Gender and job mobility among rural to urban temporary migrants in the Pearl River Delta in China

Abstract

Previous studies have found that there is a female disadvantage among rural migrants in the urban labour market in China. It remains unclear whether migrant women also lag behind migrant men in job mobility, an important channel for rural migrants to improve their labour market outcomes. Using data from a large-scale survey conducted in the Pearl River Delta region, one of the most important migration destinations in China, we examine gender gaps in job mobility of rural migrants from 1979 to 2006. Focusing on job mobility, this paper sheds new light on the changing gender dynamics among rural migrants in China. Most of the model results lend support to our hypotheses concerning the gendered job mobility patterns of rural migrants. We find that migrant women are less likely to change jobs for work-related reasons and more likely to engage in family-centered job mobility. Results of fixed-effects models of monthly wage further reveal that the positive effect of work-centered job mobility on rural migrants’ wages is smaller for migrant women. We also find that marriage does not disadvantage migrant women more than men in either work centred or family centred job mobility, and that there is a declining trend of female disadvantage in family-centered job mobility, which all points to the transformative role migration plays for rural migrants.

Keywords

China gender inequality job mobility labour market rural-to-urban temporary migrants

Introduction

Despite past measures to promote women’s status in China, gender remains a strong force of social stratification and gender inequality has been on the rise since the socioeconomic reform in the late 1970s (Bauer et al., 1992; Hannum, 2005; Hershatter, 2011; Maurer-Fazio et al., 1999). With the urban-rural divide, rural women occupy a particularly vulnerable position in China’s social stratification system. Migration empowers women, but its role in promoting women’s welfare is limited in the sense that migrant women constantly find themselves in an inferior position to men in the destination labour market (Hondagneu-Sotelo, 1992; Mahler and Pessar, 2006). Rural women in China also see their conditions improve greatly by participating in migration. However, consistent with the extant literature, studies of the China case have found that there is a systematic female disadvantage among migrant workers in the urban labour market in terms of occupational attainment, wages, gender segregation of migrant jobs, and a strong preference for young and single migrant women on the part of employers (Duan et al., 2010; Fan, 2003; Huang, 2001; Liang and Chen, 2004). In light of the persistent female disadvantage, it is important to investigate whether the path to better labour market outcomes is also obstructed for migrant women in the destination labour market.

The present study examines gender gaps in job mobility patterns and the gendered effect of job mobility on migrant wage growth. We argue that job mobility is an important prism to approach the issue of female disadvantage among rural migrants in China. Firstly, frequent job change is an important feature of migrant jobs in China. According to one study, rural migrants’ job mobility rate is six times higher than that of urban workers (Knight and Yueh, 2004). Secondly, at a time when migrant employment is not under proper state protection, ‘voting with feet’ is an important way for rural migrants to escape bad jobs that tend to violate their labour rights (Cui, 2007; Liu et al., 2006; Sun and Yang, 2012; Zhang, 2011). More importantly, despite the fact that rural migrants change jobs mostly in the secondary sector, they do benefit wage-wise through changing jobs (Knight and Yueh, 2004; Li and Liang, 2012; Liu et al., 2006). Thus focusing on job mobility, one of the few channels for rural migrants in China to improve their labour market outcomes, our study can further inform the understanding of migrant women’s labour market position.

This paper seeks to answer three questions. First, are migrant women disadvantaged in job mobility patterns as compared to migrant men? Second, how do gender differences in job mobility patterns change over time? Third, to reveal consequences of gender differences in job mobility, we investigate whether job transitions affect migrant women’s wage growth differently from men’s. Data analyses are based on a large-scale survey of rural migrants conducted in the Pearl River Delta region of China. Thanks to the job history data collected in the survey, we join recent efforts to model migrants’ job mobility using event history methods (for example, Li and Liang, 2012; Zhang, 2011). To examine whether the effect of job mobility on wage growth varies by gender, we adopt fixed-effects models so that more valid causal inferences can be made than existing cross-sectional studies on the relationship between job change and migrant wage.

Gender differences through the lens of job mobility

Job mobility/labour mobility/job shifting/job change refers to the movement from one job to the next. One can leave a job for another with either the same employer or a different employer (Rosenfeld, 1992). Distinctions are also made between voluntary and involuntary job changes (Tuma, 1976). Voluntary job mobility occurs when a person quits a job, while involuntary job mobility results from factors such as factory closing, lay-off and illness. In this research, we focus on inter-employer voluntary job mobility.

Job mobility in a market economy is viewed as a good thing as it leads to better person-job matches, and in the process human capital is allocated more efficiently (Mincer and Jovanovic, 1981). From the perspective of the individual job quitters, voluntary job mobility is also self-evidently good in that only when a person finds the current job no longer suitable for him/herself and the next job more satisfactory does he/she decide to quit, either out of career considerations or due to other reasons.

However, to the extent that the high rate of job mobility among rural migrants could simply be a manifestation of their inferiority in China, it is not entirely clear why job mobility is beneficial for them. According to the theory of labour market segmentation, the labour market is divided into the primary sector where human capital is highly rewarded and jobs are more stable and the secondary sector where human capital is poorly rewarded and jobs are highly unstable (Reich et al., 1973). Different from countries where labour market segmentation along lines of race/ethnicity and nativity status is prominent, people’s hukou status is key to labour market segmentation in China (Fan, 2002; Meng and Zhang, 2001). Without local hukou registration status, most rural migrants can only have access to so-called dirty and dead-end jobs shunned by urban workers (Roberts, 2002; Wang et al., 2002; Yang and Guo, 1996). In the earlier days of the massive internal migration in China, explicit measures were even taken by local governments to exclude temporary migrants from certain desirable jobs (Chan and Zhang, 1999). Temporary migrants also face poor employment conditions and frequent violations of labour rights (Chan, 2003; Lu and Wang, 2013). Being discriminated and confined to the secondary sector, rural migrants constantly find their current jobs unsatisfactory, and changing jobs gradually becomes a new ‘normal’ for them (Knight and Yueh, 2004; Liu et al., 2006).

In this context, we argue that job mobility can first serve as a vehicle through which we examine how migrant women differ from men in their ability to escape unpleasant working conditions. Although chances of finding a good job are slim, rural migrants still tend to quit a job once it becomes too demanding. The meaning of voluntary job changes for migrants’ wellbeing can be further appreciated if we take into account the fact that quitting a job voluntarily is not even an option for some migrants, especially in the earlier days, in that some employers illegally seize their personal identification cards (shen fen zheng) or withhold part of their wages to keep them from leaving (Chan, 2003).

Job mobility is also a lens through which we can observe whether migrant women differ from migrant men in pursuing the limited career goals afforded them by China’s hukou system. As a direct manifestation of the role job mobility plays in improving jobs for rural migrants, previous studies show that for every job change, there is a corresponding wage increase (Knight and Yueh, 2004; Li and Liang, 2012; Liu et al., 2006). Also contributing to the career advancement role of job mobility are two recent developments, namely, the increased government investment in the skill training of migrants since the 2000s and the emergence of more government-regulated migrant employment agencies (Cui, 2007). Regarding the former, the literature suggests that personal resources such as human capital are important in realising the upward mobility motivation behind job changing behaviour (Tuma, 1976). As with the latter development, it is known that job opportunities are key to a person’s evaluation of whether he/she can benefit from quitting (Sørensen, 1977; Tuma, 1976). Though social networks do not necessarily help rural migrants get better jobs in China (Liu et al., 2006; Liu and Zhang, 2007), rural migrants rely heavily on them mostly because job opportunities learned through social networks are more reliable than those obtained through employment agencies in the open market, which tend to over-charge rural migrants and even offer false opportunities. The emergence of more government-regulated employment agencies can help rural migrants access a wider range of reliable job opportunities.

Gendered job mobility patterns and their consequences on rural migrants

To study gendered job mobility patterns, we follow the approach adopted by Cao and Hu (2007) in their study of gender and job mobility of urban workers. Based on reasons for job changes, we distinguish between work-centered job mobility and family-centered job mobility. Quitting a job for work-related reasons implies the explicit pursuit of better jobs and greater chances of upward mobility, while changing jobs for family-related reasons requires compromises between work and family and a higher probability of downward mobility (Cao and Hu, 2007). It is confirmed empirically that changing jobs for family-related reasons does not lead to an increase in income or leads to lower wage growth than changing jobs for work-related reasons (Cao and Hu, 2007; Keith and McWilliams, 1999). Following earlier discussions, both work-centered and family-centered job mobility are voluntary and thus are deemed as beneficial for rural migrants. We make this distinction because each type of job mobility informs our understanding of gender inequality from a unique perspective. On the one hand, gender differences in work-related job mobility help reveal how migrant women differ from men in the pursuit of economic wellbeing. On the other, gender differences in family-centered job mobility can help further reveal whether and the extent to which women are more likely to accommodate work with family needs. We hypothesise that migrant women are less likely to change jobs for work-related reasons, and more likely to make compromises between work and family and engage in family-centered job mobility.

The proposed female disadvantage in job mobility is, first of all, rooted in the deeply ingrained patriarchal culture of the place from where they originate. To the extent that gender inequality was not eliminated in the Mao’s era in urban China, the political campaigns to promote gender equality had an even smaller impact on people’s lives in rural areas (Bauer et al., 1992; Fan, 2003; Hershatter, 2011; Huang, 2001). Through 77 interviews with rural women in Shaanxi over 10 years, Hershatter (2011) demonstrates that despite the strong state narrative of women’s liberation and the actual measures taken to promote gender equality, gender discrimination was widespread in rural areas in the 1950s, which is considered as the most egalitarian period in P.R. China.

Patriarchal culture influences rural migrants’ labour market performance first through resources allocation within the household. Under a restricted budget, rural families tend to invest limited family resources in boys’ education or training (Hannum, 2005; Huang, 2001; Zhang et al., 2007; Zhou et al., 1998). This education/skill advantage elevates men’s expectation of finding better jobs, which eventually can increase the probability of making an actual job change (Tuma, 1976). As a result, gender difference in education investment in early years has long-term consequences for women in the job market.

Patriarchal culture also influences job mobility patterns through household division of labour. While migrant men are encouraged to take full advantage of opportunities, more explicit expectations are placed on single migrant women to take care of their parent families by remitting and even by sacrificing their career goals (Diamond, 1979; Fan, 2004a; Jin, 2010). Once married, the socially accepted norm is that the wife takes care of the newly formed nuclear family and even the extended family, while the husband can continue economic activities with little disruption (Cao and Hu, 2007; Fan, 2003; Jin, 2010; Zhang et al., 2008). In fact, studies based in a variety of national contexts, including developed countries, have found that marriage has a negative effect on job mobility rate and the negative effect is greater for women than for men (Felmlee, 1982; Fuller, 2008; Sousa-Poza and Henneberger, 2004). Therefore, through the uneven household division of labour, patriarchal culture may affect migrant women’s job changing behaviour by making them less ambitious workers and more prone to sacrifice careers for the sake of their families.

Working far away from home and in an urban environment does not put women on an equal footing with men. Rather, migrant women are faced with ‘double’ disadvantages in that they are exposed to forces of gender stratification not only in rural areas, but also at urban destinations. Migrant women’s disadvantage in job mobility at urban destinations is first closely related to the economic structure of export-oriented economies. In fact, female workers have always been the much sought-after candidates to work on assembly lines for their cheap labour and more docile character, whether in the Pearl River Delta region over the past 30 years or in places such as Hong Kong and Taiwan before the global manufacturing industry shifted to mainland China (Diamond, 1979; Lee, 1995; Lim, 1983; Mills, 2003; Ngai, 2005; Salaff, 1981). Labour disciplines are strict, and worker management measures are tough for jobs on the assembly line. For migrant women, no matter how driven they are to escape bad jobs, the prospect of finding better ones is dim. In other words, the very nature of the macro economic structure determines that women’s access to better jobs is severely restricted, which thwarts their motivation to change jobs and ultimately lowers the probability of work-centered job changes (Felmlee, 1982; Rosenfeld, 1992). Moreover, the harsh working conditions and strict disciplines of feminised migrant jobs also imply that it is very hard for migrant women to take on other roles (e.g. wife or mother). When role conflicts arise, it is not a surprise if they have to quit for less demanding jobs.

For migrant women outside highly feminised job niches, they are faced with the gendered urban labour market. Although women’s labour force participation rate is higher in urban China than in other developing countries, gender equality has never been achieved (Bauer et al., 1992). Evidence suggests that women are less likely to be employed; for those employed, they earn less than men; and the negative relationship between marriage and wage is more pronounced among women (Zhang et al., 2008). Cao and Hu’s (2007) study on the job mobility of urban resident workers finds that women are less likely to engage in career-oriented job transitions and more likely to engage in family-oriented job transitions. In a study of rural migrants in Shenzhen, it is found that marriage is a constraint for migrant women in the attainment of professional or managerial jobs, but married migrant men have an advantage in obtaining these positions (Liang and Chen, 2004). It is likely that the urban labour market at the place of destination poses the same, if not more, challenges for migrant women in China.

Moreover, gender gap in labour market outcomes is widening in China. In the pre-reform era, reduction in gender inequality in the urban labour market was achieved through a highly centralised economic system. However, scholars have observed a widening gender gap in wages and job mobility patterns following China’s market transition (Cao and Hu, 2007; Fan, 2003; Gustafsson and Li, 2000; Liu et al., 2000; Maurer-Fazio et al., 1999). A similar trend of increasing gender inequality is also documented in rural China (Hannum, 2005). We expect to find the same trend in gendered job mobility patterns among rural migrants. That is, with the progression of marketisation in China, women are even less likely to change jobs for job-related reasons and even more likely to change jobs for family-related reasons.

Finally, in line with findings that changing jobs leads to an increase in wages for both urban residents and rural migrants (Knight and Yueh, 2004; Li and Liang, 2012; Liu et al., 2006), we also expect to find the same pattern in the current research. More importantly, in light of the gendered labour market that migrant women are facing, female disadvantage in job mobility is further manifested in how job mobility may reward women and men differently.

Based on the preceding discussion, we derive the following hypotheses:

Hypothesis 1: Migrant women are disadvantaged to men in job mobility patterns.

Hypothesis 1a: Migrant women are less likely to change jobs for work-related reasons and more likely to do so for family-related reasons.

Hypothesis 1b: Over time, migrant women are increasingly less likely to change jobs for work-related reasons and increasingly more likely to do so for family-related reasons.

Hypothesis 1c: Marriage decreases women’s probability of work-centered job mobility more than men’s and increases women’s probability of family-centered job mobility more than men’s.

Hypothesis 2: Job mobility’s positive effect on wages is smaller for migrant women than for migrant men.

Data and methods

Data

Data come from a 2006 survey conducted in the Pearl River Delta region (PRD) in Guangdong, a coastal province in southeastern China. Respondents of the survey are inter-county rural-to-urban migrants employed in this region and with an education level of three-year college or less. Due to the lack of complete registration of the floating population in China, random sampling is difficult to implement. In this survey, quota sampling was employed. The research team selected nine out of 11 cities that appear in the official definition of the PRD, based on criteria such as local economic development level and size of the migrant population. A budgeted total sample size is allocated to each city by the size of migrant population in each city. Also controlled in the sampling process are sex ratio and distribution across economic sectors. The complete sample size is 3085. A description of the data in terms of basic socioeconomic characteristics and other variables used in the analyses can be found in Appendix 1.¹

Modeling strategies

Piecewise exponential models of job mobility

Event history analysis models the hazard rate at which an event is happening. An event in event history analysis refers to a change or transition from one state to another. The event we are interested in is job termination, which represents a transition from being employed to leaving the current job for another. The event history analysis technique we used in this research is the piecewise exponential model. An advantage of this method is that it allows coefficients of independent variables to vary across time periods, making it possible to examine how gendered job mobility patterns change across time (Blossfeld et al., 2007). The model to be estimated is defined as:

\log r_{jkp} (t) = x (t) β_{jkp}

(1)

where x(t) denotes a set of independent variables; and $β_{jkp}$ indicates coefficients of independent variables for a specific type of job termination (i.e. work-centered job termination and family-centered job termination) in a given historical period.

In equation (1), p represents a certain historical period. The time axis of this study is divided into three historical periods: 1979–1992, 1993–2003 and 2004–2006. 1979 is the earliest year reported by respondents in the data and 2006 is the year the survey was conducted. We define these three periods to reflect the progression of marketisation as well as the changing policy context facing temporary migrants in China.

In the late 1970s, population mobility started to rise in China. However, it is not untill around 1993 that the so-called ‘tidal wave’ of internal migration came into full force (Liang, 2001). With the rapid economic development in coastal regions of China, 1993–2003 is a period of massive internal migration. Though the hukou system was relaxed to allow population mobility in this time period, discrimination against temporary migrants was severe, and harsh employment and living conditions were widely documented (Lu, 2003). Starting from around 2004, there have been some noticeable improvements in rural migrant workers’ employment and living conditions as a result of the government’s increased awareness of the migrant problem in China (Qiu and Wen, 2007; Shi, 2008). Different from the previous hands-off approach to migrant employment, the government begins to take a series of measures to tackle key problems suffered by rural migrant workers, such as lack of job training, sub-standard working conditions and unlawful employment service.²

Job history data collected in the survey contain information on up to six jobs held by the respondents.³ The respondents were also asked to select one or more reasons for each job termination they experienced. Since we focus on voluntary job mobility, job terminations due to reasons such as factory relocation, factory shutdown and lay-off are excluded in the event history analysis. As long as ‘marriage and family’ are cited as the reasons for quitting a job, it is classified as family-centered job change, no matter whether other reasons are involved. Job changes due to reasons such as low pay, poor working conditions and no prospect for promotion are treated as work-centered. Since a person contributes more than one job spell to the data, the potential correlation among records within an individual is corrected by using robust variance estimates.

There are two types of independent variables in our models. Values of time-invariant independent variable stay the same throughout the records for each individual in each job spell. Gender, education level, number of previous jobs, period of labour market entrance, location of a given job, job search method and company ownership type are treated as time-invariant variables. Time-variant independent variables change values during a job spell. With the information on the timing of marriage in the data, marital status is treated as a time-variant variable in the analysis.

Work-centered job mobility and family-centered job mobility are modeled separately, which means there are a total of four piecewise exponential models. For each type of job mobility, the first model tests Hypothesis 1a on the overall gender difference in job mobility rate. We pay close attention to the estimated coefficient of gender and how it changes across time (Hypothesis 1b). The second model adds an interaction term between marital status and gender, the coefficient of which helps test Hypothesis 1c on whether marriage enlarges female disadvantage or not.

Fixed-effects models of wage growth

We adopt the fixed-effects modeling method to examine the gendered effect of job changes on wage growth (Allison, 2009).

y_{it} = μ_{t} + β X_{it} + γ Z_{i} + α_{i} + ε_{it}

(2)

where i represents different individuals and t represents different time points. Thus y_it is the value of the dependent variable at different time points; $X_{it}$ represents values of independent variable X for individual i at different time point t, such as age and marital status; and $Z_{i}$ is characteristic of the individual i whose value does not change over time, such as gender. This model has two error terms: $α_{i}$ and $ε_{i}$ . $α_{i}$ represents all the unobserved characteristics of individual i. $ε_{i}$ is equivalent to the traditional error term we encounter in OLS regressions. By dismissing $γ Z_{i}$ and $α_{i}$ in equation (2), this technique controls for unobserved characteristics of the respondents and thus corrects for selection bias.

Since job changes in the data cover a period of nearly 30 years, we have to take inflation into account. With this adjustment, the dependent variable in fixed-effects models is monthly wage (log) of each job in the value of the 2006 price. To do the adjustment, we use historical CPI (consumer price index) data from 1979 to 2006 obtained from the website of the National Bureau of Statistics of China.⁴

We create three independent variables indicating the number of work-centered, family-centered and involuntary job transitions the respondents experienced preceding a given job. Other independent variables include gender, marital status, education level, time period at the start of a given job, location of a given job, job search method and company ownership type. Independent variables with fixed values across all jobs (e.g. gender and education) are excluded automatically in the estimation process.

We first estimate a model with all independent variables. To test Hypothesis 2 on whether the effect of job transitions on wage growth differs by gender, we include three interaction terms between gender and the number of previous work-centered/family-centered/involuntary job transitions in the second model. Inclusion of interaction terms is the only way to model the effect of fixed characteristics within the framework of fixed-effects models (Allison, 2009).

Results

Results of piecewise exponential models of work-centered job mobility

Table 1 presents results of piecewise exponential models of work-centered job mobility. It is found that during 1979–1993, there is no statistically significant difference between women and men in the rate at which they quit jobs for work-related reasons. However, as time proceeds, women become more restricted in the pursuit of better jobs. To be specific, as shown in Model 1, gender does not make a statistically significant difference in work-centered job mobility rate. In the time periods of 1993–2003 and 2004–2006, migrant women’s rate of work-centered job mobility is significantly lower than men’s. Therefore, Hypothesis 1a is confirmed that migrant women are disadvantaged in worked-centered job mobility as compared to men. The results also lend support to Hypothesis 1b on the increasing gender inequality in work-centered job mobility. That is, as time goes by, female disadvantage emerges in work-centered job mobility.

Table 1.

Estimates of piecewise exponential models of work-centered job mobility.

	Model 1			Model 2
	1979–1992	1993–2003	2004–2006	1979–1992	1993–2003	2004–2006
	B	B	B	B	B	B
Key Independent Variables
Male	0.149	0.195***	0.189**	13.743***	0.178*	0.189**
Married	0.761**	0.161*	−0.480***	14.230***	0.142	−0.479**
Married*Male	−	−	−	−13.65***	0.031	−0.003
Control Variables
Age	0.010	−0.054***	−0.024**	0.009	−0.054***	−0.024**
Education
Junior high school or less (reference)
Senior high school or more	−0.307	0.309***	0.233**	−0.283*	0.308***	0.233***
Monthly Wage	−0.000	−0.001***	−0.001**	−0.000	−0.001***	−0.001**
Period of Labour Force Entry
1979–1992 (reference)
1993–2002	−	1.159***	2.989***	−	1.169***	2.989***
2003–2006	−	−	3.909***	−	−	3.908***
Job Search Method
Market (reference)
Social Network	−0.202	−0.249***	−0.228***	−0.189	−0.249***	−0.228***
Others	−0.346	−0.117	0.033	−0.357	−0.111	0.033
Number of Previous Jobs	−0.086	0.197***	0.185***	−0.087	0.197***	0.185***
Company Ownership Types
Public (reference)
Private	0.537**	0.091	0.395***	0.541**	0.092	0.395***
Foreign	0.121	−0.001	0.159	0.159	−0.001	0.159
Others	0.321	−0.043	0.264	0.339	−0.043	0.264
Not Clear	−0.085	−0.031	0.142	−0.055	−0.033	0.142
Job Location
Outside Pearl River Delta (reference)
Pearl River Delta	−0.052	−0.556***	−0.108***	−0.038	−0.556***	−1.076***
Number of jobs	7299				7299
Number of work-centred job transitions	2884				2884
-Log Likelihood	−6788.182				−6785.422

Note: *p < 0.1, **p < 0.05, ***p < 0.01.

We then look at the main effect of marital status on the rate of work-centered job mobility. Different from existing findings (Felmlee, 1982; Fuller, 2008; Sousa-Poza and Henneberger, 2004), Model 1 in Table 1 reveals that marriage increases the hazard rate of work-centered job mobility during both 1979–1993 and 1993–2003. Only during the most recent period does it decrease the hazard rate. It is likely that in the earlier years of internal migration in China when migrant wages were very low⁵ and permanent settlement at the place of destination was extremely selective (Sun and Fan, 2011), the positive association between getting married and work-centered job mobility rate is a result of increased pressure for married migrants not only to support the newly formed family, but also to save up for life back at home. However, when wages were raised significantly and the place of destination started to become more receptive of migrants during the period of 2004–2006, the pressure to change jobs is released so that job mobility rate is lower for married migrants than for singles.

Turning to the interaction term between marital status and gender in Model 2 in Table 1, we know that the positive effect of marriage on work-centered job mobility rate is significantly smaller for migrant men than for women before 1993. During 1994–2003 and 2004–2006, the effect of marriage does not vary by gender. Therefore, our results suggest that marriage does not disadvantage women more in work-centered job mobility, failing to support Hypothesis 1c. However, it is worth noting that the greater likelihood for married migrant women to change jobs for work-related reasons during the earliest time period does not necessarily mean that marriage encourages women’s career progression more than men’s. This result points again to the financial burden and the hastened return migration, especially the return migration of wives, that marriage brings to migrants under the circumstances of very low migrant wages and hostile reception at the place of destination (Jin, 2010).

Results of piecewise exponential models of family-centered job mobility

Table 2 present results of piecewise exponential models of family-centered job mobility. Model 1 shows that all else being equal, men are less likely to experience family-centered job mobility than women, and the negative coefficients of gender are statistically significant across all time periods. That is, confirming Hypothesis 1a, migrant women are disadvantaged to men in family-centered job mobility. We also observe that women are gaining equality in family-centered job mobility over the years. In the three time periods, men are 70.44%, 58.3% and 41.1% less likely to quit jobs for family-related reasons. Thus, contrary to what is expected in Hypothesis 1b, women’s disadvantage in family-centered job mobility is on the decline.

Table 2.

Estimates of piecewise exponential models of family-centered job mobility.

	Model 1			Model 2
	1979–1992	1993–2003	2004–2006	1979–1992	1993–2003	2004–2006
	B	B	B	B	B	B
Key Independent Variables
Male	−1.219*	−0.858***	−0.529*	−2.403**	−0.144	−0.358
Married	14.037***	1.272***	−0.662	13.268***	1.685***	−0.379
Male*Married	−	−	−	1.184	−0.941*	−0.741
Control Variables
Age	0.109*	−0.065***	0.027	0.109*	−0.066***	0.027
Education
Junior high school or less (reference)
Senior high school or more	−2.280**	0.126	0.275	−2.280**	0.161	0.295
Monthly Wage	0.001***	−0.000	−0.000	0.001***	−0.000	−0.000
Period of Labour Force Entry
1979–1992 (reference)
1993–2002	−	0.577**	1.866**	−	0.556**	1.803**
2003–2006	−	−	2.208***	−	−	2.164***
Job Search Method
Market (reference)
Social Network	0.685	−0.192	−0.109	0.685	−0.194	−0.108
Others	−1.812	−0.826*	0.506	−1.812	0.822*	0.473
Number of Previous Jobs	0.198	0.154*	0.250**	0.198	0.152*	0.243**
Company Ownership Types
Public (reference)
Private	−0.007	−0.135	0.628	−0.007	−0.149	0.599
Foreign	−0.404	−0.099	0.486	−0.404	−0.100	0.471
Others	−0.302	0.069	0.729	−0.302	0.074	0.707
Not Clear	1.535*	−0.276	0.819	1.535*	−0.255	0.798
Job Location
Outside Pearl River Delta (reference)
Pearl River Delta	−1.024	−0.645**	−1.097*	−1.025	−0.654**	−1.099*
Number of jobs	7295			7295
Number of work-centred job transitions	252			252
-Log Likelihood	−1113.492			−1109.945

Note: *p < 0.1, **p < 0.05, ***p < 0.01.

Regarding the main effect of ‘marital status’ in family-centered job mobility models, Model 1 in Table 2 shows that being married increases the hazard rate of family-centered job mobility in the first two periods. Surprisingly, the coefficient of marital status is not statistically significant in the most recent time period, which may be attributed to changes in the living arrangement of migrants and the overall policy context for temporary migrants in urban China. On the one hand, there is evidence that family migration is becoming more common over the years (Roberts, 2002). This new trend in migration patterns suggests that instead of separating, married migrants start to seek long-term settlement as migrant families in cities. On the other hand, more measures are taken to facilitate the integration process of rural migrants in urban areas, such as more access to compulsory education for migrant children (Shi, 2008). With these changes, it may become easier for married migrants to accommodate work with family needs so that they do not have to change jobs for family reasons more than unmarried migrants. Given these results, the question is whether marriage makes migrant women even more likely to change jobs for family-related reasons, as predicted by Hypothesis 1c. Model 2 shows that during the period of 1994–2003, being married increased women’s likelihood of experiencing family-centered job mobility more than men.

Results of fixed-effects models of wage growth

Table 3 presents the results of the fixed-effects models. The first fixed-effects model of monthly wage reveals that, consistent with the current literature, the more work-centered job transitions a migrant has experienced, the higher his/her wage. For every work-centered job change a migrant experiences, his/her wage grows by almost 16%. An interaction term between gender and the number of work-centered job transitions is added in Model 2. Confirming Hypothesis 2, it shows that migrant men benefit more from the number of work-centered job transitions than migrant women. The result suggests that women are not merely less likely to experience work-centered job mobility. In fact, for every job change they are also rewarded less than their male counterparts.

Table 3.

Coefficients of fixed-effects models of inflation-adjusted monthly wage (logged).

	Model 1	Model 2
Independent variables	B	B
Age	0.093***	0.095***
Age Squared	−0.002***	−0.002***
Number of work-centred job transitions	0.160***	0.112***
Number of family-centred job transitions	0.075	0.091
Number of involuntary job transitions	−0.082	0.072
Male* Number of work-centred job transitions	−	0.074**
Male* Number of family-centred job transitions	−	−0.017
Male* Number of involuntary job transitions	−	−0.192
Job Search Method
Market (reference)
Social Network	−0.001	−0.014
Others	−0.004	−0.017
Company Ownership Type
Public (reference)
Private	−0.097*	−0.100*
Foreign	0.025	0.025
Others	−0.049	−0.059
Not Clear	0.035	0.025
Time Period
1979–1992 (reference)
1993–2002	0.127
2003–2006	0.154
Job Location
Pearl River Delta	0.039
Outside Pearl River Delta (reference)
Constant	5.003***	5.009***

Note: *p < 0.1, **p < 0.05, ***p < 0.01.

Conclusion and discussion

Under the hukou system of China, rural migrants are trapped in dead-end jobs in the secondary labour market. Wages are kept at a very low level, and working conditions are harsh, which is described vividly as a ‘race to the bottom’ (Chan, 2003). Though employment instability is a manifestation of rural migrants’ vulnerabilities in the destination labour market, quitting is nonetheless a leverage migrants have to improve jobs. Moreover, we find that changing jobs does lead to an increase in wage for rural migrants in China. Therefore, as an intrinsic feature of migrant jobs in China, job mobility is an important angle to approach the issue of gender inequality among migrant workers in the urban labour market.

To recapitulate, we asked three questions in this paper. Are migrant women disadvantaged in job mobility as compared to their male counterparts? How do gender differences in job mobility patterns change over time? Related to the first question, if migrant women are disadvantaged in job mobility, does this disadvantage have real consequences for their economic wellbeing? Consequences of job mobility in this paper are evaluated by gains/losses in wages. By investigating these questions, we make two major contributions to the literature.

Our first contribution is the finding of female disadvantage in job mobility and its effect on wage growth. We find that migrant women are less likely to engage in work-centered job mobility, the type of job mobility that is likely to result in job improvement, while more likely to engage in family-centered job mobility, the type of job mobility that may require compromises between work and family needs and lead to downward mobility. Moreover, to the extent that marriage increases one’s likelihood of changing jobs for family-related reasons, women followed this path more than men during 1993–2003. Model results also suggest that with the unfolding of marketisation and the increasingly gendered destination labour market, women’s disadvantage in work-centered job mobility emerges. These findings fit well with the much told story that migrant women are exploited wherever ‘world factory’ takes root, that their careers are set to be retarded by the patriarchal culture, and that they are discriminated in the destination labour market. Moreover, results of fixed-effects models suggest that the number of previous work-centered job transitions, which is the most prevalent type of job transitions among migrant workers, rewards migrant men more than women in generating wage growth.

We also contribute to the literature by uncovering some unexpected patterns of gendered job mobility. We find that women’s disadvantage in family-centered job mobility is on the decline over time. This suggests that though migrant women find it increasingly hard to achieve equality with men in the urban labour market, they are gaining equality within the household in the sense that over time migrant men are more likely to accommodate work with family needs. This departs from the experience of women in urban China whose disadvantage in family-oriented job mobility remains constant over time (Cao and Hu, 2007). Moreover, different from women workers who are disadvantaged in their career paths by marriage (Fuller, 2008; Sousa-Poza and Henneberger, 2004), marriage does not burden migrant women in the PRD more than men. In times of need, it even encourages women more than men to pursue better jobs. Therefore, though patriarchal culture still persists among rural migrants in China, these findings point to the transformative role migration plays in the realm of family relations among migrants (Foner, 1998; Hondagneu-Sotelo, 1992; Jin, 2010; Mahler and Pessar, 2006).

Finally, we should note some limitations of the current study. First, return migrant selectivity biases studies of migrants based on destination samples (Chunyu et al., 2013). To the extent that migrant women tend to return home either for marriage or for family duties more than men do (Fan, 2004b), the survey we use misses out migrant women who return to rural hometowns for these reasons. Considering that migrant women who decide to stay and continue work at the place of destination differ from those who choose to return in certain characteristics, such as the motivation for economic independence, gender inequality observed in this paper is likely to be an underestimate. Second, the survey did not cover rural migrants in the self-employed sector, which is an important channel of employment for many rural migrants in urban China. Thus patterns of job transition for the self-employed may or may not resemble what we report in this paper. Third, since the survey was conducted in the PRD, rural migrants in the manufacturing sector are overrepresented if compared to nationally representative samples. Also relating to the location of the survey, the sample is not representative of the whole country. Therefore, it would be interesting to examine gendered job transition patterns and other related issues using national survey data in future research. To the authors’ knowledge, some national surveys start to collect job history data, such as the China Labour Dynamic Survey (CLDS), sponsored by the Center for Urban Studies at Sun Yat-sen University.⁶ Their availability to researchers will certainly benefit research efforts in this area.

Footnotes

Appendix 1

Descriptive statistics of major variables used in multivariate analyses.

Categorical Variables	Frequency (%)
Gender
Male	1639 (53.13)
Female	1446 (46.87)
Education
Junior high school or less	2155 (69.85)
Senior high school or more	930 (30.15)
Marital Status
Married	1742 (56.49)
Single	1342 (43.51)
Have ever changed job(s)
Yes	2315 (75.04)
No	770 (24.96)
Job Search Method of Current Job
Market	1220 (39.65)
Social Network	1765 (57.36)
Others	92 (2.99)
Company Ownership of Current Job
Public	336 (10.93)
Private	1575 (51.22)
Foreign	682 (22.18)
Others	369 (12.00)
Not Clear	113 (3.67)
Period of Labour Force Entry
1979–1992	158 (5.22)
1993–2002	1051 (34.72)
2003–2006	1818 (60.06)
Continuous Variables	Mean (S.D.)
Age	27.452 (8.647)
Monthly wage at time of survey	1113.559 (1024.924)
Total number of job changes of those who experienced at least one job change	2.713 (3.164)

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

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