Giving Compass' Take:
- When researchers fail to disaggregate their data by racial groups, it can erase the experiences of entire communities and offer an incomplete picture.
- How will disaggregating data improve funding and services to various racial and ethnic groups?
- Learn more about ways to accomplish disaggregated data.
What is Giving Compass?
We connect donors to learning resources and ways to support community-led solutions. Learn more about us.
Surveys, datasets, and published research often lump together racial and ethnic groups, which can erase the experiences of certain communities. Combining groups with different experiences can mask how specific groups and communities are faring and, in turn, affect how government funds are distributed, how services are provided, and how groups are perceived.
Large surveys that collect data on race and ethnicity are used to disburse government funds and services in a number of ways. The US Department of Housing Urban Development, for instance, distributes millions of dollars annually to Native American tribes through the Indian Housing Block Grant. And statistics on race and ethnicity are used as evidence in employment discrimination lawsuits and to help determine whether banks are discriminating against people and communities of color.
Despite the potentially large effects these data can have, researchers don’t always disaggregate their analysis to more racial groups. Many point to small sample sizes as a limitation for including more race and ethnicity categories in their analysis, but efforts to gather more specific data and disaggregate available survey results are critical to creating better policy for everyone.
To illustrate how aggregating racial groups can mask important variation, we looked at the 2019 poverty rate across 139 detailed race categories in the Census Bureau’s annual American Community Survey (ACS). The ACS provides information that helps determine how more than $675 billion in government funds is distributed each year.
Recognizing that aggregate categories can mask significant variation across groups could help improve service and benefit provision across the country. Researchers collecting and using data can take a few steps to better represent the variation in their data.
- Carefully inspect detailed race and ethnicity data to better understand the variation across all groups.
- Seek out organizations led by people and communities of color to learn from their expertise (PDF) and weave that expertise into the analysis.
- Make an effort to collect more data from smaller and underrepresented communities.
Read the full article about disaggregated data by Jonathan Schwabish and Alice Feng at Urban Institute.