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The Returns from Classroom Training
for Displaced Workers
Louis S. Jacobson, Robert J. LaLonde and
Daniel G. Sullivan

3

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Working Papers Series
Macroeconomic Issues
Research Department
Federal Reserve Bank of Chicago
December 1994 (W P -94-27)

FEDERAL RESERVE BANK
OF CHICAGO

The Returns from Classroom Training for Displaced Workers

Louis S. Jacobson
Westat Inc.
Robert J. La L o n d e
University of Chicago
and
N.B.E.R.
and
Daniel G. Sullivan
Federal Reserve B a n k of Chicago

October 1994

T h e authors thank the C o m m u n i t y College of Allegheny County, the Pennsylvania
Department of Labor, a nd Allegheny County Planning Department for providing the data
and technical assistance for this project. W e have benefited from c o m m e n t s by J o h n H a m
a nd participants at the H S F R Conference on U n e m p l o y m e n t a n d Labor Market Policy, in
Swed e n . W e thank the Graduate School of Business, University of Chicago for financial
support. S o m e of this research w a s funded under U.S. Department of Labor E T A contract
99-0-0584-75-055-01. T h e views expressed in this paper are solely those of the authors.




Abstract

Administrative data on workers’ w a g e s a nd salaries are m e r g e d with administrative
data from a community college to estimate the returns from classroom training for
displaced workers under the Displaced Workers Educational Training Program. Results
indicate that a year of training eventually raises displaced workers’ earnings six to
seven percent above what they would have been in the absence of training. However,
this earnings increase c o m e s at the cost of substantial foregone earnings while workers
are in training a n d in the period immediately after training. M ost workers received less
than a year of training. T h u s the effect of program participation o n earnings is
correspondingly smaller than six to seven percent and, in particular, m u c h less than the
average earnings loss that accompanies displacement.




I Introduction
.
Recent studies using longitudinal data have s h o w n that worker displacement is
associated with substantial long-term earnings losses (Ruhm, 1991; Jacobson, LaLonde,
and Sullivan, 1993). T h e s e losses are substantial for both males a n d females a n d for
workers employed in a variety of industries. A s the costs of worker displacement have
b e c o m e increasingly apparent, a nd the incidence of displacement has b e c o m e m o r e widely
spread a m o n g industrial sectors, policy makers have devoted m o r e attention toward aiding
these workers. O n e concrete step that they have taken is to increase resources for
retraining displaced workers. In the U.S., the Clinton Administration proposes to expand
public expenditures on e m p loyment a nd retraining services, a nd in Europe s o m e
commentators a n d analysts believe such programs m a y be a promising w a y to reduce
chronically high u n e m p l o y m e n t rates (Saint-Paul, 1994).
Heightened support for these retraining programs has occurred even though there
is l t l evidence about their likely impacts. B y contrast to the m a n y studies of public sectorite
sponsored training for disadvantaged persons, there have b e e n relatively few evaluations
of its impacts on dislocated workers. This lack of evidence largely reflects the greater
emphasis that U.S. policy makers have placed in the past o n training economically
disadvantaged (low income) persons.
M u c h of our knowledge about of h o w training affects displaced workers c o m e s from
several demonstration programs conducted during the 1980s (Leigh, 1990). Another m o r e
recent study examines the effect of training o n a relatively small subset of displaced
workers w h o lost their jobs as a result of increased import competition (Corson, Decker




1

Gleason, a nd Nicholson, 1993). Together, these studies indicate that displaced workers
benefit from job search assistance because they found jobs sooner than similarly skilled
nonrecipients; that access to classroom vocational instruction or on-the-job training usually
has lit l effect o n subsequent earnings; a nd that female displaced workers probably benefit
te
m o r e from e m p l o y m e n t a n d training services than d o their male counterparts.
This paper a dds to the relatively sparse literature on retraining programs for
displaced workers by analyzing the long-term effects of a classroom instruction program
operated for residents of Allegheny County, Pennsylvania during the mid-1980s. Although
this w a s a small isolated program, i w a s significant for several reasons. First, i subsidized
t
t
m o r e intensive an d longer-term services than are usually available to displaced workers.
Second, its design included several features that are part of current proposals to improve
retraining services for the displaced. Third, unlike the earnings data available for nearly all
other training evaluations, the data available for this evaluation allow us to follow
participants for up to eight years after leaving training. Fourth, program operators have
provided us with substantially m o r e detailed information about the type, intensity, a n d
duration of classroom training than has usually been available for other evaluations. Finally,
because w e are studying a classroom training program for displaced workers, w e also can
estimate the returns to postsecondary schooling received by prime-age adults. T h e returns
to schooling for this group has received li t e attention in the literature, yet the likely success
tl
of m ost existing a n d proposed retraining programs requires that these returns be
substantial.
W e find that displaced workers benefited from classroom training. T h e results for




2

m e n suggest that a year of schooling raised their long-term earnings by 6 to 7 percent. T h e
effects on w o m e n ’ earnings were s o m e w h a t smaller. Further, like other studies, w e also
s
found that males benefited modestly from job search assistance.
Although w e find evidence that classroom training raises earnings, the gains
associated with a year of schooling fall far short of the losses incurred by workers w h e n
they were displaced. Moreover, despite having their training subsidized, m o s t participants
did not acquire even a years worth of schooling. Consequently, the effect of program
participation o n subsequent earnings is even smaller than the foregoing estimates would
suggest. O n e reason w h y participants m a y not stay in the program longer is that longer
participation is associated with lower earnings during training. O n e interpretation of this
result is that retraining displaced workers is m o r e costly than training the economically
disadvantaged because foregone earnings losses are greater.
T h e plan for the remainder of the paper is as follows: Section I provides s o m e
I
institutional background on public sector-sponsored retraining programs for displaced
workers, a nd describes the program and data analyzed in this paper. Section I I presents
I
our econometric model for estimating the effects of training for displaced workers. Section
IV presents our estimates a n d s o m e concluding remarks follow in Section V.

I. Retraining Programs for Displaced Workers
I
A. Existing Public Sector-Sponsored Programs
Ever since Congress established the Trade Adjustment Assistance (TAA) Program
in 1962, U.S. policy ma k e r s have provided limited assistance to displaced workers. This
particular program targets the relatively small fraction of displaced workers w h o lost their




3

jobs as a result of trade liberalization or import competition. Currently, T A A participants
m ust be in enrolled in a training program as a prerequisite for receiving extended
u n e m p l o y m e n t benefits. B e c a u s e Congress designed T A A to serve only those displaced
as a result of foreign trade, the program serves relatively few persons. For example, in
1990, approximately 19,000 persons enrolled in T A A sponsored training programs at a cost
of $ 8 0 million.
In recent years, m o s t displaced workers w h o have received public sector-sponsored
retraining services have participated either in programs authorized under the Economically
Displaced Wo r k e r Adjustment Act ( E D W A A ) or in the Pell Grant program.1 E D W A A
programs provide clients with a diverse set of services that last for a n average of 14 weeks.
Displaced workers m a y receive job search assistance, on-the-job training, or classroom
instruction in vocational, remedial, or college level skills. E D W A A also provides for a rapid
response service designed to counsel recently laid off workers about alternative programs
a n d to help t h e m develop realistic adjustment plans. Eligibility for these services extends
to all long-term u n e m ployed so that in principle as m a n y as on e million workers per year
are eligible to receive them. Unlike programs funded under T A A , however, E D W A A
participants are not eligible to receive extended u n e m p l o y m e n t insurance benefits or
stipends while they participate in training. In practice, funding constraints have limited
annual participation in E D W A A

programs to about 120,000 workers, at a cost of

1 D W A A amended Title II of JTPA, which in 1981 provided retraining of displaced workers. Unlike
E
I
programs for the disadvantaged, Title II requires that state governments "match” federal funds dollar for
I
dollar. States unwilling to do so generally would not receive federally funding and as a result would provide
few retraining services for displaced workers.




4

approximately $ 2 0 0 million.
T o provide for these training services, the federal government allocates E D W A A
funds for training to state a n d local authorities, w h o in turn, usually subcontract training
services from nonprofit a n d private organizations. Under E D W A A , states receiving federal
funds for retraining displaced workers must “mat c h ” the federal contribution with funds from
their o w n budgets. Although local authorities m a y subcontract for training services from
community organizations, proprietary schools, vocational institutes, a n d private employers,
in practice o ne of the most c o m m o n providers of training services are two year community
or “junior” colleges, 4 year colleges, a nd universities. For example, approximately 40
percent of those enrolled in T A A sponsored programs providing job-skill training, a n d 73
percent of those enrolled in T A A sponsored programs providing general education,
received these services at o n e of these academic institutions (Corson, Decker .Gleason,
a nd Nicholson, 1993).
B y contrast to T A A a nd E D W A A programs, which Congress designed specifically
for displaced workers, Congress designed the Pell Grant program for a ny low income
person seeking aid in order to further their post secondary schooling. Until recently, a
special provision in the program’ rules waived the normal limit on an applicant’ assets
s
s
and based eligibility on current instead of the previous years income. A s a result of this
provision, displaced workers have been able to receive grants to cover the tuition costs of
retaining a nd schooling. M a n y displaced workers have taken advantage of this provision.
During the 1990-91 academic year over 75,000 displaced workers received Pell Grants.
Approximately 3 0 % of displaced Pell grantees attended proprietary schools, another 1 0 %




5

attended four year colleges, a nd the remainder enrolled in c o m m unity colleges.

B. The Role of Community Colleges
T h e prominent role played by academic institutions, especially c o m m u n i t y colleges,
in providing public sector-sponsored training mirrors the increased role that these
institutions have played during the last 2 0 years in providing vocational training.
C o m m u n i t y colleges continue to offer academically inclined students college-level courses
that m a n y public a n d private four year colleges accept toward a Bachelor’ Degree.
s
However, students w h o d o not wish to transfer to a four year college can acquire jobspecific skills that traditionally have b een provided by proprietary schools a n d vocational
institutes (Freeman, 1974). C o m m u n i t y colleges often offer programs in areas as diverse
as computer information systems, food preparation a n d m a n a g e m e n t , real estate, wo r d
processing, respiratory therapy, a n d automobile repair. Moreover completion of such
courses can lead to certification in a particular trade or occupation or allow students to b e
eligible to take state licencing exams.
B e c a u s e c o m m unity colleges already have vocational programs in place, they are
natural subcontractors for public sector-sponsored retraining programs. Often c o mmunity
colleges design special noncredit courses that are tailored to the special problems faced
by displaced workers. For example, these programs m a y include c o m p o n e n t s that teach
job search skills a n d help displaced workers choose n e w careers. Although states spe n d
most of their E D W A A funds on displaced workers receiving these relatively short-term
noncredit services, they also spend s o m e of their funds o n participants enrolled in regular
community college programs.




6

A s discussed above in the introduction, relatively little is k n o w n about the
effectiveness of classroom training programs for displaced workers. O n e recent study of
the returns from community college schooling for teenagers a nd young adults indicates
that an additional year of such schooling has the s a m e effect on earnings as an additional
year spent in a four year college (Kane and Rouse, 1993).

However, available data

sources offer li t e opportunity to examine the returns from post secondary schooling
tl
received by prime-age persons such as displaced workers.

C. The D W E T P Program
Beca u s e w e have access to unique sources of administrative data, this paper can
address this question for a classroom training program established by the government of
Allegheny County, Pennsylvania. This county, located in the western part of the state, has
a population of approximately 2 million persons a nd includes the city of Pittsburgh, the
traditional “capital” of the U.S. steel industry. Beginning in the late 1970s the local e c o n o m y
began to decline a nd by the 1982-83 recession, the county u n e m p l o y m e n t rate stood at
16.2 percent. At that time federal and state authorities provided few direct resources for
retraining displaced workers. A s a result, the local government established the Displaced
Workers Educational Training Program ( D W E T P ) .
T o initiate this program, the county government granted the C o m m u n i t y College of
Allegheny County ( C C A C ) $1 million dollars to provide recruiting a nd counseling services,
to develop relevant curricula, a n d to pay participants’out-of-pocket expenses. C C A C also
received an additional $2.4 million for the program through the federal government’ Pell
s
Program. B e c a u s e most displaced workers were eligible to receive federal financial aid,




7

CCAC required eligible participants to apply for and use their Pell grants to pay for courses
taken at the community college (Bednarzik and Jacobson, 1994).
Upon applying for the program, eligible displaced workers received career
counseling from DWETP administrators. Although DWETP allowed participants to choose
their own course of study including the duration of their participation and whether they
attended classes full-or part-time, DWETP counselors assisted participants in devising a
training plan. For example, applicants without high school degrees and with poor general
skills might be advised to enroll in CCAC courses that prepared them for the Graduate
Equivalency Diploma (GED) exam. Alternatively, those with poor quantitative skills who
wished to participate in CCAC’s computer information systems program first might be
encouraged to take courses to improve their mathematics skills. In addition, to providing
counseling, DWETP administrators also developed noncredit classes teaching job search
skills and career development techniques. Finally, CCAC made an effort to schedule more
of its regular credit classes at times that would allow DWETP participants both to go to
school and to hold a regular job.
Enrollment in DWETP was not continuous, but took place during several “windows”
between April 1983 through August 1985. During those times, county residents who were
without full-time work, who had been laid off from their jobs, and who had received
unemployment benefits after August 1981 were eligible to receive program services. The
program paid for all tuition, fees, and supplies for as long as the participants remained at
CCAC and did not work more than 30 hours per week. Aid from DWETP did not affect
eligibility for unemployment insurance or welfare benefits. Participants could remain in the




8

program after securing full-time work, but they had to pay the tuition and other program
costs themselves. Participation did not have to be continuous, students could leave for a
semester or two and still return to the program and receive subsidized tuition.
DWETP appears to have increased total enrollment at CCAC during the period when
the program was in effect. This result is important because if DWETP participants simply
displaced other potential students, the program’s social benefits would be greatly
diminished. As shown by Table 1, new enrollments at CCAC rose to 14,418 students during
the 1982-83 recession and subsequently began to decline in 1985. Among non-DWETP
enrolles this decline began in 1984, the year when DWETP enrollments were at their peak.
This decline is evident both among teenage and young adult students. As further evidence
that DWETP students did not displace other students, the table indicates that as DWETP
enrollments declined starting in 1985, enrollments of non-DWETP students did not
increase.
D. The Sample of D W E T P Participants
We used information from two administrative data files first to analyze the types of
classroom instruction that DWETP participants received at CCAC and then to estimate the
effect of this training on their subsequent earnings. First, from CCAC we obtained machine
readable records of participants’ social security numbers and demographic characteristics,
listings of the credit and noncredit courses that they enrolled in, the grades they received
in courses taken credit, and whether they received a degree or occupational certificate.
Second, from the Pennsylvania Department of Labor we obtained the quarterly earnings
records of these persons from 1974 through 1991, and for each calendar year their firms




9

4 digit SIC code, location, and number of employees. Because the program operated
during the middle of the time period covered by our data, we have an unusually long
earnings panel for each participant both prior to and following training. Previous evaluations
of economically disadvantaged persons suggest that such series are crucial for identifying
the impact of training (Ashenfelter and Card, 1985; Heckman and Hotz, 1989).
Our CCAC file had records for 8,229 DWETP participants and we were able to find
quarterly earnings records for all but 19 persons. However, an additional 432 persons had
either invalid or missing information on their year of birth, gender, and marital status or
they did not permanently separate from their firms between 1978 and 1985. Among the
remaining 7,778 persons, more than 3,000 were not consistently attached to
Pennsylvania’s wage and salary work force throughout the period covered by our study.
For the purposes of this study, we excluded these persons from our sample because policy
makers’ interest in displaced workers has been in those with significant work experience
and tenure at their previous jobs.2 Therefore our study does not examine the effects of
DWETP on those workers who did not maintain strong attachment to the work force in
Pennsylvania
Accordingly, our sample is limited to DWETP participants who were permanently

2Some of these 3,000 persons were workers who had been consistently attached to Pennsylvania’s work
force during the 1970s and never reappeared in the sample after leaving DWETP. Because our administrative
data only covers wage and salary earnings in Pennsylvania, it is possible that some of these persons moved
out of the state or started their own businesses. As a result, our estimates of the effect of classroom training
on DWETP participants may be over (or under) estimated to the extent that those who left the state or
became self-employed tended to benefit less (more) from training than other participants. In practice, we
believe these concerns are minor because we find that the vast majority of workers excluded from the sample
at this stage of the analysis exhibited relatively little attachment to Pennsylvania’s work force during the
1970s.




10

displaced between 1978 and 1985 from a job that had lasted 3 or more years. This
restriction allows us to include in our sample persons who may have lost a long-standing
job in the late 1970s or early 1980s, and moved between receiving unemployment
insurance and other marginal jobs of shorter duration until they enrolled in DWETP.
Further, we excluded persons who had two or more consecutive calendar years without
earnings between 1978 and 1991, except during the periods surrounding the 1982-83
recession and the training period. As a result, our sample of “ high-tenure” displaced
workers includes 2,720 males and 912 females. For purposes of comparison, we also
analyzed the effects of DWETP for a sample of 641 “low-tenure” males who remained
attached to Pennsylvania’s work force. These males were displaced from jobs in which they
had been employed for fewer than three years.
E. Characteristics of D W E T P Participants
In Table 2 we present the means of participants’ characteristics and the amount of
classroom training that they received. The table allows for comparisons (i) between the full
sample of DWETP participants and the sample of those who had worked for their employer
three or more years when they were displaced; (ii) between female and male DWETP
participants; and (iii) between those who did not complete any credit courses and those
who completed at least one credit course.
As shown by the table, DWETP participants are relatively old for community college
students, with an average age of approximately 34. Participants in the high tenure sample
are older, have more tenure, and are more likely displaced from jobs in the primary metals
and other heavy manufacturing industries than are participants in the full sample. Finally,




11

the characteristics of DWETP participants who did not complete any credit classes are
similar to those who completed at least one such class.
The bottom half of Table 2 reveals that the average time of enrollment into DWETP
was during the fourth quarter of 1983. At that time, they enrolled in a wide range of credit
and noncredit academic and vocational courses. As the table indicates, DWETP
participants enrolled on average in approximately one noncredit course. These courses
usually taught job search skills, career development techniques, or repair skills. At the
same time, DWETP participants enrolled in and completed on average approximately five
credit classes, with women completing more classes than men. Unlike many classroom
training services offered under EDWAA, DWETP participants took credit classes with
regular community college students. As the table indicates, both males and females were
most likely to complete courses that taught business or clerical skills, or taught subjects
in the humanities. The most striking difference between the genders in the distribution of
classes occurred in courses teaching trade or repair skills, which women were less likely
to complete.
Finally, Table 2 reveals that approximately 23 percent of the male and 29 percent
of the female participants earned a two year degree or an occupational certificate.
Approximately, 14 percent of the males and 18 percent of the females received an
Associate of Arts (AA) or Associate of Science (AS) degree. The credits earned toward
these degrees can be transferred to many four year colleges. An additional 5 percent of
both males and females received an Associate of Applied Science (AAS) degree. Because
CCAC allows students to use some noncollege level courses toward these degrees, credits




12

earned by these students are not easily transferred to four year colleges. Accordingly,
students earning an AAS degree usually acquire job-specific skills. Finally, another 5
percent of both males and females received a certificate certifying their competence in a
particular occupation. Students earning a certificate spend less time at CCAC because
certificates require fewer credits and a much narrower field of study than does an AA or AS
degree.
Before discussing our econometric model of earnings and training, we examine the
quarterly earnings of male and female DWETP participants. As shown by Figure 1, male
participants’ real quarterly earnings rose during the 1970s before starting to decline in
1979. After a modest recovery during 1981, these workers earnings fell sharply after their
displacements. As late as 1991, their subsequent quarterly earnings remained $2,000 or
33 percent below their peak levels during the 1970s.
This substantial long-term loss is not unexpected (Jacobson, LaLonde, Sullivan,
1993b). What is more surprising is the difference between the earnings histories of males
who did not complete any credit classes (the dotted line in the figure) and those who
completed at least one such class (the black line in the figure). Prior to their displacements,
the earnings of the two groups were essentially identical, suggesting that their skills also
were similar. However, after their displacements, the earnings of two groups diverged while
whose that completed classes continued in the program. By 1987, the earnings of the
“ completers” had caught up with those of the “ noncompleters,” and by 1991 the earnings
of the completers were slightly above those of the noncompleters.
This difference is surprisingly small because the completers acquired nearly a year




13

of additional schooling. By contrast, conventional studies of the returns to schooling
suggest that an additional year of schooling is associated with an 8 percent rise in earnings.
Applying this rate of return to the present case would suggest that DWETP completers'
quarterly earnings should be approximately $300 higher than those of the noncompleters.
Of course there are reasons why additional schooling many have been effective in spite of
completers’ earnings being lower than expected. Among these reasons are (i) that
completers lost more specific skills following their job losses, or (ii) that they were less likely
to receive a job offer. Our econometric model developed below and the empirical work
that follows accounts for these and other possibilities and shows that simple comparisons
between the mean earnings of training completers and noncompleters yields misleading
estimates of the effect of training.
Turning to Figure 2, we find evidence that DWETP may have raised the earnings of
female participants. As the figure shows, prior to their displacement, the completers had
lower quarterly earnings than the noncompleters. However, by the early 1990s, the
earnings of the completers exceeded those of the noncompleters. These earnings patterns
are consistent with the contention that DWETP raised the earnings of female participants.
Finally, for purposes of comparison, Figure 3 presents the quarterly earnings of male
workers who were displaced from jobs lasting less than three years. Although their earnings
fell sharply around the time of their displacement, the earnings of those in the low tenure
sample were substantially higher by the 1990s than were their earnings during the late
1970s. This result stands in contrast to workers in the high tenure sample. Further, like the
female participants, the figure suggests that DWETP may have significantly raised the




14

earnings of those who completed classes.
III. Specification of the Econometric Model
To estimate the impact of classroom training on earnings we must specify a
statistical model to represent workers’ earnings histories and identify the effects of training
with some subset of the model’s parameters. In addition, this specification should take into
account the fact that experienced displaced workers usually endure a substantial and
permanent break in their earnings histories at the time of their job loss. Finally, our
specification should exploit a principal strength of our administrative data - the fact that they
cover a long period of time prior to and after DWETP - so as to obtain a detailed picture of
the pattern of earnings gains associated with training.
Accordingly, we begin this section by defining the effect of displacement on workers’
earnings. Next, we turn to specify the training effect and show how our specification differs
from that usually employed in the program evaluation literature. Lastly, we include these
two components into a standard model of worker earnings and discuss the likely
importance of different sources of bias on our estimates of the training effect.
A. Specifying the Displacement Effect
In earlier work, we showed that displaced workers’ earnings histories exhibit several
systematic features (Jacobson, LaLonde, and Sullivan, 1993a). First, the events that lead
firms to reduce their work forces alters the time-series pattern of workers’ earnings before
as well as after the date of their separations. Before separation, worker^’ earnings decline
as a result of hours reductions, real wage cuts, or temporary spells of unemployment. Prior
research indicates that these events begin to depress workers’ earnings as early as three




15

years prior to their separations. Second, after their displacement, workers’ earnings decline
and remain below their previous levels as a result of prolonged unemployment, greater
prevalence of part-time work, or a substantial loss of firm-specific or “ match” capital
resulting in lower wages in their new jobs. Finally, workers’ earnings decline at different
rates prior to displacement and recover at different rates following separation depending
on their demographic characteristics or former industries. These differing rates may be
correlated with their propensity to participate and remain in training.
In this study, we begin by pooling information for workers who were permanently
displaced between 1978 and 1985 from a job lasting three or more years. Because the
effect of displacement varies with time relative to the date of job loss, we can not specify
the event of displacement as a single dummy variable that is equal to 1 after separation
and to 0 prior to separation. Instead, we must characterize displacement by more than one
variable so as to allow its effects on earnings to vary with time. In the most general
specification, we would represent displacement by a vector of dummy variables, Df, where
k = -m,

-(m -1 ) ,..., 0 ,1 , 2 , . . . n, that for k > 0 equal 1 if in time period t, worker i had

been displacement k quarters earlier.3 Similarly, when k < 0, D-"* = 1 if, in time period t,
worker i will be displaced m quarters later. The vector of coefficients, 5“, associated with
these variables measures the effect of displacement on a worker’s earnings k quarters
following (or prior to) its occurrence.

3 In this study we ignore the possibility that training may have varying effects on different cohorts of
displaced workers. Therefore, for any year t, Df,= 1 if worker / was displaced in quarter f - A. If workers were
c
displaced from more than two jobs between 1978 and 1985, we dated their quarter of separation, s, as the
quarter they separated from the job with the most tenure. (See Jacobson, LaLonde, and Sullivan, 1993b,
Chapter 4).




16

The problem with this characterization of displacement is that because we have such
long earnings histories, it leads to a large number of parameters in our statistical model.
Therefore, in the empirical work below we restrict the vector of displacement parameters,
5* to grow or decline along a quadratic trend during the 12 quarters prior to workers’
separations, to be constant during the six quarters following their separations, and to grow
or decline along another quadratic trend during the period that begins six quarters after
their separations. To formally implement this scheme, we redefine the vector of
displacement variables as follows:
D1 = t - (s-12) if worker i is displaced at time s, and s-12 < t < s, and D1 = 0 otherwise;4
*
*
D2 = D V D 1
h
*;

D3 = 1 if worker i is displaced at time s, and s<t, and D3 = 0 otherwise;
*
*
D4 = t - (s+6) if worker i is displaced at time s, and s+6<t, and D4* = 0 otherwise;
*
D5 = DVD4 .
*
*
In empirical work not reported below, we also allowed the displacement effects to vary
during each of the first six quarters after displacement, but found that this less restrictive
specification did not change our results.
We also found in our earlier work that the pattern of “ displacement effects” varied
among workers depending on their age and previous industry. This finding was especially
true for workers displaced from the primary metals industries, who experienced larger
losses, and for workers displaced from the Finance (FIRE) and other services industries,

4For example in the 11th quarter prior to a worker’s displacement D’» = (s-11) - (s-12) = 1; in the 10th
quarter prior to displacement D \ = 2; and in the quarter of displacement D1 = s - (s-12) = 12.
,




17

who experienced smaller losses. Accounting for these differing effects may be important
if workers’ earnings losses were correlated with the amount of classroom training that they
acquired while in DWETP. To control for this possibility, we interact a dummy variable
denoting a workers’ age category or former industry, E*. with a vector, F * that has five
components, representing the quarters (i) during the predisplacement period, (ii) at the time
of and just following separation, and (iii) during the period more than six quarters after
separation. In the empirical work that follows, the coefficients, (j^j, associated with these
interactions measures how the pattern of group j’s earnings deviates from the pattern for
the base group-persons born prior to 1950 who were displaced from industries other than
primary metals, or FIRE and services. Formally, to account for varying effect that
displacement may have on earnings, we define the following variables for groups j = 1,2,
...J-1:
FV = t - (s-13) if worker i from group j is displaced at time s, and s-12 < t < s, and F1 = 0
*
otherwise;
R .. - p ..* F 1 •

F3K= 1 if worker i from group j is displaced at time s, and set, and F3 = 0 otherwise;
j
jit
F4 = t - (s+6) if worker i from group j is displaced at time s, and s+6<t, and F4 = 0
jit
*
otherwise;
. - R
.
P jit — »

jit

*F4„
■ jit*

The vector c* coefficients associated with these terms as well as the displacement terms
defined in the previous paragraph, {S1 82, 83 84,8s, <1 ^ < %
,
,
j>j,
!>
that displacement has on quarterly earnings.




18

^ }, describe the effect

B. Specifying the Training Effect
Having specified the effect that displacement has on workers’ quarterly earnings,
we turn to specify the effect that training has on earnings. The difference between the
specification of the training effect in this paper and specifications used elsewhere is that
we can exploit our long earnings histories and allow this effect to vary with time. By
contrast, in most evaluations of training programs, the sampling frames are relatively short
so that the program’s impact can be estimated only for one or two years after training. In
such instances, it is reasonable to specify the “training effect” as the coefficient of a
dummy variable equal 1 in the periods after training, and zero otherwise. This specification
assumes that training shifts earnings (presumably) upward by an amount equal to the
estimated coefficient. In general, however, there is no reason to believe that the effect of
training will be constant during the years after participants leave the program. The training
effect may increase with time because more skilled workers are likely to receive more
private sector training than their less skilled counterparts. Alternatively, the training effect
may dissipate with time as persons who did not receive public sector-sponsored training
acquire the same skills through other means.
Accordingly, we specify training to have separate effects on earnings during the
periods when participants are in and out of training, and for those effects to rise (or fall)
along a quadratic trend in the post-training period. The estimated “training effect” while
workers are in the program measures the foregone earnings costs. In this study, we divide
the training period into two parts. The first part accounts for instances when participants
have begun training before permanently separating from their old employers. These




19

workers became eligible for and entered the program while collecting unemployment
insurance benefits during a temporary layoff. The second part accounts for the period
following their permanent displacements when the worker was enrolled in DWETP.
The estimated “training effect” after workers leave the program measures its
benefits. These benefits may not be positive in each period after training, because it may
take several years for the earnings of those with more training to “catch-up” and “overtake”
the earnings of those with less training. Therefore, we allow training to have either a
positive or negative effect on earnings when participants leave the program and for this
effect to grow or decline at increasing or decreasing rates in subsequent years.
To express these ideas more formally, we define the following terms to capture the
training effect both during and after participants leave the program:
TA1» = 1, if worker i is displaced at time s, and fstqtr < t < s, where fstqtr denotes the first
calendar quarter in training, and TA1* = 0 otherwise;
TB1 „ = 1, if worker i is displaced at time s, s < t, and fstqtr < t <lstqtr, where Istqtr denotes
the last calendar quarter in training, and TB1 * = 0 otherwise;
T2« = 1, if worker i is displaced at time s, and Istqtr < t, and T2* = 0 otherwise;
T3j, = (t - Istqtr), if worker i is displaced at time s, and Istqtr < t, and T3* = 0 otherwise;
T4» = T3j,*T3i, ;
Based on these definitions, the coefficients associated with the first two terms measure the
lower earnings associated with the training period. These lower earnings may result from
the “ investment” that workers make in their human capital. Alternatively, they may result
simply from the correlation between low earnings and the propensity to take training. By




20

contrast, the estimated coefficients with the final three terms measure the effects of training
on post-program earnings. They allow for the possibility that after leaving the program
workers’ earnings start out below (above) where they would have been without training, but
rise (fall) along a quadratic trend.
Participants in DWETP did not receive a homogenous treatment of fixed length. In
this study we have more information about the type of training received by the participant
than is available in other program evaluations. To account for differences among
participants in the training that they received, we interact the five “training” variables, {TAI*,
...,T4»}, with measures years of schooling, whether the participant received a degree or
occupational certificate, the type of vocational or academic program that they enroll in,
whether they enrolled in noncredit classes, such as the special DWETP job search
assistance or career development courses, and their grade point averages(GPA). For
example, we estimated the effect of an additional year of DWETP subsidized schooling by
interacting each of the “training” variables, {TA1» ...,T4J, with years of schooling acquired
,
in the program, SCHOOL. Consequently, the effect of schooling q quarters after the
participant left the program is given by:
(1)

x3*T2i S C H O O L + T**T3i,*SCHOOL + x6 *T4«*SCHOOL
t
*

= x ^ S C H O O L + x4
*q*SCHOOL + x5*q2
*SCHOOL,

where xk, k = 1,.... 5, are the coefficients associated with the interactions between each
of the training variables and schooling.
Under some assumptions, we can interpret these estimated coefficients as
estimates of the effect of that particular component of training on earnings. One clear




21

exception to this interpretation are for estimates associated with interactions between the
training variables and participants’ GPAs. One interpretation of students’ GPAs is that all
other things equal, higher GPAs may imply that the participant worked harder in the
program and as a result received more training. Alternatively, students’ GPAs may merely
measure their inherent ability, particularly of skills that they were not compensated for prior
to their job losses. Under this interpretation, including the GPA interaction in our model
simply provides a better control for worker’s ability.
C. Specifying the Statistical Model of Earnings Histories
The displacement and training effects defined above are among the principal
components of our model of workers’ quarterly earnings histories. In addition, we allow
earnings for displaced worker i at date t to depend on fixed and time varying
characteristics, and on time varying unobserved characteristics as follows:
(2) Yit= o j +
t

+

X«p +

£ D ki S +
tk

£ E j (F1 ( ^ + . . + F sj|j5 )
i
t
^ ji
.
j<>j

+ TA1j,x1 + TB1j,x2 + T 2 itx3 + T ^ x 4 + T 4 M5 + £#,
x
where Ejit is a dummy variable denoting whether worker i belongs to group j. In (2), the
nontraining related variables are defined as follows: the vector X„ consists of workers’
observed time varying characteristics, which in this paper are limited to a fourth order
polynomial in age. The impact of permanent differences among workers’ observed and
unobserved characteristics is summarized by the “fixed effect” a,. The

y ,’s

are the

coefficients for a set of quarterly dummy variables that capture the general time pattern of
earnings among displaced workers in Allegheny County. In the empirical work below, we
control for these time effects by including in our model 72 quarterly dummy variables




22

covering the 18 years of earnings data available for this study. Finally, the error term, e«,
is assumed to have constant variance and to be uncorrelated across individuals and time.
To measure these effects of training, we estimate (2) using a fixed effect estimator.
Our framework holds constant workers’ displacement experience and labor market activity
during training to estimate the effect of completing additional classroom training on the
temporal patten of earnings. Even though our sample contains only displaced workers who
apply for DWETP, we can identify the parameters in (2) because of the variation in the
timing of workers’ layoffs and in the amount of training that they receive. The effect of
completed training is identified both because a significant fraction of applicants did not
complete any courses and because of the significant variation in the number of classes
completed among those who completed at least one class. Intuitively, the program
dropouts and no shows to the credit classes are the comparison group, and essentially
“ identify” estimates of the nontraining parameters in the model. Variation in the number of
classes taken by those who complete at least one class “ identify” estimates of the model’s
training parameters.
D. Potential Biases
The foregoing statistical framework addresses several sources of bias that may arise
in a nonexperimental evaluation of training. In particular, our framework accounts for the
possibility that persons with lower predisplacement earnings or with larger post­
displacement earnings losses are more likely to receive training and to participate in the
program for a longer period of time. However, even in our framework, biases may arise if
workers with lower (permanent) rates of earnings growth or whose performance was




23

unusually poor in the quarters prior to separation are more (or less) likely to participate in
training or to remain in the program a for longer period of time. In the case of lower
predisplacement earnings growth, we would likely understate the effects of training
because the estimated growth in earnings associated with training, (x4and t 6) would likely
be too low.
In the case of unusually poor performance in the quarters prior to separation, the
source of bias is less serious when the errors are correlated over time but are covariance
stationary.5 When the errors are stationary, the spurious effects of displacement are
symmetric about the date of displacement.6 For example, if it turns out that the estimated
displacement effects are zero, say more than 3 years before workers lose their jobs, the
spurious effects of displacement must also be zero 3 years after workers’ separations.
Accordingly, when firms displace workers partially on the basis of the error in (1), our
displacement estimates will “ regress to the mean” following workers’ separations.
Consequently, such biases are likely to be unimportant for measuring the long-term effects
of training.
A more serious problem for estimating training effects arises when the error is
nonstationary. In this case, when firms discharge recent poor performers there is no
reason to expect their earnings to recover after their displacements. If workers with
especially large “ residuals” are more likely to remain in training, (because it is less costly)
we are likely to understate both the short-term and long-term effects of the program.

sFor example, if the errors are serial correlated by following an AR1 pattern.
®See Heckman and Robb (1985) for a similar argument advocating the use of a symmetric differences
estimator, when estimating the earning impact of employment and training programs.




24

However, when we refer to these workers as poor performers we do not necessarily refer
to changes in their behavior that caused their productivity to decline. More likely workers’
performances suddenly declined because a portion of their skills became obsolete. Indeed,
because we restrict our analysis to workers with three or more years tenure prior to their
separations, and because a majority of these persons were laid off from firms experiencing
mass layoffs, it is unlikely that many workers in our sample left their firms as a result of their
own poor performance. However, short of having experimental data, we see no obvious
way of controlling for this potential source of bias in our analysis.
I . Empirical Results
V
A. Alternative Specifications
This section presents the empirical results based on the econometric model
developed in section III. We use up to eight variables to describe DWETP participants’
program experiences. The one that we focus the most attention on is years of schooling
at CCAC. This variable, denoted as “ School” in the tables, is formed by computing the total
number of credits that the participant earned while in the program and dividing that total by
30, the number of credits that a full time student would earn in a year. In addition to the
schooling variable, in some specifications we also include interactions between whether
the observation falls during the training or post-training periods and whether the participant
enrolled in the DWETP job search assistance or career development courses, or in a
noncredit “ repair” class, whether the student received an AA or AS degree, an AAS
degree, or an occupational certificate, and the student’s GPA.
Despite the restrictions that we impose on our model, the most detailed specification




25

still generates 126 estimated coefficients. Accordingly, we present results only for those
parameters related to DWETP participation. For example, in Table 3 we present the
coefficients associated with the interactions between years of schooling and whether the
observation falls during the DWETP or post-DWETP periods. The row labeled “ DWETP
prior to displacement” refers to the period when some participants have enrolled in the
program before they have permanently separated from their old employer. Although all
these persons eventually were displaced, they enrolled in the program while they were
temporarily unemployed.
The label “ DWETP after displacement” in the second row of Table 3 refers to the
period following participants’ displacements when they were enrolled in the program.
During this time, we might expect the estimated coefficient to be negative if more schooling
was associated with lower earnings. This relationship might result because participants who
acquire more schooling are turning down potential offers in order to “ invest” in their human
capital. Alternatively, it might result because schooling is less costly to those workers who
do not receive many job offers.
Finally, the last three rows of Table 3, capture the effect of training after participants
leave DWETP. As indicated above, our specification allows the effect of schooling to vary
during the 6 to 8 years that we can follow participants after they leave the program.
Accordingly, we can use the estimated coefficients associated with the post-DWETP period
to plot the estimated effects of the program for each quarter during that time span.
The table reports results for both male and female displaced workers based on five
specifications of equation (1). In the first column, the only program variable accounted for




26

in the underlying specification is the schooling variable. In particular, this specification
excludes the terms allowing the displacement effects to vary according to participants'
former industries or their ages (i.e. the

terms in (1) are set equal to zero.) Their inclusion

in the second column of the table allows for the possibility that those who are older or
displaced from certain industries may incur greater earnings losses and at the same time
acquire more schooling. Without these controls for participants’ former industries, we might
tend to understate the effects of additional schooling.
In the third column of the table, we also include interactions between the training
variables and participants’ GPA’s. The data reveals that those with higher GPA’s also
acquire more schooling. Therefore, without these controls some of the estimated returns
to schooling might reflect returns to a dimension of ability not controlled through the “fixedeffect.” Of course, higher GPA’s may also mean that the participant acquired more skills
while they were in the program.
In the fourth column of the table, we add interactions for the three most popular
noncredit classes: job search assistance, career development, or one of the repair classes.
These interactions allow for the possibility that participants may substitute between credit
and noncredit classes. Without these controls we might understate the effects of schooling,
because those with less of it actually enrolled in more noncredit courses. Table 2 provides
some weak evidence of this phenomena, especially for men. Men who took no credit
classes on average enrolled in 0.42 more noncredit classes than those men who completed
at least one credit class. Finally, in column 5 of the table, we add controls for whether the
person received an AA/AS or AAS degree or a certificate from CCAC. These variables




27

account for the possibility that part of the schooling effect that we measure is the effect of
having received a credential.
B. Estimated Returns From Schooling
As shown by Table 3, subsidized schooling under DWETP lowers earnings during
the training period, but raises them in later periods after participants have left the program.
During the training period prior to displacement, male participants’ quarterly earnings were
$118 to $190 lower per year of acquired schooling. Although one interpretation of these
lower earnings is that they represent the investment associated with training, they may
result because workers on longer temporary layoff acquired more schooling. In the next row
of the table we see that during the training period following displacement, males’ quarterly
earnings were $342 to $402 lower per year of acquired schooling. Once again, this “ loss”
may be the forgone earnings cost associated with classroom training, but it also might imply
that participants without job offers stay in school longer. Turning to the right hand panel of
Table 3, we see that these results also hold for women following their displacements.
After participants have left the program their earnings increase above the levels
we would expect had they not acquired any subsidized schooling. The estimated
coefficients for both males and females reveal that when they first leave the program their
earnings are below their expected levels (row three of the table) but that their earnings rise
with years since leaving the program at a decreasing rate. As can be seen by comparing
the estimated coefficients to their standard errors, the coefficients determining this pattern
are statistically significant at conventional levels.
Even with only three coefficients representing the effect of training during the 6 to




28

8 years that we follow participants after they leave the program, it is still difficult to envision
from the coefficients in Table 3 what the pattern looks like. Therefore, we plot the
relationship between the “training effect” and quarters since leaving the program. As
shown by Figure 4, the fixed-effect estimates indicate that when male participants first
leave the program their quarterly earnings are $100 to $300 below their expected levels.
This difference might be due to lost labor market experience while in school. However, after
that point, their returns to a year of schooling rise to approximately $200 to $400 per
quarter by the 7th year (28th quarter) after leaving the program. Observe that controls for
former industry have little effect on the results, but that controls for GPA (the dotted line in
the figure) lower the estimated effects essentially by a constant amount during the entire
post-training period. This result is consistent with the view that without controls for GPA the
returns to schooling are biased upward because those who are more able acquire more
schooling.
These estimates that control for GPA suggest that male DWETP participants earned
a return to schooling that is comparable to that found in the literature. If, as the figure
suggests, the returns have leveled off to approximately $250 per quarter by the 7th year
after training and that this effect persisted throughout the remainder of a worker’s career,
then a crude estimate of the long-term effect of DWETP subsidized schooling on earnings
is approximately 6.3% ($250 divided by the average quarterly earnings of $4,000).7 Of

7Because we control for age in our earnings equation, this estimated return understates somewhat the
return that we would have estimated had we controlled for worker’s potential experience. The amount that
we understate the returns to schooling depends on the returns to labor market experience for workers in their
mid-30s.




29

course as Table 3 and Figure 4 indicate, despite the subsidy that participants received
from the DWETP program, this return was achieved at a cost of lower earnings during the
training period and during the first nine quarters of the post training period.
We found a similar pattern of training effects for female participants. As seen in
Figure 5, the effects for women are initially negative, but rise at a decreasing rate. Like the
estimates for males, those that control for GPA are lower than those that do not control for
this variable. By the 7th year after leaving the program, the effects for women are lower
than those of males and have been declining for several quarters. Thus, contrary to the
pattern implied by the mean quarterly earnings in figures 1 and 2, the fixed-effect estimates
suggest that the returns to schooling for women are lower than are those for men. By
contrast, Figure 6 suggests that the returns to schooling for low tenure males are still rising
during the 7th year after leaving the program and that these returns substantially exceeds
our estimate for high tenure men. However, we should note the sample size of low tenure
men is relatively small and that the coefficients used to generate the trend in Figure 6 are
only marginally significant.
C. Estimated Returns From Other Program Components
Our specification also allows us to examine the relationship between post-program
earnings and whether the participant enrolled in a noncredit course or received a degree.
As shown by Figure 7, enrollment in job search assistance appears to have had a modest
impact on the earnings of male participants. After controlling for years of schooling, whether
the participant received a degree, whether they enrolled in other noncredit courses, and
their GPA, we find that the quarterly earnings of males who enrolled in the DWETP job




30

search assistance course were approximately $150 higher than what they would have been
otherwise. This effect appears to persist throughout most of the post training period. This
result is consistent with results found in the rest of the literature which indicate that
displaced workers benefit from job search assistance. However, as shown by Figure 8,
DWETP women did not appear to benefit from this course.
The estimated effects of the other noncredit courses on males’ and females’
earnings is more ambiguous than is the evidence on job search assistance. As shown by
Figures 7 and 8, career development is associated on the whole with lower earnings for
males and essentially no gains for females, whereas the opposite holds for noncredit repair
courses. Finally, as shown by Figures 9 and 10, there is not a systematic positive or
negative effect of having an AA/AS or AAS degree on males’ and females’ post program
earnings. For males however, the coefficients generating the patterns depicted in Figure
9 are statistically significant at conventional levels. By contrast to the results for AA/AS and
AAS degrees, there is a consistently negative earnings effect of having received a
certificate on post program earnings. This result suggests that participants would have
been better off taking all the courses associated with attaining a certificate, but not
bothering to collect to it from CCAC. However, another explanation for this finding is that
our underlying model is misspecified. This explanation suggests that those who took
classes toward attaining a certificate would have had lower earnings after their
displacements even if they had never participated in DWETP. For example, those who
enrolled in such programs may have been workers who experienced especially large
earnings losses as a result of their displacements.




31

D. Testing the Underlying Econometric Specification
In this paper we take two different approaches to “test” our underlying specification
and to examine the impact that any misspecification of our statistical model is likely to have
on our results. First, we allow for the possibility that the program variables, such as
schooling, affect earnings not only during and after the program, but also prior to workers’
displacements. If our model was specified correctly we would not expect that years of
DWETP subsidized schooling, enrollment in noncredit classes, or whether participants
attained a degree to be correlated with their earnings and their earnings “ dips” prior to their
job losses.
As shown by Table 4, the program variables are correlated with workers’ earnings
declines prior to displacement. In the upper portion of the table we allow participants’
earnings to deviate from their expected levels during the period 0 to 3 years prior to their
displacements and for this deviation to depend on years of DWETP subsidized schooling,
GPAs, and other program variables. The results indicate that both males and females who
subsequently acquired more schooling experienced somewhat larger declines in their
predisplacement earnings. For example, the -5.146 figure in the first column implies that
a male displaced worker who acquired one year of additional DWETP subsidized schooling
had earnings that fell by approximately $5 per quarter faster than an otherwise comparable
worker. When we introduce other program variables into the model, the effects of schooling
on predisplacement earnings declines become statistically insignificant at conventional
levels.
In the lower panel of Table 4, we allow participants’ earnings to deviate from their




32

expected levels during the period 0 to 6 years prior to their displacements. During this
period, the effects of additional DWETP schooling on predisplacement earnings declines
are similar to the results obtained for the 3 year period prior to job loss.8 Because those
who subsequently acquired more schooling had somewhat slower earnings “ growth” prior
to their job losses, they may be more likely to have had slower earnings growth after their
job losses. As a result, estimates from our “fixed-effects” model likely understate the effect
of DWETP schooling. Our fixed effects model does not control for differences among
workers “trend” rates of earnings growth.
The other results reported in Table 4 indicate significant correlations between the
size of workers’ predisplacement earnings declines and their GPAs, whether they enrolled
in noncredit classes, and whether they received an AAS degree or an occupational
certificate. These results for GPA and for the two credentials are consistent with each
other. For example the 3.105 figure in the first column of Table 4 indicates that a male,
whose GPA was one point higher than an otherwise comparable worker, had earnings that
declined by approximately $3 per quarter less prior to his job loss. This result is consistent
with the view that DWETP participants who had higher GPAs were more able as indicated
by their higher rate of earnings “ growth” prior to their displacements. Consequently,
interpreting the returns to higher GPAs as an effect of DWETP would tend to overstate the
returns to a student of working harder to obtain better grades. Similarly students who

■In results not reported in the table, we allowed workers’ predisplacement earnings to deviate from their
expected levels along a quadratic (instead of a linear) trend depending on the amount of DWETP schooling
that they subsequently completed. During the three years prior to their displacements, we found that males
who later acquired more schooling initially had a significantly steeper decline in predisplacement earnings.
However, the coefficient associated with the “squared term” indicated that by the quarter of their
displacements this gap has vanished.




33

subsequently received AAS or occupational certificates had substantially larger earnings
declines prior to their job losses. These degrees require less rigorous studies than are
required for an AA or AS degree, and likely are acquired by less able students. Therefore,
the larger earnings declines prior to job loss for students who receive an AAS or an
occupational certificate may result from these persons having a lower “tend” rate of
earnings growth. This finding would explain why we found in figures 9 and 10 that these
credentials had negative effects on postprogram earnings.
A second approach for “testing” our econometric specification examines whether
all CCAC classes have the same effect on earnings. It might be that participants receiving
certificates enrolled in courses with lower returns than those who did not receive a
certificate. We can test this possibility by replacing the schooling variable in our model with
variables denoting the type of credit classes that participants completed while in DWETP.
Accordingly, we examined the separate returns from completing what we define as “ hard”
classes and “ easy” classes. We designated academic math and science courses, and
vocational courses in nursing, other health related fields, trades and repair, and computer
information systems as hard courses. All other vocational and academic courses we
classified as easy courses. Approximately 45 percent of the credit classes taken by male
DWETP participants and 27 percent of those taken by female DWETP participants fell into
the “ hard” category.
Figure 11 presents the estimated returns associated with completing one class in
each category after controlling for whether the participant enrolled in noncredit courses and
their GPA. As shown by the figure, there are apparently substantial returns associated with




34

completing a “ hard” classes and no gain associated with completing an “ easy” class.
These results suggest that although our previous estimates provided a measure of the
average return to schooling, they masked considerable variation in the returns to different
classroom programs. Notice that for females the returns to completing one “ hard” class
equals $100 per quarter. At that rate, completing 10 such classes would essentially
eliminate the losses associated with worker displacement (See Figure 2).
We find evidence that this difference between the returns to “ hard" and “ easy”
classes results at least in part from differences between the “ investments” that individuals
taking these classes make in their human capital. We find that while males are in training
an additional “ hard” class is associated with a $54 decline in quarterly earnings. This figure
compares with only a $22 decline for an additional “ easy” class. Therefore, during training
the quarterly earnings of a male who completed one years worth of “ hard” classes would
be $322 less than that of a comparable male who completed a years worth of “ easy"
classes. We find that completing additional “ hard” classes has a similar, although smaller,
impact on the quarter earnings of females.
V. Concluding Remarks
The foregoing discussion of alternative specifications indicates the value of
implementing a richer specification of earnings and program impacts in subsequent
versions of this paper. Among directions we plan to explore are allowing our model to
include individual-specific trends and for the returns to schooling to vary according to the
participants’ academic or vocational concentrations. In addition, we also plan to add to the
analysis several comparison groups. The first group includes workers displaced from firms




35

in Allegheny country who did not participate in DWETP. A second group includes displaced
workers who were not eligible to participate in DWETP, but enrolled in CCAC courses on
their own. The third and fourth groups includes nondisplaced workers who did and did not
enroll in CCAC courses. With these additional data, we should be able to better identify
the effects of displacement, the effects of classroom training, and the effects of subsidized
schooling on displaced workers subsequent earnings.
At this stage, our study indicates that DWETP classroom training raised displaced
workers earnings. This result suggests that prime-age students benefit from community
college schooling, just as younger students appear to benefit from such schooling.
However, these gains are small compared to the earnings losses associated with
displacement.

Further, unlike training the economically disadvantaged, or educating

teenagers and young adults, retraining displaced workers is relatively costly, because time
spent training is associated with larger foregone earnings.
As a result, despite the apparent gains associated with the program, it is unclear
whether DWETP was a productive social investment. For example, males who
accumulated a years worth of schooling were in the program for approximately five
quarters. One interpretation of the figures in Table 3 is that time spent in the program is
associated with an earnings loss of $1,785 ($357*5). Next, if we discount (at a 5 percent
rate) the earnings gains and losses (in Figure 4) associated with a year of schooling after
training, we arrive at a benefit of $2,430 dollars. Finally, adding the foregone earnings to
the postprogram benefits we have a private net benefit to DWETP of approximately $645.
Because DWETP subsidized tuition and supplies, the social benefits of the program may




36

be close to zero even seven years after the program ended. We will readdress this
question of the private and social benefits of classroom training in future versions of the
paper.




37

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Jacobson, Louis S.; LaLonde, Robert J.; and Sullivan, Daniel G. (1993a). “ Earnings Losses
of Displaced Workers,” American Economic Review, 83(4):685 - 709.
— (1993bL The Costs of Worker Dislocation. Kalamazoo, Michigan: W.E. Upjohn Institute
for Employment Research.
Kane, Thomas; and Rouse, Cecila (1993). “ Labor Market Returns to Two- and Four- year
College,” mimeograph Princeton University.
Leigh, Duane E. (1990). Does Training Work for Displaced Workers: A Survey of Existing
Evidence. Kalamazoo, Michigan: W.E. Upjohn Institute for Employment Research.
Ruhm, Christopher (1991), “Are Workers Permanently Scarred by Job Displacements?”
American Economic Review. 81:1,319-323.
Saint-Paul Gilles (1994). “Out in the Cold,” European Economic Perspectives. London:
Centre for Economic Policy Research, 4: 5-6.




38

F ig u r e 1 : E a r n in g s o f M a le D W E T P P a r t ic ip a n t s

Quarterly Earnings 1982-84 $

Quarterly Earnings of males with earnings in each TW O calendar years from 1978 to 1991,
except for 1982 through 1985, and 3 or more years tenure when displaced from any firm.
Program Enrollment begins 8/83. Sample Sizes: No Classes-1081, Classes-1740

Completes One or More Classes

j I

Completes No Classes

F ig u r e 2 : E a r n in g s o f F e m a le D W E T P P a r t ic ip a n t s

Quarterly Earnings 1982-84$

Quarterly Earnings of males with earnings in each TW O calendar years from 1978 to
1991, except for 1982 through 1985, and 3 or more years tenure when displaced from
any firm. Program Enrollment begins 8/83. Sample sizes: No Classes-344, Classes-639




Figure 3: Earnings of Low-Tenure
Male DW EPT Participants
Quarterly Earnings of males with earnings in each TW O calendar years from 1978 to 1991,
except for 1982 through 1985, and less than 3 years tenure when displaced from firm.
Program Enrollment begins 8/83. Sample Sizes: No Classes-209, C lasses-423




F i g u r e 4: I m p a c t s o f S c h o o l i n g o n

Male Displaced

Deviation From Expected Quarterly Earnings

Impact of Additional Year of Vocational or Academic Schooling on Earnings
5
0
to

00
!00
00
No controls for former industry or gpa a

Deviation from Expected Earnings

I

Controls for former industry




[Z3

Controls for industry, and gpa

Controls for all including credential effect

F i g u r e 6: I m p a c t o f S c h o o l i n g o n

L o w Tenure Displaced

Impact of Additional Year of Vocational or Academic Schooling on Earnings

No additional Controls EZ3 controls for former industry

*■ 3

controls for industry, gpa

[ZB

controls for industry, gpa, credentials

Figure 7: Impact of Noncredit Courses on Male Displaced

Deviation From Expected Earnings

Impact of Enrolling in noncredit D W ETP Job Search Assistance,
Career Development, and Repair courses on Earnings




Job search h*H Career Deveolpment

\ m J.

Repair

Figure 8: Impact of Noncredit
C o u rses on Female
Impact of Enrolling in noncredit D W ETP Job Search
Assistance, Career Development, and Repair courses on

Figure 9: The Impact of Credentials on Displaced Males
Based on model that controls for years of D W EP T subsidized schooling, former industry,
noncredit courses, and gpa. Graphs measure deviation from expected quarterly earnings.




Figure 10: Impact of Credentials on Displaced Fem ales

I

Deviation from Expected Earnings

Based on model that controls for years of D W EPT subsidized schooling, former industry,
noncredit courses, and gpa. Graphs measure deviation from expected quarterly earnings.

on model that controls for number of D W EPT subsidized classes, former industry, noncredit
s, degrees and gpa. Graphs measure deviation from expected quarterly earnings.

Deviation from Expected Earnings

0 m

Figure 11: Returns From “ Hard” and “ E a s y ” C la sse s




r»"i Hard Classes-Fem ales I
“3

Hard Classes-M ales

EZ3

E a sy C lasses-Fem ales
E a sy Classes-M ales

Table 1:New Enrollees In Academic Programs at the Community Co lege of Allegheny County ' 980 * 86.
1986
1984
1985
1980
1983
1981
1982
Total
10,078
14,375
11,976
12,069
12,258
14,418
13,785
A10
Non-DWETP
10,045
11,023
11,894
11,178
12,056
13,566
13,819
Age 17-20
3,343
4,035
3,835
3,577
3,841
4,615
4,756
Age 24-33
2,566
2,886
2,791
2,973
3,231
3,830
3,610
A5
DWETP
33
0
953
202
599
3197
219

Note^: Some DWETP participants enrolled at CCAC prior to entering the program.




r

iaDie^:Mean Characteristics ot female ana Male

Age at DWETP
Married (%)
Prior Tenure (quarters)
Predisplacement
Industry (%) •
...primary metals
...other durables
...TCPU
...Trades
...Other services
Measures of
Classroom Tralnina
First DWETP Quarter
# noncredit classes
# for credit classes
% that completes 1 or
more classes

Mean
Sun. D .
m
Mean
Sun. D .
m
Mean
Sun. D .
m
Mean
Sun. D .
m
Mean
Sun. D .
m
Mean
8un. D .
m
Mean
SU . D .
n m
Mean
Sun. D .
m
Mean
S nD .
ta . m
Mean
S nD .
ta . m
Mean
S n Dv
ta . a .
Mean
S nD .
ta . m
Mean
SU . D .
n m
Mean
S nD .
ta . m

ranicipants
Women

uw e ir

Men
Full
Sample
33.280
8.625
0.508
0.500
21.137
12.024

High
Tenure
34.372
8.109
0.618
0.486
27.330
9.061

No
C lasses
35.201
8.590
0.610
0.488
27.220
8.824

Classes
33.882
7.772
0.623
0.485
27.395
9.200

0.273
0.446
0.097
0.297
0.022
0.148
0.281
0.450
0.225
0.418

0.346
0.476
0.209
0.406
0.028
0.165
0.135
0.342
0.326

0.461
0.499
0.243
0.429
0.017
0.130
0.092
0.289
0.067
0.249

0.441
0.497
0.250
0.434
0.012
0.108
0.097
0.296
0.069
0.254

0.474
0.499
0.239
0.426
0.020
0.142
0.089
0.285
0.065
0.246

60.358
2.429
1.029
1.674
9.133
7.862
0.000 _____ J.00 0

59.807
2.161
0.842
1.904
4.953
6.672
0.633
0.482

60.178
2.271
0.895
2.024
4.894
6.685
0.629
0.483

60.343
2.374
1.161
1.161
o.ooo

60.081
2.202
0.738
1.739
7.785
6.970

0.000

1.000

Full
Sample
34.906
9.677
0.400
0.490
16.821
11.210

High
Tenure
36.515
9.084
0.404
0.491
24.556
9.150

No
Classes
37.156
9.571
0.385
0.487
23.703
8.959

Classes
36.222
8.845
0.412
0.493
24.946
9.217

0.181
0.385
0.087
0.282
0.021
0.145
0.277
0.448
0.328
0.470

0.285
0.452
0.115
0.319
0.022
0.147
0.268
0.443
0.215
0.411

0.311
0.464
0.154
0.361
0.021
0.144
0.238
0.426
0.385
0.487

59.806
2.252
0.985
1.539
6.518
7.856
0.694
0.461

60.300
2.407
1.061
1.636
6.269
7.771
0.686
0.464

60.175
2.357
1.133
1.550
0.000

0.121

NotesrPrior Tenure measures quarters of tenure in predisplacement job, where years of service prior to 1974 is censored. Under
prior industry are primary metals defines as firms with 2 digit SIC code of 33; other durables are firms with 2 digit SIC code of 34
through 37; TCPU are firms in the Transportation, Communications, and Public Utility sectors; trades include Wholesale and
Retail trades; other services include FIRE and business and personal services. First DWETP Quarter = 60 corresponds to the
fourth quarter of 1983.




Iquip^.iviowtn
v o g i i u u rumcnq giro i i i u»vLir r a uoipamo
iti^io i
vaq
t
Mean
# classes taken in
8U . Dv.
n e
sublect area2.113
Mean
1.887
0.000
... BUS/ADM/CLERIC
3.406
3.629
SU . D v
n e.
Mean
0.263
0.246
0.000
... NURSING
1.337
1.307
Sun. Dv
e.
0.856
Mean
0.779
0.000
... HEALTH/SCIENCE
Sun. D v
e.
2.263
2.163
Mean
0.090
0.075
0.000
... TRADES/REPAIR
0.764
SU . D v
n e.
0.699
Mean
0.764
0.586
0.000
... COMPUTER/MATH
1.457
1.264
SU . Dv
n e.
Mean
1.659
1.453
0.000
... LETTERS/HUMAN
2.731
2.308
8Ua D v.
e
Mean
0.219
0.211
0.000
... FUN STUFF/OTH
1.033
SU . Dv
n e.
1.085
Mean
0.770
0.694
0.000
... SO C SCI/PUB S E C
1.634
Sun. D v
e.
1.580
Mean
% Earning Degree or
SU . Dv
n e.
CertificateMean
0.188
0.028
0.175
Associate of Arts (AA)
0.391
SU . Dv
n e.
0.381
or Science (AS1
0.165
Mean
0.052
0.053
0.000
Associated of Applied
SU . Dv
n e.
0.222
0.223
Science (AAS1
Mean
0.049
0.048
o.ooo
Certificate
SU . Dv
n e.
0.214
0.215
Mean
2469
912
286
# of observations
SU . Dv
n e.

3.078

0.857

0.857

4.028

2.299
0.062

0.064

1.566

0.648
0.592

0.648

2.533

2.096
0.576

0.599

0.101

2.836

2.326

0.109

0.000

0.641

1.134

1.364

2.343

0.358

0.000

0.842

1.653
0.880

0.874

0.000

1.031

0 000

0.953

o.ooo1

1.390

0000

1.906

0.000

0.219

0.000

0.707

2.866
1.910

1.582

0.853

0.806

1.449

1.931

1.978

2.117

1.300

1.199

2.521

2.242
0.143

0.138

2.402

2.116

0.307

2.347

1.298

0.143

0.893

1.011

0.474

0.444

1.118

1.821

0.474

1.202

0.243

0.139

0.133

0.158

0.196

0.429

0.346

0.339

0.004

0.397

0.077

0.045

0.053

0.004

0.266

0.208

0.225

0.063

0.070

0.044

0.050

0.015

0.082
0.275
0.071

0.256

0.204

0.218

0.121

0.256

626

5309

2720

1010

1710

1.454

Notes:Prior Tenure measures quarters of tenure in predisplacement job, where years of service prior to 1974 is censored. Under
prior industry are primary metals defines as firms with 2 digit SIC code of 33; other durables are firms with 2 digit SIC code of 34
through 37; TCPU are firms in the Transportation, Communications, and Public Utility sectors; trades include Wholesale and
Retail trades; other services include FIRE and business and personal services. First DWETP Quarter = 60 corresponds to the
fourth quarter of 1983.



•f
v'
S*

Table 3: The Effects of One Year of Community College Schooling on Displaced Workers Earnings
High Tenure Females
High Tenure Males
Controls
Controls
Controls
for
Also
Also
Also
lor
Also
for
controls for controls lor industry,
No
No
industry,
controls lor controls lor Industry,
gpa, and
industry
former
additional
former
additional
Industry
gpa, and
gpa, and
industry
noncredit
and gpa
controls
controls
Industry
degrees
and gpa
noncredit
CoeHiclenl
•46.329
•118.143
79.913
20.827
-46.833
•121.369
•190.315
•166.596
DWEPT prior to
•156.002
displacement
Stan Err.
.
38662
3 .1 4
03
3 .6 3
37
3 .4 4
68
4 .6 6
44
3 .1 3
45
3 16
0.6
3 .0 6
66
36.1
10
CoeHiclenl
•283.067
•342.499
•292.177
•271.776
-270.756
•346.578
-402.398
•357.639
•359.360
DWEPT alter
displacement
Stan Err.
.
3 .1 0
07
20604
32416
2 .2 1
61
2 .0 7
63
3 .6 7
40
3 .2 1
21
7 .5 4
40
2 .4 2
60
CoeHiclenl
•85.487 ____-44.820
•136.238
•123.901
•140.249
-294.438
•133.381
•275.008
•276.692
post DWETP •
Stan Err.
.
constant term
48047
4 .6 0
66
52655
6 .5 3
23
4 .3 2
00
4 .1 4
03
5 46
26
4 .0 6
36
4 .0 0
41
CoeHIcient
37.777
50.827
37.897 J ____37.480
30.689
31.239
38.083
40.350
40.383
post DWETPlinear trend term Stan Err.
.
70 4
2
66
.6 0
7 1 ______ 7 6
.3 0
70
.0 6
.5 4
62
.6 1
66
.4 6
.1 2
67
.4 2 ___ 7 7
CoeHIcient
•1.092
•1.061
-1*042
•0.449
-1.097
post DWETP•1.139
-0.398
-0.784
-0.790
Stan E
. rr.
quadratic term
0200
00
.2 7
05
.2 0
0263
0266
0280
0206
0
.220
02
.2 0




Controls
for
industry,
gpa, and
degrees
5.248
5 .3 0
60
•238.353
4 .0 6
10
•94.932
6 .0 0
70
34.912
10062
•1.004
0355




High
Tenure
Females

High
Tenure
Males
4 to 6 years Prior to
Displacement

j

Coefficent

LowTenure
Males
i

Sun Error
14.791 I
•332

Sun Error

5.499
4967

Coefficent
Sun Error

0.561
4.194

-13.210
6.460

16381

Coefficent

16.724

25.228

Coefficent

TO
T0*School
TO*Job Search
T0*Career Dev.

TO'AAS Degree

6.162

4971

4.302

56.128

6942

1X679

Z089

-0.359

1356

-82.678
21J E 4
02

-0.869

Coefficent

9.461

-1 8 3 6 3
16934

S9S7

Sun Error
Sun Error

TO'AA/AS Degree

9.030

-13.137

Sun Error
Coefficent
Coefficent

TO'Repair

-7.071

-17.994

0.623

2376

7967

10.767

14927

29.120

Sun Error
Coefficent

-10.586

-48.569
26.910

10963

15.776

-15.876

Sun Error

3378

T0*gpa

Sun Error

10326

Coefficent

T0*Certificate

19.721
14.714

1.109
1.747

2.386
2911

0.011
4924

-12.981

Coefficent

Sun Error

1 to 3 years Prior to
Displacement

21J006

Coefficent
Sun Error
-47.847

-9.209

Sun Error

13954

18373

27.130

IT 1cn*Q/*hnnl
|i *
owmm

Coefficent

4.721

0.763

0.424

IT1i *JUU oca) u i
Inh Qparrh
Ii
i

Coefficent

Coefficent

T1'School
1

IT 1c/i'.lnh OCCII wl t
11
JUU Qparrh
;t ,

rw .,

JTl^cTCareer l/vV«
l | 1 W Q l v C l Dev
:T i*R eoair

lT ls q -Repair
l T 1* AA/AS Degree

Sun Error

I

lTlsq *A A S Degree
| T1‘ Certificate

2.619
-88.900

23j
026
2360

Sun Error
ICoeHicent
1Sun Error

|
1

-24.7791

1Coefficent
*
I Sun Error

i
J
|

—0.473
1.790

jCoe«icent
i Sun Error

|
j

I Coefficent
(Sun. Error

11.132
4356
0.4261

I Coefficent
I Coeflicent

179661

-0.6011
39 .90 0 1

_L
l

I Sun. Error
IT l-A A S Degree

1930
4.236

-2.962

Coefficent

I Sun. Error
1T isq* AA/AS Degree

1392
17.515

Sun Error

26.176|
-3.285!

32930

56.978

-2.3491

6.932

3.1951

5.603

-6 9 .0 8 8 1

166.539

269751
4.2681

45370
-14.639

2943

4.382

-167.301

20.616

69.749

8375

11.4691

-2.091

6331 |

0.044

-71.5751

12.756

35.079

49331

7.5181

-4.420

2.6121

3.5831

4.759

1Coeflicent
I Sun. Error

-24.931 i
36.4691

-99.900'
53.060 |

292.492
69.110

I Coefficent

-0.193 i
3.6271

4.5041
5367}

-22.826

106.0021

62.921

i Sun Error
Coefficent

|

-98 .0 2 3 1

6.779

Sun Error

!

34216 i

IT ls q * Certificate
i

I Coefficent
!
1Sun Error
I

j

6.243!

!

3.402 1
i

4961 I

6.744

iT f g p a
;

I Coefficent
!
iI Sun Error

i

13.411!!

9.704 i

22.521

7.751 :

13350

iT is q 'g p a

!Coefficent
j
I Sun Error

1

-1.010

-0.3161

-1.326

I

0382

0.766 i

1364

lI Coefficent
I Sun Error

i 6.5702384

F-test for joint
Significance

•

5961 !

49362 i

693G6

-12.195 i

-2.800

6.0233022 i 4.1364577
jp vUtM < j
0001 p v«U < 9001 ) p vm < 9001
km

Notes: TO equals the number of quarters between the current quarter and the quarter 6 years prior to
displacement If observation falls between 3 and 6 years prior to displacement and zero otherwise. T1
equals the difference between the current quarter and the quarter three years prior to separation if
observation falls between the displacement quarter and 3 years prior to displacement

Working Paper Series
A series o f research studies on regional eco n o m ic issu es relating to the S eventh Federal
R eserve D istrict, and on financial and eco n o m ic topics.

REGIONAL ECONOMIC ISSUES
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Philip R. Israilevich and Kenneth

WP-92-8

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WP-92-9

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WP-92-12

William A. Testa

State and L ocal G overnm ent S p e n d in g -T h e B alance
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F orecasting w ith R egion al Input-Output T ables

WP-92-20

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A Primer on G lobal A uto M arkets

WP-93-1

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Industry A pproaches to E nvironm ental P olicy
in the Great L akes R egion

WP-93-8

David R. Allardice, Richard H. Mattoon and William A. Testa

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Lean M anufacturing and the D ec isio n to V ertically Integrate
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D om estic C onsum ption Patterns and the M id w est E con om y

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Robert Schnorbus and Paul Ballew




1

W r i gpprsre cniud
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T o Trade or N o t to Trade: W ho Participates in R EC L A IM ?

WP-94-11

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WP-94-18

Paul D. Ballew and Robert H. Schnorbus

ISSUES IN FINANCIAL REGULATION
Incentive C on flict in D eposit-Institution R egulation: E vid en ce from A ustralia

WP-92-5

E d w a r d J. Kane and George G. Kaufman

Capital A d equacy and the G rowth o f U .S . Banks

WP-92-11

Herbert Baer and John McElravey

Bank C ontagion: T heory and E vid en ce

WP-92-13

George G. Kaufman

Trading A ctiv ity , Progarm Trading and the V olatility o f Stock Returns

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James T. Moser

Preferred Sources o f Market D iscip lin e: D epositors vs.
Subordinated D eb t H olders

WP-92-21

Douglas D. Evanoff

A n Investigation o f Returns C onditional
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WP-92-24

James T. Moser and Jacky C. So

The E ffect o f Capital on P ortfolio R isk at L ife Insurance C om panies

WP-92-29

Elijah Brewer I I Thomas H. Mondschean, and Philip E. Strahan
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A Fram ew ork for Estim ating the V alue and
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David E. Hutchison, George G. Pennacchi

Capital S h ock s and B ank G ro w th -1973 to 1991

WP-92-31

Herbert L. Baer and John N. McElravey

T he Im pact o f S& L Failures and R egulatory C hanges
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WP-92-33

Elijah Brewer and Thomas H. Mondschean




2

W r i gpprsre cniud
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Junk B ond H old in gs, Prem ium Tax O ffsets, and R isk
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WP-93-3

Elijah Brewer III and Thomas H. Mondschean

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WP-93-5

Paul Kofman and James T. Moser

Is There L if(f)e A fter D T B ?
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Contracts on S ynchronous M arkets

WP-93-11

Paul Kofman, Tony B o u w m a n and James T. Moser

Opportunity C ost and Prudentiality: A R epresentativeA gen t M odel o f Futures C learinghouse B eh avior

WP-93-18

Herbert L. Baer ,Virginia G. France and James T. Moser

T he O w nership Structure o f Japanese Financial Institutions

WP-93-19

Hesna Genay

O rigins o f the M odern E xch an ge C learinghouse: A History o f Early
C learing and Settlem ent M ethods at Futures E xchanges

WP-94-3

James T. Moser

T he E ffect o f B ank-H eld D erivatives on Credit A ccessib ility

WP-94-5

Elijah Brewer HI ,Bernadette A. Minton and James T. Moser

Sm all B u sin ess Investm ent C om panies:
Financial C haracteristics and Investm ents

WP-94-10

Elijah Brewer III and Hesna Genay

MACROECONOMIC ISSUES
A n E xam ination o f C hange in Energy D ep en d en ce and E fficien cy
in the S ix Largest E nergy U sin g C o u n tr ie s -1970 -1 9 8 8

WP-92-2

Jack L. Hervey

D o e s the Federal R eserve A ffe c t A sset Prices?

WP-92-3

Vefa Tarhan

Investm ent and M arket Im perfections in the U .S . M anufacturing Sector

WP-92-4

Paula R. Worthington




3

W r i gpprsre cniud
o k n ae eis otne
Business Cycle Durations and Postwar S a i i a i n of t e U.S. Economy
tblzto
h

WP-92-6

M a r k W. Watson

A Procedure f r Predicting Recessions with Leading I d c t r : Econometric I sues
o
niaos
s
WP-92-7
and Recent Performance
James H. Stock and M a r k W. Watson

Production and Inventory Control a t e General Motors Corporation
t h
During th 1920s and 1930s
e

WP-92-10

Anil K. Kashyap and David W. Wilcox

Liquidity E f c s Monetary Policy and t e Business Cycle
fet,
h

WP-92-15

Lawrence J. Christiano and Martin Eichenbaum

Monetary Policy and External Finance: I t r r t n t e
nepeig h
Behavior of Financial Flows and I t r s Rate Spreads
neet

WP-92-17

Kenneth N. Kuttner

Testing Long Run Neutrality

WP-92-18

Robert G. King and M a r k W. Watson
A

Policymaker's Guide t Ind c t r of Economic A tivity
o iaos
c

WP-92-19

Charles Evans ,Steven Strongin, and Francesca Eugeni

Ba r e s t Trade and Union Wage Dynamics
rir o

WP-92-22

Ellen R. Rissman

Wage Growth and Sectoral S i t : P i l p Curve Redux
hfs hlis

WP-92-23

Ellen R. Rissman

Excess V l t l t and The Smoothing of I t r s R tes:
oaiiy
neet a
An Application Using Money Announcements

WP-92-25

Steven Strongin

Market S r c u e Technology and th C c i a i y of Output
tutr,
e ylclt

WP-92-26

Bruce Petersen and Steven Strongin

The I e t f c t o of Monetary Policy Disturbances:
dniiain
Explaining th Liquidity Puzzle
e

WP-92-27

Steven Strongin




4

W r i gpprsre cniud
o k n ae eis otne
Earnings Losses and Displaced Workers

WP-92-28

Louis S. Jacobson ,Robert J. LaLonde, and Daniel G. Sullivan

Some Empirical Evidence of t e Effects on Monetary Policy
h
Shocks on Exchange Rates

WP-92-32

Martin Eichenbaum and Charles Evans

An Unobserved-Components Model of
Constant-Inflation P t n i l Output
oeta

WP-93-2

Kenneth N. Kuttner

Investment, Cash Flow, and Sunk Costs

WP-93-4

Paula R. Worthington

Lessons from t e Japanese Main Bank System
h
f rFinancial System Reform i Poland
o
n

WP-93-6

Takeo Hoshi, Anil Kashyap, and Gary Loveman

Credit Conditions and the Cyclical Behavior of Inventories

WP-93-7

Anil K. Kashyap ,O w e n A. Lamont and Jeremy C. Stein

Labor Productivity During the Great Depression

WP-93-10

Michael D. Bordo and Charles L. Evans

Monetary Policy Shocks and Productivity Measures
i the G-7 Countries
n

WP-93-12

Charles L. Evans and Fernando Santos

Consumer Confidence and Economic Fluctuations

WP-93-13

John G. Matsusaka and Argia M. Sbordone

Vector Autoregressions and Cointegration

WP-93-14

M a r k W. Watson

Testing f r Cointegration When Some of t e
o
h
Cointegrating Vectors Are Known

WP-93-15

Michael T. K. Horvath and M a r k W. Watson

Technical Change, Diffusion, and Productivity

WP-93-16

Jeffrey R. Campbell




5

W r i gpp rsre cniud
o k n a e eis otne
Economic A
ctivity and the Short-Term Credit Markets:
An Analysis ofPrices and Quantities

WP-93-17

Benjamin M. Friedman and Kenneth N. Kuttner

Cyclical Productivity i a Model of Labor Hoarding
n

WP-93-20

Argia M. Sbordone

The E
ffects of Monetary Policy Shocks: Evidence from t e Flow of Funds
h

WP-94-2

Lawrence J. Christiano, Martin Eichenbaum and Charles Evans

Algorithms f r Solving Dynamic Models with Occasionally Binding Constraints
o

WP-94-6

Lawrence J. Christiano and Jonas D M . Fisher

I e t f c t o and th Effects of Monetary Policy Shocks
dniiain
e

WP-94-7

Lawrence J. Christiano,Martin Eichenbaum and Charles L. Evans

Small Sample Bias i G M M Estimation of Covariance S r
n
t uctures

WP-94-8

Joseph G. Altonji and Lewis M. Segal

I t r r t n th Procyclical Productivity of Manufacturing S c o s
nepeig e
etr:
External E
ffects of Labor Hoarding?

WP-94-9

Argia M. Sbordone

Evidence on S r c u a I s a i i y i Macroeconomic Time S ries Relations
t u t r l ntblt n
e

WP-94-13

James H. Stock and M a r k W. Watson

The Post-War U.S. P i l p Curve: A Revisionist Econometric History
hlis

WP-94-14

Robert G. King and M a r k W. Watson

The Post-War U.S. P i l p Curve: A Comment
hlis

WP-94-15

Charles L. Evans

I e t f c t o of Inflation-Unemployment
dniiain

WP-94-16

Bennett T. McCallum

The Post-War U.S. P i l p Curve: A Revisionist Econometric History
hlis
Response t Evans and McCallum
o
Robert G .King and M a r k W. Watson




WP-94-17

6

W r i gp prsre cniud
o k n ae eis otne
Estimating Deterministic Trends i t e
n h
Presence of S r a l Correlated Errors
eily

WP-94-19

Eugene Canjels and M a r k W. Watson

Solving Nonlinear Rational Expectations
Models by Parameterized Expectations:
Convergence t Stationary Solutions
o

WP-94-20

Albert Marcet and David A. Marshall

The Effect of Costly Consumption
Adjustment on Asset Price V l t l t
oaiiy

WP-94-21

David A. Marshall and Nayan G. Parekh

The Implications of First-Order Risk
Aversion f rAsset Market Risk Premiums
o

WP-94-22

Geert Bekaert, Robert J. Hodrick and David A. Marshall

Asset Return V l t l t with Extremely Small Costs
oaiiy
of Consumption Adjustment

WP-94-23

David A. Marshall

Indicator Properties of the Paper-Bill Spread:
Lessons From Recent Experience

WP-94-24

Benjamin M. Friedman and Kenneth N. Kuttner

Overtime, E fort and the Propagation
f
ofBusiness Cycle Shocks

WP-94-25

George J. Hall

Monetary p l c e i t e e r y 1990s—r f e t o s
oiis n h al
elcin
of th e r y 1930s
e al

WP-94-26

Robert D. Laurent

The Returns from Classroom Training f r Displaced Workers
o
Louis S. Jacobson ,Robert J .LaLonde and Daniel G. Sullivan




WP-94-27

7