Evidence map›Paper›PMID 39170514›Full record

ArticleHeliyon2024

Social network effect on land transfer willingness of the rural elders: Evidence from China.

Jianliang Nie, Ziyue Dong, Le Tang, Jinlin Liu, Yufeng Wu

Abstract read
In one paragraph

Article in Heliyon, 2024. 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

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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

5 authors.

Jianliang NieSchool of Public Administration, Northwest University, Shaanxi, 710127, China.
Ziyue DongSchool of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai, 200433, China.
Le TangSchool of Public Administration, Northwest University, Shaanxi, 710127, China.
Jinlin LiuState Key Laboratory of Marine Geology, Tongji University, Shanghai, 200092, China.
Yufeng WuSchool of Public Administration, Northwest University, Shaanxi, 710127, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The elders in China's rural areas are facing challenges in maintaining agricultural production due to the outflow of rural laborers. The Transfer of land could alleviate the burden of land-based livelihoods for rural elders, but their decisions regarding land transfer are influenced by their social networks within the context of Chinese rural society. This study investigates how social networks impact the willingness of rural elders to transfer land. Using survey data from 782 rural elders in 32 villages across 11 provinces in China, this paper applies multilinear and binary logistic regression models. The results indicate that the willingness of rural elders to transfer land is affected by their social neteork: (1) Internal network scale, network heterogeneity, and frequency of external network relationships have a significantly positive influence on rural elders' willingness to transfer land, while frequency of internal network relationships has a significantly negative influence. (2) There are group differences in the above impacts, and these significant impacts occur only among male elderly individuals aged 60-69 years old or living in central and western regions. (3) Social networks primarily influence rural elders' willingness to transfer land through three mechanisms: information consultation, interpersonal trust, and material resource acquisition. A larger internal social network scale, higher heterogeneity within the network, and more frequent interactions with members of external networks lead to greater access to useful information, higher levels of trust in others, increased material resources availability, and an increased likelihood of transferring land. These findings can inform government policies aimed at improving practices related to land transfers and old age security for rural elders.

Indexed as

ChinaRural eldersSocial networkWillingness to transfer land

Identifiers

PMID39170514
PMCPMC11336273

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LicenceCC BY-NC
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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.