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Management and Human Resource Research Journal Vol.9, No.2; February-2020;

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Management and Human Resource Research Journal

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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),

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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



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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



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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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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



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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


∑ 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


∑ β4𝑛𝑖=0 ΔLn (GCF)t − 1 + ∑ β5𝑛


(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


(Four). 𝛥Ln (GCF)t = 0 + ∑ β1𝑛𝑖=0 ΔLn (GCF)t − 1 + ∑ β2𝑛


∑ β4𝑛𝑖=0 ΔTOP t − 1 + ∑ β5𝑛


(Five).𝛥Ln (EDEP)t = 0 + ∑ β1𝑛𝑖=0 ΔLn (EDEP)t − 1 + ∑ β2𝑛


∑ 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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pg. 16

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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