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Does foreign aid volatility increase international migration?

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Abstract

Scholars have long debated the relationship between foreign aid and international migration, with some arguing that foreign aid can deter emigrants and others contending that aid enables them. We contribute to this discussion by exploring whether negative aid shocks affect migration patterns. We theorize that these shocks, which occur when there are large and abrupt decreases in aid disbursement to a given country, lead individuals in aid-recipient countries to emigrate. Such shocks lead to reductions in the provision of public and community services that are funded by aid. These reductions in turn force individuals who depend on these services to seek better lives abroad. We expect this effect to be especially evident among low-skilled individuals, who are most reliant on the services that are supported by foreign aid. We use country-level panel data to test hypotheses derived from these arguments and we find, first, that negative aid shocks are accompanied by heightened emigration rates and, second, that this effect is driven by the emigration of relatively unskilled individuals.

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Notes

  1. For more general analysis of anti-immigrant sentiment in Western countries, see Polakow-Suransky (2017).

  2. For more examples, see de Haas (2007: 820–827).

  3. See Clemens and Postel (2018) for a review of this literature.

  4. Hudson (2015) reiterates this point about macroeconomic management, while emphasizing the significance of project-level volatility.

  5. Interestingly, they find that this is the case of both negative and positive aid shocks.

  6. While these studies find negative effects of foreign aid shocks on economic growth, it should be noted that the literature has not produced a consensus as to the effects of aid on economic growth (see Qian 2015).

  7. The effects of aid shocks on government budgets may be contingent on the type of aid in question. For example, aid directed to food or to NGOs may have little or no impact on government spending.

  8. This is consistent with the impacts of economic shocks more generally (Savun and Tirone 2012).

  9. See Jain (2007) for discussion of project assistance versus aid for budget support.

  10. Figure A1, in the Online Appendix, provides a visual representation of our hypotheses.

  11. Indeed, international migrants are often not the poorest members of the countries from which they migrate (de Haas 2019).

  12. Nielsen et al. (2011) use aid commitments, instead of disbursements, due to concerns about reliability of disbursement data (although they note that disbursements and commitments are highly correlated). In contrast, we use disbursements, as significant gulfs may exist between aid commitments and disbursements (Celasun and Walliser 2008; Diarra 2010).

  13. See Table A1 in the Online Appendix for descriptive statistics for each variable used in this study.

  14. Of course, donors may also provide aid for strategic reasons (e.g. Dreher et al. 2008).

  15. Unfortunately, unemployment data for developing countries is notoriously unreliable, owing to the fact that unemployment surveys typically only cover urban areas and fail to adequately capture employment in informal sectors (see Gibson 2008). For that reason, we did not include unemployment as a control variable.

  16. On the other hand, researchers have argued that higher incomes actually increase emigration from developing countries, as individuals with higher incomes are more readily able to afford the costs of migration (de Haase 2007).

  17. There is some concern that population density may be largely time-invariant, making it incompatible with our use of fixed effects. However, the variable is more time variant than one might think, particularly when spaced out in five-year intervals, as is the case in the models used here. For example, average population density for sample countries was approximately 80 (per square kilometer of land) in 1995, 88 in 2000, and 96 in 2005. Additionally, our main results (presented in Table 1) hold when we drop population density from the model or when we simply replace it with population (these results are available on request).

  18. See Table A2, in the Online Appendix, for a list of countries in our sample.

  19. We conduct a second set of models with this variable because of the effect that its inclusion has on the sample size.

  20. Data for this variable was drawn from the World Economics and Politics Dataverse (Graham and Tucker 2017; Graham et al. 2018), which in turn drew it from Bauer et al. (2012) and Vreeland (2003).

  21. Terrorism deaths are drawn from the RAND Database of Worldwide Terrorism Incidents: https://www.rand.org/nsrd/projects/terrorism-incidents.html.

  22. This GDP data comes from EuroMonitor International: http://www.euromonitor.com

  23. Data for this variable comes from Nielsen et al. (2011). Also see de Ree and Nillesen (2009).

  24. We draw migration stock data from the World Bank’s Global Bilateral Migration database.

  25. A higher number corresponds to a more restrictive policy.

  26. Because some of these variables vary across time, but not across sample countries (specifically, those measuring terrorism deaths in OECD countries, OECD and donor GDP, and migration policy in donor countries), this set of statistical models does not include year fixed effects.

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Gamso, J., Lu, J. & Yuldashev, F. Does foreign aid volatility increase international migration?. Rev Int Organ 16, 581–598 (2021). https://doi.org/10.1007/s11558-020-09400-2

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  • DOI: https://doi.org/10.1007/s11558-020-09400-2

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