The establishment of the African Continental Free Trade Area has raised a new question regarding the link between gross domestic product (GDP) per capita and trade openness among economists and policymakers.
The purpose of this research is to provide an understanding of the potential free trade agreement between Economic Community of Western African States (ECOWAS) countries.
Trade liberalisation is seen as an avenue for African countries to achieve social and economic advancement. Therefore, establishing the contribution of trade to economic growth is of paramount importance to society, especially for developing economies.
This study used two econometric tools – the autoregressive distributed lag (ARDL) bound testing approach and the pool mean group (PMG) model – to assess the link between trade openness and GDP per capita in ECOWAS. The data set covers the 15 ECOWAS member countries over the period 1990–2016.
The study indicates the existence of a long-run relationship between the variables at 1% in all countries except for Ghana, Guinea-Bissau, Mali, Senegal and Togo. This implies that the variables are cointegrated; hence, the PMG can be used. The estimation reveals that trade openness has a negative impact on GDP per capita in the long run. The findings have important implications for policymakers in the ECOWAS region and other developing countries.
The paper invites policymakers in the region to carefully consider the outcome of the agreement in each member country and adjust accordingly with tariff barriers.
Even though trade liberalisation can be beneficial, the lifting of trade barriers in all sectors among ECOWAS members will not contribute to growth. ECOWAS countries must decide the sectors they want to liberalise and also add value to their production of goods and services in order to fight poverty and boost income.
Trade openness is seen as an avenue that can boost economic development in a developing country. As a result, developing countries have become more active in regional trade agreements (Dicaprio, Santo-Paulino & Sokolova
It is well known that developing countries have enormous constraints in what they can bring to global trade and investment. Economic Community of Western African States (ECOWAS) countries export primary commodities that make them vulnerable to external shocks. Inadequate infrastructure and the small size of their domestic markets often limit their access to foreign markets (Clark, Dollar & Micco
The relationship between trade liberalisation and economic growth has been extensively examined. Previous studies of economic growth theories have indicated that trade openness has the potential to boost economic growth in the long run (Edwards
In his neoclassical model, Romer (
East Asian economies have achieved remarkable growth in manufacturing, human and physical capital and macroeconomic stability through the promotion of trade (World Bank
However, the endogenous growth model asserts that the contribution of trade for economic growth rests on whether the force of comparative advantage orientates the economy’s resources towards activities that generate long-run growth or away from such activities. The theory therefore suggests that technological and financial limitations do not allow developing countries to effectively adopt the new technologies of advanced economies (Zahongo
Some theoretical studies note that trade openness may hamper long-run growth if the economy specialises in sectors with dynamic comparative disadvantage in terms of productivity growth or technological change (Haussmann, Hwang & Rodrik
Developing countries export primary commodities which make them vulnerable to external shocks. Nevertheless, international trade is seen as an avenue that can boost economic development in a developing country.
Several studies have pointed out the positive growth effect of trade liberalisation (Asfaw
An early economic theory model, such as the classical theory of factor proportions developed by Heckscher-Ohlin, suggests that countries with similar supply structures or comparative advantages do not to contribute significantly to bilateral trade. However, economies of scale or increasing returns are grounds for countries with similar supply structures to benefit from trade (Feenstra
Emerging economies like India and China, for instance, have benefitted immensely from trade liberalisation, with their global gross domestic products (GDPs) moving up from 3.6% and 4.14%, respectively, in 1990 to 7.62% and 18.33% respectively in 2017 (IMF
The above findings are inconclusive because of the different proxies used for openness and the different methodologies employed.
Also, African leaders are set to establish the African Continental Free Trade Area. The agreement was signed by 44 African countries. The biggest absent was Nigeria, which hesitated after consulting unions and business leaders. Hence, the main objective of this study is to estimate the impact of trade openness on GDP per capita in ECOWAS, a free trade agreement that is already effective. This can serve as a guideline for a larger free trade agreement, such as the African Continental Free Trade Area.
In the long run, technology developed by leading economies determines the world’s growth rate. Hence, a country open to trade would be able to learn from the technology used by these leading economies.
With this research agenda, the contribution of this article is twofold. Firstly, it includes new time series data from 1980 to 2016 which was not used in the previous studies. Secondly, it uses the autoregressive distributed lag (ARDL) cointegration model and the pool mean group (PMG) estimation model which leads to a more robust and consistent result for both the long-run and short-run relationships between growth and trade openness. This model is more appropriate than the fixed and random effects model used in a previous study (see Iyoha & Okim
Trade openness has received significant attention and has been widely discussed in both theoretical and empirical research. However, there is no consensus about the possible effects of trade openness on economic growth. The literature is divided into two categories: theoretical and empirical literature studies.
The theoretical studies on the effect of trade barriers on economic growth have revealed mixed and vague results. Comparative advantage is seen as the main reason for countries to engage in trade. Countries tend to benefit from the specialisation of goods in which they have a comparative advantage. Early endogenous growth theories claim that less developed economies tend to converge towards advanced economies through trade and technological diffusion (Barro & Sala-i-Martin
Krugman (
Barro and Sala-i-Martin (
On the other hand, Grossman and Helpman (
Some theorists argue that trade openness is prejudicial to economic growth when countries specialise in sectors in which development and research are not the core activities (Almeida & Fernandes
Empirical evidence on the positive effects of trade openness on growth is abundant (Chang et al.
In contrast, Yanikkaya (
Iyoha and Okim (
Lloyd, Ogundipe and Ojeaga (
So far, studies have looked at the effect of exports and export diversification on economic growth of the ECOWAS countries. Data were assumed to be stationary or partially stationary and to exhibit a long-run relationship during the periods of study. This article will investigate the long-run relationship between the variables and the effects of trade openness on GDP per capita in the ECOWAS region.
With the purpose of assessing the effects of trade openness on economic growth in the ECOWAS region, this section covers the data employed, the scope of the study and the model specification.
The variables used in this study include the following: GDP per capita growth rate, labour force, investment, financial development, exchange rate, external debt and trade openness. These variables are selected because these are important determinants of economic growth.
All variables were sourced from IMF (
The article hypothesises that trade openness generates economic growth in the ECOWAS region. Founded on the neoclassical growth model, initiated by Solow (
The empirical research comprises two steps. The first step aims to test the presence of a long-run relationship between the variables for each country in the ECOWAS region. The second step examines the stationarity of the variable using unit root tests after the panel regression model has been determined.
The study used the PMG model developed by Pesaran, Shin and Smith (
The estimators of the PMG method are less sensitive to outliers. They are also consistent and efficient when parameter homogeneity holds. Under this condition, the article addresses the issue of endogeneity by augmenting the PMG parameters with lags of regressors. This helps to reduce the bias of the estimators and to ensure that the regression residuals are not serially correlated.
The PMG model takes into account trade openness, heterogeneity of the coefficients and other controlled variables and can be specified for the ECOWAS countries during the period 1990–2016 as follows:
OPENNit = trade openness (measured by the ratio of exports and imports to GDP) at time
LBit = labour force at time
INVit = investment (proxied by gross capital formation) at time
FDit = financial development (proxied by domestic credit provided by financial institutions) at time
ERit = official exchange rate at time
DEBTit = external debt stock at time
The ARDL dynamic specification for this relationship is expressed as follows:
The ARDL bounds testing approach to cointegration is based on the following error correction model:
Trade openness is measured by the ratio of imports and exports to GDP. It is expected to be positively correlated with GDP per capita.
Labour force comprises people aged 15 years and above who supply labour for the production of goods and services. It is important to note that the study uses the log of labour force in the model estimation. Labour force is expected to be positively correlated with GDP per capita. It is an important contributor to economic growth through domestic technology capability building and foreign technology spillover (Banerjee & Roy
In many empirical studies, investment has been used as a contribution to capital accumulation. In this study, it is measured as gross capital formation as a percentage of GDP. It is an indicator of basic economic infrastructure, such as machinery, roads and land improvement (World Bank
Financial development is measured by the domestic credit offered by financial institutions as a percentage of GDP. The lack of a well-developed financial system and a dependence on primary products do not encourage sustainable growth in developing countries. Instead, the latter results in low real income and a tendency for borrowers to default. Hence, financial development is expected to be negatively correlated with GDP per capita.
Exchange rate refers to the price of South African currency (Rand) value against the US dollar. Hence, it measures the competitiveness of a currency. The exchange rate is expected to be negatively correlated with economic growth as suggested by the literature (Yan, Lin & Li
This article also captures the effects of debt on GDP per capita. Debt is the total external debt stock. The study uses the log of total external debt stock. Debt is expected to be inimical to economic growth.
The ARDL model is used to derive the long-run relationship in each of the 15 ECOWAS countries. The long-run relationship of the underlying variables was observed through the F-statistic or the Wald coefficient. The ARDL model used an OLS to evaluate the long-run relationship between trade openness and other controlled variables.
The ARDL bounds approach can be used irrespective of whether the underlying variables are I(0), I(1) or mutually cointegrated. This implies that the bound testing cointegration procedure does not require classification of the variables into I(0) and I(1) and is robust when there is a long-run relationship between the underlying variables. The representation of ARDL error correction becomes relatively more efficient when the F-statistic establishes a single long-run relationship between the underlying variables (Pesaran et al.
Once the long-run relationship had been established, the article used the PMG model. The latter is a cointegration technique that deals with variables that are integrated of different orders such as I(0) and I(1) (Pesaran et al.
The PMG model estimates a dynamic heterogeneous panel by looking at the long-run equilibrium relations between the underlying variables.
The null hypothesis of no cointegration is given as:
Gross domestic product (GDP) per capita and trade openness across the 15 Economic Community of Western African States countries, 1990–2016.
The descriptive statistics and correlation matrix are shown in
Descriptive statistics and correlation matrix.
Variables | GDP per capita | Debt | FD | INV | LB | ER | OPENN |
---|---|---|---|---|---|---|---|
Mean | 1.247 | 4.72 | 35.312 | 19.050 | 14.74 | 715.08 | 67.56 |
Median | 1.343 | 1.85 | 19.838 | 19.454 | 14.92 | 994.41 | 62.33 |
Maximum | 91.648 | 3.99 | 2066.180 | 49.780 | 17.86 | 8959.71 | 311.35 |
Minimum | −50.230 | 1.34 | −0.001 | −2.420 | 11.64 | 7.87 | 21.12 |
SD | 7.800 | 7.30 | 107.750 | 9.260 | 1.32 | 1208.55 | 32.21 |
GDP per capita | 1 | - | - | - | - | - | - |
Debt | −0.040 | 1 | - | - | - | - | - |
FD | 0.080 | 0.10 |
1 | - | - | - | - |
INV | 0.250 |
−0.11 |
0.15 |
1 | - | - | - |
LB | 0.050 | 0.77 |
−0.11 |
0.0090 | 1 | - | - |
ER | 0.025 | −0.16 |
−0.29 |
0.0269 | 0.268 |
1 | - |
OPENN | −0.090 | 0.05 | 0.59 |
0.2560 |
−0.130 |
−0.22 |
1 |
GDP, gross domestic product; FD, financial development; INV, investment; LB, labour force; ER, official exchange rate; OPENN, trade openness; SD, standard deviation.
indicate 1% and 5% significance level, respectively.
Unit root tests.
Variables | Levels |
First difference |
||
---|---|---|---|---|
Intercept | Trend and intercept | Intercept | Trend and intercept | |
GDP per capita | 219.79 |
205.68 |
377.93 |
2162.95 |
Debt | 15.17 | 11.04 | 187.55 |
150.68 |
FD | 58.69 |
87.37 |
250.52 |
795.90 |
INV | 43.80 |
45.75 |
317.00 |
472.84 |
LB | 40.89 |
24.27 | 34.90 | 28.05 |
ER | 27.85 | 10.90 | 163.46 |
130.53 |
OPENN | 72.40 |
86.90 | 324.85 |
625.67 |
GDP per capita | 101.45 |
84.44 |
255.67 |
200.15 |
Debt | 21.87 | 15.50 | 100.97 |
72.63 |
FD | 23.27 | 36.10 |
139.71 |
127.86 |
INV | 29.09 | 31.65 | 177.24 |
142.96 |
LB | 15.48 | 225.85 |
86.63 |
42.35 |
ER | 46.93 |
29.67 | 171.37 |
138.46 |
OPENN | 77.36 |
106.25 |
266.98 |
200.01 |
GDP per capita | −13.44 |
−12.61 |
−23.20 |
−20.81 |
Debt | 1.91 | 1.26 | −11.70 |
−9.92 |
FD | −1.92 |
−6.11 |
−19.55 |
−19.32 |
INV | −2.5 |
−3.14 |
−16.35 |
−13.38 |
LB | 4.88 | −7.18 |
−5.21 |
−0.37 |
ER | 0.45 | 0.27 | −11.42 |
−9.97 |
OPENN | −3.87 |
−6.09 |
−17.19 |
−12.88 |
GDP per capita | −13.15 |
−11.79 |
−21.69 |
−16.75 |
Debt | 1.78 | 0.43 | −11.03 |
−9.44 |
FD | −1.80 |
−5.27 |
−20.26 |
−16.68 |
INV | −2.63 |
−3.12 |
−18.43 |
−13.70 |
LB | 0.73 | −10.04 |
−7.05 |
1.70 |
ER | 1.65 | −1.23 | −12.36 |
−10.09 |
OPENN | −2.74 |
−3.58 |
−15.83 |
−9.69 |
GDP, gross domestic product; FD, financial development; INV, investment; LB, labour force; ER, official exchange rate; OPENN, trade openness.
indicate 1%, 5% and 10% significance level, respectively.
The correlation analysis was conducted using the Spearman’s rank-order test. The correlation matrix shows a positively significant relationship between investment and GDP per capita, which follows
Several unit root tests were performed to test the order of integration of the series (see
The bounds test results are shown in
Bounds tests.
Country | F-statistic |
---|---|
Benin | 3.66 |
Burkina Faso | 5.98 |
Cape Verde | 16.63 |
Cote d’Ivoire | 6.65 |
Gambia | 14.19 |
Ghana | 2.42 |
Guinea-Bissau | 2.70 |
Guinea | 9.84 |
Liberia | 32.21 |
Mali | 2.73 |
Niger | 4.71 |
Nigeria | 7.34 |
Senegal | 1.09 |
Sierra Leone | 5.94 |
Togo | 1.80 |
Critical value bounds.
Significance (%) | I0 bound | I1 bound |
---|---|---|
10 | 2.12 | 3.23 |
5 | 2.45 | 3.61 |
1 | 3.15 | 4.43 |
A diagnostic test is also conducted for each of the ECOWAS member countries using the ARDL models. The results are shown in
Diagnostic tests.
Country | Heteroscedasticity |
Serial correlation |
Normality |
Ramsey test |
||||
---|---|---|---|---|---|---|---|---|
F-stat | Prob. | F-stat | Prob. | JB. | Prob. | F-stat | Prob. | |
Benin | 0.57 | 0.83 | 10.97 | 0.0090 | 1.290 | 0.52 | 0.570 | 0.4700 |
Burkina Faso | 0.28 | 0.97 | 1.71 | 0.3100 | 2.270 | 0.32 | 0.270 | 0.6300 |
Cape Verde | 0.37 | 0.94 | 5.46 | 0.0700 | 1.760 | 0.41 | 1.460 | 0.2700 |
Cote d’Ivoire | 0.74 | 0.68 | 2.20 | 0.1500 | 0.250 | 0.87 | 0.330 | 0.5700 |
Gambia | 0.26 | 0.98 | 1.16 | 0.4600 | 4.120 | 0.12 | 0.030 | 0.8600 |
Ghana | 1.71 | 0.20 | 3.28 | 0.0900 | 0.900 | 0.63 | 48.570 | 0.0001 |
Guinea-Bissau | 1.10 | 0.51 | 0.57 | 0.6300 | 0.160 | 0.91 | 5.510 | 0.1000 |
Guinea | 0.81 | 0.66 | 15.03 | 0.0200 | 0.270 | 0.86 | 0.001 | 0.9700 |
Liberia | 1.95 | 0.18 | 34.54 | 0.0012 | 0.003 | 0.99 | 1.700 | 0.2300 |
Mali | 0.30 | 0.97 | 1.58 | 0.2500 | 1.790 | 0.40 | 3.900 | 0.0700 |
Niger | 0.36 | 0.95 | 3.74 | 0.0700 | 0.030 | 0.78 | 0.870 | 0.3700 |
Nigeria | 1.19 | 0.42 | 4.77 | 0.0600 | 0.450 | 0.79 | 3.880 | 0.0900 |
Senegal | 1.58 | 0.27 | 8.25 | 0.0200 | 0.010 | 0.99 | 0.005 | 0.9400 |
Sierra Leone | 2.42 | 0.11 | 6.79 | 0.0300 | 0.790 | 0.67 | 1.830 | 0.2200 |
Togo | 3.35 | 0.01 | 0.01 | 0.9800 | 0.680 | 0.71 | 0.810 | 0.3800 |
JB, Jarque-Bera.
The PMG results are shown in
Pool mean group results.
Variables | Coefficient | SE | T-statistic | Prob. |
---|---|---|---|---|
Debt | −1.87 | 0.105 | 17.70 | 0.000 |
FD | −0.12 | 0.004 | −25.41 | 0.000 |
INV | −0.11 | 0.018 | −6.35 | 0.000 |
LF | 6.60 | 0.634 | 10.40 | 0.000 |
ER | −0.90 | 0.185 | −4.87 | 0.000 |
OPENN | −0.03 | 0.001 | −19.51 | 0.000 |
COINTEQ01 | −0.89 | 0.300 | −2.94 | 0.004 |
D(GDP per capita(-1)) | −0.12 | 0.270 | −0.44 | 0.650 |
D(GDP per capita(-2)) | −0.02 | 0.160 | −0.14 | 0.880 |
D(Debt) | 3.46 | 3.470 | 0.99 | 0.320 |
D(Debt(-1)) | 1.93 | 3.000 | 0.64 | 0.520 |
D(Debt(-2)) | −2.68 | 4.160 | −0.64 | 0.520 |
D(FD) | 0.19 | 0.230 | 0.81 | 0.410 |
D(FD(-1)) | 0.01 | 0.110 | 0.14 | 0.880 |
D(FD(-2)) | −0.02 | 0.080 | −0.24 | 0.800 |
D(INV) | 0.38 | 0.160 | 2.35 | 0.020 |
D(INV(-1)) | 0.36 | 0.260 | 1.38 | 0.160 |
D(INV(-2)) | 0.09 | 0.150 | 0.60 | 0.540 |
D(LF) | −390.00 | 362.740 | −1.07 | 0.280 |
D(LF(-1)) | 254.00 | 377.130 | 0.67 | 0.500 |
D(LF(-2)) | 171.00 | 321.320 | 0.54 | 0.580 |
D(ER) | −13.92 | 12.830 | −1.08 | 0.280 |
D(ER(-1)) | −9.81 | 12.390 | −0.79 | 0.430 |
D(ER(-2)) | −0.04 | 4.120 | −0.01 | 0.990 |
D(OPENN) | −0.03 | 0.060 | −0.49 | 0.620 |
C | −102.52 | 40.010 | −2.56 | 0.010 |
GDP, gross domestic product; FD, financial development; INV, investment; LF, labour force; ER, official exchange rate; OPENN, trade openness; LF, Labour Fource; SE, standard error.
A possible explanation for the negative impact of trade openness on GDP per capita is that ECOWAS countries are not able to take full advantage of exports diversification, which is a necessary condition to support economic growth.
From
Surprisingly, investment has a negative relationship on economic growth, which is against the
The error correction term is significant and negative, indicating that there is a stable long-run relationship between the variables. The coefficient suggests that 89% of the disequilibrium level of output in the short run is corrected in the long run. In the short run, trade openness has a negative relationship with GDP per capita; however, it is not statistically significant.
The heterogeneous PMG results are presented in
Pool mean group short-run coefficients.
Country | D (GDP(-1)) | Debt | FD | INV | LF | ER | OPENN | Adjust |
---|---|---|---|---|---|---|---|---|
Benin | 0.53 | 0.00 | −0.11 | −0.20 | −0.00003 |
0.06000 | 0.37 | −1.14 |
Burkina Faso | −0.44 |
−0.00 |
0.32 | −0.07 | 0.00000 |
−0.01000 | −0.260 | −0.85 |
Cape Verde | 0.89 |
−0.00 | −0.11 | −0.03 | −0.02000 |
0.45000 |
−0.570 |
−1.17 |
Cote d’Ivoire | 0.56 |
0.00 |
0.62 |
1.29 |
0.00001 | 0.00001 | −0.050 |
−1.79 |
Gambia | 0.37 | −0.00 |
−1.65 |
0.10 | 0.00050 | 1.82000 |
−0.120 |
−2.41 |
Ghana | 0.49 | −0.00 | −0.33 |
0.09 | −0.00000 | −0.03000 |
0.050 | −1.24 |
Guinea-Bissau | 0.30 | −0.00 | 0.97 | 0.96 |
−0.00000 | 0.04000 | −0.440 |
−0.51 |
Guinea | 0.86 |
0.00 | 0.22 | −0.22 |
−0.00010 |
−0.00100 |
0.030 | −1.65 |
Liberia | 0.08 | 0.00 | 0.008 |
−0.93 |
0.00000 | −0.38000 | 0.060 |
−1.19 |
Mali | 0.44 | −0.00 | 0.51 | 0.45 | −0.00000 | 0.00400 |
−0.180 |
−2.39 |
Niger | 0.95 |
0.00 | −0.91 | 0.50 | 0.00000 | −0.00700 | −0.380 |
−3.10 |
Nigeria | −0.20 | 0.00 |
−0.15 |
0.82 |
−0.00000 |
0.01000 | −0.120 |
−0.40 |
Senegal | 0.99 |
0.00 |
−1.61 |
1.82 |
0.00005 |
0.03000 |
−0.914 |
−1.99 |
Sierra Leone | 0.46 | −0.00 | 0.01 | 0.01 | 0.00016 | −0.03000 |
0.080 | −2.24 |
Togo | 0.38 | 0.00 | −0.45 | 0.33 | −0.00030 |
0.05000 |
0.320 | −0.61 |
GDP, gross domestic product; FD, financial development; INV, investment; ER, official exchange rate; OPENN, trade openness.
indicate 1%, 5% and 10% significance level, respectively.
This article presents a systematic analysis of a dynamic GDP per capita across ECOWAS countries. The analysis focuses on the 15 ECOWAS member countries over the period 1990–2016. The empirical analysis employs the ARDL bounds testing approach to cointegration to test whether there is a long-run relationship between GDP per capita, debt, financial development, investment, labour force, exchange rate and trade openness. The results validate the existence of a long-run relationship between the variables at 1% level except for Ghana, Guinea-Bissau, Mali, Senegal and Togo.
Furthermore, the results reveal that trade openness has a significantly negative impact on GDP per capita in the long run. This implies that ECOWAS economies should be careful in depending heavily on trade liberalisation as their primary source of economic growth. Countries that have signed the African Continental Free Trade Agreement should be cautious in liberalising all their sectors to trade. This is in line with the infant industry argument.
However, this does not mean that trade liberalisation is harmful; rather, it invites developing countries to take advantage of trade openness to facilitate the imports of goods in which they do not have a comparative advantage and also capital goods that will help in the transformation of their economies. Labour is positively correlated with GDP per capita. Hence, investing in human capital by supporting productivity and innovation is vital for developing countries to tackle the cycle of poverty.
A limitation of this study is that it uses an aggregate value to capture the impact of trade openness on GDP per capita. Thus, it ignores the positive impact a specific sector can have on the economy. A useful continuation of this research would be to examine the effects of agricultural liberalisation on ECOWAS countries. Also adding other important variables, such as inflation and institutional quality, would improve the estimation technique and reduce the omitted variable bias.
The authors acknowledge the contribution of Nelson Mandela University for providing the materials for the study.
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
K.M.G. wrote the article and performed the computations. He verified the analytical method and provided the conclusion. P.L.R. supervised the study and provided guidance throughout the completion of the research.
This article followed all ethical standards for research without direct contact with human or animal subjects.
This research received no specific grant from any funding agency in the public, commercial or non-profit sectors.
Data sharing is not applicable to this article as no new data were created or analysed in this study.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.