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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.
References
Bahmani-Oskooee, M and Brooks, T.J. (1999): Bilateral J-curve between US and her trading partners,
Weltwirtschaftliches Archiv, 135, 156-165.
Baltagi, B. H. (2005). Econometric Analysis of Panel Data (3rd ed.), Ch. 12. New York: John Wiley.
Baltagi, B. H. and C. Kao (2000). Nonstationary panels, cointegration in panels, and dynamic panels: a
survey. Nonstationary Panels, Panel Cointegration, and Dynamic Panels. In B.H. Baltagi ed.
Advances in Econometrics, vol. 15: 7-52. Amsterdam: JAI Press.
Banerjee, A. (1999). Panel data unit roots and cointegration: an overview. Oxford Bulletin of Economics
and Statistics (special issue, supplement), 61 (4): 607-629.
9
Ben-David, D. and M.B. Lowey (1998). Free trade, growth, and convergence. Journal of Economic
Growth, 3: 143-170.
Breitung, J. (2000). The local power of some unit root tests for panel data. Nonstationary Panels, Panel
Cointegration, and Dynamic Panels. In B.H. Baltagi ed. Advances in Econometrics, vol. 15.:
161-178. Amsterdam: JAI Press.
Coe, D. T., E. Helpman, And A.W. Hoffmaister (1997) “North-South R & D Spillovers,” The Economic
Journal, 107(440): 134-49.
Edwards, S. (1993) “Openness, Trade Liberalization, and Growth in Developing Countries,” Journal of
Economic Literature, 31(3): 1358-93.
Edwards, S. (1998). ‘Openness, productivity and growth: What do we really know?’, Economic Journal,
108, 383-398.
Engle, R. F. and C. W. J. Granger (1987). Co-integration and error correction: representation, estimation
and testing. Econometrica, 55: 251-276.
Fosu, O.A.E. and F.J. Magnus, (2006). “Bounds testing approach to cointegration: An examination of
foreign direct investment, trade and growth relationship” American Journal of Applied Sciences
3 (11): 2079-2085.
Greenaway, D., W. Morgan, and P. Wright (2002). Trade liberalization and growth in developing
countries. Journal of Development Economics, 67: 229-244.
Im, K.S., M.H. Pesaran, and Y. Shin (1997). Testing for unit roots in heterogeneous panels. discussion
paper. Department of Economics, University of Cambridge, June. Also published in 2003,
Journal of Econometrics, 115: 53-74.
Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector
autoregressive models. Econometrica, 59: 1551-1580.
Johansen, S. (1992). Cointegration in partial systems and the efficiency of single-equation analysis.
Journal of Econometrics, 52: 389-402. Johansen, S. and K. Juselius (1990). The full information
maximum likelihood procedure for inference on cointegration - with applications to the demand
for money. Oxford Bulletin of Economics and Statistics, 52: 169-210.
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and
Control, 12: 231-254.
Krueger, A. (1978) Foreign Trade Regimes and Economic Development, volume X: Liberalization
Attempts and Consequences, (Cambridge, Massachusetts: Ballinger for the National Bureau of
Economic Research).
Krueger, A. (1997) “Trade Policy and Economic Development: How We Learn,” The American
Economic Review, (87)1: 1-22.
10
Krueger, A. (1998) “Why Trade Liberalisation Is Good For Growth”. The Economic Journal, (108)450:
1513-1521
Krugman, P. and Obstfeld, M. (2001) International Economics: Theory and Policy, 5th ed., Addison-
Wesley, New York.
Levin, A., C-F. Lin, and C-S.J. Chu (2002). Unit root tests in panel data: asymptotic and finite-sample
properties. Journal of Econometrics, 108: 1-24.
Maddala, G.S. and S. Wu (1999). A comparative study of unit root tests with panel data and a new
simple test. Oxford Bulletin of Economics and Statistics, (special issue, supplement), 61 (4):
631-652.
Author, O. (2009) “Export-Led growth hypothesis: Further econometric evidence from Nigeria” Pakistan
journal of social sciences 6(4): 219-223, 2009
Pedroni (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests
with an application to the PPP hypothesis. Econometric Theory, 20: 597-625.
Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple
regressors. Oxford Bulletin of Economics and Statistics, 61 (4): 653-670.
Pedroni, P. (2000). Fully modified OLS for heterogeneous cointegrated panels. Nonstationary Panels,
Panel Cointegration, and Dynamic Panels, in B.H. Baltagi ed. Advances in Econometrics vol.
15: 93-130. Amsterdam: JAI Press (Elsevier Science).
Sachs, J. And A. Warner (1995) “Economic Reform and the Process of Global Integration,” Brookings
Papers on Economic Activity 1.
Shafaeddin M (2005). Trade Policy at the Crossroads: The Recent Experience of Developing Countries.
London, Palgrave MacMillan.
Srinivasan, T.N., And J. Bhagwati (1999) “Outward-Orientation and Development: Are the Revisionists
Right?” Economic Growth Center, Yale University: New Haven, Center Discussion Paper no
806.
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
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
Gam
bia
0
2E+10
4E+10
6E+10
8E+10
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
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
19
94
19
97
20
00
20
03
20
06
Bu
rkin
a Fa
so
0
5E+09
1E+10
1.5E+10
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
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
19
94
19
97
20
00
20
03
20
06
Co
te d
'Ivo
ire
0
50
100
150
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
0
50
100
150
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
Gam
bia
0
20
40
60
80
100
120
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
Nig
eria
0
20
40
60
80
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
Sier
ra L
eon
e
0
5
10
15
20
25
30
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
Togo
0
500000000
1E+09
1.5E+09
19
70
19
74
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
Mal
i 0
200000000
400000000
600000000
800000000
19
70
19
74
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
Bu
rkin
a Fa
so
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
Sen
egal