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Comparing the Performance of Uganda’s Intra-east African Community Trade and Other Trading Blocs: A Gravity Model Analysis

Working paper by Shinyekwa, Isaac and Othieno, Lawrence, 2013

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This paper examines factors that determine Uganda’s trade flows and specifically compares the impact and performance of the different trade blocs on Uganda’s trade patterns and flows. The empirical question is whether Uganda’s trade is getting more integrated in the East African Community(EAC) region or is still dominated by other trading blocs, namely European Union (EU), Asia and Common Market for Eastern and Southern Africa (COMESA). Two analytical approaches are used, namely: trade indicators and estimation of the gravity models using data extracted from COMTRADE for the period 2001–2009 (panel). The results suggest a strong relationship between belonging to a trading bloc and trade flows.

RESEARCH SERIES No. 100




Comparing the Performance of Uganda’s
Intra-East African Community


Trade and Other Trading Blocs: A Gravity
Model Analysis


ISAAC SHINYEKWA
LAWRENCE OTHIENO


APRIL 2013






RESEARCH SERIES No. 100


Comparing the Performance of
Uganda’s Intra-East African Community


Trade and Other Trading Blocs: A
Gravity Model Analysis


ISAAC SHINYEKWA
LAWRENCE OTHIENO


APRIL 2013




Copyright © Economic Policy Research Centre (EPRC)


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Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


TABLE OF CONTENTS


Abstract ii
1. Introduction 1
2. Uganda’stradeflowswiththedifferenttradingblocs 3
3. Reviewofrelatedliterature 6
4. AnalyticalFrameworkandMethods 9
4.1 Theoretical foundations of the gravity equation 9
4.2 Gravitational model 10
4.2.1 The estimation procedure 12
4.2.2 Diagnostic tests 13
4.3 The data 13
5. Descriptiveanalysis 15
5.1 Regional trade concentration versus dispersion index 15
5.2 Trade Intensity indexes 16
5.3 Trade Complementarity Index (TCI) 18
5.4 The share of Uganda’s trade flows in different markets 19
6. GravityModelEstimationResults 22
6.1 Exports – the Static RE and Dynamic RE Models 22
6.2 Imports – the Static RE and Dynamic RE Models 25
7. Conclusion and Emerging Policy Issues 28
References 29
APPENDIX 33
EPRC RESEARCH SERIES 40


LIST OF TABLES
Table 1: Trade integration of Uganda into the different trade blocs - Trade
Entropy Indicators 16
Table 2: Trade Intensity between Uganda and its trading Partners 18
Table 3: Trade Complementarity between Uganda and its trading partners 19
Table 4: Results for the exports -2001-2009 (Random effect, Dynamic Random Effect) 23
Table 5: Results for the imports -2001-2009 (Random effect, Dynamic Random Effect) 25
Table A 1: Uganda’s Exports to the different trading blocs 2001- 2009 (millions USD) 33
Table A 2: Uganda’s Imports from the different trading blocs 2001- 2009 33
Table A 3: Uganda’s Leading Export Markets and 3 leading Competitors, 2011 33
Table A 4: Uganda’s Leading Export Markets and 3 leading Competitors, 2010 34
Table A 5: Uganda’s share of leading export markets by product 35
Table A 6: Uganda’s Export Market Share 36
Table A 7:Trade Partners’ Share in Uganda’s Imports 37
Table A 8: Results for the exports -2001-2009 (Random effect, Dynamic Random
Effect and the IV GMM) 38
Table A 9: Results for the imports -2001-2009 (Random effect, Dynamic Random
Effect and the IV GMM) 38




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Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


ABSTRACT


This paper examines factors that determine Uganda’s trade flows and specifically compares


the impact and performance of the different trade blocs on Uganda’s trade patterns and flows.


The empirical question is whether Uganda’s trade is getting more integrated in the East African


Community (EAC) region or is still dominated by other trading blocs, namely European Union


(EU), Asia and Common Market for Eastern and Southern Africa (COMESA)? Two analytical


approaches are used, namely: trade indicators and estimation of the gravity models using data


extracted from COMTRADE for the period 2001 – 2009 (panel). We estimate determinants of


export and import trade flows separately using static random, dynamic random and IV GMM


models. The results suggest a strong relationship between belonging to a trading bloc and


trade flows. Likewise, Uganda’s import and export trade flows have conspicuously adjusted


to the gravitational forces of the EAC during the progress of the integration. Whereas exports


are being integrated more in the EAC and COMESA regions, imports are more integrated in


the Asian and EU trading blocs. Therefore, strong links with trading blocs outside the EAC


(i.e. EU and Asia) with regards to imports still exist. The trade indicators demonstrate that


Uganda exports largely primary products and imports manufactured products. It is imperative


for Uganda to target implementation of regional trade agreements to expand the country’s


export markets. The EAC region should attract investment in production of high technology


products to increase intra-EAC imports and reduce imports from Asia and the EU.


Keywords: Gravity model, imports, exports, intra, trade intensity index, trade indices, trade
flows, trade shares, blocs, regional integration, panel, random and fixed effects.




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1. INTRODUCTION


The East African countries of Uganda, Kenya and Tanzania have a strong historical relationship


characterized by phases of economic and political cooperation that date back to the early


twentieth century (Shinyekwa and Othieno 2011). The latest phase is the treaty that established


the East African Community (EAC) in 2000 with the Republic of Uganda, the Republic of Kenya


and the United Republic of Tanzania being the initial members. Membership to the EAC has


grown to five partner states after Burundi and Rwanda joined the EAC in July 2007 and there


are prospects of South Sudan joining the community in the near future.


The main objective of the EAC is to attain economic, social and political integration in East


Africa. The Customs Union (CU) protocol highlights the commitment of Partner States to


support export promotion schemes in the community to accelerate development, promote


and facilitate export oriented investments, produce export competitive goods, promote export


schemes and attract foreign direct investment. The removal of tariffs on intra-regional trade


also referred to as Internal Tariffs (IT) and the efforts to reduce Non-Tariff Barriers (NTBs) and


improvement in trade facilitation are among the on-going initiatives to boost intra-EAC trade.


Ideally, formation of a CU should increase intra-trade within the EAC implying that Uganda’s


trade with the EAC partner states should increase both proportionally and in value terms.


While this is the intention among the EAC regional economic integrating countries, there is a


tendency to trade more with countries outside the regional bloc than among partner states


as will be discussed latter. This is as a result of weak infrastructure; supply side constraints,


limited value addition capacity and poor road connectivity that have remained a major


impediment to increase in intra-regional trade. There is also the phenomenon of overlapping


membership that poses a challenge given the divergence in the respective trade regimes. It


may discourage rather than promote greater trade liberalization within the EAC trading bloc


as it is diversionary in nature and problematic1.


Trade liberalization has been an important part of East Africa’s policy agenda since the


countries embarked on several structural adjustment efforts. The emphasis during the 1990s


was on multilateral liberalization, with both import tariffs and quantitative restrictions (for


example quotas) falling dramatically. However, the pace of multilateral reforms slowed at


the end of the last decade and the countries shifted their liberalization efforts in favour of


bilateral and regional agreements with major trading partners2. Although Uganda has the


liberty to further liberalize trade within the EAC framework, the key question is the extent


to which Uganda’s trade is getting integrated in order to reap the benefits especially after


forfeiting customs revenue. Tariff revenues make substantial contribution (on average 11


1 Uganda belongs to both COMESA and EAC and Tanzania a member of the EAC is also a member of the South African Development Coop-
eration (SADC).


2 This could be a results of the stalled WTO negotiations of the Doha Round that has led to proliferation of bilateral agreements globally




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Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


percent3) to total tax revenues for Uganda, whose removal puts more pressure on the country


to meet fiscal responsibilities. If the country is not integrating its trade within the EAC, in


order to benefit from the gains, then foregoing customs revenue is a loss.


The paper applies the gravity model to establish the determinants of Uganda’s trade flows.


Specifically, the paper seeks to establish whether Uganda’s trade is getting more integrated


into the EAC region or is still dominated by other trading blocs. To what extent has Uganda


taken advantage of the reduction in tariffs on intra-EAC regional trade and the reduction of


non-tariff barriers to expand the country’s regional trade? What is the nature of Uganda’s


regional and global traded products? This paper contributes to informed policy formulation


for Uganda to deepen the county’s regional trade integration within the EAC context.


The rest of the paper is organised as follows: The next section provides a brief of the trends and


patterns of Uganda’s trade flows with different trading blocs. Section 3 provides a critical review


of the relevant literature regarding determinants of trade flows. The analytical framework


and methods is the subject of Section 4. Section 5 focuses on descriptive analyses prior to


the presentation and discussion of the gravity model results in Section 6. The conclusions and


emerging policy issues are discussed in Section 7.


3 The data are sourced from the Uganda Bureau of Statistics Abstracts. The foregone revenue from the EAC partner states is part of the 11
percent.




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2. UGANDA’S TRADE FLOWS WITH THE DIFFERENT TRADING BLOCS4


Since the implementation of the EAC integration in 2001, Uganda has in general registered


an increase in the value of total exports to all the trading partners within the EAC bloc as


demonstrated in Figure 1 from about 20 percent in 2001 to 27 percent in 2009. However,


other trading blocs like the European Union (EU) still play a significant role with regards to


Uganda’s exports and account for over 30 percent of total export trade.


Figure1:TheproportionsofUganda’sexportstothedifferenttradingblocks,2001-2009


(%)


Source of data: COMTRADE, 2012


It is noted that the EU as a key destination of Uganda’s exports registered a decline in exports


from 51 percent in 2002 to about 31 percent in 2009. On the other hand, exports, worth


US$194.5 million were destined for the Common Market for Eastern and Southern Africa


(COMESA)5in 2009, whose imports contribution have grown from 6 percent in 2001 to 13


percent in 2009. From a regional perspective, Uganda’s exports to EAC and COMESA combined


have grown from 26 percent in 2000 to 58 percent in 2009 underlining the increasing role of


regional export trade. Likewise, it suggests an increasing role of the COMESA and the EAC in


Uganda’s export trade pattern (for details see Table A 1).


The proportion of Uganda’s imports from the EAC region declined significantly from 29 percent


in 2000 to 13 percent in 2009 as demonstrated in Figure 2. Apparently this decline was mainly


4 Details on import and export value are presented in Table A 1 and Table A 2.
5 The COMESA in this analysis excludes the EAC countries (Kenya, Burundi and Rwanda) for the sake of analyzing EAC trade flows. In reality


COMESA Membership accounts for the largest proportion of Uganda’s exports




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experienced in 2006 and the years thereafter, following the implementation of the CU. In


value terms, Uganda’s imports from the EAC partner states increased from US$288million


in 2001 to US$547million in 2009. This suggests that although the value of imports from the


EAC partner states doubled, the proportion to total imports declined implying that Uganda


is increasingly depending on other trading blocs for the country’s imports. COMESA without


the EAC partner countries contributes a small proportion that has increased from 2 percent in


2001 to 3 percent in 2009. During the same period, the EU maintained a constant proportion


of exports to Uganda with an average of about 21 percent of the total imports for the country.


This suggests that the implementation of the EAC treaty has so far not reduced Uganda’s


imports from the EU (for details see Table A 2).


Figure2:TheproportionsofUganda’simportsfromthedifferenttradingblocs2001–2009


(%)


Source of data: COMTRADE


Uganda has experienced a tremendous growth in imports from Asia, from 26 percent of total


imports in 2001 to about 37 percent of total imports in 2009 and Asia is likely to remain a


dominant exporter to Uganda for the foreseeable future since the EAC region does not produce


the type of goods currently imported from Asia. In monetary terms, imports from Asia have


increased from US$259million in 2001 to US$1.576 billion in 2009. This reveals the increasing


role the Asian region plays on the Ugandan economy. The statistics suggest that the decline


in the proportion of regional imports is explained by the increasing imports from Asia. It is


demonstrated in Figure 2 that the EU a major source of imports largely maintained its regional


proportion. The increase in imports especially from Asia and Europe is primarily explained


by the growth in private sector imports of capital and consumer goods such as petroleum




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products, iron and steel, mineral fuels, electrical machinery, pharmaceutical products and


sugar. The analysis to shed more light on the composition of exports is in Section 4.5. The


increase in the import bill of petroleum products is attributed to the increase in the local


demand for oil arising from shortage of hydro power and rising international oil prices. The


analysis to shed more light on the composition of imports is in Section 4. Overall, most of the


imports in Uganda originated from the Asian region and the EU, while the EAC experienced


a decline proportionally. What emerges from this analysis is that although in absolute value,


Uganda has increased imports from the EAC region (specifically Kenya), in terms of proportion,


there has been a notable decline in favour of Asia in particular.




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3. REVIEW OF RELATED LITERATURE


The recent proliferation of Regional Trade Agreements (RTAs) among countries characterised


by overlapping tendencies known as the ‘spaghetti bowl’ has generated debate on the future


of the stalled multilateral process given the growing regionalism. RTAs have spread and


deepened across both the North and South. Yeats (1997) raised empirical questions whether


RTAs stimulate growth and investment, facilitate technology transfer, shift comparative


advantage towards high value activities, induce political stability or divert trade in inefficient


channels and undermine the multilateral trading system. Trade theories explain the sources


and possible scenarios that underpin this proliferation.


Trade theories that explain gains from integration are as old as when trade shifted from autarky


to international trade. The theories explain why countries seek to integrate: The classical trade


theory put forward by Richardo argues that trade raises a country’s potential income (welfare)


compared to autarky through specialization according to comparative advantage. Countries


thus shift resources to production of goods where they efficiently produce and import goods


where they are less efficient. However, the existence of tariff and non-tariff barriers distorts


the final consumer price. Although the model explains the source of comparative advantage


which motivates countries to trade; it assumes that labour is the only factor of production


which is not true. It assumes perfect competition and yet imperfection exists, and many


countries are small and are price takers. Furthermore, the assumption that transport costs do


not exist is unrealistic.


The Heckscher-Ohlin (H-O) model on the other hand, explains international trade based on the


country’s factor endowments, that is, the relative quantities of capital and labour available


for production. It assumes that countries have access to the same technology. In this way,


countries with relatively large quantities of labour will shift production to labour-intensive


production and export these goods and import capital-intensive goods. This implies that


developed countries that are capital intensive will always dominate developing countries that


are likely to be labour intensive. This perhaps explains why South-South RTA dominated by


production of labour intensive goods and importing capital intensive products are likely to stall


intra-trade. The model assumes that factors of production are only mobile within a country


and immobile outside the country, implying that it is even more difficult for labour intensive


countries to access capital intensive technology. Raul Prebisch and Hans Singer6 explained the


disadvantage of countries being segmented into exports of either manufacturers or primary


commodities. Accordingly, countries exporting primary commodities will suffer terms of trade


decline driven by low income and price elasticities of demand.


6 http://en.wikipedia.org/wiki/Singer%E2%80%93Prebisch_thesis




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Other theories that incorporate market imperfections and explain the role of economies of


scale in trade have been proposed. DeRosa (1998) extensively reviewed regional integration


literature spanning the modern static theory and the extensions. For small countries the


author argues that intra-regional trade will increase among member countries as long as they


are predominantly least-cost producers of export goods. However, in cases where diversion


will increase costs, non-member countries are likely to continue supplying imports to member


countries which will negatively impact intra-trade. The following examples shed light on the


empirical evidence over time.


Frankel et al. (1995) establish that intra-regional trade as a share of total trade among the


Andean countries7 increased between 1965 and 1990 from 0.8 percent to 2.6 percent. There


is a higher performance among the East Asian countries from 20 percent to 29 percent during


the same period. The intra-EC trade as a share of total EC exports increased from 35 percent


in 1960 to 49 percent in 1970 (DeRosa, 1998). After the expansion of the EC from 6 to 9


countries8 from 1970 to 1981, intra- EC trade as a share of total trade grew from 49 percent to


52 percent. The EC12 saw even more intra-trade. The decomposition the effects using a gravity


model reveal a highly statistically significant relationship between regional integration and


growth in intra-regional trade. A number of other studies have demonstrated an increase in


intra-regional trade among integrating countries (for example, Vollrath, 1998 for Association


of Southeast Asia Nations (ASEAN) Free Trade Area (AFTA), Asia-Pacific Economic Cooperation


(APEC), Canada-United States Free Trade Agreement (CUSTA) and the European Union (EU).


Sherman and Karen (1999) observe that much of the increases in trade occurred among


developed countries, with developing and developed countries experiencing limited trade


and developing countries experiencing even less intra-regional trade.


Other studies have painted a rather pessimistic picture of RTAs especially in developing


countries (South-South). It is argued that the similarity of resource endowment of the partner


members and the frequent failure by these countries to implement fully the terms of their


regional integration agreement makes it hard for them to increase intra-regional trade. In


some cases there has been deliberate undermining of the integration agreements. Naya and


Plumber (1991) reported that the ASEAN after a decade of existence failed to increase intra-


bloc trade much above its level of 15 percent to 20 percent of total ASEAN trade. In Latin


America, the expansion of intra-regional trade in manufactures and all goods failed to match


that in the EC and out-ward oriented East Asian Newly Industrialized Countries (NICs) of Korea,


Hong Kong, Singapore and Taiwan. Nogues and Quintanilla (1993) report that the intra-


regional trade in manufactures during 1965 to 1990 by the out-ward-oriented Asian NICs grew


from 2 percent of GDP to 6.9 percent of GDP and the intra-regional trade in manufactures


7 The Andean Community of Nations is a trade bloc comprising the South American countries of Bolivia, Colombia, Ecuador and Peru
8 The countries that joined include Denmark, Portugal and United Kingdoms




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during the same period by the ANDEAN9 Pact countries grew from 0.1 percent of GDP to 0.6


percent of GDP.


A World Bank study on regionalism argues that South–South RTAs are non-edifying (World


Bank 2001). Rather than reaping economic benefits like increase in intra-trade, they generate


trade diversion which reduces welfare in circumstances when tariffs are high. Yeats (1998)


looking at trade data from Sub-Saharan Africa argues that intra-regional trade has a potential


to create adverse effects especially on third party member countries and concludes that intra-


trade is likely not to make an important impact on the partner countries and may negatively


impact Africa’s industrialization. A most radical view about RTAs in the South was put forward


by Schiff (1997). He argues that RTAs between small countries increase the likelihood of


partners switching from cheaper imports from low cost third party members to higher cost


partner members. Perhaps Park (1995) argument like Derosa (1998) reveals the source of this


problem. The authors argue that when the intra-regional trade shares are small in total trade,


there are more chances of trading blocs diverting trade.


A number of studies assessing the impact of trade liberalisation on Uganda have been


done, although they do not specifically examine intra-regional trade. DeRosa et al. (2002)


demonstrates the implications of the New EAC CU on Uganda’s trade, industrial competitiveness


and economic welfare. Mbabazi (2002) uses a CGE model to examine tariff liberalisation on


the welfare of Uganda and reveals that exports increase and thereafter decline while imports


increase with an overall increase in the negative trade balance. Bauer and Mugisha (2001) use


a CGE model to analyse the impact of the reduction of both import and export tariffs on the


Uganda Economy and highlight the worsening of the trade balance due to imports increasing


at a higher rate than exports. Sangeeta et.al (2009) and Othieno and Shinyekwa (2011) assess


the impact of Internal tariff reduction on trade, revenue and welfare of Uganda. It is revealed


that there is more trade creation than diversion10, hence a positive trade effect. These studies


do not specifically examine Uganda’s intra-EAC trade. Buigut (2012) estimates the trade effects


of the EAC CU on individual member countries. This study is revealing, however, it does not


cover the impact of other trading regions on Uganda which this paper contributes to. This


present paper focuses on Uganda.


9 Comprised of Bolivia, Chile, Colombia, Peru and Venezuela - Chile left the pact in 1976.
10 Trade diversion occurs when a free trade area (in this case the EAC CU) diverts trade, away from a more efficient supplier outside the EAC


region, towards a less efficient supplier within the FTA, for example Kenya, Tanzania, Burundi and Rwanda. This is likely to reduce Uganda’s
national welfare, however in some instances the national welfare may improve despite the trade diversion. Trade creation occurs when a
free trade area (in this case the EAC CU) creates trade that would not have existed otherwise without the formation of the FTA. In this case
as a result, supply will come from a more efficient producer of the concerned product. Gains occur if higher-cost domestic production is
replaced by cheaper imports from one/all EAC partner states. Unlike trade diversion, in all cases trade creation raises a country’s national
welfare.




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4. ANALYTICAL FRAMEWORK AND METHODS


4.1 Theoretical foundations of the gravity equation


The paper uses the gravity model to estimate the determinants of Uganda exports and imports.


The application of the gravity model to assess and analyse international trade flows was first


applied in the 1960s by Tinbergen (1962) and Poyhonen (1963). Since then, gravity models


have been widely used in various economic disciplines to assess and forecast the impact of


distance on the intensity of economic relations. In the latter half of the twentieth century,


gravity models (details in section 4.2) were used to explain migration and other social flows


in term of gravitational forces of human interaction (Eita, 2007). Initially, the theoretical


foundations of applying gravity models to economic interchange and trade was heavily


criticised as lacking basis and foundation from trade theory although the models exhibited


high statistical explanatory power (Matyas et al., 2000). The model was criticized for lacking


the ingredients of the prominent models of international trade that included the Ricardian


model, (differences in technology) and the Heckscher-Ohlin (H-O) model (differences in factor


endowments) as the basis for trade (UNCTAD, 2012). This view does not hold anymore given


the advancement made in the empirical work and literature.


Anderson (1979) made the first attempt to give a theoretical basis for gravity models. This


was done in the context of a model where goods are differentiated by country of origin


commonly known as the Armington assumption. Accordingly, consumers in a country with


a given price/s will consume at least some of every good from every country owing to the


existence of imperfect substitutability among goods. Given that all commodities are traded


and all countries trade, in equilibrium, national income is made of both home and foreign


demand for the unique good that each country produces. As such, larger countries export and


import more and trade costs that include transport and others reduce trade flows.


Furthermore, Bergstrand (1985, 1989) argue that the gravity model is embedded in a


monopolistic competition developed by Krugman (1980). The model has identical countries


that trade in differentiated goods because consumers have a preference for variety thus


overcoming the undesirable feature of Armington models that differentiate goods by location


of production. Deardorff (1995, 1998) further demonstrates consistency of the gravity model


with a wide range of trade models including the Heckscher-Ohlin-model, either with frictionless


or with impeded trade. Furthermore, Eaton and Kortum (2002) derive a gravity-type equation


from a Ricardian type of model, and Helpman et al., (2008). Finally, Chaney (2008) resorts to a


theoretical model of international trade in differentiated goods with firm heterogeneity.


In the literature, the gravity models have been used to analyse bilateral trade and in all cases,




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the authors argue that it is not difficult to justify even the simplest forms of gravity equations


from standard trade theories. This underlines the fact that gravity models demonstrate a


strong relation between bilateral trade flows and their determinants. Matyas et al., (2000)


modelling the export activity of eleven Asia-Pacific Economic Cooperation (APEC) countries


established that the various members’ propensities to import and export are sufficiently high.


Laaser and Schrader (2006) analysing Baltic trade flows revealed that Estonia, Lativia and


Lithuania have rapidly integrated into the international division of labour especially with the


EU. Eita (2007) estimated the determinants of Namibian exports and concluded that increases


in the importer’s GDP and Namibia’s GDP led to an increase in the country’s exports. It was


demonstrated that sharing a common border increases exports. To the contrary, increase in


distance and importers’ per capita income are associated with decrease in exports. Zarzozo


and Lehmann (2003) applied the gravity trade model to assess Mercosur-EU trade and trade


potential following the agreements reached between the two trade blocs. It was established


that importer and exporter incomes are positively associated with bilateral trade flows.


Whereas, the exporter population has a negative impact on trade flows, importer population


has a large positive effect on exports. Other variables like infrastructure, exchange rates are


important determinants of bilateral trade flows.


4.2 Gravitational model


The standard gravity model explains bilateral trade flows (imports and exports) as a function


of the trading partners’ market sizes and their bilateral barriers to trade. In its general form,


trade flows between countries are explained by their economic size (GDP), population, geo-


graphical distance and a set of dummies. The model specification follows conventional paths


widely used in the literature (see for example, Tinbergen 1962; Poyhonen 1963; Eita 2007;


and UNCTAD 2012). The general specification of the gravity model is expressed in equation (1).


The dependent variables ln(Tij)t are trade flows, which are either imports ln(Mij)t or exports


ln(Xij)t of Uganda (with subscript i indicating Uganda, j the trading partners and t time). Ln(Yij)t


is the GDP per capita income of Uganda and the trading partners, ln(Pij)t are the populations of


Uganda and the trading partners, respectively. In(Dij), measures the distance between the two


capitals of Uganda and the trading partners, In(DUMij) is a set of dummies that assume value


of one and zero, and is the error term.


In the empirical literature (for example, in Zarzoso and Lehmann 2003) a number of variables


are used to capture trade barriers that include: transport costs captured by distance between


countries; countries being islands, landlocked and border dummies to reflect that transport


costs increase with distance. It is anticipated that transport costs are higher for landlocked




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Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


countries and islands, and are lower for neighbouring countries. According to Nordås and


Piermartini (2004), information costs are generally captured by a dummy for common lan-


guage between the trading partners. Therefore the value taken on is equal to one if the trad-


ing partner is an island, landlocked, borders Uganda, has a common language with Uganda


respectively, and zero otherwise.




A study by Bougheas et al., (1999) shows the limitation of the traditional gravity model which


uses distance to model transport costs. It is argued that transport costs are not only a function


of distance but also private and public infrastructure. They thus augment the model through


introduction of infrastructure variables. They predict a positive relationship between the level


of infrastructure and the volume of trade. This paper adopts infrastructure for Uganda and


the partner country by computing a set of indices11. In the model, infrastructure is treated as


In(INFRij)t for Uganda and trading partner.


Foreign currency reserves are important indicators of ability to repay foreign debt and for


currency defence, and are used to determine credit ratings of nations. In this context foreign


reserves are a measure of the ability to import and therefore have a positive relationship with


trade flows. In the model, they are represented by In(FCRij)t for Uganda and trading partners.


In(RERij)t denotes the real exchange rate between Uganda and trading partners calculated as


the average of the national currency unit of country j per US dollar divided by the annual aver-


age of the national currency unit of i per US dollar.


Finally, we add the dummy variables for the different trading blocs that Uganda trades with and


these include: the EAC, Asia, EU and COMESA. Since membership overlaps for Kenya, Rwanda


and Burundi, these were treated as primarily EAC countries and not COMESA countries. These


dummies explain the significance of Uganda’s trade with either of the trading blocs. With


these addition variables, the equation (1) is re-expressed as in equation (2).


EACij DV, = 1, if trade flow between Uganda and EAC partner states , = 0, if not


ASIAij DV, = 1, if trade flow between Uganda and ASIA states , = 0, if not


EUij DV, = 1, if trade flow between Uganda and EU states , = 0, if not


COMESAij DV, = 1, if trade flow between Uganda and COMESA states , = 0, if not; and
The rest of the variables are as described before.


11 Infrastructure in each country is measured by an index constructed by taking the mean over four variables (Km of roads and railway den-
sity per 100 Km squares) and phone tele-density and internet users per 100 people. Details regarding modelling infrastructure on gravity
models can be referred to (Nordås and Piermartini 2004)




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4.2.1 The estimation procedure


The paper estimates three models using panel data for the period 2001-2009. A very important


property in the panel data estimation is the individual effects which are treated either as


fixed or random depending on conditions pertaining. Practically, Random Effects (RE) is


appropriate for estimating trade flows between randomly drawn samples of trading partners


from a large population. Fixed Effects (FE) is most appropriate for estimating trade flows


between ex ante predetermined selection of countries (Eita, 2007). However, the FE model


is plagued by the limitation that variables that do not change over time cannot be estimated


directly because the inherent transformation wipes out such variables. FE models are used


whenever the analyst is interested in estimating the impact of variables that vary over time.


This is because FE will not work well with data for which within-cluster variation is minimal or


for slow changing variables over time and at the extreme non-varying variables.


We use the Hausman test to choose between the FE and RE models. The choice is made by


running the Hausman test where the null hypothesis is that the preferred model is RE versus


the alternative - the FE model. It tests whether the unique errors (ui) are correlated with


the repressors. Since the P-value is 0.3665, we accept the null hypothesis that the preferred


model is the RE. Since the RE model had the correct specification for the trade flows, we


conducted the Breusch-Peagan Langrange Multiplier (LM) to decide between a RE regression


and a simple OLS regression. The null hypothesis says that the variances across entities are


zero implying that there is no significant difference across units, that is, no panel effect in


which case OLS suffices. The results show a very significant difference (P-value 0.0000) in


which case we reject the null hypothesis and conclude that RE is the appropriate model to


estimate. There is a strong evidence of the significant difference across the countries and


therefore we cannot run a simple ordinary least squares (OLS).


Trade patterns between Uganda and the country’s trading partners at one time is a function


of trade in the past for various reasons, such as bilateral agreements and trade preferences


that are likely to have a lag hence the need to apply dynamic models. Dynamic panel models


are increasingly being used in panel data estimation partly due to increase in panel data


availability and the vast array of economic theories fronting some form of partial adjustment


of economic variables to an equilibrium level (Harris and Matyas, 1996). These are models


which include lagged value(s) of the endogenous variable as explanatory variables. This paper


therefore estimates a dynamic RE model in addition to the static RE to gauge the impact of


previous trade flows on current trade flows.


It is argued that while FE models suffer short time series component, RE are often biased due to


the correlation between the equation’s disturbance terms and the lagged dependent variable


(Sevestre and Trognon 1985). Harris and Matyas (1996) suggest that consistent estimators




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Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


for both specifications do exist and generally take the form of instrumental variables (IV). It


is argued that IV estimation involves making use of certain orthogonality properties, that the


instruments are asymptotically uncorrelated with the equation’s disturbance terms. The use


of such a wide set of orthogonality properties has propelled estimation to the more general


area of Generalised Methods of Moments (GMM) estimation.


4.2.2 Diagnostic tests


We checked multi-collinearity in the model by conducting the simple correlation test that


reveals the coefficients between the explanatory variables. It is demonstrated that the values


of the correlation coefficients between explanatory variables are lower than 0.80. Following


Studenmund (200112) who argues that below such a threshold the model is fine, we concluded


that there is no serious problem.


Unit root tests are conducted to determine a potentially co-integrated relationship between


the variables. Whereas if all the variables are stationary, the traditional estimation methods can


be used to estimate the relationship between the variables, if the variables are non-stationary,


a test for co-integration is required. We conducted the Levin et al., (2000)13 test of panel unit


roots that assume that the autoregressive parameters are common across countries. Levin,


Lin and Chu used a null hypothesis of a unit root that states that the panels contain unit roots


and the alternative that the panels are stationary. The test results indicate that all variables


are stationary (the null unit root is rejected). As a result of this the co-integration test is not


required to estimate the model.


4.3 The data


The data used in this paper were drawn from different sources and compiled to suit the


analysis. The trade flow data were extracted from the COMTRADE and World Integrated


Trade Solutions (WITS) databases. In this respect, 174 countries that trade with Uganda


were included and further categorised into the trading blocs/regions of EAC, COMESA, EU,


America, Asia and the rest of the world. The data for distances were extracted from the


distance calculator website14. The distance is defined as direct distance from Kampala to the


capital city of the trading partner without taking into consideration the actual routes by either


forms of transport(“as the crow flies”). The per capita, real exchange rate, infrastructure


(rail, road, mobile telephone phone tele-density, and internet connectivity) and population


data for Uganda and the trade partner states were taken from the World Bank Development


Indicators. The data on whether, a country is land-locked or not, is an island or not, borders


12 Studenmund AH (2001) Using Econometrics – A Practical Guide, San Francisco, CA, Addision Wesley Longman
13 Levin, A, Lin, C F and Chu (20020 Unit Root Tests in Panel Data: Asymptotic and Finite Sample Properties, Journal of Econometrics , 108.


1-1-24
14 http://www.timeanddate.com/worldclock/distanceresult.html?p1=115&p2=17




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Uganda or not and has the same official language or not were extracted from the Centre


d’Etudes Prospectivesetd’ Informations Internationales (CEPII)15 gravity dataset. The trading


blocs and regions are constructed from existing information on Regional Trade Areas from the


World Trade Organization. The analysis is done for the period 2001 to 2009, the period during


the implementation of the EAC regional integration FTA and CU.


15 CEPII make available a “square” gravity dataset for all world pairs of countries, for the period 1948 to 2006. This dataset was generated by
Keith Head, Thierry Mayer and John Ries to be used in the following paper: HEAD, K., T. MAYER AND J. RIES(2010)




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5. DESCRIPTIVE ANALYSIS


In this section, we focus on descriptive analysis prior to the discussion of the gravity model


estimates. There are a number of trade indicators which can provide information on the level


of and changes in regional trade pattern or direction of trade flows. The trade indicators


help explain which economies are the most important export destinations of a country,


measure the geographical concentration or diversification of a country’s export profile among


others. A number of trade indicators can be computed, however this paper outlines three


which complement the gravity model estimates namely: regional trade concentration versus


dispersion index; trade intensity and complementarity indicators.


5.1 Regionaltradeconcentrationversusdispersionindex


The emerging pattern of Uganda’s trade during the 2000s gives evidence of the country’s


growing participation in the EAC. The quality of trade integration into different trading blocs


can be measured by trade entropy indicators (Marwah 1995; Marwah and Klein, 1995) which


give information on the spatial concentration of trade relations. This is based on the notion


that a country which is trading with many other countries can be considered to be more


deeply integrated into other trading blocs than a country trading with only a few partner


countries (Schrader and Laaser, 2006). In numerical terms of a trade concentration indicator,


trade with many countries means relatively low and equally distributed shares of trading


partners’ exports or imports in a country’s aggregate trade figures. On the other hand, trading


with few countries means unevenly distributed shares. In that respect, while some shares


will be very high, others will be equal to zero. Whereas a low concentration record for a


trading bloc implies that Uganda is less integrated into it, a high concentration record means


that the country is more integrated into the respective trading bloc. Trade concentration is


specified as follows for imports shares aij, of trading partner’s j of country i and exports shares


bij, respectively. This is expressed in equations (3) and (4) respectively. These equations are


used to measure the degree of dispersion of the statistical distribution of all the import and


export shares.


The geographical trade dispersion of Uganda’s trade during the implementation of the EAC


compared to the other trading blocs/regions reveal that the country is getting more integrated


into the region with regards to exports and less with regard to imports. Table 1 illustrates the


trends in the geographical trade dispersion indicators for imports and exports. For imports,


the trend reveals that Uganda is more integrated into Asia, the EAC and EU than COMESA, the




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America and other African countries. On average the indices over the years are similar for Asia


the EAC. On the other hand, exports reveal more integration into the EAC, COMESA and the


EU than the other trading blocs/regions. It is noted that whereas the indices slowly decrease


over the years for the EU, they increase for the EAC and COMESA further emphasising the


growing integration of Uganda’s exports in the EAC region.


Table1:TradeintegrationofUgandaintothedifferenttradeblocs-TradeEntropy


Indicators


Bloc 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009


Imports


AMERICA 0.16 0.14 0.14 0.18 0.19 0.15 0.13 0.14 0.14 0.21


ASIA 0.34 0.35 0.35 0.36 0.36 0.35 0.36 0.37 0.37 0.28


COMESA 0.07 0.06 0.06 0.06 0.08 0.08 0.08 0.07 0.07 0.05


EAC 0.36 0.36 0.36 0.35 0.34 0.36 0.30 0.29 0.26 0.37


EU 0.32 0.32 0.30 0.31 0.31 0.31 0.31 0.33 0.32 0.29


OTHER AFRICA 0.19 0.19 0.20 0.19 0.21 0.19 0.17 0.17 0.19 0.24


ROW 0.24 0.26 0.26 0.24 0.25 0.27 0.34 0.31 0.32 0.22


Total 1.68 1.68 1.67 1.70 1.73 1.71 1.69 1.67 1.66 1.65


Exports


AMERICA 0.09 0.07 0.04 0.10 0.11 0.09 0.07 0.08 0.05 0.08


ASIA 0.21 0.23 0.22 0.19 0.21 0.19 0.16 0.15 0.16 0.17


COMESA 0.13 0.09 0.16 0.16 0.21 0.28 0.28 0.32 0.33 0.33


EAC 0.30 0.31 0.31 0.33 0.34 0.31 0.30 0.33 0.33 0.34


EU 0.37 0.36 0.36 0.35 0.36 0.36 0.35 0.34 0.35 0.33


OTHER AFRICA 0.24 0.26 0.13 0.22 0.10 0.10 0.09 0.14 0.09 0.09


ROW 0.33 0.34 0.37 0.35 0.35 0.34 0.37 0.34 0.32 0.34


Total 1.67 1.66 1.58 1.71 1.67 1.68 1.62 1.70 1.65 1.68


Source: calculations based on COMTRADE data, 2012


5.2 TradeIntensityindexes


Trade intensity index (T) is a complementary method of measuring and analysing bilateral trade


flows to the gravity model. It measures trade performance between two countries. It was


pioneered by Brown (1949) and developed and popularized by Kojima (1964) and Drysdale


and Garnaut (1982). Hill (1985) applied T to analyse and explain the pattern, composition and


trends in Australia-Philippine trade over the two decades 1962-81. Hill noted that overall, the


T increased substantially since the early 1960’s, especially in the case of Philippine exports to


Australia. Similarly, Bano (2002) employed the T to examine the strength of trade relations


between New Zealand and its major trading partners (Australia and selected Asia-Pacific


nations) for the period 1981-1999. He found that bilateral trade flows between New Zealand,


Australia and other countries had become more intense indicating that trading relations were




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strengthening but in some cases the bilateral trade flows between the two countries had


decreased. Bhattacharya and Bhattacharyay (2007) equally applied T to measure the trade


potential between China and India. The study reveals that India and China possess a significant


bilateral trade potential, which remains untapped.


Unlike the gravity model, the index abstracts from the effects of the size of the exporting and


importing countries, and focuses on variations in bilateral trade levels. According to Drysdale


and Garnaut (1982), the T measures the share of one country’s trade with another country (or


region) as a proportion of its share of world trade. For the country’s exports to the country,


the index (T) is define as the share of exports to in its total exports (Xij/xi) relative to the share


of import in world imports, net of imports ()16. The Index is written as in equation (5).


(5)


Where: = The exports of country i to country j


= Total exports of country i


= Total imports of country j


= The total world imports


= The total imports of country i


= This is the proportion of exports sent to trading partner relative to


what is exported in totality.


= It is the foreign country’s total imports as a proportion of total world


imports less the import of the domestic economy.


The T takes a value between 0 and+∞. Values greater than 1 indicate an ‘intense’ trade


relationship. Countries which import at proportionally high levels from the same country to


which they send most of their exports will have a high T. Conversely, a country with diverse


markets that is not reliant on any one country for their imports will have a low T. The limitation


with this index is that with trade shares, high or low intensity indices and changes over time


may reflect numerous factors other than trade policy changes between or among trading


partners (UN ESCAP 2012).


The overall trade intensity index for Uganda’s bilateral trade with its partners presented in


Table 2 for the period 2005-2010 suggests a considerable increase in the index.


16 Mj is subtracted from Mw given that a country cannot export to itself, thus the only share of world imports it can have is a share of all
countries’ imports other than its own.




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Table2:TradeIntensitybetweenUgandaanditstradingPartners


Country RECS 2005 2006 2007 2008 2009 2010


Burundi EAC/COMESA 1041.7 994.3 1394.5 1378.9 1221.0 1053.3


Rwanda EAC/COMESA 1157.7 833.1 1460.6 1383.4 1224.5 1351.7
DR Congo SADC/COMESA 466.0 185.4 352.2 288.2 382.4 384.9


Sudan COMESA 102.4 159.1 231.5 280.4 201.5 244.1
Kenya EAC/COMESA 166.0 151.3 138.2 156.4 143.6 153.4


Tanzania EAC/COMESA 59.7 45.1 60.8 45.0 47.1 49.5


Eritrea COMESA 8.9 19.2 1.6 7.1 4.8 3.6
Mauritius SADC/COMESA 4.9 9.5 1.6 0.3 1.1 2.8


South Africa SADC/SACU 2.3 2.0 1.5 1.7 3.0 1.2


Netherlands EU 2.8 1.7 1.3 1.2 1.4 1.5


Belgium EU 1.5 1.6 1.5 1.5 1.1 1.1


Malawi COMESA/SADC 1.4 1.2 0.9 0.8 2.3 0.4


Ethiopia COMESA 1.2 0.8 1.7 0.6 4.4 4.1
Spain EU 0.8 0.7 0.7 0.6 0.8 1.1
Swaziland1 SACU/COMESA/SADC 31.7 0.7 2.5 36.6 20.5 0.8


Source: Own computation based on the WITS Data base, 2012


It is important to note that this result indicates a strong trade flow between Uganda and


some of the regional trade partners especially Burundi, Rwanda, DRC, Sudan, Kenya, Tanzania,


Eritrea, Mauritius, Malawi, Ethiopia and Swaziland. These are either EAC partners or COMESA


member states. This implies that these countries proportionately import goods from


Uganda to which they also send most of their exports, thus a high trade intensity indicator.


Considerably, the analysis also shows high trade intensity with some EU member states such


as Spain, Netherlands and Belgium.


5.3 TradeComplementarityIndex(TCI)


The TCI measures the degree to which the export pattern of a given country matches the


import pattern of another. TCI provides the framework under which the trade flow between


two countries or groups of countries could be ascertained to be more or less compatible


despite the low or high intensity between or among them. A high degree of complementarity


is assumed to indicate more favourable prospects for a successful trade arrangement. It is


possible, for example, for the composition of two countries’ exports and imports to be similar.


Accordingly, Drysdale (1982) notes that the index takes account of the commodity composition


of the countries’ trade, and one which reflects the intensity of trade in the commodities which


are traded. The Index is specified in equation (6).


(6)


Where, is the share of good d in global exports of country s and is the share of good d in all


imports of country w. The inner bracket in the index is the sum of the absolute value of the




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Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


difference between the sectoral import shares of one country and the sectoral export shares


of the other. Dividing the summation results by two converts this to a number between 0 and


1, with zero indicating all shares matched and 1 indicating none. Subtracting from one reverses


the sign, and multiplying by 100 puts the measure in percentage terms (UN ESCAP, 2012). The


TCI results lie on the range 0-100, with 100 indicating perfect overlap (that is, export and


import shares exactly match), whereas a zero would imply that no goods are exported by one


country or imported by the other.


The results in Table 3 reveal relatively high level of complementarity of Uganda’s exports with


imports of its trade partners within the respective regional blocs of EAC, COMESA, SACU, SADC,


EU, ASEAN and USA. This suggests that Uganda’s trade profile is becoming more compatible


with these trade blocs, notably SACU, EAC, SADC, COMESA and ASEAN. This corroborates


with the results of the trade intensity analysis discussed earlier. This result could be explained


by the nature of products that Uganda exports to the respective blocs and what these blocs


import or produces locally. Uganda largely exports commodities/raw materials which do not,


to a great extend, complement the imports of EU-27 or USA from other trade partners.




Table3:TradeComplementaritybetweenUgandaanditstradingpartners


SADC EAC COMESA SACU EU-27 USA ASEAN


2005 44.8 42.1 41.5 57.6 34.9 32.9 39.9


2006 41.6 40.3 42.3 47.0 32.0 26.7 35.0


2007 37.1 39.3 35.9 45.6 30.8 30.5 33.6


2008 36.8 36.8 34.3 47.7 28.4 25.7 30.6


2009 39.8 45.5 39.3 45.4 30.6 26.5 33.6


2010 38.9 39.6 38.5 45.7 29.7 25.1 32.6


Source: Own computation based on the WITS Data base, 2012


5.4 TheshareofUganda’stradeflowsindifferentmarkets


Table A 3 - Table A 5 identify leading importers of Uganda’s products and the share of the


country’s respective commodities of imports in those markets, as well as the top three leading


exporters of the given products in these markets. It is notable that in the year 2011, coffee,


not roasted or decaffeinated formed the largest proportion of Uganda’s originated exports


to the world accounting for about 18 percent share of total exports of the top 36 products.


Switzerland provided the largest market share for coffee exports accounting for about 4.2


percent of the total exports in 2011 unlike 2010 where it was 2.6 percent share. Likewise,


coffee accounted for the greatest proportion of commodity exports from Uganda in 2010


accounting for about 13.3 percent of the top 33 major domestic exports in 2010.


In addition, Uganda’s considerable coffee markets include Switzerland, Sudan, Germany,




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Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


United Kingdom, Spain and Singapore with market shares of about 5.5 percent, 4.9 percent, 3.1


percent, 2.7 percent and 2.2 percent respectively. Other considerable markets for Uganda’s


coffee include: Italy, Poland, Belgium, Netherlands and United States of America. However,


among all the eleven markets, Uganda only features as a major player of coffee exports to


Sudan and Poland. Products like roses, whether grafted or not, as well as un-rooted cuttings


and slips are largely exported to the Netherlands taking a market share of 2.8 percent and 1.5


percent respectively. Netherlands provides the export market for more than 96 percent of the


country’s flower exports to the World. The leading exporters of roses in this market include


Poland, South Africa and China; however, Uganda emerges among the top three leading


exporters of un-rooted cuttings and slips with Kenya and China. Other major commodity


exports across the years include black tea, Portland cement, tobacco, vegetable fats and oil,


tubes and pipes and maize seed largely to the regional markets. Whereas, other commodities


including, gold, carded cotton, cobalt, roses, cocoa beans and tobacco are exported to other


markets such as the EU, South Africa, USA and United Arab Emirates.


It is equally observed that Uganda’s gold in other semi manufactured form to the United


Arab Emirates (UAE) worth US$ 111.6 million in 2009 accounted for about 15.6 percent of


the market share. The UAE market alone accounts for 99.8 percent of Uganda’s gold exports


to the world. Likewise, Uganda ranks among the leading top three exporters of gold to UAE


including Australia and Kazakhstan. The country’s second major export commodity in 2009


was black tea (fermented or partly fermented) to Kenya with a market share of about 7


percent. Similarly, Kenya accounts for more than 99 percent of Uganda’s black tea exports to


the world. This implies that Kenya is the single importer of black tea from Uganda; however,


this is attributed to the presence of the auction market at Mombasa being in Kenya. The key


players in the black tea export to Kenya include: Uganda, Malawi, Tanzania and Mozambique.


Another important export product for Uganda is carded or combed cotton. It is largely exported


to Singapore with a market share of about 2 percent and the share of the country’s total


exports to the world in this market is 69.4 percent. The major players of cotton exports in the


Singapore market are Taiwan, India and Malaysia. Other major product specific markets for


Uganda’s exports illustrated in Table A 3 - Table A 5 include: South Africa, Kenya, Democratic


Republic of Congo, Rwanda and Sudan for products such as; tobacco, maize corn seed, flat


rolled metallic bars, vegetable fats and oil, soap and beer made from malt. In essence Uganda


exports are dominated by regional countries and a few countries in Europe, Middle East and


Asia.


Table 6 in Appendix A1 identifies Uganda’s share of exports to top thirty respective trading


partners. It further illustrates the growth rate of Uganda’s exports to those respective


markets, as well as the share of the respective partner countries in world imports. In the first


instance, the results in this table correlate well with that in the previous analysis. Uganda’s




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key major markets include, inter alia, Sudan17 which accounts for 12.9 percent, Kenya (11.8


percent), DR Congo (11.4 percent), Rwanda (9.3 percent) and these countries constitute the


EAC and COMESA trade blocs where Uganda is both a partner and a member respectively.


Other considerable export markets for Uganda include: United Arab Emirates which account


for about 7.5 percent of the country’s share of export markets, Netherlands (5.6 percent),


Germany (4.5 percent), Switzerland (3.6 percent) and Burundi (3.2 percent) among others.


The markets where Uganda registered more than a 20 percent growth rate penetration within


the period 2006-2010 include Rwanda (44 percent), DR Congo (39 percent), China (28 percent),


Italy (27 percent), Tanzania (24 percent), Burundi (23 percent), Kenya (20 percent) and Sudan


(21 percent). This trend suggests that China in the East is the emerging potential market for


Uganda’s exports while it also reflects strong regional trade considering the proportion of


the EAC partner states’ growth rate. In addition, it further justifies the last column in Table


6 (Appendix A1) which illustrates China as among the top importers second to United States


of America (USA) in the world with the greatest share of imports. Where USA accounts for


12.9 percent of world imports, China comes second with 9.2 percent share, hence a potential


export market. Other countries with big export shares include Germany (7 percent), France


(3.9 percent), United Kingdom (3.7 percent), Italy (3.2 percent), Hong Kong China (2.9 percent),


and Netherlands (2.9 percent).


On the other hand, Table A 7 illustrates more importantly the trade partner’s share in


Uganda’s imports and the import growth rate of the respective trade partners. The analysis


shows that India is the leading exporter to Uganda with 14.7 percent market share. Other


leading exporters include: Kenya (11 percent), China (8.9 percent), United Arab Emirates (8.4


percent), Japan (6.6 percent), South Africa (5.4 percent) and South Arabia (5.1 percent). This


suggests that within the two regional trade blocs of EAC and COMESA where Uganda is both


a partner and a member, Kenya is the only largest single exporter to Uganda. Tanzania and


Egypt that emerged among the leading thirty (30) exporters to Uganda, only accounted for 1.2


percent and 1 percent respectively in the share of Uganda’s imports. Asia presents the bigger


share in Uganda’s imports by more than 50 percent of total imports. This perhaps suggests


that there is less trade within the current regional trade groupings in Eastern Africa. Table A 7


further shows trade partners whose share in Uganda’s imports registered substantive growth


within the period 2006 to 2010. Those that registered a growth rate of more than 20 percent


include: Indonesia with 75 percent, Kuwait (62 percent), Saudi Arabia (54 percent), Republic


of Korea (36 percent), India (32 percent), China (29 percent) and Netherlands (26 percent).


17 This includes South Sudan




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6. GRAVITYMODELESTIMATIONRESULTS


This section presents and discusses the estimation results. The ultimate purpose of this


study is to compare the performance of Uganda’s intra-EAC trade with that of the other


trading blocs/regions. To this end, care is taken to examine the levels of significance and


coefficients of the estimations representing the different blocs/regions, to make comparisons.


In addition, pertinent variables are interpreted in respect of their impact on Uganda’s overall


trade - imports and exports. The results are based on the different estimations undertaken as


discussed in section 4.2.1. The discussion is based on the static RE and dynamic RE. We omit


the results of the IV GMM (details in Table A8 in the Appendix) in the discussion because they


are similar to those of the dynamic RE.


6.1 Exports–theStaticREandDynamicREModels


The results for exports (Table 4) demonstrate that an increase in the importer’s income


(per capita GDP) have significant (<1 percent) relationship with Uganda’s exports. Whereas


increasing the importer’s GDP by 10 percent will lead to a 3.9 percent increase in Uganda’s


exports (RE), it is 3.3 percent under the dynamic RE model. Similarly, the population of the


importer has a significant impact on Uganda’s export at less than one percent level for both


the RE and dynamic RE. Increasing the importer’s population by 10 percent leads to 7.7


percent in Uganda’s exports (RE) and 6 percent under the dynamic RE model. The coefficients


of the two variables are positive as anticipated and highly statistically significant which


is consistent with the theoretical expectation of the gravity models. This suggests that an


increase in the income and population of Uganda’s trading partners leads to increase in the


amount that Uganda exports. Since Uganda’s export destination is dominated by the regional


countries (neighbours), specifically, belonging to COMESA and the EAC, growth in their GDP


and population is very important to the country’s exports.


As expected the lagged exports added to the list of predictor variables is statistically significant


(less than 1 percent), moreover with the expected positive sign and the coefficient is high.


This suggests that lagged exports exert a positive and highly significant impact on current


export flows. It is argued that trade relations to increase trade once cultivated are likely


to respond with time suggesting that exports in the previous year impact on exports in the


current year. In the context of the analysis, the different trade agreements signed by Uganda


and the country’s trading partners are taking effect. This implies that the growth of Uganda’s


exports to the country’s trading partners is positive and is determined by previous exports.




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Table4:Resultsfortheexports-2001-2009(Randomeffect,DynamicRandomEffect)


Variable RE Dynamic RE


Constant -191.2 (208.7) -209.6 (240.2)


Exports lagged 0.504 (0.0233) ***
Distance -0.365 (0.34) -0.184 (0.14)
Importer’s GDP 0.393 (0.114) *** 0.330 (0.0917) ***
Importer’s population 0.774 (0.119) *** 0.604 (0.096) ***
Uganda’s GDP -3.406 (9.793) -1.522 (10.92)
Uganda’s population 11.48 (13.98) 11.93 (16.04)
Importer’s infrastructure -0.0364 (0.157) -0.107 (0.14)
Uganda’s infrastructure -0.0107 (0.876) -0.541 (0.98)
Importer’s FCR 0.393 (0.142) ** 0.142 (0.0992)
Real Exchange rate -0.116 (0.743) 0.914 (0.907)
EAC 5.724 (2.588) * 3.026 (1.061) **
Asia 2.178 (1.063) * 0.765 (0.454)
COMESA 4.286 (1.117) *** 2.269 (0.477) ***
European Union 3.795 (0.803) *** 2.079 (0.37) ***
Border 4.944 (2.227) * 2.105 (0.922) *
Island -1.265 (0.852) -0.655 (0.325) *
Locked -1.561 (0.776) * -0.22 (0.362)
Language 2.615 (0.698) *** 0.997 (0.295) ***


R squared
Overall 0.43 0.60
Between 0.60 0.91
Within 0.05 0.015


Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001


Distance has a negative coefficient and this is consistent with a priori expectation. The distance


to Uganda’s export partners negatively impacts the amount exported although in the static


RE model, it is insignificant. In the literature, distance is one of the factors that express multi-


lateral resistance terms. Targeting regional countries to mitigate difficulties associated with


distance is a reasonable policy option although this depends on the existing complementarity


among the country’s regional export trade partners. Uganda’s infrastructure and that of the


destinations for the country’s exports is not significant in the model.


The level of the foreign currency reserves of Uganda’s export trade partner has a significant


impact on the country’s exports. This suggests that foreign currency reserves difficulties in


these countries reduce Uganda’s exports. However, the real exchange rate is insignificant


which is explained by the fact that Uganda being a small country exporting largely non-


industrial products experiences the small country effect.


The variables of interest in the estimation with regard to the study objective are the dummy


variables representing the different trade blocs/regions. Results reveal that the most important


blocs are EAC, COMESA and EU with larger coefficients of 5.7, 4.3 and 3.8 respectively, and are




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highly statistically significant. This suggests that an export trading partner belonging to the


three main blocs increases Uganda’s export trade flows. It is also noted that although Uganda


is integrating the country’s export trade in the EAC region, the EU and COMESA still play a very


significant role. This suggests that Uganda’s intra EAC exports although growing remains less


in comparison to EU and COMESA combined. Even then, Uganda’s exports to the EAC region


are very significant. As exhibited in Figure 1, the share of exports to the EU has dramatically


reduced from 47 percent in 2001 to 31 percent in 2009. Asia as a region is also significant (RE)


but not as important as the other three regions. However, in the dynamic models, the Asian


region although positively related has an insignificant relationships with Uganda’s export flows.


Overall, the results of the regional blocs/regions using the dynamic models do not significantly


differ from the static RE as the EAC, COMESA and EU regions respectively maintain the highest


impact on Uganda’s export trade flows.


A set of contingent dummy variables were modelled to explain the impact of proximity,


location and communication. The dummies of border, island, land locked and language


have the expected signs, with high explanatory power and significance levels. Uganda is a


landlocked country that experiences high costs of exporting goods. The model shows that


bordering Uganda specifically, Kenya, Rwanda, Tanzania, Sudan and the Democratic Republic


of Congo increases export trade. The coefficient is about 5 and positive and the variable is


significant at 5 percent. The export destination being an Island decreases the amount of


commodities exported from Uganda to the respective destination (except for the RE model).


This is similar to an export destination being land locked (except for the dynamic RE model).


Communication has been simplified by technology and it is argued that this has increased the


volume of trade globally. The results suggest that having the same language (English) with the


export partner increases Uganda’s exports. The coefficients are positive, high and significant


at less than 1 percent. The overall picture suggested by the dummies is that Uganda’s exports


are likely to; reduce with increasing distance to an export destination, increase with proximity


to importers, reduce with poor linkages to markets and increase with ability to communicate


to importers.


The empirical question is whether the EAC regional integration is bearing fruits of deepening


trade. From the foregone discussion, it is evident that regional integration is helping to


increase Uganda’s intra-EAC regional trade. The reduction of internal tariffs, reduction


of non-tariff barriers and adoption of a common external tariff is paying off (Othieno and


Shinyekwa 2011; Shinyekwa and Mawejje 2013). The trade indicators previously discussed


further demonstrates that particular commodities have specific destinations which explains


the continued big impact of the other trading blocs on Uganda’s aside the EAC.




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6.2 Imports–theStaticREandDynamicREModels


Results in Table 5 suggest that an increase in the exporter’s income and population leads


to increase in Uganda’s imports and the two demonstrate significant gravitational forces


on Uganda’s import flows consistent with theory. The coefficients of the two variables are


positive as expected a priori and statistically significant. An increase in the exporter’s GDP by


10 percent leads to 2.8 percent imports of the partner’s goods (RE) and 2.2 percent (dynamic


RE). On the other hand, when the exporter’s population increases by 10 percent, Uganda’s


imports increase by 2.3 percent (RE) and 3.8 percent (dynamic RE).The lagged imports added


to the list of predictor (RE dynamic) variables is statistically significant (less than 1 percent),


with the expected positive signs and large coefficients. This suggests that lagged imports


exert a positive and highly significant impact on current import flows. This is not surprising


given that Uganda’s imports are currently three times the value of exports evidenced by the


persistent growing negative trade balance.


Distance has a negative coefficient and this is consistent with the a priori expectation. The


distance to Uganda’s partners impacts the amount imported negatively although in the static


RE model it is insignificant. For the dynamic RE which is significant, a 10 percent increase in


distance between Uganda and the partner reduces imports from Uganda’s partners by 2.9


percent. It is evident that although the relationship is significant, it is inelastic owing to the


nature of Uganda’s imports – mainly manufactured and intermediate goods. Uganda mainly


relies on Asia and Europe in addition to the EAC for such imports. The Middle East and Asia


have become very significant in contributing to Uganda’s imports. Uganda has experienced a


tremendous increase in imports from Asia from 26 percent in 2005 to 37 percent in 2009. The


increase in imports especially from Asia and Europe is primarily explained by the growth in


private sector imports of capital and consumer goods such as petroleum products, iron and


steel, mineral fuels, electrical machinery, pharmaceutical products and sugars.


Table5:Resultsfortheimports-2001-2009(Randomeffect,DynamicRandomEffect)


Variable RE Dynamic RE


Constant 102.2 (-183.1) 186.5 (216.3)


Lagged imports 0.614 (0.0209) ***


Distance -0.23 (0.348) -0.288 (0.125) *


Exporter’s GDP 0.282 (0.0964) ** 0.220 (0.0642) ***


Exporter’s Population 0.229 (0.0955)* 0.376 (0.063) ***


Uganda’s GDP 5.101 (9.702) 5.639 (11)


Uganda’s Population -7.156 (12.29) -12.47 (14.34)


Exporter’s Infrastructure 0.633 (0.126) *** 0.515 (0.106) ***


Uganda’s Infrastructure 0.443 (0.777) 0.544 (0.892)


Uganda’s FCR -0.282 (0.938) -0.391 (1.332)


EAC dummy 5.981 (2.701) * 2.229 (0.962) *




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Asia dummy 5.735 (0.965) *** 1.450 (0.379) ***


COMESA dummy 4.010 (1.151) *** 1.613 (0.419) ***


EU dummy 3.503 (0.807) *** 0.822 (0.317) **


Border dummy 2.45 (2.328) 0.639 (0.827)


Island dummy -3.300 (0.857) *** -0.724 (0.323) *


Locked dummy -2.735 (0.758) *** -0.53 (0.275)


Language dummy 1.880 (0.713) ** 0.633 (-0.257) *


R squared


Overall 0.382 0.673
Between 0.488 0.953


Within 0.0577 0.0217


Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001


Although as Uganda’s per capita GDP increases, imports increase, the coefficient is not


statistically significant. Similarly, Uganda’s population does not have a significant impact


on the country’s imports. Uganda’s foreign currency reserves have insignificant coefficients


in this model although they have the expected signs. The infrastructure in Uganda and the


country of source for imports have positive impact on Uganda’s imports. However, it is only the


latter’s infrastructure that is highly significant for Uganda’s import. This suggests that Uganda


imports from countries with well-developed infrastructure. Note that the bulk of Uganda’s


imports come from industrialised countries with developed infrastructure which suggests that


Uganda’s imports are a reflection of the country’s low technology. Uganda’s foreign currency


reserves have a negative impact on the country’s imports although it is insignificant.


The trading blocs/regions have positive signs as expected and have high significant levels.


However, from a comparative perspective, the magnitudes significantly differ. Whereas the


EAC is significant at 5 percent, the rest of the regions are significant at one and less than


one percent. Furthermore, Asia has the largest coefficient among the remaining three. The


results suggest that Asia remains the most dominant trading region with regard to imports


and the EAC is the least dominant when it comes to Uganda’s imports. Although Uganda has


considerably increased the volume and value of imports from the EAC partner states, owing to


technological deficits in the EAC region, all the countries still heavily rely on the industrialised


countries for high technology products. This suggests that Uganda’s intra-EAC import trade


although growing is proportionally less in comparison to Asia and EU.


The dummy variables (border, Island, land locked and language) to explain the impact of


proximity; location and communication reveal consistent results to a priori expectation.


Although the border dummy has a positive relationship with imports, it is insignificant,


explaining the limited imports from Uganda’s immediate neighbours in comparison to


the other blocs. Bordering Uganda specifically, Kenya, Rwanda, Tanzania Sudan and the


Democratic Republic of Congo has a positive but insignificant impact on Uganda’s imports.




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Being an island and/or land locked reduces imports from the respective countries, with the


former being significant. English as a language of communication also increases the amount


of imports from the respective countries and is significant. Concerning language (English).


results suggest that having the same language with the import partner increases Uganda’s


imports. The coefficients are positive and highly significant.




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Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


7. CONCLUSION AND EMERGING POLICY ISSUES


Gravity models were estimated to investigate and explain factors that determine Uganda’s


trade flows from/to the different trade blocs/regions. This was done to compare the impact


of the different trade blocs/region on Uganda’s trade flows. The testing of the intra-bloc trade


effects demonstrated positive signs and statistically significant levels suggesting that belonging


to either of them fosters trade. It is concluded that Uganda’s import and export trade flows


have conspicuously adjusted to the gravitational forces of the EAC during the progress of the


integration. There is thus compelling evidence that Uganda’s foreign trade flows are slowly


getting integrated into the EAC region. Therefore, regional integration is helping to increase


intra-regional trade. The reduction of internal tariffs, reduction of non-tariff barriers and


adoption of a common external tariff is paying off.


Whereas comparing the intra-bloc/regional effects depicts export trade being integrated more


in the EAC and COMESA regions than other trading blocs/regions, it is clear that Uganda’s


imports are more integrated in the Asian and EU blocs/region than the EAC. Strong links


remain in the other blocs outside the EAC with reasonable variations between exports and


imports arising from the nature of commodities. This is partly caused by technological deficits


in the EAC region that make it heavily rely on the industrialised countries for high technology


products while exporting primary products.


The trade indicators analysis of Uganda’s trade flows corroborates the gravity model estimation


results that during the implementation of the EAC CU, Uganda’s export trade got more


integrated into the EAC. The trade indicators also underpin the strong integration of Uganda’s


import trade into the Asian region and the EU bloc. The trade indicators further demonstrate


that Uganda exports largely primary products and imports, manufactured products.


In light of these findings:(i) Uganda should target regional destinations for the country’s exports


in addition to other blocs; (ii) Regional trade agreements should adequately be implemented


to promote intra-EAC trade; (iii) Given the composition of Uganda’s exports and imports, to


increase intra-EAC regional trade, Uganda, and other EAC partner states should attract and


channel investment in production of high technology products. This can be done through


deliberate government involvement and attraction of strategic foreign direct investment.


Additionally, Uganda should actualise the education, skills, technology development strategies


in the National Development Plan to increase the stock of skills; (iv) There is need for Uganda


and the EAC region to improve infrastructure such as roads railways to reduce transport costs


and improve on trade facilitation to boost trade. Revamping the railway system from Uganda


to Mombasa targeting reduced unit transport costs is extremely crucial.




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APPENDIX


TableA1:Uganda’sExportstothedifferenttradingblocs2001-2009(millionsUSD)


Bloc 2001 2002 2003 2004 2005 2006 2007 2008 2009 Total


AMERICAs 9,483 11,784 14,934 18,970 19,867 17,381 26,032 20,140 39,345 177,936


ASIA 46,880 37,550 40,557 53,222 56,916 66,227 67,363 95,415 96,583 560,713


COMESA 25,944 13,502 19,785 29,404 59,611 99,960 162,205 253,292 194,511 858,214


EAC 87,147 86,418 115,143 131,854 144,770 152,830 274,818 377,437 398,792 1,769,209


EU 203,322 230,800 219,375 297,175 336,499 314,716 417,757 622,016 448,566 3,090,226


OAFRICA 42,405 62,713 58,556 54,562 83,885 68,667 157,194 166,919 191,570 886,471


ROW 15,454 13,918 27,996 40,563 92,613 205,825 193,767 141,344 99,303 830,783


Source: COMTRADE. Notes: AMERICA is the AMERICAS, OAFRICA is other African countries and ROW is the rest of the world


TableA2:Uganda’sImportsfromthedifferenttradingblocs2001-2009


Bloc 2001 2002 2003 2004 2005 2006 2007 2008 2009 Total


America 44,806 45,250 93,446 144,279 137,179 109,287 159,865 197,772 192,586 1,124,470


Asia 259,705 292,491 382,083 499,558 534,222 713,439 1,133,985 1,537,664 1,576,815 6,929,962


COMESA 13,809 15,618 20,444 32,006 40,675 48,932 60,036 80,278 73,046 384,844


EAC 288,491 321,804 368,678 415,685 551,441 430,179 504,078 570,604 546,954 3,997,914


EU 234,097 212,285 273,275 329,741 414,968 587,514 835,151 1,072,882 894,547 4,854,460


OAFRICA 74,448 85,255 101,549 146,626 150,777 160,159 211,833 320,669 255,769 1,507,085


ROW 89,922 100,873 135,509 152,073 224,352 506,521 587,815 744,970 693,212 3,235,247


Source: COMTRADE. Notes: AMERICA is the AMERICAS, OAFRICA is other African countries and ROW is the rest of the world


TableA3:Uganda’sLeadingExportMarketsand3leadingCompetitors,2011


HSCODE DESCRIPTION Us Dollar Value PARTNER COUNTRY LeadingExportersinthese
markets


(%)Total
ExportShare,


0901.11.00 Coffee, not roasted or decaffeinated 90,262,732 SWITZERLAND Brazil, Colombia,Guatemala 4.2


0901.11.00 Coffee, not roasted or decaffeinated 74,897,465 GERMANY Brazil, Viet Nam, Colombia 3.5


0901.11.00 Coffee, not roasted or decaffeinated 61,657,135 SUDAN Uganda 2.9


2523.29.00 Portland cement (excl. white) 41,272,217 RWANDA Uganda, Tanzania 1.9


0902.30.00
Black tea (fermented) and partly fermented tea
in packings


36,121,820 KENYA Uganda, Tanzania, Malawi 1.7


0901.11.00 Coffee, not roasted or decaffeinated 35,635,346 ITALY Brazil, India, Viet Nam 1.7


5203.00.00 Cotton, carded or combed 34,548,029 SINGAPORE 1.6


0902.40.00
Black tea (fermented) and partly fermented
tea, nes


33,752,676 KENYA Uganda, Tanzania, Malawi 1.6


0901.11.00 Coffee, not roasted or decaffeinated 34,003,799 SPAIN Brazil, Colombia, Germany 1.6


0901.11.00 Coffee, not roasted or decaffeinated 33,585,246 BELGIUM Brazil, Colombia, France 1.6


1701.11.90 --- Other 28,919,788 SUDAN Uganda 1.3


0304.19.00 Other 29,102,928 BELGIUM 1.3


2523.29.00 Portland cement (excl. white) 25,157,827 D.R.CONGO Uganda, Tanzania 1.2


0602.10.00 Unrooted cuttings and slips 24,045,760 NETHERLANDS Kenya, Uganda, Tanzania 1.1


0901.11.00 Coffee, not roasted or decaffeinated 23,855,545 SINGAPORE 1.1


1516.20.00
Vegetable fats and oils and their fractions,
hydrogenated, et


23,254,582 RWANDA Uganda, USA, Malaysia 1.1


2523.29.00 Portland cement (excl. white) 23,164,601 SUDAN Uganda, Kenya 1.1


1511.90.30 --- Palm olein, RBD 21,144,842 RWANDA Uganda, Kenya, DRC 1.0


5203.00.00 Cotton, carded or combed 20,986,579 UK 1.0


1701.99.90 -- Other 19,644,642 SUDAN 0.9




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HSCODE DESCRIPTION Us Dollar Value PARTNER COUNTRY LeadingExportersinthese
markets


(%)Total
ExportShare,


0602.40.00 Roses 19,178,782 NETHERLANDS Kenya, Uganda, Tanzania 0.9


2203.00.90 --- Other 17,668,090 SUDAN 0.8


0901.11.00 Coffee, not roasted or decaffeinated 17,618,732 UNITED STATES Colombia, Brazil,Guatemala 0.8


8105.20.00
- Cobalt mattes and other intermediate products
of cobalt


16,362,62 NETHERLANDS Germany 0.8


1511.90.30 --- Palm olein, RBD 14,364,440 SUDAN 0.7


0304.19.00 Other 14,140,068 NETHERLANDS 0.7


7214.20.00
Iron/steel bars & rods, hot-rolled, nes, with
deformations


13,066,138 SUDAN Uganda 0.6


0901.11.00 Coffee, not roasted or decaffeinated 12,824,609 INDIA 0.6


5203.00.00 Cotton, carded or combed 12,297,914 SWITZERLAND 0.6


1801.00.00 Cocoa beans, whole or broken, raw or roasted 12,107,780 UK Cote d’Ivoire, Nigeria, Ghan 0.6


1005.10.00 Maize seed of agricultural seed for sowing 11,288,885 KENYA Uganda, Zambia, S. Africa 0.5


7214.20.00
Iron/steel bars and rods, hot-rolled, nes, with
deformations


10,940,705 D.R.CONGO Uganda, S. Africa, China 0.5


7210.41.00
Rolled iron/steel, >=600mm wide, plated. with
zinc, corrugat


10,595,550 D.R.CONGO Uganda, S. Africa, China 0.5


1701.11.90 --- Other 10,493,319 RWANDA 0.5


2401.20.00 Tobacco, partly or wholly stemmed/stripped 10,527,345 NETHERLANDS Brazil, USA, India 0.5


1516.20.00
Vegetable fats and oils and their fractions,
hydrogenated,


9,914,036 SUDAN Uganda, Kenya 0.5


Source: Calculated based on UBOs Statistics, 2012


TableA4:Uganda’sLeadingExportMarketsand3leadingCompetitors,2010


HSCODE Description Us Dollar Value Partner Country LeadingExportersInthese
Markets


Share of
Total (%)
Exports


0901.11.00 Coffee, not roasted or decaffeinated 59,181,922 GERMANY, FED RE Brazil, Peru, Viet Nam 3.7


0901.11.00 Coffee, not roasted or decaffeinated 46,919,862 SUDAN 2.9


0901.11.00 Coffee, not roasted or decaffeinated 42,666,289 SWITZERLAND
Brazil, Colombia,
Guatemala


2.6


0902.30.00
Black tea (fermented) and partly fermented tea
in packings


36,437,557 KENYA Uganda, Tanzania, Malawi 2.3


2523.29.00 Portland cement (excl. white) 33,675,646 RWANDA Uganda, Tanzania, Kenya 2.1


7108.13.00
Semi-manufactured gold (incl. gold plated with
platinum),


30,065,039 UAE
Australia, Uganda,
Kazakhsta


1.9


0902.40.00
Black tea (fermented) and partly fermented
tea, nes


27,347,652 KENYA 1.7


0901.11.00 Coffee, not roasted or decaffeinated 23,550,609 ITALY Brazil, Peru, Viet Nam 1.5


0304.19.00 Other 22,364,387 BELGIUM


0602.40.00 Roses 20,146,229 NETHERLANDS 1.2


0901.11.00 Coffee, not roasted or decaffeinated 20,070,799 SPAIN Brazil, Peru Viet Nam 1.2


2523.29.00 Portland cement (excl. white) 19,978,156 D.R.CONGO Uganda, Tanzania, Kenya 1.2


1701.11.90 --- Other 19,029,746 SUDAN Kenya, Uganda, Tanzania 1.2


0602.10.00 Un rooted cuttings and slips 18,866,424 NETHERLANDS 1.2


0901.11.00 Coffee, not roasted or decaffeinated 16,467,494 BELGIUM 1.0


0304.19.00 Other 15,537,661 NETHERLANDS 1.0


2401.20.00 Tobacco, partly or wholly stemmed/stripped 15,485,136 KENYA Uganda, DRC 1.0


1516.20.00
Vegetable fats and oils and their fractions,
hydrogenated,


14,441,369 RWANDA Uganda, Kenya, Zimbabwe 0.9


1801.00.00 Cocoa beans, whole or broken, raw or roasted 14,022,824 UNITED KINGDOM
Cote d’Ivoire, Nigeria,
Ghana


0.9


2203.00.90 --- Other 13,811,587 SUDAN 0.9




35Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


HSCODE Description Us Dollar Value Partner Country LeadingExportersInthese
Markets


Share of
Total (%)
Exports


1701.11.90 --- Other 13,256,385 D.R.CONGO 0.8


1701.11.90 --- Other 12,720,026 RWANDA 0.8


8105.20.00
Cobalt mattes & other intermediate pdts of
cobalt & article


11,137,740 NETHERLANDS Germany 0.7


2523.29.00 Portland cement (excl. white) 10,506,407 SUDAN 0.6


7214.20.00
Iron/steel bars & rods, hot-rolled, nes, with
deformations/


10,241,149 D.R.CONGO Uganda, S. Africa, Tanzania 0.6


0304.19.00 Other 10,018,127 SPAIN 0.6


7306.90.00
Tubes, pipes and hollow profiles, riveted, of
iron or steel,


9,498,074 D.R.CONGO Uganda, S. Africa, Tanzania 0.6


1511.90.30 --- Palm olein, RBD 9,446,354 RWANDA Uganda, Kenya, DRC 0.6


2401.20.00 Tobacco, partly or wholly stemmed/stripped 9,290,404 NETHERLANDS Brazil, USA, India 0.6


1005.10.00 Maize seed of agricultural seed for sowing 9,286,938 KENYA
Uganda, Zambia, South
Africa


0.6


0901.11.00 Coffee, not roasted or decaffeinated 9,208,747 UNITED STATES 0.6


0401.20.00
Milk & cream of >1% but =<6% fat, not
concentrated or sweetened


7,555,438 KENYA 0.5


1005.90.00 Other maize (not seeds), and corn 7,501,704 KENYA 0.5


Source: Calculated based on UBOs Statistics, 2012


TableA5:Uganda’sshareofleadingexportmarketsbyproduct


Product
code


Productdescription Leading
importing
country from
Uganda


Leading
importer’s
imports from
Uganda. Value
2009,US$‘000


Share of
Leading
importer’s
imports from
Uganda,2009
(%)


Marketshare
of Uganda’s
Exportstothe
World,2009
(%)


Top3leadingexportersto
the leading importer


710813
Gold in other semi-manufactured form
n-monetary(inc gold plated w platinum)


UAE 111,595 15.6 99.8
Australia, Uganda,
Kazakhstan


90240
Black tea (fermented) & partly fermented
tea in packages exceeding 3 kg


Kenya 41,321 5.8 99.8 Uganda, Tanzania, Malawi


90111 Coffee, not roasted, not decaffeinated Switzerland 39,632 5.5 21.1 Brazil, Colombia, Guatemala


90111 Coffee, not roasted, not decaffeinated Sudan 35,356 4.9 18.8 Uganda


90111 Coffee (excl. roasted and decaffeinated) Germany 22,572 3.1 12.0 Brazil, Viet Nam, Colombia


60240 Roses, whether or not grafted Netherlands 20,074 2.8 96.4 Poland, South Africa, China


90111 Coffee (excl. roasted and decaffeinated) UK 19,525 2.7 10.4 Colombia, Viet Nam, Brazil


90111 Coffee, not roasted, not decaffeinated Singapore 15,517 2.2 8.3 Indonesia, Taiwan, Malaysia


90111 Coffee (excl. roasted and decaffeinated) Spain 14,489 2.0 7.7 Brazil, Colombia, Germany


520300 Cotton, carded or combed Singapore 13,791 1.9 69.4 Taiwan, India, Malaysia


60210 Un-rooted cuttings and slips Netherlands 10,773 1.5 96.5 China, Kenya, Uganda


710812 Gold in unwrought forms non-monetary UAE 10,424 1.5 97.0 USA, Turkey, Canada


90230
Black tea (fermented) & partly in packages
not exceeding 3 kg


Kenya 8,560 1.2 99.9 Uganda, Mozambique, UK


90111 Coffee (excl. roasted and decaffeinated) Italy 8,359 1.2 4.4 Brazil, India, Viet Nam


220300 Beer made from malt Sudan 6,683 0.9 75.7
Uganda, Netherlands, S.
Africa


240120
Tobacco, partly or wholly stemmed or
stripped, unmanufactured


South Africa 6,519 0.9 29.0 Brazil, Zimbabwe, Malawi


151620
Vegetable fats &oils &
fractionshydrogenated,cinter/re-
esterifid,etc,refined/not


Rwanda 6,156 0.9 52.5 Uganda, USA, Malaysia


260500 Cobalt ores and concentrates Belgium 6,016 0.8 33.3 Netherlands, Germany,


240120
Tobacco, partly or wholly stemmed or
stripped,


Germany 4,999 0.7 22.2 USA, Brazil, Malawi


260500 Cobalt ores and concentrates Netherlands 4,918 0.7 27.2 Germany,


90111 Coffee (excl. roasted and decaffeinated) Poland 4,710 0.7 2.5 Viet Nam, Brazil, Uganda


90111 Coffee (excl. roasted and decaffeinated) Belgium 4,698 0.7 2.5 Brazil, Colombia, France


100510 Maize (corn) seed Kenya 4,505 0.6 40.4
Uganda, Zambia, South
Africa


90111 Coffee (excl. roasted and decaffeinated) Netherlands 4,325 0.6 2.3 Brazil, Viet Nam, Honduras




36 Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


Product
code


Productdescription Leading
importing
country from
Uganda


Leading
importer’s
imports from
Uganda. Value
2009,US$‘000


Share of
Leading
importer’s
imports from
Uganda,2009
(%)


Marketshare
of Uganda’s
Exportstothe
World,2009
(%)


Top3leadingexportersto
the leading importer


100590 Maize (corn) nes Kenya 4,234 0.6 87.5
Mexico, Uganda, South
Africa


721041
Flat rolled prod,i/nas,pltd or ctd w
zinc,corrugated,>/=600m wide,nes


Rwanda 4,057 0.6 30.9 Uganda,


90111 Coffee, not roasted, not decaffeinated USA 3,809 0.5 2.0 Colombia, Brazil, Guatemala


721041
Flat rolled prod,i/nas,pltd or ctd w
zinc,corrugated,>/=600m wide,nes


DRC 3,540 0.5 26.9
Uganda, South Africa,
Zambia


340119
Soap & orgn surf prep, shapd, nes; papers
& nonwovens


DRC 3,330 0.5 65.7
Uganda, South Africa,
Tanzania


Source: MacMap Calculation based on UNCTAD COMTRADE, 2012


TableA6:Uganda’sExportMarketShare


Importers Exported value
2010 (US$ ‘000)


Trade balance
2010 (US$


‘000)


Share in
Uganda’s
exports (%)


Exported growth in
value 2006-2010 (%
p.a.)


Ranking
of partner
countries
in world
imports


Share of partner
countries in world
imports (%)


World 1,618,603 -3,045,735 100.0 13 100.0


Sudan 208,567 204,739 12.9 20 108.0 -


Kenya 190,301 -321,230 11.8 21 90.0 0.1


DR Congo 183,992 176,714 11.4 39 138.0 -


Rwanda 149,345 141,956 9.2 44 173.0 -


UAE 120,889 -270,151 7.5 -15 27.0 1.0


Netherlands 89,865 -43,333 5.6 10 10.0 2.9


Germany 73,641 -54,937 4.5 12 4.0 7.0


Area Nes 72,182 72,182 4.5 26


Switzerland 57,536 38,459 3.6 5 25.0 1.2


Burundi 51,333 50,246 3.2 23 192.0 -


Belgium 41,834 6,995 2.6 -1 13.0 2.6


Tanzania 37,612 -18,916 2.3 24 106.0 0.1


United Kingdom 36,871 -97,598 2.3 4 7.0 3.7


Spain 36,394 26,719 2.2 13 14.0 2.1


Italy 31,389 -37,743 1.9 27 8.0 3.2


Singapore 23,983 -65,964 1.5 -7 15.0 2.0


China 21,988 -392,670 1.4 28 3.0 9.2


USA 21,442 -84,088 1.3 16 2.0 12.9


H. K, China 18,865 -16,690 1.2 14 9.0 2.9


India 13,905 -670,505 0.9 76 17 1.8


Poland 12,688 -16,156 0.8 33 24 1.2


France 12,370 -52,135 0.8 -23 6 3.9


Viet Nam 11,140 -1,635 0.7 26 33 0.6


Portugal 10,768 10,420 0.7 16 38 0.5


South Africa 10,269 -240,115 0.6 7 36 0.5


Israel 6,889 -898 0.4 -11 45 0.4


Denmark 6,503 -12,425 0.4 113 35 0.6


Turkey 5,477 -17,621 0.3 43 21 1.2


Russian Fed. 5,226 -44,173 0.3 37 19 1.6


Somalia 3,720 3,720 0.2 184 0


Source: ITC Calculation based on UNCTAD COMTRADE, 2012




37Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


Table A 7:Trade Partners’ Share in Uganda’s Imports


Exporters Imported value
2010(US$‘000)


Trade balance
2010(US$‘000)


Share in Uganda’s
imports (%)


Imported
growth
invalue2006-10
(%,p.a.)


Rankingofpartner
countriesinworld
exports


Share of
partner
countries in
worldexports
(%)


World 4,664,338 -3,045,735 100 15 100


India 684,410 -670,505 14.7 32 19 1.5


Kenya 511,531 -321,230 11 6 104 0


China 414,658 -392,670 8.9 29 1 10.5


UAE 391,040 -270,151 8.4 4 28 1


Japan 305,533 -303,170 6.6 14 4 5.1


South Africa 250,384 -240,115 5.4 12 39 0.5


Saudi Arabia 239,295 -238,990 5.1 54 17 1.7


U.K 134,469 -97,598 2.9 3 10 2.7


Netherlands 133,198 -43,333 2.9 26 6 3.3


Germany 128,578 -54,937 2.8 13 3 8.5


Indonesia 113,541 -112,341 2.4 75 27 1


USA 105,530 -84,088 2.3 2 2 8.5


Malaysia 100,507 -99,461 2.2 18 21 1.3


Singapore 89,947 -65,964 1.9 24 14 2.3


Rep. of Korea 80,660 -77,091 1.7 36 7 3.1


Italy 69,132 -37,743 1.5 19 8 3


France 64,505 -52,135 1.4 17 5 3.4


Kuwait 62,523 -62,522 1.3 62 46 0.4


Tanzania 56,528 -18,916 1.2 18 113 0


Brazil 50,325 -50,093 1.1 68 22 1.3


Russian Fed. 49,399 -44,173 1.1 8 12 2.7


Thailand 49,003 -48,901 1.1 27 24 1.3


Ukraine 48,345 -46,807 1 28 52 0.3


Sweden 45,508 -43,364 1 -2 26 1.1


Egypt 44,952 -44,017 1 27 61 0.2


H. K, China 35,555 -16,690 0.8 11 11 2.7


Belgium 34,839 6,995 0.7 -3 9 2.7


Poland 28,844 -16,156 0.6 56 25 1.1


Pakistan 26,500 -26,434 0.6 22 66 0.1


Turkey 23,098 -17,621 0.5 19 33 0.8


Source: ITC Calculation based on UNCTAD COMTRADE, 2012




38 Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


TableA8:Resultsfortheexports-2001-2009(Randomeffect,DynamicRandomEffectand


the IV GMM)


Variable RE Dynamic RE IV GMM


Constant -191.2 (208.7) -209.6 (240.2) -213 (238.1)


Exports Lagged 0.504 (0.0233) *** 0.504 (0.0275) ***


Distance -0.365 (0.34) -0.184 (0.14) -0.184 (0.218)


Importer’s GDP 0.393 (0.114) *** 0.330 (0.0917) *** 0.331 (0.102) **


Importer’s Population 0.774 (0.119) *** 0.604 (0.096) *** 0.604 (0.0878) ***


Uganda’s GDP -3.406 (9.793) -1.522 (10.92) -1.602 (11.19)


Uganda’s Population 11.48 (13.98) 11.93 (16.04) 12.15 (16.06)


Importer’s Infrastructure -0.0364 (0.157) -0.107 (0.14) -0.107 (0.152)


Uganda’s Infrastructure -0.0107 (0.876) -0.541 (0.98) -0.549 (0.955)


Importer’s FCR 0.393 (0.142) ** 0.142 (0.0992) 0.142 (0.104)


Real Exchange rate -0.116 (0.743) 0.914 (0.907) 0.914 (1.022)


EAC dummy 5.724 (2.588) * 3.026 (1.061) ** 3.026 (0.843) ***


Asia dummy 2.178 (1.063) * 0.765 (0.454) 0.765 (0.444)


COMESA dummy 4.286 (1.117) *** 2.269 (0.477) *** 2.268 (0.47) ***


EU dummy 3.795 (0.803) *** 2.079 (0.37) *** 2.079 (0.366) ***


Border dummy 4.944 (2.227) * 2.105 (0.922) * 2.105 (0.612) ***


Island dummy -1.265 (0.852) -0.655 (0.325) * -0.655 (0.321) *


Locked dummy -1.561 (0.776) * -0.22 (0.362) -0.221 (0.366)


Language dummy 2.615 (0.698) *** 0.997 (0.295) *** 0.997 (0.268) ***


R squared 0.60


Overall 0.43 0.60


Between 0.60 0.91


Within 0.05 0.015


Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001


TableA9:Resultsfortheimports-2001-2009(Randomeffect,DynamicRandomEffectand


the IV GMM)


Variable RE Dynamic RE IVGMM


Constant 102.2 (-183.1) 186.5 (-216.3) 188.5 (-207.4)


Lagged imports 0.614 (-0.0209) *** 0.614 (-0.0259) ***


Distance -0.23 (-0.348) -0.288 (-0.125) * -0.288 (-0.169)


Exporter’s GDP 0.282 (-0.0964) ** 0.220 (-0.0642) *** 0.220 (-0.0791) **


Exporter’s Population 0.229 (-0.0955)* 0.376 (-0.063) *** 0.377 (-0.0785) ***


Uganda’s GDP 5.101 (-9.702) 5.639 (-11) 5.653 (-10.78)


Uganda’s Population -7.156 (-12.29) -12.47 (-14.34) -12.6 (-13.68)


Exporter’s Infrastructure 0.633 (-0.126) *** 0.515 (-0.106) *** 0.515 (-0.123) ***


Uganda’s Infrastructure 0.443 (-0.777) 0.544 (-0.892) 0.549 (-0.871)


Uganda’s FCR -0.282 (-0.938) -0.391 (-1.332) -0.386 (-1.383)


EAC dummy 5.981 (-2.701) * 2.229 (-0.962) * 2.229 (-0.596) ***


Asia dummy 5.735 (-0.965) *** 1.450 (-0.379) *** 1.450 (-0.328) ***




39Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


COMESA dummy 4.010 (-1.151) *** 1.613 (-0.419) *** 1.613 (-0.402) ***


EU dummy 3.503 (-0.807) *** 0.822 (-0.317) ** 0.822 (-0.293) **


Border dummy 2.45 (-2.328) 0.639 (-0.827) 0.639 (-0.458)


Island dummy -3.300 (-0.857) *** -0.724 (-0.323) * -0.724 (-0.337) *


Locked dummy -2.735 (-0.758) *** -0.53 (-0.275) -0.53 (-0.297)


Language dummy 1.880 (-0.713) ** 0.633 (-0.257) * 0.633 (-0.232) **


R squared 0.673


Overall 0.382 0.673


Between 0.488 0.953


Within 0.0577 0.0217


Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001




40 Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


EPRC RESEARCH SERIES


Listing of EPRC Research Series published since 2005 to date. Full text format of these and


earlier papers can be downloaded from the EPRC website at www.eprc.or.ug


Series No. Author(s) Title Date


98 Annet Adong,
Francis Mwaura
Geofrey Okoboi


What factors determine membership to farmer
groups in Uganda? Evidence from the Uganda
Census of Agriculture 2008/9.


May 2012


97 Geoffrey B.
Tukahebwa


The Political Context of Financing Infrastructure
Development in Local Government: Lessons from
Local Council Oversight Functions in Uganda


December
2012


96 Sarah Ssewanyana
And
Ibrahim Kasirye


Causes Of Health Inequalities In Uganda: Evidence
From The Demographic And Health Surveys


October
2012


95 Ibrahim Kasirye HIV/AIDS Sero-Prevalence And Socioeconomic
Status:


September
2012


94 Ssewanyana Sarah
and Kasirye Ibrahim


Poverty And Inequality Dynamics In Uganda:
Insights From The Uganda National Panel Surveys
2005/6 And 2009/10


September
2012


93 Othieno Lawrence &
Dorothy Nampewo


Opportunities And Challenges In Uganda’s Trade In
Services


July 2012


92 Annet Kuteesa East African Regional Integration: Challenges In
Meeting The Convergence Criteria For Monetary
Union: A Survey


June 2012


91 Mwaura Francis and
Ssekitoleko Solomon


Reviewing Uganda’s Tourism Sector For Economic
And Social Upgrading


June 2012


90 Shinyekwa Isaac A Scoping Study Of The Mobile Telecommunications
Industry In Uganda


June 2012


89 Mawejje Joseph
Munyambonera Ezra
Bategeka Lawrence


Uganda’s Electricity Sector Reforms And Institutional
Restructuring


June 2012


88 Okoboi Geoffrey and
Barungi Mildred


Constraints To Fertiliser Use In Uganda: Insights
From Uganda Census Of Agriculture 2008/9


June 2012


87 Othieno Lawrence
Shinyekwa Isaac


Prospects And Challenges In The Formation Of The
Comesa-Eac And Sadc Tripartite
Free Trade Area


November
2011


86 Ssewanyana Sarah,
Okoboi Goeffrey &
Kasirye Ibrahim


Cost Benefit Analysis Of The Uganda Post Primary
Education And Training Expansion And Improvement
(Ppetei) Project


June 2011


85 Barungi Mildred
& Kasirye Ibrahim


Cost-Effectiveness Of Water Interventions: The Case
For Public Stand-Posts And Bore-Holes In Reducing
Diarrhoea Among Urban Households In Uganda


June 2011


84 Kasirye Ibrahim &
Ahaibwe Gemma


Cost Effectiveness Of Malaria Control Programmes
In Uganda: The Case Study Of Long Lasting
Insecticide Treated Nets (Llins) And Indoor Residual
Spraying


June 2011


83 Buyinza Faisal Performance And Survival Of Ugandan
Manufacturing Firms In The Context Of The East
African Community


September
2011




41Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


Series No. Author(s) Title Date


82 Wokadala James,
Nyende Magidu,
Guloba Madina &
Barungi Mildred


Public Spending In The Water Sub-Sector In Uganda:
Evidence From Program Budget Analysis


November
2011


81
Bategeka Lawrence
&Matovu John Mary


Oil Wealth And Potential Dutch Disease Effects In
Uganda


June 2011


80 Shinyekwa Isaac &
Othieno Lawrence


Uganda’s Revealed Comparative Advantage: The
Evidence With The Eac And China


September
2011


79 Othieno Lawrence &
Shinyekwa Isaac


Trade, Revenues And Welfare Effects Of The Eac
Customs Union On Uganda: An Application Of Wits-
Smart Simulation Model, Eprc Research Series


April 2011


78 Kiiza Julius, Bategeka
Lawrence &
Ssewanyana Sarah


Righting Resources-Curse Wrongs In Uganda: The
Case Of Oil Discovery And The Management Of
Popular Expectations


July 2011


77 Guloba Madina,
Wokadala James &
Bategeka Lawrence


Does Teaching Methods And Availability Of Teaching
Resources Influence Pupil’s Performance?: Evidence
From Four Districts In Uganda


August
2011


76 Okoboi Geoffrey,
Muwanika Fred,
Mugisha Xavier &
Nyende Majidu


Economic And Institutional Efficiency Of The
National Agricultural Advisory Services’ Programme:
The Case Of Iganga District


2011


75 Okumu Luke & Okuk
J. C. Nyankori


Non-Tariff Barriers In Eac Customs Union:
Implications For Trade Between Uganda And Other
Eac Countries


December
2010


74 Kasirye Ibrahim &
Ssewanyana Sarah


Impacts And Determinants Of Panel Survey Attrition:
The Case Of Northern Uganda Survey 2004-2008


April 2010


73 Twimukye Evarist,
Matovu John Mary
Sebastian Levine &
Birungi Patrick


Sectoral And Welfare Effects Of The Global
Economic
Crisis On Uganda: A Recursive Dynamic Cge Analysis


July 2010


72 Okidi John
& Nsubuga Vincent


Inflation Differentials Among Ugandan Households:
1997 - 2007


June 2010


71 Hisali Eria Fiscal Policy Consistency And Its Implications For
Macroeconomic Aggregates: The Case Of Uganda


June 2010


70 Ssewanyana Sarah &
Kasirye Ibrahim


Food Security In Uganda: A Dilemma To Achieving
The Millennium Development Goal


July 2010


69 Okoboi Geoffrey Improved Inputs Use And Productivity In Uganda’s
Maize Sector


March
2010


68 Ssewanyana Sarah &
Kasirye Ibrahim


Gender Differences In Uganda: The Case For Access
To Education And Health Services


May 2010


67 Ssewanyana Sarah Combating Chronic Poverty In Uganda: Towards A
New Strategy


June 2010


66 Sennoga Edward &
Matovu John Mary


Public Spending Composition And Public Sector
Efficiency: Implications For Growth And Poverty
Reduction In Uganda


February.
2010


65 Christopher Adam The Conduct Of Monetary Policy In Uganda: An
Assessment


September
2009




42 Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


Series No. Author(s) Title Date


64 Matovu John Mary,
Twimukye Evarist,
Nabiddo Winnie &
Guloba Madina


Impact Of Tax Reforms On Household Welfare May 2009


63 Sennoga Edward,
Matovu John Mary &
Twimukye Evarist


Tax Evasion And Widening The Tax Base In Uganda May 2009


62 Twimukye Evarist &
Matovu John


Macroeconomic And Welfare Consequences Of High
Energy Prices


May 2009


61 Matovu John &
Twimukye Evarist


Increasing World Food Price: Blessing Or Curse? May 2009


60 Sennoga Edward,
Matovu John &
Twimukye Evarist


Social Cash Transfers For The Poorest In Uganda May 2009


59 Twimukye Evarist,
Nabiddo Winnie &
Matovu John


Aid Allocation Effects On Growth And Poverty: A Cge
Framework


May 2009


58 Bategetka Lawrence,
Guloba Madina &
Kiiza Julius


Gender And Taxation: Analysis Of Personal Income
Tax (PIT)


April 2009


57 Ssewanyana Sarah Gender And Incidence Of Indirect Taxation: Evidence
From Uganda


April 2009


56 Kasirye Ibrahim &
Hisali Eria


The Socioeconomic Impact Of HIV/AIDS On
Education Outcomes In Uganda: School Enrolment
And The Schooling Gap In 2002/03


November
2008


55 Ssewanyana Sarah &
Okidi John


A Micro Simulation Of The Uganda Tax System
(UDATAX) And The Poor From 1999 To 2003


October
2008


54 Okumu Mike,
Nakajjo Alex & Isoke
Doreen


Socioeconomic Determinants Of Primary Dropout:
The Logistic Model Analysis


February.
2008


53 Akunda Bwesigye
Denis


An Assessment Of The Casual Relationship Between
Poverty And Hiv/Aids In Uganda


September.
2007


52 Rudaheranwa
Nichodemus, Guloba
Madina & Nabiddo
Winnie


Costs Of Overcoming Market Entry Constraints To
Uganda’s Export-Led Growth Strategy


August
2007


51 Kasirye Ibrahim Vulnerability And Poverty Dynamics In Uganda,
1992-1999


August
2007


50 Sebaggala Richard Wage Determination And Gender Discrimination In
Uganda


May 2007


49 Ainembabazi J.
Herbert


Landlessness Within The Vicious Cycle Of Poverty In
Ugandan Rural Farm Household: Why And How It Is
Born?


May 2007


48 Obwona Marios &
Ssewanyana Sarah


Development Impact Of Higher Education In Africa:
The Case Of Uganda


January
2007


47 Abuka Charles, Egesa
Kenneth, Atai Imelda
& Obwona Marios


Firm Level Investment: Trends, Determinants And
Constraints


March
2006




43Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis


Series No. Author(s) Title Date


46 Okidi A. John,
Ssewanyana Sarah
Bategeka Lawrence
& Muhumuza Fred


Distributional And Poverty Impacts Of Uganda’s
Growth: 1992 To 2003


December
2005


45 Okidi John A ,
Ssewanyana Sarah,
Bategeka Lawrence
& Muhumuza Fred


Growth Strategies And Conditions For Pro-Poor
Growth: Uganda’s Experience


December
2005


44 Obwona Marios ,
Wasswa Francis &
Nambwaayo Victoria


Taxation Of The Tobacco Industry In Uganda: The
Case For Excise Duty On Cigarettes


November
2005


43 Obwona Marios &
Ndhaye Stephen


Do The HIPC Debt Initiatives Really Achieve The
Debt Sustainability Objectives? Uganda’s Experience


August
2005


42 Rudaheranwa
Nichodemus


Trade Costs Relating To Transport Barriers On
Uganda’s Trade


May 2005




44 Economic Policy Research Centre - EPRC


Comparing the Performance of Uganda’s Intra-East African Community Trade and Other Trading Blocs: A Gravity Model Analysis







Economic Policy Research Centre


Plot 51, Pool Road, Makerere University Campus


P.O. Box 7841, Kampala, Uganda


Tel: +256-414-541023/4, Fax: +256-414-541022


Email: eprc@eprc.or.ug, Web: www.eprc.or.ug




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