Export Performance of Linseed and Its Determinant in Ethiopia

This empirical study investigates the determinants of linseed export performance of Ethiopia over a period 19832017. The study identified four factors influencing export performance in terms of export earned like GDP, gross capital formation as a ratio of GDP, openness to trade and world price. As a first step unit root analysis was conducted to test the stationarity of the variables. The variables were non stationary at levels but stationary at their first difference. Second co integration test were employed to check the existence of long run relationship between non stationary variables and found that on co integrating relationship exists in the long run. As a last step vector error correction model was employed to describe the dynamic interrelationship between variables both in the short and long run. The results derived from this study suggest that all variables are significantly influencing on the export in the long run. In the long run, GDP and openness to trade are found to have a positive impact whereas; world price and capital formation had a negative impact. In the short run capital formation and world price had significantly influencing export performance whereas: GDP and openness to trade was insignificant

good export potential earning like sesame and Niger seed. Increasing world demand for oilseeds and specialized product given the available production capacity made oilseed as important export crop (Bennet, 2004). As indicated in FAO data base world major linseed exporter includes Canada, Belgium, UK and USA. In the world market Ethiopia's major linseed trading partners include China, Belgium and Vietnam. The world market for oilseed is dominated by sesame but now a days Linseed is also exported to different world and contribute in export earnings. Thus analysis of determinants of export performance of linseed is important in identifying the possible factors affecting export performance in terms of earnings.
Therefore, the objectives of this study therefore to evaluate the performance of linseed export and its short run and long run determinant in Ethiopia using annual macroeconomic annual time series data of UNCTAD, ERCA, WBDI and FAO from a period of 1983-2017. The study considered limited factors including world price, gross capital formation, GDP and openness to trade. Moreover, Goldstein and Khan (1985) imperfect substitution model were applied. Fundamental assumption underlying the imperfect substitutes model is that neither imports, nor exports can be considered perfect substitutes for the domestic products. Perfect substitutes model, on the other side, assumes perfect substitutability between domestic and foreign goods and is typically used in the case of highly disaggregate data set. Since under the key assumption of the perfect substitutes model each country would be only an exporter or an importer of a traded good but not both, which is not observed in the real world, this model has attracted much less attention in the empirical studies than the imperfect substitutes model. The two models are usually perceived as competitors, but Goldstein and Khan (1985) suggest their possible coexistence: one should be applied in the case of aggregate and the other in the case of highly disaggregates data.
The export performance of an economy depends largely on GDP of a country. This can be argued that the output capacity of an economy is an indication for future supply capacity. Thus an increase in output will improve export earnings. According to Hailegiorgis, 2011 the output of the economy increases the supply capacity and export earnings improved. Increased world price had an effect on export earnings through increased incentive and competitiveness Openness of trade also had impact on export performance. The relative openness of a country to international trade would increase the export earnings and indicates the integration of a country into the world market. The openness of trade can be approximated by the sum of export and import of goods and service to GDP ratio.
Gross capital formation is an outlay on fixed asset of the economy and net changes in the level of inventories. It is one of the indicators of investment in the domestic economy (Mehrara and Musaai, 2013) usually included investment on infrastructural development. Infrastructure is a key determinant of export performance. In this study capital formation as a ratio of GDP is proxy for quality of infrastructure. Bhavan, 2016 found a negative coefficient of capital formation in the short run and insignificant capital formation coefficient in the long run.

RESEARCH METHODOLOGY Model Specification
The study was based on secondary data sources from different data base sources. Many macro-economic time series variable is expected to influence the export performance of oilseed crops, linseeds. But based on availability of the data selected factors were used for further analysis. Thus, time series data were extracted from FAO stat and World Bank data base. Annual macroeconomic time series were also extracted from different source. The study covers 34 years of data from 1983-2017.
As a first step, Unit root analysis was performed in order to test stationary properties of the variables because variables with non-stationary properties may produce spurious result if regression analysis is employed to test long run elasticities. Therefore, in order to overcome this issue the variables were tested by employing the ADF test method. As a second step, Co-integration Johansen maximum likelihood method was employed to test the long run relationship among the non-stationary variables used in this study. As a third step, the Vector Error Correction Model (VECM) was employed to describe the dynamic interrelationship among the variables. Therefore, the following model is expressed to analyze the determinants of the export performance of Linseed in Ethiopia.
The study identified export performance (value of exported commodity) of linseed in Ethiopia as a function of capital formation, openness of the country, world price and real output of a country. Imperfect substitution model by Goldstein and Khan (1985) were adopted here to express the analysis.
Linseeds export performance (EX) = f (cap, openness, Pw and RGDP) The log transformed function were used for the above equation for estimation and differentiating with respect to time give the variability in export earning as = + + + + + Where: Log EX is the value of exported commodity (export earned at time t), Log cap is the gross capital formation as a percentage of GDP, Log openness is export plus import as percentage of GDP (a proxy for degree of openness in log form), Log Pw is the price of commodity in the world and Log GDP is GDP of exporting country. β' s are the unknown parameter to be estimated, t is time in years from 1983-2017 and ε is the random term error that are independently and identically distributed with mean zero and constant variance. The above equation is estimated using time series analysis in Eview 10.

RESULT AND DISCUSSION
The classical linear regression model (CLRM) and VECM (vector error correction model) were used to estimate the data. The estimation begins with the testing of variables for unit root to determine whether they can be considered as a stationary or non-stationary process. Table 1 presents the Augmented Dickey Fuller (ADF) tests of variables. The tests showed that all the variables were non-stationary at level. The variables were stationary at their first difference. Critical values for tests were found to be -1. 95 and -2.63 at 5% and 10% respectively. Annex Table 1(a-e) gives details of unit root test outputs of variables. The notion behind co integration test assumed if there is a long-run relation-ship between two or more nonstationary variables, deviations from this long-run path are stationary. As all variables are integrated of the same order, the co-integration analysis can be performed to test the long run relationship between the variables. A Johannsen multi variate co integration test was employed to test for possible co integration amid variables. Trace test and Max Eigen criteria used in determining the number of co integrating equation. The maximum Eigen value test starts with the null hypothesis of at most r co-integrating vector against the alternative of r+1. The result for maximum Eigen value test confirms the rejection of the null hypothesis; i.e., no co-integrated vectors. Consequently, Max Eigen and trace value statistics indicates one co integrating equation at 5% significance level. In other words accepting there is one co integrating vector since in both trace and max Eigen criteria test statistic (86.8 and 42.8) is greater than critical value at 5%(69.8 and 33.8) respectively. Thus we can reject the null of more than one co integrating vector ( Table 2). The integrated variables were also examined to check for co integration in the long run. The normalized co integration equation also depicted long run coefficients of the model (table 3). As indicated above, by changing their sign, log openness and log rgdp are positive determinant of export performance of linseed in the long run while log pw and log cap are negative determinant. All variables are used in the logarithmic form thus; the estimated coefficients can directly be interpreted as long term elasticity.
Since that all determinants incorporated into the model are statistically significant at 1% level having a long run relationship. Thus, according to Johansen's method by reversing the signs of the coefficients, the model for Ethiopia's linseed export can be specified as follows: = −40.066 + 5.689 log + 5.89 & − 5.91 − 6.903 ) GDP and openness are positively related with export performance while negative relation was found between capital formation and world price. The result for impact of GDP of a country is also in accordance with Macroeconomic theories. As the output of the given economy increases more would be supplied to the world market. The coefficient for gross domestic product for the country is 5.68 which mean that a one percent change on real out GDP of country results 5.69 percent increase in export earnings. Similarly Openness as a measure of degree of involvement in international trade had a positive coefficient implied a 1% increase in openness of trade associated with a 5.89% increase in export earnings.
World price had negative and significant long term impact on export performance. This could be due to the case that an increase in the world price resulted in increasing the demand for quality product by importing countries which is usually unable to be met by developing countries exporters.
Capital formation as a proxy for government investment in infrastructure had significant negative impact at 1 percent significance level in decreasing export earnings in the long run. A 1% increase in gross capital formation resulted in a 6.9% decrease in export earnings. This may be due to capital formation affect the economy in different path other than the export sector After identifying the co integration equation our lagged residual for error correction term is identified. The equations were also tested for unit root and found to be integrated of order (I) and passes the ADF units root test (Annex Table 5). The final estimation was made after error correction term entered into VECM model (Annex Table 4). The equation identified ECT= log openness -0.378 log pw + 0.229log rgdp -0.556 log cap  Table 4) that world price, capital formation and the error correction term is significant and positive at 5% while the remaining factors are insignificant. The implication for their significance is to emphasize on the importance of this variable in explaining export performance of linseed in the short run. The ECM output indicated the coefficient 1.98 and 5.78 for world price and capital formation respectively show the short run impact of the two variables that cause changes in export earnings of linseed. That is world price and capital formation in the previous year affects current export performance. This is due to the fact that as world price increase, the incentive for domestic producers to produce more would increase similarly which in turn affects the export earnings. Capital formation also make domestic producers product easily available to the world market. The insignificance of GDP and openness of trade imply neither of them had a short run impact on export Developing Country Studies www.iiste.org ISSN 2224-607X (Paper) ISSN 2225-0565 (Online) Vol.9, No.12, 2019 14 performance. The coefficient of ECT indicates that the speed of adjustment to the long run equilibrium is significant and can be concluded that 76% of deviation would be eliminated annually.

CONCLUSION
The objective of this paper of this study was to evaluate the long run and short run impacts of determinants on linseed export performance over a period 1983-2017. The study investigates empirically the trend of export performance by analyzing world price, GDP, gross capital formation and openness to trade. The evidence from this study suggests that capital formation and world price significantly influence linseed export performance. The long run impact on export performance was explained by all the four factors. It was inferred that the short run implication of the significant variable clear that the incentive derived due to the increased world price usually accompanied with a high quality demand for imported commodities to developed world. This quality demand world market has to be satisfied by backup the export sector of the economy. One way of achieving this goal is investment in infrastructural development, facility and fixed asset. In the long run the increment in real out output of the economy and country's engagement in international trade will improve the export performance. Moreover investment on fixed asset capital towards the export sector also had positive impact. Finally other studies that incorporate more factors like foreign direct investment and importing countries variables would be important for further analysis of export performance of linseed.   Included observations: 33 after adjustments D(LOGEX) = C(1)*( LOGEX(-1) -5.68913177445*LOGRGDP(-1) -5.89350444465*LOGOPENESS(-1) + 5.90626346556*LOGPW(-1) + 6.90322022951*LOGCAP(-1) + 40.066530169 ) + C(2)*D(LOGDV(-1)) + C(3)*D(LOGRGDP(-1)) + C(4)*D(LOGOPENESS(-1)) + C(5) *D(LOGPW(-1)) + C(6)*D(LOGCAP(-1)) + C (7) Coefficient Std. Error t-Statistic Prob.