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The effects of state coercion on voting outcome in protest movements: a causal forest approach

Published online by Cambridge University Press:  06 December 2021

Weiwen Yin Open the ORCID record for Weiwen Yin [Opens in a new window] ,
Weidong Huo  and
Danyang Lin
Weiwen Yin
Affiliation:
Department of Asian and Policy Studies, The Education University of Hong Kong, Hong Kong SAR, China
Weidong Huo
Affiliation:
Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China
Danyang Lin*
Affiliation:
Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Guangzhou, China
*
*Corresponding author. Email: lindy23@mail.sysu.edu.cn
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Abstract

In this research note, we examine how Hong Kong voters respond to police violence in the recent social movement. We use causal forests, a machine learning algorithm, to estimate the impact of tear gas usage specific to each constituency. Based on the 2019 District Council Election outcome, we find that there is heterogeneity in the effect of state coercion on the vote share of pro-democracy candidates, depending on many socioeconomic characteristics of the constituency. The results imply that economic concerns still matter in the struggle to obtain democracy: citizens who sense economic insecurity in social unrest show less disapproval of state violence.

Keywords

Asian politicscomputational modelselections and campaignsvoting behavior
Type
Research Note
Information
Political Science Research and Methods , Volume 11 , Issue 3 , July 2023 , pp. 645 - 653
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the European Political Science Association

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References

ANTIELAB Research Data Archive (2020) URL: https://antielabdata.jmsc.hku.hk/.Google Scholar
Athey, S, Tibshirani, J and Wager, S (2019) Generalized random forests. The Annals of Statistics 47, 1148–1178.CrossRefGoogle Scholar
Aytaç, SE, Schiumerini, L and Stokes, S (2018) Why do people join backlash protests? Lessons from Turkey. Journal of Conflict Resolution 62, 1205–1228.CrossRefGoogle Scholar
Beiser-McGrath, J and Beiser-McGrath, LF (2020) Problems with products? Control strategies for models with interaction and quadratic effects. Political Science Research and Methods 8, 707–730.CrossRefGoogle Scholar
Brambor, T, Clark, WR and Golder, M (2006) Understanding interaction models: improving empirical analyses. Political Analysis 14, 63–82.CrossRefGoogle Scholar
Dorsch, MT and Maarek, P (2019) Democratization and the conditional dynamics of income distribution. American Political Science Review 113, 385–404.CrossRefGoogle Scholar
Feenstra, RC and Hanson, GH (2004) Intermediaries in entrepot trade: Hong Kong re-exports of Chinese goods. Journal of Economics & Management Strategy 13, 3–35.Google Scholar
Hainmueller, J, Mummolo, J and Xu, Y (2019) How much should we trust estimates from multiplicative interaction models? Simple tools to improve empirical practice. Political Analysis 27, 163–192.CrossRefGoogle Scholar
Independent Police Complaints Council (2020) A Thematic Study by the IPCC. URL: https://www.ipcc.gov.hk/en/public_communications/ipcc_thematic_study_report.html.Google Scholar
Lee, FLF, Yuen, S, Tang, G and Cheng, EW (2019) Hong Kong's summer of uprising: from anti-extradition to anti-authoritarian protests. China Review 19, 1–32.Google Scholar
Lupu, N and Peisakhin, L (2017) The legacy of political violence across generations. American Journal of Political Science 61, 836–851.CrossRefGoogle Scholar
Robins, JM, Rotnitzky, A and Zhao, LP (1994) Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 89, 846–866.CrossRefGoogle Scholar
Robins, JM, Rotnitzky, A and Zhao, LP (1995) Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90, 106–121.CrossRefGoogle Scholar
Rozenas, A and Zhukov, Y (2019) Mass repression and political loyalty: evidence from Stalin's “Terror by Hunger”. American Political Science Review 113, 569–583.CrossRefGoogle Scholar
Sullivan, CM and Davenport, C (2017) The rebel alliance strikes back: understanding the politics of backlash mobilization. Mobilization 22, 39–56.CrossRefGoogle Scholar
Tertytchnaya, K and Lankina, T (2020) Electoral protests and political attitudes under electoral authoritarianism. The Journal of Politics 82, 285–299.CrossRefGoogle Scholar
Wang, Y and Wong, SH-W (2021) Electoral impacts of a failed uprising: evidence from Hong Kong's umbrella movement. Electoral Studies 71, 1–12.CrossRefGoogle Scholar
Wasow, O (2020) Agenda seeding: how 1960s black protests moved elites, public opinion and voting. American Political Science Review 114, 638–659.CrossRefGoogle Scholar
Wong, B (2019 a) Why the mutually assured destruction rhetoric in Hong Kong is dangerous. The Diplomat. URL: https://thediplomat.com/2019/09/why-the-mutually-assured-destruction-rhetoric-in-hong-kong-is-dangerous/.Google Scholar
Wong, SH-W (2019 b) Gerrymandering in electoral autocracies: evidence from Hong Kong. British Journal of Political Science 49, 579–610.CrossRefGoogle Scholar
Wong, SH-W, Ma, N and Lam, WM (2018) Immigrants as voters in electoral autocracies: the case of mainland Chinese immigrants in Hong Kong. Journal of East Asian Studies 18, 67–95.CrossRefGoogle Scholar
Young, LE (2019) The psychology of state repression: fear and dissent decisions in Zimbabwe. American Political Science Review 113, 140–155.CrossRefGoogle Scholar
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Yin et al. Dataset

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https://doi.org/10.7910/DVN/CX1OGZ
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