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Self-Confidence and Search

  • Falk, Armin

    ()

    (University of Bonn)

  • Huffman, David B.

    ()

    (University of Oxford)

  • Sunde, Uwe

    ()

    (University of Munich)

Standard search theory assumes that individuals know, with certainty, how they compare to competing searchers in terms of ability. In contrast, we hypothesize that searchers are uncertain about relative ability, with important implications for search behavior. We test our hypotheses in a laboratory experiment. The first main finding is that people are substantially uncertain about whether they are a type with a high or low probability of success, determined by being above or below the median in terms of ability. Self-confidence, defined as an individual’s self-assessed probability of being a high type, is too high (above zero) for many low types, and too low (below 1) for many high types. Second, people update beliefs based on search outcomes. Self-confidence increases or decreases in the right direction, but is less sensitive to new information than predicted by Bayes’ rule. Third, updating affects future search decisions: people are less likely to search as confidence about being a high type falls. Fourth, some search too little, and others search too much, due to wrong beliefs. Fifth, at the end of the experiment a substantial fraction turn down the chance to learn their exact rank. These are overwhelmingly those with low ability, suggesting an aversion to learning that one is one of the worst performers. Given that people are uncertain even in the simple setting of our experiment, our evidence strongly suggests that uncertainty about ability is relevant in more complex, real-world search settings, including search for a job or search for a mate. Focusing on the case of job search, we discuss how our findings can provide a new explanation for various important stylized facts from field evidence.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 2525.

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Length: 44 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:iza:izadps:dp2525
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  1. Bowlus, A.J., 1995. "A Search Interpretation of Male-Female Wage Differentials," UWO Department of Economics Working Papers 9504, University of Western Ontario, Department of Economics.
  2. Stephen R. G. Jones & W. Craig Riddell, 1999. "The Measurement of Unemployment: An Empirical Approach," Econometrica, Econometric Society, vol. 67(1), pages 147-162, January.
  3. Stephen Machin & Alan Manning, 1998. "The causes and consequences of long-term unemployment in Europe," LSE Research Online Documents on Economics 20255, London School of Economics and Political Science, LSE Library.
  4. Schotter, Andrew & Braunstein, Yale M, 1981. "Economic Search: An Experimental Study," Economic Inquiry, Western Economic Association International, vol. 19(1), pages 1-25, January.
  5. Cox, James C & Oaxaca, Ronald L, 1989. " Laboratory Experiments with a Finite-Horizon Job-Search Model," Journal of Risk and Uncertainty, Springer, vol. 2(3), pages 301-29, September.
  6. Charles A. Holt & Susan K. Laury, 2002. "Risk Aversion and Incentive Effects," American Economic Review, American Economic Association, vol. 92(5), pages 1644-1655, December.
  7. Botond Köszegi, 2006. "Ego Utility, Overconfidence, and Task Choice," Journal of the European Economic Association, MIT Press, vol. 4(4), pages 673-707, 06.
  8. Gerdtham, Ulf-G. & Johannesson, Magnus, 2003. "A note on the effect of unemployment on mortality," Journal of Health Economics, Elsevier, vol. 22(3), pages 505-518, May.
  9. Paul Frijters & Bas van der Klaau, 2003. "Job Search with Nonparticipation," Paul Frijters Discussion Papers 2003-3, School of Economics and Finance, Queensland University of Technology.
  10. El-Gamal, Mahmoud A. & Grether, David M., 1995. "Are People Bayesian? Uncovering Behavioral Strategies," Working Papers 919, California Institute of Technology, Division of the Humanities and Social Sciences.
  11. Grether, David M., . "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," Working Papers 245, California Institute of Technology, Division of the Humanities and Social Sciences.
  12. Hey, John D., 1982. "Search for rules for search," Journal of Economic Behavior & Organization, Elsevier, vol. 3(1), pages 65-81, March.
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