Disaggregation Is Not Segregation: Why Breaking Down Racial Classifications Helps Vulnerable Communities

Last week, the Massachusetts state legislature scrapped H3361, a bill that sought to improve state data collection on Asian Americans. Proposed by State Representative Tackey Chan, the bill would have required state agencies to report data by individual ethnic group when collecting data on Asian Americans and Pacific Islanders.

In its place, the Joint Committee on State Administration and Regulatory Oversight substituted legislation to “investigate and study the feasibility” of collecting disaggregated data for all racial and ethnic groups in the state, according to a statement released by State Representative Jen Benson. It’s not yet clear what that statement means in terms of a real outcome, but if investigating and studying actually leads to disaggregated data collection, it will be an important step for Massachusetts in being able to provide services to vulnerable communities.

H3361 in its original form was supported by a coalition of over fifty civil rights organizations, service providers, and multiethnic groups, and for good reason—collecting disaggregated data serves to highlight the discrepancies between communities that get masked by the broad umbrella of “Asian American.” Asian American and Pacific Islander (AAPI) Data, a project led by University of California, Riverside Professor Karthick Ramakrishnan, showcases examples of how ethnicity-specific data can paint a different picture of a community. For instance, about 13% of Asian Americans are uninsured, but when the data is disaggregated, 22% of Nepalese Americans lack health insurance, compared to about 6% of Japanese Americans. This places Nepalese Americans at a higher uninsured rate than African Americans and second only to Latinos among all ethnic groups. For community organizations and healthcare providers, this kind of information is crucial to developing effective service programs.

Within Massachusetts specifically, Asian-American communities have disparities in poverty level, educational attainment, and English proficiency—the Census’s 2010-2014 American Community Survey (ACS) found that 61.2% of Vietnamese Americans in Massachusetts reported speaking English “less than very well,” compared to 21.3% of Indian Americans. Per capita income is over $16,000 higher for Chinese Americans than for Cambodian Americans. But while the ACS provides enough data to show that disaggregation is useful and necessary, nationally collected data does not provide granular information on language proficiency or use of social services, which is why disaggregation is needed at the state level.

Opposition to H3361 was mostly grassroots-based, and centered on two fears: first, that this bill was a form of institutionalized racial profiling, and second, that it would further disadvantage Asian Americans who believe they are harmed by affirmative action programs in college admissions. In a political climate that is unquestionably hostile towards immigrants, the first fear makes sense—having to report specific, race-based data about you and your family sounds sinister, particularly when your president has voiced support for the idea of a Muslim Registry. For some protestors, the bill was reminiscent of policies like the 1882 Chinese Exclusion Act and Japanese Internment.

However, it is incorrect to paint the current administration as the main source of protestor fears—when California passed a similar data disaggregation bill in 2016, protestors made similar arguments. Ramakrishnan theorizes that backlash to data disaggregation is due to a “socioeconomic and cultural disconnect” between more recent, wealthier immigrants from mainland China and the Asian-American communities needing and receiving the kinds of services that would be aided by disaggregated data. This kind of disconnect is also reflected in the affirmative action debate, where different coalitions have either pushed back against or supported the Common Application’s practice of providing ten options for applicants to describe their “Asian background.”

The backlash to data disaggregation provides one look at the tensions surrounding Asian-American identity. At the end of the day, the classification of “Asian American” tries to encapsulate the experiences of people whose families come from 48 different countries—and as American cultural identities go, it’s fairly recent. For example, the 1900 Census only allowed individuals to self-identify as White, Black, Chinese, Japanese, and American Indian. While “Asian American” is an umbrella term for groups that may increasingly have different needs, there are benefits to panethnic identity for political mobilization. If Asian Americans want to reap the benefits of panethnic identity, they need to also consider that data disaggregation will allow for more resources and support to reach groups under the umbrella that need it. To this end, it’s possible that increased community education about its benefits will reduce backlash.

In an open letter in support of H3361, Ramakrishnan and co-author Janelle Wong stated that “to reject the collection of high-quality, detailed data on AAPIs is to reject the representation of all members of our growing community.” Disaggregating data is not a racist proposal. Rather, it will confirm that structural inequalities exist and give lawmakers and community organizations a chance to address issues faced by currently invisible segments of the Asian-American population.

Written by

Alisha is a 1L at HLS from San Diego. She graduated from Yale with a BA in Political Science and worked before law school at a public interest firm on fair housing and fair lending cases. This summer, she will be working at the ACLU of Southern California in Los Angeles.

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