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orKing raper series
W a g e Differentials for T e m p orary Services
Work: Evidence from Administrative Data
Lewis M. Segal and Daniel G. Sullivan
Working Papers Series
Research Department
Federal Reserve Bank of Chicago
December 1998 (WP-98-23)
FEDERAL RESERVE BANK
OF CHICAGO
Wage Differentials for Temporary Services Work:
Evidence from Administrative Data
Lewis M. Segal
Infometrics
Daniel G. Sullivan
Federal Reserve Bank of Chicago
December, 1998
The view s expressed in this paper are so lely those o f the authors and are not official p osition s o f the Federal Reserve
Bank o f C hicago or the Federal R eserve System . Thanks are ow ed to Ken H ousinger for very capable assistance and
to sem inar participants at the Federal R eserve System R egional System C om m ittee and the A m erican E conom ics
A ssociation m eetings, esp ecially D aniel Ham mermesh.
Abstract
We use administrative data from the unemployment insurance system of the Sate of Washington
to study the magnitude of the wage differential associated with work in the temporary services
industry. We find that temp wage rates are 15% to 20% below the levels that might have been
expected based on trends during other periods in workers’ careers even after controlling for differ
ences between temps and other workers. Comparing temp wages to wages immediately before
and after temp work or to the wages on non-temp jobs begun during the same period as workers
were in the temp industry yields estimates of the temp work penalty as low as 10%.
I. Introduction
Employment in the temporary services industry has grown very rapidly over the last quarter cen
tury. Indeed, according to the Bureau of Labor Statistics’ (BLS) Current Employment Survey
(CES), the industry’s employment has increased at an annual rate of over 11 percent since 1972,
bringing its share of total U.S. employment from essentially zero to over two percent. This rapid
growth has raised concerns because many view temporary service positions as “bad jobs.” For
instance, CES data show that average hourly earnings for production and nonsupervisory workers
in the industry are 25% or more below national averages.1 However, such simple comparisons fail
to account for what may be substantial differences in skill levels and other factors between tempo
rary services workers and those employed in other industries, a defect that we attempt to remedy
in this paper.
From at least one perspective, a wage penalty for temporary services work would be surprising.
Temporary services workers - hereafter “temps”- bear more risk of unemployment than other
workers and one might reasonably expect that risk to be compensated by h ig h er wages. The
industry’s workers, who overwhelmingly work under the direction of client firms on what are
often short assignments, usually have no guarantee that they will be offered further work when
those assignments are complete. As a result, temps are more likely than other workers to face
unemployment or fewer than desired hours of work. For instance, in previous work (Segal and
Sullivan (1997)) using matched data from Current Population Surveys, we found that temps were
more than twice as likely (6.5% versus 2.6%) as other workers to be unemployment a year later
and that in a given week they were four times as likely (20% versus 5%) to find themselves invol
untarily working part time. As observers since Adam Smith have noted, workers, such as those in
the construction trades, facing similar risks often earn compensating differentials.2
Of course, temps differ from construction and other workers who may receive compensating dif
ferentials for unemployment risk in a number of dimensions. In particular, unionization is virtu
ally nonexistent in the temporary services industry. More generally, the typically very short job
1. M oreover, in previous work using the B L S ’s Current Population Survey (C P S) (S egal and Sullivan
(1 997)), w e have show n that tem ps are m uch less likely to receive benefits such as health insurance from
their em ployers.
2. For Sm ith ’s analysis see B ook I, Chapter 10 o f The Wealth ofNations (N ew York: M od em Library, 1937).
For som e evid en ce on com pensating differentials for unem ploym ent risk see, for exam ple, A bow d and A shenfelter (1 9 8 1 ) and Topel (1 984).
1
tenures of temps preclude the formation of groups of “insiders” along the lines of Lindbeck and
Snower (1988). Without the kind of bargaining power unionization or the existence of entrenched
insiders brings, temps may not be able to capture compensation for unemployment risk. Of
course, this would run counter to the standard theory of compensating differentials which assumes
a competitive labor market.
If temps do suffer a wage penalty, one explanation may be that it is compensated for by a positive
and more salient job amenity - increased acquisition of human capital. Though, many critics of
the temporary services industry claim that temp work not only is undesirable in the short term
because of low current wages, but even more undesirable in the long term because its short job
spells are inconducive to on-the-job training, industry advocates maintain that temps receive a
o
good deal of training. This latter view is supported by some survey evidence reported in Krueger
(1993) and by Bureau of Labor Statistics data analyzed in Autor (1998). A large portion of the
training provided by temporary services firms is in g e n e ra l skills, for instance in the use of com
puter software.3
4 In addition to technical skills, temp workers may be able to acquire useful infor
mation about how well suited they are to a particular field, knowledge that is harder to obtain in
conventional jobs. It is thus entirely possible that p e r
u n it o f tim e w o rk ed ,
temps acquire more
human capital that most other workers, a long-term advantage that may offset the short-term dis
advantage of lower wages and benefits. From this perspective, whether temps earn positive or neg
ative wage differentials depends on the importance of two partially offsetting job amenities:
increased risk of unemployment versus more rapid accumulation of general human capital.
As we noted above, simple comparisons of temp worker wages to those of workers in other indus
tries may not be indicative of true wage differentials associated with temp work. Temp workers
differ from the norm in a number of dimensions. For instance, they are typically younger, more
likely to be women, and are less likely to have a college degree, factors associated with lower
wage rates.5 In previous papers (Segal and Sullivan (1995, 1997)) we presented evidence suggest
ing that a significant part of the wage gap between temps and other workers was due to differences
3. S ee, for exam p le, N A T SS (1 9 9 4 , 1996a, and 1996b).
4. A utor (1 9 9 8 ) notes that the fact that m uch o f the general training is provided upfront, before tem ps g o on
assignm ents for their em ployers, presents a ch allen ge for so m e versions o f the theory o f human capital
b ecause workers cou ld c h o o se to take the training w ithout ultim ately accepting any assignm ents, leavin g the
tem porary services firm w ithout anyw ay to recoup its training exp en ses.
5. S ee, for exam ple, Segal and Sullivan (1 9 9 5 , 1997).
2
in various observable worker characteristics, as well as to other characteristics of jobs such as
part-time status and coverage by a collective bargaining agreement. Moreover, we found that
when we controlled for worker-specific fixed effects that the estimated wage penalty associated
with temp work dropped to around 3%.
However, the above results relied on a matched CPS outgoing rotation group sample in which the
identification of temp status was problematic because of the frequency with which temp workers
misreport their industry. This problem of measurement error in the temp indicator was likely
exacerbated in the fixed effect specifications since the fact that workers are only observed twice,
one year apart, implies that the effects of measurement error are amplified relative to both levels
regressions and fixed effects specifications with more than two time periods. We argued that the
relatively high frequency of transitions between temp and perm work reduced the possible magni
tude of this bias. Nevertheless, the issue of how much of the temp-perm wage gap is explained by
worker characteristics is worth revisiting.
In this paper, we use a new data source, administrative files from the Unemployment Insurance
(UI) system of the state of Washington, to study the effect of temporary services employment on
workers’ wages and earnings. As we discuss further below, administrative data has a number of
important advantages for studying these issues. These include large sample sizes and long and
complete records of workers’ career histories. There are also disadvantages. Most importantly, we
have no demographic or occupational information about the workers we study, which means that
we cannot study how results differ according to workers’ occupation, a factor we found to be
important in our previous work.
We find that temp wage rates are 15% to 20% below the levels that might have been expected
based on trends during other periods in workers’ careers even after controlling for differences
between temps and other workers. However, we also find that the periods in which workers take
jobs in the temporary services industry tend to be ones in which their wages likely would have
been lower in non-temp jobs as well. Comparing temp wages to wages immediately before and
after temp work or to the wages on non-temp jobs begun during the same period as workers were
in the temp industry yields lower estimates of the temp work differential. In the latter case the dif
ferential is approximately -10%.
3
II. Data
The primary data source for this paper is a 10% sample of quarterly wage records from Washing
ton State covering the years 1984 to 1994. This sample was created as part of the Continuous
Wage and Benefit History (CWBH) program that collected Unemployment Insurance (UI) data
from several states for late 1970s and early 1980s.6 Of the states that participated in the original
CWBH program, Washington is one of the few to have continued to create data samples for use by
researchers. Moreover, to our knowledge, Washington is the only state that provides administra
tive data on hours of work.
Each quarter, employers covered by the state’s UI system are required to report total earnings and
hours worked for each of their employees. The main categories of workers not covered are the
self-employed and federal government workers. Our 10% sample of these records is based on the
last two digits of workers’ Social Security Numbers (SSN). This file, which includes worker and
firm IDs, the four digit SIC code of the employer, and worker earnings and hours, contains nearly
100 million records. Large sample sizes are very helpful because temporary service workers are
still only a small fraction of the labor force. Using the SIC code on the UI administrative data, we
are able to identify about 1,400 temporary services workers in the first quarter of 1984, a figure
that rises to over 6,000 by the last quarter of 1994.7
Using the UI data allows us to follow workers’ careers at a quarterly frequency over an eleven
year span from 1984 to 1994. Thus we are able to observe workers’ wages for significant periods
before and after their period of temporary services employment. We also get a nearly complete
record of workers’ employment relationships. This is important because temporary services jobs
are frequently second jobs and thus would be missed in data sources that only record workers’ pri
mary jobs. Finally, because the records are used to compute benefit eligibility and levels, mea
surement errors are likely to be fewer than in survey data sources in which inaccuracies have no
consequences for those reporting the data.
6. S ee, for exam ple, A nderson and M eyer (1 9 9 4 ).
7. Temporary services firms are those w ith SIC cod e 7 3 6 2 up until 1986. In 1987 and after they are in SIC
7363 alon g w ith em p lo y ee leasin g firms also know n as P rofessional E m ployer O rganizations, or P EO s. A s
w e d iscu ss below , the m ism easurem ent caused by the p o ssib le con fu sion o f leased and temporary workers is
likely to be m inim al in W ashington State.
4
There are, of course, also drawbacks to using administrative data. As already mentioned, a major
one is the lack of any demographic or occupational information on workers. We compensate for
the lack of the typical human capital controls in our wage equations by relying on the longitudinal
nature of our data to estimate models with fixed effects and individual-specific time trends. Such
strategies should eliminate most sources of bias in our estimates of wage differentials. However,
lack of demographic and occupational data does prevent us from determining whether our results
for temporary services wage gaps differ according to workers’ age, race, sex, or occupation, the
latter being a factor we found in previous work to make a significant difference to estimated wage
differentials and mobility patterns.
o
Another difficulty associated with the use of administrative data is the lack of any direct means of
distinguishing between cases in which workers are unemployed for a full quarter, are working in
the uncovered sector, are working under another social security number, or have moved out of
state. All of these possibilities result in there being no record for the worker’s SSN in a particular
quarter. For our analysis of average wage rates, this inability to distinguish missing data from
truly zero earnings does not represent a major problem. However, we also present some evidence
on total earnings levels associated with temp work whose interpretation depends on how we treat
the lack of a wage record.
Yet another difficulty is that although firms are required to report hours, in practice they some
times do not. In fact, about 8% of quarterly wage records do not report positive hours. Unfortu
nately, temporary services firms fail to report hours information about three times more often than
this overall rate. Without valid information on hours we are unable to compute an average wage
rate and thus cannot use such observations in wage comparison models. If the true wage rates
associated with these missing observations were very different from the norm, our results would
be potentially misleading. We know of no reason to think that these missing wage rates would be
unusual. Missing hours data, however, is more common when earnings levels are low, probably
indicating that job tenure was very short.
8. Segal and Sullivan (1 9 9 5 , 1997) found significant differences betw een w hite-collar, pink-collar and bluecollar tem ps. For exam ple, for w h ite collar workers, the w age penalty associated w ith temp work w as less
and tem ps w ere m ore likely to remain tem ps one year later. For blue-collar workers, the w age penalty was
larger and tem ps w ere less likely top remain tem ps a year later. R esults for pink-collar tem ps w ere generally
in betw een those for w hite- and blue-collar temps.
5
Finally, despite the fact that these data are used for administrative purposes, what appear to be
keypunch or other errors do occur. For instance, very high or very low implied wage rates are
sometimes observed as are cases in which earnings rise then fall by a factor of ten or more over a
three quarter period, suggesting that a decimal place was shifted. We excluded from our analyses
cases that appear to be measurement errors.
Table 1 shows the growth of temporary services employment levels and employment shares in
Washington State and nationally. The rate of growth of-temporary services employment in Wash
ington has been slightly faster than that of the nation as a whole, but the pattern over time is fairly
similar. The shares of employment accounted for by the industry in Washington State, which are
shown in Figure 1, are also reasonably similar to those for the nation.9 This is reassuring since it
suggests that our findings for Washington State may generalize to the nation as a whole. More evi
dence in this regard comes from Washington State Department of Employment Security (1997)
which compares the occupational shares in temporary help supply in the Seattle metropolitan area
to those for the nation as a whole using the BLS’s Occupational Compensation Survey: Tempo
rary Help Supply Services for 1989 and 1994. They find that employment shares for most occupa
tions in Seattle are similar to those of the nation. In particular, shares in executive, administrative
and managerial; sales and marketing; and clerical and administrative support are very similar,
though shares for professional specialty and technical and related support are somewhat higher
than nationally, while those for blue-collar occupations are somewhat lower.
A final difference between Washington State and the rest of the nation is the lower fraction of
leased workers in SIC 7363. The SIC 7363 category contains both temporary services firms and
employee leasing firms, also known as professional employer organizations (PEOs). This latter
group of firms assume the existing work forces of other firms, performing all the administrative
work associated with employing workers, such as writing pay checks and paying taxes, but have
no role in recruiting or training workers. Their employees are typically long-term workers tied to
the firms they are leased to. Since our interest is in temporary services employment, we view it as
9. The som ew hat higher fractions show n for W ashington State m ay be partially due to the fact that the rates
are for work so m e tim e in a quarter w h ile those for the nation are for work so m e tim e in a m onth. B ecau se
turnover in the industry is esp ecially high, fractions o f workers em ployed in the industry rise relatively rap
idly w ith the length o f tim e interval. For instance, in other work (S egal and Sullivan 1997), w e have found
that the fraction o f workers em p loyed so m e tim e during a tw o year interval is approxim ately 5% in W ashing
ton State.
6
a plus that the 1992 Census of Services Industries reported that only about 3% of SIC 7363 work
ers in Washington are leased, compared to about 23% nationally.10
III. Effects of Temp Work on Wages
In this section we present our evidence on the magnitude of temp wage differentials using the
Washington State administrative data. As noted above, aberrant data values occasionally occur
that would tend to obscure the main message in the data. So we eliminated observations that
seemed likely to be mistakes.1011 In particular, we eliminated observations with quarterly hours
above 1,040 (=13 times 80), quarterly earnings above $50,000, average hourly wages below $1 or
above $100, or which implied an hourly wage that is a factor of ten or more away from a worker’s
average over the whole 1984-1994 period. In order to keep the computations manageable we also
limited the data on workers who were never temps to a 10% sample. The resulting data set, in
which dollar figures were converted to real 1990 levels using the standard CPI-U, had about 8.9
million observations.
As noted above, our empirical strategy is to control for differences between temps and other
workers by estimating models containing individual-specific constants and time trends. However, to facilitate comparisons to unadjusted differences in means we begin by presenting estimates of
the following simple statistical model:
(!)
y u,
=
+
+
where y ij l is the log of the wage for worker i in job j in quarter
endar quarters,
D ij t
t,
the P; are fixed effects for cal
is a dummy variable that is one when the worker is employed by a temporary
services firm and zero otherwise,
y
is the impact of temp work, and e„f is an error term with the
usual ideal properties. The Pf control for the tendency of wages to grow over time as well as sea
sonal patterns and recessions. Otherwise, however, model (1) is equivalent to a cross-sectional
difference in mean wages between temps and other workers.
10. W ashington State Department o f E m ploym ent Security (1997).
11. T hough outliers appear in administrative data, one advantage is that they tend to be extrem ely w ild outli
ers that are easy to distinguish from valid data and thus outlier bounds can be set quite w ide.
7
The estimate of y in model (1) (shown in the top-left of Table 2) is -0.391 (with a standard error of
0.002).12 This difference, which in terms of simple percentages translates into a 47.8% wage dif
ferential, is significantly larger than those found in national CES data for production workers. One
reason may be the inclusion of non-production workers. Almost all temps count as production
workers in the CES, but in other industries, 20% or more of the highest paid workers are elimi
nated from the CES. However, it seems likely that the true, cross-sectional difference is at least
somewhat higher in Washington State than nationally.
As we have noted, temps differ from other workers in numerous dimensions, so estimates of
model (1) are unlikely to capture the true wage differential associated with temp work. Any per
manent differences in the characteristics of temp and other workers can be controlled for by esti
mating a standard fixed-effect specification:
(2)
y ijt
= <x .+ P, + y D ijt
+ e.j t
which differs from (1) by the inclusion of separate constants for each worker. The effects of any
variables which, for a given worker, do not change over time, would be absorbed into these
worker-specific constants. The estimate of y in model (2) (shown in the middle row of Table 2) of
-0.167 (standard error 0.002) is considerably smaller than that for model (1). This must reflect the
fact that workers who hold temp jobs typically have lower earnings even when they are employed
in other industries.
By holding constant any unchanging, individual-specific differences between temps and other
workers, model (2) comes closer to capturing the true wage differential associated with temp
work. However, it may not go far enough. There may be other unobserved differences between
temps and other workers that are not constant over time. If these differences are changing at a
nearly constant rate over time, however, they may be accounted for by a model containing individ
ual-specific time trends in addition to individual-specific constants:
12. The standard errors show n in Table 2 are probably som ew h at optim istic. In particular, if in contrast to the
ideal assum ption m ade about the error term in m odel (1 ), there are error com ponents that are com m on to all
jo b s in a quarter o f a given type - i.e. tem p and other - then the estim ated standard errors in Table 2 are too
sm all. H ow ever, w hen w e lim it ourselves to a data set in w h ich all tem p jo b s are averaged together to form a
sin gle observation and all perm jo b s are averaged together to form another observation, and re-estim ate the
analogue o f m od el (1), w e obtain very sim ilar point estim ates and estim ated standard errors that are on ly
about 25% higher than those in Table 2 (though the 0 .0 0 2 s d o ch an ge to 0 .0 0 3 s after rounding). W e prefer to
w ork w ith the data set in w hich observations correspond to jo b s because it facilitates the estim ation o f so m e
what richer m o d els below.
8
(3)
y ijt =
a- + ° y + P,+
which differs from (2) by the inclusion of individual-specific time slopes.
Because of our lack of the standard human capital controls, it may be especially important to
employ model (3). For instance, the standard human capital specification v/ould include a qua
dratic in experience and experience squared, or equivalently (in this context) age and age squared:
(4)
y ,j, =
a,. + |3, + 0 |A.j + 02A^ + ... + e,v,
where A it is worker i ’s age at time
t.
However, if the worker’s birth date is
b{
then A it =
t —b {.
Substituting this relation into (4) introduces worker-specific time trends:
(5)
y ijt =
a / + p , ' - 2 0 2V + - + e(/,
Model (3) has been found by Heckman and Hotz (1989) to yield improved nonexperimental esti
mates of training programs and by Jacobson, LaLonde, and Sullivan (1993a) to be useful in the
analysis of data similar to that employed here.
The estimate of y based on model (3) (shown in the bottom row of Table 2) - -0.152 (standard
error 0.002) - is slightly lower in magnitude than that based on fixed effects alone. Since workers
are more likely to have spells of temp work later in the sample period, this evidently reflects the
fact that those who work as temps tend to have lower wage time slopes in addition to lower wage
levels.
Controlling for individual-specific constants and time trends greatly reduces the magnitude of the
estimated wage differential associated with temp work. However, a log wage penalty of over 15%
would, in the context of the standard competitive model of compensating differences, imply a sig
nificant positive amenity related to faster human capital acquisition or some other factor. The esti
mated wage differential is also considerably higher than those we obtained using CPS data in our
previous work (Segal and Sullivan (1997)). This would be consistent with major biases due to
measurement error in our previous work. In addition, some of the difference may be due to differ
ences between Washington State and the rest of the nation, a factor hinted at by the high unad
justed differential obtained from model (1).
Estimates based on model (3) show that temp wage rates are considerably lower than might have
been expected based on trends observed earlier and later in workers’ careers. However, anecdotal
9
and other evidence suggests that workers frequently turn to temp work after having suffering
some career setback such as loss of a job due to a layoff or plant closing.1^ Such events may be
associated with substantial reductions in wages and earnings that would not reflect temp work p e r
se,
but the circumstances that led them to accept temp work.14 In this case, the trends observed at
times significantly removed from the dates at which workers take temp jobs may not yield a valid
comparison.
One way to begin to this issue is to allow for “effects” of temp work in periods immediately
before and after their spells of temp work.To keep things relatively simple, we eliminated from
our sample workers who had more than one spell of temp work, where a temp spell is defined as a
sequence of consecutive quarters in which a worker held at least one temp job. The right hand col
umn of Table 2 shows the effect of this sample restriction on the estimates of the models we have
already discussed. For the individual-specific trends specification, the estimate - -0.160 (with a
standard error of 0.003) - is just slightly higher in magnitude when we limit the sample to workers
with at most one spell of temp employment.
We then created a series of dummy variables representing the number of quarters before or after
£
the temp spell, D ^ = 1 if the quarter t is k quarters after the temp spell. If k is negative then
D ijt
= 1 that many quarters before the temp spell starts. In particular, the dummy
ous specifications is denoted by
D ° ijt .
D t -t
in previ
We then estimated models of the form
8
(6)
y ijt =
««+° v + P , + X
° k^ k
+v
k>- 8
The parameters
yk
now measure the effect of temp work k quarters after the temp spell. The
model is identified by the assumption that
yk
= 0 in period more than eight quarters removed
from the temp work spell.
13. For exam ple, Farber (1 9 9 8 ) finds that workers reporting displacem ent in the 1994 D isp la ced Worker
Supplem ent to the C PS are som ew hat m ore lik ely to report being tem ps in a m atched extract from the 1995
C ontingent Worker CPS Supplem ent.
14. E vidence on the adverse con seq u en ces o f jo b displacem ent can be found in, for exam p le, Topel (1 9 9 0 ),
Ruhm (1 9 9 1 ), Jacobson, LaLonde, and Sullivan (1 9 9 3 a , 1993b, 1993c) and is surveyed in F a llick (1 9 9 6 ) and
K letzer (1998).
10
Estimates of the y k for k = - 8 to k = 8 are plotted in Figure 2. Several features of the plot are
notable. First, the estimates of the wage differential associated with periods immediately before
and after temp work are negative. This indicates that these periods are associated with events lead
ing to workers’ having lower wages even when they are not working as temps. These effects tend
to zero as the period is further removed from the time of the temp spell.15 This suggests that the
choice of an eight quarter “window” in model (6) is not particularly restrictive. Indeed we obtain
very similar results with windows of six or ten quarters. Second, the estimate associated with
temp quarters themselves (shown in the top left of Table 3) is slightly larger in magnitude than
that based on model (3) - -0.161 (.004) versus -0.160 (.003). This is because the quarters of non
temp work that are inside the eight quarter window, during which wages tend to be lower, are
removed from the effective comparison group. However, when we compare the temp work indica
tor coefficients to the coefficients for the quarter before and the quarter after temp work, the dif
ference is smaller than the simple estimate based on model (3).
A simple, upper bound estimate of the true wage differential associated with temp work taking
account the special circumstances in which workers accept temp jobs is
y01 = Y0 - (y_j + Yj) / 2 , the difference between the coefficients on the temp work indicator and
the average of the quarters right around the temp work spell. This quantity is an upper bound for
the magnitude of the temp work effect because the
approaches zero. Thus using
k =
\k\
yk
coefficients become more negative as
k
= 1 rather than the theoretically preferable, but unobservable,
0, understates the size of the drop in non-temp wages that would have occurred in the quar
ters workers accept temp jobs. The estimate of Y0i based on model (6) is -0.140 (.005).
Model (6) assumes that the temp work wage differential is constant over time. This assumption is
relaxed in the model whose estimates are shown in the second column of Table 3 (labeled Model
(6a)). In this specification a time trend is interacted with the temp job indicator so that the indica
tor coefficient measures the differential in the first quarter of temp work and the coefficient on the
time trend interaction shows by how much the differential changes each additional quarter the
temp spell lasts. The estimates indicate that the differential tends to be larger at the beginning of
temp spells, shrinking about 1.4 percentage points each quarter the job lasts. At such a rate it
15. This effect is clearer in Figure 3, which as we discuss below is based on a richer specification that more
satisfactorily represents the data.
11
would still take several years of temp work before the differential shrunk to zero. The fact that the
temp work wage differential shrinks as temp spells last longer is also consistent with the fact that
the differential increased when we limited the sample to workers with at most one temp work
spell.
We argued that y01 = yQ- (y_} +
y 1) / 2
was likely an over estimate of the temp wage differen
tial after taking into account the circumstances that lead to workers accepting temp jobs because
the temp wage effect in non-temp work period was increasing as the quarter approached the temp
period. To get an estimate of where workers wages in other jobs were headed in the temp period,
we added an indicator for a job being a “new perm job” - that is, for the job being outside the tem
porary services industry and having begun during the period the worker was a temp. We excluded
continuing perm jobs because they likely would have included many jobs temps would have
recently been forced to leave and thus would not be indicative of the kind of jobs temp workers
would have been able to get.
Results of adding this indicator are shown in the third column of Table 3; in the fourth column the
interactions of time trends with the temp and new perm indicators are included. For the latter, Fig
ure 3 also plots the new estimates of the
yk
coefficients, adding the level of the new perm coeffi
cient to the plot. As can be seen, when the new perm indicator is added to the model, the estimates
of the temp indicator increase in magnitude. This is because the perm jobs that are taken out of the
comparison group - those beginning during the quarters of the temp spell - are ones of abnor
mally low wages. Indeed, the coefficient on the new perm indicator in the last column is -0.111
(.006), indicating that perm jobs begun in the same quarters workers were employed in the tempo
rary services industry were about 11% below expectations based on trends in the periods before
and after temp work.
The difference between the temp work indicator and the new perm indicator - -0.107 (.006)
would seem to be a reasonable estimate of the true temp wage differential once account is taken of
the special circumstances likely surrounding the period of workers’ employment in the temporary
services industry. The coefficients on the time trend interactions indicate that the differentials
between both temp jobs and new perm jobs and what would have been expected on the basis of
period outside the eight quarter window shrink over time. The differential closes very slightly
12
more slowly - 0.134 (.0017) versus 0.139 (.0045) for temp jobs than new perm jobs, a difference
that is marginally statistically significant.
IV. Conclusion
We found that there is a definite negative wage differential associated with temp work. This is true
even after we control for worker-specific fixed effects and time trends. Comparing temp wages to
what would have been expected on the basis of wages trends at other times in workers’ careers
suggests a differential of 15% to 20%. But, up to half of this effect appears to be due to factors
associated with temp work rather than to temp work p e r
se.
When we compare temp wages to
more reasonable indicators of the non-temp opportunities temp workers might have had, the dif
ferential is only around 10%.
Of course, even a wage penalty of 10% is quite significant. Interpreted in terms of the competitive
theory of compensating differentials, it would indicate that temps significantly value the increased
opportunity to acquire human capital or some other non-wage aspect of temp work, especially
given the increased risk of unemployment that it entails. Alternatively, the wage penalty may be a
manifestation of temp workers’ lack of bargaining power.
13
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14
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15
Table 1: Temporary services employment levels and shares, U.S. and Washington State
Washington State
Total U.S.a
Period
Employment1*
Sharec
Employment
Share
1984:Q4
17.04
0.95
674.00
0.70
1985:Q4
20.03
1.0913
773.67
0.79
1986:Q4
21.92
1.1422
880.33
0.88
1987:Q4
32.08
1.4898
1045.00
1.01
1988:Q4
34.32
1.5969
1137.33
1.09
1989:Q4
41.34
1.7345
1236.33
1.14
1990:Q4
43.67
1.7578
1279.33
1.17
1991:Q4
40.91
1.6334
1300.00
1.20
1992:Q4
44.59
1.7688
1494.33
1.37
1993:Q4
49.14
1.8855
1785.33
1.60
1994:Q4
60.14
2.24
2125.00
1.84
1984:Q4 to 1994:Q4
253%d
1.29e
a. Average of October, November, and December.
b. In 1,000s
c. In percent of employment.
d. Percentage growth
e. Change in share
16
215%
1.14
Table 2: Estimates of the temp log wage differential - temp dummy only
Control variables
Full sample
Workers with at
most one temp spell
Quarter dummies
-0.391
(.002)
-0.390
(.002)
Quarter dummies and worker fixed
effects
-0.167
(.002)
-0.180
(.002)
Quarter dummies, worker fixed
effects, and worker-specific time
trends
-0.152
(.002)
-0.160
(.003)
17
Table 3: Estimates of log wage effects3
Variable
Model (6)
Model (6a)
Model
(6b)
Model (6c)
Temp job indicator
-0.161
(.004)
-0.186
(.004)
-0.195
(.004)
-0.218
(.004)
Indicator for one quarter before
temp job
-0.025
(.005)
-0.025
(.005)
-0.050
(.005)
-0.050
(.005)
Indicator for one quarter after
temp job
-0.017
(.005)
-0.017
(.005)
-0.047
(.005)
-0.048
(.005)
Temp job time slope
0.0139
(.0017)
Indicator for new perm job
0.0134
(.0017)
-0.093
(.004)
Perm job slope
Temp job indicator minus average
of indicators for one quarter before
and after.
-0.111
(.006)
0.0139
(.005)
-0.140
(.005)
temp job indicator minus new
perm job indicator.
-0.165
(.005)
-0.146
(.005)
-0.169
(.005)
-0.102
(.005)
-0.107
(.006)
a. Sample restricted to workers with at most one temp spell. All model include quarter- specific
fixed effects and worker-specific fixed effects and time trends.
18
Figure 1: Employment share of Temporary Services, monthly U.S. and quarterly Washing
ton State
YEAR
U. S . :
solid
Wa s hi ngt on:
dashed
19
Figure 2: Estimates of temp effect in period before and after temp work
estimate:
solid
K
confidence i n t e r v a l :
20
dashed
Figure 3: Estimates of temp effect in quarters before and after temp work
estimate:
solid
K
confidence i n t e r v a l :
21
da s he d
@FedFRASER