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Identifying Preferences under Risk from Discrete Choices

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  • Pierre-Andre Chiappori
  • Amit Gandhi
  • Bernard Salanie
  • Francois Salanie

Abstract

When studying consumption choices, economists have often relied on the abstraction of a representative agent. Such an agent can indeed be shown to exist and to replicate the aggregate consumers' demand under standard, but not necessarily convincing assumptions (Kirman (1992)). There was also ajustifiable reluctance to introducing heterogeneous preferences, as such a step may seem ad hoc when trying to explain different consumption behaviors. The rise of empirical studies based on micro data has opened new perspectives. The micro-economic importance of uninsurable risks is now recognized, and threatens the foundations of the representative agent hypothesis often used in macroeconomics. The continuing controversies surrounding the question of individual attitudes towards risk has motivated many empirical studies and observations; most of them conclude to a bewildering diversity of individual preferences. This paper proposes to check the conditions under which heterogeneous individual attitudes toward risk can be non-parametrically identified from discrete data on choices under risk. Our main result establishes that given data that is usually available (essentially market shares of the different options, plus the realizations of the final outcomes of agents), the analyst can recover the whole distribution of individual preferences if this can be indexed by a one-dimensional parameter, provided that a fairly weak single-crossing condition holds. We then discuss several applications of our general methodology.

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Paper provided by LERNA, University of Toulouse in its series LERNA Working Papers with number 09.11.287.

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Date of creation: May 2009
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Handle: RePEc:ler:wpaper:09.11.287

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  1. Bruno Jullien & Bernard Salanié, 1997. "Estimating Preferences under Risk : The Case of Racetrack Bettors," Working Papers 97-39, Centre de Recherche en Economie et Statistique.
  2. Sherwin Rosen, . "The Value of Changes in Life Expectancy," University of Chicago - Population Research Center 87-14, Chicago - Population Research Center.
  3. W. Kip Viscusi & Joseph E. Aldy, 2003. "The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World," NBER Working Papers 9487, National Bureau of Economic Research, Inc.
  4. Alma Cohen & Liran Einav, 2005. "Estimating Risk Preferences from Deductible Choice," Discussion Papers 04-031, Stanford Institute for Economic Policy Research.
  5. Syngjoo Choi & Raymond Fisman & Douglas Gale & Shachar Kariv, 2007. "Consistency and Heterogeneity of Individual Behavior under Uncertainty," American Economic Review, American Economic Association, vol. 97(5), pages 1921-1938, December.
  6. repec:reg:rpubli:282 is not listed on IDEAS
  7. Bruno Jullien & Bernard Salanié & François Salanié, 2007. "Screening risk-averse agents under moral hazard: single-crossing and the CARA case," Economic Theory, Springer, vol. 30(1), pages 151-169, January.
  8. Pierre-André Chiappori & Bruno Jullien & Bernard Salanié & François Salanié, 2002. "Asymmetric Information in Insurance : General Testable Implications," Working Papers 2002-42, Centre de Recherche en Economie et Statistique.
  9. repec:rje:randje:v:37:y:2006:i:4:p:783-798 is not listed on IDEAS
  10. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
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Cited by:
  1. Brian Blackburn & Aprajit Mahajan & Alessandro Tarozzi & Joanne Yoong, 2009. "Bednets, Information and Malaria in Orissa," Discussion Papers 08-025, Stanford Institute for Economic Policy Research.
  2. Benjamin R. Handel, 2011. "Adverse Selection and Switching Costs in Health Insurance Markets: When Nudging Hurts," NBER Working Papers 17459, National Bureau of Economic Research, Inc.
  3. Daniel J. Henderson & Qi Li & Christopher F. Parmeter, 2013. "Gradient Based Smoothing Parameter Selection for Nonparametric Regression Estimation," Working Papers 2014-01, University of Miami, Department of Economics.

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