About

Alex Hollingsworth is an associate professor at The Ohio State University with joint appointments in the Department of Agricultural, Environmental, and Development Economics; the Department of Economics; and the John Glenn College of Public Affairs. He is a Research Associate at the National Bureau of Economic Research, a co-editor at the Journal of Policy Analysis and Management, and an associate editor at the Journal of Health Economics. Hollingsworth is an applied microeconomist who examines how regulations affect health, with interests in environmental economics, population health, substance abuse, and access to care. His research has been published in prominent outlets such as the American Economic Journal: Economic Policy, the Journal of Public Economics, and the Journal of Human Resources, and has been covered by Scientific American, the Washington Post, CNBC, the Atlantic, VOX, and the Los Angeles Times. Additionally, he co-hosts a podcast called The Hidden Curriculum with Sebastian Tello-Trillo, which focuses on topics relevant to academic life that are not formally taught in graduate school.

Research topics

• Political Science
• Medicine
• Law
• Environmental health
• Psychology
• Psychiatry
• Social Science
• Sociology
• Computer Science
• Business
• Internet privacy
• Engineering
• Pharmacology
• Biology
• Economics
• Public economics
• Chemistry
• Waste management
• Toxicology
• Internal medicine

Selected publications

• Economic Geography and Air Pollution Regulation in the United States

  Journal of Political Economy Microeconomics · 2025-03-14 · 1 citations

  article1st authorCorresponding

  We develop a quantitative economic geography model with endogenous emissions, amenities, trade, and labor reallocation to evaluate the spatial impact of the leading air quality regulation in the United States: the National Ambient Air Quality Standards (NAAQS). We find that the NAAQS generate $40 billion in annual welfare gains, first-best emissions pricing would increase this by an additional $70 billion, gains are concentrated in a small set of cities, and improved amenities attract nonmanufacturing workers. Atmospheric transport of emissions, labor reallocation, and trade are first-order factors for quantifying the level and distribution of both costs and benefits of the NAAQS.

  PublisherDOI

• Stacked Difference-in-Differences

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• Stacked Difference-in-Differences

  National Bureau of Economic Research · 2024-01-01 · 88 citations

  reportOpen accessSenior author

  This paper introduces the concept of a "trimmed aggregate ATT," which is a weighted average of a set of group-time average treatment effect on the treated (ATT) parameters identified in a staggered adoption difference-in-differences (DID) design.The set of identified group-time ATTs that contribute to the aggregate is trimmed to achieve compositional balance across an event window, ensuring that comparisons of the aggregate parameter over event time reveal dynamic treatment effects and differential pre-trends rather than compositional changes.Taking the trimmed aggregate ATT as a target parameter, we investigate the performance of stacked DID estimators.We show that the most basic stacked estimator does not identify the target aggregate or any other average causal effect because it applies different implicit weights to treatment and control trends.The bias can be eliminated using corrective sample weights.We present a weighted stacked DID estimator, and show that it correctly identifies the target aggregate, providing justification for using the estimator in applied work.

  PublisherDOI

• The Impact of Lead Exposure on Fertility, Infant Mortality, and Infant Birth Outcomes

  Review of Environmental Economics and Policy · 2024-06-01 · 8 citations

  article

  This article reviews the quasi-experimental literature on lead and fertility, lead and infant mortality, and lead and infant birth outcomes. It then discusses the relevance of these studies for policy. In contrast to the large amount of literature on children’s blood lead levels and health and development outcomes, there are fewer studies on lead and fertility, lead and infant mortality, and lead and infant health, despite their social and economic importance. Although removal of lead in gasoline generated enormous public health benefits, lead exposure remains significant in both developed and developing countries. Thus, causal estimates from quasi-experimental studies of the relationships between lead and fertility, lead and infant mortality, and lead and infant health are critical for policy. Specifically, they can be used to generate estimates of benefits used in regulatory benefit–cost analyses.

  PublisherDOI

• The Gift of a Lifetime: The Hospital, Modern Medicine, and Mortality

  American Economic Review · 2024-06-27 · 12 citations

  article1st authorCorresponding

  We explore how access to modern hospitals and medicine affects mortality by leveraging efforts of the Duke Endowment to modernize hospitals in the early twentieth century. The Endowment helped communities build and expand hospitals, obtain state-of-the-art medical technology, attract qualified medical personnel, and refine management practices. We find that Duke support increased the size and quality of the medical sector, fostering growth in not-for-profit hospitals and high-quality physicians. Duke funding reduced both infant mortality—with larger effects for Black infants than White infants—and long-run mortality. Finally, we find that communities aided by Duke benefited more from medical innovations. (JEL I11, I12, J13, J15, L31, N32, O31)

  PublisherDOI

• Designing Difference-in-Difference Studies with Staggered Treatment Adoption: Key Concepts and Practical Guidelines

  Annual Review of Public Health · 2024-01-26 · 59 citations

  articleOpen access

  Difference-in-difference (DID) estimators are a valuable method for identifying causal effects in the public health researcher's toolkit. A growing methods literature points out potential problems with DID estimators when treatment is staggered in adoption and varies with time. Despite this, no practical guide exists for addressing these new critiques in public health research. We illustrate these new DID concepts with step-by-step examples, code, and a checklist. We draw insights by comparing the simple 2 × 2 DID design (single treatment group, single control group, two time periods) with more complex cases: additional treated groups, additional time periods of treatment, and treatment effects possibly varying over time. We outline newly uncovered threats to causal interpretation of DID estimates and the solutions the literature has proposed, relying on a decomposition that shows how the more complex DIDs are an average of simpler 2 × 2 DID subexperiments.

  PublisherDOI

• Designing Difference in Difference Studies with Staggered Treatment Adoption: Key Concepts and Practical Guidelines

  SSRN Electronic Journal · 2023-01-01 · 5 citations

  articleOpen access
  PublisherDOI

• Excess Emissions: Environmental Impacts, Health Effects, and Policy Debate

  Review of Environmental Economics and Policy · 2023-01-01

  article1st authorCorresponding

  The US Environmental Protection Agency classifies air pollution releases that are due to accidents, malfunctions, or unanticipated facility start-ups and shutdowns as excess emissions, which violate the Clean Air Act. Despite this, states have historically granted emitting facilities exemptions, shielding facilities from enforcement and penalties. We outline recent research that documents the incidence, magnitude, environmental impacts, and health effects of these emissions to inform the considerable policy debate surrounding their regulation. The majority of prior research focuses on Texas because it is the only state that provides access to detailed data on excess emissions that can be easily used for research. This data limitation creates uncertainties about the incidence, magnitude, and impacts of these emissions outside of Texas. We argue that a requirement for detailed data reporting in all states would best enable policy makers to design an effective regulatory framework.

  PublisherDOI

• The Impact of Lead Exposure on Fertility, Infant Mortality, and Infant Birth Outcomes

  National Bureau of Economic Research · 2023-06-01 · 5 citations

  reportOpen access

  Lead exposure has detrimental effects on fertility, infants, children, and adults.Despite the success in removing lead from on-road gasoline, industrial and aviation emissions continue to pose a substantial global challenge.Other major sources of exposure include dust, soil resuspension, and consumption of contaminated water or food.Both animal studies and evidence from humans support claims of an adverse relationship between lead pollution and human health.Since lead exposure is not randomly assigned, quasi-experimental studies play a crucial role in this knowledge base.Among these studies, extensive research links elevated blood lead levels in children to academic and behavioral outcomes, but more limited attention has been given to lead's impact on fertility, infant mortality, and infant health.This paper examines the existing quasi-experimental literature on lead and fertility, infant mortality, and infant birth outcomes, highlighting key results, methods, and implications for policymakers.

  PublisherDOI

• Designing Difference in Difference Studies With Staggered Treatment Adoption: Key Concepts and Practical Guidelines

  National Bureau of Economic Research · 2023-11-01 · 13 citations

  reportOpen access

  Difference-in-Difference (DID) estimators are a valuable method for identifying causal effects in the public health researcher's toolkit.A growing methods literature points out potential problems with DID estimators when treatment is staggered in adoption and varies with time.Despite this, no practical guide exists for addressing these new critiques in public health research.We illustrate these new DID concepts with step-by-step examples, code, and a checklist.We draw insights by comparing the simple 2 × 2 DID design (single treatment group, single control group, two time periods) with more complex cases: additional treated groups, additional time periods of treatment, and with treatment effects possibly varying over time.We outline newly uncovered threats to causal interpretation of DID estimates and the solutions the literature has proposed, relying on a decomposition that shows how the more complex DID are an average of simpler 2X2 DID subexperiments.

  PublisherDOI

Frequent coauthors

• Ivan Rudik

  116 shared

• Carl Kitchens

  83 shared

• Taylor Jaworski

  83 shared

• Kosali Simon

  Indiana University Bloomington

  42 shared

• Melissa A. Thomasson

  41 shared

• Anthony Wray

  University of Southern Denmark

  40 shared

• Chris Karbownik

  Athens State University

  36 shared

• Nicholas J. Sanders

  32 shared