Effects of Corruption on Economic Growth - Empirical Study of Asia Countries

Abstract

The paper is to analyze the impact of corruption on economic growth by using data of 19 Asian countries in the period of 2004-2015 with D-GMM data processing techniques and quantile regression. The study results show the corruption is a hindrance to economic growth of those Asian countries. In addition, economic growth is impacted by different levels of the corruption at different quantiles, specifically, at the quantile level from 0.1 and 0.5, corruption impacts positively on economic growth, or vice versa, from level of 0.75 and 0.90, it is negative. In addition, the result shows that institutional quality, democracy freedom and economic freedom play important roles in economic growth.

Full text

Imperial Journal of Interdisciplinary Research (IJIR)
Vol-3, Issue-7, 2017
ISSN: 2454-1362, http://www.onlinejournal.in
Imperial Journal of Interdisciplinary Research (IJIR) Page 791
Effects of Corruption on Economic Growth -
Empirical Study of Asia Countries
Nguyen Ngoc Thach1, Mai Binh Duong2, Tran Thi Kim Oanh3,
1Banking University Ho Chi Minh City, Vietnam.
2Van Lang University, Vietnam
3Ho Chi Minh College of Economics, Vietnam.
Abstract : The paper is to analyze the impact of
corruption on economic growth by using data of 19
Asian countries in the period of 2004-2015 with D-
GMM data processing techniques and quantile
regression. The study results show the corruption is
a hindrance to economic growth of those Asian
countries. In addition, economic growth is
impacted by different levels of the corruption at
different quantiles, specifically, at the quantile
level from 0.1 and 0.5, corruption impacts
positively on economic growth, or vice versa, from
level of 0.75 and 0.90, it is negative. In addition,
the result shows that institutional quality,
democracy freedom and economic freedom play
important roles in economic growth.
Keywords: corruption, institution, economic
growth, quantile regression.
1. Introduction
Recently, the anti-corruption issue is one of the top
priorities in the institutional reform agenda for
development in countries including Asia. For last
decades, many scientists and scholars have paid
their interests by their studies on evaluation to
effects on corruption in countries, especially with
the impact of corruption on economic growth.
However, to date, this matter is still in dabates in
terms of ethical and economic implications. Mauro
(1995) shows the impact of corruption results
negatively to the investment and therefore, it
causes negatively affects to the economic growth.
This also gains high agreement of Brunetti &
Weder (1998), Mo (2001), Choe et. al (2013).
However, with other researchers’s concepts,
corruption is not completely pointing to its
detrimental consequences but sometimes it causes
benificial to the growth. Bardhan (1997) illustrates
cases where corruption results to the promotion to
economic development in Europe and America.
Beck & Maher (1986) and Lien (1986) argue that
corruption induces a more efficient provision of
government services. Leff (1964), Huntington
(2006) and Leys (1965) also point out corruption
has a positive impact on economic growth by
minimizing obstacles from administrative
procedures, lackage of transparency of the system
juridical. From this perspective, corruption acts as a
lubricant that smoothes operations especially for a
bureaucracy paradigm and, hence, raises the
efficiency of an economy by reducing barriers to
investment and economic growth.
At the begining of the reform, corruption levels in
countries around the world including Asia have
developed with negative way, increased its scale
and and gain its diversification (Campos &
Pradhan, 2007). The root of this matter is supposed
in relation with low levels of democracy, limted
economic freedom, poor institutional quality. In
addition, the imposition of political power and the
influence of civil servants on socio-economic
activities are great, thus, the money is used as a
lubricant unavoidably. At the time, this lubricant is
also seen to have a positive effect on economic
efficiency because it enables the operation of the
bureaucratic governement and accelerates
economic growth through the "speed money”
mechanism (Aidt, 2009). Therefore, the purpose of
the study is to provide one more evidence
empirically of the impact of corruption on
economic growth on the both of positive of
negative basis by using D-GMM processing. In
addition, the study also uses a quantile regression
to understand the effect of corruption on economic
growth in different quantiles. As a result, the
recommendations are made accordingly.
2. Related literature review
2.1 Corruption overview
Corruption has a diverse impact on the economic,
cultural and social aspects, therefore, in the 1990s,
Corruption is wide spread in real life and now also
it is a popular topic in economic research. It is
present in all the countries of the world. Other
society. For this reason, many studies on corruption
such as Treisman (2000), Glaeser & Saks (2006),
Del Monte & Papagni (2007), Billger & Goel
(2009), etc. come into the debate. However, there is
no universal and agreed upon definition of
corruption. Corruption is defined as transfer of
interest from public to private sector. World Bank
defines corruption as “The abuse of govenment
office for private gain. Corruption is every

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transaction between actors from the private and
public sectors through collective utilities that are
illegally transformed into private gains”. As in
Oxford dictionary (2000), corruption is described
as fraudulent or illegal behavior, especially by
those in government or the act of changing ethical
standards into unethical behavior. Therefore, three
crucial factors are included in the concept of
corruption: ethical, behavioral and empowerment.
Gould (1991) argues that defining corruption as the
ethical i.e it is a unethical phenomena contrary to
ethical norms which include a set of moral
deviations from the moral social standards.
According to Transparency International (TI -
2009), “corruption is the abuse of entrusted power
for private gain”. The abuse owns a very broad
sense. However, “government office is abused for
private gain when an official accepts, solicits, or
extorts a bribe. It is also abused when private
agents actively offer bribes to circumvent
government policies and processes for competitive
advantage and profit. Government office can also
be abused for personal benefit even if no bribery
occurs, through patronage and nepotism, the theft
of state assets, or the diversion of state revenues
(http://www1.worldbank.org/publicsector/anticorru
pt/corruptn/cor02.htm)”. An act of corruption can
be characterized by the value of the transaction
concerned. Although this is a continuous variable,
the analytical distinction usually made is between
low value (“petty”) and large value (“grand”)
corruption. The petty corruption normally occurs
where low- and mid-level government officials
have their interactions with ordinary citizens such
as schools, hospitals, police offices, government
departement and local authorities, etc ... transaction
size is often small and mainly affects individuals
(June et al., 2008). Typically, the larger the value
of the corrupt transaction, the higher the position in
the government hierarchy of the government
official(s) involved at where there are acts
committed at a high level of government that
distort policies or the central functioning of the
state, enabling leaders to benefit at the expense of
the public good. Sometimes this is similar to
political corruption (Rohwer, 2009).
From the above analysis, the term corruption
covers a broad range and diverse which depends on
the research objectives and methodology. The
paper aims to study the impact of corruption on
economic growth through analyzing the effects of
institutional quality including the quality of
political institutional quality which represented by
democratic quality indicators and economic
institutional quality which is represented by the
economic freedom indicator (Heckelman & Powell,
2010; Saha & Gounder, 2013). In line with this
objective of the study, the term corruption adopted
in this paper implies that the government civil
servants as their position and authority are abused
to change / violate the governement rules and/or
circumventing precribed government procedures
for their benefits and make harm the competitive
business environment.
2.2 Economic growth overview
Economic growth is a very popular concept from
classical to updated (modern) studies. Economic
growth can be examined and undertood into 2
approaches. Those are the reproducible or
production function approaches. Most of modern
studies are done with the second approach which is
the review of the economic growth at the surface
through the numbers through indicators. According
to World Bank (2004), economic growth is
“quantitative change or expansion in a country's
economy”. In addition, the World Bank (2004)
contended that “economic growth is conventionally
measured as the percentage increase in gross
domestic product (GDP) or gross national product
(GNP) during one year”. As in research of Nafziger
(2006) Economic growth is an increase in a
country’s per capita output. Thus, economic growth
is the increase in value of the goods and services
produced by an economy over a period of time.
The nature of economic growth is to ensure the
increase of both production output and production
output per capita. In general, economic growth can
be measured by Gross Domestic Product (GDP),
Gross National Product (GNP), national income
(Naional Income) and Gross National Product
(GDP) per capita and Per Capita Income (PCI).
The relationship between corruption and economic
growth
2.3.1 Literature review on negative effects
of corrutiopn on economic growth
According to the Neoclassical economics as
represented by Solow (1956) and Swan (1956),
neoclassical theory thus implies that economists
can take the long-run growth rate as given
exogenously from outside the economic system
such as capital, labor force, and technological
progress. However, the government intervention is
not taking into account and therefore, it cannot
make a direct analysis of the impact of corruption
on economic growth. Consequently, later, many
economists place the role of the government into
neo-classical growth models such as Barro's model
of endogenous growth theory (1991). Barro's
(1997) endogenous growth theory assumes average
GDP per capita is based on average private
investment and average government expenditure. A
Cobb-Douglas specific form for equation changes
to:

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Y= A L1-α KαG1-α (1)
where: 0< α<1;
 Y = total production
 L is the amount of labor used
 K is the amount of capital used
 G: government expenditure
(spending)
 A is a parameter describing
technology
In addition, according to Haque & Kneller (2008),
the elasticity of average output and government
expenditure in Barro's (1991) production function
depends on the corruption factor: 1-α = γ (1 - φ)
where φ is the index of corruption in the
government sector (Haque & Kneller, 2008). If φ is
larger, the effect of government expenditure on
economic growth decreases. If φ = 0, government
expenditure reaches to theoretical elasticity. This
implies that corruption is a hinderance to economic
growth, and this concept gain high agreement of
Buchanan & Tullock (1962) and Rose-Ackerman
(1999). Especially for Shleifer & Vishny (1993)
studies on the corruption methods in government
officials’ perspectives, the corruption arised when
government officials are always seeking to exploit
whenever possible based on economic and legal
constraints. The term "the grabbing hand" appears
to refer to the negative impact of corruption on
economic growth.
2.3.2 Literature review on positive effects of
corrutiopn on economic growth
Leff (1964) studied economic development through
administrative corruption. And the hypothesis
arises from the study that corruption implies that
corruption may be beneficial or it is also
understood to be a lubricant for the wheels of
growth. This idea is only primitive. However, it is
considered as the foundation of "corruption-
promoting" theories for later studies such as Lui
(1985), Beck & Maher (1986) and Aidt & Dutta
(2008).
In addition, the other researchers and scholars have
other contrast ideas on that idea and the corruption
impacts of corruption on growth must be based on
many other social and economic factors. As Levine
& Renelt (1992), the theoretical framework
identifies four variables that strongly influence to
economic growth, including investment to GDP
ratio, the rate of population growth, the initial level
of GDP per capita, and human capital. The first two
variables belong to the growth component and the
others belong to the development component. Also,
Levine & Renelt (1992) defines the rate of growth
of productivity as follows:
y = y(cpi, y0, human)
Where
CPI means corruption
percerption indexs
y0 means the initial level of
GDP per capita
human means the human
capital
The expectation of the initial level of GDP per
capita is from the convergence of knowledge gaps
among nations which is already addressed in
theories of endogenous growth. Countries with
larger knowledge gaps will be more likely to
increase their productivity through technical
learning, imitation and knowing from the
developed economies (Barro, 1991). According to
Benhabib & Spiegel (1994), human capital has a
positive impact on aggregate productivity growth
because trained workforce is better at learning,
creating and implementing with new techniques
which can thereby promote higher productivity
growth. However, this theoretical framework is
difficult to raise any comment The expectation of
corruption of the aggregate productivity factor.
2.3 Empirical studies
Over the past decades, many researches have been
done into the impact of corruption on economic
growth. Generally, in all empirical studies
conducted it has been observed that corruption has
two separate effects: positive or negative impacts.
Therefore, this paper is also conducted upon to that
result.
2.4.1 Empirical negative impact of
corruption on economic growth
Some studies provide results that in case, the
uncertainty occurs, genereally, corruption make the
economic growth. Therefore, these empirical
researches give high support to the theory of "the
grabbing hand". Mo (2001) makes a study on
impact on economic growth through transmission
channels based on cross - sectional studies in the
years 1970-1985 with the 2 separate periods: 1960-
1995 with data of 54 nations and 1996-2000 with
data of 49 nations by OLS and 2 SLS methods of
estimation to control for endogeneity to examine
the model sustainability. Results show there is a
negative impact on economic growth through
transmission channels on the investment and
human capital and corruption has a positive and
significant effect on political instability through
transmission channels
As a research of Ugur & Dasgupta (2011), 1,002
studies have been found and obtained on
corruption. The study provides a synthesis of the
existing evidence on the relationship between
corruption and economic growth - controlling for
effect type, data sources, and country groupings.
The study is done with low-income countries and

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high-income countries names. However, the
findings indicate that corruption has a negative
effect on GDP per-capita growth overall and
corruption is relatively more detrimental in mixed
countries as opposed to low-income countries only
and that indirect effects of corruption on economic
growth (through the human capital and public
finance channels) are larger than its direct effects.
Also, in low income countries, a one-unit fall in the
perceived corruption index can be expected to lead
to an increase of 0.59 percentage-points in the
growth rate of its GDP per-capita. For the mixed-
country group (i.e, for country groups that include
both LICs and Non-LICs), the total (direct and
indirect) effect on GDP growth per capita is higher
- at -0.86)
Aa a consequence of the review on the literature
and empirical studies on the relationship between
corruption and economic growth, the institutional
quality of a country will effect to research results.
Aidt et al. (2008) has developed a model of
interdependence between corruption and
institutionalization, the technique of panel data
analyses through the threshold effect of
distinguishing between high quality institutions and
low quality institutions. As a consequence, it is also
found no relationship between corruption and
growth in countries with low quality political
institutions but they reach conflicting conclusions
in countries with high quality political institutions
Venard (2013) analyzes the relationship between
institutional quality, corruption level, and economic
development using cross-national data of 120
countries developed by the World Bank on
perceived levels of corruption, institutional
framework quality and economic development.
Data has been collected for four years 1998, 2001,
2004 and 2007 and the method of estimation the
Partial least squares (PLS) is used to evaluate the
proposed scheme. The empirical result shows the
impact of both institutional framework quality and
corruption on economic development is negative.
At the same time, the study also found the
interaction between corruption and institutional
quality to growth. Improvement of institutional
quality and corruption reduction are more effective
for economic development in countries with lower
institutional quality than those of high institutional
quality. This empirical research supports the ‘sand
in the wheel’ school of thought in relation to the
effects of corruption on economic development.
Tarek & Ahmed (2013) examines the impact of
corruption on the growth of 30 developing
countries in the period 1998-2011. The result show
that corruption has a detrimental effect on
economic activities and corruption level is higher
and more serious in low-income and weak
economy-integrated countries. Corruption will be
more serious in developing countries as a weak
legal system and low income levels of civil
servants are existing.
2.4.2 Empirical positive impact of corruption
on economic growth
On the constrast with the above studies, there are
many other researches as evidence proving the
corruption served as a helping hand, Grease of
wheel.
Méon & Sekkat (2005) assesses the relationship
between the impact of corruptionon growth and
investment and the quality of governance in a
sample of 63 to 71 countries between 1970 and
1998. Variables of corruption are used from
sources of World Bank and Transperancy
International. The result shows corruption has a
negative impact on growth independently from its
impact on investment. These impacts are, however,
different depending on the quality of governance.
Aslo, it is concluded that corruption not only
impacts growth through reduced accumulation of
capital but also through other channels that have
yet to be determined. In particular, the marginal
impact of corruption on growth is positive in less
politically and/ or politically or politically. In other
words, corruption is positively correlated with
efficiency in countries with “ineffective"
institutions This result is again confirmed by Méon
& Weill (2010).
Egger & Winner (2005) using data from 73
developed and underdeveloped countries to
understand the relationship of corruption as a
stimulus to attract FDI since corruption helps
businesses avoid cluttered regulations and
administrative constraints. The general idea is that
corruption facilitates beneficial transactions which
should not have happened. As its consequence, it
enhances the efficiency of the economy by
allowing individuals in the private sector to correct
or eliminate government failures.
Aidt & Dutta (2008) develops a theoretical model
of the impact of corruption on economic growth
with different institutional structures. The results
show that the nature of the impact of corruption
depends on the specific regimes and countries are
divided into different regimes based on their
institutional quality. In particular, in countries with
good institutional quality, the impact of corruption
on growth is negative, whereas in countries with
poor institutional quality, the effect is positive (or
less negative).
Heckelman & Powell (2010) study the relationship
between corruption and economic growth as well
as the institutional environment (democracy and
economic freedom). Data from 83 countries during

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1995-2005 are used and processed with the
Weighted Least Squares. The study shows out very
special result and this relationship mainly depends
on the institutional quality of the countries.
Specifically, corruption was found to be beneficial
for economic growth in high democracy countries.
This result seems to be quite special but it is
consistent with studies by Méon (2005) and
Méndez (2006). In addition, the study also is
considered as evidence of corruption which
promotes economic growth in countries with low
levels of economic freedom and this positive effect
will be reduced as economic freedom improves.
3. Research data and methodology
3.1 Research data
Based on the above theoretical and empirical
studies, the model of the impact of corruption on
economic growth is established as follows:
Table 1: Variables and measurement
Code
Variables
Expectation
Interpretation
Studies
Sources
Dependent variables
gdppcit-1
The lagged
dependent
variable
Real GDP per capita
the natural logarithm of
GDP per capita ($)
corit
Corruption
-
Corruption perception
index
Pellegrini & Gerlagh (2004),
Aidt (2009), Ugur &
Dasgupta (2011), Venard
(2013), Saha & Gounder
(2013), Tarek & Ahmed
(2013).
Transparency
International
- TI
+
Méon & Sekkat (2005),
Egger & Winner (2005),
Aidt & Dutta (2008),
Heckelman & Powell
(2010).
demoit
Democracy
index
+
democracy freedom
index
Schumpeter (2012), Kotera
et al. (2011) Heckelman &
Powell (2010), Saha &
Gounder (2013).
Freedom
House
ecoit
Economic
freedom
index
+
the average of
economics freedom
index into
five areas upon to the
World Report
Heckelman & Powell (2010)
and Peev & Mueller (2012).
The
economic
freedom of
the world –
EFW
Control variables
investit
Investment
capital
+
Investment/GDP ratio
Ekanayake & Chatrna
(2010)
Work Bank
popit
growth rate
of population
-
the percentage of
annual population
growth
Barro & Sala-i-Martin
(2004), Sachs (2008).
Work Bank
topit
Trade
openness
the percentage of
of the import and
export upon to GDP
Wacziarg & Welch (2008),
Wang & Liu (2006),
Okuyan et al. (2012).
Work Bank
schoolit
a measure of
education
+
the percentage of
pupils enrolled in
primary schools (%)
Bergheim (2005),
Boughanmi (2009).
Work Bank
govit
Government
expenditure
-
The percentage of the
government
expenditure on GDP
Landau (1983), Marlow
(1988), Fölster & Henrekson
(2001).
Work Bank
Source: authors’ research.

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To collect data on variables, the unbalanced data
panel is used and some of which are "missing" in
variable schoolit. Data are collected from 19
countries in Asia, in the period 2004-2015, from
famous and prestigous websites such as Economic
Freedom of the World, Freedom House
Transparency International and World Bank. Table
2 shows the results describing the mean of
variables included in the study.
Table 2: Decription of variables
Code
N
Mean
Min
Max
Std. Dev.
LNGDPPC
228
4.1018
3.2018
4.9437
0.4221
COR
228
6.0424
0.6000
8.5000
1.9259
DEMO
228
2.6609
0.9188
5.5070
1.0735
ECO
228
6.8348
4.1800
8.9400
0.7673
INVEST
228
27.4110
14.1206
57.9905
7.7315
GOVE
228
12.6943
5.0393
27.4451
4.9681
TOP
228
83.5131
0.1674
430.3580
62.2991
POP
228
1.9348
-0.2003
15.0326
2.3064
SCHOOL
215
0.9856
0.7329
1.2100
0.0538
source: authors’ calculation.
3.2 Research model
With variables in Table 1, static panel data regressions model on the table is used to analyze the impact of
corruption on economic growth as follows:
 =  +++++(1)
where: i = 1, 2,.., N; t = 1, 2, …, T
 N is the number of the countries
 T is the observed time in the model
  is the constant effect of the nation i and equally distributed independence errors  ≈ i.i.d (0, бe2),
E(/) = 0.
With static panel data regressions model, the three
most commonly used methods are Pooled, FEM
and REM, however, each method has its own
advantages and disadvantages. In the Pooled
method, it is seen all the nations are homogeneous
which is not practical because each country has its
own institutional characteristics that are almost
unchanged over time but this can be correlated with
variables. Thus, the Pooled method can lead to
erroneous estimates when these particular effects
are not controlled.
For FEM or REM methods, these separate effects
can be controlled. However, if these separate
effects are correlated with independent variables,
the most appropriate method is FEM, or in contrast
case, the REM model is more appropriate. To
select Pooled or FEM, F test is used by using the
Lagal Lagrange Multiplier (LM), and to select
REM or FEM, Hausman test is used. Results show
that inconsistencies occurs in the three method
selection: Pooled, FEM and REM. Therefore, in
this study, FEM model is selected because it best
fits the sampled data. However, the Breush - Pagan
test shows that the variance of the FEM model was
not homogeneous. Therefore, obtained estimates
from FEM are ineffective. In order to improve the
effectiveness of estimation, the Generalized Least
Squares (GLS) in the data panel proposed by Beck
& Katz (1995) are applied.
In addition, in the study of economic growth,
attention should be paid to the interaction between
the growth values over time. Thus, growth models
are usually built under the autoregressive model.
When datasets are panel data, the appropriate
model to produce unbiased and stable estimates is
the dynamic panel data model (Barro, 1997).
Moreovers, endogenous variables are normally
included in growth models. For example, when
investment is high, it will lead to high growth, then
high growth will promote more investment. Saha &
Gounder (2013) suspects endogenousness of
corruption happens when the measurement of this
variable has strong correlation and increases with
economic development level. Stimulously, this

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leads to the deviation of traditional estimates. D-
GMM is estimation technique that uses all lagged
dependent variables and predetermined variables as
instruments. This technique gains many advantages
in comparison with the traditional estimates (FGLS
and 2SLS). Traditional estimates (FGLS and 2SLS)
may result in bias in the presence of altered
endogenous variances. D-GMM uses moment
conditions that produce accurate estimates even
when there is an inconsistency of cross-sections
(Hansen, 2010; Hayashi, 2000). Therefore, the
model (1) will be changed as follows:
 =  + ++++  (2)
Subsequently, D-GMM is continued to conduct for
compliance tests, including probabilities of
autocorrelation of model errors and instrumental
variables. Arellano-Bond test on correlation of the
hypothesis H0: None self-correlated and applied to
differential error. The null hypothesis is often
refused in the test of AR (1) process in first-order
variance. Therefore, test of AR (2) will be done and
it is more important because it tests self-correlation
at different levels. The validity of instrumental
variables used in the D-GMM estimation is tested
by Sargan statistics. The Sargan test is an
overidentifying measure. Sargan test is done with
the H0 hypothesis of which the instrumental
variable is an exogenous variable. This means the
correlation is not occuring with the error in the
model. Therefore, the Sargan statistic value is as
large as possible. Also, the heterogeneity of error
variance is existing in the FEM model. Therefore,
the use of the quantile regression to study for the
different quantiles of the growth distribution
function is appropriate. The quantile regression is
introduced by Koenker & Bassett (1978) and is
widely used in the world. The advantage of this
approach is to examine in detail the impact of
corruption on economic growth on a per-quantile
(unit) basis to reinforce the evidence of impacts
between low and high income countries.
4. Findings and discsussion
The regression result of the function (1) is shown in
Table 3. Columns 1, 2, 3 of this table indicate the
estimated results of Pooled OLS, FEM and REM.
Chow test results and the Hausman test in Table 3
show that the FEM model is best suited to the data
collected. However, the test results show that there
is an error variance of the FEM model. To improve
the effectiveness of estimation, the FGLS method
is used as shown in Column 4 of Table 4. However,
as discussed above, FGLS still owns some
limitations, an as a consequence, results of D-
GMM estimation are collected as presented in
Column 5 of Table 3 and used for analysis.
Table 3: Regression output of corruption and institutional impacts on economic growth
Independence variables
POOLED
FEM
REM
FGLS
D-GMM
COR
0.000711
0.00193***
0.00190***
0.000965***
-0.000067**
[0.86]
[7.97]
[7.83]
[2.87]
[-1.52]
DEMO
0.214***
0.228***
0.234***
0.206***
0.00524*
[7.67]
[9.11]
[9.99]
[13.12]
[1.81]
ECO
0.0137
0.0227
0.0252*
0.0528**
0.00523***
[0.34]
[1.49]
[1.66]
[2.51]
[2.90]
INVEST
0.00202
-0.00338***
-0.00338***
0.00379***
0.000637***
[0.86]
[-3.06]
[-3.05]
[2.82]
[4.98]
GOVE
0.0188***
-0.00982***
-0.00813***
0.0147***
0.000505*
[4.83]
[-3.23]
[-2.71]
[7.44]
[1.74]
TOP
0.00108***
-0.00017
-0.00014
0.000600***
0.0000557**
[3.53]
[-1.52]
[-1.19]
[3.81]
[2.53]
POP
0.0459***
0.00442
0.00532*
0.0417***
-0.00025***
[5.39]
[1.46]
[1.74]
[6.56]
[-3.67]
SCHOOL
0.178
0.167
0.152
0.129
-0.00701
[0.52]
[1.17]
[1.06]
[0.66]
[-0.36]
L.lngdppc
0.948***

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[83.34]
Blocking factor
2.781***
3.369***
3.320***
2.565***
0.163***
[7.27]
[18.48]
[17.27]
[12.12]
[4.20]
Observations
215
215
215
215
196
Determined correlation coefficient
0.6290***
0.4497***
Chow Test
172.73***
Hausman test
32.71***
Variance deviation test
261.16***
Autocorrelation test
259.196***
Sargan test
0.853
AR(2) test pvalue
0.442
Note: *, **, *** represent significance at the 1%, 5% and 10%; [] is value of the standard error
Source: authors’ calculation
Results in Column 5 of table 3 shows the extent
and direction of the impact of corruption and
institutions on economic growth. In addition, to
clarify the effect of these factors on the quantiles of
economic growth variables, there are results of
quantile regression on function-formed table (1)
which are also shown in Table 4.
Table 4: Regression output of corruption and institutional impacts on economic growth
Independence variables
Quantile regression
0,1
0,25
0,5
0,75
0,90
COR
0.00218**
-0.00167
0.00309***
-0.00303**
-0.00492*
[2.27]
[-1.38]
[2.64]
[-0.71]
[-1.93]
DEMO
0.281***
0.287***
0.203***
0.175***
0.136***
[12.55]
[9.14]
[4.23]
[2.75]
[3.76]
ECO
0.0156
-0.00994**
0.106*
0.0169**
0.0511***
[0.60]
[-0.20]
[1.42]
[0.18]
[0.97]
INVEST
-0.00335*
-0.00055
0.00557*
-0.00143
0.00462
[-1.65]
[-0.12]
[1.69]
[-0.39]
[1.39]
GOVE
0.0182***
0.0236***
0.0243***
0.000422
-0.00622
[5.44]
[5.27]
[4.50]
[0.05]
[-1.12]
TOP
0.000906**
0.000987**
0.000852***
0.00163***
0.00170***
[2.58]
[1.99]
[3.25]
[2.73]
[3.19]
POP
0.0496***
0.0453***
0.0488***
0.0264*
0.0291
[4.44]
[2.99]
[4.32]
[1.79]
[1.65]
SCHOOL
-0.224
-0.497*
-0.477
1.683*
2.100***
Nation dummy variables
Yes
Yes
Yes
Yes
Yes
Blocking factor
[-1.27]
[-1.97]
[-0.89]
[1.90]
[3.46]
-47.27***
-49.10***
-62.81***
1.865**
1.357**
Observations
215
215
215
215
215
Note: *, **, *** represent significance at the 1%, 5% and 10%; [] is value of the standard error
Source: authors’ calculation
The corruption perception index (CPI) was created
in 1995 by Transparency International. This is done
"by their perceived levels of corruption, as
determined by expert assessments and opinion
surveys. It ranks on a scale of zero to 10, with zero
indicating high levels of corruption and 10

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ISSN: 2454-1362, http://www.onlinejournal.in
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indicating low levels. The corruption level reflects
the frequency of payments for corruption and
business barriers (Lambsdorff, 2005). In this study,
COR variable is an index of corruption measured
by the CPI and adapted from Transparency
International. This indicator is measured on a scale
from 0 to 10, in which the smaller the country, the
less corruption and vice versa. Thus, for this study,
it is adjusted to be the larger the value getting the
less corruption by subtracting 10 points from the
CPI. In case, the institutional framework
(democratic and economic freedom variables) and
socio-economic factors are controlled, column 4 of
Table 3 shows that COR coefficient of the negative
variable is negative and its tatistical significance is
at 1%. This result again confirms that corruption is
hindering economic growth in Asian countries.
Specifically, if a country raises its anti-corruption
to 1% point, the GDP growth rate will decrease by
0.000067%. Indeed, corruption can undermine
economic growth through multiple channels at two
levels.
at the micro level, many empirical studies suggest
that corruption reduces efficiency in the allocation
and use of production factors (Dal Bo & Rossi,
2007). The poor suffer the most from corruption.
As concerned in the study of De Soto (2000), the
bribes required by government officials such as
taxes are harmful to businesses because they
account for a larger proportion of income in small
businesses in comparison with large businesses. In
addition, corruption devastates the provision of
government services as health care and education,
which are important services for the lives of the
poor. In places where officials claim bribes to
provide services, the poor cannot even gain
accessibility to low quality services.
(i) at the macro level, corruption impacts
negatively on GDP per capita and
economic growth (Mauro, 1997; Ades &
Di Tella, 1999). Indeed, countries with
many erroneous policies, ineffective
government expenditures, and much
corruption cause the damage to
macroeconomic development, negatively
effects to property ownership, competitive
reduction, ineffective resource allocation,
degraded infrastructure and reduced
educational expenditures (Murphy et al.,
1991).
And table 4 shows that the extent of the COR
variable impact on economic growth is different at
different quantile 0.1; 0.25; 0.50; 0.75 and 0.90 of
the distribution function of economic growth. In
low quantiles such as 0.1 and 0.5 of the growth
variable, corruption has a positive impact on
economic growth at a 10% significance level, and
at the higher quantile, the impact is stronger. This
result is same as a study of Lui (1985) in which
advocate the corruption role in helping economic
subjects avoid consequences of ineffective policies.
In particular, momentum can be created from
bribes for officials to speed up the process when
administration is slow and corrupted officials make
faster decisions. In the context of delayed
administration and government's harsh regulations,
corruption promotes the efficiency of the economy
and positively impacts to economic growth. Bayley
(1966) argued that corruption can overcome the
bureaucracy system by improving institutional
quality and could help private businesses avoid
public policy hindrances to their businesses and
thereby, assist them find positive and appropriate
solutions. This also makes the efficiency of public
policy to be improved. Moreover, according to
Bayley (1996), it should place in government
targets. As found, in the context of the institutional
environment and other socio-economic factors,
corruption reaches a positive impact on economic
growth and at the higher quantiles of growth
variable, the impact of corruption is stronger.
As a result, the D-GMM results force to show
corruption has a negative impact on economic
growth. Thus, by using the quantile regression,
there exists a difference tendency of the impact of
corruption on economic growth in the different
quantiles of the growth distribution function. At
low quantiles, corruption reaches a negative impact
on economic growth and conversely, at the higher
quantiles, the impact of corruption is stronger.
Moreover, in high quantiles of 0.75 and 0.90 of the
distribution function of economic growth,
corruption has a negative impact on economic
growth and reaches significant at 5%. This result is
favor and supportive to "The Grabbing Hand"
theory - corruption hinders the economic growth of
the endogenous growth theory model (Barro,
1991). This has also been found in studies by
Heckelman & Powell (2010), Ugur & Dasgupta
(2011), Venard (2013), Saha & Gounder (2013),
Tarek & Ahmed (2013).
As in column 5 of table 3, it shows the regression
coefficients of the DEMO and ECO variables are
positive and gain statistically significant. This
means institutional quality has a positive impact on
economic growth. This finding is to demonstrate
the important role institutional quality in the
economic growth, especially in Asian countries
(Heckelman & Powell, 2010; Lee, 2008).
According to the results expressed in Table 4, in
general, the institutions have a positive effect on
economic growth. At the higher quantiles, the
impact is stronger and all reach statistical
significant. Also, the two factors: democracy
freedom and economic freedom, the democracy
freedom and democracy needs to be more
concerned in Asian countries.

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Indeed, most of Asian countries are developing
countries with low democracy and economic
freedom. The democracy expansion and the
reformb boost toward the free economy will bring
beneficial in enhancement of economic growth and
income improvement in these countries in which
democracy freedom is considered as a measure of
the quality of the legal system and political stability
of a country. This is essential to ensure a clean
legal system which can remove obstacles in foreign
investment attraction, stimulates technological
change and contributes to economic growth
(Campos & Prahan, 2007; Kaufmann & Kraay,
2002; Rivera-Batiz, 2002).
As shown in column 5 of table 3, the investment
rate has a positive effect on national growth with
high statistical significance. This result
reconfirms the important role of material capital
in growth models mentioned in many previous
studies. This means economic growth in Asian
countries is still dependent on capital growth. The
result reflects the current situation of the
development level and the abilities of limited
application of science and technology in
production. This finding is consistent with the
empirical results of Ekanayake & Chatrna (2010).
The same results also are found in the quantile
resgression. At the quantile 0.5, investment has a
positive impact on economic growth, and vice
versa, at the low quantile of 0.1, it is negative and
gains statistical significance at 1%. This shows,
in countries with economic growth, at low
quantile, investment is not effective and
corruption should be controlled and investment
should be more selective
Government expenditure variable gains
statistically significant and positive. This
interpretes government expenditure has a positive
impact on growth in the surveyed countries. It
can be seen that the control of government
expenditure in these Asian countries is quite
effective and contributes to the positive effects of
growth promotion. This is in accordance with
studies of Bose et al. (2007) and Acosta et al.
(2013). The results of the regression analysis is
supportive to the above observation. However,
the government expenditure management is not
identical and same as countries. Specifically, in
countries with high economic growth in high
quantiles, government expenditure has a strong
impact on economic growth. This means that in
these countries, control of government spending
is better than that of countries with low growth
rates. Indeed, in low-growth countries with high
corruption and limited institutional quality,
government expenditures make hinderance to
growth unavoidably (Marlow, 1988; Fölster &
Henrekson, 2001).
The regression coefficient of POPG variable is
negative and reach statistically significant and
this is consistent with Barro & Sala-i-Martin
(2004), Sachs (2008). That means the population
growth rate in Asian countries has a negative
impact on economic growth. This implies
economic growth does not depend on labor
growth. By contrast, population growth has
hindered the progress of economic development
in these countries. This means the SHOOL
variable is not guaranteed, and it is not affecting
economic growth. Therefore, in case, population
continues to increase which supplement high
qualified labor force for society, this gains no
meaning for economic growth and sometimes it is
seen as pressure and increase in terms of socio-
economic costs, and lastly, it leads to a decline in
public investment (Headey & Hodge, 2009).
Finally, the empirical results show that the
regression coefficient of the TOP variable
reaches statistically significant and positive. This
has same result with in many previous empirical
studies of the positive role of trade openness in
economic growth. This result is consistent with
Wang & Liu (2006), Wacziarg & Welch (2008)
and Okuyan el al. (2012).
5. Conclusion, implications and
recommendations
5.1 Conclusion
The paper uses data from 19 countries in Asia from
2004 to 2015 to study the impact of corruption on
economic growth. D-GMM panel data and quantile
regession are applied in this study to analyze
further and detailed impact of corruption on
economic growth through each quantile. The
results support the hypothesis that corruption
hinders economic growth in Asian countries. In
addition, the impact level of corruption on growth
is different from each quantile of the distribution
function of growth variables. In detailed, in low
quintiles as 0.1 and 0.5, corruption has a positive
impact on economic growth and vice versa, in high
divisions as 0.75 and 0.90, the impact of corruption
is negative. Also, in the context of the different
institutional and other socio-economic
environment, the impact level of corruption on
economic growth is different. The higher quantiles
will gain the the higher the level of impact.
Institutional quality, democracy freedom and
economic freedom play an important role in
economic growth in Asian countries. On the other
hand, according to results, generally, the institution
impacts positively on economic growth. At the
higher quantiles, the impact level is stronger and
reaches statistically significant and the democracy
freedom in Asian countries should be prioritized.
The remaining variables of investment rate,
government expenditure and trade openness have a

Imperial Journal of Interdisciplinary Research (IJIR)
Vol-3, Issue-7, 2017
ISSN: 2454-1362, http://www.onlinejournal.in
Imperial Journal of Interdisciplinary Research (IJIR) Page 801
positive impact on the growth of Asian countries
and however, the impact of population growth is
negative.
5.2 Research implications
For Asian countries with low economic growth
from 0.1 and 0.5 quantile ranges, corruption
impacts positively on economic growth. However,
this impact is uncertain for all countries where it
appears only in some circumstances; for example,
in the poor quality institutional quality.
Therefore, in that case, economic growth is likely
to minimize the value of time costs for the
economy. Bribes for officials are as the impetus for
speeding up the process in a low administration
system and officials’ bribes which can make
decisions faster. Besides, with corruption, the
bureaucracy can be reduced by improving
institutional quality and can help private businesses
avoid a public policy hindering their businesses and
assiting them to find positive and appropriate
solutions. This could help improve the efficiency of
public policy. In contrast, at high quantiles of 0.5
and 0.9, corruption has a negative impact on
economic growth. Therefore, with Asian countries,
effective institutional system should be established
to control and identify the focus of the anti-
corruption strategy in which widening the
democracy level is also an important factor for the
national institution system. Thus, countries should
establish a framework for political institutions
aimed at expanding democracy through free
elections, fairness and press freedom which help to
prevent and eliminate corruption more effectively.
Inhabitants are free to demonstrate their right in
right candidate elections, dismissal and replace
when officials show unfulfillness of their
responsibilities or abusive behavior for public
benefit. These rights as a factor motivate them to
monitor activities of the government and etc with
hope to curb corruption. As a result, it contributes
to improve the quality and efficiency of the use of
public resources and facilitate economic
development. Press freedom is also enhanced by
allowing media agencies to gain more opportunity
to publicize, condemn the errors of government
officials and prevent future recurrence. More of
that, democracy level should be extended through
fair elections and free speech to eliminate
corruption effectively. And the economic freedom
index should be improved in these countries to
reach transparent and open economic environment.
Also, law enforcement and execution should be
improved and upgraded. Singapore is a good
example as an anti-corruption country by their
strict controls and disciplines.
5.3 Research reco mmendation
Firstly, the empirical results show that capital
investment is as a factor accelerating to the
growth of Asian countries. Therefore, domestic
and foreign capital should be utilized. Capital is
seen as a key driver of growth in developing
countries and of poverty reduction. Also, it is
used to shift its economic structure to promote
faster growth and integrate effectively into the
global economy. The role of capital has been
proved in many studies throught economic
models with reliable empirical results. At present,
with increase of globalization and economic
integration, the attraction of foreign direct
investment (FDI) has been receiving much
attention, especially for developing countries.
FDI plays an important role in creating a growth
mechanism for countries receiving investment
capital. Thus, in these countries the legal system
should be enhanced in the direction of
development and and stimulation of the attraction
of investment capital for economic growth.
Secondly, the effective government expenditure
management and control system should be built
to enhance higher efficiency. The empirical result
shows there is a mutual relationship between
investment and economic growth. However, in
government expenditure, especially in public
investment, it is necessary to have a clear
definition of the mechanism for allocatin g
financial resources in line with the priority
objectives of the development strategy.
Government expenditure must be alligned with
the creation of common conditions for
development in priority to important national
projects, especially large scale transportation and
urban infrastructure, national and regional
strategic projects. In order to promote investment,
investment capital should also be diversified and
private capital should be attracted. Therefore,
governments should remain and enhance
accountability and transparency in government
expenditure management assuring effectiveness
of public goods delivery programs. Furthermore,
to promote economic growth in Asian countries,
an open economy should be established to
increase imports and exports of goods and
services and improve domestic technology which
make economic growth faster. Therefore, the
production process can be more efficient and
higher productive. Additionally, trade openness
should also be increase to gain competitive
advantage. Besides, it can also indirectly
encourage growth through other channels such as
technology transfer, product diversification,
expansion of scale of economies and effective
resource distribution and allocation in the
economy and interaction with partners.
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