| Field | Before | After |
|---|---|---|
| Field Trial Start Date | Before December 10, 2021 | After January 17, 2022 |
| Field Trial End Date | Before January 31, 2024 | After December 31, 2022 |
| Field Last Published | Before January 11, 2022 12:12 AM | After January 25, 2022 09:06 PM |
| Field Intervention (Public) | Before 1) Baseline: No affirmative action for the minority group in selecting the candidate pool. 2) Baseline – Type (2): No affirmative action for the minority group in selecting the candidate pool. No ethnic type informed. 3) Treatment (Intervention) - AA policy - minority (3): There is a soft AA for minorities in selecting the candidate pool. Ethnic type informed. 4) Controlled- AA policy – Lucky (4): There is a soft AA for a random group (to have priority) in selecting the candidate pool. No ethnic type informed but the priority status will be informed. | After |
| Field Intervention Start Date | Before December 31, 2021 | After January 18, 2022 |
| Field Intervention End Date | Before January 01, 2024 | After December 01, 2022 |
| Field Primary Outcomes (End Points) | Before 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. | After The main variable of interest is the individual hiring and estimation decisions between majority and minority candidates across the experimental treatments Secondary outcome variables are the in-group differences in hiring and estimation decisions across different treatments to understand the nature of spillover effects of an AA policy. |
| Field Primary Outcomes (Explanation) | Before | 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. |
| Field Public analysis plan | Before No | After Yes |
| Field Pi as first author | Before No | After Yes |
| Field | Before | After |
|---|---|---|
| Field Document | Before |
After
Analysis+plan+.docx
MD5:
105468d440fe0e02aff4b0946aa0eab7
SHA1:
2be53f3459776823edda61b1806ecac79d95c37b
|
| Field Title | Before | After Analysis plan |
| Field | Before | After |
|---|---|---|
| Field Affiliation | Before | After Queensland University of Technology |