Data Disaggregation 101

On Wednesday, March 27th, Asian American Organizing Project (AAOP) joined the Coalition of Asian American Leaders (CAAL) at the Capitol to voice our support for data disaggregation.

AAOP staff and volunteers with CAPI-USA staff in support of data dissaggregation.

AAOP was among a crowd of data disaggregation supporters including EdAllies, Sewa – Asian Indian Family Wellness, Asian Media Access, Teach For America – Twin Cities, The SEAD Project, Hmong American Partnership (HAP), African American Leadership Forum, CAPI-USA,Foster Advocates, Voices For Racial Justice, Karen Organization of Minnesota (KOM), Hispanic Advocacy and Community Empowerment through Research, Children’s Defense Fund-MN, NavigateMN, and Sejong Academy.

What is Data Disaggregation?

Data disaggregation means to break down data into small subcategories, such as including information about income level or grade levels versus elementary, middle, and high school.

A new bill in the Senate is proposing to repeal the All Kids Count Act. All Kids Count includes language that supports the break down of data, specifically within the educational system. This new bill seeks to erase that.

3 Compelling Reasons to Protect Data Disaggregation

However, data disaggregation is important for several reasons.

Data disaggregation provides more meaningful data about our communities. By breaking down data into subpopulations, researchers are able to better understand the full story of each community. An example would be the breakdown of the various ethnic groups listed in the Asian racial group. Asian as a racial group encompasses many different Asian ethnicities, all of whom have a vast array of experiences and stories. Japanese Americans, who have immigrated to the U.S. and were affected by the Japanese internment camps have different experiences than many Southeast Asian communities who have immigrated to the U.S. generally as refugees.
Breaking down data by subpopulations reveals underlying insights, patterns, and trends about each community. Patterns are important in telling a story about a community and in understanding how a community might be struggling or not. Asian women, for example, have the lowest rates of cervical cancer screenings. However, Asian women have high rates of cervical cancer and disaggregated data informs us that Chinese and Vietnamese women are most affected.
Data disaggregation allows us to re-allocate resources to struggling communities. For example, while data shows that 50% of Asian Americans have a bachelor’s degree or higher, disaggregated data shows that that number ranges depending on each ethnic group. 75% of Japanese have a bachelor’s degree compared with 11% of Bhutanese. Disaggregated data would allow more focus on marginalized communities like the Bhutanese so that they are able to attain higher education.

“Only 1 out of 5 Asian students are passing advanced classes. We are not the Model Minority. We need data! Count us.”

While our examples are focused on the Asian American community, there are other ways data dissaggregation can help as well. Data dissaggregation would be able to tell us the number of communities earning less than $15,000 a year are attaining higher education and how economic levels can affect that. It would be able to tell us how education level can play a role in political views or even who is represented in higher education.

While our examples are focused on the Asian American community, there are other ways data disaggregation can help other communities as well. Data disaggregation would be able to tell us the number of communities earning less than $15,000 a year are attaining higher education and how economic levels can affect that. It would be able to tell us how education level can play a role in political views or even who is represented in higher education.

Other communities such as African immigrants and the many communities that speak Spanish also benefit from data disaggregation because it allows researchers, funders, and analysts to better understand the historical context. It would differentiate recent African American immigrants with black Americans who have descended from slavery because these communities have very different needs.

Those who speak Spanish or are from countries with a history of being colonized by Spain also have very different needs as well and data disaggregation would be able to give that fuller picture.

All of this information is important in helping us, as a whole, better understand the world around us. What are other ways you think data dissaggregation can help us better understand?

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