The Okada & Samreth(2012, EL) finding that aid deters corruption could have an important influence on policy and academic debates. This paper partially negates their criticism of the mainstream approach to the aid-development nexus. Using updated data(1996-2010) from 52 African countries we provide robust evidence of a positive aid-corruption nexus. Development assistance fuels(mitigates) corruption(the control of corruption) in the African continent. As a policy implication, the Okada & Samreth(2012, EL) finding for developing countries may not be relevant for Africa.Foreign Aid; Political Economy; Development; Africa

Simplice A, Asongu

English

Research Papers in Economics

On the effect of foreign aid on corruption 
Simplice A. Asongu
E-mail: asongusimplice@yahoo.com
Tel: 0032 473613172
HEC-Management School, University of Liège.
Rue Louvrex 14, Bldg. N1, B-4000 Liège,  Belgium
1On the effect of foreign aid on corruption
Abstract
The Okada & Samreth(2012, EL) finding that aid deters corruption could have an 
important influence on policy and academic debates. This paper partially negates their 
criticism   of   the   mainstream   approach   to   the   aid-development   nexus.   Using   updated 
data(1996-2010) from 52 African countries we provide robust evidence of a positive aid-
corruption   nexus.   Development   assistance   fuels(mitigates)   corruption(the   control   of 
corruption) in the African continent. As a policy implication, the Okada & Samreth(2012, EL) 
finding for developing countries may not be relevant for  Africa. 
JEL Classification: B20; F35; F50; O10; O55
Keywords: Foreign Aid; Political Economy; Development; Africa
21.Introduction
The purpose of this comment is to stress some policy and methodological issues 
resulting from Okada & Samreth(2012). The methodological basis of the paper is the 
following: “previous research 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 & Goel,2009)”(p.240).  To confirm this assertion we peruse 
Billger & Goel(2009) and find the following: “ many previous studies of the determinants of 
corruption   employ   OLS   estimation,   therefore   reporting   parameters   estimates   at   the 
conditional mean of corruption. While mean effects are certainly important, we expand upon 
such findings using quantile regression. In addition, on underlying assumption for OLS 
regression is that the error term and the dependent variables are normally distributed…..OLS 
estimation can yield unreliable estimates, but quantile regression does not require a normally 
distributed error term”(pp.300-301). Three facts result from this cross-examination:
- Billger & Goel(2009) do not invalidate panel instrumental variable estimation techniques;
-if the classical conditions for the validity of OLS are satisfied, that is, if the error term is 
independently and identically distributed, conditional on the independent variables, then 
quantile regression  is redundant: all the conditional quantiles of the dependent variable will 
march in lockstep with the conditional mean;
-while the Okada & Samreth(2012) criticism is valid with respect to OLS, it is short of 
substance when extended to some instrumental and dynamic panel estimation techniques. 
In this comment we assess the effect of foreign aid on corruption using two panel 
estimation techniques in the context of Africa. The choice of Africa is based on the substantial 
reliance of the continent on the ‘Big-Push’ development(poverty-reduction) policy.  The rest 
3of the paper is organized as follows. Section 2 presents data and outlines the methodology. 
Section 3 covers the empirical analysis. Section 4 concludes. 
2.Data and Methodology
2.1 Data
We investigate a panel of 52 African countries with data from African Development 
Indicators   (ADI)   of   the   World   Bank  (WB)   ranging   from   1996   to   2010.   Okada   & 
Samreth(2012) have used data(1995 to 2009) from 120 developing countries. The outcome 
variables are   the ‘control of corruption’ and the ‘corruption perception’ indexes. The 
explaining variable is Net Official Development Assistance(NODA). For robustness checks 
we use total NODA, NODA from Multilateral donors and NODA from the Development 
Assistance Committee(DAC) countries. In the estimations we control for openness(trade), 
autocracy and democracy. The choice of control variables is contingent on the degrees of 
freedom necessary for overidentifying restrictions tests at second-stage regressions(more than 
two control variables will result in exact or under-identification; meaning instruments are 
either equal to or less than the number of endogenous explaining variables respectively). The 
aid and trade variables are in percentage of GDP. Instrumental variables include: legal-
origins, income-levels and religious-dominations. These instruments have been substantially 
documented in the economic development literature (La Porta et al., 1997; Beck et al., 2003). 
2.2 Methodology 
2.2.1 Endogeneity 
While development assistance affects the quality of institutions in the recipient 
countries, some foreign-aid is also contingent on the  quality of institutions in the beneficiary 
countries. We are thus faced with an important issue of endogeneity owing to reverse-
causality and omitted variables. To address this concern we shall assess the presence of 
4endogeneity with the Hausman-test and selection of  estimation technique will depend on the 
outcome of the test. 
2.2.2 Estimation techniques  
a) HAC  Two-Stage Least Squares(TSLS) Instrumental Variables(IV) 
The TSLS is preceded by the Hausman test for endogeneity. The null hypothesis of 
this test is the stance that OLS estimates are efficient and consistent; therefore a rejection of 
this null hypothesis points to the presence of endogeneity and hence an estimation approach 
that incorporates it. Before estimation we verify that the instruments are exogenous to the 
endogenous   components   of   explaining   variables(aid   channels)   conditional   on   other 
covariates(control variables). Borrowing from Beck et al.(2003) with use the TSLS-IV with 
Heteroscedasticity and Autocorrelation Consistent(HAC) standard errors. The validity of the 
instruments is assessed by the Sargan Overidentifying Restrictions(OIR) test. The null 
hypothesis of this test is the position that the instruments are not correlated with the error term 
in the equation of interest(do not suffer from endogeneity). 
b) System Generalized Methods of Moments(Dynamic Panel) 
Blundell & Bond(1998) proposed another approach to the issue of endogeneity with 
an   application   of   the  Generalized   Method   of   Moments(GMM)   that   exploits   all   the 
orthogonality conditions between the dependent lagged variables and the error term. We 
prefer the second-step GMM since it corrects the residuals for heteroscedasticity. In the first-
step  the residuals are homoscedastic. The hypothesis of no auto-correlation in residuals is 
crucial as past differenced  variables are to be used as instruments for the dependent variables. 
This concern is addressed with the second-order autocorrelation test: AR(2). Also the 
estimation depends on the assumption that the lagged values of the outcome variable and 
5other explaining variables are valid instruments in the estimation. The validity of the 
instruments is investigated by the Sargan over-identifying restrictions  test(OIR).
2.2.3 Robustness checks  
To ensure robustness of the analysis, the following checks will be carried out: (1) 
usage of alternative NODA indicators ; (2) employment of two distinct interchangeable sets of 
moment conditions that encompass every category of the instruments; (3) usage of alternative 
corruption indicators; (4) account for the concern of endogeneity; (5) estimation with robust 
Heteroscedasticity and Autocorrelation Consistent(HAC) standard errors;(6) application of 
restricted and unrestricted regressions. 
3.Empirical results 
3.1 Instrumental panel(TSLS)
Table 1 below presents results in HAC standard errors for restricted(panel A) and 
unrestricted(panel B) TSLS-IV regressions. Rejection of the null hypothesis of the Hausman 
test in all regressions confirms the presence of endogeneity and hence the choice of the IV 
estimation approach. Failure to reject the hull hypothesis of the Sargan-OIR test lends credit 
to the validity of the instruments. Clearly, it could be noticed that foreign aid significantly 
diminishes the control of corruption and the CPI. Reduction in the  CPI indicates increase in 
corruption(see  Transparency International  CPI computation). These results are robust to the 
alternative set of instrumental variables. 
3.2 Dynamic Panel(System GMM) 
Table 2 presents dynamic panel system GMM estimation results for restricted(panel 
A) and unrestricted(panel B) regressions. Failure to reject the null hypotheses of the AR(2) 
and Sargan-OIR tests for the most part confirms the absence of autocorrelation in the 
6residuals and validity of the instruments respectively. The results broadly confirm those in 
Table 1. 
Table 1: Two-Stage Least Squares Instrumental Variable regressions 
Panel A: Restricted  regressions(HAC standard errors)
Control of Corruption Corruption Perception Index(CPI)
NODAgdp -0.035*** --- --- -0.032* --- ---
(0.000) (0.060)
NODAMDgdp --- -0.082*** --- --- -0.074* ---
(0.000) (0.068)
NODADACgdp --- --- -0.062*** --- --- -0.058*
(0.000) (0.057)
Democracy  0.101* 0.119* 0.087 0.261 0.275 0.248
(0.086) (0.078) (0.116) (0.105) (0.104) (0.110)
Autocracy  -0.032 -0.000 -0.058 0.171 0.200 0.145
(0.773) (0.999) (0.575) (0.516) (0.459) (0.577)
Trade  -0.005 -0.007 -0.004 0.027* 0.025* 0.028**
(0.223) (0.169) (0.322) (0.050) (0.075) (0.035)
Hausman 234.028*** 255.223*** 233.669*** 501.364*** 495.951*** 504.967***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Sargan-OIR 0.024 0.109 0.000 2.122 2.290 1.982
(0.875) (0.741) (0.996) (0.145) (0.130) (0.159)
Adjusted R² 0.106 0.098 0.094 0.180 0.177 0.178
Fisher  16.099*** 14.177*** 17.011*** 148.337*** 158.260*** 138.526***
Observations  488 488 488 368 368 368
Panel B: Unrestricted  Regressions(HAC standard errors)
Control of Corruption Corruption Perception Index
Constant  -0.631*** -0.649*** -0.621*** 2.782*** 2.727*** 2.813***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
NODAgdp -0.023** --- --- -0.068*** --- ---
(0.014) (0.000)
NODAMDgdp --- -0.053** --- --- -0.150*** ---
(0.017) (0.000)
NODADACgdp --- --- -0.041** --- --- -0.125***
(0.013) (0.000)
Democracy  0.105** 0.107** 0.104** 0.255*** 0.259*** 0.252***
(0.017) (0.017) (0.018) (0.000) (0.000) (0.000)
Hausman 49.346*** 50.302*** 49.910*** 115.635*** 118.12*** 118.09***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Sargan-OIR 0.039 0.695 0.214 2.383 2.086 3.825
(0.980) (0.706) (0.898) (0.303) (0.352) (0.147)
Adjusted R² 0.177 0.172 0.167 0.241 0.235 0.225
Fisher  6.416*** 6.315*** 6.400*** 21.499*** 20.853*** 21.255***
Observations  514 514 514 388 388 388
First-Set of Instruments  Constant; English ; Christianity; Middle  Income; Lower Middle Income 
Second-Set of Instruments  Constant; French; Islam; Lower Income; Upper Middle Income
*;**;***: significance levels of 10%, 5% and 1% respectively. OIR: Overidentifying Restrictions test. NODAgdp: NODA on GDP. 
NODAMD: NODA from Multilateral Donors on GDP.  NODADACgdp: NODA  from DAC  countries on GDP. OIR: Overidentifying 
Restrictions test. P-values in brackets. 
 
7Table 2: Dynamic System GMM regressions 
Panel A: Restricted  regressions
Control of Corruption Corruption Perception Index
Initial  0.785*** 0.789*** 0.790*** 0.873*** 0.870*** 0.874***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
NODAgdp -0.005*** --- --- 0.005 --- ---
(0.004) (0.108)
NODAMDgdp --- -0.010** --- --- 0.015* ---
(0.042) (0.081)
NODADACgdp --- --- -0.007*** --- --- 0.008
(0.001) (0.130)
Democracy  0.001 0.001 0.0005 0.045* 0.045** 0.046*
(0.576) (0.694) (0.873) (0.055) (0.022) (0.055)
AR(2) 1.324 1.272 1.366 1.812* 1.821* 1.799*
(0.185) (0.203) (0.171) (0.069) (0.068) (0.072)
Sargan-OIR 47.079 46.156 45.410 44.902 44.891 44.769
(0.347) (0.383) (0.413) (0.966) (0.966) (0.967)
Wald 547.996*** 420.894*** 648.423*** 6836.4*** 6437.15*** 6876.4***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Observations  334 334 334 335 335 335
Panel B: Unrestricted  regressions
Control of Corruption Corruption Perception Index
Initial  0.681*** 0.668*** 0.689*** 0.776*** 0.776*** 0.780***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Constant  -0.250*** -0.267*** -0.248*** 0.597*** 0.594*** 0.582***
(0.000) (0.000) (0.000) (0.001) (0.003) (0.000)
NODAgdp -0.001*** --- --- -0.003 --- ---
(0.005) (0.148)
NODAMDgdp --- -0.003 --- --- -0.008 ---
(0.133) (0.153)
NODADACgdp --- --- -0.002*** --- --- -0.005
(0.005) (0.144)
Democracy  0.021*** 0.023*** 0.020*** 0.026** 0.026** 0.024**
(0.000) (0.000) (0.000) (0.016) (0.027) (0.019)
AR(2) 1.353 1.332 1.347 1.943* 1.933* 1.949*
(0.175) (0.182) (0.177) (0.051) (0.053) (0.051)
Sargan-OIR 46.024 45.935 45.431 44.569 44.553 44.759
(0.388) (0.392) (0.412) (0.969) (0.969) (0.967)
Wald 175.78*** 137.485*** 172.401*** 377.631*** 376.473*** 385.711***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Observations  334 334 334 335 335 335
*;**;***: significance levels of 10%, 5% and 1% respectively. OIR: Overidentifying Restrictions test. NODAgdp: NODA on GDP. 
NODAMD: NODA from Multilateral Donors on GDP.  NODADACgdp: NODA  from DAC  countries on GDP. OIR: Overidentifying 
Restrictions test. AR(2): Second order auto correlation test. Wald: statistics for joint significance of estimated coefficients. Initial: lagged 
endogenous variable. P-values in brackets.
4. Conclusion 
The Okada & Samreth(2012, EL) finding that aid deters corruption could have an 
important influence on policy and academic debates. This paper partially negates their 
criticism   of   the   mainstream   approach   to   the   aid-development   nexus.   Using   updated 
8data(1996-2010) from 52 African countries we provide robust evidence of a positive aid-
corruption   nexus.   Development   assistance   fuels(mitigates)   corruption(the   control   of 
corruption) in the African continent. As a policy implication, the Okada & Samreth(2012, EL) 
finding for developing countries may not be relevant for  Africa. 
9References
Blundell, R.W., & Bond, S.R., (1998),  “Initial Conditions and Moment Restrictions in 
Dynamic Panel Data Models”, Journal of Econometrics, 87, pp.115-143.  
Beck, T., Demirgüç-Kunt, A., & Levine, R.,(2003), “Law and finance: why does legal origin 
matter?”, Journal of Comparative Economics, 31, pp. 653-675.
Billger, S.M.,  & Goel, R.K., (2009), “Do existing corruption levels matter in controlling 
corruption?cross-country quantile regression estimates”, Journal of Development
Economics, 90, pp. 299–305.
La   Porta,   R.,   Lopez-de-Silanes,   F.,   Shleifer,   A.,   &   Vishny,   R.W.,   (1997).  “Legal 
Determinants of External Finance”, Journal of Finance, 52, pp. 1131-1150.
Okada, K., & Samreth, S.,(2012), “The effect of foreign aid on corruption: A quantile 
regression approach”, Economic Letters, 11, pp.240-243. 
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