“The Electricity-Growth Nexus: A Dynamic Panel Data Approach,” by Nadia S. Ouedraogo

8/10/2019 The Electricity-Growth Nexus: A Dynamic Panel Data Approach, by Nadia S. Ouedraogo

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THE JOURNAL OF ENERGY

AND DEVELOPMENT

Nadia S. Ouedraogo,

The Electricity-Growth Nexus:

A Dynamic Panel Data Approach,

Volume 39, Number 2

Copyright 2014


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et al. found that for developed countries there is a strong correlation between

increases in wealth over time and increases in energy consumption.9 Moreover,

there is a stronger correlation between electricity use and wealth creation than

there is between total energy use and wealth.10 Hence, the lack of access to

electricity is a serious hindrance not only to human, social, and economic de-velopment but also to technological progress.

Nevertheless, there are still billions of people without access to electricity or

cooking facilities. A quarter of humanity still lacks access to electricity world-

wide, almost all of whom live in developing countries.11 Yet, given the importance

of electricity to wealth creation and economic growth, the ambitious goals that

have been set to eradicate extreme poverty can never be fully reached without

acknowledging and confronting electricity deprivation. Thus, electricity access is

increasingly at the forefront of governments concerns for the poorest countries

and has given a renewed stimulus to research interest in the linkages between

energyspecifically electricityand economic performance at a national or re-

gional level.

Indeed, the existence and the direction of a causal relationship between elec-

tricity consumption and economic growth have significant implications for

a government in the design and implementation of its energy policy.12 The di-

rections of this causal relationship could be categorized into four types, each of

which has important implications for electricity policy.

First, if there is unidirectional causality running from electricity consumptionto economic growth, a reduction in electricity consumption may lead to a fall in

economic growth.

Second, if unidirectional causality runs from economic growth to electricity

consumption, it implies that policies for reducing electricity consumption may be

implemented with little or no adverse effects on economic growth.

Third, if bidirectional causality is detected, economic growth may stimulate the

demand for electricity while, in turn, greater electricity consumption may induce

economic growth. Electricity consumption and economic growth complement

each other and energy conservation measures may negatively affect economicgrowth.

Last, the absence of causality in either direction indicates that policies for

increasing or reducing electricity consumption do not affect economic growth and

an increase in real income may not affect electricity consumption.

Therefore, knowledge of the direction of causality between electricity con-

sumption and economic growth is of primary importance if appropriate energy

policies and measures to improve electricity access are to be devised. Numerous

studies have been undertaken over the past decade to investigate the relationship

and direction of causality between electricity consumption and economic variablessuch as gross national product (GNP), gross domestic product (GDP), income,

employment, or energy prices in several countries.

THE JOURNAL OF ENERGY AND DEVELOPMENT230


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Although the relation between economic growth and electricity consumption

has been investigated elaborately in numerous studies for different nations span-

ning various time periods, case studies for Sub-Saharan African are rare. The main

goal of this paper is to fill the gap by examining any causal effect between per-

capita electricity consumption and real GDP for a group of 15 developing coun-tries in Sub-Saharan Africa. These countries are Benin, Burkina Faso, Cape Verde,

Ivory Coast, Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Niger, Nigeria,

Senegal, Sierra Leone, and Togo.

The contribution of our study to the existing literature on the causal relation-

ship between electricity consumption and economic growth among Sub-Saharan

African countries is based upon two differentiating aspects. First, the majority of

the existing literature on this issue with regard to Africa is single country studies,

and the problem of short data periods in single country studies further reduces the

power of unit root and cointegration tests to provide reliable results. In our study,

we employ panel unit root and panel cointegration tests that combine cross-section

and time-series data and allow for heterogeneity across the countries to increase

the reliability of their results.13 Second, our study contributes to the existing panel-

based studies. To the best of our knowledge, there are only a few studies using

panel unit root and cointegration approaches to examine causality between elec-

tricity and economic growth in a large group of developing countries in Africa.

The remainder of the paper is organized as follows. We begin with a discussion

of the electricity sector of Sub-Saharan African countries. This is followed bya review of the literature on causality studies of electricity consumption and eco-

nomic growth. An overview of the methodology adopted and the data employed is

presented in the subsequent section, which is followed by an explanation of the

empirical findings. Some policy implications and concluding remarks are made in

the final portion.

Background: The Sub-Saharan African Energy Situation

The use of energy around the world is very uneven. Sub-Saharan African

countries (SSA) represent approximately 11 percent of the global population but

consume only 3 percent of the worlds commercial energy. Therefore, SSA has the

lowest per-capita energy consumption in the world. Its per-capita energy con-

sumption is 0.59 tons of oil equivalent (toe) per year versus the global average of

1.76 toe, 4.31 toe in Western Europe, and 8.46 toe in North America.

The International Energy Agency (IEA) predicts that under a business-as-

usual scenario, the problem will persist and even deepen in the longer term. The

average energy consumption of SSA should continue to fall (from 0.47 toe peryear) due to population growththe average population growth rate is about 1.9

percent per year in Africa and 1 percent in the rest of the world. The continents

SUB-SAHARAN AFRICA: THE ELECTRICITY-GROWTH NEXUS 231


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total consumption is expected to reach 687 million toe for 1.5 billion people in

2030 (or 5.9 percent of global consumption for 17.6 percent of the planets

population).14

In addition, electricity deprivation is by far most prevalent in Sub-Saharan

Africa (SSA) compared with other parts of the world. Around 1.5 billion people,more than one-fifth of the worlds population, still do not have access to electricity

and more than 500 million of them live in SSA, mainly in rural areas. Thus, SSA

clearly has the lowest rate of electrification in the world.

While transition economies and countries belonging to the Organization for

Economic Co-operation and Development (OECD) have virtually universal ac-

cess, North Africa has an access rate of 99 percent, Latin America 93 percent, East

Asia and the Pacific 90 percent, and the Middle East 89 percent. By contrast, South

Asia has an electrification rate of 60 percent and Sub-Saharan Africa only 29

percent.15 The populations without electricity in these two regions account for 83

percent of the total world population without electricity.

Moreover, inequalities in electricity access exist between urban and rural areas

(table 1). The access rate to electricity in rural areas is only 16 percent for SSA

against a world average of 65 percent (25 percent for India, 74 percent for Latin

America, 45 percent for South East Asia, and 80 percent for China).

Table 1ACCESS TO ELECTRICITY, 2009

a

Region

Population

without

Electricity

(in millions)

Electrification

Rate

(in %)

Urban

Electrification

Rate

(in %)

Rural

Electrification

Rate

(in %)

Africa 587 41.9 68.9 25.0

North Africa 2 99.0 99.6 98.4

Sub-Saharan Africa 585 30.5 59.9 14.3Developing Asia 799 78.1 93.9 68.8

China & East Asia 186 90.8 96.4 86.5

South Asia 612 62.2 89.1 51.2

Latin America 31 93.4 98.8 74.0

Middle East 22 89.5 98.6 72.2

Developing countries 1,438 73.0 90.7 60.2

Transition economies &

OECD 3 99.8 100.0 99.5

World 1,441 78.9 93.6 65.1

a OECD = Organization for Economic Cooperation and Development.

Source: International Energy Agency (IEA), World Energy Outlook 2011 (Paris: IEA, 2011).



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Furthermore, there are also persistent inequalities in electricity access across

regions in Sub-Saharan Africa, especially to the detriment of those nations in the

western portion of the continent. Southern Africa accounts for almost half of the

consumption of the continent and North Africa for 35 percent. In some Western

African countries 80 percent of the population has no access to electricity (seetable 2).

Without a major change, the situation probably will continue to deteriorate

further. In regard to the future access to electricity, the IEA predicts that by the

year 2030 the number of people without access to electricity is going to increase

from about 561 million (in 2008) to 600 million and those using biomass in SSA

will rise from 615 million to over 700 million.

These differences in electrification levels reflect the low level of development

of SSA. States with low-levels of income tend to have low energy access, limited

access to modern energy (petroleum products and electricity), and a high pro-

portion of the population relying on traditional biomass. At the same time, limited

and unreliable energy access is a major impediment to economic growth.16 Lack of

access to electricity imposes significant costs on households and can limit eco-

nomic, educational, and social activities; moreover, unreliable electricity supplies

impose direct costs on African economies in terms of lost productive output.17

Increasing supply and improving reliability will facilitate economic growth and

increase income levels.

Table 2REGIONAL ELECTRICITY CONSUMPTION PER CAPITA AND

ELECTRIFICATION RATES IN AFRICA, 2008a

Regions

Electricity

Consumption

(kWh/hbt/year)

Total

Electrification

Rate

(in %)

Urban

Electrification

Rate

(in %)

Rural

Electrification

Rate

(in %)

North Africa 961 94 97 93

West Africa 128 40 64 19

Central Africa 92 18 37 6

East Africa 351 41 43 30

Southern Africa 1,010 37 46 16

Southern Africa,

(South Africa excluded) 254 15 36 6

a kWh/hbt/year = kilowatt-hours per inhabitant per year.

Source: Calculated by the author based upon the World Bank, World Bank Key Development

Data & Statistics(Washington, D.C.: The World Bank, 2009) and the International Energy Agency

(IEA), World Energy Outlook 2010 (Paris: IEA, 2010).



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Literature Survey on the Electricity-Growth Nexus

The causal relationship between energy consumption and economic growth has

been investigated extensively since the seminal study of J. Kraft and A. Kraft;

however, a consensus has not been reached among energy economists.18 Whilea variety of studies have been conducted for numerous countries utilizing different

econometric methodologies, time periods, and proxy variables, the evidence from

empirical research is still providing mixed results and controversy in terms of the

direction of the causality and the intensity of the impacts of energy consumption

on economic growth.

The empirical findings on the relationship between electricity consumption and

economic growth also show a lack of consensus among economists. Indeed, the

empirical evidence is inconclusive with regard to causality issues between elec-

tricity consumption and economic growth. Several studies support bidirectional or

unidirectional causality, while other investigations find no evidence of causality.

For example, the existence of a causality running from electricity consumption

to economic growth is found by A. Shiu and P. Lam for China as a whole and by

Y. Wolde-Rufael for the region of Shanghai.19 Y. Wolde-Rufael also has found

a causality running from electricity to economic growth for Benin, the Democratic

Republic of Congo, and Tunisia.20 Most recently, P. K. Narayan and A. Prasad

have detected the same relationship in Australia, the Czech Republic, Iceland,

Italy, Portugal, and the Slovak Republic.

21

On the other hand, unidirectional causality runs from economic growth to

electricity consumption as revealed by S. Ghosh for India and Y. Wolde-Rufael for

Cameron, Ghana, Nigeria, Senegal, Zambia, and Zimbabwe.22 K. Fatai et al. and

P. K. Narayan and R. Smyth have found this type of relationship for Australia.23

Further, P. Mozumder and A. Marathe have addressed the case of Bangladesh.24

I. Ouedraogo has found univariate causality running from economic growth to

electricity consumption with significant feedback for Burkina Faso.25

In contrast, H. Yang, C. Jumbe, R. Morimoto and C. Hope, S.-H. Yoo, and P. K.

Narayan and S. Prasad have found bidirectional causality between electricity con-sumption and economic growth in Taiwan, Malawi, Sri Lanka, Korea, and the

United Kingdom, respectively.26 More recently, N. Odhiambo has investigated the

relationship between electricity consumption, employment, and economic growth

for South Africa.27 The results of his trivariate causality study indicate bidirectional

causality between electricity consumption, employment, and economic growth.

Lastly, the absence of causality between electricity consumption and economic

growth is found for Algeria, the Republic of Congo, Kenya, South Africa, and

Sudan by Y. Wolde-Rufael.28

The recent existing literature on the causal relationship between electricity andgrowth can be summarized in two categories: panel studies and country case

studies (selected works are given in table 3).29



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Table3

SUMMARYOFLITERATUREREV

IEWONCAUSALITYBETW

EENELECTRICITYCONSU

MPTIONANDECONOMICG

ROWTHa

Authors

(Year)

Period

Country

Methodology

Results

Studysurveyonelectricityconsumption(ELEC)-growthnexusforcountr

y-specificstudies

H.Ramc

harran(1990)

19701986

Jamaica

Grangercausality

ELEC

!GDP

H.Yang

(2000)

19541997

Taiwan

StandardGranger

causalitytest,Hsiaos

Granger

ELEC

4GDP

C.Jumbe(2004)

19701999

Malawi

Grangercausality,ECM

GDP!ELEC(Grangercausality)

ELEC

4GDP(ECM)

S.Yooa

ndY.Kim

(2006)

19712002

Indonesia

Eng

leGranger,VAR

ELEC

!GDP

P.MozumderandA.

Marathe(2007)

19711999

Bangladesh

Cointegrationtestand

VECM

ELEC

!GDP

C.F.Tang(2009)

19722003

Malaysia

ECMbasedF-test,

ARDLtest

ELEC

4GDP

J.-H.Yuanetal.(2008)

19632005

Taiwan

Johansencointegration,

VECspecifictests

ELEC

!GDP

A.Akinl

o(2009)

19802006

Nigeria

Johansen-Juselius,

coin

tegration,VECM

ELEC

!GDP

N.Odhia

mbo(2009a)

19712006

Tanzania

ARDLBoundstest

ELEC

!GDP

N.Odhia

mbo(2009b)

19712006

SouthAfrica

Grangercausality

ELEC

4GDP

I.Ouedraogo(2010)

19632008

BurkinaFaso

ARDLBoundstesting

GDP

!ELEC

(continued)



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Table3(continued)

SUMM

ARYOFLITERATUREREVIEWONCAUSALITYBETWEENELECTRICITYCONSUM

PTIONANDECONOMICGR

OWTHa

Authors

(Year)

Period

Country

Methodology

Results

P.K.Na

rayanandA.

Prasad

(2008)

19712002

19702002

19652002

19602002

30OECD

countries

Boo

tstrappedcausality

testingapproach

ELEC

!GDP(Australia,Italy,Slova

k

Republic,CzechRepublic,Portugal)

GDP!ELEC(Finland,Hungary,

Netherlands)

ELEC

4GDP(Iceland,Korea,Unite

d

Kingdom)

Nocausality(remaining19countries

)

P.K.Na

rayanand

Smyth

(2009)

19742002

Iran,Israel,

Kuwait,Oman,

Syria,Saudi

Arabia

Panelcointegration,

VECM

ELEC

4GDP

a

GD

P!ELECindicatesthattheca

usalityrunsfromgrowthtoelectricityconsumption;ELEC!

GDPindicatesthatthecausalityrunsfrom

electricityconsumptiontogrowth;EL

EC4GDPindicatesthatbid

irectionalcausalityexistsbet

weenelectricityconsumption

andgrowth;

GDP;ELECindicatesthatnocausality

existsbetweenelectricitycons

umptionandgrowth;VAR=v

ectorautoregressivemodel;VE

CM=vector

error-cor

rectionmodel;ARDL=autoregressivedistributedlag;andEC

M=error-correctionmodel.

Sources:H.Ramcharran,Electricity

ConsumptionandEconomicGrowthinJamaica,

EnergyEconomics,vol.12,no.1(1990),pp.6570;H.Y.

Yang,A

NoteontheCausalRelationshi

pbetweenEnergyandGDPinT

aiwan,

EnergyEconomics,vol.22,no.3(2000),pp.30917;C.B.L.Jumbe,

Cointeg

rationandCausalitybetweenElectricityConsumptionandGDP:EmpiricalEvidencefromMala

wi,EnergyEconomics,vol.26,no.1(2004),

pp.618

;S.H.YooandY.Kim,ElectricityGenerationandEcono

micGrowthinIndonesia,En

ergy,vol.31,no.14(2006),pp.289099;

P.MozumderandA.Marathe,CausalityRelationshipbetweenElectr

icityConsumptionandGDPin

Bangladesh,EnergyPolicy,vol.35,no.1

(2007),p

p.395402;C.F.Tang,ElectricityConsumption,Income,ForeignDirectInvestment,andP

opulationinMalaysia:NewEvidenceFrom

MultivariateFrameworkAnalysis,JournalofEconomicStudies,vol.36

,no.4(2009),pp.37182;J.-H.Yuan,J.-G.Kang,C.-H.Zhao,andZ.-G.Hu,

Energy

ConsumptionandEconomicGrowth:EvidencefromChinaatB

othAggregatedandDisaggregatedLevels,EnergyEconomics,vol.30,no.6

(2008),pp.307794;A.E.Akinlo,E

lectricityConsumptionandEconomicGrowthinNigeria:E

videncefromCointegrationan

dCo-feature

Analysis,

JournalofPolicyModeling,vol.31,no.5(2009),pp.6819

3;N.M.Odhiambo,EnergyC

onsumptionandEconomicGro

wthNexusin

Tanzania

:AnARDLBoundsTestingApproach,EnergyPolicyvol.37

,no.2(2009a),pp.61722;N.

M.Odhiambo,ElectricityConsumptionand



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All of the series are in natural logarithm form, and the start and end of the

period of the series are based on the data availability for all the series, especially

the availability of electricity data.

Table 4, figure 1, and figure 2 show an overview of GDP and electricity trends,

access to electricity, and per-capita electricity consumption, respectively, over theperiod 19802008 for the selected nations of the panel.

Econometric Methodology:The test of the causal relationship between economic

growth (GDP) and energy consumption is conducted in three stages. First, we test

for the order of integration in the GDP, electricity consumption, and price series.

Next, we employ panel cointegration tests to examine the long-run relationships

among the variables. Finally, we use dynamic panel causality tests to evaluate the

short-run cointegration and the direction of causality among the variables.

Panel Unit Root Tests: To conduct the Granger causality test, the time series of

the variables are required to be stationary. In the context of a time series, sta-

tionary refers to a condition wherein the series have constant mean and constant

variance. Most of the time-series data reflect trend, cycle, and/or seasonality. As

the use of non-stationary data in causality tests can yield spurious causality results,

these deterministic patterns must be removed to make the series stationary.31

Therefore, following Engle and Granger, we first test the unit roots of variables to

confirm the stationarity of each of them.32 If any variable is found to be non-

stationary, we must compute the differences and then apply the causality test withthe differenced data.

To check whether or not the variables under consideration are stationary, this

study uses five different panel unit roots tests including A. Levin, C.-F. Lin, and

C.-S. Chu; K. Im, M. Pesaran, and Y. Shin, referred to as IPS; G. S. Maddala and

S. Wu; I. Choi; J. Breitung; and K. Hadri.33 All tests were used to check the

robustness of the results.

For each estimation technique, the unit root is testing for a model with a constant

and a deterministic trend stationarity and a model with a constant and no trend.

The Levin, Lin, and Chu test (LLC) proceeds from the assumption of a ho-mogenous panel;biis identical across countries. The model is expressed as follows:

Dyit ai biyit1 Xpi

j1piDyitj eit: 1

Dis the first difference operator,yitis the series of observations for country i,

andt= 1,,Ttime periods. The test has the null hypothesis ofbi=b = 0 for all

i against the alternative ofH1 = b = bi < 0, which presumes that all series are

stationary.There are two major shortcomings of the LLC test. First, it relies on the as-

sumption of the independence across units of panel where a cross sectional



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H1: b1 < 0 for at least one i. IPS define a t-bar statistic as the average of the

individual ADF statistic:

t 1=NXi

n1t tbi 4wheretbiis the individual t-statistic for testing H0:bi= 1 for all i in equation

(4). The t-bar statistic has been shown to be normally distributed under

H0 and the critical values for given values of N and T are provided in

K. Im et al.36

J. Breitung showed that when individual-specific trends are included, the IPS

test can suffer from a loss of power due to bias correction.37 He proposes an al-

ternative test unit root, which corrects for the loss of power and shows that it has

greater power than the IPS test. The null hypothesis of Breitungs test is that thepanel series exhibit non-stationary difference and the alternative hypothesis as-

sumes that the panel series is stationary.

Figure 2PER-CAPITA ELECTRICITY CONSUMPTION AND GDP IN THE 15 SELECTED

SUB-SAHARAN AFRICAN COUNTRIES, 2008

Source: Authors calculations based on ENERDATA, www.enerdata.net, 2011.



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In contrast to the IPS test, which is a parametric and asymptotic test, G.

Maddala and S. Wu and I. Choi proposed a non-parametric and exact test that is

based on the Fisher test, and combining the P-values from individual unit root

tests.38 This test is superior compared to the IPS test.39 Its advantage is that its

value does not depend on different lag lengths in the individual ADF regressions.The test statistic is expressed as follows:

p 2XN

i1lnbi: 5

The null hypothesis is that each series in the panel has a unit root, i.e., H0:bi=

0 for alli and the alternative hypothesis is that not all of the individual series has

a unit root, i.e.,H1:bi< 0 fori= 1,..,N1andri= 0 fori=N1+ 1,,N. In addition,

I. Choi demonstrated that40

Z 1. ffiffiffiffi

Np XN

i1F1 pi ;N 0; 1 6

whereF1 is the inverse of the normal cumulative distribution function.

Finally, we employ the Hadri test that is a residual-based Lagrange Multiplier

(LM) test, where the null hypothesis is that the time series for each cross-section

member are stationary around a deterministic trend.41

The difference between the Hadri test and the other tests above is the null

hypothesis. The Hadri test uses a reverse null hypothesis and the panel test statisticis given as

ZffiffiffiffiN

p LMi j

z ! N 0; 1; fori 1and2 7

where LMi= 1=NPN

i = 1 Si t 2=

T2=fi0

; j = 1/6; and j = 1/45 for the only-

constant model, otherwise j= 1/15 andj= 11/6300; andfi0is the average of the

individual estimators of the residual spectrum.

Panel Cointegration: After testing for unit roots, the long-run relationship be-

tween electricity consumption and GDP is investigated, using the panel cointe-

gration technique derived from the work of P. Pedroni.42 The Pedroni technique

allows for heterogeneity among individual members of the panel. The cointe-

gration relationship is specified as follows:

LGDPit a9it b9i t d91iLEC d92iLPX e9it 8

The observable variables are in natural logarithm form;t= 1,

,Ttime periods;i = 1,.....Nmembers of the panel; ai is the country-specific effects; di is the de-

terministic time trends; andeit is the estimated residual. The estimated residual



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indicates the deviation from the long-run relationship. With the null of no coin-

tegration, the panel cointegration is essentially a test of unit roots in the estimated

residuals of the panel. P. Pedroni has shown that there are seven different statistics

for this test.43 The first four statistics (panelv-statistic, panelr-statistic, panel PP-

statistic, and panel ADF-statistic) are panel cointegration statistics and are basedon the within approach. The last three statistics (group rho-statistic, group PP-

statistic, and group ADF-statistic) are group panel cointegration statistics and are

based on the between approach. In the presence of a cointegrating relationship, the

residuals are expected to be stationary. The panelv-test is a one-sided test; the null

of no cointegration is rejected when the test has a large positive value. The other

statistics reject the null hypothesis of no cointegration when they have large

negative values.

In order to avoid spurious regression results, in addition to the Pedroni coin-

tegration test, the Fisher hypothesis utilizing the Johansen-Juselius cointegration

procedure is undertaken. The Fisher test is a non-parametric test that does not

assume homogeneity in the coefficients; it aggregates the p-values of the in-

dividual Johansen maximum likelihood cointegration test statistics. It allows

assessing the presence of long-run cointegrating relationships between the vari-

able of interest both at panel and country level.

Estimating the Long-Run Cointegration Relationship in a Panel Context: If the var-

iables are cointegrated, the long-run relationship between electricity consumption

and GDP is estimated by using the fully-modified ordinary least squares (FMOLS)

estimators. FMOLS is a non-parametric approach that takes into account the

possible correlation between the error term and the first differences of the re-

gressors, as well as the presence of a constant term, to deal with corrections for

serial correlation.

Our model is based on the regression such as suggested in P. Pedroni:44

Yit ai biLELECit Xki

kki gikDLELECitk mit; i 1; 2; . . . . . . . . . ; T

9whereYitis the log of GDP per capita andLELECitis the log of electricity con-

sumption per capita.YitandELECitare cointegrated with slopes bi,which may or

may not be homogeneous across i.

Following from equation (5), let jit= mitDELECitbe a stationary vector con-

sisting of the estimated residuals from the cointegrating regression and differences

in electricity consumption.

Also letWit= limT !Eh

T1PT

t1jiT PT

t1jit i

the long-run covariance

for this vector process that can be decomposed into Wit=W0

it+ Gi+ Gi= where

W0it is the contemporaneous covariance and Gi is a weighted sum of auto-

covariances. The FMOLS estimators are given as



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bfmols N1XN

1

XTt1 ELECit ELECi

2h i1XT

t

1

ELECit ELECi

yit T ^gih i

: 10

Panel Granger Causality Tests: For the countries without any evidence of coin-

tegration, the standard Granger causality test is performed but the error-correction

model (ECM) approach is applied if cointegration is found, to determine the di-

rection of causality between GDP and ELEC. To test for Granger causality in the

long-run relationship, the residuals from the long-run model are first estimated,

and then the estimated residuals are fitting as a right-hand variable in a dynamic

error-correction model. The dynamic error-correction model used is specified as

follows:

DLGDPit a1i Xq

k1u11ikDLGDPitk Xq

k1u12ikDLELECitk

Xqk1u13ikDLPXitk l1ECit1 m1it 11a

DLELECit a2i Xq

k1u21ikDLELECitk Xq

k1u22ikDLGDPitk

Xqk1u23ikDLPXitk l2ECit1 m2it: 11b

D is the difference operator andECTthe lagged error-correction term derived from

the long-run cointegrating relationship. ai, ui, andli are adjustment coefficients

andmis the serially uncorrected error term.

The sources of causation are identified by testing for the significance of the

coefficients on the lagged dependent variables for the equations (11a) and (11b).

First, the weak Granger causality will be evaluated by testingHA=u12=u13for

all iin equation (11a), orHB= u22= u23for all iin equation (11b).

The weak Granger causality is referred to as the short-run causality because the

dependent variable responds only to the short-term shocks to the stochastic en-vironment.45 The long-run causality can be tested by looking at the significance of

the coefficient of the error-correction term for the equations (11a) and (11b).

Second, the strong Granger causality test will be performed by testing the joint

hypothesis ofHA: l1 = u12= u13i in equation (11a) andHB: l2= u22= u23i in

equation (11b). The joint test indicates which variables bear the burden of short-run

adjustment to re-establish long-run equilibrium following a shock to the system.46

Finally, to test for the presence of a long-run relationship, we test: HA: l1 =

0 for alli in equation (11a) andHB:l2= 0 for alli in equation (11b). Ifliis zero,

then GDP does not respond to deviations from the long-run equilibrium in theprevious period.l1=l2= 0 for alliis equivalent to both Granger non-causality in

the long run and the weak exogeneity.



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Results and Policy Implications

The Results of the Unit Root Tests: Table 5 reports the results of the IPS, LLC,

Maddala and Wu, and Hadri panel unit root tests for the level and first differenced

series of variablesLGDP,LELEC, andLPX. All the variables are found to be non-

stationary (integrated of order one, I(1)) since the null hypothesis of a unit root

cannot be rejected for the variables in level form at the 1-percent level of sig-

nificance. Therefore, the panel cointegration tests can be performed.

The Results of Panel Cointegration Tests:Table 6 shows the results of the panel

cointegration tests. The tests reject the null hypothesis of no cointegration at the

1-percent significance level. Thus, there exists a long-run relationship between

GDP, electricity consumption, and prices.

As cointegrating relations exist forGDP,ELEC, andPX, the Johansen-Fisher

test for panel of countries and for individual countries are used to examine the

cointegrating vectors. Before performing the Johansen cointegration tests, the

Akaike information criterion (AIC) and the Schwarz information criterion (SIC)

are performed to determine the optimum lag length. The result indicates an op-

timum lag length of 2.

Tables 7 and 8 provide a summary of the results of the trace and maximum

Eigen value cointegration tests. The results of the Fisher tests support the

presence of at least one cointegrating vectors among the three variables for theentire panel. For the individual countries, the results overall indicate at least

one cointegrating relationship in half of the cases: Cape Verde, Gambia,

Ghana, Guinea Bissau, Niger, Nigeria, and Togo. Our results show that there

are cointegration relationships among GDP, ELEC, andPXvariables for the

entire panel and for individual countries. The FMOLS estimation is used to

estimate these relationships.

The FMOLS Estimation: Equation (10) has been estimated by the FMOLS where

the dependent variables are electricity consumptions. Intra-dimension as well asbetween dimensions of the entire panel are modeled. The intra-dimension (within)

takes into account the heterogeneity of individuals in their temporal dimension

and/or individual, while the within estimator eliminates the individual effects

(persistent differences between the countries over the period), favoring the tem-

poral information. For our panel of countries, the within-dimension results do not

differ from between-dimension results.

Table 9 reports the estimated long-run elasticities for the panel as well as in-

dividual countries. The coefficient of the panel estimation is positive and statis-

tically significant at the 5-percent level. Since all variables are expressed in naturallogarithms, the coefficients can be interpreted as elasticity estimates. They in-

dicate that a 1-percent increase in energy usage increases real GDP by 0.25



21/37

Table5

PANELUNITROOTRES

ULTSFORREALGROSSDO

MESTICPRODUCTPERCAPITA(LGDP),ELECTRICITY

C

ONSUMPTIONPERCAPITA

(LELEC),ANDOILPRICE(LPX)a

Null:Unit

Root

Null:NoUnitRoot

Variable

Levin,Lin

&

Chu(LLC

)

Breitung

t-stat

Im,

Pesaran

&Shin(IPS)

W-stat

MW-ADF

FisherChi-

square

MW

-PP

Fisher

Chi-square

Hadri

Z-stat

Hetero

scedastic

Consist

entZ-stat

Level

LGDP

3.5902

7

(0.9998)

-

3.0841

9

(0.9990)

19.0651

(0.9386)

13.5

080

(0.9958)

11.3987*

(0.0000)

7.71484*

(0.0000)

LELEC

2.5600

1*

(0.0052)

-

0.83659

(0.2014)

44.4341

(0.0435)

41.5

332

(0.0784)

13.5546*

(0.0000)

11.7286*

(0.0000)

LPX

1.48861

(0.0683)

0.3971

0.50419

(0.3071)

24.9870

(0.7257)

25.7

398

(0.6883)

5.08383*

(0.0000)

5.08383*

(0.0000)

First

differenc

e

DLGDP

12.153

4*

(0.0000)

0.346

15.094

9*

(0.0000)

261.839*

(0.0000)

294.865*

(0.0000)

1.01560

(0.8451)

1.6

7546

(0.0469)

DLELEC

19.868*

(0.0000)

-

19.910

5*

(0.0000)

343.734*

(0.0000)

429.690*

(0.0000)

1.61194

(0.0535)

1.9

5148

(0.0255)

DLPX

19.9956*

(0.0000)

16.244

15.925

0*

(0.0000)

241.758*

(0.0000)

241.758*

(0.0000)

11.4121*

(0.0000)

11.4121*

(0.0000)

a

*re

presentssignificanceatthe1-percentlevelofsignificance;Disthefirstdifferenceoperator;thenullhypothesisisthatthevariablefollows

aunitrootprocesswiththeexceptionoftheHadriZ-statandtheHeteroscedasticConsistentZ-stat;p-valuesaregiveninparentheses;probabilitiesfor

theFishe

r-typetestsarecomputedusing

anasymptoticChi-squaredistribution;andallothertestsassumeasymptoticnormality.



22/37


23/37

percent. With energy prices, a 1-percent increase in per-capita electricity con-

sumption leads to an increase of real GDP by 0.27 percent instead of 0.25 percent.

The results are unexpected, but this is most probably due to the structure of

energy consumption in our panel of countries. Indeed, access to modern fuels in

these nations including access to electricity is still very low. The population is

highly dependent on traditional biomass (wood, agricultural residues, and otherprimitive energy sources) for domestic purposes. On average, only about 20 percent

of the population has access to electricity and per-capita electricity consumption is

88 kWh. This per-capita electricity consumption varies from 346 kWh in countries

such as Benin and Senegal to 22 kWh in Burkina Faso and Niger. There is also

a large gap between access to electricity in urban (40 percent on average) and rural

areas (about 6 to 8 percent on average), as can be seen in table 4 earlier.

The power sector in these countries is characterized by excessive costs, low

service quality, poor investment decisions, and lack of innovation in supplying

customers. This contributes to the extremely low access rate to electricity. More-over, oil consumption accounts for less than 20 percent of total energy and the

industrial sector is the regions largest oil user, although, industrial consumers ap-

pear to be relatively price inelastic.

In addition, it must be noted that subsidies for oil products such as diesel,

kerosene, or liquefied petroleum gas are common in these countries, particularly

during periods of high and volatile oil prices. Thus, taking into account the var-

iable price in the model improves the overall significance of the estimation but

does not change the direction and the magnitude of the impact of electricity

consumption on GDP.We also use FMOLS to check the long-run cointegrating relationship between

electricity consumption and GDP for individual countries. The results of the estimation

Table 7PANEL COINTEGRATION TEST RESULTS OF A FISHER-TYPE TEST USING AN

UNDERLYING JOHANSEN METHODOLOGY FOR ELECTRICITY

CONSUMPTION PER CAPITA (LELEC), REAL GROSS DOMESTIC PRODUCT

PER CAPITA (LGDP), AND OIL PRICE (LPX)

a

Null Hypothesis Alternative Hypothesis

Fisher* Statistic

(Trace)

Fisher* Statistic

(Max-Eigen)

r = 0 r > 1 72.87* 65.20*

r 1 r > 2 31.99 29.17

r 2 r > 3 33.96 33.96

a Asymptotic p-values are computed using a Chi-square distribution; * indicates that the test

statistics are significant at the 1-percent level; and Fishers test applies regardless of the dependentvariable.



24/37


25/37

of individual FMOLS are also reported in table 9. Prices seem to be less influential for

most of the countries and its inclusion in the model does not give conclusive results.

The estimates of the electricity equation for the countries with evidence of

cointegration show a significant and positive long-run relationship with GDP in

the case of Cape Verde, Ghana, and Nigeria, with income elasticities of 0.32, 0.11,

and 0.44, respectively. The long-run relationship in the case of Niger is not sig-

nificant; for Gambia, Guinea Bissau, and Togo there is a negative and significant

relationship between electricity consumption and real GDP.

The Granger Causality Test: As the variables are cointegrated, the Granger

causality tests can be performed by employing the panel error-correction model

(ECM). Granger causality results in tables 10 and 11 summarize the causality

estimates for the full panel and individual countries.

The results of the significance of the estimated coefficients of lagged values of

the change in real GDP, along with the ECT in equation (11a) and lagged values of

the change in the electricity consumption in conjunction with the ECT in equation

(11b), are not consistent with the presence of strong Granger causality, whichindicates that there is no short-run relationship running from electricity con-

sumption to economic growth (GDP) or from GDP to electricity.

Table 8 (continued)RESULTS OF THE JOHANSEN COINTEGRATION TESTS FOR ELECTRICITY

CONSUMPTION PER CAPITA (LELEC), REAL GROSS DOMESTIC PRODUCT PER CAPITA

(LGDP), AND OIL PRICE (LPX)a

Countries

Null

Hypothesis

Alternative

Hypothesis

Trace

Statistic

Maximum-Eigen

Statistic

Nigeria

r = 0 r> 1 26.8419* 44.6879*

r 1 r >2 17.8461** 17.3542**

r 2 r >3 0.4919 0.4919

Senegal

r = 0 r> 1 23.6339 15.2906

r 1 r >2 7.9949 8.3432

r 2 r >3 0.3483 0.3483

Sierra Leoner = 0 r> 1 23.7391 15.6889r 1 r >2 8.0502 6.2287

r 2 r >3 1.8215 1.8215

Togo

r = 0 r> 1 30.6359** 19.6968**

r 1 r >2 10.9391 8.3447

r 2 r >3 2.5944 2.5944

a Asymptotic p-values are computed using a Chi-square distribution; * indicates that the test

statistics are significant at the 1-percent level; ** indicates that the test statistics are significant at the

5-percent level; and Fishers test applies regardless of the dependent variable.



26/37


27/37

Table 10THE RESULTS OF THE GRANGER CAUSALITY TEST

a

Source of causation (independent variable)

Short Run Joint(Short Run / Longrun) Long Run

Countries

Dependent

Variables

DLGDPt DLELECt

DLGDPt-1,EC t-1

DLELECt-1,

ECt-1 ECt-1Wald x2-Statistics

T-Statistics

Cape Verde ALGDP

2.513

(0.503)

1.721

(1.685)

0.069***

(0.026)

ALELEC

0.398***

(0.059)

0.012**

(0.026)

0.074***

(0.099)

Gambia ALGDP

7.320

(3.326)

0.095

(0.454)

0.056***

(0.075)

ALELEC

0.137***

(0.078)

0.064***

(0.071)

0.102

(0.026)

Ghana ALGDP

2.903

(0.573)

0.899

(0.795)

0.088

(0.132)

ALELEC

0.345**

(0.044)

0.021**

(0.042)

0.736

(0.178)

Guinea Bissau ALGDP

2.903

(0.573)

0.114

(0.068)

0.397

(0.167)

ALELEC

0.063**

(0.044)

0.899

(0.795)

0.7360

(0.178)

Niger ALGDP

0.018**

(0.011)

ALELEC

5.772

(1.739)

0.027***

(0.085)

Nigeria ALGDP

0.830

(0.594)

0.263

(0.630)

0.081***

(0.039)

ALELEC

1.206

(0.317)

0.051

(0.070)

0.272

(0.129)

Togo ALGDP

0.008*

(0.037)

ALELEC

2.545

(0.603)

0.087

(0.100)

(continued)



28/37

Overall, there is no evidence of a short-run relationship between electricity

consumption and GDP, but in the long run, there exists unidirectional causality

running from electricity consumption to real GDP for the entire panel.The results of the estimation of individual Granger causalities are presented in

table 10. The results from Cape Verde and Ghana imply a negative unidirectional

causality running from income to electricity consumption in the short run. The

results from Cape Verde further indicate that both electricity consumption and

income adjust to restore the long-run equilibrium relationship whenever there is

a deviation from equilibrium cointegrating relationship. For Gambia, there is

a positive unidirectional causality running from income to electricity consumption

in the short run. Furthermore, GDP seems to restore the long-run equilibrium

relationship alone. For Guinea Bissau, in the short run there is a negative causalityrunning from electricity consumption to income. Finally, for Niger, Nigeria, and

Togo, there is no evidence of causality between energy consumption and income,

indicating neutrality between energy consumption and income in the short run.

Furthermore, GDP levels appear to bear the burden of adjustment toward the long-

run equilibrium in response to a short-run deviation in these two countries.

As shown in table 11, at the 10-percent level of significance, we cannot reject the

null hypothesis of the absence of cointegration (R = 0) in the cases of Burkina Faso,

Benin, Ivory Coast, Guinea, Liberia, Mali, Senegal, and Sierra Leone. Thus, for these

countries, a long-run relationship does not exist between electricity consumption andreal GDP. In other words, in these eight countries, electricity consumption and real

GDP are not cointegrated. If the series of two variables are non-stationary and the

Table 10 (continued)THE RESULTS OF THE GRANGER CAUSALITY TEST

a

Source of causation (independent variable)

Short Run Joint(Short Run / Longrun) Long Run

Countries

Dependent

Variables

DLGDPt DLELECt

DLGDPt-1,EC t-1

DLELECt-1,

ECt-1 ECt-1Wald x2-Statistics

T-Statistics

Full panel ALGDP

2.04

(0.30)

0.317

(0.57)

0.002*

(0.004)

ALELEC

1.21

(0.30)

0.613

(0.43)

0.051*

(0.008)

a The numbers in parentheses are p-values calculated under the null hypothesis of causation;

* represents significance at the 1-percent level; ** represents significance at the 5-perecent level;

LELEC = logarithm of electricity consumption per capita; LGDP = logarithm of real GDP per capita;

LPX = logarithm of oil price; and EC = error-correction term.



29/37

linear combination of them is also non-stationary, then the standard Granger causality

test, rather than error-correction modeling, should be employedTable 11 shows that there is unidirectional Granger causality running from

electricity to GDP per capita for four countries (Benin, Burkina Faso, Guinea, and

Senegal) and from GDP per capita to electricity for two countries (Mali and Sierra

Leone). Moreover, there exists bidirectional Granger causality between electricity

consumption and income for the Ivory Coast and Liberia.

To sum up, there is no evidence of a short-run relationship between electricity

consumption and GDP, but in the long run there exists unidirectional causality

running from electricity consumption to real GDP for the full panel. Moreover, the

empirical results for individual countries present mixed and conflicting resultsacross countries.

The results of the study show a unidirectional causality runs from electricity

consumption to economic growth in Cape Verde and Ghana without any feedback

effect in the long run and a negative causality running from electricity consumption

to GDP in the short run.

The short-run negative correlation between electricity consumption and GDP

can be attributed to the diminution of energy intensity due to increases in income.

Indeed, the electrification rates of these two countries are slightly above the rates

of other countries of the panel (68 percent for Cape Verde and 54 percent forGhana). As their GDP increases, these countries will seek to expand the quality of

electricity services. The improvement of efficiency will reduce the amount of

Table 11INDIVIDUAL REGRESSION RESULTS (TIME-SERIES REGRESSION)

a

Countries Direction of Causation F-Statistic Probability

Benin ELEC!GDP 1.821 0.186Burkina Faso ELEC!GDP 1.838 0.183

Ivory Coast GDP!ELEC

ELEC!GDP2.402

1.895

0.114

0.174

Guinea ELEC!GDP 1.958 0.165

Liberia GDP!ELEC

ELEC!GDP3.167

1.045

0.062

0.369

Mali GDP!ELEC 1.505 0.244Senegal ELEC!GDP 1.271 0.300Sierra Leone GDP!ELEC 3.281 0.057

a X !Y means variable X Granger causes variable Y; GDP = economic growth; and ELEC =electricity consumption.



30/37


31/37

conditioners, pumps, telecommunications equipment, and computers) that are

predicted to consume large amounts of electricity may explain this negative re-

lationship. The excessive electricity consumption in unproductive sectors may be

another explanation of this negative causality.

The observed cross-country diversity in the causal pattern is not at all un-expected. As energy consumption structures and policies differ across states, it is

natural to expect a certain degree of cross-country variation in the causal re-

lationship between electricity consumption and economic growth. One of the

explanations for these heterogeneities can be the differences in economic levels

across countries. These differences are reflected in the diversity of industrial

structures and household consumption among countries.

Policy Implications and Concluding Remarks

This paper has documented evidence from 15 African countries with regard to

the causality between electricity consumption and economic growth as well as the

direction of causality. We covered Benin, Burkina Faso, Cape Verde, Ivory Coast,

Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Niger, Nigeria, Senegal,

Sierra Leone, and Togo for the period from 1980 to 2008.

The results of the panel causality tests between real GDP and electricity con-

sumption for the 15 countries have revealed a unidirectional long-run causalityrunning from electricity consumption to real GDP while a relationship between

electricity consumption and GDP has not been found in the short run. These results

have some policy implications. The different results between long- and short-run

causality mean that different energy policies can be implemented.

First, the existence of no causal relationship running from electricity con-

sumption to economic growth in the short run implies that electricity conservation

policies may not have negative impacts on economic activity.

However, as populations of these countries have a limited access to electricity,

conservation policies are not feasible. Moreover, causality between electricityconsumption and growth has been found in the long run. So the long-term eco-

nomic performance of these countries may be threatened by electricity conser-

vation policies. Nonetheless, energy efficiency and demand-side management

policies can be initiated with no adverse effect on economic growth; such energy

efficiency policies will reduce the wastage of electricity and, thus, the electricity

consumption without affecting the end-use benefits.

Indeed, the above empirical findings also may be due to the occurrence of

a wasteful use of electricity in almost all African countries. The electricity intensities

(defined as the amount of electricity consumption per GDP) in African countries arehigher than the values in the developed countries.47 Higher electricity intensities in

these countries reflect inefficient usage in industry as well as the commercial and



32/37

household sectors. Thus, there are considerable opportunities for energy efficiency

improvements in the residential, tertiary, and industrial sectors in Sub-Saharan Africa.

If the share of the residential sector in the total electricity used is more or less

important depending on the country, the energy savings potentials are great in this

sector in Sub-Saharan Africa. A large number of households are depending onlow-performance equipment (e.g., cooking equipment). Adoption and use of high-

performance equipment will allow higher yields and electricity savings. In addi-

tion, buildings are not always adapted to the climatic conditions of Africa; that

leads to overconsumption and losses in energy as waste heat. Energy savings in the

residential sector can be realized through various initiatives, such as energy cer-

tification of buildings, billing of heating and cooling costs according to con-

sumption, thermal insulation of new buildings, regular inspection of boilers, and

energy audits of energy-intensive industries.

Moreover, there are clearly savings potentials in the regions power sector. Sub-

Saharan Africas electricity infrastructure is the least developed, least accessible, least

reliable, most costly to operate, and highest priced of any region in the world.48 Major

reasons for operational deficiencies concern the increasingly overextended use of

decades-old generation infrastructure and the inability to contain costs and technically

balance electricity supply and demand. Rehabilitation and maintenance have received

low budget priorities by Sub-Saharan African governments for at least a decade. As

a result, engines wear out prematurely, fuel and lubricant usage rises, and capital costs

per unit of electricity produced also increase.

49

Furthermore, there are significanttechnical and non-technical losses in the distribution infrastructures in Sub-Saharan

Africa. While the international standard for losses ranges from 10 to 12 percent, some

countries in the region have experienced losses exceeding 30 percent. Non-technical

lossesillegally taking electricity from distribution lines, theft of distribution

equipment, tampering with electricity meters, etc.,are also high in these nations.50

For countries that cannot always meet their electricity requirements, such massive

losses affect distribution companies and exacerbate the situation.

Thus, power-sector reforms in Sub-Saharan Africa are vital to improve energy

efficiency and to increase the populations access to electricity. This requiressustained and concerted action on three strategic priorities: regional scaling-up of

generation capacity, improving the effectiveness and governance of utilities, and

expanding access through sector-wide engagement.

Second, the panel results suggest that a high level of electricity consumption

leads to a high level of real GDP in the long run. This means that the electricity

sector plays an important role in these states and contributes to GDP. Therefore,

supply-side management policies are needed for increasing electricity access. In-

deed, the rise in electricity demand will require an increase in the production of

electricity, which implies the renewal and expansion of electricity infrastructure.51

Therefore, African countries must make the necessary efforts to expand investments

in electricity infrastructure. They also should encourage their industries to adopt



33/37

new technologies to minimize carbon-dioxide emissions in order to abide by the

recommendations of the Kyoto Protocol.

One of the best solutions for increasing electricity availability, while partici-

pating in climate change mitigation efforts, is by raising investments in hydro-

electric infrastructure. Hydroelectric development could provide energy at lowcosts and with zero carbon emissions for many Sub-Saharan African nations. The

technically exploitable hydropower potential of Sub-Saharan Africa is estimated

to amount to 937 terawatt-hours (TWh)/year (1,834 TWh/year for the entire con-

tinent of Africa). However, only 5 percent (8 percent at Africas scale) of this

potential is presently exploited.52 But hydropower development projects are con-

strained by large up-front investment requirements. Moreover, uneven distribution

of available water resources within countries and the region exist alongside in-

efficient and unreliable electricity infrastructure, presenting few viable solutions to

many Sub-Saharan African governments individually. Therefore, increasingly so-

lutions that focus on regional development of large-scale generation projects and

cross-border electricity transmission and distribution infrastructure are emerging.

In order to take advantage of economies of scale to combat regional power

shortages, regional power pools have been formed in Central (CAPP), East

(EAPP), Southern (SAPP), and West (WAPP) Africa that seek to improve the

supply of reliable, stable, sustainable, and affordable electricity. Increased in-

vestments in the development of these pools can enhance reliability and lower the

cost of electricity across the region and improve conditions on the supply side.Operational costs are lower, due to investment in least-cost power generation

plants on a regional basis. Improving the supply side will contribute to increased

reliability, shared power generation reserves for the interconnected power grids,

and greater robustness to deal with local droughts or other unexpected events.53

Overall, it can be said that our results highlight the fact that access to modern

energy, measured here by the electricity consumption rate, is a prerequisite for

economic growth. Although our work provides considerable detail on the correla-

tion of electricity consumption and economic growth in some countries of Sub-

Saharan Africa, there are aspects that could be improved upon in later studies. First,in order to avoid bias caused by the omission of relevant variables, other variables

(such as exports, capital, or labor) could be added to turn the study into a trivariate or

multivariate investigation. Moreover, this work has used the first generation of unit

roots; later studies need to consider the second generation or third generation of

panel unit root tests that can adequately account for cross-sectional dependence.

NOTES

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2This energy form is the most vital input in production processes and the delivery of social

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4Ibid.

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6Ibid.

7N. Rosenberg, The Role of Electricity in Industrial Development,The Energy Journal, vol.

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8O. J. Ebohon, op. cit., and N. Rosenberg, op. cit.

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10Ibid.

11World Bank,World Bank Key Development Data & Statistics (Washington, D.C.: The World

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“The Electricity-Growth Nexus: A Dynamic Panel Data Approach,” by Nadia S. Ouedraogo

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