Unpacking Broad Racial Labels: The Disaggregation of Data on Race and Ethnicity

Abstract

Racial groups and identities are meaningful to individuals and organizations. However, broad labels may conceal diversity and disparities within a racial group. Data disaggregation, an approach that analyzes racial data at a more granular level, enables policymakers and practitioners to identify and address areas where challenges faced by certain groups may otherwise be obscured. The first section of the paper discusses how group labels conceal the educational and health disparities faced by Asian Americans and Middle Eastern and North African (MENA) Americans. The diverse Asian ethnic groups face different disparities, such as uneven rates of college graduation and cancer prognosis, that are hidden by the overly broad Asian American label and the associated model minority stereotype. For MENA Americans, their experiences and disparities are unrecognized when officially classified under the White category. The second section offers policy-relevant recommendations and cautions regarding the collection, analysis, interpretation, and protection of disaggregated data.

Keywords

race Asian Americans MENA Americans disaggregation health disparities education disparities

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