Status

This standard was approved as a recommended standard on May 26, 2020.

Table of contents

• Overview
• Background
• Composition, nature and purpose of the CRDC
  • Type of Activity (TOA) Classification
  • Field of Research (FOR) Classification
  • Socioeconomic objective (SEO) Classification
• Structure of the CRDC
• Use of the CRDC
• Guidelines for classifying with the CRDC
  • Classifying by type of activity (TOA)
  • Classifying by field of research (FOR)
  • Classifying by socioeconomic objective (SEO)
• Definition of R&D
• Scope of R&D
• R&D unit or object to be classified
• Relationship with other national statistical classifications
• Relationship with relevant international standard classifications
• Updates or revisions to the CRDC
• CRDC Products
• Further information

Overview

The Canadian Research and Development Classification (CRDC) was jointly developed by the federal research granting agencies, including the Canadian Foundation for Innovation (CFI), the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), the Social Sciences and Humanities Research Council (SSHRC), and Statistic Canada (STC).

The CRDC is the collective name for a set of three related classifications developed for use in the measurement and analysis of research and experimental development (R&D) undertaken in Canada. The three constituent classifications included in the CRDC are: Type of Activity (TOA), Fields of Research (FOR), and Socio-economic Objective (SEO).

Although research and development as an economic activity can be measured using classifications such as the North American Industry Classification (NAICS) and the North American Product Classification (NAPCS), the CRDC 2020 represents the first in its kind and introduces a new dedicated framework for measuring research and development activities in Canada. This first version is officially called CRDC 2020 Version 1.0.

The use of the three constituent classifications in the CRDC ensures that R&D statistics collected are useful to governments, educational institutions, international organisations, scientific, professional or business organisations and enterprises, community groups and private individuals in Canada.

The development of the CRDC by the agencies was undertaken in co-operation or consultation with major academic and research organisations, experts of specific fields and users of research information in Canada, in particular during seminars, direct exchanges and other consultative methods such as a public consultation. This comprehensive consultative process aimed to ensure that the CRDC is widely accepted and used as the national standard classification in Canada, not only in the compilation of R&D statistics, but also in the study of research and development in Canada in general.

In this introduction, we provide a summarized background to the development of the classification; an explanation of the conceptual basis of the CRDC, including the composition, nature, purpose and structure of the CRDC; the use of the CRDC and guidelines for classifying with the CRDC; the definition and scope of R&D; an outline of what constitutes a unit of R&D for data and reporting purposes.

The CRDC largely follows the guidelines prescribed in the Organisation for Economic Co-operation and Development (OECD), "Frascati Manual 2015", Guidelines for collecting and reporting data on research and experimental development.

The CRDC is also very close to the Australian and New Zealand Standard Research Classification (ANZSRC) in terms of their underlying concepts and their usage, although some groupings might be different, therefore, large content of this introduction will be similar to that of the ANZSRC.

Background

Canada's research funding agencies were using a number of different research classifications across their programs.  In most cases, these classifications only covered the mandate of a specific agency rather than all sectors of research, had not been updated in many years, were not aligned with international standards, no longer accurately represent today's research landscape, and only partially met the needs of different end-users.  In addition, a growing emphasis on interdisciplinary research, increased international collaboration, the rapid evolution of some research fields, and the increased desire for consistent inter-agency reporting are important additional drivers behind the development of the CRDC.

In 2017, the federal research granting agencies jointly started the development of the CRDC, with Statistics Canada serving as the custodian of the new standard and providing its expertise on statistical standards development and maintenance. To learn more about the development process, we refer the users to two document: 'what we learned' (from the consultation process) and a working paper on the CRDC (in English only) published in June 2019.

The CRDC provides a number of benefits such as the ability to produce an up-to-date, relevant and conceptually sound classification, a common approach to classifying research (including multi- or interdisciplinary research) across research organizations and governments, and will assist in communication, consistent reporting, identification of gaps and opportunities, stronger collaborations, and optimized support for new and innovative research.

In addition, the CRDC provides a framework which enables comparisons with other classifications used nationally and internationally.

To support international comparisons and rely on a sound conceptual base, the definition, scope and classification of R&D activities contained in CRDC largely follow the guidelines prescribed in the Organisation for Economic Co-operation and Development (OECD), "Frascati Manual 2015", Guidelines for collecting and reporting data on research and experimental development. For users with the intention of using this classification thoroughly, it is recommended to read that manual as well.

Composition, nature and purpose of the CRDC

The three classifications in the Canadian Research and Development Classification (CRDC) are:

• Type of Activity (TOA);
• Fields of Research (FOR); and
• Socio-economic Objective (SEO).

They can be used in official statistics to analyse the nature of R&D and in conjunction with industrial and institutional sector classifications to produce a set of official statistics that support a variety of user interests.

Type of Activity (TOA) Classification

This classification allows R&D activity to be categorized according to the type of research effort, namely basic or fundamental research (which groups pure basic research and strategic basic research other split in the Frascati Manual 2015), applied research and experimental development.

The types of activity are defined as follows:

Basic research: refers to experimental and theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view. It includes pure basic research (i.e., experimental and theoretical work undertaken to acquire new knowledge without looking for long term benefits other than the advancement of knowledge) and strategic basic research (experimental and theoretical work undertaken to acquire new knowledge directed into specified broad areas in the expectation of practical discoveries). It provides the broad base of knowledge necessary for the solution of recognized practical problems.

Applied research: refers to original investigation undertaken in order to acquire new knowledge.  It is, however, directed primarily towards a specific, practical aim or objective. It is undertaken either to determine possible uses for the findings of basic research or to determine new ways of achieving some specific and predetermined objectives.

Experimental development: refers to systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products, materials, policies, behaviours or outlooks, or new processes, systems and services or to improving substantially those already produced or processed/installed.

Field of research (FOR) Classification

This piece of the CRDC allows R&D activity to be categorized or classified according to the field of research (FOR); it is the methodology used in the R&D that is being considered.

The categories within this classification include major fields of research based on the knowledge sources, the objects of interest, and the methods and techniques being used.

Socioeconomic objective (SEO) Classification

This classification allows the categorization of R&D according to the purpose or outcome of the R&D as perceived by the data provider (researcher). It consists of discrete economic, social, technological or scientific domains for identifying the principal purposes of the R&D. The attributes applied to the design of the SEO classification comprise a combination of industries, processes, products, health, education, culture, ethics and other social and environmental aspects of particular interest.

Structure of the CRDC

As noted in section 2, the TOA has 3 main categories with no hierarchy between them, although they can be considered a continuum in the R&D process; for basic research to experimental development.

CRDC 2020 Version 1.0 – Type of Activity (TOA)

Level	Level name	Number of digits (truncated – Full codes are alphanumerical and start with RDT)	Count
1	Division	2	3

The FOR and SEO classifications follow a hierarchical structure.

The FOR has four hierarchical levels, namely Divisions (at the broadest level), Groups, Classes and Subclasses or Fields (at the lowest level). The Division represents a broad subject area or research discipline and is closely aligned with the 'broad classification' levels (6 in total) as identified in the Frascati Manual 2015, with some adjustments made based on comments and feedback from consultations with experts and general public (See: Comparison between Frascati Manual 2015 – Broad Classification (FOR) and CRDC 2020 Version 1.0 – Division levels (FOR)). Groups are closely aligned with the 'Second-level classification' (42 in total) as also identified in the Frascati Manual 2015, also with few adjustments (See: Comparison between Frascati Manual 2015 – Second level classification (FOR) and CRDC 2020 Version 1.0 – Group levels (FOR)). While classes and fields were added in the CRDC to represent the increasingly detailed dissections of R&D activities in Canada. Divisions, Groups, Classes and Fields are assigned unique 2-digit truncated codes (full codes for all FOR are alphanumerical starting with RDF); 3-digit truncated codes; 5-digit truncated codes and 7-digit truncated codes respectively. The FOR classification in CRDC 2020 version 1.0 has 6 Divisions, 43 Groups, 168 Classes and 1663 Subclasses or Fields.

Each Division is based on a broad discipline. Groups within each Division are those which share the same broad methodology, knowledge domain and/or perspective as others in the Division. Each Group is a collection of classes. Groups, Classes and Subclasses (Fields) of research are categorized to the Divisions sharing the same methodology rather than the Division they support.

CRDC 2020 Version 1.0 – Field of Research (FOR)

Level	Level name	Number of digits (truncated - Full codes are alphanumerical andstart RDF)	Count
1	Division	2	6
2	Group	3	43
3	Class	5	168
4	Subclass (Field)	7	1663
Total	n/a	n/a	1880

Example of the hierarchical structure of the FOR

Level	Code	Title
Division	RDF20-21	Engineering and technology
Group	RDF203	Electrical engineering, computer engineering, and information engineering
Class	RDF20303	Data analytics and signal processing
Subclass (Field)	RDF2030302	Artificial intelligence engineering

The SEO is a two level hierarchical classification with Division at the broadest level and Group at the bottom. SEO categories allow the categorization of R&D based on the purpose or outcome of the R&D as perceived by the researchers. The Division in the SEO classification is identified by a 2-digit truncated code and all full codes start with RDS. This level of the SEO classification of the CRDC is closely aligned with the 'Chapters' found in the Nomenclature for the Analysis and Comparison of Scientific Programmes and Budgets (NABS) 2007 (See: Comparison between NABS 2007 Chapters (SEO) and CRDC 2020 Version 1.0 – Division levels (SEO)) developed by Eurostat and it can also be linked to the Frascati Manual 2015 which specifically refers to the NABS when exposing on the socioeconomic objectives for R&D.

Groups form the lowest level of the SEO for CRDC 2020 version 1.0. They were put together by the funding agencies following up the scope of the NABS 2007 and other considerations such as the experience and uses from funding agencies and Statistics Canada collecting that type of information or data. It is already anticipated that more levels might be added in future revisions of the CRDC's SEO, after more consultations and analysis. Groups are assigned a unique 5-digit truncated code and all full codes start with RDS.

The SEO classification has 12 Divisions and 85 Groups. The Groups are more specific in terms of SEO and they are supplemented with illustrative examples which can represent more specific types of objective within the group. These examples can later be used to create additional levels of SEO.

Each Division is based on a broad research objective. Groups within each Division are those which are aligned towards the same objective as the Division. Each Group is a collection of related research objectives. Groups are categorized to the Divisions with which they are most closely aligned.

CRDC 2020 Version 1.0 – Socio-Economic Objective (SEO)

Level	Level name	Number of digits (truncated - Full codes are alphanumerical and start with RDS)	Count
1	Division	3	12
2	Group	5	85

Example of the hierarchical structure of the FOR:

Level	Code	Title
Division	RDS111	Political and social systems, structures and processes
Group	RDS11110	Social justice

Use of the CRDC

The CRDC provides a three way matrix of classification. Each R&D activity can be classified by Type of Activity (TOA), Fields of Research (FOR) and Socio-economic Objective (SEO).

The CRDC provides a significant degree of flexibility in meeting the needs of a wide variety of users. The hierarchical structure of both the FOR and SEO classifications enables them to be applied to particular purposes at various levels. The CRDC also helps classify multi-disciplinary research, where several disparate areas of the FOR are usually brought together to address one area, or closely related areas of the SEO.

The complexity of issues addressed by R&D is such that questions of public policy often arise in a manner which cannot be readily seen in advance. The detail available in both the FOR and SEO classifications would be sufficient to facilitate the provision of statistics that can be used in a variety of contexts. For example, areas of key technological significance could generally be assessed using an aggregate of appropriate FOR classes and subclasses (fields). The use of the CRDC for R&D surveys minimises the need for separate one-off R&D surveys aimed at narrow areas.

Guidelines for classifying with the CRDC

Classifying by type of activity (TOA)

Where possible, a research project or research program should be allocated to a single type of activity (basic research, applied research or experimental development). If the project or program is large and involves multiple types of activity, then each relevant activity category should be attributed a proportion of resources relative to the project's or research program's total R&D expenditure.

Classifying by field of research (FOR)

The research should first be considered in its broadest sense and in terms of the discipline that the research relates. The research is to be allocated to a FOR in a hierarchical manner. This is achieved by:

• first determining the most relevant division in which the largest component of the R&D is being performed; then
• determining the most relevant group within that division; then
• determining the most relevant class within that group; and then
• determining the most relevant subclass or field within that class.

Many R&D projects will be a homogenous body of work in a specific field. These are more straightforward to categorize. However, the emergence of new interdisciplinary and multidisciplinary fields of research is a feature of the modern R&D environment. The categorization of such fields within a hierarchical and exclusive classification system can pose difficulties for users of the FOR. The use of multiple fields to classify a research project ensures that this research is accommodated within the classification structure.

For example, to classify multidisciplinary research, granting agencies in Canada will each identify how they will classify them during their implementation of the CRDC. One could for instance collect 3 to 5 fields of research (FOR) to describe a particular research project.

If the research is sufficiently large or complex then multiple fields should be selected and attributed with a proportion of resources relative to the total expenditure of the R&D. If the disaggregation is difficult, consideration of relative importance may indicate a primary objective only.

Where a defined field cannot be identified within a class, the 'not elsewhere classified (n.e.c.)' category at the field level is to be used.

Classifying by socioeconomic objective (SEO)

The research should first be considered in its broadest sense and in terms of the dominant beneficiary of the research output. The research is to be allocated to an SEO in a hierarchical manner. This is achieved by:

• first determining the most relevant division corresponding to the largest component of the R&D being performed and the socioeconomic objective which covers that research and experimental development (R&D) activity; then
• determining the most relevant group or objective within that division;
• illustrative examples can be used to determine the most relevant objective within the group.

The appropriate SEO should reflect the industry, process, product, health, education or other social and environmental aspect that R&D activity aims to impact, improve or measure. The appropriate SEO may reflect the aspirations of the researchers and it may help to understand the goals of the research.

Many R&D projects will be a homogenous body of work directed towards a specific objective. These are more straightforward to categorize. However, if the R&D is sufficiently large or complex then multiple fields should be selected and attributed with a proportion of resources relative to the total expenditure of the R&D. If the disaggregation is difficult, consideration of relative importance may indicate a primary objective only.

Where a defined objective cannot be identified within a group, the 'not elsewhere classified (n.e.c.)' or residual category at the objective level is to be used.

In terms of objectives leading towards 'expanding knowledge', for this first version of the CRDC, it was decided to include them in residual categories (n.e.c.). 'Expanding Knowledge' is for the categorization of R&D which does not have an identifiable socio-economic objective. This is usually the case for basic research (as defined in the Type of Activity classification). Applied research and experimental development, by definition, have an identified socio-economic objective and therefore should not be categorized as 'expanding knowledge'.

Definition of R&D

Research and Development (R&D) is defined according to the OECD standard (Frascati Manual 2015) as comprising creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of human, culture, society and environment, and the use of this stock of knowledge to devise new applications.

An R&D activity is characterized by originality. It has investigation as a primary objective, the outcome of which is new knowledge, with or without a specific practical application, or new or improved materials, products, devices, processes or services. R&D ends when work is no longer primarily investigative.

Scope of R&D

As indicated in the Frascati Manual and as experience has shown, there are difficulties in delineating the point which clearly separates the culmination of R&D investigative work and the beginning of the implementation phase of the innovations or recommendations resulting from R&D. Errors at this point are particularly significant because, although R&D programmes require large outlays of resources, the costs of implementing innovations or recommendations resulting from R&D may also be as high, or higher, in many instances.

There is also a wide range of scientific and related activities that are not R&D, but that are closely linked to R&D in terms of organisation, resource allocation, institutional affiliation and the use or flow of information. However, activities conducted solely or primarily for the purposes of R&D support are included in R&D.

The activities which do not have clear boundaries with R&D are listed below.

(i) Education and training of personnel and students

Postgraduate research, including supervision of the research, is considered to be R&D. The development of new teaching methods is also regarded as R&D. However, teaching and training students, using established methods and subject knowledge, is excluded.

(ii) Specialised scientific and technical information services

Specialised scientific and technical information services which are undertaken solely in support of R&D are regarded as R&D. Examples of these are scientific data collection, coding, recording, classification, dissemination, translation, analysis and bibliographic services.

These specialised services are excluded if they are undertaken independently and not solely in support of R&D.

(iii) General purpose or routine data collection

Collecting data in support of R&D work is included in R&D.

However, data collection of a general nature is excluded. This is normally carried out by government agencies to record natural, biological, economic or social phenomena of general public or government interest. Examples are national population censuses, surveys of unemployment, topographical mapping and routine geographical or environmental surveys.

(iv) Maintenance of national and international standards

Routine testing and analysis of materials, components, products, processes, soils, atmospheres, etc. for standard compliance is excluded from R&D.

(v) Feasibility studies

Feasibility studies undertaken in support of R&D are included. However, a feasibility study that involves gathering information about existing conditions, for use in deciding whether or not to implement a project, is excluded, e.g. a study to determine the viability of a petrochemical complex in a particular location.

(vi) Specialized medical care

R&D includes the development of new treatments and procedures, including such developments in conjunction with advanced medical care and examinations usually carried out by university hospitals.

However, routine investigations or normal application of specialized medical knowledge, techniques or equipment are excluded from R&D. Examples of these are pathology, forensic and post-mortem procedures.

(vii) Clinical trials

Phase 1, 2 and 3 clinical trials are included in R&D. Phase 4 clinical trials are excluded from R&D, unless they bring about further scientific or technological advance.

(viii) Patent and licence work

Patent work connected directly with R&D projects is included in R&D. However, commercial, administrative and legal work associated with patenting, copywriting and licensing, is excluded.

(ix) Policy or program related studies

The boundary between certain policy related studies as described in the Frascati Manual and R&D is complex. In the Frascati Manual, policy related studies cover activities such as the 'analysis and assessment of existing programmes, continued analysis and monitoring of external phenomena (e.g. defence and security analysis), legislative inquiry concerned with general government departmental policy or operations'. Rigour is required to separate policy related studies that are not R&D from true R&D policy work.

Studies to determine the effects of a specific national policy or program to a particular economic or social condition or social group may have elements of R&D. Routine management studies or efficiency studies are excluded.

(x) Routine software development

Software development is an integral part of many projects which in themselves may have no element of R&D. The software development component of such projects, however, may be classified as R&D if it leads to an advance in the area of computer software.

For a software development to be considered as R&D, its completion must be dependent on a scientific or technological advance, and the aim of the project must be the systematic resolution of a scientific and/or technological uncertainty.

The following are examples of software development which are considered to be R&D:

• Development of internet technology
• Research into methods of designing, developing, deploying or maintaining software
• R&D on software tools or technologies in specialized areas of computing (e.g. image processing, artificial intelligence, character recognition)
• R&D producing new theorems and algorithms in the field of theoretical computer science

The following are examples of software development which are not considered to be R&D:

• Routine computer and software maintenance
• Business application software and information system development using known methods and existing software
• Adding user functionality to application languages
• Adaptation of or support for existing software

(xi) Marketing and market studies

Market research and opinion polls are excluded from R&D.

(xii) Mineral exploration

The development of new or vastly improved methods of data acquisition, processing and interpretation of data is included as R&D. Surveying undertaken as an integral part of an R&D project to observe geological phenomena is also regarded as R&D. However, the search for minerals using existing methods is excluded from R&D.

(xiii) Prototypes and pilot plants

The design, construction and testing of prototypes generally falls within the scope of R&D. However, trial production and copying of prototypes are excluded from R&D.

The construction and operation of pilot plants is part of R&D provided that these are used to obtain experience or new data for evaluating hypotheses.

Pilot plants are excluded from R&D as soon as the experimental phase is over or as soon as they are used as normal commercial production units, even if they continue to be described as 'pilot plants'.

If a pilot plant is used for combined operations, the component used for R&D is to be estimated.

(xiv) Other activities

All other activities that are ancillary or consequential to R&D are excluded. Examples of these are interpretative commentary using existing data, forecasting, operations research as contributing to decision making and the use of standard techniques in applied psychology to classify or diagnose human characteristics.

R&D unit or object to be classified

There are some inherent difficulties in formulating a definition of what constitutes a unit of R&D, due to the lack of uniformity in organizational structures and considerable variation in the way organizations allocate resources to R&D activities. From a statistical viewpoint it is desirable that R&D expenditure be reported in the smallest cluster that can be classified to a single TOA and FOR, which for the purposes of this classification is defined to be an R&D unit. The extent to which it is not practicable to provide this detail will reduce the validity and usefulness of the classification, and the resulting R&D statistics.

The most common real world references to R&D activities are Research Program and Research Project. These focal units seldom approximate the idealized R&D unit as outlined above, although they could be regarded as an aggregation of these units.

We refer to the Frascati Manual 2015 for more details about the best way to identify R&D units.

Relationship with other national statistical classifications

The CRDC is the first Canadian research classification designed to be dedicated to R&D and inclusive of all current sectors of research in Canada.  While contributing to a greater alignment with international standards, it is comprehensive enough to support a wide range of needs within the Canadian research and development ecosystem.

It might be possible to combine the CRDC and the North American Industry Classification (NAICS) Canada when collecting, analyzing and disseminating R&D data. There are also some approximations to be made with the Classification of instructional programs (CIP) Canada and the North American Product Classification (NAPCS) Canada, although direct comparison should be made with care. In the case of NAPCS for example, it is a transaction and output-based classification which may not be suited to all situations where R&D information needs to be collected and disseminated, for example 'intra-muros R&D'.

Relationship with relevant international standard classifications

The CRDC aligns with international standards to collect and report on research and development, namely with the recommendations from the Organization for Economic Cooperation and Development's (OECD) Frascati Manual 2015, the Nomenclature for the Analysis and Comparison of Scientific Programmes and Budgets (NABS) 2007 and was modeled on the Australian and New Zealand Standard Research Classification (ANZSRC) 2008.

See the comparison tables for fields of research (FOR):

Comparison between Frascati Manual 2015 – Broad Classification (FOR) and CRDC 2020 Version 1.0 – Division levels (FOR)

Frascati Manual 2015 -Broad Classification (FOR) - Code	Frascati Manual 2015 -Broad Classification (FOR) - Title	CRDC 2020 Version 1.0 – Division levels (FOR) -  Code	CRDC 2020 Version 1.0 – Division levels (FOR) - Title	Explanatory notes
1	Natural sciences	RDF10	Natural sciences
2	Engineering and technology	RDF20-21	Engineering and technology
3	Medical and health sciences	RDF30	Medical, health and life sciences	Difference in the title with addition of 'and life sciences' in the CRDC
4	Agricultural and veterinary sciences	RDF40	Agricultural and veterinary sciences
5	Social sciences	RDF50	Social sciences
6	Humanities and the arts	RDF60	Humanities and the arts

Comparison between Frascati Manual 2015 – Second level classification (FOR) and CRDC 2020 Version 1.0 – Group levels (FOR)

Frascati Manual 2015 – Second level classification (FOR)  Code	Frascati Manual 2015 – Second level classification (FOR)Title	CRDC 2020 Version 1.0 – Group levels (FOR) - Code	CRDC 2020 Version 1.0 – Group levels (FOR)- Title	Explanatory notes
1.1	Mathematics	RDF101	Mathematics and statistics	Difference in the title with addition of 'statistics' in the CRDC
1.2	Computer and information sciences	RDF102	Computer and information sciences
1.3	Physical sciences	RDF103	Physical sciences
1.4	Chemical sciences	RDF104	Chemical sciences
1.5	Earth and related environmental sciences	RDF105	Earth and related environmental sciences
1.6	Biological sciences	RDF106	Biological sciences
1.7	Other natural sciences	RDF107	Other natural sciences
2.1	Civil engineering	RDF201	Civil engineering, maritime engineering, transport engineering, and mining engineering	Difference in the title with addition of 'maritime engineering, transport engineering, and mining engineering' in the CRDC
2.2	Electrical engineering, electronic engineering, information engineering	RDF203	Electrical engineering, computer engineering, and information engineering
2.3	Mechanical engineering	RDF202	Industrial, systems and processes engineering	CRDC identifies this category as important for Canada and necessity to elevate at the Frascati second level classification. Though, the category is part of Mechanical engineering in the Frascati Manual 2015
2.3	Mechanical engineering	RDF204	Mechanical engineering
2.4	Chemical engineering	RDF205	Chemical engineering
2.5	Materials engineering	RDF206	Materials engineering and resources engineering	Difference in the title with addition of 'resources engineering' in the CRDC
2.6	Medical engineering	RDF207	Medical and biomedical engineering	Difference in the title with addition of 'biomedical' in the CRDC
2.7	Environmental engineering	RDF208	Environmental engineering and related engineering	Difference in the title with addition of 'and related engineering' in the CRDC
2.8	Environmental biotechnology	RDF209	Environmental biotechnology
2.9	Industrial biotechnology	RDF210	Industrial biotechnology
2.10	Nano-technology	RDF211	Nano-technology
2.11	Other engineering and technologies	RDF212	Other engineering and technologies
3.1	Basic medicine	RDF301	Basic medicine and life sciences	Difference in the title with addition of 'and life sciences' in the CRDC
3.2	Clinical medicine	RDF302	Clinical medicine
3.3	Health sciences	RDF303	Health sciences
3.4	Medical biotechnology	RDF304	Medical biotechnology
3.5	Other medical science	RDF305	Other medical  sciences
4.1	Agriculture, forestry, and fisheries	RDF401	Agriculture, forestry, and fisheries
4.2	Animal and dairy science	RDF402	Animal and dairy sciences
4.3	Veterinary science	RDF403	Veterinary sciences
4.4	Agricultural biotechnology	RDF404	Agricultural biotechnology and food sciences	Difference in the title with addition of 'and food sciences' in the CRDC
4.5	Other agricultural sciences	RDF405	Other agricultural sciences
5.1	Psychology and cognitive sciences	RDF501	Psychology and cognitive sciences
5.2	Economics and business	RDF502	Economics and business administration	Difference in the title with addition of 'administration' in the CRDC
5.3	Education	RDF503	Education
5.4	Sociology	RDF504	Sociology and related studies	Difference in the title with addition of 'and related studies' in the CRDC
5.5	Law	RDF505	Law and legal practice
5.6	Political science	RDF506	Political science and policy administration	Difference in the title with addition of 'and policy administration' in the CRDC
5.7	Social and economic geography	RDF507	Social and economic geography
5.8	Media and communications	RDF508	Media and communications
5.9	Other social sciences	RDF509	Other social sciences
6.1	History and archaeology	RDF601	History, archaeology and related studies	Difference in the title with addition of 'and related studies' in the CRDC
6.2	Languages and literature	RDF602	Languages and literature
6.3	Philosophy, ethics and religion	RDF603	Philosophy	Difference in the title with the removal of 'ethics and religion' in the CRDC; these words were added in the definition of the category
6.4	Arts (arts, history of arts, performing arts, music)	RDF604	Arts (arts, history of arts, performing arts, music), architecture and design	Difference in the title with addition of 'architecture and design' in the CRDC
6.5	Other humanities	RDF605	Other humanities

See the comparison table for socioeconomic objectives (SEO):

Comparison between NABS 2007 Chapters (SEO) and CRDC 2020 Version 1.0 – Division levels (SEO)

NABS 2007 Chapters (SEO) - Code	NABS 2007 Chapters (SEO) - Title	CRDC 2020 Version 1.0 – Division levels (SEO) - Code	CRDC 2020 Version 1.0 – Division levels (SEO)  - Title	Explanatory notes
1	Exploration and exploitation of the Earth	RDS101	Exploration and exploitation of the earth
2	Environment	RDS102	Environmental protection	Difference in the title with addition of 'protection' in the CRDC
3	Exploration and exploitation of space	RDS103	Exploration and exploitation of space
4	Transport, telecommunication and other infrastructures	RDS104	Transport, telecommunication and other infrastructures (including buildings)	Difference in the title with addition of 'including buildings' in the CRDC
5	Energy	RDS105	Energy (except prospecting)	Difference in the title with addition of 'except prospection' in the CRDC
6	Industrial production and technology	RDS106	Industrial production and technology
7	Health	RDS107	Health
8	Agriculture	RDS108	Agriculture (including fisheries and forestry)
9	Education	RDS109	Education
10	Culture, recreation, religion and mass media	RDS110	Culture, recreation, religion and media	Difference in the title with the removal of 'mass' for media in the CRDC
11	Political and social systems, structures and processes	RDS111	Political and social systems, structures and processes
12	General advancement of knowledge: R&D financed from General University Funds (GUF)			No direct equivalent. This category is spread across different residual categories in the CRDC
13	General advancement of knowledge: R&D financed from other sources than GUF			This category is spread across different residual categories in the CRDC
14	Defence	RDS112	Defence

Updates or revisions to the CRDC

An important consideration when developing a statistical classification is the need to build in sufficient robustness to allow for long-term usage. This robustness facilitates meaningful time series analysis of data assigned to that classification. However, there is also a need for the classification to remain contemporary to capture changes happening in the R&D sector and to provide data relevant to users' needs.

In order to achieve a balance between these two competing objectives, Statistics Canada as the custodian of the CRDC, and its close partner funding agencies, intend to undertake minor revisions every year or two, and major revision every five years. In fact, all parties already agreed that the first CRDC 2020 version 1.0 will be revised within 2 years of its first release date, and on a five-year cycle after that, with possibility of 'evergreening' for minor changes once a year to reflect the changes in the research fields.

Although the CRDC is official once published by Statistics Canada, the dates of its implementation depend entirely on the entities, organizations or individuals who decide to use it. Statistics Canada has its own internal policies on statistical standards and informing users that may influence implementation dates by the Agency's statistical programs.

CRDC Products

Correspondences (or concordances) between newest versions and older versions of the CRDC will be provided along with the classification after it revised. This is the first official version of the CRDC, therefore no correspondence table will be released. Other correspondences will be considered for development in the future, including full correspondences between CRDC FOR and OECD's Fields of Science (See: Comparison between Frascati Manual 2015 – Broad Classification (FOR) and CRDC 2020 Version 1.0 – Division levels (FOR) and Comparison between Frascati Manual 2015 – Second level classification (FOR) and CRDC 2020 Version 1.0 – Group levels (FOR)), between CDRC's SEO and the Nomenclature for the Analysis and Comparison of Scientific Programmes and Budgets (NABS) 2007 (See: Comparison between NABS 2007 Chapters (SEO) and CRDC 2020 Version 1.0 – Division levels (SEO)), and potentially develop crosswalks between the CDRC's FOR and SEO, and the North American Industry Classification (NAICS), between the CRDC's FOR and SEO, and the North American Product Classification (NAPCS) Canada, and between the CRDC's FOR and SEO, and the Classification of Instructional Programs (CIP). But these crosswalks might take time to develop and will depend on available resources, as the focus will be the deployment of the CRDC and experiment of its usage across the country for some years after the first release.

Further information

For more information about the CRDC contact Statistics Canada:

Statistics Canada
150 Tunney's Pasture Driveway
Ottawa, Ontario
Canada    K1A 0T6

Telephone
(toll free) 1-800-263-1136
(international) 1-514-283-8300
Fax
1-514-283-9350
TTY
1-800-363-7629

Email for the CRDC: statcan.crdc-ccrd.statcan@statcan.gc.ca
Email for General enquiries: infostats@statcan.gc.ca

Web site: Canadian Research and Development Classification (CRDC) 2020 Version 1.0
Catalog number: 89260004

Deflation of wholesale sales

Introduction

With the November 2018 release of the Monthly Wholesale Trade Survey (MWTS) results (reference month September 2018), the base year and reference year of the deflated wholesale sales have been updated from 2007 to 2012.

The purpose of this document is to present an overview of the deflation methodology used for producing the volume measures of sales from the MWTS.

Purpose of deflation

Changes in the value of sales collected at current prices (i.e. at the time the sales took place) may be attributable to changes in prices or quantities sold, or both. To study the activity of the wholesale sector, it is often desirable to remove the variations due to price changes from the values at current prices in order to obtain an indicator of the changes in the quantities sold, i.e. an indicator of the volume of sales. This process is known as deflation.

Derivation of wholesale sales price indices

The main price indices used to deflate wholesale sales are the selling price indices obtained from the Wholesale Services Price Index (WSPI) program. However, the WSPI data are not available in time to deflate the most recent observations of wholesale sales as the WSPI program produces monthly data that are released on a quarterly basis with about a four-month lag.

It is thus necessary to use derived price indices to extend the WSPI-based ones for the most current months, until the WSPI data become available, at which time the derived price indices are replaced by the WSPI-based ones.

In what follows, we describe how price indices, with base year 2012, are computed for the deflation of wholesale sales. We first describe how the WSPI data are used, and then how the derived price indices are constructed.

Price indices based on the WSPI

From the WSPI program, monthly selling price indices are available at the five-digit North American Industry Classification System (NAICS) industry level. These selling price indices are weighted together using the Paasche formula to obtain a sale price index for each of the wholesale trade industries published by the MWTS.

The weights used to combine the selling price indices into an industry price index are the proportions of the sales of the five-digit NAICS industries within each industry. These weights are obtained from the Annual Wholesale Trade Survey (AWTS). They vary from year to year; i.e. the 2012 proportions of sales are used in 2012, those of 2013 in 2013, and so on. For the two most recent years, the last available annual data from the AWTS are used.

Derived price indices

To extend the WSPI-based price indices, a derived price index for each industry had to be constructed based on assumptions that capture the main elements thought to affect wholesalers' selling prices. These derived price indices are based on the prices of the NAPCS which are a part of each of the 5-digit NAICS (in the AWTS commodity file) that are normally collected from the WSPI, and on the proportion of the fluctuations in the exchange rate of the dollar that is immediately passed on to the trade group's customers.

a) Main assumptions

Wholesalers trade a portion of the total supply of a commodity in Canada. The total supply is the sum of domestic production and imports. For the products whose prices are determined by the IPPI, a wholesale price index is obtained by combining a domestic production price index with an import price index from IATD.

Wholesalers sell domestically and on export markets with perhaps differentiated prices. It is assumed, however, that they set their prices according to the changes in the prices of the commodities that they trade whether the commodities are exported or not.

It is also assumed that the variations in the price of a commodity are the same across wholesale industries. This means that a commodity sold by various industries has the same price index, but the weight of that commodity will vary across industries.

b) Wholesale commodity prices

A wholesale price index for each commodity is obtained by a weighted combination of a domestic production price index with an import price index.

Most of the domestic production prices are taken from the Industrial Product Price Index program. The Raw Materials Price Index, Commercial Software Price Index, and Consumer Price Index are used to fill in the data-gaps for products where the IPPI lacks coverage.

For the import components, the current-weighted (Paasche) import price indices on a customs basis from the International Trade Price Index program are used.

c) Industry prices

The commodity price indices are then weighted together using the Paasche formula to obtain a sale price index for each industry. The weights used are based on the proportion of the industry total sales accounted by each commodity according to the AWTS.

d) Adjustment for the exchange rate of the dollar

Many of the import prices used in the derivation of the wholesale commodity price indices fully and immediately reflect the exchange rate fluctuations of the dollar. However, wholesalers do not necessarily adjust their prices immediately to compensate for those fluctuations; generally, they will change their prices to reflect only a proportion of them, and maybe with a lag.

A comparison of the industry's price indices with the selling price indices from the WSPI program showed that the price indices for many industries required an adjustment to account for the incomplete pass-through of the fluctuations in the exchange rate of the dollar.

These pass-through adjustments were evaluated and applied, when necessary, to the industry price indices.

These adjusted industry price indices are the derived price indices.

Derivation of the volume of wholesale sales

Two measures of the total volume of wholesale sales are computed. One is the volume of sales at constant prices, the other is the volume of sales in chained dollars. Both are seasonally adjusted.

Volume at constant prices (Laspeyres formula)

The volume of sales at constant prices uses the relative importance of the products' prices in a previous period, currently the year 2012, to evaluate the change in the quantities sold. This year is called the base year. The resulting deflated values are said to be "at 2012 prices". Using the prices of a previous period to measure current activity provides a representative measurement of the current volume of activity with respect to that period.

The price indices used to obtain the volume of sales at constant prices are the extended price indices, i.e. the WSPI-based price indices extended with the derived price indices described earlier.

The nominal (current dollars) sales of each industry are divided by their respective extended WSPI-based price index, and then the total volume of sales at constant prices is obtained by adding the volume of sales across the 25 industries covered by the MWTS.

Chained volume index (Fisher formula)

The chained index of the volume of total sales is the geometric mean of two evaluations of the change in the quantities sold between two consecutive months. One evaluation uses the prices of the previous month to evaluate the change; the other uses the prices of the current month.

Since the general tendency for commodity prices is to increase, the evaluation based on the prices of the previous month tends to overstate the change in quantities; i.e. as price increases, buyers tend to buy more of a cheaper commodity. Therefore, using the prices of a previous period to value the quantities bought currently may lead to an overstatement of the change in quantities.

Similarly, the evaluation of the change in the quantities sold using the prices of the current month will tend to understate the change in quantities as this approach gives more weight to the lower priced commodities than to the higher priced ones.

Hence, the geometric average of the two evaluations of the monthly change in quantities (with the previous and current monthly prices) mitigates these under- and over-statements. The chained index of the volume of total sales thus captures the effect of the most recent price changes in the change in volume, as it combines the changes in volume measured with respect to both the current and previous month's prices.

The geometric average of the changes in volume of total sales is computed monthly, and then the monthly variations are chained to provide a time series of the changes in volumes. The time series is then scaled to be equal to the total value of wholesale sales in current dollars for the year 2012.

As the only monthly price and quantity information available are the price and volume data for the 25 industries covered by the MWTS, the chained volume index of sales is only computed for the Wholesale Trade sector as a whole.

Volume of wholesale sales for 2004-2011

Above, we described how the volume of wholesale sales at 2012 prices was obtained for the period starting with January 2012. But the MWTS data based on NAICS begin in January 2004.

In order to provide an as long as possible time series of the volume of wholesale sales, we linked the data for the period 2004 to 2011 at 2007 constant prices (for the Laspeyres series), and at 2007 prices (for the Fisher chained series), to the current period starting in 2012.

This linking preserves the monthly growth rates of the data published at 2007 prices.

Why do we conduct this survey?

The purpose of this survey is to measure the size, structure and economic performance of the trucking industry and to analyze its impact on the Canadian economy.

It is the most complete source of information on the financial performance and characteristics of the trucking industry in Canada. The results will be used as inputs to the Canadian System of National Accounts. Federal and provincial governments will use the data to formulate policies and to monitor the trucking industry in Canada. Trucking companies and associations will use the published statistics for benchmarking purposes.

Your information may also be used by Statistics Canada for other statistical and research purposes.

Your participation in this survey is required under the authority of the Statistics Act.

Other important information

Authorization to collect this information

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

Confidentiality

By law, Statistics Canada is prohibited from releasing any information it collects that 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 only.

Record linkages

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

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, Québec, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia and the Yukon. The shared data will be limited to information pertaining to business establishments 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, specifying the organizations with which you do not want Statistics Canada to share your data and mailing it to the following address:

Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6

You may also contact the Statistics Canada Help Desk by email or by fax at 613-951-6583.

For this survey, there are Section 12 agreements with the statistical agencies of Prince Edward Island, the Northwest Territories and Nunavut, as well as the Newfoundland and Labrador Department of Transportation and Works, the Nova Scotia Utility and Review Board, the Quebec Department of Transportation, the Ontario Ministry of Transportation and the Alberta Ministry of Transportation.

For agreements with provincial and territorial government organizations, the shared data will be limited to business establishments located within the jurisdiction of the respective province or territory.

Statistics Canada has also entered into a Section 12 agreement with Transport Canada whereby the information collected will be provided to Transport Canada pursuant to the Canada Transportation Act and the Carriers and Transportation Undertakings Information Regulations. In this case, respondents do not have the right to object to sharing their information since the party to the Agreement is authorized by law to require the respondent to provide the information.

Business or organization and contact information

1. Verify or provide the business or organization's legal and operating name and correct where needed.

Note: Legal name modifications should only be done to correct a spelling error or typo.

Legal Name

The legal name is one recognized by law, thus it is the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name as fixed by its charter or the statute by which the corporation was created.

Modifications to the legal name should only be done to correct a spelling error or typo.

To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.

Operating Name

The operating name is a name the business or organization is commonly known as if different from its legal name. The operating name is synonymous with trade name.

• Legal name
• Operating name (if applicable)

2. Verify or provide the contact information of the designated business or organization contact person for this questionnaire and correct where needed.

Note: The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire.

• First name
• Last name
• Title
• Preferred language of communication
  • English
  • French
• Mailing address (number and street)
• City
• Province, territory or state
• Postal code or ZIP code
• Country
  • Canada
  • United States
• Email address
• Telephone number (including area code)
• Extension number (if applicable)
  The maximum number of characters is 10.
• Fax number (including area code)

3. Verify or provide the current operational status of the business or organization identified by the legal and operating name above.

• Operational
• Not currently operational (e.g., temporarily or permanently closed, change of ownership)
  Why is this business or organization not currently operational?
  • Seasonal operations
    • When did this business or organization close for the season?
      • Date
    • When does this business or organization expect to resume operations?
      • Date
  • Ceased operations
    • When did this business or organization cease operations?
      • Date
    • Why did this business or organization cease operations?
      • Bankruptcy
      • Liquidation
      • Dissolution
      • Other
    • Specify the other reasons why the operations ceased
  • Sold operations
    • When was this business or organization sold?
      • Date
    • What is the legal name of the buyer?
  • Amalgamated with other businesses or organizations
    • When did this business or organization amalgamate?
      • Date
    • What is the legal name of the resulting or continuing business or organization?
    • What are the legal names of the other amalgamated businesses or organizations?
  • Temporarily inactive but will re-open
    • When did this business or organization become temporarily inactive?
      • Date
    • When does this business or organization expect to resume operations?
      • Date
    • Why is this business or organization temporarily inactive?
  • No longer operating due to other reasons
    • When did this business or organization cease operations?
      • Date
    • Why did this business or organization cease operations?

4. Verify or provide the current main activity of the business or organization identified by the legal and operating name above.

Note: The described activity was assigned using the North American Industry Classification System (NAICS).

This question verifies the business or organization's current main activity as classified by the North American Industry Classification System (NAICS). The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS, are suitable for the analysis of production-related issues such as industrial performance.

The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.

The associated NAICS should reflect those activities conducted by the business or organizational units targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.

The NAICS classification contains a limited number of activity classifications; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.

Please note that any modifications to the main activity through your response to this question might not necessarily be reflected prior to the transmitting of subsequent questionnaires and as a result they may not contain this updated information.

The following is the detailed description including any applicable examples or exclusions for the classification currently associated with this business or organization.

Description and examples

• This is the current main activity
  • Provide a brief but precise description of this business or organization's main activity
    e.g., breakfast cereal manufacturing, shoe store, software development
• This is not the current main activity

Main activity

5. You indicated that is not the current main activity.

Was this business or organization's main activity ever classified as: ?

• Yes
  • When did the main activity change?
    Date
• No

6. Search and select the industry classification code that best corresponds to this business or organization's main activity.

Select this business or organization's activity sector (optional)

• Farming or logging operation
• Construction company or general contractor
• Manufacturer
• Wholesaler
• Retailer
• Provider of passenger or freight transportation
• Provider of investment, savings or insurance products
• Real estate agency, real estate brokerage or leasing company
• Provider of professional, scientific or technical services
• Provider of health care or social services
• Restaurant, bar, hotel, motel or other lodging establishment
• Other sector

Business type

1. Did this business transport freight by truck in 2019?

• Yes
• No

2. Is this a business that primarily moved used household and office goods in 2019?

e.g., a moving company

• Yes
• No

3. Did this business work as a subcontractor in a long term contract for a van line company in 2019?

e.g., as an agent

• Yes
  • Specify the name of the van line company this business provided moving services for.
    Name of van line company
• No

4. Did this business work under the operating authority of a road carrier in a long term contract in 2019?

e.g., as an owner operator

A long term contract is defined as 30 days or more.

• Yes
  • Specify the name of the main road carrier this business provided transportation services to in 2019.
    Name of road carrier
• No

Operating statistics

A trucking shipment represents a movement of a commodity by truck from one origin to one destination.

Note:

• Full or partial truckload picked up from or delivered to multiple destinations constitutes multiple shipments.
  • drop off trips (one origin, but multiple destinations)
  • pick up trips (multiple origins, but one destination)
  • trips involving multiple commodities e.g., from Less Than Truck load (LTL) carriers.
• Full or partial truckload should be counted as a single shipment only if all the commodities on the truck are from the same origin headed to the same destination.

5. How many trucking shipments were made by this business in 2019?

Include shipments made by owner operators on your behalf.

Number of trucking shipments

6. What was the total weight of all trucking shipments made by this business in 2019?

Include shipments made by owner operators on your behalf.

Total weight in metric tonnes

7. What was the total distance travelled by the trucks in 2019?

Include the distance travelled when trailers were empty.

Report in kilometres.

Total distance travelled by your company trucks (owned or leased)

Total distance travelled by owner operators working on your behalf

Total distance
Sum of the two amounts above.

8. How much fuel was consumed by trucks in 2019?

Total fuel consumed (in litres) by your company trucks (owned or leased)

Total fuel consumed (in litres) by owner operators working on your behalf

Total fuel consumed (in litres)
Sum of the two amounts above.

Financial statistics

9. Report your operating revenue and expenses in 2019.

Exclude GST/HST, PST and QST from all revenue and expense figures.

Report dollar amounts in Canadian dollars (CAN$).

Report your operating revenue and expenses in 2019

	CAN$
Revenue
a. Operating revenue from trucking
b. All other operating revenue
Total operating revenue Sum of a. and b.
c. Investment revenue
Total revenue Sum of total operating revenue and investment revenue.
Expenses
a. Employee salaries, wages and benefits
b. Payments to owner operators
c. Purchased transportation services
d. Vehicle fuel expenses
e. Repairs and maintenance
f. Depreciation of tangible and intangible assets e.g., depreciation of vehicles, equipments, machinery, etc.
g. All other operating expenses
Total operating expenses Sum of a. to g.
h. Other expenses
Total expenses Sum of total operating expenses and other expenses.

Operating breakdowns

10. Report the distribution of your trucking revenue by type of trucking activity in 2019.

Report in percentages up to two decimal places.

Type of trucking activity

	Percentage
a. Local trucking i.e., trucking services within a metropolitan area and its suburbs, generally, same-day return trips
b. Long distance trucking i.e., trucking services between metropolitan areas, generally, trips are not same-day return
Total Sum of a. and b. should add up to 100%.

11. Report the distribution of your trucking revenue by type of trucking service in 2019.

Report in percentages up to two decimal places.

Types of trucking service

	Percentage
a. General Freight, Truck Load (TL)
b. General Freight, Less Than Truckload (LTL)
c. Specialized trucking e.g., trucking service performed using specialized equipment such as tankers, grain tank, dump trucks, refrigerated vans, motor vehicle haulers, etc.
d. Moving used household and/or used office goods
Total Sum of a. to d. should add up to 100%.

12. Report the distribution of your trucking revenue according to the region your business picks up the commodities in 2019.

Report in percentages up to two decimal places.

The region your business picks up commodities:

	Percentage
a. Newfoundland and Labrador
b. Prince Edward Island
c. Nova Scotia
d. New Brunswick
e. Quebec
f. Ontario
g. Manitoba
h. Saskatchewan
i. Alberta
j. British Columbia
k. Yukon
l. Northwest Territories
m. Nunavut
n. Outside Canada
Total Sum of a. to n. should add up to 100%.

13. Report the distribution of your trucking revenue according to where your business moved shipments in 2019.

Report in percentages up to two decimal places.

Report the distribution of your trucking revenue according to where your business moved shipments in 2019

	Percentage
a. Domestic movements within a province or territory i.e., intraprovincial shipments
b. Domestic movements between provinces or territories i.e., interprovincial shipments
c. International movements into Canada e.g., from the United States or Mexico
d. International movements out of Canada e.g., to the United States or Mexico
Total Sum of a. to d. should add up to 100%.

14. Report the distribution of the trucking revenue of this business according to the commodities hauled in 2019.

Report in percentages up to two decimal places.

Report the distribution of the trucking revenue of this business according to the commodities hauled in 2019

	Percentage
a. Agricultural products e.g., crops or produce, live animals, bulked cereal grains, animal feed
b. Prepared food e.g., meat, fish, seafood, dairy products, bakery products, fats and oils, beverages, tobacco products
c. Minerals e.g., stone, sand, gravel and crushed stone, metallic ores
d. Coal
e. Petroleum and gaseous productse.g., fuel oils, gasoline, crude petroleum and products of petroleum refining, coal products and hydrocarbons, asphalt, lubricating oils and greases, coal coke, water, liquefied natural gas, propane
f. Pharmaceutical products
g. Chemical, plastic and rubber products e.g., chlorine, paint, pesticide, fertilizers, vinyl, acrylic polymers, tires
h. Wood products e.g., forest products, lumber, sawmill product, logs and wood in the rough
i. Paper products e.g., pulp, paper, newsprint, paperboard, printed products
j. Metallic and non-metallic products e.g., base metals in primary, semi-finished or finished form, articles of base metals, gypsum, asphalt shingles, ceramic and glass products, bricks, concrete pipe
k. Vehicles, parts and other transportation equipment
l. All other manufactured goods and miscellaneous products
m. Waste and scrap
n. Moving goods (used household and office goods)
Total Sum of a. to n. should add up to 100%.

Fleet and employments

15. Report the number of power units and trailers in service at the end of 2019.

Include units owned, financed or leased by your business.

Exclude vehicles owned by carriers working on your behalf (i.e., owner operator).

Report the number of power units and trailers in service at the end of 2019

	Number
a. Straight trucks i.e., truck class 4 to 6
b. Road tractors i.e., truck class 7 to 8
c. Trailers e.g., semi-trailer, flat deck, tank, etc.

16. Report the average number of people employed during 2019.

Include full-time, part-time and temporary employees and employees absent with pay.

Report the average number of people employed during 2019

	Number
a. Drivers
b. All other employees
Total employees Sum of a. and b.
c. Owner operators hired by this business e.g., carriers working under your operating authority in a long term contract as an owner operator

Your shipment management system

17. Would your computer system allow you to provide data in an electronic format for each shipment?

e.g., providing for each shipment, a weight, a commodity description, an origin (pick up address) and a destination (delivery address)

• Yes
• No

Changes or events

18. Indicate any changes or events that affected the reported values for this business or organization, compared with the last reporting period.

Select all that apply.

• Strike or lock-out
• Exchange rate impact
• Price changes in goods or services sold
• Contracting out
• Organizational change
• Natural disaster
• Recession
• Sold business or business units
• Expansion
• New or lost contract
• Acquisition of business or business units
• Merger of business or business units
• Vacation or maintenance periods
• Equipment failure
• Seasonal operations
• Increased or decreased market demand
• Dissolution
• End of business activities
• Change in business activity
• Other
  • Specify the other changes or events:
• No changes or events

Contact person

19. Statistics Canada may need to contact the person who completed this questionnaire for further information. Is [Provided Given Names], [Provided Family Name] the best person to contact?

• Yes
• No

Who is the best person to contact about this questionnaire?

• First name:
• Last name:
• Title:
• Email address:
• Telephone number (including area code):
• Extension number (if applicable):
  The maximum number of characters is 5.
• Fax number (including area code):

Feedback

20. How long did it take to complete this questionnaire?

Include the time spent gathering the necessary information.

• Hours:
• Minutes:

21. Do you have any comments about this questionnaire?

1.0 Description

The Canadian Perspectives Survey Series (CPSS) is a set of short, online surveys beginning in March 2020 that will be used to collect information on the knowledge and behaviours of residents of the 10 Canadian provinces. All surveys in the series will be asked of Statistics Canada's probability panel. The probability panel for the CPSS is a new pilot project initiated in 2019. An important goal of the CPSS is to directly collect data from Canadians in a timely manner in order to inform policy makers and be responsive to emerging data needs. The CPSS is designed to produce data at a national level (excluding the territories).

The survey program is sponsored by Statistics Canada. Each survey in the CPSS is cross sectional. Participating in the probability panel and the subsequent surveys of the CPSS is voluntary.

The third survey of the CPSS is CPSS3 – Resuming Economic and Social Activities During COVID-19. It was administered from June 15, 2020 until June 21, 2020.

Any question about the survey, the survey series, the data or its use should be directed to:

Statistics Canada

Client Services
Centre for Social Data Integration and Development
Telephone: 613-951-3321 or call toll-free 1-800-461-9050
Fax: 613-951-4527
E-mail: statcan.csdidclientservice-ciddsservicealaclientele.statcan@statcan.gc.ca

2.0 Survey methodology

Target and survey population

The target population for the Canadian Perspectives Survey Series (CPSS) is residents of the 10 Canadian provinces 15 years of age or older.

The frame for surveys of the CPSS is Statistics Canada's pilot probability panel. The probability panel was created by randomly selecting a subset of the Labour Force Survey (LFS) respondents. Therefore the survey population is that of the LFS, with the exception that full-time members of the Canadian Armed Forces are included. Excluded from the survey's coverage are: persons living on reserves and other Aboriginal settlements in the provinces; the institutionalized population, and households in extremely remote areas with very low population density. These groups together represent an exclusion of less than 2% of the Canadian population aged 15 and over.

The LFS sample is drawn from an area frame and is based on a stratified, multi-stage design that uses probability sampling. The LFS uses a rotating panel sample design. In the provinces, selected dwellings remain in the LFS sample for six consecutive months. Each month about one-sixth of the LFS sampled dwellings are in their first month of the survey, one-sixth are in their second month of the survey, and so on. These six independent samples are called rotation groups.

For the probability panel used for the CPSS, four rotation groups from the LFS were used from the provinces: the rotation groups answering the LFS for the last time in April, May, June and July of 2019. From these households, one person aged 15+ was selected at random to participate in the CPSS - Sign-Up. These individuals were invited to Sign-Up for the CPSS. Those agreeing to join the CPSS were asked to provide an email address. Participants from the Sign-Up that provided valid email addresses formed the probability panel. The participation rate to the panel was approximately 23%. The survey population for all surveys of the CPSS is the probability panel participants. Participants of the panel are 15 years or older as of July 31, 2019.

Sample Design and Size

The sample design for surveys of the CPSS is based on the sample design of the CPSS – Sign-Up, the method used to create the pilot probability panel. The raw sample for the CPSS – Sign-Up had 31,896 randomly selected people aged 15+ from responding LFS households completing their last interview of the LFS in April to July of 2019. Of these people, 31,626 were in-scope at the time of collection for the CPSS - Sign-Up in January to March 2020. Of people agreeing to participate in the CPSS, that is, those joining the panel, 7,242 had a valid email address. All panel participants are invited to complete the surveys of the CPSS.

Sample Design and Size

Stages of the Sample	n
Raw sample for the CPSS – Sign-Up	31,896
In-scope Units from the CPSS – Sign-Up	31,628
Panelists for the CPSS (with valid email addresses)	7,242
Raw sample for surveys of the CPSS	7,242

3.0 Data collection

CPSS – Sign-Up

The CPSS- Sign-Up survey used to create Statistics Canada's probability panel was conducted from January 15th, 2020 until March 15th, 2020. Initial contact was made through a mailed letter to the selected sample. The letter explained the purpose of the CPSS and invited respondents to go online, using their Secure Access Code to complete the Sign-Up form. Respondents opting out of joining the panel were asked their main reason for not participating. Those joining the panel were asked to verify basic demographic information and to provide a valid email address. Nonresponse follow-up for the CPSS-Sign-Up had a mixed mode approach. Additional mailed reminders were sent to encourage sampled people to respond. As well, email reminders (where an email address was available) and Computer Assisted Telephone Interview (CATI) nonresponse follow-up was conducted.

The application included a standard set of response codes to identify all possible outcomes. The application was tested prior to use to ensure that only valid question responses could be entered and that all question flows would be correctly followed. These measures ensured that the response data were already "clean" at the end of the collection process.

Interviewers followed a standard approach used for many StatCan surveys in order to introduce the agency. Selected persons were told that their participation in the survey was voluntary, and that their information would remain strictly confidential.

CPSS3 – Resuming Economic and Social Activities During COVID-19

All participants to the pilot panel for the CPSS, minus those who opted out after previous iterations of CPSS, were sent an email invitation with a link to the CPSS3 and a Secure Access Code to complete the survey online. Collection for the survey began on June 15th, 2020. Reminder emails were sent on June 16th, June 18th and June 20th. The application remained open until June 21st, 2020.

3.1 Disclosure control

Statistics Canada is prohibited by law from releasing any data which would divulge information obtained under the Statistics Act that relates to any identifiable person, business or organization without the prior knowledge or the consent in writing of that person, business or organization. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

4.0 Data quality

Survey errors come from a variety of different sources. They can be classified into two main categories: non-sampling errors and sampling errors.

4.1 Non-sampling errors

Non-sampling errors can be defined as errors arising during the course of virtually all survey activities, apart from sampling. They are present in both sample surveys and censuses (unlike sampling error, which is only present in sample surveys). Non-sampling errors arise primarily from the following sources: nonresponse, coverage, measurement and processing.

4.1.1 Nonresponse

Nonresponse errors result from a failure to collect complete information on all units in the selected sample.

Nonresponse produces errors in the survey estimates in two ways. Firstly, non-respondents often have different characteristics from respondents, which can result in biased survey estimates if nonresponse bias is not fully corrected through weighting. Secondly, it reduces the effective size of the sample, since fewer units than expected answered the survey. As a result, the sampling variance increases and the precision of the estimate decreases. The response rate is calculated as follows:

[ Responding units / (Selected units – out-of-scope units) ] x 100%

The following table summarizes the response rates experienced for the CPSS3 – Resuming Economic and Social Activities During COVID-19. Response rates are broken down into two stages. Table 4.1.1a shows the take-up rates to the panel in the CPSS- Sign-Up and Table 4.1.1b shows the collection response rates for the survey CPSS3 – Resuming Economic and Social Activities During COVID-19.

Table 4.1.1a Participation to the Pilot Probability Panel for the CPSS – Sign-Up

	Stages of the Sample for the CPSS – Sign-Up
	Raw sample for the CPSS – Sign-Up	In-scope Units from the CPSS – Sign-Up	Panelists for the CPSS (with valid email addresses)	Participation Rate to the Panel for CPSS
n	31,896	31,628	7,242	22.9%

Table 4.1.1b Response Rates to the CPSS3 – Resuming Economic and Social Activities During COVID-19

	Stages of the Sample for the CPSS3 – Resuming Economic and Social Activities During COVID-19
	Panelists for the CPSS (with valid email addresses)	Respondents to CPSS3 – Resuming Economic and Social Activities During COVID-19	Collection Response Rate to CPSS3 – Resuming Economic and Social Activities During COVID-19	Cumulative Response Rate
n	7,242	4,209	58.1%	13.3%

As shown in Table 4.1.1b, the collection response rate for the CPSS3 – Resuming Economic and Social Activities During COVID-19 is 58.1%. However, when nonparticipation in the panel is factored in, the cumulative response rate to the survey is 13.3%. This cumulative response rate is lower than the typical response rates observed in social surveys conducted at Statistics Canada. This is due to the two stages of nonresponse (or participation) and other factors such as the single mode used for surveys of the CPSS (emailed survey invitations with a link to the survey for online self-completion), respondent fatigue from prior LFS response, the inability of the offline population to participate, etc.,.

Given the additional nonresponse experienced in the CPSS3 – Resuming Economic and Social Activities During COVID-19 there is an increased risk of bias due to respondents being different than nonrespondents. For this reason, a small bias study was conducted. Please see Section 6.0 for the results of this validation.

4.1.2 Coverage errors

Coverage errors consist of omissions, erroneous inclusions, duplications and misclassifications of units in the survey frame. Since they affect every estimate produced by the survey, they are one of the most important type of error; in the case of a census they may be the main source of error. Coverage errors may cause a bias in the estimates and the effect can vary for different sub-groups of the population. This is a very difficult error to measure or quantify accurately.

For the CPSS, the population covered are those aged 15+ as of July 31, 2019. Since collection of the CPSS3 – Resuming Economic and Social Activities During COVID-19 was conducted from June 15th-21st, 2020, there is an undercoverage of residents of the 10 provinces that turned 15 since July 31, 2019. There is also undercoverage of those without internet access. This undercoverage is greater amongst those age 65 years and older.

4.1.3 Measurement errors

Measurement errors (or sometime referred to as response errors) occur when the response provided differs from the real value; such errors may be attributable to the respondent, the questionnaire, the collection method or the respondent's record-keeping system. Such errors may be random or they may result in a systematic bias if they are not random. It is very costly to accurately measure the level of response error and very few surveys conduct a post-survey evaluation.

4.1.4 Processing errors

Processing error is the error associated with activities conducted once survey responses have been received. It includes all data handling activities after collection and prior to estimation. Like all other errors, they can be random in nature, and inflate the variance of the survey's estimates, or systematic, and introduce bias. It is difficult to obtain direct measures of processing errors and their impact on data quality especially since they are mixed in with other types of errors (nonresponse, measurement and coverage).

4.2 Sampling errors

Sampling error is defined as the error that results from estimating a population characteristic by measuring a portion of the population rather than the entire population. For probability sample surveys, methods exist to calculate sampling error. These methods derive directly from the sample design and method of estimation used by the survey.

The most commonly used measure to quantify sampling error is sampling variance. Sampling variance measures the extent to which the estimate of a characteristic from different possible samples of the same size and the same design differ from one another. For sample designs that use probability sampling, the magnitude of an estimate's sampling variance can be estimated.

Factors affecting the magnitude of the sampling variance for a given sample size include:

1. The variability of the characteristic of interest in the population: the more variable the characteristic in the population, the larger the sampling variance.
2. The size of the population: in general, the size of the population only has an impact on the sampling variance for small to moderate sized populations.
3. The response rate: the sampling variance increases as the sample size decreases. Since non-respondents effectively decrease the size of the sample, nonresponse increases the sampling variance.
4. The sample design and method of estimation: some sample designs are more efficient than others in the sense that, for the same sample size and method of estimation, one design can lead to smaller sampling variance than another.

The standard error of an estimator is the square root of its sampling variance. This measure is easier to interpret since it provides an indication of sampling error using the same scale as the estimate whereas the variance is based on squared differences.

The coefficient of variation (CV) is a relative measure of the sampling error. It is defined as the estimate of the standard error divided by the estimate itself, usually expressed as a percentage (10% instead of 0.1). It is very useful for measuring and comparing the sampling error of quantitative variables with large positive values. However, it is not recommended for estimates such as proportions, estimates of change or differences, and variables that can have negative values.

It is considered a best practice at Statistics Canada to report the sampling error of an estimate through its 95% confidence interval. The 95% confidence interval of an estimate means that if the survey were repeated over and over again, then 95% of the time (or 19 times out of 20), the confidence interval would cover the true population value.

5.0 Weighting

The principle behind estimation in a probability sample such as those of the CPSS, is that each person in the sample "represents", besides himself or herself, several other persons not in the sample. For example, in a simple random 2% sample of the population, each person in the sample represents 50 persons in the population. In the terminology used here, it can be said that each person has a weight of 50.

The weighting phase is a step that calculates, for each person, his or her associated sampling weight. This weight appears on the microdata file, and must be used to derive estimates representative of the target population from the survey. For example, if the number of individuals who smoke daily is to be estimated, it is done by selecting the records referring to those individuals in the sample having that characteristic and summing the weights entered on those records. The weighting phase is a step which calculates, for each record, what this number is. This section provides the details of the method used to calculate sampling weights for the CPSS3 – Resuming Economic and Social Activities During COVID-19.

The weighting of the sample for the CPSS3 – Resuming Economic and Social Activities During COVID-19 has multiple stages to reflect the stages of sampling, participation and response to get the final set of respondents. The following sections cover the weighting steps to first create the panel weights, then the weighting steps to create the survey weights for CPSS3 – Resuming Economic and Social Activities During COVID-19.

5.1 Creating the Panel Weights

Four consecutive rotate-out samples of households from the LFS were the starting point to form the panel sample of the CPSS. Since households selected from the LFS samples are the starting point, the household weights from the LFS are the first step to calculating the panel weights.

5.1.1 Household weights

Calculation of the Household Design Weights – HHLD_W0, HHLD_W1

The initial panel weights are the LFS subweights (SUBWT). These are the LFS design weights adjusted for nonresponse but not yet calibrated to population control totals. These weights form the household design weight for the panel survey (HHLD_W0).

Since only four rotate-outs were used, instead of the six used in a complete LFS sample, these weights were adjusted by a factor of 6/4 to be representative. The weights after this adjustment were called HHLD_W1.

Calibration of the Household Weights – HHLD_W2

Calibration is a step to ensure that the sum of weights within a certain domain match projected demographic totals. The SUBWT from the LFS are not calibrated, thus HHLD_W1 are also not calibrated. The next step is to make sure the household weights add up to the control totals by household size. Calibration was performed on HHLD_W1 to match control totals by province and household size using the size groupings of 1, 2, or 3+.

5.1.2 Person Panel weights

Calculate Person Design Weights – PERS_W0

One person aged 15 or older per household was selected for the CPSS – Sign-Up, the survey used to create the probability panel. The design person weight is obtained by multiplying HHLD_W2 by the number of eligible people in the dwelling (i.e. number of people aged 15 years and over).

Removal of Out of Scope Units – PERS_W1

Some units were identified as being out-of-scope during the CPSS – Sign-Up. These units were given a weight of PERS_W1 = 0. For all other units, PERS_W1 = PERS_W0. Persons with a weight of 0 are subsequently removed from future weight adjustments.

Nonresponse/Nonparticipation Adjustment – PERS_W2

During collection of the CPSS – Sign-Up, a certain proportion of sampled units inevitably resulted in nonresponse or nonparticipation in the panel. Weights of the nonresponding/nonparticipating units were redistributed to participating units. Units that did not participate in the panel had their weights redistributed to the participating units with similar characteristics within response homogeneity groups (RHGs).

Many variables from the LFS were available to build the RHG (such as employment status, education level, household composition) as well as information from the LFS collection process itself. The model was specified by province, as the variables chosen in the model could differ from one province to the other.

The following variables were kept in the final logistic regression model: education_lvl (education level variable with 10 categories), nameissueflag (a flag created to identify respondents not providing a valid name), elg_hhldsize (number of eligible people for selection in the household), age_grp (age group of the selected person), sex, kidsinhhld (an indicator to flag whether or not children are present in the household), marstat (marital status with 6 categories), cntrybth (an indicator if the respondent was born in Canada or not), lfsstat (labour force status of respondent with 3 categories), nocs1 (the first digit of National Occupational Classification code of the respondent if employed, with 10 categories), and dwelrent (an indicator of whether the respondent dwelling is owned or rented). RHGs were formed within provinces. An adjustment factor was calculated within each response group as follows:

$\frac{\text{Sum of weights of respondents and nonrespondents}}{\text{Sum of weights of respondents}}$

The weights of the respondents were multiplied by this factor to produce the PERS_W2 weights, adjusted for panel nonparticipation. The nonparticipating units were dropped from the panel.

5.2 Creating the CPSS3 weights

Surveys of the CPSS start with the sample created from the panel participants. The panel is comprised of 7,242 individuals, each with the nonresponse adjusted weight of PERS_W2.

Calculation of the Design Weights – WT_DSGN

The design weight is the person weight adjusted for nonresponse calculated for the panel participants (PERS_W2). No out-of-scope units were identified during the survey collection of CPSS3 – Resuming Economic and Social Activities During COVID-19. Since all units were in-scope, WT_DSGN =PERS_W2 and no units were dropped.

Nonresponse Adjustment – WT_NRA

Given that the sample for CPSS was formed by people having agreed to participate in a web panel, the response rates to the survey were relatively high. Additionally, the panel was designed to produce estimates at a national level, so sample sizes by province were not overly large. As a result, nonresponse was fairly uniform in many provinces. This resulted in having only one response homogeneity group (RHG) in Prince Edward Island. For the other provinces, the RHGs were formed by some combination of age group, sex, education level, rental status, LFS status, whether or not children are present in the household, eligible household size, and the first digit of the National Occupational Classification (NOC) code for respondents who are employed. An adjustment factor was calculated within each response group as follows:

$\frac{\text{Sum of weights of respondents and nonrespondents}}{\text{Sum of weights of respondents}}$

The weights of the respondents were multiplied by this factor to produce the WT_NRA weights, adjusted for survey response. The nonresponding units were dropped from the survey.

Calibration of Person-Level Weights – WT_FINL

Control totals were computed using LFS demography projection data. During calibration, an adjustment factor is calculated and applied to the survey weights. This adjustment is made such that the weighted sums match the control totals. Most social surveys calibrate the person level weights to control totals by sex, age group and province. For CPSS3, calibration by province was not possible, since there were very few respondents in some categories in the Atlantic and Prairie Provinces. In addition, there were very small counts for male respondents aged 15 to 24 in the Atlantic Provinces. For this reason, the control totals used for CPSS3 – Resuming Economic and Social Activities During COVID-19 were by age group and sex by geographic region, where the youngest age group for males in the Atlantic region was collapsed with the second youngest. The next section will include recommendations for analysis by geographic region and age group.

5.3 Bootstrap Weights

Bootstrap weights were created for the panel and the CPSS3 – Resuming Economic and Social Activities During COVID-19 survey respondents. The LFS bootstrap weights were the initial weights and all weight adjustments applied to the survey weights were also applied to the bootstrap weights.

6.0 Quality of the CPSS and Survey Verifications

The probability panel created for the CPSS is a pilot project started in 2019 by Statistics Canada. While the panel offers the ability to collect data quickly, by leveraging a set of respondents that have previously agreed to participate in multiple short online surveys, and for whom an email address is available to expedite survey collection, some aspects of the CPSS design put the resulting data at a greater risk of bias. The participation rate to the panel is lower than typically experienced in social surveys conducted by Statistics Canada which increases the potential nonresponse bias. Furthermore, since the surveys of the CPSS are all self-complete online surveys, people without internet access do not have the means to participate in the CPSS and therefore are not covered.

When the unweighted panel was compared to the original sample targeted to join the panel, in particular there was an underrepresentation of those aged 15-24, those aged 65 and older, and those with less than a high school degree. These differences were expected due to the nature of the panel and the experience of international examples of probability panels. Using LFS responding households as the frame for the panel was by design in order to leverage the available LFS information to correct for the underrepresentation and overrepresentation experienced in the panel. The nonresponse adjustments performed in the weighting adjustments of the panel and the survey respondents utilised the available information to ensure the weights of nonresponding/nonparticipating units went to similar responding units. Furthermore, calibration to age and sex totals helped to adjust for the underrepresentation by age group.

Table 6.1 shows the slippage rates by certain domains post-calibration of CPSS3 – Resuming Economic and Social Activities During COVID-19. The slippage rate is calculated by comparing the sum of weights in the domain to that of the control total based off of demographic projections. A positive slippage rate means the sample has an over-count for that domain. A negative slippage rate means the survey has an under-count for that domain. Based on the results shown in Tables 6.1 and 6.2, it is recommended to only use the data at the geographical levels and age groups where there is 0 slippage. That is nationally, by geographic region (Maritime Provinces, Quebec, Ontario, Prairie Provinces, and British Columbia), and by the four oldest age groups.

Table 6.1 Slippage rates by geographic region

Area	Domain	n	Slippage Rate
Geography	CanadaTable 6.1 Footnote 1	4,209	0%
	Prince Edward Island	98	12.2%
	Newfoundland and Labrador	119	-7.1%
	Nova Scotia	231	3.4%
	New Brunswick	194	-1.9%
	Quebec	693	0%
	Ontario	1,232	0%
	Manitoba	342	-2.1%
	Saskatchewan	290	6.5%
	Alberta	459	-1.0%
	British Columbia	551	0%
Footnote 1 Based on the 10 provinces; the territories are excluded Return to table 6.1 footnote 1 referrer

Table 6.2 Slippage rates by age group

Area	Domain	n	Slippage Rate
Age group	15 to 24 years	236	3.2%
	25 to 34 years	510	-2.7%
	35 to 44 years	711	0%
	45 to 54 years	678	0%
	55 to 64 years	924	0%
	65 years and older	1,150	0%

After the collection of CPSS3 – Resuming Economic and Social Activities During COVID-19, a small study was conducted to assess the potential bias due to the lower response rates and the undercoverage of the population not online. The LFS data was used to produce weighted estimates for the in-scope sample targeted to join the probability panel (using the weights and sample from PERS_W1). The same data was used to produce weighted estimates based on the set of respondents from the CPSS3 survey and the weights WT_FINL. The two set of estimates were compared and are shown in Table 6.3. The significant differences are highlighted.

Table 6.3 Changes in estimates due to nonparticipation in the CPSS and the COVID-19 survey

Subject	Recoded variables from 2019 LFS	Estimate for in-scope population (n=31,628)	Estimate for W3 of CPSS (n=4,209)	% Point Difference
Education	Less than High School	15.5%	13.7%	-1.7%
	High School no higher certification	25.9%	25.5%	-0.4%
	Post-secondary certification	58.6%	60.8%	2.2%
Labour Force Status	Employed	61.2%	61.6%	0.5%
	Unemployed	3.4%	3.3%	-0.1%
	Not in Labour Force	35.3%	35.0%	-0.3%
Country of Birth	CanadaTable 6.3 Footnote 1	71.7%	75.0%	3.3%
Marital Status	Married/Common-lawTable 6.3 Footnote 1	60.4%	61.1%	0.7%
	Divorced, separated, widowedTable 6.3 Footnote 1	12.8%	11.6%	-1.2%
	Single, never married	26.9%	27.3%	0.5%
Kids	Presence of childrenTable 6.3 Footnote 1	31.7%	34.6%	2.9%
Household Size	Single person	14.4%	13.9%	-0.6%
	Two person HH	34.8%	37.3%	2.5%
	Three or more people	18.4%	18.7%	0.4%
Eligible people for panel	One eligible person aged 15+	15.9%	15.5%	-0.4%
	Two eligible peopleTable 6.3 Footnote 1	49.3%	53.2%	3.9%
	Three or more eligible people	34.8%	31.4%	-3.5%
Dwelling	Apartment	12.1%	11.8%	-0.3%
	RentedTable 6.3 Footnote 1	24.8%	25.0%	0.2%
Occupational Code	Management occupations (NOC0)	6.0%	5.9%	-0.1%
	Business Finance and Administration (NOC1)	10.7%	10.9%	0.2%
	Natural and Applied Sciences and related occupations (NOC2)	5.2%	6.1%	0.8%
	Health Occupations (NOC3)	4.7%	4.5%	-0.2%
	Occupations in education, law and social, community and government services (NOC4)	7.6%	8.2%	0.6%
	Occupations in art, culture, recreation and sports (NOC5)	2.5%	2.8%	0.3%
	Sales and service occupations (NOC6)	16.6%	16.8%	0.2%
	Trades, transport and equipment operators and related occupations (NOC7)	9.6%	10.0%	0.4%
	Natural resources, agriculture and related production occupations (NOC8)	1.6%	1.8%	0.2%
	Occupations in manufacturing and utilities (NOC9)	2.9%	2.3%	-0.6%
Footnote 1 Estimates that are significantly different at α= 5%. Return to first table 6.3 footnote 1 referrer

While many estimates do not show significant change, the significant differences show that some bias remains in the CPSS3 – Resuming Economic and Social Activities During COVID-19. There is an underrepresentation of those where there were three or more eligible participants for the panel. And there is an overrepresentation of people born in Canada, households with two persons in total, and those where there were two eligible participants for the panel. These small differences should be kept in mind when using the CPSS3 – Resuming Economic and Social Activities During COVID-19 survey data. Investigation about differences in estimates is ongoing, and as evidence of differences are identified, strategies are being tested to improve the methodology from one wave of the survey to the next.