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Trading favors? UN Security Council membership and subnational favoritism in aid recipients

Abstract

We test the hypothesis that aid recipient governments are better able to utilize aid flows for political favoritism during periods in which they are of geo-strategic value to major donors. We examine the effect of a country’s (non-permanent) membership on the United Nations Security Council (UNSC) on the subnational distribution of World Bank aid. Specifically, we analyze whether World Bank projects are targeted to subnational regions in which the head of state was born, or to regions dominated by the same ethnic group as that of the head of state. We find that all regions within a recipient country, on average, receive a greater number of aid projects during UNSC membership years. Moreover, a leader’s co-ethnic regions (but not birth regions) receive significantly more World Bank projects and loan commitments during UNSC membership years compared to other years. This effect is driven chiefly by interest-bearing loans from the International Bank for Reconstruction and Development (IBRD). Most importantly, we find stronger subnational political bias in aid allocation for aid recipients whose UNSC votes are fully aligned with those of the United States, indicating that exchanges of aid for favors occur in multilateral settings.

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Data availability

Datasets generated for and analyzed in the current study are available at the Dataverse Project repository, https://doi.org/10.7910/DVN/KTLSQT, and the Review of International Organizations’ webpage.

Notes

  1. This is by no means limited to aid from traditional donors. Chinese aid, for example, appears susceptible to political “capture” by recipient-country leaders (Dreher et al., 2019).

  2. Another donor analyzed in the literature is China. Unfortunately, there is virtually no overlap between Chinese aid disbursement and UNSC non-permanent membership. Only 14 countries among those who have at some point been UNSC members received any Chinese aid during our observation period, and only one, Vietnam, received Chinese aid while on the UNSC, in 2009. Therefore we cannot in a meaningful way incorporate Chinese aid in our analysis.

  3. Dreher et al. (2019), for example, comparing favoritism in allocation between Chinese aid and World Bank aid, argue that strict project appraisal policies, designed to target development outcomes and to prevent personal or domestic political reasons to play a role, explains why they find no diversion of World Bank project aid in contrast to Chinese aid.

  4. For more details on the UNSC and the exchange of aid for political support see, e.g., Vreeland and Dreher (2014).

  5. The multilateral channel is preferred when awarding a loyal supporter that is looked upon less favourably by a domestic US political opinion.

  6. A complicating factor is that a single World Bank project may be implemented across subnational units, and there are limited data on sub-project geographic allocations. When analyzing total commitments, therefore, we must assume that resources are evenly distributed across participating subnational units. Although this may introduce noise in our data, there is no reason why sub-project resources should be systemically allocated towards certain regions once they are included in a project. Nevertheless, dollar commitments still need to be interpreted with some caution.

  7. These data come from the same source used in, e.g., Alesina et al. (2016) and de Luca et al. (2018), but we match information on ethnographic regions to ADM1 boundaries.

  8. This is, obviously, an approximation that relies on the assumption that larger groups in population also occupy larger geographic areas.

  9. As we show in the online appendix, due to leader turnover, the leader’s birth region of a country changes between once every 7 and once every 4 years, while the ethnicity of the leader—and thus the identity of co-ethnic regions—changes less than once every decade. This implies that the co-ethnic region indicator changes at least once in most countries over our twenty years period, and the birth region indicator more than that. Therefore, there is enough variation in our ConnectRegijt indicators for specifications to include subnational fixed effects. The online appendix is available at the Review of International Organizations’ webpage.

  10. Note that concerns that the sign of the interaction coefficient may change along the distribution of a continuous variable in non-linear models (e.g., Ai and Norton 2003 and Greene 2010) does not apply in the case where two dummy variables are interacted, where the combination of coefficients actually spans the whole space of potential outcomes.

  11. An alternative is the negative binomial model in order to relax the assumption on variation imposed by the Poisson distribution (sometimes referred to as overdispersion). However, the Poisson model has been shown to be the better option in a fixed-effects panel setting, with the negative binomial model being valid only under more restrictive distributional assumptions (Allison, 2009; Guimarães, 2008). Poisson models for panel data have the additional advantage of eliminating what is known as “incidental parameter bias”, as incidental parameters for each individual in the panel are conditioned out of the likelihood function (Allison, 2009). So even though estimating an unconditional fixed-effects model we are achieving the same result. Moreover, given that our sample spans 19 years (T = 19) the bias in the unconditional estimator should be negligible as shown by simulations in Katz (2001).

  12. This is in contrast to two analyses of Kenya in the 1980’s and 1990’s, which found that the allocation of aid projects to co-ethnic regions increased during electoral cycles. Given the ethnically-contentious nature of Kenyan politics, governments tend to be rewarded for directing public spending and government jobs to ethnic coalitions (Briggs, 2014; Jablonski, 2014). There is, however, little other aid-focused research on the role of co-ethnic regions beyond just Kenya during this time period. Our results suggest that those earlier findings, while true for Kenya during that time period, may not be representative of World Bank aid in general.

  13. This differential effect is consistent with the finding in Briggs (2021) that aid projects to leaders’ birth regions are less likely to be approved by World Bank leadership, while no such effect is apparent for co-ethnic regions.

  14. In the online appendix we show that the dynamics of turnover in co-ethnic regions are remarkably similar across continents and regime type; these results do not appear to be driven by a particular subset of countries.

  15. In Dreher et al. (2009a) the impact of UNSC membership becomes marginally insignificant in both sub-samples when they separate between IDA and IBRD lending. In our extended data set, statistical significance is thus stronger, as would be expected.

  16. We report the full list of these countries in the online appendix.

  17. We have also tested using the average levels over the whole time period and it yields very similar results.

  18. See the online appendix for further details.

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Acknowledgements

We are grateful for comments and suggestions from Axel Dreher, Ryan Jablonski, Lennart Kaplan, B. Peter Rosendorff, Jonathan Strand, David Strömberg, Jennifer Tobin, and three anonymous reviewers. Earlier versions of this article benefited from comments of participants at the Political Economy of International Organization (PEIO) annual conference and the Political Economy Workshop at Uppsala University. We greatly appreciate data on UNSC voting generously provided by Axel Dreher, Valentin Lang, B. Peter Rosendorff, and James Raymond Vreeland. The authors acknowledge financial support from the Swedish Research Council, grant number 2018-01342. The statements made herein are solely the responsibility of the authors, and all errors and omissions are the authors’ own.

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Correspondence to Raj M. Desai.

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Author contributions: all authors contributed to research design and conceptualization; statistical analysis: M.P.B (60%), R.D. (20%), A.O. (20%); writing: M.P.B. (20%), R.D. (40%), A.O. (40%). The order of authors is chosen alphabetically.

Supplemental information may be found in an online appendix available on the Review of International Organizations’ webpage.

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Berlin, M.P., Desai, R.M. & Olofsgård, A. Trading favors? UN Security Council membership and subnational favoritism in aid recipients. Rev Int Organ (2022). https://doi.org/10.1007/s11558-022-09467-z

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Keywords

  • Foreign aid
  • United Nations Security Council
  • World Bank

JEL Classification

  • F35
  • F53
  • O19