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Field
Last Published
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Before
November 02, 2021 11:22 PM
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After
November 08, 2021 01:46 AM
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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. 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 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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Field
Secondary Outcomes (Explanation)
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Before
In the secondary outcomes, we will use the difference-in-difference method to compare the differences in soft AA minority(3) and baseline type(2), and the differences in soft AA lucky(3) and baseline(1). This is to determine the differential effects between a AA policy for a minority group and a AA policy for a random group, and therefore understand whether a AA policy itself has negative spillover effects.
We have two possible outcomes:
1) Hypothesis 1: Behaviour story: The hiring decisions are not consistent with estimation. We expect there is a large unfairness impact (dominate all other possible effects) - employers hire fewer minorities but estimated the same. The differences of soft AA minority(3) - baseline type(2) > The differences of soft AA lucky(3) - baseline(1) in hiring decision.
- In the hiring decision: The negative spillover effect is much larger in the soft AA policy minority(3) than in the soft AA policy lucky(4). We expect the likelihood of a minority being hired is much lower than that of a majority because unfairness effects dominate all other effects, and unfairness only exists in soft AA policy minority(3), if unfairness is stronger with the out-group than with a random group.
- In the estimation decision: Expect no difference in soft AA policy minority(3) and soft AA policy lucky(4). This is because the signal effects and exposure effects are negligible.
2) Hypothesis 2: Rational story: The hiring decisions are consistent with estimation. We expect there is a negligible unfairness impact - employers will hire indifferent between two groups but estimated less for the minority/affirmed group within soft AA minority(3) and soft AA lucky(4). The differences of soft AA minority(3) - baseline type(2) = the differences of soft AA lucky(4) - baseline(1) in estimation decision.
- In the hiring decision:
1) Exposure effects: We can examine exposure effects under soft AA minority(3). But it will not occur under soft AA lucky(4).
2) Frequency effects: We can examine frequency effects under both soft AA minority(3) and soft AA lucky(4).
We expect the likelihood of a minority/affirmed group is slightly higher than (because a AA policy brings higher frequency) or indifferent to the likelihood of a majority (because a AA policy eliminates the natural bias).
- In the estimation decision.
3) Signals effects: We can examine the signal effects caused by AA policy itself by comparing soft AA lucky(4) and baseline(1). And through the comparison between the differences of soft AA minority(3) - baseline type(2) and the differences of soft AA lucky(4) - baseline(1) in estimation decision, we can find evidence on whether the signal effect is primarily caused by the affirmative action policy-regardless of what type of AA policy it is.
We expect 1) mean estimated scores of minorities/affirmed group < mean estimated scores of majorities/unaffirmed group 2) Multipliers of minorities/affirmed group < Multipliers of majorities/unaffirmed group 3) b2 is negative and significant in both soft AA minority(3) and soft AA lucky(4). The value of b2 should have no difference under these two treatments.
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After
In the secondary outcomes, we will use the difference-in-difference method to compare the differences in soft AA minority(3) and baseline type(2), and the differences in soft AA lucky(4) and baseline(1). This is to determine the differential effects between a AA policy for a minority group and a AA policy for a random group, and therefore understand whether a AA policy itself has negative spillover effects.
We have two possible outcomes:
1) Hypothesis 1: Behaviour story: The hiring decisions are not consistent with estimation. We expect there is a large unfairness impact (dominate all other possible effects) - employers hire fewer minorities but estimated the same. The differences of soft AA minority(3) - baseline type(2) > The differences of soft AA lucky(4) - baseline(1) in hiring decision.
- In the hiring decision: The negative spillover effect is much larger in the soft AA policy minority(3) than in the soft AA policy lucky(4). We expect the likelihood of a minority being hired is much lower than that of a majority because unfairness effects dominate all other effects, and unfairness only exists in soft AA policy minority(3), if unfairness is stronger with the out-group than with a random group.
- In the estimation decision: Expect no difference between the majority candidates and the minority candidates in soft AA policy minority(3) and between the lucky candidates and unlucky candidates in soft AA policy lucky(4). This is because the signal effects and exposure effects are negligible.
2) Hypothesis 2: Rational story: The hiring decisions are consistent with estimation. We expect there is a negligible unfairness impact - employers will hire indifferent between two groups but estimated less for the minority/affirmed group within soft AA minority(3) and soft AA lucky(4). The differences of soft AA minority(3) - baseline type(2) = the differences of soft AA lucky(4) - baseline(1) in estimation decision.
- In the hiring decision:
1) Exposure effects: We can examine exposure effects under soft AA minority(3). But it will not occur under soft AA lucky(4).
2) Frequency effects: We can examine frequency effects under both soft AA minority(3) and soft AA lucky(4).
We expect the likelihood of a minority/affirmed group to be hired is slightly higher than (because a AA policy brings higher frequency) or indifferent to the likelihood of a majority to be hired (because a AA policy eliminates the natural bias).
- In the estimation decision.
3) Signals effects: We can examine the signal effects caused by AA policy itself by comparing soft AA lucky(4) and baseline(1). And through the comparison between the differences of soft AA minority(3) - baseline type(2) and the differences of soft AA lucky(4) - baseline(1) in estimation decision, we can find evidence on whether the signal effect is primarily caused by the affirmative action policy-regardless of what type of AA policy it is.
We expect 1) mean estimated scores of minorities/affirmed group < mean estimated scores of majorities/unaffirmed group 2) Multipliers of minorities/affirmed group < Multipliers of majorities/unaffirmed group (with the same given scores) 3) b2 is negative and significant in both soft AA minority(3) and soft AA lucky(4). The value of b2 should have no difference under these two treatments.
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