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Making the List: Reevaluating Political Trust and Social Desirability in China

Published online by Cambridge University Press:  07 November 2022

STEPHEN P. NICHOLSON Open the ORCID record for STEPHEN P. NICHOLSON [Opens in a new window]  and
HAIFENG HUANG Open the ORCID record for HAIFENG HUANG [Opens in a new window]
STEPHEN P. NICHOLSON*
Affiliation:
University of Georgia, United States
HAIFENG HUANG*
Affiliation:
University of California, Merced, United States
*
Stephen P. Nicholson, Professor, Department of Political Science, University of Georgia, United States, snicholson@uga.edu.
Haifeng Huang, Associate Professor, Department of Political Science, University of California, Merced, United States, hhuang24@ucmerced.edu.
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Abstract

We examined sensitive questions on political trust and regime support in China using indirect methods. We replicated previous list experiment results confirming that a majority trusts the central government despite overreports. We also conducted novel list experiments on trust in local government and support for removal of the presidential term limit and found evidence of overreporting for both. The point estimate for local government also suggests majority trust, but the central government is both more trusted and feared. However, we did not find evidence of majority support for removing the term limit at the time of the removal, indicating that the public may negatively evaluate government, albeit indirectly, in some circumstances. Last, examining self-monitoring, a personality trait for examining social desirability, we found that it only influenced overreporting trust in the central government. The results reveal meaningful variation in political trust and regime support in the world’s largest authoritarian society.

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Type
Letter
Information
American Political Science Review , Volume 117 , Issue 3 , August 2023 , pp. 1158 - 1165
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the American Political Science Association

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References

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Nicholson and Huang Dataset

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https://doi.org/10.7910/DVN/KAHHWQ
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