Abstract
Internal migration and the provision of social benefits in China are restricted by the institutional policy, commonly called hukou. Hukou status is mainly determined by place of origin. It creates a two-tier system that exacerbates inequality across Chinese households—rural versus urban hukou. We apply coarsened exact matching methods and propensity score models to estimate the impact of obtaining an urban hukou on rural-to-urban migrants’ health outcomes. Our results indicate that migrants with urban hukou maintain lower levels of blood pressure and are less likely to develop hypertension or nutritional conditions compared to rural hukou migrants. We do not find significant results on self-rated health. Our findings show that, in the short-medium term, there are differences in health that are prevalent for migrants with different hukous.
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Source: Authors’ own elaboration based on RUMiC survey (difference in means t test p value = 0.106)
Source: Authors’ own elaboration based on RUMiC survey (95% asymptotic and bootstrap confidence intervals for kdensity. Difference in means t test p value = 0.007)
Source: Authors’ own elaboration based on RUMiC survey (95% asymptotic and bootstrap confidence intervals for kdensity. Difference in means t test p value = 0.010)
Source: Authors’ own elaboration based on RUMiC survey
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Notes
Migrants have a higher likelihood than urban citizens of answering positively to questions related with health outcomes to avoid negative discrimination. This might introduce estimation bias when using migrant’s self-reported health.
See Gong et al. (2012) for an extensive survey and discussion on the effects of urbanization on health.
She uses self-rated health status as dependent variable. She reduces the five possible health status to a dichotomous variable in which excellent and good health status take the value one. Fair, poor and unknown health status take the value of zero. This approximation highly reduces variation within the dependent variable. She also uses two additional binary variables for health-related behavior: being ill or no-ill, and no-activity limiting illness. It is not clear form the paper what is the control group or group of reference. The OLS specifications find positive and significant effects of urban hukou on self-reported health and no-illness but no effect on no-activity. The study does not control for occupation, or priors to change in hukou status: such as wealth or savings, homeownership, or professional training.
Some of the individuals with rural hukou surveyed do not receive income because they are employed as housekeepers without pay, which decreases the average income. In our empirical analysis, we control for migrants’ working status.
We exclude children and the elderly as these two groups require different treatments to analyze effects on health.
We attempt an instrumental variables strategy—using spouse’s hukou and mother’s education as instruments—but the instruments are too weak to obtain credibly causal results. In this case, it is better to present OLS results; therefore, we do not include IV results in Table 2 but are available under request.
The healthy migrant hypothesis establishes that those individuals with better health migrate from rural areas leaving the less healthy behind. As our sample is only composed by migrants, the self-selection comes from distinctive characteristics that can influence the ability of migrants to change hukou status from rural to urban.
Regarding homeownership, 5.43% of migrants with rural hukou have built or purchased a house versus 6.79% of urban hukou migrants.
The function cem treats categorical and numerical variables differently. For numerical variables, we can use the cutpoints option Thus, for example, in the Chinese educational system, the following discretization of years of education corresponds to these levels: 0–3 years is kindergarten, 4–6 is primary school, 7–11 years is middle school, 12–17 years secondary school and from 18 years old is college education. We choose those cutpoints but also the program offers an automated way to compute these solutions, for a detailed analysis see Iacus et al. (2011).
Kernel to kernel match is not always possible as it requires many observations for both groups. In our analysis, we show the results for all health conditions. We additionally estimate the effect of hukou on obesity (results available under request). In this case, we could not use the k-to-k-match when assessing the effect of hukou on being obese due to lack of observations that match for both treatment and control group.
Results for the nearest neighbor matching with replacement are available under request but do not change any of the significant results.
Keele (2010) establishes that studies in social sciences are not insensitive to hidden bias (or large values of Γ) and recommends choosing values between 1 and 2. For a binary outcome Rosembaum (2002) test is based on McNemar’s test. For ordered outcomes or continuous outcomes, the Rosebaum’s test is based on Wilcoxon sign rank.
We do not report the estimated coefficients of the added controls and fixed effects to increase clarity when presenting the table. They are available by request.
Blood pressure has been negatively coded to provide easier interpretation of the coefficients.
All observations within a stratum contain treated and control units that are by definition within the common support. Following Blackwell et al. (2009) to improve the quality of the inference, we restrict the data to common support by drooping in which the cem_matched==0.
The survey uses a self-rated health scale that goes from 1 to 5, in which 1 is best and 5 is worst. To ease the interpretation of the coefficients, we code the variable negatively: from − 5 (worst) to − 1 (best). Therefore, a positive estimator means that the variable affects self-rated health positively. Our results show that the treatment—urban hukou—exerts a negative and nonsignificant effect on self-rated health, which is consistent with the results in Table 2.
The ATT and ATE refer by definition to different portions of the population of interest. ATE refers to the effect of the treatment across all individuals in the population. Results for the ATE are reported in the notes of Tables 3, 4, 5, 6 and 7. In observational studies, we do not expect that the ATT and the ATE will be equal as in randomized studies.
The lower bound is not interesting in our study as the urban hukou effect is positive on the health outcomes. We are interested in knowing the sensitivity of our results to an overestimation of the positive effect of urban hukou on health.
See the work of Liu et al. (2018).
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Acknowledgements
We would like to thank the Institute for the Study of Labor (IZA) for providing access to the Rural Urban Migration data survey. We are very grateful to the two anonymous reviewers and the guest editor who provided valuable comments leading to the improvement in the manuscript. We would also like to acknowledge the insightful comments and suggestions by Hillel Rapoport, David McKenzie, Isabel Ruiz-Olaya, Jason Barr, Daniel Maxwell, Joseph Pelzman, Sara Tonini, Balding, Yue Li and Ramya Shankar. We are also grateful to participants at various conferences and seminars where the paper was presented including the Paris School of Economics Migration workshop, the WIDER Conference on ‘Migration and Mobility,’ the International Trade and Finance Association Meeting, Western Economic Association Meeting, ASSA meeting, Eastern Economic Association annual meeting, and seminars at the City University of New York, Cape Town University, and University of Johannesburg. Excellent research assistance has been provided by David Dam. All errors remain our own.
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Bengoa, M., Rick, C. Chinese Hukou Policy and Rural-to-Urban Migrants’ Health: Evidence from Matching Methods. Eastern Econ J 46, 224–259 (2020). https://doi.org/10.1057/s41302-019-00158-z
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DOI: https://doi.org/10.1057/s41302-019-00158-z