Published online by Cambridge University Press: 13 February 2019
How should we study citizenship in authoritarian regimes? We propose studying how citizenship is performed using the “public transcript”—communication between ordinary citizens and political authorities. The stakes of these strategic communications allow us to observe the roles citizens play to elicit assistance from authoritarian elites. We use this technique to study citizenship in contemporary China, analyzing evidence from an original database of over eight thousand appeals to local officials. These public transcripts reveal three ideal-type scripts of citizenship. First, we observe individuals performing subjecthood, positioning themselves as subalterns before benevolent rulers. We also identify an authoritarian legal citizenship that appeals to the formal legal commitments of the state. Finally, we find evidence for a socialist citizenship which appeals to the moral duties of officials to provide collective welfare. This approach eschews a classification scheme based on regime types, instead acknowledging that diverse performances of citizenship can coexist within a single state.
Replication data are available from the Harvard Dataverse at: https://doi.org/10.7910/DVN/KE2AQU
The authors thank Margaret Boittin, Sarah Eaton, Peter Evans, Mary Gallagher, Christian Göbel, Genia Kostka, Cheol-Sung Lee, Sida Liu, Rana Mitter, Kevin O’Brien, Elizabeth Perry, Molly Roberts, Ed Steinfeld, Sidney Tarrow, Whitney Taylor, Jessica Teets, Rory Truex, Juan Wang, Melissa Williams, Dingxin Zhao, Ezra Zuckerman, and seminar participants at the American Political Science Association, Association of Asian Studies, Association of Chinese Political Studies, Brown University, Free University of Berlin, Göttingen University, National Sun Yat-Sen University, University of Vienna, and Zhejiang University for generous feedback on this research. They are also grateful for research assistance from Jiarui Cai, Elizabeth Ding, Emile Dirks, Yuxiao He, Yangyi Li, Yisen Lin, Grace Lin, Haoyao Ruan, Sharlene Song, and Yi Xie.