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Hindsight biased policy evaluation

Author

Listed:
  • Florian Schuett
  • Alexander K. Wagner

Abstract

Hindsight bias is a cognitive deficiency that leads people to overestimate ex post how predictable an event was. In this paper we develop a political-agency model in which voters are hindsight-biased and politicians differ in ability, defined as information concerning the optimal policy. When public information is not too accurate, low-ability politicians sometimes gamble on suboptimal policies: in an attempt to mimic the high-ability type, who has superior private information, they go against public information and choose a policy whose expected payoff to society is negative. We model hindsight bias as a memory imperfection that prevents voters from accessing their ex ante information about the state of the world. We show that the bias can act as a discipline device that reduces policy gambles and can therefore be welfare enhancing. Although it is well known that restrictions on information acquisition can be beneficial for a principal, our contribution is to show that a psychological bias can have such an effect.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Florian Schuett & Alexander K. Wagner, 2008. "Hindsight biased policy evaluation," LERNA Working Papers 08.08.252, LERNA, University of Toulouse.
  • Handle: RePEc:ler:wpaper:08.08.252
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    File URL: http://www2.toulouse.inra.fr/lerna/travaux/cahiers2008/08.08.252.pdf
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    Citations

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    Cited by:

    1. David Danz & Dorothea Kübler & Lydia Mechtenberg & Julia Schmid, 2015. "On the Failure of Hindsight-Biased Principals to Delegate Optimally," Management Science, INFORMS, vol. 61(8), pages 1938-1958, August.
    2. Lockwood, Ben & Le, Minh & Rockey, James, 2024. "Dynamic electoral competition with voter loss-aversion and imperfect recall," Journal of Public Economics, Elsevier, vol. 232(C).
    3. Herz, Holger & Kistler, Deborah & Zehnder, Christian & Zihlmann, Christian, 2022. "Hindsight Bias and Trust in Government: Evidence from the United States," FSES Working Papers 526, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    4. Bonaccorsi, Andrea & Apreda, Riccardo & Fantoni, Gualtiero, 2020. "Expert biases in technology foresight. Why they are a problem and how to mitigate them," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    5. Holger Herz & Deborah Kistler & Christian Zehnder & Christian Zihlmann, 2022. "Hindsight Bias and Trust in Government," CESifo Working Paper Series 9767, CESifo.
    6. Levy, Raphaël, 2014. "Soothing politics," Journal of Public Economics, Elsevier, vol. 120(C), pages 126-133.

    More about this item

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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