- I. Introduction
- II. Instructions
- III. Definitions
- IV. Suggestions
- Appendix A: CIP grouping list for TLAC
-
I. Introduction
A. Description
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).
If you require further information or assistance with completing the questionnaire, please contact: statcan.education-education.statcan@statcan.gc.ca.
II. Instructions
General
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.
statcan.education-education.statcan@statcan.gc.ca
Appendix A: Classification of Instructional Programs (CIP)
TLAC CIP
- 01 - Education
- 13. Education
- 13.01 Education, General
- 13.02 Bilingual, Multilingual and Multicultural Education
- 13.03 Curriculum and Instruction
- 13.04 Educational Administration and Supervision
- 13.05 Educational/Instructional Technology
- 13.06 Educational Assessment, Evaluation and Research
- 13.07 International and Comparative Education
- 13.09 Social and Philosophical Foundations of Education
- 13.10 Special Education and Teaching
- 13.11 Student Counselling and Personnel Services
- 13.12 Teacher Education and Professional Development, Specific Levels and Methods
- 13.13 Teacher Education and Professional Development, Specific Subject Areas
- 13.1301 Agricultural teacher education
- 13.1302 Art teacher education
- 13.1303 Business teacher education
- 13.1304 Driver and safety teacher education
- 13.1305 English/English language arts teacher education
- 13.1306 Aboriginal and foreign language teacher education
- 13.1307 Health teacher education
- 13.1308 Family and consumer sciences/home economics teacher education
- 13.1309 Technology teacher education/industrial arts teacher education
- 13.1310 Sales and marketing operations/marketing and distribution teacher education
- 13.1311 Mathematics teacher education
- 13.1312 Music teacher education
- 13.1314 Physical education teaching and coaching
- 13.1315 Reading teacher education
- 13.1316 Science teacher education/general science teacher education
- 13.1317 Social science teacher education
- 13.1318 Social studies teacher education
- 13.1319 Technical teacher education
- 13.1320 Trade and industrial teacher education
- 13.1321 Computer teacher education
- 13.1322 Biology teacher education
- 13.1323 Chemistry teacher education
- 13.1324 Drama and dance teacher education
- 13.1325 French language/French language arts teacher education
- 13.1326 German language teacher education
- 13.1327 Health occupations teacher education
- 13.1328 History teacher education
- 13.1329 Physics teacher education
- 13.1330 Spanish language teacher education
- 13.1331 Speech teacher education
- 13.1332 Geography teacher education
- 13.1333 Latin teacher education
- 13.1334 School librarian/school library media specialist
- 13.1335 Psychology teacher education
- 13.1337 Earth science teacher education
- 13.1338 Environmental teacher education
- 13.14 Teaching English or French as a Second or Foreign Language
- 13.99 Education, Other
- 13. Education
- 02 - Visual and Performing Arts, and Communications Technologies
- 50. Visual and Performing Arts
- 50.01 Visual, Digital and Performing Arts, General
- 50.02 Crafts/Craft Design, Folk Art and Artisanry
- 50.03 Dance
- 50.04 Design and Applied Arts
- 50.0401 Design and visual communications, general
- 50.0402 Commercial and advertising art
- 50.0404 Industrial and product design
- 50.0406 Commercial photography
- 50.0407 Fashion/apparel design
- 50.0408 Interior design
- 50.0409 Graphic design
- 50.0410 Illustration
- 50.0411 Game and interactive media design
- 50.05 Drama/Theatre Arts and Stagecraft
- 50.06 Film/Video and Photographic Arts
- 50.07 Fine Arts and Art Studies
- 50.09 Music
- 50.10 Arts, entertainment, and media management
- 50.99 Visual and Performing Arts, Other
- 10. Communications Technologies/Technicians and Support Services
- 10.01 Communications Technology/Technician
- 10.02 Audiovisual Communications Technologies/Technicians
- 10.03 Graphic Communications
- 10.99 Communications Technologies/Technicians and Support Services, Other
- 50. Visual and Performing Arts
- 03 - Humanities
- 16. Aboriginal and Foreign Languages, Literatures and Linguistics
- 16.01 Linguistic, Comparative and Related Language Studies and Services
- 16.02 African Languages, Literatures and Linguistics
- 16.03 East Asian Languages, Literatures and Linguistics
- 16.04 Slavic, Baltic and Albanian Languages, Literatures and Linguistics
- 16.05 Germanic Languages, Literatures and Linguistics
- 16.06 Modern Greek Language and Literature
- 16.07 South Asian Languages, Literatures and Linguistics
- 16.08 Iranian/Persian Languages, Literatures and Linguistics
- 16.09 Romance Languages, Literatures and Linguistics
- 16.10 Aboriginal Languages, Literatures and Linguistics
- 16.11 Middle/Near Eastern and Semitic Languages, Literatures and Linguistics
- 16.12 Classics and Classical Languages, Literatures and Linguistics
- 16.13 Celtic Languages, Literatures and Linguistics
- 16.14 Southeast Asian and Australasian/Pacific Languages, Literatures and Linguistics
- 16.15 Turkic, Ural-Altaic, Caucasian and Central Asian Languages, Literatures and Linguistics
- 16.16 Sign Language
- 16.17 Second Language Learning
- 16.99 Aboriginal and Foreign Languages, Literatures and Linguistics, Other
- 23. English Language and Literature/Letters
- 23.01 English Language and Literature, General
- 23.13 English Rhetoric and Composition/Writing Studies
- 23.14 English Literature
- 23.99 English Language and Literature/Letters, Other
- 24. Liberal Arts and Sciences, General Studies and Humanities
- 24.01 Liberal Arts and Sciences, General Studies and Humanities
- 30. Multidisciplinary/Interdisciplinary Studies
- 30.13 Medieval and Renaissance Studies
- 30.21 Holocaust and Related Studies
- 30.22 Classical and Ancient Studies
- 30.29 Maritime Studies
- 38. Philosophy and Religious Studies
- 38.00 Philosophy and Religious Studies, General
- 38.01 Philosophy, Logic and Ethics
- 38.02 Religion/Religious Studies
- 38.99 Philosophy and Religious Studies, Other
- 39. Theology and Religious Vocations
- 39.02 Bible/Biblical Studies
- 39.03 Missions/Missionary Studies and Missiology
- 39.04 Religious Education
- 39.05 Religious/Sacred Music
- 39.06 Theological and Ministerial Studies
- 39.07 Pastoral Counselling and Specialized Ministries
- 39.99 Theology and Religious Vocations, Other
- 54. History
- 54.01 History
- 55. French Language and Literature/Letters
- 55.01 French Language and Literature, General
- 55.13 French Rhetoric and Composition/Writing Studies
- 55.14 French Literature
- 55.99 French Language and Literature/Letters, Other
- 16. Aboriginal and Foreign Languages, Literatures and Linguistics
- 04 - Social and Behavioural Sciences, and Legal Studies
- 05. Area, Ethnic, Cultural, Gender, and Group Studies
- 05.01 Area Studies
- 05.02 Ethnic, Cultural Minority, Gender and Group Studies
- 05.99 Area, Ethnic, Cultural, Gender and Group Studies, Other
- 09. Communication, Journalism and Related Programs
- 09.01 Communication and Media Studies
- 09.04 Journalism
- 09.07 Radio, Television and Digital Communication
- 09.0701 Radio and television
- 09.0702 Digital communication and media/multimedia
- 09.0799 Radio, television and digital communication, other
- 09.09 Public Relations, Advertising and Applied Communication
- 09.0900 Public relations, advertising and applied communication, general
- 09.0901 Organizational communication, general
- 09.0902 Public relations/image management
- 09.0903 Advertising
- 09.0904 Political communication
- 09.0905 Health communication
- 09.0906 Sports communication
- 09.0907 International and intercultural communication
- 09.0908 Technical and scientific communication
- 09.0999 Public relations, advertising and applied communication, other
- 09.10 Publishing
- 09.99 Communication, Journalism and Related Programs, Other
- 19. Family and Consumer Sciences/Human Sciences
- 19.00 Work and Family Studies
- 19.01 Family and Consumer Sciences/Human Sciences, General
- 19.02 Family and Consumer Sciences/Human Sciences Business Services
- 19.04 Family and Consumer Economics and Related Services
- 19.05 Foods, Nutrition and Related Services
- 19.06 Housing and Human Environments
- 19.07 Human Development, Family Studies and Related Services
- 19.09 Apparel and Textiles
- 19.99 Family and Consumer Sciences/Human Sciences, Other
- 30. Multidisciplinary/Interdisciplinary Studies
- 30.05 Peace Studies and Conflict Resolution
- 30.10 Biopsychology
- 30.11 Gerontology
- 30.14 Museology/Museum Studies
- 30.15 Science, Technology and Society
- 30.17 Behavioural Sciences
- 30.20 International/Global Studies
- 30.23 Intercultural/Multicultural and Diversity Studies
- 30.25 Cognitive Science
- 30.26 Cultural studies/critical theory and analysis
- 30.28 Dispute resolution
- 30.31 Human computer interaction
- 30.33 Sustainability studies
- 42. Psychology
- 42.01 Psychology (general)
- 42.01 Psychology, General
- 42.27 Research and experimental psychology
- 42.2701 Cognitive psychology and psycholinguistics
- 42.2702 Comparative psychology
- 42.2703 Developmental and child psychology
- 42.2704 Experimental psychology
- 42.2705 Personality psychology
- 42.2706 Physiological psychology/psychobiology
- 42.2707 Social psychology
- 42.2708 Psychometrics and quantitative psychology
- 42.2709 Psychopharmacology
- 42.2799 Research and experimental psychology, other
- 42.28 Clinical, counselling and applied psychology
- 42.2801 Clinical psychology
- 42.2802 Community psychology
- 42.2803 Counselling psychology
- 42.2804 Industrial and organizational psychology
- 42.2805 School psychology
- 42.2806 Educational psychology
- 42.2807 Clinical child psychology
- 42.2808 Environmental psychology
- 42.2809 Geropsychology
- 42.2810 Health/medical psychology
- 42.2811 Family psychology
- 42.2812 Forensic psychology
- 42.2813 Applied psychology
- 42.2814 Applied behaviour analysis
- 42.2899 Clinical, counselling and applied psychology, other
- 42.99 Psychology, other
- 42.99 Psychology, Other
- 42.01 Psychology (general)
- 45. Social Sciences
- 45.02 Anthropology
- 45.03 Archeology
- 45.04 Criminology
- 45.05 Demography and Population Studies
- 45.06 Economics
- 45.07 Geography and Cartography (Geomatics BA/BSc, Geographic Information Systems/Science BA/BSc)
- 45.09 International Relations and National Security Studies
- 45.10 Political Science and Government
- 45.11 Sociology
- 45.12 Urban Studies/Affairs
- 45.13 Sociology and anthropology
- 45.14 Rural Sociology
- 45.99 Social Sciences, Other
- 22. Legal Professions and Studies
- 22.00 Non-professional General Legal Studies (Undergraduate)
- 22.03 Legal Support Services
- 22.99 Legal professions and studies, other
- 05. Area, Ethnic, Cultural, Gender, and Group Studies
- 05 - Law
- 22. Legal Professions and Studies
- 22.0101 Law (LLB, JD, BCL)
- 22.0201 Advanced legal research/studies, general (LLM, MCL, MLI, MSL, LLD, JSD/SJD)
- 22.0202 Programs for foreign lawyers (LLM, MCL)
- 22.0203 American/US law/legal studies/jurisprudence (LLM, MCJ, LLD, JSD/SJD)
- 22.0204 Canadian law/legal studies/jurisprudence (LLM, MCJ, LLD, JSD/SJD)
- 22.0205 Banking, corporate, finance and securities law (LLM, LLD, JSD/SJD)
- 22,0206 Comparative law (LLM, MCJ, LLD, JSD/SJD)
- 22.0207 Energy, environment and natural resources law (LLM, MS, MSc, LLD, JSD/SJD)
- 22.0208 Health law (LLM, MJ, LLD, JSD/SJD)
- 22.0209 International law and legal studies (LLM, LLD, JSD/SJD)
- 22.0210 International business, trade and tax law (LLM, LLD, JSD/SJD)
- 22,0211 Tax law/taxation (LLM, LLD, JSD/SJD)
- 22.0212 Intellectual property law (LLM, LLD, JSD/SJD)
- 22.0299 Legal research and advanced professional studies (post-LLB/JD), other
- 22.9999 Legal professions and studies, other
- 22. Legal Professions and Studies
- 06 - Business, Management and Public Administration
- 30. Multidisciplinary/Interdisciplinary Studies
- 30.16 Accounting and Computer Science
- 44. Public Administration and Social Service Professions
- 44.00 Human Services, General
- 44.02 Community Organization and Advocacy
- 44.04 Public Administration
- 44.05 Public Policy Analysis
- 44.07 Social Work
- 44.99 Public Administration and Social Service Professions, Other
- 52. Business, Management, Marketing and Related Support Services (excluding the MBA programs)
- 52.01 Business/Commerce, General
- 52.02 Business Administration, Management and Operations
- 52.03 Accounting and Related Services
- 52.04 Business Operations Support and Assistant Services
- 52.05 Business/Corporate Communications
- 52.06 Business/Managerial Economics
- 52.07 Entrepreneurial and Small Business Operations
- 52.08 Finance and Financial Management Services
- 52.09 Hospitality Administration/Management
- 52.10 Human Resources Management and Services
- 52.11 International Business/Trade/Commerce
- 52.12 Management Information Systems and Services
- 52.13 Management Sciences and Quantitative Methods
- 52.14 Marketing
- 52.15 Real Estate
- 52.16 Taxation
- 52.17 Insurance
- 52.18 General Sales, Merchandising and Related Marketing Operations
- 52.19 Specialized Sales, Merchandising and Marketing Operations
- 52.20 Construction Management
- 52.21 Telecommunications Management
- 52.99 Business, Management, Marketing and Related Support Services, Other
- 71. Cannabis
- 71.0106 Cannabis health policy analysis
- 71.0110 Cannabis selling skills and sales operations
- 71.0111 Cannabis marketing and marketing operations
- 30. Multidisciplinary/Interdisciplinary Studies
- 07 - Physical and Life Sciences and Technologies
- 26. Biological and Biomedical Sciences
- 26.01 Biology, General
- 26.02 Biochemistry/Biophysics and Molecular Biology
- 26.03 Botany/Plant Biology
- 26.04 Cell/Cellular Biology and Anatomical Sciences
- 26.05 Microbiological Sciences and Immunology
- 26.07 Zoology/Animal Biology
- 26.08 Genetics
- 26.09 Physiology, Pathology and Related Sciences
- 26.10 Pharmacology and Toxicology
- 26.11 Biomathematics, Bioinformatics, and Computational Biology
- 26.12 Biotechnology
- 26.13 Ecology, Evolution, Systematics and Population Biology
- 26.14 Molecular Medicine
- 26.15 Neurobiology and Neurosciences
- 26.99 Biological and Biomedical Sciences, Other
- 30. Multidisciplinary/Interdisciplinary Studies
- 30.01 Biological and Physical Sciences
- 30.18 Natural Sciences
- 30.19 Nutrition Sciences
- 30.27 Human biology
- 30.32 Marine sciences
- 40. Physical Sciences
- 40.01 Physical Sciences, General
- 40.02 Astronomy and Astrophysics
- 40.04 Atmospheric Sciences and Meteorology
- 40.05 Chemistry
- 40.06 Geological and Earth Sciences/Geosciences
- 40.08 Physics
- 40.10 Materials Sciences
- 40.99 Physical Sciences, Other
- 26. Biological and Biomedical Sciences
- 08 - Mathematics, Computer and Information Sciences
- 11. Computer and Information Sciences and Support Services
- 11.01 Computer and Information Sciences and Support Services, General
- 11.02 Computer Programming
- 11.03 Data Processing and Data Processing Technology/Technician
- 11.04 Information Science/Studies
- 11.05 Computer Systems Analysis/Analyst
- 11.06 Data Entry/Microcomputer Applications
- 11.07 Computer Science
- 11.08 Computer Software and Media Applications
- 11.09 Computer Systems Networking and Telecommunications
- 11.10 Computer/Information Technology Administration and Management
- 11.99 Computer and Information Sciences and Support Services, Other
- 25. Library Science
- 25.01 Library Science and Administration
- 25.03 Library and archives assisting
- 25.99 Library Science, Other
- 27. Mathematics and Statistics
- 27.01 Mathematics
- 27.03 Applied Mathematics
- 27.05 Statistics
- 27.99 Mathematics and Statistics, Other
- 30. Multidisciplinary/Interdisciplinary Studies
- 30.06 Systems Science and Theory
- 30.08 Mathematics and Computer Science
- 30.30 Computational science
- 11. Computer and Information Sciences and Support Services
- 09 - Engineering
- 14. Engineering
- 14.01 Engineering, General
- 14.02 Aerospace, Aeronautical and Astronautical/Space Engineering
- 14.03 Agricultural Engineering
- 14.04 Architectural Engineering
- 14.05 Bioengineering and Biomedical Engineering
- 14.06 Ceramic Sciences and Engineering
- 14.07 Chemical Engineering
- 14.08 Civil Engineering
- 14.09 Computer Engineering
- 14.10 Electrical, Electronics and Communications Engineering
- 14.11 Engineering Mechanics
- 14.12 Engineering Physics/Applied Physics
- 14.13 Engineering Science
- 14.14 Environmental/Environmental Health Engineering
- 14.18 Materials Engineering
- 14.19 Mechanical Engineering
- 14.20 Metallurgical Engineering
- 14.21 Mining and Mineral Engineering
- 14.22 Naval Architecture and Marine Engineering
- 14.23 Nuclear Engineering
- 14.24 Ocean Engineering
- 14.25 Petroleum Engineering
- 14.27 Systems Engineering
- 14.28 Textile Sciences and Engineering
- 14.32 Polymer/Plastics Engineering
- 14.33 Construction Engineering
- 14.34 Forest Engineering
- 14.35 Industrial Engineering
- 14.36 Manufacturing Engineering
- 14.37 Operations Research
- 14.38 Surveying Engineering (geomatics, geodetic)
- 14.39 Geological/Geophysical Engineering
- 14.40 Paper science and engineering
- 14.41 Electromechanical engineering
- 14.42 Mechatronics, robotics, and automation engineering
- 14.43 Biochemical engineering
- 14.44 Engineering chemistry
- 14.45 Biological/biosystems engineering
- 14.99 Engineering, Other
- 15. Engineering technologies and engineering-related fields
- 15.00 Engineering technology, general
- 15.01 Architectural engineering technology/technician
- 15.02 Civil engineering technology/technician
- 15.03 Electrical and electronic engineering technologies/technicians
- 15.04 Electromechanical and instrumentation and maintenance technologies/technicians
- 15.05 Environmental control technologies/technicians
- 15.06 Industrial production technologies/technicians
- 15.07 Quality control and safety technologies/technicians
- 15.08 Mechanical engineering related technologies/technicians
- 15.09 Mining and petroleum technologies/technicians
- 15.10 Construction engineering technology/technician
- 15.11 Engineering-related technologies
- 15.12 Computer engineering technologies/technicians
- 15.13 Drafting/design engineering technologies/technicians
- 15.14 Nuclear engineering technology/technician
- 15.15 Engineering-related fields
- 15.16 Nanotechnology
- 15.99 Engineering technologies and engineering-related fields, other
- 14. Engineering
- 10 - Architecture
- 04. Architecture and Related Services
- 04.02 Architecture (BArch, BA, BS, BSc, MArch, MA, MS, /MSc, PhD)
- 04.03 City/Urban, Community and Regional Planning
- 04.04 Environmental Design/Architecture
- 04.05 Interior Architecture
- 04.06 Landscape Architecture (BS, BSc, BSLA, BLA, MSLA, MLA, PhD)
- 04.08 Architectural History and Criticism
- 04.09 Architectural Sciences and Technology
- 04.0902 Architectural and building sciences/technology (BArch, BA, BS, BSc, MArch, MA, MS, MSc, PhD)
- 04.99 Architecture and Related Services, Other
- 30. Multidisciplinary/Interdisciplinary Studies
- 30.1201 Historic preservation and conservation, general
- 30.1202 Cultural resource management and policy analysis
- 04. Architecture and Related Services
- 11 - Agriculture, Natural Resources and Conservation
- 01. Agriculture, Agriculture Operations and Related Sciences
- 01.00 Agriculture, General
- 01.01 Agricultural Business and Management
- 01.02 Agricultural Mechanization
- 01.03 Agricultural Production Operations
- 01.04 Agricultural and Food Products Processing
- 01.05 Agricultural and Domestic Animal Services
- 01.06 Applied Horticulture/Horticultural Business Services
- 01.07 International Agriculture
- 01.08 Agricultural Public Services
- 01.09 Animal Sciences
- 01.10 Food Science and Technology
- 01.11 Plant Sciences
- 01.12 Soil Sciences
- 01.99 Agriculture, Agriculture Operations and Related Sciences, Other
- 03. Natural Resources and Conservation
- 03.01 Natural Resources Conservation and Research
- 03.0103 Environmental Studies
- 03.0104 Environmental Science
- 03.02 Natural Resources Management and Policy
- 03.03 Fishing and Fisheries Sciences and Management
- 03.05 Forestry
- 03.06 Wildlife and Wildlands Science and Management
- 03.99 Natural Resources and Conservation, Other
- 03.01 Natural Resources Conservation and Research
- 71. Cannabis
- 71.0101 Cannabis product processing and inspection
- 71.0102 Cannabis production operations and management
- 71.0103 Cannabis product development and plant breeding
- 01. Agriculture, Agriculture Operations and Related Sciences
- 12 - Medicine
- 51. Health Professions and Related Programs
- 51.12 Medicine (MD)
- 51. Health Professions and Related Programs
- 13 - Other health, Parks, Recreation and Fitness
- 31. Parks, Recreation, Leisure and Fitness Studies
- 31.01 Parks, Recreation and Leisure Studies
- 31.03 Parks, Recreation and Leisure Facilities Management
- 31.0302 Golf course operation and grounds management
- 31.05 Health and Physical Education/Fitness
- 31.0501 Health and Physical Education, General
- 31.0505 Kinesiology and Exercise Science
- 31.99 Parks, Recreation, Leisure and Fitness Studies, Other
- 51. Health Professions and Related Clinical Sciences
- 51.00 Health Services/Allied Health/Health Sciences, General
- 51.01 Chiropractic (DC)
- 51.02 Communication Disorders Sciences and Services
- 51.0201 Communication sciences and disorders, general
- 51.0202 Audiology/Audiologist
- 51.0203 Speech language pathology/pathologist
- 51.0204 Audiology/audiologist and speech-language pathology/pathologist
- 51.07 Health and Medical Administrative Services
- 51.09 Allied Health Diagnostic, Intervention and Treatment Professions
- 51.10 Clinical/Medical Laboratory Science/Research and Allied Professions
- 51.14 Medical Scientist (MS, MSc, PhD)
- 51.15 Mental and Social Health Services and Allied Professions
- 51.19 Osteopathic Medicine/Osteopathy (DO)
- 51.21 Podiatric Medicine/Podiatry (DPM)
- 51.22 Public Health
- 51.23 Rehabilitation and Therapeutic Professions
- 51.27 Medical Illustration and Informatics
- 51.31 Dietetics and Clinical Nutrition Services
- 51.3101 Dietetics/dietitian (RD)
- 51.3102 Clinical nutrition/nutritionist
- 51.32 Bioethics/Medical Ethics
- 51.33 Alternative and Complementary Medicine and Medical Systems
- 51.34 Alternative and Complementary Medical Support Services
- 51.35 Somatic Bodywork and Related Therapeutic Services
- 51.36 Movement and Mind-Body Therapies
- 51.37 Energy-based and Biologically-based Therapies
- 51.99 Health Professions and Related Clinical Sciences, Other
- 71. Cannabis
- 71.0107 Cannabis abuse/cannabis addiction counselling
- 71.0108 Cannabis public health
- 71.0109 Cannabis health professions and related clinical sciences, other
- 31. Parks, Recreation, Leisure and Fitness Studies
- 14 - Personal, Protective and Transportation Services
- 43. Security and Protective Services
- 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
- 43.0111 Criminalistics and criminal science
- 43.0116 Cyber/computer forensics and counterterrorism
- 43.0117 Financial forensics and fraud investigation
- 43.0302 Crisis/emergency/disaster management
- 43.0399 Security and protective services, specialized programs, other
- 49. Transportation and Materials Moving
- 49.01 Air Transportation
- 49.02 Ground Transportation
- 49.03 Marine Transportation
- 49.99 Transportation and Materials Moving, Other
- 43. Security and Protective Services
- 15 - Other
- 30.9999 Multidisciplinary/Interdisciplinary studies, other
- 16 - Dentistry
- 51. Health Professions and Related Programs
- 51.04 Dentistry (DDS, DMD)
- 51.05 Advanced/Graduate Dentistry and Oral Sciences (Cert., MS, MSc, PhD)
- 51. Health Professions and Related Programs
- 17 - Nursing
- 51. Health Professions and Related Programs
- 51.3801 Registered nursing/registered nurse (RN, ASN, BSN, BScN, MSN, MScN)
- 51.3802 Nursing administration (MSN, MS, MScN, MSc, PhD)
- 51.3803 Adult health nurse/nursing
- 51.3804 Nurse anesthetist
- 51.3805 Primary health care nurse/nursing and family practice nurse/nursing
- 51.3806 Maternal/child health and neonatal nurse/nursing
- 51.3807 Nurse midwife/nursing midwifery
- 51.3808 Nursing science (MS, MSc, PhD)
- 51.3809 Pediatric nurse/nursing
- 51.3810 Psychiatric/mental health nurse/nursing
- 51.3811 Public health/community nurse/nursing
- 51.3812 Perioperative/operating room and surgical nurse/nursing
- 51.3813 Clinical nurse specialist
- 51.3814 Critical care nurse/nursing
- 51.3815 Occupational and environmental health nurse/nursing
- 51.3816 Emergency room/trauma nurse/nursing
- 51.3817 Nursing education
- 51.3818 Nursing practice
- 51.3819 Palliative care nurse/nursing
- 51.3820 Clinical nurse leader
- 51.3821 Geriatric nurse/nursing
- 51.3822 Women's health nurse/nursing
- 51.3823 Registered psychiatric nurse/nursing
- 51.3899 Registered nursing, nursing administration, nursing research and clinical nursing, other
- 51. Health Professions and Related Programs
- 18 - Pharmacy
- 51. Health Professions and Related Clinical Sciences
- 51.2001 Pharmacy (PharmD, BS, BSc, BPharm)
- 51.2002 Pharmacy administration and pharmacy policy and regulatory affairs (MS, MSc, PhD)
- 51.2003 Pharmaceutics and drug design (MS, MSc, PhD)
- 51.2004 Medicinal and pharmaceutical chemistry (MS, MSc, PhD)
- 51.2005 Natural products chemistry and pharmacognosy (MS, MSc, PhD)
- 51.2006 Clinical and industrial drug development (MS, MSc, PhD)
- 51.2007 Pharmacoeconomics/pharmaceutical economics (MS, MSc, PhD)
- 51.2008 Clinical, hospital and managed care pharmacy (MS, MSc, PhD)
- 51.2009 Industrial and physical pharmacy and cosmetic sciences (MS, MSc, PhD)
- 51.2010 Pharmaceutical sciences
- 51.2011 Pharmaceutical marketing and management
- 51.2099 Pharmacy, pharmaceutical sciences and administration, other
- 51. Health Professions and Related Clinical Sciences
- 19 - Veterinary Medicine
- 51. Health Professions and Related Programs
- 51.2401 Veterinary medicine (DVM)
- 51.2501 Veterinary sciences/veterinary clinical sciences, general (Cert., MS, MSc, PhD)
- 51.2502 Veterinary anatomy (Cert., MS, MSc, PhD)
- 51.2503 Veterinary physiology (Cert., MS, MSc, PhD)
- 51.2504 Veterinary microbiology and immunobiology (Cert., MS, MSc, PhD)
- 51.2505 Veterinary pathology and pathobiology (Cert., MS, MSc, PhD)
- 51.2506 Veterinary toxicology and pharmacology (Cert., MS, MSc, PhD)
- 51.2507 Large animal/food animal and equine surgery and medicine (Cert., MS, MSc, PhD)
- 51.2508 Small/companion animal surgery and medicine (Cert., MS, MSc, PhD)
- 51.2509 Comparative and laboratory animal medicine (Cert., MS, MSc, PhD)
- 51.2510 Veterinary preventive medicine, epidemiology and public health (Cert., MS, MSc, PhD)
- 51.2511 Veterinary infectious diseases (Cert., MS, MSc, PhD)
- 51.2599 Veterinary biomedical and clinical sciences (Cert., MS, MSc, PhD), other
- 51. Health Professions and Related Programs
- 20 - Executive MBA (graduate programs)
- 52. Business, Management, Marketing and Related Support Services
- (Specifically the MBA compressed graduate programs for executives)
- 52. Business, Management, Marketing and Related Support Services
- 21 - Regular MBA (graduate programs)
- 52. Business, Management, Marketing and Related Support Services
- (Specifically Graduate MBA programs in the regular stream)
- 52. Business, Management, Marketing and Related Support Services
- 22 - Optometry
- 51. Health Professions and Related Programs
- 51.17 Optometry (OD) – optometrist, optometry doctor of optometry (OD)
- 51. Health Professions and Related Programs
Accommodation Services: CVs for operating revenue - 2019
| Geography | CVs for operating revenue |
|---|---|
| percent | |
| Canada | 0.08 |
| Newfoundland and Labrador | 0.00 |
| Prince Edward Island | 0.00 |
| Nova Scotia | 0.08 |
| New Brunswick | 0.00 |
| Quebec | 0.08 |
| Ontario | 0.22 |
| Manitoba | 0.03 |
| Saskatchewan | 0.20 |
| Alberta | 0.02 |
| British Columbia | 0.21 |
| Yukon | 0.00 |
| Northwest Territories | 0.00 |
| Nunavut | 0.00 |
Management, scientific and technical consulting services: CVs for operating revenue - 2019
| 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.
| 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.
- Date modified:
Monthly Survey of Manufacturing: National Level CVs by Characteristic - December 2020
| Month | Sales of goods manufactured | Raw materials and components inventories | Goods / work in process inventories | Finished goods manufactured inventories | Unfilled Orders |
|---|---|---|---|---|---|
| % | |||||
| December 2019 | 0.58 | 0.98 | 1.16 | 1.39 | 1.06 |
| January 2020 | 0.64 | 0.99 | 1.26 | 1.32 | 1.10 |
| February 2020 | 0.63 | 1.02 | 1.22 | 1.36 | 1.08 |
| March 2020 | 0.68 | 0.99 | 1.17 | 1.41 | 1.10 |
| April 2020 | 0.87 | 0.99 | 1.20 | 1.41 | 1.10 |
| May 2020 | 0.80 | 1.04 | 1.13 | 1.37 | 1.06 |
| June 2020 | 0.69 | 1.05 | 1.19 | 1.38 | 1.06 |
| July 2020 | 0.69 | 1.02 | 1.15 | 1.43 | 1.10 |
| August 2020 | 0.64 | 1.05 | 1.23 | 1.50 | 1.20 |
| September 2020 | 0.67 | 1.05 | 1.22 | 1.54 | 1.20 |
| October 2020 | 0.69 | 1.02 | 1.18 | 1.53 | 1.15 |
| November 2020 | 0.72 | 1.09 | 1.19 | 1.46 | 1.34 |
| December 2020 | 0.70 | 1.03 | 1.22 | 1.45 | 1.37 |
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
|
||||
Monthly Survey of Manufacturing: National Weighted Rates by Source and Characteristic - December 2020
| Data source | ||
|---|---|---|
| Response or edited | Imputed | |
| % | ||
| Sales of goods manufactured | 88.1 | 11.9 |
| Raw materials and components | 77.2 | 22.8 |
| Goods / work in process | 84.4 | 15.6 |
| Finished goods manufactured | 78.7 | 21.3 |
| Unfilled Orders | 92.5 | 7.5 |
| Capacity utilization rates | 74.6 | 25.4 |
Federal tax expenditures
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.
Supplementary information tables
Departmental Sustainable Development Strategy
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 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 |
|
Motivate suppliers to reduce the environmental impact of their goods, services and supply chains. |
|
|
| Support for green procurement will be strengthened, including guidance, tools and training for public service employees |
|
Motivate suppliers to green their goods, services and supply chain. |
|
||
|
|||||
Gender-based analysis plus
Institutional GBA+ Capacity
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-recovered Statistical ServicesFootnote 1
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.
Supporting information on the program inventory
Supporting information on planned expenditures, human resources and results related to Statistics Canada's program inventory is available in the GC InfoBase.