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Effects of incentive-based population policies on sustainability of China’s recent maternity insurance system reform: a system dynamics simulation

Health Res Policy Syst. 2022 Dec 28;20(1):140. doi: 10.1186/s12961-022-00945-9.

ABSTRACT

BACKGROUND: This paper seeks to assess the sustainability of the reformed maternity insurance system and the extent to which China’s current maternity insurance system can support different levels of fertility incentives in the future. Our findings will serve as a reference for countries in a similar demographic predicament and those about to face it.

METHODS: This study used a combination of qualitative and quantitative methods. In the qualitative assessment, we used a grounded theory model to generalize the factors influencing the sustainability of maternity insurance funds. For the quantitative analysis, we used a novel and comprehensive system dynamics model to visualize the status of the combined operation of maternity and health insurance. Data are mainly derived from the historical data of the Statistical Yearbook of Jiangsu Province and the National Bureau of Statistics of China.

RESULTS: In the short term, fertility incentive payments can be set to motivate people to have children. It is therefore recommended that when the scope of the fertility incentive policy is limited to two children, and an average amount above RMB 10 000 could be set, it would be prudent to set the amount at a level not exceeding RMB 10 000 when the scope of the fertility incentive policy is for all newborns. In the long term, a system of incentives for childbirth should be built from education policy, house price regulation, tax relief and childcare services.

CONCLUSION: Our research not only highlights the significance of improving the resilience of maternity insurance by combining maternity insurance and health insurance funds, but also suggests a way to economically incentivize beneficiaries to have children so as to mitigate the decline in China’s birth rate and cope with the crisis of an ageing population.

PMID:36578006 | DOI:10.1186/s12961-022-00945-9

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