Primary Outcomes (explanation)

In an earlier survey study, we found evidence that larger means and lower variance and minimums, relative to the other applicant, led to increased chance of being hired. We thus hypothesize that these patterns will follow in the experiment:

Hypothesis 1a: When mean differences are small metrics other than the mean (i.e. variance, range, maximum, minimum, outliers, skew) will have predictive power when determining which out of a pair of applicants is chosen to be hired.

Hypothesis 1b: When mean differences are small an applicant having a higher mean or maximum will increase their likelihood of being hired, while a higher variance will decrease their likelihood of being hired.

Small mean differences are defined based off of the three evaluation sets per pair that generated the smallest mean difference while retaining trade-offs between the two applicants, i.e. that one applicant did not strictly or weakly dominate the other applicant in terms of evaluations. The largest mean difference in our sample is 5.33 out of a possible range of 0-100. Because we focus on this case of smaller mean differences, we acknowledge that our results may not be generalizable to cases where the difference in means of the evaluations are more substantial.

Furthermore, based on this earlier evidence, we hypothesize the following treatment interactions:

Hypothesis 2: The predictive power of the evaluation metrics will be diminished when gender is known, compared to when gender is not known.

Hypothesis 3: When gender is known conditional on mean, we expect that metrics will have a less positive (or more negative) impact on hiring decisions for women than for men.

Hypothesis 4: The benefit (cost) from having a higher (lower) mean and a lower (higher) variance will be amplified for men (women) in mixed-gender pairs relative to when gender is not known.