Work

The poverty prevention effects of health insurance: evidence from China's basic medical insurance program

Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: China's Basic Medical Insurance (BMI) system is designed to alleviate illness-induced poverty. This study aims to evaluate the effectiveness of BMI in pre-venting households from falling into poverty, uncover its underlying mechanisms, and propose a preferential subsidy policy to address existing inequities. Methods: Panel data from the 2010-2018 China Family Panel Studies (CFPS) were used. We employed Probit and Heckman models to assess how BMI enrollment influences poverty likelihood, focusing on rural-urban differences, total healthcare expenditures, and out-of-pocket (OOP) spending. We also proposed a tiered, progressively subsidized policy-"income segmentation with progressive reimbursement"-as a potential strategy to bolster poverty prevention among low-income households. Results: Our findings indicate that BMI enrollment significantly reduces the probability of house-holds becoming poor, with more pronounced effects in rural areas than in urban ones. Following enrollment, total healthcare expenditures rise substantially and health outcomes improve, yet OOP expenses do not show a corresponding decline and can even increase for urban residents. Moreover, higher-income groups benefit more from BMI, whereas low-income families face financial constraints that limit their ability to fully utilize these benefits. Conclusions: Despite BMI's success in improving health out-comes, moral hazard remains a concern, particularly in urban settings. A tiered, progressively subsidized approach can more effectively support low-income households, lowering their risk of falling back into poverty due to illness. These results underscore the importance of refining insurance policies to ensure that vulnerable populations can fully benefit from healthcare coverage.
Language: English
Subfield: Finance
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4409747013
FWCI: 4.695
Cites: 34
Cited by: 5
Related to: 10