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Guide offers clarity on using race & ethnicity variables in administrative data research


As part of its commitment to anti-racism research, the BC government has published a guide on using race and ethnicity variables for administrative and survey data users. This follows the passing of the Anti-Racism Data Act in 2022 and the release of 12 anti-racism research priorities earlier this year.

Anti-racism research plays an important role in the government’s commitment to addressing systemic racism, according to Tatiana Kim, Manager of Anti-Racism Data Projects at BC Stats. Kim, who is part of the team at BC Stats that supports research aimed at understanding and addressing systemic racism in government programs and services, was the inaugural speaker at HDRN Canada’s Big IDEAs About Health Data Speaker Series.

Kim discussed a range of barriers faced in the development of the Guide on Using Race & Ethnicity Variables. “There’s uncertainty around when and how to use race and ethnicity data and there was a lack of policy structures that would support safe use of this data,” she said. “Passing the Anti-Racism Data Act was itself a first step towards removing some of these barriers. It built a foundation for safe collection and use of race-based data in research and reporting.”

The guide was designed for users of existing provincial administrative and survey data sets, such as those available through BC ’s Data Innovation Program, statistical programs, government records management systems and business applications. Its aim is to iron out differences in the way race and ethnicity data are used and interpreted. “The increased availability and use of race-based data carries a risk because we know it has been and still is misused, and used inconsistently, both within and outside of government,” explained Kim. A key reason that race and ethnicity data are misused is simple: “the concepts themselves are flawed. They’re socially constructed and their meanings and definitions change as a result. The way they are measured also varies and changes” making it challenging to use data consistently and appropriately, she continued.

The need to support data users with guidance on the appropriate and safe use of race and ethnicity data was clear, Kim affirmed: “Not only does the misuse of race and ethnicity data diminish the quality of research, it can also harm racial and ethnic groups by perpetuating stereotypes, stigmatizing racial and ethnic groups, blaming them for their own marginalization, and misinforming program and policy solutions.”

According to Kim “fuzzy variables” posed something of a challenge when developing the guide. “Race and ethnicity concepts and their respective variables are loosely defined and there is no consensus on what they mean. They are often used together or interchangeably or as proxies for related concepts such as nationality or culture or language.” The guide meets this challenge head on in its first recommendation, which calls for the definition of race and ethnicity as distinct social concepts requiring justification for their use. “This encourages users to decide whether they want to use either race or ethnicity, then to define what they mean by this concept even before they choose their data set, and finally, to justify why it is relevant in their case. Concepts should not be used out of curiosity or convenience, or as proxies for other concepts because it creates risks of perpetuating stereotypes or coming to unfounded conclusions,” she added.

While the guide’s nine recommendations are distinct, they share a general call for thoughtfulness and precision in defining, describing and discussing race and ethnicity, as well as in evaluating their role in research findings. “Reflecting on what the data means and doesn’t mean will help consumers of [the research] understand the limitations of the findings and use them appropriately. It will also help discourage deficit narratives and other harmful interpretations.”

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