This project examines the level of alignment between employer and employee beliefs about the accessibility of paid leave. It takes advantage of a unique data source, The Shift Project, which samples low-wage, service-sector workers from within a set of large retail and food service employers across the United States and allows the team to match employee responses with individual employers. The research will pair quantitative analyses of Shift Project data and in-depth interview data from interviews with twenty human resources staff members at firms in the sample to provide important information about how employer practices may mediate awareness and take-up of paid leave benefits.

This project will examine the effect of both state-provided paid medical leave and city- and state-level sick pay mandates on the provision of paid leave. The proposed project will use restricted-use National Compensation Survey data with geographic identifiers and a difference-in-differences approach to determine whether employers react to the mandated provision of sick leave and state paid leave social insurance programs by reducing their voluntary provision of medical leave, private group disability insurance, and other forms of paid leave such as family leave. No other study has comprehensively studied the interactions and interdependencies of state-level sick pay mandates, employer provisions of paid leave, and state-run medical leave systems.

This project seeks to understand how access to paid family leave influences the provision of eldercare and labor market outcomes among individuals in midlife and whether the effects vary by individual and care recipient characteristics. To examine these issues, the research team will pool data from 11 waves of the Health and Retirement Survey to examine the experiences of respondents aged 51 to 65 with at least one living parent. They will survey responses to determine the intensity of caregiving provided, as well as the intensity of labor force participation, and use a difference-in-differences approach to compare the experiences of individuals residing in states with operational paid leave social insurance programs (California, New Jersey, and Rhode Island) to those who reside elsewhere.

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.