European Journal of Economic and Financial Research
ISSN: 2501-9430
ISSN-L: 2501-9430
Available on-line at: http://www.oapub.org/soc
Volume 2 │ Issue 1 │ 2017
doi: 10.5281/zenodo.438146
CAUSALITY BETWEEN INSTITUTIONAL QUALITY AND
ECONOMIC GROWTH: EVIDENCE FROM SUB-SAHARAN AFRICA
Jeleta Gezahegne Kebede,
Paul Owusu Takyii
National Graduate Institute for Policy Studies (GRIPS), Japan
Abstract:
This paper analyses the causal relationship between institutional quality and economic
growth to investigate whether institutional quality is the outcome or the cause of
economic growth in Sub-Saharan Africa. It uses annual panel data of 27 countries for
the period spanning 1996 to 2014 by employing Pedroni panel co-integration, Wald
panel causality, and the system GMM techniques. The co-integration test results show
that there is a long-run relationship between institutional quality and economic growth.
Also, the causality test results show a unidirectional causality from economic growth to
institutional quality but not the other way round. Furthermore, the study found that
institutional quality, trade openness, financial development, and debt positively affect
economic growth. Also, economic growth and freedom are found to be important
determinants of institutional quality. However, debt servicing and dependence on
natural resources negatively affect economic growth and institutional quality
respectively. It is, therefore, recommended that enhancement of institutional quality,
openness, and financial development; while downsizing of debt servicing is crucial in
achieving desired level economic growth in the region.
JEL: R12, O47, H63, N57, O13, F15
Keywords: economic growth, institutional quality, causality, co-integration
1. Introduction
The question of why Sub-Saharan Africa is one of the poorest regions in the world
continues to be an intriguing one. The region’s tardy development pace and its lack of
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convergence with their developed counterparts in terms of income and productivity has
been academically arousing. What is readily observable is that despite the large number
of studies on this question, there is no consensus among researchers on the underlying
factors of this sluggish economic performance of the region. The literature is rife with
several varying explanatory theories and postulations. According to Kilish et al. (2013),
institutional and policy thesis, geographic thesis, cultural and historical thesis, and
trade thesis have emerged in recent time to explain growth differences across African
countries. According to the first thesis, Africa is poorer because of its weak institutions
and due to the choice of wrong policy (Kilish et al. 2013). The literature concerned with
the second thesis argues that Africa is poor because of its geographical disadvantage
(Sachs, 2015; Kilish et al. 2013; Collier, 2007). According to the third thesis culture and
historical antecedences are responsible for the lagging behind of growth in Africa
(Kilish et al. 2013, Acemoglu, Johnson, & Robinson, 2000). The last, trade thesis,
literature argues that Africa is lagging behind because it is engaging less in
international trade (Kilish et al. 2013; Baltagi et al. 2009).
Interestingly, there is a growing body of literature recently that places a
disproportionate emphasis on the role of institutions in explaining the growth
difference across countries. In effect, this body of literature has shifted the question
from getting prices right to getting institutions right . The focus on institutional
analysis and the underlying role of institutions in economic growth has birthed the
stream of economics labeled the new institutional economics (NEI), needless to say,
Douglas North’s
99
seminal work laid the foundation for the NIE. The NIE attempts
to extend neoclassical economics by incorporating institutional analysis, giving
attention to the due role of institutions in determining long time economic
development. Kilishi et al. (2013) argue that the poor economic performance of SubSaharan Africa cannot be explained by the conventional neoclassical growth model.
According to them institutions, are very important in explaining the economic
performance of the region.
Further, they explain that during the period between 9
and
, “frica’s
economic performance was the least compared to all other regions of the world.
Excluding South Africa and Nigeria during the same period, sub-Saharan Africa
experienced the least economic performance in terms of per capita income. In 2008, the
sub-Saharan African and the Pacific regions ranked least in the world of regions in
terms of government effectiveness. In terms of regulatory quality, sub-Saharan Africa
performed only better than Central and West Asia in the same year (Zhuang et al. 2010)
What is rather worthy of note is that despite the large number of studies on the effect of
institutions on economic development and the effect of economic development on
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institutional quality, which, between institution and economic development comes first
remain a void in the literature. Put differently, there is a burning question that bothers
on whether or not one, between institutions and economic development must be
prioritized and mostly importantly, which of them must be prioritized over the other?
This remains a yawning gap in the NIE literature and needs to be explored. Indeed, this
is germane to the Institution-Economic development discourse, especially with the subSaharan African context, for policy reasons. An understanding of this issue could fastpace sub-Saharan “frican countries’ economic development and institution building.
As argued by Butkiewicz and Yanikkaya (20
, good institutions create an environment
that promotes economic activity, inventiveness, and growth and development. Bad institutions
typically result in economic stagnation . The question that is left unanswered in the region
is whether institutional quality is the outcome of economic growth or the cause of
economic growth. Thus, the trust of this paper is to analyze the causal relationship
between institutional quality and economic growth and further throw light on the
simultaneous causal effect of the far variables.
2. Literature Review
Economic institutions theory is premised on the existence of significant market failures
resulting from economies of scale in production scope, incomplete market or absence of
a market in some sectors, the prevalence of externalities, and asymmetric information in
transactions. Though these market failures are common in any economy, they are more
pronounced in developing countries like sub-Saharan Africa and hence calling for more
public regulation (Jalilian et al. 2007). Therefore, the type of regulations in these
developing countries is expected to take into account the structural and institutional
characteristics of these countries so as to deploy effective regulatory mechanisms to
achieve equitable and sustainable economic development. Accordingly, the outcome of
a regulatory system can be assessed against efficiency and effectiveness. Effective
regulation achieves goals like sustainable development and reduces poverty levels
while efficient regulations achieve these set goals at minimum economic costs.
According to Mundial (2001), a strong regulatory institution is a crucial determinant of
economic development through its effectiveness in minimizing market imperfections.
Economies with strong and developed institutional quality are able to implement
effective and efficient regulations contributing to economic development. Conversely,
weak and less regulatory capacity may adversely affect economic growth of a country.
North
99
defines institutions as
rules of the game , that is, the human
devised formal and informal constraints that shape human interactions. Formal
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institutions refer primarily to constitutions, statutes, and explicit government rules and
regulations, codified and enforced by impersonal mechanisms, most importantly, the
state with its coercive power and organization. Informal institutions or constraints, on
the other hand, include unwritten rules such as traditions, norms, and codes of
behaviors, taboos, and other social mechanisms based and enforced through
interpersonal ties and relations. One of the important challenges, in this case, is the
vagueness of the concept of institution and the fact that it is multidimensional.
Accordingly, identifying which dimension of an institution is more important in
explaining the difference in economic performance across countries is subject to debate.
Also, according to Jalilian et al. (2007), government effectiveness and regulatory
quality which are two of the six World Bank worldwide governance indicators can be
used to capture regulatory institutional quality. According to them, these two variables
capture both the quality of the outcome and process dimensions of regulation. The
regulatory quality index can be taken as a proxy for the quality of the outcome of
applying regulatory instruments as it measures the regulatory burden on business
associated with inefficient quantitative controls.
Consequent to the seminal work of North (1990), several strands of thought has
emerged. Some attempt to extend the neoclassical economics by incorporating
institutional analysis, focusing on the role of institutions in explaining long term
economic performance and hence causing economic growth (Zhuang et al. 2010). Others
contend that for the reverse causality; higher economic performance has a long term
institutional quality and hence causing it (Paldam & Gundlach, 2008). According to
them using the causality between economic growth and corruption, it is found that
causality runs from economic growth to corruption; which shows that low growth
causes corruption.
The main determinant of the differences in economic growth across countries is
economic institutions which are collective choices and are outcomes of political
processes (Acemoglu et al. 2005). The nature of political institution and distribution of
political power in a society determines economic institutions. These economic
institutions not only result in different levels of economic growth through different
degrees of economic efficiency but also results in different degrees of distributions of
the economic gains across individuals and different social groups in a society affecting
economic opportunity and allocation efficiency. Moreover, political competitions like
checks and balances restrict the ability of governments to engage in rent seeking while
accountability of governments to the taxpayers leads to more business friendly and
rules and regulations and hence improves government effectiveness and regulatory
quality (North, 1990).
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The dependence on natural resource measured by revenue from natural resource
to GDP ratio is one of the main determinants of an institution of a given country. In
resource-rich countries, ruling elites are likely to be particularly opposed to
strengthening institutions as stronger institutions impose checks and balances that
make it more difficult for them to misappropriate natural resource rents (Karl, 1997).
Natural resource abundance can also have a positive effect on institutional
developments via income effect. Natural resource wealth can be used to strengthen
implementation capacity of governments, pursue basic business environment reforms
and reduce petty corruption by improving the salary of officials and regulators.
Again, as explained by Schweinker et al. (2011), a country’s openness to
international economic interaction determines institutional quality. Openness to
international economy creates demand for better institutions and also improves the
transfer of skills and knowledge from international best practices. Education also counts
as an important variable in improving institutional quality. It is argued that educated
workers are very important to internalize positive externality from openness to
international economy and to adapt them to domestic reality.
Empirically, Alexiou, Tsaliki, and Osman (2014) investigated the short-run and
long-run relationships between institutional and economic growth in Sudan from 19722008.They found that the quality of the institutional environment is one of the most
important factors in determining economic growth. Also, Asghar, Qureshi, and
Nadeem (2015) examined the impact of institutional quality on economic growth in
developing economies in Asia using a panel annual data from 1990-2013 for 13 selected
developing economies. Their results of Panel ARDL show that institutional quality
positively impacts economic growth. Again, their panel causality test results show that
there is unidirectional causality running from institutional quality to economic growth.
They, therefore, concluded that there is a need to improve institutional quality in these
developing countries to ensure high economic growth. Kilishi et al. (2013) did an
empirical investigation in sub-Saharan Africa to find out whether institutions really
matter for growth in the region and if it does, which of them matters most? Their results
show that institutions really matter for economic performance, among which regulatory
quality appeared to be the most important and they recommended that economic
performance of the region could be enhanced by improving regulatory quality.
Moreover, Siddiqui and Ahmed (2009) investigated the relationship between
institutional quality and economic performance in Pakistan by employing the JohansenJuselius cointegration technique and Granger causality test. The results from their
cointegration test indicate that there exist a long run relationship between institutional
quality and economic growth. Also, their Granger causality test results show that the
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causality between institutional quality and economic growth is unidirectional with the
causality running from institutional quality to economic growth. Other studies by
Kauffman et al. (2005), Rodrik et al. (2004), Acemoglu, Johnson, and Robinson (2000)
and Olson et al. (1998) have found evidence to support the causality from institutional
quality to economic growth. Specifically, they explained that a country with better
institutions leads to a higher growth rather than the causation being a reverse.
Furthermore, Chong and Calderon (2000) analyzed the direction of causality between
institutional measures and economic growth. They found a bidirectional causality
between institutional quality and economic growth. Specifically, they found that the
poorer the country, and the larger the wait, the higher the effect of institutional quality
on economic growth but stated that economic growth also causes institutional quality.
From the above review, the results of the causal relationship between
institutional quality and economic growth are mixed and specifically so, few studies
have been conducted thin sub-Saharan Africa context. Thus, this study seeks to add to
the debate in the literature on the causal relationship between the two variables and to
make policy recommendations to serve as a guide for policymakers in the region.
3. Data and Methodology
The data set used in this study is an annual cross-country panel data comprising of 27
sub-Saharan African countries for the period spanning 1996-2014. The choice of the
study period and the number of countries is based on the availability of data on the key
variables of interest. The data were mainly sourced from World ”ank’s World
Development Indicators, World ”ank’s Worldwide Governance Indicators and
Freedom House.
The study followed Holtz-Eakin et al. (1988) and Pedroni (1999) to undertake the
panel cointegration and causality tests respectively. Also, it employed the System GMM
(generalized method of moments) for the estimation procedure of both the economic
growth model and institutional quality model. The System GMM estimation procedure
enables us to address many econometric problems like the fixed effect by considering
the presence of unobserved country specific effects due to differences in initial
conditions or possible omitted variable bias which is persistent over time (Bond et al,
2001). Moreover, according to Sot (2009), by exploiting the time series dimension,
system GMM increases the degree of freedom and reduces collinearity between
variables leading to more efficiency of the estimates. In addition to this, the system
GMM is preferred to other estimation procedures like standard GMM in growth models
as the instruments used in standard GMM may behave poorly when explanatory
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variables present a strong autoregressive component such as income or capital and
hence using system GMM estimation leads to lower bias and more efficiency.
3.1 Co-integration and Causality tests
This study employs panel co-integration test which is developed by Pedroni (1999). This
test provides a technique that enables us to use panel data and also overcome the
problems related to small a sample. Moreover, it has the advantages of taking into
account the heterogeneity in the intercepts and slopes of the co-integrating equation.
By follow Holtz-Eakin et al. (1988), equations (1) and (2) is estimated to carry out
ln GDPCi ,t 0 k ln GDPCYi ,t k j ln INSTi ,t j i ui ,t
the panel causality test.
n
m
k 1
j 1
ln INSTi ,t 0 j ln INSTi ,t j k ln GDPCi ,t k i i ,t
Where,
k , j , j
m
n
j 1
k 1
(1)
(2)
and k are coefficients of the respective variables. Also, i and i are
individual specific effects of the countries whereas, whiles
ui ,t
and
i ,t are the error
terms under the two equations for GDPC and INST respectively. The causal relationship
is undertaken using the Wald causality test between the two variables under both
equations independently.
3.2 Empirical model for economic growth and institutional quality
We followed the theoretical framework based on human capital augmented neoclassical
model which was developed by Mankiw et al. (1992) and adopted it so that the role of
institutions can be captured by including institutional quality. Also, the institutional
quality equation is modelled after Jonathan et al. (2014) and Antonia et al. (2010).
Macroeconomic variables which are theoretically supported to be determinants of
economic growth and institutional quality such as financial development, education,
investment, debt, trade openness, population growth rate, and inflation are also
incorporated. As system GMM is dynamic model in nature, the lags of both economic
growth and institutional quality are also included. Accordingly, the empirical
regression model for economic growth and institutional quality are respectively shown
ln GDPCi ,t 0 1k ln GDPCi ,t k 2 ln INSTi ,t 3 ln INFi ,t 4 ln DEBTi ,t
in equations (3) and (4):
n
k 1
5 ln INVTi ,t 6 ln OPENi ,t 7 ln AUGLi ,t 8 ln FINDi ,t i i ,t
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ln INSTi ,t 0 1k ln INSTi ,t k 2 ln GDPCi ,t 3 ln PLCVi ,t 4 ln NAREi ,t
m
k 1
5 ln EDUCi ,t 6 ln OPENi ,t 7 ln FINDi ,t i i ,t
(4)
Where i represents individual countries, t is time, ln is natural log, i and i capture
the individual country’s fixed effect with 1 through 8 , and 1 through 7 being the
elasticity coefficients to be estimated. Here, GDPC is GDP (Gross Domestic Product) per
capita used as a proxy for economic growth, INST is institutional quality proxied by the
index of government effectiveness and regulatory quality, NARE is natural resource
measured by total natural resource revenue to GDP ratio used as proxy for dependence
on natural resource and FIND is financial development measured by domestic credit to
private sector to GDP ratio. Also, DEBT is external debt servicing to export ratio, INF is
inflation measured as consumer price index in constant of 2010 US Dollar, INVT is
investment measured by fixed capital formation as a percentage to GDP ratio, EDUC is
education as proxy for human capital measured by public expenditure on education to
GDP ratio, PLCV freedom measured by index of political right and civil liberty, and
AUGL is augmented population growth.
4. Results and Discussion
4.1 Panel Unit root test
The results of the panel unit root test shown in Table 1 indicate that all variables with
the exception lnAUGL and lnNARE are non-stationary at their levels but stationary
after first difference.
Table 1: Panel unit root test results
Level
Variable
Without time trend
First Difference
With time trend
Without time trend
With time trend
lnGDPC
5.567.6
0.59422
-6.57351***
-5.56550***
lnINST
0.93931
2.76707
-9.59932***
-6.42704***
lnOPEN
0.25342
-0.68531
-9.37450***
-6.63258***
lnFIND
1.03170
0.05186
-7.34760***
-4.92551***
lnINF
6.19413
0.28936
-8.35313***
-7.10110***
lnAUGL
-16.2666***
-21.1282***
-
-
lnEDUC
-0.88434
-0.31442
-2.89365***
-0.54596***
lnDEBT
2.44446
1.49555
-5.33755***
-2.27833**
lnINVT
0.39667
0.77163
-8.29627***
-6.39250***
-2.51161***
-2.31033**
-
-
lnNARE
Note: *** and ** indicate the rejection of the null hypothesis of non-stationary at 1% and 5% significance
level respectively
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4.2 Co-integration and causality test
The co-integration results in Table 2 shows that there is long run relationship between
economic growth and institutional quality. The fact that they have long run relationship
implies that there is at least one directional causality between the two. However, cointegration result doesn’t tell us the direction of the causality. Thus, we conducted a
causality test and the results are shown in Table 3.
Table 2: Co-integration test results between institutional quality and economic growth
Weighted
Statistic
Prob.
Statistic
Panel v-Statistic
-2.562577
[0.9948]
-1.593141
[0.9444]
Panel rho-Statistic
-17.63280
[0.0000]***
-13.05994
[0.0000]***
Panel PP-Statistic
-46.56612
[0.000]***
-27.11115
[0.00000]**
Panel ADF-Statistic
-3.083803
[0.0010]**
1.012981
[0.8445]
Group rho-Statistic
-9.058288
[0.000]***
Group PP-Statistic
-29.70497
[0.000]***
1.916275
[0.9723]
Group ADF-Statistic
Prob.
Note: *** and ** indicate the rejection of the null hypothesis of no long-run relationship at 1% and 5%
significance level respectively
The causality result is discussed by dividing the analysis into different cases depending
on per capita income levels. First, the causality test is undertaken for the whole sample.
Then, it is undertaken by dividing the countries into 14 low income countries and 13
middle income countries. Finally, the causality test is done by categorizing the middle
income countries into 10 lower middle income countries and 3 upper middle income
countries. Classifying this way and considering case by case enables us to consider if
the direction of causality between economic growth and institutional quality changes
across countries with different level of economic performance.
The causality result for the whole sample shows that we fail to reject the null
hypothesis that institutional quality does not cause economic growth. On the contrary,
we reject the null hypothesis that economic growth does not cause institutional quality
at 5% level of significance. These two results show that the causality is unidirectional,
running from economic growth to institutional quality and not the other way round.
We conduct further analysis by classifying the countries into two low-income
and middle-income countries to ascertain whether or not the result of causality between
economic growth and institutional quality changes. It is evident from this analysis that
we fail to reject the null that the institutional quality doesn’t cause economic growth but
reject the null that economic growth does not cause institutional quality under both
low-income countries and middle-income countries implying that the result shows
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unidirectional causality running from economic growth to institutional quality and not
the other way round. Causality analysis is further undertaken by dividing the middle
income countries into lower middle-income countries and upper-middle income
countries.
The test result shows that under both lower middle income and upper middle
income countries cases, we fail to reject the null that institutional quality does not cause
economic growth but reject the null that economic growth causes institutional quality.
This implies that institutional quality does not have a lasting effect on economic
growth. In other words, economic growth at a given time period is not significantly
affected by the long term past institutional quality in the region. Generally, the causality
analysis shows that the causality runs unidirectional from economic growth to
institutional quality in all the cases. This result concurs with the predictions by Paldam
and Gundlach (2008) who explained that higher level of economic development
(growth) generates the need for and lead to better institutions.
Also, the causality from economic growth to institutional quality is in line with
the findings by Chong and Calderon (2000).
Table 3: Results of causality test between economic growth and institutional quality
Null hypothesis
All sample
ᵡ
countries
p-value
Low
ᵡ
income
p-value
Middle
ᵡ
income
p-value
Lower middle
ᵡ2
income
p-value
Upper middle
ᵡ
income
p-value
2
2
2
2
Institutional quality does not
Economic growth does not cause
cause growth
institutional quality
[2.53334]
[3.75390]
[0.8137]
[0.0167]**
[2.67220]
[3.98077]
[0.7290]
[0.0442]**
[1.63836]
[11.7253]
[0.3426]
[0.0000]***
[1.96536]
[11.0856]
[0.1633]
[0.0000]***
[0.54838]
[13.8574]
[0.5689]
[0.0000]***
Note: *** and ** indicate the rejection of the null hypothesis of no causality at 1% and 5% significance
level respectively.
4.3. Effect of institutional quality on economic growth
Table 4 reports the regression results of the effect of institutional quality on economic
growth. It can be seen that the coefficients of the first and second lags of economic
growth are both positive and statistically significant at 1% level of significance. This
implies that current growth responds to previous economic performance. Also, the
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coefficient of institutional quality, which is the main variable of interest, is positive and
statistically significant at 1% level of significance. This means that improvement in
institutional quality improves the economic growth in the region. This result is in line
with what has been found by some authors in the literature (Asghar, Qureshi, &
Nadeem, 2015; Alexiou, Tsaliki, & Osman, 2014; Kilish et al. 2013; Fabro & Aixalá, 2009;
Kandil, 2009). However, from the causality test results in Table 3, institutional quality
does not cause economic growth which poses a question which is beyond the scope of
this paper as to why institutional quality is a significant determinant of economic
growth but doesn’t have lasting effect in the long run on economic growth in the region.
Moreover, openness which is a proxy for the region’s economic interaction with
the rest of the world has its coefficient being positive and statistically significant at 1%
level of significance. This implies that the more the region is integrated with the rest of
the world economically, the more its economic activities improves. Also, financial
development which measures financial deepening is found to be a significant
determinant of economic growth at 10% significant level indicating that improving
financial deepening is beneficial for economic growth. Furthermore, debt proxied by the
external debt servicing to gross domestic product ratio is found to be a negative and a
significant determinant of economic growth at 10% implying that debt servicing is
negatively affecting the economic growth in the region.
Table 4: GMM estimation results of institutional quality and economic growth
Dependent Variable: lnGDPC
Regressor
lnGDPC(-1)
lnGDPC(-2)
Coefficient
Standard Error
Z
P-values
0.8272993***
0.0551429
15.00
[0.000]
0.149229***
0.0547068
2.73
[0.006]
0.0422489***
0.0123807
3.41
[0.001]
-0.0054274
0.0079408
-0.68
[0.494]
LnDEBT
-0.0099858***
0.0036594
-2.73
[0.006]
LnINVT
0.0148993
0.0095872
1.55
[0.120]
LnOPEN
0.0348734***
0.0112666
3.10
[0.002]
LnAUGL
-0.0015494
0.008458
-0.18
[0.855]
LnFIND
0.0137603*
0.0071322
1.93
[0.054]
Constant
0.1035212
0.0887966
1.17
[0.244]
LnINST
LnINF
AR(1), p-value: [0.0519]*
AR(2), p-value: [0.2898],
Sargan test, p-value: [0.5640]
Note: ***, **, and * denote significance level at 1%, 5% and 10% respectively
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4.4. Effect of economic growth on institutional quality
The results of the impact of economic growth on institutional quality are displayed in
Table 5. From the results, the coefficients of the first and second lags of institutional
quality are both positive and statistically significant at 1% level of significance.
Economic growth, which is the variable of interest, has its coefficient being positive and
statistically significant at 10% level of significance. This means that institutional quality
responds positively to the improvement in economic growth. Thus, economic growth is
accompanied by institutional improvements and this result is consistent with most
empirical finding in the literature (Alonso & Garcimartín, 2013; Fabro & Aixalá, 2009;
Rigobon & Rodrik, 2004; Islam & Montenegro, 2002).
Table 5: GMM estimation results of economic growth and institutional quality
Dependent Variable: lnINST
Regressor
Coefficient
Standard Error
Z
P-values
lnINST(-1)
0.6540217***
0.1130693
5.78
[0.000]
lnINST(-2)
0.2253973**
0.0992179
2.27
[0.023]
lnGDPC
0.024255*
0.0142306
1.70
[0.088]
lnPLCV
0.038326 *
0.0216463
1.77
[0.077]
lnNARE
-0.0097048*
0.0052095
-1.86
[0.062]
lnEDUC
0.0026191
0.0243669
0.11
[0.914]
lnOPEN
-0.0109518
0.0278528
-0.39
[0.694]
lnFIND
-0.0275056
0.0190633
-1.44
[0.149]
-0.2837768*
0.1663272
-1.71
[0.088]
Constant
AR(1), p-value: [0.0534]*
AR(2), p-value: [0.3050]
Sargan test, p-value: [0.4033]
Note: ***, **, and * denote significance level at 1%, 5% and 10% respectively
In addition, the coefficient of freedom is positive and statistically significant at 10%
significance level which means institutional quality positively responds to freedom and
right and civil liberty. Moreover, natural resource is found to be a negative and a
significant determinant of institutional quality at 10% significance level. Thus,
dependence on natural resource is undermining the development of institutional
quality in the region.
5. Conclusion and policy recommendation
There are ample evidences which show that sub-Saharan African countries are among
the least performing economies when measured in terms of per capita income. The
region is also classified among regions with least institutional quality in the world. The
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CAUSALITY BETWEEN INSTITUTIONAL QUALITY AND ECONOMIC GROWTH:
EVIDENCE FROM SUB SAHARAN AFRICA
fact that there are sound theoretical and empirical arguments on the relationship
between economic growth and institutional quality poses a question on why this region
performs poorly in terms of both variables and what policy measure to follow if it
wants to improve them. Following from this, this paper addresses the nature of the
relationship between economic growth and institutional quality and their respective
determinants in sub-Saharan countries using 27 countries: 14 low income, 13 middle
income (10 lower middle income and 3 upper middle income) countries from 1996 to
2014.
Using system GMM, the economic growth and institutional quality are modeled
independently so as to understand what determines them in the region. Accordingly,
the economic growth model shows that its own lags, institutional quality, openness to
the rest of the world economy and financial development positively and significantly
affect economic growth whereas debt servicing negatively affects it. The finding for the
institutional model shows that its own lags, economic growth, and freedom positively
affect institutional quality whereas dependence on natural resource affects institutional
quality negatively. Also, the panel co-integration test result shows that economic
growth and institutional quality are co-integrated in the long run implying the existence
of long-run relationship between them. Moreover, the Wald causality test result shows
that there is a unidirectional causality between the two variables with the causality
running from economic growth to institutional quality.
From the discussions above, it recommended that policies to enhance
institutional quality, openness, and financial development; while policies to reduce debt
servicing to achieve desired economic growth are paramount. Also, there is the need to
design appropriate policies that promote economic growth (as institutional quality
responds positively to the improvement in economic growth) and enhance freedom;
and policies which reduce the dependence on natural resources to bring about the
desired level of institutional quality. Therefore, African countries need to concentrate
on policies to promote growth first since growth enhances institutional quality.
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EVIDENCE FROM SUB SAHARAN AFRICA
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EVIDENCE FROM SUB SAHARAN AFRICA
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CAUSALITY BETWEEN INSTITUTIONAL QUALITY AND ECONOMIC GROWTH:
EVIDENCE FROM SUB SAHARAN AFRICA
Appendix 1: Countries
Low income
Lower middle income
Upper middle income
Benin
Cote d’Ivoire
Botswana
Burkina Faso
Cameroon
Gabon
Burundi
Congo, Rep.
Mauritius
Congo, Dem.
Ghana
Kenya
Lesotho
Madagascar
Mauritania
Mali
Senegal
Mozambique
Sudan
Malawi
Swaziland
Niger
Zambia
Rwanda
Tanzania
Togo
Uganda
Source: World Bank list of economies (July 2016)
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EVIDENCE FROM SUB SAHARAN AFRICA
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