Ball State University
RESEARCH METHODS POLS 626
GOVERNMENT EFFECTIVESS AND ECONOMIC GROWTH IN SUBSAHARA AFRICA.
By:
Nii Narku Nortey
Spring, 2021
1
EXECUTIVE SUMMARY
The region of Sub-Saharan Africa has experienced low growth rates for an extended period.
Though the region abounds in natural resources, its economic growth has been generally subpar
compared to countries in the developed world. There have been several studies that examined the
growth determinants of the economies of sub-Saharan Africa countries. However, many of these
studies are focused on the traditional growth determinants with very little attention on the role of
the World Governance Indicators on countries' economic growth in sub-Saharan Africa. In recent
times, the focus has shifted to determining the economic performance of countries in the subregion. Therefore, the crux of this study is to examine the impact of government effectiveness on
the economic growth of sub-Saharan Africa. The study made use of panel data analysis using a
sample of 43 countries from1996 to 2016. The study revealed that government effectiveness
does not have any significant effect on economic growth of countries in sub-Saharan Africa.
Among the five governance indicators included in the model as controls, only political stability
significantly affected economic growth in sub-Saharan Africa. The study recommends that
governments in sub-Saharan Africa should give priority to all the WGI indicators variables. All
these indicators need to be enhanced to improve economic growth in sub-Saharan Africa. The
study further recommends a public-private partnership between government agencies and the
private sector to enhance the efficient allocation of resources through innovative ideas and
policies. Additionally, the government should invest in research and development to advance
innovative solutions to the complex challenges that hinder productivity in the public sector at
local, regional, national, and international levels.
2
CHPATER ONE
INTRODUCTION
Sub-Saharan Africa is known for its abundant natural resources, but it has lagged in development
and growth. The region's growth rates have been unsustainable and among the lowest globally,
but research into the dynamics of change has concentrated chiefly on traditional economic
determinants (Acemoglu et al. 2004). Following Barro's (1991) pioneering work, several other
economists have made significant efforts to define the fundamental factors that account for
economic growth differences between and within countries and continents worldwide. For
instance, Sawyer (2010) attests that SSA's economic growth has slowed due to low total factor
productivity growth (TFP). Dollar and Kraay (2003), whiles throwing their support for
macroeconomic policies, averred that trade shares have a positive long-run effect on economic
performance. Stern (1993) incorporated employment and capital in a model and found that
increased energy consumption results in growth in real GDP. Similarly, Dawson (2003) finds
that the economy can improve economic performance through the positive effect on investment.
The earnest desire to understand the development needs of this deprived continent of the globe
indicates that the critical determinants of economic growth might not account for the dynamics
of countries in SSA (Easterly and Levine, 1995). Through the Solow growth model in a multicountry sample, several scholars have carried out studies and found an inverse and significant
effect of other factors affecting growth aside from the traditional growth determinants. Levine
and Renelt (1992) found that other variables such as income distribution, government size, and
trade openness are critical drivers to economic growth. Other scholars also found institutional
quality as a driver of economic growth and (Sala-I Martin, 1997a, 1997b; Barro, 1991, 1997;
Bloom and Sachs 1998). According to Fayissa and Nsiah (2013), good governance, or the lack
3
thereof, contributes to the disparities in African development. Good governance is a strong
determinant of economic growth, and it helps to understand why countries have different levels
of development.
According to Schneider (1999), good governance is the exercise of authority or power over a
country's affairs and wealth. On the other hand, the United States Agency for International
Development (USAID, 2002) describes good governance as a complex system of interaction
among institutions, practices, functions, and processes characterized by values of accountability,
openness, and participation. The UNDP (2002) defines good governance as striving for the rule
of law, transparency, equity, effectiveness /efficiency, accountability, and strategic vision in
political, economic, and administrative exercise. Many studies have used the World overall
indexes of World Governance Indicators as a proxy for governance (Fayissa and Nsiah 2013;
Muhammad et al. 2015; Kilishi et al. 2013) and institutional quality (Levine and Renelt 1992;
Barro, 1991, 1997; Bloom and Sachs 1998), while controlling for other macroeconomic
determinants of growth. The World Governance Indicators are voice and accountability, political
stability, government effectiveness, regulatory quality, the rule of law, and control of corruption.
According to existing literature, the role of governance indicators in institutions on economic
growth has not been clearly defined with support of relevant evidence (Pistor, 1995; Eweld,
1995). Additionally, only fewer studies have considered how each of these indicators affects subSaharan Africa while controlling for the other governance indicators, the traditional determinants
of growth (labor and capital), and macroeconomic factors of development (Example,
unemployment, inflation, trade, etc.). Therefore, there exists a gap in the literature concerning
the exact role each governance indicator plays in the economic growth dynamics of SSA. Thus,
4
the study examines the relationship between government effectiveness and economic growth in a
sample of 43 countries in SSA spanning from 1996-2016. Specifically, the study aims to:
1. To estimate the impact of government effectiveness on the economic growth of SSA
from 1996 to 2016.
Henceforth, the study intends to find answers to the following questions;
1. Does government effectiveness have any impact on economic growth in sub-Saharan
Africa from 1996 to 2016?
5
CHAPTER TWO
LITERATURE REVIEW
According to contemporary growth theories, differences in economic growth between countries
and regions can be attributed to factors such as institutional efficiency (Easterly et al., 2004;
Acemoglu et al., 2003a). According to North (1990), governmental institutions and entities can
be defined as humanly-devised constraints that mold people's interactions and influence and
shape the incentives of economic operators. In the presence of good governance, there are higher
chances of prospering economic growth due to the promotion of more sharp divisions of labor,
faster implementation of economic and social policies, and more productive investments (Alam,
Kiterage, & Bizuayehu, 2017). Higher economic growth is facilitated by good governance,
which encourages more effective labor units, more profitable investment, and faster
implementation of social and economic policies (United Nations 2005). According to Hall and
Jones (1999), institutions and government policies shape the economic climate in which people
acquire skills and businesses acquire capital and generate production. Although good
governments can boost economic development by efficiently providing social infrastructure that
prevents diversion, bad governments can cause a public diversion in an economy through
expropriation, confiscatory taxes, and inadequate regulations and laws (Hall and Jones 1999).
Alam et al. (2018) examined the influence of government effectiveness on the economic
development of a panel of 81 countries using a Generalized System Method of Moments (System
GMM) technique. The study discovers that government effectiveness has a substantial positive
impact on economic development. In a survey spanning 73 developed and developing countries
from 1975 to 1990, Adkins and Savvides (2002) found that countries with greater economic
6
freedom have higher growth rates. In a similar vein, Dawson (2003) finds that economic
efficiency can be improved through a positive impact on investment. According to Bassam
(2013), the associations between economic growth and governance quality are highly dependent
on levels of human development and the metrics used to measure governance quality. North
(1990) demonstrates that institutional structure strongly influences how economic and political
results are connected.
Kaufmann and Kraay (2002) analyzed 175 countries in 2000/01, concluding that good
governance is needed for high per capita income and economic growth. Knack came to the same
conclusion (2002). Huynh and Jacho-Chavez (2009) investigated the relationship between
governance and growth using a nonparametric approach. Their results show that three of the six
indices of governance are economically and statistically significant: voice and transparency,
political stability, and the rule of law, while regulatory regulation, corruption control, and
government effectiveness are insignificant. Emara and Jhonsa (2014) investigated the
interrelationship between improved government efficiency and increased per capita income using
the Two-Stage Least Square method for a cross-sectional dataset of 197 countries. Their results
show that the standard of governance has a clear positive and statistically meaningful
relationship with per capita income.
Guisan (2009) compared European countries to the United States and Canada from 2000 to 2007,
looking at the relationship between government efficacy, education, and economic growth. The
findings of the author demonstrate the significance of government effectiveness in economic
growth. Cebula and Foley (2011) examined the impact of high-quality government regulation on
per capita real GDP by using panel data and PLS estimates for OECD countries throughout
7
2003-06. The authors conclude that better regulatory efficiency is positively correlated with
economic growth.
8
CHAPTER THREE
THEORY AND HYPOTHESES
In recent times, some amount of work has been done, especially concerning the role institutions
tend to play in determining the growth of countries. Given this, several academic researchers
have been conducted to provide further evidence on the impact of governance indicators and how
they affect economic progress. The role of these indicators has not been well defined in some of
the past studies (Pistor, 1995). Therefore, it is essential and timely to determine the exact role of
these governance indicators and other traditional growth determinants in the growth matrix of
SSA countries. Based on the above, the study developed the following hypotheses:
The debate about whether governance indicators have a positive or negligible impact on
economic growth is raging.
Hypothesis (1): The study hypothesized that government effectiveness positively impacts
economic growth in Sub-Saharan Africa.
Hypothesis (2): The study hypothesized that political stability, voice, and accountability,
regulatory qualities, raw of law have a positive impact on economic growth in Sub-Saharan
Africa.
Hypothesis (3): The study hypothesized that control of corruption negatively impacts economic
growth in Sub-Saharan Africa.
The effects of human and physical resources on economic development are debatable. Although
several scholars claim that human and physical capital foster economic growth (Barro, 1992),
9
others argue that human capital and labor do not explain economic growth (Barro, 1992).
(Benhabib & Spiegel, 1994)
Hypothesis (4): The study hypothesized that physical capital positively impacts economic
growth in Sub-Saharan Africa.
Hypothesis (5): The study hypothesized that human capital positively impacts economic growth
in Sub-Saharan Africa.
Although some economists (e.g., Yanikkaya, 2003) argue that net trade (trade openness) harms
economic growth, especially in developing countries, others (e.g., Romer & Frankel, 1999) say
that it has significant positive effects.
Hypothesis (6): The study hypothesized that net trade positively impacts economic growth in
Sub-Saharan Africa.
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CHAPTER FOUR
DATA AND ESTIMATION
4.1 Introduction
The chapter focused on various strategies that were adopted to achieve the goals of the study.
Specifically, this chapter looks at the model specification, variable definitions, empirical or
estimation approach, and data sources.
4.2 Model Specification
According to the Solow model, production or growth is a function of capital and labor. In this
model, production is dependent on the amount of capital stock and labor applied in the
production process in the economy (Solow, 1956).
Therefore, the basic model is stated as follows:
Y= f(K, L ).................................................................................................3.1
Where Y=Economic Growth (Real G.D.P.), K= Capital Stock, and L= Labor
Equation 3.1 is adjusted to account for government effectiveness and the selected control
variables. According to Acemoglu et al. (2003a), Apart from the traditional growth determinants
such as capital stock and labor, institutional Quality also constitutes the reasons for the
differences in economic growth between countries and regions. The study assumed a linear
relationship between growth, factors of production, government effectiveness, and control
variables. Therefore, to account for the linearity, the regression model in 3.1 is modified as
follows;
Y = β0+ β1GE+ β2PS+ β3VA+ β4CCo+ β5RL+ β6RQ+ β7K+ β8L+ β9NT+ β10EM+U….3.2
11
GE=Government Effectiveness, PSAV=Political Stability and Absence of Violence, VA= Voice
and Accountability, CCo= Control of Corruption, RL= Rule of Law, RQ= Regulatory Quality,
NT=Net Trade, EM=Employment.
Economic growth in real G.D.P. is the dependent variable. Government Effectiveness is the
primary explanatory variable, U is the error term, and all other variables are the control variables
for the study.
4.3 Variables Definition
The study used the Worldwide Governance Indicators (W.G.I.) and World Development
Indicators (W.D.I.). The variables are defined below.
4.3.1 Economic Growth
G.D.P. is a measure of the total economic value of final goods and services in an economy
(Todaro and Smith, 2011). It is the continuous increase in the output of an economy resulting in
an expansion of economic activities a country. It involves the increase in the inflation-adjusted
market value of the goods and services produced in an economy over time.
4.3.2 Government Effectiveness
Government effectiveness measures the superiority of public services, the Quality and degree of
independence from political demands of civil service, the Quality of policy formulation and
implementation, and the reliability of the government’s ability to ensure the sustainability of
policies.
4.3.3 labor
Labor refers to the effort of a man used in the production process of goods and services. It is the
aggregate of all human action used in the creation of goods and services. These factors determine
12
economic growth in an economy. Ghosh (2011) proved that there is a long-run relationship
between labor and economic growth.
4.3.4 Physical Capital
Capital refers to the machinery, tools, and buildings used to produce goods and services in a
country. It could take the form of computers, buildings, and vehicles used differently based on
the kind of labor the work requires. The amount of capital accumulation in an economy
determines its level of growth. Ellahai (2011) included capital in his model to measure the effect
on economic growth.
4.3.5 Political Stability
Political stability and absence of violence is an index that measures the perceptions of the
likelihood that the government will be destabilized or overthrown by unconstitutional or violent
means, including domestic violence or terrorism.
4.3.6 Voice and Accountability
Voice and accountability show how a country’s citizens can choose their government and enjoy
the freedom of speech, freedom of association, and free media. The level of participation by both
men and women indicates that freedom of association and expression and an organized civil
society are vital to achieving voice and accountability.
4.3.7 Control of Corruption
Control of corruption measures the magnitude to which public power is used for private gain. It
also includes petty and grand forms of corruption and “capture” of the state by elites and
personal interests.
13
4.3.8 Rule of Law
The rule of law measures the extent to which agents have confidence in and abide by society’s
rules, particularly the Quality of contract (private and government) enforcement, the police, and
the courts, as well as the likelihood of crime and violence.
4.3.9 Regulatory Quality
Regulatory Quality measures the capability of the government to formulate and apply
comprehensive policies and regulations that allow and encourage private sector development.
4.3.10 Net Trade
Trade openness involves transferring goods produced in one country to another, either for further
processing or for consumption. Shahbaz et al. (2010) included net trade as a control to estimate
the effects of energy on economic growth.
Table 4.1: Variable Description, Expected Sign of the Coefficient and Data Source
Variable Code
Variable Definition
GDP
K
Annual Gross Domestic Product growth rate (%) is
a proxy for economic growth
Capital is measured as a percentage of GDP
+
WDI, 2017
L
Labor is measured as a percentage of GDP
+
WDI, 2017
NT is measured as the sum of real exports and
imports and a percentage of G.D.P.
EM is measured by the total labor force employed
expressed as a percentage of the total population.
Government effectiveness is measured as a proxy
of good governance
Control of Corruption is measured as a proxy of
good governance
Political Stability is measured as a proxy good
governance
Rule of Law is measured as a proxy of good
governance
+
WDI,2017
+
WDI,2017
+
WGI, 2017
-
WGI, 2017
+
WGI, 2017
+
WGI, 2017
NT
EM
GE
CCo
PS
RL
14
Expected Sign
Source
WDI, 2017
RQ
Regulatory Quality is measured as a proxy of good
governance
VA
Voice and Accountability is measured as a proxy of
good governance
Source: Author’s Construct, 2021
+
WGI, 2017
+
WGI, 2017
Note: World Governance Indicators take a value between -2.5 to 2.5
3.4 Data Sources
Data on economic growth and traditional growth-determinant variables was obtained from World
Bank’s Development Indicators (W.D.I.). The institutional quality indicators were gathered from
World Bank’s Governance Indicators (W.G.I.) from 1996 to 2016. The institutional quality
variables are political stability and absence of violence, voice and accountability, control of
corruption, the rule of law, regulatory Quality, and government effectiveness. G.D.P. per capita
growth (annual percentage) is the dependent variable, government effectiveness is the primary
explanatory variable, capital stock, labor, and the other institutional quality indicators constitute
the control variables.
3.5 Data Type, Estimation Strategy, and Analytical Tool
The study made use of a panel data set and simple regression analysis as the estimation strategy.
3.5.1 Data Type
The nature of the variables under observation meant a panel data set was ideal for this type of
study. A panel data set spanning 20 years (1996 – 2016) gathered from 43 Sub-Saharan African
countries were put used for the analysis. As Wooldridge points out, a multiple observation on the
same units makes it easy to control for specific unobserved characteristics in a variable
(Wooldridge, 2016). He went on to say that using multiple observations of a variable from
different times will help with causal inference in cases where inferring causality would be
complex with just a single cross-section data (Wooldridge, 2016).
15
3.5.2 Estimation Strategy
Multiple regression analysis was the estimation strategy for the study. The researcher found this
estimator the most appropriate because it allows for controlling other explanatory variables that
jointly affect the dependent variable. Thus, it is easy to apply the ceteris paribus principle
(keeping other variables constant) to our interpretation. When dealing with nonexperimental
results, the use of ceteris paribus is essential for testing economic theories and assessing policy
effects (Wooldridge, 2016). The multiple regression analysis was used to deal with economic
variables and government policies, including several control variables.
3.5.2 Data Analytical Tool
The regression analysis was computed using the Statistical Package for Social Sciences (SPSS).
In the social sciences, it is the most commonly used programming tool for statistical analysis.
SPSS is an incredibly effective software for manipulating and deciphering survey data, thanks to
its focus on statistical data analysis.
16
CHAPTER FIVE
RESULT AND DISCUSSION
5.1 Heteroskedasticity Test
The researcher performed the Breusch-Pagan test to confirm the presence or absence of
heteroskedasticity. None of the variables explained the square residuals at the 5% critical value.
Additionally, the F-test for joint statistics was 1.67 with a p-value of 0.082 (See Appendix A). It
is not significant at the 5% critical value, indicating a very small or the absence of
heteroskedasticity.
5.2 Descriptive Statistics
The descriptive statistics cover only the mean, standard deviation, minimum and maximum
values. Table 4.1 reveals these results.
Table 5.1 Descriptive Statistics
Variables
(N)
Min
Max
Mean
Std. Deviation
GDP
903
-36.83
140.5
2.45
7.93
Capital (K)
725
-294.16
2357.68
12.83
94.85
Labor (L)
861
Voice & Accountability (VA)
903
-2.23
9.85
-.622
.79
Government Effectiveness (GE)
903
-2.19
1.11
-.70
.68
Regulatory Quality (RQ)
903
-2.29
1.13
-.65
.61
Control of Corruption (CCo)
903
-1.81
1.22
-.64
.59
Political Stability (PS)
903
-2.04
1.28
-.55
.89
Rule of Law (RL)
903
-2.13
1.08
-.70
.64
Employment (EM)
882
88.94
64.28
13.41
Net Trade (NT)
862
311.36
73.13
36.46
Valid N (Listwise)
692
453006.0 848442
35.28
17.85
17
631350.3 119482.2
The standard deviation (7.93) for GDP shows a considerable variation from the mean (2.45). It
implies that there is a wide variation in growth rates among the countries in SSA. Therefore, we
can describe the growth rate across all countries in the sample as unstable and inconsistent. The
minimum (-294.16) and maximum (2357.68) values of capital are very far away from the mean
(94.85), indicating a wide discrepancy in the data set for all the countries in SSA. The
widespread among the variables is further confirmed by a standard deviation of 94.85. It
indicates variations and inconsistency in the performance of capital as a factor of production.
The mean (631350.3) for labor shows that it plays a perfect role in performance across all subregion’s countries. The standard deviation (119482.2) is below the mean, indicating that the data
is spread close to the mean. It shows a consistent performance of labor in all the countries across
the region.
5.2 Regression Analysis
Table 5.2 Effects of Government Effectiveness on Economic Growth in Sub-Sahara Africa
Variables
Model 1
Model 2
Model 3
Government Effectiveness (GE)
0.096 (.390)
0.933 (.786)
0.893 (.878)
Voice & Accountability (VA)
0.115 (.500)
0.052 (.722)
Regulatory Quality (RQ)
-1.174(.917)
-0.670 (1.148)
Control of Corruption (CCo)
-0.671(.889)
-0.484(.964)
Political Stability (PS)
1.083 (.473)**
1.626 (.577) **
Rule of Law (RL)
-.786 (1.288)
-1.155 (1.563)
Capital (K)
.007 (.003)**
Log-Labor (L)
0.014 (.380)
Employment (EM)
0.028 (.025)
Net Trade (NT)
-0.002 (.010)
Constant
2.521 (.381)***
2.029 (.414) ***
0.562 (3.123)
(Adj.) R2
-.001
.005
.013
N
902
902
695
18
*: P< .10, **: P< .05, ***: P< .01 Note: Standard Errors are in paratheses
Dependent Variable: GDP
5.2.1 Discussion of Result
Table 2 shows the regression results for the three models. Model 1 tested only the primary
explanatory variable (government effectiveness) on GDP per capita. The result shows that
government effectiveness does not explain GDP in Sub-Sahara Africa. Model 2 tested all the
World Governance Indicators on GDP. The result revealed that only political stability is
statistically significant with GDP at the 5% critical value. The relationship was also positive.
Model 3 tested all the explanatory variables on GDP. The result showed that political stability
and capita GDP have positive and significant effects on GDP at the 5% critical value. The rest of
the explanatory variables were insignificant to GDP; thus, they could not explain the variable in
GDP in the region.
The regression results showed an insignificant and positive relationship between Government
Effectiveness and real GDP per capita growth in SSA. This finding does not conform to the
study of Kilishi et al. (2013) and Guisan (2009). Their studies on Africa predicted a positive and
significant relationship between government effectiveness and economic growth in Africa.
Similarly, Guisan (2009) compared European countries to the United States and Canada from
2000 to 2007 and found a significant relationship between government effectiveness and
economic growth.
The positive and statistically significant relationship between political stability and GDP per
capita is inconsistent with the findings of Yildirim et al. (2015) but consistent with Sokoloff and
Engerman (2000). Yildirim et al. found political stability to have a negative impact on the
macroeconomic performance of developing countries. However, Sokoloff and Engerman (2000)
opined that stable democracy reduces inequality and enhances the economic prosperity of
19
developing countries. Therefore, this study’s findings on political stability confirm the
conclusions of Sokoloff and Engerman (2000).
The positive and statistically significant relationship between capital and GDP per capita means
that a capital increase will increase per capita GDP growth in SSA. The coefficients of physical
capital being significant and positive conform with the a priori expectation that capital
contributes positively to economic growth. The results confirm the relevance of the augmented
Solow model in explaining Africa’s growth (Solow 1956). Investment in physical and human
capital is vital in promoting rapid growth in Africa.
20
CHAPTER SIX
CONCLUSION AND RECOMMENDATIONS
6.1 Conclusion
The study attempted to estimate the effect of government on economic growth in sub-Saharan
Africa. Based on the results, the study concludes that government policies and economic
regulations do not impact economic growth in Sub-Sahara Africa. The study further concludes
that only political stability out of the six World Governance Indicators (WGI) is crucial to
economic growth and development in Sub-Sahara Africa.
6.2 Recommendations
In the first place, governments in sub-Saharan Africa should prioritize all the WGI indicators
variables since all the indicators showed an insignificant relationship with GDP except for
political stability. All these indicators need to be enhanced to improve economic growth in subSaharan Africa.
The study further recommends a public-private partnership between government agencies and
the private sector to enhance the efficient allocation of resources through innovative ideas and
policies. Additionally, the government should invest in research and development to advance
innovative solutions to the complex challenges that hinder productivity in the public sector at
local, regional, national, and international levels.
21
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24
APPENDIX A
ANOVAa
Sum of
Mean
Model
Squares
df
Square
F
1
Regression 8631993.256
10 863199.326
1.677
Residual
318654385.7
619 514788.992
50
Total
327286379.0
629
07
a. Dependent Variable: Ressquare
b. Predictors: (Constant), VA, LogL, K, NT, EM, Cco, PS, GE, RQ, RL
Coefficientsa
Unstandardized
Standardized
Coefficients
Coefficients
Model
B
Std. Error
Beta
1
(Constant)
356.063
308.403
GE
71.014
83.765
.069
K
.146
.285
.020
LogL
-43.430
37.835
-.054
RQ
-84.181
109.747
-.073
Cco
-3.083
92.749
-.003
PS
109.357
55.437
.131
RL
-192.468
152.071
-.168
EM
-.068
2.464
-.001
NT
-1.382
.978
-.066
VA
-11.181
71.693
-.011
a. Dependent Variable: Ressquare
25
t
1.155
.848
.513
-1.148
-.767
-.033
1.973
-1.266
-.028
-1.412
-.156
Sig.
.082b
Sig.
.249
.397
.608
.251
.443
.973
.059
.206
.978
.158
.876