How and when group identity affects economic behavior

On 23 August 2024 Daniel Carvajal-Zuniga will hold a trial lecture on a prescribed topic and defend his thesis for the PhD degree at NHH.

Daniel Carvajal-Zuniga´s thesis explores how and when group identity—defined as a person’s sense of self derived from perceived membership in social groups—affects economic behavior. In three chapters, Carvajal-Zuniga addresses these questions using experimental methods, incentivized measures, and large-scale surveys.

The first chapter studies prosocial behavior in a large-scale U.S. sample, where participants are exogenously exposed to social contexts with varying levels of nationality diversity. The candidate finds that diverse contexts amplify participants' ingroup bias—the tendency to favor one's own group—driven by increased allocations towards fellow nationals and decreased allocations to foreigners, relative to giving in homogeneous contexts where such bias is not present. A change in perceptions of social proximity corresponds to a driver of the effect of diversity in allocations.

Essays in Industrial Organization of Spatially Differentiated Markets

On Tuesday 20 August 2024 Alina Ozhegova will hold a trial lecture on a prescribed topic and defend her thesis for the PhD degree at NHH.

The second chapter extends this research and examines how an individual’s majority-minority status in an immediate social context influences their social preferences. Participants from a U.S. sample are randomly assigned to roles as members of a majority or a minority. The findings reveal that majority-minority status does not significantly affect these social preferences. Moreover, ingroup affinity, defined as whether an individual feels closer to the ingroup relative to the outgroup, is a crucial factor in the emergence of ingroup biases in social preferences.

The third chapter focuses on a different identity, gender, and examines gender differences in generative AI adoption. Carvajal-Zuniga and his coauthors document a substantial gender gap among students at a top business school in Norway, with female students, particularly top students, opting out of AI use. They identify potential factors influencing this gap, from which identity concerns emerging from stereotypes could play a role. Finally, they provide causal evidence on policy tools to close this gap. Their findings show generative AI could widen existing gender gaps in the labor market, but appropriate policies can prevent this outcome.