Evidence map›Paper›PMID 41719366›Full record

ArticlePloS one2026

Unveiling nonlinear effects of Digital Inclusive Finance on urban-rural integration: A threshold panel analysis of China.

Ying Song, Yuanping Cao, Zhiyi Zhuo, Feifei Yang

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.

Ying SongGreen Development Research Center, Wuyi University, Fujian, China.
Yuanping CaoGreen Development Research Center, Wuyi University, Fujian, China.
Zhiyi ZhuoSchool of Economics and Management, Zhejiang Ocean University, Zhejiang, China.ORCID https://orcid.org/0000-0002-1282-0431
Feifei YangGreen Development Research Center, Wuyi University, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper examines the nonlinear effects of Digital Inclusive Finance (DIF) on urban-rural integration (URI) using a provincial panel for mainland China (31 provinces, 2011-2023). We construct a multidimensional URI index and decompose DIF into coverage breadth (D1), usage depth (D2) and digitalization level (D3). Estimation proceeds with two-way fixed-effects models and Hansen-style panel threshold regressions with bootstrap inference; robustness checks include placebo tests and instrumental-variable specifications. The evidence shows that DIF's impact on URI is regime-dependent: marginal returns are limited at low development levels but increase sharply once DIF and complementary institutional conditions cross empirically identified thresholds. Disaggregation reveals that usage depth (D2) consistently promotes integration, whereas the benefits of coverage (D1) and digitalization (D3) materialize mainly in digitally mature regimes. Traditional finance exhibits declining marginal contribution beyond its effective range, underlining the catalytic role of digital systems. We document heterogeneity across regions and show that negative baseline coefficients on openness and education reflect spatial concentration rather than intrinsic harms. The findings reconcile mixed results in prior work and imply that policy should be threshold-aware: prioritize foundational access where coverage is low, while in advanced contexts emphasize usage, platform interoperability, and regulatory safeguards to manage platform concentration and distributional risks.

Indexed as

Rural PopulationUrban PopulationChinaDigital MediaHumans

Identifiers

PMID41719366
PMCPMC12923127

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.