Determinants of the dynamics of electricity consumption in Nigeria

Determinants of the dynamics of electricityconsumption in Nigeria

Obinna Ubani

Lecturer, Department of Urban and Regional Planning, University of Nigeria, Enugu Campus, Enugu,Nigeria. Email: [email protected]

Abstract

The persistent electricity supply problems in Nigeria are attributable to the inability of energy plan-ners to accurately forecast the effect of the various socioeconomic and physical factors that influencethe electricity consumption across the country. The major problem with the existing forecasts ofelectricity consumption is often assumed to be associated with poor identification of these socioeco-nomic and physical factors upon which they are based. The goal of this study is to empirically deter-mine the various factors that affect the electricity consumption rate in Nigeria. The study covers anannual time series data from 1985 to 2005. Data for the study were collected through secondarysources. Multiple linear regression was used to test the research hypothesis. The result suggests thatelectricity consumption was significantly related to 6 of the 12 socioeconomic and physical factorsof this consumption rate studied at 0.01 level (R2 = 0.992). These are degree of urbanisation, popu-lation density, number of manufacturing industry, number of households with electricity, employ-ment rate and distance to nearest power generating station. These six variables are significantly indetermining the electricity consumption in Nigeria. Policies based on the outcome of this study willproduce positive and sound policy actions.

1. Introduction

Energy demand, and in particular, electricity consumption in Nigeria, has been growingat a very rapid rate over the decades. In the 34 years spanning the period 1970–2004,consumption of electricity in this nation increased from 752 million kwh to 8576.3million kwh (CBN, 2006). Given current trends in population growth, industrialisation,urbanisation, modernisation and income growth, electricity consumption is expected toincrease substantially in the coming decades as well. However, despite Nigeria’s vast oilwealth, much of the country’s citizens do not have access to uninterrupted supplies ofelectricity. Nigeria has approximately 5900 megawatts of installed electric generatingcapacity. Power outages are frequent and the power sector operates well below itsestimated capacity. A fundamental reason offered is the low-generating capacity of theNigerian power sector relative to installed capacity. Consequently, the sector had toundergo some reforms to increase power generation and distribution. Among the reforms

149

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is the setting up of the National Electricity Regulatory Commission, unbundling of theelectricity industry and entry of independent power producers among others (Babatundeand Shuaibu, 2009). Furthermore, there has been continuous increase in the budget allo-cation given to the electricity industry (Newswatch, 2005), and the government hasimported many power transformers, circuit breakers and high tension cables (CBN,2002). All these have not adequately improved the electricity crisis experienced acrossthe country.

The persistent electricity supply problems can be attributable to the inability of energyand urban planners to accurately understand as well as forecast properly the socioeco-nomic and physical determinants of electricity consumption in Nigeria. (Ubani, 2011).Although much explanation has been offered on the supply of electricity in Nigeria, quitea little is known about the fundamentals of electricity consumption. (Ekpo et al., 2011)The essence for more accurate estimates of key electricity consumption parameters areexplained by these reasons: firstly, the critical importance in the projection of future elec-tricity consumption; secondly, the fact that understanding electricity consumption dynam-ics through improved and more robust estimates of electricity consumption parameters isessential for more informed and successful electricity policy decision-making and imple-mentation in Nigeria. Often times and for various reasons in Nigeria, unduly strong deter-minants have been associated with electricity consumption rate and pattern; consequently,the estimated models are likely to have produced spurious parameter results. Obviously,policies based on such determinants are more likely to result in wrong policy actions. Thisis a situation that needs to be reversed. This fact is important due to the need to meet theincreasing electricity requirements arising from rapid urbanisation and an upsurge in com-mercial and industrial electricity consumption in the country.

Generally, however, variables like income, price per unit of electricity, degree ofurbanisation, population, land area and number of houses, level of commercial activity,level of industrial activity and distance from each state to power plants have been assumedto be associated with the dynamics of electricity consumption. Fundamentally, it is uncer-tain to what extent these variables (and others if any) are relevant to the Nigeria situation.The goal of this study is to empirically determine the various socioeconomic and physicalfactors that affect the electricity consumption rate in Nigeria. This study will give a soundbasis for planning of the country’s electricity production and consumption.

Answers to the following research questions were sorted in this study. Firstly, whatare the socioeconomic and physical determinants of electricity consumption in Nigeria?Secondly, to what extend do these determinants relates to the dynamics of electricityconsumption in Nigeria? For the purpose of this study, answers to these questions willprovide the basis for the work. The work utilised detailed data for residential, commer-cial and industrial electricity consumption aggregated on the basis of the 36-state struc-ture in Nigeria including Abuja. These aggregated data on state level shed light on the

Obinna Ubani150

OPEC Energy Review June 2013 © 2013 The Author.

OPEC Energy Review © 2013 Organization of the Petroleum Exporting Countries

determinants (socioeconomic and physical factors) of electricity consumption in thecountry. It was hypothesised in the study that electricity consumption in Nigeria is notsignificantly related to the aforementioned socioeconomic and physical electricity con-sumption determinants.

2. Literature review

A considerable literature exist attempting to model and examine the determinantsof energy demand functions within the context of developed and developing countries.Specifically, recent studies have focus on electricity as an important energy input foraccelerating the growth process. Most of the studies on the determinant of electricityconsumption have been considered at a disaggregated level with emphasis on thedemand for residential electricity within the context of household production theory.Such studies have concentrated on non-African countries, for instance, Halicioglu(2007) for Turkey, Zachariadis and Pashourtidou (2007) for Cyprus, Narayan and Smyth(2005) for Australia, Galindo (2007) for Mexico, Holtedahl and Joutz (2004) forTaiwan, Filippini and Pachauri (2004) for India, Hunt et al. (2003) for the UnitedKingdom, Sa’ad (2009) for South Korea, Donatos and Mergos (1991) for Greece amongothers. A paucity of evidence exist for developing countries particularly for Africancountries except for studies such as De Vita et al. (2006) for Namibia, Ziramba (2008)for South Africa, and to the best of my knowledge, Arimah (1993), Ayodele (1998) andEkpo et al. (2011) for Nigeria. However, an aggregated analysis that incorporates otheruses of electricity such the industrial and commercial sectors to obtain robust estimatesof electricity consumption determinants for policy decision-making is considered perti-nent with this paper filling the vacuum. Arimah (1993), while undertaking a study in thespatial variation of electricity consumption in Nigeria, found out that the country wasdivided into two geographical zones of high and low electricity consumption, and these,according to him, are the southwestern and northeastern geographical zones of thecountry, respectively. He posited that spatial variation of electricity consumption isaccounted for by difference in: the states’ income, price per unit of electricity, degree ofurbanisation, population, land area and the number of houses in the case of residentialconsumption, the level of commercial activity in the case of commercial consumption,the level of industrial activity in case of industrial consumption, and the distance fromeach state to Kanji Dam. He later came up with the following demand function for elec-tricity consumption which over time has been used by policy makers in electricity indus-try in Nigeria:

ELECT f Y P URBAN POP LAND HOUSE Com Mfg DISij i ij i i i i i i= ( ) ( ), , , , , , , , TTKi( )

Determinants of the dynamics of electricity consumption in Nigeria 151

OPEC Energy Review June 2013© 2013 The Author.


ELECTij is the mean annual electricity consumed in state i for class j, Yi is a measure ofincome, Pij is the price per unit of electricity, URBANi is the degree of urbanisation, POPi isthe population, LANDi is the land area, HOUSEi is the number of residential units, Comi islevel of commercial activities, Mfgi is measure of manufacture activity and DISTK is dis-tance to Kanji Dam. UnlikeAyodele (1998) who used the variable, urbanisation, as the rateof population growth over the years under consideration, Arimah (1993) measured urbanareas as the population living in cities with 20,000 or more people. However, the finaldemand function for electricity consumption model adapted by him was expressed as adouble-log model. In summary, he concluded that the main determinants of the spatialvariation in electricity consumption are the degree of urbanisation, income and populationand that the demand for the different classes of electricity with respect to their respectiveindependent variables is inelastic. This study by Arimah has some shortcomings. Firstly,one of the variables of the model specification was no longer valid at present, i.e. thevariable—distance to Kanji Dam—in the model, cannot be realistic at present becausethere are presently other functional power stations apart from Kanji Dam, which was theonly functional power station as at the time of Arimah’s study. Secondly, as at the time ofthe study, Nigeria in 1992 had only 21 states, but at present, it has 36 states includingAbuja. In light of this, various structural, physical, demographical and political changeshave occurred over time. Nigeria is presently divided into six geopolitical zones and notjust the southwestern and northeastern geopolitical zones as were used in the analysis.Thirdly, the use of commercial banks as the only surrogate for measuring commercialactivities is very unrealistic.Additional variable like regional markets should be added as avalid parameter for determining commercial activities as seen in other studies done indeveloped countries.

Ekpo et al. (2011) in their study on dynamic of electricity demand and consumption inNigeria using the Bounds of Testing Approach observed that population and industrialoutput significantly drives electricity consumption in the long and short run, while elec-tricity price is not a significant determinant. In the short run, industrial output has acrowding-out effect on the demand for electricity. They posited further that that states’income per capita is the major determinant of electricity demand in Nigeria.

Primarily, the consumption for electricity is influenced by two basic variables,namely, income and electricity prices (Sa’ad, 2009). The income level, which proxiesthe level of economic activity as well as standard of living, is perhaps the most impor-tant determinant of electricity demand. The demand for electrical goods and services(e.g. television, refrigerators, air conditioners, etc.) increases as income rises. This putssignificant pressure on the demand for electricity for their usage. This implies that apositive correlation exist between income and electricity consumption. The price ofelectricity is another factor affecting electricity consumption. Higher prices causessubstantial reduction in the demand for electricity particularly in the short-term,

Obinna Ubani152



while in the long-term, it stimulates the purchase of more efficient alternative energyappliances.

Zhenmin (2001) looked at characteristics of energy consumption in rural Chinesehouseholds from the 1960s through the 1990s. He compared the shares of commercialenergy consumption across different regions controlling family income and heatingdegree needs. According to him, the share increases with economic development associ-ated with access to different fuel sources and the construction of rural power supplynetwork.

Dahl and Erdogan (1994) briefly discussed the level of urbanisation and industrialisa-tion in explaining energy demand in developing countries. However, their economicmodels also used some other variables like prices, income, number of households withelectricity, number of shopping centres and population as the core determinants in explain-ing electricity consumption indices.

Holtedahl and Joutz (2004) formulated a general model for electricity consumptionfor developing countries as follows:

Kwh f Population Income Price kwh Price of oil Unbanisation We= , , , , , aather( )

The dependent variable kwh is electricity consumption, Income is real disposableincome, Price/kwh is the real price of electricity per kilowatt hour, Price of oil is the worldprice of oil and is used as a proxy for electricity energy, Urban is the degree of urbanisationexpressed as the percentage of people living in urban areas with population in excess of10,000 and Weather is an important determinant in the short run and utilisation models. Inanalysing the previous model, because electricity is a normal good (service), higherincome increases consumptions.The specification of the previous model treats the price ofelectricity, disposal income and urbanisation as exogenous variables. When Holtedahl andJoutz (2004) transformed the previous variables to natural logarithms with the exceptionof the urbanisation variable as it is in percentage, the following model was got:

Lkmwpc A lydpc LRP LRPoil Urban CDD Et t tB

tB

tB

tB

tB= ⋅ ⋅ ⋅ ⋅ ⋅ ⋅1 2 3 4 5

Lkwhpct is the quantity consumed in billions of kilowatts hour.The result suggested thatshort- and long-term effects are separated through the use of an error correction model. Inthe long-run, the income elasticity is unit elastic, and price is negative and inelastic. Coolingdegree-day effects have a positive impact also on short-run consumption.

The previous literature review has identified some other explanatory variables thatinfluence aggregate electricity consumption. For this study that focuses on modelling thedynamics of electricity consumption in Nigeria, the following variables will be consid-ered: per capita income, price per unit of electricity in state, degree of urbanisation in state,




population density, land area, number of residential units in state per capita, number ofbanks per capita, number of manufacturing industry per state (surrogates for industrialunits), households with electricity per capita, employment rate per capita, number ofmarkets per state (surrogates for commercial units) and distance of each state to the closestfunctional electricity power generating station.

3. Methodology

This study covers a time frame of 21 years with annual time series data from 1985 to 2005.Data for the study were collected through secondary sources. In the following are the sec-ondary data and their sources used in this study: the total electricity consumed within thestudy period, and it serves as the dependent variable. The sources of the data were NationalElectric Power Authority/Power Holding Company of Nigeria (2006), Lagos. Other datainclude internally generated revenue per state in naira; average price per unit of electricity inkobo (defined as the ratio of total revenue to quantity consumed per state); urbanisation, thepercentage of the state’s population living in cities with more than 20,000 people; popula-tion density per state; Land-total land area per state (km2); and number of households perstate. Furthermore, other secondary data include number of households with electricity perstate, number of major commercial banks per state, number of major markets per state: gotfrom National Bureau of Statistics, number of major manufacturing firms (industries) perstate and the distance to nearest power stations (km2). These data were the socioeconomicand physical determinants used in the study, and they served as the independent variables.The model specification for this study was the multiple linear regression (MLR) technique,and itwasused to test theearlier statednullhypothesis.Themodel specificationwasadoptedbecause the data for the study fitted into MLR as there were no non-linear relationships.Theresulting Multiple Linear Regression equation is shown as:

ELECON a b Pop density b HH elect b Area b Bkb Urba

= + ( ) + ( ) + ( ) + ( )+

1 2 3 4

5 nn b Empl b DIST b IGRPA b Indb Price b HH

( ) + ( ) + ( ) + ( ) + ( )+ ( ) + (

6 7 8 9

10 11 )) + ( ) +b Mk e12

where:ELECON or electricity consumption rate is the dependent variable, a is the constant ofregression equation and b1 . . . b12 is the regression coefficient.

The independent variables in the equation include the following: IGRPA is the incomefor state, converted to per capita income, Price is the price per unit of electricity in state,Urban is the degree of urbanisation in state, Pop density is the population density, Area isthe land area (density), HH is the number of households per capita, Mk is the number ofmarket/state, Bk is the number of banks per capita, Ind is the number of manufacturing

Obinna Ubani154



industry per capita, HH elect is the number of households with electricity per capita, Emplis the employment rate per capita, DIST is the distance of each state to power generatingstation and e is the residual error term.

4. Data presentation and analysis

Table 1 shows the electricity consumption rate and the 12 socioeconomic andphysical determinants of electricity consumption pattern identified in this research. Theaverage electricity consumption rate in Nigeria for the study period is 417 mwh.However, the mean internally generated revenue per state in naira is 29,765,786,987,average price per unit of electricity in kobo is 4 kobo (Nigerian currency), mean per-centage of the state’s population living in cities with more than 20,000 people is 31.6per cent, mean population density per state is 184.05 persons/hectare, average land areaper state is 24591 km2, average number of households per state is 84,1954 person, meanpercentage of households with electricity per state is 51.4 per cent, mean number ofmajor commercial banks per state is 325 banks, average number of major markets perstate is 3.4 markets, average number of major manufacturing firms (industries) per stateis 57 and average distance of state capital to the nearest electricity power stations is338 km2.

5. Results

The results of the test of the hypothesis of this research suggest that electricity consump-tion in Nigeria is significantly related to the aforementioned socioeconomic and physicaldeterminants at 0.01 level. The parameters are as shown in Table 2. It was observed fromthe result that one of the explanatory variable—price of electricity—has the problem ofhomoscedasticity and as such was excluded from the result.The results shows that six vari-ables, namely, degree of urbanisation, population density, number of manufacturingindustry, number of households with electricity, employment rate and distance to nearestpower generating station, were significant at 0.05 level as shown in Table 3. The other fivevariables, namely, income for states, land area (density), number of households per capita,number of regional markets/state and number of banks/state were not significant. Theoutput discussed previously is seen in Table 6. These later non-significant five variableswere excluded from subsequent analysis, and the hypothesis was tested again with the sixsignificant socioeconomic and physical variables, and the results were as shown inTable 3. The final analysis showed that the relationship between electricity consumptionand each of the aforementioned six independent variables were significant at 0.01 level.The parameters are as seen in Table 4.




Tab

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PL

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KT

TC

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Abi

a36

479

1,03

4,10

049

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13,8

241,

341

396

37.7

512

01,

621,

778

708

632

Ada

maw

a59

2162

6,25

238

,700

487

1,69

5,31

519

922

.15

1027

1,40

1,67

44

397

Akw

aIb

om38

948

910,

485

6900

41,

671,

342,

410

301

12.1

113

81,

549,

989

212

410

Ana

mbr

a53

489

1,08

3,08

048

654

47,4

60,7

80,0

0049

561

.94

442,

006,

260

826

662

Bau

chi

6723

778,

711

49,1

194

5,34

4,85

5,00

029

416

.01

591

2,82

7,30

68

252

4B

ayel

sa62

4026

5,18

990

594

3,30

1,34

1,74

120

822

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333,

421

163

311

Ben

ue84

2776

7,56

130

,800

42,

072,

638,

909

296

16.5

235

31,

741,

011

92

201

Bor

no36

4279

2,66

372

,609

42,

309,

829,

812

316

35.6

310

551,

687,

923

63

281

C/r

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9143

740,

248

21,7

874

2,47

0,34

1,24

141

225

.08

841,

259,

247

322

320

Del

ta14

467

1,05

6,10

617

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410

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401

33.1

55

1,80

0,17

111

24

611

Ebo

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152

2540

0,12

564

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826,

253,

106

262

16.8

161

1,05

4,32

28

340

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483

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919

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482

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464

45.4

413

91,

500,

396

633

614

Eki

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372

436,

875

5435

41,

349,

772,

711

293

28.6

310

1,76

9,55

44

343

2E

nugu

254

4961

7,72

975

344

18,1

69,3

85,5

5539

641

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109

2,06

3,46

117

14

612

Gom

be76

3045

1,16

117

,100

428

3,03

4,60

629

323

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21,

340,

097

52

115

Imo

458

951,

153,

498

5288

431

,298

,871

,150

362

32.6

798

1,75

3,00

410

14

572

Jiga

wa

127

1782

5,06

223

,287

423

2,40

2,47

175

6.94

783

1,97

2,97

46

380

Kad

una

9154

1,15

4,02

242

,481

441

,412

,487

,134

412

40.7

730

02,

574,

163

422

831

Kan

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160

1,31

9,41

820

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417

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,187

,054

486

39.8

855

03,

882,

082

216

257

1K

atis

na14

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1,03

8,28

123

,561

47,

087,

071,

142

332

30.3

253

32,

396,

797

163

238

Obinna Ubani156



Keb

bi49

4361

6,25

336

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45,

191,

417,

741

6912

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456

1,29

7,70

415

111

0K

ogi

6643

792,

218

27,7

474

751,

850,

287

274

35.2

512

01,

415,

617

62

301

Kw

ara

4178

615,

079

35,7

054

1,67

0,43

0,24

136

342

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306

1,06

1,83

310

229

9L

agos

1712

99.9

2,49

7,41

936

714

85,0

76,9

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1153

293

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84,

168,

548

768

1018

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102

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9,98

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3335

781,

568

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44.7

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875

162

610

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264

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26

696

Sok

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7427

653,

684

27,8

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21,2

48,2

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213

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2,89

8,35

316

419

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181

551,

221,

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15,8

204

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23,3

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840

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101

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211

338

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2725

446,

579

56,2

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3143

376,

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2247

689,

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37,9

314

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9,86

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5175

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433

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411

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9,52

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184.

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1,95

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6. Discussions

The analysis shows that the results of the hypothesis suggests that there is a verystrong relationship between dynamics of electricity consumption and 6 of the 12socioeconomic and physical determinants of this consumption rate identified in thisstudy in Nigeria. These are degree of urbanisation, population density, number of manu-facturing industry, number of households with electricity, employment rate and distance

Table 2 The parameters

R2 = 0.909 F-call = 22.67Adjusted R2 = 0.869 P = 0.000Standard error = 0.786 a significant = 0.01

Source: SPSS output (IBM Corporation, Armonk, NY, USA) (Field Survey, 2012).

Table 3 The relationship between electricity consumption and socioeconomic/physical factors

Variable

Standardisedcoefficients(b) T P a-Sign Remark

Income for states 0.039 0.493 0.626 0.05 Not SignificantDegree of urbanisation 0.113 2.078 0.046 0.05 SignificantPopulation density 0.284 2.938 0.002 0.05 SignificantLand area (density) 0.103 1.053 0.302 0.05 Not SignificantNumber of households per capita 0.070 0.553 0.587 0.05 Not SignificantNumber of markets/state -0.010 -0.103 0.919 0.05 Not SignificantNumber of banks 0.166 1.424 0.167 0.05 Not SignificantNumber of manufacturing industry 0.274 3.078 0.002 0.05 SignificantNumber of households with electricity 0.051 5.923 0.000 0.01 SignificantEmployment rate 0.196 7.648 0.012 0.05 SignificantDistance to power generating station -0.255 -2.821 0.009 0.05 Significant

Source: Field Study, 2012.

Table 4 The outputs

R2 = 0.992 F-call = 87.53Adjusted R2 = 0.981 P = 0.000Standard error = 0.86866 a significant = 0.01

Source: SPSS output (Field Survey, 2012).

Obinna Ubani158



to nearest power generating station (R2 = 0.992 significant at 0.01 per cent). This impliesthat these aforementioned variables explained 99.2 per cent of changes in electricityconsumption in Nigeria. However, variables like income for states, land area (density),number of households per capita, number of regional markets/state and number ofbanks/state, all of which were used in existing study in Nigeria contributed insignifi-cantly to the dynamics of electricity consumption(see Table 3). Table 5 in the followingshows the relationship between electricity consumption and the six significant explana-tory variables.

Further comparison was made in the study to actually ascertain if there were differ-ences in the results of the former commonly used socioeconomic and physical electricityconsumption determinants with that of the six significant explanatory socioeconomicand physical variables. When the hypothesis was repeated with the eight commonlyused socioeconomic and physical electricity consumption determinants, namely, states’income; price per unit of electricity; degree of urbanisation; population density, number ofhouses, level of commercial activities (banks), level of industrial activity (regionalmarkets) and distance from each state to electricity power plants. The result is seen inTable 6, and it has its output as R2 = 0.676, adjusted R2 = 0.615, standard error = 45.786,F-call = 112.32, significant at <0.01.

The results in Table 6 indicates that the eight commonly used socioeconomic andphysical electricity consumption variables explained only 61.5 per cent, whereas the sixsignificant explanatory socioeconomic and physical variables used in this study explained99.2 per cent.

The improvement in this result is due to inclusion of two new variables (employmentrate and number of households with electricity) and the exclusion of some other variablesthat do not significantly contribute to electricity consumption as found in the research, and

Table 5 The relationship between electricity consumption and socioeconomic/physical factors

Variable

Standardisedcoefficients(b) T P a-Sign Remark

Degree of urbanisation 0.203 13.138 0.000 0.01 SignificantPopulation density 0.524 9.041 0.012 0.05 SignificantNumber of manufacturing industry 0.444 20.548 0.002 0.01 SignificantNumber of households with electricity 0.952 16.003 0.000 0.01 SignificantEmployment rate 0.111 3.328 0.001 0.01 SignificantDistance to power generating station -0.345 -31.531 0.000 0.01 Significant

Source: Field Study, 2012.




these include the following variables: income for states, land area (density), number ofhouseholds per capita, number of markets/state, number of banks and price of electricity.Hence, the implication of this result is that the six significant explanatory socioeconomicand physical factors used in this analysis are better determinants of the dynamics of elec-tricity consumption in Nigeria.

7. Conclusion

This study has identified the determinants of the dynamics of electricity consumption inNigeria. The results of the study indicates that 99.2 per cent changes in electricity con-sumption rate was explained by these six factors, namely, degree of urbanisation, popula-tion density, number of manufacturing industry, number of households with electricity,employment rate and distance to nearest power generating station. The major finding ofthis study is that variables like income for states, land area (density), number of house-holds’ per capita, number of regional markets/state and number of banks/state are inad-equate for determining electricity consumption in Nigeria. The study identified newfactors, namely, employment rate and number of households with electricity that areuseful and effective for determining electricity consumption in Nigeria. The study hasgiven a breakthrough to solving the persistent electricity supply problems that has largelybeen attributed to the inability of energy and urban planners to accurately understand aswell as forecast the determinants of electricity consumption in Nigeria.

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Table 6 Comparison of the results of the eight commonly used socioeconomic and physical elec-tricity consumption determinants and the six significant explanatory socioeconomic and physicalvariables used in this study

Using the eight commonlyused determinants

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R2 0.676 0.992Adjusted R2 0.615 0.981Standard error 45.786 0.86866F-call 112.32 87.53P 0.00 0.000Significant level 0.01 0.01

Source: SPSS output (Field Survey, 2012).

Obinna Ubani160



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Determinants of the dynamics of electricity consumption in Nigeria