This project seeks to determine the feasibility of a paid family and medical leave insurance (PFMLI) program with an opt-out mechanism. While state PFMLI programs in the United States vary somewhat in program parameters, all essentially require eligible workers to participate and are funded through payroll contributions. Some allow employers to self-insure and opt out of the public program. Generally, employees do not have that option. But proposed legislation in New Hampshire would create a program that contains an employee opt-out mechanism. There is a large gap in understanding of this design choice. Which individuals would opt out? And which would opt out initially and then opt in later? And how would that interact with the employer self-insurance opt-out? The answers have lasting implications for the cost of the program, the level of benefits that can be offered, the possibility of implicit or explicit bias against low-wage women workers of reproductive age, and the ultimate success of the program. The researcher will create, distribute, and analyze a survey of New Hampshire workers in an effort to predict their behavior if such a program were to be implemented.

This project seeks to identify the impact of workforce characteristics on the employer costs of implementing paid leave policies. The researchers will perform an empirical examination of the way New York City’s paid sick days law has affected covered businesses. They will undertake an employer survey of 350 firms, analyze the impact of workforce characteristics on costs, firm profitability, the share of the workforce that has access to paid sick days, and on the number of paid sick days available to specific categories of workers before and after the law.

This research will analyze how state-level paid family and medical leave and Medicaid expansions influence drug-related outcomes. The research team will draw from a variety of administrative data sources, including restricted individual-level mortality data from the Centers for Disease Control; data from the Healthcare Cost and Utilization Project’s National Inpatient Sample and Nationwide Emergency Department Sample; and the Treatment Episode Data Set-Admissions. Drawing from these rich data sources in combination with information on population demographic characteristics and state policies, the team will employ a variety of causal inference techniques to examine whether access to paid family and medical leave can help reduce the abuse of opioids. While a large literature has examined the impacts of paid family and medical leave on parental leave-taking, labor market outcomes, and child health, there is no research to date on whether it can influence drug abuse and treatment. Similarly, there is little research on the effect of Medicaid coverage on drug-related deaths or measures of drug-related morbidity.

The project seeks to identify the causal impact of paid leave on older workers. This is particularly important since older people face greater barriers to re-entering the labor force, so better understanding how public policy can help people stay attached to the labor force is important. This is especially true for women who tend to bear the responsibility of caregiving and are likely to live longer. The research team will use the American Community Survey to span the pre- and post-law period in order to provide evidence on the causal effect of California’s paid family leave policy. They will consider three aspects of work—weeks worked, hours worked, and earnings—to provide a fuller picture of how the labor market experience is impacted.

This project will partner directly with a warehousing firm in order to: document the risks of certain schedules for subjective well-being and self-reported health for workers and specific demographic groups; document the relationships between work schedules and turnover for workers and for specific demographic groups; quantify the costs of current scheduling practices for the organization by building a more systemic view of the interdependencies of scheduling, absenteeism, productivity, and turnover; and develop and evaluate a workplace intervention targeting workers’ control over their schedules using a cluster-randomized trial.

This project aims to understand how firms accommodate increases in the minimum wage by using granular compensation data that the research team will construct by merging the individual tax returns of all employees and contractors of a firm to the business tax return of the firm. These data allow for an exploration of how changes in the minimum wage affect the full distribution of employee and contractor compensation within affected firms, including changes in employment and income. They will look at which kinds of workers, if any, lose or gain employment following increases in the minimum wage and how the incomes of covered and uncovered workers, as well as firm owners, are affected. Notably, the 17-year data panel will enable them to study dozens of minimum wage increases legislated at the federal, state, and substate level.