A Cross-National and Cross-Actor Comparative Analysis of Violent and Nonviolent Interactions Between State and Civil Society Actors

Monday, July 14, 2014: 11:30 AM
Room: 411
Oral Presentation
Takeshi WADA , Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
Yoshiyuki AOKI , The University of Tokyo, Tokyo, Japan
Hiromi MAKITA , Area studies, The University of Tokyo, Tokyo, Japan
Hiroyuki SUZUKI , The University of Tokyo, Japan
While civil violence is almost always countered by state violence, the opposite is not true. It is unclear, according to Christian Davenport, whether state violence triggers or contains civil violence. In fact, one of the most difficult issues in the study of contentious politics is to predict civil society actors’ reactions to state violence. Will those who have witnessed state repression pick up a weapon in anger or quiet down in fear and despair?

This paper tackles this repression-dissent problem. The literature on collective violence emphasizes the importance of institutional and structural contexts—e.g., political regime characteristics, state capacity, ethnic, religious, and class cleavages, levels of socioeconomic development, etc.—to understand the repression- dissent dynamics. A major stumbling block in the effort to carry out such a research agenda is methodological. On one hand, small-N case studies, while good at revealing exact processes of state and civil society interactions, cannot measure adequately the effect of multiple contextual factors. On the other, typical quantitative analyses of annual event counts, while good at estimating the contextual effects, fail to detect interactive dynamics.

By conducting a cross-national comparison of contentious event sequences, this study attempts to examine both the contextual effects and the interactive dynamics at the same time. We use a data set of 10 million events world-wide, reported by Reuters, between 1990 and 2004. This unique data set records event information at a daily—instead of yearly—basis and, thus, helps us detect interactive dynamics. Moreover, we distinguish the actors who are more likely to resort to violence after state repression from the actors who are more likely to give up any further attempts to make claims by using multilevel analysis of actor-target interactions. This paper presents an original analysis of national contexts, actor characteristics, and actor-target interactive dynamics.