Journal of International Trade, Logistics and Law, Vol. 8, Num. 1, 2022, 121-132 121 IMPACT OF ELECTRICITY CONSUMPTION ON ETHIOPIAN ECONOMIC GROWTH: AN EMPIRICAL ANALYSIS Gelana DAMENA Development Center, Addis Ababa, Ethiopia Leta SERA Jimma University, Ethiopia Negese Tamirat MALATU Jimma University, Ethiopia Received: April 14, 2022 Accepted: May 27, 2022 Published: June 01, 2022 Abstract: Electricity consumption plays a vital role in achieving sustainable economic growth. This study attempts to investigate the impact of electricity consumption on Ethiopian economic growth, time-series data was ranging from 1980 to 2018. The Autoregressive Distributed Lag Model (ARDL) was developed for the data analysis. The results of ARDL revealed that the economic growth was significantly affected by electricity consumption, energy consumption, foreign direct investment, and electricity price both in the long run and short run. While also affected by urbanization and government consumption in the long run. The speed of adjustment revealed that 60.12% of the short-run adjustment is made per year towards long-run equilibrium. Water, mine, and energy office should give important attention to electricity wastage control and integrated monitoring strategies to electricity consumption which are crucial for improving Ethiopian economic growth. The summary of electricity consumption by policymakers and plan designers could bring better improvement on economic growth. Keywords: Economic Growth, Electricity Consumption, Energy Consumption, Ethiopia, Long-run Equilibrium 1. Introduction Economic growth is a continuous rise in the real gross product over the years which are influenced by investment and urbanization. Electricity is a key factor contributing an important role to economic stability. Hence, inadequate supply of energy affects all aspects of economic and social development in less developed countries because growth and development in less developed countries have been focused on energy consumption. In Ethiopia, unlike developed countries access to modern electricity services is an enormous challenge. Particularly, the recent estimates suggest that above 60 million peoples is lack access to electricity. The production process requires a significant amount of energy which is expected to create a strong interaction between economic and energy policies to accelerate economic growth [1]. Accelerate the economic growth of the country by thoroughly investing in the electricity sector as a pertinent option to achieve sustainable economic growth [2]. Electricity plays a crucial role in the economic growth performance of the countries. Ethiopian rapid economic growth demand for electricity has been steadily increasing. The main rationale behind the current investigation carried out in Ethiopia is evidence of empirical analysis. Hence, the existing empirical studies that have been highlighted and intensely discussed detail on the contribution of energy in economic growth [3]. The detail is on the very crucial role of energy consumption in accelerating economic development and redirected energy as a vital ingredient of sustainable economic growth. This indicated that the link between energy consumption and economic growth in Ethiopia is still mixed in terms of causality and magnitude of the impact of electricity consumption on Gelana DAMENA & Leta SERA & Negese Tamirat MALATU 122 economic growth. However, in Ethiopia, there are some inconsistencies in the empirical result of energy consumption's impact on economic growth. Time series analysis determining the order and fractional integration for the series carried out through particular unit root tests [4]. Analysis of causal effect neglected to apply vigorous FMOLS estimation techniques to overcome the existence of estimation problem [5]. May previous empirical investigation with autoregressive distributed lag model subjected to the serious methodological problem [6]. Their studies do not take into account for bias of omitted variables such as urbanization and foreign direct investment [7]. The study does not develop several potential factors behind energy consumption and economic growth technique. Most of them were incompleteness of basic growth model and suffered from a short span of data 5 years on average which lowering power of the test. For certain sample sizes, the power is based on the period of the data and the power is greater when the span is large [8]. Despite the great role of electricity consumption on economic growth has been ignored to some extent by most literature in Ethiopia. Still, a questionable issue that needs focus in Ethiopia is how large the magnitude of the impact of electricity consumption is on economic growth. In lightening of this, the paper aims to test the impact of electricity consumption on the Ethiopian economy and pinpointed co-integration between employed variables. Therefore, the general objective of this study is to empirically investigate the impact of electricity consumption on Ethiopian economic growth by employing time series data set for the periods ranging from 1980 to 2018. Understanding the factors underlying electricity consumption is crucial in terms of achieving sustainable economic growth, alleviating poverty and food insecurity. There is a growing body of literature focusing on electricity consumption and its impact on economic growth [9 – 14]. As the studies conducted by [15 – 25], empirical outcomes evident electricity consumption has positive impacts on economic growth using a different variables like electricity consumption, cost of fuel or gas, foreign direct investment, urbanization, non–renewable energy, renewable and viable energy consumption, and oil energy. Their findings advise that it is better to keep employment of new policies to progress of electricity consumption to attract more foreign investment and achieve rapid and sustainable economic growth. Building on existing studies, we extend the analysis by considering a comprehensive set of gross domestic product growth. To this end, the current study was developed to investigate the effects of electricity consumption on Ethiopia's economic growth. 2. Materials and Methods 2.1. Types and Sources of Data Time series data was developed for the study ranging from 1980/81 to 2018/19 time period. Analysis of the study was depending on the availability and quality of secondary data. Data like real growth domestic product, government consumption, and rate of inflation were collected from the national bank of Ethiopia and the ministry of finance and economic cooperation. Data on electricity consumption, energy consumption, electricity production, electricity customers, and electricity price were collected from Ethiopian electricity sectors and the ministry of water resource, irrigation, and energy. Foreign direct and domestic investment data was collected from the Ethiopian investment commission and urbanization data from the Ethiopian central statistical agency. The remaining data were collected from the international monetary fund, world development indicator, World Bank group report, United Nations conference for trade and development, international energy agency, and global economy websites. 2.2. Model Specification To achieve the objective of the study basic growth econometric model was developed. To examine the long-run and short-run relationship between electricity consumption and sustainable economic growth the multivariate framework was employed. Among these multivariate frameworks, the Cobb-Douglas production function with constant returns to scale was the preferable one for this study. Cobb-Douglas production function can be expressed: Y= AKLβ..................................................................................................................................................... (1) Where Y is a real GDP, A is a factor of productivity technology, K is real capital and L is labor. The above-mentioned Cobb-Douglas production function (equation1) by including different determinant factors of electricity consumption such as technology, urbanization, foreign direct investment, domestic investment, aggregate Impact of Electricity Consumption on Ethiopian Economic Growth: An Empirical Analysis 123 energy consumption per capita, electricity production, government consumption, electricity customer, electricity price, rate of inflation and its impact on economic growth can be expressed: RGDP=AELCONᵝ1URBAEᵝ2 FDIAEᵝ3DIAEᵝ4ENCOᵝ5ELPROᵝ6 GCONᵝ7 ELCUSᵝ8 ELPRᵝ9 INFR ᵝ10 e......... (2) Where RGDP is a real gross domestic product, A is technology, ELCON is electricity consumption, URBAE is urbanization, FDIAE is foreign direct investment, DIAE is domestic investment, ENCO is aggregate energy consumption per capita, ELPRO is electricity production, GCON is government consumption, ELCUS is electricity customer, ELPR is electricity price, INFR is inflation and e is an error term assumed to normally and independently distributed as e ~Ni (0, δ2) with zero mean and constant variance δ.2 Finally, ᵝ1, ᵝ2, ᵝ3, ᵝ4, ᵝ5, ᵝ6, ᵝ47, ᵝ8, ᵝ9, ᵝ10 are returned to scale parameters of the econometrics model. To linearize the parameters Cobb-Douglas production function is transformed into log-linear form. The natural logarithm is important to study the association between real gross product and energy consumption to obtain efficient and consistent estimates. Cobb-Douglas production function in log-linear form is presented: LNRGDPt = LNA+ᵝ1LNELCONt+ᵝ2LNURBAEt+ᵝ3LNFDIAEt+ᵝ4LNDIAEt+ᵝ5LNENCOt +ᵝ6LNELPROt+ ᵝ7LNGCONt +ᵝ8LNELCUSt+ᵝ9LNELPRt+ᵝ10LNINFRt + et.......................................................... (3) The above log linear function (equation 3) modified by including two indicator such as PtDumm and DtDumm can be expressed: LNRGDPt = βo + ᵝ1LN (ELCON t) +ᵝ2(URBAEt)+ᵝ3(GRFDIAEt)+ᵝ4(DIAEt)+ᵝ5(LNENCOt) +ᵝ6(LNELPROt)+ ᵝ7(LNGCONt)+ᵝ8(LNELCUSt)+ᵝ9(LNELPRt)+ᵝ10(INFRt) +ᵝ11(DtDumm)+ ᵝ12(PtDumm) + et.........(4) Where LNRGDPt is the Natural logarithm of real GDP, LNELCON t is the Natural logarithm of electricity consumption, LNURBAEt is Natural logarithm urbanization, GRFDIAEt is the Growth rate of foreign direct investment, LNDIAEt is Natural logarithm domestic investment, LNENCOt is the Natural logarithm of energy consumption per capita, LNELPROt is Natural logarithm of electricity generation, LNGCONt is Natural logarithm of government consumption, LNELCUSt is Natural logarithm of electricity customer, LNELPRt is Natural logarithm of electricity price, LNINFRt is Natural logarithm of inflation, PtDumm is Drought time indicator, DtDumm is a Policy time indicator, βo – β12 is the coefficients as model parameters and et is an error term. Drought time indicator (DtDumm) was negatively correlated with the economy, especially in developing countries. In Ethiopia, drought affects economic growth, aggregate water resources, and electricity production. It contributes to diminishing electricity production capacity which is tangible in the current electricity utilization of the country. Policy time indicator (PtDumm) was positively correlated with the economy. It is important to estimate short and long-run relationships between macro variables including time dummy of drastic policy change events. 2.3. Method of Data Analysis Time series data was developed for the study ranging from 1980/81 to 2018/19 time period. An econometric model such as the autoregressive distributive lags model was developed for the data analysis. Autoregressive Distributed Lag (ARDL) model is a dynamic distributed-lag model in which the effect of a regressor x on y occurs over some time rather than all at once and the model is developed for the means and variances are constant and not depending on time. The selected ARDL (k) model long-run equation can be expressed: Y𝑡 =∑ 𝛼 𝑘 𝑖=1 I x1t +∑ 𝛼 𝑘 𝑖=1 2X2t + ∑ 𝛼 𝑘 𝑖=1 3X3t +∑ 𝛼 𝑘 𝑖=1 4X4t +… ∑ 𝛼 𝑘 𝑖=1 nXnt+𝑣1𝑡; s (𝑋1𝑡, 𝑋2𝑡, 𝑋3𝑡…𝑋n𝑡) These are the explanatory variables is the number of optimum lag order. Gelana DAMENA & Leta SERA & Negese Tamirat MALATU 124 Unit root tests for sensitivity analysis, post model estimation diagnostic tests to check the stability of the parameters in the model, and structural tests for a known structural break and unknown point of the structural break were developed. Data analysis was done with the latest version of gretl-2019c, R-studio of version 2019, and Eviews-11. 3. Results and Discussion 3.1 Descriptive analysis 3.1.1. Modalities of Electricity Production and Consumption in Ethiopia Figure 1 describes and represented the time series interaction for Growth rate Electricity Consumption and electricity production over the study periods. Hence there is a related trend seen between the two factors except at the starting years of the investigation there was 35.30% in the amount of consumed since 1980/81 with unmatched with power generated which recorded as 10.23% with the same year. The maximum rate of electricity production since 2012/13 was shown as maximum growth rate in generation capacity and with the same clarification, the power consumed was counted as 33.03% growth rate almost relatively with power produced which shown an increasing trend. From 2016/17 up to 2018/19, the amount of electricity consumption displayed a constant trend while the amount of production was raised from 9.93% to 19.84% Growth rate the back declined to 9.95% to the end of the study periods with relatively declining trends as seen on the time series plot. Country following the successful launch and rolling implementation of Ethiopia’s First National Electrification Program in 2017, the Government of Ethiopia has achieved significant milestones in connecting 33% of its population with on-grid electrification and 11% with off-grid pre-electrification, with the combined achievement of 44% of electricity access. Although substantial progress has been made in the sector, Ethiopia’s electrification needs are still significant. More than half of Ethiopia’s populations 56%-60% still do not have access to electricity. According to Minister Water resource and energy by 2025, Ethiopia desires to attain middle-income country status, rural and urban electricity access is targeted to achieve 100%. Achieving Ethiopia’s development vision of transformative growth and widely shared prosperity requires the timely provision of adequate, affordable, and reliable electricity access to all. Clean electricity access is an essential pillar of sustainable development, economic growth, and environmental development. Figure 1: Trends of Electricity Production and Electricity Consumption in Ethiopia Source: Ethiopian Electricity power production and Power utility 2020 3.1.2. Urbanization and Economic Growth in Ethiopia Ethiopia has one of the fastest-growing urban populations in the world with the number of people living in cities expected to nearly triple in the next two decades. This demographic dividend presents a real opportunity to change the structure and location of economic activity from rural agriculture to more diversified and much larger urban industrial and service sectors. For urbanization to contribute fully to economic growth and transformation it will have to be managed well. Ethiopia already benefits from high rates of economic growth but among most other countries at similar levels of urbanization, it has the lowest gross national income. Moreover, growth has been driven mainly by public investment and agriculture, and rapid urbanization has not been accompanied by the structural transformation of the economy. According to United Nation Development Program (2018), Ethiopia is still predominantly a rural country with only 20% of its population living in urban areas. But this is set to change dramatically in parallel with rapid urbanization. For example, Addis Ababa is Ethiopia’s only city with municipal sewerage which serves only 10% of the population. Solid waste management remains a challenge often dumped into -50 0 50 Trends for Electricty production and Electrict Consumption in Ethiopia (1980-2019) ELCONGR ELPRO Impact of Electricity Consumption on Ethiopian Economic Growth: An Empirical Analysis 125 open areas, endangering public health. Figure 2 based on the data obtained from the Ethiopian central statistical agency indicated that the highest rate of urbanization was since 2004/05 was registered as 13.57% was followed the urbanization rate registered during 1996/97 was computed to be 13.53%. The lowest growth rate of urbanization during 1992/1993 was scored to be 21.00%. To sum up, the average total urbanization registered was 14.40 over the study period 1980/81 up 2018/19. The Ethiopian context rate of urbanization is still said to be exhibited the constant trend exceptional for the previous years included in this study periods which was exhibited the declining. nd Figure 2: Trends Economic Growth a rate of Urbanization in Ethiopia 3.1.3. Trends of Inflation and Economic Growth in Ethiopia According to classical theories, inflation occurs in an economy when the overall price level increases and the demand for goods and services increases. Based on the Keynesian theory’s inflation occurs when demand exceeds the potential output of the economy. Currently, the word inflation is a major problem and the issue of more detention around the world. In the Ethiopian context, from figure 3 the inflation was counted since the starting of this study period was 4.48% which is assumed to be related to the real GDP growth measured by $2010 later one year more since 1981/82 the inflation rate was increased to 6.13% reverse to real GDP growth which declined to 3.08% again after one year shown a decreasing trend up 1986/87 increasing rate. In General, when compared to the rises in inflation in Ethiopia since 1991/92 and 2011/12 with the rate of 35.72% and 33.25% respectively the highest amount of inflation count during 2007/08 up to 2008/09 was 44.35% with the almost constant trend of GDP during the time which was 10.78 since 2010 USA dollar was counted for a growth rate of real GDP in Ethiopia based on data obtained from an indicated institution in the above-reflected source. Figure 3: Trends for rate Inflation and Economic Growth in Ethiopia Source: United Nations Conference Trade and Development 2020 -30 -20 -10 0 10 20 1 9 8 0 /8 1 1 9 8 1 /8 2 1 9 8 2 /8 3 1 9 8 3 /8 4 1 9 8 4 /8 5 1 9 8 5 /8 6 1 9 8 6 /8 7 1 9 8 7 /8 8 1 9 8 8 /8 9 1 9 8 9 /0 0 1 9 9 0 /9 1 1 9 9 1 /9 2 1 9 9 2 /9 3 1 9 9 3 /9 4 1 9 9 4 /9 5 1 9 9 5 /9 6 1 9 9 6 /9 7 1 9 9 7 /9 8 1 9 9 8 /9 9 1 9 9 9 /0 0 2 0 0 0 /0 1 2 0 0 1 /0 2 2 0 0 2 /0 3 2 0 0 3 /0 4 2 0 0 4 /0 5 2 0 0 5 /0 6 2 0 0 6 /0 7 2 0 0 7 /0 8 2 0 0 8 /0 9 2 0 0 9 /1 0 2 0 1 0 /1 1 2 0 1 1 /1 2 2 0 1 2 /1 3 2 0 1 3 /1 4 2 0 1 4 /1 5 2 0 1 5 /1 6 2 0 1 6 /1 7 2 0 1 7 /1 8 2 0 1 8 /1 9 Trends in Growth rate Real GDP and Urbanization in Ethiopia (1980- 2019) GDP Growth rate URBAN GR -40 -20 0 20 40 60 1 9 8 0 /8 1 1 9 8 1 /8 2 1 9 8 2 /8 3 1 9 8 3 /8 4 1 9 8 4 /8 5 1 9 8 5 /8 6 1 9 8 6 /8 7 1 9 8 7 /8 8 1 9 8 8 /8 9 1 9 8 9 /0 0 1 9 9 0 /9 1 1 9 9 1 /9 2 1 9 9 2 /9 3 1 9 9 3 /9 4 1 9 9 4 /9 5 1 9 9 5 /9 6 1 9 9 6 /9 7 1 9 9 7 /9 8 1 9 9 8 /9 9 1 9 9 9 /0 0 2 0 0 0 /0 1 2 0 0 1 /0 2 2 0 0 2 /0 3 2 0 0 3 /0 4 2 0 0 4 /0 5 2 0 0 5 /0 6 2 0 0 6 /0 7 2 0 0 7 /0 8 2 0 0 8 /0 9 2 0 0 9 /1 0 2 0 1 0 /1 1 2 0 1 1 /1 2 2 0 1 2 /1 3 2 0 1 3 /1 4 2 0 1 4 /1 5 2 0 1 5 /1 6 2 0 1 6 /1 7 2 0 1 7 /1 8 2 0 1 8 /1 9 Trend for rate of Infilation and Economic Growth in Ethiopia (1980- 2019) GDP Growth rate INFR Gelana DAMENA & Leta SERA & Negese Tamirat MALATU 126 Table 1 in Ethiopia the mean value of the electricity consumption period between 1980/81 - 2018/19 was 21.299 and the maximum and minimum values were 22.896 and 20.007 respectively. Similarly, the mean value of LNRGDP is 5.4124 and minimum of 6.9228, and a maximum of -21.0030. The standard deviation of electricity consumption is 0.8438. These results revealed that there is a low variety of electricity consumption from time to time in Ethiopia. The data of electricity consumption was positively skewed, which indicated that the mean of the data is greater than the median. Table 1 Summary Statistics for Variables, Using the Observations 1980 - 2018 Variable Mean Std.dev. Mini. Max. Median C.V. Skewness Kurtosis LNRGDP 5.4124 7.1560 21.0030 13.5740 6.9228 1.3221 -1.4905 2.9395 LNELCON 21.299 0.8438 20.007 22.896 21.042 0.0396 0.51706 -0.85224 URBAE 14.803 2.8761 10.41 20.698 14.553 0.19429 0.33394 -0.86551 GRFDIAE 1.7088 1.9498 -0.0372 5.673 0.78548 1.1410 0.75951 -0.88858 LNDIAE 21.889 8.7866 10.591 39.422 20.221 0.40141 0.86633 -0.46329 LN ENCO 8.6312 0.02273 8.6073 8.6935 8.6205 0.0026 1.4112 1.0435 LNELPRO 21.422 0.93664 20.000 23.3480 21.193 0.0437 0.51847 -0.69782 LNGCON 7.881 0.78615 6.8430 9.4089 7.6331 0.0997 0.65115 -0.88636 LNELCUS 13.4470 0.7128 12.4090 14.9620 13.2990 0.0530 0.26461 -1.1485 LNELPR 13.9650 2.9568 8.7010 18.9470 14.3400 0.2117 -0.28572 -0.92437 INFR 8.8662 10.968 -9.8092 44.3570 7.8174 1.2371 1.2648 2.3577 Source: Own Computation with gretl 2020a (2020) *Total 39 Observation 3.2 Unit Root Test Analysis Test for stationarity is crucial to address unit root problems in the data series. Unit root tests were carried out to determine the degree of fractional integrations for each series in a specified model. The tests were served as a way to account for the robustness of the result. Conditions unit root tests were developed before any sort of action was taken. Unit root test results revealed that URBAE and LNELCUS became stationary at a 5% significant level. The only variables became stationary after their first difference with a similar significant level while the rest all variables were stationary at their first difference which is said to be integrated of order one I (1) by both modified Dickey- Fuller GLS and Kwiatkowski-Phillips-Schmidt’s-shin at 1% significant level. Table 2 revealed that the fractional integration unit root test was developed to test the hypothesis for an alternative to the null hypothesis. This states that there is no long-run dependency in the series of each variable included in the estimated model. All variables except, LNGDP (0.396) and LNELCON (1.001) with an estimated degree of integration were much far from zero indicated that the presence of the long-range dependency. Moreover, the significance of all variables in the estimated degree of integration is the likeness of a high degree of dependency in the series for the Local Whittle Estimator (LWE) unit root tests. In detail for Geweke-Porter-Hudak (GPH) unit root test for all variables and estimated degree of integration are statistically significant with the presence of long- range dependency of series. This indicates that the rejection of the null hypothesis of no long-run dependency between series is said to be there is the highest degree of dependency between the series. Impact of Electricity Consumption on Ethiopian Economic Growth: An Empirical Analysis 127 Table 2 Test for Unit Root (Fractional Integration) Variable Local Whittle Estimator (LWE) Geweke-porter-Hudak (GPH) Estimated degree of integration The test statistic(z) Estimated degree of integration The test statistic(t) LNRGDP 0.396 2.239** 0.250 0.638 LNELCON 1.001 5.661*** 1.030 21.276** URBAE 0.939 5.313*** 0.966 32.961** GRFDIAE 0.776 4.392*** 0.031 2.493** LNDIAE 0.946 5.353*** 1.129 3.310** LNENCO 0.914 5.171*** 0.937 11.992*** LNELPRO 0.908 5.137*** 0.939 14.748*** LNGCON 0.989 5.596*** 1.004 11.900*** LNELCUS 0.928 5.247*** 0.958 13.221*** LNELPR 0.939 5.310*** 0.968 17.260*** INFR 0.541 3.059*** 0.502 3.872*** Source: Own computation with gretl 2020a (2020); “***, **, *” signs indicate statistical level of significance at 1%,5% and 10% respectively 3.3. Sensitivity Analysis Sensitivity analysis was developed by employing a selected fully modified form of augmented dicky fuller unit root test. Sensitivity analysis with unit root tests was cheeked to determine which variable is trend stationary and difference stationery by applying the decision-based level of significance. The summarized results as real LNGDP, INFR, LNGCON, LNELPRO are found to be trend stationery for 2.5, 12.5 values of c, and x*o values for both ADF and ADF-GLS tests. Whereas, ELCON, LNDIAE, LNELPR, and LNENCO were found to be trend stationery for all c and x*o values for both unit root test applied GRFDIAE, LNELCUS and became trend stationery at the value of c= 17.5, 12.5. Finally, the variable that became different stationery was URBAE and LNEPR for ADF values and became trend stationery at 12.5 c value for ADF-GLS Unit root tests. 3.4. ARDL Bound Test for Cointegration Analysis Ho (Null Hypothesis) revealed that there is no long-run cointegration between electricity consumption and economic growth. Ha (Alt. Hypothesis) indicated that there is long-run cointegration between electricity consumption and economic growth. The results showed that the computed value of the F-statistic is much higher than the upper-bound critical value (at the 1%, 2.5%, 5%, and 10% significant levels) when the real GDP is the dependent variable. Therefore, these ensured the existence of cointegration relationships among electricity consumption and economic growth [13]. F-statistic exceeded the critical value at 1% (i.e., 6.7997 >3.61), this implied that rejection of H0 no cointegration or long-run relationship for the model tested. Thus, indicated that there existed a cointegration between electricity consumption, urbanization, foreign direct investment, and domestic investment and real GDP. Akaike’s information criterion (AIC) is better model for this study with a smaller value of information criteria. 3.5. Empirical Results and Discussions Table 3 revealed that ARDL bound testing for cointegration between electricity consumption, urbanization, foreign direct investment, and domestic investment as a result of electricity consumption, electricity production, energy consumption per capita, government consumption, electricity customer, electricity price, and inflation rate. Table 3 revealed that electricity consumption was positively significant at the short-run (10%) and long-run (5%) significant level. It implied that with an increase in electricity consumption by 1%, the real GDP of a country will increase by 0.2839% in the long run. The finding is consistent with their studies of [12]. The coefficient of urbanization was positively significant at 1%. It revealed that an increase in urbanization by 1% will bring up about a 0.4813% increase in real GDP. Foreign direct investment was positively correlated with real GDP at a 1% significant Gelana DAMENA & Leta SERA & Negese Tamirat MALATU 128 level both in the short-run and long run. Foreign direct investment increase by 1% in the long term leads to a 7.20E- 05 % increase in real GDP. This study is similar to their studies of [9]. Table 3 Long Run Coefficient Analysis Matching Algorithm ARDL Cointegrating And Long Run Form Date: 04/30/2020 Time: 11:36 Dependent Variable: LNRGDP Sample: 1980 – 2018 Selected Model: ARDL (1,0,2,0,0,1,1,2,1,2,0) Included observations: 37 Variables Coefficients Std. Error t-Statistic Prob. LNELCON 0.283967 0.110777 2.563403 0.0216 URBAE 0.481332 0.124422 3.868529 0.0015 GRFDIAE 0.000072 0.000020 3.559955 0.0029 LNDIAE -0.007914 0.006532 -1.211447 0.2445 LNENCO 4.113616 1.514569 2.716030 0.0159 LNELPRO -0.207395 0.161439 -1.284661 0.2184 LNGCON 0.328101 0.111016 2.955427 0.0098 LNELCUS 0.043416 0.184457 0.235371 0.8171 LNELPR -0.377928 0.080200 -4.712315 0.0003 INFR -0.001230 0.001226 -1.002934 0.3318 PtDumm 0.340125 0.127281 2.672237 0.0174 DtDumm -0.076547 0.036966 -2.070741 0.0561 C -32.357370 13.427639 -2.409759 0.0293 Source: Own Computation with Eviews-11(2020) Aggregate energy consumption per capita was positively significant at 1% and 5% in the short-run and long-run respectively. An increase in aggregate energy consumption per capita by 1% will lead to an increase in real GDP by 4.1136%. The long-term coefficient of aggregate energy usage per capita has a less deviation from that of the short- run coefficient (4.50). The finding is in line with their studies of [10, 14, 26]. According to table 4, real government consumption negatively correlated with real GDP at a 1% significant level. GDP increased by 0.3281% as a 1% increase in government consumption. The short-term and long-term estimated coefficient of inflation rate displayed a negative insignificant effect on economic growth. Electricity price and electricity customer were statistically significant at 1% in the short term and long term. The finding is consistent with the study of [26] Finally, policy time indicator (PtDumm) and drought time indicator (DtDumm) were significant at 5% and 10% respectively. Drought time indicators were negatively correlated with economic growth whereas policy time indicators positively correlated. Particularly, the drought time indicator decreases the amount of aggregate electricity consumption directly. GDP decreased by 0.0765% as a 1% increase in drought. Finally, the long run established model has be expressed: LNRGDP = -32.3573 +0.2839 (LNELCON) + 0.4813 (URBAE) + 0.000072 (GRFDIAE) - 0.0079 (LNDIAE) (0.1107) (0.1244) (0.000020) (0.0065) 4.1136 (LNENCO) - 0.2073 (LNELPRO) + 0.3281 (LNGCON)+ 0.0434 (LNELCUS) - (1.5145) (0.1614) (0.1110) (0.1844) 0.377928 (LNELPR) - 0.0012 (INFR) + 0.3401 (PtDumm) - 0.0765 (DtDumm) (0.0802) (0.0012) (0.1272) (-0.076547) Impact of Electricity Consumption on Ethiopian Economic Growth: An Empirical Analysis 129 Table 4 Short Run Coefficient Analysis ARDL Cointegrating And Long Run Form Date: 05/05/2020 Time: 11:20 Dependent variable: RGDP Sample 1980 – 2018 Selected Model: ARDL (1, 0, 2, 0, 0, 1, 0, 2, 1, 2, 0) Included observations: 37 Cointegrating form Variables Coefficients Std. Error t-Statistic Prob. D (LNRGDP (-1)) 0.398702 0.124803 3.194647 0.0060 D (LNELCON) 0.170749 0.056523 3.020874 0.0086 D (URBAE) -0.074567 0.064543 -1.155303 0.2660 D (URBAE (-1)) 0.191715 0.084815 2.260391 0.0391 D (URBAE (-2)) 0.172276 0.082815 2.080251 0.0551 D (GRFDIAE) 0.000043 0.000014 3.049393 0.0081 D (LNDIAE) -0.004758 0.003751 -1.268740 0.2239 D (LNENCO) 4.505704 0.927036 4.860333 0.0002 D (LNENCO (-1)) -2.032193 0.995961 -2.040434 0.0593 D (LNELPRO) -0.124706 0.094970 -1.313105 0.2089 D (LNELPRO (-1)) 0.015317 0.058281 0.262813 0.1633 D (LNGCON (-1)) 0.059269 0.042943 1.380193 0.1878 D (LNGCON (-2)) -0.032118 0.027129 -1.183913 0.2549 D (LNELCUS) -0.220850 0.101550 -2.174792 0.0461 D (LNELCUS (-1)) 0.246956 0.093237 2.648691 0.0182 D (LNELPR)) -0.154700 0.038407 -4.027876 0.0011 D (LNELPR (-1)) 0.104399 0.044348 2.354070 0.0326 D (LNELPR (-2)) -0.176946 0.048500 -3.648331 0.0024 D (INFR) -0.000739 0.000710 -1.041897 0.3140 D (PtDumm) 0.204517 0.086047 2.376813 0.0312 D (DtDumm) -0.046028 0.024718 -1.862082 0.0823 CointEq (-1) -0.601298 0.124803 -4.817981 0.0002 Cointeq = LNRGDP- (0.2840*LNELCON + 0.4813*URBAE + 0.0001*GRFDIAE - 0.0079*LNDIAE + 4.1136*LNENCO -0.2074*LNELPRO +0.3281*LNGCON + 0.0434*LNELCUS - 0.3779*LNELPR -0.0012*INFR +0.3401*PtDumm -0.0765*DtDumm -32.3574) R-squared 0.943781 Mean dependent var 0.050248 Adjusted R-squared 0.919044 S.D. dependent var 0.073870 S.E. of regression 0.021018 Akaike info criterion -4.630256 Sum squared reside 0.011044 Schwarz criterion -4.107796 Log-likelihood 97.65973 Hannan-Quinn criteria -4.446064 F-statistic 38.1535 Durbin-Watson stat 1.905882 Prob(F-statistic) 0.000000 Source: Own Computation with Eviews-11(2020) For the study diagnostic checkups such as tests for serial correlation, normality, and alternative tests for heteroskedasticity were developed. To test the stability of Long run cumulative sum of recursive residuals (CUSUM) and cumulative sum of squares residuals (CUSUMSQ) were used. Top qualified diagnostic tests Chow test and Zivot-Andrews test were conducted to check the structural break model. Gelana DAMENA & Leta SERA & Negese Tamirat MALATU 130 Serial correlations computed value of F-statistics and Obs*R-squared were above standard 5% significant level of the p-value. This is good to reject the null hypothesis. The P-value associated with to test statistic was greater than the standard significant (i.e., 0.8700>0.05 and 0.7865>0.7865). Godfrey LM-testing for serial correlation is applied. Jarque-Bera and probability are applicable to check histogram-normality. This test is sufficient to reject the null hypothesis of residual in models that are not normally and independently distributed. The conclusion of residual terms was normally and independently distributed based on the determined value of Jarque-bera and computed probability value (5.290 >0.05, 0.070 > 0.05) respectively. The diagnostic for heteroskedastic was carried out that the study could not obtain evidence of rejecting null hypotheses such as Breusch-pagan-Godfrey, Harvey, Glejser, ARCH, and white. This test was carefully checked up heteroskedasticity option tests to detect the problem. Therefore, all p-value associated with each test statistics and chi-square were identified to be larger than the standard significance level (0.8936>0.05, 0.2205>0.05, 0.8673>0.05, 0.4610>0.05, and 0.9154>0.05) for each of Godfrey, found Harvey, Glejser, ARCH, and white heteroskedasticity tests respectively. Test for model stability was developed on instabilities problem to reject the null hypothesis. The result mentioned that the estimated ARDL model and p-value were obtained from statistical tests and standardized ones (0.3140>0.05). The developed ARDL model was a well and correctly specified functional form. ARDL model was free from serious parameters instability, CUSUMSQ of the corrected model with dummies and model is completely stable. For the developed Chow test and structural break, the value of F-statistics was greater than the standardized p-value 0.05 for all series. The P-value for log-likelihood ratio and Wald statistics became above standard P-value this leads to the same conclusion with F – statistics. Therefore, all summary statistics failed to reject the null hypothesis of no breaks at specified breakpoints for each series. To test different structural breaks quadrant Andrews test for the structural break was developed. Andrew test was carried out to test a structural break for unknown structural breakpoints among all regressor in estimation. Wald F- statistics (0.0975), LR F-statistics (0.2140) were greater than the p-value of 5% level of significance. These failed to reject the null hypothesis of no structural break at 30% and 35% within 15 and 11 possible numbers of a break compared respectively. 4. Conclusions and Policy Implications 4.1 Conclusion This study principally aimed to investigate the effect of electricity consumption on Ethiopian economic growth. Electricity consumption is pinpointed as one of the main motivators and being a pillar for modern economies. Auto- Regressive Distributed Lag (ARDL) model was developed to analyze short-run and long-run relationships. Alternative unit root tests and fractional integration unit root tests were conducted to test the long memory of dependency series. Urbanization and foreign direct investment were positively correlated with economic growth. The weak magnitude of foreign direct investment and domestic investment attract foreign direct investment inflow. Aggregate energy consumption per capita was significant both in the short-run and long-run. Coefficients aggregate energy consumption per capita was found to have a strong positive correlation with economic growth whereas the rate of inflation and drought time indicator were negatively correlated. The speed of adjustment indicated that 60.12% of the short-run adjustment is made per year towards long-run equilibrium. Without a strong electricity supply by the concerned body, the electricity demand might not be decline by swiftly promoting urbanization and inviting more foreign investors. 4.2 Policy Implications Given these findings, several implications could emerge from the analysis upon which important suggestions could be made as key recommendations. Unstable and inadequate electricity supply leads the country to significant loss on a certain amount of GDP. The government of Ethiopia and concerned bodies should formalize the spread out of electricity supply which is a key for income generation in an urban area. Effective implementation and regulation of limited electric supply ensure sustainable economic growth in the long run. The Ethiopian electricity sector better designed sustainable electricity wastage control and integrated monitoring in electricity generation, supply, and consumption to minimize power loss. It is better to attract foreign direct investment policymakers through Impact of Electricity Consumption on Ethiopian Economic Growth: An Empirical Analysis 131 considering more active measures such as low-cost availability of electricity by stabilizing the tariff in electricity price both in the short-run and long-run. Ethiopian energy policymakers better reinforce and boost the existing energy policy by checking up on previous policy in terms of adequacy. 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