(This post is part 2 in a series)

BY SONO SHAH AND KARTHICK RAMAKRISHNAN

The White House Office of Management and Budget (OMB) has invited public comments on potential revisions to its 1997 standards. Currently, federal agencies are not required to count detailed data for Asian, Native Hawaiian and Pacific Islander Americans. In many cases, reporting by racial group can mask important differences among Asian and NHPI sub-groups. Thus, AAPI communities often remain misrepresented, under-funded, and left out of policy and program decisionmaking.

In order to address this problem, it is vital to address not only the consistent collection of disaggregated data, but also its reporting and accessibility. Please consider joining the #CountMeIn campaign organized by a coalition of national AAPI organizations.

At AAPI Data, we seek to contribute to this conversation on federal data standards by illustrating the importance of AAPI data disaggregation. We do so in a series of blog posts this week focusing on particular outcomes, continuing with education.

Educational Attainment

The model minority stereotype often frames AAPIs in terms of their high educational attainment and advanced degrees. This notion is misguided and is driven by an incomplete picture of AAPI educational attainment. Among AAPIs there is substantial variation between groups. Just 1 in 4 Burmese in the United States have a Bachelor’s degree or higher compared to about 3 in 4 Taiwanese and Indian Americans. On the other end of the education scale, nearly 62% of Bhutanese and 50% of Burmese in the United States lack a high school degree, outcomes that are worse than African Americans and Latinos.

Without access to disaggregated data, institutions and researchers will not be able to properly identify groups that are marginalized and underrepresented in higher education. Recently, states such as Oregon, Minnesota, and Washington have taken steps to collect and report disaggregated data to match census reporting categories. In California, the University of California and California State University systems have committed to releasing disaggregated education data, even though attempts to include education in a state data disaggregation bill stalled in 2016.

One final note : the analyses above rely on microdata from the American Community Survey which is administered by the U.S. Census Bureau. We believe it is critical for the Census to continue collecting this data, expanding the number of detailed categories, and making the collection of detailed categories standard across federal datasets. Finally, and perhaps most importantly, this data needs to be more user-friendly and publicly accessible, rather than being hidden under a maze of FactFinder tables or requiring statistical software and knowledge to work with individual-level microsample data.

References and Links:

For those interested in more detail about AAPI educational attainment please scroll below to find more estimates by race and detailed origin. The tables and graphs are based on the 2011-2015 ACS Public Use Microdata Sample which is available in CSV form here. Interested users can also download this data from IPUMS from the University of Minnesota.

Center for American Progress & AAPI Data collaborated on a series of reports on AAPIs, including one on Education, you can find the consolidated report here.

For more research and data on this topic, please visit our Education page at AAPI Data.

Please consider joining the AAPI #CountMeIn commenting campaign.

Less than High School Diploma HS Diploma or GED Some College or AA BA or Higher White 11.40 28.23 29.42 30.95 Black 16.23 31.33 33.06 19.37 Latino 35.25 27.02 23.51 14.21 AAPI 13.95 15.98 19.78 50.29

Less than High School Diploma HS Diploma or GED Some College or AA BA or Higher Asian Indian 8.20 8.84 10.37 72.59 Bangladeshi 16.34 18.43 17.48 47.75 Bhutanese 61.95 17.08 9.94 11.03 Burmese 49.49 15.30 10.44 24.77 Cambodian 33.63 25.97 23.69 16.71 Chinese 18.49 14.62 14.37 52.52 Filipino 7.39 15.16 29.57 47.87 Hmong 31.21 22.84 29.05 16.89 Indonesian 5.83 18.44 27.97 47.75 Japanese 4.83 18.99 26.68 49.50 Korean 7.46 18.08 20.34 54.12 Laotian 29.13 30.46 26.36 14.04 Malaysian 14.84 15.85 14.21 55.10 Mongolian 6.44 11.36 23.62 58.57 Nepalese 28.00 14.86 14.07 43.07 Pakistani 13.24 15.45 18.07 53.24 Sri Lankan 8.00 15.80 19.01 57.19 Taiwanese 4.42 7.17 13.30 75.11 Thai 16.45 17.31 22.36 43.88 Vietnamese 27.90 21.93 22.94 27.23