|
Field
Trial End Date
|
Before
October 02, 2021
|
After
October 30, 2021
|
|
Field
Last Published
|
Before
September 10, 2021 03:32 PM
|
After
October 08, 2021 09:29 AM
|
|
Field
Intervention Start Date
|
Before
September 13, 2021
|
After
September 05, 2021
|
|
Field
Intervention End Date
|
Before
September 30, 2021
|
After
October 30, 2021
|
|
Field
Primary Outcomes (End Points)
|
Before
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.
|
After
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 on the performance of the FM.
T4: In the message treatment, the FM can suggest what the SM should report. SM receives information on the performance of the FM.
T5: In the cheating treatment, the FM can misreport their outcome as well. Otherwise, T5 is identical to T2.
T6: In the cheating-information treatment, the FM can misreport their outcome as well. Otherwise, T6 is identical to T3.
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.
|
|
Field
Primary Outcomes (Explanation)
|
Before
|
After
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).
|
|
Field
Randomization Method
|
Before
We will perform the treatments for the tedious and the challenging tasks 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.
|
After
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.
|
|
Field
Planned Number of Observations
|
Before
1040 individuals, thereof 520 first-movers and 520 second-movers
|
After
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.
|
|
Field
Sample size (or number of clusters) by treatment arms
|
Before
130 second-movers per treatment who are each matched sequentially with a first-mover.
|
After
130 second-movers per treatment who are each matched sequentially with a first-mover. We should have a total of approximately 1,040 participants.
|