Gender-Wage Difference in Japanese Affiliated Subsidiaries in
China, a Statistical Analysis Using Intra-firm Personal Data
Graduate School of Economics, Waseda University
Fengye Sun
Sep.2008
Abstract
This paper examines male-female wage determination and gender wage
discrimination in Japanese affiliated subsidiaries in China using intra-firm
personal data. It is found that female wages in a Japanese enterprise can be
explained by the human capital model, but that gender wage gaps not explained by observed differences in productivity still exist, accounting forabout 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 tofirms in Japan, marriage has a negative effect on female wages. But returns
on tenure and education level seem to have no gender differences, considering that the proportion of female managerial staff in China is larger thanmost 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. Introduction
2. Previous studies and hypothesis
3. Firm introduction and data
4. Methodology
5. Empirical result
6. Conclusions
1. Introduction
Substantially lower wages are paid to female workers than to maleworkers in many countries, especially Japan. Among developed countries,
Japan and Korea’s gender wage gaps are the largest. The wage differential iscaused by many factors, two of which stand out from the previous literature.

1

One, this paper’s explainable component, is a difference in productivity; theother, this paper’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. This process makes the trend of the female labor supply similar to the shape of the letter M. Because of shorter female tenures comparedto male employees, employers are not willing to investment in females since
their 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 other conditions are the same, wage differences in China would be expected to besmaller than in Japan. However, because this is not the case, we can say thatJapanese enterprises exploit female workers more in China than in Japan.
We are interested specifically in gender differences in wage and treatment
within Japanese enterprises embarking in China.
In this paper, we will research a Japanese firm embarking in China byinterview 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 theprevious studies on gender wage difference in Japan and China. Section 3
introduces the firm under investigation and the data. Section 4 discusses theresearch 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 differencesin Japan and China. What is the cause of it? Can it be explained in terms ofhuman 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 positive effect on male wage but a negative effect on

2

female wage. Sano (2005) and Kawaguchi (2007) tested the existence of employer discrimination using Japanese firm level panel data. Their results failto reject the hypothesis that employer discrimination is a source of themale-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 genderwage gap is due to differences in gender productivity. However, Nakada(1997) found that the gender productivity gap is due to the 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, the accumulated productivity of females is smaller
compared to males.
Yashiro (1980), classifying factors for the gender wage gap into level 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 the effect of market liberalization on the earnings of Chinese females.
For example, Meng(1998) examined male-female wage determination andgender 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 themarket. Dong, Fiona, and Bowles (2004) argued that labor segmentation exists along gender lines in the newly privatized township and village enterprises in rural China. The result based on surveys indicates that there is aclear gender bias in favor of males.
The labor market in China is very complicated compared to Japan. The
gender wage gap is related not only to differences in productivity, but also toregion within China, type of occupation, size of firm, and type of firm. Furthermore, the size of the female labor supply is higher than in Japan. Mat-3

sushige.,Xu (2002) found no gender gap in promotion opportunities or wages
among managerial staff using survey data from four Japanese firms in Dalian. Ma (2007) estimated the causes of the gender wage gaps within Japanand 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 inlength of tenure between Japanese and Chinese females.
However, there is still no data on female employees working at Japanese affiliated subsidiaries in China. Intra-firm personal data, although difficult to obtain, is necessary to study this issue. This study attempts to estimate the gender wage difference by intra-firm personal micro data from aJapanese firm in Shandong, China. First, the causes of gender wage gaps aretested by estimating the wage equation. Then, the composition of the genderwage gap is analyzed using the method of Oaxaca (1973), splitting the total
gender wage differential into two components, an explained component andan 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 359 Chinese employees and 2 Japanese managers. The descriptive statistics of Chinese employees by gender ispresented in Table 1. We can see that the mean hourly wage of females is
5.08 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 of males and females
is similar, about 25 years and 2.5 years, respectively. But the mean education
between genders is quite different. The mean level of education of females isabout 1.64 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 287 female employees, and 72 male employees. Yet, regarding themanagerial staff, 31 are female and 28 are male, which means that mostfemale employees are working 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

4can take a five-month vacation and enjoy one hour per day to nurse until thebaby 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 vacationends. Although females are somewhat more complicated than males, the firm
likes female employees and employs females as an aggressive means to atarget.
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 about 2.5 years, which isshorter than the seven-year history of the firm. On the job training is notconsidered 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 inrecent years because the headquarters in Japan has not sent orders. At thesame time, more and more plants and companies have been established in
the area. This raises the demand of the labor market, making it difficult to
sew up a large enough cheap labor force. People change 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 their families.
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 datacollected from the firm in summer 2007; the data covers age, length of tenureat 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

5method.
klog wi      j xij  ui(i  1, 2,, n)

(1)

j 1

log wiNatural log hourly wagexi

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
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iui

Cross term of marital status and gender
Cross term of high school education and gender
Cross term of university education and gender
Coefficients of x iError term

The dependent variable log wi is the log hourly wage derived by dividing themonthly wage by time worked per month. For the independent variables x iyears of experience and its square, years of tenure and its square, dummy
variables of education level are used 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 that the wage structure isdifferent between females and males in the firm if any coefficient of genderand its cross term has a significant result. A positive coefficient indicatesthat the estimate for females is lower than males, and a negative coefficient6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 less than males. How did this gap occur? According to the human resource method (Becker 1973), 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 capital an employee has accumulated, the higher that person’s wage is. The lower average education levelof 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 a wage 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 attributableto gender differences in observed productivity characteristics, and the otheris the residual gap attributable to differences in male and female returns tothese observed characteristics. This residual, or unexplained, component of
the wage gap is generally attributed to discrimination, but it could be due todifferences 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 thenondiscriminatory wage structure, as is likely in Chinese labor market, arepresented. 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 human capital.
klog wi   f    jf xij  ui(i  F )(2)(i  M )

(3)

j 1
k

log wi   m    jm xij  ui
j 1

Where F is female employees set and M is male employees
set. F  M   , F  M  1,2,..., n.
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.

7

klog w f  ˆ f   ˆ jf x jf
j 1
klog wm  ˆ m   ˆ jm x mj
j 1

Where











log w f  E iF log wi , log wm  E iM log wi , x jf  E iF xij

 , x  E
m
j

iM

xij



Then

log wim  log wif   m    im x i   f    i f x i
m

f

(4)

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

log wim  log wif  [  im x i  x i ]  [ x i ( im   i f )   m   f ]
m

f

f

(5)

The first term on the right side of equation (5) is the differential attributableto gender differences in observed productivity characteristics, while the left
terms yield the unexplained component: the wage gap due to differences inmale and female returns on these observed 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 two components.
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 apositive value. This indicates that wage increases with a lot of experience, along tenure, or a high level of education for both males and females, and thepositive 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 ofworkers. However, the rates of return on the observed productivity charac-

8

teristics are not all the same between males and females. As can be seen, theresults of gender cross terms, cross terms of experience and gender and crossterms of marital status and gender are statistically significant for negativevalues, indicating that the returns on experience and marital status for females are lower compared to males, and the coefficient of the cross terms oflength of tenure and gender is negative even though it is not statisticallysignificant at the 10% level. Cross terms of managerial position and gender,
cross terms of number of children and gender, and cross terms of educationlevel 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 agender 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 the natural 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
gender wage 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 genderwage discrimination in a Japanese affiliated subsidiary in China using intra-firm personal 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 other studies about China’s gender wage gaps. Considering that the firm examined is located in a new town that was in the
countryside before the economic reform, the lower returns perhaps proceedfrom the character of the regional labor market. Males seek work in the
major cities to supplement their income, while females remain in their hometowns to take care of their families. Thus, there is a larger female labor
supply.

9

Employers in the region might have a special bias against female employees, paying lower wages to them than to male employees. Consideringthat Chinese females do not resign after marrying or becoming pregnant as
Japanese females, statistical discrimination might be weaker compared toJapan. However, as said by the personal 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 theirproductivities, 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 the wage 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 theJapanese firm takes advantage of its female employees in China.
Finally, all the conclusions are based on estimated results fromcross-section data. There might be a bias regarding latent personal ability
that could not be controlled for, which may influence the value and statisticalsignificance of the coefficients. Panel data is 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 Japaneseaffiliated subsidiaries in China in the future.

10

Acknowledgement
The author would particularly 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 nameremains anonymous. Finally, this research received a subsidy from Constructing Open Political-Ecomomic Systems, Centre of Execellence Program
for the 21st Century at Waseda University.
Appendix
Table 1．descriptive statistics of the firm
Variable

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

tenure

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

5.08

2.33

0.00

24.53

age

25.06

4.83

16.00

49.00

9.68

1.43

9.00

16.00

tenure

2.61

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
Male

72

education years
Female

Total

287

315

manager

28

31

59

11

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

12

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

13Tble4. 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 014

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