Estimating Urban Residential Water Demand: Effects ...

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Department of Economics, University of North Texas, Denton. Water demand ...... Berk, R. A., D. J. LaCivita, K. Sredl, and T. F. Cooley, Water. Shortage, Abt ...
WATER RESOURCES RESEARCH, VOL 28, NO. 3, PAOES 609-615, MARCH 1992

Estimating Urban Residential Water Demand: Effects of Price Structure, Conservation, and Education MICHAEL

L.

NIESWIADOMY

Department of Economics, University of North Texas, Denton Water demand equations are estimated using the .most current American Water Works Association (1984) survey of 430 (of 600 largest) U.S. utilities. The data set was auginented by monthly rainfall and temperature data from the National Oceanic and Atmospheric Administration's climatological data. Demographic data were obtained from the U.S. Department of Corilmerce (1988). Besides the usual endogeneity problems involving block price structures this paper also examines the possible endogeneity of conservation and education programs. Three types of models were used: a marginal price model, an average price model, and Shin's (1985) price perception model. The results generally show that price elasticity is higher in the South and the West. Conservation does not appear to reduce water use, but public education appears to have reduced water usage in the West. The Shin (1985) tests in this study indicate that consumers react more to average than marginal prices in all regions.

1,

INTRODUCTION

In recent years, water policy officials, faced with growing water demands, have explicitly addressed demand management strategies. Rate structures have been infrequently used as a management tool, as opposed to the traditional methods of education and conservation programs. While the latter two strategies have been emphasized, pricing strategies have recently been receiving attention. The impact of all of these strategies must be examined in a simultaneous framework in any comprehensive demand management program. This paper has two main purposes. The first is to estimate urban water demand in the United States using the most current American Water Works Association (AWWA) [1984] survey of 430 (of600 largest) U.S. utilities. It is essential to use the most recent data since customers may be responding differently to higher rates than in the past. The most recent studies [e.g., Williams, 1985] rely on the AWWA [1970] survey. This water demand model will explicitly incorporate the impact of conservation programs, something few other studies have done. The second purpose is to test if consumers respond to average prices (AP) or marginal prices (MP) using Shin's [1985] model. Section 2 reviews some of the water demand estimation issues in the literature. Section 3 discusses the data used in this study. Section 4 presents the models used in this study, in particular, a price perception model [Shin, 1985]. Section 5 analyzes the results. Section 6 concludes the paper.

2.

WATER DEMAND ESTIMATION ISSUES

Although it is essential to understand the key determinants of water usage [Hirshleifer et al., 1960], a consensus on the proper estimation methodology has not been reached. Early water studies ignored block rates by using an average price [Gottlieb, 1963; Young, 1973; Foster and Beattie, 1979]. Later, Nordin [1976] modified Taylor's (1975] specification to include a marginal price and a difference variable tO account for the effects of inframarginal rates and fixed fees. Lately, some researchers have implemented Nordin's theoCopyright 1992 by the American Geophysical Union. Paper number 91 WR02852. 0043-1397 /92/9 I WR-02852$05.00 609

retical model, but with little success [Billings and Agthe, 1980; Howe, 1982; Jones and Morris, 1984; Nieswiadomy and Molina, 1988, 1989]. Others claimed that the measure(s) of price to which consumers respond is (are) an empirical question [Foster and Beattie, 1981; Opaluch, 1982; Chicoine and Ramamurthy, 1986]. Since the price of water both determines, and is determined by, use, ordinary least squares (OLS) estimation may yield biased and inconsistent estimates. Several authors have used instruniental variables techniques to address this problem in electricity demand [Wilder and Willenborg, 1975; McFadden et al., 1977] and in water demand estimation [Jones and Morris, 1984; Deller et al., 1986]. Many different types of data sets have been used, ranging from household data to aggregate data. Since this study uses a large national data set on cities in the United_ States (the AWWA 1984 survey), a brief review of the studies using similar data sets will be given. The two most recent studies to use AWWA's data [Male et al., 1979; Williams, 1985] used the 1970 survey. Williams' [1985] marginal price elasticities ranged from -0.26 in the South to -0.54 in the West. Two other studies used the 1960 survey [AWWA, 1960]; Foster and Beattie [1979] estimated the price elasticity in the range of -0.27 to -0.76, and Hittman Asso~iates, lnc.'s [1970] measure of elasticity was -0.44. Two studies used the 1955 survey [AWWA, 1955]: Conley [1967] calculated price elasticity as -1.02 for southern California, using average prices, and Seidel and Baumann [1957] did not calculate price elasticity. It does not appear that any study has used the AWW A's most recent data set [AWWA, 1984] to estimate water demand. Since much has changed in the water industry between 1970 (the data period used in the most recent studies) arid 1984, it is important to update our knowledge of water demand on a national level. Furthermore, the techniques for handling the simultaneity problems of block structures have improved. Thus an analysis of recent data using recent techniques should provide useful information to public policy officials. A problem which has received very little attention in the economic literature is the impact of conservation and public education programs on water demand. For example, there

6!0

NIESWIADOMY: RESIDENTIAL WATER DEMAND-PRICE, CONSERVATION, AND EDUCATION

exist some engineerihg studies [U.S. Department of Housing and Urban Development; 1984] that have estimated the water savings Of retrofitting houses with water-saving toilets and faucets, but few ecOnomic studies Of the actual impact on water demand. (Notable exceptions are MOncur [.1987], Mercer and Morgan [1985], and Berk et al. [1981]. Moncur [1987] found that water rates had a significant impact even during a drOught in Hawaii.) For example, if a utility installs a device that saves 100 gal./yr (379 L/yr), the customer may react by using mOre water on the lawn, theieby partially offsettihg the conservation impact of the devi_ce. The actual impact is_ ah einpirical question. The flaW in the_ engineering approach is tha~ it does not allow for changes in consumer behaVior. A similar problem persists with public education prOgi'ams that encourage people to save water, which may convince people to conserve water out of a sense of public du~y; But if the plea for conservation is not accompanied by an increase in water rates, the plan is likely to fail in the long run. For example; in Tucson in the 1970s; a ''Beat the Peak'' (conservation) campaign was si.tccessful in decreasing per capita use for a few years. Howeve.r, a few years later, use increased back to its previous level beCaU:se real water rates did not rise. Martin et al. (1984, p. 66] state, in surriinarizing the Tlicson situation, "Major decreases in water use per capita occur only where a majof price increase is accompanied by major public awareness of the action surrounding the passage of the increased price schedule."

TABLE 1.

Definitions Mincharge Income Conserve Water

Total bill Pu bed MarginP Avgcap Maxcap Pop8086

Persons Homel939

OwnocCup Julytemp Annrain Cooldays Rainavg Tempavg AvgP North Central

3.

DATA

The basis for the data in this study is the A WWA [1984] survey of 430 water utilities in the United states, with populations over 10,000. If complete informatiOn was not available on any city, it was dropped from the data set. (All data are from 1984, unless otherWise stated.) Some sumriiafy definitions of the variables are included in Table 1. Only cities using 100% metering were included. The primary data extracted from the survey included average monthly water usage per household (Water), the typical bills at 3750, 7500, and 75,000 gal. (1 gal. ~ 3.7854 L), the minimum charge (Mincharge), and the average monthly bill. The average price was constructed as the average monthly bill diVided by the average monthly use. Because the actual inarginal rates were not available, the marginal price was apprOximated by the ratio of discrete changes. For example, the marginal price for the average consumer in the 3750-7500 gal. block was calculated as the typical bill at 7500 gal. minus the typical bill at 3750 gal. divided by (7500 - 3750). (Water is measured in thousands of gallons.) The other variables obtained ffoin the A WW A survey are Pubed, Conserve, Avgcap, ahd Maxcap. Additionally, this data set was augmented by gathering derriographic data and weather data for each city. The following variables were obtained from the U.S. Departinent of Commerce, [1988]: Income, Pop8086, Persons, Horilel939, Ownoccup, Julytemp, Annrain, and Cooldays. (Some of the AWW A survey cities were not listed by the U.S. Department of Commerce [1988], thereby reducing ihe size of the complete data set. The non-A WWA data were matched tO city in which the watef utility was located. It should be noted that Some water utilities sell soine water outSide of the city liinits, so the matching Of the water and nonwater data will never be completely accurate.) Monthly

Definitions of Variables

Northeast South West

minimum monthly water charge per capita monthly income (1985) dummy variable; 1 is conservation program, 0 otherwise water use per household per month total water bill per month dummy variable; 1 is public education program, 0 otherwise_ marginal price (in dollars) of water per 1,000 gal. average day usage as a percentage of city's capacity maximum day usage as a percentage of city's capacity populatiOn growth rate from 1980 to 1986 nllmber of persons per household (1980) percentage of homes built before 1939 percentage of homes that are owner occupied (1980) 1951-1980 average July temperature 1951-1980 average anntial rainfall 1951-1980 aver_age annual cooling-degree days

average monthly rainfall for months between last Spring freeze month artd first fall freeze month average tf:mperature for months between last Spring freeze month and first fall freeze month avetage price (in dollars) of water per 1,000 gal. Iowa, Ill., Indiana, Kansas, Mich., _Mo., N.I)., Nebr., Ohio, S.D., and Wis. Conn,, Mass., Maine, N.H., N.J., N.Y., Penn~. R.I., and Vt. Ala:, Arkansas, Del., Ga., Ky., La., Md,, Miss., N.C., Okla., S.C., Tenn., Tex., Va., and W.V. Alaska, Ariz., Calif., Colo., Hawaii, Idaho, Mont., N.M., Nev., Oreg., Utah, Wash., and Wyo.

temperature and rainfall data were obtained fro in the National Oceanic and Atmospheric Administration (NOAA). Rainavg was defined as the average monthly rainfall (in inches (1 inch ~ 2.54 cm)) in the months in between the last spring freeze month and the first fall freeze month in 1984. Tempavg was defined as the average monthly temperature (in Fahrenheit) in between the last spring freeze month and the first fall freeze month ii1 1984. Some mean statistics are provided in Table 2.

4.

MODELS

Several different models have been used iri ~fie literatllre. Thtee of these models are examined in this study ... 0.Qe t)rpe of model Uses the marginal price as the price vafiable; The second type of model uses the average price as the· price variable. The third model used in this study is Shin's [1985] price perception model, which tests whether conSumers i"eact to a-Verage or marginal prices. All three models Were estimated for each of four regions in the United States. Income (and other demographic factors as discussed below) and weather variables are included as explanatory variables. Because a standard log-lOg model is estimated for the marginal and average price models, it does not need to be described. However, because the Shin [1985] model is not

NIESWIADOMY: RESIDENTIAL WATER DEMAND-PRICE, CONSERVATION, AND EDUCATION

Mincharge, $ Income,$ Conserve Water Total bill, $

Pubed MarginP, $ Avgcap Max cap Pop8086

Persons Home1939 Ownoccup Julytemp Annrain Cooldays Rainavg Tempavg AvgP, $

Block type, % Decreasing Uniform Increasing

TABLE 2.

Mean Statistics

North Central

Northeast

South

West

3.53 880.80 0.15 6,917.34 9.44 0.75 1.23 0.48 0.74 -1.37 2.57. 34.76 61.90 74.17 34.49 893.60 3.31 68.68 1.49

4.29 984.05 0.50 7,240.48 11.67 0.69 1.16 0.50 0.70 -1.56 2.47 48.61 54.08 73.76 41.24 771.95 3.89 69.68 1.69

3.90 931.26 0.47 9,370.34 11.57 0.86 1.28 0.54 0.76 8.90 2.54 18.39 57.00 80.09 44.47 2,201.14 4.08 74.65 1.45

4.14 990.01 0.64 13,544.40 14.63 0.72 0.97 0.53 0.88 11.93 2.59 13.95 57.57 71.75 19.45 906.69 0.88 69.29 1.19

47.8 30.4 21.7

35.8 34.0 30.2

48.1 33.7 18.3

24.7 35.8 39.5

< 1. However, if k is greater than 1 or less than 0, it has different interpretations under different block structures. In a decreasing block rate scheme, k > 1 implies that P* > AP >MP, and k < 0 implies that P* I implies that P* < AP < MP, and k < 0 implies that P* > MP > AP. (See Nieswiadomy and Molina [1991]

for further discussion and diagrams.) The model in this paper is specified in (2). It should be noted that another variable

(often called the difference variable or rate structure premium) is sometimes used in the marginal price model, as described by Nordin [1976], to account for the effect offixed fees and inframarginal prices. Theoretically, it should be equal in magnitude and opposite in sign of the income effect. It does not enter Shin's [1985] model directly because it affects the consumer's perception of price rather than entering the analysis through an income effect. In Q

price perception model is that it is costly for consumers to determine the actual rate schedule. There are several reasons that make this process costly. First, it is difficult to determine one's water (e.g., vis-a-vis electricity) use during the month because water meters _are generally more difficult to read. This makes it difficult for the consumer to know when he has switched from one block to another. A second difficulty faced by water consumers is the inclusion of a sewage charge in their water bill which may or may not be shown separately. The consumer may also confuse the sanitation (solid waste) charge with the sewage charge. Also, many sewage charges are determined by winter usage, but consumers may be unaware of this rule. Sewage fees are not included in this study since the A WW A data do not include

specific measures of sewage fees. If the expected marginal benefit of learning the true nature of the rate schedule is less than the expected marginal cost, it is likely that the consumer will react to some proxy marginal price, such as an ex post calculated average price from a recent bill, as Shin [1985] notes. However, if the expected marginal benefits are greater than the expected marginal costs, the consumer will investigate the problem more carefully and will probably react to the true rate schedule. If the consumer stops searching for information when expected marginal benefit equals expected marginal cost, perceived price (P*) may lie between marginal and average price since some part of the difference variable may be embedded in the perceived price. P* is a function of the marginal price, average price, and a price perception parameter k as follows: P* = MP(AP/MP)'

(1)

The price perception parameter k, shown in (1), is expected to be nonnegative. If the consumer only responds to MP, then k = 0. If the consumer only responds to AP, then k = I. If the perceived price is between AP and MP, then 0 < k

~

{3 0 +

/3 1 In Income+ /32

+ f3 2k In (AP/MP) +

In Margin P

/3 3 In Rainavg + /3 4 In Tempavg

+ {3 5 In Persons+ {3 6 ln Home 1939 + {37 ln Ownoccup +

well known, it will be presented briefly here for the benefit of the reader. The basic concept underlying the Shin [1985]

611

/3 8

Conserve +

/3 9 Pubed

(2)

where Q is the monthly wateruse (in thousand gallons) of the

average household in the city and AP/MP is the ratio of average monthly price of the average household to the marginal price. Other variables are defined in Table 1. Note that the marginal price (MarginP) model is specified similarly as the Shin model (equation (2)), with the exception that the

AP/MP variable is not included. The average price model is specified similarly as the marginal price model, with the exception that the average price (AvgP) variable is substituted for MarginP.

One econometric issue of model specification that must be considered is the potential endogeneity of several variables. While the endogeneity of the price variables (depending on

which model is used) has been thoroughly discussed in the literature, the potential endogeneity Of the Conserve (conservation) and the Pubed (public education) variables have not received much attention in the literature. Since the second purpose of this project is to estimate the effectiveness of conservation programs, this issue will be briefly discussed here. A survey of the literature [Boland et al., 1984] does not reveal any studies that have analyzed the

impact of conservation programs using a national crosssectional data set. The A WWA data set contains separate dummy variables on water conservation programs and public education programs. Obviously, it would be helpful to have more specific details on the conservation and education programs. Nevertheless, an analysis of the effect of these programs will be useful. A city which indicates that it does not have either program probably is not encouraging conservation as much as a city which indicates it does have a Pubed program or a Conserve program (or both programs). Thus these variables can be used as proxies for the strength of the conservation programs. It is important to note that these conservation and public education variables may be endogenous. This endogeneity may arise because water administration officials may implement a conservation program in response to a perceived excessive water demand. Thus the conservation variable may be a function of the dependent

612

NIESWIADOMY: RESIDENTIAL WATER DEMAND-PRICE, CONSERVATION, AND EDUCATION

variable water use, as well as water use being a function of conservation. Since there are potentially three right-hand side endogenous variables in the model, a' Hausmann test will be conducted. A two-stage least squares (2SLS) technique will be used to obtain the consistent estimates needed for calculating the Hausmann test statistic. The test statistic is m = /l'