Published and Accepted Papers
How the 1963 Equal Pay Act and 1964 Civil Rights Act Shaped the Gender Gap in Pay
(with Martha Bailey and Bryan Stuart, Quarterly Journal of Economics, 139(3): 1827-1878, 2024)
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
(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 \textit{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 treatment to identify the Average Treatment Effect on the Treated (ATT). We develop a framework to estimate the treatment effect when the dose takes effect only after a cutoff value, the Minimum Effective Dose (MED), and introduce the average treatment effect on the effectively treated (ATET) as our target estimand. We propose a sample splitting estimator of the ATET and MED under non-parametric assumptions on the dose response function. First, in a hold-out sample, we borrow methods from the pharmacological literature to estimate the MED in a model selection step. This is then used to estimate the ATET with the remaining observations in a second step. This estimator is asymptotically conservative: it does not erroneously identify any treated units as untreated in the limit even if the MED is on the boundary of the parameter space, but as a result, it provides an attenuated estimate of the ATET. We use the bootstrap procedure in Efron (2014) to construct standard errors for the ATET estimate that reflect uncertainty over the value of the MED. Our simulations suggest that this estimator performs well in finite samples.
Non-Peer-Reviewed Publications
Changes in the US Gender Gap in Wages in the 1960s
(with Martha Bailey and Bryan Stuart, AEA Papers and Proceedings, 111: 143-148, 2021)
COVID-19 and stay-at-home orders: identifying event study designs with imperfect testing
(with Jaedo Choi, Elird Haxhiu, Nishaad Rao and Taeuk Seo, COVID Economics, 76: 23 April 2021)
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
Women in Law and the Draft (with Benjamin Pyle)
Addressing Racial Disparities in Medical Education and Science: The Role of the Civil Rights Movement (with Francesca Truffa and Ashley Wong)
Medical Schools, Physician Maldistribution, and Mortality (with Caitlin Carroll and Tyler Radler)