Evidence map›Paper›PMID 40777907›Full record

ArticleIranian journal of public health2025

Empowering the Aging Workforce: The Impact of Medical Insurance on Labor Decisions in Rural China.

Cheng Qin, Yuchen Zhu

Abstract read
In one paragraph

Article in Iranian journal of public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Cheng QinSchool of Economics, Guangxi University, Nanning, China.
Yuchen ZhuCollege of Economics and Management, China Agricultural University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Understanding how enhanced medical insurance benefits influence labor decisions of rural elderly individuals is crucial for addressing the challenges posed by population aging on labor market. This study investigated the effects of enhanced welfare levels resulting from medical insurance integration on labor decisions of rural elderly individuals. The objective was to offer policy recommendations to the government to mitigate the decline in the working-age population and sustain the momentum and vibrancy of economic and social development. Methods: This study conducted an empirical analysis, utilizing two waves of data from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2015 and 2018. This analysis employed the Difference-in-Differences (DID) approach, treating the enhancement of medical insurance benefits for rural residents, due to China's medical insurance integration as a quasi-natural experiment. Results: Enhancing medical insurance benefits increased the labor supply of older rural individuals, as evidenced by higher labor participation rates and increased hours of work. It also facilitates migrant work while decreasing the willingness to work indefinitely. Medical insurance benefits indirectly influence labor decisions by alleviating healthcare burdens and improving the health status. Notably, the effect is more pronounced among females, higher-income groups, and individuals with poorer health status. Conclusion: The enhancement of medical insurance benefits resulting from China's medical insurance integration increased the social labor supply and reduced the necessity for elderly individuals to continue working indefinitely. The integration provides disadvantaged groups with improved social security, thereby allowing them to enjoy their later years more fully.

Indexed as

Elderly peopleLabor migrationLabor supplyMedical insuranceRural China

Identifiers

PMID40777907
PMCPMC12325851

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.