# Study Notes: Data Disaggregation and the Debate Over AAPI Identity ## Overview Efforts to disaggregate data for Asian American and Pacific Islander (AAPI) communities have faced numerous challenges, including government bureaucracy, community disagreements, and debates over identity. However, the demand for more precise data has grown, especially during the COVID-19 pandemic, which highlighted disparities masked by aggregated data. --- ## Key Concepts ### What is Data Disaggregation? - **Definition**: The process of breaking down aggregated data into smaller, more specific subgroups. - **Purpose**: To reveal disparities and nuances within diverse populations, enabling targeted policies and interventions. For instance, during the COVID-19 pandemic, Native Hawaiians and Pacific Islanders (NHPIs) experienced disproportionately high infection and death rates. However, these trends were obscured because NHPIs were grouped with Asian Americans in federal data. --- ## The Origins of AAPI Identity ### Early Self-Identification - Before the 1960s, individuals of Asian descent in the U.S. identified primarily by their ethnic subgroup (e.g., Chinese, Filipino, Japanese). - The derogatory term "Oriental" was commonly used by policymakers until it was formally removed from federal law in 2015. ### The Birth of "Asian American" - Coined in 1968 by activists at UC Berkeley, the term "Asian American" was inspired by the Black Power Movement and aimed to unify diverse Asian subgroups for political advocacy. - The term gained prominence after events like the 1982 killing of Vincent Chin, which galvanized pan-Asian solidarity. The term "Asian American" was initially radical and political, designed to challenge stereotypes and build collective power. --- ## Federal Involvement in Racial Classification ### Directive No. 15 (1977) - Issued by the Office of Management and Budget (OMB), this directive standardized racial and ethnic categories for federal data collection. - Created the "Asian or Pacific Islander" (API) category, grouping diverse populations from Asia and the Pacific Islands. The API category was not based on cultural or scientific similarities but on geographic and political considerations, leading to oversimplification. ### The Push for Disaggregation - Activists criticized the API category for masking disparities and perpetuating the "model minority" myth. - In 1997, OMB revised Directive 15, splitting the API category into: - **Asian American** - **Native Hawaiian or Other Pacific Islander (NHPI)** This change allowed for more accurate representation of NHPIs, whose needs and challenges often differ significantly from those of Asian Americans. --- ## The Case for Data Disaggregation ### Benefits 1. **Revealing Disparities**: Disaggregation highlights differences in health, education, and economic outcomes among subgroups. - Example: Poverty rates and English proficiency vary widely between Asian Indian, Vietnamese, and Chinese Americans. 2. **Informed Policy**: Enables targeted interventions tailored to specific communities. 3. **Empowering Communities**: Provides visibility and recognition to smaller, often overlooked groups. Hawaii, with its detailed disaggregated data, has been able to better address the needs of its diverse population, including NHPIs. ### Challenges 1. **Cost and Complexity**: Implementing disaggregation requires additional resources and infrastructure. 2. **Privacy Concerns**: Smaller sample sizes can make it difficult to protect individual identities. 3. **Resistance**: Some groups fear disaggregation could weaken collective political power or lead to racial profiling. Critics often conflate data disaggregation with affirmative action or racial quotas, leading to misinformation and opposition. --- ## Historical and Political Context ### The Role of Activism - Advocacy groups have long pushed for disaggregation to combat stereotypes and improve resource allocation. - The 1990s saw increased lobbying for changes to OMB's racial categories, with Native Hawaiians leading efforts to be recognized separately. ### Federal and State Efforts - **Federal Initiatives**: - The Obama administration launched the iCount campaign to improve AAPI data collection. - The Biden administration established the Equitable Data Working Group to expand disaggregated data collection. - **State Laws**: - States like New York and California have passed laws mandating detailed data collection on AAPI subgroups. Despite federal progress, state-level initiatives often lead the way in implementing comprehensive disaggregation practices. --- ## Real-World Applications ### Public Health - Disaggregated data is critical for identifying health disparities. - Example: Guam used disaggregated data to allocate healthcare resources during the pandemic. ### Education - Disaggregation helps identify gaps in educational attainment and access among AAPI subgroups. - Example: Federal grants have supported studies on AAPI student outcomes by ethnicity. ### Advocacy and Policy - Organizations like Stop AAPI Hate use disaggregated data to track and address hate incidents against specific AAPI subgroups. Disaggregated data is not just about numbers—it's about ensuring that every community's unique challenges are addressed. --- ## Common Misconceptions 1. **"Disaggregation is divisive."** - Reality: It highlights diversity within groups, ensuring no subgroup is overlooked. 2. **"It’s only for AAPIs."** - Reality: Disaggregation benefits all racial and ethnic groups by providing more accurate data. 3. **"It’s too expensive."** - Reality: The long-term benefits of informed policymaking far outweigh the initial costs. Assuming aggregated data is sufficient can lead to policies that fail to address the needs of marginalized subgroups. --- ## Key Takeaways 1. **Data disaggregation is essential for equity.** It ensures that the unique needs of diverse subgroups are visible and addressed. 2. **Historical context matters.** The creation of pan-ethnic categories like "Asian or Pacific Islander" was politically motivated and not reflective of cultural realities. 3. **Progress is slow but ongoing.** Federal and state efforts, along with community advocacy, continue to push for better data practices. 4. **Challenges remain.** Resistance, costs, and methodological issues must be addressed to fully realize the benefits of disaggregation. When analyzing data, always ask: "Who is being included, and who might be left out?" --- ## Practice Questions 1. **Why is data disaggregation important for AAPI communities?** 2. **What were the main changes introduced by OMB’s 1997 revision of Directive 15?** 3. **What are some challenges associated with implementing data disaggregation?** 4. **How has the COVID-19 pandemic highlighted the need for disaggregated data?** **Question**: How does disaggregated data help combat stereotypes like the "model minority" myth? **Answer**: By breaking down data into subgroups, disaggregation reveals disparities within the AAPI community, such as higher poverty rates among certain groups, challenging the notion that all AAPIs are uniformly successful. --- By understanding the history, benefits, and challenges of data disaggregation, we can advocate for more equitable policies that truly reflect the diversity of AAPI communities.