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Cited by 481

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Lying Aversion and the Size of the Lie

Open access
Gneezy, Uri, Kajackaite, Agne, Sobel, Joel·2018·American Economic Review
DOI 10.1257/aer.20161553
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Abstract

This paper studies lying. An agent randomly picks a number from a known distribution. She can then report any number and receive a monetary payoff based only on her report. The paper presents a model of lying costs that generates hypotheses regarding behavior. In an experiment, we find that the highest fraction of lies is from reporting the maximal outcome, but some participants do not make the maximal lie. More participants lie partially when the experimenter cannot observe their outcomes than when the experimenter can verify the observed outcome. Partial lying increases when the prior probability of the highest outcome decreases. (JEL C91, D12, D90, Z13)

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Metadata

Authors
Gneezy, U., Kajackaite, A., & Sobel, J.
Venue
American Economic Review
Publisher
American Economic Association
Published
2018-02-01
Volume
108
Issue
2
Pages
419-453
ISSN
0002-8282 (ISSN-L)
DOI
10.1257/aer.20161553
OpenAlex
W2568738663
Cited by
481
References
23
FWCI
90.36
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Lying Aversion and the Size of the Lie. https://app.cochat.ai/papers/10.1257/aer.20161553
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Gneezy, U., Kajackaite, A., & Sobel, J. (2018). Lying Aversion and the Size of the Lie. American Economic Review. https://doi.org/10.1257/aer.20161553

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.