We found substantial evidence of gender bias with open committee deliberation and tested several mechanisms and test two interventions for open deliberation. Our main experimental treatments involve open committee deliberation. We find significant gender biases under open committee deliberation. After deliberation 60 percent of ratings received by men are revised upwards compared to only 25 percent of ratings received by women. As a consequence women are ranked on average three positions lower after deliberation. We tested two further interventions both designed to reduce gender bias in the presence of open deliberation. The first intervention randomized the order of speaking in the committee. This intervention was unsuccessful and in fact produced weakly larger gender biases compared to our baseline open deliberation treatment. The second intervention we tested is an information intervention, where participants are made aware of gender bias in previous sessions prior to entering their ratings. Similar interventions have sometimes been shown to be successful in non-committee decision-making. We also find that this intervention is successful. There is no gender bias in this treatment. These results carry potentially actionable policy consequences. Our interventions have shown that care must be taken when designing rules for committee deliberation. Changes designed to reduce bias, such as randomizing the order of speaking in a committee, can have unintended consequences and in our case led to very strong gender bias. On the other hand our information intervention was successful and did not lead to gender bias (neither against men nor women). We also did not find evidence that this intervention would lead to greater polarization of opinions.
In a subproject on statistical discrimination we use theoretical modelling and two experiments to study the implications of such failures of Bayesian rationality on discrimination in the labour market. Our research shows not only that wrong beliefs matter, but moreover that the source of these wrong beliefs is crucial for our understanding of discrimination and of workers' human capital investments. We show that naive employers discriminate against disadvantaged groups much more often than bayesian employers. Such irrational discrimination makes workers from the disadvantaged group less willing to pursue education, further exacerbating the initial inequalities. Compared to the Bayesian benchmark, we observe excess discrimination especially when signals are highly informative. An example where we encounter this pattern is women and computer science. There are many fewer women studying computer science than men and---in line with this pattern---women are perceived as ``on average worse'' in coding than men. However, conditional on having programming knowledge, there is suggestive evidence that women are better coders than men. These are the situations where Bayesians and non-Bayesians will make different decisions. In particular, since conservatives neglect the education signal, they will be less likely to hire the disadvantaged group than Bayesians. We refer to this type of discrimination as irrational statistical discrimination. We then design two experiments that allow us to test the intuitions developed in the theory. We find substantial evidence of conservatism, with a larger share of decisions being consistent with conservatism than with Bayesian reasoning. As a result, the disadvantaged group is hired around 52 percent less frequently compared to what we would expect if all employers were Bayesian. We also find that---conditional on their real productivity---workers from the disadvantaged group seek education much less frequently than others. This finding is important not only because market exit further exacerbates imbalances between the two groups and implies a substantial welfare loss due to the loss of high-skilled individuals in the work-force, but also because the fact that many high quality disadvantaged workers exit the market early makes it harder for employers to learn. We do indeed not find any evidence that the quality of employers' decisions improves over time.
