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Aid and Governance: Negative Returns?

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Abstract

Recent literature has come to little consensus on the impact of aid flows on governance in recipient countries. This article adds to the debate by developing a theoretical and empirical argument to help resolve the contradictory claims. The article suggests that the aid–governance relationship need not be linear, but rather, that aid may simultaneously improve and hinder governance. This relationship might be akin to an aid–governance ‘aid dependence’ Laffer curve wherein ‘too much’ aid can lead to counter-productive results. Inserting non-linear aid terms in established techniques for examining aid and governance reveals significant support for the potential of negative returns in aid and governance.

Abstract

Le corps d’études récent ne montre aucun consensus en ce qui concerne l’impact de l’aide au développement sur la gouvernance des pays récipiendaires. Cet article contribue au débat en développant un argument théorique et empirique pour aider à résoudre les contradictions sur le sujet. L’article suggère que la relation entre l’aide au développement et la gouvernance nationale n’est pas forcément linéaire, mais que l’aide au développement peut simultanément améliorer et détériorer la gouvernance nationale. La courbe de Laffer peut servir à illustrer la relation entre ces deux variables, ainsi que la dépendance sur l’aide au développement, idée selon laquelle « trop » d’aide peut être contre-productif pour un pays. L’utilisation de terminologie non-linéaire dans les techniques établies pour examiner la relation entre aide et gouvernance révèle un fort potentiel d’effets négatifs dans le rapport entre l’aide et la gouvernance.

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Notes

  1. Although Kurtz and Schrank (2007) go on to point out that the relationship between governance and growth may show a similar endogeneity to governance and aid – growth may cause good governance rather than the reverse.

  2. Both articles suggest ways for refining governance interventions that would appear to presuppose that governance interventions can be effective. Grindle (2007) in particular exerts significant effort in supporting the claim that governance leads to development, but extends virtually no effort to substantiating that aid can improve governance.

  3. Considering the WGIs and other measures including the Varieties of Democracy Project, the ICRG and the Quality of Governance Institution, Fukuyama (2013) laments the conceptual ambiguity of ‘governance’. He suggests refinement along the lines of returning to a more explicit focus on state capacity, defining governance as ‘a government’s ability to make and enforce rules, and deliver services’ (Fukuyama, 2013, p. 350). Yet as refreshing as Fukuyama’s call for conceptual clarity may be, the bureaucratic-oriented conceptualization of governance he focuses on is not the conceptualization that has been widely employed in considering the relationship between governance and development.

  4. Some examples of papers using the WGI conceptualization of governance include Shepherd (2010), Winters (2010) and Demetriades and Fielding (2012).

  5. While there is reliable GDP data for almost all aid-recipient countries, reliable government expenditure data for countries with high levels of aid receipt is difficult to obtain. As I expect the non-linear relationship between aid and governance to become manifest at ‘high’ levels of aid I chose ODA/GDP as the measure to keep as many countries in my sample as possible.

  6. While a number of scholars have discussed the advantages of using aid disbursement data over aid commitment data when examining aid impact (for an example of such a discussion, see Michaelowa, 2004), the main drawback is that aid disbursement data is not broken down by sector. While at first glance it might appear attractive to restrict the analysis to the relationship between governance aid and governance, any aid that could be used as a ‘reward’ for good governance might spur a positive relationship between aid and governance. Conversely, any type of aid may have the potential to be used as a ‘selective benefit’, undermining good governance. Accordingly, I use total aid disbursements as my aid measure.

  7. The one exception in the controls is the measure of Battle Deaths, which while negative and significant for ‘political stability’ is positive and significant for ‘voice and accountability’ and ‘government effectiveness’.

  8. For example, OECD CRS data shows almost double the standard deviation (18.5 versus 9.6 per cent) in per cent changes in ODA from 2005 to 2012 in the Federated States of Micronesia when compared to official government statistics (www.pitiviti.org/initiatives/economics/fsm.php). In 2008 CRS data shows a 22 per cent drop in ODA compared with a 3 increase in official government statistics.

  9. The Sargan χ2 values in Table 3 suggest valid instruments in each of Models 3.I–3.VIII. For Model 3.I (ICRG) the 5th lag was needed to find valid instruments, while for Model 3.III the 7th lag was needed to not reject the Sargan null hypothesis. For all other models either the 2nd or 3rd lag was used.

  10. While this measures includes, as component parts, measures from the WGIs, the variables in the following models are sufficiently transformed (both through differencing of the outcome variable and through the interaction between aid and the CIAC measure) to mitigate concerns of endogeneity and/or multicollinearity.

  11. In two instances (Figures 3(c) and (g)) the ‘linear’ aid coefficient moves from ‘positive’ to ‘insignificant’ at higher levels of the CIAC. This finding is not entirely inconsistent with the discussion above given the relatively small density at the higher levels of the CIAC distribution.

  12. Instead, aid data is collected by aggregate commitment or disbursement. Aid expenditure data is not accessible for a number of reasons, including the fact that aid is often mixed with local revenues such that local budget reports cannot disentangle local-revenue expenditures from aid-financed expenditure.

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Authors and Affiliations

  1. University College Dublin, Dublin, Ireland

    Samuel Brazys

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Appendix

Appendix

Data

The data on aid used in all regression models and in Figures 1 and 2 was obtained from the OECD Creditor Reporting Database (CRS) from 1995 to 2008 available at stats.oecd.org/Index.aspx?DatasetCode=CRSNEW. Data on Gross Domestic Product (GDP), Battle Deaths, Population and Oil Rents are from the World Bank’s World Development Indicator database, available at data.worldbank.org/data-catalog/. Data on the WGIs is from the World Bank’s Worldwide Governance Indicators project available at info.worldbank.org/governance/wgi/index.asp. The ICRG data is the ‘governance quality’ indicator and is available at www.prsgroup.com/icrg.aspx. The CIAC data was provided most graciously by Professor Simon Feeny. Summary statistics are presented in Table A1.

Table A1 Summary statistics
Full size table

All regressions were run in STATA 12 using reg, and xtbond2 and STATA were used to create the interactive Figures 3 and 4. The marginal effects plots were created using Microsoft Excel. Missing data was dropped from the regression. For a full list of missing data points, or for a full replication data set, please contact the author.

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Brazys, S. Aid and Governance: Negative Returns?. Eur J Dev Res 28, 294–313 (2016). https://doi.org/10.1057/ejdr.2014.77

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