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Heterogeneous Effects of Health Insurance on Rural Children’s Health in China: A Causal Machine Learning Approach

Author

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  • Hua Chen

    (School of Insurance, Central University of Finance and Economics, Beijing 102206, China)

  • Jianing Xing

    (School of Insurance, Central University of Finance and Economics, Beijing 102206, China)

  • Xiaoxu Yang

    (School of Insurance, Central University of Finance and Economics, Beijing 102206, China)

  • Kai Zhan

    (School of Finance, Guangdong University of Foreign Studies, Guangzhou 510410, China)

Abstract

This paper investigates the impact of Urban and Rural Resident Basic Medical Insurance (URRBMI) on the health of preschool and school-age children in rural China using data from the 2018 wave of the China Family Panel Studies (CFPS). We employ the propensity score matching approach and causal forest to evaluate impacts. Results show that the URRBMI has significantly improved the health status of preschool children. However, the health improvement of school-age children by URRBMI is only limited to obese children, and this effect is not significant. In addition, this paper identifies important variables related to heterogeneity through the causal forest and evaluates the heterogeneity of the impact of URRBMI on the health of two types of children. For preschool children, we find disadvantaged mothers (i.e., with lower wealth, lower educated, or in rural areas) benefit more from the URRBMI. No significant heterogeneity is found for the school-age children. Our study demonstrates the power of causal forest to uncover the heterogeneity in policy evaluation, hence providing policymakers with valuable information for policy design.

Suggested Citation

  • Hua Chen & Jianing Xing & Xiaoxu Yang & Kai Zhan, 2021. "Heterogeneous Effects of Health Insurance on Rural Children’s Health in China: A Causal Machine Learning Approach," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:18:p:9616-:d:634018
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    References listed on IDEAS

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    2. Luís Lourenço & Luciano Weber & Leandro Garcia & Vinicius Ramos & João Souza, 2024. "Machine Learning Algorithms to Estimate Propensity Scores in Health Policy Evaluation: A Scoping Review," IJERPH, MDPI, vol. 21(11), pages 1-12, November.

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