Available via license: CC BY 4.0
Content may be subject to copyright.
Page 1/10
The Impact of Corruption On Economic Growth: A
Nonlinear Evidence
Mohamed Ali Trabelsi ( daly1704@yahoo.fr )
University of Tunis El Manar https://orcid.org/0000-0003-2307-323X
Research
Keywords: Corruption, Economic Growth, Panel data, PCSE estimator.
Posted Date: November 29th, 2021
DOI: https://doi.org/10.21203/rs.3.rs-1021805/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Page 2/10
Abstract
Several cross-country studies have found that corruption slows growth, but these ndings are not
universally robust. Therefore, the questions to be addressed are to what extent corruption can be tolerated
and at what threshold it has a detrimental effect on an economy.
This article investigates the impact of corruption on economic growth by testing the hypothesis that the
relationship between these two variables is nonlinear. In this article, a panel data analysis has been used
to examine 65 countries over the 1987 to 2011 period.
Our ndings are that corruption can have a positive effect on growth. The results indicate that beyond an
optimal threshold, both high and low corruption levels can decrease economic growth. Under this optimal
threshold, a moderate level of corruption, dened by the point of reversal of the curve of the marginal
corruption effect on growth, could have advantages for economic growth.
JEL: B23, C51, D73, O47.
Introduction
Empirical literature in the eld has consistently reported a negative correlation between economic growth
and corruption. These studies have shown that developed countries are known by low corruption levels
and a relatively high growth rate (Cooper & al, 2006), and by contrast most developing countries are
known by high poverty and corruption levels (Chetwynd & al, 2003; Umbreen and Saadat, 2015).
The novelty of the empirical contribution is that we estimate a non-linear growth model that allows for
threshold effects. To this end, we will use the method proposed byBeck and Katz (1995) who suggested
estimating linear models of time-series-cross-section (TSCS) data by ordinary least squares (OLS). For
this, they proposed the panel-corrected standard errors model (PCSE).
The paper is structured as follows: section 1 presents a review of both the theoretical and empirical
literature; section 2 presents the econometric model and the main results followed by a discussion of the
ndings in the nal section.
1. Literature Review
The theoretical and empirical literature on corruption has generated a rich debate over the last 40 years.
This literature can be summarized in two opposing theories. The rst assumes that corruption "lubricates
the economic cycle" or "greases the economic wheel" and produces the most ecient
economies(Acemoglu and Verdier, 2000; Barreto, 2000; Egger and Winner, 2005; Méon and Weill, 2010;
Heckelman & Powell, 2010 and Johnson & al, 2014).In contrast, the second theory blames corruption and
sees it as a factor that slows down economic activity(Mo, 2001; Mironov, 2005; Méon and Sekkat, 2005
and Mushq, 2011).
Page 3/10
Mauro (1995) detects a weak statistical signicance between corruption and economic growth. However,
this signicance disappears once investment rate is introduced in the model. Mo (2001) nds that
corruption negatively affects economic growth. However, the additional introduction of variables like
investment to GDP ratio, political stability and human capital weakens or eliminates the signicance of
this negative impact.
Aidt et al. (2008) show that the impact of corruption on economic growth depends on institutional quality.
Moreover, they show that when political institutions are of low quality, corruption has little impact on
growth. In the other hand, Mendez & Sepúlveda (2006) nd that high quality political institutions result in
corruption being harmful to growth. In accord with Mendez & Sepúlveda, Heckelman & Powell (2010) nd
that at the lowest levels of democracy, corruption is harmful to growth but becomes less harmful and
eventually benecial as the level of democracy increases.
Méon and Weill (2010) emphasize the hypothesis of the lubricating effect of corruption by studying the
interaction between institutional quality, corruption and production eciency, thereby validating the
hypothesis that corruption may have a positive effect on economic activities.
Mushq (2011) tests corruption-growth relationship in a non-linear framework. He shows that corruption
increases growth even at a higher level of corruption. In the same context, Allan and Roland (2012) use
linear and non-linear panel methods over the period 1998 to 2009 for determining the causal relationship
between economic growth and corruption in 42 developing countries. Moreover, Aghion et al. (2016) show
that corruption affects the marginal effect of taxation on growth.
Trabelsi & Trabelsi (2020)show that beyond an optimal threshold, both high and low corruption levels
can decrease economic growth.Under this optimal threshold, a moderate level of corruption, dened by
the point of reversal of the curve of the marginal corruption effect on growth, could have advantages for
economic growth.
All these studies indicate that corruption may have either positive or negative effects on economic
growth, making the issue ambiguous and conrming the non-linearity of the relationship between
corruption and growth. However, one must ask to what extent can corruption be tolerated and from what
threshold would it become destructive to the economy. The questioning is motivated by the fact that
studies don’t test whether there is a growth-enhancing or growth-reducing level of corruption and not one
study thoroughly identied the corruption level that will allow an optimal growth.
2. Research Methodology
2.1. Description of data
Corruption is not the only factor that affects economic growth (Barro, 1991 and Brunetti, 1997,
Lambsdorff, 1999). Other control variables are also relevant (Fernando & al, 2016). According to theory
Page 4/10
and on the basis of arguments cited in the literature, we propose economic growth depends mainly on
investment, ination and trade openness.
The study is based on a panel data set over the period 1987-2011 for 65 countries taken from the World
Development Indicators (Growth rate,Foreign direct investment, Ination & Trade). The ICRG index has
been obtained from the Quality of Government Institute, the Transparency International and International
Country Risk Guide published by Political Risk Services group. It measures the risk involved in corruption
rather than the perceived level of corruption.
The descriptive analysis for the full set of 65 countries appear in Table 1. It shows that average economic
growth is 3.68% with an average corruption index of 3.32.
Table 1: Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max
growth 1625 3.686523 3.713523 -17.14604 21.82889
Fdi 1625 2.869559 4.119356 -12.20843 33.56602
Inf 1625 5.757924 7.254553 -11.68611 59.46156
Trade 1625 77.77811 50.94521 10.74832 439.6567
Icrg 1625 3.321103 1.441531 0 6
Where:
Growth: Annual growth rate of GDP per capita
Fdi:Percent ofForeign direct investment per GDP
Inf: Consumer price index ination (annual %)
Trad: Exports plus imports as share of GDP.
Icrg:International Country Risk Guide index of corruption, scaled 0-6. Higher values indicate lower
corruption
These results do not specify the dependency relationship between growth and corruption. To further
probe this dependency relationship, an econometric study of the relationship between growth and
corruption is necessary.
2.2. Empirical model
Empirical studies generally opt for the non-linear approach to study the impact of corruption on economic
growth (Méon and Sekkat (2005), Mendez & Sepúlveda , 2006; Aidt et al, 2008; Mushq, 2011; Allan &
Page 5/10
Roland, 2012; Eatzaz & al, 2012; Saha & Gounder, 2013 and Kolstad & Wiig, 2013). This is a quadratic
function based on the hypothesis that the impact of corruption on growth is not always negative and that
a moderate corruption level could have advantages for economic growth.
In order to verify this, a cross-sectional framework is used in which growth rate and the ICRG index are
observed only once for each country. The scatter plot (gure below), using the tted Kernel curve
illustrates and conrms the hypothesis that the relationship between corruption and economic growth
(tted values) is nonlinear. The curve is clearly increasing in the middle range of corruption and
decreasing where corruption is least and most.
Therefore, we propose the following quadratic model. Subscripts i (i=1,…..,65) and t (t=1987,….,2011)
denote index country and time, respectively.
Past studies have used a panel of 5-year averages and the system GMM estimator because this choice
reduces, in general, short run uctuations and resolves the endogeneity due to time invariant effects; but
this method will not address endogeneity due to the possible interactions between higher growth rates
and greater resources to combat corruption, or other time varying effects. Levin & Satarov (2000) and
Paldam (2002) have presented evidence for the existence of both types of endogeneities.
Recently, the empirical studies characterized by having repeated observations over time on some
countries are resolved by others models. In this study, we will follow the Beck and Katz (1995)
methodology who suggested estimating linear models of time-series-cross-section (TSCS) data by
ordinary least squares (OLS) and they proposed the panel-corrected standard errors (PCSE) estimator.
The results for GDP growth using the PCSE estimator are reported in Table 2.
It can be seen that corruption negatively affects (-0.9967357) economic growth unlike the square
coecient of corruption which positively affects (0.1793806) economic growth. The signicance of Icrg²
coecient conrms the non-linearity of this model and shows the presence of a threshold above which
there will be a change of sign.
Table 2:Panels Corrected Standard Errors (PCSE)
Page 6/10
Growth Coef. Std. Err. t P > | t | [95% Conf. Interval]
Fdi 0.0606709 0.0238888 2.54* 0.011 0.0138497 0.1074921
Inf -0.0321519 0.0128872 -2.49* 0.013 -0.0574104 -0.0068934
Trade 0.0093146 0.0022877 4.07* 0.000 0.0048308 0.0137984
Icrg -0.9967357 0.3167777 3.15* 0.002 0.3758627 1.617609
Icrg2 0.1793806 0.0464706 -3.86* 0.000 -0.2704613 -0.0883
Cons 2.002278 0.5132159 3.90* 0.000 0.9963933 3.008163
*: test-statistic is signicant at the 1% level.
3. Results And Discussion
The concave function (Figure) may be interpreted in the following way. Corruption, that facilitates tax
evasion, has two types of effects in economics. It offers households an opportunity of tax savings that
can be consumed or invested, as tax evasion leads to a transfer of public resources to private agents
(Tanzi & Davoodi, 2000 and Cerqueti & Coppier, 2011). This could improve growth up to a certain
threshold. The optimal threshold represents the reversal point of the curve; otherwise, the country may
suffer underdevelopment like several countries immersed in corruption.
This corruption, if signicant, will reduce state resources because of productive public spending which
will lead to a loss in economic growth, which sooner or later will lead to an uprising calling for
establishing democratic principles and good governance.
This result may surprise those who advocate the negative effects of corruption but it can be explained by
the fact that administrative delays resulting from absence of "bribes" paid in a corrupt economy may
dampen economic growth and reduce economic development.
4. Conclusion
The aim of this paper is to examine the impact of corruption on economic growth. The empirical literature
that reported a linear relationship between corruption and economic development failed to differentiate
between growth-enhancing and growth-reducing levels of corruption.
In our study, we have presented evidence that suggests the existence of hump-shaped relationship
between corruption and growth, which shows the existence of a non-linear relationship between these two
variables. This non-linear results show that growth increases at middle-corruption and decreases as
nations’achieve higher level of governance (low corruption). In other words, the results indicate that
higher or lower levels of corruption negatively affect growth. Minimum corruption can be benecial to
economic growth. This conrms some theories that assume that corruption "lubricates the economic
Page 7/10
cycle" and produces the most ecient economies. However, this lubricating effect has a threshold beyond
which it becomes a threat to economic growth. Conversely, lack of corruption may be a mechanism that
slows down growth.
Declarations
Ethics approval and consent to participate: not applicable
Consent for publication: not applicable
Availability of data and materials: yes
Competing interests: not applicable
Funding: not applicable
Authors' contributions:100%
Acknowledgements :not applicable
Authors' information:Professor & Head of Unit Research: Econometrics Applied to Finance, Faculty of
Economics and Management of Tunis, University of Tunis El Manar, Tunisia.
ORCID : https://orcid.org/0000-0003-2307-323X
References
1. Acemoglu D. and Verdier T. (2000). The Choice between Market Failures and Corruption,
The
American Economic Review,
90 (1), pp. 194-211.
2. Aghion P., Akcigit J. and Kerr W.R. (2016). Taxation, Corruption, and Growth,
National Bureau of
Economic Research
, NBER Working Papers: 21928.
3. Aidt T., Dutta J. and Sena V. (2008). Governance regimes, corruption and growth: Theory and
evidence,
Journal of Comparative Economics
, 36, pp. 195-220.
4. Allan S. W. and Roland C. (2012). Economic Growth and Corruption in Developing Economies:
Evidence from Linear and Non-Linear Panel Causality Tests, Journal of Business, Finance and
Economics in Emerging Economies, 7 (2), pp. 21-43.
5. Barreto R. A. (2000). Endogenous Corruption in a Neoclassical Growth Model,
EuropeanEconomic
Review
, 44 (1), pp. 35-60.
. Barro R.J (1991). Economic growth in a cross section of countries,
Quarterly Journal of Economics
,
106, pp. 407-443.
7. Beck N. and Katz J. (1995). What to do (and not to do) with Time-Serie Cross-Section Data,
American
Journal of Political Science
, 89 (3), pp. 634-647.
Page 8/10
. Brunetti A. (1997). Political Variables in Cross-Country Growth Analysis,
Journal of Economic Survey
,
11, pp. 163-190.
9. Cerqueti R. and Coppier R. (2011). Economic Growth, Corruption, Tax Evasion,
Economic Modelling
,
28, pp. 489-500.
10. Chetwynd E., Chetwynd F. and Spector B. (2003). Corruption and Poverty: A Review of Recent
Literature,
Management Systems International
, Final Report, Washington.
11. Cooper Drury A., Krieckhaus J. and Lusztig M. (2006). Corruption, Democracy and Economic Growth,
International Political Science Review
, 27 (2), pp.121-136.
12. Eatzaz A., Muhammad A.U and Muhammad I.A. (2012). Does Corruption Affect Economic Growth?
Latin American Journal of Economics
, 49 (2), pp. 277-305.
13. Egger P. and Winner H. (2005). Evidence on Corruption as an Incentive for Foreign Direct Investment,
European journal of political economy
, 21 (4), pp. 932-952.
14. Fernando D., Carlos D. and Marà a angeles C. (2016). Growth, Inequality and Corruption: Evidence
from Developing Countries,
Economics Bulletin
, 36 (3), pp. 1811-1820.
15. Heckelman J.C. and Powell B. (2010). Corruption and the institutional environment for growth,
Comparative Economic Studies
, 52, pp. 351-378.
1. Johnson N.D., Ruger W., Sorens J. and Yamarik S. (2014). Corruption, Regulation and Growth: an
empirical study of the United States,
Economics of Governance
, 15 (1), pp. 51-69.
17. Kolstad I. and Wiig A. (2013). Digging in the dirt? Extractive industry FDI and corruption,
Economics
of Governance
, 14 (4), pp. 369-383.
1. Lambsdorf f J.G. (1999). Corruption in Empirical Research - A Review,
Transparency International
Working Paper
, Berlin.
19. Levin M. and Satarov G.A. (2000). Corruption and institutions in Russia.
European Journal of
Political Economy,
16, pp. 113– 132.
20. Mauro P. (1995). Corruption and Growth,
Quarterly Journal of Economics
, 60 (3), pp. 681-712.
21. Méon P. G. and Sekkat.K. (2005). Does Corruption Grease or Sand the Wheels of Growth?
Public
Choice
, 122 (1), pp. 69-97.
22. Méon P. G. and L. Weill (2010). Is Corruption an Ecient Grease?
World Development
, 36 (3), pp. 244-
259.
23. Méndez F. and
Sepúlveda F. (2006).
Corruption, growth and political regimes: Cross country evidence,
European Journal of Political Economy
, 22, pp. 82-98.
24. Mironov M. (2005). Bad Corruption, Good Corruption and Growth.
University of Chicago
. Mo P. H.
(2001). Corruption and Economic Growth,
Journal of Comparative Economics
, 29, pp. 66-79.
25. Mushq S. (2011). Economic Growth with Endogenous Corruption: an Empirical study,
Public Choice
,
146, pp. 23-41.
2. Paldam M. (2002). The cross-country pattern of corruption: economics, culture and the seesaw
dynamics.
European Journal of Political Economy
, 18, pp. 215– 240.
Page 9/10
27. Saha S. and Gounder R. (2013).Corruption and economic development nexus: Variations across
income levels in a non-linear framework,
Economic Modelling
, 31, pp. 70-79.
2. Tanzi V. and Davoodi H.R. (2000). Corruption, Growth and Public Finances,
International Monetary
Fund working paper
.
29. Trabelsi M.A. and Trabelsi H. (2020). At what level of corruption does economic growth decrease?,
Journal of Financial Crime
, 28 (4), pp. 1317-1324.
30. Umbreen J. and Saadat F. (2015). Corruption Pervades Poverty: In Perspective of Developing
Countries,
Research Journal of South Asian Studies,
30 (1), pp. 175-187.
Figures
Figure 1
Page 10/10
The scatter plot (gure above), using the tted Kernel curve illustrates and conrms the hypothesis that
the relationship between corruption and economic growth (tted values) is nonlinear. The curve is clearly
increasing in the middle range of corruption and decreasing where corruption is least and most.
Therefore, we propose the following quadratic model. Subscripts i (i=1,…..,65) and t (t=1987,….,2011)
denote index country and time, respectively.
