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Foreign aid, bilateral asylum immigration and development

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Abstract

This paper measures the links between aid from 14 rich to 113 developing economies and bilateral asylum applications during the years 1993 to 2013. Dynamic panel models and Sys-Generalized Method of Moments are used. The results show that asylum applications are related to aid nonlinearly in a U-shaped fashion with respect to the level of development of origin countries, although only the downward segment proves to be robust to all specifications. Asylum inflows from poor countries are significantly and negatively associated with aid in the short run, with mixed evidence of more lasting effects, while inflows from less poor economies show a positive but non-robust relationship to aid. Moreover, aid leads to negative cross-donor spillovers. Applications linearly decrease with humanitarian aid. Voluntary immigration is not related to aid. Overall, the reduction in asylum inflows is stronger when aid disbursements are conditional on economic, institutional and political improvements in the recipient economy.

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Fig. 1

Notes

  1. 1.

    The real impact of aid on growth and institutions remains elusive, as it depends on diverse factors, among which the incentives of recipients and donors in transferring and receiving aid. Several studies find that a substantial part of the aid provided by rich economies is unrelated to the real needs of recipient countries (Boone 1996; Alesina and Dollar 2000; Collier and Dollar 2002; Lancaster 2007; Fuchs et al. 2014; Jones 2015). However, there is a certain degree of agreement on a change of approach of Western donors after the end of the cold war. While during the Cold War, the political allegiance of recipients was decisive, afterwards, their economic and institutional development became more important.

  2. 2.

    More aid from a country can intensify the attractiveness of the donor among alternative destinations. The presence of a donor in the recipient country, or projects funded by the donor, creates opportunities for contacts between the local population and the donor. More generally, it provides knowledge on the donor’s social norms, institutions and culture, which can decrease migration costs.

  3. 3.

    This can apply especially, but not only, to countries of origin with strong internal divisions determined by religion or ethnicity. Political divisions may also matter. Some evidence suggests that refugees from Latin America who flew their countries during the dictatorships of the 1970s of the last century scarcely interact with economic immigrants from their home countries who arrived later.

  4. 4.

    Part of foreign aid is concessional in character and conveys a grant element (OECD). As an effect of interest repayment, some figures are negative. However, they are a very small proportion of total observations and have been substituted by zeros.

  5. 5.

    Following Alesina et al. (2013), the share of variation in asylum application explained by foreign aid can be calculated by excluding Bilateral aid from the regression of column1. This makes the R2 to shrink from 0.871 to 0.868. Hence, Bilateral aid accounts for 0.3% of the total variation in asylum applications and 2.3% of the residual variation left unexplained by the control variables. The latter is calculated as (0.871–0.868)/(1–0.868). The same procedure shows that the exclusion of Bilateral aid and (Bilateral aid) × (pc GDP origin) produces almost identical figures.

  6. 6.

    In regressions, not shown to save space, I also added the ‘affinity’ IV proposed by Alesina and Dollar (2000), based on dyads’ coincidence of votes at the United Nations, but it failed first-stage tests.

  7. 7.

    The size of the panel is N = 1582 (country-pairs), T = 21 (years). Although there might seem to be a high number of number of instruments, it is always lower than N (Roodman 2009b).

  8. 8.

    I thank an anonymous reviewer for suggesting this test.

  9. 9.

    Bilateral aid as a share of the per-capita income of the recipient country decreases with the country’s level of development. It varies from 0.13% in the lowest income quintile to less than 0.003% in the highest quintile. Detailed figures are available from the author upon request.

  10. 10.

    Hatton (2009, p. 187) reports that ‘[o]nly a small proportion of those who are displaced become asylum seekers in Western countries and fewer still are accepted as genuine refugees. The applications to industrialised countries are on average less than 5% of the refugee stock [during 1970–2005]. Most of those who are counted as refugees by the UNHCR are displaced into neighbouring countries and often into the poverty and squalor of refugee camps near the border.’

  11. 11.

    The negative impact of Bilateral aid on applications from poor countries could be compatible with a different interpretation if, as some studies hypothesise, the relationship between development and asylum outflows was bell-shaped and if aid had a detrimental effect on either the growth or level of income. I tested for non-linearity in the relation between per-capita income and asylum flows, as well as for the effect of Bilateral aid on income growth. Results show that, as in all specifications in this study, the relation between per-capita income at home and asylum applications is linearly negative and significant. Moreover, Bilateral aid is positively related to growth in recipient countries. Hence, in poor economies, more aid and more income strengthen the incentives to stay. They are reinforced by aid both directly and indirectly. Regression results are available from the author upon request.

  12. 12.

    A world economy where countries minimize the expenditure in aid for given levels of social welfare functions and negative aid spillovers can be characterised by multiple equilibria. Given other countries’ transfers to a specific destination, a donor can choose to reduce its own attraction effect by reducing its aid transfers, and benefit from the attraction to the other donors. However, a generalised move of this kind would produce inferior equilibria: by worsening living conditions in poor countries, it would lead to higher aggregate asylum inflows (Table 1). Jones (2015) finds evidence of positive bandwagon effects, especially among larger donors.

  13. 13.

    There is only one country-pair-year—in 33,222—with zeros for both asylum seekers and bilateral aid (Denmark-Comoros). The proportion of zeros in the variable of interest, bilateral aid, is 4.5%.

  14. 14.

    Nyberg Sørensen et al. (2003) state that ‘aid selectivity tends to allocate development aid to the well performing countries and humanitarian assistance to the crisis countries and trouble spots. However, development aid is more effective than humanitarian assistance in preventing violent conflicts, promoting reconciliation and democratization, and encouraging poverty-reducing development investments by migrant diasporas.’ (p. 6).

  15. 15.

    I thank an anonymous reviewer for suggesting this analysis.

  16. 16.

    I thank an anonymous reviewer for suggesting this test.

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Acknowledgements

I thank the participants to the Bari Conference, Economics of Global Interactions, 2017. I am particularly indebted to Elisabetta Lodigiani, Massimiliano Bratti, Fréderic Docquier, Anzelika Zaiceva, Clementina Crocé and three anonymous reviewers for useful suggestions.

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Financial support was received from a UNIMORE FAR 2017 Grant.

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Correspondence to Marina Murat.

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Appendix

Appendix

Table 7 presents some further robustness tests. Regressions include all covariates, but, to save space, only the coefficients of the variables considered are reported. In column 1, the logs on the dependent variable are taken without adding 1. This implies that about 18% of observations, corresponding to zeros, are lost. Column 2 includes controls for multilateral resistance to migration. They are as follows: origin-time effects, which should capture all time-varying terms that are constant across destinations d and only vary by year and country of origin; destination-time effects, meant to capture time-varying terms that are constant across origins, o, but vary year and country of destination; destination-origin dummies, which absorb all time-invariant dyadic variables that affect asylum applications; and destination fixed effects, which account for factors of the destination country that are invariant or change very slowly along time, such as culture or institutions and origin fixed effects which absorb similar factors of the origin country. This is a very demanding specification, where measurement is entirely concentrated on within country-pair time variations.

It has been hypothesised that some applicants could be ‘bogus’ asylum seekers (Neumayer 2005). For example, irregular immigrants who correctly foresee they will not be eligible for the refugee status might nonetheless apply for asylum, only to avoid deportation during time needed for the application to be processed. To control for this possibility, I restrict the sample to countries of origin with above average levels of political terror. Presumably, they are more likely to generate flows of ‘genuine’ asylum applicants. Large geopolitical shocks, such as the Afghan and Iraq wars, might affect Western countries’ policies on asylum. In column 4, the Asylum Policy Index (previously tested in column 3, Table 3) is interacted with time dummies. Column 5 includes the Dreher et al. IV among the instruments of the Sys-GMM specification. Results in columns 1 to 5 are as in previous tests.

Former colonial links between origin and destination country might alter the choices of asylum seekers among potential destinations, as well as those of donors among potential aid recipients. In column 6, Bilateral aid and the interacted term are multiplied, first, by a dummy taking value 1 if the origin country was a donor’s colony in 1945 and 0 otherwise, and, second, by a dummy taking opposite values. Results show that coefficients on the variable of interest, which split between former colonies and other developing countries, are as in previous regressions; also, they do not differ between them at a statistically significant level. Results (not shown) do not change with the dummy Colonies included among regressors.

Balli and Sørensen (2013) find that the coefficients of interaction terms could be biased in settings where fixed effects are used.Footnote 16 The solution they propose is to de-mean the components of the interaction term within the groups for which the fixed effects are included. Hence, I did de-mean Bilateral aidt−1 within each origin-destination dyad and year, as well as pc GDP origin within each origin country and year (column 7). Column 8 reports coefficients when the OLS regression is run after excluding outliers. Results are as in previous regressions.

Table 5 Data definitions and sources. List of countries
Table 6 Summary statistics
Table 7 Further robustness tests for bilateral asylum applications
Table 8 Further lags for asylum applications. Poor and less poor countries. Sys-GMM

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Murat, M. Foreign aid, bilateral asylum immigration and development. J Popul Econ 33, 79–114 (2020) doi:10.1007/s00148-019-00751-8

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Keywords

  • Foreign aid
  • Asylum seekers and refugees
  • Development

JEL classification

  • F35
  • F22
  • J15