Effect of price information on residential water demand

Abstract

Microeconomic theory predicts that people decrease consumption when price increases, the magnitude of the effect depending on price elasticity. The law of demand, however, implicitly assumes that consumers know prices, an assumption that is not always satisfied in markets with ex post billing. When prices are not transparent, elasticity estimates are potentially lower than their full information potential. Evidence of low price elasticity abounds in residential water demand studies, limiting the effectiveness and desirability of using price signals as a conservation tool. It is hypothesized that resident's sluggish response to price is partly due to the absence of price information on water bills. Differences in the informational content of bills are documented for the first time on the basis of sample bills collected from 383 utilities across the USA. A standard aggregate water demand model is augmented with qualitative variables describing differences in billing information, allowing such variables to affect the intensity with which consumers respond to price signals. No evidence is found that non-price information items affect price elasticity but there is a statistically significant effect in the case of price-related information; in our sample, price elasticity increases by 30% or more when price information is given on the bill.

Acknowledgements

I wish to thank Adam Greeney for invaluable assistance in the data collection process and the Mellon Foundation for financial support. I also thank Barbara Craig, Hirschel Kasper, Kenneth Kuttner, Celine Nauges, Steven Renzetti, John Swinton, seminar participants at McMaster University and the Lawrence Berkeley National Laboratory, as well as two anonymous referees for comments on earlier drafts. All remaining errors are mine.

Notes

¹ The average estimate of price elasticity from 18 studies of annual residential water demand reported in Hanemann (1997) is 0.46 (absolute value) with a mean elasticity of 0.36 for winter demand and 0.70 for summer demand (computed from Hanemann, 1997, Table 2.5, pp. 67–72). Espey et al. (1997), in a meta-analysis of residential water demand studies, report a median short run price elasticity of 0.38 and a median long-run price elasticity of 0.64.

² Baumann and Boland (1997) dismiss the ‘water is a necessity’ argument as a water management myth (p. 21).

³ The Safe Drinking Water Act Amendments of 1996 mandated EPA to publish guidelines for water conservation to be used by public systems. These guidelines are purely informative and available on the EPA website. ‘Understandable’ refers to the inclusion of price and quantity information on the bill; ‘informative’ refers to additional quantity information such as comparison with previous usage and the inclusion of conservation tips.

⁴ Egan et al. (1996) compare the effectiveness of different graphical displays to provide feedback on own energy use and find that consumers react differently to different displays. Matsukawa (2004) finds that the use of monitors providing continuous feedback to customers on their energy consumption promotes energy conservation, and that the more the monitor is used, the greater the price elasticity of demand.

⁵ Espey et al. (1997) found no significant differences between long-run elasticity estimates calculated using single period cross sectional data and other types of data.

⁶ See Renzetti (2002) for a review of the literature.

⁷ Nieswiadomy and Molina (1991) found evidence that, for a true increasing block rate structure – i.e. without large quantity allowances included in the fixed fee – the role of marginal prices dominated average price; a recent article by Taylor et al. (2004) attributes the response to average price to the magnitude of fixed fees.

⁸ We do know the cost to consumers of 3750 gallons and 7500 gallons per month but a pseudo-marginal price calculated from this information is not likely to be the appropriate marginal price for the level of water consumed.

⁹ A better specification in cases when researchers are interested in changes in price elasticity along the demand curve would require functional forms such as a Stone-Geary form or flexible forms that allow decreasing price elasticity with decreasing quantity and increasing price (Gaudin et al., 2001). In terms of demand elasticities at the mean, however, Espey et al. (1997) find that the choice of functional form does not systematically affect price elasticity estimates.

¹⁰ Coefficients on household size and number of hot days are left in their raw form to be conveniently interpreted as semi-elasticities. A similar regression with the two variables in their log form reduced the overall fit but did not affect the level and significance of other parameter estimates.

¹¹ AWWA membership in 1996 consisted of about 4000 utilities. Although there are approximately 56 000 utilities in the USA, most of them are small utilities with a customer base lower than 500. Over two-thirds of the 500 largest utilities are represented in the AWWA data. Although another survey was conducted in 1999, it did not include enough information for our analysis.

¹² When a copy of a 1995 bill could not be found but someone with an accurate recollection of the information could be located, we asked the utility to answer a simple questionnaire. If there was any doubt about the accuracy of the information, the observation was dropped.

¹³ The price structure is as reported by the utility in the AWWA survey. As indicated in the previous section, we do not know the details of the price schedule. In our phone interview we asked utilities that still had a decreasing block structure the quantity level that would push consumers into the lower priced second block and found that in most cases, the second block was too high to be reached by any single-family household. Such a price structure is effectively similar to the uniform rate for the household.

¹⁴ Sixty utilities had either changed ownership (most due to the American Water Company merger in 2000) or could not be located; 24 did not have records or recollection of their 1995 bill; 5 had bills changed in the 1995–96 period; 3 were removed for miscellaneous reasons, such as a utility serving only summer homes. Twenty utilities refused to participate.

¹⁵ Among utilities that gave price information on the bill, 32% gave simple history and 13% gave more detailed history (compared to 23 and 6% in the full sample), while 40% of all bills with consumption history included price information (compared to 20% in the full sample).

¹⁶ Eight per cent of the bills with price information gave conservation messages (compared to 10% for the full sample) while 16% of the bills with messages gave price information (compared to 20% for the full sample).

¹⁷ Stevens et al. (1992) find that higher frequency billing decreases price elasticity while Kulshreshtha (1996) did not find conclusive results on billing frequency (see Arbués et al., 2003).

¹⁸ Billing frequency in our sample varied from twice a year (only two utilities) to 12 times a year (200 utilities); 92 utilities billed four times a year and 88 billed six times a year. There were no clear differences in billing frequency by rate structure.

¹⁹ However, the presence of detailed quantity information on the bill could also complicate the bill and obscure price information, leading to a reverse effect.

²⁰ Variables were also created for simple history, advanced history, and others quantity related information to test individual effects.

²¹ Additional dummy variables were created to test the individual effects of sewer charges and energy charges as well as different billing frequencies.

²² The 2000 US Census was used because the 1990 Census did not allow us to match a good number of service areas because of changes in zip codes or names of places.

²³ These reasons are somewhat different from usual simultaneity issues in demand estimation since the supply side of the market is regulated to balance revenues and expenses each period. In effect, this means that a higher price cannot be interpreted as an incentive for utilities to supply more water.

²⁴ To gauge the appropriateness of the instruments, we use them to obtain predicted values for the average price variable. Correlation between the predicted and the original average price variable is 0.65. A linear regression of the predicted value of AP on all the predetermined endogenous variables of the demand equation produces residuals that do not significantly explain the variation in Q (p-value of 0.57).

²⁵ For example, it is likely that consumers in areas with low annual precipitation are more sensitized to water conservation than those in water abundant areas.

²⁶ Note that the sample size decreases significantly, thus affecting standard errors and test statistics.

²⁷ The estimation was run with median income instead; other parameter estimates were not affected significantly and the income elasticity was 0.24.

²⁸ For all models with information we test the joint significance of (1) all non-price variables interacted with ln(AP) with and without IB, (2) history and otherinfo interacted with ln(AP) with and without IB, (3) power and sewer interacted with ln(AP), (4) all non-price information variables interacted with ln(AP) plus the message. We found no evidence of joint significance as F-test values ranged between 0.01 and 0.92 with probability values from 0.99 to 0.43.

²⁹ Recall that utilities that report using and increasing block rate structure may have free allowances that would effectively create a decreasing rate structure for lower consumption levels. Again, the structure of the AWWA data does not allow us to identify such nuances in rate structures.

³⁰ At the suggestion of a referee, we ran the same model taking account of the possibility that price elasticity was not constant for all income levels. We created dummy variables for four income levels around the mean. All results were largely statistically insignificant except for communities in the highest 10% of the income distribution for which the price elasticity was reduced by 0.12 (with a standard error of 0.07 and a p-value of 0.083).

³¹ In Shin's article (1985) individuals respond to a perceived price P* = MP × (AP/MP) ^k , where MP is marginal price and AP is average price. The price perception parameter k is between 0 and 1. A decrease in k indicates that individuals’ price perception is closer to marginal price than average price.