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Results from single-donor analyses of project aid success seem to generalize pretty well across donors

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Abstract

Much research on foreign aid presents claims that apply to aid in general but tests these claims using data from one or a small number of donors. This makes it difficult to know if we have learned something about aid, or merely something about one donor. For example, the literature on project aid success has found that per capita GDP growth rates or Freedom House scores in recipient countries correlate with project success. However, these claims have been tested against data from only multilateral donors and often against data from only the World Bank. I re-examine these analyses using a dataset of harmonized project outcome scores for seven diverse donors. Most donors seem to be similarly influenced by recipient-level and project-level factors, though a few notable exceptions exist. Analyses of project aid success that focus on single donors may be able to produce knowledge about aid in general.

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Notes

  1. If country-year data suffice then the OECD DAC database has reasonable coverage for their members for many aid flows.

  2. As noted by Neumayer (2003, p. 102) in a discussion of bilateral aid, “there is little doubt that economic, political, and sometimes military-strategic interests of donors play a significant and sometimes dominating role for practically all donors.” For further discussion, see Briggs (2017, p. 189).

  3. The sector fixed effects use the OECD-DAC’s 5-digit purpose classification codes. This means that sectors specify something precise like ‘Tuberculosis control’ rather than the more aggregated ‘Basic health.’ The results are very similar if I use the cruder 3-digit codes for the fixed effects.

  4. The dataset includes 8 donors, but the UK’s DFID lacks information on start and end dates and so I ignore it here. The first year in the database in which a project was started is 1956 and the last year is 2012, but the data are densest between 1975–2005.

  5. Japan’s lack of poverty sensitivity is partially due to the fact that “Japanese aid is concentrated on (relatively advanced) Asian neighbours” (Nunnenkamp and Thiele 2006, p. 1191).

  6. One may worry that project outcome ratings are biased by, for example, political factors. Kilby and Michaelowa (2016) examine WB project outcome ratings for bias and find that most geopolitical variables do not seem to cause bias in IEG ratings. Only non-permanent membership in the UN Security Council seems to influence IEG ratings.

  7. For more information, see Honig (2018, p. 58–68) and Honig (2019). Section two of Bulman et al. (2017) also has a useful discussion of the potential challenges when comparing outcome scores across different donors. Every time that I make cross-donor comparisons I normalize the data by donor (through the addition of donor fixed effects, for example).

  8. Past work also used (private) WB CPIA scores as a measure of the institutional environment of a recipient country (Denizer et al. 2013; Bulman et al. 2017). Publicly available data on CPIA scores runs from 2005 to 2016, and only 7% of the projects in the PPD start on or after 2005. For this reason, I do not examine the relationship between CPIA scores and project outcomes.

  9. I drop GiZ and IFAD from this analysis as both donors have very few projects and so I expect them to produce very unreliable results.

  10. The dataset runs from 1956 to 2012, so starting on years that end in five creates a better spread of projects over decadal periods than starting on years that end in zero.

  11. The results are quite similar if I use five year periods instead of ten year periods.

  12. I report scatter plots of some of the correlations in the online appendix.

  13. Note that in calculating the Pearson correlation coefficients the data are demeaned by donor. Thus, I am never making a comparison between absolute levels of projects success scores across donors. This is important as different donors very likely have different baseline levels of project success ratings.

  14. The p-values are shown to two significant digits.

    Fig. 2
    figure 2

    Cross-donor correlations between project success per recipient-period (numbers in cells are p values testing zero correlation)

    Full size image

  15. Clustering on periods and sectors generally produced similar standard errors. Clustering on (the small number of) donors produced smaller standard errors. Thus, clustering on recipients seems conservative.

  16. More precisely, the AsianDB is the base term in the interaction, so its estimate in Fig. 3 is β. The interaction terms (each donor’s γ) then are added to β to show effect for each donor.

    Fig. 3
    figure 3

    Relationship between GDP per capita growth and project success

    Full size image

  17. Their coefficient comes from Table 3, Panel B, Model 6. They add a series of sector fixed effects and some related interactions, but they notably do not add country fixed effects as I do in the right pane of Fig. 3.

  18. If I add the fixed effects sequentially, the point estimates all move toward zero when the recipient country fixed effects are added. The year and sector fixed effects do very little in this case.

  19. In the limiting case, if each recipient receives one project then the recipient dummies will explain 100% of the variation in project outcomes. This issue is noted in footnote 5 of Bulman et al. (2017) and they respond to it by dropping some of their data.

  20. It is important not to read this result in causal terms, as sample selection is a major issue. Given that a donor is active in a country, local conditions appear to not explain much of the variation in outcomes across its projects. Of course, donors may choose not to operate in places with particularly bad conditions because of the causal effect of those conditions on outcomes.

  21. There does not appear to be much sector-level heterogeneity in the relationship between project duration and outcome scores. For evidence supporting this claim, see the appendix.

  22. This discussion brackets research on the effects of aid and instead focuses on aid as a dependent variable. Informally, there seems to be more heterogeneity in the effects of aid on various outcomes than there is on the effects of various factors on aid.

  23. For an extended discussion of how these bargains work themselves out in practice, see Swedlund (2017b).

  24. The names of people in TTL-equivalent roles could be use to test the degree to which variation in outcomes is associated with fixed features of team leaders (Denizer et al. 2013).

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Authors and Affiliations

  1. Guelph Institute of Development Studies and Department of Political Science, University of Guelph, Guelph, Canada

    Ryan C. Briggs

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Correspondence to Ryan C. Briggs.

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I’d like to thank Aart Kraay for comments on an earlier draft and Dan Honig for sharing data and for comments.

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Briggs, R.C. Results from single-donor analyses of project aid success seem to generalize pretty well across donors. Rev Int Organ 15, 947–963 (2020). https://doi.org/10.1007/s11558-019-09365-x

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