaum and Rubin

(1983) recommend the use of a propensity score to reduce the dimensionality of the

problem and to condition on a scalar variable. A propensity score is defined as the

probability of assignment to the treatment group given a set of observed covariates:


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p(X) =P(w= 1 |X). (10)

While the propensity value is unobserved (all that is observed is the value w= 1 or w=

0), it can be estimated using a probit or logit regression model.11

Conditional Difference-in-Differences Matching

Up until this point, I have focused upon estimators that evaluate causal effects at the

cross-section. Even if a nonparametric estimation strategy is used, such as propensity

score matching, such estimators assume that after conditioning on a set of observable

characteristics, mean outcomes are conditionally independent of displacement. This

estimation strategy can be problematic due to remaining systematic differences (such as

unmeasured characteristics) between treated and control units.

To construct a counterfactual in the cross-section, data on non-treated persons is

used. There is another source of information that can be used to construct the required

counterfactual: data on the treated prior to treatment. A major utilization of panel data

for estimating the effects of events is to obtain two or more time-separated measures of

selected outcomes and use pre-treatment data to impute counterfactual outcomes for the

treated. In the two-period panel data situation, letting t represent a time period before the

11Rosenbaum and Rubin (1983) prove that when Y0outcomes are independent of treatment conditional on

X, they are also independent of treatment conditional onp(X); that is, Y0w|p(X). To see this, suppose

that we have the following regression model: Y = 0+ 1 w+ 2X. The bias of omittingXfrom the

regression on wis equal to (2 *) where is the coefficient on win a regression ofXon w. By

conditioning on the propensity score, the correlation betweenXand wis removed.


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event and t a time period after an event, the outcome variable is measured at two distinct

points in time, Yt and Yt, where the treated experience the event (job displacement)

between the two measurements. Several studies of job displacement, especially since the

use of the Displaced Workers Survey limits the construction of a comparable control

group, have used a simple before-after approach to establish causality. A drawback of

the simple before-after estimation strategy is that identification of the causal effect breaks

down in the presence of time-specific intercepts, such as life-cycle wage growth or from

the business cycle (Smith and Todd 2003). To the extent that the earnings of non-

displaced workers are rising, the simple before-after earnings change for displaced

workers will underestimate the true earnings loss displaced workers suffer. A simple

before-after estimation of earnings losses will also assume that any change in earnings is

the result of displacement. This is a strong assumption, however, considering the

multifarious possibilities of earnings trajectories.

A difference-in-differences (DID) estimator measures the effect of the treatment

by the difference between the treated and nontreated in the before-after difference in

outcomes. It uses both pre- and post-program data (t and t data, respectively) on w= 1

and w= 0 units. In contrast to the before-after estimator, the DID estimator allows for

time-specific intercepts that are common across groups. The difference-in-differences

estimator explicitly takes into account earnings growth displaced workers would have

experienced had they not been displaced.12

The traditional way to accommodate

12Difference-in-differences estimators have been usefully employed in studies of job displacement that

have data that make such estimators feasible (Farber 1993; Jacobson et al. 1993).


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covariates in the DID model is to introduce them linearly in a parametric model. This

may not be appropriate if the treatment has different effects for different groups in the

population (Abadie 2002). Ideally, covariates should be treated non-parametrically;

when the number of covariates required for identification is large, integration such as the

propensity score is necessary.

The conditional difference-in-differences matching estimator (CDIDM) formally

extends propensity score matching to a longitudinal setting. CDIDM estimators compare

the conditional before-after outcomes of displaced and non-displaced workers. The

assumptions that justify CDIDM estimation are weaker than the assumptions invoked to

justify conventional matching estimators. The less demanding mean independence

assumptions are assumptions about differences:

E(Y0t Y0t|X, w= 1) =E(Y0t Y0t|X, w= 0); (11)

i.e., in the absence of treatment, the average outcomes for treated and controls would

have followed parallel paths. If we assume that this assumption is true, we can estimate a

nonparametric conditional difference-in-differences matching average treatment effect on

the treated by the following:

ATTDD= (Y1t Y0t|p(X), w = 1) -E(Y0t Y0t|p(X), w = 0). (12)

A conditional difference-in-differences estimator is effective in eliminating bias,

especially when it is due to temporally-invariant omitted variables (Heckman et al. 1997).

The CDIDM estimator is robust to temporally-persistent separable components of bias


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invariant set of covariates. By dividing displacement into 6 time periods, I am also able

to construct time-varying pretreatment propensity covariates that correspond to the year

immediately prior to the 3-year displacement period. For each displacement period

propensity score equation, I include the following set of time-varying covariates: class of

worker (private, government, self-employed), industry (agriculture, goods-producing,

trade), occupation (professional/managerial, sales/clerical, blue-collar), tenure (and

tenure squared), full-time employment status, pension, occupational earnings, and sex

interactions for most of these variables.15 The use of tenure squared is intended to

capture the diminishing marginal effect of tenure on the probability of job displacement.

Heckman et al. (1997) find that matching on propensity scores works well if it is

based on a high-quality model of the probability for treatment. Unfortunately, propensity

score matching methods essentially offer no guidance as to which variables to include or

exclude in the conditioning sets, and the choice of which variables to include can

influence results (Heckman and Navarro-Lozano 2003). This is a potential weakness of

the use of matching methods. Inclusion of additional variables in the propensity score

equation is not guaranteed to improve the effectiveness of the matching estimator.

To estimate propensity scores, I split the sample into kequally spaced intervals of

the propensity score, and within each interval test that the average propensity score of

15The 1990-basis occupational earnings score is the percentage of persons in the 1990 Census in a category

who earned at least $14.30 per hour in 1989. Hauser and Warren (1997) recommend that a started logit

transformation of this percentage be used to correct for heteroskedasticity: SL(oe) = ln ((oe+1)/(100-

oe+1)), where oe is the occupational earnings.


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treated and control units do not differ. If the test in one interval fails, the interval is split

in halves and tested again. This process is continued until, in all intervals, the average

propensity score of treated and control units do not differ. Within each interval, the

means of each characteristic are tested for differences between treated and control units.

This is a necessary condition for the balancing hypothesis (see Becker and Ichino 2002).

This series of steps can also be restricted to the region of common support to improve

upon the quality of matches used to estimate the treatment effects. It is rare to find two

units with exactly the same propensity score; the objective is to match a treated unit to the

control unit whose propensity score is sufficiently close so as to regard them as

approximately the same.

Outcomes Variables

I assess the effects of job displacement on career outcomes. I go beyond the standard

economic evaluation of the impact of job displacement that has primarily focused on

earnings and employment status and include outcome variables that incorporate

sociological notions of the importance of a wider range of job desirability characteristics

(Jencks, Perman, and Rainwater 1988). First, I graphically examine month-by-month

employment status 5 years before and 5 years after the time of job displacement. I obtain

outcomes for both hourly wages and yearly earnings for workers current job, and for

pension and health insurance on current or last job. I also include two measures of

occupational status, occupational education and occupational income (Hauser and Warren

1997). I include measures of job autonomy, job authority, and cleanliness: Job autonomy


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indicates that a worker does not have a boss that supervises what or how much he or she

produces; job authority indicates that a worker supervises the work of others; and

cleanliness indicates that a worker does not get at all dirty on the job. I also include an

overall measure of subjective evaluation of ones job, job satisfaction, based upon a 4-

point scale where 4 indicates a worker is very satisfied with his or her job. For all of

these career outcomes, I have data in both 1975 and in 1992/3, allowing for difference-in-

differences estimation of effects.

RESULTS

Displacement Centered Non-employment

The first career outcome that should be assessed in any analysis of job displacement is

employment status. Unemployment is a stigmatized condition in American society. It

restricts an employee from constructing an appealing image of him or herself to a

potential employer, and creates considerable anxiety, insecurity, and shame (Newman

1988). Since it is somewhat arbitrary whether WLS workers were employed in 1975

and/or 1992/3, I assess employment status by centering time on job displacement. Table

6 explains how the centering of displacement is accomplished. I examine 30 possible 2-

month periods before and after the displacement event, for a total of 5 years before (b1-

b30) and 5 years after displacement (p1-p30).

TABLE 6 ABOUT HERE


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employed). This trend generally continues until about year 4. Here there is about an

equal to slightly higher non-employment level among white-collar workers (22% upper-

white collar compared to 20-21% other). In year 5, white-collar workers have the highest

non-employment level (19% compared to 16%). One plausible interpretation supported

by qualitative accounts of white-collar displaced workers is that while white-collar

workers are the most likely to find reemployment in the initial years after displacement,

they are also likely to hold out for better quality jobs as the years pass (Newman 1988).

A similar occupational differences story can be told for women, although lower-white

collar and blue-collar women tend to have similar non-employment rates 5 years pre- and

5 years-post displacement. Still, the first year after displacement lower-white and blue-

collar women have a 50% non-employment rate. Upper-white collar women go from a

13-18% non-employment rate pre-displacement to 43% in year 1, to still 25% 5 years

post.

Regression Estimates

I assess the effect of job displacement on career outcomes using both cross-sectional

and longitudinal methods in order to see how the introduction of longitudinal data affects

the cross-sectional results. Longitudinal here refers to difference-in-differences

estimators based upon outcomes observed in 1975 (at time t) and outcomes observed in

1992 (at time t). I first estimate the effect of job displacement using ordinary least

squares (OLS) regressions. The OLS regressions are regressions of the outcome variable

Yion job displacement and the set of pretreatment covariates:


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Yi= + (wi)+ (Xi) + . (13)

Whether the coefficient in this model is estimating an ATE or an ATT depends upon

the interpretation we make of the parameter. Ordinary least squares regressions typically

assume that the average treatment effect is equal to the average treatment effect for the

treated and that these effects are constant across individuals. Table 7 reports the results

of running OLS regressions for each of the 10 outcome variables, and for each of the 6

displacement periods, as well as regressions for displacement across the entire

observation period, 1975-1992/3. Wages and earnings are for current job; workers that

were unemployed at the time of the interview but in the labor force receive a value of 0

for wages and earnings. By measuring wages and earnings in this way, I capture not only

characteristics of jobs displaced workers may obtain upon reemployment, but the overall

economic state of displaced worker regardless of employment status. This is a more

powerful and telling measure of displaced workers economic condition. If I eliminated

displaced workers that were never reemployed, as many studies of displacement have

done, I would obtain estimates of job displacement only for workers that were able to

find jobs, and not for the general population of displaced workers.

Career outcomes in Table 7 other than wages and earnings refer to the current or

last job a worker held. If a displaced worker was never reemployed following

displacement, he or she is excluded from these outcomes. There is no reasonable

assignment of scores to job characteristics for workers that are not reemployed. These

estimates therefore are not for the general population of displaced workers, but for the

population of displaced workers that were able to find reemployment. About 10% (110


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out of 1,136 displaced workers) were never reemployed in the observation period. Over

half of those not reemployed were displaced in the 1990-92/3 displacement period.17

TABLE 7 ABOUT HERE

Just looking at the overall effect of displacement, that is, displacement occurring

anytime between 1975-1992/3, there are statistically significant losses for displaced

workers on every outcome except job autonomy and cleanliness. Displaced workers have

substantially lower hourly wages, lower yearly earnings, are less likely to have a pension

or health insurance on reemployed jobs, have lower occupational status, have less job

authority, and lower job satisfaction than workers that have not been displaced from a

job, conditional on the set of observed covariates. There is a 36% decline in hourly

wages and a 46% decline in yearly earnings. Looking at the second to last column,

workers displaced between 1975 and 1977, there are no statistically significant effects;

however, there are statistically significant wages losses for workers displaced in 1978-

1980, i.e. workers that have been displaced for 12-14 years. This is a result of some

consequence. No study of job displacement has analyzed job displacement effects

beyond about 5 years post-displacement. In general, the longer a worker has been

displaced, the smaller and less significant his or her losses tend to be. Pension and health

insurance and occupational education and occupational income have significant effects

only for 0-2 years following displacement. Still, displaced workers are less likely to have

job authority as much as a decade after job displacement occurs.

17I do adjust for the inverse Mills ratio based upon Heckman selection models in the next section for the

CDIDM estimates to try to account for some of these selection effects.


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Table 8 includes the difference-in-differences regression estimates of job

displacement on career outcomes. These estimates almost always show larger losses for

displaced workers than the cross-sectional estimates. An exception to this is job

satisfaction; taking into account pretreatment job satisfaction reduces the loss of

satisfaction experienced by displaced workers. The reason is that displaced workers were

less likely than non-displaced workers to be satisfied with their jobs prior to

displacement. Moreover, all workers are less satisfied with their jobs between the 1975

measure of job satisfaction and the 1992 measure. While about 59% of non-displaced

workers were very satisfied with their jobs in 1975, 53% were very satisfied in 1992;

likewise for displaced workers, 56% were very satisfied in 1975 and 48% were very

satisfied in 1992. In general, the employment relationship has been changing such that

all workers, displaced and non-displaced, have more job insecurity and less job

satisfaction (Fligstein and Shin N.d.).

TABLE 8 ABOUT HERE

Matching Estimates

Traditional pairwise, or single nearest-neighbor matching without replacement selects as

the match the control unit with the value of p(Xj)that is closest to the treated unit with

p(Xi). Matching with replacement involves a bias-variance tradeoff; allowing

replacement increases the average quality of the matches (assuming some matches are

used more than once), but reduces the number of unique control units used to construct

the counterfactual mean, increasing the variance of the estimator. If the data being used


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autonomy for workers displaced in 1984-86 and 1990-1992/3. Workers displaced

between 1978 and 1980 experienced particularly significant losses on wages,

occupational education, occupational income, and job autonomy. This may be the result

of the early 1980s recession; workers displaced in this time period may have never

recouped the losses they endured when trying to find reemployment in a bleak labor

market. This may also be true for workers displaced in 1990-92/3, during the early 1990s

recession.

Table 10 provides CDIDM estimates for outcomes for which displaced workers

were previously eliminated from the analysis because they were not reemployed in the

observations period; that is, for all outcomes except wages and earnings. While there is

no reasonable assignment of scores for these workers, they do not need to be excluded

from the analysis. They can be treated as censored observations that are subject to

corrections for selection bias. This amounts to having two equations, one for a binary

variable that indicates reemployment, and then another for the dependent variable of

interest with a correction term added, i.e. the inverse of Mills ratio based upon a

Heckman selection model (Heckman 1979). This second equation includes variables that

may affect whether the dependent variable in the equation of interest is observed or

missing. Because the Heckman selection model depends strongly on model correctness, I

only perform this additional correction in this final set of estimates. Results based upon

these models are not substantively different than those in Table 9.

TABLE 10 ABOUT HERE


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Table 11 provides estimates for wages and earnings using several matching

algorithms. All results pertain to the overall effect of displacement 1975-1992/3.

TABLE 11 ABOUT HERE

The first column provides estimates of single nearest neighbor matching of the (sample)

average treatment effect (SATE) on a randomly selected worker from the population. I

do this to better determine whether the OLS regression estimates appear to making

implausible assumptions. On the contrary, SATE results are very similar to SATT

results, suggesting that the effect of being displaced conditional on the observed

covariates is fairly constant across workers.19

The second column provides single nearest

neighbor SATT estimates. In comparison to estimates based upon 4 matches, replicated

from Table 9 in column 3 of Table 11, single nearest neighbor matching minimizes bias,

but sacrifices efficiency. Still, the fourth column adds a bias-correcting adjustment, using

the same set of covariates used in the propensity score equation, entering linearly in the

regression function. These results are almost identical to that of third column suggesting

little benefit from the covariate adjustment. Because results in Table 11 are based upon

the full set of displaced workers, there are substantially fewer possible unique controls for

every treated unit. Therefore, using 4 matches in contrast to a single match should not

have increased bias for most results in Table 9.

19An example of when the average treatment effect is clearly different than the average treatment effect on

the treated can be found in Brand and Halaby (2003) for the study of elite college effects on career

outcomes.


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Quantile Treatment Effects

Most of the literature on treatment effects has focused on the estimation of average

treatment effects. There are situations, however, such as when the effects of a treatment

are heterogeneous, varying along the outcome distribution, that it is useful to learn about

the distributional effects of a treatment. Seitchik (1991) argues that one of the most

important findings in the job displacement literature is the variation of economic losses

following job loss among displaced workers. It is interesting to summarize in detail the

earnings of displaced and non-displaced workers in 1974 and in 1992.

CHART 4 ABOUT HERE

Chart 4 shows that differences in the distribution of earnings between displaced and non-

displaced workers before displacement are not dramatically different. In 1992, however,

we see large differences in the distribution of earnings, especially at the upper tails of

distribution. Moreover, displaced workers tend to have lower earnings in 1992 than in

1974 (in real dollars), while non-displaced workers have increased earnings from 1974 to

1992 at every percentile of the earnings distribution.

We can describe the center of the distribution of the treatment by estimating a

median treatment effect (MTE), i.e. the quantile treatment effect for the 50th

percentile.

Like the ATE, this is a central measure of the treatment effect. Unlike the ATE,

however, the MTE is robust to the presence of data outliers. Median regression finds a

line through the data that minimizes the sum of the absolute residuals rather than the sum

of the squares of the residuals, as in ordinary regression. Table 12 provides quantile

treatment effects of job displacement on wages and earnings.


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TABLE 12 ABOUT HERE

Median regression estimates do capture a different measure of the central tendency. They

are considerably smaller, although still highly statistically significant. These estimates

also tell a consequential story: the effect of job displacement on wages and earnings

endures as much as 14 years after a worker is displaced, and the median displaced worker

12-14 years after displacement is suffering a 22% reduction in hourly wages and a 14%

reduction in yearly earnings. Moreover, the nearer the displacement event (and

consequently the older the displaced worker and the further that worker is in his or her

career), the larger the wages and earnings loss he or she might experience. Just looking

at the yearly earnings difference-in-differences losses: 2% loss 15-17 years after

displacement, 14% loss 12-14 years later, 23% loss 9-11 years later, 28% loss 6-8 years

later, 38% loss 3-5 years later, and 66% loss 0-2 years later. This is an incredibly

compelling picture of the extent of career earnings loss a displaced worker endures.

DISCUSSION

Downward mobility is a widespread and persistent disquiet in American society. Job

displacement is increasingly affecting the security of long-term steady employment

breeding an uneasy concern over the ability of workers to sustain a successful career

characterized by upward mobility. Beyond individual consequences of unemployment

and career losses are the societal consequences, such as the failure to realize the social

investment in human capital made through the educational system, a loss of tax revenue,

and increased outgoings in unemployment benefits. The economic recessions of the


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1980s and 90s brought post-Depression high levels of unemployment. Workers were

permanently displaced from lifetime jobs and often left with obsolete specific skills and

training. Job loss might not be such a serious problem if there were many replacement

jobs that displaced workers could obtain with similar levels of earnings, benefits, and

characteristics. But this is not the case; jobs created are increasingly poorly paid ones in

contrast to jobs that workers are displaced from. This does not bode well for displaced

workers. It further restricts their ability to replace a lost job with an equivalent position

and sustain a rewarding career. There is evidence to support the troubling notion that

downsizing is profitable partly because it is an effective way to hold down wages,

transferring income from workers to owners.

This study has been an effort to analyze the effect of job displacement on both

traditional economic outcomes as well as job characteristics that are important

components of the sociological literature on job quality and career attainment. It utilizes

WLS data, which solves many of the problems studies have encountered using other data

sets, and allows for a truly long-term assessment of the effects of job displacement on

career outcomes. This study is effective in minimizing evaluation bias, using conditional

difference-in-differences matching estimation, and divides displacement into six 3-year

time periods controlling for both time-invariant and time-varying covariates. I find that

displaced workers suffer considerable non-employment levels, such that the average non-

employment rate 5 years after displacement is significantly greater than the rate in the

years prior to displacement. I find that displaced workers suffer significant economic

losses as a result of job displacement; these effects consistently wane with the passing of


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time, but remain large and significant as much as more than a decade after the

displacement event occurs. Other characteristics of jobs that have been used to determine

whether jobs are bad, such as pensions and health insurance, suggest that displaced

workers tend to recoup losses after about 2 years. As much as 8 years after displacement,

however, displaced workers still have less job autonomy and authority than they would

have had had they not been displaced. I also find that losses displaced workers endure

can be particularly significant and persistent when displacement occurs amidst a bleak

labor market.

Job Displacement is affecting the nature of work and opportunity in America.

Again, this is not simply a question of growing wage differentials, but of what it means to

hold a job and shape a career. The average worker that is displaced from a job endures a

career characterized by years of unemployment, no real earnings growth, and job quality

losses on reemployed jobs. These results also suggest that displaced workers suffer large

cumulative effects on financial resources, diminishing career expectations,

disappointment, self-blame, and insecurity. The American employment relationship has

been changing, and so to have the lives of American workers.


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Table 2. Characteristics of WLS Workers by Sex and Displacement Status

Displaced Non-displaced Displaced Non-displaced

n = 542 n = 3,235 n = 543 n = 3,288

Background

Parental Income 6.34 6.33 6.33 6.33

Father's (head's) occ. status 34.64 32.65 32.11 33.15

Mother's education 10.51 10.57 10.13 10.38

Cognitive Ability 100.97 101.61 100.86 101.6

Highest Level of Educational Attainment

High School Graduate 56.5 54.2 78.1 66.8

Some College 17.9 13.6 12.2 13.1

College Graduate 16.1 14 6.6 13.5

More than College 9.6 18.2 3.1 6.5

Class of Worker first job spell

Private 85.2 65.9 77.7 65.6

Government 6.3 20.3 11.6 22.5Self 8.5 13.4 9.2 10.1

Industry / Sector first job spell

Agriculture, Forestry, Fisheries 1.7 5.4 2.2 2.9

Manuf./Mining/Construction 49.4 42.1 22.5 13.2

Transportation - Public Utilities 10 8.2 2.2 3.1

Wholesale and Retail Trade 17.9 11.1 34.6 21.2

Finance, Insurance, Real Estate 5.4 5.3 4.6 6.8

Services 6.8 4.1 8.3 7.5

Professional Services 7 16.6 24.1 42.1

Public Administration 1.8 7 1.5 3.1

Occupation first job spellProfessional/Managerial 40.2 44.4 23.9 34.7

Sales/Clerical 22.9 19.7 60.4 53.7

Crafts/Operators/Laborers 34.9 30.7 13.6 9.2

Economic Status in 1974

Wages/hour $6.01 $6.06 $2.41 $2.35

Earnings/year $14,970.49 $15,953.22 $2,981.82 $3,117.43

Men Women


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Table 3. Timing of Job Displacement in the WLS

Year first Number Percentage

Displaced

1975-1977 121 10.7%

1978-1980 196 17.3%

1981-1983 195 17.2%

1984-1986 209 18.4%

1987-1989 178 15.7%

1990-1992/3 235 20.7%

Total 1,134 100%


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Percentage 1975-77 1978-80 1981-83 1984-86 1987-89 1990-92/93

College Educated 17.4 11.7 22.1 18.2 14.0 21.5

Goods-Producing Ind. 41.3 30.6 36.9 43.5 32.6 38.6

Upper white collar Occ. 29.8 29.6 34.9 34.4 33.1 36.5Lower white collar Occ. 52.9 46.4 45.6 35.9 41.6 45.1

Blue collar Occ. 22.3 28.1 21.5 30.6 21.9 19.7

Table 4. Education, Industry, Occupation by Displacement Period

Year of Displacement


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Table 5. Descriptive Statistics of WLS Worker Tenure Prior to Displacement Period

Displacement Period

on-

displaced

sp ace

men

on-

displaced

sp ace

women

1975-77 8.41 4.97 4.56 4.02

1978-80 10.42 8.13 5.51 4.26

1981-83 12.29 9.98 6.80 6.75

1984-86 14.38 13.52 8.38 8.64

1987-89 16.18 14.65 9.47 9.39

1990-92/3 17.95 13.32 10.98 6.28

Men Women


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Table 6. Example of Conversion to Displacement Periodsa

Calendar year statusb Displacement periodc

Year Person Person Person Person1 2 1 2

1975 non-displaced non-displaced b10-b15 ----------

1976 non-displaced non-displaced b4-b9 ----------

1977 displaced non-displaced b1-b3; p1-p3d ----------

1978 displaced non-displaced p4-p9 ----------



1981 displaced non-displaced p22-p27 b28-b30

1982 displaced non-displaced p28-p30 b22-b27

1983 displaced non-displaced ---------- b16-b21



1986 displaced displaced ---------- b1-b3; p1-p3

1987 displaced displaced ---------- p4-p9





aDisplacement period refers to a two month period, such that within each calendar year, there are 6

periods.b

These workers may become reemployed after they are displaced; for the purposes of evaluating post-displacement outcomes, however, they are considered a displaced worker from the time of displacement

to the end of the observation period.cPerson 1 is left truncated such that out of 30 pre-displacement periods, this worker has information on 15,

or 2 years before displacement. Person 2, because of the timing of displacement has information for all

30 pre- and post-displacement periods, or 5 years pre- and post-displacement. There is also a third

possibility in which a worker would be right truncated, such that he or she would have information on theentire set of pre-displacement periods and lack information for some of the post-displacement periods.dThe loss of a job is assumed to occur in the middle of the calendar year.


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1990-92/3 1987-89 1984-86 1981-83 1978-80 1975-77 1975-92/3

Wages -0.852*** -0.573*** -0.411*** -0.118 -0.297** 0.196 -0.356***

(11.35) (6.40) (4.90) (1.35) (3.25) (1.82) (8.50)

Earnings -1.118*** -0.883*** -0.456*** -0.159 -0.233 0.217 -0.457***

(10.98) (7.25) (4.00) (1.35) (1.88) (1.48) (8.05)

Pension -0.083*** 0.016 0.018 0.018 0.003 0.048 -0.064***

(4.62) (0.64) (0.71) (0.66) (0.09) (1.29) (4.18)

Health insurance -0.072* 0.053 -0.018 -0.025 0.008 0.04 -0.035*

(2.57) (1.65) (0.62) (0.83) (0.27) (1.09) (2.34)

Occ. Education -0.167* -0.123 0.03 -0.024 -0.118 0.148 -0.095*

(2.22) (1.46) (0.40) (0.31) (1.45) (1.49) (2.45)

Occ. Income -0.293*** -0.061 -0.124 0.014 -0.242** 0.182 -0.097*

(4.18) (0.78) (1.73) (0.19) (3.04) (1.89) (2.49)

Autonomy -0.047 0.018 -0.011 -0.046 -0.082* -0.012 -0.012

(1.38) (0.50) (0.35) (1.40) (2.40) (0.30) (0.77)

Authority -0.142** -0.077 -0.108** -0.113** -0.06 0.025 -0.073***

(3.42) (1.76) (2.82) (2.92) (1.54) (0.54) (4.03)

Cleanliness -0.001 -0.083* 0.016 -0.001 -0.028 0.047 -0.012

(0.02) (2.01) (0.44) (0.02) (0.76) (1.08) (0.69)

Job Satisfaction -0.093 -0.140* -0.083 -0.111* 0.018 -0.045 -0.076**

(1.69) (2.41) (1.59) (2.07) (0.33) (0.69) (3.07)

*p


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1990-92/3 1987-89 1984-86 1981-83 1978-80 1975-77 1975-92/3

Wages -1.059*** -0.627*** -0.436*** -0.308** -0.407** 0.082 -0.444***

(9.58) (4.94) (3.79) (2.62) (3.35) (0.60) (8.26)

Earnings -1.226*** -0.676*** -0.555*** -0.470** -0.277 0.007 -0.513***

(9.13) (4.35) (3.86) (3.19) (1.81) (0.04) (7.49)

Pension -0.161*** -0.057 0.016 -0.04 0.006 0.046 -0.067***

(4.42) (1.43) (0.46) (1.11) (0.16) (1.19) (4.20)

Health insurance -0.093** 0.011 -0.048 -0.052* -0.05 0.033 -

enduring effects of job displacement on career outcomes

Documents