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Dataset . 2024
License: CC BY
Data sources: Datacite
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From Fear to Love: Dissecting Political Trust in China

Authors: Liu, Yu; Su, Yu-Sung;

From Fear to Love: Dissecting Political Trust in China

Abstract

The remarkably high level of political trust observed in Chinese surveys has ignited enduring debates regarding whether fear has inflated these numbers. The opposing sides share one assumption: fear and trust are inherently contradictory. Our study proposes an alternative view. Grounded in cognitive dissonance theory, which holds that induced compliance can lead to sincere attitude change, we argue that fear has in fact increased political trust in China. We take Xi’s sweeping anti-corruption campaign as a quasi-natural experiment to test this theory. Using 2002-2021 survey data and employing a Difference-in-Differences model, we take the campaign as treatment and regime insiders as the treated group, finding that political trust among regime insiders increased faster than among outsiders during the campaign. This finding withstands various robustness checks. Our study not only addresses the fear-love conundrum in China studies but also uncovers a general mechanism of legitimacy production in deeply authoritarian states.

The data is about the paper "From Fear to Love: Dissecting Political Trust in China"

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average