Research

Since 2009, the United States has experienced persistent shortages of off-patent injectable drugs, defying the expectation that competitive pressures would quickly resolve excess demand. This paper shows that these shortages arise from two key market failures in a globalized supply chain: (i) pervasive traceability problems that hide where and how drugs are manufactured, and (ii) an institutionalized assumption that all generic versions are perfect substitutes. Together, these frictions leave buyers unable to identify and reward reliable suppliers, fueling adverse selection once markets open to offshoring. I document the causal impact of offshore facilities on U.S. shortages by constructing a novel dataset that, for the first time, maps the exact manufacturing location of drug products sold in the U.S., thereby resolving longstanding traceability issues. Using these data, I then develop and estimate a partial-equilibrium model of global procurement in which manufacturers trade off cost-cutting and reliability, endogenously translating into higher disruption risks in offshore locations. The inability to capture and reward supply resilience under the current procurement system, combined with short-term pricing and capacity rigidities, generates persistent shortages once markets open to global competition. The model explicitly accounts for potential excess demand in logit-based estimations, enabling to recover location-specific disruption probabilities and cost parameters. Counterfactual simulations reveal that reshoring subsidies modestly curtail shortages and result in substantial price hikes. In contrast, reforming procurement contracts to recognize variation in production quality significantly lowers disruptions and increases consumer welfare by incentivizing reliability investments. These findings highlight the importance of market design and transparency in global supply chains and show how failing to capture and reward manufacturing quality can lead to persistent disruptions even in highly contestable, commodity-like markets.

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.

Large language models have the potential to transform the landscape of online labor marketplaces, not just through the channels of supply and demand but also the matching technology itself. In this paper, we use data from Freelancer.com, a major online labor platform, to investigate exactly how the advent of large language models (LLMs) affects the matching process in online labor markets. Our first major contribution is to develop a new measure of “content fit” that uses a LLM to quantify how well a cover letter responds to a given job description. We show that this measure is significantly predictive of labor demand, and that job posters have a high willingness to pay for increases in the “content fit” of cover letters. Motivated by this finding, we develop a Spence (1973) style model of signaling, in which Freelancers spend time (costly effort) to increase their cover letters’ levels of “content fit”, and the freelancers with a greater match quality with a given job posting are more easily (less marginally costly) able to expend that effort. This model along with our descriptive findings lend evidence to the theory that job posters are using “content fit” as a noisy but predictive signal of freelancer match quality. With this theory in hand, we then ask what happens to the efficiency of this signaling equilibrium when LLMs make it cheaper to increase “content fit”, thus garbling the signal. To do so, we estimate how the relationship between time spent writing and “content fit” changes after the introduction of LLMs onto the platform, and then simulate a signaling equilibrium, in which supply and demand are held fixed as they are in the pre-LLM time periods, but the signaling technology of the post-LLM time periods is used instead. One of our key contributions is to develop estimation and identification strategies of scoring auctions where one of the dimensions of the score is a signal for quality whose weighting in the score is determined by the equilibrium itself. These strategies allow us to recover both supply and demand, as well as the relevant parameters of the signaling equilibrium.

Enforcement of quality regulations in the Global North may decrease the quality of goods distributed to the Global South, due to the rerouting by manufacturers of "sub-quality" products to less regulated markets. This paper studies the global redistribution of products recalled in highly regulated markets, such as the United States and Europe, when the manufacturers are global firms. Are new global manufacturing forces differentially allocating their products based on differentials in regulatory enforcement among destination markets? To answer these questions, I digitized new U.S. data on pharmaceutical product recalls and warning letters to foreign manufacturing facilities, which I merge with detailed descriptions extracted from Indian customs manifests. Using text mining techniques, I trace global exports of drug produced by Indian drug manufacturers and study what happens to global exports after an Indian product is recalled in the U.S. After a recall, I find evidence that firms reduce U.S. sales and reallocate production to other (less regulated) markets, such as African and South-Asian markets. Such behavior may result in an equilibrium in which high-quality products are traded with the North and sub-optimal quality products are diverted to the South. This may have important policy implications if more stringent quality enforcement in the North widens the differential in regulatory standards with the South. Abilities for global manufacturers to arbitrage exports based on differentials in global regulatory standards may lead to more product differentiation, resulting in lower-quality batches of products being sent to South countries with less regulated markets. This may also have implications for product availability in the North, as suppliers now located in the South may have lower incentives to quickly correct manufacturing quality issues if they can still distribute these products to South markets.

This paper provides a first empirical study of how openness to international trade in news products, a phenomenon largely facilitated by the digitalization of newspapers, may impact the characteristics of the information products available to consumers. Based on a case study of the entry of French media firms in Francophone Africa, I build a new dataset of more than 800 000 newspaper articles over the period 2005 to 2017. Using text mining techniques, I construct a five-dimensional set of newspaper characteristics to qualitatively analyze newspaper data. My evidence suggests that the entry of French digital newspapers, which produce a relatively low share of local news and whose websites are generally uniformly targeting all Francophone African countries, lead to a significant decrease of the diversity of subjects treated by Francophone African newspapers, characterized by a strong and significant increase of the share of local news, and a small decrease of the total number of articles related to France. I also find a slight but significant impact of the penetration of French digital newspapers on two indicators of newspaper format, namely a small increase of the average frequency of publication of Francophone African newspapers, and a very small decrease of the mean wordcount per articles.