2008-E-27

Gender-Wage Difference in Japanese Subsidiaries in China, a Statistical Analysis Using Intra-firm Personnel Data
Fengye Sun
Graduate School of Economics,
Waseda University
February 2009

Abstract
This paper examines male-female wage determination and gender wage discrimination in Japanese affiliated subsidiaries in China using intra-firm personnel data. It isfound that female wages in a Japanese enterprise can be explained by the human capitalmodel, but that gender wage gaps not explained by observed differences in productivity
still exist, accounting for about 71% by the Oaxaca (1973) decomposition method. The
estimated results of the wage equation indicate that female employees receive lower returns on experiences compared to their male counterparts and, similar to firms in Japan,
marriage has a negative effect on female wages. But returns on tenure and educationlevel seem to have no gender differences, considering that the proportion of female managerial staff in China is larger than most firms in Japan. It seems that Japanese firms
make better use of female employees in China than they do in Japan.
Key words: Gender wage differences; Discrimination; Female employment

11. Introduction
Social status of women is different among Asian countries, thus making it a very
interesting topic to study. The treatment of women in firms is one of the ways to estimate the social status of women. In this study, in order to evaluate whether Japanesesubsidiaries lower gender inequality in China, we examine the wage gap between men
and women in a Japanese subsidiary in China. Although the common thinking is “thesame work, the same pay”, substantially lower wages are paid to female workers compared to male workers in many countries, especially Japan. According to International
Labor Organization (ILO), Japan’s gender wage gap is the largest among developed
countries. According to the wage census conducted by the Ministry of Internal Affairs
and Communications of Japan, in 2005 the wage for women was about 64.9% of wage
for men. On the other hand, in China the situation differs from Japan. According to the2000 Population Census of China, women’s wage is from 74.7% to 88.1% of men’s
wage. What is the cause of the wage gap between men and women? Can the gap be attributed to firm HRM system or country context? Do the same gaps exist in Japanesesubsidiaries in China?
To analyze this topic, we firstly think about the cause of gender gaps. The wage
differential is caused by many factors, two of which stand out from previous literature.
One, this paper’s explainable component, is a difference in productivity; the other, thispaper’s unexplained component, is often referred to as discrimination. So called statistical discrimination is a major contributor to the large wage gap in Japan. Most Japanese
females work in their twenties, resign when they marry or become pregnant around 30
years old, and then return to work later. As we could find in Figure 1, this process
makes the trend of the female labor supply similar to the shape of the letter M. Becauseof shorter female tenures compared to male employees, employers are not willing to
make investment in females since the expected returns are smaller. Compared to Japan,
most female employees in China do not resign after marrying or becoming pregnant.
They continuously work until retirement like males. For this reason, assuming that otherconditions are the same, wage differences in China would be expected to be smaller
than in Japan. We can say that Japanese enterprises are exploiting female workers morein China than in Japan. We are interested specifically in gender differences in treatment
within Japanese enterprises embarked in China.

2

Figure1. Female Labor Supply in Japan and China*The data for Japan are from Japanese Ministry of Internal Affairs and Communications, Statistics Bureau, The data for
China are from the National Bureau of Statistics of China, 2000’s Population Census of China.

In this paper, we will research a Japanese firm embarked in China by interview
and statistical analysis. We will examine the wage difference by estimating the wage
function, and then decompose the two factors, the explained component and the unexplained component.
The rest of the paper is organized as follows. Section 2 lays out previous studies
on gender wage difference in Japan and China. Section 3 introduces firm under investigation and data. Section 4 discusses the research method. Section 5 shows the estimation results, and conclusions are discussed in Section 6.
2. Previous studies and hypothesis
There are many empirical studies that analyze gender wage differences in Japanand China. What is the cause of it? Can it be explained in terms of human capital, statistical discrimination, employer discrimination, or discrimination due to choice of occupation? (Abe 2005) The answer is different for each country.
To analyze the cause of gender wage difference in Japan, Kawaguchi (2005)
tested the effects of marriage and children. It was found that marriage has a positive effect on male wage but no effect on female wage, while having children has a positiveeffect on male wage but a negative effect on female wage. Sano (2005) and Kawaguchi(2007) tested the existence of employer discrimination using Japanese firm level panel3

data. Their results fail to reject the hypothesis that employer discrimination is a sourceof male-female wage gap. However, the estimated effect on profit of female employees
is one twentieth of the predicted coefficient, which they calculated based on the assumption that the gender wage gap is a product of gender discrimination. This result
suggests that the large portion of the gender wage gap is due to differences in gender
productivity. However, Nakada(1997) found that the gender productivity gap is due tothe tenure gap between males and females. Most Japanese females retire after marriage
or having children, which leads to shorter tenure compared to males. Thus, employers
are more willing to invest in male employees than female employees. As a result, theaccumulated productivity of females is smaller compared to males.
Yashiro (1980), classifying factors for the gender wage gap into levels of education, size of firm, and length of tenure, found that tenure can explain 46.8% of the gap.
Ogura (2004), researching a firm where the tenure of females had become as long as
males, found that the gender wage gap had clearly decreased. However, the main factor
in raising the average female wage was the birth of the high wage female employee.
Wages of other female employees were still as low as before.
Regarding the cause of the gender wage gap in China, most studies discuss theeffect of market liberalization on the earnings of Chinese females. For example,
Meng(1998) examined male-female wage determination and gender wage discrimination in the newly developed rural industrial sector in China using survey data. It was
found that since the labor market was liberalized, wage discrimination has accounted for
all non-market gender wage differences, while accounting for only two thirds of differences within the market. Dong, Fiona, and Bowles (2004) argued that labor segmentation exists along gender lines in the newly privatized township and village enterprises inrural China. The result based on surveys indicates that there is a clear gender bias in favor of males.
The labor market in China is very complicated compared to Japan. The genderwage gap is related not only to differences in productivity, but also to region within
China, type of occupation, size of firm, and type of firm. Furthermore, the size of thefemale labor supply is higher than in Japan. Matsushige.,Xu (2002) found no gender
gap in promotion opportunities or wages among managerial staff using survey data fromfour Japanese firms in Dalian. Ma (2007) estimated the causes of the gender wage gaps
within Japan and China using survey data. It was found that the gender wage gap in Japan is larger than in China, the most important factor being the difference in length oftenure between Japanese and Chinese females.
However, there is still no data on female employees working at Japanese affiliated4subsidiaries in China. Intra-firm personnel data, although difficult to obtain, is necessary
to study this issue. This study attempts to estimate the gender wage difference by intra-firm personnel micro data from a Japanese firm in Shandong, China. First, the causes of gender wage gaps are tested by estimating the wage equation. Then, the composition of the gender wage gap is analyzed using the method of Oaxaca (1973), splitting
the total gender wage differential into two components, an explained component and an
unexplained component, by observed productivity characteristics.
3. Firm introduction and data
The firm under investigation is a Japanese capital enterprise in rural Qingdao,
Shandong Province, China. It was established in April 2000 with $7,390,000, 92% of
which is from a manufacturing firm and 8% from a trading company. The firm produces
electronic parts and mainly supplies Japanese firms located in the area. There are 358
Chinese employees and 2 Japanese managers. The descriptive statistics of Chinese employees by gender is presented in Table 1. We can see that the mean hourly wage of females is 5.1 yuan (about 80 yen), which is less than the mean male hourly wage of 8.35
yuan (about 130yen). The age and length of tenure are similar between males and females. But the mean education between genders is quite different. The mean level of
education of females is about 1.66 years less than males. About 31% of males and 44%
of females have been married. 31% of males and 33% of females have children. Finally,
there are 286 female employees, and 72 male employees. Yet, regarding the managerial
staff, 31 are female and 28 are male, which means that most female employees areworking at the production lines.
To understand the method for female wage determination in the firm, it would be
useful to first introduce the female child-care leave system. Like most firms in Shandong Province, female employees who become pregnant can take a five-month vacationand enjoy one hour per day to nurse until the baby is one year old. In this firm, females
are moved to a suitable position before their vacation and return to a reasonable position
when the vacation ends. Although females are somewhat more complicated than males,
the firm likes female employees and employs females as an aggressive means to a target.
The strategy of a firm usually affects its human resource management. The firm’s
chief embarkation target in China is to decrease production cost. As we can see from
Table 1, the labor cost is by far cheaper than in Japan, where the hourly wage is about
700 yen. Following its embarkation strategy, this firm employs a starting pay of minimum wage. Therefore, employee attachment is not strong; the mean tenure is only about52.5 years, which is shorter than the seven-year history of the firm. On-the-job training is
not considered as important as it is in Japan; new recruits only receive basic skill training. Salary increases and promotions are not performed every year like they are in Japan.
Instead, employees can be promoted at any time they are seen competent enough.
As explained by the general manager, the firm has been stagnant in recent years
because the headquarters in Japan has not sent orders. At the same time, more and more
plants and companies have been established in the area. This raises the demand of thelabor market, making it difficult to sew up a large enough cheap labor force. Peoplechange jobs for higher wages, especially young males seeking work in the city to supplement their income; young females often remain in their hometowns to care for theirfamilies. This leads to a larger supply of females compared to males, which might decrease the wage level for females. In the following section, the gender wage difference
will be analyzed.
The empirical analysis of this paper is based on personnel micro data collectedfrom the firm in summer 2007, the data covers age, length of tenure at the firm, level of
education, gender, job duties, monthly wage, monthly working hours, overtime working
hours, marital status, numbers of children, various allowances, and deductions.
4. Methodology
1) Model 1
The following model is used to estimate the wage equation by OLS method.
klog wi = a + å b j xij + ui

(i = 1, 2, , n)j =1

log wiNatural log hourly wageaxi

Constant termYears of experiencei
Squared years of experience
Cross term of experience and genderYears of tenure
Squared years of tenure
Cross term of tenure and gender
Dummy variable of genderii
Dummy variable of high school education
Dummy variable of university education
Dummy variable of managerial position
6

(1)

bi
ui

Cross term of managerial position and years of tenure
Cross term of managerial position and gender
Number of children
Cross term of number of children and gender
Dummy variable of marital status
Cross term of marital status and gender
Cross term of high school education and gender
Cross term of university education and gender
Coefficients of xi
Error term

The dependent variable log wi is the log hourly wage derived by dividing the monthly
wage by time worked per month. For the independent variables xi years of experienceand its square, years of tenure and its square, dummy variables of education level areused as observed productivity characteristics. Also, dummy variables of gender and its
cross terms are used to test differences in the gender wage structure. This is to say thatthe wage structure is different between females and males in the firm if any coefficientof gender and its cross term has a significant result. A positive coefficient indicates thatthe estimate for females is lower than males, and a negative coefficient indicates that it
is higher than males.
2) Model 2
As shown in Table1, the mean hourly wage of females is about 3.27 yuan lessthan males. How did this gap occur? According to the human resource method (Becker1973), it is due to the quantity gap in observed productivity characteristics: for example,
age, years of tenure, years of experience and education level. The more human capitalan employee has accumulated, the higher that person’s wage is. The lower average education level of females might be one of the factors leading to lower wages for females.
At the same time, different evaluations of the same human capital could also lead to awage difference. Therefore, the respective importance of the two factors must be investigated．
Here the procedure developed by Oaxaca (1973) to analyze the composition of
the gender wage gap, which splits the total gender wage differential into two components, is employed. One part of the differential is attributable to gender differences inobserved productivity characteristics, and the other is the residual gap attributable todifferences in male and female returns to these observed characteristics. This residual,7

or unexplained, component of the wage gap is generally attributed to discrimination, but
it could be due to differences in unobserved productivity characteristics. In this analysis,
decompositions of the gender wage gap calculated using the Oaxaca (1973) method under the assumption that the male wage structure would be the nondiscriminatory wage
structure, as is likely in Chinese labor market, are presented. In the first step, the wage
equation is regressed by gender using OLS method like model 1, except for the dummy
variable of gender and its cross terms, to get the evaluation of male and female humancapital.
klog wi = a f + å b jf xij + ui

(i Î F )(2)(i Î M )

(3)

j =1
k

log wi = a m + å b jm xij + ui
j =1

Where F is female employees
set. F Ç M = f , F È M = {1,2,..., n} .

set

and

M

is

male

employees

In the second step, the two factors of explained component and unexplained
component are calculated based on the male employees using regressed results as following.
klog w f = aˆ f + å bˆ jf x jf
j =1
klog wm = aˆ m + å bˆ jm x mj
j =1

Where

[

]

[

[

]

log w f = E åiÎF log wi , log w m = E åiÎM log wi , x jf = E åiÎF xij] , x = E[å
m
j

iÎM

xij

]

Then
log wim - log wif = a m + å b im x i - a f - å b i f x i
m

f

(4)
m

log wim and log wif are the average male and female hourly wages, respectively. x ifand x i are the average observed productivity characteristics for males and females,
respectively. Equation (4) could be rewritten as follows:()

log wim - log wif = [å b im x i - x i ] - [å x i ( b im - b i f ) + a m - a f ]
m

f

f

(5)

The first term on the right side of equation (5) is the differential attributable to genderdifferences in observed productivity characteristics, while the left terms yield the unex8

plained component: the wage gap due to differences in male and female returns on theseobserved productivity characteristics, which is generally attributed to discrimination.
There is an index number issue regarding the possibility that equation (5) could also be
written base on the female standard, which would yield different results for the twocomponents.
5. Empirical results
This study first examines the wage profile difference between males and females.
Table 2 reports the results of the wage equation estimated. As Becker’s human resource
method indicates, the rates of return on experience, length of tenure, and education level
are all statistically significant for a positive value. This indicates that wage increases
with a lot of experience, a long tenure, or a high level of education for both males andfemales, and the positive effect of tenure and experience decreases as time goes by. It
can also be estimated that the wages of managers are higher than the wages of workers.
However, the rates of return on the observed productivity characteristics are not all thesame between males and females. As can be seen, the results of gender cross terms,
cross terms of experience and gender and cross terms of marital status and gender are
statistically significant for negative values, indicating that the returns on experience andmarital status for females are lower compared to males, and the coefficient of the crossterms of length of tenure and gender is negative even though it is not statistically significant at the 10% level. Cross terms of managerial position and gender, cross terms ofnumber of children and gender, and cross terms of education level and gender were also
not statistically significant at the 10% level. It seems that female employees receive
similar treatment inside this firm.
Nevertheless, the female employees are exploited as there is still a gender wage
gap. As can be seen from the estimated results, the female employees receive lower returns compared to males. The next estimate determines how much the female penalty is.
Table 4 shows the results from a decomposition analysis following Oaxaca’s (1973)
procedure. The total predicted gender wage differential of 0.4364 (as calculated in thenatural logarithm) was divided into an explained and unexplained component. The explained component was estimated to add up to 0.1240, about 29% of the total genderwage gap, and the unexplained component summed up to 0.3104, about 71% of the total
gender wage gap.
6. Conclusions
This paper examines male-female wage determination and gender wage discrimi9

nation in a Japanese affiliated subsidiary in China using intra-firm personnel data. First,
the wage difference is examined by estimating the wage equation. Then, the factors of
the explained and unexplained components are decomposed.
From the wage regression by the OLS method, it appears that compared to males,
returns on experience is lower for female employees. This result corresponds with otherstudies about China’s gender wage gaps. Considering that the firm examined is locatedin a new town that was in the countryside before the economic reform, the lower returns
perhaps proceed from the character of the regional labor market. Males seek work in the
major cities to supplement their income, while females remain in their hometowns totake care of their families. Thus, there is a larger female labor supply.
Employers in the region might have a special bias against female employees,
paying lower wages to them than to male employees. Considering that Chinese females
do not resign after marrying or becoming pregnant as Japanese females, statistical discrimination might be weaker compared to Japan. However, as said by the personnel department manager during an interview, female employees on maternity leave or nursing
babies are given positions that make it easy for them to care for their children according
to the labor law. This period might cause female employees to increase their productivities, making their returns on tenure lower than male employees. However, the estimate
coefficient is not statistically significant at the 10% level. Other factors that might influence different returns are marital status, number of children, and level of education. It
was found that female married employees receive lower wages than male married employees, but level of education and the number of children have no gender difference.
The gender wage gaps were also decomposed according to the Oaxaca (1973)
decomposition method for the explained and unexplained components. About 71% of
the wage gaps could not explain by observer productivity characteristics.
From these results, it appears that female employees receive poor treatment compared to male employees when entering the firm. However, estimated results of thewage equation fail to support that there is gender discrimination inside the firm. Considering that there are 31 female managers and 28 male managers, as presented in Table 1,
it seems that the Japanese firm takes advantage of its female employees in China.
Finally, all the conclusions are based on estimated results from cross-section data.
There might be a bias regarding latent personal ability that could not be controlled for,which may influence the value and statistical significance of the coefficients. Panel datais necessary to obtain a better estimate in the future. In addition, this paper researched
only one enterprise. The results cannot be interpreted as representative for all possible
cases. More studies are expected to analyze the gender wage different in Japanese affi10

liated subsidiaries in China in the future.

Acknowledgement
The author would like to thank Hiroshi Saito, Hitoshi Miyabe and Takeshi Miyahara for introducing the research objective, and appreciates the firm offering valuable
data. As promised, here the firm’s name remains anonymous. She would like to thankProfessor Mitsuhide Shiraki (Waseda University) and two referees for helpful commentand suggestions. She takes responsibility for any remaining errors.

11Appendix
Table 1．descriptive statistics of the firm
Variable

Male

Female

Total

Obs

Mean

Std. Dev.

Min

Max

hourly wage

8.35

4.38

3.78

23.98

Age

25.71

6.62

17.00

53.00

11.32

1.91

9.00

16.00

2.42

2.13

0.08

6.92

married person

0.31

0.46

0.00

1.00

kids

0.31

0.55

0.00

2.00

hourly wage

5.10

2.32

2.33

24.53

Age

25.06

4.83

16.00

49.00

9.66

1.41

9.00

16.00

2.62

2.39

0.08

7.17

married person

0.44

0.50

0.00

1.00

kids

0.33

0.51

0.00

2.00

education years
tenureeducation years
tenure

72

286

358

manager

28

31

59

12

Table2. Result estimates of the wage equation
dependent variable: natural log hourly wage
Coef.

Std. Err.

Sign.level

Experience

0.03710.0111***

Squared years of experience-0.0006

0.0002***

Cross term of experience and gender-0.02070.0095**

Tenure0.10350.0341***

Squared years of tenure-0.0060

0.0030**

Cross term of tenure and gender-0.0391

0.0350female dummy

0.02080.0837

Manager dummy

0.24780.1061

Cross term of manager and gender

0.07140.1401

Cross term of tenure and manager

0.02500.0254

Kids dummy-0.35320.2172

Cross term of kids and gender

0.35150.2189Marriage dummy

0.35110.2215

Cross term of marriage and gender-0.4074

0.2232*High school dummy

0.1431

0.0696**

University dummy

0.74510.1055***

Cross term of High school and gender-0.00850.0767

Cross term of university and gender

0.02220.1245Constant term

1.1877

0.0931*****

Adjusted R-squared : 0.7624
Number of obs: 358
t-statistics reported in parentheses are calculated using heteroskedasticity-constant standard errors.
*** Significant at the 1% level,* *Significant at the 5% level,* Significant at the 10% level
High school dummy and University dummy are based on middle school

13

Table3. Result estimates of the wage equation by gender
Coef.

Std. Err.

Sign.level

Experience

0.01160.0063*

Squared years of experience-0.0004

0.0002*

FemaleTenure0.07550.0178***286

Squared years of tenure-0.0076

0.0027***

Manager dummy

0.49670.0462***

Cross term of tenure and manager-0.00560.0019***Kids dummy

0.00350.0263

Marriage dummy-0.0580

0.0323*High school dummy

0.13470.0394***

University dummy

0.7471

0.0972***

Constant term

1.21590.0519***Experience

0.04110.0153***

Squared years of experience-0.0008

0.0004**

Tenure0.00340.0783

Squared years of tenure

0.01190.0123

Manager dummy

0.32400.1076

Male

Cross term of tenure and manager-0.0006

0.0131

72

Kids dummy-0.2715

0.2188Marriage dummy

0.25680.2220High school dummy

0.14480.0800*

University dummy

0.68570.1386***

Constant term

1.37950.3131***

Adjusted R-squared : 0.729
Number of obs: 286***

Adjusted R-squared : 0.6707
Number of obs: 72
t-statistics reported in parentheses are calculated using heteroskedasticity-constant standard errors.
*** Significant at the 1% level,* *Significant at the 5% leve,l* Significant at the 10% level
High school dummy and University dummy are based on middle school

14

Table4. Detail decomposition of the wage differential
Explained component

Unexplained component

Total

Experience

0.20000.1692-0.0307

Squared years of experience-0.0314-0.0382-0.0068

Tenure-0.1886-0.1892-0.0007

Squared years of tenure

0.24490.2187-0.0262

Manager dummy-0.0187

0.07240.0910

Cross term of tenure and manager-0.1077-0.10750.0003

Kids dummy-0.0901-0.08410.0060Marriage dummy

0.13930.1041-0.0352

High school dummy

0.0016

0.05180.0502

University dummy-0.0026

0.07350.0761

Constant term

0.00000.1636

0.1636Total

0.12400.31040.4344

%

29%

71%

100%

i
ii

Experiences=age-educated years-tenure-6
Female is 1,male is 015

Reference
1. Masahiro Abe(2005）「DANJYO NO KOYOUKAKUSA TO CHINKIN KAKUSA」 NIHON RODO
KENKYU ZASSI , No.538/May P15-P31
2. Shinpe Sano(2005）「DANJYOKAN CHINKIN KAKUSA WA SIKOU NI YORU SABETU GAGENNIN KA」NIHON RODO KENKYU ZASSI, No.540/July 2005 P55-P67
3. Yoshihumi Nakada（1997）「NIHON NI OKERU DANJYOKAN CHINKIN KAKUSA NO YOUYIN
BUNSEKI」Hiroyuki Tyuma& Terukazu Suruga,NIHON NO KOYOU KANKOU TO ROUDOUSIJYO,PP170-200，TOYOKEIZAISINHOSYA
4. Akira Kawaguchi（2005）「KEKKON TO SYUSSAN WA DANJYONOCHINKIN NI DO-NOYOUNI EIKYOU WO OYOBOSHITEIRUKA 」 NIHON RODO KENKYU ZASSI,
No.535/January

P42-P55

5. Naohiro Yashiro(1980）「DANJYOKAN CHINKIN SABETU NO YOUYIN NI TUITE――SONO
GOURITEKI KAIMEI TO TAISAKU」 NIHON KEIZAI KENKYU, DAI９GOU,

PP17-31

6. Syouko Ogura(2004）「JYOSEI NO TYOUKI KINZOKUKA NI YORU DANJYOKAN CHINKIN
KAKUSA NO DOUKOU」

OHARASYAKAIMONDAIKENKYUJYOZASSI,

No.546/2004.5

P22-P37
7. Hisakazu Matsushige& Xuemei Xu(2002）「TYUGOKU NO NIKKEIKIGYO NI OKERU TYUKANKANRISYOKUNOSYOUSINTO

CHINKIN」

OSIPP

Discussion

per:DP-2002-J-019 ,August 23,2002
8. Xinxin Ma（2007）「NITTYU NI OKERU DANJYOKANCHINKIN NO SAGI NI KANSURUYOUYINBUNSEKI」NIHON RODO KENKYU ZASSI,No.560/special Issue 2007

P96-P107

9. Dong, X.-Y.,Fiona, Mscphall.,Paul, Bowles（2004） 「Gender Segmentation at Work in China’s
Privatized Rural Industry :Some Evidence from Shandong and Jiangsu」

World Development Vo-

lume32,Issue6,June 2004 , pp. 979-998
10. Oaxaca,Ronald(1973) ‘‘Male-female Wage differentials in Urban Labor Market.’’ International Economic Review 14(3):693-709
11. Becker ,G.S(1964) Human Capital: Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education. Chicago: University of Chicago Press
12. Daiji Kawaguchi(2006) ‘‘A market test for sex discrimination: Evidence from Japanese firm-level
panel data ’’ International Journal of International Organization” 25(2007)P441-P460

13. Meng, X(1998)「Male-female wage determination and gender wage discrimination in China's
rural industrial sector」Labor Economics,5(1)pp.67-89

16