This article is part of Mochi’s Fall 2022 issue on politics. In the late ’60s, the slogan “The personal is political” came out of a second-wave feminist movement, presenting an alternative thought that politics is about one’s dignity and agency. As Asian Americans, we know this to be true, since even our identities are political. In this issue, we talk about what politics mean to us — from the politics of our identities to political dynamics in our relationships and communities. Check out the rest of our issue here! And if you like what you are reading, please support us by buying us a boba through Ko-fi.
“Why would you want to divide Asian Americans?”
“Why do you want to put us in an ethnic registry?”
Natalie Truong remembers over 300 raised hands from an opposition group asking these questions. They “flooded the public testimony [of a state committee] for hours,” she says, “about why [the state committee members, representatives, and senators] shouldn’t pass the bill.” The bill in question proposed to disaggregate, i.e. split, racial data on Asian Americans into ethnic data, with the intention that this would allow different ethnic groups to receive the necessary resources for their specific needs. And although there was “beautiful, cross-subgroup, cross-ethnic support for the bill” — from Southeast Asian American, South Asian, Chinese, and Korean coalitions — the opposition group was also Asian, and they protested that this bill would infringe on our privacy and personal rights, divide Asian Americans in spirit, and take away our children’s existing opportunities.
Truong has dedicated her career to battling these misconceptions and fighting for data disaggregation policies as the Education Policy Manager at the Southeast Asia Resource Action Center. “This isn’t a zero sum game. We don’t want other Asian American subgroups to have their resources taken away or not have what they deserve,” she says. “All children deserve to be successful. It’s more about uplifting those who haven’t had as much voice in this conversation.”
What Exactly Is Data Disaggregation and Why Should Asian Americans Care About it?
By splitting the collection of data into more specific categories, data disaggregation hopes to reveal and therefore address data inequity and allocate resources to vulnerable and historically underserved communities more accurately. For Asian Americans in particular, the umbrella term often perpetuates a stereotype of a pan-ethnic monolith and the model minority. However, data disaggregation calls for the standardized collection of data on Chinese, Indian, Korean, Vietnamese, Thai, Lao, Hmong, Japanese, Filipinx, Native Hawaiian, Pacific Islander, and so many other communities. This is especially relevant in education and healthcare, as well as politics, all of which often have varying outcomes depending on each community’s unique histories, cultural beliefs and practices, and traumas. And we’re only scratching the surface of intersectionality, and how gender and sexual orientation are significant factors as well.
By acknowledging and factoring these differences and challenges into qualitative and quantitative research, data disaggregation hopes to guide federal and state decision making and funding, ultimately addressing institutionalized racism and dismantling systemic barriers.
The Empowering History of “Asian American” Identity — and Who it Leaves Out
The term “Asian American” was intentionally adopted in the 1960s by UC Berkeley student activists Emma Gee and Yuji Ichioka for their Asian American Political Alliance organization. In an effort to fight against the inaccurate use of “Oriental” (which was actually used in many legal and governmental documents until the Meng Bill in 2016) and inspired by the Black Power movement, these students sought to unite Asians under a shared identity to protest against the Vietnam War and fight for racial equality.
However, much of the movement focused on East Asian communities, leaving out South Asian voices, and overlooking Pacific Islander and Native Hawaiian issues entirely.
And while solidarity is important, lumping all Asian Americans into a single umbrella group often perpetuates the stereotype to non-Asians that all Asian American groups are universally and inherently successful, wealthy, healthy, and educated. And yet, not only do we all speak different languages and have different cultural beliefs and practices, but we also have the highest income gap, education gap, and healthcare gap compared to other racial groups, with some Asian ethnic groups doing three to four times better than other Asian ethnic groups.
In fact, this is precisely why Native Hawaiians disaggregated from the Asian American umbrella in 1997. The Office of Management and Budget (OMB) had previously classified Native Hawaiians and Pacific Islanders as Asians, despite their unique and ongoing history of colonization. Like many indigenous communities around the world, Native Hawaiians face higher rates of poverty, houselessness, incarceration, and death. And yet, because they were being included with Asian ethnic groups, Native Hawaiian high school students had lower chances of being accepted into mainland colleges with affirmative action policies, and the indigenous communities’ specific health issues were being obscured in statistics. Senator Daniel K. Akaka (the first U.S. Senator of Native Hawaiian ancestry) lobbied to decolonize this data so these specific issues could be addressed, and on October 30, 1997, the OMB distinguished Native Hawaiian and Other Pacific Islanders as their own category.
It’s important to note, however, that the fight for “data justice” and to improve the wellbeing and cultural preservation of Native Hawaiians is an ongoing one in both Hawaii and the mainland.
What Exactly Are the Benefits of Data Disaggregation?
Much of the conversation on data disaggregation revolves around health and education, with the end goal to provide more resources for the most statistically vulnerable communities.
By looking at different children with different needs, we can allocate resources towards specific actions — adding effective educators in schools, specific language support, and culturally relevant curriculum, just to name a few — and address the education gap.
Data disaggregation can also shift the focus to preventative health; for example, targeting diabetes rates in Pacific Islander and Native Hawaiian communities, addressing mental health issues in Southeast Asian communities like higher rates of anxiety and depression stemming from generational trauma from war and genocide, or providing language access to government programs to Korean communities who are less likely to have health insurance.
Additionally, Asian Americans have had the lowest voter turnout compared to other racial groups because many believe these policies are not relevant to their needs, as voting materials are sometimes not translated, and because politicians often ignore Asian Americans as a voting bloc — assuming that we’ll vote Democratic, or that we won’t vote at all. And yet there is immense understudied and untapped potential in Asian American civic engagement. Different Asian American communities vote differently because of different beliefs and histories, and by addressing this, campaigners and policy makers can minimize voter disenfranchisement.
Truong adds, “There isn’t targeted recruitment; there’s no targeted voter turnout. Even for languages, they’ll just do the top three or four Asian languages. They’re not really targeting specifically who’s in their community. If you’re in Minnesota, you really should target the Hmong community, and not just translate everything into Chinese.”
Data disaggregation will give both policy makers and community members more information on why certain groups vote a certain way, and therefore address those voting practices. For example, Truong says, “Vietnamese Americans tend to lean a little bit more Republican based on the rhetoric of the candidates” and that there’s often misinformation circulating in Vietnamese. She urges fellow Asian Americans with access to robust information to help “counteract some of this false information [and] hold our communities accountable,” ensuring that the policies behind the rhetoric actually benefits our communities.
Why Has There Been Pushback From Asian Americans Against Data Disaggregation?
It’s no coincidence that the pushback against data disaggregation within Asian American communities often comes from ethnic groups who have been in the United States for longer, immigrants who qualified for H-1B visas, a.k.a skilled worker visas, those experiencing differing circumstances in their home countries, and or groups who are on the wealthier end of the Asian American spectrum.
“Compared to Southeast Asian Americans who came over here as refugees, there’s a huge gap in their [educational and economic] attainment,” reminds Truong. “So when we are advocating on behalf of Southeast Asian Americans, and we see other ethnic groups not wanting to call that equity issue out, for the most part, it’s because they already know the game. They play the game well; they think all you have to do is work hard.”
It’s this same meritocratic belief that leads these groups who oppose affirmative action to oppose data disaggregation as well, a belief that students who “work hard” deserve to get into elite schools, regardless of their unique experiences and backgrounds.
Truong explains, “Overall, it’s a myth that there’s not enough resources for my child and that my child just has to do whatever they can to compete and win in America. And that in itself has been perpetuated more by white society, and some Asian groups have bought into it. That’s where a lot of the pushback comes from: this very sincere fear for their children and wanting to do what’s best for them without considering how some of these policies actually support a wider net of all other types of Asian Americans and especially low-income ones.”
Other myths Truong has heard include being put on an ethnic registry and tracked, but she often reassures policymakers and communities that data disaggregation does not change privacy laws and that no personal identifying information is collected. This, too, is a fear that needs to be assuaged.
And while data disaggregation policies aren’t perfect and still have much room for improvement, we will only truly be able to improve these policies by implementing and learning from them.
Why Is Aggregate Data Also Useful?
There are, of course, times when aggregate data is crucial. Most recently, the pan-ethnic Asian American solidarity in anti-AAPI Hate protests continues to be a beautiful showcase of the collective. After all, the individuals who hold these prejudices and carry out these hate crimes don’t see us as distinct ethnic groups. And there is power in numbers.
It’s also crucial that data sets are large enough to draw significant conclusions, and that population sizes aren’t so small (especially since ethnicity is largely self-reported) that incorrect conclusions are formed or disregarded altogether. At the beginning of the COVID-19 pandemic in 2020, Native Hawaiian and Pacific Islander communities across the United States were disproportionately devastated by the virus more than any other racial or ethnic group, and yet it went largely underreported because the data set was so small and therefore not considered in the larger data analysis. If the data had been aggregated and they had been included as AAPI, then the experiences of these communities would have also been counted.
Ultimately, Truong says, “We should have data systems that are so robust that we can have two conversations at once. We can have a conversation that is broad that says, ‘In general, Asian Americans feel this way about this specific topic.’” That way, policy makers have a general understanding of trends. On the other hand, there should also be conversations about the specific differences within that aggregate data, which will give us actionable information. “You can’t have policies in the general,” she adds. “They don’t actually benefit anybody when they’re trying to benefit the middle. And whoever the middle is, usually isn’t Asian American anyway.”
What Does the Future of Data Disaggregation Look Like?
Over the last decade, there has been exciting federal and state movement for data disaggregation in health and education. Truong points out, “We’ve had nine states that have either proposed or passed a bill on data disaggregation and the three of them [New York, New Jersey, and Massachusetts] have been just this year in 2022 alone.” West Coast states such as Washington, Oregon, and California have passed health data disaggregation bills, but are still working on similar education bills. According to Truong, this is all thanks to “the ground efforts by community advocates [ … ] who work with our communities every day and know what languages need to be translated and which groups need to be represented.”
This particular election year, it’s up in the air as to which direction the House and the Senate are going to swing and whether it’ll be favorable to data disaggregation. Truong remains optimistic. “Even if Congress itself may not have all the progressive or even nonprogressive members who would push and champion data disaggregation, we have enough champions to still have these conversations at the Department of Education or in the White House who have been favorable to data disaggregation. We’re reaching a tipping point where interesting bills can be introduced. But we’ll still have to see how this election year shapes up in terms of if we’ll have enough votes to get it through!”
As we approach midterm elections and beyond, it’s not only our responsibility to educate ourselves and our community members on these issues, but for those of us who can vote, it’s important to vote in leaders who care about each and every community to ensure no one falls through the cracks. Data disaggregation isn’t meant to divide Asian Americans; it’s meant to celebrate our differences, while recognizing our unique challenges, and to truly fight for social equity together.
Cover photo: Alexander Sinn/Unsplash
Last modified: September 19, 2022