Blog: Special Series — Using Race-Based Data as a Form of Transparency and Accountability
The last in our series of blog posts on anti-racism focuses on race-based data, sometimes disaggregated data. Race-based data is used as an indicator to assess and understand the extent of racism and discriminatory practice. This data can be used to advocate for changes in practice, policies, and to substantiate (or refute) narratives around racism. Used properly, race-based data can increase transparency and accountability within institutions and systems to track discrimination and inequity (Nerenz, 2005; Owusu-Bempah & Millar, 2010; Hasnain-Wynia, Weber, Yonek, Pumarino, & Mittler, 2012).
Currently in Canada, racial and ethnic data is collected across various settings using differing methodologies. Examples of collection include, but are not limited to, the Census of Canada, provincial health care systems, immigration, crime and justice, and less often, social services, education, and social inclusion. Even within each sector, the collection and use of racial and ethnic data changes over time. Ontario, for example, recently created “Data Standards for the Identification and Monitoring of Systemic Racism” as an extension of the Anti-Racism Act of 2017. In Canada, there is a lack of population-wide, disaggregated, high-quality race-based data (that is, rigorously validated, collected regularly, and appropriately used).
In order to better understand why this is the case, it is necessary to examine the history behind the use and collection of racial and ethnic data in Canada. In 1994, the Canadian Journal of Law and Society released a special issue that captured arguments against collecting race-based data, specifically within the context of crime and policing. These arguments can be briefly summarized as follows:
- Routine race-based data collection would normalize racism by creating and perpetuating artificial categories. The idea of “race” was not perceived to be based on sound biological research, and so race-based data would entrench social constructs that were inherently flawed (Johnston, 1994). In addition, researchers of the time be feared that data may be abused to further justify racist attitudes or beliefs, especially by media or members of the public.
- Without concrete recommendations, race-based data would not lead to actionable change in programming or policy (Hatt, 1994; Roberts, 1994). On its own, race-based data could not inform what new programs or policies would be needed.
- The creation of race classifications is a balancing act: too many racial categories can be confusing for participants and challenging to analyze and make sense of; too few categories, and the data may no longer be useful in specific contexts, or lack explanatory power to be meaningful (Gabor, 1994; Hatt, 1994; Johnston, 1994). Further challenges included how exactly to define each category, especially groups which included a complex diaspora. Debates persisted around using the different uses of Country of Origin, Place of Birth, Ethnicity, or even vague terms such as “African,” “Asian,” or “Mixed.”
Without clear evidence that the collection of race-based data would not be abused, or even provide insight, the consensus among academics was that it would be unethical (Johnston, 1994), in addition to unfeasible, to collect systematic and rigorous race-based data.
In the 25 years that have since elapsed, some of these challenges are still in play (Bhopal, 2006; Wolf, 2006; Aspinall, 2009; Derose, Contreras, Coleman, Koebnick, & Jacobsen, 2013; Charmaraman, Woo, Quach, & Erkut, 2014). However, critical pivots have altered the modern discourse. As outlined by Owusu-Bempah and Millar (2010), Black, Indigenous, and People of Colour (BIPOC) activists and organizations have started to demand the systematic collection of race-based data. Racism and discrimination have persisted, and not collecting race-based data did not benefit marginalized communities of colour. Political discourse has also changed, with scholars and policy-makers in Canada, the United States (Derose et al., 2013), and the United Kingdom (Song, 2018) beginning to regularly collect and use population-wide and rigorous race-based data sets (Aspinall, 2018).
Closer to home, Black Lives Matter Edmonton, the Institute for the Advancement of Aboriginal Women, and the Stolen Sisters Awareness Movement requested racialized data from the Edmonton Police Services regarding the practice of street checks (Wakefield, 2017). Most recently, Students4Change advocated for collection of race-based data at Norquest College (Norquest College, 2020). The Edmonton Public School Board has also voted to begin collecting race-based data in their school district, the first one in Alberta to do so (Junker, 2020).
On the one hand, collecting race and ethnicity data allows for breakdowns according to different populations and communities. On the other hand, race-based data needs to be collected in a way that is meaningful to the communities that the data is about, reflects their interests, and is not based on a racialized “essentialism” that is rooted in stereotypes and white supremacy. In order to move forward, it is important that all attempts to collect race-based data are tied to:
- The creation of an overarching national framework and strategy to address racism in Canada that would guide data collection practices (Hasnain-Wynia et al., 2012). Currently data practices are fractured (Millar & Owusu-Bempah, 2011), poorly examined for rigor and quality (Aspinall, 2018), and lack high-level goals that inform how the data is collected and used. This framework would also safeguard against racial and ethnic data misuse. The Anti-Racism Data Standards from Ontario provide one such starting point.
- Engagement of BIPOC communities so that they are looked to as leaders who inform the use and ownership of racial and ethnic data (Hasnain-Wynia et al., 2012; Owusu-Bempah & Millar, 2010). One example are the First Nations principles of OCAP (ownership, control, access, and possession), as outlined by Schnarch (2004), that provide a political response to colonial approaches to data collection.
Aspinall, P. J. (2009). The future of ethnicity classifications. Journal of Migration and Ethnic Studies, 35(9), 1417–145. doi 10.1080/13691830903125901
Aspinall, P. J. (2018). What kind of mixed race/ethnicity data is needed for the 2020/21 global population census round: the cases of the UK, USA, and Canada. Ethnic and Racial Studies, 41(11), 1990–2008. doi: 10.1080/01419870.2017.1346267
Bhopal, R. (2006). Responsible use from epidemiological and public health perspectives. Journal of Law, Medicine and Ethics, 34(3), 500–507.
Charmaraman, L., Woo, M., Quach, A., & Erkut, S. (2014). How have researchers studied multiracial populations? A content and methodological review of 20 years of research. Cultural Diversity and Ethnic Minority Psychology, 20(3), 336–352. doi: 10.1037/a0035437
Derose, S. F., Contreras, R., Coleman, K. J., Koebnick, C., & Jacobsen, S. J. (2013). Race and ethnicity data quality and imputation using U.S. census data in an integrated health system: The kaiser permanente Southern California experience. Medical Care Research and Review, 70(3), 330–345. doi:10.1177/1077558712466293
Gabor, T. (1994). The suppression of crime statistics on race and ethnicity: The price of political correctness. Canadian Journal of Criminology, 36(2), 153–163.
Government of Ontario. (2020). Ontario’s anti-racism strategic plan. Retrieved from https://www.ontario.ca/page/ontarios-anti-racism-strategic-plan
Hasnain-Wynia, R., Weber, D. M., Yonek, J. C., Pumarino, J., & Mittler, J. N. (2012). Community-level interventions to collect race/ethnicity and language data to reduce disparities. The American Journal of Managed Care, 18(6 Suppl), s141-147
Hatt, K. (1994). Reservations about race and crime statistics. Canadian Journal of Criminology, 36(2), 164–165.
Johnston, J. P. (1994). Academic approaches to race-crime statistics do not justify their collection. Canadian Journal of Criminology, 36(2), 166–174.
Junker, A. (2020, September 23). Edmonton Public Schools becomes first jurisdiction in Alberta to commit to collecting race-based data. Edmonton Journal. Retrieved from https://edmontonjournal.com/news/local-news/edmonton-public-schools-becomes-first-jurisdiction-in-alberta-to-commit-to-collecting-race-based-data
Nerenz, D. R. (2005). Health care organizations’ use of race/ethnicity data to address quality disparities. Health Affairs, 24(2), 409–416. DOI: 10.1377/hlthfaff.24.2.409
Norquest College. (2020). NorQuest College takes steps to sustain success of black students. Retrieved from https://www.norquest.ca/media-centre/news/2020/norquest-college-takes-steps-to-sustain-success-of-black-students.aspx
Owusu-Bempah, A., & Millar, P. (2010). Research note: Revisiting the collection of “justice statistics by race” in Canada. Canadian Journal of Law and Society, 25(1), 97–104. DOI: 10.1017/S0829320100010231
Roberts, J. V. (1994). Crime and race statistics: Toward a Canadian solution. Canadian Journal of Criminology, 36(2), 175–186.
Schnarch, B. (2004). Ownership, control, access, and possession (OCAP) or self-determination applied to research. Journal of Aboriginal Health 1(1), 80–95.
Song, M. (2018). Why we still need to talk about race. Ethnic and Racial Studies, 41(6), 1131–1145. DOI: 10.1080/01419870.2018.1410200
Wakefield, J. (2017, June 27). Black people, aboriginal women over-represented in “carding” stops. Edmonton Journal. https://edmontonjournal.com/news/local-news/black-people-aboriginal-women-over-represented-in-carding-police-stops
Wolf, S. M. (2006). Debating the use of racial and ethnic categories in research. Journal of Law, Medicine and Ethics (34)3, 483–486. DOI: 10.1111/j.1748-720X.2006.00059.x