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The Effect of Self-awareness on Dishonesty
Last registered on July 08, 2019

Pre-Trial

Trial Information
General Information
Title
The Effect of Self-awareness on Dishonesty
RCT ID
AEARCTR-0004407
Initial registration date
July 04, 2019
Last updated
July 08, 2019 10:19 AM EDT
Location(s)

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Primary Investigator
Affiliation
University of Warwick
Other Primary Investigator(s)
PI Affiliation
University of Warwick
Additional Trial Information
Status
In development
Start date
2019-07-21
End date
2020-07-01
Secondary IDs
Abstract
In his seminal work, Becker (1968) showed that the gain from behavior that harms others, the cost of that behavior and the probability of getting caught have an effect on the decision to commit criminal acts. However, these mechanisms are not enough to prevent crime in general and certainly not enough to curb more modest anti-social behavior such as dishonesty. Other than the monetary cost of dishonesty, people also experience psychological distress from dishonest behavior. This comes about through the cognitive dissonance or mental discomfort which arises because of the contradiction between a person’s beliefs about herself and her behavior in real life. A person might well like to think of herself as honest but behaving in conflict with this ideal would create cognitive dissonance and put that self-awareness under threat. In this study we attempt to measure and understand the role of self-awareness (both in terms of better understanding and modifying the ideal self) as a tool to explain and possibly mitigate dishonest behaviour, using an online experiment.
External Link(s)
Registration Citation
Citation
Cibik, Ceren Bengu and Daniel Sgroi. 2019. "The Effect of Self-awareness on Dishonesty." AEA RCT Registry. July 08. https://doi.org/10.1257/rct.4407-2.0
Former Citation
Cibik, Ceren Bengu and Daniel Sgroi. 2019. "The Effect of Self-awareness on Dishonesty." AEA RCT Registry. July 08. https://www.socialscienceregistry.org/trials/4407/history/49442
Experimental Details
Interventions
Intervention(s)
We will recruit Amazon MTurk workers who will be randomized among multiple treatments. Each treatment requires the subject to write a description about a real life event involving honesty or dishonesty which entailed different combinations of benefit and cost to themselves and/or others. There is also a control group who do not write descriptions. After this, they all face a series of tasks designed to reveal attitudes towards honesty and self-awareness.
Intervention Start Date
2019-07-21
Intervention End Date
2020-07-01
Primary Outcomes
Primary Outcomes (end points)
Multiple measures of honesty, measures of self-awareness (personality, integrity, fairness, other-regarding preferences), text.
Primary Outcomes (explanation)
The various honesty measures include decisions made during a coin-tossing game (number of coins claimed to land hands) which is our main measure (and power calculations are based on this) as well as supplementary measures based on a matrix puzzle game (in which subjects have the opportunity to lie) and a cheap talk game. Personality is derived from the Big Five Inventory test. We use a separate questionnaire for integrity and fairness. The main other regarding preference to be assessed is altruism using a dictator game. We will also analyse the text used in the descriptions to reveal other psychological variables relevant to dishonesty and self-awareness.
Secondary Outcomes
Secondary Outcomes (end points)
Demographics, risk preferences.
Secondary Outcomes (explanation)
Basic demographics (gender, age, education, etc.).
Experimental Design
Experimental Design
In the MTurk experiment we first randomize into different groups each of which requires the subject to write a description about a real life event involving honesty or dishonesty. After this they all face a series of tasks designed to reveal dishonesty and self-awareness. They then face a number of tests and questionnaires designed to reveal attitudes and self-awareness, with a demographic questionnaire at the end. There is also a control group who do not write a description but face the same subsequent tasks.
Experimental Design Details
Not available
Randomization Method
Randomization done by computer.
Randomization Unit
For the online experiment, it will be at the individual level.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
No clustering.
Sample size: planned number of observations
604 individuals.
Sample size (or number of clusters) by treatment arms
70 individuals in the baseline control, 168 in each of the three honesty/dishonesty treatments. These numbers are based on power calculations for the coin flipping task.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Given the sample size, for the coin flipping task the minimum detectable effect size for the main outcome is 2 additional coins (40% higher than the control group).
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Internal Department of Economics Approval Process
IRB Approval Date
2019-06-11
IRB Approval Number
ECONPGR 05/18