Better Data for Better Early Learning Equity

Early learning programs are collapsing under the financial and logistical strain of the COVID-19 pandemic. In a July survey, 40% of child care providers, and half of providers of color, reported that they were certain they would close permanently without public assistance. Now, about half of providers report having to take on debt and raise their tuition by 11% to 14% to stay open due to pandemic-related expenses. If families who are already struggling financially during the pandemic cannot afford increased tuition, early learning programs will continue to permanently close. Black and Latino families already had inadequate access to high-quality early learning programs before the pandemic, and these new child care challenges will reduce this access even more.

A resource from the Prenatal-to-3 Policy Impact Center further illuminates racial inequities in early learning programs: the State Policy Roadmap is an evidence-based guide for state leaders that drills down on the most effective policies and strategies to build up systems of care for infants and toddlers. In examining 20 key outcome measures for each state, the roadmap found that access to services varied considerably by race and ethnicity, worsening the inequities in overall well-being between Black and Latino infants and toddlers and their White peers. The roadmap illuminates the choices states make to either increase equity for Black and Latino children or further the divide and reinforce systemic racism.

Ed Trust has examined the availability of race and ethnicity data for preschoolers and found that the quality of state early learning data is again insufficient for measuring equity:

• No state has a data system that allows clear, transparent racial equity measurement, by access and quality, starting in early childhood.
• No state publicly reports child demographics and quality at the individual ECE program level.
• States with ECE funding have primarily built strong horizontal data systems (i.e., across agencies and systems) without developing strong vertical systems (i.e., following children over time) that include preschool data, which makes it difficult to assess equity across the many systems that serve young children, and over time, as students grow from preschool through high school and beyond.
  • The two main types of early learning-related data systems are the Early Childhood Integrated Data System (ECIDS), which is a horizontal data system, and the P-20W+ statewide longitudinal data system (SLDS), which is a vertical data system that collects data from preschool through postsecondary outcomes. These P-20W+ vertical data systems typically do not actually collect meaningful data at the preschool level, but rather begin at the kindergarten level.
• Too often, preschool access data are reported at the district/county level, and not at the individual preschool program level. But the program quality ratings are reported at the program level, making it impossible to see how access to quality varies by race or ethnicity. To meaningfully assess the racial equity of a district’s preschools, information about access and quality is required. It’s possible that districts have this data, but do not publicly report it.

States that show promise in building data systems that allow us to measure whether children of color, children from low-income families, children with disabilities, and dual language learners are attending high-quality early learning programs are Kentucky, Minnesota, North Carolina, Utah, and Washington.

These states have the following characteristics:

• A good horizontal data system or a good “K-20W+” vertical data system, while taking one or both of these critical next steps:
  • Turning their “K-20W+” vertical data system into a P-20W+ system by including useful preschool data for measuring equity. Too often, states’ vertical data systems are “K-20W+” data systems, meaning that they begin gathering data in kindergarten rather than in preschool. To truly measure equity, states must be able to track data from preschool through postsecondary outcomes.
  • Connecting a strong horizontal early childhood data system with their vertical data system, even if that vertical system is a work in progress.
• An understanding that just having both of these systems is not sufficient: As states design both horizontal and vertical data systems simultaneously, it’s important for them to establish the distinct purpose of each system and to leverage the commonalities and share best practices and lessons learned.
• A statewide commitment and ability over time to developing metrics, cross-sector matching methods and data sharing protocols, longitudinal links, and statewide reporting at the service level by subgroup.
• Long-term financial sustainability from local funding commitments and long-term leadership support for cross-sector data sharing and transparent, actionable reporting.

When it comes to maximizing opportunities for our country’s children, the earlier, the better. Making real progress toward racial equity requires comprehensive, transparent data systems that allow us to easily identify and eradicate inequities in early learning.