[组织经济学Seminar]“Thugs-for-Hire”: State Coercion and Everyday Repression in China
发文时间:2016-03-14
中国人民大学企业与组织研究中心 组织经济学Seminar   总第69期 【OE201601】  
组织经济学(Organizational Economics)Seminar由中国人民大学企业与组织研究中心(CFOS)主办。CFOS的宗旨是,利用现代经济学方法研究中国的企业、政府、市场和非营利组织的重大问题,推动企业理论、契约理论和制度经济学的研究与教学。关注CFOS,请访问http://CFOS.ruc.edu.cn。
 
时间:2016年3月17日(周四)12:00-13:30
地点:明德主楼734会议室
主讲:王慧玲
主题:“Thugs-for-Hire”: State Coercion and Everyday Repression in China(“鲍鱼之肆”的雇佣:国家强制与镇压)
摘要:This paper examines “thugs-for-hire” (TFH) as a form of state coercion and everyday repression. Violence is central to what TFH do and what they deliver. TFH serves as an extension of the state, implementing illegal measures or unpopular policies upon a recalcitrant population. They lend support to the state’s coercive capacity and bolster its “despotic power”. Taking the shape of third-party violence, TFH constitutes a form of privatized covert repression, which allows the state to evade responsibility. Drawing comparisons with the Mafiaosi, this study underlines why TFH exist, who they are, what they deliver, how they function and their relationship with the state. Third-party violence is commonly deployed by local states to evict homeowners, enforce one-child policy, collect exorbitant exactions, and deal with petitioners and protestors in China. This study contributes to state repression literature by elaborating on the role of thugs and gangsters as a repressive measure. The fact that TFH are commonly hired by local states to get job done illustrates the distinctive fragmented authoritarian nature of the Chinese state.
 
演讲者简介:Lynette H. Ong is an associate professor of political science at the Asian Institute, Munk School of Global Affairs, The University of Toronto. Her publications have appeared in Comparative Politics, International Political Science Review, China Quarterly, Foreign Affairs, among others. She is the author of Prosper or Perish: Rural Credit and Fiscal Systems in China, Cornell University Press, 2012.
 
 
中国人民大学经济学院 人大企业与组织研究中心 国发院新政治经济学研究中心 人大微观数据与实证方法研究中心 2016年3月14日