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: Partisanship and Attention in Videos
(Job Market Paper)
Abstract
Political news today is consumed in a fragmented, attention-scarce, and predominantly video-based media environment. Yet existing measures of media partisanship focus almost exclusively on text and assume attentive information processing. This paper analyzes how partisanship emerges and operates in video news when information is conveyed jointly through images and text, and attention is limited. I train a multimodal model on political advertisements that embeds text and images in a shared semantic space, to quantify video partisanship and decompose each modality’s contribution. Applying this model to video news, I show that the relative informational strength of each modality depends on attention: images convey partisanship quickly through emotional cues, while text transmits information slowly through deliberative content. A survey experiment with real news footage shows how these properties drive viewers' responses to political videos: partisan images elicit rapid emotional responses, while partisan text shifts policy attitudes only with sustained exposure. These results characterize partisanship in modern video media as multimodal and attention-dependent, with images and text affecting different types of audiences. Efforts to improve the quality and delivery of media content must therefore account for their distinct roles.
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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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Bonus or Budget? Equity-Efficiency Tradeoff in Performance-Based Transfers
(with Luigi Caloi)
Abstract
To improve service delivery, central governments can tie intergovernmental transfers to local policy performance. While performance-based transfers incentivize local governments and generate efficiency gains, they also shift transfers from low-capacity to high-capacity governments, which leads to equity losses. We study this equity-efficiency trade-off using a bundle of transfer reforms 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, which translated to disparities in expenditures across sectors. In contrast, contemporaneous reforms to unconditional transfers had negligible effects on student outcomes. We use a simple model of optimal transfers to interpret these findings. Our results suggest that the introduction of performance-based transfers delivered large efficiency gains, limited equity costs, and was welfare enhancing. 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