We Cannot Disregard Data: How Opposition to Data Disaggregation Hurts AAPI

Protesters rally behind #AllCACounts (Photo Credit: SEARAC)
Protesters rally behind #AllCACounts (Photo Credit: SEARAC)

In California, a battle over data disaggregation has reached a fevered pitch.

Earlier this year, Asian American and Pacific Islander (AAPI) advocates worked tirelessly in conjunction with state legislators to draft and advance Assembly Bill 1726 (AB1726, nicknamed “The AHEAD Act”), which would disaggregate healthcare and higher education data pertaining to the AAPI community using the same guidelines as the federal Census Bureau. AB1726 is the second effort to pass such a law in the state of California; Governor Jerry Brown vetoed an earlier data disaggregation bill passed with near unanimous support in 2015.

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.

So, how can one possibly oppose The AHEAD Act?

AB1726 enjoys strong bipartisan support from both within and outside the AAPI community: it is co-sponsored by the Asian Pacific Islander American Health Forum (APIAHF), the Southeast Asia Resource Action Center (SEARAC) and Empowering Pacific Islander Communities (EPIC), and is further backed by 112 California-area organizations, most of them AAPI-focused. Yet, a number of Chinese Americans lead by State Senator Bob Huff of California’s 29th District, have become increasingly vocal — and vitriolic — in their opposition to The AHEAD Act.

Opponents’ rhetoric have also become increasingly bizarre. In debate before the Education Committee meeting in June, Senator Huff suggested that earlier data disaggregation had enabled Chinese Exclusion and Japanese American incarceration (pdf), and that The AHEAD Act might usher in similar efforts. Huff later wrote a condescending letter (pdf) to the Japanese American Citizens League (JACL), presuming to educate the Japanese American advocacy group that was founded to oppose Japanese American incarceration on its own history. Today, Huff and his supporters — which mostly includes Chinese American groups founded to counter race-conscious affirmative action in California — will rally on the steps of the California State Assembly to vocalize their fears that The AHEAD Act will invite racial profiling on the scale of Chinese Exclusion.

Although Huff is correct to point out that the Census disaggregated Chinese and Japanese ethnic groups in the late 1800’s, he frames those events incorrectly. Huff forgets that these changes occurred during a profound period of racial demographic upheaval, when the boundaries of race were particularly uncertain and pliable when it comes to the Asian community.

Throughout the late 1800’s, Asian immigration constituted predominantly people of Chinese nationality, as well as immigrants from Japan, Korea, India, and the Philippines. All of these early Asian immigrants entered into a racialized America where race existed primarily along a black-white binary. Thus, prior to 1870, Asian immigrants occupied an uncertain racial space, legally and politically “Colored” (an official classification that ensured the denial of basic legal rights) while demographically lumped in with Whites in early Census reports. Faced with the overt illogic of their simplistic racial taxonomy, the Census experimented through the late 1800’s with myriad ways to count America’s Asians — a process that was not only confusing to its Asian respondents but which also raises fundamental questions of what constitutes race in America. For example, in 1870, when the Census first included a box for “Chinese”, they noted that this group included immigrants from Japan. Just a few decades later, Japanese people had a separate box in the Census.

The Census’ efforts to reorganize its collection of demographic data to count Asian immigrants as separate from White reflected the country’s overall recognition that Asians were already understood to be “Colored.” Asians at this time were explicitly unable to vote, to naturalize as American citizens, or to testify in a court of law. But, Huff is incorrect to suggest that Chinese Exclusion and other anti-Chinese laws passed during this same time only occurred after the federal government had begun to count — and therefore recognize — America’s growing Chinese population.

Huff forgets that America’s anti-Asian racists needed no Census numbers to fuel their racism; anti-Chinese laws were being passed in the state of California in the 1850’s, long before the Census first recognized Asians (or Chinese) as a distinct group. Anti-Asian sentiment might have influenced 19th century efforts to disaggregate Asians from Whites in the Census, but it would be illogical and ahistorical to say that the Census’ disaggregation of Asian from White, itself, caused the anti-Asian sentiment that eventually resulted in Chinese Exclusion or Japanese American incarceration.

Two centuries ago, Americans disaggregated Asians from Whites to underscore the perpetual foreigner status of Asian immigrants — a status that was already officially codified in American law. Today, the opposite is true: The AHEAD Act seeks to improve access for — not to disenfranchise — our country’s underserved Asian communities. That distinction matters. The AHEAD Act is an important civil rights bill for AAPIs.

Sadly, mainstream media ignores The AHEAD Act’s backing by hundreds of AAPI groups to focus their narrative on the few AAPIs who oppose the bill. According to their own Committee testimony, these opponents fear that data disaggregation is a “backdoor” to passage of affirmative action. If so, they tie themselves into an unusual rhetorical knot: to argue that if one believes that The AHEAD Act will boost support for affirmative action, one must also believe that the bill’s disaggregation of California’s higher education data will yield an inconvenient result. What could they be afraid we will see in a disaggregated dataset, I wonder?

Opponents of The AHEAD Act demand that the AAPI community embrace invisibility. They suggest that more data about AAPIs is wrong; worse yet, that to better understood the nuances of our community is dangerous. Yet, racism doesn’t emerge from knowledge — it is ignorance that breeds hatred.

Writes the White House back in 2012:

Disaggregation of data is a necessary step in fully understanding the needs of the AAPI community and many other pan-ethnic communities.

To support The AHEAD Act, check out this website hosted by SEARAC.

Update: I received permission to make available the following documents

  • JACL’s statement in response to Bob Huff’s remarks during debate over AB1726 in Committee (pdf)
  • Huff’s astounding and offensive response (pdf)

We need data disaggregation, if for no other reason than because we should be suspicious of the motives of someone like Huff who would presume to lecture an organization like JACL about the discrimination against AAPIs that led to their own founding. Bob Huff needs to sit all kinds of down.

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  • Josh

    Interesting how you would never see this level of self-targeting with other communities of color. Not saying your view is necessarily wrong, just interesting to see that the Asian community seems much more compromising to other causes/groups( maybe because Asian girls think Asian men are too privileged?)

  • Observer157

    This blog pretends that it represents Asian Americans. It neither represents Asian Americans nor even Asian American women. It is the viewpoint of someone who happened to be born Asian but #1 is an anti-Asian male Amy Tan feminist who reinforces white privilege and black politics above all. Basically another Stacey Dash or Michelle Malkin of the community

  • Myra Esoteric

    Data disaggregation isn’t about this though. Data disaggregation is important for Asian Americans because stuff like income and education level statistics is bimodal or trimodal for Asians, while it’s a normal distribution (like a bell curve shape) for whites, most Blacks (though that is changing) and various other communities.

    Like there are way, way more low income / low education Asians than the average would indicate.

  • Guest

    Nice job as usual, Jen

  • Chinese

    Look at this stupid cunt thinking she speak for the Asian community. Bitch you ain’t

  • Chinese

    Why can’t the Asian community just split already? So we don’t deal with this none sense.

  • Thanks!

  • Keith

    Cool story bro.

  • LOL

  • Josh

    No trolling allowed. Please read comment policy

  • Josh

    lol. You didn’t actually say anything. I just said that this view is not necessarily wrong; please address my main point thnx 🙂

  • Josh

    No trolling. Please read comment policy thanks!

  • Actually, Myra has laid out an important and insightful point about how the AAPI community, unlike other communities, experiences bimodal or trimodal patterns in most metrics of economic mobility and success. This would argue quite strongly for disaggregation, since current usages of broad racial categories assume a normal distribution of data within that group in order for that group classification to be meaningful.

    Would you care to actually address that point, rather than to distract from the conversation with an (unsupported, inflammatory, speculatory) finger-pointing about “Asian girls”, which has nothing to do with this conversation (if for no other reason than the fact that data disaggregation is an issue being championed by SEA and PI advocates regardless of gender)?

  • Myra Esoteric

    Sometimes I look at the comments and see huge amounts of salty guys, bringing their dating grievances into a conversation on how to help poor and elderly people with stuff like health disparities and accessing education for their families. Smh

  • Vincent

    Let’s assume that you got your data, and then what? Deprive the resource from one and give it to another just because they come from diffrent subgroups of Asian? Instead of looking into actual situations you choose to look for their races? Correct me if there is any misunderstanding, thanks