IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/22503.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    Note: EEE IO
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w22503.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yen, Steven T. & Chern, Wen S. & Lee, Hwang-Jaw, 1991. "Effects Of Income Sources On Household Food Expenditures," 1991 Annual Meeting, August 4-7, Manhattan, Kansas 271167, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Magnus, Jan R., 2007. "The Asymptotic Variance Of The Pseudo Maximum Likelihood Estimator," Econometric Theory, Cambridge University Press, vol. 23(5), pages 1022-1032, October.
    3. Corradi, Valentina & Swanson, Norman R., 2004. "A test for the distributional comparison of simulated and historical data," Economics Letters, Elsevier, vol. 85(2), pages 185-193, November.
    4. Hao Wu & Michael Browne, 2015. "Random Model Discrepancy: Interpretations and Technicalities (A Rejoinder)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 619-624, September.
    5. Liang Lu & David Deller & Morten Hviid, 2019. "Price and Behavioural Signals to Encourage Household Water Conservation: Implications for the UK," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 475-491, January.
    6. In-Koo Cho & Kenneth Kasa, 2015. "Learning and Model Validation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(1), pages 45-82.
    7. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    8. Michael O'Donnell & Robert P. Berrens, 2018. "Understanding Falling Municipal Water Demand in a Small City Dependent on the Declining Ogallala Aquifer: Case Study of Clovis, New Mexico," Water Economics and Policy (WEP), World Scientific Publishing Co. Pte. Ltd., vol. 4(04), pages 1-40, October.
    9. Otsu, Taisuke & Whang, Yoon-Jae, 2011. "Testing For Nonnested Conditional Moment Restrictions Via Conditional Empirical Likelihood," Econometric Theory, Cambridge University Press, vol. 27(1), pages 114-153, February.
    10. Anna Gottard & Maria Iannario & Domenico Piccolo, 2016. "Varying uncertainty in CUB models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 225-244, June.
    11. Wang, Qingbin & Halbrendt, Catherine & Johnson, Stanley R., 1996. "A non-nested test of the AIDS vs. the translog demand system," Economics Letters, Elsevier, vol. 51(2), pages 139-143, May.
    12. Paarsch, Harry J., 1997. "Deriving an estimate of the optimal reserve price: An application to British Columbian timber sales," Journal of Econometrics, Elsevier, vol. 78(2), pages 333-357, June.
    13. Xiaojia Bao, 2016. "Water, Electricity and Weather Variability in Rural Northern China," Working Papers 2014-07-02, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    14. Zhang, Zibin & Cai, Wenxin & Feng, Xiangzhao, 2017. "How do urban households in China respond to increasing block pricing in electricity? Evidence from a fuzzy regression discontinuity approach," Energy Policy, Elsevier, vol. 105(C), pages 161-172.
    15. Eklof, Matias & Lunander, Anders, 2003. "Open outcry auctions with secret reserve prices: an empirical application to executive auctions of tenant owner's apartments in Sweden," Journal of Econometrics, Elsevier, vol. 114(2), pages 243-260, June.
    16. Susanne M. Schennach & Daniel Wilhelm, 2017. "A Simple Parametric Model Selection Test," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1663-1674, October.
    17. Gouriéroux, Christian, 1994. "Modèles économétriques : utilisation et interprétation (les)," CEPREMAP Working Papers (Couverture Orange) 9423, CEPREMAP.
    18. Fontaine, Charles & Frostig, Ron D. & Ombao, Hernando, 2020. "Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials," Econometrics and Statistics, Elsevier, vol. 15(C), pages 85-103.
    19. Bettin, Giulia & Lucchetti, Riccardo & Zazzaro, Alberto, 2012. "Endogeneity and sample selection in a model for remittances," Journal of Development Economics, Elsevier, vol. 99(2), pages 370-384.
    20. Komunjer, Ivana & Ragusa, Giuseppe, 2016. "Existence And Characterization Of Conditional Density Projections," Econometric Theory, Cambridge University Press, vol. 32(4), pages 947-987, August.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:22503. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.