Subsidized Child Care in Varying State Policy Contexts: Evidence from Administrative Data

Session Submission Type: Panel

Abstract

Subsidized child care programs in the United States reflect both state choices and federal constraints. They also exist within varying safety net contexts. This panel brings together innovative uses of state administrative data to examine questions key to individual states and central to the overarching goals of the Child Care and Development Fund (CCDF): assisting low-income families in accessing affordable, high-quality child care to support parental work and education, while strengthening child development. 

The first paper focuses on the Massachusetts context and examines policy changes that increased payment rates to subsidized providers in 2023 and 2024 to provide early evidence about whether higher rates in fact expand the supply of subsidized care, using quasi-experimental methods and conducting analyses by region, child age, and provider type. 

The second (Virginia) and third (California) papers leverage CCDF, SNAP, and TANF program data linked to UI-covered wage records to draw insights about state-level policy changes. In the Virginia context, state policy changes in 2018 delayed and raised the income level at which families face a “benefit cliff” for subsidized child care eligibility. The authors use difference in difference methods to pinpoint effects of these changes on parental labor force participation and earnings, subsidy receipt, and family income. 

In the early years of the covid-19 pandemic, California committed to expanding a full year of pre-kindergarten for 4-year olds and to expanding subsidized care, including CCDF-funded programs. The authors of the third paper assess changes in the characteristics of families and children participating in subsidized care as these expansions were unfolding, leverage age cut-offs to look at impacts of expanding prekindergarten on subsidized care participation, and provide new evidence about safety net participation and parental labor force participation and earnings. 

The final paper uses machine learning methods to test small-area forecasting tools that promise to aid state agencies in better understanding where demand for subsidized child care may be growing, and where it may be shrinking. CCDF funds are block granted and typically do not stretch to serving either all eligible families or all families who apply. Assisting states in better understanding how to allocate scarce child care dollars promises to deploy them more efficiently. 

To provide an adequate and accessible supply of subsidized child care to improve children’s outcomes and support parents’ employment, states need policies that support families, providers, and the government entities that administer subsidies. Together, these papers provide new evidence on all three of these perspectives, promising to help states improve the effectiveness of their subsidized programs

Policy Area

• Family and Child Policy

Chair

• Colleen M Heflin, Syracuse University

Discussants

• Leslie Hodges, USDA - Economic Research Service

• Elizabeth E. Davis, University of Minnesota - Twin Cities

Organizer

• Caroline Danielson, Public Policy Institute of California

Individual Presentations

• The Impact of State Child Care Subsidy Payment Policies on Provider Participation in Subsidy Programs - Presenting Author: Yoonsook Ha, Boston University

• Changes in Subsidized Child Care after California’s Expansion of Subsidized Slots and Public Preschool - Presenting Author: Brett Guinan, Public Policy Institute of California; Non-Presenting Co-Author: Patricia Malagon, Public Policy Institute of California; Non-Presenting Co-Author: Caroline Danielson, Public Policy Institute of California

• Child Care Subsidy Policy Changes and Parental Employment and Earnings Outcomes - Non-Presenting Co-Author: Colleen M Heflin, Syracuse University; Presenting Author: Taryn W. Morrissey, American University; Non-Presenting Co-Author: Clay Fannin, University of Wisconsin-Madison; Non-Presenting Co-Author: Hyeryung Jo, Syracuse University

• Forecasting Child Care Subsidy Receipt Using Linked Administrative Data and Machine Learning Models: A Massachusetts Study - Non-Presenting Co-Author: Sangmi Kim, University of Tennessee (Knoxville); Presenting Author: Kyle Xu, Boston University