Research

 Abstract: How does FDA regulation affect innovation and market concentration? I examine this question by exploiting FDA deregulation events that affected certain medical device types but not others. I collect comprehensive data on medical device innovation, device safety, firm entry, prices, and regulatory changes and enhance these data using text analysis methods. My analysis of these data reveals three key findings. First, deregulation events significantly increased the quantity and quality of new technologies in affected medical device types relative to controls. These increases are particularly strong among small and inexperienced firms. Second, these events increased firm entry and reduced prices for medical procedures that utilize affected medical device types. Finally, rates of serious injuries and deaths attributable to defective devices did not significantly increase following these events. Interestingly, deregulating certain device types was associated with reduced adverse event rates, possibly due to firms increasing their emphasis on product safety in response to increased litigation risk.

Abstract: We investigate the effects of substantial Medicare price reductions in the medical device industry, which amounted to a  61% decrease over 10 years for certain device types. Analyzing over 20 years of administrative and proprietary data, we find these price cuts led to a 25% decline in new product introductions and a 75% decrease in patent filings, indicating significant reductions in innovation activity. Manufacturers decreased market entry and increased outsourcing to foreign producers, associated with higher rates of product defects. Our calculations suggest the value of lost innovation may offset the direct cost savings from the price cuts. We propose that better-targeted pricing reforms could mitigate these negative effects. These findings underscore the need to balance cost containment with incentives for innovation and quality in policy design.

 Abstract: We analyze wartime prosthetic device patents to investigate how procurement policy affects the cost, quality, and quantity of medical innovation.  Analyzing whether inventions emphasize cost and/or quality requires generating new data.  We do this by first hand-coding the economic traits emphasized in 1,200 patent documents.  We then train a machine learning algorithm and apply the trained models to a century's worth of medical and mechanical patents that form our analysis sample.  In our analysis of these new data, we find that the relatively stingy, fixed-price contracts of the Civil War era led inventors to focus broadly on reducing costs, while the less cost-conscious procurement contracts of World War I did not.  We provide a conceptual framework that highlights the economic forces that drive this key finding. We also find that inventors emphasized dimensions of product quality (e.g., a prosthetic's appearance or comfort) that aligned with differences in buyers' preferences across wars.   Finally, we find that the Civil War and World War I procurement shocks led to substantial increases in the quantity of prosthetic device patenting relative to patenting in other medical and mechanical technology classes.  We conclude that procurement environments can significantly shape the scientific problems with which inventors engage, including the choice to innovate on quality or cost.

Abstract: In a California field experiment, I investigated the impact of a Facebook outreach campaign aimed at increasing enrollment in the Supplemental Nutrition Assistance Program (SNAP). The campaign used a promising marketing strategy known as "retargeting," where ads were delivered to a randomly selected subset of over 16,000 eligible non-participants who had nearly completed the SNAP application process, while a control group remained unaffected. Despite leveraging ad content developed in collaboration with non-profit and government partners, the campaign did not produce statistically or economically significant increases in enrollment, even when considering the extreme values of estimated confidence intervals.

Research Question: Are there ways to embed economics into AI models used in health care settings? Our research takes a cost-benefit approach to optimize the use of an AI algorithm that alerts healthcare providers to sepsis cases within a specific diagnostic group, such as heart disease. Our simulations show potential cost savings of $4.6 billion and higher accuracy using our implementation.