On the effect of foreign aid on corruption
Simplice Asongu (PhD)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.
Related papers
The Evolving Debate on the Effect of Foreign Aid on Corruption and Institutions in Africa
This policy chapter summarises an evolving debate on the effect of foreign aid on corruption and institutions. It entails a series of publications that have been successively motivated by feedbacks from academic and policy making circles. The plethora of papers explores debates sustaining the direct, conditional and indirect effects of foreign aid on institutions. Moreover, another debate on the incidence of foreign aid distortions on corruption is also assessed in light of a recently celebrated literature on development assistance. Overall, the findings show that the effects of foreign aid on corruption and institutions are: directly positive; conditionally positive with a magnitude dependent on initial institutional capacity levels; contingent on fundamental characteristics of development due to heterogeneity and; indirectly positive or negative depending on the transmission mechanism. While the impact of foreign aid uncertainty on corruption is also positive, the sign on governance could change in light of governments’ commitment to increase its dependence on local tax revenues.
On the dynamic effects of foreign aid on corruption
We assemble more pieces on the puzzle of the aid-corruption nexus. In essence, we extend the debate on the effect of foreign aid on corruption by providing evidence on dynamic effects of wealth, legal origin, religious-domination, regional proximity, openness to sea, natural resources and politico-economic stability. The empirical evidence from dynamic panel GMM estimation is based on 53 African countries for the period 1996-2010. The findings show that the positive effect of foreign aid on corruption is most significant in: Middle-income, French civil-law, Christian-dominated, non-oil exporting and landlocked countries. Moreover, there is also some scanty evidence of foreign aid increasing corruption-control in Lower Middle income and Not-landlocked countries. Justifications for the dynamics are discussed.
On the channels of foreign aid to corruption
The debate by Okada & Samreth (2012, EL) and Asongu (2012, EB; 2013, EEL) on ‘the effect of foreign aid on corruption’ in its current state has the shortcoming of modeling corruption as a direct effect of development assistance. This note extends the debate by assessing the channels of foreign aid to corruption in 53 African countries for the period 1996-2010. Two main findings are established to unite the two streams of the debate. (1) Foreign aid channeled through government’s consumption expenditure increases corruption. (2) Development assistance channeled via private investment and tax effort decreases corruption. It follows that foreign aid that is targeted towards reducing corruption should be channeled via private investment and tax effort, not through government expenditure. Our results integrate an indirect component and reconcile the debate by showing that, the effect could either be positive or negative depending on the transmission channel.
Effect of foreign aid on corruption: evidence from Sub-Saharan African countries
International Journal of Social Economics, 41(11), pp. 1131-1155, 2014
Purpose – The purpose of this paper is to examine whether initial levels in GDP growth, GDP per capita growth and inequality adjusted human development matter in the impact of aid on development. In substance its object is to assess if threshold development conditions are necessary for the effectiveness of foreign aid in Africa. Design/methodology/approach – The panel quantile regression technique enables us to investigate if the relationship between development dynamics and development assistance differs throughout the distributions of development dynamics. Findings – Three main findings are established. First, with slight exceptions, the effectiveness of aid in economic prosperity (at the macro level) increases in positive magnitude across the distribution. This implies high-growth countries are more likely to benefit from development assistance (in terms of general economic growth) than their low-growth counterparts. Second, the positive nexus between aid and per capita economic growth displays nonlinear patterns across distributions and specifications, with the correlations broadly higher in top quantiles than in bottom quantiles after controlling for the unobserved heterogeneity. Third, the aid-human development nexus is negative and almost similar in magnitude across distributions and specifications. Practical implications – As a policy implication, there is need to improve management of aid funds destined for health and education projects in the sampled countries. Moreover, given the magnitude of the nexuses, while blanket aid initiatives could be applied for policies targeting the human development index (due to the absence of significant differences in the magnitude of estimated coefficients), such are unlikely to succeed for aid targeting economic prosperity at macro and micro levels. From the weight of the findings, given a policy of balancing the impact of aid, it could be inferred that low-growth countries would need more aid than their high-growth counterparts because of the less positive effects in the former countries. Originality/value – This paper contributes to existing literature on the effectiveness of foreign aid by focussing on the distribution of the dependent variables (development dynamics). It is likely that high- and low-growth countries respond differently to development assistance.
2010, Aid and Corruption: Do Donors Use Development Assistance to Provide the “Right” Incentives? Paper presented at the AidData Conference
2020
In this paper, we focus on the determinants of the relationship between aid and corruption. We propose a static principal-agent model where a donor faces the problem of giving aid to a recipient country characterized by wide spread corruption. The model describes four possible equilibria: one, in which foreign aid is set by the donor independently of the level of corruption and a second one, where the donor uses aid to induce the "right" incentives on local elites. In the third and forth equilibria, the donor either cannot impose an incentive mechanism or prefers not to. In these last two cases, the aid-corruption relationship depends only on the preferences of the donor. We then use the restrictions implied by our theoretical framework to test a model of aid allocation. For a group of donors (Scandinavian countries plus Germany and Japan), we find a negative relationship between aid and corruption, which shows a high concern for recipients needs. For a second group of don...
Aid and Corruption: Do Donors Use Development Assistance to Provide the 'Right' Incentives?
SSRN Electronic Journal, 2008
RPP Bab I Nilai Mutlak
RPP nilai mutlak untuk semester 1 kelas X SMK
ĐỀ CƯƠNG MÔN KINH TẾ HỌC VĨ MÔ
(Thời lượng 3 tín chỉ) I.Lý do nghiên cứu và tầm quan trọng của môn học:Thứ nhất , nghiên cứu kinh tế học nói chung và kinh tế học vĩ mô nói riêng sẽ giúp cho chúng ta hiểu được thế giới mà chúng ta đang sống. Thứ hai, giúp cho chúng ta sắc sảo hơn khi tham gia vào nền kinh tế.Thứ ba, giúp cho chúng ta có sự hiểu biết tốt hơn về tiềm năng lẫn giới hạn của các chính sách kinh tế. II.Mục tiêu của kinh tế học vĩ mô là trang bị cho người học những kiến thức cơ bản trong lĩnh vực kinh tế vĩ mô, để từ đó có thể giải thích những thay đổi kinh tế có ảnh hưởng đồng thời đến nhiều hộ gia đình, nhiều doanh nghiệp và các thị trường III.Nội dung: Kinh tế học vĩ mô nghiên cứu các hoạt động diễn ra trên phạm vi tổng thể toàn bộ nền kinh tế, nó nghiên cứu trên quy mô toàn cục các biến số kinh tế vĩ mô như: lạm phát, thất nghiệp, tăng trưởng GDP, cán cân thương mại, cán cân thanh toán….. IV. Phương pháp giảng dạy và học tập:-Những kiến thức về kinh tê học vĩ mô sẽ được giảng viên truyền đạt trên giảng đường kết hợp với học trực tuyến LMS.-Sau mỗi nội dung của các chương, giảng viên sẽ tóm tắt nội dung và đưa ra các tình huống, bài tập và câu hỏi ôn tập của chương đó-Kết thúc môn học giảng viên sẽ tổng ôn những nội cơ bản của môn học V.Các chính sách và thủ tục sẽ được áp dụng trong môn học.-Điểm toàn học phần gồm có điểm quá trình và điểm kết thúc học phần-Điểm quá trình có thang điểm là 10 chiếm 30%, điểm kết thúc học phần có thang điểm là 10 chiếm 70% trong điểm toàn học phần-Điểm quá trình sẽ bao gồm điểm kiểm tra giữa kỳ, điểm phát biểu trên lớp, điểm tiểu luận theo nhóm…. VI.Phương tiện vật chất cần thiết cho việc tham gia lớp học-Giaó trình môn kinh tế học vĩ mô (N. GREGORY MANKIW)-Các tài liệu học tập tham khảo khác về kinh tế học vĩ mô-Máy vi tính để học trực tuyến và thu thập dữ liệu để làm tiểu luận VII.Những yêu cầu đối với sinh viên để hoàn thành tốt môn học.-Sinh viên phải thường xuyên tham dự các buổi giảng của giảng viên tiếp thu các kiến thức kinh tế học vĩ mô-Sinh viên có thể vào thư viện để đọc các tài liệu kinh tế vĩ mô có liên quan.-Làm các bài tập , các câu hỏi ôn tập sau mỗi chương.-Cần tranh thủ hỏi giảng viên về các vấn đề còn khúc mắc, chưa hiểu để giảng viên giải thích.-Trao đổi tranh luận với các bạn trong nhóm và giảng viên về các nội dung trong môn học.-Kết thúc chương nào cần ôn lại và nắm nội dung chính của chương đó
AFRICAN GOVERNANCE AND DEVELOPMENT
INSTITUTE
A G D I Working Paper
WP/12/031
On the effect of foreign aid on corruption
Simplice A. Asongu
African Governance and Development Institute,
P.O. Box 18 SOA/ 1365 Yaoundé, Cameroon.
E-mail: asongusimplice@yahoo.com
1
© 2012 African Governance and Development Institute
WP/12/031
AGDI Working Paper
Research Department
On the effect of foreign aid on corruption
Simplice A. Asongu1
February 2012
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 aidcorruption 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
1
Simplice A. Asongu is Lead economist in the Research Department of the AGDI (asongus@afridev.org).
2
1. 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
3
of 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: legalorigins, 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 reversecausality and omitted variables. To address this concern we shall assess the presence of
4
endogeneity 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 firststep 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
5
other 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
6
residuals 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)
NODAgdp
Control of Corruption
-----
NODAMDgdp
-0.035***
(0.000)
---
NODADACgdp
---
-0.082***
(0.000)
---
Democracy
0.101*
(0.086)
-0.032
(0.773)
-0.005
(0.223)
234.028***
(0.000)
0.024
(0.875)
0.106
16.099***
488
0.119*
(0.078)
-0.000
(0.999)
-0.007
(0.169)
255.223***
(0.000)
0.109
(0.741)
0.098
14.177***
488
Autocracy
Trade
Hausman
Sargan-OIR
Adjusted R²
Fisher
Observations
---0.062***
(0.000)
0.087
(0.116)
-0.058
(0.575)
-0.004
(0.322)
233.669***
(0.000)
0.000
(0.996)
0.094
17.011***
488
Corruption Perception Index(CPI)
-----0.032*
(0.060)
-----0.074*
(0.068)
-----0.058*
(0.057)
0.261
0.275
0.248
(0.105)
(0.104)
(0.110)
0.171
0.200
0.145
(0.516)
(0.459)
(0.577)
0.027*
0.025*
0.028**
(0.050)
(0.075)
(0.035)
501.364***
495.951***
504.967***
(0.000)
(0.000)
(0.000)
2.122
2.290
1.982
(0.145)
(0.130)
(0.159)
0.180
0.177
0.178
148.337***
158.260***
138.526***
368
368
368
Panel B: Unrestricted Regressions(HAC standard errors)
Control of Corruption
Constant
Corruption Perception Index
-0.649***
(0.000)
---
-0.621***
(0.000)
---
NODAMDgdp
-0.631***
(0.000)
-0.023**
(0.014)
---
---
NODADACgdp
---
-0.053**
(0.017)
---
-0.041**
---0.125***
(0.013)
(0.000)
0.107**
0.104**
0.255***
0.259***
0.252***
(0.017)
(0.018)
(0.000)
(0.000)
(0.000)
50.302***
49.910***
115.635***
118.12***
118.09***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.695
0.214
2.383
2.086
3.825
(0.706)
(0.898)
(0.303)
(0.352)
(0.147)
0.172
0.167
0.241
0.235
0.225
6.315***
6.400***
21.499***
20.853***
21.255***
514
514
388
388
388
Constant; English ; Christianity; Middle Income; Lower Middle Income
Constant; French; Islam; Lower Income; Upper Middle Income
NODAgdp
Democracy
0.105**
(0.017)
Hausman
49.346***
(0.000)
Sargan-OIR
0.039
(0.980)
Adjusted R²
0.177
Fisher
6.416***
Observations
514
First-Set of Instruments
Second-Set of Instruments
2.782***
(0.000)
-0.068***
(0.000)
---
2.727***
(0.000)
---
2.813***
(0.000)
---
-0.150***
(0.000)
---
---
*;**;***: 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.
7
Table 2: Dynamic System GMM regressions
Panel A: Restricted regressions
Initial
Control of Corruption
0.789***
0.790***
(0.000)
(0.000)
-----
NODAMDgdp
0.785***
(0.000)
-0.005***
(0.004)
---
NODADACgdp
---
-0.010**
(0.042)
---
Democracy
0.001
(0.576)
1.324
(0.185)
47.079
(0.347)
547.996***
(0.000)
334
0.001
(0.694)
1.272
(0.203)
46.156
(0.383)
420.894***
(0.000)
334
NODAgdp
AR(2)
Sargan-OIR
Wald
Observations
---0.007***
(0.001)
0.0005
(0.873)
1.366
(0.171)
45.410
(0.413)
648.423***
(0.000)
334
Corruption Perception Index
0.873***
0.870***
0.874***
(0.000)
(0.000)
(0.000)
0.005
----(0.108)
----0.015*
(0.081)
----0.008
(0.130)
0.045*
0.045**
0.046*
(0.055)
(0.022)
(0.055)
1.812*
1.821*
1.799*
(0.069)
(0.068)
(0.072)
44.902
44.891
44.769
(0.966)
(0.966)
(0.967)
6836.4***
6437.15***
6876.4***
(0.000)
(0.000)
(0.000)
335
335
335
Panel B: Unrestricted regressions
Control of Corruption
Initial
NODAMDgdp
0.681***
(0.000)
-0.250***
(0.000)
-0.001***
(0.005)
---
NODADACgdp
---
-0.003
(0.133)
---
Democracy
0.021***
(0.000)
1.353
(0.175)
46.024
(0.388)
175.78***
(0.000)
334
0.023***
(0.000)
1.332
(0.182)
45.935
(0.392)
137.485***
(0.000)
334
Constant
NODAgdp
AR(2)
Sargan-OIR
Wald
Observations
0.668***
(0.000)
-0.267***
(0.000)
---
0.689***
(0.000)
-0.248***
(0.000)
-----0.002***
(0.005)
0.020***
(0.000)
1.347
(0.177)
45.431
(0.412)
172.401***
(0.000)
334
Corruption Perception Index
0.776***
(0.000)
0.597***
(0.001)
-0.003
(0.148)
---
0.776***
(0.000)
0.594***
(0.003)
---
0.780***
(0.000)
0.582***
(0.000)
-----
---
-0.008
(0.153)
---
0.026**
(0.016)
1.943*
(0.051)
44.569
(0.969)
377.631***
(0.000)
335
0.026**
(0.027)
1.933*
(0.053)
44.553
(0.969)
376.473***
(0.000)
335
-0.005
(0.144)
0.024**
(0.019)
1.949*
(0.051)
44.759
(0.967)
385.711***
(0.000)
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
8
data(1996-2010) from 52 African countries we provide robust evidence of a positive aidcorruption 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.
References
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.
9
Economics Bulletin, 2012, Vol. 32 No. 3 pp. 2174-2180
1. 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, an 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
of 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).
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Economics Bulletin, 2012, Vol. 32 No. 3 pp. 2174-2180
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 reversecausality and omitted variables. To address this concern we shall assess the presence of
endogeneity with the Hausman-test and selection of estimation technique will depend on the
outcome of the test.
2.2.2 Estimation techniques
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).
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 lagged dependent variables and the error term. We
prefer the second-step GMM since it corrects the residuals for heteroscedasticity. In the firststep, 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
other 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.
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Economics Bulletin, 2012, Vol. 32 No. 3 pp. 2174-2180
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 Corruption Perception Index (CPI). Reduction
in the CPI indicates an 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
residuals and validity of the instruments respectively. The results broadly confirm those in
Table 1.
2177
Economics Bulletin, 2012, Vol. 32 No. 3 pp. 2174-2180
Table I: Two-Stage Least Squares Instrumental Variable regressions
Panel A: Restricted regressions (HAC standard errors)
NODAgdp
Control of Corruption
-----
NODAMDgdp
-0.035***
(0.000)
---
NODADACgdp
---
-0.082***
(0.000)
---
Democracy
0.101*
(0.086)
-0.032
(0.773)
-0.005
(0.223)
234.028***
(0.000)
0.024
(0.875)
0.106
16.099***
488
0.119*
(0.078)
-0.000
(0.999)
-0.007
(0.169)
255.223***
(0.000)
0.109
(0.741)
0.098
14.177***
488
Autocracy
Trade
Hausman
Sargan-OIR
Adjusted R²
Fisher
Observations
---0.062***
(0.000)
0.087
(0.116)
-0.058
(0.575)
-0.004
(0.322)
233.669***
(0.000)
0.000
(0.996)
0.094
17.011***
488
Corruption Perception Index (CPI)
-0.032*
----(0.060)
---0.074*
--(0.068)
-----0.058*
(0.057)
0.261
0.275
0.248
(0.105)
(0.104)
(0.110)
0.171
0.200
0.145
(0.516)
(0.459)
(0.577)
0.027*
0.025*
0.028**
(0.050)
(0.075)
(0.035)
501.364***
495.951***
504.967***
(0.000)
(0.000)
(0.000)
2.122
2.290
1.982
(0.145)
(0.130)
(0.159)
0.180
0.177
0.178
148.337***
158.260***
138.526***
368
368
368
Panel B: Unrestricted Regressions (HAC standard errors)
Control of Corruption
Constant
NODAMDgdp
-0.631***
(0.000)
-0.023**
(0.014)
---
NODADACgdp
---
NODAgdp
Democracy
0.105**
(0.017)
Hausman
49.346***
(0.000)
Sargan-OIR
0.039
(0.980)
Adjusted R²
0.177
Fisher
6.416***
Observations
514
First-Set of Instruments
Second-Set of Instruments
Corruption Perception Index
-0.649***
(0.000)
---
-0.621***
(0.000)
---
-0.053**
(0.017)
---
---
2.782***
(0.000)
-0.068***
(0.000)
---
2.727***
(0.000)
---
2.813***
(0.000)
---
-0.150***
(0.000)
---
---
-0.041**
---0.125***
(0.013)
(0.000)
0.107**
0.104**
0.255***
0.259***
0.252***
(0.017)
(0.018)
(0.000)
(0.000)
(0.000)
50.302***
49.910***
115.635***
118.12***
118.09***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.695
0.214
2.383
2.086
3.825
(0.706)
(0.898)
(0.303)
(0.352)
(0.147)
0.172
0.167
0.241
0.235
0.225
6.315***
6.400***
21.499***
20.853***
21.255***
514
514
388
388
388
Constant; English ; Christianity; Middle Income; Lower Middle Income
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.
2178
Economics Bulletin, 2012, Vol. 32 No. 3 pp. 2174-2180
Table II: Dynamic System GMM regressions
Panel A: Restricted regressions
Initial
Control of Corruption
0.789***
0.790***
(0.000)
(0.000)
-----
NODAMDgdp
0.785***
(0.000)
-0.005***
(0.004)
---
NODADACgdp
---
-0.010**
(0.042)
---
Democracy
0.001
(0.576)
1.324
(0.185)
47.079
(0.347)
547.996***
(0.000)
334
0.001
(0.694)
1.272
(0.203)
46.156
(0.383)
420.894***
(0.000)
334
NODAgdp
AR(2)
Sargan-OIR
Wald
Observations
---0.007***
(0.001)
0.0005
(0.873)
1.366
(0.171)
45.410
(0.413)
648.423***
(0.000)
334
Corruption Perception Index
0.873***
0.870***
0.874***
(0.000)
(0.000)
(0.000)
0.005
----(0.108)
--0.015*
--(0.081)
----0.008
(0.130)
0.045*
0.045**
0.046*
(0.055)
(0.022)
(0.055)
1.812*
1.821*
1.799*
(0.069)
(0.068)
(0.072)
44.902
44.891
44.769
(0.966)
(0.966)
(0.967)
6836.4***
6437.15***
6876.4***
(0.000)
(0.000)
(0.000)
335
335
335
Panel B: Unrestricted regressions
Control of Corruption
Initial
NODAMDgdp
0.681***
(0.000)
-0.250***
(0.000)
-0.001***
(0.005)
---
NODADACgdp
---
-0.003
(0.133)
---
Democracy
0.021***
(0.000)
1.353
(0.175)
46.024
(0.388)
175.78***
(0.000)
334
0.023***
(0.000)
1.332
(0.182)
45.935
(0.392)
137.485***
(0.000)
334
Constant
NODAgdp
AR(2)
Sargan-OIR
Wald
Observations
Corruption Perception Index
0.668***
(0.000)
-0.267***
(0.000)
---
0.689***
(0.000)
-0.248***
(0.000)
-----0.002***
(0.005)
0.020***
(0.000)
1.347
(0.177)
45.431
(0.412)
172.401***
(0.000)
334
0.776***
(0.000)
0.597***
(0.001)
-0.003
(0.148)
---
0.776***
(0.000)
0.594***
(0.003)
---
0.780***
(0.000)
0.582***
(0.000)
-----
---
-0.008
(0.153)
---
0.026**
(0.016)
1.943*
(0.051)
44.569
(0.969)
377.631***
(0.000)
335
0.026**
(0.027)
1.933*
(0.053)
44.553
(0.969)
376.473***
(0.000)
335
-0.005
(0.144)
0.024**
(0.019)
1.949*
(0.051)
44.759
(0.967)
385.711***
(0.000)
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 data
(1996-2010) from 52 African countries we provide robust evidence of a positive aidcorruption 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.
2179
Economics Bulletin, 2012, Vol. 32 No. 3 pp. 2174-2180
References
Blundell, R. W and Bond, S. R. (1998) “Initial Conditions and Moment Restrictions in
Dynamic Panel Data Models” Journal of Econometrics, 87, 115-143.
Beck, T., Demirgüç-Kunt, A and Levine, R. (2003) “Law and finance: why does legal origin
matter?” Journal of Comparative Economics, 31, 653-675.
Billger, S. M and Goel, R. K. (2009) “Do existing corruption levels matter in controlling
corruption? cross-country quantile regression estimates” Journal of Development
Economics, 90, 299–305.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A. and Vishny, R. W. (1997) “Legal
Determinants of External Finance” Journal of Finance, 52, 1131-1150.
Okada, K. and Samreth, S. (2012), “The effect of foreign aid on corruption: A quantile
regression approach” Economic Letters, 11, 240-243.
2180
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