To what extent are judges responsive to litigants under authoritarianism? Research has elaborated on selective responsiveness in authoritarian governments (Su and Meng, 2016), but it remains unclear whether policy priorities proposed by governments or issue attention displayed by citizens drives responsiveness, with even more scant evidence from judicial politics. This project will answer this question with evidence from two sources: (1) the Message Board for China’s Judges – the largest nationwide online forum on which litigants can engage with local judges, and (2) the annual government reports across Chinese local governments since the 2010s. Two threads of inquiries drive this research: (1) whether judicial responsiveness more closely corresponds to policy priorities or public attention; and (2) what explains the variation in judicial responsiveness. This study will use semi-supervised machine learning methods to classify litigants’ discussion and policy documents into validated topics across time and regions. We will then construct a novel measure for multifaceted judicial responsiveness including specificity, timeliness, length, and follow-up actions. We will employ a vector autoregression (VAR) method to study whether policy priorities or citizen preferences more strongly predict judicial responsiveness, and explore whether individual-level characteristics, temporal and regional differences explain the variation in judicial responsiveness.
PhD Student, Department of Political Science, University of Washington