Education and Economic Growth in Uganda: A cointegration approach

Education and Economic Growth in Uganda: A Cointegration Approach
Education and Economic Growth in Uganda: A
Cointegration Approach
James Kizza1, David Amwonya2, Nathan Kigosa3
Kyambogo University, Department of Economics and Statistics
This study examines the impact of the quantity of education on economic growth using gross
enrolment ratio of primary, secondary and tertiary education as a proxy for the quantity of
education. The annual data over the period 1985 to 2017 was used. Unit root tests, cointegration
and causality tests were conducted following the Johansen and Juselius cointegration
approach. The results indicate that the higher the education level attained the more likely the
contribution to Uganda’s economic growth. The study variables were found to be integrated of
order one using the ADF test for unit root. The long run causality test detected the existence of
long run causality at all levels of education with GDP. The paper contributes to the ongoing
debate as to whether education contributes to economic growth, and if it does which level is
likely to contribute more to a country’s growth and under what conditions. The paper
recommends the need for policy makers to provide an enriched curriculum that trains learners
to be creative and productive right from primary education. The government is urged to increase
the budget allocation to education as a percentage of GDP to at least 5.4% to ensure acquisition
of the necessary education infrastructure to promote quality education.
Keywords: Education, Economic growth, Co-integration, Granger causality, Gross enrolment ratio
JEL codes: C32; E62; H52; 015
INTRODUCTION
Education is a fundamental human right that is essential
for the exercise of all other rights as enshrined in the
Universal declaration of human rights (UN-Article 26). The
literature identifies three basic principles that underlie a
good education system. These are: equity, access and
quality. The role of education in a country’s development
has been acknowledged by many throughout ages. Goal 2
of the millennium development goals (MDGs) aimed at
countries achieving universal primary education. The
focus was on quantity and largely ignored the quality
aspect of education. The evaluations commissioned to
assess the attainment of this goal produced results that left
a lot to be desired. It was reported that in many low and
lower middle income countries; many children were
completing primary school without becoming literate!! In
Ghana, it was reported that over half of women and over
one third of men aged 15 to 29 who had completed six
years of school could not read a sentence at all in 2008
(UNESCO 2012). In light of this, the sustainable
development goals (SDGs) evolved. The sustainable
development goals (SDGs) include among others Goal 4
that stress the need for countries to lay emphasis on
quality education that is easily accessible to all (SDG 4).
SDG 8 talks of focusing on sustainable economic growth.
Economic theory posits that human capital as measured
by the level of education attainment in an economy is an
important factor in enhancing and promoting
output/economic growth.
Education enables countries to sustain competitive
markets, lower unemployment rate if the right skills are
imparted to those in the school system, and helps to
sustain democracies.
*Corresponding Author: James Kizza, Kyambogo
University, Department of Economics and Statistics.
*E-mail: kizzajames2016@gmail.com
2
E-mail: david.amwonya@gmail.com
2
E-mail: nathankigosa@yahoo.com
Research Article
Vol. 6(2), pp. 195-206, December, 2020. © www.premierpublishers.org. ISSN: 3012-8103
World Journal of Economics and Finance

James et al 196
Hanushek (2016) is in support of this paradigm shift from
quantity to quality and the provision of more education that
is focused by arguing how the quality of basic skills is key
to other levels of education and also adds that higher
education without the necessary basic skills is worthless.
The justification of more and better education is also
supported by Njong (2010) who is in support of enriching
the curriculum at all levels of education aimed at producing
a productive labor force. According to the World Economic
Forum (2016), education enables sustainable economic
growth mainly through three channels 1) the worker is
enabled to execute tasks fast and preciously 2) more
education eases knowledge transfer 3) knowledge transfer
enables new knowledge. A more educated labor can easily
adapt to new tasks, skills and technologies. In essence,
education increases labor productivity, an active labor is
less prone to crime as it neutralizes the adage “an idle
mind is a devil’s workshop”.
Denison (1962) defines economic growth as the increase
of real GDP or GDP PC measured in constant prices.
Boldeanu and Constantinescu (2015) hold the same view
on economic growth which they define as the increase in
the growth rate of GDP. Boldeanu and Constantinescu
(2015) further labor to identify the four major determinants
of economic growth that include: human resources,
natural resources, capital formation and technology.
Robert Solow (1957) attributed the growth of New York to
three sources: increases in stock of physical capital,
increase in the size of the labor force, and a residual
representing all other factors. Solow observed that
increasing levels of education were one of the factors
contributing to this growth. We can note that education
features most among the key determinants of growth in
form of human resources and capital formation. It is also
worth noting that education plays a key role in the proper
utilization of the other determinants of economic growth,
namely, natural resources and technology. Hanushek and
Wopman (2010) identifies education as one of the key
determinants of economic growth provided the necessary
facilitating factors, such as, functioning institutions for
markets and legal systems are in place. Prichett (2001)
decried the poor policies and institutions responsible for
hampering growth in many developing countries where
quality skilled labor is deployed into relatively unproductive
activities.
Acemoglu (2009) distinguishes between economic and
non-economic determinants of growth. Among the non-
economic determinants of growth identified includes
factors like institutions, governance and social factors. In
the study on the role of governance on economic growth
for 71 developed, developing and transition countries
between 1996 and 2003, Arusha (2009) reached a
conclusion that countries with high governance grow faster
compared with those with weak governance. Todaro and
Smith (2015) argue that education which is a form of
human capital is among the key factors influencing the
country’s level of economic growth and social change.
According to Becker (1975) education loaded with virtues
like punctuality and honesty are a form of human capital;
for the more educated one is, the more earnings he/she is
likely to obtain. Researches by several economists attest
to the notion that more and better education leads to
improved wealth and welfare (Denison 1962; Cooray
2009; Barro 1991; Sala-i-Martin et al.2004). Research
suggests that societies with a large number of highly
skilled workers generate more ideas and consequently
grow more (Romer 1990; Grant 2017;WEF 2016). A highly
educated population is able to transform the society and
according to Grant 2017 help the country to sail through
the middle income trap to a high income country status.
The more educated tend to earn more than the less
educated, and research findings support the existence of
a linear relationship between education and earnings
(Njong 2010).
The World Economic Forum (WEF 2016) defines
education as the stock of skills, competencies and other
productivity -enhancing characteristics. Education is
considered to be a public good that the state should avail
to the citizens (OIDEL, 2018). In many countries, the
provision of education is provided by the government
(public sector) and individual entrepreneurs (private
sector). Whether education is provided by the government
or individuals, the regulation of the education enterprise is
largely in the hands of the central government to ensure
issues of equity and quality are well catered for. Given the
resource envelop limitations in many developing countries;
it is essential that any amount of public spending is
efficiently allocated to prioritized sectors. William., Isabel.,
and Jeffrey (2006) in a policy brief #153 analyze the impact
of high quality universal preschool policy on economic
growth. William et al submit that education spending
estimated at a cost of $59 billion by 2080 could add up to
$2 trillion to annual US GDP by 2080. Studies relating
government expenditure on education and economic
growth have yielded mixed results; with some studies
positing a positive relationship (Baladacci et al. 2008;
Cooray 2009; Babalola 2011), other positing a negative
relationship (Devarajan et al.1996) and other studies
positing a no relationship (Prichett, 2001).
Literature suggests that a higher education level (skilled
labour) is an essential component of human capital. A well
trained labor is associated with quality output and
increased productivity. The UNESCO reports of 2010,
2011 seem to support the need for more schooling when
they attribute improved livelihood of children and general
poverty reduction to the acquisition of more years of
schooling by mothers. It is estimated that 1.8 million
children’s lives could have been saved in 2008 if their
mothers had at least secondary education representing a
41 percent reduction (UNESCO 2011). William., Isabel.,
and Jeffrey (2006) evaluated the effects of investing in
early education on economic growth based on research

World J. Econs. Fin. 197
using models where growth is endogenous. William et al.
(2006) reached a conclusion that the direct and indirect
effects of education are substantially large, where for
instance, the direct impact of a 10 percent increase in the
amount of education that people get could be as much as
7 or 8 percent and an increase in investment in education
could produce a permanent increase in the rate of growth.
According to Cooray (2009), a one percent increase in the
primary enrolment ratio is associated with a 0.12 percent
increase in income per capita, a one percent increase in
the secondary enrolment ratio is associated with a 0.17
percent increase in income per capita while a one percent
increase in schooling life expectancy is associated with a
0.19 percent increase in per capita income. Cooray’s study
seem to corroborate studies that more years of schooling
leads to higher per capita income and associated high
levels of economic growth. Grant (2017) studied the
contribution of education to economic growth and found
that investment in secondary education provides a clear
boost to economic development much more than it can be
achieved by UPE alone. Grant (2017) made a
recommendation to low and lower middle income countries
to allocate at least 3.4 percent of GDP on pre-primary,
primary and lower secondary education or 5.4 percent
across all education levels. Based on this, we can safely
justify the need to carry out more studies justifying
increased public spending on education in designated
contexts.
The state of education in Uganda
The Uganda government adopted universal primary
education (UPE) for all her school going age children in
1997 and later in 2006 introduced universal secondary
education (USE). The main objective of this initiative was
to eliminate illiteracy among the population and reduce
poverty through skilling of the young generation.
Cuaresma and Raggi (2014) assessed changes in returns
to education at the subnational level in Uganda using the
Uganda National Household Surveys for 2002/2003 and
2005/2006. The findings indicated that average returns to
schooling tended to converge across regions in the last
decade. Ahaibwe (2017) explored the links between
education attainment, age of marriage and child birth, and
labour market among young Ugandans (15 -24 years of
age). The study found that young women leave school
early, give birth and/or get married before the legal age of
18 years, enter the labour market early with limited skills,
while a good number are inactive (neither in the labour
market nor in school). Mugizi (2018) explored how higher
education can contribute towards achieving Uganda Vision
2040 and found that higher education influences the
development of improved technology, knowledge transfer,
promotes national unity, democracy, supports innovation
and increases productivity. Despite this supported
evidence crediting education with economic growth, there
are growing worries on the quality of education offered to
our learners in schools especially at the lower levels of
education.
The Uwezo (2018,p10) report on the quality of learning in
refugee contexts in Uganda decry the poor learning
outcomes across refugee and non refugee contexts where
findings with primary three pupils showed that more than
90 percent were unable to read, comprehend and divide.
Earlier Uwezo reports depicted the same poor quality of
learning outcomes where many of those who drop out
before completing the primary cycle never mastered the
intended basic skills Uwezo (2015,p19-21). The Uwezo
(2016,p20) reported that on average, 8 out of 10 primary 3
pupils and 2 out of 10 primary 7 pupils are unable to read
a primary 2 level story!! This poor state of education may
be attributed to low budget allocation to education as a
percentage of GDP which over the last ten (10) years has
stood below a miserable 3 percent (UNESCO Institute for
Statistics (http://uis.unesco.org). The specific objectives
that guided this current study were: 1) To identify the level
of contribution of different levels of education to the
country’s GDP 2) To check either there is a long run
relationship of education with economic growth or not 3)
To test the existence of causality between the different
levels of education and GDP.
The UNESCO (2020) report refers to issues of quality
education when it refers to inclusive education to be not
just a result but a process. A result would put emphasis on
the quantity but a process will take care of the quality
aspects embedded in the education system. The
measures for quality of education usually provided in the
literature include: survival rates, repetition rates, student-
teacher ratios, schooling life expectancy, trained teachers
and test scores in numeracy especially at the level of
primary education (Cooray 2009). The school environment
and other factors like parent-teacher meetings, community
involvement, local government coordination structures and
ministerial structures are identified as key ingredients in
support of a quality education system. Quality education is
contextualized as education that prepares the young to be
productive members of the global citizenry. Quality
education is manifest where graduates of the school
system exhibit proficiency in literacy and numeracy, have
an appreciation for the respect of human rights, respect for
cultural diversity and have the relevant skills and attitudes
required in the job market. Quality education is a joint effort
of all stakeholders that strive to ensure that children in
schools achieve the required skills and mindset that mirror
the societal aspirations.
1. Theoretical and Empirical Review
Nowak and Dahal (2016) investigated the long run
relationship between education and economic growth in
Nepal between 1995 and 2013 through the application of
Johansen cointegration technique and ordinary least
squares (OLS). The results from OLS showed that

James et al 198
primary, secondary and tertiary education contributes
significantly to the real Gross domestic product per capita
(GDP PC) in Nepal. The cointegration test results
confirmed the existence of a long run relationship between
education and real GDP PC. Kwabena et al. (2006)
investigated the effect of higher education human capital
on economic growth in African countries. The study found
that all levels of education, human capital including higher
education human capital, have positive and statistically
significant impact on the growth rate of per capita income
in African countries.
Mankiw et al (1992) and Barro (1991) investigated the
relationship between education and economic growth.
They examined the variation in school enrolment rates,
using a single cross section of both the industrialized and
low developing countries. From the study findings, they
concluded that schooling has a significant positive impact
on the rate of growth of real GDP. Aghion et al. (2009)
investigated the causal impact of education on economic
growth in the United States and the results supported the
existence of a positive causal impact between education
and economic growth.
Odit et al. (2010) investigated the impact of investment in
education on economic growth in Mauritius. The research
findings revealed that human capital plays a key role in
economic growth mainly as an engine of improving of the
output level. Babalola (2011) evaluated the impact of
education on economic growth in Nigeria. The study
findings revealed the existence of a long run relationship
between education and economic growth. The causality
test results indicated uni –directional causality that runs
from economic growth to education.
Mercana and Sezer (2014) investigated the effect of
education expenditure on economic growth in Turkey for
the period 1970-2012 and the study findings revealed a
positive relationship between education expenditures and
economic growth. Musila and Belassi (2004) employed the
time series technique and Johansen cointegration
procedure to investigate the relationship between
government education expenditure and economic growth
in Uganda during the period 1965 -1999. The results
showed that education expenditure per worker has a
positive and significant impact on economic growth in both
the short run and long run. Cooray (2009) examined the
effects of the quantity and quality of education on
economic growth in a cross section of low and medium
income countries. The study found that education quantity
measured by enrolment ratios has a positive influence on
economic growth.
The millennium development goals (MDGs) provide
indicators of quantity of education that have been adopted
by several researchers. These include: enrolment ratios
(Mankiw, Romer and Weil 1992; Barro 1991; Cooray
2009), average years of schooling (Hanushek and
Woessmann, 2008), and the funding allocated to
education activities (Baladacci et al, 2008; Cooray 2009).
The UNESCO (2012) report highlights how for every US$
1 spent on education, as much as US$10 to US$15 can be
generated in economic growth. On the other hand, the
measures for quality of education usually provided in the
literature include: survival rates, repetition rates, student-
teacher ratios, schooling life expectancy, trained teachers
and test scores in numeracy especially at the level of
primary education (Cooray 2009). Hanushek et al (2010)
found the quality of education to be significantly related to
long run economic growth. Hanushek et al (2010) caution
that if developing countries are to achieve their long run
economic performance, they have to focus on the
improvement of the quality of their school system.
The UNESCO(2020) report decries the unequal
distribution of education opportunities and the increasing
barriers to quality education especially among the
disadvantaged groups that has been worsened by school
closures in the face of the Covid-19 pandemic. Many would
be school going children estimated at about one in five
have been excluded from the school system widening
societal inequalities. The report recommends the need for
countries to adopt inclusive education that focuses on the
elimination of all forms of exclusion. The report
acknowledges the essential contribution of education
towards building inclusive, social cohesion and democratic
societies. Inclusive and quality education are given as a
key ingredient in exploiting fully the benefits of any
country’s human potential and thus a clear justification for
increased education funding (UNESCO,2019;2020).
2. METHODOLOGY
We apply the Johansen cointegration approach to test the
existence of a long run relationship between education and
economic growth. The Johansen cointegration approach is
justified where variables to be tested have the same level
of integration I (1) (Naidu,Pandaram and Chand,2017).
The unit root tests were performed using the Augmented
Dickey-Fuller (ADF) where it was found that all variables
were I (1) thereby qualifying our choice of Johansen
cointegration approach. The verification of unit root status
of variables is necessary given that regression based on
non stationary variables is spurious and could undermine
policy implications (Engle and Granger, 1987). In order to
test the existence of short run causality between the
different levels of education and GDP, we perform the
Wald test; and to test for the existence of long run
causality, we perform the long-run Granger Causality test.
Descriptive statistics are provided for the key variables
under study. To identify the level of contribution of the
different levels of education to the country’s GDP, we
conduct the Ordinary Least Square (OLS) estimation. The
OLS is used because under certain assumptions, namely,
the equation to be estimated is linear in parameters, is non

stochastic, has zero mean value, possess equal variance
of distribution makes the model a powerful method of
regression analysis. We test the robustness of the model
used in the study by carrying out various diagnostic tests
including normality, multicollinearity, serial correlation and
heteroskedasticity tests.
Model specification
The growth model for the study takes the form of the Cobb
–Douglas function:
)(edufgdp  (1.1)
Where gdp is the gross domestic product and edu
represents the gross enrolment ratio of the various levels
of education under study.
Gross enrolment ratio for primary education is defined as
total enrolment in primary education regardless of age,
expressed as a percentage of the population of official
primary education age, and this can exceed 100 percent
due to the inclusion of over aged and under aged students
because of early or late school entrance and grade
repetition. Gross enrolment ratio for secondary education
is defined as total enrolment in secondary education
regardless of age, expressed as a percentage of the
population of official secondary education age. Gross
enrolment ratio for tertiary education is defined as total
enrolment in tertiary education regardless of age,
expressed as a percentage of the total population of the
five year age group following on from secondary school
leaving (green data-
https://knoema.com/atlas/uganda/GER).
This is expressed in a linear form as:
ttt edugdp   loglog 10
(1.2)
0 and 01 
The OLS specification of the model estimated is
tXXXLGDP   3322110
(1.3)
Where 31, ii represent the regression coefficients,
1X represents primary education gross enrolment ratio,
2X represents secondary education gross enrolment ratio
3X represents tertiary education gross enrolment ratio
while 𝜀𝑖~𝑖𝑖𝑑(0, 𝛿2
is the model error term).
The literature posits that time series data are non
stationery (have a stochastic trend) and regressing non
stationery series on each other is bound to produce
spurious results (Engle and Granger, 1987). Non
stationery variables are made stationery by differencing. If
ttt UpYY  1 and ,11  p where tY is the
variable of interest and tU is white noise error term, then
it is concluded that tY has unit root and thus non stationery
provided p=1. We handle the subject of unit root of our
time series variable through the use of the Augmented
Dickey-Fuller (ADF). Given our interest in establishing the
long run relationship between the study variables, it was
necessary to test for cointegration. Granger (1987)
advises that a test for cointegration be done as a pre-test
to avoid spurious regression results. Cointegration in
economics would imply the existence of long run or
equilibrium relationship between two or more variables
(Babalola, 2011). In this study as discussed earlier on, the
Johansen cointegration approach is adopted.
In the building of forecasting models, it is essential to test
for causality as widely popularized by economists like
Granger (1969). One variable )( tX is said to granger
cause another variable )( tY if the lagged values of )( tX
can predict tY and the reverse also holds. In this study,
the Granger causality test was performed using the vector
autoregressive model below:
If causation runs from education to gdp, we have:
 


 
n
m
tjtj
n
i
tit edugdpgdp
1
1
1
1 logloglog 
(1.4)
If causation runs from gdp to education, we have:
 


 
n
m
tjtj
n
i
tit gdpeduYedu
1
2
1
1 logloglog 
(1.5)
Where t1 and t2 are assumed to be uncorrelated.
The decision rule:
From equation (1.4), jtedu log Granger causes
tgdplog if the coefficient of the lagged values of edu as a
group j is significantly different from zero based on F-
test, and from equation (1.5) jtgdp log Granger causes
tedulog if  is statistically significant.
Data sources
The study employed secondary data with reference period
of 1985-2017 sourced from the World Bank data, World
Development Indicators, UNESCO Institute for statistics
(http://uis.unesco.org); Uganda Bureau of Statistics,
Ministry of Education and Sports and the National Council
for Higher Education.

James et al 200
Empirical Results and Discussion
This section covers the empirical results of the study and
the subsequent discussion therefrom.
Study Descriptives
The descriptives for the variables under study are
represented in Table 1
Table 1: Summary statistics
Variable Mean Std. Dev. Min Max
Primary 103.52 26.30 63.7 138.4
Secondary 17.62 7.03 9.1 28
Tertiary 2.79 1.44 0.7 5
GDP 1.30E+10 7.80E+09 4.12E+09 2.86E+10
Unit root tests
The real values of the variables were converted to logs and tested for stationarity using the Augmented Dickey Fuller test.
Table 2: Augmented Dickey Fuller (ADF) test results before differencing
Variable Test Statistic 1% Critical
value
5% Critical value 10% Critical P-value Conclusion
Lgprimary -1.316 -3.702 -2.98 -2.622 0.6218 Non stationary
Lgsecondary -0.98 -3.702 -2.98 -2.622 0.7604 Non stationary
Lgtertiary -2.206 -3.702 -2.98 -2.622 0.2042 Non stationary
Lgdp -0.038 -3.702 -2.98 -2.622 0.9553 Non stationary
All variables were found to be non stationary at level. We
proceeded to difference all variables and the results
showed that all variables became stationery at first
difference as represented in Table 3.
Table 3: Augmented Dickey Fuller (ADF) test results after first differencing
Variable Test Statistic 1% Critical value 5% Critical value 10% Critical P-value Conclusion
Dlgprimary -5.213 -3.71 -2.98 -2.62 0.000 Stationary
Dlgsecondary -4.129 -3.71 -2.98 -2.62 0.001 Stationary
Dlgtertiary -6.184 -3.71 -2.98 -2.62 0.000 Stationary
Dlgdp -4.877 -3.71 -2.98 -2.62 0.000 Stationary
Optimal lag selection
Since all the variables were found to be stationary at I (1),
it meant the existence of a long run relationship between
them. A Johansen cointegration test using the Johansen –
Julius maximum likelihood cointegration that is very
sensitive to the choice of lag length was used. The VAR
model was fitted to the time series data to establish an
appropriate lag structure and the results are presented in
Table 4
Table 4: VAR lag order selection criteria
Lag LL LR Df P FPE AIC HQIC SBIC
0 150.54 3.30E-10* -10.47* -10.41* -10.28*
1 157.74 14.39 16 0.569 6.40E-10 -9.83819 -9.55 -8.89
2 178.97 42.46 16 0.000 4.70E-10 -10.2118 -9.69 -8.50
3 187.04 16.15 16 0.443 1.00E-09 -9.64558 -8.89 -7.17
4 200.51 26.95* 16 0.042 2.00E-09 -9.46507 -8.48 -6.23

Endogenous: dloggdp, dlogpri, dlogsec, dlogter
Exogenous: _cons
LR: sequential modified LR test static (at 5% level)
From Table 4, lag 4 was chosen and used in subsequent analysis.
Testing for long run association by Johansen Cointegration
Both the Trace and Maximum Eigen value tests were run
Table 5: Unrestricted Cointegration rank test (Trace)
maximum rank Parms LL Eigenvalue Trace statistic 5% critical value
0 20 156.39 . 66.59 47.21
1 27 170.97 0.62 37.43 29.68
2 32 181.48 0.50 16.41 15.41
3 35 187.34 0.32 4.69 3.76
4 36 189.68 0.14
Since the eigen values are less than the trace static, this
indicates the existence of a long run relationship between
the explanatory variables and the dependent variable. The
null is thus rejected at 5% level.
Table 6: Maximum rank
maximum rank Parms LL Eigenvalue Maximum statistic 5% critical value
0 20 156.39 . 29.17 27.07
1 27 170.97 0.62 21.02 20.97
2 32 181.48 0.50 11.72 14.07
3 35 187.34 0.32 4.69 3.76
4 36 189.68 0.14
Since the eigen values are less than the maximum
statistic, this indicates the existence of a long run
relationship between the explanatory variables and the
dependent variable. The null is thus rejected at 5% level.
From the cointegration Tables 5 and 6, both the trace
statistic and maximum eigen value statistic indicate the
presence of cointegration at 5% level of significance
implying the existence of a log run relationship between
the explanatory variables and the dependent variable.
Johansen and Juselius (1990) suggest that where the
Trace statistic and Maximum eigen value tests produce
different results, it is preferable to use the results of the
trace test. In our study, they both yield the same results
and therefore, there was no need to make a choice
between them.
Granger causality test
Table 7: Short run Granger Causality (Wald test)
To detect the Short-run Granger Causality, one looks at the P-value if it is significant or not (Kigosa, 2014).
Causal variable Coefficient P-value Null hypothesis Decision
Primary education 0.13 0.031 Primary education does not
Granger Cause GDP
Reject the null
hypothesis
Tertiary education -0.12 0.018 Tertiary education does not
Granger Cause GDP
Reject the null
hypothesis
Secondary
education
0.8 0.005 Secondary education does not
Granger Cause primary education
Reject the null
hypothesis
Reject the null
hypothesis
GDP 2.01 0.058 GDP does not
Reject the null
hypothesis

Primary education -0.81
0.000
Primary education does not
Granger Cause secondary education
Reject the null
hypothesis
Tertiary education 0.58 0.002 Tertiary education does not
Reject the null
hypothesis
Reject the null
hypothesis
Secondary
education
0.5 0.053 Secondary education does not
Granger Cause tertiary education
Reject the null
hypothesis
Granger Cause tertiary education
Reject the null
hypothesis
From the Table 7, it can be seen that both Primary and
Tertiary School Education Granger Cause GDP implying
that you can use the lagged/ passed values of the above
two variables to predict/ forecast growth. In the same vein,
GDP does Granger Cause both Primary and Tertiary
School Education meaning that there is a two-way
causality between GDP and Primary School Education,
and between GDP and Tertiary School Education. A two-
sided Granger Causality was also observed between
Primary and Secondary School Education. Only a
unidirectional Granger causality from Tertiary to Primary
School Education was detected. Lastly, a two-sided
Granger Causality was observed between Secondary and
Tertiary School Education.
Long-run Granger Causality test
To determine if there is a long-run causality, the coefficient
is analyzed. If the coefficient is negative and significant, it
means a long-run causality exists (Kigosa and Rudi,
2014). The results of the long-run Granger Causality are
presented in Table 8
Table 8: Long-run Granger Causality test
Causal variable Coefficient P-value Null hypothesis Decision
Primary education -0.72 0.000 Primary education does not
Granger Cause GDP
Reject the null
hypothesis
Secondary
education
-1.24 0.037 Secondary education does not
Granger Cause Primary education
Reject the null
hypothesis
Granger Cause Secondary education
Reject the null
hypothesis
From Table 8, it can be seen that a long-run causality from
Primary School Education to GDP was detected because
the coefficient was negative and significant. This means
that the lagged values of Primary School Education can
help in predicting economic growth in the long run.
Similarly, a long-run causality was detected from
Secondary Education to Primary Education. Lastly, it was
also found that Tertiary Education Granger Causes
Secondary Education.
Error correction model
The literature posits that cointegration is a necessary
condition for an error correction model to hold (Engle and
Granger, 1987). Figure 1 reveals evidence for the model
to be error correcting in the long run after short-run
disturbance.

Figure 1: Error correction model
OLS model estimation
The results of the ordinary least square regression as drawn from equation (1.3) are presented in Table 9.
Table 9: OLS Model estimates
Lggdp Coef. Std. Err. t P>t
Lgprimary -0.29 0.15 -2.00 0.055
lgsecondary 0.37 0.13 2.75 0.010
Lgtertiary 0.87 0.10 8.28 0.000
Cons 22.66 0.70 32.33 0.000
R-squared 0.96 R-adjusted 0.95
F-value 215.97 0.000
From Table 9, it can be seen that the coefficient of primary
school education is negative though weakly significant at
5% level. The implication of the negative sign of the
coefficient is that primary school education negatively
affects the growth of the economy. This can be attributed
to the poor quality of primary school education in Uganda
where most learners can hardly read and write (Uwezo,
2015). The coefficient of the Primary school education
above means that if the level of the Primary school
education increases by one percent, the level of growth
decreases by 0.29%. The coefficients of the secondary
and tertiary school education on the other hand are
positive and significant implying that these two variables
positively contribute to economic growth. The coefficient of
the secondary school education above means that if the
level of the secondary school education increases by one
percent, the level of growth increases by 0.37%. Similarly,
if the level of tertiary school education increases by one
percent, the level of growth increases by 0.87%. The
adjusted R-squared implies that the explanatory variables
explain 96 percent of the variation in the level of growth
and other factors outside this model only account for 4
percent of the change in the level of growth.
The estimated model is thus specified as
321 87.037.029.066.22 XXXGDP 
(1.3)*
We thus reject the null and accept the alternative for X2
and X3 that secondary and tertiary education has a
significant positive effect on growth. We otherwise accept
the null for X1 that primary education does not have a
significant positive effect on growth.
Diagnostic tests
Test for multicollinearity
This was done to test whether the variables tested have
correlation among or between each other
-.2
0
.2.4.6
1980 1990 2000 2010 2020
Year
dlggdp dlgprimary
dlgsecondary dlgtertiary

James et al 204
Table 10: Multicollinearity test results
Variable VIF 1/VIF
Lgtertiary 7.14 0.14
Lgsecondary 5.32 0.19
Lgprimary 2.74 0.36
Mean VIF 5.07
Table 10 results show that VIF (5.07) is greater than 5
and less than 10. The collinearity tolerance (1/VIF) for all
predictor variables were greater than 0.1 (10%) with the
corresponding variable inflation factor (VIF) for all
variables ranging between 2.74 and 7.14 which is within
the recommended interval of 1 and 10 implying
nonexistence of multicollinearity.
Test for Serial correlation
Table 11: Breusch-Godfrey LM test for autocorrelation
lags(p) F df Prob > F
1 1.286 (1,27) 0.2667
No serial correlation
Test for normality
The Jack –Bera (JB) test was used to test the normality of
the model. The statistics of the model is given as





 

24
)3(
6
22
KS
nJB
Where n is the sample size, S stands for skewness and K
stands for Kurtosis coefficient. For distribution of the
variable, S=0 and K=3, therefore the JB test of normality is
a test of joint hypothesis where skewness and kurtosis are
0 and 3 respectively. The worth of the JB statistic is
considered to be 0 in this case (Gujrati,2004)
Table 12: Jack- bera test for normality
Equation chi2 Df Prob > chi2
Lggdp 4.13 2 0.1266
Lgprimary 12.33 2 0.0021
Lgsecondary 4.67 2 0.09689
Lgtertiary 2.74 2 0.25395
ALL 23.87 8 0.00241
The normality results show that GDP as the dependent
variable is normally distributed as the corresponding p-
value from the test statistic was greater than 5 percent
level of significance. Similarly, all explanatory variables
with the exception of primary education were found to be
normally distributed.
Test for heteroskedasticity
Ho: Constant variance
Table 13: Breusch-Pagan / Cook-Weisberg test for
heteroskedasticity
Stat chi2 (1) Prob > chi2
Output 0.35 0.5562
The Chi square value was 0.35. Prob  Chi square value
at 0.5562. This means there is no heteroskedasticity, so
the null hypothesis was upheld.
CONCLUSIONS AND POLICY IMPLICATIONS
This study examines the impact of the quantity of
education on economic growth using gross enrolment ratio
of primary, secondary and tertiary education as a proxy for
the quantity of education. The results indicate that the
higher the education level attained the more likely the
contribution to Uganda’s economic growth. The study
variables were all found to be integrated of order one using
the ADF test for unit root. The long run causality test
detected the existence of long run causality at all levels of
education with GDP. The study confirms the existence of
long run relationship between education and GDP as
shown from the Johansen cointegration test results. The
paper contributes to the on going debate as to whether
education contributes to economic growth, and if it does
which level is likely to contribute more to a country’s growth
and under what conditions. The paper recommends the

need for policy makers to provide an enriched curriculum
that trains learners in skills needed to make labor more
creative and productive right from the early stages of
education. The school environment should be improved
especially at the lower levels of education to ensure that
graduates at the primary level meet the required
competences in literacy and numeracy as per UNESCO
guidelines. The Government of Uganda should consider
increasing her spending on education as a percentage of
GDP across all education levels to near the 5.4%
recommended by Grant (2017) to facilitate the acquisition
of the necessary education infrastructure to promote
quality education. In line with SDG 4, the government
should prioritize inclusive education as a way of minimizing
the widening inequalities in society and as a means of
ensuring the maximal efficient harnessing of all her human
potential. It is also recommended that stakeholder
engagements in the education system be strengthened to
ensure learners in schools are trained in accordance with
the aspirations of the society, in which case, this requires
regular monitoring and support of what goes on in our
schools by all stakeholders.
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Accepted 26 November 2020
Citation: James K, David A, Nathan K, (2020). Education
and Economic Growth in Uganda: A cointegration
approach. World Journal of Economics and Finance, 6(2):
195-206.
Copyright: © 2020 James et al. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.

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