Introducing the Government-Sponsored Mass Expulsion Dataset

Meghan Garrity
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Meghan Garrity: Department of Political Science, University of Pennsylvania

Journal of Peace Research, 2022, vol. 59, issue 5, 767-776

Abstract: This article introduces the Government-Sponsored Mass Expulsion (GSME) dataset documenting cross-border mass expulsion episodes around the world from 1900 to 2020. This new dataset focuses on mass expulsion policies in which governments systematically remove ethnic, racial, religious or national groups, en masse . The GSME dataset disaggregates mass expulsion from other exclusionary politics concepts to isolate policies of intentional group-based population removal. This allows for a systematic examination of governmental expulsion policies, distinct from policies aimed at annihilation (genocide), control (massacre) or cultural elimination (coercive assimilation). The GSME dataset documents 139 expulsion episodes since 1900, affecting over 30 million citizens and non-citizens across all world regions. The data are drawn from archival research conducted at the United Nations High Commissioner for Refugees and the International Committee of the Red Cross, as well as secondary sources and extant datasets. This article presents an empirical overview of the data including information on the expelling country, onset, duration, region, scale, category of persons expelled, and frequency. Although mass expulsion is a rare event, it is a reoccurring rare event. Its consistent use – with over two million people expelled in the last five years alone – demands additional empirical and theoretical investigation. The GSME dataset contributes to the study of exclusionary politics as a dependent variable, but it also offers promise as an explanatory variable for those studying phenomena affected by mass expulsion.

Keywords: demographic engineering; ethnic cleansing; forced migration; mass expulsion; quantitative data (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:joupea:v:59:y:2022:i:5:p:767-776

DOI: 10.1177/00223433211068633

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