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Panel Data Analysis of Japanese Residential Water Demand Using a Discrete/Continuous Choice Approach

Author

Listed:
  • Koji Miyawaki

    (National Institute for Environmental Studies)

  • Yasuhiro Omori

    (Faculty of Economics, University of Tokyo)

  • Akira Hibiki

    (National Institute for Environmental Studies)

Abstract

Block rate pricing is often applied to income taxation, telecommunication services, and brand marketing in addition to its best-known application in public utility services. Under block rate pricing, consumers face piecewise-linear budget constraints. A discrete/ continuous choice approach is usually used to account for piecewise-linear budget constraints for demand and price endogeneity. A recent study proposed a methodology to incorporate a separability condition that previous studies ignore, by implementing a Markov chain Monte Carlo simulation based on a hierarchical Bayesian approach. To extend this approach to panel data, our study proposes a Bayesian hierarchical model incorporating the individual effect. The random coefficients model result shows that the price and income elasticities are estimated to be negative and positive, respectively, and the coefficients of the number of members and the number of rooms per household are estimated to be positive. Furthermore, the AR(1) error component model suggests that the Japanese residential water demand does not have serial correlation.

Suggested Citation

  • Koji Miyawaki & Yasuhiro Omori & Akira Hibiki, 2010. "Panel Data Analysis of Japanese Residential Water Demand Using a Discrete/Continuous Choice Approach," CIRJE F-Series CIRJE-F-717, CIRJE, Faculty of Economics, University of Tokyo.
  • Handle: RePEc:tky:fseres:2010cf717
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    References listed on IDEAS

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    2. Koji Miyawaki & Yasuhiro Omori & Akira Hibiki, 2006. "Bayesian Estimation of Demand Functions under Block Rate Pricing," CIRJE F-Series CIRJE-F-424, CIRJE, Faculty of Economics, University of Tokyo.
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    Cited by:

    1. Havranek, Tomas & Irsova, Zuzana & Vlach, Tomas, 2016. "Publication Bias in Measuring the Income Elasticity of Water Demand," MPRA Paper 75247, University Library of Munich, Germany.
    2. Mónica Maldonado-Devis & Vicent Almenar-Llongo, 2021. "A Panel Data Estimation of Domestic Water Demand with IRT Tariff Structure: The Case of the City of Valencia (Spain)," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    3. Lin, Boqiang & Chen, Xing, 2018. "Is the implementation of the Increasing Block Electricity Prices policy really effective?--- Evidence based on the analysis of synthetic control method," Energy, Elsevier, vol. 163(C), pages 734-750.
    4. Tomas Havranek & Zuzana Irsova & Tomas Vlach, 2018. "Measuring the Income Elasticity of Water Demand: The Importance of Publication and Endogeneity Biases," Land Economics, University of Wisconsin Press, vol. 94(2), pages 259-283.
    5. Kenneth A. Baerenklau & Kurt A. Schwabe & Ariel Dinar, 2014. "The Residential Water Demand Effect of Increasing Block Rate Water Budgets," Land Economics, University of Wisconsin Press, vol. 90(4), pages 683-699.
    6. Darío F. Jiménez & Sergio A. Orrego & Felipe A. Vásquez & Roberto D. Ponce, 2017. "Estimating water demand for urban residential use using a discrete-continuous model and disaggregated data at the household level: the case of the city of Manizales, Colombia," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 86, pages 153-178, Enero - J.
    7. Baerenklau, Kenneth A. & Schwabe, Kurt & Dinar, Ariel, 2014. "Do Increasing Block Rate Water Budgets Reduce Residential Water Demand? A Case Study in Southern California," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170019, Agricultural and Applied Economics Association.
    8. 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.
    9. Kenneth A. Baerenklau & Kurt A. Schwabe & Ariel Dinar, 2014. "The Residential Water Demand Effect of Increasing Block Rate Water Budgets," Land Economics, University of Wisconsin Press, vol. 90(4), pages 683-699.
    10. Ming-Feng Hung & Bin-Tzong Chie, 2013. "Residential Water Use: Efficiency, Affordability, and Price Elasticity," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 275-291, January.
    11. Martins, Rita & Quintal, Carlota & Cruz, Luís & Barata, Eduardo, 2016. "Water affordability issues in developed countries – The relevance of micro approaches," Utilities Policy, Elsevier, vol. 43(PA), pages 117-123.
    12. Maamar Sebri, 2014. "A meta-analysis of residential water demand studies," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 16(3), pages 499-520, June.
    13. Ming-Feng Hung & Bin-Tzong Chie & Tai-Hsin Huang, 2017. "Residential water demand and water waste in Taiwan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(2), pages 249-268, April.
    14. Jiménez, Darío F. & Orrego, Sergio A. & Vásquez, Felipe A. & Ponce, Roberto D., 2016. "Estimación de la demanda de agua para uso residencial urbano usando un modelo discreto-continuo y datos desagregados a nivel de hogar: el caso de la ciudad de Manizales, Colombia," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 86, pages 153-178, December.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water

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