Urban Wire Disaggregating Data Is Critical to Dismantling the Model Minority Stereotype
Paige Sonoda, Heather Hahn
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The “model minority” stereotype labels Asian Americans, Native Hawaiians, and Pacific Islanders (AANHPIs) as naturally hardworking, successful, and free of hardship. Though these traits are seemingly positive, the model minority narrative has racist roots and distills the AANHPI community into a monolith—rendering their vast diversity invisible.

Aggregate data on AANHPI economic well-being have further perpetuated the model minority narrative. Measures such as income and education show AANHPIs to be better off than any other racial or ethnic group, yet these findings obscure the vastly different social, economic, and cultural histories (PDF) that inform the experiences of the more than 50 ethnic groups that make up the AANHPI community. Only considering aggregate data also prevents decisionmakers from developing accurate solutions that can meet the needs of vulnerable groups within the AANHPI population.

Researchers and policymakers have been calling for disaggregated data for years as one step toward rectifying the problem. A few research considerations and best practices can help them build a more representative database that accurately depicts the realities AANPHIs experience so policymakers can address their needs. 

The emergence of the “model minority” stereotype

Before the rise of the model minority narrative, AANHPIs were largely framed as perpetual foreigners, never seen as fully American regardless of length of time in the US or nativity.

In 1965, Congress passed the Immigration and Nationality Act, putting an end to a series of laws that had banned immigrants from Asia, including the Chinese Exclusion Act, the Immigration Act of 1917, and the Immigration Act of 1924. The Immigration and Nationality Act gave preference to family reunification and skilled laborers, resulting in an influx of highly educated AANHPI immigrants seeking employment in well-paying fields.

Thus began the model minority myth that grouped all AANHPIs in the same category, a term popularized through a 1966 New York Times article, “Success Story, Japanese American Style” (PDF).

The model minority myth doesn’t just harm the AANHPI community. It is also rooted in anti-Blackness and has been used to pit AANHPIs against Black Americans by emphasizing hard work and perseverance as remedies to structural racism. Making disaggregated data more widely available not only reflects the nuance of experiences within the AANHPI community, but reveals this racist premise as false.

The harms of aggregate data on AANHPI communities

Aggregate data on AANHPI economic well-being shows that they have the highest income and educational attainment of any racial or ethnic group in the US, painting a rosy picture of AANHPI economic well-being that suggests they do not need safety net supports.

These data dangerously overlook the vast diversity and inequality that exists within the AANHPI community. Disaggregation reveals the deep inequities between national-origin subgroups.


For example, Burmese Americans have the lowest median household income ($47,061), compared with all AANHPIs ($91,828) and Asian Indians, who have the highest ($125,319). Median household income is just one of many measures that can be used to understand the economic well-being of AANHPIs. Smaller Pacific Islander subgroups, such as Marshallese Americans, similarly have a median household income ($50,128) well below the aggregate AANHPI.

Median household income is just the tip of the iceberg. Exploring differences across indicators of economic and social well-being will reveal the unique needs of different AANHPI communities. It’s also important to note that disparities in economic well-being exist within AANHPI subgroups as well. For example, despite Chinese Americans having a relatively high median household income ($84,215), 28.6 percent of Chinese seniors in New York City are experiencing poverty (PDF).

The future of disaggregated data and research

Making disaggregated data more widely available will help decisionmakers create evidence-based policy recommendations that accurately reflect the experiences and the needs of the most vulnerable AANHPIs. This will require the following:

  • Greater data privacy considerations, especially for people of color with low incomes, who are most vulnerable to private data exposure.
  • Community engagement. Conversations about data disaggregation should include members of the AANHPI community to help inform decisionmaking and center community needs, especially around potential privacy risks.
  • Ethical safeguards around disaggregated data use. For example, these would help avoid situations like when Census Bureau data were used to identify Japanese Americans and send them to internment camps during World War II.
  • Disaggregated data across other economic indicators, such as English language proficiency, nativity, gender, age, geography, and more. An Urban Institute-National CAPACD study is currently examining AANHPI economic well-being in depth across a comprehensive range of indicators, disaggregated by ethnic subgroup.

In addition to data disaggregation, future research should explore alternate methods, like qualitative case studies or surveys in Asian languages, to deepen our understanding of AANHPI economic well-being. Additional research methods should elevate narratives that get lost in quantitative data, such as the experiences of Southeast Asians refugees, NHPIs living in overcrowded households (PDF), or Compact of Free Association migrants who lack access to safety net supports.

Disaggregated data and deeper qualitative dives can give voice to a population that has historically been forgotten, challenge the predominant model minority stereotype, and provide critical evidence that can inform more inclusive and effective policies.

Research Areas Race and equity
Tags Race, gender, class, and ethnicity
Policy Centers Center on Labor, Human Services, and Population
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