Economics seminar paper

1. HARAMAYA UNIVERSITY
Postgraduate Program Directorate
College of Business and Economics
Department of Economics
Program: MSc in Development Economics
Seminar Title: THE NEXUS BETWEEN CORRUPTION AND
ECONOMIC GROWTH IN SUB-SAHARAN AFRICA
BY: Urgessa Firomsa
Advisor: Abdella Mohammed (PhD)
JANUARY, 2024
HARAMAYA, ETHIOPIA

2. i
Table of Contents
Table of Contents............................................................................................................................. i
List of Acronyms and Abbreviations.............................................................................................. ii
Abstract..........................................................................................................................................iii
CHAPTER ONE............................................................................................................................. 1
1. INTRODUCTION .................................................................................................................. 1
1.1. Background of the study .................................................................................................. 1
1.2. Statement of the Problem................................................................................................. 2
1.3. Seminar Objectives .......................................................................................................... 4
1.3.1. The general objectives of Seminar............................................................................ 4
1.3.2. The specific objective of the Seminar....................................................................... 4
CHAPTER TWO ............................................................................................................................ 5
2. RELATED LITERATURE REVIEWS.................................................................................. 5
2.1. Theoretical Literature Review of Corruption ............................................................... 5
2.2. Empirical Studies on Corruption and Economic growth in SSA..................................... 6
2.3. Conceptual framework of the study ................................................................................. 9
CHAPTER THREE ...................................................................................................................... 11
3. METHODOLOGY ............................................................................................................... 11
3.1. Data source ................................................................................................................. 11
3.2. Description of Variable and its measurements............................................................... 11
3.3. Methods of data analysis................................................................................................ 13
3.4. Econometric model specification................................................................................... 13
3.4.1. Generalized Method of Moment (GMM) Model for dynamic panel data.............. 15
3.5. Test for Dynamic Panel data Model........................................................................... 17
CHAPTER FOUR......................................................................................................................... 18

3. ii
4. RESULS AND DISUSIONS ................................................................................................ 18
4.1. The GMM Result and Discussions .................................................................................... 18
CHAPTER FIVE .......................................................................................................................... 21
5. CONCLUSION AND RECOMMENDATION.................................................................... 21
5.1. Conclusion...................................................................................................................... 21
5.2. Recommendation............................................................................................................ 21
References........................................................................................................................................I
List of Acronyms and Abbreviations
CPI Corruption Perception Index
GDP Gross Domestic Product
GE Government effectiveness
GMM Generalized Method of Moment
FE Fixed effect
FDI Foreign Direct Investment
INF Inflation rate
PS Political Stability
RE Random effect
SSA Sub Saharan Africa
UNCAC United Nations Convention Against Corruption
UN Unemployment

4. iii
Abstract
The objective of this seminar was to examine the nexus between Corruption and Economic
growth in forty (40) SSA countries, by utilizing the dynamic panel data from 2008 to 2022,
collected from online World Bank data set. The paper reviewed the relevant theoretical and
empirical literature on corruption and its relationship with economic growth in the region. It
also discussed the conceptual framework and methodology used in the study, including data
sources, variables, and econometric model specifications. In order to capture the endogenity
problem, the two step System Generalized Method of Moments (SGMM) model was employed for
the econometric analysis of dynamic panel data. The two step SGMM revels that Corruption is
negatively affect the economic growth in SSA, which means the result support the finding of
previous researcher those argued as corruption hinders growth (the "sands of the wheels"
hypothesis).However the combined value of coefficient of the interaction of corruption and
unemployment support with that corruption and unemployment can enhance economic efficiency
(the "grease the wheels" hypothesis).Additionally the study was conducted the autocorrelation
test for second order differences (AR-2) and test of instrumental and model identification test.
The output for these tests respectively shows that there is no autocorrelation and it also revealed
that the instrument is valid and model is just identified.
Key words: Corruption, Dynamic panel data, Economic growth, SSA, System GMM

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CHAPTER ONE
1.INTRODUCTION
This chapter presents the background of the study, the statement of the problem and objectives of the
Seminar on the nexus between Corruption and Economic Growth in Sub Saharan Africa.
1.1. Background of the study
Today the issue of misusing public resources for self-benefits, being without a job and general price
hike is the major problem of the world. Sub-Saharan Africa (SSA) has facing continues economic
hardships until today than other continents, because of sluggish economic expansion, high rates of
joblessness, rampant corruption, and rise of the general consumer price index. These elements have
hampered the region's economic growth and inversely affect the living standard of people. Many
scholars forwarded the problems of the above macroeconomic variables. For instance among the
devastating macroeconomic problems that had received serious attention from economists, financial
analysts, policymakers, and the monetary officials in both developed and developing countries of the
world is the relationship between the Corruption, unemployment, inflation and economic growth
(Ndoricimpa, 2017; Seleteng et al., 2013; Van Eyden et al., 2015)
Corruption is a base for all the ills of a nation which weakens the foundation and the economic
performance of a country. Several socio-economic issues like poverty, inflation, unemployment,
income inequalities, and commercial limitations are the well-known factors that cause corruption
(Akçay, 2001). According to Gazdar (2010) investigated, there is a positive relationship between
corruption and Economic growth, when the quality of governance is very low.
The study conducted by Mo (2001) and Tanzi et al., (2000) shows how corruption and economic
growth are related; regardless of the main performance measure or statistical variables employed in
these investigations. The majority of the above-mentioned studies indicate the negative relationship
between corruption and economic growth. During his studies Mo (2001) concluded that there is an
inverse relationship between economic growth and corruption. Consequently, it is hypothesized that
less corrupted countries have better levels of institutional transparency and economic progress than
more corrupted countries of the world.
According to Gründler and Potrafke (2019) mentioned the nexus between corruption and economic
growth has been examined for a long period of time. Many empirical studies measured corruption by
the reversed Transparency International's Perception of Corruption Index (CPI) and ignored that the
CPI was not comparable over time. The CPI is comparable over time since the year 2012. They
employ new data for 175 countries over the period 2012–2018 and re-examine the nexus between
corruption and economic growth. The cumulative long-run effect of corruption on growth is that real
per capita GDP decreased by around 17% when the reversed CPI increased by one standard deviation.
The effect of corruption on economic growth is especially pronounced in autocracies and transmits to
growth by decreasing FDI and increasing inflation.

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Theoretically, the literature reaches no agreement about the effect of corruption on economic growth.
Some researchers suggest that corruption might be desirable (Dreher & Gassebner, 2013) Corruption
works like piece rate pay for bureaucrats , induces a more efficient provision of government services,
and it provides a leeway for entrepreneurs to bypass inefficient regulations. From this perspective,
corruption acts as a lubricant that smoothers operations and, hence, raises the efficiency of an
economy. Accordioning to Dreher and Gassebner (2013) finding shows corruption facilitates firm
entry in highly regulated economies. For example, the 'greasing' effect of corruption kicks in at around
50 days required to start a new business. Their results thus provide support for the 'grease the wheels'
hypothesis.
In their research Qureshi et al.,(2021) looked again at how corruption and foreign direct investment
affected economic growth in 54 industrialized and developing nations. They apply panel
Autoregressive (PVAR) model to annual data covering 1960–2018 and find that control of corruption
affects economic growth. Additionally, they note that for developing countries, economic growth and
corruption have a positive bidirectional causal relationship and a negative unidirectional correlation
for developed nations.
1.2. Statement of the Problem
The finding of many studies on the impact of corruption on economic growth is contradictory. Some
papers indicate that corruption can important for economic growth as supporting “the grease of the
wheels” hypothesis whereas, many scholars argues that corruption harms economic growth and firms’
performance that supports the “sands of the wheels” hypothesis. For instance some researchers like
(Houston, 2007; P. G. Méon & Sekkat, 2005; P. Méon & Weill, 2008) continue to argue that corruption
may be economically justified as it provides opportunities to avoid inefficient regulations and red tape,
and allows the private sector to correct government failures and inefficiency. As such, it could
potentially motivate economic growth by eliminating bureaucratic barriers to entry and dropping
companies’ transaction costs when trying to comply with extreme regulations.
Event thought corruption is an economic evil, another scholars also support “the grease of the wheels”
hypothesis as corruption enhances efficiency by removing rigidities imposed by the government which
interfere with economic decision and hinder investment (Dreher & Gassebner, 2013; Huang, 2016;
Nguyen & Bui, 2022; Omoteso & Mobolaji, 2014)
Many countries in sub-Saharan Africa (SSA) experience impeded economic development and corrupt
public administration. As the United Nations Convention Against Corruption (UNCAC) focuses on
implementation mechanisms in SSA, there is a need to examine the causes of the setbacks affecting
these mechanisms, looking at the current trends of corruption and their impact on socioeconomic
development (Senu, 2020)
Th study conducted by Shittu et al. (2018) on the impact of external debt and corruption on economic
growth in the selected five Sub-Saharan African (SSA) countries, from 1990 to 2015 shows a positive
relationship between corruption and economic growth, thus their finding support the grease of the
wheels hypotheses. They used a Fully modified OLS and dynamic OLS techniques are also employed
to examine the long-run coefficients of the variables of the model, as well as panel Granger causality
test, in order to examine the direction of causality among the variables.

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The advocators of “sands of the wheels” believe that corruption is an obstacle to economic growth
and development (Aidt, 2009; Amoh et al., 2022; Bardhan, 2017; Ibrahim et al., 2015; Mohammed et
al., 2022; Sharma & Mishra, 2022).
Corruption has a negative impact on investment and growth in addition to static efficiency. A customs
officer allowing contraband through, a tax auditor purposefully ignoring a case of tax evasion, and so
on are just a few examples of instances where corruption benefits both the official and his client. There
is a component in the corruption literature, contributed both by economists and non-economists,
suggesting that, in the context of pervasive and cumbersome regulations in developing countries,
corruption may actually improve efficiency and help growth (Bardhan, 2017)
The only studies that shows the combined impact of corruption, unemployment and inflation on
economic growth by using panel data from 79 developing countries was conducted by Uddin and
Rahman (2023). The main objective of their empirical study was to examine the impact of corruption,
unemployment and inflation on economic growth for seventy-nine (79) developing countries of the
world for the period from 2002 to 2018. The authors used Panel unit root tests
(PUT), Pooled Mean Group (PMG), Fully modified ordinary least square (FMOLS), and
Dynamic least square (DOLS), for the data estimation. The result shows
that corruption, unemployment and political stability have negative effect on GDP
per capita, while Inflation, governance effectiveness and rule of law have positive effect
on GDP per capita.
Even though there is growing attention to the issue of corruption and economic growth globally, there
is limitation within the Sub-Saharan African (SSA) and it is not have been much broadly studied.
Thus, this study investigates the impact of corruption on economic growth in Sub-Saharan Africa by
using panel data from 2008-2022 and try to fill the gap that did not covered by former studies. In this
study, for addressing the gabs and analyzing the panel data of fifteen (15) years, the System
Generalized Method of Moments (GMM) model used in order to capture the short term and long-
term relationship between the variables and also these models are particularly useful when studying
the impact of variables that might have lagged effects on economic growth, such as corruption,
unemployment, and inflation. This model also addresses endogeneity concerns, provide efficient
estimates and allow us to examine the impacts of corruption, on economic growth, as well as their
combined effect by accounting the time varying effects and control for unobserved heterogeneity
across countries. In order to fill the gabs, this paper provide a valuable insight by examining the nexus
between corruption and economic growth in SSA, and it will also give evidence-based
recommendations for policymakers and stakeholders in Sub-Saharan Africa.

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1.3. Seminar Objectives
1.3.1. The general objectives of Seminar
The Primary objective of this study is to analyze the relationship between Corruption and Economic
Growth in Sub-Saharan Africa.
1.3.2. The specific objective of the Seminar.
The specifically the seminar is aims to:
 To examine the effect of corruption on economic growth in SSA.
 To analyze the interactive effect of Corruption and Unemployment on Economic
growth in SSA
 To provides the empirical evidence that can inform policymakers and stakeholder in
designing effective strategies to address the issues of corruption in SSA with the aim of
promoting economic growth.

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.
CHAPTER TWO
2. RELATED LITERATURE REVIEWS
This chapter discusses the theoretical and empirical literatures related to corruption and economic
growth as a general and Sub-Saharan Africa in particular
2.1. Theoretical Literature Review of Corruption
Misuse of public office for personal benefit is a valid explanation for public corruption. Naturally,
misuse usually entails the application of a legal standard. If corruption were defined in this manner, it
would include things like bribery and misappropriation of public funds, kickbacks in public
procurement, and the sale of government property by officials (Acaravcet al. 2023; Anh et al., 2016;
Azam, 2022;Ghauri et al., 2022; Svensson, 2005).
Two theories about how corruption affects economic growth are covered in the article of Gründler
and Potrafke (2019), the "sand the wheels" theory contends that corruption impedes innovation and
efficient production, while the "grease the wheels" theory contends that corruption can boost economic
growth by getting around ineffective regulations. The idea that corruption hinders economic growth is
supported by the actual data in the article, particularly in nations with low investment rates and poor
governance.
In case of Africa; Money laundering, illicit financial flows, bribery and kickbacks, embezzlement and
theft, state capture, and other financial crimes are all common forms of corruption that have a negative
impact on the continent's socioeconomic growth. Due to the increasing tendency in the severity of
these crimes, corruption continues to have a major detrimental influence on Africa's prospects for
present and future growth. The size of these many corruption channels must be measured and regularly
monitored in order to create rules that minimize these detrimental consequences(Hope, 2020)
One of the most dangerous social ills for many society, which eats into the political and economic
growth of any country and as well abolishes the functioning of various societies organs is called
corruption(Mongi & Saidi, 2023).
Complexities in arriving at a universally accepted definition of corruption due to cultural, historical,
and political differences, reflecting poorly defined moral and unethical acts (Transparency
International). Even though Various forms of corruption like nepotism, bribery, embezzlement, and
patronage, that all need different investigative and corrective strategies to control, in certain situations,
it has a positive impact on economic growth by serving as a mechanism for avoiding inefficient
regulations or facilitating transactions in countries with weak institutional frameworks(Aidt, 2009)

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Corruption is considered one of the biggest threats to humanity in both developing and developed
countries because it distorts economic growth, lowers foreign direct investment, and decreases
productivity on a firm level due to inefficient allocations of contracts, and also Corruption impedes the
general societal and economic environment because it reduces voluntary contributions to public goods,
increases inequality, facilitates emigration of highly skilled people and creates inefficiencies in the
sport sector(Dimant et al., 2016)
2.2. Empirical Studies on Corruption and Economic growth in SSA
There is a plethora of information and debates about the relationship between corruption and economic
growth in the empirical literature. Here bellows are some main points that empirical research has
validated. The studies by Afonso and de Sá Fortes Leitão Rodrigues (2022) evaluates the impact of
corruption on economic activity and emphasizes the significance of government size by considering a
panel of 48 nations and incorporating the dynamic models and the generalized method of moments
methodology. The Transparency International Corruption Perceptions Index is used as a measure of
corruption, and it is available for data from 2012 to 2019. The authors discover that corruption has a
considerable negative impact on GDP per capita levels and rates of growth, but that large governments
gain less from reduced corruption. Moreover, developing economies gain less from decreasing
corruption, independent of government size, and the influence of government size alone cannot be
explained.
Gründler and Potrafke (2019) paper investigates the connection between economic expansion and
corruption. It draws attention to the fact that earlier empirical research frequently used Transparency
International's Perception of Corruption Index (CPI) to measure corruption, ignoring the CPI's lack of
cross-validation. The authors reexamine the relationship between corruption and economic growth
using fresh data for 175 countries from 2012 to 2018. Their finding suggested that, economic progress
is significantly hampered by corruption. When one standard deviation is added to the inverted CPI,
which measures corruption, the cumulative long-run effect of corruption on growth is projected to be a
drop of approximately 17% in real per capita GDP. Autocracies are more likely to experience the
negative effects of corruption on economic growth, which are mostly manifested as lower foreign
direct investment (FDI) and higher inflation.

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The impact of corruption on economic growth in the BRICS nations is investigated by Simo-Kengne
and Bitterhout (2023) using a panel dataset covering the years 1996–2014. According to empirical
findings, there is a negative correlation between output growth and the corruption index when only
heterogeneity or fixed effect is taken into account. But when endogeneity and heterogeneity by
considering the GMM parameters are taken into consideration, the corruption index shows a strong
and positive correlation with economic growth. Although this result defies most empirical evidence,
with a small exception, which has shown that corruption has a negative effect on economic growth, the
effect of corruption on growth does, in fact, diminish with increasing levels of corruption. This raises
the possibility of a corruption threshold, below which the relationship could have the opposite effect.
Li and Kumbhakar (2022) explore the impact of democracy, corruption, and economic freedom on
economic growth for more than a hundred nations using a unique dataset compiled from multiple
sources. By using a quantile regression technique to account for diverse impacts the study finding offer
some fresh insights while also corroborated by some previous research findings. Quantile regression
estimates frequently diverge significantly from GMM estimations. They discover that the beneficial
impact of democracy is smaller in nations with faster GDP per capita growth rates and similarly,
countries with the lowest GDP per capita growth; corruption “sands the wheels” whereas for the
highest GDP per capita growth, corruption "greases the wheels".
The study conducted on the effects of corruption on economic growth, human development and
natural resources in Latin American and Nordic countries by utilizing the Bayesian panel Vector Auto
Regression (VAR) model is estimated by (Urbina & Rodríguez, 2022).The result of this article
indicates that there was some applicable differences by considering panel error correction VAR model
and an asymmetric panel VAR model in addition the above mentioned model. The empirical finding of
the authors recommends that in Latin America there is support for the “sand the wheels hypothesis in
Bolivia and Chile and it support for the “grease the wheels” hypothesis in Colombia and also there is
no significant impact of corruption on growth in Brazil and Peru, while in Nordic countries the reply
of growth to shocks in corruption is negative in all cases. corruption tends to spur natural resources
sector in Latin American countries, while it is detrimental for natural resources sector in Nordic
countries.
The effect of corruption on economic growth across provinces in Indonesia over the 2004–2015
period was examined by (Alfada, 2019). The study investigates whether corruption works to the

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benefit of the provinces with low-corruption levels by supporting their economic growth when the
number of corruption cases is below the corruption threshold and in contrast, corruption worsens
economic growth in provinces with high levels of corruption when corruption exceeds the threshold.
The researcher was utilized the threshold model developed and for controlling endogeneity problem he
used the instrumental variable two-stage least squares (2SLS) estimator. The finding describes that the
impact of corruption is a growth-deteriorating effect for provinces with corruption levels below the
threshold of 1.765 points, and the destructive effect of corruption appears stronger for provinces with
corruption levels above the threshold.
Employing the fixed effect model and the random-effects model Mohammed et al.(2022)study about
the organized crime, corruption and their challenges to the economic growth of the Economic
Community of West African States (ECOWAS) by using secondary time series data that covers the
period 2000 to 2019 for 11 countries in the ECOWAS region and finds that corruption significantly
reduces economic growth.
Furthermore in order to determine if corruption had a detrimental effect on economic growth in
thirteen Asia-Pacific nations between 1997 and 2013, Huang (2016) used the bootstrap panel Granger
causality approach, which takes into account both cross-sectional dependence and heterogeneity across
countries. According to his empirical findings, there is no significant causal relationship between
corruption and economic growth for the other countries, but there is a significantly positive causal
relationship between corruption and economic growth in South Korea and a significantly positive
causal relationship between economic growth and corruption in China. The empirical findings refute
the widely held belief that corruption hinders economic growth in any of the thirteen Asia-Pacific
countries. In contrast, the study's findings point to support for South Korea for the "grease the wheels"
theory.
The analysis of how corruption control affects government spending's effect on economic growth was
examined by covering the panel data of 2002–2019, gathered from sixteen Asian Emerging Markets
and Developing Economies(Nguyen & Bui, 2022).The authors utilized the Generalized method of
moments (GMM) and threshold model to estimate research models. The outcome shows that
government spending and efforts to combat corruption have a detrimental effect on economic growth.
An intriguing finding of this study is that government spending and corruption control interact to
lessen the degree to which these two factors negatively affect economic growth. Furthermore, the

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findings of the threshold model estimation show that corruption control has two threshold values,
which are -0.61 and 0.01 respectively, in contrast to earlier research.
The study conducted by Shittu et al. (2018) on the impact of external debt and corruption on
economic growth in the selected five Sub-Saharan African (SSA) countries, from 1990 to 2015 shows
a positive relationship between corruption and economic growth and also the uni-directional causality
running from economic growth through corruption. They used a Fully modified OLS and dynamic
OLS techniques are also employed to examine the long-run coefficients of the variables of the model,
as well as panel Granger causality test, in order to examine the direction of causality among the
variables.
2.3.Conceptual framework of the study
The conceptual framework for this study can be developed from the reviewed literature, and also from
the standing point of views of various economic theories that indicates the impact of corruption and
other variables on economic growth in Sub Saharan Africa. The institutional economics offers a
framework for examining how institutions influence the connections between inflation,
unemployment, corruption, and economic growth. Inadequate institutions and governance frameworks
impede the development of jobs, heighten inflationary pressures, and lead to high levels of corruption.
These negative impacts can be lessened by strengthening institutions ,encouraging openness, and
improving accountability (Chavance, 2008).
The conceptual framework of this study indicates the relationship between the regressed, Gross
domestic product per capita and the regressors such as Corruption, Unemployment, inflation, Political
Stability and Absence of Violence, Government Effectiveness, and Foreign direct investment and
country`s legal system

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Figure 1: Conceptual framework of the study.
Source: Own conception based on the literature
Gross
Domestic
Product per
capita
Corruption
inflation
Legal
system
Political
Stability and
Absence of
Violence
Government
Effectivenes
s
Foreign
direct
investment
.
Unemploy
ment

15. Page | 11
CHAPTER THREE
3. METHODOLOGY
3.1. Data source
This study covers about forty (40) sub-Saharan Africa countries. The sample countries were selected
based on data availability. However, the selections of countries encompass all regions of sub-Saharan
Africa. This indicates that the sample is representative of the whole sub-Saharan Africa countries that
helps to make an inference for all sub-Saharan Africa countries. The annual panel data covering from
2008-2022 was collected from different sources, for instance some data collected from the World
Bank`s World development indicators (2022) such as gross domestic product per capita,
unemployment, inflation and foreign direct investment whereas some data was gathered from the
World governance indicators (2023) such as Political Stability and Absence of Violence, and
Government Effectiveness, Corruption perception index was collected from the Transparency
international (TI) organization and the countries legal system obtained from the website of them, for
the selected the Sub-Saharan African nation based on data availabilities.
3.2. Description of Variable and its measurements
Gross Domestic Product per capita (GDP pc)- is a dependent variable, and this gross domestic
product per capita (GDP.pc) is a measure of the economy’s output per population of the countries
(Uddin & Rahman, 2023). This data obtained from World Bank
Corruption (CPI): Corruption is the activity conducted by government officials, politicians, or by
someone who is on the positions that abuse of trusted power, for personal gain or it is a misuse of
public resource for private benefit. The level of corruption perception index that will be measured
using the index such as corruption perception index (CPI) provided by the Transparency international.
The index ranges from 0 to 100 with higher value indicating lower level of corruption and the lower
value indicating the higher level of corruption(Transparency, 2020)
Unemployment (UN): unemployment rate will be obtained from the World development indicator of
the World Bank, based on Interinstitutional Labor Organization (ILO) estimates. The unemployment
rate represents the percentage of labor force that is actively seeking employment but unable to find it
(Akinyele et al., 2023; International Labour Organization (ILO), 2023)

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Inflation (INF): The inflation indicates that the continuous rises of the general price level of a
commodities or goods and service over a period of time. Data for inflation rate was collected from
World Bank, world development indicators. The inflation rate measures the percentage in the general
price level over a specified period and is mostly measured by the change in the consumer price index
(CPI) for a given time period(Uddin, 2021)
Political stability and absence of violence (PS): is a situation in which a notions or regions live in
peaceful and secure the political environments without civil war, armed conflicts, political upheavals
and any other part of violence. Political stability and absence of violence will be measured using an
established index such as World governance indicators (WGI) provided by the World bank (Uddin &
Rahman, 2023).
Government effectiveness (GE): Can be measured using the established index obtained from the
World Governance Indicators (WGI). It shows the perceptions of the quality of public services or the
civil service, the quality of policy formulation and implementation, the degree of independence from
political pressures, and the competence of the civil service.(Uddin & Rahman, 2023)
Foreign direct investment (FDI): The FDI is the capital accumulation or investment engaged by
investors of abroad country and flow in to another country. It plays a key role for economic relation
and growth by providing job opportunities, technologies and markets for the country. The data of FDI
collected from World bank`s data of the World Development Indicators (WDI). FDI represents the net
inflows of the investment to a country (Mouzam, 2020)
Countries legal system (LSD): The degree of corruption in a nation can be impacted by its legal
system. While it's crucial to recognize that there are many variables that might affect the complex
relationship between the legal system and corruption, some research indicates that particular legal
systems may be linked to lower levels of corruption. For example, legal frameworks that are more
structured and predictable may be found in civil law systems, which are frequently distinguished by
codified laws, unambiguous legal procedures, and a heavy emphasis on legal formalism. This may
lead to fewer opportunities for bribery and corruption as well as increased accountability and openness
in the legal system. In this study the legal system (LSD) dummy variable is represented a value of 1
for civil law and 0 for other legal systems, to evaluate the significance of the types of legal system in
explaining differences in corruption levels among nations

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3.3. Methods of data analysis
The descriptive statistics such as mean, standard deviation, minimum and maximum values are used to
describe Corruption, Unemployment, inflation, Political Stability and Absence of Violence,
Government Effectiveness, and Foreign direct investment that affect economic growth in SSA.
Using Fixed effect and Arellano- bond GMM dynamic panel model estimation, this study analyze the
influence of explanatory variables on the dependent variable. A class of estimators, which is nowadays
most commonly used estimator for dynamic panels with fixed effects, is generalized methods of
moments (GMM) estimator by Arellano and Bond (1991)
3.4. Econometric model specification
This paper investigates the relationship between Corruption and Economic growth in SSA, including
other independent variables like Unemployment, inflation, Political Stability and Absence of Violence,
Government Effectiveness, Foreign direct investment and Legal system. If we may be assumed pooled
OLS for this study, the model can be specified as follows:
𝑌𝑖𝑡 = 𝛽0 + 𝛽𝑋𝑖𝑡 + 𝜀𝑖𝑡 -----eq (1) Where: i = 1,2,.…., N & t = 1,2, …………., N, 𝑌𝑖𝑡 = the dependent
variable, 𝑋𝑖𝑡 is a vector of explanatory variables for group i at time t 𝛽 is the vector of parameter to
be estimated which represents the coefficients that specify the relationship between the predicted
variable and the predictor variables 𝜀𝑖𝑡 = is the error term. However the panel data decompose the
error term in to two, as 𝜀𝑖𝑡 = 𝜃𝑖 + 𝜇𝑖𝑡-----------eq (2) where the 𝜃𝑖 represent the time constant/time
invariant observable or unobservable individual effect or it is a country specific fixed effect and 𝝁𝒊𝒕
indicates the error term which has zero mean and constant variance both across countries and over
time. Therefore, the general linear panel data model is: 𝑌𝑖𝑡 = 𝛽0 + 𝛽𝑋𝑖𝑡 + 𝜃𝑖 + 𝜇𝑖𝑡---------------eq (3).
from this equation;𝐶𝑜𝑣(𝜇𝑖, 𝑋𝑖𝑡) ≠ 0. Thus, Pooled OLS does not take in to account the 𝜃𝑖, hence it is
inconsistent and biased to use pooled OLS model.
The fixed effect estimation takes the country specific effects as regressors rather than attributing them
to the error term, thereby minimizing omitted variable bias (krifa-schneider et al., 2010). In this work
the Hausman(1978) specification tests will be is conducted to choose fixed or random effects. In the
Hausman test, the null hypothesis states that random effect model is appropriate to fixed effect model
and consequently, rejecting the null hypothesis suggested that the appropriate model is a fixed effect
model. Consequently, following (Bell & Jones, 2015; Damodar N, 2004; Gujarati, 2022; Marquering
& Verbeek, 2004) the study conducts a panel data regression model.

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The problem is that how panel data control for the unobserved heterogeneity across the unit? Here the
easiest way to control the problem of 𝜃𝑖 is to include the dummies for all individuals ( 𝑖`𝑠,) but this
approach is problematic with large N, so panel data offer techniques to remove 𝜃𝑖 problem. Using
panel data, the study will control the specific heterogeneities of the countries by using the fixed or
random effects panel data models. Based on the P value of Hausman’s test if it will be greater than
0.005, the Random effect will be use consistent and efficient.
𝑌𝑖𝑡 = 𝛽0𝑖 + 𝛽𝑋𝑖𝑡 + 𝜇𝑖𝑡---------------eq (4) from this equation we assume 𝛽0𝑖 is a random variable with
mean value of 𝛽0 (no subscript 𝑖 here) and the intercept value of individual countries can be identified
as 𝛽0𝑖 = 𝛽0 + 𝜀𝑖 ---------------- eq (5)
Where the 𝑖 = 1,2,3 … 𝑁 𝜀𝑖 = random error term with mean value of zero and variance of 𝜎2
𝜀. RE
model do not consistent and unbiased if there is strong exogeneity problem is exist in the model, when
the 𝐶𝑜𝑣(𝜇𝑖, 𝑋𝑖𝑡 ≠ 0 and this model cannot control the omitted variable bias, but Fixed effect model
(FEM) can control it. Then we can employe the FE model if the P value of Hausman’s test will be less
than 0.005, because the Fixed effect is consistent and efficient. The linear panel data model for FE is
given as: 𝑌𝑖𝑡 = 𝛽0𝑖 + 𝛽𝑋𝑖𝑡 + 𝜃𝑖 + 𝜇𝑖𝑡---------------eq (6), where the 𝐶𝑜𝑣(𝜇𝑖, 𝑋𝑖𝑡) ≠ 0. Despite of
controlling the time invariant differences between the individuals cannot estimate the coefficient of
time invariant variables and it ignores the variation across the units, rather than consider the variation
within the units. Based on the literature and the above model, this study will estimate the following
equations, which is the functional form of the model. In the following functional expression of the
model GDP pc is the predicted variable and all the right-hand side variables are predictor variables of
the model. Based on the work of perivenous researchers, for example by ( Canh et al., 2020; Eregha,
2019; krifa-schneider et al., 2010) the above equation can be formulate as follows:
𝐆𝐃𝐏𝐏𝐂 = 𝜷𝟎+𝛃𝟏𝐂𝐏𝐈𝐢𝐭+𝛃𝟐𝐔𝐍𝐢𝐭+𝛃𝟑𝐈𝐍𝐅𝐢𝐭+𝛃𝟒𝐅𝐃𝐈𝐢𝐭+𝛃𝟓𝐆𝐄𝐢𝐭 +𝛃𝟔𝐏𝐒𝐢𝐭+ 𝛃𝟕𝐃𝐢𝐭+ 𝜽𝒊+ 𝛆𝐢𝐭---------eq(7)
Where:
GDPPC = is gross domestic product per capita
CPIit= corruption perception index in a country i at time t
INFit= inflation rate in country i at time t
UNit=unemployment rate in country i at time t

19. Page | 15
FDIit= is foreign direct investment inflows in % of GDP in country i at the time t
GEit =is government effectiveness estimated in country i at time t
PSit= is political stability estimated in country i at time t
Dit = is a dummy variable as {
1 𝑖𝑓 𝑡ℎ𝑒 𝑙𝑒𝑔𝑎𝑙 𝑠𝑦𝑡𝑒𝑚 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑖 𝑖𝑠 𝑐𝑖𝑣𝑖𝑙 𝑙𝑎𝑤 𝑎𝑛𝑑
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝑖. 𝑒; 𝑐𝑜𝑚𝑚𝑢𝑛𝑎𝑙, 𝑑𝑢𝑎𝑙, 𝑚𝑖𝑥𝑒𝑑
}
εit =is error term
The most often used panel data estimate approaches include Pooled OLS (Ordinary Least Square),
Fixed Effects (FE), Random Effects (RE), Generalized Least Squares (GLS), Difference-GMM, and
System-GMM. Nevertheless, the endogeneity issue raised by the inclusion of the lagged dependent
variable of the given models necessitates careful attention to address it. As expected, pooled OLS
assumes exogeneity, which means that OLS cannot be utilized because endogeneity results in an
inconsistent estimate. Moreover, parameter estimates produced by OLS application to dynamic panel
models are skewed and unreliable (Baltagi, 2015). This is mostly because the country-specific effects
and the lag dependent variable are correlated, which goes against the fundamental presumption needed
for OLS consistency. Additionally, Generalized Least Squares (GLS), FE and RE model can not
address the problem of endogeneity in the above specified equation (7), the GMM model will be
employed for this study but for some identification we can use the Fixed effect and Arellano- bond
GMM dynamic panel model for some estimation purpose. A class of estimators, which is nowadays
most commonly used estimator for dynamic panels with fixed effects, is generalized methods of
moments (GMM) estimator by Arellano and Bond (1991)
3.4.1. Generalized Method of Moment (GMM) Model for dynamic panel data
The Arellano-Bond GMM dynamic panel data estimator addresses the problem of the autocorrelation
of the residuals, as the lagged dependent variable is included as an additional regressor (krifa-
schneider et al.,2010). The problems related to simultaneity bias, inverse causality, and omitted
variables are corrected by GMM dynamic panel data estimators (Ngono, 2023). This estimator, often
referred to as the difference generalized method of moment’s estimator, takes the first difference of the
data and then uses lagged values of the endogenous variables as an instrument.
Therefore, the main idea of the Arellano bond first differenced GMM is to take first differences to
remove time-invariant country-specific unobserved effects. The instruments build the variables on the

20. Page | 16
right-hand side in the first difference equations using series levels and one lagged period or more. In
addition, the assumptions of time-varying disturbances in the original level equations are not serially
correlated (Bond et al., 2001). In this study the panel data set has a short time dimension (T=15 years)
and a larger country dimension (N=40 countries). To solve these econometric aspects, the (Arellano &
Bond, 1991) dynamic panel GMM estimation will be applied in this study.
Due to the FE and RE model can not address the problem of endogeneity in the above specified
equation (7), the GMM model will be employed for this study. The Arellano-bond dynamic panel
GMM (Panel Generalized Method of Moments at first difference) estimation techniques is used to
measure impacts of the independent variables on Economic growth in SSA economies by for other
variables.
The System GMM model is designed for situations with “small T, means that few time periods, and
when large N” means many individuals or groups are required, and the independent variables do not
have to be strictly exogenous, which means that they are correlated with past and possibly current
realizations of the error term, and also system GMM overcomes the problems of heteroskedasticity,
autocorrelation and fixed effects, within individuals. The model specification using the system -GMM
involves incorporating lagged variable and instrumental variables to address the potential endogeneity
issue. According to the former researcher Roodman (2009) the model of system GMM can be
estimates as the following:
𝐆𝐃𝐏𝐩𝐜𝐢𝐭 = 𝛃𝟏𝐆𝐃𝐏𝐩𝐜𝐢𝐭−𝟏+𝛃𝟐𝐂𝐏𝐈𝐢𝐭+𝛃𝟑𝐔𝐍𝐢𝐭+𝛃𝟒𝐈𝐍𝐅𝐢𝐭+𝛃𝟓𝐅𝐃𝐈𝐢𝐭+𝛃𝟔𝐆𝐄𝐢𝐭+𝛃𝟕𝐏𝐒𝐢𝐭+𝛃𝟖Dit+𝛆𝐢𝐭------eq (8)
Where: 𝐆𝐃𝐏𝐩𝐜𝐢𝐭= is represent the economic growth in country i at time t
𝐆𝐃𝐏𝐩𝐜𝐢𝐭−𝟏= is the lagged value of economic growth to capture the dynamic effects
𝐂𝐏𝐈𝐢𝐭= is level of corruption perception index in country i at time t
𝐔𝐍𝐢𝐭= is represent the unemployment rate in country i at time t
𝐈𝐍𝐅𝐢𝐭= is the inflation rate in country i at time t
𝐅𝐃𝐈𝐢𝐭= is represent the foreign direct investment inflow in to country i at time t
𝐆𝐄𝐢𝐭= shows the government effectiveness in country i at time t
𝐏𝐒𝐢𝐭= indicates the political stability and absence of violence in country i at time t

21. Page | 17
𝐃𝐢𝐭 = is a dummy variable as {
1 𝑖𝑓 𝑡ℎ𝑒 𝑙𝑒𝑔𝑎𝑙 𝑠𝑦𝑡𝑒𝑚 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑖 𝑖𝑠 𝑐𝑖𝑣𝑖𝑙 𝑙𝑎𝑤 𝑎𝑛𝑑
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝑖. 𝑒; 𝑐𝑜𝑚𝑚𝑢𝑛𝑎𝑙, 𝑑𝑢𝑎𝑙, 𝑚𝑖𝑥𝑒𝑑
}
𝛃𝟏 , 𝛃𝟐 , 𝛃𝟑 , 𝛃𝟒 , 𝛃𝟓 , 𝛃𝟔 , 𝛃𝟕 ,and 𝛃𝟖 = are parameters and 𝛆𝐢𝐭= is error term
From equation (8) above we can develop the GMM model in order to address the endogeneity
problem. The GMM estimator allows us to use the lagged value of dependent variable and
instrumental variables as instrument for the endogenous variable. To address the problem of
endogeneity lagged and differenced variables can be used as the instruments and the Moment
condition of the GMM.
3.5. Test for Dynamic Panel data Model
For the GMM to be valid at this point, it is essential to assume that the instruments are exogenous and
that autocorrelations do not exist. Arellano and Bond(1991) and Arellano and Bover (1995)
recommended the following tests to assess these assumptions:
i. Arellano-Bond (AR) Test
One of the GMM estimators is Arellano-Bond (AR) test that used for assessing autocorrelation test.
This test based on the assumption that the error term in the model is serially uncorrelated. In contrast
to the alternative hypothesis of serial correlation, the test looks at the null hypothesis, which states that
the error term is not serially correlated. If the test statistics exceeds a critical value, shows the
existence of serial correlation, that tell us the estimated model is biased and inefficient. The presence
of serial correlation, also known as autocorrelation, in the residuals of dynamic panel data models is
investigated using the Arellano-Bond tests, namely the AR (1) and AR (2) tests. First order serial
autocorrelation, AR (1) must exist in the residuals for the GMM estimator to function, but no second
order serial autocorrelation AR(2) is required (Arellano & Bond, 1991)
ii. Sargan-Hansen test
The SGMM) estimation process uses the Sargan-Hansen test, sometimes referred to as the Sargan test
or Hansen test, as a statistical tool in econometrics to evaluate the soundness of the over identification
constraints. This test identifies whether the moment conditions indicated by the instruments are
correctly specified and whether the instruments are valid. The 𝐇𝟎 indicates that instruments are not
serially correlated with the disturbance term, revealing that the over identification restriction hold
valid. The significant Sargan-Hansen test result mean that the instruments are poor, that the model has
been mis specified, or that there may be endogeneity in the data. In these situations, it is crucial to
reassess the tool set, take into account substitute tools, or investigate additional estimation
techniques(Sato & Söderbom, 2017)

22. Page | 18
CHAPTER FOUR
4. RESULT AND DISCUSSIONS
4.1. The GMM Result and Discussions
In this seminar study the following System GMM result is obtained using the STATA-15, and by using
panel data collected from the World bank data set of 2023. The following table presents the results of
two different SGMM panel data estimation techniques.
Table-4.1 is the SGMM results
Dependent variable: GDPpc in natural logarithm(lnGDPpc)
Explanatory variables One step System GMM Two step System GMM
LlnGDPpc 0.60424
(0.00**)
0.7532
(0.00**)
CPI -0.0022
(0.2310
-0.0337
(0.04**)
UN 0.03332
(0.00**)
-0.0033
(0.881)
INF -0.0004
(0.027**)
0.00054
(0.352)
FDI 0.00303
(0.576)
0.01305
(0.037**)
GE 0.30818
(0.00**)
0.75696
(0.002**)
PS 0.00214
(0.894)
-0.0401
(0.633)
CPI*UN -0.0002
(0.05**)
0.00052
(0.255)
LSD -0.0474
(0.03**)
0.51927
(0.064*)
Cons 2.86426
(0.00**)
3.10103
(0.00**)
Results of post-estimation tests
Observation= 600
Arellano-Bond test for
AR(1)
P-value =0.050
P-value =0.00
Arellano-Bond test for
AR(2)
P-value =0.141
P-value =0.759
Sargan test of overid
restrictions
P-value = 0.257
P-value =0.278
. Source: own computations by using Stata15.
The P-value** and * shows that the variable is significant at 5% and 10% of significance level
respectively

23. Page | 19
The above table (table 4.1) consist the result of dynamic panel data model employed, by using one step
and two step SGMM estimation techniques for investigating the relationship between the Corruption
and Economic growth in SSA counties. For this Study the Two step SGMM result is interpreted in the
following section, since two step SGMM can provide more reliable and robust estimates by
considering the robustness to misspecification and instrumental variable selections.
 Interpretation for Two step SGMM result
 The coefficient of the LlnGDPpc is about 0.7532 (0.00**), this means that ceteris paribus, a
one unit increases in LlnGDPpc leads to a 75.32% unit increases in the current year value of
Economic growth in the SSA, and it is increases is significant at 5%. This shows that there is a
high persistence or memory in the Economic growth.
 The value of coefficient of CPI is -0.0337(0.04**) which shows the negative relationship
between the GDPpc and CPI in SSA, means that a one unit increase in CPI associated with a
3.37% decreases in GDP per capita of the SSA countries, holding other variables constants.
And the result also show the effect of CPI on GDP per capita is significant at 5%
 The coefficient value of UN is -0.0033(0.881), which shows that the one unit increase in UN
lead to a 0.337% decreases in GDP per capita of the SSA countries, holding other variables
constants, but this result is not significant on the Economic growth of the region.
 The coefficient value of INF is 0.00054 (0.352), ceteris paribus, a one unit increasing of INF
leads to a 0.054% unit increase in the value of Economic growth in the SSA, but this is not
significant
 FDI:0.01305(0.037**) shows that, other factors being constant, a one unit increase in FDI
leads to 1.305% increases of Economic growth(GDPpc) in SSA and it is statistically
significant at 5%
 GE= 0.75696(0.002**), this indicates that a one unit increase in the GE leads to 75.696%
increases the GDPpc of the SSA, other variables remain constants. This GE is statistically
significant at 5% of significance level
 The value of PS is -0.0401(0.633) show that there is negative relationship between GDPpc and
PS, ceteris paribus
 CPI*UN 0.00052(0.255), this reveals that the interaction of CPI&UN is positively related to
the GDPpc of SSA countries.
 LSD is 0.51927(0.064*) this reveals that , ceteris paribus, being a Civil law legal system
country compared to the Mixed law legal system country lead a 51.9269% more of GDPpc of
the SSA countries those who followed Civil law legal system, and this effect is significant at
10% of significance level or we can say that, the legal system(LSD) is positively and
marginally/slightly significant related to the lnGDPpc, this means that the countries with a
Civil law legal system(coded as 1) have on average a 0.51927 unit higher lnGDPpc compared
to a countries with a Mixed law legal system (coded as 0), and it’s significance level is 0.064
which is slightly significant at 10% 0f significance level.
 The constant value/term (3.10103) is the intercept of the model, which indicates that when the
effect of all independent variables on lnGDPpc incorporated in the model is zero, the other
factors that did not included in the study affect the economic growth of the SSA countries
 In this study, a set of year dummy variables was incorporated in order to capture the time
specific effects. From these year dummy some of them are statistically significant, which
shows that the specific years had an impact on the Economic growth of the SSA countries.

24. Page | 20
 Interpretation for Post-estimation test of dynamic panel data model
As we observed from the above table 4.1, there were some post-estimation test conducted in order to
check the Autocorrelation of the variables and validity of the model identifications or over
identification restrictions test.
 According to the Arellano-Bond tests, there isn't autocorrelation test AR(2) in the model means
that no second-order autocorrelation, since the P-value is greater than 0.05 which is a P-value
=0.759
 The Sargan test of over identification restrictions show that model's instruments are valid, on
the other hands we cannot reject the Null hypothesis, since P-value =0.278 is greater than
0.05, so that the instruments used in the model are valid.

25. Page | 21
CHAPTER FIVE
5. CONCLUSION AND RECOMMENDATION
5.1. Conclusion
This section presents the overall conclusion of the findings of this Seminar study on the nexus
between the Corruption and Economic growth in SSA Countries from the 2008 to 2022. In
order to reach the Primary objective of the seminar that intended to analyze the relationship
between Corruption and Economic Growth in 40 Sub-Saharan Africa countries and its specific
objective, the panel data was collected from World Bank data set via online, from 2008 -2022
and filled the gap that did not covered by former studies. In this study, for addressing the gabs
and analyzing the panel data of fifteen (15) years, the System Generalized Method of Moments
(SGMM) model used in order to capture the short term and long-term relationship between the
variables and also these models are particularly useful when studying the impact of variables
that might have lagged effects on economic growth, such as corruption, unemployment, and
inflation. This model also addresses endogeneity concerns, provide efficient estimates and
allow us to examine the impacts of corruption, on economic growth, as well as their combined
effect by accounting the time varying effects and control for unobserved heterogeneity across
countries. The result of two step SGMM shows that the value of coefficient of CPI is -
0.0337(0.04**) which indicates that there is a negative relationship between the economic
growth and Corruption in SSA countries, means that a one unit increase in CPI associated with
a 3.37% decreases in GDP per capita of the SSA countries, holding other variables constants.
And the result also show the effect of CPI on GDP per capita is significant at 5%. However the
result show that corruption and unemployment jointly increase the SSA economic growth,
which means that there CPI*UN 0.00052(0.255), this reveals that the interaction of CPI&UN is
positively related to the GDPpc of SSA countries. The study also show that there are other
independent variables which positively affect the SSA growth such as FDI, GE, LSD and INF
5.2. Recommendation
Based on the findings the following recommendation was forwarded;
 In most SSA countries, Corruption is one of the major factor that hinder the economic growth
in the region, so effective governing system that can reduce corruption should be constructed in
the region in order to address corruption and promote economic growth in SSA. These include
strengthening governance, improving transparency and accountability, and implementing anti-
corruption measures.
 The major economic variable that can affect growth in SSA is Unemployment, this
unemployment even can be happen because of the corruption in the area, so all concerned body
have to see this issues in serious and try to boost the economic growth in the SSA
 The FDI plays a key role for economic relation and growth by providing job opportunities,
technologies and markets for the country, so the government should increases this capital

26. Page | 22
accumulation or investment engaged by investors of abroad country and flow in to a home
country for economic growth of SSA
 The SSA countries have to work on the Political stability, Government effectiveness and on
their legal system effectively in order to reduce corruption and increases economic growth

27. I
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