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Field
Primary Outcomes (End Points)
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Before
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.
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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 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.
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