Replication Data for: The Effects of State Coercion on Voting Outcome in Protest Movements: A Causal Forest Approach

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.

本研究简报探讨了香港选民对近期社会运动中警方暴力行为的反应。本研究采用机器学习算法因果森林（Causal Forests），估算各选区特定的催泪弹使用所产生的影响。基于2019年香港区议会选举结果，本研究发现，国家强制行为对亲民主派候选人得票率的影响存在异质性，且该异质性取决于选区的多项社会经济特征。研究结果表明，在争取民主的斗争中，经济关切仍具有重要意义：在社会动荡中感受到经济不安全感的民众，对国家暴力行为的反对程度相对更低。