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Econometrics and Data Science

Econometrics and Data Science

The Econometrics and Data Science Research Group covers a wide number of topics within the areas of modern econometric theory and applications, as well as data science in economics. On the econometrics side, the group’s research interests include: the econometrics of networks, panel data econometrics, identification and semiparametric econometrics, macroeconometrics and financial econometrics. On the data science side, the group is interested in, among other topics, machine learning, artificial intelligence, high-dimensional econometrics and text analysis. Such research is often motivated and applied to problems in other fields, including those in industrial organisation, labour economics, political economy, macroeconomics and finance.

The group organises an Econometric seminar that takes place every two weeks on Mondays at 2pm. The group also participates in the CAGE seminar in applied economics, which runs every two weeks on Tuesdays at 2pm, and engages with other seminars in the Department. Students and faculty of the group present their work in progress in two brown bag seminars which run weekly on Tuesdays and Wednesdays at 1pm. The group also co-organises annual workshops, including the Econometrics Workshop, which is a one-day event coupled with an econometrics masterclass.

Our activities

Econometrics Seminar

Monday afternoons
For faculty and PhD students at Warwick and other top-level academic institutions across the world. For a detailed scheduled of speakers please see our upcoming events.
Organisers: Kenichi Nagasawa and Ao Wang

Work in Progress Seminars

Tuesdays and Wednesdays: 1.00-2.00pm
Students and Faculty of the group present their work in progress in two brown bag seminars. For a detailed scheduled of speakers see our upcoming events.
Organiser: Chris Roth

People

Events

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Applied Economics, Econometrics & Public Policy (CAGE) Seminar - Gordon Dahl (UCSD)

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Location: S2.79

Title: Diversity and Discrimination in the Classroom, joint with Dan Anderberg, Christina Felfe, Helmut Rainer and Thomas Siedler.

Abstract: What makes diversity unifying in some settings but divisive in others? We examine how the mixing of ethnic groups in German schools affects intergroup cooperation and trust. We leverage the quasi-random assignment of students to classrooms within schools to obtain variation in the type of diversity that prevails in a peer group. We combine this with a large-scale, incentivized lab-in-field-experiment based on the investment game, allowing us to assess the in-group bias of native German students in their interactions with fellow natives (in-group) versus immigrants (out-group). We find in-group bias peaks in culturally polarized classrooms, where the native and immigrant groups are both large, but have different religious or language backgrounds. In contrast, in classrooms characterized by non-cultural polarization, fractionalization, or a native supermajority, there are significantly lower levels of own-group favoritism. We find empirical evidence that culturally polarized classrooms foster negative stereotypes about immigrants' trustworthiness and amplify taste-based discrimination, both of which are costly and lead to lower payouts. In contrast, accurate statistical discrimination is ruled out by design in our experiment. Consistent with a simple model, discrimination in culturally polarized classrooms is associated with lower levels of intergroup friendship and larger identity gaps. Taken together, these findings suggest that extra efforts are needed to counteract low levels of inclusivity and trust in culturally polarized environments.

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