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Regional Economic Integration, Growth and Income Distribution: a GTAP/CGE Impact Analysis of ECOWAS

1. Introduction Increasingly, the Economic Community of West African States (ECOWAS) Commission

is moving forward to achieving its ultimate objective of a region-wide common market, where there would be unimpeded free flows of goods, services, capital and labor as well as socio-economic policy coordination and harmonization. Considerable progress has been made in the area of the removal of tariffs and non-tariff barriers as well as the development of common trade policies.

While without doubt, ECOWAS has had one of the most constructive experiences with economic integration in Africa and is being touted as the most successful, the most ambitious and the largest trading bloc in Africa (Mistry, 2000), whether the benefits and results derived commensurate with the efforts and costs expended remain to be seen, particularly as it relates to growth and income distribution among the member states. The irony is that despite the modest gains in both intra- and inter-trade liberalization policies over the last three decades, the West Africa region remains one of the poorest in the world. Whereas over 52 percent of its almost 300 million population lives below US$1.25 (PPP) per day, all its members, with the exception of Cape Verde and Ghana, are at the bottom of the Low Human Development Index of the about 169 countries. The same indicators also show the GDP per capita of members ranges from US$3,402 (PPP) for Cape Verde to as low as US$641 (PPP) for Niger. These are suggestive of how widespread poverty and income inequality are within ECOWAS.

A 2011 report commissioned by the ECOWAS Commission on the harmonisation of convergence criteria within ECOWAS indicates that some of the areas lacking convergence are growth rates and living standards in ECOWAS countries. It observes that despite the generalisation of the Structural Adjustment Programmes (SAP) and the adoption of the ECOWAS Monetary Cooperation Programme (EMCP), the productive structures of ECOWAS economies do not converge. ECOWAS has a monetary union bloc and a group of countries which aspires to create an alternate monetary union. Of these two, the West African Economic and Monetary Union (WAEMU)1, also known as the CFA zone, has a lower average growth rate compared to the non-monetary union group called the West African Monetary Zone (WAMZ). Real average annual growth rates range from 0.4% (Cote d’Ivoire) to 9.4% (Nigeria), indicating rather uneven/unequal growth performance. Whereas Cape Verde (a member of WAMZ) and Niger (a member of WAEMU) which, having respectively had the highest and the lowest standards of living at the beginning of the decade they each recorded the same average annual real GDP per capita growth rate (13.7%) over the 2001-2008 period. It therefore cautions that if this past trends continue, it would take decades for national disparities to be eliminated within ECOWAS.

The issue is that whether the national economies of member states could, over time, converge towards their own GDP equilibrium level, given their economic base and their own factor endowment., and/or whether the ongoing economic integration can bring about convergence in economic growth and national income or will further cause a divergence. Lama (2011) has observed that it is not certain that the process of integration will, in and of itself, lead

1 The WAEMU is a customs union and monetary union with the following ECOWAS members: Benin,

Burkina Faso, Côte d’Ivoire, Mali, Niger, Senegal, Togo and Guinea‐Bissau. The ECOWAS CET, based on that of WAEMU, was adopted in Niamey (Niger) on 12 January 2006 by the 29th ordinary session of the Authority of Heads of State and Government.

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to convergence towards the average per capita GDP of the sub-region unless a change in economic structures and relative factor endowment of the various countries could help reduce disparities. Given the theoretical justification that regional trade arrangements have both winner and loser effects, the question that the present study seeks to answer is what is the contribution of regional economic integration to growth and income distribution of member states. More specifically, do the creation of an Free Trade Area (FTA) and Common External Tariffs (CET) among countries of unequal size and economic power, promote an economic growth pattern that bridges the income gap or it further deepens the already income disparities in the sub region? The main thrust of this paper is to seek answers to these questions by embarking on a simulation exercise of the various tariff adjustments and potential international relationships scenarios for the purpose of achieving regional economic integration.

While previous studies relating economic integration to growth and income distribution for member states, have either concentrated on partial equilibrium analysis, neglecting certain important sectors of the economy, or focused the impact on only one country (Aka 2006; Bhasin and Obeng 2006), the present study employs the GTAP standard model to investigate the effect key member states, blocs within and the region as a whole. We argue that as negotiations are underway to move beyond an FTA and institute a CET with an eventual common market by 2015 within ECOWAS, the findings from this study will crystallize the growth and inequality effects of any trade arrangements either within or outside the region leading policymakers to design appropriate counter or complementary policy measures to address any unintended consequences.

The rest of the paper is organized as follows. Section two presents a brief overview of ECOWAS and the growth trends of its members. Section three reviews the extant literature of regional integration, growth and income distribution. Section four presents the methodology and data source while section five focuses on the presentation and discussion of the simulation results. The last section, section six, concludes with some policy recommendations.

2. A Brief Overview of ECOWAS and Growth Trends

The Economic Community of West Africa States (ECOWAS), which was created in May 1975, is a sub-regional group of fifteen countries whose main aim is to promote economic and monetary integration and foster improved trade relations among its members. Considered one of the pillars of the African Economic Community, the organization was founded in order to achieve "collective self-sufficiency" for its member states by creating a single large trading bloc through an economic and trading union, as well as a peacekeeping force in the region. Among some of its mandated treaty provisions are to: (a) eliminate between member states customs duties and other charges of equivalent effect on imports and exports –; (b) eliminate quantitative and administrative restrictions on trade among members; (c) establish a common external tariff structure and commercial policy towards non-member countries.

On that score, while ECOWAS has been successful with creating an FTA and has undertaken the application of a harmonized customs regime through the CET within the WAEMU and the implementation of a Community industrial policy to promote productive investment and industrial development, the Community’s efforts have been most frustrating in the area of market integration. Indeed, the trade liberalization scheme is not yet operational as reflected by inadequate intra regional trade, which is less than 12 %.

The entire fifteen member states of ECOWAS have a combined population of roughly 300 million people and with GDP which is slightly above US$300 billion as at 2010, resulting in a per capita income of about US$1,000. However, it is noteworthy that the Community comprises a heterogeneous group of countries. The economies in the region are made up of a continental powerhouse Nigeria, constituting about 68% of the region’s GDP and hosting more than half the region’s population. Côte d’Ivoire, Ghana, and Senegal are the second, third, and

http://en.wikipedia.org/wiki/African_Economic_Community

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fourth largest economies, though significantly smaller (4% to 6% of the region’s GDP). Table 1 reveals the wide per capita income disparities which exist in the ECOWAS. While the richest countries in the region, Cape Verde, with a GDP per capita of approximately US$3,247 and Ghana, Nigeria, and Côte d’Ivoire, with GDP per capita of US$1,363, US$1298.14 and US$1,042, respectively as at 2010, Sierra Leone and Liberia have per capita income of 325.76 and US$ 229.44, respectively.

Table 1: GDP Per Capita, Current Prices (U.S. Dollars)

Source: International Monetary Fund, World Economic Outlook Database, September 2011

Again, in terms of growth rate over the 2001-2009 period, it has been fairly uneven. Average annual real GDP growth rates vary between 0.4% (Cote d’Ivoire) and 9.4% (Nigeria). In almost all the countries, the rates have followed a roller coaster pattern (Lama, 2011). WAMZ countries generated a far higher average growth rate than WAEMU countries. On average, agriculture has made the greatest contribution to growth in ten of the fifteen countries of the sub-region, including six WAEMU countries (Benin, Burkina Faso, Cote d'Ivoire, Mali, Niger, Togo) and four WAMZ countries (Ghana, Guinea, Nigeria and Sierra Leone). However, this contribution is the most variable in all the countries. This is one of the main reasons for the unstable and low growth in these countries. In the coastal countries and in Niger, trade activities made a substantial contribution to growth. Niger’s case stems in part from its privileged position as a transit country between Nigeria and hinterland countries such as Mali and Burkina Faso.

Figure 2 shows that real convergence assessment within ECOWAS, WAEMU and the group of WAMZ countries based on per capita GDP standard variations gives contrasting results up to 2002 and marked divergent trends for the rest of the period. Variations in WAMZ were found to be the lowest with WAEMU having the highest variations.

2004 2005 2006 2007 2008 2009 2010

Benin 498.16 518.81 544.87 620.29 730.74 704.17 681.71

Burkina Faso 378.36 417.32 452.71 495.01 589.50 582.01 609.76

Cape Verde 1,966.52 2,035.82 2,288.15 2,711.28 3,146.64 3,174.52 3,247.46

Côte d'Ivoire 837.92 862.73 888.27 983.48 1,132.25 1,051.99 1,042.52

The Gambia 410.86 436.26 441.82 532.95 639.85 587.30 611.77

Ghana 714.67 833.40 952.74 1,126.86 1,266.11 1,116.45 1,363.85

Guinea 405.55 318.53 308.41 432.36 459.36 452.40 448.49

Guinea-Bissau 363.95 389.68 384.27 448.86 537.53 518.20 508.34

Liberia 139.60 157.50 168.90 197.52 216.04 212.95 229.44

Mali 431.85 464.46 505.72 576.66 690.93 690.82 700.67

Niger 238.11 268.68 281.87 321.89 391.97 371.21 375.42

Nigeria 662.47 823.82 1,038.76 1,153.40 1,401.24 1,111.75 1,298.14

Senegal 706.30 748.23 784.62 924.39 1,066.37 997.61 979.96

Sierra Leone 216.34 237.64 270.01 306.96 351.07 325.66 325.76

Togo 324.01 344.58 349.98 391.25 479.72 466.26 457.14

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Figure 2: Per Capita real GDP Standard Deviations

Source: ECOWAS Commission, 2011

Notes: CEDEAO is the French abbreviation ECOWAS; UEMOA is WAEMU and ZMAO is WAMZ

3. Literature Review

The drive towards regional economic integration has been informed by the desire by countries to enhance trade and improve the general welfare of their peoples. Regional economic integration can take many forms: FTAs, customs unions, common markets or monetary unions. FTAs are an economic arrangement whereby countries agree to remove all tariff barriers between them but each country is allowed to determine tariffs on imports on goods from other countries. Like FTAs, customs unions have zero tariffs within the zone; however, there is a common external tariff system in place for goods that come in from outside the region amidst greater customs cooperation. A common market, on the other hand, is a customs union which permits the free movement of the factors of production across borders within a zone; while a monetary union involves an irrevocable fix of the currencies in a zone with the ultimate being hallmarked by the use of a single currency issued by a common regional central bank.

The advantages of regional economic integration are well documented with studies by Rose (2000), Frankel and Rose (1998 and 2004), Asenso (2009 and 2012) and Engel and Rose (2002) confirming the positive role monetary unions, for example, play in enhancing intra-regional trade through the reduction of transaction costs. Furthermore, the removal of internal tariff barriers widens the scope of intra-regional trade while the non-zero external tariff serves to

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CEDEAO UEMOA ZMAO

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insulate domestic businesses from the effects of dumping. On the other hand, monetary unions lead to the substitution of one-size-fits-all monetary policies for independent monetary policies while the removal of tariff and non-tariff barriers could lead to loss of tax revenues and the collapse of small businesses due to competition from bigger ones.

There is a plethora of literature that border on the effects of regional integration on economic growth, trade enhancement and income distribution. While the above mentioned studies employed the gravity model to assess the impact monetary unions have on reciprocal trade, others like Ezaki and Nguyen (2008) have employed computable general equilibrium (CGE) models to assess the impact of economic integration on income distribution, growth and poverty. The one advantage CGE models have over such partial equilibrium models is their ability to simulate various scenarios in a general equilibrium framework which makes it possible to ascertain the effects of policy changes on various sectors of an economy. As a result, studies on regional economic integration and its effects on various economies can best be conducted using CGE models.

A number of studies have been conducted to ascertain the effects of trade liberalization on poverty and income distribution using CGE models. Hérault (2005) analyzes the effects of trade liberalization on poverty and inequality in South Africa using a macro-oriented CGE model and a micro-simulation (MS) model. The study finds that trade liberalization leads to poverty reduction with white South Africans emerging as the main winners as a result of the policy. Nguyen and Ezaki (2007) assess the effects of regional economic integration on growth, poverty and income distribution in Vietnam using a global CGE model. The study finds that regional economic integration is generally good for Vietnam because it enhances welfare and improves income distribution. Furthermore, the study finds that regional economic integration leads to increases in household income and consumption with rural households benefitting more than urban high income groups.

A study by Aka (2006) attempts to quantify the effects of removing trade taxes and instituting fiscal reform on poverty and income distribution in Côte d’Ivoire. It analyzes income distribution for various socioeconomic groups using an absolute poverty line based on the constant basic needs approach. It analyzes the impact of the elimination of taxes on agricultural exports and imports combined with a change in the domestic tax rate on poverty, inequality and welfare using a CGE model. The results show that poverty increases for all households, but depending on the simulations, the situation is diversified among socioeconomic groups. Furthermore, the removal of tax on exports leads to an increase in domestic prices of agricultural and industrial goods, resulting in an increase in the consumer price index and a decrease in households’ disposable income and, thus, in their consumption.

Bhasin and Obeng (2006) likewise used a CGE model to examine the impact of trade liberalization in Ghana, in which lost tariff revenue is compensated for by increased foreign borrowing, on the poverty and income distributions of various categories of households. The study finds that elimination of trade-related import duties accompanied by an increase in foreign borrowing reduces the incidence, depth, and severity of poverty, whereas the elimination of export duties accompanied by an increase in foreign borrowing, increases the incidence, depth and severity of poverty. It is also shown that the income distributions of households improve when elimination of trade-related import duties are accompanied by an increase in foreign borrowing, whereas the income distributions of households worsen when elimination of export duties are accompanied by an increase in foreign borrowing.

While the above studies could be regarded as country-based studies of the effects of trade liberalization on poverty, neither of them had looked into the issue of regional integration and its impact on member countries’ employment and poverty reduction efforts especially within SSA. A few available have either cantered on regions outside ECOWAS or has not taken all the issues on board, particularly assessing welfares impacts of bi/multilateral trade agreements within a broader context of regional integration or multilateral agreements. Ezaki and Nguyen (2008)

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studied the effects of regional economic integration on growth, income distribution and poverty in four East Asian countries: China, Indonesia, Thailand and Vietnam. The study utilizes a global CGE model which links country or regional CGE models based on the data and structure of the Global Trade Analysis Project (GTAP) model. To be able to generate micro-simulations, the study includes household data of income and expenditures for the four countries and extends the GTAP model accordingly. It finds that East Asian FTAs generally have positive effects on growth, enhance income distribution and leads to poverty reduction.

McDonald and Walsmley (2003), studying the impact of the EU-Republic of South Africa Free Trade Agreement on Botswana, argue that the free trade agreement between the EU and the Republic of South Africa (EU-RSA FTA) is clearly one of the first fruits of the Cotonou Agreement to trade relationships. However, there is no evidence that the design of the EU-RSA FTA incorporated a comprehensive general equilibrium evaluation of the agreement for either the signatories or the other southern African nations. Their analyses from a GTAP CGE simulation report indicate that while the EU-RSA FTA may substantially benefit the signatories, there are appreciable negative impacts for other states, especially the RSA’s immediate neighbours. Moreover, the reluctance of the EU to fully liberalize trade in food and agriculture commodities results in a major reduction in the benefits for the RSA without ameliorating substantively the adverse implications for other nations.

In another related study, Yousouf (2008) used the standard GTAP model to assess the prospective economic and social effects of the proposed EU-ECOWAS Economic Partnership Agreements (EPAs) in Cote d’Ivoire. These impacts along with proposed average tariff reductions were particularly utilized to forecast the potential revenue gain or loss resulting from the establishment of an EPA. Their simulation results show that full reciprocity will be costly for Cote d’Ivoire in terms of revenue losses and adjustment costs associated with de-industrialization. However, unrestricted market access for Cote d’Ivoire into the EU-25, taking into account the issue of sensitive products and fiscal compensations, promise positive gains while resulting also in welfare gains. However, this study and the others do not take into consideration the wider growth and income inequality effects of various regional economic integration scenarios such as customs union, particularly within the ECOWAS region which has made substantial progress towards CET and an eventual common market.

4. Methodology

The study aims to ascertain the impacts of various economic agreements and regional economic integration measures on economic growth and poverty in the ECOWAS with Ghana as a case study. The methodology is premised on a Computable Generalised Equilibrium (CGE) model derived from the Global Trade Analysis Project (GTAP).

Model: GTAP Standard Model and Closure Rules

The standard GTAP model is a multi-region, multi-sector CGE model that uses is a numerical tool based on the CGE theory (Hertel, 1997). The CGE theory analyzes how multiple markets – consumers and governments and sectors such as industry, agriculture and services, and factors of production such as land, labor and capital – come into equilibrium simultaneously. This involves an algebraic framework resulting from imposing the conditions of producer and consumer maximization on the accounting framework of the social accounting matrix (SAM). Like the CGE model, under the standard GTAP closure, the prices of goods, factors and services adjust until all markets are cleared.

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The GTAP model is based on a standard neo-classical hypothesis, with perfect competition and constant returns to scale assumed throughout. Trade between the different regions in the model is incorporated through an Armington specification – products are differentiated by country of origin, representing the only important departure from the standard neoclassical Framework. The Armington specification introduces imperfect substitution between domestic and imported sources of supply. Furthermore, the model assumes that there is a regional household that collects all income and allocates across private consumption, government, and saving. At a macroeconomic level, the standard model requires that the difference between national savings and national investment is exactly equal to the current account surplus. However, the GTAP does not include observations on net transfers. The macroeconomic closure therefore collapses to its simplest form, whereby net national savings are equal to the trade balance. According to Zahariadis (2007), the principal implication of this specification is that any change in trade flows following a policy shift will require the trade balance to adjust in maintaining the simplified macro identity.

The study adopts this standard model but with a few adjustments to the standard closure rules that reflects the socio-economic peculiarities of ECOWAS member states. Following McDonald and Walsmley (2003) and (Zahariadis, 2007) and others, three basic changes are made to the closure rules. The first relates to fixing the trade balance, the second to employment of unskilled labor and the third to the prices and quantities on the world market of certain special commodities. More specifically, first, we adopt a fixed trade balance to account for the fact that developing countries, like the ones in the ECOWAS region, follow a flexible or a floating exchange rate regime. Their trade balances are fixed, while savings change. Second, we allow for the incorporation of unemployment in our model because of the presence of excess supply of unskilled labor in these countries which makes the assumption of full employment inappropriate. In this regard, the real wage for unskilled labour ECOWAS was fixed, while endogenizing the supply of labor. This allows us to take unemployment into account. Third, we account for fixed prices in the market for the countries’ major commodity exports. The export prices of these commodities are generally determined in the world market and the entire ECOWAS region is only a price-taker.

The study focuses on the effects import tariff (tmirk) adjustments necessitated by economic agreements will have on growth and income distribution. Such adjustments are translated through the price mechanism which is stated thus:

)1(**$ irkrirkirk tmERPMPMS (1)

where i represents good type; (r, k) represent countries or regions, PMS is the domestic price of imports by sources of imports; ER is the exchange rate and PM$ is the world price of imports. The effect of the changes in import tariffs will be translated through PMSirk which will then cause changes in the variables mentioned below as follows:

i li lirlirliririrr PRWKLKRKYH )**(

(2) The above equation measures the impact of import tariff changes on income distribution

among the various households. YHr represents household income, WKlir is wage rates by sector and types of labour; PRlir; LKlir is labour demand by types of labour; Kir is capital demand by sector; Rir is capital rents and i and r are as previously defined.

i i irirjrijri iriri irirr PXVPNMXiocfPGMGPCMCGDPN

(3)

i ik irkirkirir PMMSPKMID $

i irirPEE $

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Equation (3) is the nominal GDP (GDPNr) equation from which the effects of tariff changes on growth will be deduced. Gir and PGMir represent government consumption and market prices of public goods respectively; Xjr is the quantity of composite goods; PNM is market prices for intermediate goods; iocfijr is the intermediate input coefficient of good j in industry i; Vir is demand for inventory investment; PXir is output prices; IDir is demand for capital goods; PKMir is market prices of capital goods; Eir is export supply; PE$ir is the world price of exports and MSirk and PM$irk are as previously defined.

As signatories to the ECOWAS Protocols, all members are enjoined to implement the agreed common external tariffs. Members of the WAEMU have started implementing aspects of the CET with the WAMZ is expected to follow soon to facilitate the formation of a West African Monetary Union.

The study ascertains the likely impacts a CET on selected members of ECOWAS, WAEMU, WAMZ, namely, Côte d’Ivoire, Senegal, Ghana, Nigeria and four other ECOWAS countries. It also ascertains the likely impact of an enhanced FTA on the sub-region and individual countries. Specifically, the study seeks to ascertain the impact the operation of an FTA or a CET in ECOWAS will have on growth and income distribution in the region. It is also interested in finding out whether these trade arrangements could lead to income convergence in the region or otherwise. This has been necessitated by the fact that there are wide disparities in economic size and growth patterns, a situation which does not augur well for shocks asymmetry.

The model is based on the theory that trade liberalization reduces the cost of doing business through the removal of tariff and non-tariff barriers which in turn reduces prices of goods and services. The removal of tariffs leads to lower domestic prices of imported commodity which enhances real income and consumption.

4.1 Data Sources

The study makes use of the GTAP Africa Data Base. The GTAP Africa Data Base includes data for 39 regions (30 African regions and 9 other aggregated regions) and 57 sectors. Latest additions are Cameroon, Cote d'Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya and Sudan. For West Africa, the aggregation includes Côte d’Ivoire and Senegal (and the rest of WAEMU) and Ghana and Nigeria (and the rest of ECOWAS).

5. Estimation Results

The study aggregates four regions – the West African Economic and Monetary Union (WAEMU), the Rest of ECOWAS, WAMZ and the Rest of the World – and three sectors: Agriculture, Industry and Services. It lumps all agriculture, industry and services related activities in the respective sectors and brings Côte d’Ivoire, Senegal, Benin and Burkina Faso under WAEMU while Ghana, Nigeria, The Gambia and Cape Verde are categorized under WAMZ. Subsequently, the individual countries, say Cape Verde and Gambia (RestWAMZ) as well as Benin and Burkina Faso (RestWAEMU), are matched against the rest of ECOWAS (RestECOWAS) on one hand, and the Rest of the World to ascertain the impact of the policy simulations on the individual countries. RestECOWAS is made up of RestWAEMU and RestWAMZ.

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The study runs two main simulations:

1. An FTA simulation for all ECOWAS countries which involves the removal of all tariff barriers within the region for regional goods and services; and

2. A CET simulation which involves a zero percent tariff in the ECOWAS for all regional goods and services and a 20 percent tariff for all non-ECOWAS goods and services.

These two simulations are run for the consolidated WAEMU and RestECOWAS simulations as well as the individual countries bringing the number to 14 simulations.

5.1 Performance after Simulations

5.1.1 WAEMU and WAMZ

The FTA simulation results in relatively favorable income levels for WAEMU as opposed to WAMZ as can be seen from Tables A4, A5 and A6 in comparison with the baseline data in A1, A2 and A3. This means that an FTA of this nature will be more favorable to WAEMU than WAMZ. The reason is that WAEMU countries are somewhat efficient producers of agricultural and industrial goods than their WAMZ counterparts and a sudden change in tariff policy would benefit them more since WAMZ has a bigger population. As a result, they have a stronger export presence in the sub-region. With the removal of tariff barriers, they will be emboldened to export more to the WAMZ and other WAEMU countries. Furthermore, the FTA leads to a slight reduction in total factor incomes in the WAMZ with all factor incomes, including labor, in the agriculture sector declining. Overall, an FTA does not auger well for the WAMZ even though the WAEMU benefits from it. It leads to a decline in labour incomes, particularly, unskilled labor incomes in the zone.

However, the CET simulation (as indicated in Tables A7, A8 and A9) benefits both WAEMU and WAMZ with aggregate total factor and sectoral (save the agriculture sector in WAEMU) incomes increasing in both sub-regions as can be seen in Tables A7, A8 and A9. This could be attributed to the fact that intra-ECOWAS products become cheaper with the institution of a CET relative to non-ECOWAS products, thus, raising the competitiveness (but not necessarily quality) of ECOWAS goods and services. However, WAEMU unskilled labor income is slightly below the FTA simulation results with skilled labor income falling below pre-simulation levels. Furthermore, the bloc’s skilled and unskilled labor incomes in the industrial sector also experience declines. For the WAMZ, CET seems a better option than FTA after it experiences income mark-up across sectors. However, it also experiences declines in industrial labour incomes.

5.1.2 Ghana and ECOWAS

The FTA simulations indicate that an FTA between Ghana and the rest of ECOWAS would be mutually beneficial as evidenced in Tables B4, B5 and B6 in comparison with the baseline data in Tables B1, B2 and B3. Both Ghana and the rest of ECOWAS experience moderate increases in sectoral incomes across sectors. In effect, both of them take advantage of the FTA to enhance trade relations. This is particularly the case for Ghana which is surrounded by WAEMU members. Factor and sectoral incomes in Ghana increase significantly with the FTA. The same holds for the rest of ECOWAS except for a marginal decline in agricultural capital returns. With the FTA, it becomes cheaper for Ghana to trade with such direct neighbors as Côte d’Ivoire, Burkina Faso and Benin, which are member of WAEMU and operate a CET.

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The CET simulations (in Tables B7, B8 and B9) also exhibit a marked overall improvement in income levels for both Ghana and the rest of ECOWAS. Both skilled and unskilled labour experience moderate increases in incomes across sectors for Ghana. A similar trend emerges for the rest of the ECOWAS countries except for declines in agricultural capital returns and industrial labour and capital returns. However, the changes brought about by the introduction of a CET are higher than those the FTA brings. This indicates that an all-out customs union, marked by the introduction of a CET, works better than an FTA since it results in higher mutual benefits for both Ghana and the rest of ECOWAS.

5.1.3 Nigeria and ECOWAS

The case of Nigeria and the rest of ECOWAS is a bit different from that of Ghana and the rest of ECOWAS. While the rest of ECOWAS experiences an increase in income levels after the application of the FTA, Nigeria suffers a reduction in same with private consumption and government expenditure leading the way. The FTA would also lead to an increase in imports, probably, mainly from the rest of ECOWAS owing to the removal of tariff and non-tariff barriers which restricts the flow of goods from the sub-region into the largest single market on the African continent (i.e. Nigeria). Though an FTA would increase factor incomes slightly, there is indication that agricultural incomes could suffer. From the results, incomes to skilled and unskilled labour as well as rent from the agriculture sector experience a decline even though labour incomes increase in the other sectors. This could be as a result of a move of labour from the agriculture sector to industry owing to its attractiveness or the laying off of the sector’s workers due to its non-competitiveness compared with sub-regional agricultural products.

The introduction of the CET leads to much bigger positive income changes for both Nigeria and the rest of ECOWAS. For example, in the case of Nigeria, private consumption and all the other components of GDP increase under the CET simulation while private consumption plummeted in the FTA scenario. Furthermore, factor incomes experience a significant general increase. Contrary to the FTA simulation, factor incomes from agriculture increase when compared to the baseline data. However, labour incomes from the industrial sector experience a slight plunge. This could be good for overall welfare since most people are engaged in agricultural activities.

5.1.4 The Rest of WAMZ and ECOWAS

The FTA augurs well for the rest of the WAMZ (Cape Verde and The Gambia), however, the same cannot be said of the rest of ECOWAS which experiences a slight fall in incomes. The fall is preceded by falls in private and public consumption and investment. In the rest of the WAMZ, factor incomes increase across sectors with both skilled and unskilled labour benefiting quite considerably. The story is a bit different when it comes to the rest of ECOWAS with slight declines in agricultural factor incomes. There are slight declines in skilled and unskilled labour incomes and returns on capital in the agriculture and services sectors in the rest of ECOWAS, however, industrial factor incomes record positive changes.

The CET, on the other hand, is favourable for the rest of ECOWAS but unfavorable for the rest of WAMZ. That, notwithstanding, industrial incomes decline for factors such as skilled and unskilled labour as well as capital for the rest of ECOWAS. Interestingly, factor incomes in the industrial and services sectors fall in both Cape Verde and The Gambia (RestWAMZ) as a result of the CET. On this score, it is arguable that a CET that is cast in this mould might not be beneficial to the Gambia and Cape Verde.

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5.1.5 Côte d’Ivoire and ECOWAS

An ECOWAS FTA with Côte d’Ivoire leads to mutual benefits to both of them. However, while overall factor incomes increase in Côte d’Ivoire, the same declines in the rest of ECOWAS with wholesale industrial sector factor income declines, drops in agricultural capital and unskilled labor incomes and further drops in labor and capital incomes in the services sector. The decline in skilled and unskilled labor incomes in the agriculture, industry and services sectors is primarily responsible for the general decline in factor incomes in the rest of ECOWAS.

Like the FTA, the CET leads to increased income levels in both the ECOWAS and Côte d’Ivoire. However, overall factor incomes reduce in Côte d’Ivoire while they increase in the ECOWAS sub-region. The decline in factor incomes in Côte d’Ivoire is heralded by declines in labour and capital incomes across sectors. These same factors decline only in the industrial sector in the ECOWAS sub-region. From the foregoing, while an FTA gives better results for Côte d’Ivoire, the CET is more favorable to the rest of ECOWAS.

5.1.6 Senegal and ECOWAS

Senegal experiences a decline in incomes after the FTA while the rest of ECOWAS experience marginal increases in income after the introduction of an FTA. However, they both experience a marginal rise in post-FTA factor incomes with no factor being worse off, even though the increases are relatively modest.

With the introduction of the CET, both Senegal and the rest of ECOWAS experience positive changes in income with overall increases in factor incomes. However, both of them experience declines in labor and capital incomes in the industrial sector while the rest of ECOWAS experiences a decline in agricultural capital returns.

5.1.7 Rest of WAEMU and ECOWAS

The FTA simulation for the rest of WAEMU (Benin and Burkina Faso) and the rest of ECOWAS leads to a slight fall in income in ECOWAS and a rise in incomes in WAEMU. Whereas private and public consumption increases in the rest of the WAEMU, they fall in the rest of ECOWAS. The FTA portends a general rise in factor incomes in the rest of the WAEMU, with skilled and unskilled labor experiencing increases across sectors. However, agricultural labor and capital incomes fall within the rest of ECOWAS.

The CET simulation posts a similar result with the rest of ECOWAS incomes suffering a decline while those of the rest of WAEMU increase Interestingly, overall factor incomes increase in both areas with only agricultural capital and industrial labor and capital incomes in the rest of ECOWAS declining. All in all, the CET proves to be a better option than the FTA in increasing general and factor incomes.

5.1.8 Status of Income Convergence under FTA and CET Simulations

One of the main arguments that stand against ECOWAS integration has been the seeming lack of convergence. For quite some time now, the countries have failed to meet most of the convergence criteria, even though current trends are beginning to show encouraging signs of convergence. It must be pointed out, however, that it is not possible for all the countries to be of the same economic size. What is possible is for the countries that are lagging behind to post higher economic growth in order to catch up to those ahead.

From the results in Table H, it is obvious that Ghana benefits the most from the FTA policy, posting a 20.80 percent increase over its baseline GDP with Nigeria recording a negative

12

growth. Benin and Burkina Faso (together) also record a 4.82 percent increase with the rest posting modest gains.

The CET scenario leads to better growth than the FTA scenario. Apart from Côte d’Ivoire, all the countries perform better under this scenario than under the FTA scenario. Ghana continues to post an impressive result (23.46 percent) while a combined Benin and Burkina Faso grows at 9.16 percent. In this scenario, the four country groups recorded higher economic growth than the two leading economies, Nigeria and Côte d’Ivoire, except Cape Verde and Gambia (combined) which equals Nigeria’s growth.

From the foregoing, it is obvious that a CET would help countries behind to catch up faster than an FTA due to the fact that weaker countries post stronger growth than the large countries in the region.

6. Conclusion

The study set out to ascertain the probable impact regional integration policies could

have on economic growth and income distribution. This was done using FTA and CET as

policy variables in the ECOWAS and its two sub-blocs: WAEMU and WAMZ. Furthermore,

simulations were run for individual ECOWAS countries which are part of the GTAP data

base as well as joint simulations for Cape Verde and The Gambia (RestWAMZ) and Benin and Burkina Faso (RestWAEMU).

The results indicate that whereas an FTA would generally increase individual and regional

incomes, a CET would be better in most cases. Income growth rates were higher for the CET

simulations for all the countries, except for Côte d’Ivoire where FTA performed better. Moreover, the CET provides better opportunities for smaller economies to catch-up with larger ones in the economic bloc, thus, allowing for possible future growth convergence. However, the results for income distribution are somewhat mixed with some factors experiencing income declines across sectors.

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http://www.uneca.org/aria/aria3/index.htm

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14

APPENDICES

Appendix A FTA and CET Simulations Results of the WAMZ and WAEMU

Baseline Data

Table A1 Gross Domestic Product (Expenditure Approach)

Table A2 Sources of Factor Incomes (WAEMU)

Table A3 Sources of Factor Incomes (WAMZ)

FTA Simulation Results


GDPEXP 1 cons 2 inv 3 gov 4 export 5 imp Total

1 WAEMU 21,414.40 4,448.10 2,937.60 7,794.50 (10,622.20) 25,972.40

2 WAMZ 29,298.30 13,838.90 11,610.70 21,120.70 (23,954.20) 51,914.40

3 RestofWorld 19,280,962.00 6,723,682.00 5,190,226.50 7,117,914.00 (7,112,252.50) 31,200,532.00

Total 19,331,674.70 6,741,969.00 5,204,774.80 7,146,829.30 (7,146,829.00) 31,278,418.80

EVFA 1 Agric 2 Indust 3 Serv 4 CGDS Total

1 Land 822.50 - - - 822.50

2 UnSkLab 5,182.30 2,422.80 3,732.30 - 11,337.40

3 SkLab 41.80 469.30 2,250.60 - 2,761.70

4 Capital 1,192.00 2,182.60 4,475.20 - 7,849.80

5 NatRes 165.40 64.10 - - 229.50

Total 7,403.90 5,138.80 10,458.10 - 23,000.80


1 Land 1,067.80 - - - 1,067.80

2 UnSkLab 6,548.20 1,360.10 3,155.60 - 11,063.90

3 SkLab 52.80 314.90 3,558.00 - 3,925.70

4 Capital 1,890.90 12,798.30 10,417.90 - 25,107.10

5 NatRes 197.20 4,354.90 - - 4,552.10

Total 9,756.90 18,828.20 17,131.60 - 45,716.60

GDPEXP 1 cons 2 inv 3 gov 4 exp 5 imp Total

1 WAEMU 21,778.80 4,583.10 2,988.30 7,960.40 (10,927.30) 26,383.40

2 WAMZ 29,221.90 13,871.20 11,591.50 21,261.20 (24,140.80) 51,805.00

3 RestofWorld 19,280,708.00 6,723,425.00 5,190,154.50 7,117,896.50 (7,112,050.50) 31,200,133.50

Total 19,331,708.80 6,741,879.40 5,204,734.30 7,147,118.10 (7,147,118.60) 31,278,321.90

15



CET Simulation Results




1 Land 846.70 - - - 846.70

2 UnSkLab 5,242.60 2,477.00 3,794.30 - 11,513.90

3 SkLab 42.50 479.00 2,280.80 - 2,802.30

4 Capital 1,214.70 2,226.90 4,533.30 - 7,974.80

5 NatRes 170.30 65.40 - - 235.70

Total 7,516.80 5,248.20 10,608.40 - 23,373.30


1 Land 1,044.40 - - - 1,044.40

2 UnSkLab 6,501.50 1,366.30 3,161.00 - 11,028.80

3 SkLab 52.60 316.10 3,559.80 - 3,928.40

4 Capital 1,883.50 12,844.50 10,419.40 - 25,147.30

5 NatRes 192.90 4,369.10 - - 4,562.00

Total 9,674.90 18,895.90 17,140.20 - 45,710.90


1 WAEMU 22,310.30 4,324.60 3,065.30 7,181.90 (9,786.20) 27,095.90

2 WAMZ 30,167.70 13,862.10 11,971.00 20,073.50 (22,638.70) 53,435.50

3 RestofWorld 19,279,658.00 6,723,858.00 5,189,843.50 7,116,600.50 (7,111,430.50) 31,198,529.50

Total 19,332,135.90 6,742,044.70 5,204,879.70 7,143,855.90 (7,143,855.30) 31,279,060.80


1 Land 747.00 - - - 747.00

2 UnSkLab 5,277.30 2,408.20 3,821.20 - 11,506.70

3 SkLab 41.70 469.10 2,326.50 - 2,837.30

4 Capital 1,183.60 2,184.90 4,638.70 - 8,007.20

5 NatRes 150.30 64.40 - - 214.60

Total 7,399.90 5,126.60 10,786.50 - 23,312.90

16


Appendix B FTA and CET Simulations Results of Ghana and the rest of ECOWAS

Baseline Data

Table B1 Gross Domestic Product (Expenditure Approach)

Table B2 Sources of Factor Incomes (Ghana)

Table B3 Sources of Factor Incomes (Rest of ECOWAS)


1 Land 1,035.30 - - - 1,035.30

2 UnSkLab 6,676.60 1,314.40 3,278.00 - 11,269.00

3 SkLab 53.80 304.40 3,698.40 - 4,056.60

4 Capital 1,886.60 12,444.00 10,937.30 - 25,267.90

5 NatRes 191.20 4,260.40 - - 4,451.60

Total 9,843.40 18,323.20 17,913.70 - 46,080.40


1 RestECOWAS 46,427.00 16,979.80 13,701.00 26,754.20 (31,276.40) 72,585.50

2 Ghana 4,285.70 1,307.20 847.20 2,161.10 (3,300.00) 5,301.20

3 RestofWorld 19,280,962.00 6,723,682.00 5,190,226.50 7,117,914.00 (7,112,252.50) 31,200,532.00

Total 19,331,674.70 6,741,969.00 5,204,774.80 7,146,829.30 (7,146,829.00) 31,278,418.80

EVFA 1 Agric 2 Indus 3 Servs 4 CGDS Total

1 Land 134.60 - - - 134.60

2 UnSkLab 914.80 801.90 765.90 - 2,482.60

3 SkLab 7.40 177.40 425.70 - 610.40

4 Capital 209.20 620.80 456.70 - 1,286.80

5 NatRes 39.50 3.80 - - 43.30

Total 1,305.60 1,603.80 1,648.30 - 4,557.60


1 Land 1,755.70 - - - 1,755.70

2 UnSkLab 10,815.70 2,981.10 6,122.00 - 19,918.70

3 SkLab 87.20 606.80 5,382.90 - 6,077.00

4 Capital 2,873.60 14,360.10 14,436.40 - 31,670.10

5 NatRes 323.10 4,415.20 - - 4,738.30

Total 15,855.30 22,363.20 25,941.40 - 64,159.80

17








1 RestECOWAS 46,576.30 17,081.50 13,746.40 26,987.90 (31,589.10) 72,803.00

2 Ghana 5,308.90 2,635.40 1,061.70 2,086.30 (4,688.20) 6,404.00

3 RestofWorld 19,280,184.00 6,721,932.50 5,190,006.00 7,118,722.00 (7,111,520.50) 31,199,324.00

Total 19,332,069.20 6,741,649.40 5,204,814.00 7,147,796.20 (7,147,797.80) 31,278,531.10


1 Land 153.40 - - - 153.40

2 UnSkLab 968.30 1,078.30 935.30 - 2,982.00

3 SkLab 8.70 231.10 491.90 - 731.70

4 Capital 252.50 804.50 523.00 - 1,580.10

5 NatRes 45.10 4.80 - - 49.90

Total 1,428.10 2,118.80 1,950.30 - 5,497.10


1 Land 1,762.00 - - - 1,762.00

2 UnSkLab 10,833.00 3,000.30 6,151.80 - 19,985.10

3 SkLab 87.60 610.40 5,403.10 - 6,101.00

4 Capital 2,886.00 14,441.70 14,487.30 - 31,815.00

5 NatRes 324.30 4,439.40 - - 4,763.70

Total 15,892.90 22,491.80 26,042.20 - 64,426.80


1 RestECOWAS 47,968.50 16,943.20 14,173.20 24,963.30 (29,057.60) 74,990.70

2 Ghana 5,423.40 2,474.70 1,086.20 2,012.50 (4,452.00) 6,544.70

3 RestofWorld 19,279,290.00 6,722,432.00 5,189,737.00 7,117,462.00 (7,110,931.00) 31,197,990.00

Total 19,332,681.90 6,741,849.90 5,204,996.40 7,144,437.80 (7,144,440.60) 31,279,525.40

18



Appendix C FTA and CET Simulations Results of Nigeria and the rest of ECOWAS

Baseline Data

Table C1 Gross Domestic Product (Expenditure Approach)

Table C2 Sources of Factor Incomes (Nigeria)


1 Land 141.90 - - - 141.90

2 UnSkLab 971.70 1,043.00 953.00 - 2,967.70

3 SkLab 8.60 224.40 504.90 - 738.00

4 Capital 246.90 784.00 539.80 - 1,570.60

5 NatRes 41.70 4.70 - - 46.40

Total 1,410.80 2,056.20 1,997.70 - 5,464.60


1 Land 1,687.50 - - - 1,687.50

2 UnSkLab 11,039.70 2,876.50 6,324.90 - 20,241.10

3 SkLab 88.40 586.70 5,581.20 - 6,256.30

4 Capital 2,863.60 13,950.40 15,091.30 - 31,905.30

5 NatRes 310.60 4,317.10 - - 4,627.70

Total 15,989.80 21,730.80 26,997.40 - 64,718.00


1 RestECOWAS 31,960.00 7,270.70 4,539.60 12,675.60 (19,932.40) 36,513.40

2 Nigeria 18,752.70 11,016.40 10,008.60 16,239.60 (14,644.00) 41,373.40

3 RestofWorld 19,280,962.00 6,723,682.00 5,190,226.50 7,117,914.00 (7,112,252.50) 31,200,532.00

Total 19,331,674.70 6,741,969.00 5,204,774.80 7,146,829.30 (7,146,829.00) 31,278,418.80


1 Land 805.80 - - - 805.80

2 UnSkLab 4,806.90 190.80 1,964.00 - 6,961.70

3 SkLab 38.80 72.70 2,847.00 - 2,958.40

4 Capital 1,480.60 11,534.00 8,911.00 - 21,925.60

5 NatRes 126.10 4,305.60 - - 4,431.70

Total 7,258.20 16,103.10 13,721.90 - 37,083.30

19

Table C3 Sources of Factor Incomes (Rest of ECOWAS)






1 Land 1,084.50 - - - 1,084.50

2 UnSkLab 6,923.60 3,592.10 4,924.00 - 15,439.60

3 SkLab 55.80 711.50 2,961.60 - 3,729.00

4 Capital 1,602.20 3,446.90 5,982.20 - 11,031.20

5 NatRes 236.50 113.40 - - 349.90

Total 9,902.60 7,863.90 13,867.70 - 31,634.20


1 RestECOWAS 33,417.20 7,911.50 4,752.30 13,173.20 (21,216.00) 38,038.30

2 Nigeria 18,613.10 11,056.20 9,944.00 16,535.70 (15,062.30) 41,086.70

3 RestofWorld 19,280,188.00 6,722,519.00 5,189,962.00 7,118,124.50 (7,111,555.50) 31,199,238.00

Total 19,332,218.40 6,741,486.70 5,204,658.30 7,147,833.40 (7,147,833.80) 31,278,363.00


1 Land 782.80 - - - 782.80

2 UnSkLab 4,745.20 192.10 1,974.80 - 6,912.10

3 SkLab 38.60 73.00 2,852.10 - 2,963.70

4 Capital 1,473.70 11,584.80 8,924.30 - 21,982.70

5 NatRes 122.50 4,323.20 - - 4,445.70

Total 7,162.70 16,173.10 13,751.20 - 37,087.00


1 Land 1,113.00 - - - 1,113.00

2 UnSkLab 7,044.40 3,829.70 5,151.40 - 16,025.40

3 SkLab 57.90 754.40 3,069.00 - 3,881.40

4 Capital 1,665.50 3,652.70 6,193.10 - 11,511.30

5 NatRes 242.70 119.80 - - 362.60

Total 10,123.50 8,356.60 14,413.50 - 32,893.60

20





Appendix D FTA and CET Simulations Results of the Rest of WAMZ and the Rest of

ECOWAS

Baseline Data

Table D1 Gross Domestic Product (Expenditure Approach)


1 RestECOWAS 33,930.20 6,724.30 4,831.10 11,405.40 (18,048.00) 38,843.00

2 Nigeria 19,142.10 11,256.10 10,188.10 16,460.50 (14,959.30) 42,087.50

3 RestofWorld 19,279,012.00 6,723,724.50 5,189,690.50 7,116,239.50 (7,111,099.00) 31,197,567.50

Total 19,332,084.30 6,741,705.00 5,204,709.60 7,144,105.40 (7,144,106.30) 31,278,498.00


1 Land 803.20 - - - 803.20

2 UnSkLab 4,897.00 186.90 2,095.20 - 7,179.00

3 SkLab 40.30 70.80 3,006.30 - 3,117.40

4 Capital 1,501.20 11,314.10 9,528.60 - 22,343.80

5 NatRes 125.70 4,252.30 - - 4,378.00

Total 7,367.40 15,824.00 14,630.10 - 37,821.50


1 Land 944.20 - - - 944.20

2 UnSkLab 7,075.70 3,571.40 5,012.80 - 15,659.80

3 SkLab 55.20 714.00 3,063.70 - 3,832.90

4 Capital 1,574.90 3,464.80 6,207.30 - 11,246.90

5 NatRes 206.00 114.30 - - 320.20

Total 9,856.00 7,864.30 14,283.70 - 32,004.10


1 RestECOWAS 44,452.90 16,771.60 13,793.50 26,195.30 (28,566.30) 72,647.00

2 RestWAMZ 6,259.80 1,515.40 754.80 2,720.00 (6,010.20) 5,239.80

3 RestofWorld 19,280,962.00 6,723,682.00 5,190,226.50 7,117,914.00 (7,112,252.50) 31,200,532.00

Total 19,331,674.70 6,741,969.00 5,204,774.80 7,146,829.30 (7,146,829.00) 31,278,418.80

21

Table D2 Sources of Factor Incomes (Rest of WAMZ)

Table D3 Sources of Factor Incomes (Rest of ECOWAS)





1 Land 127.40 - - - 127.40

2 UnSkLab 826.50 367.50 425.70 - 1,619.70

3 SkLab 6.70 64.90 285.30 - 356.90

4 Capital 201.00 643.50 1,050.30 - 1,894.70

5 NatRes 31.50 45.50 - - 77.10

Total 1,193.10 1,121.30 1,761.30 - 4,075.70


1 Land 1,762.90 - - - 1,762.90

2 UnSkLab 10,903.90 3,415.40 6,462.20 - 20,781.60

3 SkLab 87.90 719.30 5,523.30 - 6,330.50

4 Capital 2,881.80 14,337.40 13,842.90 - 31,062.10

5 NatRes 331.10 4,373.50 - - 4,704.60

Total 15,967.70 22,845.70 25,828.40 - 64,641.70


1 RestECOWAS 44,444.70 16,772.50 13,790.70 26,204.10 (28,578.30) 72,633.60

2 RestWAMZ 6,316.50 1,537.30 761.70 2,725.70 (6,059.90) 5,281.20

3 RestofWorld 19,280,940.00 6,723,628.50 5,190,220.50 7,117,940.50 (7,112,231.00) 31,200,498.50

Total 19,331,701.10 6,741,938.20 5,204,772.90 7,146,870.30 (7,146,869.20) 31,278,413.30


1 Land 128.80 - - - 128.80

2 UnSkLab 830.00 370.50 430.20 - 1,630.60

3 SkLab 6.70 65.30 287.70 - 359.70

4 Capital 202.70 647.90 1,059.00 - 1,909.60

5 NatRes 31.90 45.80 - - 77.70

Total 1,200.10 1,129.60 1,776.80 - 4,106.50

22







1 Land 1,763.00 - - - 1,763.00

2 UnSkLab 10,903.20 3,415.60 6,462.10 - 20,780.90

3 SkLab 87.90 719.40 5,523.00 - 6,330.30

4 Capital 2,881.90 14,337.90 13,842.20 - 31,061.90

5 NatRes 331.10 4,373.60 - - 4,704.70

Total 15,967.10 22,846.40 25,827.30 - 64,640.90


1 RestECOWAS 45,417.50 16,777.80 14,099.00 24,933.00 (27,057.30) 74,170.00

2 RestWAMZ 6,223.30 576.40 751.50 2,099.90 (4,320.80) 5,330.30

3 RestofWorld 19,280,444.00 6,724,913.50 5,190,066.00 7,116,332.00 (7,111,987.00) 31,199,768.50

Total 19,332,084.90 6,742,267.60 5,204,916.50 7,143,364.80 (7,143,365.10) 31,279,268.80


1 Land 95.00 - - - 95.00

2 UnSkLab 829.40 283.80 339.30 - 1,452.50

3 SkLab 5.60 52.80 249.10 - 307.40

4 Capital 165.90 525.10 921.10 - 1,612.10

5 NatRes 23.50 37.40 - - 61.00

Total 1,119.40 899.10 1,509.50 - 3,528.00


1 Land 1,714.30 - - - 1,714.30

2 UnSkLab 11,054.80 3,286.50 6,697.70 - 21,039.00

3 SkLab 89.20 692.00 5,722.80 - 6,504.10

4 Capital 2,873.10 13,861.40 14,465.70 - 31,200.10

5 NatRes 322.00 4,257.10 - - 4,579.10

Total 16,053.40 22,097.00 26,886.20 - 65,036.60

23

Appendix E FTA and CET Simulations Results of Côte d’Ivoire and the rest of

ECOWAS

Baseline Data

Table E1 Gross Domestic Product (Expenditure Approach)

Table E2 Sources of Factor Incomes (Côte d’Ivoire)

Table E3 Sources of Factor Incomes (Rest of ECOWAS)




1 RestECOWAS 42,977.00 17,252.70 13,596.10 24,696.50 (31,074.80) 67,447.50

2 CIV 7,613.60 1,128.60 932.60 4,431.50 (3,855.50) 10,250.80

3 RestofWorld 19,280,962.00 6,723,536.00 5,190,228.50 7,118,079.00 (7,112,277.50) 31,200,528.00

Total 19,331,552.50 6,741,917.30 5,204,757.20 7,147,207.00 (7,147,207.80) 31,278,226.30


1 Land 376.50 - - - 376.50

2 UnSkLab 2,318.10 1,015.10 2,000.70 - 5,333.80

3 SkLab 18.70 202.70 1,127.60 - 1,349.00

4 Capital 509.90 543.50 1,322.00 - 2,375.40

5 NatRes 41.90 19.80 - - 61.70

Total 3,265.00 1,781.10 4,450.30 - 9,496.40


1 Land 1,513.80 - - - 1,513.80

2 UnSkLab 9,412.40 2,767.90 4,887.20 - 17,067.50

3 SkLab 75.90 581.50 4,681.00 - 5,338.40

4 Capital 2,573.00 14,437.30 13,571.10 - 30,581.40

5 NatRes 320.80 4,399.10 - - 4,719.90

Total 13,895.90 22,185.90 23,139.40 - 59,221.10


1 RestECOWAS 42,977.60 17,253.90 13,596.30 24,700.10 (31,079.70) 67,448.20

2 CIV 7,765.90 1,213.10 952.40 4,504.20 (3,999.00) 10,436.50

3 RestofWorld 19,281,018.00 6,723,475.50 5,190,245.50 7,118,168.50 (7,112,294.50) 31,200,613.00

Total 19,331,761.40 6,741,942.50 5,204,794.30 7,147,372.70 (7,147,373.20) 31,278,497.70

24







1 Land 413.00 - - - 413.00

2 UnSkLab 2,326.20 1,038.80 2,066.90 - 5,431.90

3 SkLab 19.40 205.60 1,147.30 - 1,372.20

4 Capital 529.00 551.00 1,343.60 - 2,423.60

5 NatRes 46.00 20.10 - - 66.10

Total 3,333.50 1,815.60 4,557.70 - 9,706.90


1 Land 1,513.20 - - - 1,513.20

2 UnSkLab 9,409.40 2,767.50 4,886.60 - 17,063.50

3 SkLab 75.90 581.40 4,680.20 - 5,337.60

4 Capital 2,572.40 14,435.30 13,568.80 - 30,576.50

5 NatRes 320.60 4,398.50 - - 4,719.10

Total 13,891.40 22,182.80 23,135.70 - 59,209.90


1 RestECOWAS 44,359.80 17,153.50 14,044.00 22,935.90 (28,909.10) 69,584.10

2 CIV 7,647.30 756.30 937.80 3,837.00 (2,803.60) 10,374.80

3 RestofWorld 19,280,128.00 6,724,228.00 5,189,975.50 7,116,654.00 (7,111,713.00) 31,199,272.50

Total 19,332,135.10 6,742,137.80 5,204,957.30 7,143,426.90 (7,143,425.70) 31,279,231.30


1 Land 299.00 - - - 299.00

2 UnSkLab 2,272.90 949.60 1,955.00 - 5,177.60

3 SkLab 17.60 191.90 1,124.80 - 1,334.30

4 Capital 474.00 516.20 1,325.90 - 2,316.10

5 NatRes 33.30 18.90 - - 52.20

Total 3,096.80 1,676.60 4,405.60 - 9,179.00

25


Appendix F FTA and CET Simulations Results of Senegal and the rest of ECOWAS

Baseline Data

Table F1 Gross Domestic Product (Expenditure Approach)

Table F2 Sources of Factor Incomes (Senegal)

Table F3 Sources of Factor Incomes (Rest of ECOWAS)


1 Land 1,499.90 - - - 1,499.90

2 UnSkLab 9,659.80 2,655.10 5,034.40 - 17,349.30

3 SkLab 77.30 559.10 4,841.40 - 5,477.80

4 Capital 2,573.80 13,949.50 14,156.30 - 30,679.60

5 NatRes 317.80 4,279.00 - - 4,596.80

Total 14,128.60 21,442.70 24,032.10 - 59,603.50


1 RestECOWAS 46,564.90 17,221.10 14,013.10 27,774.80 (32,332.60) 73,241.40

2 Senegal 4,147.80 1,065.90 535.20 1,140.40 (2,243.90) 4,645.40

3 RestofWorld 19,280,962.00 6,723,682.00 5,190,226.50 7,117,914.00 (7,112,252.50) 31,200,532.00

Total 19,331,674.70 6,741,969.00 5,204,774.80 7,146,829.30 (7,146,829.00) 31,278,418.80


1 Land 86.30 - - - 86.30

2 UnSkLab 579.40 170.70 387.20 - 1,137.40

3 SkLab 4.70 33.80 347.50 - 386.00

4 Capital 145.00 607.20 1,556.30 - 2,308.60

5 NatRes 40.60 6.20 - - 46.80

Total 856.00 818.00 2,291.00 - 3,965.00


1 Land 1,804.00 - - - 1,804.00

2 UnSkLab 11,151.00 3,612.20 6,500.70 - 21,263.90

3 SkLab 89.90 750.40 5,461.20 - 6,301.40

4 Capital 2,937.80 14,373.60 13,336.80 - 30,648.20

5 NatRes 322.10 4,412.80 - - 4,734.90

Total 16,304.80 23,148.90 25,298.70 - 64,752.40

26




Table F6 Sources of Factor Incomes (Rest of ECOWAS)





1 RestECOWAS 46,565.40 17,222.30 14,013.20 27,778.40 (32,337.30) 73,242.00

2 Senegal 4,156.30 1,069.40 536.30 1,144.30 (2,252.30) 4,654.10

3 RestofWorld 19,280,956.00 6,723,673.00 5,190,223.50 7,117,914.00 (7,112,246.50) 31,200,520.00

Total 19,331,677.70 6,741,964.70 5,204,773.00 7,146,836.80 (7,146,836.10) 31,278,416.10


1 Land 86.50 - - - 86.50

2 UnSkLab 580.20 171.30 388.10 - 1,139.60

3 SkLab 4.70 33.90 348.10 - 386.70

4 Capital 145.40 609.20 1,559.10 - 2,313.70

5 NatRes 40.70 6.20 - - 46.90

Total 857.50 820.70 2,295.30 - 3,973.50


1 Land 1,804.10 - - - 1,804.10

2 UnSkLab 11,151.20 3,612.40 6,501.00 - 21,264.60

3 SkLab 89.90 750.40 5,461.30 - 6,301.70

4 Capital 2,937.90 14,374.60 13,337.20 - 30,649.70

5 NatRes 322.10 4,413.10 - - 4,735.20

Total 16,305.20 23,150.50 25,299.50 - 64,755.30


1 RestECOWAS 47,866.60 17,013.60 14,422.30 25,793.70 (29,797.80) 75,298.50

2 Senegal 4,340.10 1,041.40 559.90 978.70 (2,051.90) 4,868.20

3 RestofWorld 19,280,080.00 6,724,082.50 5,189,962.50 7,116,739.00 (7,111,662.50) 31,199,201.50

Total 19,332,286.70 6,742,137.50 5,204,944.70 7,143,511.40 (7,143,512.10) 31,279,368.20

27


Appendix G FTA and CET Simulations Results of the Rest of WAEMU and the rest of

ECOWAS

Baseline Data

Table G1 Gross Domestic Product (Expenditure Approach)

Table G2 Sources of Factor Incomes (Rest of WAEMU)

Table G3 Sources of Factor Incomes (Rest of ECOWAS)


1 Land 86.00 - - - 86.00

2 UnSkLab 600.60 164.70 390.40 - 1,155.70

3 SkLab 4.70 32.90 354.50 - 392.10

4 Capital 145.90 590.80 1,592.30 - 2,329.10

5 NatRes 40.40 6.10 - - 46.50

Total 877.70 794.50 2,337.20 - 4,009.50


1 Land 1,716.90 - - - 1,716.90

2 UnSkLab 11,334.70 3,465.00 6,680.30 - 21,480.00

3 SkLab 90.50 721.80 5,638.80 - 6,451.10

4 Capital 2,907.10 13,892.60 13,886.50 - 30,686.30

5 NatRes 306.60 4,292.00 - - 4,598.60

Total 16,355.70 22,371.40 26,205.70 - 64,932.70


1 RestECOWAS 41,163.00 15,935.60 13,091.80 26,481.30 (29,701.40) 66,970.30

2 RestWAEMU 9,549.70 2,351.40 1,456.50 2,434.00 (4,875.00) 10,916.50

3 RestofWorld 19,280,962.00 6,723,682.00 5,190,226.50 7,117,914.00 (7,112,252.50) 31,200,532.00

Total 19,331,674.70 6,741,969.00 5,204,774.80 7,146,829.30 (7,146,829.00) 31,278,418.80


1 Land 359.70 - - - 359.70

2 UnSkLab 2,284.80 1,237.00 1,344.40 - 4,866.20

3 SkLab 18.40 232.80 775.50 - 1,026.80

4 Capital 537.10 1,031.80 1,596.90 - 3,165.80

5 NatRes 83.00 38.10 - - 121.00

Total 3,282.90 2,539.70 3,716.80 - 9,539.40

28








1 Land 1,530.60 - - - 1,530.60

2 UnSkLab 9,445.70 2,545.90 5,543.50 - 17,535.10

3 SkLab 76.20 551.40 5,033.10 - 5,660.60

4 Capital 2,545.80 13,949.00 13,296.30 - 29,791.00

5 NatRes 279.70 4,380.90 - - 4,660.60

Total 13,877.90 21,427.30 23,872.90 - 59,178.00


1 RestECOWAS 41,120.70 15,967.90 13,083.00 26,610.20 (29,869.80) 66,912.00

2 OtherWAEMU 10,044.10 2,539.20 1,532.80 2,560.60 (5,234.10) 11,442.70

3 RestofWorld 19,280,666.00 6,723,321.00 5,190,145.50 7,117,966.50 (7,112,033.50) 31,200,065.50

Total 19,331,830.80 6,741,828.10 5,204,761.30 7,147,137.40 (7,147,137.50) 31,278,420.20


1 Land 400.90 - - - 400.90

2 UnSkLab 2,376.80 1,304.80 1,413.50 - 5,095.10

3 SkLab 19.50 244.30 808.00 - 1,071.80

4 Capital 570.50 1,082.00 1,661.90 - 3,314.40

5 NatRes 92.50 39.90 - - 132.40

Total 3,460.10 2,671.00 3,883.40 - 10,014.50


1 Land 1,518.20 - - - 1,518.20

2 UnSkLab 9,419.70 2,553.80 5,550.30 - 17,523.70

3 SkLab 76.10 552.80 5,035.80 - 5,664.70

4 Capital 2,543.40 13,984.70 13,300.50 - 29,828.60

5 NatRes 277.40 4,391.10 - - 4,668.50

Total 13,834.80 21,482.40 23,886.60 - 59,203.80

29



Appendix H

Table H Baseline GDP (in $millions) and GDP Growth after FTA and CET

Scenarios


1 RestECOWAS 42,310.10 15,803.90 13,476.00 24,750.20 (27,485.50) 68,854.80

2 OtherWAEMU 10,458.70 2,524.30 1,599.10 2,331.60 (4,996.90) 11,916.80

3 RestofWorld 19,279,694.00 6,723,638.00 5,189,856.00 7,116,796.00 (7,111,394.00) 31,198,590.00

Total 19,332,462.90 6,741,966.20 5,204,931.10 7,143,877.80 (7,143,876.30) 31,279,361.60


1 Land 390.90 - - - 390.90

2 UnSkLab 2,447.70 1,287.50 1,455.30 - 5,190.50

3 SkLab 19.80 242.10 838.10 - 1,100.00

4 Capital 575.50 1,074.00 1,728.40 - 3,377.80

5 NatRes 90.20 39.80 - - 130.00

Total 3,524.00 2,643.40 4,021.80 - 10,189.20


1 Land 1,441.60 - - - 1,441.60

2 UnSkLab 9,572.40 2,447.90 5,705.50 - 17,725.80

3 SkLab 76.60 531.30 5,199.30 - 5,807.20

4 Capital 2,516.20 13,506.50 13,853.20 - 29,876.00

5 NatRes 263.40 4,268.60 - - 4,532.00

Total 13,870.30 20,754.20 24,758.00 - 59,382.50

Country Baseline GDP

FTA CET

CIV 10,250.80 1.81 1.21

Ghana 5,301.20 20.80 23.46

Nigeria 41,373.40 -0.69 1.73

Senegal 4,645.40 0.19 4.80

Cape Verde and Gambia 5,239.80 0.79 1.73

Benin and B. Faso 10,916.50 4.82 9.16

GDP Growth

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