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Federal Science Expenditures and Personnel 2023/2024: Activities in the natural sciences and engineering

Information for respondents

Authority to publish

I hereby authorize Statistics Canada to disclose any or all portions of the data supplied on this questionnaire that could identify this department after the tabling of the forecast year Main Estimates.

  • Yes
  • No

Respondent Information:

  • Name of person authorized to sign
  • Signature
  • Official position
  • Program
  • Department or agency
  • E-mail address
  • Telephone number

Enquiries to be directed to:

  • Name
  • Date
  • Position title
  • Telephone number
  • Email address
  • Fax number

Purpose

This survey collects financial and operating data on expenditures and full-time equivalent personnel on the scientific activities of Federal Government Public Administration in Canada.

Additional information

The data collected are used by federal and provincial science policy analysts, and are also part of the gross domestic expenditures on research and development (GERD). Your information may also be used by Statistics Canada for other statistical and research purposes.

Authority

Collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.

Completion of this questionnaire is a legal requirement under this Act.

Confidentiality

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Statistics Canada will use the information from this survey for statistical purposes.

Data-sharing agreements

To reduce respondent burden, Statistics Canada has entered into data-sharing agreements with provincial and territorial statistical agencies and other government organizations, which have agreed to keep the data confidential and use them only for statistical purposes. Statistics Canada will only share data from this survey with those organizations that have demonstrated a requirement to use the data.

Section 11 of the Statistics Act provides for the sharing of information with provincial and territorial statistical agencies that meet certain conditions. These agencies must have the legislative authority to collect the same information, on a mandatory basis, and the legislation must provide substantially the same provisions for confidentiality and penalties for disclosure of confidential information as the Statistics Act. Because these agencies have the legal authority to compel businesses to provide the same information, consent is not requested and businesses may not object to the sharing of the data.

For this survey, there are Section 11 agreements with the provincial and territorial statistical agencies of Newfoundland and Labrador, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia, and the Yukon.

The shared data will be limited to information pertaining to federal departments and agencies located within the jurisdiction of the respective province or territory.

Section 12 of the Statistics Act provides for the sharing of information with federal, provincial or territorial government organizations. Under Section 12, you may refuse to share your information with any of these organizations by writing a letter of objection to the Chief Statistician and returning it with the completed questionnaire. Please specify the organizations with which you do not want to share your data.

For this survey, there are Section 12 agreements with the statistical agencies of Prince Edward Island, the Northwest Territories, Nunavut and Industry Canada.

The shared data will be limited to information pertaining to federal departments and agencies located within the jurisdiction of the respective province or territory.

Record linkage

To enhance the data from this survey and to minimize the reporting burden, Statistics Canada may combine it with information from other surveys or from administrative sources.

Security of emails and faxes

Statistics Canada advises you that there could be a risk of disclosure during the transmission of information by facsimile or e-mail. However, upon receipt, Statistics Canada will provide the guaranteed level of protection afforded all information collected under the authority of the Statistics Act.

Return procedures

Please forward the completed questionnaire and listing of extramural performers through the Electronic File Transfer service (EFT).
For further inquiries:

Thank you for your co-operation.

FSEP - Introduction

This introduction is intended to provide an overview of the process of collecting science expenditure data; definitions of and explanatory notes on natural sciences and engineering, social sciences, humanities and the arts, scientific and technological activities, performance sectors, and other terms used are given in subsequent sections.

The collection of science expenditure data is organized by the Investment, Science and Technology Division (ISTD) of Statistics Canada. This exercise was formerly conducted under the aegis of the Treasury Board of Canada Secretariat but is now solely a Statistics Canada survey.

Collection is undertaken to gather essential data describing the recent, current and proposed state of the federal resources allocated to science. Federal science expenditures data are provided to Industry Canada who in turn use the data in the development of advice to the Assistant Deputy Ministers' Steering Committee on the Management of S&T, their Minister and the Treasury Board of Canada Secretariat, as well as in policy development and in monitoring the implementation of science policies. Statistics Canada maintains historical expenditure series in natural sciences and engineering dating back to 1963 and to 1971 in the social sciences, humanities and the arts. These data are available through the Investment, Science and Technology Division (ISTD) or through special requests.

The basic reporting unit is the budgetary program of a department or agency. Each budgetary program forms the subject of separate scientific expenditure reports for the natural and for the social science activities within it. Both the program and the program activities within it may be scientific in whole or in part only. Only expenditures on the scientific components of a program or its activity are reported. In some programs it will be difficult to distinguish between the natural and social sciences. However, some allocation must be made and in determining this allocation, the dominant orientation of the projects and the area of expertise of the personnel involved must be considered. Detailed definitions are given on the following pages.

On the questionnaires, the identified expenditures are looked at from several different viewpoints and in various subdivisions. Expenditures on research and development (R&D) and related scientific activities (RSA) are subdivided to provide an indication of the "what" of a department's scientific effort. Expenditures in each category of scientific activity are further subdivided into "current" and "capital" segments. Current expenditures are additionally subdivided by sector, to indicate the "where" and "by whom" the activity is performed (e.g., in business enterprise, in higher education).

The human resources allocated to scientific activities are summarized in terms of the involved categories of personnel (scientific and professional, technical, etc.) and the principal focus of their efforts (R&D, RSA and, administration of extramural programs).

When completed, checked for consistency with previous reports, entered into the database and totaled along the various dimensions, these data provide snapshots of the federal resources allocated to science, supporting not only the work of central agencies but also the submissions of departments and agencies requesting resources.

Purpose

This survey collects financial and operating data on expenditures and full-time equivalent personnel on the scientific activities of Federal Government Public Administration in Canada.

Question 1: Expenditures by activity and performer

General

The natural sciences and engineering consist of disciplines concerned with understanding, exploring, developing or utilizing the natural world. Included are the engineering and technology, mathematical, computer and information sciences, physical sciences, medical and health sciences, and agricultural sciences, veterinary sciences and forestry.

Expenditures by activity and performer

Scientific and technological (S&T) activities can be defined as all systematic activities which are closely concerned with the generation, advancement, dissemination and application of scientific and technology knowledge in all fields of science and technology, that is the natural sciences and engineering, and the social sciences, humanities and the arts.

The central activity is scientific research and experimental development (R&D). In addition there are a number of activities closely related to R&D, and are termed related scientific activities (RSA). Those identified as being appropriate for the federal government in the natural sciences are: scientific data collection, information services, special services and studies and education support.

The performer is equivalent to the sector in which the scientific activity is conducted. The basic distinction is between intramural and extramural performance. Extramural payments are classified on the basis of the performance sectors to which they are made. The appropriate extramural performers are business enterprise, higher education, Canadian non-profit institutions, provincial and municipal government, and foreign performers.

I. Performers

lntramural activities include all current expenditures incurred for scientific activities carried out by in-house personnel of units assigned to the program; the related gross fixed capital expenditures (acquisition of land, buildings, machinery and equipment for scientific activities); the administration of scientific activities by program employees; and, the purchase of goods and services to support in-house scientific activities (include royalties or licences for the use of patents and other intellectual property rights, the lease of capital goods (machinery and equipment, etc.) and the rental of buildings to support scientific activities performed by the statistical unit in the reference year).

The intramural expenditures reported for scientific activities are those direct costs, including salaries, associated with scientific programs. The costs should include that portion of a program's contribution to employee benefit plans (e.g., superannuation and compensation) which is applicable to the scientific personnel within the program. The summation of intramural R&D activity is synonymous with the performance of R&D for the entire economy (GERD).

Extramural performers are groups being funded by the federal government sector for S&T activities. In this survey the extramural performers include:

  • Business enterprise – business and government enterprises including public utilities and government-owned firms. Both financial and non-financial corporations are included. Incorporated consultants or unincorporated individuals providing scientific and engineering services are also included. Industrial research institutes located at Canadian universities are considered to be in the higher education sector.
  • Higher education – comprises all universities, colleges of technology and other institutes of post-secondary education, whatever their source of finance or legal status. It also includes teaching hospitals (non-teaching hospitals are in the Canadian non-profit sector) all research institutes, centers, experimental stations and clinics that have their scientific activities under the direct control of, or administered by, or associated with, the higher education establishments.
  • Canadian non-profit institutions – charitable foundations, voluntary health organizations, scientific and professional societies, non-teaching hospitals (teaching hospitals are in the higher education sector) and other organizations not established to earn profits. Non-profit institutions primarily serving or controlled by another sector should be included in the controlling sector.
  • Provincial and municipal governments – departments and agencies of these governments as well as provincial research organizations. Government enterprises, such as provincial utilities are included in the business enterprise sector, and non-teaching hospitals in the Canadian non-profit institutions sector.
  • Foreign performers – all foreign government agencies, foreign companies (including foreign subsidiaries of Canadian firms), international organizations, non-resident foreign nationals and Canadians studying or teaching abroad.

II. Research and experimental development (R&D)

Research and experimental development - comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.

R&D activities may be aimed at achieving either specific or general objectives. R&D is always aimed at new findings, based on original concepts (and their interpretation) or hypotheses. It is largely uncertain about its final outcome (or at least about the quantity of time and resources needed to achieve it), it is planned for and budgeted (even when carried out by individuals), and it is aimed at producing results that could be either freely transferred or traded in a marketplace.

For an activity to be an R&D activity, it must satisfy five core criteria:

  • To be aimed at new findings (novel);
  • To be based on original, not obvious, concepts and hypothesis (creative);
  • To be uncertain about the final outcome (uncertainty);
  • To be planned and budgeted (systematic);
  • To lead to results that could be possibly reproduced (transferable/or reproducible).

Examples of R&D:

  • A special investigation of a particular mortality in order to establish the side effects of certain cancer treatment is R&D.
  • The investigation of new methods of measuring temperature is R&D, as is the study and development of new models for weather prediction.
  • Investigation on the genetics of the species of plants in a forest in an attempt to understand natural controls for disease or pest resistance.
  • The development of new application software and substantial improvements to operating systems and application programs.

R&D is generally carried out by specialized R&D units. However, an R&D project may also involve the use of non R&D facilities (e.g., testing grounds), the purchase or construction of specialized equipment and materials, and the assistance of other units. Costs of such items, attributable to the project, are to be considered R&D costs.

R&D may also be carried out by units normally engaged in other functions (e.g. a marine survey ship used for hydrological research, a geological survey team may be directed to work in a certain area in order to provide data for a geophysical research project). Such effort is part of an R&D project and, again, so far as is practical, the costs should be assigned to R&D expenditures.

On the other hand, R&D units may also be engaged in non R&D activities such as technical advisory services, testing, and construction of special equipment for other units. So far as is practical, the effort devoted to such operations should be included in the related scientific activities (RSA).

1. In-house R&D – R&D performed by personnel of the reporting program. It may include R&D carried out on behalf of another program or federal government department.

2. R&D contracts – R&D contracts to an outside institution or individual to fund R&D performed by the institution or individual. The criterion is: would the performer report the R&D contract as in-house (intramural) R&D that is government-funded? If the answer is yes the activity would be an R&D contract. If no, and the funding is to provide goods and services necessary to support the in-house R&D of the federal government it should be reported as In-house R&D.

3. R&D grants and contributions – awards to organizations or individuals for the conduct of R&D and intended to benefit the recipients rather than provide the program with goods, services or information. These funds are normally identical to that portion of the budgetary "grants and contributions" line object of expenditure which is devoted to R&D activities.

4. Research fellowships – awards to individuals for advanced research training and experience. Awards intended primarily to support the education of the recipients should be reported as "education support".

5. Administration of extramural programs – the costs of identifiable units engaged in the administration of contracts and grants and contributions for scientific activities that are to be performed outside the federal government. These expenditures should be broken down by the type of scientific activity supported, i.e. R&D or RSA.

6. Capital expenditures – the annual gross amount paid for the acquisition of fixed assets that are used repeatedly or continuously in the performance of scientific activities for more than one year. They should be reported in full for the period when they took place, whether acquired or developed in house, and should not be registered as an element of depreciation.

The most relevant types of assets used for capital expenditures are:

  • Land and buildings
  • Machinery and equipment
  • Capitalized computer software
  • Other intellectual property products

III. Related scientific activities (RSA)

Related scientific activities (RSA) are all systematic activities which are closely concerned with the generation, advancement, dissemination and application of scientific and technological knowledge. The types of related scientific activities for the natural sciences and engineering are described below.

7. In-house RSA – RSA performed by personnel of the reporting program. It may include RSA carried out on behalf of another program or federal government department.

In-house RSA activities include all current expenditures incurred for scientific activities carried out by in-house personnel of units assigned to the program; the purchase of goods and services to support in-house scientific activities (include royalties or licences for the use of patents and other intellectual property rights, and the rental of buildings to support scientific activities performed by the statistical unit in the reference year). Also include expenses of persons who provide ancillary services such as security, cleaning and maintenance work, finance and administration that are proportional to the RSA being conducted. However, the personnel providing these services are not to be included in the in-house personnel counts (see Section 2. Personnel).

The intramural expenditures reported to RSA are those direct costs, including salaries, associated with scientific programs. The cost should include that portion of a program's contribution to employee benefit plans (e.g., superannuation and compensation) which is applicable to the scientific personnel within the program. Also include the costs of self-employed individuals, consultants and researchers who are working on-site on the departments' RSA projects.

8. RSA contracts – contracts to an outside institution or individual to fund RSA performed by the institution or individual. The criterion is: would the performer report the RSA contract as in-house (intramural) RSA that is government-funded? If the answer is yes the activity would be an RSA contract. If no, and the funding is for the purchase goods and services to support the in-house RSA of the federal government department, it should be reported as In-house RSA (Item 7).

Contracts to other federal government departments should be reported as a transfer of funds in question 3A (i) and 3A (ii) of the questionnaire.

9. RSA grants and contributions – awards to organizations or individuals for the conduct of RSA and intended to benefit the recipients rather than provide the program with goods, services or information. These funds are normally identical to that portion of the budgetary "grants and contributions" line object of expenditure which is devoted to RSA.

In-house RSA, RSA Contracts and RSA grants and contributions can include the following items:

  • Scientific data collection – the gathering, processing, collating and analyzing of data on natural phenomena. These data are normally the results of surveys, routine laboratory analyses or compilations of operating records.
    Data collected as part of an existing or proposed research project are charged to research. Similarly, the costs of analyzing existing data as part of a research project are R&D costs, even when the data were originally collected for some other purpose. The development of new techniques for data collection is also to be considered a research activity. Examples of RSA scientific data collection are: routine geological, hydrographic, oceanographic and topographic surveys; routine astronomical observations; maintenance of meteorological records; and wildlife and fisheries surveys.
  • Information services – all work directed to collecting, coding, analyzing, evaluating, recording, classifying, translating and disseminating scientific and technological information as well as museum services. Included are the operations of scientific and technical libraries, S&T consulting and advisory services, the Patent Office, the publication of scientific journals and monographs, and the organizing of scientific conferences. Grants for the publication of scholarly works are also included.
    General purpose information services or information services directed primarily towards the general public are excluded, as are general departmental and public libraries. When individual budgets exist, the costs of libraries which belong to institutions otherwise entirely classified to another activity, such as R&D, should be assigned to information services. The costs of printing and distributing reports from another activity, such as R&D, are normally attributed to that activity.
  • Sub category under Information services:
    • Museum services – the collecting, cataloguing and displaying of specimens of the natural world or of representations of natural phenomena. The activity involves a systematic attempt to preserve and display items from the natural world; in some ways it could be considered an extension of information services. The scientific activities of natural history museums, zoological and botanical gardens, aquaria, planetaria and nature reserves are included. Parks which are not primarily restricted reserves for certain fauna or flora are excluded. In all cases the costs of providing entertainment and recreation to visitors should be excluded (e.g. restaurants, children's gardens and museums).
      When a museum also covers not only natural history but also aspects of human cultural activities, the museum's resources should be appropriated between the natural and social sciences. However, museums of science and technology, war, etc., which display synthetic or artificial objects and may also illustrate the operations of certain technologies, should be considered as engaged in museum services in social sciences.
    • Special services and studies – work directed towards the establishment of national and provincial standards for materials, devices, products and processes; the calibration of secondary standards; non-routine quality testing; feasibility studies and demonstration projects.
  • Sub categories under Special services and studies include:
    • Testing and standardization – concerns the maintenance of national standards, the calibration of secondary standards and the non-routine testing and analysis of materials, components, products, processes, soils, atmosphere, etc. These activities are related scientific activities (RSA). The development of new measures for standards, or of new methods of measuring or testing, is R&D. Exclude routine testing such as monitoring radioactivity levels or soil tests before construction.
    • Feasibility studies – technical investigations of proposed engineering projects to provide additional information required to reach decisions on implementation. Besides feasibility studies, the related activity of demonstration projects are to be included. Demonstration projects involve the operation of scaled-up versions of a facility or process, or data on factors such as costs, operational characteristics, market demand and public acceptance. Projects called "demonstration projects" but which conform to the definition of R&D should be considered R&D. Once a facility or process is operated primarily to provide a service or to gain revenue, rather than as a demonstration, it should no longer be included with feasibility studies. In all demonstration projects, only the net costs should be considered.
    • Education support – grants to individuals or institutions on behalf of individuals which are intended to support the post-secondary education of students in technology and the natural sciences. General operating or capital grants are excluded. The activity includes the support of foreign students in their studies of the natural sciences at Canadian or foreign institutions. Grants intended primarily to support the research of individuals at universities are either R&D grants or research fellowships.
      Awards intended primarily to support the education of the recipients should be reported as "education support".

10. Administration of extramural programs – the costs of identifiable units engaged in the administration of contracts and grants and contributions for scientific activities that are to be performed outside the federal government. These expenditures should be broken down by the type of scientific activity supported, i.e. R&D or RSA.

11. Capital expenditures – the annual gross amount paid for the acquisition of fixed assets that are used repeatedly or continuously in the performance of scientific activities for more than one year. They should be reported in full for the period when they took place, whether acquired or developed in house, and should not be registered as an element of depreciation.

The most relevant types of assets used for capital expenditures are:

  • Land and buildings
  • Machinery and equipment
  • Capitalized computer software
  • Other intellectual property products

Question 2: Personnel

Full-time equivalent (FTE) – the ratio of working hours actually spent on scientific activities during a specific reference period divided by the total number of hours conventionally worked in the same period by an individual or a group. For example, an employee who is engaged in scientific activities for half a year has a full-time equivalence of 0.5. Personnel data reported should be consistent with expenditures data.

Scientific and professional – researchers and professionals engaged in the conception or creation of new knowledge. They conduct research and improve or develop concepts, theories, models, techniques instrumentation, software or operational methods. They require at least one academic degree or a nationally recognized professional qualification, as well as those with equivalent experience.

Technical – technicians and equivalent staff are persons whose main tasks require technical knowledge and experience in one or more fields of engineering, the physical and life sciences, or the social sciences, humanities and the arts. They perform scientific and technical tasks involving the application of concepts and operational methods and the use of research equipment, normally under the supervision of researchers.

Other – other supporting staff includes skilled and unskilled craftsmen, and administrative, secretarial and clerical staff participating in science and technology projects or directly associated with such projects.

Gender – refers to current gender which may be different from sex assigned at birth and may be different from what is indicated on legal documents.

Personnel in full time equivalent for intramural scientific and technological activities:

  • Column A: Personnel engaged in Research and experimental development (R&D)
  • Column B: Personnel engaged in Related scientific activities (RSA)
  • Column C: Personnel engaged in the administration of extramural R&D programs
  • Column D: Personnel engaged in the administration of extramural RSA programs
  • Column E: Total personnel

Question 3: Sources of funds

Question 3A (i). Transfers for natural sciences and engineering activities

Include payments and recipients for contracts, transfers and joint programs from/to other federal government departments. Please identify the amount and names of the origination and recipient programs.

Question 3A (ii). Sources of funds for total scientific and technological activities

This question identifies the sources of funds for expenditures on scientific activities reported for all three years. It will help to ensure that work funded from outside the department is not overlooked.

  • Departmental S&T budget – that portion of the total departmental budget which was spent on natural science and engineering activities.
  • Revenues to / from other federal departments – money transferred from this program to another federal department or money transferred into this program from another federal department for activities in the natural sciences and engineering.
  • Provincial government departments – all funds from the provincial government used for natural science and engineering activities. The funds are referred to as payments, contributions, transfers, etc. Also include provincial portions of federal-provincial cost sharing programs performed by the department program.
  • Business enterprises – all funds from business enterprises used for natural science and engineering activities performed by the department.
  • Other – all funds for natural sciences and engineering activities from other sources not specified above.

Question 4: Socio-economic objectives

Intramural and extramural scientific and technological expenditures by socio-economic objective for the reporting year by activity (research and experimental development, related scientific activities, and total).

  • 1. Exploration and exploitation of the Earth
  • 2. Infrastructure and general planning of land use:
    • 2.1: Transport
    • 2.2: Telecommunications
    • 2.3: Other
  • 3. Control and care of the environment
  • 4. Protection and improvement of human health
  • 5. Production, distribution and rational utilization of energy
  • 6. Agricultural production and technology:
    • 6.1: Agriculture
    • 6.2: Fishing
    • 6.3: Forestry
  • 7. Industrial production and technology
  • 8. Social structures and relationships
  • 9. Exploration and exploitation of space
  • 10. Non-oriented research
  • 11. Other civil research
  • 12. Defence

1. Exploration and exploitation of the Earth – scientific activities with objectives related to the exploration of the Earth's crust and mantle, seas, oceans and atmosphere, as well as on their exploitation. It also includes climatic and meteorological research, polar exploration (under various headings, as appropriate) and hydrology.

Examples:

  • General scientific activities
  • Mineral, oil and natural gas prospecting
  • Exploration and exploitation of the sea-bed
  • Earth's crust and mantle excluding sea-bed and studies of soil for agriculture (objective 6)
  • Hydrology - excludes scientific activities on: water supplied and disposal (objective 2) and water pollution (objective 3)
  • Sea and oceans
  • Atmosphere
  • Other scientific activities on the exploration and exploitation of the earth

Excludes: scientific activities on pollution (objective 3), soil improvement (objective 2), land-use and fishing (objective 6).

2. Infrastructure and general planning of land use – scientific activities on infrastructure and land development, including research on the construction of buildings. More generally, it covers all scientific activities relating to the general planning of land use. This includes scientific activities into protection against harmful effects in town and country planning but not scientific activities into other types of pollution (objective 3).

2.1 Transport systems – covers scientific activities on transport systems, including road accident prevention and ancillary services such as electronic traffic aids and radar stations. Also included is general scientific activities on transport systems, road and rail traffic, inland waterway and sea transport, air traffic, pipeline transport systems, works transport systems, combined transport systems and scientific activities on the potential effects on the environment of the planning and operation of transport systems. Scientific activities on transport equipment is included only when it forms part of the co-ordinated programs for the development of improved and safer transport systems, otherwise, such research is classified in objective 7.

2.2 Telecommunications system – covers scientific activities on telecommunications services and the planning and organization of telecommunications networks. It includes, in particular, general scientific activities on telecommunications systems, telephones, telex, data transmission, radio and television (including cable TV).

2.3 Other scientific activities – covers scientific activities on the infrastructure and general planning of land-use.

Examples:

  • General scientific activities
  • General planning of land-use
  • Construction and planning of buildings
  • Civil engineering - excludes scientific activities on building materials and industrial processes (objective 7)
  • Water supply

3. Control and care of the environment – covers scientific activities aimed at improving the control of pollution, including the identification and analysis of the sources of pollution and their causes, and all pollutants, including their dispersal in the environment and the effects on humans, species (fauna, flora, microorganisms) and the biosphere. The development of monitoring facilities for the measurement of all kinds of pollution is included. The same is valid for the elimination and prevention of all forms of pollution in all types of environment.

Examples:

  • General scientific activities on the environment
  • Protection of atmosphere and climate
  • Protection of ambient air
  • Solid waste
  • Protection of ambient water
  • Protection of soil and groundwater
  • Noise and vibration
  • Protection of species and habitats
  • Protection against natural hazards
  • Radioactive pollution
  • Other scientific activities on the environment

4. Protection and improvement of human health – scientific activities aimed at protecting, promoting and restoring human health, broadly interpreted to include health aspects of nutrition and food hygiene. It ranges from preventative medicine, including all aspects of medical and surgical treatment, both for individuals and groups, and the provision of hospital and home care, to social medicine and pediatric and geriatric research.

Examples:

  • General scientific activities
  • Medical scientific activities, hospital treatment, surgery
  • Preventive medicine
  • Biomedical engineering and medicines
  • Occupational medicine
  • Nutrition and food hygiene
  • Drug abuse and addition
  • Social medicine
  • Hospital structure and organization of medical care
  • Other medical scientific activities

5. Production, distribution and rational utilization of energy – covers scientific activities aimed at improving the production, storage, transportation, distribution and rational use of all forms of energy. It also includes scientific activities on processes designed to increase the efficiency of energy production and distribution, and the study of energy conservation.

Examples:

  • Fossil fuels and their derivatives
  • Nuclear fission
  • Radioactive waste management including decommissioning with regard to fuel/energy
  • Nuclear fusion
  • Renewable energy sources
  • Rational utilization of energy

6. Agricultural production and technology – covers all scientific activities on the promotion of agriculture, forestry, fisheries and foodstuff production, or further knowledge on chemical fertilizers, biocides, biological pest control and the mechanization of agriculture, as well as concerning the impact of agricultural and forestry activities on the environment. Also covers scientific activities on improving food productivity and technology.

6.1 Agriculture – covers scientific activities on animal products, veterinary medicine, crops, food technology and other scientific activities on agricultural production and technology.

6.2 Fishing – covers scientific activities on fishing, salting, drying, and initial freezing of products (but not on preparation and canning (objective 7)), scientific activities on fish-farming, exploration of new fishing grounds, exploration and development of new and unconventional sources of seafood.

6.3 Forestry – covers scientific activities into the ecological and economic aspects of forestry and timber production.

7. Industrial production and technology – covers scientific activities on the improvement of industrial production and technology. It includes scientific activities on industrial products and their manufacturing processes except where they form an integral part of the pursuit of other objectives (e.g. defence, space, energy, agriculture).

Examples:

  • Increasing economic efficiency and competitiveness
  • Manufacturing and processing techniques
  • Petrochemical and coal by-products
  • Pharmaceutical products
  • Manufacture of motor vehicles and other means of transport
  • Aerospace equipment manufacturing and repairing
  • Electronic and related industries
  • Manufacture of electrical machinery and apparatus
  • Manufacture of non-electronic and non-electronical machinery
  • Manufacture of medical and surgical equipment and orthopaedic appliances
  • Manufacture of food products and beverages
  • Manufacture of clothing and textiles and leather goods
  • Recycling

8. Social structures and relationships – scientific activities on social objectives, as analyzed in particular by social and human sciences, which have no obvious connection with other objectives. This analysis includes quantitative, qualitative, organizational and forecasting aspects of social problems.

Examples:

  • Education – covers scientific activities aimed at supporting general or special education, including training, pedagogy, didactics, and targeted methods for specially gifted persons or those with learning disabilities. Applied to all levels of education as well as to subsidiary services to education.
  • Culture, recreation, religion and mass media – covers scientific activities aimed at improving the understanding of social phenomena related to culture activities, religion and leisure activities so as to define their impact on life in society, as well as to racial and cultural integration and on socio-cultural changes in these areas. The concept of "culture" covers sociology of science, religion, art, sport and leisure, and also comprises inter alia R&D on the media, the mastery of language and social integration, libraries, archives and external cultural policy.
  • Political and social system, structures and processes – covers scientific activities aimed at improving the understanding and supporting the political structure of society, public administration issues and economic policy, regional studies and multi-level governance, social change, social processes and social conflicts, the development of social security and social assistance systems, and the social aspects of the organization of work.

9. Exploration and exploitation of space – all civil space scientific activities relating to the scientific exploration of space, space laboratories, space travel and launch systems. Although civil space research is not in general concerned with particular objectives, it frequently has a specific goal, such as the advancement of knowledge (e.g. astronomy) or relates to particular applications (e.g. telecommunications satellites or earth observation).

Examples:

  • General scientific activities
  • Scientific exploration of space
  • Applied research programs
  • Launch systems
  • Space laboratories and space travel
  • Other research on the exploration and exploitation of space

10. Non-oriented research – basic activities motivated by scientific curiosity with the objective of increasing scientific knowledge. It also includes funding used to support postgraduate studies and fellowships.

Examples:

  • Mathematics and Computer Sciences
  • Physical Sciences
  • Chemical Sciences
  • Biological Sciences
  • Earth and Related (Environmental) Sciences
  • Engineering Sciences
  • Medical Sciences
  • Agricultural Sciences
  • Social Sciences
  • Humanities

11. Other civil research – civil scientific activities which cannot (yet) be classified to a particular objective.

12. Defence – covers scientific activities for military purposes. It also includes basic research and nuclear and space research financed by the Department of National defence. Civil scientific activities financed by ministries of defence, for example, in the fields of meteorology, telecommunications and health, should be classified in the relevant objectives.

Question 5: Expenditures and personnel by region

Scientific and technological expenditures and personnel of federal organizations for the reference year, including current and capital expenditures for intramural R&D and RSA and by scientific and professional and total personnel for R&D and RSA.

Regions include:

  • Newfoundland and Labrador
  • Prince Edward Island
  • Nova Scotia
  • New Brunswick
  • Quebec (excluding NRC - Quebec)
  • National Capital Region (NCR) - Quebec
  • Ontario (excluding NRC - Ontario)
  • National Capital Region (NCR) - Ontario
  • Manitoba
  • Saskatchewan
  • Alberta
  • British Columbia
  • Yukon, Northwest Territories and Nunavut
  • Canada Total

In September 2022, questions measuring the Labour Market Indicators were added to the Labour Force Survey as a supplement.

Question wording within the collection application is controlled dynamically based on responses provided throughout the survey.

Labour Market Indicators

ENTRY_Q01 / EQ1 – From the following list, please select the household member that will be completing this questionnaire on behalf of the entire household.

WFH_Q01 / EQ2 – At the present time, in which of the following locations (do/does) (Respondent’s name/this person/you) usually work as part of (his/her/their/your) main job or business?

WFH_Q02 / EQ3 – Last week, what proportion of (his/her/their/your) work hours did (Respondent name/this person/you) work at home as part of (his/her/their/your) main job or business?

CAR_Q01 / EQ4 – Over the last 12 months, how often did (Respondent’s name/this person/you) perform the following household tasks?

Helping children with homework or homeschooling.

CAR_Q02 / EQ5 – Over the last 12 months, how often did (Respondent’s name/this person/you) perform the following household tasks?

Taking children to or from school, a bus stop, or a day care centre.

CCP_Q01 / EQ6 – During the last 12 months, did (Respondent’s name/this person/you) do any of the following because of child care responsibilities?

CCP_Q02 / EQ7 – Prior to the COVID-19 pandemic, did (Respondent’s name/this person/you) ever do any of the following because of child care responsibilities?

Monthly Survey of Manufacturing: National Level CVs by Characteristic - July 2022

National Level CVs by Characteristic
Table summary
This table displays the results of Monthly Survey of Manufacturing: National Level CVs by Characteristic. The information is grouped by Month (appearing as row headers), and Sales of goods manufactured, Raw materials and components inventories, Goods / work in process inventories, Finished goods manufactured inventories and Unfilled Orders, calculated in percentage (appearing as column headers).
Month Sales of goods manufactured Raw materials and components inventories Goods / work in process inventories Finished goods manufactured inventories Unfilled Orders
%
July 2021 0.79 1.05 1.46 1.69 1.45
August 2021 0.74 1.04 1.53 1.81 1.50
September 2021 0.79 1.03 1.54 1.83 1.41
October 2021 0.76 1.03 1.52 1.73 1.46
November 2021 0.73 1.00 1.62 1.57 1.34
December 2021 0.75 1.01 1.81 1.56 1.46
January 2022 0.78 1.12 1.82 1.85 1.43
February 2022 0.73 1.14 1.64 1.77 1.38
March 2022 0.71 1.13 1.52 1.66 1.44
April 2022 0.69 1.19 1.51 1.62 1.49
May 2022 0.67 1.16 1.53 1.68 1.44
June 2022 0.69 1.15 1.55 1.76 1.44
July 2022 0.70 1.11 1.69 1.48 1.38

Variant of the North American Product Classification System (NAPCS) Canada 2022 Version 1.0 for Agricultural goods (extension variant) - Background information

Status

The variant of the North American Product Classification System (NAPCS) Canada for agricultural goods has been approved as a departmental standard. The first agricultural goods variant was based on NAPCS Canada 2012, yet it was not implemented. The NAPCS 2017 Version 2.0 was approved as departmental standard on November 27, 2017. An updated variant has been developed and adapted to NAPCS Canada 2022 Version 1.0, which was approved on September 24, 2021.

Changes to NAPCS Canada 2022 version 1.0

In the past, the standard classification for agricultural goods was not used by the Agriculture Division. An existing classification that included agriculture goods in the Annual Survey of Manufactures (ASM 2004 – List of Goods), did not meet the needs of the Agriculture Division's programs. At the same time, the standard classification of NAPCS Canada did not have enough detail to meet the needs of all the Agriculture Division programs. Thus, an extension variant for agricultural goods has been developed to meet those specific needs. This variant is generally an extension of the four levels of the standard NAPCS Canada.

Three subclasses of the standard classification have had their titles enhanced and definitions improved: NAPCS 115141- Seeds of flowers, shrubs and trees; NAPCS 115143-Floriculture products (except seeds); and NAPCS 115144- Nursery products (except seeds).

This variant will continue to be updated as agriculture data form part of Statistics Canada's Integrated Business Statistics Program (IBSP).

The standard NAPCS Canada has been adopted by most Statistics Canada programs that have a product dimension.

There are several benefits to converting to NAPCS Canada:

  • Most Statistics Canada programs with a product dimension will adopt NAPCS Canada. As a result, Statistics Canada data will be coherent and consistent.
  • Some products produced in Canada today did not exist a few years ago. Converting to a new classification system will allow products to be classified more accurately.
  • By using NAPCS Canada, we insure alignment with the macro-economic accounts, the prices programs and the merchandise accounts.
  • Statistical programs in the United States and Mexico may also release data based on NAPCS. In these cases, international comparisons of product data will be easier.

Variant of NAPCS Canada for agricultural goods (extension variant)

The NAPCS Canada 2022 Version 1.0 contains 158 groups (3-digits), of which only 8 groups are related to agricultural goods. These groups from code 111 to 121 and group 213 are included in this variant; in most cases these goods represent farm output. Processed agricultural goods are not included in the variant. However, the variant does include unprocessed fishery products.

There are two additional levels of detail below the standard four-level NAPCS Canada, to meet the needs of the Agriculture Division. The first variant level below the detail level, which is made of 8-digits codes, comprises 283 categories, while the second level, which is made of 9-digits codes, comprises 378 categories.

See "Hierarchical structure" for a detailed presentation of the levels and code structures of the variant.

Hierarchical structure

The structure of the variant of NAPCS Canada 2022 Version 1.0 for agricultural goods is hierarchical. It is composed of six levels.

level 1: group (three-digit standard codes or four-character alphanumeric variant codes)
level 2: class (five-digit standard codes or six-character alphanumeric variant codes)
level 3: subclass (six-digit standard codes)
level 4: detail (seven-digit standard codes)
level 5: detail (eight-digit variant codes)
level 6: detail (nine-digit variant codes)

Variant of the North American Product Classification System (NAPCS) Canada 2022 Version 1.0 for Farm Product Price Index - FPPI (regrouping variant) - Background information

Status

The variant of the North American Product Classification System (NAPCS) Canada for the prices of farm products was approved as a departmental standard on November 27, 2017. This variant is based on NAPCS Canada 2022 Version 1.0, which was approved on September 24, 2021.

Transition to NAPCS Canada 2022 Version 1.0

In the past, the standard classification for agricultural goods was not used by the Agriculture Division. An existing classification that included agriculture goods in the Annual Survey of Manufactures (ASM 2004 – List of Goods), did not meet the needs of the Agriculture Division's programs. At the same time, the standard classification of NAPCS Canada did not have enough detail to meet the needs of all the Agriculture Division programs. An extension variant for the agricultural goods has been developed to meet the specific detail needs, while new aggregates were created by regrouping 3, 5, 6 and 7 digits categories for the price index. The Farm price index (FPPI) is therefore a regrouping variant, using also the 8 digit detail level of the agricultural goods extension variant.

This variant will continue to be updated as agriculture data form part of Statistics Canada's Integrated Business Statistics Program (IBSP).

The standard NAPCS Canada has been adopted by most Statistics Canada programs that have a product dimension.

There are several benefits to converting to NAPCS Canada:

  • Most Statistics Canada programs with a product dimension will adopt NAPCS Canada. As a result, Statistics Canada data will be coherent and consistent.
  • Some products produced in Canada today did not exist few years ago. Converting to a new classification system will allow products to be classified more accurately.
  • By using the classes of NAPCS Canada, we insure alignment with the macro-economic accounts, the prices programs and the merchandise accounts.
  • Statistical programs in the United States and Mexico may also release data based on NAPCS. In these cases, international comparisons of product data will be easier.

NAPCS Canada variant for farm product price index (FPPI)

The NAPCS Canada 2022 Version 1.0 contains 158 groups (3-digits), of which only 8 groups are related to agricultural goods. Six of these groups, from code 111 to 116, are included in this variant; in most cases these goods represent farm output. Processed agricultural goods are not included in the variant.

This variant defines a new aggregate level (sections coded from A11 to A25) by regrouping 3-digit categories of NAPCS Canada 2022 Version 1.0 covering farm products. Other aggregate levels were also created to regroup 5, 6 and 7 digits categories, where needed (see the list of groupings below). An additional level of detail (8 digit) was kept as it is shared with the agricultural goods extension variant. The new detailed level, which is made of 8-digits codes, comprises 153 categories.

For the FPPI, here are other aggregates (except the new sections) created by modifying the standard NAPCS Canada for the variant:

NAPCS Canada variant for farm product price index (FPPI)
Code Title
115AA Corn for grain, oats, barley and rye
11511AA Corn for grain, oats, barley and rye
115115AA Rye
115AB Soybeans and flaxseed
11512AA Soybeans and flaxseed
115122AA Flaxseed
114AA Chickpeas, lentils, dry beans and dry peas
11431A Chickpeas, lentils, dry beans and dry peas
115AC Canary seeds, sunflower seeds and mustard seeds
11511AB Canary seeds
115115AB Canary seeds
1151155A Canary seeds
11512AB Sunflower seeds
115122AB Sunflower seeds
11513A Mustard seeds
115139A Mustard seeds
114AB Fresh fruit
11411A Fresh fruit
114AC Fresh vegetables (except potatoes and pulse crops)
114AD Fresh potatoes
111AA Cattle and calves
111AB Hogs
111AC Chickens, turkeys, chicks, poults
11113A Chickens, turkeys, chicks, poults
116AA Eggs in shell
116AB Unprocessed milk

See "Hierarchical structure" for a detailed presentation of the levels and code structures of the variant.

Hierarchical structure

The structure of the NAPCS Canada 2022 Version 1.0 variant for the prices of farm products is hierarchical. It is composed of six levels.

level 1: section (three-character alphanumeric variant codes)
level 2: group (three-digit standard codes or five-character alphanumeric variant codes)
level 3: class (five-digit standard codes or six or seven-character alphanumeric variant codes)
level 4: subclass (six-digit standard codes or seven or eight-character alphanumeric variant codes)
level 5: detail (seven-digit standard codes or eight-character alphanumeric variant codes)
level 6: detail (eight-digit variant codes)

2021 Census: New Indigenous content

Video - 2021 Census: New Indigenous content

For the first time, the 2021 Census of Population included a question on Enrollment under an Inuit land claims agreement and one on membership in a Métis organization or Settlement. These questions will help to fill data gaps on Indigenous peoples.

Statistics Canada to hold news conference to present 2021 Census data on First Nations people, Métis and Inuit in Canada and Canada's housing

Media advisory

September 14, 2022, OTTAWA, ON -

On September 21, 2022, Statistics Canada will release the fifth set of results from the 2021 Census. This release will focus on First Nations people, Métis and Inuit in Canada as well as Canada's housing portrait.

The release will be published in Statistics Canada's Daily at 8:30 a.m. eastern time on September 21, 2022. Information about subsequent releases throughout 2022 is available here.

Statistics Canada officials will hold a news conference to present high-level national, provincial, and territorial findings for the fifth release from the 2021 Census. Officials will be available to answer questions from the media following their remarks.

On September 21 and the following days, Statistics Canada will also grant interviews regarding this 2021 Census data release. Members of the media are invited to submit their requests for interviews and/or custom tabulations ahead of the release date to the Media Hot Line.

Date

September 21, 2022

Time

9:30 AM to 10:30 AM (EDT)

Location

Participation in the question and answer portion of this event is for accredited members of the Canadian Parliamentary Press Gallery only. Media who are not members of the Press Gallery may contact pressres2@parl.gc.ca to request temporary access. A teleconference line is also available for media who wish to listen to the event:

Toll-free dial-in number (Canada/US): 1-866-206-0153
Local dial-in number: 613-954-9003
Participant passcode: 6871810#

Associated link:

2021 Census of Population – Backgrounder for Media

Contact:

Media Relations
Statistics Canada
STATCAN.mediahotline-ligneinfomedias.STATCAN@statcan.gc.ca

Monthly Survey of Manufacturing: National Weighted Rates by Source and Characteristic - July 2022

National Weighted Rates by Source and Characteristic - July 2022
Table summary
The information is grouped by Sales of goods manufactured, Raw materials and components, Goods / work in process, Finished goods manufactured, Unfilled Orders, Capacity utilization rates (appearing as row headers), and Data source as the first row of column headers, then Response or edited, and Imputed as the second row of column headers, calculated by percentage.
  Data source
Response or edited Imputed
%
Sales of goods manufactured 88.2 11.8
Raw materials and components 77.3 22.7
Goods / work in process 79.6 20.4
Finished goods manufactured 77.2 22.8
Unfilled Orders 79.2 20.8
Capacity utilization rates 67.9 32.1

Text Classification of Public Service Job Advertisements

By: Dominic Demers and Jessica Lachance, Public Service Commission of Canada

Introduction

The Public Service Commission (PSC) is an independent agency mandated to promote and safeguard a non-partisan, merit based public service that is representative of all Canadians. Among its many responsibilities, the PSC also oversees over 50,000 hiring activities that fall under the Public Service Employment Act (PSEA), each year.

This rich data environment includes over a million resumes and 8,000 job advertisements yearly. Some of the data are structured, like the organization name or the position's group and level. But, most human resource (HR) data collected by the PSC is unstructured. The unstructured data, such as job advertisements or screening questions, can be used for analytical purposes.

The PSC's Data Services and Analysis Division is responsible for data requests, statistical studies, surveys, forecasting models and data visualizations for staffing and recruitment activities that fall under the PSEA.

This article will give an overview of two natural language processing (NLP) techniques used by our team to extract valuable insights from two open-ended fields – Educational Requirements and the Area of Selection variables. We'll also explain how they were subsequently used to feed the Applications Forecasting Tool, a data visualization tool that reports on job advertisements.

Applications Forecasting Tool

In 2019, the PSC developed the Applications Forecasting Tool to help managers and HR advisors within the Government of Canada prepare for selection processes. Users can select the characteristics of a job advertisement and get an estimate on the number of candidates, based on similar jobs that were previously advertised.

The first version of the tool only worked with structured data from the job advertisement. But, the PSC received feedback about two open-ended fields users wanted to use to obtain a better estimate of the number of candidates for their selection process. These fields included the level of education in the essential qualifications; and for internal processes, details about the Area of Selection such as the department, location or classification.

As such, the PSC used text classification techniques for the education and Area of Selection fields to structure the information into categories that fed into the Applications Forecasting Tool. These algorithms enabled more precise and useful reporting capabilities for the PSC.

Text Classification

Text classification is a subset of problems that fall under NLP. The goal of text classification is to take open-ended fields and assign each text a label from a limited set of options.

In our case, we explored two different models to reach our goal. For the education variable, we used a rules-based approach using regular expressions. For the Area of Selection, we used a machine learning based approach called Name-entity-recognition (NER).

Although text classification using any model can produce good results, the capability of the algorithm to extract information from text is not always reliable. As such, we had to evaluate the algorithm's efficacy in extracting the correct information. We evaluated the model using a test dataset and examined metrics to determine how the classifier performed.

Evaluating text classification models

To evaluate the performance of our text classification algorithms, we used a confusion matrix. A confusion matrix is a table that describes the performance of the classification model on a set of test data for which the true values are known.

The number of correct and incorrect predictions are summarized in a table, and include count values. It also summarizes the number of errors made by our classifier and, most importantly, error type.

The confusion matrix is comprised of four types of predicted and actual value combinations. In our text classification context, the algorithm will provide a "true" (or "positive") value when the text is predicted as part of the classification. For example, if the text is classified as "high school diploma" it will return "true" (or "positive") for this classification.

The four categories are described below.

Figure 1: Confusion Matrix

Figure 1: Confusion Matrix
Description - Figure 1: Confusion Matrix

Quadrant diagram with four combinations of predicted and actual values.
Positive Predicted Value + Positive Actual Value = True Positive
Positive Predicted Value + Negative Actual Value = False Positive
Negative Predicted Value + Positive Actual Value = False Negative
Negative Predicted Value + Negative Actual Value = True Negative

The True Positive (TP) Combination: The classification is predicted as true and is correct.
The True Negative (TN) Combination: The classification is predicted as false and is correct.
The False Positive (FP) or Type 1 Error: The classification is predicted as true but is incorrect.
The False Negative (FN) or Type 2 Error: The classification is predicted as false but is incorrect.

Using these combinations, we derived the following performance metrics:

  • Accuracy: The percentage of texts that were categorized with the correct classification. Used to determine how many classifications the model got right.
  • Precision TPTP+FP: The percentage of texts correctly classified out of the total number of texts classified as positive. Used to determine the proportion of positive identification that were correct.
  • Recall TPTP+FN: The percentage of actual positive values that are predicted correctly. Used to determine the proportion of actual positives that were correctly identified.
  • F1 Score: The harmonic mean of precision and recall.

In the context of this article, these statistics will be used to evaluate the performance of classifying two variables – Educational Requirement and the Area of Selection.

Educational Requirement field

In accordance with the PSEA, the Treasury Board Secretariat established Qualification standards for the core public administration by occupation group or classification. The qualification standards give the minimum educational requirements for each occupational group. Job advertisements for positions under the PSEA must include this merit criteria.

Managers generally use the qualification standard as their essential requirement. But they have the ability to set higher educational levels when required. For example, a hiring manager might require that an EC-06 senior policy analyst have a masters degree, even though the minimum requirement is a bachelors degree.

We might expect less candidates that have a masters instead of a bachelors. Parsing the level of education would allow us to give users of the Applications Forecasting Tool more relevant estimates and historical job advertisements.

Method

There are just over 100 qualification standards across all occupational groups which are also written in natural language. We decided that these standards could be summarized as belonging to one of eight education levels:

  • Some high school
  • High school
  • Some post-secondary
  • Post-secondary
  • Professional degree (e.g. Law degree, medical degree)
  • Master's degree
  • Ph.D. or above
  • Education unknown/not listed

To label the job advertisements according to education level, we used regular expressions to find key phrases and then apply the label. Regular expressions are a sequence of characters that specify a pattern in text. To analyze the education level we:

  • found key phrases, using regular expressions, which signal a type of education
  • mapped these phrases to a common level
  • labeled the education requirements text with one of these common levels

In total, we used 30 different rules to map the job descriptions to the eight education levels. These rules were created manually, using an iterative process. We started with regular expressions that capture the sentence structure and key phrases used in many qualification standards. Then, we added additional rules to capture cases which did not follow the qualification standards.

Here's a visual representation of what this looks like:

Figure 2: Educational requirement classification

Figure 2: Educational requirement classification

Description - Figure 2: Educational requirement classification

**Please read section *Other information*

EDUCATION – COMMON TO ALL STREAMS

Successful completion of two years of an acceptable post-secondary educational program in computer science, information technology, information management or other specialty relevant to the position to be staffed.

-Indeterminate period incumbents of positions in the CS group on May 10, 1999, who do not possess the education prescribed above, are deemed to meet the minimum education standards based on their education, training and/or experience; They must be accepted as having met the minimum education standard whenever this standard is called for when staffing positions in the CS group.

-It is a recognized educational institution (e.g., community college, CÉGEP or university) that determines if the courses taken by a candidate correspond to two years of a post-secondary program at the institution.

IMPORTANT:

-It is the responsibility of the candidates to provide proof od their education. Note that your original diploma will be required during the process;

-Candidates with foreign educational credentials are required to provide proof of Canada equivalency. Please consult the Canadian Information Centre for International Credentials for further information. Any applicable fees are the responsibility of the candidate. Candidates who are unable to provide proof that they meet this essential qualification as requested will be eliminated from the process.

…years of post-secondary…

Some post-secondary

In this image, the first section represents our input. The segment highlighted in green states the relevant portion of the text related to the educational requirement. "Successful completion of two year of an acceptable post-secondary educational program in computer science, information technology, information management or other speciality relevant to the position to be staffed".

Then the second block represents the rule which was applied to the text using regular expressions. The text was flagged containing the phrase "… years of … post-secondary".

This flag, and the absence of a flag from a higher qualification (e.g. "degree", "doctorate") means this job advertisement was labelled as having the level of education as "Some post-secondary".

Model evaluation

To evaluate the model, we extracted a sample of 1,000 advertisements from the 2019-2020 Fiscal Year and manually labelled the level of education. The table below presents the precision, recall and f1-score of our rules-based algorithm, for each of the eight levels of education.

Table 1: Educational requirements model evaluation results
  Sample size Precision Recall F1-score
Education level unknown/not listed 45 97.7% 95.6% 96.6%
Some high school 30 100.0% 100.0% 100.0%
High school 418 99.3% 98.3% 98.8%
Some post-secondary 72 94.4% 94.4% 94.4%
Post-secondary 391 96.0% 97.7% 96.8%
Professional degree 17 100.0% 88.2% 93.8%
Master's degree 17 83.3% 88.2% 85.7%
Ph. D or above 10 100.0% 90.0% 94.7%

Results

We applied the algorithm to a total of 18,055 job advertisements between April 1, 2016 and March 31, 2019. The following table provides a breakdown of the EX-01 job advertisements, by the level of education derived from the algorithm. As shown below, the vast majority require either a high school education or a post-secondary education.

Table 2: Educational requirement for EX-01 positions (April 1, 2016, to March 31, 2019)
Educational Requirement Number of Job Advertisements %  Total
Post-secondary 676 83%
Master's degree 81 10%
Some post-secondary 27 3%
Education level unknown/not listed 16 2%
High school 13 2%
Professional degree 2 0%
Total 815 100%

Using this methodology, when accessing the AFT to estimate the number of job applications, users can filter results on this new education field. For instance, since April 1, 2015, 921 EX-01 jobs were advertised with a median of 30 applicants. Out of those advertisements, 806 required a post-secondary degree and had a median of 32 applicants.

Area of Selection field section

Background

In accordance with PSEA article 34 (1), for the purpose of eligibility in an appointment process, an organisation may limit the Area of Selection for internal job processes by establishing geographic, organizational, or occupational criteria. This restriction is written in the "Who can apply" field of a job advertisement.

Having a restricted Area of Selection will reduce the pool of potential applicants. Users of the Applications Forecasting tool wanted to know how many applicants they could expect if they only limited their Area of Selection to at-level employees in their department, as oppose to all public servants in Canada.

Method

Our objective was to parse the Area of Selection field to extract the department(s), location(s), and level(s) mentioned by using a technique called name-entity recognition (NER). An NER model is an NLP technique that identifies “entities” in a block of text, such as proper nouns (a person’s name, a country) or category of things (animals, vehicles).

In our case, the entities extracted are

  • organizations (e.g. “Transport Canada”, “the Federal Public Service”),
  • locations (e.g. “Canada”, “Atlantic Region”, “a 40 km radius of Winnipeg, MB”)
  • occupation classifications (e.g. “EC-04”, “EX-01”)

To apply the NER model we used spaCy, a free, open-source library used for advanced NLP in Python.

SpaCy's NER algorithm includes the entities “ORG” (organization), “LOC” (location) and “GPE” (Geopolitical).

To reduce the amount of manual tagging, we took an iterative approach to building our training set. First, we used SpaCy's default algorithm to tag a random sample of 1000 Area of Selections. Then, we made the following changes:

  1. Merged the “LOC” and “GPE” tags into one “LOC” tag
  2. Added a “LEVEL” tag which identifies occupational classifications
  3. Corrected any other issues with the “ORG” and “LOC” tags

Building off this, we created an additional 200 training examples, which were targeted to include additional examples of the “LEVEL” tag, and other cases the initial algorithm consistently failed to identify.

With the training set ready, the SpaCy NER algorithm performs the following tasks:

  1. Creates a prediction model using a portion of the labeled training data
  2. Sends an unlabeled version of another portion of the training data to model and predicts the entities
  3. Compares predicted labels to true labels
  4. Updates model to account for incorrect labels. The amount of change between models is called the gradient.
  5. Repeat until gradient is small and model predictions change very little between iterations

This process resulted in a final model that can identify the different criteria in an Area of Selection. The following image illustrates an example of the tagging the model performed:

Figure 3: Area of Selection classification

Figure 3: Area of Selection classification
Description - Figure 3: Area of Selection classification

Employees of the public service at the PM-04 or an equivalent classification who occupy a position within 40km of Edmonton, Alberta. Employees of the public service ORG at the PM-04 LEVEL or an equivalent classification who occupy a position within 40km of Edmonton, Alberta LOC.

At the top of the image, we have the complete text of the Area of Selection, then at the bottom of the image, we have our three “entities” highlighted. “the public service” is labelled as ORG, “PM-04” is labelled as LEVEL and “within 40km of Edmonton, Alberta” is labelled “LOC”

Model evaluation

We evaluated the model using a random sample of 465 Area of Selection statements which we manually labeled. The following table shows the precision and recall scores for each entity typeFootnote 1

 
Entity tag Precision Recall F1-score
ORG 92.6% 90.8% 91.7%
LOC 80.2% 74.9% 77.5%
LEVEL 95.0% 76.0% 84.4%

Results

Using the results of the model, we produced the following exploratory analysis. This analysis is based on of 13,362 internal job postings between April 1, 2016 and March 31, 2019.

Figure 4: Venn diagram of Area of Selection field, by organization, occupational group and geography

Figure 4: Venn diagram of Area of Selection field, by organization, occupational group and geography
Description - Figure 4: Venn diagram of Area of Selection field, by organization, occupational group and geography

Venn diagram of an Area of Selection field split into three.

Organizational (Dep't.) = 6.6% Organizational & Occupational share 0.4%
Occupational = 1.6% Occupational & Geographic share 2.2%
Geographic = 41.5% Geographic & Organizational share 37.9%
All three share 0.9% Open area of selection = 8.9%

What we found is that most internal advertisements chose to use at least one of the filters outlined in the PSEA and that most of the areas of selection with a geographic filter were for “Persons employed by the Public Service occupying a position in the National Capital Region (NCR)”.

However, we realized that some areas of selection proved to be harder to parse. These included:

1) Employees of Transport Canada who occupy a position in Calgary, Edmonton, Saskatoon, Winnipeg, Whitehorse, Yellowknife or Churchill.

2) Should an insufficient number of applicants be identified, persons employed in the Public Service, who occupy a position within 40km of Winnipeg, Manitoba or within 40km of Edmonton or Calgary, Alberta may be considered without re-advertising. Therefore, applicants in this expanded area of selection are encouraged to apply.

Our model performed well, but due to multi-criteria areas, we decided to use our analysis with a broader set of categories. Previously in the Applications Forecasting Tool, users could only select “internal job advertisement” or “external job advertisement”. Now, users have more precision for internal job advertisements. They can select:

  • Internal job advertisements, open to all public servants
  • Internal job advertisements, open to public servants in the NCR
  • Internal job advertisement, other areas of selection

This addition improved our model and allowed users to search a narrower set of advertisements to find any that matched their intended selection process.

Conclusion

Open-ended fields are a valuable way of collecting information and shouldn't be excluded from forms or surveys. It allows for a catch-all response when questions don't allow for users to give information within a fixed set of choices.


But this flexibility will come at the cost of accuracy of the classifications. Classification systems can generate the right predictions (true positives and true negatives), but can also make the wrong ones (false positives, false negatives). Cross validating the performance of your algorithm will be essential in determining if the classifications are sufficiently accurate for your reporting purposes.

This article showed methods to structure information from open-ended fields for reporting purposes in the Application Forecasting Tool. The categories derived from the area of selection and level of education fields were used to populate to drop-down menus allowing users to fine-tune their search results.

You're encouraged to visit the Application Forecasting Tool, or our other data visualization tools on the PSC's Data Visualization Hub.

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