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Incorporating Quadratic Scale Curves in Inverse Demand Systems

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  • Robert H. Beach
  • Matthew T. Holt

Abstract

In this paper we introduce inverse demand systems that include quadratic scale terms. These systems are similar to regular quadratic demand systems introduced by Howe, Pollak, and Wales. A unique feature of these specifications is that they maintain linear scale curves as a special case. For illustrative purposes we estimate the Normalized Quadratic Inverse Demand-Quadratic Scale System using monthly South Atlantic fish landings and valuation data, 1980-1996. In estimation concavity is maintained locally, and the rank reduction procedures advocated by Diewert and Wales (1988a) are employed. The estimated model is then used to obtain welfare estimates associated with catch restrictions. Copyright 2001, Oxford University Press.

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  • Robert H. Beach & Matthew T. Holt, 2001. "Incorporating Quadratic Scale Curves in Inverse Demand Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(1), pages 230-245.
  • Handle: RePEc:oup:ajagec:v:83:y:2001:i:1:p:230-245
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    File URL: http://hdl.handle.net/10.1111/0002-9092.00150
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    Cited by:

    1. Dey, Madan Mohan & Garcia, Yolanda T. & Kumar, Praduman & Piumsombun, Somying & Haque, Muhammad Sirajul & Li, Luping & Radam, Alias & Senaratne, Athula & Khiem, Nguyen Tri & Koeshendrajana, Sonny, 2008. "Demand for fish in Asia: a cross-country analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 1-18.
    2. Shr, Yau-Huo & Zipp, Katherine, 2016. "Valuing complementarity between environmental goods and housing attributes with the benefit function: An application to flood hazards," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235961, Agricultural and Applied Economics Association.
    3. Vasiliki Fourmouzi & Margarita Genius & Vangelis Tzouvelekas, 2006. "The Demand for Organic, Integrated-Agriculture, and Conventional Fresh Vegetables: A Censored Inverse Almost Ideal Demand System," Working Papers 0618, University of Crete, Department of Economics.
    4. K. K. Gary Wong & Hoanjae Park, 2018. "Consumption dynamics in inverse demand systems: an application to meat and fish demand in Korea," Agricultural Economics, International Association of Agricultural Economists, vol. 49(6), pages 777-786, November.
    5. Chris Moore & Charles Griffiths, 2018. "Welfare analysis in a two-stage inverse demand model: an application to harvest changes in the Chesapeake Bay," Empirical Economics, Springer, vol. 55(3), pages 1181-1206, November.
    6. Keith R. McLaren & K. K. Gary Wong, 2009. "The Benefit Function Approach to Modeling Price-Dependent Demand Systems: An Application of Duality Theory," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(4), pages 1110-1123.
    7. Michele Baggio & Jean-Paul Chavas, 2006. "On the Consumer Value of Environmental Diversity," Working Papers 35/2006, University of Verona, Department of Economics.
    8. Liu, Kang Ernest, 2006. "A Quadratic Generalization of the Almost Ideal and Translog Demand Systems: An Application to Food Demand in Urban China," 2006 Annual meeting, July 23-26, Long Beach, CA 21387, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Toshinobu Matsuda, 2007. "Linearizing the inverse quadratic almost ideal demand system," Applied Economics, Taylor & Francis Journals, vol. 39(3), pages 381-396.
    10. Lee, Min-Yang A. & Thunberg, Eric M., 2012. "An Inverse Demand System for New England Groundfish: Welfare Analysis of the Transition to Catch Share Management," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 123879, Agricultural and Applied Economics Association.
    11. Gary K.K. Wong & Keith R. McLaren, 2002. "Regular and Estimable Inverse Demand Systems: A Distance Function Approach," Monash Econometrics and Business Statistics Working Papers 6/02, Monash University, Department of Econometrics and Business Statistics.

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