Secondary Outcomes (end points)
Data collection by the survey provider began on January 15th, 2026, and is expected to conclude by the end of January or early February. We have not yet received access to the data, which will be made available once the data collection is complete.
We plan to test heterogeneities by:
- age groups (18-34, 35-44, 45-65),
- empathy (dummy coded 1 for respondents who answered “disagree” or “totally disagree”),
- right-leaning political views as proxied by a dummy coded 1 if the respondent answers “Totally agree” or “agree” to the statement “Unemployed individuals should accept any available job or lose their unemployment benefits.” Respondents who said “don’t know” will be coded as missing.
- Has already received a training on sexual harassment prevention or gender equality at work (dummy coded to 1 if received any of these two trainings). A robustness check will focus on training specific to sexual harassment with a dummy coded 1 if received the training on sexual harassment.
- By confidence in priors about the prevalence of sexual harassment, measured on a five-point scale from 1: “Very unsure” to 5: “Very sure”.
- sexist views: dummy coded to 1 if answered “Totally agree” or “Agree” to any of the two statements measuring sexism. “Don’t know” will be coded as missing.
- By prior inability to recognize an inappropriate gender-based situation: dummy coded to 1 if the respondent answered to the vignette one “humor” or “neither”, and 0 if answered “sexism” or “sexual harassment”. To further explore heterogeneity, we will also construct separate dummy variables for each response category, provided that at least 10% of respondents select that category, to ensure sufficient statistical power.
- Measures of prior beliefs on prevalence of sexual harassment: (1) a dummy coded to 1 if answered 27 or less, 0 if answered 28 or more; (2) one dummy coded to 1 if answered 22 or less, one dummy coded to 1 if answered between 23 and 33, one dummy coded to 1 if answered 34 or more; (3) one dummy coded to 1 if strictly below the median, 0 if equal or above the median answer; (4) one continuous variable between 0 and 100.
- Measures of prior beliefs on consequences for victims: (1) a dummy coded 1 if the respondent selects “Neither of the previous ones”, 0 otherwise; (2) an index ranging from 0 to 4 constructed as the sum of binary indicators for whether the respondent selected each of the following items: anxiety, depression, trouble sleeping, chronic stress. Each item is coded as 1 if selected, 0 otherwise. “Don’t know” and “Neither of the previous ones” are coded as zero.
- Measures of prior beliefs on costs for harassers: (1) dummy coded to 1 if answered “Moderately often”, “Very often” or “Extremely often”, 0 otherwise; (2) dummy coded to 1 if answered “Very often” or “Extremely often”, 0 otherwise; (3) variable coded on a 1–5 scale, with higher values indicating a belief that sexual harassers are punished more frequently.
- Index on prior misbeliefs about sexual harassment: dummy coded 1 if measure (1) of prior beliefs on prevalence of sexual harassment is equal to 1 and measure (1) of prior beliefs on consequences for victims is equal to 1 and measure (2) of prior beliefs on costs for harassers is equal to 1; 0 otherwise.
- prior beliefs on respondent’s self-rated knowledge of good practices in case of sexual harassment: dummy coded 1 if answered “Good” or “Very good”.
- Actual prior knowledge of good practices for firms: dummy coded to 1 if respondent responded correctly to all 5 items; continuous variable from 1 to 5 for each correct answer.
- prior beliefs about the prevalence of false accusations: (1) dummy coded to 1 if answered “Moderately common”, “Very common” or “Extremely common”. “Don’t know” are coded as missing; (2) dummy coded to 1 if answered “Very common” or “Extremely common”. “Don’t know” are coded as missing.
Since only half of the respondents were presented with this question, we will also test for potential priming effects of seeing this question on the treatment effects by verifying, for each treatment, if effects are similar for respondents who saw that question and those who did not see it.
- working in a male-dominated firm: dummy coded to 1 if answered “Exclusively or almost exclusively men” or “Majority of men”. “Prefer not to answer” will be coded as missing.
- experience of sexual harassment (or discrimination) as a victim [or a witness]: dummies coded to 1 if experienced [or witnessed] any sexual harassment situation (or gender discrimination)
We will further analyze petition support using the same linear model as the main specification with an ordinal outcome variable coded as 0 if the respondent refuses to sign, 1 if they sign anonymously, and 2 if they sign with their name. As a robustness check, we will also estimate an ordered logit model. To isolate public support for the petition, we will additionally estimate our linear model using as outcome a dummy variable equal to 1 if the respondent signs with their name and 0 otherwise.
If applicable, we want to test for potential substitution and moral licensing effects. We distinguish moral licensing from substitution by separately analyzing (i) the likelihood and amount of donating overall and (ii) the allocation of donations across organizations. So, we will analyze also the following outcomes:
- Any donation: dummy variable coded to 1 if the respondent agrees to donate to any of the three charities offered, 0 if he doesn’t wish to give.
- Total donation: continuous variable between 0 and 100 euros representing the amount given to any of the 3 charities. People who refused to give will be coded as 0.
- Other donation: dummy coded to 1 if the respondents agree to donate to any of the 2 charities non-related to sexual harassment (ONCE or Cruz Roja), 0 if they chose AVA or not to donate.
- Amount other: continuous variable between 0 and 100 representing the amount given to any of the 2 charities non-related to sexual harassment (ONCE or Cruz Roja).
If relevant, we will also test for a potential backlash effect with a dummy coded 1 if the respondent answered “Agree” or “Strongly agree” to the question “Some people believe that sexual harassment and discrimination against women are discussed too often in the workplace. To what extent do you agree or disagree with this statement?”. Similarly, we might also test for zero-sum thinking with a dummy coded 1 if the respondent answered “Agree” or “Strongly agree” to the question “Some people believe that when women advance in the labor market, it is often at the expense of men. To what extent do you agree or disagree with this statement?”
To test mechanisms, we also code a dummy equal to 1 if respondent answered “Agree” or “Strongly agree” to the statement “Donations to specialized organizations are an effective way to help victims of sexual harassment at work.” We also code a dummy equal to 1 if respondents answered “Quite useful” or “Very useful” to the question about the usefulness of the training, and a dummy equal to 1 if they answered “Quite applicable” or “Very applicable” to the question about the applicability of the training.
For the conjoint experiment, to test the robustness of the results, we will also reproduce the analysis restricting the sample to the first choice of profiles offered to respondents.
To explore policy preferences further, we construct a continuous variable for each question capturing respondents’ preferred strictness of sanctions related to workplace sexual harassment. The variable will take values from 1 to 5, with higher values representing preference for stricter sanctions. To deal with the issue of multiple hypotheses testing, we will also construct an index defined as Sanction Strictness Indexi=1/2 * (Ziprison+Ziemployer) where the variables are respectively the z-scores for the prison policy question and the employer policy question.
We will address multiple hypotheses testing in two ways: (1) the use of indices as described in the different sections and (2) accounting for the False Discovery Rate using the “sharpened q-value approach” (Benjamini et al. 2006; Anderson 2008) as a robustness check, over the 3 main families of outcomes defined in section 3: belief updating, real-stake outcomes and policy preferences.
As the bystander training treatment is a bit longer than the two others, we will also test whether the time spent on the 3 filler questions after the treatments is not significantly higher than for the other treatment groups. We will also use the filler questions to analyze whether respondents in male-dominated firms are working in less supportive working environments.