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.

This grant is co-funded by the Russell Sage Foundation. This research seeks to empirically disentangle and quantify job moves that occur within versus between employers. It utilizes two main sources of data: the Current Population Survey and restricted versions of the Survey of Income and Program Participation. The project investigates the extent to which aggregate trends in wage inequality are the result of increased within-organization job mobility and whether this shift has disproportionately benefited high-income/high-skill workers. It will also account for heterogeneity of outcomes among low-skilled/low-educated workers by exploring under what conditions less-educated workers benefit from internal labor markets and whether these conditions vary systematically by industry, occupation, or region.

The research team will develop the Washington Merged Longitudinal Administrative Data, which will link demographic information to employment records, and public program administrative data. It will construct households from these state-level data, including the creation of detailed documentation and testing that will allow other scholars to replicate these methodological innovations for analysis of a host of policy interventions on household income and program participation.