1

Trade Liberalisation and Economic Growth in ECOWAS Countries

ABSTRACT: This paper examines the effects of trade liberalization in forms of trade openness on

economic growth in selected ECOWAS countries. More specifically, the idea is to examine the long run

relationship between trade openness and growth in these countries. The study employs recent panel unit

root tests and panel cointegration tests framework that allow for heterogeneity across selected countries

using annual panel data on these countries. In particular, we implement several panel unit root tests

(Maddala and Wu, 1999; Levin, Lin and Chu, 2002; Im, Pesaran and Shin, 2003) and panel cointegration

tests (Pedroni, 1999, 2004). Our estimation period covers from 1980 to 2008. Three different measures

of openness are used in this study. The results from various panel unit root tests demonstrate that the

panel data under consideration are stationary at first difference. Again, apart from the fact that the three

core growth models employed (following different measures of openness) unarguably demonstrates that

there exists long run relationship among the variables in the respective model, the panel cointegration

regression result reveals that trade openness and liberalization are germane to growth prospect in the

selected countries. This, therefore, lends credence to the growth-enhancing effects hypothesis of trade

liberalization and openness in the ECOWAS countries. Hence, more trade enhancing policies through

regional integration among member states in the ECOWAS would be beneficial for growth.

Keywords: Trade liberalization, trade openness, economic growth, panel cointegration

JEL Classification: C22, C23, F1, F15

1. Introduction

For more than two decades, there have been different strands of theoretical and empirical studies

aimed at investigating the relationship between trade liberalisation and growth both in developed and

developing countries. The potential benefits of free trade, more specifically, those accruing from

comparative advantage and specialization have been well documented in the classic theories of

international trade (Krugman and Obstfeld, 2011)1. The traditional opinion of gains from globalization

fundamentally rests on believe that when countries open up their economies, specialization and

economic competitiveness are encouraged. However, changes that have occurred in the field of

economic development and international trade in the last two decades, such as modification from

inward-oriented policies to export-promoting strategy, are believed to have been majorly responsible for

the vast and extensive empirical literature concerning trade openness and growth on the international

scale.

Also, the renewed emphasis on the need for regional trade integration and liberalization

especially among developing countries has been argued to generally been traceable to the collapse in the

multilateral trade agreements among nations in the World Trade Organisation. In fact, trade liberalisation

has been a prominent component of policy advice to developing countries for the last two decades.

Among the benefits claimed to spring from it, economic growth is probably the most important. Given

1 Concerted efforts by nations at establishing both bilateral trade links and subsequent formation of regional trading

blocs, for instance EMU, NAFTA, MERCOSUR, SADC, GCC among others, are the upshot of this widely held

view.

2

that free trade is fundamental to achieving the objective of regional trade agreement in most countries,

especially the developing ones, trade liberalisation has often been implemented with the expectation of

enhancing and promoting growth among countries (Greenaway et al., 2002). Openness to trade has been

seen as an important element of a sound economic policy and trade liberalization as a necessary element

of achieving it2.

As the push for trade openness at the multilateral level becomes more and more contentious,

various governments have been focusing on negotiating regional pacts as means to enhancing policy

credibility and accelerating trade and investment liberalization in the hopes of spurring domestic growth.

The fundamental rationale for this degree of commitment to programmes of trade reform is the obvious

belief that liberalization is a pre-requisite to a transition from a relatively closed to a relatively open

economy. For instance, it has been argued in the trade literature that export expansion is expected to lead

to better resource allocation, economies of scale, production efficiency through knowledge and

technological transfer, capital formation, employment creation and thus, economic growth and

development (Author, 2009). Export promotion, which is a product of trade liberalization, is also seen in

most developing countries as a way of correcting imbalances in the external sector and at the same time

assisting them in ensuring that their domestic economies made a full recovery. West African Countries

are faced with just such a challenge in announcing creation of the Economic Community of West

African States (ECOWAS).

The effects of economic integration primarily on national output, among other economic

fundamentals, have been rigorously and extensively research. More specifically, the attention of various

empirical researchers has been focused on investigating the impact of trade liberalisation on different

economies. This research upsurge, unarguably, is premised on the fact that economies the world over

have become increasingly interdependent. While closer ties among nations has been partly driven by the

rise in the movement of labour, services and capital- with the latter amid barriers to complete mobility-

across national borders, it is trade in goods that is arguably at the core of economic relations among

independent states (Bahmani-Oskooee and Brooks, 1999). However, while there is a consensus that

developing countries have a great deal to gain from free trade (Krueger, 1978; 1997 and 1998;

Srinivasan and Bhagwati, 1999), a critical theoretical argument has also emerged that the sub-regional

integration arrangements in Africa have failed to date to substantially increase trade (in terms of market

access) and economic growth both within the region and in the world.

Empirical studies which are mainly devoted to investigating growth enhancing effects of trade

liberalization abound in the literature. For instance, Edwards (1993) provides a survey of trade and

2 Trade liberalization does not have a specific definition; nonetheless, some authors have attempted to define it.

Shafaeddin (2005) defines trade liberalization ‘as any act that would make the trade regime more neutral -nearer to

a trade system free of government intervention.’ Edwards (1998) defines trade liberalization ‘as any policy that

reduces the degree of anti-export bias.’

3

growth studies covering the 1970s and 1980s3. In the empirical literature, the relationship between trade

and economic growth has two distinct strands. First, the pre-1990s studies focus on the relationship

between exports and growth. With some qualifications, the regression analyses were consistent in their

conclusions that growth of exports was significantly correlated with growth of output. Second, the post-

1990s studies, such as Dollar (1992), Ben-David (1993), and Sachs and Warner (1995) focus on the

relationship between openness and growth. These studies focus either on the direct impact of trade on

output growth or total factor productivity growth (examples are Edwards, 1998; Coe et al, 1997 and

Author, 2009). Their results and hence policy intuitions, at best, are mixed. Several reasons emanating

from different theoretical paradigm have been rigorously argued.

Though this study aims at contributing to this ongoing empirical exercise with the eventual

purpose of investigating the long run relationship between trade liberalization and growth in ECOWAS

countries, its path is charted differently. The motivation for this study is centered on the need to examine

the viability of trade performance in the light of growth dynamics in the ECOWAS region. One of the

focal objectives of ECOWAS lies in the need to foster trade performance and growth not only among the

country members but also with the rest of the world. It is expected, meanwhile, that this empirical

exploration would not only help in guiding policy makers in formulating sound and more informed

policies with respect to international trade performance, but also contribute immensely to the literature.

Consequently, the overall objective of this paper is to investigate the long run relationship between trade

liberalization on growth in the ECOWAS region employing different measures of openness.

The remainder of this study is, therefore, structured as follows: While the issue of methodology

involving data employed and model specification is extensively detailed in section 2, empirical analysis

and results are presented in section 3. Finally, conclusion is made in section 4.

2. Methodology

2.1. Data Sources and Measurement

With the overall aim of examining the impact of trade liberalization on growth in the ECOWAS

region, this section delves into issues concerning data employed, study scope and model specification

3 The U.S. International Trade Commission, USITC (1997) also provides a summary review of the literature on the

dynamic effects of trade liberalization.

4

among other things. The variables used in this study include the following: real GDP per head; ratio of

gross domestic investment to GDP; labour force participation rate; population growth rate; liberalization

episode dummy and “volumes” trade openness measures4. All variables are sourced from World Data

Indicator (2009) and the International Financial Statistics (various issues). The study scope ranges from

1980 to 20085. We employ annual panel data on 9 ECOWAS countries. The countries included in our

analysis are: Burkina Faso, Cote d’Ivoire, The Gambia, Ghana, Mali, Nigeria, Senegal, Sierra Leone and

Togo.

2.2. Model Specification

Given that over time, trade liberalization and growth especially in the developing countries have

been subjected to rigorous empirical investigation and following recent developments in time series

econometrics a number of authors have been able to model various determinants of core growth models

augmented with indicators of trade liberalization and openness. Until now, these varied specifications

reflect mainly differences in data employed and theoretical underpinnings. Following the work of Levine

and Renelt (1992) which searched for a set of robust variables to model growth, most models include as

explanatory variables the following: investment; population growth; initial per capita GDP; and initial

human capital. Basically, this study shall employ the Aggregate Production Function (APF) framework.

This production function which has been widely applied in the analysis of trade liberalization, trade

openness and economic growth assumes unconventional inputs such as trade liberalisation and trade

openness along the conventional inputs of labour and capital in the model. The approach used in this

study follows that of Fosu and Magnus, 2006. The aggregate growth model is thus specified thus:

(1)

From equation (1), Yt represents the aggregate production of the economy (proxied by GDP) at time t;

At, Kt and Lt also denote the total factor productivity (TFP), capital stock and labour stock at time t

respectively. Consequently, TFP is therefore specified thus:

(2)

Hence, the model used in this study not only reflects theoretically enriched but also parsimonious

specification models of core growth. In this study, the effect of trade liberalisation through the

4 One of the key difficulties in trying to assess the relationship between liberalisation and growth concerns an

accurate measurement of the point at which a country is deemed to have liberalised. Clearly such a process does

not happen instantaneously but it is possible to suggest when moves toward greater market freedoms occurred. The

simplest option is to use a statement of intent, signals the beginning of reform. Thus, our first indicator of

liberalisation is a dummy variable, which is activated at the time of a country’s first World Bank intervention. 5 The choice of 1980 to 2008 is primarily due to the availability of data among the countries under investigation

5

introduction of structural adjustment policy in these countries is examined. Thus a dummy variable is

included in (2) which thus become:

(3)

Therefore, to estimate [3], we take the natural logs of both sides which result in the following equation:

(4)

where = real GDP per head;

= the ration of gross domestic investment to GDP; LABOR =

labour force participation rate; = population growth rate; = liberalization episode dummy;

= “volumes” trade openness measure. The term is the error term bounded with the

classical statistical properties. Equation (4) is rightly represented within a panel setting by incorporating

a subscript ‘i’ depicting each of the countries in the sample.

3. Empirical Result and Interpretation

As earlier stated, the overriding objective of this paper is to empirically examine the panel

cointegration relationship among ECOWAS countries. For ease of rendition, this section presents the

results of the methodological and analytical processes followed in this study. This section reports the

results of time series and panel unit root tests on the data variables, panel cointegration tests.

3.1. Panel unit root test

The starting point is to examine whether the variables under consideration contain a panel unit

root. Table 1 displays the results of five distinct panel unit root tests, namely Levin, Lin, and Chu

(LLC)'s t*, Breitung's t, Im, Pesaran, and Shin (IPS)'s W, and Maddala and Wu (MW)'s ADF- and PP-

Fisher statistics on the level and first difference variables in our model. While LLC and Breitung, tests

are based on the common unit root process assumption that the autocorrelation coefficients of the tested

variables across cross sections are identical, IPS and ADF- and PP-Fisher tests solely rely on the

individual unit root process assumption that the autocorrelation coefficients vary across cross sections.

Table 1 reports all the unit root tests applied to all variables in our model for the panel of 9

ECOWAS countries. The results indicate that the variables contain a panel unit root. It is evident from

our result that the null of panel unit roots cannot be rejected by LLC, Breitung, IPS, and both PP- and

ADF-Fisher tests for all level variables under consideration. Thus, we may conclude that there exist unit

roots in all the level variables considering the panel unit root test results. However, the result also

indicates that all variables become stationary at first difference.

Table 1: Panel Unit Root Result Common unit root process Individual unit root process

6

Variables LLC t*-stat:

Ho: unit root

Breitung-stat:

Ho: unit root

IPS W-stat:

Ho: unit root

ADF-Fisher:

Ho: unit root

PP-Fisher:

Ho: unit root

At level At 1

st

Diff. At level

At 1st

Diff. At level

At 1st

Diff. At level

At 1st

Diff. At level

At 1st

Diff.

Y 0.153 0.000 0.911 0.000 0.450 0.000 0.166 0.000 0.053 0.000

0.998 0.000 0.973 0.000 0.354 0.000 0.760 0.000 0.010 0.000

0.910 0.000 0.999 0.000 0.931 0.000 0.991 0.000 0.836 0.000

1.000 0.000 0.770 0.014 0.146 0.000 0.064 0.000 0.829 0.000

(Trade) 0.520 0.000 0.048 0.000 0.411 0.000 0.442 0.000 0.268 0.000

(Import) 0.923 0.000 0.998 0.000 0.445 0.000 0.268 0.000 0.759 0.000

(Export) 0.647 0.000 0.905 0.000 0.333 0.000 0.220 0.000 0.000 0.000

Notes: Figures in the Table are the statistics of 5 distinct panel unit root tests. Numbers in parentheses are marginal

significance levels (p-values). *=significant at 10%, **=significant rejection of the null of unit roots at or below

5%.

3.2. Panel Cointegration Tests

Panel cointegration primarily focuses on addressing whether there exists a long-run equilibrium

relationship between the variables of interest if the variables contain a panel unit root. Hence, using

Pedroni’s (2004) test that allows for heterogeneity in the intercepts and slopes of the cointegrating

equation, we test for panel cointegration. Actually, Pedroni (2004) provides seven statistics for the test of

the null of no cointegration in heterogeneous panels. One group of tests is termed ‘within dimension’

(panel tests) and the other group of tests is ‘between dimension’ (group tests). The ‘within-dimension’

tests take into account common time factors and allow for heterogeneity across countries. The ‘between-

dimension’ tests are ‘group mean cointegration tests’ and allow for heterogeneity of parameters across

countries. The seven Pedroni (2004) panel cointegration test statistics that we employ are as follows:

Within dimension (panel tests): (a) Panel v-statistic, (b) Panel Phillips–Perron type r-statistics, (c) Panel

Phillips–Perron type t-statistic, (d) Panel augmented Dickey–Fuller (ADF) type t-statistic. Between

dimension (group tests): (e) Group Phillips–Perron type r-statistics, (f) Group Phillips–Perron type t-

statistic, (g) Group ADF type t-statistic. These seven statistics are based on the estimated residuals from

the following panel cointegration regression:

Table 2: Pedroni’s Panel Cointegration Test Result Test Statstics Model 1 Model 2 Model 3

Within dimension (panel tests)

7

v-statistic 0.5704 ( 0.2842) -2.0072( 0.9776) -1.5203( 0.9358)

Phillips–Perron t-statistic 1.5681 (0.9416) 0.4662( 0.6795) 0.2259( 0.5894)

Phillips–Perron statistic -1.865 (0.0145) -1.6446**( 0.0500) -2.2072**( 0.0136)

ADF t-statistic -3.8140** ( 0.0001) -2.6024**( 0.0046) -2.6164**( 0.0044)

Between dimension (group tests)

Phillips-Perron -statistic 1.9855 ( 0.9765) 1.0310( 0.8487) 0.9978( 0.8408)

Phillips–Perron t-statistic -1.8366**( 0.0331) -2.9904**( 0.0014) -2.9973**( 0.0014)

ADF t-statistic -3.4041**( 0.0003) -3.1756**( 0.0007) -4.2755**( 0.0000)

Notes: Probability values are in parenthesis; * and ** denote statistical significance at the 5 percent and 1 percent

levels, respectively.

Having established that all variables are stationary at first difference, we proceed to test whether

there is a long run relationship between the variables using the Pedroni’s (2004) heterogeneous panel

cointegration test. The idea is to examine whether there is a cointegration relationship between the

dependent variable and explanatory variables for our models. The results for the seven different panel

test statistics suggested by Pedroni are reported in Table 2. The statistical significance of these test

statistics is provided in parenthesis in the form of p-values. Each of the seven-test statistics (with the

exception of panel v and PP-t statistics) suggests that our growth models are exhibits long run

cointegration relationship irrespective of the choice of trade openness used.

4. Summary and Conclusion

As the push for trade openness at the multilateral level becomes more and more contentious,

various governments have been focusing on negotiating regional pacts as means to enhancing policy

credibility and accelerating trade and investment liberalization in the hopes of spurring domestic growth.

The fundamental rationale for this degree of commitment to programmes of trade reform is the obvious

belief that liberalization is a pre-requisite to a transition from a relatively closed to a relatively open

economy. This study aims at contributing to this ongoing empirical exercise with the eventual purpose of

investigating the long run relationship between trade liberalization and growth in ECOWAS countries.

The motivation for this study is centered on the need to examine the viability of trade performance in the

light of growth dynamics in the ECOWAS region. One of the focal objectives of ECOWAS lies in the

need to foster trade performance and growth not only among the country members but also with the rest

of the world.

This paper examines the long run relationship between trade liberalization and economic growth

in 10 ECOWAS countries. Hence, the study employs recent panel unit root tests and panel cointegration

tests framework that allows for heterogeneity across selected countries using annual panel data on these

countries. In particular, we implement several panel unit root tests (Maddala and Wu, 1999; Levin, Lin

and Chu, 2002; Im, Pesaran and Shin, 2003) and panel cointegration tests (Pedroni, 1999, 2004). Our

estimation period covers from 1980 to 2008. Three different measures of openness are used in this study.

The results from various panel unit root tests demonstrate that the panel data under consideration are

8

stationary at first difference. Again, the three core growth models employed (following different

measures of openness) unarguably demonstrates there exists long run relationship among the variables in

the respective models. This lends credence to the growth-enhancing effects hypothesis of trade

liberalization and openness in the ECOWAS countries. Hence, more trade enhancing policies through

regional integration among member states in the ECOWAS would be beneficial for growth.

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Appendix: Trends in Economic Growth and Trade in the Selected ECOWAS Countries

Growth Performance in ECOWAS Countries: Trend Analysis (1970-2008)

0

2E+09

4E+09

6E+09

8E+09

1E+10

1.2E+10

19

70

19

74

19

78

19

82

19

86

19

90

19

94

19

98

20

02

20

06

Co

te d

'Ivo

ire

0

2E+09

4E+09

6E+09

8E+09

19

70

19

73

19

76

19

79

19

82

19

85

19

88

19

91

19

94

19

97

20

00

20

03

20

06

Gh

ana

11

0

200000000

400000000

600000000

19

70

19

74

19

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19

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19

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19

94

19

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02

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Gam

bia

0

2E+10

4E+10

6E+10

8E+10

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70

19

73

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76

19

79

19

82

19

85

19

88

19

91

19

94

19

97

20

00

20

03

20

06

Nig

eria

0

500000000

1E+09

1.5E+09

2E+09

19

70

19

74

19

78

19

82

19

86

19

90

19

94

19

98

20

02

20

06

Sier

ra L

eon

e

0

500000000

1E+09

1.5E+09

2E+09

19

70

19

74

19

78

19

82

19

86

19

90

19

94

19

98

20

02

20

06

Togo

0

2E+09

4E+09

6E+09

8E+09

19

70

19

74

19

78

19

82

19

86

19

90

19

94

19

98

20

02

20

06

Mal

i 0

2E+09

4E+09

6E+09

8E+09

19

70

19

73

19

76

19

79

19

82

19

85

19

88

19

91

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94

19

97

20

00

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03

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06

Bu

rkin

a Fa

so

0

5E+09

1E+10

1.5E+10

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70

19

73

19

76

19

79

19

82

19

85

19

88

19

91

19

94

19

97

20

00

20

03

20

06

Sen

egal

12

Trade Performance in ECOWAS Countries: Trend Analysis (1970-2008)

0

50

100

150 1

97

0

19

73

19

76

19

79

19

82

19

85

19

88

19

91

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50

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19

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bia

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120

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Nig

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Sier

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0

5

10

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30

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Togo

0

500000000

1E+09

1.5E+09

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70

19

74

19

78

19

82

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86

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90

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02

20

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Mal

i 0

200000000

400000000

600000000

800000000

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74

19

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