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Risk and punishment revisited. Errors in variables and in the lab

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  • Christoph Engel

    (Max Planck Institute for Research on Collective Goods)

  • Oliver Kirchkamp

    (University Jena, School of Economics)

Abstract

We provide an example for an errors in variables problem which might be often neglected but which is quite common in lab experimental practice: In one task, attitude towards risk is measured, in another task participants behave in a way that can possibly be explained by their risk attitude. How should we deal with inconsistent behaviour in the risk task? Ignoring these observations entails two biases: An errors in variables bias and a selection bias. We argue that inconsistent observations should be exploited to address the errors in variables problem, which can easily be done within a Bayesian framework.

Suggested Citation

  • Christoph Engel & Oliver Kirchkamp, 2016. "Risk and punishment revisited. Errors in variables and in the lab," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2016_11, Max Planck Institute for Research on Collective Goods.
    • Handle: RePEc:mpg:wpaper:2016_11
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    References listed on IDEAS

    as
    1. Maria Montero & Martin Sefton & Ping Zhang, 2008. "Enlargement and the balance of power: an experimental study," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 30(1), pages 69-87, January.
    2. Engel, Christoph, 2014. "Social preferences can make imperfect sanctions work: Evidence from a public good experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 108(C), pages 343-353.
    3. Catherine C. Eckel & Philip J. Grossman, 2008. "Forecasting Risk Attitudes: An Experimental Study Using Actual and Forecast Gamble Choices," Monash Economics Working Papers archive-01, Monash University, Department of Economics.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Risk; lab experiment; public good; errors in variables; Bayesian inference;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

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