The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Federal Reserve Bank of Chicago
The Effect of Part-Time Work on
Wages: Evidence from the Social
Security Rules
Daniel Aaronson and Eric French
REVISED July, 2003
WP 2001-20
The Effect of Part-Time Work on Wages:
Evidence from the Social Security Rules
Daniel Aaronson and Eric French
Federal Reserve Bank of Chicago
July 18, 2003
Abstract
This paper identi es the part-time wage e ect using hours variation caused by the
Social Security rules. We show that work hours and wages drop sharply at ages 62 and
65. We argue that the hours decline causes the wage decline, resulting in a 25 percent
wage penalty for men who cut their workweek from 40 to 20 hours. However, we nd
little evidence for such an e ect among women. We also show that models that fail to
account for the joint determination of hours and wages will understate the labor supply
response to a tax change by about 26 percent.
Comments welcome at efrench@frbchi.org and daaronson@frbchi.org. We thank Kirti Kamboj and Ken Housinger for their great assistance and Dan Sullivan and Ruilin Zhou for helpful discussions. The views of the authors do not necessarily re ect those of the Federal Reserve Bank
of Chicago or the Federal Reserve System.
Recent versions of the paper can be obtained at
http://www.chicagofed.org/economists/EricFrench.cfm/. Author correspondence to Daniel Aaronson or
Eric French, Federal Reserve Bank of Chicago, 230 S. LaSalle St., Chicago, IL 60604. Telephone (312)322-6831,
Fax (312)322-2357.
1
1 Introduction
Labor supply models typically assume that a worker receives a xed wage o er, then
chooses the number of hours to work given that wage. However, the wage o ered to workers
may be determined by the number of hours worked by an employee.1
The purpose of this paper is two-fold. First, we show how taxes a ect hours worked in
a model where hours and wages are jointly determined. Standard labor supply elasticities
measure the relationship between labor supply and the wage. Tax analysts use these estimated
labor supply elasticities to predict the labor supply response to a tax change. However, tax
analysts usually fail to account for the e ect of hours worked upon the wage. This failure
creates a problem because a tax increase not only lowers the after-tax wage because of a
change in the marginal tax rate, it also indirectly lowers the pre-tax wage through the tax
change's e ect on hours worked. Therefore, failure to account for this latter e ect leads
to an underestimate of the e ect of tax changes on the post-tax wage and consequently an
underestimate of the e ect of tax changes on labor supply.
The second purpose of this paper is to provide new estimates of the e ect of work hours
upon the wage. In order to estimate the e ect of hours worked upon the wage, we must
overcome an important identi cation problem. It is not clear whether changes in hours a ect
wages or whether changes in wages a ect hours. Previous studies that try to measure the
part-time wage e ect often use an instrumental variables strategy that employs the number
of young children in the household and other childbearing demographics as instruments for
hours worked in samples of working women.2 Presumably, increases in the number of children
cause reductions in a woman's work hours. Researchers interpret di erences in wages between
women with and without children as resulting from di erences in work hours. However,
this is a valid strategy only if young children reduce a mother's available time for work
and do not directly a ect her productivity. Furthermore, if young children also restrict the
mother's job opportunities, perhaps because she needs a exible work schedule, then her
1
See Barzel (1973), Rosen (1976), MoÆtt (1984) and Ermisch and Wright (1993) for descriptions of why
wages may vary with hours worked.
2
Rosen (1976), MoÆtt (1984), Simpson (1986), Blank (1990), Hotchkiss (1991), and Ermisch and Wright
(1993) use this strategy. Lundberg (1985) nds that lagged hours has predictive ability for wages, and argues
that this predictive power is evidence in favor of tied wage-hours o ers. We are aware of no other identi cation
strategy.
2
wages are lower not because she is a part-time worker but because she faces other work
restrictions. This would lead to an overestimate of the e ect of part-time work upon wages.
Nevertheless, examples using such instruments result in estimates of the part-time/full-time
wage di erential that are all over the board.3
In order to identify the part-time wage di erential, we take advantage of what we believe
is a better instrument for hours worked, that is the work disincentives of the Social Security
system.
At ages 62 and 65, individuals face incentives to reduce their work hours. During our
sample period, most individuals aged 62 and older are eligible for Social Security bene ts but
face an earnings test until age 69. Above the Social Security earnings test threshold level,
individuals face a high marginal tax rate on earnings. Between ages 62 and 64, bene ts lost
through the earnings test are replaced in the form of higher bene ts in the future, resulting
in about a dollar of higher bene ts in present value in the future for every dollar lost through
the earnings test. However, if individuals are liquidity constrained, it may not be until age 62,
when the early retirement provision of the Social Security rules applies, that they will have
suÆcient nancial resources to reduce their work hours. After age 65, bene ts lost through
the Social Security earnings test result in only small increases in future bene ts. Therefore,
the Social Security earnings test results in a strong incentive to reduce work hours by age
65.4
A further reason the Social Security system provides incentives to reduce work hours by
age 65 is that for many workers, health insurance is included with their job. Many individuals
who would reduce their work hours would also lose their health insurance, exposing them to
the risk of facing health problems without insurance. At age 65, all individuals who are
eligible for Social Security are also eligible for Medicare. This means that most individuals
who are age 65 and older have reasonable quality health insurance even if they lose their
employer provided insurance.
While we believe our identi cation strategy is a useful addition to the literature, a disadvantage to this approach is that we are working with the oldest of workers. Therefore, our
results are not necessarily representative of other populations of workers.
Nevertheless, using these instruments and data from the Health and Retirement Survey
3
4
See Blank (1990) for a review.
For formal evidence, see French (2000).
3
(HRS), the Panel Study of Income Dynamics (PSID), and Current Population Survey (CPS),
we nd evidence that male part-time workers earn lower wages than male full-time workers.
Depending on the speci cation and the data employed, our estimates imply that cutting
hours from 40 to 20 hours per week lowers wages by as much as 25 percent for men. Many,
but certainly not all, of our estimates are signi cant at the 10 percent level or higher and
point estimates are similar across the di erent datasets. The point estimates appear to be
relatively robust to attempts to control for confounding factors that might in uence changes
in wages at ages 62 and 65. However, the evidence for a part-time penalty among female
workers is weak.
2 Estimating the Intertemporal Elasticity of Substitution with
Tied Wage-Hours O ers
In this section, we present a standard life-cycle labor supply model augmented to include
tied wage-hours o ers. Solving the model illuminates a fundamental model misspeci cation
problem.5 An increase in the post-tax wage from a tax cut potentially leads to an increase
in hours worked. Additionally, this increase in the workweek leads to an increase in the
pre-tax wage through the tied wage-hours e ect, further increasing hours worked. Therefore,
there is a larger labor supply response to a tax change than to an equally large wage change.
Most models do not account for tied wage-hours o ers and thus ignore this latter e ect.
Therefore, the model misspeci cation problem causes tax analysts to understate the labor
supply response to a tax change.
5
A potential simultaneous equation bias is also present since hours depend upon wages and wages depend
on hours worked. Given that an increase in hours results in an increase in the wage and an increase in the
wage results in an increase in hours, the simultaneous equations bias results in an upward bias for the labor
supply elasticity if the econometrician uses OLS. However, this bias is overcome using standard instrumental
variables procedures.
4
2.1 The Intertemporal Labor Supply Model
We begin with the canonical intertemporal labor supply model,6 as in MaCurdy (1985),
augmented to account for tied wage-hours o ers. Preferences take the form:
U = E0
T
X
t=1
t
v(cit ) exp( "it =)
1
1+
hit
1 + 1
(1)
where U is the expected discounted present value of lifetime utility, cit is consumption, v(:) is
some increasing concave function, hit is hours worked, and "it is the person and year speci c
preference for work. The parameter is the intertemporal elasticity of substitution, which is
the usual object of interest in intertemporal labor supply studies. De ne Ait as assets, rt the
interest rate, and Wit (log hit ) the post-tax wage which potentially depends on hours worked:
log Wit (log hit ) = log wit (log hit ) + log(1
t );
log wit (log hit ) = it + log hit
(2)
(3)
where wit (log hit ) represents the pre-tax wage, it represents an individual's underlying productivity or technology during a speci c year and t is the tax rate. We assume that the tax
rate depends on neither wages nor hours worked. The function log hit maps work hours into
the wage. In one of the few papers that provide a theoretical explanation for the existence
of tied-wage hours o ers, Barzel (1973) suggests that at low levels of hours worked, an additional work hour increases the hourly wage because the xed costs of work (such as time
spent in training) are spread over a longer workweek, i.e. > 0:7
Maximization of (1) subject to equations (2), (3) and the dynamic budget constraint
Ait+1 = (1 + rt )(Ait + Wit (log hit )hit
cit )
(4)
6
The key results from this section do not depend on whether the model is static or dynamic. However, the
intertemporal model simpli es the analysis because it allows us to focus more on the substitution e ect of a
tax change. In static models and models with liquidity constraints, tax changes cause an additional change in
the marginal utility of wealth.
7
If > 0 then the budget set is not convex. However, equation (5) still represents an equilibrium condition
so long as < 1: This condition is satis ed for reasonable parameter values.
5
results in the labor supply function:
log hit = log
@Wit (log hit )hit
@hit
+ log it + "it
where it is the marginal utility of wealth.
hit @Wit (log hit )
Taking a rst order Taylor's series approximation around log 1 + W
@hit
it
log
@Wit (log hit )hit
@hit
(5)
h @Wit (log hit )
@ log Wit (log hit )
log Wit (log hit ) +
;
@hit
@ log hit
it
= log Wit (log hit ) 1 + it
W
(6)
noting that
@ log Wit (log hit )
= ;
@ log hit
(7)
and combining (5), (6), and (7) results in
log hit = log Wit (log hit ) + + log it + "it :
(8)
The term in square brackets, parameterized by ; is the logarithm of the opportunity cost of
time. The opportunity cost of time has two parts. The rst part arises because of increased
income from increases in hours worked, holding the wage xed. The second part arises
because of increased income from a higher hourly wage when the individual works more
hours. If changes in hours of work do not change the wage, then = 0 and equation (8)
becomes the standard estimating equation in intertemporal labor supply models.
2.2 Biases Caused by Model Misspeci cation
We are not the rst to point out that the labor supply function must be augmented to
account for the marginal e ect of work hours upon wages.8 However, we believe that we are
the rst to show analytically why failure to account for tied wage-hours o ers will produce
labor supply elasticities that are smaller than the elasticity of interest. We describe the
8
Rosen (1976), MoÆtt (1984), and Lundberg (1985) point out the same problem in static labor supply
models
6
di erence below.
Wage changes a ect hours changes in the following way:
d log it
d log hit
= 1+
d log Wit
d log Wit
!
(9)
log it
9
Assuming ddlog
Wit = 0; the labor supply response to the wage change is
d log hit
d log Wit
it
= :
(10)
log hit
is an unbiased estimator of :10
Therefore, ddlog
Wit
it
However, the parameter is no longer of interest if wages are tied to hours worked.
hit
Macroeconomists are interested in the comovement of hours and technology, d log
d it : Tax
d log hit : The labor supply
analysts are interested in the e ect of taxes on labor supply, d log(1
t )
response to both technology changes and tax changes are the same. For convenience, we
consider the e ect of a tax change on a change in hours:
!
d log hit
d log it
d log hit
= 1+
+
:
d log(1 t )
d log(1 t ) d log(1 t )
(11)
There are three objects on the right hand side of equation (11), re ecting three di erent
labor supply incentives from a tax change. The rst term arises from changes in the post-tax
wage, holding the pre-tax wage xed. A reduction in taxes causes an increase in the post-tax
wage, which in turn a ects labor supply. This is the usual object of interest in intertemporal
labor supply studies. The second term arises from the e ect of hours worked upon the wage.
If > 0; reductions in taxes cause increases in hours worked, which in turn increases the
9
The labor supply elasticity holding the marginal utility of wealth constant, or the Frisch elasticity, is
the usual object of interest in intertemporal labor supply studies. Changes in the marginal utility of wealth
are potentially correlated with wage changes. Assuming away precautionary behavior and potential liquidity
constraints, Browning et al. (1985) and MaCurdy (1985) both show that anticipated wage changes should not
be correlated with the marginal utility of wealth.
10
This result relies on the assumption that the log wage increases linearly in log hours. However, Barzel
(1973) speculates that at very long work weeks, an increase in hours might lower wages as exhaustion reduces
log hit
productivity, so w00 (log hit ) < 0: In an earlier version of this paper we showed that ddlog
is a downWit
it
ward biased estimator of if w00 (log hit ) < 0: Nevertheless, the existence of tied-wage hours o ers need not
necessarily lead to inconsistent estimates of : It is non-linearity in the wage-hours relationship that causes
inconsistent estimates of :
7
pre-tax wage (because of tied wage-hours o ers). Because the pre-tax wage increases, hours
worked increases further. The nal term is the e ect of the tax change on the marginal utility
d log it = 0; we can rearrange equation (11) as
of wealth. Assuming that d log(1
t )
d log hit
d log(1 t )
it
=
1
(12)
Comparing equations (10) and (12) shows that the labor supply response to a one percent
increase in 1
t is larger than the labor supply response to a one percent wage increase.
This result is important for two reasons. First, the empirical strategy for estimating labor
supply elasticities becomes material. Speci cally, studies that use tax changes (e.g. Eissa and
Liebman (1996)) to proxy for wage changes should nd larger labor supply elasticities than
studies that use wage changes (e.g. MaCurdy (1981), Altonji (1986), and Browning et al.
(1985)) if wages depend on hours worked. Second, analysts who use labor supply elasticities
are usually interested in the labor supply response to tax and technology changes. Labor
supply elasticity estimates obtained using wage changes will be smaller than the elasticity of
interest.
3 Empirical Strategy
Our empirical strategy is fairly transparent from gure 1. The top panel reports the
change in the average number of hours worked for all working men in the Panel Study of
Income Dynamics (PSID), Health and Retirement Survey (HRS), and the March supplement
and Outgoing Rotation Groups (ORG) of the Current Population Survey. These pro les are
constructed using xed-e ects estimators and are computed from samples of workers aged 50
to 70. The next section describes the data and sample restrictions in more detail.
Hours slowly begin to decline around age 55 but the biggest drops occur after age 61.
For example, among PSID workers between ages 61 and 62, annual work hours decline 10
percent. Hours continue to fall at ages 63 and 64 before another large 10 percent drop at
65. After age 65, hours declines are smaller in magnitude, although sample sizes are so small
that wage growth is not reliably estimated. Big drops after age 61, particularly at ages 62
and 65, are observed in the other three datasets as well.11
11
The ORG shows a less severe drop in hours at ages 62 and 65 because the ORG measures changes in the
8
The bottom panel displays average wage growth for the same male workers. Wages remain
relatively at, with perhaps some modest overall decline, between ages 50 and 61. But at
age 62, the average wage among PSID workers drops by 4.5 percent. This decline continues
from ages 63 through 66, with the biggest drop, 6.5 percent, occurring at 65. Again, these
patterns are relatively consistent with those observed for the other data sets.
Taken together, the two panels in gure 1 suggest a possible causal relationship between
hours and wages. The biggest drop in wages occurs at 62 and 65, the same ages at which
the Social Security work disincentives begin. This suggests to us that turning age 62 and 65
are possible instrumental variables that can be used to identify the part-time/full-time wage
di erential.
In order to estimate a part-time di erential, we use a standard instrumental variables
model that allows for individual-speci c xed e ects.
log hit = fi +
K
X
k=1
k
k ageit +
log wit = zi +
\
62
I fageit 62g +
K
X
k=1
65
I fageit 65g + eit
\
Æk agekit + log hit + uit
(13)
(14)
where log hit is the predicted hours level for the ith individual at time t using equation (13),
ageit is the individual's age, I fageit 62g and I fageit 65g are 0-1 indicators equal to one
when the individual is greater than 62 and 65, respectively, fi and zi are individual-speci c
xed e ects, and the error terms eit and uit are white noise.12 Note that we prefer to use
workweek. The other three surveys measure changes in the work year, thus accounting for both changes in
the number of weeks worked and changes in the average workweek.
12
The functional forms in equations (13) and (14) are consistent with the model described in Section 2 under
the following assumptions:
it
log(1
t ) = 0 +
K
X
k=1
= zi +
K
X
k=1
Æk agekit + uit ;
k agekit + 62 I fageit 62g + 65 I fageit 65g
"it = !i0
K
X
k=1
!k agekit +
9
it ;
(15)
(16)
(17)
hours worked, rather than a part-time indicator, to avoid concerns about the ad-hoc nature
of a part-time/full-time threshold and because we believe that the hours measure allows us to
exploit more informative variation.13 However, a continuous measure of hours may introduce
measurement error that biases downward our part-time wage estimate. This is a particular
problem in cases where we estimate equation (13) in di erenced form. Therefore, we present
results that use both hours worked and the part-time dummy. In light of Hotchkiss (1991)
and Ermisch and Wright (1993), we use part-time/full-time hours thresholds ranging from
30 to 38 hours per week.
Another noteworthy part of the estimating equations is the xed-e ect term. If there
is an unobserved quality di erence between part-time and full-time workers, cross-sectional
studies of the part-time wage e ect will be biased, as the part-time dummy will proxy for
latent worker quality. Failure to account for the xed e ect will lead to an omitted variables
bias in the cross section. Nevertheless, most studies of the part-time wage e ect that we are
aware of have been estimated on cross-sectional samples.14
Note that because the xed-e ect model is identi ed from wage changes, composition bias
problems (i.e. the question of whether high wage or low wage individuals become part-time
workers) are addressed if wage growth is the same for workers and non-workers. However, if
where uit and it are white noise. The parameters in equation (13) have the following interpretation:
1
= [Æ1 + 1 + log( (1 + r))] + !1 ;
(18)
= [Æk + k ] + !k ; k = 2; :::; K;
(19)
62
= 62
(20)
65
= 65
(21)
k
fi = [ + 0 + zi + log i0 ] + !i0
eit = [uit + (log it
log E0 it )] + it
(22)
(23)
where log it follows a random walk with drift term (1 + r) (MaCurdy (1985)). The drift term is captured
in 1 :
13
Hotchkiss (1991) estimates this threshold within a switching model with sample selection. She nds that
the proper average hours cuto is signi cantly higher, around 38 hours, than the standard 35 hour workweek,
and that this cuto varies across industry. The survey used in Ermisch and Wright (1993) reports woman's
own de nition of part-time. With the exception of teachers, most women's de nition of part-time corresponds
to less than 30 hours per workweek.
14
For example, Rosen (1976), Simpson (1986), Blank (1990), and Hotchkiss (1991). Ermisch and Wright
(1993) use a cross-section that includes recall of work history. However, longitudinal analyses are presented
in Lundberg (1985), Blank (1998) and Hirsch (2001).
10
individuals leave the market because of a sudden wage drop, such as from job loss, we will
not be able to include the new potential wage of those individuals. This problem will bias
wage growth upward. Because more individuals drop out of the labor force at ages 62 and
65 than at other ages, this upward bias should be more severe at these ages. Therefore, the
wage declines depicted in Figure 1 understate the true wage declines at these ages and our
estimate of the part-time wage e ect could be biased towards zero.
Because of the xed e ect, the speci cation of equations (13) and (14) is parsimonious,
re ecting only essential time-varying confounding factors. One such factor is the natural
aging process. Human capital theory posits that near the end of the life cycle, workers
should invest less in skill development, as they have fewer years left in the labor market to
recoup the investment. Therefore, wages should decline as remaining skills decline in value.
Declining wages at the end of the life cycle potentially induces declining hours worked at the
end of the life cycle (Heckman (1976)).
To solve this problem, we assume that productivity declines smoothly with age, so the
e ects of declining productivity and the declines in hours that result from declining productivity can be captured by a fourth order age polynomial. We use indicators for ages greater
than 62 and greater than 65 to capture the e ects of the Social Security System on hours
and wages. These variables should capture the change in hours and wages at the exact ages
of 62 and 65.
Other potential problems with our estimation strategy are discussed in the results section.
4 Data
We use four data sets of workers in their 50s or 60s { the Panel Study of Income Dynamics
(PSID), the outgoing rotation (ORG) of the Current Population Survey (CPS), the March
supplement of the CPS, and the Health and Retirement Survey (HRS) { to deal with our
concerns. Each data set has particular strengths for our purpose.
The long, panel structure of the PSID is particularly useful for accounting for individualspeci c attributes that might in uence wages. However, the number of workers older than
61 is limited. Between 1968 and 1997, we have 11,493 person-year observations on 1,436
separate men and 4,816 person-year observations on 685 separate women. At age 62, we
11
observe 468 men working and 163 women working. At age 65, we observe 245 men working
and 76 women working. Our PSID sample, unlike those from the other surveys, does not
include nonmarried women but does include nonmarried men.
Alternatively, the HRS has been following an older cohort, those aged 51 to 61 in 1992,
for close to a decade. After ve waves of data, the last in 2000, the full panel includes 9,545
worker-year observations on 2,945 men and 9,725 worker-year observations on 2,912 women.
The oldest cohort is 69 in 2000 but many of the respondents are still younger than 62 in
the latest waves. Furthermore, since the survey occurs every other year, respondents' labor
market activity is observed at age 62 or 65 but not both. Therefore, we only have 766 and
329 workers at ages 62 and 65, respectively. However, the detail of the questions about work
and retirement far exceeds those in the PSID.
To alleviate concerns about sample size, we used the CPS. In particular, we report results
from two samples of CPS workers { those that are in the March supplements and those in
the outgoing rotation groups (ORG). CPS respondents follow a speci c sampling timeframe;
they are surveyed for four months, are o for eight months, and then are interviewed again
for four months. Starting in 1979, respondents are in the ORG during the last month of
each four month cycle. This means all CPS respondents have two observations in the ORG,
spaced one year apart. Since there are only two observations per person, rather than add a
person-speci c xed e ect to the statistical model, we work in rst di erences. Regardless,
the ORG samples far exceed those from the PSID or HRS. We are able to match over 193
thousand workers aged 50 to 70 between 1980 and 1999, of which 7,244 are aged 62 and 3,455
are 65 during the sampling frame. The drawback to the ORG is its limited range of questions.
For example, we have no information about job tenure or bene ts.
A convenient compromise between the survey breadth of the HRS and the sample sizes of
the ORG is the March supplement of the CPS. The March surveys include a series of questions
about job characteristics, including the existence of pension plans and employer-provided
health insurance. It also includes a question about how many employers the respondent has
had over the last year, which allows us to denote job switchers and job stayers. Sample sizes
are larger than those found in the PSID and HRS but are smaller than the ORG. Roughly
87 thousand workers aged 50 to 70 are included in the matched March samples between 1979
12
and 1999, of which 3,570 are aged 62 and 1,841 are 65.15 Like the ORG, we are restricted
to only two observations per person. However, an important distinction between the two
CPS surveys is that the ORG asks about labor market activity last week, while the March
supplement describes wages and hours over the previous calendar year. These distinctions,
as we shall see, can lead to di erent inferences.
To account for the life-cycle wage pro le, all samples are restricted to workers between
the ages of 50 and 70. A worker-year is included if an individual toils between 10 and 89
hours per week (or 500 to 4,500 hours per year) and has a real wage between $3 and $100.16
These restrictions result in the loss of 11, 4, 13, and 5 percent of age 50 to 70 workers in our
PSID, HRS, March CPS, and ORG samples.17
Appendix 1 reports descriptive statistics for the key variables in our analysis. Means in
the PSID, particularly for the real hourly wage, are di erent than the other surveys because
of the greater composition of men.
5 Results
Table 1 reports the basic instrumental variables results. Each panel displays the rst
stage estimates for the age 62 and age 65 instruments (
62
and
65
in equation (13)) and
the second stage (equation (14)) parameter of interest, , the part-time wage e ect. All
regressions also include a fourth order age polynomial18 and year or survey dummies. The
results are reported separately for men and women. The top panel reports ndings when hit
is a continuous measure of hours. Alternatively, the bottom panel shows how robust these
results are to a commonly used discrete measure of part-time status, whether the individual
worked more than 35 hours per week or 1,750 hours per year.19 All standard errors are
15
However, the 1985-86 and 1995-96 March les cannot be matched. See appendix S of the Unicon Research
Corporation (1999).
16
In the CPS, we also discard workers with more than a 400 percent wage change across years. This a ects
a small minority of respondents and has very little impact on the results.
17
The di erence between samples that exclude 4 or 5 versus over 10 percent is due to when part-year workers
(who work over 10 hours per week) are dropped. For work-related questions based on last week's activity (ORG
and HRS), many part-year workers are excluded since they would have zero hours at the survey date. For
questions based on last year's activity (March CPS and PSID), we drop workers who work fewer than 500
hours per year. Therefore, part-year workers in the March CPS and PSID might be excluded due to our hours
restrictions.
18
Both the Schwarz and Akaike information criterion, described in Judge et al. (1985), suggest either a
second or a third order polynomial.
19
The results are not sensitive to using other reasonable cuto s, such as 30 or 38 hours per week.
13
Huber-White and corrected to account for multiple observations within individuals.
As we already saw in gure 1, table 1 shows the high degree of association between age
62 and 65 indicators and our measures of hours worked. For men, hours drop at least an
additional two percent per year, and in some instances as much as ten percent, at these older
ages, relative to what is expected based on the fourth order age polynomial. Furthermore,
t-statistics usually exceed three for each of the age instruments, suggesting that these drops
are not only economically but also statistically important. For women, the change in hours
at ages 62 and 65 are not as striking. With the exception of the PSID, hours drop by roughly
1 to 2 percent above what would be expected by the age polynomial, with mixed statistical
importance.
The row labeled \predicted hours change" show our estimate of the causal impact of hours
on wages. For men, the point estimates are fairly stable across three of the four datasets,
clustered tightly between 0.40 and 0.50 for all but the ORG and statistically signi cant at the
ten percent level for all but the HRS. Furthermore, when using the discrete hours threshold,
the results remain relatively similar and, in all cases, are di erent from zero at standard
signi cance levels.20
However, for women, the evidence is less clear. Part-time coeÆcients are statistically
signi cant only when using the ORG, suggesting that the causal impact of hours on wages for
female workers is quite weak. While the PSID estimate is large, precision is poor. Combined
with the nonexistent e ects arising from the HRS and CPS March data, we conclude that
there is little evidence for a causal impact of hours on wages for women, given the available
data.
It is interesting to note that the ORG appears to be an outlier among both genders. We
believe this is due to di erences in the period being examined. In the ORG, respondents
are asked about work activity in the last week. For the other three surveys, respondents are
asked about their experience in the last year.21 Rosen (1976) also nds a larger e ect when
using hours last week than when using hours last year. He speculates that many individuals
working fewer than 1750 hours per year are not part-time workers, but full time workers who
are only working part of the year. Therefore, \part-time" workers using the last year measure
20
When we estimate the wage-hours relationship with xed e ects using OLS, the point estimates are of the
wrong sign and statistically signi cant. This is not a surprise, as a number of studies have found that OLS is
biased downwards in the presence of measurement error. We discuss this issue in more detail below.
21
The HRS hours measure is hours worked last week multiplied by weeks usually worked per year.
14
are actually a mixture of part-time and full-time workers using the last week measure. An
alternative explanation is that the last year query could bias our results towards zero since
the survey mixes hours and wage measures across ages (e.g. the age 62 indicator is actually
a combination of a year when the respondent is 61 and 62). In fact, when we run the March
CPS regressions but use respondents in the outgoing rotation months (thus allowing us to
use the last week variables), the point estimates are very similar to those reported for the
ORG, albeit with much less precision. We take this as evidence that the 0.4 estimates for
men are perhaps a lower bound of the true relationship between hours and wages.
We also attempted two variations on the instrument choice. First, we added an interaction
between age 65 and whether the individual had employer-provided health insurance at age 64.
Many individuals with employer-provided health insurance will lose their health insurance
if they leave their job or cut their work hours. Upon eligibility for Medicare at age 65,
an individual's health insurance is no longer tied to full-time job status. Therefore, this
interaction takes advantage of variation in hours associated with the advent of Medicare at
age 65 for those who rely on employer-provided health insurance prior to 65. However, the
presence of employer-provided health insurance could be a proxy for the quality of a job and
therefore be an invalid instrument. Nevertheless, the results are virtually identical to those
reported in table 1. Second, we interact the age instruments with indicators for changes in
the Social Security earnings test in 1990 and 1996. These speci cations were run on the CPS,
the only dataset that had suÆcient samples during the di erent policy years. But again, the
results were virtually identical to those reported in table 1.
Still, there may be alternative explanations for why wages decline at ages 62 and 65.
First, within a job, the workweek is often xed. Therefore, much of the variation used to
identify hours changes could come from job switchers. Ruhm (1990) nds that many older
workers switch to part-time bridge jobs, potentially in a di erent industry than their career
job. Therefore, wage changes could be due to productivity changes that may result from the
loss of industry-, rm-, or job-speci c human capital.
We address this issue by restricting the sample to workers who have not switched employers in the last year.22 Therefore, identi cation is based on within-employer variation in hours
22
The March CPS supplement asks respondents to self-report how many employers they had during the
previous year, where multiple part-time employers are counted as one employer. We de ne an employer switch
as having more than one employer over the year. The HRS de nition is based on a similar question. The
15
worked. For the CPS, this meant throwing out any worker-year observation that involved an
employer change.23 For the HRS and PSID, we control for employer changes by including a
full set of employer dummy variables that separately identi es hours and wages changes that
are coming from employer switches and from nonswitches.24
Table 2 reports the results of such an exercise using the men from the PSID, HRS, and
March CPS. The results generally con rm our earlier ndings. When we limit identi cation
to men who stay at their employers, point estimates remain in the 0.40 range, relatively
similar to those reported in table 1. Given the smaller sample sizes, standard errors rise,
causing statistical signi cance to be severely impacted in some cases. But given how stable
our estimates appear to be across datasets, we believe that larger samples would show that
the part-time e ect survives this test.
Alternatively, we can control for changes in industry and occupation to account for any
loss of rm- and industry-speci c human capital. While this is a less satisfactory way to deal
with this problem, it is another robustness check of the importance of latent changes in a
worker's endowments. The results appear robust to such controls. For example, reestimating
the CPS outgoing rotation group regressions for men but including controls for changes in
occupation and industry alters our point estimates (and standard errors) from 0.95 (0.36)
to 0.85 (0.39). Likewise, among CPS March respondents, occupation and industry controls
changes our estimates from 0.42 (0.22) to 0.48 (0.22). Stratifying the sample to exclude
industry and occupation switchers also has no discernable e ect.
A second concern about the estimation procedure is that rms may structure their compensation and pension plans in order to encourage workers to leave by age 62 or 65. Lazear
(1981) implies that employers o er below-productivity compensation when a worker is young
but reward her with above-productivity compensation at the end of her career. This large
PSID asks how long the individual has been with the current employer. If the individual has been with that
employer less than one year, we consider her to have taken a new employer.
23
This could result in not enough variation to identify the part-time e ect. If this were the case, standard
errors would blow up. However, while the amount of hours variation within employers is obviously smaller,
it is hardly inconsequential. Among male CPS respondents between ages 50 and 70, the mean log change in
annual hours worked for employer stayers and employer switchers is -0.018 and -0.075. Among 62 and 65 year
olds, the mean hours change for an employer stayer is roughly half of that for an employer changer.
24
An alternative strategy is to analyze a group of workers who were displaced involuntarily at older ages
and returned to the labor market. Any wage loss that might result from another employer switch would likely
have occurred after their initial displacement. However, displacement rates for older workers are low, and
those that are displaced often do not return to the labor force. Therefore, such an analysis is not feasible on
standard data sets such as the Displaced Worker Survey.
16
payday when old motivates young workers to work hard. However, this scheme potentially
causes the worker to remain with the rm longer than is optimal. In order to induce the
worker to leave, rms often o er low pension accrual to employees in their 60s (Gustman
et al. (1998)). This induces workers to leave their old, high wage job for new jobs with
potentially lower wages. Therefore, there may be a drop in wages at ages 62 and 65 because
workers are moving to jobs with lower wages, not because they are working fewer hours.
To sidestep this problem, we look at samples of workers who do not have pension plans.
Moreover, our results on workers who remain at the same rm should inform us about the
severity of this problem. The second panel of columns in table 2 shows the results on a
restricted sample of male workers without pension plans. With the HRS, we restrict the
sample to those without de ned bene t plans. For the March CPS, we restrict the sample
to those without any pension plan. Naturally, this increases the standard errors. Still our
inferences, while less assured, are similar in nature. The nal panel of columns in table 2
combines the job stayers and pension restrictions. Again, although precision is hampered by
the small sample size, we see little reason to recast inferences based on these results. Clearly,
we are less con dent of our ndings, but this is likely due to a lack of older workers that
are not switching jobs and do not have pension plans, than any instability in the parameter
estimates.
A third consideration is the presence of measurement error. Because variation in measured
hours and wage changes is dominated by measurement error (Altonji (1986), Bound et al.
(2000)), measurement error is of particular concern in studies of the inter-relation between
hours and wages. This problem is especially severe because the wage is constructed by
dividing earnings by hours, meaning that measurement error in wages and hours are negatively
correlated by construction. This causes the OLS estimate to be biased downwards, and is
a similar problem to the \division bias" problem in the labor supply literature. Because we
use age to instrument for wage and hours changes, our instrument should be uncorrelated
with measurement error in hours and wage changes at the population level. Although age is
uncorrelated with measurement error at the population level, age will in general be correlated
with measurement error in the small sample (Nelson and Startz (1990), Staiger and Stock
(1997)).
To address the small sample bias problem, we vary our sample sizes and re-estimate the
17
part-time wage e ect on the smaller sample using a \jackknife" procedure (Efron (1982)). If
the small sample bias problem is unimportant, then reducing the sample size should make
our estimates less precise, but should not a ect the point estimates in any predictable way.
However, if small sample bias is important, reducing the sample size should bias results
towards the OLS estimate (Nelson and Startz (1990)).
We ran our two-stage regressions 1,000 times each on random samples of 10, 20, and 30
thousand March CPS men between the ages of 50 and 70. Over the 1,000 repetitions, a
random sample of 10,000 men results in a part-time penalty point estimate that averages
0.41 but has a standard deviation around that average of 1.09. The impact of doubling the
sample to 20,000 is to alter the point estimates ever so slightly, to 0.43, but substantially
reduce the standard deviation around the point estimate to 0.33. Finally, when the sample is
raised to 30,000 men, the average coeÆcient is again barely a ected (0.43), but the standard
deviation drops to 0.20. This pattern is consistent with that seen across datasets with varying
sample sizes, as well as the sample restrictions reported in table 2. Point estimates remain
remarkably stable, although precision can be seriously hindered by small samples, suggesting
that small sample bias is relatively unimportant.
A nal concern is that compensation packages change with age. At older ages, workers
may demand nonpecuniary bene ts at the cost of lower wages. While it is important to note
that one of these key bene ts, reduced hours, is precisely the parameter that we are trying
to identify, other forms of compensation are not included in our wage measure.
We attempt to control for this problem by inferring the value of employer-provided health
insurance and pensions using the March CPS and HRS, the only two datasets that have
information on whether respondents earn these bene ts.25 Since these datasets do not report
the dollar value of bene ts, we infer their value using average employer-paid health plan costs
reported in EBRI (1999, tables 3.3 and 4.2) and age-speci c pension accrual values from
Gustman et al. (1998).26 Table 3 reports results using this more complete compensation
measure. For both the CPS and HRS, the results are similar to those reported in table 1.
25
According to the BLS' annual Employer Costs for Employee Compensation release, our measure of wages,
which should include paid leave and bonuses, comprises 82 percent of total compensation. Health insurance
and retirement savings encompass another 6 and 3.5 percent. The remaining 8.5 percent of compensation
consists almost entirely of legally required bene ts that should not vary much in our sample.
26
Because we lack data on employer-provided health plan costs by demographic group, each person with a
plan is assigned the average cost, as computed by EBRI. However, pension values are age-speci c.
18
Using the hours measure, elasticity point estimates continue to be around 0.40. Therefore,
we believe o setting nonwage compensation is unlikely to be signi cantly biasing our results.
Furthermore, while changes in other latent nonmonetary bene ts, such as a less stressful
workplace or more exible schedule, are potentially problematic, many of our results suggest that this is unlikely to explain the main results. First, inclusion of an age polynomial
will account for compensation mix choices that change smoothly with age. While it is still
plausible that there are discrete changes in non-pecuniary bene ts at ages 62 and 65, we
believe that our speci cation somewhat limits this concern. Second, including industry and
occupation dummies, which made very little di erence to our results, may eliminate obvious
career changes that would be consistent with the nonpecuniary bene ts story. Finally, our
results are robust to restricting the sample to job stayers. Again, this does not eliminate the
possibility that workers are trading wages for nonpecuniary bene ts even within- rms, but
the robustness of the results to these checks suggests to us that our results are consistent
with the existence of a part-time wage penalty for older men.
6 Conclusions
This paper assesses the impact of wage-hours ties on the intertemporal labor supply elasticity. We present new empirical evidence on wage-hours ties among older workers that takes
advantage of the work disincentives of the Social Security system. Using this identi cation
strategy, we nd evidence that male part-time workers earn lower wages than male full-time
workers, although there is little evidence of such an e ect among women. Depending on the
speci cation and the data employed, our estimates imply that cutting older mens' hours from
40 to 20 hours per week lowers wages by up to 25 percent.
Not accounting for such a relationship leads to an underestimate of the e ect of tax
changes on the post-tax wage and consequently labor supply. Table 4 quanti es this e ect.
In particular, equation (12) shows the relationship between the labor supply response to
d log hit
changes in taxes (or equivalently, technology), d log(1
t )
log hit
changes in the wage, ddlog
Wit
it
it
; the labor supply response to
= ; and the tied wage-hours coeÆcient : Recall that
log hit
most studies measure ddlog
and nd the variable to usually be between 0 and 0.5 for
Wit
it
continuously employed men, but are often greater than 1 for women (e.g. Heckman and
19
MaCurdy (1980)). Most of our estimates of the tied wage-hours coeÆcient are in the
log hit
neighborhood of 0.4. Assuming ddlog
= :5 and = :4; equation (12) shows that the
Wit
it
labor supply response to a technology change is 26% greater (0.5 versus 0.63) than the labor
supply response to a wage change for men. However, using an estimate of the wage elasticity
of 1 for women, the di erence could be substantial, perhaps a 67% di erence (1 versus 1.67),
although it is important to note that we nd very weak evidence of any wage-hours tie for
women. Of course, our estimates are based on older cohorts of workers and therefore may not
be representative of a wage-hours tie for prime-age workers. Nevertheless, if older workers
are representative of the population, it suggests that the interrelationship between hours and
the wage is economically important to labor supply estimation.
20
References
[1] Altonji, Joseph. \Intertemporal Substitution in Labor Supply: Evidence from Microdata." Journal of Political Economy 94 (June 1986): S176-S215.
[2] Barzel, Yoram. \The Determination of Daily Hours and Wages." Quarterly Journal of
Economics 87 (May 1973): 220-38.
[3] Blank, Rebecca. \ Are Part-Time Jobs Bad Jobs?" In A Future of Lousy Jobs? The
Changing Structure of U.S. Wages, edited by Gary Burtless, pp. 123-155. Washington
DC: Brookings Institution, 1990.
[4] Blank, Rebecca. \Labor Dynamics and Part-Time Work." Research in Labor Economics
17 (1998): 57-93.
[5] Blomquist, N.. \Labour Supply in a Two-Period Model: The E ect of a Nonlinear
Progressive Income Tax." Review of Economic Studies 52 (July 1985): 515-524.
[6] Bound, John; Brown, Charles; and Mathiowetz, Nancy. \Measurement Error in Survey
Data." In Handbook of Econometrics 5, edited by James Heckman and Ed Leamer,
forthcoming.
[7] Browning, Martin; Deaton, Angus; and Irish, Margaret. \A Pro table Approach to
Labor Supply and Commodity Demands Over the Life-Cycle." Econometrica 53 (May
1985): 503-543.
[8] Efron, Bradley, The Jacknife, The Bootstrap, and Other Resampling Plans. Philadelphia: Society for Industrial and Applied Mathematics, 1982.
[9] Eissa, Nada and Liebman, Je ery. \Labor Supply Response to the Earned Income Tax
Credit." Quarterly Journal of Economics 111 (May 1996): 605-37.
[10] Employee Bene t Research Institute, EBRI Health Bene ts Databook, Washington, DC:
EBRI-ERF, 1999.
[11] Ermisch, John and Robert Wright. \Wage O ers and Full-Time and Part-Time Employment by British Women." Journal of Human Resources 28 (Winter 1993): 111-133.
21
[12] French, Eric, \The E ects of Health, Wealth, and Wages on Labor Supply and Retirement Behavior." working paper 00-2, Federal Reserve Bank of Chicago, 2000.
[13] Gustman, Alan; Mitchell, Olivia; Samwick, Andrew; and Steinmeier, Thomas. \Evaluating Pension Entitlements." Unpublished manuscript. Hanover, NH: Dartmouth College,
1998.
[14] Gustman, Alan, and Steinmeier, Thomas. \A Structural Retirement Model," Economet-
rica54 (May 1986): 555-584.
[15] Heckman, James. \A Life-Cycle Model of Earnings, Learning, and Consumption." Jour-
nal of Political Economy 84 (August 1976): S11-S44.
[16] Heckman, James and MaCurdy, Thomas. \A Life Cycle Model of Female Labour Supply." Review of Economic Studies 47 (January 1980): 47-74.
[17] Hirsch, Barry. \Why do Part-Time Workers Earn Less? The Role of Worker and Job
Skills." Unpublished manuscript. San Antonio, TX: Trinity College, 2001.
[18] Hotchkiss, Julie. \The De nition of Part-Time Employment: A Switching Regression
Model with Unknown Sample Selection." International Economic Review 32 (November
1991): 899-917.
[19] Lazear, Edward. \Agency Earnings Pro les, Productivity, and Hours Constraints."
American Economic Review 71 (September 1981): 606-620.
[20] Lettau, Michael. \Compensation in Part-Time Jobs versus Full-Time Jobs: What If the
Job Is the Same?" Economics Letters 56 (September 1997) 101-106.
[21] Lundberg, Shelly. \Tied Wage-Hours O ers and the Endogeneity of Wages." Review of
Economics and Statistics 67 (August 1985): 405-10.
[22] MaCurdy, Thomas. \An Empirical Model of Labor Supply in a Life-Cycle Setting."
Journal of Political Economy 89 (December 1981): 1059-85.
[23] MaCurdy, Thomas. \Interpreting Empirical Models of Labor Supply in an Intertemporal
Framework with Uncertainty." In Longitudinal Analysis of Labor Market Data , edited
by J. Heckman and B. Singer. Cambridge: Cambridge University Press, 1985.
22
[24] MoÆtt, Robert. \The Estimation of a Joint Wages-Hours Labor Supply Model." Journal
of Labor Economics 2 (October 1984): 550-566.
[25] Nelson, Charles and Startz, Richard. \Some Further Results on the Exact Small Sample
Properties of the Instrumental Variables Estimator." Econometrica 58 (July 1990): 967976.
[26] Rosen, Harvey. \Taxes in a Labor Supply Model with Joint Wages-Hours Determination." Econometrica 44 (May 1976): 485-507.
[27] Ruhm, Christopher. \ Bridge Jobs and Partial Retirment." Journal of Labor Economics
8 (October 1990): 482-501.
[28] Simpson, Wayne. \Analysis of Part-Time Pay in Canada." Canadian Journal of Eco-
nomics 19 (November 1986): 798-807.
[29] Staiger, Douglas and Stock, James. \Instrumental Variables with Weak Instruments."
Econometrica 65 (May 1997): 557-586.
[30] Unicon Research Corporation. \Current Population Survey: March Data Dictionary."
College Station, TX: Stata Press,1999.
23
24
-0.046
(0.033)
-0.082
(0.046)
-0.051
(0.039)
-0.101
(0.049)
0.612
0.678
(0.369) (0.606)
-0.066
(0.025)
-0.095
(0.035)
0.517
0.863
(0.304) (0.748)
-0.082
(0.021)
-0.097
(0.031)
-0.041
(0.015)
0.002
(0.029)
-0.044
(0.021)
0.021
(0.038)
0.498
-0.012
(0.357) (0.280)
-0.030
(0.019)
-0.077
(0.030)
0.371
0.024
(0.313) (0.315)
-0.043
(0.015)
-0.076
(0.026)
HRS
Males Females
Table 1:
Basic FE, IV Estimates
-0.015
(0.009)
-0.026
(0.012)
-0.013
(0.012)
-0.061
(0.016)
37,618
0.282
-0.007
(0.130) (0.239)
-0.052
(0.009)
-0.097
(0.012)
0.424
0.176
(0.224) (0.492)
-0.035
(0.007)
-0.055
(0.010)
CPS March
Males Females
Sample size
11,493 4,816
9,545
9,725
49,590
Notes:
Regressions include a fourth order age polynomial and year or survey dummies.
Part-time threshold is based on a 35 hour, 50 week workyear.
Second stage: hours e ect
from wage regression
worker is age 65
worker is age 62
First stage: Point estimates for
instruments used in hours regression
B. Using part-time dummy
(35 hour workweek cuto )
Second stage: hours e ect
from wage regression
worker is age 65
worker is age 62
First stage: Point estimates for
instruments used in hours regression
A. Using continuous hours measure
PSID
Males Females
0.691
(0.306)
-0.027
(0.008)
-0.034
(0.011)
0.663
(0.386)
-0.023
(0.005)
-0.014
(0.007)
104,485 89,200
1.127
(0.429)
-0.018
(0.007)
-0.033
(0.009)
0.949
(0.362)
-0.021
(0.004)
-0.027
(0.006)
CPS ORG
Males Females
A. Using continuous hours measure
First stage: point estimates for
instruments used in hours regression
worker is age 62
worker is age 65
Second stage: hours e ect
from wage regression
Employer stayers only
PSID
HRS March
CPS
Workers
Employer stayers
without pensions without pensions
HRS
March
HRS
March
CPS
CPS
-0.073
(0.025)
-0.058
(0.043)
-0.048
(0.022)
-0.092
(0.033)
-0.042
(0.015)
-0.070
(0.028)
-0.034
(0.007)
-0.046
(0.010)
-0.029
(0.015)
-0.040
(0.019)
-0.048
(0.022)
-0.094
(0.033)
-0.024
(0.015)
-0.036
(0.019)
0.314
0.401
0.413
0.235
0.972
(0.340) (0.336) (0.254) (0.341) (0.640)
0.246
1.205
(0.336) (0.813)
-0.082
(0.032)
-0.080
(0.054)
-0.051
(0.028)
-0.092
(0.040)
B. Using part-time dummy
(35 hour workweek cuto )
First stage: point estimates for
instruments used in hours regression
worker is age 62
worker is age 65
Second stage: hours e ect
from wage regression
-0.029
(0.019)
-0.073
(0.031)
-0.048
(0.009)
-0.088
(0.013)
-0.051
(0.028)
-0.091
(0.036)
-0.060
(0.018)
-0.069
(0.023)
0.372
0.490
0.298
0.219
0.470
(0.315) (0.374) (0.148) (0.333) (0.289)
Sample size
6,961
9,234
46,625 4,775
16,160
Notes:
Regressions include a fourth order age polynomial and year or survey dummies.
Part-time threshold is based on a 35 hour, 50 week workyear.
Table 2:
Robustness Checks: Males only
25
-0.057
(0.018)
-0.075
(0.023)
0.231
0.518
(0.329) (0.294)
4,775
14,794
HRS
A. Using Continuous hours measure
Second stage: hours e ect from wage regression
CPS March
0.533
0.421
(0.337) (0.222)
B. Using part-time dummy (35 hour workweek cuto )
Second stage: hours e ect from wage regression
0.725
0.311
(0.424) (0.137)
Sample size
8,235
Notes:
Compensation includes wages, pensions, and health insurance.
See text for details.
Table 3:
39,647
Basic FE, IV Estimates Using Compensation Measures: Males only
d log hit
d log Wit
0
.5
1
1.5
Table 4:
Value of
0
0
.5
1
1.5
.2
0
.56
1.25
2.14
d log hit
d log(1 t )
26
.4
0
.63
1.67
3.76
it
=
.6
0
.71
2.5
15
:
1
1
log hit
ddlog
Wit
it
Appendix 1
Descriptive Statistics
Means (Standard Deviation in Parentheses)
PSID
HRS
CPS
March
ORG
Hours
average, ages 50-70
age 62
age 65
1,941
1,835
1,611
1,981
1,842
1,625
2,000
1,896
1,738
39.4
38.0
35.0
Part-time dummy
average, ages 50-70
age 62
age 65
0.303
0.372
0.560
0.199
0.282
0.398
0.205
0.280
0.412
0.205
0.245
0.377
Real hourly wage (96 dollars)
average, ages 50-70
17.26
age 62
16.55
age 65
15.26
16.07
15.26
13.46
16.08
15.29
14.74
14.93
14.39
12.64
0.705
0.741
0.704
0.516
0.535
0.567
0.568
0.576
0.557
0.543
0.536
0.528
16,309
631
321
19,270
766
329
87,208
3,570
1,841
193,685
7,244
3,455
Male
average, ages 50-70
age 62
age 65
Sample Size
age 50-70
age 62
age 65
Years covered
1969-96 1992-2000 1979-99 1979-99
Notes:
Hours are an annual measure for the PSID, HRS, and March CPS
and a weekly measure for the ORG. For the part-time threshold,
the annual measure is based on less than 1,750 hours
and the weekly measure on less than 35 hours.
27
Hours
Growth,
by
Age
=
=
~
=
~
~
2
~
"'
~
0
=
=
=
~
d·::--~-~
1'.
j I
I
~
=
=
=
=
=
'
PSID
CPS March
CPS ORG
HRS
'
6
'
6
50
52
54
56
58
60
62
64
66
68
70
'
Age
Wage
Growth,
by
Age
=
=
/
~
=
~
~
0
0
w
~
~
=
=
=
=
=
~
...;/......
',
......... ;
I
.
I Y.
'i._!
'
=
=
=
'
6
'
I
I
\1
I
PSID
CPS MarTh
CPS ORG
HRS
I
I
I I
I
6~~~==~~~~~~~~~~
I 50
52
54
56
58
60
62
64
66
68
70
Age
Figure 1:
Hours and Wage Growth
28
Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Plant Level Irreversible Investment and Equilibrium Business Cycles
Marcelo Veracierto
WP-98-1
Search, Self-Insurance and Job-Security Provisions
Fernando Alvarez and Marcelo Veracierto
WP-98-2
Could Prometheus Be Bound Again? A Contribution to the Convergence Controversy
Nicola Cetorelli
WP-98-3
The Informational Advantage of Specialized Monitors:
The Case of Bank Examiners
Robert DeYoung, Mark J. Flannery, William W. Lang and Sorin M. Sorescu
WP-98-4
Prospective Deficits and the Asian Currency Crisis
Craig Burnside, Martin Eichenbaum and Sergio Rebelo
WP-98-5
Stock Market and Investment Good Prices: Implications of Microeconomics
Lawrence J. Christiano and Jonas D. M. Fisher
WP-98-6
Understanding the Effects of a Shock to Government Purchases
Wendy Edelberg, Martin Eichenbaum and Jonas D. M. Fisher
WP-98-7
A Model of Bimetallism
Francois R. Velde, and Warren E. Weber
WP-98-8
An Analysis of Women=s Return-to-Work Decisions Following First Birth
Lisa Barrow
WP-98-9
The Quest for the Natural Rate: Evidence from a Measure of Labor Market Turbulence
Ellen R. Rissman
WP-98-10
School Finance Reform and School District Income Sorting
Daniel Aaronson
WP-98-11
Central Banks, Asset Bubbles, and Financial Stability
George G. Kaufman
WP-98-12
Bank Time Deposit Rates and Market Discipline in Poland:
The Impact of State Ownership and Deposit Insurance Reform
Thomas S. Mondschean and Timothy P. Opiela
WP-98-13
Projected U.S. Demographics and Social Security
Mariacristina De Nardi, Selahattin ¤mrohoro—lu and Thomas J. Sargent
WP-98-14
Dynamic Trade Liberalization Analysis: Steady State, Transitional and
Inter-industry Effects
Michael Kouparitsas
WP-98-15
1
Working Paper Series (continued)
Can the Benefits Principle Be Applied to State-local Taxation of Business?
William H. Oakland and William A. Testa
WP-98-16
Geographic Concentration in U.S. Manufacturing: Evidence from the U.S.
Auto Supplier Industry
Thomas H. Klier
WP-98-17
Consumption-Based Modeling of Long-Horizon Returns
Kent D. Daniel and David A. Marshall
WP-98-18
Can VARs Describe Monetary Policy?
Charles L. Evans and Kenneth N. Kuttner
WP-98-19
Neighborhood Dynamics
Daniel Aaronson
WP-98-20
Inventories and output volatility
Paula R. Worthington
WP-98-21
Lending to troubled thrifts: the case of FHLBanks
Lisa K. Ashley and Elijah Brewer III
WP-98-22
Wage Differentials for Temporary Services Work:
Evidence from Administrative Data
Lewis M. Segal and Daniel G. Sullivan
WP-98-23
Organizational Flexibility and Employment Dynamics at Young and Old Plants
Jeffrey R. Campbell and Jonas D. M. Fisher
WP-98-24
Extracting Market Expectations from Option Prices:
Case Studies in Japanese Option Markets
Hisashi Nakamura and Shigenori Shiratsuka
WP-99-1
Measurement Errors in Japanese Consumer Price Index
Shigenori Shiratsuka
WP-99-2
Taylor Rules in a Limited Participation Model
Lawrence J. Christiano and Christopher J. Gust
WP-99-3
Maximum Likelihood in the Frequency Domain: A Time to Build Example
Lawrence J.Christiano and Robert J. Vigfusson
WP-99-4
Unskilled Workers in an Economy with Skill-Biased Technology
Shouyong Shi
WP-99-5
Product Mix and Earnings Volatility at Commercial Banks:
Evidence from a Degree of Leverage Model
Robert DeYoung and Karin P. Roland
WP-99-6
2
Working Paper Series (continued)
School Choice Through Relocation: Evidence from the Washington D.C. Area
Lisa Barrow
Banking Market Structure, Financial Dependence and Growth:
International Evidence from Industry Data
Nicola Cetorelli and Michele Gambera
WP-99-7
WP-99-8
Asset Price Fluctuation and Price Indices
Shigenori Shiratsuka
WP-99-9
Labor Market Policies in an Equilibrium Search Model
Fernando Alvarez and Marcelo Veracierto
WP-99-10
Hedging and Financial Fragility in Fixed Exchange Rate Regimes
Craig Burnside, Martin Eichenbaum and Sergio Rebelo
WP-99-11
Banking and Currency Crises and Systemic Risk: A Taxonomy and Review
George G. Kaufman
WP-99-12
Wealth Inequality, Intergenerational Links and Estate Taxation
Mariacristina De Nardi
WP-99-13
Habit Persistence, Asset Returns and the Business Cycle
Michele Boldrin, Lawrence J. Christiano, and Jonas D.M Fisher
WP-99-14
Does Commodity Money Eliminate the Indeterminacy of Equilibria?
Ruilin Zhou
WP-99-15
A Theory of Merchant Credit Card Acceptance
Sujit Chakravorti and Ted To
WP-99-16
Who’s Minding the Store? Motivating and Monitoring Hired Managers at
Small, Closely Held Firms: The Case of Commercial Banks
Robert DeYoung, Kenneth Spong and Richard J. Sullivan
WP-99-17
Assessing the Effects of Fiscal Shocks
Craig Burnside, Martin Eichenbaum and Jonas D.M. Fisher
WP-99-18
Fiscal Shocks in an Efficiency Wage Model
Craig Burnside, Martin Eichenbaum and Jonas D.M. Fisher
WP-99-19
Thoughts on Financial Derivatives, Systematic Risk, and Central
Banking: A Review of Some Recent Developments
William C. Hunter and David Marshall
WP-99-20
Testing the Stability of Implied Probability Density Functions
Robert R. Bliss and Nikolaos Panigirtzoglou
WP-99-21
3
Working Paper Series (continued)
Is There Evidence of the New Economy in the Data?
Michael A. Kouparitsas
WP-99-22
A Note on the Benefits of Homeownership
Daniel Aaronson
WP-99-23
The Earned Income Credit and Durable Goods Purchases
Lisa Barrow and Leslie McGranahan
WP-99-24
Globalization of Financial Institutions: Evidence from Cross-Border
Banking Performance
Allen N. Berger, Robert DeYoung, Hesna Genay and Gregory F. Udell
WP-99-25
Intrinsic Bubbles: The Case of Stock Prices A Comment
Lucy F. Ackert and William C. Hunter
WP-99-26
Deregulation and Efficiency: The Case of Private Korean Banks
Jonathan Hao, William C. Hunter and Won Keun Yang
WP-99-27
Measures of Program Performance and the Training Choices of Displaced Workers
Louis Jacobson, Robert LaLonde and Daniel Sullivan
WP-99-28
The Value of Relationships Between Small Firms and Their Lenders
Paula R. Worthington
WP-99-29
Worker Insecurity and Aggregate Wage Growth
Daniel Aaronson and Daniel G. Sullivan
WP-99-30
Does The Japanese Stock Market Price Bank Risk? Evidence from Financial
Firm Failures
Elijah Brewer III, Hesna Genay, William Curt Hunter and George G. Kaufman
WP-99-31
Bank Competition and Regulatory Reform: The Case of the Italian Banking Industry
Paolo Angelini and Nicola Cetorelli
WP-99-32
Dynamic Monetary Equilibrium in a Random-Matching Economy
Edward J. Green and Ruilin Zhou
WP-00-1
The Effects of Health, Wealth, and Wages on Labor Supply and Retirement Behavior
Eric French
WP-00-2
Market Discipline in the Governance of U.S. Bank Holding Companies:
Monitoring vs. Influencing
Robert R. Bliss and Mark J. Flannery
WP-00-3
Using Market Valuation to Assess the Importance and Efficiency
of Public School Spending
Lisa Barrow and Cecilia Elena Rouse
WP-00-4
4
Working Paper Series (continued)
Employment Flows, Capital Mobility, and Policy Analysis
Marcelo Veracierto
Does the Community Reinvestment Act Influence Lending? An Analysis
of Changes in Bank Low-Income Mortgage Activity
Drew Dahl, Douglas D. Evanoff and Michael F. Spivey
WP-00-5
WP-00-6
Subordinated Debt and Bank Capital Reform
Douglas D. Evanoff and Larry D. Wall
WP-00-7
The Labor Supply Response To (Mismeasured But) Predictable Wage Changes
Eric French
WP-00-8
For How Long Are Newly Chartered Banks Financially Fragile?
Robert DeYoung
WP-00-9
Bank Capital Regulation With and Without State-Contingent Penalties
David A. Marshall and Edward S. Prescott
WP-00-10
Why Is Productivity Procyclical? Why Do We Care?
Susanto Basu and John Fernald
WP-00-11
Oligopoly Banking and Capital Accumulation
Nicola Cetorelli and Pietro F. Peretto
WP-00-12
Puzzles in the Chinese Stock Market
John Fernald and John H. Rogers
WP-00-13
The Effects of Geographic Expansion on Bank Efficiency
Allen N. Berger and Robert DeYoung
WP-00-14
Idiosyncratic Risk and Aggregate Employment Dynamics
Jeffrey R. Campbell and Jonas D.M. Fisher
WP-00-15
Post-Resolution Treatment of Depositors at Failed Banks: Implications for the Severity
of Banking Crises, Systemic Risk, and Too-Big-To-Fail
George G. Kaufman and Steven A. Seelig
WP-00-16
The Double Play: Simultaneous Speculative Attacks on Currency and Equity Markets
Sujit Chakravorti and Subir Lall
WP-00-17
Capital Requirements and Competition in the Banking Industry
Peter J.G. Vlaar
WP-00-18
Financial-Intermediation Regime and Efficiency in a Boyd-Prescott Economy
Yeong-Yuh Chiang and Edward J. Green
WP-00-19
How Do Retail Prices React to Minimum Wage Increases?
James M. MacDonald and Daniel Aaronson
WP-00-20
5
Working Paper Series (continued)
Financial Signal Processing: A Self Calibrating Model
Robert J. Elliott, William C. Hunter and Barbara M. Jamieson
WP-00-21
An Empirical Examination of the Price-Dividend Relation with Dividend Management
Lucy F. Ackert and William C. Hunter
WP-00-22
Savings of Young Parents
Annamaria Lusardi, Ricardo Cossa, and Erin L. Krupka
WP-00-23
The Pitfalls in Inferring Risk from Financial Market Data
Robert R. Bliss
WP-00-24
What Can Account for Fluctuations in the Terms of Trade?
Marianne Baxter and Michael A. Kouparitsas
WP-00-25
Data Revisions and the Identification of Monetary Policy Shocks
Dean Croushore and Charles L. Evans
WP-00-26
Recent Evidence on the Relationship Between Unemployment and Wage Growth
Daniel Aaronson and Daniel Sullivan
WP-00-27
Supplier Relationships and Small Business Use of Trade Credit
Daniel Aaronson, Raphael Bostic, Paul Huck and Robert Townsend
WP-00-28
What are the Short-Run Effects of Increasing Labor Market Flexibility?
Marcelo Veracierto
WP-00-29
Equilibrium Lending Mechanism and Aggregate Activity
Cheng Wang and Ruilin Zhou
WP-00-30
Impact of Independent Directors and the Regulatory Environment on Bank Merger Prices:
Evidence from Takeover Activity in the 1990s
Elijah Brewer III, William E. Jackson III, and Julapa A. Jagtiani
WP-00-31
Does Bank Concentration Lead to Concentration in Industrial Sectors?
Nicola Cetorelli
WP-01-01
On the Fiscal Implications of Twin Crises
Craig Burnside, Martin Eichenbaum and Sergio Rebelo
WP-01-02
Sub-Debt Yield Spreads as Bank Risk Measures
Douglas D. Evanoff and Larry D. Wall
WP-01-03
Productivity Growth in the 1990s: Technology, Utilization, or Adjustment?
Susanto Basu, John G. Fernald and Matthew D. Shapiro
WP-01-04
Do Regulators Search for the Quiet Life? The Relationship Between Regulators and
The Regulated in Banking
Richard J. Rosen
WP-01-05
6
Working Paper Series (continued)
Learning-by-Doing, Scale Efficiencies, and Financial Performance at Internet-Only Banks
Robert DeYoung
The Role of Real Wages, Productivity, and Fiscal Policy in Germany’s
Great Depression 1928-37
Jonas D. M. Fisher and Andreas Hornstein
WP-01-06
WP-01-07
Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy
Lawrence J. Christiano, Martin Eichenbaum and Charles L. Evans
WP-01-08
Outsourcing Business Service and the Scope of Local Markets
Yukako Ono
WP-01-09
The Effect of Market Size Structure on Competition: The Case of Small Business Lending
Allen N. Berger, Richard J. Rosen and Gregory F. Udell
WP-01-10
Deregulation, the Internet, and the Competitive Viability of Large Banks and Community Banks
Robert DeYoung and William C. Hunter
WP-01-11
Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards
Christopher R. Knittel and Victor Stango
WP-01-12
Gaps and Triangles
Bernardino Adão, Isabel Correia and Pedro Teles
WP-01-13
A Real Explanation for Heterogeneous Investment Dynamics
Jonas D.M. Fisher
WP-01-14
Recovering Risk Aversion from Options
Robert R. Bliss and Nikolaos Panigirtzoglou
WP-01-15
Economic Determinants of the Nominal Treasury Yield Curve
Charles L. Evans and David Marshall
WP-01-16
Price Level Uniformity in a Random Matching Model with Perfectly Patient Traders
Edward J. Green and Ruilin Zhou
WP-01-17
Earnings Mobility in the US: A New Look at Intergenerational Inequality
Bhashkar Mazumder
WP-01-18
The Effects of Health Insurance and Self-Insurance on Retirement Behavior
Eric French and John Bailey Jones
WP-01-19
The Effect of Part-Time Work on Wages: Evidence from the Social Security Rules
Daniel Aaronson and Eric French
WP-01-20
7
@FedFRASER