I didn’t know I was Asian until I moved to the United States. As a 4-year-old immigrant, my American journey of “finding myself” was directed by the check boxes available to me—check boxes on school enrollment forms, medical forms, Social Security forms, field trip slips. These collection points about my life and existence introduced “Asian” to my vocabulary. While in the Philippines, we were Pilipino—but not Intsik or Tshino (Chinese), not Koreano (Korean), not Hapones (Japanese), and not Indiyano (Indian). In America, though, we were all the same.
Although I built the muscle memory to check off the “right” box, I eventually learned to question my trained response. Who benefits when our ethnic identities are collapsed as Asian? What do we internalize when we believe that I am the same type of Asian person as someone who is Pakistani, Vietnamese, Cambodian, Taiwanese, or Hmong? What gets lost when Asians in America are considered in the same clump of people?
It's not just Asian communities who lose in the laziness of our data systems.
Betraying its reputation for inclusion, my home state of California, like much of the United States, remains behind the times in its understanding of its Asian population. While many Californians have learned to identify cultural differences between Asian groups, government agencies continue to analyze data as if these differences didn’t exist—or worse, as if our communities didn’t experience different life outcomes. When we “average” Asian data, we lose key variances in everything from health disparities to wage gaps, from disproportionate engagement with the prison system to inequities in access to generational wealth. Like a stereotype applied to us on a systemic level, we are apparently all alike.
And it’s not just Asian communities who lose in the laziness of our data systems. We lose when “Latino or Hispanic” erases differences between Hondurans, Nicaraguans, Mexicans, and Panamanians. We lose when “African-American” is the only option for Jamaicans, Eritreans, or Haitians. Our communities all face the consequences when data generalize about our needs and challenges.
Regularly disaggregating data by ethnicity would allow public agencies to identify underrepresented groups and create strategies or programs to ensure that all those in need are able to have their right needs met.
On May 28—30 years after I enrolled as a kindergartner in the Los Angeles Unified School District—our nation’s second-largest school district finally became just the second California school district to adopt this common-sense practice. The school board passed the Everyone Counts resolution, a move that institutes data disaggregation policies for all students and employees of color in L.A. Unified, including students who might otherwise be generalized as “Latino or Hispanic” or “African-American.” Among other commitments, the resolution also directs the superintendent to provide an unprecedented annual update on the state of AANHPI AMEMSA (that is, Asian American, Native Hawaiian, Pacific Islander, Arab, Middle Eastern, Muslim, and South Asian) students and employees. This resolution is especially significant given that a third of all Asian Americans in the United States live in California.
This is not the end of this movement for the more than 40 organizations that pushed for this resolution to pass in Los Angeles. For us, the next stop is Sacramento to push our state to support the needs of our different communities with more nuance. That begins with resolving to have student data disaggregated. Our hope as both community advocates and the ultimate beneficiaries of such policies is that our policymakers do whatever it takes to get smarter with their lawmaking. If they’re going to make decisions about our lives, they have to know who those decisions are impacting and how.
Since 2015, states including Washington, Minnesota, and Rhode Island have modeled the way for lawmakers across the nation to act on data disaggregation from the state level, rather than forcing already-stretched advocacy groups to fight their way district-by-district. Washington state situated data disaggregation efforts among a larger strategy to support students from marginalized backgrounds.
Hawaii has inched closer to full disaggregation of data by beginning to examine some of their largest ethnic groups, including students from Native Hawaiian, Pacific Islander, and Filipino backgrounds.
What might’ve been different in my education journey if I had known my community counted from the first moments I had enrolled in school? How might schools have served me differently if they could distinguish my strengths and needs from the strengths and needs of other Asian groups? Where would my life be now if data had permitted a deeper understanding of my community?
All of that would be a mere guess for me. But for our future generations, it doesn’t have to be.
