Anna Sandberg's research while affiliated with Stockholm University and other places

Images of half-dressed women are ubiquitous in advertising and popular culture. Yet little is known about the potential impacts of such images on economic decision making. We randomize 648 participants of both genders to advertising images including either women in bikini or underwear, fully dressed women, or no women, and examine the effects on risk taking, willingness to compete and math performance in a lab experiment. We find no treatment effects on any outcome measure for women. For men, our results indicate that men take more risk after having been exposed to images of half-dressed women compared to no women.

Twenty-nine teams involving 61 analysts used the same data set to address the same research question: whether soccer referees are more likely to give red cards to dark-skin-toned players than to light-skin-toned players. Analytic approaches varied widely across the teams, and the estimated effect sizes ranged from 0.89 to 2.93 (Mdn = 1.31) in odds-ratio units. Twenty teams (69%) found a statistically significant positive effect, and 9 teams (31%) did not observe a significant relationship. Overall, the 29 different analyses used 21 unique combinations of covariates. Neither analysts’ prior beliefs about the effect of interest nor their level of expertise readily explained the variation in the outcomes of the analyses. Peer ratings of the quality of the analyses also did not account for the variability. These findings suggest that significant variation in the results of analyses of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy in which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective, analytic choices influence research results.

I use data from the Olympic sport of dressage to explore in-group biases among judges. Dressage – the only international sport with subjective performance evaluations in which men and women compete as equals – provides a rare opportunity to identify multiple in-group biases in the same naturally occurring setting. While, on average, judges are not biased in favour of either gender, they exhibit substantial biases in favour of (i) athletes of their own nationality, and (ii) athletes of the same nationality as the other judges in the competition. Heterogeneity across competitions suggests that biases increase as group identity becomes more salient. (JEL J15, J16, J71, L83) This article is protected by copyright. All rights reserved.

Twenty-nine teams involving 61 analysts used the same dataset to address the same research question: whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players. Analytic approaches varied widely across teams, and estimated effect sizes ranged from 0.89 to 2.93 in odds ratio units, with a median of 1.31. Twenty teams (69%) found a statistically significant positive effect and nine teams (31%) observed a non-significant relationship. Overall 29 different analyses used 21 unique combinations of covariates. We found that neither analysts' prior beliefs about the effect, nor their level of expertise, nor peer-reviewed quality of analysis readily explained variation in analysis outcomes. This suggests that significant variation in analysis of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy by which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective analytic choices influence research results. Currently available at: https://psyarxiv.com/qkwst/

Twenty-nine teams involving 61 analysts used the same dataset to address the same research question: whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players. Analytic approaches varied widely across teams, and estimated effect sizes ranged from 0.89 to 2.93 in odds ratio units, with a median of 1.31. Twenty teams (69%) found a statistically significant positive effect and nine teams (31%) observed a non-significant relationship. Overall 29 different analyses used 21 unique combinations of covariates. We found that neither analysts' prior beliefs about the effect, nor their level of expertise, nor peer-reviewed quality of analysis readily explained variation in analysis outcomes. This suggests that significant variation in analysis of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy by which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective analytic choices influence research results. Currently available at: https://psyarxiv.com/qkwst/

Robustness checks. (PDF)

Ethics statement. (PDF)