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Last Published	January 25, 2022 09:06 PM	May 06, 2022 01:06 AM
Primary Outcomes (Explanation)	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+a1minority_it +b1scores_it +b2scores_itminority_it (b2scores_itlucky_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.	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 majority candidates and minority 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+a1minority_it +b1Task B's score_it +b2Task B's score_itminority_it (b2scores_itlucky_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. The predicted scores by using the regression will be considered as "expected task C scores" based on the given information.
Secondary Outcomes (Explanation)		1) Exposure effects: - Hiring: The percentage of advantaged candidates in each treatment. We expect the percentage of minority candidates is higher in the AA minority treatments than in the Baseline type treatments while the percentage of lucky candidates is higher in the AA lucky treatments than in the Baseline treatments. 2) Singal effects: - The difference in average scores of Task A between advantaged groups and disadvantaged groups. We expect the difference should be positive and significant in AA minority treatments (majority-minority) and in AA lucky treatments (unlucky - lucky) while the difference should be close to zero in Baseline treatments and Baseline type treatments. - The difference in estimated scores between the disadvantaged group and advantaged group should be significantly greater in the AA minority and AA lucky treatments. - The contribution of Task B's scores to the estimated scores should be smaller in AA minority and AA lucky treatments. This is because TaskB's score is less effective in the context of Affirmative action policy. 3) Fairness effects: The answer to the first question in the post-experimental survey. We expect participants will perceive the pre-screen is less fair in AA minority treatments and in AA lucky treatments than in Baseline treatments and Baseline type treatments. 4) Overall effects: - The percentage of a minority candidate (who pass the pre-screen process) being hired: There are more minority candidates passing the pre-screen process but the percentage of a minority candidates being hired will be lower in AA minority treatments. In addition, there are more lucky candidates passing the pre-screen process but the percentage of a lucky candidate being hired will be lower in AA lucky treatments. - The probability of an advantaged candidate being hired, holding all other factors constant. The probability of a minority candidate being hired should be significantly smaller in the AA minority treatments than in Baseline type treatments while the probability of a lucky candidate being hired should be significantly smaller in the AA lucky treatments than in Baseline treatments.