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 increasingly consumed in attention-scarce, video-based environments. Yet existing research on media partisanship remains text-centric and implicitly assumes long-form, attentive processing. This paper studies how partisanship operates in video news through the joint transmission of multimodal signals—images and text—under limited attention. I develop a multimodal measure of video partisanship to quantify partisan content in video news and decompose the contribution of each modality. I show that the informational strength of each channel depends on attention: images convey partisanship rapidly through affective cues, while text operates through substantive information that requires sustained exposure to accumulate. A survey experiment using real news footage shows how these properties shape viewers’ responses to political videos. Partisan images elicit immediate emotional responses even under brief exposure, whereas partisan text shifts policy attitudes only with sustained exposure. Overall, the results underscore that in low-attention video environments, images are a more efficient vehicle for partisan information, operating primarily through affective channels. Efforts to improve media quality must therefore account for the asymmetric roles of visual and textual content.
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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 incentivizes local governments and generate efficiency gains, they also shift transfers from low- to high-capacity governments, creating 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 0.16 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 improvements in the quality of education inputs and suggestive evidence of reduced corruption.
Resources
Contact
Email: ac4790@columbia.edu