Collective Impacts: PFML Interacts with State Policies to Predict Maternal Employment

• In Event: Broadening the Evidence on State Paid Leave Access and Outcomes

Abstract

In the United States, the policy environment for families welcoming a new child is impoverished compared to peer countries, presenting challenges to the health, employment, and economic security of working families.. As of March 2025, 24 jurisdictions implemented or enacted Paid Family and Medical Leave (PFML) policies to support infant bonding and physical recovery among those welcoming a new child. Research on the effects of PFML on employment has thus far focused on California, finding increases in short term labor force participation and employment.  A paucity of evidence from other states leaves open the question of whether these effects generalize outside of California, and little is known about the interaction of PFML policies with other state-level policies to support working families.

We harness two complementary approaches to estimate the net causal effect of gaining access to PFML on leave-taking, labor force participation, and employment in six treated cohorts of states (7 states) that implemented a PFML policy between 2001 and 2021. We identify a sample of women ages 18-45 years who reported giving birth in the previous 12 months in the 2001 to 2021 American Community Survey. We employ a two-way fixed effects approach estimated using the Callaway Sant’Anna framework to account for policy and treatment heterogeneity associated with staggered treatment adoption. Additionally, the causal random forest approach enables the identification of key state policies that may either compliment or substitute for PFML. Two-way fixed effects models control for basic demographics and maternal characteristics (race/ethnicity, age, educational attainment, number of children under age 5 in the household, number of adults in the household, and marital status). Standard errors are clustered at the state level, and in the context of Callaway Sant’Anna specifications we test the inclusions of both never-treated and not-yet-treated states as controls. Causal random forest models (CRFM) consider 83 individual and state-level characteristics and continuous measures that capture the inclusivity and generosity of nine key social policies and programs (TANF, child support, SNAP, SSDI, preschool and early education programs, targeted job assistance, health insurance/Medicaid, unemployment insurance, and child care), the state EITC rate, and binary indicators for the presence of paid sick leave, pregnancy accommodation laws, temporary disability insurance.

After demonstrating that PFML increases leave-taking post-birth, we show that PFML is associated with a 4.57 percent increase in employment and a 5.13 percent increase in labor force participation among potentially treated mothers with infants. Results from CFRM suggest that the effects of PFML on both employment and labor force participation are substantially larger in states with more inclusive child-care subsidy programs. Yet, we also find that the effects of PFML on employment and labor force participation are smaller in states with more generous unemployment insurance and child-care subsidy programs. Our findings show that PFML programs increase mothers with infants’ employment in the short-term, and that importantly, the effects of PFML on employment might depend on other state policies that support work or allow a reprieve from work.

Authors

• Jessica Erin Pac, University of Wisconsin-Madison
  Presenting Author

• Alejandra Ros Pilarz, University of Wisconsin-Madison
  Non-Presenting Co-Author

• Meredith Slopen, Stony Brook University
  Non-Presenting Co-Author

• Runshi Tang, University of Wisconsin-Madison
  Non-Presenting Co-Author