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Field
Primary Outcomes (End Points)
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Before
Hiring decisions: 1) percentage % of minorities candidates being hired 2) probability of a minority candidate being hired, controlled by ethic type, scores(rank), age(different from the employer's age). We compare these two outcome variables in different treatments.
Estimation decisions: 1) mean estimated scores for majority candidates and minorities candidates in different treatments 2) Multiplier, measured by (estimated scores/given scores) -1 (given scores is the score of Task B - please see the section of experiment design). We will compare the average value of the multiplier among the majorities candidates and minorities candidates in four treatments. 3) For each treatment, we will run a linear regression on estimated scores_it (i for an individual candidate, t for the session) = a0+a1*minority_it +b1*scores_it +b2*scores_it*minority_it (b2*scores_it*lucky_it in treatment (4))+b3*age+eit. b2 captures the ethnic/priority difference in the impact of given scores on estimated scores (signal effects). Therefore, we can capture the signal effects via b2. If b2 is negative and different from 0 under AA policy minority (3) and AA policy lucky(4), the signal effect is significant, and the positive productivity signals of a minority/affirmed candidate are less effective than that of a majority/unaffirmed candidate.
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After
Hiring decisions: 1) percentage % of minorities candidates being hired 2) probability of a candidate being hired, controlled by ethic type, scores(rank), age(different from the employer's age). We compare these two outcome variables in different treatments.
Estimation decisions: 1) mean estimated scores for majority candidates and minorities candidates in different treatments 2) Multiplier, measured by (estimated scores/given scores) -1 (given scores is the score of Task B - please see the section of experiment design). We will compare the average value of the multiplier among the majorities candidates and minorities candidates in four treatments. 3) For each treatment, we will run a linear regression on estimated scores_it (i for an individual candidate, t for the session) = a0+a1*minority_it +b1*scores_it +b2*scores_it*minority_it (b2*scores_it*lucky_it in treatment (4))+b3*age+eit. b2 captures the ethnic/priority difference in the impact of given scores on estimated scores (signal effects). Therefore, we can capture the signal effects via b2. If b2 is negative and different from 0 under AA policy minority (3) and AA policy lucky(4), the signal effect is significant, and the positive productivity signals of a minority/affirmed candidate are less effective than that of a majority/unaffirmed candidate.
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