The impact of corruption on economic growth in developing countries and a comparative analysis of corruption measurement indicators

Figures & data

Table 1. Studies that find positive/negative /no relation with alternative indicators on corruption-economic growth models

Author	Countries	Period	Main objective	Corruption	Major Findings
Mo, 2001	54 Countries	1970–1985	The impact of corruption on the growth and the transmission channels	CPI	A 1% increase in the corruption level reduces the growth rate by about 0.72%.
Li et al., 2000	46 Countries	1980–1992	Corruption and how it affects income destitution and economic growth	ICRG	Corruption retard economic growth. But the effect is less pronounced than the one found in Mauro (1995).
Gupta et al., 2002	38 Countries	1980–1997	The effect of corruption on income, income inequality, and poverty	ICRG	A one-sd increase in the growth rate of corruption reduces income growth by 4.7 %s per year.
Méon & Sekkat, 2005.	63–71 Countries	1970–1998	The relationship between the impact of corruption on growth and investment	CPI, CCI	Corruption harms growth. These impacts are different depending on the quality of governance.
Méndez & Sepúlveda, 2006.	40–85 Developing Countries	1960–2000	Corruption, growth, and political regimes	ICRG, CPI	Corruption benefits economic growth at low levels of political freedom and is detrimental at high.
Aidt et al., 2008	120 Countries	1970–2000	Governance regimes, corruption, and growth.	CPI, CCI	Corruption improves efficiency by allowing individuals to circumvent the worst institutional deficiencies.
Mocan, 2008	49 Countries	1975–1995	Does corruption have a direct impact on growth?	CPI, CCI, ICRG	The corruption indexes were not significant determinants of growth.
Anokhin & Schulze, 2009	64 Countries	1996–2002	Entrepreneurship, innovation, corruption, and productivity	CCI	Better control of corruption is associated with rising productivity through innovation and entrepreneurship.
Kunieda et al., 2014	109 Countries	1985–2009	Corruption, capital account liberalisation, and economic growth	ICRG	Government corruption and financial openness significantly and negatively impact economic growth.
Menard & Weill, 2016	71 Developing Countries	1996–2009	Understanding the link between aid and corruption through economic growth	CCI	There is no significant causal relationship between the variables.
Cieślik & Goczek, 2018	142 Countries	1994–2014	Control of corruption, international investment, and economic growth.	CPI, ICRG	The empirical results confirm that corruption hinders growth by limiting investment.
Shittu et al., 2018	5 SSA Developing Countries	1990–2015	The nexus between external debt, corruption and economic growth	ICRG	Corruption in these countries rather increases the level of production.
Chakravorty, 2019	91–98 Countries	2000–2011	The effect of financial development on renewable energy consumption	ICRG, CPI	CPI, in some cases significant and negative correlated with economic growth, ICRG insignificant.
Gründler & Potrafke, 2019	175 Countries	2012–2018	The nexus between corruption and economic growth	CPI, ICRG	The effect of corruption on growth is that real pc GDP decreased by 17% when it increased by one sd.
Song et al., 2020	142 Countries	2002–2016	Corruption, economic growth, and financial development	CCI	Corruption deteriorates economic growth and financial development.
Cao et al., 2021	111 Countries	2005–2018	A Sentiment-Enhanced CPI and the correlation with economic growth	SE-CPI	The SECPI is negatively correlated with the business environment, institutional quality, and econ. growth.
Malanski & Póvoa, 2021	30 Developing Countries	2000–2017	The effects of corruption on growth for different levels of economic freedom	CPI	Corruption damages countries with greater ec freedom in LA and Asia and favours less liberal countries.

Table 2. Categorisation of the processes of corruption measurement

	FIRST-GENERATION INDICATORS	SECOND-GENERATION INDICATORS	THIRD-GENERATION INDICATORS
MEASUREMENT CHARACTERISTICS	Local investigations Individual domains Nonmeasurable and noncomparable sizes	General surveys Wide range of countries Averages or weighted averages (aggregate indicators)	A) Field surveys, individual domains, Analytical measurements of financial flows, etc. B) Media surveys
INDICATOR NAMES	—	CPI, CCI (part of WGI), ICRG, GCB, and NIS	Α) DAC index, PEFA, BEEPS, PETS, QSDS, and BPI Β) CRI, Corruption Conviction Index and NIC
PERIOD	1950–1990	1990–	2001–
BASIC AUTHORS	Wraith and Simpkins (1963), Leys (1965), Scott (1969), Heidenheimer et al. (1989)	Kaufmann et al. (1999), Lambsdorff (2002), Treisman (2000), Kaufmann and Kraay (2007)	Reinikka and Svensson (2006), Seligson (2006), Olken (2007), Hlatshwayo et al. (2018), Dincer (2020)

Note: CPI: Corruption Perceptions Index, CCI: Control of Corruption Index, WGI: World Governance Index, ICRG: International Country Risk Guide, GCB: Global Corruption Barometer, NIS: The National Integrity System, DAC: Development Assistance Committee index, PEFA: Public Expenditure and Financial Accountability, BEEPS: Business Environment and Enterprise Performance Survey, PETS: Public Expenditure Tracking Surveys, QSDS: Quantitative Service Delivery Survey, BPI: Bribe Payers Index, CRI: Corruption Reflection Index, NIC: News flow Indices of Corruption.

Table 3. Variables, symbols, period, price scale, sources, and literature

Symbols	Variables	Time	Data Sources	Literature
LGDP	GDP per capita at fixed $US prices, 2011 (PPP)	2012–2018	World Bank, 2019	Aidt, 2009; Cieślik & Goczek, 2018
CPI	Corruption Perception Index	2012–2018	Transparency International	Gründler & Potrafke, 2019
CCI	Control of Corruption Index	2012–2018	World Bank	Menard & Weill, 2016
ICRG	International Country Risk Guide (Corruption Index)	2012–2018 (0 − 6)	Transparency International	Tanzi & Davoodi, 1998
INVT	Total Investments (% GDP)	2012–2018	World Bank, Gl. Economy	Mauro, 1995; Méon & Sekkat, 2005
FDI	Foreign Direct Investment, inflows (% GDP)	2012–2018	World Bank UNCTAD	Anokhin & Schulze, 2009
POP	Population growth (annual %)	2012–2018	World Bank	Barro & Sala-i-Martin, 2004
SEDU	Percentage of citizens who participated in Sec Education	2012–2018	Global Economy	Méndez & Sepúlveda, 2006
GEXP	General government final cons. expenditure (% GDP)	2012–2018	World Bank	Easterly & Rebelo, 1993
TROP	Total exports and imports (% GDP), at constant prices	2012–2018	Global Economy	Méon & Sekkat, 2005; Chakravorty, 2019
BRM	Broad Money (M3/GDP)	2012–2018	WB, IMF, OECD	Li et al., 2000; Song et al., 2020

Table 4. Minimum, maximum, averages, and standard deviations of the variables

Variables	Observations	Minimum	Maximum	Average	Standard Deviation
GDP pc, PPP	581 (83 countries)	706,37	28.176,40	8.766,98	6.319,60
LGDP	581 (83 countries)	6,56	10,25	8,73	0,92
CPI	581 (83 countries)	35	89	67,06	9,09
CCI	581 (83 countries)	31,33	83,27	62,51	9,45
ICRG	581 (83 countries)	2,0	5,5	4,01	0,56
FDI	581 (83 countries)	−37,16	84,89	3,91	6,71
POP	581 (83 countries)	−1,79	6,57	1,71	1,15
SEDU	581 (83 countries)	10,00	132,82	69,78	26,65
INVT	581 (83 countries)	5,89	47,08	23,32	7,28
TROP	581 (83 countries)	19,10	208,31	71,42	31,39
BRM	581 (83 countries)	10,75	264,39	54,52	39,92
GEXP	581 (83 countries)	4,40	182,18	16,27	14,84

Table 5. Long-term effects of augmented Solow and endogenous models with fixed effect AR (1) dynamic estimation method

Dependent Variable	Logarithm of GDP per capita: LGDP 581 observations 83 countries
Corruption	CPI			CCI			ICRG
Model	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9
CPI	−0.23293** (0.038)	−0.21854* (0.052)	−0.19592* (0.078)
CCI				−0.10594 (0.417)	−0.07846 (0.547)	−0.05782 (0.652)
ICRG							−1.81072 (0.170)	−2.08848 (0.112)	−2.24973* (0.083)
POP		−2.60624** (0.013)	−2.37028** (0.022)		−2.49238** (0.018)	−2.26099** (0.030)		−2.60938** (0.013)	−2.38609** (0.021)
SEDU		0.10692 (0.220)	0.12725 (0.140)		0.12494 (0.150)	0.14179* (0.097)		0.13434 (0.125)	0.15013* (0.082)
INVT		0.17469** (0.036)	0.19201** (0.020)		0.17785** (0.033)	0.19506** (0.018)		0.18269** (0.030)	0.19782** (0.017)
FDI		0.0141 (0.781)	0.01218 (0.811)		0.01598 (0.753)	0.01194 (0.814)		0.01947 (0.706)	0.01421 (0.783)
BRM			0.06466* (0.086)			0.06143 (0.103)			0.05855 (0.121)
TROP			−0.04438 (0.197)			−0.05082 (0.137)			−0.05535 (0.110)
GEXP			−0.11755** (0.024)			−0.12221** (0.019)			−0.12097* (0.083)
$R^2$	0.1265	0.1385	0.3784	0.1322	0.1928	0.4691	0.0427	0.1813	0.5966

*, ** and *** mean the significance level of 10%, 5%, and 1%, respectively, the numbers in parentheses show the p values following the normal distribution as T and N tend to infinity. Variables are the same as in Table 3. The reported coefficient on all variables is multiplied by 100. Columns 1–3 present results using the CPI indicator. Columns 4–6 present results using the CCI indicator. Columns 7–9 present results using the ICRG indicator. It used the autocorrelation of residuals method. We control for a complete set of country and year fixed effects in all specifications. Sources: World Bank, IMF, Global Economy, OECD, UNCTAD, Transparency International, and authors’ calculations.

Table 6. Long-term effects of augmented Solow and endogenous models with FMOLS estimation method

Dependent Variable	Logarithm of GDP per capita: LGDP 581 observations 83 countries
Corruption	CPI			CCI			ICRG
Model	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9
CPI	−4.76937*** (0.000)	−0.73984* (0.089)	−1.23233*** (0.006)
CCI				−4.4633*** (0.000)	−0.3428 (0.421)	−0.61251 (0.202)
ICRG							−4.4587*** (0.001)	−8.05609 (0.370)	−8.0918 (0.348)
POP		−2.5318 (0.524)	−9.27229** (0.024)		−1.81291 (0.649)	−7.23347 (0.105)		−5.15419 (0.328)	−9.33002* (0.065)
SEDU		2.79798*** (0.000)	2.64465*** (0.000)		2.93131*** (0.000)	2.71806*** (0.000)		2.88138*** (0.000)	2.65442*** (0.000)
INVT		0.69893 (0.159)	1.25797** (0.014)		0.43214 (0.389)	1.18041** (0.036)		0.29079 (0.659)	0.64163 (0.309)
FDI		0.69418 (0.194)	1.18685** (0.042)		0.27458 (0.607)	−0.46264 (0.467)		0.09906 (0.889)	0.37128 (0.606)
BRM			0.45376*** (0.000)			0.50907*** (0.000)			0.3669*** (0.003)
TROP			−0.50828*** (0.000)			−0.4945*** (0.000)			−0.25639 (0.103)
GEXP			−0.75458*** (0.003)			−0.53848* (0.053)			−0.1364 (0.664)
$R^2$	0.1007	0.4097	0.1418	0.0469	0.3348	0.2450	0.0606	0.6070	0.5618

*, ** and *** mean the significance level of 10%, 5%, and 1%, respectively, the numbers in parentheses show the p values following the normal distribution as T and N tend to infinity. Variables are the same as in Table 3. The reported coefficient on all variables is multiplied by 100. Columns 1–3 present results using the CPI indicator. Columns 4–6 present results using the CCI indicator. Columns 7–9 present results using the ICRG indicator. In FMOLS, lags 2.00 are used, and the linear and quadratic eqtrend effect is controlled (2). Sources: World Bank, IMF, Global Economy, OECD, UNCTAD, Transparency International, and authors’ calculations.

Table 7. Effects of corruption on economic growth with AR (1) and FMOLS (dynamic) estimation method, using CPI Corruption indicator in five regions according to the world bank’s classification

Dependent Variable	Logarithm of GDP per capita: LGDP 581 observations 83 Countries
Regions, Count Observations	ECA, 77 obs, 11 countries Europe and Central Asia		LAC, 133 obs, 19 countries Latin America and Caribbean		EAP, 91 obs, 13 countries East Asia and the Pacific		MENA, 70 obs, 10 countries Middle East and North Africa		SSA, 210 obs, 30 countries Sub Saharian Africa
Model	Model 1 AR (1)	Model 2 FM-OLS	Model 3 AR (1)	Model 4 FM-OLS	Model 5 AR (1)	Model 6 FM-OLS	Model 7 AR (1)	Model 8 FM-OLS	Model 9 AR (1)	Model 10 FM-OLS
CPI	−0.21643** (0.030)	−0.39877*** (0.000)	0.7501* (0.061)	1.94965*** (0.001)	−0.46877 (0.132)	−6.48255*** (0.000)	0.52162 (0.293)	4.09648 (0.153)	−0.3942** (0.026)	−1.61215*** (0.001)
POP	24.94453*** (0.000)	9.99586** (0.000)	1.75411*** (0.000)	0.91806 (0.191)	−4.779677*** (0.000)	−4.68768*** (0.000)	−1.36059** (0.022)	−1.682251*** (0.000)	−1.40004 (0.702)	−1.062095 (0.271)
SEDU	0.7945** (0.017)	8.09192*** (0.000)	0.16564 (0.336)	2.18875*** (0.000)	0.30045 (0.790)	0.90513*** (0.000)	0.20015** (0.035)	2.90117*** (0.000)	0.18325** (0.022)	1.91346*** (0.000)
INVT	0.96602*** (0.002)	4.24203*** (0.001)	0.52186*** (0.000)	1.38896** (0.022)	0.4954* (0.076)	8.12644* (0.086)	0.21195 (0.662)	1.36586*** (0.000)	0.00940 (0.923)	2.39979*** (0.000)
FDI	−0.28346 (0.231)	−3.19239*** (0.002)	0.17638 (0.409)	1.14075 (0.524)	−0.11009 (0.880)	−1.352257 (0.153)	0.01212 (0.992)	1.313742*** (0.000)	0.03239 (0.589)	2.07218*** (0.000)
$R^2$	0.4764	0.5525	0.6004	0.1437	0.5124	0.8080	0.5376	0.4932	0.3534	0.1856
*, ** and *** mean the significance level of 10%, 5%, and 1%, respectively, the numbers in parentheses show the p values. Al abbreviations and sources are the same as in Table 3 with the authors’ calculations. In FMOLS, lags 2.00 are used, and the linear and quadratic eqtrend effect is controlled (2). The reported coefficient on all variables is multiplied by 100

Figure 1. Dispersion chart between countries for Corruption Perceptions Index–Bribe Payers Index for 2011. Source: Transparency International (www.transparency.org).

Table

	Second Generation Indexes	Third Generation Indexes
Advantages	They cover a wide range of countries They are a collection of several datasets and individual indicators They allow for calculating marginal errors and econometric calculations (Kaufmann & Kraay, 2007).	They measure the facts and the experiences They use plenty of data They measure many different aspects of the phenomenon
Disadvantages	They measure the perceptions As a measurement of a social and hidden phenomenon, there are errors They measure different types of corruption and consist of different sources for each country There is a lack of systematic anti-corruption reaction processes based on perceptual indicators	They are field research in individual sectors (local) In a lot of cases, they don’t measure corruption, but the judiciary performance, or the freedom of the media they are volatile in corruption crises they are very costly methods

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