Determinants of corruption in Nigeria: evidence from various estimation techniques

Figures & data

Table 1. A Summary of empirical literature on the determinants of corruption.

Authors	Country/Region	Method/Model	Findings
Kolstad and Wiig (2015)	Cross-country (1946–2008)	OLS and IV	Democracy reduces corruption.
Busse and Gröning (2013)	129 countries (1984–2007)	System-GMM	Exports of natural resources encourage corruption, but income reduces corruption.
Kotera et al. (2012)	82 countries (1995–2008)	OLS, IV and GMM	Government size, democracy (average of political rights and civil liberties), government size and democracy interaction, and income level are negatively related to corruption.
Nur-Tegin and Czap (2012)	113–115 countries (2000–2009)	OLS and Leamer’s Extreme-Bounds Analysis	Corruption is less in unstable democracies compared to stable autocracies. In addition, income level is negatively related to the level of corruption.
Elbahnasawy and Revier (2012)	150 countries (1998–2005)	Hausman and Taylor which takes into account effects	Greater law enforcement, freedom of expression and accountability, high income have a negative effect on corruption.
Iwasaki and Suzuki (2012)	Transition economies (1996–2006)	Random effects and fixed effects	Greater rule of law, democracy and progress in marketisation are effective in the control corruption.
Arezki and Bruckner (2011)	30 oil exporting countries (1992–2005)	System-GMM and least squares	Oil rents do promote corruption, have a negative effect on political rights, and positive impact on civil liberties.
Evrensel (2010)	154 countries (1998–2000)	Cross-section OLS	Corruption is high in countries with high inflation rates, totalitarian political regimes, ineffective judicial systems, slow economic growth and low levels of education.
Goel and Nelson (2010)	100 countries (1995–1997, 1998–2000, 2001–2003)	Random effects	Income level, democracy (captured by the sum of political rights and civil liberties) and common law are negatively related to the level of corruption.
Billger and Goel (2009)	99 countries (2001–2003)	OLS and quantile regression	Greater democracy (captured by the sum of political rights and civil liberties) reduces the level of corruption.
Serra (2006)	62 countries (1990–1998)	Leamer’s Extreme-Bounds Analysis	High income and greater democracy (political rights and civil liberties) reduce corruption. But corruption is high in political unstable countries.
Gokcekus and Knörich (2006)	133 countries	OLS and IV	Openness, free press and income level have a reducing effect on the level of corruption.
Ades and Di Tella (1999)	Cross-country (1980–1983, 1989–1990)	OLS, TSLS and fixed effects	Rents are positively related with corruption, while income level and competition reduce corruption.

Note: OLS = Ordinary Least Squares; IV = Instrument Variables; GMM = Generalized Method of Moments; and TSLS = Two Stage Least Squares.

Table 2. Results of unit root tests.

	ADF		PP
Variable	Level	First difference	Level	First difference
COR	–1.9513	–2.7591*	–1.4136	–2.6725*
LGDPC	–2.0036	–4.0572***	–1.9554	–4.0442***
POL	–2.5187	–9.2254***	–2.3295	–18.6006***
LMLE	–1.1635	–6.4868***	–0.9502	–6.4225***
RENT	1.4268	–4.8525***	2.3342	–4.8523***
OPEN	–4.1420***	–6.1504***	–4.1420***	–17.8096***
CIV	–3.4884**	–4.3318***	–2.1687	–6.1409***

Asterisks *, ** and *** indicate a rejection of the null hypothesis of no unit root at 10%, 5%, and 1%, respectively. L denotes logarithm.

Source: Authors’ calculation.

Table 3. Results of bounds tests to cointegration.

Dependent Variable			Function			F-Statistic
COR			F(COR/LGDPC,POL,LMLE,RENT, OPEN, CIV)			18.4932***
Critical Values Bounds
	10%		5%		2.5%		1%
	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
	1.99	2.94	2.27	3.28	2.55	3.61	2.88	3.99

*** Denotes statistical significance at 1% level.

Source: Authors’ calculation.

Table 4. Results of ARDL model.

Panel A: Long-run Coefficients - Dependent variable is COR
Regressor	Coefficient	Standard Error	T-Ratio	Prob.
C	–39.7409	9.1996	–4.3198	0.0025
LGDPC	2.5255	0.5703	4.4283	0.0022
POL	–0.0922	0.0382	–2.4122	0.0424
LMLE	0.3976	0.0877	4.5335	0.0019
RENT	–24.3326	4.9896	–4.8754	0.0012
CIV	0.0846	0.0484	1.7463	0.1189
OPEN	3.2554	0.9751	3.3385	0.0103
Panel B: Short-run Coefficients - Dependent variable is ΔCOR
Regressor	Coefficient	Standard Error	T-Ratio	Prob.
ΔCOR–1	0.6113	0.0571	10.7022	0.0000
ΔLGDPC	1.4052	0.1344	10.4543	0.0000
ΔLGDPC–1	–1.7891	0.1895	–9.4391	0.0000
ΔPOL	–0.0469	0.0107	–4.3811	0.0023
ΔLMLE	–0.2727	0.0323	–8.4383	0.0000
ΔLMLE–1	–0.3110	0.0329	–9.4277	0.0000
ΔRENT	–2.9383	1.2609	–2.3301	0.0481
ΔRENT–1	15.860	1.8408	8.6158	0.0000
ΔCIV	0.0883	0.0121	7.2901	0.0001
ΔCIV–1	0.0708	0.0134	5.2787	0.0007
ΔOPEN	0.3832	0.1335	2.8692	0.0209
ΔOPEN–1	–1.2499	0.1373	–9.0986	0.0000
ECM–1	–0.8141	0.0488	–16.6553	0.0000
R^2 0.9725

Δ is the first difference operator. L denotes logarithm.

Source: Authors’ calculation.

Table 5. ARDL diagnostic tests.

LM Test Statistic	Results
Serial Correlation: χ^2	4.8476 [0.0559]
Normality (Jarque-Bera)	0.7738 [0.6791]
Heteroscedasticity: χ^2	3.0977 [0.0529]

Probability values are in brackets.

Source: Authors’ calculation.

Figure 1. Cumulative sum of recursive residuals plots. Source: Authors’ calculation.

Figure 2. Cumulative sum of squares of recursive residuals plots. Source: Authors’ calculation.

Table 6. Results of FMOLS and CCR models.

Panel C: FMOLS - Dependent variable is COR
Regressor	Coefficient	Standard Error	T-Ratio	Prob.
C	–46.597	7.9388	–5.8696	0.0000
LGDPC	3.0426	0.4849	6.2741	0.0000
POL	–0.1286	0.0526	–2.4420	0.0231
LMLE	0.4475	0.0802	5.5767	0.0000
RENT	–17.641	4.8194	–3.6604	0.0014
CIV	0.1252	0.0582	2.1516	0.0427
OPEN	0.7483	0.6994	1.0698	0.2963
R^2	0.8083
Panel D: CCR - Dependent variable is COR
Regressor	Coefficient	Standard Error	T-Ratio	Prob.
C	–48.923	10.567	–4.6297	0.0001
LGDPC	3.1703	0.6398	4.9545	0.0001
POL	–0.1230	0.0640	–1.9202	0.0679
LMLE	0.4706	0.1067	4.4070	0.0002
RENT	–19.545	6.3834	–3.0619	0.0057
CIV	0.1414	0.0705	2.0066	0.0572
OPEN	1.0539	1.0876	0.9690	0.3431
R^2 0.8038

L denotes logarithm.

Source: Authors’ calculation.

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