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Applied Microeconomics

Applied Microeconomics

The Applied Microeconomics research group unites researchers working on a broad array of topics within such areas as labour economics, economics of education, health economics, family economics, urban economics, environmental economics, and the economics of science and innovation. The group operates in close collaboration with the CAGE Research Centre.

The group participates in the CAGE seminar on Applied Economics, which runs weekly on Tuesdays at 2:15pm. Students and faculty members of the group present their ongoing work in two brown bag seminars, held weekly on Tuesdays and Wednesdays at 1pm. Students, in collaboration with faculty members, also organise a bi-weekly reading group in applied econometrics on Thursdays at 1pm. The group organises numerous events throughout the year, including the Research Away Day and several thematic workshops.

Our activities

Work in Progress seminars

Tuesdays and Wednesdays 1-2pm

Students and faculty members of the group present their work in progress in two brown bag seminars. See below for a detailed scheduled of speakers.

Applied Econometrics reading group

Thursdays (bi-weekly) 1-2pm

Organised by students in collaboration with faculty members. See the Events calendar below for further details

People

Academics

Academics associated with the Applied Microeconomics Group are:


Natalia Zinovyeva

Co-ordinator

Jennifer Smith

Deputy Co-ordinator


Events

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CWIP (CAGE work in progress) - Mirko Draca

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Title: The Returns to Viral Media: The Case of US Campaign Contributions (with Johannes Böken, Arianna Ornaghi and Nicola Mastrorocco)

Abstract: This paper provides estimates of the impact of social media attention on US campaign contributions. Our setting is a daily dataset of campaign contributions and Twitter activity for Members of Congress over the 2019-2020 period. Our average elasticity of 0.01 for the Contributions-Likes relationship is driven entirely by the top tail (90th percentile and above) of `viral' Tweets. In turn, both negative sentiment and member ideology are strong predictors of virality. The relationship with regard to ideology is U-shaped - members on the far right and far left systematically receive more Likes relative to those in the middle. As part of our identification strategy, we develop a `news pressure' strategy based on the overlap of followers between political and non-political media accounts on the network.

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