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.