Why was this analysis done?

In 2021, the Canadian Privy Council Clerk made a Call to Action on Anti-Racism, Equity, and Inclusion, urging the Government of Canada to take action against the effects of racism and discrimination within the Canadian Public Service. In particular, the call to action recommends that specific and targeted measures be taken to address the lack of diverse leadership. This analysis addresses one of the Privy Clerk’s recommended actions, which is:

“Measuring progress and driving improvements in the employee workplace experience by monitoring disaggregated survey results and related operational data (for example, promotion and mobility rates, tenure)…”

What does the “Non-Visible Minority” category entail?

The Non-Visible Minority (non-VM) category contains data from any employee who has not self-identified as a visible minority.

A few caveats are that it is possible that this category includes:

• employees who are visible minorities that have chosen not to self-identify
• members of other equity deserving groups.

In 2022, this group made up approximately 85% of all core Public Service employees.

Are Indigenous employees included in the Visual Minority (VM) Group?

Indigenous employees are not included in the VM group. Due to the Employment Equity Act’s classifications of the group, Indigenous employees’ data is collected separately from the VM group.

Are Black employees included in the Visual Minority (VM) Group?

Yes, Black employees are a subset of the VM group.

What about the effects of level of education, age, tenure, ability to speak official languages, and other covariates? How do they play into Black employees’ representation? Is it possible they explain the differences we see?

To examine the effects of covariates, the Disproportionality Index (DI) would be need to be calculated directly from microdata (individual data) rather than aggregated counts, which is what is available on the Treasury Board site. This microdata contains personal and confidential information, and must be handled by personnel with the required security clearance. We highly recommend that DI be calculated on the complete and original dataset.

The Treasury Board reports data on employee:

• Location
• Occupational group/category
• Number of executives
• Salary
• Age
• Tenure
• Language

Depending on data linkages and format, it could be possible to quantify the effects of these covariates.

Are the observed differences statistically significant?

As with the previous question, to quantify uncertainty and determine the statistical significance of the differences between groups, the Disproportionality Index (DI) would be need to be calculated directly from microdata. This microdata contains personal and confidential information, and must be handled by personnel with the required security clearance. We highly recommend that DI be calculated on the complete and original dataset.

If the differences are due to covariates related to differences in demographics (ex: level of education, age, tenure, ability to speak official languages, etc), does that mean that the differences don’t need to be addressed?

The Employment Equity Act and the concept of Equality at large in Canada operate within the legal framework of equal outcomes, which means that employers have an obligation to ensure that employees success is not dependent on their race, gender, or any other factor related to the designated employment equity groups. This data illustrates that equal outcomes are not being achieved in the core Public Service, and therefore we defer to the recommendations outlined in the Privy Council Clerk’s Call to Action on Anti-Racism, Equity, and Inclusion in the Federal Public Service for further action on resolving the career marginalization that Black public servants face.