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Refugee gravitation

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Abstract

This paper makes use of a gravity model to investigate the determinants of global forced migration. We find that omitting zero counts results in parameter estimates that underestimate covariate effects on refugee counts. We compare the pooled regression, which does not account for unobserved heterogeneity, and the fixed-effects method, which does not identify the effect of time-invariant covariates, and cannot shed much light when covariates vary little. We propose the pre-sample mean generalized method of moments (PSM-GMM) estimator, which accommodates the zeros, accounts for unobserved heterogeneity, but does not have the drawbacks of the fixed-effects methods when covariates exhibit little variation. In addition, using recently developed methods to estimate standard errors that are robust to dyadic correlation, we find evidence suggesting that previous findings based on underestimated standard errors mostly remain valid after properly adjusting standard errors; in particular, conflict and civil liberties in the source country and proximity are found to be significant determinants of forced migration. However, some covariates previously found to be significant, such as sharing a common language or having a colonial relationship, lose significance. In addition, we find a significant positive influence of the level of civil liberties in the destination country on the number of refugees it receives, a mechanism not explored before.

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Notes

  1. The term asylum/residence is used by the UNHCR to account for those refugees to whom asylum status has been granted and also those to whom such status has been denied or who simply did not apply for it. In what follows, we will use the term “destination countries” to refer to asylum/residence countries.

  2. See Neumayer (2004, 2005a, 2005b) and Barthel and Neumayer (2015).

  3. Crisp (1999) explain these data.

  4. The US data exclude those refugees who flee to Canada, which are abnormally large owing to the Safe Third Country Agreement between Canada and the United States. Under that agreement, refugee claimants in each country are required to request refugee protection in the first safe country in which they arrive, regardless of whether their final destination is another country.

  5. Note that the theoretically feasible number of dyads is the square of the number of sovereign states, which is a larger number than what we call the potential number of dyad observations.

  6. In order to see this, average Eq. 1 over destinations (respectively, origins) during the pre-sample period.

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Acknowledgments

We would like to thank Daniel Arce, Martin Gassebner, Eric Neumayer and Todd Sandler and the 2016 University of Texas at Dallas Political Violence and Policy Conference participants for very helpful comments. Jon Echevarria acknowledges financial support from the Basque Government (PRE-2015-1-0073). Javier Gardeazabal acknowledges financial support from the Basque Government (IT783-13) and the Ministerio de Economía y Competitividad y Fondo Europeo de Desarrollo Regional ECO2015-64467-R.

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Authors and Affiliations

  1. Departamento de Fundamentos del Análisis Económico II, University of the Basque Country (UPV/EHU), Lehendakari Aguirre 83, 48015, Bilbao, Spain

    Jon Echevarria & Javier Gardeazabal

Authors
  1. Jon Echevarria
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  2. Javier Gardeazabal
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Correspondence to Javier Gardeazabal.

Appendix A: Data sources

Appendix A: Data sources

The dataset compiled is a blend of the following datasets:

  1. 1.

    Persons of concern dataset. United Nations High Commissioner for the Refugees (UNHCR). Time frame: 1960–2014. Variables used: Refugees. Availability: http://popstats.unhcr.org/en/time_series.

  2. 2.

    Armed conflict dataset. Uppsala Conflict Data Program (UCDP) Uppsala University / Peace Research Institute Oslo (PRIO). Time frame: 1946–2014. Variables used: Location. The name(s) of the country/countries whose government(s) have a primary claim to the issue in dispute. Year of observation. The date when the conflict activity reached 25 battle-related deaths in a year. The date when conflict activity ended. Availability: https://www.prio.org/Data/Armed-Conflict/UCDP-PRIO/.

  3. 3.

    Freedom in the world dataset. Freedom House. Time frame: 1972–2015. Variables used: Civil Liberties. Availability: https://freedomhouse.org.

  4. 4.

    POLITY IV and MAJOR EPISODES OF POLITICAL VIOLENCE (MEPV) datasets. Center for Systemic Peace. Time frame: 1960–2014. Variables used: POLITY2 (Revised Combined Polity Score). ACTOTAL: Total summed magnitudes of all (societal and interstate). Integer-valued scale ranging from 0 to 14. Transformed into a 0-8 scale by grouping categories (7, 8 and 9) and (10, 11, 12, 13 and 14) of the original scale into categories 7 and 8 of the new one. Availability: http://www.systemicpeace.org/inscrdata.html.

  5. 5.

    Geographical and distance dataset. CEPII, SciencesPo Department of Economics. Diadic dataset. Variables used: Contiguous countries indicator. Simple distance (most populated cities, km). Common official language indicator. Colonizer: dummy variable which takes value equal to 1 if country of destination has ever been colonizer of the country of origin (a transformation of the original colony variable). Availability: http://econ.sciences-po.fr/staff/thierry-mayer.

  6. 6.

    World development indicators. The World Bank. Population. GDP per capita, PPP (current international dollars). Availability: http://databank.worldbank.org/data/home.aspx.

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Echevarria, J., Gardeazabal, J. Refugee gravitation. Public Choice 169, 269–292 (2016). https://doi.org/10.1007/s11127-016-0367-y

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