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Outside the racial checkbox: Disaggregating data on Asian American, Native Hawaiian, and Pacific Islanders.

  • Writer: Vikash Reddy, Ph.D.
    Vikash Reddy, Ph.D.
  • May 1
  • 5 min read

A persistent and pernicious problem in American data collection is the tendency to treat the Asian American, Native Hawaiian, and Pacific Islander (NHPI) community as a single, monolithic category. Asian and NHPI Americans generally have high levels of education and income, but the averages that get reported obscure meaningful differences in educational attainment, income, and myriad other measures. This tendency to report group averages helps perpetuate and reinforce the model minority myth, which holds that Asian and NHPI Americans are uniformly high-performing populations. As a result, the specific, very different circumstances of dozens of distinct communities — from Afghan refugees to Native Hawaiians to Hmong elders — disappear into the average.

 

Aggregated Asian American and NHPI data conceals stark educational disparities that become visible when you disaggregate to the subgroup level. To be sure, there is tremendous power in the collective identity of a shared Asian American and NHPI community. Much like a mosaic relies on the collective impact of its individual tiles, the Asian American and NHPI community can find power in community. But there has long been tension within the panethnic coalition that gave rise to the AAPI category — a political achievement of the civil rights era — and the ways that same aggregation now obscures heterogeneity that matters for policy and practice. The critique is established. What's been harder to find is an accessible, interactive way to see it.

 

That is why I created this dashboard. The data is available for folks who want to get down and dirty with Census data, but I want this to be in folks’ hands!


 

A note on who gets counted

Even in disaggregated analyses, one issue tends to get missed: a substantial portion of the Asian American and NHPI community identifies as multiracial — Asian or NHPI in combination with another race. 74% of Native Hawaiians identify with two or more races, compared with 53% of Pacific Islanders. When analysts (and I have to admit, I’ve done this myself) use standardized single-race categories, they miss most of the Native Hawaiian community entirely.

 

This isn't a rounding error. The way federal data systems classify race actively misrepresents NHPI students — most undergraduates who identify as Native Hawaiian or Pacific Islander end up classified as "Two or More Races" or "Hispanic or Latino" under standard IPEDS logic, effectively erasing them from NHPI counts. Research focused specifically on Hawai’i has shown that reporting "Native Hawaiian alone" as the primary measure reduces the Native Hawaiian-identifying population by over two-thirds. The dashboard shows both alone and in-combination figures for every state, so readers can see the gap between the headline number and the fuller picture. (Also, I'm actively thinking about better ways to visualize smaller populations — suggestions welcome!)

 

A few findings that struck me 

I’ve used these data in many different contexts, but I continue to find stories that strike me. The gap between the best and worst-off Asian American and NHPI subgroups is large enough that it should give anyone pause before citing the aggregate. Asian Indian households have a median income nearly three times that of Marshallese households. Afghan Americans experience poverty at 32% — comparable to the highest poverty rates of any group in the country. Filipino Americans experience poverty at 5% -- among the lowest of any racial/ethnic subgroup. All three communities are "Asian" in most published statistics.

 

68% of Asian alone adults in the United States were born outside the country. Burmese Americans arrived largely as refugees. Filipino Americans have a documented presence in this country going back to the 1700s, while more recent waves of immigrants have come from eastern and southern parts of Asia. Their histories, circumstances, and outcomes are not comparable — and treating them as a single data point tells us nothing useful about any of them.

 

How to use this dashboard

Click any state on the map to filter all seven panels to that geography. The map can toggle between share of population and total AANHPI population — these tell very different stories. California has the largest AANHPI population in the country by count; Hawai‘i has the highest share. Use the comparison dropdowns on the Overview tab to add up to three specific ethnic communities to the educational attainment and poverty charts. Only subgroups with reliable sample sizes for the selected state will appear as options.

 

The full subgroup charts — one each for education, poverty, income, nativity, language, and health insurance — show all available communities for the selected state. (I’m working on fixing a glitch that makes the map toggling a bit finicky as users move between tabs. To reset the map within a tab, click on a state to filter to it, then on that same state again to release it.)

 

What this dashboard can't yet show

Asian Americans make up roughly 6% of unauthorized immigrants in the United States. That figure rarely surfaces in immigration debates, which tend to treat undocumented status as a predominantly Latinx issue. A future version of this dashboard will incorporate those estimates. For now, I'm naming the gap rather than pretending it doesn't exist.

 

About this project

One of the core missions of CollegeReddy LLC is helping organizations disaggregate their data and tell the stories that aggregated numbers hide. If your institution or organization is working with Asian American and NHPI data — or any population where the headline numbers aren't telling the full story — get in touch!

 

Acknowledgments

Audrey Dow and Kristine Jan Cruz Espinoza reviewed early versions of this dashboard and blog post. Both offered feedback that sharpened both the framing and the methodology. Any errors or omissions are my own.

 

References

 
 
 

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