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BEGIN:VEVENT
DTSTAMP:20260428T190626Z
DTSTART;VALUE=DATE-TIME:20260304T130000
DTEND;VALUE=DATE-TIME:20260304T140000
SUMMARY:AMES (Applied Microeconomics Early Stage) Workshop - Anwesh Mukho
 padhyay & Yanjun Gao (PGRs)
TZID:Europe/London
UID:20260304-8ac672c79c03da45019c0949329a1553@warwick.ac.uk
CREATED:20260302T085947Z
DESCRIPTION:There will be two x 30 minutes presentations: i) Anwesh will 
 be presenting Media Bias and Information Bubbles: Evidence from Reportin
 g of Pre-Election Polls on YouTube Abstract: A large share of the econom
 ics literature on media bias focuses on framing or slant\, rather than i
 nformation selection. At the same time\, growing concerns about informat
 ion bubbles and the “polarisation of reality”\, particularly in the US w
 here media markets have strong partisan sorting\, suggest that agenda se
 tting may play an equally important role. I study the existence of such 
 information gaps in the context of pre-election polling\, where the unde
 rlying information is verifiable\, but media outlets remain free to choo
 se which polls to report. I construct novel data on poll reporting on Yo
 uTube\, one of the most widely used news platforms in the United States.
  Using transcripts from 94 YouTube channels covering U.S. news and polit
 ics\, together with an LLM-based extraction filter\, I build a structure
 d dataset of all polling-related information reported in each video. I d
 ocument three main findings. First\, at any given point in time\, Republ
 ican-leaning channels report more information on polls where Trump is ah
 ead 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 abo
 ut polls\, these information bubbles are relatively more driven by the i
 ntensive margin -- channels selectively sampling from different ends of 
 the distribution\, than mechanically through the amount of information i
 n each video. ii) Yanjun will be presenting From Calories to Calcium: Re
 duced-Form and Structural Evidence on Soda–Milk Substitution from U.S. S
 canner Data Abstract: This paper examines the substitution patterns betw
 een milk and soda\, with particular attention to demographic heterogenei
 ty. Using the Nielsen Retail Scanner dataset\, I estimate demand paramet
 ers through a novel share-to-share regression framework. The results ind
 icate that while soda and milk appear nearly independent at the store le
 vel\, 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 a
 dopt a structural approach by estimating a multinomial logit demand mode
 l using household-level scanner data. This demand model allows for riche
 r individual heterogeneity\, and the resulting structural estimates clos
 ely mirror the reduced-form findings. Taken together\, these findings su
 ggest that milk and soda are strong substitutes\, especially flavored mi
 lk 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 pro
 vide new insights into the evaluation of sugar tax policies
LOCATION:S2.79
CATEGORIES:CAGE-AMES Workshop
LAST-MODIFIED:20260302T085947Z
ORGANIZER;CN=Gill Gudger:
END:VEVENT
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