ABSTRACT
This study uses the conditional logit model to explore the impact of anti-corruption publicity on the probability of choosing a specific city among internal migrants in China. By leveraging China’s Corruption Investigations Dataset and China Migrants Dynamic Survey, we find that anti-corruption publicity significantly decreases the probability of migrants choosing the city. The effects are driven by the mechanism whereby anti-corruption publicity increases the individuals’ perceptions of corruption and reduces their trust in local government officials. The negative effects remain after the migrants move to the city. Exposure to anti-corruption publicity is associated with decreased settlement intention and social health insurance participation. Our findings strongly indicate the necessity for the government to reconsider its communication strategies concerning anti-corruption efforts.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/00036846.2023.2273236
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
Notes
1 The Section-Head level is the primary level cadre in China’s civil service system, and its government position is equivalent to a mayor of towns. Datasource: http://sc.people.com.cn/n2/2021/0629/c345460–34799133.html
4 Due to a scarcity of anti-corruption news releases by the government since 2016, it becomes impossible to construct empirical regressions based on anti-corruption propaganda data from that period. In the Appendix A3, we present regressions using data from 2013, 2014, and 2015. These regressions consistently demonstrate that anti-corruption propaganda decreases the likelihood of migrants choosing certain localities as their destination. And the dimensions grow gradually ovgouzer time from 2013–2015.
5 We show an example that how to construct choice sets in Appendix B.