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Robust pricing with refunds

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  • Toomas Hinnosaar
  • Keiichi Kawai

Abstract

Before purchase, a buyer of an experience good learns about the product's fit using various information sources, including some of which the seller may be unaware of. The buyer, however, can conclusively learn the fit only after purchasing and trying out the product. We show that the seller can use a simple mechanism to take best advantage of the buyer's post‐purchase learning to maximize his guaranteed‐profit. We show that this mechanism combines a generous refund, which performs well when the buyer is relatively informed, with non‐refundable random discounts, which work well when the buyer is relatively uninformed.

Suggested Citation

  • Toomas Hinnosaar & Keiichi Kawai, 2020. "Robust pricing with refunds," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1014-1036, December.
    • Handle: RePEc:bla:randje:v:51:y:2020:i:4:p:1014-1036
    DOI: 10.1111/1756-2171.12348
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    References listed on IDEAS

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    Cited by:

    1. Qianjun Lyu, 2022. "Optimal Refund Mechanism," ECONtribute Discussion Papers Series 214, University of Bonn and University of Cologne, Germany.
    2. Rodivilov, Alexander, 2021. "A note on robust procurement contracts," Economics Letters, Elsevier, vol. 201(C).
    3. Pham, Hien, "undated". "a reprendre_ WP annulé," TSE Working Papers 21-1263, Toulouse School of Economics (TSE).
    4. Casner, Ben, 2020. "Seller curation in platforms," International Journal of Industrial Organization, Elsevier, vol. 72(C).

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    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other
    • D42 - Microeconomics - - Market Structure, Pricing, and Design - - - Monopoly

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