The impact of peer pressure on effort and cheating: An experimental approach

Last registered on October 09, 2021

Pre-Trial

Trial Information

General Information

Title
The impact of peer pressure on effort and cheating: An experimental approach
RCT ID
AEARCTR-0008201
Initial registration date
September 07, 2021
Last updated
October 09, 2021, 4:44 AM EDT

Locations

Region
Region

Primary Investigator

Affiliation
Victoria University of Wellington

Other Primary Investigator(s)

PI Affiliation
HSE University
PI Affiliation
Victoria University of Wellington

Additional Trial Information

Status
In development
Start date
2021-06-01
End date
2021-10-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We plan to test experimentally the impact of peer effects in groups. The payoff of both group members is identical and depends on their aggregated performance. Subjects are randomly assigned to groups of two and perform a real-effort task sequentially. We consider two real effort tasks, a tedious task (counting the occurrence of letters in senseless paragraphs) and an intellectually challenging task (solving Raven’s matrices).
For each of the two real effort tasks, we perform two treatments: In both treatments, we distinguish between a first mover (FM) and a second mover (SM). FMs can never misreport their actual performance. In the first treatment, SMs will be informed about the performance and sex of FMs before they perform the task. Identical to FMs, SMs cannot misreport their performance in this treatment. In the second treatment, SMs will learn the FMs actual performance and sex only after they also performed the task. Thereafter, SMs will have the opportunity to misreport their own performance.
Our experiment extends the analysis of peer effects to situations in which the interests of group members are fully aligned. Our original hypotheses before we performed a pilot were as follows:
(i) In the first treatment, the actual performance of SMs increases in the performance of first movers. This effect is more pronounced for the challenging task.
(ii) In the second treatment, the reported performance of SMs (and hence their degree of misreporting) increases in the performance of first movers. This effect is more pronounced for the challenging task.
Our pilots, however, suggests that all of our hypotheses will be rejected by the data.
External Link(s)

Registration Citation

Citation
Dudek, Thomas, Eberhard Feess and Yuriy Timofeyev. 2021. "The impact of peer pressure on effort and cheating: An experimental approach." AEA RCT Registry. October 09. https://doi.org/10.1257/rct.8201-2.1
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-09-05
Intervention End Date
2021-10-30

Primary Outcomes

Primary Outcomes (end points)
NEW
We now consider the following treatments with cheating possibilities for the second mover (SM) (Treatment 1 is the treatment without cheating possibility).
T2: In the no-information treatment, the SM receives no information on the performance of the FM.
T3: In the information treatment, the SM receives information about the performance of the FM after the SM has finished the task but before the SM enters their outcome.
T4: In the message treatment, the FM can suggest what the SM should report. SM receives information about the actual performance of the FM.
T5: In the cheating treatment, the FM can misreport their outcome as well. Otherwise, T5 is identical to T2. The SM receives no information about the reported performance of the FM.
T6: In the cheating-information treatment, the FM can misreport their outcome as well. Otherwise, T6 is identical to T3. SM receives information about the reported performance of the FM after the SM has finished the task but before they enter their outcome.

In all treatments, our primary outcome is the report of SMs. We will compare SM reports among treatments, both with non-parametric statistics (Wilcoxon Rank Sum Test) and regression analysis. Specifically, we compare the SM behavior among treatments as follows:
Between treatment comparisons
Comparing the no-information treatment (T2) and the information treatment (T3) gives us the overall impact of information revelation on the degree of misreporting.
Comparing the message treatment to the information treatment reveals whether allowing the FM to send a message to the SM increases or decreases the overall degree of misreporting.
Comparing the cheating treatment (T5) and the cheating-information treatment (T6) gives us the overall impact of information revelation on the degree of SM misreporting in a setting where FMs can cheat as well.
Within treatment analyses
For the treatments with information revelation, our main independent variable of interest is the true performance (T3) or reported performance (T6) of the FM. In the message treatment T4, the other independent variable is the FM message.
In all regression, we also analyze gender effects and interact gender with the variables of interest. For gender, we analyze the impact of the SM’s own gender and the gender SMs are matched with on SM behavior.
In the message treatment T4, we run a regression of the FM report on the FM performance, gender, and there interaction.


OLD
Our primary outcome is always the regression coefficient for the FMs’ performance on the SMs’ performance. For all our analyses, we will run regressions with different combinations of covariates as robustness checks.
(i.a) Treatment 1: We run regressions of the SMs’ actual performance in Counting Letters as the dependent variable on the actual performance of FMs in the same task.
(i.b) Treatment 1: We run regressions of the SMs’ actual performance in Raven’s matrices as the dependent variable on the actual performance of the FMs in the same task.
(ii.a) Treatment 2: We run regressions of the SMs reported performance in Counting Letters as the dependent variable on the FMs’ actual performance in the same task
(ii.b) Treatment 2: We run regressions of the SMs reported performance in Raven’s matrices as the dependent variable on the FMs’ actual performance in the same task.
Primary Outcomes (explanation)
Between 5 September and 23 September, we collected data and as initially expected, our treatments had no effect. We tested only treatments in which we let participants solve Raven’s matrices. We so far have not collected data from a letter counting task. Performance differences are insignificant or significant as expected for those who were able to cheat in the task (participants in treatments where cheating was possible have higher average performance than participants where cheating was impossible).
These results confirm the results of the pilot that our current treatments will not lead to any significant results. We have decided to modify the approach in order to utilize our remaining funds more efficiently and to analyze more interesting behavioral insights. Our adjustments are as follows:
(i) We focus on the treatments with cheating, as the data shows that there will be no impact of the information revelation on effort.
(ii) For the cases with cheating, we add additional treatments that may potentially lead to more interesting results compared to our current treatments.
(iii) For budget reasons, we restrict attention to one real effort task. We have decided to take Raven’s matrices, as it seems reasonable to assume that, compared to a task in which participants count letters, subjects should intrinsically care more about their outcome, their self-image and about social image (reputation effects towards the FM).

Secondary Outcomes

Secondary Outcomes (end points)
(i) Our first secondary outcome is the comparison between FM performance and SM performance in treatment 1.
(ii) Our second secondary outcome is how demographics and personal attitudes influence the behavior. We will collect data on sex, age, education, personal income, and the traits risk tolerance and competitiveness.
(iii) Our third secondary outcome is the comparison between the impacts of the FMs performance in Counting Letters compared to Raven’s matrices.
(iv) Our fourth secondary outcome is the comparison of differences in actual/reported performance of SMs who had a same-sex or opposite-sex teammate.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment will be conducted online and participants will be recruited using Prolific.
For the tedious task, we use a task for which participants count the occurrence of letters in senseless paragraphs. For the intellectually challenging task, we use Raven’s matrices. We adopt a between-subject design, i.e. each participant performs just one real effort task as either the First-Mover (FM) or Second-Mover (SM). We adopt a two-by-two design with the two variables “misreporting for SM possible/ misreporting for SM not possible” and “tedious task/intellectually challenging task”. In the treatment with misreporting possibility for SMs, SMs will learn about this possibility only after they have worked on the real effort task. This excludes that the misreporting possibility influences the actual performance.
We will run the experiments with the tedious and the intellectually challenging task separately. FMs will perform the task first, and their performance will be utilized for determining whether the average performance is larger with or without information on the partner’s performance. SMs will then be randomly assigned to FMs.
Details:
Subjects will have nine minutes to work on counting letters or to work on Raven’s matrices. The chance of receiving a fixed bonus payment of GBP 1.75 increases by 2 percentage points for each correctly counted letter or for each correctly solved matrix (depending on the assigned treatment). A pre-test showed that the expected payoff is then approximately the same for both tasks. Our payoff structure ensures that the marginal financial benefit from each correctly solved task is independent of the performance of the partner. This means that any impact of the FMs performance can be attributed to peer effects.
Experimental Design Details
Not available
Randomization Method
We perform the treatments for the challenging task (Raven's matrices) separately. Furthermore, FMs will perform the task before SMs. Still, our matching procedure ensures full random assignment as all participants receive an identical HIT when they decide to take part in the experiment.
Randomization Unit
Individual. No clustering.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
0
Sample size: planned number of observations
1,040 individuals, thereof 520 first-movers and 520 second-movers We aim to collect 130 First-Movers (FMs) and 130 Second-Movers (SMs) for each treatment. Data collection takes approximately one week per treatment.
Sample size (or number of clusters) by treatment arms
130 second-movers per treatment who are each matched sequentially with a first-mover. We should have a total of approximately 1,040 participants.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Human Ethics Committee Victoria University of Wellington
IRB Approval Date
2021-06-01
IRB Approval Number
29506