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      Demographic Engineering and International Conflict: Evidence from China and the Former USSR

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      International Organization
      Cambridge University Press (CUP)

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          Abstract

          When and where do states coercively alter their internal demography? We build a theory that predicts under what conditions states alter the demographic “facts on the ground” by resettling and expelling ethno-national populations. We predict that, under particular scope conditions, states will employ demographic engineering to shore up control over (1) nonnatural frontiers, and (2) areas populated by ethnic minorities who are co-ethnics with elites in a hostile power. We then substantiate our predictions using new subnational data from both China and the USSR. Causally identifying the spatially differential effect of international conflict on demographic engineering via a difference-in-differences design, we find that the Sino-Soviet split (1959–1982) led to a disproportionate increase in the expulsion of ethnic Russians and resettlement of ethnic Han in Chinese border areas lacking a natural border with the USSR, and that resettlement was targeted at areas populated by ethnic Russians. On the Soviet side, we similarly find that the Sino-Soviet split led to a significant increase in expulsion of Chinese and the resettlement of Russians in border areas, and that resettlement was targeted at areas populated by more Chinese. We develop the nascent field of political demography by advancing our theoretical and empirical understanding of when, where, and to whom states seek to effect demographic change. By demonstrating that both ethnic group concentration and dispersion across borders are endogenous to international conflict, our results complicate a large and influential literature linking ethnic demography to conflict.

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          Most cited references28

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          Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models

          Yiqing Xu (2017)
          Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller 2010) with linear fixed effects models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modeling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is easy to implement. An empirical example of Election Day Registration and voter turnout in the United States is provided.
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            Geography as Motivation and Opportunity

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              Fighting at Home, Fighting Abroad

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                Author and article information

                Journal
                applab
                International Organization
                Int Org
                Cambridge University Press (CUP)
                0020-8183
                1531-5088
                2019
                April 16 2019
                2019
                : 73
                : 02
                : 291-327
                Article
                10.1017/S0020818319000067
                2606a030-71f1-4013-9738-117f24586b07
                © 2019

                https://www.cambridge.org/core/terms

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