Gender and Income: Differences in the Migration Experience of males and females
In Shanghai
Shuang Zhou
Institute of Population Research
Peking University
[email protected]
Abstract
According to China’s censuses in 2010 and 2000, there were a growing number of female
floating people every year in Shanghai. Females are playing increasingly important role
in migration stream due to expanded employment opportunities. This paper explores
gender difference in income of floating people in Shanghai.
Data used is from the dynamic monitoring survey of the floating population in Shanghai,
conducted in 2012, which examined the living and development conditions of 14993
floating people at the age of 15 to 59 and 44675 their family members. The interest of this
study lies in gender based income difference. First, we check whether there is income
difference between males and females, by using multiple linear regression controlling for
potential confounders (age, occupation, education).Second, the comparison of income
difference in gender perspective is conducted both before and after floating experience of
floating people.
Introduction
In the process of economic development in Shanghai，China, floating people has become
an indispensable diving force in employment market. According to China’s national
census in 2000, the number of floating population in employment market took up 27.37%
of all working population in Shanghai. In this case, almost three out of ten employees in
employment market are floating people. In the employment course of floating people, the
employment rate of females is higher than the local people. In some industries, female
floating people played an increasingly important role in economic activities of local social
development. However, do they receive the equal treatment as that of males?
This paper is trying to check the income of floating people in gender perspective.
Firstly, whether there is gender difference in income of floating people in Shanghai?
Secondly, we will compare the income difference between men and women before
and after their floating experience in Shanghai.
Data and method:
The dynamic monitoring survey of the floating population in Shanghai, conducted in
2012, examined the living and development conditions of 14993 floating people at the
age of 15 to 59 and 44675 their family members. This paper uses 14993 floating
people as analysis object, consisting of 50.7% males and 49.3% females.
This study firstly examines whether there is income difference between males and
females of floating people. So in the multiple linear regression, the dependent variable
is set as monthly salary of floating people. The analysis considers a selection of
variables that could influence the income of floating people, and conducts separate
analysis for males and females. The variables are: gender, age, education level, and
occupation. Age, education level are selected according to wage appraisal system in
China, both in public institutions and enterprises. Education level is categorized into
eight classes, illiteracy, primary school, middle school, high school, vocational school,
junior college, undergraduate college and postgraduate. Occupation is selected
referring to literature review. It is classified into eighteen categories, government
officer, professional, civil servant, businessman, small retailer, attendant, domestic
worker, cleaner, decorate personnel, other commercial services personnel, farming,
forestry, husbandry and fishing water conservancy production personnel, production
personnel, transportation worker, builder, equipment operator, security, unoccupied
person, others.
This paper describes the gender difference in income by comparing the average
monthly salary of men and women. In addition, change of occupation distribution
between men and women before and after their floating experience according to their
occupations is also conducted. The gender based distribution of occupation according
to the various income levels is displayed. Occupations are categorized into four
classes based on their average income. The forth category, also the highest one,
comprises government officer, professional, civil servant, businessman. Within the
group, average income level is above 4500RMB. The third category includes decorate
personnel, transportation worker, builder (average income is between 3800 and
4500RMB). The second one is consist of attendant, other commercial services
personnel, transportation worker, farming, forestry, husbandry and fishing water
conservancy production personnel and production personnel (average income is
between 2500-3800RMB). The first one includes domestic worker, cleaner,
unoccupied person, security and others (average income is under 2500RMB). The
figure can be seen below.
Figures 1 the occupation distribution between male and female Obviously, the income of floating people increase as the occupational class goes up
fromⅠto Ⅳ. There was a tiny gap between the income of female floating people and
that of male before their migration. However, after they flow into Shanghai, the
occupation of males developed dramatically, while occupational development of
females was gently. The phenomenon was especially prominent among professionals
and executives.
Result:
Table 1 presents the results from the multiple linear regression models that examine
whether there is gender difference in income of floating people in Shanghai. We
confirmed that gender has a significant (p < .05) effect on the income.
Table 2 and Figure 1 describe the gender based changes in occupation distribution
before and after migration experience of floating people in shanghai. We confirmed
that men’s average income is higher than women. What’s more, they are more likely
to have better job with larger amount of salary. Table 1 the multiple linear regression
Coefficientsa
Standardized
Unstandardized Coefficients
Model
1 (Constant)
B
Std. Error
2616.709
472.884
gender
734.769
64.015
e1
461.895
e2
Coefficients
t
Beta
Sig.
5.534
.000
.099
11.478
.000
254.844
.040
1.812
.070
1065.856
247.006
.144
4.315
.000
e3
1682.081
257.911
.158
6.522
.000
e4
1786.545
276.351
.118
6.465
.000
e5
2790.613
268.213
.217
10.404
.000
e6
4148.592
273.701
.308
15.157
.000
e7
6885.743
402.865
.177
17.092
.000
j1
-2016.694
365.681
-.182
-5.515
.000
j2
-2526.251
405.248
-.104
-6.234
.000
j3
322.603
371.716
.026
.868
.385
j4
-1733.764
385.127
-.103
-4.502
.000
j5
-2321.679
377.481
-.169
-6.150
.000
j6
-2667.076
482.861
-.068
-5.523
.000
j7
-3368.798
413.043
-.138
-8.156
.000
j8
-3441.383
444.265
-.106
-7.746
.000
j9
-1587.491
399.811
-.073
-3.971
.000
j10
-2306.984
367.559
-.227
-6.276
.000
j11
-2915.212
443.371
-.092
-6.575
.000
j12
-2791.976
368.168
-.306
-7.583
.000
j13
-1942.553
387.882
-.108
-5.008
.000
j14
-1712.138
390.474
-.090
-4.385
.000
j15
-2745.054
381.118
-.175
-7.203
.000
j16
-3027.743
567.066
-.056
-5.339
.000
j17
-2173.004
451.646
-.063
-4.811
.000
39.312
3.822
.095
10.286
.000
age
Table 2 gender difference in mean monthly salary
standard
gender
mean
N
error
female
3301.25
5551
3154.277
male
4214.98
7105
4027.387
total
3814.20
12656
3697.853
Conclusions
Based on the discussion above, we can draw conclusions as follows. Firstly, there is an
approximately linear relationship between gender and income. Secondly, male’s
average income is higher than that of female. Thirdly, there was little difference
between men and women in distribution of occupation pre-migration. While, after they
flow into Shanghai, the percentage of men’s occupation with higher income is much
higher than that of women.
The wide disparity between males’ income and that of females showed an unbalanced
development among floating people in gender perspective. This disparity hindered the
career development of female floating people and had a bad impact on rational
exploitation of their human resources.
References
[1] Becker, G. S. , The Economics of Discrimination. Chicago : University of Chicago Press , 1957.
[2] Becker, G. S.,“Trends in Women’s Work , Education , and Family Building”, Journal of L abor
Economics ,1985,3, S33 —S58.
[3] Bernasco, W. ,“Effects of Spouse’s Resources on Occupational Attainment in the Netherlands”,
European Sociology Review , 1998, 14,15 —31.
[4] Bian, Yanjie ,“BringStrong TiesBack In: Indirect Connection,Bridges,andJobSearchinChina”,
American Sociological Review , 1997 , 62 (3) , 266 —285.
[5] Bian , Yanjie ,“Chinese Social Stratification and Social Mobility”, Annual Review of Sociology ,
2002, 28,91 —116.
[6] Bian, YanjieandL. ,John,“Market Transition and Persistence of Power : The Changing Stratifica2
tion System in Urban China”, American Sociological Review , 1996 , 61 (5) , 739 —758.
[7] Blau, P. M. , and O. D. Duncan. TheAmericanOccupational Structure. New York : Free Press ,
1967.
[8] Blinder, A. “Wage Discrimination: Reduced Form and Structural Estimates”, Journal of Human
Resources , 1973 , 8 (4) , 436 —455.
[9] Bobai Li and A. G. Walder ,“CareerAdvancementasPartyPatronage: SponsoredMobilityintothe
Chinese Administrative Elite , 19491996”, AmericanJournal of Sociology , 2001 , 106 (5) , 1371
— 1408.
[10] Burdett , K. ,“A Theory of Employee Job Search and Quit Rates”, A merican Economic Review ,
1978 , 68 (1) , 212 —220.
[11] Davis , D. S. ,“Job Mobility in Post2Mao Cities: Increase on the Margins”, The China Quarterly ,
1992 , 132 (4) , 1062 —1085.
[12] Farber, H. S, “The Analysis of Inter2Firm Worker Mobility”, Journal of L abor Economics , 1994 ,
12 (4) , 554 —593.
[13] Groot , W. and H. Maassen van den Brink ,“Glass Ceilings or Dead ends : Job Promotion of Men
and Women Compared”, Economics Letters , 1996 , 53 (2) , 221 —226.
[14] Hersch,J. and W. K. Viscusi,“Gender Differences in Promotions and Wages”, Industrial Rela2
tions , 1996 , 35 (4) , 461 —472.
[15] Hiller, D. V. and W. W. Philliber ,“The Division of Labor in Contemporary Marriage : Expecta2
tions , Perceptions and Performance”, Social Problems , 1986 , 33 (3) , 191 —201.
[16] Honig E. and G. Hershatter , Personal Voices . Stanford , CA : Stanford University Press , 1988.
[17] Jones , D. R. and G. H. Makepeace ,“Equalworth, EqualOpportunities: PayandPromotionin An
Internal Labour Market”, Economic Journal , 1996 , 106 (435) , 401 —409.
[18] Jovanovic, B. “Job Matching and the Theory of Turnover”, Journal of Political Economy, 1979,
87(5), 972- 990.
[19] Kahn S. and H. Griesinger , “Female Mobility and the Returns to Seniority : Should EEO Policy
be Concerned with Promotion?” American Economic Review, 1989, 79(2), 300- 304.
[20] Keith, K and A. McWilliams, “The Returns to Mobility and Job Search by Gender: Additional
Evidence from the NLSY”, Industrial and Labor Relations Review, 1999, 52(3), 460-447.