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Welfare Measures when Agents can Learn: A Unifying Theory

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  • Jinhua Zhao
  • Catherine L. Kling

Abstract

We extend Graham's (1981) welfare analysis under uncertainty to a dynamic environment where the agent can delay and obtain information. The dynamic willingness to pay locus unifies the concepts of option price, dynamic WTP, commitment costs and quasi-option value. Option price (dynamic WTP) corresponds to the "ex ante" WTP when the agent ignores (recognises) the learning opportunity. The commitment cost, or the difference between option price and dynamic WTP, equals the annualised and monetised quasi-option value. State contingent contracts have added value by allowing trading among agents with heterogeneous access to future information and delay opportunities. Copyright © The Author(s). Journal compilation © Royal Economic Society 2009.

Suggested Citation

  • Jinhua Zhao & Catherine L. Kling, 2009. "Welfare Measures when Agents can Learn: A Unifying Theory," Economic Journal, Royal Economic Society, vol. 119(540), pages 1560-1585, October.
  • Handle: RePEc:ecj:econjl:v:119:y:2009:i:540:p:1560-1585
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    Cited by:

    1. Krüger, Niclas A. & Svensson, Mikael, 2009. "The impact of real options on willingness to pay for mortality risk reductions," Journal of Health Economics, Elsevier, vol. 28(3), pages 563-569, May.
    2. Richard T. Carson & Miko_aj Czajkowski, 2014. "The discrete choice experiment approach to environmental contingent valuation," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 9, pages 202-235, Edward Elgar Publishing.
    3. Amiran, Edoh Y. & Hagen, Daniel A., 2018. "What do income tests tell us about the gap between WTA and WTP for public goods?," Journal of Environmental Economics and Management, Elsevier, vol. 90(C), pages 134-146.
    4. Catherine L. Kling & Daniel J. Phaneuf & Jinhua Zhao, 2012. "From Exxon to BP: Has Some Number Become Better Than No Number?," Journal of Economic Perspectives, American Economic Association, vol. 26(4), pages 3-26, Fall.

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