Despite the well-known gains from trade, the effects of trade openness are a priori ambiguous. For this reason it’s important to establish effects of trade openness on both aggregate and disaggregated import demand. This study sought to establish the effects of trade openness on disaggregated imports. A panel data cointegration technique that uses the Fully Modified Ordinally Least Squares and Dynamic Ordinally Least Squares was employed. The data are annual cross country panels of EAC countries covering the period 1994-2012. The data were obtained from the IMF’s International Finance Statistics, the World Bank’s World Development Indicators as well as the World Integrated Trade Solution. The findings on the effects of trade openness on import demand show that an increase in the tariff rate reduces imports both at the aggregate and disaggregated levels. An increase in income positively influences the aggregate and disaggregated levels of imports. An increase in prices positively influences the aggregate and disaggregated levels of imports. Exports positively influence the aggregate and disaggregated levels of imports. Lastly the real effective exchange rate negatively influences aggregate and disaggregated imports. The policy implications is that governments of EAC countries could use trade openness reforms, particularly the tariff rate to minimize the importation of goods that can be produced locally, this will help in managing the balance of trade.
There has been substantial expansion in trade flows, capital movements as well as mobility of labour across borders over the latter part of the 20th century. During this period world trade has grown from about US $6.199 trillion in 1994 to approximately US $26.02 trillion in 2012 [
The three major reasons for the growth in world trade are, first, improvements in the technology of transportation and communication, second, increasing convergence of tastes and preference and thirdly, the global economic cooperation that has led to trade openness [
This theoretical relationship has been examined by [
Secondly, [
In the case of EAC countries, despite implementing trade openness reforms, hitherto there are no studies that investigated the effects of trade openness on disaggregated imports and different categories of taxes. Studies by [
Furthermore, according to [
Example of trade openness reforms implemented in EAC countries include; implementation of tariff reforms under the World Trade Organization and General Agreement on Tariffs and Trade (GATT), tariff reforms under the IMF and World Bank Structural Adjustment Program and tariff reforms under the EAC customs union protocol [
Additionally, an analysis of the volume of imports to the EAC countries shows that during the period from 1994 to 2012 imports have dramatically increased from 2002 to 2012 (
importance to EAC countries, similarly the performance of imports measured by the import growth rate shows that for the period from 1994 to 2012, EAC imports grew at 88.3 percent while the sub-Saharan Africa imports grew at 83.1 percent [
From literature, it’s clear that higher levels of import demand could be driven by increased levels of trade openness. Based on literature this study attempts to answer the question how does trade openness affect disaggregated imports in EAC countries?
Statement of the ProblemIt’s argued that the reduction or removal of barrier to free trade, such as import tariffs lowers import prices. The gains from removal of barriers to free trade are expected to increase domestic output through the use of better imported skills and technology to foster high productivity at both firm and industrial level; thereby lowering import for certain categories of imports.
However, in the case of EAC countries, despite implementing trade openness reforms the performance of imports measured by the growth rate shows that EAC countries are experiencing higher import growth as compared to the averaged sub Saharan African countries. This is likely to cause a balance of trade problems, if the high import demand is not managed. For instance; EAC countries will have to review their trade openness policies in order to avoid problems associated with balance of trade deficits such as unemployment, low per capita income, exchange rate shortage, raising inflation, increasing government debt among others. Thus, it’s imperative that evidence regarding the influence of trade openness on import demand is provided given the strategic importance of imports to development of EAC countries.
The theoretical linkage between trade openness and imports has been explained by a range of theories, however there are three leading theories that explain demand for imports. First, is the theory of comparative advantage or neoclassic trade theory, second is the perfect substitute’s model or Keynesian trade multiplier and third is the imperfect competition theory also known as the new trade theory [
The first theory is the comparative advantage theory, the theory focuses on how the volume and direction of international trade are affected by changes in relative prices. The volume and direction of trade are explained by differences in factor endowments between countries. The theory is not concerned with the effects of changes in income on trade as the level of employment is assumed to be fixed and output is assumed to be on a given production frontier. This suggests that import demand is based on the assumptions of neoclassic microeconomic consumer behavior and general equilibrium theory. The models predict that movement towards openness can temporarily increase imports due to short run gains from re-allocation of resources within the economy. The implication is that trade openness has a positive relationship with imports [
The second theory is the perfect substitute’s model or Keynesian import demand function, which is based on the macroeconomic multiplier analysis. In this model, relative prices are assumed to be rigid while employment is variable. The model assumes international capital movements which passively adjust to restore the trade balance. The relationship can be defined by the average and marginal propensity to import and the income elasticity of imports. The perfect substitute’s model is based on the assumption that traded goods are perfectly substitutes. But in reality, traded goods are not perfect substitutes hence both imported goods and locally produced goods coexist in the same market [
The theory assumes that the state intervenes in international trade through the use of trade controls. The theory is based on the assumptions that traded goods are perfectly substitutable and can be traded across countries. The theory further assumes that international capital movements will passively adjust to restore the trade balance and it identifies two mechanisms in which trade openness might affect imports [
The third theory is the imperfect competition theory. The theory focuses on intra-industry trade and explains the effects of economies of scale, product differentiation and monopolistic competition on international trade. The theory uses three approaches to try and define effects of imperfect competition on international trade these include the Marshallian, Chamberlinian and Cournot approaches. First the Marshallian approach assumes constant returns at the firms level but increasing returns at the industry level, secondly the Chamberlinian approach assumes that an industry consists of many monopolistic firms and new firms are able to enter the market and differentiate their products from existing firms so that any monopoly profit at the industry level is eliminated. Lastly the Cournot approach assumes a market with only a few imperfectly competitive firms where each firm output is taken as given [
The theoretical literature suggests three theories that influence import demand, however two theories are commonly used in estimating the import demand function. These are the imperfect substitute model and the perfect substitute model [
There are various studies estimating import demand elasticities for different countries. Some of the studies use aggregate analysis whereas others use disaggregate analysis. Some of the studies estimating the aggregate import demand elasticities are reviewed below. First, a study by [
A study by [
A study by [
Reference [
An investigation into the behavior of the import demand function for India using annual data from 1975 to 2003 by [
Some empirical studies that have used the Autoregressive Distributed Lag (ARDL) bounds test approach to examine import demand functions include; [
Furthermore, a study by [
Among the studies conducted on import demand functions for East African countries include studies by [
A study by [
Some of the studies that have included a trade openness variable in the aggregate import demand function include studies by [
Reference [
A similar study by [
In contrast to the studies using aggregate analysis other studies have used disaggregated analysis. Some of the studies using disaggregated are reviewed below. Reference [
A study on the US import demand by [
Reference [
Reference [
Some of the studies that have included a trade openness variable in the disaggregate import demand function include studies by [
The section provides a synthesis of literature on studies investigating aggregate and disaggregate import elasticities. The literature suggests two leading theories that explain demand for import, these are the perfect substitute model and the imperfect substitute model [
The studies have used the following data estimation techniques to estimate import elasticities i.e., Error Correction Model, Vector Error Correction Models, Cointegration technique, Auto Regressive Distributed Lag models, Fixed and Random Effects models as well as the General Method of Moments. The findings shows that aggregate and disaggregated demand for import is explained by income, relative prices, exports, real effective exchange rate, trade openness, government and household expenditure and inflation [
According to [
M t = f ( P M t / P D t , Y t ) (1)
where;
M t = Imports.
P M t = Import prices.
P D t = Domestic prices.
P M t / P D t = Relative import prices.
Y t = Gross Domestic Product.
t = is a time subscript
We rewrite Equation (3.1) into an econometric form i.e.,
M t = α 1 + α 2 ( P M t / P D t ) + α 3 Y t + e t . (2)
where;
α ( 1 , 2 and 3 ) = are coefficients for the import demand variables.
e t = is the error term.
The imports are considered as an endogenous variable while the relative import price and GDP are considered as exogenous variables. Equation (2) is transformed into Equation (3) where the variables are transformed into natural logs and also a lag structure is introduced to capture the lagged effect of import demand. The purpose is to make the relationships between variables linear as well as capture the lagged structure of import demand. Hence, the import demand function for consumer, intermediate, capital goods and aggregate goods can be expressed as follows:
ln M t = a 1 + a 2 ln Y t + a 3 ln R P M t + a 4 ln M t − 1 + U t (3)
where,
ln M t = import categories of consumer, intermediate, capital and aggregate goods.
ln M t − 1 = lagged effect of consumer, intermediate, capital and aggregate goods.
ln Y t = real GDP at time t.
ln R P M t = relative import prices for the respective import categories i.e., consumer, intermediate, capital and aggregate goods.
U t = the error term.
t = is a time subscript.
To establish the effects of trade openness on imports on EAC countries, we convert model 3.3 to a panel estimation and also introduce the average tariff rate alongside other control variables. These variables are drawn from literature as proposed by [
ln M i t = a 1 + a 2 ln Y i t + a 3 ln R P M i t + a 4 ln A T R i t + a 5 X i t + a 6 ln R E E R i t + a 7 ln M i t − 1 + U i t (4)
where,
ln M i t = imports of consumer, intermediate, capital and aggregate goods.
ln M i t − 1 = lagged effect of consumer, intermediate, capital and aggregate goods.
ln Y i t = Real GDP at time t.
ln R P M i t = relative import price for consumer, intermediate, capital and aggregate goods.
ln A T R i t = average tariff rate.
ln X i t = exports.
ln R E E R i t = real effective exchange rate.
U i t = is the error term.
t = is a time subscript.
i = 1, 2,3,4,5.
The variables presented in this section are drawn from theoretical and empirical literature. It’s expected that an increase in the income of the importing country will raise import demand substantially. If the income elasticity of import demand is high, other things being equal, this would lead to a deterioration of the balance of payments. However an increase in income may lead to a rise in the production of goods and services domestically. In this case, one may expect imports to fall in the face of an increase in income which means that the relationship between volume of imports and income may be either negative or positive [
According to economic theory as well as studies by [
We introduce the average tariff rate as a measure for trade openness, this is in line with studies by [
The other variable considered in the extended import demand function is the values of exports. Exports are a crucial component of a country’s economy. Not only do exports facilitate international trade, they also stimulate domestic economic activity by creating employment, production, foreign exchange rate and taxes. The ability to export goods helps an economy to grow, by selling more goods and services. Reference [
We also include the real effective exchange rate into the extended import demand function. The use of the real effective exchange rate variable is in line with studies by [
This section provides a description of the data appearing in the four import demand equations. The study employs a cross country panel which includes the following countries, Burundi, Kenya, Rwanda, Tanzania and Uganda. The following explanatory variables are used to explain aggregate and disaggregated import demand; Real GDP per capita, relative import prices for the different import categories, average tariff rate, trade ratio to GDP, exports and real effective exchange rate. Data on imports of aggregate goods (M), consumer goods (Co), intermediate goods (I) and capital goods (K) were obtained from the World Bank’s World Integrated Trade Solution (WITS). Data on the GDP per capita, average tariff rate and exports was obtained from the World Bank’s World Development Indicators (WDI). Data on the relative import price (RPM) and the real effective exchange rate (REER) was obtained from the IMF’s International Finance Statistics (IFS)
Variable | Source |
---|---|
Imports (M) of aggregate goods and services | Constant 2000 US Dollars (USD), Source; WITS, World Bank, April 2014. |
Import of consumer goods and services (Co) | Constant 2000 US Dollars (USD), Based on the Broad Economic Categories (BEC) Source; WITS, World Bank, April 2014 |
Import of intermediate goods and services (I) | Constant 2000 US Dollars (USD), Based on the Broad Economic Categories (BEC) Source; WITS, World Bank, April 2014 |
Imports of capital goods and services (K) | Constant 2000 US Dollars (USD), Based on the Broad Economic Categories (BEC) Source; WITS, World Bank, April 2014 |
Real GDP per capita (Y) | Constant 2000 US Dollars (USD). Source; World Bank, World Development Indicators (WDI), July 2014. |
Relative import price (RPM). | International Monetary Fund’s (IMF) International Finance Statistics (IFS), July 2014. Constructed by dividing imports prices by domestic prices. |
Average tariff rate (ATR) | Weighted average tariff rate. Source; World Bank, World Development Indicators (WDI), July 2014. |
Trade ratio to GDP | World Bank, World Development Indicators (WDI), July 2014. |
Exports in values (X) | World Bank, World Development Indicators (WDI), July 2014. |
Real effective exchange rate (REER) | International Monetary Fund’s (IMF) International Finance Statistics (IFS), July 2014. Constructed by dividing disaggregated imports by the price indices. |
The use of panel data offers several advantages in econometric analysis, first, panel data contains more degrees of freedom and more sample variability, hence improving the efficiency of econometric estimates. Second, panel data has a greater capacity for capturing the complexity of import demand behavior than a single time series data. It is frequently argued that the reason that a researcher finds or does not find certain causal effects in econometric analysis is due to omission of certain variables in one’s model specification which are correlated with the included explanatory variables. However, since panel data contain information on both the inter-temporal dynamics and the individuality of the entities, it is capable of controlling for the effects of missing or unobserved variables.
Reference [
The study uses Stata 14 to estimate the panel unit root tests, panel cointegration test as well as the related import demand panel cointegration regressions i.e., consumer, intermediate, capital and aggregate goods.
The first step in the analysis is to ascertain the order of integration of the variables. We use two panel unit root tests to establish the orders of integration of the variables. We use a panel unit root test that allows for cross sectional dependence and another that allows for cross sectional independence. A variable is integrated of order d, written as I(d), if it requires differencing d times before it becomes stationary. To test the variables order of integration, we use the [
The imports of aggregate goods, consumer goods, intermediate goods and capital goods are taken as dependent variables while the per capital GDP, relative import price, average tariff rate, real effective exchange rate, exports are taken as independent variables. Imports and the respective relative import prices are transformed into natural logarithms to make the relationships between variables linear. If the data series is stationary at level, it is called I(0) if the data series is stationary at first difference, it’s referred to as integrated of order one, i.e., I(1) and if the data series is stationary at second difference, then it’s referred to as integrated of order two, i.e., I(2). First, we perform unit root tests at levels and where necessary we carry out higher order tests. The results for the [
The results under
Im, et al. (2003) | Maddala and Wu (1999) | Order of integration | ||
---|---|---|---|---|
Variable | T-bar | Z (T-bar) | Modified Chi Squared Pm | |
Consumer goods | −1.13 | 0.911 | −1.66 | I(1) |
ΔConsumer goods | −4.35* | −4.59* | 6.11* | |
Intermediate goods | −0.903 | 1.49 | −1.67 | I(1) |
∆Intermediate goods | −4.19* | −4.39* | 5.43* | |
Capital goods | −1.03 | −1.107 | −1.71 | I(1) |
∆Capital goods | −4.61* | −4.78* | 11.0* | |
Aggregate goods | −0.14 | 3.62 | −1.18 | I(1) |
∆Aggregate goods | −3.86* | −4.09* | 4.29* | |
GDP per capita | −0.958 | −1.403 | 1.10 | I(1) |
∆GDP per capita | −3.03* | −2.39* | 1.34* | |
Average tariff rate | −5.91* | −5.24* | 2.39* | I(0) |
Consumer goods relative import price | −2.209* | −1.871* | 2.83* | I(0) |
Intermediate goods relative import price | −2.358* | −1.459* | 4.53* | I(0) |
Capital goods relative import price | −2.644* | −2.361* | 4.34* | I(0) |
Aggregate goods relative import price | −2.173* | −1.066* | 3.85* | I(0) |
Exports | −4.240* | −4.525* | 5.54* | I(0) |
REER | −1.271 | 0.543 | −0.71* | I(1) |
∆REER | −0.462* | 2.669* | −1.94* |
We use constant & trend as deterministic terms. We use two lag for consumer goods, intermediate goods, capital goods, aggregate goods, relative import prices, exports and GDP per capita. On the other hand we use a lag of 1 for the real effective exchange rate and the average tariff rate for the IPS and Fisher-ADF Test. (*) denotes rejection of the null hypothesis at 99%.
roots at level for the following variables; aggregate goods, consumer goods, intermediate goods, capital goods, real effective exchange rate and GDP per capita. This suggests that the variables are non-stationary at level. On the contrary the [
The results of the [
In testing for cointegration in panel data, we use the Kao panel cointegration tests. The test is suitable for panels with small T. The results from the four import models show that the ADF test statistic rejects the null hypothesis of no cointegration at 1 percent level of significance. This implies that there exists a long-run relationship in the variables. From the results we conclude that the variables in the four import demand functions have a long-run cointegration relationship.
The results from the panel unit roots test and the panel cointegration tests show that the variables across the four import models are integrated of I(0) and I(1) but also cointegrated. According to [
In our specific case i.e., T = 19 and N = 5. We have a relatively small T and small N panel that is integrated of I(0) and I(1) but cointegrated. From the literature we adopt the FMOLS and DOLS estimators. Pedroni (2000) shows that the FMOLS and DOLS estimators performs well in small samples. The FMOL and DOLS model is considered superior to other estimation techniques because it inherently correct for endogeneity, serial correlation and asymptotic bias.
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
---|---|---|---|---|---|---|---|---|
VARIABLES | Consumer | Consumer | Intermediate | Intermediate | Capital | Capital | Aggregate | Aggregate |
FMOLS | DOLS | FMOLS | DOLS | FMOLS | DOLS | FMOLS | DOLS | |
GDP per capita | 1.857* | 1.4623* | 1.8500* | 1.6855* | 1.8167* | 1.6831* | 1.8975* | 1.5208* |
(0.191) | (0.296) | (0.168) | (0.198) | (0.199) | (0.238) | (0.046) | (0.242) | |
Relative import price | 0.169* | 0.3915* | 0.1251* | 0.2470* | 0.1576* | 0.2540* | 0.1594* | 0.3583* |
(0.046) | (0.110) | (0.043) | (0.075) | (0.050) | (0.086) | (0.186) | (0.088) | |
Observations | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 |
R-squared | 0.94 | 0.97 | 0.94 | 0.98 | 0.94 | 0.98 | 0.95 | 0.98 |
Note: Standard errors in parentheses ***p < 0.10, **p < 0.05, *p < 0.01. The dependent variable for Models (1) and (2) is consumer goods (3) and (4) intermediate goods (5) and (6) capital goods while (7) and (8) is aggregate goods. The relative import price variables represents the respective relative import prices for consumer, intermediate, capital and aggregate good.
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
---|---|---|---|---|---|---|---|---|
VARIABLES | Consumer | Consumer | Intermediate | Intermediate | Capital | Capital | Aggregate | Aggregate |
FMOLS | DOLS | FMOLS | DOLS | FMOLS | DOLS | FMOLS | DOLS | |
Lag (1) dependent variable | 0.687* | 0.466* | 0.564* | 0.401* | 0.633* | 0.412 | ||
(0.069) | (0.071) | (0.064) | (0.067) | (0.079) | (0.084) | |||
GDP per capita | 0.099* | 0.405* | 0.131* | 0.398* | 0.230* | 0.257 | 0.073* | 0.215 |
(0.182) | (0.187) | (0.147) | (0.155) | (0.309) | (0.298) | (0.196) | (0.209) | |
Relative import prices | 0.099* | 0.105* | 0.042* | 0.039** | 0.155* | 0.131* | 0.089* | 0.088* |
(0.022) | (0.023) | (0.019) | (0.020) | (0.038) | (0.041) | (0.024) | (0.025) | |
Exports | 0.337* | 0.414* | 0.379* | 0.439* | 0.776* | 0.724* | 0.405* | 0.478* |
(0.091) | (0.093) | (0.071) | (0.075) | (0.134) | (0.133) | (0.094) | (0.100) | |
Average tariff rate | −0.013** | −0.013* | −0.011* | −0.009** | −0.019** | −0.015 | −0.01** | −0.012* |
(0.006) | (0.006) | (0.005) | (0.005) | (0.010) | (0.010) | (0.006) | (0.007) | |
REER | −0.156* | −0.125* | −0.124* | −0.051 | −0.230* | −0.22* | −0.083* | −0.075 |
(0.043) | (0.044) | (0.035) | (0.036) | (0.073) | (0.069) | (0.047) | (0.050) | |
R-squared | 0.97 | 0.97 | 0.98 | 0.98 | 0.96 | 0.95 | 0.98 | 0.98 |
Note: Standard errors in parentheses ***p < 0.10, **p < 0.05, *p < 0.01. The dependent variable for Models (1) and (2) is consumer goods (3) and (4) intermediate goods (5) and (6) capital goods while (7) and (8) is aggregate goods. The relative import price variables represents the respective relative import prices for consumer, intermediate, capital and aggregate goods.
results in
The results under
The results under
The coefficient of relative import price or price elasticity across the four models is positive and statistically significant. The positive relationship implies that an increase in relative import prices increases imports of aggregate and disaggregated goods. The results shows that the price elasticities are inelastic and concentrated between 0.15 and 0.09 range. Furthermore, disaggregated price elasticities show that consumer goods have the highest price elasticity, followed by capital goods and lastly intermediate goods. These result contradicts economic theory which predicts that as prices increase demand for goods decreases. This result is not expected but it is supported by findings from [
The results of the first lag of the consumer, intermediate and aggregate good models are positive and statistically significant. This result implies that imports of consumer, intermediate and aggregate goods in the previous period are an important determinant for imports in the present period. This result supports economic theory which predicts that demand for imports in the previous period influence demand for imports in the current period [
The coefficient of average tariff rate which is used as a measure for trade openness is negative and statistically significant for the aggregate and disaggregated import models. The negative relationship implies that an increase in the tariff rate leads to decrease imports of aggregate and disaggregated goods. The average tariff rate elasticity are inelastic and concentrated between −0.009 and −0.019 range. The results are expected and are in line with theoretical literature which predicts that a decrease in tariff rates leads to an increase in import demand [
As a robustness test, in
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
---|---|---|---|---|---|---|---|---|
VARIABLES | Consumer | Consumer | Intermediate | Intermediate | Capital | Capital | Aggregate | Aggregate |
FMOLS | DOLS | FMOLS | DOLS | FMOLS | DOLS | FMOLS | DOLS | |
Lag (1) dependent variable | 0.643* | 0.432* | 0.506* | 0.506* | 0.446* | 0.281* | 0.566* | 0.357* |
(0.066) | (0.072) | (0.066) | (0.066) | (0.119) | (0.126) | (0.075) | (0.077) | |
GDP per capita | 0.642* | 0.432* | 0.613* | 0.665* | 0.261** | 0.298* | 0.790* | 0.572* |
(0.136) | (0.071) | (0.125) | (0.132) | (0.107) | (1.048) | (0.135) | (0.175) | |
Relative import prices | 0.0871*** | 0.106* | 0.0747 | 0.043* | 0.0848** | 0.0478* | 0.0797 | 0.088* |
(0.048) | (0.022) | (0.0465) | (0.019) | (0.0377) | (0.673) | (0.0491) | (0.022) | |
Exports | 0.509* | 0.198* | 0.563* | 0.267* | 0.647* | 0.655* | 0.539* | 0.231* |
(0.0640) | (0.008) | (0.0620) | (0.070) | (0.0545) | (0.726) | (0.0646) | (0.087) | |
Trade ratio to GDP | 0.0153* | 0.008* | 0.010* | 0.008* | 0.021* | 0.022 | 0.013* | 0.011* |
(0.0045) | (0.002) | (0.004) | (0.002) | (0.003) | (0.043) | (0.004) | (0.002) | |
REER | −0.242* | −0.050* | 0.071*** | −0.007* | −0.144* | 0.144 | −0.165* | −0.004 |
(0.0431) | (0.041) | (0.0399) | (0.033) | (0.034) | (0.353) | (0.0423) | (0.042) | |
Observations | 95 | 95 | 95 | 95 | 95 | 95 | 95 | 95 |
R-squared | 0.936 | 0.962 | 0.867 | 0.971 | 0.960 | 0.978 | 0.902 | 0.965 |
Note: Standard errors in parentheses ***p < 0.10, **p < 0.05, *p < 0.01. The dependent variable for Models (1) and (2) is consumer goods (3) and (4) intermediate goods (5) and (6) capital goods while (7) and (8) is aggregate goods. The relative import price variables represents the respective relative import prices for consumer, intermediate, capital and aggregate goods.
import demand. The purpose is to find out whether using another measure of trade openness the results remain valid. The results from this measure show that with respect to aggregate and disaggregated goods the coefficient for trade ratio to GDP is positive and statistically significant. This results supports results obtained under the average tariff rate which implies that trade openness positively influences import demand. The results are expected and are in line with theoretical and empirical literature which predicts that an increase in trade openness leads to an increase in import demand [
The coefficient for exports is positive and statistically significant for the aggregate and disaggregated import models. The positive relationship implies that an increase in exports increases imports of aggregate and disaggregated goods. The export elasticity are inelastic and concentrated between 0.77 and 0.33 range. The size of the disaggregate export elasticities are higher than aggregate export elasticities, which suggests that aggregated elasticities could mask important characteristics in data [
The coefficient of real effective exchange rate is negative and statistically significant for the aggregate and disaggregated import models. The negative relationship implies that an increase in the real effective exchange rate decreases imports of aggregate and disaggregated goods. The results are expected and are in line with empirical literature by [
Despite the well-known gains from trade, the effects of trade openness are a priori ambiguous. For this reason it’s important to establish effects of trade openness on both aggregate and disaggregated import demand for any country opening its borders to trade. This paper establishes the effects of trade openness on aggregate and disaggregated import elasticities for EAC countries. The paper uses the FMOL and DOLS panel data analysis techniques to estimate import elasticities, which is an important contribution of the paper to trade openness literature.
After testing for robustness of the results, our main findings show that the average tariff rate used as a measure for trade openness is negative and inelastic with respect to aggregate and disaggregated imports. This result implies that tariff rates negatively influence demand for imports. The policy implications from this result are that governments of EAC countries should use trade openness reforms, particularly the tariff rate to minimize the importation of goods that can be produced locally. This will help in managing the balance of trade.
The policy implications from this result are that governments of EAC countries should use trade openness reforms, particularly the tariff rate to minimize the importation of goods that can be produced locally. This will help in managing the balance of trade.
In this study, we have found that there are a number of directions which could be explored in future research. First, the use of highly disaggregated sector specific data. This will provide a more complete picture of effects of trade openness on disaggregated import demand. Furthermore; so many others factors that influence import demand could be analysed i.e., how institutional factors such as customs procedures affect import demand. Lastly the data set used in this study covers the period in which EAC countries had implemented the EAC Customs Union i.e. 1994 to 2012, this period could be extended to cover the implementation of the EAC Common Market.
Gaalya, M.S., Ed- ward, B. and Eria, H. (2017) Trade Openness and Disaggregated Import Demand in East African Countries. Modern Economy, 8, 667-689. https://doi.org/10.4236/me.2017.85048