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Migration control policy and parent–child separation among migrant families: evidence from China

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Abstract

This paper investigates the effects of a migration control policy in mega cities after 2014 in China on parent–child separation. One of the key initiatives of the policy is to restrict the access of migrant children to public education in cities. We employ two empirical approaches: one that leverages variations in policy implementation pressure across cities, and another that exploits variations in restrictions on migrant children’s access to education across cities and over time. We find that the policy contributes to an increased probability of children being left behind. The impact is most salient among children from families of low socio-economic status and at the compulsory-education age. Little evidence indicates that the policy causes a change in the composition of the migrant families in cities.

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Data Availability

The Chinese Migrant Dynamic Monitoring Survey (2011–2018, CMDMS) data is obtained from the China National Health and Family Planning Commission. Data use agreement is required by the China National Health and Family Planning Commission.

Notes

  1. Unlike harsh actions such as deporting undocumented cross-border immigrants, internal migration initiatives usually dis-incentivize the inflow of rural immigrants by limiting their access to public welfare and urban services such as access to health insurance and education. The welfare loss of migrant families associated with internal migration policies in developing countries is often overlooked.

  2. Among all children separated from their migrant parents, there are 40.51 million left-behind children in their rural hometowns, 28.26 million left-behind children in their urban hometowns and 18.84 million left-behind children who migrate to other cities where they do not have hukou residence.

  3. The NBS defines migrants as those who move to live for more than six months in cities where they do not have local hukou. Hence, the permanent migrants who manage to obtain hukou in the destination cities are counted as local residents. In the 2020 census data, the migrant population reached 376 million, though how much of the statistics is due to the increase of the actual migrants or the measurement issue in the previous surveys is still under debate.

  4. http://ghs.ndrc.gov.cn/zttp/xxczhjs/ghzc/201605/t20160505/_800839.html.

  5. Starting from 2014, more and more cities started to implement a points-based system, whereby points can be accumulated according to a migrant’s education level, skill specialization, and occupation, as well as social security and tax payment. A migrant's right to access public service in the city is based on their points. However, it is almost impossible for low-skilled migrants to obtain the necessary points.

  6. For details about the educational rights of migrant children in China, see a literature review by Chen et al. (2019).

  7. In many cities, such as Shanghai, migrant schools are only available for primary education, but not for middle school education.

  8. The only exception is to have a flexible employment certificate, which applies to some specific and local urgently needed occupations, such as carers in hospitals or nursing homes, and some employees in the agricultural sector. However, this flexible employment certificate is difficult to obtain and requires a large number of documents. There are also many hidden rules, and the difficulty of obtaining these certificates increases with the population control pressures in local districts.

  9. For details on the development of migrant schools in Shanghai before 2014, please refer to Chen and Feng (2017).

  10. Chen and Feng (2017) find that around 80% of migrant parents think that the quality of public schools in Shanghai is better than the quality of schools in their hometown, and around 50% think that migrant schools in Shanghai are better compared to those in their hometown. Lai et al. (2014) compares the math scores of students in migrant schools in Beijing with those in rural public schools in Shaanxi and finds that migrant schools in Beijing outperform rural public schools in Shaanxi.

  11. In practice, it is possible that the parent may send his child to a smaller city for education, rather than leave them in rural hometown. Our model can incorporate this case by assuming that education costs are lower in cities with no migration control policy.

  12. More generally, we can assume there is a positive cost of education \({\uptheta }_{r}\left(X,{d}_{r}\right)>0\). This will allow the framework to be applied to scenarios where migrant family leave its child to another locations including hometown or other cities.

  13. The existence of the interior solution requires that \(\frac{{\upbeta }_{3}}{1+{\upbeta }_{3}}\frac{\mathrm{E}\left({\mathrm{w}}_{\mathrm{c}}|\mathrm{X}\right)}{\mathrm{E}\left({\mathrm{w}}_{\mathrm{c}}|\mathrm{X}\right)-\mathrm{E}\left({\mathrm{w}}_{\mathrm{r}}|\mathrm{X}\right)}<1\).

  14. In China, as required by the Compulsory Education Law, children must attend primary school when they reach the age of six and complete nine years of education. Children between three to six years old are usually enrolled in preschools, but this is optional. Youth older than 16 years old are eligible to be employed.

  15. This left-behind variable measures a child’s exposure to the absence of at least one parent. We replicate our result by defining a left-behind child if he does not live in the same city with both migrant parents and obtain similar results. Besides, the left-behind children in this paper include those left behind either in their rural hometown or in other cities.

  16. The CMDMS data do not include information on cities of origin. To the best we can do, we control for the fixed effect of migrants’ home province.

  17. We use the multi-way clustering in the child analysis because the children within a particular age group may be in the exposure to a common shock from national education policy. For example, children at age six are mandated to enroll in primary school.

  18. Our results remain robust when we separately add each city characteristic to the specification in Table A.1 in the Online Appendix.

  19. The mean of policy pressure index over the 11 mega cities is 0.836 and the standard deviation is 0.0935, and the mean of children being left behind prior to the policy implementation is 0.416. The coefficient in column (5) reflects the policy effect if the policy pressure index increases from 0 to 1. Thus, a 1 standard deviation increase in the policy pressure index is estimated to increase the likelihood of children being left behind by 4.31 ((= 0.0935/1 × 0.461) × 100) percentage points, equivalent to 10.4 ((0.0431/0.416) × 100) percent.

  20. The Assumption 4 in Callaway et al. (2021) specifies the canonical parallel trend assumption when treatment D is continuous. We rephrase it below. For any level of policy pressure d:

    \(\mathrm{ E}\left(\mathrm{Y}{\left(0\right)}_{\mathrm{t}}-\mathrm{Y}{\left(0\right)}_{\mathrm{t}-1}|\mathrm{D}=\mathrm{d}\right)=\mathrm{E}\left(\mathrm{Y}{\left(0\right)}_{\mathrm{t}}-\mathrm{Y}{\left(0\right)}_{\mathrm{t}-1}|\mathrm{D}=0\right)\).

  21. As noted by Wolfers (2006), when the policy effect is time-varying, the city-specific trends could pick up both preexisting trends and policy effects. Since the included trend parameters are identified in part by the post-policy data, controlling trends in the model could be “over-controlling” when the policy effect also varies over time, leading to biased policy effect estimates (Lindo and Parkham 2017).

  22. As long as a child finishes his compulsory education in the ninth grade and reaches 16 years of age, he can choose to stay with his/her parents and work in a city rather than separating from his parents to enter high school in his home province. For preschool children under six, their parents can choose not to send them to kindergarten, as preschool education is not mandatory.

  23. The mean of policy pressure index over the 11 mega cities is 0.836 and the standard deviation is 0.0935. Thus, a one standard-deviation increase in the policy pressure index is estimated to increase the education barrier score by 0.571 (= 0.0935/1 × 6.111) standard deviations.

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Acknowledgements

The authors are grateful to editor Kompal Sinha, three anonymous reviewers, Kevin Lang, Jennifer Hunt, Junsen Zhang, and Min Lu, as well as to participants at the 2017 Meeting of International Labor Economics Symposium, the 2018 Conference of Migrant Children at Jinan University, the 2019 Summer Institute of CCER at Peking University, and seminar participants at Fudan University for their comments to earlier drafts. All errors are our own.

Funding

Chen received financial support from the National Natural Science Foundation of China (Grant No. 71773074, Grant No. 72273081) and partial financial support from the Key Program of the National Natural Science Foundation of China (Grant No. 72034006).

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Authors and Affiliations

  1. Institute for Advanced Research, Shanghai University of Finance and Economics, Shanghai, China

    Yuanyuan Chen

  2. Key Laboratory of Mathematical Economics, Ministry of Education, Beijing, China

    Yuanyuan Chen

  3. Department of Health Management and Systems Sciences, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA

    Wei Fu

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Chen, Y., Fu, W. Migration control policy and parent–child separation among migrant families: evidence from China. J Popul Econ 36, 2347–2388 (2023). https://doi.org/10.1007/s00148-023-00971-z

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