Statistical Discrimination Against Minority Groups

19 Oct 2023

10:00 -12:00

Times are shown in local time.

Open to: Members of the University of Cambridge

Room W4.05 (Cambridge Judge Business School)

Trumpington St



United Kingdom

Join us for an organisational behaviour seminar

Members of the University of Cambridge are invited to join this Oranisational Behaviour Seminar Series event David Hagmann, Assistant Professor, Hong Kong University of Science and Technology.

About the seminar topic

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When employers make hiring decisions, they have to predict a job candidate’s performance on the basis of observable attributes. Demographic characteristics, such as gender and race, affect these assessments even when they are not predictive of performance.

In this paper, we propose that a simple cognitive mechanism can lead people to form false beliefs about performance differences after receiving true information. Specifically, we suggest that people who learn about the demographic characteristics of top performers fail to adjust for the prevalence of people with those demographics in the pool from which the top performers emerge. This process systematically generates statistical discrimination against minority groups. Across 2 preregistered experiments in which participants make incentivised hiring decisions, we find that people who receive demographic information about the top performers fail to adjust for the demographic composition of the pool they receive information about.

  • Study 1 (n = 3,002) uses a pool composition unbalanced toward male workers, reflective of some high-profile industries. Receiving information about the top performers’ gender leads participants to infer gender differences where none exist.
  • Study 2 (n = 2,000) shows the effect also occurs in a sample representative of the US population, where there are inherently fewer Black or Asian than White candidates. Here, participants infer performance differences across race that are opposite to actual performance differences.

Speaker bio

David Hagmann is an Assistant Professor in the Department of Management at The Hong Kong University of Science and Technology. His research examines how people’s complex relationship with information leads to decisions that deviate from the standard economic accounts. David’s research shows that people actively avoid information to protect their beliefs, even when the information could help them make better decisions. Increasing political polarisation can spill over into work, as people distrust those presenting information challenging closely held beliefs. David’s research finds that sharing personal narratives may be one way to reduce distrust even though, amusingly, personal stories are easier to fabricate.

David’s research in the area of behavioural public policy finds that “nudges,” small changes to the environment that do not affect economic incentives, can crowd out support for more heavy-handed, but also more effective, policies. Moreover, when people’s attention is drawn to individual behaviour, such as recycling, they become less supportive of systemic interventions, such as carbon taxes. Companies may strategically take advantage of such limited attention, which may explain why BP introduced the carbon footprint calculator, and why Coca-Cola is a major funder of research emphasising the importance of exercise (but not nutrition) for weight loss.

Most recently, David’s work examines how errors in statistical reasoning can lead people to draw incorrect inferences from accurate information. As a result, they can make worse decisions than people who do not receive information. Such errors can also be costly for others, including by inducing statistical discrimination against minority groups.

David received a BA in Mathematics and Economics from Fordham University, and his MS and PhD in Behavioral Decision Research from Carnegie Mellon University. Prior to joining HKUST, he was a postdoctoral fellow at the Kennedy School of Government at Harvard University.


To register and have more information, please email Luke Slater.