Getting the information we need to advance the conversation on paid leave in the United States
Over the course of their careers, most workers will experience a life event—whether it is a serious personal medical issue, the birth of a child, or the need to care for a loved one who is ill—that they will need to address outside of work. Paid leave provides the right to time off with pay so that workers can continue to cover the electricity bill and put food on the table while they focus their attention on addressing their needs or the needs of their family members. Yet only 16 percent of civilian workers in the United States have access to paid family leave through their employers.
To address this gap, six states and the District of Columbia have paid family leave laws on the books, and six bills on paid leave have been introduced at the federal level. Research funded by Equitable Growth finds that 63 percent of small- and medium-sized employers in New York and New Jersey support the paid leave programs in their states. Other research finds that these programs affect labor supply: California’s paid leave program is associated with an 18 percentage point increase in new mothers’ likelihood of working a year after giving birth.
Still, much remains to be learned about existing paid leave policies that could help legislators and administrators implement strong policies. Are there unintended consequences of paid leave programs? How does personal medical leave affect health outcomes?
Researchers can’t answer key questions about paid leave without the data that tell the story. So, last week, the Washington Center for Equitable Growth brought together researchers who study paid leave and related topics, state data administrators, and experts in federal and private-sector data systems to talk about data. The day-long meeting was part of Equitable Growth’s Paid Leave Research Accelerator project.
Paid leave is a topic that touches outcomes that are often studied in siloes. The researchers in attendance were experts in topics ranging from women’s labor supply to public health, from childhood poverty to gerontology, and from sick days to Temporary Disability Insurance. They shared information about an alphabet soup of surveys that contain valuable information on caregiving in the United States, among them NHANES (National Health and Nutrition Examination Survey), NHATS (National Health and Aging Trends Study), ATUS (American Time Use Survey), HRS (Health and Retirement Study), PSID (Panel Study of Income Dynamics), MOPS (Management and Organizational Practices Survey), and NSOC (National Study of Caregiving). Stay tuned for a report from Equitable Growth that will share more information on each data source—what it contains and how to access it—so that researchers can break disciplinary siloes and get the data they need to answer pressing policy questions on paid leave.
When the topic turned to administrative data, the attendees at the Equitable Growth event benefitted tremendously from the presence of paid leave administrators in states large (California, Washington) and small (Rhode Island, New Jersey). The administrators and academics brought together important perspectives on the equity implications of research on paid leave as they considered how much data state programs should collect. First, they considered the “more is more” perspective: They outlined the promise of administrative data for answering pressing policy questions, argued for the collection of key demographic variables needed to understand the equity implications of paid leave, and discussed strategies for accessing and building administrative datasets.
Second, they considered the “less is more” perspective: They discussed the fears constituents have about the use of their data, the importance of streamlined enrollment procedures from a behavioral science perspective, and the idea that when less data is collected, more members of vulnerable groups may access benefits. These two lines of thought lead to an equity research paradox: To understand who is accessing benefits and why, we need detailed demographic data. Yet the most vulnerable groups of applicants may be the most likely to be deterred from accessing benefits by requests for demographic information.
At the end of the day, attendees gained insight into new data sources to use, a deeper understanding of the ethical considerations around administrative data use, and ideas of how to move forward in fully understanding the effects of these policies. New research shows the unintended consequences of some paid leave policies. To better understand these impacts and design smart policies, researchers and policymakers alike need good data. In the coming year, Equitable Growth will release a report that fleshes out these ideas in more depth and offers tools and resources to help researchers access the right data to answer their research questions.