A closer look at who’s in Canada’s nursing workforce – and how their earnings compare
Author: DataNB
Posted on Sep 2, 2026
Category: DataNB
If you walk into almost any healthcare setting in Canada, you’ll meet nurses from a wide range of backgrounds. National data reflect this diversity in new detail. The 2021 Canadian Population Census was the first large-scale survey in the world to capture information on gender diversity. Drawing on these data, alongside detailed information on ethnic diversity and professional labour, research supported by DataNB shows who is represented in nursing, who isn’t, and how earnings compare across groups.
Nearly nine in ten nurses ages 20-64 in Canada are cisgender women. The new census data reveal that transgender women are more represented in the nursing workforce than in the general population it serves, while transgender men and non‑binary people are identified in nursing less often. The share of visible minority nurses was found to have reached proportional representation relative to the population overall, with some ethnic groups more highly represented, including nurses from Filipino and Black communities. Nurses of Indigenous identity are under-represented.
Earnings differences tell another part of the story. Even after accounting for education and other factors, cisgender women, despite being the strong majority in nursing, earned less on average than cisgender men. Transgender men also faced a notable earnings gap, although not transgender women or non-binary nurses. Among racial‑ethnic minority groups, nurses who were of West Asian, Arab, South Asian, Latin American, Black, Korean, or Filipino origin earned less than their White counterparts, while those of Indigenous, Chinese, Japanese, or Southeast Asian origins showed no discernible differences.
Taken together, these findings show rich demographic diversity in the nursing workforce, but that experiences are not even across communities – as illustrated by compensation differences even when similar types of work are being done. By using nationally representative census data to reach these conclusions, this study highlights why data disaggregation matters. When people’s identities are detailed, the nuances become visible, and so do the conversations needed to understand and address them.
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