Data Disaggregation Deconstructed: AANHPI Communities
By Kathleen Malloy and Terry Ao Minnis
To ensure our democracy works for all of us, it must represent all of us — and that representation begins with how our communities are reflected in data. Data can highlight or hide communities’ unique traits and needs. For Asian American, Native Hawaiian, and Pacific Islander (AANHPI) communities, it has hidden them for too long.
AANHPIs include more than 50 detailed race groups; the U.S. Census Bureau reports data annually on at least 22 distinct, self-identified AANHPI groups, each with unique linguistic, cultural, and historical differences. However, data collection often masks this diversity by aggregating data points into one AANHPI label that creates a misleading and uniform story about AANHPI people in the United States. This is why data disaggregation, which refers to the collection and reporting of data by detailed AANHPI subgroups, is so important.
For example, AANHPI women are paid an average of 86 cents for every dollar a White man is paid — but disaggregated data demonstrates that Native Hawaiian women are paid only 66 cents; Vietnamese, Laotian, and Samoan American women 61 cents; Burmese American women 53 cents; and Bhutanese American women only 38 cents for every dollar a White man is paid. The percentage of Asian Americans living below the poverty line ranges from 6.8 percent of Filipino Americans to 39.4 percent of Burmese Americans. In education, aggregated data hides the fact that the barriers to college access and success that Southeast Asian American (SEAA) students face are more akin to those faced by Black and Latino students than other groups of Asian American students.
Unfortunately, AANHPI communities are often thought of and treated as homogeneous, a mistaken belief reinforced by the way the government (and other entities) approach data collection. Aggregated data points in areas such as health, education, and elsewhere perpetuate the model minority myth — that all Asian Americans have high levels of income, homeownership, education, and health — by not allowing for deeper analysis of differences between subgroups. The aggregation of two racial groups — Asian Americans and Native Hawaiian Pacific Islanders — further exacerbates inequalities and disparities, particularly for NHPI communities.
Indeed, the reason NHPIs became a separate racial group in the 1997 OMB Revisions to the Standards was to help “facilitate the production of data to describe their social and economic situation and to monitor discrimination against Native Hawaiians in housing, education, employment, and other areas.” By making the diversity and unique needs of AANHPI subgroups invisible, aggregated data inform (or misinform as the case may be) policies that exacerbate disparities within AANHPI communities — disparities that are in turn masked again in aggregated data, thereby starting the cycle all over again.
To build policy that best serves the unique needs of AANHPI communities, federal, state, and local governments need disaggregated data that will illuminate, not hide, the distinct AANHPI subgroups. When data are disaggregated, a much more complex story emerges across key social indicators such as health care, employment, and housing.
When government agencies report only aggregate data under the “Asian” category or the “Native Hawaiian Pacific Islander” category (or worse yet, in a combined “Asian Pacific Islander” category), they conceal significant differences and inequities among the many distinct AANHPI groups. Disaggregating data on AANHPI groups helps policymakers figure out how to best support diverse communities in education, health, and more. Without data on how many people live in each community, who comprises that community, and what their unique needs are, policymakers cannot know how to address community needs effectively and successfully.
For example, knowing the disaggregated data on the higher-than-average rates of cervical cancer among Hmong Americans helps health policymakers and practitioners provide better, more targeted care for Hmong Americans than if they only had cervical cancer rates for one large “Asian” group. As another example, for Southeast Asian Americans, the mass collective trauma from war, genocide, displacement, and the stressors associated with relocation — like English language difficulties, cultural conflicts, and racism — affect health and socioeconomic outcomes. Disaggregating AANHPI data makes visible the needs of Southeast Asian American communities and helps to ensure that they receive their fair share of resources and support.
To achieve racial equity for AANHPI communities, policymakers must see and understand the diversity of AANHPI subgroups. The Interagency Committee on Statistical Policy’s new AANHPI data catalog is one example of how policymakers can better disaggregate AANHPI data. The tool, launched last month, provides a searchable catalog of federal datasets that include disaggregated data about Asian American, Native Hawaiian, and Pacific Islander populations. The catalog is a positive first step, but we encourage governments at all levels to show the same level of granularity in their data collection. This granularity is crucial to ensuring that AANHPI communities are served by the institutions that represent them.
Kathleen Malloy is the census and data equity program assistant at The Leadership Conference on Civil and Human Rights. Terry Ao Minnis is the senior director of the census and voting programs for Asian Americans Advancing Justice | AAJC.
This blog is the first in the series ”Data Disaggregation Deconstructed,” which explores how data disaggregation in various policy areas can enhance equity. The series is based on The Leadership Conference Education Fund’s report “Information Nation: The Need for Improved Federal Civil Rights Data Collection.” The report urges the Biden administration to restore and expand the scope, frequency, and public accessibility of federal data collections in order to identify equity gaps and solutions to remedy them.