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(Sex) Crime and Punishment in the #MeToo Era: How the Public Views Rape

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One of the core tasks of a well-functioning state is providing fair and adequate criminal justice. Recent events have raised concerns that the US exhibits a “culture of rape,” wherein victims are often disbelieved and blamed. Scholars have not yet examined how the public understands rape and how it should be punished, despite the important role that public pressure has played in the #MeToo era. We present an empirical conceptualization of rape culture to generate predictions for how various attributes of rape incidents affect the likelihood that they are perceived as punishable crimes. In a series of conjoint experiments, we demonstrate that details relating to the victim’s consent and credibility significantly decrease participants’ propensities to support reporting to police or to recommend a severe punishment for the perpetrator. The results show that emphasizing certain legally irrelevant features of rape strongly affect whether the public views an incident as severe or worthy of punishment.

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  1. For simplicity, we use the terms “victim” and “perpetrator” throughout because these are standard within the criminal justice system. We acknowledge some people who have been assaulted prefer the term “survivor.”.


  3. In one recent case, voters removed an elected judge over his decision in a rape trial: Judge Aaron Persky in California was recalled following the controversy over his lenient sentence for Stanford swimmer Brock Turner. The law professor who organized the recall campaign stated that she hoped the recall of the judge would serve as a national model “…for how to respond to bias against women in the legal system.” (see: More generally, see Pickett (2019), who outlines mechanisms through which public opinion shapes criminal justice policy, including elections for chief prosecutors, judges and sheriffs, as well as ballot propositions and referendums.

  4. Replication data and code for this project can be found at:

  5. For instance, a Human Rights Watch (2013) analysis of factors that influenced Washington, DC police handling of victims’ reports of sexual assault cases included victims’ drug and alcohol use, and relationship to the perpetrator.

  6. Rape is the most underreported violent crime; over half of rape cases never enter the criminal justice system and are excluded from official FBI crime statistics (Tjaden and Thoennes 2006).

  7. We focus on preferences over sanctioning to reveal potential distortions in—and public support for—sentencing of convicted perpetrators.

  8. Legally irrelevant case parameters are superfluous details that do not concern matters of evidence to establish whether a crime occurred, and that should not matter for determining guilt, crime severity and level of punishment. We exclude several potentially legally relevant factors (such as alcohol consumption by the victim) because their legal implications can differ substantially across states (Kruttschnitt et al. 2014: Chapter 2). However, see Footnote 14 for a description of a secondary study on the influence of alcohol.

  9. Michael Martinez and Gigi Mann. 2015. “Former Oklahoma City police officer Daniel Holtzclaw found guilty of rape,” CNN ( ).


  11. A related implicit belief is that if a woman consented to sexual acts in the past—especially with the perpetrator—she likely consented again (Feild 1979)

  12. Most research focuses on the impact of the victim’s morality and persona on prosecutorial and courtroom decisions, including risk-taking and (illegal) behaviors such as hitchhiking or drug use (Beichner and Spohn 2005)

  13. Situational relevance, or “the degree of probability that the observer will find himself [or herself] someday in similar circumstances” (Chaikin and Darley 1973: 269), may affect how people perceive acquaintance rape scenarios. If individuals consider themselves to be unlikely to face a given scenario, such as being assaulted at a party, they typically perceive it as less threatening, and thus as less severe of a crime (Workman 1999; Grubb and Harrower 2009). By contrast, being raped by a stranger in a one’s home may appear more threatening to many respondents since they can imagine themselves in the role of the victim.

  14. Exceptions include studies of the prevalence of prison rape (Wolff et al. 2006).

  15. In a secondary study, using Amazon Mechanical Turk, we provided approximately 10% (n = 125) of participants with information about the victim’s alcohol consumption (either three drinks or no drinks) prior to the incident, in addition to the eight standard attributes. These participants were 17.1 percentage points (SE = .074) less likely to select a case for a more severe punishment when the victim had three drinks (vs. no drinks) before the assault.

  16. See 2015 FBI Uniform Crime Reporting (UCR) statistics ( See also Socia et al. (2019), who found in a survey experiment that respondents recommended more lenient punishments for female perpetrators of sex crimes relative to male perpetrators.

  17. While conjoint experiments allow researchers to test more attributes at once than traditional experimental designs (using vignettes), there is still a limit to the number of attributes that can be included to avoid “satisficing” effects on the participant level (Bansak et al. 2019).

  18. In addition to this qualitative approach, we conducted a more systematic mediation analysis by randomly assigning participants in our main AmeriSpeak study to one of four rating-based questions, asking them how severe they found each case, how blameworthy they perceived the victim and perpetrator, and how trustworthy they found the victim’s account. Because traditional mediation analyses that add post-treatment mediators as right-hand side regressors tend to bias the estimation of the direct treatment effects, we followed Gerber and Green’s (2012: Chapter 10) approach and simply treated these mediation measures as additional outcomes. As shown in Online Appendix, Sect. 2.6, these questions did not generate a systematic pattern, which is why we conducted the follow-up study with an opened-ended question to gain richer insights into the reasoning behind participants’ choices.

  19. We also employed the 11-point version of the Ambivalent Sexism Scale (Glick and Fiske 1996) as well as the ANES racial resentment scale. Due to space limitations, however, we restricted our subgroup analyses in the main text to respondents’ political ideology as this is, for our purposes (and arguably for political scientists in general), the most theoretically interesting respondent characteristic. However, we present results from treatment-covariate interaction models for the remaining scales in our Online Appendix, Sect. 2.7.

  20. As shown by Hainmueller et al. (2014: 14–15), the AMCE estimator can conveniently be implemented by a linear regression. Hence, we use OLS regression models throughout our analysis to estimate our results.

  21. Since conjoint experiments allow researchers to simultaneously analyze numerous hypotheses, one possible risk is false positives. We guarded against this risk in three ways. First, we pre-registered our analysis plan on Open Science Framework. Second, we replicated our main findings on multiple independent samples drawn from MTurk and NORC’s AmeriSpeak Panel, suggesting that our reported results are not artifacts of sampling variability. Third, as robustness checks, we adjusted our main results for multiple comparisons using a Holm correction (see Online Appendix, Sects. 2.3 and 2.4 for additional information on the adjustment procedure as well as the adjusted findings, respectively). Our main results remained robust.

  22. We also pre-registered interaction models for respondent gender, partisan identification and ideology, past crime victimization as well as two attitudinal scales (ambivalent sexism and racial resentment). We report results from these additional models in Online Appendix, Sect. 2.5.

  23. In one pilot study, we allowed participants to choose both or neither of the cases. As expected, many simply selected both, which increased standard errors and widened confidence intervals. However, even in this “soft choice” variant, point estimates were very similar to the contrasting “forced choice” alternative. We report results from this pilot in Online Appendix, Sect. 3.2.1.

  24. Note that, in accordance with NORC’s guidelines, participants in our main study were not forced to answer the case selection question. Only a handful refused to answer the question altogether, and in those cases, both crime profiles were coded as “not chosen.” We discuss these “refusers” in more detail in the Qualitative Analysis section.

  25. In the Online Appendix Sect. 5, we document the results from this analysis. In particular, we transformed all outcomes into binary variables, indicating for each case whether or not it was chosen for reporting or severe punishment. We then estimated AMCE for each condition. To assess whether response patterns differed overall across conditions, we conducted F-tests using nested models with interaction effects for whether or not a respondent saw the binary choice outcome measure.

  26. The weighted AAPOR cumulative response rate for this study was 8.6%.

  27. See Online Appendix, Sect. 2.1 for detailed demographic characteristics of the sample as well as sample size per condition.

  28. Full regression tables for the main results are displayed in the Online Appendix, Sect. 2.4.

  29. Rather than interpreting interaction terms of single case attributes with crime type, we present an F-test to determine whether overall participants tend to evaluate crimes differentially, depending on the nature of the crime (see also Gerber and Green 2012: Chapter 9). We chose this inferential strategy mainly because we did not have a priori theoretical predictions for how participants would respond to specific attributes of the cases depending on crime type, and developing such predictions was beyond the scope of this project. In both conditions, the F-statistic was statistically significant at the 0.01 level.

  30. We used a self-reported measure of political ideology to identify conservative, moderate, and liberal participants. Future research can explore more direct measures of gender ideology; see, e.g., Davis and Greenstein (2009) for a review of the concept—defined as “individuals’ levels of support for a division of paid work and family responsibilities that is based on the belief in gendered separate spheres”—and its measurement.

  31. Treatment-covariate interaction models include an array of standard demographic controls such as respondent gender, age, race/ethnicity, education and region of residence.

  32. The F-statistic was significant in both the reporting condition (1.53, p = 0.04) and the punishment condition (2.55, p < .001) in the punishment condition. We find similar results for the robbery punishment condition (not shown).

  33. Absent a priori theoretical predictions for how liberals vs. conservatives would respond to specific attributes of the rape cases, we present an F-test to determine whether overall participants tend to evaluate crimes differentially, depending on their political ideology.

  34. Comments were randomly divided between two human coders. Intercoder reliability statistics, based on 341 comments classified by both coders, meet or exceed conventional standards of agreement (see the Online Appendix, section 4.3).



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The authors thank Jessica Fournier for excellent research assistance, and Don Green, Dan Hopkins, Josh Kertzer, Neil Malhotra, Tali Mendelberg, Marc Ratkovic, Maya Sen and Ariel White, the anonymous reviewers and editors for feedback on previous drafts. Many thanks to the participants of the HKS Junior Political Science Faculty workshop, the APSA 2016 panel on Experiments on Gender & Sexuality, and the Yale ISPS Experiments Workshop for their helpful feedback. We gratefully acknowledge financial support from the Women and Public Policy Program at the Harvard Kennedy School, the Foundations of Human Behavior Initiative at Harvard University, and the reviewers and directors of the Time-sharing Experiments for the Social Sciences, funded by the National Science Foundation. This study received human subjects approval from the Harvard University Committee on the Use of Human Subjects (IRB-15-4024) and was pre-registered on Open Science Framework.

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Schwarz, S., Baum, M.A. & Cohen, D.K. (Sex) Crime and Punishment in the #MeToo Era: How the Public Views Rape. Polit Behav 44, 75–104 (2022).

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