A WITS partial equilibrium model is used to perform simulations on Uganda’s trade and revenue effects with the EAC countries, DRC and Sudan using highly disaggregated HSC six-digit level trade data. At the zero percent tariff rate, tariff revenue effects in all country cases were small. At the 25 percent tariff rate, tariff revenue effects in all country cases simulated were large. This indicates that the revenue implications of changes of applied rates depend on the applied tariff rate on imports. High tariff rates show larger revenue effects, while low tariff rates show lower revenue effects. However higher tariff rates show lower trade volumes and lower tariff rates high trade volumes.
Trade and tariff revenue effects are an important area of study for low incomes countries liberalizing their trade regimes. In regard to Uganda, the EAC customs union 2006 to 2010 stands out as a major trade liberalization period. The EAC Customs Union eliminated intra-regional tariff and adopted a Common External Tariff (CET) for EAC member states. This raises a lot of questions given that the partner countries are at different levels of economic development and considerably rely on customs tariffs for trade and tax revenue. Uganda’s elimination of intra-regional tariff would have its economic performance harmed with respect to trade and tariff revenue, [
According to [
This study therefore contributes to the debate on trade creation, trade diversion and revenue effects in trade literature by undertaking a quantitative study that investigates these effects for Uganda. This study presents empirical analysis of trade effect and revenue effects for Uganda under alternative tariff liberalization scenarios. The alternative policy scenarios can be broadly seen as encompassing regional tariff liberalization options under the EAC framework. The valuations provide insights into questions such as what are the advantages of Uganda entering an Economic Partnership Agreement (EPA) with DRC Congo and Sudan, which are Uganda’s key trading partners. What are the potential gains of further and more comprehensive regional liberalization with these countries? This study uses a partial equilibrium model to analyze the alternative tariff liberalization options using highly disaggregated harmonized commodity coding system (HS) six-digit level trade data.
Theoretical work on trade and revenue effects was introduced by [
Viner’s framework and subsequent theoretical modifications of the framework have provided a methodology for the analysis of both trade and welfare effects. The outcome from formation of a customs union is empirically evaluated by estimating the net effect of trade creation and trade diversion effects. In the analysis, if trade creation exceeds trade diversion then a customs union is considered to be advantageous and welfare enhancing. On the other hand if trade diversion exceeds trade creation then a customs union is considered to be disadvantageous and welfare decreasing. Viner’s theoretical framework uses comparative static analysis that is based on a series of restrictive assumptions. The restrictive assumptions facilitate in the estimation of resource allocation, welfare changes and the trends in trade flows. The assumptions are drawn from works of [
The challenge to Viner original theoretical framework is his inability to estimate economies of scale, imperfect competition and terms of trade issues. The three issues were left unattended to, despite having been recognised by Viner. The studies that have preceded Viner’s work have dealt with the challenges pointed out by Viner, in addition to covering other topics of trade liberalization. For example [
Another study that modified Viner’s work is a study by [
Ref [
The literature initiated by Viner and substantially enriched by [
In summary trade liberations can be fully estimated by trade creation and trade diversion effects as proposed by [
There are mainly three methods commonly used in empirical studies of trade agreements and trade liberalization these include econometrics models, computable general equilibrium (CGE) models and partial equilibrium (PE) models. The methodologies are employed depending on the study to be under taken either ex-ante or ex-post. The ex-ante analysis simulates the likely outcomes of the probable post-liberalization scenarios. While ex-post analysis is performed after implementation of trade liberalization and use historical data to ascertain the outcomes of liberalization. The objective of ex-ante and ex-post analysis is to estimate trade flows of a hypothetical condition, termed as the anti-monde, based on the absence of a trade agreement and compare them to conditions prevalent after trade liberalization. The three methodologies are discussed below.
Econometrics models particularly gravity models were introduced by [
Gravity models have proven effective in predicting trade flows and have exhibited good explanatory power. The model has also proven to be easy to work with, as it is clear, simple and intuitive. In addition, data requirements are fairly limited and these data are usually available. However, some features of gravity models have raised questions about its validity as a methodology. The primary critique levelled has been that they lack a theoretical foundation based on traditional international trade theory [
Computable General Equilibrium (CGE) models are constructed on the basis of general equilibrium theory and offer a framework of analysis that is rigorous as well as theoretically consistent. The models consider the workings of an economy and the changes that would follow specific policy implementation to perform simulations. CGEs models have played a significant role in ex-ante modelling of international trade Piermartini and the, (2005). CGE simulations can be used to evaluate trade policy alternatives. The alternatives involve modelling trade and welfare effects of economic integration agreements. The CGE model presents the overall aggregate trade, terms of trade effects, factor prices, trade creation and trade diversion within an economy-wide model.
The CGE model builds inter-sectoral linkages, estimates of prices, wages, and exchange rates that lead to equilibrium in product and factor markets and the balance of trade figures. The estimates for prices, wages and exchange rates as well as the balance of trade are considered in the estimation of parameters for the model. The analysis is based on the differences in values in the pre and post-implementation situations, this allows for the evaluation of alternative scenarios. The scenarios relate to the identified gains, losses and distributional effects from policy actions [
Although highly detailed and sophisticated, CGE models have some limitations and they have equally drawn some criticism. Taking a general equilibrium approach and considering that they are an economy wide model, CGE models have an inherent burdensome data requirement problem. CGE models require a full account of trade flows, tariff rates, macroeconomic data as well as a wealth of additional government data on various variables. Data is often missing or inconsistent thus posing some difficulties. Based on this burdensome and problematic data requirement, researchers are moving toward the use of alternative methodologies [
In addition, when dealing with preferential trading arrangements analysts must take into account that CGE models, by virtue of generally being static, cannot adequately adjust when different phases of RTAs enter into effect [
The static partial equilibrium ex-ante and ex-post models are used to predict or simulate the effects of trade policy changes when tariff rates are expected to be modified, as is the case in the formation of trade agreements or other trade liberalization schemes. The partial equilibrium models have been used in discriminatory trade analysis following the pioneering work by [
Ref [
Other studies by [
Among the studies conducted on trade and revenue effects for the EAC region include studies by [
Ref [
Ref [
The empirical model applied in this analysis is the WITS/SMART partial equilibrium model developed by the World Bank. The model was introduced under the literature review in chapter two along with other trade policy analysis models. The model is employed to estimate trade effects and tariff revenue changes contingent on the specification of alternative trade policy scenarios. The WITS model uses the Armington assumption of product differentiation, it assumes that the source of imports for any given product induce imperfect substitution. The model makes additional assumptions such as trade flow changes do not affect incomes or exchange rates, infinite supply elasticities and iso-elastic import demand functions. These assumptions, although restrictive, are not unreasonable for the case of small open economies.
The model follows the theoretical formulation by [
The import demand function for Uganda is represented by Equation (1), below;
where;
M = Imports
Y = National Income
P = Price
j = Importing country in this case Uganda
I = Commodities imported
k = Preference beneficiary country in this case EAC countries, DRC and Sudan. (We consider the period before the new South Sudan and Sudan were born, therefore in this study we consider data for the former Sudan.)
The export supply function of preference beneficiary country is simplified as
where Xijk = Exports of commodity i by country k to country j.
Exports of preference beneficiary countries and import to Uganda give the standard partial equilibrium equation in (3), which is obtained by equating Equation (1) and (2) to give Equation (3). In this equation we assume exports are equal to imports, if we assume a preferential trade area without taxes, i.e. the domestic price of commodity in the importing country is equal to the price in the exporting country. However this is not always the case since in most cases commodities have an extra portion in form of import taxes plus transport and insurance charges.
If we assume a preferential trade agreement where taxes are eliminated, the domestic price of commodity i in the importing country j will be equal to the exporting country k’s export price. When taxes are imposed the price will rise by the amount equal to the ad valorem tariff incidence as shown in Equation (4).
where tijk = Tariff rate.
The trade creation effect is defined as the increased demand for the imports from a preference country. In this case the increased demand for the imports of commodity i into Uganda or country j from preference country k in Equation (1). The increased demand for imports is resulting from the price decrease arising from the price changes when tariff are eliminated [
Equations (4) and (5) are then substituted into the elasticity of import demand equation i.e. Equation (5.1) to get Equation (6)
where
Taking the right hand side of the equation and differentiating it with respect to world prices we get Equation (7)
In Equation (7)
where TCijk = Trade creation.
The expression TCijk represents the sum of trade created over I commodities affected by tariff change. Mijk Represents the current levels of import demand of the given commodity i. In this, case trade creation will depend on the current level of imports, the import demand elasticity and the relative tariff change.
Where;
If
where TCijk the sum of trade is created in millions of dollars over i commodities affected by tariff change and
Trade diversion occurs in a preferential trade agreement when efficient producers from outside the preferential trade agreement are displaced by less efficient producers in the preferential trade agreement, [
where
where TDijk are the trade diversion effects.
Equation (11) can be simplified and expressed as Equation (12) to reflect the trade preference, [
where
The total trade effect is derived by adding Equation (9) and (12) or the trade creation and diversion to give the total trade effect [
In theory, tariff revenue is given as the product of the tax rate and the value of the commodity as the tax base. In estimating the revenue effects, tariff revenue is calculated for the pre and post implementation period of the preference agreement. In this tariff liberalization process the value of imports is taken as the tax base to which the tax rate is applied to get the tariff revenue. To obtain the revenue effects we obtain the tariff revenue using the tariff rates used before the implementation of the trade agreement and subtract the tariff revenue obtained using tariff rates used after the trade agreement. Thus, the revenue, obtained before the trade agreement is given by the expression below
After the change in the tariff rate, the new revenue is given by the expression below
The net revenue as a result of the implementation of the trade agreement would then be the net between R1 and R0 which is given by the Equation (13) below, the net will either be a positive or negative indicating a revenue increase or loss.
Trade agreements, as any type of trade negotiation, contain provisions for gradual phased tariffs reductions. Although phased liberalization processes are usually the norm, the simulations conducted in this study assume the complete and immediate elimination of all import tariffs. This assumption allows the calculation of static effects under the most radical type of liberalization that would reflect long term and perhaps future end scenarios.
The analysis and simulations of the empirical model were carried out for the EAC member as a group and each for the DRC Congo and Sudan. Three alternative possible trade liberalization scenarios were simulated using high and low elasticity values. The analysis was conducted using data available for five different years and averages for those years are presented in the results section. The first scenario considers Uganda completely eliminating existing import tariffs on all four members of the EAC. The second scenario considers Uganda completely eliminating existing import tariffs on DRC imports. DRC is a major trading partner for Uganda, Rwanda, Burundi and Tanzania. The third scenario considers Uganda completely eliminating existing import tariffs on Sudan imports, Sudan is a major trading partner with Uganda and Kenya.
The empirical model obtains values for trade creation, trade diversion and changes in import duty revenue. The simulations and analysis provide a means of comparing and understanding the implications of alternative policy scenarios for Uganda. The results are very useful for researchers, trade negotiators and policy makers as they discuss the reduction phases in actual trade agreement negotiations.
Data Analysis TechniqueThe data utilized in the application is based on the harmonized commodity coding system (HSC) at the six-digit trade level. At this level of aggregation, Uganda has identified (33,833) tariff lines. A calculation at the six-digit level avoids the aggregation of tariff rates and allows for individual tariff line analysis. The information calculations at this level allows for identification of product categories most affected by tariff elimination. The data performed in June 2013.
To examine Uganda’s trade creation, trade diversion and revenue effects, we use a SMART/WITS partial equilibrium model. Under the SMART/WITS we first select the country (in this case Uganda) that is going to change its tariff. In the next step we select each year for the tariff and import data to be used in the simulations, the simulation need a starting point in order to interpolate the consequences of the tariff reform. In this simulation we select six separate years 2006 to 2011 for the simulations. Subsequently we select the products involved in the simulation during the tariff reform, the products are those items for which the tariff is changed by the scenario. In this case all products are considered for tariff reform. The above procedure allows in setting up a scenario for simulation. A scenario is made of four sets of parameters as stated below;
1) Partners; These include Burundi, Kenya, Rwanda, Tanzania, Sudan and the DRC.
2) Products; All products are considered in these simulations.
3) Formula; the tax rate considered is zero percent, a Swiss Formula is used in order to reduce tariffs which is defined as
4) Elasticities; these define behaviors and affect the magnitude of the scenario impact.
The import demand elasticity values used in the scenarios are by default in SMART model and have been empirically estimated for each country for every HS 6-digit product. The substitution elasticity value between partners entails a product by product simulation, which is based on the assumption that any product is independent of another product.
The SMART model uses 1.5 as the default import demand elasticity value. The supply elasticity or the export supply elasticity value is by default and SMART uses 99 for an infinite elasticity for all products and partners. The reason is that we are dealing with a single-country simulation tool, so one country is too small to have an impact on the price level of the rest of the world.
The simulation is done year-by-year and it is independent of the amount of products being chosen. The simulation automatically takes place at the 6-digit level. To simulate effects involving other countries cutting their tariffs at the same time, we run several separate simulations of the selected countries i.e. (Burundi, Kenya, Rwanda, Tanzania, Sudan and the DRC are considered).
Trade effects from the simulations of alternative tariff liberalization scenarios are presented under Appendix 1. Tables 1-13 with values for trade creation, trade diversion, revenue and total trade effects relative to total imports are presented under Appendix 1. Equation (9) and (13) allows for the quantification of the trade creation and trade diversion following the elimination of tariff barriers. Estimation of the equations present simulation results for the alternative tariff liberalization scenarios i.e. Uganda and the individual EAC partner states, Uganda and the DRC and lastly Uganda and Sudan.
Total trade effects as a percentage of baseline imports, are relatively small in both the low and high elasticities values. Using the low elasticity value total trade effects as a percentage of baseline imports ranged between −0.40 percent and 0.10 percent. Using the high elasticity scenario total trade effects as a percentage of baseline imports ranged from −0.17 percent to 0.86 percent. At both high and low elasticity value Rwanda and Burundi showed the largest total trade effects while Kenya and Tanzania showed the least total trade effects. In terms of trade in monetary value, the total trade values were largest in Kenya while the lowest were in Burundi. Total trade value for the two elasticity values ranged from US $ 581.9 million to US $ 1.42 million for the EAC partner states, with an average of US $ 928,100 for the period. Overall the results indicate that the trade liberalization between Uganda and EAC partner states could lead to more trade for Uganda.
The trade effects from the second scenario, where liberalization is extended to the DRC, are presented in
The DRC scenario shows less trade diversion effects compared to the EAC liberalization scenario, however compares to Sudan the DRC scenario shows more trade diversion effects. The total trade effects as a percentage of baseline imports are still small as in the first scenario. They range from 33 percent to 9.5 percent in low elasticity values, in the high elasticity value the total trade effects as a percentage of baseline imports range from 38 percent to 4.2 percent. The total trade effects as a percentage of total imports are similar to the EAC scenario but they are relatively higher. Hence, compared to EAC countries, the DRC is the most affected by trade diversion. In general on account of higher trade creation effects and higher total trade effects as a percentage of total imports, the results indicate that the trade liberalization between Uganda and DRC could lead to more trade.
The trade effect from the third scenario, where liberalization is extended to the Sudan is presented under
In terms of the total trade effects as a percentage of baseline imports, the total trade effects as a percentage of baseline imports are still relatively small under the high and low elasticities. In the case of the low elasticity scenario, the total trade effects as a percentage of baseline imports range from −2.4 percent to −0.30 percent. In the high elasticity scenarios, trade effects as a percentage of total imports range from −5.3 percent to −0.10 percent. In general the results indicate that an EPA between Sudan and Uganda would be trade creating.
The removal of import duties from the assortment of international trade taxes could have effects on overall government revenues. Equation (13) allows the quantification of the total change in revenue following the elimination of import duties. Although it is clear that revenues derived from import tariffs could be eliminated in the simulation, the effect on total trade revenue is not clearly defined given that the other trade taxes such as import VAT, Withholding tax, Environmental levy among others are not eliminated. In fact, an increase in the revenues generated by the other trade taxes is possible if trade liberalization leads to significant increases in the value of trade creation. Trade created is subject to levies imposed by the entire array of other international taxes. Therefore revenue effects evaluated in this study focus on the primary effects that result from the loss of import tariff revenue.
The discussion that follows takes into account the average values obtained for the high and low elasticity. The revenue effects from the elimination of tariffs in the first scenario simulating tariff liberalization for the EAC partner states are summarized in
The first scenario involving tariff liberalization with the EAC partner states is presented in
Overall a large proportion of the negative revenue effects are from Burundi, while a large proportion of positive revenue effects is from Rwanda, Kenya and Tanzania. Uganda’s international trade revenue from Burundi decreases while Rwanda, Kenya and Tanzania’s international trade revenue to Uganda increase. A large proportion of the increase in revenue effects could be attributed to an increase in trade values between EAC partner’s states. The revenue effects from the elimination of tariffs on EAC imports are small but positive for all the countries apart from Burundi which has negative revenue effects. Burundi’s negative revenue effects represent tariff revenue losses, while Rwanda, Tanzania and Kenya’s positive revenue effects represent revenue increases.
Second scenario involving tariff liberalization with the DRC
The second scenario considers the revenue effects that result from the elimination of duties on imports from DRC, the results are presented in
Trade liberalization in this scenario, which includes tariff elimination on goods coming from the DRC, has the largest negative revenue effects. This scenario shows that an EPA between Uganda and DRC would lead to decline in trade revenue, however considering the small revenue effects, one could conclude that the negative revenue effects don’t warrant any alarm. Therefore it’s more likely that an EPA between Uganda and DRC would lead to an increase in trade volumes which would facilitate in abating the negative revenue effects.
Third scenario involving tariff liberalization with Sudan
The third scenario considers the revenue effects that result from the elimination of duties on imports from Sudan are presented in
Using the WITS model with highly disaggregated six-digit level import data, permits the identification of products that have the largest trade and revenue effects. This information is useful to researcher, policymakers and trade negotiators when in the process of negotiating trade liberalization agreements, especially in the context of special and differential treatment.
The first scenario considers the top five most affected products for each individual EAC partner states. These were sorted and ranked by trade and revenue effects (see
The analysis involving Kenya shows that the top five most affected products with the highest trade and revenue effects to Uganda comprised of Portland cement, salt/pure sodium chloride, coils of iron, un-denatured ethyl alcohol, and medicaments of mixed and unmixed products. The products most affected by tariff liberalization in terms of largest trade and revenue effect from Kenya are primarily products where Uganda has limited domestic production. This suggests that the above products could be selected for reduced tariff in order to supplement the limited domestic production. This could improve the welfare of consumers.
The scenario involving Rwanda shows that the top five most affected products with the highest trade and revenue effects to Uganda comprised of electricity, vacuum flasks, road tractors for semi-trailer, wheat not for agricultural sowing, house hold and toilet articles. The products most affected by tariff liberalization in terms of largest trade and revenue effect for Rwanda are primarily products where Uganda has limited domestic production. This suggests that the above products could be selected for reduced tariff in order to supplement the limited domestic production this could improve consumer welfare.
The analysis involving Tanzania shows that the top five most affected products with the highest trade and revenue effects to Uganda comprised of light oil preparations, knitted and orchestrated bed spreads, carboys bottles, flasks, stoppers, lid and others, motor spirits and transformers. The products most affected by tariff liberalization in terms of largest revenue effect from Tanzania are primarily products where Uganda has limited domestic production. This suggests that the above products could be selected for reduced tariff in order to supplement the limited domestic production. This will increase consumer welfare, however some of these products have domestic production and these could call for preferential tariff treatment of the domestic producers.
The scenario involving DRC shows that the top five most affected products with the highest trade and revenue effects to Uganda comprised of beauty make up preparations, wood sawn and chipped, specified tropical woods, vanilla, wood sawn and chipped clockwise and motorcycles. The products most affected by tariff liberalization in terms of largest trade and revenue effect from DRC are primarily products where Uganda has limited domestic production. This suggest that the above products could be selected for reduced tariff in order to provide for the limited domestic production which will increase welfare for consumers, however some of these products have domestic production and such products could call for preferential tariff treatment.
The scenario involving Sudan shows that the top five most affected products with the highest trade and revenue effects to Uganda comprised of light oil preparations, knitted and orchestrated bed spreads, carboys bottles, flasks, stoppers, lid and others, motor spirits and transformers. The products most affected by tariff liberalization in terms of largest revenue effect from Sudan are primarily products where Uganda has limited domestic production. This could suggest that the above products could be selected for reduced tariff in order to provide for the limited domestic production which will increase welfare for consumers. However compared to other country cases Sudan shows the products with the least domestic production.
Overall the country analysis of imports most affected by largest trade effects and revenue effects show that the commodities from Burundi and Rwanda are domestically produced in Uganda. This could call for preferential treatment focussing on protecting local producers. Commodities from Kenya and Tanzania are products with limited domestic production in Uganda, this could call for reduced tariff rate to improve domestic consumption of these products. The products most affected by revenue and trade effects in scenarios involving DRC and Sudan suggest that Uganda’s domestic producers could benefit, since the products are not produced in Uganda.
The chapter has applied a partial equilibrium model to estimate the likely trade and revenue effects on Uganda under EAC. The chapter also estimates trade and revenue effects for the DRC and Sudan. The trade liberalization scenarios simulated provide insight into the effects of tariff elimination, although it is clear that simulations performed were limited to the removal of import duties while a broad array of other border charges remained, results are nevertheless noteworthy.
The EAC trade liberalization scenario leads to trade creation effects. Among the EAC partner states, Rwanda has the largest trade creation effects. DRC has larger trade creation effects compared to Sudan. Small trade diversion effects are evident among the EAC partner states, Burundi has the largest trade diversion effects among EAC partner states and between Sudan and DRC, and the DRC has the largest trade diversion effects. The analysis on DRC and Sudan suggests that trade agreements between Uganda and the two countries would increase Uganda’s trade and revenue. Tariff revenue effects in all scenarios simulated were small, the revenue implications of removal of applied rates depend on the price elasticity of imports. High elasticity shows larger trade and revenue effects compared to low elasticity.
It is identified that products most affected by largest trade creation effects are primarily products that are not produced on the local market. However, there are a few items that could be produced locally. One could propose that these products should be earmarked for preferential tariff treatment. Such products include garments of other textiles, knitted or crocheted, polythene or polypropylene, breeches of other textiles, t-shirts, singlet’s knitted or crocheted, sacks and bags, used for packing goods, of other textiles and some other foods among agricultural and raw materials. The commodities could be included on the sensitive list and charged a higher tariff rate as a way of protecting local producers.
The products with largest revenue effects include worked monumental or building stone, woven fabrics of synthetic filament yarn, flat-rolled products of iron or non-alloy steel, textiles; wearing apparel and dressing and rubber and plastic products. Tariffs on these products could be reduced or completely removed to enable growth in the construction industry.
The results obtained in this study are similar to results by [
The study’s major conclusions are that Uganda is likely to record trade creations effects, small trade diversion effects and small positive revenue effects over the existing levels. However the trade values are bound to increase significantly over the existing levels.
The elimination of import duties in the EAC could lead to an increase in trade and revenue. The identification of products most affected by tariff revenue cuts, or products that have the largest trade and revenue effects, can be very useful in setting timetables and liberalization phases, however, beyond usefulness in many settings, the analysis performed can be viewed in the broader issue of trade liberalization as policy direction.
The findings provide empirical support in assessing the more appropriate courses of action for Uganda under the EAC trade agreement. In light of the findings, the more appropriate courses of action for Uganda would be to advocate for the inclusion of DRC and Sudan under the EAC trade agreement, or Uganda entering economic partnership agreement with both Sudan and DRC.
These results coincide with the broader theoretical arguments and policy suggestions made by [
I would also like to express my gratitude to Associate Professor Eria Hisali and Dr. Edward Bbale for the very helpful comments and constructive suggestions on econometric analysis.
0 percent Rate | |||||
---|---|---|---|---|---|
Years | Trade Value in 1000 USD | Trade Creation Effect in 1000 USD | Trade Diversion Effect in 1000 USD | Trade Total Effect in 1000 USD | Trade Effects Relative to Total Imports |
2010 | 83.36 | 0.00 | 0.00 | 0.00 | 0.00 |
2009 | 660.74 | 0.00 | 0.00 | 0.00 | 0.00 |
2008 | 660.74 | 0.00 | 0.00 | 0.00 | 0.00 |
2007 | 459.08 | 0.01 | 0.12 | 0.22 | 0.00 |
2006 | 11.39 | −1.08 | −2.55 | −6.05 | −0.53 |
2005 | 15.34 | 0.00 | 0.00 | 0.00 | 0.00 |
351.96 | −0.13 | −0.30 | −0.73 | −0.07 |
0 percent Rate | |||||
---|---|---|---|---|---|
Years | Trade Value in 1000 USD | Trade Creation Effect in 1000 USD | Trade Diversion Effect in 1000 USD | Trade Total Effect in 1000 USD | Trade Effects Relative to Total Imports |
2010 | 456967.3 | 0.00 | −187.17 | −312.0 | 0.00 |
2009 | 123424.6 | 0.00 | −3.35 | −5.6 | 0.00 |
2008 | 138207.7 | 25.04 | 60.56 | 142.7 | 0.00 |
2007 | 121215.2 | 207.2 | 325.8 | 888.3 | 0.01 |
2006 | 105304.8 | −9825.9 | −11559.7 | −35642.6 | −0.34 |
2005 | 102444.3 | 143.6 | 175.2 | 531.3 | 0.01 |
148,121 | −1181.3 | −1,399 | −4299.8 | −0.04 |
0 percent Rate | |||||
---|---|---|---|---|---|
Year | Trade Value in 1000 USD | Trade Creation Effect in 1000 USD | Trade Diversion Effect in 1000 USD | Trade Total Effect in 1000 USD | Trade Effects Relative to Total Imports |
2010 | 356.02 | 0.00 | −0.35 | −0.58 | 0.00 |
2009 | 1046.13 | 0.00 | −0.02 | −0.03 | 0.00 |
2008 | 1046.13 | 0.00 | −0.02 | −0.03 | 0.00 |
2007 | 888.07 | 3.15 | 7.02 | 16.95 | 0.02 |
2006 | 258.65 | −11.01 | −22.73 | −56.23 | −0.22 |
2005 | 355.33 | 10.31 | 20.59 | 51.50 | 0.14 |
1119.85 | 0.31 | 0.56 | 1.45 | −0.01 |
0 percent Rate | |||||
---|---|---|---|---|---|
Year | Trade Value in 1000 USD | Trade Creation Effect in 1000 USD | Trade Diversion Effect in 1000 USD | Trade Total Effect in 1000 USD | Trade Effects Relative to Total Imports |
2010 | 53420.4 | 0.0 | −9.7 | −16.1 | 0.00 |
2009 | 12691.8 | 0.0 | −0.5 | −0.8 | 0.00 |
2008 | 12691.8 | 0.0 | −0.5 | −0.8 | 0.00 |
2007 | 8244.7 | 0.0 | −10.0 | −16.7 | 0.00 |
2006 | 14425.4 | −862.2 | −915.3 | −2962.4 | −0.21 |
2005 | 12188.2 | 0.0 | −2.0 | −3.3 | 0.00 |
17280.2 | −107.8 | −117.3 | −375.1 | −0.03 |
0 percent Rate | |||||
---|---|---|---|---|---|
Year | Trade Value in 1000 USD | Trade Creation Effect in 1000 USD | Trade Diversion Effect in 1000 USD | Trade Total Effect in 1000 USD | Trade Effects Relative to Total Imports |
2010 | 1190.92 | 62.57 | 1447.78 | 2517.25 | 2.11 |
2009 | 300.11 | 25.01 | 64.32 | 148.88 | 0.50 |
2008 | 300.11 | 25.01 | 64.32 | 148.88 | 0.50 |
2007 | 28.80 | 1.32 | 3.44 | 7.94 | 0.28 |
2006 | 18.23 | −1.36 | −3.92 | −8.80 | −0.48 |
2005 | 40.60 | 0.77 | 1.74 | 4.19 | 0.10 |
1878.77 | 113.31 | 1577.69 | 2818.33 | 3.00 |
0 percent Rate | |||||
---|---|---|---|---|---|
Year | Trade Value in 1000 USD | Trade Creation Effect in 1000 USD | Trade Diversion Effect in 1000 USD | Trade Total Effect in 1000 USD | Trade Effects Relative to Total Imports |
2010 | 116.67 | 0.00 | −0.03 | −0.05 | 0.00 |
2009 | 83.96 | 0.00 | 0.00 | −0.01 | 0.00 |
2008 | 83.96 | 0.00 | 0.00 | −0.01 | 0.00 |
2007 | 162.28 | 0.00 | 0.00 | 0.00 | 0.00 |
2006 | 3.47 | 0.00 | 0.01 | 0.02 | 0.01 |
2005 | 176.24 | 0.00 | −0.01 | −0.02 | 0.00 |
3760.84 | 0.00 | −0.04 | −0.07 | 0.006 |
Years | Trade Value in 1000 USD | Revenue Effect in 1000 USD |
---|---|---|
2010 | 10,870 | 0.00 |
2009 | 4260 | 0.00 |
2008 | 9090 | −12.50 |
2007 | 7870 | −02.00 |
2006 | 170 | −1.30 |
Years | Trade Value in 1000 USD | Revenue Effect in 1000 USD |
---|---|---|
2010 | 5,115,310 | 0.00 |
2009 | 5,026,590 | 0.00 |
2008 | 5,113,340 | 0.00 |
2007 | 4,956,870 | 0.00 |
2006 | 4,080,150 | 0.00 |
Years | Trade Value in 1000 USD | Revenue Effect in 1000 USD |
---|---|---|
2010 | 73,890 | 0.1 |
2009 | 30,710 | 0.00 |
2008 | 28,790 | 0.00 |
2007 | 37,860 | 0.20 |
2006 | 4880 | 0.00 |
Years | Trade Value in 1000 USD | Revenue Effect in 1000 USD |
---|---|---|
2010 | 5,652,800 | 0.00 |
2009 | 407,980 | 0.00 |
2008 | 554,830 | 0.00 |
2007 | 308,000 | 0.00 |
2006 | 289,950 | 0.00 |
Years | Trade Value in 1000 USD | Revenue Effect in 1000 USD |
---|---|---|
2010 | 25,900 | −138.76 |
2009 | 8980 | −62.76 |
2008 | 3220 | −130.73 |
2007 | 410 | 35.36 |
2006 | 390 | 67.06 |
Years | Trade Value in 1000 USD | Revenue Effect in 1000 USD |
---|---|---|
2010 | 31,560 | 0.00 |
2009 | 2570 | 0.00 |
2008 | 960 | 0.00 |
2007 | 1740 | 0.00 |
2006 | 40 | 0.00 |
HSC Code | Imports Most Affected by Trade Effects | |
---|---|---|
BURUNDI | ||
1 | 40210 | Milk and cream in solid form |
2 | 90111 | Coffee not roasted or decaffeinated |
3 | 281121 | Iodine |
4 | 310520 | Mineral or chemical fertilizers with nitrogen |
5 | 520100 | Cotton not carded or combed |
KENYA | ||
1 | 252329 | Portland cement |
2 | 250100 | Salt/pure sodium chloride |
3 | 720918 | Coils of iron |
4 | 220710 | Un-denatured ethyl alcohol |
5 | 300490 | Medicaments of mixed and unmixed products |
RWANDA | ||
1 | 271600 | Electricity |
2 | 961700 | Vacuum flasks |
3 | 870120 | Road tractors for semi-trailer |
4 | 100190 | Wheat not for agricultural sewing |
5 | 392490 | House hold and toilet articles |
TANZANIA | ||
1 | 271019 | Light oil preparations |
2 | 630491 | Knitted and orchestrated bed spreads |
3 | 701090 | Carboys bottles, flasks, stoppers, lid and others |
4 | 271011 | Motor spirits |
5 | 850433 | Transformers |
DRC | ||
1 | 330499 | Beauty make up preparations |
2 | 440729 | Wood sawn and chipped |
3 | 440721 | Specified tropical woods |
4 | 90500 | Vanilla |
5 | 440799 | Wood sawn and chipped clockwise |
6 | 871120 | Motorcycles |
SUDAN | ||
1 | 890590 | Light vessels, fire floats and floating cranes |
2 | 120430 | Waste and scrap of iron and steel |
3 | 841182 | Gas turbine |
4 | 720410 | Waste and scrap of cast iron |
5 | 842959 | Self-propelled bull dozers and excavators |