By Alycia Hardy and Alyssa Fortner

Introduction

Data collected from across the child care and early education (CCEE) field, both during and prior to COVID-19, have been critical for identifying and scaling public resources to meet the increasing needs of children, families, early educators, and providers. Much of the data collected throughout the global health crisis demonstrated how the pandemic has exacerbated long-standing inequities for communities with low incomes.¹ This has been particularly true for Asian American Pacific Islander (AAPI), Black, Indigenous, Latinx, immigrant, and other communities of color with low incomes who are subjected to intersecting economic and racial inequities.

While data collection and other data cycle processes are powerful tools used by researchers and policymakers to directly inform key policy decisions, these data processes were created within the same systems, institutions, and structures that have been shaped by the historic and present impacts of white supremacy culture and systemic racism.² This means that each component in the data processing cycle—including data planning, collection, analysis, interpretation, contextualization, and dissemination—all collectively and individually reflect and uphold systemic inequities that center on race. Yet, these data processes that deeply impact decision making do not always consider, account for, or contextualize those historic and present impacts. And the people with lived experiences who could inform practices and use data to help tailor resources and supports to meet specific community needs are often disconnected from those processes. Ultimately, this creates siloed data processes and decision making. These silos are devoid of people who possess the knowledge, experiences, and expertise to preemptively identify practical implications of harmful policies as well as identify and provide restorative practices. These practices can address present and historical harms related to which services and supports policymakers choose to fund; how much funding is needed; who has access; and how families can access those supports and services.

Within CCEE, our growing reliance on data to inform policy and shape resources will require equal reliance on informed data practices that center equitable community engagement strategies in addressing long-standing, and presently exacerbated, inequities. Current processes related to the planning, collection, analysis, and contextualization of data — which drive decision making — are often far removed from equitable community engagement practices and almost exclusively involve researchers, analysts, administrators, and policymakers.

This perpetuates the longstanding gap or disconnect between those who are deeply impacted by policy decisions and those with the power to make those decisions. In addition, data are primarily analyzed within the context of a single program, which fails to address how children, families, and providers access resources across state agencies and programs as well as what access looks like across communities of color. Thus, decision makers create policy that falls short of equitably meeting needs across all communities. And this, in turn, amplifies the inequities and harms experienced by communities of color.

Central to closing this disconnect and moving toward a more equitable system are equitable community engagement frameworks that center and elevate the voices and needs of those who are directly impacted.³ Researchers must also include these frameworks in qualitative and quantitative data planning, collection, analysis, and interpretation. Doing so will strengthen expanded data strategies to move beyond disaggregating data by incorporating practices that intentionally shift power dynamics to support leadership from within directly impacted communities at each phase of the data process.

While researchers and policy analysts need to expand their strategies to move beyond disaggregating data, simply collecting more data is insufficient. Instead, researchers, policy analysts, and administrators must combine these expanded data strategies with an equitycentered community engagement framework. By expanding data strategies to include the following recommendations and centering equitable community engagement frameworks as the core of these strategies, policymakers can have a lasting impact on how states support the range of CCEE needs across communities—and specifically within communities of color.

• Integrating data across state agencies to better understand and meet community needs and create aligned CCEE resources.
• Using data to physically map resources through spatial analyses to gauge and increase equitable access to appropriate resources.
• Making data accessible, usable, and inclusive for practitioners, families, and advocates; not
  just researchers, administrators, policy analysts, and policymakers.

The health, racial, and economic implications of the global health crisis greatly increased the number of children, families, early educators, and providers who need additional CCEE supports as well as the severity of those needs. To address these exacerbations of long-standing issues around access, affordability, and availability, Congress passed the Coronavirus Aid, Relief, and Economic Security Act (CARES Act), the Coronavirus Response and Relief Supplemental Appropriations Act (CRRSA), and American Rescue Plan Act (ARPA) in 2020 and 2021, which collectively allocated more than $50 billion in federal CCEE funding across states.⁴ Yet, to address increased needs equitably, states must prioritize funding to address the gaps and disconnects in how communities of color are engaged in decision making and the data processes that inform those decisions—both now and beyond COVID-19. Failing to do so will further exacerbate the long-standing harms and inequities inflicted on communities with low incomes and especially communities of color that have low incomes.

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>>Read the executive summary

^{1 A person or a family with low income is defined as those whose income is at or below the 200% Federal Poverty Line or 85% State Median Income. However, this is not a comprehensive definition that incorporates the number of ways families and communities experience poverty.}

² The data processing cycle consists of the individual steps and stages that are used to collect raw data and turn it into practical information from which inferences can be drawn and observations can be made. Some of these stages include data generation, planning, acquisition/collection, cleaning, coding, storing, management, analysis, visualization, interpretation, contextualization, and dissemination.

³ Alycia Hardy, Child Care Coronavirus Recovery Conversations: Equitable Approaches to Elevating Parent Voices, CLASP, 2020, https://www.clasp.org/blog/child-care-coronavirus-recovery-conversations….

⁴  Included in the figure for more than $50 billion in child care and early education funding are the Coronavirus Aid, Relief, and Economic Security (CARES) Act, which provided $3.5 billion to CCDBG and $750 million for Head Start; Consolidated Appropriations Act, 2021, which provided $10 billion in CCDBG funding; and the American Rescue Plan Act of 2021, which provided $39 billion in relief and stabilization funding, $1 billion for Head Start, increases in CCDBG mandatory funding, and child-related tax credit improvements.