The Impact of Corruption on Economic Growth: A Nonlinear Evidence

Abstract

Several crosscountry studies have found that corruption slows growth, but these findings 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 2021 period. Our findings 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, defined by the point of reversal of the curve of the marginal corruption effect on growth, could have advantages for economic growth.

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
Chapter
The Impact of Corruption on
Economic Growth: A Nonlinear
Evidence
Mohamed AliTrabelsi
Abstract
On basis of the lubricating effect hypothesis of corruption (grease-the-wheels
hypothesis), the impact of corruption on growth seems ambiguous. Therefore, the
question that arises is to what extent corruption can be tolerated and at what threshold
it has detrimental effect on an economy. This chapter investigates the impact of cor-
ruption on economic growth by testing the hypothesis that the relationship between
these two variables is nonlinear, and we assess whether the belief that corruption has
detrimental effects on the economy is always true. In this chapter, a panel data analy-
sis has been used to examine 65 countries over the 1987–2021 period. Our findings 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, defined by the
point of reversal of the curve of the marginal corruption effect on growth, could have
advantages for economic growth.
Keywords: corruption, economic growth, panel data: PCSE estimator
. Introduction
Empirical literature in the field has consistently reported a negative correlation
between economic growth and corruption. These studies have shown that devel-
oped countries are known by low corruption levels and a relatively high growth
rate [1], and by contrast, most developing countries are known by high poverty and
corruption levels [2, 3].
The novelty of the empirical contribution is that we estimate a nonlinear growth
model that allows for threshold effects. To this end, we will use the method proposed
by Beck and Katz [4], 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 chapter is structured as follows: Section 1 presents a review of both the
theoretical and empirical literature; Section 2 presents the research methodology and
the main results followed by a discussion of the findings in the final section.

Corruption - New Insights

. 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 first assumes that corruption “lubricates the economic cycle” or “greases the
economic wheel” and produces the most efficient economies [5–10]. In contrast, the
second theory blames corruption and sees it as a factor that slows down economic
activity [11–14].
Mauro [15] detects a weak statistical significance between corruption and
economic growth. However, this significance disappears once investment rate is
introduced in the model. Mo [13] finds that corruption negatively affects economic
growth. However, the additional introduction of variables such as investment to GDP
ratio, political stability, and human capital weakens or eliminates the significance of
this negative impact.
Aidt etal. [16] 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. On the other hand, Méndez
and Sepúlveda [17] find that high-quality political institutions result in corruption
being harmful to growth. In accord with Méndez and Sepúlveda [17], Heckelman
and Powell [8] find that at the lowest levels of democracy, corruption is harmful to
growth but becomes less harmful and eventually beneficial as the level of democracy
increases.
Méon and Weill [10] emphasize the hypothesis of the lubricating effect of cor-
ruption by studying the interaction between institutional quality, corruption, and
production efficiency, thereby validating the hypothesis that corruption may have a
positive effect on economic activities. In the same context, Kato and Sato [18] provide
evidence supporting the “greasing the wheels” hypothesis and argue that corruption
enhances economic growth.
Mushfiq [14] tests corruption-growth relationship in a nonlinear framework. He
shows that corruption increases growth even at a higher level of corruption. In the
same context, Allan and Roland [19] use linear and nonlinear panel methods over the
period 1998–2009 for determining the causal relationship between economic growth
and corruption in 42 developing countries. Moreover, Aghion etal. [20] show that
corruption affects the marginal effect of taxation on growth.
Huang [21] examines the causal relationship between corruption and economic
development in 13 Asia-Pacific countries and finds that South Korea and China are
experiencing economic advancement despite high-corruption levels.
Trabelsi and Trabelsi [22] 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, defined 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 confirming 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 do
not test whether there is a growth-enhancing or growth-reducing level of corrup-
tion, and not one study thoroughly identified the corruption level that will allow an
optimal growth.


The Impact of Corruption on Economic Growth: A Nonlinear Evidence
DOI: http://dx.doi.org/10.5772/intechopen.108876
. Research methodology
. Description of data
Corruption is not the only factor that affects economic growth [23–25]. Other
control variables are also relevant [26]. According to theory and on the basis of
arguments cited in the literature, we propose economic growth depends mainly on
investment, inflation, and trade openness.
The study is based on a panel data set over the period 1987–2021 for 65 countries
taken from the World Development Indicators (Growth rate, Foreign direct invest-
ment, Inflation & 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 appears in Table . It shows
that average economic growth is 3.63% with an average corruption index of 3.35.
Where:
Growth: Annual growth rate of GDP per capita.
Fdi: Percent of Foreign direct investment per GDP.
Inf: Consumer price index inflation (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.
. Empirical model
Empirical studies generally opt for the nonlinear approach to study the impact
of corruption on economic growth (Méon and Sekkat [11]; [14, 16, 17]; Allan and
Roland [19]; [27–29]). 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 (Figure ),
using the fitted Kernel curve, illustrates and confirms the hypothesis that the relation-
ship between corruption and economic growth (fitted values) is nonlinear.
Var i a b l e Obs Mean Std. Dev. Min Max
growth 2275 3.631514 3.694122 −17.14604 21.82889
Fdi 2275 2.792351 4.098536 −12.20843 33.56602
Inf 2275 5.787994 7.268143 −11.68611 59.46156
Trade 2275 81.67821 51.23418 10.74832 439.6567
Icrg 2275 3.351098 1.462316 0 6
Table 1.
Descriptive statistics.

Corruption - New Insights

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,…,2021) denote index country and time, respectively.
αβ γ µ δ λ ε
=++ ++ + +
2
it i it it it it it it
Growth Inf Trad Fdi Icrg Icrg (1)
Past studies have used a panel of 5-year averages and the system GMM estimator
because this choice reduces, in general, short run fluctuations and resolves the endo-
geneity 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 and Satarov [30] and Paldam
[31] 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 [4] 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 .
It can be seen that corruption negatively affects (−1.0853466) economic growth
unlike the square coefficient of corruption, which positively affects (0.1982614)
economic growth. The significance of Icrg2 coefficient confirms the nonlinearity
of this model and shows the presence of a threshold above which there will be a
change of sign.
Figure 1.
Growth and corruption: countries distribution.


The Impact of Corruption on Economic Growth: A Nonlinear Evidence
DOI: http://dx.doi.org/10.5772/intechopen.108876
. Determining the threshold
We will determine the governance level that allows for achieving maximum
growth. The resulting model is:
= + −+ +
2
Growth 2.153 – 0.0391 0.011Trad 1.08Icrg 0.198Icrg 0.062FdiInf
(2)
In deriving growth through governance, we get:
∂=−+ =
∂1.08 0.396Icrg 0
Grow
Icrg (3)
Relationship (3) shows that an optimum is achieved by Icrg=1.08/0.396=2.73.
This indicates that up to a corruption index of 2.73, the trend of the bell-shaped curve
(Figure ) increases showing that there is a positive relationship between corruption
and economic growth.
This bell-shaped curve (Figure ) is interpreted by the fact that corruption,
through tax evasion, has two types of effects in economics.
First, it offers households a tax that can be consumed or invested, and therefore, it
could improve growth up to a certain threshold. This optimal threshold represents the
reversal point of the curve otherwise the country can be found in an underdevelop-
ment trap like several countries that are immersed in corruption. This corruption, if
significant, will reduce state resources because of productive public spending, which
will lead to a loss in economic growth that sooner or later will lead to an uprising call-
ing for establishing democratic principles and good governance.
These results indicate that low of corruption (Icrg <2) negatively affects
economic growth. This result disappears in the presence of corruption (Icrg >3).
However, for an average corruption of (2≤Icrg ≤3), we will be at an optimum level
of growth (Figure ).
This result may surprise those who advocate lack 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.
Growth Coef. Std. Err. tP > | t | [ Conf. Interval]
Fdi 0.0618651 0.0238888 2.59* 0.008 0.0150430 0.1086871
Inf −0.0392218 0.0128872 −3.04* 0.003 −0.0644807 −0.0139629
Trade 0.0112539 0.0022877 4.92* 0.000 0.0067700 0.0157378
Icrg −1.0853466 0.3167777 −3.43* 0.001 −1.7062309 −0.4644623
Icrg 0.1982614 0.0464706 4.27* 0.000 0.1071790 0.2893438
Cons 2.153168 0.5132159 4.19* 0.000 1.1472648 3.1590712
*test statistic is significant at the  level.
Table 2.
Panels corrected standard errors (PCSE).

Corruption - New Insights

The results obtained are derived from static panel model, which has some
shortcomings. One of the reasons is not to take into account growth’s lag operator.
Indeed, economic growth is attributed to the results obtained a year earlier, and
therefore, it is desirable to include this variable in the model. Therefore, a dynamic
panel is needed.
. The dynamic model
The dynamic panel model we propose is defined as follows:
αρ β γ µ δ λ ε
−
=+ ++ + + + +
2
1
it i it it it it it it it
Growth Growth Inf Trad Fdi Icrg Icrg (4)
Where Growthit-1 represents per capita lagged GDP growth rate.
The results of the estimation are reported in Table , which shows that H0
hypothesis of the validity of the instruments is not rejected (the probability of Sargan
statistics exceeds 5%, which means that instruments are in all exogenous). Similarly,
there is no order 2 serial autocorrelation (probability of Arellano & Bond AR test (2)
is greater than 5%). This allows us to assert that the GMM system model is appropri-
ate and specifies well the instruments, with no heteroscedasticity or autocorrelation
problems.
This method is more robust than the previous one. Table  confirms our hypoth-
esis that corruption negatively affects growth (−1.86). However, square corruption
positively affects growth (0.33). The results obtained by the two methods (static and
dynamic) confirm the positive impact of investment on growth.
The estimated model is written as follows:
−
=+ + −+
++
2
1
Growth 2.885 0.0505 0.0419Trad 1.865Icrg 0.332Icrg
0.0213 0.098Growth
Inf
Fdi (5)
Figure 2.
Bell-shaped curve of growth through governance.


The Impact of Corruption on Economic Growth: A Nonlinear Evidence
DOI: http://dx.doi.org/10.5772/intechopen.108876
By analogy to Section 2.3, determining the threshold effect shows that an optimum
is achieved by Icrg=1.865/0.664=2.81. This value confirms our hypothesis on the
relevance of moderate corruption to achieve an optimal growth value.
. Results and discussion
The concave function (Figures  and ) may be interpreted in the following way.
Corruption, which 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 [32, 33]. 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 significant, will reduce state resources because of produc-
tive 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.
. 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
Coef. Std. Error zP > z [ Conf. Interval]
Growth L 0.0987477 0.0229037 4.31* 0.000 0.0538573 0.143638
Inf 0.0505543 0.0260105 1.94** 0.052 −0.0004253 0.1015339
Fdi 0.0212885 0.0311189 0.68** 0.049 −0.0397035 0.0822805
Trade 0.041935 0.0092438 4.54* 0.000 0.0238175 0.0600525
Icrg −1.864927 0.5426194 −3.44* 0.001 −2.928442 −0.8014126
Icrg0.3316179 0.0813414 3.20* 0.001 0.1011917 0.4200442
_Co n s 2.885563 1.041519 2.77* 0.006 0.844223 4.926903
AR(2) Sargan Test
(1.0000)*** (0.1428)***
AR (): Arellano and Bond test of null of zero second-order serial correlation, distributed N (, ) under null. Sargan
test: is a statistical test used to check for over-identifying restrictions in a statistical model. t-statistics are displayed in
parentheses under the coefficient estimates.*test statistic is significant at the  level.
**test statistic is significant at the  level and.
***the numbers in parentheses are p-values.
Table 3.
Estimation of the model by GMM.

Corruption - New Insights

Author details
Mohamed AliTrabelsi
Faculty of Economic Sciences and Management ofTunis, University of Tunis El
Manar, Tunisia
*Address all correspondence to: daly1704@yahoo.fr
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
nonlinear relationship between these two variables. This nonlinear result shows 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 ben-
eficial to economic growth. This confirms some theories that assume that corruption
“lubricates the economic cycle” and produces the most efficient 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.
Statements and declarations
On behalf of myself as the alone author, I state that there is no conflict of interest
and I declare that no funds, grants, or other support was received during the prepara-
tion of manuscript. Data are available from the author on reasonable request.
Additional information
Unreviewed version of the chapter published on Research Square preprint server.
JEL: B23, C51, D73, O47
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of
the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited.

The Impact of Corruption on Economic Growth: A Nonlinear Evidence
DOI: http://dx.doi.org/10.5772/intechopen.108876

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