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Exact Estimation of Demand Functions under Block-Rate Pricing

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  • Koji Miyawaki
  • Yasuhiro Omori
  • Akira Hibiki

Abstract

This article proposes an exact estimation of demand functions under block-rate pricing by focusing on increasing block-rate pricing. This is the first study that explicitly considers the separability condition which has been ignored in previous literature. Under this pricing structure, the price changes when consumption exceeds a certain threshold and the consumer faces a utility maximization problem subject to a piecewise-linear budget constraint. Solving this maximization problem leads to a statistical model in which model parameters are strongly restricted by the separability condition. In this article, by taking a hierarchical Bayesian approach, we implement a Markov chain Monte Carlo simulation to properly estimate the demand function. We find, however, that the convergence of the distribution of simulated samples to the posterior distribution is slow, requiring an additional scale transformation step for parameters to the Gibbs sampler. These proposed methods are then applied to estimate the Japanese residential water demand function.

Suggested Citation

  • Koji Miyawaki & Yasuhiro Omori & Akira Hibiki, 2016. "Exact Estimation of Demand Functions under Block-Rate Pricing," Econometric Reviews, Taylor & Francis Journals, vol. 35(3), pages 311-343, March.
    • Handle: RePEc:taf:emetrv:v:35:y:2016:i:3:p:311-343
    DOI: 10.1080/07474938.2013.806857
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    References listed on IDEAS

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    Cited by:

    1. Diego Maria André & José Carvalho, 2014. "Spatial Determinants of Urban Residential Water Demand in Fortaleza, Brazil," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2401-2414, July.
    2. Jean-Philippe Terreaux & Mabel Tidball, 2020. "Can Nonlinear Water Pricing Help to Mitigate Drought Effects in Temperate Countries?," Post-Print halshs-02283100, HAL.

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