Abstract This research seeksto examine the relationship between the energy consumption and economic growth withinTanzania.
This intended study will investigatethe causal relationship on how energy consumption affects the economic growth withinTanzania. With the existing literature indicating that there is a positive causalrelationship between economic growth and energy consumption, aggregate anddisaggregate data on electricity, energy and oil will be used to examine therelationship between energy consumption in Tanzania between 1990 and 2017. Toachieve this, Granger causality and ARDL boundary approaches will be employed.In the first part, a general understanding of global energy consumption will bewill be discussed briefly. Second, data on annual energy consumption andeconomic growth series of Tanzania between 1990 and 2017 will be examined.
Ourcontribution will be to provide policymakerswith a new dimension of approaching economic growth through increasing energyconsumption. The research tend to havefive chapters including introduction, Energy Consumptionand Economic Growth in Tanzania empirical literature on the causal relationshipbetween energy consumption and economic growth, methodology, data and estimate results,conclusion and Bibliography, Appendices. Keywords: Tanzania, energyconsumption, economic growth, panel cointegration, panel causality.
IntroductionGlobal energy consumption overview; the global energyconsumption has been increased by 1% in 2016, following the growth rate of 0.9%in 2015 and also 1% growth rate in 2014, this is related to 10-year which is averageof 1.8% a year (BP; June 2017). The Global energy consumption have been doubled for the last three decadesof the past century. In 2004, about 77.8% of the primary energy consumption isfrom fossil fuels (32.8% oil, 21.
1% natural gas, 24.1% coal), 5.4% from nuclearfuels, 16.5% from renewable resources, of which the main one is hydroelectric,5.
5%, whereas the remaining 11% consists of noncommercial biomasses, such aswood, hay, and other types of fodder, that in rural-economies still constitutethe main resource Beretta, G. P. (2007).Africa energy consumption overview; the Africa energyconsumption; in 1991, per capita consumption in Africa was estimated to be 12GJ which is exactly indicate less than ahalf of that South-America and less than one-tenththat of Europe (World Resources Institute, 1994c). Despite the fact thatAfrican Continent has got potentiality environmentalfor energy sources for energy production butsurly the energy and electricity consumption are very low (Karekezi and Kimani,2002; ECA, 2004). Therefore there is lessamount of energy consumption with regard to an average person used energy in England more than a century ago(Davidson and Sokona, 2002). Even more glaring in energy consumption is the widedisparity within African countries themselves. For example, in Ghana, 62% of the urban population has accessto electricity while only 4% of the rural population has access to electricity(Saghir, 2002).
Electrification rates range from as low as 3.7% in Uganda, 4.7%in Ethiopia and 5.
0% in Malawi to as high as 45% in Ghana, 50% in the IvoryCoast and 66% in South Africa (IEA),2002). Also electric power consumption per capita ranged from as high as 556kWhin Zambia, 698kWh in Gabon and 845kWh in Zimbabwe to low as 22kWh in Ethiopia,47kWh in the Democratic Republic of the Congo and 58kWh in Tanzania (WorldBank, 2003). The average per capita electricity consumption for Sub-SaharanAfrica (excluding South Africa) was 112.8kWh in 2000, representing a mere 5% ofthe world average.2 With only 23% of its population electrified compared to theworld average of 73%, Africa has the lowest electrification rate of any majorworld region (IEA, 2002). Although the African continent has 14.1 percent ofthe world’s total population lives in but, the continent consumes only 4.
2percent of world processed energy for industrial uses in 2007 (IEA, 2010).Tanzania Energy consumptionOverview; Tanzaniahas abundant energy sources that areuntapped, the energy sources include biomass, hydro, uranium, natural gas,coal, geothermal, solar and wind 10. Odhiambo, N.M. (2009). The primary energyincludes biomass (90%); petroleum (8%); electricity (1.5%), and the remaining(0.
5%) is contributed by coal and renewable energy sources. Infect about 80% ofthe energy that delivered from biomass isconsumed in rural, while the importation of oil costs about 25% to 35% of thenation’s foreign currency earnings Msyani, C. M.
(2013). Recently, only about 18.4% of the country’s populationhas gained access to electricity.
Extending the National Grid to many parts ofthe country including rural areas is not financially and economically feasible,Msyani, C. M. (2013).The high rate ofdependence on biomass energy indicatesthat the government has not paid as much attention to energy development as it supposedto be.
This may be one among the reason why Tanzania having very slow ratedevelopment process in the economic region due to depending much on Biomass forenergy production which is inefficient for country investment and domestic holds . It causes some concernnot only for the government perspective butalso for environmentalists owing to continuinguse of biomass has adverse consequences on soil fertility and the environment in general Goreau, T. J., & deMello W. Z. (1988) .The Annual Energy Consumption for Tanzania is 5,740.84GWh(2012) The Highest Energy Demand Stands at 16.
9 GWh/Day. Only 14% of theCountry is Electrified (12% of Urban And 2% of Rural) Access to electricity is about18.4%. Current Total Number of Customers Is 1,032,000 Maximum Number ofConnections per annum achieved is 90,000. Problem Statement andjustifications; Pattern of energy consumption in Tanzania is beingaffected by the energy prices.
The serious problems of fuel and increasingenergy consumption have brought the focusof Tanzania on the role of energy in economic performance. However, the growing concerns over energyscarcity and environmental costs of energy have attracted the interest of the government in Tanzania to declared thata variety of energy strategies are implemented to promote the rational andefficient use of energy. Thus, the causal relationship between energy andeconomy has undergone investigation. Whether energy consumption leads toeconomic growth or economic growth stimulates energy consumption have beenexamined in a number of studies. The causality in either direction between energyconsumption and economic growth may have a significant impact on energy-savingpolicies. If causality runs from economic growth to energy consumption in acountry, this indicates economic of the country is less dependent on energy.Energy saving policies may have no adverse effect on economic growth. However,if causality runs in the reverse direction, this suggests an energy-dependenteconomy in which a shortage of energy may adversely affect income (Narayan andSmyth, 2008).
The economic growth and energy consumption are highly dependentand energy conservation measures may negatively affect economic growth (Asghar,2008). Hence, the study to investigatethe causality relationship between economic growth and energy consumption isvital so that the energy conservation policies may be pursued without adverselyaffecting the Tanzanian economy.The significance of the study findings:The findings from the studywill help to contribute to reform Tanzanian energy strategy efforts towardsadvanced energy consumption and consequently curb environmental problems and fostereconomic growth. Therefore Policy makers will make use of the findings fromthis study to devise short-term,medium-term and long-term strategies for sustainable natural resourcemanagement.Why focus on the energyconsumption and economic growth? : At the advancedlevel, both energy consumption and economic growth are on consumers asthe ultimate target. While economics deals with the allocation of scarceresources among consumers’ competing wantWood TS, Baldwin S(1985), people’s welfare is the centralconcern of the economic growth h systems.
Why study the householdsector? : The household sector consumes the greatest proportion of total energyacross the country. In Tanzania, the householdsector accounts for 80-91% of total energy consumption in the country Hysen B (2011). Statisticsfurther reveal that in sub-Saharan Africa (SSA) household cooking alone takesup to 60-80 percent of the total nationalenergy use . In SADC region, households consume 97 per-cent of wood energyfor cooking, heating and cottage industries 14.
AndreaB, Goldemberg J (1996).Different studies have pointed out factors thataffecting energy consumption thatrelated to economic growth especiallythey focus on fuel accessibility, fuel affordability, fuel reliability, fuelflexibility, Oil prices, household type and effective household size, climatic conditions, dwelling technology technics and ownership, stock of liquid assets(wealth); future income expectation, urban-rural location differences, andlevel of consumer indebtedness. Objectives of the studyThe specific objectives of the study were to:a. Analyze patterns of energy consumption and economic growth in Tanzaniab. Analyze factors affecting energy consumptionand economic growth in Tanzania c. Investigation of households’ preferences to wood fuel from natural forests.
Hypotheses of the studyThis studyputs forward the following main hypotheses; the feedback hypothesis suggeststhat energy consumption and growth are interrelated and complement each other.Literature survey on energy consumption andEconomic growthThehypotheses mentioned above have motivated scholars to put more effort for investigating the causal relationship betweenenergy consumption and economic growth. Therefore, some studies attempt toexamine the causal relationship between energy consumption and economic growth.The previous researches definitely basedon time-series data of specific countries and, apply the Engle and Grangerresidual-based cointegration test (1987) and its maximum test based on Johansen(1988) and Johansen and Juselius (1992). For example, by employing Grangercausality test, Ebohon (1996) shows the causal relationship between energy consumptionand economic growth. The previous advanced time series in last decades,analysis techniques have evolved and the energy consumption and economic growthrelationships are carried out by using the Toda and Yamamoto tests of Grangercausality (1995). For example, Wolde-Rufael(2005) investigate the long-runrelationship between per capita energy use and per capita GDP for 19 Africancountries and finds mixed results, ranging from negative causality to bidirectionalcausality. Akinlo (2008), by using the Autoregressive Distributed Lag (ARDL)bounds and Granger causality tests based on Vector Error Correction Model(VECM), explores the causal relationship between energy consumption andeconomic growth for 11 Sub-Saharan African countries and finds mixed results.
He reveals that economic growth and energy consumption are cointegrated and,there is a bidirectional relationship between energy consumption and economicgrowth for 3 countries and a unidirectional causality running from economicgrowth to energy consumption for 2 countries. The “neutrality hypothesis” for theenergy–income relationship is confirmed in respect of 5 countries. With thesame method, Odhiambo (2009b) finds a unidirectional causality running fromenergy consumption to economic growth in Tanzania.
Wolde-Rufael (2009), in amultivariate framework including labor and capital as additional variables and,by using Granger causality test of Toda and Yamamoto, re-examines the causalrelationship between.MethodologyGrangercausality (Granger, 1969) can be used to analysethe extent that change of past values of one variable account for the later variationof other variables. Usually, Grangercausality exists between variables and if by using the past values ofvariable the variable can be predicted with a better accuracy, andrelating to a case when past values of variables are not being used, with an assumption thatother variables stay unchanged. Granger causality test usually analyses twovariables together, testing their interaction. Gelo,T.
(2009). All of the possible permutations of the two variables are:•Unidirectional Granger causality from variables to variables,•Unidirectional Granger causality from variables, to variables • Bi-directionalcasualty,• Nocausality.In all conditions, the possible common assumption isthat the data are stationary. Stationary of a data in random Process indicates that its statistical property does not change (constant) with time. If theGranger causality not in non-stationary time data can lead to false casualrelation (Cheng, 1996). Economic and energetic time series usually have some challengesof non-stationary series. This is due to the fact that most often lies in the constant change of legal and technicalregulations and rules, and is making changes in the economic relations, which causesthe change of time series.
Infarct the change of regulations can affect thestationary time series, but in that case the relation between variables before andafter the changes is stable. Non-stationary time series are trying stationarywith certain mathematic procedures, for example,differentiation of variables.Granger test of causality analyzes if theequation= + + ? Where 0 ? i, j ? T Gives better results than equation:Where is = 0 (the null hypothesis,).If the hypothesis is rejected where = = … = = 0, than it can be implied that according to Granger causality causes variable. The statementwhich implies that x according to Granger does not cause y, is gained if the current value if x better explains the current value y, and the past values of x and y, than just pastvalues of y.
Granger causalitytest explains which variable is dependent and which is independent in theequation, and in the energy economics most often the long term relation isformed between energy consumption and income of a country, and it is expressedthrough the gross domestic product. According to Granger (1986), the test isvalid if the variables are not cointegratedThe second important element is the analysis of laglength.The result of Granger causality test is very sensitive to the selection of laglength. If the chosen lag length is less than the true lag length, the omissionof relevant lags can cause bias. If the chosen lag length is more the true laglength, the irrelevant lags in the equation cause the estimates to beinefficient and does not give expected results Gelo, T. (2009).
Ifthey share common trends i.e. they have long-runequilibrium relationships thus two or more variables are said to becointegrated. But this technique of cointegration involves three steps such as Determinationof the order of integration of the variables of interest. For this purpose twopopular tests are used: namely Dickey Fuller (DF) and Augmented Dickey Fuller (ADF) test that is based on expanded Dickey Fuller test; Perron (Perron 1988), Philipsand Perron (1988) made a PP test, which is considered more useful for aggregatesdata; Kwiatkowski et al.
(Kwiatkowski at al., 1992) KPSS test; Perron (Perron 1989) with PB testwhich is considered more useful when there structural breaks time series datawith PB test, which is considered better than other tests, if there are structuralbreaks in time series data Gelo, T. (2009). Combining these tests,there are four different results to be considered:1. Rejection with ADF andPP tests and the acceptance with KPPS test offer firm proof stationary of theanalyzed data2.
The acceptance of ADFand PP tests and the rejection of KPPS test offers firm indication I(1)3. Acceptance of all thetests indicates that the data with insufficiently long series of data is notrepresentative enough.4. Rejection of all thetests indicates that series of data is not I (1) nor I (0).Innormally the Dickey-Fuller (DF) test andAugmented Dickey-Fuller (ADF) test(Dickey and Fuller 1979, 1981) are commonly applied on: is not I (0), whichare given by the following equations:(DF) ? = a + b + Where denotes the variables GDP, energy consumption(total energy consumption or energy consumption of specific c for, of energylike electricity, oil, gas…
). All the variables are real and in log form. ?is the difference operator, and b are parameters to be estimated. (ADF) ? = a + b + ? + ADF test: a,b,and care the parameters to be estimated, yis element t. The tests are based onthe null hypothesis (): is not I(0),If the calculated DF and ADF statistics are less than their critical valuesfrom Fuller’s table, then the null hypothesis ()is rejected and the series are stationary or integrated. In the second stepco-integration between variables is estimated using variables:· Engle and Grangertechnique (Engle and Granger 1987), or· Johansen maximumlikelihood approach (Johansen 1988; Johansen and Juselius 1990, 1992) Theco-integration equation estimated by the OLS method is given as: = + + Whereand are the income and energy consumption.In the third step residuals () from the cointegration regressionare subjected to the Stationarity test based on the following equations:(DF)? = ? + + (ADF)?= ? + t ? + Where, is the residual from equation gained by OLS method?If b is negative and the calculated DF or ADF statistics is less than thecritical value from Fuller’s table, the null hypothesis of non-stationarity isrejected.
On the other hand, if the null hypothesis of non-stationarity isrejected and the variables are not cointegrated then the standard Grangercausality test is appropriate.In the third step vector, error-correction modeling and exogenous variables test are used.Engle and Granger introduced a new method for the analysis of time series in1987. The assumption for their modelingis that they are stationary.
Time series is a stationary, if its arithmetic means does not depend on time, and if itsvariance does not change systematically through time. That implicates that thevalue of variance is a definite number. Therefore, time series return to themiddle of the series and fluctuate around it, around its constant range. Inpractice, that mostly is not the case.
Time series can be transformed, butworking with such series leads to cases where it is difficult or almostimpossible to interpret gained results. By overcoming such circumstances, Engleand Granger have proven that if the independent series is integrated by thesequence I(d), and if the residual linear regression are among these variablesintegrated by the same order, I(d-b), then the series are coin grated sequencesd,b, CI(d,b). In order to detect integration, it is necessary to note the orderof the integration of variables x and y.Non-stationary series is causing problemswhen unit rooted, which equals them, being integrated into the first order.
Such series are series of a random walk, according to which the futurevalue equals the past value increased by error. Random walk series are difficultfor predicting future. Therefore, it is necessary for them to be tested forunit roots, and it is necessary to discover the order of integration.
Causalityin econometric relates to the possibility of one variable, predicting (andtherefore causing) the rest of the variables. Therelation between these variables can be described by the VAR models. Inthis case is possible that variable influences, that influences, as well as there exists mutualinfluence of these variables, or that these variables are nondependent of each other.Granger causality test comes down to the estimation of following VAR mode With the assumption of beingcorrelated and producing white noise.
All the variables, used inanalysis have unit roots, approximately of 5% of significance. Non-stationary hasbeen removed by differentiation. The hypothesis for Grangercasualty test are:.
Does notinfluence …. Influence Data usedTotal primary energy consumption (TPEC) and the fluctuation of economicactivity (GDP) are connected the observed variables. Therefore total primary energyconsumption is the result of the consumption graph of particular forms ofprimary energy (coal and coke, liquid Fuels, natural gas, hydropower, biomass, electricity, waste andrenewable).
The variables will be used to display the facts of the cumulativegraphs that depict the report of energy consumption and economic growth inTanzanian country.Results Thefinders will be obtained due to the realizing connection between the energy andGDP, the first Granger test has been conducted, that applies to the relation of the total primary energyconsumption, and gross domestic product. Also,the Review can be used to analyze the estimated data.
The econometric test is used to determine whether economicgrowth affecting the energy consumption or energy consumption affect the economic growth. The model of analysis will be estimated andlater the Granger causality test will be takenas shown from the following models.VARModel is:Log= + logpe + logLog= + loge + logThe hypothesis for the Granger causalitytest are:…. Does not influence,.
… Influence,The Variable GDP is logarithmic, and the original GDP series had unit rootslggdp has differentiated and also beingtransformed into lggdpd if. The totalprimary energy consumption variable is logarithmic, lgtpec, and since it had unit roots it as well differentiated, lgtpecsd if was gained therefore a test of the unit root without thedifferentiation for the total primary energy consumption. Also, the P-values can be used to determine theconclusion values which leads to acceptance or rejections.The significance or insignificance values of gained or rejections of theresults can be seen in the fact that GDP is the cause of the change of total primary energy consumption orincreasing the primary energy consumption that is normally being caused due to the change in GDP.ConclusionsFrom the hypothesis concept detriment,the relation between energy consumption how it affectsthe economic growth in Tanzanian by increasing or decreasing or it tends to exist proportional.The result that obtained using the Granger causality tests andcointegration analysis normally indicate some relationshipfor Tanzania from primary energyconsumptions to gross domestic products, or fromgross domestic products to primary energy consumption.