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

Weiwen YIN, Weidong HUO, Danyang LIN

Research output: Contribution to journalArticlespeer-review

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. Copyright © 2021 The Author(s).
Original languageEnglish
JournalPolitical Science Research and Methods
Early online dateDec 2021
DOIs
Publication statusE-pub ahead of print - Dec 2021

Citation

Yin, W., Huo, W., & Lin, D. (2021). The effects of state coercion on voting outcome in protest movements: A causal forest approach. Political Science Research and Methods. Advance online publication. doi: 10.1017/psrm.2021.70

Keywords

  • Asian politics
  • Computational models
  • Elections and campaigns
  • Voting behavior

Fingerprint

Dive into the research topics of 'The effects of state coercion on voting outcome in protest movements: A causal forest approach'. Together they form a unique fingerprint.