The United Nations High Commissioner for Refugees (UNHCR) Populations of Concern Time Series dataset contains annual dyadic flows between countries for populations of concern. This dataset tracks 7 distinct populations:
Refugees: These are individuals recognized under the Geneva Convention of 1951 as refugees or persons in “refugee like situations.” These persons are defined as 1) being present outside of their home country, 2) have a well-founded fear of persecution, and 3) lack the protection of their own country.
Asylum-seekers: Individuals seeking international protection, but whose refugee status has yet to be verified.
Internally Displaced Persons (IDPs): Indivuals or groups of people forced from their home as a result of armed conflict, natural or man-made disasters, or human rights violations, but have not crossed an international border.
Returned Refugees: Former refugees that returned to their country of origin, but have not integrated back into society.
Returned IDPs: Former IDPs under UNHCR protection who returned to their home region or original residence.
Stateless Persons: Individuals defined under international law not considered as belonging to any Nation-State.
Others of Concern: Individuals who are not covered by any of the aforementioned legal declarations, but are under UNHCR protection or receiving UNHCR assistance.
As the premier source for global annual dyadic flows of refugee and asylum seekers, the UNHCR time series dataset is regularly featured in high impact peer reviewed research and serves as the basis for international policy making. Abel et al. (2019) recently featured the UNHCR time series in their examination the confounding interactions of conflict, climate, and forced migration. Barthel and Neumayer (2015) investigated spatial dependency of countries in close proximity to host countries with laxed refugee and asylum policies. While most research focuses on root causes of refugee flows, Salehyan and Gleditsch (2006) tested refugee flows as a driver of conflict.
Although the UNHCR time series dataset is heavily featured in academic research, users should consider potential systematic biases in reporting. A large proportion of reported dyadic flows are not attributed to an origin country. These values are often removed from analysis, Marbach (2018) presents methods for imputing unreported values. Moreover, due to the imprecise nature of observing migration flows, especially in underdeveloped countries, Azose and Raftery (2019) suggests that global migration may be substantially larger than previously estimated.
Screenshot or Representative Figure:
Abel, Guy J., Michael Brottrager, Jesus Crespo Cuaresma, and Raya Muttarak. 2019. “Climate, Conflict and Forced Migration.” Global Environmental Change 54 (January): 239–49. https://doi.org/10.1016/j.gloenvcha.2018.12.003.
Azose, Jonathan J., and Adrian E. Raftery. 2019. “Estimation of Emigration, Return Migration, and Transit Migration Between All Pairs of Countries.” Proceedings of the National Academy of Sciences 116 (1): 116–22. https://doi.org/10.1073/pnas.1722334116.
Barthel, Fabian, and Eric Neumayer. 2015. “Spatial Dependence in Asylum Migration.” Journal of Ethnic and Migration Studies 41 (7): 1131–51. https://doi.org/10.1080/1369183X.2014.967756.
Marbach, Moritz. 2018. “On Imputing UNHCR Data.” Research & Politics 5 (4): 2053168018803239. https://doi.org/10.1177/2053168018803239.
Salehyan, Idean, and Kristian Skrede Gleditsch. 2006. “Refugees and the Spread of Civil War.” International Organization 60 (02). https://doi.org/10.1017/S0020818306060103.