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Lucy Msall

(last name pronounced ‘muh-sal’)

I am a PhD student in Economics at the University of Chicago’s Booth School of Business and a Master of Legal Studies candidate at the University of Chicago Law School.

My research interests are in Public Finance. Prior to graduate school, I worked as a political campaign staffer, government employee, and NBER Research Assistant.

I can be reached via email at lmsall [at]

Working Papers

Sex, Drugs, and R&D: Missing Innovation from Regulating Female Enrollment in Clinical Trials with Valerie Michelman

Abstract: This paper considers the consequences of unequal representation in research. From 1977-1993, the Food and Drug Administration (FDA) issued guidance that “women of childbearing potential” should not be included as human subjects in early-stage clinical trials. We study how the pharmaceutical industry’s response to the guidance shaped the course of innovation serving men and women. We develop a model of drug development which predicts that the guidance leads to less innovation for female-focused drugs. Compliance with the guidance decreases the informativeness of clinical trials for drugs intended to treat predominantly female diseases, resulting in higher expected costs. To bring our theory to the data, we link biomedical patents, commercial data on drug development, and FDA records of approved drugs. By exploiting the contrast between drug and non-drug biomedical patents, we estimate that the guidance resulted in a sex-specific drug innovation gap of 14%. We test for downstream effects on drug development and approval. Our results inform current policy tradeoffs about underrepresentation of racial minorities, pregnant people, and older adults in clinical research.

Presentations: NBER SI Innovation (2022), Eastern Economic Association (2022), University of Nebraska England-Clark Conference (2022) 

Consumer-Financed Fiscal Stimulus: Evidence from Digital Coupons in China with Jing Ding, Lei Jiang & Matthew Notowidigdo

Abstract: We study a new form of fiscal stimulus undertaken by municipalities across China starting in 2020: government-issued digital coupons designed to encourage spending in certain categories such as restaurants, groceries, and entertainment. Using unique account-level and transaction-level data from a large online shopping platform that distributed many of the government coupons, we estimate the effect of the coupons on spending for many different types of coupons with different spending thresholds (e.g., “Spend at least ¥X, get ¥Y off”). We identify the effects of the coupons on spending using a bunching estimator that uses the transaction-level spending distribution in the weeks before each coupon is distributed as the counterfactual, and we validate the bunching estimates by using the random assignment of a subset of the coupons in our data. We first present visual evidence of sharp bunching around coupon-specific thresholds during the weeks that the coupons are distributed, but not in the weeks before and after. We find that the coupons cause large and persistent increases in spending in the targeted spending categories, and we do not find evidence of any substitution away from spending on the platform in “non-targeted” spending categories. We estimate that the consumer spending increases by 2.7-2.8 yuan per yuan spent by the government. As a result, we conclude that the digital coupons increased spending substantially in the targeted spending categories at very low fiscal cost. We show that a standard consumption model can generate these results since the coupons’ spending thresholds create “notches” that lead to large spending responses from consumers. We calibrate the consumption model to match our empirical results, and we find that the coupons generate about half of the increase in consumer welfare as an equivalent amount of fiscal stimulus distributed as cash. We then use the calibrated model to simulate alternative coupon designs, and we find that lower coupon thresholds and higher coupon discounts would be less cost-effective but would deliver greater aggregate stimulus.

Online Appendix

Pre-Doctoral Publications

Decreases In Readmissions Credited To Medicare’s Program To Reduce Hospital Readmissions Have Been Overstated with Christopher Ody, Leemore Dafny, David Grabowski & David Cutler. Health Affairs, January 2019. Vol. 38, Issue 1.

Abstract: Medicare’s Hospital Readmissions Reduction Program (HRRP) has been credited with lowering risk-adjusted readmission rates for targeted conditions at general acute care hospitals. However, these reductions appear to be illusory or overstated. This is because a concurrent change in electronic transaction standards allowed hospitals to document a larger number of diagnoses per claim, which had the effect of reducing risk-adjusted patient readmission rates. Prior studies of the HRRP relied upon control groups’ having lower baseline readmission rates, which could falsely create the appearance that readmission rates are changing more in the treatment than in the control group. Accounting for the revised standards reduced the decline in risk-adjusted readmission rates for targeted conditions by 48 percent. After further adjusting for differences in pre-HRRP readmission rates across samples, we found that declines for targeted conditions at general acute care hospitals were statistically indistinguishable from declines in two control samples. Either the HRRP had no effect on readmissions, or it led to a systemwide reduction in readmissions that was roughly half as large as prior estimates have suggested.

Popular Press Coverage: The New York Times, Chicago Booth Review