Information for respondents

Authority to publish

Section 17 of the Statistics Act allows for the disclosure of certain information relating to an organization. For this survey, Statistics Canada will release the names of federal departments and agencies whose science and technology (S&T) expenditures surpass 2% of the total federal S&T expenditures for the 2025/2026 reference year. This applies to departments that previously authorized publication in the 2024/2025 collection cycle. For each of these departments, data on expenditures and personnel will be published.

Respondent Information:

• Name of person who approved the data reported
• 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, Innovation Science and Economic Development Canada (ISED), as well as the Office of the Chief Science Advisor (OCSA) of ISED. 

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:

• Phone Number: toll free at 1-888-659-8229
• Fax number: toll free at 1-800-755-5514
• Email: statcan.fsepsurv-enqdmsf.statcan@statcan.gc.ca

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 Centre for Innovation, Technology and Enterprise Statistics (CITES) 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 Innovation, Science and Economic Development 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 Centre for Innovation, Technology and Enterprise Statistics (CITES) 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 (R&D) - 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 – 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 for the purchase of goods and services to support the in-house R&D of the federal government department, it should be reported as In-house R&D (Item 7).

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 of 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.

Question 2: Personnel by gender

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. Categories include: man, woman, and non-binary person.

Man – this category includes persons whose reported gender is male. It includes cisgender (cis) and transgender (trans) men. 

Woman – this category includes persons whose reported gender is female. It includes cisgender (cis) and transgender (trans) women. 

Non-binary person – this category includes persons whose reported gender is not exclusively male or female.

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. Environment 
• 3. Exploration and exploitation of space
• 4. Transport, telecommunication and other infrastructures
• 5. Energy
• 6. Industrial production and technology
• 7. Health
• 8. Agriculture (include forestry and fisheries)
  • 8.1: Agriculture
  • 8.2: Fishing
  • 8.3: Forestry
• 9. Education 
• 10. Culture, recreation, religion and mass media
• 11. Political and social systems, structures and processes
• 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. It does not include scientific activities related to soil improvement (objective 4), land use or fishing (objective 8), or pollution (objective 2).

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 8)
• Hydrology - excludes scientific activities on: water supplied and disposal (objective 4) and water pollution (objective 2)
• Sea and oceans
• Atmosphere
• Other scientific activities on the exploration and exploitation of the earth

Excludes: scientific activities on pollution (objective 2), soil improvement (objective 4), land-use and fishing (objective 8).

2. 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, micro-organisms) and the biosphere.

The development of monitoring facilities for the measurement of all kinds of pollution is included, as is scientific activities for the elimination and prevention of all forms of pollution in all types of environment.

3. Exploration and exploitation of space – covers all civil space scientific activities relating to the scientific exploration of space, space laboratories, space travel and launch systems. Although civil space scientific activities 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). This chapter does not include corresponding scientific activities for defence purposes.

4. Transport, telecommunication and other infrastructures – Transport, telecommunication and other infrastructures - covers scientific activities aimed at infrastructure and land development, including the construction of buildings. More generally, this objective 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 research into other types of pollution (objective 2). This objective also includes scientific activities related to transport systems; telecommunication systems; general planning of land use; the construction and planning of buildings; civil engineering; and water supply.

5. 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. It does not include scientific activities related to prospecting (objective 1) or scientific activities into vehicle and engine propulsion (objective 6).

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. 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

7. Health - covers scientific activities aimed at protecting, promoting and restoring human health broadly interpreted to include health aspects of nutrition and food hygiene. It ranges from preventive 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 paediatric 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

8. Agriculture (include forestry and fisheries) – 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.

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

8.2 Fishing – covers scientific activities on fishing, salting, drying, and initial freezing of products (but not on preparation and canning (objective 6)), scientific activities on fish-farming, exploration of new fishing grounds, exploration and development of new and unconventional sources of seafood.

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

9. 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.

10. 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.

11. 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.

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 NCR - Quebec)
• National Capital Region (NCR) - Quebec
• Ontario (excluding NCR - Ontario)
• National Capital Region (NCR) - Ontario
• Manitoba
• Saskatchewan
• Alberta
• British Columbia
• Yukon, Northwest Territories and Nunavut
• Canada Total

Presentations

Presenters in invited sessions are allowed approximately twenty-five minutes for their presentations and a period of 5 minutes is allowed for questions and discussion at the end of their presentation.

Presenters in contributed sessions are allowed fifteen minutes for their presentations and a period of fifteen minutes is allowed for questions and discussion at the end of the session.

Presenters in special sessions, please refer to information provided by your session's chair.

We strongly recommend that you submit a Microsoft PowerPoint version of your presentation. Please verify with us in advance if you would like to use any other software. PowerPoint is preferred in order to produce a translated version with the same appearance as the original, especially when the presentation includes tables, graphics or equations. We also ask that you avoid any PowerPoint animation. During translation, the hidden animation control code can sometimes be accidentally deleted, resulting in translated presentations that are out of synch. Please use at least a 24-point font that can easily be read from a distance and that is part of the standard Microsoft suite of fonts. The bilingual presentations will be loaded onto Statistics Canada's laptops. If we do not have the font that you use, we will not be able to show your presentation.

Registration

Please remember that you must register for the conference by September 30, 2024.

Proceedings manuscripts

Proceedings of the conference will be eventually published on our website. Please note that the final version of your manuscript is due by January 10, 2025, in order to be translated and included in the Symposium's Proceedings. Including references and appendices, the manuscript must not exceed:

• eight pages in length for invited sessions (sessions A and special sessions)
• six pages in length for contributed sessions (sessions B)

We strongly encourage you to use Microsoft Word for your manuscript. If planning to use any other software, please check with us first. A Style Guide for Manuscripts is available in the Manuscript Style Guide section of the website.

Please send the final version of your manuscript to statcan.symposium2024-symposium2024.statcan@statcan.gc.ca no later than January 10, 2025.

All times listed in the schedule refer to Eastern Daylight Time (EDT): UTC-4

Wednesday October 30, 2024

08:45 – 09:00
Opening Remarks
Simon Goldberg Room

• Éric Rancourt, Assistant Chief Statistician, Strategic Data Management, Methods and Analysis Field, Statistics Canada, Canada

09:00 – 10:00
Session 1 – Keynote Address
Simon Goldberg Room

• The evolving role of National Statistical Institutes – Challenges and Opportunities
  Pádraig Dalton, Former Director General, Central Statistics Office, Ireland
  • Abstract

    The demand for data continues to grow unabated. The range of subject matter areas on which evidence and insight is required continues to expand while in addition users want more timely, more frequent and more granular data. All of this is happening in a context where National Statistical Institutes (NSIs) are just one part of an ever-growing data market and where fake news and alternative facts present significant risks to decision makers and indeed the general public. National Statistical Institutes must step forward and play a strong leadership role and establish their point of difference from other unregulated data providers and create an understanding of the value-add that can and must be delivered from within the Official Statistical System. To meet the growing range of demands in this challenging environment NSIs will need to be agile, adaptive and innovative while adhering to our fundamental principles and core values. We will also need to exploit all of the opportunities offered by advancements in technology, methodology and the ongoing emergence of new data sources. This presentation will look at the evolving context within which NSIs now find themselves and consider the associated opportunities and challenges this presents.

10:00 – 10:30
Morning Break

10:30 – 12:00
Session 2A – Balancing Disclosure Risk and Analytic Utility with Synthetic Data
Simon Goldberg Room

• Inference from Synthetic Data: Challenges and Solutions
  Anne-Sophie Charest, Université Laval, Canada
  • Abstract

    As concerns over privacy and data security mount, the generation of synthetic data emerges as a promising avenue for mitigating disclosure risks associated with personal data. But while there is a rapidly growing body of literature on methods to generate such synthetic datasets, the focus has predominantly been on generating more complex and accurate data, rather than the implications of synthesis for downstream analysis.

    This presentation aims to address this gap by providing an overview of current research efforts in the field, including some of my own contributions. Two main approaches to obtain accurate uncertainties will be discussed: using combining rules with multiple synthetic datasets as is sometimes done with missing data, and leveraging direct knowledge of the synthesis methodology, which is available for example in the case of differential privacy. We will discuss the advantages and limitations of both and offer some thoughts on practical implications.

• Generating and Analyzing Synthetic Data
  Khaled El Emam, University of Ottawa, Canada
  • Abstract

    Synthetic data can be a privacy protective way for sharing microdata, and are increasingly seen as a more robust form of non-personal information. This presentation will summarize the learnings from a series of real data and simulation studies on the generation and statistical analysis of synthetic data: (a) common metrics to assess the privacy risks of synthetic data are attribution and membership disclosure, (b) valid statistical analysis requires the generation of 10 synthetic dataset and using combining rules to get the parameter estimates and standard errors, (c) data amplification has marginal benefit, (d) understanding the performance of generative models relative to the number of variables in the training dataset, (e) when and how to augment microdata to maximize model performance, (f) using synthetic data generation to mitigate bias in real-world data.

    These results will be illustrated on health datasets across multiple types of generative models: sequential synthesis with decision trees, Bayesian networks, generative adversarial networks, and variational autoencoders.

• Generating Select Synthetic Survey Data
  Minsun Riddles, Westat, USA
  • Abstract

    As the demand for microdata access grows alongside privacy concerns, interest in synthetic data has surged. For example, synthetic data is recognized as offering a promising solution for sharing vast amounts of health data to accelerate research while safeguarding privacy. However, generating synthetic data presents challenges, particularly when it comes to survey data, in balancing reducing disclosure risk with retaining original data integrity. One approach to addressing these challenges is through the 'select' data synthesis approach, which involves synthesizing select variables of select records with high disclosure risks. In this paper, we address these challenges and propose solutions for generating select synthetic data in two U.S. large-scale national surveys. Additionally, we introduce a replication variance estimation method to appropriately measure the additional variation introduced by data synthesis.

10:30 – 12:00
Session 2B – Modern Approaches for Different Data Sources
Jean Talon Conference Room

• Low response rate from merchants? No problem, just ask consumers! An application of indirect sampling to consumer payment diary data
  Joy Wu, Heng Chen, Bank of Canada, Canada
  • Abstract

    Merchant surveys traditionally have very low response rates. An alternative would be to leverage consumer surveys, but there are two issues: the representativeness of consumers and the accuracy of their responses. This paper addresses both issues through applying indirect sampling and survey weight calibration. When survey practitioners face the challenge to directly sample from the target population for which either the sampling frame is difficult to compile, or response rates are very low, indirect sampling can help by leveraging the links between the frame population and the target population. We apply this estimator to generate indirect estimates of (target
    population) Canadian-merchants' cash, debit, and credit acceptance from consumer survey data (frame population), which contains transaction-level details recorded in the diary section of the data. Not only do we leverage the consumer-merchant transaction data to construct the merchant weights through the generalized weight share method (GWSM), but we also construct the payment acceptance variables of these linked merchant from both consumers' revealed and stated payment choices. We do so without needing to interact with merchants, overcoming the challenge of traditionally low response rates.

    We also innovate on the existing GWSM methodology by using an external data set to estimate the total number of visits received by a merchant (a key component in GWSM), which is underestimated in the consumer data due to its structure. Lastly, our GWSM weights are also calibrated so that our final indirect merchant sample is representative of the merchant population. Our results show that the direct and indirect estimates of merchant payment acceptance are very similar.

• The Field Data Collector Labor Force: Considerations for the Future of In-Person Data Collection
  Brad Edwards, Rick Dulaney, Jill Carle, Tammy Cook, Westat, USA
  • Abstract

    In-person data collection is critical for the success of many large government-sponsored surveys. Despite response rate declines and increasing costs, the mode remains the gold standard for meeting the most rigorous survey requirements for national survey programs, particularly as part of a multimode data collection strategy (Schober 2018). However, over the last ten years critical labor market and workforce changes, exacerbated by the pandemic, have made in-person data collection efforts prohibitive for many largest survey organizations. New ideas about job flexibility and job satisfaction coupled with the increasingly technical role and demanding nature of the job have impacted recruitment and retention for survey organizations across the U.S. and Europe (Charman et al 2024; Carle et al 2023). Indeed, some European countries have abandoned in-person data collection altogether for lack of work to sustain a field data collector (FDC) labor force.

    This presentation will summarize trends in U.S. FDC employment over the past decade and outline key challenges in recruiting and retaining high quality FDCs. Through surveys of current and exiting FDCs and a unique administrative dataset of over 27,000 FDCs across more than 80 large survey projects, we will highlight key takeaways from an ongoing research program, including the impact of changing demographics on FDC retention, and how training mode efficacy differs by FDC experience. We will discuss considerations for the future of in-person data collection, including supplementing field data collection with multimode alternatives such as video interviewing, professionalizing the FDC role, and updating value propositions for respondents.

• Constructing a synthetic population to evaluate redesign options through simulation, for a rotating panel survey
  Pauline Summers, Andrew Brennan, Statistics Canada, Canada
  • Abstract

    The methodology of Canada's Labour Force Survey (LFS) undergoes a thorough review every ten years. For the current review, we developed a simulation system that reproduces the complex LFS survey process, from sampling to estimation, for investigating alternative methodologies or identifying places for improvement. The complexity of the LFS – its rotating panel design, regression composite estimator, and other features – made the simulation system both challenging to construct and extremely useful for understanding how the various components interact. Since its development, it has proved to be an indispensable tool, and has helped us generate knowledge specific to the LFS that would otherwise have been very difficult or impossible to obtain.

    This presentation describes the methodology we used to construct the synthetic population that is the basis for those simulations. We faced a series of unique modelling challenges, juggling requirements with respect to variable specification, cross-sectional consistency, and longitudinal consistency, to support simulations of the complex LFS survey process. Our solution is a "rotating panel population," modelled over a six-year period using a combination of cross-sectional and longitudinal modelling techniques.

    The synthetic population consists of six sets of parallel "clones" of a base population, where each "clone" unit is longitudinally modelled for six months, and then reset; this produces the six-month series required for each simulated LFS respondent, while mitigating the drift that arises from projecting a population over an extended period. We developed an innovative methodological procedure to generate the data in multiple stages, using several different statistical tools and techniques, and implemented it in R.

• Improving the automated capture of Survey of Household Spending receipts using advanced machine learning techniques
  Joanne Yoon, Oladayo Ogunnioki, Statistics Canada
  • Abstract

    The Survey of Household Spending (SHS) conducted by Statistics Canada collects paper diaries and shopping receipts as a source of household expenditure data. An auto-capturing algorithm was created for SHS 2023 to reduce statistical clerks' manual work of extracting important information from scanned receipts of common store brands. The algorithm used Tesseract optical character recognition (OCR) to extract text characters from images of receipts, and it identified store and product entities using regular expressions, also known as regex. The goal of this study was to enhance the current auto-capture algorithm by experimenting with more advanced OCR and machine learning methods. As a result, PaddleOCR, an open-source OCR toolkit, was selected as the new default OCR engine due to its overall performance in recognizing texts, especially digits, accurately across receipts of various qualities. Additionally, entity classifiers based on support vector machines were trained on historical SHS records and existing regex patterns. By using classifiers to categorize different elements present on receipts instead of relying solely on regex patterns, product and store recognition improved. It is expected that this new algorithm will be used for SHS 2025 to improve the auto-capture quality and reduce the manual burden associated with capturing receipt variables.

• Using LLMs for Automating the Analysis of Alternative Data for the Physical Flow Account of Plastic Materials
  Alexandre Istrate, Oladayo Oggunnioki, Statistics Canada
  • Abstract

    The Physical Flow Account for Plastic Material (PFAPM) pilot program aims to track the flow of plastic materials throughout the Canadian economy. Within this program, analysts rely heavily on alternative data sources consisting in a diverse array of annual reports sourced from various companies and organizations. These are essential to conduct thorough research and validate activities pertinent to the account.

    Due to their unstructured format and diversity, analyzing annual reports from various companies and organizations is a laborious and inefficient process, requiring significant time and effort from analysts. To address this, the project leverages advanced natural language processing (NLP) techniques, notably Large Language Models (LLMs), to automate two key objectives: sector classification and COVID-19 impact summarization.

    The goal was to develop an algorithmic pipeline that can ingest PDF documents and based on their content, classify companies into distinct sectors (Residential, Commercial, Institutional, Industrial, and Construction) and summarize the impact of the COVID-19 pandemic on plastic-related activities such as collection and recycling rates, logistical disruptions, etc.

    By automating these tasks, the project seeks to enhance the efficiency of data extraction, reduce manual workload, and improve the quality of insights derived from document analysis. The ultimate objective is to contribute to the advancement of environmental-economic research and inform sustainable plastic resource management strategies. By harnessing the power of LLMs, the project aims to unlock the potential of alternative data sources, providing more accurate and effective insights to shape environmental-economic policy and decision-making processes.

12:00 – 13:30
Lunch

13:30 – 15:00
Session 3A – Small Area Estimation: Extensions, Applications and New Developments sponsored by the International Association of Survey Statisticians (IASS)
Simon Goldberg Room

• Hierarchical Bayes small area estimation for county-level health prevalence to having a personal doctor
  Andreea Erciulescu, Westat, USA
  • Abstract

    The complexity of survey data and the availability of data from auxiliary sources motivate researchers to explore estimation methods that extend beyond traditional survey-based estimation. The U.S. Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.

    (BRFSS) collects a wide range of health information, including whether respondents have a personal doctor. While the BRFSS focuses on state-level estimation, there is demand for county-level estimation of health indicators using BRFSS data. A hierarchical Bayes small area estimation model is developed to combine county-level BRFSS survey data with county-level data from auxiliary sources, while accounting for various sources of error and nested geographical levels. To mitigate extreme proportions and unstable survey variances, a transformation is applied to the survey data. Model-based county-level predictions are constructed for prevalence of having a personal doctor for all the counties in the U.S., including those where BRFSS survey data were not available. An evaluation study using only the counties with large BRFSS sample sizes to fit the model versus using all the counties with BRFSS data to fit the model is also presented.

• New Twists on Old Tricks: Applications and Extensions of Traditional Small Area Models
  Emily Berg, Iowa State University, USA
  • Abstract

    The mixed model is a widely accepted tool for small area estimation. The thesis of this talk is that this vetted approach remains well-suited to addressing the challenges that arise in modern small area estimation problems. As evidence of this, we review applications and extensions of area-level and unit-level small area models. We first discuss the use of multivariate Fay-Herriot models for small area estimation of crime victimization rates. This application uses data from the National Crime Victimization Survey and illustrates the value of area-level models in an application of current interest.  We then review approaches to estimating nonlinear small area parameters, such as the median or the quartile, in the context of the unit-level model. In this setting, we discuss methods for informative sampling and extensions to exponential dispersion family models.

    The review encompasses modest innovations to well established procedures. The methods illustrate that the basic construct of the mixed model remains a useful tool for addressing small area problems of recent interest.

• Reverse-Engineering a Hypothetical Raking Process for the Estimation of Mean Squared Error of Raked Small Area Estimates
  François Verret and Braedan Walker, Statistics Canada, Canada
  • Abstract

    Small area estimation (SAE) of total employment and unemployment rate is performed monthly for the Labour Force Survey (LFS) using the Fay-Herriot model. These estimates are required for a mapping of the ten provinces defined by Census Metropolitan Areas, Census Agglomerations and a complementary geography called Self-contained Labour Areas.

    To protect against model inadequacies and for consistency, total employment small area estimates are raked to their provincial direct/published estimate, which are of good quality by design. To get an uncertainty estimate for the raked small area estimates, a parametric bootstrap procedure making the usual assumption of independence between area-level direct estimates results in an MSE for the aggregate of the raked estimates at the provincial level considerably greater than the variance of the provincial direct estimate, especially for smaller provinces. This is because (province-level) LFS weight calibration introduces negative correlations between sub-provincial direct estimates. Ignoring this negative correlation thus results in MSE estimates for the raked estimates that should be artificially inflated.

    Instead, a parametric bootstrap is used with a working covariance matrix of the direct estimates obtained by smoothing the variance components (as in the independence case) and by reverse-engineering a hypothetical raking process to derive the covariance terms. Application of the resulting theory gives a variance of the raked SAE estimates that is consistent with that of the provincial direct estimates. The MSE estimates of the raked SAE estimates obtained are in turn reduced and more reasonable.

13:30 – 15:00
Session 3B – Evaluating and Improving Surveys
Jean Talon Conference Room

• Correcting Selection Bias in a Non-probability Two-phase Payment Survey
  John Tsang, University of Ottawa, Canada
  Heng Chen, Bank of Canada, Canada
• • Abstract

    This presentation extends the Pseudo Maximum Likelihood (PML) estimator to the non-probability two-phase sampling by leveraging the probability sample at the individual level. Using the Bank of Canada 2020 Cash Alternative Survey Wave 2, we compare the performance of our proposed method to alternative methods, which either do not account for a two-phase sampling design or do not explicitly model the selection probability. The results show that the PML-based approach performs better than raking in reducing the selection bias for both phases' payment-related variables, especially for the low-response youth group. Furthermore, the two-phase PML weighting scheme accounting for both phases' selection mechanisms has a smaller bias than Phase-2-Only alternatives of ignoring Phase 1's design and simply treating Phase 2 as a standalone single-phase design. The presentation will end with a discussion about variance estimation for both phases.

• Recruitment and Collection of Web Panels at Statistics Canada
  Krista MacIsaac, Cilanne Boulet, Marnie Thomas, Statistics Canada
  • Abstract

    In 2020, Statistics Canada started to use probabilistic web panels as an alternate method of collecting official statistics. In a web panel, respondents to other surveys are asked for contact information to participate in future short surveys. This presentation will highlight Statistics Canada's experience with panels after 4 years, including what has been learned about the recruitment of panel participants and how to subsequently collect data using panel surveys. For instance, recruitment questions have been displayed in various ways, resulting in very different rates of participation. Moreover, the wealth of auxiliary information available on the recruitment survey can not only be incorporated into the creation of weights, it can also be used to actively manage collection operations, by predicting the probability of response to target follow-up efforts.

• A Bias Evaluation for Probabilistic Web Panels at Statistics Canada
  Anne Mather, Cilanne Boulet, Statistics Canada, Canada
  • Abstract

    Statistics Canada began to implement probabilistic web panels in 2020.

    Participants for these panels are recruited via questions on other Statistics Canada surveys that ask those willing to participate to provide their contact information. Web panels are a timely, cost-effective collection method to meet emerging data needs. However, they have lower response rates than are typically observed with traditional survey methods. Despite the lower response rates, the recruitment surveys provide a lot of auxiliary information for both respondents and non-respondents to the panel, which can be used in non-response adjustments.

    A study was carried out to explore the potential bias associated with these lower response rates and the degree to which it can be corrected during weighting. We will present the highlights of that study.

• Life in the FastText Lane: Harnessing Linear Programming Constrained Machine Learning for Classifications Revision
  Justin Evans, Laura Wile, Statistics Canada, Canada
  • Abstract

    Statistics Canada's Labour Force Survey (LFS) plays an essential role in the estimation of labour market conditions in Canada. Periodically, LFS revises its data to the most recent industry and occupational classification versions. Differences in versions can be extensive, including high-level and unit-group structural changes, creations, deletions, split-offs and combination of classification units (classes). Historically, to reconcile split-off classes - where one class splits into multiple classes - a sample of LFS split-off records would be manually recoded to the new classification version. Based on the split-off proportion observed in the recoded sample, a random allocation method would be applied on all data to reflect the changing Canadian labour market over time. This article proposes using machine learning (fastText), constrained to split-off proportions using linear programming, to revise industry and occupation classifications in LFS.

    The hybrid framework benefits from a text-based revision mechanism while adhering to traditional proportions driven estimates, thus ensuring a minimal impact on the comparability of published labour market indicators.

• Data-Driven Imputation Strategies and their Associated Quality Indicators in Economic Surveys
  Matei Mireuta, Ahalya Sivathayalan, Stephen Styles, Statistics Canada, Canada
  • Abstract

    Majority of economic surveys at Statistics Canada are processed in the Integrated Business Statistics Program (IBSP). The IBSP framework relies on the generalized system BANFF for editing and imputation (E&I) and on the generalized system G-EST for estimation and variance estimation. Currently, variance estimation is carried out analytically using the System for the Estimation of Variance due to Nonresponse and Imputation (SEVANI) which is part of G-EST.

    Classical E&I strategies for economic surveys are typically based on composite linear imputation given the difference in availability of auxiliary data among units. However, this can lead to tens (and often more) of possible imputation models for a given variable which makes implementation, support and analysis of a survey's overall imputation strategy very difficult. As part of recent modernization initiatives within the agency, our team has investigated several machine learning alternatives and examined their application and quality of imputation in the context of economic surveys.

    This presentation will cover some of these results as well as the challenges faced in the estimation of the variance due to imputation of these methods.

    The authors will describe two approximate fit-for-use methods for the estimation of total variance and their advantages/disadvantages from the point of view of a harmonized processing framework such as IBSP.

15:00 – 15:30
Afternoon Break

15:30 – 17:00
Session 4 – Collection Initiatives in Challenging Situations
Simon Goldberg Room

• Collection of Social Data – designing and changing
  Fiona O'Riordan, Central Statistics Office, Ireland
• • Abstract

    Social data is key to a properly functioning society. Our age, our sex, our educational attainment, do we work? do we have a warm coat? have we experienced discrimination? is data that informs government, researchers, and the citizen about our lives. Asked continually, we can see how we are changing and how our requirements are changing.

    The task of collecting social data has changed considerably over the last5-10 years. Field interviewers still have a role but its time now to supplement this mode with other ways of collecting this data. Innovation is key - understanding the respondent and designing campaigns that are efficient and, in some way, appealing is now necessary for success. The Office uses the concept of adaptive design to ensure continued success in the collection of household data, this is where the design of the survey is adapted to suit the requirements of the respondent.

    The Central Statistics Office (Ireland) is moving to a multi-mode environment, the Office has reviewed and continues to review its sample design for all surveys, it is trying to augment and keep the sample frame as current as possible by using admin data; behavioural science, analysis of previous response behaviours and the use of panel surveys are some of the key projects that are in progress at present.

    The Office is adapting to societal changes and societal requirements by using different tools and methods to continue to collect quality social data.

• The challenges of collecting data in remote locations: The example of data collection for the Québec Health Survey of High School Students in Nunavik – UVIKKAVUT QANUIPPAT?
  Catherine Côté, Marcel Godbout, Institut de la statistique du Québec, Canada
  • Abstract

    Since 2010, the Institut de la statistique du Québec (ISQ) has been conducting the Québec Health Survey of High School Students (QHSHSS) every six years for the Ministère de la Santé et des Services sociaux (MSSS). There were editions in 2010/2011, in 2016/2017 and in 2022/2023. This survey collects data on the physical and mental health, lifestyle habits, and social adjustment of high school students in Québec's regions. These three editions of the survey did not cover Nunavik, a region in Québec's Far North that comprises 14 communities, because the health needs and realities are different from those in the southern regions of the province. In 2016, the Nunavik Regional Board of Health and Social Services (NRBHSS), the School Board of Nunavik (Kativik Ilisarniliriniq) and the MSSS indicated to the ISQ that they would like a survey similar to the QHSHSS to be conducted just for Nunavik. The ISQ therefore began planning this survey that same year. However, there were many challenges along the way and it was only conducted in 2022. Collection was done in all communities, and the high school students in each one were enumerated because of their low numbers. The QHSHSS questionnaire was used for this survey, but was largely adapted to reflect the particularities of the region. In this presentation of the survey, called Québec Health Survey of High School Students in Nunavik – UVIKKAVUT QANUIPPAT?, we will examine the various challenges encountered (geographic, logistic, context, etc.) during data collection and how they were overcome.

• Measuring urban Indigenous health using respondent driven sampling
  Lisa Avery and Sara Wolfe, University Health Network (Toronto) and Aboriginal Health and Wellness Centre, Canada
  • Abstract

    Our Health Counts (OHC) uses respondent-driven sampling (RDS) that combines a peer-to-peer recruitment strategy with statistical methods to sample from populations that are difficult to reach because they lack a sampling frame. Embedding the sampling process within a community enables the collection of comprehensive socio-demographic data that might otherwise be underreported in a standard census by leveraging the strong social connections within a group. The survey process gathers information about social connectivity that, alone, or in combination with information about the ties in the recruitment chain, is used to adjust and obtain unbiased estimates of the population parameters by accounting for unequal sampling probability and homophily in the social network. An initial set of 10-12 seeds from the target population is selected to complete the OHC survey, who then refer family and friends. The first wave of recruits is identified through these referrals. Referrals are provided a unique coupon serial number, honourarium, and survey participation is facilitated. The second wave of participants are recruited based on the first wave's referrals, and so on, until the target sample size has been reached. Child recruitment occurs through custodial caregivers who complete the survey. Every OHC project is Indigenous-led, and the data remains community-owned and governed. OHC successfully produces meaningful, culturally relevant health data for Indigenous adults and children and has completed its data collection phase in Winnipeg, Manitoba, led by Aboriginal Health and Wellness Centre of Winnipeg. Key to the OHC approach are the principles of reciprocity, relationality, and local community self-determination.

Thursday October 31, 2024

9:00 – 10:00
Session 5 – Waksberg Award Winner Address
Simon Goldberg Room

• Waksberg Lecture 2024: Sample Design Using Models
  Richard Valliant, Research Professor Emeritus, University of Michigan and Joint Program in Survey Methodology, University of Maryland, USA
  • Abstract

    Joseph Waksberg was an important figure in survey statistics mainly through his applied work in the design of samples. He took a design-based approach to sample design by emphasizing uses of randomization with the goal of creating estimators with good design-based properties. Since his time on the scene, advances have been made in the use of models to construct designs and in software to implement elaborate designs. This paper reviews uses of models in balanced sampling, cutoff samples, stratification using models, multistage sampling, and mathematical programming for determining sample sizes and allocations.

10:00 – 10:30
Morning Break

10:30 – 12:00
Session 6A – Recent Advances in Time Series Modelling
Simon Goldberg Room

• Multilevel times series model for mobility trends in the Netherlands
  Harm Jan Boonstra, Maastricht University, Netherlands
  Jan van den Brakel, Statistics Netherlands, Maastricht University, Netherlands
  • Abstract

    The purpose of the Dutch Travel Survey (DTS) is to produce reliable estimates on mobility of the Dutch population. In this paper a multilevel time-series model is proposed to estimate mobility trends at several aggregation levels. The method is developed to solve different problems. The sample size for many publication domains is so small that direct estimates for target parameters are very noisy and unreliable. The time series model is developed as a form of small area estimation to obtain more precise domain estimates and smoother trend series.

    Another problem are systematic shocks in the sample estimates, which are the result of three major survey process redesigns. These shocks or discontinuities disturb comparability with figures published in the past. The time series model accounts for the discontinuities through an intervention component such that uninterrupted series of trend estimates are obtained. Finally, the coronavirus had a major impact on mobility, which requires additional adjustments of the model.

    The DTS is a multipurpose survey that produces many different output tables. Instead of developing separate models for each individual output table, one multivariate time series model is developed for a breakdown of the population parameter at the most detailed level in about 700 domains. This breakdown is based on the cross-classification of all output tables of interest. Predictions at higher aggregation levels are obtained by aggregation of the predictions of these 700 domains. This result in a numerically consistent set of estimates for all target variables, which are corrected for the different discontinuities.

    Modelling time series at the most detailed level requires random effect components to avoid overfitting of the time series, in particular the discontinuities. Additionally, non-normally distributed random effects such as Laplace and Horseshoe distributions are used as a regularization method to suppress noisy model coefficients and at the same time to allow large effects that are sufficiently supported by the data. The model is developed in hierarchical Bayesian framework and fitted with MCMC simulation. The method is implemented in production for publication of official statistics on mobility.

• The impact of environmental disasters on Canadian personal debt
  Cristina Agatep, Bank of Canada, Canada
  • Abstract

    Canada has been experiencing an increase in the occurrence of natural disasters in recent years, resulting in a growing interest in the effect that weather events have on the personal finances of its citizens. Tracking individual consumer debt incurs a high privacy cost. Instead, the distribution of consumer debt is tracked over time to balance the need for privacy with strong insights into the behaviour of the population.

    The focus of this talk is on the impact of wildfires on debt distribution through a causal inference lens, where the evolution of densities across time is treated as functional data objects. A synthetic control model is used to account for the counterfactual evolution of debt distribution arising from the 2016 Fort McMurray wildfires.

• Privacy Mechanisms That Balance Utility for Time Series Data
  Anindya Roy, University of Maryland Baltimore County and United States Census Bureau, USA
  • Abstract

    Ensuring privacy in released data is of paramount importance for data-producing agencies. Privacy and confidentiality issues in data collection and data release mechanisms have seen revolutionary changes in recent years. The procedures guaranteeing the desired privacy of released data mostly use noise addition to achieve privacy goals. While appealing and appropriate for general databases, noise addition for time series data typically changes the sample autocorrelation structure, thereby compromising data utility for time series data. We propose a privacy mechanism that satisfies the dual objectives of privacy and data utility for time series. The proposed mechanism uses noise convolution instead of noise addition to achieve a privacy-utility trade-off. In the context of our proposal, we investigate filter design that allows us to define quantities that remain invariant under the mechanism, thereby excluding such data functionals from the privacy budget. We also propose generalizations that make the filtering-based privacy mechanism model-agnostic as well as those that are applicable to multiple time series.

10:30 – 12:00
Session 6B – Data Ethics and Confidentiality
Jean Talon Conference Room

• Advancing Equitable Data Collection: Insights from Statistics Canada's Statistical Integration Methods Division Disaggregated Data Action Plan (DDAP) Research Project
  Andrew Pearce, Kenza Sallier, Christiane Laperrière, Statistics Canada, Canada
  • Abstract

    In an era marked by the advocacy for Indigenous rights, racial justice, and economic equity, Statistics Canada has embarked on a transformative journey through the Disaggregated Data Action Plan (DDAP). This initiative aims to modernize data collection methods to better understand and address the challenges faced by diverse population groups, including women, Indigenous peoples, racialized communities, and individuals with disabilities. In this context, the Statistical Integration Methods Division of Statistics Canada has undertaken a comprehensive investigation of the ethical and practical implications inherent in evolving survey designs and data sources.

    The research culminated in the creation of "Guiding Principles: Leveraging the 2021 Census of Populations Data for DDAP Groups of Interest". This document summarizes valuable insights drawn from our investigation and literature review. It explains the organizational framework of DDAP within Statistics Canada, navigates existing data sources, addresses ethical considerations, and rigorously examines sampling methods tailored for DDAP initiatives.

    Drawing upon theoretical frameworks and applications based on Statistics Canada's experience, the findings highlight the importance of accounting for population characteristics such as hiddenness and social connectedness in selecting appropriate sampling methods. Through concrete examples and a detailed analysis of pros and cons, the guiding principles equip decision-makers with a comprehensive toolkit for navigating the intricacies of data collection in DDAP contexts.

    This presentation will discuss the methodological gaps that untraditional sampling methods can fill in the context of DDAP, discuss the importance of clear standards when creating and using such methods as well as the practical considerations to implement them in the context of a National Statistical Organization.

• On the interplay of legal requirements, quality aspects and ethical risks when using ML in official statistics
  Florian Dumpert, Federal Statistical Office of Germany, Germany
  • Abstract

    Ethics makes statements about how we should act. Ethical questions and ethical risks can also arise when machine learning is used in official statistics. More generally speaking: When methods and technologies are used to produce official statistics. The presentation deals with the interplay and the dependencies of such ethical risks, legal requirements and quality aspects and discusses the approach of German official statistics to address the topic "Ethics of Machine Learning".

• Statistical Disclosure Control Analysis for Small Area Estimation
  Cissy Tang, Statistics Canada, Canada
  • Abstract

    Currently, Statistics Canada has no official guidance on confidentiality rules for releasing small area estimates and no official study has yet been conducted on the subject. In recent years, there has been increasing demand from Research Data Centres (RDC) researchers for comprehensive confidentiality guidelines such that they can publish small area estimates in their research. This confidentiality analysis applies to area-level small area estimation.

    A simulation study is conducted in R to create simulated populations from which samples are selected. The simulated population contains an auxiliary variable, a variable of interest, and domain information. The strength of the relationship between the auxiliary variable and the variable of interest is controlled through an "error" variable with a random component.

    Stratified random sample are drawn, and area-level small area estimates are calculated using the "sae" R package (Molina and Marhuenda 2015). The simulation is run for various sampling rates and various auxiliary variable strength levels to identify potential areas of disclosure risk. The risk of disclosure of the small area estimate is compared against the direct Horvitz-Thompson estimate to demonstrate that small area estimates are inherently less risky than direct estimates, especially when sampling rates are extremely low. The results are then analyzed and finally, comprehensive confidentiality guidelines for the release of area-level small area estimates are proposed. The presentation will outline the simulation process and discuss justifications for the proposed confidentiality guidelines.

• Synthetic Data Disclosure Risk Assessment
  Zhe Si Yu, Statistics Canada, Canada
  • Abstract

    The adoption of synthetic data generation as a confidentiality measure is increasing in statistical agencies worldwide, including at Statistics Canada. This approach provides an alternative to the traditional dissemination of anonymized public microdata files, offering both privacy protection and data utility. However, the creation of synthetic data presents challenges in assessing and mitigating disclosure risks. This paper reviews the different types of disclosure risks, that being attribute, membership and identity disclosure, and presents some of the associated methods for measuring risk. Identity disclosure is recognized as a non-issue for fully synthetized data, but remains an issue for partially synthesized data. The paper presents prominent risk assessment metrics and discusses practical methods for disclosure control in data synthesis. Methods for assessing disclosure risks usually produce a metric that can be used to gauge the risk, but there is little consensus on threshold values for these metrics. It is also important to focus on importance of balancing utility and confidentiality, which needs further discussion in context of these methods.

    The paper concludes by offering insights and recommendations about managing disclosure risk while creating synthetic data as well as providing some ideas on future directions for research and practical implications for managing disclosure risks in synthetic data.

• Exploration of Deep Learning Synthetic Data Generation for Sensitive Utility Data Sharing
  Julian Templeton, Benjamin Santos, Rafik Chemli, Statistics Canada, Canada
  • Abstract

    Utilities hold crucial information about energy usage and building characteristics which can be utilized by government agencies to improve their corresponding analytics. However, this data is associated with private customer records and thus the building data and energy usage may be too sensitive to share. Often, high-level aggregated versions of this data are shared through robust contracts, limiting the statistics that can be derived.

    With the advancement of generative machine learning techniques, Statistics Canada and Natural Resources Canada have explored the feasibility of using these models to produce synthetic versions of utility data which may be shared in full to requesting organizations. These synthetic datasets can be created by a utility through a locally run program and the outputs can be approved before being sent. This work has identified that certain generative models can feasibly be used by utilities to generate new versions of a dataset and has identified the issues which must be addressed prior to implementing this in practice. Both tabular and time-series models have been tested for different data sharing scenarios, where the TimeGAN model successfully captured the general energy peaks and valleys over a given day with reasonable computational requirements. Although this process takes days for annual energy amounts over thousands of customer records, this can enable new data sharing initiatives between utilities and National Statistical Offices while managing privacy risks. As work progresses in future phases with real utility partners, trust can be built for these approaches, and they can begin being tested on real data by actual data holders.

12:00 – 13:30
Lunch

13:30 – 15:00
Session 7A – Strategies to mitigate potential nonresponse bias in social surveys
Simon Goldberg Room

• Strategies to battle bias when preparing for digital first collection and a smaller geographical footprint for people surveys in Australia
  Anders Holmberg, Australian Bureau of Statistics, Australia
  • Abstract

    Although non-probability data sources are not new to official statistics, a revived interest in the topic has emerged from pressures due to falling survey response rates, increasing data collection costs and a desire to take advantage of new data source opportunities from the ongoing societal digitalisation. Due to the exclusion of certain segments of the target population, inference derived solely from a non-probability data source is likely to result in bias. This work approaches the challenge of addressing the bias by integrating non-probability data with reference probability samples. We focus on methods to model the propensity of inclusion in the non-probability dataset with the help of the accompanying reference sample, with the modelled propensities then applied in an inverse probability weighting approach to produce population estimates. The reference sample is sometimes assumed as given. In this presentation however, we pursue an objective of finding an optimal strategy, that is, the combination of a data integration-based estimator and sample design for the reference probability sample. We discuss recent work in which we take advantage of the good unit identification possibilities in business surveys to study an estimator based on propensities and derive optimal (unequal) selection probabilities for the reference sample.

• Dealing with non-response and attrition bias in a Transformed LFS
  Petya Kozhuharova, Office for National Statistics, United Kingdom
  • Abstract

    The Office for National Statistics UK is undergoing a transformation of its Labour Force Survey. The key changes include switching to an online first mode, implementing an adaptive survey design and changing the structure of the longitudinal component. Extensive testing of the attrition and non-response biases present in the TLFS quarterly files (at GB level) has been carried out. Prominent non-response biases were detected by tenure, age and geographically less affluent areas. A pre-calibration step was included in the weighting to offset this non-response and adjust distributions before final weighting. The ONS also implemented an Adaptive Survey Design and targeted field forces to areas of known non-response. This step has successfully increased response rates as intended, complimenting methods adjustments.

    Investigations showed different longitudinal attrition patterns in the new survey. Previous employment status in TLFS is a significant predictor of longitudinal attrition even after controlling for other predictors, with employed and self-employed people dropping out significantly more from subsequent waves. Thus, following preliminary calibration as a non-response adjustment for Wave 1, for waves 2-5 we adjust individual design weights by calculating sequential attrition probabilities calculated via logistic regression models. These wave-specific weights are then combined before final calibration. Final calibration is done by combinations of age groups, sex, local authority, regions and country. Without the addition of attrition with included previous economic status, the quarterly estimate was biased towards economically inactive people who are more likely to answer in later waves.

    Therefore, the inclusion of attrition adjustments increases employment compared to weighting just including non-response adjustment.

• Investigations into administrative data for measuring persistent child poverty
  Adam O'Neill, Keith McLeod, Robert Templeton, Statistics New Zealand, New Zealand
  • Abstract

    Using information from the Household Economic Survey (HES) Stats NZ provides yearly estimates of child poverty rates in Aotearoa (New Zealand). To extend this, a new longitudinal survey was developed to inform on rates of persistent child poverty - the Living in Aotearoa Survey.

    Collection challenges associated with increasing non-response, along with keeping to budgeted costs, posed significant risk to the quality of downstream estimates of persistent child poverty. This highlighted a need to explore alternative avenues for estimating poverty persistence.

    Advances in the construction of households using administrative data has been a large focus at Stats NZ. Current admin-based households place 90% of individuals into the right address. At the population-level this still leaves, however, notable room for intra-household discrepancies.

    Leveraging off the high coverage of low-income families observed through administrative welfare data, we show that the formation of these admin-based households can be improved for the purposes of measuring persistent child poverty in Aotearoa. Specifically, family welfare data is coupled with HES households, and high confidence admin-based households. Household income derived from administrative sources allows the poverty status of children to not only be determined for the current year, but previous three years as well.

    Validations of these households along with the resulting poverty rates will also be presented.

13:30 – 15:00
Session 7B – Record Linkage
Jean Talon Conference Room

• Efficient Record Linkage for large datasets by Business Names
  Hanan Ather, Statistics Canada, Canada
  • Abstract

    Record linkage across diverse datasets presents significant challenges in big data applications, particularly when business names serve as unique identifiers. Traditional linkage methods often face challenges with the variability in formatting, abbreviations, and errors within administrative and alternative data sources. Addressing this challenge, the introduced approach is a robust and user-friendly method that effectively links business records from external datasets to SBR, thereby overcoming the computational constraints of previous practices.

    The system utilizes a suite of string-matching algorithms, including edit distance, n-gram, and Jaro-Winkler, to facilitate record linkage and statistical matching. It distinguishes between true matches and false matches by calculating similarity measures, s(x, y), for entities within the SBR and any external list of businesses. By establishing a scalar threshold, t, we refine the linkage criteria, enhancing the precision of match declarations.

    Our method enhances data integration efforts by enabling a precise, yet computationally efficient identification of matches. Moreover, our approach is evaluated on accuracy metrics, balancing between sensitivity to detect true cases and specificity to minimize false positives.

    The strategic advantage of our approach lies in its efficiency, providing rapid processing times without sacrificing the accuracy of the matches. This efficiency is especially advantageous in big data environments, where quick data processing and efficient use of computational resources are critical.

    Our methodology is designed to contribute to improved standards in record linkage and statistical matching, aiming to enhance both the speed and precision of matching across databases.

    Evaluating the linkage accuracy when linking records in waves
    Abel Dasylva, Arthur Goussanou, Statistics Canada, Canada

    At Statistics Canada, many data sets are linked with quasi-identifiers such as the first name, last name, or address. In such cases, linkage errors are a potential concern and must be measured. In that regard, previous studies have shown that the evaluation may be based on modeling the number of links from a given record while accounting for all the interactions among the linkage variables and dispensing with clerical reviews, so long as the decision to link two records does not involve other records. In this communication, the methodology is adapted for a class of practical strategies, which violate this constraint by linking the records in consecutive waves, where a given wave links a subset of the records that are not linked in previous waves. In particular, the linkage may be based on a deterministic wave followed by a probabilistic one.

• Evaluating the accuracy when linking records in waves
  Abel Dasylva, Arthur Goussanou, Statistics Canada, Canada
  • Abstract

    At Statistics Canada, many data sets are linked with quasi-identifiers such as the first name, last name, or address. In such cases, linkage errors are a potential concern and must be measured. In that regard, previous studies have shown that the evaluation may be based on modeling the number of links from a given record while accounting for all the interactions among the linkage variables and dispensing with clerical reviews, so long as the decision to link two records does not involve other records. In this communication, the methodology is adapted for a class of practical strategies, which violate this constraint by linking the records in consecutive waves, where a given wave links a subset of the records that are not linked in previous waves. In particular, the linkage may be based on a deterministic wave followed by a probabilistic one.

• Model Based Threshold Selection for Agricultural Probabilistic Linkages
  Christian Arsenault, Statistics Canada, Canada
  • Abstract

    With the increasing importance of administrative data usage in producing official statistics, conducting quality probabilistic record linkages have become paramount to the success of many programs at Statistics Canada. Under the Fellegi and Sunter methodology, the weight threshold for determining a match is a critical parameter on which the optimality of the procedure depends. Solutions for setting this parameter up to this point have had major limitations, either relying on overly optimistic assumptions, or requiring training data or clerical review. However, a new model was developed which estimates the linkage error based on the number of links from a given record, accounting for all the interactions among the linkage variables. This error model serves as the foundation for evaluating different algorithms for setting the linkage threshold. Between exhaustive search, binary search, or a more sophisticated recursive partitioning procedure, each method offers distinct advantages in terms of runtime and the quality metrics produced. Using real farm data, we were able to validate the results of the error model with goodness-of-fit tests, as well as weigh the practical considerations of each of these methods by experimenting with different datasets. Although automating the selection of linkage thresholds presents challenges with the varying quality, structure, and size of agricultural data, this work provides a practical path forward to navigate these issues.

• The T1 Partnership Process: leveraging clustering and graphing methods
  Shaundon Holmstrom, Statistics Canada, Canada
  • Abstract

    All individuals who own a sole proprietorship or partnership business must complete a statement of business activities when filing their personal income taxes (T1 General Form). A T1 partnership is a collection of filers who form a single business but file their share individually. With the T1 business population it can be challenging to determine if a filer is a sole proprietorship or a partnership, and in most cases, there are no linkage keys available to identify the collection of filers belonging to a given partnership. Therefore, a T1 partnership identification process has been developed with the goal of the performing internal record linkage within T1 business population to identify partnerships. Failing to identify valid partnerships can lead to duplication in the population of T1 businesses (overcoverage) while incorrectly linking individuals as partners can lead to businesses being incorrectly removed from the population of T1 businesses (undercoverage). The T1 processing system is currently undergoing a redesign.

    The new T1 partnership identification process leverages numerical clustering using the DBSCAN algorithm, then compares matching fields to trim down potential clusters in the set of pairwise comparisons. When the final set of pairwise comparisons is complete, graph theory is used to create the set of all finalized partnerships.

• Effects of a two-category gender variable (men+ and women+) in the development of linkage-adjusted weights for the 2021 Canadian Census Health and Environment Cohort (CanCHEC)
  Eric Hortop, Yubin Sung, Statistics Canada, Canada
  • Abstract

    The 2021 CanCHEC consists of the 2021 Census long-form sample records linked to the Derived Record Depository (DRD) with a linkage rate of 96%. The DRD includes persons from Statistics Canada's survey and administrative data, such as the Census and deaths. Thus, the linkage facilitates research examining sociodemographic factors influencing the health of Canadians. The 2021 CanCHEC is highly anticipated because the 2021 Census collected data specifically about gender for the first time, differentiating from sex at birth, enabling an examination of health outcomes among gender diverse persons. Linkage-adjusted record weights allow researchers to estimate and evaluate the variance of summary measures of the Canadian population in the presence of missed links. To create the final weights, we construct response homogeneous groups using the linkage propensity score from a logistic model fitted for the long-form sample records. We then use a cell calibration procedure that further adjusts the weights such that the weighted totals for a multivariate tabulation of the cohort match the weighted totals from the long-form sample. The tabulation variables include the two-category gender variable among other characteristics. We examine slippage on estimates of cisgender, transgender and non-binary populations to evaluate our practical weighting procedure.

    Decisions in the weighting procedure can affect the accuracy of estimates for small population groups such as gender-diverse persons. This presentation will highlight the importance of routinely measuring slippage.  A limitation is that our linkage-adjusted weights only consider the linkage of the cohort to the DRD while many population health analyses would need to account for additional linkages to health administrative data.

15:00 – 15:30
Afternoon Break

15:30 – 17:00
Session 8 – The Future of National Statistical Organizations
Simon Goldberg Room

• The Future of National Statistical Organisations - the Longer-Term Role and Shape of NSOs
  Osama Rahman, Office for National Statistics, United Kingdom
  • Abstract

    The rise of a data-driven world is radically reshaping the context in which National Statistical Offices (NSOs) operate. There is rising demand from both policymakers and wider society to provide information that is more accurate, more timely and more granular compared to previous decades. At the same time, NSOs face increased competition from the emergence of many new sources of analysis and statistics, which is challenging their previously pre-eminent role as one of the most relevant and trusted sources of information. The increasing granularity and velocity of new forms of data and data science techniques are also leading to a reduction in the traditional gaps between policy development, implementation and delivery, and monitoring and feedback. This then leads to question to what extent the gap between statistics/research and operational analysis/use of management information needs to reduce and what role NSOs have here.

    In response, NSOs are investing considerable effort in initiatives to remain relevant to their users. They are innovating by developing new methods and using new sources of data to produce more robust statistics and analysis that better meet the demands of government and society. Some NSOs are also looking to the longer-term, developing their vision for what their work will need to look for their organisations to remain relevant in ten to twenty years' time.

    This session will discuss how NSOs are already adapting and ways they may need to continue to adapt to succeed in an increasingly data-driven world.

    Other members of the panel:

    André Loranger, Chief Statistician of Canada, Statistics Canada

    Francesca Kay, Central Statistics Office, Ireland

    Anders Holmberg, Australian Bureau of Statistics, Australia

Friday November 1, 2024

8:30 – 10:00
Session 9A – Young Statisticians from Statistics Canada's Modern Statistical Methods and Data Science Branch
Simon Goldberg Room

• • Abstract

    The 2024 Statistics Canada International Methodology Symposium on "Shaping the Future of Official Statistics" will feature a special invited session designed to showcase the innovative thinking and strategic vision of young statisticians. Indeed, part of envisioning the future of official statistics demands an investment in tomorrow's statistical leaders. By cultivating a diverse and healthy pool of talent equipped with both technical expertise and essential soft skills, we can ensure a strong succession for the future of official statistics. Selected through a competitive process that took place within Statistics Canada Modern Statistical Methods and Data Science Branch, winners will present their strategic insights on pressing issues facing National Statistical Organizations (NSOs). This session presents a unique opportunity to gain fresh viewpoints with the hope of stimulating strategic thinking, encouraging dialogue, and contributing to the advancement of NSOs. Ultimately, shaping the future of statistical practices, the advancement of statistical methodology, and promoting data driven decision making at both national and global levels.

    Presentation 1: How do you foresee the evolving role and significance of National Statistical Organizations (NSOs) such as Statistics Canada over the next 25 years?". [Assuming the speed of data and IT evolution remains the same as in the last years]
    Namita Chhabra, Johan Fernandes, Craig Hilborn and Joshua Miller, Statistics Canada, Canada

    Presentation 2: What would be the key characteristics, skills, abilities and capacities of the ideal (perfect) future statistician” (or employee of Statistics Canada) in 25 years.
    Neal Jin, Andrew Jay, Andrew Pearce and Abhishek Singh, Statistics Canada, Canada Presentation 3:

    How can Statistics Canada, effectively and explicitly leverage ethical principles and democratic values to all Canadians particularly in the context of social and economic polarization?
    Marc Beauparlant, Bassirou Diagne and Beni Ngabo Nsengiyaremye, Statistics Canada, Canada

    Presentation 4: What are the issues facing Statistics Canada in reconciling the imperative of collecting disaggregated data for minority subgroups with the need to uphold privacy, data ethics, and historical sensitivities?
    David Ahn, Alexandre Istrate, Samuel Sombo and Nicholas Wilker, Statistics Canada, Canada

9:00 – 10:00
Session 9B – Nowcasting for Economics Statistics
Jean Talon Conference Room

• High Frequency Data Collection at the U.S. Census Bureau - the Business Trends and Outlook Survey (BTOS)
  Cory Breaux, Kathryn Bonney, U.S. Census Bureau, USA
  • Abstract

    The Business Trends and Outlook Survey is an experimental data product from the U.S. Census Bureau, intended to capture high-frequency changes in economic conditions, first launched in July 2022. The BTOS draws from a large sample of nearly 1.2 million businesses to provide representative, bi-weekly data on economic conditions and trends. The survey collects information on a wide range of business conditions including current performance, changes in revenue, employment, demand, and prices, operating status, the impact of natural disasters, and Artificial Intelligence usage. Firms are asked about the previous two weeks and for a six-month projection.

    From December 2023 to February 2024, the BTOS added supplemental content which provides a detailed real- time look at U.S. businesses' use of AI.

    During this period, bi-weekly estimates of AI use rose from 3.7% to 5.4%, with an expected use rate of about 6.6% by early Fall 2024. AI usage was found to vary across sector and geography, with the highest use rates occurring in the Information sector and states in the western U.S. Amongst AI-using businesses, the most common applications include marketing automation, virtual agents, natural language processing, and data/text analytics. Many businesses report using AI to replace worker tasks and existing equipment/software, though there is little evidence that AI use is associated with decline in firm employment. Businesses were also asked why they do not expect to use AI; the predominant reason reported was the inapplicability of the technology to their business. Future research can integrate BTOS data with other Census survey and administrative data, exploring the connection between AI use and firm performance.

• Improving Nowcasts for the U.S. Census Bureau Index of Economic Activity (IDEA)
  Elizabeth Marra, Rebecca L Weaver, William R Bell, Tucker S McElroy, Valerie E Pianin, Jose Asturias, Rebecca J Hutchinson, U.S. Census Bureau, USA
  • Abstract

    The U.S. Census Bureau Index of Economic Activity (IDEA) is an experimental data product that was first released in February of 2023. It is constructed from 15 of the Census Bureau's primary monthly economic time series, providing a single time series reflecting the variation of the full set of component series over time. The component series are monthly measures of activity in retail and wholesale trade, manufacturing, construction, international trade, and business formations. One of the challenges of producing a monthly economic indicator from 15 different component series is that not all the series are released on the same day in a month. To account for these varying release dates, the index is calculated daily, incorporating the most recently released values from the component series for the current release month. For component series whose values have not yet been released for the current month, we predict (nowcast) their values using a multivariate autoregressive time series model. We estimate index weights for these 15 series using Principal Components Analysis applied to the standardized monthly growth rates. Series with larger weights will have more influence over the index, compared to those with smaller weights. If nowcasts for a highly weighted series are inaccurate, then when a new monthly estimate for the series is released, the index could exhibit a large revision for that month. This presentation reviews the index, discusses the nowcasting procedure, and presents a potential improvement to the nowcasting.

• Leveraging Transformers for Now-Casting Canadian Labor Indicators
  Luke Budny, Aziz Al-Najjar, Tariq El Bahrawy, Carleton University, Canada
  • Abstract

    In the rapidly advancing data-centric world, the acuity of labor market analytics is pivotal for economic strategy and policy-making. This study explores the application of the Lag-Llama model, a state-of-the-art transformer-based architecture, for nowcasting key Canadian labor indicators such as employment, job vacancies, payroll, and hours worked. Addressing the challenge of delayed or incomplete labor data from Statistics Canada surveys, the study proposes a forecasting methodology that leverages both historical trends and contemporary data inputs. The model is pre-trained on diverse datasets across multiple domains, enhancing its predictive robustness and interpretability. Through systematic fine-tuning of hyperparameters and judicious selection of external variables via forward and backward selection techniques, the Lag-Llama model achieves significant performance gains over classical time series forecasting methods. Experimental results exhibit substantial improvements in Mean Absolute Percentage Error (MAPE) for both point estimates and prediction intervals, especially in the All Industries MAPE for earnings data. The study also identifies certain industry-specific forecasting challenges and discusses potential solutions including separate models. These findings highlight the potential of transformer models like Lag-Llama in macroeconomic forecasting and set a new benchmark for future analyses in this domain.

10:00 – 10:30
Morning Break

10:30 – 12:00
Session 10A – The use of machine learning in official statistics
Simon Goldberg Room

• Statistical Inference in the Presence of Non-Response and Machine Learning Methods: Some Recent Works
  David Haziza, University of Ottawa, Canada
  • Abstract

    In recent years, machine learning methods have generated increasing interest within national statistical institutes. These methods allow for precise predictions by analyzing large datasets and identifying complex patterns and relationships. These predictions can be applied at various stages of a survey, particularly for handling missing data and small area estimation.

    In this presentation, we will present the results of recent or ongoing work on inference in the presence of item non-response. We will begin by discussing statistical inference when random forests are used to impute missing values. Then, we will address doubly robust estimation methods that incorporate predicted response probabilities and imputed values—obtained using any machine learning method—into the construction of the estimators. We will highlight the advantages of doubly robust methods within the framework of machine learning and discuss their practical implementation. We will show how to estimate the variance of doubly robust estimators. Finally, we will present the results of simulation studies aimed at evaluating the performance of point and variance estimators.

• Fitting Classification Trees Accounting for Complex Survey Design
  Minsun Riddles, Westat, USA
  • Abstract

    Survey practitioners have increasingly embraced the benefits of modern machine learning techniques, including classification and regression tree algorithms. These methods, which do not require a predefined functional relationship between outcomes and predictors, offer a practical means of conducting variable selection and deriving interpretable structures that link predictors with a target outcome. However, when applying these algorithms to survey data, it is common to overlook crucial factors like sampling weights, as well as sample design features such as stratification and clustering. To bridge this oversight, we propose an extension of the well-known Chi-square Automatic Interaction Detector (CHAID) approach. Our enhancement incorporates a Rao-Scott correction into the splitting criterion, accounting for the survey design. We discuss the statistical properties of the resulting algorithm under a design-based framework. Using data from the U.S. American Community Survey, we illustrate the use of the method and evaluate its performance through comparisons with existing weighted and unweighted algorithms.

• Tree-Based Algorithms for Official Statistics
  Daniell Toth, US Bureau of Labor Statistics, USA
  • Abstract

    Over the past decade, machine learning algorithms have been increasingly employed to produce official statistics. This trend has been particularly notable following the adaptation of various algorithms for use with survey data collected from an informative sample design. Among these techniques are several tree-based methods such as regression and boosted trees, random forest models, and Bayesian tree models. These models have been utilized in analyzing survey data for purposes such as assessing data quality and nonresponse, as well as estimating official statistics through small area estimation, model-assisted estimators, and data imputation. In this article, we discuss how these tree-based methods have been adapted for use with survey data and explore their significance in the production of official statistics.

10:30 – 12:00
Session 10B – Society and Official Statistics
Jean Talon Conference Room

• A Safe and Inclusive Approach to Disseminating Statistical Information about the Non-binary Population in Canada
  Claude Girard, France-Pascale Ménard, Statistics Canada, Canada
  • Abstract

    In 2022, Canada became the first country to release statistical information about its transgender and non-binary populations based on data collected from the 2021 Census of Population. Moreover, following a recent government-wide directive, Statistics Canada's surveys have begun to collect and disseminate information about gender rather than sex at birth.

    Due to the small size of the transgender and non-binary populations – according to the 2021 Census, these represent 0.3% of individuals aged 15 or older in Canada – disseminating safe statistical information about them at detailed sociodemographic or geographical levels poses a challenge.

    The dissemination strategy adopted for the 2021 Census, which was subsequently adapted and recommended for surveys, is centered on a new two-category gender variable (Men+ and Women+) that include non-binary individuals, and which is to be used at all but the highest dissemination levels. In this talk, we retrace the methodological considerations that have gone into creating and adopting this novel approach, which is deemed both inclusive of non-binary people and statistically safe.

• One-Stop-Shop for AI/ML for Official Statistics - Methodology at the Heart
  Francesca Kay, Brendan O'Dowd, Central Statistics Office, Ireland
  • Abstract

    In a world of constant change, new and improved statistical products and services have become a necessity for official statistics. On the one hand, demand from users is evolving, as they ask for more granular, more timely and better integrated data. From a methodology perspective that means Official Statisticians have to reconsider what users need and how we can provide fit for purpose and methodologically sound information. On the other hand, technical opportunities appear at such a pace that it is very challenging to provide new and robust methodologies that ensure these opportunities can be integrated in the production of official statistics whille maintaining the understanding, transparency and trust in official statistics as a trusted source of information.

    In April 2024, Eurostat awarded a grant to a consortium of 14 countries led by the Central Statistics Office of Ireland to create a One-Stop-Shop for AI/ML in Official Statistics (AIMLOS4). The potential for AI/ML is still being developed and the aim of the One-Stop-Shop is to enable systematic learning, sharing of experiences, identification of good practices and reuse of solutions. At the heart of the consortium is both researching and creating new methodologies to support the application of AI/ML solutions as well as the standardisation of methodologies and best practice to facilitate the scaling up or reuse of existing solutions.

    The presentation will highlight some of the key objectives of the AIMLOS4 project and how it will look to develop innovative new methodologies in AI/ML to meet the evolving needs and challenges in the production of official statistics.

• Citizen-generated data and its impact on official statistics
  Haoyi Chen, United Nations Statistics Division, United Nations, New York, USA
  • Abstract

    Citizen participation throughout the data value chain is increasingly recognized in addressing data gaps for marginalized communities and enhancing data fairness, inclusiveness, openness, accountability and transparency. The global official statistical community has partnered with other data stakeholders including civil society organizations, human rights institutions and academia marching forward with the draft "Copenhagen Framework on Citizen Data", to support the sustainable and use of data generated by citizens and communities.

    The paper discusses how bringing citizen generated data (CGD) into the official statistics community will impact the national official statistics systems and transform the role of national statistical offices as data stewards. The areas that will be covered include: (a) discussing the role of CGD to fill data gap and to improve inclusivity of official statistics; (b) reassessing the relevance of existing quality frameworks for official statistics to allow data to be used fit-for-purpose; (c) challenges and opportunities in integrating CGD into official statistics and (d) discussing how the role of national statistical offices need to evolve in leveraging the power of the citizen generated data.

• Exploration of approaches to small area estimation with measurement errors; and their application to Indonesian household surveys
  Ika Yuni Wulansari, University of Technology Sydney, Australia, Politeknik Statistika STIS, Indonesia, Statistics Indonesia, Indonesia
  Stephen Woodcock, University of Technology Sydney, Australia
  James J Brown, University of Technology Sydney, Australia
  • Abstract

    The UN's SDGs require highly disaggregated data on indicators that are typically available only from household surveys. However, this presents an issue as the required level of dis-aggregation is beyond what surveys can support through direct estimation. Therefore, NSIs are turning to small area approaches. However, that is not without its issues, as often the auxiliary variables that might be used in estimation models are themselves estimated from surveys, introducing the additional complication of measurement errors.

    We aim to apply the approach of Ybarra and Lohr (2008) to adjust for measurement errors in a classic Fay-Herriot area-level model. We use a comprehensive simulation study to explore the impact of measurement error on a single auxiliary variable, and the situation where there are two auxiliary variables, one with and one without measurement error. The results demonstrate the robustness of the standard approach and ignoring measurement error, but show there are specific scenarios where correct adjustment for measurement errors is beneficial. We apply the approach to a case-study example utilising Indonesian household survey data, estimating at the sub-district level. In the case study, we estimate per capita household expenditure from the National Socio-Economic Survey (SUSENAS) as variable of interest and use village potential data (PODES) for auxiliary variables.

12:00 – 13:30
Lunch

13:30 – 15:00
Session 11 – Special Session in Honour of J.N.K. Rao
Simon Goldberg Room

• J.N.K. Rao's Contributions to Survey Research
  Sharon Lohr, Arizona State University, USA
  • Abstract

    J.N.K. Rao has contributed to almost every subdiscipline of survey research. In this nontechnical talk, I will touch on some of Rao's work on unequal-probability and two-phase sampling, variance estimation, regression and categorical data analysis, small area estimation, and data integration.

    For each of these topics, Rao's work anticipated and led future research directions, and I will discuss his contributions in the context of broader trends and current challenges in survey research.

• Celebrating J.N.K. Rao's Legacy in Small Area
  Mahmoud Torabi, University of Manitoba, Canada
  • Abstract

    This talk pays homage to the remarkable contributions of J.N.K. Rao to the field of small area estimation (SAE). Rao's pioneering research has profoundly influenced the development and application of SAE methods, revolutionizing how statisticians tackle inferential challenges in areas with limited data representation. Beginning with an overview of SAE's significance across various domains, from official statistics to public health, the presentation highlights Rao's key methodological advancements.

    These include his instrumental work in model-based approaches, notably the area-level and unit-level models, and estimation of mean squared prediction error of small area parameters. Furthermore, the talk explores Rao's enduring legacy in fostering interdisciplinary collaborations and promoting the practical adoption of SAE techniques in decision-making processes.

    Through an examination of Rao's influential papers and collaborations, the presentation illustrates his pivotal role in shaping the theoretical foundations and real-world applications of SAE. Additionally, building upon Rao's legacy, it discusses contemporary challenges and recent advancements in SAE research. Ultimately, this talk serves as a tribute to J.N.K. Rao's indelible mark on SAE, inspiring continued innovation and impact in the field for generations to come.

• Contributions of J.N.K. Rao to complex survey multilevel models and composite likelihood
  Mary Thompson, University of Waterloo, Canada
  • Abstract

    With H. O. Hartley, J. N. K. Rao was an early contributor to multilevel modeling with survey data, through methods of inference for variance components. In recent years, he has returned to this area of research. With F. Verret and M. Hidiroglou he proposed a weighted composite likelihood approach to inference under a two-level model (Survey Methodology, 2013). This method and its impact, applications and later extensions will be outlined.

15:00 – 15:30
Afternoon Break

15:30 – 17:00
Session 12A – Integration of probability and non-probability sample data
Simon Goldberg Room

• Some Theoretical and Practical Issues in and Strategies for Dealing with Non-Probability Samples
  Changbao Wu, University of Waterloo, Canada
  • Abstract

    We provide an overview of recent developments in statistical inference for non-probability survey samples. We discuss issues arising from methodological developments related to inverse probability weighting and model-based prediction and concerns with practical applications. Three procedures proposed in the recent literature on the estimation of participation probabilities, namely, the method of Valliant and Dever (2011) based on the pooled sample, the pseudo maximum likelihood method of Chen, Li and Wu (2020), and the method of Wang, Valliant and Li (2021) using a two-step computational strategy, are examined under a joint randomization framework. The inexplicit impact of the positivity assumption on model-based prediction approach is examined, and the main issue of undercoverage is highlighted. We discuss potential strategies for dealing with standard assumptions and undercoverage problems in practice.

• Comparison of recent techniques of combining probability and non-probability samples
  Julie Gershunskaya, U.S. Bureau of Labor Statistics, USA
  • Abstract

    We compare several recent quasi-randomization methods for inferences from non-probability samples. The considered techniques are developed under the assumption that the sample selection is governed by an underlying latent random mechanism and that it can be uncovered by combining non-probability survey data with a "reference" probability-based sample, obtained from the same target population. Challenges prompting the development of alternative procedures include (i) non-probability sample participation indicators are available only on the observed sample units and (ii) it is not generally known which units from the underlying population belong to both the non-probability and reference samples. We consider the ways different procedures address these challenges, discuss theoretical properties of the methods and compare them using simulations.

• Propensity Score Estimation and Optimal Sampling Design when Integrating Probability Samples with Non-probability Data
  Anders Holmberg, Australian Bureau of Statistics, Australia
  Lyndon Ang, Australian National University and Australian Bureau of Statistics, Australia
  Robert Clark, Bronwyn Loong, Australian National University, Australia
  • Abstract

    Although non-probability data sources are not new to official statistics, a revived interest in the topic has emerged from pressures due to falling survey response rates, increasing data collection costs and a desire to take advantage of new data source opportunities from the ongoing societal digitalisation. Due to the exclusion of certain segments of the target population, inference derived solely from a non-probability data source is likely to result in bias. This work approaches the challenge of addressing the bias by integrating non-probability data with reference probability samples. We focus on methods to model the propensity of inclusion in the non-probability dataset with the help of the accompanying reference sample, with the modelled propensities then applied in an inverse probability weighting approach to produce population estimates. The reference sample is sometimes assumed as given. In this presentation however, we pursue an objective of finding an optimal strategy, that is, the combination of a data integration-based estimator and sample design for the reference probability sample. We discuss recent work in which we take advantage of the good unit identification possibilities in business surveys to study an estimator based on propensities and derive optimal (unequal) selection probabilities for the reference sample.

15:30 – 17:00
Session 12B – Challenges in Production of Official Statistics
Jean Talon Conference Room

• Using Non-Binary Gender to Calibrate Survey Weights for the Canadian Long-Form Census Sample
  Alexander Imbrogno, Statistics Canada, Canada
  • Abstract

    In 2021, Canada became the first country to collect and publish data on gender in a national census giving Canadians the option to choose male, female, or non-binary. Due to their small sizes, non-binary population totals were excluded from the 2021 long-form sample calibration due to the risk of increasing the variance of estimates. This talk presents an alternative long-form calibration procedure which aggregates sub-provincial non-binary totals into a large provincial total to protect against variance inflation. Artificial sub-provincial non-binary totals are introduced as a tool to decompose the resulting provincial level calibration back into independent sub-provincial problems, maintaining the computational efficiencies of the usual long-form calibration. An algebraic expression for the artificial totals under the chi-squared distance is derived. Simulation results are presented demonstrating the benefits of non-binary calibration on data quality for the non-binary domain.

• A New Origin-to-Destination Table of Canadian Manufacturing Sales: Challenges with Imputing a Distribution from Annual Survey Data
  Nicholas Huliganga, Statistics Canada, Canada
  • Abstract

    Until now, detailed data on the destination of manufacturing sales have not historically been available to Canadians. Through integration of annual survey (ASML) data, a destination of sales table by industry and province of origin was developed for the manufacturing annual and monthly surveys at Statistics Canada.

    While not a question on the monthly survey, the respondents for the annual survey are asked for their distribution of sales as a percentage across 15 destinations. To tackle the difficulty of generating an establishment-level distribution for multi-province respondents, three approaches were compared:
    using the respondents' total distribution for all their establishments, using optimization, and using the distributions of the single-province respondents. The imputed distribution of destination sales from the annual data was then applied to the monthly sales value. Monthly establishments not linked to annual respondents were imputed with a strategy involving the aggregate distribution of their industry-province group in the annual survey.

    Finally, point estimates and a sampling variance (and CV) were successfully generated for the entire origin-destination of sales table and for each industry in the monthly survey.

    This presentation delves into challenges faced with imputing the destination sales (especially for respondents with establishments in multiple provinces), ensuring sales match marginal origin province totals, and allocating a distribution of destinations based on data from the annual program to the monthly estimates.

• The Usage of the ReliefF Algorithm for Edit & Imputation in the Canadian Census of Population
  Irwin Khuu, Statistics Canada, Canada
  • Abstract

    Historically, the Canadian Census of Population Edit & Imputation (E&I) process has operated using a nearest-neighbour donor imputation methodology wherein the distance between a failed unit and a potential donor is obtained through a weighted combination of auxiliary variables. Revision to the model between cycles can be a complicated and time-consuming process given there is no standard approach to variable selection and weighting between topics. This presentation will illustrate the potential of the ReliefF feature selection algorithm to create a machine learning-driven approach to variable selection and weighting that is standardized and comparable between Census cycles and among the many topics of the Census. An overview on how this process may be applied in practice will be presented, followed by results on a diverse set of topics that indicate a general improvement over previous methods.

• Factors affecting response propensity, with an interest in units sampled multiple times – an empirical study using social surveys at Statistics Canada
  Noah Johnson, Catherine Deshaies-Moreault, Cilanne Boulet, Statistics Canada, Canada
  • Abstract

    As the need for data has grown over the past number of years, the effect and burden of repeatedly sampling the same units for multiple surveys has become an increasing concern. Response burden is generally assumed to contribute to decreasing response rates, however there are few empirical studies looking into this question. One such study was undertaken by Statistics Canada, aggregating data on response to social surveys conducted between 2020 and 2023. It investigates factors contributing to the observed response patterns, including the effect of having been previously selected on response propensity. This presentation will describe the study, share its principal results, and address how they can inform decisions about sample coordination among household surveys at Statistics Canada.

17:00 – 17:15
Closing Remarks
Simon Goldberg Room

• Wesley Yung, Director General, Modern Statistical Methods and Data Science Branch, Statistics Canada, Canada

• I. Preamble
  • A. Reconciliation to Audited Financial Statements
  • B. Limitations
• II. Reporting Practices
  • A. Prescribed Reporting Practices
  • B. Uniform Reporting Practices
    • 1. Basis of Consolidation
    • 2. Funds
    • 3. Accrual Concept
    • 4. Funds Flow Approach
    • 5. Guidance on Use of the Correct Fund
    • 6. Capital Assets
    • 7. Vacation Pay, Pension Costs and Future Benefits
    • 8. Sales and Cost Recoveries
    • 9. Interfund Transfers
    • 10. Gifts-In-Kind
    • 11. Borrowing and Principal Repayment
    • 12. Full Costing of Ancillary Services
    • 13. Use of Estimates
• III. Detailed Instructions for Institutions Reporting Financial Data
  • A. Comparable Financial Data
  • B. Annual Return
  • C. Definitions, Explanations and Examples
    • 1. Funds
    • 2. Income by Fund (Table 1)
    • 3. Expenditures by Fund (Table 2)
    • 4. General Operating Expenditures by Function (Table 4)

I. Preamble

Financial Information of Universities is an annual survey conducted by Statistics Canada to provide a basic source of reference for the financial data of universities and degree-granting colleges in Canada.

The Guidelines are intended to assist both users and preparers of the financial data reported in the annual survey (or "return"), and are organized as follows:

Section II provides general information for both users and preparers of the annual return. This section discusses financial reporting by institutions and identifies users of the annual return and their needs, as well as the relationship of generally accepted accounting principles to the financial data and the prescribed reporting practices underlying that data.

This section will assist users and preparers of the annual return to appreciate the differences between accounting principles for audited financial statements and prescribed reporting practices for the annual return.

Section III provides detailed instructions for institutions reporting financial data. This is the "how-to" section for preparers to refer to when completing the forms, and will be of interest to users who seek additional information on specific terms or particular line items used in the annual return.

A. Reconciliation to Audited Financial Statements

A copy of your audited financial statements is requested for submission along with your input return. If a copy is not available please advise us of the date on which the audited financial statements will be forwarded.

B. Limitations

Notwithstanding the use of detailed Guidelines to assist preparers, there are limitations in the comparability of the data because of differences in the underlying accounting practices followed by institutions. Even the most stringent of reporting guidelines cannot eliminate differences resulting from different underlying accounting practices. As well, interregional comparisons must recognize differences such as various sources of funding, fiscal year-end dates varying from March 31^st to June 30^th, and variations in provincial policies and provincial funding responsibilities.

Specific examples where differences between institutions result in limitations in the comparability of financial data include:

• Definition of research – The definition or research used by an institution will determine the income and expenditures that are reported in the Sponsored research fund. For example, clinical trials may or may not be defined as research and therefore may or may not be reported as sponsored research expenditures.
• Hospitals and hospital based medical research – The amount and level of detail reported by institutions for hospitals and for hospital based medical research varies depending upon the corporate relationship between the institution and the hospital.
• Canada Foundation for Innovation (CFI) Provincial matching grants – while an institution separately reports certain specific provincial government grants that are earmarked as CFI matching grants, not all provincial CFI matching grants are separately reported because not all are specific and earmarked.
• Internal sales and cost recoveries – Depending upon particular management information systems and business practices, an institution may report amounts by reducing offsetting expenditures or as internal cost recoveries.
• Computing and communication costs – The amount reported by institutions for computing and for communication costs will vary depending upon whether an institution has a centralized or decentralized structure for computing and for communications.

In addition, comparisons of financial data over multiple years should be done with caution because of changes in generally accepted accounting principles that could alter the underlying data and changes in the Guidelines that govern the reporting of the data.

II. Reporting Practices

This section will assist users and preparers of the annual return to appreciate the differences between accounting principles for audited financial statements and prescribed reporting practices for the annual return.

A. Prescribed Reporting Practices

The audited financial statements of reporting institutions are prepared in accordance with generally accepted accounting principles (GAAP). Adherence to GAAP results in consistency of reported financial results from one year to the next.

In certain situations, however, GAAP permits individual institutions to choose between equally acceptable alternatives. As an example, institutions can choose either the deferral or restricted fund method of revenue recognition, and reporting nuances of each method may make comparisons between institutions difficult.

In addition, the users of the annual return may require, in certain situations, financial data based on an accounting practice that deviates from GAAP. For example, users of capital expenditure data generally require line item reporting of income and expenditures based on the flow of funds, rather than on capitalized and amortized amounts.

By way of highlights, users and preparers of the financial data should note the following points that apply to the annual return, even though they may represent differences from the practices normally followed by individual institutions in reporting financial information:

• Restricted funds include both external and internal restrictions, rather than external only.
• Certain restricted income not expended in the year, such as income in the Sponsored research fund, is reported on the funds flow approach, rather than deferred (see Section II.B.4).
• Capital expenditures are reported on the funds flow approach, rather than capitalized and amortized (see Section II.B.6).
• Certain expenditures, such as vacation pay, pension costs and future benefits, are reported on the cash basis, rather than accrued (see Section II.B.7).
• Institutions are encouraged to minimize interfund transfers by reporting income and the corresponding expenditures in the same fund (see Section II.B.9).
• Users require income and expenditure data, only; therefore, a complete set of financial statements is not reported.

These Guidelines are not intended to conform an institution's annual return to its financial statements or its internal management reports. The prescribed practices, including the uniform reporting practices that follow, may or may not be in accordance with generally accepted accounting principles. These Guidelines are intended to promote consistency of financial data.

B. Uniform Reporting Practices

For consistency of financial data, reporting institutions and the preparers of the annual return within those institutions must comply with the Guidelines in general, and specifically with the uniform reporting practices. The uniform reporting practices, and the detailed instructions that follow in Section III, have been developed recognizing that balance is required between the information requirements of the users of the annual return and the response burden that is placed on the preparers. The uniform reporting practices are as follows:

1. Basis of Consolidation

For related and affiliated entities, each institution is to report financial data in the annual return on the same basis as that used for its consolidated financial statements. If the financial data for the entity are only reported in the notes to the consolidated financial statements, then the financial data are not reported in the annual return. For instance, the financial data for a Charitable Foundation will only be included in the annual return if the Charitable Foundation is consolidated in the financial statements of the institution.

2. Funds

The financial data will be reported following a form of fund accounting. Fund accounting classifies resources for accounting and reporting purposes in accordance with activities or objectives as specified by donors, in accordance with regulations, restrictions, or limitations imposed by sources outside the institution, or in accordance with directions issued by the governing body of the institution.

A fund is an accounting entity with a self-balancing set of accounts for recording assets, liabilities, a fund balance, and changes in the fund balance. Funds have been identified as either unrestricted or restricted. Restricted funds, other than Endowment, account for resources that may be used for current purposes, but with some limitations imposed by external or internal sources.

For accounting and reporting purposes, institutions combine the funds with similar characteristics into distinct fund groups. The fund groups reported in the annual return, with a brief explanation of each, are as follows:

General operating is an unrestricted fund that accounts for the institution's primary operating activities of instruction and research, other than sponsored research.

Special purpose and trust is a restricted fund. The funds, including donations, may be restricted by external sources, or internally restricted by the institution's governing body, for purposes other than sponsored research (Sponsored research fund), or capital (Capital fund).

Sponsored research is a restricted fund that accounts for income and expenditures for all sponsored research. Amounts are separately reported for entities consolidated and entities not consolidated (see Section II.B.1).

Ancillary is an unrestricted fund that separately accounts for all "sales-producing" operations or "self-supporting" activities that are supplementary to the institution's primary operating activities of instruction and research.

Capital is a restricted fund that accounts for resources provided to the institution for capital purposes and not reported in any other fund.

Endowment is a restricted fund that accounts for the capitalization of externally or internally restricted amounts, primarily donations, which cannot be spent.

Section III.C.1 provides additional information and explanatory comments on each of the above funds.

3. Accrual Concept

As a general reporting practice, institutions follow the accrual, rather than the cash basis of accounting. The accrual concept refers to the method of recording transactions where income is reported in the period in which the income is considered to have been earned, rather than received; and expenditures, in the period in which the expenditures are considered to have been incurred, rather than disbursed. An example of the application of this concept to an income item is the accrual for interest earned, but not received; and, to an expenditure item, is the accrual for retroactive salary costs earned, but not paid.

Exceptions in the annual return to the accrual concept include

• the funds flow approach for reporting income in the Special purpose and trust, and Sponsored research funds (see Section II.B.4),
• the funds flow approach for reporting income and expenditures for capital asset transactions (see Section II.B.4), and
• the cash basis for reporting vacation pay, pension costs and future benefits (see Section II.B.7).

4. Funds Flow Approach

For specific types of activities, income will be reported in the annual return following a funds flow approach; that is, for both Special purpose and trust, and Sponsored research (see Section III.C.1), the funds are reported as income in the period in which the funds are received or receivable. The corresponding expenditures, on the other hand, are reported consistent with the accrual concept; that is, in the period in which the expenditures are incurred. For example, when an institution is awarded a research contract, the income is reported when the funds are received or receivable under the terms of the contract.

Income and the corresponding expenditures are to be reported in the same fund (see Section II.B.9).

5. Guidance on Use of the Correct Fund

For all funds the matching principle applies; that is the revenue and related expenditure should be recorded in the same fund. It is not as straightforward to decide whether the revenue or expenditure source should dictate the fund where they are recorded. Depending upon the fund, there is not one method that says that expenditures should be recorded in the same fund as the revenue (expenditures follow revenues) or vice versa (revenues follow expenditures). Other reporting considerations have taken precedence over this consideration. However, while the applicable method may not be consistent across all funds, it is consistent within a given fund. The following shows the method to follow for each fund:

Operating Fund – expenditures follow revenues; Special Purpose & Trust Fund – expenditures follow revenues; Sponsored Research Fund – expenditures follow revenues; Ancillary Fund – expenditures follow revenues; Endowment Fund – revenues follow expenditures; Capital Fund – expenditures follow revenues.

6. Capital Assets

The uniform reporting practice in the annual return for capital expenditures is to follow the funds flow approach, rather than to capitalize and amortize. Funds received to acquire capital assets are reported as income in the period in which the funds are received or receivable. Funds used to acquire capital assets are reported as expenditures in the period in which the funds are paid or payable.

Capital expenditures are to be reported in the same fund as the corresponding income. Specifically, capital expenditures are only reported in the Capital fund when the corresponding income is reported in the Capital fund.

7. Vacation Pay, Pension Costs and Future Benefits

Vacation pay, pension costs and future benefits, including benefits arising as a result of early retirement, are to be reported on the cash basis. The cash basis refers to the method of recording transactions where expenditures are reported in the period in which cash is disbursed.

8. Sales and Cost Recoveries

The practices followed by institutions in reporting sales and cost recoveries in their financial records vary significantly and, for the most part, are dependent upon the particular management information systems and business practices of the respective institutions.

For the annual return, as a general practice, sales and cost recovery amounts are to be reported at "gross", rather than "net". "Gross" means that the sales and the corresponding cost are reported as separate items. "Net" means that the sales and corresponding cost are combined, and the difference is reported as a separate item. Reporting amounts at "gross" provides users of the financial data with better information than reporting at "net".

Sales and cost recovery transactions can generally be classified as external sales, internal sales, external cost recoveries and internal cost recoveries.

• (a) External sales and external cost recoveries – "third party" transactions, where the price to the external party is determined based on either the commercial value of the services or product, or the cost of the services or product. The price may or may not include a profit component.
• (b) Internal sales – transactions between funds or functions, where the price to the internal party is determined based on either the commercial value of the services or product, or the cost of the services or product. The price includes a profit component. Internal sales exclude transactions based specifically on indirect or overhead costs. For the purposes of the annual return, internal sales will be categorized by those sales originating from ancillary services (see Section III.C.1 – Ancillary) and those sales originating from other funds or functions.
• (c) Internal cost recoveries – the recovery, allocation, charge-out or transfer of costs between funds or functions. Internal cost recoveries refers specifically to indirect or overhead costs.

External sales, external cost recoveries and internal sales originating from ancillary services are to be reported as sale of services and products. (See Section III.C.2 – line 25.)

As an exception to reporting amounts at "gross", and also to avoid double counting of income and expenditures, the preferred method of reporting internal sales, other than those originating from ancillary services, is to report the amounts at "net". To report at "net", income in the fund or function selling the services or product is netted against the expenditures in that same fund or function. The fund or function purchasing the services or product reports the expenditure. Alternatively, where "netting" is not possible or feasible within a fund or function, the internal sales can be reported separately under an expenditure line item (a recovery) in both the fund or function selling the services or product and the fund or function purchasing the services or product. (See Section III.C.3 – line 20.)

Internal cost recoveries are also to be reported in such a manner as to avoid double counting of expenditures. The preferred method is direct allocation – that is, by reducing the expenditure types in the fund or function from which the costs are allocated, offset with a corresponding increase in the same expenditure types in the fund or function to which the costs are allocated. This approach provides users with better functional comparisons of individual expenditure line items. Alternatively, where direct allocation is not possible or feasible, the internal cost recoveries can be reported separately under an expenditure line item (a recovery) in the fund or function from and to which the costs are allocated. (See Section III.C.3 – line 20.)

9. Interfund Transfers

Situations arise where in the normal course of operations, an institution reports income in one fund, but reports the corresponding expenditure in another fund. In such situations, the institution records a transfer from the fund in which the income was received, to the fund in which it is expended. This transfer is referred to as an interfund transfer. The transfer of an operating surplus from the Ancillary fund to the General operating fund is an example of an interfund transfer.

These Guidelines encourage institutions to report income and the corresponding expenditure in the same fund. For example, capital expenditures are to be reported in the same fund as the corresponding income and investment income earned on trust and endowment funds is to be reported in the same fund as the corresponding expenditures. This approach provides users with better financial data to calculate statistics such as the relationship between income and expenditures, by fund.

10. Gifts-In-Kind

Gifts-in-kind that are recorded in an institution's audited financial statements will be reported in the annual return as both an income and expenditure item.

11. Borrowing and Principal Repayment

Interest payments will be reported as expenditures in the appropriate fund. The borrowing and repayment of principal will not be reported as income or expenditure.

12. Full Costing of Ancillary Services

Ancillary services (see Section III.C.1 – Ancillary) should include all direct expenditures and cost allocations related to ancillary operations. Cost allocations, for example, should include a reasonable allocation for utility (unless the utility is an ancillary service) and plant maintenance, and for the institution's management and administrative support. Cost allocations to ancillary services are internal cost recoveries (see Section II.B.8) in the fund or function from which the costs are allocated.

13. Use of Estimates

To complete the annual return in accordance with these uniform reporting practices, costs may have to be allocated among funds and functions. Where cost allocations are required, the allocations can be based on best estimates.

III. Detailed Instructions for Institutions Reporting Financial Data

This section provides detailed instructions for institutions reporting financial data. This is the "how-to" section for preparers to refer to when completing the annual return, and will be of interest to users who seek additional information on specific terms or particular line items used in the annual return. Preparers of the financial data should review the previous sections of the Guidelines before proceeding.

A. Comparable Financial Data

Normally, the criteria for placement of a particular income or expenditure item within a fund or function in the annual return is the same as that used by an institution in its financial statements or internal management reports. However, where the Guidelines specifically designate the placement of an item, the item must be shown under the designated heading regardless of the institution's practice. Consequently, the classification of activities or items of income and expenditure in the annual return may differ from the classification used by an institution in its financial statements or internal management reports. For example, health services and athletics are to be reported in the Student services function in the annual return (see Section III.C.4 – Student services) although they may be reported as ancillary services in the institution's financial statements or internal management reports.

The financial data reported by each institution will be more useful when the data have been prepared consistently over time. In order to satisfy user information needs, preparers must comply with these Guidelines.

B. Annual Return

The detailed financial data requested in the annual return are reported in Tables 1, 2 and 4. (Note that Table 3 pertains to a more detailed survey conducted with other institutions and is not part of this package). The contents of the annual return are as follows:

• General Information and Instructions
• Table 1. Income by Fund
• Table 2. Expenditures by Fund
• Table 4. General Operating Expenditures by Function

In certain situations, an institution may determine that while it has complied with the Guidelines, it has provided financial data that may exceptional. In such situations, the institution can provide either accompanying notes of explanation, or observations and comments in the space provided at the bottom of each Table. This additional information would be useful for Statistics Canada in its review of the annual return for reasonableness. Examples could be any "material" extraordinary or non-recurring income or expenditure item included in a fund and/or functional area.

An institution may also use the space provided at the bottom of each Table for any observations and comments that the institution wishes to make regarding items not covered in the annual return.

Preparers should recognize that users of the annual return are prepared to accept reasonable allocations where exact numbers are not available (see Section II.B.13).

C. Definitions, Explanations and Examples

The funds are discussed first, to assist the preparer to segregate the various income and expenditure items for reporting purposes. Following the discussion of funds, the financial data to be reported on the applicable lines in each Table are discussed. The financial data should be reported by fund in Tables 1 and 2 of the annual return.

1. Funds

Fund accounting (see Section II.B.2) classifies resources for accounting and reporting purposes in accordance with activities or objectives as specified by donors, in accordance with regulations, restrictions, or limitations imposed by sources outside the institution (external restrictions) or in accordance with directions issued by the governing body (internal restrictions). Funds have been identified as either unrestricted or restricted. Restricted funds, other than Endowment, account for resources that may be used for current purposes, but with some limitations imposed by external or internal sources.

For accounting and reporting purposes, institutions combine the funds with similar characteristics into distinct fund groups. For the annual return, the fund groups are General operating, Special purpose and trust, Sponsored research, Ancillary, Capital, and Endowment.

Preparers should note the following:

• restricted funds include both external and internal restrictions,
• income and expenditure within Sponsored research is separately reported for entities consolidated and entities not consolidated (see Section II.B.1),
• interfund transfers should be minimized by reporting income and the corresponding expenditure in the same fund (see Section II.B.9).

General operating is an unrestricted fund that accounts for the institution's primary operating activities of instruction and research, other than sponsored research. The general operating fund includes the costs of privately funded and non-credit programs.

Fund income includes provincial government grants (including research other than sponsored research), student tuition and other fees (for credit and non-credit courses), and income from private and other unrestricted sources. Fund income also includes investment income, if the corresponding expenditures are reported in the General operating fund.

Fund expenditures are for the general operating costs of the institution including instruction and research (other than sponsored research), academic support services, library, student services, administrative services, plant maintenance, external relations and other operating expenditures of the institution. Fund expenditures also include the purchase of capital assets, if the corresponding income is reported in the General operating fund.

Special purpose and trust is a restricted fund. The funds, including donations, may be restricted by external sources, or internally restricted by the institution's governing body, for purposes other than sponsored research (Sponsored research fund), or capital (Capital fund). Income is to be reported following the funds flow approach (see Section II.B.4).

Fund income includes designated gifts, benefactions and grants. Fund income also includes investment income, if the corresponding expenditures are reported in the Special purpose and trust fund.

Fund expenditures include the purchase of capital assets, if the corresponding income is reported in the Special purpose and trust fund.

Sponsored research is a restricted fund that accounts for income and expenditures for all sponsored research. Amounts are separately reported for entities consolidated and entities not consolidated (see Section II.B.1). Income is to be reported following the funds flow approach (see Section II.B.4).

Fund income includes funds to support research paid either in the form of a grant or by means of a contract from a source external to the institution. Income sources include government, private industry and donors. The federal grant allocation for Indirect Costs of Research would be included here. The corresponding expenditures should be reported as an internal cost recovery between the Operating and Sponsored Research Funds, similar to the treatment of overheads. Fund income also includes investment income, if the corresponding expenditures are reported in the Sponsored research fund.

Fund expenditures include activity funded from Sponsored research income and exclude activity funded from the General operating fund. Fund expenditures include the purchase of capital assets, if the corresponding income is reported in the Sponsored research fund. Fund expenditures also include internal cost recoveries (see Section II.B.8).

Funds from Canada Foundation for Innovation, along with applicable matching funds, are to be reported as Sponsored research income. The corresponding expenditures, including the purchase of capital assets, are to be reported as Sponsored research expenditures.

Funding related to Canada Research Chairs are to be reported as Sponsored Research income. The corresponding expenditures, including the purchase of capital assets, are to be reported as Sponsored Research expenditures.

Within the Sponsored research fund, the first column in the applicable Tables is used to report income and expenditures for entities consolidated, and the second column, for entities not consolidated. Both columns combined represent the total Sponsored research reported by the institution. For the first column, "Entities Consolidated", reported amounts are based on the financial data of entities included in the consolidated financial statements of the institution.

For the second column, "Entities not Consolidated", institutions are permitted to separately report sponsored research, including hospital based medical research funding, that is granted to academic staff of the reporting institution, but conducted in entities that are not consolidated. Reporting of the sponsored research is permitted if all the following four conditions are met:

• the entity not consolidated must be an affiliated institution as established by an affiliation agreement with the reporting institution.
• academic staff from the reporting institution lead the sponsored research project and conduct the research at the non-consolidated affiliated institution,
• the financial data (income and expenditure) for the sponsored research are reported in the financial statements of the non-consolidated affiliated institution, and
• the sponsored research would be reported in the Sponsored research fund had the research been conducted at the reporting institution, rather than at the affiliated institution.

In addition, for "Entities not Consolidated", the amounts reported as income (Table 1, line 27, column 4) must equal the amounts reported as expenditures (Table 2, line 24, column 4).

To provide financial data that are comparable, the income and expenditure items for sponsored research for entities not consolidated are to be reported in accordance with these Guidelines. Although this financial data have not been subject to audit by the reporting institution, there is an expectation that the data have adequately documented support.

Ancillary is an unrestricted fund that separately accounts for all "sales-producing" operations or "self-supporting" activities that are supplementary to the institution's primary operating activities of instruction and research. Ancillary services exist to provide goods and services to students, faculty, staff, and others. Ancillary services charge a fee directly related to, although not necessarily equal to, the cost of the goods or services.

Ancillary services typically include bookstores, food services (dining hall, cafeterias, vending machines), residences and housing, parking, university press, publishing, laundry services, property rentals, university facility rentals, theaters, and conference centers.

All sales, external and internal, from ancillary services are reported as income (see Section II.B.8).

To report expenditures, full costing of ancillary services is required (see Section II.B.12). The preferred method of reporting internal cost recoveries or cost allocations is direct allocation, but where direct allocation is not possible or feasible, the internal cost recoveries can be reported under a separate expenditure line item (see Section II.B.8). Any capital items purchased directly from Ancillary income are to be reported in the Ancillary fund on the appropriate expenditure line.

Capital is a restricted fund that accounts for resources provided to the institution for capital purposes and not reported in any other fund. Income and expenditures are to be reported following the funds flow approach for capital assets (see Section II.B.6).

Fund income includes grants and related investment income, donations, and other resources made available to the institution by external funding sources, such as government and donors, specifically for capital purposes.

Fund expenditures include building programs, acquisitions of major equipment and furniture, major renovations and alterations, space rental and buildings, land and land improvements.

Because capital expenditures are to be reported in the same fund as the corresponding income, not all capital expenditures will be reported in the Capital fund. For example, funds from Canada Foundation for Innovation, along with applicable matching funds, are to be reported as Sponsored research income. The corresponding expenditures, including the purchase of capital assets, are to be reported as Sponsored research expenditures.

Endowment is a restricted fund that accounts for the capitalization of externally or internally restricted amounts, primarily donations, which cannot be spent.

Investment income generated by endowments may be used for various purposes, with these purposes often restricted by donors. Investment income should be reported in the same fund as the corresponding expenditures. Expenditures, excluding those incurred to earn investment income, are to be reported in an appropriate fund other than the Endowment fund. Expenditures incurred to earn investment income are to be reported "net" of the investment income.

Investment income that is used to preserve the capital value of the Endowment fund is reported as income in the Endowment fund.

2. Income by Fund (Table 1)

The funds described in Section III.C.1 are reported in columns 1, 2, 5, 6, 7 and 8 in Table 1, with the total of the funds reported in column 9. Column 5 reports the sub-total for the Sponsored research fund. Within Sponsored research, column 3 reports "Entities Consolidated" and column 4 reports "Entities not Consolidated".

The types of income to be reported in Table 1 are identified on the left-hand side of the Table. If there is uncertainty as to which line to use to report a type of income, report the income on the line best describing the activity. For example, government funds to pay tuition fees for participants in a non-credit program should be reported on line 13 (Non-credit tuition), rather than under government grants and contracts. Furthermore, where the designation of a particular type of income in this Table differs from that used by an institution in its financial statements or its internal management reports, the type of income must be shown per the Guideline instructions regardless of the institution's practice.

As a general reporting practice, institutions follow the accrual, rather than the cash basis of accounting (see Section II.B.3). For reporting income, exceptions to the accrual concept in the annual return include the funds flow approach for reporting funds received to acquire capital assets (see Section II.B.6) and for reporting income in the Special purpose and trust, and Sponsored research funds (see Section II.B.4).

Income includes gifts-in-kind that are recorded in an institution's audited financial statements (see Section II.B.10).

The six major categories of income are:

• government departments and agencies – grants and contracts,
• tuition and other fees,
• donations, including bequests
• non-government grants and contracts,
• investment, and
• other (including sale of services and products, and miscellaneous).

(i) Government departments and agencies - grants and contracts

Lines 1 to 11 include grants from, and contracts with, federal government departments and agencies, provincial government departments and agencies, and municipal governments. Grants and contracts from other provincial governments and from foreign governments are also reported in this category.

Government grants provide financial support to institutions and the grants may or may not be restricted.

Government contracts provide financial support to institutions under certain stipulations and conditions, including the provision of a deliverable product, such as a piece of equipment, a service, or a report. A contract normally includes provisions for institutions to recover certain indirect or overhead costs, with the contract specifying or documenting the basis for the calculation of the recoverable costs.

Federal

Lines 1 to 7 include all research grants, research contracts, grants and contributions from the Government of Canada and its departments and agencies, including the federal portion of capital and other grants that flow through a provincial government. Income received from the six major federal government agencies is reported on lines 1 to 6, as applicable.

The line items under "federal" are as follows:

Line 1 Social Sciences and Humanities Research Council (SSHRC)

Line 2 Health Canada

• Income from Health Canada not reported under Line 4 – Canadian Institutes of Health Research (CIHR) – should be reported in this line.

Line 3 Natural Sciences and Engineering Research Council (NSERC)

Line 4 Canadian Institutes of Health Research

Line 5 Canada Foundation for Innovation (CFI)

• CFI income is reported under the Sponsored research fund.

Line 6 Canada Research Chairs

• Funding for Canada Research Chairs is reported under the Sponsored Research Fund.

Line 7 Other federal

• Income from all other federal government departments and agencies is reported on this line. This would include grant allocations for the Indirect Costs of Research.

Other

Lines 8 to 11 include all grants from, and contracts with, the province and its departments and agencies, municipal governments, other provinces, and foreign governments.

The line items under "other" are as follows:

Line 8 Provincial

• Income from provincial government departments and agencies, including provincial CFI matching grants, is reported on this line.
• Provincial CFI matching income from the Ministry responsible for the institution is reported under the Sponsored research fund.

Line 9 Municipal

• Examples of income to be reported on this line include grants from urban transit, communication and parking authorities.

Line 10 Other provinces

• This line includes grants from, and contracts with, provinces other than the province with jurisdiction.

Line 11 Foreign

• Examples of income to be reported on this line include grants from the National Endowment for Humanities, National Institutes of Health, and the National Science Foundation.

(ii) Tuition and other fees

The types of revenue (Lines 12 to 14) include credit course tuition, non-credit tuition and other fees.

Line 12 Credit course tuition

• Credit courses are courses of instruction or programmed learning that are offered within a degree program; or, that may be granted status equivalent to a credit course within a degree program.
• Credit courses are offered during the fall and winter sessions of a semester type operation, all three terms of a trimester operation and the year round operation of graduate schools and include intersession, spring session and summer session credit courses and credit extension.
• Credit course tuition includes tuition and other mandatory fees related to the instruction of the courses, such as computer and laboratory fees.
• Credit course tuition also includes fees for “make up” or special courses that are related to the credit offerings of the institution, and fees for auditing in credit courses.
• Credit course tuition should be reported on this line whether the cost of the credit course is subsidized or fully recoverable.

Line 13 Non-credit tuition

• Non-credit programs are courses of instruction or programmed learning that are not credit courses (see line 12).
• Non-credit tuition includes fees for lectures, courses and similar activities that are not recognized by the institution for the purpose of granting credit. Non-credit programs are usually offered through continuing education units.
• Government funds to pay tuition for participants in a non-credit program should be reported as non-credit tuition, rather than as government grants and contracts.

Line 14 Other fees

• Other fees include all compulsory and non-compulsory fees charged to students such as health services, athletics, library, applications, late registrations, lockers and transcripts. These fees would be reported under the General operating fund.
• Other fees exclude fees collected by the institution acting in an agency capacity. An example would be student fees collected on behalf of student controlled and administered activities such as student councils or federations.

(iii) Donations, including bequests

Donations are a voluntary transfer of cash or negotiable instruments made without expectation of return or benefits of any kind to the donor. Bequests flow from wills. Donations, including bequests, are considered to be gifts for tax purposes. Amounts received that are eligible to be receipted as charitable donations for federal income tax purposes are to be reported on lines 15 to 17, as applicable.

Lines 15 to 17 categorize "donations, including bequests" by individuals, business enterprises, foundations and not-for-profit organizations.

In addition, donations designated for specific purposes and donations that cannot be spent are reported in the Endowment fund (see Section III.C.1 – Endowment). Donations also include gifts-in-kind that are recorded in an institution's audited financial statements (see Section II.B.10).

With the exception of circumstances outlined in the preceding paragraph, donations are to be reported in the same fund as the corresponding expenditures (see Section II.B.9).

Line 15 Individuals

• This line includes families.

Line 16 Business enterprises

• Business enterprises include unincorporated businesses as well as privately or publicly incorporated companies that are operated for profit and derive revenue mainly from the sale of goods and services. The common forms of unincorporated businesses are sole proprietorships and partnerships, and examples include farmers and professional practitioners.

Line 17 Not-for-profit organizations

This includes foundations and other not-for-profit organizations.

• A foundation is an entity that can either be a corporation or a trust constituted and operated exclusively for charitable purposes. Funds contributed to an institution by a non-consolidated charitable foundation would be reported here.
• Not-for-profit organizations include associations or societies, and examples include religious organizations, labour unions, professional organizations and fraternal societies.

(iv) Non-government grants and contracts

Non-government grants and contracts provide financial support under certain specific stipulations and conditions, including the provision of a deliverable product, such as a piece of equipment, a service, or a report. The amounts received by an institution are not considered as charitable donations for tax purposes and therefore are ineligible to be receipted as charitable donations for federal income tax purposes.

Lines 18 to 20 categorize "non-government grants and contracts" by individuals, business enterprises, foundations and not-for-profit organizations.

Line 18 Individuals

• This line includes families.

Line 19 Business enterprises

Business enterprises include unincorporated businesses as well as privately or publicly incorporated companies that are operated for profit and derive revenue mainly from the sale of goods and services. The common forms of unincorporated businesses are sole proprietorships and partnerships, and examples include farmers and professional practitioners.

Line 20 Not-for-profit organizations

This includes foundations and other not-for-profit organizations.

• A foundation is an entity that can either be a corporation or a trust constituted and operated exclusively for charitable purposes.
• Not-for-profit organizations include associations or societies, and examples include religious organizations, labour unions, professional organizations and fraternal societies.

(v) Investment Income

Investment income includes income from dividends, bonds, mortgages, short-term notes and bank interest. Bond interest would include an accrual for stripped bonds (see Section II.B.3). Investment income also includes realized and unrealized gains and losses on investment transactions, if the gains and losses are reported in the audited financial statements, regardless of how investments have been designated by the institution (held for trading or not).

Investment income excludes income from a non-consolidated charitable foundation. Income from a non-consolidated charitable foundation should be reported on line 17 (Not-for-profit organizations).

Included in this section are endowment and other investment income (Line 21 and 22).

Line 21 Endowment

• Investment income earned on endowment funds is reported on this line under the same fund as the corresponding expenditures.
• Investment income earned on endowment funds and used to preserve the capital value of the Endowment fund is reported on this line under the Endowment fund.
• Expenditures incurred to earn investment income, such as the cost of an investment manager(s) to manage the endowment funds, are to be reported "net" of the investment income.

Line 22 Other investment

• Investment income earned on all funds other than endowment funds is reported on this line under the same fund as the corresponding expenditures.
• Other investment income also includes charges for deferred or installment payments and for unpaid student tuition and other fees.
• Any significant non-recurring items should be explained by way of accompanying notes or in the observations and comments section at the bottom of Table 1.

(vi) Other

Other income (Lines 23 and 24) includes sale of services and products, and miscellaneous.

Line 23 Sale of services and products

• This line includes external sales and external cost recoveries (see Section II.B.8).
• External sales and external cost recoveries include sales to outside organizations, such as those for laboratory tests, space rental, utilities and incidental income (including athletic gate receipts, parking fees, conferences and various medical clinics).
• This line also includes rental income from residences and parking.
• Payments received from non-consolidated federated or affiliated entities for the provision of instructional, administrative or other services are reported as sale of services and products.
• For ancillary services (see Section III.C.1 – Ancillary), this line includes both external and internal sales (see Section II.B.8).
• Internal sales, other than those originating from ancillary services, and internal cost recoveries are not reported as income.

Line 24 Miscellaneous

• Miscellaneous income includes commissions, royalties and fees from the use of institution owned rights or properties, or fees for services rendered. Miscellaneous also includes library and other similar fines, rentals, net gain or loss on sale of fixed assets and any type of income not identified in the other categories of income.
• Payments received from non-consolidated federated or affiliated entities for the provision of instructional, administrative or other services are reported as sale of services and products (line 23).

3. Expenditures by Fund (Table 2)

The funds described in Section III.C.1 are reported in columns 1, 2, 5, 6, 7 and 8 in Table 2, with the total of the funds reported in column 9. Column 5 reports the sub-total for the Sponsored research fund. Within Sponsored research, column 3 reports "Entities Consolidated" and column 4 reports "Entities not Consolidated".

The types of expenditures to be reported in Table 2 are identified on the left-hand side of the Table. Where the designation of a particular expenditure in this Table differs from that used by an institution in its financial statements or its internal management reports, the expenditure must be shown under the designated Table heading regardless of the institution's practice.

As a general reporting practice, institutions follow the accrual, rather than the cash basis of accounting (see Section II.B.3). For reporting expenditures, exceptions to the accrual concept in the annual return include the funds flow approach for reporting funds used to acquire capital assets (see Section II.B.6) and the cash basis for reporting vacation pay, pension costs and future benefits (see Section II.B.7).

Expenditures include gifts-in-kind that are recorded in an institution's audited financial statements (see Section II.B.10).

The repayment of principal will not be reported as an expenditure (see Section II.B.11).

Lines 1 to 20 report expenditures that are generally recurring, with a sub-total for lines 1 to 20 reported on line 21. Lines 22 and 23 report significant periodic expenditures such as those for buildings, land and land improvements (line 22) and unusual or non-recurring expenditures, referred to as lump sum payments (line 23), such as those for special assisted early retirement programs. The total of all expenditures is reported on line 24.

The types of expenditures to be reported in Table 2, by line, are as follows:

Lines 1 – 3: Salaries and wages

Salaries and wages are categorized as academic salaries (lines 1 and 2) and other salaries and wages (line 3). Academic salaries are reported by academic ranks (line 1) and by other instruction and research (line 2).

The following types of payments are to be reported as salary and wage expenditures:

• compensation payments, such as payments for salary continuance during sick leave or maternity leave,
• severance payments as a result of terminations in the normal course of business, and
• vacation pay (see Section II.B.7).

Certain lump sum payments for current and future fiscal periods to employees who have terminated employment with the institution are reported on an accrual basis as lump sum payments (line 23).

With the exception of vacation pay, the amounts to be reported as salaries and wages in the annual return are to be calculated following the same practices as those used by the institution for its audited financial statements.

Lines 1 – 2: Academic salaries

Academic salaries are reported by academic ranks and by other instruction and research.

Line 1 Academic ranks

• This line includes payments to both full and part time staff members who hold an academic rank at the reporting institution and are engaged in instruction and research activities.
• The academic ranks include deans, professors, associate professors, assistant professors and lecturers.
• Academic salaries also include payments to staff members in the academic ranks for various types of leave such as administrative, academic or sabbatical.

Line 2 Other instruction and research

• This line includes payments to both full and part time staff and non-staff members without academic rank at the reporting institution, but who are engaged in instruction and research activities.
• The staff and non-staff members include instructors, tutors, markers, laboratory demonstrators, teaching assistants, research assistants, invigilators, clinical assistants, post-doctoral fellows, and others.
• Other instruction and research salaries also include payments made to graduate and undergraduate students undertaking instruction and research activities.

Line 3 Other salaries and wages

• This line includes salaries and wages not reported on lines 1 and 2. Specifically, other salaries and wages includes payments to all full and part time non-instructional (support) staff including among others, technicians, teaching and research laboratory technicians, clerical and secretarial, professional and managerial, janitorial, trades and maintenance.
• Other salaries and wages also includes payments to individuals who may hold an academic rank, or equivalent thereto, but are engaged in activities other than instruction and research. Examples of such individuals include the president, vice-presidents, certain professional librarians and computing center personnel.

Line 4 Benefits

• Pension costs and future benefits, including benefits arising as a result of early retirement, are to be reported on the cash basis (see Section II.B.7). Otherwise, the amounts to be reported as benefits in the annual return are to be calculated following the same practices as those used by the institution for its audited financial statements.
• Benefits include the cost of an institution's contributions (with respect to salaries) for pensions (including payments for actuarial deficiencies and past service liability), group life insurance, salary continuance insurance, dental plans, workers' compensation, health taxes, tuition remission, employment insurance and other costs of an employee benefit programs.
• Benefits also include the cost of benefits paid during early retirement periods, as well as the cost of post retirement benefits.
• Whenever an institution pays a premium or sets aside a negotiated amount for an employee, these amounts should be included as Benefits.
• Memberships or other perquisites of employment are not reported as Benefits.

Line 5 Travel

• Travel includes expenditures on recruitment, travel, moving and relocation of staff, field trips and all other types of travel necessary for the operation of the institution.

Line 6 Library acquisitions

• Library acquisitions include all purchases of, and access to (including electronic access), books, periodicals and other reference materials for the institution's main branch and faculty or departmental libraries.
• Cost of binding may also be included if normally considered part of the acquisition cost.

Line 7 Printing and duplicating

• This line includes expenditures that would normally be consumed in the fiscal year such as printing, duplicating, photocopying, reproductions, illustrations, publishing and the related supplies.

Line 8 Materials and supplies

• Materials and supplies include expenditures that would normally be consumed in the fiscal year such as sports supplies, stationery, computer and other office supplies.
• Also included are material and supplies for teaching and laboratories. Laboratory supplies include chemicals, instruments, animals, feed and seed.
• Small dollar value equipment and computer software items should be reported under furniture and equipment purchase (line 18).

Line 9 Communications

• Communications includes telephone, data communications, mailing and courier, but excludes expenditures reported as equipment rental and maintenance (line 19).
• Telephone includes watts lines, line services, long distance and other charges.

Line 10 Other operational expenditures

• This line includes space rental, property taxes, institutional membership fees, insurance, meals, advertising and promotion, and doubtful accounts.
• Space rental includes the cost of renting space and land on a long-term basis.
• Property taxes include all taxes paid directly to municipalities by the institution, whether assessed on property values or based on student population.
• Institutional membership fees include fees paid by the institution to outside organizations in lieu of membership.
• This line includes all other expenditures that are not reported elsewhere.

Line 11 Utilities

• Utilities include expenditures for items such as electricity, water, natural gas, fuel and sewer.
• Utilities also include the generating costs for electricity, steam, water, and natural gas.

Line 12 Renovations and alterations

• This line includes expenditures for renovations and alterations to the existing space of the institution, whether the expenditures are internally performed or externally contracted.

Line 13 Scholarships, bursaries and prizes

• This line includes payments to students (except those for which the student is required to perform service for the payment) such as those for fee remission, prizes and awards.
• Payments for which the student is required to perform service for the payment are reported as other instruction and research (line 2), and include payments to graduate and undergraduate students who are instructors, tutors, markers, laboratory demonstrators, teaching assistants, research assistants, invigilators, clinical assistants, post-doctoral fellows, and others.

Line 14 Externally contracted services

• This line includes all expenditures for services contracted to external agencies except for renovations and alterations (line 12), professional fees (line 15), equipment rental and maintenance (line 19), and buildings, land and land improvements (line 22).
• Examples of expenditures to be included are cleaning contracts, security services, snow removal and similar time and material contracts, and food services.
• Where food services are contracted, the contract amount in total should be shown on this line and not as cost of goods sold (line 16) or any other expenditure types, even though the contractor may provide a breakdown of costs.

Line 15 Professional fees

• Professional fees include all fees paid to legal counselors (including retainers for the negotiations of collective agreements), auditors, and computer, human resource and other consultants.
• This line excludes consulting fees for renovations and alterations (line 12), equipment rental and maintenance (line 19), and buildings, land and land improvements (line 22).

Line 16 Cost of goods sold

• Cost of goods sold is to be used where an inventory method of accounting is normally employed, (e.g. bookstore, food services) and should include the laid down cost of goods purchased for resale only. The remaining costs of operating the service, such as salaries and supplies, are to be shown in their respective expenditure types.
• Where a service is externally contracted, particularly for ancillary services, the total costs of the contract should be included in externally contracted services (line 14). For example, contracted food services are to be reported on line 14, under the Ancillary fund.
• The cost of goods sold is to be reported under the same fund as the income from the sale of the product (see Section III.C.2 – line 25).

Line 17 Interest

• This line includes all interest expenditures to service debts of the institution. Examples include bank interest, mortgage or debenture interest and related charges, and the interest component of installment or lease payments
• Repayments of principal such as principal reductions on loans, mortgages, debentures or repayable grants are not reported as expenditures (see Section II.B.11).

Line 18 Furniture and equipment purchase

• This line includes laboratory equipment (other than consumables), computing equipment and computer software packages, administrative equipment and furnishings (including carpets and drapery), copying and duplicating equipment, and maintenance equipment. Installation expenditures for the above items are to be included as part of their cost.
• This line also includes installment payments and payments under lease purchase contracts, where the lease is a capital lease for accounting purposes. The interest component of any such payments should be reported on line 17.
• This line includes small dollar equipment and computer software items that would normally be expensed in the accounting records of the institution.
• Furniture and equipment purchases are reported under the same fund as the corresponding income (see Section II.B.6). For example, purchases made from CFI grants are reported under Sponsored research (see Section III.C.1 – Sponsored research). Purchases made or to be made from current or future ancillary services income are to be reported under Ancillary (see Section III.C.1 – Ancillary).
• Amortization is not reported as an expenditure.
• Provisions for the replacement of furniture and equipment are considered to be transfers to appropriation or reserve accounts; consequently, such provisions are not to be reported as expenditures.

Line 19 Equipment rental and maintenance

• This line includes all rental and maintenance expenditures for furniture and equipment including laboratory equipment (other than consumables), administrative equipment and furnishings (including carpets and drapery), copying and duplicating equipment, computing equipment, maintenance equipment and telephone equipment.
• This line also includes lease purchase contracts, where the lease is an operating lease for accounting purposes.
• This line also includes expenditures for equipment repairs and maintenance contracted to external agencies.

Line 20 Internal sales and cost recoveries

• The preferred method of reporting internal sales, other than those originating from ancillary services, is to report the amounts at "net" (see Section II.B.8). The preferred method of reporting internal cost recoveries is direct allocation (see Section II.B.8). Where the preferred method is not possible or feasible, this expenditure type can be used, but when it is used, the internal sales and cost recoveries for all funds, when added together, must equal zero.
• This line includes internal sales, other than those originating from ancillary services, and internal cost recoveries (see Section II.B.8).
• Internal sales originating from ancillary services are to be reported as sale of services and product (see Section III.C.2 – line 25).
• Common examples of internal cost recoveries include the overhead recovery of administrative costs and the indirect costs of research between the General operating fund and the Ancillary and Sponsored research funds, and the overhead recovery of utility (unless the utility is an ancillary service) and maintenance costs between the General operating fund and the Ancillary fund.
• To provide better functional comparisons of types of expenditures, institutions are asked to minimize the use of this line to the extent possible.

Line 21 Sub-total

• This line is the sub-total of all expenditures reported on lines 1 to 20.

Line 22 Buildings, land and land improvements

• Buildings include all expenditures that are normally considered part of the construction cost as well as costs incurred during the construction period such as utilities. Land and land improvements include acquisition costs and site preparation such as landscaping, sewers, tunnels and roads. All fees and planning costs related to buildings, land and land improvements are also included.
• Furniture and equipment purchases are reported on line 18.
• The expenditures for buildings, land and land improvements are reported under the same fund as the corresponding income (see Section II.B.6). For example, purchases made from CFI grants are reported under Sponsored research (see Section III.C.1 – Sponsored research). Purchases made or to be made from current or future ancillary services income are to be reported under Ancillary (see Section III.C.1 – Ancillary).
• Amortization is not reported as an expenditure.
• Provisions for the replacement of buildings are considered to be transfers to appropriation or reserve accounts; consequently, such provisions are not to be reported as expenditures).

Line 23 Lump sum payments

• This line includes certain lump sum payments for current and future fiscal periods to employees who have terminated employment with the institution. The characteristics of the payments are such that similar transactions or events are not expected to occur frequently over several years, or do not typify normal business activities of the institution.
• Lump sum payments are reported on an accrual basis.
• Examples of lump sum payments include payments under downsizing or special assisted early retirement programs.
• Severance payments as a result of terminations in the normal course of business are reported as salary and wage expenditures (lines 1 to 3).

4. General Operating Expenditures by Function (Table 4)

Expenditures by Fund (see Section III.C.3) and this section of the Guidelines are very similar in that types of expenditures are identified on the left-hand side of both Tables. However, unlike Table 2 (which is organized by fund), Table 4 is organized by operational or functional areas, within the General operating fund, that represent the major areas of institutional activity. The functions are Instruction and non-sponsored research, Non-credit instruction, Library, Computing and communications, Administration and academic support, Student services, Physical plant and External relations. These functions are reported in columns 1 to 8, with the total of the functions reported in Column 9. The amounts in Column 9 should be identical to the amounts in Table 2, Column 1 (General operating).

This section provides details to assist preparers to segregate, by function, the various activities and types of expenditures under the General operating fund. Unless otherwise indicated, the definitions, explanations and examples presented in Section III.C.3 for types of expenditures also apply to this section. In addition, as noted previously, where the designation of a particular expenditure in this Table differs from that used by an institution in its financial statements or its internal management reports, the expenditure must be shown under the designated heading regardless of the institution's practice. For example, health services and intramural and intercollegiate athletics are to be reported under the Student services function although they may be reported as ancillary services in the institution's financial statements or its internal management reports.

In reporting General operating fund expenditures by function, preparers should be familiar with the uniform reporting practices (see Section II.B). In particular, preparers should be familiar with the practices on internal and external cost recoveries (see Section II.B.8) and use of estimates (see Section II.B.13).

The functions in the General operating fund are as follows:

(i) Instruction and non-sponsored research

The Instruction and non-sponsored research function in the General operating fund includes all direct costs of faculties, academic departments (including salaries of academic deans and their offices), graduate school, summer school, credit extension, and other academic functions and expenditures attributable to this function.

(ii) Non-credit instruction

The Non-credit instruction function in the General operating fund includes lectures, courses and similar activities that are not recognized by the institution for the purpose of granting credit. Non-credit programs are usually offered through continuing education units. Normally where there is non-credit tuition income reported on line 13 under the General operating fund in Table 1, the corresponding expenditures (not necessarily equal to the income) will be reported under this function.

(iii) Library

The Library function in the General operating fund includes the institution's Archives and other activities related to the institution's main branch and faculty or departmental libraries. The expenditures include the salary and wage costs of providing the library services as well as the cost of books and periodicals.

(iv) Computing and communications

The Computing and communications function in the General operating fund includes only the activities of centralized computing and communication facilities.

A centralized computing facility refers to computer related activities and resources that have been organized under the management of a central administration. The computing facility is usually seen as an institutional resource that is available on an institution-wide basis and is the most effective way of providing certain services supportive of the institution's research and administrative activities. Such a facility usually results from factors including economies of scale, a large number of users who require a wide variety of services, and a high degree of technical expertise required in computer operations.

This function does not include the activities of local or decentralized stand-alone computer installations that are under the management of, and were established for the main purpose of providing services to a single division or department. The expenditures for decentralized computing facilities are to be included under the related functions and funds, as appropriate.

A centralized communications facility includes the costs of telephone equipment rental, service, acquisition and switchboard, including related personnel and other costs. The expenditures for decentralized communications facilities are to be included in the related functions and funds, as appropriate.

If an institution employs a charge-out system for central computing time or communications equipment usage, expenditures should be combined and reported under this function.

Any sales to, or recoveries from, other functional areas or funds, or outside users, are considered to be either an internal or external cost recovery and are to be reported according to the uniform reporting practice for internal and external cost recoveries (see Section II.B.8).

(v) Administration and academic support

The Administration and academic support function in the general operating fund covers expenditures in the two broad areas of academic support and other support services. Other support services include administration. These areas are combined and reported in Table 4 under Administration and academic support.

The academic support area of the Administration and academic support function includes all activities provided by an institution in direct support of Instruction and non-sponsored research. This area includes the following types of activities:

• the positions of vice-president academic and research (or their equivalents) and their offices
• faculty and instructional support services
• research administration (including grants and contracts administration)
• registrar's and graduate students office (including calendars, admissions, student records and related reporting)
• convocation and ceremonies
• co-op program administration
• central animal services
• central shops for instruction and research (machine shop, glass blowing, electronics shop)
• distance education support
• instructional technology and audio visual services
• academic class scheduling

The administration area of the Administration and academic support function includes the following activities:

• administration, planning and information costs and activities associated with the positions of president and vice president (or their equivalents) and their offices, except for the positions of vice-president academic and research (or their equivalents) and their offices, which are included in the academic support area. Administrative costs for activities such as fundraising, development, alumni and external communications are included in the external relations area.
• finance, including investment management, internal audit and accounting
• human resources (personnel)
• institutional research
• board and senate secretariat
• printing and duplicating services.

Specific types of expenditures in the administration area include the following:

• professional fees including legal, audit, human resource and other consulting fees that are not specifically attributable to another function. Computer consulting fees are included if the computing facilities are decentralized
• general university memberships
• liability and E & O insurance (fire, boiler and pressure vessel, and property insurance are reported under the Physical plant function).

The appropriate reporting for computing, communications, purchasing, receiving and stores will depend upon whether the institution operates with centralized or decentralized facilities. If the institution has centralized facilities for computing and communications, the activities should be reported under the Computing and communications function. If the institution has centralized facilities for purchasing, receiving and stores, the activities should be included in the administration area of the Administration and academic support function. If any of computing, communications, purchasing, receiving or stores is decentralized, then these activities should be included under the related functions and funds, as appropriate.

(vi) Student services

The Student services function in the General operating fund includes the cost of services (other than direct teaching, research and administrative services) provided to students by the institution. Generally, these services will include:

• the dean of students and the dean's office
• counseling and chaplaincy services
• career guidance and placement services
• intramural and intercollegiate athletics (not physical education)
• student health services
• student accommodation services (not residences)
• student transportation services
• student financial aid administration
• bursaries, scholarships and prizes
• grants to student organizations, including the student union
• student programs, including music, drama and student center
• student day care center
• any other student services, social or cultural activities funded by the institution

These services may be provided from the General operating fund income in whole, or in part by a specific fee included in the student incidental fee structure. Where an institution acts in an agency capacity, however, and collects student fees on behalf of student controlled and administered activities such as student councils or federations, the fees collected by the institution are to be excluded from income of the institution. The amount turned over to the benefit of the student council or federation is to be excluded from expenditures of the institution.

(vii) Physical plant

The Physical plant function in the General operating fund includes expenditures related to the physical facilities of the institution. The expenditures include the physical plant office, space planning, maintenance of buildings and grounds, custodial services, utilities, vehicle operations, security and traffic, repairs and furnishings, renovations and alterations, mail delivery services, long-term space and property rental, and municipal taxes (including those for which compensatory grants are received from government).

Physical plant also includes fire, boiler and pressure vessel, and property insurance. All other insurance is reported in the administration area of the Administration and academic support function.

(viii) External Relations

The external relations area includes all activities provided by an institution in support of ongoing external relations. These activities include fundraising, development, alumni, public relations and public information or external communications. The related administrative costs from the office of the vice-president(s), or equivalent, responsible for one or more of these activities should be included in this area.