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Oilfields, Mosques and Violence: Is There a Resource Curse in Xinjiang?
Published online by Cambridge University Press: 05 February 2018
Abstract
How does natural resource extraction affect ethnic violence in a strong authoritarian state? This study investigates the effects of oil and natural gas development on violent incidents in Xinjiang, China, using data from its eighty-six counties. Contrary to the resource curse claim, we find that areas with larger quantities of resource production have lower rates of violence. The analysis of reserves data confirms that this finding is not driven by endogeneity between violence and resource production. This soothing effect of resources subsides, however, in areas with high mosque density. While we find no supporting evidence that drastic ethno-demographic changes or strengthening of public security are associated with resource extraction, the analysis shows that resource development contributes to improved local economic conditions, particularly with respect to employment and the incomes of employees of state-owned enterprises.
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Footnotes
Division of Social Science, Hong Kong University of Science and Technology (E-mail: [email protected]); Department of Government, University of Texas at Austin (E-mail: [email protected]). We thank Xun Cao, Ting Chen, Haiyan Duan, Enze Han, Yue Hou, James Kung, Chuyu Liu, Glenn Palmer, James Piazza, Victor Shih, Kellee Tsai, Erik H. Wang, Yuhua Wang, Lingwei Wu, Xiaogang Wu, Vera Zuo, and the participants of the 2016 Midwest Political Science Conference, the 2016 Peace Science Society (International) Conference in East China University of Political Science and Law and the seminar at Fudan University for helpful comments and discussion. We thank Xun Cao, Haiyan Duan, Chuyu Liu, James Piazza, and Yingjie Wei for sharing the violence data. Ji Yeon Hong acknowledges that this project is supported by the Early Career Scheme grant sponsored by the Research Grants Council, Hong Kong (Reference No. ECS26600915). Data replication sets are available in Harvard Dataverse at: https://dx.doi.org/10.7910/DVN/SIREAP and online appendices are available at: https://doi.org/10.1017/S0007123417000564
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