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Pricing Diagnostic Information

Author

Listed:
  • Ashish Arora

    (Carnegie Mellon University, Hamburg Hall 1108, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213)

  • Andrea Fosfuri

    (Universidad Carlos III de Madrid, Madrid 126--28903, Getafe, Madrid, Spain, and Center for Economic Policy Research (CEPR), London, United Kingdom)

Abstract

Diagnostic information allows an agent to predict the state of nature about the success of an investment project better than the prior. We analyze the optimal pricing scheme for selling diagnostic information to buyers with different, privately known, ex ante success probability. Investment costs and returns of successful projects are assumed to be the same for all buyers. The value of diagnostic information is the difference in expected payoffs with and without it, and we show that the willingness to pay for diagnostic information is nonmonotonic in the ex ante success probability. When the information seller can offer only one quality level, and negative payments are not allowed, we find that the optimal menu of (linear) contracts is remarkably simple. A pure royalty is offered to buyers with low ex ante success probability, and a pure fixed fee is offered to buyers with high ex ante success probability.

Suggested Citation

  • Ashish Arora & Andrea Fosfuri, 2005. "Pricing Diagnostic Information," Management Science, INFORMS, vol. 51(7), pages 1092-1100, July.
    • Handle: RePEc:inm:ormnsc:v:51:y:2005:i:7:p:1092-1100
    DOI: 10.1287/mnsc.1050.0362
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    References listed on IDEAS

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    Cited by:

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    4. Li, Yongquan & Zhu, Kaijie, 2009. "Information acquisition in new product introduction," European Journal of Operational Research, Elsevier, vol. 198(2), pages 618-625, October.
    5. Wright, Brian D. & Pardey, Philip G. & Nottenburg, Carol & Koo, Bonwoo, 2007. "Agricultural Innovation: Investments and Incentives," Handbook of Agricultural Economics, in: Robert Evenson & Prabhu Pingali (ed.), Handbook of Agricultural Economics, edition 1, volume 3, chapter 48, pages 2533-2603, Elsevier.
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    8. J. A. Garcia & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2021. "The editor-manuscript game," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4277-4295, May.
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