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A method to estimate mean lying rates and their full distribution

Published online by Cambridge University Press:  01 January 2025

Ellen Garbarino ,
Robert Slonim  and
Marie Claire Villeval Open the ORCID record for Marie Claire Villeval [Opens in a new window]
Ellen Garbarino*
Affiliation:
Department of Marketing, University of Sydney Business School, Abercrombie Building, Sydney, NSW 2006, Australia
Robert Slonim*
Affiliation:
Department of Economics, University of Sydney, Merewether Building, Sydney, NSW 2006, Australia IZA, Bonn, Germany
Marie Claire Villeval*
Affiliation:
Univ Lyon, CNRS, GATE (UMR5824), 93, Chemin Des Mouilles, 69130 Ecully, France IZA, Bonn, Germany
*
ellen.garbarino@sydney.edu.au
obert.slonim@sydney.edu.au
e-mail: villeval@gate.cnrs.fr
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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 the existing methods.

Keywords

DishonestyLyingEconometric estimationSampling errorsExperimental economics

JEL classification

C91: Laboratory, Individual Behavior C81: Methodology for Collecting, Estimating, and Organizing Microeconomic Data • Data Access
Type
Original Paper
Information
Journal of the Economic Science Association , Volume 4 , Issue 2 , December 2018 , pp. 136 - 150
Copyright
Copyright © 2018 Economic Science Association

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