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Restraining the Huddled Masses: Migration Policy and Autocratic Survival

Published online by Cambridge University Press:  05 March 2018

Abstract

What determines citizens’ freedom to exit autocracies? How does this influence global patterns of migration and democratization? Although control over citizen movement has long been central to autocratic power, modern autocracies vary considerably in how much they restrict emigration. This article shows that autocrats strategically choose emigration policy by balancing several motives. Increasing emigration can stabilize regimes by selecting a more loyal population and attracting greater investment, trade and remittances, but exposing their citizens to democracy abroad is potentially dangerous. Using a half-century of bilateral migration data, the study calculates the level and destinations of expected emigration given exogenous geographic and socioeconomic characteristics. It finds that when citizens disproportionately emigrate to democracies, countries are more likely to democratize – and that autocrats restrict emigration freedom in response. In contrast, a larger expected flow of economic emigration predicts autocratic survival and freer emigration policy. These results have important implications for autocratic politics, democratic diffusion and the political sources of migration.

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Copyright © Cambridge University Press 2018

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Footnotes

*

Department of Political Science, George Washington University (email: mkm2@gwu.edu); Department of Political Science, University of California at Los Angeles (email: mepeters@ucla.edu). We would like to thank Hans Lueders for his research assistance. We also thank Lisa Blaydes, Joe Wright, David Steinberg, Jeff Colgan, Andrew Kerner, David Singer, David Bearce, Tom Pepinsky, Hein Goemans, Lilly Frost, Michael Joseph, audiences at APSA 2014, IPES 2014, Yale University, and the University of Michigan, Editor René Lindstädt, and three anonymous referees for their helpful comments on earlier drafts. Data replication files are available in Harvard Dataverse at: https://dx.doi.org/10.7910/DVN/KGL6YO and online appendices at: https://doi.org/10.1017/S0007123417000680.

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