Management and Human Resource Research Journal Vol.9, No.2; February-2020;
ISSN (3363 – 7036);
p –ISSN 4244 – 490X
Impact factor: 7.22
Management and Human Resource Research Journal
Official Publication of Center for International Research Development Double Blind Peer and Editorial Review International Referred Journal; Globally index
Available www.cird.online/MHRRJ: E-mail: [email protected]
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THE IMPACT OF TRADE OPENNESS ON HUMAN CAPITAL
DEVELOPMENT AND ECONOMIC GROWTH IN ETHIOPIA
Asnake Getie Asmare and Liu Haiyun Huazhong University of Science and Technology Postal Code: 430074 Luoyu Road 1037-Wuhan, China,
Corresponding author: Asnake Getie Asmare (ORCID: 0000-0003-0624-7728)
Abstract: The theoretical and empirical associations between trade openness and economic growth have been a subject of
debates among scholars. Most of the previous empirical literature investigated the effects of trade openness on economic
growth. Studying the impacts of trade openness on human capital accumulation and economic growth is an interesting issue.
This study applied the Autoregressive Distributed Lag and Error Correction Model estimation techniques. The main findings
of this empirical study are: 1. A long-run cointegration among the variables. 2. A long-run positive and significant effect of
trade openness on GDP growth of Ethiopia. 3. A positive and significant long-run effects of trade openness on human capital
accumulation. 4. A positive but not significant long-run effect of human capital on GDP growth. 5. A positive and significant
long-run effect of human capital on trade openness. 6. A positive and significant short-run and long-run effects of physical
capital on GDP growth. 7. Positive and significant short-run and long-run effects of labor force on GDP growth. 8. Positive
long-run effects of and real exchange rate on GDP growth. This study suggests that increasing trade openness can facilitate
human capital development and the long-run GDP growth of Ethiopia.
Keyword: Trade Openness; Human Capital; GDP Growth
1. Introduction
Economists have been concerned about the determinant
factors of longrun economic growth. The theoretical and
empirical literature has been stressed the importance of
human capital and trade openness for its longrun impact on
the economic growth of countries. The theoretical and
empirical associations between trade openness and
economic growth have been a subject of debates without
established clear consensus among scholars. In the
contemporary progressive knowledge based
interdependent global economy, a higher rate of trade
openness to a global market and a welleducated human
capital can be the main drivers of economic growth.
Related to this issue, the main contributors to economic
growth have been established within the background of the
endogenous and the new growth theories. These theories
and literature have been emphasized on the theoretical and
empirical findings, emerged in the late 1980s as a new
challenge for the popular neoclassical growth model. The
new growth theories have been delivering a convincing
logical argument about the importance of human capital,
knowledge, and technological progress for the
sustainability of the economic growth of countries. An
economic growth model contributed by Lucas (1988),
Management and Human Resource Research Journal Vol.9, No.2; February-2020;
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pg. 2
argued that the major source of longrun economic growth
can be through the contribution of human capital
accumulation (Mustafa, Rizov, and Kernohan, 2017). The
available literature is not enough to reach an explicit
conclusion about the relationship between trade openness,
human capital and economic growth of countries
especially in least developed countries of Africa
(Malefane, Odhiambo, 2018). Moreover, some of the
existing empirical literature findings about the relationship
between the openness of trade and economic growth have
been criticized by other researchers. Although theoretical
studies supported the contributions of trade openness for
the economic growth of countries, some researchers
claimed the harmful effects of trade openness on the
economic growth of countries. Trade openness may not
enhance the economic growth of countries when it leads to
economic specializations on the disadvantaged sectors of
country's economy (Huchet-bourdon and Mou, 2018).
Trade openness can facilitate the economic growth of
countries through improving the accumulation of human
capital that results knowledge spillover effects. This
argument is supported by the endogenous growth models
which imply that human capital is an important
determinant factor for accomplishing longrun economic
growth (Gonza, 2015; Audretsch, 2000). This empirical
study is intended to deliver empirical insights about the
contributions of trade openness on human capital
accumulations and for the economic growth of Ethiopia.
Policymakers would be benefited in preparing trade and
growth policies if they got explicit evidence on the longrun
effects of trade openness for the growth of human capital
and economic growth of countries. In the least developed
countries lack of skilled human capital, less investment in
research and development hinders the innovation and
adaptation of new technologies from the global market.
Based on the opines of UNCTAD (2005) developing
countries international trade participation level is still very
low compared to developed countries (Kim, 2011a). The
limited knowledge on the main drivers of trade and
economic growth can significantly influence policymakers
to formulate effective policies (Patrick, Amelia, and
Dogan, 2018; UNCTAD, 2007). Although empirical
literature on the relationship between trade openness and
economic growth is enormous, literature in Africa and
specifically in Ethiopia is very scanty. Moreover, some of
the previous empirical studies have been criticized related
to using cross country data which may not cover for
specific countries special situations, measurement issues,
data quality problem, less attention on time series data
stationarity and endogeneity nature of variables (Chang,
Kaltani, and Loayza, 2009). This study paper mainly
investigated the shortrun and longrun effects of trade
openness on human capital accumulations and the
economic growth of Ethiopia using a time series data from
1981-2017. Investigating the impacts of trade openness on
the enhancement of human capital for the longrun
economic growth of Ethiopia is important for contributing
empirical evidences for policymakers of Ethiopia and
researchers on similar areas of study. Considering most of
the common methodological shortcomings and
suggestions of literature this empirical research paper
employed an Autoregressive Distributed Lag (ARDL)
model and Error Correction Model (ECM) estimation
techniques of Pesaran (2001) (Camarero and Mart, 2016).
Using the Autoregressive Distributed Lag and Error
Correction Model estimation techniques can resolve most
of the common empirical estimation bias and
shortcomings.
1.2. General Overviews on Trade, Human Capital and
Economic Growth in Ethiopia
After 1992 Ethiopia started new policy reforms by
liberalizing its economy to the rest of the world. The main
objectives of this opening policy reform were to increase
its economic growth and to create a stable macroeconomic
condition. This policy reform measures are creating short
and simple licensing processes, reducing exchange rate
government intervention, tariff, and quota reduction
measures, and liberalizing government-owned business
Management and Human Resource Research Journal Vol.9, No.2; February-2020;
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pg. 3
sectors. The government of Ethiopia has also adopted a
new trade liberalization policy and institutional reforms.
These trade liberalization measures resulted in increasing
both export and imports of goods and services. The
economic growth rate of Ethiopia was increased with an
average real GDP growth rate of 10.4% from 2003-2011.
The growth of trade was also expanding with an average
export growth rate of 7% per annum from1981-2008. The
growth of export and import of goods and services was
mainly caused by the economic liberalization reform
measures such as the devaluation of the foreign exchange
rate, government encouragement in export sectors, and
other structural macroeconomic policy program
adjustments. However, the economic growth of Ethiopia
has been sometimes subject to fluctuation related to the
global market price fluctuations and climate changes,
especially in agricultural commodities. Although the
participation of international trade has been increasing, the
trade openness index of Ethiopia is still low compared to
other developing countries trade openness index. This
lower trade openness index indicated that Ethiopia should
improve the participation of trade in the global market to
facilitate its long-run economic growth. Recently the
Ethiopian government adopted an economic growth
strategy called agricultural development led
industrialization strategy. This economic growth strategy
focuses on increasing agricultural sector productivity
followed by raising labor intensive industrialization. The
main objective of this economic growth policy is using
agricultural sector growth as the main driving force for
achieving the growth objectives of industrialization
strategy. Moreover, the Ethiopian government has given
high emphasis on the expansion of education mainly after
1995 education access program policy implementation.
After the implementation of education access program the
primary school enrolment rate was increased from 22%
enrolment rate in 1995 to 87.5% in 2013. The secondary
school enrolment rate in Ethiopia was also increased
especially after 1999 from 13.64% to 39.3% in 2013.
However, the enrolment rate of tertiary education in
Ethiopia is still low compared to the primary and
secondary school enrolment growth rates. The tertiary
school enrolment rate was 0.96% in 1999 increased to
7.4% enrolment rate in 2013 (World Development
Indicator, 2014). The Ethiopian government has also given
high emphasis on education and health expenditures by
giving the highest share from the total government
expenditures. The growth rate of education expenditure
was increased from 11.5% share of the total government
budget in 1999 to 25.2% in 2013, which fulfills the
minimum education expenditure requirement suggestion
rate by UNESCO of 25% from the total budget. Moreover,
Ethiopia made significant growth in expanding the health
sector coverage mainly in the health of women and
children by implementing its own health extension
programs. After implementing the expansion of successful
education and health policy in the last few decades,
Ethiopia becomes one of the 10 countries in the world that
achieved the highest improvement in the human
development index. The remaining parts of this study
paper are organized as part two covered empirical
literature review and theories. Part three, discussed
research data, applied models and estimation techniques.
Part four discussed empirical findings and discussions, and
the last part mentioned conclusions and policy
implications.
2. Literature Review
2.1 Theoretical Framework
Theoretical literature has been investigated about the
relationship between human capital, trade openness and
economic growth. The theoretical relationship between
trade openness, human capital, and economic growth has
been supported by the development of the endogenous and
the new growth theories. However, in the neoclassical
growth theory, the relationship between the openness of
trade, human capital, and economic growth was not fully
recognized. It states that longrun economic growth is
determined by technology not by trade. However, the
Management and Human Resource Research Journal Vol.9, No.2; February-2020;
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p –ISSN 4244 – 490X
Impact factor: 7.22
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pg. 4
endogenous and the new growth theories implied that
opening trade and increasing the accumulation of human
capital can promote economic growth by increasing the
augmented technology spillover through international
trade, skilled and trained human capital, and new ideas
(Silajdzic & Mehic, 2017). The endogenous and new
growth theories have been emerged as a reaction to the
neoclassical economic growth theory. The new economic
growth theories contributed a convincing argument about
the longrun effects of human capital, knowledge and
technological progress for the sustainability of economic
growth. The economic growth model built by Lucas
(1988), investigated the contribution of human capital
accumulation as a major determinant of economic growths
of countries. Romer (1986 and 1990) investigated positive
longrun effects of human capital accumulation on the
economic growth of countries through knowledge
spillovers across firms and individuals. Moreover, trade
openness may improve human capital through knowledge
and technology spillovers for promoting domestic research
and development of countries. Human capital and trade
openness can increase economic growth through educated
and skilled labor force and knowledge spillover from the
global market to the domestic economy (Idris, Yusop, &
Habibullah, 2016). During the 1980s various theoretical
and empirical studies have been emerged as a response to
the neoclassical economic growth model. The new
economic growth theories provided an important argument
that economic growth can be sustained by knowledgebased
human capital and technological progresses. The
accumulation of human capital can determined the
capacity to innovate and the speed of technological
diffusion (Zahonogo & Zahonogo, 2019). A model
prepared by Nelson and Phelps (1966) contributed the gap
between technology frontier characterized by the country
leader and the followers level of productivity depends on
the accumulation of human capital. Based on different
theoretical and empirical evidence the accumulation of
human capital affects the growth rate of domestic
innovations on technological goods. Countries with a
higher level of trade openness have a higher capability of
absorbing innovated technology from developed countries
(Goswami, 2013). Trade openness can create exposures for
internationally innovated technological goods, new
production systems, new ideas and competitions among
global firms (Huchet-bourdon & Mou, 2018b). Trade
openness creates technology transfer through the exchange
of new ideas from traded goods and from the flow of
hightech knowledge through traded capital goods mainly
machinery equipment (Turnbull, Sun, and Anwar, 2016).
The endogenous economic theory opened a new viewpoint
on the determinants of a nation’s economic growth. It
states that the main determinants of economic growth is
internal factors such as human capital and technology
innovations rather than external factors. The ability and
speed of nations to innovate and technological spillovers
are mainly determined by its stock of human capital and
are open to the international world (Zahonogo and
Zahonogo, 2019). A growth model formulated by Nelson
and Phelps (1966) contributed that the main development
difference between technologically advanced countries
and developing countries is usually caused by the
differences in its human capital stock (Hofmann, 2013).
The availability of educated and skilled labor force can
determine both new technology innovation capacity and
adopting foreign technology for domestic production
processes. The theoretical and empirical importance of this
model is that the economic growth rate of countries is
different due to the differences in the stock level of human
capital, rather than the growth rates. Trade openness can
create a higher capacity for technology absorption created
by advanced countries and human capital creativity and
adaptation ability through the flow of ideas in the global
world (Goswami, 2013; Grossman and Helpman, 1991).
Their empirical study result concluded that least developed
countries can increase their economic growth by adopting
more foreign technologies through increasing the
participations of international trade. The new growth
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theory mainly stresses on the significance of the
accumulation of human capital that can result in the growth
of entrepreneurship, knowledge, innovation, and
technology advancements. The accumulation of
knowledge is preserved as an asset to economic growth
that is not lead to restrictions and diminishing returns. The
new growth theory argued that the innovation of new
technology and new ways of doing things are determined
by the number of people that seeks technology and
innovations. The vital determinant factor for the
innovation of new technologies is the accumulation of
human capital or knowledge capital through quality of
education and the choice of people what to study and how
hard to study. The most important feature of the new
economic growth theory is that the accumulation of
knowledge is considered as a vital intangible asset for
economic growth that is not subject to diminishing returns.
To encourage internal innovations through new concepts
and technological advancements the government and
private sectors should create new opportunities and
resource availability within organizations are important.
Investing in human capital mainly by improving the
quality of education can sustain countrys objective of
creating knowledge driven longrun economic growth.
Generally, the new economic growth theory mentioned
that governments are the major primary player for
encouraging and facilitating quality and better education
including giving incentives and supports for the private
sector research and development. Based on Grossman and
Helpman's (1991) descriptions about the benefits of trade
openness through different channels concluded that
outward oriented economic policies experience higher
economic growth rates than inwardoriented economic
policies through different trade channels (Silajdzic and
Mehic, 2017). These channels are discussed as follows:
The first way is through communication of ideas, technical
information, innovated products, and the new way of
production, techniques. The second channel is trade
openness creates international competition among firms of
different countries that initiate entrepreneurs to create new
products, ideas and technologies. The third channels of
trade openness is that it can create a large global market for
different countries producers. Rivera-Batiz and Romer
(1991) investigated that technology and knowledge can be
transferred through the exchange of ideas from traded
goods and through traded capital goods mainly machinery
and equipment that can create opportunity to new
knowledge that raises economic growth (Journal and
Gruyter, 2015).
2.2 Empirical Literature Review
Most of the empirical studies support the benefits of trade
openness for the economic growth of countries through
facilitating human and capital accumulations, promoting
industrial sectors, and advancements in knowledge transfer
and technology spillovers. A research done on 93 countries
by Soderbom and Teal (2003) studied the effects of human
capital and trade openness for productivity growth implied
that trade openness supports technical progress and human
capital has significant effects on income (Evans, 2018).
International trade can benefit economic growth through
imported capital and intermediate goods which can be used
in domestic manufacturing, (Silajdzic and Mehic, 2017;
Lee, 1995). An empirical study by Kraay (1999) found a
prominent learning effect from the export based industries
of China using panel data (Edwards and Edwards, 2018).
An empirical study about global research and development
transfer among 21 OECD countries and Israel by Coe and
Helpman (1995) confirmed that trade openness has
positive effects on technology transfer (Leite and Silva,
2019). According to Rivera-Batiz's explanation, the benefit
of trade openness from innovated technology may be
negative for economic growth if the domestic human
capital is incapable to grasp effectively and efficiently
(Kose, Meredith, and Towe, 2004). An empirical study
investigated the relationship between trade openness,
human capital and individual incomes on Mexico studied
by Krebs (2005) found that trade openness has no
significant relationship with the income of individuals both
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pg. 6
on lower and higher levels of human capital (Levchenko,
2008). Another empirical study investigated by Utkulu
and Ozdemir (2004) about the effects of trade openness on
economic growth of Turkey implied that trade openness
has significant effect on the shortrun and longrun
economic growth of Turkey (Kahya, 2011). An empirical
study done by Effiom et al. (2011) investigated the effects
of trade openness on the economic growth of Nigeria using
two different models for human capital as a proxy variable
for the first model using education expenditure and for the
second model using literacy rate (Zeem, 2015). His
empirical study result implied that there is a positive and
significant effects of trade openness on human capital
when he used literacy rate as a proxy variable for human
capital. Maksymenko and Rabbani (2011) studied an
empirical study in the economy of India and South Korea
showed that human capital has positive effects on the
economic growth of India and South Korea (Zeem, 2015).
Although, a number of empirical studies have been done
about the relationship between human capital and
economic growth the results were inconsistent and there
were shortcomings in applied empirical estimation
techniques (Huchet-bourdon and Mou, 2018b; Huchet-
bourdon and Mou, 2018a). Most of the past literature
mainly focuses on the relationship between trade openness
and economic growth. This empirical study investigated
the effects of trade openness on human capital and
economic growths of Ethiopia. This empirical study used
an Autoregressive Distributed Lag and Error Correction
model estimation techniques which can estimate
consistence and efficient results by solving most of
previous study estimation errors and shortcomings.
3 Research Data, Models, and Methodology
3.1 Research Data
This empirical research paper used a time series data of
Ethiopia from 1981-2017. The applied variables are
economic growth, human capital, physical capital, trade
openness, real exchange rate, and labor forces. The total
GDP of Ethiopia is used as a proxy variable for the
economic growth, human capital is represented by
secondary school enrollment rate and total education
expenditure, physical capital is represented by fixed capital
formation, trade openness is used using the sum of total
export and imports divided by total GDP, real exchange
rate is taken as average real exchange rate, and labor force
is proxied by active populations from the age of 15-64
years old. The source of the input data is from the World
Bank-Development Indicators database.
3.2 Model Specification
The research model is designed to find the shortrun and
longrun relationships among the variables of economic
growth, trade openness, human capital, physical capital,
and labor forces. To find the relationship among these
variables the endogenous growth model can be used. The
endogenous growth model has similarity with the
neoclassical growth model viewpoints and it can provide
the significance of human capital accumulation that is not
subject to decreasing returns to scale (Mankiw 1992; Lucas
1988) (Alvarado, Iñiguez, and Ponce, 2017;
Q.Muhammad, 2015b). The other perception of the new
endogenous growth model is that research and
development which can be increased through the quality of
education and skilled human capital is considered as an
engine of economic growth (Q. Muhammad, 2015b).
Understanding the endogenous theory can have
significances mainly in developing and least developed
countries to understand the importance of human capital
both for adopting and using technologies created by
developed countries and for new technology innovations
(Kim, 2011b; Q.Muhammad, 2015a). This empirical study
used a classical economic growth model originally
proposed by Solow (1956) and augmented by Mankiw
(1992) to include human capital and trade openness
(Huchet-bourdon & Mou, 2018a; Ranjbar, Li, Chang, and
Lee, 2014). The model can be described in the production
model as follows: (1). YT=At Kɑt H
t L1-ɑ-t 𝓔t. Where YT is
aggregate economic production at time period t, At is total
factor production at time t, ɑ is elasticity of total production
Management and Human Resource Research Journal Vol.9, No.2; February-2020;
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p –ISSN 4244 – 490X
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pg. 7
with respect to capital, is elasticity of production with
respect to human capital, 1-ɑ- is elasticity of production
respect to labor force participation. Kt is capital stock at
time t, Ht is human capital stock at time t, Lt is employed
labor force at the time, and 𝓔t is the error term that is
independent of all explanatory variables. The production
function in equation (1) can be described as a function of
trade openness and other explanatory variables. (2). At=f
(TOP t, Ct, 2t). Equation (2) can be rewritten as follows: (3).
At=TOPt, Ct, 𝓔2t.where At is total production at time t and
TOP t is trade openness at time t, and 𝓔2t is the error term
that is independent of all explanatory variables. We can
combine the two equations to incorporate trade openness
in the first equation and illustrated as follows: (3). YT= Ct,
K ɑ t, H
t, Lt1-ɑ-
, TOPt, 𝓔3t. Where 𝓔2t is the error term that
is independent of all explanatory variables, ɑ is elasticity
or percentage change of total production with respect to
capital, is elasticity of production with respect to human
capital, is elasticity of production with respect to trade
openness, 1-ɑ- is elasticity of production respect to labor
force participation. Equation 3 can be transformed into
natural logarithm forms as follows: (4). Ln Y t=C1t+ ɑ ln
K t+ln H t+ TOP t+ ln Lt+ 𝓔3t. Where C1t is constant
parameter, all coefficients such as ɑ, , , and δ are
constant elasticities. Based on these equations and the
classical economic growth model first used by Solow and
Augmented by Mankiw, we prepared the following model.
(5). Ln GDP=1+2TOP+3H+4LnGFC+5LnAP+ 𝓔.
Where 1,2,3,4,5 are coefficients, Ln GDP is the
natural logarithmic form of gross domestic product which
is a dependent variable, TOP, H, Ln GCF, and Ln AP are
explanatory variables and 𝓔 is the error term. Economic
growth is defined as the gross domestic product produced
by an economy over a period of time. The higher growth
rate level of economic growth or GDP growth can lead to
higher human capital growth as shown by Effiom (2001)
(Zeem, 2015). Trade openness is taken as the sum of total
export and imports divided by total Gross Domestic
Product (GDP).The relationship between trade openness
and economic growth is expected to be positive as
investigated by different reseahers. Human capital can be
defined as the stock of knowledge, capabilities, creativity
of labor to increase productivity, economic growth, and
innovation. We used the secondary school enrollment rate
and total education expenditure as a proxy variable to
human capital, as most of economic theories used the
education enrolment rate and education expenditure for
explaining human capital development relationships with
economic growth. Physical capital is defined as a physical
production factor such as machinery, computers,
telecommunication and electric power infrastructures,
buildings, etc., which can be used in the production
processes of a country. Labor force which is proxied by
economically active populations is also included in the
model as it is one of the basic ingredients of economic
growth.
3.3 Estimation Methodology
This empirical research paper investigated the relationship
between economic growth, trade openness, human capital,
physical capital, and economically active populations
using a time series data of Ethiopia from 1981-2017. This
empirical study used the Autoregressive Distributed Lag
model and Error Correction model estimation systems
based on the Pesaran (2001) descriptions (F. Muhammad,
2017). This empirical study starts with testing the
stationarity of the variables. If the test result leads to the
application of the Autoregressive Distributed Lag model
estimation technique, then investigating the longrun
relationship or cointegration of the variables can be done.
After investigating the longrun cointegration tests among
the variables, estimating the longrun and shortrun
relationships between the variables can be done using the
Autoregressive Distributed Lag (ARDL) and the Error
Correction Model (ECM) estimation techniques. These
estimation techniques are efficient and effective estimation
techniques for investigating the shortrun and longrun
relationship and cointegration among the variables. The
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variables stationarity test is done by using the Augmented
Dickey Fuller (ADF) tests and the Phillip Perron (PP) tests.
Dickey and Fuller prolonged their stationarity tests of
variables suggesting an augmented form of the test
including additional lagged terms of the explained variable
to eliminate autocorrelation. The optimum lag length on
extra terms can be determined by Akaike Information
Criterion (AIC) or other techniques that are necessary to
whiten the residuals. The variables stationarity testing by
ADF and PP test can be done with intercept, trend and
intercept, and none of them. For the investigation of the
cointegration among the variables in the longrun and for
estimation of the longrun coefficients of the variables, we
used the Autoregressive Distributed Lag model bound
testing technique formulated by Pesaran (2001)
(Odhiambo, 2012). Using the Autoregressive Distributed
Lag model and Error Correction Model estimation
techniques have many advantages for solving most of the
common empirical studies' shortcomings. These are: The
Autoregressive Distributed Lag model bounds testing can
be applicable whether the variables are I (0) or I (1). The
Autoregressive Distributed Lag model estimation
technique can provide efficient and consistent estimation
results using small sample data. The Autoregressive
Distributed Lag model estimation assumes all variables are
endogenous so it can reduce the endogeneity explanatory
variable estimation bias. The dynamic unrestricted error
correction model can be derived from the Autoregressive
Distributed Lag model with a simple linear transformation.
The Error Correction Model can give information’s about
the shortrun dynamics with the longrun equilibrium or the
speed of adjustment. The Error Correction Model can
estimate the shortrun relationship between variables and it
is free from estimation errors. Two sets of critical values
can be determined within a given significant level based on
the assumption of all the variables are I (0) and the second
assumption is all the variables are I (1). If the calculated F-
statistics value is greater than the upper critical value, it
confirms that there is a longrun relationship among the
variables. If the result of the F-statistics value is less than
the lower critical value, it confirms that there is no longrun
relationship among the variables. If the F-statistics value is
between the upper and lower critical values we cannot
determine whether there is a longrun relationship or not
among the variables. After confirming the existence of
longrun relationships among the variables then the next
step is estimating the longrun and shortrun coefficients
using the Autoregressive Distributed Lag model bound test
and Error Correction Model estimation techniques. The
formulations of the augmented classical growth model are
used to estimate the longrun relationship between
variables. GDP t = f (HC t, PYC t, TOP t, ACTP t), this
model can be explained in the following form as follows:
Ln (GDP)t = 0 + β1SSER t + β2Ln(EDEP)t +
β3Ln (GCF)t + β4TOP t + β5Ln (ACTP)t + β6EXRt +
ℰt. Where 0 is constant term, β1, β2, β3, β4, β5, and β6
are coefficients, t is time series dimensions of variables, Ln
(GDP) is the natural logarithm form of economic growth,
SSER represents secondary school enrolment rate and Ln
EDEP is the natural logarithm form of education
expenditure used as a proxy variable for human capital
accumulations, Ln (GCF) represents gross fixed capital
formations used as a proxy variable for physical capital,
TOP represents trade openness, Ln (ACTP) represents
economically active populations, EXR is used to represent
real exchange rate, and 𝓔 represents the error term. After
the stationarity test result of the variables which indicates
that some variables are stationary at the level I(0) and some
are stationary at first differences I(1) or some are mixed,
then the appropriate estimation technique can be the
Autoregressive Distributed Lag model bound test
estimation system. The longrun estimation Autoregressive
Distributed Lag model can be described as follows:
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Model.(One).𝛥Ln (GDP)t = 0 + ∑ β1𝑛𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β2𝑛
𝑖=0 ΔSSER t − 1 + ∑ β3𝑛𝑖=0 ΔLn (GCF)t − 1 +
∑ β4𝑛𝑖=0 ΔTOP t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (EDEP)t − 1 + δ1ΔSSER t + δ2 ΔLn (GCF)t +
δ3ΔTOP t + δ4 ΔLn (ACTP)t + δ5ΔLn (EDEP) t + ℰt.
Model:(Two).𝛥TOP t = 0 + ∑ β1𝑛𝑖=0 ΔTOP t − 1 + ∑ β2𝑛
𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β3𝑛𝑖=0 ΔSSER t − 1 +
∑ β4𝑛𝑖=0 ΔLn (GCF)t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (EDEP)t − 1 + δ1ΔLn (GDP)t + δ2ΔSSER t +
δ3Δ Ln (GCF) t + δ4ΔLn (ACTP) t + δ5ΔLn (EDEP) t + ℰt.
Model:(Three). 𝛥SSER t = 0 + ∑ β1𝑛𝑖=0 ΔSSER t − 1 + ∑ β2𝑛
𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β3𝑛𝑖=0 ΔLn (GCF)t − 1 +
∑ β4𝑛𝑖=0 ΔTOP t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (EDEP)t − 1 + δ1ΔLn (GDP) t + δ2Δ Ln (GCF) t +
δ3Δ TOP t + δ4ΔLn (ACTP) t + δ5ΔLn (EDEP) t + ℰt.
Model:(Four).𝛥Ln (GCF)t = 0 + ∑ β1𝑛𝑖=0 ΔLn (GCF)t − 1 + ∑ β2𝑛
𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β3𝑛𝑖=0 ΔSSER t − 1 +
∑ β4𝑛𝑖=0 ΔTOP t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (EDEP)t − 1 + δ1ΔLn (GDP) t + δ2ΔSSER t +
δ3Δ TOP t + δ4ΔLn (ACTP) t + δ5ΔLn (EDEP) t + ℰt. Model:(Five).𝛥Ln (EDEP)t = 0 + ∑ β1𝑛
𝑖=0 ΔLn (EDEP)t − 1 + ∑ β2𝑛𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β3𝑛
𝑖=0 ΔSSER t − 1 +
∑ β4𝑛𝑖=0 ΔTOP t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (GCF)t − 1 + δ1ΔLn (GDP)t + δ2ΔSSER t +
δ3Δ TOP t + δ4ΔLn (ACTP)t + δ5Δ Ln (GCF) t + ℰt.
Note:We assumed that similar model can be added if other
variab is included such as real exchange rate.
Where β0 is the intercept and β1, β2, β3, β4, β5, and β6 are
short-run coefficients, δ1, δ2, δ3, δ4 and δ5 are long-run
coefficients, and 𝓔t is the error term.
Before using the Autoregressive Distributed Lag model
bound testing estimation techniques, testing the
cointegration relationship among the variables is done. The
cointegration test is done to check the existence of a linear
combination for nonstationary processes of the variables.
From the Autoregressive Distributed Lag model bound test
model the null hypothesis can be done by HO: There is no
cointegration among the variables as, (HO: δ1= δ2= δ3=
δ4= δ5=0), and the alternative hypothesis (H1) of there is
a cointegration among the variables or there is a longrun
relationship among the variables can be described as: (H1:
δ1≠δ2≠ δ3≠ δ4≠ δ5≠0). The result of the Autoregressive
Distributed Lag model bound test of cointegration is
determined by the computed F-statistics value which has a
nonstandard distribution, regardless of whether the
variables are integrated at the level I(0) or at first
differences, I(1) compared with the critical values
formulated by Pesaran (2001). After confirmation of
longrun cointegration among the variables, the Error
Correction Model estimation system can be applied. The
dynamic Error Correction Model (ECM), which is derived
from the Autoregressive Distributed Lag model through a
simple linear transformation that gives information about
the shortrun dynamics with the longrun equilibrium. The
coefficient of the error correction term should be negative
and significant. It indicates how fast the variables are
returned to the longrun equilibrium. We used the Error
Correction Model (ECM) for estimating the shortrun
coefficients and to find the speed of adjustments to the
longrun equilibrium level.
(One). 𝛥Ln (GDP)t = 0 + ∑ β1𝑛𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β2𝑛
𝑖=0 ΔSSER t − 1 + ∑ β3𝑛𝑖=0 ΔLn (GCF)t − 1 +
∑ β4𝑛𝑖=0 ΔTOP t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (EDEP)t − 1 + ECM t − 1 + ℰt.
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(Two).𝛥TOP t = 0 + ∑ β1𝑛𝑖=0 ΔTOP t − 1 + ∑ β2𝑛
𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β3𝑛𝑖=0 ΔSSER t − 1 +
∑ β4𝑛𝑖=0 ΔLn (GCF)t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (EDEP)t − 1 + ECM t − 1 + ℰt.
(Three). 𝛥SSER t = 0 + ∑ β1𝑛𝑖=0 ΔSSER t − 1 + ∑ β2𝑛
𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β3𝑛𝑖=0 ΔLn (GCF)t − 1 +
∑ β4𝑛𝑖=0 ΔTOP t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (EDEP)t − 1 + ECM t − 1 + ℰt.
(Four). 𝛥Ln (GCF)t = 0 + ∑ β1𝑛𝑖=0 ΔLn (GCF)t − 1 + ∑ β2𝑛
𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β3𝑛𝑖=0 ΔSSER t − 1 +
∑ β4𝑛𝑖=0 ΔTOP t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (EDEP)t − 1 + ECM t − 1 + ℰt.
(Five).𝛥Ln (EDEP)t = 0 + ∑ β1𝑛𝑖=0 ΔLn (EDEP)t − 1 + ∑ β2𝑛
𝑖=0 𝛥Ln (GDP)t − 1 + ∑ β3𝑛𝑖=0 ΔSSER t − 1 +
∑ β4𝑛𝑖=0 ΔTOP t − 1 + ∑ β5𝑛
𝑖=0 ΔLn (ACTP)t − 1 + ∑ β6𝑛𝑖=0 ΔLn (GCF)t − 1 + ECM t − 1 + ℰt.
Where ECM t − 1 is the error correction term which
denotes the speed of adjustment to the longrun equilibrium
level, β0 is the intercept and β1, β2, β3, β4, β5, and β6 are
shortrun coefficients. The coefficients of the Error
Correction Model measures the departure from the longrun
equilibrium which can be corrected in the shortrun. To test
the presence of shortrun relationship between the variables
which is described in equation (Four) Error Correction
model the null hypothesis H0: β1=β2=β3=β4=β5= β6=0 or
(H0: There is no shortrun relationship) and the alternative
hypothesis H1: β1≠β2≠β3≠β4≠β5≠β6≠ 0 or (H1: There is
a shortrun relationship between the variables). This should
be done to check the existence of shortrun relationships
between the variables. Using the Autoregressive
Distributed Lag (ARDL) estimation model should fulfill
the assumptions of the model normality test, the functional
forms, the serial correlation tests, and the
heteroscedasticity tests. The stability tests of the model are
done by using the Cumulative Sum (CUSUM) and the
Cumulative Sum of Squares (CUSUMSQ) within the
acceptable critical value.
4. Empirical Result and Discussion
4.1 Unit Root Test Results
This empirical study used a time series data of Ethiopia
from 1981-2017. Using time series data needs to test a unit
root test in order to test for the time series properties of the
variables. We employed two univariate methods of unit
root tests such as the Augmented Dickey Fuller (ADF) and
the Phillip Perron (PP) applied for testing each variable
which is used in our estimation models. The Augmented
Dickey Fuller (ADF) which is a robust method for testing
the presence of unit roots is applied to test the non
stationarity of variables of the null hypothesis (H0:
Variables are non stationary) and the alternative hypothesis
(H1: Variables are stationary). The other applied unit root
test is the Phillip Perron (PP) for testing the time series
properties of variables with a null hypothesis (H0:
Variables has a unit root, and alternative hypothesis H1:
Variables does not have unit root).The ADF and PP unit
root test results showed that some of the variables are
stationary at I (0) and others are stationary at I (1) and some
are mixed as illustrated in Table 4.1.
Table 4. 1: Unit Root Test Results Using ADF and PP
Test Type ADF At
Level
First
Difference
PP At
Level
At First
Difference
Variable t-statistics t-statistics t-statistics t-statistics
Ln (GDP) Constant - -4.23* - -4.17*
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Constant &
trend
- -5.33* - -5.72*
None - -2.85* - -2.72*
Ln
ACTPOP
Constant - -3.37** - -3.37**
Constant &
trend
-3.59** -3.45*** - -3.24***
None 3.51** - - -
TOP Constant - -7.11* - -7.11*
Constant &
trend
-89.72* -7.02* - -7.02*
None - -6.83* - -6.83*
SSER Constant - -2.83*** - -3.27**
Constant &
trend
-3.28*** -3.44*** - -3.37***
None - -2.15** - -3.01*
Ln GCF Constant - -8.90* - -8.90*
Constant &
trend
-4.42* -9.82* - -9.82*
None - -7.08* - -7.08*
Constant - -6.82* - -6.83*
Ln EDEP Constant &
trend
- -6.85* - -6.94*
None - -6.16* - -6.16*
Constant -3.24** - - -3.48**
EXR Constant &
trend
-3.97** - - -3.43***
None - -2.79* - -2.79*
Note: * ,** and *** represent a rejection of the null hypothesis of the presence of unit root at 1%, 5%, and 10% respectively.
Source: Author’s Calculations Using Eviews.
Table 4.2: VAR Lag Order Selection Using Akaike Information Criterion (AIC).
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Note: * indicates lag order
selected by the criteria.
Source: Author’s
Calculations Using Eviews.
The lag length selection and optimum lag choice of the
model are done by using the Akaike Information Criterion
(AIC) based on the suggestion of Pesaran (2001). The
optimum lag for our models is selected at lag 2 based on
AIC which is illustrated in Table 4.2.
4.2 Cointegration Test Result
The cointegration test is necessary to check whether there
is a longrun relationship exists or not among the variables.
The cointegration test is done by using the Autoregressive
Distributed Lag bound test method using each variable as
a dependent variable and others as an independent variable
interchangeably for each of the variables in all models. The
cointegration test result confirmed the existence of a
longrun relationship or cointegration among the variables.
The detail cointegration test result is illustrated in the
following Table 4.3.
Table 4.3: Cointegration Test Result
Dependent
Variable
ARDL F-Statistics Outcome Decision
Ln (GDP) (2,1,2,0,2,0,
0)
6.12* Cointegrat
ion
Reject HO
TOP (1,0,1,1,0,1,
0)
4.01* Cointegrat
ion
Reject HO
Ln (GCF) (2,0,0,0,2,2,
2)
5.74* Cointegrat
ion
Reject HO
Ln (ACTPOP) (2,1,0,2,2,0,
0)
7.74* Cointegrat
ion
Reject HO
SSER (1,0,0,0,1,0,
1)
4.46* Cointegrat
ion
Reject HO
Ln EDEP (1,0,0,0,0,1,
2)
5.35* Cointegrat
ion
Reject HO
EXR (2,2,0,0,0,0,
0)
4.51* Cointegrat
ion
Reject HO
Critical Value 1% Lower 1% Upper 5% Lower 5% Upper
Actual Sample
Size 35
3.71 5.32 2.69 3.96
Lag Log L LR FPE AIC SC HQ
0 26.74 NA 7.63e-10 -1.13 -0.82 -1.02
1 353.97 504.86 1.01e-16 -17.03 -14.54 -16.17
2 442.70 101.41* 1.49e-17* -19.29* -14.63* -17.69*
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Note:* & ** indicates the rejection of the null hypothesis of no co-integration at 1% and 5% level of significance,
respectively. The values in parenthesis are selected the number of lags using the AIC criterion. Source: Author’s
Calculations Using Eviews.
4.4 The Longrun and Shortrun Empirical Results.
The longrun and shortrun relationship between the variables are estimated using the Autoregressive Distributed Lag and
Error Correction model estimation techniques and the estimation results of the variables are illustrated in the following
Table 4.4.
Table 4.4: The Longrun and Shortrun Estimation result using the Autoregressive Distributed Lag Bound Test and Error
Correction Models.
Independent Variables Dependent
Variables
At Levels Equation D(LnGDP)
(2,1,2,0,2,0,0)
D(TOP)
(1,0,1,1,0,1,0)
D(SSER)
(1,0,0,0,1,0,1)
D(Ln EDEP)
(1,0,0,0,0,1,2)
Ln GDP - 0.07 (0.52) 195.24 (0.22) 1.15 (1.26)
TOP 0.69 (2.07)*** - -16.68 (-0.06) 3.39 (2.72)**
Ln GCF 0.24 (3.71)* -0.17 (-3.12)* 15.39 (0.21) 0.74 (2.69)**
Ln EDEP 0.02 (0.38) 0.04 (1.02) -65.91 (0.21) -
LnACP 0.25 (3.14)* 0.03 (0.49) 97.94 (0.20) 0.03 (0.06)
SSER 0.002 (0.39) 0.02 (4.53)* - -0.04 (-1.39)
EXR 0.29 (5.10)* 0.01 (0.72) -11.65 (-0.22) -0.09 (-2.01)***
C 13.65 (7.21)* 0.54 (0.21) -4889.35(-0.22) -23.17 (-1.32)
Conditional EC
Regression results
Ln GDP(-1)* -0.94 (-5.78)* - -
TOP(-1)* - -0.79 (-4.22)* -
Ln GDP** - 0.06 (0.50) 5.42 (1.32)
TOP - - -0.46 (-0.08) 2.52 (2.86)*
Ln ACP** - 0.027 (0.51) - 0.02 (0.06)
EXR** 0.03 (6.68)* 0.004 (0.74) -0.32 (-
1.75)***
-0.07 (-2.04)***
Ln EDEP** 0.017 (0.375) - -
SSER** 0.002 (0.39) - - -0.03 (-1.39)
D(TOP) 0.02 (0.08) - -
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D(Ln GCF) 0.09 (1.95)*** -0.06 (-
1.75)***
- 0.11 (0.48)
D(Ln ACP) -12.17 (-
2.05)***
- -237.85 (-
2.27)**
D(SSER) - 0.003 (0.07) -
D(Ln EDEP) - 0.07 (2.54)** -0.02 (-0.02)
C 12.83 (4.42)* 0.43 (0.21) -135.75 (-
1.97)***
-17.19 (-1.33)
ECM Regression D(LnGDP)
(2,1,2,0,2,0,0)
D(TOP)
(1,0,1,1,0,1,0)
D(SSER)
(1,0,0,0,1,0,1)
D(Ln EDEP)
(1,0,0,0,0,1,2)
D(Ln GDP(-1)) 0.39 (3.57)* - - -
D(Ln GDP) - - - 0.05 (0.11)
D(TOP) 0.02 (0.14) - - -
D(SSER) - 0.004 (0.12) - -
D(Ln EDEP) - 0.07 (3.83)* -0.003 (-0.004) -
D(Ln GCF) 0.09 (3.21)* -0.06 (-2.78)** - 0.11 (0.68)
D(Ln ACP) -12.17 (-3.42)* - -237.85 (-
6.13)*
-
Coint Eq(-1)* -0.94 (-8.08)* -0.79 (-6.41)* -0.03 (-6.73)* -0.74 (-7.44)*
R-Squared 0.81 0.65 0.59 0.63
Breusch - Godfrey Serial
Correlation LM Test: Ho:
No Serial Correlation
Accepted (0.45) Accepted
(0.99)
Accepted
(0.94)
Accepted (0.99)
Heteroscedasticity test:
(H0: Homoscedasticity)
Accepted (0.18) Accepted
(0.69)
Accepted
(0.12)
Accepted (0.56)
Normality (Jarque–Bera
test)
Normal
(0.73)
Normal (0.68) Normal (0.16) Normal (0.12)
Note: *, **, and *** represents coefficients are significant at 1%, 5%, and 10% level of significance. Source: Author’s
Calculations Using E-views 10.
This empirical research paper longrun estimation result
illustrated in Table 4.4 indicated that trade openness has
positive and significant longrun effects on the GDP growth
in Ethiopia. The longrun effects of trade openness on the
growth of GDP indicate that a one unit increase in the rate
of trade openness results in a 0.69% increase in the growth
of GDP in Ethiopia at a 10% level of significance. When
trade openness is a dependent variable the longrun effects
of GDP growth on the rate of trade openness are also
positive but not statistically significant. The shortrun
effects of trade openness on the growth of GDP in Ethiopia
are not statistically significant. This empirical study
indicated that there is a positive longrun effect of human
capital accumulation on the growth of GDP in Ethiopia but
not statistically significance in both using secondary
school enrollment rate and education expenditure used as
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pg. 15
a proxy variable for human capital. This result implied that
in the longrun human capital accumulation has a positive
impacts on the GDP growth of Ethiopia. On the other side,
the longrun effects of GDP growth in Ethiopia on human
capital accumulations using secondary school enrolment
rate and education expenditures are also positive but not
statistically significant. When human capital is a
dependent variable using education expenditure as a proxy
variable the shortrun effects of GDP growth on human
capital accumulation in Ethiopia is positive but not
significant. The longrun effects of trade openness on the
accumulation of human capital in Ethiopia using education
expenditure as a proxy variable for human capital indicated
that there is a positive and significant longrun effect of
trade openness on human capital accumulation at 5% level
of significance. This empirical study also found positive
and significant effects of human capital using secondary
school enrolment rate as a proxy variable for human capital
on trade openness at 1% level of significance. The shortrun
effects of human capital on the rate of trade openness in
Ethiopia is positive and significant at 1% level of
significance using education expenditure as a proxy
variable for human capital . This empirical research
estimation result indicated that the shortrun and longrun
effects of physical capital formation on the GDP growth of
Ethiopia is positive and significant both at 1% level of
significance. The longrun effects of the labor force using a
proxy variable of economically active populations on the
GDP growth of Ethiopia are found positive and significant
at a 1% significance level. Moreover, the longrun effects
of real exchange rate on the growth of GDP in Ethiopia is
positive and significant at 1% significance level. The ECM
coefficient estimation results which is illustrated in Figure
4.4 for both models have the correct sign (negative) and
statistically significance at 1% level of significance, which
indicated that the system corrects its previous period
disequilibrium at a speed of adjustments 93.9%, 79.1%,
2.8%, and 74.2% for GDP growth, trade openness,
secondary school enrolment rate, and education
expenditure is taken as a dependent variables, respectively.
The models diagnostic test result which is illustrated in
Table 4.4 indicated that all models have correct functional
forms and the models residuals are serially uncorrelated,
there are no heteroscedasticity problems, and the models
are normally distributed. The models stability test result
also indicated that all the models are stable based on the
Cumulative Sum (CUSUM) and the Cumulative Sum of
Squares (CUSUMSQ) tests within the acceptable 5%
critical value.
5. Conclusion
The importance of trade openness and human capital
accumulation for facilitating the economic growth of
countries has been an important research issue for the last
decades. The contribution of trade openness on economic
growth can be related to facilitating human capital
accumulations through knowledge and technology
transfer. The arguments mentioned above advocates
researchers give more attention to trade openness and
human capital as a vital element of economic growth. Most
of the previous empirical literature investigated the effects
of trade openness on economic growth. The main purpose
of this empirical study is to investigate the contributions of
trade openness on human capital accumulation and for the
economic growth of Ethiopia. This empirical study used
the Autoregressive Distributed Lag (ARDL) model and
Error Correction Model (ECM) using a time series data of
Ethiopia from 1981-2017. This empirical study applied the
Augmented Dickey Fuller (ADF) stationarity tests and the
Phillip Perron (PP) unit root testing techniques to check the
time series properties of the variables. The stationarity test
result of the variables indicated that the variables are
integrated at the level I (0), at first differences I (1), and
others are mutually integrated. Choosing the number of
lags and selecting the optimum lag of the models is done
by using Akaike Information Criterion (AIC). This study
cointegration test result of the models implied that the
variables have longrun relationships. This study found that
trade openness has positive and significant longrun effects
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on the economic growth of Ethiopia. When trade openness
is a dependent variable the longrun effects of GDP growth
on the rate of trade openness are also positive but not
statistically significant. The longrun effects of human
capital accumulation using secondary school enrolment
rate and education expenditure as a proxy variable of
human capital on the growth of GDP are positive but not
statistically significant. On the other hand, the longrun
effects of GDP growth on the accumulation of human
capital represented by education expenditure and
secondary school enrolment rate is positive but not
significant. This empirical study also found positive and
significant shortrun and longrun effects of physical capital
accumulation on the GDP growth of Ethiopia. Labor force
using a proxy variable of economically active populations
has positive and significant effects on the GDP growth of
Ethiopia, while in the shortrun its effect is negative. The
longrun effects of real exchange rate on the GDP growth
of Ethiopia is positive and significant. This empirical study
found a positive and significant longrun effects of trade
openness and human capital accumulation in Ethiopia
using education expenditure as a proxy variable for human
capital. The study also found a positive and significant
longrun effects of human capital on trade openness in
Ethiopia using secondary school enrolment rate as a proxy
variable for human capital. The shortrun effects of human
capital using education expenditure on trade openness is
also positive and significant. The Error Correction Model
coefficient estimation results for both models have the
correct sign and statistically significance, which indicated
that the system corrects its previous period disequilibrium
at a speed of adjustments 93.9%, 79.1%, 2.8%, and 74.2%
for the models of GDP growth, trade openness, secondary
school enrolment rate, and education expenditure is taken
as a dependent variables, respectively. To increase the
contribution of human capital for the longrun economic
growth of Ethiopia increasing the openness of trade, more
investments to increase quality education and human
capital is important. The policy implications from this
empirical study recommended that increasing trade
openness can facilitate the human capital accumulations
and the longrun economic growths of of Ethiopia. Ethiopia
can be benefitted by following further outward oriented
trade policy to support its economic growth through
facilitating the growth of human capital accumulations,
knowledge and technology transfers. Trade openness can
increase human capital development through knowledge
and technology transfer which can increase the longrun
sustainable economic growth of Ethiopia..
Disclosure Statement
There is no potential conflict of interest in this research
paper.
Fund
This research paper didn’t receive any financial grant
from governmental, commercial or not-for-profit
financial agencies.
Research Data
The sources of the data that supports this research paper
result are openly available at:
[https://databank.worldbank.org/data/source/world-
development-indicators].
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