Loss Aversion and Lying Behavior: Theory, Estimation and Empirical Evidence

67 Pages Posted: 29 Nov 2016 Last revised: 19 Jul 2017

See all articles by Ellen Garbarino

Ellen Garbarino

The University of Sydney

Robert Slonim

The University of Sydney; IZA Institute of Labor Economics

Marie Claire Villeval

GATE - CNRS; IZA Institute of Labor Economics

Multiple version iconThere are 3 versions of this paper

Date Written: July 18, 2017

Abstract

We theoretically show that loss-averse agents are more likely to lie to avoid receiving a low payoff after a random draw, the lower the ex-ante probability of this bad outcome. The ex-ante expected payoff increases as the bad outcome becomes less likely, and hence the greater is the loss avoided by lying. We demonstrate robust support for this theory by reanalyzing the results from the extant literature and with two new experiments that vary the outcome probabilities and are run double-anonymous to remove reputation effects. To measure lying, we develop an empirical method that estimates the full distribution of dishonesty.

Keywords: Loss Aversion, Dishonesty, Lying, Econometric Estimation, Experimental Economics

JEL Classification: C91, C81, D03

Suggested Citation

Garbarino, Ellen and Slonim, Robert and Villeval, Marie Claire, Loss Aversion and Lying Behavior: Theory, Estimation and Empirical Evidence (July 18, 2017). Available at SSRN: https://ssrn.com/abstract=2875989 or http://dx.doi.org/10.2139/ssrn.2875989

Ellen Garbarino

The University of Sydney ( email )

Robert Slonim

The University of Sydney ( email )

University of Sydney
Sydney, NSW 2006
Australia

IZA Institute of Labor Economics ( email )

P.O. Box 7240
Bonn, D-53072
Germany

Marie Claire Villeval (Contact Author)

GATE - CNRS ( email )

35 rue Raulin
LYON, 69007
France
+33 688314656 (Phone)

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
274
Abstract Views
1,639
Rank
81,106
PlumX Metrics