Proponents of maternity leave policies contend that paid maternity leave produces positive benefits for both the mother and child, including improved parent-child bonding, child and mother health, employment stability and financial security. However, most of the current evidence is based on analyses of European and Canadian programs. This is due in part to the fact that while pregnant American workers can receive up to 12 weeks of job-protected, unpaid leave through the federal Family and Medical Leave Act (FMLA), there is no national paid leave policy. Only 12 percent of workers have access to paid leave through private sector employers, but women in five states (California, Hawaii, New Jersey, New York and Rhode Island) can qualify for maternity leave benefits through state-based Temporary Disability Insurance (TDI) programs.
Justine Hastings and Eric Chyn will analyze linked administrative data from Rhode Island and document the extent to which mothers use maternity leave, and the extent to which state-sponsored paid maternity leave improves outcomes for mothers and children. They will address three key questions: First, to what extent does paid leave affect health or labor market outcomes of low-income mothers? Second, to what extent does paid leave affect child outcomes of low-income mothers at birth or in early childhood? Third, in the absence of program-wide random assignment of paid leave, Hastings and Chyn will use high-dimensional data and machine-learning techniques to explore the extent to which paid leave affects maternal and child outcomes for middle and higher-income mothers.