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ong-term earnings losses of
high-seniority displaced workers
lost effective control of urban smog:
a report of a conference held at the
Federal Reserve Bank of Chicago, June 7-8, 1993
Index for 1993



Call for
1994 Conference on
Bank Structure
& Competition

Long-term earnings losses of
high-seniority displaced workers................................................................. 2
Louis S. Jaco b so n, R o b ert J. LaLonde, and D aniel G. Sullivan

During the 1980s, many high-seniority workers lost jobs
because of plant closings and mass layoffs. The authors
examine the long-term earnings losses suffered by such
workers and the extent to which assistance programs
can offset those losses.

Call for conference papers


Cost effective control of urban smog:
a report of a conference held at the
Federal Reserve Bank of Chicago, June 7-8, 19 93..................................22
Richard F. Kosobud, W illia m A . T esta, and D onald A . Hanson

With the move toward market-based incentives, public
policy to control urban smog is headed in promising
new directions. The authors give a brief overview of
the issue and of a recent conference on the subject at
the Chicago Fed.

Index for 1993



N o vem ber/D ecem ber 1993 V o lum e X V II, Issue 6

Karl A. Scheld, Senior Vice President and

the Research Department of the Federal Reserve
Bank of Chicago. The views expressed are the
authors’ and do not necessarily reflect the views
of the management of the Federal Reserve Bank.
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Director o f Research
Editorial direction
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Kathryn Moran, assistant editor

ISSN 0164-0682

Long-term earnings losses of
high-seniority displaced workers

Louis S. Jacobson, Robert J. LaLonde,
and D aniel G. Sullivan

The recent recession and con­
tinuing slow employment
growth have focused renewed
attention on the plight of dis­
placed workers—workers
whose job loss results from the plant closings
and mass layoffs associated with economic
restructuring. It has long been clear that such
workers suffer short-term earnings losses if they
are forced into unemployment while searching
for new jobs.1 What has been less clear, howev­
er, is the magnitude of any long-term losses
from reduced earnings on these new jobs. This
article provides evidence that for displaced
workers who had substantial seniority with their
former employers, these long-term earnings
losses are highly significant and, in fact, cumula­
tively much larger than the earnings losses they
suffer while unemployed.
Displacement is, of course, not limited to
recessions. Rather, displacement is a normal
feature of a dynamic economy in which techno­
logical progress and changing consumer tastes
constantly necessitate economic restructuring.
The Congressional Budget Office recently esti­
mated that during the 1980s, 20 million workers
lost jobs because of plant closings and perma­
nent layoffs. Even in the relatively strong labor
market that prevailed in 1988, 1.5 million work­
ers lost jobs in this way.2 The aggregate econo­
my benefits from this frequent restructuring.
Nevertheless, many have shown a concern for
the losses suffered by some of those who do not
benefit—the displaced workers.
Concern has been especially great for work­
ers who are displaced as a result of changes in

government policy. For instance, trade liberal­
ization is almost certainly good for the country
as a whole. In addition to lowering prices for
consumers, it increases opportunities for exportproducing sectors of the economy and is likely
to result in a net increase in jobs for U.S. work­
ers. Despite these benefits, however, some
workers are likely to be displaced as a result of
increased imports.3 Policymakers have tried to
help such workers, either because they believe
fairness requires it or because they believe such
assistance is the only way to win enough politi­
cal support for freer trade. In either case, they
have an interest in knowing the full losses suf­
fered by job losers.
Assessing the full costs borne by displaced
workers is even more important when the net
benefits of policy changes that lead to displace­
ment are less clearly positive than those of freer
trade. In such cases, policymakers need to be
concerned not only with compensating the los­
ers, but also with determining whether the policy
change should be made at all. For instance, from
an economic perspective, additional environ­
mental regulations should only be imposed if the
benefits of such regulations exceed their full
costs, including the costs borne by any workers
who lose their jobs. Similarly, much debate over
appropriate monetary and fiscal policy can be
cast in terms of a trade-off between inflation and



Louis S. Jacobson is senior economist with Westat
Incorporated. Robert J. LaLonde is an associate
professor in the University of Chicago Graduate
School of Business. Daniel G. Sullivan is a senior
research economist and research officer with the
Federal Reserve Bank of Chicago.

unemployment. If some of this unemployment
takes the form of permanent job loss, then the
net benefits of further reductions in inflation
depend on the magnitude of any long-term earn­
ings losses experienced by affected workers.
If workers’ skills were perfectly general in
the sense that they were equally valuable to all
employers and if earnings only reflected work­
ers’ accumulated skills, then displacement might
not have any long-term consequences. Howev­
er, workers possessing skills that were especially
well-suited to their old positions are likely to be
less productive, at least initially, on their subse­
quent jobs. Such a fit between workers’ skills
and the requirements of their old jobs could have
resulted from on-the-job investment in firmspecific human capital or from costly search
resulting in particularly good matches with their
old firms.4
Moreover, workers losing jobs that paid
wage premiums that didn’t merely depend on
their accumulated skills are likely to have long­
term losses if their subsequent jobs pay standard
wages. Such wage premiums could have arisen
because of direct or threatened effects of unions,
or because employers voluntarily chose to pay
higher wages because doing so directly raised
workers’ productivity.5
Finally, displaced workers’ long-term earn­
ings will be lower if, on their previous jobs, they
had accepted wages below their level of produc­
tivity in return for higher earnings later in their
careers. Economists have interpreted internal
labor market phenomena and promotion-fromwithin policies as attempts by firms to imple­
ment such deferred compensation schemes.
Workers might have accepted such earnings
paths in order to enhance their employers’
incentive to invest in their human capital.6
These theoretical considerations suggest
that displaced workers could suffer earnings
losses after they find new jobs. They do not,
however, tell us how large those losses are likely
to be or how long the losses might persist.
Empirical analysis is required to determine
whether displacement is a relatively temporary
setback from which workers quickly recover
once they find new jobs or, alternatively,
whether it is an essentially permanent blow
to workers’ living standards.
In this article, we examine the long-term
consequences of displacement for workers who
had accumulated substantial seniority with their
former employers. Using newly developed data


from the administrative records of the state of
Pennsylvania, we study the earnings histories of
a group of workers who left declining firms
between 1980 and 1986 after working for those
firms since at least 1974. By observing these
workers’ earnings for several years before and
after their separations, as well as the earnings
of workers whose employment relationships
endured throughout our sample period, we are
able to assess displaced workers’ long-term
earnings losses much better than has previously
been possible.
Workers with seniority as high as those we
study make up only a small portion of all lay­
offs. Nationally, we estimate that during the
period 1980-86, approximately 350,000 such
workers were displaced per year, about 18 per­
cent of all displacements. Nevertheless, highseniority workers are likely to account for a
significant fraction of earnings losses because
they are more likely to have accumulated firmspecific skills or to have been particularly well
matched to their former employers. Likewise,
because earnings premiums and deferred com­
pensation decrease quit rates, high-seniority
workers are more likely than others to experi­
ence earnings losses due to displacement for
these reasons as well.
Our principal finding is that high-seniority
workers suffer large, seemingly permanent
earnings losses as the result of displacement.
Even five years after their separations, the aver­
age annual losses of the workers we studied
were approximately 25 percent of their 1979
earnings. We also observe several important
patterns to the earnings losses. For instance,
workers losing jobs in depressed labor markets
and those formerly employed in highly union­
ized durable goods industries experience espe­
cially large losses. Nevertheless, we find that
workers of nearly every description experi­
enced significant losses. Even workers who
found new jobs in the same industry had major
earnings losses, a finding that suggests the
importance of highly firm-specific features in
workers’ employment relationships.
In the next section we describe the data
that underlie our analysis. Next we discuss the
specific definition of earnings losses due to
displacement that is adopted in this article
and present some simple estimates of those
losses for the cohort of displaced workers who
separated from their former firms in the fourth
quarter of 1981. The subsequent section


presents results derived from our full data set
showing how losses vary over time and across
workers. This is followed by a discussion of
some of the steps policymakers have taken to
assist displaced workers. Finally, we offer
some brief conclusions.
T h e P e n n sylva n ia data

The empirical work described in this article
assesses the magnitude and temporal pattern of
earnings losses suffered by workers displaced in
Pennsylvania between 1980 and 1986. We have
limited our analysis to these workers in order to
take advantage of a rich set of administrative
data on Pennsylvanian workers and their firms.
By combining quarterly earnings histories for a
5 percent sample of the state’s workers with
their firms’ employment data, we have created a
data set that contains workers’ quarterly earnings
from 1974 through 1986 as well as information
about their firms, including employment levels
and growth, geographic location, and four-digit
SIC industry.7 By observing changes in the
sources of earnings, we were able to date with
high accuracy the quarter in which some work­
ers separated from firms experiencing substan­
tial employment declines. We also identified
other workers who remained continuously em­
ployed by a single firm.
These administrative data have several
advantages for assessing the losses suffered by
displaced workers. First, they track workers’
earnings over a relatively long period of time.
This allows us to distinguish short-term from
long-term losses and also to be more confident
that our results are free of statistical biases.8
Second, they contain information on a large
number of displaced workers. This allows us to
provide useful results for relatively narrowly
defined groups of workers. Third, they include
information on employment changes in workers’
firms. This allows us to identify workers who
separated from distressed firms. Such workers
are likely to have been displaced rather than to
have quit or been dismissed for cause. Finally,
they contain information on a large number of
nondisplaced workers. This allows us to borrow
statistical techniques from the program evalua­
tion literature in order to obtain more reliable
estimates of the cost of displacement, including
the loss of earnings growth that would have
occurred in the absence of job loss.
Our Pennsylvania data set also allows us to
avoid two problems inherent in the use of stan­


dard survey-based data sets. First, earnings in
data such as the Current Population Survey
(CPS) and Panel Study of Income Dynamics
(PSID) are reported by workers with significant
error, while our data are based on firms’ reports
that are used to calculate tax liabilities and are
presumably virtually free of measurement error.9
Second, workers in data sets such as the Dis­
placed Worker Supplements (DWS) to the CPS
are less likely to report instances of job loss the
longer ago the displacement occurred. If, as
seems likely, the less severe setbacks are the
ones not reported, it becomes difficult to use this
data to determine the rate of recovery from job
loss. By contrast, our administrative data allow
us to identify all of workers’ separations.
There are also some disadvantages to using
these data. Most obviously, they report only on
Pennsylvanian workers. Although we cannot be
sure that their experiences reflect the experienc­
es of displaced workers generally, it is worth
noting that Pennsylvania is a large state with a
diverse industrial base. Further, during the peri­
od covered by this study, the economic perfor­
mance of the eastern half of the state, which
shared in the growth experienced by the other
Middle Atlantic states and New England, was
considerably better than that of the western half,
which experienced double-digit unemployment
rates.1 This variation in labor market conditions
allows us to assess the impact of such conditions
on earnings losses, which in turn helps us assess
the importance of our restriction to Pennsylva­
nian workers.
Another disadvantage of our data is that
demographic information on workers is limited
to their sex and year of birth. By comparison,
data sets such as the CPS and the PSID include a
wider array of characteristics including, work­
ers’ educational attainments, occupations, and
marital and union statuses. The statistical tech­
niques that we employ account for unobserved
heterogeneity in ways that ensure that our lack
of such information does not lead to any biases
in our estimates of average earnings losses.1
However, lack of data does limit our ability to
measure differences in earnings losses across
demographic groups. Similarly, lack of data
prevents us from decomposing earnings losses
into those due to lower wages and those due to
reduced hours. However, even given these
limitations, we are able to provide a substantially
more complete assessment of the determinants


of long-term earnings losses than has previously
been possible.
Another possible shortcoming of our data is
that they do not explicitly identify whether
workers’ separations resulted from quits, dis­
charges for cause, or displacements.1 The fact
that the former two reasons for separation are
likely to have quite different consequences for
workers’ earnings motivates our focus on work­
ers separating from distressed firms. Specifical­
ly, we limited our analysis to a mass-layoff
sample that includes separators whose firms’
employment in the year following their depar­
ture was 30 percent or more below their maxi­
mum levels during the late 1970s.1 This defini­
tion encompasses firms that closed around the
time of workers’ separations as well as firms
with large employment declines. Although
some workers from this sample may have quit or
been discharged for cause, the vast majority
probably were displaced from their firms for
economic reasons.
Finally, our data’s most important disadvan­
tage is that they do not allow us to distinguish
between workers who left Pennsylvania’s wage
and salary work force and those who remained
unemployed for long periods of time.1 In these
data, both groups of workers have zero earnings.
For the unemployed, those earnings are their
actual earnings. But for workers who moved out
of state, became self-employed, or worked under
a different Social Security number, zero clearly
understates their actual earnings.1 Therefore, to
avoid overstating workers’ earnings losses, we
have eliminated from our sample the approxi­
mately 25 percent of high-tenure separators who
subsequently never showed positive earnings in
our data.
Because some of the workers we eliminate
probably were unemployed, we believe that this
decision biases downward our displacement cost
estimates. Without this sample restriction, our
estimates of losses would be approximately 15
percentage points larger. Alternatively, with this
sample restriction, we might overstate losses if
the most resilient workers are significantly more
likely to move out of state. However, we dis­
count this possibility because before their sepa­
rations, the excluded workers had characteristics
similar to the rest of the sample. Moreover, in
results not reported in this article, we find that
displaced workers who move within Pennsylva­
nia actually experience somewhat larger-thanaverage losses.


To summarize, to construct the specific
sample analyzed in this article, we first included
only those workers with six or more years of
tenure by the beginning of 1980. Second, we
restricted our sample to workers for whom we
had information on age and sex and, to avoid
complications associated with early retirement,
to workers bom between 1930 and 1959. Third,
we selected those workers who separated from
firms that had experienced employment declines
of 30 percent or more from their 1974-79 peaks,
as well as those workers who maintained a stable
employment relationship through at least 1986.1
Finally, to reduce biases due to workers’ disap­
pearing from the sample, we included only
workers who had received some wage or salary
earnings during each calendar year.
Table 1 displays some characteristics of the
age and earnings distributions of the displaced
workers that we study, as well as of workers
who continued working for a single employer
through at least 1986. The median displaced
worker was 37 years old in 1979, only one year
less than the median nondisplaced worker. In
addition, 80 percent of all workers were within
ten years of the median age for their group.
The earnings figures in table 1 indicate that
the median displaced worker earned $23,593
(1987 dollars) in 1979, about 5 percent less than
the median stably employed worker’s earnings
of $24,867. A similar gap between displaced
and nondisplaced workers’ 1979 earnings holds
within each of the groups displayed in the table.
It is also worth noting that, on the one hand,
displaced workers were more likely to be male,
to come from the manufacturing sector, and to
work in western Pennsylvania, characteristics
associated with higher earnings. On the other
hand, they were somewhat more likely to work
for smaller firms, a characteristic associated with
lower earnings.
T he nature o f e a rn in g s lo sse s

Our concept of the losses caused by dis­
placement focuses on the consequences to the
worker. Specifically, we define the loss as the
difference between workers’ actual earnings and
their expected earnings in the absence of the
events that led to their job loss. We derive esti­
mates of this quantity from a statistical model
(see box) that represents the dependence of
workers’ earnings on a number of factors includ­
ing the event of displacement.



Sample characteristics
Displaced workers

1979 age

percentile'1 Mean

Stably employed workers

Median" percentile"













Median* percentile'

1979 earningsb






























M anufacturing










Nonm anufacturing











Eastern Pennsylvania











Western Pennsylvania











1979 firm size < 2,000











1979 firm size > 2,000











“Values represent the level below which 10%, 50%, or 90% of the respective group falls.
bln 1987 dollars.

Before examining such estimates, however,
it is useful to discuss a number of subtleties in
our definition of earnings losses that can be
more easily appreciated in the context of the
simple earnings history data displayed in figure
1. That figure shows the mean earnings over the
sample period of the group of displaced workers
that separated from their former employers in
the fourth quarter of 1981 as well as the mean
earnings of workers who remained stably em­
ployed throughout the sample period.
Figure 1 illustrates a number of characteris­
tics that proved to be general features of the
experience of displaced workers:
1) From the beginning of the sample
period until a year or two before
workers’ separations, there was a
relatively constant gap between
the earnings of displaced and
nondisplaced workers.

relatively constant, but larger than at the
beginning of the sample period.
To estimate the losses suffered by displaced
workers, we track the earnings growth of nondis­
placed workers to infer by how much displaced
workers’ earnings would have changed if they
had not been displaced. Because the gap be­
tween the two groups’ earnings was relatively
constant for several years prior to the period
surrounding separation, it is reasonable to as­
sume that gap would have remained constant
throughout the sample period if the one group of
workers had not been displaced. This assump-


Earnings histories of workers displaced in 1981: Q4 and
workers stably employed through 1986
thousands of 1987 dollars, quarterly


2) During the year or two immedi­
ately prior to workers’ separa­
tions, this gap began to widen.
3) Displaced workers’ earnings took
a sharp drop when they separated
from their former firms.
4) During the next few quarters,
displaced workers’ earnings
recovered somewhat relative to
nondisplaced workers.
5) Finally, for the rest of the sample
period, the earnings gap remained


1981 :Q4


Stably employed



... i ... i ... i ... i ... i






tion in turn implies that the increase in the earn­
ings gap is a reasonable estimate of the effect of
displacement on earnings.
For example, the losses suffered during
1986 by the workers displaced in the fourth
quarter of 1981 (1981:Q4) could be estimated by
( y S6D - y 86S ^ ' - ^ b a s e D - y baseS ^
^ 8 6 D ~ ybaseD

baseS ^

y8 = mean earnings of displaced workers
in 1986,
y8 = mean earnings of stably employed
workers in 1986,
ybaseD = mean earnings of displaced workers in
the base period, and
ybaseS = mean earnings of stably employed
workers in the base period.

mediately prior to separation. Table 2 suggests
that such a measure could misrepresent the im­
portance of displacement to workers in two
ways. First, the events that eventually lead to
job loss may decrease workers’ earnings even
before their final separations. This can occur
through reduction in overtime hours, temporary
layoffs, or real wage decreases. Table 2 presents
evidence of such pre-separation earnings de­
clines for the 1981 :Q4 cohort. In particular, the
difference-in-differences estimator of losses in
1981 is $2,138. Using 1981 earnings as the base
in a calculation of earnings losses would de­
crease loss estimates for subsequent years by this
same amount.
The second reason why the simple change
in displaced workers’ earnings from levels im­
mediately prior to separation may fail to capture
the importance of displacement to workers is
that it does not account for the loss of earnings
growth that would have occurred in the absence
of displacement. Such losses of potential earn­
ings reduce workers’ welfare just as meaningful­
ly as do actual declines in earnings, and thus
ought to be included in any measure of lost
earnings. Lost earnings growth is not a signifi­
cant factor in the estimation of losses in the first

Table 2 presents such difference-in-differ­
ences estimates.1
Over the period 1974-79, the displaced
workers who separated in 1981:Q4
had average annual earnings of
$21,868. This was $2,804 less than
the average earnings of the nondisEarnings means for workers displaced in 1981:Q4
placed workers. By contrast, in
and stably employed workers
1986 the displaced workers earned
(in 1987 dollars)
an average of $19,759, which was
$8,008 less than the average for
displaced in
displaced workers
1981 :Q4
nondisplaced workers. Thus the gap
between the two groups’ earnings
Earnings level
increased by $5,203. Alternatively,
Annual average in
this estimate of the earnings loss
-$ 2 ,8 0 4
base period (1974-79)
caused by displacement is equal to
-4 ,9 4 2
the difference between displaced
and nondisplaced workers’ earnings
-9 ,0 7 9
growth from the base period (1974(684)
79) to 1986. For the displaced
-8 ,0 0 8
workers, earnings growth was
-$2,108 and, for the nondisplaced
Earnings changes
workers, it was $3,095. Again, the
-1 ,6 0 9
-2 ,1 3 8
difference was $5,203. Similar
-6 ,1 9 9
-6 ,2 7 5
calculations yield an estimated loss
during the first year after separation
-2 ,1 0 8
-5 ,2 0 3
(1982) of $6,275.
Some studies define the losses
Note: Numbers in parentheses are standard errors.
due to displacement as the decline in
workers’ earnings from levels im­



year after the 1981:Q4 cohort’s separation be­
cause the annual earnings of nondisplaced work­
ers’ had grown by only $76 dollars from the
base period. Over the five years following sepa­
ration, however, the picture changes consider­
ably. Ignoring the $3,095 earnings growth expe­
rienced by nondisplaced workers for the period
ending five years after the 1981 :Q4 cohort’s

separation reduces the estimated losses of the
latter group by more than half.
A final point to note is that we have inferred
the growth in displaced workers’ earnings that
would have occurred in the absence of displace­
ment from the average experience of all nondis­
placed workers. An alternative would be to
make this inference only on the basis of the


Statistical models for estimating earnings losses
Our estimates of earnings losses are derived from a statistical model that represents the dependence
of workers’ earnings histories on displacement and other factors.' This model exploits two of the prin­
cipal strengths of our data set—that it covers a long period of time and that it contains data on many
individuals—so as to yield a detailed picture of the pattern of earnings losses across time and across
To produce such a detailed picture, we pool information for all workers displaced between 1980
and 1986. A convenient way to do this is by introducing a series of dummy variables for the number of
quarters before or after workers’ separations. We let D*= 1 if, in period t, worker i had been displaced
k quarters earlier (or, if k is negative, worker i was displaced -k quarters later). Otherwise, D*= 0. By
restricting attention to these dummy variables, we formalize the idea that a worker displaced in 1982
was in much the same position in 1985 as a worker displaced in 1981 was in 1984.
Our specification assumes that workers’ earnings at a given date depend on displacement through
the set of previously defined dummy variables and on some controls for fixed and time varying char­
( 1)

y. = a.i + Y + xi t r(3 + £ D* 5, + 8..

J it

k > -m

In this equation, the dummy variables D*, k = -m, -{m - 1 ) , . . . , 0, 1 , 2 , . . . jointly represent the
event of displacement. In particular, 8; is the effect of displacement on a worker’s earnings k quarters
after its occurrence. In the empirical work described in this article, we allow displacement to affect
earnings up to 20 quarters before separation.2 The vector xjt consists of the observed time-varying char­
acteristics of the worker, which in this article are limited to the interactions among sex, age, and age
squared. The parameter y, is the coefficient of a dummy variable for the quarter t in the sample period;
these quarter dummies jointly capture the general time pattern of earnings in the economy. The “fixed
effect,” a., summarizes the impact of permanent differences among workers in observed and unob­
served characteristics. Finally, the error term e.( is assumed to have constant variance and to be uncor­
related across individuals and time.
We estimate the parameters of equation 1, including the fixed effects, by least squares. Thus, no
matter how workers’ permanent characteristics are related to their displacement status, our estimates of
the displacement effects are unbiased. This estimation approach generalizes the “difference-in-differences” technique which uses a comparison group to estimate the earnings changes that would have
occurred in the absence of displacement, by accounting for the effects of time-varying variables and by
allowing the effects of displacement to vary by the number of quarters relative to separation.
The foregoing model describes the temporal pattern of displaced workers’ earnings losses in a
highly flexible manner. It must, however, be modified to summarize how this pattern varies among
different groups of workers. The most straightforward such modification interacts each displacement
dummy variable, D*, with variables indicating workers’ sex, age, industry, or region. The problem with
this approach is that it leads to a very large number of parameters. Fortunately, after examining such
estimates, we observed that differences among groups in the time pattern of earnings losses occurred



experiences of nondisplaced workers who were
highly similar to the displaced workers. In fact,
the estimates presented in the next section do
allow workers’ expected earnings growth to
depend on their age and sex. It is possible to go
still further and compare displaced workers only
to others who kept jobs in their former industries

or firms. But our interest is in the full effects of
the events that lead to displacement. A compari­
son of displaced workers’ earnings only to those
of workers retaining jobs in firms or industries
affected by displacement does not capture these
full effects if those same events cause those who
retain their jobs in affected firms or industries to

mainly along just three dimensions: the rate at which earnings dip in the period before separation, the
size of the drop that occurs at the time of separation, and the rate of recovery in the period following
To construct a more parsimonious representation of losses across time and workers, we use the
fact that differences in the losses among groups can be summarized by three magnitudes. Specifically
we define
FI = t - (5 - 13), if worker i is displaced at time s and s - 12 < t < s, and F!f = 0 otherwise;
P = 1, if worker i is displaced at time s and t > s + 1, and Fr = 0 otherwise; and
P = t - (5 + 6), if worker i is displaced at time s and t > s + 7, and P.t - 0 otherwise.
Then, if c. is a vector of characteristics of individual 1, our parsimonious model takes the form
v 7

y. = a 1 + y + rB +

J it

L D* 8. + F'c.cp, + Pi t cm 2 -1- Pitc mp, -1 £.,
i 3 - ir
i t 1*1


where cpp cp,, and cp3 are parameter vectors giving the effect of workers’ characteristics on the dip, the
drop, and the recovery, respectively. To implement this specification we include the full set of displace­
ment dummies but only allow for interactions between worker characteristics and the three variables F\t,
P , and P . Specification 2 forces the gap between the estimated losses of two workers to 1) be zero in
the period more than three years before separation, 2) grow or decline linearly during the period from
three years before separation until the quarter of separation, 3) be constant during the period from one to
six quarters after displacement, and 4) grow or decline linearly from its value six quarters after separa­
tion until the end of the sample period. Accordingly, the losses k quarters after separation for a worker
with characteristics c. take the following form:
5^ if k < -1 3 ;
bk + c (p,(& + 13) i f -12 < k < 0;
5; + c.cp2 if 1< k < 6; and
5, + c (p2 + c.cp3 - 6) if k < 7.
The loss estimates presented in table 3 are derived from model 2 for the cases in which c consists
of dummy variables for sex, birth cohort, industry, and firm size, and for the case in which c. summariz­
es local labor market conditions by including a region’s unemployment rate, its trend growth rate of
employment growth, and the deviation of its employment from trend in the quarter in which the worker
was displaced.
'Similar statistical models are often used to evaluate the earnings impact of public sector training programs. See Ashenfelter
(1978), Heckman and Robb (1985), and LaLonde (1986).
2To identify the parameters of model 1, we must observe the earnings o f at least some displaced workers more than m quarters
prior to their displacement. The choice o f m - 20 presents us with no problems o f identification, for even our first cohort of
displaced workers, who separated from their firms in the first quarter o f 1980, have six years o f pre-displacement data.
3Elsewhere we consider models that allow for a worker-specific time trend in addition to the worker-specific constant in model
1. (See Jacobson, LaLonde, and Sullivan [1993a].) Estimated displacement costs are slightly higher under this alternative



suffer their own earnings declines. Instead, it
captures only the effects specifically associated
with the separation.
We have chosen to focus here on the work­
ers most affected by their firms’ distress—the
workers who actually lost jobs. Yet workers
who kept jobs in distressed firms and whose
earnings declined relative to those who kept
jobs in nondistressed firms suffered meaningful
losses too. Elsewhere we estimate that these
earnings losses are about 20 percent as large as
those suffered by workers who lost their jobs.1
Thus the choice of comparison group, while
significant, is not crucial; even when it is limit­
ed to workers who remained employed in dis­
tressed firms, the estimated earnings losses due
to displacement are still 80 percent of those
reported here.
E stim a ted ea rn in g s lo sse s

In this section we present our estimates of
the earnings losses associated with displacement
as derived from the statistical model described in
the box. Like the difference-in-differences esti­
mates computed in the previous section, these
estimates account for the loss of earnings growth
that displaced workers experience and allow for
permanent differences in the level of earnings
across workers. They extend the difference-in­
differences estimates by
1) pooling information from all cohorts of work­
ers displaced from 1980 to 1986;
2) allowing individual workers’ earnings growth
to vary by age and sex;
3) making the base period, in which
displacement effects are assumed
to be absent, end five years before
workers’ actual separations;
4) allowing the effects of displace­
ment to vary by length of time
since separation; and

after their separations. To facilitate the exposi­
tion, we plot these estimated effects against the
number of quarters before or after workers’
separations. We also show 95 percent confi­
dence bounds for each quarter’s estimate.
As figure 2 shows, high-tenure prime-age
workers endured substantial and persistent earn­
ings losses when they were displaced from firms
with substantial employment declines. Even in
the fifth year after separation, their quarterly
earnings remained $ 1,600 below expected lev­
els. This loss corresponds to approximately 25
percent of their 1979 earnings.1 Further, be­
cause the estimated losses do not decline signifi­
cantly following the third year after separation,
there is little evidence that displaced workers’
earnings will ever return to expected levels.
Clearly, displacement is a major setback for
experienced workers.
We also found evidence that the events
which led to job loss caused workers’ earnings
to depart from their expected levels well before
these workers actually left their firms. In fact,
their quarterly earnings began to diverge mean­
ingfully from expected levels approximately
three years before separation. That divergence
accelerated as separation approached, so that by
the quarter immediately before separation, these
workers’ quarterly earnings were approximately
$ 1,000 below expected levels. Although we
cannot determine from our data whether these
pre-separation earnings losses resulted from cuts
in real wages or in weekly hours, elsewhere we


Overall earnings losses of displaced workers by time
relative to separation
thousands of 1 9 8 7 dollars, qua rte rly

5) allowing the magnitude of work­
ers’ losses to vary by sex, birth
cohort, former industry, former
firm size, and conditions of the
local labor market at the time of
their separation.
We begin by reporting estimates
of the average effect of displacement
on displaced workers’ earnings for
each quarter beginning with the
twentieth quarter prior to, and end­
ing with the twenty-sixth quarter


years since disp la c em e n t


present evidence that temporary layoffs for
which workers received unemployment insur­
ance benefits can account for about half of these
pre-separation losses.2
The average present discounted value of
workers’ earnings losses during the period from
three years before to six years after their separa­
tions amounted to approximately $50,000.2 If,
as seems likely, these workers’ earnings losses
remain at about $6,000 per year until their retire­
ment at age 65, their losses’ present value rises
to approximately $80,000. Workers’ average
earnings losses during the period up to six quar­
ters after their separations were approximately
$20,000. Virtually all workers had found stable
employment by this time. Even during this
period, far from all of these losses are attribut­
able to workers’ unemployment. But, even if
unemployment was responsible for all of these
losses, it would still account for only about 25
percent of workers’ cumulative earnings losses.
Table 3 displays estimates of earnings loss­
es for several categories of workers. Each group
of estimates corresponds to a version of model
(2) of the box for a particular choice of the vec­
tor c 2 Losses are shown for the first and fifth
years after separation. The former reflect both
lower earnings on workers’ initial jobs after
separation and earnings losses due to unemploy­
ment. By the fifth year after separation, howev­
er, workers have had a significant amount of
time to adjust to their displacement. Losses at
that time reflect almost exclusively lower earn­
ings on jobs that those workers are likely to hold
for some time. To aid interpretation, losses are
presented in 1987 dollars and as a percentage of
workers’ 1979 earnings.
Table 3 indicates that men had larger dollar
losses than women. However, because their pre­
displacement earnings were much less than those
of men, women’s smaller dollar losses actually
were larger percentages of their pre-displace­
ment earnings. In the first year after job loss,
men’s earnings were more than $10,500 less
than expected and even in the fifth year were
still $7,100 less than expected. These figures are
39 percent and 26 percent, respectively, of their
1979 earnings. For women, the losses were
$6,700 and $4,700 in the first and fifth years
after job loss, or 45 percent and 32 percent of
1979 earnings. On the one hand, the lower
dollar losses for women suggest that before
displacement, they possessed fewer firm-specific
skills or were less likely to have been receiving


wage premiums. On the other hand, their higher
percentage losses suggest that a greater fraction
of women’s earnings were attributable to firmspecific skills or wage premiums.
The birth cohort estimates in table 3 indi­
cate that workers of widely different ages had
remarkably similar long-term losses. In the first
year after separation, workers bom in the 1950s
had losses more than $1,000 higher than those of
workers bom earlier. By the fifth year after
separation, however, their losses were less than
$600 higher than those of the older workers.
The modest narrowing of the differences in
losses across age cohorts may reflect a greater
willingness of younger workers and their new
employers to invest in obtaining new skills.
This greater willingness, in turn, is consistent
with the longer time they will have to recoup the
benefits of such investments.
Table 3 also indicates that long-term losses
due to displacement are substantial for workers
in almost every industry. However, losses were
especially large in the primary metals industries.
In the first year after separation, workers in these
industries were earning $17,600 less than ex­
pected. Five years after separation their losses
were still $12,100, or 40 percent of their 1979
earnings. These workers’ large losses may re­
flect the loss of union wage premiums that kept
earnings on their old jobs especially high. How­
ever, loss of union premiums cannot be the
whole explanation of earnings losses among
displaced workers. Even workers in the whole­
sale and retail trade industries, where unioniza­
tion rates are relatively low, experienced long­
term losses equal to approximately 29 percent of
their 1979 earnings.
The only industry group for which long­
term losses were not a significant fraction of
previous earnings was finance, insurance, and
real estate, where losses five years after separa­
tion averaged only 3.5 percent of 1979 earn­
ings.2 Experienced workers in these industries
may have skills that are more easily transferred
from one employer to another. Another possi­
bility is that because employment in these indus­
tries was growing relatively rapidly, displaced
workers may have found it easier to find new
jobs with similar firms. However, we show
below that returning to the same industry did
not, in general, shield workers from losses.
Another indication that losses are somewhat
higher for unionized workers is the larger losses
experienced by workers displaced from very



Earnings losses by worker characteristics
(in 1987 dollars)



First year
after separation


Fifth year
after separation

































Nondurable manufacturing



Primary metals



Fabricated metals



Nonelectrical machinery



Electrical machinery



Transportation equipment



Other durable manufacturing



communication, and
public utilities







Wholesale and retail trade



Finance, insurance, and
real estate
Professional, business, and
entertainment services




































Decade of birth


Mining and construction

Firm size

Local labor market

aLoss as a percentage of 1979 earnings.
Note: Numbers in parentheses are standard errors.



large firms. Workers from firms
with over 5,000 employees in 1979
Earnings losses of displaced workers by
had fifth year losses of 36 percent of
sector of new job
their 1979 earnings. By contrast,
(in 1987 dollars)
average losses of workers in smaller
First year after
Fifth year after
firms were at most 25.4 percent.2
Finally, table 3 indicates that
the size of earnings losses depended
substantially on the state of the local
labor market when workers were
Same SIC
displaced. We divide Pennsylvania
Same sector
into 13 distinct regions and summa­
rize local labor market conditions in
Different sector
those regions with three variables: 1)
the trend rate of employment growth
Same SIC
over the sample period, 2) the devia­
tion of employment growth from
Same sector
that trend in the quarter in which the
Different sector
worker separated, and 3) the unem­
ployment rate in the quarter in
which the worker separated.
“Loss as a percentage of 1979 earnings.
Note: Numbers in parentheses are standard errors.
We further summarize these
effects by presenting estimates of
losses for a particularly weak labor market
As table 4 shows, in the fifth year after separation,
(Pittsburgh in 1982) and a particularly robust
the losses of those who left the manufacturing
labor market (Philadelphia in 1985). Losses
sector were 38 percent of their 1979 earnings.2
were 13 percentage points higher in the weaker
However, for those who found new jobs in the
labor market. Even in the robust market, how­
manufacturing sector, it did not matter as critically
ever, losses still averaged over 19 percent of
whether they found a job in their old four-digit
1979 earnings. Therefore, while labor market
SIC industry. In the fifth year after their separa­
conditions are a significant determinant of dis­
tions, manufacturing workers’ losses were 17
placed workers’ losses, even those who separate
percent of 1979 earnings if they found new jobs in
from distressed firms in prosperous times experi­
the same four-digit SIC industry, compared with
ence large losses.
19 percent if they found new manufacturing jobs
Our data also allow us to assess the impor­
in different four-digit SIC industries.
tance of the sector of workers’ post-displace­
The findings for displaced nonmanufacturing
ment jobs for the size of their losses.2 If the
workers are similar, though the dependence on
skills required on two jobs are more similar
new industry is less pronounced. For those who
when the jobs are in the same industry, and if the
found new jobs in the same four-digit SIC indus­
loss of specialized skills is an important determi­
try, earnings losses in the fifth year after separa­
nant of workers’ losses, then displaced workers
tion were 21 percent of 1979 earnings. That figure
returning to the same industry should experience
rose to 26 percent when the new jobs were in
smaller losses than those whose new jobs lie in a
different four-digit SIC industries but still in the
different industry. Accordingly, we examined
same sector. For those who found new jobs in the
the earnings losses of workers whose new jobs
manufacturing sector, fifth-year earnings losses
were 1) in the same four-digit SIC industry as
were 31 percent of 1979 earnings.
their old job, 2) in the same sector (manufactur­
It is clear, then, that among both manufactur­
ing or nonmanufacturing) but in a different four­
ing and nonmanufacturing workers, even those
digit SIC industry, or 3) in a different sector.
who found jobs in the same four-digit SIC indus­
The earnings losses of manufacturing work­
try experienced large and persistent losses. This
ers depended crucially on whether those workers
finding suggests that something intrinsic to the
obtained new jobs in the manufacturing sector.



employment relationship itself is lost
when workers are displaced. If it is
workers’ skills that are lost, these skills
must be firm-specific, not merely in­
dustry-specific. Alternatively, such
earnings losses may result from the
workings of internal labor markets.
Though a number of interesting
patterns appear in tables 3 and 4, work­
ers’ earnings losses appear to be more
similar than different. Large long-term
losses appear to be the rule when expe­
rienced workers are forced to leave
declining firms.


Earnings losses and losses in total income including
UI and TAA benefits
thousands of 1987 dollars, quarterly

P u b lic p o lic ie s to a ssist
d isp la ce d w o rk e rs

Assistance for displaced workers
comes in several forms.2 Unemploy­
years since displacement
ment insurance (UI) provides income
replacement while workers are unem­
ployed. For some workers, these benefits are
figure 3 demonstrates, UI and TAA do relative­
supplemented by Trade Adjustment Assistance
ly little to reduce displaced workers’ cumula­
(TAA), which provides additional benefits to
tive losses. Figure 3 compares losses in earn­
workers whose job loss is the result of import
ings as shown in figure 2 with a measure of
competition. Other programs aim to speed dis­
displaced workers’ losses in income including
placed workers’ return to work and raise their
UI and TAA benefits. Clearly, UI significant­
skills so that they will have higher earnings in
ly reduces losses in the period when they are
the future. For instance, the Economic Disloca­
most severe but has no impact on workers’
tion and Worker Adjustment Act (EDWAA)
long-term welfare.
provides certain displaced workers with exten­
Of the displaced workers we study who
sive job search assistance, counseling, and class­
did receive UI benefits, many received them for
room training. Unfortunately, as we shall argue,
long periods. Nearly two-thirds received 26 or
the existing assistance programs do
not and probably cannot eliminate
more than a small fraction of the
losses suffered by workers such as
Earnings losses for workers not collecting UI, collecting
fewer than 26 weeks, and collecting 26 weeks or more
those we report on here.
In the previous section we ob­
thousands of 1987 dollars, quarterly
served that most of the cumulative
losses experienced by displaced
workers occurred after they had
become re-employed. This finding
obviously implies that a benefit such
as UI, whose receipt is tied to being
unemployed, cannot eliminate a
large fraction of workers’ losses. In
any case, only a little over 40 per­
cent of the displaced workers we
study in this article received any UI
payments in the quarter of their
separation or the one thereafter.2
Thus it is not surprising that, as
years since displacement



The fact that these workers’
losses are so large and persistent,
Distribution of weekly UI benefits relative to weekly
however, makes providing that
earnings before and after displacement
help in the form of longer maxi­
(in 1987 dollars)
mum benefit durations potentially
costly to the economy because
doing so may substantially delay
the beneficiaries’ return to work.
Did not collect UI
Previous weekly earnings
In order to give unemployed work­
Subsequent weekly earnings
ers strong incentives to find new
Percentage change6
jobs, policymakers have limited UI
Less than 26 weeks of UI
benefits to a little less than half
Previous weekly earnings
their previous earnings. Yet even
Subsequent weekly earnings
such benefit levels may represent a
Percentage change6
substantial fraction of the earnings
UI weekly benefit
Benefit relative to
that workers can eventually expect
previous earnings6
to get on their new jobs, since new
Benefit relative to
jobs tend to be lower-paying than
subsequent earnings6
previous ones.
26 or more weeks of UI
Previous weekly earnings
Table 5 compares estimates
Subsequent weekly earnings
of workers’ weekly earnings on
Percentage change6
pre- and post-displacement jobs
UI weekly benefit
with their weekly UI benefits.
Benefit relative to
We estimated weekly earnings on
previous earnings6
new jobs by dividing by 13 work­
Benefit relative to
subsequent earnings6
ers’ earnings in the second quarter
Values represent the level below which 25%, 50%, or 75% of the
after their separations in which
respective group falls.
they had positive earnings and
bEntries are the respective percentiles of the distribution of individual
percentage changes, not the percentage difference between the
received no UI benefits. We
corresponding percentiles of the distributions of previous and subsequent
estimated weekly earnings on old
cEntries are the respective percentiles of the distributions of individual
jobs in the same manner from
benefit-to-earnings ratios, not the ratio of the percentiles.
workers’ earnings in the last quar­
ter before their separations in
which they received no UI payments.
more weeks of UI in the four years surrounding
The table shows that workers who collected
their separations. This finding is not unique to
26 or more weeks of UI generally had much
our data. Other researchers have noted an in­
larger drops in weekly earnings than other work­
creased tendency for certain groups of workers
ers and that UI benefits were a significantly
to exhaust 26 weeks of UI eligibility. This fact
larger fraction of subsequent than of previous
has prompted a number of policymakers and
earnings. For workers collecting fewer than 26
analysts to advocate substantially lengthening
weeks of UI, the median ratio of benefits to
the standard maximum duration of benefits.2
earnings was 42 percent for previous earnings
Figure 4 displays estimates of losses sepa­
and 49 percent for subsequent earnings. For
rately for workers who
workers collecting at least 26 weeks of benefits,
1) received no UI benefits,
however, the two median ratios were 45 percent
2) received fewer than 26 weeks of benefits, and
and 78 percent. Indeed, for more than a third of
3) received 26 or more weeks of benefits.3
the latter group, benefits exceeeded their earn­
As can be seen, workers who collected
ings on post-displacement jobs.3
many weeks of UI had especially large earnings
It seems possible, then, that providing long­
losses. Indeed, in the fifth year after separation
er periods of eligibility for UI benefits will
their losses averaged nearly $10,000. Clearly,
increase unemployment durations not only be­
workers who collect many weeks of UI are
cause appropriate jobs for such workers are
among those who policymakers should most
scarce, but also because many workers will have
want to help.



relatively little incentive to take those jobs. For
many displaced workers, lengthening the dura­
tion of benefits might simply postpone the
inevitable—taking a job at substantially lower
earnings. Elsewhere, we suggest that assistance
could be better provided to such workers by
offering an earnings subsidy that would replace
a fraction of the difference between earnings on
their pre- and post-separation jobs. Such a sub­
sidy would direct the most assistance to those
suffering the largest losses without at the same
time eliminating displaced workers’ incentives
to return to work.3
Because UI has an obviously limited capaci­
ty to reduce displaced workers’ long-term losses,
policymakers have also designed programs to
raise these workers’ earnings once they are re­
employed. These include training programs that
upgrade workers’ skills and job search assistance
programs that better match workers’ existing
skills with the needs of employers. Unfortunate­
ly, a good deal of research suggests that these
efforts historically have not raised workers’
earnings by enough to come close to compensat­
ing for losses of the size we estimated.3 This
lack of success in raising workers’ earnings may
reflect the relatively modest duration and inten­
sity of traditional subsidized training programs.3
There is also reason to question whether
the resources that are available for assisting
displaced workers are allocated wisely. Eligibil­
ity for EDWAA services theoretically extends
to millions of workers per year. In reality, how­
ever, funding constraints have limited participa­
tion to about 120,000 workers annually.3 Thus
in determining the mix of services provided
under this program, policymakers face an inevi­
table choice between breadth and depth. More
specifically, they can provide large numbers of
workers with relatively basic and inexpensive
job search assistance, or they can provide a
smaller number of workers with job training
which, while relatively modest in duration and
intensity, is still several times more expensive.
Presumably, the decision should depend on two
considerations: 1) the respective rates of return
that these two choices offer in the form of in­
creased earnings on workers’ subsequent jobs,
and 2) their respective implications for equity
among workers.
Our reading of the available evidence sug­
gests that job search assistance has a substantial­
ly higher rate of return than the kind of training

that has been traditionally provided to displaced
workers. In a recent survey of research on train­
ing and job search assistance programs for the
displaced, Leigh (1990) concluded that job
search assistance strongly improves a variety of
labor market outcomes, including earnings.
Given its low cost per worker, it also appears to
be cost effective.3 Later, however, Leigh notes
that “classroom training fails to have a sizable
incremental effect on earnings and employment
above that of job search assistance only. In
particular, it certainly does not appear that the
additional affect of classroom training is large
enough to offset the higher cost of these servic­
es.”3 Concentration on job search assistance
would also allow more workers to be served and
hence seems more equitable as well. Thus, the
stipulation in EDWAA’s enabling legislation
that half of all funds be spent on classroom train­
ing may be unfortunate.
The results of the recent New Jersey Unem­
ployment Insurance Re-employment Demonstra­
tion are typical of the evidence on the relative
rates of return to training and job search assis­
tance.38 This demonstration used an experimen­
tal design to study whether mandatory job search
assistance and referrals for retraining raised
displaced workers’ earnings. The demonstration
targeted UI claimants over 25 years old, with at
least three years’ tenure with their former em­
ployer, and who had been laid off without a
recall date for more than four weeks. A random
sample of this group was required to participate
in a two-week job search assistance workshop.
Afterwards, a random sample of these partici­
pants was referred to training.
The evaluation indicated that job search
assistance raised participants’ earnings by $450
during a one-year period some months after the
programs ended. However, the earnings gains
of those who received both job search assis­
tance and retraining referrals were not signifi­
cantly larger than the gains of those who re­
ceived job search assistance alone. To make
the case for retraining even worse, job search
assistance cost only a few hundred dollars per
participant, whereas training cost at least
$2,300 per participant.
As we noted, the evidence suggests that
traditional subsidized training programs have not
significantly reduced displaced workers’ earn­
ings losses. Nevertheless, it is instructive to ask
how much it might cost for a hypothetical, well­



functioning training program to eliminate dis­
placed workers’ $6,000 annual long-term earn­
ings losses. Suppose that such a program were
able to generate a 12 percent rate of return on its
investment—a high rate compared to invest­
ments in other forms of human capital such as
schooling. Even such a program could generate
a permanent earnings gain of $6,000 per year
only at a cost of $50,000. For this price, one
could allow participants to spend two years out
of the labor force and forego $15,000-$ 19,000
per year in earnings in a full-time retraining
program with direct costs of $6,000-$ 10,000
per year. This would be equivalent to paying
the tuition, books, and other expenses for a dis­
placed worker with a high school diploma to
go back to school full time to acquire an associ­
ates degree. To date, policymakers have not
been willing to commit this level of resources
to retraining displaced workers; typical pro­
grams last only a few months and cost a few
thousand dollars.
In summary, the existing programs designed
to aid displaced workers provide modest short­
term relief but do little to reduce long-term loss­
es. No existing program provides the costly,
long-lasting assistance that might come close to
offsetting these losses fully. Although current
programs could probably be substantially im­
proved through reorganization and in some cases
additional funding, it is doubtful whether they
could ever fully restore workers’ lost earnings
potential. It may be more efficient to introduce
some form of earnings subsidy that would re­
place a fraction of the difference between work­
ers’ earnings on their pre- and post-displacement
jobs. Such a program might effectively provide
substantial assistance to those most severely
affected by job loss without at the same time
creating strong disincentives to work.
C o n c lu s io n

Displacement clearly has substantial long­
term consequences for high-seniority workers.
Even several years after separation, such work­
ers’ losses are still approximately 25 percent of
their pre-displacement earnings. Losses vary in
important ways across groups of workers; they
are larger for workers in highly unionized dura­
ble goods manufacturing industries and for those
losing jobs in depressed labor markets. But
workers from almost every industrial sector and
in every labor market condition appear to suffer


significant losses. Even workers returning to the
same industry experience significant losses.
Current programs to assist displaced work­
ers offset only a small fraction of the losses of
high-seniority workers. Even so, the current
structure of the UI system may delay significant­
ly displaced workers’ return to work, since bene­
fit levels for those most adversely affected by
job loss are often relatively close to earnings on
post-displacement jobs. Job search assistance
appears to be highly cost effective, but our find­
ing that even workers who return to the same
industry suffer large losses suggests that these
programs are limited in their capacity to aid
workers. The job training traditionally offered
to displaced workers does not appear to come
close to eliminating their losses and may not
even be cost effective. Whether more ambitious
training programs would have larger effects is an
open question. Given the current resources
devoted to assisting displaced workers, however,
shifting resources from training to job search
assistance would probably contribute to both
greater equity and greater efficiency.
If policymakers wish to offset a substantial
portion of displaced workers’ losses, they will
almost certainly have to commit substantially
more resources than they have done heretofore.
Whether they should make this commitment is
obviously a political question. But when dis­
placement is the result of policies such as trade
liberalization, whose net benefits to society are
likely to be large, it may be worth insuring that
those who suffer losses receive assistance if for
no other reason than to insure the political via­
bility of the policy.
When policies that entail job loss are less
clearly beneficial, as in some cases of proposed
environmental regulation, policymakers will
need to weigh carefully the full consequences of
the resulting dislocation. Similarly, if lower
inflation can be achieved only at the cost of
permanently displacing workers, then the magni­
tude of the long-term losses documented in this
article suggests caution in evaluating the net
gains from further reductions in inflation. Only
a small fraction of workers’ total losses occur
while they are actually unemployed. The major­
ity of their losses occur in the form of lower
earnings on subsequent jobs. Thus the trade-off
is not strictly between inflation and unemploy­
ment, but between inflation and a more compre­
hensive measure of labor market disruption that
includes the long-term effects of displacement.


'See Chapter 2 o f Jacobson, LaLonde, and Sullivan (1993b)
for a review o f some o f the previous empirical literature
documenting displaced workers’ earnings losses.
2See Congressional Budget Office (1993).
3In the case o f the North American Free Trade Agreement
(NAFTA), a widely quoted estimate from the Institute for
International Economics is that 150,000 jobs will be lost in
the ten years after ratification. This figure is easily offset by
the 325,000 new jobs predicted to be created as the result of
the treaty. See Congressional Budget Office (1993).
4For example, on the former possibility see Becker (1975)
and on the latter possibility see Jovanovic (1979).
5For example, on the former possibility see Lewis (1986)
and on the latter possibility see Stiglitz (1974).

l4The wage and salary work force consists o f those covered
by the unemployment insurance system. The primary
group of workers excluded are those that are self-em ­
ployed. Potential sample selection problems are not unique
to studies using administrative data. For example, in the
1984 DWS, wage data were unavailable for the approxi­
mately 40 percent o f the sample that was not employed at
the survey date. See Flaim and Seghal (1985).
l5Tannery (1991) used U.S. Social Security Administration
data to study the rates at which workers left the Pennsylva­
nia wage and salary work force between 1979 and 1987.
Although his sample is not restricted to high-tenure work­
ers, he found that among those who left the Pennsylvania
wage and salary labor force for reasons other than retire­
ment, 60 percent had earnings outside the state. However,
among those who left the state by 1987, over one-half had
1979 earnings less than $3,000 and less than 8 percent had
earnings above $20,000.

6See, for example, Lazear (1981).
7For details on how we constructed our data, see Jacobson,
LaLonde, and Sullivan (1993b).
8The statistical issues associated with estimating earnings
losses due to displacement are similar to those involved in
the estimation o f the impact o f programs such as those
providing subsidized training to workers. One interpretation
o f the exchange between LaLonde (1986) and Heckman and
Hotz (1989) is that reliable nonexperimental estimation of
such programs’ impacts requires data on workers well
before the time o f their participation.
9See Duncan and Hill (1985) and Bound and Krueger
l0See Jacobson (1988).
"See Jacobson, LaLonde, and Sullivan (1993a) and Chapter
4 o f Jacobson, LaLonde, and Sullivan (1993b) for discus­
sion o f statistical issues in the estimation of earnings losses,
including a description o f circumstances under which our
earnings loss estimators could possibly be biased.

1 Workers who separated from firms that did not experience
large employment declines or who worked for firms with
fewer than 50 employees in 1979 were not used in the
analysis described in this article.
"The difference-in-differences technique has been fre­
quently employed in the program evaluation literature.
See, for example, Ashenfelter (1978), Ashenfelter and Card
(1985), Heckman and Robb (1985), LaLonde (1986), and
Card and Sullivan (1988).
l8See Jacobson, LaLonde, and Sullivan (1993b).
l9Although not shown, the quarterly employment rates of
the displaced workers in our sample differ only slightly
from their expected levels except in the year after separa­
tion. This is not surprising because our sample excludes
workers with extremely long spells without wage and
salary earnings. Thus the substantial earnings losses shown
in figure 2 are largely due to lower earnings for those who
work, rather than to an increase in the number o f workers
without quarterly earnings.
20See Jacobson, LaLonde, and Sullivan (1993b).

l2In related research, Jacobson (1991) found that between
1977 and 1987, the rate o f separations for workers from
Allegheny County (Pittsburgh) was 80 percent for workers
with less than one year of tenure, 43 percent for workers
with one year o f tenure, 24 percent for workers with two to
three years o f tenure, and 13 percent for workers with four
or more years o f tenure. For those with four or more years
o f tenure, he estimated that one-half were retirements and
one-third were displacements. Thus the quit rate for that
group would be about 2 percent per year.
l3This categorization is less sensible for workers in small
firms. Accordingly, we further restricted our sample to
workers whose firms had at least 50 employees in 1979. We
have experimented with other, similar definitions o f mass
layoff and obtained results similar to those presented here.


2lThis assumes a 4 percent real discount rate.
“ Elsewhere we explore the relative importance o f the
various factors displayed in table 3 in determining workers’
losses, for instance, the extent to which the differences in
men’s and wom en’s earnings losses are explained by
differences in the industries in which they work. See
Jacobson, LaLonde, and Sullivan (1993a).
23The variation in losses across industries is significantly
less when the alternative comparison group discussed at the
end of the previous section is used to estimate losses. For
instance, the earnings losses o f displaced primary metals
workers and displaced finance, insurance, and real estate


workers are both about 25 percent when the comparison
group is limited to workers in the displaced workers’
former industries. This convergence in loss estimates
reflects the relatively rapid earnings growth o f workers who
remained employed in finance, insurance, and real estate
and the significant earnings reductions experienced by
workers who remained employed in primary metals. See
Chapter 6 o f Jacobson, LaLonde, and Sullivan (1993b).
24In Jacobson, LaLonde, and Sullivan (1993a) we show that
the large losses o f workers from large firms remain even
after we control for other factors such as the industrial
makeup o f these firms.
25In keeping with this study’s focus on displacement’s long­
term impact, we would like to assess the relationship
between earnings losses and the industry of workers’ new
jobs several years after separation. For workers displaced
in 1985 and 1986, however, such an assessment is impossi­
ble because we have post-separation data for only a few
quarters. Accordingly, we examined the relationship
between earnings losses and industry o f new job for work­
ers separating from distressed firms between 1980 and
1983. Industry o f new job refers to the workers’ primary
employer in 1986, or three to six years after separation.
26This finding showing greater losses when displaced
workers switch sectors does not result because workers
with jobs in the nonmanufacturing sector have been dis­
placed for a shorter period of time. The mean quarter of
separation for those who switch sectors is the same as for
those who remain in the manufacturing sector.
27For a fuller discussion o f assistance policies see Jacobson,
LaLonde, and Sullivan (1993b).

28Most of these workers appear to have found new (lowerpaying) jobs relatively quickly. Very few o f them had even
a single quarter without earnings.
29See, for example, Topel (1991).
30The estimates in figure 4 were obtained from a model that
interacted dummy variables for the three categories of
workers with dummies for the number o f quarters relative
to separation and thus do not satisfy the constraints im­
posed by the parsimonious model described in the box.
3'Moreover, for most of the period covered by our study,
unemployment insurance benefits received favorable tax
treatment. Note that the figures given are medians o f the
distributions of the ratio o f benefits to earnings. This is not
necessarily the same as the ratio of the medians.
32See Jacobson, LaLonde, and Sullivan (1993b).
33See Leigh (1990) for a comprehensive survey o f research
on the effectiveness o f training programs for displaced
34Such programs, which are often run through community
colleges, seldom last more than six months.
35A recent Congressional Budget Office study (1993) notes
that funding levels have recently risen to levels consistent
with participation of around 200,000 workers per year.
36Leigh (1990), p. 102.
37Leigh (1990), p. 103.
“ See Corson et al. (1989).

Ashenfelter, Orley, “Estimating the effect of
training programs on earnings,” Review o f Eco­
nomics and Statistics, Vol. 60, February 1978, pp.

Card, David, and Daniel Sullivan, “Measuring
the effects of CETA participation on movements
in and out of employment,” Econometrica, Vol.
56, No. 3, May 1988, pp. 497-530.

Ashenfelter, Orley, and David Card, “Using the
longitudinal structure of earnings to estimate the
effect of training program, ” Review o f Economics
and Statistics, Vol. 67, No. 4, November 1985, pp.

Congressional Budget Office, Displaced work­
ers: trends in the 1980s and implications fo r the
future, Washington, DC, 1993.

Becker, Gary, Human Capital, New York: Na­
tional Bureau of Economic Research, second
edition, 1975.
Bound, John, and Alan Krueger, “The extent of
measurement error in longitudinal earnings data:
Do two wrongs make a right?,” Journal o f Labor
Economics, Vol. 9, No. 1, January 1991, pp. 1-24.


Corson, Walter, Shari Dunstan, Paul Decker,
and Anne Gordon, “New Jersey Unemploy­
ment Insurance Re-employment Demonstration
Project,” unemployment insurance occasional
paper, No. 89-3, U.S. Department of Labor,
Duncan, Greg J., and Daniel H. Hill, “An
investigation of the extent and consequences of
measurement error in labor-economics data on


earnings,” Journal o f Labor Economics, Vol. 3,
No. 4, October 1985, pp. 508-532.
Flaim, Paul, and Ellen Seghal, “Displaced
workers of 1979-83: How well have they fared?”
U.S. Department of Labor, Bureau of Labor
Statistics, Bulletin, No. 2240, 1985.
Heckman, James, and V. Joseph Hotz,
“Choosing among alternative nonexperimental
methods for estimating the impact of social
programs: the case of manpower training,” Jour­
nal o f the American Statistical Association, Vol.
84, No. 408, December 1989, pp. 862-74.
Heckman, James, and Richard Robb, “Alter­
native methods for evaluating the impact of
interventions,” in Longitudinal Analysis o f La­
bor Market Data, J.J. Heckman and B. Singer
(eds.), Cambridge: Cambridge University Press,
1985, pp. 156-245.
Jacobson, Louis, “Structural change in the
Pennsylvania economy,” W.E. Upjohn Institute
for Employment Research, Kalamazoo, MI,
____________ , “The dynamics of the Pittsburgh
labor market,” W.E. Upjohn Institute for Em­
ployment Research, Kalamazoo, MI, 1991.

Jovanovic, Boyan, “Job matching and the theo­
ry of turnover,” Journal o f Political Economy,
Vol. 87, No. 6, December 1979, pp. 972-90.
LaLonde, Robert, “Evaluating the econometric
evaluations of training programs with experi­
mental data,” American Economic Review, Vol.
76, No. 4, September 1986, pp. 604-20.
Lazear, Edward, “Agency, earnings profiles,
productivity, and hours restrictions,” American
Economic Review, Vol. 71, No. 4, September
1981, pp. 606-20.
Leigh, Duane E., Does Training Work fo r Dis­
placed Workers?: A Survey o f Existing Evi­
dence, W.E. Upjohn Institute for Employment
Research, Kalamazoo, MI, 1990.
Lewis, H. Gregg, Union relative wage effects: a
survey, Chicago: University of Chicago Press,
Stiglitz, Joseph, “Alternative theories of wage
determination and unemployment in LDs: the
labor turnover model,” Quarterly Journal of
Economics, Vol. 88, No. 20, May 1974, pp. 194227.

Jacobson, Louis S., Robert J. LaLonde, and
Daniel G. Sullivan, “Earnings losses of dis­
placed workers,” American Economic Review,
Vol. 83, No. 4, September 1993a, pp. 685-709.

Tannery, Frederick J., “Labor market adjust­
ments to structural change: comparisons be­
tween Allegheny County and the rest of
Pennsylvania 1979-87,” unpublished working
paper, Economic Policy Institute, University of
Pittsburgh, 1991.

____________ , The Costs o f Worker Disloca­
tion, W.E. Upjohn Institute for Employment
Research, Kalamazoo, MI, 1993b.

Topel, Robert H., “Unemployment and insur­
ance,” testimony before the U.S. Senate Finance
Committee, April 1991.



The 30th Annual Conference on Bank Structure and Competition
May I 1-13, 1994

Call for Papers


T h e Role of Banking
The Federal Reserve Bank of Chicago invites the sub­
mission of research and policy-oriented papers for its
30th annual Conference on Bank Structure and Compe­
tition which will be held at the Westin Hotel in Chicago,
Illinois, May 11-13,1994.
The theme of the conference is the widely held
but debatable perception that the commercial banking
industry is in a state of decline. By some measures,
including employment and inflation-adjusted assets,
this decline has been absolute as well as relative. On
the other hand, the decline is less obvious when more
comprehensive measures are used that take account of
banks’ enormous expansion into off-balance-sheet ac­
tivities. Moreover, the importance of banking in the
implementation of public policy may be largely inde­
pendent of the size of the industry.
Whatever its magnitude, the perceived decline
has raised a plethora of questions on the part of bank­
ers, their nonbank competitors, and public officials.
For example, does the performance of many tradition­
al banking services by nonbank competitors expose
the economy to disturbances that previously would
have been absorbed by the bank safety net? Is the de­
cline a consequence of overregulation that could be
eliminated without significant adverse effects on finan­
cial stability? Or is it the result of inefficient manage­
ment and inappropriate strategies on the part of the
banks? What if anything should public policy do to
slow down or reverse the decline? What can bank
management do? What do banks’ declining shares

of assets and deposits portend for the Federal Reserve’s
ability to affect money and credit and, therefore, total
spending in the economy?
Although much of the program will address is­
sues related to the primary theme of the conference,
we also welcome papers on the following topics:
■ the impact of regulatory changes in FDICLA;
■ the consolidation movement in banking, with
an emphasis on interstate expansion;
■ depositor preference legislation;
■ risk management and derivative financial
■ financial issues associated with community
development; and
■ other topics related to the structure and regulation
of the financial services industry.
If you would like to present a paper at the confer­
ence, please submit three copies of the completed
paper or an abstract with your name, address, and
telephone number, and those of any coauthors, by
December 15, 1993. Correspondence should be
addressed to:
Conference on Bank Structure and Competition
Research Department
Federal Reserve Bank of Chicago
230 South LaSalle Street
Chicago, Illinois 60604-1413
For additional information, call Douglas Evanoff
312-322-5814 or Larry Mote 312-322-5809.

Cost effective control of urban smog:
a report of a conference held at the
Federal Reserve Bank of Chicago, June 7-8, 1993

Richard F. Kosobud, W illia m A. Testa, and
D onald A . Hanson

recent years have seen the emergence of two
apparently opposing trends: a heightened inter­
est in reducing urban smog concentrations,
which remain high, and a growing apprehension
that improved air quality will require increasing
costs per unit of improvement.
What explains this shift from optimism
about having achieved certain environmental
goals to the more recent apprehension of an
environment-prosperity trade-off? Perhaps
some of the more tractable environmental prob­
lems have been solved and the less costly pollu­
tion abatements have been achieved, leaving
those complex environmental problems that will
be very costly to remedy. One of the remaining
problems is the quantity of low-level airborne
ozone, perhaps the most important component of
urban smog. After twenty years of efforts such
as modifications to automobiles, many urban
areas still fail to meet national standards for
ambient ozone.
Given the difficulties in attaining national
ozone standards, it is natural to ask whether the
goals of current ozone legislation can be justified
within a cost-benefit framework. In the minds
of many of the conference participants was an
earlier and influential study by the economists
Alan Krupnick and Paul Portney (1991), which
estimated that the costs of a one-third reduction

The new environmental man­
dates set forth in the Clean Air
Act Amendments of 1990
(CAAA ‘90) are expected to
cost the nation $20 to $30
billion annually through the end of the decade.
These costs will fall particularly hard on Seventh
District metropolitan areas such as Chicago,
Milwaukee, and Muskegon, Michigan, which
are classified as severe nonattainment areas.
Responding to these expectations, a group
of academics, business people, government
regulators, and environmentalists gathered on
June 7 and 8, 1993, for a conference at the Fed­
eral Reserve Bank of Chicago sponsored by the
Chicago Fed, the Workshop on Market-Based
Approaches to Environmental Policy of the
University of Illinois at Chicago, and the Chica­
go Council on Foreign Relations. The confer­
ence was designed to evaluate the promise and
the potential shortcomings of urban smog con­
trol strategies from various perspectives, ranging
from the impact on human health to the potential
effects on regional economies. The conference
proceedings reflect this diversity of topics and
explore ways of crafting environmental policy
that will improve air quality while minimizing
the extent of economic disruption.
During the past twenty-five years, most
regions of the United States have experienced
both growing per capita standards of living (as
measured by national income) and improved air
quality. Environmental policy measures have
brought about reduced atmospheric concentra­
tions of lead, particulate matter, and sulfur diox­
ide. In contrast to this improvement, however,

R ic h a rd F. K o s o b u d is p r o fe s s o r o f e c o n o m ic s a t th e
U n iv e r s ity o f Illin o is a t C h ic a g o , W illia m A . T e s ta is
a re s e a rc h o ffic e r a n d s e n io r re g io n a l e c o n o m is t at
th e F e d e ra l R e s e rv e B a n k o f C h ic a g o , a n d D o n a ld A.
H a n s o n is m a n a g e r o f th e E n e rg y P o lic y S e c tio n at
A r g o n n e N a tio n a l L a b o ra to ry . T h e a u th o r s w is h to
th a n k th e ir a s s o c ia te e d ito rs , P a m e la P in n o w ,
J e n n ife r Z im m e r m a n , a n d J e ff C a m p .



of volatile organic compounds, a precursor of
ground-level ozone, far exceeded the benefits
associated with this reduction—by a factor of
eight or more.1 Calculations for the Los Angeles
area, that “superbowl” of smog, reduced the
factor but left the ratio above three. These find­
ings were consistent with those of earlier re­
search. Yet the Clean Air Act Amendments of
1990 (CAAA ‘90) set new and even more strin­
gent goals for the nation that could require more
expensive control measures.
Recent research suggests that the benefits of
reducing smog are greater than previously esti­
mated. This shift in thinking is due to new dis­
coveries about the health impacts of ozone, as
well as its adverse effects on agriculture and
material contamination—primarily vehicle tires.
Moreover, as new market-based approaches to
controlling emissions are tried, the smog cleanup
costs, both for volatile organic compounds and
nitrogen oxides, appear to be decreasing or in­
creasing less rapidly per unit of improvement. If
this is true, the new legislation might be even
closer to the mark than previously thought.
Some observers view Title I of CAAA ‘90
as a renewed effort by the federal government to
attain cleaner urban air, but in the most costefficient fashion so as to allow continued im­
provement in both living standards and air quali­
ty. The legislation sets more stringent require­
ments for reducing ozone concentrations, yet it
provides for new, flexible, market-based ap­
proaches to controlling those ozone precursors
generated by human activity. Such approaches
hold out the promise of more cost effective and
innovative control of air pollution. Among the
responses to the legislation are programs that
allow firms to trade rights to emit prescribed
levels of the precursors of urban ozone, and
“cash-for-clunkers” programs that offer bounties
to car owners who scrap their high-emitting,
often older, automobiles.
Incentive systems such as these have long
appealed to economists. In theory, given cost
variability within and among firms, market
incentives allow firms to realize significant cost
savings by choosing the cheapest, most efficient
methods of reducing their own emissions. In
addition, programs of tradeable emission credits
give firms an incentive to search out-of-house
for the most cheaply reduceable emission sourc­
es to control first, such as motor vehicles. But
perhaps the most significant benefit of incentive
systems is that they stimulate advances in en­


vironmental control technologies and promote
practices that lead to additional cost savings and
emissions reduction.
Clearly, incentive systems hold out the
promise of substantial savings in resources that
would be welcome in an era of increasing de­
mands. The only hitch is that they are relatively
untried and untested. A heavy load of program
design, institution creation, monitoring, and
enforcement problems remains to be resolved
before the promise of incentive systems can be
fulfilled. Additionally, many of the parties con­
cerned with environmental policy are uneasy
with market-based approaches. This includes
not only some environmental groups, but also
segments of the business and government regu­
latory communities.
An important objective of the June confer­
ence, therefore, was to contribute to a full airing
of these disparate views. Several contributions
to the conference bear on this point. The direc­
tor of the Illinois Environmental Protection
Agency and the president of Commonwealth
Edison Company announced the initiation of a
new market-based program whereby emitters in
the Chicago region can trade nitrogen oxide
emission credits. A senior economist with the
Environmental Defense Fund voiced support for
this program, illustrating the potential for coop­
eration among groups previously in opposing
environmental camps.
Such signs of cooperation are welcome at
this time. The debate leading up to CAAA ‘90,
both inside and outside Congress, revealed a
dramatic widening of the range of interest
groups demanding a say in the legislative pro­
cess. Groups with differing points of view and
conflicting historical positions on environmental
policy—particularly, the business and environ­
mental communities— seemed to be modifying
previous positions and opening up tentative new
lines of communication and cooperation. At the
local and regional level, such cooperation will be
needed if these innovative policies are to be
sucessfully designed and implemented. The
conference aimed to nurture the development of
these new cooperative relationships, which can
ultimately fashion the most cost effective poli­
cies for solving the ozone abatement problem.
'A la n J. K ru p n ick and P au l R. P o r tn ey , “ C o n tr o llin g urban
air p o llu tio n : a b e n e fit -c o s t a s s e s s m e n t ,”


V o l. 2 5 2 ,

1 9 9 1 , pp. 5 2 2 -2 8 .


Proceedings of the Conference on Cost Effective Control of Urban Smog

David R. Allardice,

F e d e r a l R e s e r v e B a n k o f C h ic a g o

Ed ito rs' in tro d u ctio n

Richard F. Kosobud, U n iv e r s ity o f I llin o is a t C h ic a g o ;
William A. Testa, F e d e r a l R e s e r v e B a n k o f C h ic a g o ; a n d
Donald H. Hanson, A r g o n n e N a tio n a l L a b o r a to r y
Special address

Samuel K. Skinner,

C o m m o n w e a lth E d is o n C o m p a n y

The challenges fa c in g Illinois: achieving b alance b etw ee n a clean er en viro n m e n t
and econ o m ic g ro w th

Mary A. Gade,

I llin o is E n v ir o n m e n ta l P r o te c tio n A g e n c y

The urban ozone a b a te m e n t problem

George Tolley, U n iv e r s ity o f C h ic a g o a n d R C F , In c .;
Jeffrey Wentz, H a r d in g - L a w s o n A s s o c ia te s ;
Steven Hilton, R C F , In c .; a n d
Brian Edwards, R C F , Inc.
Karl A. McDermott, I llin o is C o m m e r c e C o m m is s io n
The status o f th e m o d elin g o f ozone fo rm a tio n and geographic m o ve m e n t in th e M id w e s t

Stephen L. Gerritson, L a k e M ic h ig a n
Mark E. Femau, S ig m a R e s e a r c h
Peter A. Scheff, U n iv e r s ity o f I llin o is

A i r D ir e c to r s C o n s o r tiu m

a t C h ic a g o

C ost e ffe ctive n e ss o f re m o te sensing o f vehicle em issions

Winston Harrington, R e s o u r c e s f o r th e F u tu re ; a n d
Virginia D. McConnell, U n iv e r s ity o f M a r y la n d a n d R e s o u r c e s f o r
Wynn Van Bussmann, C h r y s le r C o r p o r a tio n
Thomas R. Wallin, I llin o is E n v ir o n m e n ta l P r o te c tio n A g e n c y
James D. Boyd, C a lif o r n ia A i r R e s o u r c e s B o a r d

th e F u tu r e

Incen tives and th e car

Daniel J. Dudek, E n v ir o n m e n ta l D e f e n s e F u n d
Thomas F. Walton, G e n e r a l M o to r s C o r p o r a tio n
Elmer W. Johnson, K ir k la n d & E llis
H ealth im p acts o f ozone

John D. Spengler, H a r v a r d U n iv e r s ity
Victoria W. Persky, U n iv e r s ity o f I llin o is a t C h ic a g o
Richard A. Wadden, U n iv e r s ity o f I llin o is a t C h ic a g o
Emissions o ffs e t tra d in g program s in th e N o rth ea st and M id -A tla n tic states

Bruce S. Carhart,

O z o n e T r a n s p o r t C o m m is s io n

M o b ile source em issions redu ctio n cred its as a cost e ffe c tiv e m easure
fo r co n tro llin g urban a ir p ollution

James D. Boyd, C a lif o r n ia A i r R e s o u r c e s
Tom Tietenberg, C o lb y C o lle g e

B oard

Regional econ o m ic im p acts o f m a rk etab le p erm it program s: th e case o f Los A ngeles

Kelly Robinson,

R u tg e r s U n iv e r s ity

T itle I o f th e Clean A ir A c t A m en d m en ts o f 1 9 9 0 and im p licatio n s fo r m arket-b ased strategies

John Calcagni,

E 3 V e n tu r e s I n c o r p o r a te d

For a complimentary copy of these proceedings, write or phone

P u b lic A f f a ir s D e p a r tm e n t,

F e d e r a l R e s e r v e B a n k o f C h ic a g o , P .O . B o x 8 3 4 , C h ic a g o , I llin o is 6 0 6 9 0 - 0 8 3 4 , te le p h o n e 3 1 2 - 3 2 2 - 5 1 1 1 .







Reducing credit risk in over-the-counter derivatives
John P. Behof.............................................................................................................. .Jan/Feb


Consumer debt and home equity borrowing
Francesca Eugeni........................................................................................................ . Mar/Apr


Recent trends in corporate leverage
Paula R. Worthington.................................................................................................. May/Jun


Capital shocks and bank growth— 1973 to 1991
Herbert L. Baer and John N. McElravey.................................................................... .Jul/Aug


Why the life insurance industry did not face an “S&L-type” crisis
Elijah Brewer III, Thomas H. Mondschean, and Philip E. Strahan............................. Sep/Oct



Assessing global auto trends
Paul D. Ballew and Robert H. Schnorbus..................................................................., Mar/Apr


Economic development policy in the 1990s—are state
economic development agencies ready?
Richard H. Mattoon.................................................................................................... .May/Jun


Long-term earnings losses of high-seniority displaced workers
Louis S. Jacobson, Robert J. LaLonde, and Daniel G. Sullivan................................. Nov/Dec



Indicators, performance, and policy in the 1930s and today
Robert D. Laurent....................................................................................................... .Jan/Feb



NAFTA: a review of the issues
Linda M. Aguilar........................................................................................................ .Jan/Feb


Trends and prospects for rural manufacturing
William A. Testa........................................................................................................., Mar/Apr


How lean manufacturing changes the way we understand
the manufacturing sector
Thomas H. Klier......................................................................................................... . May/Jun


Shaping the Great Lakes economy: a conference summary
Richard H. Mattoon and William A. Testa.................................................................. Jul/Aug


Tracking Midwest manufacturing and productivity growth
Philip R. Israilevich, Kenneth N. Kuttner, and Robert H. Schnorbus........................ ..Sep/Oct


Cost effective control of urban smog: a report of a conference
held at the Federal Reserve Bank of Chicago, June 7-8,1993
Richard F. Kosobud, William A. Testa, and Donald A. Hanson............................... . Nov/Dec


To order copies of any of these issues, or to receive a list of other publications,
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Chicago, IL 60690-0834

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