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Income inequality has become one of our greatest obstacles to economic mobility, as U.S. residents today face unequal opportunities and access to the American Dream. Some people have it better than others: Whites earn higher incomes and greater access to education and health care than communities of color. But there are large variations even between different communities of color, with African Americans, Latinos, and Native Americans—as well as multiracial Americans and Asian Americans and Pacific Islanders, or AAPI—all facing different challenges. There are further differences within these individual populations, particularly among AAPIs. As policymakers craft interventions to best address inequality, it is vital that their data are robust and their analysis is performed thoughtfully. This will ensure not only that policy solutions efficiently address the problem but also that they successfully acknowledge the diversity within different communities.

While not the only criterion, efficiency is very important to the design of public policy. More efficient public policy means that more government services and social programs can help Americans who need assistance. For programs to be efficient, however, their target audiences must be clearly identified; this is not always a simple task. In the United States, identifying target audiences to determine the distribution of public services often requires a working definition of race and ethnicity, as communities of color frequently struggle with economic disadvantages that require these services. But population data that are broken down by race and ethnicity often only exist at highly aggregated levels, meaning that groups of people with very different cultural, social, and historical backgrounds end up being lumped into one larger group. For example, people of Chinese, Indian, Pakistani, Vietnamese, Cambodian, and Laotian descent— among many others—make up the Asian American population, even though their socioeconomic experiences vary widely. Therefore, programs and services targeted toward only the broader Asian American population may struggle to meet the specific needs of some subpopulations.

This report discusses some of the data available on Asian Americans. It then presents and explains the challenges associated with the data and offers policy recommendations to address them. During our research, we discovered that:

• Asian Americans are a very diverse population group. The term “Asian” in official government statistics is a racial category based on the history of U.S. migration and race relations. It encompasses immigrants from Asia and people of Asian descent born in the United States. Asians come from Chinese, Indian, Pakistani, Bangladeshi, Cambodian, Vietnamese, and Thai backgrounds, among many others. Native Hawaiian and Pacific Islander has been a different racial category in the decennial census since 2000, and the category was added for data collected by all federal agencies no later than January 1, 2003.
• People of Asian descent are the fastest-growing population in the United States. The portion of the U.S. population that self-identifies as Asian grew 46 percent from 2000 to 2010. The Asian American population grew by 2.9 percent in 2012, compared to the Hispanic population, which grew 2.2 percent. However, the total population of Hispanics is still markedly bigger at 53 million people; there are still only 18.9 million Asian Americans.
• Asian Americans have highly varied economic experiences. A substantial share of Asian American subpopulations struggle with high poverty and a lack of health insurance, but these struggles are often masked by the high employment and incomes of other, larger Asian American subpopulations.

To both increase the number of respondents willing to identify their race and ethnicities and better disseminate disaggregated data, we recommend that the federal government do the following:

• Conduct surveys in the most common languages of relevant subpopulations
• Encourage the Census Bureau and other federal statistical agencies to continue researching more ways to capture subpopulation data, including national origin
• Oversample respondents from subpopulations that are likely to underreport
• Generate disaggregated data in addition to its aggregated data whenever possible
• Create a central data repository on communities of color, including—but not limited to—Asian Americans

Farah Ahmad is a Policy Analyst for Progress 2050 at the Center for American Progress. Christian E. Weller is a Senior Fellow at the Center and a professor in the Department of Public Policy and Public Affairs at the McCormack Graduate School of Policy and Global Studies at the University of Massachusetts, Boston.