The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal inference poses many challenges in DID designs. In this article, we review key features of DID designs with an emphasis on public health policy research. Contemporary researchers should take an active approach to the design of DID studies, seeking to construct comparison groups, sensitivity analyses, and robustness checks that help validate the method's assumptions. We explain the key assumptions of the design and discuss analytic tactics, supplementary analysis, and approaches to statistical inference that are often important in applied research. The DID design is not a perfect substitute for randomized experiments, but it often represents a feasible way to learn about casual relationships. We conclude by noting that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach.

The U.S. population receives suboptimal levels of preventive care and has a high prevalence of risky health behaviors. One goal of the Affordable Care Act (ACA) was to increase preventive care and improve health behaviors by expanding access to health insurance. This paper estimates how the ACA-facilitated state-level expansions of Medicaid in 2014 affected these outcomes. Using data from the Behavioral Risk Factor Surveillance System, and a difference-in-differences model that compares states that did and did not expand Medicaid, we examine the impact of the expansions on preventive care (e.g., dental visits, immunizations, mammograms, cancer screenings), risky health behaviors (e.g., smoking, heavy drinking, lack of exercise, obesity), and self-assessed health. We find that the expansions increased insurance coverage and access to care among the targeted population of low-income childless adults. The expansions also increased use of certain forms of preventive care, but there is no evidence that they increased ex ante moral hazard (i.e., there is no evidence that risky health behaviors increased in response to health insurance coverage). The Medicaid expansions also modestly improved self-assessed health.

We make several contributions to understanding the socio-demographic ramifications of the COVID-19 epidemic and policy responses on employment outcomes of subgroups in the U.S., benchmarked against two previous recessions. First, monthly Current Population Survey (CPS) data show greater declines in employment in April and May 2020 (relative to February) for Hispanics, younger workers, and those with high school degrees and some college. Between April and May, all the demographic subgroups considered regained some employment. While in most cases the re-employment in May was proportional to the employment drop occurred through April, we show that this was not the case for Blacks. Second, we show that job loss was larger in occupations that require more interpersonal contact and that cannot be performed remotely. Third, we see that consistent with theories of occupational segregation, the extent to which workers of certain demographic groups sort (pre-COVID-19) into occupations and industries can explain a sizeable portion of the gender, race, and ethnic gaps in recent unemployment. However, there remain substantial unexplained differences in employment losses across groups even in these detailed decompositions. We also demonstrate the importance of tracking workers who report having a job but are absent from work, in addition to tracking employed and unemployed workers. We conclude with a discussion of policy priorities and future research needs implied by the disparities in labor market losses from the COVID-19 crisis that we identify.