Evidence map›Paper›PMID 41719298›Full record

ArticlePloS one2026

Does Internet use alleviate household financial vulnerability? An empirical analysis based on panel data from China Family Panel Studies (CFPS).

Zeliang Yu, Heyu Li, Pei Guo, Lin He

Abstract read
In one paragraph

Article in PloS one, 2026. 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

4 authors.

Zeliang YuCollege of Economics and Management, China Agricultural University, Beijing, China.
Heyu LiCollege of Management, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China.ORCID https://orcid.org/0009-0007-0170-1537
Pei GuoCollege of Economics and Management, China Agricultural University, Beijing, China.
Lin HeCollege of Management, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As digital technologies increasingly shape household financial behavior, understanding their role in buffering financial shocks has become an important policy and research concern. Drawing on panel data from the 2016, 2018, and 2020 waves of the China Family Panel Studies (CFPS), this paper investigates whether and how Internet use mitigates household financial vulnerability. The empirical results indicate that Internet use significantly lowers both the likelihood and severity of household financial vulnerability, a conclusion that holds after a series of robustness tests and corrections for endogeneity. Mechanism analysis reveals that this effect operates primarily through three channels: boosting income growth, promoting wealth accumulation, and enhancing risk management capabilities. Further heterogeneity analysis shows stronger mitigating effects in western regions, rural areas, and among households headed by individuals under the age of 60. These findings suggest several policy implications: deepen digital development by improving infrastructure and raising digital literacy to broadly reduce financial vulnerability; expand household income and wealth accumulation channels through innovative, technology-driven financial products that strengthen risk management; and prioritize equitable digital advancement, focusing on western regions, rural households, and older populations to narrow the digital divide and reduce systemic financial risk.

Indexed as

Family CharacteristicsInternetInternet UseChinaHumansIncomeRural PopulationSocioeconomic Factors

Identifiers

PMID41719298
PMCPMC12923019

What OpenQuestion holds

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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.