WPS7049
Policy Research Working Paper 7049
Reducing Trade Costs in East Africa
Deep Regional Integration and Multilateral Action
Edward J. Balistreri
David G. Tarr
Hidemichi Yonezawa
Development Research Group
Trade and International Integration Team
September 2014
Policy Research Working Paper 7049
Abstract
There is substantial evidence that with the progressive global multilaterally would increase the gains between two and
decline in tariffs over several decades, trade costs are a more seven times, depending on the country. that the analysis also
significant barrier to trade than tariffs, especially in Sub- finds that reducing nondiscriminatory services barriers in
Saharan Africa. This paper decomposes trade costs into three Kenya and Tanzania would increase welfare even more than
categories: costs that can be lowered by trade facilitation, multilateral reduction of discriminatory services barriers.
nontariff barriers, and the costs of business services. The The paper is innovative both conceptually and empirically.
paper develops a 10-region, 18-sector, global trade model It contains foreign direct investment in services and is the
that includes Kenya, Tanzania, Uganda, and Rwanda of the first paper to numerically assess liberalization of barriers
East African Customs Union. The analysis finds that deep against domestic and multinational service providers in
integration in the East African Customs Union that lowers a multi-sector, multi-region, applied general equilibrium
these trade costs results in significant gains for the four coun- model. The paper uses new databases of the ad valorem
tries, especially from improved trade facilitation. Extending equivalents of barriers in services and the time in trade costs.
the lowering of nontariff barriers and services liberalization Both databases are shown to be important to the results.
This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a
larger effort by the World Bank to provide open access to its research and make a contribution to development policy
discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
The authors may be contacted at dtarr@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Reducing Trade Costs in East Africa: Deep Regional Integration and Multilateral Action
by
Edward J. Balistreri
David G. Tarr and
Hidemichi Yonezawa*
*Balistreri: Colorado School of Mines; Tarr, Consultant, The World Bank; Yonezawa,
University of Ottawa.
Keywords: trade facilitation; trade costs; services liberalization; non-tariff barriers; regional
integration; Tripartite Free Trade; East Africa; Kenya. Tanzania.
JEL categories: F14; F15; F17; O55; F55.
Contents
1. Introduction ................................................................................................................................. 1
2. Review of the Applied General Equilibrium Literature on Regional Agreements and Foreign
Direct Investment Liberalization in Services .................................................................................. 4
2.1 Applied General Equilibrium Literature Assessing Goods Market Preferential
Liberalization. ............................................................................................................................. 4
2.2 Applied General Equilibrium Literature Assessing Foreign Direct Investment in Services 5
3. Overview of the Model ............................................................................................................... 6
3.1 Perfectly competitive goods and services sectors ................................................................. 7
3.2 Goods produced subject to increasing returns to scale ......................................................... 8
3.3 Service sectors in which foreign direct investment occurs ................................................... 9
4. Data of the Model and Evidence for Key Elasticities .............................................................. 10
4.1 Ad Valorem Equivalents (AVEs) of the Barriers Against Foreign Suppliers of Business
Services. .................................................................................................................................... 10
4.1.1World Bank Services Trade Restrictiveness Indices Database. .................................... 11
4.1.2 Methodology based on the Australian Productivity Commission Methodology. ........ 11
4.2 Ad Valorem Equivalents (AVEs) of the Non-Discriminatory Barriers Against Suppliers of
Business Services in Kenya and Tanzania ................................................................................ 13
4.3 Estimates of the Ad Valorem Equivalents of the Costs of Time in Exporting and Importing
................................................................................................................................................... 13
4.4 Estimates of the Ad Valorem Equivalents (AVEs) for Non-Tariff Measures (NTMs) for the
Regions of our Model ................................................................................................................ 14
4.5 Tariff Data ........................................................................................................................... 14
4.5.1 Kenya. ........................................................................................................................... 14
4.5.2 Tanzania........................................................................................................................ 15
4.5.3. Regions other than Kenya or Tanzania........................................................................ 16
4.6 Social Accounting Matrices ................................................................................................ 16
4.7 Trade Data by Regional Partner and Sector ........................................................................ 16
4.8 Share of Market Captured by Foreign Direct Investors in Services and by Cross-Border
Sales of Services........................................................................................................................ 17
4.8.1 Kenya and Tanzania. .................................................................................................... 17
4.8.2 Regions other than Kenya and Tanzania. ..................................................................... 17
4.9 Share of Expatriate Labor Employed by Multinational Service Providers. ........................ 18
4.10 Key Elasticities .................................................................................................................. 18
5. Results for East African Customs Union Preferential and Multilateral .................................... 19
Policies to Reduce Trade Costs..................................................................................................... 19
5.1 Deep Preferential Integration Within the East African Customs Union (EACU) .............. 19
5.1.1 Aggregate Welfare Effects of Deep Preferential Integration by the East African
Customs Union. ..................................................................................................................... 20
5.1.2 Preferential Reduction of Time in Trade Costs by EACU. .......................................... 20
5.1.4 Reduction of Non-Tariff Barriers within the EACU .................................................... 21
5.1.5 Preferential Reduction of Barriers against EACU Service Providers .......................... 21
5.1.6 Why the Lack of Trade Diversion. ............................................................................... 22
5.2 EACU Multilateral Liberalization....................................................................................... 23
ii
5.3. Reduction of Non-Discriminatory Barriers in Services in Kenya and Tanzania ............... 24
5.4 The Tripartite Free Trade Area: EACU Deep Integration with COMESA and SADC ...... 25
5.5 Sector Impacts: Diverse Trade Facilitation Impacts and the Political Economy of
Regionalism ............................................................................................................................... 26
5.5.1 Trade Facilitation Impacts on Sector Output vary with the Ad Valorem Equivalents at
the Sector Level. .................................................................................................................... 26
5.5.2 Political Economy of Regional Trade Liberalization ................................................... 27
6. Sensitivity Analysis .................................................................................................................. 27
6.1 Impact of Rent Capture Assumption ................................................................................... 28
6.2 Piecemeal Sensitivity Analysis ........................................................................................... 29
7. Conclusions ............................................................................................................................... 30
8. References ................................................................................................................................. 31
Tables ............................................................................................................................................ 39
Appendices .................................................................................................................................... 83
Appendix A: Mapping from the GTAP Sectors and Regions to the Sectors and Regions of our
East Africa-Global Model ............................................................................................................. 83
Appendix B: Estimates of the Ad Valorem Equivalents (AVEs) for Non-Tariff Measures
(NTMs) for the Regions of our Model .......................................................................................... 87
Appendix C: Estimates of the Ad Valorem Equivalents of Poor Trade Facilitation .................... 94
Appendix D: Estimates of Insurance ownership shares in Kenya, Tanzania, Uganda, Rwanda,
SADC and COMESA ................................................................................................................. 101
Appendix E: Telecommunications Ownership Shares in Kenya, Tanzania, Uganda, Rwanda,
COMESA and SADC ................................................................................................................. 105
iii
Reducing Trade Costs in East Africa: Deep Regional Integration or Multilateral Action
by
Edward J. Balistreri
David G. Tarr and
Hidemichi Yonezawa 1
1. Introduction
Evidence is now substantial that with the progressive global decline in tariffs over several
decades, trade costs are often a much more substantial barrier to trade than tariffs. 2 Moreover,
trade costs are especially high in Sub-Saharan Africa compared to other regions in the world. For
example, the World Economic Forum (2012) found that it is still considerably more expensive to
trade with Africa than with other regions, and, in many cases, the cost of trading is a more
important obstacle to trade development than trade policies. 3 Some Sub-Saharan countries,
notably the members of the East African Customs Union (EACU, also known as the East African
Community) 4 are addressing the high trade costs through regional initiatives, which may or may
not become multilateral. In this paper, we assess the impacts of reducing trade barriers among
the EACU members and also assess how much more there is to gain if the trade cost reduction
1
Corresponding author: David G. Tarr, is Consultant and Former Lead Economist, the World Bank. His email is:
dgtarr@gmail.com. Edward Balistreri is Associate Professor at the Colorado School of Mines and Hidemichi
Yonezawa is with the University of Ottawa. The authors gratefully acknowledge the financial support of the
Government of the Netherlands under the Bank-Netherlands Partnership Program, project TF012466, entitled
“Reducing Trade Costs in East Africa: Analytical Development and Capacity Building." The authors thank: Ana
Margarida Fernandes Yaghoob Jafari, Zoryana Olekseyuk, Josaphat Kweka, John Randa, George Gandye, Paul
Brenton, Maryla Maliszewska, Yutaka Yoshino, Jacques Morisset and Victoria Cunningham and an anonymous
referee for their contributions. The views expressed are those of the authors and do not necessarily represent those of
the World Bank or its Executive Directors or those acknowledged.
2
See, for example, Hummels (2007) or Hummels et al., (2007).
3
Brenton and Isik (2012) have also documented the high costs of trading in Sub-Saharan Africa. See also, the
estimates of Hummels et al., (2007) and Minor (2013).
4
The Treaty on the Establishment of the East African Community among Kenya, Tanzania and Uganda came into
force on July 7, 2000. It stipulated that the three countries would continue to trade preferentially. On March 2, 2004,
however, the Protocol for the Establishment of the East African Customs Union was signed by the Heads of State of
Kenya, Tanzania and Uganda. Rwanda and Burundi joined the Customs Union in 2008. See
http://www.customs.eac.int/index.php?option=com_content&view=article&id=123&Itemid=78. Although the five
country grouping of Kenya, Tanzania, Uganda, Rwanda and Burundi is sometimes referred to as the East African
Community (EAC), given the later developments of 2004 and 2008, we typically refer to the five country grouping
as the East African Customs Union (EACU).
1
initiatives could be extended multilaterally or to a wider regional grouping, namely the proposed
Tripartite Free Trade Area among EACU, COMESA and SADC.
We decompose trade costs into three categories: costs that can be lowered by trade
facilitation; non-tariff barriers; and the costs of business services. Trade facilitation
addresses costs such as delays at border crossing, roadblocks for trucks and the necessity to pay
bribes. Regarding non-tariff barriers, recent work by Cadot and Gourdon (2012) has shown
that the old command and control non-tariff barrier measures have significantly declined, but
standards as barriers to trade have supplanted them in importance. Further, poor business
services for trade are also a problem. Improvements in a wide range of business services such as
banking, insurance, communication and professional services such as legal, auditing, engineering
and computer services would also lower trade costs. This also includes poor transportation
services, such as very poor or non-existent freight train services in many countries of Sub-
Saharan Africa, delays at ports, poor air freight services in many countries.
In this paper we build a ten region, 19-sector global trade model, with a focus on the
members of the EACU. The model contains Kenya, Tanzania, Uganda, Rwanda (the four EACU
countries included in the GTAP 8.1 data set), plus COMESA, SADC, the US, EU, China and
Rest of the World.
Since the early 1990s, regional trade agreements have surged; 377 are in force and have
been notified to the WTO as of January 2014. 5 Policy makers have expressed considerable
demand for analysis of their actual or potential regional agreements. Applied modelers have
responded with applied general equilibrium models that focus on goods. So the literature now
contains a substantial number of good studies (summarized below) that examine regional
agreements in goods. But the literature does not contain any global modeling studies of regional
arrangements that involve commitments to multinational firms who will undertake foreign direct
investment in services. Given the inclusion of services in modern FTA agreements negotiated
with the EU, the US and in some other agreements, economists need to be able to assess the
impact of services commitments as part of their advice to governments regarding preferential
trade agreements. We attempt to fill that gap in this paper.
5
http://www.wto.org/english/tratop_e/region_e/region_e.htm. This counts goods, services and accessions separately,
but does not include a significant number that are in force but which have not been notified to the WTO.
2
This paper is innovative both conceptually and empirically. The conceptual innovation is
that it is the first global trade model to numerically assess regional liberalization of barriers to
foreign direct investors in services and to assess barriers against both domestic and multinational
service providers. Given the importance of services in trade costs, we retain all seven business
services sectors from the GTAP 8.1 database and ten services sectors overall.
The paper builds on the following four databases, the first two of which have not been
used before in a general equilibrium model: (i) services barriers--for this project, the authors
developed a new database of the ad valorem equivalents of barriers in eleven business services
sectors in 103 countries, based on the newly released World Bank survey information on these
11 sectors in 103 countries; (ii) trade facilitation—the paper employs the database recently
posted on the GTAP website entitled, “The Value of Time in Trade: GTAP Database of AVEs
for Estimating the Impacts of Swift Customs Clearance and Shipping V8.1,” by Peter Minor
based on work of David Hummels; (iii) for foreign affiliate sales-- “Global Database of Foreign
Affiliate Sales” by Fukui and Lakatos; and (iv) estimates of the ad valorem equivalents of non-
tariff measures by Kee, Nicita and Olarreaga (2008; 2009). Although a central finding of the
studies by Hummels, Minor and their co-authors is that the AVE of time in trade varies across
products, most computable general equilibrium modeling of trade facilitation issues have used a
single AVE across all products. By basing our estimates on the work of Hummels and Minor, we
improve on the sector accuracy of the benefits of trade facilitation. For example, we find that the
agriculture sector in Uganda expands relative to other sectors in Uganda when there are
improvements in trade facilitation.
In recent years, the East African Customs Union (EACU) has initiated several steps at
deep integration. In particular, the EACU is moving to improve trade facilitation, reduce non-
tariff barriers and reduce barriers to foreign providers of services within the EACU. 6 There are
also other deep integration initiatives being negotiated in Sub-Saharan Africa, including the
Tripartite Free Trade Area being negotiated among the East African Customs Union (EACU),
the Common Market of East and Southern Africa (COMESA) and South African Development
Community (SADC). We assess these initiatives and compare the results to broader multilateral
liberalization by the EACU.
6
The World Bank (2012) has argued that regional integration in the East African Community is a promising path
that would lead to the reduction of barriers that increase trade costs among the member countries of the EACU.
3
The paper is organized as follows. We begin in section 2 with a brief review of the
applied general equilibrium literature of regional arrangements. In section 3 we provide an
overview of the model. In section 4 we explain the data that we have used in constructing this
model and what the data needs are for the final report. Results based on the present data set are
presented in section 5. Sensitivity analysis and conclusions are presented in sections 5 and 6,
respectively. The appendices provide documentation of the background data work and further
detail on the model.
2. Review of the Applied General Equilibrium Literature on Regional Agreements and
Foreign Direct Investment Liberalization in Services
The previous studies that are most closely related to our studies are those that assess
liberalization of foreign direct investment in services. We begin this section, however, with a
review of the more notable studies of preferential liberalization of goods markets.
2.1 Applied General Equilibrium Literature Assessing Goods Market Preferential
Liberalization
The formation of the Canada-US free trade agreement led to the path-breaking work of
Harris (1984) and Cox and Harris (1986) in incorporating imperfect competition into a small
open economy applied general equilibrium model. They showed that if the agreement leads to a
more competitive pricing strategy by Canadian firms, there would be substantial welfare gains
from rationalization. The creation of the single market in the European Union led to innovative
analysis that required the use of multi-region models with imperfect competition or dynamic
effects in order to capture the impacts of the key features of the single market (Harrison,
Rutherford and Tarr, 1996; Smith and Venables, 1988; Baldwin, Forslid and Haarland, 2000).
The North American Free Trade Agreement (NAFTA) led to a large number of CGE studies
summarized in the Francois and Shiells (1994) volume. Among these, Levy and van Wijnbergen
(1995) use their dynamic CGE model to argue that dynamic incentive problems in adjustment
policies for Mexican agriculture imply that adjustment policies should focus on increasing the
4
value of the assets of poor farmers, not their incomes. Preferential arrangements of the European
Union with its Mediterranean neighbors led to policy maker requests for CGE analysis. Using
small open economy models of the developing country under perfect competition (Harrison,
Rutherford and Tarr (1997a) for Turkey; Rutherford, Rutstrom and Tarr (1993) for Morocco; and
Rutherford, Rutstrom and Tarr (1995) for Tunisia), these North-South arrangements were
estimated to be beneficial to the developing country due to the introduction of competition into
the Southern markets. Finally, Chile has adopted a strategy of negotiating preferential
arrangements with all potential partners (called “additive regionalism” or “competitive
regionalism”). This strategy has been controversial within Chile regarding preferential
arrangements with its Southern neighbors. Using a multi-region perfect competition model,
Harrison, Rutherford and Tarr (2002) estimated that Chile would lose from individual
preferential arrangements with Southern neighbors unless it lowered its then 11 percent uniform
tariff. But these authors show that the agreements with Southern partners are beneficial to Chile
in the context of Chile’s additive regionalism strategy due to substantial estimated terms of trade
gains to Chile in partner markets and the reduction of trade diversion costs if the Northern
partners are included in the network of agreements. 7 Rutherford and Tarr (2003) showed that
simply making the Chilean model dynamic will not increase the estimated gains from these
agreements if there are no endogenous productivity effects.
2.2 Applied General Equilibrium Literature Assessing Foreign Direct Investment in
Services
Our paper is more closely related to studies that incorporate foreign direct investment in
services. This includes the following. Markusen, Rutherford and Tarr (2005) developed a
stylized model where foreign direct investment is required for entry of new multinational
competitors in services, but they did not apply this model to the data of an actual economy.
Jensen, Rutherford and Tarr (2007; 2010), Rutherford and Tarr (2008; 2010) and Balistreri,
Rutherford and Tarr (2009) developed small open economy applied general equilibrium models
in Russia, Kenya and Tanzania based on the Markusen, Rutherford and Tarr methodology.
Konan and Maskus (2006) assessed services liberalization in Tunisia. But these models could not
7
Harrison, Rutherford, Tarr and Gurgel (2004) found similar results for Brazil.
5
assess regional preferences in services. Brown and Stern (2001) and Dee et al. (2003) employ
multi-country numerical models with many of the same features of Markusen, Rutherford and
Tarr. Their models contain three sectors, agriculture, manufacturing and services, and are thus
also rather stylized.
The model described in this paper is closest to the small open economy models developed
by Balistreri, Jensen and Tarr (2011) and especially Jensen and Tarr (2012). Balistreri, Jensen
and Tarr (2011) have shown that there is an imperfect competition analogy to trade diversion in
goods whereby preferential commitments in to foreign investors in services could be
immizerising. Jensen and Tarr (2012) extended the analysis to include the impact of improved
trade facilitation and the reduction of non-tariff barriers in Armenia. But since Balistreri, Jensen
and Tarr (2011) and Jensen and Tarr (2012) employed small open economy models, they were
not capable of endogenously assessing the terms of trade gains from improved market access in
preferential trade arrangements. 8
3. Overview of the Model
This paper builds on the algebraic structure of the model of Jensen and Tarr (2010; 2012)
and of Balistreri, Jensen and Tarr (2011). Here we provide a general description of the structure
described there and provide more details where we depart from that structure. We employ the
GTAP 8.1 data set as our basic data set.
The key extension of the earlier models we developed of Armenia, Kenya and Tanzania
is that we adopt a multi-region model, rather than a small open economy model, since we need to
consider the possible effects on the member countries of the EACU. That is, we need to account
for the “market access” effects on Kenya, Tanzanian, Ugandan and Rwandan exports of a
reduction of import tariffs by the partner countries and by agreements to facilitate trade, lower
non-tariff barriers within the free trade area or allow access to service providers within the FTA.
Although the general theory of the welfare effects of preferential trading arrangements
does allow for the impact of changes in partner country tariffs on the home country’s market
8
Wonnacott and Wonnacott (1981) have demonstrated the theoretical importance of assessing improved market
access in regional agreements, and Harrison, Rutherford and Tarr (2002) have shown numerically that assessing
market access is very important in determining the value of a preferential trade agreement.
6
access to partner countries and for the impact on the terms-of-trade, 9 small open economy
models cannot assess them endogenously. Our framework allows us to explicitly evaluate the
importance to Kenya, Tanzania and the EACU members of improved market access or reduced
trade costs, as well as losses EACU members may suffer as partner countries may raise export
prices to each other.
There are 18 sectors in the model. The mapping from the 57 sectors in the GTAP 8.1 data
set to sectors of our model is shown in appendix A. There are three categories of firms: (1) four
perfectly competitive goods and services sectors: (2) seven imperfectly competitive goods
sectors; and (3) seven services sectors in which there is foreign direct investment. The cost,
production and pricing structures in the three categories differ widely.
Primary factors are skilled labor, unskilled labor, capital (including land) and natural
resources. Regarding capital, there is mobile capital and sector-specific capital in imperfectly
competitive goods sectors and services sectors with FDI; and primary inputs imported by
multinational service providers, reflecting specialized management expertise or technology of
the firm. There is some sector specific capital for each imperfectly competitive firm (and for
firms in services sectors with FDI) for each region of the model. In the sectors where there is
sector specific capital, there are decreasing returns to scale in the use of the mobile factors and
supply curves in these sectors slope up. We calibrate the elasticity of substitution between sector
specific capital and other inputs in each sector so that the elasticity of supply of the firms is
consistent with econometric evidence that indicates that the supply response depends on the level
of development and the technological complexity of the product. We also conduct sensitivity
analysis with respect to the sector elasticities of supply.
3.1 Perfectly Competitive Goods and Services Sectors
Regardless of sector, all firms minimize the cost of production. In the competitive goods
and services sectors, goods or services are produced under constant returns to scale and where
price equals marginal costs with zero profits. This includes agriculture, utilities, trade and “other
services.” 10 In these sectors, products are differentiated by country of origin, i.e., we employ the
Armington assumption. All goods producing firms (including imperfectly competitive firms) can
sell on the domestic market or export. Firms optimize their output decision between exports and
9
See Wonnacott and Wonnacott (1981) and Harrison, Rutherford and Tarr (2002).
10
Economies of scale are typically estimated to be small in agriculture, trade and other services.
7
domestic sales based on relative prices and their constant elasticity of transformation production
function. Having chosen how much to allocate between exports and domestic sales, firms also
optimize their output decision between exports to the three possible export regions, based on
relative prices the three regions and their constant elasticity of transformation production
function for shifting output between the regions.
3.2 Goods Produced Subject to Increasing Returns to Scale
The cost, production and competition structure for firms in this group of industries
follows Helpman and Krugman (1985). Goods are differentiated at the firm level. We assume
that manufactured goods may be produced domestically or imported from firms in any region in
the model. Demand in all countries for these goods is characterized by the constant elasticity of
substitution demand function. As the marginal utility of a good goes to infinity as the quantity
goes to zero, if a variety of the good is produced anywhere, some of it will be consumed in all
regions of the model. Firms in these industries incur a fixed cost of production and set prices
such that marginal cost (which is constant with respect to output) equals marginal revenue; and
there is free entry, which drives profits to zero. Costs are defined by observed primary factor and
intermediate inputs to that sector in the base year data. The cif import price of foreign goods is
simply defined by the import price, and, by the zero profits assumption, in equilibrium the import
price must cover fixed and marginal costs of foreign firms. Firms set prices using the
Chamberlinian large group monopolistic competition assumption within a Dixit-Stiglitz
framework, which results in constant markups over marginal cost for both foreign firms and
domestic firms.
In this model, all imperfectly competitive domestic firms (both goods and services
producers) face a downward sloping demand curve in each of their nine export markets. It
follows from symmetrically applying the Dixit-Stiglitz demand structure in all regions of the
model, where there is imperfect competition, that the elasticity of demand in each of the export
markets is the Dixit-Stiglitz elasticity of demand. Firms then set marginal revenue equal to
marginal costs in each of their nine export markets; then the export markets contribute to the
quasi-rents of the firm and affect the entry and exit decisions of firms.
For simplicity we assume that the ratio of fixed to marginal costs is constant with respect
to the non-output variables and parameters in the model in all firms producing under increasing
8
returns to scale (in both goods and services). This assumption in our Dixit-Stiglitz based
Chamberlinian large-group model assures that output per firm for all firm types remains
constant, i.e., the model does not produce rationalization gains or losses.
The number of varieties affects the productivity of the use of imperfectly competitive
goods based on the standard Dixit-Stiglitz formulation. The effective cost function for users of
goods produced subject to increasing returns to scale declines in the total number of firms in the
industry. But, since all countries consume some of any variety that is produced, the number of
varieties is determined by global demand and one country can affect the number of varieties only
insofar as it affects global demand.
3.3 Service Sectors in Which Foreign Direct Investment Occurs
These sectors are telecommunications, insurance services, other financial services, water
transportation services, air transportation services, other transportation services and professional
business services. In these services sectors, we observe that some services are provided by
foreign service providers on a cross border basis analogous to goods supply from abroad. But a
large share of business services is provided by service providers with a domestic presence, both
multinational and local. 11 Our model allows for both types of provision of foreign services in
these sectors.
The cost, production, demand and competition structure for firms in this group of
industries follows the same structure as the imperfectly competitive goods firms with two
differences. 12 The first difference is that we allow multinational service firms to establish a local
presence to compete with local firms directly. Multinational service firms produce a home region
specific variety, which is differentiated from domestic and other home region varieties. The
second difference, which is in contract to Balistreri, Jensen and Tarr (2011), is that downstream
firms do not experience a productivity increase from additional varieties, i.e., no variety
externality. Given that there are no rationalization gains or variety externalities, the model
exhibits an equivalence with respect to our policy changes to one in which all multinationals
11
One estimate puts the world-wide cross-border share of trade in services at 41% and the share of trade in services
provided by multinational affiliates at 38%. Travel expenditures 20% and compensation to employees working
abroad 1% make up the difference. See Brown and Stern (2001, table 1).
12
See Balistreri, Jensen and Tarr (2011) for greater detail.
9
from a specific home region are in perfect competition with each other. 13 That is, it is analogous
to the Armington structure, except that production also takes place in the host country.
For domestic firms, costs are defined by the costs of local primary factors and
intermediate inputs. When multinationals service providers decide to establish a local presence,
they will import some of their technology or management expertise. That is, foreign direct
investment generally entails importing specialized foreign inputs. Thus, the cost structure of
multinationals differs from national-only service providers. Multinationals incur costs related to
both imported primary inputs and local primary factors, in addition to intermediate factor inputs.
Foreign provision of services differs from foreign provision of goods, since the service providers
use local primary inputs. For multinational firms, the barriers to foreign direct investment raise
their costs of production. The reduction of the barriers lowers these costs, freeing the capital and
labor that was used to overcome the barriers for use elsewhere in the economy. Thus, the
reduction in the constraints on foreign direct investment allows the domestic economy to capture
rent rectangles. In addition, reducing barriers induces foreign entry until profits are driven to
zero, so there are also triangles of efficiency gains.
4. Data of the Model and Evidence for Key Elasticities
4.1 Ad Valorem Equivalents (AVEs) of the Barriers Against Foreign Suppliers of Business
Services
A new database of the ad valorem equivalents of barriers against foreign providers of
services in eleven business services sectors in 103 countries was developed for this project. We
also commissioned new surveys of the regulatory regimes in services of Kenya and Tanzania as
a basis of estimating both the discriminatory barriers against foreign service providers as well as
the non-discriminatory regulatory barriers that impact both domestic and foreign suppliers of
services. This work is documented in Jafari (2014a; 2014b; 2014c; 2014d and 2014e).
13
Development of a multi-region model version that includes the full monopolistic competition structure for
services found in the small-economy model of Balistreri, Jensen and Tarr (2011) is a challenging goal for future
research. The formulation would require significant first-order data development, however, that reconciles the
multi-region GTAP accounts on cross-border service provision with country-level data. Faced with the data
challenges we are more comfortable in this report to make the conservative assumption that there are no variety-
induced productivity impacts in the business services sectors.
10
4.1.1World Bank Services Trade Restrictiveness Indices Database. The data for the
estimates of the barriers faced by foreign suppliers of services comes from the recently released
World Bank database of survey information on these 11 sectors in 103 countries. The World
Bank survey is a 169 page questionnaire of the regulatory regimes of these 11 sectors that was
completed by law firms resident in each of the 103 countries. 14 Combining the data and
methodology, they produced “Services Trade Restrictiveness Indices,” for all eleven sectors in
all of the 103 countries.
4.1.2 Methodology based on the Australian Productivity Commission Methodology.
Although we use the data of the World Bank database, we are unable to use the Borchert et al.
Services Trade Restrictiveness Indices, since they did not transform these into ad valorem
equivalents. Our methodology builds on a series of studies supported by the Australian
Productivity Commission, which develop alternate Services Trade Restrictiveness Indices and
also convert these indices into ad valorem equivalents. We rely especially on the papers by
Warren (2000) in telecommunications, Kalirajan et al., (2000) in financial services, Kang (2000)
in transportation services and Nguyen-Hong (2000) in engineering services. For each of these
service sectors, the authors first developed a matrix to evaluate and score the regulatory
environment in the sector. They collected data and assessed the regulatory regimes of many
countries. Evaluations of each criterion were transformed into a quantitative score and weights
were assigned to each criterion so that the regulatory regimes of each country were transformed a
“restrictiveness index.” They then regressed the price of services against their restrictiveness
index and other relevant variables to determine the impact of the regulatory barriers on the price
14
The database is available at http://iresearch.worldbank.org/servicetrade/. See Borchert, Gootiiz and Mattoo (2012)
for a guide to the database. They also developed a methodology for evaluating the restrictiveness of the services
regimes against foreign suppliers.
11
of services. 15 Through this regression, it is possible to obtain ad valorem equivalents of the
regulatory barriers in the countries of their sample.
Our methodology defines a mapping from the World Bank database to the scoring
matrices of the Australian authors. In this manner, we score the regulatory regimes of the 11
sectors in the 103 countries according to the Australian authors’ criteria. We adopt the World
Bank terminology and call these scores Services Trade Restrictiveness Indices (STRIs).
Our methodology assumes that the international regressions estimated by the Australian
authors to get AVEs applies to our 103 countries, and this allows us to convert our STRIs into
AVEs. The estimates and documentation are available in Jafari (2014c).
Since our model contains the eight business services sectors of the GTAP database, not
all 11 of the World Bank data set, we had to aggregate fixed line and mobile telecommunications
into communications; road and rail transport into transport not otherwise classified; and legal and
accounting and auditing services into business services, not otherwise classified. In addition,
since our model contains ten regions, we had to aggregate regions according to our mapping in
appendix A. The results for our six African regions are in tables 4a to 4f. Jafari (2014d) explains
the methodology and data used in these aggregations to arrive at the ad valorem equivalents of
the sectors and regions of our model.
Kenya and Tanzania. We refer the reader to Balistreri, Jensen and Tarr (2011) for a
summary of the key institutional and policy issues in telecommunications, banking, insurance
and transportation in Kenya; and to Jensen, Rutherford and Tarr (2010) for Tanzania. We note,
however, that there have been substantial changes in the services regulatory regimes in both
countries in the past few years. Probably the most significant development has been the failure in
both countries of the private company commissioned to regenerate freight train traffic. Cargo
traffic on the roads is very overloaded, as the volume of freight traffic vastly exceeds the
capacity of the facilities.
15
Warren estimated quantity impacts and then using elasticity estimates was able to obtain price impacts.
12
4.2 Ad Valorem Equivalents (AVEs) of the Non-Discriminatory Barriers Against Suppliers
of Business Services in Kenya and Tanzania
Some regulatory barriers, such as complete bans on entry, are non-discriminatory in that
sense that they impose costs on both domestic suppliers of services and foreign suppliers of
services. In the cases of Kenya and Tanzania, we also estimate the AVEs of non-discriminatory
barriers. We commissioned new updated surveys (using the World Bank survey instrument) and
obtained supplemental information on the regulatory regimes. 16 Based on these surveys and
supplementary information in both countries, Jafari (2014a; 2014b) scored the regulatory
regimes according to the Australian methodology to develop services trade restrictiveness
indices (STRIs) for the non-discriminatory barriers to services providers in the 11 sectors of the
World Bank survey in Kenya and Tanzania. He then used these STRIs and the Australian
regressions to estimate the ad valorem equivalents of the non-discriminatory barriers in Kenya
and Tanzania. The results for the AVEs are in tables 4a and 4b.
4.3 Estimates of the Ad Valorem Equivalents of the Costs of Time in Exporting and
Importing
In order to estimate the impact of improved trade facilitation, in this paper we apply a
new data set based on the path-breaking work of David Hummels and his co-authors (Hummels,
2007; Hummels and Schaur, 2013; Hummels et al., 2007). These authors estimate the time cost
of trade by product and country in two steps: (i) they first estimate the cost of one day delay by
product; (ii) they combine their estimates of the per day cost of trade by product with the Doing
Business database on the number of days to export or import by country. This yields the costs
Using the estimates of Hummels and his co-authors, Peter Minor (2013) provided estimates for
the regions and products in the GTAP database on a bilateral basis. We use estimates from Peter
Minor, which we aggregate to the sectors and regions of our model, yielding the cost of trade by
16
We thank Ms. Sonal Sejpal of the law firm of Anjarwalla & Khanna Advocates in Nairobi and Cyril Pesha and his
law firm associates in Dare es Salaam for leading this research effort in Kenya and Tanzania, respectively.
13
product and country on a bilateral trade basis. 17 Documentation of the steps we have taken, a
brief explanation of the methodology and a detailed explanation of our aggregation methodology
are in appendix C.
4.4 Estimates of the Ad Valorem Equivalents (AVEs) for Non-Tariff Measures (NTMs) for
the Regions of our Model
Our estimates of the AVEs of NTMs are based on the estimates of Kee et al., (2008;
2009), which in turn are based on the theoretical developments of Anderson and Neary (1996;
2003). Kee et al. estimate the AVEs of NTMs for 105 countries at the 6 digit level. These
estimates, as well as aggregated estimates for manufacturing and agriculture for the 105
countries, are available on the World Bank website. 18
The measure we use from Kee et al. is the uniform tariff equivalent that generates the
same level of import value for the country in a given year. 19 Kee et al. provide estimates based
on both applied and MFN tariffs; the measure we use is based on applied tariffs, which take into
account bilateral trade preferences. At the six digit level, the estimates of Kee et al. are
sometimes subject to a substantial margin of error that may lead to misleading results in a CGE
model policy analysis. Consequently, we have chosen to use the aggregated estimates of Kee et
al. at the sector level, i.e., for each country, we have two AVEs for each country: one AVE of the
NTMs in manufacturing and one AVE of the NTMs in agriculture. We then further aggregate
these values for 93 countries to the regions of our model. Details are available in appendix B.
4.5 Tariff Data
4.5.1 Kenya. Most-Favored Nation (MFN) tariff rates at the eight digit level were taken
from the website of the Kenyan government: www.kra.go.ke/customs/customsdownloads.php.
These tariff rates were then aggregated to the 55 sectors of the model of Balistreri, Rutherford
17
We thank Peter Minor for his cooperation with us in this process. The estimates are subject to a margin of error
for various reasons, including that the procedure relies on the Doing Business database and thus assumes the same
number of days for all products in a country throughout the year.
18
The data set is available at
http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:22574446~pagePK:
64214825~piPK:64214943~theSitePK:469382,00.html
19
Specifically, we take the difference between the Overall Trade Restrictiveness Index (OTRI) and for the Tariff-
only OTRI (OTRI_T), which gives us the AVE of the NTMs.
14
and Tarr (2009), using simple averages. Since these are MFN tariff rates, they exceed the
collected tariff rates due to tariff preferences to regional partners and due to other preference
items or tariff exemptions. Thus, they exaggerate the protection received by Kenyan industry and
agriculture. Consequently we obtained the value of overall customs duties and other taxes
applied only on imports from the Kenyan Economic Survey for 2006. We then took the ratio of
the total taxes on imports to the total value of imports to obtain the average value of import taxes
in the Kenyan economy. In 2005, this was 8.4 percent. That is, on average, Kenyan importers
paid 8.4 percent of the value of imports on import taxes that did not apply to domestic
production. We then scaled all the MFN tariffs in the model of Balistreri, Rutherford and Tarr
(2009) so that the weighted average import tax is 8.4 percent. For the present model, we mapped
the 55 sectors of Balistreri, Rutherford and Tarr (2009) to the 18 sectors of our model and took a
trade weighted average of the tariff rates of the 55 sectors of Balistreri, Rutherford and Tarr
(2009) to obtain the tariff rates for the 18 sectors of the present model. The results are presented
in table 4a.
4.5.2 Tanzania. We were fortunate to receive unusually detailed collected tariff data
from the Tanzania Revenue Authority. That is, we received data on collected import duties
(tariffs) and import values at the eight digit tariff line level. The collected tariff rates for the
sectors in our model are obtained by first aggregating the eight digit tariff line level tariff
collections and import values to the 52 sectors of our the model of Tanzania by Jensen,
Rutherford and Tarr (2010). The ratio of tariff collections to import values for each sector of the
model of Jensen and Tarr (2010) is then calculated to give estimates of the collected tariff rates.
Applying these tariff rates across all sectors implies that tariff revenue in the revised database is
about 1.3% of GDP, which is consistent with collected revenues in Tanzania. 20 The data for
import tariffs are replaced with collected tariff rate data for the year 2006. For the present model,
we mapped the 52 sectors of Jensen, Rutherford and Tarr (2010) to the 18 sectors of our model
and took a trade weighted average of the tariff rates of the 52 sectors of Jensen, Rutherford and
Tarr to obtain the tariff rates for the 18 sectors of the present model.
Given that Tanzania participates in preferential trade areas with the East Africa Customs
Union and the South African Development Community, it was necessary to make further
adjustments. That is, since, in principle, tariff rates should be zero within these preferential trade
20
For the year 2006, aggregate data from Tanzania show that tariff collections are 1.47 percent of GDP.
15
areas, we set tariff collections on imports from SADC, Kenya, Uganda and Rwanda at zero. We
then increased the tariff rates for the other regions in our model so that the overall weighted
average collected tariff rate is unchanged. We used the trade flow data, disaggregated by regions
and sectors of our model to weight the tariff rates. This adjustment has the impact of raising the
collected tariff rates for the regions in our model where positive tariff rates apply. The resulting
adjusted tariff rates are reported in Table 4b.
4.5.3. Regions other than Kenya or Tanzania. Tariff rates for the eight regions other
than Kenya and Tanzania in our model are taken from the GTAP 8.1 database. GTAP 8.1 uses
the third version of the MAcMap-HS6 database. This database is jointly developed by the Paris
based think tank CEPII and the International Trade Center (ITC) of Geneva and is documented
in Guimbard, Jean and Mimouni (2011). It is based on the ITC’s raw data. The authors
calculated applied protection, including incorporating preferential tariff protection in 2007 and
the ad valorem equivalents of specific tariffs. They estimate the world average applied protection
level in 2007 to be 4.4 percent. Compared to 2004, this is a decline by nearly 0.7 percentage
points, mainly due to unilateral liberalizations and to new preferential trade agreements. The
increasing share of developing countries in world trade, where protection is higher, tended to
raise the world average, but this was counterbalanced by the decline in the ad valorem equivalent
of specific tariffs, linked to the surge in world prices of agricultural products. The results for our
sectors and African regions other than Kenya and Tanzania are in tables 4c, 4d, 4e and 4f.
4.6 Social Accounting Matrices
The core data of the model comes from the GTAP 8.1 data set, described on the GTAP
website: https://www.gtap.agecon.purdue.edu/databases/default.asp. The GTAP 8.1 data set
contains 57 sectors and 129 regions. We explain our aggregation of the data set in appendix A.
4.7 Trade Data by Regional Partner and Sector
To obtain data on imports and exports from the different regions of our model, we used
trade data from the GTAP 8.1 data set. Although trade data are available from WITS access to
the COMTRADE database, the data must constitute a balanced data set that satisfies all
accounting identities. For example, exports of any product from a region of our model must
equal the imports of the rest of the world for this product from that region. And for each region
16
and product, exports plus domestic consumption must equal imports plus domestic production.
The GTAP data set satisfies all accounting identities.
4.8 Share of Market Captured by Foreign Direct Investors in Services and by Cross-Border
Sales of Services
It was necessary to calculate the market share of foreign direct investors by source region
in the services sectors of our model by host region. That is, take the banking sector in Tanzania
as an example. We need to know the share of the market captured by banks from all the regions
of our model, i.e., Tanzania, Kenya, Uganda, Rwanda, COMESA, SADC, the EU, US, China
and the Rest of the World. The results of our calculations for our African regions are available in
tables 6a to 6f. It is also necessary to calculate the share of cross border services in each of our
regions for these seven sectors. For cross-border sales of services, we use the data from the
GTAP 8.1 data set.
4.8.1 Kenya and Tanzania. In the cases of Kenya and Tanzania, we built the data set
for this project. Detailed commercial databases were employed, where available. This included
Bankscope for banking and Axco for insurance, which list all companies active in Kenya and
Tanzania in these sectors. For ownership shares of the companies by region of our model, the
websites of the companies often had to be consulted. In telecommunications, the
telecommunication regulatory authorities in Kenya and Tanzania list all active companies and
their market shares in mobile and fixed line services. Details and documentation of the
calculations are available in Jafari (2014e), who built on and updated earlier work by Worley
(2009). The results for Kenya and Tanzania are presented in tables 6a and 6b.
4.8.2 Regions other than Kenya and Tanzania. Other than for our seven business
services sectors in Kenya and Tanzania as discussed above, and insurance and communications
services in our Africa regions, the basic data source for foreign affiliate sales is the database
developed by Fukui and Lakatos (2012). This database has some advantages over earlier efforts
to develop databases of foreign affiliate sales. First, it is based on sales data, rather than using
investment data as a proxy for sales data. Second, Fukui and Lakatos use the Eurostat database of
data on foreign affiliate sales in 41 countries (including the United States), rather than relying on
data from the United States alone. Despite having data for 41 countries, there are a large number
17
of countries in the GTAP data set for which the foreign affiliate sales data are missing. Fukui and
Lakatos use an econometric model to estimate the missing values and thus produce estimates for
all regions and sectors in the GTAP data set. For the share of sales in the sector by the host
country, we use the GTAP data set for total sales in the sector; we subtract the total of foreign
affiliate sales from total sales to obtain the host country share of sales.
In the case of insurance services in African regions, we used the Axco database as our
primary data source. Documentation is in appendix D. In the case of telecommunications
services in our six African regions, we used communications commission data and other publicly
available sources, taking South Africa as our proxy for SADC. Details are in appendix E.
4.9 Share of Expatriate Labor Employed by Multinational Service Providers
The impact of liberalization of barriers to foreign direct investment in business services
sectors on the demand for labor in these sectors will depend on the share of expatriate labor used
by multinational firms. We explain in the results section that despite the fact that multinationals
use domestic labor less intensively than their domestic competitors, if multinationals use mostly
domestic labor, their expansion is likely to increase the demand for domestic labor in these
sectors. 21 For this version of the paper, we used representative estimates from earlier studies of
the share of expatriate labor or specialized technology not available to domestic firms. In earlier
studies we have found that multinational service providers use mostly local primary factor inputs
and only small amounts of expatriate labor or specialized technology.
4.10 Key Elasticities
Our key elasticities are shown in table 7. Dixit-Stiglitz elasticities in services are based
on estimates from Broda and Weinstein (2006). Dixit-Stiglitz elasticities in goods are the
estimates of the elasticity of substitution for imports from different regions in the GTAP data set.
Armington elasticities of substitution of domestic for foreign are from the GTAP data set.
Armington elasticities of substitution of imports from different regions in the four CRTS sectors
listed in table 7 are based on the estimates of Reidel (1988). Supply elasticities are based on the
estimates of Schiff, Wang and Olarreaga (2002) and Schiff and Wang (2006); the methodology
21
See Markusen, Rutherford and Tarr (2005) for a detailed explanation on why FDI may be a partial equilibrium
substitute for domestic labor but a general equilibrium complement.
18
and additional empirical and theoretical justification for the supply elasticities is provided in
Jensen and Tarr (2010).
5. Results for East African Customs Union Preferential and Multilateral
Policies to Reduce Trade Costs
5.1 Deep Preferential Integration Within the East African Customs Union (EACU)
We execute several scenarios in our multi-region trade model to assess the impacts of the
reduction in trade costs by the East African Customs Union on Kenya and Tanzania, as well as
on Uganda and Rwanda. 22 We decompose the trade costs into three categories: excessive trade
facilitation costs; non-tariff barriers; and barriers on foreign providers of services. In the case of
trade facilitation, as we have explained in more detail in appendix C, there are several reasons to
take modest cuts in these barriers. These include that the most efficient countries in the world,
such as Singapore, the Republic of Korea and Hong Kong SAR, China, have not cut the time
cost of trade to zero; and part of the costs are due to infrastructure deficiencies which can’t be
addressed through policy alone. There are, however, some collaborative projects and plans
among members of the EACU (see East African Community Secretariat, 2011), such as common
customs posts, designed to cut the time costs of trade. Consequently, we assume a 20 percent cut
in the ad valorem equivalents of the time cost of trade within EACU. Since there is likely a
spillover benefit of these measures that will cut the time costs of trade outside of the EACU, we
take a 5 percent cut in these costs for trade with countries outside of the EACU.
Similarly, under the auspices of the East African Community, the member countries are
undertaking collaborative efforts to reduce non-tariff barriers (see, for example, East African
Community, 2012). Non-tariff measures, however, have become much more subtle in the post-
Uruguay Round world. Most measures have a legitimate regulatory function and distinguishing
the legitimate regulations from protective or inefficient regulations is complicated.
Consequently, we take a more modest 20 percent reduction in the ad valorem equivalent of these
barriers.
22
Burundi is a fifth member of the EACU. Since Burundi is not represented as a separate region in the GTAP 8.1
data set, it is not a separate region of our model and we do not have separate results for Burundi.
19
Finally, on July 1, 2010, the East African Community adopted a Common Market
protocol that called for the free movement of services within the five member states, along with
the free movement of goods, capital and labor. 23 We take a 50 percent cut in these barriers.
5.1.1 Aggregate Welfare Effects of Deep Preferential Integration by the East
African Customs Union. Our aggregate results for Kenya, Tanzania, Uganda and Rwanda are
presented in table 8. Under the column labeled “EACU Central,” we report our findings for the
impacts of combined cuts in trade facilitation, non-tariff barriers and services barriers. The
welfare gains are presented as Hicksian equivalent variation as a percent of consumption. We
find that all four countries gain from this deep integration, with gains ranging from a low of 0.9
percent of consumption in the case of Tanzania to a high of 1.4 percent of consumption in the
case of Rwanda. To examine the source of these gains, we execute three additional scenarios in
which we allow only one of the reforms to be implemented in each case.
5.1.2 Preferential Reduction of Time in Trade Costs by EACU. In the case of deep
preferential integration within the East African Community (EAC), the reduction in time in trade
costs constitute the largest share of the gains—about two-thirds of the total gains in the cases of
Kenya and Tanzania, but over 80 percent of the gains in the cases of Uganda and Rwanda. We
assume that the time in trade costs consume capital and labor in the home country equal to the ad
valorem equivalent of the time costs of the imported product (from a country) times the
benchmark value of the imports plus the time costs of the exported product (to a country) times
the benchmark value of the exports. Reduction of the time in trade costs by 20 percent within
the EACU and by 5 percent for countries outside the EACU, leads to freeing up of 20 percent of
the capital and labor devoted to overcoming the time costs of trade within the EACU on both
imports and exports and 5 percent of the capital and labor devoted to overcoming the time costs
of trade outside the EACU on both imports and exports. To provide concrete values for these
estimates, in table 9 we show the value of the rents recaptured by any of the policies simulated.
In the case of improved trade facilitation, we see that rents recaptured as a percent of domestic
consumption are 0.37 percent in Kenya and 0.41 percent in Tanzania. These are “rectangles” of
gains. The reduction of the costs of trade results in an increase in the returns to exporting relative
to domestic sales and a decrease in the cost of imports relative to domestic production. As a
23
For the text of the protocol, see: http://www.eac.int/commonmarket/index.php. See also Dihel, Fernandes, Mattoo
and Strychacz (2010) for a discussion of liberalization of professional services in East Africa.
20
result, there are also “triangles” of efficiency gains from increased trade. Aggregate trade
increases in all four EACU countries, ranging from 2.7 percent in Tanzania to 8.8 percent in
Rwanda. Rents as a share of the total welfare gain range from 50 percent in the case of Uganda
to about 83 percent in the case of Rwanda. .
5.1.4 Reduction of Non-Tariff Barriers within the EACU. For Tanzania and Kenya,
the next most important source of gains is the reduction of non-tariff barriers within EACU by 20
percent. Hicksian equivalent variation increases by 0.1 percent in the case of Kenya and 0.17
percent in the case of Tanzania. As with the time costs of trade, we assume that the non-tariff
barriers result in a loss of capital and labor in the home country devoted to overcoming these
barriers. Total rents from the non-tariff barriers are equal to the ad valorem equivalent of the
non-tariff barrier of the product times the benchmark value of the imports. Since we assume a 20
percent reduction in barriers, 20 percent of the benchmark value of the rents are captured and are
“rectangles” of gains. Unlike improved trade facilitation, non-tariff barriers only result in
captured rents on the import value. We also limit the reduction in non-tariff barriers to partner
countries within the EACU. We can see from table 9 that there are substantially fewer rents
affected by the reduction of non-tariff barriers, compared with trade facilitation. Recaptured rents
are equal to .016 percent of consumption for Kenya and 0.218 percent of consumption in the case
of Tanzania. Since the ad valorem equivalents of the non-tariff barriers are not large in the cases
of Uganda and Rwanda, the welfare gains are only .04 and .03 percent of consumption,
respectively. Analogous to the reduction in trade costs, the reduction of the non-tariff barriers
results in a decrease in the cost of imports relative to domestic production. As a result, there are
also “triangles” of efficiency gains from increased trade. Trade increases in all four countries,
with the maximum increase of 1.8 percent in Tanzania.
5.1.5 Preferential Reduction of Barriers against EACU Service Providers. Fifty
percent preferential liberalization of services barriers results in gains of .04 percent of
consumption in the case of Kenya and .03 percent of consumption in the case of Tanzania. Only
in the case of Rwanda within the EACU are the gains from services liberalization greater than
reduction of non-tariff barriers. In our central scenario, we assume that it takes domestic capital
and labor to overcome the costs of the barriers against foreign providers of services, both those
that supply the domestic markets through FDI and also through cross-border services. Thus,
there are potentially rectangles of recaptured rents from reducing the barriers on EACU foreign
21
suppliers of services in other EACU markets. We say potential, since if there are no sales of
services from partner countries initially, there are no rents to be recaptured. In table 9, we show
separately the recaptured rents on FDI and the recaptured rents on cross-border sales of in
services. The recaptured rents on FDI from partner countries in the EACU are equal to .026
percent of consumption in the case of Kenya and .006 of consumption in the case of Tanzania.
There are very small flows of cross-border trade in our business services within EACU, however,
so only Rwanda has a positive rent rectangle, when measured at three digits. With the reduction
in the barriers on EACU suppliers of services within EACU, there are production and
consumption efficiency gains, which explain the difference between the total welfare gains and
the recaptured rents.
5.1.6 Why the Lack of Trade Diversion. Economic theory of preferential tariff
liberalization has demonstrated the possibility of losses to the member countries from
preferential reduction of tariffs. Imports from countries that are not partner countries will be
displaced and the welfare loss in consumers’ surplus analysis may be measured by the loss of
tariff revenue on those imports. The higher the tariffs on excluded countries, the greater the
possibility of welfare losses from preferential tariff liberalization. Nonetheless, in the three
preferential reforms that we have considered, we have shown gains for all four countries for all
three reforms. Although there is no tariff revenue that is lost from any of these three reforms,
Jensen and Tarr (2011) have shown that if some of the rents from the barriers are captured by
domestic agents, then these rents play the same role as tariffs in the welfare analysis of
preferential trade policy. Key to the explanation is that in our central scenario, we have assumed
that the rents are dissipated, so there is no welfare loss on displaced imports or services from
suppliers of excluded countries. In the sensitivity analysis, we assess the implications of the rent
capture assumption.
In Balistreri, Jensen and Tarr (2011), we have shown that there is the possibility of
welfare reducing preferential liberalization of barriers against FDI in services. If the excluded
countries are more efficient suppliers of services or have better technologies that increase
productivity more, then this could result in welfare reducing preferential liberalization of
services. Unlike the driving mechanism of Balistreri, Jensen and Tarr (2011), in the present
model, additional foreign suppliers of services do not induce productivity increases in the
country that has the additional suppliers of services. So this mechanism for potential welfare
22
reducing preferential liberalization of FDI is not present. Balistreri, Jensen and Tarr (2011) have
shown, however, that preferential liberalization of services can be expected to increase welfare
unless there is initial rent capture of the barriers against FDI. So again, we will investigate the
important issue of initial rent capture in the sensitivity analysis below.
5.2 EACU Multilateral Liberalization
While the above estimates indicate that there are gains from deep integration within the
EACU, with a combined GDP of only $85 billion, the EACU is not a large market and economic
theory indicates that there should be substantially greater gains from integrating more deeply into
the world trading environment. In the scenario labeled “EACU Liberal,” we assess the extent of
these larger gains. In EACU Liberal, we extend the liberalizations of non-tariff barrier and
services barriers implemented in “EACU Central” to all trading partners in the world. That is, we
assume a 20 percent cut in the ad valorem equivalents of the non-tariff barriers applied on
imports from all regions in the model into Kenya, Tanzania, Uganda and Rwanda. And we
assume that the member countries of the EACU implement reforms that result in a 50 percent cut
in the ad valorem equivalents of the barriers on foreign providers of services from all regions. In
the case of the time in trade costs, we do not extend these multilaterally on the grounds that the
improvements that can be made are primarily regional and we already convey a 5 percent cut in
these barriers for countries outside of the EACU.
We see that the gains for all four of our EACU countries increase substantially. For
Kenya and Uganda, the gains are about twice as large; for Rwanda the gains increase
substantially from 1.4 percent to 4.95 percent of consumption. The biggest increase in welfare is
for Tanzania; the welfare gain dramatically increases from 0.95 percent of consumption to 7.11
percent of consumption.
We execute two new policy scenarios to decompose the policy changes in EACU Liberal.
We assess EACU Liberal with only services liberalization and EACU Liberal with only non-
tariff barrier liberalization. The trade facilitation results are the same as under EACU Central
(only Trade Facilitation), so are already reported and discussed above. In the case of Tanzania,
the big increase in welfare is clearly due to the broader liberalization of non-tariff barriers. The
wider liberalization of non-tariff barriers results in a welfare gain of more than 5 percent of
consumption, whereas the welfare gains were only 0.17 percent of consumption in the EACU
23
central case. This large increase is explained by two factors: (i) as shown in table 4b, the ad
valorem equivalents of the non-tariff barriers are 47.4 percent in manufacturing and 22.2 percent
in agriculture. This is substantially higher than the estimates for the other EACU countries; and
(ii) as shown in table 5b, the vast majority of Tanzania’s trade is with countries outside of the
EACU. Thus, the reduction of NTB barriers impacts a much larger share of trade, generating
more recaptured rents and greater efficiency gains.
The other country in EACU to see much larger gains is Rwanda, but in the case of
Rwanda it is due to wider services liberalization. We see in table 4d, that the ad valorem
equivalents of the non-tariff barriers are less than 5 percent, so the gains from NTB liberalization
are much smaller than in Tanzania. But the ad valorem equivalents of barriers to foreign service
providers are substantial, with four sectors having AVEs of between 25 and 62 percent. From
table 6d, we see the market share of EACU services firms is zero except of Kenyan insurance
firms, but there is substantial FDI in Rwandan services from regions outside of EACU; so the
broader liberalization has a much larger impact.
5.3. Reduction of Non-Discriminatory Barriers in Services in Kenya and Tanzania
In table 10, we present the results of two scenarios where we assess the impacts in Kenya
and Tanzania, respectively, of reform of domestic regulations that impose costs on both domestic
and foreign suppliers of business services in a non-discriminatory manner. We implement two
symmetric scenarios. In the scenario for Kenya (Tanzania), we assess the impacts in Kenya
(Tanzania) of a 50 percent reduction of the ad valorem equivalents of the non-discriminatory
barriers to suppliers of services in Kenya (Tanzania). We see that the 50 percent reduction in the
non-discriminatory barriers result in gains in Hicksian equivalent variation equal in Kenya equal
to 1.4 percent of domestic consumption and equal to 2.2 percent of domestic consumption in the
case of Tanzania. Comparing results in table 10, this exceeds the gains from the 50 percent
reduction in discriminatory barriers in the EACU Liberal scenario, where Kenya is seen to gain 1
percent of consumption and Tanzania gains 1.2 percent. Thus, while the discriminatory barriers
are important and are the focus of international negotiation, the regulatory barriers that impose
costs on domestic suppliers and foreign suppliers of services in a non-discriminatory manner are
quantitatively more important in these cases.
24
The larger gains from the reduction of non-discriminatory barriers are due to the larger
base from which the barriers are reduced. The non-discriminatory barriers affect all providers of
services, so the base of the recaptured rents and the distortion triangle is larger. That is, as in our
central scenarios above, we assume that it takes domestic capital and labor to overcome the costs
of the barriers. Thus, when there are positive AVEs of the barriers, there are “rectangles” of
recaptured rents from reducing the barriers; and there are recaptured rents from all forms of
supply of services: domestic sales, FDI and cross-border supply. There are also “triangles” of
efficiency gains as there will be more business services supplied at a lower price when the
inefficient regulatory barriers are reduced.
5.4 The Tripartite Free Trade Area: EACU Deep Integration with COMESA and SADC
In the Tripartite preferential liberalization scenario, all six of our African regions execute
identical preferential liberalization, analogous to the EACU Central scenario. Take Kenya as an
example. We assume that the ad valorem equivalents of the trade facilitation barriers (on both
exports and imports) are reduced by 20 percent for partner countries (Tanzania, Uganda,
Rwanda, COMESA and SADC) and 5 percent for non-partner countries (USA, EU, China and
ROW). We assume that the ad valorem equivalents of the non-tariff barriers are reduced by 20
percent for partner countries in the Tripartite grouping, but no reduction for non-partner
countries. In the case of discriminatory barriers against foreign investors in services, we assume
that the initial ad valorem equivalents of the discriminatory barriers against foreign suppliers of
services are reduced by 50 percent of their benchmark value in the seven business services
sectors for partner countries; we assume no change in the barriers for excluded regions. Finally,
we impose zero tariffs in trade within the Tripartite region. Kenya already practices tariff free
trade with EACU and COMESA countries, so this implies a lowering of tariffs against SADC
countries that were not members of COMESA, and improved market access for Kenyan
exporters in SADC countries that were not members of COMESA. We assume that non-tariff
barriers apply to all countries, so in this scenario, the cuts in non-tariff barriers result in improved
market access for the members of the Tripartite Free Trade Area to each other’s markets. In
order to assess the relative importance of each of the three components of the trade costs
25
reduction scenario, we execute three additional scenarios where each of the three components is
liberalized separately, and where we simulate preferential tariff reduction.
The results are presented in table 11. For all four EACU countries, the aggregate welfare
gains exceed deep integration within the EACU alone, but are considerably less than multilateral
liberalization by the EACU. In the cases of Tanzania and Rwanda, the gains are very
significantly smaller than with multilateral liberalization. In the case of preferential liberalization
of services alone, the gains are very significantly reduced compared with the multilateral
liberalization scenarios due to the relatively low market shares of partner countries in the
services sectors of the Tripartite countries. The exception to this pattern is Kenya, which has a
significant share of the insurance market in COMESA, where we estimate a very high ad
valorem equivalent of the barriers to services providers. The improved market access for Kenyan
insurance suppliers under the protected umbrella of very high barriers creates substantial gains
for insurance services suppliers from Kenya in COMESA markets. We verified this this
explanation by executing a scenario in which we preferentially liberalize services barriers within
the Tripartite area, but exclude preferential reduction in insurance services barriers. In this
scenario, the estimated gains to Kenya fall dramatically to 0.2 percent of consumption,
significantly less than multilateral liberalization of services. The trade facilitation gains are larger
than in the EACU Central or EACU Liberal scenarios, since the reductions in the AVE of time
costs of trade extend to two additional regions of the model. In the case of Kenya, trade diversion
dominates the tariff reduction scenario and there is a slight loss from tariff liberalization, despite
the improved market access.
5.5 Sector Impacts: Diverse Trade Facilitation Impacts and the Political Economy of
Regionalism
In tables 12a-12e, we present the results at the sector level for output in the six African
regions of our model. These results are presented for all the scenarios discussed above. Given
that we assume that total employment and the capital stock are fixed in the medium term, if labor
expands in some sectors, it must contract in other sectors. There are two key insights from the
results on the changes in sector output.
5.5.1 Trade Facilitation Impacts on Sector Output vary with the Ad Valorem
Equivalents at the Sector Level. First, our more accurate database on the time costs of trade
26
leads to a much more diverse and accurate assessment of the output changes at the sector level.
Previous efforts at simulating sector output changes from trade facilitation used uniform ad
valorem equivalents across sectors, which led to more uniform impacts on sector output. The
data set we have used has the barriers varying by product and by country of origin or destination
(due to the product mix of exporting and importing). Consequently, the predicted changes in
output at the sector level vary considerably. For example, in Uganda, one of the sectors with the
highest AVE of the time costs of exporting (importing) within EACU is agricultural products,
where the Ugandan AVE is about 40 (30) percent, depending on the destination (origin)
country. 24 Reduction of the time costs of trade barriers then leads to an expansion of agriculture
in Uganda relative to other sectors.
5.5.2 Political Economy of Regional Trade Liberalization The trade liberalization
scenario we consider is the liberalization of non-tariff barriers. We find that preferential
liberalization results in a substantially muted output change compared with multilateral
liberalization. Take Tanzania as an example (which has the highest AVEs of its NTBs in our data
set). The gains from multilateral reduction of NTB barriers alone are about 25 times greater than
the gains from liberalization within EACU alone. But, the maximum output decline at the sector
level from NTB liberalization within EACU alone is 2 percent, compared with output declines of
9.5 percent for textiles and apparel, 11.8 percent for other manufacturing and 13.8 percent for
wood and paper products. Thus, although the welfare gains of preferential liberalization are
dramatically smaller than multilateral liberalization, the adjustment costs are also smaller.
Empirical studies have shown that the adjustment costs of trade liberalization are dramatically
smaller than the welfare gains. 25 However, policy makers often receive strong lobbying from
those who suffer adjustment costs from trade liberalization, while those who gain are more
diverse and may not realize they gain from trade liberalization, and typically do not lobby for
liberalization or lobby much less vigorously. Thus, these results explain some of the appeal of
regional liberalization to policy makers, despite the larger net gains of broader liberalization.
6. Sensitivity Analysis
24
The partner country AVE is also relevant in assessing impacts.
25
Matusz and Tarr (2000) summarize the evidence on the adjustment costs of trade liberalization.
27
In this section we assess the impact of parameter values and the key modeling assumption
of rent capture on the results. Through our “piecemeal sensitivity analysis” we will determine the
most important parameters for the results, and we will assess how important the rent capture
assumption is for the results. We examine the two most important aggregate policy scenarios:
EACU Deep Integration and EACU Multilateral Liberalization. Our results are presented in
tables 13a-13d.
6.1 Impact of Rent Capture Assumption
In our central scenarios we assume that it takes capital and labor to overcome the barriers,
that the rents from the barriers are “dissipated,” and are recaptured by the domestic economy in
the central scenarios. The value of the capital and labor freed up by the reduction of the barriers
we call the rents available in the scenario and these values are displayed in table 9. It is possible,
however, that some of the barriers are not dissipated, but instead generate rents that are captured
by domestic agents in our initial equilibrium. In the counterfactual scenario then, the rents that
are captured rents initially by domestic agents are not available as a net welfare gain since they
are a loss to domestic agents. The “triangle” of efficiency gains will remain, but there will not be
a gain of rents if they are initially captured by domestic agents, so the welfare gains should be
smaller when there are initial rents captured by domestic agents.
28
In tables 13a-13d, the row labeled θr represents the share of rents captured initially by
domestic agents. We retain all other modeling and parameter assumptions, but allow the initial
rent capture share to be either zero (central value ) or 1 (upper value). In the case of Kenya, we
see that the welfare gains do not change considerably in the case of deep integration within
EACU (reflecting small available rents), but the welfare gains fall by more than 50 percent in the
case of EACU liberal. This reflects large available rents in the case of services and non-tariff
barriers. In the case of Tanzania, the impact is very dramatic. In the case of EACU multilateral
liberalization, the welfare gain falls from more than 7 percent of consumption to about 1 percent
of consumption, deriving mostly from the large rents that are impacted by multilateral
liberalization of NTBs in Tanzania. Results for Uganda and Rwanda are between Kenya and
Tanzania in terms of percentage reduction of the gains due to initial rent capture.
6.2 Piecemeal Sensitivity Analysis
We see that central results are rather robust with respect to most of the parameters. In the
case of EACU Central (deep integration within EACU), the parameter that has the strongest
impact on the results is the elasticity of substitution between firm varieties in imperfectly
competitive goods sectors, σ(qi, qj). In the case of Kenya (Tanzania), the welfare gains as a
percent of consumption vary from 0.78 (0.85) to 1.16 (1.06) percent of consumption, with
greater gains at the upper elasticity values. Following from the Le Chatelier principle, larger
elasticities typically lead to larger welfare gains in response to welfare improving reforms, as the
economy can adapt more readily. In the case of this parameter, however, there are offsetting
impacts. Lower values of this elasticity imply that varieties are less close to each other, so
additional varieties are worth more. In the case of multilateral liberalization in Kenya, the variety
impact slightly dominates the Le Chatelier principle in interpreting the results, but the Le
Chatelier principle still dominates in Tanzania. The elasticity of substitution between imports
from different regions in CRTS sectors, σ(M, M), also has a modest impact. The gains are larger
in the EACU Liberal case for Tanzania, Uganda and Rwanda, as the larger elasticities allow
substitution toward the most efficient supplier. In the case of EACU Central, however, there is a
29
preference induced substitution away from non-EACU suppliers and trade diversion slightly
dominates in the cases of Kenya and Tanzania, but not for Uganda and Rwanda.
Finally, we examine the impact of varying the seven vectors of elasticities of firm supply
with respect to price for imperfectly competitive goods and services sectors. (We have one
vector for each of our ten regions, but there are only seven distinct vectors since all four EACU
regions are assumed to have identical vectors for these elasticities of firm supply.) In the EACU
liberal case, the welfare gains are about 5 percent larger in Kenya, Uganda and Rwanda with
larger elasticities of firm supply. 26
7. Conclusions
In this paper we have developed an innovative multi-region computable general
equilibrium model with a focus on Kenya, Tanzania and their trade partners in the East Africa
Customs Union. We have assessed three kinds of deep integration for these countries: trade
facilitation, services liberalization and reduction of non-tariff barriers. We assessed the impacts
of these reforms within the EACU alone; within a Tripartite agreement among the EACU,
COMESA and SADC; and multilaterally. The analysis is based on two new data sets of the ad
valorem equivalents of the barriers against foreign suppliers of services and the time costs of
trade, which we have shown are important to the results.
We estimate that deep integration within the EACU alone will produce benefits, and if
expanded to include COMESA and SADC would produce larger benefits for all four of our
EACU countries. Schiff and Winters (2003) have found that the largest gains from regional
integration come from the deep aspects of the agreements, so our results are consistent with the
broader empirical and theoretical literature.
We find that extending the same three reforms multilaterally generates substantially
larger gains than EACU only reform, ranging from twice the size of the gains for Kenya to seven
times the gains for Tanzania. We estimate that the impact on sector output, however, is
substantially greater with multilateral reform, leading to larger adjustment costs of trade
liberalization. Despite the fact that the evidence shows that the adjustment costs of trade
26
In Tanzania the gains are approximately unchanged. If available varieties in IRTS goods fall, the welfare gain is
smaller. In the EACU Central case, possible trade diversion leads to ambiguous results.
30
liberalization are dramatically smaller than the welfare gains, lobbying interests are much
stronger among those who anticipate a negative adjustment. Thus, our results show why there
can be a political economy appeal to regionalism, despite the larger net gains of multilateral
reform.
We have also assessed the impact of reform of domestic regulations that impose costs on
both domestic and foreign suppliers of business services in a non-discriminatory manner. We
found that while the discriminatory barriers are important and are the focus of international
negotiation, the regulatory barriers that impose costs on domestic supplier and foreign suppliers
of services in a non-discriminatory manner are quantitatively more important. The larger gains
from the reduction of non-discriminatory barriers are due to the larger bases from which the
barriers are reduced. The non-discriminatory barriers affect all providers of services, so the bases
of the recaptured rents and the distortion triangles are larger.
In our central scenarios, we assume that the barriers result in costs that are dissipated and
liberalization releases labor and capital for productive purposes in the domestic economy. In our
sensitivity analysis, we have shown that the magnitudes of the gains are in some cases very
strongly impacted by this assumption. If rents are captured by domestic agents, the gains are
reduced by as much as 80 percent in one case.
8. References
Anderson, James and J. Peter Neary (1996), "A new approach to evaluating trade policy," Review of
Economic Studies, 63 (1), 107-125.
Anderson, James and J. Peter Neary (2003), “The Mercantilist index of trade policy,” International
Economic Review, 44, 627--649.
Baldwin, Richard E., Forslid, Rikard and Haaland, Jan (1999), "Investment Creation and Investment
Diversion: Simulation Analysis of the Single Market Programme," in R. Baldwin and J. Francois
(eds.), Dynamic Issues in Applied Commercial Policy Analysis, Cambridge: Cambridge
University Press.
Balistreri, Edward J. and James Markusen (2009), “Sub-national differentiation and the role of the firm
in optimal international pricing,” Economic Modeling, 26(1), 47 - 62.
31
Balistreri, Edward J., Jesper Jensen and David G. Tarr (2011), “Services Liberalization in Preferential
Trade Arrangements: the case of Kenya,” World Bank Policy and Research Working Paper No.
5552, January.
Balistreri, Edward J., Thomas F. Rutherford and David G. Tarr (2009), “Modeling Services
Liberalization: The Case of Kenya,” Economic Modeling, Vol. 26 (3), May, 668-679.
Borchert, Ingo, Batshur Gootiiz and Aaditya Mattoo (2012), “Guide to the Services Restrictions
Database,” World Bank Policy and Research Working Paper Number 6108, June. Available at:
http://econ.worldbank.org/external/default/main?ImgPagePK=64202990&entityID=000158349_
20120628130854&menuPK=64210521&pagePK=64210502&theSitePK=544849&piPK=64210
520
Brenton , Paul and Gozde Isik (2012), De-fragmenting Africa: Deepening Regional Trade Integration in
Goods and Services, Washington DC: The World Bank.
Broda, Christian and David E. Weinstein (2006), “Globalization and the Gains from Variety,” Quarterly
Journal of Economics, Vol. 121 (2), 541-585.
Brown, Drusilla and Robert Stern (2001), “Measurement and Modeling of the Economic Effects of
Trade and Investment Barriers in Services,” Review of International Economics, 9(2): 262-286.
Cadot, Olivier and Julien Gourdon (2012), “Assessing the price-raising effect of non-tariff measures in
Africa,” mimeo.
Cox, David and Richard G. Harris (1986), “A Quantitative Assessment of the Economic Impact on
Canada of Sectoral Free Trade with the United States,” The Canadian Journal of Economics,
Vol. 19 (3), August, 377-394.
Dee, Philippa, Kevin Hanslow and Tien Phamduc (2003), “Measuring the Costs of Barriers to Trade in
Services,” in Trade in Services in the Asia-Pacific Region, Takatoshi Ito and Anne Krueger
(eds.), Chicago: University of Chicago Press.
Dihel, Nora, Ana Margarida Fernandes, Aaditya Mattoo and Nicholas Strychacz (2010), “Reform and
Regional Integration of Professional Services in East Africa,” Economic Premise No. 32,
September, The World Bank. Available at: www.worldbank.org/economicpremise.
Dixit, A. and J. Stiglitz (1977), “Monopolistic Competition and Optimum Product Diversity,” American
Economic Review, 76(1):297-308.
East African Community Secretariat (2011), “East African Transport Strategy and Regional Road Sector
Development Program and East African Trade and Transport Facilitation Project,” September.
East African Community (2012), “Status of Elimination of Non-Tariff Barriers in the East African
Community,” Vol. 5, December.
32
Fernandes, Ana M. (2009), “Structure and performance of the services sector in transition economies,”
Economies of Transition, Vol. 17(3), 467-501
Fernandes, Ana M and Caroline Paunov (2012), “Foreign direct investment in services and
manufacturing productivity: evidence for Chile,” Journal of Development Economics, Vol. 97
(2), March, 305-321.
Francois, Joseph and Bernard Hoekman (2010), “Services Trade and Policy,” Journal of Economic
Literature, Vol. 48 (September), 642-692.
Francois, Joseph and Clint Shiells (eds., 1994), Modeling Trade Policy: Applied General Equilibrium
Assessments of North American Free Trade, Cambridge: Cambridge University Press.
Fukui, Tani and Csilla Lakatos (2012), “A Global Database of Foreign Affiliate Sales,” GTAP Research
Memorandum No. 24, July.
Guimbard, Houssein, Sébastien Jean and Mondher Mimouni (2011), "GTAP 8.1 Data Base
Documentation - Chapter 10.D: MAcMap-HS6 2007, an exhaustive and consistent measure of
applied protection in 2007."
Available at: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3761
Harris, Richard (1084), “Applied General Equilibrium Analysis of Small Open Economies with Scale
Economies and Imperfect Comp,” The American Economic Review, Vol. 74 (5), December,
1016-1032
Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1993), "Trade Reform in the Partially
Liberalized Economy of Turkey," The World Bank Economic Review, May.
Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr. (1996), “Increased Competition and
Completion of the Market in the European Community: Static and Steady-State Effects,” Journal
of Economic Integration, 11(3), September 1996, 332-365.
Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997), “Quantifying the Uruguay
Round,” Economic Journal, Vol. 107 (444), September, 1405-1430.
Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997a), “Economic Implications for
Turkey of a Customs Union with the European Union,” European Economic Review, Vol. 41(3-
5), 861-870.
Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (2002), “Trade Policy Options for Chile:
The Importance of Market Access,” World Bank Economic Review, Vol. 16, Number 1.
Harrison, Glenn H., Thomas F. Rutherford, David G. Tarr and Angelo Gurgel (2004) “Trade Policy and
Poverty Reduction in Brazil,” The World Bank Economic Review, Vol. 18, 289-317, 2004.
Hummels, David L. (2007), "Transportation Costs and International Trade in the Second Era of
Globalization," Journal of Economic Perspectives, Vol. 21(3), 131-154.
33
Hummels, David L. and G. Schaur (2013), “Time as a Trade Barrier,” American Economic Review, Vol.
103, 1-27.
Hummels, David L., Peter Minor, Matthew Reisman and Erin Endean (2007), “Calculating Tariff
Equivalents for Time in Trade,” Arlington, VA: Nathan Associates Inc. for the United States
Agency for International Development (USAID). Available at:
http://www.krannert.purdue.edu/faculty/hummelsd/research/tariff_equivalents.pdf
Jafari, Yaghoob (2014a), “Ad Valorem Equivalents of Barriers to Services Providers in Kenya,” mimeo,
March 22, 2014. Available at: https://drive.google.com/folderview?id=0B0V-t-Bs4-
hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing
Jafari, Yaghoob (2014b), “Ad Valorem Equivalents of Barriers to Services Providers in Tanzania,”
mimeo, March 22, 2014. Available at: https://drive.google.com/folderview?id=0B0V-t-Bs4-
hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing
Jafari, Yaghoob (2014c), “Foreign Discriminatory Barriers to Services Providers and Estimates of the
Ad Valorem Equivalents (AVEs) of the Regulatory Barriers in the 11 Services Sectors for 103
countries,” mimeo. March 22, 2014. Available at:
https://drive.google.com/folderview?id=0B0V-t-Bs4-
hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing
Jafari, Yaghoob (2014d), “Calculation of the Weighted Average AVEs of the Services Barriers in Ten
Regions,” mimeo, March 18, 2014. Available at: https://drive.google.com/folderview?id=0B0V-
t-Bs4-hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing
Jafari, Yaghoob (2014e). “Different Region’s Ownership Shares of Tanzanian and Kenyan Services
Sectors,” mimeo, January 13. Available at: https://drive.google.com/folderview?id=0B0V-t-Bs4-
hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing
Jensen, Jesper, Thomas Rutherford and David Tarr (2010), “Modeling Services Liberalization: the Case
of Tanzania,” Journal of Economic Integration, Vol. 25 (4), December, 644-675.
Jensen, Jesper, Thomas Rutherford and David Tarr (2007), “The Impact of Liberalizing Barriers to
Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade
Organization,” Review of Development Economics, Vol. 11 (3), August, 2007, 482-506.
Jensen, H. G. and R. Sandrey, 2011, The Tripartite Free Trade Agreement: A computer analysis of the
impacts, Chapter 4 in “Cape to Cairo: Making the Tripartite Free Trade Area Work”, Trade Law
Center for Southern Africa, National Agricultural Marketing Council 2011.
Jensen, H. G. and R. Sandrey, 2013, A New Approach to a regional Free Trade Agreement in East
Africa: willing participants, TRALAC Working Paper No. S13WP09/2013, June. Available at:
http://www.tralac.org/files/2013/07/S13WP092013-Jensen-Sandrey-A-new-approach-to-a-
regional-Free-Trade-Agreement-in-east-Africa-20130626-fin.pdf
34
Jensen, Jesper and David G. Tarr (2012), “Deep Trade Policy Options for Armenia: The Importance of
Trade Facilitation, Services and Standards Liberalization,” Economics: The Open Access-Open
Assessment E-Journal, Vol. 6, 2012-1. http://dx.doi.org/10.5018/economics-ejournal.ja.2012-1.
Jensen, Jesper and David G. Tarr (2010), “Regional Trade Policy Options for Tanzania: the importance
of services commitments,” Policy and Research Working Paper Number 5481. Available at:
http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK
=64165421&menuPK=64166093&entityID=000158349_20101117083006.
Kang, Joog-Soon (2000), “Price Impact of Restrictiveness on Maritime Transportation Services,” in
Impediments to Trade in Services: Measurement and Policy Implications, Christopher Findlay
and Tony Warren (eds), London: Routledge.
Karingi S. and B. Fekadu, 2009, Beyond Political Rhetoric – the Meaning of the Grand Eastern and
Southern Africa FTA, UN Economic Commission for Africa, draft 13 April 2009.
Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga. (2008), "Import Demand Elasticities and
Trade Distortions", Review of Economics and Statistics, 90 (4), 666—682.
Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga (2009). "Estimating trade restrictiveness
indices," Economic Journal, Vol. 119, 172--199.
Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004a), “Estimating the Ad Valorem Equivalent
of Barriers to Foreign Direct Investment in the Telecommunications Services Sectors in
Tanzania .” Available at http://www.worldbank.org/trade/russia-wto.
Konan, Denise Eby and Keith E. Maskus (2006), “Quantifying the impact of services liberalization in a
developing country,” Journal of Development Economics, Vol. 81, 142-162.
Levy, Santiago and Sweder van Wijnbergen (1995), "Transition Problems in Economic Reform:
Agriculture in the North American Free Trade Agreement," American Economic Review, Vol.
85(4), September, 738-54.
Markusen, James R, Thomas Rutherford and David Tarr (2005), Trade and Direct Investment in
Producer Services and the Domestic Market for Expertise,” Canadian Journal of Economics,
Vol. 38 (3), 758-777.
Marshall, J.N. (1988), Services and Uneven Development, London: Oxford University Press.
Matoo, Aaditya and Carsten Fink (2003), “Regional agreements and trade services - policy issues,
Volume 1,” World Bank Policy and Research Working Paper Number 2852. Available at:
http://www-
wds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2002/07/09/000094946_0206
2104102344/Rendered/PDF/multi0page.pdf.
35
Matoo, Aaditya and Pierre Sauve (2008), “Regionalism in Services Trade,” in A Handbook of
International Trade in Services, Aaditya Mattoo, Robert M. Stern and Gianni Zanini (eds.),
Oxford: Oxford University Press.
Matusz, Steven and David G. Tarr (2000), “Adjusting to Trade Liberalization,” in Anne O. Krueger
(ed.), Economic Policy Reform: The Second Stage, Chicago: The University of Chicago Press.
Mevel S. and S. Karingi, 2012, Deepening Regional Integration in Africa: A Computable General
Equilibrium Assessment of the Establishment of a Continental Free Trade Area followed by a
Continental Customs Union, 7th African Economic Conference Kigali, Rwanda, 30 October - 2
November, 2012.
McGuire, Greg and Michael Schuele (2000), “Restrictiveness of International Trade in Banking
Services,” in Impediments to Trade in Services: Measurement and Policy Implications,
Christopher Findlay and Tony Warren (eds), London: Routledge.
McKee, D.L. (1988), Growth, Development, and the Service Economy in the Third World, New York:
Praeger Publishers.
Minor, Peter (2013), “Time as a Barrier to Trade: A GTAP Database of ad valorem Trade Time Costs,”
ImpactEcon, Second Edition, October. Available at: http://mygtap.org/wp-
content/uploads/2013/12/GTAP%20Time%20Costs%20as%20a%20Barrier%20to%20Trade%20
v81%202013%20R2.pdf.
Nguyen-Hong, D. (2000), “Restrictions on Trade in Professional Services”, Productivity Commission
Staff Research Paper, Ausinfo, Canberra. Available at:
www.pc.gov.au/research/staffresearch/rotips
Reidel, James (1988), “The Demand for LDC Exports of Manufactures: Estimates from Hong Kong,”
Economic Journal, 98, March, 138-148.
Rutherford, Thomas F. and David G. Tarr (2010), “Regional Impacts of Liberalization of Barriers
against Foreign Direct Investment in Services: the case of Russia’s accession to the WTO,”
Review of International Economics, Vol. 18(1), February, 30-46.
Rutherford, Thomas F. and David Tarr (2008), "Poverty Effects of Russian WTO accession: modeling
“real” households with endogenous productivity effects,” Journal of International Economics,
Vol. 75 (1), 131-150.
Rutherford, Thomas F. and David Tarr (2003), "Regional Trading Arrangements for Chile: Do the
Results Differ with a Dynamic Model?" Integration and Trade, Vol. 7, Number 18, 117-139.
Rutherford, Thomas F. and David G. Tarr (2002), "Trade Liberalization and Endogenous Growth in a
Small Open Economy," Journal of International Economics, 56 (2), March, 247-272
36
Rutherford, Thomas F., Rutstrom, E.E. and Tarr, David G. (1993), “Morocco's free trade agreement with
the European Community: A quantitative assessment,” Economic Modeling, Vol. 14, No. 9, 237-
269.
Rutherford, Thomas, Rutstrom, E.E. and Tarr, David (1995), “The free trade agreement between Tunisia
and the European Union,” World Bank: Washington, D.C. Mimeo. Reprinted as Rutherford,
Thomas F.; Rutstrom, E.E.; and Tarr, David G. (2000) “A Free Trade Agreement Between the
European Agreement and a Representative Arab Mediterranean Country: A Quantitative
Assessment,” in Bernard Hoekman and Jamel Zarrouk (eds.), Catching Up with the Competition:
Trade Opportunities and Challenges for Arab Countries, Ann Arbor: University of Michigan
Press.
Schiff, Maurice and Yanling Wang (2006), “North-South and South-South Trade Related Technology
Diffusion: An Industry Level Analysis of Direct and Indirect Effects,” Canadian Journal of
Economics, Vol. 39 (3), August, 831-844.
Schiff, Maurice, Yanling Wang and Marcelo Olarreaga (2002), “Trade Related Technology Diffusion
and the Dynamics of North-South and South-South Integration,” World Bank Policy and
Research Working Paper 2861, June.
Schiff, Maurice and L. Alan Winters (2003), Regional Integration and Development, Oxford: Oxford
University Press and the World Bank.
Smith, Alasdair, and Venables, Anthony J. (1988), "Completing the Internal Market in the European
Community: Some Industry Simulations," European Economic Review, 32, 1501-1525.
Tarr, David (2002), "On the Design of Tariff Policy: Arguments for and Against Uniform Tariffs,” in B.
Hoekman, A. Mattoo and P. English (eds.), Development, Trade and the WTO: A Handbook,
Washington: World Bank.
Warren, Tony (2000), “The Impact on Output of Impediments to Trade and Investment in
Telecommunications Services,” in Impediments to Trade in Services: Measurement and Policy
Implications, Christopher Findlay and Tony Warren (eds), London: Routledge.
Willenbockel, D. (2013), General Equilibrium Analysis of the COMESA-EAC-SADC Tripartite FTA,
Draft Final Report, August 2013.
Wonnacott, Paul and Ronald Wonnacott (1981), “Is Unilateral Tariff Reduction Preferable to a Customs
Union? The Curious Case of the Missing Foreign Tariffs,” American Economic Review, Vol. 71
(4), 704-714.
World Bank (2012), East African Community: Reshaping Economic Geography of East Africa: From
Regional to Global Integration, Report No. 6599-AFR, Poverty Reduction and Economic
Management Unit, Africa Region, June.
37
World Economic Forum (2012), The Global Enabling Trade Report, 2012: Reducing Supply Chain
Barriers, edited by Robert Lawrence, Margareta Drzeniek Hanouz and Sean Doherty. Available
at www.weforum.org.
Worley, Chris (2009), “Documentation of the Calculation of Ownership Shares for Kenya,” mimeo.
Available at: https://drive.google.com/folderview?id=0B0V-t-Bs4-
hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing
Worley, Chris (2010), Documentation of Ownership Share Data for Tanzania,” mimeo. Available at:
https://drive.google.com/folderview?id=0B0V-t-Bs4-
hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing
38
Tables
Table 1: List of Sectors, Regions and Factors of Production in the East Africa Model
Business Services with FDI Dixit-Stigliz Goods
Air Transport Chemicals Mineral and Metal Products
Communication Energy and Minerals
Insurance Food Products
Business Services nec Petroleum and Coal Products
Financial Services nec Other Manufacturing
Transport nec Textile, Apparel and Leather Products
Water Transport Wood and Paper Products
CRTS Goods and Services Regions
Agriculture and Forestry Kenya
Other Services Tanzania
Trade Uganda
Utilities Rwanda
COMESA
Factors of Production SADC
Skilled labor USA
Unskilled labor European Union (EUR)
Capital China
Natural Resources Rest of the World (ROW)
39
Table 2a. Sector Value Added in Kenya (Percentage unless otherwise indicated)
Labor GDP
Skilled Unskilled Capital Natural USD in % of total
labor labor Resources billion
Business Services
Air Transport 9.6 41.6 48.8 0.0 0.3 1.3
Communication 12.0 17.5 70.6 0.0 0.6 2.5
Insurance 27.0 39.3 33.8 0.0 0.2 0.6
Business Services nec 24.1 35.0 40.9 0.0 0.8 3.3
Financial Services nec 24.7 36.0 39.2 0.0 0.5 2.0
Transport nec 10.4 45.0 44.6 0.0 0.8 3.4
Water Transport 9.1 39.5 51.5 0.0 0.1 0.4
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 6.6 35.2 58.3 0.0 0.7 2.9
Energy and Minerals 6.8 47.1 21.9 24.2 0.0 0.1
Food Products 4.5 23.5 71.9 0.0 7.1 28.3
Petroleum and Coal Products 8.2 37.8 54.0 0.0 0.0 0.0
Other Manufacturing 5.8 34.7 59.5 0.0 0.5 2.1
Textile and Apparel 9.1 60.0 31.0 0.0 0.3 1.3
Wood and Paper Products 7.5 46.0 46.5 0.0 0.5 2.2
CRTS
Agriculture and Forestry 0.6 68.7 28.7 2.1 6.1 24.2
Other Services 33.2 34.4 32.4 0.0 4.8 19.2
Trade 7.1 30.8 62.1 0.0 1.3 5.2
Utilities 26.0 49.2 24.8 0.0 0.3 1.2
Source: Authors’ calculations based on the GTAP 8.1 data set.
40
Table 2b. Sector Value Added in Tanzania (Percentage unless otherwise indicated)
Labor GDP
Skilled Unskilled Capital Natural USD in % of total
labor labor Resources billion
Business Services
Air Transport 10.3 44.3 45.5 0.0 0.0 0.1
Communication 15.5 22.6 61.9 0.0 0.1 0.8
Insurance 24.9 36.5 38.5 0.0 0.0 0.1
Business Services nec 0.0 74.3 25.7 0.0 1.0 6.7
Financial Services nec 22.6 33.1 44.3 0.0 0.0 0.0
Transport nec 11.0 47.7 41.3 0.0 0.2 1.1
Water Transport 8.2 35.3 56.5 0.0 0.1 0.8
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 4.4 23.9 71.7 0.0 0.6 4.5
Energy and Minerals 0.2 1.5 88.8 9.4 0.3 2.4
Food Products 8.3 43.5 48.2 0.0 1.1 7.5
Petroleum and Coal Products 0.0 0.0 0.0 0.0 0.0 0.0
Other Manufacturing 2.7 14.1 83.2 0.0 0.1 0.8
Textile and Apparel 6.8 45.6 47.6 0.0 0.1 1.0
Wood and Paper Products 3.4 23.3 73.3 0.0 0.1 0.4
CRTS
Agriculture and Forestry 0.5 60.4 38.1 1.0 4.6 32.2
Other Services 32.3 46.5 21.2 0.0 2.9 20.3
Trade 10.6 45.6 43.8 0.0 2.6 18.0
Utilities 6.9 12.9 80.2 0.0 0.5 3.3
Source: Authors’ calculations based on the GTAP 8.1 data set.
41
Table 2c. Sector Value Added in Uganda (Percentage unless otherwise indicated)
Labor GDP
Skilled Unskilled Capital Natural USD in % of total
labor labor Resources billion
Business Services
Air Transport 8.9 38.8 52.3 0.0 0.0 0.1
Communication 20.2 29.5 50.3 0.0 0.1 1.1
Insurance 0.0 17.5 82.5 0.0 0.0 0.1
Business Services nec 36.9 53.7 9.4 0.0 0.5 4.7
Financial Services nec 35.4 51.5 13.1 0.0 0.2 1.5
Transport nec 7.4 32.4 60.2 0.0 0.2 2.0
Water Transport 3.2 13.8 83.0 0.0 0.0 0.0
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 3.3 17.8 78.9 0.0 0.3 2.8
Energy and Minerals 2.7 15.4 43.2 38.7 0.8 7.7
Food Products 9.5 47.0 43.5 0.0 0.5 4.9
Petroleum and Coal Products 6.5 30.1 63.3 0.0 0.0 0.0
Other Manufacturing 5.0 26.9 68.1 0.0 0.2 1.9
Textile and Apparel 6.2 41.0 52.8 0.0 0.1 0.6
Wood and Paper Products 7.6 45.1 47.3 0.0 0.0 0.3
CRTS
Agriculture and Forestry 0.6 74.1 22.6 2.7 2.6 24.4
Other Services 24.8 22.6 52.7 0.0 3.5 32.7
Trade 6.7 29.2 64.0 0.0 1.3 12.6
Utilities 4.2 7.9 87.9 0.0 0.3 2.8
Source: Authors’ calculations based on the GTAP 8.1 data set.
42
Table 2d. Sector Value Added in Rwanda (Percentage unless otherwise indicated)
Labor GDP
Skilled Unskilled Capital Natural USD in % of total
labor labor Resources billion
Business Services
Air Transport 13.5 58.7 27.8 0.0 0.0 0.3
Communication 23.5 34.3 42.2 0.0 0.0 1.6
Insurance 32.1 46.7 21.2 0.0 0.0 0.8
Business Services nec 32.6 47.6 19.8 0.0 0.1 3.0
Financial Services nec 30.3 44.2 25.4 0.0 0.1 4.2
Transport nec 14.1 61.4 24.5 0.0 0.1 2.9
Water Transport 13.1 57.0 29.9 0.0 0.0 0.1
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 3.2 17.3 79.6 0.0 0.0 1.2
Energy and Minerals 2.9 18.0 38.0 41.1 0.2 6.0
Food Products 5.5 34.7 59.8 0.0 0.1 4.7
Petroleum and Coal Products 7.1 33.0 59.9 0.0 0.0 0.0
Other Manufacturing 4.7 24.6 70.8 0.0 0.0 0.7
Textile and Apparel 7.9 53.4 38.7 0.0 0.0 0.3
Wood and Paper Products 5.0 33.2 61.8 0.0 0.0 0.3
CRTS
Agriculture and Forestry 0.6 70.0 28.6 0.9 1.2 39.8
Other Services 26.3 32.6 41.2 0.0 0.7 23.9
Trade 9.9 42.8 47.3 0.0 0.3 9.4
Utilities 12.2 23.0 64.9 0.0 0.0 0.9
Source: Authors’ calculations based on the GTAP 8.1 data set.
43
Table 2e. Sector Value Added in COMESA (Percentage unless otherwise indicated)
Labor GDP
Skilled Unskilled Capital Natural USD in % of total
labor labor Resources billion
Business Services
Air Transport 10.7 49.0 40.3 0.0 0.7 0.4
Communication 18.3 24.9 56.8 0.0 5.5 3.1
Insurance 36.4 48.7 14.9 0.0 1.7 1.0
Business Services nec 32.1 43.6 24.3 0.0 7.9 4.4
Financial Services nec 34.1 45.6 20.3 0.0 4.0 2.3
Transport nec 12.0 54.9 33.1 0.0 6.6 3.7
Water Transport 11.8 53.4 34.8 0.0 0.5 0.3
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 7.1 36.8 56.1 0.0 10.0 5.6
Energy and Minerals 1.8 9.5 54.5 34.2 24.2 13.5
Food Products 7.2 38.8 54.0 0.0 9.8 5.5
Petroleum and Coal Products 1.9 8.9 89.2 0.0 1.0 0.6
Other Manufacturing 9.8 49.1 41.1 0.0 5.4 3.0
Textile and Apparel 4.8 31.4 63.8 0.0 8.5 4.8
Wood and Paper Products 7.5 43.6 48.9 0.0 2.2 1.2
CRTS
Agriculture and Forestry 0.7 60.1 36.9 2.3 28.1 15.7
Other Services 27.0 31.6 41.4 0.0 42.0 23.5
Trade 10.9 49.9 39.2 0.0 17.3 9.7
Utilities 5.1 9.9 85.0 0.0 3.5 2.0
Source: Authors’ calculations based on the GTAP 8.1 data set.
44
Table 2f. Sector Value Added in SADC (Percentage unless otherwise indicated)
Labor GDP
Skilled Unskilled Capital Natural USD in % of total
labor labor Resources billion
Business Services
Air Transport 11.9 49.2 38.9 0.0 1.6 0.5
Communication 18.2 25.9 55.9 0.0 7.6 2.2
Insurance 24.3 34.3 41.4 0.0 12.7 3.7
Business Services nec 20.9 29.7 49.4 0.0 23.8 6.9
Financial Services nec 24.1 34.4 41.5 0.0 5.9 1.7
Transport nec 12.5 51.4 36.1 0.0 7.8 2.3
Water Transport 11.4 47.0 41.6 0.0 0.7 0.2
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 6.7 32.2 61.2 0.0 31.6 9.1
Energy and Minerals 2.5 14.7 48.9 33.9 42.4 12.3
Food Products 7.3 35.6 57.1 0.0 12.2 3.5
Petroleum and Coal Products 6.3 25.5 68.2 0.0 0.3 0.1
Other Manufacturing 7.9 37.1 54.9 0.0 13.6 3.9
Textile and Apparel 8.9 55.8 35.3 0.0 5.5 1.6
Wood and Paper Products 9.7 51.2 39.1 0.0 5.2 1.5
CRTS
Agriculture and Forestry 0.4 41.7 52.2 5.6 21.4 6.2
Other Services 33.7 30.9 35.4 0.0 99.9 28.8
Trade 9.6 39.7 50.7 0.0 44.8 12.9
Utilities 12.2 22.9 64.9 0.0 9.4 2.7
Source: Authors’ calculations based on the GTAP 8.1 data set.
45
46
Table 3a. Trade Flows of Kenya
Imports Exports
USD in USD in
billion % of total % of supply billion % of total % of output
Business Services
Air Transport 0.1 0.8 17.5 0.6 9.6 69.1
Communication 0.1 1.4 12.6 0.3 4.8 40.5
Insurance 0.0 0.5 12.4 0.0 0.5 10.2
Business Services nec 0.2 2.2 9.8 0.0 0.6 2.4
Financial Services nec 0.0 0.4 3.9 0.0 0.5 4.3
Transport nec 0.1 0.7 4.5 0.1 1.8 7.3
Water Transport 0.0 0.2 10.2 0.1 2.2 58.9
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 2.2 23.7 53.3 0.8 12.2 33.8
Energy and Minerals 0.8 8.9 91.8 0.1 0.9 68.6
Food Products 0.6 5.8 3.7 0.7 10.9 4.9
Petroleum and Coal Products 0.9 9.1 44.0 0.1 1.7 11.1
Other Manufacturing 2.9 30.4 57.1 0.3 4.0 14.6
Textile and Apparel 0.5 5.5 23.1 0.5 7.2 22.7
Wood and Paper Products 0.3 3.4 21.1 0.1 1.8 9.1
CRTS
Agriculture and Forestry 0.4 3.7 3.8 1.8 27.3 16.7
Other Services 0.2 2.5 2.4 0.9 13.9 8.6
Trade 0.1 0.5 2.6 0.0 0.1 0.3
Utilities 0.0 0.0 0.3 0.0 0.2 1.1
Source: Authors’ calculations based on the GTAP 8.1 data set.
47
Table 3b. Trade Flows of Tanzania
Imports Exports
USD in USD in
billion % of total % of supply billion % of total % of output
Business Services
Air Transport 0.0 0.9 50.1 0.0 1.0 47.6
Communication 0.0 0.4 7.7 0.0 1.1 16.8
Insurance 0.1 1.0 95.3 0.0 0.8 104.0
Business Services nec 0.2 3.0 8.3 0.3 7.1 14.4
Financial Services nec 0.0 0.6 89.0 0.0 0.2 80.8
Transport nec 0.2 3.0 39.7 0.2 4.8 49.1
Water Transport 0.0 0.1 26.1 0.0 0.2 2.0
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 1.3 23.6 58.9 0.8 23.4 54.0
Energy and Minerals 0.0 0.3 7.4 0.3 7.6 60.0
Food Products 0.4 7.7 13.6 0.5 12.8 15.2
Petroleum and Coal Products 0.6 11.9 90.0 0.0 0.0 0.0
Other Manufacturing 1.6 29.6 78.6 0.1 2.2 23.2
Textile and Apparel 0.3 5.5 51.1 0.1 4.0 45.7
Wood and Paper Products 0.1 2.6 51.8 0.0 1.3 31.4
CRTS
Agriculture and Forestry 0.2 2.9 3.0 0.8 22.2 13.9
Other Services 0.3 5.1 4.7 0.2 5.0 3.2
Trade 0.1 1.7 2.7 0.2 5.8 5.8
Utilities 0.0 0.0 0.3 0.0 0.4 1.9
Source: Authors’ calculations based on the GTAP 8.1 data set.
48
Table 3c. Trade Flows of Uganda
Imports Exports
USD in USD in
billion % of total % of supply billion % of total % of output
Business Services
Air Transport 0.1 3.2 69.4 0.0 0.1 6.0
Communication 0.0 0.6 5.5 0.0 1.1 12.3
Insurance 0.0 1.8 36.9 0.0 0.5 16.2
Business Services nec 0.1 3.7 13.4 0.0 0.6 2.7
Financial Services nec 0.0 0.4 2.0 0.0 0.4 2.4
Transport nec 0.0 1.3 7.8 0.1 4.6 31.6
Water Transport 0.0 0.1 40.8 0.0 0.0 20.4
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 0.7 23.8 45.7 0.3 8.8 28.8
Energy and Minerals 0.0 0.9 14.2 1.2 40.3 90.5
Food Products 0.2 6.9 8.5 0.3 11.1 14.6
Petroleum and Coal Products 0.3 11.0 64.9 0.0 0.2 4.7
Other Manufacturing 0.8 30.4 62.8 0.0 1.4 9.8
Textile and Apparel 0.1 4.9 40.4 0.0 1.1 16.8
Wood and Paper Products 0.1 3.7 31.5 0.0 0.5 6.6
CRTS
Agriculture and Forestry 0.1 2.6 2.8 0.6 18.9 18.6
Other Services 0.1 2.1 1.1 0.2 6.6 3.8
Trade 0.1 2.0 3.0 0.1 2.6 4.3
Utilities 0.0 0.6 3.0 0.0 1.4 8.2
Source: Authors’ calculations based on the GTAP 8.1 data set.
49
Table 3d. Trade Flows of Rwanda
Imports Exports
USD in USD in
billion % of total % of supply billion % of total % of output
Business Services
Air Transport 0.0 1.4 33.3 0.0 0.5 16.1
Communication 0.0 0.5 4.5 0.0 0.6 5.9
Insurance 0.0 0.4 9.0 0.0 0.2 3.7
Business Services nec 0.1 9.7 36.7 0.0 0.5 3.1
Financial Services nec 0.0 0.1 0.6 0.0 0.1 0.5
Transport nec 0.0 2.3 9.0 0.1 7.4 23.6
Water Transport 0.0 0.2 9.6 0.0 0.2 11.4
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 0.2 21.5 48.5 0.0 1.1 4.7
Energy and Minerals 0.0 1.6 33.2 0.4 60.8 95.2
Food Products 0.1 12.8 18.0 0.0 3.6 5.1
Petroleum and Coal Products 0.1 10.3 71.2 0.0 0.4 13.9
Other Manufacturing 0.2 23.6 58.3 0.0 0.7 5.0
Textile and Apparel 0.0 2.8 35.2 0.0 0.5 8.7
Wood and Paper Products 0.0 3.3 46.4 0.0 0.1 2.9
CRTS
Agriculture and Forestry 0.0 1.7 1.1 0.1 9.9 5.1
Other Services 0.0 4.1 3.0 0.1 10.4 6.2
Trade 0.0 3.3 7.2 0.0 1.6 3.3
Utilities 0.0 0.6 6.3 0.0 1.3 12.4
Source: Authors’ calculations based on the GTAP 8.1 data set.
50
Table 3e. Trade Flows of COMESA
Imports Exports
USD in USD in
billion % of total % of supply billion % of total % of output
Business Services
Air Transport 1.3 2.0 49.2 2.1 4.0 63.4
Communication 0.4 0.6 4.1 1.3 2.4 16.3
Insurance 1.0 1.5 26.8 0.3 0.5 10.5
Business Services nec 2.9 4.4 16.1 1.7 3.2 11.6
Financial Services nec 0.2 0.3 2.9 0.4 0.7 7.1
Transport nec 1.2 1.8 8.5 8.1 15.4 44.2
Water Transport 0.2 0.3 7.4 0.2 0.4 7.1
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 13.7 20.9 32.1 6.6 12.4 19.9
Energy and Minerals 1.7 2.6 7.9 14.6 27.5 42.8
Food Products 4.4 6.7 11.7 2.0 3.8 6.0
Petroleum and Coal Products 3.4 5.2 15.9 3.3 6.2 15.8
Other Manufacturing 20.1 30.7 49.4 1.7 3.1 8.5
Textile and Apparel 3.6 5.5 14.7 3.1 5.9 13.6
Wood and Paper Products 2.6 3.9 25.6 0.6 1.1 7.9
CRTS
Agriculture and Forestry 5.0 7.6 11.2 2.7 5.1 6.5
Other Services 3.0 4.6 3.6 3.6 6.8 4.2
Trade 0.9 1.4 3.1 0.8 1.5 2.8
Utilities 0.1 0.1 0.5 0.1 0.2 0.8
Source: Authors’ calculations based on the GTAP 8.1 data set.
51
Table 3f. Trade Flows of SADC
Imports Exports
USD in USD in
billion % of total % of supply billion % of total % of output
Business Services
Air Transport 2.5 2.1 32.8 2.5 1.8 37.1
Communication 0.1 0.1 0.4 0.6 0.4 3.0
Insurance 0.8 0.6 2.3 0.8 0.6 3.1
Business Services nec 7.8 6.6 10.4 2.4 1.7 4.2
Financial Services nec 0.8 0.7 4.6 0.9 0.6 6.3
Transport nec 1.8 1.5 6.2 2.9 2.0 10.8
Water Transport 0.5 0.4 17.2 0.5 0.3 18.8
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 21.4 18.0 21.5 36.8 25.6 32.9
Energy and Minerals 8.8 7.4 39.5 60.2 41.9 83.2
Food Products 6.5 5.5 10.5 4.6 3.2 7.9
Petroleum and Coal Products 4.3 3.6 21.6 1.4 0.9 8.5
Other Manufacturing 43.8 36.9 37.9 13.2 9.2 16.6
Textile and Apparel 4.8 4.0 18.3 3.4 2.4 14.8
Wood and Paper Products 2.9 2.4 12.9 2.4 1.7 11.5
CRTS
Agriculture and Forestry 1.6 1.3 4.3 4.8 3.3 11.9
Other Services 6.3 5.3 3.3 4.3 3.0 2.3
Trade 3.7 3.1 4.1 1.4 0.9 1.6
Utilities 0.7 0.6 3.5 0.5 0.4 2.5
Source: Authors’ calculations based on the GTAP 8.1 data set.
52
Table 4a: Benchmark Distortions in Kenya (Ad valorem values in percentage)
Ad Valorem Values or Equivalents
Barriers Against Service Providers
Discriminatory Non-
Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW Discriminatory
Business Services
Air Transport 38.0 38.0 38.0 38.0 38.0 38.0 38.0 38.0 38.0 0.0
Communication 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 3.4
Insurance 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 11.6
Business Services nec 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 13.0
Financial Services nec 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 18.6
Transport nec 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
Water Transport 42.0 42.0 42.0 42.0 42.0 42.0 42.0 42.0 42.0 0.0
Tariff Rates on Goods Non-Tariff
Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW Measures
Goods
Chemicals Mineral and Metal Products 0.0 0.0 0.0 0.0 7.5 7.5 7.5 7.5 7.5 0.3
Energy and Minerals 0.0 0.0 0.0 0.0 1.1 1.1 1.1 1.1 1.1 0.3
Food Products 0.0 0.0 0.0 0.0 19.0 19.0 19.0 19.0 19.0 0.3
Petroleum and Coal Products 0.0 0.0 0.0 0.0 9.3 9.3 9.3 9.3 9.3 0.3
Other Manufacturing 0.0 0.0 0.0 0.0 16.1 16.1 16.1 16.1 16.1 0.3
Textile and Apparel 0.0 0.0 0.0 0.0 13.0 13.0 13.0 13.0 13.0 0.3
Wood and Paper Products 0.0 0.0 0.0 0.0 8.4 8.4 8.4 8.4 8.4 0.3
Agriculture and Forestry 0.0 0.0 0.0 0.0 17.5 17.5 17.5 17.5 17.5 14.6
Barriers to Efficient Trade Facilitation on Exports
Chemicals Mineral and Metal Products 21.4 22.3 21.5 21.7 27.0 16.9 20.1 18.3 21.2
Energy and Minerals 12.6 12.6 12.6 12.6 12.5 12.3 12.6 12.6 12.2
Food Products 22.3 13.5 21.2 11.8 16.3 15.1 16.3 12.8 20.3
Petroleum and Coal Products 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9
Other Manufacturing 16.2 15.7 14.5 15.8 14.2 11.1 11.5 15.9 8.7
Textile and Apparel 7.1 8.1 8.0 8.6 8.2 10.0 7.8 7.9 7.8
Wood and Paper Products 15.9 23.1 18.0 13.7 19.1 11.5 13.3 14.8 15.2
Agriculture and Forestry 21.3 26.6 21.0 15.9 25.3 22.7 25.9 23.5 19.1
Barriers to Efficient Trade Facilitation on Imports
Chemicals Mineral and Metal Products 25.6 45.9 42.9 9.5 30.7 4.3 6.8 13.4 14.1
Energy and Minerals 14.3 18.9 19.8 4.6 16.0 2.8 4.4 8.1 9.9
Food Products 20.1 27.2 22.9 14.7 19.4 4.6 7.6 13.2 10.5
Petroleum and Coal Products 31.9 41.9 43.9 36.8 35.5 6.0 12.3 18.0 21.4
Other Manufacturing 12.6 22.7 24.6 7.2 15.5 1.7 4.3 8.6 9.5
Textile and Apparel 9.4 12.2 14.9 4.6 20.6 1.8 7.4 5.9 7.0
Wood and Paper Products 12.9 10.0 17.3 10.7 30.4 3.8 8.5 12.0 14.4
Agriculture and Forestry 23.1 41.4 24.8 18.3 35.0 8.0 8.3 20.8 21.2
53
Table 4b: Benchmark Distortions in Tanzania; Ad valorem values in percentage.
Barriers Against Service Providers
Discriminatory Non-
Kenya Uganda Rwanda COMESA SADC USA EUR China ROW Discriminatory
Business Services
Air Transport 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 0.0
Communication 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 3.1
Insurance 37.0 37.0 37.0 37.0 37.0 37.0 37.0 37.0 37.0 17.9
Business Services nec 22.0 22.0 22.0 22.0 22.0 22.0 22.0 22.0 22.0 22.0
Financial Services nec 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 14.7
Transport nec 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Water Transport 51.0 51.0 51.0 51.0 51.0 51.0 51.0 51.0 51.0 0.0
Tariff Rates on Goods Non-Tariff
Kenya Uganda Rwanda COMESA SADC USA EUR China ROW Measures
Goods
Chemicals Mineral and Metal Products 0.0 0.0 0.0 4.4 0.0 4.4 4.4 4.4 4.4 47.4
Energy and Minerals 0.0 0.0 0.0 3.2 0.0 3.2 3.2 3.2 3.2 47.4
Food Products 0.0 0.0 0.0 13.4 0.0 13.4 13.4 13.4 13.4 47.4
Petroleum and Coal Products 0.0 0.0 0.0 3.2 0.0 3.2 3.2 3.2 3.2 47.4
Other Manufacturing 0.0 0.0 0.0 6.3 0.0 6.3 6.3 6.3 6.3 47.4
Textile and Apparel 0.0 0.0 0.0 29.7 0.0 29.7 29.7 29.7 29.7 47.4
Wood and Paper Products 0.0 0.0 0.0 11.6 0.0 11.6 11.6 11.6 11.6 47.4
Agriculture and Forestry 0.0 0.0 0.0 11.9 0.0 11.9 11.9 11.9 11.9 22.2
Barriers to Efficient Trade Facilitation on Exports
Chemicals Mineral and Metal Products 16.2 16.1 14.2 14.0 16.1 12.9 13.7 15.9 16.2
Energy and Minerals 9.0 7.6 9.0 9.0 5.4 5.4 8.6 9.0 8.4
Food Products 12.6 15.3 16.4 12.1 13.8 7.6 14.7 9.8 13.9
Petroleum and Coal Products 19.9 19.9 19.9 19.9 19.9 19.9 19.9 19.9 19.9
Other Manufacturing 7.8 6.4 13.1 10.2 10.2 9.4 9.3 9.8 9.6
Textile and Apparel 5.9 5.7 5.8 5.6 6.3 6.8 7.2 6.6 7.5
Wood and Paper Products 8.3 15.1 11.2 10.8 9.5 5.8 6.3 4.3 15.2
Agriculture and Forestry 14.3 18.2 28.7 12.5 17.6 17.9 15.2 13.1 18.0
Barriers to Efficient Trade Facilitation on Imports
Chemicals Mineral and Metal Products 19.9 38.6 58.8 9.2 29.3 4.8 5.9 14.8 13.5
Energy and Minerals 11.7 18.9 19.8 4.5 17.3 2.6 3.9 8.2 9.2
Food Products 20.7 30.7 27.9 41.9 19.7 4.8 4.8 11.3 10.8
Petroleum and Coal Products 25.9 41.9 43.9 38.1 36.0 6.0 14.1 18.0 17.8
Other Manufacturing 15.1 26.9 42.6 3.4 16.3 2.0 4.1 8.6 8.2
Textile and Apparel 6.5 12.8 13.8 5.3 15.0 2.1 3.9 5.9 7.2
Wood and Paper Products 14.7 17.6 49.9 10.8 33.9 2.6 7.9 9.6 9.7
Agriculture and Forestry 19.8 34.7 54.2 20.4 30.3 8.7 9.0 17.9 15.6
54
Table 4c: Benchmark Distortions in Uganda; Ad valorem values in percentage.
Barriers Against Service Providers
Discriminatory Non-
Kenya Tanzania Rwanda COMESA SADC USA EUR China ROW Discriminatory
Business Services
Air Transport 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Communication 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 0.0
Insurance 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 0.0
Business Services nec 43.0 43.0 43.0 43.0 43.0 43.0 43.0 43.0 43.0 0.0
Financial Services nec 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0
Transport nec 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 0.0
Water Transport 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Tariff Rates on Goods Non-Tariff
Kenya Tanzania Rwanda COMESA SADC USA EUR China ROW Measures
Goods
Chemicals Mineral and Metal Products 0.0 0.0 0.0 0.0 6.2 6.6 5.2 10.7 9.6 0.0
Energy and Minerals 0.0 0.0 0.0 0.0 3.3 1.8 1.3 0.1 2.8 0.0
Food Products 0.0 0.0 0.0 0.0 66.4 19.4 19.6 21.5 20.9 0.0
Petroleum and Coal Products 0.0 0.0 0.0 0.0 10.0 6.5 4.1 0.0 7.1 0.0
Other Manufacturing 0.0 0.0 0.0 0.0 7.3 5.6 4.1 7.0 11.2 0.0
Textile and Apparel 0.0 0.0 0.0 0.0 21.7 20.8 20.8 24.6 20.6 0.0
Wood and Paper Products 0.0 0.0 0.0 0.0 15.8 13.6 10.5 21.1 20.0 0.0
Agriculture and Forestry 0.0 0.0 0.0 0.0 15.8 14.8 17.5 14.4 3.1 3.9
Barriers to Efficient Trade Facilitation on Exports
Chemicals Mineral and Metal Products 52.4 44.9 48.4 44.5 51.7 28.7 32.6 29.1 36.8
Energy and Minerals 21.6 21.6 21.6 21.6 22.1 21.6 21.7 21.6 21.6
Food Products 31.1 35.1 38.0 24.6 24.3 31.9 38.2 23.1 30.5
Petroleum and Coal Products 47.9 47.9 47.9 47.9 47.9 47.9 47.9 47.9 47.9
Other Manufacturing 25.9 30.7 23.8 34.2 21.4 18.4 24.1 30.9 26.6
Textile and Apparel 14.1 14.6 13.5 14.2 14.3 14.7 15.9 12.8 15.4
Wood and Paper Products 11.4 20.1 21.3 32.8 14.1 22.6 23.6 19.8 17.1
Agriculture and Forestry 46.6 39.8 35.1 36.1 36.5 36.8 35.9 38.7 34.7
Barriers to Efficient Trade Facilitation on Imports
Chemicals Mineral and Metal Products 20.8 25.7 35.7 15.1 24.7 4.3 5.3 13.6 12.9
Energy and Minerals 11.7 12.1 19.8 5.1 16.3 2.7 4.2 8.1 7.8
Food Products 12.4 24.4 17.2 7.7 18.5 4.4 6.1 14.2 11.7
Petroleum and Coal Products 25.9 31.9 43.9 11.3 35.8 6.0 10.8 18.0 14.4
Other Manufacturing 14.6 10.2 31.4 5.0 16.6 2.0 3.8 6.8 9.7
Textile and Apparel 7.5 9.2 9.1 5.1 17.7 2.2 4.0 4.9 5.0
Wood and Paper Products 21.4 23.8 8.7 11.3 29.4 3.0 6.7 6.5 12.6
Agriculture and Forestry 25.8 29.4 35.8 27.9 40.9 8.2 6.0 19.8 24.1
55
Table 4d: Benchmark Distortions in Rwanda; Ad valorem values in percentage.
Barriers Against Service Providers
Discriminatory Non-
Kenya Tanzania Uganda COMESA SADC USA EUR China ROW Discriminatory
Business Services
Air Transport 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Communication 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 0.0
Insurance 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 0.0
Business Services nec 62.0 62.0 62.0 62.0 62.0 62.0 62.0 62.0 62.0 0.0
Financial Services nec 44.0 44.0 44.0 44.0 44.0 44.0 44.0 44.0 44.0 0.0
Transport nec 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 0.0
Water Transport 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Tariff Rates on Goods Non-Tariff
Kenya Tanzania Uganda COMESA SADC USA EUR China ROW Measures
Goods
Chemicals Mineral and Metal Products 0.0 0.0 0.0 0.0 16.3 14.8 14.9 20.7 10.6 4.8
Energy and Minerals 0.0 0.0 0.0 0.0 2.8 0.0 3.0 0.2 4.6 4.8
Food Products 0.0 0.0 0.0 0.0 16.4 22.7 20.2 19.9 20.7 4.8
Petroleum and Coal Products 0.0 0.0 0.0 0.0 15.2 0.0 9.6 0.0 15.1 4.8
Other Manufacturing 0.0 0.0 0.0 0.0 17.8 18.8 19.3 16.8 18.8 4.8
Textile and Apparel 0.0 0.0 0.0 0.0 6.6 25.4 23.4 26.5 22.6 4.8
Wood and Paper Products 0.0 0.0 0.0 0.0 11.3 22.3 19.7 21.1 18.5 4.8
Agriculture and Forestry 0.0 0.0 0.0 0.0 4.6 4.9 5.4 0.7 5.1 0.0
Barriers to Efficient Trade Facilitation on Exports
Chemicals Mineral and Metal Products 39.0 53.4 32.5 29.7 27.3 27.7 27.3 35.6 30.2
Energy and Minerals 18.0 18.0 18.0 18.4 18.0 18.0 18.0 18.0 18.0
Food Products 20.8 23.3 15.8 29.3 16.1 9.0 16.4 16.2 19.2
Petroleum and Coal Products 39.9 39.9 39.9 39.9 39.9 39.9 39.9 39.9 39.9
Other Manufacturing 22.3 38.2 28.5 14.5 18.4 16.3 17.9 24.2 16.7
Textile and Apparel 13.5 12.5 8.3 8.0 10.9 19.5 10.6 8.6 12.2
Wood and Paper Products 16.0 45.1 7.9 28.5 19.4 9.6 15.3 32.3 17.9
Agriculture and Forestry 22.6 47.1 31.5 57.0 29.5 31.6 30.3 44.4 25.3
Barriers to Efficient Trade Facilitation on Imports
Chemicals Mineral and Metal Products 20.0 22.7 42.1 11.8 24.3 3.7 5.5 14.1 10.7
Energy and Minerals 11.7 14.4 18.9 18.0 16.3 2.7 4.3 8.3 19.9
Food Products 19.8 26.3 33.3 15.2 27.9 4.9 4.3 9.1 8.7
Petroleum and Coal Products 25.9 31.9 41.9 11.0 36.8 6.0 9.4 18.0 14.1
Other Manufacturing 13.6 21.0 21.0 16.7 23.6 2.4 3.8 8.3 8.1
Textile and Apparel 7.4 9.3 11.7 9.9 5.2 1.9 3.2 5.3 3.8
Wood and Paper Products 16.7 17.4 18.6 9.1 20.3 2.5 6.6 9.3 10.0
Agriculture and Forestry 19.4 46.1 32.9 39.8 9.6 8.7 5.0 14.9 26.4
56
Table 4e: Benchmark Distortions in COMESA; Ad valorem values in percentage.
Barriers Against Service Providers
Discriminatory Non-
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW Discriminatory
Business Services
Air Transport 45.0 45.0 45.0 45.0 0.0 45.0 45.0 45.0 45.0 45.0 0.0
Communication 40.0 40.0 40.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 0.0
Insurance 102.0 102.0 102.0 102.0 0.0 102.0 102.0 102.0 102.0 102.0 0.0
Business Services nec 36.0 36.0 36.0 36.0 0.0 36.0 36.0 36.0 36.0 36.0 0.0
Financial Services nec 101.0 101.0 101.0 101.0 0.0 101.0 101.0 101.0 101.0 101.0 0.0
Transport nec 25.0 25.0 25.0 25.0 0.0 25.0 25.0 25.0 25.0 25.0 0.0
Water Transport 56.0 56.0 56.0 56.0 0.0 56.0 56.0 56.0 56.0 56.0 0.0
Tariff Rates on Goods Non-Tariff
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW Measures
Goods
Chemicals Mineral and Metal Products 0.0 14.3 0.0 0.0 0.0 10.5 7.7 8.9 13.3 7.8 20.1
Energy and Minerals 0.0 8.8 0.0 0.0 0.0 4.4 1.4 0.7 2.3 0.3 20.1
Food Products 0.0 26.7 0.0 0.0 0.0 35.7 11.5 84.2 25.6 9.0 20.1
Petroleum and Coal Products 0.0 0.0 0.0 0.0 0.0 11.4 6.2 6.7 9.2 4.4 20.1
Other Manufacturing 0.0 12.6 0.0 0.0 0.0 13.4 6.3 7.4 10.5 13.0 20.1
Textile and Apparel 0.0 22.2 0.0 0.0 0.0 18.0 15.3 20.6 30.2 13.5 20.1
Wood and Paper Products 0.0 20.1 0.0 0.0 0.0 7.6 8.2 9.4 22.6 8.3 20.1
Agriculture and Forestry 0.0 12.0 0.0 0.0 0.0 2.4 2.3 5.3 14.6 3.9 27.5
Barriers to Efficient Trade Facilitation on Exports
Chemicals Mineral and Metal Products 9.4 9.0 12.7 10.0 10.5 10.8 8.4 9.3 14.2 11.3
Energy and Minerals 4.5 4.5 4.9 12.4 4.6 4.6 6.2 5.0 5.5 4.7
Food Products 12.5 30.3 7.7 11.3 13.9 20.7 8.7 10.2 13.1 9.2
Petroleum and Coal Products 25.8 26.5 10.7 10.5 25.2 17.4 10.8 10.2 14.3 10.8
Other Manufacturing 6.0 3.3 4.6 13.3 5.5 6.3 7.2 4.6 8.6 5.4
Textile and Apparel 4.6 5.3 5.0 7.2 10.1 4.9 3.6 3.7 4.3 4.7
Wood and Paper Products 10.7 10.9 11.3 8.9 7.3 7.8 4.6 5.9 5.7 6.7
Agriculture and Forestry 16.9 15.9 19.7 26.5 21.5 26.6 19.9 17.8 14.8 15.6
Barriers to Efficient Trade Facilitation on Imports
Chemicals Mineral and Metal Products 20.2 22.5 39.0 32.8 10.9 37.0 5.1 8.2 14.3 14.3
Energy and Minerals 11.7 14.4 18.9 20.2 4.6 9.8 2.7 4.2 8.1 8.3
Food Products 10.9 19.3 21.2 32.2 13.8 15.9 5.0 6.6 12.8 13.1
Petroleum and Coal Products 25.9 31.9 41.9 43.9 35.8 27.9 6.0 12.0 18.0 22.1
Other Manufacturing 14.8 16.4 30.2 16.0 6.2 17.0 2.1 4.4 7.9 8.0
Textile and Apparel 8.0 9.0 12.5 8.2 11.4 9.5 2.1 5.1 7.0 7.4
Wood and Paper Products 12.4 17.3 28.7 31.5 7.3 21.5 5.1 7.2 6.8 11.0
Agriculture and Forestry 14.7 19.9 31.7 63.1 20.3 37.5 7.1 10.7 16.9 17.8
57
Table 4f: Benchmark Distortions in SADC; Ad valorem values in percentage.
Barriers Against Service Providers
Discriminatory Non-
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW Discriminatory
Business Services
Air Transport 30.0 30.0 30.0 30.0 30.0 0.0 30.0 30.0 30.0 30.0 0.0
Communication 53.0 53.0 53.0 53.0 53.0 0.0 53.0 53.0 53.0 53.0 0.0
Insurance 22.0 22.0 22.0 22.0 22.0 0.0 22.0 22.0 22.0 22.0 0.0
Business Services nec 38.0 38.0 38.0 38.0 38.0 0.0 38.0 38.0 38.0 38.0 0.0
Financial Services nec 15.0 15.0 15.0 15.0 15.0 0.0 15.0 15.0 15.0 15.0 0.0
Transport nec 27.0 27.0 27.0 27.0 27.0 0.0 27.0 27.0 27.0 27.0 0.0
Water Transport 7.0 7.0 7.0 7.0 7.0 0.0 7.0 7.0 7.0 7.0 0.0
Tariff Rates on Goods Non-Tariff
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW Measures
Goods
Chemicals Mineral and Metal Products 1.5 0.0 13.8 0.6 2.5 0.0 4.7 4.2 8.5 3.7 0.4
Energy and Minerals 4.2 0.0 0.0 0.0 6.9 0.0 2.5 0.2 3.4 0.2 0.4
Food Products 1.1 0.0 17.3 0.0 2.5 0.0 13.9 11.6 10.1 10.1 0.4
Petroleum and Coal Products 1.9 0.0 9.7 0.0 1.2 0.0 2.3 6.1 1.4 5.6 0.4
Other Manufacturing 1.5 0.0 5.1 1.9 3.8 0.0 4.9 5.5 6.0 10.0 0.4
Textile and Apparel 6.5 0.0 10.0 0.0 19.7 0.0 21.0 11.1 26.1 18.7 0.4
Wood and Paper Products 3.9 0.0 15.9 15.6 8.4 0.0 4.8 6.1 15.7 8.7 0.4
Agriculture and Forestry 12.8 0.0 32.0 0.1 4.6 0.0 3.2 3.0 6.6 5.1 4.5
Barriers to Efficient Trade Facilitation on Exports
Chemicals Mineral and Metal Products 17.4 16.5 14.2 14.2 21.8 21.3 16.0 17.6 20.0 18.5
Energy and Minerals 8.9 9.7 9.1 9.1 7.7 14.0 11.1 12.5 10.0 11.8
Food Products 11.0 11.5 10.5 18.3 9.9 16.1 9.0 9.6 13.4 12.7
Petroleum and Coal Products 19.9 20.1 19.9 22.6 18.1 20.2 35.3 21.9 20.0 23.7
Other Manufacturing 8.7 9.2 9.2 13.3 9.6 11.2 11.8 8.3 8.6 13.8
Textile and Apparel 12.8 8.6 10.0 3.5 6.1 7.8 7.5 5.4 8.8 7.9
Wood and Paper Products 16.9 18.9 16.4 11.4 12.7 13.4 11.6 11.5 20.2 13.8
Agriculture and Forestry 20.0 16.9 21.9 5.5 23.7 20.0 20.0 23.6 18.5 23.3
Barriers to Efficient Trade Facilitation on Imports
Chemicals Mineral and Metal Products 24.9 25.9 45.6 30.1 11.7 34.8 4.4 6.9 12.6 13.9
Energy and Minerals 11.6 8.7 19.3 19.8 4.7 21.6 2.7 3.7 8.1 12.1
Food Products 15.3 22.2 21.1 17.7 28.9 26.0 4.5 7.0 14.5 11.9
Petroleum and Coal Products 25.9 31.9 41.9 43.9 22.5 36.1 6.0 9.5 18.0 18.6
Other Manufacturing 13.4 16.7 19.5 20.2 7.8 19.3 2.8 5.0 8.8 8.5
Textile and Apparel 7.5 10.1 12.2 11.9 4.9 12.8 1.8 4.0 5.7 5.7
Wood and Paper Products 17.4 15.3 12.3 19.8 7.9 22.4 4.2 6.9 5.8 9.9
Agriculture and Forestry 24.0 28.1 31.8 32.5 26.3 33.1 6.6 7.5 20.0 15.9
58
Table 5a—Trade Flows by Trading Partner of Kenya (in percentage)
Imports Exports
Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW
Business Services
Air Transport 0.0 0.0 0.0 0.3 0.4 1.0 58.2 0.3 40.2 0.0 0.1 0.0 0.4 1.3 20.7 39.1 2.1 36.4
Communication 0.1 0.0 0.0 1.3 0.2 13.7 53.6 1.9 29.3 0.0 0.0 0.0 0.7 0.0 11.6 60.7 1.6 25.4
Insurance 0.0 0.0 0.0 0.2 0.0 18.6 49.5 1.4 30.3 0.0 0.0 0.0 0.0 0.0 20.6 36.8 4.5 38.1
Business Services nec 0.0 0.0 0.0 0.2 0.3 8.6 53.8 1.6 35.5 0.0 0.0 0.0 0.6 1.1 12.3 33.7 2.0 50.3
Financial Services nec 0.0 0.0 0.0 0.3 0.6 25.2 38.6 1.2 34.0 0.1 0.0 0.0 0.5 0.8 12.5 41.6 3.2 41.3
Transport nec 0.0 0.0 0.0 0.0 1.2 6.0 39.5 4.4 48.9 0.0 0.0 0.0 0.5 0.7 12.4 45.2 3.7 37.5
Water Transport 0.0 0.0 0.0 0.0 0.5 1.8 47.4 0.6 49.7 0.0 0.0 0.0 0.2 0.2 0.8 45.9 0.2 52.7
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 0.3 0.2 0.0 1.2 21.7 2.2 21.2 10.6 42.5 12.8 24.0 4.2 13.1 7.6 0.8 2.8 0.8 33.9
Energy and Minerals 0.1 0.1 0.0 0.1 1.4 0.1 0.1 0.0 98.1 11.4 25.9 8.6 1.8 0.6 0.3 9.6 9.3 32.5
Food Products 4.7 5.5 3.0 14.1 13.2 8.2 11.1 0.9 39.4 6.4 11.7 2.1 13.2 13.0 3.3 31.6 0.7 18.0
Petroleum and Coal Products 0.0 0.0 0.0 1.7 1.3 0.2 2.8 0.1 94.0 7.1 42.0 11.8 8.1 2.1 3.0 12.3 0.8 12.9
Other Manufacturing 0.3 0.1 0.0 0.4 3.2 14.5 35.2 12.2 34.2 12.3 17.5 4.2 25.6 4.0 4.6 13.6 0.3 18.0
Textile and Apparel 2.9 0.7 0.0 1.1 3.2 0.9 4.9 51.2 35.2 3.9 6.4 1.0 3.4 2.1 50.6 13.9 1.6 17.1
Wood and Paper Products 8.0 0.3 0.0 8.1 12.3 1.7 29.5 9.7 30.4 14.3 28.8 6.4 13.3 6.1 2.4 6.4 0.4 22.0
CRTS
Agriculture and Forestry 11.3 18.7 0.3 2.4 4.7 14.1 6.7 0.5 41.3 0.6 0.6 0.1 13.4 0.9 2.8 48.1 0.5 32.9
Other Services 0.0 0.1 0.0 0.8 0.9 38.8 30.1 2.0 27.3 0.1 0.0 0.0 0.9 0.9 33.7 26.0 1.7 36.6
Trade 0.1 0.0 0.0 0.5 0.3 11.0 37.9 3.0 47.2 0.0 0.0 0.0 0.6 1.2 12.9 29.7 1.8 53.8
Utilities 0.2 19.1 0.0 0.2 1.8 7.0 41.4 3.2 27.2 0.0 0.1 0.0 0.4 1.9 9.4 47.9 3.4 36.9
Source: GTAP 8.1 data set.
59
Table 5b—Trade Flows by Trading Partner of Tanzania (in percentage)
Imports Exports
Kenya Uganda Rwanda COMESA SADC USA EUR China ROW Kenya Uganda Rwanda COMESA SADC USA EUR China ROW
Business Services
Air Transport 0.0 0.0 0.0 1.7 1.4 11.5 42.4 1.7 41.9 0.0 0.0 0.0 0.5 1.0 16.3 40.1 3.1 38.9
Communication 0.3 0.0 0.0 2.1 0.0 16.6 46.4 3.9 30.7 0.2 0.0 0.0 0.7 0.6 11.4 58.6 1.7 26.8
Insurance 0.0 0.0 0.0 0.3 0.9 19.8 48.7 1.5 28.8 0.0 0.0 0.0 0.8 0.8 33.2 24.9 6.3 33.9
Business Services nec 0.0 0.0 0.0 0.0 0.0 9.1 60.0 2.5 28.3 0.0 0.0 0.0 0.4 1.2 10.7 51.6 2.4 33.7
Financial Services nec 0.1 0.0 0.0 0.5 0.6 24.9 41.2 2.5 30.3 0.0 0.0 0.0 0.5 0.8 13.0 41.9 2.3 41.5
Transport nec 0.0 0.0 0.0 1.8 0.9 7.2 40.0 4.4 45.7 0.0 0.0 0.0 0.5 0.6 16.3 44.7 3.7 34.2
Water Transport 0.0 0.0 0.0 1.7 0.5 10.2 19.9 10.7 56.9 0.0 0.0 0.0 0.6 0.8 12.0 42.0 3.9 40.6
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 8.0 0.6 0.0 1.1 15.6 0.9 21.9 11.8 40.2 0.9 0.4 0.7 0.7 22.0 0.9 3.8 4.2 66.5
Energy and Minerals 46.7 0.4 0.0 3.9 13.3 0.4 1.4 2.0 31.9 0.2 0.0 0.0 0.2 7.9 1.5 10.0 47.0 33.1
Food Products 11.1 3.1 0.0 0.2 8.5 1.4 11.7 0.8 63.2 5.6 3.5 1.5 0.9 2.2 4.4 52.0 1.7 28.3
Petroleum and Coal Products 1.2 0.0 0.0 1.9 2.7 0.4 1.3 1.1 91.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Other Manufacturing 2.0 0.1 0.0 1.1 8.7 7.2 41.8 13.9 25.3 12.0 1.7 1.1 2.2 22.7 6.9 24.3 1.5 27.7
Textile and Apparel 6.1 0.5 0.0 0.3 2.5 1.0 3.6 48.7 37.4 10.5 2.1 0.3 2.0 12.4 7.1 28.5 3.9 33.2
Wood and Paper Products 11.4 0.2 0.0 1.6 19.6 1.2 23.6 9.9 32.4 55.0 1.0 0.5 1.6 3.5 1.5 7.5 6.5 22.8
CRTS
Agriculture and Forestry 6.9 0.2 0.0 0.3 5.5 11.3 2.7 0.6 72.5 4.9 0.2 0.1 0.6 1.7 6.9 41.0 3.5 41.0
Other Services 0.2 0.0 0.0 0.8 1.0 27.5 35.9 4.4 30.1 0.1 0.0 0.0 0.7 0.8 20.3 38.0 3.1 37.1
Trade 0.0 0.0 0.0 0.6 0.3 13.0 44.3 3.5 38.4 0.0 0.0 0.0 0.5 0.7 12.2 45.6 3.9 37.1
Utilities 0.2 0.4 0.0 0.3 3.1 16.5 35.8 3.5 40.1 0.0 0.0 0.0 0.5 0.7 12.2 45.9 3.9 36.7
Source: GTAP 8.1 data set.
60
Table 5c—Trade Flows by Trading Partner of Uganda (in percentage)
Imports Exports
Kenya Tanzania Rwanda COMESA SADC USA EUR China ROW Kenya Tanzania Rwanda COMESA SADC USA EUR China ROW
Business Services
Air Transport 0.4 0.0 0.0 2.2 1.1 18.0 44.5 2.2 32.7 0.0 0.0 0.0 0.5 1.1 9.0 22.9 1.9 64.5
Communication 0.5 0.1 0.0 1.6 0.0 15.7 59.2 2.5 20.5 0.2 0.0 0.0 0.6 0.6 11.5 57.1 1.9 28.0
Insurance 0.0 0.0 0.0 0.2 0.2 18.9 50.5 1.2 29.1 0.0 0.0 0.0 0.8 0.8 33.7 24.1 6.2 34.3
Business Services nec 0.0 0.0 0.0 0.2 0.4 9.8 54.5 2.3 32.8 0.0 0.0 0.0 0.4 0.8 12.5 37.7 2.5 46.2
Financial Services nec 0.1 0.0 0.0 0.4 0.7 0.0 55.5 2.0 41.4 0.0 0.0 0.0 0.2 0.5 16.0 51.8 0.7 30.8
Transport nec 0.1 0.1 0.0 1.6 1.4 0.0 41.2 4.7 50.9 0.0 0.0 0.0 0.5 0.7 13.8 45.0 3.7 36.2
Water Transport 0.1 0.1 0.0 1.2 1.0 4.8 26.1 7.8 58.8 0.0 0.0 0.0 0.6 1.1 9.1 21.9 2.0 65.4
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 29.5 0.5 0.2 1.0 12.0 0.9 20.7 4.5 30.7 1.8 2.9 19.1 21.0 1.7 3.1 2.8 1.1 46.5
Energy and Minerals 68.9 0.3 3.7 0.1 1.5 0.7 2.6 2.2 19.9 0.1 0.0 0.0 0.0 0.5 25.8 27.2 5.2 41.2
Food Products 44.7 8.7 0.0 2.0 19.7 6.6 11.7 0.4 6.2 9.1 3.8 10.9 11.8 6.8 1.3 37.0 0.6 18.5
Petroleum and Coal Products 15.2 0.0 0.0 1.2 0.6 0.6 0.9 0.1 81.4 0.1 1.0 10.2 1.2 23.5 7.5 30.8 2.0 23.7
Other Manufacturing 5.5 0.2 0.0 0.7 7.7 4.5 37.9 11.0 32.5 4.7 2.5 6.5 10.6 25.7 2.1 14.9 0.7 32.3
Textile and Apparel 22.9 2.3 0.0 0.2 2.2 0.5 3.2 46.1 22.6 11.9 4.5 7.0 12.6 2.5 5.0 8.8 24.4 23.3
Wood and Paper Products 32.8 0.5 0.1 4.4 9.0 0.7 21.4 5.9 25.3 6.5 1.7 25.0 13.9 2.7 4.1 11.3 1.6 33.1
CRTS
Agriculture and Forestry 16.1 2.8 0.5 0.9 1.9 26.4 9.8 0.2 41.4 11.5 0.1 1.6 9.6 1.6 2.5 45.4 0.8 27.0
Other Services 0.3 0.1 0.0 0.7 1.2 33.8 31.4 3.3 29.2 0.1 0.1 0.0 0.8 0.9 26.7 32.2 2.4 36.9
Trade 0.0 0.1 0.0 0.2 0.5 5.3 47.5 9.3 37.1 0.0 0.0 0.0 0.4 1.0 8.2 47.3 7.5 35.6
Utilities 0.0 0.0 0.0 0.2 1.8 3.7 59.8 3.8 30.6 1.4 0.0 0.2 0.1 0.3 7.6 55.2 1.2 34.0
Source: GTAP 8.1 data set.
61
Table 5d—Trade Flows by Trading Partner of Rwanda (in percentage)
Imports Exports
Kenya Tanzania Uganda COMESA SADC USA EUR China ROW Kenya Tanzania Uganda COMESA SADC USA EUR China ROW
Business Services
Air Transport 0.1 0.0 0.0 0.4 0.8 1.2 52.0 0.4 45.2 0.0 0.1 0.0 0.5 0.9 13.8 39.8 3.2 41.7
Communication 0.3 0.0 0.0 1.7 0.0 2.5 53.9 3.0 38.6 0.2 0.0 0.0 0.6 0.5 11.6 56.2 1.8 28.9
Insurance 0.0 0.0 0.0 0.4 0.0 14.8 50.7 2.0 32.1 0.0 0.0 0.0 0.7 0.7 27.4 24.9 5.0 41.3
Business Services nec 0.0 0.0 0.0 0.2 0.3 13.4 53.5 2.3 30.3 0.0 0.0 0.0 0.6 1.1 11.7 31.7 2.1 52.7
Financial Services nec 0.2 0.0 0.0 1.7 1.9 0.0 0.0 8.5 87.7 0.0 0.0 0.0 0.6 1.0 12.8 24.9 2.0 58.6
Transport nec 0.1 0.1 0.1 2.8 1.2 9.9 34.5 5.0 46.4 0.0 0.0 0.0 0.4 0.5 25.0 42.1 3.2 28.8
Water Transport 0.1 0.1 0.0 1.2 1.0 2.1 44.4 8.2 42.9 0.0 0.1 0.0 0.5 0.7 12.4 40.4 3.4 42.5
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 19.1 3.4 28.6 1.8 3.1 1.1 24.1 3.1 15.6 4.4 0.1 13.7 10.1 0.2 3.1 15.0 3.5 50.0
Energy and Minerals 41.0 0.2 1.5 4.3 0.3 0.2 1.0 1.7 49.8 0.1 0.0 0.2 0.0 0.4 21.8 31.2 9.6 36.7
Food Products 14.3 6.4 34.6 9.5 7.4 7.6 8.6 0.1 11.5 66.9 0.0 0.3 0.8 0.1 8.7 6.6 0.5 16.2
Petroleum and Coal Products 15.1 0.0 0.8 0.2 0.1 1.3 2.1 0.2 80.1 0.0 0.0 0.0 93.3 0.0 0.8 4.2 0.2 1.5
Other Manufacturing 5.6 0.5 1.4 2.3 5.1 2.6 43.9 12.9 25.7 0.6 0.5 4.4 9.5 1.8 13.3 23.4 1.4 45.1
Textile and Apparel 20.3 1.8 9.8 2.0 2.7 1.2 9.1 9.0 44.2 0.0 0.0 1.1 6.3 0.2 5.1 40.5 8.5 38.3
Wood and Paper Products 27.2 0.8 12.8 5.8 6.2 1.0 28.6 4.8 12.9 0.3 0.2 12.5 4.3 0.2 51.2 16.7 0.6 14.0
CRTS
Agriculture and Forestry 7.2 5.9 62.9 2.8 4.3 2.0 2.6 0.5 11.8 1.5 0.0 0.6 2.7 8.5 10.1 52.2 0.6 23.9
Other Services 0.3 0.0 0.1 0.8 1.0 33.1 32.8 3.4 28.5 0.1 0.0 0.0 1.0 1.0 39.7 20.3 1.2 36.6
Trade 0.0 0.1 0.0 0.3 0.5 5.4 48.9 9.7 35.1 0.0 0.0 0.0 0.2 1.5 2.6 49.8 12.8 33.1
Utilities 0.0 0.0 1.5 0.2 1.7 4.2 60.5 4.1 27.6 0.0 0.0 0.0 0.0 0.2 5.2 63.3 1.3 29.9
Source: GTAP 8.1 data set.
62
Table 5e—Trade Flows by Trading Partner of COMESA (in percentage)
Imports Exports
Kenya Tanzania Uganda Rwanda SADC USA EUR China ROW Kenya Tanzania Uganda Rwanda SADC USA EUR China ROW
Business Services
Air Transport 0.2 0.0 0.0 0.0 0.8 8.3 50.4 1.2 39.0 0.0 0.0 0.0 0.0 1.2 20.2 39.3 2.6 36.7
Communication 0.5 0.1 0.0 0.0 0.0 18.3 57.2 3.1 20.8 0.1 0.0 0.0 0.0 0.6 11.9 55.4 2.4 29.5
Insurance 0.0 0.0 0.0 0.0 0.0 18.9 50.6 1.3 29.2 0.0 0.1 0.0 0.0 0.0 22.7 35.3 5.0 36.9
Business Services nec 0.0 0.0 0.0 0.0 0.4 12.3 53.0 2.5 31.9 0.0 0.0 0.0 0.0 1.1 12.1 49.2 2.6 35.0
Financial Services nec 0.1 0.0 0.0 0.0 1.1 0.2 37.7 4.4 56.4 0.0 0.0 0.0 0.0 0.6 14.9 46.9 2.7 34.8
Transport nec 0.1 0.1 0.1 0.0 1.2 11.7 34.7 5.3 47.0 0.0 0.0 0.0 0.0 0.4 26.0 42.4 3.2 27.9
Water Transport 0.2 0.0 0.0 0.0 0.7 0.0 64.5 4.0 30.6 0.0 0.1 0.0 0.0 0.9 10.4 44.7 3.1 40.8
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 0.8 0.0 0.4 0.0 1.8 6.1 34.7 12.7 43.4 0.4 0.2 0.1 0.0 0.7 4.8 40.3 0.8 52.6
Energy and Minerals 0.1 0.0 0.0 0.0 0.2 4.8 10.2 0.9 83.6 0.0 0.0 0.0 0.0 0.0 4.2 11.9 34.5 49.4
Food Products 2.2 0.1 0.9 0.0 1.2 7.3 19.9 1.5 66.9 3.9 0.0 0.2 0.5 2.6 3.9 30.3 0.9 57.7
Petroleum and Coal Products 0.3 0.0 0.0 0.1 0.3 1.9 7.2 5.9 84.4 0.4 0.4 0.1 0.0 0.5 4.8 49.6 0.2 43.9
Other Manufacturing 0.3 0.0 0.0 0.0 1.0 11.4 41.2 14.4 31.7 0.6 1.1 0.4 0.3 1.1 7.0 36.2 1.1 52.3
Textile and Apparel 0.5 0.1 0.1 0.0 0.3 1.2 11.0 44.5 42.3 0.2 0.0 0.0 0.0 0.3 31.5 46.7 2.0 19.2
Wood and Paper Products 0.6 0.0 0.1 0.0 1.0 6.4 38.4 9.3 44.2 4.6 0.4 0.8 0.3 0.8 7.8 28.7 1.5 55.1
CRTS
Agriculture and Forestry 4.8 0.1 1.1 0.0 1.1 34.3 8.2 1.0 49.3 0.3 0.0 0.0 0.0 0.4 4.5 38.6 4.1 52.0
Other Services 0.3 0.0 0.1 0.0 0.9 30.8 34.7 3.4 29.7 0.1 0.1 0.0 0.0 1.6 16.4 38.5 3.1 40.2
Trade 0.0 0.1 0.0 0.0 0.3 10.8 43.7 5.2 39.8 0.0 0.1 0.0 0.0 0.8 11.9 45.2 4.6 37.4
Utilities 0.1 0.1 0.1 0.0 2.0 8.2 52.7 3.9 32.9 0.0 0.0 0.0 0.0 1.6 5.5 49.8 2.1 41.0
Source: GTAP 8.1 data set.
63
Table 5f—Trade Flows by Trading Partner of SADC (in percentage)
Imports Exports
Kenya Tanzania Uganda Rwanda COMESA USA EUR China ROW Kenya Tanzania Uganda Rwanda COMESA USA EUR China ROW
Business Services
Air Transport 0.3 0.0 0.0 0.0 2.3 11.7 48.4 2.3 36.3 0.0 0.0 0.0 0.0 0.4 18.3 40.9 2.7 37.6
Communication 0.0 0.2 0.2 0.0 7.5 17.5 0.0 15.1 59.4 0.0 0.0 0.0 0.0 0.0 12.4 52.9 2.6 32.0
Insurance 0.0 0.0 0.0 0.0 0.0 13.4 50.9 2.0 33.6 0.0 0.1 0.0 0.0 0.0 21.6 36.5 5.0 36.8
Business Services nec 0.0 0.0 0.0 0.0 0.2 12.5 56.6 2.6 28.1 0.0 0.0 0.0 0.0 0.4 13.0 45.6 3.2 37.8
Financial Services nec 0.0 0.0 0.0 0.0 0.3 17.1 44.9 1.9 35.8 0.0 0.0 0.0 0.0 0.2 16.6 51.3 1.7 30.3
Transport nec 0.0 0.1 0.1 0.0 2.0 7.7 47.1 5.3 37.7 0.0 0.1 0.0 0.0 0.5 12.7 45.1 3.7 37.9
Water Transport 0.1 0.0 0.0 0.0 0.5 0.0 53.9 2.7 42.8 0.0 0.0 0.0 0.0 0.3 4.8 46.5 1.4 47.0
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 0.3 0.9 0.0 0.0 0.2 6.3 40.3 10.3 41.7 1.3 0.5 0.2 0.0 0.7 13.9 28.3 7.0 48.1
Energy and Minerals 0.0 0.2 0.1 0.0 0.0 0.5 8.6 0.2 90.4 0.0 0.0 0.0 0.0 0.0 31.1 23.2 24.4 21.3
Food Products 1.4 0.2 0.4 0.0 0.8 4.7 32.8 2.7 57.1 1.6 0.8 0.8 0.2 1.2 5.4 54.4 1.1 34.6
Petroleum and Coal Products 0.1 0.0 0.0 0.0 0.4 11.9 13.1 3.8 70.7 0.8 1.3 0.1 0.0 0.7 20.7 27.4 3.5 45.5
Other Manufacturing 0.0 0.0 0.0 0.0 0.0 10.4 49.4 11.1 29.0 0.7 1.1 0.5 0.1 1.5 15.5 40.9 3.3 36.5
Textile and Apparel 0.2 0.4 0.0 0.0 0.2 1.3 15.0 50.9 32.0 0.5 0.2 0.1 0.0 0.3 31.5 51.8 1.5 14.2
Wood and Paper Products 0.2 0.1 0.0 0.0 0.2 5.5 50.8 12.8 30.4 1.7 1.2 0.4 0.1 1.1 4.0 40.6 2.9 48.1
CRTS
Agriculture and Forestry 1.0 0.8 0.6 0.4 0.7 18.2 14.7 7.0 56.6 0.3 0.2 0.0 0.0 1.2 4.3 51.3 10.1 32.5
Other Services 0.1 0.0 0.0 0.0 0.9 20.0 40.5 4.6 33.8 0.0 0.1 0.0 0.0 0.6 16.3 41.3 3.4 38.3
Trade 0.0 0.0 0.0 0.0 0.2 4.0 47.0 10.6 38.2 0.0 0.0 0.0 0.0 0.2 4.2 49.3 11.9 34.2
Utilities 0.0 0.0 0.0 0.0 0.2 3.5 54.9 3.4 37.9 0.0 0.0 0.1 0.0 0.2 7.2 48.1 2.5 41.9
Source: GTAP 8.1 data set.
64
Table 6a—Market Shares of Output Produced in Kenya in Sectors with FDI (in percentage).
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW
Business Services
Air Transport 31 5 0 0 5 1 0 30 0 28
Communication 33 0 0 0 0 1 0 36 0 31
Insurance 85 0 0 0 0 4 5 4 0 3
Business Services nec 98 0 0 0 0 0 0 2 0 0
Financial Services nec 65 0 0 0 0 0 5 27 0 4
Transport nec 74 4 6 1 12 0 0 2 0 2
Water Transport 45 9 0 2 5 0 2 25 5 8
Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.
Table 6b—Market Shares of Output Produced in Tanzania in Sectors with FDI (in percentage).
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW
Business Services
Air Transport 5 31 0 0 0 5 0 30 3 26
Communication 0 29 0 0 0 24 0 25 0 22
Insurance 48 43 0 0 0 8 0 1 0 0
Business Services nec 0 65 0 0 4 3 7 13 0 9
Financial Services nec 2 62 0 0 0 10 10 15 0 2
Transport nec 5 86 1 0 0 3 0 2 0 3
Water Transport 9 46 0 0 0 5 0 25 6 10
Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.
65
Table 6c—Market Shares of Output Produced in Uganda in Sectors with FDI (in percentage).
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW
Business Services
Air Transport 0 0 5 0 0 0 72 19 0 4
Communication 0 0 1 0 0 55 0 14 0 31
Insurance 60 0 12 0 0 4 15 5 0 5
Business Services nec 0 0 78 0 0 0 9 9 0 4
Financial Services nec 0 0 50 0 0 0 50 0 0 0
Transport nec 0 0 67 0 0 0 18 8 0 7
Water Transport 0 0 39 0 0 0 61 0 0 0
Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.
Table 6d—Market Shares of Output Produced in Rwanda in Sectors with FDI (in percentage).
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW
Business Services
Air Transport 0 0 0 5 0 0 2 92 0 1
Communication 0 0 0 54 0 36 11 0 0 0
Insurance 60 0 0 12 0 4 15 5 0 5
Business Services nec 0 0 0 78 0 0 6 15 0 0
Financial Services nec 0 0 0 50 0 0 26 24 0 0
Transport nec 0 0 0 66 0 0 6 20 0 7
Water Transport 0 0 0 40 0 0 13 0 0 47
Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.
66
Table 6e—Market Shares of Output Produced in COMESA in Sectors with FDI (in percentage).
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW
Business Services
Air Transport 0 0 0 0 5 0 2 92 0 1
Communication 0 0 0 0 15 40 0 19 0 26
Insurance 32 0 0 0 32 17 12 3 0 4
Business Services nec 0 0 0 0 78 0 6 15 0 0
Financial Services nec 0 0 0 0 50 0 26 24 0 0
Transport nec 0 0 0 0 66 0 6 20 0 7
Water Transport 0 0 0 0 40 0 13 0 0 47
Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.
Table 6f—Market Shares of Output Produced in SADC in Sectors with FDI (in percentage).
Kenya Tanzania Uganda Rwanda COMESA SADC USA EUR China ROW
Business Services
Air Transport 0 0 0 0 0 32 68 0 0 0
Communication 2 0 0 0 0 53 5 28 0 13
Insurance 3 0 0 0 1 81 6 6 0 3
Business Services nec 0 0 0 0 0 80 6 2 0 13
Financial Services nec 0 0 0 0 0 70 12 18 0 0
Transport nec 0 0 0 0 0 74 3 0 0 23
Water Transport 0 0 0 0 0 21 62 0 0 18
Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.
67
Table 7—Model Elasticities
Armington elasticities Supply elasticities
Domestic vs Foreign Foreign vs Foreign Dixit-Stiglitz Africa SADC China EUR and ROW USA
Business Services
Air Transport 3.0 1.9 3.9 6.0 10.0 15.0
Communication 3.0 2.5 5.2 8.0 13.4 20.0
Insurance 3.0 3.3 3.3 3.3 3.3 10.0
Business Services nec 3.0 2.5 5.2 8.0 13.4 20.0
Financial Services nec 3.0 3.3 3.3 3.3 3.3 10.0
Transport nec 3.0 1.9 2.3 2.6 3.3 10.0
Water Transport 3.0 1.9 3.9 6.0 10.0 15.0
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 6.8 1.9 3.9 6.0 10.0 15.0
Energy and Minerals 11.6 3.3 3.3 3.3 3.3 10.0
Food Products 5.1 3.3 3.3 3.3 3.3 10.0
Petroleum and Coal Products 4.2 3.3 3.3 3.3 3.3 10.0
Other Manufacturing 7.7 1.9 3.9 6.0 10.0 15.0
Textile and Apparel 7.6 3.3 3.3 3.3 3.3 10.0
Wood and Paper Products 6.3 3.3 3.3 3.3 3.3 10.0
CRTS
Agriculture and Forestry 2.5 30.0
Other Services 1.5 30.0
Trade 1.5 30.0
Utilities 2.8 30.0
Source: Dixit-Stiglitz elasticities in services are based on estimates from Broda and Weinstein (2006). Dixit-Stiglitz elasticities in goods are the
estimates of the elasticity of substitution for imports from different regions in the GTAP data set. Armington elasticities of substitution of domestic
for foreign are from the GTAP data set. Armington elasticities of substitution of foreign for foreign are based on the estimates of Reidel (1988).
Supply elasticities are based on the estimates of Schiff, Wang and Olarreaga (2002) and Schiff and Wang (2006).
68
Table 8: East Africa Customs Union (EACU) Policies: Deep Integration and Multilateral
Liberalization (Results are percentage change from initial equilibrium)
EACU
Liberal:
(Multilateral
services,
NTM
refrom plus
EACU Central:
(Trade EACU (only Trade EACU EACU
Facilitation plus Trade EACU (only EACU (only Facilitation Liberal: (only Liberal: (only
services and NTB Facilitation) services NTB within services NTB
Scenario definition Benchmark liberalization) * liberalization) liberalization) EACU) liberalization) liberalization)
Trade faclitation: 20% reduction of trade costs within EACU countries only No Yes Yes No No Yes No No
Trade faclitation: 5% reduction in trade costs with non-EACU countries No Yes Yes No No Yes No No
Services Liberalization: 50% reduction of discriminatory FDI barriers within EACU countries No Yes No Yes No Yes Yes No
Services Liberalization: 50% multilateral reduction of discriminatory FDI barriers by EACU co No No No No No Yes Yes No
Non-Tariff Barriers: 20% reduction of NTB costs within EACU countries No Yes No No Yes Yes No Yes
Non-Tariff Barriers: 20% multiltateral reduction of NTB costs by EACU countries No No No No No Yes No Yes
Aggregate welfare
Welfare (EV as % of consumption)
Kenya 0.96 0.66 0.04 0.10 1.81 0.98 0.14
Tanzania 0.95 0.63 0.03 0.17 7.11 1.24 5.06
Uganda 1.24 1.13 0.04 0.04 2.79 1.54 0.03
Rwanda 1.40 1.21 0.13 0.03 4.95 3.35 0.27
Aggregate trade
Aggregate exports
Kenya 6.08 4.46 0.02 1.04 7.74 2.55 0.46
Tanzania 5.57 2.70 -0.03 1.83 16.65 2.51 10.49
Uganda 5.17 4.88 0.02 0.06 7.97 2.96 0.05
Rwanda 9.60 8.83 0.17 0.40 20.12 9.92 1.04
Factor earnings
Kenya
Capital 1.38 0.86 0.03 0.31 1.17 0.17 0.09
Unskilled labor 1.56 1.40 0.01 0.08 1.67 0.24 0.02
Skilled labor 0.24 0.13 0.01 0.04 0.11 -0.01 0.05
Resource 2.84 2.78 0.01 0.04 3.49 0.63 0.04
Tanzania
Capital 0.05 0.35 0.04 -0.21 -0.40 0.33 -1.04
Unskilled labor 0.61 0.62 0.00 0.07 1.29 0.32 0.35
Skilled labor 0.26 0.18 0.01 0.03 1.64 0.13 1.13
Resource -0.32 0.58 0.00 -0.62 -0.03 0.31 -0.62
Uganda
Capital 1.60 1.32 0.04 0.10 2.01 0.67 0.05
Unskilled labor 4.65 4.23 0.01 0.29 4.73 0.22 0.02
Skilled labor 1.18 1.04 0.02 0.03 1.17 0.27 0.02
Resource 1.58 1.70 0.02 -0.05 2.52 0.70 0.00
Rwanda
Capital -0.47 -0.56 0.16 -0.07 3.04 3.69 -0.20
Unskilled labor 1.11 0.95 0.06 0.00 2.24 1.40 0.00
Skilled labor 0.48 0.43 0.07 0.02 1.46 0.76 0.09
Resource 2.90 2.74 0.12 0.06 7.40 4.76 0.18
* Trade facilitation within EACU is part of the "EACU liberal" scenario also.
Source: Authors' estimates
69
Table 9: Rents Captured from Deep Integration of the East African Customs Union and the Tripartite
FTA (values are percentage of initial consumption of the relevant country)
Tripartite
EACU Liberal: regional
(Multilateral integration,
EACU Central: services and central: Trade Tripartite Tripartite Tripartite
(Trade NTB refrom Facilitation plus regional regional regional
Facilitation plus EACU (only EACU (only EACU (only plus Trade EACU Liberal: EACU Liberal: services and integration integration (only integration (only
Benchmark: services and NTB Trade services NTB Facilitation (only services (only NTB NTB (only Trade services NTB
Scenario definition Existing rent liberalization) Facilitation) liberalization) liberalization) within EACU) liberalization) liberalization) liberalization Facilitation) liberalization) liberalization)
Rent affected by the policy
Foreign Direct Investment
Kenya 3.210 0.026 0.000 0.026 0.000 0.486 0.486 0.000 0.060 0.000 0.060 0.000
Tanzania 5.012 0.006 0.000 0.006 0.000 0.680 0.680 0.000 0.124 0.000 0.124 0.000
Uganda 1.646 0.044 0.000 0.044 0.000 0.823 0.823 0.000 0.152 0.000 0.152 0.000
Rwanda 3.030 0.116 0.000 0.116 0.000 1.515 1.515 0.000 0.184 0.000 0.184 0.000
COMESA 6.802 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.758 0.000 0.758 0.000
SADC 5.891 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.094 0.000 0.094 0.000
Trade Faciliatation
Kenya 9.338 0.369 0.369 0.000 0.000 0.369 0.000 0.000 0.675 0.675 0.000 0.000
Tanzania 8.721 0.407 0.407 0.000 0.000 0.407 0.000 0.000 0.680 0.680 0.000 0.000
Uganda 6.348 0.564 0.564 0.000 0.000 0.564 0.000 0.000 0.709 0.709 0.000 0.000
Rwanda 7.480 1.011 1.011 0.000 0.000 1.011 0.000 0.000 1.111 1.111 0.000 0.000
COMESA 6.966 0.005 0.005 0.000 0.000 0.005 0.000 0.000 0.245 0.245 0.000 0.000
SADC 6.222 0.002 0.002 0.000 0.000 0.002 0.000 0.000 0.184 0.184 0.000 0.000
Non-Tariff Barriers
Kenya 0.375 0.016 0.000 0.000 0.016 0.075 0.000 0.075 0.022 0.000 0.000 0.022
Tanzania 18.910 0.218 0.000 0.000 0.218 3.782 0.000 3.782 0.625 0.000 0.000 0.625
Uganda 0.034 0.001 0.000 0.000 0.001 0.007 0.000 0.007 0.002 0.000 0.000 0.002
Rwanda 1.136 0.073 0.000 0.000 0.073 0.227 0.000 0.227 0.090 0.000 0.000 0.090
COMESA 7.616 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.040 0.000 0.000 0.040
SADC 0.182 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Cross-Border Services
Kenya 3.053 0.000 0.000 0.000 0.000 0.351 0.351 0.000 0.002 0.000 0.002 0.000
Tanzania 3.515 0.000 0.000 0.000 0.000 0.403 0.403 0.000 0.004 0.000 0.004 0.000
Uganda 0.766 0.000 0.000 0.000 0.000 0.383 0.383 0.000 0.004 0.000 0.004 0.000
Rwanda 2.140 0.001 0.000 0.001 0.000 1.070 1.070 0.000 0.009 0.000 0.009 0.000
COMESA 2.256 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.006 0.000
SADC 1.884 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.000 0.007 0.000
Total
Kenya 15.976 0.411 0.369 0.026 0.016 1.281 0.836 0.075 0.759 0.675 0.062 0.022
Tanzania 36.158 0.631 0.407 0.006 0.218 5.272 1.083 3.782 1.433 0.680 0.129 0.625
Uganda 8.794 0.610 0.564 0.045 0.001 1.777 1.206 0.007 0.867 0.709 0.156 0.002
Rwanda 13.786 1.200 1.011 0.116 0.073 3.823 2.585 0.227 1.394 1.111 0.193 0.090
COMESA 23.639 0.005 0.005 0.000 0.000 0.005 0.000 0.000 1.049 0.245 0.764 0.040
SADC 14.179 0.002 0.002 0.000 0.000 0.002 0.000 0.000 0.285 0.184 0.101 0.000
Source: Authors' calculations.
70
Table 10: 50 Percent Reduction of Non-Discriminatory Services Barriers in Kenya and Tanzania
Results are percentage change from initial equilibrium
Scenario definition Kenya Tanzania
50% reduction in ad valorem equivalents of non-discriminatory services barriers in Kenya Yes No
50% reduction in ad valorem equivalents of non-discriminatory services barriers in Tanzania No Yes
Aggregate welfare
Welfare (EV as % of consumption)
Kenya 1.4 0.0
Tanzania 0.0 2.2
Aggregate trade
Aggregate exports
Kenya 1.3 0.0
Tanzania 0.0 3.8
Factor earnings
Kenya
Capital 0.9 0.0
Unskilled labor 0.3 0.0
Skilled labor 0.2 0.0
Resource 0.6 0.0
Tanzania
Capital 0.0 1.7
Unskilled labor 0.0 0.5
Skilled labor 0.0 0.3
Resource 0.0 0.3
Source: Authors' estimates
71
Table 11: Deep Integration in the Tripartite Agreement (Results are percentage change from
initial equilibrium)
Tripartite
regional
integration,
central:
(Trade
Facilitation
plus services Tripartite Tripartite Tripartite
and NTB regional regional regional
liberalization integration integration (only integration (only
and tariff (only Trade services NTB
Scenario definition Benchmark reform) Facilitation) liberalization) liberalization)
Trade faclitation: 20% reduction in trade costs with Tripartite countries* No Yes Yes No No
Trade faclitation: 5% reduction in trade costs with non-Tripartite countries No Yes Yes No No
Services Liberalization: 50% reduction of discriminatory FDI barriers for Tripartite countries* No Yes No Yes No
Non-Tariff Barriers: 20% reduction of NTB costs with Tripartite countries* No Yes No No Yes
Aggregate welfare
Welfare (EV as % of consumption)
Kenya 3.1 1.3 1.4 0.2
Tanzania 2.2 1.1 0.1 0.6
Uganda 2.4 1.9 0.2 0.1
Rwanda 2.3 1.7 0.2 0.1
COMESA 1.5 0.3 1.0 0.0
SADC 0.8 0.6 0.2 0.0
Aggregate trade
Aggregate exports
Kenya 5.9 6.3 -2.3 1.5
Tanzania 10.1 4.9 0.2 3.4
Uganda 7.7 6.9 0.1 0.1
Rwanda 12.9 10.9 0.4 0.4
COMESA 3.4 1.8 0.8 0.3
SADC 2.7 2.3 0.1 0.1
Factor earnings
Kenya
Capital 4.5 1.4 2.2 0.7
Unskilled labor 4.1 2.6 0.3 1.3
Skilled labor 0.9 0.4 0.2 -0.1
Resource 7.3 5.0 0.2 2.7
Tanzania
Capital 0.6 0.9 0.1 -0.3
Unskilled labor 1.4 1.1 0.0 0.2
Skilled labor 0.8 0.4 0.0 0.2
Resource 0.1 0.9 0.0 -0.6
Uganda
Capital 3.5 2.4 0.1 0.3
Unskilled labor 10.7 7.8 0.0 1.2
Skilled labor 3.1 2.1 0.1 0.1
Resource 0.7 1.3 0.0 -0.2
Rwanda
Capital 1.8 0.8 0.2 0.0
Unskilled labor 11.1 7.8 0.1 0.2
Skilled labor 0.0 -0.3 0.1 0.0
Resource -0.3 0.7 0.2 0.0
COMESA
Capital 0.3 0.1 0.2 0.0
Unskilled labor 0.6 0.4 0.2 0.0
Skilled labor 0.7 0.2 0.4 0.0
Resource 0.4 0.4 0.0 0.0
SADC
Capital 0.9 0.6 0.2 0.1
Unskilled labor 1.1 0.7 0.1 0.1
Skilled labor 0.5 0.4 0.1 0.0
Resource 0.6 0.6 0.0 0.0
*Tripartite countries are EAC, COMESA and SADC
Source: Authors' estimates
72
Table 12a. Output Impacts in Kenya from EACU Regional Deep Integration and Multilateral Liberalization
(results are percentage change from initial equilibrium)
Tripartite
regional
integration,
central: (Trade Tripartite
EACU Central: EACU Liberal: Facilitation plus regional
(Trade (Multilateral services and Tripartite integration Tripartite Tripartite
Facilitation plus services, NTM and NTB regional (only regional regional
services and EACU (only EACU (only EACU (only tariff refrom plus EACU Liberal: EACU Liberal: liberalization integration services integration integration
NTB Trade services EACU (only NTB unilateral tariff Trade Facilitation (only services (only NTB and tariff (only Trade liberalizatio (only NTB (only tariff
Scenario definitions (see tables 8 and 10). liberalization) Facilitation) liberalization) liberalization) liberalization) within EACU) liberalization) liberalization) reform) Facilitation) n) liberalization) reform)
Business Services
Air Transport -0.5 -0.3 -0.2 -0.1 0.2 -1.7 -1.4 0.1 -1.5 -0.6 -0.3 -0.6 0.3
Communication -0.5 -0.4 0.0 -0.1 0.2 0.2 0.6 0.1 -1.3 -0.8 0.2 -0.8 0.2
Insurance 0.4 0.3 0.0 0.1 0.1 -2.8 -3.1 0.1 1.4 0.5 0.9 -0.1 -0.1
Business Services nec 0.5 0.3 0.0 0.1 0.2 -2.9 -3.3 0.1 0.5 0.4 0.1 -0.1 0.2
Financial Services nec 0.7 0.5 0.0 0.1 0.1 0.3 -0.3 0.1 1.4 0.8 0.6 0.0 0.1
Transport nec 0.1 0.1 0.0 0.0 0.2 0.5 0.4 0.1 -0.1 0.1 0.1 -0.3 0.3
Water Transport 0.4 -0.2 0.7 -0.1 0.2 -0.4 -0.3 0.1 -0.7 -0.4 0.2 -0.6 0.3
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 4.1 2.1 0.0 1.4 0.3 3.5 1.1 0.2 2.3 1.3 -0.3 0.6 -1.6
Energy and Minerals 9.3 5.0 0.0 3.9 0.4 10.4 1.6 3.5 7.2 4.6 -0.6 2.1 0.9
Food Products 0.4 0.0 0.0 0.2 0.1 1.2 0.9 0.2 0.6 0.0 0.4 -0.2 -0.7
Petroleum and Coal Products 1.3 0.9 0.0 0.3 0.2 1.6 0.8 0.0 0.4 0.4 0.1 -0.2 -0.2
Other Manufacturing 0.8 -0.1 0.0 0.6 0.3 0.7 0.9 -0.1 0.0 -0.1 -0.3 -0.1 -0.9
Textile and Apparel 0.2 -0.3 0.0 0.4 0.2 1.9 2.1 0.1 -1.2 -0.8 -0.1 -0.4 0.2
Wood and Paper Products 0.4 -0.4 0.0 0.7 0.3 0.8 1.4 -0.2 -3.7 -3.0 -0.3 -0.6 -1.1
CRTS
Agriculture and Forestry 1.7 1.9 0.0 -0.2 -0.1 2.7 0.8 -0.1 4.4 3.3 0.0 1.8 1.3
Other Services -1.2 -1.0 0.0 -0.2 0.3 0.2 1.2 0.0 -2.6 -1.7 -0.2 -1.3 0.2
Trade 0.9 0.7 0.0 0.1 0.1 1.2 0.4 0.1 1.7 1.3 0.1 0.4 0.5
Utilities 0.8 0.3 0.0 0.3 0.2 1.4 0.9 0.1 0.6 0.2 0.3 -0.2 -0.3
*Tripartite partners are EAC, COMESA and SADC.
Source: Authors’ estimates
73
Table 12b. Output Impacts in Tanzania from EACU Regional Deep Integration and Multilateral Liberalization
(results are percentage change from initial equilibrium)
Tripartite
regional
integration,
central: (Trade Tripartite
EACU Central: EACU Liberal: Facilitation plus regional
(Trade (Multilateral services and Tripartite integration Tripartite Tripartite
Facilitation plus services, NTM and NTB regional (only regional regional
services and EACU (only EACU (only EACU (only tariff refrom plus EACU Liberal: EACU Liberal: liberalization integration services integration integration
NTB Trade services EACU (only NTB unilateral tariff Trade Facilitation (only services (only NTB and tariff (only Trade liberalizatio (only NTB (only tariff
Scenario definitions (see tables 8 and 10). liberalization) Facilitation) liberalization) liberalization) liberalization) within EACU) liberalization) liberalization) reform) Facilitation) n) liberalization) reform)
Business Services
Air Transport 0.8 0.2 0.1 0.3 0.2 1.3 -3.8 4.6 1.4 0.3 0.2 0.6 -0.1
Communication 0.6 0.1 0.0 0.2 0.2 6.6 2.2 4.0 1.6 0.2 0.5 0.6 -0.2
Insurance 1.2 -0.2 0.9 0.2 0.2 8.5 5.4 3.0 2.4 -0.4 2.0 0.5 -0.2
Business Services nec 0.7 0.1 0.0 0.3 0.2 7.0 2.4 4.1 1.6 0.2 0.3 0.7 -0.2
Financial Services nec 0.5 -0.1 0.2 0.2 0.2 6.0 2.7 3.2 2.0 -0.2 1.4 0.5 -0.2
Transport nec 0.6 0.1 0.0 0.2 0.2 4.6 0.5 3.8 1.1 0.2 0.1 0.6 -0.1
Water Transport 0.5 0.0 0.0 0.2 0.2 3.3 0.2 3.0 0.7 0.0 0.0 0.5 -0.1
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products -0.5 -0.1 0.0 -0.4 0.3 -0.5 0.8 -1.7 0.9 1.9 0.0 -1.1 -0.3
Energy and Minerals -2.0 -0.5 0.0 -1.4 0.5 0.8 0.7 0.9 -2.7 -0.9 0.0 -1.4 -0.7
Food Products -0.9 0.0 0.0 -0.6 0.2 -1.6 0.9 -2.0 -0.8 0.0 0.1 -0.7 0.1
Petroleum and Coal Products 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Other Manufacturing -0.3 -0.5 0.0 0.0 0.3 -11.0 0.6 -11.8 -2.3 -0.6 0.0 -1.6 -0.4
Textile and Apparel -0.6 -0.3 0.0 -0.7 0.5 -8.9 0.6 -9.5 -0.7 -0.3 0.1 -0.6 0.7
Wood and Paper Products -1.8 0.3 0.0 -2.0 0.6 -11.8 0.9 -13.8 -11.6 -4.5 0.2 -5.6 -0.9
CRTS
Agriculture and Forestry 1.8 1.4 0.0 0.5 -0.3 4.5 0.2 2.9 3.3 2.0 0.0 0.9 0.6
Other Services 0.5 0.0 0.0 0.2 0.2 4.9 0.9 3.5 0.9 0.0 0.0 0.6 -0.2
Trade 0.2 -0.2 0.0 0.1 0.3 3.8 1.2 2.8 0.3 -0.3 0.1 0.3 -0.3
Utilities 0.3 0.1 0.0 0.0 0.2 3.6 1.2 2.1 0.7 0.3 0.1 0.0 -0.2
*Tripartite partners are EAC, COMESA and SADC
Source: Authors’ estimates
74
Table 12c. Output Impacts in Uganda from EACU Regional Deep Integration and Multilateral Liberalization
(results are percentage change from initial equilibrium)
Tripartite
regional
integration,
central: (Trade Tripartite
EACU Central: EACU Liberal: Facilitation plus regional
(Trade (Multilateral services and Tripartite integration Tripartite Tripartite
Facilitation plus services, NTM and NTB regional (only regional regional
services and EACU (only EACU (only EACU (only tariff refrom plus EACU Liberal: EACU Liberal: liberalization integration services integration integration
NTB Trade services EACU (only NTB unilateral tariff Trade Facilitation (only services (only NTB and tariff (only Trade liberalizatio (only NTB (only tariff
Scenario definitions (see tables 8 and 10). liberalization) Facilitation) liberalization) liberalization) liberalization) within EACU) liberalization) liberalization) reform) Facilitation) n) liberalization) reform)
Business Services
Air Transport -1.3 -0.9 0.1 -0.3 0.3 1.0 2.0 0.0 -5.7 -3.5 0.2 -0.9 -8.2
Communication 0.0 0.0 0.1 -0.1 0.1 2.8 2.9 0.0 0.3 -0.2 0.9 -0.2 -2.9
Insurance 3.2 -1.0 4.3 -0.1 0.2 2.5 3.5 0.0 1.5 -2.2 4.9 -0.5 -5.5
Business Services nec -0.5 -0.4 0.0 -0.1 0.2 -0.3 0.5 0.0 -1.6 -1.1 0.1 -0.3 -5.0
Financial Services nec -0.1 -0.1 0.1 -0.1 0.2 1.1 1.2 0.0 -1.1 -0.8 0.3 -0.3 -4.9
Transport nec -0.5 -0.4 0.0 -0.1 0.1 -0.6 -0.2 0.0 -1.7 -1.1 0.1 -0.3 -3.0
Water Transport -1.7 -1.3 0.0 -0.2 0.3 -0.3 1.1 0.0 -5.9 -3.8 0.1 -0.8 -6.8
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 0.1 -0.8 0.0 0.3 0.3 1.5 2.1 0.0 1.0 0.4 0.2 0.4 -9.4
Energy and Minerals -2.3 -1.8 0.0 -0.3 0.5 0.1 2.0 0.0 -8.9 -5.7 0.1 -1.3 -11.8
Food Products 0.6 0.1 0.0 0.2 0.3 2.6 1.9 0.3 -1.4 -1.1 0.2 0.2 -23.9
Petroleum and Coal Products -13.1 -11.2 0.0 -0.3 0.3 -9.1 1.4 -0.2 -19.1 -14.4 0.1 -0.9 -10.8
Other Manufacturing -3.6 -3.5 0.0 -0.2 0.4 -1.5 2.1 0.0 -8.2 -6.1 0.1 -0.8 -10.4
Textile and Apparel -3.6 -3.8 0.0 0.1 0.4 -1.7 2.0 0.0 -9.0 -7.1 0.2 -0.3 -13.1
Wood and Paper Products -7.4 -7.3 0.1 -0.1 0.3 -4.6 2.4 0.0 -13.4 -11.6 0.3 -0.5 -13.9
CRTS
Agriculture and Forestry 6.5 6.1 0.0 0.4 -0.7 7.6 0.8 0.0 14.2 10.8 0.0 1.8 24.9
Other Services -1.2 -1.1 0.0 -0.1 0.2 0.0 1.3 0.0 -2.2 -1.8 0.1 -0.5 -3.1
Trade -1.3 -1.3 0.1 -0.1 0.3 0.9 2.8 0.0 -2.2 -1.9 0.2 -0.6 -5.8
Utilities -1.8 -1.5 0.0 -0.2 0.3 0.2 1.6 0.0 -4.4 -3.2 0.1 -0.7 -6.7
*Tripartite partners are EAC, COMESA and SADC
Source: Authors’ estimates
75
Table 12d. Output Impacts in Rwanda from EACU Regional Deep Integration and Multilateral Liberalization (results are percentage
change from initial equilibrium)
Tripartite
regional
integration,
central: (Trade Tripartite
EACU Central: EACU Liberal: Facilitation plus regional
(Trade (Multilateral services and Tripartite integration Tripartite Tripartite
Facilitation plus services, NTM and NTB regional (only regional regional
services and EACU (only EACU (only EACU (only tariff refrom plus EACU Liberal: EACU Liberal: liberalization integration services integration integration
NTB Trade services EACU (only NTB unilateral tariff Trade Facilitation (only services (only NTB and tariff (only Trade liberalizatio (only NTB (only tariff
Scenario definitions (see tables 8 and 10). liberalization) Facilitation) liberalization) liberalization) liberalization) within EACU) liberalization) liberalization) reform) Facilitation) n) liberalization) reform)
Business Services
Air Transport 1.6 1.5 0.1 0.1 0.0 4.4 2.5 0.3 -4.4 -2.8 0.2 -0.1 0.2
Communication 1.4 1.3 0.1 0.1 0.0 5.9 3.9 0.4 -2.9 -2.5 1.0 -0.1 0.2
Insurance 3.7 1.6 1.9 0.1 0.0 2.5 0.4 0.3 -0.3 -1.3 2.2 0.0 0.2
Business Services nec 1.3 1.3 0.1 0.1 0.1 -4.5 -6.1 0.3 -6.6 -4.2 0.0 -0.1 0.3
Financial Services nec 1.3 1.3 0.0 0.1 0.0 0.9 -0.8 0.3 -2.8 -1.7 0.0 0.0 0.2
Transport nec 0.8 0.7 0.0 0.0 0.0 1.6 0.5 0.2 -3.4 -2.2 0.1 0.0 0.1
Water Transport 0.8 0.7 0.1 0.0 0.0 3.6 2.3 0.2 -4.1 -2.7 0.2 -0.1 0.1
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products -19.9 -18.9 0.1 -0.6 0.1 -15.5 3.9 -1.7 -32.2 -27.3 0.2 -0.6 -1.1
Energy and Minerals 3.9 3.6 0.3 0.1 0.1 18.8 14.5 0.4 -8.3 -4.8 0.4 -0.1 0.5
Food Products -3.9 -3.5 0.1 -0.3 -0.1 -0.2 3.8 -0.5 -10.8 -8.7 0.3 -0.2 0.5
Petroleum and Coal Products -8.9 -7.8 0.2 -0.2 0.1 1.0 2.7 -2.6 -2.0 -1.6 0.3 1.0 0.1
Other Manufacturing 0.1 0.2 0.0 0.0 0.1 -0.3 1.0 -1.6 -13.0 -8.7 0.0 -0.3 -2.7
Textile and Apparel -0.3 0.0 0.2 -0.3 0.0 7.1 7.9 -1.4 -11.1 -7.5 0.4 -0.3 0.3
Wood and Paper Products -8.7 -7.8 0.2 -0.7 0.1 -1.5 8.2 -2.2 -26.4 -20.8 0.5 -1.0 -1.1
CRTS
Agriculture and Forestry 2.8 2.5 0.1 0.1 -0.1 4.4 2.0 0.2 15.4 11.5 0.1 0.3 -0.1
Other Services 1.2 1.1 0.1 0.1 0.1 4.9 3.2 0.4 -4.5 -3.4 0.2 -0.1 0.3
Trade -1.9 -1.8 0.1 -0.1 0.0 2.1 3.7 -0.2 -6.8 -5.5 0.1 -0.2 -0.1
Utilities 0.6 0.6 0.1 0.1 0.1 13.7 8.0 1.2 -13.5 -9.8 0.3 -0.2 0.4
*Tripartite partners are EAC, COMESA and SADC
Source: Authors’ estimates
76
Table 12e. Output Impacts in COMESA from the Deep Integration in the Tripartite Free Trade Agreement
(results are percentage change from initial equilibrium)
Tripartite
regional
integration,
central: (Trade Tripartite
EACU Central: EACU Liberal: Facilitation plus regional
(Trade (Multilateral services and Tripartite integration Tripartite Tripartite
Facilitation plus services, NTM and NTB regional (only regional regional
services and EACU (only EACU (only EACU (only tariff refrom plus EACU Liberal: EACU Liberal: liberalization integration services integration integration
NTB Trade services EACU (only NTB unilateral tariff Trade Facilitation (only services (only NTB and tariff (only Trade liberalizatio (only NTB (only tariff
Scenario definitions (see tables 8 and 10). liberalization) Facilitation) liberalization) liberalization) liberalization) within EACU) liberalization) liberalization) reform) Facilitation) n) liberalization) reform)
Business Services
Air Transport 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 0.1 0.8 0.1 0.1
Communication 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.3 0.2 1.9 0.0 0.0
Insurance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.6 0.2 13.3 0.0 0.0
Business Services nec 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.2 0.9 0.0 0.0
Financial Services nec 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.2 0.9 0.0 0.0
Transport nec 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 0.1 0.8 0.0 0.1
Water Transport 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.1 0.7 0.0 0.1
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.8 0.8 -0.1 -0.1
Energy and Minerals 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.1 0.6 0.1 0.1
Food Products 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.9 0.0 -0.4
Petroleum and Coal Products 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 -0.3 0.9 0.1 0.0
Other Manufacturing 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 -0.6 0.8 0.0 -0.2
Textile and Apparel 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.2 0.9 0.1 0.1
Wood and Paper Products 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 -0.6 1.2 0.0 0.0
CRTS
Agriculture and Forestry 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 1.0 0.8 -0.1 0.0
Other Services 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.1 0.7 0.1 0.1
Trade 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.9 0.0 0.0
Utilities 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 0.0 0.9 0.0 0.0
*Tripartite partners are EAC, COMESA and SADC.
Source: Authors’ estimates
77
Table 12f. Output Impacts in SADC from Regional Deep Integration through the Tripartite Free Trade Agreement
(results are percentage change from initial equilibrium)
Tripartite
regional
integration,
central: (Trade Tripartite
EACU Central: EACU Liberal: Facilitation plus regional
(Trade (Multilateral services and Tripartite integration Tripartite Tripartite
Facilitation plus services, NTM and NTB regional (only regional regional
services and EACU (only EACU (only EACU (only tariff refrom plus EACU Liberal: EACU Liberal: liberalization integration services integration integration
NTB Trade services EACU (only NTB unilateral tariff Trade Facilitation (only services (only NTB and tariff (only Trade liberalizatio (only NTB (only tariff
Scenario definitions (see tables 8 and 10). liberalization) Facilitation) liberalization) liberalization) liberalization) within EACU) liberalization) liberalization) reform) Facilitation) n) liberalization) reform)
Business Services
Air Transport 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.1 0.1 0.0 0.0
Communication 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.3 0.4 0.0 0.1
Insurance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.3 0.1 0.0 0.1
Business Services nec 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.1 0.1 0.0 0.1
Financial Services nec 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.3 0.1 0.0 0.0
Transport nec 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.2 0.1 0.0 0.1
Water Transport 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.2 0.1 0.0 0.0
Dixit-Stigliz Goods
Chemicals Mineral and Metal Products 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.4 0.1 0.0 0.2
Energy and Minerals 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.7 -0.1 0.0 -0.1 -0.2
Food Products 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.2 0.0 1.1
Petroleum and Coal Products 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.2 0.1 0.0 0.0
Other Manufacturing 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.2 -1.0 0.1 0.0 0.3
Textile and Apparel 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.8 -0.7 0.2 0.0 0.0
Wood and Paper Products 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.4 -0.3 0.1 0.0 0.2
CRTS
Agriculture and Forestry -0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.0 3.9 0.1 0.3 -0.6
Other Services 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.1 0.1 0.0 0.0
Trade 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.1 0.1 0.0 0.2
Utilities 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.6 -0.4 0.1 -0.1 0.0
*Tripartite partners are EAC, COMESA and SADC.
Source: Authors’ estimates
78
Table 13a: Piecemeal Sensitivity Analysis: Impact on Kenya of EACU Deep Integration
and Multilateral Liberalization
Equivalent Variation as a % of Consumption
Parameter Value EACU Deep Integration EACU Liberalization
Parameter Lower Central Upper Lower Central Upper Lower Central Upper
σ(qi, qj) – services sectors 2 3 4 0.96 0.96 0.96 1.81 1.81 1.83
σ(qi, qj) – goods sectors see below 0.78 0.96 1.16 1.90 1.81 1.87
σ(va, bs) 0.625 1.25 1.875 0.96 0.96 0.96 1.79 1.81 1.83
σ(D, M) see below 0.95 0.96 0.97 1.80 1.81 1.83
σ(M, M) GTAP values 30 1.06 0.96 NA 1.86 1.81 NA
σ(L, K) 0.5 1 1.5 0.96 0.96 0.96 1.82 1.81 1.81
σ(A1,…An) 0 0 0.25 NA 0.96 0.96 NA 1.81 1.81
εEACU, εCOMESA, εSADC Lower (upper) values are 0.5 (1.5) central
0.94 0.96 0.99 1.67 1.81 1.90
εEU , εROW, εUSA, εCHINA values. See table 7 for central values.
θr 0 0 1 NA 0.96 0.91 NA 1.81 0.77
θm 0.025 0.05 0.075 0.96 0.96 0.96 1.81 1.81 1.81
σ(qi, qj) – IRTS goods Parameter Value σ(D, M)--CRTS sectors Parameter Value
chemicals and metals 3.4 6.8 10.2 Lower Central Upper
energy and minerals 5.8 11.6 17.4 agriculture and forestry 1.3 2.5 3.8
food products 2.6 5.1 7.7 other services 0.8 1.5 2.3
petroleum and coal prod. 2.1 4.2 6.3 trade 0.8 1.5 2.3
other manufacturing 3.9 7.7 11.6 utilities 1.4 2.8 4.2
textiles, apparel and leather 3.8 7.6 11.4
wood and paper products 3.2 6.3 9.5
Key:
σ(qi, qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors
σ(va, bs): Elasticity of substitution between value-added and business services
σ(D, M): Elasticity of substitution between domestic goods and imports in CRTS sectors
σ(M, M): Elasticity of substitution between imports from different regions in CRTS sectors
σ(L, K): Elasticity of substitution between primary factors of production in value added
σ(A1,…An): Elasticity of substitution in intermediate production between composite Armington aggregate goods
εROW, εEU, εCHINA, εUSA εEACU, εCOMESA, εSADC: Vectors of elasticities of imperfectly competitive firms' supply in the Rest
World, EU, China. USA, EACU, COMESA and SADC with respect to the price of their outputs.
θr: Share of rents in services sectors captured by domestic agents
θm: Shares of value added in multinational firms due to specialized primary factor imports
Source: Authors’ estimates.
79
Table 13b: Piecemeal Sensitivity Analysis: Impact on Tanzania of EACU Deep
Integration and Multilateral Liberalization
in Equivalent Variation (EV) as a percentage of consumption Equivalent Variation
Parameter Value EACU Deep Integration EACU Liberalization
Parameter Lower Central Upper Lower Central Upper Lower Central Upper
σ(qi, qj) – services sectors 2 3 4 0.95 0.95 0.95 7.13 7.11 7.11
σ(qi, qj) – goods sectors see below 0.85 0.95 1.06 6.76 7.11 7.64
σ(va, bs) 0.625 1.25 1.875 0.95 0.95 0.95 7.08 7.11 7.14
σ(D, M) see below 0.94 0.95 0.95 7.10 7.11 7.13
σ(M, M) GTAP values 30 0.98 0.95 NA 7.04 7.11 NA
σ(L, K) 0.5 1 1.5 0.95 0.95 0.95 7.12 7.11 7.11
σ(A1,…An) 0 0 0.25 NA 0.95 0.95 NA 7.11 7.11
εEACU, εCOMESA, εSADC Lower (upper) values are 0.5 (1.5) central
0.97 0.95 0.93 7.10 7.11 7.09
εEU , εROW, εUSA, εCHINA values. See table 7 for central values.
θr 0 0 1 NA 0.95 0.68 NA 7.11 1.05
θm 0.025 0.05 0.075 0.95 0.95 0.95 7.11 7.11 7.11
σ(qi, qj) – IRTS goods Parameter Value σ(D, M)--CRTS sectors Parameter Value
chemicals and metals 3.4 6.8 10.2 Lower Central Upper
energy and minerals 5.8 11.6 17.4 agriculture and forestry 1.3 2.5 3.8
food products 2.6 5.1 7.7 other services 0.8 1.5 2.3
petroleum and coal prod. 2.1 4.2 6.3 trade 0.8 1.5 2.3
other manufacturing 3.9 7.7 11.6 utilities 1.4 2.8 4.2
textiles, apparel and leather 3.8 7.6 11.4
wood and paper products 3.2 6.3 9.5
Key:
σ(qi, qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors
σ(va, bs): Elasticity of substitution between value-added and business services
σ(D, M): Elasticity of substitution between domestic goods and imports in CRTS sectors
σ(M, M): Elasticity of substitution between imports from different regions in CRTS sectors
σ(L, K): Elasticity of substitution between primary factors of production in value added
σ(A1,…An): Elasticity of substitution in intermediate production between composite Armington aggregate goods
εROW, εEU, εCHINA, εUSA εEACU, εCOMESA, εSADC: Vectors of elasticities of imperfectly competitive firms' supply in the Rest
World, EU, China. USA, EACU, COMESA and SADC with respect to the price of their outputs.
θr: Share of rents in services sectors captured by domestic agents
θm: Shares of value added in multinational firms due to specialized primary factor imports
Source: Authors’ estimates.
80
Table 13c: Piecemeal Sensitivity Analysis: Impact on Uganda of EACU Deep
Integration and Multilateral Liberalization
in Equivalent Variation (EV) as a percentage of consumption Equivalent Variation
Parameter Value EACU Deep Integration EACU Liberalization
Parameter Lower Central Upper Lower Central Upper Lower Central Upper
σ(qi, qj) – services sectors 2 3 4 1.25 1.24 1.24 2.75 2.79 2.82
σ(qi, qj) – goods sectors see below 1.09 1.24 1.35 2.90 2.79 2.79
σ(va, bs) 0.625 1.25 1.875 1.24 1.24 1.24 2.74 2.79 2.83
σ(D, M) see below 1.22 1.24 1.27 2.77 2.79 2.81
σ(M, M) GTAP values 30 1.24 1.24 NA 2.76 2.79 NA
σ(L, K) 0.5 1 1.5 1.27 1.24 1.23 2.81 2.79 2.78
σ(A1,…An) 0 0 0.25 NA 1.24 1.24 NA 2.79 2.79
εEACU, εCOMESA, εSADC Lower (upper) values are 0.5 (1.5) central
1.25 1.24 1.24 2.58 2.79 2.91
εEU , εROW, εUSA, εCHINA values. See table 7 for central values.
θr 0 0 1 NA 1.24 1.19 NA 2.79 1.38
θm 0.025 0.05 0.075 1.24 1.24 1.24 2.79 2.79 2.79
σ(qi, qj) – IRTS goods Parameter Value σ(D, M)--CRTS sectors Parameter Value
chemicals and metals 3.4 6.8 10.2 Lower Central Upper
energy and minerals 5.8 11.6 17.4 agriculture and forestry 1.3 2.5 3.8
food products 2.6 5.1 7.7 other services 0.8 1.5 2.3
petroleum and coal prod. 2.1 4.2 6.3 trade 0.8 1.5 2.3
other manufacturing 3.9 7.7 11.6 utilities 1.4 2.8 4.2
textiles, apparel and leather 3.8 7.6 11.4
wood and paper products 3.2 6.3 9.5
Key:
σ(qi, qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors
σ(va, bs): Elasticity of substitution between value-added and business services
σ(D, M): Elasticity of substitution between domestic goods and imports in CRTS sectors
σ(M, M): Elasticity of substitution between imports from different regions in CRTS sectors
σ(L, K): Elasticity of substitution between primary factors of production in value added
σ(A1,…An): Elasticity of substitution in intermediate production between composite Armington aggregate goods
εROW, εEU, εCHINA, εUSA εEACU, εCOMESA, εSADC: Vectors of elasticities of imperfectly competitive firms' supply in the Rest
World, EU, China. USA, EACU, COMESA and SADC with respect to the price of their outputs.
θr: Share of rents in services sectors captured by domestic agents
θm: Shares of value added in multinational firms due to specialized primary factor imports
Source: Authors’ estimates.
81
Table 13d: Piecemeal Sensitivity Analysis: Impact on Rwanda of EACU Deep
Integration and Multilateral Liberalization
in Equivalent Variation (EV) as a percentage of consumption Equivalent Variation
Parameter Value EACU Deep Integration EACU Liberalization
Parameter Lower Central Upper Lower Central Upper Lower Central Upper
σ(qi, qj) – services sectors 2 3 4 1.40 1.40 1.40 4.91 4.95 4.99
σ(qi, qj) – goods sectors see below 1.32 1.40 1.44 5.32 4.95 4.90
σ(va, bs) 0.625 1.25 1.875 1.42 1.40 1.39 4.88 4.95 5.03
σ(D, M) see below 1.41 1.40 1.40 4.95 4.95 4.95
σ(M, M) GTAP values 30 1.32 1.40 NA 4.87 4.95 NA
σ(L, K) 0.5 1 1.5 1.41 1.40 1.40 4.94 4.95 4.95
σ(A1,…An) 0 0 0.25 NA 1.40 1.40 NA 4.95 4.95
εEACU, εCOMESA, εSADC Lower (upper) values are 0.5 (1.5) central
1.46 1.40 1.36 4.45 4.95 5.27
εEU , εROW, εUSA, εCHINA values. See table 7 for central values.
θr 0 0 1 NA 1.40 1.18 NA 4.95 1.66
θm 0.025 0.05 0.075 1.40 1.40 1.40 4.95 4.95 4.95
σ(qi, qj) – IRTS goods Parameter Value σ(D, M)--CRTS sectors Parameter Value
chemicals and metals 3.4 6.8 10.2 Lower Central Upper
energy and minerals 5.8 11.6 17.4 agriculture and forestry 1.3 2.5 3.8
food products 2.6 5.1 7.7 other services 0.8 1.5 2.3
petroleum and coal prod. 2.1 4.2 6.3 trade 0.8 1.5 2.3
other manufacturing 3.9 7.7 11.6 utilities 1.4 2.8 4.2
textiles, apparel and leather 3.8 7.6 11.4
wood and paper products 3.2 6.3 9.5
Key:
σ(qi, qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors
σ(va, bs): Elasticity of substitution between value-added and business services
σ(D, M): Elasticity of substitution between domestic goods and imports in CRTS sectors
σ(M, M): Elasticity of substitution between imports from different regions in CRTS sectors
σ(L, K): Elasticity of substitution between primary factors of production in value added
σ(A1,…An): Elasticity of substitution in intermediate production between composite Armington aggregate goods
εROW, εEU, εCHINA, εUSA εEACU, εCOMESA, εSADC: Vectors of elasticities of imperfectly competitive firms' supply in the Rest
World, EU, China. USA, EACU, COMESA and SADC with respect to the price of their outputs.
θr: Share of rents in services sectors captured by domestic agents
θm: Shares of value added in multinational firms due to specialized primary factor imports
Source: Authors’ estimates.
82
Appendices
Appendix A: Mapping from the GTAP Sectors and Regions to the Sectors and Regions of
our East Africa-Global Model
We employ the GTAP 8.1 data set as the basis of the data set for our multi-region trade
model. This was the most recent release of the GTAP data set at the time we built the model. It is
documented in Badri Narayanan et al. (2012). 27 GTAP 8 contains 57 sectors and 129 regions.
We aggregate both the sectors and the regions of the GTAP 8 data set to an 18 sector and 8
region model that is most relevant for analyzing the impact on trade costs of Kenya and
Tanzania.
Sector Aggregation
Given that access to business services constitutes an important aspect of the analysis of
trade costs, we retain all the business services sectors of the GTAP data set such as
communications, financial services and various transportation services. We aggregate the goods
sectors into their important and common aggregates. The mapping may be found in table A1.
27
See Badri Narayanan, G., Angel Aguiar and Robert McDougall, Eds. (2012), Global Trade, Assistance, and
Production: The GTAP 8 Data Base, Center for Global Trade Analysis: Purdue University. Available at:
https://www.gtap.agecon.purdue.edu/databases/v8/default.asp.
83
Table A1: Mapping from the GTAP Sectors to the Sectors of our Model, and IRTS/CRTS
classification
GTAP Codes and Sector Descriptions SECTOR IN OUR MODEL
Number Code Description
1 PDR Paddy rice 1 AGRICULTURE AND FORESTRY CRTS
2 WHT Wheat 1 AGRICULTURE AND FORESTRY
3 GRO C ereal grains nec 1 AGRICULTURE AND FORESTRY
4 V_F Vegetables, fruit, nuts 1 AGRICULTURE AND FORESTRY
5 OSD Oil seeds 1 AGRICULTURE AND FORESTRY
6 C_B Sugar cane, sugar beet 1 AGRICULTURE AND FORESTRY
7 PFB Plant-based fibers 1 AGRICULTURE AND FORESTRY
8 OCR C rops nec 1 AGRICULTURE AND FORESTRY
9 CTL Bovine cattle, sheep and goats, horses 1 AGRICULTURE AND FORESTRY
10 OAP Animal products nec 1 AGRICULTURE AND FORESTRY
11 RMK Raw milk 1 AGRICULTURE AND FORESTRY
12 WOL Wool, silk-worm cocoons 1 AGRICULTURE AND FORESTRY
13 FRS Forestry 1 AGRICULTURE AND FORESTRY
14 FSH Fishing 1 AGRICULTURE AND FORESTRY
15 COA C oal 2 ENERGY AND MINERALS IRTS
16 OIL Oil 2 ENERGY AND MINERALS
17 GAS Gas 2 ENERGY AND MINERALS
18 OMN Minerals nec 2 ENERGY AND MINERALS
19 CMT Bovine meat products 3 FOOD PRODUCTS IRTS
20 OMT Meat products nec 3 FOOD PRODUCTS
21 VOL Vegetable oils and fats 3 FOOD PRODUCTS
22 MIL Dairy products 3 FOOD PRODUCTS
23 PCR Processed rice 3 FOOD PRODUCTS
24 SGR Sugar 3 FOOD PRODUCTS
25 OFD Food products nec 3 FOOD PRODUCTS
26 B_T Beverages and tobacco products 3 FOOD PRODUCTS
27 TEX Textiles 4 TEXTILES, APPAREL and Leather IRTS
28 WAP Wearing apparel 4 TEXTILES, APPAREL and Leather
29 LEA Leather products 4 TEXTILES, APPAREL and Leather
30 LUM Wood products 5 WOOD AND PAPER PRODUCTS IRTS
31 PPP Paper products, publishing 5 WOOD AND PAPER PRODUCTS
32 P_C Petroleum, coal products 6 PETROLEUM AND COAL PRODUCTS IRTS
33 CRP C hemical, rubber, plastic products 7 CHEMICALS, MINERAL AND METAL PRODUCTS IRTS
34 NMM Mineral products nec 7 CHEMICALS, MINERAL AND METAL PRODUCTS
35 I_S Ferrous metals 7 CHEMICALS, MINERAL AND METAL PRODUCTS
36 NFM Metals nec 7 CHEMICALS, MINERAL AND METAL PRODUCTS
37 FMP Metal products 7 CHEMICALS, MINERAL AND METAL PRODUCTS
38 MVH Motor vehicles and parts 8 OTHER MANUFACTURES IRTS
39 OTN Transport equipment nec 8 OTHER MANUFACTURES
40 ELE Electronic equipment 8 OTHER MANUFACTURES
41 OME Machinery and equipment nec 8 OTHER MANUFACTURES
42 OMF Manufactures nec 8 OTHER MANUFACTURES
43 ELY Electricity 9 UTILITIES CRTS
44 GDT Gas manufacture, distribution 9 UTILITIES
45 WTR Water 9 UTILITIES
46 CNS C onstruction 10 OTHER SERVICES CRTS
47 TRD Trade 11 TRADE CRTS
48 OTP Transport nec 12 TRANSPORT NEC IRTS
49 WTP Water transport 13 WATER TRANS;PORT IRTS
50 ATP Air transport 14 AIR TRANSPORT IRTS
51 CMN C ommunication 15 COMMUNICATION IRTS
52 OFI Financial services nec 16 FINANCIAL SERVICES NEC IRTS
53 ISR Insurance 17 INSURANCE IRTS
54 OBS Business services nec 18 BUSINESS SERVICES NEC IRTS
55 ROS Recreational and other services 10 OTHER SERVICES
56 OSG Public Administration, Defense, Education, 10 OTHER SERVICES
57 DWE Dwellings 10 OTHER SERVICES
84
Aggregation of Regions
Kenya, Tanzania and Uganda. Given our interest in Kenya and Tanzania, we, of
course, retain Kenya and Tanzania as two separate regions of the model. The East African
Customs Union (EACU) is comprised of Kenya, Tanzania, Uganda, Burundi and Rwanda.
Uganda and Rwanda are also regions in the model, but Burundi is not a region of GTAP 8.1
Consequently, we retain Uganda and Rwanda as a separate regions of our model, but not
Burundi.
South African Development Community (SADC). Given the potential merger of the
Common Market for East and Southern Africa (COMESA), SADC and the EACU, we include
COMESA and SADC in our model and define regions as close as possible to these regions.
Some countries are members of both SADC and COMESA. In these cases, we included them in
the SADC region of our model. This included Madagascar, Mauritius, Malawi, the Democratic
Republic of the Congo, Zambia and Zimbabwe.
COMESA. Since we have to choose where to place countries that are in both SADC and
COMESA, our COMESA region is comprised of only eleven countries. Egypt and Ethiopia are
individual countries in the GTAP 8.1 data set that are not in SADC or EAC, so we include them
in COMESA. The remaining nine countries in our COMESA region are in the GTAP 8.1 region
called “Rest of East Africa.” With the exception of Somalia and the territory of Mayotte, all of
these nine countries or territories are members of COMESA. Although Burundi is also a member
of both the EAC and COMESA, and Seychelles is a member of SADC, this region is dominated
by COMESA members, so we “Rest of East Africa” into the COMESA region of our model.
United States, European Union, China and Rest of the World. Given potential
alliances with the European Union or the United States and the growing importance of China in
Africa, we retain the USA, China and the 27 country European Union as separate regions of the
model. All other countries of the world are aggregated into an aggregate Rest of the World
region in our model.
Table A2. In table A2 we show the details of our mapping from the regions of the GTAP
8.1 database to the regions of our model for the five regions in Eastern and Southern Africa. The
mapping for the other three regions of our model should be clear from the previous paragraphs.
85
Table A2: Mapping from the GTAP Regions to the Regions of our Model
GTAP code GTAP 8 Region Region in our Model
102 Egypt
COMESA
113 South Central Africa all SADC
- Angola
- Congo the Democratic
Republic of the
114 Ethiopia
COMESA
115 Kenya
Kenya
116 Madagascar
SADC
117 Malawi
SADC
118 Mauritius
SADC
119 Mozambique
SADC
120 Tanzania United Republic
of Tanzania
121 Uganda
Uganda
122 Zambia
SADC
123 Zimbabwe
SADC
124 Rest of Eastern Africa
- Burundi COMESA
- Comoros COMESA
- Djibouti COMESA
- Eritrea COMESA
- Mayotte COMESA
- Rw anda Rwanda
- Seychelles COMESA
- Somalia COMESA
- Sudan COMESA
125 Botswana
SADC
126 Namibia
SADC
127 South Africa
SADC
128 Rest of South African
Customs Union all SADC
- Lesotho
- Sw aziland
86
Appendix B: Estimates of the Ad Valorem Equivalents (AVEs) for Non-Tariff Measures
(NTMs) for the Regions of our Model
Our estimates of the AVEs of NTMs are based on the estimates of Kee et al., (2008;
2009). Kee et al. estimate the AVEs of NTMs for 105 countries at the 6 digit level. These
estimates, as well as aggregated estimates for manufacturing and agriculture for the 93 countries,
are available on the World Bank website. 28 At the six digit level, the estimates are sometimes
subject to a substantial margin of error that may lead to misleading results in a CGE model
policy analysis. Consequently, we have chosen to use the aggregated estimates of Kee et al. at
the sector level, i.e., for each country, we have one AVE of the NTMs in manufacturing and one
AVE of the NTMs in agriculture. For seven of the ten regions of our model (Kenya, Tanzania,
Uganda, Rwanda, the United States, China and the European Union) estimates are available in
the data set of Kee et al. For COMESA, SADC and our Rest of the World, we further aggregate
these values for 105 countries to the regions of our model.
Specifically, we use the values for the Overall Trade Restrictiveness Index (OTRI) and
for the Tariff-only OTRI (OTRI_T) at the aggregated sector level. The OTRI measures the
uniform tariff equivalent of the country’s tariff and NTMs that would generate the same level of
import value for the country in a given year. The OTRI_T focuses only on tariffs of each
country. We use the values based on applied tariffs which take into account the bilateral trade
preferences. Both indices are available for 105 countries 29 and for two different types of
aggregated products: agricultural goods and manufacturing goods. Calculating the difference
between OTRI and OTRI_T gives us an AVE for NTMs only for each product and county.
Aggregation of resulting AVEs to the regions of our model includes two steps:
1. Aggregation of 105 countries to the GTAP 8.1 countries and regions.
2. Aggregation of GTAP countries to the specific regions of our model.
28
The data set is available at
http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:22574446~pagePK:
64214825~piPK:64214943~theSitePK:469382,00.html
29
There are more countries available in the data set, but the values are missing for some countries due to non-
availability of a tariff schedule for the reported year in the source database (TRAINS).
87
There are 129 countries and regions in the GTAP database, but we have data for only 105
countries. The GTAP data set, however, includes the EU countries as independent countries in
the model. We assume, as Kee does, that there is a unique ad valorem equivalent of the non-tariff
measure for all EU countries. For the single countries of the GTAP data set, which are also
available in the Kee et al. (2009) data, we simply assign the calculated AVEs for the EU. Several
GTAP regions are aggregates of smaller countries. These GTAP regions, the countries that
comprise the regions and data availability for the countries within the region from Kee et al. are
listed in table B1. We obtain estimates for these regions as follows. For Rest of Oceania, Rest of
East Asia, Rest of South Asia, Rest of Central America, Caribbean, Rest of Europe and Rest of
South African Customs Union, the AVEs are zero for all countries within these regions for which
estimates from Kee et al. (2009) are available. Consequently, we report zero AVEs for these
countries. For Rest of EFTA, Rest of North Africa and Central Africa we have estimates for only
one country in the region from Kee et al. (2009); for these regions, we assume the values of the
one country apply to the region. We compute simple averages for Rest of Western Asia, Rest of
Western Africa and Rest of Eastern Africa 30 using AVEs for the available countries. There are
no data available for several of the smaller regions and countries in the GTAP aggregation. After
this aggregation step, we receive positive AVEs for 90 31 GTAP countries, zero AVEs for 27
countries and 18 missing values. A detailed description of our results is given in Table B2.
In the final step we aggregate the GTAP countries to the model specific regions using the
mapping described in Appendix A. For the single countries we simply assign the calculated
values. For SADC, COMESA, EU and ROW we compute weighted averages using GTAP
countries’ total imports at market prices as weights. The AVEs for SADC are calculated without
taking Madagascar, Zimbabwe and rest of South Central Africa into account as these values are
missing. Table B3 presents the resulting AVEs applied in our model.
Table B1: Data availability for the GTAP regions
No. Code Description Countries No. Code Description Countries
available available
30
Kenya, Tanzania, Rwanda and Uganda are all regions of our model. The aggregation for Rest of Eastern Africa is
based on the available data for Burundi, Djibouti and Sudan.
31
As the EU is a single region in the data set of Kee et al. (2009), we assume the same value for all EU member
countries which apply a common trade policy.
88
from Kee from Kee
et al. et al.
3 XOC Rest of Oceania - 84 XEE Rest of Eastern Europe -
- American Samoa - - Moldova Republic of -
- Cook Islands - 85 XER Rest of Europe -
- Fiji FJI - Andorra -
- French Polynesia - - Bosnia and Herzegovina BIH
- Guam - - Faroe Islands -
- Kiribati - - Gibraltar -
- Marshall Islands - - Guernsey -
- Micronesia Federated States of - - Holy See (Vatican City State) -
- Nauru - - Isle of Man -
- New Caledonia - - Jersey -
- Macedonia the former Yugoslav
- Niue - MKD
Republic of
- Northern Mariana Islands - - Monaco -
- Palau - - Montenegro MNE
- Papua New Guinea - - San Marino -
- Pitcairn - - Serbia -
- Samoa - 86 KAZ Kazakhstan -
- Solomon Islands - 87 KGZ Kyrgyzstan KGZ
- Tokelau - 88 XSU Rest of Former Soviet Union -
- Tonga TON - Tajikistan -
- Tuvalu - - Turkmenistan -
- United States Minor Outlying
- - Uzbekistan -
Islands
- Vanuatu VUT 101 XWS Rest of Western Asia -
- Wallis and Futuna - - Iraq -
10 XEA Rest of East Asia - - Jordan JOR
- Korea Democratic Peoples
- - Lebanon -
Republic of
- Macao MAC - Palestinian Territory Occupied -
19 XSE Rest of Southeast Asia - - Syrian Arab Republic SYR
- Brunei Darussalam - - Yemen YEM
- Myanmar - 105 XNF Rest of North Africa -
- Timor Leste - - Algeria DZA
25 XSA Rest of South Asia - - Libyan Arab Jamahiriya -
- Afghanistan - - Western Sahara -
- Bhutan - 111 XWF Rest of Western Africa -
- Maldives MDV - Cape Verde CPV
29 XNA Rest of North America - - Gambia GMB
- Bermuda - - Guinea-Bissau GNB
- Greenland - - Liberia -
- Saint Pierre and Miquelon - - Mali MLI
40 XSM Rest of South America - - Mauritania -
- Falkland Islands (Malvinas) - - Niger NER
- Saint Helena, Ascension and
- French Guiana - -
Tristan Da Cunha
- Guyana - - Sierra Leone -
- South Georgia and the South 112 XCF Central Africa -
-
Sandwich Islands
- Suriname - - Central African Republic -
47 XCA Rest of Central America BLZ - Chad -
- Belize - - Congo -
48 XCB Caribbean - - Equatorial Guinea -
- Anguilla - - Gabon GAB
- Antigua & Barbuda ATG - Sao Tome and Principe -
- Aruba - 113 XAC South Central Africa -
- Bahamas - - Angola -
- Congo the Democratic Republic
- Barbados - -
of the
- Cayman Islands - 124 XEC Rest of Eastern Africa -
- Cuba CUB - Burundi BDI
- Dominica - - Comoros -
89
- Dominican Republic - - Djibouti DJI
- Grenada - - Eritrea -
- Haiti - - Mayotte -
- Jamaica - - Seychelles -
- Montserrat - - Somalia -
- Netherlands Antilles - - Sudan SDN
- Puerto Rico - 128 XSC Rest of South African Customs Union -
- Saint Kitts and Nevis KNA - Lesotho LSO
- Saint Lucia - - Swaziland SWZ
- Saint Vincent and the Grenadines - 129 XTW Rest of the World -
- Trinidad and Tobago - - Antarctica -
- Turks and Caicos Islands - - Bouvet Island -
- Virgin Islands British - - British Indian Ocean Territory -
- Virgin Islands U.S. - - French Southern Territories -
76 XEF Rest of EFTA -
- Iceland ISL
- Liechtenstein -
Table B2: Ad Valorem Equivalents for Non-Tariff Measures for
Agricultural Goods and Manufacturing Goods for the GTAP countries 32
Country Agricultural Manufacturing
goods goods
ALB Albania 7,0% 0,6%
ARG Argentina 7,1% 3,9%
AUS Australia 28,8% 4,2%
BFA Burkina Faso 19,2% 0,5%
BLR Belarus 17,7% 4,6%
BOL Bolivia, Plurinational Republic of 19,6% 5,1%
BRA Brazil 20,8% 11,6%
CAN Canada 11,4% 2,4%
CHE Switzerland 14,9% 0,7%
CHL Chile 17,2% 1,3%
CHN China 6,1% 5,1%
CIV Cote d'Ivoire 27,8% 18,8%
CMR Cameroon 5,8% 1,1%
COL Colombia 30,5% 6,4%
CRI Costa Rica 0,7% 0,1%
EGY Egypt 29,9% 23,7%
ETH Ethiopia 0,0% 1,1%
GHA Ghana 10,9% 1,1%
GTM Guatemala 36,1% 5,5%
32
Table B3 includes only values different from zero.
90
HKG Hong Kong SAR, China 20,3% 0,8%
HND Honduras 7,2% 0,0%
IDN Indonesia 11,5% 0,5%
IND India 26,2% 4,8%
JPN Japan 23,6% 3,8%
KEN Kenya 14,6% 0,3%
KOR Korea, Rep. of 0,6% 0,1%
LKA Sri Lanka 0,0% 0,3%
MAR Morocco 39,3% 3,8%
MEX Mexico 26,1% 12,3%
MUS Mauritius 26,6% 3,3%
MWI Malawi 21,5% 1,4%
MYS Malaysia 44,8% 22,1%
NGA Nigeria 42,5% 22,2%
NIC Nicaragua 29,6% 3,1%
NOR Norway 18,4% 0,8%
NZL New Zealand 23,0% 7,3%
OMN Oman 36,1% 0,6%
PER Peru 22,5% 2,9%
PHL Philippines 34,3% 15,4%
PRY Paraguay 37,8% 3,7%
RUS Russian Federation 16,9% 9,2%
RWA Rwanda 0,0% 4,8%
SAU Saudi Arabia 1,1% 0,5%
SEN Senegal 33,9% 33,4%
SGP Singapore 52,3% 12,7%
SLV El Salvador 1,3% 4,8%
THA Thailand 24,9% 0,6%
TUR Turkey 6,0% 5,2%
TZA Tanzania United Republic of 22,2% 47,4%
UGA Uganda 3,9% 0,0%
UKR Ukraine 3,3% 19,4%
URY Uruguay 25,8% 5,8%
USA United States of America 14,8% 3,3%
VEN Venezuela 35,1% 3,7%
91
ZAF South Africa 4,8% 0,3%
ZMB Zambia 5,1% 0,0%
XEF Rest of EFTA 15,5% 0,8%
XWS Rest of Western Asia 4,6% 5,9%
XNF Rest of North Africa 36,4% 20,7%
XWF Rest of Western Africa 2,8% 0,4%
XCF Central Africa 0,0% 0,1%
XEC Rest of Eastern Africa 9,7% 8,8%
Table B3: Applied AVEs for Agricultural Goods and Manufacturing Goods for the regions
of our model
Agricultural Manufacturing
goods goods
China 6,1% 5,1%
Kenya 14,6% 0,3%
Rwanda 0,0% 4,8%
Tanzania 22,2% 47,4%
Uganda 3,9% 0,0%
USA 14,8% 3,3%
SADC 4,5% 0,4%
COMESA 27,5% 20,1%
EU 27,0% 2,3%
ROW 17,4% 5,0%
References
Anderson, James and J. Peter Neary (1996), "A new approach to evaluating trade policy,"
Review of Economic Studies, 63 (1), 107-125.
92
Anderson, James and J. Peter Neary (2003), “The Mercantilist index of trade policy,”
International Economic Review, 44, 627--649.
Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga. (2008), "Import Demand Elasticities
and Trade Distortions," Review of Economics and Statistics, 90 (4), 666—682.
Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga (2009). "Estimating trade
restrictiveness indices," Economic Journal, 119, 172--199.
Kee, Hiau Looi, Cristina Neagu and Alessandro Nicita (2013). “Is Protectionism on the Rise?
Assessing National Trade Policies during the Crisis of 2008,” The Review of Economics
and Statistics, 95(1), 342-346.
93
Appendix C: Estimates of the Ad Valorem Equivalents of Poor Trade Facilitation
Our estimates of the ad valorem equivalents (AVEs) of poor trade facilitation are based
on the path-breaking work of David Hummels and his co-authors (Hummels, 2007; Hummels
and Schaur, 2013; Hummels et al., 2007). Using the estimates of Hummels and his co-authors,
Peter Minor (2013) provided estimates for the regions and products in the GTAP database. We
use estimates from Peter Minor, which we aggregate to the sectors and regions of our model. 33
Documentation of the steps we have taken and a brief explanation of the methodology are
explained below. (See Minor (2013) for a fuller explanation of the methodology.)
Although a central finding of the above studies is that the AVE of time in trade varies
across products, most computable general equilibrium modeling of trade facilitation issues have
used a single AVE across all products. By basing our estimates on the work of Hummels and
Minor, we improve on the sector accuracy of the benefits of trade facilitation, and show that the
results are dependent on these sector estimates. We summarize the steps in the estimation of
Minor and our aggregation below.
1. Estimation of the value of one day saved in transit for over 600 HS4 products (“the per-
day value of time savings” by product)
The crucial first step is the estimation of the value of one day saved in transit for each
product (“the per-day value of time savings” by product). The key to the estimation is the
premium in shipping costs that firms are willing to pay for air shipping to avoid an additional
day of ocean shipping. The premium that firms are willing to pay for air shipping varies
considerably across products. At one extreme, we have products like crude oil, coal and
fertilizers with an AVE of zero for one day saved in transit. Evidently no significant amounts of
these products are shipped by air, which reflects no willingness to pay to save time. On the other
hand, a significant share of fruits and vegetables are shipped by air, reflecting a willingness to
pay to save time in shipping. Hummels et al., (2007, p. 8) estimate that for an aggregate of all
fruits and vegetables the AVE of one day saved in 0.9 percent; that is, one additional day in
33
We thank Peter Minor for his cooperation with us in this process.
94
transit cost almost one percent of the value of the fruits and vegetables. Hummels has
statistically significant estimates of the AVE of one day saved in transit for slightly more than
600 HS4 products. The AVE of one day of time saved in shipping is independent of the country.
The data for Hummels estimates come from the U.S. Merchandise Imports database
1991-2005, and a database on shipping times between ports. Hummels calculates average
shipping times between ports around the world and U.S. ports. As such, the AVE estimates of
one day saved in shipping are based solely on U.S. data and assumed to apply to all countries.
2. Estimation of the value of one day saved in transit for the GTAP database (“the per day
value of time savings” by GTAP product category)
To obtain estimates of the AVE of one day saved in transit for the GTAP product categories,
Peter Minor (2013) aggregated from the HS4 categories for which Hummels has provided
estimates, to the 57 product categories of the GTAP database. The 600 plus HS4 product
categories for which Hummels has statistically significant estimates, however, is less than the
number of HS4 categories underlying the GTAP database. The missing HS4 categories account
for about 38 percent of the value of trade, based on the MacMap 2007 database. 34 Minor
proposes three methodologies to address the lack of estimates for the missing HS4 categories and
provides estimates using all three methodologies. We believe the first two methodologies are
biased down and Minor shows evidence of this. We believe the third of his three methodologies
is theoretically unbiased (although Minor shows it may be empirically biased up), and we chose
the third -- he calls it “tau-3.” In tau-3, where there is a missing estimate at the HS4 level, Minor
replaces the missing value with the average for the same GTAP product category based on
values that exist from Hummels. Minor then aggregates from HS4 to the GTAP product
categories using trade weights from the MacMap 2007 data set for GTAP. The trade weights in
the MacMap data set vary by country; so, despite the fact that there is a unique AVE for the
value of one day saved in trade at the HS4 level for each of the 600 plus product categories from
Hummels, due to differing trade weights across countries, the value of one day saved in transit
varies across countries at the GTAP 57 product level. Minor’s full data set of results is available
at: http://mygtap.org/resources/.
34
See Minor (2013, table 1).
95
3. Calculating the AVE of Time in Importing and Exporting for the GTAP sectors and
countries.
Following Hummels et al. (2007), Minor combines the above data set with the World
Bank’s Doing Business data set for 2012. The Doing Business data set shows the number of days
in transit in each country for importing and for exporting goods. The Doing Business data set is
not distinguished by product, so it is assumed the same number of days applies to all products.
Combining the Doing Business data set with the Minor data set mentioned in step 2 above, yields
the AVE equivalents of the cost of time to export or import by product and country in the GTAP
data set for imports and exports, where the AVEs are bilateral depending on the partner country.
4. Aggregating the AVE of the time in trade to the products and regions of our model.
We start with the estimates of Minor (2013) described in step 3 of the bilateral ad
valorem equivalents (AVEs) of the time in trade for exports and imports. We then aggregate
these estimates to the products and regions of our model. The mapping of the GTAP sectors
and regions to the sectors and regions of our model is described in Appendix A. The weights
we use for the aggregation are bilateral trade weights, taken from the GTAP 8.1 data set,
which is based on 2007 trade weights. There are four steps in the aggregation, which we
describe in both words and mathematics. We specify the mathematics for exporting; the
importing aggregation is fully symmetric and is omitted.
(i) Total time costs of exporting product k from region r to region s. We calculate the
value of the total time costs of exporting product k from region r to region s by
multiplying the AVEs of time costs of exporting product k from region r to region s by
the bilateral exports if product k from region r to region s. The trade flow data are
taken from GTAP 8.1 with the base year 2007.
Let = the value of exports of product k from region r to region s from the GTAP data set.
Let = the bilateral trade weighted ad valorem equivalent of the time in trade in exporting
product k from region r to region s (from step 3 above).
96
Define = the total costs of time in exporting product k exported from
GTAP region r to GTAP region s.
(ii) Total time cost of exporting products within one of the model’s sectors and regions.
Then for any sector and region of our model, we aggregate these total costs for all
subsectors and subregions –simply summing up the values for GTAP sectors which
belong to the one sector of our model and the same for the regions) – according to
the mapping given in Appendix A.
Let R be the set of all 129 regions in the GTAP data set. Our model contains ten regions, , v=
1,…10. The GTAP regions that belong to are defined in appendix A. The regions are non-
overlapping subsets of R, the union of which is all the GTAP regions. That is, we have R =
, with , where is an alternate index for the elements
of . Let the elements of R be indexed by both and .
Similarly, let K be the set of all 57 GTAP goods and services. Our model contains 18 sectors,
, w= 1,…18. The GTAP sectors that belong to are defined in appendix A. Our sectors are
non-overlapping subsets of K, the union of which is all the GTAP products. That is, we have K =
,
with , where and is an alternate index for the elements of .
For any product group and any pair of regions of our model, we aggregate the total
cost of exporting across the sub-products of from the sub-regions of to the sub-regions of
. That is, the aggregate or total time cost of exporting products within product group from
region to region is:
(iii) Total value of exports within one of the model’s sectors from one of the model’s
regions to another. In the same way we aggregate the value of all exports . The
97
total value of all exports within product group from one sub-region of to
another sub-region of region is:
(iv) Model specific AVEs of the cost of time in exporting one of the model’s sectors from
one of the model’s regions to another. At the end we calculate the new AVEs for
our model. In particular, we divide the value of total costs of time to export (import)
by the value of exports (imports) for each sector and country pair of our model (we
use here already aggregated values from (ii) and (iii).
In the last step we calculate the model-specific AVEs of time in trade to export
the commodities within product group from one region to another region of our
model:
98
5. Interpretation and Caveats
If using these estimates in a simulation exercise of policy changes to facilitate trade, we
believe that it is prudent to simulate modest percentage cuts, rather than cuts of 50 to 100
percent. In our policy scenarios, the maximum cuts in the time in trade costs that we
implement are 20 percent. The reasons are as follows.
(i) The time in trade can’t be cut to zero. The world average for shipping a container for
exporting or importing is about 23 days, down about two days compared with 2009. 35
However, the most efficient country in the world in the Doing Business data is
Singapore. Based in the 2014 Doing Business data, it takes six days to export a
shipment on average and four days to import a shipment in Singapore. This is likely a
lower bound for most countries to achieve.
(ii) Policies can’t change infrastructure. Many of the changes responsible for the global
decline in the time in trade to ship a container in the past few years are policies such
as: improved customs administration; introduction or improvement in electronic
submission and processing; introduction of the electronic single window; introduction
or improvement in risk management procedures. But poor roads, ports, rail facilities,
airports and pipelines also significantly contribute to delays. If polices are being
simulated, they can’t be expected to improve infrastructure.
(iii)Potential double counting. In a Small Open Economy model, we clearly need to impose
the distortion on both imports and exports. But there may be components of double
counting if we follow the tradition in this field and impose trade facilitation
distortions on both imports and exports for both countries in a bilateral relationship in
a multi-country model. Take the example of exports of food products from Kenya to
Tanzania. We have an export distortion that we impose in Kenya that reflects the
AVE of the time of exporting from Kenya to Tanzania. In addition, we impose an
import distortion in Tanzania on food products from Kenya. For there to be no double
counting, the distortions should be independent. One could argue the distortions are
independent. That is, the exporting time lost for Kenyan food to Tanzania comes from
factors under the control of Kenya (like Kenyan roads, ports, customs procedures);
35
In 2009 the world average to export a standard containerized cargo by sea transport was 23.5 days, and 25.9 days to import.
Today it takes 21.8 days on average to export and 24.2 days to import (World Bank, 2014, p.107).
99
and the Tanzanian time lost on importing food from Kenya is due to Tanzanian roads,
ports and customs procedures that are under the control of Tanzania. Then the
distortions would be independent and should both be included. We have chosen to
include the distortions on both imports and exports, but take modest cuts in both
recognizing that there is a potential for double counting.
References
Djankov, Simeon, Caroline Freund and Cong S. Pham (2010), “Trading on Time,” The Review of
Economics and Statistics, MIT Press, vol. 92(1), pp. 166-173, February.
Hummels, David L. and G. Schaur (2013), “Time as a Trade Barrier,” American Economic
Review, vol. 103, 1-27.
Hummels, David L., Peter Minor, Matthew Reisman and Erin Endean (2007), “Calculating
Tariff Equivalents for Time in Trade,” Arlington, VA: Nathan Associates Inc. for the
United States Agency for International Development (USAID). Available at:
http://www.krannert.purdue.edu/faculty/hummelsd/research/tariff_equivalents.pdf
Hummels, David L. (2007), "Transportation Costs and International Trade in the Second Era of
Globalization," Journal of Economic Perspectives, 21(3), 131-154.
Minor, Peter (2013), “Time as a Barrier to Trade: A GTAP Database of ad valorem Trade Time
Costs,” ImpactEcon, Second Edition, October. Available at: http://mygtap.org/wp-
content/uploads/2013/12/GTAP%20Time%20Costs%20as%20a%20Barrier%20to%20Tr
ade%20v81%202013%20R2.pdf.
World Bank (2014), Doing Business Report, 2014. Available at:
http://www.doingbusiness.org/reports/global-reports/doing-business-2014.
100
Appendix D: Estimates of Insurance ownership shares in Kenya, Tanzania, Uganda,
Rwanda, SADC and COMESA
All market share data come from Axco country reports. 36 Ownership data comes from
Axco reports and company reports.
Given our focus on Kenya and Tanzania, we investigate these countries in detail and
calculate market shares in insurance in Kenya and Tanzania for the regions in our model. This
work is documented in Jafari (2014). Given that Uganda is a separate region in our model and we
have the Axco country report, we also calculate the market share for Uganda in detail as
explained below.
Given the relative size of South Africa in SADC, we calculate the market share by region
in South Africa and take that as representative of SADC. For COMESA, we take an average of
Zambia and Uganda as representative of COMESA. Zambia had a state monopoly in insurance
prior to opening the market in 1992. Partly as a result of the late opening of the market, Zambia
has a rather large domestic share of the insurance market. Uganda, which has been very open to
foreign direct investment, has a much smaller domestic share. It is likely that neither market is
representative of COMESA, so an average of the two is more appropriate. Without data on
Rwanda, we take Uganda as representative of Rwanda, where Rwanda’s domestic share in
Rwanda is assumed equal to Uganda’s domestic share in Uganda.
Tanzania
In 2013 there were 23 non-life companies (including health insurers) registered and two
composites, including one that is state-owned. There are also two registered life only companies.
The market shares of these companies, based on written premiums, were obtained from the Axco
company report for Tanzania. See Jafari (2014) for details.
Having been a monopolistic insurance system for more than 30 years, Tanzania has
operated as a free market since 1998. There are two state-owned insurers: National Insurance
Corporation of Tanzania (NIC) and Zanzibar Insurance Corporation (ZIC).
36
Axco country reports are available at: https://www.axco.co.uk/. Accessed on January 9, 2014.
101
At the end of December 2011, 19 non-life and composite insurers had foreign
shareholders, most of which are regionally based, largely in Kenya. That said, South African
interests are now having a greater impact. Recent market entrants have, however, largely been
locally owned, but they are small, and there are no major local insurance groups operating in
Tanzania.
With market shares from 25.4% to 10.7%, the six largest companies (listed according to
market share) are Jubilee, Heritage, Alliance, AAR, Phoenix and Momentum.
Jafari (2014e) provides details of who owns the insurance companies of Tanzania, and calculates
the market shares by region. The resulting market shares by regions of our model are as follows:
SADC, 7.6%; Kenya, 48. 2%; Rest of the World, 4.2%; European Union, 3.2%; others 0%.
Kenya
There were more than 35 insurance companies operating in Kenya in 2013. The largest
companies and their market shares are the following: Jubilee (11.3%); CIC General (9. 2%);
UAP (8.3%); APA (7.8%); ICEA Lion (5.6%); Heritage (4.8%); Kenindia (4.7%); AIG Kenya
(4.5%); Britam (4.4%); First Assurance (4.1%).
Who owns these companies and their market shares by region of their model is explained
in Jafari (2014e). The resulting market shares by regions of our model are as follows:
SADC, 4. 2%; USA, 4.7 %; Kenya, 84.6%; Rest of the World, 2.8%; European Union,
3.7%; others 0%.
Uganda
The largest insurance companies operating in Uganda, with their market shares and their
primary country of ownership, are as follows: Jubilee (Kenya, 26.0%); AIG (USA, 14.5%);
UAP (Kenya, 13.1%); Goldstar (Uganda, 6.3%); Lion (Kenya, 5.5%); Insurance Company of
East Africa (Kenya, 5.4%); Phoenix of Uganda (Kenya, 4. 2%); East Africa Underwriters (South
Africa and Kenya, 43.8%); APA (Kenya and South Africa, 3.5%); and Niko (Malawi, 2.3%).
This identifies 86.7% of the market. Although we have not identified ownership from India,
there are reportedly Indian ownership interests, so we allocate some ownership share of the
unidentified portion to the rest of the world. Since it is likely that investors from Uganda, the
102
rest of the world and the EU have minority shares in many of the companies, we assume that the
unidentified market shares are allocated as follows: European Union, 5%; Rest of the World 5%;
Uganda, 4.3%; and COMESA, 1%.
This yields the following shares for the regions of our model: Uganda, 10.6%; Kenya,
60. 2%; European Union, 5%; COMESA, 1%; SADC, 3.7%; USA, 14.5%; Rest of the
World, 5%; others 0%.
South Africa
There were 83 active short term insurance companies licensed and operating in South
Africa in 2012. The ten largest are: Santam, Mutual and Federal, Hollard, OUTsurance,
Guardrisk, Zurich, Absa, Auto and General, Centriq and AIG. Collectively, their market share is
63%. Lloyds of London is the only company permitted to write insurance directly without
registering as a South African company.
There are several large South African financial conglomerates the control the insurance
market, involving complex capital structures and multiple cross holdings. Santan is a subsidiary
of the Sanlam Financial Group of South Africa. Absa is 62% owned by Barclays Bank of the
UK, with the remainder sold on stock markets. Auto and General is owned by the financial
group formed by the merger of Royal Group and South Africa Mutual and Fire—both South
African companies. Hollard is owned by the Hollard Group of South Africa. Mutual and Federal
is owned by the Mutual Group of South Africa. Outsurance is owned by the First Rand group of
South Africa. Guardrisk was acquired in 2013 by MMI Holdings of South Africa. Zurich and
Centriq are South African companies. AIG is a subsidiary of the large insurer from the United
States.
For the remaining 37% market share, we increase the market shares of the US, European
Union, Rest of the World and Kenya by 2 percent each and 1 percent for COMESA. We then
scale the market shares of all regions proportionately so the sum of shares equals 100 percent.
This yields the following shares by the regions of our model.
SADC, 80.6%; USA, 6. 2 %; European Union, 6.3%; Kenya, 2.8%; COMESA, 1.4%; Rest
of the World, 2.8%; others 0%.
Zambia
103
Between 1970 and 1992, insurance in Zambia was controlled by a state-owned
monopoly, Zambia State Insurance Corporation (ZSIC). ZSIC, which is still a state company,
remains one of the three major companies in the market.
The leading insurance companies of Zambia (with their nationality and market shares) are
as follows: Professional (24.2%, Zambia); Madison General (23%, Zambia with minority USA);
ZSIC (21%, Zambia); Niko (11.9%, Malawi); Hollard (7.3%, South Africa); Goldman (5%,
Zambia); Diamond (3.8%, Zambia); Phoenix (2%, Kenya); Mayfaiar (1%, Kenya).
Malawi is in both COMESA and SADC. We allocate Malawi to SADC, and the unidentified
0.8% to Rest of the World. This yields the following market shares for the regions of our model
in Zambia.
SADC, 19.2%; USA, 2 %; Kenya, 3%; COMESA, 75%; Rest of the World, 0.8%; others
0%.
COMESA
For the share of COMESA, we take the average of Zambia’s COMESA share. plus
Uganda’s COMESA share. We then scale all shares (except for Kenya, which is part of
COMESA) so they sum to unity. This yields the following shares of the insurance market for
COMESA countries.
SADC, 16.8%; USA, 12.1 %; Kenya, 31.6%; COMESA, 31.7%; Rest of the
World, 4.2%; European Union, 3.2%; others 0%.
104
Appendix E: Telecommunications Ownership Shares in Kenya, Tanzania, Uganda,
Rwanda, COMESA and SADC
Kenya
Based on data from the Kenyan Communications Commission, there are now more than
30 million telephone subscribers in Kenya, where all but about 200,000 are mobile telephone
subscribers. The companies providing telephone services in Kenya and their market shares are:
Safaricom, 65.5%; Airtel, 17.0%; Essar Telekom Kenya, 9.9%; and Telekom Kenya, 7.6%
(combined mobile and fixed line subscribers) and TTCL, 1%. 37
According to the Safaricom website, 38 the corporate structure in 2013 is 40% owned by
Vodafone UK, 35% Government of Kenya and is 25% publicly traded open stock. We allocate 1
percent of this open stock to SADC and leave the remainder unidentified. Airtel is 95% owned
39
by its Indian parent company. Kenyan businessman Naushad Merali owns 5 percent. Essar
Telekom Kenya is wholly owned by the Essar Group of India. 40 Telekom Kenya is 49% owned
by the government of Kenya and the remainder is owned by France Telekom. 41 This identifies
84.6% of the ownership. Scaling all identified shares up proportionately so that the sum of the
shares is 100%, yields the following shares for the regions of our model: EU = 35.5%;
Kenya = 32.5%; Rest of World = 30.7%; SADC = 1.2%; others = 0.
Tanzania
The Tanzanian Communications Regulatory Authority indicates that the companies
providing telephone services in Tanzania and their market shares are: Vodacom, 37%; Airtel,
37
Communications Commission of Kenya (2013), “Quarterly Sector Statistics Report, April-June 2013,” Available
at: http://www.cck.go.ke/resc/downloads/Q4_201213_STATISTICS_final_25th_oct_2013.pdf.
38
http://www.safaricom.co.ke/about-us/investor-relations/investor-dashboard/corporate-fact-sheet.
39
/Airtel-freed-from-20-per-cent-local-ownership-rule/-/539550/1632440/-/mkvcm0z/-/index.html A Kenyan court
ruling has freed Airtel from the 20% local ownership share requirement.
.http://www.businessdailyafrica.com/Corporate-News.
40
http://www.businessdailyafrica.com/Corporate-News/Yu-confirms-search-for-new-investors/-/539550/1880876/-
/h20lebz/-/index.html
41
See “Tanzanian govt considers plan to take 100% ownership of TTCL,” TeleGeography, February 13, 2013.
Available at: http://www.telegeography.com/products/commsupdate/articles/2013/02/13/tanzanian-govt-considers-
plan-to-take-100-ownership-of-ttcl/
105
32%; Millicom (marketed as Tigo), 23%; Zantel, 7%; and TTCL, 1%. 42 As documented in
Worley, Vodacom Tanzania is a subsidiary of South Africa-based Vodacom (Pty) Ltd, which
owns 65% of the company. The remainder is held by Tanzanian companies Planetel
Communications (16%) and Caspian Construction (19%). Regarding MIC Tanzania (also known
as TIGO), Luxemburg-based Millicom International Cellular (MIC) owned 84% of the company
until it assumed 100% ownership in early 2006 when it bought out other minority shareholders.
The Indian company Airtel bought the shares of the Zain group in Celtel Tanzania. Celtel
Tanzania Ltd was 60% owned by Celtel International and 40% owned by the Tanzanian
government. Regarding Zanzibar Telecommunications Corporaton (Zantel), Etisalat of the UAE
acquired a 34%. stake, with the government retaining 18%. The other shareholders are Kintbury
Investment of the Channel Islands (24%) and MEECO International of Tanzania (24%). Finally,
TTCL is 65% owned by the government of Tanzania and 35% owned by Airtel.43
This implies the following shares for the sectors of our model: Tanzania, 29%; European
Union 24.7%; SADC, 24.1%; and Rest of the World, 22.2%.
Rwanda
Rwandatel has an estimated 11 percent of the fixed plus mobile market and MTN
Rwanda has 89 percent. Rwandatel is 99 percent owned by a consortium of US investors, with
the remaining 1 percent Rwanda owned. MTN Rwanda is a South African company with
headquarters in Johannesburg.
Based on these data, the ownership shares of communications sector in Rwanda are: USA=
10.9%; SADC= 35.6%; Rwanda= 53.5%.
Uganda
The Uganda Communications Commission (2012) reported the active mobile and fixed line
service suppliers in 2011-2012, along with their assets. Based on their assets, the telecom
companies and their market shares in 2011 were the following: Airtel, 15.4%; MTN Uganda,
42
Tanzanian Communications Regulatory Authority, “Quarterly Telecom Statistics,” September 2013. Available at:
http://www.tcra.go.tz/images/documents/telecommunication/telecomStatsSept13.pdf. In addition, Benson had a
miniscule share of 0.002%.
43
See: http://www.telegeography.com/products/commsupdate/articles/2013/02/13/tanzanian-govt-considers-plan-to-
take-100-ownership-of-ttcl/
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54.9%; Warid, 14.7%; Orange, 14.0%; and Smile, 1%. 44 Airtel is an Indian based company and
MTN is headquartered in South Africa (although MTN Uganda is a subsidiary of MTN
International, which is headquartered in Mauritius. Warid was acquired in 2013 by Airtel. 45
Orange is owned by France Telecom. Finally, Smile is owned by companies from Saudi Arabia,
Capital Works (a consortium of domestic and international institutional investors) and Venere,
whose nationality is unknown. 46 Based on the owners of Smile, we assign 0.4% of the market to
nationals of Saudi Arabia, 0.1% to nationals of the United States, 0.25% of the market to
Ugandan nationals, 0.10% to nationals of COMESA, 0.10% to nationals of Kenya and 0.05% to
nationals of Tanzania.
For the regions in our model, this means that we have the following ownership shares:
SADC= 54.9%; ROW= 30.5%;United States, 0.1%,; Uganda, 0.25%; COMESA, 0.1%;
Kenya, 0.1%; and Tanzania, 0.05%.
South Africa--SADC
We take South Africa as representative of SADC. Five companies provide mobile
telephone services. The five cellular providers and the number of their subscribers in millions are
Vodacom, 29.3; MTN, 25; Cell C, 11.7;, 0.5; and Telkom Mobile, 1.54. 47
Regarding fixed line telephone services, in 1997, the South African telecommunications,
was partly privatized. Telkom has a reported 3 million fixed line subscribers. A Second Network
Operator was licensed to compete with Telkom across its spectrum of services and operates
44
Data are from: Uganda Communications Commission (2012), “Annual Post, Broadcasting and
Telecommunications Market Review 2011/2012.” Available at :
http://www.ucc.co.ug/files/downloads/2011%2012%20Annual%20post%20and%20Telecom%20market%20review
%20report-Final.pdf
45
Saturday Monitor , “Airtel Buys Warid Telecom,” April 23, 2013. The article notes that MTN claims to have 7.7
million subscribers as of December 2012 and claims a 51 per cent market share while Airtel says it will have 7.4
million after adding Warid’s 2.8 million customers. We base our market share calculations on the asset data, which
is complete.
46
Smile shareholders are listed on the company’s website at: http://www.smilecoms.com/~smlcoms/pages/about-
us/shareholders.php
47
See http://en.wikipedia.org/wiki/Telecommunications_in_South_Africa; market share data are calculated from
data on subsribers available in Qunton Bronkhorst, “Mobile subscribers in SA: who’s eating whose lunch?”
Business Tech, August 14, 2013. Available at:
http://businesstech.co.za/news/mobile/44164/mobile-subscribers-in-sa-whos-eating-whose-lunch/
107
under the name, Neotel. It has a reported 132.4 thousand fixed line subscribers in late November
2012. 48
Market shares of the combined fixed line and subscriber base are: Telekom ((combining
Telkom and Telekom Mobile), 6.4%; Neotel, 0.2%; Vodacom, 41.2%; MTN, 35.1%; Cell C,
16.4%; and Virgin Mobile, 0.7%.
The ownership of these compnaies is as follows. The shareholders of Telekom are: the
government of South Africa and an investment corporation of the South African government
(50.3%), South African billionaire Allan Gray (5.4%), open stock purchases for 42.3% and 2% is
TelekomTreasury stock. 49 The US company SBC sold its shares in 2007. Vodaphone UK owns
65% of the stock of Vodacom and the remainder is publicly traded due to the Telekom spin off. 50
MTN Group is reported to be South African. 51 Cell C is 75% controlled by the Saudi Arabian
company (Saudi Oger) and 25% by a broad-based black economic empowerment entity of South
Africa representing over 30 black empowerment companies and trusts.52 Virgin Mobile is
100% owned by Richard Branson’s group of the UK. Neotel, is 68.%% owned by the Indian
company Tata, 19% owned by a Nexus Connexion, a South African black economic
empowerment partner and 12.5% owned by Communitel, a consortium of South African and
Namibian companies. 53
Taking weighted average of the identified owners, the ownership shares for the regions of our
model are SADC, 49.35%; EU, 27.5%; Rest of World, 12.5%. The balance of 17.1% is publicly
traded due to the Telekom and Vodacom public holdings. We allocate the ownership of the
publicly traded shares as follows: United States, 5%; Kenya, 2%; COMESA, 1%, Tanzania, 1%;
48
Nicola Mawson, “Neotel Gains on Telekom,” ITWeb, November 7, 2012. Available at
http://www.itweb.co.za/?id=59858:Neotel-gains-on-Telkom
49
http://www.telkom.co.za/about_us/company_information/shareholding.html
50
http://en.wikipedia.org/wiki/Vodacom.
51
http://en.wikipedia.org/wiki/MTN_Group.
52
This combines the shares of Oger Telekom South Africa and Lanum Securities, both of which are wholly owned
by Saudi Oger. See http://en.wikipedia.org/wiki/Cell_C
53
See
https://www.neotel.co.za/wps/portal/!ut/p/c5/04_SB8K8xLLM9MSSzPy8xBz9CP0os3gL52AnczcPIwMLMw9DA0
_vAB9_M2NjY39Hc_1wkA6cKty9DdHk3d2NLYHygYGG7r7-BgbuxgTkTSDyBjiAo4G-n0d-bqp-
QXZ2kEe5oyIAehRj4A!!/dl3/d3/L2dJQSEvUUt3QS9ZQnZ3LzZfOENTQjdGSDIwODZIMTBJS1BMTzYzMzNP
UTE!/?WCM_GLOBAL_CONTEXT=
108
Rwanda, 0.25%; Uganda, 0.5%; China. 1%; and SADC, 6.35%. We take the weighted average
of the identified owners and add the publicly traded shares allocation.
This gives us the following shares for the regions in our model: SADC, 49.35%; EU, 27.5%;
Rest of World, 12.5%; United States, 5%; Kenya, 2%; COMESA, 1%, Tanzania, 1%;
Rwanda, 0.25%; Uganda, 0.5%; China. 1%.
COMESA
Clearly SADC and the Rest of the World are represented well in the communications
sector of COMESA countries. Airtel (of India) and MTN (of South Africa) are companies that
are well represented in COMESA countries. Among COMESA countries, Airtel is located in the
Democratic Republic of Congo, Madagascar, Malawi, Seychelles and Zambia 54, while MTN is
located in Democratic Republic of Congo, Sudan, Swaziland and Zambia. 55 Vodacom (of South
Africa) provides telephone service in the Democratic Republic of Congo as well as several
SADC countries. We take an average of the Tanzanian and Ugandan market shares as
representative of COMESA countries. This yields the following market shares:
COMESA, 14.65%; SADC, 39.5%; EU, 19.35%; USA, 0.1%; Rest of the World, 26.35%;
Kenya, 0.05%.
54
http://en.wikipedia.org/wiki/Airtel_Africa.
55
http://en.wikipedia.org/wiki/MTN_Group.
109