Demand for Electricity in the Philippines: Implications for Alternative Electricity Pricing Policies by Ronald Yacat

DEMAND FOR ELECTRICITY IN THE PHILIPPINES Implications for Alternative Electricity Pricing Policies

Clodualdo

R. Francisco

PHILIPPINE

FOR DEVELOPMENT

INSTITUTE

STUDIES

All Rights Reservedby THE PHILIPPINE INSTITUTE FOR DEVELOPMENT STUDIES, 1988

The Philippine Institute for DevelopmentStudies is a non-stock, non-profit, government research institution engaged in long-term, policy-oriented research.It was establishedon September26, 1977 by virtue of PresidentialDecree No. 1201. Through the Institute's activities, it is hoped that policyoriented researchon Philippine social and economic developmentcan be expanded in sucha manneras to more directly and systematicallyassistthe government in planningand policy making. PIDS publishesthe output of its researchprogram as part of its effort to promote the utilization of research findings and recommendations.The views expressed in published reports, however, do not necessarilyreflect thoseof t he Institute.

ISBN 971-128_)14-0 Printed in the Philippines by Kolortech, Co.

Acknowledgements

The author acknowledges with deep gratitude the support and assistance of many persons and institutions in the preparation of this study. Maureen Ceniza provided efficient and diligent researchassistanceduring the initial and data gathering phase. Leah Gutierrez provided expert and cheerful research assistancein additional data collection, analysis, computer and editorial work. The comments on a previous draft by Edna Angeles-Reyes,Ruperto Alonzo, Robert Dohner, Mario Lamberte, Rodolfo Quetua, Shayam Rungta, Margarita Songco and Ofelia Templo have helped improve the study. I am grateful to Marcelo Orense, Ivan dela Pena and Chit Arceo for their help in facilitating data gathering. The cooperation of MERALCO in providing data for the study is specially noted and acknowledged. The assistanceand encouragement of Mario Feranil, Jennifer Liguton, Filologo Pante, jr. and Boy Puno during the various stagesof the study have been invaluable. Also, Dale Jorgensonisacknowledged for the office spaceand researchfacilities he arrangedfor me at Harvard University which enabled me to make substantial revisionsof the previousdraft, taking into account some recent developments in the energy economicsliterature. Emma Pizarro-Cincoprovided accurateand patient typing services.Financial support from the Philippine Institute for Development Studiesand the United Nations for Development Programme are gratefully acknowledged. I am also thankful to my wife Catherine for her encouragementand understandingand which made the researchfulfilling and rewarding. There are many other persons and'_in_titutionswho have helped me in one way or another. To all of them, I am most grateful. The omissionsand possibleerrors, both in fact and in analysis in this study are, however, my own and soleresponsibility.

Summary

This study estimates the demand elasticities of electricity in the Philippines focusing on theManila Electric Company franchise area. The features of electricity as a commodity are described in detail and the demand model specified, taking into account the developments in the electricity economics literature. Demand for electricity by residential, commercial and industrial consumers is postulated to be a function of the price of electricity, the price of substitutes, the price of electricity consuming equipment, income and environmental variables. Long-run price and income elasticities are found to be generally larger in magnitude than short-rum elasticities and environmental variables have varying degreesof effects on demand for electricity. Estimates of the peakload elasticity coefficients of demand provide a measure of the relative contribution of ,residential, commercial and industrial consumers in the evolution of the system peakload. The implications of the results on alternative pricing policies are discussed.At the end of the study, conclusions and some suggestionsfor further study are made.

vii

Contents

Acknowledgement ................................................ Summary ...................................................... Chapter 1 - Introduction Purpose of the Study .......................................... Some Methodological Issuesand Limitations ....................... Organization ................................................ Chapter 2 - The Electricity Demand Model ............................ Features of Electricity asa Good ................................ Two Related Components of Electricity ....................... Pricing of Electricity ...................................... Demand for Electricity isa Derived Demand.................... Modelling the Consumer Demand Function ........................ The Block Pricing of Electricity ............................. The Short-Run Demand for Electricity ....................... The Long-Run Demand for Electricity ....................... The Electricity Demand .Model ............................. Chapter 3 - Residential Consumer Demand Functions ................... Residential Models I and II .................................... Theoretical Considerations ................................ Model Specifications ..................................... The Data .............................................. Test Results............................................ Positive Price Elasticities of Demand: An Explanation ................. . .......................... The Evolution of the MERALCO Rate Structure ............... Demand Responsesof MERALCO Residential Consumers.......................................... Structure of Electricity Consumption .................... Structure of the Number of Consumers................... Per Capita KWH Consumption ......................... An Explanation of the Perverse Finding on Price Elasticities................................... Tests of the Residential Model UsingAnnual Data .................. Short-run Versus Long-Run Demand Behavior ................. Test Results............................................ Chapter 4 - Commercial Consumer Demand Functions .................. Modification of the BasicModel Specification ..................... The Data ..................................................

v vii 1 1 4 7 7 7 7 8 8 8 10 10 11 13 13 13 15 16 17 20 20 27 27 30 41 43 43 43 44 51 51 53 ix

Test Results. _.......................... ........ _ ............ X-1 Commercial Consumers ................................ XMD Commercial Consumers.............................. Primary Account Commercial Consumers..................... Secondary Account Commercial Consumers. .................. Aggregate Commercial Demand Function Using Annual Data ..............................................

55 55 60 67 67

Chapter 5 - Industrial Consumer Demand Functions .................... Model Specification........ ................................... The Data .... . ............................................. Test Results.. .............................................. XMD Industrial _........................................ GP Primary Industrial .......................... ........... GP Secondary IndUsti'ial.................................... Aggregate Consumer Demand Functions: Industrial Consumers.. ............... .................

79 79 79 80 80 82 84

Chapter 6 - Implications on Alternative Pricing•Policies.................. Price, Income and Other Explanatory Variables .................... Own Price Variables ............. "........................ Income Variables......................................... Substitutesfor Electricity ........................... _ ..... Electricity ConsumingEquipment.. ........................ Environmental Variables. •. ............................... System Peakload....................................... Short-run and Long-run Elasticities............................. •Own Price Elasticities.... ............................... IncomeElasticities ..................................... Price of Substitutes............ ............. ............ Electricity ConsumingEquipment ....... ..... .............. A Dynamic Reformulation of the Demand Model: Some Implications........................................... Pricing Policy Implications ................................... The Subsidy and the Tariff Structure ....................... Stability in Price of Electricity. .... ....................... Environmental Variables....... .......................... Peakload Elasticity Coefficients ........................... Chapter 7 - Summary, Conclusionsand Suggestionsfor Further Research ...................................... Summary..................... . .............. •............. Conclusionsand Suggestionsfor Further Research.................

86 91 91 91 97 99 101 104 106 108 110 110 111 112 112 117 118 119 119 120 121 121 125

References.................................................... Appendices...................................................

127 131

List of Figures Number 2.1 3.1a 3.1b 3.2

3.3. 3.4

3.5 3.6 3.7 3.8 3.9 3.10

3.11 3.12 -_ 3.13 3.14 3.15

Description Indifference Curve Analysis of Consumer Demand for Electricity .... Block DecreasingPrice Schedule............................ Block IncreasingPriceSchecule............................. MERALCO Price Schedule By Blocks(Deflated) Residential, May 1970 -- Dec 1984 (1978 = 100) ........................................ MERALCO Relative Indexed Price by KWH Blocks Residential, 1970-1984 ................................ MERALCO Residential Consumers, Percent Distribution of KWH Consumption by KWH Blocks, May 1971 to Jun 1984 ..................... Structural Shifts in Number of Customersand KWH Consumption, ME RALCO Residential ............... 1971 KWH Consumption (%), Frequency Distribution, ME RALCO Residential ..................... 1975 KWH Consumption (%), Frequency Distribution, ME RALCO Residential ..................... 1980 KWH Consumption (%), Frequency Distribution, MERALCO Residential ..................... 1984 KWH Consumption (%), Frequency Distribution, ME RALCO Residential ..................... MERALCO Residential Consumers,Percent Distribution of the Number of Consumers by KWH Blocks, May 1971 to Jun 1984 ................... 1971 Number of Customers (%), Frequency Distribution, MERALCO, Residential ..................... 1975 Number of Customers (%), Frequency Distributi6n, MERALCO, Residential ..................... 1980 Number of Customers (%), Frequency Distribution, MERALCO, Residential..... ................ 1984 Number of Customers (%), Frequency Distribution, MERALCO, Residential..................... Mean KWH of PercentShare, Number of Customers

9 14 15

25 26

29 31 32 33 34 35

36 37 38 39 40 xi

4.1

4.2 4.3

4.4

4.5

4.6

and KWH Consumption, MERALCO Residential ......................................... MERALCO Price Schedule by Blocks (Deflated) Commercial (XI), May 1970 - Dec 1984 (1978=100) ......................................... MERALCO Relative Indexed Price by KWH Blocks Commercial (XI), May 1970-Dec. 1984 .................... MERALCO Commercial Consumers (XI) Percent Distribution of KWH Consumption by KWH Blocks, May 1971 to ]un 1984 .......................... MERALCO Commercial Consumers (XI) Percent Distribution of Number of Customers by KWH Blocks, May 1971 to lune 1984 ..................... MERALCO Price Schedule By Blocks (Deflated) Commercial XMD, May 1970 - Dec. 1984 (1978=100) ......................................... MERALCO Price Schedule By Blocks (Deflated) General Power, May 1970-Dec 1984 (1978=100) .........................................

57 58

List of Tables Number 3.1

3.2

3.3 3.4 3.5

3.6

xii

Description Resultsof RegressionAnalysis(OLS) Residential Consumers,MERALCO (Jan 1971 to Nov 1984) ............................... Results of RegressionAnalysis (OLS) Residential Consumers,MERA LCO (Jan 1971 to Dec 1984) ............................... Average Prices- MERALCO, Residential Deflated ........................................... MERALCO Residential, Adjustments at I_/KWH jan 1974 - Dec 1984 (Deflated) ......................... MERA LCO Residential Consumers, Percentage Breakdown By KWH Blocks (1971, 1975, 1980, 1984) ........................................ Estimates of Mean KWH and Standard Deviations: Consumer, Consumption and Revenue Distribution, MERALCO Residential

21 23 24

3.7

3.8

3.9

4.1

4.2

4.3 4.4

4.5 4.6

4.7 4.8

4.9 4.10

Consumers (1971,1975, 1980, 1984) ..................... Results of RegressionAnalysis (OLS) MERALCO Residential, Annual Data (1971-1984) ........................................ Results of RegressionAnalysis (OLS) MERALCO Residential, Annual Data (1971-1984) ........................................ Comparative Estimatesof Priceand Income Elasticities: Short-run (Monthly) Versus Long-run (Annual) MERALCO, Residential (1971-1984) .............. ................. MERALCO Commercial Consumers(X-l) Percentage Breakdown By KWH Blocks (1971, 1975, 1980, 1984) ........................................ ME RALCO Commercial Consumers(X-l) Percentage Breakdown By KWH Blocks (1971,1975, 1980, 1984) ........................................ Resultsof RegressionAnalysis (OLS) X-1 Commercial Consumers, ME RALCO (Jan 1971 to Nov 1984) ................................ Average Prices- MERALCO Commercial (X1), Deflated (BaseYear: 1978) ............................. Resultsof RegressionAnalysis (OLS) XMD Commercial Consumers,ME RALCO (Jan 1971 to Nov 1984) ............................... Average Prices- MERALCO Commercial XMD Deflated (BaseYear: 1978) ............................. Resultsof RegressionAnalysis (OLS) Primary Account (GP) Commercial Consumers, MERALCO (Jan 1971 to Nov 1984) ...................... Average Prices- MERALCO General Power Deflated (BaseYear: 1978) ............................. Resultsof RegressionAnalysis (OLS) Secondary Account (GP) Commercial Consumers,MERALCO (jan 1971 to Nov 1984) .......................................... Proportions of Commercial Type Consumers MERALCO (1971,1975, 1980, 1984) .................... Resultsof RegressionAnalysis (OLS) MERALCO Commercial Consumers

56 59

63 65

68 69

71 74

째째. Xlll

5.1

5.2

5.3

5.4

5.5 6.1 6.2

6.3 6.4

6.5

6.6 6.7 6.8

xiv

(1971-1984) ......................................... Results of Regression Analysis (OLS) XMD Industrial Consumers, MERALCO (Jan 1971 - Nov 1984) ................................ Results of RegressionAnalysis (OLS) GP-Primary Industrial Consumers, MERALCO (jan 1971 - Nov 1984) ...................... Results of Regression Analysis (OLS) GP-Secondary Industrial Consumers, MERALCO (Jan 1971 - Nov 1984) ............ .......... Results of Regression Analysis (OLS) MERALCO, Industrial Consumers (1971-1984) ........................................ Proportions of Industrial Type of Customers, MERALCO (1971,1975, 1980, 1984) .................... Own Price Elasticities for Type Consumers, MERALCO .......................................... Meansand Rangesof Monthly Per Capita KWH Consumption of Type Consumers for Indicated Periods, MERALCO ........................... Income Elasticities for Type Consumers, MERALCO ...................................... Elasticities of Prices of Substitutes for Electricity By Type Consumers (MERALCO) ....................................... Elasticities of Prices of Electricity Consuming Equipment By Type Consumers (MERALCO) .............................. Elasticities of Environmental Variables By Type Consumers (MERALCO) ....................... Peakload Elasticity Coefficient of Type Consumers (MERALCO) .............................. Ranked Estimates of Peakload Elasticity Coefficients and Load Factors MERALCO, Monthly Data (Jan 1971-Nov 1984) ................................

87 89 92

95 ...

100

102 105 107

109

List of Appendices Number 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9a 3.9b 3.9c 3.9d 3.10 3.11 3.12 3.13 3.14 4.1 4.2 4.3 4.4 4.5a

Description MERALCO-Residential Monthly Total Number of Customers .... MERALCO-Residential Monthly Total MWH Consumption ...... MERALCO Adjusted Revenues,Residential (1970-1984) ........ Schedule of Adjustments, MERALCO-Residential, 1976-1984 ......................................... Residential Adjustments to Revenue, 1974-1975 .............. Summary of MERALCO Rate Schedulesby KWH Blocks, Residential Consumers,1970-1985 (In Pesos).............. ConsumerPrice Index, Fuel, Light & Water (Jan 1974-Dec 1984) ................................. Indexed Laborers'AverageDaily BasicWageRates in Industrial Estatesin Metro Manila ..................... 1971 Revenuein %, MERALCO Residential.................. 1975 Revenue in %, MERALCO Residential.................. 1980 Revenue in %, MERALCO Residential.................. 1984 Revenue in %, MERALCO Residential.................. MERALCO ResidentialAnnual Per Capita KWH Consumption, 1970-1984 ............................. Resultsof RegressionAnalysis (OLS) Residential Consumers (MERALCO), Jan 1971 to Nov 1984 ........... Resultsof RegressionAnalysis(OLS) Residential Consumers(MERALCO), jan 1971 to Nov 1984 ........... Resultsof RegressionAnalysis (OLS) Residential Consumers(MERALCO), 1971-1984 .................... Resultsof RegressionAnalysis (OLS) Residential Consumers(MERALCO), 1971-1984 .................... MERALCO Commercial, MWH Consumption, Jan 1970- Dec 1984 ............................. ... ME RALCO Commercial Number of Customers, Jan 1970- Dec 1984 ................................ MERALCO Adjusted Revenues,Commercial (1970-1984) ....................................... MERALCO Commercial Adjustments at f_/KWH, Jan 1974 - Dec 1984 (Deflated) .................. 1971 KWH Consumption (%), MERALCO Commercial(X-1) ...................................

133 134 135 136 137 138 140 141 143 144 145 146 147 148 149 150 151 152 153 154 155 156 xv

4.5b 4.5c 4.5d 4.6a 4.6b 4.6c 4.6d 4.7a 4.7b 4.7c 4.7d 5.1 5.2 5.3 5.4 5.5 5.6 5.7 6.1 6.2

xvi

1975 KWH Consumption (%), MERALCO Commercial (X-l) ................................... 1980 KWH Consumption (%), MERALCO Commercial (X-1) ................................... 1984 KWH Consumption (%), MERALCO Commercial (X-l) ................................... 1971 Number of Customers(%), MERALCO Commercial (X-1) ................................... 1975 Number of Customers (%), MERALCO Commercial (X-1) ................................... 1980 Number of Customers (%), MERALCO Commercial (X-1) ................................... 1984 Number of Customers(%), MERALCO Commercial (X-1) ................................... 1971 Revenues(%), MERALCO Commercial (X-1) ................................... 1975 Revenues(%), MERALCO Commercial (X-1) ................................... 1980 Revenues(%), MERALCO Commercial (X-1) ................................... 1984 Revenues(%), MERALCO Commercial (X-1) ................................... MERALCO Industrial MWH Consumption, Jan 1970- Dec 1984 ................................ MERALCO Industrial, Number of Customers, Jan 1970- Dec 1984 ................................. MERALCO Industrial, Net Adjustments to Revenue Jan 1974- Dec 1984 ................................ MERALCO Industrial, Per KWH Adjustments Jan 1974 - Dec 1984 (Deflated) ........................ MERALCO Industrial, Adjusted Revenues, Jan 1970 - Dec 1984 ................................ MERALCO Industrial, Growth Ratesof MWH Consumption, Jan 1970 - Dec 1984 ..................... MERALCO industrial, Growth Ratesof Number of Customers,Jan 1970 - Dec 1984 ....................... Resultsof RegressionAnalysis (OLS) Residential Consumers(MERALCO), Jan 1971 to Nov. 1984 ........... Resultsof RegressionAnalysis (OLS) Residential Consumers(MERALCO), 1971- 1984 ....................

157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176

6.2a 6.2b 6.3 6.4 7.1

Resultsof RegressionAnalysis (OLS) Residential Consumers(MERALCO), 1971-1984 .................... Resultsof RegressionAnalysis (OLS) Residential Consumers (MERALCO), 1971-1984 .................... Resultsof RegressionAnalysis (OLS) X-1 Commercial ConsumersMERALCO, Jan 1971 to Nov 1984 ............. Resultsof RegressionAnalysis (OLS) Residential Consumers(MERALCO), Jan 1971 to Nov. 1984 ........... List of Variables ........................ ................

177 178 179 180 181

xvii

Chapter 1

Introduction

I. Purpose of the Study This study attempts to estimate the demand elasticities of electricity in the Philippines with focus on the Manila Electric Company (MERALCO) franchise area. The results of the analysis are usedto derive some broad implications for alternative electricity pricing policies. Despite the importance of these parameters for planning, forecasting and policy formulation, there seemsto be a dearth of research work done in this specificarea of study. 1 The estimatesof demand elasticitiesare by no means definitive due to data limitations and the ensuing difficulties at econometric estimation. However, despite these limitations, the results provide some fairly reasonableand theoretically plausible explanation of electricity consumption behavior. Moreover, these estimatesare comparable with the evidence in the literature. The use of price and income elasticities for forecasting demand is wellknown. Since it takes several years of lead time to put up power plants, the importance of forecasting demand for electricity cannot be overemphasized. If the demand forecast is lessthan the demand realized, then the resulting loss-ofload or power brown-outs results in economic losses.If the demand forecast is much more than the demand realized, then the ensuingover-capacity of the power plants installed alsoresults in economic losses. II. Some Methodological Issuesand Limitations In a survey of the econometric research on the demand for electricity by Taylor (197S), where he summarized the results of 11 demand for electricity studies in the United States, he concludesthat "... not one study hasrecognized that completely proper treatment of price in this context requires that inter1/A numberof MERALCO(1973).

unpublished studies,however,have beendone. See,for instance, 7

Introduction marginal prices as well as the marginal price be represented in the demand function." This observation arises from the multistep block pricing for electricity. Clearly, the question on which price should enter the demand function becomes an important econometric consideration. Taylor (1977b) further elaborated on the issuesinvolved here. Another important issueraised by Taylor (1975) is the difference between demand for electricity in the short-run and in the long-run. This difference arises from the nature of electricity as a good. Unlike ordinary goods which are consumed directly to derive satisfaction, the demand for electricity is a derived demand. In other words, one has to use an electricity-- consuming equipment to obtain satisfaction or utility from electricity. In the short-run, it is assumed that the stock of electricity-consuming equipment is fixed, while in the longrun, the stock of electricity-consuming equipment is assumedto be Variable. In his review of the literature, Taylor (1975) observesthat there are evidencesto show that lOng-run price and income elasticities tend to be greater in magnitude than short-run elasticities. Further elaboration on this appears in an expanded review by Taylor (1977a) and Bohi (1981). Succeeding demand studies have attempted to overcome the econometric problems associated with block pricing, and the difference between short-run and long-run demand elasticities, requiring explicit consideration of the dynamics of stock adjustments. A cursory review of the electricity demand literature in the U.S. shows a veritable explosion of researchwhich perhaps has been partly motivated by the 1973-74 and the 1979 energy shocks. Nordhaus (1977) provides a compilation of some of the earlier works while Bohi (1981) made a review of some major demand studies from 1962 to 1978. The work of Murray, et. al. (1978); Acton, et. al. (1980); Houthakker (1980); Parti and Parti (1980); Barnes, et. al. (1981); Beierlein (1981); Ramanathan and Mitchan (1982); Donnelly (1984); Lilliard and Aigner (1984); Westley (1984); Dubin (1985); JeongShik Shin (1985); Chi-Keung Woo (1986); Engle, et. al. (1986); Garbacz (1986); Veall (1986); and Taylor and Schwarz (1.986) are more recent studies in the United States. The literature in the issue of pricing has gradually evolved taking into account the problemsassociatedwith block pricing for kilowatt-hours used,and the "demand" chargeswhich-are usually priced in blocks of kilowatt demanded. Also, the effects on consumption of the time component of the price of electricity has been investigated as an offshoot of the enactment of the U.S. Public Utility Regulatory Policies Act in 1978 which requires electric utilities to usetimeof-day (TOD) pricing or show causeif they cannot. 2

Introduction On the difference between short-run and long-run elasticities, and consequently the need to explicitly consider the dynamics of stock adjustmentsaffecting the demand for electricity, the useof electricity-consumingequipment stock data results in the development of modelsof demand basedon appliance-specific information. One important offshoot of these studies is the accumulation of evidence on the important effects of environmental variables such as ambiant temperature on electricity consumption. This study postulatesthat the demand for electricity by residential,commercial and industrial consumers is basically a function of five setsof explanatory variables, namely, own price variables, income, price of substitutes for electricity, price of electricity consuming equipment, and environmental variables. The own price variables used take into account the block pricing of the rate schedule including the demand charge as the price per kilowatt used. Due to the absenceof data on the stock of electricity-consumingequipment, a static model is used.2 However, the prices of electricity-consuming equipment are included as additional set of explanatory variables to overcome this deficiency. The environmental variables are included based on casual observation and on recent evidence in the literature. In the latter part of the study, the system peakload is used as an additional explanatory variables to derive a statistical measure of the degreeof contribution of each type of consumer in the evolution of the system peakload. This measureis considered important in the design of tariff policies and rate schedules.Finally, the income and price of substitute variables are included based on the traditional theory of consumer demand. There is no attempt here to specifically model the production behavior of commercial and industrial consumersin their demand for electricity as an input to production. However, it is postulated that the utilization rate of their existing stock of electricity consuming equipment contributes to output, revenuesand, hence, utility. Also, the use of a general functional relationship for residential, commercial and industrial consumersservesas a convenient basisfor comparing demand behavior across type of consumers in the design of tariff policies.3 _2/In the absence of data on the stockof electricityconsuming equipment,thelagged dependentvariableis usuallyemployed.However,asidefromautocorrelation andothereconometricproblems of estimation,thisprocedure istheoreticallyunappealing dueto theabsence of an underlying theorywhichsupportsthespecificform of stockadjustment assumed (Bohi, 1981,p. 19). __/ The Philippinegovernmentis at presentseriouslyconsideringthe institutionof major policy reformsin the electricity sector.MERALCO,which accountfor approximatelytwothirdsof total Philippineelectricity consumption,will be significantlyaffected bythispolicy 3

Introduction Due to the absence of adequate and comparable data for all the seven power grids in the Philippines, this study focuses on the Luzon grid using MERALCO data. MERALCO is a utility which distributes electricity. It purchasesall its power needs from the National Power Corporation, a government-controlled electric utility tasked with the primary responsibility of generating and transmitting electricity. III.

Organization

After the introduction in Chapter i, the second chapter provides a brief theoretical discussion of the electricity demand model used. The two related components of electricity, power and energy, the two-part tariff and multiblock pricing of electricity, and the derived nature of demand for electricity are considered. Chapter 3 specifies the residential consumer demand function for electricity. Two models are tested. The difference between these two models arise from the suggested specifications of Taylor (1975) and Nordin (1976) of the inframarginal price variables. Both monthly and annual data were used to test the model. Attempts are made to explain the results. Chapter 4 provides specification of the commercial consumer demand model. As in the residential model, there are two models specified and tested due to two different specifications of the inframarginal prices. Under the commercial consumer category, there are four types of consumers based on MERALCO classification. These are X-l, XMD, primary account, and secondary account commercial consumers. An additional feature of the commercial consumer demand function is the introduction of the peakload and demand charge variables. The results are then analyzed and explained. Similarly, Chapter 5 specifies the industrial consumer demand function. Again, there are two models tested due to the different specifications of the inframarginal price variables. Under the industrial category, there are three types of consumers based on MERALCO classification. These are XMD, primary account, and secondary account industrial consumers. The consumer demand function specified is tested for each type of consumer and the results are explained. Except for the specification of the explanatory variable for industrial activities, the specification of both the commercial and industrial demand functions are similar. reform sinceitsfranchiseareaincludesMetroManilawhichis the centerof Philippineeconomicactivities. 4

Introduction Chapter 6 integrates the results of the preceding three chapters. The results for each of the five sets of explanatory variables are analyzed across various types of consumers. Also, the results of the peakload elasticity coefficients and some added explanatory variables are discussed.The implications of the shortand long-run estimates of the coefficients of price, income and other variables to include the results on stock adjustments are presented using a dynamic reformulation of the demand model. Thereafter, the implication for alternative electricity pricing policiesare derived. Finally, Chapter 7 summarizes the results of the study and states some conclusions, and suggeststopics for further research. Appendix 7.1 provides a complete list of all the variablesused in the study.

Chapter 2

The Electricity Demand Model

Featuresof Electricity as a Good

To be able to adequately model the consumer's demand for electricity, it is necessaryto have a good understanding of the nature of this particular kind of consumer good. Three related features are briefly discussedhere: the components of electricity; the price at which it issold; and the way it is utilized. A.

Two Related Components of Electricity

Electricity as it is being utilized has two related components. These are power and energy. Power is usually expressed in kilowatts (1000 watts) and energy is in kilowatt-hours. Power is the instantaneous amount of energy available. One kilowatt-hour is equivalent to the energy of one kilowatt acting for one hour. The demand meter measuresthe amount of power used while the energy meter measuresthe quantity of kilowatt-hour consumed. B.

Pricing of Electricity

Most utilities sell electricity at different prices at different levels of consumption. These consumption levels are in blocks of kilowatts (kw) or kilo_ watt-hours (kwh). In other words, there is block-pricing for both demand and energy. The demand and energy sets of block prices comprise the parts of the so-called two-part tariff for electricity. Price schedulesfor residential consumers usually involve only the energy charges in pesos per kwh. Industrial and commercial consumers are usually charged for both the demand (pesosper kw) and energy charges. The reason is that residential consumers usually have relatively small power requirements as contrasted with industrial and commercial con. sumers. 7

Electricity demand model C.

Demand for Electricity

is a Derived Demand

Unlike most other goods, the use of electricity does not directly provide utility and satisfaction. It is the service or output from an electricity-consuming equipment which is of real concern. The demand therefore for electricity is a derived demand. Since the demand for electricity suming equipment, electricity cannot

is through the use of an electricity-conb6 utilized by an individual consumer if

no stock of such equipment is available. Moreover, this equipment has power rating, i.e.; it can only utilize a maximum level of power. Since energy is the product of power and time, flie total energy consumption is limited by the power rating of the equipment. The consumption of electricity is therefore a function of both stock of electrical equipment and its rate of utilization. In the short-run, it is usually assumed that the stock of electricity-consuming capital is fixed. The short-run consumer's behavior therefore can be viewed in terms of the rate of utilization of the existing stock. In the long-run, the electricity-consuming stock of capital changes. In modelling the consumer's demand behavior, one may have to consider the dynamics of stock adjustments over time.

II.

Modelling the Consumer Demand Function Traditional

consumer demand theory postulates that the demand for a good

is determined by its price, the price of other goods, and the consumer's income. In specifiying the demand function for electricity, this traditional result in consumer demand theory has to be extended to cover the two special features of electricity as a good, i.e., block pricing and its utilization modelled as a derived demand.

The Block Pricing of Electricity

Let q be the quantity of electricity in kwh consumed per unit of time. 4 Suppose the price at which it is sold is given by the following price structure:

_/ For simplicity of exposition, we initially assumeout the demand chargepart of the tariff. This portion'of the discussiondraws from Taylor (1975, 1977h). 8

Electricitydemandmodel Quantity Block (kwh)

Price (@kwh)

O<q_ql

ql < q _ q2

Given the income level of the consumer, his budget constraint is piece-wise linear as given by ABCD in Figure 2.1. It is assumed that the price schedule is blockdecreasing, i.e., Pl > P2 > P3.

Figure 2. I Indifference Curve Analysis of Consumer Demand for Electricity

C' T'

O ql

q"_*

D' ,_ T, q(kwh) 9

Electricity demand model

Suppose the consumer'slutility is optimal at consumption level q* as shown by his indifference curve IC tangent at point T on segment BC of the budget constraint. Thus, for this particular consumer, P2 is the marginal price, Pl is inframarginal and P3 superfluous. Changes in the marginal price induces both income and substitution effects. Changes in the inframarginal price results in an income effect. However, changes in the superfluous price have neither income nor price effect, hence the name. These imply that in modelling consumer demand behavior for electricity, price variables must be able to take into consideration these characteristics of the price of electricity. B.

The Short-Run

Demand for Electricity

In the short-run, it is assumed that the stock of electricity-consuming is fixed. Suppose, the existing stock is given by: 5/ sl = q2

capital

(2.1)

The rate of utilization of the existing stock is therefore q*/q2" Further, the rate of utilization is a function of the price structure of electricity, price of other products, income, temperature, and other factors. These other factors will be specified below. C.

The Long-Run Demandfor

Suppose over time

Electricity

the consumer's

income level increases. Assuming that

there is6no change in prices, the consumer's budget constraint is then shifted upward. The optimal point is at T' at the consumption level q**. However, the existing stock allows the consumer consumption level up to q2 at which his utility level is lower. There is therefore a motivation to add electricity-consuming equipment to the existing stock. Since the demand for demand for electricity

5J The stock is usually measured in power rating of kw. Given a fixed time period, however (e.g. 24 hours),the kwh consumptionis still determinedand constrainedby the kw power rating of the stock of appliances.This simplifiesthe representationof the conceptof rate of stock utilization. A reduction in the price of electricity, an increasein price of substitutes,changesin demographic,environmentaland other factors could also induce consumersto increasetheir electricity-consumingstock. 10

Electricitydemandmodel consuming equipment is a function of its price, among others, this providesthe basisfor including the price of electricity-consumingequipment in the consumer demand function. D.

The Electricity Demand Model

Based on the foregoing discussions,we postulate the following demand model for electricity: qt = qt (Pt, Bt, At, Zt, Yt)

(2.2)

where: Pt is a vector of pricesof electricity at period t, Bt isa vector of pricesof substitutesfor electricity, At is a vector of pricesof electricity-consumingequipment, Zt isa vector of environmental variables;and Yt isthe incomevariable. More specifically, let lnqt=

a+bÂ˘ _;ilnPit

+em _;mZmt

+ cj _;j In Bit+

+ flnYt

+ uT

dk _;kl n Ak t (2.3)

where a, bi , cj, dk, em and f are the parametersto be estimated and ut representsidentically distributed random errors with mean0 and variance0 u. In the short-run, when the stock level of electricity-consumingequipment is fixed, the parameter estimatesfor the previousequation provide the short-run elasticities. A one-month period is considered reasonablefor the purposeof this study. Over a one-year period, however, stock levelsmay have beenfully adjusted and the indirect effects of changes in the relevant variables may have been realized, Thus, using annual data for the same equation could provide estimates for long-runelasticities.7/

7/ Somelimitationsof this procedurewill be discusssed in the contextof a dynamic reformulation of (2.3) in chapter6. 11

Electricitydemandmodel This procedure, which in effect takes snap shots at consumer behavior over a one-montn and a one-year period, is an attempt to overcome the need to develop a dynamic model which inevitably leads to the need for data on electricity-consuming equipment stock or the use of lagged dependent variables. Data on stock of electricity-consuming equipment are not available while the use of lagged dependent variable has not been empirically rewarding due to both theoretical and econometric consideration. The same equation could be viewed as a general relationship, and with appropriate modifications could be applied to residential, commercial, industrial or any class of electricity consumers. There is no attempt here to specifically model the production behavior of commercial and industrial consumers in their demand for electricity. This is an important area for further investigation given the availability of more specific and relevant data. It is, however, postulated that the rate of utilization of their existing stock of electricity-consuming equipment contributes to output, revenues, and hence, utility. Also, using a single functional relationship provides a better appreciation of the demand behavior of a particular class of consumer in relation to others, and serves as a convenient basis for evolving pricing and other relevant economic policies.

Chapter 3

Residential Consumer Demand Functions In this chapter, two residential consumer demand functions are estimated taking into account developments in the literature. The two functions are referred to as models I and II. The details of the model specificationsare describedbelow. The resultsof the testsare analyzed in succeedingsections. I.

Residential Models I and II A.

Theoretical Considerations

One of the problems associated with attempts in estimating consumer demand functions for electricity is the existence of block pricing. As the consumption level changesover blocks of consumption, the price also changes.This situation is illustrated in Figure 3.1a where a block decreasing price schedule isassumed. At consumption blocks AJ, JK and at levels greater than K, the prices are D, C and B respectively. This means that the consumer does not face a single market price. If the consumer is in equilibrium at consumption level I_, then C is the price which is marginal, D is inframarginal and B is superfluous. Changes in the inframarginal price result in an income effect; changes in the marginal price result in both income and substitution effects; and changesin the superfluous price have no effect on demand, hence the name.8 In estimating the associated consumer demand functions, a question is raisedas to which price should be used.Taylor (1975) suggeststwo setsof prices: the price which is marginal to the consumer; and the average price for blocks other than the final one, or the total payment for blocks other than the final one. __JFor moredetailson theseobservations, seeTaylor(1975, 1977b)andMurray,et. al. (1978). 13

Residentialconsumerdemandfunctions Nordin

(1976)

disagrees with

Taylor

(1975)

with

respect to the second

price variable. He suggests that the apropriate variable to use is the lump sum payment the customer must make before buying as many units as he wants at the marginal price. In Figure 3.1a, it is assumed that the consumption level is at L such that C is the price which 'is marginal to the particular consumer. Taylor's suggestion is to use the average price over consumption levels A to J, or the total payment equivalent to area ABCDEFIJ. Nordin's suggestion is to use the total payment equivalent to area CDEF. In F_igure 3.1b a block-increasing price schedule is shown. Assuming again that the consumer is in equilibrium at consumption level L, Taylor's inframarginal price could be represented by B or payment ABIJ, while that of Nordin could be represented by B implying a net subsidy.

C, or payment

BCGI which are negative quantities

Figure Block Decreasing

3. la Price Schedule

Price

....

---r---

I I

I !

.I I

I I

A 14

Residentialconsumerdemandfunctions Figure

3. I b

Block Increasing Price Schedule

Price E D

I I B

--I-i

i i I

i I i

1 L

i K

As far as the writer

is aware of, no test has been made on which inframarginal price variable is more appropriate. Nordin's specification, however, appears to be more theoretically appealing. The models following Taylor's and Nordin's specifications of the inframarginal price variables are referred to here as model I and II. B.

Model Specifications

Following the genera ! specification in equation 2.3, it is postulated that qt, the demand for electricity by residential consumers at time t, is a function of the following variables: Plt=

marginal price of electricity;

P2t = (Model I) the total payment at prices other than the marginal block; (Model II) the lump sum that the customer must pay before being allowed to buy as many units as he wants at the marginal price Pl ; 15

Residential consumer demandfunctions Blt=

price of liquified petroleum gas(LPG);

B2t =

price of firewood;

Yt =

level of employment.

Further, it is postulated that the residential demand for electricity is a function of: Alt =

price of flat iron;

A2t = price of refrigerator; Zlt =

maximum temperature;and,

Z2t =

relative humidity.

The variables P1 nd P2 are the own price variables, B 1 and B2 representâ&#x20AC;˘ prices of substittues, Y is an income surrogate, A 1 and A 2 represent prices of electricity-consuming equipment, and Z1 and Z2 are environmental variables.9 C.

The Data

The Manila Electric Company (MERALCO) provided monthly data from 1970 to 1984 on the total number of residential customers, the total monthly kwh consumption corresponding revenues. These are shown in Appendix 3.1, 3.2 and 3.3 respectively. The monthly revenues shown in Appendix 3.3 were adjusted taking into account adjustments in fuel cost and other adjustments. The adjustments to revenues are shown in Appendix 3.4. (From 1970 to 1973). There are no adjustments from 1970 to 1973 but from 1976 to 1984, there are monthly adjustments broken down by category of consumers (residential, commercial, industrial). For 1974 and 1975, the revenue adjustments are not broken down by type of consumers. The 1976 to 1984 data on revenue adjustments are used to estimate the adjustments for 1974 and 1975. The 1976 to 1984 averageproportions and revenue adjustments for 1974 and 197_5are shown in Appendix 3.5. The rate schedules over the same period are also provided by MERALCO. The rates at various kwh blocks with the effectivity dates are summarized in Appendix 3.6. _2/The priceof crudeoil assubstitutefor electricitywas initially considered.However, sincethe priceof electricityis largelydependenton the priceof oil, thesetwo variablesare not independent. This isthemainreasonwhy crudeoil isnotusedasasubstitutein thisstudy. 16

Residential consumerdemandfunctions Monthly data on the prices of LPG, firewood, flat iron and refrigerator are taken from the National Census and Statistics Office (NCSO). Quarterly data on employment, taken from NCSO, were interpolated to get the corresponding monthly employment levels. The marginal price P1 is based on the rate schedule at the averageper capita kwh consumption for the month. The inframarginal lump sum payments based on the suggestionsof Taylor (1975) and Nordin (1976) are computed based on the rate schedule and the average per capita kwh consumption for a given month. The variables P1, P2, B1 and B2 are deflated by the price index for electricity, fuel, light and water with 1978 as the base year. The price indices used are shown in Appendix 3.7. Since there are no monthly income data over the period covered, the national level of employment is usedasan income surrogate. Monthly average maximum temperature at the Manila International Airport (reading and recording point) and monthly average relative humidity data were provided by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). 10 These temperature and relative humidity data are considered representative of the ME RALCO franchise area. D.

Test Results

The results of the regressionruns using ordinary least squaresfor models I and II are shown in Table 3.1. For model I, the price variable P1 (marginal) is insignificant while P2 (inframarginal) is significantly different from zero and the estimated coefficient has a positive sign.11 For model II, the price variable P1 is significant with a positive sign while P2 is insignificant but with the correct sign. The model is in a double logarithmic form so that the coefficients of P1 and P2 represent price elasticities. These results shall be further validated below by introducing the average price variable. In the next section, an attempt will be made to explain this perverse finding of positive price elasticity of demand for electricity. The price of LPG (B1) and firewood (B2) are insignificant. These mean that these substitutes for electricity hardly enter into the residential consumers' PAGASAmakesdaily recordof minimumandmaximumtemperature.Thedataused here is the monthlyaverage of the daily maximumtemperature.The sameis true for relative humidity. The levelof significance usedhereand all succeeding inferencesfrom the resultsisat least10%usinga two-tailedt-test. 17

Table 3.1

_O.

Resultsof Regression Analysis(OLS)a ResidentialConsumers, ME.RALCO (January1971 to November1984)

o = O

Dependent Variable: CAPL 3

INFRAL Re_.Coeff. Constant MARGL MANFRAL PRLPGL FIREL Model I: 1.0420 0.0097 0.1283 .0809 .0.0035 Model I1: 3.0326 0.0893 0.0385 .0939 -0.0073

A1 LABORFRL IRONL 0.2842 -0.0190 0.1930 .0.0640

FRIDGEL TEMPMIAL HUMIDITL -0.1342 0.3932 0.1812 -0.2272 0.3900 0.1893

= _ E_ O

StY. Error Model I: Model Ih

2.8126 3.8770

0.0259 0.0190

0.0308 0.0606

.0782 .0846

0.0472 0.0508

0.2074 0.3211

0.0686 0.0716

0.1147 0.1228

0.1191 0.1247

0.0914 0.0971

0.3705 0.7822

0.3735 4.7027

4.1589 0.6364

1.0341 1.1104

.0.0740 -0.i442

1.3702 0.6011

-0.2774 -0.8943

-1.1697 -1.8495

3.3021 3.1278

1.9820 1.9488

•T-Value Model I: Model I1:

Model I

Model II

Muitiple Correlation R2

• :

0.8330 0.6939

0.8136 0.6620

R2, Adjusted StandardError of Estimate Durbin-WatsonStatistic

: : :

0.6763 0.0573 1.8499

0.6425 0.0603 1.8289

Rho Estimate

0.6289

0.6506

F-Value

7.7606

5.1634

aThe Cochrane-Orcuttprocedureisappliedfor all results.

Residential consumer demandfunctions demand for electricity. This is exPected since the shift to substitutes for electricity could be difficult, if not impossible, given the stock of electricity-consumingequipment designedfor specific use. The income elasticity, as measured by the level of employment as income surrogate, is insignificant for both model I and II. These results, however, do not necessarily mean that the income variable is insignificant. They only show that the income surrogate used may not be appropriate for this type of consumer to capture their income levels as a whole. In other words, these results are constrained by data limitation. The price of flat iron (A 1) is insignificant for both models and the price of refrigerator (A2) is significant for model II but insignificant for model I. It is recalled that A 1 and A2 are included to capture the effects of stock adjustment behavior. The estimated coefficients, however, have the correct sign. These mean that increasesin the prices of these electrical equipment, while they could result in reduction in electricity consumption, have small effects on the demand fo electricity by residential consumers. The temperature (Z1) and relative humidity (Z2) are both significant with t values of 3.3021 and 3.1278, respectivelyfor model I and 1.9820 and 1.9488 for model II. The estimates of the "temperature elasticities of demand for electricity" are 0.3932 and 0.3900 for model I and Ii, respectively. These mean that for residential consumers, a one percentage point change in the temperature results in approximately 0.4 percentage point change in demand for electricity. The estimates of the "relative humidity elasticities of demand for electricity" are 0.1812 and 0.1893 for models I and II respectively. These are approximately half of the estimates of the temperature elasticities. This finding on the significant effect of temperature and relative humidity points to the possible significant effect of electricity consumption for ventilation. Air-conditioners and electric fans are the commonly used equipment for ventilation. Since air-conditioners consume more electricity than electric fans, the price of an air-conditioner is added as another independent variable (A3). Moreover, the perversesign of the marginal and inframarginal price variables necessitated the introduction of another price variable. For this purpose, the ex-post average price of electricity is made as an additional independent variable (P3)" Since the price schedulesover the sample period are both block-decreasing and block-increasing, as well as relatively flat for some block levels, the problms of simultaneity and identification as recognized by Halvorsen (1975) and Taylor (1975) could be minimal. Also, the ex-post averageprice has the advantage of being independent of the marginal and inframarginal prices. Moreover, 19

Residentialconsumerdemandfunctions the recent study of Jeong-Shik Shin (1985) indicates that residential electricity consumers may take into account in their demand behavior the cost-benefit considerations of getting additional information based on the published price schedules. Otherwise, if the cost of additional information does not justify the benefits, their demand behavior could be based on a notion of an average price. The ex-post average price could serve as an adequate measure of this consumer's notion of average price. The results of the regression runs for models I and II with the two additional independent variables included are shown in Table 3.2. As expected, the price of an air-conditioner (A3) is significant with coefficients of-0.2172 and -0.2974, and t values of -2.5232 and -3.3046 for models I and II, respectively. These mean that a one percentage point increase in the price of air-conditioners results in approximately one-fifth to one-third percentage drop in the residential demand for electricity. These further demonstrate the significant effect of environmental variables, as measured by temperature and relative humidity, on residential consumption of electricity. The average price variable (P3) is found to be insignificant but with a positive sign. Moreover, the inframarginal price coefficients for model I are still positive. These confirm the previous results. Except for the results on the two additional variables P3 and A3 and the increase in the t values of P2, B1 and Y in model II, the results are generally the same with the previous results shown in Table 3.1. To test for possible lag effects, models I and II were tested using lagged price variables (one month). The explanatory powers of the relevant variables did not improve but in fact worsened. II. Positive Price Elasticities of Demand: An Explanation The attempt here to explain the perverse finding of a positive price elasticity of demand for electricity looks into the evolution of the rate schedule structure of MERALCO, and the demand for electricity given the changes in the rate structure over the period covered. A.

The Evolution of the MERALCO Rate Structure

Appendix 3.6 shows the basic rate schedules of MERALCO for residential consumers from 1970 to 1984. Over this 15-year period, there were five basic rate schedule changes made. Table 3.3 summarizes these rate schedules at various 20

Table 3.2 Resultsof Regression Analysis(OLS)a ResidentialConsumers(MERALCO) (January1971 to December1984) DependentVariable: CAPL RegCoeff

Constant P1

B._].I

Model h Model [1:

2.8602 3.3913

0.0235 0.0989

0.1152 -0.0852

0.0794 0.0881

0.0943 0.1217

0.0305 0.0349

.StclError Model ]: Model 1h

2.8457 3.6493

0.0263 0.0194

0.0306 0.0570

0.0548 0.0568

0.0763 0.0785

1.0051 0.9293

0.8939 5.1075

3.7597 -1.4948

1.4499 1.5524

1.2359 1.5508

_A3

0.3076 0.4034

-0.0238 -0.0646

-0.1225 -0.2264

-0.2172 -0.2974

0.3713 0.3771

0.1672 0.1821

0.0482 0.0501

0.2021 0.3036

0.0680 0.0695

0.1138 0.1186

0.0861 0.0900

0.1176 0.1222

0.0911 0.0950

0.6327 0.6960

1.5219 1.3288

-0.3503 -0.9291

-t .0766 -1.9087

-2.5232 -3.3046

3.1582 3.0850

1.8352 1.9173

T-Value Mode] I: Modetth

Model I

Model II t_

Multiple Correlation R2 R2, Adjusted

: : :

0.8420 0.7090 0.6882

0.8289 0.6870 0.6647

StclError of Estimate Durbin-WatsonIStatisUc Rho Estimate F-Value

: : : ;

0.0563 1.8759 0.6198 7,6123

0.0584 1.8441 0.6074 6.2555

_-_:

_J Q.

aTheCochrane-Orcuttprocedureisappliedfor resultsof modelII. (,_

__. o

Residentialconsumer demandfunctions block levels. These basic rate schedules exclude price adjustments due to exchange rate fluctuations, changes in fuel and steam costs and other adjustments. The minimum payment for consumption at the initial block (usually the first 10 kwh) is converted to the corresponding average price by dividing the minimum monthly bill by the block length. The more relevant prices, however, are the real prices. In fact in the preceding tests, all prices were deflated using 1978 as the base year. In Table 3.3, therefore, both the current and deflated rate schedules are shown. Also shown in Table 3.3 are the corresponding indexed prices at various block levels using the price at the first block equal to unity. These indexed prices provide indications of the relative price of electricity at various blocks of con. sumption, and the type of rate schedule the residential consumers face, whether they are block increasing, block decreasing or a combination of both. Figure 3.2 shows the plot of those deflated rate schedules. Three sets of related observations are evident from this. First, the basic structure of the rate schedules is both block decreasing and block increasing, exhibiting a "V-type" schedule where the change from block decreasing to block increasing occurs somewhere in the 100-120 kwh block. This is particularly evident for the rate schedules of May 1970, October 1972 and September 1974. Since the 1974 schedule was revised in December 1981, it is reasonable to assume that the V-type rate schedule could have been in effect at least over the 1970-1981 period. 12 The second observation is that the real price of electricity has been steadily going down over the last 1.5 years for blocks of consumption up to 200 kwh per month. For consumption greater than 200 kwh per month, this declining real price of electricity is also true except in September 1974 when the real price increased. However, in December 1981, the real price also went down. 13 Table 3.4 shows the average monthly real price adjustments in pesos per kwh from 1974 to 1984. These are arrived at by dividing the total monthly revenue adjustments by the corresponding total monthly kwh consumption. It

1__ The inframarginal price based on Nordin's (1976) definition for a block increasing price schedule is negative. To be able to use this for the double logarithmic specification, the origin is shifted by a positive constant making all the prices positive. This could have an effect on the intercept but not on the parameter estimates. Accordingly, the correct sign of the coefficient of P2 is shown. 13/In the U.S., for instance, Hogan (1985) shows that the relative price of electricity for residential, commercial and industrial consumers have declined from 1960 to 1980, relative to non-electric energy resulting in the interfuel substitution in favor of electricity. 22

T_ 3.3 AVERAGE PRICES - MERAiLCO e RESiDENTiAL - DEFLAIED (BASE YEAR: 1978]

MAY 1970 KWH INDEXED RLOCK_ PRICE DEFLATED DEFLATED AP AP

PRICE DEFLATED AP

0.-10 11-14 13-S0 :51-60 61-100 101-120 121-130 151-200 201-2$0 251-7:50 751-2000 _'?O00

OCT

197_:

SEPT

INDEXED DEFLATED AP

1914

PRICE DEFI-ATED AP

DEC LNDEXEO DEFLATED AP

1901

PRICE DEFLATED AF

.1429 ,1429 ,125 .123 .07 .07 .1

.4023 .4025 3522 .3522 .1972 .1972 ._18

? 1 ,873 .875 .49 .48 .7

,1429 ,1429 .123 .123 .07 .07 .14

.2459 .2456 .2149 .2149 .[203 .3203 ,2401

1 1 873 ,875 .49 .48 98

3429 .1429 325 .123 .07 .07 ,14

.1548 .1548 .1354 .1384 .0758 .0755 .1517

1 l ,873 .81S .49 .49 .98

.1429 .1429 .12$ .15 .1S .2 .2

,0639 ,0639 ,0539 .0671 .0671 .0894 _0894

,1 .t .1 .09

.2818 .2818 .2818 .2536

7 .7 .7 .63

.14 .128 .125 .11

2407 .3149 .2149 .1891

.98 875 .875 .77

.14 .35 .35 .35

.1317 .3792 .3792 .3792

.98 2.48 2.4:5 2.45

.2 .363 .365 .355

.0894 3632 .1632 .1632

.ID_

.22S4

.56

.1719

.3_,

.3792

-2.45

.365

.1632

DEC INDEXED DEFLATED AP I 1 ,$78 .10:S .105 1.4 1.4

1984

PRICE DEFLATED AF

r19?_NDEXED INDEXED ,AVERAGED AVERAGED DEFLA'PED DEFLATED DEFLATED AP AF AP

.2 .2 .2 .25 .28 .2S ,25

.0392 .0392 ,0392 .049 .049 .049 .049

1 1 I ["25 1.2:5 1.2S 1.25

.1812 .1812 ,1595 .1637 .1019 .1064 1623

1 1 .8804 .9033 J623 .S$7 .8969

1.4 2-¢549 2.5548 2.5549

.2$ 2.36 2.36 2.36

.049 .4623 .4623 .4623

1.25 11.8 11.8 11.8

.1625 J003 .3003 .2895

,8969 1.6872 1.6:572 1.5976

2.5.549

"9-..36

.4623

11.8

.2804

1.5476

_," e_ ,L't,

"Prices arein pesosper kilowaZl-hO_r

i,o

:x_

o "_

Tabie 3,4 ADJUSTMENTS

MERALCO RESIDENTIAL. AT PfKWH, JAN 1974 - DEC 1984 (OEFLATED)

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

JAN U A RY FEB RUA RY

.0256 .0547

.00_ .0011

.0001 -.001

.0011 -.002

.0064 -.003

-.001 .0035

.0063 .0003

-.019 -.007

-.020 .0576

-.017 .0164

-.018 .0334

"-,

MARCH

.0201

.0031

.0057

.0050

.0003

.0443

-.005

-5e-4

.0014

-.01%

APRIL MAY

-.014 .0248

.0015 -.001

-.002 -.003

.0075 .0044

.0050 .0058

.0%31 .0174

.0551 .0197

-.008 .0199

.0045 .0070

.0087 .0089

l UN E

-.017

.0031

.0065

.0036

.0011

.0095

.0136

.0154

-.001

.0167

.0587

JU LY A UG UST

-.026 -.004

.0068 .0071

-3e-4 .0008

.0009

-.003 .0008

.0002 -.003

-.001 -.005

-.024 -.010

-.024 .0035

-.003 .0172

.0080 .0680

SEPTEMBE R

-.008

.0081

.00t 8

-.003

-.002

-.00t

-.010

.0023

-.032

OCTOB E R

-.001

.0032

-.003

-.001

.0034

-.006

-.009

.0225

.0268

N OV EM BE R DECEMBER

.0070 .0011

-.003 .0017

.0003 -.004

-.009

-.002 .0057

.0151 .0157

-.005 .0666

-.010 -.028

-.005 -.006

.0649 -.019

.0657 .1200

M EA N

.0050

.0030

.0007

.0010

.0009

.0056

.0163

-.007

-2e4

.0101

.0258

STD. DEV'N.

.0229

.0033

.0032

.0042

.0039

.0079

.0251

.0134

.0205

.0216

.0463

Source of basic electricity data: MERALCO Source of price index data: NCSO Prices Division

.0325 -.031

Residential consumer demandfunctions is recalled that from 1970 to 1973 there are no revenue adjustments. Table 3.4, for instance, also shows that the annual average price adjustments are O.OOS, -0.007 and 0.027 in 1974, 1981 and 1984, respectively. Except for the adjustments which increased in 1984, these price adjustments are minimal to affect the rate structure asshown in Figure 3.2. It is important to point out that residential consumption up to 200 kwh per month is exempted from these price adjustments. This means that the portions of the rate schedulesshown in Figure 3.2, which will be altered if price adjustments are taken into account, are those to the right of the 200 kwh level. Finally, the third observation is that the real rate schedulesshow a shift from a basically block decreasingto a block increasingstructure. This is evident if we compare the generally downward slopingschedule of May 1970 with the generally upward sloping schedule of December 1984. This means that over the 15-year period, there was a major restructuring of the residential rate schedulein favor of the small residential consumers.This restructuring is basically due to the MERALCO subsidy program. Figure 3.3. shows the indexed price schedules.Aside from showing the relative prices by blocks, it also indicates the increasing magnitude of the subsidy burden which the other consumers have to bear. Figure

3.2

MERAI.CO Price Schedule By Blocks (Deflated) Residential, May 1970 - Dec 1984 ( 197B= I00) Prlae (P-/KWIt_

I1_o 1984

â&#x20AC;˘_'_

Sep 1974

"_. ..

""_,

"_._._\

1972

DeaIsel

Residentialconsumerdemandfunctions Figure MERALCO

3.3

Relative Indexed. Price by KWH Blocks Residential ,. 1970-1984 DeG 1_4

IndexedPrice I1,0

I0.0

9.0

?.0

6.0

5.0

4.0

3.0

Dec 1981

/I 2.0

o 26

Sep1974

....... i0

ioo

120

150

200

250

7502000

' >2000

! kwh

Residential consumer demandfunctions B.

Demand Responsesof MERALCO Residential Consumers

Given the evolution of the MERALCO residential tariff structure, a brief analysis of the correspondingdemand data over the same period could provide indications of the nature of consumer demand behavior. More specifically,the behavior of the structure of electricity consumption and revenuesat variousblock levels and the corresponding structures of the number of customersare analyzed. Thereafter, the perverse finding of positive price elasticitiesof demand for electricity might be explained. 1. The Structure of Electricity Consumption Table 3.5 shows the percentage breakdown by kwh blocks of the number of customers,the kwh consumption and the revenuesfor May 1971, May 1975, April 1980 and June 1984. These are based on the bill frequencies done by MERALCO for the indicated month of the year. Due to time and cost considerations, MERALCO only occasionally undertakes bill frequency analysis.Over the 15-year period covered by the study, 1971, 1975, 1980 and 1984 are the only years wherein the company hassuchdata for a representive month in a given year. It is evident from Table 3.5 that the distribution of kwh consumptionsrests heavily on the higher consumption block. However, over the years there are some observablestructural changeswhich can be seenin Figure 3.4. More specifically, three patterns can be discerned from the graphs.The first is the absenceof an appreciable change in the percentagekwh consumption over th 15-year period (stable). This pattern is exhibited by the 81-120 and 351-650 kwh blocks. The second is the pattern of decreasingpercentagesharesand this is exhibited by blocks 0-10 to 51-80, 651-1050 and _>1050. The third pattern is an increasing percentage share of kwh consumption exhibited by blocks 121150 to 201-350. These observationsare summarizedin Figure 3.5 below. It is recalled that the real price of electricity is generally declining over the 15-year period under study, especially for blocks of consumption up to and including the 100-120 block which falls on the baseof the V-type rate schedules. This is evident from Table 3.3 and Appendix 3.7 where the current price schedule and the consumer price index for fuel, light and water as deflators are shown, respectively.

Residentialconsumerdemandfunctions

Table 3.5 MERALCO ResidentialConsumers PercentageBreakdownBy KWH BIoÂ˘ksa (1971,1975, 1980, 1984)

1971 KWH Blocks 0-10 11-30 31*50 51-80 81-120 121-150 151).200 201-350 351-650 651-1050 1050-above

No. of Customers 3.9 16.8 12.6 11.8 11.1 7.6 10.0 13.4 6.0 3.1 3.7

KWH Consumption 0.100 1.591 2.266 3.429 4.972 4.601 7.809 15,576 12.619 11.332 35.705

1975 Revenue0.349 2.210 2.997 4.266 5.280 4.620 7.848 15.668 12.705 11.289 32.768

No. of Customers 2.7 13.1 10.9 11.1 11.9 9.5 13.6 15.6 5.6 2.9 3.1

1980 KWH Blocks 0-10 11-30 31.50 51-80 81-120 121-150 151-200 201-350 351-650 651-1050 1050-above

No. of Customers 1.8 5.0 5.7 7.8 11.4 10.4 17.4 26.9 8.5 2.8 2.3

KWH Consumption 0.028 0.454 0.983 2.128 4.864 5.903 1').784 28.871 16.273 9.336 18.376

KWH Consumption 0.O74 1.284 2.047 3.329 5.581 5.962 11.058 18.319 12.333 10.839 29.173

0.116 0.797 1.211 1.851 2.646 2.806 5 ,596 13.691 13.921 14.316 43.048

1984 Revenue 0.075 0.302 0.625 1.267 2.473 2.988 6.967 23.525 19.515 13.238 29.024

No. of Customers 2.1 3.5 3.8 6.1 10.8 10.6 21.8 30.2 7.3 2.2 1.6

KWH Consumption 0.030 0.326 O.681 1.787 4.855 6.402 16.959 33.741 14.731 7.751 12.737

asourceof data; MERALCO. Computedfrom the bill frequenciesfor May 1971,1975, April 1980 and June1984.

Revenue

Revenue 0.1O0 0.328 0.640 1.273 3.362 4.540 12.359 30.635 17.622 10.515 18.725

Residentialconsumerdemandfunctions

Figure

:3.4

MERALCO RESIDENTIAL CONSUMERS Percent Distribution of KWH Consumption By KWH Blocks May 1971 to June 1984 _t

> I O5O

. ;_ol-SSo \

" \

X\ _.

_,o,-,_o ._

_"-""

35i -SSO.-_651-1050

*--

IO"

_-----.-,-

.._'_

>1050

_.__. _.____.

81- 120 ._'--_ 121-1E0 "--

0 -I0

_,o

. ----""

51 --80

J5,-2oo

71 72 73

121--150 81-

120

............

....................... • _

_--_.-

....... •

74 75 76

78 79

83 84

II _30 =_ 0-10

Year 29

Residential consumer demandfunctions Assuming that over the 15-year period, the averagereal incomeof consumers at all income levels did not significantly change, 14 the declining real price of electricity results in an income effect. Consumersin the low 0-80 kwh block could increasetheir electricity consumption to move up to the higher block levels, and therefore, the percentagekwh consumption at the low block level decreases asshown in Figure 3.5. Similarly, kwh consumption at the 120-650 block levels could also increase. The MERALCO subsidy program actually subsidizesconsumption up to percentagekwh consumption at that level decreases.The stable 650 kwh consumption in a lesserdegree. However, beyond the 650-1050 kwh block, there is a negative income effect such that the blocks of 80-120 and 650-1050 kwh serve as the boundaries between the upward movement in the lower block regions, and the downward movement in the higher block regions. Similarly, it is evident from Table 3.5 that there is a correspondingshift in the percentage revenuesat different block levels. For instance, while the 121350 kwh block accounts for only 28.1% of the total revenueshare in 1971, this increased to 47.5% in 1984. On the other hand, the percentage revenue share in the 1050 kwh block fell from 37.5% in 1971 to 18.7% in 1984. These shifts in percentagerevenue sharesis basically due to the change in the tariff structure from block decreasingto block increasingand the subsidy program. A frequency table was estimated for the distribution of kwh consumption at various block levels. The results are plotted for 1971, 1975, 1980 and 1984 and are shown in Figures3.6, 3.7, 3.8 and 3.9, respectively. Over the years, the weight of the distribution _sgradually moving towards the 120-350 blocks of consumption. This demand behavior is in accordance with, and in responseto, the changingtariff structure of electricity asshown earlier. 2. The Structure of the Number of Consumers Table 3.5 also shows the changes in the corresponding structure of the percentage shares of the number of customers. The details of the changes in the structure are shown in Figure 3.10. Figure 3.10 shows the percentage sharesat the 81-120 and 351-650 kwh blocks are stable and remain practically unchanged. For the 0-80 and > 650 14_4] Appendix3.8 showsthe wageindicesof skilledand unskilledindustrial workersin MetroManila,the averageindicesof the manufacturingsector,and the wageindexof nonagriculturalsectorof theNationalCapitalRegion.Theseindicesindicatethat theassumption isreasonable. 30

Figure 3.5 Structural Shifts in Number of Customers and KWH Consumption, MERALCO Residential

INCREASING

STABLE 0

_1 a.

DECREASING

_. ('_

E 3 0

120

150 200

o - Percentage of Number of Customers x - Percentage of kwh Consumption

350

650 1050 > 1050 kwh Blocks

=,, E_: 5"

Figure 3 6

1971.KWH CONSUMPTIONIN %

_,,

MERALCO, Residential

_. _o

3 ¢3.

t3.

> t_

bJ 03 m

o a. Ig z o o -r v

o.?

0.4

o.F=

(Thousonds) KWH MIDPOINTS

0.8

Residentialconsumerdemandfunctions

% NI SNOIIVA_I3SSO 34

NOll,=IINf'ISNO0

HM>I

Residentialconsumerdemandfunctions

Residential consumer demand functions

Figure 3.10 MERALCO RESIDENTIAL CONSUMERS Percent Dlstrlbutio n of the Num ber of Consumer s by KWH Blocks Moy I_)71 to June 1984 PercIflt

201-300

KWH

/ 20-

11"50 "_.._

_01-380 â&#x20AC;˘ """ 31-50 ' _-m'lc.v I0'

151-2ooxw.

,_/

._ _

:\ ._

01 - 120 KWH

161-200'

,2,-,_o-_

_--,,.-_

121"I60KWH

"_ _-_<,---__ â&#x20AC;˘ _....__ _"_---_ ---__ sn,-S.OKW. _* 81 -80 KWH _ _ _ -'_ _-.-_

31-80

KWH

)IO_KWH 0-10 KWH

blocks, the percentage share decreased while those for the 120-350 kwh blocks increased. In effect, these structural changes match those of the changes in the percentage shares of kwh consumption as indicated in Figure 3.5. Similarly , a frequency table was estimated for the distribution of the number of customers over the various block levels. The results are plotted and shown in Figures 3.11, 3.12, 3.13 and 3.14. As in the case of kwh consumption, it is seen that the peak and central tendency are moving away from the origin towards the 120-350 blocks of consumption. The same observations can be made for the corresponding revenue distributions which are shown in Appendix 3.9. 36

Figure

3. il

1971 NO. OF CUSTOMERSIN % MERALCO, Resldentla I

9 8

z m 6" ¢0 n, Lu =E 5" 0 I¢o u 0

_ I'D

w "_

T"__T

0.2

0.4

0.6 (Thousands) KWH MIDPOINTS

0.8

..t

o" -"

Figure 3.12

1975 NO. OF CUSTOMERSIN % 7

_,,.

MERALCO, Residenfiol

_. 0

3 _

3 -"I

N _z

_: bl Ig l-

_, _. -"I

째 ) U

0.2

0.4

o'.6

(Thousands) KWH M IDPO!NTS

0.8

;

Figure 3.13

1980 NO. OF CUSTOMERS IN% MERALCO, Residential

3.5

3.0

2.5

_z 03 Iv

,,, 2.0 0 .I-(/) :D

1.0

0 ..,=

3 _*

0.5

-0

_ 0.2

0.4

0.6 (Thousands) KWH MIDPOINTS

0.8

l _" _=

% NI

F I

.._._ 0

"ON

_ _13 INo±sno

Residentialconsumerdemandfunctions

o_ Z

n_ UJ -_

_;.

g 8 :p,

0 Z 00

•

¢0

-6

Residentialconsumer demandfunctions 3. Per Capita KWH Consumption Basedon the frequency distributions, asshown in the preceding figures, the mean kwh level or the central tendency of the percentage shares of kwh consumption, number of customers and revenue distributions are shown in Table 3.6. The estimates of the corresponding standard deviations are alsoshown. Table 3.6 provides an estimate of the corresponding behavior of per capita kwh consumption. This is done by dividing the kwh mean for kwh percentage consumption share by the kwh mean for the percentage share of the number of customers. It is observed that the ratio is decreasingover the period covered. This implies that the means of two distributions are approaching each other over time as shown in Figure 3.15. Mean per capita kwh consumption therefore could decreaseover time. Appendix 3.10 shows the residential annual mean per capita kwh consumption from 1970 to 1984. While there are indications of decreasing per capita kwh consumption in some years, merely looking at the overall per capita kwh consumption arrived at by dividing total kwh consumption by the total number of customers, as done in Appendix 3.10, may not reveal the details in the changes in the demand structure for electricity by residential consumers as previously shown.

Table3.6 Estimates of MeanKWHandStandardDeviations Consumer, Consumption and Revenue Distribution MERALCOResidential Consumers (1971,1975,1980,1984) 197__1

1975

198Q

1984

Numberof Customers: (1) Mean: Std. Dev:

187.358 247.189

191.161 231.687

228.808 213.123

224.614 189.224

KwhConsumption:

(2) Mean: Std.Dev:

607.241 400.688

548.183 393.904

471.006 348.204

411.452 317.219

Revenue:

(3) Mean: Std.Dev:

577.943 403.303

701.171 376.690

600.830 361.879

296.810 2S7.677

3.241

2.868

2.059

1.832

KwhConsumption (2): No.of Customers (1)

Residentialconsumerdemandfunctions

Figure MERALCO,

:3.15 Residential

Mean KWH of %Share of Number

1971

ioo

Mean

of Customers

,,l,i

_oo

300

KWH of % Share

of KWH

Consumption

400

_' _

500

6c_ I I I I I I I !

I f 1975

' 0

, I00

i, 2'00 I I I t I t

• 300

.... 400

500 I / I I

t It I

,oo

600

19BO

! i

zooI

/ / / i

•

soo

400

t ! I

/soo

•

eoo

/ / I I I I I I I

19_ o

,oo zoo

300

400

•

500

eoo

kwh

Residentialconsumerdemandfunctions C.

An Explanation of the PerverseFinding on Price Elasticities

The finding of positive price elasticities can be explained by the preceding analysis of the changes in the distribution structure of the number of customers and kwh consumption at various blocks, and the evolution of the price structure of MERALCO. Over the 15-year period the real price of electricity has been declining, and assumedly, also per capita consumption of electricity. Since both price (marginal P1 and averageP3) and per capita consumption are moving in the samedirection, and since the regression analysis was done using per capita kwh as the dependent variable, the finding on positive marginal price elasticities of demand for electricity is explained. Moreover, the elasticity of demand for the inframarginal price (P2) based on Taylor's definition (Model I) is also positive. As shown in Appendix 3.8, the real income of MERALCO consumers could be reasonably assumed to have remained stable or decreased slightly over the 1S-year period. However, over the years, the real price of electricity has been declining and this includes blocks of consumption at lower blocks. This means that the inframarginal price (Taylor's 1975) definition also declined. Again since the dependent variable per capita kwh consumption and the inframarginal price (P2) move in the same direction, the finding of a positive price elasticcity is expected. In the caseof Model II, the negative estimate of the inframarginal price coefficient confirms a priori expectations. Thus, the perverse finding of positive price elasticities of demand for electricity is explained within the context of standard consumerdemand theory. 15 II1. Tests of the Residential Model UsingAnnual Data A.

Short-run Versus Long.run Demand Behavior

The preceding tests of the residential models use montly data. Over a onemonth period, it is reasonable to assumethat the stock level of electricity-con-

The price indexfor fuel, lightandwater closelyapproximates the consumerprice index(CPI) exceptfor the lasttwo to threeyearsof the sampleperiod.Theformerisusedto be ableto makemoremeaningfulcomparisons of demandresponses amongresidential, commercialandindustrialconsumers. However,usingtheCPI asdeflatoryieldssimilartestresults asshowninAppendix3.11and3.12. 43

Residentialconsumerdemandfunctions suming equipment is fixed.Thus, the preceeding parameter estimates using monthly data could be considered as estimates for short-run elasticities of demand for electricity. Over a one-year period, however, it is possible that stock level adjustments may have been accomplished and most of the indirect effects of changes in price, income and other relevant variables may have been realized. Thus, using annual data for equation 2.3 could provide estimates for long-run elasticities. The specification of the consumer demand function basically follows the general formulation of equation 2.3. More specifically, the annual average residential per capita kwh consumption is a function of the price of electricity, income, price of electricity substitutes, price of electricity-consuming equipment and environmental variables. The data used are the annualized version of the monthly data used in the previous tests. B.

Test Results16

The results of the regression analysis using annualized data over the same period of 1971-1984 are summarized in Table 3.7. The average price elasticity of demand for electricity is -0.5409 and -0.4646 for Model I and Model II, respectively. Both are highly significant and with the correct sign. This means that the long-run behavior of residential consumers indicate that in fact per capita kwh consumption decreases as price increases. However, due to the MERALCO subsidy, which was started sometime in September 1974, and the distortion having increased in degree in the succeeding years, monthly or short-run demand behavior could have resulted in the perverse finding of positive price elasticities. However, the marginal price is insignificant for Model I but significant for Model II. The positive marginal price elasticity of 0.1990 confirms the previous results using monthly data. Also, the inframarsinal price is insignificant for both Model I and II. The positive sign for Model I also confirms the previous results using monthly data. Employment level is insignificant for Model I and II. This implies that employment level may not be an appropriate income surrogate. The price of LPG and that of an air-conditioner are both insignificant. Both variables are also insignificant using monthly data as shown in Table 3.2. In the precedingsections, the presentationof the results usesthe definitions of variablesin equation2.3. For convenience,the variablesare hereby spelledout or basedon computerprintouts.Appendix7.1 listsall the variablesin this study. 44

Residentialconsumerdemandfunctions

Table 3.7 Resultsof Regression Analysis(OLS) MERALCOResidential, AnnualData (1971-1984) DependentVariable:RESPERAN Average Marginal Inframar- EmploymentPrice Constant Price, , Price , _inal Price Level LPG

Price Maximum Aircon Te,mperatu_

Re_Coeff. Modelh ModelIh

3.8699 -0.5409 -5.6962 -0.4646

0.0501 0.1990

0.0253 0.0592

0.0231 0.6568

-0.0171 0.1855

0.0950 0.0749

0.5700 0.2526

StdError Modelh Model II:

3.5318 7.1498

0.1402 0.1254

0,0738 0.0960

0.1071 0.0399

0.1514 0.4542

0.1731 0.1994

0.1378 0.1185

0.5796 0.5114

1.0957 -3,8590 -0.7967 -3.7049

0.6781 2.0724

0.2373 1.4829

0,1524 1.4460

-0.0989 0.9302

0.6894 0.6325

0.9834 0.4940

T-Value Modelh ModelIh

ModelI

ModelII

MultipleCorrelation R2

: 0.9373 0.8785

0.9545 0.9111

R2, Adjusted Std Errorof Estimate Durbin-Watson Statistic

: : :

0.7368 0.0375 2.8637

0.8074 0.0321 2,8622

Rho Estimate F.Value

: .0.6137 : 6.1988

-0.4791 8.7836

Residential consumer demandfunctions Similarly, the maximum temperature representingthe environmental variable is insignificant. However, this does not mean that temperature does not affect demand for electricity in the long-run. The data used is the maximum temperature for each year and annual variations in maximum temperature are relatively small compared to monthly variations within a given year. The preponderanceof significant results for Model II could be interpreted to mean that Nordin's (1976) specification of the inframarginal price variable is better and in accordancewith theory. As another income surrogate, annual GNP data was used insteadof employment data. The results are generally the same with those shown in Table 3.7. GNP is insignificant for Model I but significant for Model II. The resultsare summarized in Table 3.8.17 Empirical evidence in the United States show that short-run price and income elasticities of demannd for electricity are relatively smaller in magnitude than long-run values. Table 3.9 summarizes the "long-run" and "short-run" elasticities, the standard error estimatesof the coefficients and T-values are also shown for convenience. For the price elasticities, annual valuesare greater (absolute values) than monthly estimates. The same observation could be made for income elasticitiesalthough the differences are not as pronounced as in the case of price elasticities.These findings confirm previousresultsin the literature. These results imply that while the immediate change in demand for electricity due to a given price change might be relatively small, the long-run effects on the demand for electricity by residentialconsumerscould be larger. Also, the immediate change in demand for electricity due to a given changein income could be relatively small compared to the changein demand in the long-run. One important implication for policy of these results is that utilities especially since the power sector isa highly regulatedsector) should not only consider the immediate effects on demand for electricity by a given price change, but more importantly, with the long-run effects on demand. The same observation holds true for the effects on demand by changesin income levels.Thus, efforts should be done to maintain a stable price of electricity. 18 17/ Test results,basedon the specificationshownin Table 3.7 and 3.8 andusinginstead the CPI aspricedeflatorfor thepriceof electricityandLPG,areshownin Appendix3.13and 3.14 respectively.The resultsare generallythe samewith thoseshownin Table3.7 and3.8 respectively. 1_ Thisfindingprovidesa strongbasisfor a tariff structurebasedon long-runmarginal costsincethiscostisinsulatedfromshort-run costvariations. 46

Table 3.8 Resultsof RegressionAnalysis(OLS) MERALCO Residential,Annual data (1971-1984) DependentVariable: RESPERAN

Constant Re_Coeff Model I: 4.0463 Model I1:3.3104

Averase Price

Marginal InframarPrice _inal Price

-0.5190 -0.5243

0.0390 0.1358

0.0441 0.0409

0.0468 0.3324

-0_0122 0.0272

0.0702 0.0428

0.5339 .0.0268

GNP

Price LPG

Price Maximum Aircon Temperature

Std Error Model I: Model Ih

2.6673 2.0570

0.1304 0.1020

0.0759 0.0507

0.1051 0.0243

0.1069 0.1991

0.1633 0.1333

0.1427 0.1202

0.5790 0.5695

T-Value Model I: Model I1:

t.5170 1.6094

-3.9805 -5.1392

0.5130 2,6770

0.4194 1.6817

0.4376 1.6692

.0.0748 0.2039

0.4922 0.3559

0.9221 .0.0470

Multiple Correlation R2

: :

r,.

Model I

Model I.I.

0.9382 0.8803

0.9573 0.9164

r2, Adju, : Std Error of Estimate :

0.7,,O6 00.0311 .8188 0.0372

Durbin-WatsonStatistic: Rbo Estimate : F-Value : â&#x20AC;˘

2.8415 -0.5994 6.3025

2.9790 -0.5059 9.3925

o. _'= 2. 0

O -t

Table 3.9 ComparativeEstimatesof Priceand Incomea ElastiCities:Short-run(Monthly) Versus Long-run(Annual) MERALCO, Residential(1971-1984)

_c_ Q.

Price Elasticities (Average) . Monthly

Annual

Income Elasticities(Employment Level) Monthly

â&#x20AC;˘

Annual

Coefficient Model h Model II:

0.0794 0.0881

4).5409 -0.4646

0.3076 0.4034

0.0231 0.6568

0.0548 0.0568

0.1402 0.1254

0.2021 0.3036

0.1514 0.4542

1.4499 1.5524

-3.8590 -3.7049

1.5.219 1.3.288

0.1524 1.446f3

'Std.Error Model I: Model I1: T-Val_ Model I Model I1: "

aBasedon Tables3.2 and 3.7.

_= f_ o"

Residential consumerdemandfunctions Within the context of the basic consumer demand model given by equation 2.3, these resultsare expected since the demand for electricity isa function of the stock of electricity-consuming equipment. In the short-run, where the stock is assumedfixed, the level of electricity consumption is a function of the utilizzation rate of the existing stock. Changesin this utilization rate could be "sticky" due to a number of factors such as habitual behavior and perhapsthe tendency to maintain a given levelof electricity consumption and lifestyle. In the long-run,however, where the level of stock changes_changesin consumptionare expected to be larger.

Chapter 4

Commercial Consumer Demand Functions

This chapter attempts to estimate the demand functions of commercial consumers. Four categories of consumers are considered: X-l, primary, secondary, and X-MD. The X-1 and X-MD are general service type of consumerswith connected loadsof 1 to 5 kw and _ S kw, respectively. The primary and secondary consumersare general power type of consumersthat guarantee a billing demand of not lessthan 40 kw. A consumer is classifiedprimary or secondarydepending on the nature of the load and the price discount provided. This classification is based on the MERALCO system of classification of its commercial consumers for which monthly data are available.19 The consumer demand functions are estimated for each category. The resultsof the regressionruns are presentedand analyzed. I.

Modification of the BasicModel Specification

The specification of the model follows the general formulation in equation 2.3. However, there is an additional feature of the demand function for commercial consumers (except for x-1 which is similar to that of residential consumers) not presentin that of residentialconsumers'.Thisadditional feature isthe existence of an additional price variable, the demand charge, in pesos per kw. Correspondingly, this also requiresdata on kw load. This data is usually not availablefor eachtype of consumer.There, are however, monthly data on system peak demand for MERALCO. Since it is the system peakload which puts pressureon the utility to install more power generation and transmission as well as distribution facilities, and For addeddetails,seethe MERALCOrate schedules for eachcategoryof consumers. The categorization of the commercial consumers hereis essentially dictatedby theavailability of data. 51

Commercialconsumerdemandfunctions given the relative scarcity of capital resources, the crucial question is: How does the elasticity demand behavior of a particular consumer or group of consumers contribute to the system peakload? More specifically, is it possible to statistically measure the Consumer's contribution to the evolution of the system peak? Using the system peakload as an additional explanatory variable might provide as answer to this question. Strictly, it is the timing of the decisions of electricity users in switching their appliances and the power rating of these appliances which determine system peakload, taking into account the diversity factor. 20 However, assuming that the consumption of a particular consumer is relatively small compared to the system load over a month or a year this procedure may not be unreasonable. Now, assume that the average load is a proportion of the peakload. More specifically, â&#x20AC;˘ Average Load = Peakload

(4.1)

where:â&#x20AC;˘ ol<1. The average load multiplied by time, say 730 hours, gives the total kwh consumed for a given month. This means that the average load is proportional to the kwh consumed for a given month. This means that the average load is proportional to the kwh consumed and therefore also proportional to per capita kwh consumption. Taking the logarithm

on both sides of (4.1) yields:

Ln(Average Load) = _ Ln (Peakload), Thus, if the system peakload is introduced as an additional variable in logarithmic form of equation 2_3, the resulting estimated coefficient measure of how the electricity demand of each type of consumer is or affected by the system peak, referred to as the "peakload elasticity for electricity" or "peak coefficient".

(4.2) the double provides a influenced of demand

2_.0./Thediversity factor measures the divergence or spreadingof individualpeak demands (load) over time. It is the ratio of the sum of the individual peak demands to the combined or simultaneous peak demand. The diversity factor, therefore, is greater than or equal to unity. 52

Commercialconsumerdemandfunctions The interpretation of the magnitude of this coefficient as it varies somewhere in the neighborhood of 0 to 1 is the higher the coefficient value, the greater the kwh consumption is affected by the movements of the system peak. The lower the value of the coefficient, the smaller the effect of the system peak on kwh consumption. These mean that the peak coefficient is inversely related to the load factor. The higher the peak coefficient, the lower the load factor and vice-versa.Thus, for residential consumers, a higher value of _ is expected since they have relatively lower load factor. For industrial consumers,a lower value of _ isexpected. The peak coefficient, therefore, is an extremely useful parameter in the design of tariff policies and scheduleswhich cuts acrossvarious types of consumers. II. The Data Data on the total monthly kwh consumption, number of customers and revenuesfor commercial consumers to include system peak demand are provided by MERALCO. There is no separate monthly data for each type of commercial consumers except for X-1. However, MERALCO has bill frequencies for May 1971, May 197.5, April 1980 and June 1984 which show the kwh consumption, number of customers and revenue per type of consumer at each block level of kwh consumption. Based on these bill frequencies, the percentage breakdown of the number of customers, kwh consumption and revenue by blocks are computed. The results for X-1 are shown in Table 4.1. Similar computationsfor XMD and primary and secondary account consumers are done. These percentage breakdowns are usedto estimate the monthly proportions over the January 1971November 1984 period and which are, in turn, used to estimate the monthly kwh consumption, revenues and number of customers per type. The per capita kwh consumption and monthly average prices are thereafter computed. Monthly commercial data on the mwh consumption, the number of customers,and revenues are shown in Appendix 4.1, 4.2 and 4.3, respectively. Also, the revenue adjustments in pesos per kwh as shown in Appendix 4.4 are relatively small. These are included in the computations of the averageprices. As in the caseof the residential consumers, the marginal and inframarginal prices are based on the price schedulesfor each type of comrfiercial consumer and the average monthly per capita kwh consumption. All price data aredeflated using the consumer price index for electricity, fuel, and light and water with 1978 as the base year. Except for the demand charge, system peak, and the index of commercial sales (1978=100) used as income surrogate, the other variables remain asbefore. 53

Table 4.1 MERALCO CommercialConsumers(X-l) PercentageBreakdownBy KWH Blocksa (1971, 1975, 1980, 1984)

No. of KWH Blocks.. 0-I 0 11-30 31-50 51-80 81-120 121-150 151-200 201o350 351-650 651-1050 1050-above

KWH Blocks 0-10 11-30 31-50 51-80 81-120 121-150 t51-200 201-350 351-650 651-1050 1050-_ove

Customers

1971 KWH Consumption

No. of Revenue

Customers

(7 o =f =1 1975 KWH

_--

Consumption

Revenue

r= 3

0.107 0.863 1._522 3.223 5.328

0.220 0.485 0.844 1.780 3.325

e, 3

7.5 10.1 9.1 I 1.7 11.8

0.113 0.919 1.617 3.327 5.108

0.484 1.120 1.949 3.989 6.106

7.3 9.6 8.5 11.3 12.2

6.8 8.7 14.9 11.2 5.3 2.9

4.020 6.572 17.250 23.012 18.607 19.453

4.799 7.840 19.290 22.864 16.424 15.135

7.5 9.7 15.7 10.4 4.8 3.0

4.426 7.383 18.046 21.501 17.117 20.484

3.401 6.386 17.658 23.090 19.178 23.634

No. of Customers

1984 KWH _Consumption

Revenue

No. of Customers 6.5 6.4 6.2 8.9 11.6 7.4 10;9 19.0 13.8 5.7 3.6

1980 KWH Consumption 0.063 0.486 0.933 2.145 4.292 3.704 6.995 t8.503 24.031 17.079 21.769

Revenue 0.163 0.268 0.507 1.159 2.639 2.789 5.925 17.726 25.266 18.803 24.753

7.0 6.2 6.4 10.0 13.0 8.2 10.9 17.7 1%.8 5.0 3.8

0.064 0.488 0.995 2.503 5.004 4.265 7.257 17.854 21.261 15.691 24.617

aSourceof daM: MERALCO; computed from the bill frequenciesfor May 1971, 1975, April }980 and June 1984.

0.257 0.360 0.706 1.726 3.671 3.526 6.471 17.315 22.041 16.846 27.085

Commercial consumer demandfunctions III. Test Results A.

X-1 Commercial Consumers

Table 4.2 shows the results of the regressionruns for the X-1 commercial consumers.The model specification is similar with that of the residential consumers. Again, models I and II refer to the specification of the inframarginal price suggestedby Taylor (1975) and Nordin (1976) respectively. The marginal price (X1CMA) is significant for both models I and II with positive signs for the estimated coefficients. These results are similar with that of the residential consumers.Figure 4.1 shows the real price of electricity for X-1 commercial consumers has been declining over the 1970-1984 period snd the structure changed from block-decreasing to block-increasing.This is more apparent in Figure 4.2 which shows the relative indexed price by blocks. It is noted that the data used in the test are only up to November 1984 and excludes the December 1984 price changes.(For details on the price schedules,see Table 4.3). The subsidy for the 1-200 kwh per month consumption block is evident. A similar explanation, as in the case of residential consumers,for a positive marginal price elasticity could be made. The estimated coefficient of the inframarginal price for model i (Xl MARTL) has a positive sign and is significantbut insignificant for model II (X1CMARNL). The same explanation could also be made for the positive coefficient of X1MARTL. For X1MARL, the insignificant result could be explained by the relatively flat scheduleup to and including the average monthly kwh consumption. Based on Nordin's (1976) definition, the magnitude and variations of the inframarginal payments are, therefore, relatively small and giving insignificant results.Unlike the residentialconsumers,21 however, the averageprice (X1CAVERE) is significant and the coefficient hasthe correct sign. Since the distortions in the price structure for X-1 commercial consumersis not as pronounced as that of residential consumers,this result may not be unexpected. The movements in the price of substituteslike LPG (PRLPGL) and firewood (FIREL) do not affect demand for electricity by X-1 commercial consumers. Meanwhile, the level of employment (LABORFRL) is significant with a positive sign of the estimated coefficient. The coefficients are 0.8109 and 0.6505 for models I and II, r_spectively. It is recalled that the employment level is used as an incomesurrogate. It isrecalledthat.theaverage pricefor residential consumers hasa positivecoefficient for monthlytestsbutwith negative coefficientusingannualdata. S5

o 3 3 Table 4.2 Resultsof RegressionAnalysis(OLS) X-1 CommercialConsumers,MERALCO (January1971 to November1984)

E o O = 3 o "1

Uepenaent var_aole:_IJ'ERCAL

c_ 3 XIMARTL c_.

Constant

X1CMA

X1CMARNL

XICAVERE

PRLPGL

FIREL

LABORFRL

FRJDGEL

AIRCOML

TEMPMIAL

HUMIDITY

Reg Coeff

_. O

Model I: Model 11: Std Error

-2.0554 -0.4523

0.1023 0.0619

0.0291 0.0099

-0.0477 .0.0662

0.0096 -0.0256

0.033,% 0.0374

0.8190 0.6505

-0.1503 -0.1523

-0.0661 -0.0480

0.2363 0.2251

0.1609 0.1402

Model h Model II: T-Value

2.1918 2.6957

0.0338 0.0359

0.0096 0.0463

0.0382 0.0387

0.0588 0.0615

0.036I 0.037t

O.1730 0.2259

0.0811 0.0817

0.0696 0.0720

0.0848 0.0863

0.0656 0.0672

Model I: Model ll:

-0.9378 -0.1678

3.0126 1.7238

3.0480 0.2130

-1.2493 -1.7118

0.1640 -0.4164

0.9241 1.0082

4.6885 2.8800

-1.8527 -1.8640

-0.9499 .0.6663

2.7849 2.6076

2.4546 2.0874

Model I

Model II

Multip|e Correlation R2 R2, Adjusted Std Error of Estimate Durbin-Watson Statistic Rho Estimate F-Value

0.9582 0.9182 0.9129 0.0407 1.7913 0.6506 18.9669

0.9558 0.9135 0.9079 0.0418 1.7963 0.6875 13.4112

Commercialconsumer demandfunctions

Figure

4. I

MERALCO Price Schedule By Blocks (Deflofed) Commercial (XI), Moy 1970- Dec 1984 (1978 = I00)

Price

1>/KWH)

0.50 970"

....

Dec 1984

0.40 --;--"

" Sep 1974

o.so Moy 1970

o.zo-

0.10

i974.

-'------ .......

reel.

"_-

Oct 1972 Dec 1984

19 84 ....

• I0

, 12

, 14

, 50

, 90

•, L' 200 400

• J. I 750 2000 >2000

KWH

Commercial consumer demand functions Figure

4.2

MERALCO Relative Indexed Price by KWH Blocks Commercial (XI), May 1970- Dec 1984 Indexed Price 8 Dec 1984

Sep 1974 --.

Dec 1981

1994 "_"-----_"'-; tO 58

+ .t2

: )4

_ SO

: "" SO 200

;_ ; ; 400 750 2000

Oct 1972 Mey 1970 , 2000

KWH

Table 4.3 AVERAGE PRICES - MERALCO COMMERCIAL (Xlj, DETLATED (BASE YEAR: 1978!

MAy

19T0

KWH

OCT

19TZ

INDEXE_

BLOCKS YBtCL

OE_-'LATEID DEFLATED AP AP

PRICE DEFLATEO AP

0-)0 I]-lu I_-12

.1667 .16_7 . _Eb.:

I_.14

.16

4_08

._'6

.2751

1%5L, 51-90 91.200

.le. .'t6 _16

.4S08 .450_ .4508

.96 .96 .96

.16 .16 185

.2751 ,2751 .318

201-4DO 401 75 O

.12 .1

.3381 .-'_ t¢_

.'?,2 .6

.17 .125

.2922 .2149

__ L-2000 )'2000

.09 .08

.2536 .2254

.54 .48

.tl 1

.1891 ,1719

,4696 .468.N

SEPT

1974

INDEXED

1 1

.1667 .166 1

.2BE5 .7._,65

DEC

1981

I_DEXED

DEFLATEO AP

1 1

PRICE UEfLATEO AP

OET'LATEO PRICE DEFLATED AP AP

I 1

DEC

1984

INDEXED

.1_.61 ,1667

.1_04 ,1804

.2143 .214]

.09";8 ,09S8

.]b

.;732

.96

.2 t43

.O_JS 8,

.96 .98 1.11

.16 .16 .35

.1732 .173"2 ,3788

._6 .96 2.1

.21 .21 .365

.0939 .093S" .1632

].G2 .15

.35 .3'_

.3788 .3788

2,1 .21

,3ES .365

.1632 .I 63"2

.66 .6

.35 .35

.3788 .3788,

2.1 2.1

.365 .365

_,.632 .1632

DEFEA1ED AP

PRICE DEFLATED AP

_970-|9_ 1NDEXED

AVERAGED A'VEAAGED

DEFLAT£D AP

DEFLATEO Alp

DEFLATED AP

.3 28

.0588 .0548

.9333

.21R2 _217_I

,28

.0548

.9._33

.20S9

.962

.SB .98 1.703

.2B .28 2.345

.054g .0548 .4594

.9333 .9333 7.gl 7

.2096 .2096 35,4

.9603 .9603 1.622

1.703 _.703

2.345 2.345

.45 SI4 .4594

7.817 7.817

.3263 .2996

1.495 1.373

.1703 1.703

.2.345 2.345

.45_4 .4594

7.817 7.817

.2888 .2797

1.324 1.28"2

¢'_ C'

1 _

1 .gt963 (e.} 3 ¢i_

3 e_

--i

Commercial consumer demand functions The price of electricity-consuming (FRIDGEL) has a negative and significant

equipment such as refrigerator effect for both models. The results

show that for every one percent increase in price, demand for electricity falls by 0.15%. The price of airconditioners (AIRCONL) is insignificant, unlike the case of residentisl consumers where it is highly significant. Finally, both temperature (TEMPMIAL) and relative humidity (HUMIDITL) are highly significant and positively correlated with the demand for electricity. Figure 4.3 shows the percent distribution of kwh consumption by kwh blocks for MERALCO X-1 commercial consumers. Unlike the case of the residential consumers, there are no significant changes in the percent distribution over the 1971-1984 period. Similar observations can be made for the percent distribution of the number Of customers as shown in Figure 4.4. Like the case of the residential consumers, a frequency table was estimated for the distribution of kwh consumption at various kwh block levels. The results are plotted for years 1971, 1975, 1980 and 1984. These are shown in Appendix 4.5. Appendix 4.6 and 4.7 show similar plots for the number of customers and the revenue distributions, respectively. The observed relative stability in the distribution structures of the kwh consumption, number of customers and revenues, as contrasted with those of residential consumers, could perhaps provide an explanation why the coefficient of the average price for X-1 has the correct (negative) sign while that of the residential consumers has a (positive) perverse sign. B.

XMD Commercial Consumers

Table 4.4 shows the results of the regression runs for XMD commercial consumers, with the demand charge (DEMXDL), system peakload (PKLOADML) and index of commercial sales (LINDEXF) included as additional variables. The marginal price (XDMARL) is insignificant for models i and II. This means that the price marginal to an XMD commercial consumer has negligible effect on its level of demand. This could perhaps be explained by the relatively flat price schedule as shown in Figure 4.5. The inframarginal price (XDFRATL for model I and XDMARNL2 for model II) are, however, significant but the signs of the estimated coefficients are different, i.e., negative for model I and positive for model II. The price schedule as shown in Table 4.5 is depicted in Figure 4.5 where the real price schedule during the entire period is block decreasing. Based on Taylor's (1975) definition of the inframarginal price, the estimated negative 6O

Commercial consumer demand functions

Figure 4.3 MERALCO COMMERCIAL CONSUMERS(XI) PercentDistributionof KWH Consumptionby KWH Blocks May 1971to June 1984 Per¢_ :nt

_ I050

s5,45o 20.

-"'_ 3 51-650

loso 651 - 105G....._....._ 201-35 O''"--

201-350 _GS1-1050

t I0 .__

181-IE)O

151_200 81-120

/§1 -120 ...---------121-150

121-150 51-80

51 -80

31-50

31 -50

,,0-10

, 1971 72

"' 81

.I 82

Year

Commercialconsumerdemandfunctions

Figure

4.4

MERALCO COMMERCIAL CONSUMERS ( Xl ) Percent Distribution of Number of Customers by KWH Blocks May 1971 to June 1984 Percent'

20 .存

201-350

2OI -350.

81-120_

5_-80__

81--120

35_-sso

3_'=__.ov_-_= "=^ i0.

31-5o -- _ 151 -200_ 0 - I0 _ 121 t GSI -1050

__'-"--_

---

--,5,-200

._---

51-80

121 - 150 0 - 10 II- 30

__"--,-._ _._ _'----.,_'--..._

.....

_'__

31-50 651 - 1050

-_----_--------__

1050 _1050

1971

7'3

1=10

_r Year

coefficient is expected. However, based on Nordin's (1976) definition of the inframarginal price, the positive coefficient may be explained by the price schedules which decreased and flattened over the period. In other words, the inframarginal payment which the XMD commercial consumer hasto pay before being able to buy an unlimited quantity at the marginal price, has been declining over th years. Moverover, the relatively flat schedule provides no disincentive for higher per capita consumption. Thus, the positive coefficient may not be unexpected. 62

Commercial consumer demand functions

Table4.4 Resultsof Regression Analysis(OLS)a XMD CommercialConsumers,MERALCO (January1971 to November1984) DependentVariable:XDPERCAL Independent Variables

StandardError Model I Model II

ComputedT Value Model I Model II

Constant

7,7248

7.3411

1.9250

2.3513

4.0129

3.1222

XDMARL

0,1029

-0.1499

0.1363

0.1553

0.7549

-0.9649

XDFRATL/ XSMARNL2

-0,3218

0.9321

0.0892

0.2858

-3.6056

3.2618

XDAVEREL

-0,0521

.0.0516

0.0333

-1.5651

1.5471

0,0239

0.0183

0.0485

0.0489

0.4925

0.3745

FI REL

.0,0343

-0.0264

0.0301

0.0305

-1.1399

-0.8637

LABORFRL

-0,3410

-0.4601

0.0245

0.2155

-1.6675

-2.1351

FRIDG EL

-0.0178

.0.0438

0.0732

0.0751

-0.2428

.0.5827

AI RCONL

-0.1490

-0.1600

0.0588

0.0592

-2.5356

-2.7041

TEMPMIA L

0.0378

0.0223

0.0845

0.0849

0.4477

0.2630

HUMIDITL

0.1039

0.0894

0.0587

0.0591

1.7712

1.5121

DEMXDL

0,2918

-0.7160

0.1560

0.2235

1.8703

-3.2036

PKLOADML

0.6496

0.6627

0.0881

0.0893

7.3757

7.4245

LINDEXF

0.0223

0,0194

0.0302

0.7387

0.6403

Model I

Model II

PRLPGL

;

RegressionCoefficients Model I Model II

MultipleCorrelation

0.8775

0,8773

0.7700

0.7693

R2, Adjusted

0.7503

0.7500

Std Error of Estimate

0.0364

Drrbin-WatsonStatistic

2.0587

2.0356

Rho Estimate

0.5247

0.5272

F-Value

9.7597

9.5075

aTheCochrane-Orcuttprocedureisappliedfor all results.

Figure

4.5

3 3

MERALCO Price Schedule By Blocks(Deflated) Commercial X-MD, May 1970 - Dec 1984 (1978 = I00)

_Â˘1 0

Price _-/KWH

0.5'

o. 1970

0.4.

_--,.

1974

] 1970

02!1981

1972

I 0,1

1981

1984

1984 I

I00

I50

200

250

300

>300

KWH

Table 4.S AVERAC-E PRICES - MERALCO COMMERCIAL XMD DEFLATED (BASE YEAR: 19')'8}

MAY

1970

OCT

1972

SEPT

1974

DEC

1981

DEC

1984

1970-1984

1. ENERGY CHARGE KWH BLOCKS PRICE DEFLATED AP

INDEXED DEFLATED AP

PRICE DEFLATED AP

tNDEXED DEFLATED Ap

pRICE DEFLATED AP

INDEXED DEFLATED AF

PRICE DEFLATED AP

INDEXED DEFLATED AP

IpR1CE DEFLATED AP

INDEXED DEFLATED AP

AVERAGED DEFLATED AP

0-SQ 5 I-IO0 101"150

.16 .12 .l

.4505 r33_1 .2818

1 ._ S .625

.185 .1S .125

.3_8 .2579 .2149

I ,8108 .6757

.3 .3 .25

.325 .325 .2709

1 1 ,8333

.33 .33 .28

,1476 .1476 ,_252

I 1 ,8485

325 ,325 ,25

.0637 _0637 .049

1 I .7692

.261 2664 .1583

1 ,E67;S .7215

151-200 20l .300

.OB .138

.2254 .2254

.5 .5

.1 ,I

,'r 719 .I 719

5405 .$405

.2$ .22

.2709 .2394

.8333 ,7333

.28 .25

.'L252 ,1118

.8485 .7576

.25 .22

.049 ,0431

.7692 .6769

,1685 .1SS1

.6454 .6057

) 320

.08

.2254

,1719

.540:5

.2_67

,6667

.23

,I029

.697

._95

.0382

.IS_

.$785

1t, DEMAND

CHARGE

Iper Xwp

N' IlL GENERATION CHARGE (eltective Dec. 19B4)

appllcabL_for tl_ monlh: PI,985 per kwh

(_ O e-

3 3

Commercial consumer demand funclions The average price for models I and II is insignificant but with the correct sign. The t-ratios are, however, -1.5651 and -1.5471, and estimated coefficients are -0.0521 and -0.0516 for models I and II, respectively. These average price coefficients appear to be relatively small. These results are similar to that of X-1 commercial consumers. No dramatic changes are observed over the 1971-1984 period in the distribulion structure of kwh consumption, and number of customers and revenues for XMD commercial consumers. Both the LPG (PRLPGL) and firewood (FIREL) coefficients are insignificant as in the case of the X-1 commercial consumers. The employment level (LABORFRL), however, is significant but with a negative sign. Previously, this variable was originally used as an income surrogate. For residential and X-1 commercial consumers, this variable might be an acceptable income surrogate for it showed significant positive signs of the estimated coefficients. However, for XMD commercial electricity users, this assumption may not hold. The variable is retained as additional explanatory variable and used for comparative interpretation. Since labor and electricity are two important inputs to commercial and industrial activities, it may not be unreasonable to hypothesize that in this case, labor and electricity are substitutes. In fact, the negative sign of the estimated coefficient of the level of employment could mean a possible substitution between electricity an labor. 22 The income surrogate given by the index of gross commercial sales (LINDEXF) is, however, insignificant. 23 The price of refrigerators (FRIDGEL) is insignificant for both models. However, the price of airconditioners is significant for both models and the estimated coefficients have negative signs. [he estimate shows that a one percentage point increase in the price of airconditioner as an electricity-consuming equipment results in approximately 0.15 to 0.16 percentage point reduction in XMD commercial consumers' demand for electricity. The temperature changes do not affect electricity consumption. However, changes in the relative humidity appear to have a positive effect on demand, The demand charge is significant for both model I and II but the estimated coefficients have opposite signs. This could perhaps be explained by the different specifications of the two models. Finally, the estimates of the peakload elasticity coefficient are 0.65 and 0.66 for models I and II, respectively and are significant.

2__ For a brief review of the evidence in the United States on substitution behavior between labor and electricity, see]orgenson (1984). The Central Bank stopl_ed publishing the index of gross commercial sales/receipts in 1980. The data used from 1981 to 1984 are forecast values basedon the 1971-1980 data. 66

Commercial consumer demand functions These mean that for every one percentage point increase in the system peak, XMD commercial consumers' per capita kwh consumptio'n increases by approximately 0.65%. C.

Primary Account

Commercial

Consumers

The same explanatory variables as in XMD commercial in estimating the consumer demand functions of primary customers. The results are shown in Table 4.6. It

is seen that

all the

marginal

(GPMARL),

consumers were used account commercial

inframarginal

(GPFRATL/

GPFRANL) and average prices (PRAVEREL) are insignificant. Tile prices of LPG (PRLPGL) and firewood (FIREL), which are possible substitutes for electricity, are also insignificant. Also, the price of refrigerators (FRIDGEL) is insignificant. However, the estimated coefficient of the price of airconditioners (AI RCONL) is negative, significant and of approximately the same magnitude as that for XMD as shown in Table 4.4. Temperature (TEMPMIAL) appears to be less significant than relative humidity (HUMIDITL). The demand charge (GPDEML), minimum charge (GPMINL), and index of commercial sales (LINDEXF) which is used as income surrogate, are all insignificant. The estimate of the peak elasticity coefficient as given by the coefficient of the system peak load (PKLOADML) is approximately 0.52, slightly lower than that for XMD. These results indicate the electricity consumption of primary account commercial customers are generally unaffected by the explanatory variables used, except for the price of airconditioners, relative humidity level, and to a certain extent, temperature. It is interesting to note that these are all environmental variables. It is recalled that the primary account commercial consumers are the general power type of consumers that guarantee billing demand of not less than 40 kw. Thus, these types of consumers are the relatively large commercial users with loads that are relatively stable. The price schedules used are shown in Table 4.7 and Figure 4.6 provides a graphic interpretation of these schedules. D.

Secondary Account Commercial Consumers

The results of the regression runs for the secondary account commercial consumers are shown in Table 4.8. The same set of independent variables are used. The marginal price (GPMARL) is significant with positive coefficients for Model I but insignificant for Model II. This could perhaps be explained by the 67

Commercial consumer demand functions

Table4.6 Resultsof Regression Analysis(OLS)a PrimaryAccount(GeneralPower)CommercialConsumers, MERALCO (January1971 to November1984) DependentVariable:PRPERCAL Independent Variables

_Regression Coefficients Model I Model II

StandardError Model I Model II

ComputedT Value Model I Model II

Constant

15.2720

12.3430

5,4431

2.5300

2.8058

4.8790

GPMARL

_0,0907

0.2076

0.3254

0.1799

-0.2788

1.1539

GPFRATL/ GPFRANL

0.2668

0.2677

0.5729

0.2665

0.4657

1,0045

PRAVEREL

0.0042

0.0089

0.0314

0.0316

0.1325

0.2820

PRLPGL

-0.0574

-0.0593

0.0499

0.0503

-1.1500

-1,1774

FI REL

-0.0325

-0.0349

0.0316

0.0317

-1.0259

-1.1021

LABORFRL

-0.4336

-0.2392

0.2252

0.2076

-1.9259

-1,1524

FRI DGEL

-0.0199

0.0380

0.0762

0.0?36

-0.2616

0.5165

AI RCONL

-0.1714

-0.1611

0.0596

0.0599

-2.8780

-2.6891

TEM PMIA L

0.1028

0.1200

0.0841

0.0842

1.2221

1.4249

HUMiDTI L

0.1437

0.1664

0.0589

0.5839

2.4399

2.8499

GPDEML

0.3964

-0.3745

0.6003

0.7192

0.6603

-0.5207

PKLOADML

0.5157

0.5105

0.0895

0.0905

5.7598

5.6418

LINDEX F

0.0290

0.0319

0.0299

0.0300

0.9713

1.0616

GPMINL

-0.5425

-0.0481

0.4444

0.3893

-1.2206

-0.1236

Model I

Model II

Multiple Correlation R2

: :

0.8675 0.7526

0.8658 0.7496

R2, Adjusted StandardError of Estimate Durbin-WatsonStatistic Rho Estimate

: : :

0.7297 0.0361 1.9332 0.5400

0.7263 0.0364 1.9567 0.5429

F-Value

7,4597

7,3165

aTheCochrane-Orcuttprocedureis applied for all results.

Table 4.7 AVERAGE FRICES - MERALCO GENERAL POWER -- DEFLATED (BASE YEAR: 1978)

MAY

1970

OCT

1972

SEPT

t974

DEC

1981

DEC

1984

1970-t984

I. ENERGY CHARGE INDEXED KWH BLOCKS PRICE DEFLATED AP

0-200 201-400 401-$00 $0t -600 600

.065 .06 .04S .038 .0,)

.1832 .1691 .1268 ,1071 .0845

INDEXED DEFLATED AP

1 ,9231 .6923 .5846 .461._

PRICE DEFLATED AP

.082 .0?S .0$6 .047 .03"/

.141 ._289 .0963 .OS08 .0636

6.6S

11.43

tNDEXED DEFLATED Af"

1 ,9146 .392 .329 ,259

PRICE DEFI.ATED AP

.24 `23 `22 ,21 ,2

.28 `2492 `2384 ,2"275 _'167

INDEXED DEFI-ATED AP

1 .9583 .9167 .87S ,833S

PRICE DEFLATED AP

.2_ .2E .25 `24 ,23

.1207 .1163 .1148 .1073 .1029

42._

S.63

INDEXED DEFLATED AP

1 .963 .92';9 .8889 .8S19

PRICE DEFLATED AP

.25 `23 .22 .21 .2

.049 .0451 .0431 .0411 .0392

12.6

2.47

II, DEMAND CHARGE (per kw)

5,5

INDEXED DEFLATED AP

1 .02 ,,88 .84 .8

AVERAGED AVERAGED DEFLATED DEFLATED AF AP

.15@8 .14_7 .1233 .1128 .10;4

"b .9398 .8476 .741_ ,6724 ,me

15.5

42.6

13+6:5

IlL MINIMUM CHARGE Iper month: demand charge or the amc_Jn[]_ [his row, whi_he_er is h_gher} 40O 1127,08.

_, 1 ('J 450

773.59

900

975.08

900

402.47

900

176.3

Figure 4.6

3 3 __.

MERALCOPrice Schedule By Blocks ( Deflated ] General Power, May 1970 -Decl984 (1978= I00)

1974

8ep 1974 0.2

o _=

1970

1 1972 1981

O. I

Dec 1981

May 1970

Oat 1972

1984 I, s

2' =

460

500

6()0

Dec 1984

> 600

Commercial consumer demand functions

Table 4.8 Resultsof Regression Analysis(OLS)a SecondaryAccount(GeneralPower)CommercialConsumers, MERALCO (January1971 to November1984) DependentVariable:SCPERCAL Independent Variables

R.egression Coefficients Model,i, Model Ii

StandardError ModelI Model II

ComputedT Value ModelI Model II

Constant

24.4960

7.2103

6.9375

2.2481

3.5310

3.2073

GPMARL

0.7110

-0.0326

0.4077

0.1529

1.7437

.O.2134

GPFRATL/FRANL

.1.2988

0.0600

0.7071

0.2303

-1.8368

0.2607

SCAVEREL

-0.0612

.0.0506

0.0323

0.0314

-1.8952

-1.6118

PRLPGL

.0.0412

0.0011

0.0501

0.0452

-0.8222

0.0242

FI REL

.0.0161

.0.0267

0.0315

0.0290

.0.5104

-0.9208

LABORFRL

.0.8364

.0.3502

0.2454

0.1745

-3.4078

2.0074

FRIDGEL

-0.0814

0.0433

0.0809

0.0707

-1.0059

0.6125

AIRCONL

-0.1637

.0.1179

0.0603

0.0548

-2.7137

-2.1501

TEMPMIAL

0.0333

O.1456

0.0858

0.0825

0.3882

1.7643

HUMIDITL

0.0799

0.1733

0.0610

0.0549

1.3112

3.1585

GPDEML

1.2898

.0.2211

0.6930

0.6299

1.8612

-0,3511

PKLOADML

0.5966

0.5518

0.0897

0.0807

6.6524

6.8413

LINDEXF

0.0264

0.0320

0.0299

0.8837

1.0680

GPMIN L

-0.6625

0.3142

0.4842

0.3547

-1.3682

0.8859

Model I

Model Ii

MultipleCorrelation

0.8584

0.8654

0.7368

0.7490

R2, Adjusted

0.7124

0.7256

Std Errorof Estimate

0,0362

0.0354

Durbin-Watson Statistics

1.9768

1.9552

Rho Estimate

0.5371

F-Value

9.0940

0.4220 13.430

aTheCochrane-Orcuttprocedureisappliedfor all results.

Commercial consumer demandfunctions decreasing real price and the structure of rates shown in Figure 4.6. Also, the inframarginal price is significant, and with the correct sign for Model I but insignificant for Model II. Since the rate structure is relatively flat, changesin the inframarginal payments based on Nordin's (19"/6)definition could be relatively small. This could perhapsexplain the insignificant result for Model II. The average price, however, is significant with a negative sign. This result issimilar to that of the other commercial consumers. The prices of LPG (PRLPGL), firewood (FIREL) and refrigerators (FRIDGEL) are insignificant. The level of employment (LABORFRL) is significant but with a negative sign. This result is similar to that of XMD commercial consumers where there is a tendency to substitute electricity for labor. The price of airconditioners (AIRCONL) is significant and negatively correlated with electricity consumption. As in the case of the other commercial consumers, humidity level (HUMIDITL) is significant but temperature (TEMPMIAL) is less significant. The index of commercial sales (LINDEXF) as an income surrogate is insignificant. The unavailability of appropriate income data does not necessarily mean that income, per se, of this type of commercial consumer does not affect demand for electricity. The minimum charge (GPMINL) is also found to be insignificant. The estimates for the peak elasticity coefficients of secondary account commercial consumers range from 0.55 to 0.60. These are slightly higher than that of the primary account consumers but lower than that of the XMD commercial consumers. These results again demonstrate that in addition to the price variables, the environmental variables have significant correlation with electricity consumption. While the price of airconditioners (AIRCONL) is not an environmental variable, the useof this electricity-consuming equipment is dependent to a certain extent on changes in temperature and humidity levels. Thus, environmental variables play a significant role in commercia_ consumer demand for electricity. Moreover, the negative coefficients of the labor force for XMD, primary and secondary account commercial consumers indicate that over the years, due to the changes in the price of electricity relative to the price of labor, there seemsto be a tendency for substitution between labor and electricity. This is not a remote possibility given the availability of electrical equipment which could reduce the need for human work if not entirely replace labor. IV. AggregateCommercial Demand Function Using Annual Data The preceding tests to estimate the demand function for each category of commercial consumers used data based on estimates from the MERALCO bill 72

Commercial consumer demandfunctions frequencies and proportions by type of consumers. This is one limitation which should be taken into account in interpreting the results of the preceding tests. In this section, the aggregatecommercial consumer demand function is estimated using the annualized version of the total monthly kwh consumption, total monthly number of customers and corresponding revenues. From these data, the per capita kwh consumption per year and averageprices are estimated. Table 4.9 shows the relative proportions of the different types of commercial consumers, i.e., X-l, Ă&#x2014;MD, primary and secondary account. The proportions24 in terms of the number of customers, kwh consumption, and revenues provide indications on what type of consumer would have predominant effects on the succeedingtest results. The model used follows the general formulation of equation 2.3 in which demand for electricity is a function of five setsof variables,namely: the price of electricity; income; price of substitute; price of electricity - consuming equipment; and, environmental variables. The results of the regressionanalysis are shown in Table 4.10. The average price (COMAVEPL) is significant and with the correct sign. Over the 1971-1984 period, the averageprice elasticity of demand for electricity by commercial consumers is _.2515. The income surrogate used for the test is the annual GNP (GPNPKL). The value of the estimated coefficient is 1.1880 with a t-value of 2.4088. Except for LPG which turned out to be a significant substitute for electricity, the rest of the variables such as maximum temperature (MAXTEMPL) and maximum humidity (MAXHUME), and price of airconditioner (LAIRCON), representing the environmental variables, and price of electricity consuming equipment, respectively,are all insignificant. The insignificant result for maximum temperature and humidity is the same with that for the residentialconsumersusingannual data. One possibleexplanation is that annual variations in maximum temperature and humidity are relatively smaller than monthly variations. These mean that monthly and seasonalvariations in environmental variables, instead of annual changes,have strong positive effects on demand for electricity. The result for the price of LPG meanthat over the long-run,this variable has a significant effect on demand for electricity. This is reasonable since over a Theseproportionswere adjustedfor changesin the categorization of consumers especiallyfrom the May 1971 to the May 1975 priceschedules. Theseadjustedproportions weretheonesusedincalculating percapitamonthlyfiguresforeachtypeof consumer. 73

_-J

£h 3 3 Table 4.9 Proportionsof CommercialType Consumers MERALCO (1971,1975, 1980, 1984)

_-..[

E 3 MAY 1971 No. of Customers KWH Consumption Revenues May 1975 No. of Customers KWH Consumption Revenues April 1980 No. of Customers KWH Consumption Revenues June 1984 No. of Customers KWH Consumption Revenues

X-1' 59323 12737208 17_3457

PRIMARY .8193 .0953 .167l

X-1 65548 15038832 4386910

.8024 .1047 .1003

Source of Basic Data: MERALCO

108 39225561 10797518

.001322 .273197 .246910

PRIMARY .7953 .1091 .1035

X-t 95822 25033857 8126632

.001008 .197669 .16"7349

PRIMARY

X-I 78825 21439525 6385455

73 26409979 1716509

SECONDARY

147 52570812 12384470

.001483 .267513 .200761

PRIMARY .7988 .1196 .1211

1_49 53534697 14462682

.001242 .255763 .215575

X-MD

TOTAL

.0246 .2688 .3946

11230 58551845 2780077

.1551 72407 .4382 133607049 .2710 10257032

SECONDARY

X-MD

TOTAL

1.781 35908017 4046989

.2047 62868950 19729199

.0251 .4379 .4512

13991 26446277 8816955

SECONDARY

X-MD

TOTAL

.0291 .4407 .5010

17260 35900037 12010483

.1741 99114 .1827 196516735 .1947 61687662

SECONDARY

X-MD

TOTAL

2882 86606361 30907254

3400 90617004 29580719

.0283 .4329 .4409

20592 40128023 1 4918707

.1713 .1842 .2016

81694 143579620 43730582

.1717 119963 .1917 20931.3581 .2224 67088740

t_.

3 c. E' _"

Commercialconsumerdemandfunctions

Table 4.10 Resultsof RegressionAnalysis(OLS) MERALCO, CommercialConsumers (1971-1984) DependentVariable: COMPERAN Independent Variables

StandardError of Reg. Coeff.

Computed T-Value

4.2966

2.6538

1.6190

-0.2515

-0.0665

-3.7824

1.1880

0.4932

2.4088

MAXTEMPL

-0.2160

0.5110

.0.42"27

MAXHUME

-0.3699

0.4814

-0.7684

Constant COMAVEPL GPNPKL

LAI RCON

Regression Coefficients

0.0221

0.1438

0.1541

LPGL

-0.1892

0.1108

-1.7079

PKLOADAL

-0.5995

0.3748

.1.5997

Multiple Correlation

0.9664

0.9338

R2, Adjusted

0.8567

Std Error of Estimate

0.0267

Durbin-WatsonStatistic

2.6351

Rho Estimate

-0.5326

Commercialconsumer demandfunctions longer period of time commercial consumers could shift from one stock of energy-usingequipment to another. The price of airconditioner is, however, also insignificant usingannual data. The average peak load variable (PKLOADAL) was also included to verify the responsivenessof annual demand to the average annual peak. The result is insignificant but the peak coefficient is negative with a t-ratio of -1.599-/. One interpretation of this result is that averageannual peak movements are generally opposed to variations of per capita kwh commercial consumption. This means that the changesin the annual average system peak has negligible, and in fact, negative correlation with average kwh consumption. One important implication of this is that commercial consumers,on the average,appears not to contribute to system peakload evaluation. This has further implications on the allocation of capacity costs. In brief, the preceding results how that only the average price, income (GNP) and price of substitute (I_PG) variables are significant. These show that estimating demand elasticitiesfor electricity usingannualdata hidesa lot of information which could only be seen if monthly and more detailed data are available. This is dramatized by the highly significant effects of environmental variables and the price of air conditioners. Using hourly data could perhaps further reveal more information on consumer demand behavior since hourly variations in temperature and relative humidity are greater than variations in monthly maximum values. On the other hand, using only monthly data may not reveal long-run behavior such as the finding on LPG as substitute for electricity. Thus, there isa need to consider both short-run and long-run demand behavior of electricity consumers. The "long-run" estimate of the average price elasticity for commercial consumersis -0.2515. This is greater in magnitudethan the "short-run" estimates which range from -0.0662 to -0.0477, taking into account all the estimatesof the average price elasticities of X-l, XMD, primary and secondary account commercial consumers. These results are similar with the results for residential consumersand are in accordancewith the evidence in the literature. The results on the "long-run" and "short-run" income elasticities are, however, not very definitive. One reason is that for the monthly tests, the employment level is used while for the annual test, GNP is usedasthe income surrogate. However, using employment level in the annual test instead of GNP yields a value of -0.1249, a value greater than that for commercial consumerswhich 76

Commercial consumer demandfunctions range from -0.8364 to -0.2392. However, the positive coefficient for X-1 could have reduced the negativeeffect of the variable for the test usingannual aggregate data. It is recalled that while the employment level might be a suitable income surrogatefor X-l, it may not be the casefor the other commercial types of consumers. Thus, while the 1.1880 GNP elasticity estimate using annual data is relatively large compared to monthly estimates, a definitive statement on the relative magnitudesof the "long-run" and "short-run" income elasticitiescannot be made due to data limitations. Nevertheless,these results provide indications that annual elasticitiescould be largerthan monthly estimates.

Chapter 5

Industrial Consumer Demand Functions In this chapter, attempts are made to estimate the demand functions of industrial consumers. Three categories of consumers are considered: XMD; General Power (GP) primary; and, GP secondary industrial consumers. These categoriesare based on the MERALCO system of classificationof its industrial consumersfor which monthly data are availabe. The model specification, data usedand resultsof the regressionruns are discussedand analyzed.

Model Specification

The specification of the demand function for industrial consumersfollows the general formulation in equation 2.3. Five sets of independent variables are included in the specification. These are the own price of electricity, price of substitute, income, price of electricity-consuming equipment, and environment variables. Like in the commercial consumers, the demand charge, index of industrial sales, and system peak are included as additional explanatory variables. Since there is minimum chargefor GP industrial usersthis pricevariable (GPMINL) is also included. As before, model I and II refer to the specification of Taylor (1975) and Nordin (1976) of the inframarginal prices. II. The Data Monthly data on industrial consumerson kwh consumption, number of customers and revenues to include their adjustments are provided by MERALCO. Thedata on monthly kwh consumption and number of customers are shown in Appendix 5.1 and 5.2 respectively. The net adjustments to revenuesare shown in Appendix 5.3. The corresponding monthly per kwh price adjustments are shown in Appendix 5.4. The resulting monthly adjusted revenues are shown in Appendix 5.5 taking into account these monthly revenueadjustments. 79

Industrialconsumer demandfunctions The price schedule for XMD industrial is the same with that for XMD commercial as shown in Table 4.5. The General Power price schedule shown in Table 4.7 applies for GP primary and GP secondary industrial consumers. The marginal and inframarginal prices are based on these price schedules and the average monthly kwh consumption. Also, the minimum chargesare based on the GP rate schedule. The sales index on manufacturing is taken from the Central Bank. Since there are no separate monthly data on kwh consumption, number of customers and revenues for each type of industrial consumers, the bill frequencies for May 19"/1, May 1975, April 1980 and June 1984 are used to estimate these data for each type of industrial consumers as was done in the"case of commercial consumers. From these estimates, the monthly average per capita kwh consumption and monthly average prices are estimated. The tests here for the different types of industrial consumers, therefore, have the same data limitations as in the tests for different types of commercial consumers. All other data are the same as in the caseof commercial consumers. III. Test Results A. XMD Industrial Table 5.1 shows the results of the regression runs for XMD industrial consumers. The marginal price (XDMARL) is insignificant for model II but significant for model I with a positive sign. This significant result for model I could perhaps be explained by the declining real marginal price over the 1971-1984 period and the decreasing average per capita kwh consumption, noted at 3022 kwh per month in January 1971 but steadily fell down to a level below 2000 kwh per month by November 1984 and to 1758 kwh per month by December 1984. This declining per capita kwh consumption is also evident from the relatively lower growth rates in mwh consumption compared with the growth rates in the number of customers asshown in Appendix 5.6 and 5.7. The inframarginal prices are insignificant for both models. This could perhaps be explained by the relatively flat price schedules. Similarly, the average price is insignificant for XMD commercial consumers, as well as the prices of LPG (PRLPGL) and firewood (FIREL). This is expected since substitution between electricity and these other energy sources could be minimal for this type of industrial consumer. Both employment level (LABORFRL) and manufacturing sales index (I'INDMFGF) are insignificant. For the electricity-consuming equipment, 8O

Industrial consumer demand functions

Table 5.1 Resultsof Re_ression Analysis(OLS)a XMD IndustrialConsumers,MERALCO (January1971 to November1984)

DependentVariable: IXDPERL Independent Variables Constant XDMARL

RegressionCoefficients Model I Model II

Sam:lardError Model I Model II

ComputedT Value Model I Model LI 1.1729

0.6413

6.2937

3.8599

5.3660

6.O189

0.6664

0,3359

0.3225

0.4122

2.0660

0.8148

XDFRATL/ XDMARNL2

.0.3910

1.2435

0.3143

0.9870

-1.2440

1.2599

IXDAVELPL

0.0537

0,0547

0.0547

0.9803

0,9807

PRLPGL

-0.0530

.0.0546

0.1185

0.1186

-0.4476

-0.4606

FI REL

.0.0567

-0.0567

0,0634

0.0634

-0.8932

-0.8944

0.0300

0.0294

0.5346

0.5361

0.0561

0.0549

LABORFRL FRIDGEL

0.1440

0.1441

0.1345

1.0708

1,0716

AI RCONL

-0.1798

-0.1808

0.1208

1.4982

-1.4975 0.0441

TEMPMIAL

0.0065

0.0061

0.1388

0.1387

0.0466

HUMIDITL

0.1090

0.1091

0.1015

1.0734

1.0746

DEMXDL

0.0035

-1.3029

0.4772

0.7093

0.0073

-1.8370

PKLOADML

0.5247

0.5258

0.1776

2.9535

2.9598

LINDMFGF

0.0896

0.0898

0.0839

1.0673

1.0706

Model,I,

Model II

MultipleCorrelation R2

: :

0.9788 0.9598

0.9788 0.9580

R2, Adjusted Std Errorof Estimate

: :

0.9544 0.0672

Durbin-WatsonStatistic

2.6321

2.6343

Rho Estimate

0,9574

0.9576

F-Value

1.8299

1.8342

aTheCochrane-Orcuttprocedureisappliedfor all results. 81

Industrialconsumerdemandfunctions the price of airconditioners (AIRCONL) is insignificant but with a correct negative sign. The price of refrigerators (FRIDGEL) is also insignificant. The two environmental variables, temperature (TEMPMIAL) and relative humidity (HUMIDITL), are both insignificant. The estimte of the peak elasticity of demand is 0.5247. B. GP Primary Industrial Table 5.2 shows the regression results for the General Power primary industrial consumer. The same explanatory variables are used as in the case of XMD. The results show that the inframarginal and average prices are all highly significant with the expected negative sign. The average price elasticity of demand ranges from -0.30 to -0.35 while inframarginal price elasticities range from -2.9 to -4.0. These indicate that GP Primary industrial consumers are highly sensitive to both inframarginal payments and average prices. This is expected since this type of industrial consumer has relatively large monthly consumption. The marginal price is significant but with opposite signs for model I and II. For model II, the marginal price elasticity is -1.34 with the correct sign. Again, this indicates that GP primary industrial consumers are highly sensitive to changes in marginal price. This is expected due to their relatively large monthly consumption. For instance, monthly per capita kwh consumption ranges from 450,000 to 820,000 kwh as contrasted with the XMD industrial monthly per capita kwh consumption which ranges from 1,700 to 3,000 kwh. The positive marginal price elasticity for model I could perhaps be explained by the relatively large effects of a decreasing real price of electricity given Taylor's (1975) definition. The per capita kwh consumption per month have not significantly increased or decreased over the 1971-1984 period. Thus, the positive marginal price elasticity may not be unexpected. The prices of LPG (PRLPGL) and firewood (FIREL) which are used to represent possible substitutes for electricity are both significant but with different signs. LPG appears to be a substitute but firewood is not in the sense that the estimated coefficient has a positive sign. The prices of electricityconsuming equipment are insignificant except for refrigerators (FRIDGEL) which has a negative and significant coefficient for model II. The index of manufacturing sales is insignificant. This result, however, does not necessarily mean that income is not a significant variable since this variable may not be an appropriate income surrogate for this type of consumer. There seems to be no substitution between labor and electricity given the insignificant result for the employment level. 82

Industrial consumer demand functions

Tab_=5.2 Resultsof RegressionAnalysis(OLS) GP-PrimaryIndusuial Consumers,MERALCO (January1971 m No.tuber 1984) DependentVariable:IPRPERL Independent Variables Constant GPMARL

...Regression Coefficients Model I Model II

StandardError Model I Model II

ComputedT Value Model I Model II' 5.1192

5.7477

45.9940

29.9120

8.9847

5,2041

2.7923

-1.3368

0.5054

0,2855

5.5254

-4.6821

GPFRATL/ GPRRANL

-3:9999

-2.8664

(18960

0.4316

-4.4645

-6.6415

IPRAVEPL

-0.3528

-0.2929

0.0536

0.0532

-6.5793

-5.5038

PRLPGL

-0.2822

-0.3030

0.0873

0,0818

-3.2322

-3.7034

FI RE L

0.1194

0.0886

0.0569

0.0556

2.0970

1.5946

LABORFRL

0.4685

0.5010

0.4189

0,3922

1.1185

1.2773

FRI DGEL

-0.1746

-0.3177

0.1740

0.1626

-1.0034

-1.9544

AI RCONL

-0.0010

-0.0520

0.0943

0.0920

-0.0109

-0.5652

TEMPMIAL

0.4844

0.5266

0.1896

0,1840

2.5543

2.8616

HUMIDITL

0.3806

0.3864

0.1199

0.1160

3.1750

3.3297

GPDEML

6.0401

10.8950

0.8430

1.2516

7.1647

8.7047

PKLOADML

0.3017

0.2557

0.1676

0,1629

1.7996

1.5696

LINDM FGF

0.0224

0.0147

0.0817

0.0790

0.2742

0.1854

GPMINL

-4.6919

-6.5740

0.6354

0.7106

-7.3841

-9.2518

Model i

Model II

MultipleCorrelation

0.9840

0.9849

0,9682

(19701

R2, Adjusted

0.9653

0.9673

Std Errorof Estimate

0.0835

0.0810 1.3847

Durbin-WatsonStatistic

1.4560

Rho Estimate

0.2505

0.2846

F Value

: 330.3216

351.7288

Industrial consumerdemandfunctions The demand charge (GPDEML) is highly significant but with a positive sign. This means that as the demand charge increases,monthly per capita kwh consumption of GP primary industrial consumers increases.The data, however, indicate that the real demand charge has been declining over the 1971-1984 period. Per capita monthly kwh consumption could have also decline so this result may not be unexpected. However, the minimum charge (GPMINL) is significant with the expected negative coefficient. The estimate of the peak elasticity of demand is0.30, lower than that of XMD. This result is expected sinceGP primary are bulk industrial users, hence, their peak contribution is relatively lower due to their relatively high load factor. C. GP Secondary Industrial Table 5.3 shows the regressionresults for GP secondary industrial consumers. The same explanatory variables are used as in the case of XMD and GP primary industrial consumers. The results indicate that the marginal price is significant but with opposite signsand is negative for model I and positive for model II. This is in contrast with the resultsfor GP primary where the signsare reversed. In accordancewith theory, the case of model I has negative marginal and average prices. In the case of model II, however, the sign of the coefficient of the marginalprice ispositive. This is perhaps explained by the declining real marginal price and also a declining monthly per capita kwh consumption. GP secondary monthly average per capita kwh consumption has gone down from approximately 51,000 kwh in January 1971 to 31,000 in November 1982. These, perhaps, could also provide explanation why the coefficients of the inframarginal prices are positive for both models I and II. The price of LPG is significant for model II with positive sign but insignificant for model I. Firewood as a possible substitute in insignificant. The prices of electricity-consuming equipment are all significant but with positive signs indicating some complementary behavior of the demand for electricity and the pricesof these electricity-consuming equipment. Both the temperature and relative humidity variables are insignificant which indicate that GP secondary industrial usersare less sensitiveto environmental factors. The demand charge is significant and with the expected negative sign. However, the minimum charge (GPMINL) is significant but with a positive sign. Again, these results are opposite to that of the GP primary industrial users. 84

Industrial consumer demand functions

Table5.3 Resultsof Regression Analysis(OLS) GP-SeeondaryIndustrialConsumers,MERALCO (January 1971 to November1984) DependentVariable: ISCPERL Independent Variables

RegressionCoefficients. Model I Mocl..elII

StandardErrors Model I Model II

ComputedT Value Model I Model II,

Constant

-11.4160

-9.0648

7.3843

4.1896

-1.5459

GPMARL

-0.8106

0.3694

0.4236

0.2136

-1.9138

1.7298

GPFRATL GPFRANL

1.0272

0.9606

0.7241

0.3404

1.4186

2.8219

ISCAVEPL

-0.2331

-0.2116

0.0406

0.0394

-5.7355

-5.3750

-2.1636

PRLPGL

0.0894

0.1155

0.0711

0.0652

1.2583

1.7223

FIREL

-0.0176

-0.0062

0.04.50

0.0429

.0.3905

-0.1441

LABORFRL

-0.3598

.0.2844

0.3252

0.2980

-1.1065

-0.9547

FRIDGEL

0.2852

0.3810

0.1397

0.1282

2.0422

2.9706

AI RCONL

0.1140

0.1290

0.0741

0.0705

1.5391

1.8298

TEMPMIAL

0.1130

0.1335

0.1407

0.1336

0.8034

0.9995

HUMIDITL

0.0318

0.0689

0.0835

0.0801

0.3808

0.8594

GPDEML

-2.6851

-4.3604

0.5688

0.8786

-4.7208

-4.9629

PKLOADML

0.5193

0.5409

0.1330

0.1265

3.9038

4.2759

LINDMFGF

.0.0221

-0.0156

0,0645

0.0610

.0.3429

.0.2560

GPMIN L

2.6672

3.2582

0.4125

0,4593

6.4667

7_0936

Model I

Model II

Multiple Correlation

0.9769

0.9793

0.9544

0.9590

R2, Adjusted

0.9502

0.9552

Std Error of Estimate

0.0659

0.0625

Durbin-WatsonStatistic

1.4096

1.3940

Rho Estimate

0.2940

0.3013

F-Value

: 227.1327

253.7500

Industrialconsumer demandfunctions Since both type of customers faces the same price schedule; their level of per capita kwh consumption could result in different demand responsesas they fall at different blocks in the block-type tariff schedule. Both the level of employment and the index of manufacturing salesare insignificant. The latter may not be the appropriate income surrogate so this resultshould be interpreted with caution taking into account data limitations. The estimate of the peak elasticity coefficient is0.52. This isapproximately equal to that of XMD industrial but higher than that of GP primary. D. AggregateConsumer Demand Functions: Industrial Consumers In this section, the aggregate consumer demand function for industrial consumers is estimated using the annualized version of the total monthly kwh consumption, total number of customers and total revenues. From these data, the annual per capita kwh consumption and averageprice are estimated and used in the analysis. As in the caseof commercial consumers, the model usedfollows the general formulation of equation 2.3 in which demand for electricity is a function of five sets of explanatory variables like the price of electricity, price of substitute, income, price of electricity-consuming equipment and environmental variables. Table 5.4 shows the results of the regression runs. The averageprice is significant and its coefficient has the expected sign. The averageprice elasticity is --0.2411. This falls somewhere between the average price elasticities of GP secondary using monthly data which are --0.2116 and -0.2331 for model II and I, respectively, and that of GP primary which are -0.2929 and -0,3528 for model II and I, respectively. It is recalled that the average price elasticity usingmonthly data for XMD industrial consumeris insignificant. The GNP elasticity of demand for electricity is 1.2877 and insignificant at the ten percent level but with a t-ratio of 1.5293. This is a bit larger than the GNP elasticity of demand for electricity by commercial consumers which is at 1.1880. Per capita kwh demand by industrial consumersis elastic. The environmental variables, maximum temperature and relative humidity are both insignificant. This is expected since the changes in maximum annual temperature and relative humidity could be minimal compared to monthly changes. It is recalled that these variables are significant for GP primary using monthly data. The average price of airconditioner (AIRCONL) is significant but with a positive coefficient similar to that of GP secondary. It is recalled that AIRCONL 86

Industrialconsumerdemandfunctions

Table 5.4 Resultsof RegressionAnalysis(OLS) MERALCO, Industrial Consumers (1971-1984) DependentVariable: INDPERAN Independent Variables

Regression Coefficients

StandardError of gej_.Coeff.

Computed T Value 1.8251

Constant

8.3655

4.5835

I NDAVE PL

-0.2411

-0.0880

GPNPKL

-2.7416

1.2877

0.8421

1.5293

MXTEMPAL

-0,5570

0.8693

-0,6407

MXHUMEAL

-0.2011

0.8115

-0.2478

LAI RCON

0,5593

0.2431

2.3007

LPGL

-0.2749

0.1857

-1.4807

PKLOADAL

-1.3139

0.6306

-2.0836

Multiple Correlation

0.9706

0.9421

R2, Adjusted

0.8746

Std Error of Estimate

0.0456

Durbin-WatsonStatistic

2.8359

Rho Estimate

-0.7120

F.Value

: 13.9496

Industrialconsumer demandfunctions is insignificant for GP primary and XMD with a negative sign. This overall result could mean that GP secondary effect on demand for electricity predominates. Table 5.5, which shows the proportions of industrial type customers,kwh consumption and revenues could provide indications on which type of industrial customercould havethe predominating effect. For LPG, the coefficient is negative but insignificant. LPG appearsto serve as a substitute for electricity by the industrial users. It is recalled that LPG is insignificant for XMD; significant with positive sign for GP secondary; and significant with negative sign for GP primary. The effect of GP primary appears to predominate in the overall results. To derive some indications on the annual peak elasticity coefficient, the annual average of monthly system peaks was included. The peak coefficient is -1.3139 and significant. This means that the movements in averageannual peak is opposed to movement in annual average per capita kwh consumption by industrial consumers. In other words, consumption by industrial consumers appears to be opposed to the growth of system peak demand. This relatively large negative value of the coefficient could be explained by the steadily decreasing annual average per capita kwh consumption but a steadily increasing average annual system peak except for the fall in 1984. Thus, over the period covered, it appears that the per capita kwh consumption of industrialconsumers may not have significant effect on the system peak. This might be expected given the high load factor of industrial consumers.This result is similar to that for commercial consumers and has important pricing implications, especially in the allocation of capacity costs. The "long-run" estimate of the averageprice elasticity of demand for electricity by industrial consumers is -0.2411. This is approximately the same as the estimate of the "long-run" average price elasticity for commercial consumers which is -0.2515, but approximately one-half that of the residential consumers' which are -0.5409 and -0.4646 for model I and II, respectively. Unlike the residential and commercial consumers,which havedifferent estimated values for long-run and short-run average price elasticities, the results show that the estimate of -0.2411 falls somewhere between the "short-run" estimates for GP secondarywhich are -0.2331 and --0.2116 for model I and II, respectively. The estimatesfor GP primary are -0.3528 and -0.2929 for model I and II, respectively. It is recalled that the averageprice is found insignificant for XMD industrial. These results indicate that for industrial consumers,there seem to be no difference between long-run and short-run averageprice elasticitiesof demand. 88

TaMe 5.5 Proportions of Industrial Type Customers MERALCO (1971, 1975, 1980, 1984) May 1971 No. of Customers KWH Consumption Revenues

PRIMARY 158 9511096 6301120

May 1975

PRIMARY

No. of Customers KWH Consumption Revenues

| 85 i i 4981047 30972918

April 1980

PRIMARY

No. of Customers

SECONDARY .10604 .16166 .61935

1223 49068410 3836602

.8208 .8340 .3771

X-MD

TOTAL

109 254881 36110

.073154 .004332 .003549

1490 58834387 10173832

X-MD

TOTAL

.044171 .000832 .001045

1947 195446924 5 4473797

X-MD

TOTAL

SECONDARY .09502 .58830 .56858

1676 80303295 234439 44

.8606 .4100 .4304

86 162582 56935

SECONDARY

206

.069 41

2589

.8723

173

.058288

2968

152405755

.55989

119331108

.4384

467856

.001719

272204719

Revenues

34777612

.52094

31821794

.4767

159713

.002392

66759119

June 1984

PRIMARY

No. of Customers KWH Consumption Revenues

221 141119852 35173978

KWH Consumption

SECONDARY .06231 .55476 .50366

3113 112770644 34481234

X-MD .8776 .4433 .4937

213 489760 182194

.060051 .001925 .002609

TOTAL

3547 254380256 69837406

_. _a o.

Sourceof BasicData: MERALCO O kO

Industrialconsumer demandfunctions Since all the tests usingmonthly data showed insignificant resultsfor income, no comparison can be made on short-run and long-run income elasticites. It should be borne in mind, however, that the GNP elasticity for industrial consumers using annual data is elastic (i.e. 1.2877). Over the long-run, industrial consumers may purchase additional stock of electricity-consuming equipment as income increases,and correspondingly increase per capita kwh consumption. Thus, it is possible that long-run income elasticities could be larger than shortrun income elasticitiesfor industrial consumers.

Chapter 6

Implications on Alternative Pricing Policies

This chapter integrates the results of the preceding three chapterson residential, commercial and industrial consumers.The resultsfor each of the five sets of explanatory variables, such as own price of electricity, income, price of substitute, price of electricity-consuming equipment and environmental variables, are discussedacross the various types of electricity consumers. Also, the results on the peakload elasticity coefficients and some explanatory variables added in the regression analysis are summarized. The implications of the short-and long-run estimates of the coefficients of price, income and other variables to include the implications on the dynamics of stock adjustmentsare presented.Thereafter, the implications for alternative electricity pricing policies are derived. Some specific pricing policy recommendationsare proposed. I.

Price, Income and Other Explanatory Variables A. Own Price Variables

There are three sets of own price for electricity used.The justifications for the use of these prices are discussedin chapters 2 and 3. Table 6.1 summarizes the results for the regression analysis for residential, commercial and industrial consumers. I. MarginalPrice For the tests using monthly data, five out of eight tests for model I are significant using a two-tailed t-test at ten percent level. Of these five, four have positive marginal price coefficients and one has a negative sign. For model II, out of the eight tests, four are significant and of these four, three have positive coefficients and one negative. These coefficients represent the marginal price elasticitiesof demand for electricity. 91

Table &l Own PriceElasticities for Type Consumers (MERALCO)

0' O

Dependent Variable: PerCapita KWH Consumption INDEPENDENT VARIABLES I.

RESIDENTIAL

;_r

X-1

C 0 M M XMD

E R C m A L Prim Ac_t

1 N Second Acet

XMD

0.7110 (1.743?) -0.0326

0.6664 (2.0660) 0,3359

U S T GP PHm

R I

A L rap .Second

"_ i. <

Monthly Data {Jan '71-Nov. '84) a Marginal

II : Inframarginal

II : Average

I1 :

fO 0235 (0.8939) 0.0989

0.1023 Â˘3.0216) 0,0619

0,1029 (0.75 49) -O,1499

-0.0907 (-0.2788) 0.2076

{5.1075) 0.1152 (3.7597) 0.0852 (1.49_) 0.0794 (1.4499) 0.0881 (1.5524)

(1.7238) 0.0291 (3.0480) 0.0099 {0.2130) -0.0477 -1.2493 -0.0662 (-1.7118}

(-0.9649) -0.3218 (-3.6056) 0.9321 {3.2618) -0,0521 ,1.5651 -0.0516 (-1 __47T)

{1.1 S39) 0.2668 (0.4657) 0.2677 {1.0045) 0.0042 0.1325 0.0089 (0.2820)

IL Annual Data 1971-1984) b Marginal

I II

Inframarginal

I I1 :

Average

II :

0.0501 (0.6781) 0.1990 (2.0724} 0.0254 (0.3373) 0,0592 (1.4829) -0.5409 {-3.8590) -0.4646 (-3.7049)

-(0.21 34) -1.2988 (-1.8368) 0.0600 (0.2607) -0.0612 -1.8952 -0.0506 (-1.6118)

(0.8148) -0.3910 (-1.2440) 1.2435 _1.2599) 0.0537 0.9803 0.0537 (0.9807)

2,7923 (S.5254) -1.3368 (-4.6821) -3.9999 (-4.4645) -2.8664 (_.641S) -0,3528 (-6.5793) -0,2929 {-5.5038}

Commercial

Industrial

-0.2515 (-3.'/8241

-0.2411 (-2.74t6t

aBasedon Tables 3.2, 4.2, 4.4, 4.6, 4.8, 5.1,5.2, and5.3. Figuresin parenthesisare estimated t values. bBasedon Tables 3.7_ 4.10 and 5,4, Models I and II do not appfy to aggregatecommercial and indestria/annual tests,

-0.8106 C4.9138) 0.3694 (1.7298) 1.0272 ( 1.4186) 0.9606 (2.8219) -0.2331 (-5.735S) -0.2116 (-5.3750)

_. 5" OQ

Implicationson alternativepricingpolicies The residential and X-1 commercial consumers have all positive coefficients. This finding is explained by the declining real price of electricity, especially for the subsidized kwh block levels, the change in the structure from basically blockdecreasing to block-increasing and the demand responses of consumers at the prices marginal to them. For residential and X-1 commercial consumers, the shift to lower monthly per capita kwh consumption while overall monthly kwh consumption remained stable or had increased, means that the rate of increase in the number of customers is faster than the rate of increase in the total monthly kwh consumption. This implies three possibilities. First, the occurrence of multiple metering where one household or X-1 commercial unit applies for more than one meter to break the monthly overall consumption into two or more parts, depending on the number of meters, to be able to take advantage of the subsidized consumption level. The second possibility, due to the subsidy which is not available to nonMERALCO residential and X-1 commercial consumers, is a movement of population towards the urban areasto take advantage of this relative lower price of electricity. Also, large commercial and industrial consumers may move out of the ME RALCO franchise area further reducing the overall averagemonthly per capita kwh consumption. Finally, this subsidy program could provide signals to consumers outside but at the fringes of the MERALCO franchise area to request for similar subsidy or ask the government to let MERALCO take over the private utilities or electric cooperatives serving these consumers.25 In each case, these possibilities will increase the averagecost of distribution of electricity to include technical and non-technical losses. Since the marginal price is based on the tariff schedule and the average monthly per capita kwh consumption, the finding on positive marginal price elasticities indicates that the tariff schedulesdo not provide the correct price signals so electricity as a scarce resource is accordingly not efficiently allocated. The availability of detailed data at variouskwh blocks on residential and X-1 commercial consumers provide the needed information to analyze and explain their demand responses, not otherwise revealed by aggregated and lessdetailed data. Since no detailed data is available on the other commercial and the industrial consumers, it is difficult to provide precise explanation why positive and negative marginal price elasticities are obtained. 2=_]Forfurtherdetailson theseobservations, seeFrancisco(1984,pp.190-191). 93

Implications onalternative pricingpolicies Table 6.2 shows the mean, standard deviation, maximum and minimum monthly per capita kwh consumption for all types of consumersconsidered.The figures inside the parenthesis for minimum and maximum values refer to the corresponding month and year on which the value occurred. Three periods are considered: January 1971 to August 1974; September 1974 to August 1983; and September 1983 to November 1984. The first covers the presubsidy period, the second is from the start of the subsidyto the August 21, 1983 incident which triggered economic and financial crisis, and the third covers the post-August 21 incident. For residential consumers, the overall figures do not evidently show that there was a reduction in per capita kwh consumption. In chapter 3, however, the changes in demand structure at various kwh blocks are clearly shown. Also, we have noted that the changes in the demand structure at various kwh blocks for X-1 commercial are relatively small compared to that of the residential con.sumers. For the other commercial and industrial types of consumers,the monthly per capita kwh consumption are estimated from monthly aggregate data using the bill frequencies for May 1971, May 1975, April 1980and June 1984. Given these limitations, attempts were made to explain the results for the other types of consumers. Ă&#x2014;MD and primary account commercial consumers have insignificant marginal price elasticities for both models I and II as shown in Table 6.1. For the secondary account, the result for model II is insignificant while that for model I is significant and positive. Since the real marginal price of electricity is decreasing over the period, and as shown in Table 6.2, also the monthly per capita kwh consumption, this result may not be unexpected. For model I, the sameobservation could be made for Ă&#x2014;MD industrial. However, the opposing signsand significant results for both model I and II for GP primary and GP secondaryindustrial consumers might be difficult to explain. One possiblereason for this i_ perhaps the data limitations previously described. Also, the different marginal price levels which they face at different blocks of consumption and their corresponding price movements may have influenced a specific demand responsefor each type of consumer. The preponderance, however, of positive marginal price elasticities indicate that the existing MERALCO tariff structures may not provide the correct marginal price signals.For residentialconsumers,the resultsusingannual data confirms this asshown in Table 6.1. 94

Tab le 6.2 M,_ns and Rangesof MonthLy PerCapita KWH Contraption of Type Consumersfor indicated Periods (MERALCO)

RESIO_ERIOD I.

.ENTIAL

Jan'71 -- Aug'?4 Minimum : Mean : Std Dev : Maximum :

C O X-1

XMD

E R C |

A L

Prim Acct

I N Second Acct

XMD

O U $ T

GP Prim

I A

GP Second

179 (2/71)a 210 19 249 {6,/731

167 (2/71) 211 16 245 (7,/731

1805 (1/74) 2173 160 2489 (9/7t)

284310 ('2/71) 352275 24356 405401 (7,/73)

28429 (1./74) 33256 2081 37152 (9,/71)

3008 (1./74} 4557 943 6219 (6,/71)

102329 (1,/71) 298876 116654 528046 (8/74)

52798 (1,/74) 78001 15924

t06595(1771)

II. Sap '74 -- Aug '83 Minimum Mean

: :

16t (2176) 219

209 (t/76) 267

1701 (2`/76) 2091

320458 (2`/81) 377953

26401 (2./81) 31108

1679 (1`/76) 2404

449566 (1,/75) 667084

32391 (1/82) 44993

Std Dev Maximum

23 269 (5/81)

27 323 (7./83)

t50 2422 {7./83)

448775 24373 (7,/83)

1640 34513 (5./78)

269 3010 (6./'/9)

820691 65488 (6/79)

4693 56579 (9./74) 0

11t.Sep'83- Nov '84 Minimum : Mean : Std Oev : Maximum :

200 {3184) 216 12 235 (5./84)

255 (1`/84) 273 12 291 {4./84),

1908 (1/84} 2041 92 2170 (4,/84)

359229 (1./84) 387420 15601 412018 (4,/84)

26201 (11`/84) 28019 1390 29875 (il./83)

aFiguresIn parenthesisrefer to the month and year of the indicated per capita monthly kwh consumption.

1850 (1./84) 2180 182 2495 (10./83)

511472 (1./84) 603236 49657 689387 {10,/83)

29295 (1,/84) 34371 3025 39701 (10./83)

0 ._ lu ¢>_"

¢) "10 ,-i

2. Oct O

Implicationson alternativepricingpolicies 2. inframarginal Price Two alternative definitions of the inframarginal price variables are used in the tests based on Taylor's (1975) and Nordin's (1976) suggestion. This necessitated the use of model I and II. In effect, this study also provides an attempt at empirical resolution of this issue. Table 6.1 shows that for model I, out of the eight tests, five are significant and of these, two values are positive: residential and X-1 commercial. The other three have negative signs. For model II, three of the eight tests are significant and of these three, two havepositive signs. Again the results fo the residential and X-1 commercial consumers provide indications of the nature of the price signals taking into account the definitions of the inframarginal price specific to models I and II and the data used for the tests. The results for the other types of consumers may reflect their peculiar demand responses to the inframarginal price signals as well as the data limitations earlier pointed out.

3. Average Price The initial findings on the marginal and inframarginal price variables, some of which are perverse, necessitated the introduction of the average price to validate the results. Table 6.1 shows that using monthly data, the average price elasticities are with the correct signs except for the residential consumers which are still positive but insignificant for both models I and II. This result provides some level of confirmation for the perverse finding on the positive marginal and inframarginal price elasticities. For the other types of commercial consumers, however, the estimates of the average price elasticities are relatively small in magnitude ranging from -0.0477 to -0.0662. Those for the industrial consumers are relatively larger in magnitude ranging from --0.2116 to --0.3528. The tests using annual data, however, show that all the average price elasticities have correct signs. Annual demand behavior from 1971 to 1984 appears to predominate over the monthly perverse demand behavior from September 1974 when the subsidy program was started. The positive marginal and inframarginal price elasticities using annual data further confirms the previous finding that the tariff structure, which exhibits the marginal and inframarginal prices, does not provide the correct price signals. In fact, the nature of the tariff schedule distorts the overall price structure. 96

Implications on alternative pricing policies B. Table

Income Variables 6.3 shows the estimates

of the income

elasticities

for the different

types of consumers. For lack of an appropriate monthly income series, the employment level is used as an income sufrogate. This might be adequate for residential and X-1 commercial consumers. The coefficients are allsignificant and positive for both model I and model II for X-1 commercial consumers. The results for residential are, however, insignificant but has the correct sign. A one percentage point increase in employment level, for instance, means a 0.65 to 0.81% increase in X-1 commercial demand for electricity, respectively. However, the results for XMD, primary account and secondary account commercial consumers are opposite to those of the residential and X-1 commercial consumers, i.e., the coefficients of the employment level are negative. For XMD, for instance, a one percentage point increase in employment results in a 0.34 to 0.46% fall in electricity consumption. This seems to point to the possibility of substitution between electricity (capital) and labor. If the price of electricity increase relative to labor, then more of the latter is used. Similarly, if the price of labor increases relative to electricity, is used. This may not be improbable considering the labor- saving electrical equipment. These results indicate that the level of employment income surrogate for XMD, primary and secondary industrial consumers. The commercial and industrial added as explanatory

then more of electricity fact that there are many may not be an adequate account commercial and sales index are therefore

variables to represent income. The results for the monthly

tests using both models I and II are all insignificant. These results, however, do not necessarily indicate that income, per se, does not affect the level of demand for electricity. This merely indicate that these variables may not be appropriate as income surrogates. In the annual tests, the employment level is insignificant for both models I and II for residential consumers. Using GNP as the income surrogate for residential consumers, the GNP elasticity of demand for electricity is 0.3324 and is significant. The higher employment elasticity compared for residential consumers is plausible considering that

to the GNP elasticity employment is more

directly contributory to the purchasing power of residential consumers. Appendix 6.2 shows the test results for residential consumers where GNP is used instead of the employment level. The GNP elasticity for commercial and industrial consumers are 1.1880 and 1.2877, respectively. The value for commercial consumer is significant while that for industrial is insignificant and has a t ratio of 1.5293. 97

3 "o D, O Table 6.3 Income Elasticitiesfee Type Consumers ( MERA LCO)

O lu ;=r

Dependent Variable: Per Capita KWH Consumptioa INDEPENDENT VARIABLES

RESIDENTIAL

_e_. < C O M X-1

XMD

E R

C I

A L

Prim Ar,,Â˘t

1 N SecondAcct

XMD

U S T

GP I_rim

R I

A L GP Second

I. Monthly Data (Jan '71-Nov '84} a, Labor Force

|1 : Sales Index

0.3076 (1.5219) 0.4034 (1.3288)

II, Annual Data {1971-1984) b Labor Force

GNP

--_, 5" "O O

0.8109 (4.6,885) 0.6505 (2.0800)

-0.3410 (-1.6675) -O.4601 (-2.13S1 ) 0.0223 (0.7_7) 0.0194 10.6403)

-0.4336 (-1.9259) -0,2392 (-I,1S24} 0,0290 (0.9713) 0.0319 (1,0616)

-0.8364 (-3.4070) -0,3502 (.2.0074} 0.0264 (0.8837) 0.0320 {I.0600)

Commercial 0,0231 (0.1524) 0.6568 (1.4460) 0.0468 (0.4376) 0.3324

1.1880 (2._88)

aBa._edon Tabia_ 3.2, 3.7, 3.8, 4.2, 4.4_ 4.6, 4.8, 5.1,5.2 a,_d5.3. Figuresin perenthesisare estimated t value. bB_ed on Tables 3.7_3.8, 4.10 and S.4. Models I and II do not apply to aBgreB_tecommercial and industrial annual tests.

0.0300 (0.0561) 0,0294 (0.0549) 0.0896 (1.0673) 0.0898 (1.0706) Industrial

1.28"/7 (1.5293}

0.4685 (1.1 t85) 0,5010 {_.2773) 0.0224 (0.2742) 0.0147 10.1854)

-0.3598 (-1.106S) .0.2844 (-0.9547) -0.0221 (-0.34291 -0.0156 (-0.2560)

_" _"

Implications onalternative pricingpolicies C.

Substitutesfor Electricity

Table 6.4 summarizes the estimates of the coefficients of the prices of substitutes for electricity. Liquefied petroleum gas (LPG) and firewood were used as possible substitutes for electricity. These choices are partly dictated by data availability. 1. LPG For residential consumers, LPG is found insignificant but with positive coefficients for both models I and II usingmonthly data. This indicatesthat LPG is in fact not a substitute but a complement to electricity. This is reasonablesince in the short-run, substitution of electricity by I_PG may not be technically possible and that more consumption of electricity could mean more activities which also require more use of LPG. Moreover, the price of LPG is alsoregulated or subjected to price control. Thus, the complementary relationship between electricity and LPG for residentialconsumersmay not be unexpected. For commercial consumers, the results are all insignificant for all types. Changesin the price level of LPG do not seem to affect monthly per capita kwh consumption of electricity by commercial consumers. For GP primary industrial consumers,however, LPG appearsto be a substitute for electricity. The estimates of the coefficients are -0.2822 and -0.3030 for models I and II, respectively. These mean that a one percentage point increase in the price of LPG results in approximately 0.3% increase in demand for electricity. These results are expected since GP primary industrial consumersare bulk users of electricity as Table 6.2 shows. Industrial users may have the capability to shift from one type of energy source to another. For GP secondary industrial consumers however, LPG appears to be a complement to electricity. The coefficient for model I is 0.1155 and significant. All the other results, using monthly data, are insignificant to include that of XMD industrial consumers. Using annual data, LPG is insignificant for residential consumers. However, for commercial and industrial consumers, the coefficients are -0.1892 and -0.2749, respectively. These show that in the long-run, LPG does not appear to be a substitute for electricity for residential consumers. However, LPG is a substitute for electricity in so far ascommercial and industrial consumers are concerned. For commercial consumers, the results show that, again, elasticities in the long-run elasticities are relatively larger than short-run. Monthly tests show the estimates of the coefficients are relatively small and insignificant. Over the long99

--t O 9_ m Table 6.4 Elasticities

of Prices of Substitutes

By Type

Consumers

for Electricity

(MERALCO}

Â˘D Dependent

Variable:

Per Capita KWH

INDEPENDEINT

RESID-

VARIABLES

ENTIAL

Monthly LPG

Data (lanI '71ZNov'84) :

Firewood

II. Annual

Consumption

"_ __.

C X-1

IJr_,mAcct

I SecorLd Acct

GP Prim

"_ O

a 0.0943

0.0096

0.0239

-0.0574

-0.0412

-0.0530

`0.2822

0.0894

IO.1640_

(0.4925)

I-1.1500)

t-O.S2_2)

(-0.4470)

(-3.2322)

0.25S3)

0.1217

-0.0256

0.01.83

-0.0593

0.0011

-0,0546

-0.3030

0.1155

(1.5508) 0.0305

I`0.4164) 0.0334

(0.3745) -0.0343

(-1.| 774) -0.0325

10.02421 -0.0161

(-0.46061 -0.0567

(-3.7034} 0.1194

(1.7223) -0.0176

I0.6327) 0.0349

(0.9241) O.03"_4

(1.1399) -0.O264

(-I,02591 42.0349

(-0.5104) -0.0267

I`0.8932) -0.056"_

(2.0970) 0.0886

I`0.3905) `0.0062

(0.6960)

(I.0082)

(-1.1021)

(`0.92_)

(-0.8944)

(1.5946)

(_.8637}

Commereia;

Industrial

-0.G171

-0/1892

-0.2749

(-_.7079)

(1.4S07)

(-0.0989) 0.1885 (0.9302)

3.2_ 3.7, 4.2, 4.4_ 4.6, 4.8, 5.1_ 5.2 and S.3.

bBased on Tables 3.7, 4.10 and 5.4, Models

Figures in parenth_is

I and 11 do not apply

are the estimated

to aggregate commercial

t values.

and industrial

annual

1est,.

_;_

GP Second

abased on Tables

XMD.

_1.2359)

Data (1971-1984J b

LPG

XMD

__,. <

-0.!441

Implications onalternative pricingpolicies run, a one percentage point increasein the price of LPG resultsin approximately 0.2% increasein demand for electricity by commercial consumers.However, the short-run and long-run estimates of the coefficients of LPG for industrial consumers are both approximately 0.3, indicating the rate of substitution in the short- and long-run between LPG and electricity is the same. 2.

Firewood

Firewood as a possiblesubstitute for electricity is found to be insignificant for residential, commercial and industrial consumers except for GP primary industrial. In the case of GP primary industrial consumers,the coefficient estimates are 0.1194 and 0.0886 for models I and II respectively, indicating that firewood is a complement to electricity. Due to thesegenerally poor resultsand taking into account the number of the degreesof freedom in the regressionanalysis,firewood was not included in the annual tests. D.

Electricity-Consuming Equipment

Table 6.5 summarizes the estimates of the coefficients of the prices of electricity-consuming equipment. The equipments included in the tests are flat iron, refrigerator and air conditioner. For the annual tests, only the air conditioner is used taking into account the resultsof the test usingmonthly data and degreesof freedom consideration. 1. Flat Iron Flat iron is included as explanatory variable only for residentialconsumers. The resultsare insignificant indicatingthat changesin the price of flat iron do not haveany significant effect on residentialdemand for electricity. 2. Refrigerator For model I, the price of refrigerator is found to be insignificant for all consumers except for X-1 commercial and GP secondary industrial. For X.1 commercial, a one percentage point increase in the price of refrigerator results in a 0.1503% point decreasein demand for electricity. For GP secondary industrial consumer, however, a one percentage point increasein the price of refrigerator resultsin a 0.2852% point increasein demand for electricity. 101

"10

O Table 6.5 Elestleit|esof Pricesof Elect_¢ity Consuming Equipment By Type Consumers (MERALCO)

Dependent Variable: Per Capita KWH Consumption INDEPENDENT RESIDVARIABLES ENTIAL

X-1

C O M XMD

E R C t A L . Prim Acct

O _'

! SecondAc_

XMD

U S T GP Prim

R I

A L GP Second

T:"

F," J. f. Monthly Data (Jan '71-Nov. '84) a Flat Iron

II : Refrigerator

II : AirConditiom_" I

II :

-0.0238

(-0.3s03) -0.0646 (-0.9291) -0.1225 (-1.0766) -0.2264 (-1.9087) -0.2172 (-2.5232_ -0.2974 4-3.3046)

II. Annual Data _1971-1984) b Airr Conditioner

II :

OQ O

_" ,0.1503 (-1.8527) -0.1523 (-1.$640) 0.0661 (-0.9499) -0.0480 (.0.6663}

-0.0178 (-0.2428) -0.0438 {-0.5827) -0.1490 (-2.5356) -0.1600 (-2.7041)

-0.0199 {-0.2616) 0.0380 (0.5165) -0.17t4 (-2.8780) -0.1611 (-2.6891}

-0.0814 [-1.0059) 0.0433 (0.6125) -0.1637 (-2.7137) -0.1179 (-2.1501}

Commercial 0,0950 (0.6894) 0,0749 (0.6325)

0.0221 (0.1541 )

abased on Tables 3.2, 4.2. 4.4, 4.6, 4.8, 5.1,5.2 and 5.3. Figures in parenthesisare the estimated t vatues. bBa.sedon Tables 3.7, 4.10 and 5.4. Models l ¢qd II do not apply to asgregatacommercial and industrial annual r_sts.

0,1440 (1.0708) 03441 (1.0716) -0.1798 I-1.4892) -0.t808 (-1.4975)

-0.1746 (-1.0034) -0.3177 (-1.9544) -0.0010 (.0.0109) -0.0520 (.0.5652) Industrial 0.5593 (2.3007)

0,2852 (2.0422) 0.3810 (2.9'706) 0.t140 (1 _391 0.1290 (1.8298)

Implications onalternative pricingpolicies For model II, residential, X-l, GP primary and GP secondary are significant with coefficient estimates of-0.2264, -0.1523, -0.3177 and 0.3810. These results, which are consistent in signs with model I and II, show that increases in the price of refrigerator as an electricity-consuming equipment dampens the residential, X-1 commercial and GP primary industrial demand for electricity. The effect for GP secondary industrial consumers,however, is complementary. The effects on demand for electricity by other types of consumersare insignificant. 3. Air Conditioner The price of air conditioner is found to be significant for all types of consumers except X-1 commercial, XMD and GP primary industrial with the signs of the coefficients consistentfor models I and II. The coefficients for residential consumers are -0.2172 and -0.2974 for models I and II. These are relatively larger in magnitude than those estimatesfor the other types of consumerswhich are -0.1714 and -0.1611 for primary account, and -0.1637 and -0.1179 for secondary account commercial consumers. It is noted that X-1 hasthe smallest magnitude of the estimated coffiecient. However, the cofficients for GP secondary are positive with valuesfor models I and II ofO.1140 and 0.1290. These results indicate that changes in the price of air conditioner have a significant effect on the demand for electricity. For residential consumers,the effect on demand is greatest with a 0.22 to 0.3_0%fall for every percentagepoint increase in the price of air conditioner. For others, except GP secondary industrial and GP primary, the effect of a one percentageincreasein the price of air conditioner is a reduction in electricity demand of approximately 0.12 to 0.19%. Again, due to the number of degreesof freedom in the regressionanalysis, only air conditioner is used in the annual tests. In the long-run, price changes in air conditioner do not have a significant effect on the demand for electricity by residential consumers.The same finding is observed for commercial users. These indicate that stock adjustments for this type of electricity-consuming equipment could be done within the month or "instantaneously" since the use of air conditioner is seasonalespecially for residential users. If the price of air conditioner increases,then demand for air conditioner falls resulting in a corresponding fall in electricity consumption. The results, however, for industrial users indicate that in the long-run,there is a positive correlation between the price of air conditioner and the demand for electricity. A one percentagepoint increasein the price of air conditioner results in a 0.5593% point increase in demand for electricity. This could perhaps be 103

Implicationsonalternativepricingpolicies explained by the process which results in something referred to as a "feedback effect", wherein the increasing price of air conditioner results in increasing incomes of industrial users, who may directly or indirectly, manufacture the equipment or benefit from its production through various linkages in the economy, thereby increasing their demand for electricity. The relatively high and elastic income elasticity of demand for electricity by industrial consumers provides support for this possible explanation. E.

Environmental Variables

Table 6.6 shows the estimates of the coefficients of the environmental explanatory variablesl Maximum temperature and relative humidity are used to represent the environmental variables. 1. Temperature For monthly data, residential demand for electricity is significantly and positively correl_tted with maximum monthly temperature. A one percentage point increasein temperature results in 0.37% increasein demand for electricity. For X-1 commercial consumers, the same finding is observed. However, the temperature elasticity of demand for electricity is lower comparedto that of residential consumers ranging from 0.2251 to 0.2363 for models I and II, respectively. XMD and primary account commercial consumers' demand for electricity appear not to be significantly affected by temperature changes.Secondary account commercial consumers' demand for electricity is, however, positively correlated with maximum temperature and a coefficient estimate of 0.1456 for model II. The result for model I is insignificant. For the industrial consumers, it is only the GP primary which is sensitive to temperature changes with coefficient estimates of 0.4844 and 0.5266 for models I and I1. These mean that a one percentage point increasein ambient temperature results in approximately 0.5% increasein demand for electricity. For ithe annual tests, maximum temperature is found to be insignificant for residential, commercial and industrial consumers. The most plausible explanation for this is the use of the maximum of the monthly temperaturesfor each year in the annual tests. Since the variations in the annual maximum are relatively small, these insignificant results may not :be unexpected. Using the annual averageof monthly maximum temperatures alsoyield insignificant results_ 104

Table 6.6 Elasticities of Environmental Variables By Type Consumers(MERALCO)

Dependent Variable: Per Capita KWH Commption INDEPENDENT RESIDVARIABLES ENTIAL

X-1

C O M XMD

E R

C I A L Prim AÂ˘ct

Second Acct

XMD

I N

U S T GP Prim

0.0:333 (0.38821 0.1456 (1.7643) 0.0799 (1.3112) 0.t733 (3.1585)

0.0065 {0,0466) 0.0061 (0.04411 0.1090 (1.0734) 0.1091 (1.0T46)

0.4844 (2,5543) 0.5266 (2.8616) 0.3806 (3.1750) 0.3864 {3.3297)

R I

A L GP Second

I. Monthly Data (tan '71 to Nov "841a Max Temperature ! : II : Rel Humidity

1 : II :

0.3713 (3.1582) 0.3771 (3.0850) 0.;672 (1.8352) 0.182t (1.9173)

IL Annual Data (1971-1984) b Max Temperature l : II :

0.2363 (2.76491 0.2251 (2.60"/6) 0.1609 (2.4546) 0.1402 (2.0874)

0.0378 (0.4477) 0.0223 (0.26301 0.1039 (1.7712) 0.0894 (1.5121)

0.1028 (_ .2221 ) 0.1200 (42491 0.;437 (2.4399} 0.1664 (2.0499)

Commuciai 0.5700 (0.9834) 0.2526 (0.4940)

-0.2160 (-0.4227) -0.3699 (-0.7684)

aB_ed on Tables 3.2, 4.2, 4.4, 4.6, 4.8, 5.t, 5.2 and 5.3. Figures in parehetheslsare the estimated t values bBasedon Table 3.7, 4.10 and 5.4. Models I and II do not apply to asgregatecommercial and lndustria| annual tests.

lndusvial _.5570 (-0.6407) -0.201 "i (-0.2478)

0.1130 (0.80341 0.1335 (0,9995) 0,0318 (0.3808) 0.0689 (0.0594)

"_ _e_ O O _r "_ -_, < "O

.-L

Implicationson alternativepricingpolicies 2. Relative Humidity Table 6.6 shows that relative humidity is significantly and positively correlated with monthly per capita kwh consumption for all consumers except for XMD and GP secondary industrial consumers. The estimates of the relative humidity coefficients range from 0.0894 and 0.1039 for XMD commercial to 0.1672 to 0.1821 for residential. The coefficients for GP primary industrial are, however, larger with values of 0.3806 and 0.3864 for models I and II, respectively. Like in the case of maximum temperature, these indicate that electricity demand of industrial consumers, specifically GP primary, are more sensitive to environmental variables than residential and commercial consumers. F.

System Peakload

Table 6.7 shows that peakload elasticity coefficient estimates for all types of consumers. The monthly system peakload is introduced as an independent variable to derive some measures of type consumers' contribution to the evolution of the system peakload. The higher the values of the estimated coefficient, the higher the monthly per capita kwh consumption responds to the system peak. The peakload elasticity coefficient is therefore inversely related with the load factor. Since the system peakload was not included in the tests for residential consumers, regression analysis was done where this variable is included. The results are shown in Appendix 6.1. 26 The test results for X-1 commercial are shown in Appendix 6.3. Using monthly data, the largest estimates are those for residential, f wed by X-1 commercial, then XMD and secondary account commercial, GP secondary and XMD industrial, primary account commercial, and GP primary industrial in that order. These results are summarized in Table 6.8. It is recalled that equation 4.1 postulates that Average load = Peakload c_ where (_ _ I, since the average load could not be equal to or greater than the system peakload. The average load of a consumer is directly proportional to his n_onthly per capital kwh consumption. Since 0_ is inversely related to the load factor, a crude statistical approximation to the load factor is thus postulated. 27 2_/ The corresponding results using the CPI, instead of the price index for fuel, light and water, as the deflator for the price of electricity, LPG and flat iron are shown in Appendix 6.4. The resultsaregenerallythe samewith thoseshownin Appendix 6.1. Given the crude estimatesof the load factor and the useof per capitakwh consumption to approximate the averageconsumer load, these estimatescould be refined or adjusted if reliableestimatesof the diversity factor for eachtype of consumeris available. 106

Table 6.7 PealdoadElasticity Coefficient of Type Consumers (MEP.ALCOI Dependent Variable: PerCrapitaKWH Consumption INDEPEI_DENT VARIABLES

RESIDENTIAL

I. Monthly Data I_an ";'t-Nov'B41a Peakload I : 0.7911 {6.4132) 11 : 0.7797 (5.8880) II. Annual Data (t971-1984) b Avera_ Peak

II :

0.9114 (1.2117) 0,7111 (1.1523)

X-1

0.6996 (9.0527) 0.7047 (8.4551)

C O M M E R C I A L XMD Prim Acct.

0.6496 (7.575?) 0.6627 (7,4245)

0.5157 (5.7598) 0.5105 (5.6418)

SecondAcct

0.5966 (6.6524) 0.5518 6.84131

XMD

I N D U S T R I A L GP Prim GP Second

0.5247 (2.9535) 0.5258 (2.959B)

0,3017 (1.7996) 0.2557 (1.5696)

05193 (3.9058) 03409 (4.2759)

Commercial

Industrial

-0.5996 (-1.5997)

-1.3139 (-2.0836)

abasedon Tables4.4, 4.6, 4.10, 5.1,5.2, 5.3, 5.4, and Appendix 6.1, and 6.3. Fisures in parenthesisare the estimated t values. bBasedon Appendix 6.2 and Tables4.10 and 5.4.

O 0 _a _"

3 =.., 3" ,el) "O '-,i n.

0Â˘1 O

Implications onalternativepricingpolicies Based on this equation, the last two columnsof Table 6.8 provides crude statistical estimates of the load factor of each of the type of consumers.These estimates, in effect, take into account historical values from January 1971 to November 1984. Table 6.8 shows a pattern that confirms casual observations. Residential and X-1 commercial consumers have the first two highest values of peakload elasticity coefficients while GP primary industrial and primary account commercial have the first two lowest peakload coefficients. Correspondingly, the estimates of their load factors show that GP primary industrial and primary account commercial consumersare the first two in rank in terms of load factor while residentialand X-1 commercial are the lastin rank. For residential consumers, using the annual average of monthly system peak, Table 6.7 shows a peakload coefficient of 0.7111 and 0.9114 for models II and I, respectively. However, these results are not significant although the t ratios are approximately 1.2. However, the estimatesof the peakload coefficients for the aggregatecommercial and industrial consumersusingannual data are negative.One interpretation for this is that on a long-term basisover the 1971-1984 period, increasesin system peakload do not contribute to increasesin averageannual per capita kwh consumption but apparently results in their reduction. One practical inferene from this finding is that commercial and especially industrial demand for electricity may have negligible impact on the long-termevolution of the system peak, and that residential consumersin fact are the ones who primarily contribute to the evolution of the system peakload. This has important implications for designing tariff policies and rate schedules,especially in the designof demand and energy changes. I1. Short-run and Long-run Elasticities The tests using monthly and annual data is used to differentiate between short-run and long-run elasticities. In the relatively shorter period of time, say one month, electricity consumers may or may not be able to fully adjust their stocks of electricity-consuming equipment to their desired stock levels.Within a longer period, however, say one year, they are expected to adjust their stocks to the desired levels. Since electricity consumption is a function of the utilization rate of existing stocks, changesin consumption due to changes in price, income and other variables in the short-run are expected to be smaller than changesin consumption in the long-run. Short-run elasticities are therefore expected to be smaller in magnitude than long-runelasticities. 108

Table6.8 Ranked Estimatesof PeakloadElasticity Coefficientsand Load Factors MERALCO, Monthly Data {Jan '71-Nov '84) Load Factor Estimate PeakCoefficient Rank

Type Consumer

Peakload Coefficient Model I Model il

_ (Crude) Model I Model II

Load Factor Rank

Residentiala

0.7911

0.7797

0.2089

0.2203

X-1 Com'fb

0.6996

0.7047

0.3004

0.2953

XMD Com:l

0.6496

0.6627

0.3504

0.3373

Sec AcctCom'l 0.5966

0.5518

0.4034

0.4482

GP SecInd'l

0.5193

0.5409

0.4807

0.4591

XMD Ind'l

0.5247

0.5258

0.4753

0.4742

Prim Acct Corn 0.5157

0.5105

0.4843

0.4895

GP Pi'im Ind't 0.3017

0.2557

0.6983

0.7443

aBasedon Appendix 6.1. bBasedon Appendix6.3

Implications onalternative pricingpolicies A.

Own Price Elasticities

Table 6.1 shows the estimates of the own price elasticities using monthly and annual data. For residential consumers,the short-run and long-run marginal price elasticities are 0.0989(11) and 0.1990(11), respectively. 28 For inframarginal price, the short-run elasticity is 0.1152(I) while the long-run elasticity is 0.0592 (11). For the average price, the short-run elasticities are 0.0794 (I) and 0.0881(11) while the long-run estimates are-0.5409(I) and -0.4646(11). Except for the inframarginal price, long-run estimates for residential consumers are relatively larger in magnitudethan short-run price elasticities. For commercial consumers,there are no estimatesfor marginal and inframarginal prices using annual data for reasonsearlier stated. The short-run and long-runaverage price elasticitiesare therefore the only onescompared. For shortrun elasticities, the estimates range from -0.0612 (I, secondary account) to -0.0662 (11, X-l), For the long-run, the estimate is -0.2515. These show that the long-runaverage price,elasticity is larger than short-run elasticities. Similarly, for industrial consumers, no estimates for marginal and inframarginal price elasticities were made for reasonsearlier stated. Only the short-run and long-run average price elasticities are therefore compared. For the shortrun, the estimatesare .0.2929(11) and -0.3528(I) for GP primary and-0.2116(11) and -0.2331(I) for GP secondary. For the long-run, the estimate is -0.2411. This value falls somewhere between those of GP secondary and GP primary. For industrial consumers,there is no significant difference between short- and longrun average price elasticities. Thus, unlike the residential and commercial consumers, monthly changes in the price of electricity appear to be immediately translated into corresponding changes in demand levels. While the apparent interpretation of this result implies that the industrial consumers' adjustments to their desired stock level could be "instantaneous", a more logical interpretation of this is that industries adjust capacity utilization rate to meet desired output levels. Given the observed relatively low rate of capacity utilization and overcapacity in the economy, this explanation appears plausible. B.

Income Elasticities

In Table 6.3, the estimates of the income elasticities using monthly and annual data for varioustypes of consumersare summarized. 2_ For expositoryconvenience, the figure insidethe parenthesis refersto the model used. 110

Implications onalternative pricingpolicies For residential consumers, the short-run income elasticity using employment level or labor force as income surrogate is 0.3076(I). The long-run estimate is 0.6568(11). Thus, the long-run estimate is more than twice as much as the short-run income elasticity. To be able to make a comparisonwith the annual resultsfor commercial and industrialconsumers,the GNP was also usedas income surrogate giving an elasticity of 0.3324(11). This higher employment elasticity compared to the GNP elasticity is plausible consideringthat employment could be more directly contributory to the purchasingpower of residential consumers than GNP. For commercial Consumers,labor force is found to have negativecofficients except for X-1. This means that employment level may not be an appropriate income surrogate. Using index of commercial salesyields insignificant results. For annual data, the GNP was used as income surrogate giving an estimate of 1.1880. While there is no short-run estimatesto compare with, this elasticGNP coefficient indicates that long-run income elasticity is possibly larger than shortrun elasticity. Also, commercial GNP elasticity is larger than that of the residential consumers. In the case of industrial consumers,employment level and industrial sales index are both insignificant for all types of consumers.The annual GNP elasticity of demand for electricity is 1.2877. Again, this estimate could be possiblylarger than short-run valueseven if there are no short.run estimates. Thus, like in the case of the price of electricity, the coefficients of the income surrogatesused indicate that long-run income elasticitiescould be larger than short-run elasticities. These results confirm theory and are in consonance with the evidencein the literature.29 C.

Priceof Substitutes

As shown in Table 6.4, LPG and firewood were usedaspossiblesubstitutes for electricity. Firewood is found to be insignificant for all types of consumers _/In a surveyof studiesof the demandfor electricityin the UnitedStatesby Bohi (1981), priceelasticities for residentialconsumers basedon 25 studiesrangefrom -0.03 to 0.54 in the short-runandfrom -0.45 to -2.20 in the long-run.Incomeelasticities rangefrom -0.32 to 2.00 in the short-runand from 0.12 to 2.20 in the long-run.For fivecommercial consumerdemandstudies,priceandincomeelasticities rangefrom _).17 to -1.18 and0.10 to 0.72, respectivelyin the short-runandfrom -0.56 to -1.60 and0.88 to 1.15 in thelong-run. For eightindustrialconsumerdemandstudies,priceand incomeelasticities rangefrom -0.04 to -1.36 and0.06 to 0.87, respectively in the short-runand -0.51 to -1J_2and0.51 to 0.73 in thetong-run. 111

Implications onalternative pricingpolicies using monthly data and was not usedfor the annual test. LPG is alsoinsignificant for all types of consumersusing monthly data except for GP primary and GP secondary industrial consumers.The estimatesof the coefficients for GP primary are -0.2822(I) and -0.3030(11). Except for that of GP secondary which is 0.1155 (I I), theseestimatesare closeto the annual estimatesof .0.2749. For the commercial consumer, the long-run price elasticity of LPG as a substitute for electricity is significant with an estimate of-0.1892. Thus, for commercial consumers,it is possible that long-run elasticity could also be larger than short-run elasticity. However, for industrial consumers,short- and long-run elasticitiesappear to be of the same magnitude. This is similar to the results in price and income elasticities.30 D.

Electricity ConsumingEquipment

Table 6.5 shows the estimates of the coefficients of flat iron, refrigerator and air conditioner which were usedto represent the electricity-consumingequipment. For residential consumers,coefficients of air conditioner are negativeand significant with estimated values of-0.2172(I) and -0.2974(11). However, annual results are insignificant. Similarly, for commercial consumers, while monthly tests show some significant and negative coefficients, the annual resultsare also insignificant. For industrial consumers, the short-run coefficients range from .0.1798(I) and -0.1808(11) for XMD to 0.1140(I) and 0.1290(11) for GP secondary. The long-run elasticity is 0.5593. Thus, changesin the price of airconditioner as an electricity-consuming equipment have greater effects on demand for electricity in the long-run than in the short-run. There are no annual coefficient estimates for flat iron and refrigerator. Hence no comparison of short- and longrun elasticitiescan be made.

III. A Dynamic Reformulation of the Demand Model: Some Implications The model used in this study is essentiallya static one and therefore is unable to capture the dynamics of adjustments in the stock levels of consumers' electricity-consuming equipment. However, despite some limitations, it is conThis resultfor industrialconsumers, whichshowsthat there is no largedifference betweenshort-runand long-runpriceand incomeelasticities,is similarto theoverallresults in theUnitedStatesasreviewedbyBohi(1981,p. 84). 112

Implications onalternativepricingpolicies sidered adequate for the present purpose. Usinga one-month and one-year period as characterization of short-run and long-run behavior, respectively, implicitly assumesthat over a one-month period the stock of electricity-consumingequipment is fixed, while over a one-year period, consumershaveadjusted their stocks to desired levels and that the indirect effects of chargesof all relevant variables have beenfully realized. As pointed out earlier, the use of a static model is dictated by the absence of data on stocks of electricity-consuming equipment. Supposethat in fact we have such data, then based on the discussionin chapter :2, the demand model given by equation 2.3 can be reformulated. More specifically, following Taylor (1975) and Murray, et. al. (1978), let qt =

rt st

(6.1)

where rt is the rate of utilization of stock st during time period t. Also let

and

rt =

rt (Pt, Bt, Zt, Yt, Wt)

st = st (At, Wt)

(6.2)

(6.3)

where Pt, Bt, Zt, Yt and At are as previously defined and Wt is a set of variables which jointly determine rt and st and that Wt is a sub-setof the set of all the explanatory variables. This is done because it is difficult if not impossible to determine which of the explanatory variables Pt, Bt, Zt, Yt and At determine rt and st. Now, let Et and V t be vectors of variables with elements Pt, Bt, Zt, Yt, Wt and At, Wt, respectively. Assume that rt is a long-linear function of Et. Then (6.1) can be written as In qt = _hi

lnEit +

In st

(6.4)

where hi are parameters. Define s*t as the desired stock level of electricity-consuming equipment for period t. Also, assume that s*t is a log-linear function of Vt and that a consumer continually undertakes partial adjustment toward this desired stock level. Then, 113

.Implications onalternativepricingpolicies In s* t

Z; w In j= 1 j

V it'

(6.5)

where wj are parameters. Assuming that the partial adjustment process takes the form

In st

-In

st. I

=Â˘ (In s* t -

In st.1) ,

(6.6)

where 0 < ##< 1, then the demand relationship can be expressed as In

qt =

Z; hi E i=l it

+ (I-##)

t-1

## In

S* t.

(6.7)

Substituting equation 6.5 in 6.7 yields In qt

= +

_;h ilnEit

(1-Â˘

) In

+ ## _;

j=l

st.1

j (6.8)

Except for the introduction of the dynamics of the stock adjustment process,this equation is similar to equation 2.3. Therefore, using equation 6.8, to interpret some of the preceding test results could provide some indications of the limitations of the test procedure used. The parameter _ in equation 6.6 might be referred to asthe "stock adjustment parameter". If its value is close to 0, then the adjustment to the desired stock level is very slow. If its value is close to 1, the adjustment to the desired stock is very fast. But this speed of adjustment is dependent on the length of the time period chosen and the type of consumer. More specifically, a one-month period for some residential, commercial or even industrial consumers of electricity might be long enough to purchase their desired appliances but not long enough for others. Similarly, a one-year period for some residential, commercial or industrial consumers might be long enough but not for others. Also, the timing of decisions to alter stock levels of economic agents may not coincide with the observed monthly and annual data. Aggregating over each type consumers further complicates the problem. Moreover, the characterization of short-run (one month) 114

Implicationson alternativepricingpolicies and long-run (one year) may not be able to take over which the indirect effects on consumption variables are realized. This consumption, in turn, consuming equipment. These potential problems interpreting the results. Suppose it is assumed that h In

qt = i=1

In i

into account the length of time by changes in the explanatory affects purchases of electricitymust be borne in mind when

¢ _- 1. Then equation 6.8 becomes E it

3_ w j=l j

V jt

+ u

(6.9) t

This yield equation 2.3 after substituting variables for E t and V t. In this case, ordinary least squares which is used in this study is an appropriate test procedure. If ¢ <_ 1, then the correct relationship is given by equation 6.8. However, if equation 2.3 is estimated instead of equation 6.8, then there is a downward bias on the parameter estimates of the explanatory variables which determine the demand for stock of appliances due to the presence of ¢ as a multiplier. This could explain why tests using monthly data show that short-run demand elasticities are generally smaller than long-run elasticities for residential and commercial consumers. In the case, however, of industrial consumers, due to the observed existence of overcapacity, they could be more concerned with the rate of utilization of existing stocks rather than with additions to stock. In effect _ 1 and short-run and long-run demand elasticities become approximately equal thus providing explanation for the test results. Moreover, the presence of 1 n st. 1 is a case of an omitted variable problem. 31 This results in a biased as well as inconsistent parameter estimates. Also_ the coefficient of determination as a measure of regression fit could be reduced. However, as the value of

approaches unity, these problems disappear.

Estimating equation 6.8 is not possible due to the absence of stock data. Using proxy variables may not necessarily give better estimates since the choice of a proxy variable and the availability of data would still pose some problems. 32 Besides, if equation 6.5 holds, the stock of the previous period might have a More specifically, the last two terms in equation 6.8 becomethe error term if equation 2.3 is estimatedgiven that _1. This is a caseof a non-linearerror-in-variablemodel. Hwang (1986, p. 680), for instance,observesthat at present, not much isknown on the linear error-in-variablemodel. 3_j For problemsassociated with this procedure,seeMaddala(1977, p. 304). 115

implications on alternative pricing policies correlation with the V t vector of variables, and in effect serve as proxy variables. 33 Also, (1-_) could be small so that the effect of the omitted variable is reduced. On the other hand, a reformulation of equation 6.8 using a lagged dependent variable eliminates the stock variable. However, this procedure has not been empirically rewarding due to autocorrelation and other econometric problems. The preceding discussion provides indication of the potential limitations of equation 2.3 and the reasons why such a simple demand model is used. Based on equation 6.8, if the value of the stock adjustment parameter approaches unity, then the omitted variable problem disappears and a better regression fit, as measured by the coefficient of determination, is expected. For residential consumers, the results in Chapter 3 indicate that there are significant improvements in the values of the annual over the monthly coefficients of determination. This could mean that residential consumers are unable to adjust to their desired stock level during a one-month period but are able to do so during a longer period of one year. This result is consistent with the finding on price and income wherein long-run coefficients are larger than short-run estimates. In the case of commercial consumers, the results in Chapter 4 show that there is significant improvement in the values of the annual over the monthly coefficients of determination, except for X-1. A similar conclusion as in the residential consumers could be arrived at regarding adjustment to desired stock levels for a one-month or a one-year period. This result is also consistent with the finding on price and income variables where their long-run coefficients are smaller than the short-run coefficients. Finally, the results in Chapter 5 for industrial consumers show that in general, there is no significant improvement in the values of the annual over the monthly coefficients of determination. In fact, there is a slight reduction in the annual coefficient of determination compared with those for the monthly tests. Given the observed existence of overcapacity in the industry, these results may not be unexpected since industrial users are more concerned with capacity utilization rather than capacity additions. This means that changes in the values of the independent variables, especially price and income, are immediately translated into changes in demand for electricity as industries adjust their rate of utilization of existing stocks. The partial adjustment process given by

3_ Appliances and other forms of electricity-consuming equipment are usually purchased on deferred payment basis. Thus, it is possible that st = st (Vt_l) or st.1 = st.1 (Vt). Pursuingthis line of inquiry, therefore, may not provide better parameter estimates. 116

Implications onalternative pricingpolicies equation 6.6 does not hold and qt = rt s, where s is a fixed stock level. Thus, short-run and long-run coefficients are expected to be the same. The preceding results confirm this where estimatesof the price and income elasticitiesfor industrial consumersare of similar magnitudesfor the short-run and long-run. IV.

Pricing Policy Implications 34

The present price structure of MERALCO basically reflects the price structure of the National Power Corporation (NPC) from which MERALCO purchases electricity for distribution. This price structure is essentially based on averagecost pricing. The price schedulefor a particular type of consumerwhich may result in a return lessthan the averagecost is recoveredfrom the other consumers.This is the case, for instance,for the small residentialconsumerswho are subsidizedby the large residential,commercial and industrial consumers. 35 An alternative pricing policy is the marginal cost pricing. This is presently being considered by both the power sector and by government economic policymakers. Theory suggeststhat the marginal cost reflects the economic cost of a scarce resource. In the case of electricity, however, the application of the principle of marginalcost pricing is not straightforward. This is dictated by the special nature of electricity as a commodity which was briefly describedin Chapter 2. Since electricity cannot be stored, the marginal cost of supply dependson the level of demand at a given time. The higher the demand, the higher the marginal cost of supply since the more expensiveplants from the existing stockswill have to be dispatched. This cost representsthe marginal fuel or energy cost and is basically a short-run marginal cost sinceplant capacity is assumedfixed. Over time, however, as peak demand grows, more generatingplantsand the appurtenant transmissionand distribution sub-systemshave to be installed. Since power generating plants are characterized by indivisibilities of investments,the

Dueto thevariouslimitations, bothin thedemandmodelandthedata,whichdictated the use of specification searches(Learner,1978), it is evidentthat the policy implications derivedhereonly provideindications of the possible directionsfor policy.Sincethe study is unableto providerigorousboundsfor the parameterestimates as suggested by Learner (1983), careshouldbe exercisedin usingtheestimatedparameters for forecasting electricity demand. NPC,for instance,alsodoesthis amonggrids.The Mindanaoand Luzongridssubsidizethe Visayangrids,wherethecostof electricityisrelativelyhigher,to arriveat anoverall rateof return(ontheratebase)of at least8%. 117

Implications onalternativepricingpolicies resulting marginal capacity cost when the plant is being installed becomes relatively high, and after it has been installed, the marginal capacity cost becomes zero or relatively small. A pricing policy based on marginal energy and this mar. ginal capacity cost will introduce large fluctuations and are not acceptable to consumers.To resolve this difficulty, the long-run marginal cost is used. It considers both the marginal energy cost and the marginal capacity cost neededto avert power shortages. Reference to marginal cost pricing, hereafter, shall mean long-run marginal cost pricing, unlessstated otherwise. A.

The Subsidy and the Tariff Structure

The preponderanceof positive marginal and inframarginal price elasticities, especially for the residential and X-1 commercial consumers,indicates that the tariff schedule do not provide the correct signalsto the consumers.In fact, the tariff schedule introduces distortions in the price signalsresulting in an apparently perverse behavior of consumers. However, a deeper analysis of the demand responsesreveal that consumers in fact responded to the price signals in a rational manner. Thus, the pricing policy implication is clear, i.e., to remove the subsidy. If this cannot be entirely eliminated, a subsidy structure need to be designedto minimize the distortions in the price signals. Electricity is the most versatile and widely usedform of energy. It is alsoa scarce resource. Since a strong positive correlation between energy use and eco. nomic growth exists, using electricity pricing as a means of income distribution distorts price signalsand could be detrimental to the overall and long-term economic growth process.36 Over time, from 1971 to 1984, the structure of the price schedulefor residential and X-1 commercial consumersgradually shifted from block-decreasing to block-increasing. This is a direct consequenceof the subsidy program. While this may have to be empirically validated, a block-decreasingtariff schedule is more conducive to a more efficient utilization of electricity. 37 Thus, in removing the subsidy, the tariff scheduleshould be revisedto its former block decreasing structure. 3__In the UnitedStates,for instance,Jorgenson (1984, 1986) findsthat theuseof electricity playsan importantrole in productivitygrowth.Whiletheenergyshocksof 1973-74and 1979 haveresultedto lesseruseof primaryenergyfor GNP growth,this isnot the casefor electricity.Sioshansi(1986) showsthat this so-called "decouplingof energyandGNP" does notnecessarily applyto electricityandGNP. Fora theoretical discussion of theissues involved,seeFrancisco (1987). 118

Implications onalternative pricingpolicies In the case of the commercial and industrial consumers,while the price schedule remains block-decreasing,the changesover the years have made them practically flat at all block levels. This meansthat the marginaland inframarginal prices may not be the effective price signalsas compared with the averageprice. This perhaps provide an overall explanation for someof the positive and negative marginal and inframarginal price elasticities for various types of commercial and industrial consumerswhile all averageprices are consistently significant and with correct negative signs.A relatively flat price scheduleat all block levelsdoes not encourageefficient useof electricity. B.

Stability in Priceof Electricity

Except for industrial consumers,the long-run price elasticitiesare found to be larger than short-run elasticities. For income, long-run elasticities are larger than short-run elasticities for all types of consumers.These mean that changes in prices of electricity and in the level of incomehave relatively small immediate effect on the demand for electricity. However, over a long period, the effect on demand by these price and income changesare relatively larger. This result confirms the well-known requirement for a stable price of electricity. For instance, the present pricing policy of NPC, which is in turn passedon to the consumersby MERALCO, incorporates in their monthly power bills the changesin the cost of generation_andtransmissiondue to fluctuations in the cost of oil, steam, and exchangerate. In turn, MERALCO further introduces its own adjustments. While the immediate effects of these changesin prices are small, the long-run effects are expected to be relatively larger. This is an implication of the larger long-run elasticities. Since the power sector has very strong linkages while the entire economy, the effects on the long-term expectationsof economic agentswould not be conducivefor a sustainedeconomic growth. Thus, these provide empirical basesfor the use of a policy where the price of electricity is made relatively stable over time. Marginal cost pricing, which takes into account the short-run marginal energy cost and the long-run capacity cost, isa pricing policy which might meet this requirement. C.

Environmental Variables

The significant and positive correlation of electricity demand with temperature and relative humidity changesis consideredan important set of results. It demonstrates that electricity consumption by all types of consumers& resi119

Implications on alternative pricing policies dential, commercial and industrial -- is determined also by environmental variables. Since maximum temperature and relative humidity change over the different months of the year, these significant results show that monthly changes in demand for electricity are affected by these changes in the environmental variables. While the test are unable to use hourly

data due to data limitations,

tem-

perature and relative humidity changes over the hours of the day are relatively larger compared to changes in maximum temperature and relative humidity among months of the year. Thus, it could be expected that hourly changes in demand for electricity during the day could be influenced by changes in temperature and relative humidity. While this requires empirical verification, it may not be unreasonable to postulate that the daily load curve of electricity consumers could be also influenced by temperature and relative humidity changes. 38 These results again provide an empirical basis for seasonal and time-of-daypricing. Since marginal cost pricing takes into account the seasonal as well as hourly marginal cost of supplying electricity, it appears that marginal cost pricing could be a suitable alternative to the existing average cost pricing policy. D.

Peakload Elasticity Coefficients

The findings on the peakload elasticity coefficients for the different categories of consumers serve as basis for making crude statistical estimates of the load factor. Also, it provides an alternative basis for assigning peak responsibilities to various types of consumers in designing tariff structure based on marginal cost pricing. 39 A more refined tariff proposal based on marginal cost pricing could thereafter be developed.

38_/For some recent U.S. studies showing the effects of environmental variables on demand for electricity, see Lilliard and Aigner (1984), Dubin (1985) and Engle,et. al. (1986). For instance, in determining the load factor and peak responsibility of each customer class for MERALCO in the design of a tariff structure based on long-run marginal cost, a sampleof a representativeand typical loadcurvefor eachcustomerclassisused. SeeEiectricite de France International, Electricity Tariff Study, Vol. 3, December 1985, Annex 2. The choice of the representativeor typical load curve might be questionable. Accordingly, the resulting tariff proposals,which is applied to all customersin a given category,may not be accurate. 120

Chapter 7

Summary, Conclusions and Suggestions for Further Research

Summary

This study is an attempt to provideestimatesof the elasticitiesof electricity demand and derive some implicationsfor alternative pricing policies. In chapter 2, the consumerdemand function is modelled taking into account peculiar features of electricity as a consumer good and developments in the literature. The model usesfive setsof explanatory variableswhich are own prices of electricity, income, prices of substitutes for electricity, prices of electricityconsumingequipment, and environmental variables.Short- and long-run demand behavior are captured assuming a one-month and one-year time period, respectively. The data usedwere taken from MERALCO, PAGASA, NCSO and NEDA. Chapter 3 specifiesthe consumerdemand model for residentialconsumers. The own price initially used are the marginal and inframarginal prices.The marginal price is based on the price schedule and the average monthly per capita kwh consumption. The inframarginal price is basedon the suggestionsof Taylor (1975) and Nordin (1976), and accordingly two models are tested. These are models I and II which use the definitions of Taylor (1975) and Nordin (1976), respectively. Due to the absenceof adequate per capita disposableincome data, the income surrogateused is the level of employment. The substitutesfor electricity used are firewood and liquified petroleum gas (LPG) while the electricityconsuming equipment used are flat iron and refrigerator. Maximum monthly temperature and relative humidity are used to represent environmentalvariables. When these environmental variables were initially found to be significant, air conditioner was further added asan electricity-consumingequipment. Using monthly data, the marginal and inframarginal price elasticitiesare found to be positive and significant. To further validate this, the average price 121

Summary,conclusions and suggestions whose coefficient is found to be also positive is used. However, while the coefficients of the marginal and inframarginal pricesare positive and significant using annual data, the average price coefficient is significant with a negative sign. Long-run price elasticities are found to be larger than short-run estimates, Attempts were made to explain and interpret these results. Labor force is found to be positively correlated with demand for electricity by residential consumers for both monthly and annual tests. Firewood is not a substitute for electricity while LPG is a complement. However, using annual data, LPG isfound insignificant. Changes in the price of flat iron have no significant effect on residential demand for electricity. However, changes in the price of refrigerator and air conditioner have significant and negative effects on demand for electricity. On the other hand, maximum temperature and relative humidity are positively correlated with the demand for electricity. The estimate of the residential peak elasticity coefficient is approximately 0.8. Correspondingly, a crude estimate of the load factor ismade. Chapter 4 attempts to estimate the consumer demand functions of four types of commercial consumersusing monthly data. For annual data, the consumer demand function for the aggregateof the commercial consumers is estimated. The own price variables are the same with that of the residential model. Similarly, this necessitatedthe use of models I and II. The index of commercial sales is used as an income surrogate while employment level was included as an additional explanatory variable. LPG and firewood are used as substitutes. However_ only refrigerator and air conditioner are used as electricity-consuming equipment. Maximum temperature and relative humidity represent the environmental variables. Except for X-1 commercial consumer, another price variable in the form of a demand charge is introduced. Accordingly, the system peakload is also added as an independent variable to measure the responsivenessof electricity-consumption to movements in the system peak. This isdone for all the commercial aswell asindustrial consumers.Regressionrunsfor residential consumerswith the system peakload added were accordingly done enabling comparisonsacross consumer classes. The marginal and inframarginal price elasticities for commercial X-1 are positive. These results are similar with those of the residential consumers. For the other Wpes of commecial consumers,some have positive while others have negative elasticities. However, the average price elasticities arc negativeand significant. The annual test shows that the average price elasticity is also negative. 122

Summary, conclusionsand suggestions However, it is larger than the estimates of the tests using monthly data. Except for X-1 which has a positive coefficient for employment level, the other types of commercial consumers have negative coefficients indicating possible substitution between electricity and labor. The index of commercial sales representing income is found to be insignificant. For the test using annual data, GNP is used as income surrogate. The GNP elasticity is positive, significant and elastic. LPG and firewood are found to be generally insignificant as substitutes for electricity using monthly data. However, using annual data, LPG is a significant substitute for electricity by commercial consumers. Regarding the electricity-consuming equipment, changes in prices of refrigerator have significant negative effect on electricity demand by X-1 commercial consumers. However, for the other types of commercial consumers, the results are insignificant. For air conditioner, the coefficients are insignificant for X-1 but significant annd negative for all the other commercial consumers. For the environmental

variables,

changes in maximum

temperature

are

significant and positively correlated with commercial demand for electricity except for XMD and primary account. Relative humidity is, however, significant and positively correlated with electricity demand for all types of commercial consumers. The system peakload elasticity coefficients for commercial consumers are highest for X-1 and lowest for primary account with the estimates for XMD and secondary account in between. These estimates are lower than that of residential consumers. Annual peakload elasticity coefficient is negative which appears to indicate that in the long-run, the movements in the system peakload is negatively correlated with the average load. While this has to be interpreted with caution, it provides indications for determining peak responsibility among types of consumers. Chapter 5 provides estimates for the consumer demand functions of industrial consumers using monthly and annual data. The variables included are similar with those used for the commercial consumers. As income surrogate, the index of industrial sales is used while employment level is included as an additional explanatory variable. The marginal price is found to be significant.

However, some coefficients

are positive while others are negative. These could perhaps be explained by the different demand responses at various block levels of the tariff schedules faced by a particular

consumer. Also, the data limitations

may have influenced these

poor results. For the inframarginal price, GP primary has negative and significant 123

Summary, conclusions and suggestions coefficients while GP secondary has positive and significant estimates. XMD shows insignificant results. The average price elasticities are, however, negative and significant for GP primary and GP secondary. The results for XMD are still insignificant. Using annual data, the average price is negativeand significant with a value approximately equal to the estimatesusing monthly data. For the income variables, both the employment level and the index of industrial sales are found to be insignificant. Using annual data, however, the GNP elasticity of demand for electricity is positive and elastic. LPG appears to be a substitute for electricity for GP primary but a complement for GP secondary. The results for XMD are insignificant. Firewood is a complement for GP primary but not a substitute for electricity in so far as XMD and GP secondary industrial consumers are concerned. Using annual data, LPG is a substitute with a coefficient estimate approximately equal in magnitude with the monthly estimates for GP primary. Changes in the price of refrigerator have insignificant effect on electricity demand of XMD, negative effect on electricity demand of GP primary and positive effect on electricity demand of GP secondary industrial consumers. Changes in the price of air conditioner have negative effect on electricity demand by XMD, insignificant effect for GP primary and positive effect for GP secondary industrial consumers. Tests using annual data yields a positive elasticity for air conditioner. With regards to the maximum temperature and relative humidity variables, the demand for electricity by GP primary industrial consUmer is positively correlated with changes in these environmental variables. The results for the other types of industrial consumers are insignificant. The system peakload elasticity coefficients for industrial consumers ranges from 0.2557 (11) to 0.5409 (11) for GP primary and GP secondary, respectively. GP primary has the lowest peakload coefficient not only among industrial consumers but also among all types of consumers. These peakload coefficients for industrial consumers are generally lower than those of the commercial consumers except the primary account. The peakload coefficients for residential consumers are highest. As in the caseof the commercial consumers, the peakload coefficient estimate for industrial consumers using annual data is significant and negative but with a relatively larger magnitude. Residential estimates are positive but insignificant. Again, while these could provide indications and basis for determining peakload responsibility over the long-run, caution should be made in interpreting these results possibly due to data limitations. Chapter 6 integrates the results of the preceding three chapters wherein the 124

Summary, conclusions andsuggestions findings for each of the five sets of explanatory variablesare discussedacrossthe various types of consumers.Also, the results on the peakload elasticity coefficients and some added variablesare presented.The implications of the differences between short- and long-run price, income and other elasticitiesare also discussed in the context of a dynamic reformulation of the demand model. Some limitations of the demand model are described. Finally, the implications for pricing policy are derived. II. Conclusionsand Suggestionfor Further Research This study demonstratesthat setting up a tariff structure for electricity without having a clear ideaon the demand behavior of the varioustypes of consumers could result in price signals which may not help in the efficient allocation of electricity. This conclusion is particularly evident from the resulting consumer demand behavior arising from the institution of the MERALCO subsidy program which was initiated sometime in 1974. This further demonstrates that using electricity pricing asa means for income distribution distorts the price signals of a commodity which has strong linkagesin the economy. This could be detrimental to the economicgrowth process. The evidence that long-run price and income elasticitiesare generally larger than short-run elasticities provides a clear warning to utilities and economic policy makers in the power sector that they should not allow the price of electricity to unnecessarilyfluctuate. Rather, extra efforts should be exerted to maintain a stable price over the long-run, providing a clear signal to all the economic agentsin the economy so that sustainedgrowth not only in the power sector but probably in the entire economy could be realized. The significant and positive effects of temperature and relative humidity on demand for electricity only serveto reinforce the important yet often disregarded influence of the environment especiallythe ambient temperature and humidity to power requirements. This finding, together with the need for a stable price of electricity over time, providesempirical basisfor the application of marginal cost pricing. In establishingtariff structure based on marginal costs, the estimatesof the peakload elasticity coefficients arrived at in this study could perhaps serve as an alternative basisfor determining peak responsibility of the various categories of consumers. The Electricite de France International (1985) study adjuststhe estimated marginal costs ("theoretical tariffs") to satisfy the revenue constraints of the utility firm based on its averagefinancial cost. The difference between the theo125

Summary,conclusions andsuggestions retical tariff and the average financial cost determines the "adjustment coefficient". This adjustment coefficient is neutrally applied on the theoretical tariff at all voltage levels to arrive at the proposed tariff. Given the revenue constraint, this proposed tariff is not optimal (or "given-optimal" in the sensethat it is in the area of second best) since consumers at different voltage levels, e.g. residential, commercial and industrial, have different price elasticities as shown in this study, Thus, 40 an important area for further research is to reestimate the proposed tariff rates taking into account the demand elasticities of the various types of consumers to arrive at a quasi-optimal, and hence, economically efficient tariff. Finally, since hourly changes in temperature and relative humidity are greater than monthly changes, it is postulated that hourly changesin these environmental variables have significant effects on demand for electricity. If this is empirically established, then the policy implications of the results could be applied not only in the power sector but also in other related policy areassuch as daylight saving time, transportation management and labor overtime policies. Thus, another important area for further research is to empirically determine to what extent do environmental variables influence the daily load curve.

For proof,seefor instance, BaumolandBradford(1970). 126

References

Acton, J.P., B.M. Mitchell and R. Sohlberg(1980),"Estimating ResidentialElectricity Demand Under Declining-BlockTariffs: An Econometric Study Using Micro-Data," Applied Economics (April), 145-161. Barnes, Roberta, Robert Gillingham and Robert Hageman(1981),"The Short-run Residential Demand for Electricity", The Review of Economics and Statistics, Vol. LXIII, No. 4 (November),541-551. Baumol, William J. and David F. Bradford, "Optimal Departuresfrom MarginalCost Pricing", The American Economic Review, Vol. LX, No. 3, June1970, 265-283. Beierlein, JamesG., James W. Dunn and JamesC. McConnan, Jr. (1981). "The Demand for Electricity and Natural Gas in the Northeastern United States", The Review of Economics and Statistics, Vol. LXIII, No. 3 (August), 403-408. Bohi, Douglas R. (1981), Analyzing Demand Behavior: A Study of Energy Elasticities, Resourcefor the Future, Inc., The Johns Hopkins University Press,Baltimore, Maryland, 177 p. Cantoria, Filomena, M. (1977), "Socialized Pricing: A CaseStudy of MERALCO" Professional Chair Lecture MonographNo. 34, Universtiyof the Philippines,Universityof the PhilippinesPress,QuezonCity, 26 p. Caves,D., L. Christensenand J. Herriges(1984), "Modelling Alternative ResidentialPeak-Load Electricity..Rate Structures", Journal of Econornetrics, 24 (March), 249-268. Chi-KeungWoo, Philipp Hanser and Nate Toyama (1986), "Estimating Hourly Electric Load with Generalized Least Square Procedures",The Energy Journal, Vol. 7, No. 2 (April), 153-170, Donnelly, W.A. (1984), "Residential Electricity Demand Modelling in the AustralianCapital Territory: Preliminary Results", The Energy Journal, Vol. 5, No. 2 (April), 119-132. Dubin, Jeffrey A. (1985), Consumer Durable Choice and the Demand for Electricity, NorthHolland, Amsterdam,265 p. Electricite' de France(1985), "Electricity Tariff Study", December,7 Volumes. EnglÂ˘, Robert F., C.W.J. Granger, john Rice and Andrew Weiss (1986), "Semiparametric Estimates of the Relationship Between Weather and Electricity Sales", Journal of American 5tatisticalAssoc/ation, Vol. 81, No. 394 (June), 310-320. Francisco,Clodualdo R. (1984), "Electricity Pricing in the Philippines", PhilippineInstitute for DevelopmentStudies,Mimeo, 297 p. 127

(1987), "Residential Demand for Electricity and Pricing Policy Implications in a Developing Economy: The Case of the Philippines", Energy and Environmental Policy Center, john F. Kennedy School of Government, Harvard University, Mimeo, 43 p. Garbacz, Christopher A. (1986), "Seasonal and Regional Electricity Demand", The Energy Journal, Vol. 7, No. 2 (April), 121-]34. Halvorsen, R. (1975), "Residential Demand for Electric Energy", Review of Economics ond Statistics, Vol. 57, No. I (February), 12-18. Hogan, William W. (1985), "Energy Demand and the Outlook for Electricity", Discussion Paper Series No. E-85-09. Energy and Environmental Policy Center, John F. Kennedy School of Government, Harvard University, August, 21 p. Houthakker, Hendrick S. (1980), "Residential Electricity Revisited", The Energy journol (January), 29-41. Hwang,Jiunn T. (1986), "Multiplicative Errors-in-Variables Models with Applications to Recent Data Released by the U.S. Department Energy", journal of American Statistical Association, Vol. 81, No. 395 (September), 680-698. jeong-Shik Shin (1985), "Perception of Price when Price Information is Costly: Evidence from Residential Electricity Demand", The Review of Economics and Statistics, Vol. LXVII, No. 4 (November), 591-598. ]orgenson, Dale W. (1984), "The Role of Energy in Productivity Growth", The EnergyJourna/, Vol. 5, No. 3 (July), 11-25. Lawrence, Anthony and Dennis Aigner (eds.) (1979), "Modelling and Forecasting Time-of-Day and SeasonalElectricity Demands", Journal of Econometrics, Vol. 9. Learner, Edward E. (1978), Specification Searches: Ad HOCInference with Nonexperimental Data, john Wiley & Sons, New York, 370 p. (1983), "Reporting the Fragility of RegressionEstimates", The Review of Economics ondStotistics, Vol. LXV, No., 2 {May), 306-317. Lilliard, L.A. and D.J. Aigner (1984), "Time-of-Day Electricity Consumption Responseto Temperature and the Ownership of Air Conditioning Appliances", ]oul'nd of Business and Economic Statistics, Vol. 2, No. 1,40.53. Maddala, G.S. (1977), Econometrics, McGraw-Hill Book Company, New York, 516 p. Manila Electric Company (MERALCO) (1973), "The Effect of Rate changeson Residential and Industrial KWH Sales", mimeo, 26 p. Murray, Michael P., Robert Spann, Lawrence Pulley and Edward Beauvais(1978), "The Demand for Electricity in Virginia", The Review of Economics and Statistics, Vol. LX, No. 4 (November), 585-600. Nordin, john A. (1976), "A Proposed Modification of Taylor's Demand Analysis: Comment", The Bell Journal of Economics and Management Science, Vol. 7, No. 2 (Autumn), 719-721. Nordhause, William D. (ed.) (1977). International Studies of the Demand for Energy, North. Holland Publishing Company, Amsterdam, 340 p. 128

Parti, Michael and Cynthia Parti (1980), "The Total and Appliance-SpecificConditional Demand for Electricity in the Household Sector", The Bell Journal of Economics and ManegementScience, Vol. XI, No. 1 (Spring), 309-321. Ramanathan, Ramu and Allen Mitchen (1982), "Econometric and Computational Issuesin Estimating Demand for Energy by Time-of-Day", The Review of Economics and Statistics, Vol. LXIV, No. 2 (May), 335-338. Taylor, Lester D. (1975), "The Demandfor Electricity: A Survey", The Bell journal of Economics and ManagementScience, Vol. 6, No. 1 (Spring),74-110. (1977a), "The Demand for Energy: A Survey of Price and Income Elasticities", in Nordhaus,W. D. (ed.), International Studies of the Demand for Energy, North-Holland PublishingCompany, Amsterdam,1977, 3-43. (1977b), "Decreasing Block Pricingand the Residential Demandfor Electricity", in Nordhaus,W.D. (ed.), 1977, 65-80. Taylor, Thomas N. and Peter M. Schwarz(1986), "A ResidentialDemandChange: Evidence from the Duke Power Time-of-Day Pricing Experiment", The Energy Journal, Vol. 7, No. 2 (April), 135-151. Veall, Michael R. (1986), "On Estimating the Effects of Peak Demand Pricing", JournÂ˘lof Applied Econometrics, Vol. 1, No. 1 (January), 81-83. Westley, Glenn D. (1984), "Electricity Demand in a DevelopingCountry", The Review of Economics andStatlstics, Vol. LXVI, No. 3 (August),459-467.

129

Appendices

Aplamndlx3.1 MER.ALCO- RESIDENTIAL MONTHLY TOTAL NUMBER OF CUSTOMERS

JANUARY FEBRUARY MARCH APR1L MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER

1970

1971

474083 475696 476072 477805 479396 481672 483491 486034 486339 487354 488919 488778

492153 495770 499451 502623 504279 505934 506806 509690 510944 512102 514262 517836

Sourceof Bas|cData: MERALCO

1972

1973

1974

519566 522056 522317 526153 528319 528063 529459 529626 530774 532792 534831 536796

539799 547063 549762 551999 554722 558543 561084 562847 563857 565033 567404 569180

571798 573727 574765 577090 577278 579253 579792 584796 588828 590654 591478 592902

1975

1976

595763 594055 596534 597053 599184 601463 603030 606095 608024 609124 611253 614281

614703 616742 620023 621699 622410 621010 623719 627260 630765 633498 636896 638757

1977 641212 644273 644813 647586 649947 651172 649736 652812 653983 655609 655497 657606

1978

1979

656105 662630 664084 668630 668925 670926 695058 700061 701676 703178 705382 712003

714883 721438 725436 728071 732248 735067 736986 741366 746035 750629 754515 758632

1980

1981

762729 826565 765873 830245 757443 837318 774553 842757 776877 846947 776877 851345 784632 858736 ?94374 859665 795321 873158 798721 882212 803316 885983 822456 881854

1982 886194 901271 909928 916939 921828 922527 931113 939265 94.5238 950836 956728 962172

1983 " 967180 968845 1009081 1022068 1020529 1024126 1036840 1042899 1049017 1058597 1071169 1078896

1984 1083144 1086215 1085634 1085797 1098895 i104084 1111439 1123488 1133714 1146265 1155880 1163795

Appendix3,2 MERALCO - RESIDENTIAL MONTHLY TOTAL MWHCONSUMPTION

JANUARY FEBRUARY MARCH APRIL MAY JUNE IULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER

1970

1971

104497 94284 98467 115029 122769 114314 109427 _08723 105652 109290 104360 74966

102238 88684 97768 104278 111881 111881 111643 111120 119755 112671 111639 105462

Sourceof BasicData: MERALCO

1972

1973

110179 113347 9109 104595 101964 106133 117949 112897 128726 137348 127361 139018 115975 135943 109416 124231 110495 120625 115996 116058 114928 124724 109558 112246

1974 107710 100556 103977 107852 117370 113607 113942 104984 106883 113883 108281 102909

1975

1976

1977

108246 99775 104423 115424 129729 132359 122960 122866 121011 124350 121441 115301

112218 99441 109431 118328 137326 129475 13t725 130556 135470 140132 139100 134782

126768 121452 117439 131755 144980 148803 t44787 137962 135470 140132 139100 134782

1978

1979

136474 120920 130829 164101 167852 157373 161986 149370 148297 153133 148179 147973

159996 144026 153245 171787 181414 177476 184779 162901 169517 172963 177071 159783

1980 170330 166467 165040 187884 195443 203907 190269 187824 188086 191621 191405 188413

1981

1982

184693 151742 177425 205990 227961 213995 197279 202997 214161 217671 195782 206035

196630 175042 192110 222158 237549 238965 218224 210734 211960 223338 225626 217496

1983 229283 203581 219831 265011 257800 264425 267141 231362 233128 234163 242441 229914

1984 227685 220483 216630 254575 258586 252982 234087 243166 228236 240999 233992 242552

AiP0endtx 3.3 MERALCO AD,IUSTED REVENUES RESIDENTIAL (1970,-1984) 19?0

1971

1972

?973

1974

JANUARY FEBRUARY

8248758 7572997

10250731 8999569

11285361 10309849

13986623 12883934

16038032 17872820

22927581 20563683

24270867 20378440

28558706 26726394

3_762427 25775406

37163759 325_7206

42465893 42459687

MARCH APRIL MAY

7819205 8813906 9363031

9543786 10279806 10716345

10531212 12062395 13077855

13094820 13885328 16891300

19163043 23072780 30477333

22467603 26028673 30932501

24882115 26913980 34348574

26084953 31480139 368309_5

30029631 42204134 44495357

35608224 45051726 42581342

JUNE JULY AUGUST SEPTEMBER OCTOBER

10734022 10951894 10809108 10536889 10861369

11152884 11056130 11050868 11845011 11238559

14888965 _$663556 14733259 14912116 15021753

17082710 16702735 15282283 14808823 14279"158

26657389 26235045 23654878 23652697 22059852

31816673 28924308 28623022 28609717 29623734

31358467 31466300 31_08321 29492888 31660_95

37974350 36194426 33600962 31788046 33555292

39065631 39653232 34952607 33718365 33790610

NOVEMBER DECEMBER

10415231 74628¢5

11133399 10815196

14129391 13496181

15348617 13795762

22974309 21421275

28193904 25796949

32071210 27071804

32571649 29451650

33862855 33989052

Source of Baste Data: MERALCO Adjustments for 1974-'b975, s_e next p_ge

1975

1976

1977

1978

19?9

1980

1981

1982

1983

1984

57421095 46790283

68356813 64719361

92133543 85651649

118123076 127072615

S0648863 61936989 69287617

57246751 76802598 96617932

67302268 86288600 99344835

95198235 135853430 125223334

116666270 172495965 157941543

47591866 49439328 40016618 42202352 43709536

69356270 63612942 613266?8 61729714 64823980

82841385 68349213 72930569 82287025 79773788

98334402 82327848 81745241 83956687 91157347

135462043 132842065 107021579 114042448 120577426

170481686 140497387 173074680" 149877888 171346944

42351920 47503566

6077737? 75566068

64670193 92656691 67876193129386276

139017836 119700264

175923733 284772703

Appendix 3.4 SCHEDULE OF ADJ USTMENTS MERALCO - RESIDENTIAL 1976-1984 1976

1977

1978

1979

1980

1981

1982

1983

1984

JANUARY FEBRUARY MA RCH APRIL

16013 -111648 618585 -215244

135402 -191176 587754 993763

870980 ..405040 37681 817935

-88376 498196 2253070

t 081796 57292 7315593 10361428

-3576664 -1180996 -1046627 -1662359

-3870361 10081838 -89986 1001 635

-3790081 3347564 316860 2299105

-4152357 7367693 -2291421 8339995

MAY JUN E JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER

-413273 841187 45935 100374 242656 454439 47873 -533238

641976 528013 125115

975960 180207 -520912 114380 .484409 .435789 -233200 838371

3153080 t 691489 40951 -463545 -358588 -204739 2669021 2504591

3844732 2773342 -216379 -1003126 -334891 643442 -1036550 12542721

4526911 3297406 -4785925 -2089020 -243997 -1202654 -2042064 -4615955

1667658 -216875 -5200235 737111 -2079320 -2075766 -1233965 -1247128

2285132 4410219 -688435 3985418 531352 5261276 1.5723519 -4306806

-7944597 14854982 1872047 16545426 -7379216 6458788 15382459 29105184

-1252896

Source of basic data: MERALCO

•

Appendix 3.5 RESIDENTIAL ADJUSTMENTS TO REVENUE 1974-1975

Proportion of cost adjustments to revenue {1975-1984)

1976-1984

1978

1979

1980

1981

1982

1983

1984

JANUARY FEBRUARY MARCH APRIL MAY

.1126 .0822 .0985 .1149 .1451

.1159 .0974 .03 .1163 .1355

.1182 .09 1045 1,449 .t445

.1169 .0932 1346 .t384

.123 .1102 .1035 .1306 .1429

.1283 .0909 .1081 .1424 .1631

.1293 .0937 ,1139 .146 ,1613

.1479 .! 115 .1204 .1613 .1574

.1353 .1168 .1122 .1537 .1637

.1253 .0984 .1071 .1383 .1502

22016470 55882407 19488420 -11253403 19375618

3983931 1103950 3036079 1238789 -631234

2758884 5498829 2087210 -1556346 2910218

499187 108629 325164 170218 -94811

JU NE JULY AUGUST •SEPTEMBE R OCTOBER NOVEMBER DECEMBER

.1345 .1241 .1211 .1135 .124 .121 .1016

.1347 .1317

.1327 .1299 .1172 .1107 .t224 .1177 .106

.127 .1343 .1153 .1181 .1205 .1258 .1071

.1317 .132t .1262 .1244 .1274 .1207 .1182

o1454 .1271 .1277 .t 227 .1342 .1198 .1184

.1591 .1329 .1202 .1212 .1332 .1303 .127

.161 .1591 .1303 .132 .1286 .1313 .1226

.1481 .|263 .1305 .127 .1329 .1245 .119

.1416 .1331 .1236 1225 .1279 .1238 .1276

-13502741 -24155877 -3363483 -7268166 -126202Z 6100169 850749

2895413 6271313 7101440 8037452 3138043 -3138223 1514525

-i 911988 -3215147 -415726 -890350 -161413 755201 108556

409992 834712 877730 984598 401356 °388512 193253

,101

Source of basic data: MERALCO

-,j

;974

1975

Res'| Adjustments

1977

Average

AD.I USTMENTS

1976

'1974

1975

.....a go

Appendix 3.6 Summaryof MERALCO Rate Schedulesby KWH Blocks ResidentialConsumers,1970-1985 (In Pesos) 1970

1972

1974

1981

(New Area) 1983

KWH Blocks

May 21

Oct 1

Sept 1

Dec 4

Jan

0-10

2.000 a

5.000 a

11-14

2.000 a

0.500

15-50

0.125

0.500

51-60

0.125

0.150

0.550

61-100

0.070

0.150

0.550

I01-120

0.070

0.200

0.600

121-150

0.100

0.140

0.200

0.600

t 51-200

0.1 O0

0.1 40

0.200

0.600

201-250

0.100

0.125

0.350

0.365

0.360

251-750

0.I O0

0.125

0.350

0.365

0.360

751-2000

0.900

0.110

0.350

0.365

0.360

2000-above

0.080

0.1 O0

0.350

0.365

0.360

Appendix 3.6 (cont'd.)

Gen

1984 (Dec 1) Dist Gen

Dist

1985 (Feb 1) Dist Gen

Gen

Dist

C,har_e Charge (Old Area)

Charge Charge (New Area)

Charge

0-I 0

2.000 a

0.0

5.700 a

0.0

5.000

0.0

2.0

0.0

11-14

0.200

0.00

0.570

0.0

0.500

0.0

0.20

0.0

15-50

0,200

0.0

0.570

0.0

0.500

0.0

0.20

0.0

51-60 61-100

0,250 0,250

0.0 0.0

0.870 0.870

0.0 0.0

0.870 0.870

0,0 0.0

0,25 0.25

0.0 0.0

10t -120 121-150

0.250 0,250

0.0 0.0

0.920 0.920

0.0 0.0

0.920 0.920

0.0 0.0

0.25 0.25

0.0 0.0

151-200 201-250 251-750 751-2000 2000-above

0,250 1.985b 1,985 b 1.985 b 1,985 b

0.0 0.375 0.375 0:375 0.375

0,920 1.985 b 1.985 b 1.985 b 1,985 b

0.0 0.375 0.375 0.375 0,375

0.920 1.81-2.0825 c 1.81-2.0825 c ] .81-2.0825 c 1,81-2,0825 c

0.0 1.81-2.0825 c '().3751.81-2.0825 c 0.375 1.81-2.0825 c 0.375 1.81-2.0825 0,375 1.81-2,0825

KWH Blocks

Charge Charge Char_, (Old Area) (New Area)

aNet bill shall not be lessthan this amount, i.e, a fixed chargefor the indicated block. bAs computed for the month of December1984. CRangeof valuescomputedfor the variousmonths of 1985. Source of Basic Data: MERALCO

0.0 0.375 0.375 0.375 0.375

• Appendix 3.7 CONSUMER PRICE INDEX FUEL, LIGHT

& WATER (JAN 1974-DEC

1974

1975

1976

1977

1978

JANUARY FEBRUARY MA RCH APRIL

.702 .759 .864 .900

.881 .880 .881 .910

.9433 .9631 .9631 .9631

.9641 .9641 .9652 .9697

.9837 .9837 .9837 .9837

1.0782 1.0782 1.1081 1.9929

MAY UNE JULY AUG UST

:968 .946 .977 .971

.928 .932 .928 .928

.9635 .9631 .9631 .9631

.9847 .9847 .9847 .9847

.9837 .9844 .9844 .9844

SEPTEMBER OCTOBER NOVEMBER

.923 .924 .924

.928 .928 .929

.9631 .9631 .9631

.9847 .9847 .9847

D ECEM BE R

.924

.932

.9631

.9847

Source: NCSO Prices Division

1979

1980

1984)

1981

1982

1983

1984

1.4358 1.4660 1.6436 1.7062

1.9664 13670 t .9935 2.0134

2.3194 2.1818 2. t 920 2.1943

2.3948 2.4064 2.4357 2.4642

3.2784 3.3282 3.3838 33669

1.2183 1.2462 1.2501 1.4287

1.7903 1.7967 1.8023 1.922

2.0135 2.0138 2.0503 2.0606

2.2042 2.1843 2.3722 2.3601

2.4552 2.4490 2.5614 2.6060

3.4077 3.813 :4.1,244 4.1802

.9844 1.048 1.048

1.4287 1.4287 1.4286

1.9231 1.9384 1.941 6

2.1071 2.1087 2.1,032

2.3751 2.3874 2.3880

2.5449 2.6912 3.t 105

4.2764 4.3405 4.7743

1.048

1.4347

1.9458

2.2362

2,3936

3.2057

5.1050

Appendix 3.8 INDEXED LABORERS' AVERAGE DAILY BASIC WAGE RATES IN INDUSTRIAL ESTATES IN M MLA YEAR 1970 1971 1972 1973 19"/4 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984

SKILLED

UNSKILLED

122.2 110.1 1 9().88 73.74 71.88 67.56 67.79 71.02 66.76 60.30 59.24 55.46 54.36 51.10

119.2 109.1 1 88.55 70.99 72.12 68.54 65.52 63.66 57.23 50.50 49.61 46.45 45.52 42.79

Source of data: CB StatisticalBulletin Data for 1981-1984 were estimatedwith the application of growth rates in legislatedwages.

141

Appendix 3.8 (cont'd.) REAL INDICES NCR YEAR 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984

WAGES IN NON-AGRI SECTOR

AVERAGE EARNINGS MFG SECTOR

99.69 87.59 81.82 80.59 91.28 100.31 100 120.02 130.96 124.57 111 132.31 102.15

aNon-agri sectordata source:National WagesCouncil bMfg sectordata source: NEDA

107.8 98.9 I O0 94.5 74.1 84.9 85.3 84.3 89.9 88.1 91.2 92.9 95.7 96.5 77.7

Appendix

3.9a

1971 .REVE NUE IN % :34

MERALCO, Residential

32 30 28 26 24 22 z

20 18

16 Z

i., > ,., iX:

14 12 I0 8 6 4 2 0

0 ._

0.2

0.4

0.6 (Thousands) KWH MIbPOINTS

0.8

•

Appendix

3.9b

1975 REVENUE IN % MERALCO, Residential

4540-

30 z bJ

z tu 20 > tlJ n, 15

_ 0

,-_r_-_r_--_ 0.2

0.4

0.6 (Thousonds) KWH MIDPOINTS

Appendix

3.9c

1980 REVENUE IN % MERALCO, Residential

3028 26 24 22 2O z -- 16 hi 14 Z IU >12 w n,, I0 8 6 4 2 _

o'.2

0.4

0.6

(Thousands) KWH MIDPOINTS

0.8

;

Appendix

.--t

3,9d

1984 REVENUE IN % M ERALCO ,Residential

19 18 17 16 15 14 13 Z

z w >

9 8

llg

6 5 4 3

/-"

0.2

/ 0,4

0.6 (Thousands) KWH MIDPOINTS

0.8

Appendix 3.10 MERALCO ResidentialAnnual PerCapitaa KWH Consumption,1970.1984 Year 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1970-1984

Mean 218.1628 211.8916 214.7663 216.2180 186.4732 195.8966 202.0355 207.8620 217.6543 227.7584 236.5402 233.9654 230.4972 233.1422 213.4220 216.4191

S_ndard Deviations 25.5871 14.7411 16.1541 20.4196 9.2013 15.6551 19.5834 13.7079 19.9310 15.5917 13.1586 19.6831 18.1333 18.8665 12.7952 21.7853

aComputedfrom monthly total kwh consumptionand total number of customers. Sourceof basicdata: MERALCO

147

,.a

Appendix 3.11 Resultsof RegressionAnalysis(OLS)a ResidemialConsumers,MERALCO (January1971 to November1984) DependentVariable: RegCoeff

Constant

Marginal

CAPLb InfraMarginal

Model I Model II:

2.116 0.0587

0;0041 0.0971

0.1429 0.I 627

2.8528 4.8906

0.0256 0.0197

0.7416 0.0120

0.1615 4.9270

. Employment Fiat Firewood Level Iron

Refrigerator

Temperatur_ e

Humidity

0.0614 0.0947

0.0160 0.0039

0,1655 0.4445

-0.0349 -0.0843

-0.1135 -0.2620

0.3865 0.3985

0,1737 0.2135

0.0314 0.0910

0.0655 0.0722

0.0477 0.0508

0.2t 40 0,3977

0.0678 -0.0702

-0.1156 0,1253

0,1190 0.1256

0.0909 0.0983

4.5526 1.7872

0.9376 1.3131

0.3355 0.0765

0,7735 1,1177

-0.5138 -1.1860

-0.9824 -2.0914

3.2493 3.1737

1.9114 2.1725

LPG

Std Error Model 1: Model I1: T-Value Model 1: Model I1:

Model I MuItipleCorrelation R2 R2, Adjusted Std Error of Estimate Durbin-Watson Statistic Rho Estimate F-Value aThe Cochr_e-Orcutt

0.8353 0.6978 0.6803 0.0570 1.8397 0.6115 8.5601

Model II 0.8158 0.6655 0.6462 0.0599 1.8040 0.6040 6.1974

procedure is applied for all results.

bThe regression analysis here are the same with that for the results shown in Table 3.1 except that the price deflator used here for the prices of electricity, LPG and firewood is the Consumer Price Index instead of the index for fuel, light and water.

Appendix 3.12 Resultsof RegressionAnalysis(OLS)a ResidentialConsumers,MERALCO (January1971 to November1984) DependentVariable:CAPLb

Re Co

Con ., P!

Model I: Model iI:

3.4230 1.0678

0.0129 0.1032

0.1302 0.1797

0.0678 0.0680

0.0293 0.0558

0.0316 0.0344

0.1916 0.5431

-0.0399 -0.0894

-0.1071 -0.2383

3.0809 4.7744

0.0266 0.0200

0.0316 0.0903

0.0533 0.0556

0.0694 0.0733

0.0501 0,0518

0.2263 0.3880

0.0681 0.0696

1.1110 0.2237

0,4841 5.1588

4.t 131 1.9905

1.2723 1.2240

0.4225 0,7617

0.6312 0.6637

0.8465 t.3998

.0.5864 -1.2859

__v A1

_5_ A3

.0.1366 -0.2067

0.354t 0.3595

0.1578 0.1881

0.1179 0.1241

0.0866 0,0873

0,1177 0.1235

0.0919 0,0981

-0.9086 -1,9208

-1,5763 -2,3684

3.0080 2.9095

1.7172 1.9181

Std Error Model I: Model II: T-Vatue Model I: Model I1"

MultipleCorrelation R" R2,Adjusted 5td Error of Estimate Durbin-Watson Statistic Rho Estimate F-Value

: : : : : : :

Model I

Model II

0.8401 0.7058 0.6848 0.0566 1.8709 0.6445 7.0231

0.8253 0.6811 0.6583 0.0589 1.8186 0.6198 5.7413

aTheCothrane-Orcutt procedure isapplied forresults of ModelsI andII. bTheregression anatysis herearethesame withthatfortheresults showninTable3,2,exceptthatthepricedeflatorusedherefortheprices of electricity,LPGandfirewood istheConsumer Priceindexinstead of theindexforfuel,lightandwater.

Appendix 3.13 Results of RegressionAnalysis(OLS)a MERALCO Residential,Annual Data (1971-1984)

DependentVariable: RESPERAN Reg Coeff Model I: Model II:

Average

Marginal Inframarginal

Constant 3.2652 2.9017

Price -0.5195 -0.5288

Price 0.0028 0.1163

Price

Maximum

GNP 0.1355 0.3879

LPG 0.1268 0.1261

Aircon -0.0507 43.0466

Temperature 0.6389 0.0482

2.4369 1.9431

0.1213 0.0992

0.0701 0.0568

0.0957 0.0236

0.0882 0.1749

0.0885 0.0748

0.1180 0.0988

0.5457 0.5640

1.3399 1.4934

-4.2835 -5.3316

0.0395 2.0462

0.6374 1.7161

1.5361 2.2178

1.4329 1.6750

-0.4298 -0.4719

1.1707 0.0855

Modeil

â&#x20AC;˘ Model II

: :

0.9425 0.8882

0.9591 0.9199

R2, Adjusted : Std Error of Estimate : Durbin-Watson Statistic: Rho Estimate : F-Value :

0.7578 0.0359 2.7500 -0.5678 6.8099

0.8264 0.0304 2.8764 -0.4583 9.8430

Price 0.0610 0.0405

Price

Std Error Model I: Model I1: T-Value Model I: Model I1:

Multiple Correlation R2

a'l-hespecificationof the variablesand data usedfor the resultshere are the samewith thoseshownin Table 3.8 except that the price deflator usedherefor electricity and LPG is the CPI insteadof the price index for fueT,light and water.

Appendix 3.14 Resultsof ResressionAnalysis(OLS) MERALCO Residential,Annual Data (1971-1984) DependentVariable: RESPERAN

Re_ Coeff Model I: Model I1:

Constant 2.2403 -0.9635

Average Price .0.$203 -0.5153

Marginal |nframarginal Employment Price Price Price Level . LPG 0.0166 0.0548 0.1618 0.1511 0.1673 0.0488 0.5702 0.1954

Price Aircon -0.0184 0.0561

Maximum Temperature 0.7992 0.4440

2.5460 2.8085

0.1246 0.0996

0.0692 0.0747

0.0973 0.0269

0.1147 0.2551

0.0833 0.0734

0.1137 0.1034

0.5349 0.4629

0.8799 -0.3431

_4.1758 -5.1733

0.2394 2.2398

0.5630 1.8131

1.4106 2.2350

1,8139 2,6614

.0.1621 0,5433

1.4942 0.9592

Std Error Model I: Model I1: T-Value Model h Model I1:

Model I

Model II

Multiple Correlation R2

: 0.9396 : 0.8829

0.9596 0.9208

R2, Adjusted Std. Error of Estimate Durbin-WatsonStatistic Rho Estimate F-Value

: : : : :

0.8284 0.0303 3.1695 -0.6963 9.9619

0.7463 0.0368 2.7080 .0.5952 6.4640

Appendix 4.1 MERALCO COMMERCIAL MWH CONSUMPTION,

JAN 1970-DEC.

1970

1971

1972

1973

1974

1975

1976

JANUARY FEBRUARY MARCH APRIL MAY

108154 105749 110379 109214 138409

110778 101164 116652 115578 119838

1"J8569 115002 122074 123099 134812

1.28802. 126217 ]26377 132929 143293

121218 124550 131998 129600 137350

133479 134746 137357 141482 160433

138016 138506 152013 156290 161355

JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER

121642 12|292 121351 116590 120127 107719 90811

1.19838 122386 125017 131795 127058 ]19591 120875

133425 128777 124261 128927 132263 131797 128793

147738 150220 144034 138961 140918 140844 128175

141.597 144187 139456 137468 147190 138555 135753

158630 157477 158074 160701 161291 156364 152266

158728 166516 170764 169614 187130 182999 176204

1.14290.2 119214.2

126816.6

M EA N

Source of Basic Data: MERALCO

137575.7 135743.5

" 1977

1984

1978

1979

1980

1'981

1982

1983

1984

168572 175849 174336 176448 I78682

174581 173621 182619 205154 206746

200158 192050 198842 203825 216238

199471 199221 "207454 220652 222248

207739 190166 210830 221470 236962

212317 201307 217931 228748 242308

231736 270389 238768 259053 263073

214958 220084 220120 239522 230819

1.90961 190279 183658 181036 188233 186677 182010

200816 209137 197771 192505 194616 187898 197178

217540, 225952 206459 208876 216853 215562 205997

232086 222065 229050 239674 244269 224574 221791

244970 236338 234125 237432 242353 248859 239479

266861 271533 247971 239469 241623 245956 237086

235887 227259 233473 223259 223997 221271 216886

247793.2

225627.9

151025 163177.9181395.1

193553.5

232947 222423 " 221638 227760 225951 219015 218366

209029.3218095.5

225951

223389.7

Appendix 4.2 ME RALCO COMMERCIAL NO.OF CUSTOMERS

JAN 1970 -DEC 1984

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

JANUARY FEBRUARY MARCH APRIL MAY

67704 68086 68085 68885 68551

69876 70004 70314 70964 70722

72155 72471 72284 72800 73068

73566 73989 74_72 74592 75112

78196 78661 78940 79391 79280

82135 82373 83177 83495 83809

86889 87262 87611 88571 88994

92620 93603 93517 93633 92450

91308 93965 94414 95736 95619

99590 10.t280 101924 101974 102804

104097 105684 104252 106858 107129

111470 t09288 110012 110066 110196

111351 113595 114971 115543 115356

117849 118510 120982 121406 121336

125029 125128 123982 122935 124262

JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER

68912 68927 69145 69361 69514 69783 68707

70480 78533 70765 70690 71083 71591 71900

72734 72555 72572 72619 72837 73072 73414

75795 76021 76498 76466 76663 7731 ! 77866

79437 79656 80387 80676 81448 8t407 81771

83910 84_82 84906 85132 85576 86050 86207

88746 89483 90079 90228 90663 91319 91793

93830 95517 92797 98599 93597 99207 93023 99095 93388 99430 93111 99460 95587 102810

102733 102767 103108 103498 104497 104624 105530

107149 107374 107382 108116 107461 107633 111488

110444 110905 111020 112565 113198 112826 114096

114671 115656 116515 116638 116927 117427 120666

_22208 123496 123606 124012 124005 125768 128944

124765 125188 126351 127296 129129 129312 132385

68738.33

70743.5

72715.08

75670.92

79937.5

107051.9

111340.5

M E A N

Source of Basic Data: MERALCO

I./,i

84246

89303.17

93429.67

97096.67

102860.8

115776.3

122676.8

128313.5

Appe_ldlx 4.3 MERALCO ADJUSTED REVENUES COMMERCIAL (1970-1984) 1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

t982

1983

1984

JANUARY FEBRUARY MARCH APRIL MAY

7245575 7197618 7266350 7343888 10303762

10283474 9831808 10750228 10747754 10945784

11382709 11211848 11595976 !.1796588 12632658

15188663 15050181, 15147672 15692330 16731293

23508398 36109096 33958357 34207341 48151900

40888853 40459821 43024128 43216787 47803629

45923237 46442371 52597267 52104707 54990020

57200283 58070345 62207490 62_,04583 63273138

51994236 59432059 64892757 72649?49 75125486

69144254 71105385 70883837 77917773 83773990

89450522 87618081 117748496 127683361 133540447

"b27339854 126148280 142705562 153554946 176484562

149118498 199826997 173050166 177328293 188698601

179875879 204368980 218320417 238982999 233591590

268106142 33499"_327 312343380 362797962 306538909

.pUNE JULY AUGUST

8856583 10935451 11071699

11143814 11225807 11412767

15098381 "16877721 16555465

17207048 17313187 18808675

41014662 39778311 45255034

48794079 49589681 50582784

54652813 35970265 57477656

67524504 66608002 63885561

69577461 72343835 69197286

86129148 86817555 79133790

139829568 128398149 130590539

167545086 156195049 165658122

188630324 172345266 188444514

248118?54 246005520 238638888

365928038 347959232 429945213

SEPTEMBER OCTOBER NOVEMBER DECEMBER

10721790 11063601 10115750 8523480

11837574 11629140 11080581 11366604

16895733 _.6467847 15551003 15288137

18431399 18537245 16617005 15367824

40346558 36138505 41481120 40238405

53200643 52509185 51019454 50410677

56775935 62322330 61051559 57319752

62664770 65493483 63334823 58427380

66450234 68089767 64880535 71487508

86417599 80461897 92522863 88190399

138695569 14,0712109 134725960 185023545

18_.618722 177216357 154756710 157321410

189697172 191578033 197504124 215745995

249902104 271081748 317734978 292379893

373753883 433838029 451782460 623293647

Soulce of basic dita: MERALCO

Appendix 4.4 MERALCO COMMERCIAL (Xl) AD] USTMENTS AT 1P'IKWH,JAN 1974 - DEC 1984 (DEFLATED)

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

JAN UA RY FEBRUARY MARCH APRI L MAY

.4016 .2211 .0647 -.036 .0546

.0133 .0035 .0035 .0036 -.002

.0004 -.003 .0143 -.004 -.005

.0028 -.004 .0137 .0183 .0095

.0162 -.010 .0007 .0105 .0122

-.001 .0091 0 .0249 .0305

.0117 .006 .0716 .0785 .02 49

-.027 -.013 -.003 -.010 .0221

-.025 .0959 -.001 .0054 .0076

%019 .0221 .0018 .0087 .0089

-.019 .0353 -.011 .0273 -.025

JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER

%038 -.085 -.010 -.022 -.004 .0185 .0027

.0073 .0163 .0167 .0246 .0084 -.009 .0042

.0144 -.001 .0018 .0047 .0072 .0008 -.012

.0074 .0018 0 0 0 0 -.025

.0025 %007 .0019 -.008 -.007 -.004 .0143

.0173 .0004 -.005 -.004 -.002 .0256 .0295

.0165 -.002 -.007 -.002 .0044 -.008 .0953

.0187 -.032 -.014 -.001 -.007 -.014 -.032

-.001 -.028 .0044 -.012 -.011 -.005 ..007

.0163 -.002 .0192 .0025. .0247 .0618 -.019

.0445 .0265 .0530 -.025 .02i 3 .0494 .1070

M EA N STD DEV'N.

.0241 .0782

.0079 .0065

0015 .0079

.0020 0109

.0019 .0094

.0105 .0141

.2239 .0366

-.010 .0172

.0019 .0316

.0105 .0216

.0220 .0394

Sourceof basicelectricity data: MERALCO Sourceof price index data: NCSO PricesDivision

156

..-

...._ 04

I 1." 'l

="' !

% NI NOIld_nSNOO

I .....i

Hh_M

! ' tr I

LgL

O--

IN %

I_ (d 4=, OI O_ -'.40_ ¢.OO -- I_ O,I .Ib OI 0_.,,4 00 _

KWH CONSUMPTION

Appendix

1980 22

4.5c

KWH CONSUMPTION MERALCO, Commercial (X - I ]

18 16 z_ z 0 Io. :E :3 u) z 0

u -I-

[4 12 I0

8 6 4

2 F 0

\ ,

0.2

0.4

0.6 0.8 (Thousands) KWH CONSUMPTION BLOCK MIDPTS

Appendix

4.6a

1971 NO. OF CUSTOMERS 8,

MERALCO, Commercia I (X-I)

U) n* • bJ

=E 4 0 I(n ::) 30

, 0

l !

/, '

0.2

0.4

I I

0.6 0.8 (Th0usands) KWH CONSUMPTION BLOCK MIDPTS

1 I

Appendix

4.6b

1975 NO.OF CUSTOMERS MERALCO, C0mmerciol (X-l) 8

6 z m rY bJ :E o lu) o " o

d z

, 0 0

/ .......

0_2

, 0.4

0.6

(Thousands] KWH CONSUMPTION BLOCK MIDPTS

O.B

0 _

-J

_ I

O_ I ,

.I_ I

NO. OF CUSTOMERS

01 _.t.

IN %

O_ J

_,._

_9L

Appendix

4.6d

t984 NO. OFCUSTOMERS MERALCO, Commercial

(X -I)

5 z ¢o o_ w m

0 iu L_ 0

d z

0 c_o

_ 0.2

0.4

0.6 0.8 (Thousands) KWH CONSUMPTION BLOCK MIDPTS

Appendix C_

4.7a

1971 REVENUE S I6

MERALCO, Commercial (X - I )

15 14 , 13 12 II I0, z

,,, ;>. "' n,

7 6 5 4, 3 2.

o fr -, 0

\ |

0.2

\ I

0.4

0.6 0.8 (Thousands) KWH CONSUMPTION BLOCK MIDPTS

Appendix

4.7b

1975 REVENUES MERALCO, Commercial

(X - I )

22 20 le 16 z bJ Z > n,

14 12 I0 8 6 4 2 '

-0

0.2

0.4

0.6 0.8 (Thousands) KWH CONSUMPTION BLOCK MIDPTS

1980 26

Appendix

4.7c

REVENUES

MERALCO, Commefciol ( X- I )

24 22 20 18' 16 Z

14.

zhi

"' ns

I0 8 6 4 2 0 0

0.2

0.4

0.6 0.8 (Thousand s) KWH CONSUMPTION BLOCK MIDPTS

Appendix

4.7d

1984 RE VE N UE S MERALCO, CommercloI ( X- I ) "

28 26 24 22 20 18 z z u_ > uJ a_

16 14 12 I0 8 6 4 2 0

0 "J

O. 2

0.4

0.6 0.8 (Thousands) KWH CONSUMPTION BLOCK MIDPTS

Append'ix 5.1 MERALCO INDUSTRIAL •- •

JANUARY

MWH CONSUMPTION, 1970

1971

1972

1973

1974

1975

1976

JA N 1970-DEC

1977

1978

1984 |979

1980

1981

1982

1983

1984

126680

128767

149177

155898

161342

155074

173747

190679

204874

255524

245822

226393

21342.8

255396

205998

. 135321

138693

162337

17508"b

185094

t91382

204713

228586

235265

277960

278069

268954

248776

252955

237903

MARCH

150501

153586

175518

172464

186042

_180672

209654

237362

238994

282875

278790

260346

246225

268140

240039

APRIL MAY _UNE.

146949 158347 155596

I4_895 t'55538 164180

156755 177656 132640

180395 181727 192580

180819 191273 198961

182266 196247 201020

210408 205736 201919

229308 230650 240394

237914 250081 252249

265763 '291563 302674

275302 .273880 280886

267589 268333 272380

245413 261321 262262

280178 _278223 289836

244029 224895 2.50686

JULY AUGUST

152968 155279

163117 166924

158217 144748

192305 191579

197652 197003

204890 218409

225909 225764

238899 233244

273296 267276

292810 272311

291461 270912

261715 261363

257100 267417

284052 264540

236266 248802

SEPTEMBER OCTOB E R

135691 141233

169746 171002

159105 173378

183419 194112

195444 189790

215090 2 18284

226854 228763

232473 238451

257934 252780

283342 280766

274953 269234

280136 276867

270521 259830

264457 275805

234395 223904

NOVEMBER DECEMBER

134631 115045

162078 159908

'i68637 166450

" 196"f60 185772

186444 178072

214614 207484

230908 226320

223707 240070

250591 268637

272779 270726

258258 261584

253434 256337

274069 258225

275776 256123

220616 194553

FEBRUARY

Source of Basic Dat*:

ME RALCO

Appendix 5.2 MERALCO INDUSTRIAL NUMBER OF CUSTOMERSf

1970

1971

1972

1973

JANUARY FEBRUARY

1448 1451

1483 149t

1704 1720

MARCH APRIL

1456 1423

1498 1506

MAY

1458

JUNE JULY AUG UST

1464 1,476 1476

SEPTEMBER OCTOBER NOVEMBER DECEMBER

1984

1974

1975

1976

1977

1978

1979

1980

1981

1982

1866 1880

1990 1998

i 988 1994

2t 36 2142

2286 2290

2469 2469

2741 2756

2949 2976

3190 3205

3319 3331

1729 1731

1891 1902

1998 2034

1983 1985

2173 2183

2312 2330

2464 2488

2746 2786

2984 3022

3225 3243

" 3348 3343

1528

1753

1905

1977

1989

2181

2336

2509

2810

3044

3241

3363

1549 1577 1606

1778 1796 1800

1916 1927 1941

1957 1951 1953

2001 2013 2033

2187 2197 2218

2359 2374 2387

2553 2652 2662

2822 2835 2870

3024 3090 3108

3251, 3264 3304

3368 3368 3377

1468 1476 1493

1638 1645 1,660

1837 1838 1859

1940 1958 1967

1947 1966 1983

2052 2074 2101

2223 2382 2253

2402 2410 2437

2673 2704 2713

2886 2893 2935

3132 3147 3164

3307 3305 3327

3416 3407 3399

1467

1681

1859

1975

1982

2122

2279

2723

2713

2950

3189

3307

30,24

Source of Basic Data: MERALCO

JAN 1970-DEC.

Appendix 5.3 MERALCO INDUSTRIAL NET ADJUSTMENTS TO REVENUE JANUARY 1974-OECEMBER 1984 1974

1975

1976

JAN UA RY FEBRUARY MARCH APRIL MAY

10232909 28560153 9820754 -5328067 9007823

185 T 668 564202 1529964 582733 -293464

JUNE JULY AUGUST

.6419402 -115260t8 -1626395

1376522 2992368 3433865

3130346 -189878 418557

-3546081 -601730 2927794

3921410 1496215 1506199

1109523 1865658 197636

418228

730298

-2710908

SEPTEMBER OCTOBER NOVEMB E R DECEMBER

71942 -762297 3410395 -974383 -1399553

Source of basic data: MERALCO The 1974 and 1975 adjustmentsare esZimatesonly. See next table for the proportions used.

1977

1978

548815 -1003035 3452085 4316315 2364705

3490141 -2332433 184523 2555537 3105152

1950713 459150

648600 -1943243 486012

-6261775

1979 -356205 2699252

1980

1981

1982

1983

4270702 270374 34447061 38701322 12924231

-12940166 -6916342 .4883036 -5485184 12509921

.13514822 54762664 -378914 3095645 4569062

6813146 150363 -2046176

10333438 -799944 -3864260

10500552 -17819596 -7733616

-604478 -18103196 2932827

12191994 -1900866 14081 314

45041588 6761311 58173562

-2251869 -1743578 -985 611

-1562566 -853105 10453781

-1313562 2418096 -4190425

-871035 -4153016 -8106380

.8035491 -7127488 -4402239

1878073 19457486 56218062

-26623325 21296731 55823793

4044320

11928809

52336868

-18494875

-4515618

-16367578

100426544

8261366 11361422

-10947271 14227614 1250952 6331514 6398252

1984 -13321799 29594903 -9659243 23701133 -20391291

Appendix 5.4 M ERALCO -- INDUSTRIAL PER KWH ADJUSTMENTS JAN 1974- DEC 1984 (DEFLATED) 1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

JANUARY FEB RU A RY

.0903 .2033

.0136 .0034

.0004 -.004

.0030 -.005

.0173 -.010

-.001 -0090

-0121 .0007

-.029 --013

-.027 .1009

-.O18 .0234

-.020 .0374

MARCH APRI L MAY

.0611 -.033 .0487

.0096 .0035 -.002

.O169 %005 -.007

.0151 .0194 .0104

-0008 .0109 ,0126

.O261 .0320

.0752 .0824 .0264

-009 -.010 .0232

-.001 .0057 .0079

.0019 ,0092 .0094

-.012 .0288 -,027

J UNE

-.034

.0073

.0161

.0082

,0026

.0181

.0205

.0191

-.001

.O172

,0471

JU L Y A U GUST SEPTEMBER

%060 -.009 -.020

.0157 .0169 ,0196

-.O01 ,0019 .0051

,0020

-.007 .0018 -.009

.0004 .-005 -.004

-.002 %007 --002

--033 --014 -,001

..030 .0046 -.013

-.003 ,0204 ,0027

.0069 .05 S9 .027

OCTOBER NOVEM BER DECEMBER

-.003 ,0t 70 -0025

.0074 --008 .0038

.0085 .0009 -.O12

-.026

-.007 -.004 .0144

-_002 .0268 .0307

.0046 -.008 .1028

-.007 -.015 -.032

-.O11 -.007 -,007

,0262 0655 -.020

.0219 .0530 .1011

MEAN

.0221

.0076

.0017

.0034

.0020

.0119

.0254

-.010

.0019

.0113

.0223

Source of Basic Data: MERALCO

..j

Appe.d!x 5,5 MERALCO INDUSTRIAL ADJUSTED 1970 JANUARY FEBRUARY MARCH APRIL MAY

1971

1972

1973

1974

1975

REVENUES, 1976

JAN 19701977

DEC 1984 1978

1979

1980

1981

1982

1983

1984.

5843415 6161220 6494812 6482198 9681696

8858565 9457976 9800936 9857086 10211813

10456915 11_78626 11677044 10997676 11842434

14098238 15373184 15260184 15594157 15998189

25497890 45015369 44703176 41379526 58258232

43961681 51381241 91591657 9096513l 54352446

53815171 62389968 69034080 64007268 64805837

65008608 68880850 76599619 73298916 75804953

68296-143 73624421 78524627 77302922 84112489

81777634 92345268 95592894 90056259 103317990

103541472 113393942 152435970 153787804 160048042

136061197 172224316 173185198 180291372 197428778

148922514 245813997 193932733 188204427 202510251

175080014 226928642 245750869 259607000 246900955

2647.00894 372652122 347013754 379006844 304240399

JUNE JULY AUGUST SEPTEMBER OCTOBER

7155159 10007588 t0078990 9_52551 9398656

10566540 10659257 108892_2 11101434 11_50556

15173711 16916917 14899846 15916303 15486389

16930927 16594825 16586208 16041898 16776812

50471207 49490469 58426157 48858701 42857897

55745013 58644442 62046926 64819637 64047946

64308201 68459507 68675289 69109154 71406441

78876490 "16989275 75708411 73710987 76]88190

80090781 86740894 85433032 81302579 81171274

111140265 10492931.1 96827820 101000734 100899098

163388882 163088298 153620697 163203090 162610103

.]92452928 179714987 190481171 210346445 197311390

199520363 186051_,00 213710082 217;683181 204345755

269265493 9.57366651 256045533 278619377 313229907

396747757 371575343 473366074 404164.572 437962872

NOVEMBER DECEMBER

8924047 7390478

10776487 11020129

14867721 14717784

16884565 16363845

48780571 47953120

63672872 62977919

71029268 66036916

70845549 69881710

79748131 88487176

109692044 109188254

156165631 222296]20

172259755 _8026684

215932009 248940165

361884444 322522876

479792039 575194221

Sourr.e of Basic Data: MERALCO

Appendix 5.6 ME RALCO INDUSTRIAL GROWTH RATES OF MWH CONSUMPTION, JAN 1970 -- DEC 1984 (%) 1970

1971

1972

19"73

1974

1975

1976 " 1977

1978

1979

1980

1981

1982

1983

1984

JANUARY FEBRUARY MARCH APRIL MAY

6.599 10.63 -2.39 7.470

11.27 7.426 10.20 -4.45 5.717

-6.95 8.454 7.807 -11.3 12.52

-6..55 11.60 -.1.51 4.498 .7357

-14.1 13.73 .5109 -2.85 S.621

-13.8 21.04 -5.76 .8784 7.391

-17.9 16.40 2.385 .3590 -2.25

-17.1 18.13 3.767 -3.45 .5835

-15.9 13.83 1.573 -.453 4.988

-5.00 8.416 1.753 -6.24 9.265

-9.65 12.33 .2590 -1.26 °.518

-14.4 17.23 -3.25 2.744 .2777

-18.3 15.33 -1.03 -.330 6.281

-t.10 -.960 5.830 4.392 -.700

-21.8 14.40 .8938 1.649 -8.17

2UNE .TULY AUG UST SEPTEMBER OCTOBE R NOVEMBER

-1.75 -1.70 1.499 -13..5 4.003 -4.79

5.407 -.650 2.307 1.676 .7372 -5.36

-2.8§ -8.72 .8.90 9.457 8.591 -2.77

5.801 -.143 -.378 -4.35 5.666 1.050

3.941 -.660 -.329 -.795 -2.94 -1.78

2.403 1907 6.390 -1.53 1.474 -1.70

-1.87 11.23 -.064 .4816 .8380 .9333

4.138 -.624 -2.40 -.331 2.5 39 -6.38

.8632 8.014 -2.23 -3.56 -2.02 -.870

3.740 -3.31 -7.26 3.971 -.913 -2.89

2.526 3.696 -7.31 1.481 -2.10 -4.16

1.497 -.3.99 -.135 6.936 -t .17 -.884

.3594 -1.99 3.934 1.154 -4.03 5.335

4.089 -2.02 -7.12 -.031 4.202 -.011

10.86 °5.92 5.170 -5.96 4.58 -1.48

DECEMBER

-15.7

-1.35

-1.31

-5.44

-4.59

-3.20

-2.01

7.059

6.954

-.755

1.280

1.139

-'5.95

-7.39

-12.6

M EA N STD DEV'N

-.876 8.281

2.744 5.37

.3341 8.528

.9152 5.275

-.353 6.575

1.288 8.368

.7095 8.t48

.4915 8.347

.9389 7.357

.0646 S,451

-.286 5.601

-.169 7,808

-.068 4.26

-2.29 9.933

Sourceof BaslcData: MERALCO

.0612 8.046

Appendix 5.7 MERALCO INDUSTRIAL GROWTH RATES OF NO.OF CUSTOMERS, JAN 1970-DEC 1984 (%) 1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

•JANUARY FEBRUARY MARCH APRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER

.2070 .3440 -2.29 2.430 .4107 ,8163 0 -.543 ,5435 1.145 -1.76

1.085 .5380 .4684 .5326 1.450 1.365 1.791 1.822 1.973 .4264 .9077 i.257

1.359 .9346 "5219 .1156 1.263 t.416 1.007 .2225 2.035 .0544 1.136 0

.3758 .7475 ,5834 ,5800 .1576 ,5758 "5725 .7239 -.052 .9236 .4586 .4059

.7566 .4012 0 ,1.786 -2.84 -1.02 -.307 .1025 -.308 .9711 .8610 -.050

.3023 .3014 -.553 .1008 .2013 .6015 .5979 .9886 .9302 1.066 1.293 .9946

.6576 .2805 1.437 .4591 -.092 .2747 .4562 .9513 .225 2 6.098 -5.57 1.147

.3067 .1748 ,9561 .7755 .2572 .9798 .6338 .5461 .6264 .3325 1.114 11.10

-9.79 0 -.203 .9693 .8405 1.738 3.804 .3764 .4124 1.153 .3323 0

1.027 5458 -.364 1.446 .8578 .4261 .4596 1.227 ,5559 ,2423 1.441 "5098

-.034 .91i4 .2685 1.265 .7254 -.659 2.159 .5808 .7692 .4778 .5387 .7870

.0314 .4691 .6221 .5566 -.062 .3081 .3991 1.218 .0908 -.060 .6635 -.603

.3622 .3609 ,5091 -.149 ,5965 .1486 0 .2669 1.148 -.264 -.235 .7328

.5243 .9254 -.404 .0867 .2884 .6315 .1144 .2854 .1424 -.228 .4553 ].912

-1.12 .2813 .1964 .1121 .2238 -084 .5021 .-.028 -.028 -.084 .2505 -.223

M EA N STD DEV'N

.'i185 1.303

1,135 "5635

.8287 .651

"5044 .2632

.0295 1.164

.5688 .5238

.5948 2.694

1.483 3.043

-.031 3.259

.6979 "5283

.5492 .6834

.3028 .4647

.2897 .4227

.3945 "599

0 .4063

Sourceof BasicData: MERALCO

Appendix 6.1 Resultsof Regression Ar, qysis(OLS) ResidentialConsumecs(MERALCO) (Jan 1971 to Nov 1984) . DependentVariable: CAPL Independent , Variable

Rc,gr,e,ssionCoefficient Model I Model II

StandardError Model I Model ii

,,Computed T Value Model I Model II

Constant

5.9365

9,0724

2,5001

3.8145

2.3745

2.3784

MARGL INFRAL/ MANFRAL

-0.0032 0.1235

0,0756 0.0651

0,0237 0.0270

0.0184 0.0493

-0.1350 4.5665

4.1081 1.3194

AVEPRL PRLPGL FI REL LABORFRL

0.0489 0.0638 0.0275 -0.6329

0,0686 0.0782 0.0366 -0.7017

0,0489 0,0587 0.0406 0.2136

0.0515 0.0645 0.0444 0.3458

1.0017 1.0862 0.6762 --2.9629

1,3327 1.2117 0.8243 -2.0291

IRON L F RIDGEL AI RCONL

0.0294 0.0219 -0.2290

4).0288 -0.1328 -0.3132

0,0608 0,1034 0.0692

0.0625 0.1128 0.0754

0.4828 0.2121 -3.3089

-0.4614 -1.1768 -4.1565

TEMPMIAL HUMIDITL

-,1478 0.1296

-.1258 0.1160

0,1183 0,0805

0.1271 0.0885

1.2499 1.6104

0.9891 1.3107

PKLOADML

0,7911

0.7797

0,1234

0.1324

6.4132

5.8880

Model I

Model II

Multiple Correlation R2

: :

0.8748 0.7652

0_599 0.7394

R2, Adjusted Std Error of Estimate

: :

0.7468 0.0507

0.7190 0.0534

Durbin-WatsonStatistic Rho Estimate

; :

1.9384 0.4697

1.8622 0.4815

F Value

15.0411

11.9862

175

â&#x20AC;˘ Appendix 6.2 Resultsof RegressionAnalysis(OLS)a ResidentialConsumers(MERALCO) (1971.1984) DependentVariable: RESPERAN Independent Variable

Regression Coefficient Model I Model II

StandardError Model I Model_II

ComputedT Value Model I Model II

Constant

2.5281

4.0854

5.2950

3.1925

0.4775

1.2797

AVEPL MARPL

-0.2754 0.0473

-0.2891 0.1512

0.2498 0.0888

0.2024 0.0474

-1.1024 0.5328

-1.4248 3.1900

INFRATPL/ INFRANPL

0.0614

0.0466

0.1346

0.0235

0.4558

1.9802

GPNPKL LPGL

-0.9124 0.2317

-0.3546 0.2012

0.8117 0.2905

0.7167 0.2169

-1.1241 0.7978

-0.4948 0.9276

LAI RCON MXTEMPAL MXHUMEAL

0.1932 0.4400 0.9188

0.0971 -0.1061 0.2285

0.1938 0.7553 0.8705

0.1606 0.6572 0.6735

0.9965 0.5826 1.0556

0.6048 -0.1615 0.3392

PKLOADAL

0.9114

0.7111

0.7522

0.6171

1.2117

1.1523

Model I

Model II

176

Multiple Correlation R2

: :

0.9577 0.9173

0.9723 0.9454

R2, Adjusted Std Error of Estimate Durbin-WatsonStatistic

: : :

0.7311 0.0379 3.2113

0.8226 0.0308 2.8409

Rho Estimate F Value

: :

-0.7437 4.9267

-0.4253 7.6989

Appendix 6.2a Resultsof RegressionAnalysis(OLS) ResidentialConsumers(MERALCO) (T971-1984) DependentVariable: RESPERAN Independent Variable

RegressionCoefficients Model I Model II

StandardError Model I Model I!

Computed T Value Model I Model II

Constant

4.1676

4.6621

5.5380

2.2924

0.7526

2.0337

AVE PL

.0.4874

.0.3589

0.1782

0.1100

-2.7359

-3.2625

MARPL

0.0327

0.1530

0.0955

0.0360

0.3429

4.2484

INFRAT.PL/ INFRANPL

0.0613

0.0511

0.1463

0.0165

0.4191

3.0992

MAXTEMPAL

0.2523

-0.2237

0.8012

0.4747

0.3149

-0.4742

MXHUMEAL

0.2111

-.0560

0,6577

0.2749

0.3210

-0.2037

LAI RCON LPGL

0.0622 -0.0104

0.0455 0.1234

0.1692 0.2125

0.0941 0.1145

0.3676 -0.0491

0.4833 1.0777

PKLOADA L

0.0742

0.4179

0.1164

0.1319

0.6374

3.1673

Model I

Model II

Multiple Correlation R2

: :

0.9368 0.8776

0.9800 0.9604

R2, Adjusted Std Error of Estimate

: :

0.6819 0.0412

0.8970 0.0234

Durbin-WatsonStatistic

2.9221

2.6999

Rho Estimate F Value

: :

.0.6691 4.4829

-0.3573 15.1528

Appendix6.2b Resultsof RegressionAnalysis(OLS) a ResidentialConsumers(MERALCO) (1971.1984) DependentVariable: RESPERAN Independent Variable

RegressionCoefficient Model I Model Ii

StandardError Model I. Model II

Constant

2.829 4

4.5482

5.1292

2.5670

0.5516

AVE2 L

-0.4890

-0.3897

0.1674

0.1121

-2.9218

-3.4771

MP2L

0.0006

0.I377

0.0900

0.0500

0.0066

2.9301

INF RAT2 L/ INFRAN2L

0.0789

0.0469

0.1345

0.0179

0.5870

2.6155

MXTEMPAL

0.3846

-0.1836

0.7808

0.5410

0.4925

.0.3395

MXHUMEAL LAI RCON LPGY2 L

0.3520 -0.0191 0.1202

.0.0198 0.0352 0.1222

0.6310 0.1576 0.1146

0.3198 0.0935 0.0679

0.5578 -0.1215 1,0494

.0.0620 0.3767 1.8010

PKLOADAL

0.1418

0.3983

0.0928

0.1154

1.5278

3.4511

Model I

Model It 0.9741 0.9481

Multiple Correlation R2

: :

0.9349 0.8741

R2, Adjusted Std Error of Estimate Durbin.WatsonStatistic Rho Estimate F Value

: : : : :

0.6727 0.0418 2.8882 .0.6748 4,3400

. ComputedT Value ,ModelI Model II 1.7718

0.8673 0,0266 2.8618 _3.4561 11.6164

aThespecificationof the variablesanddata usedfor the resultshereare the samewith thoseshownin Appendix6,2a except that the price deflator for electricity and LPGis the CPI insteadof the price index for fuel, lightandwater.

178

Appendix 6.3 Results of RegressionAnalysis(OL$) a X.1 CommercialConsumers,MERALCO (January1971 to November1984) DependentVariable: XIPERCAL Independent Variable

RegressionCoefficients Model I Model II

StandardError Model I Model II

ComputedT Value Model I Model II

Constant

-0.0728

2.6365

1.7255

2.1388

.0.0422

1.2327

X1CMA

0.1100

0.0902

0.0225

0.0241

4.8950

3.7 418

X1CMARTL/ X1CMARNL

0.0306

.0.0244

0.0076

0.0291

4.0027

.0.7021

X1CAVERE PRLPGL

.0.0189 -0.0125

.0.0600 .0.0730

0.0312 0.0412

0.0321 0.0413

-0.6068 -0.3030

-1.8708 -1.7687

FIREL LABORF RL FRIDGEL AIRCONL TEMPMIAL

0.0248 0,0268 -0.0221 -0,0967 0.0735

0,0321 .0.2118 -0.0328 -0.0837 0.0661

0.0273 0.1521 0.0666 0.0520 0.0798

0.0287 0.1962 0.0675 0.0553 0.0840

0.9073 0.1761 -0,3319 -1.8613 0,9210

1.1157 -1.0797 -0.4853 -1.5129 0,7865

HUM IDITL PKLOADML

0.1686 0.6996

0.1507 0,7047

0.0529 0.0773

0.0567 0.0833

3.1853 9,0527

2.6566 8,4551

Model I

Model II

Multiple Correlation R2

: :

0.9710 0.9428

0.9681 0,9372

R2, Adjusted Std Error of Estimate Durbin-WatsonStatistic

: : :

0.9387 0.0341 1.9457

0.9327 0.0358 1.9424

Rho Estimate F Value

: :

0.4129 78.5787

0.4459 62.6544

aThe Cochrane-Orcuttprocedureisappliedfor all results.

179

Appendix6.4 Resultsof RegressionAnalysis(OLS)a ResidentialConsumers(MERALCO) (Jan 1971 to Nov 1984) DependentVariable: CAPLb !ndependent Variable

Regression Model I

Coefficient Model II

StandardError Model I Model II

ComputedT Value Model I Model II

Constant MARG2L

5.5000 -0.0191

3.1052 0.0799

2.3859 0.0236

3.0221 0.0187

2.3052 -0.8100

1,0275 4.2773

INFRA 2L/ MARN2L.

0.1502

0.1897

0.02798

0.0611

5.3659

3.1021

AVE2L LPG2L

0.0387 0.0901

0.0505 0.0533

0.0463 0.0504

0.0498 0.0564

0.8363 0.5751

1.0156 0.9445

FI RE 2 L LABORFRL

0.0247 -0.7082

0.0147 -0.2802

0.0400 0.2050

0.0428 0.2835

0.6165 -3.4538

0,3444 .0.9882

IRONL FRI DGEL AIRCONL

0.0167 0.0692 -0.1460

-0.0581 -0.0873 -0.2208

0.0603 0.1009 0.-653

0.0620 0.1085 0.0700

0.2773 0.6860 -2.2371

43.9362 .0.8043 -3.1538

TEMPMIAL HUMIDITL

0.1224 0.1225

0.1232 0.1523

0.1174 0.0796

0.1243 0.0854

1.0421 1.5385

0.9908 1.7835

PKLOADM L

0.8197

0.7770

0.1206

0.1294

6.7973

6.0058

Model I

Model II

Multiple Correlation R2

: :

0.8754 0.7665

0.8591 0.7380

R2, Adjusted Std Error of Estimate

: :

0.7481 0.0506

O.7174 0.0536

Durbin-Watson Statistic Rho Estimate F-Value

; : :

1.9468 0.4623 15.4603

1.8952 0,4850 11.8179

a'fhe specification of the variables and data used for the results here are the same with those shown in Appendix 6.1 except that th e price deflator used here for electricity, LPG and firewood is the CPI instead of the price index for fuel, light and water. bThe Cochrane-Orcutt

180

procedure is applied for all results.

Appendix 7.1 LIST OF VARIABLES I. RESIDENTIAL

VARIABLES:

VARIABLE NAME

DESCRIPTION

SOURCE

CAPCONS

Per capita residentialconsumption- '

Derivedfrom â&#x20AC;˘ Meralcodata

CAPL

Ln (CAPCONS)

MARGLPR

Marginalprice

MARGL

Ln (MARGLPR/(CPIFUEL/100))

INF RAMAR

Inframarginal Lumps-sumpayment usingTaytor's definition

INFRAL

Ln (INF RAMAR/(CPt FUEL/I00))

MARNFRA

rnframarginal Lump-sumpayment usingNordin's definition

Derived from Meratcoprice schedule

AVEPR

Averageprice

Derived from Meralco data

AVEPRL

Ln (AVEPR/(CPIFUELfl00)

Derivedfrom Meralco price schedule

)

De_'ivedfrom Meralcoprice schedure

PERIOD

FILE NAME

1/70-12t84

CLODF

1/70-12184

RESDEN

1/70-12/84

RESDEN

I/70.12184

REVISED 2

1/70-12/84

RESDEN

1/70-12/84

REVISED 2

1/70-12/84

RESDEN

1/70-12/84

CLODF

1/70_12/84

RESDEN

II. COMMERCIAL VARIABLES: GO

VARIABLE NAME

DESCRIPTION

SOURCE

PERIOD

FILE NAME

SALESMW

Total MWH consumption,commercial

Meralco

1/70-12/84

COMAt.4T

NOCUST

Total numberof customers, commercial

Meralco

i/70-12/84

COMAL4T

REV

Revenuesfrom commercial REV*1000 to get true revenues

Meratco

1/70-12/84

COMAL4T

PX1KWHC

Proportion of X-1 KWH consumption in total commercial consumption

Interpolated from Meralco data

1/70-12/84

COMAL4T

PPRKWHC

Proportion of primary kwh consumption in total commercial consumption

Interpolated from Meralco data

1/70-12/84

COMAL4T

PSECKWHC

Proportion of secondary kwh consumption in total commercial consumption

Interpolated from Meralco data

1/70-12/84

COMAL4T

PXMDKWHC

Proportion of X-MD kwh consumption in total commercial consumption

Interpolated from Meralco data

1/70-12/84

COMAL4T

PX1 REV

Proportion of X-1 revenues in totat commercial revenues

Interpolated from Meralco data

I/70-12/84

COMAL4T

PPRfMREV

Proportion of primary revenues in total commercialrevenues

Interpolated from Meralco data

1/70-12/84

COMAL4T

PSECREV

Proportionof secondaryrevenues in total commercialrevenues

Interpolated from Meralco data

1/70-12/84

COMAL4T

VARIABLE NAME

_.O

DESCRIPTION

SOURCE

PERIOD.

FILE NAMI_

PXMDREV

Proportionof X-MD revenuesin ' total commercialrevenues

Interpolated from Meraico data

1/70-12f84

COMAL4T

PXICUST

Propotion of X-1 customersin total commen;ialcustomers

Interpolated from Meralco data

1t70-12/84

COMAL4T

PPRMCUST

Proportion of primary customers in total commercialcustomers, PPRMCUSTfl00 to get true figure

Interpolated from Meralco data

1f70-12f84

COMAL4T

PSECCUST

Proportion of secondarycustomers in total commerciat customers, PSECI_UST/i0 to get true figure

Interpolated from Mera|co data

1/70-12t84

COMAL4T

PXMOCUST

Proportionof X-MD customersin total commereia[customers, PXMDCUST/IO to get true figure

Interpolated from Meralco data

i/70-12/84

COMAL4T

X1KWHC

PX110NHC* SALESMW in MWH

I/70-12/84

COMAL4T

PRKWHC

PPRKWHC" SALESMW in MWH

1F/0-12184

COMAL4T

SECKWHC

PSECKWHC* SALESMW in MWH

!/70-12/84

COMAL4T

XMDKWHC

PXMDKWHC * SALESMW in MYtH

1/70-12/84

COMAL4T

X1REV

PX1REV * REV

1/70-12/'84

COMAL4T

PRIMREV

PPRIMREV * REV

1/70-12/84

COMAL4T

SECREV

PSECREV * REV

1/70-12/84

COMAL4T

XMDREV

PXMDREV * REV

1/70-12/84

COMAL4T

X1CUST

PX1CUST * NOCUST

1/70-12/84

COMAL4T

._.

VARIABLE NAME

DESCRIPTION

PRIMCUST

SOURCE

PERIOD

FILE NAME

PPRMCUST * NOCUST/100

1/70-12/84

COMAL4T

SECCUST

PSECCUS'r* NOCUST/IO

1/70-12/84

COMAL4T

XMDCUST

PXMDCUST * NOCUST/10

1/70-12/84

COMAL4T

X 1AV ER EV

X I R EV/X 1KWHC

1/70-t 2/84

COMA L4T

PRAVEREV

PRIMREV/PRKWHC

1./70-12/84

COMAL4T

SCAVEREV

SECREV/SECKWHC

1/70-12/84

COMAL4T

XDAVE REV

XM DREV/XM DKWHC

1/70-12/84

COMAL4T

X1PERCAP

(X1KWCH *1000)IX1CUST

1/70-12/84

COMAL4T

PRPERCAP

(PRKWHC *1000}/PRIMCUST

1/70-12/84

COMAL4T

SCPERCAP

(SECKWHC * 1000)/SECCUST

1/70-12/84

COMAL4T

XDPERCAP

(XMDKWHC *1000)/XM DCUST

1/70.12/84

COMAL4T

Xt PERCAL

Ln (X1PE RCAP)

1/70-12/84

COM-DAT1

XDPERCAL

Ln (XDPERCAP)

1/70-12/84

COM-DAT1

PRPERCAL

Ln (PRPERCAP)

1/70-12/84

COM-DAT1

SCPERCAL

Ln (SCPERCAP)

1/70-12/84

COM-DATT

X1CAVERE

Ln (X 1AVE REV/(CPI, FUEL/100))

1/70-12/84

COM-DAT1

XDAVEREL

Ln (XDAVE REV/CP]FUE L/100))

1/70-12/84

COM-DATI

PRAVEREL

Ln (PRAVEREV/(CP[FU EL/100)

1/70-12/84

COM-DAT1

SCAVEREL

Ln (SCAVEREV/(CP]FUE L/100))

1/70-12/84

COM-DAT1

III. INDUSTRIAL VARIABLES: VARIABLE NAME_

DESCRIPTION

PERIOD

FILE NAME

[NMWHSAL

Total MWH consumption,industrial

Meralco

1/70.12184

LNNOCUST

Total number of customers, industrial

Meraico

1/70-12/84

tREVN

Total revenues',industrial

Meratco

1170-12/84

IPRCONS

Proportion of primaryconsumption in total consumption,industrial

Interpolated from Meralco data

1170-12/84

1:SCCONPR

Proportionof secondaryconsumption in total consumption,industrial

Interpolated from Meralco data

1/70-12/84

D(DCONPR

Proportionof X-MD consumptionin total consumption, industrial

Interpolated from Meralco data

1f70-12t84

IPRCUSPR

Proportion of primary customersin total numberof customers, industrial

Interpofated from Meralco data

l/70.12f84

ISCCUSPR

Proportionof secondarycustomers in total numberof customers, industrial

Interpobted from Meralco data

1170-12f84

IXDCUSPR

Proportion of X-MD customersin tota| number of customers, industrial,[XDCUSPR/10 to get true figure

Interpolated from Meralco data

1/70-12t84

,it O0

VARIABLE NAME

DESCRIPTION

SOURCE

PERIOD

FILE NAME

IPRREVPR

Proportion of primaryrevenuein total revenues,industrial

Interpolated from Meralco ¢}ata

1/70-12/84

_SCREVPR

Proportionof secondaryrevenuein total revenues,industrial

Interpolated from Meralco data

1/'/0-t2/84

LXDREVPR

Proportionof X-MD revenuesin total revenues,industrial IXDREV PR/100 to get true figure

Interpolated from Meralco data

1/70.12/84

IPRCON

[PRCONS * INMWHSALi in MWH

1/70.12/84

ISCCON

[SCCONPR * ?_IMWHSAL;in MWH

1/'70.-12184

[XDCON

IXDCONPR * INMWHSAL; in MWH

1j70.12/g4

IPRCUS

IPRCUSPR * INNOCUST

1I70-12/84

ISCCUS

ISCCUSPR * ]NNOCUST

1/70-12/84

IXDCUS

IXDCUSPR * 1NNOCUST

1/70-12/84

IPRREV

IPRREV PR * ]REVN; ]TRREV*1000 to get true figure

1/70-12/84

ISCREV

ISCREVPR * ]REVN; ISCREV* 1000 to get true figure

1/70-12`/84

IXDREV

IXDREVPR * IREVN

_,/70-12/84

1"2

IrpRPER

('IPRCON* 1000)/_RCUS; in KWH

1/70-12,/84

VARIABLE NAME

DESCRIPTION

SOURCE

PERIOD

FILE NAME

]SCPER

(LSSCON* 1000)t[SCCUS; in KWH

1/70-12184

]XDPER

]XDCON/]X DCUS

1170-12/84

"_2

IPRAVEP

IPRREV/]PRCON

1t70-12/84

ISCAVEP

ISCREV//SCCON

1/70-12184

IXDAV EP

LXDREV/I'XDCON

1/70-12184

_2.

]PRPERL

Ln (IPRPER)

1/70-12t84

1"2

ISCPERL

Ln (ISCPER)

1170.12/84

ZXDPERL

Ln (T.XDPER)

1,/70-12/84

]TRAVEPL

Ln (IPRAVEP/(CPI"FUEL/100))

1/70-12/84

ISCAVEPL

Ln (]SC.AVEP/(CPTFUEL/IO0))

1/70-12/84

1"2

IXDAV EPL

Ln (IXDAVEPI(CPIFUE Lf100))

1I70-12/84

/.2

IV. EXPLANATORY VARIABLES: GO

GO VARIABLE NAME

DESCRIPTION

FRID GE

Priceof refrigerator

FRIDGEL

Ln (FRIDGE/'WPILA/100})

AI RCON

Priceof air-conditioner

A IRCON L

Ln (AIRCONI(WPI LA/100))

FIREWOOD

Priceof firewood

FIREL

Ln (FI REWOOD/(CPIFU ELIt 00))

PRLPG

Priceof liquefied petroleumgas

PRLPGL

Ln (PRLPGI(CFLFUEL/IO0))

TEMPMIA

Monthly maximumtemperaturein MI_,

TEM PMIA L

Ln (TEMPMIA)

HUMIDITY

Relativehumidity

HUM]])ITL

Ln (HUMIDITY)

LABORFR

Laborforce; interpolationsdone between quarterly figures; in thoudands

LABORFRL

Ln (LABORFR)

PKLOADMW

Peak load in MW

PKLOADML

Ln (PKLOADMW)

SOURCE NCSO

NCSO

PAGASA

NCSO

Meralco

pERIOD.

FILE NAME

1/71-12/84

VAR

l/71-12/84

VAR

1/71A2/84

VAR

1/71-12/84

REVISED 2

1/71-12/84

VAR

1/71-12/84

VAR

1/71-12/84

VAR

1/71-12/84

VAR

1/70-12/84

VAR

1/70.t 2)84

VAR

1/70-12/84

VAR

1/70.12/84

VAR

1/70-12/84

VAR

1,/70-12/84

VAR

1/70-12/84

VAR

1/70-12/84

VAR

VARIABLE NAME

.--L O0 _D

DESCRIPTION

SOURCE

PERIOD

FIL_. NAME

CB Bulletin

1/70-12/84

VAR

TOUT

Manufacturingsalesand receipts figuresfor 1/81 andbeyondwere estimatedusingtime-series forecast

IOUTK

Realmanufacturingsalesand receipts

i/70-12/84

VAR

I./NDMFGF

Ln (_OUTK) whereIOUTK wasrebased to 1978-100

1/70-12/84

VAR

I"NDEXCOM

Real commercialsalesandreceipts figuresfor 1/81 andbeyondwere estimatedusingtime-series forecast

1F/0-12/84

VAR

L]NDEXF

Ln (1NDEXCOM) whereZNDEXCOM was rebasedto 1978 =100

1/70-12/84

VAR

WP_LA

Wholesaleprice index (Manila), equipment;1978.=100

NCSO, NEDA

1t7.0-12/84

VAR

CPITUE L

Consumer'sprice indexfor water, light andfuel; 1978=100

NSCO,NEDA

1/70-12/84

VAR

CB Bulletin

ANNUAL

DATA

.--h _D

V. EXPLANATORY VARIABLES VARIABLE NAME

DESCRIPTION

GNPK

realGNP (1972 =100), in millions

GPNPKL

Ln (GNPK), GNPK rebasedto 1978=100

MAXTEMP

maximum temperaturefor the year at MIJ_

MAXTEMPL

Ln (MAXTEMP)

MAXHUMEL

Maximum relativehumidity for the year

MAXHUME

SOURCE NEDA

PERIOD

FILE NAME

1971-1984

LUZG

1971-1984

MRINDAV

1971-1984

LUZG

t971-1984

MRINDAV

1971-1984

LUZG

Ln (MAXHUME)

1971-1984

MRINDAV

MXHUMEAV

Averageof monthly maximum humidity for the year

1971-1984

MRAVEVAL

MXHUMEAL

Ln (MXHUMEAV)

1971-1984

MRAV EVAL

MXTEMPAV

Average of monthly maximum temperaturefor-the year (recordedat MIA)

1971-1984

MRAVEVAL

MXTEMPA L

Ln (MXTEMPAV)

1971 -t984

M RAVEVA L

PKLOADYR

Maximum of the monthly maximum peak Ioacls

1971-1984

LUnG

PKLOADYL

Ln (PKLOADYR)

t971-1984

MRINDAV

PKLOADAV

Averageof the monthly maximum peak load for theyear

1971-1984

MRAVI_AL

PAGASA

VARIABLE NAMI_

..a _O

,DESCRIPTION

SOURCE

pERIOD

FILE NAME

PKt.OADAL

Ln (PKLOADAV)

1971-1984

M RAVEVAL

A_TRCONA

Averageprice of airconditionersfor the year

1971-1984

LUZG

LAXRCON

Ln (AI RCONAfMt-AWPIfl00)

1971-1984

MRINDAV

LPG

Averageprice of LPGfor the year

1971-1984

LUZG

LPGL

Ln (LPGf)(CPIFUELI100))

1971-1984

MRINDAV

LABORF

Labor Forcein May of eachyeaF,in '000

1971-1984

LUZG

LABORF L

Ln (LABORF)

1971-1984

MRINDAV

CP_UEL

Averagedmonthly CPI

1971-1984

LUZG

MLAWP!

Averagedmonthly WPI (Manila)

1971-1984

LUZG

VI.

SECTORAL VARIABLES

VARIABLE NAME

DESCRIPTION

SOURCE

PERIOD

FILE NAME

RESPERAN

Ln (Total annualresidential consumptiondivided by annualaverage of residentialnumberof customers

1971-1984

MRINDRES

AVE

Total annual residentialrevenues/ annualresidentialkwh consumption

1971-1984

LUZG8

AVEPL

Ln (AVE/(CPIFUE L/100) )

1971-1984

MRINDRES

COMPERAN

Ln (Total annualcommercial kwh consumptiondividedby annualaverage of commercialnumber of customers)

1971-1984

MRINDAV

COMAVEPL

Ln ( (Total annualcommercial revenues divided by the total industrial kwh consumption)/(CPlFUEL/_00))

1971-1984

MRINDAV

INDPERAN

Ln (Total annual industrialkwh consumptiondividedby the annual industrialaveragenumberof customers)

1971-1984

MRINDAV

].NDAVEPL

Ln ((Total annualindustrialrevenues divided by total annual industrial kwh consumption/(CP1FUEL/100))

1971-1984

MR.TJqDAV

XICMAR

Marginalprice - Xl Comercial

1/70-12/84

COM-X1

X1CMA

Ln (XlCMAR)

1/70-12/84

COM-X1

X1CMART

InframarginaLprice- X1 Commercial Taylor definition

1/70-12]84

COM-X1

XICMARTL

Ln (XICMARTL)

it70-12J84

COM-X1

Dervivedfrom Meralco rate schedules

Derivedfrom Meralcorate schedules

VARIABLE NAME

.DESCRIPTION

X1CMARN

lnframarginalprice - X1 Commercial Nordin definition

X1CMARNL

Ln (XICMARN)

GPDEM

Demand chargefor generalpower

GPDEML

Ln (GPDEM)

GPM_TN

Minimum charge-general power

GPMINL

Ln (GPMZN)

GPMAR

Marginal price- generalpower

GPMARL

Ln (GPMAR)

GPFRAN

Inframarginalprice- genera]power Nordin definition

GPFRANL

Ln (GPFRAN)

GPFRAT

Inframarginalprice -general power Taylor definition

GPFRATL

Ln (GPFRAT)

XDMAR

Marginalprice - XMD

SOURCE Derivedfrom Mera|co rate schedules

Derived from Meralco rate schedules

Derived from Meralcorate schedules

Derivedfrom Meralco rate schedules

Derivedfrom Mera]corate schedules

Derivedfrom Meralcorate schedules

,PERIOD

FILE NAME,

1/70-12184

COM-Z1

1/70-12/84

COM-X1

I/70-12/84

1/70-12184

1/70-12/84

1I70-12f84

1I70-12184

1/70-t2f84

1170-12/84

1/70-12184

Z2.

1/70-12/84

VARIABLE NAME

DESCRIPTION

XDMARL

Ln (XDMAR)

XDFRAT

tnframarginalprice - XMD Taylor Definition

XDFRATL

Ln (XDFRAT}

XDN

Inframarginalprice - XMD Nordin definition

XDMARNL2

Ln (XDN)

DEMXD

Demandcharge- XMD

DEMXDL

Ln (DEMXD)

SOURCE

Derivedfrom Meralco rate schedules

Derivedfrom Meralcorate schedules

Derived from Meralcorate schedules

,PERIOD

FILE NAME,

1/70-12/84

1170-12/84

1/70.12j84

1/70-12/84