The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
LIES IN DISGUISE-AN EXPERIMENTAL STUDY ON CHEATING
Abstract
We present a novel experimental design to measure honesty and lying. Participants receive a die which they roll privately. Since their payoff depends on the reported roll of the die, the subjects have an incentive to be dishonest and report higher numbers to get a higher payoff. This design has three advantages. First, cheating cannot be detected on the individual level, which reduces potential demand effects. Second, the method is very easy to implement. Third, the underlying true distribution of the outcome under full honesty is known, and hence it is possible to test different theoretical predictions. We find that about 20% of inexperienced subjects lie to the fullest extent possible while 39% of subjects are fully honest. In addition, a high share of subjects consists of partial liars; these subjects lie, but do not report the payoff-maximizing draw. We discuss different motives that explain the observed behavioral pattern.
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Cites
z-Tree: Zurich toolbox for ready-made economic experiments
Fischbacher, U. (2007) · Experimental Economics · doi:10.1007/s10683-006-9159-4 · Cited by 9,510
Self-citationRandomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Warner, S. L. (1965) · Journal of the American Statistical Association · doi:10.1080/01621459.1965.10480775 · Cited by 3,255
Intrinsic and extrinsic motivation
Benabou, R., & Tirole, J. (2003) · Review of Economic Studies · doi:10.1111/1467-937x.00253 · Cited by 2,913
The Dishonesty of Honest People: A Theory of Self-Concept Maintenance
Mazar, N., Amir, O., & Ariely, D. (2008) · Journal of Marketing Research · doi:10.1509/jmkr.45.6.633 · Cited by 2,875
Deception: The Role of Consequences
Gneezy, U. (2005) · American Economic Review · doi:10.1257/0002828053828662 · Cited by 1,729
Self-Confidence and Personal Motivation
Benabou, R., & Tirole, J. (2002) · The Quarterly Journal of Economics · doi:10.1162/003355302760193913 · Cited by 1,539
Charness, G., & Dufwenberg, M. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00719.x · Cited by 1,491
Exploiting moral wiggle room: experiments demonstrating an illusory preference for fairness
Dana, J., Weber, R. A., & Kuang, J. X. (2007) · Economic Theory · doi:10.1007/s00199-006-0153-z · Cited by 1,445
DePaulo, B. M., Kashy, D. A., Kirkendol, S. E., Wyer, M. M., & Epstein, J. A. (1996) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.70.5.979 · Cited by 1,062
DePaulo, B. M., Kashy, D. A., Kirkendol, S. E., Wyer, M. M., & Epstein, J. A. (1996) · PubMed · Cited by 1,052
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Cohn, A., Fehr, E., & Maréchal, M. A. (2014) · Nature · doi:10.1038/nature13977 · Cited by 687
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Shalvi, S., Dana, J., Handgraaf, M. J. J., & De Dreu, C. K. W. (2011) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2011.02.001 · Cited by 515
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Lammers, J., Stapel, D. A., & Galinsky, A. D. (2010) · Psychological Science · doi:10.1177/0956797610368810 · Cited by 375
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Gibson, R., Tanner, C., & Wagner, A. F. (2013) · American Economic Review · doi:10.1257/aer.103.1.532 · Cited by 324
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Let’s be honest: A review of experimental evidence of honesty and truth-telling
Rosenbaum, S. M., Billinger, S., & Stieglitz, N. (2014) · Journal of Economic Psychology · doi:10.1016/j.joep.2014.10.002 · Cited by 288
Bilen, E., & Matros, A. (2021) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2020.12.004 · Cited by 237
Motivated Bayesians: Feeling Moral While Acting Egoistically
Gino, F., Norton, M. I., & Weber, R. A. (2016) · Journal of Economic Perspectives · doi:10.1257/jep.30.3.189 · Cited by 208
AI increases unethical consumer behavior due to reduced anticipatory guilt
Kim, T., Lee, H., Kim, M. Y., Kim, S., & Duhachek, A. (2023) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-021-00832-9 · Cited by 188
Salvato, C., Reuer, J. J., & Battigalli, P. (2017) · Academy of Management Annals · doi:10.5465/annals.2014.0001 · Cited by 86
Cohn, A., Gesche, T., & Maréchal, M. A. (2022) · Management Science · doi:10.1287/mnsc.2021.3985 · Cited by 62
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Metadata
- Authors
- Fischbacher, U., & Föllmi-Heusi, F.
- Venue
- Journal of the European Economic Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06
- Volume
- 11
- Issue
- 3
- Pages
- 525-547
- ISSN
- 1542-4766 (ISSN-L)
- OpenAlex
- W1544763516
- Cited by
- 1,243
- References
- 43
- FWCI
- 166.48
- Type
- journal article
- Language
- en
- Open access
- closed
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