Causes of Electricity Consumption in Bombay
Piyush Tiwari,Housing Development Finance Corporation
The energy use increases rapidly due to growth in population, urbanization, and economic growth. Residential electricity consumption has an important place in the rapidly growing electricity consumption in India. Most of the studies that are carried out for energy planning in India, use macrolevel data for the estimation of price and income elasticities of electricity demand. The present study analyses the short-run residential demand for electricity using household survey data for Bombay. The study concludes that the price and income elasticit-ies of residential electricity demand are20.70 and 0.34, respectively. 2000 Society for Policy Modeling. Published by Elsevier Science Inc.
Key Words: Residential electricity demand; End-use model.
1. INTRODUCTION
The energy use in the developing countries is increasing rapidly. The factors that drive this rapid increase in energy use include the population growth, the economic growth, and urbanization. With increasing urbanization, towns, cities, and metropolitan areas of varying sizes develop. Each type of urban area has different energy consumption patterns, partly due to income and cultural differences. However, the general trend is that with urbanization the energy consumption increases. It is increasingly costlier to add new electricity generation capacity. In India, the share of outlay in energy sector has been around 28 percent of the total eighth 5-year plan (Planning Commission, 1992). In the eighth plan, the share of power in the energy sector is 70 percent. The residential sector is an important sector contributing to the rise in electricity demand, and its share in India is 23 percent of the total electricity
Address correspondence to P. Tiwari, Housing Development Finance Corporation, Ra-mon House, 169 Backbay Reclamation, Mumbai 400 020, India.
We thank P. Pushpa for helping us in structuring the manuscript. Received February 1997; final draft accepted September 1997.
Journal of Policy Modeling22(1):81–98 (2000)
consumption (Planning Commission, 1992). The demand for elec-tricity in this sector is basically due to lighting and comfort (heating and cooling) for the residents. As the income rises, the consump-tion of electricity also increases because a larger share of increased income is spent on electric appliances, which are directly linked with comfort. Most of the studies carried out for energy planning in India like the Fuel Policy Committee report (1976), the Working Group on Energy Policy report (1979), the Advisory Board on Energy report (1985), Parikh (1981), Regional Energy Develop-ment Programme (1991), etc., use aggregate data. Until now, households level data are not used for energy demand estimation. The primary reason is the nonavailability of data. In this paper the analysis has been carried out using household level data for Bombay, a metropolitan city in India. Empirical research that provides reliable information on energy demand elasticities by households is important for several reasons. First, empirical evi-dence and the use of policy instruments go hand in hand. Energy conservation and energy efficiency are two important policy issues in the current energy debate, for example, if the utilities offer tax credits to households to encourage them to make energy saving improvements. Any response to the above policy concerns re-quires estimates of the responsiveness of energy demand to price and income.
Second, though there exist numerous studies for developed countries to estimate price and income elasticities (Acton et al., 1976; Burgess and Paglin, 1981; Garbacz, 1984, 1983a, 1984b), etc., the number of studies for developing countries, particularly India, are practically none. Admittedly, it is very difficult to use mac-rolevel elasticities to infer into the nature of demand response to price change for specific households.
for household energy demand is most often used in linear or logarithmic form. Dahl’s (1990) work favours logarithmic over linear demands models. Hsing (1990) shows that the conventional log-linear form is appropriate.
2. DETERMINANTS OF RESIDENTIAL ELECTRICITY DEMAND IN BOMBAY
The household data used in this analysis are drawn from the 1987–88 household survey of Bombay Metropolitan Regional De-velopment Authority (BMRDA, 1990). The BMRDA household survey has categorized the residential electricity consumption into five categories. There are some problems with the accuracy and completeness of the raw data. This required fairly extensive clean-ing, and resulted in exclusion of some observations because of unrealistic or missing values for one or more variables. This re-sulted in a sample of size 6358 households. The data regarding housing1indicates that 19 percent of the households stay in huts,
8 per cent dwell in chawls, while the households staying in flats having less than five floors constitute 42 percent of the total survey population. Ten percent of the sample households stay in flats having more than five floors. Five percent reside in bungalows. The rest of the sample households are from tenements and row houses. The mean monthly expenditure on electricity in Bombay is Rs. 80, and the maximum monthly expenditure of the households is Rs. 417. The distribution of households according to residential electricity expenditure, classified with respect to type of dwelling units and the mother tongue of the household, is given in Table 1. Ninety-two percent of the hut dwellers have the residential electricity consumption between Rs. 13 to Rs. 105. About 3 percent
P.
Tiwari
Table 1: Distribution of Residences and Residents According to Residential Electricity Consumption
Monthly electricity
expenditures (Rs.) 4.17 12.5 25.0 45.84 70.84 104.17 166.67 312.5 416.67
Residences
Hut 0.6 1.7 5.0 5.8 3.0 2.2 0.7 0.3 0.02
Chawl 0.2 0.7 1.5 2.3 1.4 0.9 0.6 0.4 0.03
Flat,5 floors 0.8 1.8 4.7 8.8 7.5 9.1 6.7 2.0 0.4
Flat.5 floors 0.3 1.0 1.1 1.2 1.0 2.4 2.3 0.9 0.2
Bungalows 0.3 0.9 0.8 0.9 0.5 0.8 0.6 0.3 0.02
Residents
Marathi 1.7 5.8 10.6 13.4 7.8 6.9 3.9 1.5 0.3
Gujrati 0.2 0.6 1.5 2.9 2.4 3.2 2.7 0.9 0.1
of them have electricity consumption that is less than Rs. 13. Eighty-five of the chawl dwellers report their electricity expendi-ture ranges between Rs. 13 to Rs. 105. Three percent of them have residential electricity consumption below Rs 13. The 76% of the dwellers of the flats below five floors have their residential electricity consumption within the range Rs. 13 to Rs. 105. Only 2% of them have electricity consumption below Rs. 13. Sixty-four percent of the dwellers of the flats above five floors have their residential electricity consumption in the range of Rs. 13 to Rs. 105. Three percent of them have electricity consumption below Rs. 13. The 76% of the bungalow occupants have electricity con-sumption in the range of Rs. 13 to Rs 105. Six percent of them have electricity consumption below Rs. 13. The social pattern of the sample population is as follows: Maharashtrians—52%, Gujratis—15%, South Indians—7%, while the rest of 26% are from North India. Three percent of the Maharashtrians have elec-tricity consumption below Rs 13. Eighty-five percent of them have residential electricity consumption that lies in the range of Rs. 13 to Rs. 105. Compared to this, 73 percent of the Gujratis have electricity consumption between Rs. 13 to Rs. 105 and only 1 percent have below Rs. 13. The South Indians have higher residen-tial electricity consumption. Seventy-eight percent of them have it between Rs. 13 to Rs. 105, while only 1 percent of them have it below Rs. 13. The average monthly household income in Bom-bay in Rs. 2550. The monthly expenditure on electricity of a household is only 3 percent of the total monthly income. The mean household size as indicated by sample survey in Bombay is 5.24. As the household size increases, the electricity consumption also increases. The mean number of rooms in Bombay is 2.2. The average age of the buildings in Bombay is 31 years, although some structures are as old as 100 years.
The BMRDA survey reports the appliance characteristics of the households. The possession of various appliances of households in Bombay is as follows. Sixty-nine percent of the households in Bom-bay have a television set. The VCR is possessed by 11 percent of the households. Fifty percent of the Bombay households have a tape-recorder. Thirty-seven percent of the households have a refrig-erator, while 61 percent of the households have an electric iron.
3. THE STRUCTURE OF ELECTRICITY DEMAND
expressing the quantity of electricity consumption as a function of its current relative price and real income. Several restrictions have been incorporated into the model in its wide application of studies of household electricity demand to improve its perfor-mance and increase its flexibility (see, e.g., Anderson, 1973; Bales-tra, 1967; Chern and Bouis, 1988; Chern et al., 1982; Houthakker et al., 1974; Wills, 1981). Among the problems that are encoun-tered in the study of household electricity demand a major one is the high degree of correlation between several explanatory variables in the demand equation with important implications for its econometric estimation. The previous research bypasses this problem using systems of equations and appropriate restrictions (e.g., Garbacz, 1983; Halvorsen, 1975; Houthakker, 1980). Do-natos and Mergos (1991) used a single-regression equation even under the conditions of multicollinearity using the “Ridge Regres-sion Technique” (RR). The problem of heteroskedasticity also persists besides the multicollinearity with the cross-sectional data. This paper proposes an alternate functional form of a single equa-tion that avoids the problem of heteroskedasticity and multicollin-earity. The functional form takes care of the heteroskedasticity problem, and it uses the RR technique of estimation to get over with the problem of multicollinearity.
The choice of variables depend on the determinants the residen-tial electricity consumption demand in Bombay. Other studies for example, Hirst et al. (1981), find that the demand for comfort is a major determinant of monthly consumption: the key variables are heating degree days and floor area (a proxy for energy needed to heat the residence). Family size, income, and age of the house were also the significant determinants of residential electricity consumption. Garbacz (1983a) excludes dwelling size, focussing on an index of appliance stock. Others have examined auxiliary heating (Reilly and Shankle, 1988), preferred thermostat settings (Kushman and Anderson, 1986), or continuous occupancy (Cap-per and Scott, 1982). The presence or absence of a particular appliance has also been used in models of electricity consumption. For example, Parti and Parti (1980) use zero-one dummies and interaction terms to determine the electricity consumption associ-ated with ownership of individual appliances.
demand for the residential electricity. In the short run, the intensity with which consumers use electric appliances depends on their income, housing unit structure, demographic characteristics, sea-sonal variations, weather, and electricity prices. The area covered in the present study is Bombay, which does not have much varia-tions in its climatic condivaria-tions over the year. The residential elec-tricity price structure, which is slab type, is the same throughout Bombay.
To formalize the discussion, the household’s problem is to max-imize utility (1):
U(E;B), (1)
where E is the electricity consumption and B represents other goods. The household is constrained by the existing stock of appli-ances and their income. Utility maximization subject to these constraints generates a residential demand for energy that is the function of income and prices, and may be written as (2):
Ei5F(Yi,Pi,APINDXi,Di,Hi). (2)
Besides the price of electricity (P) and income (Y), the other ex-planatory variables in the demand equation fall into three groups, housing characteristics, demographic characteristics, and appliance holdings of the household.
We use expenditure on housing, in the form of expenditure on electricity, as a surrogate for consumption as the measurement of the dependent variable related to the quantity of electricity consumed. The expenditure Electiis, however, a product of unit price (P) and quantity consumed (E); and the relation postulated above becomes (3):
Electi5PiEi5f(Pi,Y,APINDXi,Di,Hi), (3)
where Electiis the monthly expenditure on electricity by ith house-hold. The total annual income is used as proxy for permanent income, consistent with permanent income hypothesis (Friedman, 1957; Houthakker and Taylor, 1970). APINDXi is the appliance index,Diare the demographic variables,Hiare house characteris-tics, andPiis the price of electricity per unit.
4. VARIABLES CONSIDERED FOR ANALYSIS
The various variables considered for analysis of residential elec-tricity demand in Bombay are as follows.
Household Electricity Expenditure (Elect)
The household monthly electricity expenditure as reported by the households is taken as dependent variable. Because the weather condition in Bombay is the same throughout the year, the weather or season does not affect the electricity expenditure. Although there is some cooling load in summer and postmonsoon period, it has been ignored.
Structure Type
There are four dummies that have been used for the structure type. These are dummies of a one-zero type for hut, chawl, flat with less than five floors, flat with more than five floors, and bungalows. These variables are introduced to capture the effects of design, materials, construction techniques, etc., on residential electricity consumption.
Age of the House
This variable is a proxy for a low energy-efficiency thermal enve-lope associated with old houses. It is likely that this variable is also capturing effects related to improvements in design, materials, construction techniques, and appliance efficiency in addition to higher insulation values (Berg and Taylor, 1994).
Rooms
This is a house structure variable. The more the number of rooms the more is the requirement of electric fixtures.
Demographic Variables
have been included are of one-zero type for maharashtrians, gujra-tis, and south Indians.
APINDX
The electricity consumption of the household depends on the appliance holding of the household. A methodology to construct the appliance index (APINDX) is discussed in Appendix 1.
Income (Y)
The income of households is the single most important determi-nant of residential electricity consumption. This variable captures, besides a number of behavioural effects related to variations in income, the number and size of appliances that a household pos-sess. The annual income of the household is considered for the analysis.
Price per Unit of Electricity (P)
The price of electricity that has been used in the present estima-tion is the average price for the household. The use of an average price as a determinant of electricity is based on the assumption that the consumer responds to the price that he perceives from his total electricity bill (Wilder and Willenborg; Shin, 1985; Branch, 1993).
5. EMPIRICAL MODEL
Estimation of Equation 2 using ordinary least square (OLS) failed because of heteroskedasticity and multicollinearity. The Goldfield-Quandt test for a simple semi-log functional form shows that the cross-sectional data has the problem of heteroskedaticity. The func-tional form in Equation 2 is corrected for heteroskedasticity (4).
Ln Electi
the household has the determinants and zero otherwise: Yi 5 Permanent income, proxied by total expenditures over a year;
Dik 5 Household is demographic characteristics (these include household size, age of the head of household, three dummy vari-able for social characteristics: Maharashtrian, Gujarati, and S. Indian); Hi 5 Structure-related variables that include structure type, number of rooms, and age of the structure; APINDXi5It is a vector of appliances (a dummy is used for this variable, which is one if the appliance is present—otherwise zero);Pi5Price of electricity per unit.
Using the Farrar-Clauber test we found the presence of strong multicollinearity. To overcome this problem Equation 3 is esti-mated using ridge regression suggested by Hoerl and Kennard (1970). Using this method the system stabilized and had the gen-eral character of the orthogonal system. The coefficients that had improper signs in OLS changed to proper ones.
To estimate price elasticity from Equation 4:
LnE5a1EyLnY1EpLnP
Ln Elect5Ln (P.E)5a1EyLnY1(11Ep) LnP.
In Equation 4,b 5 Eyand x 5 (1 1Ep). So, price elasticity5 coefficient of the price term minus one.
6. ESTIMATION RESULTS The results are shown in Table 2.
The model fits the data well. The signs for variables can be best summarized by saying that they tend to have the a priori expected signs. A five-member family will have 23 percent more electricity expenditure compared to a two-member family. With an addi-tional household member the electricity expenditure increases by 7.7 percent. An additional room in the house leads to 11 percent more electricity expenditure. A 10-year-old house has 4 percent more electricity requirements. The parameters for age of reference person and APINDX have position coefficients. A 10 percent in-crease in APINDX causes a 10 percent inin-crease in residential electricity expenditure.
The most important result that we obtain from this study are the estimated elasticities of income and price (Table 3).
Table 2: Regression Results Dependent Variable: Log(Elect)
Variable Parameter T-Value
Dummy for hut 0.407 5.34
Dummy for chawl 0.611 43.34
Dummy for flat,5 floors 0.623 56.63
Dummy for flat.5 floors 0.852 48.4
Dummy for bungalows 0.880 6.7
Dummy for maharashtrians 0.734 69.96
Dummy for gujrati 20.605 225.58
Dummy for south indians 0.429 29.2
Household size 0.099 94.95
Rooms 0.114 67.41
Age of the house 0.0004 2.07
Log(Y) 0.047 206.6
Log(P) 0.293 222.12
Age of household head 0.008 67.77
Appliance Index 0.01 96.89
Constant 0.387E111 5.74
R2 0.98
Note: All the variables except constant term are divided by Income22.5.
elasticities for different income classes in Bombay. The mean monthly income and mean monthly electricity expenditures for these income classes are tabulated in Table 4.
The share of electricity expenditures in monthly income of the household decreases with higher income class. The shares are: lower income class—6.5 percent, middle income class—4.2 per-cent, upper middle income class—3.3 perper-cent, and upper income class—2.2 percent.
The income elasticity increases with increase in income. How-ever, the price elasticities indicate that with increase in price the group that will respond favorably are lower- and upper-income groups. The results are indicated in Table 5.
Income and price elasticities from this study are compared with results of selected short-run residential electricity-demand studies
Table 3: The Estimated Elasticities
Variables Elasticities
Income 0.34
Table 4: Mean Monthly Income and Electricity Expenditures
Mean income Mean electricity Sample
Income groups (Rs) expenditure (Rs) size
Lower income
(up to Rs. 1000) 788.4 51.1 1274
Middle income
(Rs. 1000–2000) 1600.0 65.1 2318
Upper middle income
(Rs. 2000–3500) 2768.3 92.2 1542
Upper income group
(above Rs. 3500) 5676.7 120.3 1224
Note: The income classes are defined with respect to monthly household income.
and are within a reasonable range of the selected estimates (Table 6). Some of the differences between the estimates of income and price elasticities in this study and the selected ones are due to differences in methodology and data, as shown in Table 5.
7. CONCLUSIONS AND ENERGY POLICY IMPLICATIONS
In this paper we have tried to estimate a comprehensive residen-tial electricity demand equation for the Bombay Metropolitan Region. This is the first effort to estimate the demand function for residential electricity, using household level data in India, which, besides incorporating the income, household characteristics incorporates the shelter-related characteristics of the house occu-pied by households. If progress is to be made in designing effective
Table 5: Income and Price Elasticities for Different Income Classes
Income groups Income elasticity Price elasticity
Lower income
(up to Rs. 1000) 0.28 20.76
Middle income
(Rs. 1000–2000) 0.19 20.65
Upper middle income
(Rs. 2000–3500) 0.28 20.61
Upper income group
ELECTRICITY
CONSUMPTION
IN
B
OMBAY
93
Table 6: Comparison with Other Studies
Income Price Income
Study/year elasticity Price type Data type measure Method Area
Present (1987–88) 0.34 20.70 AP Bombay Total annual RR/semi log Bombay household expenditure
Branch3(1985) 0.23 20.2 AP Household Total EC(GLS)/ML National monthly expenditure double log
Donatos and Mergos4 1.50 20.58 National annual Per capita Ridge regression National (1961–86)
Hsiao and Mountain8 0.17 Utility annual Real per capita LSDV/Koyck Ohio State
(1960–80) flow adjustment
double log
Barnes, Gillingham, and 0.20 20.55 MP Household Real per capita IV semi log National Hagemann2(1972–73) monthly
Houthakker5(1964–76) 0.14 20.11 MP State annual Real per capita EC log/flow National adjustment
Murray et al.6 0.69 21.01 to State
20.61 subdivision
Taylor et al.9 0.35 20.54 State McFadden et al.7 0.99 20.71 State
0.20 20.37 Household
P.
Tiwari
Table 6: Continued
Income Price Income
Study/year elasticity Price type Data type measure Method Area
Wilder et al.10 0.16 21.0 Household
Acton, Mitchell and 0.40 20.35 MP Small geograph- Real mean Weighted OLS Los Angeles
Mowill1(July ’72 to ical monthly County
June ’74)
1 Acton, J., Mitchell, B., and Mowill, R. (1976)Residential Demand for Electricity in Los Angeles: An Econometric Study of Disaggregated Data. The Rand Corporation, R-1899-NSF.
2 Barnes, R., Gillingham, R., and Hagemann, R. (1981) The short run residential demand for electricity.Review of Economics and Statis-tics, 63:541–551.
3 Branch, E.R. (1993) Short run income elasticity of demand for residential electricity using consumer expenditure survey data.The Energy Journal, 14:111–121.
4 Donatos, G.S., and Mergos, G.J. (1991) Residential demand for electricity: The case of Greece:Energy EconomicsJan. 41–47. 5 Houthakker, H.S., and Taylor, L.D. (1970)Consumer Demand in the United States: Analysis and Projections, Cambridge: Harvard Univer-sity Press.
6 Murray, M., Spann, R., Pulley, L., and Beauvais, E. (1978) The demand for electricity in Virginia.Review of Economics and Statistics, 60:585–600.
7 McFadden, D., Puig, C., and Kirschner, D., et al.A Simulation Model for Electricity Demand. Cambridge Systematics Inc., Final Report, Aug. 1977.
8 Hsiao, C., and Mountain, D. (1980) Estimating the short run income elasticity of demand for electricity using cross-sectional categorised data.Journal of American Statistical Association, 80:259–265.
energy policies to promote energy conservation among house-holds, reliable estimates of price elasticities of residential demand for energy must be available to the policy makers.
This paper estimates the residential electricity demand with the econometric estimation of a single equation that takes care of the problem of heteroskedasticity. To overcome the problem of multicollinearity, we used ridge regression technique, which gave efficient estimation results.
The main conclusions of the analysis is that the residential electricity consumption is inelastic with respect to both income and price. The price elasticity is20.70, while the income elasticity is 0.34. An interesting finding of the study is that the price elasticity of upper middle income class is 20.84, which is highest among all income classes. If the price of electricity is increased in the upper slab, this group is expected to respond favorably.
The results also indicate that housing unit size, structure type, age of the building, and demographic characteristics (such as household size, age of reference person, and social background) have a significant effect on residential electricity consumption.
REFERENCES
Acton, J., Mitchell, B., and Mowill, R. (1976)Residential Demand for Electricity in Los Angeles: An Econometric Study of Disaggregated Data. The Rand Corporation, R-1899-NSF.
Advisory Board of Energy. (1985)Towards a Perspective on Energy Demand and Supply in India in 2004/05.Government of India, May.
Anderson, K.P. (1973) Residential Demand for Electricity: Econometrics Estimates for California and the United States.Journal of Business46:526–553.
Balestra, A.D. (1967)The Demand for Natural Gas in the United States. Amsterdam: North Holland.
Berg, S.V., and Taylor, C. (1994) Electricity Consumption in Manufactured Housing.
Energy Economics16:54–62.
BMRDA. (1990)Multipurpose Household Survey.Bombay, India.
Branch, E.R. (1993) Short Run Income Elasticity of Demand for Residential Electricity Using Consumer Expenditure Survey Data.The Energy Journal14:111–121. Burgess, G., and Paglin, M. (1981) Lifeline Electricity Rates as an Income Transfer Device.
Land Economics41–47.
Capper, G., and Scott, A. (1982) The Economics of House Heating: Further Findings.
Energy Economics4:134–138.
Chern, W.S., and Bouis, H.E. (1988) Structural Change in Residential Electricity Demand.
Energy Economics10:213–222.
Dahl, C.A. (1990) Do Gasoline Demand Elasticities Vary.Land Economics58:373–382. Donatos, G.S., and Mergos, G.J. (1991) Residential Demand for Electricity: The Case of
Greece.Energy Economics41–47.
Friedman, M. (1957)A Theory of the Consumption Function. Princeton University Press. Fuel Policy Committee. (1976)Report.Planning Commission, Government of India, New
Delhi.
Garbacz, C. (1984) A National Micro-Data Based Model of Residential Energy Demand: New Evidence on Seasonal Variation.Southern Economic Journal235–249. Garbacz, C. (1983a) A Model of Residential Demand for Electricity Using a National
Household Sample.Energy Economics124–128.
Garbacz, C. (1983b) Electricity Demand and the Elasticity of Intra-marginal Price.Applied Economics699–701.
Halvorsen, R. (1975) Residential Demand for Electric Energy.Review of Economics and Statistics12–18.
Hirst, E., Goeltz, R., and Carney, J. (1981) Analysis of Disaggregated Data.Energy Economics4:74–82.
Hoerl, A.E., and Kennard, R.W. (1970) Ridge Regression: Biased Estimation for Non-Orthogonal Problems.Technometrics55–82.
Houthakker, H.S., Verleger, P.K., and Sheeham, D.P. (1974) Dynamic Demand Analyses for Gasoline and Residential Electricity.American Journal of Agricultural Econom-ics56:412–418.
Houthakker, H.S. (1980) Residential Electricity Revisited.The Energy Journal1:29–41. Houthakker, H.S., and Taylor, L.D. (1970)Consumer Demand in the United States: Analysis
and Projections.Cambridge: Harvard University Press.
Hsing, Y. (1990) On the Variable Elasticity of the Demand for Gasoline.Energy Economics
12:132–136.
Kushman, J.E., and Anderson, J.G. (1986) A Model of Individual Household Temperature Demand and Energy Related Welfare Change Using Satiety.Energy Economics
8:154–174.
Parikh, J.K. (1981)Modelling Energy Demand for Policy Analysis.Government of India: Planning Commission.
Parti, C., and Parti, M. (1980) The Total and Appliance Specific Conditional Demand for Electricity in the Household Sector.Bell Journal of Economics, Spring.
Planning Commission (1992)Eighth Five Year Plan.New Delhi: Government of India. Regional Energy Development Programme. (1991)Sectoral Energy Demand in India,
Planning Commission, Government of India, New Delhi, August.
Reilly, J.M., and Shankle, S.A. (1988) Auxilliary Heating in Residential Sector.Energy Economics10:29–41.
Shin, J.S. (1985) Perception of Price Information is Costly: Evidence from Residential Electricity Demand.Review of Economics and Statistics67:591–598.
Thursby, J., and Thursby, M. (1984) How Reliable Are Single Equation Specifications of Import Demand.Review of Economics and Statistics120–128.
Wilder, R., and Willenborg, J. Residential Demand for Electricity: A Consumer Panel Approach.Southern Economic Journal42:212–217.
Wills, T. (1981) Residential Demand for Electricity.Energy Economics249–255. Working Group on Energy Policy. (1979)Report.Government of India: Planning
APPENDIX A
The BMRDA reports the survey that contains the ownership pattern of home appliances. The average power requirement of these appliances that is used in calculation of index is given in A.1 below.
The APINDX is created as follows (A.1):
APINDXi5
1
o
61
BkOi,k
2
*100o
61 BkCk
, (A.1)
whereOi,kis the number ofkth home appliances owned by house-holdi. The denominator of APINDX is obtained by multiplying the average power of each home appliance with the maximum number of each home appliance in the survey. The numerator is obtained by multiplying the average power of each home appliance with the number of the each home appliance owned by the house-holdi. This index captures the composition of home appliances, depending on the average power requirement of each one, rather than just counting the number of the appliances.
Table A1: Average Power Consumption of Various Home Appliances
Average power
Appliance consumption (W)
Television 200
Video 240
Tape recorder 900
Radio 20
Refrigerator 150
Table A2: Mean and Standard Deviation of Variables Regression
Variable Mean Standard deviation
Annual income (Rs.) 30594 28350
Dummy for hut 0.19 0.39
Dummy for chawl 0.08 0.27
Dummy for flat,5 floors 0.42 0.49
Dummy for flat.5 floors 0.10 0.30
Dummy for bungalows 0.05 0.21
Dummy for maharashtrians 0.52 0.50
Dummy for gujrati 0.15 0.35
Dummy for south indians 0.07 0.25
Household size 5.24 2.32
Males 2.73 1.49
Females 2.50 1.42
Married couples 1.12 0.62
Rooms 2.20 1.20
Age of the house (years) 31.44 26.98
Monthly consumption of
electricity (Rs.) 79.46 72.36
Price of electricity/unit 0.79 4.50
Age of household head 41.16 11.97
