We are happy to announce that all trial registrations will now be issued DOIs (digital object identifiers). For more information, see here.
Back to History Current Version
Loss Aversion in Social Image Concerns
Last registered on February 21, 2019


Trial Information
General Information
Loss Aversion in Social Image Concerns
Initial registration date
October 15, 2018
Last updated
February 21, 2019 4:10 AM EST
Primary Investigator
Düsseldorf Institute for Competition Economics
Other Primary Investigator(s)
PI Affiliation
Düsseldorf Institute for Competition Economics
PI Affiliation
Düsseldorf Institute for Competition Economics
Additional Trial Information
Start date
End date
Secondary IDs
Loss aversion is widely studied in the monetary domain as well as with respect to material goods. However, little is known about its relevance in the non-material domain. We plan to conduct a laboratory experiment to explore whether the concept of loss aversion applies to social image concerns, a non-material good. Social image concerns are important in an individual decision-making since people typically care about their reputation. We plan to analyze whether an exogenous improvement or harm to one's reputation follows the same pattern as gaining and losing money or material goods. Our experimental design attempts to quantify the effect of loss aversion in social image concerns via the scope of lying.
External Link(s)
Registration Citation
Petrishcheva, Vasilisa, Gerhard Riener and Hannah Schildberg-Hörisch. 2019. "Loss Aversion in Social Image Concerns." AEA RCT Registry. February 21. https://doi.org/10.1257/rct.3422-4.0.
Former Citation
Petrishcheva, Vasilisa et al. 2019. "Loss Aversion in Social Image Concerns." AEA RCT Registry. February 21. https://www.socialscienceregistry.org/trials/3422/history/41812.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Key outcomes are whether the individuals lie and to which extent, i.e. average reported dice rolls for reference group and oneself by treatment (HardEasy versus EasyHard). Another crucial outcome is Rank 1. It indicates the reference point of each subject.

We investigate how HardEasy and EasyHard subjects differ in the reported dice difference conditional and unconditional on their reference points. We propose several specifications where we also include measures of subjects' risk aversion, loss aversion, social image concerns as well as various sociodemographic characteristics and their interactions with the main treatment variables.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our design encompasses two treatments (HardEasy and EasyHard). In both treatments, subjects work on Raven's Progressive Matrices which are commonly used to measure fluid intelligence. We split the matrices in two parts such that one part is easier (Easy) and another one is harder (Hard). Matrices in part Easy and part Hard do not repeat or overlap. All participants receive a show-up fee, but no additional payment for correct answers on the matrices.

Subjects are randomly assigned to one of the two treatments or the role of being Observer (2 subjects per session, one female and one male). In treatment HardEasy, subjects work on the Hard part in Stage 1 and the Easy part in Stage 2. In treatment EasyHard, subjects complete the two parts in the reverse order, i.e. they first work on the Easy part and then the Hard one. Stage 1 establishes a within-subject reference point with respect to performance on the matrices. Since subjects know that their answers on the matrices reflect a measure of their IQ, their performance feedback (rank) is expected to be image relevant. We calculate a subject's rank as a percentile compared to a predetermined reference group (university students who answered the same matrices in a previous experiment). In both treatments, subjects solve Part 1, observe their Rank 1 and report it privately to the two observers (who can verify the report). In Stage 2, participants do Part 2 of the quiz. For subjects in treatment EasyHard, Part 2 is more complicated than Part 1. Thus, we expect them to perform worse on average than in Part 1, such that on average their rank decreases. For subjects in treatment HardEasy, on the contrary, the average rank increases compared to Rank 1. Construction of Rank 2 is based on exactly the same matrices (part Hard and Easy) for all subjects, so we do not expect any absolute difference between Ranks 2 in treatments HardEasy and EasyHard. After Part 2 is completed, the own Ranks 1 and 2 are displayed privately to each subject, so that subjects see whether they performed better or worse than in Part 1. The only expected difference is their reference points, i.e. Rank 1. Then we suggest subjects to throw a dice twice privately and report the numbers they got. The first reported number is added to the number of correctly solved matrices of each person in the reference group, the second reported number to their own number of correctly solved matrices (a scope for lying). Observers know about the existence of a further task in Stage 2, but not the exact nature of the dice rolls. Once the reported dice rolls are added and the Overall Rank is updated, participants go to Observers again and report their final ranks privately.

We test the following hypothesis: subjects in treatment EasyHard (who on average experience a loss in social image since their ranking deteriorates from Part 1 to Part 2) lie more than subjects in treatment HardEasy (who on average experience a gain in social image since their ranking improves from Part 1 to Part 2). We compare the average reported difference in dice roll reports (average reported number to be added to own performance minus average reported number to be added to reference group's performance) from treatments HardEasy and EasyHard. If this difference is significantly higher in treatment EasyHard than in treatment HardEasy, this provides evidence for loss aversion in social image concerns because it implies that subjects who risk loosing social image are ready to lie more than those with social image gains.
Experimental Design Details
Randomization Method
Randomization done in the experimental laboratory by letting participants draw a cabin number (which assigns them randomly to either treatment HardEasy or EasyHard, or the role of Observer).
Randomization Unit
Each participant of the laboratory experiment is considered as one independent observation
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
Approximately 168 observations for HardEasy and EasyHard treatments (6 laboratory sessions with approximately 30 subjects each, 6x2 Observers are not part of the treatment conditions)
Sample size (or number of clusters) by treatment arms
We aim at a balanced design in which about 50% of observations are assigned to treatment EasyHard and about 50% to treatment HardEasy, i.e. about 80 observations per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We define the key variable DiceDiff = DiceSubject – DiceSample, where DiceSubject is a reported dice roll for oneself (on a scale of 1 to 6), DiceSample is a reported dice roll for the reference group (on a scale of 1 to 6), and DiceDiff is a reported dice roll difference (on a scale -5 to 5, larger differences are more favorable for a subject).
IRB Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers