What Do Child Care Deserts Really Measure? Exploring Demand-Sensitive Alternatives

• In Event: Understanding ECE Access: New Evidence on the Dynamics of U.S. Child Care Supply and Demand

Abstract

The most widely used measure of access to child care is the “child care deserts" measure, based on the number of children per licensed child care slot within a geographic area. This measure is appealing for its simplicity. However, as a supply-based measure, it does not account for variation in demand. A high child-to-slot ratio may not represent a shortage of care if demand for formal care is low. This paper compares the desert measure to two alternatives that incorporate demand: (1) provider vacancy rates and (2) deviations between observed supply and predicted supply based on community characteristics associated with demand.

If the desert metric aligns with these demand-sensitive alternatives, it may serve as a reasonable approximation of care access. If not, it could misidentify some areas as underserved or overlook others with inadequate supply. Understanding how these measures relate can improve efforts to identify areas where demand exceeds supply, helping policymakers better target investments.

Desert data comes from the Center for American Progress's 2018 tract-level data and includes counts of licensed providers and slots as well as the number of children under age 5. The data includes some community characteristics with others are incorporated from external sources (poverty, urbanicity, educational attainment, etc.). Vacancy rate data is from the National Survey of Early Care and Education (NSECE) 2019, a nationally-representative survey of child care providers (home- and center-based) and households with young children. Using restricted NSECE data with geographic identifiers, I will merge these datasets to examine the relationship between vacancy rates and child care deserts. I have received approval for the restricted data and will have access to the data within the next month. Preliminary analysis of the two data sets separately suggests a disconnect between the measures: children in high-poverty areas are more likely to live in child care deserts, yet providers in these areas more often have high vacancy rates.

Vacancy rates provide a more direct signal of excess demand (at market prices) but are resource-intensive to collect and update. As a more scalable alternative, I construct a measure of the gap between predicted supply and observed supply. Observed supply below predicted supply may indicate undersupply. Predicted supply is constructed using a leave-one-out approach based on community characteristics likely to influence demand (e.g., religiosity, education levels). I will test whether this measure better predicts vacancy rates than the child care desert metric.

The goal is to evaluate tradeoffs across the three measures – desert status, vacancy rates, and predicted undersupply – in terms of accuracy and ease of calculation. The predicted undersupply metric may offer a practical middle ground: more predictive of unmet demand than desert status, while more scalable than vacancy rates. Refining how we measure access to care is key for policymakers and nonprofit leaders seeking to expand access where it’s most needed.

Author

• Jessica H. Brown, University of South Carolina
  Presenting Author