Elsevier

Economics Letters

Volume 115, Issue 2, May 2012, Pages 240-243
Economics Letters

The effect of foreign aid on corruption: A quantile regression approach

https://doi.org/10.1016/j.econlet.2011.12.051Get rights and content

Abstract

This paper investigates the effect of foreign aid on corruption using a quantile regression method. We show that foreign aid generally reduces corruption, and its reduction effect is greater in less corrupt countries. Moreover, this effect is different by different donor countries.

Highlights

► We examine the effect of foreign aid on corruption using a quantile regression method. ► Foreign aid generally decreases corruption. ► The reduction effect is larger in less corrupt countries. ► The effect of foreign aid on corruption is different by different donor countries.

Introduction

Poverty reduction in developing countries is a theme that has been addressed in many international arenas. Although fighting poverty requires various policy prescriptions, foreign aid in both multilateral and bilateral forms is considered an important element of poverty alleviation (e.g., Agénor et al., 2008; Chong et al., 2009; Masud and Yontcheva, 2005).

Foreign aid may also impact the quality of governance, particularly corruption, in recipient countries.1 Since corruption impedes economic growth, as pointed out by researchers such as Mauro (1995) and Mo (2001), investigating the impact of foreign aid on corruption is important. Previous research (e.g., Alesina and Weder, 2002; Knack, 2001; Svensson, 2000; Tavares, 2003) has primarily been based on ordinary least squares (OLS), instrumental variables, and panel estimation. These approaches have disadvantages, as they only estimate the parameters of interest at the mean evaluation by a conditional distribution of the dependent variable (Billger and Goel, 2009).2 Unlike previous studies, this paper investigates the effect of foreign aid on corruption in recipient countries using the quantile regression (QR) methodology developed by Koenker and Bassett (1978). This method enables us to examine this effect at different intervals throughout the corruption distribution. Furthermore, we examine the effect of multilateral and bilateral foreign aid in addition to total foreign aid, because Alesina and Dollar (2000) demonstrate that the amount of aid is more related to cultural and historic proximity between countries than to economic performance in recipient countries; thus, the characteristics and impact of bilateral and multilateral aid may differ.

Section snippets

Estimation methodology and data

This paper employs the QR approach to examine the effect of foreign aid on corruption. The OLS results are also reported for comparison purposes. The quantile estimator is obtained by solving the following optimization problem: minβ∈Rk[∑i∈{i:yi≥xi′β}θ|yi−xi′β|+∑i∈{i:yi<xi′β}(1−θ)|yi−xi′β|], for the θth quantile (0<θ<1), where yi is the dependent variable and xi is a k by 1 vector of the explanatory variables. The QR estimation approach is more robust than the OLS approach with a presence of

Empirical results

Table 2 presents the estimation results using a share of aggregate net disbursements of ODA to GDP as an aid variable. Robust standard errors for the OLS estimates and the QR results from the 10,000 bootstrapping repetitions are reported to obtain heteroskedasticity-robust estimates. The QR results in columns (2)–(6) illustrate that foreign aid generally has a reduction impact on corruption. In particular, its effect is greater in countries with lower levels of corruption. A possible

Conclusion

This paper examines the effect of foreign aid on corruption using a quantile regression analysis. The results show that foreign aid generally decreases corruption level and that its reduction effect is greater in less corrupt countries. In addition, while multilateral aid has a reduction impact on corruption, bilateral aid from the world’s leading donor countries, except Japan, but including France, the UK, and the US, has no significant effect. Our results are robust even with different

Acknowledgments

We are indebted to Akihisa Shibata, Go Kotera, and an anonymous referee for their insightful comments and suggestions. Any remaining errors are our own.

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