Pan-Asian Student Classifications in Higher Education: What the Data Do and Don’t Tell Us

December 13, 2017

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By Jennifer R. Crandall

This post is part of the series Beyond the Margins: Meeting the Needs of Underserved Students.

In supporting an inclusive campus environment at colleges and universities, Asian Americans are an often-overlooked part of the equation. Reasons for this vary. But as demographics shift, issues of diversity increase in importance and institutional policies and practices need to address the complexity of populations that fall under pan-ethnic categorizations such as Asian American.

To deconstruct the notion of Asian American students as a monolith and advocate for data disaggregation to support their educational experience and success, we must first understand popular misconceptions that feed these stereotypes.

The Model Minority Myth

Asian Americans are often excluded from dominant discussions on race, heightened by the black-white racial paradigm that tends to exist in the United States. When they are included in discussions on race, it is usually as a model minority. In these instances, their success is hailed as evidence of equal opportunity. This narrative, by default, has veiled implications for educational access and equity:

One, it disparages other minorities. The model minority stereotype originated in the mid-1960s during a period of racial unrest in the United States. The term first appeared in a New York Times article, “Success Story Japanese American Style,” in which journalist William Peterson praised Japanese Americans for not being a “problem minority.” A concept such as model minority cannot exist without the opposite.

Two, it takes attention away from the issues and needs of Asian Americans, especially those who fall far from the stereotype. Because Asian Americans are often portrayed as the model minority—hence high achieving students free of problems—higher education institutions have had difficulty supporting their educational needs, which research has shown differs by ethnicity and socioeconomic class.

Three, it minimizes discrimination faced by Asian Americans who could be seen as embodying the stereotype. Those who on the surface seem to embody the image still have to deal with the “negative consequences of the ostensibly positive image,” namely, prejudice and discrimination not only from outside their racial group but at times within.

Four, it creates pressure for young Asian Americans to excel academically, which has shown to lead to mental health problems, including suicide. Asian American students can feel extreme pressure from parents, instructors, and peers to perform, which for some can result in strained relationships and academic performance and psychological problems.

When the dominant culture depicts Asian Americans as the model minority, the stereotype reinforces not only the rhetoric of the achievement ideology, but also educational policies that focus on outcomes at the expense of all else.

Diversity Abounds

The Asian American pan-ethnic label itself obscures the diversity of a population that represents a wide range of languages, religions, socioeconomic levels, political leanings, English proficiency levels, and cultures. In fact, Asian American as a category does not exist in Census data. Rather, “Asian” is used to denote someone with origins from the Far East, Southeast Asia, or the Indian subcontinent. Within the Asian population, over 20 ethnic subgroups exist, which can further be delineated by language.

According to the 2010 American Community Survey, over 18 Asian languages are represented on the 2010 Census Language Assistance Guides. In 2010, the largest groups represented in the United States were Chinese, Asian Indian, Filipino, Vietnamese, and Korean, followed by Japanese, Pakistani, Cambodian, Hmong, Laotian, and Thai.

Projected Growth

The largest population increases in the early years of the 21st century have been in the Hispanic and Asian populations (see Figure 1). Census data of Asian groups indicate that from 2000 to 2010, the Hmong, Cambodian, and Vietnamese populations experienced a higher percentage of growth compared to the Chinese, Japanese, and Korean populations. By the year 2060, the Census Bureau projects the Asian population will be almost 39 million, over 9 percent of the total U.S. population.

Figure 1: Population growth for largest minority groups in the U.S., 2000-10

pan-asian-fig1Source: Humes, K.R.; Jones, N.A.; and Ramirez, R.R. (2011)

Educational Attainment

Asians rank highest in terms of high school graduation and college entrance, persistence, and completion. But relying on aggregate data presents a simplistic and inaccurate picture that ignores disparities within the pan-Asian group. It is not until categories are broken down into subgroups that striking differences emerge. Although Asians are more likely than whites, blacks or Hispanics to complete college, only 11 percent of Laotians and 13 percent of Hmong and Cambodians earn a degree, compared to 34 percent of Korean and 26 percent of Chinese students (see Figure 2).

Figure 2: Educational attainment of selected groups in the U.S., ages 25 and over


Note: Missing from table are figures for “some college” and ‘graduate/professional degree”
Source: U.S. Census Bureau, American Community Survey (2015)

Socio-Economic Status

The median Asian family income is higher than for all racial groups, which could be attributed to a higher proportion of Asian Americans with college degrees. According to data from the 2013 Current Population Survey Annual Social and Economic Supplement, the 2012 national average income for people living in poverty was 15 percent. Asians fared better than most with around 12 percent below poverty compared to almost 10 percent for non-Hispanic whites, less than 26 percent for Hispanics (of any race), and approximately 27 percent for blacks.

But similar to educational attainment data, aggregating income data masks challenges of certain subgroups within the Asian population. Disaggregating 2010 Census data shows that the poverty rate is highest among Hmong (27 percent) and Cambodians (22 percent).

So What?

The projected growth and diversity of the pan-Asian population calls for data disaggregation and more research so higher education can ensure educational access and equity for all.

Ethnically disaggregated data allows institutions to tailor programming, services, events and information to their student and family populations. This includes orientation, graduation, financial aid, and scholarships in specific Asian languages. Knowledge of ethnicity aids institutions in developing culturally relevant mentoring and training for student affairs, faculty, career services, and other professionals highly engaged with students. It arms institutions with data on how Asian ethic groups utilize or under-utilize educational and support services so they can strategically deploy resources. Institutions also can gain a better, more nuanced understanding of campus climate and inclusion by looking at ethnic bias and racially or culturally divisive incidents.

However, not all stakeholders are on board with disaggregating data by ethnicity. The Asian American Coalition for Education contends that disaggregating data by ethnicity for some racial groups and not others is discriminatory. To this end, they recently called for a stop to disaggregating Asian groups in tools such as the Common Application, an undergraduate admissions application for over 700 member institutions. This tension presents an opportunity to step back to consider some fundamentals of data collection in higher education.

Data used by colleges and universities usually fall into two categories: accountability data and administrative data. The Integrated Postsecondary Education Data System, commonly known as IPEDS, can be classified as accountability data. Institutions that participate in federal student financial aid programs are required to report demographic, educational attainment, and other data each year to the federal government. It is aggregate, standardized, publically available data that lends itself to high-level comparisons at the federal, state, system, and institution levels. Such data are valuable for key policy and decision makers who tend to think about economics of scale related to student success, for example. What is missing from aggregate level data, however, are variations that can be localized.

For institutions and students, value also lies in more personalized data, which can inform administrative and academic systems. As previously mentioned, disaggregating administrative data can assist postsecondary institutions in promoting diversity and equity through policies and programs designed for these individual groups. One initiative that sought for better administrative data is the successful “Count Me In” campaign UCLA undergraduates initiated in the early 2000s. Students launched a nine-month campaign for the university to further disaggregate data by ethnicity data on Asian American and Pacific Islander (AAPI) students so the institution could more effectively support their educational health.

In recognizing that different data are needed for different purposes, we must also consider that size matters. The degree of disaggregation depends on its use. Minneapolis, home to a large Hmong population, might use disaggregate race/ethnicity data for research to inform local policy that supports needs specific to its population. Similarly, school districts with large ethnic populations might disaggregate by language spoken at home to plan and implement education services. The “actionable N”, according to Penn State’s Alicia Dowd in an upcoming paper, “is the number that grabs a person’s attention and motivates them to action where action is needed in their own area of work to ensure quality, equity and effectiveness.”

With an eye toward data disaggregation and the “actionable N,” institutions must also be aware of data challenges. Institutional actors in particular need to be savvy data collectors and users:

  • Consider data for whom. Institutions need to understand what data are aggregated or disaggregated and how. If institutions collect data on ethnic subgroups and enter categorization territory, they need to be aware of the power politics inherent in determining racial/ethnic classifications and ask themselves for what purpose subgroups are being aggregated. The same holds true if pan-racial/ethnic categorizations are disaggregated. For this reason, institutions might want to use the Census Bureau’s established racial categories.
  • Be careful not to conflate issues. Institutions also need to be careful not to conflate student-level issues with institutional ones. If troubling trends exist across or within racial/ethnic groups, institutions should consider systemic problems that could be contributing to such trends. For instance, look first to deficiencies in policies and programs. Fortunately, tools such as the Equity Scorecard exist to aid institutions in identifying inequitable policies and practices.


Despite the critics, research advocates for disaggregate data to help institutions promote diversity and equity and assess effectiveness of education policies and programs for populations that fall under pan-racial categorizations.

Without disaggregate data, Asian subgroups can get lost in a system blind to their presence and needs and be denied access to resources that could direct them toward a credential or degree path. An institution’s overall student population loses out in this equation as well. At a basic level, the presence of a population as diverse as Asians supports an institution’s diversity agenda by broadening student, faculty, and staff understanding of diversity—religion, language, immigration status, socioeconomic level—and enriching the collective college experience.


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