x

NEW UPDATE: Completed trials may now upload and register supplementary documents (e.g. null results reports, populated pre-analysis plans, or post-trial results reports) in the Post Trial section under Reports, Papers, & Other Materials.
Attitudes towards hiring decisions
Last registered on July 28, 2020

Pre-Trial

Trial Information
General Information
Title
Attitudes towards hiring decisions
RCT ID
AEARCTR-0005064
Initial registration date
May 11, 2020
Last updated
July 28, 2020 10:50 PM EDT
Location(s)

This section is unavailable to the public. Use the button below to request access to this information.

Request Information
Primary Investigator
Affiliation
Victoria University of Wellington
Other Primary Investigator(s)
PI Affiliation
Victoria University of Wellington
PI Affiliation
Victoria University of Wellington
Additional Trial Information
Status
In development
Start date
2020-05-18
End date
2020-12-31
Secondary IDs
Abstract
This paper investigates attitudes towards hiring decisions.
External Link(s)
Registration Citation
Citation
Feess, Eberhard, Jan Feld and Shakked Noy. 2020. "Attitudes towards hiring decisions." AEA RCT Registry. July 28. https://doi.org/10.1257/rct.5064-2.0.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2020-05-18
Intervention End Date
2020-06-30
Primary Outcomes
Primary Outcomes (end points)
1. “pro-women attitude” in base scenario.
2. “pro-woman attitude” after reflection.
3. Effect of holding discrimination constant on pro-women attitudes (separately for “pro-women”, “neutral” and “pro-men” respondents)
4. Effect of holding effort constant on pro-women attitudes (separately for “pro-women”, “neutral” and “pro-men” respondents)
5. Effect of holding suffering constant on pro-women attitudes (separately for “pro-women”, “neutral” and “pro-men” respondents)
6. Effect of holding discrimination, effort and suffering constant on pro-women attitudes. (separately for “pro-women”, “neutral” and “pro-men” respondents)
Primary Outcomes (explanation)
Terminology: We measure “pro-women attitude” as “attitude towards discriminating against a man” minus “the attitude towards discrimination against a woman”. For example, a respondent who finds discrimination against a man neutral (50, where 0 is very morally wrong and 100 is very morally right) and discrimination against a woman is somewhat morally bad (40) has a pro-women attitude of 10. Negative pro-women attitudes reflect that respondents find discrimination against men more morally objectionable than discrimination against women. As shorthand, we will refer to respondents’ as “pro-women” if they have a pro-women attitude >1, “neutral” if they have a pro-women attitude of -1, 0 or 1, and “pro-men” if pro-women attitude <-1.

Primary outcome 1: Average pro-women attitude in the sample.

Primary outcome 2: We measure explicit attitudes toward gender discrimination as respondents’ answers to questions following up on answering these two scenarios. In this question, respondents can indicate whether they find 1) discrimination against women more morally objectionable, 2) discrimination against men more morally objectionable, or 3) both equally morally objectionable. A random subset of 50% of respondents are shown this question. Our second primary outcome is the percentage of respondents who choose each option.

Background primary outcomes 3-6 are based on an experiment embedded in the survey. In this experiment, all respondents see four additional pairs of scenarios. A scenario pair shows two scenarios that are identical expect for that in one a women is discriminated against and in the other a man is discriminated against. In the control group, respondents see four scenario pairs that are similar to the base scenarios, except that they are describing the geographical location of the job (urban, suburban, rural, major city). In the treatment groups, respondents see scenarios in the same geographical locations with four additional information treatments, that is, texts that hold potential reasons for judging discrimination differently constant. The information treatment for primary outcomes 3-5 state that “ The job is in an industry where there is no gender discrimination”, “The man and the woman have worked equally hard in their career”, and “The man and the woman would suffer equally much from not getting the job.” The information treatment for primary outcome 6 combines the previous three information treatments.

To illustrate how this experiment leads to causal estimates, consider the following example. Respondents in the control group see a first pair of scenarios in which they are asked to judge discrimination against men and women for jobs in urban areas. Respondents in one of the treatment groups are asked to judge discrimination against men and women for jobs in urban areas (same as control group) in an industry without gender discrimination. This design allows us to compare evaluations for scenarios that are identical except for the information treatment. To estimate the effect of the information treatment “no discrimination”, we can take the average pro-women bias in the control group minus the average pro-women bias in this treatment group.

Primary outcome 3: Differences in average pro-women attitude in control scenarios and average pro-women attitude in “no-discrimination” information scenarios. We will show these differences separately for respondents we classified as pro-women, neutral, and pro-men; based on their answers in the base scenario.

Primary outcome 4: Differences in average pro-women attitude in control scenarios and average pro-women attitude in “same effort” information scenarios. We will show these differences separately for respondents we classified as pro-women, neutral, and pro-men; based on their answers in the base scenario.

Primary outcome 5: Differences in average pro-women attitude in control scenarios and average pro-women attitude in “same suffering” information scenarios. We will show these differences separately for respondents we classified as pro-women, neutral, and pro-men; based on their answers in the base scenario.

Primary outcome 6: Differences in average pro-women attitude in control scenarios and average pro-women attitude in “no-discrimination, same effort, and same suffering” information scenarios. We will show these differences separately for respondents we classified as pro-women, neutral, and pro-men; based on their answers in the base scenario.
Secondary Outcomes
Secondary Outcomes (end points)
1. Average pro-women attitude (between sample)
2. Average pro-women attitude by gender
3. Average pro-women attitude by education
4. Average pro-women attitude by income
5. Average pro-women attitude by political leanings
6. Relationship between pro-women attitude in base and belief that women suffered more in hypothetical scenarios.
7. Relationship between pro-women attitude in base and belief that women worked harder in their career in hypothetical scenarios.
8. Relationship between pro-women attitude in base and belief that women worked harder in general in hypothetical scenarios.
9. Relationship between pro-women attitude in base and belief that women are more discriminated against in hypothetical scenarios.
Secondary Outcomes (explanation)
Secondary outcome 1. One concern is that respondents might evaluate the first scenario following their intuition and then answer the second scenario with the goal of wanting to be consistent with their first answer. Randomly selected 50% respondents first see a scenario in which a women is discriminated against whereas the remaining 50% of respondents first see a scenario in which a man is discriminated against. Secondary outcome 2 is the average pro-women attitude based on respondents’ answers to the first scenario. This between-person design allows us to estimate pro-women attitude which is not driven by concerns about wanting to be consistent.

Secondary outcomes 2-5 are pro-women attitudes, separated by women and men (2), the following five categories of education: high school, some college, 2-year college, 4-year college, professional degree or postgrad (3), below median income and above median income (4), and democrats, independents and republicans (5).

Secondary outcomes 6-9 are based on questions asked to the control group after the survey experiment. The questions are about the hypothetical scenarios.

For secondary outcome 6, respondents are asked: “In the previous scenarios, which of the candidates do you think would have suffered more from not getting the job?” The answer options range from 0 (“The women would have suffered much more”) over 50 (“Both would suffered equally much”) to 100 (“The men would have suffered much more”). We transform each respondent’s answer into a “women suffered more” score where positive values reflect that women would have suffered more, 0 reflects both would have suffered equally and negative values reflect that men would have suffered more. Secondary outcome 6 shows the average women suffered more score for respondents we classified as pro-women, neutral pro-men based on their answers in the base scenario.

The procedure for estimating secondary outcomes 7-9 is analogous to the procedure outlined for secondary outcome 6.

Secondary outcome 7 is based on this question: Who do you think would have worked harder to get where they are in their career?

Secondary outcome 8 is based on this question: Who do you think is generally more hard-working (in their career and other aspects of their life)?

Secondary outcome 9 is based on this question: Who do you think would be more discriminated against in the labor market?
Experimental Design
Experimental Design
Nothing to report yet.
Experimental Design Details
Not available
Randomization Method
Randomization will be carried out in the Qualtrics Survey by a computer.
Randomization Unit
Respondents.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
450 respondents

***Update July 29, 2020***
additional 1,300 respondents only for the survey experiment
Sample size: planned number of observations
For primary outcomes 1-2: 450 observations (1 observation per respondent) For primary outcomes 3-6: 1,800 observations (1 per respondent-scenario combination - each respondent sees 4 scenarios) ***Update July 29, 2020*** For primary outcomes 3-6, we will collect an additional 5,200 observations (1 per respondent-scenario combination)
Sample size (or number of clusters) by treatment arms
For primary outcomes 1 and 2:
Treatment arm 1: approx. 225 respondents (50%) will first see the scenario in which a woman is discriminated against
Treatment arm 2: approx. 225 respondents (50%) will first see the scenario in which a man is discriminated against
For primary outcomes 3-6:
• Control group: approx. 225 respondents (50%)
For ease of exposition, we will number the information treatments as same suffering (treatment 1), worked equally hard (2), no discrimination (3), same suffering, worked equally hard and no discrimination (4).
• Treatment group 1: order of information treatments (1,2,3,4), approx. 37.5 respondents (8.3%)
• Treatment group 2: order (1,3,2,4), approx. 37.5 respondents (8.3%)
• Treatment group 3: order (2,1,3,4), approx. 37.5 respondents (8.3%)
• Treatment group 4: order (2,3,1,4), approx. 37.5 respondents (8.3%)
• Treatment group 5: order (3,1,2,4), approx. 37.5 respondents (8.3%)
• Treatment group 6: order (3,2,1,4), approx. 37.5 respondents (8.3%)

***Update July 29, 2020***
For primary outcomes 3-6, the additional respondents will be assigned to treatment groups in the same proportions as stated above.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Victoria University Human Ethics Committee
IRB Approval Date
2020-05-10
IRB Approval Number
0000028526