Evidence map›Paper›PMID 40556916›Full record

ArticleFrontiers in public health2025

From caregiving burden to income transformation: intergenerational employment effects of long-term care insurance.

Zhiying Li, Longhua Zheng, Hong Xu

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

3 authors.

Zhiying LiSchool of Government, University of Chinese Academy of Social Sciences, Beijing, China.
Longhua ZhengEnglish Department, Jining Shiqiao Middle School, Jining, China.
Hong XuSchool of Public Administration and Policy, Shandong University of Finance and Economics, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Long-term care insurance (LTCI) has been introduced in China to address the challenges posed by an aging population. This study examines its impact on the income of older adult households, using data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2020. The analysis further explores heterogeneity and implications for income inequality. Methods: A Difference-in-Differences (DID) approach is employed, with 12 pilot cities as the treatment group and non-pilot cities as the control group. The analysis is based on 33,435 valid observations of individuals aged 60 and above. The main outcome variable is per capita household income, which has been deflated to 2011 constant prices using the Consumer Price Index (CPI). Demographic and regional characteristics are controlled for robustness. Results: LTCI significantly increases the income of older adult households, particularly in multi-child families and among younger older adult (under 75). Its impact is weaker in low- or no-child households and for those aged 75 and above. Additionally, LTCI contributes to reducing income inequality by enhancing income distribution among low-income older adult households. Discussion: The findings highlight the role of LTCI in improving economic security and narrowing income disparities among older adult households. To enhance its effectiveness, policy adjustments should account for family structure and age composition. These insights provide empirical support for optimizing LTCI to foster a more inclusive social security system.

Indexed as

CaregiversEmploymentIncomeInsurance, Long-Term CareIntergenerational RelationsAgedAged, 80 and overChinaFemaleHumansLongitudinal StudiesMaleMiddle AgedDifference-in-Differencesemployment promotionincome inequalitylong-term care insuranceolder household income

Identifiers

PMID40556916
PMCPMC12185542

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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.