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Designing Nonlinear Price Schedules for Urban Water Utilities to Balance Revenue and Conservation Goals

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  • Frank A. Wolak

Abstract

This paper formulates and estimates a household-level, billing-cycle water demand model under increasing block prices that accounts for the impact of monthly weather variation, the amount of vegetation on the household’s property, and customer-level heterogeneity in demand due to household demographics. The model utilizes US Census data on the distribution of household demographics in the utility’s service territory to recover the impact of these factors on water demand. An index of the amount of vegetation on the household’s property is obtained from NASA satellite data. The household-level demand models are used to compute the distribution of utility-level water demand and revenues for any possible price schedule. Knowledge of the structure of customer-level demand can be used by the utility to design nonlinear pricing plans that achieve competing revenue or water conservation goals, which is crucial for water utilities to manage increasingly uncertain water availability yet still remain financially viable. Knowledge of how these demands differ across customers based on observable household characteristics can allow the utility to reduce the utility-wide revenue or sales risk it faces for any pricing plan. Knowledge of how the structure of demand varies across customers can be used to design personalized (based on observable household demographic characteristics) increasing block price schedules to further reduce the risk the utility faces on a system-wide basis. For the utilities considered, knowledge of the customer-level demographics that predict demand differences across households reduces the uncertainty in the utility’s system-wide revenues from 70 to 96 percent. Further reductions in the uncertainty in the utility’s system-wide revenues in the, range of 5 to 15 percent, are possible by re-designing the utility’s nonlinear price schedules to minimize the revenue risk it faces given the distribution of household-level demand in its service territory.

Suggested Citation

  • Frank A. Wolak, 2016. "Designing Nonlinear Price Schedules for Urban Water Utilities to Balance Revenue and Conservation Goals," NBER Working Papers 22503, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:22503
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    References listed on IDEAS

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    1. Koichiro Ito, 2014. "Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing," American Economic Review, American Economic Association, vol. 104(2), pages 537-563, February.
    2. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    3. Olmstead, Sheila M. & Michael Hanemann, W. & Stavins, Robert N., 2007. "Water demand under alternative price structures," Journal of Environmental Economics and Management, Elsevier, vol. 54(2), pages 181-198, September.
    4. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
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    Cited by:

    1. Pavanini, Nicola & Feger, Fabian & Radulescu, Doina, 2017. "Welfare and Redistribution in Residential Electricity Markets with Solar Power," CEPR Discussion Papers 12517, C.E.P.R. Discussion Papers.
    2. El-Khattabi, Ahmed Rachid & Eskaf, Shadi & Isnard, Julien P. & Lin, Laurence & McManus, Brian & Yates, Andrew J., 2021. "Heterogeneous responses to price: Evidence from residential water consumers," Journal of Environmental Economics and Management, Elsevier, vol. 107(C).
    3. Elinder, Mikael & Hu, Xiao & Liang, Che-Yuan, 2021. "Water conservation and the common pool problem: Can pricing address free-riding in residential hot water consumption?," CERE Working Papers 2021:12, CERE - the Center for Environmental and Resource Economics.
    4. Patrick Bigler & Doina Maria Radulescu, 2022. "Environmental, Redistributive and Revenue Effects of Policies Promoting Fuel Efficient and Electric Vehicles," CESifo Working Paper Series 9645, CESifo.
    5. Fabian Feger & Nicola Pavanini & Doina Radulescu, 2022. "Welfare and Redistribution in Residential Electricity Markets with Solar Power," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(6), pages 3267-3302.
    6. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    7. Fuente, David, 2019. "The design and evaluation of water tariffs: A systematic review," Utilities Policy, Elsevier, vol. 61(C).
    8. Fabra, Natalia & Reguant, Mar, 2024. "The energy transition: A balancing act," Resource and Energy Economics, Elsevier, vol. 76(C).

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    More about this item

    JEL classification:

    • L38 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - Public Policy
    • L5 - Industrial Organization - - Regulation and Industrial Policy
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • L95 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Gas Utilities; Pipelines; Water Utilities

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