Examining Predictors and Outcomes of U.S. Quality Maternity Leave
Maternity leave includes the time that mothers take off from work to care for their baby and heal after childbirth. Despite the growth of mothers in the U.S. workforce, the U.S. lags behind other countries in offering paid maternity leave, resulting in poor quality leave for working mothers. Scholars have continually examined maternity leave as an objective construct and this method of measurement, while important, may be inadequate in capturing mothers’ experiences. Quality maternity leave (QML) is a novel construct that captures mothers’ subjective leave experiences and includes time off, benefits, coworker support, flexibility, and an absence of workplace discrimination and microaggressions. However, little is known regarding individual predictors and outcomes of QML. Therefore, I will discuss prevalent societal-level, work-level, and individual- level predictors of QML and well-being and work-related outcomes of QML. I will also integrate these into a conceptual framework that researchers can use understand what may affect and result from QML. This review has important practical implications for US policymakers and organizations regarding their support of mothers in society and the workplace. Future research should continue to build on this framework to ensure that mothers are provided the QML they need to thrive.
Maternity leave is a critical part of decent work when mothers are able to heal from childbirth, care for their newborn, attend medical appointments, and integrate their identities. However, the United States is one of few countries that does not offer paid maternity leave and instead offers job-protected unpaid time off from work, despite the importance of maternity leave for important maternal work and well-being outcomes. Scholars have typically examined maternity leave with objective indicators (e.g., days off from work) instead of investigating mothers’ subjective experiences of the quality of their maternity leave (QML), contributing to a lack of understanding regarding what leads to and results from QML attainment. Therefore, in the present study, I drew upon a framework that I created through a thorough review of the literature to examine privilege and access to power and resources, workplace culture and support, and work characteristics as predictors of QML. Additionally, this study primarily explored work-related outcomes including organizational commitment, turnover intentions, and job satisfaction. I hypothesized that the former variables would indirectly predict the latter variables through QML. The findings of this study inform inclusive, equitable, and adequate organizational and U.S. maternity leave policies.