Can Data Disaggregation Resolve Blind Spots in Policy Making? Examining a Case for Native Hawaiians

• In Event: Dis-Orienting Asian Pacific America: Emerging Perspectives on the Asian American and Pacific Islander Educational Experience

Abstract

Objectives:
Today, large volumes of data are much easier to capture, manage, and process, enhancing its application for a wider range of organizations. Given that a broad spectrum of educational institutions and governmental agencies have already embraced Big Data and data science, will this trend help towards effective policy-making when it comes to addressing educational challenges faced by Asian American and Pacific Islanders (AAPI)?
By examining those who identified as Native Hawaiians in the U.S. Census, this study can uniquely reveal the promises and limits of data-driven policy-making that affect AAPI communities. Given the rich yet complicated history of Hawaiʻi (McGregor, 2002; Trask, 1993), these particular forms are especially unique for Native Hawaiians as compared to other AAPI groups. According to Spickard (2002), “Pacific Islanders historically have constructed their ethnic identities rather more complexly than many other peoples. Pacific Islanders have long had a greater consciousness than other American groups of being mixed peoples, of having multiple ethnic identities” (p. 43). Because there are numerous problems in determining the primary cultural affiliation of individuals in a multicultural society, those figures have led to disagreement among agencies on how ethnicity should be determined.
Thus, our purpose is to examine patterns, based on the complex attributes of Native Hawaiians, which can help illuminate the extent Big Data informs policy-making and addresses educational opportunity gaps for AAPIs.

Methods/Data:
To examine whether the trend toward embracing data-driven decision-making can better serve Native Hawaiian communities at the policy level, we analyzed data collected by the Census, specifically the American Community Survey. The sample included those who self-identified as Native Hawaiian and further disaggregated by ancestry and geographic location. For the latter, we chose Hawaiʻi and California, states with the largest numbers of Hawaiians. The analyses compared these disaggregated Hawaiian groups across various policy relevant indicators (educational attainment, educational enrollment, household income, unemployment, and poverty rates).

Results/Significance:
Key patterns emerged for those who identified as Native Hawaiians as data was further disaggregated. First, those who reside in California tended to report higher levels of income and educational attainment, whereas their counterparts in Hawaiʻi reported higher rates of unemployment and poverty. Second, those who identified as Native Hawaiian Only tended to do worse than their state counterparts, having among the highest unemployment and poverty rates and lowest median household income and educational attainment rates. While those who identified as Native Hawaiian, Asian, and White in California consistently reported doing much better than everyone else.
Variations by geography and ancestry confirm heterogeneity of this population, which has implications for data-driven policy-making. This study addressed whether those efforts concerning data disaggregation at the most basic level stand to improve the capacity of data-driven policy-making to address challenges faced by AAPI communities. While previous studies already pointed out the importance of disaggregating data by ethnic groups within the AAPI population, our study took this call for disaggregation further. Our findings show more granular level disaggregation reflects different trends within the Native Hawaiian population, which can complicate policy-making.

Authors

• Mike Hoa Nguyen, University of California - Los Angeles

• Kapua Chandler, University of California - Los Angeles

• Mitchell J. Chang, University of California - Los Angeles