Abstract through increasing energy consumption. The research tend

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.