Why Disaggregating Data by Race is Important for Racial Equity

Updated on August 18, 2020 and originally posted August 9, 2016 by the Annie E. Casey Foundation

Children in Extreme Poverty by Race and Ethnicity: Minnesota 2013; White: 3%, Latino: 9%, American Indian: 21%, African American: 18%, Asian/Pacific Islander: 8%, Two or more races: 9%, Total: 6%

Iden­ti­fy­ing race equi­ty prob­lems can be chal­leng­ing with­out the prop­er col­lec­tion or use of data. The Foundation’s recent case study, By the Num­bers: Using Dis­ag­gre­gat­ed Data to Inform Poli­cies, Prac­tices, and Deci­sion-Mak­ing, shows how tak­ing apart data and pre­sent­ing the infor­ma­tion in a new way can change the way sta­tis­tics look and prob­lems are solved:

Break­ing down aggre­gat­ed data can uncov­er hid­den racial inequities

The kind of data col­lect­ed mat­ters and can help unearth a prob­lem masked by aggre­gate data — even data already bro­ken down by basic racial cat­e­gories. For exam­ple, con­sid­er 2010 U.S. Cen­sus Bureau sta­tis­tics that showed more than half of Asian Amer­i­cans had a bachelor’s degree or high­er by the age of 25, the high­est pro­por­tion among racial cat­e­gories. Yet when the data are dis­ag­gre­gat­ed to focus specif­i­cal­ly on South­east Asian Amer­i­cans, a dif­fer­ent pic­ture emerges. Just 15% of Cam­bo­di­an Amer­i­cans, 14% of Hmong Amer­i­cans, 12% of Laot­ian Amer­i­cans and 26% of Viet­namese Amer­i­cans over the age of 25 had a bachelor’s degree, the cen­sus report­ed. The rates for Cam­bo­di­an Amer­i­cans, Hmong Amer­i­cans and Laot­ian Amer­i­cans were low­er than the 18% rate report­ed for African Amer­i­cans, and the rate for Laot­ian Amer­i­cans fell below the 13% rate report­ed for Lati­nos. As these dis­ag­gre­gat­ed data show, South­east Asian Amer­i­cans expe­ri­ence bar­ri­ers to edu­ca­tion­al attain­ment on par with their African-Amer­i­can and Lati­no peers, a phe­nom­e­non that could eas­i­ly have been over­looked with less spe­cif­ic data.

Dis­ag­gre­gat­ing data also helps in iden­ti­fy­ing the specifics of an estab­lished prob­lem. The W. Hay­wood Burns Insti­tute applies REG­GO”, a strat­e­gy to break down juve­nile jus­tice sys­tem data based on race, eth­nic­i­ty, gen­der, geog­ra­phy and offense. When apply­ing REG­GO to Ven­tu­ra Coun­ty juve­nile cor­rec­tions data, Burns and coun­ty offi­cials were able to doc­u­ment the need for report­ing cen­ters with evening tutor­ing or pro­fes­sion­al devel­op­ment in Lati­no com­mu­ni­ties. This inter­ven­tion meant 53% few­er Lati­no youth admit­ted into deten­tion cen­ters. Tak­ing data apart or even apply­ing a strat­e­gy like REG­GO to data can help reframe race equi­ty issues and prompt tar­get­ed inter­ven­tions in com­mu­ni­ties with lim­it­ed resources.

The pre­sen­ta­tion of dis­ag­gre­gat­ed data also mat­ters for racial and social equity

Once data are dis­ag­gre­gat­ed, pre­sen­ta­tion of data can change how peo­ple view the sever­i­ty or salience of a prob­lem. With­in our case study, we high­light­ed the Kir­wan Institute’s use of Oppor­tu­ni­ty Map­ping.” The Oppor­tu­ni­ty Map­ping process gath­ers data relat­ed to edu­ca­tion, health and jobs and maps them onto indi­ca­tors of oppor­tu­ni­ty with­in com­mu­ni­ties. In so doing, the Insti­tute sit­u­ates social prob­lems with­in the con­text of a spe­cif­ic com­mu­ni­ty. The process cul­mi­nates with a visu­al heat map” that shows where oppor­tu­ni­ties are high or low in ref­er­ence to pop­u­la­tion den­si­ty of spe­cif­ic eth­nic groups. The Insti­tute found that the visu­al rep­re­sen­ta­tion of social prob­lems with­in the com­mu­ni­ty elicit­ed dif­fer­ent, stronger reac­tions than just the pre­sen­ta­tion of raw num­bers. For exam­ple, the Kir­wan Insti­tute found that Oppor­tu­ni­ty Map­ping of child mor­tal­i­ty with­in Ohio helped state offi­cials see the sever­i­ty of the problem.

Learn more about racial equi­ty data col­lec­tion and the impor­tance of disaggregation

Dis­ag­gre­gat­ing data and pre­sent­ing it in a mean­ing­ful way can help bring atten­tion and com­mit­ment to the solv­ing of social and racial equi­ty prob­lems. For more impor­tant take­aways on why data dis­ag­gre­ga­tion and a data-dri­ven approach mat­ter, review our full report.

Relat­ed Article:

The Pop­u­la­tion With a Bachelor’s Degree or High­er by Race and His­pan­ic Ori­gin: 2006 – 2010 (Amer­i­can Com­mu­ni­ty Sur­vey Briefs)

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