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The effect of domestic currency devaluation on trade balance in ethiopia
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The prime objective of the study was to investigate the effect of domestic currency devaluation on trade balance in Ethiopian over the period 1974-2016. To address the objective, ARDL and Error Correction Model were applied. The ARDL bound test result shown that There is a long run relationship between real effective exchange rate, real domestic income and lending interest rate.

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Research Journal of Finance and Accounting www.iiste.org<br />
ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)<br />
Vol.11, No.1, 2020<br />
<br />
<br />
The Effect of Domestic Currency Devaluation on Trade Balance<br />
in Ethiopia<br />
Weldeslasie Teklencheal Berhe<br />
Ethiopian Police University college, student admission and registrar office<br />
<br />
Kidanemariam Gidey Gebrehiwot<br />
Ethiopian Civil Service University; College of Finance, Management and Development; department of<br />
development Economics and Management<br />
<br />
Abstract<br />
The prime objective of the study was to investigate the effect of domestic currency devaluation on trade balance<br />
in Ethiopian over the period 1974-2016. To address the objective, ARDL and Error Correction Model were applied.<br />
The ARDL bound test result shown that There is a long run relationship between real effective exchange rate,<br />
real domestic income and lending interest rate .Specifically, real effective exchange rate, real domestic income and<br />
lending interest rate have a significant and positive effect on trade balance; whereas money supply and government<br />
expenditure deteriorated trade balance while deposit interest rate was insignificant in the long run. This clearly<br />
shows that both elasticity and absorption approaches improved the trade balance but monetary measures have<br />
deteriorated it. Hence; the government should take follow contractionary monetary policy and absorption approach<br />
through productivity improvement, diversification of export sectors and expansion of import computing industries<br />
to overcome trade deficit.<br />
Keywords: Ethiopia, Trade Balance, Currency Devaluation, ARDL Bound test.<br />
DOI: 10.7176/RJFA/11-1-03<br />
Publication date: January 31st 2020<br />
<br />
1. Introduction<br />
Our world faces insufficient match between limited resource and unlimited human wants. To solve this mismatch,<br />
many countries are trading their production in international market by determining their currency value with<br />
respect to US dollar. This international trade critically affected by exchange rate system and developing countries<br />
are vulnerable to this system (Kibret, 1994).<br />
Issue of currency devaluation or appreciation levels and their relationship with economic variables have a<br />
great deal of discussion since mid-2000s, while global imbalances started to widen (Hacioglu and Dincer, 2013).<br />
Because import and export orders are processed several months in advance the devaluation of currency is mostly<br />
synonymous with deterioration of trade balance in the short run (Paul and Maurice, 2012). The economic concepts<br />
of J-curve and depreciation of currency closely related which worsen the trade balance in short run since volume<br />
of the imports remains stable but in the long run there is increase in exports and reduction in imports (Magee,<br />
1973). The exchange rate affects trade balance through its effects on competitiveness; the appreciation of domestic<br />
currency increases cost of production and the country decreases its competitiveness in the global market and this<br />
leads to trade deficit (Morck et al., 2000). During the three successive regimes in Ethiopia pre 1974, totalitarian<br />
Derg period and post 1991 shown that the country’s external trade policies were free trade, controlled trade policies<br />
and once again back to free trade policy and the exchange rate was regarded as fixed in pre 1990 and post 1990<br />
was floated. Under the structural adjustment program with the support of international monetary fund and World<br />
Bank during 1992/93 the Ethiopian birr was devaluated from 2.07 birr to 5 birr per US dollar and its devaluation<br />
was continuously reached about 27.41 in 2017 with objective of balancing external sector in general and improving<br />
account balance and boost exports specifically and to stay competitive on price (NBE, 2018).<br />
<br />
2. Literature Review and Problem Statement<br />
The economic reason for currency devaluation is for contractionary effects of devaluation on aggregate demand<br />
and to increases the price of imports relative to domestic goods and lowering imports and stimulating the demand<br />
for exports. As a result of currency devaluation domestic price level will go up and this price level has also two<br />
consequences. First, it reduces private spending and aggregate demand. Second, it also provokes the redistribution<br />
of income because it shifts income from wage earners to profit recipients (Setzer, 2006).<br />
Developing countries’ economy was suffered to various economic problems like current account deficit,<br />
declining foreign currency reserve, high inflation at home market. To handle these problems some countries<br />
impressed to stabilization and Structural Adjustment Program (SAP) with support of IMF and World Bank<br />
(Edwards and Savastano, 1999).<br />
Ethiopia’s export is characterized by high commodity and geographic concentration, susceptibility to external<br />
shocks, dependence on agricultural export that in turn depends on vagaries of nature, high price and low-income<br />
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elasticity of demand, and low supply response. On the other hand, imports intrinsically are highly price inelastic<br />
which are either necessities in production or consumption or very strategic commodity and are invariably required<br />
by the country. Therefore, the gap between export-import leads to a persistent deficit trade balance in the country.<br />
The existence of persistent trade deficit is fundamentally structural in nature in Ethiopia, because imports are<br />
essential that they are price inelastic and cannot simply be discouraged or easily substituted while exports are<br />
highly concentrated on agricultural primary commodities that are very sensitive to whether condition and price<br />
shocks (Lencho, 2013).<br />
Since Ethiopia depends on export of agricultural product and import of capital goods; devaluation deteriorates<br />
trade balance even if increasing currency devaluation was expected to encourage the export sector and still the<br />
country’s trade balance shows continuous trade deficit (MOFED, 2010). This case leads to ambiguity for the<br />
currency devaluation policy measurement. Whatever the case is, Ethiopian government still used currency<br />
devaluation approach from among the absorption and monetary approaches as macroeconomic policy to improve<br />
trade balance and many researchers were dealt about it and the ambiguity for the currency devaluation is continued.<br />
There are also arguments among researchers about the effects of currency devaluation on trade balance of<br />
Ethiopia, such studies conducted by Kibre (1994), Asmamaw (2008), Befirdu (2014) and Fassil (2017) shown that<br />
birr devaluation improved Ethiopian trade balance but in other round studies conducted by Kidane (1999) and<br />
Alemu(2008) indicated that birr devaluation deteriorated trade balance since the country’s rate of import is much<br />
higher than the rate of export.<br />
Studies that have done on the relationship of currency devaluation and trade balanc; such as effect of exchange<br />
rate movement on trade balance in Ethiopia (Lencho, 2013) and trade balance and exchange rate (Gebeyehu, 2014)<br />
were studied the effects of currency devaluation on trade balance using Johnson’s Co-integration technique.<br />
However; the Johnson’s Co-integration technique is one of the widely used methods of time series analysis,<br />
its outcome could not be reliable for small sample size (Pesaran and Shin, 1997; Narayan, 2005) relatively, the<br />
Autoregressive distributed lag method of co-integration has more advantage over the Johnsons method (Pesaran<br />
and Shin, 1997; Pesaran and Shin, 1999; Pesaran, Shin, and Smith, 2001; Harris and Sollis, 2003; Narayan, 2005<br />
and Chaudhry, 2006). Hence this paper has used Autoregressive distributed lag method to provide valid empirical<br />
evidence and to avoid the problem of biasness that arises from small sample size.<br />
Therefore, these arguments, government measurement of currency devaluation to improve trade balance and<br />
the methodological gaps motivated the researcher for the problem statement of this study that considered<br />
investigating whether currency devaluation improves trade balance based on empirical analysis of trade balance<br />
responsiveness for birr devaluation in short run and long run. The objective of this research was to empirically<br />
investigate the effect of domestic currency devaluation on trade balance in Ethiopia in both in the short run and<br />
long run3. Materials and Methods<br />
According to Magazzino et al (2012), there are various aspects to time series analysis but the most common<br />
is to fully exploit the dynamic structure in the data which extracts as much information as possible from the past<br />
history of the series. The key purpose of time series analysis is to capture and examine the dynamics of the data.<br />
In the context of time series regression, the idea that historical relationships can be generalized to the future is<br />
formalized by the concept of stationarity (Binh, 2013).<br />
According to Gujarati (2004), the so-called stationary stochastic process means mean and variance are<br />
constant over time and the value of the covariance between the two periods depends only on the distance or gap<br />
or lag between the two time periods and not on the actual time at which the covariance is computed.<br />
The concept of co integration was first introduced by Granger (1981) and elaborated further by Engle and<br />
Granger (1987), Engle and Yoo (1987), Phillips and Ouliaris (1990), Stock and Watson (1988), Phillips (1986 and<br />
1987), and Johansen (1988, 1991, and 1995). It is known that trended time series can potentially create major<br />
problems in empirical econometrics due to spurious regressions. One way of resolving this is to difference the<br />
series successively until stationary is achieved and then use the stationary series for regression analysis.<br />
<br />
3.1. Time series models /ARDL, VECM and VAR<br />
In a time series data type there are different models which mainly categorize based on the variables order<br />
stationarity and existence of co integration. If a group of time series variables are individually integrated of the<br />
same order and if at least one linear combination of these variables is stationary, then the variables are said to be<br />
co-integrated (Harris, 1999 and Enders, 2004). This tells us a long-run relationship between the variables. Vector<br />
auto regression (VAR) was introduced by Sims (1980) as a technique that could be used by macroeconomists to<br />
characterize the joint dynamic behavior of a collection of variables without requiring strong restrictions of the kind<br />
needed to identify underlying structural parameters. It has become a prevalent method of time-series modeling.<br />
Both Engle-Granger Approach and Johansen co integration techniques used tests of co integration variables<br />
of the same order of staionarity and large sample size. Pesaran and Shin (1997, 1999, and 2001) have developed<br />
a new ARDL model which has more advantages than both Engle-Granger Approach and Johansen co integration<br />
techniques and this approach can solve the problems which created during small sample size and variables of<br />
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different order of co integration I(0) and I(1). The ARDL co integration test uses bound tests rather than Johansen<br />
co integration test. ARDL procedure provides statistically significant result in small samples that means, it avoids<br />
the problem of biasness that arises from small sample size (Chaudhry, 2016).<br />
<br />
3.2. Model Specification<br />
In an open economy financial markets different scholar have designed different conceptual frameworks that<br />
incorporate currency devaluation as one of the determinant factor of trade balance of a given country. Among those<br />
scholars, Fassil (2017) and, Abebe (2014) had accommodated birr devaluation as an independent factor of trade<br />
balance of Ethiopia in their empirical analysis as well as money supply, real gross domestic product and interest<br />
rates also used as determinant factors for trade balance. These researchers had employed log linear transformation<br />
model as their framework specification to analyze relationship between the dependent and independent variables.<br />
The overall objective of this study was to test the effect of currency devaluation on the trade balance of Ethiopia<br />
for the period 1974-2016 using Autoregressive Distributed Lags (ARDL) because of small sample size and<br />
variables are co integrated of I (0) and I (1). Therefore, ARDL is advantageous over the Engle-Granger and Vector<br />
Error Correction Models (Pesaran and Shin, 1997, 1999, and 2001). Since the objective of this study was<br />
consolidated about the trade balance improvement approaches such that elasticity, absorption and monetary<br />
approaches the model was derived based on these three approaches in line with methodology at hand (Piontkivsky,<br />
1999):<br />
Using the synthesis equations of the trade balance approaches (the absorption, elasticity and monetary<br />
approaches) based on the international fiancé and by splitting interest rate in to lending and deposit interest rates<br />
the specified model for this study is:<br />
TBt=ß0 + ß1 REERt + ß2 Yt + ß3 Gt + ß4 MSt + ß5 RLt + ß6 RDt<br />
Since the study focused on responsiveness of trade on the change of domestic currency value they shall be<br />
transformed in to logarithm in order to deal the elasticity issues of currency devaluation on Ethiopian trade balance<br />
over the period 1974 to 2016. Note that, TB is negative value but it was considered as positive, only for the purpose<br />
of logarithm and in the interpretation case the negative sign was granted. Therefore;<br />
LTBt=ß0 + ß1 LREERt + ß2 LYt + ß3 LGt + ß4 LMSt + ß5 LRLt + ß6 LRDt +Ut<br />
Where, LTBt = Logarithm of trade balance at time t.<br />
LREER t = Logarithm of real effective exchange rate at time t.<br />
LY t = Logarithm of domestic real income at time t.<br />
LG t = Logarithm of government expenditure at time t.<br />
LMSt =Logarithm of money supply at time t.<br />
LRL t = Logarithm of lending interest rate at time t.<br />
LRD t = Logarithm of deposit interest rate at time t.<br />
Ut = The random error at time t.<br />
The regression of the trade balance as it appears in Equation 8 has derived from the perspective of three<br />
approaches trade balance improvement (elasticity, absorption and monetary). The elasticity approach focuses on<br />
real effective exchange rate (REERt) as the determinants of the trade balance and thus the relevant coefficient is<br />
β1 . The monetary approach assumes money supply (MSt) as the determinants of the trade balance and hence the<br />
relevant coefficient is β4 and absorption approach considers domestic real income (Yt) as key determinants of the<br />
trade balance and hence the relevant coefficient is β2 . In this study of effect of currency devaluation on trade<br />
balance the variable REERt is targeted and its coefficient β1 is our key interest and expected signs of β2 and β4<br />
is negative whereas β1 is undetermined (Salvatore, 2001).<br />
<br />
3.3. Method of Data Analysis<br />
The descriptive statistics and econometric methods have employed throughout this study to discuss and analyze<br />
the time series data. Initially examining the characteristics (stationarity and long run relationship behaviors of<br />
variables) of the time series data is mandatory before any model estimation. Auto Regressive Distributed Lag<br />
(ARDL) used to examine characteristics of the time series data rather than Vector Error Correction Model (VECM)<br />
/Johnson because in this study the observations are small which is 43 and variables were I (0) and I (1). Before<br />
running any time series regression analysis testing variable stationarit is required. Main objective of stationarity<br />
test is to get variable which has a constant mean, variance and covariance because regression results from time<br />
series data may generate spurious regression if the variables are non-stationary. To check whether long run<br />
relationship between all variables exist or not bound test/co-integration has applied and Augmented Dickey-<br />
Fuller(ADF ) was used for test of stationarity or unit root test based on the null hypothesis that a unit root is exist<br />
in the time series data(Guajarati, 2004).<br />
ADF tested the unit root both at level and at difference for the variables which included in the model. Data<br />
have analyzed using EViews 9 software tool. The econometric methods that investigated effects of independent<br />
variables on dependent variable were treated in logarithmic form in order to minimize inflationary effect and to<br />
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explain the response in percentage/elasticity.<br />
Table 3.1: Tests of time series data<br />
Diagnostic Test Type of Test Null Hypothesis Test Category<br />
Stationarity ADF unit root Test There is unit root Pre- estim.<br />
Co integration Bound Test No Co integration Pre- estim.<br />
Normality Jarque-Bera Normally distributed Post- estim.<br />
Serial Correlation Breush –Godfrey LM No Serial Correlation Post- estim.<br />
Heteroscedasticity Breush–Pagan-Godfrey No Heteroscedasticity Post- estim.<br />
Model specification Ramsey RESET Test Model specified well Post- estim.<br />
Model stability CUSUM Test ——————– Post- estim.<br />
Source: Guajarati (2004). Note: estim. represents estimation<br />
The above tests have undertaken to check the robustness of the estimated models which have specified in<br />
equations (17), (18) and (19) but test of multicolinearity left out because it is soundless test in lagged same variable<br />
of time series data as well as finally data results, interpretation and finding have summarized accordingly.<br />
To check a structural breakpoint over the 1974 to 2016 on Ethiopian trade balance due to the exchange rate<br />
regimes of Dergue (Fixed rate) and EPDRF (managed floating rate); graphical stability diagnostics tests has been<br />
tested by applying the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of squares of<br />
recursive residuals (CUSUMSQ) test. These stability tests have recommended by Pesaran and Shin (1999, 2001).<br />
Model stability test using cumulative sum of recursive residuals (CUSUM test) could be replaced for Chow test<br />
of structural breakpoint.<br />
<br />
4. Result and Discussion<br />
1.4<br />
<br />
<br />
1.2<br />
<br />
<br />
1.0<br />
<br />
<br />
0.8<br />
<br />
<br />
0.6<br />
<br />
<br />
0.4<br />
<br />
<br />
0.2<br />
<br />
<br />
0.0<br />
1975 1980 1985 1990 1995 2000 2005 2010 2015<br />
<br />
LTB LREER<br />
Source: National Bank of Ethiopia (2018)<br />
Fig.4.1. Trend of trade balance (LTB) and real effective exchange rate (LREER) [1974 to 2016])<br />
Figure 4.1. shown that both trade balance and real effective exchange rate increased over the period 1974 to<br />
2016. Over the period 1974 to 1981 whatever exchange rate was constant trade balance is still increased due to<br />
other macro variables.<br />
<br />
4.1. Optimum lag selection criteria using AIC, SIC and HQIC<br />
The second step after test of stationarity using ADF unit root is determining the optimum lag. In the time series<br />
data analysis, there is a tradeoff between differencing of data till the data become stationary and as a result<br />
information also lost. This process of removing fear of spurious regression and information lost tradeoff become<br />
a challenge in time series data. To overcome this problem of tradeoff selection of optimum lag length is required.<br />
The ARDL model used information criterias AIC, SIC and HQIC with the principle that, the lowest value is the<br />
best ARDL model in that lag and that lag has considered as a optimum lag of the model.<br />
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Table 4.2: Optimum lag selection criteria using AIC, SIC and HQIC.<br />
Number of lags AIC SIC HQIC Selected Optimum lag<br />
Lag 1 -2.073 -1.742 -1.952<br />
Lag 2 -2.117 -1.615 -1.934<br />
Lag 3 -2.389 -1.544 -2.084<br />
Lag 4 -6.187* -4.737* -5.667 Lag 4<br />
Lag 5 -6.027 -4.231 -6.157<br />
Lag 6 -6.154 -4.526 -6.891<br />
Lag 7 -6.002 -3.987 -7.301*<br />
Lag 8 -5.689 -3.456 -7.021<br />
Note: More IC is granted and the low the value of IC is the best the model is in that lag number. Akaike info<br />
criterion (AIC), Schwarz info criterion(SIC) and Hanan-Quinn info criterion (HQIC)<br />
Therefore the optimum lag length for the ARDL is lag 4 in which values of AIC and SIC are lowest at lag 4<br />
while value of HQIC is lowest at lag 7 but both AIC and SIC are granted than HQIC alone and it obliged to accept<br />
this lag due to the principle stated.<br />
<br />
4.2. Long run ARDL bounds test for co-integration<br />
Based on the specified optimum lag length the ARDL bounds test approach of co-integration is applied. In this<br />
study Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) were taken as a guide and a<br />
maximum lag order of 4 was chosen for the conditional ARDL model. Then F-test through the Wald-test (bound<br />
test) is performed to check the joint significance of the coefficients. The Wald test is conducted by imposing<br />
restrictions on the estimated long-run coefficients of trade balance, real effective exchange rate, real domestic<br />
income, government expenditure, money supply, lending interest rate and deposit interest rate. The hypothesis<br />
tests defined as:<br />
H0: The given variables have not long run association ship / there is no co integration<br />
H0: π1=π2 = π3= π4= π5= π6 = π7 =0.<br />
H1: The given variables have long run association ship / there is co integration<br />
H1: π1 ≠ π2 ≠ π3 ≠ π4 ≠ π5 ≠ π6 ≠ π7 ≠ 0<br />
To decide whether H0 reject or fail to reject the computed F-statistic value is compared with the lower bound and<br />
upper bound critical values tabulated in Table CI (III) case IV of Pesaran, Shin, and Smith (2001).<br />
Table 4.3: ARDL bounds test and Pesaran et al. (2001) lower and upper bound cv<br />
Bounds Test for Co integration analysis Pesaran (2001) critical values for K=6<br />
Description Value At 1% LS At 5% LS<br />
Included obserbvation 39 Lower bound 3.15*** Lower bound 2.45**<br />
Optimum Lag length of the Model 4 Upper bound 4.43*** Upper bound 3.61**<br />
Calculated F-statistic 8.11<br />
Source: Pesaran, Shin, and Smith (2001) table and own computation (2018)<br />
Note: The *** and ** sign indicates the rejection of the null hypothesis of no long- run relationships exist at 1%<br />
and 5% significant level respectively. Critical Values are cited from Pesaran et al. (2001) Table and K =6 is the<br />
number of regressors in ARDL long –run model.<br />
As shown above, table 5 is the combination of two tables of ARDL bounds test and Pesaran et al. (2001)<br />
lower and upper bound critical values with an intercept and trend. It depicted that the calculated F statistics 8.11<br />
is higher than the Pesaran, Shin, and Smith (2001) upper bound critical value (4.43) at 1 percent level of<br />
significance. This implied that the null hypothesis of no long- run relationships exist is rejected rather than its<br />
alternative hypothesis (there is long-run relationship) is accepted based on the Pesaran, Shin, and Smith (2001)<br />
critical value at 1 percent level of significance. This concluded that the variables of the ARDL model trade balance,<br />
real effective exchange rate, gross domestic product, government expenditure, money supply, lending interest rate<br />
and deposit interest rate are moving together in the long-run. Therefore, the ARDL model could be estimated<br />
because all these macro variables have a long run association ship.<br />
<br />
4.3. Long-run ARDL model estimation.<br />
The above table 5 results had shown the existence of a long-run relationship among trade balance, real effective<br />
exchange rate, gross domestic product, government expenditure, money supply, lending interest rate and deposit<br />
interest rate. After confirming the existence of long-run co-integration among these variables, the estimated long-<br />
run relationship between the variables have estimated and the estimated coefficients after normalizing on trade<br />
balance have reported in table 4.4 below:<br />
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Table 4.4: Estimated long run coefficients using ARDL (4, 4, 3, 4, 4, 4, 4) (TB is DV)<br />
Regressors Coefficinets Std.Error t-Statistic Prob<br />
LREER -0.5288 0.1215 -4.3523 0.0428**<br />
LY -0.6473 0.2622 -2.4687 0.0319**<br />
LG 1.3326 0.2291 5.8191 0.0432**<br />
LMS 3.5411 0.3975 8.9093 0.0009***<br />
LRL -3.6721 0.2606 -14.0886 0.0411**<br />
LRD 3.3267 0.2203 15.1023 0.1025<br />
Constant -3.4993 1.5477 -2.2609 0.0866<br />
Trend 0.0065 0.0236 0.2771 0.7954<br />
Long run model is LTB=-3.4993 -0.5288LREER – 0.6473LY + 1.3326LG+3.5411LMS – 3.6721LRL + 3.3267<br />
LRD<br />
R-Squared 0.9518 Mean of dep. Var 0.5118<br />
Adjusted R-Squared 0.9225 S.D. of dep. Var 0.1604<br />
S.E of Regression 0.0142 RSS 0.00806<br />
F-Statistic 142.7082 Log likelihood 155.018<br />
Prob(F-Statistic) 0.00103 AIC -6.1518<br />
DW-Stat 2.2237 SIC -4.6618<br />
Source: Own computation using Eviews9 package (2018)<br />
Note: The *** and ** sign indicate the rejection of the null hypothesis at 1% and 5% significant level respectively.<br />
The coefficient covariance matrix method is Ordinary<br />
The above report (table 6) depicted that the long-run model is statistically significant/adequate at 1 percent<br />
level of significance since prob (F- statistic) 0.00103 less than 0.01and the coefficient of determination (R-squared)<br />
is high explaining that about 95.18 percent of variation in the trade balance attributed to variations in the<br />
explanatory variables included in the model and there is no evidence of spurious regression since DW-Stat (2.2237)<br />
is greater than R-squared (0.9518).<br />
The estimated coefficients of real effective exchange rate and real domestic income have the expected signs<br />
but money supply have unexpected sign. This study concentrated on the devaluation theories such that elasticity<br />
(coefficient of LREER), absorption (coefficient of LY) and monetary (coefficient of LMS) theories. Therefore, in<br />
the trade balance of Ethiopia both elasticity and absorption approaches of trade balance deficit problem solving<br />
mechanisms have matched with theoretical signs where as sign of monetary approach has not fitted in Ethiopia as<br />
compared with the economic theory. And the estimated coefficients of real effective exchange rate (at 5 percent<br />
level), real domestic income (at 5 percent level), government expenditure (at 5 percent level), money supply (at 1<br />
percent level) and lending interest rate (at 5 percent level) are statistically significant while deposit interest rate is<br />
not statistically significant.<br />
The trade balance model has specified in a log-linear form under this study, and then coefficient of the<br />
dependent variable trade balance can be interpreted as elasticity/percentage change with respect to these<br />
explanatory variables. The coefficient of log real effective exchange rate is -0.5288, this indicates that, in the long<br />
run, holding other things constant, a one percent change in real effective exchange rate (appreciation or<br />
depreciation of birr against US dollar) brought 0.5288 percent change in trade balance. With understood<br />
characteristics of Ethiopian trade deficit behavior the negative sing of LREER indicates that, as birr devaluated by<br />
one percent against US dollar the trade balance improved by 0.5288 percent. But, when birr is appreciated by one<br />
percent against US dollar the trade balance has been deteriorated by 0.5288 percent.<br />
More than of the real effective exchange rate, real domestic income has significant long run impact on the<br />
Ethiopian trade balance. This indicates that, in the long run, holding other things constant, a one percent change in<br />
real domestic income brought 0.6473 percent change in trade balance. Under the Ethiopian trade deficit<br />
characteristics, the negative sing of LY also indicates that, as Ethiopian real income increased by one percent the<br />
trade deficit improved by 0.6473 percent while if it decreased by one percent the trade balance has been<br />
deteriorated by 0.6473 percent.<br />
The coefficient of log money supply (LMS) is 3.5411. This indicates that, in the long run, holding other things<br />
constant, a one percent change in money supply brought 3.5411 percent change in trade balance. With the<br />
Ethiopian trade deficit behavior, the unexpected positive sign of MS indicates that, as money supply increased by<br />
one percent trade balance has been deteriorated by 3.5411 percent while if it decreased by one percent the trade<br />
deficit improved by 3.5411 percent.<br />
Finding of this study along the long run effect of real effective exchange rate is consistent with the positive<br />
effect of birr devaluation on trade balance with respect to the researches made in Ethiopia by Haile (2008), Abebe<br />
(2014) and Fassil (2017). But the unexpected sign of the coefficient of money supply (MS) contradicts with<br />
monetary approach of trade deficit improvement mechanism. The reason behind being contradicted with the<br />
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monetary approach devaluation theory due to the unexpected sign of money supply may as well as unexpected<br />
sign of government expenditure could be due to data and/or valuation problem, but it is difficult to justify the exact<br />
reason behind such unexpected signs using this research. Hence, further detailed research should be done to<br />
identify the reason behind such unexpected sign of money supply coefficient.<br />
Both government expenditure and lending interest rate have maximum significant effect on trade balance<br />
with positive and negative coefficient sign respectively. The positive sign of government expenditure deteriorated<br />
the trade balance in the long run when government expenditure increased. While lending interest rate has a negative<br />
coefficient sign that improved Ethiopian trade balance at the time percentage increment of lending interest rate.<br />
This depicted that, in the long run, holding other things constant, a one percent change in government expenditure<br />
and lending interest rate brought 1.3326 and 3.6721 percent change in trade balance respectively.<br />
15<br />
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10<br />
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5<br />
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0<br />
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-10<br />
<br />
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-15<br />
94 96 98 00 02 04 06 08 10 12 14 16<br />
<br />
CUSUM 5% Signific anc e<br />
Fig 4.1: Cumulative sum of recursive residuals<br />
<br />
1.4<br />
<br />
1.2<br />
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1.0<br />
<br />
0.8<br />
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0.6<br />
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0.4<br />
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0.2<br />
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0.0<br />
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-0.2<br />
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-0.4<br />
94 96 98 00 02 04 06 08 10 12 14 16<br />
<br />
CUSUM of Squares 5% Significance<br />
Fig 4.2: Cumulative sum of squares of recursive residuals<br />
Source: Own computation using EViews 9(2018)<br />
Note: The straight lines represent critical bounds at 5% significance level<br />
To check whether there was a structural breakpoint over the 1974 to 2016 on Ethiopian trade balance due to<br />
the exchange rate regimes graphical stability diagnostics tests has been tested by applying the cumulative sum of<br />
recursive residuals (CUSUM) and the cumulative sum of squares of recursive residuals (CUSUMSQ) test. The<br />
graphical stability tests used not only identifying their significance but also it tells us at what point of time a<br />
possible structural break (instability) has occurred. Here, model stability test using cumulative sum of recursive<br />
residuals (CUSUM) could be replaced for Chow test structural breakpoint. If the plot of CUSUM / CUSUMSQ<br />
statistic moves between the critical bounds/red lines (at 5 percent significance level), then the estimated<br />
coefficients or the model/system are/is said to be stable in the long run / short run. The graphical plots of CUSUM<br />
and CUSUMSQ have shown below in Fig. 4.1 and Fig. 4.2 respectively.<br />
Figure 4.1 reported that the plot of CUSUM test did not cross the lower and upper red lines critical limits<br />
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which indicated that, the estimate is stable and there is no any structural break in the long run. But CUSUMSQ<br />
test reported in Figures 4.2 shown that the graph crosses the lower and upper red lines critical limits around the<br />
year 2008 and 2012 and immediately recovered. Therefore, we can conclude the short run estimate is not stable<br />
there are structural breakpoints in the year 2008 and 2012. Since our target is the long run estimation the results<br />
of the estimated long run model was stable, reliable and efficient.<br />
<br />
4.4. Short run error correction estimation<br />
Since the long run model coefficients of the trade balance have discussed above; the short run correction model<br />
estimation necessary in order to deal the rate of adjustment and to see the responsiveness of trade balance over the<br />
currency devaluation in short run. The below report (table 4.5) depicted that the short run model is statistically<br />
significant/adequate at 1 percent level of significance since prob (F- statistic) 0.000008 less than 0.01. The<br />
coefficient of determination (R-squared) is high explaining that about 89.92 percent of variation in the trade<br />
balance attributed to variations in the explanatory variables included in the model and there is no evidence of<br />
spurious regression since DW-Stat (2.1808) is greater than R-Squared (0.8992).<br />
The error correction model coefficient was matched with the hypothesized sign and magnitude. Thus, it<br />
estimated – 0.27056 which is statistically significant at 1 percent level of significance since p-value 0.0001 is less<br />
than 0.01 which indicated that the system is getting adjusted for the long run disequilibrium. Approximately 27.056<br />
percent of the disequilibrium from the previous year’s shock converges back to the long-run equilibrium in the<br />
current year. This expected sign and significant error correction term is also additional evidence for the existence<br />
of a stable long run relationship.<br />
In short run all the explanatory variables were significant because in all independent variables P-value is less<br />
than 5 and 1 percent significant level and there is no evidence of spurious regression since DW-Stat (2.180813) is<br />
greater than R-squared (0.899160) but in long run deposit interest rate was not significant. There is a significant<br />
effect of real effective exchange rate at 1 percent significant level in the first, third and fourth lags and at 5 percent<br />
significant level in the second lag on trade balance. This implying that the current real effective exchange rate<br />
would still affect the trade balance in the next three and four years as well as the current real domestic income,<br />
government expenditure, money supply, lending and deposit interest rates would affect the trade balance for the<br />
coming three and four years. A positive sign of real effective exchange rate coefficients in lag 1 and lag 2 implied<br />
that depreciation of Ethiopian birr against US dollar deteriorated trade balance of the country however, a negative<br />
coefficient in lag 3 and 4 shows improvement in the other way around.<br />
In econometrics, an endogeneity problem occurs when an explanatory variable is correlated with the error<br />
term. Endogeneity can arise as a result of measurement error, autoregression with autocorrelated errors,<br />
simultaneous causality, omitted selection, and omitted variables. Dependent variable generated within a model and,<br />
therefore, a variable whose value is changed (determined) by one of the functional relationships in that model. But<br />
in this research model there is no evidence of existence of endogeneity problem based on the result of covariance<br />
matrix<br />
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Table 4.5: Error correction representation for ARDL model (4, 4, 3, 4, 4, 4, 4) (DV is ∆DLTB )<br />
Variable Coefficient Std. Error t-Statistic Prob.*<br />
<br />
<br />
∆LREER(-1) 0.659644 0.158107 4.172146 0.0087***<br />
∆LREER(-2) 0.514201 0.192826 2.666659 0.0445**<br />
∆LREER(-3) -1.737974 0.207143 -8.390228 0.0004***<br />
∆LREER(-4) -0.557236 0.093644 -5.950557 0.0019***<br />
∆LY(-1) -2.512076 0.246553 10.18877 0.0002***<br />
∆LY(-2) -2.772354 0.172853 -16.03883 0.0000***<br />
∆LY(-3) -0.688404 0.319302 -2.155963 0.0836*<br />
∆LG(-1) -1.776721 0.224002 -7.931713 0.0005***<br />
∆LG(-2) 0.549056 0.186568 2.942931 0.0321**<br />
∆LG(-3) 1.224293 0.177496 6.897572 0.0010***<br />
∆LG(-4) 0.783109 0.095581 8.193130 0.0004***<br />
∆LMS(-1) 0.862695 0.334950 2.575596 0.0497**<br />
∆LMS(-2) -1.779603 0.344054 -5.172459 0.0035***<br />
∆LMS(-3) -1.897248 0.270130 -7.023458 0.0009***<br />
∆LMS(-4) 0.390057 0.184771 2.111022 0.0885*<br />
∆LRL(-1) 3.044785 0.265903 11.45071 0.0001***<br />
∆LRL(-2) -1.501056 0.222782 -6.737784 0.0011***<br />
∆LRL(-3) -1.706466 0.200488 -8.511543 0.0004***<br />
∆LRL(-4) -1.273022 0.151834 -8.384272 0.0004***<br />
∆LRD(-1) -1.401507 0.221706 -6.321477 0.0015***<br />
∆LRD(-2) 0.457893 0.117546 3.895436 0.0115**<br />
∆LRD(-3) 1.076085 0.148677 7.237755 0.0008***<br />
∆LRD(-4) 0.856770 0.111392 7.691488 0.0006***<br />
Constant -3.914122 0.355552 -11.00859 0.0001***<br />
Trend 0.006546 0.023617 0.277178 0.7954<br />
ECM(-1) -0.270558 0.257809 -4.540405 0.0001***<br />
R-squared 0.899160 F-statistic 180.3245<br />
Adj. R-squared 0.794520 DW- stat 2.180813<br />
Prob(F-statistic) 0.000008 AIC -6.1518<br />
Where, ECM =LTB+0.5288*LREER+0.6473*LY-1.3326*LG-3.5411*LMS+3.6721*LRL-<br />
3.3267*LRD+0.3.4993*Constant-0.0065*Trend<br />
Source:Own computation using Eviews 9(2018)<br />
Note: The *** and ** sign indicates the rejection of the null hypothesis at 1% and 5%<br />
<br />
5. CONCLUSION AND POLICY IMPLICATION<br />
5.1. Conclusion<br />
The main finding of this paper is that in the long run birr devaluation has improved the trade balance by 0.5288<br />
percent when birr is devaluated by 1 percent against US dollar but in the short run it deteriorated trade balance in<br />
which 1 percent birr devaluation brought 0.6597 and 0.5142 percent decreased in trade balance at the immediate<br />
two years after devaluation and improves over time.<br />
Real domestic income of the country is a major contributor to improve trade deficit problem rather than birr<br />
devaluation in the short and long run; in the short run it brought about 2.5121 and 2.7723 percent in the first two<br />
years and in long run about 0.6473 percent improvement of trade balance due to 1 percent increment. While money<br />
supply and government expenditure are negatively affected trade balance during the study period which<br />
deteriorated by 5.54 and 1.33 percent for 1 percent of a percentage increase respectively as well as percentage<br />
increase of lending interest rate has contributed on trade balance improvement.<br />
Therefore, the results revealed that absorption approach is better than elasticity approaches to improve<br />
Ethiopian trade balance in both short run and long run, then it could be concluded that birr devaluation approach<br />
is less appropriate tool to improve trade balance as compared absorption approach.<br />
In the short run, the coefficient of error correction term is -0.27056 which suggested that the system getting<br />
adjusted annually about 27.056 percent towards the long run equilibrium. CUSUM model stability test and Ramsey<br />
RESET model specification test revealed that existence of stable long run model and the model was specified as<br />
well. This system stability means that there is no any evidence of structural break in the long run due to exchange<br />
rate regimes of Dergue and the current governments but in short run there were two breakpoint years (in 2008 and<br />
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2012) but they recovered immediately.<br />
In the short run estimation model real domestic income affects negatively trade balance by 2.5121 percent<br />
deterioration for a percentage increase in one period lagged value whereas in two and three period lagged values<br />
it affects positively by 2.7723 and 0.6881 percent improvement for a percentage increase respectively. Real<br />
effective exchange rate improves trade balance in the long run but in the short run at one and two period lagged<br />
values deteriorates by 0.6589 and 0.5142 percent for one percent birr devaluation and starts improvement in the<br />
third and fourth period lagged values by 1.7379 and 0.5572 for one percent birr devaluation respectively. Money<br />
supply deteriorates trade balance in the long run but in the short run it improves at two and three period lagged<br />
values by 1.7796 and 1.8972 percent for a percentage increase respectively.<br />
The finding revealed that trade balance of Ethiopia can be improved in the long run by using birr devaluation<br />
as macroeconomic policy measurement but it deteriorated in the short run. This finding is consistent with the<br />
findings of Kibret (1994), Asmamaw (2008), Befirdu (2014) and Fassil (2017). Since birr devaluation approach<br />
deteriorated trade balance in the short run and as compared to real domestic income it is less appropriate. Therefore,<br />
for the Ethiopian trade deficit problem, the best approach is absorption approach by focusing on productivity<br />
improvement rather than currency devaluation whereas monetary approach deteriorates trade because the<br />
unexpected sign of money supply happened in the ARDL model. There reason behind unexpected sign of money<br />
supply may due to e data and/or valuation problem and it needs further detailed research to identify the reason<br />
behind of unexpected sign.<br />
Generally from the research findings the research questions concluded that in the short run birr devaluation<br />
has negative effect /deteriorated trade balance but in the long run birr devaluation has positive effect/ improved<br />
trade balance and birr devaluation is an appropriate tool to improve trade balance but it is less as compared to real<br />
domestic income/absorption approach as well as the empirical implications of the trade balance improvement<br />
approaches revealed that both elasticity and absorption approaches improved trade balance but monetary approach<br />
has deteriorated the trade balance and the policy implications derived from the empirical analysis have been<br />
explained below.<br />
<br />
5.2. Policy Implication<br />
The behavior of trade deficit happening in Ethiopia negatively affects the country’s foreign trade and economic<br />
growth in general due to shortage of foreign reserve. Therefore, results of this study draw some important policy<br />
recommendations. In order to minimize Ethiopian trade deficit it is better to prioritize the absorption approach due<br />
to real domestic production level rather than birr devaluation through elasticity approach as long run<br />
macroeconomic policy measurement tool. And also lending interest rate increment is recommended as an<br />
additional mechanism for trade balance improvement. Government/national bank should take contraction policy<br />
measurement on money supply because the result shows money supply deteriorates trade balance.<br />
Therefore, demand side theory/elasticity approach/ of market adjustment suggested to shift in to supply<br />
side/absorption approach/ by producing competitive commodities in the world market since in the short run<br />
devaluation deteriorate the economy as well as its contribution is lower in the long run as compared to supply<br />
side/absorption approach. To boost export performance of Ethiopia and thereby lead to improvement in trade<br />
balance the first, is through the rise in export productivity and diversification in the area of agriculture, agro-<br />
investment and mining industries which will improve the country’s trade balance and foreign exchange earnings<br />
and second, give attention to the import computing industries. This helps to improve Ethiopian trade balance by<br />
decreasing dependence on imported goods.<br />
Therefore, government and macroeconomic policy makers have to focus on policies that encourage<br />
productivity improvements, diversification of the export sectors and expansion of import computing industries<br />
rather than birr devaluation measurement for the long run Ethiopian trade balance improvement.<br />
<br />
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