The Distribution of 1st and 2nd Rolls in Lying Experiments: Distinguishing Between Competing Theories
Last registered on December 23, 2019

Pre-Trial

Trial Information
General Information
Title
The Distribution of 1st and 2nd Rolls in Lying Experiments: Distinguishing Between Competing Theories
RCT ID
AEARCTR-0003547
Initial registration date
November 08, 2018
Last updated
December 23, 2019 5:06 AM EST
Location(s)
Primary Investigator
Affiliation
UEA
Other Primary Investigator(s)
PI Affiliation
CUHK
Additional Trial Information
Status
Completed
Start date
2018-11-11
End date
2018-11-23
Secondary IDs
Abstract
We will test 2 leading theories of lying, using new die rolling experiments.

More details are in the pre-analysis plan, attached in pdf form.
External Link(s)
Registration Citation
Citation
Clist, Paul and Ying Hong. 2019. "The Distribution of 1st and 2nd Rolls in Lying Experiments: Distinguishing Between Competing Theories." AEA RCT Registry. December 23. https://doi.org/10.1257/rct.3547-4.0.
Former Citation
Clist, Paul and Ying Hong. 2019. "The Distribution of 1st and 2nd Rolls in Lying Experiments: Distinguishing Between Competing Theories." AEA RCT Registry. December 23. http://www.socialscienceregistry.org/trials/3547/history/59496.
Sponsors & Partners

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Request Information
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2018-11-11
Intervention End Date
2018-11-23
Primary Outcomes
Primary Outcomes (end points)
The distribution of reports.
Primary Outcomes (explanation)
I.e. roll 1 and roll 2 claimed roll.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We will run a dice rolling experiment.
Experimental Design Details
Randomization Method
By Z-tree.
Randomization Unit
Individual.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
The data is not clustered.
Sample size: planned number of observations
c. 250 subjects.
Sample size (or number of clusters) by treatment arms
125
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
UEA International Development Ethics Committee
IRB Approval Date
2017-05-09
IRB Approval Number
N/A
Analysis Plan
Analysis Plan Documents
PaP.pdf

MD5: 708e45ec404762488ba71b6f270b1a9f

SHA1: e316bb6f4d516d79bdb33a09888c1a35eb2533d2

Uploaded At: November 08, 2018

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
November 23, 2018, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
November 23, 2018, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
2 groups (though the difference between them isn't the main element of interest)
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
231 people
Final Sample Size (or Number of Clusters) by Treatment Arms
127 in the 2 screen treatment 104 in the 1 screen treatment
Data Publication
Data Publication
Is public data available?
Yes
Program Files
Program Files
Yes
Reports and Papers
Preliminary Reports
Relevant Papers
Abstract
Lying is an important human behaviour that has received unprecedented empirical attention in recent years due to a new experimental paradigm in which subjects are incentivised to misreport a die roll for financial gain. Amongst theoretical attempts to explain results, Justified Dishonesty (JD) is a theory with impressive support and a plausible psychological foundation. JD predicts subjects will swap a paid and unpaid roll of a die whenever financially beneficial, as this feels less like lying. However, JD’s predictions are virtually identical to a competing economic model. In Dufwenberg & Dufwenberg’s (DD) subjects have perceived cheating aversion, incurring a cost of lying that is in proportion to the amount they are perceived to cheat. Current evidence is unable to distinguish between these two distinct mechanisms. Here we show that JD often makes accurate predictions, but is a poor explanation. We perform a placebo test, finding that JD is more accurate when it should be irrelevant. Furthermore, we elicit the second (unpaid) roll, strongly rejecting a direct corollary of JD. Our results demonstrate that the role of justifications and desired counterfactuals have been overstated. The simple idea that subjects dislike others perceiving them as liars in proportion to the size of the lie is sufficient to explain patterns of lying behaviour.
Citation
Clist, P and Hong, YY (2019) Why Do We Lie? Distinguishing Between Competing Lying Theories, CBESS Working Paper 19(03)