Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji 884.full
The Gender Wage Gap among Young
Adults in the United States
The Importance of Money versus People
Nicole M. Fortin
abstract
Using two single-cohort longitudinal surveys, the NLS72 and the NELS88,
I investigate the impact of four noncognitive traits—self-esteem, external
locus of control, the importance of money/work and the importance of people/
family—on wages and on the gender wage gap among these young workers.
I find that gender differences in these noncognitive factors, especially the
importance of money/work, have a modest but significant role in accounting
for the gender wage gap. Methodologically, this paper proposes a correction
to the Oaxaca-Blinder-Ransom decomposition that results in a truly
decomposable approach compatible with the simple pooled regression that
includes a gender dummy.
I. Introduction
After spectacular gains in the 1970s and 1980s, the gender wage gap
has shown slower progress during the 1990s (O’Neill 2003; Blau and Kahn 2004).
Given the realized promises of the 1980s that convergence in human capital (O’Neill
and Polachek 1993; O’Neill 2003) would be key to the closing of the gender pay gap,
this stagnation was disappointing. In the 1980s and 1990s, dramatic increases in educational attainment among younger cohorts allowed women to enter professions, especially managerial professions, previously dominated by men. These gains were
concurrent with increases in labor market experience among older cohorts, which
Nicole Fortin is professor of economics at the University of British Columbia and a fellow of the
Canadian Institute for Advanced Research (CIFAR). She would like to thank Francine Blau, David
Card, Peter Dolton, Thomas Lemieux, Markus Mobius, Nathaniel Wilcox, anonymous referees, and
seminar participants at the 2002 and 2006 AEA Meetings, at the 2005 SOLE Meetings, at UC-Berkeley
and at the CIFAR Workshop on Social Interactions, Identity and Well-Being for helpful comments on
this and earlier versions of the manuscript. The early stages of the project benefited from Michael
Coelli’s diligent research assistance. The data used in this article can be obtained beginning May 2009
through April 2012 from Nicole Fortin, Dept. of Economics, UBC, nifortin@interchange.ubc.ca.
Financial support was provided by SSHRC Grants #410-99-0959 and #501-2002-0118.
½Submitted May 2006; accepted February 2007
ISSN 022-166X E-ISSN 1548-8004 Ó 2008 by the Board of Regents of the University of Wisconsin System
T H E JO U R NAL O F H U M A N R ES O U R C ES
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also contributed to the narrowing of gender gap (Blau and Kahn 2000). Thus perhaps
unsurprisingly, Blau and Kahn (2004) attribute the 1990s slowdown in the gender
convergence in pay to factors, other than educational attainment and labor market
experience that contributed to changes in the unexplained gender pay gap. These factors include changes in labor market selectivity, changes in gender differences in unmeasured characteristics and labor market discrimination, as well as less favorable
supply and demand shifts.1
Following a renewed interest in the impact of behavioral and personality traits on
earnings (Bowles, Gintis, and Osborne 2001), this paper attempts to move some of
the typically unmeasured characteristics into the measurable dimension.2 The main
goal of the paper is to assess the contribution of noncognitive factors in accounting
for the gender pay gap among workers in their early thirties in the mid-1980s. A second goal of the paper is to shed some light on the sources of changes in the gender
pay gap over the last two decades of the twentieth century among young workers.
The recent literature on personality and earnings has incorporated noncognitive
factors from the Rosenberg self-esteem and the Rotter locus of control scales (Goldsmith, Veum, and Darity 1997; Osborne Groves 2005; Heckman, Stixrud, and Urzua
2006; Manning and Swaffield 2008; Waddell 2006), and from the Five-Factor Model
(FFM) of personality structure (Mueller and Plug 2006; Nyhus and Pons 2005).3
Like the papers in this first group, I consider the role of personality traits such as
self-esteem and external locus of control, but the gender differences in these traits
are not very large.
Because this paper is concerned with the gender wage gap, I put more emphasis on
noncognitive factors where there are important gender differences, namely those associated with differentiated gender roles. In this sense, this study complements that
of Mueller and Plug (2006), which focuses on personality traits that are prototypically masculine and feminine. Consistent with the traditional roles of men as ‘‘breadwinners,’’ responsible for the financial welfare of the family, and of women as
‘‘homemakers’’ or ‘‘caregivers,’’ entrusted with the care of children and the support
of relationships, I pay special attention to gender differentials in valuing money and
work on the one hand, and in valuing people, usefulness to society, and family on the
other.4 These traits also are easily tied to the economic literature on the household
division of labor (Becker 1985), as argued in Section II. In addition, an important focus of the recent experimental literature has been on gender differences in
altruism.5
1. Much recent research on the gender pay gap has focused on the impact of labor market selectivity, an
issue outside of the scope of the present paper.
2. Early research in this area goes back to the late 1970s and early 1980s; a review can be found in Bowles,
Gintis, and Osborne (2001) and Osborne Groves (2005).
3. The Rosenberg self-esteem scale is a widely used measure of self-esteem. The Rotter scale indicates
whether an individual is more external or fatalistic, believing that hard work and effort are not rewarded,
but rather that luck, fate, or good fortune determine success and achievement. The five personality traits
included in the FFM are extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience.
4. Various terminologies have been used to describe these potentially conflicting values: career-life
choices, work-family balance, etc.
5. The experimental literature also focuses on gender differences in risk taking and competiveness, because
experimental methods are more appropriate to study these traits than career-life choices.
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Ample anecdotal evidence suggests that women give greater importance in their
choice of job to its value to society and to personal interactions, and less importance
to money and prestige than men, but few studies investigate this path directly.6 Yet, a
recent hypothesis in the gender pay gap literature, called the negotiating divide hypothesis (Babcock and Laschever 2003), argues that women may have less of a sense
of entitlement to higher wages and are less likely to initiate negotiation or bargain.7
I utilized data from two widely used high school-cohort surveys: the National Longitudinal Study of the High School Class of 1972 (NLS72) and the National Education Longitudinal Study of 1988/94 (NELS88). The longitudinal aspect of these
surveys allows me to use noncognitive traits measured when the individuals were
in their teens (or early twenties)—in fact, many years before the labor market data
was collected. The wage data was recorded when the individuals were in their
mid-twenties (in 1979 for the NLS72, in 2000 for the NELS88) and in their early
thirties (in 1986 for the NLS72). The measured noncognitive traits cannot suffer
from a reverse causality problem since they are recorded before the respondents’
wages are observed. Further there is relatively little time for these noncognitive traits
to affect the labor market outcomes of individuals in their mid-twenties by an indirect behavioral route, such as a more altruistic mother taking longer maternal leaves,
working part-time and getting lower pay then a career-oriented mother working fulltime with minimal work interruptions and getting higher pay. I thus argue that for
these young workers the impact of noncognitive traits is relatively untainted by direct
feedback effects from the labor market, rather these traits can be thought off as predetermined factors.8 While considering these younger workers presents an advantage
with respect to the identification of the impact of noncognitive traits, it is a disadvantage in terms of a deeper understanding of the gender pay gap in the whole economy.
Finally to the extent that there are cohort effects in the gender pay gap, looking at
single cohorts rather than cross-sectional data arguably provides a cleaner assessment of changes in the gender pay gap within and between cohorts.9
I assess the importance of these noncognitive traits on wages, appealing to a hedonic wage function framework, and of gender differentials in these traits on the
mean gender wage gap. Because of the nature of the noncognitive traits, I cannot
claim to identify the ‘‘causal’’ effects of these factors on wages; rather I am interested in their impact as omitted variables on potential biases in the gender dummy
of a standard wage regression.10 To assess the impact of the traits on gender wage
differentials, I propose a correction to the Blinder-Oaxaca-Ransom wage gap decomposition that makes it fully compatible with the simple pooled regression that
6. Filer (1986) evaluates the impact of various personality traits and job characteristics, including ‘‘contribution of job to society,’’ on occupational structure. Chevalier (2004) investigates the impact of gender differences in motivation on the gender wage gap in the United Kingdom among recent graduates.
7. Säve-Söderbergh (2003) indeed finds that not only were Swedish female university graduates asking for
lower wages, but they also were receiving lower counter-offers.
8. This case, of course, is not as clear for workers in their early thirties. In their mid-twenties, women in my
samples have on average about one year of parental experience, but more than five years of parental experience in their early thirties.
9. On this issue, see for example Blau and Kahn (2000) and Weinberger and Kuhn (2006).
10. To simplify the exposition, at times the word ‘‘effect’’ or ‘‘impact’’ is used when what is estimated is
merely a partial correlation.
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includes a gender dummy, and for which standard errors are easily computed. The
correction also solves the problems resulting for the choice of the nondiscriminatory
wage structure (Oaxaca and Ransom 1994) and includes recent propositions (Gardeazabal and Ugidos 2004) to correct the identification problem caused by the
left-out reference group, acknowledged in Oaxaca and Ransom (1999).
The main finding of the paper is of a modest but significant role (1.9 log points out
of 22.9 log points) for gender differences in noncognitive traits, in particular valuing
money/work and people/family (1.7 log points), to account for the gender wage gap
in 1986 (among workers in their early thirties from the NLS72). This role is larger
than the role of gender differences in educational attainment and cognitive skills
(math score) combined, about 1.2 log points. It is almost as important in magnitude
as the role of gender differences in labor market experience and job tenure (2.4 log
points), which is however lower than typically found in cross-sectional data.
Considering changes in the gender pay gap, from 1979 to 2000, among workers in
their mid-twenties, the raw gender pay has decreased by a 5.6 log points (from 23.7
to 18.1 log points). The human capital variables accounted for 2.8 log points of the
gap in 1979 and -2.2 log points in 2000, thus accounting for -5.0 log points (-2.2–2.8)
of the change in the gap. The noncognitive traits accounted for an additional -0.6 log
points (0.9–1.5) of the change. Essentially the gender convergence in educational attainment, cognitive skills, valuing money, and work can explain away all of the gender convergence in pay between young workers from the NLS72 and NELS88
cohorts. This result suggests that the clear progression across cohorts of women’s
preparation for the labor market is responsible for the decrease in the gender pay
gap. The within-cohort decrease in the gender gap pay of less than 1 log points (from
23.7 log points to 22.9 log points for the NLS72 cohort) from age 25 to age 32 further suggests a rather small role for within-cohort relative earnings growth. These
findings help clarify the deliberations of Blau and Kahn (2000) on which factor—
within-cohort, between-cohort changes or lower discrimination—is more important
in accounting for the gender convergence in pay among young workers.
The remainder of the paper is organized as follows. The next section reviews the
literature on gender differences in work values and presents a hedonic wage function
framework in which noncognitive factors are thought to influence wages. Section III
addresses detailed data issues. Section IV presents the update on the Oaxaca-BlinderRansom methodology. The wage regression and decomposition results are discussed
in Section V. Finally, I conclude with a review of the findings in Section VI.
II. The Influence of Work Values on Economic Behavior
Historically gender differences in work values, which were perceived
as a rationalization for occupational gender segregation, have been deemphasized in
the sociological literature (Bielby and Bielby 1984; Rowe and Snizek 1995). More
recently, in an effort to account for the unexplained part of the gender pay gap
and increasing returns to unobserved skills, there has been a renewed interest in economics for ‘‘soft’’ skills or noncognitive traits.
Experimental studies in behavioral economics have noted important differences
between men and women in individual attitudes toward altruism and greed (Andreoni
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and Vesterlund 2001), leadership, and competitiveness (Gneezy, Niederle, and Rustichini 2003). Empirical studies have found a substantial impact of these and related
traits on wages. Kuhn and Weinberger (2005) find sizeable positive returns, among
white males, to leadership skills ranging from 4 to 24 percent and an even larger impact for workers in managerial occupations.
Another trait at the opposite end of the leadership spectrum is the ‘‘external locus
of control,’’ that is a belief that one’s outcomes are controlled more by external
forces than by internal forces (or that chance and luck are more important for success
than one’s own efforts). The effect of external locus of control on earnings, often together with the effect of self-esteem, has been investigated by many authors (Goldsmith, Veum, and Darity 1997; Osborne Groves 2005; Heckman, Stixrud, and Urzua
2006; Manning and Swaffield 2008; Waddell 2006, among others). For example, using the same surveys that I use, Waddell (2006) (using the NLS72) and Coleman and
DeLeire (2003) (using the NELS88), find that low self-esteem and an external locus
of control are associated with lower investment in human capital, among other negative indicators of future labor market performance. On the other hand, Goldsmith,
Veum, and Darity (1997) who include both predicted values of self-esteem and locus
of control in their wage regressions, find that only self-esteem was statistically significant.
Psychologists believe that work effort reflects motivation, which is governed in
part by these personality factors. Individuals with an external locus of control believing that hard work and effort will be not rewarded are less likely to display high levels of effort on the job. On the positive side, self-esteem has been found to be a
strong predictor of job performance (Judge, Tippie, and Bono 2001). Brockner
(1988) argues that workers with high self-esteem will tend to be more confident
about making decisions, will use their time more effectively and more productively. Psychologists also expect that higher wages will enhance self-esteem, but that
poor self-esteem may lead to a decline in productivity and wages which may in turn
erode a person’s perception of self. Thus, theories of self-esteem formation advanced
by psychologists suggest that wages and self-perception may be jointly determined,
which could lead to an endogeneity problem. To avoid this potential problem, I use
measures of personality traits and features obtained many years before the wages are
observed.
Because the gender differences in self-esteem and locus of control are relatively
small, these commonly studied noncognitive factors will prove to be less useful
for my purpose of explaining the gender pay gap. I thus capture an additional dimension of motivation and work effort by including a measure of the importance of
money/work. Psychologists (Furnham and Argyle 1998) argue that workers’ beliefs
about money are clearly related to their actual economic behavior. The attitudebehavior relation literature (Ajzen and Fishbein 1977; Tang, Kim, and Tang 2000)
further argues that those who do value money and want to make more money (a
work-related attitude) may have a higher level of turnover (a work-related behavior)
than those who do not. Workers for whom ‘‘Making a lot of money’’ is ‘‘very important’’ may be more likely to initiate bargaining talks with employers or change jobs
for more money. Similarly, workers for whom ‘‘Being successful in work’’ in ‘‘very
important’’ may be more likely to display to work-related behaviors that lead to promotions and higher wages. Kirkcaldy and Furnham (1993) also found that attitudes
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toward money are predicted by competitiveness or the motive to outperform others.
To the extent that there are gender differences in competitiveness, these will translate
into gender differences in attitudes toward money.
Gender differences in the importance of people/family are well-known, but there
are few attempts to study directly how they affect work-related behaviors.11 In their
analysis of changes in the gender pay gap, Borghans, ter Weel, and Weinberg (2006)
model the importance of interpersonal interactions in the context of an assignment
model, where there are positive returns to interpersonal skills. Here, I combine this
dimension of altruism, the importance of ‘‘working with people rather than things,’’
with one that goes beyond interpersonal skills, the importance of ‘‘a job usefulness to
others or society’’ and I focus on the implications of women’s higher demand for altruistic rewards for earnings. Many more women than men, indeed, choose lines of
work, such as education and health care, where they perform many tasks for the love
of others and reap lower market rewards working for public and nonprofit organizations.12 Women also are more likely than men to use their time to volunteer in organizations with some altruistic purpose, such as schools, religious organizations, and
hospitals.13
I show how, in a classic hedonic wage model (Rosen 1974) the importance of
money/work can have a positive impact on earnings, while the importance of people/family can have a negative impact. By contrast with the human capital model,
the hedonic wage function model is job-oriented, and indeed assumes the workers
choose and change jobs on the basis of the characteristics of the job. The importance
of money/work, self-esteem, and external locus of control are preferences and personality features that influence the choice of wage level and the level of effort and
responsibility linked to the job. The importance of people/family influences the
choice of altruistic rewards of the job.
These noncognitive traits are thought to impact wages through a hedonic wage
function resulting from a matching equilibrium between the employers’ wage and
amenity offers, and the workers’ utility maximizing choices of job characteristics.
The worker/consumer i maximizes utility over wages, effort and responsibility,
and altruistic rewards
ð1Þ Ui ¼ UðWi ; Ri ; Ai Þ
where Wi is the wage rate, Ri is the level of effort and responsibility exerted by the
worker/consumer, and Ai is the level of altruistic rewards, which is derived from both
H
market and nonmarket activities: Ai ¼ AM
i + Ai . The marginal utility of wages and
altruistic rewards is positive, but its sign with respect to effort and responsibility
is ambiguous. To the extent that leadership and ambition denote different flavors
of effort, one can see those as positively linked to utility, but when effort and responsibility exceed some threshold they entail a disutility.
11. Daymont and Andrisani (1984), who focus their analysis of the gender pay gap on the choice of college
major, also study the effect of these values.
12. There is some evidence that men are becoming more like women on this ‘‘usefulness to others’’ dimension, as found across birth cohorts in the European Value Survey (Fortin 2005).
13. On the other hand, men are more likely to volunteer as coaches in youth sports teams, in unions, professional organizations, social and political clubs, where they can use and perfect leadership skills.
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Following Becker (1985), who argues that individuals have a limited amount of
time and energy to split between market work and homemaking, I assume a total
amount of effort and responsibility that an individual can devote to either market
H
T
T
or nonmarket activities, Ri ¼ RM
i + Ri # Ri , where Ri denotes to the total amount
H
available, fixed for each individual, and Ri denotes the amount of effort and responsibility allocated to nonmarket activities, like household work and volunteering activities. To the extent that women carry the double-shift of housework and
market work more often than men do, this implies that the marginal utility (disutility)
of market effort and responsibility is greater (less) for men than for women,
M
@Uim =@RM
im . @Uif =@Rif , and more generally for individuals who value homemaking
and volunteering more than others.
The utility function of individuals for whom ‘‘working with people’’ and feelings
of ‘‘usefulness of a job to others and society’’ are more important relative to financial
rewards will be characterized by a greater marginal rate of substitution between the
altruistic rewards and wages than other workers. To the extent this trait characterizes
women (f) more than men (m):
ð2Þ
@Uif @Uif @Uim @Uim
=
.
=
@Wif @Aif @Wim @Aim
For these individuals, working for philanthropic nonprofit organizations can take
place at a wage cost.14 Alternatively, individuals could accept a job with lower altruistic rewards and lower effort, but volunteer in altruistic organizations or become a
part-time caregiver at home. Because of the quantity constraint
on effort
and respon
sibility, when the worker/consumer chooses a job vector ðWi ; RiM ; AiM Þ to maximize
his/her utility,
he/she
simultaneously chooses a level of nonmarket effort and responH
M
T
sibility, Ri¼ Ri 2 Ri . Similarly, the individual’s demand for the altruistic market
amenity may depend on the level already provided by home and volunteering activities. These potential utility maximizing choices generate a job demand locus
ui ðW; RM ; AM Þ.
Employers are thought to be cost–minimizers, so that optimizing behavior generates an offer function, f ðW; RM ; AM Þ, indicating unit prices that employers are willing to pay on various combinations of wage, effort, and altruistic amenities.15
Because it is time-consuming, the altruistic amenity is by definition costly to provide.16 It is assumed that employers pay higher wages for higher levels of effort
and responsibility.
Subsumed in the worker/consumer job demand functions and the employers’ offer
functions are the human capital variables, which are thought to help eliminate multiple equilibria.17 I circumvent the identification problems (Ekeland, Heckman, and
14. For example, in the NELS88, 11.7 percent of women work for nonprofit organizations by contrast with
4.4 percent of men.
15. To the extent that some employers (for example, hospitals) are exert monopsony power in some markets for altruistic jobs (for example, nursing), the offer functions cannot be seen as perfectly competitive
outcomes.
16. For example, smaller classes are more rewarding to teach.
17. I could have more explicitly conditioned the utility function on the human capital variables
UðWi ; Ri ; Ai jSi ; Ei Þ.
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Nesheim 2004) associated with hedonic wage functions by including information on
usually unobserved tastes for job characteristics. I simply assume that one equilibrium is realized in the data and seek to broadly identify preferences from value statements and reported behaviors in a reduced-form approach. In the equilibrium
assignment, we thus find a wage equation,
M
ð3Þ Wi ¼ gðSi ; Ei Þ + hðRM
i Þ + tðAi Þ;
where in addition the usual human capital component, gðSi ; Ei Þi , with Si denoting
schooling and Ei labor market experience, there is a component that rewards effort
M
and responsibility hðRM
i Þ and another one that taxes altruistic amenities tðAi Þ.
My aim is to first assess the impact of work values and altruistic preferences on
wages, then I will consider the impact of gender differentials in these traits on the
gender pay gap.18 The importance of money/work will prove to have an impact on
wages more distinct from cognitive factors that self-esteem and external locus of
control. Noncognitive factors offer a unique possibility to improve the empirical
analysis of models of the household division of labor and of wage determination by incorporating variables that predate household formation and labor market
decisions.19
III. Data and Descriptive Statistics
To avoid problems of ex post rationalization, I appeal to two singlecohort longitudinal surveys conducted by the National Center for Education Statistics (NCES) to study education issues. The National Longitudinal Study of the High
School Class of 1972 (NLS72), known as the grandmother of educational longitudinal surveys, is widely used (Adelman 1994) outside of the education field. For example, Brown and Corcoran (1997) use it, in conjunction with the SIPP, to study the
impact of gender differences in the field of the highest degree on the gender wage
gap.20 The NLS72 interviewed students in their senior year of high school in the
spring of 1972, with followup surveys conducted in 1973, 1974, 1976, 1979, and finally in 1986. While the sample is not fully nationally representative because it
ignores students who dropped out before Grade 12, Krueger and Dale (2002) argue
that it is representative for college graduates.
The NLS72 contains answers to many questions on what is important in life and in
selecting a career. For example, respondents are asked, with reference to selecting a
18. In a previous version of the paper, I had shown that work values and altruistic preferences have behavioral implications of the expected signs on promotions (or training for career advancement), volunteering in
either philanthropic (hospitals, youth organization, etc.) or leadership organizations (sports teams, rotary
club, etc.) among men and women, and on the incidence of working part-time or long maternal leave
(or the number of dependents) among women.
19. Although the influences of neurological, environmental, psychological and social factors have been implicated with varying degrees in the development of gender roles, it is generally recognized that gender and
sex-role knowledge are acquired early in life (Ruble and Martin 1998).
20. Here I omit field of study because it is missing for a sizeable portion (more than a quarter) of the
sample.
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The Journal of Human Resources
career, about the importance of ‘‘Making a lot of money’’ and ‘‘The chance to be a
leader,’’ which speaks to the importance of money and ambition in work values. They
are also asked about the importance of ‘‘Opportunities to be helpful to others or useful to society,’’ ‘‘Opportunities to work with people rather than things,’’ this speaks to
the altruistic and social aspect of work. The survey also contains questions from the
well-known Rosenberg and Rotter scales. Many of these questions are asked repeatedly in each followup. Because of the longitudinal aspect of the survey, it is possible
to use preferences expressed in a time period that precedes the behavior of interest.
Wage information was recorded in the fourth followup in 1979, when most respondents were age 25, and in the fifth followup in 1986, when most respondents were age
32. Note that in 1986 more respondents were likely to have completed their studies
and there was more time for labor market experience to have an effect.
The National Education Longitudinal Study of 1988/94 (NELS88) interviewed
students in eighth grade in the spring of 1988. A sample of these respondents was
then resurveyed through four followups in 1990, 1992, 1994, and 2000, and a
refreshed sample was added in the 1990 and 1992 waves to ensure a representative
sample of high school sophomores and seniors in those years. The wage data was
recorded in 2000 when most respondents were age 24, which leaves little time for
labor market intermittency to have an effect. To enhance the comparability between
the two samples, I select only respondents from the NELS88 who reached the 12th
grade. The NELS88 was explicitly administered with the intent of maintaining comparability with the NLS72, so the major components of the two studies are very similar, including the components on values important in life.
Being very detailed and complex also means that these longitudinal surveys are at
times difficult to code. In particular, there is detailed information about employment
and unemployment spells, which are used to construct an actual work experience variable, but it is not straightforward to code.21 I retain individuals from the NLS72
(NELS88) who were working in 1979 or in 1986 (2000) and that have valid information on wage, educational attainment, high school math test, and questions from the
Rosenberg and Rotter scales.22
The means of the human capital variables and other individual characteristics are
reported separately by gender for each time period in Table 1. They show that, in the
NLS72, when workers are in their mid-twenties, women’s educational attainment is
21. In the NLS-72, the information on these spells from October 1972 to October 1979 is available in the
first to the fourth followup. The information from October 1979 to July 1986 is available in terms of spells
of employment for up to four jobs and unemployment for up to eight spells, thus individuals with more than
eight spells of unemployment were excluded. In the NELS88, detailed labor force status by month is available from June 1990 to August 1994. In 1997, 1998, and 1999, only information on whether the respondent
was unemployed for more than six months was available.
22. I retain only individuals whose wage was between $1.00 and $150 in 1986, and the equivalent in 1979
and 2000. The wage rate is computed to approximate as closely as possible an hourly wage measure. In
1986 and 2000, the earnings are reported either as hourly wages, weekly earnings, biweekly earnings,
monthly earnings or annual earnings and flagged as such. The hourly wages are thus computed correspondingly as weekly earnings divided usual weekly hours of hour, biweekly earnings divided usual weekly hours
times two, monthly earnings divided usual weekly hours times 52/12, annual earnings divided by usual
weekly hours times 52. In 1979, earnings were recorded only as weekly earnings, wages are thus computed
as weekly earnings divided by weekly hours. About 4 percent of the observations are in the edited categories (with codes in 800 and 1000 numbers) and are recoded using the information in the codebook.
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Table 1
Mean and Standard Deviations of Commonly Used Variables
Samples
Year (Age)
NLS72
1979(25)
NELS88
1986(32)
2000(24)
Variables
Male
Female
Male
Female
Male
Female
Log wage
1.803
(0.418)
1.566
(0.395)
2.521
(0.585)
2.292
(0.693)
2.621
(0.469)
2.440
(0.467)
0.006
(0.080)
0.202
(0.401)
0.112
(0.316)
0.368
(0.482)
0.267
(0.443)
0.044
(0.206)
0.001
(0.028)
0.233
(0.423)
0.087
(0.281)
0.369
(0.483)
0.269
(0.443)
0.042
(0.200)
0.002
(0.045)
0.178
(0.383)
0.155
(0.362)
0.297
(0.457)
0.252
(0.434)
0.115
(0.362)
0.003
(0.055)
0.211
(0.408)
0.133
(0.340)
0.327
(0.469)
0.233
(0.423)
0.093
(0.290)
0.006
(0.079)
0.189
(0.391)
0.099
(0.299)
0.409
(0.492)
0.267
(0.442)
0.029
(0.169)
0.006
(0.076)
0.133
(0.339)
0.092
(0.288)
0.362
(0.480)
0.362
(0.481)
0.047
(0.211)
5.269
(1.440)
1.018
(2.000)
4.747 10.851
(1.560) (2.354)
0.767
4.916
(1.738) (3.816)
9.625
(2.917)
4.084
(3.616)
4.682
(2.234)
2.194
(2.225)
4.461
(2.028)
1.988
(2.057)
Education
Less then high school
High school
Trade or vocational
Some college
College
Postgraduate
Work experience
Years
Tenure
Cognitive skills
High school math
Standardized score
Personal characteristics
Part-time
Black
Married
Parental experience
Child
Number of observations
0.091 20.114
(0.974) (0.962)
0.098 20.158
(0.979) (0.973)
0.010 20.011
(1.020) (0.977)
0.048
(0.213)
0.052
(0.222)
0.528
(0.499)
1.011
(2.071)
0.315
(0.465)
3,391
0.025
(0.156)
0.052
(0.222)
0.736
(0.441)
4.507
(4.767)
0.642
(0.479)
3,456
0.067
(0.250)
0.122
(0.327)
0.368
(0.483)
0.943
(1.992)
0.306
(0.461)
3,183
Note: Standard deviations in parentheses.
0.110
(0.312)
0.101
(0.301)
0.524
(0.499)
1.293
(2.354)
0.333
(0.471)
2,793
0.164
(0.370)
0.100
(0.300)
0.687
(0.464)
5.479
(5.476)
0.649
(0.478)
3,066
0.150
(0.357)
0.101
(0.301)
0.429
(0.495)
1.174
(2.293)
0.150
(0.356)
3,293
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The Journal of Human Resources
similar to men’s, but by their early thirties, more men than women have a postgraduate degree. In the NELS88, where the workers are in their mid-twenties, this pattern
has been reversed with not only more women holding a college degree than men, but
also more women holding a postgraduate degree. In these single cohorts of young
workers, the differences in the labor market experience of women and men are relatively small (at most 13 percent). There is thus limited scope for labor market intermittency to account for a large share of the gender wage gap. This contrasts
with the recent work of O’Neill and O’Neill (2005) were differences in labor market
experience and work interruptions for family reasons are found to account for more
than half of the gender wage gap.
Cognitive skills are captured using high school math score measured when the
respondents were in 12th grade, both in the NLS72 and NEL88. Since the samples
exclude earlier dropouts, this measure of cognitive skills generally precede the realizations of the educational attainment observed. I standardize the measure to have
zero mean and a variance of one over the entire sample in each case. Thus in
NSL72, the measure for women is negative while the measure for men is positive.
This male advantage seemed to have shrunk somewhat in the NELS88.23 Table 1 also
reports the means and standard deviations of demographic characteristics commonly
used in wage regressions.
Table 2 reports the descriptive statistics of the four composite noncognitive traits
used in the analysis: self-esteem, external locus of control, the importance of money/
work and the importance of people/family. I report the means and standard deviations of the variables by gender, as well as the differences between the male and female averages. First note that all the composite variables are scaled between 0 and 1
inclusively, and that the standard deviations of these variables are in the same range,
from 0.141 to 0.286. Also reported in the table is Cronbach’s alpha, a measure of
reliability that indicates whether the responses to each question comprising the composite are highly correlated.24 It is computed over the sample of men and women
combined.
The measures of self-esteem and external locus of control are constructed using
answers to a subset of four or five questions, asked in the surveys, from the widely
used Rosenberg self-esteem scale and the Rotter locus of control scale. The description of the precise statements used and the aggregation method is in the Appendix.
As explained in the Appendix, the responses are recoded in terms of a binary variable
(1 or 0) reflecting agreement or disagreement with the statements. In these samples,
the reliability indexes for self-esteem and external locus of control seem relatively
low; they are, however, higher than the ones of Waddell’s (2006) Attitude and
Self-Esteem indexes, also computed with the NLS72 data, which range from 0.36
to 0.50.
The measures of the importance of money/work and of people/family are constructed using subjective questions about values generally important in life, which
are asked in the base year and in each followup of the NLS72, and in selected
23. This is consistent with the literature on the gender gap in math tests.
24. The reliability of a scale (or composite variable) is one of three principles (with convergence and stability) used by psychologists to assess their validity. The measure of reliability is based on the correlations
between the items that make up the composite, relative to the variances of the items.
Table 2
Mean, Standard Deviations, and Reliability Coefficient of the NonCognitive Variables
Samples
NLS72
Year (Age)
1979(25)
Variables
Male
Self-esteema
0.851
(0.286)
External locus of controlb
Money/workc very important
People/familyd very important
Female
0.877
(0.244)
[0.7967]
0.123
0.100
(0.228)
(0.205)
[0.4529]
0.447
0.390
(0.189)
(0.167)
[0.5869]
0.326
0.389
(0.196)
(0.199)
[0.6268]
NELS88
1986(32)
2000(24)
D
Male
Female
20.026
0.935
(0.141)
0.930
(0.159)
0.005
0.023
0.096
(0.181)
0.104
(0.305)
20.008
0.057
0.456
(0.183)
0.391
(0.160)
0.065
20.063
0.322
(0.193)
0.382
(0.196)
20.060
D
[0.5629]
[0.4858]
[0.6347]
[0.6823]
Male
0.757
(0.144)
Female
0.731
(0.178)
[0.6310]
0.179
0.156
(0.247)
(0.238)
[0.5975]
0.561
0.518
(0.206)
(0.196)
[0.5487]
0.629
0.686
(0.253)
(0.254)
[0.4823]
D
0.026
0.023
0.043
20.057
Fortin
Note: Standard deviations in parentheses. Cronach’s alpha scale measure of reliability in brackets. The composite noncognitive variables are generated using STATA’s
alpha procedure, as explained in detail in the Appendix, using components measured many years before the wage data is gathered. They are on a [0,1] scale. D is equal
to the male minus the female measure.
a. The self-esteem measure is constructed using four/five questions from the Rosenberg scale.
b. The external locus of control measure is constructed using four/five questions from the Rotter scale.
c. The money/work composite is constructed using the answers coded 1 for ‘‘Very important,’’ 0 otherwise to valid answers about the importance in selecting a career of
‘‘Making of lot of money’’ and of ‘‘The chance to be a leader in one line of work’’ and about the importance in life of ‘‘Being successful at work’’ and ‘‘Having lots of money.’’
d. The people/family composite is constructed using the answers coded 1 if ‘‘Very important,’’ 0 otherwise to valid answers about the importance in selecting a career of
‘‘Opportunities to be helpful to others or useful to society’’ and ‘‘Opportunities to work with people rather than things,’’ and in life of ‘‘Helping other people in the community,’’ the ‘‘Ability to give children better opportunities,’’ and ‘‘Living close to parents and relatives.’’
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The Journal of Human Resources
followups of the NELS88 and, about values important in selecting a career, which is
asked only in the base survey of the NLS72. The values selected to capture the importance of money/work include questions about the importance in selecting a career
of ‘‘Making a lot of money’’ and of ‘‘The chance to be a leader’’ and about the importance in life of ‘‘Being successful at work’’ and ‘‘Having lots of money.’’ As
explained in the Appendix, the composite is constructed as means of the underlying
answers coded 1 if ‘‘very important,’’ 0 otherwise. Recoding the variable to capture
the intensity of preferences is consistent with the idea that workers who really think
that money is ‘‘very important’’ are more likely to initiate bargaining talks with
employers or change jobs for more money, than workers who simply think that
money is ‘‘somewhat important.’’25
The importance of people/family composite is constructed using questions about
the importance in selecting a career of ‘‘Opportunities to be helpful to others or useful to society’’ and ‘‘Opportunities to work with people rather than things,’’ and in
life of ‘‘Helping other people in the community,’’ the ‘‘Ability to give children better
opportunities,’’ and ‘‘Living close to parents and relatives.’’ This composite is constructed using the same recoding as the previous variable. The reliability indexes
for these two composites are in the same range as the indexes for the more commonly used self-esteem and locus of control composites. This nevertheless indicates
the potential for measurement error in these values, which should lead to an attenuation bias and weaker results than more precise measures would entail.
The gender differences (D) in the importance of money/work and people/family,
ranging from j0.043j to j0.065j to, are much larger than the gender differences in
self-esteem and locus of control, which range from to j0.008j to j0.026j. The gender
differences in the importance of money/work and of people/family are also much
more stable across surveys, despite the differences across surveys in the questions
underlying the composite.26 Ultimately, these gender differences will have substantial explanatory power toward the gender pay gap. Finally, note that the correlation
between the four composite variables is relatively small. If one wanted to construct a
grand composite of the four noncognitive measures, the resulting Cronbach’s alpha
would be very low around 0.25.27
IV. Decomposition of Gender Wage Differentials
In this paper, I propose and implement a modification to the OaxacaBlinder decomposition that addresses two noninvariance problems of the methodology; these are also discussed in Oaxaca and Ransom (1994) and Oaxaca and Ransom
(1999). The first one is the well-known fact that the portion of the gender wage gap
25. In a previous version of the paper, I indeed found that valuing money was a significant determinant of
‘‘having changed for a better job.’’
26. As explained in the Appendix, the greater relative weight of the ‘‘Ability to give children better opportunities,’’ which is very important for 70 percent to 90 percent of respondents, explains the large value of
people/family very important in the NELS88.
27. Given that regression coefficients are in effect partial correlations, any cross-correlations between the
composites will be reflected in changes in the regression coefficients, which are discussed below. It will
show that the more important correlations are with math scores.
Fortin
explained by differences between male and female characteristics is not invariant to
whether the male or female wage structure is chosen as the reference wage structure.
Here, I propose to use as reference wage structure the one under which the advantage
of men is equal to the disadvantage of women. The second noninvariance problem
occurs in the presence of categorical variables and entails that portions of the gender
pay gap attributable to gender differences in specific categorical variables will generally not be invariant to the choice of the omitted category. This problem is easily
solved by appealing to a restricted least squares estimator. One attractive feature of
this modified decomposition is that it is fully compatible with the usual pooled wage
regression that includes a dummy for the disadvantaged group.
Let the following denote the log wage equations estimated separately for samples
of males (m) and females (f ), and for males and females pooled together ( p)
ð4Þ ln wig ¼ b0g + Xig bg + eig ;
g ¼ f ; m; p
where the Xi is a 1 3 K vector of explanatory variables and b is K 3 1 vector of coefficients. Here, I am careful to distinguish the intercept from the other explanatory
variables. As shown below, this is important in the presence of categorical variables,
including the gender dummies.
Under the usual assumption Eðeif Þ ¼ Eðeim Þ ¼ 0, the difference between the
means of male and female log wages will be
ð5Þ ln wm 2ln wf ¼ Xm b̂m 2Xf b̂f + ðb̂0m 2b̂0f Þ:
In the familiar Oaxaca-Blinder (Oaxaca 1973; Blinder 1973) decomposition, letting
DX ¼ Xm 2Xf and Db ¼ b̂m 2b̂f , this expression can be written as either,
ð6Þ ln wm 2ln wf ¼ DXb̂m + Xf Db + ðb̂0m 2b̂0f Þ ¼ DXb̂f + Xm Db + ðb̂0m 2b̂0f Þ
depending on whether the male or female wage structure is chosen as the reference or
nondiscriminatory one, and where the first terms (DXb̂g ) capture differences due to
characteristics while the other terms denote differences to the returns to those characteristics.
A first problem with the Oaxaca-Blinder decomposition is that in practice, the
choice of the nondiscriminatory wage structure (either male or female) will yield different results (Cotton 1988; Oaxaca and Ransom 1994). One proposition is to use the
pooled wage structure as the nondiscriminatory one (Neumark 1988; Oaxaca and
Ransom 1994):
ð7Þ ln wm 2ln wf ¼ DXb̂p + ½Xm ðb̂m 2b̂p Þ + ðb̂0m 2b̂0p Þ2½Xf ðb̂f 2b̂p Þ + ðb̂0f 2b̂0p Þ;
where the second term in brackets is interpreted as the advantage of men and the
third term in brackets as the disadvantage of women. An important problem with
the Neumark method is that the pooled coefficients capture part of the ‘‘between’’
male and female effects, thus overstating the effects of variables with large gender
differences. For example, in Table 2 of Neumark (1988), the effects of schooling
(0.088) and union (0.172) in the pooled sample are larger than the same effects in
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either the male (0.072 and 0.138) or female (0.062 and 0.134) samples. This occurs
because, as shown in Table 1 of Neumark (1988), males have higher schooling and
unionization rates than women and thus their coefficients capture part of the advantage of males.28 In addition, if the advantage of men and the disadvantage of women
are not equal, the wage structure of reference can hardly be called nondiscriminatory.
A better alternative is to include gender intercept shifts, along with an identification restriction, in the regression of males and females pooled together29
ð8Þ
ln wi ¼ g0 + g0f Fi + g0m Mi + Xi g + yi
subject to g0f + g0m ¼ 0
where Fi is a female dummy and Mi ¼ 12Fi is a male dummy.30 Then
ð9Þ
ln wm ¼ ĝ0 + ĝ0m + Xm ĝ + Eðyi jFi ¼ 0Þ;
ln wf ¼ ĝ0 + ĝ0f + Xf ĝ + Eðyi jFi ¼ 1Þ;
The resulting decomposition makes the assumptions on the error terms explicit,
ð10Þ ln wm 2ln wf ¼ DXĝ + ðĝ0m 2ĝ0f Þ + ½Eðyi jFi ¼ 0Þ2Eðyi jFi ¼ 1Þ:
The first term is the familiar portion of the pay gap attributable to differences in characteristics and its standard error is computed as ðDXÞ#Sg ðDXÞi where Sg is the variance-covariance matrix of the ĝ. The second term ðĝ0m 2ĝ0f Þ corresponds to the
negative of the coefficient of a female dummy in a familiar wage regression on
the pooled sample when male if the omitted category and the intercept thus includes
the effect of the male dummy (¼ ĝ0m 2ĝ0f ). The last term vanishes,
½Eðyi jFi ¼ 0Þ2Eðyi jFi ¼ 1Þ ¼ Covðyi; Fi Þ ¼ 0:
where the first equality follows from the fact that Fi is a binary variable (as in Angrist
and Imbens 1995), and the second one is a property of ordinary least squares. This
decomposition is thus compatible with the conventional practice of including a
dummy denoting the group of interest in a pooled regression to investigate the impact
of belonging to a disadvantaged group (as in Equation 14). To the extent that ĝ0m
is positive and ĝ0f is negative, it is appropriate to say that these expressions are representing the ‘‘advantage’’ of men and the ‘‘disadvantage’’ of women.
The decomposition can also be written in terms of the Neumark-Cotton decomposition as:
28. A similar problem can be seen in the comparison of black males/white males in Table A2 of Oaxaca
and Ransom (1994) with regards to the coefficient for managers and professionals (OC1).
29. Brown and Corcoran (1997) incorporates a single gender dummy in their pooled regressions and proposes using the term D ¼ DXðb̂m 2b̂f Þ as a measure that is invariant to the choice of omitted category. The
interpretation of this term is that the elimination of differences in characteristics would lead to a pay increase D if estimated at the male returns instead of the female returns; but it is not straightforward.
30. Alternatively, it is always possible to weight the female and male dummies by their percentage in the
sample or to express variables as deviations from the pooled means to obtain a nondiscriminatory wage
structure that would maintain the same average wage where nepotism and discrimination to be eliminated.
Fortin
ð11Þ ln wm 2ln wf ¼ DXĝ + ½Xm ðb̂m 2ĝÞ + ðb̂0m 2ĝ0 Þ2½Xf ðb̂f 2ĝÞ + ðb̂0f 2ĝ0 Þ:
If the coefficients ĝ and ĝ0 truly represent a nondiscriminatory wage structure,
Adults in the United States
The Importance of Money versus People
Nicole M. Fortin
abstract
Using two single-cohort longitudinal surveys, the NLS72 and the NELS88,
I investigate the impact of four noncognitive traits—self-esteem, external
locus of control, the importance of money/work and the importance of people/
family—on wages and on the gender wage gap among these young workers.
I find that gender differences in these noncognitive factors, especially the
importance of money/work, have a modest but significant role in accounting
for the gender wage gap. Methodologically, this paper proposes a correction
to the Oaxaca-Blinder-Ransom decomposition that results in a truly
decomposable approach compatible with the simple pooled regression that
includes a gender dummy.
I. Introduction
After spectacular gains in the 1970s and 1980s, the gender wage gap
has shown slower progress during the 1990s (O’Neill 2003; Blau and Kahn 2004).
Given the realized promises of the 1980s that convergence in human capital (O’Neill
and Polachek 1993; O’Neill 2003) would be key to the closing of the gender pay gap,
this stagnation was disappointing. In the 1980s and 1990s, dramatic increases in educational attainment among younger cohorts allowed women to enter professions, especially managerial professions, previously dominated by men. These gains were
concurrent with increases in labor market experience among older cohorts, which
Nicole Fortin is professor of economics at the University of British Columbia and a fellow of the
Canadian Institute for Advanced Research (CIFAR). She would like to thank Francine Blau, David
Card, Peter Dolton, Thomas Lemieux, Markus Mobius, Nathaniel Wilcox, anonymous referees, and
seminar participants at the 2002 and 2006 AEA Meetings, at the 2005 SOLE Meetings, at UC-Berkeley
and at the CIFAR Workshop on Social Interactions, Identity and Well-Being for helpful comments on
this and earlier versions of the manuscript. The early stages of the project benefited from Michael
Coelli’s diligent research assistance. The data used in this article can be obtained beginning May 2009
through April 2012 from Nicole Fortin, Dept. of Economics, UBC, nifortin@interchange.ubc.ca.
Financial support was provided by SSHRC Grants #410-99-0959 and #501-2002-0118.
½Submitted May 2006; accepted February 2007
ISSN 022-166X E-ISSN 1548-8004 Ó 2008 by the Board of Regents of the University of Wisconsin System
T H E JO U R NAL O F H U M A N R ES O U R C ES
d
XLIII
d
4
Fortin
also contributed to the narrowing of gender gap (Blau and Kahn 2000). Thus perhaps
unsurprisingly, Blau and Kahn (2004) attribute the 1990s slowdown in the gender
convergence in pay to factors, other than educational attainment and labor market
experience that contributed to changes in the unexplained gender pay gap. These factors include changes in labor market selectivity, changes in gender differences in unmeasured characteristics and labor market discrimination, as well as less favorable
supply and demand shifts.1
Following a renewed interest in the impact of behavioral and personality traits on
earnings (Bowles, Gintis, and Osborne 2001), this paper attempts to move some of
the typically unmeasured characteristics into the measurable dimension.2 The main
goal of the paper is to assess the contribution of noncognitive factors in accounting
for the gender pay gap among workers in their early thirties in the mid-1980s. A second goal of the paper is to shed some light on the sources of changes in the gender
pay gap over the last two decades of the twentieth century among young workers.
The recent literature on personality and earnings has incorporated noncognitive
factors from the Rosenberg self-esteem and the Rotter locus of control scales (Goldsmith, Veum, and Darity 1997; Osborne Groves 2005; Heckman, Stixrud, and Urzua
2006; Manning and Swaffield 2008; Waddell 2006), and from the Five-Factor Model
(FFM) of personality structure (Mueller and Plug 2006; Nyhus and Pons 2005).3
Like the papers in this first group, I consider the role of personality traits such as
self-esteem and external locus of control, but the gender differences in these traits
are not very large.
Because this paper is concerned with the gender wage gap, I put more emphasis on
noncognitive factors where there are important gender differences, namely those associated with differentiated gender roles. In this sense, this study complements that
of Mueller and Plug (2006), which focuses on personality traits that are prototypically masculine and feminine. Consistent with the traditional roles of men as ‘‘breadwinners,’’ responsible for the financial welfare of the family, and of women as
‘‘homemakers’’ or ‘‘caregivers,’’ entrusted with the care of children and the support
of relationships, I pay special attention to gender differentials in valuing money and
work on the one hand, and in valuing people, usefulness to society, and family on the
other.4 These traits also are easily tied to the economic literature on the household
division of labor (Becker 1985), as argued in Section II. In addition, an important focus of the recent experimental literature has been on gender differences in
altruism.5
1. Much recent research on the gender pay gap has focused on the impact of labor market selectivity, an
issue outside of the scope of the present paper.
2. Early research in this area goes back to the late 1970s and early 1980s; a review can be found in Bowles,
Gintis, and Osborne (2001) and Osborne Groves (2005).
3. The Rosenberg self-esteem scale is a widely used measure of self-esteem. The Rotter scale indicates
whether an individual is more external or fatalistic, believing that hard work and effort are not rewarded,
but rather that luck, fate, or good fortune determine success and achievement. The five personality traits
included in the FFM are extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience.
4. Various terminologies have been used to describe these potentially conflicting values: career-life
choices, work-family balance, etc.
5. The experimental literature also focuses on gender differences in risk taking and competiveness, because
experimental methods are more appropriate to study these traits than career-life choices.
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The Journal of Human Resources
Ample anecdotal evidence suggests that women give greater importance in their
choice of job to its value to society and to personal interactions, and less importance
to money and prestige than men, but few studies investigate this path directly.6 Yet, a
recent hypothesis in the gender pay gap literature, called the negotiating divide hypothesis (Babcock and Laschever 2003), argues that women may have less of a sense
of entitlement to higher wages and are less likely to initiate negotiation or bargain.7
I utilized data from two widely used high school-cohort surveys: the National Longitudinal Study of the High School Class of 1972 (NLS72) and the National Education Longitudinal Study of 1988/94 (NELS88). The longitudinal aspect of these
surveys allows me to use noncognitive traits measured when the individuals were
in their teens (or early twenties)—in fact, many years before the labor market data
was collected. The wage data was recorded when the individuals were in their
mid-twenties (in 1979 for the NLS72, in 2000 for the NELS88) and in their early
thirties (in 1986 for the NLS72). The measured noncognitive traits cannot suffer
from a reverse causality problem since they are recorded before the respondents’
wages are observed. Further there is relatively little time for these noncognitive traits
to affect the labor market outcomes of individuals in their mid-twenties by an indirect behavioral route, such as a more altruistic mother taking longer maternal leaves,
working part-time and getting lower pay then a career-oriented mother working fulltime with minimal work interruptions and getting higher pay. I thus argue that for
these young workers the impact of noncognitive traits is relatively untainted by direct
feedback effects from the labor market, rather these traits can be thought off as predetermined factors.8 While considering these younger workers presents an advantage
with respect to the identification of the impact of noncognitive traits, it is a disadvantage in terms of a deeper understanding of the gender pay gap in the whole economy.
Finally to the extent that there are cohort effects in the gender pay gap, looking at
single cohorts rather than cross-sectional data arguably provides a cleaner assessment of changes in the gender pay gap within and between cohorts.9
I assess the importance of these noncognitive traits on wages, appealing to a hedonic wage function framework, and of gender differentials in these traits on the
mean gender wage gap. Because of the nature of the noncognitive traits, I cannot
claim to identify the ‘‘causal’’ effects of these factors on wages; rather I am interested in their impact as omitted variables on potential biases in the gender dummy
of a standard wage regression.10 To assess the impact of the traits on gender wage
differentials, I propose a correction to the Blinder-Oaxaca-Ransom wage gap decomposition that makes it fully compatible with the simple pooled regression that
6. Filer (1986) evaluates the impact of various personality traits and job characteristics, including ‘‘contribution of job to society,’’ on occupational structure. Chevalier (2004) investigates the impact of gender differences in motivation on the gender wage gap in the United Kingdom among recent graduates.
7. Säve-Söderbergh (2003) indeed finds that not only were Swedish female university graduates asking for
lower wages, but they also were receiving lower counter-offers.
8. This case, of course, is not as clear for workers in their early thirties. In their mid-twenties, women in my
samples have on average about one year of parental experience, but more than five years of parental experience in their early thirties.
9. On this issue, see for example Blau and Kahn (2000) and Weinberger and Kuhn (2006).
10. To simplify the exposition, at times the word ‘‘effect’’ or ‘‘impact’’ is used when what is estimated is
merely a partial correlation.
Fortin
includes a gender dummy, and for which standard errors are easily computed. The
correction also solves the problems resulting for the choice of the nondiscriminatory
wage structure (Oaxaca and Ransom 1994) and includes recent propositions (Gardeazabal and Ugidos 2004) to correct the identification problem caused by the
left-out reference group, acknowledged in Oaxaca and Ransom (1999).
The main finding of the paper is of a modest but significant role (1.9 log points out
of 22.9 log points) for gender differences in noncognitive traits, in particular valuing
money/work and people/family (1.7 log points), to account for the gender wage gap
in 1986 (among workers in their early thirties from the NLS72). This role is larger
than the role of gender differences in educational attainment and cognitive skills
(math score) combined, about 1.2 log points. It is almost as important in magnitude
as the role of gender differences in labor market experience and job tenure (2.4 log
points), which is however lower than typically found in cross-sectional data.
Considering changes in the gender pay gap, from 1979 to 2000, among workers in
their mid-twenties, the raw gender pay has decreased by a 5.6 log points (from 23.7
to 18.1 log points). The human capital variables accounted for 2.8 log points of the
gap in 1979 and -2.2 log points in 2000, thus accounting for -5.0 log points (-2.2–2.8)
of the change in the gap. The noncognitive traits accounted for an additional -0.6 log
points (0.9–1.5) of the change. Essentially the gender convergence in educational attainment, cognitive skills, valuing money, and work can explain away all of the gender convergence in pay between young workers from the NLS72 and NELS88
cohorts. This result suggests that the clear progression across cohorts of women’s
preparation for the labor market is responsible for the decrease in the gender pay
gap. The within-cohort decrease in the gender gap pay of less than 1 log points (from
23.7 log points to 22.9 log points for the NLS72 cohort) from age 25 to age 32 further suggests a rather small role for within-cohort relative earnings growth. These
findings help clarify the deliberations of Blau and Kahn (2000) on which factor—
within-cohort, between-cohort changes or lower discrimination—is more important
in accounting for the gender convergence in pay among young workers.
The remainder of the paper is organized as follows. The next section reviews the
literature on gender differences in work values and presents a hedonic wage function
framework in which noncognitive factors are thought to influence wages. Section III
addresses detailed data issues. Section IV presents the update on the Oaxaca-BlinderRansom methodology. The wage regression and decomposition results are discussed
in Section V. Finally, I conclude with a review of the findings in Section VI.
II. The Influence of Work Values on Economic Behavior
Historically gender differences in work values, which were perceived
as a rationalization for occupational gender segregation, have been deemphasized in
the sociological literature (Bielby and Bielby 1984; Rowe and Snizek 1995). More
recently, in an effort to account for the unexplained part of the gender pay gap
and increasing returns to unobserved skills, there has been a renewed interest in economics for ‘‘soft’’ skills or noncognitive traits.
Experimental studies in behavioral economics have noted important differences
between men and women in individual attitudes toward altruism and greed (Andreoni
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and Vesterlund 2001), leadership, and competitiveness (Gneezy, Niederle, and Rustichini 2003). Empirical studies have found a substantial impact of these and related
traits on wages. Kuhn and Weinberger (2005) find sizeable positive returns, among
white males, to leadership skills ranging from 4 to 24 percent and an even larger impact for workers in managerial occupations.
Another trait at the opposite end of the leadership spectrum is the ‘‘external locus
of control,’’ that is a belief that one’s outcomes are controlled more by external
forces than by internal forces (or that chance and luck are more important for success
than one’s own efforts). The effect of external locus of control on earnings, often together with the effect of self-esteem, has been investigated by many authors (Goldsmith, Veum, and Darity 1997; Osborne Groves 2005; Heckman, Stixrud, and Urzua
2006; Manning and Swaffield 2008; Waddell 2006, among others). For example, using the same surveys that I use, Waddell (2006) (using the NLS72) and Coleman and
DeLeire (2003) (using the NELS88), find that low self-esteem and an external locus
of control are associated with lower investment in human capital, among other negative indicators of future labor market performance. On the other hand, Goldsmith,
Veum, and Darity (1997) who include both predicted values of self-esteem and locus
of control in their wage regressions, find that only self-esteem was statistically significant.
Psychologists believe that work effort reflects motivation, which is governed in
part by these personality factors. Individuals with an external locus of control believing that hard work and effort will be not rewarded are less likely to display high levels of effort on the job. On the positive side, self-esteem has been found to be a
strong predictor of job performance (Judge, Tippie, and Bono 2001). Brockner
(1988) argues that workers with high self-esteem will tend to be more confident
about making decisions, will use their time more effectively and more productively. Psychologists also expect that higher wages will enhance self-esteem, but that
poor self-esteem may lead to a decline in productivity and wages which may in turn
erode a person’s perception of self. Thus, theories of self-esteem formation advanced
by psychologists suggest that wages and self-perception may be jointly determined,
which could lead to an endogeneity problem. To avoid this potential problem, I use
measures of personality traits and features obtained many years before the wages are
observed.
Because the gender differences in self-esteem and locus of control are relatively
small, these commonly studied noncognitive factors will prove to be less useful
for my purpose of explaining the gender pay gap. I thus capture an additional dimension of motivation and work effort by including a measure of the importance of
money/work. Psychologists (Furnham and Argyle 1998) argue that workers’ beliefs
about money are clearly related to their actual economic behavior. The attitudebehavior relation literature (Ajzen and Fishbein 1977; Tang, Kim, and Tang 2000)
further argues that those who do value money and want to make more money (a
work-related attitude) may have a higher level of turnover (a work-related behavior)
than those who do not. Workers for whom ‘‘Making a lot of money’’ is ‘‘very important’’ may be more likely to initiate bargaining talks with employers or change jobs
for more money. Similarly, workers for whom ‘‘Being successful in work’’ in ‘‘very
important’’ may be more likely to display to work-related behaviors that lead to promotions and higher wages. Kirkcaldy and Furnham (1993) also found that attitudes
Fortin
toward money are predicted by competitiveness or the motive to outperform others.
To the extent that there are gender differences in competitiveness, these will translate
into gender differences in attitudes toward money.
Gender differences in the importance of people/family are well-known, but there
are few attempts to study directly how they affect work-related behaviors.11 In their
analysis of changes in the gender pay gap, Borghans, ter Weel, and Weinberg (2006)
model the importance of interpersonal interactions in the context of an assignment
model, where there are positive returns to interpersonal skills. Here, I combine this
dimension of altruism, the importance of ‘‘working with people rather than things,’’
with one that goes beyond interpersonal skills, the importance of ‘‘a job usefulness to
others or society’’ and I focus on the implications of women’s higher demand for altruistic rewards for earnings. Many more women than men, indeed, choose lines of
work, such as education and health care, where they perform many tasks for the love
of others and reap lower market rewards working for public and nonprofit organizations.12 Women also are more likely than men to use their time to volunteer in organizations with some altruistic purpose, such as schools, religious organizations, and
hospitals.13
I show how, in a classic hedonic wage model (Rosen 1974) the importance of
money/work can have a positive impact on earnings, while the importance of people/family can have a negative impact. By contrast with the human capital model,
the hedonic wage function model is job-oriented, and indeed assumes the workers
choose and change jobs on the basis of the characteristics of the job. The importance
of money/work, self-esteem, and external locus of control are preferences and personality features that influence the choice of wage level and the level of effort and
responsibility linked to the job. The importance of people/family influences the
choice of altruistic rewards of the job.
These noncognitive traits are thought to impact wages through a hedonic wage
function resulting from a matching equilibrium between the employers’ wage and
amenity offers, and the workers’ utility maximizing choices of job characteristics.
The worker/consumer i maximizes utility over wages, effort and responsibility,
and altruistic rewards
ð1Þ Ui ¼ UðWi ; Ri ; Ai Þ
where Wi is the wage rate, Ri is the level of effort and responsibility exerted by the
worker/consumer, and Ai is the level of altruistic rewards, which is derived from both
H
market and nonmarket activities: Ai ¼ AM
i + Ai . The marginal utility of wages and
altruistic rewards is positive, but its sign with respect to effort and responsibility
is ambiguous. To the extent that leadership and ambition denote different flavors
of effort, one can see those as positively linked to utility, but when effort and responsibility exceed some threshold they entail a disutility.
11. Daymont and Andrisani (1984), who focus their analysis of the gender pay gap on the choice of college
major, also study the effect of these values.
12. There is some evidence that men are becoming more like women on this ‘‘usefulness to others’’ dimension, as found across birth cohorts in the European Value Survey (Fortin 2005).
13. On the other hand, men are more likely to volunteer as coaches in youth sports teams, in unions, professional organizations, social and political clubs, where they can use and perfect leadership skills.
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The Journal of Human Resources
Following Becker (1985), who argues that individuals have a limited amount of
time and energy to split between market work and homemaking, I assume a total
amount of effort and responsibility that an individual can devote to either market
H
T
T
or nonmarket activities, Ri ¼ RM
i + Ri # Ri , where Ri denotes to the total amount
H
available, fixed for each individual, and Ri denotes the amount of effort and responsibility allocated to nonmarket activities, like household work and volunteering activities. To the extent that women carry the double-shift of housework and
market work more often than men do, this implies that the marginal utility (disutility)
of market effort and responsibility is greater (less) for men than for women,
M
@Uim =@RM
im . @Uif =@Rif , and more generally for individuals who value homemaking
and volunteering more than others.
The utility function of individuals for whom ‘‘working with people’’ and feelings
of ‘‘usefulness of a job to others and society’’ are more important relative to financial
rewards will be characterized by a greater marginal rate of substitution between the
altruistic rewards and wages than other workers. To the extent this trait characterizes
women (f) more than men (m):
ð2Þ
@Uif @Uif @Uim @Uim
=
.
=
@Wif @Aif @Wim @Aim
For these individuals, working for philanthropic nonprofit organizations can take
place at a wage cost.14 Alternatively, individuals could accept a job with lower altruistic rewards and lower effort, but volunteer in altruistic organizations or become a
part-time caregiver at home. Because of the quantity constraint
on effort
and respon
sibility, when the worker/consumer chooses a job vector ðWi ; RiM ; AiM Þ to maximize
his/her utility,
he/she
simultaneously chooses a level of nonmarket effort and responH
M
T
sibility, Ri¼ Ri 2 Ri . Similarly, the individual’s demand for the altruistic market
amenity may depend on the level already provided by home and volunteering activities. These potential utility maximizing choices generate a job demand locus
ui ðW; RM ; AM Þ.
Employers are thought to be cost–minimizers, so that optimizing behavior generates an offer function, f ðW; RM ; AM Þ, indicating unit prices that employers are willing to pay on various combinations of wage, effort, and altruistic amenities.15
Because it is time-consuming, the altruistic amenity is by definition costly to provide.16 It is assumed that employers pay higher wages for higher levels of effort
and responsibility.
Subsumed in the worker/consumer job demand functions and the employers’ offer
functions are the human capital variables, which are thought to help eliminate multiple equilibria.17 I circumvent the identification problems (Ekeland, Heckman, and
14. For example, in the NELS88, 11.7 percent of women work for nonprofit organizations by contrast with
4.4 percent of men.
15. To the extent that some employers (for example, hospitals) are exert monopsony power in some markets for altruistic jobs (for example, nursing), the offer functions cannot be seen as perfectly competitive
outcomes.
16. For example, smaller classes are more rewarding to teach.
17. I could have more explicitly conditioned the utility function on the human capital variables
UðWi ; Ri ; Ai jSi ; Ei Þ.
Fortin
Nesheim 2004) associated with hedonic wage functions by including information on
usually unobserved tastes for job characteristics. I simply assume that one equilibrium is realized in the data and seek to broadly identify preferences from value statements and reported behaviors in a reduced-form approach. In the equilibrium
assignment, we thus find a wage equation,
M
ð3Þ Wi ¼ gðSi ; Ei Þ + hðRM
i Þ + tðAi Þ;
where in addition the usual human capital component, gðSi ; Ei Þi , with Si denoting
schooling and Ei labor market experience, there is a component that rewards effort
M
and responsibility hðRM
i Þ and another one that taxes altruistic amenities tðAi Þ.
My aim is to first assess the impact of work values and altruistic preferences on
wages, then I will consider the impact of gender differentials in these traits on the
gender pay gap.18 The importance of money/work will prove to have an impact on
wages more distinct from cognitive factors that self-esteem and external locus of
control. Noncognitive factors offer a unique possibility to improve the empirical
analysis of models of the household division of labor and of wage determination by incorporating variables that predate household formation and labor market
decisions.19
III. Data and Descriptive Statistics
To avoid problems of ex post rationalization, I appeal to two singlecohort longitudinal surveys conducted by the National Center for Education Statistics (NCES) to study education issues. The National Longitudinal Study of the High
School Class of 1972 (NLS72), known as the grandmother of educational longitudinal surveys, is widely used (Adelman 1994) outside of the education field. For example, Brown and Corcoran (1997) use it, in conjunction with the SIPP, to study the
impact of gender differences in the field of the highest degree on the gender wage
gap.20 The NLS72 interviewed students in their senior year of high school in the
spring of 1972, with followup surveys conducted in 1973, 1974, 1976, 1979, and finally in 1986. While the sample is not fully nationally representative because it
ignores students who dropped out before Grade 12, Krueger and Dale (2002) argue
that it is representative for college graduates.
The NLS72 contains answers to many questions on what is important in life and in
selecting a career. For example, respondents are asked, with reference to selecting a
18. In a previous version of the paper, I had shown that work values and altruistic preferences have behavioral implications of the expected signs on promotions (or training for career advancement), volunteering in
either philanthropic (hospitals, youth organization, etc.) or leadership organizations (sports teams, rotary
club, etc.) among men and women, and on the incidence of working part-time or long maternal leave
(or the number of dependents) among women.
19. Although the influences of neurological, environmental, psychological and social factors have been implicated with varying degrees in the development of gender roles, it is generally recognized that gender and
sex-role knowledge are acquired early in life (Ruble and Martin 1998).
20. Here I omit field of study because it is missing for a sizeable portion (more than a quarter) of the
sample.
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The Journal of Human Resources
career, about the importance of ‘‘Making a lot of money’’ and ‘‘The chance to be a
leader,’’ which speaks to the importance of money and ambition in work values. They
are also asked about the importance of ‘‘Opportunities to be helpful to others or useful to society,’’ ‘‘Opportunities to work with people rather than things,’’ this speaks to
the altruistic and social aspect of work. The survey also contains questions from the
well-known Rosenberg and Rotter scales. Many of these questions are asked repeatedly in each followup. Because of the longitudinal aspect of the survey, it is possible
to use preferences expressed in a time period that precedes the behavior of interest.
Wage information was recorded in the fourth followup in 1979, when most respondents were age 25, and in the fifth followup in 1986, when most respondents were age
32. Note that in 1986 more respondents were likely to have completed their studies
and there was more time for labor market experience to have an effect.
The National Education Longitudinal Study of 1988/94 (NELS88) interviewed
students in eighth grade in the spring of 1988. A sample of these respondents was
then resurveyed through four followups in 1990, 1992, 1994, and 2000, and a
refreshed sample was added in the 1990 and 1992 waves to ensure a representative
sample of high school sophomores and seniors in those years. The wage data was
recorded in 2000 when most respondents were age 24, which leaves little time for
labor market intermittency to have an effect. To enhance the comparability between
the two samples, I select only respondents from the NELS88 who reached the 12th
grade. The NELS88 was explicitly administered with the intent of maintaining comparability with the NLS72, so the major components of the two studies are very similar, including the components on values important in life.
Being very detailed and complex also means that these longitudinal surveys are at
times difficult to code. In particular, there is detailed information about employment
and unemployment spells, which are used to construct an actual work experience variable, but it is not straightforward to code.21 I retain individuals from the NLS72
(NELS88) who were working in 1979 or in 1986 (2000) and that have valid information on wage, educational attainment, high school math test, and questions from the
Rosenberg and Rotter scales.22
The means of the human capital variables and other individual characteristics are
reported separately by gender for each time period in Table 1. They show that, in the
NLS72, when workers are in their mid-twenties, women’s educational attainment is
21. In the NLS-72, the information on these spells from October 1972 to October 1979 is available in the
first to the fourth followup. The information from October 1979 to July 1986 is available in terms of spells
of employment for up to four jobs and unemployment for up to eight spells, thus individuals with more than
eight spells of unemployment were excluded. In the NELS88, detailed labor force status by month is available from June 1990 to August 1994. In 1997, 1998, and 1999, only information on whether the respondent
was unemployed for more than six months was available.
22. I retain only individuals whose wage was between $1.00 and $150 in 1986, and the equivalent in 1979
and 2000. The wage rate is computed to approximate as closely as possible an hourly wage measure. In
1986 and 2000, the earnings are reported either as hourly wages, weekly earnings, biweekly earnings,
monthly earnings or annual earnings and flagged as such. The hourly wages are thus computed correspondingly as weekly earnings divided usual weekly hours of hour, biweekly earnings divided usual weekly hours
times two, monthly earnings divided usual weekly hours times 52/12, annual earnings divided by usual
weekly hours times 52. In 1979, earnings were recorded only as weekly earnings, wages are thus computed
as weekly earnings divided by weekly hours. About 4 percent of the observations are in the edited categories (with codes in 800 and 1000 numbers) and are recoded using the information in the codebook.
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Table 1
Mean and Standard Deviations of Commonly Used Variables
Samples
Year (Age)
NLS72
1979(25)
NELS88
1986(32)
2000(24)
Variables
Male
Female
Male
Female
Male
Female
Log wage
1.803
(0.418)
1.566
(0.395)
2.521
(0.585)
2.292
(0.693)
2.621
(0.469)
2.440
(0.467)
0.006
(0.080)
0.202
(0.401)
0.112
(0.316)
0.368
(0.482)
0.267
(0.443)
0.044
(0.206)
0.001
(0.028)
0.233
(0.423)
0.087
(0.281)
0.369
(0.483)
0.269
(0.443)
0.042
(0.200)
0.002
(0.045)
0.178
(0.383)
0.155
(0.362)
0.297
(0.457)
0.252
(0.434)
0.115
(0.362)
0.003
(0.055)
0.211
(0.408)
0.133
(0.340)
0.327
(0.469)
0.233
(0.423)
0.093
(0.290)
0.006
(0.079)
0.189
(0.391)
0.099
(0.299)
0.409
(0.492)
0.267
(0.442)
0.029
(0.169)
0.006
(0.076)
0.133
(0.339)
0.092
(0.288)
0.362
(0.480)
0.362
(0.481)
0.047
(0.211)
5.269
(1.440)
1.018
(2.000)
4.747 10.851
(1.560) (2.354)
0.767
4.916
(1.738) (3.816)
9.625
(2.917)
4.084
(3.616)
4.682
(2.234)
2.194
(2.225)
4.461
(2.028)
1.988
(2.057)
Education
Less then high school
High school
Trade or vocational
Some college
College
Postgraduate
Work experience
Years
Tenure
Cognitive skills
High school math
Standardized score
Personal characteristics
Part-time
Black
Married
Parental experience
Child
Number of observations
0.091 20.114
(0.974) (0.962)
0.098 20.158
(0.979) (0.973)
0.010 20.011
(1.020) (0.977)
0.048
(0.213)
0.052
(0.222)
0.528
(0.499)
1.011
(2.071)
0.315
(0.465)
3,391
0.025
(0.156)
0.052
(0.222)
0.736
(0.441)
4.507
(4.767)
0.642
(0.479)
3,456
0.067
(0.250)
0.122
(0.327)
0.368
(0.483)
0.943
(1.992)
0.306
(0.461)
3,183
Note: Standard deviations in parentheses.
0.110
(0.312)
0.101
(0.301)
0.524
(0.499)
1.293
(2.354)
0.333
(0.471)
2,793
0.164
(0.370)
0.100
(0.300)
0.687
(0.464)
5.479
(5.476)
0.649
(0.478)
3,066
0.150
(0.357)
0.101
(0.301)
0.429
(0.495)
1.174
(2.293)
0.150
(0.356)
3,293
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The Journal of Human Resources
similar to men’s, but by their early thirties, more men than women have a postgraduate degree. In the NELS88, where the workers are in their mid-twenties, this pattern
has been reversed with not only more women holding a college degree than men, but
also more women holding a postgraduate degree. In these single cohorts of young
workers, the differences in the labor market experience of women and men are relatively small (at most 13 percent). There is thus limited scope for labor market intermittency to account for a large share of the gender wage gap. This contrasts
with the recent work of O’Neill and O’Neill (2005) were differences in labor market
experience and work interruptions for family reasons are found to account for more
than half of the gender wage gap.
Cognitive skills are captured using high school math score measured when the
respondents were in 12th grade, both in the NLS72 and NEL88. Since the samples
exclude earlier dropouts, this measure of cognitive skills generally precede the realizations of the educational attainment observed. I standardize the measure to have
zero mean and a variance of one over the entire sample in each case. Thus in
NSL72, the measure for women is negative while the measure for men is positive.
This male advantage seemed to have shrunk somewhat in the NELS88.23 Table 1 also
reports the means and standard deviations of demographic characteristics commonly
used in wage regressions.
Table 2 reports the descriptive statistics of the four composite noncognitive traits
used in the analysis: self-esteem, external locus of control, the importance of money/
work and the importance of people/family. I report the means and standard deviations of the variables by gender, as well as the differences between the male and female averages. First note that all the composite variables are scaled between 0 and 1
inclusively, and that the standard deviations of these variables are in the same range,
from 0.141 to 0.286. Also reported in the table is Cronbach’s alpha, a measure of
reliability that indicates whether the responses to each question comprising the composite are highly correlated.24 It is computed over the sample of men and women
combined.
The measures of self-esteem and external locus of control are constructed using
answers to a subset of four or five questions, asked in the surveys, from the widely
used Rosenberg self-esteem scale and the Rotter locus of control scale. The description of the precise statements used and the aggregation method is in the Appendix.
As explained in the Appendix, the responses are recoded in terms of a binary variable
(1 or 0) reflecting agreement or disagreement with the statements. In these samples,
the reliability indexes for self-esteem and external locus of control seem relatively
low; they are, however, higher than the ones of Waddell’s (2006) Attitude and
Self-Esteem indexes, also computed with the NLS72 data, which range from 0.36
to 0.50.
The measures of the importance of money/work and of people/family are constructed using subjective questions about values generally important in life, which
are asked in the base year and in each followup of the NLS72, and in selected
23. This is consistent with the literature on the gender gap in math tests.
24. The reliability of a scale (or composite variable) is one of three principles (with convergence and stability) used by psychologists to assess their validity. The measure of reliability is based on the correlations
between the items that make up the composite, relative to the variances of the items.
Table 2
Mean, Standard Deviations, and Reliability Coefficient of the NonCognitive Variables
Samples
NLS72
Year (Age)
1979(25)
Variables
Male
Self-esteema
0.851
(0.286)
External locus of controlb
Money/workc very important
People/familyd very important
Female
0.877
(0.244)
[0.7967]
0.123
0.100
(0.228)
(0.205)
[0.4529]
0.447
0.390
(0.189)
(0.167)
[0.5869]
0.326
0.389
(0.196)
(0.199)
[0.6268]
NELS88
1986(32)
2000(24)
D
Male
Female
20.026
0.935
(0.141)
0.930
(0.159)
0.005
0.023
0.096
(0.181)
0.104
(0.305)
20.008
0.057
0.456
(0.183)
0.391
(0.160)
0.065
20.063
0.322
(0.193)
0.382
(0.196)
20.060
D
[0.5629]
[0.4858]
[0.6347]
[0.6823]
Male
0.757
(0.144)
Female
0.731
(0.178)
[0.6310]
0.179
0.156
(0.247)
(0.238)
[0.5975]
0.561
0.518
(0.206)
(0.196)
[0.5487]
0.629
0.686
(0.253)
(0.254)
[0.4823]
D
0.026
0.023
0.043
20.057
Fortin
Note: Standard deviations in parentheses. Cronach’s alpha scale measure of reliability in brackets. The composite noncognitive variables are generated using STATA’s
alpha procedure, as explained in detail in the Appendix, using components measured many years before the wage data is gathered. They are on a [0,1] scale. D is equal
to the male minus the female measure.
a. The self-esteem measure is constructed using four/five questions from the Rosenberg scale.
b. The external locus of control measure is constructed using four/five questions from the Rotter scale.
c. The money/work composite is constructed using the answers coded 1 for ‘‘Very important,’’ 0 otherwise to valid answers about the importance in selecting a career of
‘‘Making of lot of money’’ and of ‘‘The chance to be a leader in one line of work’’ and about the importance in life of ‘‘Being successful at work’’ and ‘‘Having lots of money.’’
d. The people/family composite is constructed using the answers coded 1 if ‘‘Very important,’’ 0 otherwise to valid answers about the importance in selecting a career of
‘‘Opportunities to be helpful to others or useful to society’’ and ‘‘Opportunities to work with people rather than things,’’ and in life of ‘‘Helping other people in the community,’’ the ‘‘Ability to give children better opportunities,’’ and ‘‘Living close to parents and relatives.’’
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The Journal of Human Resources
followups of the NELS88 and, about values important in selecting a career, which is
asked only in the base survey of the NLS72. The values selected to capture the importance of money/work include questions about the importance in selecting a career
of ‘‘Making a lot of money’’ and of ‘‘The chance to be a leader’’ and about the importance in life of ‘‘Being successful at work’’ and ‘‘Having lots of money.’’ As
explained in the Appendix, the composite is constructed as means of the underlying
answers coded 1 if ‘‘very important,’’ 0 otherwise. Recoding the variable to capture
the intensity of preferences is consistent with the idea that workers who really think
that money is ‘‘very important’’ are more likely to initiate bargaining talks with
employers or change jobs for more money, than workers who simply think that
money is ‘‘somewhat important.’’25
The importance of people/family composite is constructed using questions about
the importance in selecting a career of ‘‘Opportunities to be helpful to others or useful to society’’ and ‘‘Opportunities to work with people rather than things,’’ and in
life of ‘‘Helping other people in the community,’’ the ‘‘Ability to give children better
opportunities,’’ and ‘‘Living close to parents and relatives.’’ This composite is constructed using the same recoding as the previous variable. The reliability indexes
for these two composites are in the same range as the indexes for the more commonly used self-esteem and locus of control composites. This nevertheless indicates
the potential for measurement error in these values, which should lead to an attenuation bias and weaker results than more precise measures would entail.
The gender differences (D) in the importance of money/work and people/family,
ranging from j0.043j to j0.065j to, are much larger than the gender differences in
self-esteem and locus of control, which range from to j0.008j to j0.026j. The gender
differences in the importance of money/work and of people/family are also much
more stable across surveys, despite the differences across surveys in the questions
underlying the composite.26 Ultimately, these gender differences will have substantial explanatory power toward the gender pay gap. Finally, note that the correlation
between the four composite variables is relatively small. If one wanted to construct a
grand composite of the four noncognitive measures, the resulting Cronbach’s alpha
would be very low around 0.25.27
IV. Decomposition of Gender Wage Differentials
In this paper, I propose and implement a modification to the OaxacaBlinder decomposition that addresses two noninvariance problems of the methodology; these are also discussed in Oaxaca and Ransom (1994) and Oaxaca and Ransom
(1999). The first one is the well-known fact that the portion of the gender wage gap
25. In a previous version of the paper, I indeed found that valuing money was a significant determinant of
‘‘having changed for a better job.’’
26. As explained in the Appendix, the greater relative weight of the ‘‘Ability to give children better opportunities,’’ which is very important for 70 percent to 90 percent of respondents, explains the large value of
people/family very important in the NELS88.
27. Given that regression coefficients are in effect partial correlations, any cross-correlations between the
composites will be reflected in changes in the regression coefficients, which are discussed below. It will
show that the more important correlations are with math scores.
Fortin
explained by differences between male and female characteristics is not invariant to
whether the male or female wage structure is chosen as the reference wage structure.
Here, I propose to use as reference wage structure the one under which the advantage
of men is equal to the disadvantage of women. The second noninvariance problem
occurs in the presence of categorical variables and entails that portions of the gender
pay gap attributable to gender differences in specific categorical variables will generally not be invariant to the choice of the omitted category. This problem is easily
solved by appealing to a restricted least squares estimator. One attractive feature of
this modified decomposition is that it is fully compatible with the usual pooled wage
regression that includes a dummy for the disadvantaged group.
Let the following denote the log wage equations estimated separately for samples
of males (m) and females (f ), and for males and females pooled together ( p)
ð4Þ ln wig ¼ b0g + Xig bg + eig ;
g ¼ f ; m; p
where the Xi is a 1 3 K vector of explanatory variables and b is K 3 1 vector of coefficients. Here, I am careful to distinguish the intercept from the other explanatory
variables. As shown below, this is important in the presence of categorical variables,
including the gender dummies.
Under the usual assumption Eðeif Þ ¼ Eðeim Þ ¼ 0, the difference between the
means of male and female log wages will be
ð5Þ ln wm 2ln wf ¼ Xm b̂m 2Xf b̂f + ðb̂0m 2b̂0f Þ:
In the familiar Oaxaca-Blinder (Oaxaca 1973; Blinder 1973) decomposition, letting
DX ¼ Xm 2Xf and Db ¼ b̂m 2b̂f , this expression can be written as either,
ð6Þ ln wm 2ln wf ¼ DXb̂m + Xf Db + ðb̂0m 2b̂0f Þ ¼ DXb̂f + Xm Db + ðb̂0m 2b̂0f Þ
depending on whether the male or female wage structure is chosen as the reference or
nondiscriminatory one, and where the first terms (DXb̂g ) capture differences due to
characteristics while the other terms denote differences to the returns to those characteristics.
A first problem with the Oaxaca-Blinder decomposition is that in practice, the
choice of the nondiscriminatory wage structure (either male or female) will yield different results (Cotton 1988; Oaxaca and Ransom 1994). One proposition is to use the
pooled wage structure as the nondiscriminatory one (Neumark 1988; Oaxaca and
Ransom 1994):
ð7Þ ln wm 2ln wf ¼ DXb̂p + ½Xm ðb̂m 2b̂p Þ + ðb̂0m 2b̂0p Þ2½Xf ðb̂f 2b̂p Þ + ðb̂0f 2b̂0p Þ;
where the second term in brackets is interpreted as the advantage of men and the
third term in brackets as the disadvantage of women. An important problem with
the Neumark method is that the pooled coefficients capture part of the ‘‘between’’
male and female effects, thus overstating the effects of variables with large gender
differences. For example, in Table 2 of Neumark (1988), the effects of schooling
(0.088) and union (0.172) in the pooled sample are larger than the same effects in
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either the male (0.072 and 0.138) or female (0.062 and 0.134) samples. This occurs
because, as shown in Table 1 of Neumark (1988), males have higher schooling and
unionization rates than women and thus their coefficients capture part of the advantage of males.28 In addition, if the advantage of men and the disadvantage of women
are not equal, the wage structure of reference can hardly be called nondiscriminatory.
A better alternative is to include gender intercept shifts, along with an identification restriction, in the regression of males and females pooled together29
ð8Þ
ln wi ¼ g0 + g0f Fi + g0m Mi + Xi g + yi
subject to g0f + g0m ¼ 0
where Fi is a female dummy and Mi ¼ 12Fi is a male dummy.30 Then
ð9Þ
ln wm ¼ ĝ0 + ĝ0m + Xm ĝ + Eðyi jFi ¼ 0Þ;
ln wf ¼ ĝ0 + ĝ0f + Xf ĝ + Eðyi jFi ¼ 1Þ;
The resulting decomposition makes the assumptions on the error terms explicit,
ð10Þ ln wm 2ln wf ¼ DXĝ + ðĝ0m 2ĝ0f Þ + ½Eðyi jFi ¼ 0Þ2Eðyi jFi ¼ 1Þ:
The first term is the familiar portion of the pay gap attributable to differences in characteristics and its standard error is computed as ðDXÞ#Sg ðDXÞi where Sg is the variance-covariance matrix of the ĝ. The second term ðĝ0m 2ĝ0f Þ corresponds to the
negative of the coefficient of a female dummy in a familiar wage regression on
the pooled sample when male if the omitted category and the intercept thus includes
the effect of the male dummy (¼ ĝ0m 2ĝ0f ). The last term vanishes,
½Eðyi jFi ¼ 0Þ2Eðyi jFi ¼ 1Þ ¼ Covðyi; Fi Þ ¼ 0:
where the first equality follows from the fact that Fi is a binary variable (as in Angrist
and Imbens 1995), and the second one is a property of ordinary least squares. This
decomposition is thus compatible with the conventional practice of including a
dummy denoting the group of interest in a pooled regression to investigate the impact
of belonging to a disadvantaged group (as in Equation 14). To the extent that ĝ0m
is positive and ĝ0f is negative, it is appropriate to say that these expressions are representing the ‘‘advantage’’ of men and the ‘‘disadvantage’’ of women.
The decomposition can also be written in terms of the Neumark-Cotton decomposition as:
28. A similar problem can be seen in the comparison of black males/white males in Table A2 of Oaxaca
and Ransom (1994) with regards to the coefficient for managers and professionals (OC1).
29. Brown and Corcoran (1997) incorporates a single gender dummy in their pooled regressions and proposes using the term D ¼ DXðb̂m 2b̂f Þ as a measure that is invariant to the choice of omitted category. The
interpretation of this term is that the elimination of differences in characteristics would lead to a pay increase D if estimated at the male returns instead of the female returns; but it is not straightforward.
30. Alternatively, it is always possible to weight the female and male dummies by their percentage in the
sample or to express variables as deviations from the pooled means to obtain a nondiscriminatory wage
structure that would maintain the same average wage where nepotism and discrimination to be eliminated.
Fortin
ð11Þ ln wm 2ln wf ¼ DXĝ + ½Xm ðb̂m 2ĝÞ + ðb̂0m 2ĝ0 Þ2½Xf ðb̂f 2ĝÞ + ðb̂0f 2ĝ0 Þ:
If the coefficients ĝ and ĝ0 truly represent a nondiscriminatory wage structure,