Empir Econ (2009) 37383–392DOI 10.1007s00181-008-0236-8.docx

Empir Econ (2009) 37:383–392
DOI 10.1007/s00181-008-0236-8
O R I G I NA L PA P E R
A note on schooling and wage inequality in the public
and private sector
Harry Anthony Patrinos · Cristobal Ridao-Cano ·
Chris Sakellariou
Received: 15 November 2006 / Accepted: 15 June 2008 /
Published online: 10 September 2008
© Springer-Verlag 2008
Abstract The few studies that have examined the wage impact of
education across
the earning distribution have focused on high-income countries
and show education
to be more profitable at the top of the distribution. The
implication is that education
may increase inequality. Extending the analysis to 16 East
Asian and Latin American
countries, in Latin America we observe a pattern similar to that
of Europe/North
America (increasing wage effects), while in East Asia the wage
effects are predo-
minantly decreasing by earnings quantile. However, once the
analysis is performed
separately for the public and the private sector, it is revealed
that the strongly decrea-
sing impact of schooling on earnings in the public sectors of

East Asian countries is
responsible for the overall observed decreasing pattern, while
the impact of schooling
on earnings in the private sectors of these countries is non-
decreasing.
Keywords Returns to education · Quantile regression
JEL Classification I21 · J31
H. A. Patrinos (B) · C. Ridao-Cano
World Bank, Washington, DC, USA
e-mail: [email protected]
C. Ridao-Cano
C. Sakellariou
Department of Economics, Nanyang Technological University,
Singapore, Singapore
123
384 H. A. Patrinos et al.
1 Introduction
There is an extensive empirical literature on estimates of the
rate of return to invest-
ment in education. Most studies estimate the mean impact of
education on earnings,
which may be interpreted as the return to additional schooling
for an individual with
mean ability; in other words, the return for the average
individual. Recently, an increa-
sing number of studies investigate the pattern of the wage

effects from an additional
year of education along the earnings distribution using quantile
regression analysis.
Estimation of the effect of education on earnings using ordinary
least squares (OLS)
disregards variation of the effect of education for workers in the
same education group.
On the other hand, quantile regression analysis, by allowing the
impact of schooling
on earnings to vary within education groups can be used to
measure inequality within
groups, since quantile results represent the wage differential
between individuals in
the same education group but at different earnings quantiles.
From the evidence available, in most countries, increasing wage
effects by quantile
have been observed. In particular, increasing wage effects have
been documented for
15 out of 16 European countries studied (Martins and Pereira
2004), the United States
(Buchinsky 1998), and whites in South Africa (Mwabu and
Schultz 1996). Evidence
for middle and low income developing countries is scarce. For
low income countries,
Girma and Kedir (2005) present evidence for Ethiopia that
shows that education is
more beneficial to the less able.
The aim of this study is to investigate the pattern of wage
effects across a mix of
countries by conducting a systematic examination of the impact
of education along the
conditional distribution of the dependent variable (log of hourly
earnings), as well as
examining the pattern within education levels for each country;

subsequently we inves-
tigate the existence of a relationship between a country’s labor
market characteristics
and the pattern of wage effects along the conditional earnings
distribution. Earnings
function estimates using quantile regressions have been
discussed in the context of
unobserved ability differences (for example, Martins and
Pereira 2004). In this paper
we will not do so, since to assume quantile regressions
implicitly assess returns to
ability requires strong assumptions.
2 Methodology and data
When it is suspected that various regressors in an earnings
function (such as schooling
and experience) influence parameters of the conditional
distribution of the dependent
variable other than the mean, quantile regression (Koenker and
Bassett 1978) is a
particularly useful approach because it allows the examination
of how the conditional
distribution of the dependent variable depends on the vector of
independent variables
at particular segments of the conditional distribution of the
dependent variable. This
approach allows us to infer the extent to which education
exacerbates or reduces
underlying inequality in wages due to other, perhaps
unobservable, factors. One, then,
can compare the results between groups of countries and
empirical evidence already
available in the literature. We are aware that OLS and quantile
regression coeffi-
cient estimates do not necessarily measure returns to schooling

when schooling is
123
A note on schooling and wage inequality in the public and
private sector 385
endogenous. While we keep this caveat in mind, this paper does
not address the
endogeneity issue and is mainly concerned with the effect of
changes in independent
variables on the conditional distribution of earnings.
Data used1 are for East Asian and Latin American countries. A
consistent sample
across countries is used, with the same variables used and few
control variables added,
to make the work comparable to the work carried out in
advanced countries. That is,
the working samples for every country consist of males
employed for wages in private
or public sector (excluded are day laborers, workers in
agriculture, self-employed and
employers), who are 25 to 65 years of age. The dependent
variable is the logarithm of
hourly wage (derived from dividing monthly earnings by the
monthly hours worked),
except for Mongolia and Thailand, for which the dependent
variable is the logarithm
of monthly earnings.
3 Results
Educational attainment in East Asia for those aged 25–65 years

and employed in the
formal sector and measured by years of schooling, ranges from
7.4 to 11.3 years for
males with an 8 country average of 9.8 years, and between 5 and
13.2 years with an
8 country average of 9.9 for females. Educational attainment
measured by years of
schooling in Latin America averages 9.0 and 10.2 years of
schooling for men and
women respectively.
Looking the wage effects of additional schooling for the
average individual (from
OLS regressions), the average estimate of the effect of an
additional year of schooling
for males in the 8 East Asian countries is comparable to the
average in the eight
Latin American countries, at about 10.2–11.6% (Table 1).
Generally speaking, wage
effects are considerable for all countries, especially Thailand
and Brazil. In only three
cases are the estimates less than 10%. The second column of
Table 1 presents the
differences in quantile regression coefficients between the
bottom and top quantile.
A negative entry implies that estimates are higher at the bottom
of the distribution,
meaning that education investments tend to equalize earnings. A
positive entry means
that estimates are higher at the higher end of the earnings
distribution, suggesting that
education tends to increase inequality. In general education
tends to equalize earnings
in East Asia while inequality tends to be enhanced through
education in Latin America.

Casual observation of the quantile regression evidence in this
study suggests that,
by and large, the regression coefficients of the schooling
variable (Table 2)2 decrease
1 Data sources: Cambodia, Socio-economic Survey of
Households 2003–2005; China, Economic, Popu-
lation, Nutrition and Health Survey 2000; Indonesia, Survei
Sosial Economi Nasional (Susenas) 2003;
Mongolia, Living Standards Measurement Survey 2002;
Philippines, Annual Poverty Indicator Sur-
vey (APIS) 1999; Singapore, Labor Force Survey (LFS) 1998;
Thailand, Socioeconomic Survey 2002;
Vietnam, Survey of Households 2001–2002; Argentina,
Encuesta Permanente a Hogares (INDEC) 2003;
Bolivia Mejoramiento de las Encuestas y Mediciones de las
Condiciones de Vida (MECOVI), 2002; Brazil,
Pesquisa Nacional Por Amostra De Domicilios (PNAD) 2002;
Chile, Encuesta de Caracterización Socioe-
conómica Nacional 2003; Colombia, Encuesta de Calidad de
Vida 2003; Guatemala, Encuesta Nacional
sobre Condiciones de Vida (ENCOVI) 2000; Mexico, Encuesta
Nacional de Ingresos y Gastos de los
Hogares 2002; Venezuela, Encuesta de Hogares por Muestro
2002.
2 Detailed results available upon request.
123
Table 1 Coefficient of years of
schooling by country (male
wage earners, 25–65 years)

Country Coefficient (OLS) 90th−10th quantile
Cambodia 3.8 4.2
China 12.1 −4.7
Indonesia 11.4 −0.9
Mongolia 8.5 −4.5
Philippines 11.6 −3.3
Singapore 11.9 4.3
Thailand 15.2 −5.3
Vietnam 7.2 −4.4
East Asia mean 10.2 −1.8
Argentina 11.0 4.2
Bolivia 10.3 6.2
Brazil 15.7 6.4
Chile 12.0 7.0
Colombia 10.4 5.5
Guatemala 12.6 5.3
Mexico 11.3 2.4
Venezuela 9.9 3.3
Latin America mean 11.6 5.0
Table 2 Pattern of wage effects by quantile and implications
Country Returns by quantile Pattern of returns Education and
Implication for
by quantile unobserved skills income inequality

Cambodia Strongly increasing U-shaped Substitutes Increase
China Strongly decreasing Fluctuations Substitutes Decrease
Indonesia Weakly decreasing U-shaped Substitutes Decrease
Mongolia Strongly decreasing Monotonic Substitutes Decrease
Philippines Mildly decreasing U-shaped Substitutes Decrease
Singapore Strongly increasing Monotonic Complements
Increase
Thailand Strongly decreasing U-shaped Substitutes Decrease
Vietnam Strongly decreasing U-shaped Substitutes Decrease
Argentina Strongly increasing Monotonic Complements Increase
Bolivia Strongly increasing Monotonic Complements Increase
Brazil Strongly increasing Monotonic Complements Increase
Chile Strongly increasing Monotonic Complements Increase
Colombia Strongly increasing Monotonic Compliments Increase
Guatemala Strongly increasing Monotonic Complements
Increase
Mexico Weakly increasing Monotonic Compliments Increase
Venezuela Mildly increasing Monotonic Complements Increase
123

private sector 387
by quantile in East Asia—except Singapore, which has patterns
similar to that of
OECD countries and Cambodia. The Latin American countries
under study on the
other hand display uniform results. While the expectation may
have been that for
countries such as Argentina and Chile the pattern will be similar
to that of high
income countries, and this is confirmed, a mixed pattern may
have been expected
for the rest. However, the results exhibit a pattern more akin to
high income countries
in every other country examined. Tests of the statistical
significance of inter-quantile
differences show that the overwhelming majority of consecutive
inter-quantile dif-
ferences are statistically significant, as are almost all the 90th–
10th quantile diffe-
rences.
We hypothesize that there might be a relationship between a
country’s develop-
ment stage (as this is reflected in their labor market
characteristics) and the impact of
schooling on earnings by quantile. In the OECD it is clear that
education is a comple-
ment to unobserved skills (or abilities), and that education
investment will increase
inequality, other things being equal. We observe cases where
the OECD pattern does

not apply. The idea behind our approach is that if the effect on
wages is higher at the
top end of the earnings distribution than at the bottom end (as is
the case for OECD
countries and, based on our findings, other high and middle
income countries), then
education tends to increase earnings inequality, since education
is a better investment
for the better off. On the other hand, if skills and education are
substitutes, then the
least skilled will benefit more from education and education
tends to reduce earnings
inequality.
The question that remains is what explains the observed patterns
(increasing or
decreasing). Explanations that have been cited in previous
studies relate to: (1) the
presence of more job mobility in developed countries. That is,
higher levels of schoo-
ling and higher levels of unobserved skills (therefore higher
levels of human capital),
allow workers to change jobs, to improve their position, and
therefore earnings. (2) The
scarcity of skills. Based on the relationship between a country’s
development stage
and the pattern of wage effects from additional schooling, the
argument might be that
the lower the human capital in a country, the more equal the
impact of schooling on
earnings by quantile. This could apply to countries such as
Ethiopia, some East Asian
countries and the case of Blacks in South Africa. Latin
American countries on the
other hand historically have higher levels of income inequality
and this may relate to

the nature of the results for Latin America. (3) Differences in
the quality of schooling.
In particular, the deviation from the OECD pattern could be due
to differential access
to quality education or distribution of quality outcomes (based
on measures such as
test scores).
However, without discounting the possible relevance of such
explanations when
considering differences between developed and less developed
countries, they don’t
seem to be convincing in our case (i.e., different patterns
between East Asian and Latin
American countries). For example, there are no significant
differences in educational
endowments between these two groups of countries. Likewise, it
is unlikely that, on
average, there are significant differences in quality of education
between the two groups
of countries. We, therefore, turn our attention to another
possible explanation for the
differences in observed patters, namely within sector (public vs.
private) differences
within each country.
123
4 Public versus private sector
The findings concerning the divide in the pattern of coefficient
estimates between

the two groups of countries may also relate to different labor
markets (as well as
different sectors within the same labor market) having a
differential exposure to market
forces and the link between pay and productivity. In particular,
in exploring within
sector differences, one can divide the data into the competitive
sector (private) and the
uncompetitive (public). Psacharopoulos (1979) looked at such a
distinction between
private and public sectors and argued that wages may exceed
productivity in the public
sector but not in the private sector. But is the overpayment of
government workers in
relation to their productivity mostly found at the lower or the
upper end of the earnings
distribution?
Many studies have examined the public sector wage premium in
various develo-
ped and developing countries. Evidence suggests that the public
sector pay premium
is declining as one moves up the conditional wage distribution,
suggesting that the
profitability of public sector employment is higher in the lower
end of the wage dis-
tribution.3 Budria (2006), instead of looking at the impact of
public sector status on
the conditional wage distribution, examines wage differences
within education groups
in the private and the public sector in 8 European countries and
compares the effects
of schooling on the conditional distribution of each sector. He
found that, while the
average impact of schooling on wages is similar across sectors,
the impact of schoo-

ling on within-groups dispersion is substantially larger in the
private sector than in
the public sector. He concludes that the effects of educational
expansion on overall
within-groups dispersion may be lower than previously thought,
as previous studies
did not consider the public sector.
Table 3 presents the pattern of the impact of years of schooling
on wages for 13
countries (for which information exists) by sector of
employment (public vs. private).
In Vietnam, Thailand the Philippines and China, we found that
the profitability of an
additional year of schooling for males 25–65 years exhibits a
declining patter, while
an increasing pattern was observed for Cambodia. However, in
the small private sector
in Vietnam, the impact on wages is clearly increasing with
higher unobserved skills,
in the Thai private sector we observe a slightly increasing
pattern, while in the private
sector of the Philippines the pattern is decreasing but only
slightly. In the case of
Cambodia, the overall increasing pattern can be attributed to the
strongly increasing
effect in the private sector, while in the much larger public
sector we observe a flat
(or slightly decreasing) wage structure. The average ninth to
first quantile difference
in public sector schooling coefficient estimates in the East
Asian countries is −5.4%
points, compared to about 2.6% points in the private sector. On
the other hand, in all
countries for which an overall increasing pattern is observed,
exhibit this increasing

pattern in both the private and public sectors (with the
exception of Mexico where
schooling coefficients in the public sector are flat); this pattern
is more pronounced
3 See Poterba and Rueben (1994) and Mueller (1998), among
others, for evidence on the United States and
Canada. Some evidence for developing countries exists in Skyt-
Nielsen and Rosholm (2001) for Zambia
and Hyder and Reilly (2005) for Pakistan, who find a sizable
public-private sector differential, however,
this differential declines monotonically with movement up the
conditional wage distribution.
123
private sector 389
Table 3 Pattern of wage effects by public-private employment—
males
Country Overall pattern Coefficient 90th−10th quantile
difference (%)
of returns
Public Private Public Private
Cambodia Increasing 5.1 10.9 −0.3 5.4
China Decreasing 10.4 a −6.3 a
Philippines Decreasing 10.8 10.8 −6.5 −1.4
Thailand Decreasing 14.6 14.4 −7.4 1.4
Vietnam Decreasing 7.9 7.2 −6.6 4.8
East Asia Mean Decreasing 9.8 10.8 −5.4 2.6

Argentina Increasing 10.2 11.1 6.3 4.8
Bolivia Increasing 13.3 8.7 2.8 8.2
Brazil Increasing 16.8 13.1 5.6 8.8
Chile Increasing 13.7 11.2 3.4 7.6
Colombia Increasing 9.5 9.9 1.4 7.3
Guatemala Increasing 12.3 11.5 2.8 7.7
Mexico Increasing 13.3 9.2 0.3 4.6
Venezuela Increasing 11.2 8.8 2.8 4.8
Latin A. Mean Increasing 12.5 10.4 3.2 6.7
a Only 122 usable observations
in the private sector (seven country average of 6.7 vs. 3.2%
points in the private and
public sectors).
The government is the largest employer in several East Asian
countries, and in most
countries (even developed ones) the two sectors use different
criteria in recruiting
and promoting workers, setting wage scales in relation to
productivity and the role
of collective bargaining and trade unions tends to be different.
These differences are
expected to result in sorting of workers into the two sectors, and
a different distribution
of earnings across sectors affecting overall within-groups wage
inequality. Schooling is
a risky investment and a measure of risk is differences in wage

effects across quantiles.
Public sector employment involves less risk as the impact of
schooling on within-
groups wage dispersion is substantially smaller in the public
sector (and in the case
of the East Asian economies examined, more schooling has an
equalizing effect);
furthermore, the public sector provides more job security.
Recent evidence shows
that in Pakistan (Hyder 2007), a year of education yields a
greater increase in wages
in the private sector than the public sector (despite more
educated workers being
attracted to the public sector of both countries) and that there is
a different reward
system in the private sector (where the earnings gap is small)
compared to the private
sector.
While it would be difficult to provide a complete explanation
for the lower within-
group dispersion, the distribution of unobservable skills and
other characteristics
between the two sectors will likely figure in any explanation.
Assume for example
that workers located at higher quantiles of the earnings
distribution have more
123
favorable characteristics (including unobserved skills such as
motivation and other

characteristics affecting productivity) and that these
characteristics interact positively
with schooling.
In the public sector, due to less pressure for wages to be
flexibly associated with
productivity, stronger unions, political appointments, and in
general certain entrenched
practices associated with the public sector, this sector will tend
to have a more homo-
geneously skilled work force, a flatter wage structure and lower
earnings at the top
of the wage distribution compared to the private sector. Under
such a wage structure,
while the public sector would be able to attract workers with
above average formal
schooling, it would be difficult to attract workers with favorable
unobserved skills
even with higher average wage offers.4
The findings in this study along with the existing empirical
evidence suggest that
increasing wage effects are generally observed in the private
sector across all coun-
tries - developed as well as developing. Thus, more schooling
unambiguously increases
within group inequality in the private sector. In the public
sector of developed coun-
tries (i.e., in Europe and North America) as well as the group of
Latin American
countries examined in this note, increasing wage effects are still
observed, but the
dispersion is considerably lower compared to the private sector.
Finally, in the public
sector of a number of low income developing countries where
the distorting pay

and employment policies in the public sector are particularly
severe, wage effects
in the public sector (and overall, when there is a large presence
of the public sector in
the economy) are decreasing with quantiles and the presence of
the public sector in the
economy reduces the impact of educational expansion on the
overall within-groups
wage dispersion.
Finally, using years of schooling in the wage equation implies
that the impact of
one additional year of schooling on within-group earnings
dispersion is the same,
irrespectively of education level. Using education levels in the
wage equation instead
of years of schooling allows for further insight on differences
across educational
qualifications. Budria and Pereira (2005) used quantile
regression analysis to estimate
the returns to different education qualifications in nine
European countries. They
find that returns to education generally increase with quantiles
and that education
(especially tertiary) has a positive impact on within group
dispersion.
Looking at the pattern of wage effects by quantile, in East
Asian countries (where
were a decreasing pattern by quantile was earlier found), the
pattern of wage effects at
the primary and secondary level is mixed, while at the tertiary
level we generally see
a decreasing pattern (with the exceptions of Singapore and
Cambodia). A consistent
finding, however, is that the effects of university education

decline through the 75th
quantile and subsequently rebound at the highest quantile. In
Latin America, generally
speaking, an increasing pattern is observed within every
education level. Exceptions
are fount in the case of primary education in Guatemala, Brazil
and Colombia and
tertiary education in Mexico (Figs. 1, 2).
4 Borjas (2002) reports that offering higher wages in the US
public sector is not enough to attract high
quality workforce due to low earnings at the top of the earnings
distribution (see also Budria 2006).
123
private sector 391
0.9
-20
-15
-10
-5
0
5
10

15
20
M
o
n
g
o
lia
V
ie
tn
a
m
In
d
o
n
e
si
a
T
h
a
ila
n
d

C
a
m
b
o
d
ia
C
h
in
a
P
h
ili
p
p
in
e
s
S
in
g
a
p
o
re
Primary Secondary Tertiary

Fig. 1 Ninth to first decile difference in annualized wage effects
by education level across Southeast Asia
-20
-15
-10
-5
0
5
10
15
20
A
R
G
C
H
IL
E
C
o
lo

m
b
ia
G
u
a
te
m
a
la
V
e
n
e
zu
e
la
M
e
xi
co
B
o
liv
ia
B

ra
zi
l
Primary Secondary Tertiary
Fig. 2 Ninth to first decile difference in annualized wage effects
by education level across Latin America
5 Conclusion
This study investigates the impact of schooling on earnings
across a mix of East Asian
and Latin American countries by first conducting a systematic
examination of the
returns to education along the conditional wage distribution, as
well as examining
the pattern of returns within education levels for each country.
It also investigates
the existence of a relationship between a country’s labor market
characteristics in the
public vs. the private sector and the pattern of returns to
education along the earnings
distribution. Our contribution is that we consider cases where
the OECD pattern may
not apply. We find evidence that, while wage effects increase
with quantiles in the
private sector across all countries, the pattern in the public
sector varies among groups
of countries. It is generally increasing in most high and middle
income countries
(i.e., Europe, North America and Latin America), but the
dispersion is considerably
lower compared to the pattern in the private sector. In several
East Asian countries,

on the other hand (and from past evidence in some low income
African countries),
the opposite pattern is observed. In general, more education
tends to increase earnings
inequality, since education is a better investment for the better
off; however, the pre-
sence of the public sector in the economy reduces the impact of
educational expansion
on the overall within-groups wage dispersion.
123
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123
Gender Pay Gap and Employment Sector: Sources
of Earnings Disparities in the United States, 1970–2010
Hadas Mandel & Moshe Semyonov
Published online: 23 August 2014
# Population Association of America 2014
Abstract Using data from the IPUMS-USA, the present research
focuses on trends in
the gender earnings gap in the United States between 1970 and
2010. The major goal of
this article is to understand the sources of the convergence in
men’s and women’s
earnings in the public and private sectors as well as the
stagnation of this trend in the
new millennium. For this purpose, we delineate temporal
changes in the role played by
major sources of the gap. Several components are identified: the
portion of the gap
attributed to gender differences in human-capital resources;
labor supply;
sociodemographic attributes; occupational segregation; and the
unexplained portion
of the gap. The findings reveal a substantial reduction in the
gross gender earnings gap
in both sectors of the economy. Most of the decline is attributed
to the reduction in the

unexplained portion of the gap, implying a significant decline in
economic discrimi-
nation against women. In contrast to discrimination, the role
played by human capital
and personal attributes in explaining the gender pay gap is
relatively small in both
sectors. Differences between the two sectors are not only in the
size and pace of the
reduction but also in the significance of the two major sources
of the gap. Working
hours have become the most important factor with respect to
gender pay inequality in
both sectors, although much more dominantly in the private
sector. The declining
gender segregation may explain the decreased impact of
occupations on the gender
pay gap in the private sector. In the public sector, by contrast,
gender segregation still
accounts for a substantial portion of the gap. The findings are
discussed in light of the
theoretical literature on sources of gender economic inequality
and in light of the recent
stagnation of the trend.
Keywords Gender pay gaps . Public sector. Private sector.
Gender discrimination
Demography (2014) 51:1597–1618
DOI 10.1007/s13524-014-0320-y
Electronic supplementary material The online version of this
article (doi:10.1007/s13524-014-0320-y)
contains supplementary material, which is available to
authorized users.
H. Mandel (*): M. Semyonov

Department of Sociology and Anthropology, Tel-Aviv
University, Tel-Aviv, Israel 69978
Introduction
One of the most significant social changes in recent decades has
been the changing
economic status of women. Since the middle of the twentieth
century, women have not
only joined the economically active labor force in ever-
increasing numbers but also
enhanced their education and improved their occupational status
and economic re-
wards. More specifically, during the last decades, women have
surpassed men in
overall rates of college graduation and have almost reached
parity with men in rates
of earning doctoral and professional degrees. In addition, levels
of sex segregation have
declined, and women have increased their representation in
male-dominated occupa-
tions, particularly in managerial and high-status professional
occupations (Blau et al.
2013; Charles and Grusky 2004; Cotter et al. 2004; DiPrete and
Buchmann 2013;
England 2010; Jacobs 1992; Weeden 2004). Consequently,
earnings disparities be-
tween men and women have been gradually declining. The
decline was especially
evident after the mid-1970s, although its pace has slowed in the
new millennium (Blau
and Kahn 1994, 1997; Cotter et al. 2004; McCall 2007; O’Neill
2003).

Students of gender-linked gender inequality have attributed the
gender earnings gap
and its decline to a number of sources. The most notable
sources are gender differences
in labor market–relevant attributes (i.e., human capital), labor
supply, occupational
segregation, and employers’ discrimination. Change in each of
these sources can lead to
a decline over time in the gender earnings gap. First, the decline
may be a result of a
relative improvement in women’s human-capital resources (i.e.,
education, skills, work
experience) or changes in the personal attributes of working
women (i.e., marital or
maternity status). Second, the decline may be a result of
changes in the number of hours
that women allocate to paid work. Third, the decline may reflect
a decrease in earnings
discrimination against women, or greater equality between men
and women on unob-
served characteristics. And fourth, the decline may be a result
of a decrease in gender
occupational segregation, reflecting the upward occupational
mobility experienced by
women (Cotter et al. 2004; Mandel 2012, 2013).
The impact of each of the sources, however, may change over
time and may play a
different role in different sectors of the economy. For example,
with the growing
educational attainment of women, differences in human-capital
resources may cease
to be one of the major sources of women’s earnings
disadvantage, as they were until the
1980s (DiPrete and Buchmann 2013). Similarly, the impact of

human-capital resources
on earnings attainment may vary across the public and private
sectors. Because the
wage structure in the public sector is more bureaucratically
regulated, the impact of
formal education on wages is expected to be more pronounced
in the public sector than
in the private sector of the economy (Asher and Popkin 1984;
Gornick and Jacobs
1998; Grimshaw 2000; Maume 1985).
Although the literature on the causes and consequences of
gender economic in-
equality and its long-term trends has grown and become
substantial, no study has yet
(to the best of our knowledge) systematically examined
temporal changes in the sources
of the gender earnings gap. Furthermore, no research has
systematically examined
whether and to what extent the decline in the earnings gap has
assumed different
patterns and different trajectories in the two economic sectors.
The major goal of the
present study, then, is to contribute to the existing literature on
the decline in the gender
pay gap by evaluating the change in the role played by different
sources of the gap in
1598 H. Mandel, M. Semyonov
the public and private sectors during the last four decades. To
do so, we first distinguish
between the explained and unexplained portions of the gender
pay gap in the two

sectors and describe their dynamics over time. We then
disaggregate the gap into the
following components: those attributed to human-capital
characteristics, personal attri-
butes, labor supply, and gender differences in occupational
distributions. Subsequently,
we compare the components of the pay gap over time and across
sectors.
The findings of the analysis reveal meaningful differences in
the size and pace of the
reduction in the gender pay gap between the two sectors as well
as differences in the sources
that account for the reduction in the gap. Personal attributes and
human-capital resources
account for only a limited portion of the gap in both sectors. By
contrast, occupational
segregation and working hours account for greater portions of
the gender pay gap. The
former is dominant in both sectors; the latter is substantial only
in the private sector. In
addition, the key factor accounting for the decline in the gender
pay gap in both sectors is
the decline in the wage penalty that women experience in the
labor market (i.e., a decline in
the unexplained portion of the gap).
In light of the sharp decline in gender earnings disparities
between 1970 and 1990
and the slowdown of the trend toward the new millennium (Blau
and Kahn 2006; Blau
et al. 2006; England 2006), the results of this study allow us to
join the ongoing debate
on whether the “Golden Age” of gender equality reached its
limit toward the end of the
twentieth century. By doing so, the present article contributes to

a better understanding
of temporal changes in the sources of the gender pay gap in
recent decades and the
mechanisms that produce gender inequality in pay.
Theoretical Considerations and Previous Research
Sources of Gender Economic Inequality
Most studies on temporal changes in earnings gaps between men
and women have been
conducted by economists, who commonly divide the gender pay
gap into two compo-
nents: the portion of the gap that is “explained” by gender
differences in work-related
characteristics, and the portion of the gap that remains
“unexplained.” The unexplained
portion of the gap is often used as a proxy for discrimination or
viewed as gender
differences in unobserved predictors (Weichselbaumer and
Winter-Ebmer 2005). In
recent decades, the size of both components—the explained and
the unexplained—
has declined in the U.S. labor market, most pronouncedly from
the mid-1970s until the
mid-1990s and to a lesser extent during the 2000s (e.g., Cotter
et al. 2004). Several
changes explain this decline: changes in labor force–relevant
attributes (such as educa-
tion and work experience) as well as changes in earnings returns
to such attributes. For
example, O’Neill (2003) contended that change in the respective
work experience of
men and women and change in returns to work experience are
the key factors underlying
the decline in gender earnings disparities between 1979 and

2001. Not only have the
disparities in work experience between men and women
narrowed, but women’s returns
to potential work experience have increased much more rapidly
than men’s returns (see
also O’Neill and Polachek 1993). Likewise, Blau and Kahn
(1994, 1997) found that
during the 1980s, changes in the qualifications of women—
especially their labor market
experience—contributed to the decline in the gender wage gap.
Gender Pay Gap and Employment Sector 1599
These changes were accompanied by changes in the two most
important
determinants of wages: working hours and educational
attainment. The rise in
women’s work supply is reflected in the growth of average
weekly working
hours that women allocate to paid work, as well as in an
increase in their
overtime work (Cha and Weeden 2014). In addition, the
educational qualifica-
tions of men and women have converged. In fact, since the
1980s, the gender
gap in college enrollment in the United States has been
reversed, in favor of
women, because of an increase in women’s educational
attainment coupled with
a slowdown in men’s attainment of professional degrees (e.g.,
DiPrete and
Buchmann 2013; Morris and Western 1999).
In addition to changes in human-capital attributes and work-

related attributes,
researchers have observed a significant decline in the size of the
unexplained
portion of the earnings gap. According to Blau and Kahn (1997,
2006), the
decrease in the unexplained portion of the gap was the major
factor accounting
for the sharp decrease in gender earnings disparities during the
1980s. This
decrease is attributed to both a decline in the unmeasured
characteristics of men
and women and a decrease in labor market discrimination
against women (Blau
and Kahn 2006).
Notwithstanding the convergence in work-related characteristics
and the
reduction in the size of the unexplained portion of the gap,
gender-based
occupational segregation has also declined in recent decades
(Blau et al.
2013; Cotter et al. 2004; England 2006). Sociologists have long
argued that
occupational segregation is one of the most significant factors
accounting for
earnings disparities between men and women. According to this
view, women’s
earnings are lower than men’s because women are sorted (either
denied access
or self-selected) into female-typed low-paying jobs and
occupations (Bielby and
Baron 1986; Petersen and Morgan 1995; Treiman and Hartmann
1981). In
recent decades, however, the American labor market has
witnessed a steady
decline in rates of occupational segregation (Blau et al. 2013;

England 2006).
England (2006), for example, reported a steady decrease in
levels of occupa-
tional sex segregation between 1960 and 2000, which was
evident, first and
foremost, among highly educated workers. The major cause of
this decline was
the growing integration of women into new occupational
domains, particularly
women’s increased participation in lucrative professional and
managerial posi-
tions, occupations from which they were traditionally absent
(Burris and
Wharton 1982; Cotter et al. 2004; Jacobs 1992; Mandel 2012;
2013; Weeden
2004).
In sum, the declining gender pay gaps in the United States in
recent decades can be
attributed to three major temporal trends: a sharp convergence
in men’s and women’s
wage-related measured and unmeasured characteristics, a
convergence in occupational
distributions of men and women (i.e., a decrease in the rate of
occupational segrega-
tion), and a decline in pay discrimination against women. In
what follows, then, we first
develop theoretical expectations regarding sources of the
temporal decline in earnings
gaps between men and women in the private and public sectors
and then empirically
estimate our expectations in the labor market as a whole and
separately in the private
and public sectors. Finally, we discuss the findings in light of
the theoretical
expectations.

Gender Earning Gaps in the Public and Private Sectors
The public sector of the economy has long been favored by
women (and
minorities) as a preferred locus of employment because of its
greater commit-
ment to universalistic criteria of recruitment, promotion, and
allocation of
rewards. The more bureaucratic nature of the public sector and
its more
regulated employment practices make it a more protected
employment realm
than other segments of the economy. The public sector is also
more likely to
rely on centralized pay arrangements and to adopt and enforce
affirmative
action policies that apply to women and minorities. That is,
equal opportunities
and antidiscrimination policy are more effectively implemented
and used in the
public sector. Consequently, the public sector has become one
of the most
attractive employment sites for women and racial minorities
because of its
protective nature and more egalitarian pay system (Asher and
Popkin 1984;
Gornick and Jacobs 1998; Grimshaw 2000; Maume 1985).
In addition to its egalitarian pay system and antidiscrimination
policy enforcement,
the public sector provides greater occupational opportunities for

women. Gornick and
Jacobs (1998), in an influential article, pointed to the relatively
large supply of good
jobs that the public sector offers women, especially in
professional occupations but also
in managerial and technical jobs (see also Kolberg 1991).
Professional jobs, which
attract women because of their relative high status and high
earnings, are much more
prevalent in the public than in the private sector. Moreover,
professional jobs not only
featured the smallest gender occupational segregation to begin
with (Burris and
Wharton 1982; Wharton 1989) but also have experienced the
greatest decline in gender
segregation in recent decades (Cotter et al. 2004).
Indeed, researchers in a variety of countries have repeatedly
demonstrated that the
pay disadvantage for women is substantially smaller in the
public than in the private
sector. For example, Arulampalam et al. (2007) showed that in
all 11 countries included
in their study, the gender pay gap is more pronounced in the
private than in the public
sector. Panizza and Qiang (2005) found similar findings for 12
(of 13) Latin American
countries. Country-specific studies have yielded similar
results—Melly (2005) for
Germany, Zweimiiller and Winter-Ebmer (1994) for Austria,
and Tansel (2005) for
Turkey—all of whom found gender pay gaps to be larger in the
private sector relative to
the public sector.
Notwithstanding the universal pay advantages of the public

sector for wom-
en, Gornick and Jacobs (1998), comparing the gender pay gaps
across seven
countries, reported marked cross-country variation in the
magnitude of the
public sector’s advantages for women. The major factor that
accounts for the
variation between countries in the public–private sectors’
gender pay gaps is the
size of the public sector; the smaller the public sector, the
larger are women’s
relative economic advantages. Thus, whereas the effect of
public sector em-
ployment on the gender wage gap is limited (or nonexistent) in
countries with a
large public sector (like Sweden), the effect is quite pronounced
in the
American labor market, where public sector employment
accounts for less than
one-fifth of the workforce (Gornick and Jacobs 1998). These
findings suggest
that variation between sectors in the gender pay gaps would be
particularly
evident in the U.S. labor market.
Empirical Expectations
Based on the literature reviewed in the previous sections, we
expect the impact of each
of the sources of gender inequality to vary across economic
sectors. Obviously, we
expect gender pay disparities in the United States, as in most

other countries, to be less
pronounced in the public sector than in the private sector. Yet,
expectations of a
differential decline of the gender gap across sectors are neither
trivial nor straightfor-
ward. On one hand, the large supply of professional occupations
in the public sector as
well as the rapid decline in rates of gender segregation in
professional occupations
might lead us to expect a more rapid decline of the gender pay
gap in the public sector.
On the other hand, a more rapid reduction might be expected to
occur in the private
sector because of the initial large gaps in this sector. Moreover,
because much of the
decline in the gender pay gap in recent decades is attributed to a
reduction in the
unexplained portion of the gap (i.e., gender discrimination), and
given that gender
discrimination is much more pronounced in the private than in
the public sector, one
might well expect a more pronounced reduction in the former
than in the latter sector.
The impact of working hours on the gender pay gap is expected
to be less dominant
in the public than in the private sector. Because of its more
rigid wage determination
system and its higher regulation of employment conditions, the
public sector is less able
to enforce a long working day and extra hours. Thus, we expect
working hours to have
a weaker effect on the gender pay gap in the public than in the
private sector. However,
the convergence in men’s and women’s working hours may
contribute to a more rapid

reduction in the gender pay gap in the private than in the public
sector.
One can arrive at two alternative expectations regarding the
impact of human-capital
attributes on the gender pay gap. First, because of adaptation of
more universalistic
criteria for promotion and allocation of economic rewards in the
public sector, human-
capital attributes may be more dominant determinants of pay
levels (compared with
other factors) in the public sector. However, with the
convergence in human-capital
attributes of men and women in recent decades, they may cease
to be a major wage
determinant in both sectors. Nonetheless, that convergence may
contribute to a more
rapid reduction in the gender pay gap in the public sector.
Data Source and Variables
Data for the present analysis were obtained from the
harmonized Integrated Public Use
Microdata Series (IPUMS) between the years 1970 and 2010.
The data for 1970 were
derived from the 1 % census samples; the data for 1980, 1990,
and 2000 were derived
from the 5 % census samples; and the data for 2010 were
obtained from the American
Community Survey (ACS). The obvious advantages of the
IPUMS data are that they
provide a large number of sampled cases and that all variables
are comparable over
time.
The variables selected for estimating earnings equations are

those traditionally used
in models predicting earnings: gender (female = 1), age (in
years), race/ethnicity (by
five dummy variables: blacks, Hispanics, Asians, other races,
and non-Hispanic whites
(the omitted category)), marital status (married = 1), nativity
status (foreign-born = 1),
number of children, presence of a young child (presence of
child under age 5 = 1), level
of education (four ordinal categories: less than high school,
high school graduate, some
college, and college graduate (the omitted category)), potential
work experience and its
squared term (age – years of schooling – 6), and weekly
working hours. Occupations
are measured in two-digit classification by the occupational
variable OCC1990. The
latter is one of two standardized occupational variables and the
one recommended by
IPUMS as preferable for analyses of the samples from 1980
onward.1 Because
occupational classification from 1970 and 2010 became more
detailed, harmonizing
the categories according to 1990 would involve some selection
or aggregation of
occupations, which can affect the results. However, because of
a statistical consider-
ation, we aggregated the variable OCC1990—originally in
three-digit classification—
to two-digit classification, creating about 80 occupational
categories in each decade.

This aggregation may reduce the problem of comparisons over
time among occupa-
tional classifications, but it may also conceal part of the impact
of occupations and
therefore part of the explained portion of the gap.
Earnings, the dependent variable, is measured by pretax wages
and salary income
for the year prior to the survey divided by the number of weeks
that a person worked in
the year prior to the survey, adjusted for inflation. Estimation of
the earnings equation is
restricted to the population aged 25–59. The list of variables
and their means, by
gender, decade, and sector, are displayed in Table S1 in Online
Resource 1.
Methodology
There are several alternative techniques for decomposing pay
gaps between groups via
the use of regression equations. One of the most popular
techniques is the procedure
proposed by Oaxaca (1973) and Blinder (1973). This technique
uses separate linear
regression models for men and women and—in a counterfactual
manner—distin-
guishes between two distinctive portions: (1) a portion that is
explained by gender
differences in work-related characteristics, such as education or
work experience (the
Xs); and (2) the unexplained portion of the gap that cannot be
accounted for by mean
differences in wage determinants. The latter is attributed to
differences in the intercepts
and differences in returns to wage determinants’ factors (the

s).2
The analysis is formulated as follows:
where and are weekly wages of men and women, respectively.
and are
means of all predictors, and and are the coefficients of these
predictors for
men and women, respectively. is the portion of the gap that is
1 More details are available online (http://usa.ipums.org/usa-
action/variables/OCC1990#description_tab).
2 The Oaxaca-Blinder decomposition also allows decomposing
the gap into three components. The third
component is the interaction term that accounts for the fact that
gender differences in characteristics and
coefficients exist simultaneously. To check this, we applied the
triple decomposition to our data. The analysis
yielded results that were very similar to those of the dual
decomposition because the interaction term was
found to be negligible. We therefore present the dual
decomposition.
explained by gender differences in mean wage-related
attributes.
is the portion of the gap attributed to gender differences
in returns to wage-related attributes (on the left side) and
gender differences in

intercepts (right side). Given that this portion cannot be
explained by wage-related
attributes, it serves as a proxy for economic discrimination.
At the first stage, we compare the explained and unexplained
portions of the gap, by
decade, in the public and private sectors. We then compare the
contribution of each
component (i.e., weekly working hours, human capital, personal
attributes, and occu-
pations) after distinguishing between the explained portion (say,
gender differences in
education levels) and the unexplained portion (say, gender
differences in returns to
education) by decade and sector.
Analysis and Findings
Decomposition of the Gender Pay Gap Over Time: Explained
and Unexplained Gaps
In Table 1, we display trends in the gender pay gaps over the
last four decades. In
column 1, we list the wage gap between average earnings of
men and women. In
columns 2 and 3, we list the two major components of the pay
gap: the explained and
unexplained portions of the gap (obtained by the Oaxaca-
Blinder decomposition
procedure).
The results show that the gross gender gap has substantially
decreased over the years
from –0.66 log units in 1970 to –0.35 log units in 2010—a
reduction of 46 % over four
decades. Although the decline in the gross gender pay gap

began in the 1970s, the trend
was especially prominent between 1980 and 2000—a 36 %
decline in only two
Table 1 Gross gender pay gaps and the Oaxaca-Blinder
decomposition, 1970–2010
Gross Gender
Wage Gaps Explained Unexplained
Year (1) (2) (3)
1970 0.657 0.133 0.524
(100) (20) (80)
1980 0.623 0.176 0.447
(100) (28) (72)
1990 0.497 0.177 0.320
(100) (36) (64)
2000 0.398 0.145 0.253
(100) (36) (64)
2010 0.356 0.150 0.206
(100) (42) (58)
% Change 1970–2010 –46 13 –61
Notes: Coefficients in columns 2 and 3 are presented in terms of
log wage. Percentages are shown in

parentheses.
decades. The coefficients presented in columns 2 and 3, which
pertain to the explained
and unexplained portions of the gap, are presented in terms of
log wage and percentage
(in parentheses). The data show that in addition to the sharp
decrease in the raw pay
gap, the explained portion of the gap has steadily increased over
the years. More
specifically, the portion of the gap that can be attributed to
gender differences in wage-
related characteristics (and hence can be viewed as the
“legitimate,” or nondiscrimina-
tory portion of the gap) doubled in four decades, rising from 20
% in 1970 to 42 % in
2010. Nevertheless, even for 2010, less than one-half of the gap
(42 %) is explained by
gender differences in work-related characteristics.
Differences in returns to wage-related characteristics ( ) and
differences in initial
starting points (intercepts) account for the other portion of the
gap. These two compo-
nents are attributed to unmeasured wage-related characteristics
as well as to differential
returns that men and women receive on their wage-related
attributes (i.e., discrimina-
tion). As shown in the table, the unexplained portions of the
wage gap have decreased
substantially in both absolute and relative terms over the years.
In 1970, the unex-

plained portion of the gap was 0.52 log units, 80 % of the total
gap. Four decades later,
the unexplained portion of the gap had shrunk by 61 %, to 0.20
log units, accounting
for 58 % of the total gap.
To further explore the unique contribution of each component to
the gender pay gap
and the extent to which that contribution has changed over the
years, we display in
Table 2 the disaggregated components of the explained (left
panel) and unexplained
(right panel) portions of the gap. For the purpose of the
presentation, earnings predic-
tors are divided into four groups: measured indicators of
human-capital resources (i.e.,
education, work experience), an individual’s sociodemographic
attributes (i.e., marital/
parental status, race/ethnicity), weekly working hours, and
occupations (at the two-digit
classification level). (See Table S1 in Online Resource 1 for the
averages of the
variables by gender and by sector.) The coefficients are
presented in terms of log wage
and percentage of the gross gender pay gap. The bottom row of
the table displays
changes in the coefficients throughout the period. Figure 1
provides a visual illustration
of the decomposition results 3; Table 4 in the appendix
presents, for each decade,
coefficients of the two separate regression equations (for men
and women) on which
the decomposition was conducted.4
In general, the data reveal that gender differences in human-
capital resources and

sociodemographic attributes do not account for the gender pay
gaps. By way of
contrast, working hours and occupations exert a significant
effect on the gender pay
gap. Specifically, Table 2 shows that gender differences in
education and work
experience explain only negligible portions of the pay gap.
Although men benefit more
than women from education and work experience in attainment
of earnings (see Table 4
in the appendix), gender differences in human-capital resources
actually conceal part of
the gender earning gaps. The suppressive effect of human
capital, although modest, has
increased from 1990 onward, reaching –0.02 log units (6 %) in
2010. This finding is
not surprising in light of the higher rates of college graduation
among women as of the
3 The table shows the disaggregated coefficients of both the
explained and unexplained portions of the gap.
However, the figure displays only the disaggregated components
of the explained portion. A visual demon-
stration of the disaggregated components of the unexplained
portion of the gap is problematic because the
intercept is dependent on the values of the coefficients and has
no substantive meaning.
4 The coefficients of occupations (about 80 in each decade) are
not presented.
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50
0.
36
8
0.
12
2
–
0.
4
07
0.
01
2
0
.1
11
0.
2
06
(1
0
0)
(–
6)

(2
)
(3
3)
(1
3
)
(4
2)
(1
04
)
(3
4)
(–
11
5)
(3
)
(3
1)
(5
8)
%
C
ha

ng
e
1
97
0
–
20
10
–
46
33
9
–
78
33
6
–
38
1
3
–
4
5
–
5
7
–

57
–
86
–
59
–
6
1
N
o
te
s:
C
oe
ff
ic
ie
nt
s
in
co
lu
m
ns
2
an
d
3
ar
e

pr
es
en
te
d
in
te
rm
s
o
f
lo
g
w
ag
e.
P
er
ce
nt
ag
es
ar
e
sh
ow
n
in
pa
re
nt

h
es
es
.
1980s (see Online Resource 1, and DiPrete and Buchmann
2013), coupled with the
positive correlation between educational level and earnings.
This finding implies that
the earnings gaps between equally educated men and women
(with identical charac-
teristics) are greater than the earnings gaps with no control for
education.5 Based on
these findings, it would be reasonable to conclude that gender
differences in level of
education per se do not account for the gender pay gaps. Rather,
the major source of the
pay gap is in the kinds of education that men and women
acquire, which is the relevant
factor for the differential occupational profiles of the two
gender groups.6
The data presented in Table 2 and Fig. 1 reveal that like human-
capital resources,
sociodemographic characteristics have a negligible effect on the
gender pay gap; they
accounted for 6 % and 4 % of the total gender pay gap in 1970
and 1980, respectively,
and for only 2 % of the gender pay gap from 1990 onward. The
data also show that
men receive higher earnings returns on sociodemographic
attributes than women in all

decades. Nevertheless, the decline took place mostly because
the two gender groups
have become very similar in their sociodemographic attributes,
owing to changes in the
sociodemographic characteristics of women in recent decades
(see Table 4 in the
appendix, as well as the right panel of Table 2).
Unlike the negligible effects of sociodemographic attributes and
human-capital
resources, the effect of working hours on the gender pay gap
has substantially inten-
sified over time, in both relative and absolute terms.7 The data
in Table 2 show that
gender differences in average working hours accounted for 4 %
of the total gap in 1970
(less than sociodemographic characteristics and occupations).
Only two decades later,
in 1990, working hours accounted for one-fifth of the gross gap;
and in 2010, working
hours already explained one-third of the gross gender pay gaps.
The increasing effect of
working time is somewhat curious in light of the convergence in
men’s and women’s
working hours. Although mean weekly working hours decreased
among men between
1970 and 2010, they steadily increased among women (from
33.8 (exp(3.52)) to 36.4
(exp(3.59))) (see Table 4). Nevertheless, the importance of
working hours as a deter-
minant of earnings—for men and women alike—has largely
increased over the years
(see Table 4, and also Cha and Weeden 2014). Thus, in spite of
the overall decline, the
remaining gap in the amount of time allocated to paid work by
men and women has

become costly to women in recent decades.
Moreover, Table 2 shows that working hours represent a much
more important
determinant of pay among working women than among working
men because the
5 The effect of work experience is negligible, perhaps because
of the problematic use of age (minus education)
as a proxy for work experience. Because women in the sample
are slightly older than men in all decades, their
work experience is slightly greater than that of men. The returns
on work experience, however, are greater for
men in all decades (see Table 4 in the appendix).
6 Indeed, the suppressing effect of education is eliminated (and
in some decades even reversed to a modest
positive effect) after we control for occupations at the three-
digit classification. Apparently, within detailed
occupations, men have slightly higher education and greater
experience than women. Unfortunately, we were
not able to implement the Oaxaca-Blinder decomposition with
the three-digit occupational classification
because the matrix was too complicated.
7 To verify the strong impact of working hours, we reanalyzed
the data using hourly earnings as the dependent
variable. We compared the results with those presented by the
present analysis in which weekly wage is used
as the independent variable. The results of the two strategies are
very similar. Specifically, the contribution of
working hours to the gender pay gap is very similar under the
two alternatives, as is the contribution of each of
the other components.

variance in weekly working hours among men is relatively small
(i.e., in all decades,
more than 90 % of men work more than 40 weekly hours),
whereas the variance among
women is much larger (more than one-third of working women
worked fewer than 40
hours). Note that a simple comparison between men’s and
women’s returns may be
somewhat misleading. Because of the interrelations between the
intercept and the
returns, higher returns for women relative to men should be
evaluated in light of the
higher intercept for men than for women.
Inclusion of occupations among the predictors of wage enables
us to disaggregate
the gender pay gap into a portion attributed to gender
occupational segregation
(explained) and a portion attributed to differential rewards to
men and women
employed in the same occupation. With only one exception
(1980), the impact of
occupations on the wage gap has remained constant in relative
terms (ranging from
11 % to 13 % of the gap) but has declined in absolute terms
(from 0.072 log units in
1970 to 0.045 log units in 2010: a decline of almost 40 %). The
decline is hardly
surprising given the reduction in the level of gender
occupational segregation from
1970 onward (Blau et al. 2013; Cotter et al. 2004; Weeden
2004). Within occupations,
however, men’s earnings are still higher than women’s, as the
unexplained portion of
the wage gap makes evident. Nevertheless, men’s earnings

advantage within occupa-
tions has been declining. The decline is quite evident in both
absolute and relative terms
(dropping from 0.082 log units to 0.012 log units between 1970
and 2010—a reduction
of 86 %). However, occupations are not controlled in detailed
categories but in two-
digit classification—that is, in approximately 80 occupational
categories in each
decade.8 This should lead to an underestimation of the role of
segregation and an
overestimation of the unexplained portion of the gap.
Nonetheless, it ought not to affect
the comparisons.9
Overall, Table 2 and Fig. 1 reveal that the effect of weekly
working hours on the
wage gap has become the dominant factor from 1990 onward, in
both relative and
absolute terms. Working hours and occupational segregation
account for almost all the
8 We used the two-digit occupational classification (instead of
three-digit (300 to 400) detailed occupational
categories) because the statistical software could not estimate
the equation using the large matrix created by the
detailed occupations.
9 An advantage of using the two-digit over the three-digit
occupational classifications, however, is that it offers
a better adjustment between decades in the case of occupational
crosswalks.
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
1970

1980
1990
2000
2010
Human capital Demographics Hours Occupations Unexplained
Fig. 1 Components of the gender pay gaps, 1970–2010
explained portion of the gender pay gap in 2010. Over the years,
the sociodemographic
attributes of working men and women have become more similar
and thus ceased to be
major determinants of the gender pay gap. Human-capital
resources, by way of
contrast, conceal part of the gap because of the higher level of
education that women
have attained in recent decades. The findings also show that at
all points in time, the
largest portion of the gender earnings gap cannot be attributed
to any of the variables
included in the regression equations.
Cross-Sector Comparison
The data presented thus far suggest that the decline over time in
earnings disparities
between men and women can be attributed partly to the effect of
working hours, partly

to gender-linked occupational segregation, and partly to a
decline in the size of the
unexplained gap (economic discrimination). As noted at the
outset in our theoretical
section, these sources of the gap are likely to operate differently
in the public and
private sectors. To uncover differences across sectors, we apply
the same method used
in the previous section for each sector separately. Results of the
analysis—the coeffi-
cients and the relative contribution (in percentage)—are
presented in Table 3. The
upper half of the table presents the explained portions of the
gap, disaggregated into
four components, and the lower half presents the same
disaggregation for the unex-
plained portion of the gap. The bottom row of each panel
displays changes in the
coefficients between 1970 and 2010. Figure 2 illustrates
graphically the contribution of
each component in terms of the coefficients (log wage) in each
sector.
In columns 1a and 1b of Table 3, we display the gross gender
earnings gap in the
private and public sectors, respectively. As expected, gender
pay disparities in all
decades are less pronounced in the public sector than in the
private sector. Yet, the
figure reveals a trend quite similar to the trend observed in the
previous analysis: a
systematic decline in the gender pay gap in both sectors, mainly
resulting from a sharp
decline in the unexplained portion of the gap. Likewise, in both
sectors, the most
pronounced decline in the gap occurred between 1980 and 1990.

Notwithstanding the similarities between the two sectors, the
data show some
meaningful differences too. First and foremost, the decline is
much more pronounced
in the private sector than in the public sector. Although the
gender pay gap in the
private sector has declined by almost one-half (46 %), it has
correspondingly declined
in the public sector by less than one-third (27 %). Apparently,
during the last four
decades, gender earnings disparities in the “less egalitarian”
private sector have become
more similar to those in the “more egalitarian” public sector. As
suggested at the outset,
a more rapid reduction is expected in the private sector, where
the initial gaps were
larger. Put differently, the relative stability of the gender pay
gap in the public sector
during the last four decades may reflect a ceiling effect, and
this ceiling effect could be
one of the causes of what England (2006) and others have
referred to as a “bottleneck”
of the gender revolution.
Another difference between the two sectors concerns the ratio
between the explained
and unexplained portions of the gap and their changes over
time. As noted, the
accelerated reduction in the private sector is mainly due to the
sharp decline in the
unexplained portion of the gap. The reduction in the gap in the
public sector, however,
is also due to a larger increase in the explained portion of the
gap (from 14 % to 44 %).

T
ab
le
3
T
he
O
ax
ac
a-
B
li
nd
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co
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po
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at
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se
ct
or
s,
19
7
0
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2
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0
G
ro
ss
G
ap
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an
C
ap
it
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D
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(1
a)
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b)
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)
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a)
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%
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%
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%
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4
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24
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7
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)
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)
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)
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)
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)
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1.
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)
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(3
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)
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6
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6
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%
C
ha
ng
e
19
70
–
2
01
0
–
6
3
–
36
–
46

–
30
–
5
7
–
61
–
61
–
3
5
–
89
–
44
–
6
2
–
5
2
N
o
te
:
P

er
ce
nt
ag
es
ar
e
sh
ow
n
in
p
ar
en
th
es
es
.
The greater reduction of the unexplained portion of the gap in
the private sector is
expected because the unexplained portion of the gap serves as a
proxy for employers’
discrimination, which is more pronounced in the private than in
the public sector. In
light of the reduction in gender discrimination in recent decades
as well as the more
pronounced level of discrimination in the private sector at the
first place, these findings

are expected. What is surprising, however, is the initially large
size of the unexplained
portion of the gap in the public sector (86 % in 1970) despite its
regulated wage
structure.
When dividing the gap into explained and unexplained portions,
it is impor-
tant to distinguish between absolute and relative changes. The
data reveal that
in the public sector, the explained portion of the gap has
dramatically increased
both in absolute and relative terms. In the private sector, by
comparison, the
explained portion has increased mostly in relative terms because
of the reduc-
tion in the unexplained portion of the gap. Whereas the
explained portion of
the gap has remained relatively stable in absolute terms in the
private sector
(increasing by only 8 %, from 0.156 to 0.170 log units), it more
than doubled
in the public sector (increasing by 122 %, from 0.062 to 0.137
log units).
These different trends are mainly the result of the initially large
size of the
unexplained gap coupled with the drastic reduction in the
unexplained portion
of the gap in the private sector.
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
1970
1980

1990
2000
2010
Human capital Demographics Hours Occupations Unexplained
Private sector
1970
1980
1990
2000
2010
Public sector
Fig. 2 Components of the gender pay gaps, by sectors, 1970–
2010
A decomposition of the gender pay gap into components raises
similarities as
well as differences between the sectors. First, similar to the
findings observed
in Table 2, gender differences in sociodemographic attributes do
not play a
major role in explaining the gender pay gap in either sector. In

both sectors,
sociodemographic characteristics have become more similar
among working
men and women. Yet, men still receive higher returns on
sociodemographic
characteristics in both sectors. Second, the suppressive effect of
human-capital
resources viewed in the previous analysis (for the labor force as
a whole) is
observed mostly in the public sector: only in 2010 does this
effect appear to
some degree in the private sector. In both sectors, men receive
higher returns
for their human-capital characteristics (not shown). However, in
the private
sector, there are almost no differences between the two gender
groups in
human-capital characteristics. By contrast, in the public sector,
differences in
educational levels between men and women have increased over
the decades in
favor of women; in the public sector, the percentage of college
graduates
reached 55 % among women versus 46 % among men in 2010.
Thus, in
2010, the gender pay gaps are 9 % larger in the public sector
when education
is controlled for. Apparently, the gender pay gaps observed in
the public sector
exist despite the higher educational levels of women.
Third, working hours has become the most important
determinant of the gender pay
gap in both sectors, mostly because of the growing returns to
working hours regardless
of sector. Between 1970 and 2010, the effect of working hours

on the gender pay gap
almost doubled in the public sector and increased almost
fourfold in the private sector.
By 2010, differences between men and women in the amount of
time allocated to paid
work accounted for one-third of the total gap in the private
sector (33 %) and for one-
quarter (26 %) of the gap in the public sector. A possible
explanation for the greater
effect of working hours on the gender pay gap in the private
sector may lie in the rising
cost of overtime work. Despite the increase in women’s average
weekly working hours
in both sectors, overtime work is still much more prevalent
among men, and the cost of
overtime work has largely increased in recent decades (Cha and
Weeden 2014). At the
outset of this article, we suggested that because of the less
regulated working conditions
in the private sector, overtime work is less prevalent in the
public than in the private
sector. Indeed, the gap between average working hours of men
and women is higher in
the private than in the public sector at all decennial points. (In
2010, the numbers were
5.1 as opposed to 3.6 hours, respectively; see Online Resource
1.)
The impact of occupational segregation also differs across
sectors. Whereas the
impact of occupational segregation decreased by one-half (52
%) in the private sector, it
has doubled in the public sector, becoming a major determinant
of the gender pay gap.
In 2010, occupational sex segregation accounted for one-quarter
of the total gender pay

gap in the public sector, compared with only one-tenth in the
private sector. Once again,
the difference between the sectors could be related to the
regulated occupational wage
structure in the public sector, which limits employers’ ability to
pay differential salaries
to men and women who are employees in the same occupation.
Moreover, although
women have increased their share in professional and
managerial occupations in the
public sector, they are still heavily concentrated in female-
dominated occupations, such
as elementary school teachers, librarians, and nurses (Cotter et
al. 2004; Wharton
1989).
Discussion and Conclusions
Aiming at understanding the convergence in men’s and women’s
earnings throughout the
last four decades, we examined the sources of the gender pay
gap between 1970 and 2010
in the American labor market as a whole, and in the public and
private sectors separately.
We decomposed the gap into the portion of the gap that can be
attributed to differential
human-capital characteristics, personal attributes, labor supply,
gender occupational seg-
regation, and the portion of the gap that remains unexplained
(i.e., unmeasured factors and
economic discrimination). Decomposition of the gender pay gap
enabled us to estimate

the unique contribution of each component as well as its
fluctuations over the years in the
labor market as a whole and in each of the two major sectors.
The findings show that working hours have become the most
dominant factor
accounting for the gender pay gap, followed by occupational
segregation. Personal
attributes and human-capital resources account for a very small
portion of the gap,
especially in recent decades. Moreover, the key factor
accounting for the decline in the
gender pay gap in both sectors is the decline in the wage
penalty that women
experience in the labor market. Although the decline in the size
of the wage penalty
component can partly be attributed to unmeasured
characteristics, it also reflects a
decline in gender-based earnings discrimination.
Differences between the two sectors of the economy are
primarily in the size and
pace of the reduction. First, the gender pay gaps and the
unexplained portion of the gap
are considerably larger in the private than in the public sector.
Second, the reduction in
the gross gender pay gaps was larger in the private sector than
in the public sector (one-
half compared with one-quarter, respectively). Third, the
reduction in the private sector
continued consistently to 2010, but it halted completely in the
public sector in 2000.
Fourth, although working hours have become the most
important factor for gender pay
inequality in both sectors, their effect is much more dominant in
the private sector.

Apparently women’s growing allocation to paid work is not
sufficient to overcome the
wage gap in light of the growing significance of working hours
for determination of
pay (Cha and Weeden 2014). Declining gender segregation over
time (Cotter et al.
2004; England 2010) may explain the lesser impact of
occupations on the gender pay
gap in the private sector. In the public sector, by contrast,
gender segregation accounts
for a greater portion of the gap in absolute as well as relative
terms.
The findings reported here join the body of research that
provides empirical
evidence for the striking decrease in gender earnings disparities
over recent decades.
Some of these studies point directly to a reduction in the
unexplained portion of the gap
(e.g., Blau and Kahn 2006); others highlight the upward
occupational mobility of
American women, implying a reduction in the tendency of
employers to prefer men
over women to fill lucrative jobs within organizations (Cotter et
al. 2004; Jacobs 1992;
Mandel 2012, 2013). However, consistent with the findings
reported here, research on
recent trends in gender-linked earnings disparities also shows
that the drastic reduction
of the gap during the 1980s and 1990s has been largely
restrained since the mid-1990s
(Blau and Kahn 2006; England 2006, 2010). On one hand, the
rapid and consistent
reduction of both the gross and unexplained gender gap supports
optimistic beliefs that
the trend toward greater gender equality will continue into the

future. Such beliefs
follow neoclassical contentions that competitive forces in the
private sector will further
erode discrimination (Jackson 1998; Polachek 2006). Indeed,
the reduction in the
gender pay gap in the private sector, although restrained, has
not halted. On the other
hand, the stagnation observed in the public sector during the
2000s, as well as the
slowdown observed in the private sector in this decade, may
point toward a less
optimistic outlook suggesting that gender equality has reached a
bottleneck that inhibits
further integration (England 2006).
Our findings may indicate where such bottlenecks lie. In
particular, they underscore
the significant role played by working hours and occupational
segregation, which are
the two dominant factors for gender inequality in contemporary
America. Meyersson
et al. (2006) referred to three “frozen pipelines.” Two of the
three pipelines are related
to time supply and occupations. As for the former, women’s
labor supply has grown
faster than men’s. However, the requirement for demanding
working hours has also
grown, as have the returns to working hours for both genders
(Cha and Weeden 2014).
In addition, occupations in which women’s representation has
most dramatically

increased—particularly professional and managerial
occupations—are those in which
overwork has become part of the occupational culture (Cha and
Weeden 2014; Epstein
et al. 1999). Because a requirement for demanding working
hours cannot meet the
domestic division of labor at home, until fundamental changes
in the latter take place,
women will not be able to equal men in terms of their labor
supply. Consequently,
working hours will continue to be a major determinant of
women’s pay disadvantage,
especially in the private sector (Cha and Weeden 2014).
The second pipeline is sex differences in educational choices,
which in turn are
reflected in patterns of gender occupational segregation. The
findings show that the
high levels of education among women do not contribute to
reducing the pay gap in the
private sector; on the contrary, education suppresses male wage
advantages because
women are highly concentrated in fields such as education,
administration, and social
science, and they tend to be absent from engineering and
technical fields (Meyersson
Milgrom and Petersen 2006). Indeed, our findings show that not
only is the unequal
distribution of men and women across occupations one of the
major barriers to gender
equality in pay, but the role of occupational segregation has
also been growing in
importance over the years in the public sector.
The two “frozen pipelines” (Meyersson Milgrom and Petersen
2006) or “bottle-

necks” (England 2006) are two challenges that working women
have to confront in
order to achieve economic equality with men. These challenges,
however, emanate
from the same source: namely, the gendered division of labor.
Gender occupational
segregation preserves the domestic division of labor, creating a
barrier to overtime
work, a crucial requirement for cracking the glass ceiling and
reducing occupational
segregation. To change this vicious circle, it is necessary to
cross rigid gender bound-
aries, which serve as key principles for the organization of labor
not only within the
labor market but also within the family (Charles and Bradley
2009; England 2006;
Ridgeway 2009). Unless those boundaries are seriously
challenged, the prospect of a
further substantial decline in the gender pay gap seems less
likely. Rather, the stagna-
tion of the gender pay gap observed in the public sector may
hint at the pace of any
future reduction in the gender pay gaps in the private sector as
well.
Acknowledgments We thank Richard Barrett, William Bridges,
Yitzhak Haberfeld, Noah Lewin-Epstein,
and Anthony Orum for advice and comments; and Yael Navon
for valuable assistance in the analysis of the
data. The project was supported by the Israel Science
Foundations (Grant No. 491/13).

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Empir Econ (2009) 37383–392DOI 10.1007s00181-008-0236-8.docx

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