The impact of local government information disclosure on county-seat development in an authoritarian context: evidence from a pilot policy in China

Yingchao Yan & Shoujun Lyu

Journal of Public Policy2026https://doi.org/10.1017/s0143814x26101081article
AJG 2ABDC B
Weight
0.50

Abstract

Authoritarian regimes have long faced governance challenges arising from decentralization, as central governments struggle to control local government behavior due to information barriers. This paper argues that promoting the disclosure of local government information to the public is an effective strategy to alleviate issues associated with decentralization. Using a policy mandating the disclosure of local government information on social governance as a quasi-natural experiment, we examine its impact on a critical governance challenge emerging from decentralization – the decline of county seats. Using China’s county-level panel data (2015–2022) and a difference-in-differences approach, we find that local information disclosure significantly promotes county seat development. Specifically, it increases land allocation for both economic and public service purposes, thereby breaking the vicious cycle between deficient public services and economic stagnation. Heterogeneity analysis indicates that these effects are more pronounced in counties with higher citizen responsiveness and more constrained fiscal capacity.

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https://doi.org/https://doi.org/10.1017/s0143814x26101081

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@article{yingchao2026,
  title        = {{The impact of local government information disclosure on county-seat development in an authoritarian context: evidence from a pilot policy in China}},
  author       = {Yingchao Yan & Shoujun Lyu},
  journal      = {Journal of Public Policy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/s0143814x26101081},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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