A GTAP model analysis of Ethiopia-COMESA Free Trade ... Union, and European Partnership Agreement....

I

A GTAP model analysis of Ethiopia-COMESA Free Trade Agreement, COMESA

Customs Union, and European Partnership Agreement.

Habtamu Shiferaw Amogne1

Abstract

Common Market for Eastern and Southern Africa (COMESA) is one of the eight “building blocks”

regional economic communities recognized by the African Union (AU) for the establishment of

Continental Free Trade Area (CFTA). This study focuses on analyzing the implication of Ethiopia-

COMESA Free Trade Agreement (Et-COMESA FTA), COMESA Customs Union (CU), and European

Partnership Agreement (EPA) on the economies of COMESA member countries. The study uses standard

Global Trade Analysis Project (GTAP) model version 9 database. The result indicates that with full FTA

among all countries new FTA member countries reported a large expansion in their GDP while Ethiopia

experiences loss in GDP. Nevertheless, compared to full FTA, customs union expands the economies of

most COMESA countries. By contrast, EPA shrinks most COMESA countries GDP with the largest loss

goes to REA, Zimbabwe, and Kenya. This higher loss in GDP comes from a drop in domestic consumption

and investment, although trade balance is positive for most countries. The trade effect on the economy of

most COMESA country is mixed, but some countries reported a growth in their export and import. Et-

COMESA FTA has positive trade effect for most member countries compared to a customs union.

Furthermore, the export and import of most COMESA countries grow with EPA, but with Brexit, the trade

effect is lesser. Finally, COMESA countries reported aggregate welfare gain with FTA while CU and EPA

result in net welfare loss. EPA reduces the well-being of most COMESA countries, but world welfare

improves due to substantial welfare gain from EU-27 and UK. This study concludes that full FTA among

COMESA countries and EPA has significant trade effect for most COMESA countries. However,

regarding welfare, FTA and CU is preferred to EPA.

JEL Classifications: F11, F13, F15

Keywords: Free Trade Agreement, Customs Union, European Partnership Agreement, GTAP, COMESA

1 Corresponding Author: Habtamu Shiferaw Amogne; Economics Department, Kobe University, Address: Apartment 401,

2-22 Ichinosancho, Nada-Ku, Kobe, P.O.Box 657-0012, JAPAN, Tel. 090 - 8366 - 4964, FAX: +81(0)78- 803 - 6877 E-mail:

[email protected].

Acknowledgments: The author acknowledges Kobe University for every cooperation and buying GTAP database. My research

advisor, Professor Taiji. Hagiwara for his extensive comments and suggestion. I thank Prof. Dr. Jong-Hwan Ko for very useful

comments and advice during my presentation at PAPAIOS Conference. Finally, the author acknowledges Reynaldo Senra and

Jane Uzunovski for their valuable comments, and help on the editing of the paper.

mailto:[email protected]

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1. Introduction

Common Market for Eastern and Southern Africa (COMESA) was formed to promote intra-regional trade

among member states with the ultimate objective of attaining sustainable growth & development of the

countries, and realization of the objective of African Economic Integration (COMESA, 2009). It is one of

the eight "building blocks" regional economic communities recognized by the African Union for the

establishment of Continental Free Trade Area (CFTA)2. In 2000, COMESA Free Trade Agreement

(COMESA FTA) was launched, and currently, 16 of the 19 COMESA member countries are the signatory

to FTA while the rest three non-FTA members namely Eritrea, Ethiopia, and Swaziland are negotiating to

sign FTA.3 This FTA provides duty-free, quota-free market access to member States on COMESA

originating products. Furthermore, in 2009 COMESA member countries launched custom union and

agreed to levy Common External Tariff (CET) against non–COMESA member countries. However, the

implementation is still under discussion, and many countries did not adjust their national tariffs according

to CET rate.

There are ongoing debates on the macroeconomic and welfare impacts of regional trade agreements.

According to Baier and Bergstrand (2007), FTA increases member countries bilateral trade. However, on

welfare ground, a customs union is always Pareto-superior to FTA (Krueger, 1997). On the other hand, a

study by Marvel and Karingi (2012), Mureverwi (2016), Yang and Gupta (2007) and Khandelwal (2004)

states that trade agreements in Africa are not sufficient to increase intra-Africa trade, and more

liberalization is needed in Africa focusing on trade facilitation, non-tariff barriers, and strengthening

domestic revenue base. The negotiations of non-FTA member countries mainly Ethiopia is important.

First, Ethiopia is the fourth largest economy in Sub-Saharan Africa (SSA), and the country has an abundant

cheap labor force, a market of over 90 million people, and an enormous underexplored hydrocarbon

potential. However, Ethiopian economy is highly protected compare to other member countries, and tariff

duties range from 0% to 35%, with an average rate of 17%. Second, from geopolitics perspective, Ethiopia

has a significant place in the Horn of Africa (Mesfin, 2012). Furthermore, more integration among

COMESA member through reduction of protection is vital for the ongoing negotiation undertaken among

Africa countries to form Continental Free Trade Agreement (CFTA) and boosting intra-Africa trade.

Therefore, any trade liberalization policy undertaken by COMESA countries through reduction of

protection has large implication for the success of CFTA.

The recent trade negotiation between the European Union (EU) and ACP countries, European

Partnership Agreement (EPA) was initiated to ensure that the ACP-EU trade relation was compatible with

WTO rules (Article XXIV). EPA provides a duty-free and quota-free market access to the EU with

2 These eight are namely: AMU, CEN-SAD, COMESA, EAC, ECCAS, ECOWAS, IGAD, and SADC.

3 The COMESA FTA member countries are Burundi, Comoros, Djibouti, D.R. Congo, Egypt, Kenya, Libya, Madagascar,

Malawi, Mauritius, Rwanda, Seychelles, Sudan, Uganda, Zambia, and Zimbabwe.

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improved rules of origin for ACP countries that have signed WTO-compatible agreements. Recently, nine

COMESA member countries have signed interim EPA agreement while the rest are still negotiating.4

Recently, there are an increasing number of studies analyzing the effects of EPA between ACP and

EU. Many African policy-makers, business representatives, and NGOs argue that the EPA agenda is too

broad and intrusive for African countries. Besides, with EPA bigger EU companies could flood the

continent with cheaper products, destroying emerging local industries. Also, cutting tariffs will lower

government revenues that Africa needs to invest in areas including agriculture, health, and education.

According to Vollmer et al. (2009), the impact of EPA differs from country to country depending on trade

relation between states and initial protection levied on import from EU. Also, full reciprocity with EU is

very costly for Africa (S. Karingi et al., 2006). Furthermore, EPA results in budgetary difficulties as a

consequence of the loss of trade tax revenue (Bilal and Roza, 2007). Therefore, it is very important to

analyze the economic and welfare impact of EPA on the economies of EPA signatory COMESA member

countries and EU. However, the United Kingdom (UK) decided to leave EU, Brexit. Therefore,

considering this situation the EPA negotiation when UK is a member of EU and the case when there is

Brexit will have different economic and welfare impact. As a result, this study analyzes the impact of EPA

on cases when UK is a member of EU and when there is Brexit.

This paper has three main objectives. First, to analyze the impact of Et-COMESA FTA with both partial

liberalization and full liberalization among all COMESA countries. Second, to estimate the effect of

implementing CU in an operational FTA on the economies of COMESA countries. Lastly, assessing the

impact of potential FTA between COMESA countries and EU under EPA scheme. The last objective

considers the situation where UK is a member of the EU and the situation after Brexit. The study mainly

focuses on macroeconomics, trade, welfare and output effects of the liberalization policy. To achieve this

objective the study uses (GTAP) model (Hertel and Thomas .W., 1997) (Version 9). Also, the baseline

GTAP database is adjusted by including tariff changes made among COMESA countries after 2011.

The rest of the paper is organized as follows: Section 2 presents an overview of COMESA economies.

Section 3 presents the relevant empirical literature. Section 4 explains the model database and model

simulations. Section 5 analyses the simulation results under different scenarios. Section 6 provides the

systematic sensitivity analysis of the model result. Finally, Section 7 concludes.

2. COMESA Economy: A descriptive exposure

2.1. Economic character of COMESA

Demographic changes across countries influence the level and composition of trade both through their

impact on comparative advantage and on patterns of demand. As can be seen in Table 1, Ethiopia, Egypt,

and D.R.Congo are most populated countries while Seychelles, Djibouti, and Comoros have a subtle

4 Out of 19 COMESA members, 17 are eligible for EPA agreement. Out of 17 countries, eight countries namely Comoros,

Djibouti, Eritrea, Ethiopia, Malawi, Sudan, Zimbabwe, and D.R. Congo are negotiating to sign EPA, while the rest countries,

Burundi, Kenya, Mauritius, Madagascar, Rwanda, Seychelles, Uganda, Zimbabwe, and Swaziland already signed interim EPA.

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number of population. Besides, Comoros, Seychelles, Djibouti and Mauritius have small arable land while

Sudan and Ethiopia have largest arable land hectares among COMESA member countries. Further, as

shown in the last column of Table 1, most countries with abundant arable land have significant agriculture

sector value added compare to industry and service sector.

Table 1 further reports the relative size of economies of COMESA member countries using GDP. PPP-

based GDP data in Table 1 shows that Egypt, Sudan, Ethiopia, and Kenya are the four largest economies

among COMESA member countries. In addition, The GDP per capital of COMESA member countries

varies widely and ranges from US$711.52 in D.R.Congo to US$ 25172.44 in Seychelles in 2014. The

largest GDP per capital for Seychelles, Mauritius, and Libya signals the growth in the economy of these

countries and tend to reflect an increase in productivity. Furthermore, Table 1 reports the trade-to-GDP-

ratio measured by the sum of exports and imports divided by GDP. This indicator measures a country's

'openness' or 'integration' in the world economy. Some COMESA countries reported the significant trade

to GDP ratio in 2014 (indicating an increasing openness). Trade constitutes 181.29 % of Seychellois's

GDP, 114.57% of Mauritius's GDP, and 147.58 % of Libya's GDP. In contrast, Sudan, Egypt, Ethiopia,

Burundi, Rwanda, and Uganda have a relatively small trade to GDP ratio of below 50% suggesting plenty

of rooms increase openness.

Table 1. Economic character of COMESA

Country Arable Land

(in Thousand

Hectares)

Population

(in

millions

,2014)

GDP

(in millions

US$,

2014)

Per Capital

GDP (in

US$

,2014)

Trade, (as %

of GDP)

(2014)

Average Value added

( as % of GDP)

(2010 - 2014)

Agri. Ind. Svces

Burundi 1200 10.8 7944.82 734.48 41.31 40.09 17.31 42.60

Comoros 65 0.8 1049.93 1363.56 79.92 38.24 11.57 50.19

D.R.Congo 7100 74.9 53238.84 711.52 80.06 22.76 33.71 43.53

Djibouti 2 0.9 2733.70 3120.04 n.a n.a n.a n.a

Egypt 2738 89.6 900147.80 10045.78 37.41 12.34 38.37 49.29

Eritrea 690 5.1 n.a n.a n.a n.a n.a n.a

Ethiopia 15119 97 138728.89 1430.8 40.74 44.84 11.50 43.66

Kenya 5800 44.9 126449.16 2818.26 51.12 29.18 20.36 50.46

Libya 1720 6.3 93133.61 14879.99 147.58 n.a n.a n.a

Madagascar 3500 23.6 32308.91 1373.19 69.38 27.24 16.24 56.52

Malawi 3800 16.7 18611.30 783.83 73.40 31.07 16.20 52.74

Mauritius 75 1.3 22365.09 17730.90 114.57 3.40 25.11 71.49

Rwanda 1182.5 11.3 17975.00 1584.21 46.17 32.96 14.18 52.83

Seychelles 0.08 0.1 2303.93 25172.44 181.29 2.24 13.23 68.59

Sudan 17220 39.4 152767.42 3882.25 19.12 27.44 23.90 48.66

Swaziland 175 1.3 10039.74 7910.84 n.a 6.75 45.49 47.76

Uganda 6900 37.8 63831.94 1689.44 46.83 26.28 19.80 53.92

Zambia 3700 15.7 56946.17 3724.53 n.a 9.96 35.28 54.77

Zimbabwe 4000 15.2 26057.36 1709.14 79.56 13.38 31.12 55.50

(Note) n.a. = not available; GDP per capital is PPP in 2014 (constant 2011, international $).

(Source) World Development Indicators (latest update, November 17, 2016)

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The breakdown of average value added by activity has changed considerably across COMESA member

countries over the period 2010-2014. Agriculture is a dominant sector with agriculture value added

constituting more than 40% for Ethiopia and Burundi. On the other hand, industry sectors account for

more than a quarter of GDP for Swaziland, Egypt, D.R. Congo, Zambia, Zimbabwe, and Mauritius. The

value added of the service sector is greater than agriculture and industry sector for most COMESA member

countries except Ethiopia. In general, Table 1 shows that the economic characteristics of COMESA

member countries are diverse and more trade liberalization in these countries would have a varied effect.

2.2. Trade and protection pattern

Table 2 below reports the import share of Ethiopia, COMESA, EU-27, and UK for GTAP aggregated

sectors to reflect the existing trade relations among negotiating regions. On the other hand, Table 3 reports

estimated average bilateral import tariffs levied by Ethiopia, COMESA, EU-27 and UK on one another's

export. The primary source of data for both import and average bilateral tariff is GTAP 9 database, the

base year 2011. Therefore, trade liberalizations among COMESA countries mainly FTA members after

2011 is not included in version nine database, and these tariff cuts are included as part of the baseline

scenarios explained in Section 4.3.

As can be seen from Table 2 below, Ethiopia's import of Petroleum & Chemical, food manufacturing,

and service from COMESA member countries is high, but Petroleum & Chemical face a low average tariff.

On the other hand, Beverage & Tobacco, Leather, Other Manufacturing, Forestry & Fishery, and

Vegetable & fruit import by Ethiopia from COMESA countries face high average tariff but constitutes

small import share. Therefore, a substantial expansion of imports by Ethiopia from COMESA member

countries are expected on highly protected sectors following Et-COMESA FTA. Furthermore, COMESA

countries have significant import share of vegetable & fruit, livestock, and other crops from Ethiopia, but

face very low average tariff from Ethiopia. However, COMESA countries have low import share of Motor

vehicle part, Forestry and Fishery, Beverage & Tobacco, Other Manufacturing, Wood Paper, and

Petroleum Chemical from Ethiopia, but face a high tariff. Therefore, significant improvement in import

by COMESA countries is expected in these sectors. The overall Ethiopia-COMESA trade share shows

that COMESA countries mainly import agricultural products from Ethiopia while Ethiopia imports mainly

petroleum and chemical products. Besides, the average import tariff levied by COMESA member

countries from Ethiopia is small compared to Ethiopia's average import tariff on goods originated from

COMESA member countries.

Table 2 and three further reports the import share between COMESA and EU-27 region. Accordingly,

Fabric Metal Equipment, service and petroleum & chemical sector constitute more than 60% of

COMESA's import from EU-27. However, these sectors are moderately protected by COMESA member

countries. On the other hand, Beverage & Tobacco, Food manufacture and other manufacturing, which

have small import share from EU-27 face very high average tariff. Therefore, a massive expansion of

imports from EU-27 to COMESA countries on Beverage & Tobacco, Food manufacture, and other

manufacturing sectors is expected due to EPA. As can be seen from Table 2, Fabric Metal Equipment,

service and petroleum & chemical industries constitute more than 70% of COMESA's imports from the

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UK while the share of other sectors is subtle, and face a reasonable average tariff. On the other hand,

beverage & tobacco, Food manufacturing, leather and other manufacturing sectors constitute a small share

of import by COMESA countries from the UK but face a high average tariff. Therefore, with Brexit, small

expansion of imports from the UK is expected on beverage & tobacco, food manufacturing, leather and

other manufacturing sectors compare to the case when UK is a member of EU.

Table 2. Composition of imports by source (Percentage Share of total import, 2011)

Commodity

Ethiopia

import

share from

COMESA

COMESA

import

share from

Ethiopia

COMESA

import

share from

EU-27

EU-27

import

share from

COMESA

COMESA

import

share from

UK

UK

import

share from

COMESA

Grains 0.00 0.87 5.27 0.07 0.20 0.19

Vegetable & Fruit 0.02 51.75 0.64 1.02 1.62 4.95

Oilseed 0.00 2.83 0.01 0.07 0.00 0.07

Other crops 0.21 10.29 0.33 2.86 0.01 7.08

Livestock 0.03 25.52 0.39 0.09 0.10 0.12

Forestry & Fishery 0.02 0.06 0.05 0.23 0.04 0.24

Coal, Oil, and Gas 0.53 0.00 0.50 56.94 0.29 21.87

Food manufacturing 4.18 1.22 6.30 2.69 2.04 5.63

Beverage & Tobacco 0.17 0.04 1.33 0.20 0.67 0.20

Textile & Apparel 0.84 0.09 1.20 2.85 0.72 9.27

Leather 0.11 0.30 0.18 0.44 0.12 0.30

Wood paper 1.80 0.09 4.69 0.35 2.04 1.83

Petroleum & Chemicals 81.65 1.60 18.94 12.39 10.75 10.99

Basic metals 2.70 0.01 6.62 2.39 3.25 0.34

Fabric metal equipment 3.19 1.56 25.44 1.13 30.28 3.70

Motor vehicle part 0.25 0.24 5.57 0.23 4.96 0.29

Other manufacturing 0.86 0.14 0.62 0.16 1.00 0.50

Services 3.41 3.35 21.93 15.87 41.92 32.42

Total import

(US$ million)

441.59 542.56 78702.74

80961.01

8929.8

7398.27

(Source) GTAP database version 9

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Table 3. Bilateral Tariff (Average Ad valorem tariff, 2011)

Commodity

Ethiopia

average tariff

on import from

COMESA

COMESA

average tariff

on import

from Ethiopia

COMESA

average tariff

on import

from EU-27

COMESA

average tariff

on import

from the UK

Grains 2.25 0.30 4.38 8.04

Vegetable & Fruit 23.44 1.03 11.27 1.56

Oilseed 0.00 2.63 6.45 1.60

Other crops 10.32 1.13 6.14 8.99

Livestock 3.28 0.15 2.27 4.21

Forestry & Fishery 25.93 12.04 3.07 5.80

Coal, Oil, and Gas 5.05 1.86 2.26 2.14

Food manufacturing 15.24 6.94 20.01 27.68

Beverage & Tobacco 31.03 18.45 83.38 235.25

Textile & Apparel 20.39 5.68 11.67 13.63

Leather 30.21 8.29 15.00 25.10

Wood paper 13.73 11.37 6.39 8.62

Petroleum & Chemicals 6.61 10.10 11.42 7.32

Basic metals 8.75 5.86 2.58 4.43

Fabric metal equipment 16.49 8.71 7.78 5.25

Motor vehicle part 15.38 19.45 11.83 14.75

Other manufacturing 26.76 14.34 14.50 23.21

Services 0.00 0.00 0.00 0.00

(Source) GTAP database version 9

3. Literature Review

The proliferation of regional trade blocks in Africa have appealed interest among academics and policy

makers in Africa. Many studies have been done to analyze the effect of trade liberalization in COMESA

and other regional trade agreements in Africa. However, the policy scenarios of trade liberalization

measures, the period of assessment and the structures of the model employed vary among these studies.

Some studies use a gravity model to analyze the trade liberalization effect while others use partial or

general equilibrium models.

Musila (2005) using gravity model examine the intensity of trade creation and trade diversion in

COMESA, ECOWAS, and ECCAS. The result indicates that the strength of trade creation is higher for

ECOWAS followed by COMESA while the trade diversion effect is weak in all regional trade agreements.

Furthermore, the result re-enforces the idea that size factors (level of GNP and population), and resistance

factors (distance and language) play an important role in the determination of flow of international trade.

Similarly, Conroy (2013) analyze the impact of trade creation and trade diversion on COMESA FTA and

MERCOSUR FTA using gravity equation. The study points out that both trade agreements have

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significant trade creating effects while MERCOSUR FTA will have a modest level of trade diversion.

Also, trade created in the COMESA FTA occurred largely in sectors in which countries have different

comparative advantages, indicating that new links are efficient.

Many studies like Balistreri et.al (2015), Karingi and Fekadu (2009), Willenbockel (2013), and

Makochekanwa (2014) use CGE modeling to investigated the impact of Tripartite Free Trade Area

(TFTA) on the economies of member states. The study by Balistreri et.al (2015) analyzes trade cost as the

primary trade barrier in (TFTA)5. The study found that deep integration among the three regions would

produce substantial gain, but the estimated gain vary across countries and depends on the trade reform.

On the other hand, Karingi and Fekadu (2009) and Willenbockel (2013) using GTAP model analyzes the

economic and welfare impact of forming TFTA. The research found an overall benefit from establishing

TFTA, but the regional level impact is unbalanced due to the difference in initial protection structure.

Similarly, Makochekanwa (2014) using WITS-SMART model analyzes the welfare implication of TFTA

and found that there is potential net trade gain, but the implementation of TFTA will lead to loss of tariff

revenues, which contribute to a significant proportion of fiscal resources for most countries.

Karingi et al.(2002) analyze the impact of implementing COMESA FTA and then forming the

Customs Union on the economies of member countries using GTAP model. The study found that

COMESA is better off with free trade. However, there is unbalanced benefit across member countries.

Besides, FTA gives good outcomes but the customs union must be preferred, and the member countries

benefit regarding real incomes and reduction of poverty from the customs union. Also, Dimaranan and

Mevel (2008) using similar methodology examine the likely impacts of COMESA customs union and

found that custom union results in expansion of trade but most COMESA countries report negative real

income. These differences across countries are due to the heterogeneity of the COMESA economies

regarding their economic structure, trade and protection patterns. Similarly, Sawkut and Boopen (2010),

found that the global welfare increases with COMESA customs union and COMESA countries benefited

from the increase, although not to the same degree from forming the Customs Union. In contrast, Nzuma

et al.(2009) found that the proposed COMESA customs union will not be beneficial to a majority of the

member countries. Besides, to benefit more from the Customs Union more liberalization is needed in the

area of harmonization of customs procedures, non-tariff barriers, infrastructural improvements,

diversification of production, and measures to include more cross-border transactions with recorded

(formal) trade among others.

There are currently few studies on the potential macroeconomic and welfare impact of EPA between

COMESA countries and EU. A study by S. Karingi et al.(2006) examines the impact of EPA, between

COMESA countries and EU on multilateral trade development using both general and partial equilibrium

models. The general equilibrium result indicates that with full reciprocity trade relations and general

welfare would register positive trends. However, these benefits can only be realized, at the cost of

significant and extensive macroeconomic adjustments. Furthermore, the partial equilibrium result shows

5 TFTA is a free trade agreement among COMESA, East African Community (EAC) and Southern African Development

Community (SADC).

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that COMESA countries undergo a customs revenue loss on EU import and a certain level of trade

diversion from their trade partners and other COMESA member countries. Besides, Vollmer et al.(2009)

analyze the impact of EU-ACP European Partnership Agreement for Sub-Saharan Africa(SSA). The

research found that some SSA countries like Botswana, Cameroon, Mozambique, and Namibia would

significantly benefit from the interim EPA agreements, while the trade effects for Côte d'Ivoire, Ghana,

Kenya, Tanzania, and Uganda are close to zero.

4. Methodology

The study employed a multi-country, multi-sector general equilibrium modeling approach. (WTO, 2012)

States that a general equilibrium analysis explicitly accounts for all the links between the sectors of an

economy – households, firms, governments, and countries. It imposes a set of constraints on these sectors

so that expenditures do not exceed income, and income, in turn, is determined by what the factors of

production earn. These constraints establish a direct link between what the factors of production earn and

what households can spend. The WTO document further states that the purpose of Computable General

Equilibrium (CGE) simulations is to determine the effects of a change in trade policy on the endogenous

variables of the model – prices, production, consumption, exports, imports, and welfare. The simulation

represents what the economy would look like if the policy change or shock had occurred. The difference

in the values of the endogenous variables in the baseline and the simulation represents the effect of the

policy change. Therefore, the model should be able to predict the effect on macroeconomic, trade, welfare

and production patterns if the trade policy was changed. Furthermore, based on the change in welfare, the

policy-maker would be able to judge whether the country benefited from the change in policy or not.

The study uses the global economy-wide model known as GTAP model (Hertel, Thomas .W., 1997).

The study uses the static GTAP model with standard macroeconomic closure to analyze the potential

impact of regional integration on COMESA regions. The standard features of the GTAP model are perfect

competition, Constant return to scale, Armington assumption in Trade flows, disaggregated import usage

by activity, non-homothetic consumer demands and explicit modeling of international trade and

investment. GTAP model has the advantage of overcoming the effects of policy changes, at national,

bilateral or multilateral levels, on production levels, input factors, volumes of trade and other induced

influences on welfare. Furthermore, GTAP model is centered on the reallocation of resources between the

sectors of the economy; it is an appropriate instrument for identifying the sectors and countries, which

gain or which lose with the change of policy induced by trade liberalization policy. The data used in this

study is the version 9 of the GTAP database (Aguiar, Narayanan, and McDougall, 2016). The reference

year for the database is 2011.

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4.1. Regional and sectoral Aggregation

The GTAP-9 database features 140 countries/regions and 57 tradeable commodities. In this study, the 140

countries/regions are mapped into 17 regions, and the 57 sectors are mapped into 18 sectors (Appendix I

& II respectively). The GTAP 9 database identifies only 10 of the 19 COMESA member countries as a

separate region while the other nine COMESA countries are aggregated into four GTAP composite

regions. As these four GTAP composite regions are almost exclusively composed of COMESA countries,

the regional aggregation structure of the database supports almost perfect analytical separation of

COMESA and Non-COMESA regions6. In addition, the aggregation allows a quite detailed analysis of

changes in intra - COMESA trade flows, which takes explicit account of the bilateral trade flows among

COMESA regions and their trade with the rest of the world. Furthermore, the regional aggregation

includes three Non-COMESA regions, EU-27, UK, and ROW. In this study, UK is included as a separate

region from EU to analyze the impact of Brexit on EPA negotiation.

4.2. Common External Tariff and Sensitive Product

The COMESA customs union was established in 2009, and the member countries agreed to impose CET

on non-member countries. The agreed-upon CET rates have four categories of commodities provided by

product line in the Common Tariff Nomenclature (CTN). Accordingly, the CTN adopts a four-band

classification where scheduled CET rates are 0% for raw material and Capital goods, 10% for intermediate

goods and 25% for finished products.

In the creation of a customs union, some of the initial tariff rates are higher than the recommended

CET rate while in other cases they had to be raised to bring them to the CET rate. As a result, a customs

union may reduce or increase protection. Therefore, the design of tariff changes from original levels to

the CET rates for customs union scenario is calculated as follow. First, COMESA CTN, specified at the

HS6 2007 classification is mapped to MAcMap-HS6 v.3 database. Second, I compute the net tax saving

for each aggregated sector at HS code classification.7Third, the weighted average tax rate is calculated for

the corresponding GTAP sectors. Finally, the weighted average tax rate is used as a shock value for custom

union experiment. Accordingly, a negative weighted average shows an increase in protection while a

positive value reflects a decrease in tariff protection. (Appendixes III)

Under most trade agreements, member countries specify a list of sensitive products that are excluded

from the sectors that will be liberalized. Countries often argue for eliminating key products from

liberalization for reasons of national interests such as tariff revenue considerations, infant industries

6. The GTAP composition of REA includes Somalia and Mayotte besides to COMESA member countries; RSAC includes

Lesotho besides to Swaziland; RSCA include Angola besides to D.R. Congo, and RNA includes Algeria and Western Sahara

besides to Libya.

7 Net tax saving is the difference between actual MFN tax rate of COMESA countries and COMESA CET bound rate at HS6

2007 product classification.

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argument, health issues, and a political and cultural importance of the sector. The framework of the

COMESA customs union also allows for exclusions of sensitive products. However, at the time of writing

this paper, only eleven countries submit a list of sensitive products. In addition, three of the eleven

countries namely, Burundi, Rwanda, and Uganda, agreed to use a similar list of sensitive products with

Kenya for the customs union.8 Therefore, for the above COMESA countries, this study excludes the

submitted list of sensitive products from CET calculation. However, for Ethiopia, Egypt, Zambia,

Zimbabwe, REA, RNA and RSCA, I select sensitive products using import revenue criteria and exclude

top 5% of goods from CET calculation.

4.3. Experiment Design

This study begins with GTAP 9 database with the base year 2011, aggregated to the set of regions and

sectors specified in section 4.1 above. A baseline scenario is created in this paper by updating the tariff

component of the database. The new benchmark contains information on the policy changes, which

includes mainly the reductions of duty among COMESA countries mainly from Uganda and D.R.Congo.

Therefore, the baseline tariff is adjusted before simulation, but the results are not interpreted in this paper.

The primary purpose of including this policy change is to develop realistic and actual policy scenarios for

the free trade experiment. Furthermore, the customs union and EPA scenarios consider sensitive products

provided by COMESA countries and are thus exempted from the CET. This paper has five different

scenarios regarding tariff reduction between the various regions. Under each scenario, tariff among

members of regional integration (FTA, CU or EPA) is removed but maintained for other regions.

Scenario 1: Et-COMESA FTA (FTA-17). All bilateral ad valorem import tariffs between Ethiopia

and COMESA FTA countries are removed. In this scenario, Eritrea and Swaziland are not the

members of COMESA FTA. The main purpose of this scenario is to analyze the separate impact

of Ethiopia’s accession to COMESA FTA on the economies of COMESA member countries.

Scenario 2: Full COMESA FTA (FTA-19). A complete removal of Ad valorem import tariff among

all COMESA member countries. As Eretria and Swaziland are negotiating to join COMESA FTA,

this scenario provides an estimate of the consequence of extending FTA to all COMESA countries.

Scenario 3: COMESA Customs Union (COMESA CU). There is full FTA among all COMESA

countries whereas CET rate is levied against all non-COMESA countries. This scenario provides

an estimate of the consequence of implementing the Customs Union by all member countries.

8Refer 2011 Gazette, Volume 16 Annex 1: list of sensitive product for Kenya, Madagascar, Malawi, Mauritius, and Swaziland.

http://www.comesa.int/wp-content/uploads/2016/06/2011Gazette-Vol.-16-Annex-II-ist-of-sensitive-products.pdf

http://www.comesa.int/wp-content/uploads/2016/06/2011Gazette-Vol.-16-Annex-II-ist-of-sensitive-products.pdf

12

Scenario 4: COMESA FTA with EU (EPA). There is a complete removal of ad Valorem import

tariff among all COMESA countries and between EU-28 and COMESA countries. However, CET

rate is maintained on imports from ROW.9 In this scenario, UK is a member of EU.

Scenario 5: COMESA FTA with EU after Brexit (EPA+Brexit). This scenario is similar to scenario

four, but UK is not a member of EU. Therefore, CET rate is maintained on imports from UK and

ROW whereas FTA is maintained among all COMESA and between COMESA and EU-27.

Table 4 Experiment Design

Scenarios Integration

FTA-17 COMESA-16 FTA + Ethiopia

FTA-19 FTA-17 + Eritrea (REA) + Swaziland (RSAC)

COMESA CU FTA-19 + CU

EPA FTA-19 + CU + EPA (EU-28)

EPA+Brexit FTA-19 + CU + EPA (EU-27)

Source: Authors’ scenario design.

5. Result and Discussion

All scenario’s results are designed as a variation of the baseline scenario. The analyses are comparative

static; hence, they do not address potentially critical questions relating to the sequencing of reforms and

potential dynamic benefits from trade liberalization. When bilateral tariffs are eliminated, relative prices

change, and in response, trade flows between countries change, which eventually affect the resource

allocations in the economy. It is expected that different sectors in the economy adjust their outputs

according to relative price shifts. (Narayanan and Sharma, 2016) States that when an importer reduces

tariffs on its partners, the degree of increase or decrease of imports from each of them would depend on

two opposite effects. First, trade creation effect enabled by overall expansion in demand for cheaper

imports. Second, trade diversion effect created by the expansion of exports by partners facing higher tariff

reduction at the cost of others accomplished in terms of a response to price differentials. The following

section explains the macroeconomic, welfare, and industry output impact of the different scenarios

described above.

9 In this scenario, Egypt and Libya (RNA) are excluded since they are not a beneficiary of EPA.

13

5.1. COMESA FTA

1. Macroeconomic effect

Table 5 indicates the macroeconomic impact of COMESA FTA under scenario one and two. When

Ethiopia joins COMESA FTA, Kenya experiences the largest growth in GDP (0.27%) whereas Ethiopia’s

economy contract by 0.23%. However, the impact on other COMESA member countries is insignificant.

Table 5 further reports that with full FTA among all COMESA countries (FTA-19), most COMESA

member countries experience growth in their GDP compared to FTA-17. RSAC records the largest growth

in GDP (3.99%) whereas Zimbabwe and Ethiopia reported contraction of the economy by 0.75% and

0.14% respectively. The main reason for the decline in the growth of GDP for Ethiopia is the reduction in

the consumption of domestic commodities and investment, although there is an expansion of the trade

balance in scenario one. This decline in domestic consumption is due to an increase in both export and

import in both scenario one and two. Thus, there is a potential for change in the production and

consumption structure; more of production is exported than in the base case, and more of consumption is

imported from Ethiopia.

Removal of import tariff among COMESA is expected to result in a significant improvement in the

level and direction of trade among member countries by reducing the domestic market price of import.

The reduction of import price results in a rise in demand for import by firms for intermediate goods,

private households as well government for consumption. Table 5 reveals that most COMESA countries

do not benefit from the import and export surge. Kenya and Ethiopia reported the largest increase in export

and import with FTA-17 whereas the impact on other COMESA member countries is tiny. However, when

the remaining COMESA member countries, Eritrea (REA) and Swaziland (RSAC), join COMESA FTA

(FTA -19) the export and import of most COMESA member countries show improvement. As expected,

new FTA member countries, Ethiopia, REA and RSAC witness large import growth when they eliminate

tariff. The availability of cheap import reduces domestic production cost and increase competitiveness in

these countries, resulting in increased export. However, the extent of the rise in both export and import

depends on the relative change in price in different sectors driven by tariff reduction. As a result, there is

a slight difference in the growth of export and import among COMESA countries.

Trade balance of most COMESA countries deteriorates with FTA. Kenya, Egypt reported significant

trade balance deficit in scenario one whereas RSAC and Mauritius reported massive deficit with scenario

two. In contrast, Ethiopia records large trade balance surplus (US$9.24 million) in scenario one but

worsens with scenario two.

14

Table 5. Macroeconomic impact of COMESA FTA scenarios.

Region

Import value (%) Export value (%) GDP (%) Change in Trade Balance

(US$ Million)

FTA-17 FTA-19 FTA-17 FTA-19 FTA-17 FTA-19 FTA-17 FTA-19

Ethiopia 0.22 0.5 0.68 0.95 -0.23 -0.14 9.24 -5.95

Egypt 0.04 0.07 0.04 0.06 0.03 0.05 -12.11 -19.59

Kenya 0.26 0.39 0.15 0.43 0.27 0.21 -29.19 -25.86

Malawi 0.01 0.28 0 0.06 0.01 0.17 -0.24 -5.44

Madagascar 0 -0.01 0 0 0 0 0 0.1

Mauritius 0 0.57 0 0.21 0 0.54 -0.04 -28.64

Rwanda 0.01 0.14 0.01 0.13 0.01 0.06 -0.03 -0.46

Uganda 0.01 0.17 0.01 0.15 0.01 0.1 -0.12 -1.64

Zambia 0 0.01 0 0 0 -0.02 0.02 -1.11

Zimbabwe 0 1.31 0.01 3.76 0 -0.75 0.09 25.4

REA -0.01 0.73 -0.01 0.72 -0.01 0.09 0.28 -16.53

RNA 0 0 0 0 0 0 0.01 -0.22

RSCA 0 0.01 0 0 0 0 -0.16 -0.25

RSAC 0 7.67 0 3.62 0 3.99 0 -46.51

EU-27 0 0 0 0 0 0 6.76 23.08

UK 0 0 0 0 0 0 0.83 0.36

ROW 0 0 0 0 0 0 24.67 103.27

(Source) Model simulation

15

Table 6 Change in GDP Components for New COMESA FTA Countries/regions (in US$ Million)

GDP

Component

Consumption

Investment

Government

Export

(-)Import

Total

Ethiopia

FTA-17 -57.7 -16.42 -6.34 28.88 -19.54 -71.11

FTA-19 -37.59 1.46 -3.54 39.81 -45.87 -45.72

REA

FTA-17 -2.95 -0.91 -0.59 -1.82 2.09 -4.17

FTA-19 47.1 30.1 12.11 143.8 -160.89 72.22

RSAC

FTA-17 0 0 0 0 0 0.01

FTA-19 170.51 97.37 43.05 107.62 -154.12 264.44


2. Welfare effect

In GTAP model, the welfare changes are measured by equivalent variation (EV). This change is the

amount of money consumer in any region would pay rather than face the changes in prices and quantities

resulting from the simulations. The net welfare impact of tariff reduction depends on the relative sizes of

trade creation, and trade diversion effects. Trade creation arises when more efficiently produced imported

goods replace the relatively inefficiently produced domestic products by increasing import demand. On

the other hand, trade diversion occurs when the sources of supply divert from the more efficiently

producing non-member countries to the less efficiently producing member countries under the tariff-free

access granted to signatory countries.

Table 7 reported that Kenya and Egypt experience substantial welfare gain under scenario one. In

contrast, Ethiopia suffers considerable welfare loss equivalent to US$ 13 million. The welfare effect for

other COMESA member countries from FTA-17 is small, but the overall result shows that aggregate

COMESA and world welfare improves by US$39.5 million and US$3.41 million respectively. On the

other hand, when Eritrea and Swaziland join the FTA (FTA-19), most COMESA member countries enjoy

substantial welfare gain. With full FTA among all COMESA, the largest welfare benefit goes to

Zimbabwe, RSAC, REA, Egypt and Mauritius. In contrast, Ethiopia and to a lesser extent Madagascar

and Zambia suffers moderate welfare loss. Table 7 further reports that, with full FTA, the aggregate

welfare for COMESA and world improve by US$328 million and US$250 million respectively.

Considering the non-COMESA regions, the study found that all regions experience a loss in welfare and

the loss increases with FTA-19 compared to FTA -17.

The welfare effect in GTAP model can be decomposed into allocative efficiency, Terms of Trade and

Investment-Savings effect. Allocative efficiency is the measured change in the ability to efficiently

allocate resource across sectors in the economy. Mathematically, this is just a collection of variations in

the tax revenue of a regional household, which represent the government of a country in the real world

(Narayanan and Sharma, 2016). Table 7 shows that most COMESA member states reported positive

16

allocative efficiency in both scenario 1&2. In contrast, Zimbabwe, REA, and RSCA get welfare loss in

scenario 1 and RNA in scenario 2. For most COMESA countries the major gain in welfare comes from

positive allocative efficiency (e.g., Kenya and Egypt in scenario 1; Zimbabwe, Kenya, and REA in

scenario 2).

Table 7. Welfare Decomposition for COMESA FTA (US$ million)

Regions

FTA-17 FTA-19

Allocative

Efficiency

Terms of

Trade

Investment

Savings

Total

(EV)

Allocative

Efficiency

Terms

of

Trade

Investment

Savings

Total

(EV)

Ethiopia 1.1 -6.15 -8.13 -13.18 4.45 -2.6 -5.52 -3.7

Egypt 2.6 10.67 5.62 18.9 6.67 16.3 8.03 31

Kenya 7.1 15.33 13.16 35.6 11.43 9.2 10.08 30.7

Malawi 0.05 0.06 0.05 0.16 1.29 1.03 0.74 3.06

Madagascar 0 0 0 0 0.19 -0.5 0.01 -0.3

Mauritius 0 0 0.01 0.02 1.72 19.3 5.18 26.2

Rwanda 0.06 -0.15 0.01 -0.1 0.46 0.04 0.06 0.56

Uganda 0.23 -0.48 0.04 -0.2 1.35 1.04 0.36 2.76

Zambia 0.06 0.09 -0.07 0.07 0.23 -1.3 0.36 -0.7

Zimbabwe -0.13 -0.06 -0.04 -0.2 174.41 -29 -27.56 118

REA -0.82 -0.48 -0.08 -1.4 36.33 4.92 1.66 42.9

RNA 0.06 -0.07 -0.13 -0.1 -0 .34 0.39 0.29 0.34

RSCA -0.08 -0.02 0.09 -0 0.01 0.48 -0.02 0.48

RSAC 0 0 0 0 31.55 69.1 -24.09 76.5

EU27 -1.17 -5.74 -1.26 -8.2 -5.69 -26 3.36 -28

UK -0.2 -1.11 -0.13 -1.5 -0.65 -1.9 -0.35 -2.9

ROW -5.39 -11.9 -9.16 -26 -12.15 -62 27.31 -47

Total World 3.41 250

(Source) Model Simulation

The terms of trade, in theory, are defined using the C.i.f price of imports relative to the f.o.b. prices of

exports. The domestic price of imports goes down when the FTA is implemented. Hence, elimination of

import tariffs automatically leads to a reduction in the import prices. Terms of trade loss result from lower

import price than export price arising from tariff elimination. Table 7 further reports that with FTA-17,

17

Egypt and Kenya experience substantial gain in their terms of trade while other COMESA member

countries reported low or negative terms of trade with the highest loss goes to Ethiopia (US$6.15 million).

However, with FTA-19, RSAC, Egypt, Mauritius and REA reported a large gain in their terms of trade.

The difference between investment and saving in a country adjusts to equate the real trade balance.

Therefore the saving – investment effect from COMESA FTA in Table 7 above moves in line with the

direction of trade balance.

5.2. COMESA Customs Union


Table 8 shows the macroeconomic and trade effect of COMESA customs union. The move towards

customs union slightly improves GDP of most COMESA member countries. Egypt, Malawi, Madagascar,

Mauritius, Zambia, REA, and RSCA reported modest growth in their GDP but lose regarding export and

import growth. On the other hand, Ethiopia, Kenya, Rwanda, and Uganda reported growth in their export

and import but loses in terms of GDP from the Customs Union scenario. For non-COMESA regions, EU-

27, UK, and ROW, there is a trade surplus, but they lose in terms of GDP.

As can be seen from Table 9, the decline in GDP for some COMESA countries is mainly due to a

flood of cheap imports and a resulting reduction in consumption of domestic commodity and investment,

although there is an improvement in trade balance (e.g., Ethiopia, Kenya, Uganda and Rwanda). However,

for other countries, the gain in GDP results from the increase in trade balance except for Zimbabwe, REA,

and RNA. For Zimbabwe, REA and RNA the main reason for the growth of GDP is an increase in domestic

consumption, investment and government consumption, although trade balance is negative. Therefore,

there will be a change in the production and consumption structure for most COMESA countries because

of the customs union; for Ethiopia, Kenya, Rwanda, and Uganda more consumption is imported, and

production is exported compared to the base year scenario.

As COMESA countries reduce import tariff against non-member regions through customs union, their

import demand increase. However, the existing tariff of some COMESA member countries is below CET

rate, and customs union may, in turn, increase protection for most COMESA countries. As a result, the

growth of export and import for most COMESA countries is negative.

18

Table 8. Macroeconomic effects of COMESA Custom Union

Region Import Value

(%)

Export Value

(%)

GDP

(%)

Change in Trade

Balance

(US$ million)

Ethiopia 0.06 0.22 -0.05 4.02

Egypt -0.18 -0.22 0.08 16.87

Kenya 0.06 0.35 -0.16 21.74

Malawi -0.14 -0.07 0.07 1.92

Madagascar -0.33 -0.27 0.08 2.4

Mauritius -0.08 -0.09 0.05 1.3

Rwanda 0.06 0.08 -0.05 0.24

Uganda 0.09 0.12 -0.02 0.74

Zambia -0.02 -0.01 0.02 0.75

Zimbabwe 0.02 -0.36 0.39 -12.26

REA -0.05 -0.07 0.16 -3.06

RNA 0.05 -0.1 0.12 -108.65

RSCA -0.12 -0.06 0.09 6.85

RSAC -0.02 0.07 0 2.48

EU27 0 0 0 33.28

UK 0 0 0 2.38

ROW 0 0 0 29.01


19

Table 9 Change in GDP Components for Customs Union Scenario (US$ million)

GDP

Components

Consumption

Investment

Government

Exports

(-) Imports

Total

Ethiopia -9.93 -8.58 -1.27 9.42 -5.4 -15.8

Egypt 165.5 -6.71 18.96 -130.5 147.4 194.6

Kenya -46.15 -19.9 -10.89 32.24 -10.5 -55.2

Malawi 3.59 -2.1 0.81 -1.33 3.26 4.22

Madagascar 6.31 -1.95 0.71 -8.53 10.92 7.46

Mauritius 4.9 -1.06 0.84 -4.36 5.65 5.97

Rwanda -2.44 -0.71 -0.26 1.14 -0.9 -3.17

Uganda -3.16 -0.78 -0.25 5.2 -4.46 -3.44

Zambia 1.77 0.54 0.59 -1.38 2.13 3.65

Zimbabwe 40.34 6.94 7.36 -10.73 -1.53 42.39

REA 96.61 16.87 13.1 -14.96 11.9 123.5

RNA 151.14 166.65 64.79 -76.75 -31.92 273.9

RSCA 71.36 23.89 19.79 -42.69 49.54 121.9

RSAC -0.37 -1.97 -0.03 2.12 0.36 0.11

EU27 -96 -60.75 -33.75 4 30 -157

UK -11.13 -3.75 -3.69 -1.88 4.25 -16.2

ROW -144 -88 -38 1 27 -242

Total 228.35 18.64 38.81 -237.9 237.6 285.5

Source: Model simulation

2. Welfare effect

Table 10 shows that COMESA customs union is welfare improving for most COMESA countries; RNA,

Zimbabwe, and Ethiopia reported substantial welfare gain while Egypt, Kenya, REA and RSCA

experience considerable welfare loss. For Ethiopia, Rwanda, and Uganda the large gain in welfare results

from an improvement in the efficiency of resources allocated to the sector. In contrast, for Madagascar,

Mauritius, Zambia, Zimbabwe and RNA, the efficiency gain results from positive terms of trade effect.

In general, the welfare impact of COMESA customs union differs from country to country depending on

the change in the efficiency of resource allocation and relative change in import and export price.

COMESA customs union reduce the aggregate welfare of COMESA and world by US$50.59 million and

US$80.66 million respectively. Besides, COMESA customs union result in a welfare loss for non-

COMESA regions.

20

Table 10. Welfare Decomposition for COMESA Customs Union (US$ million)

Region Allocative

Efficiency

Terms of Trade Investment-

Savings

Total (EV)

Ethiopia 13.09 -1.69 -6.81 4.59

Egypt -75.63 35.77 12.6 -27.26

Kenya 8.64 -11.18 -9.92 -12.46

Malawi -1.27 0.49 0.1 -0.68

Madagascar -1.53 1.54 0.07 0.08

Mauritius -0.58 1.64 0.7 1.76

Rwanda 1.67 -0.05 -0.03 1.59

Uganda 6.85 -1.15 0.15 5.86

Zambia -0.19 0.96 -0.43 0.34

Zimbabwe -2.06 3.25 12.5 13.69

REA -50.57 6.48 1.35 -42.75

RNA -3.78 17.45 6.48 20.15

RSCA -11.47 5.31 -9.21 -15.37

RSAC 0.34 -0.1 -0.38 -0.13

EU27 30.29 -44.56 -0.92 -15.19

UK 0.7 -1.2 -1 -1.5

ROW 4.91 -12.99 -5.28 -13.36

World Total -80.66


21

5.3. COMESA - EU FTA: European Partnership Agreement


As shown in Table 2, COMESA member countries have large import share from EU, particularly in

industrial and service sector. Therefore, a complete removal of import tariff from COMESA to EU will

have a potential impact on the trade between these regions. This effect comes from two sources. First, EU

imports, which were previously taxed, will enter COMESA markets duty-free once an EPA is in place.

Therefore, there will be a substitution of domestic goods for cheap imports from EU. Second, trade

liberalization under an EPA will make some EU products more affordable (since exempted from customs

duties) than products previously imported from other sources still subject to customs duties; this trade

diverted from non- EPA origin to the benefit of EU imports.

Table 11 shows that all EPA signatory COMESA member countries lose in terms of GDP except

RNA; the loss is smaller when UK exit from EU (Brexit). The largest loss in terms of GDP goes to REA,

Zimbabwe, Kenya and Ethiopia. In contrast, EU-27 and UK reported a slight increase in GDP growth, but

Brexit result in lower GDP growth for the UK compared to the case when UK is a member of EU. Table

11 further reports that EPA results in a growth of import for most COMESA countries except Mauritius

and Egypt; with Brexit, the impact is smaller. Similarly, there is an increase in export of most COMESA

countries with EPA scenario except Egypt, Mauritius, RNA, and RSAC; the impact is less with Brexit.

The largest growth in export for EPA scenario goes to Zimbabwe, REA, Kenya and Ethiopia whereas

REA, Madagascar, RSCA, and Ethiopia have large import growth. The impact of EPA differs from

country to country depending on the trade relation and initial protection imposed by the country.

Furthermore, with EPA, EU-27, and UK experience slight growth in their export and import, whereas

when UK exit from EU, UK's import decrease by 0.01%.

Table 11 indicates that Ethiopia, Egypt, Kenya, and Zimbabwe reported large trade surplus while

RSCA, RNA, Zambia, and Madagascar reported significant trade deficit with EPA. Brexit has similar

trade balance effect, but the trade balance deficit and surplus are small with Brexit. On the other hand,

EPA agreement worsens the trade balance of EU-27 and UK by US$880.17 million and US$161.13

million respectively. However, UK reported a trade balance surplus of US$57.22 million following Brexit

while the deficit to EU-27 increases further with Brexit.

As can be seen from Table 12, the main reason for the decline of GDP for Ethiopia, Egypt, Kenya,

Malawi, Mauritius, Zimbabwe and, REA are the drop in consumption of domestic commodities,

investment, and Government consumption, although trade balance is positive. For Rwanda, Uganda,

RSCA, Madagascar, Zambia and, RSAC deterioration of trade balance contributes to the decline in GDP

besides to a fall in consumption, investment and government consumption. The main reason for the

decrease in consumption of domestically produced commodity for COMESA countries is the increase in

both export and import due to the complete removal of tariff by COMESA countries for EU origin product.

Furthermore, the increase in import and export also change the production and consumption structure; the

22

change differs from country to country depending on the elasticity of substitution between imported and

domestically produced goods.

Table 11. Macroeconomic effects of EPA

Region Import value (%) Export Value (%) RGDP (%) Change in Trade

Balance (US$ Million)

EPA EPA+Brexit EPA EPA+Brexit EPA EPA+Brexit EPA EPA+Brexit

Ethiopia 1.05 0.97 3.39 3.1 -1.98 -1.82 48.76 44.31

Egypt -0.36 -0.35 -0.39 -0.39 -0.05 -0.04 61.66 59.39

Kenya 0.8 0.53 3.57 2.8 -2.46 -2.04 195.06 170.24

Malawi 0.11 0.02 0.69 0.48 -0.9 -0.69 10.72 8.72

Madagascar 2.39 2.29 1.79 1.76 -0.7 -0.72 -24.49 -21.93

Mauritius -0.42 -0.4 -0.33 -0.33 -0.22 -0.21 12.37 11.56

Rwanda 1.04 1 1.05 1.02 -1.04 -1 -1.29 -1.2

Uganda 0.33 0.23 0.24 0.16 -1.19 -1.06 -5.56 -4.11

Zambia 0.76 0.58 0.28 0.23 -0.13 -0.08 -33.31 -23.6

Zimbabwe 0.65 0.51 4.14 2.35 -2.87 -1.32 82.03 37.41

REA 3.29 3.15 3.64 3.49 -3.01 -2.89 0.83 1.27

RNA 0.02 0.02 -0.12 -0.12 0.09 0.09 -102.49 -102.85

RSCA 1.77 1.73 0.45 0.47 -1.35 -1.28 -409.91 -376.24

RSAC 0.37 0.34 -0.51 -0.38 -0.5 -0.38 -23.58 -19.19

EU27 0.06 0.07 0.05 0.05 0.06 0.06 -880.17 -932.7

UK 0.08 -0.01 0.07 0 0.05 0.01 -161.13 57.22

ROW -0.02 -0.02 -0.01 -0.01 -0.01 -0.01 1226.33 1088.45


23

Table 12 Changes in GDP Components for EPA scenario (in US$ million)

GDP

components

Consumption Investment Government Exports (-) Imports Total

Ethiopia -486.19 -126.79 -54.47 144.88 -96.13 -618.71

Egypt -83.79 -69.03 -19.35 -232.89 294.63 -110.43

Kenya -716.74 -155.21 -167.4 329.95 -134.84 -844.24

Malawi -38.59 -10.92 -12.02 13.35 -2.4 -50.59

Madagascar -55.13 16.72 -6.36 55.76 -80.25 -69.27

Mauritius -21.02 -12.39 -3.93 -16.39 28.79 -24.94

Rwanda -50.26 -9.91 -5.08 14.65 -15.94 -66.54

Uganda -136.93 -25.53 -15.66 10.14 -15.7 -183.68

Zambia -12.17 25.16 -3.8 33.67 -66.31 -23.46

Zimbabwe -297.9 -38.97 -58.65 123.72 -43.19 -314.99

REA -1720.32 -354.89 -299.31 728.85 -729.73 -2375.39

RNA 131.97 140.87 49.14 -92.54 -9.92 219.52

RSCA -912.07 -124.29 -289.37 298.76 -708.63 -1735.58

RSAC -19.52 13.44 -4.52 -15.7 -8.07 -34.37

EU27 5455 2373.5 2042.25 3162 -4047 8985.75

UK 940.62 248.09 323.63 512.62 -674.44 1350.53

ROW -3288 -2205 -847 -1328 2568 -5100

Total -1311.06 -315.15 628.09 3742.87 -3741.12 -996.37

Source: Model Simulation

2. Welfare effect

Table 13 shows that EPA results in a welfare loss for most COMESA member countries; RSCA, REA,

and Rwanda reported large welfare gain with EPA whereas Kenya, Ethiopia, and Zimbabwe experience

large welfare loss. Similarly, with Brexit, RSCA, REA & Rwanda reported welfare gain while other

countries reported welfare loss, but the loss is small compared to the case when UK is a member of EU.

As shown in Table 13, the main source of welfare loss for most of COMESA member countries is a loss

in terms of trade owing to lower import price relative to export price arising from tariff elimination. The

free trade agreement with Europe results in efficiency gain for Ethiopia, Rwanda, Zambia and REA

whereas other COMESA member countries reported large efficiency loss.

For non-COMESA regions, EU-27 reported significant welfare gain of US$1923.7 million with EPA and

the benefits increase further to US$1987.94 million with Brexit. Similarly, UK reported a welfare gain of

US$299.04 million with EPA, but with Brexit, UK reported a welfare loss of US$55.51 million. Finally,

FTA with EU reduces aggregate COMESA welfare while world welfare improve. Similarly, with Brexit,

the aggregate welfare of COMESA reduce and world welfare improve.

24

Table 13. Welfare effect by Decomposition for EPA (US$ million)

Region EPA EPA+Brexit

Allocative

Efficiency

Terms of

Trade

Investment-

Savings

Total

(EV)

Allocative

Efficiency

Terms of

Trade

Investment-

Savings

Total

(EV)

Ethiopia 7.99 -49.62 -98.46 -140.09 6.83 -45.46 -91.26 -129.89

Egypt -89.78 -4.8 -10.68 -105.27 -88.94 -3.41 -9.64 -101.99

Kenya -44.3 -136.8 -124.24 -305.34 -38.67 -112.83 -102.47 -253.96

Malawi -6.55 -5.35 -2.62 -14.53 -5.75 -4.2 -2 -11.96

Madagascar -1.94 -10.98 -1.5 -14.43 -1.97 -10.92 -1.52 -14.41

Mauritius -1.25 -8.05 -1.82 -11.12 -1.21 -7.58 -1.68 -10.47

Rwanda 4.06 -0.81 -1.42 1.83 4.07 -1.04 -1.35 1.68

Uganda -9.64 -15.72 -4.67 -30.03 -7.82 -15.17 -3.97 -26.95

Zambia 0.17 -8.46 2.81 -5.48 0.44 -6.88 1.55 -4.9

Zimbabwe -49.16 -31.95 -65.32 -146.44 -9.15 -17.86 -27.28 -54.29

REA 220.96 -159.9 -52.61 8.46 223.62 -154.42 -50.09 19.11

RNA -5.74 -6.73 8.47 -4 -5.68 -5.86 8.41 -3.13

RSCA -57.96 -90.14 306.38 158.27 -59.08 -85.13 280.1 135.88

RSAC -1.73 -6.23 5.96 -2 -1.23 -4.71 4.69 -1.26

COMESA -610.17 -456.54

EU27 357.6 1565.69 0.38 1923.67 360.17 1627 0.77 1987.94

UK 76.61 170.48 51.95 299.04 11.72 -74.61 7.38 -55.51

ROW -264.54 -1203.8 -14.79 -1483.13 -237.46 -1079.61 -13.35 -1330.43

Total 134.77 -3.16 -2.2 129.41 149.89 -2.69 -1.72 145.48


5.4. Industries Output effect for Ethiopia.

A significant effect of trade liberalization is that it causes reallocation of resources such as labor, capital,

and land, which further leads to structural adjustment to some extent in the factor market and industry

output. In many cases, the sectors protected by high tariff rates will lose their production more, when the

tariffs are reduced. In contrast, the trade liberalization brings about efficiency gains to increase in income

25

and production across the sectors through allocating additional resources to areas in which it has a

comparative advantage.

Table 14 shows that there is a growth of export and import in many sectors of Ethiopia for all scenarios.

In few sectors such as grains and service, import decline across all scenario; for grain, the decline is greater

with Custom unions and EPA scenarios. On other sectors such as vegetable and fruit, oil seeds and Motor

vehicle part, their import decrease with FTA-17 but increase with full FTA, CU and EPA scenarios. For

Coal Oil & Gas, and Basic metal sector, import decrease with both customs union and EPA scenario;

Oilseed, livestock, Food Manufacturing, Wood paper, Forestry & Fishery and Petroleum & Chemical have

similar result, but they are exception in that the first four sectors reduce import with customs union while

the last two sectors reduce import with EPA. The largest growth in import from all scenario goes to Leather

and other manufacturing sectors, although Food manufacturing, and Beverage & Tobacco sectors show

moderate improvement with EPA. Table 14 further indicates that with Ethiopia-COMESA FTA, most

sectors export grow except Other Crop sector, which shows slight reduction with full FTA. The largest

growth in export with COMESA FTA in both scenario goes to other manufacturing, Motor Vehicle Part,

Wood Paper and Food Manufacturing, Petroleum & Chemical, and Fabric Metal Equipment. In contrast,

customs union has a little or negative impact on the growth of export for most sectors. For Textile &

Apparel, Leather, Basic Metal and Coal Oil & Gas sector, there is growth in export for all scenarios, but

substantial growth is recorded with EPA. On the other hand, EPA reduces the export of Vegetable & Fruit,

Livestock, and Beverage & Tobacco sectors for Ethiopia.

Table 15 summarizes the effect tariff elimination across all scenarios on Ethiopia's industry output and

trade balance. Most manufacturing products such as Food Manufacture, Beverage & Tobacco, Textile &

Apparel, Wood Paper, and Other Manufacturing sectors trade balance deteriorate with all scenario.

However, Petroleum & Chemical and Basic metal sectors show large trade surplus with EPA. On the other

hand, few agricultural sectors such as Grains, Vegetable & Fruit, Oil Seed and Other Crop report trade

surplus with COMESA FTA; the surplus is more with EPA for Grain, oilseed, and Other Crop sectors.

Furthermore, a large trade surplus is recorded for service sector for all scenarios. Table 15 further shows

that the highest increase in output of Ethiopia from COMESA FTA goes to Oilseed and Motor Vehicle

Part while other sector reported slight or negative growth of output. Similarly, customs union reduces

many sectors output, but slight improvement is shown on Grain, Coal Oil & Gas, Food manufacturing,

wood paper, and Basic Metals sectors compared to FTA scenario. EPA has mixed result in the growth of

output for Ethiopia; Grain, Oil Seed, Other Crops, Coal Oil & Gas, leather, Petroleum Chemical and Basic

Metal show an increase in output while Other Sector output decline with EPA scenario. In general, the

difference between the percentage changes in sectoral output reflect the comparative advantage as well as

the scales of the tariff reductions across member states.

26

Table 14: Import and export Change for Ethiopia by sector

% Change in Ethiopia’s Import

% Change in Ethiopia’s Export

sectors FTA-17 FTA-19 CU EPA EPA+

Brexit

FTA-17 FTA-19 CU EPA EPA+

Brexit

Grains -0.44 -0.17 -2.52 -6.13 -5.82 0.63 0.44 1.46 1.53 1.04

VegetablFrut -0.16 0.24 1.71 0.75 0.35 0.45 1.91 0.81 -1.71 -1.28

Oilseed -0.11 0.09 -0.51 6.1 6.19 0.39 0.3 -0.01 3.06 2.8

OtherCrops 1.41 1.86 1.58 3.2 3.18 0.51 -0.15 -0.02 4.44 4.08

Livestock 0.38 0.71 -0.74 0.95 1.21 0.43 0.48 0.43 -8.05 -8.12

ForestFisher 2.75 3.43 2.07 -0.74 -0.5 0.52 0.94 0.28 4.14 3.8

CoalOilGas 0.03 1.3 -0.02 -1.12 -0.97 1.27 1.41 0.65 9.33 8.61

FoodMnfcs 2.92 3.21 -0.75 8.48 8.74 4.14 4.19 0.71 3.21 2.74

BeverTobaco 0.73 0.96 1.08 14.67 7.74 0.23 2.49 -0.15 -0.25 -0.26

TextileAppar 1.73 2.07 5.24 7.58 6.44 1.17 0.76 0.42 9.04 8.31

Leather 11.83 12.32 6.85 43.93 37.95 2.1 2.03 0.15 9.43 8.65

WoodPaper 0.89 1.05 -1.02 2.29 1.94 2.12 18.48 0.95 1.17 0.91

PetroChemica 0.01 0.4 0.01 -0.42 -0.38 1.76 25.46 0 2.07 1.96

BasicMetals 0.33 0.41 -0.67 -1.76 -1.64 1.11 0.85 0.32 8.78 8.06

FabMetalEqu 0.06 0.32 0.45 4.91 4.55 1.6 23.7 0 3.46 3.05

MotorVehpar -0.14 0.04 0.29 4.18 4.12 2.27 48.78 0.7 1.01 1.59

OtherMnfcs 19.61 20.07 0.5 9.15 7.37 12.54 12.22 0.76 1.78 1.63

Services -0.37 -0.17 -0.22 -3.15 -2.91 0.46 0.23 0.19 3.89 3.57


Table 15 Trade Balance and Output for Ethiopia by sector

Change in Ethiopia’s Trade Balance (US$ Million)

% Change in Output

sector FTA-17 FTA-19 CU EPA EPA+

Brexit

FTA17 FTA19 CU EPA EPA+

Brexit

Grains 2.01 0.85 10.58 24.94 23.51 0 0 0.29 0.15 0.15

VegetablFrut 1.8 7.43 2.75 -7.06 -5.23 0.01 0.16 0.06 -0.47 -0.41

Oilseed 1.45 1.11 -0.02 11.07 10.1 0.38 0.21 0 3.28 2.98

OtherCrops 4.98 -1.94 -0.52 44.81 41.13 0.24 -0.12 -0.06 2.27 2.08

Livestock 0.68 0.75 0.69 -12.88 -13 -0.02 0 -0.04 -0.84 -0.81

ForestFisher 0.06 0.13 0.02 0.84 0.77 -0.02 0 -0.01 -0.16 -0.15

CoalOilGas 0.3 0 0.16 2.59 2.38 0.11 -0.09 0.03 1.17 1.07

FoodMnfcs -5.9 -6.74 3.03 -24.84 -26.07 -0.3 -0.31 0.15 -1.67 -1.68

BeverTobaco -0.22 -0.21 -0.35 -4.68 -2.47 -0.08 -0.04 -0.06 -1.24 -0.82

TextileAppar -2.95 -4.14 -12.3 -9.3 -7.26 -0.24 -0.32 -1.4 -1.56 -1.36

Leather 0.97 0.8 -0.93 5.48 5.4 0.05 0.04 -0.27 0.12 0.2

WoodPaper -1.72 -1.56 2.09 -4.56 -3.87 -0.73 -0.64 1.08 -1.68 -1.37

PetroChemica 0.2 -6.13 -0.21 14.08 12.84 -0.06 -0.49 0 0.23 0.21

BasicMetals 0.03 -0.65 3.26 18.88 17.44 0.15 0.01 0.71 4.66 4.3

FabMetalEqu -0.63 2.05 -9.27 -99.9 -92.62 -0.03 0.24 -0.55 -4.43 -4.11

MotorVehpar 0.86 1.98 -1.49 -22.02 -21.7 0.26 0.7 -0.54 -4.44 -4.41

OtherMnfcs -6.28 -6.5 -0.13 -4.1 -3.29 -1.05 -1.07 -0.05 -0.82 -0.66

Services 13.63 6.6 6.71 115.49 106.29 -0.02 0 -0.05 -0.04 -0.04


27

6. Systematic Sensitivity Analysis

Results from economic models depend on many inputs, such as shock and elasticity values, which may

be uncertain. CGE modelers typically draw the elasticities from econometric work that uses time series

price variation to identify an elasticity of substitution between domestic goods and composite imports.

This approach has three problems: the use of point estimates as "truth", the downward bias in the

magnitude of the point estimates created by problems in the estimation technique, and a mismatch between

the data sample and source of variation in the econometric exercise and the policy experiment explored in

the CGE exercise (Hertel et al., 2007). In GTAP, the values of the main economic parameters in the

disaggregated database are derived from a survey of econometric work. Such estimates are most

appropriately viewed as random. Using the Gaussian quadratures technique developed by (Arndt, 1996)

and (Pearson and Arndt,1998) for the GTAP model, it is possible to calculate means and standard

deviations for the results. These results give an indication of the sensitivity of the model to parameter

changes and the degree of confidence that can be ascribed to any given result using Chebyshev’s Inequality.

The results reinforce our conclusions on the welfare impacts of the FTA among COMESA countries.

Table 16 shows the outcomes of the SSA on welfare as well as a calculation of the confidence intervals

under Chebyshev inequality. In the case of FTA-17, the obtained confidence intervals for Egypt, Kenya,

Madagascar, Ruanda, and RSAC are positive values. This means that, under Chebyshev inequality, we

can be 95% confident that the welfare impact remains positive for these countries even when Armington

elasticities vary. However, the welfare loss remains negative for Ethiopia, RNA, EU-27, UK and ROW.

The only doubt concerns Malawi, Mauritius, and Zambia, where the effects on welfare could happen to

be negative but with the greater part of the interval in the positive zone. Similarly, the welfare effect on

Uganda, Rwanda, Zimbabwe, REA, and RSCA could happen to be positive but with the greater part of

the interval in the negative zone. Table 16 further shows SSA for full FTA among all COMESA countries.

The SSA results for (+/-) 50 % shock around the default value of ESUBD indicate that welfare gains for

Egypt, Uganda, and RSAC will remain positive and lies within 95 % confidence interval the reverse are

true for EU-27. The SSA results for other regions indicate both possibilities, negative and positive, with a

greater chance of having the expected sign.

As shown in Table 17, the SSA for customs union scenario for (+/-) 50 % shock around the default

value of ESUBD indicates that welfare gains for Mauritius, Rwanda, Zimbabwe, and RNA will remain

positive and lies within 95 % confidence interval the reverse are true for Malawi, REA, and RSCA.

However, for other regions indicate both negative and positive welfare, with a greater chance of having

the expected sign. The SSA result for EPA scenario in Table 18 indicates that for (+/-) 50 % shock around

the default value of ESUBD the welfare gain for RSCA, EU-27, and the UK remain positive and lies

within 95 % confidence interval. In addition, for Ethiopia, Kenya, Malawi, Mauritius, Madagascar,

Uganda, Zimbabwe, and ROW, the welfare loss remains negative and lies within 95% confidence interval.

However, the SSA result for Egypt, Rwanda, Zambia, REA, RNA, and RSAC indicate both possibilities,

negative and positive, with a greater chance of having the expected sign.

28

Table 16. Systematic Sensitivity Analysis (Welfare Changes, US$ millions) for scenario 1 & 2.

Country

Scenario 1 SSA, ESUBD (+/-50% shock)

Scenario 2 SSA , ESUBD (+/-50% shock)

95 % C.I.

95 % C.I.

Simulation Mean SD lower upper Simulation Mean SD lower upper

Ethiopia -13.18 -13.69 2.89 -26.61 -0.77 -3.49 -4.16 2.4 -14.89 6.57

Egypt 18.89 19.78 3.16 5.65 33.91 31.04 33.33 5.37 9.33 57.33

Kenya 35.58 37.45 6.26 9.47 65.43 30.71 31.05 7.44 -2.21 64.31

Malawi 0.16 0.16 0.04 -0.02 0.34 3.06 3.27 3.03 -10.27 16.81

Madagascar 0 0 0 0.00 0.00 -0.29 -0.32 0.08 -0.68 0.04

Mauritius 0.02 0.02 0.01 -0.02 0.06 26.15 30.07 11.17 -19.86 80.00

Rwanda -0.07 -0.08 0.03 -0.21 0.05 0.56 0.63 0.51 -1.65 2.91

Uganda -0.22 -0.22 0.1 -0.67 0.23 2.78 2.67 0.53 0.30 5.04

Zambia 0.07 0.08 0.04 -0.10 0.26 -0.76 -0.7 0.54 -3.11 1.71

Zimbabwe -0.23 -0.24 0.08 -0.60 0.12 118.08 122.66 69.75 -189.12 434.44

REA -1.39 -1.39 0.34 -2.91 0.13 42.91 47.05 11.53 -4.49 98.59

RNA -0.14 -0.15 0.03 -0.28 -0.02 0.38 0.38 0.27 -0.83 1.59

RSCA -0.02 -0.02 0.02 -0.11 0.07 0.48 0.5 0.23 -0.53 1.53

RSAC 0 0 0 0.00 0.00 76.51 79.22 17.42 1.35 157.09

EU27 -8.17 -8.52 1.34 -14.51 -2.53 -27.88 -29.82 5.57 -54.72 -4.92

UK -1.45 -1.54 0.32 -2.97 -0.11 -2.88 -3.17 1.19 -8.49 2.15

ROW -26.45 -27.61 4.05 -45.71 -9.51 -47.26 -51.27 22.9 -153.63 51.09


Table 17. Systematic Sensitivity Analysis (Welfare Changes (US$ millions) for CU

Country Scenario 3 SSA , ESUBD (+/-50% shock)

95 % C.I.

Simulation Mean SD lower upper

Ethiopia 4.59 4.5 1.67 -2.96 11.96

Egypt -27.26 -26.53 18.67 -109.98 56.92

Kenya -12.46 -12.87 3.77 -29.72 3.98

Malawi -0.68 -0.69 0.15 -1.36 -0.02

Madagascar 0.08 0.08 0.29 -1.22 1.38

Mauritius 1.76 1.77 0.22 0.79 2.75

Rwanda 1.59 1.59 0.32 0.16 3.02

Uganda 5.86 5.86 1.34 -0.13 11.85

Zambia 0.34 0.35 0.1 -0.1 0.8

Zimbabwe 13.71 13.81 1.25 8.22 19.4

REA -42.75 -42.51 6.25 -70.45 -14.57

RNA 20.15 20.36 1.8 12.31 28.41

RSCA -15.37 -15.28 1.61 -22.48 -8.08

RSAC -0.13 -0.13 0.15 -0.8 0.54

EU27 -15.19 -15.77 8.07 -51.84 20.3

UK -1.5 -1.52 0.41 -3.35 0.31

ROW -13.36 -13.14 10.82 -61.51 35.23


29

Table 18. Systematic Sensitivity Analysis (Welfare Changes (US$ millions) for EPA

Country

Scenario 4 (EPA)

ESUBD (+/-50% shock)

Scenario 5 (EPA+Brexit)

ESUBD (+/-50% shock)

95 % C.I.

95.% C.I.

Simulation Mean SD lower upper Simulation Mean SD lower upper

Ethiopia -140.09 -143.68 24.11 -251.45 -35.91 -129.88 -133.33 22.76 -235.07 -31.59

Egypt -105.27 -105.75 29.86 -239.22 27.72 -101.99 -102.42 29.73 -235.31 30.47

Kenya -305.34 -314.14 44.87 -514.71 -113.57 -253.96 -262.22 38.98 -436.46 -87.98

Malawi -14.53 -14.87 1.46 -21.40 -8.34 -11.99 -12.25 1.25 -17.84 -6.66

Madagascar -14.43 -14.73 1.80 -22.78 -6.68 -14.41 -14.72 1.81 -22.81 -6.63

Mauritius -11.12 -11.23 1.68 -18.74 -3.72 -10.47 -10.57 1.63 -17.86 -3.28

Rwanda 1.83 2.00 1.03 -2.60 6.60 1.68 1.86 1.02 -2.70 6.42

Uganda -30.03 -30.48 3.04 -44.07 -16.89 -26.95 -27.37 2.89 -40.29 -14.45

Zambia -5.68 -6.03 1.71 -13.67 1.61 -5.06 -5.34 1.41 -11.64 0.96

Zimbabwe -146.57 -149.76 29.11 -279.88 -19.64 -54.37 -55.64 8.87 -95.29 -15.99

REA 41.42 43.82 63.97 -242.13 329.77 51.92 54.28 63.67 -230.32 338.88

RNA -4.00 -3.96 2.40 -14.69 6.77 -3.13 -3.09 2.40 -13.82 7.64

RSCA 158.27 157.72 13.02 99.52 215.92 135.88 135.38 12.39 80.00 190.76

RSAC -1.79 -2.08 1.03 -6.68 2.52 -1.35 -1.28 0.61 -4.01 1.45

EU27 1924.12 1943.77 140.16 1317.25 2570.29 1988.36 2010.73 145.29 1361.28 2660.18

UK 299.04 305.27 34.19 152.44 458.10 -55.51 -55.75 9.50 -98.22 -13.29

ROW -1479.87 -

1485.78

102.59 -

1944.36

-

1027.20

-1327.43 -

1331.58

90.33 -

1735.36

-927.80


30

7. Conclusion

The purpose of the study is to provide an in-depth analytical work aimed at assessing the impact of the

Ethiopia-COMESAFTA, COMESA customs union and European partnership agreement. This study uses

standard GTAP model version 9 database. Trade barriers broadly include tariff and non-tariff barriers.

However, this study considers cases where the countries take policy initiatives to eliminate only their

import tariffs. The results are interpreted in terms of GDP, trade, welfare, and industry output effect. The

simulation analysis considers five distinct trade integration scenarios that differ in their level of ambition.

It is unlikely that regional trade agreements would result in a complete removal of tariffs on all products,

but due to the difficulty to find the list of sensitive products the COMESA FTA and EPA scenarios provide

the upper bound tariff liberalization. However, for the customs union scenario, a list of sensitive products

are exempted from CET rate calculation.

This study has the following limitations. First, the regional aggregation summarizes nine COMESA

countries into four composite regions. Therefore, this study does not provide detail country level analysis

for all COMESA countries. Similarly, the study does not give detail sectoral level analysis, which may

give slightly different and detail country level result. Second, in this paper, the static version of the

standard GTAP model is used. Gilbert (2013) states that the static model has the disadvantage of

describing the time path, that is, attention in the analysis is concentrated on the end outcomes rather than

the transition. Third, recently many COMESA countries are negotiating to liberalize service trade.

However, this study focuses on the liberalization of goods sector only.

Further study may focus on the dynamic analysis of both service and goods sector liberalization. In

addition, recent trade negotiations across the world focus mainly on non-tariff barriers. Therefore, future

studies may focus on analyzing the impact of non-tariff barriers on the economies of COMESA countries.

Lastly, African countries are discussing to establish continental Free trade agreement aiming at boosting

intra-Africa trade, so extending the analysis at the continental level will have a great importance.

The simulation result indicates that Et-COMESA FTA has an insignificant impact on the economies

of most COMESA countries. However, with full FTA the new FTA member countries, REA and RSAC

reported an improvement in their GDP while Ethiopia's GDP growth shrink by 0.14% which is modest

compared to 0.23% loss under scenario one. On the other hand, COMESA customs union contract GDP

of Ethiopia, Egypt, Rwanda, and Uganda while other COMESA countries reported moderate growth in

their GDP. Furthermore, free trade with Europe (EPA) shrink the economies of all EPA signatory

COMESA countries, but the loss decrease with Brexit. In contrast, EU-27 and UK reported slight growth

in their GDP. The main reason for the loss of GDP for most COMESA countries under EPA scenario is

due to the reduction in domestic consumption and investment, although trade balance is positive.

Another interesting result from FTA and CU analysis concerns its implication for trade pattern in the

regional block. The result for scenario one (FTA-17) reported that the impact on most COMESA countries

import and export is small due to low Ethiopia - COMESA trade. In contrast, when all COMESA member

states implement the FTA, there is a significant improvement in their export and import volume. The

31

largest increase in export and import with full FTA goes to Zimbabwe and RSAC. Furthermore, compared

to full FTA the move towards custom union reduce the export volume of most COMESA countries except

Kenya and Ethiopia, which reported moderate improvement. On the other hand, the import and export

volume of most COMESA countries improves with EPA. REA, Madagascar, RSCA, and Ethiopia

reported large import growth while Egypt and Mauritius experience slight decrease in their import volume

in both EPA and EPA+Brexit scenarios. On the other hand, Zimbabwe, Kenya, Ethiopia and REA reported

moderate growth in their export volume with EPA scenario, but it decreases slightly with the exit of UK

from EU.

The simulation result indicates that with full FTA among COMESA countries, the gain in welfare

from FTA are greater compared with the case when Ethiopia only joins FTA (FTA-17). Zambia, RSCA,

and REA reported substantial welfare gain with full FTA. However, Ethiopia reported large welfare loss

in both FTA-17 and FTA-19, but the loss decrease with FTA-19. In addition, the aggregate welfare of

COMESA and world improve with both FTA-19 and FTA-17 scenarios. The move towards custom union

reported significant welfare gain for RNA, Zimbabwe, Uganda, and Ethiopia while REA, RSCA, Egypt

and Kenya experience large welfare loss. For Ethiopia, customs union reported a welfare gain of US$4.59

million. Therefore, customs union improves the welfare of Ethiopia compare to FTA scenarios. The

overall welfare effect shows that customs union reduces aggregate COMESA and world welfare by

US$50.59 million and US$80.66 million respectively.

The free trade agreement with Europe through EPA benefit RSCA, REA, and Rwanda regarding

welfare, but the welfare gain reduce with Brexit for RSCA and Rwanda while REA reported more welfare

gain with Brexit. In contrast, Kenya, Zimbabwe, Ethiopia and Egypt reported considerable welfare loss

due to EPA, but the loss decrease with the Brexit scenario. On the other hand, EPA improves the welfare

of EU-27 and UK, but with Brexit, UK experiences welfare loss of US$55.51 million. The aggregate

COMESA welfare decrease in both EPA and EPA+Brexit scenario, but the welfare loss decrease with

Brexit. In contrast, the world welfare improves with EPA and EPA+ Brexit scenario.

This study analyzes the sectoral level effect of all scenarios for Ethiopia. The result indicates that for

vegetable and fruit, oil seeds and Motor vehicle part, their import decrease with FTA-17 but increase with

FTA-19, CU and EPA scenarios. In contrast, Coal Oil & Gas, and Basic metal sector, import decrease

with both customs union and EPA scenario. In addition, with FTA-17 and FTA-19 scenarios, most sectors

export grow except Other Crop sectors, which shows slight reduction with FTA-19. Furthermore, most

manufacturing products such as Food Manufacture, Beverage & Tobacco, Textile & Apparel, Wood Paper,

and Other Manufacturing trade balance deteriorate with all scenario. In contrast, the service sector has a

large trade surplus for all scenarios.

32

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34

Appendixes

Appendixes I. Regional aggregation of GTAP 9 Database.

No. My Code Aggregated Region GTAP Regions

1 ET Ethiopia Ethiopia

2 EG Egypt Egypt

3 KE Kenya Kenya

4 MAL Malawi Malawi

5 MAD Madagascar Madagascar

6 MAU Mauritius Mauritius

7 RW Rwanda Rwanda

8 UG Uganda Uganda

9 ZA Zambia Zambia

10 ZI Zimbabwe Zimbabwe

11 RNA Libya, Algeria, Western Sahara Rest of North Africa

12 RSAC Swaziland, Lesotho Rest of South African Customs Union.

13 REA Eritrea, Seychelles, Burundi,

Comoros, Djibouti, Sudan, Somalia,

Mayotte

Rest of Eastern Africa

14 RSCA D.R. Congo, Angola South Central Africa

15 EU-27 27-European Union members Austria, Belgium, Cyprus, Czech

Republic, Denmark, Estonia, Finland,

France, Germany, Greece, Hungary,

Ireland, Italy, Latvia, Lithuania,

Luxembourg, Malta, Netherlands, Poland,

Portugal, Slovakia, Slovenia, Spain,

Sweden, Bulgaria, Croatia, Romania.

16 UK United Kingdom United Kingdom

17 ROW Rest of World All other regions

Source: GTAP 9 Data Bas

35

Appendixes II. Sectoral Aggregation

Codes Description GTAP sectors

Grains Grains Paddy rice, Wheat, Cereal grains nec.

VegetablFrut Vegetable and Fruit Vegetables, fruit, nuts.

Oilseed Oilseed Oil seeds.

Othcrops Other crops Sugar cane, sugar beet, Plant-based fibers and Crops nec.

Livestock Livestock Cattle, sheep, goats, horses, Animal products nec, Raw Milk, Wool,

silkworm cocoons.

ForestFisher Forestry & Fishery Forestry, Fishing.

CoalOilGas Coal, Oil, and Gas Coal, Oil, Gas, Minerals nec.

FoodMnfcs Food manufacturing Meat: Cattle, sheep, goats, horse, Meat products nec, Vegetable oils,

and fats, Dairy products, Processed Rice, Sugar, Food products nec.

BeverTobaco Beverage and Tobacco Beverages and tobacco products.

TextileAppar Textile & wearing Apparel Textiles, Wearing apparel.

Leather Leather Leather products.

WoodPaper Wood Paper Wood products, Paper products, publishing,

PetroChemica Petroleum & Chemical Petroleum, coal products, Chemical, rubber, plastic prods, Mineral

products nec.

BasicMetals Basic metals Ferrous Metals, Metals nec

FabMetalEqu Fabric metal Equipment Metal products, Transport equipment nec, Electronic Equipment,

Machinery, and Equipment nec,

MotorVehpar Motor vehicle part Motor vehicles and parts

OtherMnfcs Other manufacturing Manufactures nec

Services Services Electricity, Gas manufacture, distribution, Water, Construction,

Trade, Transport nec, Sea transport, Air transport, Communication,

Financial services nec, Insurance, Business services nec, Recreation

and other services, PubAdmin/Defence/Health / Educat, Dwellings.

Source: GTAP 9 Data Base

36

Appendixes III. COMESA customs union tariff shock values (%)

Sectors ET EG KE MAL MAD MAU RW UG ZA ZI REA RNA RSCA RSAC

Grains -19 -11 7 0 -4 -11 0 7 -12 -3 -19 -16 -17 -15

VegetablFrut 4 -23 0 -3 -5 -25 0 0 -5 -5 -13 4 -16 -19

Oilseed -5 -10 0 -9 -7 -10 0 0 5 -5 1 1 0 0

Othcrops 5 -8 0 -4 -6 -3 -2 0 3 -8 0 -4 -9 -14

Livestock -7 -13 0 -2 -4 -20 12 11 -2 -10 7 -15 -7 -21

ForestFisher 10 0 0 5 -15 -10 0 0 3 -18 16 -3 14 -4

CoalOilGas 4 0 0 -20 4 -9 0 0 1 2 7 2 2 -2

FoodMnfcs -2 -17 6 -8 -11 -23 18 17 -1 -2 -11 -20 -10 -10

BeverTobaco 6 6 1 -1 -5 -16 0 0 -13 -14 9 18 -6 -11

TextileAppar 9 -6 -3 -6 -6 -17 -2 1 -3 -2 -4 -14 -4 4

Leather 10 6 0 -5 -3 -15 0 0 0 13 9 -17 -5 3

WoodPaper -7 -9 2 4 0 -12 0 -1 -5 -4 -8 -5 -6 -9

PetroChemica 2 -1 -2 0 -2 -9 -1 -2 0 2 3 1 0 -2

BasicMetals -4 -2 0 -10 -8 -16 0 0 -6 -1 10 -10 0 -12

FabMetalEqu 4 -1 0 -1 0 -9 0 0 1 1 3 5 2 -4

MotorVehpar 3 -5 -9 -19 -11 -23 -13 -13 -2 -6 -1 18 -3 -3

OthMnfcs 2 -7 0 -22 -6 -23 0 0 -3 -3 -5 -11 -6 -13

Service 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(Note) A negative value indicates an increase in protection while positive values indicate a reduction of

protection.

(Source): Authors calculation using UN Comtrade and COMSTAT database

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