Economic Reforms and Gender Inequality in Urban

China

Haoming Liu∗

Department of EconomicsNational University of Singapore

ecsliuhm@nus.edu.sg+65 6516 4876

May 31, 2007

Abstract

This paper jointly examines the gender earnings gap and employment ratein urban China using a longitudinal data set that covers the period of 1989–2004. Consistent with previous studies, we find that the earnings gap increasedbetween 1989 and 1993. The rise is mainly driven by unobserved gender spe-cific factors, such as a decline in women’s unobserved skill or an increase inthe discrimination against them. After 1993, the gap fell gradually, which isachieved at the cost of less educated women’s employment. Consequently, thenarrowing gap is not necessarily a good news to all urban women. Since theexit from employment is mostly involuntary, women still lost relative to meneven in the later reform period.

Keywords: Economic reform, gender inequality, discriminationJEL classification: J3, J7, P25

∗Corresponding author: Haoming Liu, Department of Economics, National University of Sin-gapore, 1 Arts Link, Singapore 117570. Email: ecsliuhm@nus.edu.sg, Phone: +65 6516 4876,Fax: +65 6775 2646

1 Introduction

The impacts of economic reforms on the gender earnings gap in transition economies

have been examined by several authors with mixed findings. For example, Brainerd

(2000) finds that economic reforms increased the gender earnings gap in Russia

and Ukraine but reduced it in most Eastern European countries such as Hungary,

Poland, Czech Republic and Slovak Republic. Hunt (2002) shows that while the

gender earnings gap fell in East Germany after German unification, almost half

of it was attributable to the exit from employment of low skill women. Munich,

Svejnar, and Terrell (2005a) suggest that economic reforms narrowed the gap in

Czech Republic. Studies on China mostly find that economic reforms raised the

gender earnings gap. For instance, Gustafsson and Shi (2000) show that China’s

gender wage gap in urban areas rose slightly, from 15.6% in 1988 to 17.5% in 1995.

Similar pattern has also been documented by Knight and Song (2003) and Yang

(2005). Comparing with other transitional economies, the gender earnings gap in

China is small and little affected by economic reforms.

Several factors might be responsible for China’s small and relatively stable gender

earnings gap. First, although China’s economic reforms were commenced in the

late 1970s, early reforms mainly affected rural areas. The urban labor market has

not been affected utile the mid-1990s (Meng 2004). This is evident by changes in

the payroll employment share of State-Owned Units (SOU) and of Collective-Own

Enterprises (COE). The payroll employment share of SOUs is 78.4% in 1978, 73.4%

in 1988, 73.5% in 1995, and 63.1% in 2003.1 This suggests that the 1995 data is still

too early to reveal the impacts of recent fundamental urban reforms. Second, as

has been pointed out by Hunt (2002), the small increase in the gender earnings gap

might be attributable to the decline in the employment rate of low skilled workers.

Because the average skill level of women is lower than that of men, a decline in the

employment rate of low skilled workers has a larger effect on women’s than on men’s1The information is extracted from Table 1-14 of the 2005 China Labour Statistical Yearbook.

The payroll employment only includes staff-and-workers, is called “zhi gong” in Chinese. Hereafter,we will use “payroll employment” and employment interchangeably.

1

employment, which narrows the gender skill gap.

This paper uses information extracted from the China Health and Nutrition

Survey (CHNS) to tackle this issue. Comparing with previous studies that largely

used the 1988 and 1995 China Household Income Project (CHIP), the CHNS enables

us to document the evolution of the gender earnings gap over a much longer period,

between 1989 and 2004. By extending the sample coverage to post 1995 reforms, we

can compare the impacts of recent reforms that mostly affected urban areas with

these of earlier reforms. In addition, we can use CHNS to jointly examine changes

in employment probability and changes in the gender earnings gap.

Consistent with previous studies, we find that the gender earnings gap in urban

China raised from 13% in 1989 to 18% in 1993, and then stabilized around that

level for the rest of the sample period. The increase in the earnings gap in the

earlier reform period was mainly driven by the widening gap at the top of the

earnings distribution. Our quantile regression results indicate that while the gap

(9.6%) hardly changed at the 10th percentile between 1989 and 1993, it increased

from 15.4% in 1989 to 22.3% in 1993 at the 90th percentile. The stable gender

earning gap at the lower percentile in the early reform period reflects the fact that

there were no obvious losers during this period. According to Zhao and Li (1999),

while the incomes of the top 3% urban households increased by 53% from 1988 to

1995, the incomes of the bottom 20% urban households increased by 20% over the

same period. In contrast to the earlier period, the earnings gap at the bottom of the

earnings distribution increased considerably while it actually declined slightly at the

top in the later reform period. For example, while the gap rose from 8.9% in 1993

to 15.6% in 2004 at the 10th percentile, it fell from 22.3% to 7.7% over the same

period at the 90th percentile. As a result, the mean gender earnings gap hardly

changed. The evidence documented here suggests that while the gender earnings

gap in the early reform period is characterized by men gaining ground on women at

the top of the earnings distribution, high wage women regained some ground in the

later reform period.

2

Our decomposition exercise shows that the increase in the earnings gap in the

early period was mainly driven by changes in unobserved characteristics–the “gap

effect”, and their prices–the “unobserved prices effect”. The “gap effect” can be due

to either a decrease in women’s unobserved skill or an increase in discrimination.

The “unobserved prices effect” reflects the impact of rising earnings inequality. The

slight decrease in the earnings gap between 1997 and 2004 was mainly attributable

to the rising education level of employed women, which was largely due to the exit

from employment of low skilled women. Among individuals who worked in 2000,

our regression results show that a one year increase in schooling raises women’s

2004 employment probability by 5.5 percentage points, while it can only raise men’s

2004 employment probability by 1.9 percentage points. The high exit rate from

employment of less educated women is not a concern if they withdraw from the

labor market voluntarily. Unfortunately, our regression results suggest the oppo-

site. Therefore, our analysis suggests that the recent decline in earnings gap is not

necessarily a good news to urban women.

The paper is structured as follows. The next section provides some background

knowledge on the process of economic reforms in urban China and a brief discussion

of the data source. Section 3 documents the evolution of the gender earnings gap

in urban China between 1989 and 2004. Section 4 analyzes the determinants of

employment rate. A short conclusion is given in Section 5.

2 Institutional Background and the Data

Before the economic reform, both employment and compensation in China were

controlled by the state. The wage system was centrally regulated into occupational

based wage scales: administrative personnel were put into 20 salary grades, techni-

cians into 17 grades and manual employees into 8 grades (Knight and Song 2003).

Employers had little control over who they could employ and how much to pay. The

first step to increasing labor market flexibility is the reintroduction of bonuses and

piece wage in 1978. Since then, the share of bonuses in total wages for all enter-

3

prizes increased from 2% of wage bill in 1978 to around 16% in 1997 (Brooks and

Tao 2003). Although, in theory, employers were free to set bonuses level (subject

to a ceiling), they rarely used bonuses as an incentive mechanism. Actually, some

researchers (Walder 1987) argue that most employers distributed bonuses equally

among their employees so as to minimize contention.

A labor contract system was introduced in the mid-1980s, these contract workers

were largely treated the same as permanent workers. But then in the mid-1990s,

the whole institution setup changed dramatically. The radical reform, known as

xia gang, first on trial in 1994 and finally launched in 1997 (Appleton et al. 2002).

According to Brooks and Tao (2003), 25 million employees from Sate-Owned En-

terprises (SOE) and COEs were laid-off during 1998–2002.2 By the end of 1999,

the accumulated laid-off workers accounted for 13.2% of the entire urban labor force

(Appleton et al. 2002). The mass layoff has a larger negative impact on women’s

than on men’s employment. According to the statistics documented in Table 1 of

Appleton et al. (2002), the incident rate of layoff is 12% for men and 22% for women.

Although some of the male-female difference in the probability of being laid-off is

due to differences in personal characteristics, females’ probability of being laid-off

is still 5.5 percentage points higher than that of males even after controlling for a

series of observed characteristics. To make things worse, females are not only more

likely to be retrenched than males, but also more likely to be forced to retire early.

In addition, once separated from previous jobs, females have a much lower reemploy-

ment probability as well. For example, Giles, Park, and Cai (2006) show that while

44.3% of 40-50 year old males were reemployed within 12 months of leaving their

jobs, the corresponding figure for females is only 22.1%. Facing the harsh reality,

many females dropped off the labor market. The female labor force participation

rate fell from 74.4% in January 1996 to 63.1% in November 2001 while the male

labor force participation rate declined from 93.0% to 86.3% (Giles, Park, and Cai2SOE consists a subset of SOU. For example, while workers employed by government institutions,

hospitals, education and research institutions are a part of SOU employment, they are not a partof SOE employment.

4

2006, p67) during the same period. The male-female difference in the labor force

participation rate is the largest among the 40-50 year old group. The labor force

participation rate of the 40-50 year old declined by 7.9 and 14.5 percentage points

for males and females, respectively. Among individuals who are in the labor force,

females generally have to face higher unemployment rates. In November 2001, the

unemployment rate of the 40-50 year old is 10.3% for men and 17.1% for women.

Actually, among all age groups, only women of the youngest group (16-29 year old)

have a lower unemployment rate than men of the same age (Giles, Park, and Cai

2006, p67).

As a result of the mass layoff, the employment shares of SOEs and COEs have de-

creased considerably since 1997. Table 1 reports the number of payroll employment

by registration type. The data show that both total employment and the number of

employees in State-Owned Units (SOUs) increased year by year between 1984–1995.

The employment share of SOUs stayed at around 73% over this period with little

year-to-year variation. As urban economic reforms gained momentum in the mid-

1990s, both total employment and SOU employment started to fall, with the latter

outpaced the former. As a result, SOU’s employment share decreased from 73.5%

in 1995 to only 60.9% in 2004. During the same period, the employment share of

Other-Ownership Units increased from 5.9% to 31.1%. This evidence suggests that

previous studies that largely used the 1988 and 1995 data might not able to capture

the impacts of more recent radical urban reforms on gender inequality in the labor

market.

To examine the impacts of the later radical reforms, we use data extracted from

the 1989–2004 China Health and Nutrition Survey (CHNS). The CHNS is designed

jointly by the Carolina Population Center at the University of North Carolina at

Chapel Hill and the Chinese Academy of Preventive Medicine. It is implemented

by related city/county anti-epidemic stations under the organization of the Food

Inspection Services of the provinces. Although the CHNS is designed to examine

the effects of health, nutrition, and family planning policies, it does contain detailed

5

income and job related information that can be used to analyze labor market issues.

The survey took place over a 3-day period, draw a sample of about 4,400 households

with a total of 16,000 individuals in nine provinces, including Liaoning, Heilongjiang,

Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi and Guizhou. Households from

Heilongjiang were only sampled after the 1993 survey and households from Liaoning

were not surveyed in 1997.

The CHNS has a high retention rate. For example, among the 15,923 individuals

who were surveyed in 1989, 14,028 were also surveyed in 1991. Even after 15 years

of the initial survey, the CHNS still managed to survey 56.3% of the original sample

population whose community still participated in 2004. The retention rate is even

higher among these who were born between 1929 and 1969 (aged 20-60 in 1989), with

a value of 61.4%. Since 1993, all new households formed from sample households

residing in sample areas were added to the survey, resulting in a total of 13,893

individuals. Since 1997, additional households were added to replace those no longer

participating.

In this paper, we focus on individuals aged between 17 and 60 in the survey year,

were not enrolled in any types of schools, and lived in urban sites. Their earnings are

measured by self-reported average monthly wages (excluding bonus and subsidies)

of the primary job in the year prior to the survey year except for the 1989 survey.

Since this information was not collected in the 1989 survey, we have to use the

monthly wage constructed by the survey center from two questions: (1) daily wage

on the current job for workers who were paid by time; (2) piece rate for workers

who were paid by the number of pieces finished. Consequently, our wage information

covers the period of 1989–2003. The monthly wage is top coded at 999 yuan in 1991

and at 9,999 yuan between 1993 and 1999. Since only 27 observations are affected,

these observations are excluded from our analysis. In the 2000 survey, all reported

monthly earnings are lower than 9,999. However, there are 4 observations whose

reported wages are greater than or equal to 9,999 yuan in 2004 and 45 observations

whose constructed monthly wages are greater than or equal to 999 yuan in 1989.

6

To keep consistency, these observations are excluded as well.3 Because it is difficult

to measure labor income for self-employed or family workers, they are also excluded

from our analysis. The nominal wages are deflated by the CPI (1978=100) of all

urban areas.

The change in survey design prevents us from constructing a consistent measure

of the amount of subsidies and bonuses. Therefore, subsidies and bonuses are not

included in our earnings measure. The amount of subsidies and bonus were broken

into several categories in the period of 1989–1997, but only the total amount was

asked in the last two surveys. Curiously, while all urban wage earners reported some

types of subsidies between 1989–1997, only around half of the urban earners reported

a positive amount of subsidy in the last two surveys. Because the missing informa-

tion on subsidy could be either the result of not receiving any or refusing to answer

the question, we use basic monthly salary as our earnings measure throughout the

entire sample period. To gauge the potential bias induced by excluding various

subsidies, we calculate the correlation between the amount of total subsidies and

basic monthly wages among workers who reported both positive wage and subsidies.

The correlation coefficient between these two variables never exceeds 0.14, suggest-

ing that they are weakly correlated. The average amount of subsidies received by

women is equal to 91% of that received by men, which is larger than the female

to male wage ratio of 82%. The above evidence suggests that our wage regression

results should not be sensitive to whether we include subsidies, and including them

will slightly narrow the gender earnings gap.

Another labor market variable used in our analysis is the type of registration of an

employee’s work units. Although the survey question on this issue varies over time,

two groups of workers can be consistently identified throughout the entire sample

period: those worked for SOUs and those worked for COEs. Consequently, we

group all employed workers into three categories: SOU, COE, and others. The last

group largely consists of employees of private enterprises, foreign owned enterprises,3Among these excluded individuals, 63% of them are males. Including these topcoded (or would

be topcoded in some years) observations will slightly increase the gender earnings gap.

7

overseas Chinese owned enterprises and joint ventures. In the later discussion, we

will refer this group as employees in the private sector.

Other variables used in the analysis are the province of residence, years of school-

ing and potential years of experience. Years of schooling are constructed from two

variables: years of formal education completed and the highest level of education

obtained. The existence of two education related variables enables us to check

the consistency of the education information. Presumably, the reported years of

schooling are likely to be accurate if these two measures conform with each other.

Therefore, the reported years of schooling are used whenever these two variables

are consistent. If these two variables are only consistent in some survey years, then

the years of schooling in these years are used for the nearest inconsistent years. If

these two variables are inconsistent throughout the entire sample period, then the

respondent’s average years of schooling is used for each period. Average years of

schooling over the entire sample period might differ from actual schooling in a par-

ticular year for individuals whose formal education consists of several disconnected

intervals. In our urban sample, only 64 of them went back to school after leaving

school in the previous survey, and only 17 of them whose education variables were

not consistent with each other. Therefore, we are confidence that the benefits of

using our constructed years of schooling rather than the reported years of schooling

outweighs its costs. Having constructed years of schooling, the potential years of

working experience are then calculated as age minus years of schooling minus 6.

Table 2 reports the basic sample statistics by gender and survey year. The female

to male earnings ratio fell from 84.1% in 1989 to 82.6% in 1991, which is consistent

with the evidence documented by previous studies. Interestingly, women’s relative

earnings seem to have gained some grounds since 1993. Unfortunately, the narrowing

earnings gap was accompanied by a declining employment rate. The most dramatic

decline in employment rate happened in the late 1990s and early 2000s. Women’s

employment rate fell faster than men’s, which widened the difference in employment

rate between men and women. For instance, as shown in the first row of panel A

8

and B of Table 2, while men’s employment rate declined by 45.9 percentage points

between 1989 and 2004, women’s employment rate decreased by 46.3 percentage

points. This finding is consistent with the statistics documented by the National

Bureau of Statistics (2001) using the second survey on women’s status in China.

According to the National Bureau of Statistics (2001), the urban employment rate

of 18-64 year old fell from 90.0% in 1990 to 81.5% in 2000 for men, and from 76.3% to

63.7% for women. The relative high employment rate documented by the National

Bureau of Statistics (2001) is likely due to its loss definition of employment. An

individual is considered as employed as long as he has “worked for income in the last

week”. Consequently, individuals who are temporarily employed and self-employed

are also considered as employed. However, individuals are considered as employed

only if they reported a positive value on the question “on average, what was your

monthly wage/salary last year, excluding subsidies and bonuses?”, which excluded

self-employed and temporarily employed. The decline in the employment share of

SOU started a few years later than the fall in the employment rate. It changed little

between 1989 and 1993 for both men and women, but decreased by 6.7 percentage

points for men and 4 percentage points for women between 1993 and 1997. The

timing of the decline in SOU’s employment share is consistent with what has been

documented in Table 1 at the national level.

One potential reason for the decline in the employment rate is that many work-

ers started their own business and become self-employed.4 Including self-employed

workers into our sample indeed raises the employment rate, but it does not change

the downward trend. For example, the employment rate (including self-employed

workers) decreased from 90% in 1989 to 69.1% in 2004 for men and from 82.5% in

1989 to 52.8% in 2004 for women. In contrast to the decline in the employment rate,

monthly earnings of both men and women increased steadily over the entire sample

period. Men’s average earnings are 3.51 times larger in 2004 than in 1989 while

women’s average earnings are 3.63 times higher in 2004 than their 1989 level. Years4Self-employed workers are excluded from our sample.

9

of education of employed workers also increased steadily for both men and women.

It raised from 9.58 years in 1989 to 11.20 years in 2004 for men, and from 9.14 to

11.41 for women. The average education level of employed workers is always higher

than that of the entire adult population, especially for women, suggesting that bet-

ter educated individuals have a higher employment rate. The positive relationship

between education and employment should have a larger impact on women’s em-

ployment since their average years of education are lower, which in turn narrows

the gender earnings gap. Similar conclusion has been drawn by Hunt (2002) using

a German data set. The decline in employment rate and the increase in monthly

earnings of employed workers suggest that changes in the gender earnings gap need

to be jointly examined with changes in employment opportunities.

3 Wage results

To depict a general picture of the evolution of the gender earnings gap, we first use

the CHNS as 6 cross-section data sets. We run log earnings regressions for each of

the 6 waves separately. The control variables include a gender dummy (=1 for male

and 0 otherwise), years of education, potential years of experience and its square, the

registration type of work unit, and the province of residence. To help us understand

the contributions of these observed variables to the earnings gap, we add them into

our regression gradually. The regression results are reported in Table 3.

In Panel A, gender dummy is used as the only control variable. Similar to

Table 2, the largest increase in the gender earnings gap occurred between 1991 and

1993, and has declined since then. The slight difference between Table 2 and Panel

A of Table 3 is that while the former reveals the difference in the average earnings,

the latter reflects the difference in the average of log earnings. Panel B shows

that controlling for education and experience reduces the gap by 1.4% to 5.1%.5

Including types of work units in Panel C and the province of residence in Panel D5Hereafter, the coefficient on gender dummy γ will all be converted to percent using the formula

(exp(γ)− 1) as suggested by Halvorsen and Palmquist (1980).

10

have no obvious impact on the gap, suggesting gender earnings gap does not vary

much across different types of work units. After including all control variables, the

gender earnings gap measured by the coefficient on gender dummy increased from

0.1237 log points (13.2%) in 1989 to 0.1308 (14.0%) in 1991 and then to 0.1686

(18.4%) in 1993. These estimates are very close to Yang (2005) who finds that the

coefficient on gender dummy is 0.097 in 1988 and 0.155 in 1995. Interestingly, the

gender earnings gap stabilized around 0.164 till the end of the sample period.

The increase in the rate of returns to education (as reported in Panel D) started

much later than the rise in the gender earnings gap. The coefficient on years of

education stayed at around 0.02 for the period of 1989–1997, then increased to

0.0347 in 1999 and 0.073 in 2004. Our 1989–1997 estimates are comparable to

the rate of returns to education in other central planned economies, such as Czech

Republic (0.027 in 1989 Munich, Svejnar, and Terrell 2005b), Russia (0.039 in 1990

Gorodnichenko and Peter 2005), and Ukraine (0.039 in 1991 Gorodnichenko and

Peter 2005). It is also consistent with previous Chinese studies. For instance, using

the 1988 CHIP data, both Liu (1998) and Yang (2005) find that the rate of returns to

education is around 0.03 in China. However, our 1997 estimate is lower than what

have been found by Yang (2005) using the 1995 CHIP data set. His estimation

suggests that the rate of returns to education increased to 0.059 in 1995 with a

considerable variation across cities. Yang’s results show that the rate of returns

to education is higher in cities with better information infrastructures and more

foreign and joint-ventures. We suggest that the lower rate of returns to education in

our estimation is mainly driven by the fact that the CHIP contains more developed

provinces, such as Beijing, Guangdong and Jiangsu, than the CHNS does.

Another interesting result of Table 3 is that the magnitudes of the coefficients on

SOU and COE first increased from 1989 to 1993 and then declined. The coefficients

on SOU even became statistically insignificant in the last two surveys. Because

workers in the private sector are used as the reference group, these changes suggest

the wage gap between private and non-private sector widened in the early reform

11

period but narrowed recently. The gain in the relative position of SOU workers dur-

ing 1993-2004 was largely driven by the country-wide salary increase of government

employees (including workers in the government, state services and institutes), in

July 1989, July 1997, January 1999, January 2001 and October 2001.6 As govern-

ment employees account for more than half of the SOU employment, these salary

increases reduced the earnings gap between workers in the private and non-private

sectors.7 The increased competition between private and non-private sector in the

labor market could be another reason for the narrowing wage gap.

Because some workers’ monthly earnings depend on the number of hours worked,

our results might be sensitive to the variation in working hours. Unfortunately,

monthly working hours cannot be consistently calculated using the CHNS. It only

contains information on daily working hours for the entire sample period. Weekly

working hours are only available in the 1989 survey.8 Days worked per week are

only recorded in 1989–2000 surveys. To make our estimation results comparable

over time, we use daily working hours to account for variation in working hours.

Table 4 reports the estimation results. For the sake of brevity, we only report the

estimation results with the full set of control variables. Interestingly, controlling for

daily working hours only slightly reduces the coefficient on gender dummy. This is

because women’s daily working hours are only about 0.1 hours shorter than that of

men. Moreover, adding daily working hours does not affect the trend of the gender

earnings gap.

The coefficients on education and experience are not sensitive to controlling for

daily working hours either, suggesting that working hours do not vary systematically

across education and experience groups. Another interesting observation from Table

4 is that the coefficient on daily working hours is only significant at the 5% level6Outlook Weekly, 2002, No. 187According to our own calculation, government employees accounted for 63% of the total SOU

employment in 2004. Because SOE employees and government employees were grouped togetherin the earlier surveys, we cannot calculate the employment share of the government employees.However, as the downsizing mainly occurred in SOEs, the employment share of the government islikely to be smaller than the 2004 value.

8Weekly working hours in the 1991 to 2004 survey refers to the week prior to the survey weekrather than to hours per week usually worked.

12

in 1989 and 1997. This does not necessarily imply that monthly earnings do not

depend on working hours. Rather, the weak relationship between working hours

and monthly wages reveals that jobs requiring long working hours do not pay higher

wages.

Several researchers (e.g. Knight and Song 2003; Meng 2004) find that earnings

inequality has increased in the 1990s. These studies also suggest that the increase

in the rate of returns to human capital is at least partially responsible for the rise

in inequality. Because the skill distribution differs between males and females, their

impacts on the earnings gap are unlikely to be uniformly distributed across the earn-

ings distribution. Figure 1 plots female/male earnings ratios at selected percentiles.

The percentile rankings refer to each gender group’s own earnings distribution. In-

terestingly, although the overall earnings ratio declined from 0.841 to 0.808 between

1989–1993, the earnings ratios at the 20th and 40th percentile actually increased

while the earnings ratio at the 80th fell. This suggests that the widening earnings

gap in the earlier reform period was mainly driven by the deterioration in female’s

relative earnings at the top half of the earnings distribution. In contrast, it was the

females at the bottom of the earnings distribution who lost ground to men during

1997–2004. The earnings gap increased at the 20th, 40th and 60th percentile, but

decreased at the 80th percentile.

To quantitatively compare changes in the earnings gap at various percentiles, we

run a series of quantile regressions at the 10th, 25th, 50th, 75th and 90th percentiles

with the full set of controls as in Panel D of Table 3. Unlike the OLS estimates that

reveal the returns to observed characteristics at the sample mean, the estimates of

quantile regressions reveal the returns to observed characteristics at the particular

percentiles.9 plotted in Figures 2 and 3. These two figures show that the evolution

of the gender earnings gap at different percentiles takes different paths. In 1989,

the gap was almost uniformly distributed across the earnings distribution. It then

increased dramatically at the 75th and 90th percentiles in 1991, but changed little9The estimation results are reported in Tables A1 to A5.

13

at the bottom half of the earnings distribution, implying that our OLS results were

mainly driven by changes at the top half of the earnings distribution (conditional

on observables).

The difference in the degree of risk aversion between men and women (Jianako-

plos and Bernasek 1998) is likely to be responsible for the widening gap at the top

of the earnings distribution. As men are less risk aversion than women, they have a

higher probability of switching from relatively stable jobs to less stable but better

paid jobs at the early stage of urban reforms. Moreover, because working for private

firms is still not the norm in the early reform period, it is usually the husbands who

quit relatively stable SOU or COE jobs to pursue high paying private jobs while

wives stay with their non-private employers to enjoy health and housing subsidies

that private firms normally do not provide. As Figures 4–7 show, between 1989

and 1993, better-paid jobs in the private sector confer a considerable premium over

jobs in non-private sectors while the premium for lower-paid private jobs is much

smaller. Men’s higher probability of moving to the private sector will widen the

earnings gap at the top of the earnings distribution. This claim is supported by the

data. Among the 1,744 men who worked at least in two out of the 6 surveys, 14.7%

of them switched from non-private to private sector while 6.8% of them moved from

private to non-private sector. The corresponding numbers for the 1,633 women who

worked at least in two out of the 6 surveys are 13.2% and 7.2%.

As urban economic reform accelerated in the late 1990s, even jobs at SOUs are

not for lifetime. Consequently, the gender difference in terms of accepting different

type of jobs also narrowed. Actually, women were more likely to work in the private

sector than men in the 2004 survey. As more women took higher paid jobs, the

gender earnings gap at the top of the earnings distribution were reduced. This

argument is consistent with the pattern documented in Figure 1. After 1993, the

gap still trended up at the 10th, 25th and 50th percentiles, but trended down at

the 75th and 90th percentiles. This finding is consistent with Liu, Zhang, and Kung

(2004) who show that the gender wage gap increased by more that 12 percentage

14

points between 1988 and 1999 at the 10th percentile while it declined by about 5

percentage points over the same period at the 90th percentile.

Another interesting observation from these quantile regressions is that the coef-

ficients on SOUs at different percentiles also evolved differently. The earnings gap

between SOU employees and private employees was much larger at the top than at

the bottom of the earnings distribution, implying that top earners in the private

sector earned much more than their counterparts in the non-private sectors while

bottom earners in the private sector earned only slightly more than their counter-

parts in the non-private sectors. This suggests that the earnings distribution was

more compressed in SOUs than in the private sector. The cross-sector differences

have narrowed since 1993 as economic reforms progressed. Figures 4 and 5 show that

both the 90/10 and 75/25 differences have decreased since 1993 while the coefficient

on the SOU dummy increased steadily at all percentiles.

Figures 6-7 shows the coefficient on COE at different percentiles. Similar to

the coefficient on SOU, the 75/25 difference decreased gradually over the entire

sample period. However, unlike the coefficient on SOU, the 90/10 difference shows

no clear pattern. The coefficient on COE at the 90 percentile is still negative and

significantly differs from 0 at the 7% level in the last two surveys. This suggests that

earnings grow faster in SOUs than in COEs, particularly at the top of the earnings

distribution. Since women are more likely to work in COEs than men, changes in

the earnings distribution in COEs should have a larger impact on women’s earnings.

To spell out the contributions of various factors to the gender earnings gap, we

adopt the decomposition method proposed by Blau and Kahn (1997). Assuming that

males are paid fairly according to their personal characteristics, then the coefficients

from males’ earnings regression should be able to reflect the actual price of the

corresponding characteristics. Thus the gender earnings gap can be modeled as

Dt = lnwmt − lnwft = ∆Xtβt + σt∆θt (1)

where wj (j = m, f) represents monthly earnings, and X is a vector of control vari-

15

ables including education, experience, registration type of work units, and residence

of province, the subscript m means male and f female, x represents the sample

average of x, β is the corresponding estimates from male earnings regression, θ is

the standardized residual, σ is the standard deviation of the earnings residuals, and

∆ denotes the male-female difference in X and θ at their corresponding means.

The change in the earnings gap between year t and 0 can then be decomposed

as:

Dt −D0 = (∆Xt −∆X0)βt + ∆X0(βt − β0)

+ (∆θt −∆θ0)σt + ∆θ0(σt − σ0). (2)

The first term reflects the contribution of changes in characteristics to the earnings

gap when evaluated at the period t price. The second term reveals the contribu-

tion of changes in returns to characteristics evaluated at the period 0 difference in

characteristics. These two terms are called “observed X ′s effect” and “observed

prices effect” respectively by Blau and Kahn (1997). The third term represents the

contribution of changes in the percentile ranking of the female earnings residuals

while holding the standard deviation of male earnings residuals at the period t level,

called “gap effect”. Finally, the fourth term of equation (2), the “unobserved prices

effect”, measures the contribution of changes in the standard deviation of male earn-

ings residuals holding the percentile rankings of female earnings residuals at their

period 0 levels.

The last two terms of equation (2) can be rewritten as

(∆θt −∆θ0)σt + ∆θ0(σt − σ0) = [(θtm − θtf )− (θ0m − θ0f )]σtm + (θ0m − θ0f )(σtm − σ0m)

(3)

Because the averages of both θtm and θ0m are zero, expression (3) can be further

16

simplified as

(∆θt −∆θ0)σt + ∆θ0(σt − σ0) = (−θtf + θ0f )σtm − θ0f (σtm − σ0m) (4)

where θtfσtm and θ0fσ0m are the averages of female earnings residuals in year t

and 0 imputed based on the corresponding year’s males’ earnings regressions. To

impute θ0fσtm, we first estimate each woman’s percentile ranking in year 0 based on

her earnings residual in that year’s distribution of male earnings residuals. Then,

we assign her the residual of the corresponding percentile of the year t distribution

of male earnings residuals. For example, if a woman’s year 0 earnings residual is

ranked at the pth percentile in that year’s male earnings residual distribution, then

she would be assigned the residual corresponding to the pth percentile of the year t

male earnings residual distribution.

Table 5 reports the decomposition results. The β′s used in the decomposition

are estimated using only male earnings with the same control variables as what

have been used in Panel D of Table 3. Given the low male employment rate in the

2000s, it is possible that the estimates from the men’s earnings regression might also

subject to the selection bias. Therefore, we also tried to estimate β′s using the full

maximum likelihood estimates of the Heckman selection model. Our results suggest

that the estimates are not sensitive to the estimation method. This is because the

low male employment rate is mainly due to the difficulty of locating a job rather a

low labor force participation rate. For simplicity, we will focus our discussions on the

results based on the OLS estimates. To make our results comparable with existing

studies, we group the entire sample into three periods 1989–1993, 1993-1997, and

1997-2004.10 The impacts of self-selection on female’s earnings will be reflected in

either the “observed X ′s effect” or the “gap effect”.

The first column of Table 5 reports the decomposition of the changes in the10For the sake of brevity, the estimation results of the underlying wage regressions are not re-

ported. To check whether our results are sensitive to controlling for sample selection, we alsoconduct a decomposition exercise using the coefficients from the full maximum likelihood estimatesof the Heckman selection model. The results reported in Table A6 show that our conclusions arenot sensitive to whether we control for the sample selection in the men’s wage regression.

17

earnings gap between 1989 and 1993, the period when the earnings gap increased

the most. Changes in observed X ′s widened the earnings gap by 0.6 percentage

points. The increase in women’s probability of working for SOUs from 1989 to 1993

is the major contributor. The decline in the relative earnings of SOU employees and

of COE employees worked in opposite directions. On the one hand, the increase

in women’s probability of working for SOUs widened the gap by 1.5 percentage

points. On the other hand, the decline in women’s probability of working for COEs

narrowed the gap by 0.3 percentage points.

Changes in the prices of observed X ′s have a much larger impact on the earnings

gap than the “observed X ′s effect”. If nothing else had changed except for the prices

of X ′s, the gap would have narrowed by 3.8 percentage points. Again, the decline

in the relative wage of SOU employees is the major contributor for the “observed

prices effect”. As males are more likely to work for SOUs, their earnings are more

sensitive to the fall in the relative wages of SOU employees than women’s earnings.

The increase in the “gap effect” and the “unobserved prices effect” are the two

major culprits for the widening genders earning gap, each contributes 2.4 percentage

points. This is in contrast to the German evidence documented by Hunt (2002)

who finds that the “gap effect” reduced the gender earnings gap in Germany. The

increase in the “gap effect” may be the result of either a rise in the degree of

labor market discrimination against women, or a relative deterioration in women’s

unobserved skill. The latter explanation would be correct if women with higher

level of unobserved human capital have a higher exit rate than other women. Since

observed and unobserved human capital are likely to be positively correlated, this

argument also implies that women with higher level of observed human have a

higher exit rate. However, our decomposition shows that changes in education level

and experience, the two commonly used measures for skill, only have minuscule

impacts on the gender earnings gap. In addition, our later analysis on employment

rate suggests that better educated women have a higher employment rate than less

educated women. Therefor, the “gap effect” is unlike the result of a deterioration

18

in women’s unobserved skill.

Moreover, if the “gap effect” is driven by changes in the relative level of unob-

served skill, it will not affect the gender earnings gap among individuals who worked

in both 1989 and 1993. Following this rationale, we repeated the decomposition ex-

ercise using a sample individuals worked in both years. For brevity, the detailed

results are not reported. Interestingly, while the gross earnings gap only increased

by 0.15 percentage points, the “gap effect” still widened the earnings gap of the

balanced panel by 0.16 percentage points. This further confirms that the widening

gender earnings gap during this period was not driven by the exiting of high skilled

women from the labor market.

Another factor that can lead to the increase in the “gap effect” is a decline

in women’s relative productivity. If this is the case, then the returns to women’s

observed human capital should be lower as well. To test this argument, we pool

women and men together and interact gender dummy with the full set of control

variables. The coefficient on the interaction between education and gender dummy

is positive (the return is higher for men) in 1989 and negative (the return is lower

for men) in 1993. The coefficient is not statistically significant even at the 10% level

in either 1989 or 1993. This evidence does not support the productivity difference

interpretation. Therefore, the increase in the “gap effect” is likely to be the result

of an increasing in the degree of gender discrimination.

Column (2) of Table 5 shows that the gross earnings gap has declined slightly

from 1993 to 1997. This decline is mainly driven by gender specific factors, i.e. the

“observed X ′s effect” and the “gap effect”. If the prices of observed and unobserved

characteristics had not changed over this period, the gender earnings gap would have

declined by 2.8 percentage points. The “observed X ′s effect” was mainly driven by

the fact that while the employment share of COEs increased for men, it decreased

for women. If the decline is the result of women leaving COEs for better paid

jobs, it improves women’s relative earnings. If it is the result of women leaving for

unemployment, then it deteriorates women’s employment prospect. Our calculation

19

supports the latter interpretation. Among the 118 men who were employed by COEs

in 1993, 71% of them were still employed in 1997. However, among the 141 women

who were employed by COEs in 1993, only 54.6% of them were employed in 1997.

In contrast to these gender specific factors, the “observed prices effect” and

the “unobserved prices effect” widened the gap by 1.3 percentage points and 0.6

percentage points, respectively. The largest contributor is the increase in the relative

earnings of SOU workers. As shown in figures 4-3, the earnings of SOU workers

increased at a faster rate than COE workers. Because statistics reported in Table 2

show that men were more likely to work for SOUs than women, they should benefit

more from the increase in SOU workers’ earnings.

Column (3) of Table 5 shows that the gross earnings gap declined by another 0.8

percentage points from 1997 to 2004. Again, changes in observed characteristics,

which narrowed the gap by 4.6 percentage points, is the single largest contributor

to the narrowing gap. This “observed X ′s effect” was mainly driven by the rise in

female workers’ relative education level, which was because of the higher exit rate of

less educated women. For example, while 61.9% of male workers with at most lower

secondary education in 1997 were still employed in 2004, the corresponding number

was only 37.1% for females. The “observed X ′s effect” was almost counterbalanced

by the “observed prices effect”, which widened the gap by 4.1 percentage points.

Half of the “observed prices effect” was attributable to the rising rate of returns to

education. Another contributor is the increase in SOU workers’ earnings.

Unlike observed variables, unobserved factors have little impact on the earnings

gap during this period. The “gap effect” widened the earnings gap by only 1 percent-

age point, and the “unobserved prices effect” reduced it by 1.3 percentage points.

Because changes in the observed X ′s suggest that unskilled female workers have a

higher exit rate from employment, the increase in their relative unobserved skill is

also likely to be the result of the higher exit rate of women with less unobserved

skills.

Column (4) reports the decomposition results for the entire sample period. The

20

gross earnings gap in 2004 is almost identical to its 1989 level. However, this does

not suggests that nothing has happened over these 15 years. Changes in observed

characteristics, mainly driven by the increase in the education level of employed

women, narrowed the earnings gap by 4.3 percentage points, while changes in the

prices of observed characteristics widened the gap by 1.6 percentage points. If the

relative education level had been the same as it was in 1989, then the earnings gap

would have increased by 1.7 percentage points. The “gap effect” contributed another

1.5 percentage points to the increase and the “unobserved prices effect” contributed

2.6 percentage points. Because the difference in education level between employed

women and the entire female population increased from 1.769 years in 1997 to 1.979

years in 2004, it is reasonable to suspect that less educated women have a higher exit

rate from employment than well educated women. This suggests that the selective

exit from employment might mitigate the “gap effect” over the entire sample period.

4 Employment Results

In the previous section, we show that the increase in the relative education level of

women helped to close the earnings gap by 4.3 percentage points. If the increase

in the relative education level of women is the result of a faster growth in women’s

education level, then it suggests that women’s relative status has improved since

1989. However, if the increase is due to a higher exit rate of less educated women

from employment, then it implies that women’s status has deteriorated if the exit

is involuntary.

To examine whether the probability of exit from employment differs across gen-

der and skill level, we create 5 panel data sets from the total 6 surveys. Each panel

consists of two adjacent surveys, 1989–1991, 1991–1993, 1993-1997, 1997–2000, and

2000–2004. The sample of individuals are restricted to those who reported a positive

earnings in the first survey and were interviewed in the second survey.11 Figure 811To have a complete picture on the impact of changes in employment rate on the gender earnings

gap, we should compare both the exit rate and the entrance rate. However, because only a very smallnumber of individuals who did not work in the first period were employed in the second period, the

21

plots the employment rate in the second survey by workers’ earnings quintiles in the

first survey. In Panels (a) to (e), the quintiles are separately calculated for the male

and female earnings distributions. In Panels (f) to (j), the quintiles are calculated

using the pooled earnings distribution.

During the period of 1989–1993, employment rate was negatively correlated with

earnings. On average, women had a lower employment rate than men, and the

differences were larger at the top of the earnings distribution. The higher exit

rate of better-paid female workers widened the gross gender earnings gap. Because

wages largely reflected workers’ seniority in the early reform period, the negative

correlation between earnings and employment might be the result of the higher

probability of withdrawing from employment of senior workers.

During the period of 1993–1997, the relationship between employment and earn-

ings is not very clear. Nevertheless, as in the earlier period, women still had a lower

employment rate than men, and the differences at the top of the earnings distribu-

tion were larger than at the bottom of the earnings distribution.

The correlation between employment rate and earnings reverted from negative

in 1989–1993 to positive in 1997–2004 for both male and females. Moreover, the

gender differences in employment rate were larger at the lower half of the earnings

distribution in the period 1997–2004, which is also in contrast to the 1989–1993

data. The higher exit rate of lower-paid female workers helped to narrow the gross

gender earnings gap. As the narrowing earnings gap was achieved at the cost of the

declining employment opportunity of lower-paid women, it did not signal a reduction

in discrimination against women.

Although the graphic presentation is informative, it cannot reveal whether the

high exit rate of lower-paid workers is due to their lower level of human capital or

other factors that are positively correlated with their wages. For example, workers

in SOUs do not only earn less than workers in the private sector, but might also

face a higher probability of being laid off due to the large scale of downsizing in

difference in the entrance rate between men and women is unlikely to have any significant impacton the earnings gap. For the sake of concise, we focus our discussion on the exit rate.

22

the late 1990s. This generates a positive relationship between monthly earnings

and employment rate. Another potential factor that can generate similar relation-

ship is the unbalanced economic growth across provinces. The large scale of SOE

downsizing has a much stronger impact on workers in the northeastern provinces,

such as Liaoning and Heilongjiang, than on workers in the eastern coastal provinces,

such as Jiangsu. Because workers in the northeastern provinces also earn less than

workers in the coastal provinces, without controlling for the province of residence,

it is difficult to pin down the causes for the correlation between employment and

earnings.

We use a probit model to address the above issues. The probability of being

employed in the second period is modeled as

Prob(Ei = 1) = Φ(Ziα), (5)

where E = 1 if an individual is employed and 0 otherwise, Φ(·) is the standard

normal cumulative density function, Z is a vector of control variables including age,

education, registration type of work unit in the first survey, log of monthly earnings

in the first survey, number of earners (except the respondent) in the household in

the second survey, and the province of residence. To capture the potential nonlinear

relationship between employment and age, age is included as a series of dummy

variables: between 17-25, 45-49, 50-54, and 55-59. These aged between 26 and 44

are used as the reference group. As a result, coefficients on age dummies should be

interpreted as the difference between the corresponding age group and these aged

between 26 and 44. Equation (5) is estimated for males and females separately.

Table 6 reports the estimation results.12 For the sake of brevity, coefficients on

the province of residence dummies are not reported. Earnings have no significant

impacts on employment rate except for females during 1991–1993. This suggests12We have also tried to include marital status and number of children under 12 year old in the

estimation. The coefficients on these two variables are not significant even at the 10% in most of ourregression. the coefficients on other variables are not sensitive to the inclusion of these two variableseither. For brevity, we only report the estimation results without these two control variables.

23

that the relationship between earnings and employment rate shown in Figure 8 is

mainly driven by observed characteristics.

Age is the most important determinant of employment, particularly for women.

Its impact reached its peak in 1993–1997 for men and in 1991-1993 for women. Since

then, its impact has declined considerably. In 1993–1997, the employment rate of

55–60 year old men was 22.3 percentage points lower than that of prime age men,

while the difference between 55–60 year old women and prime age women was 55.4

percentage points. The difference in employment rate between 55–60 year old and

the prime age workers became insignificant for both men and women in 2000-2004.

As senior workers generally earn more than junior workers, the higher exit rate of

senior workers induces a negative correlation between earnings and employment if

ages are not controlled for. This explains why Figure 8 shows a negative relationship

between earnings and employment rate in the early reform period but the coefficients

on wages are not statistically significant in Table 6.

Years of education has a positive effect on employment. The effect is larger for

women and increases overtime. For example, a one year increase in schooling raised

women’s employment rate by only 0.9 percentage points (significant at the 1% level)

in the period of 1989–1991, but by 5.5 (significant at the 1% level) percentage points

in 2000–2004. The corresponding values for men were 0.15 (not significant even at

the 10% level) and 1.85 (significant at the 5% level). The rising impact of education

on employment suggests that it becomes harder and harder for low skilled workers to

keep their jobs as economic reform progresses. Because women have lower average

education level than men, it also suggests that the selective exit from employment

has a larger effect on women. If unobserved skills are positively correlated with years

of education, the selective exit from employment should reduce the “gap effect”. As

our analysis in the previous section finds a positive “gap effect” except for the period

of 1989–1993, it implies that economic reforms in China have likely raised the degree

of discrimination against women.

Another interesting observation from the Table 6 is that the number of earners

24

(excluding the respondent) in the household and employment probability are posi-

tive correlated. The correlation is stronger for women than for men. The coefficients

on the number of earners are always positive for both males and females, and are

statistically significant among most of the regressions. There are several possible ex-

planations for this. First, the positive coefficient might reflect the impact of family

incomes on its members’ labor force participation. Namely, more individuals have

to work to support the family if other family members earn low salaries. Second, the

positive coefficient might be the result of the endogeneity of coresidence decision.

Namely, children of poor families tend to live with their parents longer and start to

work at a younger age. Both explanations imply that women’s earnings are nega-

tively correlated with the number of earners in the family. However, if we include

the number of earners in our wage regression, its coefficient is never significant even

at the 10% level, which does not support these two arguments.

The third explanation is the tendency for household members to work in the

same work unit. If several household members are employed by the same work unit,

then a member’s employment status would also signal the work unit’s performance of

his family members. As a result, the employment statuses of household members are

likely to be positively correlated. Unfortunately, the CHNS data does not contain

enough information that allows us to identify a worker’s work unit. However, we can

roughly tell whether two household members worked in the same unit by their work

unit’s registration type and the number of employees in their work unit. Among

households with at least two earners, about 30% of them have two earners with

the same type of work unit registration and the same number of employees in their

work unit. This suggests that a substantial number of households whose members

were employed by the same employers. The evidence implies that many workers are

forced to exit from employment because of the bad performance of their work units.

Therefore, the exit from employment is unlikely to be voluntary.

Finally, results in Table 6 show that SOU workers have a lower exit rate than

workers in the private sector in the later reform period. For males, the coefficients

25

on both SOUs and COEs are positive and significant during 1997–2004. For females,

the coefficient on SOU is positive but not significant during the same period. These

results suggest that workers in the private sector faced a higher probability of losing

their jobs than SOU workers even in the period when SOEs were experiencing an

unprecedented large scale of downsizing.

5 Conclusion

In this paper, we use a Chinese longitudinal data set, the China Health and Nu-

trition Survey to examine the evolution of the gender earnings gap between 1989

and 2004. The longer period covered by the data set enables us to analyze whether

the unprecedented large scale downsizing of State Owned Enterprizes has deterio-

rated women’s relative position in the labor market. Because discrimination against

women can either take the form of lower pay or of lower employment probability,

the impacts of economic reforms on women’s relative status in the labor market can

only be fully understood by examining earnings and employment simultaneously.

Our results show that the gender earnings gap widened during the early urban

reform period (1989–1993), but has declined thereafter. Unfortunately, the narrow-

ing gender earnings gap does not necessarily convey good news to female workers.

Our decomposition analysis shows that the narrowing gap was mainly achieved by

a rising in relative education level of employed women. Without these changes in

observed characteristics, the gap would have increased by about 4 percentage points.

Moreover, our examination on the determinants of employment status shows that

the increase in the relative education level is the result of the higher probability

of exiting from employment of less educated women. If they withdraw from em-

ployment voluntary, then the selective exit can still improve welfare. However, the

empirical evidence points to the opposite direction.

Overall, our analysis finds that women have shouldered more of the burdens

of economic reforms in urban areas. The gender inequality in the labor market

was mainly in the form of unequal pay in the early period of the urban reform,

26

and in the form of unequal employment probability in the later period. Obviously,

much more future works need to be done to explain why recent urban reforms had

a stronger adverse impact on women’s than on men’s employment. However, our

results indeed show that to fully understand the impacts of economic transition on

gender inequality in the labor market, researchers need to examine earnings and

employment simultaneously.

27

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Table 1: Total number of “staff and workers”, by registration typeNumber of workers Employment share

in 10,000 in %Year Total State Collective Other State Collective Other

owned owned owned owned1984 11890 8637 3216 37 72.6 27.0 0.31985 12358 8990 3324 44 72.7 26.9 0.41986 12809 9333 3421 55 72.9 26.7 0.41987 13214 9654 3488 72 73.1 26.4 0.51988 13608 9984 3527 97 73.4 25.9 0.71989 13742 10108 3502 132 73.5 25.5 1.01990 14059 10346 3549 164 73.6 25.2 1.21991 14508 10664 3628 216 73.5 25.0 1.51992 14792 10889 3621 282 73.6 24.5 1.91993 14849 10920 3393 536 73.5 22.9 3.61994 14849 10890 3211 747 73.3 21.6 5.01995 14908 10955 3076 877 73.5 20.6 5.91996 14845 10949 2954 942 73.8 19.9 6.31997 14668 10766 2817 1085 73.4 19.2 7.41998 12337 8809 1900 1628 71.4 15.4 13.21999 11773 8336 1652 1785 70.8 14.0 15.22000 11259 7878 1447 1935 70.0 12.9 17.22001 10792 7409 1241 2142 68.7 11.5 19.82002 10558 6924 1071 2563 65.6 10.1 24.32003 10492 6621 951 2920 63.1 9.1 27.82004 10576 6438 851 3287 60.9 8.0 31.1Data Source: The information is extracted from Table 1-14 of the 2005 China Labour

Statistical Yearbook.

30

Table 2: Descriptive statistics1989 1991 1993 1997 2000 2004

Earnings ratio 0.841 0.826 0.808 0.827 0.838 0.865A: Males

Employment rate 0.904 0.865 0.835 0.704 0.610 0.445No. of earners 1.115 1.171 1.086 0.991 0.761 0.490Schooling (entire sample) 8.273 8.852 9.280 9.524 10.063 10.442Schooling (employed) 9.580 9.767 9.962 10.524 10.927 11.200Monthly earnings 43.80 53.99 73.12 97.40 147.62 197.07Age 36.44 36.62 37.80 38.23 39.69 41.38Proportion of SOU employees 0.705 0.719 0.707 0.640 0.611 0.611Proportion of COE employees 0.259 0.251 0.246 0.269 0.226 0.115Hours per day 7.938 7.979 7.851 7.890 7.919 8.008

B: FemalesEmployment rate 0.791 0.717 0.714 0.575 0.478 0.330No. of earners 1.331 1.337 1.172 1.068 0.840 0.572Schooling (entire sample) 6.823 7.469 7.815 8.323 9.023 9.433Schooling (employed) 9.138 9.444 9.466 10.092 10.833 11.412Monthly earnings 36.83 44.61 59.07 80.53 123.75 170.37Age 34.00 33.65 35.03 35.35 37.04 38.15Proportion of SOU employees 0.615 0.641 0.639 0.599 0.572 0.575Proportion of COE employees 0.343 0.329 0.321 0.276 0.247 0.120Hours per day 7.845 7.937 7.894 7.795 7.812 7.959

Note: The sample consists of individuals aged between 17 and 60. “No. of earners” refers tothe number of employed adults in the household excluding the respondent. Employmentrate, the “No. of earners” and years of schooling are calculated using the entire sample.All the other statistics are calculated using workers who reported a positive amount ofmonthly wages.

31

Table 3: Estimation results from earnings regressions1989 1991 1993 1997 2000 2004(1) (2) (3) (4) (5) (6)

A: Gender .1673 .1723 .1834 .1787 .1779 .1663(.0190)∗∗∗ (.0155)∗∗∗ (.0222)∗∗∗ (.0203)∗∗∗ (.0244)∗∗∗ (.0287)∗∗∗

B: Gender .1234 .1281 .1674 .1614 .1633 .1546(.0185)∗∗∗ (.0155)∗∗∗ (.0226)∗∗∗ (.0211)∗∗∗ (.0248)∗∗∗ (.0280)∗∗∗

Schooling .0273 .0211 .0035 .0138 .0351 .0762(.0038)∗∗∗ (.0029)∗∗∗ (.0048) (.0049)∗∗∗ (.0064)∗∗∗ (.0057)∗∗∗

Experience .0266 .0180 .0270 .0196 .0179 .0119(.0035)∗∗∗ (.0032)∗∗∗ (.0048)∗∗∗ (.0055)∗∗∗ (.0056)∗∗∗ (.0055)∗∗

Experience 2 -.0003 -.00008 -.0004 -.0003 -.0003 -.00008(.00007)∗∗∗ (.00006) (.0001)∗∗∗ (.0001)∗∗ (.0001)∗∗ (.0001)

C: Gender .1247 .1308 .1712 .1655 .1627 .1532(.0187)∗∗∗ (.0156)∗∗∗ (.0216)∗∗∗ (.0206)∗∗∗ (.0248)∗∗∗ (.0276)∗∗∗

Schooling .0288 .0262 .0152 .0184 .0340 .0747(.0036)∗∗∗ (.0029)∗∗∗ (.0049)∗∗∗ (.0051)∗∗∗ (.0066)∗∗∗ (.0060)∗∗∗

Experience .0270 .0189 .0317 .0237 .0182 .0124(.0035)∗∗∗ (.0031)∗∗∗ (.0047)∗∗∗ (.0053)∗∗∗ (.0057)∗∗∗ (.0055)∗∗

Experience 2 -.0003 -.00009 -.0005 -.0004 -.0003 -.00008(.00007)∗∗∗ (.00006) (.0001)∗∗∗ (.0001)∗∗∗ (.0001)∗∗ (.0001)

SOU employees -.0696 -.2958 -.5530 -.2653 -.0137 -.0145(.0752) (.0739)∗∗∗ (.1036)∗∗∗ (.0527)∗∗∗ (.0471) (.0386)

Collectives -.0461 -.2182 -.3213 -.2573 -.0609 -.1392(.0781) (.0764)∗∗∗ (.1050)∗∗∗ (.0555)∗∗∗ (.0535) (.0545)∗∗

D: Gender .1237 .1308 .1686 .1639 .1634 .1656(.0186)∗∗∗ (.0155)∗∗∗ (.0213)∗∗∗ (.0201)∗∗∗ (.0242)∗∗∗ (.0266)∗∗∗

Schooling .0268 .0249 .0185 .0199 .0347 .0732(.0038)∗∗∗ (.0029)∗∗∗ (.0053)∗∗∗ (.0050)∗∗∗ (.0067)∗∗∗ (.0060)∗∗∗

Experience .0277 .0197 .0295 .0253 .0197 .0187(.0035)∗∗∗ (.0031)∗∗∗ (.0047)∗∗∗ (.0049)∗∗∗ (.0051)∗∗∗ (.0052)∗∗∗

Experience 2 -.0003 -.0001 -.0004 -.0004 -.0003 -.0002(.00007)∗∗∗ (.00006)∗ (.0001)∗∗∗ (.0001)∗∗∗ (.0001)∗∗∗ (.0001)∗∗

SOU employees -.0894 -.3016 -.5405 -.2320 .0105 .0364(.0763) (.0689)∗∗∗ (.1025)∗∗∗ (.0522)∗∗∗ (.0454) (.0376)

Collectives -.0690 -.2410 -.3312 -.2951 -.0847 -.1393(.0790) (.0710)∗∗∗ (.1037)∗∗∗ (.0559)∗∗∗ (.0498)∗ (.0518)∗∗∗

Number of obs. 1535 1474 1235 1267 1203 899Note: The sample consists of individuals aged between 17 and 60. In panel D, the residence of

province is also used as a control variable. The log of real monthly average earnings isused as the dependent variable. Numbers in parenthesis are standard errors. ∗∗∗ meanssignificant at the 1% level, ∗∗ means significant at the 5% level and ∗ means significantat the 10% level. The standard errors are corrected for the potential correlation amongindividuals of the sample household.

32

Table 4: Estimation results from, control for daily working hours1989 1991 1993 1997 2000 2004(1) (2) (3) (4) (5) (6)

Gender .1198 .1271 .1671 .1632 .1681 .1628(.0186)∗∗∗ (.0154)∗∗∗ (.0212)∗∗∗ (.0200)∗∗∗ (.0246)∗∗∗ (.0271)∗∗∗

Schooling .0267 .0253 .0196 .0201 .0347 .0732(.0038)∗∗∗ (.0029)∗∗∗ (.0052)∗∗∗ (.0050)∗∗∗ (.0068)∗∗∗ (.0060)∗∗∗

Experience .0276 .0196 .0290 .0251 .0207 .0181(.0035)∗∗∗ (.0031)∗∗∗ (.0044)∗∗∗ (.0049)∗∗∗ (.0053)∗∗∗ (.0053)∗∗∗

Experience 2 -.0003 -.0001 -.0004 -.0004 -.0003 -.0002(.00007)∗∗∗ (.00006) (.00009)∗∗∗ (.0001)∗∗∗ (.0001)∗∗∗ (.0001)∗

Hours per day .0356 .0181 .0181 .0305 -.0013 .0203(.0166)∗∗ (.0157) (.0164) (.0141)∗∗ (.0152) (.0109)∗

SOU employees -.0919 -.2976 -.5520 -.2058 -.0114 .0437(.0760) (.0692)∗∗∗ (.1040)∗∗∗ (.0525)∗∗∗ (.0475) (.0378)

Collectives -.0756 -.2434 -.3435 -.2727 -.0943 -.1353(.0788) (.0717)∗∗∗ (.1051)∗∗∗ (.0564)∗∗∗ (.0523)∗ (.0524)∗∗∗

Number of obs. 1528 1460 1222 1247 1154 875Note: The sample consists of individuals aged between 17 and 60. The log of real monthly

average earnings is used as the dependent variable. The residence of province is also usedas a control variable. Numbers in parenthesis are standard errors. ∗∗∗ means significantat the 1% level, ∗∗ means significant at the 5% level and ∗ means significant at the 10%level. The standard errors are corrected for the potential correlation among individualsof the sample household.

33

Table 5: Decomposition of changes in the gender earnings gap1989-1993 1993-1997 1997-2004 1989-2004

(1) (2) (3) (4)Changes in the earnings gap 0.016 -0.009 -0.008 -0.001Observed X ′s: 0.006 -0.019 -0.046 -0.058

Schooling 0.001 -0.001 -0.042 -0.043Experience 0.008 0.006 0.022 0.034Experience 2 -0.013 -0.004 -0.014 -0.026SOU employees 0.015 0.011 -0.000 -0.002Collectives -0.003 -0.030 -0.000 -0.008Provinces -0.002 -0.001 -0.012 -0.013

Observed prices: -0.038 0.013 0.041 0.016Schooling -0.006 -0.001 0.023 0.017Experience 0.006 -0.009 -0.010 -0.010Experience 2 -0.018 0.000 0.017 -0.006SOU employees -0.038 0.016 0.017 0.023Collectives 0.017 0.006 -0.002 -0.003Provinces 0.001 0.001 -0.005 -0.005

Gap effect: 0.024 -0.009 0.010 0.015Unobserved prices: 0.024 0.006 -0.013 0.026

Mean female residual percentile1989 40.31 40.311993 39.49 39.491997 39.57 39.572004 40.03 40.03

Note: The sample consists of individuals aged between 17 and 60. The prices of observedcharacteristics used in the decomposition are estimated using male sample.

34

Tab

le6:

The

dete

rmin

ants

ofem

ploy

men

tra

teM

ales

Fem

ales

1989

-19

91-

1993

-19

97-

2000

-19

89-

1991

-19

93-

1997

-20

00-

1991

1993

1997

2000

2004

1991

1993

1997

2000

2004

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Ear

ning

sin

1st

wav

e-.00

72-.01

18.0

080

.050

9.0

429

.000

8-.07

16.0

603

.027

8.0

569

(.0126)

(.0218)

(.0505)

(.0470)

(.0448)

(.0200)

(.0375)∗

(.0655)

(.0578)

(.0545)

Age

>54

-.22

32-.15

25-.33

23-.17

90-.06

87-.55

39-.65

96-.64

64-.48

54-.17

74(.0692)∗∗∗

(.0706)∗∗

(.1083)∗∗∗

(.0800)∗∗

(.0801)

(.1286)∗∗∗

(.1267)∗∗∗

(.0780)∗∗∗

(.0992)∗∗∗

(.1133)

54≥

age

>50

-.03

24-.08

04.1

149

-.06

69.1

099

-.36

23-.42

71-.39

87-.16

81-.15

90(.0381)

(.0557)

(.0580)∗∗

(.0706)

(.0615)∗

(.1038)∗∗∗

(.1172)∗∗∗

(.1004)∗∗∗

(.1017)∗

(.0782)∗∗

50≥

age

>45

-.02

29.0

017

.100

3.1

128

.136

4-.02

31-.08

39-.12

98.0

674

-.00

23(.0278)

(.0301)

(.0508)∗∗

(.0499)∗∗

(.0593)∗∗

(.0434)

(.0586)

(.0819)

(.0633)

(.0753)

Age

<25

-.05

42-.08

46-.19

20-.03

88.1

884

.003

2-.11

73-.22

43.0

395

-.03

52(.0403)

(.0503)∗

(.1318)

(.0921)

(.1284)

(.0301)

(.0637)∗

(.1315)∗

(.0949)

(.1574)

Scho

olin

g.0

015

.000

4.0

168

.015

1.0

185

.009

0.0

087

.021

0.0

188

.055

1(.0016)

(.0028)

(.0079)∗∗

(.0074)∗∗

(.0085)∗∗

(.0026)∗∗∗

(.0040)∗∗

(.0090)∗∗

(.0084)∗∗

(.0102)∗∗∗

No.

ofea

rner

s.0

102

.052

0.1

061

.061

8.1

840

.010

0.0

565

.065

7.0

947

.091

3(.0062)

(.0112)∗∗∗

(.0291)∗∗∗

(.0275)∗∗

(.0373)∗∗∗

(.0105)

(.0153)∗∗∗

(.0308)∗∗

(.0326)∗∗∗

(.0421)∗∗

SOU

empl

oyee

s.0

383

-.04

20-.10

65.2

183

.296

4.1

867

.006

3.2

411

.062

4.1

218

(.0490)

(.0466)

(.1176)

(.0812)∗∗∗

(.0705)∗∗∗

(.0777)∗∗

(.0673)

(.1294)∗

(.0794)

(.0783)

Col

lect

ives

-.01

23-.10

10-.11

86.1

505

.174

3.0

894

.014

4.1

118

-.04

28.1

938

(.0366)

(.1171)

(.1561)

(.0643)∗∗

(.0646)∗∗∗

(.0317)∗∗∗

(.0617)

(.1198)

(.0849)

(.0795)∗∗

Num

ber

ofob

s.67

061

036

851

146

954

250

934

445

040

4N

ote

:T

he

sam

ple

consi

sts

of

indiv

iduals

aged

bet

wee

n17

and

60

who

wer

ein

two

adja

cent

surv

eys

and

work

edin

the

firs

tof

the

two

surv

eys.

The

emplo

ym

ent

statu

s(=

1if

emplo

yed

)in

the

seco

nd

surv

eyis

use

das

the

dep

enden

tva

riable

.Log

earn

ings

and

the

type

of

work

unit

regis

trati

on

refe

rto

the

job

inth

efirs

tsu

rvey

.T

he

resi

den

ceofpro

vin

ceis

als

ouse

das

aco

ntr

olva

riable

.T

he

valu

esare

marg

inaleff

ect

of

the

corr

espondin

gva

riable

s.N

um

ber

sin

pare

nth

esis

are

standard

erro

rs.

∗∗∗

mea

ns

signifi

cant

at

the

1%

level

,∗∗

mea

ns

signifi

cant

at

the

5%

level

and

∗m

eans

signifi

cant

at

the

10%

level

.T

he

standard

erro

rsare

corr

ecte

dfo

rth

epote

nti

alco

rrel

ati

on

am

ong

indiv

iduals

of

the

sam

ple

house

hold

.

35

.75

.8.8

5.9

.95

Fem

ale

s e

arn

ings/M

ale

s e

arn

ings

20 40 60 80Percentile

1989 1991

1993

.75

.8.8

5.9

.95

Fem

ale

s e

arn

ings/M

ale

s e

arn

ings

20 40 60 80Percentile

1997 2000

2004

Figure 1: Earnings ratios at selected percentiles

36

.05

.1.1

5.2

1988 1990 1992 1994 1996 1998 2000 2002 2004year

10th percentile 50th percentile90th percentile

Figure 2: The gender earnings gap at the 10th, 50th and 90th percentile

.05

.1.1

5.2

.25

1988 1990 1992 1994 1996 1998 2000 2002 2004year

25th percentile 50th percentile75th percentile

Figure 3: The gender earnings gap at the 25th, 50th and 75th percentile

37

−.8

−.6

−.4

−.2

0.2

1988 1990 1992 1994 1996 1998 2000 2002 2004year

10th percentile 50th percentile90th percentile

Figure 4: Coefficient on SOU at the 10th, 50th and 90th percentile

−.8

−.6

−.4

−.2

0.2

1988 1990 1992 1994 1996 1998 2000 2002 2004year

25th percentile 50th percentile75th percentile

Figure 5: Coefficient on SOU at the 25th, 50th and 75th percentile

38

−.6

−.4

−.2

0.2

1988 1990 1992 1994 1996 1998 2000 2002 2004year

10th percentile 50th percentile90th percentile

Figure 6: Coefficient on collective at the 10th, 50th and 90th percentile

−.6

−.4

−.2

0

1988 1990 1992 1994 1996 1998 2000 2002 2004year

25th percentile 50th percentile75th percentile

Figure 7: Coefficient on collective at the 25th, 50th and 75th percentile

39

.88

.9.9

2.9

4.9

6.9

8

1 2 3 4 5Earning Quintile

(a) 89−91

Male Female

.75

.8.8

5.9

.95

1 2 3 4 5Earning Quintile

(b) 91−93

Male Female

.55

.6.6

5.7

.75

.8

1 2 3 4 5Earning Quintile

(c) 93−97

Male Female

.5.5

5.6

.65

.7.7

5

1 2 3 4 5Earning Quintile

(d) 97−100

Male Female

.3.4

.5.6

.7

1 2 3 4 5Earning Quintile

(e) 100−104

Male Female

.85

.9.9

51

1 2 3 4 5Earning Quintile

(f) 89−91

Male Female

.7.7

5.8

.85

.9.9

5

1 2 3 4 5Earning Quintile

(g) 91−93

Male Female

.5.6

.7.8

1 2 3 4 5Earning Quintile

(h) 93−97

Male Female

.55

.6.6

5.7

.75

1 2 3 4 5Earning Quintile

(i) 97−100

Male Female

.4.5

.6.7

1 2 3 4 5Earning Quintile

(j) 100−104

Male Female

Figure 8: Employment rate of those who worked in the 1st wave, by earnings deciles

40

APPENDIX

Table A1: Quantile regression results, 10th percentile1989 1991 1993 1997 2000 2004(1) (2) (3) (4) (5) (6)

Gender .0956 .0812 .0856 .1864 .1050 .1452(.0288)∗∗∗ (.0213)∗∗∗ (.0246)∗∗∗ (.0454)∗∗∗ (.0453)∗∗ (.0352)∗∗∗

Schooling .0371 .0367 .0319 .0192 .0347 .0674(.0051)∗∗∗ (.0036)∗∗∗ (.0037)∗∗∗ (.0087)∗∗ (.0086)∗∗∗ (.0072)∗∗∗

Experience .0394 .0277 .0335 .0265 .0197 .0103(.0048)∗∗∗ (.0035)∗∗∗ (.0041)∗∗∗ (.0079)∗∗∗ (.0078)∗∗ (.0066)

Experience 2 -.0005 -.0002 -.0004 -.0005 -.0003 -.00004(.0001)∗∗∗ (.00007)∗∗∗ (.00009)∗∗∗ (.0002)∗∗∗ (.0002) (.0001)

SOU -.0066 -.1151 -.1776 -.1554 .0503 .0215(.0728) (.0612)∗ (.0557)∗∗∗ (.0850)∗ (.0683) (.0462)

COE -.0922 -.2082 -.1279 -.1960 -.0218 -.3183(.0755) (.0621)∗∗∗ (.0569)∗∗ (.0865)∗∗ (.0723) (.0618)∗∗∗

Number of obs. 1535 1474 1235 1267 1203 899Note: The sample consists of individuals aged between 17 and 60. The log of real monthly

average earnings is used as the dependent variable. The residence of province is also usedas a control variable. Numbers in parenthesis are standard errors. ∗∗∗ means significantat the 1% level, ∗∗ means significant at the 5% level and ∗ means significant at the 10%level. The standard errors are corrected for the potential correlation among individualsof the sample household.

41

Table A2: Quantile regression results, 25th percentile1989 1991 1993 1997 2000 2004(1) (2) (3) (4) (5) (6)

Gender .0984 .0800 .1103 .1464 .1622 .1975(.0157)∗∗∗ (.0126)∗∗∗ (.0211)∗∗∗ (.0258)∗∗∗ (.0244)∗∗∗ (.0475)∗∗∗

Schooling .0341 .0323 .0213 .0200 .0321 .0625(.0028)∗∗∗ (.0022)∗∗∗ (.0037)∗∗∗ (.0050)∗∗∗ (.0047)∗∗∗ (.0093)∗∗∗

Experience .0332 .0275 .0328 .0294 .0253 .0158(.0025)∗∗∗ (.0021)∗∗∗ (.0037)∗∗∗ (.0047)∗∗∗ (.0045)∗∗∗ (.0085)∗

Experience 2 -.0003 -.0002 -.0004 -.0005 -.0004 -.0002(.00005)∗∗∗ (.00004)∗∗∗ (.00008)∗∗∗ (.0001)∗∗∗ (.0001)∗∗∗ (.0002)

SOU .0235 -.1285 -.2313 -.1024 .1012 .0420(.0412) (.0358)∗∗∗ (.0517)∗∗∗ (.0452)∗∗ (.0364)∗∗∗ (.0617)

COE -.0491 -.1497 -.0784 -.1399 .0160 -.1003(.0422) (.0366)∗∗∗ (.0531) (.0484)∗∗∗ (.0393) (.0837)

Number of obs. 1535 1474 1235 1267 1203 899Note: The sample consists of individuals aged between 17 and 60. The log of real monthly

average earnings is used as the dependent variable. The residence of province is also usedas a control variable. Numbers in parenthesis are standard errors. ∗∗∗ means significantat the 1% level, ∗∗ means significant at the 5% level and ∗ means significant at the 10%level. The standard errors are corrected for the potential correlation among individualsof the sample household.

Table A3: Median regression results1989 1991 1993 1997 2000 2004(1) (2) (3) (4) (5) (6)

Gender .0958 .0757 .1364 .1449 .1667 .1336(.0178)∗∗∗ (.0184)∗∗∗ (.0324)∗∗∗ (.0217)∗∗∗ (.0284)∗∗∗ (.0399)∗∗∗

Schooling .0304 .0259 .0157 .0233 .0404 .0726(.0032)∗∗∗ (.0033)∗∗∗ (.0059)∗∗∗ (.0043)∗∗∗ (.0057)∗∗∗ (.0077)∗∗∗

Experience .0269 .0234 .0305 .0283 .0244 .0229(.0027)∗∗∗ (.0030)∗∗∗ (.0054)∗∗∗ (.0037)∗∗∗ (.0050)∗∗∗ (.0072)∗∗∗

Experience 2 -.0002 -.0001 -.0005 -.0004 -.0004 -.0003(.00006)∗∗∗ (.00006)∗∗ (.0001)∗∗∗ (.00008)∗∗∗ (.0001)∗∗∗ (.0002)∗∗

SOU -.0342 -.2897 -.5133 -.1558 .0132 .0812(.0465) (.0539)∗∗∗ (.0788)∗∗∗ (.0374)∗∗∗ (.0418) (.0496)

COE -.0466 -.2614 -.2848 -.2691 -.1094 -.0579(.0478) (.0551)∗∗∗ (.0816)∗∗∗ (.0400)∗∗∗ (.0455)∗∗ (.0691)

Number of obs. 1535 1474 1235 1267 1203 899Note: The sample consists of individuals aged between 17 and 60. The log of real monthly

average earnings is used as the dependent variable. The residence of province is also usedas a control variable. Numbers in parenthesis are standard errors. ∗∗∗ means significantat the 1% level, ∗∗ means significant at the 5% level and ∗ means significant at the 10%level. The standard errors are corrected for the potential correlation among individualsof the sample household.

42

Table A4: Quantile regression results, 75th percentile1989 1991 1993 1997 2000 2004(1) (2) (3) (4) (5) (6)

Gender .1126 .1176 .2045 .1435 .1825 .1203(.0209)∗∗∗ (.0240)∗∗∗ (.0350)∗∗∗ (.0285)∗∗∗ (.0293)∗∗∗ (.0235)∗∗∗

Schooling .0321 .0193 .0179 .0241 .0366 .0726(.0039)∗∗∗ (.0044)∗∗∗ (.0065)∗∗∗ (.0055)∗∗∗ (.0060)∗∗∗ (.0046)∗∗∗

Experience .0218 .0186 .0285 .0184 .0101 .0206(.0030)∗∗∗ (.0037)∗∗∗ (.0055)∗∗∗ (.0046)∗∗∗ (.0049)∗∗ (.0041)∗∗∗

Experience 2 -.0001 -.00007 -.0005 -.0002 -.00005 -.0003(.00006)∗∗ (.00008) (.0001)∗∗∗ (.0001)∗∗ (.0001) (.00009)∗∗∗

SOU -.2005 -.3881 -.8006 -.3130 -.0361 .0521(.0546)∗∗∗ (.0710)∗∗∗ (.0864)∗∗∗ (.0472)∗∗∗ (.0421) (.0291)∗

COE -.1482 -.3079 -.5825 -.3793 -.1793 -.0860(.0563)∗∗∗ (.0726)∗∗∗ (.0888)∗∗∗ (.0515)∗∗∗ (.0463)∗∗∗ (.0409)∗∗

Number of obs. 1535 1474 1235 1267 1203 899Note: The sample consists of individuals aged between 17 and 60. The log of real monthly

average earnings is used as the dependent variable. The residence of province is also usedas a control variable. Numbers in parenthesis are standard errors. ∗∗∗ means significantat the 1% level, ∗∗ means significant at the 5% level and ∗ means significant at the 10%level. The standard errors are corrected for the potential correlation among individualsof the sample household.

Table A5: Quantile regression results, 90th percentile1989 1991 1993 1997 2000 2004(1) (2) (3) (4) (5) (6)

Gender .1432 .1925 .2016 .1835 .1695 .0741(.0388)∗∗∗ (.0285)∗∗∗ (.0501)∗∗∗ (.0429)∗∗∗ (.0412)∗∗∗ (.0389)∗

Schooling .0212 .0210 .0136 .0247 .0323 .0759(.0078)∗∗∗ (.0052)∗∗∗ (.0086) (.0078)∗∗∗ (.0094)∗∗∗ (.0082)∗∗∗

Experience .0204 .0065 .0205 .0138 .0018 .0156(.0056)∗∗∗ (.0043) (.0077)∗∗∗ (.0071)∗ (.0069) (.0067)∗∗

Experience 2 -.0002 .0001 -.0003 -.0002 .00002 -.00003(.0001)∗ (.00009)∗ (.0002)∗ (.0002) (.0002) (.0001)

SOU -.5401 -.3984 -.8332 -.4220 -.1242 -.0607(.0986)∗∗∗ (.0800)∗∗∗ (.1162)∗∗∗ (.0688)∗∗∗ (.0588)∗∗ (.0492)

COE -.3013 -.1682 -.6117 -.5186 -.1825 -.2717(.1011)∗∗∗ (.0819)∗∗ (.1202)∗∗∗ (.0743)∗∗∗ (.0650)∗∗∗ (.0636)∗∗∗

Number of obs. 1535 1474 1235 1267 1203 899Note: The sample consists of individuals aged between 17 and 60. The log of real monthly

average earnings is used as the dependent variable. The residence of province is also usedas a control variable. Numbers in parenthesis are standard errors. ∗∗∗ means significantat the 1% level, ∗∗ means significant at the 5% level and ∗ means significant at the 10%level. The standard errors are corrected for the potential correlation among individualsof the sample household.

43

Table A6: Decomposition of changes in the gender earnings gap1989-1993 1993-1997 1997-2004 1989-2004

Changes in the earnings gap 0.016 -0.009 -0.008 -0.001Observed X ′s: 0.007 -0.029 -0.043 -0.060

Schooling 0.001 -0.001 -0.039 -0.041Experience 0.007 0.005 0.019 0.029Experience 2 -0.011 -0.003 -0.011 -0.020SOU employees 0.017 0.018 0.000 0.007Collectives -0.004 -0.046 -0.000 -0.020Provinces -0.004 -0.001 -0.011 -0.013

Observed prices: -0.038 0.015 0.040 0.012Schooling -0.006 -0.002 0.021 0.016Experience 0.001 -0.016 -0.003 -0.015Experience 2 -0.014 0.008 0.009 -0.002SOU employees -0.042 0.004 0.020 0.012Collectives 0.020 0.018 -0.002 0.006Provinces 0.002 0.002 -0.005 -0.005

Gap effect: 0.022 -0.005 0.010 0.019Unobserved prices: 0.036 0.033 0.001 0.078

Mean female residual percentile1989 40.362 40.3621993 39.471 39.4711997 39.607 39.6072004 39.954 39.954

Note: The sample consists of individuals aged between 17 and 60. Blau and Kahn (1997)decomposition of changes in gender earnings gap. The prices of observed characteristicsused in the Blau and Kahn (1997) decomposition are estimated using male sample. Weuse the full maximum likelihood estimates of the Heckman selection model to account forthe potential sample selection bias.

44