What is Data Erasure and Why It's Important

• Data approach: Our best practice is to include estimates for both ‘AIAN alone’ and ‘AIAN alone and in combination with another race’. If we are unable to include both, we compare the estimates and decide intentionally with partners which to use—with ‘AIAN alone or in combination’ often being the preferred definition. The Census Bureau also has data on urban and rural AIAN groups. The experiences of indigenous people living in an urban space versus in more rural areas or at a reservation can vary greatly and should be considered when analyzing AIAN data.  
• Why this is important: The Census Bureau’s definition of AIAN still does not include all indigenous groups. Because Native Americans have a very high percentage of multiracial people, the Census Bureau’s tendency to use strictly ‘AIAN alone’ results in exclusion of a large portion of multi-racial AIAN people. In fact, current data collection methods exclude more than 75% of Native Americans who also identify as Latinx or another race. Systemic and cultural barriers also limit data collection for this group. The 2020 Census had a significant undercount of AIAN people due to the agency’s failure to fully partner with tribal nations in data collection, among other issues.  
• Examples of AIAN Data Disaggregation: 
  "We the Resilient," California Native Vote Project, accessed August 19, 2024, https://canativevote.org/what-we-do/research/. 

Catalyst California calculations of American Community Survey (ACS) table DP05, 2017-2022 5-year estimates, We the Resilient Report Update by CNVP, Publication forthcoming. 

The lack of comprehensive data disaggregation for the Asian community is one of the most glaring examples of data erasure. While the Census Bureau’s American Community Survey includes more than 50 Asian subgroups, not all Census Bureau data tables can be disaggregated by these subgroups. Sample sizes for Asian subgroups are often too small to be statistically stable for certain population characteristics (e.g., poverty), or for local geographies, making the data difficult to use. Disaggregating Asian data is necessary to reveal the disparities that different Asian groups experience.  

Additional resources on how to get better Asian disaggregated data and why this is important: 

• Disaggregated data resource guides from AAPI Data 
• Heather C. McGhee, "State Data Disaggregation for Asian American, Native Hawaiian, and Pacific Islander Groups," Urban Institute, September 21, 2023, https://www.urban.org/urban-wire/state-data-disaggregation-asian-american-native-hawaiian-and-pacific-islander-groups. 
• Jonathan Schwabish, Alice Feng, "Combining Racial Groups in Data Analysis Can Mask Important Differences Among Communities," Urban Institute, April 12, 2022, https://www.urban.org/urban-wire/combining-racial-groups-data-analysis-can-mask-important-differences-communities.  

• Data approach: Our best practice is to include estimates for ‘NHPI alone’ and ‘NHPI alone and in combination with another race.' If we cannot include both, we review the estimates and intentionally decide with partners which is most appropriate to use. In most cases, we lean towards including 'NHPI alone and in combination.' Collecting data and disaggregating for identities within NHPI, like Native Hawaiian, Chamorro, Samoan, and Tongan, is preferred but not practiced enough. 
• Why this is important: NHPI group data is often categorized with other Asian group data. Asian and Pacific Islander data is rarely disaggregated despite this group’s size and diversity, resulting in the erasure of unique experiences in the data. For example, Asian and Pacific Islander women earn an average of 86 cents for every dollar a White man earns, but Native Hawaiian women are paid only 66 cents. Data often do not reflect that distinction. NHPI people also experienced some of the highest rates of COVID-19 infection in the country, but policymakers and interventions did not target this group because aggregated data could not highlight those racial disparities. The NHPI group is one of the most multiracial groups in the United States. Using ‘NHPI alone’ data will likely result in an undercount of many multiracial NHPI people.      
• Examples of disaggregated NHPI data: 
  Asian Americans Advancing Justice, Community of Contrasts: Asian Americans and Pacific Islanders in the United States (Washington, D.C.: Asian Americans Advancing Justice, 2014), https://www.advancingjustice-aajc.org/sites/default/files/2016-09/2014_Community%20of%20Contrasts.pdf. 

• Data approach: We use the Census Bureau’s reported ancestry field to create a custom definition for a SWANA category, by taking a list of ancestries that we define to be part of the SWANA region and aggregating estimates for those reported ancestries. This group is also commonly known as Middle Eastern or North African (MENA).   
• Why this is important: Many people from the Southwest Asian ("Middle East") and North African regions do not see themselves represented in the census, especially under the race question. As a result, they may identify as ‘White’ or ‘Other Race,’ or not identify themselves at all for lack of options. This renders invisible a huge group of people who we know face unique experiences. Wars and conflict in the Southwest Asian and North African region have driven many people to the United States as refugees, where their identities have been negatively stereotyped. Without proper data disaggregation and accurate self-identification, this group's invisibility can hinder effective policymaking and systemic change efforts. 
• Examples of SWANA data disaggregation: 
  Queer Crescent nation-wide survey of LGBTQI+ Muslims  

Catalyst California, Bold Vision Mid-Term Report, Forthcoming, 2024. www.boldvisionla.org  

Identifiers for this community have been changing rapidly in recent years. Catalyst California has chosen to use Latinx and will continue monitoring usage to determine if a change is appropriate. 

• Data approach:  In our analyses, we define Latinx as ‘Latinx alone or in combination with other groups.’ Latinx identity is multifaceted. To accurately reflect this diversity, it is also important to consider disaggregating based on ancestry, nationality, immigration status, or intersecting racial identities.   
• Why this is important: Latinx people represent more than 30 nationalities and racial groups. Not all Latinx people feel that their identity is captured in the current census race question. For example, in the 2020 Census, 8 percent of Latinx people left the race question blank, and 35 percent indicated they were of “some other race.” The Afro-Latinx community is also a group that is consistently misrepresented in large data sets. Lack of disaggregation hides several disparities between Afro-Latinx and non-Black Latinx people around health, education, poverty, and homeownership. As the Census Bureau implements the new standard to include Latinx as a race, it will become increasingly important to critically assess disaggregation for the Latinx community to ensure Latinx people of different racial identities are not undercounted.  
• Example of disaggregating for Afro-Latinx:  
  Latino Policy and Politics Initiative. Centering Black Latinidad. UCLA, April 2023. https://latino.ucla.edu/wp-content/uploads/2023/04/LPPI-116-Centering-Black-Latindad-2023-FINAL-2.pdf.