ERC Starting grant (2018-2023)
Under my ERC Starting grant "Opinion Dynamics" (2018-2023) I study how people form and revise beliefs about themselves and the world and how social identity affects this process. We study opinion dynamics in social networks, committees or other groups and ask when opinion dynamics lead to good outcomes and how they can derail to produce "collective delusions". Our research has shown among others that deliberation in committees can lead to an increase in gender bias, that social identity affects belief formation in a variety of contexts and that failures to update rationally could be behind a substantial share of observed discrimination. Social Identity can be both a boon and a bane for efficient information aggregation. On this site I periodically update about the activities and outcomes from the grant. Support from the ERC is gratefully acknowledged.
Publications:
Gender Bias in Opinion Aggregation, International Economic Review 62(3) (2021), 1055-1080.
Abstract: Gender biases have been documented in areas including hiring, promotion or performance evaluations. Many of these decisions are made by committees. We experimentally investigate whether committee deliberation contributes to gender biases. In our experiments participants perform a real effort task with subjective performance and then rate the task performance of other participants. In a 3 x2 design we vary the extent to which communication among raters is possible and whether or not the experiment is gender-blind. There is substantial evidence of gender bias with open committee deliberation. In this case 60 percent of ratings received by men are revised upwards after deliberation compared to only 25 percent of ratings received by women. As a consequence women are ranked on average three positions lower after deliberation. We explore several mechanisms and test two interventions for open deliberation. Randomizing the order of speaking does not reduce gender bias, but an information intervention where raters are informed of gender bias in prior sessions does.
Diversity in Committees (joint with N. Hughes and Z. UH Khan), SSRN working paper (2023).
Abstract: We investigate the potential of diversity to influence committee decision-making. In our model committee members receive private information on a state of the world, deliberate and vote. Committee members belong to one of two groups which may differ along two dimensions: (a) their preferences and (b) their information structures. If groups only differ in their information structures, welfare is increasing in committee diversity. If groups only differ in preferences, welfare is decreasing in diversity. If groups differ along both dimensions then, depending on parameters, welfare may be increasing, decreasing or non-monotonic in diversity. We test the model's key predictions in a laboratory experiment. As predicted, diverse committees outperform non-diverse committees when preferences are aligned. However, when preferences are misaligned diverse and non-diverse perform equally well. The reason is that participants reveal more information than theory predicts and update imperfectly.
Non Bayesian Statistical Discrimination (joint with Pol Campos Mercade), Management Science 70 (4) (2024), 2549-2567.
Abstract: Models of statistical discrimination typically assume that employers make rational inference from (education) signals. However, there is a large amount of evidence showing that most people do not update their beliefs rationally. We use a model and two experiments to show that employers who are conservative, in the sense of signal neglect, discriminate more against disadvantaged groups than Bayesian employers. We find that such irrational statistical discrimination deters high-ability workers from disadvantaged groups from pursuing education, further exacerbating initial group inequalities. Excess discrimination caused by employer conservatism is especially important when signals are very informative. Out of the overall hiring gap in our data, around 40% can be attributed to rational statistical discrimination, a further 40% is due to irrational statistical discrimination, and the remaining 20% is unexplained or potentially taste-based.
Personal Relative Position, Attribution and Social Trust (joint with A. Albertazzi and P. Lown), SSRN working paper (2023).
Abstract: Across the social sciences researchers have debated the impact income inequality has on people's perceptions, specifically on attribution and social trust. In this paper we use a combination of surveys and behavioral lab experiments to identify a causal impact of inequality on attribution and social trust. We find that higher relative position has a positive impact on belief in meritocracy and social trust, which we causally identify both using a novel incentivized lab task as well as standard survey measures. These results are in line with correlational associations we find using larger general surveys. They speak to why inequality can be so socially and economically corrosive while at the same time remaining largely unaddressed.
Identity and Learning in Social Networks (joint with V. Grimm, X. Zhou and D. Ozdemir)
Abstract: This paper and abstract will be posted soon!
Presentations:
Personal Relative Position, Attribution and Social Trust (joint with A. Albertazzi and P. Lown)
Gender Bias in Opinion Aggregation
Non-Bayesian Statistical Discrimination (joint with Pol Campos Mercade)
[Link to Video of presentation at IWET]
- Diversity in Committees (joint with N. Hughes and Z. UH Khan)
Conference
The interdidsciplinary workshop at the University of Essex on Inequality, Identity and Beliefs had to be cancelled due to Covid.