The What, Why, and How of Collecting and Analyzing Demographic Data to Improve Equity in Child Welfare


This brief summarizes practices related to collecting detailed demographic data—such as race, ethnicity, sexual orientation, gender identity, and gender expression—and other data to measure equity in child welfare. We compiled these data practices from an environmental scan of academic literature, policy documents, and other relevant sources to examine how state and local child welfare agencies and their partners determine what data they will collect, why they will collect these data, and how they will collect and use the data to measure and understand inequities.

Understanding inequities in child welfare

There is general acknowledgment in the field of child welfare that inequities exist along the child welfare continuum of services (Child Welfare Information Gateway 2021; Tajima et al. 2022). These inequities include disparities and disproportionalities among children who are involved in reports, investigations, and out-of-home placements, and who receive child welfare support services (Summers 2015). Data can play an important role in identifying where inequities exist, and which children are most affected by inequities (Annie E. Casey Foundation 2016). Therefore, many public and private child welfare agencies are searching for ways to improve how they collect and use demographic and other data to identify and address inequities.

Identifying equity-focused data practices

To help child welfare agencies and their partners understand the type and magnitude of inequities in their jurisdictions, this brief highlights several equity focused data practices for collecting demographic and other data and measuring inequities in child welfare. We identified the data practices from an environmental scan of recently published literature and federal policy documents conducted for the Child Welfare Study to Enhance Equity with Data (CW-SEED) project. Mathematica and its partners—the Center for the Study of Social Policy and the University of North Carolina School of Social Work—conducted this work under a contract with the Office of Planning, Research, and Evaluation in collaboration with the Children’s Bureau within the Administration for Children and Families.

Although there are multiple dimensions of equity, we focus on two types of demographic characteristics that were the focus of many articles identified in the environmental scan and in two recent federal executive orders on advancing equity (White House 2021 2022): (1) data on race and ethnicity and (2) data on sexual orientation, gender identity, and gender expression (SOGIE).

Although the CW-SEED environmental scan identified a wide variety of data practices, this brief is limited to providing a high-level overview of the data collection and measurement data practices that were identified in the environmental scan, which included peer reviewed and grey-literature and federal policy documents published between January 2012 and March 2022. Another brief in this series, titled “Using Data to Enhance Equity in Child Welfare: Findings from an Environmental Scan,” provides a synthesis of the environmental scan findings and describes a broader array of data practices across the data life cycle. Additional details about specific data practices may also be found in the cited sources found throughout the brief.

A framework for implementing data practices

Throughout the brief, we use a framework of key questions to guide child welfare agencies and their partners when implementing data practices related to data collection and measurement using disaggregated demographic and other data. When considering how to implement data practices, specifically for data collection and measurement, we recommend asking three key questions: What, why, and how? We use the framework of three questions throughout the brief to highlight data practices child welfare agencies could consider when improving how they use existing demographic and other data, collect demographic and other data from children and families, and better measure equity.

For example, child welfare agencies and their partner organizations could consider the following questions:

  • What demographic and other data could we use or collect to understand and enhance equity?
  • Why do we want to collect disaggregated demographic or other data or measure equity?
  • How can we improve our collection of current demographic or other data? How can we collect new data?

In addition, we highlight examples of how child welfare agencies are currently implementing data collection and measurement data practices and some important considerations agencies could use to inform decisions about implementing these data practices. While we feature many examples of data practices and share helpful issues to consider, the efforts to implement data practices are often more nuanced than what can be conveyed in this brief. However, this overview of data practices and selected examples could be used as a starting point to prompt consideration of what might be possible among child welfare agencies and their partners.

Collecting disaggregated data

Using disaggregated demographic data to understand inequities

Child welfare agencies routinely collect disaggregated data (narrow, specific subcategories of data) on child and family characteristics. For example, the U.S. Office of Management and Budget’s (OMB’s) standard, broad category of Black or African American can be broken down to reflect the diversity of the Black population in the United States (for example, people from Caribbean and African countries). Disaggregated data can be used for numerous reasons, such as matching children to age-appropriate clinical services, informing continuous quality improvement efforts, and evaluating the impact of service outcomes by demographic characteristics, which can reveal disparities between specific subcategories of child and family demographic data (Child Welfare Information Gateway 2021).


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