I am a PhD Student in Business Economics at Columbia University. I am mainly interested in political economy, media economics and organizational economics.
My research combines economics and machine learning to understand how information is produced, transmitted, and influences social and political outcomes.
I am on the 2025-2026 job market.
Research
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An Image is Worth 40.38 Words: Political Partisanship in Videos
(Job Market Paper)
Abstract
A large share of political news is now consumed through short, visually rich videos. Yet, most measures of media bias rely on textual content and ignore images. I develop a framework to quantify partisan signals from both textual and visual components of video political ads. Applied to immigration coverage in television news, omitting visuals understates the partisan gap between channels. Which modality conveys more partisanship depends on clip length: images dominate in short clips, text in long ones. Image-based predictions are also more sensitive to emotional content, while text reflects propositional content that requires longer exposure to accumulate. A survey experiment shows that these differences translate into relevant implications for political communication. Republican images immediately increase perceived Republican partisanship, raise negative emotions, and in short clips reduce pro-immigrant charity donations, especially among Republicans. Textual cues, by contrast, lose their partisan signal in short clips but, under longer exposures, shift immigration attitudes, with effects concentrated among Democratic viewers. The results reveal distinct comparative advantages of each modality: visuals act quickly through emotion and behavior, while text persuades more slowly through attitudes and beliefs.
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Fixed Effects Topic Model
(with Dan Biderman, David Blei, Wei Cai, Amir Feder & Andrea Prat)
Abstract
Social scientists wish to perform topic modeling on documents that are created by different authors in different contexts. However, the same broad topic may be expressed in different ways depending on the environment where the author operates. For example, one may wish to use employee reviews to identify broad corporate culture topics, but the language of reviews is influenced by industry-specific jargon. Existing methods attempt to control for these biases ex post, such as with traditional fixed effect regressions. But these methods cannot fully separate global themes from category-specific language within them. In this paper, we introduce the Fixed Effects Topic Model (FETM), a novel approach to disentangling broad topics from contextual influences by incorporating fixed effects directly into the generative process of language. We use the FETM to identify themes in a large corpus of Glassdoor job reviews. We show that it outperforms conventional topic models, both in interpretability and predictive accuracy.
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Today’s Bonus, Tomorrow’s Budget: Equity-Efficiency Tradeoff in Performance-Based Transfers
(with Luigi Caloi)
Abstract
To improve service delivery, central governments often tie intergovernmental transfers to local policy performance. While such performance-based transfers can raise efficiency by incentivizing municipalities, they may also create equity losses by disproportionately rewarding high-capacity governments with larger transfers. We study this equity efficiency trade-off using transfers to Brazilian municipalities. When two states tied transfers to relative educational performance, student test scores rose substantially: moving from the 25th to the 75th percentile of per capita conditional transfers increased scores by 0.13 standard deviations. However, the reform also widened funding disparities, as municipalities with higher pre-existing capacity received larger transfers. In contrast, contemporaneous reforms to passive transfers had negligible effects on student outcomes. A simple model of optimal transfers interprets these findings, suggesting that performance-based transfers deliver large efficiency gains, limited equity costs, and should constitute a sizable share of the optimal transfer mix. We find minimal evidence of multitasking distortions or score manipulation. Instead, we document increased education-related inputs and suggestive evidence of reduced corruption.
Resources
Contact
Email: ac4790@columbia.edu