Stark Choices: Work-Family Tradeoffs among Migrant Women and Men in Urban China

Abstract

China’s so-called floating population of rural-urban labor migrants includes rising numbers of couples and families migrating together. Labor market outcomes may differ for migrant men and women, in part due to family obligations, but few recent studies have investigated this possibility. This paper focuses on the relationship of labor outcomes with family obligations among migrant men and women and considers whether this relationship differs among those with higher and lower earnings potential. We perform nested logit models of employment status and ordinary least square regression analyses of income, using a nationally representative survey collected in 2013. For migrant women, childcare responsibilities are positively associated with unemployment and negatively correlated with income. In contrast, for migrant men, being coresident with children has no bearing on probability of being unemployed and is sometimes positively associated with income. Further, the “motherhood penalty” in income is most pronounced among migrant women with the least education. Results illustrate the embeddedness of individual migration decisions and outcomes within families. Findings also highlight a stark choice facing many migrant women: between earning for their children and living with them.

Notes

Acknowledgment

We thank the editor and anonymous reviewers for their comments and suggestions.

Notes

1 For simplicity, we will use the term “migrant” to refer to the floating population sampled in this study.

2 Work hours should not be longer than 8 h per day or 44 h per week (“Labor Law of the People’s Republic of China”). However, for migrant workers, work hours longer than the standard are common. According to the China Population and Employment Statistics Yearbook 2014, the weekly work hours of urban employed persons were more than 46 h per week, on average, since 2010 (National Bureau of Statistics of PRC 2014).

3 We include this category in the interest of comprehensively covering employment arrangements, at the helpful suggestion of a reviewer. However, given the small fraction of respondents who report part-time employment, we do not analyze this category fully.

4 A likelihood ratio test for the IIA assumption also supports the nested logit model over a multinomial logit model.

5 The survey did not ask directly whether children were residing with the respondents, instead they asked whether the children lived at current places, the places of origin or other places. We assume that the children who lived at the same places with the respondents were residing with the respondents.

6 In addition, as robustness tests, we add a variable indicating whether the respondent gave birth at the current place in models estimated for people who have children. We adopt this strategy because the estimates of the relationship between migrants’ economic outcomes and their family arrangements, especially whether they live with children, might be affected by migration selection. That is, people who gave birth before migration tend to be those with higher chances of being employed or earnings potential in the current place because they faced higher opportunity costs when they made the decision about migration. This means, for those who gave birth before migrating to current cities, jobs and higher income might be the main incentives and they are selected to be more advantaged than those who gave birth after migration. By including the variable measuring whether a child was born at current place, this migration selection can be partly controlled. However, another source of selection—that migrants who have higher income can afford to reside with their children—remains. Thus, if women’s economic outcomes are negatively associated with being able to live with children without accounting for the second selection, our analysis might give conservative estimates of the magnitude of the association of coresident children and migrant women’s employment and income.

One study (Min et al. 2016), which used 2010 data from the same migration survey as this study, suggested there might be potential selection bias in that the wages of migrants who are unemployed or seeking jobs at the time of survey are unobserved. They overcame this bias by adopting a Heckman two-step correction with the age of the youngest child as the identifying variable. We do not adjust for this selection, for three reasons. First, we also conduct analysis of employment status, which can give us insights about how the employment status differs between women and men. Second, we only include migrants who have stayed in their current places more than six months, leading to limited selection bias. That is, migrants who were unemployed or looking for jobs at the time of survey are likely to be those who have stayed in the receiving cities for a short period, and thus they were not included in this sample. Finally, as discussed, a large body of studies has shown that working mothers persistently suffer from family obligations and childcare responsibilities. For this reason, we treat family circumstances, including the presence of children, as analytic variables rather than identifying variables.

7 Because of the missing values in spousal characteristics, the sample size used for the analyses on married groups is smaller than the number reported in Table 1. However, the deleted individuals because of missing values only account for a small proportion (about 2 percent).

8 The proportion of being unemployed, part-time employed and full-time employed is 23.57 percent, 2.98 percent, and 73.45 percent for married women. For married men, the proportion is 3.24 percent, 3.12 percent, and 93.64 percent.

9 All the variables are not significant for the model of part-time employment after treating being unemployed as the reference category, which might result from the large standard errors of the estimates in part-time employment model.

10 We also conduct t tests on equal coefficients of these variables, the following variables are significantly different between men and women in M3: Coreside with very young children (p < 0.001), Have young children (p < 0.01), Coreside with young children (p < 0.001), Coreside with children at school age (p < 0.001).

11 The probability of having part-time jobs is a bit higher.

12 These numbers use the definition, children under 18 years of age with at least one parent migrating.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71490731]；China’s National Social Science Foundation [19CRK015] and China Postdoctoral Science Foundation [2018M641572].

Notes on contributors

Menghan Zhao

Menghan Zhao ([email protected]) is an assistant professor of demography at Renmin University of China. Her research investigates how gender intersects with family and population processes, such as childbearing behavior, divisions of labor, to shape individual wellbeing and societal inequality in China. She obtained her PhD in demography from the University of Pennsylvania.

Emily Hannum

Emily Hannum ([email protected]) is professor of sociology and associate dean for social sciences at the University of Pennsylvania. Her research focuses on education, child and youth welfare, and social inequality, particularly in China. In China, she has conducted research on gender, ethnic, and geographic disparities in education and employment, changes in the impact of education on income and occupational attainment under market reforms, rural teachers and their links to student outcomes, and children’s and adolescents’ welfare under market reforms.