The Asian American and Pacific Islander (AAPI) community is not a monolith.
Representing over 18 million people, AAPIs are a diverse, fast-growing population that includes Americans who identify with one or more of numerous East Asian, South Asian, Southeast Asian and Pacific Islander ethnic groups. Even the most populous of of AAPI sub-groups — Chinese Americans, Indian Americans, and Filipino Americans — individually comprise less than one-quarter of the total AAPI population.
And yet, the federal government still largely fails to collect data that reflect the diversity of the AAPI community; instead, most federal agencies follow an archaic standard — established in 1997 — wherein they lump together all AAPI into the two broad categories: “Asian” or “Native Hawaiian and other Pacific Islander”. Such a generalizing approach misses the nuance of the AAPI community, and washes away the specific socioeconomic challenges faced by AAPI sub-groups.
California State Assemblymember Rob Bonta, author of The Ahead Act (AB1726) which would expand data disaggregation for AAPIs in California, speaks to supporters at a rally earlier this year. (Photo Credit: Twitter)
Following years of tireless advocacy work by AAPI advocacy groups, California has signed a critical data disaggregation bill into law.
AB1726 (also called “The AHEAD Act”) was introduced before the California Legislature early this year by bill author Assemblyman Rob Bonta. Recognizing that most state and federal data generally lump all members of the nearly 50 ethnic groups that comprise the AAPI community into a single monolithic category or disaggregate by only a handful of ethnic identifiers, the bill called for the expanded disaggregation of state public health and higher education data to include at least ten more ethnic categories for AAPIs. Those new ethnic options — which include checkboxes for those who identify as Bangladeshi, Hmong, Indonesian, Malaysian, Pakistani, Sri Lankan, Taiwanese, Thai, Fijian and Tongan — were consistent with what is currently available via the National Census.
Meanwhile, the lack of disaggregated data renders invisible several achievement disparities — including increased incidence of certain treatable diseases and/or reduced education access — that disproportionately impact certain AAPI ethnic groups over others. Without the capacity to draw awareness to those inequities, no culturally- or linguistically-specific resources are devoted to addressing them.
The AHEAD Act was designed to take the first step towards helping the thousands of Asian American and Pacific Islander Californians who are currently underserved by state and federal services.
Attendees at a recent rally in support of AB1726, a data disaggregation scheduled to reach the CA Senate floor soon. (Photo Credit: @DiverseElders / Twitter )
After months of increasingly vitriolic debate that divided the AAPI community, California Assembly Bill 1726 (AB1726) was significantly amended on Friday. In its original version, AB1726 was the culmination of years of lobbying work by California’s AAPI advocacy community, and it would have put in place measures to disaggregate healthcare and higher education data to reveal disparities faced by Southeast Asian Americans and Pacific Islanders in the state. Using the same ethnic options offered by the National Census, AB1726 would have expanded the ethnic self-identification choices offered in demographic studies conducted by state departments related to healthcare and higher education.
How a data collection bill designed was supposed to circumvent California state law prohibiting race-conscious affirmative action in higher education remains unclear to me.
Yet, no one can deny this grassroots conservative Chinese American movement’s growing clout.
California State Assemblymember Rob Bonta, author of The Ahead Act (AB1726) which would expand data disaggregation for AAPIs in California, speaks to supporters at a rally earlier this year. (Photo Credit: Twitter)
By Guest Contributor: Ryan Khamkongsay, Institutional Researcher
If knowledge is power, then accurate data is the foundation of empowerment. We live in a world that is completely data driven—good data is crucial for doctors to make accurate diagnoses, for teachers to know what’s working in a classroom, for epidemiologists to stop global pandemics, and for policymakers to design effective solutions.
As an Institutional Researcher in the California Community College (CCC) system, I know that the lack of disaggregated data is a major barrier to strengthening the evidence-based practices for promoting access and equity in higher education. California Community Colleges are the quintessential “open access” college system serving the most diverse population in the nation. CCC’s are the gateway to higher education for nearly 2.6 million students annually within the 112 college campus and 71 off-campus centers. As researchers in the CCC system, we are tasked with identifying achievement and access gaps within our diverse student body, but the overly broad aggregate racial and ethnic categories that are currently used continue to mask dramatic disparities across ethnic sub-communities making them invisible to us. I also know this as a second-generation Laotian American and a California native who entered higher education through the community college system. Southeast Asian Americans, like my family, entered this country as refugees of war or genocide and have struggled with much lower than average incomes, English proficiency levels, and educational attainment. The community college system can be a gateway for Southeast Asian Americans and other low-income communities of color into both higher education and high-skilled jobs. But if researchers and policymakers are not able to “see” our community’s students, then they will not know how to adequately support them.
We must ask: Are higher education institutions using the best methodologies to properly measure equity and identify achievement gaps within our diverse student populations?
In April, I wrote about why we need data disaggregation. I noted the broad diversity of the AAPI community that creates vastly unequal access to services such as education and healthcare for many specific AAPI ethnic groups. Yet, those ethnicity-specific inequities are often lost by state and federal data collection systems that treat AAPIs as an ethnically homogenous group. That invisibility, in turn, protects and preserves structural injustices faced by many AAPIs. Data disaggregation is not just an important issue; it is one of the core civil rights issues facing AAPIs today.
As far as I’m concerned, it’s a “no brainer” for AAPI advocates to support data disaggregation. Previous efforts to disaggregate AAPI demographic data — including, most notably, successful efforts to disaggregate Native Hawaiians and Pacific Islanders in Census data as a separate racial category — have yielded a plethora of valuable data concerning these communities. Activists have subsequently mobilized to develop programs specifically focused on the NH/PI community. For a community long damaged by our invisibility, AAPI must agree: efforts to improve data collection around the AAPI community are a good thing.
