For instance, Lee and Anas (1989) showed .... ty consumed into the electricity bill. ..... private sector participation in Nigeria, as documented by Lee and Anas ...
Electricity consumption in Nigeria: a spatial analysis Dr Ben C. Arimah Centre for Urban and Regional Planning Faculty of the Social Sciences, University of Ibadan, Nigeria SINCE THE 1950s, quantitative research on electricity consumption using either time-series or cross-sectional data has proliferated, particularly in the United States and United Kingdom (Houthakker, 1951; Fisher and Keysen, 1962; Baxter and Rees, 1968; Taylor, 1975; Murray et ~ l 1978; , Battalio et al, 1979; Mayer and Horowitz, 1979). This has not been the case in developing countries (DCs). For instance, in Nigeria, apart from the studies conducted by Ayodele (1978) and Iwayemi (198 1) using time-series data, there exists a paucity of quantitative literature on electricity consumption. Furthermore, there exists in Nigeria a virtual absence of research on electricity consumption from a spatial perspective. This trend is surprising, considering the fact that information on the determinants of electricity consumption is of utmost importance when proposing solutions to numerous problems that beset the Nigerian electricity industry - the most prominent of these being poor delivery of services to consumers in terms of incessant power shortages, as well as meeting the increasing electricity requirements arising from rapid urbanization and an upsurge in commercial and industrial activity. This state of affairs can, in part, be attributed to the absence of more informative and detailed data on cross-sectional and time-series bases for electricity consumption (Iwayemi, 1981). The aim of this paper is to undertake a spatial analysis of electricity consumption in Nigeria. Specifically, the following issues are addressed. First, what is the nature of the spatial pattern of electricity consumption in Nigeria? Secondly, what are the determinants of the observed spatial pattern? The answers to these questions are crucial, as they are likely to have important implications for electricity planning and development in the country. This paper utilizes detailed data for residential, commercial and industrial classes aggregated on the basis of the Nigerian 21-state structure.l It is intended that the use of data aggregated on a state basis will shed further light on the determinants of electricity consumption in Nigeria. The author wishes to thank an anonymous assessorfor helpfil comments and suggestions.
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The Nigerian electricity industry Rior to the 1950s, the generation of electricity was in the hands of a multiplicity of bodies. These included: the public works departments, the native authorities and the municipal authorities. While the activities of the public works departments transcended their geographical boundaries, those of the latter two were restricted to their various paiitical and administrative boundaries. Given the existence of a multiplicity of bodies, Ayodele (1987) notes that there was duplication of effort, overlapping of functions, wastage of resources and excessive overcosts. The foregoing situation, coupled with the need to meet increased electricity demand, necessitated the setting up of a national electricity institution, as contained in Ordinance Number 15 of 1950. This ordinance vested electricity development in the Electricity Corporation of Nigeria (ECN),which was established as a monopolistic commercial enterprise. The broad functions of ECN as outlined in the ordinance relate to electricity generation, transmission, distribution and sales throughout the country. Following the cessation of the civil war in 1970, an upsurge in economic activity was envisaged. Indeed, the Second National Development Plan projected a growth rate of seven per cent for the 1970-74 period. This in turn required an efficient and wellstructured electricity institution. In April 1972, following the recommendation of the Shaman study of 1971, Decree Number 24 was promuIgated, in which the National Electric Power Authority (NEPA) came into existence, following the merger of the ECN and the Niger Dam Authority. The pattern of electricity consumption in Nigeria between 1964 and 1989 is shown in table 1. This reveals that total consumption increased from 752 million kilowatt hours in 1964 to 2,045 mkwh in 1973, representing an annual average growth rate of 11.8 per cent. From 1974 to 1983, an annual average growth rate of 11.1 per cent was experienced, while between 1984 and 1989 this fell to around 7.9 per cent. With respect to the structure of electricity consumption, table 1 shows that between 1964 and 1975 residential electricity constituted between 32.6 per cent and 38.8 per cent of the national total. Between 1982 and 1989, the share of residential consumption ranged between 50.6 per cent and 56.6 per cent. In the case of commercial electricity, prior to 1970, its contribution to the national total was less than 15 per cent. Between 1970 and 1980, commercial consumption accounted for no less than 21 per cent. In the 1981-89 period, the contribution varied between 10.6 per cent and 13.9 per cent. As regards industrial consumption, this sector between 1964 and 1975 was the largest consumer of electricity, accounting for between 40.0 per cent and 54.7 per cent of total consumption. However, the share of this sector from 1976 to 1989 was never more than 38.4 per cent, thus making it the second largest consumer of electricity. The fall in recent years in the industrial
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Table 1 Structure of electricity consumption in Nigeria 106 kWh
1964 1965 1966 1967 1968 1969 I970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
Residential
%
256 288 311 239 259 333 445 471 634 758 895 1,014 1,357 1,487 1,713 2,127 2,898 2,723 3,018 3,136 2.86 1 3,259 4,175 4,105 3.887 4,167
34 .O 34.0 32.6 33.3 32.7 36.1 38.8 34.5 36.1 37.1 38.4 37.5 40.9 41 .I 41 .O 42 .O 42 .O 48.4 50.6 52.3 51.4 51.9 56.6 54.9 52.0 52.0
Commercial
112 127 141 103 100
101 243 301 391 448 498 597 771 824 1,003 1,166 1,517 747 657 635 715 750 742 789 1,039 1,059
%
Industrial
%
Total
14.9 15.0 14.8 143 12.6 10.9 21.2 22 .o 223 21.9 21.4 22.1 23.2 22.9 24.0 23.0 22.0 13.3 11.0 10.6 12.8 11.9 10.1 10.6 13.9 13.2
384 432 503 376 434 489 459 594 729 839 939 1,094 1.189 1,306 1,462 1,773 2,484 2.151 2.295 2,229 1,992 2,776 2,458 2,577 2,549 2,793
51.1 51.0 52.7 52.7 54.7 53.0 40.0 43.5 41.6 41 .O 40.3 40.1 35.8 36.1 35.0 35.0 36.0 383 38.4 37.7 35.8 36.2 33.3 345 34.1 34.8
752 847 955 955 793 923 1,147 1,366 1,754 2,045 2.332 2,707 3.317 3,617 4,178 5,066 6,899 5,621 5,970 6,000 5,568 6,285 7,375 7,471 7,475 8,019
Sources: NEPA. Lanos. Central Bank of Nigeria, Economic and Financial Review for dperent years .
sector's share of national electricity consumption can be attributed to a heavy dependence on private generating plants. For instance, Lee and Anas (1989) showed that, in a survey of 179 f m s in Nigeria, every one with more than 20 employees, apart from being connected to the national power grid, had its own standby generator and invested an average of US $130,000 on generators. Currently, the Nigerian electricity industry is bedevilled with many serious technical, managerial, personnel, financial and logistic problems. Furthermore, the demand for electricity has continued to outstrip capacity; the end-result has been the delivery of poor and shoddy services, evidenced by frequent power failures. Consequently, calls are rife for the dismantling of NEPAls monopoly and its eventual privatization.
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Spatial pattern of electricity consumption The spatial pattern of electricity consumption is depicted in figure 1. The general picture painted is one which roughly divides the country into two areas of high and low electricity consumption. These are the south-western and northeastern sections, respectively. The states in the high-consumption zone include: Lagos, Ogun. Oyo, Bendel, Rivers and Anambra. The mean annual electricity consumption within this area ranges between 272 mkWh and 2,142 mkWh, with Lagos state being the highest consumer. These states also coincide with areas of rapid urbanization and high population densities and are of a generally high level of economic well-being. Major urban centres in these states include Lagos, Ibadan, Ogbornoso, Osogbo, Abeokuta, Benin, Asaba, Port Harcourt, Enugu and Onitsha. Indeed, 7 1 per cent of the country’s major urban centres are concentrated in this area. Population densities in some of these cities are as high as 1,128 persons per square kilometre. With respect to the economic base of these states, available information on the overall pattern of industrial location in the country reveals that about 35.2 per cent of total industrial investment is located within the Lagos metropolis and 8.8 per cent in the Port Harcourt area; while Benin, Sapele and Wani (in Bendel state) account for six per cent (Makinwa, 1978). States in the lowelectricity consumption axis include Gongola, with a mean of 43 mkwh, Cross River, Benue, Bauchi, Akwa Ibom, Katsina and Niger. These states are the least urbanized, have low population densities and have basically an agrarian economic base when compared with those in the former group. Urban centres in these states include Calabar, Makurdi, Bauchi, Uyo, Katsina and Minna. These cities owe their importance to the fact that they have become capitals of newly created states. For instance, Uyo and Katsina became prominent in 1987, following the creation of Akwa Ibom and Katsina states respectively. Population densities in some of these states are as low as 3 1 people per sq km. We also note the existence of pockets of high electricity consumption states in the predominantly low northern half of the country. These are Kaduna and Kano states which ostensibly are the most urbanized and have about the strongest =onomic base in the north. The civ of Kaduna was, prior to 1967, the capital of the then Northern Region. In the case of Kano city, it emerged as a major urban centre as far back as the pre-colonial era. These two cities collectively account for 15 per cent of the nation’s industrial investment (Makinwa, 1978). Figure 1 also shows that residential electricity consumption accounts for about half total consumption. The notable exceptions are Imo and Bauchi States. This in part might be attributed to the relatively low levels of urbanization in these states. A further examination of figure 1 reveals that, with the exception of Lagos, Ogun. Oyo, Bendel and Rivers states, peripherally located states tend to consume
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Kainjidam
0Industrial electricity
@ Commercial electricity
Residential electricity
Figure 1 Nigeria: mean electricity consumption, 1985-89 lo6kWh
lower units of electricity. The implication of this is that the distance to Kainji dam might be a determining factor in accounting for the observed spatial patterne2 The inference that we might draw, though tentatively, from the foregoing is that the spatial variation in electricity consumption mirrors the differences in socioeconomic and physical factors. These notions are rigorously tested in the sections that follow.
Model specification The model outlined in this section seeks to explain the variation in the pattern of electricity consumption. The model, which is based on the neoclassical theory of consumer behaviour, posits that the spatial variation in electricity consumption is accounted for by differences in: income; the price per unit of electricity; the degree of urbanization; the population; the land area and the number of houses in the case of residential consumption: the level of commercial activity, in the case of commercial consumption; the level of industrial activity, in the case of industrial consumption; and the distance from each state to Kainji dam. Linking these variables into an equation, we have the following demand function for electricity consumption:
(MFGi ), DISTKi ) where: ELECTi, is the mean annual electricity consumed in state i for class j between 1985 and 1989; Yi is a measure of income for state i; Pij is the price per unit of electricity in state i for,classj; URBANi is the degree of urbanization in state i; POPi is the population in state i; LANDi is the land area in state i; HOUSESi is the number of residential units in state i; COMi is a measure of the level of commercial activity in state i; MFGi is a measure of the level of manufacturing activity in state i; and DISTKi is the distance to Kainji dam from state i.
Specification of independent variables The independent variables utilized and their sources are presented in table 2. Three different measures of income are employed. These are: the amount of
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Table 2 Definitions and sources of data Definitions Dependent variables RESELECT Mean annual residential electricity consumed between 1985 and 1989
Source National Electric Power Authority (NFPA) Lagos, 1990
COMELECT
Mean annual commercial electricity consumed between 1985 and 1989
NEPA Lagos, 1990
INDELECT
Mean annual industrial electricity consumed between 1985 and 1989
NZPA Lagos, 1990
RESPCA
Mean annual residential electricity consumed between 1985 and 1989 per capita
Calculated using NEPA and population data
COMPCA
Mean annual commercial electricity consumed between 1985 and 1989 per capita
Caculated using NEPA and population data
WDPCA
Mean annual industrial electricity consumed between 1985 and 1989 per capita
Calculated using NEPA and population data
Independent variables Internally generated revenue per REV state in naira
First National Rolling Plan. 1990
SGDP
Percentage contribution of each state to gross domestic product
Nigeria porrs study. 1985
REVPCA
lntemally generated revenue per capita
Estimated from First National Rolling Plan, 1990
PRICE
Average price per unit of electricity in kobo. Defined as the ratio of total revenue to quantity consumed per state
Estimated from NEPA accounts, 1990
URBAN
Percentage of the state's population living in cities with more than 20.000 people
National Demographic Sample Survey (NDSS).National Population Bunau Lagos, 1990
POP
Population per state
Provisional census figures of Nigeria. National Population Commission Lagos, 199 1
LAND
Total land area per state (kmz)
Map calculations
HOUSES
Estimate of state's housing requirements for 1990
Onibokun (1990)
COMBANK
Number of commercial banks per state
Teriba (1981)
Number of manufacturing firms per State
Industrial Directory. Federal Ministry Lagos, 1980. eighth edition
Distance from Kainji dam (km)
Map measurements
MFG DISTK
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internally generated revenue per state (REX);the percentage contribution of each state to the nation’s gross domestic product (SGDP);and the internally generated revenue per capita (REVPCA).These measures of income are hypothesized to be positively related to the amount of electricity consumed in each class. The price of any good is deemed to affect the quantity consumed. One of the major problems encountered in estimating demand functions for electricity relates to specifying the price per unit of electricity. This is because electricity is purchased in multi-step blocks usually of decreasing marginal price. Consequently, two methods have been used in calculating the unit price of electricity in previous studies. The first entails calculating the average price by dividing the total units of electricity consumed into the electricity bill. The second, which is the marginal price, is defined as the price of the last unit of electricity consumed. In a decreasing block rate, Gibbs (1978) points out that the marginal price is equivalent to the lowest rate reached. In other words, the marginal price is the extra cost in a block rate pricing system. The issue of average and marginal price further raises the question as to which of these two price measures be utilized. The choice between average price and marginal price has been explicitly examined by Wilson (1971) and Uri (1975). While Wilson prefers the latter measure, estimates presented reveal that both price measures produced identical results. On the other hand, Uri (1975) utilizes average prices apparently because of their practical appeal to utility companies. In Nigeria, consumers face a price schedule from which electricity is purchased in multi-step blocks of increasing marginal price. For instance, in the case of residential consumers, the current monthly rate schedule for houses with singlephase meters is: 0-400 kWh, six kobo per k W h with a fixed charge of two naira; above 400 kWh, 18 kobo per k W h with a fixed charge of five naira. For dwelling units with three-phase meters, the monthly rate is: 0-400 kWh, eight kobo per kWh with a fixed charge of ten naira; above 400 kwh. 20 kobo per kwh, with a fixed charge of 30 naira. Given the above complex price schedules, difficulties are likely to arise in using marginal prices. Furthermore, our units of analysis are states of the federation, which essentially are purely administrative units and not households. If our units of observation were households or firms. the issue of employing marginal prices could then be explored in greater detail. This is because the household or f m is generally considered the decision-making unit, and it is the marginal and not average price that is considered relevant in decisions concerning how much more electricity to consume. Our measure of the price per unit of electricity relates to the average rather than marginal price. This is defined as the ratio of the revenue derived by the NEPA from electricity sales to quantity consumed in each class for each state of the country. We hypothesize that the price per unit of electricity will be negatively related to the different classes of electricity consumed. It is pertinent to note that.
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.since the NEPA enjoys a monopoly of electricity supply, it charges uniform rates across the states of the country. Differences only exist with respect to the rates for the different classes of consumer. This implies that price differentials across the states are determined by their levels of consumption. While we observe that, given the above situation, utilizing average price poses the danger of detecting a spurious relationship between price and consumption, the dearth of quantitative studies on the determinants of electricity consumption in DCs, especially from a spatial perspective, offers little or no guidance as to solving such problems. In this respect, our utilization of average price can be seen as exploratory. In specifying a model for electricity consumption using data aggregated on a state level, the impact of factors, such as the degree of urbanization, the population and the total land area in each state, must be considered. In a previous study of the demand for electricity in Nigeria, some of the variables were poorly specified. For instance, urbanization is the rate of population growth over the years under consideration (Ayodele, 1978). In this paper, URBAN is specified as the proportion of a state’s population living in cities with 20,000or more people. POP represents the provisional results of the 1991 census, while LAND is a map calculation. The degree of urbanization, population and total land area are hypothesized to be positively related to the quantity of electricity consumed. An additional variable likely to be positively related to residential electricity consumed is HOUSE in each state. This variable, which can be regarded as a proxy for the number of households, also measures the size of the present and potential state demand for residential electricity. In the case of commercial electricity consumption, it is hypothesized that the level of commercial activity will be positively related to the amount of electricity consumed. The number of commercial banks (COMBANK)in each state is used as a surrogate for measuring the level of commercial activity. The level of industrial activity in a state is expected to be an important factor determining the amount of industrial electricity consumed. In this paper, the number of manufacturing establishments (MFG)in each state is utilized as a proxy for the level of industrial activity. The final variable utilized is DISTK. This variable is employed because Kainji dam is the nation’s major supplier of electricity, producing about 70 per cent of total electricity consumed. Furthermore, utilizing DISTK allows for the inclusion of a purely geographic factor. Electricity consumption for each of the three classes is hypothesized to be a negative exponential function of the distance to the Kainji dam.In developed countries, the distance from the source of supply is usually incorporated into the price paid for electricity. This implies that the further away consumers are from the source of supply, the higher will be the price paid. In Nigeria, this is not the case, as consumers, irrespective of their location, pay uniform block rates depending on the quantity consumed.
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We acknowledge, however, that our list of independent variables does not take cognizance of certain important issues in the specification of demand functions. For instance, theory suggests that the demand for electricity should include the price of substitutes and complements. We know that electricity has substitutes in the form of natural gas, coal and petroleum products. Fuel choice proportions for the country reveal that petroleum products account €or 73.6 per cent of total energy consumption, natural gas 20.5 per cent, hydroelectricity 5.5 per cent and coal 0.4 per cent.3 Information on fuel mix proportions on a state basis is either difficult to come by or non-existent. This prevents us from pursuing further the issue of substitution possibilities. While electricity is complementary to other goods, in the sense that it is used with electrical appliances, information on electrical appliance ownership or the price of such appliances is not available on a state basis. In the light of the foregoing, we assume that the cross-price effects of these substitutes and complements are negligible. Another issue worth pursuing in the empirical analysis is how some of the supply constraints identified in previous sections will affect demand decisions. The inability of NEPA to meet the demand requirements of its different categories of consumer has meant that these consumers have had to seek alternative sources of power supply. In the commercial and industrial sectors, most medium- and largescale fins have their own private generators which they switch to when the quality or reliability of electricity generated by NEPA falls below a critical level (Lee and h a s , 1989). In the case of residential consumers, the use of generators is currently restricted to the high-income groups who, invariably, can afford such. While captive power, as provided by private generating plants, constitutes a ‘reasonable’ share of electricity consumption, such records do not enter into the national accounts; an analysis of electricity consumption in Nigeria implies that we are dealing with only NEPA-served consumers. This in effect precludes us from analyzing in a meaningful way the impact of supply constraints on demand decisions. Finally, it is important to point out that the spatial approach adopted in this paper implicitly assumes that the same income and price elasticity prevails for each state. This may not be the case. A more thorough comparison of electricity consumption across the states would entail the estimation of separate elasticities for each state, using time-series data. This has not been feasible in this paper, as the appropriate data are not available. As more refined data on a state basis become available, this is an area which could be addressed in future research.
-
Empirical estimation and discussion of results The functional form adopted for estimating the demand function for electricity consumption is the double-log model, in which we take the natural
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logarithms of both the dependent and independent variables. The rationale for so doing is twofold. First, it enables us to interpret the estimated coeEcients directly as elasticity measures; and secondly, the double-log model reduces the Occurrence of heteroscedasticity, thereby ensuring the efficiency of the parameter estimates. For instance, the consumption function for residential electricity is estimated as follows: Ln(RESELECT) = bo + blLn(Yi)+ b2Ln(Pi)+ b3Ln(URBANi)
(2)
+ b4Ln(POPi)+ b5Ln(LANDi) + b,Ln(HOUSESi)
+ b7Ln(DISTKi) The regression results for the residential, commercial and industrial classes of consumer are presented in tables 3 , 4 and 5 respectively. For each class of consumer, our estimation strategy was as follows. First, we estimated each model with all the relevant independent variables. This is presented in the first section of the respective tables. In the second section, the models were re-estimated without the price term. The reason for this was twofold: first, to observe what the regression results would look like assuming the existence of a spurious relationship between average price (which depends on the level of consumption) and electricity
Table 3 Residential electricity consumption regressions Dependent variable: Ln(RESELECT) Variable Ln(SGDP) Ln(PR1CE) Ln(URBAN) Ln(P0P) LnN(LAND) Ln(H0USES) Ln(D1STK)
CONSTANT R2 F-ratio N
(1)Full set of variables
(2) Full set of variables,
without the price term
0.4977( 1.84)** -0.3436(0.8 1) 0.4748( 1.38)*** 0.3 170(0.75) 4.0932(0.46) 4.0332(0.11) 4.193 l(0.56) 13.4452(2.07)** 0.8010 7.473 21
0.5066( 1.90)**
-
0.6196(2.13)** 0.3611( 1.35)*** 0.1974( 1.26) -0.1545(0.62) -0.2224(0.66) 13.9722(2.19)** 0.79 10 8.829 21
\
(3) Reduced set of variables 0.5247(2.33)**
-
0.5352(2.64)* 0.3356( 1.18)
-
-
-
10.3492(2.06)** 0.7556 17.523 21
*Significant ot the 0.01 level ond above (one-tail test) **Sign$cont ot the 0.05 level (one-tail test) ***Significantof the 0.1 level (one-tail test) Absolute t-values are in parentheses Ln = noturol logorithm - Not included in the model
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Table 4 Commercial electricity consumption regressions Dependent variable: Ln(C0MELECT) Variable Ln(REV) Ln(PRICE) Ln(URBAN) Ln(P0P) Ln(LAND) Ln(C0MBANK) Ln(D1STK) CONSTANT
R2 F-ratio N
(1) Full set of variables
(2) Full set of variables, without the price term
-0.2612(0.81) -0.4869( 1.45)*** -0.065 l(0.18) 0.4341(0.79) -0.09 18(0.38)** 0.7398(2.11)** 0.3019( 1.13) 10.5376(1.67)**
-0.2762(0.82)
-
0.1709(0.51) 0.4796(0.85) -0.2430( 1.09) 0.7938(2.19)** 0.1285(0.52) 13.4821(2.18)**
0.807 1 7.771 21
0.7758 8.074 21
(3) Reduced set of variables -0.03 lO(0.12)
-
0.0552(0.14) 0.9433(3.04)* 0.1235(0.64) 12.9826(2.18)** 0.7554 12.353 21
*Sign@cantat the 0.01 level and above (one-tail test) *+Significantat the 0.05 level (one-tail tesr) *+*Significantat the 0.1 level (one-railtest) Absolute t-values are in parentheses Ln = natural logarithm Not included in the model
-
Table 5 Industrial electricity consumption regressions Dependent variable: Ln(1NDPCA) Variable Ln( REVPCA) Ln(PR1CE) Ln(URBAN) Ln(POP) Ln(LAND) Ln(MFG) Ln(D1STK) CONSTANT
R2 F-ratio
N
(1) Full set
(2) Full set of variables,
of variables
without the price term
0.9967( 1.50)** -1.1 163(3.08)* -0.1989(0.48) 4.9917(1.27) 0.8059( 1.61)*** 0.4625(1.48)*** -0.9889(1.49)*** 7.1908(0.67)
(3) Reduced set of variables
0.5789(0.70)
0.8033( 1.32)***
0.2114(0.42) -0.4228( 1.05) 0.4567(0.74) 0.3791(1.96)** -0.7709(0.92) 11.7975(0.87)
-1.1893( 1.3 1) 0.5841( 1.11) 0.8213(2.57)* 4.9488( 1.35)*** 12.7998(0.99)
-
0.6723 3.810 21
0.4321 1.775 21
-
0.4249 2.2 17 21
*Significantat the 0.01 level and above (one-tail test) **Sign@cantat the 0.0slmel (one-tail test) ***Significantat the 0.1 lael (one-tail test) Absolute t-values are in parentheses Ln = natural logarithm Not included in the model
-
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consumption; and secondly, because in a few cases price was found to be the most important determinant of electricity consumption. Such a result appears surprising and unlikely in such a poor country where one would expect income to be the key determinant of electricity consumption. . Owing to the occurrence of multicollinearity, the insignificance and wrong signs of a number of variables, it became necessary to re-estimate the regression models with a reduced set of.variables.These results are presented in the third part of the different tables. For each of the different consumption classes, we further experimented with total electricity consumption and consumption per capita as dependent variables. For residential and commercial usage, total consumption provided results with the best fit. In the case of the industrial class, electricity consumption per capita provided plausible results. For comparative purposes, we present in the appendix results obtained when consumption per capita is used for both the residential and commercial sectors, and total usage for the industrial sector.
Residential electricity consumption The results of the regression analyses for the residential class are presented in table 3. The analysis reveals that the models account for between 75.56 per cent and 80.10 per cent of the spatial variation in residential electricity consumption. The analysis further indicates that income is an important determinant of residential electricity consumption. The income elasticity (SGDP)is about 0.498 in the full model, 0.507 when the price term is dropped and 0.525 in the model with the reduced set of variables4 This implies that, all other things being equal, the demand for residential electricity will increase by between 0.498 per cent and 0.525 per cent, given a one per cent increase in the proportion of the state's contribution to the nation's GDP.The values of our income elasticity are lower than those of Ayodele (0.819) and Iwayemi (1.145). This might be attributed to the nature of the data and income variable employed. However, our less-than-proportionalincome elasticity is consistent with that obtained in most developed countries (Taylor, 1975). URBAN is positively related to the amount of electricity consumed in each state.' Indeed, the level of urbanization is the most important factor affecting residential electricity-consumption. Specifically, the URBAN coefficients, which range between 0.475 and 0.620, indicate that the amount of residential electricity consumed at the state level will, all other things being equal, increase by between 47.5 per cent and 62.0 per cent, given a 100 per cent increase in the state's level of urbanization. Table 3 shows that POP is sigdicant in only the second model. The POP coefficient of 0.361 indicates that, on average, a one per cent increase in state population will increase residential electricity consumption by 0.36 per cent. The
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implication of a less-than-unity population elasticity is that a greater percentage of the state’s population will be associated with less than the required quantity of electricity for residential consumption. This is because the increase in the demand for residential elasticity is less than proportional to the increase in the state’s population.
Commercial electricity consumption The regression results for commercial electricity consumption are presented in table 4. The analysis indicates that the estimated models account for between 75.54 per cent and 80.71 per cent of the spatial variation in commercial electricity consumption. These models, despite all experimentation, generally exhibit a poor degree of fit as indicated by the large number of insignificant variables. In fact, the only variable that is consistently significant is COMBANK, which is indicative of the level of commercial a~tivity.~ The COMBANK coefficients reveal that the amount of electricity consumed by the commercial sector will, all other things being equal, increase by between 74.0 per cent and 94.3 per cent, given a doubling of the level of commercial activity. This finding conforms with the a priori expectation and is consistent with Iwayemi’s (1981) finding that the level of commercial activity is an important determinant of non-residential electricity consumption.
Industrial electricity consumption The regression estimates for the industrial sector, with R2 values ranging be~ in table 5. The income cotween 42.49 per cent and 67.23 per cent, a r presented efficients are significant in models 1 and 3, indicating that the demand for industrial electricity will increase by between 0.803 and 0.997 per cent, given a one per cent increase in the state’s internally generated revenue. This means that the demand for industrial electricity increases with an increase in income, but at a less-thanproportional rate. The price per unit of industrial electricity (PRICE)in model 1 is significant and conforms to the a priori expectation, in that it is negative. However, the relatively large size of the price term and its emergence as the most important variable determining industrial electricity consumption requires caution in interpretation. This is because, given the poor state of the Nigerian economy, one would expect other variables, particularly income, to emerge as the most important factor. Furthermore, non-residential price elasticity obtained using time-series data and average price has been found to be -0.419 (Iwayemi, 1981). This is far lower than our estimated value of -1.116. The foregoing observations question the reliability of our price elasticity. Consequently, the price tern is deleted in the subsequent models. It is, however, expected that, as more refined electricity and price data become available on a spatial dimension, this problem might be resolved.
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The land area of the respective states (LAND), as indicated in model 1, significantly and positively affects the amount of electricity consumed. The LAND coefficient is 0.806. This means that two states, identical in all respects, save for one having a land area ten per cent larger thaq the other, will, all other things being equal, have a higher industrial electricity consumption of 8.06 per cent. While this finding appears plausible, its consistency cannot be ascertained, as such landrelated variables were not specified in previous studies. The effect of the level of industrial activity (MFG) is positive and significant in all models. The MFG coefficients range between 0.379 and 0.821. This implies that the doubling of industrial activity will, all other things being equal, increase industrial electricity consumption by between 38 and 68 per cent. This means that the demand for industrial electricity with respect to industrial establishments is inelastic. The last significant variable is DISTK. The DISTK coefficients follow the negative exponential fashion, in that industrial electricity consumed decreases by between 0.949 per cent and 0.989 per cent, given a one per cent increase in the distance from Kainji dam. This finding is plausible and consistent with the notion that geographical phenomena often decrease with increasing distance from the point/ source of origin.
Conclusion In this paper, we have attempted to analyze the determinants of the spatial variation in electricity consumption in Nigeria. The generaI pattern depicted by the spatial distribution of electricity consumption is one in which the country is divided roughly into two zones of high and low electricity consumption. The areas of high consumption are seen to coincide with areas of high level of economic wellbeing, rapid urbanization and high population densities. Areas of low consumption display the opposite conditions. Our findings indicate that the main determinants of the spatial variation in residential electricity consumption are the degree of urbanization, income and population. For the commercial class, the major determinant is the level of commercial activity. In the case of industrial electricity, the important factors accounting for its spatial variation are the level of manufacturing activity, internally generated revenue per capita, the distance to the Kainji dam and the land area per state. Furthermore, the estimated coefficients for the above variables are below unity. This implies that the demand for the different classes of electricity, with respect to their respective independent variables, is inelastic. The results of the analyses presented in this paper have implications for the future growth in demand for electricity. A future escalation in levels of urbanization and population, an increase in income and an upsurge in commercial and industrial activity will all necessitate an increase in electricity consumption. The
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question that then arises is: how prepared is the Nigerian electricity industry to cope with such increases, in view of the supply constraints being encountered by NEPA? Given the current pallid state of Nigeria’s economy on the one hand, and the bureaucracy and numerous inefficiencies associated with running governmentowned agencies on the other, it is Uniiely that NEPA will meet the challenges of the future growth in electricity consumption. A plausible way out would be to encourage the participation of the private sector in the generation, transmission, distribution and sale of electricity. Indeed, private sector participation in Nigeria, as documented by Lee and Anas (1989) and Whittington et a1 (1989) in the spheres of transportation, postal and telecommunication services, water supply and even power generation, has been characterized by some degree of success. This is because the willingless to pay for such services is higher when consumers are guaranteed reliable services.
Footnotes I. In 1991, nine additional states were carved out of the then 21 states. We are constrained, owing to the problem of data availabiliry,to use the 21 -state structure. 2.
Kainji dam supplies about 70 per cent of the nation’s electricity requirements.
3.
These are the mean proportions of total energy consumed between 1983 and 1985, as obtained from the Central Bank of Nigeria’s Annual Report and Statement of Accounts.
4.
In running the regression analyses with total electricity consumptionfor each class as the dependent variable, we experimented with two income variables -internally generated revenue per state (REV)and the percentage contribution of each state to the countryyf GDP(SGDP). The results reported are those that provide the most plausible estimates.
5.
The simple correlation (r)between Ln ( C O M F C T ) and Ln (COMBANK)is 0.86538. This is the highest correlation between l j l (COMELECT) and variables m the independent set; which means Ln (COMBANK)alone accounts for 74.89 per cent of the spatial variation in commercial electricity Consumption.
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References Ayodele, A.I. (1978),An econometric analysis of the pattern of electricity consumption in Nigeria: 1960-75. Unpublished PhLl thesis, Department of Economics, University of Ibadan. Ayodele. A S . (1987), ‘Energy institutions’, in Ayodele, AS. (eds.), Energy development and utilization in Nigeria, Nigerian Institute of Social and Economic Research, Ibadan. Battalio, R.C.;Kagel, J.H., Winkler,R.C., and Winett, RA. (1979), ‘Residential electricity demand: an experimental study’, Review of Economics and Statistics, Val. 61, 180-189. Barter, R.E., and Rees, R. (1968), ‘An analysis of industrial demand for electricity’. Economic Journal, Vol. 78.227-298. Fisher, F., and Keysen, C. (1962),A study in econometrics: the demand for electricity in the US, North Holland Publishing Company, Amsterdam. Gibbs, K.C. (1978), ‘Price variable in residential water demand models’, Water Resources Research, Vol. 14,15-18. Houthakker, H.S. (1951). ‘Some calculations of electricity consumption in Great Britain’, Journal of the Royal Statistical Society (A),Vol. 114,351-371. Iwayemi. A. (19811, ‘An econometric analysis of the demand for electricity in Nigeria’,Research for Development, Vol. l ,23-32. Lee, S.K..and Anas, A. (1989). Manufacturers’ response to infiastructural deficiencies in Nigeria. World Bank Working Paper No.325. Makinwa, P.K. (1978), ‘Thedynamics of urban population growth’, in Sada,P.O., and Akinbode. A. (eds.), Settlement systems in Nigeria, Proceedings of the IGU Commission on National Settlement Systems, Benin City, 25-30 July 1978. Mayer, S.K., and Horowitz, CE. (19791, ‘The effect of price on the residential demand for electricity: a statistical study’, Energy, Vol. 4.87-99. Murray, P.M., Spann, R., Pulley, L., and Beauvais, E. (1978). ‘The demand for electricity in Virginia’,Review of Business and Economics, Vol. 60,585600. Onibokun. P. (1990),Urban housing in Nigeria, Nigerian Institute of Social and Economic Research,Ibadan. Taylor,L.D. (1975), ‘Thedemand for electricity: a survey’, Bell Journal of Economics,
Vol. 6, 74-110. Teriba, 0. (1981), ‘Financial institutions,financial markets and income distribution’, in Bienen. H.. and Diejomaoh, VP.(eds.), The political economics of income distribution in
Nigeria, Holmes and Meier Publishers Inc, New York.
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Uri,ND. (1975), Towards an efficient allocation of electrical energy, D.C. Health and Company,Lexington . Whim’ngton,D., Lauria, D.T.. and Mu, X . (1989).Paying for water services: a study of water vending and willingness to pay for water in Onitsha, Nigeria, World Bank Case Study Report, INU 40. Wilson,J.W. (1971). ‘Residential demand for electricity’, Review of Economics and Statistics, Vol. 11.7-22.
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Appendix 1 Residential electricity consumption regressions Dependent variable: Ln(RESPCA) Variable
Ln(REVPCA) Ln(PRICE) Ln(URBAN) Ln(POP) Ln(LAND) Ln(H0USES) Ln(D1STK)
CONSTANT R2 F-mtio N
(1)Full set of variables 0.156 l(0.36) -0.4276(0.86) 0.5650( 1.42)*** 4.5602( 1.09) -0.0855(0.25) 0.1134(0.36) -0.0282(0.07) 8.2028(1.26)
(2) Full set of variables, without the price term 0.0488(0.12)
-
0.7665(2.40)** -0.3000(0.72) -0.2692(1.04) -0.0163(0.06) -0.0275(0.07) 8.7549( 1.36)*** 0.6704 4.745 21
0.6882 4.099 21
(3) Reduced set of variables 0.3459( 1.49)***
-
0.6997(3.65)* 4.4584( 1.31)*
-
6.5467( 1.29) 0.6422 10.169 21
'Significant at the 0.01 level and above (one-tailtest) **SigniJcant at the 0.05 level (one-tail test) ***Significantat the 0.1 level (one-tail test) Absolute t-values are in parentheses Ln = natural logarithm - Not included in the model
Appendix 2 Commercial electricity consumption regressions Dependent variable: Ln(C0MPCA) Variable
Ln(REVPCA) Ln(PRICE) Ln(URBAN) Ln (POP) Ln(LAND) Ln(C0MBANK) Ln(D1STK) CONSTANT R2 F-ratio
N
(1) Full set of variables
(2) Full set of variables, without the price term
(3) Reduced set of variables
-0.2 134(0.61) -0.4599( 1.34)*** -0.0793(0.22) 4 . 7 8 Z ( 1.80)** -0.0923(0.34) 0.7239(2.05)** 0.2992( 1-11) 10.3229( 1.61)**
-0.3524(0.83)
-0.0130(0.05)
0.7460 5.454 . 21
-
0.1506(0.46) -0.7282(1.63)** 4.2876( 1.10) 0.7799(2.17)** 0.1435(0.57) 13.3431(2.17)** 0.7109 5.738 21
-
4.967 1(2.58)*
-
0.93 14(3.07)* 0.1229(0.64) 12.8637(2.18)**
0.6840 8.659 21
*Significantat the 0.01 level and above (one-tail test) **Significantat the 0.05 level (one-tail test) *+*Significantat the 0.1 Ievel (one-tail test) Absolute t-values are in parentheses Ln = natural logarithm - Not included in the model
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Appendix 3 Industrial electricity consumption regressions Dependent variable: Ln(INDELECT) Variable Ln(SGDP) Ln(PFUCE) M W A N )
Ln(P0P) Ln(LAND) Ln(MFG) Ln(DISTK) CONSTANT
R2 F-ratio N
(1)Full set of variables
(2) Full set of variables, without the price term
(3) Reduced set of variables
1.0655(1.85)** -0.9241(2.69)* -0.1997(0.53) 0.533 l(0.84) 0.1261(0.95) -0.0944(0.25) -0.5481(0.95) 10.0141(0.95)
1.2638(1.84)**
1.200(2.392)**
-
-0.078q0.17) 0.2455(0.33) 0.2260(0.95) O.lO83(0.06) -0.5 1w0.74) 15.0355(1.20)
0.7416 5.330 21
0.598 3.472 21
-
0.0675(0.21) 0.1722(0.46) -0.5457(0.90) 18.1243(3.475)* 0.5940 5.853 21
*Significant at the 0.01 level and above (one-tail test) **Significant at the 0.05 level (one-tail test) ***Significant at the 0.1 level (one-tail test) Absolute t-values are in parentheses Ln = natural logarithm Not included in the model
-
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