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:
Research Students
Events
AMES (Applied Microeconomics Early Stage) Workshop - Anwesh Mukhopadhyay & Yanjun Gao (PGRs)
There will be two x 30 minutes presentations:
i) Anwesh will be presenting Media Bias and Information Bubbles: Evidence from Reporting of Pre-Election Polls on YouTube
Abstract: A large share of the economics literature on media bias focuses on framing or slant, rather than information selection. At the same time, growing concerns about information bubbles and the 鈥減olarisation of reality鈥�, particularly in the US where media markets have strong partisan sorting, suggest that agenda setting may play an equally important role. I study the existence of such information gaps in the context of pre-election polling, where the underlying information is verifiable, but media outlets remain free to choose which polls to report. I construct novel data on poll reporting on YouTube, one of the most widely used news platforms in the United States. Using transcripts from 94 YouTube channels covering U.S. news and politics, together with an LLM-based extraction filter, I build a structured dataset of all polling-related information reported in each video. I document three main findings. First, at any given point in time, Republican-leaning channels report more information on polls where Trump is ahead relative to Democratic-leaning channels, establishing the presence of information bubbles even in a setting with hard, publicly verifiable information. Second, I find that reporting favourable information for the channel's preferred candidate generates noisy but generally positive effects on viewership. Third, I find that conditional on reporting about polls, these information bubbles are relatively more driven by the intensive margin -- channels selectively sampling from different ends of the distribution, than mechanically through the amount of information in each video.
ii) Yanjun will be presenting From Calories to Calcium: Reduced-Form and Structural Evidence on Soda–Milk Substitution from U.S. Scanner Data
Abstract: This paper examines the substitution patterns between milk and soda, with particular attention to demographic heterogeneity. Using the Nielsen Retail Scanner dataset, I estimate demand parameters through a novel share-to-share regression framework. The results indicate that while soda and milk appear nearly independent at the store level, they behave as strong substitutes at more aggregated market levels. Flavored milk, in particular, emerges as a close substitute for soda, consistent with its stronger appeal among younger consumers. I then adopt a structural approach by estimating a multinomial logit demand model using household-level scanner data. This demand model allows for richer individual heterogeneity, and the resulting structural estimates closely mirror the reduced-form findings. Taken together, these findings suggest that milk and soda are strong substitutes, especially flavored milk and particularly among households with children. Finally, I conduct a back-of-the-envelope policy simulation to evaluate how a one-cent-per-ounce sugary drink tax would affect the market shares of milk and soda, and how these effects differ across demographic groups. The results provide new insights into the evaluation of sugar tax policies