Evidence map›Paper›PMID 41984899›Full record

ArticlePloS one2026

Improving residents' satisfaction with administrative boundary changes: A comparative analysis based on the township-town merger policy.

Qiong Wang, Hao Zhao, Jie Sun, Yajie Yu, Changlin Zhang, Pingfan Hao

Abstract readComparative Study
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.

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0cells of the map it votes in
0citing papers in PubMed
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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

6 authors.

Qiong WangCollege of Foreign Languages, Nanjing Xiaozhuang University, Nanjing, Jiangsu, China.
Hao ZhaoCollege of Marxism, Hainan University, Haikou, Hainan, China.ORCID https://orcid.org/0009-0003-4152-8565
Jie SunCollege of Foreign Languages, Nanjing Xiaozhuang University, Nanjing, Jiangsu, China.
Yajie YuCollege of Foreign Languages, Nanjing Xiaozhuang University, Nanjing, Jiangsu, China.
Changlin ZhangCollege of Foreign Languages, Nanjing Xiaozhuang University, Nanjing, Jiangsu, China.
Pingfan HaoCollege of Foreign Languages, Nanjing Xiaozhuang University, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Administrative boundary changes driven by policies are pivotal instruments for improving resource allocation efficiency. Studying the factors driving residents' satisfaction with these policies is crucial for enhancing policy effectiveness. Based on field surveys in Lukou and Hengxi Subdistricts, this paper constructs a logit model to identify key determinants of residents' satisfaction with the township-town merger policy (TMP) and applies the Fairlie decomposition to quantify the sources of satisfaction disparities between subdistricts. Results indicate that employment incentives, social security, and cultural development are the primary drivers of policy satisfaction and interregional differences, exerting greater influence than individual demographic traits. Mechanistically, the study reveals that the development approaches of different regions are strongly associated with policy satisfaction. Besides, residents in communities with stronger collective identity tend to evaluate policy outcomes through temporal comparisons with their own past, leading to higher satisfaction when improvements in social security and cultural preservation are evident. Conversely, in areas with weakened collective identity, residents form higher expectations through upward social comparisons with more developed neighboring subdistricts. When actual policy outcomes fall short of these expectations, satisfaction declines. These findings offer a micro-perspective on how top-down administrative boundary reforms affect public satisfaction, providing recommendations for policy design, sustainable governance, and regional planning.

Indexed as

Personal SatisfactionPublic PolicyHumans

Identifiers

PMID41984899
PMCPMC13082704

What OpenQuestion holds

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LicenceCC BY
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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.