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A Method to Estimate Mean Lying Rates and Their Full Distribution

Author

Listed:
  • Ellen Garbarino

    (University of Sydney Business School, Department of Marketing, Abercrombie building, Sydney, NSW 2006, Australia)

  • Robert Slonim

    (University of Sydney, Department of Economics, Merewether building, Sydney, NSW 2006, Australia; IZA, Bonn, Germany)

  • Marie Claire Villeval

    (Univ Lyon, CNRS, GATE L-SE UMR 5824, F-69131 Ecully, France)

Abstract

Studying the likelihood that individuals cheat requires a valid statistical measure of dishonesty. We develop an easy empirical method to measure and compare lying behavior within and across studies to correct for sampling errors. This method estimates the full distribution of lying when agents privately observe the outcome of a random process (e.g., die roll) and can misreport what they observed. It provides a precise estimate of the mean and confidence interval (offering lower and upper bounds on the proportion of people lying) over the full distribution, allowing for a vast range of statistical inferences not generally available with existing methods.

Suggested Citation

  • Ellen Garbarino & Robert Slonim & Marie Claire Villeval, 2018. "A Method to Estimate Mean Lying Rates and Their Full Distribution," Working Papers 1816, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    • Handle: RePEc:gat:wpaper:1816
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    References listed on IDEAS

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    1. Banerjee, Ritwik & Gupta, Nabanita Datta & Villeval, Marie Claire, 2018. "The spillover effects of affirmative action on competitiveness and unethical behavior," European Economic Review, Elsevier, vol. 101(C), pages 567-604.
    2. Alain Cohn & Michel André Maréchal & Thomas Noll, 2015. "Bad Boys: How Criminal Identity Salience Affects Rule Violation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1289-1308.
    3. Houser, Daniel & Vetter, Stefan & Winter, Joachim, 2012. "Fairness and cheating," European Economic Review, Elsevier, vol. 56(8), pages 1645-1655.
    4. Benndorf, Volker & Moellers, Claudia & Normann, Hans-Theo, 2017. "Experienced vs. inexperienced participants in the lab: Do they behave differently?," DICE Discussion Papers 251, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    5. Johannes Abeler & Daniele Nosenzo & Collin Raymond, 2019. "Preferences for Truth‐Telling," Econometrica, Econometric Society, vol. 87(4), pages 1115-1153, July.
    6. Jacobsen, Catrine & Piovesan, Marco, 2016. "Tax me if you can: An artifactual field experiment on dishonesty," Journal of Economic Behavior & Organization, Elsevier, vol. 124(C), pages 7-14.
    7. Urs Fischbacher & Franziska Föllmi-Heusi, 2013. "Lies In Disguise—An Experimental Study On Cheating," Journal of the European Economic Association, European Economic Association, vol. 11(3), pages 525-547, June.
    8. Shalvi, Shaul & Dana, Jason & Handgraaf, Michel J.J. & De Dreu, Carsten K.W., 2011. "Justified ethicality: Observing desired counterfactuals modifies ethical perceptions and behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 115(2), pages 181-190, July.
    9. repec:wly:soecon:v:81:4:y:2015:p:1012-1024 is not listed on IDEAS
    10. Kröll, Markus & Rustagi, Devesh, 2017. "Reputation, honesty, and cheating in informal milk markets in India," SAFE Working Paper Series 134, Leibniz Institute for Financial Research SAFE, revised 2017.
    11. Volker Benndorf & Claudia Moellers & Hans-Theo Normann, 2017. "Experienced vs. inexperienced participants in the lab: do they behave differently?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 3(1), pages 12-25, July.
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    Citations

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    Cited by:

    1. Sandro Casal & Antonio Filippin, 2024. "The effect of observing multiple private information outcomes on the inclination to cheat," Economic Inquiry, Western Economic Association International, vol. 62(2), pages 543-562, April.
    2. Geraldes, Diogo & Heinicke, Franziska & Kim, Duk Gyoo, 2021. "Big and small lies," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 91(C).
    3. Michailidou, Georgia & Rotondi, Valentina, 2019. "I'd lie for you," European Economic Review, Elsevier, vol. 118(C), pages 181-192.
    4. David Hugh-Jones, 2019. "True lies," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(2), pages 255-268, December.
    5. Fries, Tilman & Gneezy, Uri & Kajackaite, Agne & Parra, Daniel, 2021. "Observability and lying," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 132-149.
    6. Marie Claire Villeval, 2019. "Comportements (non) éthiques et stratégies morales," Revue économique, Presses de Sciences-Po, vol. 70(6), pages 1021-1046.
    7. Chadi, Adrian & Homolka, Konstantin, 2022. "Little Lies and Blind Eyes – Experimental Evidence on Cheating and Task Performance in Work Groups," Journal of Economic Behavior & Organization, Elsevier, vol. 199(C), pages 122-159.
    8. Georgia Michailidou & Hande Erkut, 2022. "Lie O'Clock: Experimental Evidence on Intertemporal Lying Preferences," Working Papers 20220076, New York University Abu Dhabi, Department of Social Science, revised Apr 2022.
    9. Aksoy, Billur & Palma, Marco A., 2019. "The effects of scarcity on cheating and in-group favoritism," Journal of Economic Behavior & Organization, Elsevier, vol. 165(C), pages 100-117.

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    More about this item

    Keywords

    Dishonesty; lying; econometric estimation; sampling errors; experimental economics;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

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