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American Journal of  Economics and 
Business Innovation (AJEBI)

Institutional Quality, Corruption, and Income Inequality: A Panel Study of  Selected 
SAARC Economies

Faisal Abbas1*, Babar Shahzad1, Sabila Raees1

Volume 2 Issue 3, Year 2023
ISSN: 2831-5588 (Online), 2832-4862 (Print)

DOI: https://doi.org/10.54536/ajebi.v2i3.1957
https://journals.e-palli.com/home/index.php/ajebi

Article Information ABSTRACT

Received: September 10, 2023

Accepted: October 06, 2023

Published: October 17, 2023

Previous studies have primarily focused on issues related to income inequality, aiming to 
identify the underlying causes and urging swift action to mitigate such disparities. In this 
context, the current article expands upon existing literature by introducing the influence 
of  corruption and institutional quality. This study contributes to the existing knowledge by 
investigating the interplay between institutional quality, corruption, and income inequality 
within SAARC countries spanning 2000 to 2021, sourced from World Governance 
Indicators, Transparency International, Global Consumption and Income Project, and World 
Development Indicators. After analysed the properties of  data, FMOLS analytical approach 
employed. The empirical analysis validates the enduring effects of  the examined factors on 
income inequality over the long term. The findings indicate that institutional quality exerts 
a notable and favorable influence in reducing income inequality. Conversely, corruption, the 
combined impact of  corruption and institutional quality substantially and adversely affect 
income inequality. Addressing the imperative of  ensuring an equitable income distribution 
across the SAARC economies necessitates implementing comprehensive strategies to foster 
enduring institutional quality and effectively manage corruption. Study’s conceptual and 
empirical advancements carry significant implications for policy formulation within this 
region. They offer valuable insights for the region’s endeavors to ameliorate income inequality. 
This study underscores the importance of  measures to enhance institutional quality and 
combat corruption within SAARC countries. Such measures should be strategically designed 
to tackle income distribution challenges and promote greater equity. 

Keywords
Institutional Quality, Corruption, 
Income Inequality, SAARC

1 Department of  Economics, University of  Sargodha, Punjab, Pakistan
* Corresponding author’s e-mail: abbas.eco383@gmail.com

INTRODUCTION
Income inequality is a significant concern for economists 
and policymakers worldwide. It refers to the uneven 
distribution of  income within a population, often 
accompanied by wealth inequality. When resources in the 
economy are unequally distributed among its residents 
and the flow of  resources continue to grow from poor 
to rich, such situation is described as income inequality 
(Staff 2009). Recent evidence suggests that countries 
with high economic growth rates have experienced an 
increase in income inequality. Institutional processes 
have been identified as a contributing factor to rising 
income inequality, as they can lead to corruption, political 
clientelism, and other irregularities that undermine 
property rights. Poor institutional quality has been found 
to have a detrimental effect on income distribution.
Institutions, as defined by Chong and Calderón (2000), 
encompass the norms, legal and political frameworks, 
and cultural factors that shape economic activity within a 
nation. Healthy institutions are associated with economic 
development and a more equitable distribution of  income. 
Countries with higher levels of  institutional quality, such 
as Denmark, Sweden, and New Zealand, tend to have 
more equal income distributions. Conversely, countries 
with higher levels of  corruption and lower institutional 
quality, such as Bangladesh, India, and Pakistan, exhibit 
greater income inequality. 
Corruption has direct and indirect impacts on economic 

and governance aspects, magnifying the existing 
inequalities (Sanjeev et al., 1998). The issue of  corruption 
gained attention in the mid-1990s when international 
donor institutions and researchers focused on measuring 
corruption cross-country. Corruption is viewed as an 
indicator of  other governance failures, and it hampers 
economic growth and financial performance in South 
Asian countries. Political instability, poor institutional 
quality, and governance crises are key factors hindering 
further improvement in economic growth and 
performance in the region.
As societies prosper, expectations for better government 
services, rule of  law, accountability, transparency, and 
welfare improvements increase. Rising income and wealth 
inequality pose significant challenges for governments 
globally. In developing economies, public expenditure 
is prioritized over taxation mechanisms due to the small 
size of  tax revenues and the perceived low quality of  
governance and institutions. In cases where there is a 
presence of  strong institutional quality, characterized by 
minimal corruption and significant political competition, 
public expenditure can contribute to the advancement of  
a more equitable society.
In the context of  the SAARC countries, poor governance, 
political instability, income inequality, and corruption 
are prevalent issues. It is crucial to study these issues to 
identify effective solutions. This paper aims to analyze 
institutional quality, income inequality, corruption, 



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government effectiveness, and political stability in the 
SAARC countries. Historical context reveals that many of  
these countries were colonized by the British Empire, and 
the institutional frameworks inherited from the colonial 
period continue to shape their governance systems.
India held a significant place among the colonies of  the 
British Empire. The gradual establishment of  control by 
the British East India Company occurred over various 
regions of  the Indian subcontinent starting from the 
mid-18th century (Bayly, 1990). Subsequently, after the 
Indian Rebellion of  1857, direct British rule, known as 
the British Raj, was implemented across the entire Indian 
subcontinent until India finally gained independence in 
1947 (Gilmartin, 1998). 
Pakistan was also established after the partition of  British 
India in 1947, comprising two regions: East Pakistan (now 
Bangladesh) and West Pakistan (now Pakistan). Both East 
and West Pakistan were part of  the colonial administration 
of  British India until they gained independence (Talbot, 
1998).
Formerly known as East Pakistan, Bangladesh emerged 
after the partition of  British India in 1947. It remained 
under the colonial administration of  Pakistan until 
it achieved independence through the Bangladesh 
Liberation War in 1971 (Raghavan, 2013).
Previously known as Ceylon, Sri Lanka was one of  the 
colonies of  the British Empire. The British gradually 
gained control over various parts of  Sri Lanka through 
treaties with local rulers, establishing colonial rule. Sri 
Lanka remained under British colonial administration until 
it gained independence in 1948 (Wickramasinghe, 2006). 
Aim of  the study is to analyze the impact of  corruption 
and institutional quality on income inequality by using the 
panel data of  2000-2021 for SAARC countries. This study 
contributes to the burgeoning literature on institutions in 
two ways. First, our study find that institutional quality 
helps to reduce income inequality. In this article we 
consider two types of  institutional quality; institutional 
quality (simple mean of  six governance indicators), and 
institutional quality with corruption impact (interaction 
term). Second, the level of  corruption is also an important 
factor for income distribution. More corruption has the 
positive impact on income inequality. 
The structure of  the paper is outlined as follows: Section 
2 offers an extensive examination of  the existing body 
of  literature pertaining to the subject. Moving on to 
Section 3, we intricately explain the empirical model 
while also tackling any potential data-related issues. 
Section 4 showcases the empirical findings, which are 
comprehensively analyzed in Section 5. The paper 
concludes with final remarks presented in Section 6.

LITERATURE REVIEW
Batabyal and Chowdhury (2015) examined corruption, 
financial development, and income inequality in 30 
Commonwealth countries from 1995 to 2008. Using 
OLS and IV estimation techniques, they found that high 
corruption levels in Commonwealth countries hinder the 

benefits of  financial development. Financial development 
positively affects income inequality in all countries, with 
a stronger impact in low- and middle-income countries 
when corruption levels are high. The study suggests 
implementing integrated policies that tackle corruption 
and promote financial development to effectively reduce 
income inequality.
N. P. R. Deyshappriya (2017) conducted a study on 
income inequality across 33 Asian nations spanning the 
period from 1990 to 2013. Through the application of  
dynamic panel data analysis, the study revealed an inverse 
U-shaped correlation between GDP and inequality, a 
phenomenon known as the Kuznets curve. The research 
identified several factors that contributed to the reduction 
of  inequality, encompassing Official Development 
Assistance (ODA), education, and participation in the 
labor force. Conversely, inflation, political risk, terms 
of  trade, and unemployment were identified as factors 
that heightened inequality. Initial GDP growth favored 
the middle class and richest groups, but further growth 
favored middle-income and poor groups. The study 
recommended sustained economic growth, improved 
education and employment access, price stability, and 
political stability to reduce income inequality in Asia.
Chowdhury et al. (2018) investigated the connections 
between entrepreneurship, corruption, and income 
distribution in low- and middle-income countries in South 
and East Asia from 2004 to 2012. The study employed 
ordinary least squares analysis. The findings revealed 
that entrepreneurship has a positive impact on reducing 
income inequality, but the type of  entrepreneurship 
also matters. Moreover, the level of  corruption in a 
country plays a significant role. The study suggested that 
countries with lower corruption levels are more effective 
in reducing inequality through entrepreneurial activities.
Brei (2018) investigated the connection between financial 
structure and income inequality using panel data from 97 
economies spanning 1989 to 2012. The study found a 
non-monotonic relationship, where an increase in finance 
initially reduces income inequality. However, beyond a 
certain point, expanding market-based financing leads 
to a rise in inequality, while expanding finance through 
bank lending does not. These findings align with existing 
literature suggesting that deeper financial systems aid in 
reducing poverty and inequality in developing countries. 
They also align with recent evidence of  increasing 
inequality in financially advanced economies.
Saengchai (2019) investigated the association between 
government external debt, corruption, and ECNG in five 
ASEAN countries. Using secondary data from 1990 to 
2015, variables such as external debt stock, gross capital 
formation, GDP, interest on external debt, exports, and 
corruption were considered. The study revealed negative 
consequences for the economy resulting from increasing 
debt, emphasizing the importance of  addressing this issue 
through alternative capital investment sources. The study 
recommended efficient management of  public resources 
to mitigate challenges like high servicing costs, corruption, 



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and capital flight. Strategies such as promoting economic 
openness, easing import restrictions, and boosting 
valuable exports were also suggested. 
Law & Soon (2020) conducted a study that examined 
how institutional quality influences the connection 
between inflation and income inequality. The research 
employed a two-step system using the Generalized 
Method of  Moments with unbalanced panel data 
encompassing the years 1987 to 2014. This dataset 
included 65 countries, both developed and developing. 
The study’s findings indicated that higher levels of  
inflation exacerbate income inequality, whereas enhanced 
institutional quality contributes to a reduction in income 
inequality. Furthermore, the study unveiled that the 
impact of  inflation is moderated by better institutional 
quality, suggesting the presence of  a mediating effect. 
Additionally, the research highlighted that both inflation 
and institutional quality have incremental effects in 
diminishing income inequality. Drawing from these 
outcomes, policymakers are advised to prioritize the 
enhancement of  institutional quality. This improvement 
holds a dual role in influencing income inequality – 
directly and indirectly by interacting with inflation.
Daud (2020) conducted a study that delved into the role 
played by institutional quality in the correlation between 
external debt and economic growth. The study adopted 
a dynamic threshold specification approach using panel 
data encompassing 53 countries. This dataset covered the 
time span from 2005 to 2016. The estimation process 
employed the System Generalized Method of  Moments. 
The study’s findings illuminated that external debt 
exerts a negative impact on a nation’s economic growth, 
while institutional quality yields a positive influence on 
growth. Moreover, the research revealed that the extent 
of  external debt’s impact on economic growth hinges on 
the quality of  institutions. Particularly noteworthy is the 
fact that when external debt reaches high levels, the effect 
of  institutional quality on growth becomes relatively 
insignificant. In light of  these outcomes, the study’s 
conclusion underscores the persistence of  the detrimental 
effect of  external debt on a country’s economic growth.
Asamoah (2021) conducted a study that investigated the 
presence of  a threshold effect in the relationship between 
institutional quality and income inequality. This inquiry 
was carried out utilizing a dynamic panel threshold 
model. The study’s focus encompassed a panel of  both 
developing and advanced countries, spanning the period 
from 1995 to 2017. The outcomes of  the study unveiled 
varying impacts: i) When assessed through the World 
Governance Indicators, advanced countries displayed a 
quadratic effect, while developing nations consistently 
demonstrated a negative effect. This finding implies 
that enhanced institutional quality leads to a reduction 
in income inequality in developing countries. ii) Using 
a measure derived from the International Country 
Risk Guide, the study identified an inverted U-shaped 
correlation between institutions and income inequality 
in both advanced and developing countries. Interestingly, 

the threshold value for this relationship was higher in 
developing economies. The study’s results remained 
robust even when accounting for measurement and 
endogeneity concerns. These findings carry significant 
policy implications, offering valuable guidance for 
addressing income inequality within developing 
economies.
Paulo Diogo Amaro Nunes de Sousa Rego (2021) 
investigated the impact of  corruption on income 
inequality and regional variations. Using panel data from 
108 countries over 1996-2017, the study found that 
controlling corruption was associated with increased 
income inequality in Asian and Eastern European 
countries, while Western European and Latin American 
countries saw lower inequality with corruption control. 
Additionally, democratic political regimes were found to 
improve corruption control.
Biglaiser and McGauvran (2021) studied the effects 
of  debt restructurings on income distribution in 71 
developing countries from 1986 to 2016. They found that 
debt restructurings led to reduced social spending and 
lower taxes, exacerbating income inequality. The results 
remained robust across various model specifications, 
shedding light on the negative impact on the less well-off  
following debt restructurings.
Obiero and Topuz (2021) examined the impact of  
internal and public debt on income inequality in Kenya 
from 1970 to 2018 using the ARDL model. They found 
that both internal and public debt contribute to income 
inequality in the long term. Internal debt has a one-way 
causal relationship with income inequality, while no such 
relationship was observed for public debt. The study 
recommended using non-debt financing methods to 
address income inequality in Kenya, as debt financing is 
not favorable for the less privileged.
Bon’s (2022) research focusing on advanced economies 
delved into how institutional quality influences the 
relationship between public debt and income inequality. 
The study utilized data spanning the timeframe of  2002 
to 2020, encompassing 30 advanced economies. The 
research methodology comprised the utilization of  
both the system-GMM and PMG estimator techniques. 
The study’s findings illuminated intriguing dynamics: 
Individually, both public debt and institutional quality 
exhibited the potential to alleviate income inequality. 
However, the interaction between these two factors, 
as indicated by their combined effect, paradoxically 
intensified the existing inequality. Furthermore, the 
study unearthed the influential roles played by economic 
growth and unemployment in shaping income inequality, 
both factors contributing to its exacerbation. Conversely, 
the presence of  education emerged as a counteracting 
force, actively contributing to the reduction of  income 
inequality. The study underscores the critical need to derive 
actionable policy implications. It highlights the potential 
of  harnessing the intertwined influences of  public debt 
and institutional quality as a strategic avenue to effectively 
address the intricate challenge of  income inequality.



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Kunawotor et al. (2020) undertaken a study that delved 
into influence of  institutional quality on income inequality 
in the African context. They employed a dynamic 
two-step difference GMM approach to analyze data 
spanning the years 1990 to 2017. The study’s outcomes 
revealed intriguing patterns: While the overall impact of  
institutions on income inequality across Africa lacked 
statistical significance, distinct indicators of  institutional 
quality, notably the control of  corruption and stringent 
adherence to the rule of  law, demonstrated a notable 
capacity to reduce income inequality significantly. In 
contrast, other indicators such as GE, VAC, RQ, and PS 
failed to exhibit statistically significant effects on income 
inequality. The study underscored the importance of  
prioritizing measures aimed at curbing corruption and 
establishing robust rule of  law systems as pivotal factors 
in fostering a more balanced distribution of  income 
across Africa.
Berisha et al. (2023) examined the differential impact 
of  inflation on income inequality across various levels 
of  inequality in the US states. Using a quarterly dataset 
from 1990: Q1 to 2017: Q2, the study employed a panel 
quantile regression model with fixed effects. The results 
showed a negative contemporaneous effect of  inflation 
on inequality, which was more pronounced at higher 
levels of  income inequality. However, over a one-year 
period, higher inflation rates only increased income 
inequality when it was initially low.
Abbas et al., (2023) examined how well institutions 
function, the level of  education, and corruption in 
lower middle-income countries. Study discovered that 
if  institutions work better, there is less corruption. 
Furthermore, they found that having a higher level 
of  education in institutional could lead to increase the 
corruption. This study highlights the crucial importance 
of  dealing with corruption, particularly within the 
education system.

METHODOLOGY 
Data has been gathered from various sources, namely 
the World Governance Indicators, Transparency 
International, Global Consumption and Income Project, 
and World Development Indicators. The data spans from 
2000 to 2021 and includes British Colonized countries 
such as Bangladesh, India, Pakistan, and Sri Lanka. 
Income inequality is measured using the Gini Index, while 
corruption is assessed using the corruption perception 
index, with higher values indicating increased corruption. 
The mean of  six governance indicators is utilized as a 
measure of  institutional quality (Ismail and Amjad, 
2022). The study incorporated an interaction term to 
examine the influence of  poor institutional quality. This 
interaction term was created by combining corruption 
and institutional quality. Inflation was approximated 
using the GDP deflator. Government effectiveness and 
political stability were employed to assess the impact of  
the government and political stability on the economy.

Model Specification
In the era of  globalization, panel data studies often face 
challenges related to residual interdependence and the 
omission of  common factors, leading to cross-sectional 
dependence. To address this issue, the analysis begins 
with a cross-sectional dependency test. This examination 
serves the purpose of  ascertaining whether latent factors 
and disturbances embedded within the error term 
contribute to significant cross-sectional interdependence 
in models applied to panel data. Such interdependence 
can potentially yield misleading and erroneous outcomes. 
The null hypothesis for this test postulates the absence of  
cross-sectional interdependence, whereas the alternative 
hypothesis suggests its presence. Additionally, unit root 
tests are administered to establish the integration status 
of  the variables, as the existence of  unit roots can lead 
to problematic results. To ensure the robustness of  the 
findings, the Pesaran-CIPS unit root tests are employed 
for validation.
Prior to conducting the econometric model estimation, 
a crucial step involves identifying the long-term 
relationships among the considered variables. This goal is 
achieved through the utilization of  diverse cointegration 
tests, encompassing the Pedroni test (Pedroni, 1999), the 
combined cointegration test devised by Maddala (Maddala 
and Wu, 1999), and the Kao residual test (Mouelhi, 2021). 
The cointegration regression analysis utilizes the FMOLS 
method to assess long-term sensitivities. The FMOLS 
approach proves beneficial in mitigating concerns related 
to autocorrelation and endogeneity (Marimuthu et al., 
2021). In terms of  unbiased estimations, both FMOLS 
and DOLS demonstrate superiority over ordinary least 
squares (OLS) (Akbar et al., 2021a, b; Marimuthu et al., 
2021; Zhong, et al., 2022).
The objective of  the study is to examine the relationship 
among income inequality, corruption, and institutional 
quality for countries in the SAARC. The study employs a 
specific functional form for the analysis.
INCINQit = ƒ (CORRit, IQ_CORRit, IQit, INFit, GEit, PSit, ) (1)

Econometric Model
INCINQit = δi + λi + β1i CORRit + β2iIQ_CORRit + 
β3iIQit + β4iINFit + β5iGEit + β6iPSit + µi               (2)
In the provided equation, “i” represents a specific 
SAARC country, and “t” denotes the time period. Within 
this model, λi and δi represent the trends and country-
specific effects, while β1 to β6 indicate the magnitude 
of  impact for corruption, institutional quality, the 
interaction between institutional quality and corruption, 
inflation, government effectiveness, and political stability, 
respectively.

RESULTS AND DISCUSSIONS 
Descriptive Statistics
Table 1 displays descriptive statistics and a correlation 
matrix of  the variables included in the study. The statistical 
properties of  the variables align with their suitability for 



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panel data estimation. Furthermore, the presence of  
low correlation among the independent variables helps 
alleviate the problem of  multicollinearity.

Cross-Sectional Dependence Tests 
Table 2 displays the results of  the cross-sectional 
dependence tests, showing a probability value of  0.6325. 
This value exceeds the significance threshold of  0.05. Based 
on this p-value, study can finalize that there is not enough 
substantiated proof  to reject the null hypothesis. This null 
hypothesis proposes the lack of  cross-sectional dependence.

than 0.05, implying that they are integrated of  I (0). These 
results provide the basis for conducting panel regression 
analysis to examine unexpected shocks and structural 
changes.

Results of  Panel Cointegration and Long Run Estimates
Following the completion of  the unit root test, the 
subsequent stage involves investigating the existence of  
a prolonged relationship using cointegration tests. Three 
distinct cointegration tests were deployed: the Pedroni 
panel cointegration test, the Fisher-Johanson combined 
cointegration test, and the Kao residual cointegration test. 
The Pedroni panel cointegration test, established in 1999 
and further refined in 2004, encompasses seven statistical 
metrics that aid in identifying enduring relationships 
among the variables. These metrics encompass panel 
ADF statistics, panel v statistics, panel PP statistics, panel 
rho statistics, as well as three group statistics: rho, ADF, 
and PP. The outcomes of  the Pedroni panel cointegration 
test are illustrated in Table 4. The decision to reject 
the null hypothesis, which posits no cointegration, is 
contingent upon the majority of  the statistical measures 
attaining significance levels of  1%, 5%, or 10%. The 
results validate the presence of  a long-term association 
within the SAARC panel encompassing income 
inequality, corruption, institutional quality, institutional 
quality coupled with corruption, inflation, government 
effectiveness, and political stability. The p-values and 
t-statistics presented in the table indicate that six of  the 
seven statistical metrics hold significance.
Subsequently, the Fisher panel cointegration test, 
introduced by Maddala and Wu in 1999, was executed to 
validate the outcomes of  the Pedroni panel cointegration 
test. Additionally, the Kao residual cointegration test 
was invoked to further reinforce the conclusions of  
the Pedroni panel and Fisher-Johanson combined 
cointegration tests. The findings from the Fisher panel 
and Kao residual cointegration tests are showcased 
in Table 5, corroborating the existence of  enduring 

Table 1: Description of  Variables
INCINQ CORR IQ_CORR IQ INF GE PS

Mean 0.452 2.734 -1.376 -0.762 7.710 -0.383 -1.316
Median 0.431 2.600 -3.212 -1.334 6.219 -0.391 -1.25
Maximum 0.571 4.000 8.766 2.369 38.512 0.405 0.090
Minimum 0.394 0.400 -5.557 -2.409 1.921 -0.937 -2.810
Std. Dev. 0.051 0.761 4.167 1.3689 5.761 0.3458 0.668
Observations 88 88 88 88 88 88 88
INCINQ 1.000
CORR -0.207 1.000
IQ_CO -0.365 0.524 1.000
GE 0.459 0.455 0.338 1.000
INF -0.179 0.043 0.018 -0.107 1.000
IQ -0.382 0.689 0.969 0.403 0.041 1.000
PS 0.065 0.286 0.545 0.556 -0.234 0.520 1.000

Table 2: Cross-Sectional Dependence Tests 
Test Statistic d.f. Prob.
Breusch-Pagan LM 29.21791 6 0.0001
Pesaran scaled LM 6.702432 0.0000
Pesaran CD 0.434406 0.6640

Cross-Sectional Dependence Unit Root Test
Once the absence of  CSD in the data was confirmed, 
the unit root tests were conducted and the outcomes 
are presented in Table 3. The test results confirmed that 
all variables in the study are integrated of  order zero, 
indicating that they are stationary at the level. Based on 
these results, study can reject the null hypotheses of  a 
unit root for all variables. The findings demonstrate that 
variables are stationary, as indicated by p-values lower 

Table 3: Cross-Sectional Dependence Unit Root Test
Variables Pesaran-CIPS
CORR <0.01
GE <0.01
INCINQ <0.01
INF <0.01
IQ <0.01
IQ_CORR <0.05
PS <0.05



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relationships encompassing all the variables. In essence, 
all three tests concur, collectively affirming a sustained 
connection among the variables.

Results Fully Modified OLS
Upon verifying the existence of  enduring relationships 
among all variables, the analysis progresses to the 
cointegration regression analysis stage. It’s crucial to 
underline that the cointegration test’s sole objective is 
to confirm the presence of  a prolonged relationship. In 
order to investigate the interplay and causal flow within 
the SAARC panel, this study opted for the utilization of  
the FMOLS technique. The outcomes, as depicted in 
Table 6, demonstrate that within the SAARC economies, 
institutional quality exerts an adverse influence on 
income inequality, yielding a negative impact (Zehra et al., 
2021; Cheah, 2021; Náplava, 2020; Mehmet, 2017; Borja, 
2018). Based on the findings, it can be inferred that a 1 
unit increase in institutional quality will lead to a decrease 
in income inequality by 0.027%. 
The results of  the FMOLS analysis reveal a significant 
positive relationship between poor institutional quality 
and income inequality. This implies that a 1 unit increase 
in corrupted institutions leads to a 0.07% increase in 
income inequality, according to the findings. 
In 2003, Sonin presented a dynamic model that suggests 
how low-quality institutions can lead to the negative 
impact of  inequality on economic growth. The core idea 
is that such institutions tend to favor the wealthy through 
wasteful redistribution, which hampers the overall growth 
process. In a similar vein, Chong and Gradstein (2004) 
proposed a mechanism linking low institutional quality to 

the intensity of  rent-seeking behavior derived from public 
assets like technological knowledge or natural resources.
Sonin’s theoretical model demonstrates that in societies 
without strong democratic governance, where there is 
both political and wealth inequality, the rich and politically 
influential individuals tend to manipulate institutions for 
their own benefit, engaging in rent-seeking activities. 
This behavior not only leads to inefficient allocation of  
resources but also results in slower economic growth and 
increased inequality.
In contrast, the analysis demonstrates a positive and 
significant association with corruption (Bayar and 
Aytemiz, 2019; Gupta, Davoodi, and Alonso-Terme, 
2002; Policardo et al., 2019; Dwiputri, I. N., Arsyad, L., & 
Pradiptyo, R., 2018). This suggests that a 1 unit increase 
in corruption leads to a 0.019% increase in income 
inequality, according to the findings. 
Government effectiveness, in contrast, exhibits a negative 
and statistically insignificant influence on income 
inequality. The findings suggest that a 1 unit increase in 
government effectiveness is associated with a negligible 
decrease of  0.004% in income inequality, although this 
result is not statistically significant. Political stability 
shows a significant negative relationship with income 
inequality. The analysis reveals that a 1 unit increase in 
political stability is associated with a decrease of  0.022% 
in income inequality. On the other hand, inflation has 
a significant positive impact on income inequality. The 
findings indicate that a 1 unit increase in inflation leads 
to a 0.00065% increase in income inequality, according 
to the results.

Table 4: Pedroni Panel Cointegration Test Result
With Dimension Between Dimension
Statistic Prob. Statistic Prob. Statistic Prob.

Panel v-Statistic 0.3 0.382 1.194 0.116 Group rho-Statistic -1666 0.048
Panel rho-Statistic 0.999 0.159 -2.112 0.017 Group PP-Statistic -5.903 0
Panel PP-Statistic -5.499 0 -4.958 0 Group ADF-Statistic  -1.846 0.033
Panel ADF-Statistic -3.175 0 -1.588 0.056

Table 5: Fisher and Johanson Combined Cointegration and Kao Residual Cointegration Results
Fisher and Johanson combined cointegration results 
No cointegration: null hypothesis Reject criteria: p<0.05
Hypothesized No. of  CE(s) Fisher Stat.* (from trace test) Prob. Fisher Stat.* (from 

max-eigen test
Prob.

None 57.02 0 42.57 0
At most 1 30.3 0.0002 22.57 0.004
At most 2 22.3 0.004 22.3 0.004
Kao Residual Cointegration Results
No cointegration: null hypothesis Reject criteria: p<0.05  

-7.382701 (0.000)



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Table 6: Long-run dynamics
Variable FMOLS

Coefficient Prob.
IQ -0.02727 0.0067
IQ_CORR 0.017458 0
CORR 0.019121 0.0006
GE -0.00377 0.7935
PS -0.02221 0.0001
INF 0.000647 0.069

CONCLUSION 
The present study aimed to investigate the relationship 
between institutional quality, corruption, and income 
inequality in SAARC countries from 2000 to 2021. The 
researchers used several statistical methods, including 
the Padroni cointegration test, Kao cointegration, and 
Fisher test, after employing the FMOLS approach. The 
empirical analysis conducted in the study confirmed that 
the examined factors had long-term effects on income 
inequality in the SAARC countries. The results indicated 
the following: Institutional quality: It was found to have 
a significant negative impact on income inequality. This 
suggests that countries with better institutional quality 
tend to experience lower levels of  income inequality. 
Corruption: The study found that corruption had a 
significant positive impact on income inequality. This 
implies that higher levels of  corruption in a country are 
associated with increased income inequality. Institutional 
quality and corruption combined: When considering 
the effect of  corruption along with institutional quality, 
the study found a significant positive impact on income 
inequality. This suggests that the presence of  corruption 
can counteract the potential benefits of  good institutional 
quality in reducing income inequality. The study 
highlights the importance of  IQ in reducing income 
inequality in SAARC countries. It also underscores the 
detrimental impact of  corruption on income distribution. 
Moreover, the findings indicate that even countries with 
good IQ may not effectively address income inequality if  
corruption remains prevalent.

RECOMMENDATION
Strengthen Institutional Quality
Policymakers should focus on improving institutional 
quality within the SAARC economies. This involves 
enhancing the efficiency, transparency, and accountability 
of  public institutions, as well as ensuring the rule of  law 
and protection of  property rights. Strong institutions can 
help promote equitable economic growth and reduce 
income inequality.

Fight Corruption
The positive relationship between corruption and income 
inequality suggests that addressing corruption should be a 
priority. Implementing effective anti-corruption measures, 
such as promoting transparency, enforcing strict penalties 

for corrupt practices, and establishing independent 
oversight bodies, can help reduce rent-seeking behaviour 
and create a more equitable distribution of  resources and 
opportunities.

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