The Tuition and Living Accommodation Costs (TLAC) survey collects data for full-time students in degree programs at Canadian public postsecondary institutions. The survey was developed to provide an overview of tuition and additional compulsory fees, and living accommodation costs that students can expect to pay for an academic year.
TLAC survey data:
provides stakeholders, the public and students with annual tuition costs and changes in tuition fees from the previous year
contributes to a better understanding of the costs to obtain a degree
contributes to education policy development
contributes to the Consumer Price Index
facilitates interprovincial comparisons
facilitates comparisons between institutions
B. Reference period
2021/2022 academic year (September to April)
C. Population
The target population is all publicly funded degree-granting institutions (universities and colleges) in Canada.
The survey target population includes institutions that have degree-granting status for the academic year 2021/2022. Institutions that do not have degree-granting status are excluded even if they provide portions of programs that lead to a degree granted by another institution. The survey is limited to institutions whose operations are primarily funded by provincial governments. Institutions that do not receive grants from Education ministries or departments, and institutions that receive grants only from Health ministries and departments are excluded.
D. Fields of study
The field of study classification for both undergraduate and graduate programs are adapted from the 2016 Classification of Instructional Programs (CIP), Statistics Canada's standard for field of study classification. The CIP's structure comprises several groupings developed jointly by Statistics Canada and the National Center for Education Statistics (NCES) in the USA. It is based on work undertaken as part of the creation of the North American Product Classification System (NAPCS) by Canada, the United States and Mexico.
TLAC CIP groupings for Undergraduate programs:
Education
Visual and Performing Arts, and Communications Technologies
Humanities
Social and Behavioural Sciences, and Legal Studies
Law
Business, Management and Public Administration
Physical and Life Sciences and Technologies
Mathematics, Computer and Information Sciences
Engineering
Architecture
Agriculture, Natural Resources and Conservation
Dentistry
Medicine
Nursing
Pharmacy
Veterinary Medicine
Optometry
Other Health, Parks, Recreation and Fitness
Personal, Protective and Transportation Services
Other
TLAC CIP groupings for Graduate programs:
Includes all of the undergraduate program groupings with the exception of Medicine and the addition of:
Executive MBA
Regular MBA
Refer to Appendix A: CIP
Note: Dental, Medical and Veterinary Residency Programs offered in teaching hospitals and similar locations that may lead to advanced professional certification are excluded.
E. Submission Date
The completed questionnaire must be returned by June 11, 2021 by uploading the file back in the Secure Internet Site (E-File transfer Service).
Tuition fee tables disseminated by Statistics Canada are based on an academic year for full-time students with a full course load in degree programs, regardless of the number of credits.
Tuition should be reported based on the academic year (8 months, September to April) or semester (4 months) regardless of the number of credits. If it is not possible to provide tuition data for a semester or academic year, tuition should be reported per credit.
Final fees should be reported. If they have not yet been determined, report an estimate and check the box on the questionnaire to state that these are estimated fees for 2021/2022.
Part A: Tuition fees for full-time students
How to Report Tuition Fees:
Report tuition fees for full-time students in degree programs only. The degree must be conferred by your institution, which means that students start and complete their degree at your institution. DO NOT include associate degrees, diplomas and certificates.
Verify and update the previous year data (2020/2021) on each page if required.
Report fees with decimals, NO commas. Example 2415.45.
Quebec, Nova-Scotia and Newfoundland and Labrador: Lower fees represent Canadian students that have a permanent address in the province (in-province students) and the Upper fees represent Canadian students with an out-of-province permanent address.
Academic year (8 months, September to April): When tuition is reported based on the academic year, report the full cost of the program regardless of the number of credits.
Semester (4 months): When tuition is reported based on semester, report the full cost of the semester regardless of the number of credits. Semester fees will be multiplied by two to calculate tuition for the academic year (8 months).
Per Credit: Only report per credit if you cannot report based on semester or academic year regardless of the number of credits. We assume 30 credits as the minimum number of credits to calculate academic year fees. Therefore, when reporting based on per credit, tuition will be multiplied by 30 credits.
Report additional compulsory fees for materials or equipment on pages 4 (undergraduate) and 5 (graduate).
NEW degree programs must be specified in the Comments section at the bottom of page 2 (undergraduate) and page 3 (graduate).
Undergraduate Law page 2, only professional designations for Law (LLB, JD, BCL), from a Faculty of Law should be reported in this grouping.
Graduate Law page 3, only professional Law degrees from a Faculty of Law (post-LLB/JD), should be reported in this grouping.
Tuition for legal studies degree programs (non-professional Law degrees) on page 2 and page 3, should be reported under "Social and Behavioural Sciences, and Legal Studies". See Appendix A.
Only Medicine (MD, doctor of medicine) program should be reported under undergraduate Medicine, page 2 of the questionnaire. See appendix A.
Personal, Protective and Transportation Services includes:
43.0103 Criminal justice / law enforcement administration
43.0104 Criminal justice / safety studies
43.0106 Forensic science and technology
43.0107 Criminal justice / police science
Part B: Additional Compulsory fees for full-time Canadian Students
How to Report Additional Compulsory Fees:
In part B of the questionnaire, report additional compulsory fees for full-time Canadian students in the first row of the table where these fees do not vary according to their field of study for all full-time undergraduate students (page 4) and graduate students (page 5).
Important note: Health Plan and Dental Plan fees that students can opt out of with proof of comparable coverage should not be included. However, this information should be noted in the comments section of the questionnaire.
Part C: Living Accommodation costs at residences/housing
Accommodation costs should be reported wherever possible for full-time students living in residence. If it is not possible to separate the room and the meal plan costs for single students only a total should be reported.
III. Definitions
Tuition Fees
Tuition that is charged to a full-time student with a full course load, regardless of the number of credits.
Additional Compulsory fees
Additional compulsory fees collected by the TLAC survey are those that all students must pay regardless of the field of study (TLAC grouping).
These fees cover services that vary from institution to institution, year to year, faculty to faculty, or school to school within the same institution.
Additional compulsory fees may include: general fees (admission, registration, examination, internship, etc.), technology fees, student services fees, student association fees, contributions to student activities, copyright fees, premiums for compulsory insurance plans, fees for athletics and recreational facilities/activities, and other fees such as transcript, degree, laboratory, uniform, u-pass, etc.
TLAC Additional Compulsory Fee Breakdown
Athletics fees
Mandatory fees that support intercollegiate athletics, they cover athletics facilities and campus recreational activities (intramurals, fitness and recreation courses, etc.)
Health Services fees
Mandatory fees support the on-campus clinic facilities providing services of doctors and nurses. Health and dental plan fees: if students can opt out of these plans with proof of comparable coverage, these fees should be excluded from the survey.
Student Association fee
Mandatory fees support the general operating expenses of the association.
Other fees
If compulsory fees are reported in "Other please specify" you must provide further details on the types of fees reported. For example, u-pass, transcript, laboratory, technology fee, etc.
IV. Suggestions
Statistics Canada would welcome any suggestions for changes in the survey which you may wish to propose.
Accommodation Services : CVs for operating revenue - 2019
Table Summary
This table displays the results of CVs for operating revenue for 2019. The information is grouped by Geography (appearing as row headers), CVs for operating revenue (appearing as a column header) calculated using percentage units of measure.
Management, scientific and technical consulting services: CVs for operating revenue - 2019
Table summary
This table displays the results of CVs for operating revenue for 2019. The information is grouped by Geography (appearing as row headers), CVs for operating revenue (appearing as a column header) calculated using percentage units of measure.
Geography
CVs for operating revenue
percent
Canada
0.01
Newfoundland and Labrador
0.02
Prince Edward Island
0.02
Nova Scotia
0.02
New Brunswick
0.03
Quebec
0.02
Ontario
0.02
Manitoba
0.05
Saskatchewan
0.03
Alberta
0.02
British Columbia
0.03
Yukon
0.01
Northwest Territories
0.00
Nunavut
0.00
2021 Census Comment Classification
By: Joanne Yoon, Statistics Canada
Once every five years, the Census of Population provides a detailed and comprehensive statistical portrait of Canada and its population. The census is the only data source that provides consistent statistics for both small geographic areas and small population groups across Canada. Census information is central to planning at all levels. Whether starting a business, monitoring a government program, planning transportation needs or choosing the location for a school, Canadians use census data every day to inform their decisions.
Preparation for each cycle of the census requires several stages of engagement, as well as testing and evaluating data to recommend questionnaire content for the next census, as is the case for the upcoming 2021 Census. These steps include content consultations and discussions with stakeholders and census data users, as well as the execution of the 2019 Census Test (which validates respondent behaviours and ensures that questions and census materials are understood by all participants).
At the end of the Census of Population questionnaires, respondents are provided with a text box in which they can share concerns and suggestions, or make comments about the steps to follow, the content or the characteristics of the questionnaire. The information entered in this space is further analyzed by the Census Subject Matter Secretariat (CSMS) during and after the census collection period. Comments pertaining to specific questionnaire content are classified by subject matter area (SMA)—such as education, labour or demography—and shared with the corresponding expert analysts. The information is used to support decision making regarding content determination for the next census and to monitor factors such as respondent burden.
Using machine learning to classify comments
In an effort to improve the analysis of the 2021 Census of Population comments, Statistics Canada's Data Science Division (DScD) worked in collaboration with CSMS to create a proof of concept on the use of machine learning (ML) techniques to quickly and objectively classify census comments. As part of the project, CSMS identified fifteen possible comment classes and provided previous census comments labelled with one or more of these classes. These fifteen classes included the census SMAs as well as other general census themes by which to classify comments from respondents such as "experience with the electronic questionnaire," "burden of response," as well as "positive census experience" and comments "unrelated to the census." Using ML techniques along with the labelled data, a bilingual semi-supervised text classifier was trained wherein comments can be in either French or English and the machine can use labelled data to learn each class, while leveraging unlabelled data to understand its data space. DScD data scientists experimented with two ML models—the strengths of each model, along with the final model are detailed in this article.
The data scientists trained the 2021 Census comment classifier using comments from the 2019 Census Test. The CSMS team manually labelled these comments using the fifteen identified comment classes and reviewed each other's coding in an effort to reduce coding biases. The classifier is multi-class since a comment can be classified into fifteen different classes. As a result, the classifier is also multi-label since a respondent can address multiple topics within a single comment falling under multiple classes, and so the comment can be coded to one or more class.
Deterministic question and page number mapping
When a comment contains a question or page number, that number is deterministically mapped to the SMA class associated to the question and then combined with the ML class prediction in order to output the final class prediction. For example, say that a respondent completes a questionnaire where question number 22 asks about the respondent's education. In the comment box, the respondent comments on question 22 by explicitly stating the question number and also mentions the sex and gender questions without stating any question numbers. The mapping outputs the education class and the ML model predicts the sex and gender class based on the words used to mention the sex and gender questions. The program outputs the final prediction which is a union of the two outputs: education and sex and gender class. When no question number or page is explicitly mentioned, the program only outputs the ML prediction. The ML model is not trained to learn the page number mapping of each question since the location of a question can change depending on the questionnaire format. There are, for example, questions on different pages when you compare the regular font and the large print questionnaires as fewer questions fit per page in large print, and the electronic or online questionnaire does not show any page numbers.
Text cleaning
Before training the classifier, the program first cleans the comments. It identifies the language of the comment (English or French) and then corrects the spelling of unidentifiable words with a word that requires the least amount of edits and is most frequently found in the training data. For example, the word toqn can be corrected to the valid words torn or town, but is corrected to town because town was used more frequently in the training data. Also, the words are lemmatized into their root representation. The machine thus understands the words walk and walked to have the same root meaning. Stop words are not removed since helper words have meaning and imply sentiment. For example, this should be better has a different meaning from this is better, but if the program dropped all stop words (including this, should, be and is), the two sentences becomes identical with only one word left: better. Removing stop words can alter the meaning and the sentiment of a comment.
Bilingual semi-supervised text classifier
The bilingual semi-supervised text classifier learns from the labelled comments and is used to classify comments. Bilingual semi-supervised text classifier is not a single concept but rather individual pieces combined to best classify census comments.
The data scientists have trained a bilingual model where the proportion of French to English labelled comments as detected by a language detecting python program was 29% and 71%, respectively (16,062 English labelled comments and 6,597 French labelled comments). By training the model on both languages, it leveraged identical words (such as consultation, journal and restaurant) that have the same meaning in both languages to improve the accuracy of French comments which have less labels than English comments.
The model is semi-supervised. Labelled data define the knowledge that the machine needs to replicate. When given the labelled training data, the model uses maximum likelihood to learn the model's parameters and adversarial training to be robust to small perturbations. Unlabelled data are also used to expand the data space that the machine should handle with low confusion but does not teach the model about the meaning of classes. The unlabelled data are only used to lower the model's confusion using entropy minimization to minimize the conditional entropy of estimated class probabilities and virtual adversarial training to maximize the local smoothness of a conditional label distribution against local perturbation.
The text classifier starts with an embedding layer to accept words as input. A lookup table will map each word to a dense vector since the machine learns from numbers and not characters. The embedding layer will represent a sequence of words into a sequence of vectors. With this sequence, the model looks for a pattern that is more generalizable and robust than learning individual words. Also, to prevent the machine from memorizing certain expressions rather than semantic meaning, a dropout layer directly follows the embedding layer. When training, the dropout layer drops random words from the training sentence. The proportion of words dropped is fixed but the dropped words are selected at random. The model is forced to learn without some words so that it generalizes better. When using the model to classify comments, no words are dropped and the model can use all identified knowledge and patterns to make a prediction.
Comparing CNN to Bi-LSTM
The data scientists compared a convolutional neural network (CNN) to a Bi-directional-Long Short Term Memory (Bi-LSTM) network. Both networks can classify text by automatically learning complex patterns, but learn differently because of their different structures. In this proof of concept, the data scientists experimented with three different models to learn all fifteen classes: a single-headed LSTM model, a multi-headed LSTM model and a multi-headed CNN model. Overall, the single-headed LSTM model consistently predicted all the classes the most accurately and will thus be used in production.
LSTM can capture long-term dependencies between word sequences using input, forget and output gates as it can learn to retain or forget previous state's information. Previous state's information is the context made by the group of words that preceded the current word that the network is looking at. If the current word is an adjective, the network knows what the adjective is referring to because it retained that information earlier in the sentence. If the sentence talks about a different topic, the network should forget the previous state of information. Since Bi-LSTM is bi-directional, the model gathers past and future information relative to each word.
The CNN model applies a convolution filter to a sliding window of group of words and max pooling to select the most prominent information from a phrase of words rather than looking at each word independently. CNN defines the semantic context of a word using neighbouring words, whereas LSTM learns from a sequential pattern of words. Individual features are concatenated to form a single feature vector that summarizes the key characteristics of the input sentence.
A multi-headed classifier was tested with a final sigmoid layer giving a confidence distribution of the classes. The sigmoid layer will represent each class prediction confidence score as a decimal between 0-1 (i.e. 0% - 100%) where each score is independent to each other. This is ideal for the multi-label problem of comments that talk about multiple topics.
The data scientists also tested a single-headed classifier where a model only learns to identify if a single class is present in the text using a softmax activation function. The number of single-headed classifier is equal to the number of classes. An input comment can have multiple labels if multiple classifiers predict that its topic is mentioned in the comment. For example, if a comment talks about language and education, the language classifier and education classifier will predict 1 to signal the presence of the relevant SMA classes and other classifiers will predict 0 to signal the absent.
A single-headed classifier learns each class better than a multi-headed classifier which needs to learn fifteen different classes, but there is the added burden for programmers to maintain fifteen different classifiers. The burden to run the multiple classifiers is minimal since it can easily be programmed to run all classifiers in a loop and output the presence of relevant class. As shown below, the single-head Bi-LSTM model performs the best across the different classes and also in the weighted average.
Table 1: Test weighted average F1-score of different models.
Table 1: Test weighted average F1-score of different models.
F1-score
Single-head Bi-LSTM
90.2%
Multi-headed CNN
76%
Bi-LSTM
73%
Amongst the multi-headed classifiers, CNN had a 4.6% higher average test F1-score than Bi-LSTM when classifying comments into SMA classes such as language and education. On the other hand, the Bi-LSTM model's average test F1-score on general census themed classes (i.e. "unrelated to the census," "positive census experience," "burden of response," "experience with the electronic questionnaire") was 9.0% higher than CNN model. Bi-LSTM was better at predicting if a comment was relevant to the Census program or not because it knew the overall context of where the comment was directed. For example, a respondent's positive opinion on a Canadian sports team is not relevant to the census, so this type of comment would be classified under the class "unrelated to the census." In this case, the CNN model predicted the comment to be positive in nature and thus to the positive census experience class, whereas Bi-LSTM tied the positive sentiment to the context (sports teams) and since the context was unrelated to the census, it correctly labelled it to be of no value for further analysis by CSMS. CNN, on the other hand, only looks at a smaller range of words so it excels in extracting features in certain parts of the sentence that are relevant to certain classes.
Next steps
This proof of concept showed that a ML model can accurately classify bilingual census comments. The classifier is multi-class, meaning that there are multiple classes to classify a comment into. It is also multi-label, meaning that more than one class may be relevant to the input comment. The second phase of this project will be to transition this model into production. In production, French and English comments will be spell checked and stemmed to the root words depending on each comment's language. A bilingual semi-supervised text classifier will predict both the cleaned French and English comments. The labelled 2019 data will train the ML model to predict and label incoming comments from the new 2021 Census of Population and ensure that the respondent comments are categorized to and shared with the appropriate expert analysts. In the production phase, when 2021 Census comments come in, the CSMS team and data scientists will continue to validate the ML predictions and feed them back to the machine to further improve the model.
If you are interested in text analytics or want to find out more about this particular project, the Applied machine learning for text analysis community of practice (GC employees only) recently featured a presentation on this project. Join the community to ask questions or discuss other text analytics projects.
Retail Commodity Survey: CVs for Total Sales (November 2020)
NAPCS-CANADA
Month
202008
202009
202010
202011
Total commodities, retail trade commissions and miscellaneous services
0.69
0.58
1.23
0.59
Retail Services (except commissions) [561]
0.68
0.58
1.21
0.59
Food at retail [56111]
0.81
0.60
1.25
0.73
Soft drinks and alcoholic beverages, at retail [56112]
0.52
0.55
0.76
0.64
Cannabis products, at retail [56113]
0.00
0.00
0.05
0.00
Clothing at retail [56121]
1.07
1.09
1.61
1.74
Footwear at retail [56122]
2.17
1.66
1.73
2.15
Jewellery and watches, luggage and briefcases, at retail [56123]
9.08
9.18
6.60
2.34
Home furniture, furnishings, housewares, appliances and electronics, at retail [56131]
0.73
0.66
0.70
0.64
Sporting and leisure products (except publications, audio and video recordings, and game software), at retail [56141]
3.00
3.31
2.74
2.18
Publications at retail [56142]
8.50
8.32
6.44
7.28
Audio and video recordings, and game software, at retail [56143]
7.86
5.40
6.87
6.13
Motor vehicles at retail [56151]
2.58
1.95
4.73
2.02
Recreational vehicles at retail [56152]
3.79
3.95
4.42
6.47
Motor vehicle parts, accessories and supplies, at retail [56153]
1.67
1.49
2.47
1.39
Automotive and household fuels, at retail [56161]
2.13
2.23
2.40
2.24
Home health products at retail [56171]
2.26
2.53
3.32
3.73
Infant care, personal and beauty products, at retail [56172]
2.70
2.30
3.35
2.96
Hardware, tools, renovation and lawn and garden products, at retail [56181]
1.22
1.51
1.36
1.48
Miscellaneous products at retail [56191]
2.37
2.43
2.77
2.33
Total retail trade commissions and miscellaneous servicesFootnotes 1
1.65
1.66
2.38
1.56
Footnotes
Footnote 1
Comprises the following North American Product Classification System (NAPCS): 51411, 51412, 53112, 56211, 57111, 58111, 58121, 58122, 58131, 58141, 72332, 833111, 841, 85131 and 851511.
Statistics Canada's Departmental Plan does not include information on tax expenditures that relate to its planned results for 2021–22.
Tax expenditures are the responsibility of the Minister of Finance, and the Department of Finance Canada publishes cost estimates and projections for government-wide tax expenditures each year in the Report on Federal Tax Expenditures. This report provides detailed information on tax expenditures, including objectives, historical background and references to related federal spending programs, as well as evaluations, research papers and gender-based analysis. The tax measures presented in this report are solely the responsibility of the Minister of Finance.
2020 to 2023 Short-form Departmental Sustainable Development Strategy
Name of department
Statistics Canada
Date
January 2021
Context
Although Statistics Canada is not bound by the Federal Sustainable Development Act and is not required to develop a full departmental sustainable development strategy, Statistics Canada adheres to the principles of the Federal Sustainable Development Strategy (FSDS) by complying with the Policy on Green Procurement.
The Policy on Green Procurement supports the Government of Canada's effort to promote environmental stewardship. In keeping with the objectives of the policy, Statistics Canada supports sustainable development by integrating environmental performance considerations into the procurement decision making process through the actions described in the 2019 to 2022 FSDS "Greening Government" goal.
Commitments
Please refer to the table below.
Integrating sustainable development
Statistics Canada will continue to ensure that its decision-making process includes consideration of FSDS goals and targets through its Strategic Environmental Assessment (SEA) process. An SEA for policy, plan or program proposals includes an analysis of the impacts of the given proposal on the environment, including on FSDS goals and targets.
Public statements on the results of Statistics Canada's assessments will be made public and announced on its website when an initiative has undergone a detailed SEA. The purpose of the public statement is to demonstrate that the environmental effects, including the impacts on achieving the FSDS goals and targets, of the approved policy, plan or program have been considered during proposal development and decision making.
FSDS goal: Greening Government
FSDS goal: Greening Government
FSDS target
FSDS contributing actions
Corresponding departmental action(s)
Contribution by each departmental action to the FSDS goal and target
Starting point(s), target(s) and performance indicator(s) for departmental actions
Link to the department's Program Inventory
Actions supporting the Greening Government goal and the Policy on Green Procurement
Departments will use environmental criteria to reduce the environmental impact and ensure best value in government procurement decisions
Integrate environmental considerations into procurement management processes and controls.
Ensure paper purchased by Statistics Canada is made from recycled material.
Motivate suppliers to reduce the environmental impact of their goods, services and supply chains.
To reduce waste generated and minimize the environmental impacts of assets throughout their lifecycle, Statistics Canada will continue to embed environmental considerations in public procurement in accordance with the Policy on Green Procurement.
Copy paper purchased by Statistics Canada contains a minimum of 30% recycled content and has a forest certification, ECOLOGO certification or equivalent certification
Economic and Environmental Statistics
Socio-economic Statistics
Censuses
Centres of Expertise
Cost-Recovered Statistical Services
Internal Services
Support for green procurement will be strengthened, including guidance, tools and training for public service employees
Ensure that decision makers and materiel management and procurement specialists have the necessary training and awareness to support green procurement.
Ensure that key officials include support for and contributions to the Government of Canada's Policy on Green Procurement objectives.Table note 1
Motivate suppliers to green their goods, services and supply chain.
100% of specialists in procurement and materiel management have completed training on green procurement.
Performance evaluations of managers and functional heads of procurement and materiel management include support for and contributions to green procurement in the given fiscal year.
Table note 1
Reference to performance agreements of procurement materiel management senior officials has been removed. Green Procurement considerations are addressed at the requirements definition phase and have been built into templates each contracting officer must use. The templates are subject to peer review and sectional audit, with monitoring and oversight by the key official.
Statistics Canada has established a Centre for Gender, Diversity and Inclusion Statistics to report on progress made towards gender equality and address gaps in the availability of disaggregated data and analysis on gender, race, class, sexual orientation, disability and other intersecting identities. The Centre enables data users to easily access and analyze a wealth of statistical information, relevant to the evaluation of programs, policies and initiatives from a gender, diversity and inclusion perspective.
Statistics Canada is committed to creating not only a diverse and inclusive workforce, but a safe place for all employees. Statistics Canada's Diversity and Inclusion Framework and goals include:
Have a workforce that is representative of the Canadian population
Attract and retain a talented, skilled and diverse workforce
Understand inclusion barriers within the agency
Create barrier free processes, policies, practices and programs
Tracking progress and measure results
In 2021-22, the agency will continue to implement the Equity Diversity and Inclusion Action Plan and support progress in areas under five pillars; recruitment, development, increasing awareness, visible leadership and accountability, and accessibility. A few examples of the Equity, Diversity and Inclusion actions include:
Increase hiring of racialized employees for all new recruitment, promotions and acting assignments for executive positions
Calculate projected gaps to inform potential hiring goals and staffing strategies with hiring managers
Develop an accountability mechanism for Champions
Develop and put in place a mandatory pledge for support and engagement from management in relations to diversity and inclusion
Identify and implement diversity and inclusion mandatory training for all employees
Statistics Canada plans to continue to diversify its hiring practices and staffing processes to ensure it is inclusive and accessible to all Canadians. As part of the agency's commitment to inclusivity and accessibility, the agency is amending questions in the hiring process to give flexibility to candidates who may have been out of the workforce for some time, removing barriers at the outset of the hiring process, and encouraging women to apply to male dominated fields where they are under-represented.
In addition, Statistics Canada is committed to tracking employment equity gaps and in 2021-22 a new Dashboard for management will be available with more indicators, giving management a better idea of the retention rate, promotion rate, and other key information regarding employment equity and diversity and inclusion. This will better equip management to understand and address the gaps in their division.
Highlights of GBA+ Results Reporting Capacity by Program
Economic and Environmental Statistics
National Economic Accounts Program:
To support the economic participation and prosperity framework, the human resources modules for the Infrastructure Accounts and selected satellite accounts (natural resources, environment) contain detailed breakdowns for men and women. For 2021-22, work is underway to link the labour productivity program to labour force characteristics which will provide further GBA+ insights. As well, estimates of the value of unpaid household work, for women and men, will be updated, thereby contributing to GBA+ and supporting analysis related to gender inequalities in Canada.
Corporations Return Act:
This was used to construct a gender database for corporate Canada. Coupled with additional information the database does provide insight on gender distribution within senior ranks of corporate Canada and therefore inform on the Gender Results Framework pillar: Leadership and Democratic Participation. Thus far the project has provided research and further insights into gender representation of decisions makers within the corporate sector in Canada. For 2021-22, the agency would like to widen the scope of the project to include a broader diversity lens especially within the immigrant population. This research initiative will pursue a research agenda focusing on the Canadian corporate sector developments in the areas of diversity within the existing GBA+ framework. The goal is to table a report by March 31, 2022 that outlines the findings and propose potential future initiatives for analytical studies and statistical outputs.
COVID-19
New disaggregated data based on gender will be released, particularly as they relate to the impact of the economic downturn in the context of the pandemic.
Socio-economic Statistics
With a joint goal to increase knowledge and literacy under five of the pillars of the Gender Results Framework (GRF) (Economic Participation and Prosperity; Poverty Reduction, Health and Well-being; Leadership and Democratic Participation; Education and Skills Development; Gender-based Violence and Access to Justice), the Department for Women and Gender Equality (WAGE) has engaged Statistics Canada to address important gaps in the availability of data and analysis related to gender, age, sexual orientation, disability, ethnocultural characteristics and their intersecting identities. Among projects supported by WAGE, the Centre will produce a report on the assessment of adding intersectionality to the Gender Results Framework indicators. The Centre will also release a few analytical products to inform on Canada's diversity: an analytical series on the Lesbian, Gay and Bisexual (LGB) population, including an article on the linguistic and ethnocultural diversity among lesbian, gay and bisexual Canadians, a paper on the sociodemographic profile of women living in rural and remote areas of Canada (including immigrant status, Indigenous identity and ethnocultural characteristics. Further, a greater emphasis was placed on disaggregating data as much as possible so all papers will include as much information on diverse population groups as the data will allow.
Since the onset of the pandemic, the Centre released a number of articles on the impact of COVID-19 on diverse population groups, including mental health and gender, mental health for population groups designated as visible minorities, the impact of COVID-19 on LGBTQ2+ Canadians and parenting through the pandemic. An article on statistical standards used to disaggregate data was also released. The Centre has also modified the Gender, Diversity and Inclusion Statistics Hub to highlight the COVID-19 articles that used disaggregated data and also organized them by diverse population group.
The Centre has also been developing a standard for measuring sexual orientation. The first round of consultations took place in winter 2020 with experts within the federal government, academia and community organizations. The next phase took place in summer 2020 where 17 focus groups were conducted. Since late January 2021, the proposed standards have been available for review by the public. A final round of qualitative testing will take place in late March and a final report with recommendations will be prepared in the first quarter of 2021-2022.
Social Inclusion Framework
With funding from Canada's Anti-Racism strategy, Diversity and Sociocultural Statistics will continue to work on the development of its conceptual framework on social inclusion, including a large number of social inclusion indicators based on 2016 Census data (to be later updated based on 2021 Census data) and other survey data such as the General Social Survey. These indicators will be presented on the Gender, Diversity and Inclusion Statistics HUB using a new disaggregated classification of ethnocultural groups that combines the population group question with the ethnic and cultural origin question. Indicators are currently in production and a new interactive tool to present them on GDIS Hub is currently in development through what is now called The Social Indicators Visualization Project.
General Social Survey on Social Identity
Work is also being undertaken with regards to collection and dissemination of ethno-cultural statistics. For example, with support from Heritage Canada, the new cycle of the General Social Survey on Social Identity will allow for the disaggregation of some specific ethno cultural groups to allow for increased data and more targeted policy analysis with respect to the experiences of some ethno-cultural groups for most provinces/provincial regions of Canada.
Labour Force Survey
Starting in the July 2020 reference month, the Labour Force Survey started collecting information on visible minority status which can be used to report on the labour market activities of persons belonging to population groups designated as visible minorities.
Justice Statistics
The Canadian Centre for Justice and Community Safety Statistics has released a number of articles and reports on Gender Based Violence. In addition, many projects are underway to report on the experiences of diverse population groups. Two such are projects are:
Statistics Canada and the Canadian Association of Chiefs of Police publicly announced a commitment to work with the policing community and key organizations to add Indigenous identify and ethno-cultural groups to police-reported crime data. This will help inform issues of system inequities and shine light on the experiences of these populations.
A collaborative project with the Government of Saskatchewan was undertaken to respond to the growing need to better understand the pathways individuals take through and, often back into, the justice system. This includes understanding how certain population groups, such as Indigenous peoples, may be more vulnerable to repeat contacts with the system.
Census
2021 Census
Various ethnocultural concepts, such as immigration, language groups, ethnic origins, population groups designated as visible minorities and religion will be measured on the 2021 Census. The data will provide detailed and granular disaggregation of data on population groups designated as visible minorities. In addition, Statistics Canada is consulting with experts and data users with the objective of developing a more disaggregated classification of groups designated as visible minorities for dissemination and analytical purposes using Census data. This new classification (2021 Census derived variable) combines information from the question on population groups with information from the ethnic and cultural origin question.
Cost Recovery projects are reflected throughout the programs mentioned.
For example, a portion of work being done to address important data gaps in collaboration with the Department for Women and Gender Equity (WAGE), is a cost-recovery program.
Centre of Expertise
Economic Analysis Projects
Data has been collected and shared on Private Enterprises by gender of primary owner, age of primary owner and enterprise size. In 2021-22, the agency plans to undertake projects that update and expand its capacity to report on gender and diversity. Specifically, the statistics on Private Enterprises by gender of primary owner will be updated to the latest period possible (2018), and research projects will be undertaken to examine human capital by gender, gross domestic product by gender, the performance gaps between women-owned and men-owned enterprises, as well as Black business owners and persons with disabilities and business ownership in Canada.
Internal Services
Statistics Canada’s Employment Equity, Diversity and Inclusion team supports two different pillars of the Gender Results Framework:
Economic Participation and Prosperity- Increased labour market opportunities for women, especially women in under-represented groups
Leadership and Democratic Participation- More women in senior management positions and more diversity in senior leadership positions.
Initiatives are targeted towards all employment equity groups, including Indigenous, members of population groups designated as visible minority and people with disability.
Here are some of the initiatives the agency is currently working on that support the pillars and goals of the Gender Results Framework:
Increase diversity in staffing process. We are working on adding a paragraph to questions during the hiring process to give the flexibility for candidates to use their own personal experience rather than only work-related experience to meet the merit criteria.
Review of tools (Track Record) for our staffing team in order to remove barriers in the hiring process and be more diverse/inclusive at the outset.
Add specific paragraphs encouraging women to apply to under-represented fields or male dominated fields, such as IT.
Add a column in the screening board report to identify people that have self-declared during the process. We will be able to see if more women have applied to the jobs that had specific wording to encourage them to apply and self-declare with the new column on the screening board report.
Create and implement a new Dashboard for management with employment equity (EE) data and more indicators than only the gaps and the Work Force Availability. It will also give a better idea of the retention rate, promotion rate, and will contain official language information and other key information regarding EE, diversity and inclusion in order to better identify and address the gaps in their division.
Review the Self-ID form. This will be a revamp to change more specifically the gender identity section, in order to include more than just male or female. We will be able to collect more accurate data on gender including Two-Spirit and trans employees and see where they are situated in the Agency, for example, at the executive level.
Establish an integrated approach to development and talent management for career progression for equity-seeking groups, e.g. through mentoring, coaching, and sponsorship by senior leaders.
Partner with educational and training institutions to provide a direct pathway into public service jobs for Indigenous peoples in occupations and departments in which they are under-represented.
