Performance Pay, the Gender Earnings Gap and
Parental Status
John S. Heywood
Daniel Parent
Department of Economics
Department of Applied Economics
University of Wisconsin - Milwaukee
HEC Montréal
June 2014
Abstract
We show that the large gender earnings gap at the top of the distribution (the
glass ceiling) and the motherhood penalty are associated with each other and both are
uniquely associated with performance pay. The US gender earnings gap in performance
pay jobs exceeds that in non-performance pay jobs. Yet, this larger gap is driven
exclusively by the comparison between mothers and fathers with the unexplained gender
earnings gap among parents in performance pay jobs exceeding 50 percent at the top
of the earnings distribution. Mothers earn substantially less than childless females
in performance pay jobs while fathers earn substantially more than childless males in
performance pay jobs. These differences by parental status are muted or completely
absent in non-performance pay jobs. We confirm sorting patterns associated with these
differentials and suggest alternative theoretical motivations for the observed patterns.
1
Introduction
The suggestion that performance pay influences gender earnings differences has a long history. Four decades ago Gunderson (1975) argued that basing pay on objective performance
criteria should reduce gender earnings differences and this argument remains a fundamental
part of the broader and even more long standing view that formalizing organizational wage
setting shrinks gender earnings differences (Elvira and Graham (2002)). Yet, this argument
sits uneasily alongside evidence that the growth in performance pay has brought far greater
inequality. Lemieux, MacLeod and Parent (2009) demonstrate that the growth in performance pay jobs combined with the increasing returns to skill in those jobs account for nearly
all of the growth in wage inequality at the top of the US distribution over the 1980s and
1990s. Moreover, the role played by performance pay differs by race as Heywood and Parent
(2012) show that the unexplained US black earnings differential is larger in performance pay
jobs and massively so at the top of earnings distribution with the unexplained differential
reaching over 30 percent.
This sets the stage for our examination of the role of performance pay on the distribution
of the US gender earnings differential. This examination is particularly important as it has
been suggested that a glass ceiling holds back the advancement and earnings of women at
the top of the distribution (see for example Matsa and Miller (2011)) and as performance
pay is far more common at the top of the distribution. We show that the unexplained female
earnings gap is larger in performance pay jobs than in non-performance pay jobs. Crucially,
this larger gap is driven entirely by a much larger gender gap between mothers and fathers
in performance pay than in non-performance pay jobs. The earnings gap between mothers
and fathers in performance pay jobs remains about 10 percentage points larger than that in
2
non-performance pay jobs through the median of the earnings distribution and then explodes
toward the top of the earnings distribution. At the very top of the earnings distribution the
unexplained earnings gap between mothers and fathers in performance pay jobs exceeds 50
percent.
Among the childless, the female earnings gap in performance pay jobs is essentially the
same as that in non-performance pay jobs. This does, however, reflect a fascinating distributional pattern with the gap in performance pay jobs modestly larger until about the 65th
percentile of the earnings distribution and then becoming very much smaller. Indeed, at the
top of the earnings distribution, the gender earnings gap in performance pay jobs is essentially
zero among the childless while remaining at fifteen percentage points in non-performance pay
jobs.
As an important consequence of the pattern we isolate, the well known tendency of children to be associated with lower earnings for women and higher earnings for men (Waldfogel
(1998)) is uniquely tied to performance pay. We show that mothers earn substantially less
than childless females in performance pay jobs. Conversely, fathers earn substantially more
than childless males in performance pay jobs. These differences by parental status are muted
or absent in non-performance pay jobs.
Confirming these patterns raises important issues about the pattern of worker selection by
gender and family structure. We explore these using test scores, measures of education and
working hours. We find evidence that fathers with greater ability select into performance
pay jobs but that mothers with greater ability do not. We find no distinction by gender
among the childless. We also show that hours of work in performance pay jobs exceed those
in non-performance pay jobs for the childless and for fathers but not for mothers.
3
The approach taken in this paper is essentially descriptive. This seems a necessary first
step to reveal empirical patterns that may point toward specific mechanisms. A critical first
direction that emerges is surely that of labor supply. More skilled mothers appear to select
out of performance pay jobs and respond with far fewer additional hours of work when in
performance pay jobs. Doing justice to the implied labor supply decisions would require
not only specifying and estimating a dynamic labor supply model but doing so with the
added complexity of the related decisions of family formation and the method of payment.
Isolating the relevant labor-leisure choices across all these regimes seems particularly daunting
as there are unlikely to be sufficient and suitable exogenous instruments. Yet, we think
our descriptive results do call for continued study of the underlying labor supply choices
associated with home production, caregiving responsibilities and performance pay. Critically,
even if it can be confirmed that the home responsibilities were endogenously chosen and linked
to performance pay, a fundamental puzzle remains. The choices could reflect true market
signals of productivity as revealed by the performance pay scheme or they could be associated
with inferior contracts that employers provide as in a statistical discrimination equilibrium
as described by Albanesi and Olivetti (2009) or they could even reflect contracts based on
prejudice by employers – so called, caregiver discrimination. While we certainly cannot
distinguish between these in this paper, our central contribution is to isolate that the large
gender earnings gap at the top of the distribution (the glass ceiling) and the motherhood
penalty are associated with each other and both are intimately associated with performance
pay.
In what follows, we next review the theory and literature on the influence of performance
pay on the gender earnings differential. We use this to establish both the contradictory
4
theoretical suggestions and the contradictory evidence albeit largely from outside the US. In
the third section we describe our data and variable construction. The fourth section presents
our counterfactual exercise of estimating the gender earnings gap and showing its variation
by method of pay and parental status. The fifth section explores the patterns of selection into
payment methods in light of our evidence on the earnings gap and the final section concludes.
2
Past Theory and Evidence
The argument that performance pay should reduce unexplained gender differentials follows
from the view that it increases the difficulty of translating employer gender preferences
(Becker (1971)) into differential earnings. If workers are paid by the piece, at the end
of each period, the supervisor has a list of workers, their individual outputs and a preestablished wage increment for each unit of output. The basic barrier of objective fairness
is more immediate and the intensity of preference must be that much greater. Even more
critically, this improves external transparency. Those judging the earnings structure including the workers themselves and the courts are much more likely to be persuaded by gender
earnings discrepancies between workers with identical measured productivity than by time
rate earnings discrepancies between workers that supervisors may claim reflect unmeasured
productivity. Thus, the improved information on productivity associated with output-based
pay increases the expected cost of discrimination by increasing the probability of detection
and the associated penalties. In this vein, Barbezat and Hughes (1990) present a model of
earnings discrimination in which improved information on earnings and productivity reduce
the gender differential and Heywood and O’Halloran (2005) extend this model to emphasize
5
the importance of output-based pay in improving this information.
Several empirical studies support this role of output-oriented performance pay. Using
German data, Jirjahn and Stephan (2004) find that gender differentials are substantially
smaller among piece rate workers than otherwise similar workers not receiving piece rates
and Gunderson (1975) originally found that gender wage differences were smaller in those
Canadian occupations making use of incentive pay such as piece rates and commissions.
Yet, output-based pay need not routinely narrow gender differentials. Madden (2012) uses
detailed data from two Wall Street stock brokerages that paid brokers entirely by commission
and were sued for gender discrimination. She argues that this payment method created a
veneer of gender neutrality that actually made it more difficult to determine the relationship
between pay and productivity. She shows that women brokers earned substantially less in
commissions then men but not because they were less successful in generating sales from a
given account or that the quality of the accounts they initiated were lower. Instead, male
supervisors assigned lower quality accounts to women brokers than to male brokers and this
drove the gender difference in commissions. This seems an illustration of the broader point
that gender preference may influence the assignment of many complimentary inputs such
as shifts, location, equipment and co-workers that help determine earnings even when using
output-based pay.
While the influence of output based pay remains uneven, the evidence on the role of performance pay generally appears even more mixed. In the US the majority of performance
based pay is in the form of bonuses (Parent (2002)). While some of these are narrow production bonuses, many reflect an appraisal process in which there is substantial latitude for
supervisory discretion (MacLeod (2003)). Thus, Elvira and Town (2001) argue that perfor-
6
mance pay based on subjective evaluations serves to increase the latitude for supervisory
prejudice relative to time rates. The broad indicators of performance pay that many empirical researchers examine are a combination of performance pay based on differing degrees of
objective and subjective evaluation.
The empirical studies that focus on broad indicators of performance pay use different
data and methods and there seems to be no central tendency in the results. In a recent
examination of the Chinese labor market, Xiu and Gunderson (2013) find that while women
are less likely to receive performance pay, a smaller portion of the gender earnings gap is left
unexplained among workers receiving performance pay. Manning and Saidi (2010) use linked
employee-employer data from the UK to find that the influence of performance pay on men
and women is virtually identical and that the ability of performance pay to explain the gender
pay gap is “very limited.” On the other hand De La Rica, Dolado and Vegas (2010) examine
a large cross section of workers from Spain finding the unexplained gender earnings gap is
larger for performance pay than for time rates. Moreover, they find the difference in the gap
by sectors increases at the top of the distribution and conjecture, but cannot test, that this
may reflect women’s lower mobility due to their attachment to household tasks. Thus, in
three recent examinations one finds suggestions that performance pay may decrease, increase
or have no influence on the gender pay gap.
One well documented influence of performance pay has been its tendency to increase
wage dispersion within firms (Lazear (2000) and Barth, Bratsberg, Haegeland and Raaum
(2012)). Using linked employee-employer data from New Zealand, Fabling, Grimes and Maré
(2012) show that the positive influence of performance pay on wages is restricted to high
wage workers and so stretches out the distribution of earnings within firms. While their
7
individual controls are limited, they demonstrate that this happens only for the earnings of
men. Thus, the gender wage gap within firms grows at the top of the distribution in those
establishments with performance pay. They also show that the failure of performance pay to
stretch the within firm wage distribution for women remains regardless of whether the firm
is led by a man or a woman.
Our study is the first to focus on the role of performance pay in the distribution of the
gender earnings gap in the US and uniquely emphasizes the importance of family structure.
The distribution of the gender earnings gap and the importance of family structure on that
gap each have their own substantial literatures. The suggestion of a glass ceiling, a barrier for
women to top positions and earnings, has been explored in both Europe and the US. While
Arulampalam, Booth and Bryan (2007) present evidence that the unexplained earnings differential is typically largest at the top of the wage distribution for major European countries,
the US pattern appears somewhat more varied. Kassenbohmer and Sinning (2010) examine
the distribution of the gender earnings gap over time using the Panel Study of Income Dynamics. At the start of their examination in 1993 they estimate that the unexplained gender
differential at the bottom of the distribution exceeds that at the top of the distribution but
that it shows a U-shape and is smallest in the middle. This pattern is taken as evidence of
both a glass-ceiling and a sticky floor. While at the end of their examination in 2006 the
unexplained differential at the bottom of the distribution had shrunk, the U shape pattern
clearly remains.
A critical issue is the extent to which the gender earnings differential and, in particular, the
differential at the top of the distribution may be attributable to motherhood. For instance, in
their examination of 3000 MBA graduates from the University of Chicago, Bertrand, Goldin
8
and Katz (2010) show that the initial salaries of men and women are virtually identical but
diverge over time. The major source of the divergence is the reduced working hours and career interruptions associated with motherhood. More generally, estimates of the motherhood
gap confirm an earnings differential between otherwise similar mothers and single women
(Waldfogel (1998); Anderson, Binder and Krause (2003)). Felfe (2010) shows that motherhood brings with it reductions in hours and changes in working conditions that help explain
the motherhood gap (she shows these job changes often result in more flexible schedules and
reduction in self-reported "stress"). Using German longitudinal data, Felfe shows that if
the sample of mothers is restricted to those that do not change hours or measured working
conditions as a result of the birth of a child, a previously estimated double digit motherhood
gap drops to an insignificant gap of only 2.9 percent. Thus, one might imagine that mothers
move out of performance pay jobs as an example of the adjustments that Felfe highlights.
It remains important to note that not all of the changes associated with motherhood
need be freely chosen. A broad class of gender discrimination lawsuits in the United States
focus on "caregiver discrimination" (see Adams, Heywood and Miller (2013). These suits
have grown much more quickly than traditional gender suits and frequently claim that job
assignments, promotions and the associated earnings reflect stereotypes about the effort or
commitment of caregivers (primarily mothers). Thus, just as a portion of the unexplained
gender earnings gap could reflect employer prejudice, a portion of any motherhood gap could
also reflect employer prejudice and stereotyping.
In a slightly different view, the observed patterns could reflect a self-fulfilling equilibrium
based on statistical discrimination. Albanesi and Olivetti (2009) present an adverse selection
model in which home commitments raise the effort cost of work and employers believe home
9
commitments are greater for women. As a consequence, they offer women labor contracts
with lower earnings and effort (facilitated by performance pay). This reduces the opportunity
cost for home hours for women causing them to allocate more time to home production and so
confirms the belief of the employers. As caregiving is a critical component of home production,
it makes sense that such a statistical discrimination equilibrium would be concentrated among
mothers.
We also recognize that employers need not be the only source of potential discrimination.
Service workers may suffer customer discrimination that could be aggravated when tips represent a substantial source of income (See Neumark et al. (1996) and Parrett (2011)). Similarly,
the desire of co-workers to provide helping effort in the face of individual performance pay
could vary by gender (Drago and Garvey (1996)). Thus, a largely male work force that is
rewarded by commissions may not wish to help a female co-worker as they would a male
co-worker
Our work bridges gaps across this wide literature. We estimate the relationship between
performance pay and the gender earnings gap across the earnings distribution in the US.
We find that the gap is routinely larger in performance pay jobs and that the gender gap
in performance pay jobs increases dramatically at the top of the distribution. Critically,
this pattern is driven entirely by parents. Among the childless, the gender pay gap is not
routinely larger in performance pay jobs and it actually decreases to essentially zero at the
top of the distribution. As a consequence, the tendency of children to be associated with
lower earnings for women and higher earnings for men is uniquely tied to performance pay.
10
3
The Data
3.1
Sample Selection
We use the National Longitudinal Survey of Youth covering the years 1980 to 2010.1 Although
we use all the waves, we are constrained by the availability of questions regarding pay methods
and by changes in those questions over time. In the 1988, 1989, 1990, 1996, 1998 and 2000
waves of the panel, respondents report whether part of their earnings was based on job
performance, and if so, they report the form it took (bonuses, piece rates, commissions, tips).
Respondents were explicitly asked to exclude profit sharing, for which there was a separate
question. Although the NLSY has continued to include information on compensation forms
up to 2010, the questions on performance pay changed dramatically starting with the 2002
interview. For example, respondents were asked
“Some employees receive cash bonus pay in addition to their regular earnings. For example, employees sometimes receive year-end bonuses, profit-sharing bonuses, or payments for
exceeding a production quota or completing work ahead of schedule. Did you receive any cash
bonuses on your job in 2001? Please do not consider tips or commissions as bonus payments.”
This obviously combines several of the previous categories as well as profit sharing and, not
surprisingly, the fraction of individuals answering “yes” in 2002 is roughly twice as large as in
2000 (30% vs 16%) when the question explicitly focused on bonuses based on job performance,
excluding profit sharing. Consequently we only use the information on performance pay in
1
Annual interviews were conducted until 1994, followed by biennial interviews since then.
11
the earlier years when the questions were consistent from year to year. We also impose
additional sample restrictions similar to those used by Gibbons et al. (2005). For example,
self-employed and public sector workers are deleted, as are members of the NLSY military
subsample. Perhaps more importantly given our focus on gender, we use only individuals who
have made their first long term transition to the labor market, namely those who spent at
least three consecutive years primarily working, following a year spent primarily not working.
Someone is classified as primarily working if she/he has worked at least half the weeks since
the last interview and averaged at least thirty hours per week during the working weeks.
Thus we focus on those individuals who have made an initial commitment to the labor force.
We classify a job (more precisely a job match) as a performance pay job if the worker
reports receiving some form of performance pay at least once over the course of being with
the same employer. Note, however, that the limited number of years in which questions about
performance pay are asked means that we likely do not “catch” all performance pay jobs. We
nonetheless find that the incidence of performance pay jobs increases from 26.1 percent in
the late 1980s to 30 percent in the late 1990s, which is broadly consistent with the evidence
from the PSID contained in Lemieux et al. (2009).
The fact that a consistent series of questions on performance pay was asked in some years
and not in others has, of course, implications in terms of how we construct the longitudinal
sample. We could either use only the years in which those questions were asked, limiting our
sample to cover the years 1988 to 1990 and 1996 to 2000, thus dropping all the observations
before 1988, between 1991 and 1994, and after 2000. Given that the NLSY has all the job
history information necessary to link the jobs held across all years, valuable information about
the workers’ employment histories would be lost by simply discarding those observations.
12
Instead we use all the waves between 1980 and 2010 by retaining all job matches observed
during the years when there is information on performance pay (1988-1990, 1996-2000) and
exploiting the fact that many of those matches started between 1980 and 1987 (or between
1991 and 1994), and/or ended either between 1991 and 1994, or after 2000. Since we can
determine whether those job matches are performance pay jobs using the information available in 1988-1990 and 1996-2000, we simply extend that categorization of the matches to
the years in which there is no information. In other words we increase our sample size by
using the information that is available on compensation along with the job-match identifier
to identify performance pay jobs in the 1980-1987, 1991-1994, and 2002-2010 periods. When
discussing the summary statistics we discuss how this choice affects the composition of the
sample.
Naturally, if workers in those employment relationships do not receive performance pay
when that information is available, it does not follow that the jobs are non-performance pay
jobs given that some form of performance pay could have been received in 1980-1987, 19911994, or 2002-2010. So it is clear that compared to the Panel Study of Income Dynamics, we
are exacerbating the problem of falsely classifying performance pay jobs as non-performance
pay jobs. The main consequence of this is that any measured difference between the two
types of jobs will be understated relative to what we would measure if we did not have those
“holes” in the data.2
2
See Appendix 2 in Heywood and Parent (2009) for the same idea applied to the white-black wage gap
measured in the NLSY. In it we describe a simple, empirically tractable measurement framework in which
we make more concrete the nature of the biases imparted by wrongly classifying performance pay jobs as
non-performance pay jobs. We exploit the fact that tenure levels are much higher in the PSID than in
the NLSY to compute “steady-state” misclassification error correction terms using the PSID, which we then
incorporate in our analysis using the NLSY data. We show that under reasonable assumptions, the whiteblack wage gap in non-performance pay jobs is overstated and the magnitude of the overstatement increases
as one approaches the top end of the distribution. While we would expect the same qualitative impact for
13
Since we use the actual receipt of bonuses, commissions or piece rates to identify performancepay jobs, we are also likely to misclassify performance-pay jobs as non-performance-pay jobs
if some employment relationships are either terminated before performance pay is received, or
partly unobserved for being out of our sample range. Given our definition of performance-pay
jobs, we may mechanically understate the fraction of workers in such jobs at the beginning of
our sample period because many employment relationships observed in 1988 started before
1988, and we do not observe whether or not performance pay was received prior to 1988.
The same issue arises for employment relationships observed in 1996-2010: many started at
a time when either no information on performance pay was available or the questions on
performance pay changed after 2000 to an extent that prevents their use. We deal with
that end-point problem by simply including as additional controls dummies for the number
of times a job match is observed in the counterfactual exercise as well as in our selection
models.
3.2
Construction of a Skill Index
To summarize the relationship between wages and observable skills, we construct a "skill
index" for each worker, which we use below to perform counterfactual analyses in a more
compact way relative to using all its individual components. We also use the skill index to
document the patterns of selection into performance pay jobs by gender and parental status.
We first estimate a flexible log (hourly) wage equation using our sample.3 The base
the male-female wage gap, it is not possible to make the same calculations in the context of male-female
wage differentials because only heads of households are asked questions about bonuses in the PSID and the
criteria used to qualify as a head have historically been heavily tilted towards males.
3
We use the hourly earnings information on the current job at the time of the survey, provided in the
employer supplements, as our measure of wages. Respondents are instructed to include everything including
14
explanatory variables used in the log wage equation are the Armed Forces Qualifying Test
(AFQT) score, years of education, education category dummies (dropout, high school graduates, some college, at least a college degree), (actual) experience, experience squared, dummy
variables for race, gender, marital status, union status, and a set of dummies for year, industry, and occupation. We also include sets of pairwise interactions between the education
category dummies, gender, and race, as well as interactions between gender and experience,
gender and marital status, and race and experience. We then use the estimated coefficients
from that equation to predict the wage of each worker. The skill index is the predicted wage
based solely on the education, experience, and AFQT score of the worker, as well as the
interaction terms involving those variables. That is, although characteristics such as occupation, industry, union status, and demographic characteristics are included in the initial wage
equation as controls to improve estimates, they are not used to construct the skill index.
We standardize the skill index to have a mean of zero and a variance of one to facilitate
comparisons across demographics and job types.
3.3
Summary Statistics
In Figure 1 we show the incidence of performance pay jobs accross the earnings distribution.
As in the PSID (Heywood and Parent (2012)), performance pay is more common at the top
bonuses. For example, the preamble in the employer supplement questionnaire in 1988 specifies: “Now, we
would like to ask you a few questions concerning your earnings at that job. For these questions, please
include any tips, overtime, and bonuses and give me the amount you earned before deductions like taxes and
Social Security (are/were) taken out.” Also, from the documentation regarding wages: “Data on respondents’
usual earnings (inclusive of tips, overtime, and bonuses but before deductions) have been collected during
every survey year for each employer for whom the respondent worked since the last interview date. The
amount of earnings, reported in dollars and cents, is coupled with information on the applicable unit of
time, such as per day, per hour, per week, or per year.” (http://nlsinfo.org/content/cohorts/nlsy79/topicalguide/employment/wages)
15
of the distribution and this is largely generated by the increased prevalence of bonuses (see
Figure 2). Men and women have more nearly similar incidences of performance pay at the
bottom of the distribution but a substantial difference emerges further up the distribution
with the difference largest at the very top where performance pay is much more common for
men.
In Table 1 we report the sample means for a variety of socio-demographic characteristics
by gender and type of job, including measures of parental education as well as the AFQT
score. Note that because many individuals are observed in both types of jobs, the total
number of workers across job types is greater than the actual number of workers in the
sample.
Both males and females in performance pay jobs are paid more and work more hours. The
difference in pay between performance pay jobs and non-performance pay jobs is larger for
males, the average hourly earnings in performance pay jobs being 32% higher for males and
21% for females. Naturally this difference in the wage premium could result at least partly
from gender differences in the selection process into performance pay jobs. While we explore
this in detail later, Table 1 gives some early clues that males are more positively selected
than females. First, the difference in educational attainment between the two types of jobs
is considerably larger for males. Second, the pattern of differences in parental education is
also suggestive. While parents of female workers in performance pay jobs have basically the
same level of educational attainment as those of females in non-performance pay jobs, the
difference is more substantial for males. This seems particularly evident when looking at
whether the father has a B.A. degree or more. Finally, the average gap by payment type
in the Armed Forces Qualifying Test score is eleven percentiles for males compared with
16
only four percentiles for females. We return to these patterns and others after exploring the
patterns of the earnings gap.
As mentioned earlier, we keep all the observations for a given job match in the years when
there is no information on performance pay and we identify the type of job from the years
when we have consistent pay-for-performance questions. This has two main consequences.
First, as pointed out, it exacerbates the misclassification of performance pay jobs as nonperformance pay jobs, thus understating the extent to which average hourly earnings differ
across the two types of jobs. Second, it generates a sample containing more observations on
jobs of relatively longer durations. This follows as we keep job matches that overlap with
the years when the performance pay questions were asked and shorter job matches will be
observed for fewer periods.
Appendix Table 1 shows this second consequence by reporting the summary statistics
limited to the observations from the “no information” years. While most characteristics are
very similar to the overall sample, average hourly earnings (mostly in the case of males), labor
market experience, and job tenure appear different. Tenure, in particular, increases by at least
one full year for females and by over one and a half year for males. Consequently, our sample
consists disproportionately of workers who are in more stable employment relationships. The
stronger labor force attachment makes our samples of males and females more comparable
as marginally attached workers would be disproportionately females.
17
4
Examining the Gender Earnings Gap
In this section we investigate how the male-female hourly earnings gap across the distribution
changes once we adjust for composition, including skills. In the previous section we hinted
that gender differences exist in wage determinants between performance pay jobs and nonperformance pay jobs so a natural question is the extent to which such differences account
for the observed gender wage differentials. We are also interested in the extent to which
compositional effects account for within gender wage differentials across job types and across
parental status.
To perform our counterfactual analysis we use the DiNardo, Fortin and Lemieux (1996)
methodology properly extended to handle the extra conditioning involved in taking into
account the type of job (performance vs non-performance pay job) and whether at least one
child is present or not. In Appendix A of Heywood and Parent (2012) we show how this
extra conditioning affects the computation of the DFL weights relative to simply using the
subsamples corresponding to the relevant demographics. For example, if we were interested
in the male-female wage in performance pay jobs, we could use the subsample consisting
of performance pay jobs only. However, doing so imposes the restriction that the selection
process governing who works in performance pay jobs be exactly the same for males and
females. By using the full sample and thus having to model the selection process for each
gender, we do not impose that assumption. In our case, not only do we have to take into
account the selection process into performance pay jobs, but we also have to model the
selection process into parental status.4
In Figures 3 and 4 we use the full sample of males and females and examine how correcting
4
As in all decomposition exercises of this kind, we have to assume that selection is based on observables.
18
for composition influences the observed male-female wage gaps in performance and nonperformance pay jobs. Note that we first adjust simply by using the skill index as the
regressor (as well as dummies for the number of times a job match is observed, to adjust for
the end point problem). We then add the other controls: tenure and dummies for collective
bargaining agreement coverage, race, calendar years, occupations and industries5
Looking first at performance pay jobs, we see in Figure 3 that the raw wage gap increases
monotonically across the distribution. As in the case of the black white differential in Heywood and Parent (2012), there is evidence of a run up at the top of the distribution although
it is less dramatic in scale. Next, simply controlling for skills reduces the gap and the reduction gets larger at the higher end of the distribution. There remains a sizable gap left after
controlling for the skill index. Adding the other controls makes a significant difference over
most of the range of the distribution but it makes very little difference at the top.
In Figure 3A we exclude occupations and industries from the set of control variables.
Keeping the counterfactual gap using the skill index only allows for an easier comparison with
Figure 3, where we do include occupations and industries. We can see that gender differences
in industries and occupations matter less as one moves towards the top of the distribution.
Overall, Figure 3 shows that there is a significant gap remaining once all observables are
controlled for. Different selection according to skills is part of the story, but it is far from
being able to account for the raw wage differentials.
Figure 4 focuses on non-performance pay jobs and shows that differences in the skill index
are basically non-existent across the distribution and so explain none of the gender earnings
gap. Consequently, the part of the gap that can be accounted for by observables comes
5
The result would be very similar if we controlled for the individual variables used to build the skill index
instead of simply using the index.
19
exclusively from the other controls and, given Figure 4A, most of the “effect” of the other
controls stems from gender differences in occupation and industry.
Comparing Figures 3 and 4 shows that the unexplained gender earnings gap is everywhere
larger in performance pay jobs than in non-performance pay jobs. The difference between
the two unexplained gaps grows over the distribution and is approximately .11 log points
(.32 vs. .21) at the very top of the distribution.
In the next sets of figures we disaggregate the sample by parental status to illuminate
if the interaction between skills, job types and whether or not the respondents are parents
makes a substantial difference. As we will see, it does.
Looking first at Figure 5, we focus on the earnings gap between fathers and mothers in
performance pay jobs. We see that the actual wage gap is considerably larger across the
distribution when comparing mothers and fathers, relative to Figure 3 and that the run up
at the top is much more pronounced. In addition, the general pattern of an increasingly
important role for skills present in Figure 3 is also quite apparent in Figure 5. In fact,
skills alone account for roughly one-third of the raw wage gap at the top of the distribution.
Factoring in all other observables does not make much of a difference except at the top where
the counterfactual gap is basically equal to the raw gap. Remarkably, it seems that once all
observables are included, the gender wage gap in the top decile is basically left “unexplained.”
It is also enormous reaching more than .5 log points at the very top of the distribution.
Turning to parents in non-performance pay jobs in Figure 6, the raw wage gap is smaller
than in performance pay jobs and it decreases by almost 10 percentage points between the
60th and 100th percentiles. Similar to what we saw in Figure 4, skill differences have little
to do with the observed wage gap. Instead, it is the other observables that matter, except at
20
the very top where they make little difference. As we can see in Figure 6A, it again seems
clear that occupations and industries represent the main contributing factor accounting for
the explained part of the male-female wage gap in non-performance pay jobs, at least over
the first 80 percentiles.
Comparing Figures 5 and 6 shows that the unexplained gender earnings gap for parents is
everywhere larger in performance pay jobs than in non-performance pay jobs. The difference
between the two unexplained gaps grows over the distribution and is approximately .30 log
points (.58 - .28) at the very top of the distribution.
Focusing on childless individuals in performance pay (Figure 7), the raw wage gap is
much smaller than is the case for parents. It is at most around 10%, with a steep decline to
basically zero in the top decile. In non-performance pay jobs (Figure 8) the raw gap actually
increases over most of the range of the distribution and reaches approximately 15% at the
top. What is most noticeable, is that controlling for differences in skills actually makes the
gap larger, particularly in non-performance pay jobs. Yet, in the end there are only modest
differences between the raw and the full unexplained differential.
Comparing figures 7 and 8 shows that unexplained gender earnings gap for the childless
does not differ on average between performance pay jobs and non-performance pay jobs. The
gap starts out being modestly larger in performance pay jobs but then reverses at about
the 65th percentile with that for performance pay jobs falling dramatically while that for
non-performance pay jobs continues to increase. As a consequence, the initial pattern for the
full sample that the unexplained gender differential is everywhere larger in performance pay
jobs and that the difference increases at the top is driven exclusively by parents. It simply
does not apply to the childless.
21
We examine the implications of our results by focusing on within-gender comparisons
across job types. Figure 9 shows the earnings gap between females in performance pay jobs
and in non-performance pay jobs while Figure 10 shows the same earnings gap for males.
Although the earnings gain associated with performance pay increases in a fairly similar
monotonic fashion for women and men across the distribution, the unadjusted male gap is
routinely larger and also shows a much more pronounced run up at the top. The portion
of the wage gap that can be accounted for by the skill index is proportionately larger for
females. In fact skills account for only a modest fraction of the wage gap at the top for males.
Despite this, the full unexplained gaps suggest that little of the female gap is attributable
to observables. This results in the unexplained differential for men not always being larger
but the run up at the top of the male distribution becomes even more dramatic reaching a
very large 40%. Thus, the inequality enhancing role played by performance pay emerges as
far more pronounced for males than for females.
In additional estimates available from the authors we focus on the earnings gap associated
with performance pay separately for mothers, fathers, childless females and childless males.
The run up at the top of the distribution is least evident for mothers. Moreover, while
accounting for observables reduces the earnings gap for fathers and the childless, it modestly
increases the gap for mothers. Thus, if mothers in performance pay jobs had the same
observables as those in non-performance pay jobs, the return to performance pay would
be larger than the raw differential. This certainly hints at a different selection pattern for
mothers.
The final set of figures examine the earnings differential associated with parental status
separately by gender and performance pay status. Looking first at the differentials within
22
performance pay jobs in Figure 11 and Figure 12, the outcome for mothers relative to childless
females differs dramatically from that for fathers relative to childless males. Mothers’ hourly
earnings trail those of childless females by at least 20% over a wide range of the distribution
whereas the gap is everywhere positive for fathers with a very significant run up at the top
to over 30% at the top of the distribution. It also emerges that a significant portion of the
discrepancy between mothers and childless females can be accounted for simply by differences
in skills, while the same is not true for males. At the very top, the unexplained gap for females
is close to ten percent once mothers have the same distribution of observables as childless
females.
We observe smaller raw differentials between mothers and childless females in non-performance
pay jobs in Figure 13. Skills continue to be critical in closing the gap and the full unexplained
differential turns modestly positive at the top of the distribution. In Figure 14 the positive
raw differentials for males in non-performance pay jobs is smaller–a lot smaller towards the
top–but the most noticeable difference with Figure 12 is that not only is there no run up at
the top, but the opposite is actually observed. In sum this series of estimates suggests that
the influence of parental status is very muted among those in non-performance pay jobs. The
estimated gaps are not large and there are not particularly strong distributional patterns.
This contrasts with performance pay jobs in which the estimated gaps were larger and the run
up in the gap for males was very pronounced. This evidence suggests that the parental status
gaps for both genders are associated with performance pay, although in opposite directions.
The conclusion from our counterfactual exercise remains that the gender gap is larger in
performance pay jobs than in non-performance pay jobs. Moreover, the gender gap among
those in performance pay jobs increases at the top of the distribution. Both the larger gender
23
gap in performance pay jobs and the dramatic increase at the top of the distribution are driven
by parents and are largely absent among the childless. As a consequence, the tendency for
fathers to earn more than childless males and for mothers to earn less than childless females
is concentrated among those in performance pay jobs. Given these patterns we now turn to
explore the distribution of skills and other critical observables to see the extent to which they
reflect selection likely associated with the estimated differentials.
5
Evidence of Sorting
We now undertake two broad examinations. In the first we present descriptive patterns of
skills and the observables across the key indicators of performance pay status, gender and
family structure. In the second we provide estimates of the partial correlates of workers
being observed in performance pay jobs. While also largely descriptive, the estimates allow
simultaneous examination of many observables.
5.1
Descriptive Evidence
Table 2 presents the mean skill index by type of job, gender, and family structure. As
described in our data section, the index is constructed with an overall mean of zero and
variance of one. The index for females is lower than that for males and the difference is
statistically significant. Those two numbers represent separate thresholds relative to which
one can judge the degree of selectivity according to skills for both males and females. For
example in rows 2A and 2B not only can we see that parents are below the overall average
for their respective gender, but also that the value for fathers is not that far from the average
24
for all females in row 1A. At the opposite end, childless individuals have indices which
are considerably above the average (rows 3A and 3B). And although one cannot reject the
hypothesis that males and females have the same value of the index (p-value of .1788), the
value for females is actually above that males and is statistically different from the overall
male average reported in row 1A.
Turning to differences across job types we can see in rows 4A and 4B that both females
and males in performance pay jobs are above their respective overall averages. Yet, this is
particularly noticeable for males. On the other hand, there is no statistical evidence that
the skill index differs between males and females in non-performance pay jobs (rows 5A and
5B). Putting those two sets of statistics together, while it is true that females in performance
pay jobs have a higher value of the skill index than in non-performance jobs (p-value of the
difference of 1% for a t-statistic of 3.24), the evidence is even stronger for males (p-value
below .0001 for a t-statistic of 9.83).
In rows 6A, 6B, 7A, and 7B we disaggregate the sample by parental status. Mothers
appear negatively selected into performance pay jobs relative to the overall average of the
female skill index while fathers are positively selected relative to the male average. Moreover,
that difference between mothers and fathers is quite large and significant. However, the same
is not true for childless individuals. While males are more positively selected with no children
than is the case for fathers, the difference is quite dramatic when comparing childless females
and mothers in performance pay jobs.
Turning to the final two sets of rows in Table 2, there is basically no difference in the
skill indices of childless individuals in performance pay jobs (rows 8A and 8B): both are
strongly positively selected relative to their respective averages. Finally, as shown in rows
25
9A and 9B, childless females are considerably more positively selected than childless males
in non-performance pay jobs. In fact, the average skill index for childless males in nonperformance pay jobs is virtually equal to the overall average. As a result, the difference in
the degree of positive selection between childless males in performance pay jobs and those in
non-performance pay jobs is substantially larger than is the case for childless females.
In sum the pattern of skills fits closely with the estimated earnings differentials. The
gender gaps in skills shows a large positive advantage for males among parents in performance
pay jobs. At the same time, the gender gap in skills shows a large positive advantage for
females among the childless not in performance pay jobs. There are essentially no gender
differences in skills among the childless in performance pay jobs or parents in non-performance
pay jobs. Given the generally higher skills of males, this supports the pattern of earnings
differentials by showing skilled mothers selecting out of performance pay as skilled fathers
select into performance pay jobs.
Another basic look at the gender difference in selection is provided by breaking out the
distribution of AFQT scores by gender and job type. Figure 15 shows very strong positive
selection of males with high AFQT scores into performance pay jobs. This selection is far
less obvious for women. Another view is given by Figure 16 where we superimpose the
distribution of AFQT scores in performance pay jobs for mothers and childless females in
Panel A and likewise for fathers and childless males in Panel B. While there is basically no
difference in the distributions for males, it is clear that childless females are more likely to
select into performance pay jobs than mothers. Interestingly, if we show the analogous figure
for non-performance pay jobs (Figure 17), there is much less evidence of a difference between
males and females. To emphasize this point, it is not so much that Panel A in Figure 17 is
26
that different from Panel A in Figure 16. Rather, it is that there is not a great discrepancy
between the relative positions of the distributions for males and females in non-performance
pay jobs, while there is a very noticeable gender difference in Figure 16.
We now change focus from skills to examining the patterns of hours of work. In Figure 18
we plot the average annual hours worked by percentile of the hourly earnings distribution.6
Not surprisingly given the sample means reported in Table 1, males work on average a larger
number of annual hours than females across the distribution in both types of jobs, with the
larger discrepancies being observed in the bottom half. In fact, males in non-performance
pay jobs work more hours on average than females in performance pay jobs. Also, the
difference between workers in performance pay vs. non-performance pay jobs is everywhere
larger for males than for females. This figure hints that labor supply decisions in response
to performance pay operate differently for females than for males and that this difference is
pervasive across the wage distribution.
As we can see in Figure 19 in the case of childless males and females, the differences across
types of jobs are not as straightforward as in Figure 18. From approximately the middle of
the hourly earnings distribution to the top the difference in average annual hours worked
between females and males in performance pay jobs shrinks to the point of there being only a
modest difference between males and females. The same is not true for males and females in
non-performance pay jobs. From about the 50th percentile and up, the difference in annual
worked between females and males is roughly constant. Relatedly, contrary to Figure 18 it
is not true that males in non-performance pay jobs work more on average than females on
performance pay jobs throughout the distribution.
6
The information on annual hours worked is constructed using the weekly total number of hours worked
across all employers.
27
Turning to Figure 20 where we compare mothers and fathers, we can see that the general
patterns illustrated in Figure 18 are driven by this subsample. In fact, if we compare Figures
19 and 20, we can see that fathers in either type of job work more hours than childless males.
The reverse is true for females with mothers working fewer hours in either type of job than
childless females.
Taken together, Figures 18-20 make more precise the link mentioned earlier between labor
supply decisions and whether the job is a performance pay job or not. The performance pay
job “effect”, keeping in mind that the evidence shown here is purely descriptive, seems to work
through family composition with the increase in hours associated with performance pay being
smallest for mothers. This again supports the evidence from the earnings differentials that
the distinction between mothers and fathers is critical for understanding gender differences.
5.2
Selection Into Performance Pay Jobs
In this subsection we analyze the statistical correlates of employment in a performance pay
job. The goal is two-fold: first we investigate the relationship between skills and performance
pay across genders and, second, we try to address the issue of why similarly skilled females
are less likely to be working in performance pay jobs and whether the gender gap in terms
of the incidence of performance pay jobs can be accounted for by observable factors.
In Table 3 we first show the results from basic regressions linking characteristics to a
dummy indicator for whether a worker is observed in a performance pay job. In column [1],
we only use a female indicator, years of education and the AFQT score as the regressors and
other characteristics are added as we move across the columns. Column [1] simply confirms
that females are less likely to be working in performance pay jobs, even when controlling
28
for education and the AFQT test score. Conditional on having the same level of education
and AFQT cognitive skills, females are nearly nine percentage points less likely to be in
performance pay jobs. Adding the usual controls for labor market experience, tenure, sectoral
dummies, etc. leaves the qualitative conclusion unchanged.
However, as we can see in column [4], simply adding an interaction term between the
female indicator and years of completed schooling substantially changes the general impression. The negative coefficient associated with the female indicator results from the fact that
females are increasingly less likely to be working in performance pay jobs relative to males as
the level of educational attainment increases. There is no evidence of a “baseline” negative
association between gender and performance pay – it is tied to education. The role of education remains when adding an interaction between the AFQT score and the gender dummy.
Indeed, there is little evidence of a significant negative interaction term between gender and
the AFQT, controlling for education.7
In column [6] we include as two additional controls the average number of usual weekly
hours observed over the duration of a job match and the maximum of usual weekly hours
. We saw in Figure 18 that males work more hours on average in performance pay jobs.
Here we explore whether the higher average matters more or less than the maximum ever
observed over the course of an employment relationship. Indeed, the coefficient on each of the
hours variables is positive but that on the maximum is much larger and highly significant.
Adding the hours variables increases the coefficient on the female indicator suggesting that a
greater number of work hours is negatively correlated with being a female while at the same
time being positively correlated with performance pay jobs. Critically, it is not so much the
7
There is a significant AFQT x Female interaction term if we drop education.
29
fact that performance pay jobs are more demanding on average in terms of hours that is
negatively associated with the female dummy indicator, but rather it is the “surge” in hours
worked over the course of a worker-firm match that seems to be the key factor.8
In the last column we check whether the negatively signed interaction term between
education and the female indicator results from some unmeasured but fixed individual component. Although the identification of the fixed-effects linear probability model is driven
by the subsample of females who report upgrading their educational attainment (with the
accompanying measurement error), and thus may not be representative of the full sample of
females, the sign and magnitude of the interaction term’s coefficient provides little reason to
believe that some unobserved factor is driving the results.
In Tables 4A,B we further disaggregate the results by parental status. As one might expect
given the visual impression left by comparing Figures 18, 19, and 20, the difference in the
relationship between gender, skills, and being in a performance pay is quite striking. Basically,
the negative coefficient associated to the interaction of education and gender reported in Table
3 is driven by the subsample of mothers. And the sign reversal of the female dummy indicator
once the interaction terms and the hours worked controls are added is even more striking
for mothers than in the overall sample. At the same time, there is little in Table 4B that is
strongly suggestive that more educated females are less likely to be in performance pay.
The regression estimates reinforce the conclusions from the descriptive statistics. The
large gender earnings gap among parents in performance pay jobs seems to be associated
with a series of logical selection patterns. Mothers with greater education are more likely
8
For women in performance pay jobs the average of this maximum is about 45 hours per week while it
is over 52 hours per week for males. Note that we get very similar results whether we use one or the other
separately. It is when we put the two of them in horse race that the maximum emerges as the relevant factor.
30
than males to select out of performance pay. The extreme peak hours of work associated
with performance pay also appear to cause mothers to select out of performance pay.
6
Conclusion
While such selection patterns fit with the estimated differentials, they do not explain the
cause of the large gender differential between mothers and fathers in performance pay. The
differential may reflect unmeasured differences in labor force attachment or commitment
between mothers and fathers that are translated into earnings when in performance pay jobs.
Such a view takes performance pay jobs to more closely align productivity and pay. While
possible, this view ignores the possibility that performance pay, on balance, enhances the
ability to translate discriminatory preferences into earnings differences. In this alternative,
women may be given inferior opportunities to earn under objective schemes or may be given
systematically worst appraisals under subjective schemes. Yet, a critical point of our analysis
is that if performance pay enhances the ability to discriminate, it appears to be oriented more
narrowly toward mothers rather than toward women in general.
We identify a substantial distributional element in the role of performance pay. The
gender earnings gap expands at the top of the performance pay distribution. Again, that
growth at the top is generated by the comparison between parents. Thus, the glass ceiling
we identify could also be generated by either unmeasured productivity differences or by the
enhanced ability to discriminate but it is uniquely tied to performance pay and to parents.
31
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35
Figure 1. Incidence of PP Jobs by Percentile of Wage Distribution
.2
.3
Fraction
.4
.5
.6
.7
NLSY: 1980−2010
0
20
40
60
80
100
Percentile
Females
Males
Smoothed by Locally Weighted Regression
Figure 2. Incidence of Bonus Pay Jobs by Percentile of Wage Distribution
.1
.2
Fraction
.3
.4
.5
.6
NLSY: 1980−2010
0
20
40
60
80
Percentile
Females
Males
Smoothed by Locally Weighted Regression
36
100
37
20
80
100
0
100
Percentile
60
80
40
Percentile
60
80
Smoothed by Locally Weighted Regression
Smoothed by Locally Weighted Regression
Counterfact. Gap in Non−PP Jobs−Skill Index +
20
Counterfact. Gap in non−PP Jobs−Skill Index Only
0
Counterfact. Gap in PP Jobs−Skill Index +
80
Actual MF Gap in Non−PP Jobs
60
Counterfact. Gap in PP Jobs−Skill Index Only
Percentile
Actual MF Gap in PP Jobs
40
NLSY: Full Sample Occ’s and Ind’s Excluded
20
Figure 4A. Male−Female Wage Gap in Non−Performance Pay Jobs
NLSY: Full Sample Occ’s and Ind’s Excluded
Difference in Log Wages
.1
.2
.3
.4
0
Figure 3A. Male−Female Wage Gap in Performance Pay Jobs
Smoothed by Locally Weighted Regression
Counterfact. Gap in Non−PP Jobs−Skill Index +
40
Counterfact. Gap in non−PP Jobs−Skill Index Only
20
Counterfact. Gap in PP Jobs−Skill Index +
0
Actual MF Gap in Non−PP Jobs
60
Counterfact. Gap in PP Jobs−Skill Index Only
Percentile
NLSY: Full Sample
Figure 4. Male−Female Wage Gap in Non−Performance Pay Jobs
Actual MF Gap in PP Jobs
40
Difference in Log Wages
.1
.2
.3
.4
0
Smoothed by Locally Weighted Regression
0
NLSY: Full Sample
Figure 3. Male−Female Wage Gap in Performance Pay Jobs
Difference in Log Wages
.1
.2
.3
.4
0
Difference in Log Wages
.1
.2
.3
.4
0
100
100
Difference in Log Wages
Difference in Log Wages
.6
.5
.4
20
100
20
40
Percentile
60
80
40
Percentile
60
80
Smoothed by Locally Weighted Regression
Counterfact. Gap in non−PP Jobs−Skill Index +
20
Counterfact. Gap in non−PP Jobs−Skill Index Only
0
Counterfact. Gap in PP Jobs−Skill Index +
80
Actual Gap
60
Counterfact. Gap in PP Jobs−Skill Index Only
Percentile
Actual Gap
40
NLSY: Mothers and Fathers Occ’s/Ind’s Excluded
NLSY: Mothers and Fathers Occ’s/Ind’s Excluded
20
Figure 6A. Male−Female Wage Gap in Non Performance Pay Jobs
Figure 5A. Male−Female Wage Gap in Performance Pay Jobs
Smoothed by Locally Weighted Regression
Counterfact. Gap in non−PP Jobs−Skill Index +
0
Counterfact. Gap in non−PP Jobs−Skill Index Only
Smoothed by Locally Weighted Regression
0
100
Counterfact. Gap in PP Jobs−Skill Index +
80
Actual Gap
60
Counterfact. Gap in PP Jobs−Skill Index Only
Percentile
NLSY: Mothers and Fathers
Figure 6. Male−Female Wage Gap in Non Performance Pay Jobs
Actual Gap
40
Smoothed by Locally Weighted Regression
0
NLSY: Mothers and Fathers
Figure 5. Male−Female Wage Gap in Performance Pay Jobs
.6
.5
.4
.3
.2
.3
.2
.1
0
.6
.5
.4
.3
.2
.1
0
Difference in Log Wages
Difference in Log Wages
.1
0
.6
.5
.4
.3
.2
.1
0
38
100
100
Difference in Log Wages
Difference in Log Wages
.25
20
100
20
40
Percentile
60
80
40
Percentile
60
80
Smoothed by Locally Weighted Regression
Counterfact. Gap in non−PP Jobs−Skill Index +
20
Counterfact. Gap in non−PP Jobs−Skill Index Only
0
Counterfact. Gap in PP Jobs−Skill Index +
80
Actual Gap
60
Counterfact. Gap in PP Jobs−Skill Index Only
Percentile
Actual Gap
40
NLSY: Childless Individuals Occ’s/Ind’s Excluded
NLSY: Childless Individuals Occ’s/Ind’s Excluded
20
Figure 8A. Male−Female Wage Gap in Non Performance Pay Jobs
Figure 7A. Male−Female Wage Gap in Performance Pay Jobs
Smoothed by Locally Weighted Regression
Counterfact. Gap in non−PP Jobs−Skill Index +
0
Counterfact. Gap in non−PP Jobs−Skill Index Only
Smoothed by Locally Weighted Regression
0
100
Counterfact. Gap in PP Jobs−Skill Index +
80
Actual Gap
60
Counterfact. Gap in PP Jobs−Skill Index Only
Percentile
NLSY: Childless Individuals
Figure 8. Male−Female Wage Gap in Non Performance Pay Jobs
Actual Gap
40
Smoothed by Locally Weighted Regression
0
NLSY: Childless Individuals
Figure 7. Male−Female Wage Gap in Performance Pay Jobs
.25
.2
.15
.2
.15
.1
.05
0
.25
.2
.15
.1
.05
0
Difference in Log Wages
Difference in Log Wages
.1
.05
0
.25
.2
.15
.1
.05
0
39
100
100
Difference in Log Wages
.2
.4
.6
0
Difference in Log Wages
.2
.4
.6
0
20
100
20
40
Percentile
60
40
Percentile
60
Smoothed by Locally Weighted Regression
Counterfactual Gap−Skill Index +
20
Counterfactual Gap−Skill Index Only
0
Counterfactual Gap−Skill Index +
80
Actual Gap
60
Counterfactual Gap−Skill Index Only
Percentile
Actual Gap
40
80
NLSY: Males Occ’s/Ind’s Excluded
20
Figure 10A. Wage Gap Between PP Jobs and Non PP Jobs
NLSY: Females Occ’s/Ind’s Excluded
80
Figure 9A. Wage Gap Between PP Jobs and Non PP Jobs
Smoothed by Locally Weighted Regression
Counterfactual Gap−Skill Index +
0
Counterfactual Gap−Skill Index Only
Smoothed by Locally Weighted Regression
0
100
Counterfactual Gap−Skill Index +
80
Actual Gap
60
Counterfactual Gap−Skill Index Only
Percentile
NLSY: Males
Figure 10. Wage Gap Between PP Jobs and Non PP Jobs
Actual Gap
40
Smoothed by Locally Weighted Regression
0
NLSY: Females
Figure 9. Wage Gap Between PP Jobs and Non PP Jobs
Difference in Log Wages
.2
.4
.6
0
Difference in Log Wages
.2
.4
.6
0
40
100
100
Difference in Log Wages
Difference in Log Wages
.4
.3
.2
20
100
20
40
Percentile
60
80
40
Percentile
60
80
Smoothed by Locally Weighted Regression
Counterfact. Gap in non−PP Jobs−Skill Index +
20
Counterfact. Gap in non−PP Jobs−Skill Index Only
0
Counterfact. Gap in PP Jobs−Skill Index +
80
Actual Gap
60
Counterfact. Gap in PP Jobs−Skill Index Only
Percentile
Actual Gap
40
NLSY
NLSY
20
Figure 14. Gap Between Fathers and Childless Males in Non PP Jobs
Figure 12. Gap Between Fathers and Childless Males in PP Jobs
Smoothed by Locally Weighted Regression
Counterfact. Gap in non−PP Jobs−Skill Index +
0
Counterfact. Gap in non−PP Jobs−Skill Index Only
Smoothed by Locally Weighted Regression
0
100
Counterfact. Gap in PP Jobs−Skill Index +
80
Actual Gap
60
Counterfact. Gap in PP Jobs−Skill Index Only
Percentile
NLSY
100
100
Figure 13. Gap Between Mothers and Childless Females in Non−PP Jobs
Actual Gap
40
Smoothed by Locally Weighted Regression
0
NLSY
Figure 11. Gap Between Mothers and Childless Females in PP Jobs
.3
.2
.1
0
.1
−.3 −.2 −.1 0
.4
.3
.2
.1
−.3 −.2 −.1 0
Difference in Log Wages
Difference in Log Wages
−.1
−.2
.3
.2
.1
0
−.1
−.2
41
0
.01
.02
Figure 15. Distribution of AFQT Score by Type of Jobs
Panel A: Females NLSY 1980−2010
0
20
40
60
80
100
80
100
AFQT Score
Performance Pay Jobs
Non Performance Pay Jobs
0
.01
.02
Panel B: Males NLSY 1980−2010
0
20
40
60
AFQT Score
Performance Pay Jobs
Non Performance Pay Jobs
42
.02
.01
0
0
0
20
20
AFQT Score
60
80
40
AFQT Score
60
Panel B: Males NLSY 1980−2010
80
Childless Females in Performance Pay Jobs
Mothers in Performance Pay Jobs
40
Figure 16. Distribution of AFQT Score in Performance Pay Jobs
Panel A: Females NLSY 1980−2010
100
100
AFQT Score
60
80
AFQT Score
60
80
Childless Males in Non Performance Pay Jobs
40
Panel B: Males NLSY 1980−2010
Childless Females in Non Performance Pay Jobs
Mothers in Non Performance Pay Jobs
40
Fathers in Non Performance Pay Jobs
20
20
Childless Males in Performance Pay Jobs
0
0
100
100
Figure 17. Distribution of AFQT Score in Non Performance Pay Jobs
Panel A: Females NLSY 1980−2010
Fathers in Performance Pay Jobs
.01
0
.02
.02
.01
0
.02
.01
0
43
2400
0
20
40
60
80
Percentile of Hourly Earnings Distribution
NLSY: 1980−2010
100
Figure 18. Average Annual Hours Worked by Percentile of Wage Distribution
Males−−PP
Males−−Non PP
0
20
Females−−PP
Females−−Non PP
Males−−PP
Males−−Non PP
40
60
80
Percentile of Hourly Earnings Distribution
NLSY 1980−2010: Childless Individuals
100
Figure 19. Average Annual Hours Worked by Percentile of Wage Distribution
Females−−PP
Females−−Non PP
Hours Worked
2000
2200
Hours Worked
2100
2200
2300
2400
1800
2600
Hours Worked
2000
2200
2400
1800
2000
44
0
20
Females−−PP
Females−−Non PP
Males−−PP
Males−−Non PP
40
60
80
Percentile of Hourly Earnings Distribution
NLSY 1980−2010: Parents
100
Figure 20. Average Annual Hours Worked by Percentile of Wage Distribution
45
15.92
33.83
13.08
11.90
6.09
0.56
0.18
38.73
0.37
0.45
0.21
0.11
46
2,846
4,574
16,255
Average Hourly Earnings ($2008)
Age
Education
Experience
Employer Tenure
Married
Covered by CBA
Usual Hours Worked Per Week
Father high school graduate
Mother high school graduate
Father B.A.+
Mother B.A.+
AFQT (percentile)
# workers (Tot:7,384)
# Job Matches (Tot: 13,592)
# Observations (Tot: 52,395)
Non-performancepay Jobs
[1]
1,156
1,392
6,514
19.24
33.48
13.28
12.05
6.62
0.58
0.12
40.31
0.37
0.47
0.21
0.11
50
Performance-pay
Jobs
[2]
Females
3,131
5,394
18,882
19.98
33.99
12.79
13.08
6.61
0.60
0.28
43.28
0.35
0.47
0.23
0.13
46
1,755
2,232
10,744
26.43
34.32
13.63
13.54
7.67
0.66
0.15
45.88
0.34
0.50
0.27
0.15
57
Performance-pay
Jobs
[2]
Males
Non-performancepay Jobs
[1]
Table 1. Summary Statistics: National Longitudinal Survey of Youth 1980-2010
Table 2. Skill Index by Gender and Family Structure
NLSY 1980-2010
P-value of Test
of Equality
Mean of Skill Index
N
1A. All females
1B. All Males
-0.0650
0.0500
22,769
29,626
0.0005
2A. Mothers
2B. Fathers
-0.1885
-0.0155
16,869
21,111
0.0000
3A. Childless Females
3B. Childless Males
0.2564
0.1855
5,900
8,515
0.1788
4A. All Females in PP Jobs
4B. All Males in PP Jobs
0.0356
0.3033
6,514
10,744
0.0000
5A. All Females in Non-PP Jobs
5B. All Males in Non-PP Jobs
-0.1064
-0.1205
16,255
18,882
0.6827
6A. Mothers in PP Jobs
6B. Fathers in PP Jobs
-0.1095
0.2632
4,675
7,576
0.0000
7A. Mothers in Non-PP Jobs
7B. Fathers in Non-PP Jobs
-0.2197
-0.1935
12,194
13,535
0.4779
8A. Childless Females in PP Jobs
8B. Childless Males in PP Jobs
0.3747
0.3958
1,839
3,168
0.7995
9A. Childless Females in Non-PP Jobs
9B. Childless Males in Non-PP Jobs
0.2020
0.0496
4,061
5,347
0.0126
The skill index is normalized to have a mean of zero and a unit variance in the full sample of
males and females. See text for details on the construction of the index.
46
Table 3. Selection into Performance Pay Jobs: Basic Regressions
NLSY 1980-2010
Dependent Variable: Whether Individual is in a PP Job
Probit
Specification
[1]
[2]
[3]
[4]
[5]
[6]
-0.0932
(0.0142)
0.0134
(0.0037)
-
-0.0976
(0.0143)
0.0152
(0.0039)
-
-0.0896
(0.0156)
0.0107
(0.0040)
-
0.0166
(0.0031)
-
0.0120
(0.0035)
0.0098
(0.0036)
0.1599
(0.0851)
0.0181
(0.0049)
-0.0187
(0.0062)
0.0093
(0.0036)
-
Average Usual Weekly
Hours in Job Match
Maximum Usual Weekly
Hours in Job Match
Experience:
-
-
-
-
0.1527
(0.0904)
0.0176
(0.0053)
-0.0175
(0.0063)
0.0099
(0.0045)
-0.0017
(0.0063)
-
-
-
-
-
-
-
Tenure:
-
Married
-
CBA
-
Nonwhite
-
0.0000
(0.0024)
0.0092
(0.0016)
0.0190
(0.0114)
-0.0899
(0.0165)
0.0227
(0.0176)
Yes
0.0009
(0.0025)
0.0090
(0.0016)
0.0179
(0.0114)
-0.0898
(0.0165)
0.0247
(0.0177)
Yes
0.0009
(0.0025)
0.0090
(0.0016)
0.0179
(0.0114)
-0.0898
(0.0165)
0.0244
(0.0176)
Yes
0.2003
(0.0902)
0.0168
(0.0054)
-0.0183
(0.0077)
0.0105
(0.0045)
-0.0014
(0.0064)
0.0030
(0.0017)
0.0048
(0.0013)
-0.0005
(0.0025)
0.0089
(0.0016)
0.0192
(0.0114)
-0.0819
(0.0167)
0.0281
(0.0177)
Yes
Female
Years of education
Education X Female
AFQT score /10
AFQT Score /10 X Female
Linear
Fixed-Effects
[7]
0.0134
(0.0108)
-0.0135
(0.0132)
0.0014
(0.0024)
0.0047
(0.0019)
-0.0001
(0.0037)
0.0028
(0.0016)
0.0139
(0.0077)
-0.0287
(0.0097)
-
Occup. & Indust. Dummies
No
0.0022
(0.0024)
0.0085
(0.0015)
0.0201
(0.0113)
-0.1450
(0.0153)
-0.0015
(0.0171)
No
Year Dummies
No
Yes
Yes
Yes
Yes
Yes
Yes
Worker Fixed Effects
No
No
No
No
No
No
Yes
Number of observations: 52,395
Notes: Standard errors (in parentheses) are adjusted for clustering at the individual level. Marginal probability effects evaluated
at the mean of the regressors are reported.
47
Yes
Table 4. Selection into Performance Pay Jobs by Family Structure
NLSY 1980-2010
Panel A: Mothers and Fathers
Dependent Variable: Whether Individual is in a PP Job
Probit
Specification
[1]
[2]
[3]
[4]
[5]
[6]
-0.0964
(0.0164)
0.0144
(0.0044)
-
-0.0986
(0.0167)
0.0157
(0.0046)
-
-0.0835
(0.0183)
0.0138
(0.0047)
-
0.0175
(0.0036)
-
0.0124
(0.0041)
0.0109
(0.0042)
0.2529
(0.0974)
0.0235
(0.0058)
-0.0257
(0.0073)
0.0102
(0.0042)
-
Average Usual Weekly
Hours in Job Match
Maximum Usual Weekly
Hours in Job Match
Experience:
-
-
-
-
0.2403
(0.1038)
0.0225
(0.0063)
-0.0236
(0.0090)
0.0114
(0.0053)
-0.0030
(0.0074)
-
-
-
-
-
-
-
Tenure:
-
Married
-
CBA
-
Nonwhite
-
-0.0003
(0.0028)
0.0094
(0.0018)
0.0163
(0.0133)
-0.0830
(0.0189)
0.0133
(0.0200)
Yes
0.0009
(0.0028)
0.0092
(0.0018)
0.0156
(0.0133)
-0.0829
(0.0189)
0.0168
(0.0201)
Yes
0.0009
(0.0028)
0.0092
(0.0018)
0.0157
(0.0133)
-0.0829
(0.0189)
0.0165
(0.0201)
Yes
0.2760
(0.1030)
0.0212
(0.0064)
-0.0233
(0.0090)
0.0124
(0.0053)
-0.0023
(0.0074)
0.0023
(0.0020)
0.0052
(0.0014)
-0.0006
(0.0028)
0.0009
(0.0018)
0.0172
(0.0134)
-0.0755
(0.0190)
0.0207
(0.0201)
Yes
Female
Years of education
Education X Female
AFQT score /10
AFQT Score /10 X Female
Linear
Fixed-Effects
[7]
0.0221
(0.0130)
-0.0242
(0.0152)
0.0019
(0.0030)
0.0046
(0.0023)
-0.0018
(0.0044)
0.0035
(0.0020)
0.0172
(0.0089)
-0.0254
(0.0106)
-
Occup. & Indust. Dummies
No
0.0001
(0.0028)
0.0088
(0.0018)
0.0190
(0.0132)
-0.1328
(0.0177)
-0.0098
(0.0195)
No
Year Dummies
No
Yes
Yes
Yes
Yes
Yes
Yes
Worker Fixed Effects
No
No
No
No
No
No
Yes
Number of observations: 37,980
Notes: Standard errors (in parentheses) are adjusted for clustering at the individual level. Marginal probability effects evaluated
at the mean of the regressors are reported.
48
Yes
Table 4 (continued)
NLSY 1980-2010
Panel B: Childless Individuals
Dependent Variable: Whether Individual is in a PP Job
Probit
Specification
[1]
[2]
[3]
[4]
[5]
[6]
-0.0832
(0.0218)
0.0119
(0.0057)
-
-0.0971
(0.0218)
0.0149
(0.0060)
-
-0.1041
(0.0239)
0.0038
(0.0063)
-
0.0143
(0.0048)
-
0.0106
(0.0052)
0.0065
(0.0052)
-0.0160
(0.1370)
0.0064
(0.0074)
-0.0064
(0.0095)
0.0063
(0.0052)
-
Average Usual Weekly
Hours in Job Match
Maximum Usual Weekly
Hours in Job Match
Experience:
-
-
-
-
-0.0095
(0.1446)
0.0069
(0.0080)
-0.0075
(0.0118)
0.0057
(0.0065)
0.0016
(0.0096)
-
-
-
-
-
-
-
Tenure:
-
Married
-
CBA
-
Nonwhite
-
0.0008
(0.0045)
0.0087
(0.0026)
0.0231
(0.0194)
-0.1162
(0.0263)
0.0493
(0.0295)
Yes
0.0008
(0.0045)
0.0086
(0.0026)
0.0230
(0.0195)
-0.1162
(0.0263)
0.0497
(0.0295)
Yes
0.0009
(0.0045)
0.0086
(0.0026)
0.0230
(0.0195)
-0.1162
(0.0263)
0.0499
(0.0294)
Yes
0.0334
(0.1475)
0.0063
(0.0079)
-0.0082
(0.0119)
0.0056
(0.0065)
0.0004
(0.0097)
0.0046
(0.0027)
0.0040
(0.0020)
0.0000
(0.0045)
0.0081
(0.0027)
0.0244
(0.0195)
-0.1075
(0.0264)
0.0540
(0.0294)
Yes
Female
Years of education
Education X Female
AFQT score /10
AFQT Score /10 X Female
Linear
Fixed-Effects
[7]
0.0097
(0.0224)
0.0015
(0.0310)
0.0011
(0.0042)
0.0041
(0.0037)
-0.0012
(0.0103)
0.0040
(0.0032)
0.0217
(0.0167)
-0.0376
(0.0193)
-
Occup. & Indust. Dummies
No
0.0055
(0.0044)
0.0076
(0.0027)
0.0226
(0.0192)
-0.1825
(0.0238)
0.0238
(0.0284)
No
Year Dummies
No
Yes
Yes
Yes
Yes
Yes
Yes
Worker Fixed Effects
No
No
No
No
No
No
Yes
Number of observations: 14,415
Notes: Standard errors (in parentheses) are adjusted for clustering at the individual level. Marginal probability effects evaluated
at the mean of the regressors are reported.
49
Yes
50
16.68
35.02
13.11
13.21
7.61
0.57
0.20
38.99
0.38
0.45
0.20
0.10
46
2,088
2,573
8,443
Average Hourly Earnings ($2008)
Age
Education
Experience
Employer Tenure
Married
Covered by CBA
Usual Hours Worked Per Week
Father high school graduate
Mother high school graduate
Father B.A.+
Mother B.A.+
AFQT (percentile)
# workers (Tot: 5,911)
# Job Matches (Tot: 7,735)
# Observations (Tot: 27,005)
Non-performancepay Jobs
[1]
842
908
3,402
19.52
34.52
13.25
13.14
7.74
0.59
0.12
40.23
0.37
0.46
0.20
0.10
50
Performance-pay
Jobs
[2]
2,253
2,810
9,510
21.07
35.40
12.87
14.61
8.31
0.63
0.30
43.21
0.35
0.48
0.23
0.12
48
Non-performancepay Jobs
[1]
1,280
1,444
5,650
27.95
35.69
13.66
14.92
9.26
0.67
0.16
45.47
0.35
0.49
0.26
0.16
58
Performance-pay
Jobs
[2]
Appendix Table 1. Summary Statistics: National Longitudinal Survey of Youth 1980-2010
Subsample of Added Observations from 1980-87, 1991-1994, and 2002-2010
Females
Males