Association for Public Policy Analysis & Management

Unlike the comprehensive, national education system for students in kindergarten through twelfth grade, the federal government has not yet created a systematic approach for the care and education of children before they enter kindergarten. Without such a system in place, individual states are left to respond to the demand for child care as parents participate in the workforce (Blau & Currie, 2006). A fragmented set of private child care centers, Head Start centers, state Pre-K programs, and informal home care each contribute to fill this demand, resulting in substantial variability in the quality of care that young children receive.

In response, most states have adopted a voluntary Quality Rating Improvement System (QRIS), which is intended to assess, improve, and communicate the level of quality in early childhood care and education (ECCE) programs (Goffin & Barnett, 2015; Tout et al., 2010). QRIS differs from other rating scales by aiming to improve the program’s quality over time, and as a demand side intervention to inform parents of high quality options in their neighborhoods. However, participation is this system is typically voluntary for ECCE programs. We do not know who selects into these systems and whether participation varies across communities, by program characteristics, or funding source. Given current efforts to expand ECCE programs and to understand what constitutes a high-quality program, understanding what states are currently using is essential.

The present study examines the characteristics of center-based programs and surrounding communities that predict QRIS participation. Thus, we can identify characteristics of centers likely to participate in the voluntary system in order to identify the centers a QRIS initiative influence. We use data collected for the National Survey of Early Care and Education (NSECE), a nationally representative survey of program directors (N = 8,265). For our analysis, we used a logistic regression to identify which characteristics predicted participation in QRIS.

The results reveal that funding plays in integral role in predicting if a center is likely to participate in QRIS. For example, centers that report more than two funding streams and centers receiving Pre-K funding are more likely to participate in QRIS. Interestingly, NAEYC accreditation also predicted QRIS participation. Importantly, centers located in neighborhoods with low percentages of African American families are more likely to participate. Lastly, centers in high-income areas are less likely to participate, suggesting less of an engagement in QRIS policy in wealthy neighborhoods.

The purpose of our study was to understand who participates in QRIS intended to improve quality. Using a nationally representative dataset, we examined center and community characteristics associated with participation. Our results suggest that funding streams play a large role in QRIS participation, as does NAEYC accreditation. Our study is limited by unsystematic mandatory participation rates in select states and lack of time series data to infer causality. The results describe participating centers but do not identify QRIS as the cause for these activities. Regardless, a deeper understanding of the centers participating in QRIS ultimately reveals the type of programs that are touched by the QRIS initiative.