Abstract

This research seeks

to examine the relationship between the energy consumption and economic growth within

Tanzania. This intended study will investigate

the causal relationship on how energy consumption affects the economic growth within

Tanzania.

With the existing literature indicating that there is a positive causal

relationship between economic growth and energy consumption, aggregate and

disaggregate data on electricity, energy and oil will be used to examine the

relationship between energy consumption in Tanzania between 1990 and 2017. To

achieve this, Granger causality and ARDL boundary approaches will be employed.

In the first part, a general understanding of global energy consumption will be

will be discussed briefly. Second, data on annual energy consumption and

economic growth series of Tanzania between 1990 and 2017 will be examined. Our

contribution will be to provide policymakers

with a new dimension of approaching economic growth through increasing energy

consumption. The research tend to have

five chapters including introduction, Energy Consumption

and Economic Growth in Tanzania empirical literature on the causal relationship

between energy consumption and economic growth, methodology, data and estimate results,

conclusion and Bibliography

, Appendices.

Keywords: Tanzania, energy

consumption, economic growth, panel cointegration, panel causality.

Introduction

Global energy consumption overview; the global energy

consumption 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 average

of 1.8% a year (BP; June 2017). The Global energy consumption have been doubled for the last three decades

of the past century. In 2004, about 77.8% of the primary energy consumption is

from fossil fuels (32.8% oil, 21.1% natural gas, 24.1% coal), 5.4% from nuclear

fuels, 16.5% from renewable resources, of which the main one is hydroelectric,

5.5%, whereas the remaining 11% consists of noncommercial biomasses, such as

wood, hay, and other types of fodder, that in rural-economies still constitute

the main resource Beretta, G. P. (2007).

Africa energy consumption overview; the Africa energy

consumption; in 1991, per capita consumption in Africa was estimated to be 12

GJ which is exactly indicate less than a

half of that South-America and less than one-tenth

that of Europe (World Resources Institute, 1994c). Despite the fact that

African Continent has got potentiality environmental

for energy sources for energy production but

surly the energy and electricity consumption are very low (Karekezi and Kimani,

2002; ECA, 2004). Therefore there is less

amount 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 wide

disparity within African countries themselves. For example, in Ghana, 62% of the urban population has access

to 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 Ivory

Coast and 66% in South Africa (IEA),

2002). Also electric power consumption per capita ranged from as high as 556kWh

in 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 (World

Bank, 2003). The average per capita electricity consumption for Sub-Saharan

Africa (excluding South Africa) was 112.8kWh in 2000, representing a mere 5% of

the world average.2 With only 23% of its population electrified compared to the

world average of 73%, Africa has the lowest electrification rate of any major

world region (IEA, 2002). Although the African continent has 14.1 percent of

the world’s total population lives in but, the continent consumes only 4.2

percent of world processed energy for industrial uses in 2007 (IEA, 2010).

Tanzania Energy consumption

Overview; Tanzania

has abundant energy sources that are

untapped, the energy sources include biomass, hydro, uranium, natural gas,

coal, geothermal, solar and wind 10. Odhiambo, N.M. (2009). The primary energy

includes biomass (90%); petroleum (8%); electricity (1.5%), and the remaining

(0.5%) is contributed by coal and renewable energy sources. Infect about 80% of

the energy that delivered from biomass is

consumed in rural, while the importation of oil costs about 25% to 35% of the

nation’s foreign currency earnings Msyani, C. M. (2013). Recently, only about 18.4% of the country’s population

has gained access to electricity. Extending the National Grid to many parts of

the country including rural areas is not financially and economically feasible,

Msyani, C. M. (2013).

The high rate of

dependence on biomass energy indicates

that the government has not paid as much attention to energy development as it supposed

to be. This may be one among the reason why Tanzania having very slow rate

development process in the economic region due to depending much on Biomass for

energy production which is inefficient for country investment and domestic holds . It causes some concern

not only for the government perspective but

also for environmentalists owing to continuing

use of biomass has adverse consequences on soil fertility and the environment in general Goreau, T. J., & de

Mello 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 the

Country is Electrified (12% of Urban And 2% of Rural) Access to electricity is about

18.4%. Current Total Number of Customers Is 1,032,000 Maximum Number of

Connections per annum achieved is 90,000.

Problem Statement and

justifications; Pattern of energy consumption in Tanzania is being

affected by the energy prices. The serious problems of fuel and increasing

energy consumption have brought the focus

of Tanzania on the role of energy in economic performance. However, the growing concerns over energy

scarcity and environmental costs of energy have attracted the interest of the government in Tanzania to declared that

a variety of energy strategies are implemented to promote the rational and

efficient use of energy. Thus, the causal relationship between energy and

economy has undergone investigation. Whether energy consumption leads to

economic growth or economic growth stimulates energy consumption have been

examined in a number of studies. The causality in either direction between energy

consumption and economic growth may have a significant impact on energy-saving

policies. If causality runs from economic growth to energy consumption in a

country, 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-dependent

economy in which a shortage of energy may adversely affect income (Narayan and

Smyth, 2008). The economic growth and energy consumption are highly dependent

and energy conservation measures may negatively affect economic growth (Asghar,

2008). Hence, the study to investigate

the causality relationship between economic growth and energy consumption is

vital so that the energy conservation policies may be pursued without adversely

affecting the Tanzanian economy.

The significance of the study findings:

The findings from the study

will help to contribute to reform Tanzanian energy strategy efforts towards

advanced energy consumption and consequently curb environmental problems and foster

economic growth. Therefore Policy makers will make use of the findings from

this study to devise short-term,

medium-term and long-term strategies for sustainable natural resource

management.

Why focus on the energy

consumption and economic growth? : At the advanced

level, both energy consumption and economic growth are on consumers as

the ultimate target. While economics deals with the allocation of scarce

resources among consumers’ competing want

Wood TS, Baldwin S

(1985), people’s welfare is the central

concern of the economic growth h systems.

Why study the household

sector? : The household sector consumes the greatest proportion of total energy

across the country. In Tanzania, the household

sector accounts for 80-91% of total energy consumption in the country Hysen B (2011). Statistics

further reveal that in sub-Saharan Africa (SSA) household cooking alone takes

up to 60-80 percent of the total national

energy use . In SADC region, households consume 97 per-cent of wood energy

for cooking, heating and cottage industries 14. Andrea

B, Goldemberg J (1996).

Different studies have pointed out factors that

affecting energy consumption that

related to economic growth especially

they focus on fuel accessibility, fuel affordability, fuel reliability, fuel

flexibility, 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, and

level of consumer indebtedness.

Objectives of the study

The specific objectives of the study were to:

a. Analyze patterns of energy consumption and economic growth in Tanzania

b. Analyze factors affecting energy consumption

and economic growth in Tanzania

c. Investigation of households’ preferences to wood fuel from natural forests.

Hypotheses of the study

This study

puts forward the following main hypotheses; the feedback hypothesis suggests

that energy consumption and growth are interrelated and complement each other.

Literature survey on energy consumption and

Economic growth

The

hypotheses mentioned above have motivated scholars to put more effort for investigating the causal relationship between

energy consumption and economic growth. Therefore, some studies attempt to

examine the causal relationship between energy consumption and economic growth.

The previous researches definitely based

on time-series data of specific countries and, apply the Engle and Granger

residual-based cointegration test (1987) and its maximum test based on Johansen

(1988) and Johansen and Juselius (1992). For example, by employing Granger

causality test, Ebohon (1996) shows the causal relationship between energy consumption

and economic growth. The previous advanced time series in last decades,

analysis techniques have evolved and the energy consumption and economic growth

relationships are carried out by using the Toda and Yamamoto tests of Granger

causality (1995). For example, Wolde-Rufael

(2005) investigate the long-run

relationship between per capita energy use and per capita GDP for 19 African

countries and finds mixed results, ranging from negative causality to bidirectional

causality. 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 and

economic 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 economic

growth for 3 countries and a unidirectional causality running from economic

growth to energy consumption for 2 countries. The “neutrality hypothesis” for the

energy–income relationship is confirmed in respect of 5 countries. With the

same method, Odhiambo (2009b) finds a unidirectional causality running from

energy consumption to economic growth in Tanzania. Wolde-Rufael (2009), in a

multivariate framework including labor and capital as additional variables and,

by using Granger causality test of Toda and Yamamoto, re-examines the causal

relationship between.

Methodology

Granger

causality (Granger, 1969) can be used to analyse

the extent that change of past values of one variable account for the later variation

of other variables. Usually, Granger

causality exists between variables and if by using the past values of

variable the variable can be predicted with a better accuracy, and

relating to a case when past values of variables are not being used, with an assumption that

other variables stay unchanged. Granger causality test usually analyses two

variables 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-directional

casualty,

• No

causality.

In all conditions, the possible common assumption is

that the data are stationary. Stationary of a data in random Process indicates that its statistical property does not change (constant) with time. If the

Granger causality not in non-stationary time data can lead to false casual

relation (Cheng, 1996). Economic and energetic time series usually have some challenges

of non-stationary series. This is due to the fact that most often lies in the constant change of legal and technical

regulations and rules, and is making changes in the economic relations, which causes

the change of time series. Infarct the change of regulations can affect the

stationary time series, but in that case the relation between variables before and

after the changes is stable. Non-stationary time series are trying stationary

with certain mathematic procedures, for example,

differentiation of variables.

Granger test of causality analyzes if the

equation

= + + ?

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 statement

which 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 past

values of y. Granger causality

test explains which variable is dependent and which is independent in the

equation, and in the energy economics most often the long term relation is

formed between energy consumption and income of a country, and it is expressed

through the gross domestic product. According to Granger (1986), the test is

valid if the variables are not cointegrated

The second important element is the analysis of lag

length.

The result of Granger causality test is very sensitive to the selection of lag

length. If the chosen lag length is less than the true lag length, the omission

of relevant lags can cause bias. If the chosen lag length is more the true lag

length, the irrelevant lags in the equation cause the estimates to be

inefficient and does not give expected results Gelo, T. (2009).

If

they share common trends i.e. they have long-run

equilibrium relationships thus two or more variables are said to be

cointegrated. But this technique of cointegration involves three steps such as Determination

of the order of integration of the variables of interest. For this purpose two

popular tests are used: namely Dickey

Fuller (DF) and Augmented Dickey Fuller (ADF) test that is based on expanded Dickey Fuller test; Perron (Perron 1988), Philips

and Perron (1988) made a PP test, which is considered more useful for aggregates

data; Kwiatkowski et al. (Kwiatkowski at al., 1992) KPSS test; Perron (Perron 1989) with PB test

which is considered more useful when there structural breaks time series data

with PB test, which is considered better than other tests, if there are structural

breaks in time series data Gelo, T. (2009).

Combining these tests,

there are four different results to be considered:

1.

Rejection with ADF and

PP tests and the acceptance with KPPS test offer firm proof stationary of the

analyzed data

2.

The acceptance of ADF

and PP tests and the rejection of KPPS test offers firm indication I(1)

3.

Acceptance of all the

tests indicates that the data with insufficiently long series of data is not

representative enough.

4.

Rejection of all the

tests indicates that series of data is not I (1) nor I (0).

In

normally the Dickey-Fuller (DF) test and

Augmented Dickey-Fuller (ADF) test

(Dickey and Fuller 1979, 1981) are commonly applied on: is not I (0), which

are 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 energy

like 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, y

is element t. The tests are based on

the null hypothesis (): is not I(0),

If the calculated DF and ADF statistics are less than their critical values

from Fuller’s table, then the null hypothesis ()

is rejected and the series are stationary or integrated. In the second step

co-integration between variables is estimated using variables:

·

Engle and Granger

technique (Engle and Granger 1987), or

·

Johansen maximum

likelihood approach (Johansen 1988; Johansen and Juselius 1990, 1992)

The

co-integration equation estimated by the OLS method is given as:

= + +

Where

and

are the income and energy consumption.

In the third step residuals () from the cointegration regression

are 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 the

critical value from Fuller’s table, the null hypothesis of non-stationarity is

rejected. On the other hand, if the null hypothesis of non-stationarity is

rejected and the variables are not cointegrated then the standard Granger

causality 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 in

1987. The assumption for their modeling

is that they are stationary. Time series is a stationary, if its arithmetic means does not depend on time, and if its

variance does not change systematically through time. That implicates that the

value of variance is a definite number. Therefore, time series return to the

middle of the series and fluctuate around it, around its constant range. In

practice, that mostly is not the case. Time series can be transformed, but

working with such series leads to cases where it is difficult or almost

impossible to interpret gained results. By overcoming such circumstances, Engle

and Granger have proven that if the independent series is integrated by the

sequence I(d), and if the residual linear regression are among these variables

integrated by the same order, I(d-b), then the series are coin grated sequences

d,b, CI(d,b). In order to detect integration, it is necessary to note the order

of the integration of variables x and y.

Non-stationary series is causing problems

when unit rooted, which equals them, being integrated into the first order. Such series are series of a random walk, according to which the future

value equals the past value increased by error. Random walk series are difficult

for predicting future. Therefore, it is necessary for them to be tested for

unit roots, and it is necessary to discover the order of integration. Causality

in econometric relates to the possibility of one variable, predicting (and

therefore causing) the rest of the variables. The

relation between these variables can be described by the VAR models. In

this case is possible that variable influences, that influences, as well as there exists mutual

influence 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 being

correlated and producing white noise. All the variables, used in

analysis have unit roots, approximately of 5% of significance. Non-stationary has

been removed by differentiation.

The hypothesis for Granger

casualty test are:

…. Does not

influence

…. Influence

Data used

Total primary energy consumption (TPEC) and the fluctuation of economic

activity (GDP) are connected the observed variables. Therefore total primary energy

consumption is the result of the consumption graph of particular forms of

primary energy (coal and coke, liquid Fuels, natural gas, hydropower, biomass, electricity, waste and

renewable).The variables will be used to display the facts of the cumulative

graphs that depict the report of energy consumption and economic growth in

Tanzanian country.

Results The

finders will be obtained due to the realizing connection between the energy and

GDP, the first Granger test has been conducted, that applies to the relation of the total primary energy

consumption, and gross domestic product. Also,

the Review can be used to analyze the estimated data.The econometric test is used to determine whether economic

growth affecting the energy consumption or energy consumption affect the economic growth.

The model of analysis will be estimated and

later the Granger causality test will be taken

as shown from the following models.

VAR

Model is:

Log= + logpe + log

Log= + loge + log

The hypothesis for the Granger causality

test are:

…. Does not influence,

…. Influence,

The Variable GDP is logarithmic, and the original GDP series had unit roots

lggdp has differentiated and also being

transformed into lggdpd if. The total

primary 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 the

differentiation for the total primary energy consumption. Also, the P-values can be used to determine the

conclusion values which leads to acceptance or rejections.

The significance or insignificance values of gained or rejections of the

results can be seen in the fact that GDP is the cause of the change of total primary energy consumption or

increasing the primary energy consumption that is normally being caused due to the change in GDP.

Conclusions

From the hypothesis concept detriment,

the relation between energy consumption how it affects

the economic growth in Tanzanian by increasing or decreasing or it tends to exist proportional.

The result that obtained using the Granger causality tests and

cointegration analysis normally indicate some relationship

for Tanzania from primary energy

consumptions to gross domestic products, or from

gross domestic products to primary energy consumption.