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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. 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.
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    Keywords

    Risk; lab experiment; public good; errors in variables; Bayesian inference;

    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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