Working Papers

Network Competition and Exclusive Contracts: Evidence from News Agencies March 2026. [paper]

[abstract]

This paper studies exclusive vertical contracts in network industries. The setting is the market for telegraphic news agencies in the early 20th-century United States, where the Associated Press (AP) granted member newspapers exclusive territory contracts that prevented later entrants from joining AP. I show that these contracts diverted excluded newspapers to AP's competing entrant, the United Press (UP), providing the scale necessary for its growth and survival. I develop and estimate a dynamic model of newspaper entry and news agency choice using novel historical data linking newspapers to news agency subscriptions, costs, and network coverage over time. The model captures demand-side network effects and supply-side economies of scale that are quantitatively important for newspapers' decisions to join news agencies. Counterfactual simulations show that removing AP’s exclusive territory contracts causes UP to fail to reach sufficient scale and thus collapse. Removing exclusivity increases newspaper competition and consumer surplus, but reduces coverage in some markets. The results suggest that exclusive contracts can shape market structure in network industries not only by reducing competition, but also by sustaining an entrant competitor.

Urban Migration, Public Health Amenities, and Local Newspapers in 1870-1940 U.S. (w/ Anaïs Galdin) [paper]

[abstract]

We study the role of newspapers in the migration decisions of rural individuals in the 1870-1940 United States. Contributing to the urban and health economics literature, we offer new insights about how information, specifically newspaper portrayals of public health advancements like water filtration and sewage systems, shapes rural-urban migration patterns. We show that access to information about urban health conditions through newspapers played a crucial role in encouraging rural individuals to move to cities over the turn of the 20th century. By exploiting a novel linkage between full-count U.S. census data and a unique historical newspaper dataset at the county level, which leverages text-mining and Natural Language Processing (NLP) algorithms, we provide novel evidence that rural individuals responded to newspaper narratives on public health investments and disease occurrences. Rural migrants with access to information about public health investments migrated in higher proportion to sanitation-adopting cities compared to rural migrants with no access to such information, and avoided cities affected by epidemics more than their non-informed counterparts. This finding is consistent with the literature suggesting that such investments significantly reduced mortality from waterborne diseases (typhoid and diphtheria) and improved quality of life, making cities more attractive places to live. As policymakers consider strategies to revitalize urban areas in the post-pandemic era, our study highlights the potential importance of information dissemination in promoting urban growth and development.

Work in Progress

If You Can’t Beat Them, Ban Them: Hotel Market Power and Short-Term Rental Regulations

(w/ Sophie Calder-Wang, Chiara Farronato, and Karam Kang)

Publications

American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers (w/ Melissa Dell, Jacob Carlson, Tom Bryan, Emily Silcock, Abhishek Arora, Zejiang Shen, Luca D’Amico-Wong, Pablo Querubín, and Leander Heldring) [paper] [dataset] [GitHub]

Neural Information Processing Systems — Datasets and Benchmarks, 2023

[abstract]

Existing full text datasets of U.S. public domain newspapers do not recognize the often complex layouts of newspaper scans, and as a result the digitized content scrambles texts from articles, headlines, captions, advertisements, and other layout regions. OCR quality can also be low. This study develops a novel, deep learning pipeline for extracting full article texts from newspaper images and applies it to the nearly 20 million scans in Library of Congress's public domain Chronicling America collection. The pipeline includes layout detection, legibility classification, custom OCR, and association of article texts spanning multiple bounding boxes. To achieve high scalability, it is built with efficient architectures designed for mobile phones. The resulting American Stories dataset provides high quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge. The dataset could also be added to the external database of a retrieval-augmented language model to make historical information - ranging from interpretations of political events to minutiae about the lives of people's ancestors - more widely accessible. Furthermore, structured article texts facilitate using transformer-based methods for popular social science applications like topic classification, detection of reproduced content, and news story clustering. Finally, American Stories provides a massive silver quality dataset for innovating multimodal layout analysis models and other multimodal applications.