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Catch me if you care: International development organizations and national corruption

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Abstract

Many international development organizations (IDOs) have officially mandated anti-corruption criteria for aid selectivity. Substantial debate remains over whether corruption deters aid and whether anti-corruption rules are effectively implemented. We argue that the extent to which both corruption and anti-corruption mandates factor into IDO allocation depends on the composition of the donors. Using existing data on corruption alongside newly collected data on anti-corruption mandates, we demonstrate that organizations composed of corrupt donors are just as likely to adopt, but less likely to enforce, anti-corruption mandates. Organizations composed of less corrupt donors, by contrast, tend to divert aid away from corrupt states, with or without formal anti-corruption rules in place. The findings have implications for the debate over whether international efforts to institutionalize “good governance” standards are sincere or cheap talk, whether multilateral strategies are in fact less politicized than bilateral aid allocation strategies, and whether international organizations should be inclusive, open to membership by many or even all states, including those with dubious track records.

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Notes

  1. Oehler et al. (2012) show that anti-corruption programs are not always effective. See also Neumayer (2003a, b); Vreeland (2006, 2007); Clist (2011); Winters and Martinez (2014).

  2. For a canonical discussion, see Chayes and Chayes (1993).

  3. Some scholars find a positive relationship between foreign aid and economic growth (Sachs 2006; Galiani et al. 2014); some find no relationship (Burnside and Dollar 2000); and some find a negative relationship (Easterly and Pfuetze 2008; Knack 2009).

  4. That declaration was followed by a 1998 World Bank report and a seminal article by Burnside and Dollar (2000) claiming that foreign aid could only foster economic growth under the condition that recipient countries pursued “good” economic policies.

  5. Follow-up conferences in Doha (2008) and Addis Adaba (2015) further institutionalized global anti-corruption norms.

  6. Although there is some evidence that good governance in recipient countries can improve aid efforts, other studies do not find a positive effect (Svensson 1999; Zanger 2000).

  7. Along similar lines, Hafner-Burton and Schneider (2019) find that corruption can spread through international organizations and affect levels of national corruption in their member states.

  8. See also Milner (2006); Milner and Tingley (2011, 2013a).

  9. According to AidData, Ghana, Nigeria and Sudan are the biggest recipients of Chinese aid which goes primarily to infrastructure projects like oil pipelines: http://aiddata.org/. According to Transparency International, China’s government ranks as highly corrupt (http://www.transparency.org/country#CHN).

  10. Recent work, however, indicates that Chinese bilateral aid is not likely to be more politicized than Western aid (Dreher and Fuchs 2015; Strange et al. 2017; Dreher, Fuchs, Parks, Strange, & Tierney, 2018).

  11. A related strand of the literature demonstrates that donors make use of these differences when deciding whether or not to delegate to those organizations (Winters 2010; Dietrich 2013, 2016; Schneider and Tobin 2016).

  12. For all IDOs in our sample we code whether the organization in a given year had a formal, and enforceable, anti-corruption mandate in place (0,1). Online Appendix A (available from the authors and on the Review of International Organization’s website) provides the data on mandates. Online Appendix C elaborates on the definition and coding protocol. See also Lohaus (2019) for a more general historical discussion of anti-corruption mandates in international organizations.

  13. In 2015, the top five ADB donors included Japan, Canada, the United Kingdom, Switzerland and the United States.

  14. See: https://www.adb.org/site/integrity/overview.

  15. This is consistent with Pevehouse (2002), who argues that if external guarantees and threats are not credible, IOs will no longer help to foster democracy.

  16. See: http://www.caribank.org/.

  17. The sample is based on the data availability. The latest AidData research release only covers aid allocation through 2013. A list of IDOs in our data set is in Online Appendix A.

  18. Anti-corruption mandates are often dual focused on the prevention and prosecution of corruption in recipient countries. Available data on aid commitments best align with IDO policies that speak to the consideration of recipient corruption at the allocation stage. For example, the ADB’s Office of Anti-Corruption and Integrity states “Management and staff will consider issues of corruption more explicitly in the formulation of the country strategy and program” (Asian Development Bank 1998). In the World Bank’s updated Integrity Guidelines, a risk assessment relating to the potential occurrence of fraud and corruption is required before program initiation. Even where policies are more prosecutorial, provisions are explicit about how current allegations will block future commitments until complaints are resolved.

  19. There exist alternative corruption indicators, notably the corruption score of the World Governance Indicators (WGI), and Transparency International’s corruption index (CPI). The correlation between these three indicators is very high (above 0.9), and we show in Online Appendix F that our main results are robust to using these alternative corruption indicators.

  20. Graham and Tucker’s (2019) measures for GDP and population use data from the Penn World Table to supplement data missing from the World Bank’s World Development Indicators.

  21. For each variable, we isolate the top ten largest members within each IDO-year based on GDP. We then calculated the average distance from or trade with these top ten members.

  22. The use of Tobit estimation is consistent with the current standard in the aid allocation literature. See Alesina and Dollar (2000); Alesina and Weder (2002); Berthélemy and Tichit (2004); Bermeo and Leblang (2015); Bermeo (2017).

  23. See also Bermeo and Leblang (2015) and Neumayer (2003b) for an in-depth discussion of these issues.

  24. Note that the inclusion of fixed effects in Tobit models can lead to bias and incorrectly estimated standard errors. Monte Carlo simulations demonstrate that this problem is negligible in Tobit models, particularly as the frequency of censored observations grows, and if there are more than five time periods (Greene 2004).

  25. This is what we find when we estimate our model on the period 1984–1998. Results are available in Online Appendix M.

  26. According to the World Bank’s definition of high-income countries. See: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups, last accessed: September, 2019.

  27. Our interaction figure is based on Model (3) of Table 1 and generated using the code provided by Matt Golder (http://mattgolder.com/interactions#articles, last accessed: November 2016). See also Brambor et al. (2006); Berry et al. (2012).

  28. 90% confidence interval (−1.20, −0.52).

  29. 90% confidence interval (−0.22, 0.50).

  30. 90% confidence interval (0.44, 1.67).

  31. More generally, our robustness checks indicate that the positive effect of IDO Member Corruption on aid allocations to corrupt recipients is not robust to a number of model specifications. Although the effect is not robust, it presents an interesting avenue for future research to investigate how IDOs composed of more corrupt members reach these aid allocation decisions. It is beyond the scope of this paper to do so, but one possible explanation for a positive effect could be in the rent-seeking behavior of corrupt donor governments who use regional IDOs to recoup some of the resources they contribute without being detected.

  32. Estimation results available upon request.

  33. The variation for IDOs without mandates is larger than for IDOs with mandates and there are a number of IDOs that have no mandate but also very low levels of average donor corruption. While the distribution is bimodal, the average value of Average IDO Corruption for observations with (2.04) and without (1.88) a mandate is very similar. The fact that IDO-years with an anti-corruption mandate in place have a marginally higher corruption score is in line with our expectation that IDOs with more corrupt donors are just as likely to adopt anti-corruption mandates.

  34. We follow the method proposed in Brambor et al. (2006). Tabular and graphical results are reported in Appendices D and E respectively. We also split the samples by whether the IDO had a mandate or not; the results are reported alongside the triple interaction in Online Appendix D.

  35. 90% confidence interval (0.04, 0.94).

  36. 90% confidence interval (0.03, 0.37).

  37. 90% confidence interval (−0.65, −0.29).

  38. In line with the findings on multilateral aid in Winters and Martinez (2014), results for the type of aid are inconsistent and thus we do not report them here.

  39. Recent work, however, indicates that Chinese aid is not likely to be more politicized than Western aid (Dreher and Fuchs 2015; Strange et al. 2017; Dreher, Fuchs, et al., 2018).

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Acknowledgements

We thank Sarah Bermeo, Axel Dreher, Andreas Fuchs, Susanne Mueller, Jon Pevehouse, Tal Sadeh, Mike Tierney, and the participants at the PEIO conference (2017), the speaker series at the University of Konstanz (2017) and the University of Wisconsin at Madison (2018), the International Studies Association annual conference (2017) and the International Political Economy Society conference (2017) for helpful comments. Hafner-Burton gratefully acknowledges support from the MacArthur Foundation and the Laboratory on International Law and Regulation at the University of California, San Diego. Schneider gratefully acknowledges financial support from the UCSD Academic Senate (#RP85G-SCHNEIDER) and the Lifelong Learning Programme of the European Union. We thank Rachel Schoner for her research assistance.

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Authors and Affiliations

  1. Department of Political Science, University of Mississippi, Oxford, MS, USA

    Lauren L. Ferry

  2. School of Global Policy and Strategy and Department of Political Science, University of California, San Diego, CA, USA

    Emilie M. Hafner-Burton

  3. Department of Political Science, University of California, San Diego, CA, USA

    Christina J. Schneider

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  1. Lauren L. Ferry
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  2. Emilie M. Hafner-Burton
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Correspondence to Lauren L. Ferry, Emilie M. Hafner-Burton or Christina J. Schneider.

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Ferry, L.L., Hafner-Burton, E.M. & Schneider, C.J. Catch me if you care: International development organizations and national corruption. Rev Int Organ 15, 767–792 (2020). https://doi.org/10.1007/s11558-019-09371-z

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