GENDER EARNINGS DIFFERENCES IN CHINA: BASE PAY,
PERFORMANCE PAY, AND TOTAL PAY
LIN XIU and MORLEY GUNDERSON
We utilize a data set that has not been used in literature—the Life Histories and
Social Change in Contemporary China (LHSCCC)—to provide new evidence on malefemale pay differences in China. The data set not only enables us to control for a wide
range of pay-determining characteristics but also is the first to enable an analysis of
the different components of pay (e.g., base pay and performance pay) as well as for
total pay. We find: (1) Women receive about three-quarters of male pay for each of the
dimensions of base pay, performance pay, and total pay, before adjusting for the effect
of different pay-determining factors; (2) Approximately two-thirds of the gap reflect
the fact that females tend to be paid less than males for the same wage-determining
characteristics (often labeled as discrimination), while about one-third reflects the
fact that males have endowments or characteristics that tend to be associated with
higher pay, especially supervisory responsibilities, general labor market experience,
occupational skills, education, and membership in the Communist party; (3) Marriage
has a large positive effect on the earnings of women in China (and none for men), but
childcare responsibilities for children under the age of 6 have a large negative effect
on the earnings of women although these are offset almost completely if an elder family
member is present, highlighting that childcare responsibilities disproportionately fall
on women unless an elder family member is present; (4) Pay premiums for higher level
skills and higher supervisory ranks are remarkably small for both males and especially
females; (5) With respect to the unexplained or “discriminatory” portion of the gap,
females get a huge pay penalty for simply being female, but a substantial portion of this
gets offset by the higher pay premium they receive for such factors as Han ethnicity,
being married, and education. This suggests that discrimination tends to occur in the
form of a pay penalty for simply being female and not from lower returns to the same
endowments of pay-determining characteristics. (JEL J3, J7, M5)
I.
private, or foreign ownership of the firm or
the individual’s membership in the Communist
Party? How do the gender pay differences vary
after controlling for other wage-determining factors such as human capital characteristics, firm
ownership types, and the individual’s occupation
or supervisory rank in the organization? How
much of the gap is due to differences between
INTRODUCTION
Gender pay differentials in China are of
increased importance as China moves toward a
more market-oriented economy and wages are
increasingly used to allocate labor. Do gender
pay differences in China resemble those of
the more market-oriented Western economies?
How are they affected by factors that are of
particular relevance to China such as state,
Xiu: Assistant Professor, Labovitz School of Business and
Economics, University of Minnesota Duluth, 1318 Kirby
Drive, LSBE 365H, Duluth, MN 55812. Phone 218726-6721; 218-626-7218, Fax 218-726-7578, E-mail
[email protected]
Gunderson: Professor, Department of Economics, Centre for
Industrial Relations and Human Resources, University
of Toronto, 121 St. George Street, Toronto, ON M5S
2E8, Canada. Phone 416-978-5398, Fax 416-978-5696,
E-mail [email protected]
ABBREVIATIONS
CASS: Chinese Academy of Social Science
CCP: Chinese Communist Party
CHIP: Chinese Household Income Project
LHSCCC: Life Histories and Social Change in Contemporary China
NBS: National Bureau of Statistics
OLS: Ordinary Least Squares
235
Contemporary Economic Policy (ISSN 1465-7287)
Vol. 31, No. 1, January 2013, 235–254
Online Early publication February 27, 2012
doi:10.1111/j.1465-7287.2011.00307.x
© 2012 Western Economic Association International
236
CONTEMPORARY ECONOMIC POLICY
males and females in their endowments of wagedetermining characteristics and how much is due
to differences in the pay that they receive for the
same characteristics, with the later component
possibly reflecting discrimination? What is the
relative importance of the different characteristics in contributing to each of those components
as well as to the overall pay gap? How does the
picture vary by the components of pay including
base pay and performance pay as well as total
pay?
These questions are addressed through the
use of a unique data set (discussed subsequently)—Life Histories and Social Change in
Contemporary China (LHSCCC)—that has not
been used previously to analyze gender pay differences in China. The data set is well suited
to address these questions because it has information not only on total compensation but also
on the components of base pay, performance
pay, and other forms of pay. It also has a rich
array of information on other determinants of
pay including general labor market experience
and firm-specific tenure, both of which may be
particularly important for male-female pay differences to the extent that females have career
interruptions due to family responsibilities.
The paper addresses these question through
five empirical contributions to the literature:
(1) a portrayal of how the gender pay gap is
altered after sequentially controlling for differences in human capital characteristics, firm ownership types, and the individual’s occupation and
rank in the organization; (2) a portrayal of how
different wage-determining characteristics have
different effects on male and female pay; (3) a
decomposition of the pay gap into components
due to differences between males and females
in their endowments of wage-determining characteristics as well as differences in pay they
each receive for the same wage-determining
characteristics; (4) a sub-decomposition of each
of those components to illustrate the relative
importance of the different wage-determining
variables to each of those components as well as
to the overall pay gap; (5) how the above pictures vary by the different components of pay
(e.g., base pay and performance pay) as well as
for total pay. Contributions (1)–(3) have been
carried out on the basis of total compensation in
the existing literature (reviewed subsequently).1
1. As outlined subsequently, the existing literature in
China tends to focus on how the gender pay differential is
affected by using better measures of productivity to reduce
To our knowledge, the sub-decompositions of
item (4) and the separate analysis by the different components of pay as in item (5) have not
been performed in the existing literature. Also,
our new data set provides an additional source of
information on gender pay differences in China.
The paper is organized as follows. Section II
discusses the wage system reform and the existing empirical literature on gender pay differentials in China. Section III describes the data and
empirical methodology. Sections IV–VI present
the empirical results, and Section VII concludes
with a summary and general discussion.
II.
WAGE SYSTEM REFORM AND GENDER PAY
DIFFERENTIALS IN CHINA
China has a long tradition of Confucianism, which emphasizes the subordinate roles
of females in the society, as illustrated by the
famous saying “lack of talent is a virtue of
women” (nuzi wucai bianshi de). Such beliefs
have diminished during the planned economy
(1949–1978) when the Chinese central government implemented a system of national wage
scales based on the socialist egalitarian principle, whereby wage dispersion due to gender
and human capital was suppressed. Women were
also actively involved in the workforce as evidenced by the fact that the female labor participation in urban areas during this period was
more than 90%, much higher than most Western
countries (Croll 1995).
The Chinese government adopted the policy of reform in 1978, although the overall
labor market reform did not take place until
the 14th Party Congress proclaimed that China
would adopt a “socialist market economy” in
1992 (Naughton 1995). Since then, the centrally
controlled employment system was gradually
replaced by a labor contract system. Firms were
given more autonomy and discretion in making decisions on recruitment, promotion, layoffs,
and termination. In terms of the wage-setting
system, incentives were introduced to encourage
efficiency through linking the earnings of individuals to their labor productivity. The “wage
plus bonus system” was the most frequently
adopted pay scheme in the 1990s (Meng 2000).
the unexplained portion of the pay gap. See, for example,
Meng (1998); Ng (2007); Wang & Cai (2008); and Zhang
et al., (2008). A detailed analysis of the determinants of the
share of performance pay in total compensation is provided
in Xiu and Gunderson (2011).
XIU & GUNDERSON: GENDER EARNINGS DIFFERENCES IN CHINA
Since the mid-1990s, there has been a
growing literature on gender pay differentials
in China. The studies tend to find a substantial unadjusted earnings gap,2 which has been
increasing in the past two decades (i.e., the ratio
of female-to-male earnings has been declining).
However, all of these studies use either the
monthly wage or total employment earnings as
the dependent variable and none of them differentiate the gender pay gap across payment
types (base pay, performance pay, and total
pay).
For example, based on Urban Household
Survey data collected by China’s National
Bureau of Statistics (NBS), Zhang et al. (2008)
found that the gross female/male earnings ratio
declined from 86.3% in 1988 to 76.2% in 2004
with four identified periods: a fall in the mean
gender earnings gap from 1988 to 1994, a rise
from 1994 to 1998, a fall from 1998 to 2001,
and a sharp fall from 2001 to 2004. On the basis
of the same data set, Chi and Li (2008) showed
that the ratio decreased from 84% in 1987 to
83% in 1996 and 76% in 2004.
The decreasing trend in the ratio of femaleto-male earnings was also found in the study by
Gustafsson and Li (2000), Shu and Bian (2003),
Bishop, Luo, and Wang (2005), and Appleton, Song, and Xia (2005) based on the urban
household surveys conducted by the Chinese
Academy of Social Science (CASS), also called
Chinese Household Income Project (CHIP). The
CHIP survey sample is drawn from the NBS
urban household data, but contains more information about the individual’s characteristics,
such as membership in the Communist Party.
On the basis of this data set, Bishop, Luo,
and Wang (2005) and Gustafsson and Li (2000)
showed that the gross female-male earnings ratio
decreased from 84% in 1988 to 83% in 1995.
Similar patterns were documented by MaurerFazio and Hughes (2002) and Hughes and
Maurer-Fazio (2002) on the basis of data from
the Chinese Labor Market Research Project
(earnings ratio of 86% in 1992), and Wang and
Cai (2008) on the basis of the China Urban
Labour Survey (earnings ratio of 77% in 2001).
The gender wage gap was also shown to be
larger in more competitive sectors, including the
joint venture sector and the private sector, and
smaller in the less competitive ones including
2. The unadjusted or gross gap refers to the pay gap
between males and females before adjusting for the effect
of other factors, like differences in education or experience
that can affect pay.
237
the state-owned sector and collective sector (Chi
and Li 2008; Liu, Meng, and Zhang 2000;
Maurer-Fazio and Hughes 2002). The previously
cited figures refer to the gross gender earnings
gap, before adjusting for the effect of other factors that may explain at least part of the gap.
Some of the studies have decomposed the
overall pay gap into an “explained” component attributed to differences in their endowments of wage-determining characteristics such
as education and labor market experience, and
an “unexplained” portion (often labeled as “discrimination”) due to differences in the pay that
male and female workers receive for the same
wage-determining characteristics.
Most (but not all) studies find the unexplained or discriminatory component in China
to be greater than the explained component
attributable to differences in endowments of
wage-determining characteristics. This is the
case in Bishop, Luo, and Wang (2005), Gustafsson and Li (2000), Liu, Meng, and Zhang
(2000), and Wang and Cai (2008), but not in
Hughes and Maurer-Fazio (2002). For instance,
Bishop, Luo, and Wang (2005) found the “unexplained” portion of the earnings gap to be 71%
in 1998 and 61% in 1995. Gustafsson and Li
(2000) found the “unexplained” portion to be
52.5% in 1988 and 63.2% in 1995. Hughes and
Maurer-Fazio (2002), on the other hand, found
the “unexplained” portion to be around 40% in
1992.
In terms of the trend in the unexplained or
discrimination component, the results are more
mixed. For example, the unexplained component was found to decline from 71% in 1988
to 41% in 1995 in Biship et al. (2005), and
from 69% in 1988 to 41.8% in 1995 in Shu and
Bian (2003). In contrast, the unexplained component increased in Gustafsson and Li (2000),
Ng (2007), and Zhang et al. (2008). Zhang et al.
(2008), for example, indicated that the rising
returns to skills, especially the returns to education, are responsible for the increase in the
gender earnings gap.
Evidence on how the unexplained or “discriminatory” portion varied across ownership
types is mixed. Liu, Meng, and Zhang (2000)
found the discriminatory component to be
largest in the state sector, while Maurer-Fazio
and Hughes (2002) and Hughes and MaurerFazio (2002) found it to be smallest in the state
sector.
With respect to geographical areas, Gustafsson and Li (2000) and Ng (2007) generally
238
CONTEMPORARY ECONOMIC POLICY
found a larger unadjusted gap and a higher discriminatory component in the Eastern provinces.
Hughes and Maurer-Fazio (2002) found that
both the unadjusted gender wage gap and the
“unexplained” portion are larger for married
women compared with their unmarried counterparts. In particular, they found that although
married women generally earn more than single women, the gross gender wage gap is higher
for married women than for unmarried women.
Moreover, after controlling for other factors,
married women earn more (about 3%–5%) in
state-owned enterprises and collective enterprises, and earn less (about 12%) in joint venture companies than do single women. The
unexplained portion is larger for married workers than for single workers. In contrast, Qian
(1996) found that both married men and married women earned more than single men and
single women. This is attributed to the unusually
strong labor market attachment of Chinese married women3 and to the fact that employers view
married women as more stable (Meng 2000;
Hughes and Maurer-Fazio 2002) as well as the
egalitarian elements of China’s pay system.
In summary, the wage-gap studies yield the
following generalizations: there is evidence of
an overall gender pay gap; the discriminatory
or unexplained component is larger than the
explained component; the evidence is mixed as
to whether the discriminatory component has
declined over time, and whether it is greater in
the state and collective sectors as opposed to the
private sectors. The discrimination component is
greater for less educated women and for married
women.
III.
DATA, VARIABLES, AND SUMMARY STATISTICS
A. Data
Our analysis employs the urban sample of
LHSCCC. It has two main advantages relative to the two data sets that have been widely
used in the China pay differential studies—the
urban household survey data and CHIP—both
of which use total pay to include bonuses, subsidies, and other labor-related income in addition to the basic wage. First, the LHSCCC data
include measures of three types of pay: base pay,
3. In China, married women tend not to drop out of
the labor force even after they have a family. According to
Meng (2000), 95% of nonstudent women in urban China
aged between 15 and 45 are in the labor force, based on a
large survey in 1996.
performance pay, and total employment income
where total employment income includes not
only base pay and performance pay, but also
other forms of pay such as allowances, subsidies, and other forms of employment income but
not fringe benefits. While a separate question
was not asked about the magnitude of “other”
labor-related income, it can be calculated as total
pay minus the base and performance pay components. Second, the data set is ideally suited for
our analysis because it includes a rich array of
pay-determining variables including: measures
of general labor market experience as well as
specific tenure with the organization; detailed
education categories; membership in the Chinese Communist Party (CCP); the ownership
structure of the enterprise—state, collective, private, foreign owned, and public sector (e.g.,
government, government agencies and institutions, education, and health); supervisory rank
within the organization; occupation; and region.
As such, it allows for a direct measure of work
experience and job tenure rather than proxies
based on age and family responsibility.
The LHSCCC is a multistage stratified national probability sample of 6,090 adults aged
20–69 from all regions of China (except Tibet).
It is a systematic sample of 1% of the households in China, treating county-level units as
the primary sampling units. County-level units
are then divided into urban and rural portions,
forming two populations from which the samples are drawn. Further details on the survey
can be found in the codebook (Treiman 1998).
To facilitate comparisons with the literature and
because rural labor markets differ fundamentally from urban labor markets we restricted our
analysis to the urban sample of 3,087 observations. We also excluded 460 respondents who
did not have an urban registration status because
they were much less likely to answer earningsrelated questions. For example, only 39% of
nonurban registered respondents reported their
base pay, while almost 70% of urban registered respondents did so. A similar pattern was
found in the response rates of performance pay
and total employment income questions. Furthermore, following most research on gender
pay differentials, self-employed workers were
excluded due to their different earnings mechanism. Police and soldiers were also excluded
because no females in the data reported they
worked as police or soldiers. After such exclusions, the sample consisted of 1,790 observations, with 966 males and 824 females.
XIU & GUNDERSON: GENDER EARNINGS DIFFERENCES IN CHINA
B. Dependent and Independent Variables
and Missing Values
We use the logarithm of monthly base pay,
performance pay, and total pay as dependent
variables. As discussed, total pay includes not
only base pay and performance pay, but also
other forms of pay such as allowances, subsidies, and other forms of employment income but
not fringe benefits. We use monthly pay because
measures of hourly base pay are not available
in our data set and the other components of pay
are calculated on a monthly basis. We do not
believe that this affects the results because average working hours tend to be similar for men
(42.3 per week) and for women (41.6 per week)
(Li and Zax 2000).
Our explanatory variables involve three
groupings, entered sequentially so as to illustrate how male-female wage differences and
the pay gap changes after controlling for the
effect of the different factors. The first group
(model 1) involves basic personal and human
capital characteristics of education, general work
experience, firm-specific job tenure, marital status, having children under the age of 6, having
elders above 64, ethnicity, residence area, and
membership in the CCP.4 The second group
(model 2) also adds controls for the ownership type of the firm (public, state, collective,
private, and foreign owned). The third group
(model 3) adds further controls for the occupation of the individuals and their rank in the
workplace (worker, supervisor, and higher level
leader). Our data does not include information on the industry in which the individual
works. Estimating the three separate models is
instructive given the debate over whether it is
appropriate to control for such factors as the
ownership type and especially occupation and
4. There is debate in the literature as to whether party
membership should be included in the regression in part
because it, like occupation, may be a channel through
which discrimination can occur. We include it, following the
general literature that finds it is an important determinant
of pay (Appleton, Song, and Xia 2005; Bishop, Luo, and
Wang 2005; Gustafsson and Li 2000; Liu, Meng, and Zhang
2000; and Millimet and Wang 2006). Party membership may
be a form of investment in social capital and networking
(Appleton et al. 2009) or a screening mechanism to signal
conventional unobservables like ability or networks through
family status (Li et al. 2007). Including it as a regressor
enables us to determine the relative importance of party
membership in explaining the overall gender pay gap
through both its effect on pay as well as its effect through
the different proportions of males and females that are party
members.
239
rank as they may be ways in which discrimination occurs.
For observations that did not respond to the
questions on their occupation or rank in the work
unit (i.e., item nonresponses) we constructed a
separate dummy variable, coded as “no answer.”
This enabled retaining the observation for its
other information, with the coefficient on the
“no answer” category providing information on
whether those individuals who chose not to
answer have distinct characteristics in terms of
their earnings. Only two other observations had
missing values—one female was missing her
education and one was missing both experience
and education. They were assigned the mean
value for those missing values.
C. Summary Statistics and Unadjusted
Earnings Ratios
Summary statistics are shown in Table 1 and
Appendices 1 and 2. As indicated in Table 1,
which gives the mean values for the three
dependent variables (as well as the calculated
“other income” component), women receive
about three-quarters of male pay in terms of base
pay, performance pay, and total pay. For the
smallest “other pay” component, they receive
about 63% of what males receive. The share
of each component is fairly similar for males
and females, with the shares of base pay being
67.5% for males and 69.7% for females, performance pay being 17.2% for males and 17.5% for
females, and other pay being 15.3% for males
and 12.8% for females.
Means for the independent variables (Appendix 1) indicate that males tend to be slightly
older than females and they have higher education, lower firm tenure, but longer other labor
market experience. A larger portion of males are
CCP members, work in public sector or state
enterprises, work as skilled workers and managers, and are supervisors.
On an unadjusted basis, before controlling
for the effect of other wage-determining factors,
the ratio of female-to-male base pay is higher
among those who are younger, more highly educated, married, with children, of Han ethnicity,
residing in the central and west regions, and
working in the public sector (e.g., government,
education, health) or state enterprises (Appendix
2). There is considerable variability but no consistent pattern in the ratio of female-to-male
performance pay, possibly reflecting the censored nature of this variable—45.1% of males
240
CONTEMPORARY ECONOMIC POLICY
TABLE 1
Summary Statistics for Dependent Variables
Male
Means
(Yuan/month)
(%)
Variables
Base pay
Performance pay
Other pay
Total pay
N
357 (67.5)
91 (17.2)
81 (15.3)
529 (100)
966
Female
Standard
Error
6.61
4.97
7.90
11.12
Means
(Yuan/Month)
(%)
278 (69.7)
70 (17.5)
51 (12.8)
399 (100.0)
824
Standard
Error
f/m Ratio
Comparison
t-value
5.97
5.08
3.32
9.48
0.78
0.77
0.63
0.75
8.74
2.96
3.37
8.73
Notes: The total pay includes base pay, performance pay, and other forms of employment income such as allowances and
subsidies, but not fringe benefits. The “other pay” component was not asked on the survey. It is calculated here as total pay
minus the sum of base pay and performance pay.
and 51.3% of females had zero performance pay.
The pattern of the ratio of female-to-male total
pay is similar to that of the base pay.
IV.
PAY GAP EQUATIONS WITH A GENDER
DUMMY VARIABLE
Our empirical analysis involves first estimating a conventional pay equation with a
gender dummy variable to illustrate how the
male-female pay gap (separately for base pay,
performance pay, and total pay) changes as
variables are added to sequentially control for
three groupings of wage-determining variables:
individual personal and human capital–related
characteristics; firm characteristics involving
ownership type; and the individuals’ occupation
and supervisory rank within the organization.
This is followed by the estimation of separate
pay equations for males and females which are
then used to decompose the pay gap into an
“explained” component due to differences in
endowments of pay-determining characteristics
and an “unexplained” component due to differences in pay for the same endowments of wagedetermining characteristics. Sub-decomposition
analysis is then performed to indicate the relative importance of the different variables to each
of the endowment and returns component and to
the overall pay gap.
Table 2 illustrates how the gender gap in
each of the components (base pay, performance pay, and total pay) changes as variables are added to sequentially control for the
three groupings of pay-determining characteristics. For the base pay and the total pay components, the gender pay gap is simply the
coefficient for a female dummy variable in an
ordinary least square (OLS) logarithmic pay
equation estimated separately for each of those
components of pay. As such, it provides an estimate of the percent by which female pay falls
short of male pay after controlling for the various wage-determining characteristics in the different models.5
For the performance bonus pay equation we
use Tobit regression to account for the fact that
bonuses are left-censored with a clustering at a
lower bound of zero as 45% of men and 51%
of women did not receive bonuses. In order to
construct the logarithm of the amount of the
performance pay, we increase the zero values by
one unit (one yuan per month) as did Chauvin
and Ash (1994).
As discussed, we estimate three sequential
models so as to highlight the effect of adding
additional control variables: model 1 with individual personal and human capital characteristics; model 2 also including firm ownership
variables; and model 3 further including the
individual’s occupation and rank within the
organization.
For the base pay component (i.e., first row
of Table 2) the unadjusted gross gap is 25.5%
indicating that females earn 25% less than males
earn before controlling for the effect of other
wage-determining characteristics. As is common
in the literature, the gap falls after controlling for
the effect of other wage-determining characteristics. Specifically, it falls to 18% after controlling
for differences in human capital type characteristics but it falls only to 17.4% after adding
additional controls for the ownership type of
the firm and it falls only to 16.8% after adding
5. In log earnings equations, the true proportional
change is exp(β) − 1 where β is the estimated coefficient.
For low values of β the approximation is very close, underestimating the true value by about 0.02 for values of β = 0.20
which are typical in our study.
XIU & GUNDERSON: GENDER EARNINGS DIFFERENCES IN CHINA
241
TABLE 2
Gender Pay Gap, Unadjusted and Adjusted by Controlling for Various Wage-Determining
Characteristics (t-Statistics in Parentheses)
Pay Component as
Dependent Variable
Ln base pay
Ln bonus (unconditional)
Ln total pay
Unadjusted
Gross Gap
Model 2
+Ownership
Model 3 +Occ
& Rank
−0.180∗∗∗
(−6.89)
−0.328∗∗∗
(−2.93)
−0.231∗∗∗
(−9.49)
−0.174∗∗∗
(−6.67)
−0.116
(−1.07)
−0.202∗∗∗
(−8.46)
−0.168∗∗∗
(−6.30)
−0.128
(−1.15)
−0.197∗∗∗
(−8.36)
Yes
Yes
Yes
Yes
Yes
Yes
Model 1 Basic
Model
−0.255∗∗∗
(−9.65)
−0.339∗∗∗
(−2.99)
−0.299∗∗∗
(−11.79)
Controls for:
Individual characteristics
Ownership
Occupation + rank
Notes: The gender pay gap is the coefficient on the female dummy variable in the base pay and total pay regressions. For
the (unconditional) bonus regression it is the marginal effect for the female dummy variable in a Tobit regression to account
for the clustering of values at zero bonus, which occurs for approximately half of the individuals.
Model 1 includes controls for individual characteristics (education, job tenure, other labor market experience, marital
status, family composition, ethnicity, age, and CCP membership. Model 2 also includes controls for the ownership type of
the firm a (public, state, collective, private, and foreign owned). Model 3 also includes further controls for the occupation of
the individual and their rank within the organization (i.e., worker, supervisor, higher level leader).
∗ Significant at 10%; ∗∗ significant at 5%; ∗∗∗ significant at 1%.
further controls for the individual’s occupation
and rank within the organization. In essence,
controlling for the ownership type of the firm
and the occupation and rank of the individual
within the organization does little to alter the
male-female pay gap. Controlling for differences
in the human capital type variables does reduce
the gap, but even that reduction is small (i.e.,
from 25.5% to 18%).
A similar pattern prevails when comparisons are made on the basis of total compensation (third row). This is expected because, as
indicated previously, base pay constitutes over
two-thirds of total compensation. The overall
unadjusted pay gap is slightly larger at 29.9%
compared with 25.5% for base pay, but the pattern is similar: a moderate reduction to 23.1%
after controlling for the effect of human capital variables, and very little further reduction to
20.2% after further controlling for the ownership
type of the firm and to 19.7% after additional
further controls for the individuals occupation
and rank within the organization.
A somewhat different pattern prevails when
comparisons are made on the bonus or performance pay component (second row). Here, the
unadjusted gap at 33.9% is largest and there
is not much reduction in the gap (to 32.8%)
after controlling for the effect of human capital
variables. But there is a dramatic drop to 12%
(statistically insignificantly different from zero)
after controlling for the effect of the ownership
type of the firm and the individual’s occupation
and rank within the organization.
V.
SEPARATE MALE-FEMALE PAY EQUATIONS
The previous analysis illustrated how the various components of the pay gap and the total pay
gap varied after controlling for different groupings of wage-determining variables. The single
equation estimates, however, restrict the coefficients on the various wage-determining variables to be the same for males and females.
The regressions of Table 3 relax that assumption and estimate separate pay equations for
males and females so as to illustrate how various
pay-determining variables may have a different
effect on male pay and female pay. The separate
equations also enable the decomposition analysis outlined subsequently. The separate male
and female regressions are presented for total
pay as that is the most relevant measure of
compensation.
A. Model 1: Basic Regression with Personal
and Human Capital Variables
As was the case with the overall gender gap
estimated from a single pay equation with a
gender dummy variable indicator, the returns to
different personal and human capital variables
do not generally vary much across the different
models when separate equations are estimated
for males and females. For that reason and
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CONTEMPORARY ECONOMIC POLICY
TABLE 3
Ln (Total Pay) Regression: Various Models (t-Values in Parentheses)
Male (N = 966)
Model 1
Variable
Education (years)
Job tenure (years)
Other experience (years)
Married
Child(s) under 6
Elder(s) over 64
Han_ethnicity
Central region
West region
Party member
Model 2
Basic Model +Ownership
0.043∗∗∗
(8.506)
0.008∗∗∗
(3.055)
0.005∗∗∗
(3.057)
0.022
(0.291)
−0.084∗
(−1.909)
0.004
(0.043)
−0.063
(−0.824)
−0.098∗∗∗
(−2.842)
−0.115∗∗∗
(−2.675)
0.127∗∗∗
(3.629)
Ownership_state
Ownership_collective
Ownership_ private, foreign, joint
Ownership unknown
0.037∗∗∗
(7.071)
0.009∗∗∗
(3.508)
0.010∗∗∗
(5.789)
−0.042
(−0.572)
−0.058
(−1.358)
0.035
(0.427)
−0.073
(−0.968)
−0.108∗∗∗
(−3.188)
−0.130∗∗∗
(−3.095)
0.098∗∗∗
(2.820)
−0.028
(−0.711)
−0.196∗∗∗
(−3.721)
0.134
(0.900)
−0.375∗∗∗
(−6.686)
Occup._skilled manual
Occup._sales and service
Occup._office worker
Occup._management
Occup._professional
Occup. unknown
Rank_supervisor
Rank_higher level leader
Rank unknown
Constant
R2
5.638∗∗∗
(51.651)
0.116
5.772∗∗∗
(50.348)
0.169
Female (N = 824)
Model 3
+Occ &
Rank
0.030∗∗∗
(5.465)
0.008∗∗∗
(3.184)
0.010∗∗∗
(5.147)
−0.031
(−0.422)
−0.040
(−0.949)
0.030
(0.374)
−0.060
(−0.803)
−0.125∗∗∗
(−3.708)
−0.150∗∗∗
(−3.596)
0.071∗
(1.950)
−0.002
(−0.043)
−0.150∗∗∗
(−2.720)
0.295
(1.070)
0.007
(0.025)
0.200∗∗∗
(3.625)
0.040
(0.546)
0.095
(1.344)
0.095
(1.124)
0.246∗∗∗
(3.727)
−0.106
(−0.690)
0.036
(0.615)
0.229∗∗∗
(3.059)
−0.096
(−0.405)
5.658∗∗∗
(46.751)
0.199
Model 1
Model 2
Basic Model +Ownership
0.064∗∗∗
(11.455)
0.009∗∗∗
(2.712)
0.005∗∗
(2.369)
0.194∗∗
(2.447)
−0.061
(−1.217)
−0.053
(−0.623)
0.148∗
(1.720)
−0.089∗∗
(−2.181)
−0.116∗∗
(−2.431)
0.198***
(3.630)
0.050***
(8.633)
0.009∗∗∗
(2.913)
0.010∗∗∗
(4.493)
0.159∗∗
(2.078)
−0.057
(−1.187)
−0.033
(−0.398)
0.163∗∗
(1.972)
−0.110∗∗∗
(−2.767)
−0.158∗∗∗
(−3.430)
0.165∗∗∗
(3.107)
−0.081∗
(−1.729)
−0.357∗∗∗
(−6.176)
−0.002
(−0.014)
−0.404∗∗∗
(−6.920)
4.871∗∗∗
(39.845)
0.196
5.148∗∗∗
(40.420)
0.264
Model 3
+Occ & Rank
0.043∗∗∗
(7.360)
0.009∗∗∗
(2.795)
0.009∗∗∗
(4.253)
0.148∗
(1.947)
−0.056
(−1.179)
−0.007
(−0.088)
0.147∗
(1.782)
−0.116∗∗∗
(−2.944)
−0.171∗∗∗
(−3.732)
0.142∗∗∗
(2.631)
0.011
(0.218)
−0.253∗∗∗
(−4.098)
−0.600∗∗
(−2.164)
−0.769∗∗
(−2.224)
0.102
(1.515)
−0.021
(−0.295)
0.139∗
(1.768)
0.073
(0.539)
0.255∗∗∗
(3.735)
−0.139
(−0.553)
−0.041
(−0.439)
0.217∗∗
(1.974)
0.679∗∗∗
(2.745)
5.038∗∗∗
(38.277)
0.295
Notes: The omitted reference category for region is East, for ownership type is public sector (e.g., government, education
health), for occupation is unskilled manual, and for rank in the organization is ordinary worker.
∗
Significant at 10%; ∗∗ significant at 5%; ∗∗∗ significant at 1%.
XIU & GUNDERSON: GENDER EARNINGS DIFFERENCES IN CHINA
because the estimates for models 2 and 3 may
inappropriately control for variables that are the
mechanisms whereby discrimination occurs, we
focus our discussion on model 1, commenting on
the other models in the few cases where larger
differences arise.
On the basis of the separate male-female
pay equations of Table 3, the returns to education are substantially higher for females (6.4%)
compared with males (4.3%)—a common result
found in the literature.6 The returns to an additional year of job tenure at the organization
are slightly higher for females (0.9%) compared
with males (0.8%), while the returns to an additional year of other general labor market experience are the same for males and females (0.5%).
Interestingly, the effect of being married is
statistically insignificant for men but positive,
large, and statistically significant for women
(19.4%). Similar evidence was found in the
study by Qian (1996) and is likely due to
the egalitarian elements that remain in China’s
pay system as well as the unusually strong
labor market attachment of Chinese married
women and the fact that employers view married
persons as more stable (Hughes and MaurerFazio 2002; Meng 2000). Also, the one-child
policy in China and the fact that older family members often take care of the children
and household tasks (Maurer-Fazio et al. 2011)
reduce such responsibilities that would otherwise fall on women if an elder family member is not present.7 As well, our analysis
includes measures of both firm-specific tenure
and general labor market experience and hence it
controls for those channels through which differential household responsibilities may affect pay.
The effect of Han ethnicity is statistically
insignificant for men, but is large, positive, and
6. As indicated in Appendix 1, the small number of
persons who did not complete phases of education beyond
middle school precluded determining the separate effect of
completing a degree.
7. The importance of older family members caring for
children is illustrated by further regressions we conducted
interacting the variables for the presence of young children
and of older adults in the household (results available on
request). The large positive effect of being married by itself
remained for females but having a child below 6 years
was associated with a large pay penalty that dominated the
positive effect of being married, although it was essentially
eliminated if an elder parent were present. In essence, a
large childcare penalty does exist for females (but not males)
who have younger children at home but do not have an
elder family member who can care for the children. This
highlights the likely disproportionate responsibilities falling
on women for childcare if there is not an elder family
member present.
243
statistically significant for women (19.8%). A
pay premium for an ethnic majority such as Han
ethnicity (conversely a pay disadvantage for ethnic minorities) is expected but more research is
needed on why it should prevail for women and
not for men. Ethnic minorities in China have
their distinctive ethnic cultures, customs, and
value orientation, which can have an effect on
labor income. For example, China has relatively
lenient birth control policies for ethnic minorities so that the number of dependent children
is greater in minority households than in Han
households. This and other factors may lead to
greater effort devoted to household tasks and
less to labor market tasks on the part of female
minorities.
As expected, pay is significantly lower in the
Central region and even more so in the Western
region compared with the more prosperous and
growing East. The regional effects are similar
for males and females.
CCP membership is associated with a pay
premium of almost 13% for males and an even
larger 20% for females. As discussed previously,
this can reflect networks, social capital, and
contacts associated with membership as well
as possible selection effects as persons with
unobserved characteristics that influence CCP
membership may also have a positive effect on
pay. Similar evidence is found by Bishop, Luo,
and Wang (2005) and Gustafsson and Li (2000).
Clearly, “membership has its privileges.”
B. Model 2: Adding Ownership Type
When the ownership type variable is added
to the regression (i.e., model 2), the coefficients on the individual characteristics variables
of model 1 generally do not change much. With
respect to the ownership variables themselves,
pay differences between state-owned firms as
well as private, foreign, and jointly owned
firms compared to the omitted reference category of public sector (e.g., government, education, and health) are statistically insignificant for
males, albeit females receive 8.1% lower pay in
state-owned compared with public sector firms.
Pay in collectively owned firms is substantially
lower—19.6% lower for males and 35.7% lower
for females. Lower pay may reflect a compensating wage for job security and reduced stress. In
essence, after controlling for the effect of other
determinants of pay, collectively owned firms
are the outlier in terms of low pay and this is
especially the case for females.
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CONTEMPORARY ECONOMIC POLICY
C. Model 3: Adding Occupation and Rank
within the Organization
When occupation and rank in the workplace
are added (model 3) the coefficients on the
other variables generally do not change much.
The notable exception is that the negative effect
of 8.1% being in the state sector disappears
for women, and a pronounced negative effect
of 60% for women in private, foreign, and
joint-owned firms emerges relative to the public sector. The fact that the private, foreign, and
joint-owned firms did not pay wage premiums
when there were controls for occupation and
rank within the organization (model 2) suggests
that these firms disproportionately employed
women in higher paying occupations and ranks,
so that when occupation and rank were controlled for model 3 they paid substantially less
than other ownership types. Whether this suggests rents in the other ownership types or other
factors is an open question. It also leaves the
puzzle as to why such a pay deficit in private,
foreign, and joint-owned firms, after controlling
for the effect of other wage-determining variables, should prevail only for females and not
for males.
With respect to the occupation variables,
the pay premiums for higher level skills seem
remarkably small for males and especially
females. Relative to the reference category of
unskilled manual workers, the only significant
total pay premiums for males were for skilled
manual workers (only 20%) and professionals
(only 24.6%). For females, the only occupational
pay premiums were for office workers (13.9%)
and professionals (25%, the same as for males).
Clearly, an egalitarian and compressed occupational pay structure tends to prevail in China.
The same egalitarian compressed wage structure exists when comparisons are made across
different ranks within the organization. Relative
to the omitted reference category of ordinary
workers and staff, there is no pay premium for
being a supervisor, and the premium for being a
higher level leader is only about 22% for males
and for females.
VI. DECOMPOSITIONS
AND SUB-DECOMPOSITIONS
A. Decomposing Gap into Differences in
Endowments and Returns: Various Models
The estimation of separate male and female
pay equations enables decomposing the average
pay between males and females into two components: the “explained” or endowment component is attributable to gender differences in their
endowments of observable wage-determining
characteristics (mean values of their explanatory
variables); the “unexplained” or returns component is attributed to differences in the pay they
receive for the same wage-determining characteristics (differences in the regression coefficients). The later is often taken as a measure of
the discriminatory component of the pay differential as it reflects different pay for the same
wage-determining characteristics. This method
assumes that there is some nondiscriminatory
pay structure that could be used to weight
the differences in the endowments of wagedetermining characteristics (i.e., the differences
in average values of the explanatory variables).
Several suggestions have been made in the literature regarding the nondiscriminatory norm.
To see if the results are sensitive to the procedure used, we provide three separate estimates:
the male pay structure, the female pay structure
(both suggested by Oaxaca 1973), and the pay
structure from a pooled regression with a gender dummy variable (a procedure suggested by
Jann 2008 based on Neumark 1988 and Oaxaca
and Ransom 1994 and available in STATA). Our
preferred procedure is the male pay structure on
the grounds that this is generally regarded as the
nondiscriminatory norm; however, as indicated
subsequently, our results are not sensitive to the
use of the alternative procedures.
The decomposition results are shown in
Table 4 based on three dimensions: differences
between the base pay decomposition (top panel)
and total pay decomposition (bottom panel); differences between various specifications (models
1–3); and differences between different procedures for weighting the endowment differences
(weighting by the male, female, or pooled pay
structure). This gives rise to 18 estimates of the
explained and unexplained portions of the malefemale pay gap (3 pay structures × 3 models ×
2 measures of pay).
In all of the 18 estimates, the unexplained
portion of the overall male-female pay gap
attributed to differences in the coefficients (i.e.,
the discrimination component) is much larger
than the explained component. Specifically, the
unexplained component ranges from 62.4% of
the gap (based on total pay, the female pay structure, and model 2) to 80.6% of the gap (based on
total pay, the male pay structure, and the basic
model 1). Typically, about 2/3 of the overall gap
XIU & GUNDERSON: GENDER EARNINGS DIFFERENCES IN CHINA
245
TABLE 4
Decomposition: Various Payment Measures
Model 1: Base Model
Gap
Base pay
Male structure
Female structure
Pooled structure
Total pay (base pay
Male structure
Female structure
Pooled structure
0.2551
100%
0.2551
100%
0.2551
100%
+ bonus +
0.2991
100%
0.2991
100%
0.2991
100%
Explained
Unexplained
0.0796
0.1755
31.20%
68.80%
0.0701
0.185
27.48%
72.52%
0.0754
0.1797
29.56%
70.44%
allowance + others)
0.0580
0.2411
19.39%
80.61%
0.0846
0.2145
28.28%
71.72%
0.068
0.2311
22.73%
77.27%
is attributable to differences in coefficients or
returns to the same characteristics, and about 1/3
is attributable to differences in their endowments
of wage-determining characteristics.
In almost all cases, the unexplained or discriminatory component falls as more control
variables are added (i.e., moving from model
1 to 3) albeit those changes are not generally large. This reflects the fact that some
of the overall male-female pay differential
arises because females are disproportionately
employed in ownership types, occupations, and
ranks that pay lower wages. Once these factors
are controlled for in the regression, the unexplained or “discriminatory” component declines,
but only slightly. In most cases (and in all cases
based on our preferred male pay structure), the
unexplained or discriminatory component was
higher, based on total pay (bottom panel) compared with base pay (top panel).
In our preferred model (the top row of panel
2 based on total pay, the male pay structure, and
the basic model that does not control for mechanisms whereby discrimination can occur) about
80% of the gap is due to the differences in pay
for the same wage-determining characteristics.
This falls to about 68% after controlling for the
effect of ownership type, occupation, and rank
within the organization.
Overall, our decomposition results suggest
that typically, about 2/3 of the overall malefemale pay gap is attributable to differences in
coefficients or returns to the same characteristics,
and about 1/3 is attributable to differences in
Model 2: +Ownership
Model 3: +Occup. & Rank
Explained
Unexplained
Explained
Unexplained
0.0873
34.22%
0.0754
29.56%
0.081
31.75%
0.1678
65.78%
0.1797
70.44%
0.174
68.21%
0.0945
37.04%
0.0838
32.85%
0.0876
34.34%
0.1606
62.96%
0.1713
67.15%
0.1675
65.66%
0.0904
30.22%
0.1124
37.58%
0.0975
32.60%
0.2087
69.78%
0.1867
62.42%
0.2016
67.40%
0.0962
30.16%
0.1086
36.31%
0.1017
34.00%
0.2029
67.84%
0.1905
63.69%
0.1974
66.00%
their endowments of wage-determining characteristics. The amount attributable to different returns is highest (about 80%) based
on our preferred specification involving total
pay, using the male pay structure to evaluate
male-female endowments differences in paydetermining characteristics, and in the basic
model that does not control for mechanisms
whereby discrimination may occur. Nevertheless, the results are not very sensitive to differences in the pay measures (base pay or total
pay)8 or the weights used to evaluate the endowment differences (male weights, female weights,
8. Similarity of the results between base pay and total
pay reflects in part the fact that bonuses are not prominent
and they constitute similar portions of the pay for both
males (17.2%) and females (17.5%) (Table 1). Bonuses can
be in different forms that are generally not related to the
performance of the individual: annual or half-yearly bonuses
based on some notion of the performance of the company;
periodic bonuses paid to workers before major holidays
such as Chinese New Year; a lump-sum given to each
department that will then be distributed to workers; and
monthly bonuses based on employers’ discretion (Cooke
2002; Taylor 2002). Bonuses tend not to be based on
individual performance in part because egalitarianism has
been one of the most important characteristics of the
pay system in China. Corresponding to the collectivistic
values in China, seniority and equality concerns have been
emphasized more than the individual performance when
bonuses are used (Zhu et al. 2005; Du and Choi 2010).
Although research findings have shown an increasing portion
of total pay in the form of bonuses (Warner 1996; Chiu,
Wai-Mei Luk, and Li-Ping Tang 2002), most organizations
believe that differentials in the bonus payments between
individuals and between groups of individuals should be
minimized so as to maintain harmonious and stable relations
within the organization (Cooke 2001). Xiu and Gunderson
(2011) provide more detailed analysis of the determinants
of the different components of pay as well as gender
246
CONTEMPORARY ECONOMIC POLICY
or pooled weights) or the extent to which controls are added for factors such as firm ownership or occupation or rank (models 1–3). The
majority (between 2/3 and 80%) of the malefemale pay gap appears to be attributable to pay
differences for the same pay-determining characteristics.
B. Sub-Decomposing to Illustrate Relative
Contribution of Different Variables
The previous decomposition of the pay
gap into a component due to differences in
endowments of wage-determining characteristics and differences in pay for the same
wage-determining characteristics can be further
sub-decomposed to illustrate the relative importance of the different wage-determining variables to each of those components as well as
to the overall pay gap. We utilize the STATA
procedure described by Jann (2008) which is
based on the method set out in the study by
Yun (2005) which essentially structures the coefficients to be interpreted relative to the grand
mean with no omitted reference categories to get
around the concern raised by Jones (1983) that
the sub-decomposition of the coefficients component is not invariant with respect to the choice
of omitted reference category. So as to illustrate the relative contribution of the full range
of wage-determining variables, we base our subdecomposition analysis on the full regression of
model 3 using total pay and the male coefficients
as the nondiscriminatory norm to evaluate the
endowment differences.
As indicated in Table 5, the overall malefemale gap in total compensation is 0.299, of
which 0.096 or about one-third is attributable to
the fact that males have slightly greater endowments of wage-determining characteristics, and
0.203 or about two-thirds are attributable to
the higher pay that males receive for the
same endowments of wage-determining characteristics. These correspond respectively to the
“explained” and “unexplained” components in
the previous Table 4 for model 3 based on total
pay and the male pay structure.
differences in different dimensions of the performance pay
component: the probability of receiving performance pay;
the magnitude conditional upon receiving it; and their
product being the overall conditional magnitude. They
find that the discriminatory component is larger for the
conditional magnitude where discretion can be more at play,
and smaller for the probability of having a performance pay
system where discretion is less likely to be at play.
The sub-decomposition analysis illustrates
that the main factors that contribute to the
greater endowments that males have of wagedetermining characteristics are (in rank order of
importance) higher levels of: supervisory rank
in the organization, general labor market experience, occupational skills, education, and membership in the Communist Party. The relative
contribution of each of these factors to the gap
of 0.096 that is explained by the endowment
differences ranges from 14% to 29% of that
component, but because it only explains about
one-third of the overall gap, then these factors
contribute only about 4.5% to 9% of that overall gap. Furthermore, because many of these
important factors in contributing to the endowment differences themselves may reflect mechanisms through which discrimination occurs (e.g.,
supervisory rank in the organization and occupation), it is clear that differences in what could be
considered as legitimate wage-determining characteristics (education and general labor market
experience) explain only a very small portion of
the male-female wage gap.
The sub-decomposition of the unexplained
portion of the gap provides an interesting portrayal. Of the unexplained component of 0.203
that arises because of differences in pay for the
same wage-determining characteristics, 0.829
(or four times the unexplained gap) is due to
differences in the constant terms in the pay
equation. This implies that females receive a
huge pay penalty for simply being female. This
may reflect the possibility that gender discrimination takes a blunt form of a penalty for
simply being female as opposed to occurring
through differential payments for the same paydetermining characteristics. Or it could reflect
the possibility that the penalty reflects variables that are not fully controlled for in the
analysis but that are correlated with gender,
such as career ambition or differential household responsibilities dictated by cultural norms
that can deflect energy toward home and dependent care.
Differences in the pay for the same wagedetermining characteristics generally favor females as indicated by the negative sign on
many of the sub-components. For example, the
higher returns that females of Han ethnicity get
over males of Han ethnicity accounts for 97%
of the unexplained gap, their higher returns to
being married accounts for 77% of the gap,
and their higher returns they receive for education as well as their higher returns in different
XIU & GUNDERSON: GENDER EARNINGS DIFFERENCES IN CHINA
247
TABLE 5
Relative Contribution of Various Factors to the Gender Pay Gap:
Sub-Decompositions Based on Total Pay, Full Model 3, and Male Pay Structure
Variables
Total Pay Gap
Explained portion
Education
Job tenure
Other experience
Marital status
Child(s) under 6
Elder(s) over 64
Han ethnicity
Residence area
CCP party member
Industry/firm ownership
Occupation
Rank in the work unit
Unexplained portion
Education
Job tenure
Other experience
Marital status
Child(s) under 6
Elder(s) over 64
Han ethnicity
Residence area
CCP party member
Industry/firm ownership
Occupation
Rank in the work unit
Constant
% of Each
Component
Coefficients
0.2991
0.0962
0.0179
−0.0021
0.0249
0.0001
0.0006
0.0000
−0.0001
−0.0056
0.0137
−0.0007
0.0201
0.0275
0.2029
−0.1256
−0.0080
0.0020
−0.1563
0.0027
0.0334
−0.1975
−0.0026
−0.0095
−0.1259
0.0029
−0.0413
0.8286
ownership types each account for about 62%
of the unexplained gap. Furthermore, because
these different returns explain about two-thirds
of the overall pay gap, they account for a
fairly substantial portion of that overall pay gap.
In essence, females appear to get a huge pay
penalty for simply being female, but a substantial portion of this gets offset by the higher
pay premium they receive for the same endowments of pay-determining characteristics. For
example, the higher returns females receive for
the four factors cited above offset almost threequarters of the pay penalty of 0.829 that they
receive for simply being female. Nevertheless,
they do not fully offset the pay penalty so that
the lower returns for the same wage-determining
characteristics still account for about two-thirds
of the overall male-female pay gap in total
compensation. In terms of interpreting the differences in returns for the same endowments
of pay-determining characteristics as reflecting
18.6
−2.2
25.9
0.1
0.6
0.0
−0.1
−5.8
14.2
−0.7
20.9
28.6
−61.9
−3.9
1.0
−77.0
1.3
16.5
−97.3
−1.3
−4.7
−62.1
1.4
−20.4
408.4
% of the Overall
Pay Gap
32.2
6.0
−0.7
8.3
0.0
0.2
0.0
0.0
−1.9
4.6
−0.2
6.7
9.2
67.8
−42.0
−2.7
0.7
−52.3
0.9
11.2
−66.0
−0.9
−3.2
−42.1
1.0
−13.8
277.0
discrimination, this sub-decomposition analysis
would suggest that the discrimination occurs in
the form of a pay penalty for simply being
female and not from lower returns to the same
endowments of pay-determining characteristics.
A further implication of this result is that it
highlights the importance of females in China
to acquire the attributes like education or higher
ranks or CCP membership or obtain jobs in the
professions or in the public sector that can help
reduce the pay gap.
VII.
SUMMARY AND DISCUSSION
On the basis of the analysis using the
LHSCCC we find that women receive about
three-quarters of male pay for each of the dimensions of base pay, performance pay, and total
pay, before adjusting for the effect of different
pay-determining factors. Approximately twothirds of the gap reflect the fact that females tend
248
CONTEMPORARY ECONOMIC POLICY
to be paid less than males for the same wagedetermining characteristics (often labeled as discrimination), while about one-third reflects the
fact that males have endowments or characteristics that tend to be associated with higher pay,
especially supervisory responsibilities, general
labor market experience, occupational skills,
education, and membership in the Communist
Party. Marriage has a large positive effect on
the earnings of women in China (and none for
men), but childcare responsibilities for children
under the age of 6 have a large negative effect
on the earnings of women although these are
offset almost completely if an elder family member is present in the household, highlighting that
childcare responsibilities disproportionately fall
on women unless an elder family member is
present. Pay premiums for higher level skills
and higher supervisory ranks are remarkably
small for both males and especially females.
With respect to the unexplained or “discriminatory” portion of the gap, females get a huge
pay penalty for simply being female, but a substantial portion of this gets offset by the higher
pay premium they receive for such factors as
Han ethnicity, being married, and education.
This suggests that discrimination tends to occur
in the form of a pay penalty for simply being
female and not from lower returns to the same
endowments of pay-determining characteristics,
although colinearity among the variables makes
it difficult to identify what determines such a
female pay penalty.
A number of implications of policy and
practical importance flow from this analysis.
For example, the higher returns that females
can expect to receive from additional education highlights the importance of acquiring more
education, with the higher returns also providing an incentive to invest in such human capital formation that can reduce the gap in the
future. Similar issues apply to the importance
for females in China to acquire the attributes
like higher ranks in the organization or party
membership or obtain jobs in the professions
or in the public sector that can enhance their
earnings and help reduce the pay gap. As a
first step, publicly providing more information to women about the pay premiums associated with these attributes may go a long
way in providing the incentives to undertake
such investments or decisions. Whether more
aggressive policy initiatives are merited (e.g.,
subsidies to female education, quotas on party
membership or on public sector jobs) is an open
question. The same applies to issues such as
quotas or affirmative action programs for ethnic
minorities.
The fact that women (and not men) experience a large pay penalty if they have children
under the age of 6, but that this prevails only
if older family members are not available in the
household to take care of the children, highlights
that childcare responsibilities disproportionately
fall on women in China if elder members are
not available to care for the children. Traditional
gender roles and expectations within the family for childcare seem prominent even though
women in China participate extensively in the
labor market. Certainly, the common practice of
family elders caring for younger children helps
mitigate issues of both eldercare and childcare.
To the extent that elders live within the family in part out of economic necessity, and this
need dissipates with growing wealth, this can
increase the burden of childcare on women in
China and this, in turn, can have important negative implications for their wages. In such circumstance, facilitating more equal experiences
in the labor market may require initiatives to
mitigate the disproportionate burden of childcare
that women face if elders are not present. Such
initiatives could include childcare arrangements
and parental leaves as well as more flexibility in
work-time arrangements that can accommodate
childcare.
Gender discrimination tends to account for
two-thirds or more of the male-female pay gap,
largely in the form of a huge pay penalty for
simply being female, offset in part (but not fully)
by the higher pay premium females receive
for various other pay-determining attributes. As
indicated, investments in such attributes (where
feasible) can be important for reducing the
pay gap, and informing females of the importance of such decisions can be a first step.
More aggressive antidiscrimination policies in
the form of equal pay and equal employment
opportunity legislation are also possible initiatives, but their practical importance is likely to
be severely hindered by enforcement difficulties (Rawski 2006). The continued transition to
a more market-oriented economy will dissipate
discrimination by providing the market incentive to hire the more productive people relative
to their pay (Black and Brainerd 2004; Meng
2004; and references cited therein). This potential role of market forces is likely to be the most
interesting issue with respect to the future of the
gender pay gap in China.
XIU & GUNDERSON: GENDER EARNINGS DIFFERENCES IN CHINA
249
APPENDIX
TABLE A1
Summary Statistics for Independent Variables
Male
Variables
Age (Mean = 42.30)
(below 25)
25–34
35–44
45–54
55–64
65 and above
Education Level (Mean = 9.64)
(Illiterate)
Primary incomplete
Primary completed
Middle school incomplete
Middle school complete
High school completea
University completeb
Firm Tenure/Seniority (Mean = 9.31)
(less than 5 years)
≥5 years and <10 years
≥10 years and <15 years
≥15 years and <20 years
20 years and above
Other Work Experience (Mean = 12.81)
(less than 5 years)
≥5 years and <10 years
≥10 years and <15 years
≥15 years and <20 years
≥20 years and <25 years
25 years and above
Marital Status
Single
Married
Family Structure
Not having child(ren) under 6
Having child(ren) under 6
Not having elder(s) over 64
Having elder(s) over 64
Han Ethnicity
(Non-Han ethnicity)
Han ethnicity
Residence Area
(East area)
Central area
West area
Communist Party
(Not a member)
Member
Industry/Enterprise Ownership
(Public sector)
State enterprise
Collective enterprise
Private enterprise/foreign
No answer or not applicable
Female
Observed
Percentage
Observed
Percentage
41
270
266
169
153
67
4.24
27.95
27.54
17.49
15.84
6.94
51
225
259
133
121
35
6.19
27.31
31.43
16.14
14.68
4.25
39
62
75
33
312
251
194
4.04
6.42
7.76
3.42
32.3
25.98
20.08
54
48
68
39
248
286
81
6.55
5.83
8.25
4.73
30.1
34.71
9.83
323
252
178
117
96
33.44
26.09
18.43
12.11
9.94
254
204
170
126
70
30.83
24.76
20.63
15.29
8.50
319
146
90
98
97
216
33.02
15.11
9.32
10.14
10.04
22.36
309
109
106
99
76
125
37.5
13.23
12.86
12.01
9.22
15.17
124
842
12.84
87.16
104
720
12.62
87.38
812
154
100
866
84.06
15.94
10.35
89.65
681
143
86
738
82.65
17.35
10.44
89.56
41
925
4.24
95.76
37
787
4.49
95.51
438
348
180
45.34
36.02
18.63
412
252
160
50.00
30.58
19.42
650
316
67.29
32.71
714
110
86.65
13.35
251
387
135
20
173
25.98
40.06
13.98
2.07
17.91
181
283
126
23
211
21.97
34.34
15.29
2.79
25.61
250
CONTEMPORARY ECONOMIC POLICY
TABLE A1
Continued
Male
Variables
Occupation
(Unskilled manual workers)
Skilled manual workers
Sales and service workers
Administrative employees
Managers
Professionals
No answer or not applicable
Rank in the Work Unit
(Ordinary worker or staff)
Cadre or supervisor
Higher level leader
No answer or not applicable
a This
Female
Observed
Percentage
Observed
Percentage
91
254
68
95
128
159
171
9.42
26.29
7.04
9.83
13.25
16.46
17.7
84
133
101
78
24
193
211
10.19
16.14
12.26
9.47
2.91
23.42
25.61
575
96
107
188
59.52
9.94
11.08
19.46
531
34
29
230
64.44
4.13
3.52
27.91
category also contains two females who did not complete high school.
There are no university incompletes; this category contains one male who completed a post-university degree and four
males who completed an imperial degree. Imperial degree holders are those who at least passed one level of the former
Chinese imperial examination hierarchy, which applied to the older generation.
b
Age
(below 25)
25–34
35–44
45–54
55–64
65 and above
Education Level
(Illiterate)
Primary incomplete
Primary complete
Middle school incomplete
Middle school complete
High school complete
University complete
Job Tenure
(1 < 5 years)
5–9 years
10–14 years
15–19 years
20 years and above
Other Work Experience
(<5 years)
≥5 years and <10 years
≥10 years and <15 years
≥15 years and <20 years
≥20 years and <25 years
25 years and above
Marital Status
Single
Married
Family Structure
Not having child(ren) under 6
Having child(ren) under 6
Not having elder(s) over 64
Variables
254
261
280
311
291
246
234
254
225
308
254
294
370
306
263
250
276
297
257
297
268
274
278
328
244
283
280
269
261
327
350
327
352
331
337
448
379
344
352
324
373
317
328
363
360
403
413
325
362
363
327
334
Female
274
328
327
408
429
358
Male
0.772
0.823
0.782
0.749
0.783
0.811
0.905
0.738
0.761
0.690
0.794
0.809
0.765
0.711
0.851
0.796
0.717
0.725
0.688
0.874
0.769
0.874
0.827
0.930
0.794
0.858
0.762
0.679
0.689
Female/
Male
Base Pay (Yuan/Month)
93
90
100
118
77
24
35
90
64
112
99
97
89
86
80
106
82
118
99
89
95
129
86
63
87
92
96
66
85
3.24∗∗∗
2.69∗∗∗
4.58∗∗∗
1.08
5.68∗∗∗
2.99∗∗∗
2.01∗∗
3.66∗∗∗
5.11∗∗∗
5.71∗∗∗
2.44∗∗
3.12∗∗∗
4.87∗∗∗
1.06
4.47∗∗∗
4.09∗∗∗
3.62∗∗∗
3.34∗∗∗
3.61∗∗∗
8.03∗∗∗
8.72∗∗∗
2.21∗∗
2.81∗∗∗
Male
0.61
3.72∗∗∗
3.53∗∗∗
3.98∗∗∗
5.35∗∗∗
2.92∗∗∗
t-value
69
75
56
60
71
76
103
67
61
65
39
74
56
87
62
71
25
25
26
54
69
84
126
53
92
87
55
25
41
Female
0.719
1.136
0.658
0.685
0.779
0.768
1.157
0.705
0.473
0.756
0.619
0.854
0.690
0.819
0.756
0.602
0.709
0.272
0.412
0.484
0.692
0.865
1.414
0.567
1.022
0.872
0.465
0.321
1.689
Female/
Male
3.55∗∗∗
−0.45
1.94∗
1.73∗
2.58∗∗∗
2.30∗∗
−0.54
1.79∗
3.38∗∗∗
0.79
1.31
0.81
2.35∗∗
1.25
1.19
2.06∗∗
0.49
1.59
2.19∗∗
2.07∗∗
2.30∗∗
1.23
−1.48
2.13∗∗
−0.13
0.99
3.93∗∗∗
2.69∗∗∗
−0.77
t-value
Performance Pay (Yuan/Month)
TABLE A2
Female and Male Monthly Wage and Bonus and Female/Male Ratio
539
482
474
471
539
510
488
559
565
566
545
549
490
562
485
571
390
477
500
532
519
508
634
431
505
531
600
577
406
Male
398
407
347
331
409
391
454
388
381
397
398
427
381
383
374
438
272
297
287
388
375
435
590
332
423
425
416
388
301
Female
0.738
0.844
0.732
0.703
0.759
0.767
0.930
0.694
0.674
0.701
0.730
0.778
0.778
0.681
0.771
0.767
0.697
0.623
0.574
0.729
0.723
0.856
0.931
0.770
0.838
0.800
0.693
0.672
0.741
Female/
male
Total Pay (Yuan/Month)
9.32∗∗∗
1.58
4.22∗∗∗
5.12∗∗∗
7.84∗∗∗
5.96∗∗∗
0.68
5.35∗∗∗
5.52∗∗∗
3.87∗∗∗
3.02∗∗∗
3.37∗∗∗
5.86∗∗∗
5.66∗∗∗
4.15∗∗∗
3.41∗∗∗
3.71∗∗∗
2.49∗∗
2.54∗∗
3.19∗∗∗
6.11∗∗∗
4.32∗∗∗
0.77
2.62∗∗∗
2.60∗∗∗
3.70∗∗∗
5.47∗∗∗
6.57∗∗∗
2.67∗∗∗
t-value
XIU & GUNDERSON: GENDER EARNINGS DIFFERENCES IN CHINA
251
Male
∗
Significant at 10%;
∗∗
0.772
0.780
0.781
0.811
0.815
0.820
0.808
0.720
0.620
0.748
0.910
0.754
0.796
0.904
0.838
0.793
0.747
0.814
0.856
0.891
0.750
284
266
284
270
332
315
267
229
469
271
253
247
251
303
387
311
270
264
318
424
287
significant at 1%.
0.729
0.781
246
280
∗∗∗
0.779
280
Female
significant at 5%;
Having elder(s) over 64
360
Han Ethnicity
(Non-Han ethnicity)
338
Han ethnicity
358
Residence Area
(East area)
368
Central area
341
West area
364
Communist Party
(Not a member)
333
Member
408
Industry/Enterprise Ownership
(Public sector)
384
State enterprise
331
Collective enterprise
318
Private/foreign
756
No answer or not applicable
363
Occupation
(Unskilled manual worker)
278
Skilled manual worker
328
Sales and service worker
315
Ordinary office worker
335
Middle/high management
462
Professional
392
No answer or not applicable
361
Rank in the Work Unit
(Ordinary worker or staff)
325
Cadre or supervisor
371
Higher level leader
476
No answer or not applicable
382
Variables
Female/
Male
Base Pay (Yuan/Month)
90
93
93
117
77
221
26
6.25∗∗∗
3.46∗∗∗
4.23∗∗∗
5.32∗∗∗
4.49∗∗∗
1.65∗
5.05∗∗∗
104
110
122
25
104
83
75
5.42∗∗∗
6.17∗∗∗
4.58∗∗∗
6.61∗∗∗
1.29
1.13
4.02∗∗∗
83
91
2.74∗∗∗
8.40∗∗∗
78
113
107
95
102
114
28
92
8.28∗∗∗
1.31
4.71∗∗∗
2.21∗∗
0.85
1.29
4.29∗∗∗
5.06∗∗∗
Male
t-value
80
68
105
43
55
74
57
120
75
107
26
86
87
61
192
25
67
90
76
67
59
21
72
72
Female
0.772
0.619
0.866
1.675
0.702
0.659
0.536
1.260
0.742
0.936
0.936
0.917
0.743
0.792
0.869
0.991
0.743
0.962
0.732
0.803
0.787
0.247
0.790
0.780
Female/
Male
2.95∗∗∗
1.04
0.49
−1.07
1.24
2.50∗∗
2.30∗∗
−0.75
0.59
0.45
0.20
0.65
2.66∗∗∗
1.16
0.18
0.03
2.83∗∗∗
0.21
2.20∗∗
1.69∗
1.47
3.01∗∗∗
2.59∗∗∗
2.60∗∗∗
t-value
Performance Pay (Yuan/Month)
TABLE A2
Continued
515
604
701
440
423
528
481
528
687
606
423
581
551
446
1000
422
504
584
569
481
531
590
527
537
Male
404
453
637
352
349
381
330
510
530
497
319
483
414
328
683
319
386
488
425
372
376
308
404
405
Female
0.784
0.750
0.909
0.800
0.825
0.722
0.686
0.966
0.771
0.820
0.754
0.831
0.751
0.735
0.683
0.756
0.766
0.836
0.747
0.773
0.708
0.522
0.767
0.754
Female/
male
Total Pay (Yuan/Month)
t-value
8.04∗∗∗
1.28
0.93
2.58∗∗∗
2.55∗∗
5.27∗∗∗
3.60∗∗∗
0.25
1.21
4.61∗∗∗
5.39∗∗∗
4.47∗∗∗
5.34∗∗∗
4.59∗∗∗
0.98
5.35∗∗∗
6.66∗∗∗
3.37∗∗∗
6.10∗∗∗
6.87∗∗∗
3.64∗∗∗
2.99∗∗∗
8.28∗∗∗
8.09∗∗∗
252
CONTEMPORARY ECONOMIC POLICY
XIU & GUNDERSON: GENDER EARNINGS DIFFERENCES IN CHINA
253
TABLE A3
Tobit Regression: Performance Pay Amount; Full Sample Including Zero Values
Model 1
Education (years)
Job tenure (years)
Other experience (years)
Married
Child(ren) under 6
Elder(s) over 64
Han_ethnicity
Central
West
Party member
Model 2
Model 3
Male
Female
Male
Female
Male
Female
0.088∗
(1.890)
−0.034
(−1.521)
−0.084∗∗∗
(−5.585)
0.611
(0.880)
−1.096∗∗∗
(−2.751)
0.154
(0.205)
0.068
(0.100)
0.609∗∗
(1.973)
0.875∗∗
(2.299)
0.082
(0.264)
0.391∗∗∗
(7.213)
−0.008
(−0.283)
−0.079∗∗∗
(−4.047)
0.844
(1.152)
−1.124∗∗
(−2.488)
−0.360
(−0.468)
2.674∗∗∗
(3.033)
0.223
(0.623)
0.267
(0.641)
0.858∗
(1.818)
0.010
(0.210)
−0.034
(−1.585)
−0.010
(−0.598)
−0.001
(−0.002)
−0.689∗
(−1.820)
−0.046
(−0.064)
−0.068
(−0.105)
0.448
(1.526)
0.872∗∗
(2.407)
−0.056
(−0.185)
0.616∗
(1.855)
−0.588
(−1.313)
−3.530∗∗
(−2.477)
−4.816∗∗∗
(−8.776)
0.258∗∗∗
(4.703)
−0.017
(−0.632)
−0.018
(−0.879)
0.241
(0.335)
−0.942∗∗
(−2.152)
−0.052
(−0.070)
2.690∗∗∗
(3.089)
−0.034
(−0.099)
−0.003
(−0.007)
0.735
(1.593)
0.082
(0.211)
−0.595
(−1.219)
−4.217∗∗∗
(−2.897)
−3.736∗∗∗
(−6.829)
0.923
(0.952)
3.949∗∗∗
(28.572)
70.038
60.32***
966
−5.201∗∗∗
(−4.303)
3.993∗∗∗
(24.635)
127.282
2.205∗∗
(2.210)
3.696∗∗∗
(28.779)
190.871
46.38***
966
−3.174∗∗
(−2.544)
3.814∗∗∗
(24.769)
194.897
−0.026
(−0.525)
−0.039∗
(−1.821)
−0.009
(−0.528)
0.093
(0.141)
−0.624∗
(−1.661)
−0.109
(−0.154)
−0.046
(−0.072)
0.396
(1.352)
0.831∗∗
(2.297)
−0.085
(−0.267)
0.695∗
(1.953)
−0.335
(−0.720)
0.382
(0.137)
0.328
(0.118)
1.170∗∗
(2.486)
0.521
(0.826)
0.296
(0.494)
−0.424
(−0.594)
1.452∗∗∗
(2.596)
−0.660
(−0.442)
0.706
(1.442)
1.508∗∗
(2.438)
−3.645
(−1.504)
1.579
(1.498)
3.644∗∗∗
(28.803)
212.309
66.88***
966
0.214∗∗∗
(3.802)
−0.021
(−0.781)
−0.017
(−0.806)
0.313
(0.435)
−0.925∗∗
(−2.123)
−0.058
(−0.077)
2.600∗∗∗
(2.976)
−0.038
(−0.108)
−0.128
(−0.317)
0.770
(1.637)
0.640
(1.487)
0.017
(0.032)
−5.986∗∗
(−2.430)
−4.979
(−1.556)
0.379
(0.653)
0.089
(0.144)
1.037
(1.550)
0.366
(0.319)
1.624∗∗∗
(2.768)
0.262
(0.102)
−0.589
(−0.742)
−0.442
(−0.484)
1.987
(0.992)
−3.754∗∗∗
(−2.931)
3.766∗∗∗
(24.780)
208.748
Ownership_state
Ownership_ collective
Ownership_private, foreign, joint
Ownership unknown
Occup._ skilled manual
Occup._sales and service
Occup._office worker
Occup._management
Occup._professional
Occup. unknown
Rank_supervisor
Rank_higher level leader
Rank unknown
Constant
Sigma_constant
χ2
Chow test
N
∗ Significant
at 10%;
∗∗ significant
at 5%;
824
∗∗∗ significant
at 1%.
824
824
254
CONTEMPORARY ECONOMIC POLICY
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