Employment Equity Promotion Rate Study - Five-Year Update
Table of Contents
- Executive summary
- Introduction
-
Findings
- Public service-wide results
- Results by occupational categories
- Relative promotion rates within the Executive Category, from feeder groups, and to the Executive Category
- Intersectionality
- Representation of employment equity group members as internal applicants, in the federal public service, and as a share of promotions
- Conclusion
- Appendix A: Methodology
- Appendix B: Overall results
- Appendix C: Results by occupational categories
- Appendix D: Executive results
- Appendix E: Intersectionality
- Appendix F: Propensity to apply
- Appendix G: Groups categorized as Executive Equivalent and Executive Minus 1
- Appendix H: Groups in occupational categories
Key terms used in this report
“federal public service”
- departments and agencies that rely on the Commission’s authority to make appointments as set out in the Public Service Employment Act. The several positions in or under:
- the departments named in Schedule I to the Financial Administration Act;
- the organizations named in Schedule IV to that Act; and
- the separate agencies named in Schedule V to that Act.
“employees” or “public servants”
- indeterminate public servants employed in part of the public service to which the Commission has exclusive authority to make appointments.
“employment equity groups”
- under the Employment Equity Act, the 4 employment equity designated groups are women, members of visible minorities, Aboriginal people (referred to as Indigenous Peoples throughout this study) and persons with disabilities
“Comparators” and “reference group”
- a group of employees not belonging to the employment equity group being considered
- the comparators for women are men, and the comparators for each of the other 3 groups are people who did not self-identify as belonging to that group; for example, the comparators for members of visible minorities and their subgroups are people who did not self-identify as a member of visible minorities
Executive summary
This update of the Employment Equity Promotion Rate Study Footnote 1 was undertaken as part of the Public Service Commission of Canada (PSC)’s oversight mandate to assess the integrity of the public service staffing system under its responsibilities outlined in the Employment Equity Act. The main purpose of the update was to reflect the most recent employment equity data and promotion trends. The key findings from the analysis can be summarized as follows:
First, the relative promotion rates of members of visible minorities continued to increase since the 2008 to 2021 study period. Most visible minority subgroups experienced this increase in relative promotion rates. For example, the relative promotion rate of Black public servants as compared to their comparator went from not being statistically significant (‑1.1%) in the period (2008 to 2021) to a higher relative promotion rate (5.0%) in the latest study period (2010 to 2023) (see Table 1 and Table 2).
Second, persons with disabilities experienced an increase in their relative promotion rates over the same reference periods, but their relative promotion rates remain significantly below those of employees without disabilities. The relative promotion rate of persons with disabilities increased from ‑12.6% to ‑10.9% between the study period (2008 to 2021) and the most recent analysis period (2010 to 2023) (see Table 1).
Third, the relative promotion rate of Indigenous Peoples as compared to non-Indigenous peoples was -6.9% for the study period ending 2023. The relative promotion rate of Indigenous Peoples has remained below non-Indigenous peoples over all study periods reported (see Table 1).
Fourth, women in the Scientific and Professional occupational category went from a lower relative promotion rate when compared to men (-3.1%) in the 2008 to 2021 study period to a relative promotion rate that is not statistically significant (0.3%) in the most recent study period (2010 to 2023). This is the first occurrence of a positive relative promotion rate for women in the Scientific and Professional occupational category over all time periods reported (see Table 6).
Lastly, the report provides additional insights on how relative promotion rates change for employees who are members of multiple employment equity groups in the intersectional analysis section. The PSC’s 2022 update of the original study provided intersectionality results for the first time and the reader can now compare this second installment of intersectionality results to the previous results.
Introduction
The Public Service Commission of Canada (PSC) is responsible for promoting and safeguarding a merit-based, representative and non-partisan federal public service. As part of its oversight role, it undertakes investigations as well as audit and research activities to assess the integrity of the public service staffing system and its performance against intended outcomes. This study is part of these oversight initiatives and assesses the success of employment equity group members (women, members of visible minorities, Indigenous Peoples, and persons with disabilities) in seeking and obtaining promotions, relative to their comparators.
The study expands the analysis of both the original PSC’s 2019 Employment Equity Promotion Rate Study and PSC’s 2022 Employment Equity Promotion Rate Study - Three-Year Update Footnote 2 to include two additional study periods: 2009 to 2022 and 2010 to 2023. The tables displayed throughout the report includes 6 study periods from 2005 to 2018, 2006 to 2019, 2007 to 2020, 2008 to 2021, 2009 to 2022, and 2010 to 2023. However, the findings of the analysis on trends are mainly focused on changes since the last update (2008-2021).
The statistical methodology used is explained in Appendix A. The study is presented in 5 sections: government-wide results, results by occupational categories, Executives, employment equity groups’ intersectionalities, and the propensity to apply to a job advertisement.
Methodological notes
The results in this paper are reported as relative promotion rates, where a relative promotion rate above 0 indicates a higher promotion rate for a given employment equity group than its comparator. Conversely, a negative relative promotion rate indicates a lower promotion rate for an employment equity group compared to its comparator. Results presented in the tables may be accompanied by asterisks, which indicate the statistical significance of the finding. The absence of asterisks means no statistical significance, one asterisk means a statistical significance at the 5% level, and 2 asterisks denote a statistical difference at the 1% level.
Data
The dataset used for this analysis is derived from 2 data sources. The first data source, PSC’s administrative hiring and staffing data, contains transactional data gathered from the government’s pay system since April 1990. This data source was used to identify relative promotions and most explanatory variables used in the analysis, such as region and occupational category. The second data source, the Treasury Board of Canada Secretariat’s Employment Equity Data Bank, contains employment equity self-identification information provided by employees.
For the determination of employment equity status, women are identified through the federal government pay system. The other 3 groups (members of visible minorities, persons with disabilities, and Indigenous Peoples) and the visible minority subgroups are identified through the Employment Equity Data Bank. A retrospective approach was taken for members of visible minorities and Indigenous Peoples. That is, if a public servant self-identified as such after the year of hiring, then that self-identification status was used for the public servant throughout the entire study period. A similar retrospective approach could not be taken for persons with disabilities since not all disabilities may have been present before employees self-identified and may also be temporary.
Findings
Public service-wide results
Table 1 below presents the relative promotion rates for the 4 employment equity groups from the original study (2005 to 2018) and the 5 subsequent 13-year periods studied up to and including 2010 to 2023. Table 1 includes results for the largest subgroup for members of visible minorities (Black public servants) while numbers for all other visible minorities subgroups are reported in Table 2.
The most notable results from Table 1 are the following:
- After decreasing in the three periods following the original study, Women’s relative promotion rates increased over the latest 2 study periods. Women’s relative promotion rates consistently remained above those of men.
- The relative promotion rate of members of visible minorities continued to improve throughout the most recent 2 study periods. They increased from 4.4% for the 2008 to 2021 period to 9.5% for the 2010 to 2023 period.
- Black public servants followed the same trend. During the 2008 to 2021 period, the relative promotion rate of public servants who identified as Black was not statistically different from their comparator. For the period ending in 2023, Black public servants had a higher relative promotion rate (5.0%).
- Indigenous Peoples continue to have lower relative promotion rates than non-Indigenous peoples (‑6.9% for the most recent period ending in 2023).
- Persons with disabilities had lower relative promotion rates in each of the 2 time periods. Their relative promotion rate increased since the three-year update (from ‑12.6% to -10.9% for the periods ending in 2021 and 2023, respectively).
| Employment equity groups | Original study 2005 to 2018 |
2006 to 2019 | 2007 to 2020 | 2008 to 2021 | 2009 to 2022 | 2010 to 2023 |
|---|---|---|---|---|---|---|
| Women | 4.3%** | 3.2%** | 2.8%** | 2.8%** | 3.5%** | 4.6%** |
| Members of visible minorities | 0.6% | 1.8% | 2.9%** | 4.4%** | 7.4%** | 9.5%** |
| Black | -4.8%** | -4.2%** | -3.1%* | -1.1% | 2.4%* | 5.0%** |
| Indigenous Peoples | -7.5%** | -8.4%** | -8.5%** | -7.5%** | -7.3%** | -6.9%** |
| Persons with disabilities | -7.9%** | -9.4%** | -11.2%** | -12.6%** | -11.8%** | -10.9%** |
* Stands for statistical significance at 5% level; ** stands for statistical significance at 1% level
Results by occupational categories
The original study and three-year update explored the relative promotion rates of employment equity group members by occupational categories. In this section, the analysis was updated with results from 2 additional study periods (2009 to 2022 and 2010 to 2023) (see Tables 3 to 7). The analysis below focuses on key findings; however, complete results on all 5 occupational categories are presented in Appendix C.
Findings
Women continued to have higher relative promotion rates in the Administrative Support, Administrative and Foreign Service and Operational occupational categories, but have considerably lower relative promotion rates in the Technical occupational category. In the most recent 2 periods of our study (2009 to 2022 and 2010 to 2023), the relative promotion rates of women were not statistically significant as compared to men in the Scientific and Professional occupational category.
Indigenous Peoples and persons with disabilities have had lower relative promotion rates in the Administrative Support, Administrative and Foreign Services, and the Scientific and Professional categories throughout the 6 periods. For the period ending in 2023, persons with disabilities also had a lower relative promotion rate in the Technical occupational category (-10.6%). Indigenous peoples and persons with disabilities had substantially higher relative promotion rates in the Operational category (7.7% and 33.2% respectively).
Members of visible minorities had, for the most part, relative promotion rates that were comparable, or higher, across all occupational categories, with the highest relative promotion rate differential observed in the Administrative Support (23.8%), Administrative and Foreign Service (8.0%) and Operational occupational categories (19.3%).
Finally, for visible minorities subgroups, the most recent 2 periods of our study showed consistent results as the 2008 to 2021 period of our study. The main exception was Black public servants. For the 2010 to 2023 study period, they had higher relative promotion rates in the Administrative Support (12.5%), Administrative and Foreign Services (3.9%) and Operation (29.9%) occupational categories. Relative promotion rates there were not statistically significant (as compared to their comparator groups) in the Scientific and Professional and Technical occupational categories.
Relative promotion rates within the Executive Category, from feeder groups, and to the Executive Category
As in the original study and the 3-year update, this report also contains updated results on relative promotion rates of employment equity group members:
- within the Executive (EX) Group
- from feeder groups (EX-minus-1 level) to EX and EX-equivalent positions
- from feeder groups (EX-minus-1 level) to the (EX) group only
This section presents key findings on the Executive Group. Detailed results can be found in tables 8, 9 and 10 in Appendix D.
Within the Executive (EX) Group
Within the Executive Group, Women’s relative promotion rates continued to increase over the 2 additional time periods added to the study. For the period ending 2023, women’s relative promotion rate was 8.8%, whereby women’s relative promotion rates in the Executive Group were not statistically different to men’s for the period ending in 2022.
Members of visible minorities also had a steady increase in their relative promotion rate. As a group, their relative promotion rates turned positive in the period ending 2022 (3.2%) and further increased in the subsequent period (4.5%). The most notable change amongst visible minority subgroups were Black executives. They had a statistically higher relative promotion rate for the period ending 2023 (24.0%), while the relative promotion rates as compared to non-visible minority executives were not statistically significant in previous periods of our study.
Indigenous executives continued to have relative promotion rates that were not statistically significant from non-Indigenous executives over the two additional study periods (-8.3% for period ending 2022, -0.9% for period ending 2023). In the period ending 2023, executives with disabilities had a lower relative promotion rate (-15.6%).
From the feeder groups (EX-minus-1 level) to EX and EX-equivalent positions
For the period ending in 2023, women (7.4%), Black executives (31.0%) and person of mixed origin executives (22.4%) had a higher relative promotion rate. The relative promotion rates from feeder groups to EX and EX-equivalent positions for all other employment equity groups, including the other visible minority subgroups, were not statistically significant.
From the feeder groups (EX-minus-1 level) to the EX group only
For our study period 2010 to 2023, women (26.8%), Black employees (36.9%) and Indigenous employees (19.8%) had a higher relative promotion rate from EX minus 1 level to EX group. Chinese employees (-47.2%) had a lower relative promotion rate. The relative promotion rates for all other EE groups, including other visible minority subgroups, were not statistically significant.
Intersectionality
Intersectionality refers to the compounding effect of belonging to more than one employment equity group. This section presents some findings from our intersectionality analysis as an example for the reader when exploring all the interactions provided in Tables 11 and 12 of Appendix E. In addition, the reader is encouraged to make comparisons to the intersectionality tables provided in the PSC’s 2022 Employment Equity Promotion Rate Study - Three-Year Update.
Interaction between gender and identifying as a Black public servant
As presented in our main results (see Table 1), Black public servants had a higher relative promotion rate (5.0% for the 2010 to 2023 period). The results presented in Table 11a and 11b show the compounding effect of gender and of being a Black public servant on the chances of promotion. More specifically:
- Black public servant men had a higher relative promotion rate (9.9%) than men who did not identify as a member of visible minorities.
- The relative promotion rate of Black public servant women was not statistically significant (2.0%) as compared to women who did not identify as a member of visible minorities.
- The relative promotion rate of Black public servant women was not statistically significant (-2.5%) as compared to Black public servant men.
- Women who did not identify as a member of visible minorities had a higher relative promotion rate (5.0%) than men who did not identify as a member of visible minorities.
Other interactions
This study also measured interactions between other factors and employment equity status. Table 12 shows that the relative promotion rates of public servants in the National Capital Region were much higher than public servants outside the National Capital Region (52.0%). When considering the interaction with gender, we see that:
- the relative promotion rate for women compared to men was higher (9.1%) in the National Capital Region as compared to outside the region (-2.5%)
- the relative promotion rate of women in the National Capital Region compared to women outside the region (59.0%) was higher than the same comparison for men (42.1%).
Finally, note that there was no intersectionality effect on relative promotion rates for members of visible minorities and Indigenous Peoples by National Capital Region vs outside the region. Conversely, persons with disabilities have significantly lower relative promotion rates than persons without disabilities in the National Capital Region (-15.2%) versus outside the region (-3.5%). (See Table 12.)
Representation of employment equity group members as internal applicants, in the federal public service, and as a share of promotions
Relative promotion rates depend on both the success of employment equity groups during the staffing process and how likely members of the groups are to apply for opportunities. That is, an employment equity group or sub-group may be under-promoted because it encounters barriers at the application stage, during the staffing process, or both.
Table 13 Footnote 3 provides representation numbers for all employment equity groups, including visible minority subgroups as internal applicants, compared to their representation share in the federal public service for the fiscal years ending 2018 through to 2023. The main results are:
- The representation of women applicants to job advertisements and their share of promotions continue to surpass their representation in the federal public service.
- Members of visible minorities’ representation as applicants continues to be larger than both their representation in the federal public service and their share of promotions. Among visible minority subgroups, this was also notable for public servants who identified as Black, South Asian/ East Indians and Non-White West Asian, North African or Arabs.
- Indigenous Peoples had a lower representation as applicants than their representation in the federal public service over the 6 fiscal years. However, their representation in promotions continues to be higher than their representation as applicants.
- Over the most recent 4 years, persons with disabilities had higher representation as applicants compared to their share in promotions. In the most recent 2 years, their representation as applicants was also higher than their representation in the federal public service.
Conclusion
The main objective of this study was to identify potential gaps in the relative promotion rates of employment equity groups and subgroups, and to assess progress in that regard over 6 comparable periods between 2005 and 2023. This study relied on the same survival analysis techniques as both the original 2019 study and the 2022 three-year update.
This update reveals a continued increase in the relative promotion rates of members of visible minorities over the last 2 study periods. This increasing trend is also observed in the data for the Black public servants subgroup. Black public servants now have higher relative promotion rates as compared to public servants who are not a member of visible minorities.
The update also showed no marked improvement in overall relative promotion rates for Indigenous peoples as compared to non-Indigenous peoples since the three-year update. The Operational occupational category was an exception where Indigenous peoples now have a higher relative promotion rate.
Finally, this update showed that the decline in relative promotion rates of persons with disabilities observed in the three-year update has shifted to a small increase over the 2 most recent study periods but remains negative overall.
Employment equity is a shared responsibility between the PSC, the Office of the Chief Human Resources Officer and federal departments and agencies. With this in mind, we will continue to work with key stakeholders as well as members of the employment equity community to identify and address barriers to career progression.
Appendix A: Methodology
In this paper, we relied on a Cox proportional hazards survival model to investigate the effect several variables have on the time to the first promotion and between 2 consecutive promotions for Canadian federal public servants. This type of model is generally used to look at the relationship between the “survival” of a subject and various explanatory variables. This type of analysis originates from research in the field of health sciences, where the impact of various treatments for life-threatening conditions on the survival of patients are being compared. Due to technical similarities in experiment design and features of datasets, survival analysis has known considerable popularity beyond the narrow scope of health sciences, and it has been successfully used over a wide spectrum of research fields. It is particularly useful for social research aiming at following a subject until a certain event of interest occurs and discerning the factors that predict the duration to that particular event. In our case, the event of interest is the promotion of a federal public servant. Our model allowed us to compare the survival times to promotion of members of an employment equity group to their comparators.
Survival analysis has also been widely used in econometric modelling with respect to labour market analysis such as employee retention, career advancement, unemployment spells, and product life expectancy. Allison Milner et al. (2018) (English only) used survival analysis to identify employment characteristics associated with exiting work and assess if these were different for persons with disabilities versus those without disabilities. A paper by Daniela-Emanuela Danacica and Ana-Gabriela Babucea (2010) (English only) presented an application of survival analysis on the duration of unemployment using exogenous variables such as gender and age. More closely related to our work, Janet M. Box-Steffensmeier, Raphael C. Cunha, et al. (2015) (English only) used survival analysis to look at the impact of gender on the time of departure and time to promotion in academia.
Unlike logistic regression, which calculates the probability of an event happening based on explanatory variables, the response variable hi(t) in Cox Proportional Hazard models is the hazard function at a given time t. Therefore, an underpinning assumption of our analysis is that all employees, regardless of employment equity status, are eligible to obtain a promotion at any given time. However, federal public servants are only eligible to obtain a promotion if they are active in the pursuit of promotion opportunities. Our analysis of the propensities of the various employment equity groups to apply to staffing processes attempts to provide a better picture of the factors that may be at play behind promotion rate differentials across employment equity groups.
The model used in this analysis takes the form of
Text version
The function h underscript i at time t equals the function h underscript 0 at time t multiplied by the exponential function of the linear function of k variables x with their corresponding parameters β for each observation i.where h stands for the hazard function, x refers to a set of explanatory variables listed below and ranging from 1 to k, depending on each specification of the model, β represents the coefficients of these variables, the argument t stands for time, and the subscript i refers to a specific transaction (that is, a promotion or a waiting period leading to a separation) being described by the model. Here, h0(t) is the baseline hazard as hi(t) = h0(t) when all x’s are equal to zero. The baseline hazard is unspecified and can take any form. If we consider 2 different observation (i) and (i’), the hazard ratio given by
Text version
The fraction of the function h underscript i at time t to the function h underscript i prime (i’) at time t equals the fraction of the function h underscript 0 at time t multiplied by the exponential function of the linear function of k variables x with their corresponding parameters β for each observation i as the numerator and the function h underscript 0 at time t multiplied by the exponential function of the linear function of k variables x with their corresponding parameters β for each observation i prime (i’) as the denominator. Simplifying the equation by the common multiplier of the function h underscript 0 at time t, we get the fraction of the exponential function of the linear function of k variables x with their corresponding parameters β for each observation i as the numerator and the exponential function of the linear function of k variables x with their corresponding parameters β for each observation i prime (i’) as the denominator.is independent of time (t) and proportional across time. Not having to make arbitrary, and possibly counterfactual, assumptions about the form of the baseline hazard is a significant advantage of using Cox’s specification. Footnote 4
Observations
Due to a transition in data capture in April 1991, the dataset only contains employees hired into the public service after April 1, 1991, who are followed until their separation/retirement or until March 31, 2023. The rationale for the choice of the starting date is that, prior to the April 1991 transition, the data does not differentiate between a promotion and other types of staffing actions (such as new hires).
The time to promotion is calculated from the time of hire to the time of the first promotion, or the time between any 2 consecutive promotions. The datasets consist of the following: 1. observations, 2. number of indeterminate employees, 3. waiting periods to promotion and 4. interrupted waiting periods. Interrupted waiting periods are periods of time (either since last promotion, or since hiring in cases where there was no promotion) which were interrupted either by a separation or by the end of the period under observation.
| Time period | Total observations | Indeterminate employees | Waiting periods | Interrupted waiting periods |
|---|---|---|---|---|
| 2005 to 2018 | 409 468 | 230 310 | 172 125 | 237 343 |
| 2006 to 2019 | 431 970 | 242 097 | 182 708 | 249 262 |
| 2007 to 2020 | 450 428 | 252 487 | 190 639 | 259 789 |
| 2008 to 2021 | 464 026 | 263 103 | 193 695 | 270 331 |
| 2009 to 2022 | 482 447 | 275 945 | 199 265 | 283 182 |
| 2010 to 2023 | 513 736 | 295 768 | 210 560 | 303 176 |
The following variables were used in our models:
- Time: a continuous dependent variable, defined as length of time (in years) from being hired into the federal public service to first promotion, the length of time (in years) between 2 consecutive promotions or length of time with no promotion.
- Promotion: the event indicator, equal to 1 if the employee obtained a promotion; 0 otherwise.
- Women: an indicator variable, equal to 1 if indicated as such in the pay system; 0 otherwise.
- Visible minority subgroups: an indicator variable, equal to 1 if self-identified as such in the employment equity database by the end of the study period; 0 otherwise.
- Indigenous Peoples: an indicator variable, equal to 1 if self-identified as such in the employment equity database by the end of the study period; 0 otherwise.
- Persons with disabilities: an indicator variable, equal to 1 if self-identified as such in the employment equity database by the end of the fiscal year the promotion was obtained; 0 otherwise.
- Age: a continuous variable defined as age at hire or age at previous promotion.
- Salary deciles: an indicator variable, derived from current salary of public servants who were not promoted and public servants’ salary before promotion for those who were promoted for each fiscal year. This was used as a proxy of seniority. The reference group selected was salary decile 1.
- Occupational categories: an indicator variable based on Treasury Board of Canada Secretariat definitions. The reference group selected was the Executive Group.
- First official language: an indicator variable defined as 0 if Anglophone; 1 if Francophone.
- Bilingual bonus: an indicator variable based on the requirements of the position and linguistic profile of the public servant. If the position is bilingual and the public servant meets the requirements of the position, then 1; otherwise, 0. For those who did not obtain a promotion, it is based on current position; for those who were promoted, it is based on new position.
- National Capital Region: an indicator variable, defined as 1 if position is NCR; 0 otherwise. For those who did not obtain a promotion, it is based on current position; for those who were promoted, it is based on new position.
- Leave without pay: a continuous variable, defined as length of time (in years) that a public servant spent on leave without pay. For the public servants who were not promoted, it equates to all time spent on leave without pay throughout their career. For public servants who were promoted, it is the time spent on leave without pay from either the start of their career to promotion, or the time spent on leave without pay between consecutive promotions.
Models
For this paper, we analyzed 2 models for each of the 6 time periods while using a varying number of explanatory variables and interactions. Our benchmark model consisted of using all variables as listed above while considering promotions that occurred between:
- April 1, 2005, and March 31, 2018, for those hires on or after April 1, 1991
- April 1, 2006, and March 31, 2019, for those hires on or after April 1, 1992
- April 1, 2007, and March 31, 2020, for those hires on or after April 1, 1993
- April 1, 2008, and March 31, 2021, for those hires on or after April 1, 1994
- April 1, 2009, and March 31, 2022, for those hires on or after April 1, 1995
- April 1, 2010, and March 31, 2023, for those hires on or after April 1, 1996
The second model consisted of the benchmark model augmented with factors capturing the interactions of pairs of employment equity statuses as well as of each employment equity status with age, first official language, bilingual status, National Capital Region, and leave without pay.
For the same time periods and explanatory variables as indicated above, we also analyzed:
- promotions within the executive level (that is, an executive being promoted to a higher-level executive position).
- promotions to the executive level or executive equivalent level from employees who held positions one level below the executive level.
- promotions to the executive level from employees who held positions one level below the executive level.
Interpretation
Throughout the paper we refer to hazard rates as “relative promotion rates” for simplicity. This is not to be confused with other approaches, such as logistic regression for dichotomous outcomes, which calculates an event happening based on independent variables.
A hazard ratio (HR) is indeed a ratio and its interpretation in the context of this paper is as follows:
- HR = 0.5: at any particular time, half as many public servants in a given employment equity group obtained a promotion as compared to those who were not in that employment equity group.
- HR = 1: at any particular time, as many public servants in a given employment equity group obtained a promotion as compared to those who were not in that employment equity group.
- HR = 2: at any particular time, twice as many public servants in a given employment equity group obtained a promotion as compared to those who were not in that employment equity group.
The reporting of relative promotion rates (RPR) in this paper is given as RPR = HR -1. That means a (+) indicates a lower survival time (higher promotion rate) as compared to the reference groups and a (-) indicates a higher survival time (lower promotion rate) as compared to the reference groups.
Appendix B: Overall results
|
Original study |
|
|
|
|
|
|---|---|---|---|---|---|---|
Women |
4.3%** |
3.2%** |
2.8%** |
2.8%** |
3.5%** |
4.6%** |
Members of visible minorities |
0.6% |
1.8% |
2.9%** |
4.4%** |
7.4%** |
9.5%** |
Black |
-4.8%** |
-4.2%** |
-3.1%* |
-1.1% |
2.4%* |
5.0%** |
Chinese |
2.6% |
3.0%* |
2.7%* |
3.7%** |
4.9%** |
5.4%** |
Filipino |
-7.5%* |
-7.1%* |
-6.3%* |
-4.8% |
-1.4% |
1.8% |
Japanese |
-3.2% |
-1.6% |
-1.7% |
-3.3% |
-4.6% |
-2.9% |
Korean |
6.0% |
7.2% |
7.2% |
5.1% |
10.2%* |
16.6%** |
Non-white Latin American |
-6.4%* |
-1.8% |
0.2% |
3.1% |
8.3%** |
13.8%** |
Person of mixed origin |
5.5%** |
6.3%** |
5.3%** |
7.3%** |
10.5%** |
11.3%** |
South Asian / East Indian |
-0.1% |
0.5% |
2.0% |
3.5%* |
7.5%** |
9.9%** |
Southeast Asian |
-14.7%** |
-12.7%** |
-10.3%** |
-6.3%* |
-4.6% |
-1.0% |
Non-white West Asian, North African or Arab |
10.1%** |
10.7%** |
12.1%** |
14.2%** |
18.6%** |
20.4** |
Other visible minority |
4.3% |
9.1%** |
12.2%** |
12.4%** |
13.2%** |
15.3** |
Indigenous Peoples |
-7.5%** |
-8.4%** |
-8.5%** |
-7.5%** |
-7.3%** |
-6.9%** |
Persons with disabilities |
-7.9%** |
-9.4%** |
-11.2%** |
-12.6%** |
-11.8%** |
-10.9%** |
* Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level
Appendix C: Results by occupational categories
|
Original study |
|
|
|
|
|
|---|---|---|---|---|---|---|
Women |
11.5%** |
8.6%** |
6.9%** |
6.0%** |
5.6%** |
4.1%** |
Members of visible minorities |
6.2%** |
8.6%** |
11.1%** |
13.6%** |
18.5%** |
23.8%** |
Black |
-7.6%** |
-7.8%** |
-4.0% |
-0.4% |
6.4%* |
12.5%** |
Chinese |
15.7%** |
18.1%** |
18.6%** |
23.2%** |
25.9%** |
27.9%** |
Filipino |
12.6%* |
14.5%* |
14.9%* |
15.1%* |
16.6%* |
25.0%** |
Japanese |
7.8% |
14.9% |
23.5% |
38.1%* |
45.4%* |
46.2%* |
Korean |
21.4% |
24.6% |
31.0% |
37.8%* |
62.7%** |
71.7%** |
Non-white Latin American |
-7.0% |
-3.0% |
2.0% |
8.6% |
19.3%* |
29.4%** |
Person of mixed origin |
10.8%* |
14.3%** |
12.3%* |
17.5%** |
18.8%** |
19.6%** |
South Asian / East Indian |
3.7% |
9.5%* |
9.0%* |
10.3%** |
17.3%** |
23.5%** |
Southeast Asian |
14.6% |
13.0% |
18.4%* |
25.4%** |
32.0%** |
39.0%** |
Non-white West Asian, North African or Arab |
24.3%** |
24.7%** |
28.8%** |
29.1%** |
34.5** |
37.6** |
Other visible minority |
9.1% |
16.5%** |
23.8%** |
18.5%** |
16.4%** |
22.9%** |
Indigenous Peoples |
-11.6%** |
-13.8%** |
-14.1%** |
-14.7%** |
-17.5%** |
-19.3%** |
Persons with disabilities |
-20.3%** |
-21.3%** |
-23.7%** |
-24.9%** |
-25.2%** |
-23.1%** |
* Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level
|
Original study |
|
|
|
|
|
|---|---|---|---|---|---|---|
Women |
10.0%** |
8.8%** |
7.7%** |
7.3%** |
7.0%** |
8.1%** |
Members of visible minorities |
-2.2%* |
-0.1% |
1.3% |
3.5%** |
5.9%** |
8.0%** |
Black |
-5.3%** |
-3.9%* |
-3.3% |
-1.3% |
1.7% |
3.9%* |
Chinese |
-1.7% |
-0.2% |
1.5% |
3.2% |
4.0%* |
4.9%* |
Filipino |
-15.8%** |
-13.4%** |
-11.8%** |
-7.0% |
-2.1% |
0.0% |
Japanese |
-5.7% |
1.1% |
-0.1% |
-0.8% |
-5.8% |
-3.0% |
Korean |
2.2% |
3.1% |
4.2% |
3.4% |
4.1% |
11.4% |
Non-white Latin American |
-11.5% |
-4.3% |
-1.9% |
0.4% |
5.6% |
11.7%** |
Person of mixed origin |
5.9% |
6.3%* |
3.5% |
5.9%* |
7.4%** |
7.3%** |
South Asian / East Indian |
-3.1% |
-3.2% |
-1.1% |
1.2% |
3.3% |
6.3%** |
Southeast Asian |
-22.1%** |
-18.2%** |
-14.8%** |
-11.7%** |
-12.2%** |
-8.5%** |
Non-white West Asian, North African or Arab |
6.8%** |
8.6%** |
10.6%** |
14%** |
19.1%** |
21.9%** |
Other visible minority |
2.8% |
9.6%** |
12.4%** |
13.5%** |
14.5%** |
15.5%** |
Indigenous Peoples |
-6.7%** |
-7.1%** |
-8.0%** |
-7.0%** |
-6.4%** |
-5.7%** |
Persons with disabilities |
-3.3% |
-6.1%** |
-8.5%** |
-10.6%** |
-9.9%** |
-10.0%** |
* Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level
|
Original study |
|
|
|
|
|
|---|---|---|---|---|---|---|
Women |
2.2% |
1.1% |
2.1% |
5.4%* |
8.5%* |
11.0%** |
Members of visible minorities |
0.5% |
3.9% |
9.3%* |
11.3%** |
14.1%* |
19.3%** |
Black |
-0.1% |
3.2% |
12.1% |
17.0% |
20.2%** |
29.9%** |
Chinese |
28.4%* |
24.7% |
22.9% |
24.2% |
21.1% |
19.9% |
Filipino |
-16.3% |
-10.7% |
-1.5% |
3.7% |
6.1% |
8.9% |
Japanese |
2.7% |
13.4% |
1.5% |
15.9% |
28.1% |
21.3% |
Korean |
-66.8% |
-83.7%** |
-69.8%** |
-37.3% |
-12.9% |
31.0% |
Non-white Latin American |
-7.9% |
-10.7% |
-7.5% |
-2.9% |
13.5% |
22.0% |
Person of mixed origin |
3.2% |
12.2% |
11.8% |
4.9% |
19.3% |
17.5% |
South Asian / East Indian |
-12.1% |
-8.8% |
4.1% |
8.3% |
15.1% |
20.3%* |
Southeast Asian |
-17.0% |
-5.2% |
-6.6% |
-12.9% |
-13.4% |
-3.0% |
Non-white West Asian, North African or Arab |
44.4%** |
41.8%* |
49.0%** |
44.1%** |
41.2%** |
40.4%** |
Other visible minority |
2.2% |
8.3% |
6.6% |
7.3% |
0.9% |
6.5% |
Indigenous Peoples |
-0.6% |
-0.8% |
0.2% |
5.2% |
7.7%* |
7.7%* |
Persons with disabilities |
28.0%** |
23.5%** |
26.1%** |
27.0%** |
31.0%** |
33.2%** |
* Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level
|
Original study |
|
|
|
|
|
|---|---|---|---|---|---|---|
Women |
-5.7%** |
-5.8%** |
-4.1%** |
-3.1%** |
-1.0% |
0.3% |
Members of visible minorities |
-0.3% |
-0.9% |
-1.3% |
-0.5% |
3.4%* |
4.7%** |
Black |
-6.1% |
-6.3%* |
-6.8%* |
-6.4%* |
-3.6% |
-1.7% |
Chinese |
0.9% |
0.0% |
-2.6% |
-3.0% |
-1.0% |
-1.1% |
Filipino |
-8.9% |
-17.0%* |
-17.5%* |
-17.3%* |
-14.5% |
-11.7% |
Japanese |
-10.6% |
-15.9% |
-17.5% |
-26.4%* |
-26.3% |
-24.9% |
Korean |
9.8% |
10.2% |
7.9% |
1.5% |
10.0% |
15.1%* |
Non-white Latin American |
-0.8% |
2.6% |
4.2% |
8.3% |
10.2% |
13.7%* |
Person of Mixed Origin |
2.7% |
2.5% |
4.5% |
5.3% |
11.9%** |
14.4%** |
South Asian / East Indian |
0.7% |
0.0% |
0.1% |
1.0% |
6.4%* |
7.4%** |
Southeast Asian |
-12.5% |
-14.2%* |
-15.7%* |
-10.3% |
-3.4% |
-2.4% |
Non-white West Asian, North African or Arab |
1.2% |
0.4% |
0.5% |
2.5% |
6.8%* |
8.3%** |
Other visible minority |
5.7% |
5.5% |
7.6% |
9.3%* |
13.4%** |
12.2%** |
Indigenous Peoples |
-13.3%** |
-13.6%** |
-11.2%** |
-9.6%** |
-8.7%** |
-7.3%** |
Persons with disabilities |
-13.9%** |
-13.5%** |
-14.1%** |
-15.1%** |
-14.3%** |
-11.0%** |
* Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level
|
Original study |
|
|
|
|
|
|---|---|---|---|---|---|---|
Women |
-10.6%** |
-13.8%** |
-16.4%** |
-19.6%** |
-18.9%** |
-16.6%** |
Members of visible minorities |
7.0%* |
5.3% |
2.7% |
0.9% |
4.1% |
6.8%* |
Black |
4.1% |
-0.1% |
-0.6% |
2.2% |
3.8% |
6.7% |
Chinese |
6.8% |
5.7% |
2.1% |
0.4% |
7.4% |
8.9% |
Filipino |
-11.1% |
-8.6% |
-11.1% |
-21.5% |
-20.6% |
-19.3% |
Japanese |
17.3% |
17.9% |
14.1% |
-1.3% |
12.6% |
13.5% |
Korean |
5.8% |
18.6% |
-0.6% |
-21.4% |
-21.5% |
-19.5% |
Non-white Latin American |
19.1% |
10.7% |
-6.0% |
-9.7% |
-8.8% |
-13.3% |
Person of Mixed Origin |
-1.7% |
0.7% |
0.5% |
2.8% |
3.0% |
12.8% |
South Asian / East Indian |
5.1% |
3.6% |
4.5% |
1.5% |
10.4% |
14.0% |
Southeast Asian |
-18.8% |
-22.8% |
-24.0% |
-12.1% |
-11.5% |
-13.3% |
Non-white West Asian, North African or Arab |
65.5%** |
61.5%** |
55.5%** |
41.4%** |
38.7%** |
32.4%** |
Other visible minority |
-3.9% |
-4.2% |
-3.5% |
-4.6% |
8.8% |
4.5% |
Indigenous Peoples |
1.1% |
0.2% |
1.6% |
-0.9% |
-2.3% |
-2.3% |
Persons with disabilities |
-2.0% |
-3.2% |
-5.4% |
-6.9% |
-7.6% |
-10.6%* |
* Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level
Appendix D: Executive results
|
Original study |
|
|
|
|
|
|---|---|---|---|---|---|---|
Women |
-2.7% |
-2.5% |
-2.9% |
2.1% |
4.7% |
8.8%** |
Members of visible minorities |
-15.6%** |
-14.7%** |
-7.1% |
-6.0% |
3.2% |
4.5% |
Black |
2.6% |
3.7% |
11.7% |
10.7% |
20.7% |
24%* |
Chinese |
-49.5%** |
-54.0%** |
-49.5%** |
-39.2%** |
-37.9%** |
-35.0%** |
Filipino |
-56.5% |
-61.2% |
-61.3% |
-75.1%* |
-76.7%* |
-51.0% |
Japanese |
7.5% |
-67.1% |
-45.2% |
-55.2% |
-100.0% |
-74.4% |
Korean |
-30.3% |
-27.5% |
-25.2% |
-31.9% |
-27.4% |
-32.5% |
Non-white Latin American |
-11.4% |
-21.3% |
-7.8% |
-25.4% |
-32.0% |
-32.5% |
Person of mixed origin |
-37.2%* |
-37.7% |
-27.4% |
-19.1% |
-1.2% |
2.2% |
South Asian / East Indian |
6.0% |
6.6% |
14.6% |
12.1% |
24.7%* |
18.3%* |
Southeast Asian |
-60.1% |
-42.2% |
-29.3% |
-27.6% |
-4.5% |
37.3% |
Non-white West Asian, North African or Arab |
-5.4% |
12.5% |
13.1% |
18.0% |
25.8%* |
15.2% |
Other visible minority |
-24.0% |
-31.3%** |
-12.6% |
-19.3% |
-12.8% |
7.8% |
Indigenous Peoples |
-11.3% |
-13.0% |
-11.2% |
-10.8% |
-8.3% |
-0.9% |
Persons with disabilities |
-17.7% |
-12.3% |
-14.3% |
-11.2% |
-11.1% |
-15.6%* |
* Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level
|
Original study |
|
|
|
|
|
|---|---|---|---|---|---|---|
Women |
-3.6% |
-0.8% |
-1.1% |
0.7% |
4.5% |
7.4%* |
Members of visible minorities |
-0.5% |
0.6% |
-1.7% |
2.2% |
6.5% |
5.4% |
Black |
3.0% |
6.5% |
3.5% |
16.5% |
17.6% |
31.0%** |
Chinese |
28.3%** |
23.1%* |
7.0% |
-1.4% |
0.8% |
-8.0% |
Filipino |
-17.2% |
-16.4% |
-25.2% |
-40.7% |
-56.7% |
-53.0% |
Japanese |
-38.6% |
-36.6% |
-51.4% |
-31.5% |
-10.1% |
-13.3% |
Korean |
-43.1% |
-15.0% |
-6.2% |
-4.5% |
17.0% |
25.1% |
Non-white Latin American |
-35.8% |
-23.7% |
-24.3% |
-4.3% |
5.7% |
-2.2% |
Person of mixed origin |
-12.5% |
-3.1% |
-2.7% |
10.1% |
17.9% |
22.4%* |
South Asian / East Indian |
-12.2% |
-11.6% |
-12.2% |
-11.6% |
-12.1% |
-9.2% |
Southeast Asian |
-13.8% |
-15.1% |
-11.1% |
-22.8% |
-13.4% |
-15.9% |
Non-white West Asian, North African or Arab |
5.8% |
-2.1% |
7.7% |
20.0% |
28.2%** |
19.3% |
Other visible minority |
6.4% |
1.3% |
2.7% |
2.5% |
9.6% |
9.6% |
Indigenous Peoples |
-10.6% |
-6.5% |
-5.8% |
6.7% |
7.7% |
8.1% |
Persons with disabilities |
5.5% |
2.7% |
-3.4% |
-11.4% |
-9.7% |
5.3% |
* Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level
|
Original study |
|
|
|
|
|
|---|---|---|---|---|---|---|
Women |
15.6%** |
18.5%** |
18.7%** |
20.8%** |
25.8%** |
26.8%** |
Members of visible minorities |
-25.7%** |
-23.6%** |
-21.7%** |
-12.7%* |
-6.6% |
-3.4% |
Black |
-17.8% |
-13.4% |
-8.4% |
8.2% |
17.9% |
36.9%** |
Chinese |
-53.5%** |
-48.1%** |
-50.8%** |
-54.4%** |
-51.3%** |
-47.2%** |
Filipino |
-23.0% |
-9.4% |
-22.6% |
-29.7% |
-50.4% |
-41.8% |
Japanese |
-43.8% |
-39.9% |
-68.1% |
-33.4% |
-27.6% |
-37.9% |
Korean |
-25.5% |
-6.0% |
-2.7% |
3.1% |
65.1% |
57.6% |
Non-white Latin American |
-32.9% |
-20.2% |
-23.4% |
2.7% |
20.0% |
16.7% |
Person of mixed origin |
-18.9% |
-16.9% |
-17.6% |
4.3% |
10.5% |
16.8% |
South Asian / East Indian |
-15.6% |
-16.4% |
-17.2% |
-11.8% |
-14.0% |
-12.1% |
Southeast Asian |
-40.5% |
-37.2% |
-28.8% |
-37.2% |
-30.7% |
-22.3% |
Non-white West Asian, North African or Arab |
-15.7% |
-22.3% |
-10.6% |
1.2% |
10.0% |
2.9% |
Other visible minority |
-9.1% |
-11.6% |
-4.7% |
6.0% |
17.0% |
10.1% |
Indigenous Peoples |
3.5% |
9.0% |
8.8% |
21.7%* |
19.0% |
19.8%* |
Persons with disabilities |
2.4% |
6.3% |
-0.1% |
-6.7% |
-2.9% |
14.8% |
* Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level
Appendix E: Intersectionality
Comparison made (non-conditioned relative promotion rate) |
Conditioned on men |
Conditioned on women |
Conditioned on not visible minority members |
Conditioned on visible minority members |
Conditioned on Black visible minority subgroup |
Conditioned on Chinese visible minority subgroup |
Conditioned on Filipino visible minority subgroup |
Conditioned on Japanese visible minority subgroup |
Conditioned on Korean visible minority subgroup |
Conditioned on Non-white Latin American visible minority subgroup |
|---|---|---|---|---|---|---|---|---|---|---|
Women vs Men (4.6%**) |
. |
. |
5.0%** |
3.1%** |
-2.5% |
10.8%** |
11.1% |
-4.0% |
6.4% |
4.5% |
Members of Visible Minorities vs Not a Member of Visible Minorities (9.5%**) |
10.7%** |
8.8%** |
. |
. |
. |
. |
. |
. |
. |
. |
Black vs Not a Visible Minority (5.0%**) |
9.9%** |
2.0% |
. |
. |
. |
. |
. |
. |
. |
. |
Chinese vs Not a Visible Minority 5.4%**) |
1.9% |
7.6%** |
. |
. |
. |
. |
. |
. |
. |
. |
Filipino vs Not a Visible Minority (1.8%) |
-1.9% |
3.9% |
. |
. |
. |
. |
. |
. |
. |
. |
Japanese vs Not a Visible Minority (-2.9%) |
2.9% |
-5.9% |
. |
. |
. |
. |
. |
. |
. |
. |
Korean vs Not a Visible Minority (16.6%**) |
15.6%* |
17.2%** |
. |
. |
. |
. |
. |
. |
. |
. |
Non-white Latin American vs Not a Visible Minority (13.8%**) |
14.1%** |
13.6%** |
. |
. |
. |
. |
. |
. |
. |
. |
Person of mixed origin vs Not a Visible Minority (11.3%**) |
14.8%** |
9.3%** |
. |
. |
. |
. |
. |
. |
. |
. |
Other Visible Minority vs Not a Visible Minority (15.3%**) |
17.7%** |
13.8%** |
. |
. |
. |
. |
. |
. |
. |
. |
South Asian / East Indian vs Not a Visible Minority (9.9%**) |
11.9%** |
8.5%** |
. |
. |
. |
. |
. |
. |
. |
. |
South East Asian vs Not a Visible Minority (-1.0%) |
-5.1% |
2.0% |
. |
. |
. |
. |
. |
. |
. |
. |
Non-white West Asian, North African or Arab vs Not a Visible Minority (20.4%**) |
21.3%** |
19.7%** |
. |
. |
. |
. |
. |
. |
. |
. |
Indigenous Peoples vs Non-Indigenous Peoples (-6.9**) |
-5.2%** |
-7.9%** |
. |
. |
. |
. |
. |
. |
. |
. |
Persons with Disabilities vs Persons without Disabilities (-10.9%**) |
-12.9%** |
-9.6%** |
-10.5%** |
-13.0%** |
. |
. |
. |
. |
. |
. |
National Capital Region vs Outside National Capital Region (52.0%**) |
42.1%** |
59.0%** |
52.1%** |
53.2%** |
57.4%** |
47.4%** |
57.1%** |
47.7%* |
45.5%** |
56.8%** |
French vs English (-8.8%**) |
-5.3%** |
-10.9%** |
-9.7%** |
-3.8%** |
-0.7% |
2.4% |
23.9% |
-32.1% |
-23.6% |
-15.2%** |
Receive Bilingual Bonus vs Does not Receive Bilingual Bonus (32.7%**) |
30.0%** |
34.6%** |
33.1%** |
32.4%** |
30.2%** |
34.3%** |
40.6%** |
23.8% |
32.2%* |
26.4%** |
Note 1: * Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level.
Note 2: The original interaction model included multiple 2‑way interactions for each employment equity group. This led to more than 20 000 relative promotion rates to analyze. To cite actual relative promotions rates as displayed in Table 2 meant executing the model considering only one 2‑way interaction at a time.
Note 3: Empty cells in the table are due to one of the following:
- there were no interactions to cite (for example, visible minority groups as compared to non-visible minority groups conditions on visible minority groups)
- interactions between 2 employment equity groups intuitively would lead to small numbers (for example, Indigenous Peoples and persons with disabilities)
- interactions that were not included in the main interaction model (for example, region by first official language)
Comparison made (non-conditioned relative promotion rate) |
Conditioned on Person of mixed origin visible minority subgroup |
Conditioned on other visible minority subgroup |
Conditioned on South Asian / East Indian visible minority subgroup |
Conditioned on Southeast Asian visible minority subgroup |
Conditioned on Non-white West Asian, North African or Arab visible minority subgroup |
Conditioned on not Indigenous Peoples |
Conditioned on Indigenous Peoples |
Conditioned on persons without disabilities |
Conditioned on persons with disabilities |
|---|---|---|---|---|---|---|---|---|---|
Women vs Men (4.6%**) |
0.0% |
1.5% |
1.8% |
12.8%* |
3.6% |
4.7%** |
1.7% |
4.4%** |
8.4%** |
Members of Visible Minorities vs Not a Member of Visible Minorities (9.5%**) |
. |
. |
. |
. |
. |
. |
. |
9.7%** |
6.6%* |
Black vs Not a Visible Minority (5.0%**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Chinese vs Not a Visible Minority 5.4%**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Filipino vs Not a Visible Minority (1.8%) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Japanese vs Not a Visible Minority (-2.9%) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Korean vs Not a Visible Minority (16.6%**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Non-white Latin American vs Not a Visible Minority (13.8%**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Person of mixed origin vs Not a Visible Minority (11.3%**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Other Visible Minority vs Not a Visible Minority (15.3%**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
South Asian / East Indian vs Not a Visible Minority (9.9%**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
South East Asian vs Not a Visible Minority (-1.0%) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Non-white West Asian, North African or Arab vs Not a Visible Minority (20.4%**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Indigenous Peoples vs Non-Indigenous Peoples (-6.9**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
Persons with Disabilities vs Persons without Disabilities (-10.9%**) |
. |
. |
. |
. |
. |
. |
. |
. |
. |
National Capital Region vs Outside National Capital Region (52.0%**) |
51.2%** |
51.9%** |
53.4%** |
40.6%** |
41.9%** |
52.0%** |
58.2%** |
53.2%** |
34.7%** |
French vs English (-8.8%**) |
-12.3%** |
-10.7%** |
-3.0% |
-14.4%** |
6.4%* |
-9.1%** |
-2.8% |
-8.8%** |
-9.9%** |
Receive Bilingual Bonus vs Does not Receive Bilingual Bonus (32.7%**) |
22.3%** |
33.8%** |
42.5%** |
32.1%** |
26.5%** |
32.6%** |
40.0%** |
33.1%** |
29.8%** |
Note 1: * Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level.
Note 2: The original interaction model included multiple 2‑way interactions for each employment equity group. This led to more than 20 000 relative promotion rates to analyze. To cite actual relative promotions rates as displayed in Table 2 meant executing the model considering only one 2‑way interaction at a time.
Note 3: Empty cells in the table are due to one of the following:
- there were no interactions to cite (for example, visible minority groups as compared to non-visible minority groups conditions on visible minority groups)
- interactions between 2 employment equity groups intuitively would lead to small numbers (for example, Indigenous Peoples and persons with disabilities)
- interactions that were not included in the main interaction model (for example, region by first official language)
Comparison made (non-conditioned relative promotion rate) |
Conditioned on outside National Capital Region |
Conditioned on in the National Capital Region |
Conditioned on English as first official language |
Conditioned on French as first official language |
Conditioned on not receiving a bilingual bonus |
Conditioned on receiving a bilingual bonus |
Conditioned on age 25 |
Conditioned on age 35 |
Conditioned on age 45 |
Conditioned on age 55 |
Conditioned on Age 65 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Women vs Men (4.6%**) |
-2.5%** |
9.1%** |
6.7%** |
0.3% |
3.3%** |
7.0%** |
1.9%** |
4.7%** |
7.5%** |
10.4%** |
13.4%** |
Members of Visible Minorities vs Not a Member of Visible Minorities (9.5%**) |
9.0%** |
9.8%** |
7.8%** |
14.9%** |
9.7%** |
9.1%** |
5.7%** |
9.8%** |
14.1%** |
18.6%** |
23.3%** |
Black vs Not a Visible Minority (5.0%**) |
2.6% |
6.1%** |
0.5% |
10.6%** |
6.1%** |
3.6%* |
-5.6%** |
4.4%** |
15.4%** |
17.6%** |
41.0%** |
Chinese vs Not a Visible Minority (5.4%**) |
7.3%** |
3.9%* |
4.4%** |
18.3%** |
5.4%** |
6.2%* |
11.7%** |
4.6%** |
-2.2% |
-8.4%** |
-14.3%** |
Filipino vs Not a Visible Minority (1.8%) |
0.5% |
3.7% |
0.8% |
38.3% |
1.4% |
7.0% |
-3.4% |
1.9% |
7.4% |
13.2% |
19.3% |
Japanese vs Not a Visible Minority (-2.9%) |
-1.6% |
-4.4% |
-1.5% |
-25.9% |
-1.7% |
-8.6% |
-9.3% |
-3.5% |
2.6% |
9.1% |
16.0% |
Korean vs Not a Visible Minority (16.6%**) |
19.7%** |
14.4%* |
17.6%** |
-0.5% |
16.9%** |
16.0% |
22.0%** |
15.0%** |
8.3% |
2.0% |
-3.90% |
Non-white Latin American vs Not a Visible Minority (13.8%**) |
11.5%** |
14.9%** |
15.5%** |
8.4% |
16.2%** |
10.3%** |
4.6% |
13.4%** |
22.9%** |
33.3%** |
44.5%** |
Person of mixed origin vs Not a Visible Minority (11.3%**) |
11.8%** |
11.0%** |
11.9%** |
8.7%* |
15.1%** |
5.6%* |
8.3%** |
11.9%** |
15.7%** |
19.5%** |
23.5%** |
Other Visible Minority vs Not a Visible Minority (15.3%**) |
15.4%** |
15.2%** |
15.6%** |
14.2%** |
15.1%** |
15.6%** |
8.0%** |
15.9%** |
24.4%** |
33.4%** |
43.2%** |
South Asian / East Indian vs Not a Visible Minority (9.9%**) |
9.4%** |
10.3%** |
9.3%** |
17.3%** |
9.0%** |
16.6%** |
6.0%** |
10.3%** |
14.9%** |
19.6%** |
24.5%** |
South East Asian vs Not a Visible Minority (-1.0%) |
4.1% |
-3.9% |
0.1% |
-5.1% |
-0.7% |
-1.5% |
-1.3% |
-1.1% |
-0.9% |
-0.7% |
-0.5% |
Non-white West Asian, North African or Arab vs Not a Visible Minority (20.4%**) |
27.4%** |
18.8%** |
13.8%** |
34.0%** |
23.2%** |
17.0%** |
13.1%** |
21.4%** |
30.4%** |
40.0%** |
50.4%** |
Indigenous Peoples vs Non-Indigenous Peoples (-6.9**) |
-8.9%** |
-5.2%** |
-8.9%** |
-2.5% |
-8.5%** |
-3.5%* |
-12.4%** |
-6.9%** |
-1.1% |
5.1%* |
11.7%** |
Persons with Disabilities vs Persons without Disabilities (-10.9%**) |
-3.5%* |
-15.2%** |
-10.6%** |
-11.7%** |
-10.2%** |
-12.4%** |
-18.4%** |
-12.1%** |
-5.3%** |
2.0% |
9.9%** |
Note 1: * Stands for statistical significance at 5% level, ** stands for statistical significance at 1% level.
Note 2: The original interaction model included multiple 2‑way interactions for each employment equity group. This led to more than 20 000 relative promotion rates to analyze. To cite actual relative promotions rates as displayed in Table 2 meant executing the model considering only one 2‑way interaction at a time.
Appendix F: Propensity to apply
Employment equity groups |
2017 to 2018 population |
2017 to 2018 applicants |
2017 to 2018 promotions |
2018 to 2019 population |
2018 to 2019 applicants |
2018 to 2019 promotions |
2019 to 2020 population |
2019 to 2020 applicants |
2019 to 2020 promotions |
2020 to 2021 population |
2020 to 2021 applicants |
2020 to 2021 promotions |
2021 to 2022 population |
2021 to 2022 applicants |
2021 to 2022 promotions |
2022 to 2023 population |
2022 to 2023 applicants |
2022 to 2023 promotions |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Women |
54.1% |
60.7% |
59.2% |
54.2% |
62.0% |
60.3% |
54.3% |
60.7% |
61.0% |
54.8% |
61.2% |
60.5% |
55.2% |
62.8% |
62.2% |
55.7% |
62.9% |
63.2% |
Members of visible minorities |
15.4% |
21.8% |
17.1% |
16.2% |
22.7% |
18.5% |
17.1% |
24.3% |
19.6% |
18.1% |
26.7% |
20.9% |
19.4% |
27.9% |
23.0% |
20.7% |
28.9% |
24.0% |
Black |
2.8% |
5.1% |
2.9% |
3.0% |
5.1% |
3.2% |
3.2% |
5.6% |
3.6% |
3.5% |
6.0% |
4.2% |
3.8% |
6.6% |
4.7% |
4.2% |
6.8% |
5.0% |
Chinese |
3.0% |
3.6% |
3.9% |
3.0% |
3.4% |
2.8% |
3.1% |
3.4% |
2.8% |
3.2% |
3.6% |
3.1% |
3.3% |
3.6% |
3.4% |
3.4% |
3.4% |
3.4% |
Filipino |
0.5% |
0.8% |
0.5% |
0.6% |
0.8% |
0.6% |
0.6% |
0.8% |
0.6% |
0.7% |
0.9% |
0.7% |
0.7% |
1.0% |
0.7% |
0.8% |
1.0% |
0.9% |
Japanese |
0.1% |
0.2% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
0.1% |
Korean |
0.2% |
0.3% |
0.2% |
0.2% |
0.4% |
0.2% |
0.2% |
0.4% |
0.3% |
0.3% |
0.5% |
0.3% |
0.3% |
0.5% |
0.4% |
0.3% |
0.5% |
0.4% |
Non-white Latin American |
0.6% |
1.0% |
0.7% |
0.6% |
0.9% |
0.8% |
0.7% |
1.0% |
0.9% |
0.8% |
1.1% |
0.9% |
0.8% |
1.2% |
1.0% |
1.0% |
1.3% |
1.3% |
Person of Mixed Origin |
1.2% |
1.8% |
1.4% |
1.2% |
1.4% |
1.6% |
1.3% |
1.5% |
1.6% |
1.5% |
1.6% |
1.8% |
1.6% |
1.5% |
2.1% |
1.7% |
1.6% |
2.0% |
South Asian / East Indian |
2.7% |
4.7% |
2.6% |
2.8% |
4.7% |
2.8% |
2.9% |
4.9% |
3.1% |
3.2% |
5.8% |
3.3% |
3.5% |
5.9% |
3.9% |
3.7% |
6.2% |
4.2% |
Southeast Asian |
0.7% |
1.0% |
0.6% |
0.7% |
0.9% |
0.9% |
0.7% |
0.9% |
0.8% |
0.8% |
0.9% |
0.9% |
0.8% |
1.0% |
1.0% |
0.9% |
1.0% |
1.0% |
Non-white West Asian, North African or Arab |
1.7% |
3.3% |
2.4% |
1.8% |
3.2% |
2.3% |
1.9% |
3.6% |
2.7% |
2.1% |
4.0% |
2.9% |
2.3% |
4.1% |
3.1% |
2.5% |
4.6% |
3.2% |
Other visible minority |
2.0% |
0.9% |
2.6% |
2.2% |
0.9% |
3.2% |
2.3% |
1.0% |
3.2% |
2.1% |
1.1% |
2.7% |
2.1% |
1.2% |
2.7% |
2.1% |
1.2% |
2.6% |
Indigenous Peoples |
5.3% |
4.0% |
5.0% |
5.2% |
4.0% |
4.8% |
5.2% |
3.9% |
5.0% |
5.4% |
3.7% |
4.9% |
5.3% |
4.0% |
5.1% |
5.4% |
4.3% |
5.1% |
Persons with disabilities |
5.4% |
4.0% |
4.0% |
5.3% |
4.4% |
4.3% |
5.3% |
4.5% |
4.1% |
5.7% |
5.1% |
4.7% |
6.2% |
6.8% |
5.8% |
6.8% |
8.3% |
6.7% |
Appendix G: Groups categorized as Executive Equivalent and Executive Minus 1
Occupational Category |
Occupational Group |
|---|---|
Executive Equivalent |
AC 03 - Actuarial Science, AINOP06/07/08 - Air Traffic Control, AOETP 01/02 - Aircraft Operations, AR 07 - Architecture and Town Planning, AS 08 - Administrative Services, AU 06 - Auditing, CO 04 - Commerce, CS 05/IT 05 - Computer Systems, DE 03/04 - Dentistry, DS 05/06/07/08 - Defence Scientific Service, EC 08 - Economics and Social Science Services, GX – General Executive, LA 02/03 - Law, LC - Law Management, LP 02/03/04/05 - Law Practitioner, MA 07 - Mathematics, MD 02/03/04/05 - Medicine, MT 08 - Meteorology, PC 05 - Physical Sciences, SEREM - Scientific Research, SERES03/04/05 - Scientific Research, SGPEM09 - Scientific Regulation, SOMAO13 - Ships' Officers, TI 09 - Technical Inspection, UT 04 - University Teaching, VM 05 - Veterinary Medicine, WP 07 - Welfare Program |
Executive Minus 1 |
AC 02 - Actuarial Science, , AG 05 - Agriculture, AINOP 04/05 - Air Traffic Control, AOCAI 05/AOHPS 03 - Aircraft Operations, AR 06 - Architecture and Town Planning, AS 07 - Administrative Services, AU 05 - Auditing, BI 05- Biology, CH 05 - Chemistry, CO 03 - Commerce, CS 04/IT 04 - Computer Systems, DE 02 - Dentistry, DS 04 - Defence Scientific Service, EC 07 - Economics and Social Science Services, EG 08 - Engineering and Scientific Support, EL 09 - Electronics, EN 05 - Engineering and Land Survey, ES 06 - Economics and Social Science Services, FB 08 - Border Services, FI 04/CT 04 - Financial Management, FO 04 - Forestry, FS 02 - Foreign Service, GT 08 General Technical, NDADV03/NDHME05 - Nutrition and Dietetics, HR 05 - Historical Research, IS 06 - Information Services, LS 06- Library Services, MA 06 - Mathematics, MDMOF01 - Medicine, MT 07 - Meteorology, NUCHN08/NUCON 01/NUHOS08 - Nursing, OM 06 - Organization and Methods, OP 04 - Occupational and Physical Therapy, PC 04 - Physical Sciences, PE 06 - Personnel Administration, PG 06 - Purchasing and Supply, PHADR04 - Pharmacy, PM 07 - Program Administration, PR 05 - Printing Operations, PS 05 - Psychology, RO 07 - Radio Operations, SERES02 - Scientific Research, SG 07/08 - Scientific Regulation, SI 07/08 - Scientific Support, SOINS02/SOMAO12 - Ships' Officers, SWSCW05 - Social Worker, TI 08 - Technical Inspection, TR 05 - Translation, VM 04 - Veterinary Medicine, WP 06 - Welfare Program |
Appendix H: Groups in occupational categories
Occupational Category |
Occupational Group |
|---|---|
Executive |
EX – Executive |
Scientific and Professional |
AC – Actuarial Science, AG – Agriculture, AR – Architecture and Town Planning, AU – Auditing, BI – Biological Sciences, CH – Chemistry, DE – Dentistry, DS – Defence Scientific Service, EC – Economics and Social Science Services, ED – Education, EN – Engineering and Land Survey, FO – Forestry, HR – Historical Research, LA – Law, LP – Law Practitioner, LS – Library Science, MA – Mathematics, MD – Medicine, MT – Meteorology, ND – Nutrition and Dietetics, NU – Nursing, OP – Occupational and Physical Therapy, PC – Physical Sciences, PH – Pharmacy, PS – Psychology, SE – Scientific Research, SG – Scientific Regulation, SW – Social Work, UT – University Teaching, VM – Veterinary Medicine |
Administrative and Foreign Service |
AS – Administrative Services, CO – Commerce, IT (CS) – Computer Systems, CT (FI) – Financial Management, FS – Foreign Service, IS – Information Services, OM – Organization and Methods, PE – Personnel Administration, PG – Purchasing and Supply, PL – Leadership Development Programs, PM – Program Administration, TR – Translation, WP – Welfare Program |
Technical |
AI – Air Traffic Control, AO – Aircraft Operations, DD – Drafting and Illustration, EG – Engineering and Scientific Support, EL – Electronics, EU – Educational Support, GT – General Technical, PI – Primary Products Inspection, PY – Photography, RO – Radio Operations, SO – Ships' Officers, TI – Technical Inspection |
Administrative Support |
CM – Communications, CR – Clerical and Regulatory, DA – Data Processing, OE – Office Equipment Operation, ST – Secretarial, Stenographic, Typing |
Operational |
CX – Correctional Services, FB – Border Services, FR – Firefighters, GL – General Labour and Trades, GS – General Services, HP – Heat, Power and Stationary Plant Operation, HS – Hospital Services, LI – Lightkeepers, PR – Printing Operations, SC – Ships' Crews, SR – Ship Repair |
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