Published and Accepted Papers

How the 1963 Equal Pay Act and 1964 Civil Rights Act Shaped the Gender Gap in Pay

 (Forthcoming at Quarterly Journal of Economics, joint with Martha Bailey and Bryan Stuart)

In the 1960s, two landmark statutes—the Equal Pay and Civil Rights Acts—targeted the long-standing practice of employment discrimination against U.S. women. For the next 15 years, the gender gap in median earnings among full-time, full-year workers changed little, leading many scholars and advocates to conclude the legislation was ineffectual. This paper uses two different research designs to show that women’s relative wages grew rapidly in the aftermath of this legislation. The data show little short-term changes in women’s employment but some evidence that firms reduced their hiring and promotion of women in the medium to long term.

Working Papers

Health Womanpower: The Role of Federal Policy in Women's Entry into Medicine (Job Market Paper)

During the 1970s, women’s representation in medical schools grew rapidly from 9.6% of all students in 1970 to 26.5% in 1980. This paper studies the role of federal policy in increasing women’s access to medical training through two distinct channels: pressure to curb sex discrimination in admissions and a massive expansion in total enrollment through Health Manpower policy starting in 1963. To study this, I construct a novel school-by-year data set with enrollment and application information from 1960 through 1980. Using a continuous difference- in-differences design, I find that medical schools respond to the threat of losing federal contracts by increasing first year enrollment of women by 4 seats at the mean, which explains 27% of women’s gains between 1970 and 1973. Further, I provide OLS evidence that year-to-year expansions explain around 40% of women’s gains from 1970 to 1980; I verify this result in a synthetic control case study to identify the increase in women’s enrollment resulting from large jumps in capacity. 

Continuous Treatment Difference-in-Differences with Unknown Controls: A Data-Driven Approach

 (Joint with Elird Haxhiu)

This paper studies difference-in-differences (DD) research designs where all observations receive a continuous treatment (or dose) in response to an aggregate policy, so there is no group that is ex post unexposed. This setting stands in contrast to the recent literature re-examining DD estimators which typically requires that a subset of observations never receive the treat- ment to identify the Average Treatment Effect on the Treated (ATT). We develop a framework to estimate the ATT when the dose takes effect only after a cutoff value, the Minimum Effec- tive Dose (MED), and propose a sample splitting estimator of the ATT and MED under non- parametric assumptions on the dose response function: first estimate the cutoff in a hold-out sample, then use it to define and estimate the ATT with least squares. This estimator is consis- tent and the second-stage standard error is estimated with usual methods to conduct inference. We conclude with simulations comparing performance to current methods.

Non-Peer-Reviewed Publications

Changes in the US Gender Gap in Wages in the 1960s

(AEA Papers and Proceedings, 111: 143-148, 2021; Joint with Martha Bailey and Bryan Stuart)

COVID-19 and stay-at-home orders: identifying event study designs with imperfect testing

(COVID Economics, 76: 23 April 2021, joint with Jaedo Choi, Elird Haxhiu, Nishaad Rao and Taeuk Seo)

This paper estimates the dynamic effect of Stay-At-Home (SAH) orders on the transmission of COVID-19 in the United States. Identification in this setting is challenging due to differences between real and reported case data given the imperfect testing environment, as well as the clearly non-random adoption of treatment. We extend a Susceptible-Infected-Recovered (SIR) model from Epidemiology to account for endogenous testing at the county level, and exploit this additional structure to recover identification. With the inclusion of model-derived sufficient statistics and fixed effects, SAH orders have a large and sustained negative effect on the growth of cases under plausible assumptions about the progression of testing. Point estimates range from a 44% to 54% reduction in the growth rate of cases one month after a SAH order. We conclude with a discussion on extending the methodology to later phases of the pandemic.

Works in Progress

Health Manpower and the Geographic Distribution of Physicians (Joint with Tyler Radler)