Why Disaggregating Data by Race is Important for Racial Equity

Updated August 18, 2020 | Posted August 9, 2016
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: 20062010 (Amer­i­can Com­mu­ni­ty Sur­vey Briefs)

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