Table of contents

• Message from the Canadian Statistics Advisory Council
• Executive summary
• 1. Introduction
• 2. National data strategy
  • 2.1. Critical data gaps
    • Data gaps on health and health care
    • Data gaps on racialized groups and Indigenous peoples
  • 2.2. Serious imbalances in funding statistical programs
• 3. Privacy and data sharing
  • 3.1. Provincial and territorial data sharing
• 4. Microdata access
  • 4.1 Modernizing the Microdata Access Program
• Definitions
• Endnotes

Executive summary

Statistics Canada's central role as an independent national statistical organization has never been more critical to meeting the need for timely and high-quality statistics in Canada. Having an independent and trusted source of official statistics provides a solid foundation for government accountability and evidence-based decision making by both the public and the private sectors for the benefit of all Canadians.

The fast pace of social and economic change is affecting the kinds of data and analyses Canadians need. There has also been a dramatic shift in how Canadians receive information, with a proliferation of information from new sources, such as social media. New tools are being used to collect, process, transform and visualize information. For Canada to succeed in this dynamic digital economy, Statistics Canada must play a central leadership role, coordinating with governments and organizations to produce coherent and trusted national statistical information.

Canadians have provided personal data to Statistics Canada for over 100 years. The confidentiality of their information is protected under the Statistics Act, and, under federal data protection laws, Statistics Canada must respect the privacy of Canadians. There should be no conflict between respect for the privacy of Canadians and the need for Canadians to provide data to Statistics Canada.

1. Introduction

Statistics Canada's central role as an independent national statistical organization has never been more critical to meeting the need for timely and high-quality statistics in Canada. Having an independent and trusted source of official statistics provides a solid foundation for government accountability and evidence-based decision making by both the public and the private sectors for the benefit of all Canadians. These decisions affect everybody's daily lives, including their health, where they live, where they work and their wages.

Fundamental to public trust is the clear independence of the country's national statistical office, where high-quality statistics and pertinent statistical analyses are produced with objective methods and with outputs that are accessible to everyone. The requirement that statistical information not be subject to political pressure and not serve special interests must be well recognized. This way, even people who may not trust their government can trust the statistical results and, just as importantly, entrust their information to Statistics Canada.

Canadians have provided personal data to Statistics Canada for over 100 years. The confidentiality of their information is protected under the Statistics Act, and, under federal data protection laws, Statistics Canada must also respect the privacy of Canadians. There should be no conflict between respect for the privacy of Canadians and the need for Canadians to provide data to Statistics Canada.

The fast pace of social and economic change is affecting the kinds of data and analyses Canadians need. For example, with the growth of online shopping, Statistics Canada requires new methods to measure consumer spending. Data on consumer spending are used to produce the Consumer Price Index, which Canadians depend on as a measure of inflation that affects wages, pensions, the cost of goods and interest rates. There has also been a dramatic shift in how Canadians receive information, with a proliferation of information from new sources, such as social media. New tools are being used to transform and visualize information, with significant increases in the flows of information, the extent of interconnectedness, and the development of increasingly powerful artificial intelligence software.

Not all available data sources are of good quality, nor do they all take measures to protect the privacy of personal information. Big datasets (usually characterized by their high volumes of data, speed of updates and variety of formats) and web-scraped data (data extracted from websites) are important new sources of data. However, their value for statistical analysis often has significant limitations, such as the underrepresentation of people with certain social or economic characteristics.

For Canada to succeed in this dynamic digital economy, Statistics Canada's role is key. The agency not only has the mandate to produce high-quality national social and economic measures, as well as more disaggregated statistical portraits, it also must play a central leadership role in coordinating data collection and integration with governments and organizations to produce coherent national statistical information for the benefit of all Canadians. This includes supporting leading-edge analysis of this statistical information.

Statistics Canada is responding to these challenges by developing, piloting and deploying new data sources, collection techniques and modelling to add depth and agility to its statistical programs. It has also engaged with Canadians in new ways—for example, using social media to encourage participation in web panel surveys and crowdsourcing surveys. At the same time, the federal government needs to seriously commit to starting a dialogue to address persistent, systemic data gaps. In some key sectors, fragmented data and an unwillingness to share data across jurisdictions have hampered Statistics Canada's ability to create needed nationwide datasets on a timely basis to address the country's most complex and dynamic challenges.

2. National data strategy

Nationwide data are a key strong foundation for decision makers and governments at all levels, as well as the general public, to understand and address important social, health, economic, environmental and energy issues facing Canadians.

Canada does not have a proactive national data strategy that considers the information needs of both today and the future and that puts in place new data sources to inform and anticipate emerging issues and concerns. Throughout its history, Statistics Canada has continually modernized its statistical programs to provide Canadians with the nationwide data and statistical information they need.

Its current modernization initiative is in response to a rapidly evolving digital economy and society. However, there are important data gaps in sectors such as health, energy and the environment, and a lack of sociodemographic detail, including about racialized and Indigenous groups, in social and economic indicators.

New governance mechanisms are required to formally open new dialogues on national data needs and how to best collect and share this information. This must be led by Statistics Canada, in accordance with its mandate, with the full support and funding of the federal government. It must also include all levels of government and statistical organizations. Without a national data strategy, bureaucratic inertia and other hindrances to collecting and sharing statistical information across jurisdictions will continue to outweigh efforts to develop needed nationwide data accessible to all Canadians.

A national data strategy could include First Nations, Inuit and Métis organizations that are planning, implementing and exercising control over the delivery of services to their communities. The nature of the data and analytical skills they require is changing and is more specific to regional and local issues that affect their peoples. Collection for new data needs could be done in partnership with Statistics Canada and other departments. This includes, for example, the need for data to support indicators of well-being, resiliency, understanding, and measurable progress on reconciliation and economic measures.

Statistics Canada is well positioned to lead the various dialogues on national data and information needs. Its proven operational infrastructure provides an essential foundation, given that the agency has developed statistical data from hundreds of federal, provincial and territorial administrative data files. Its expertise in developing high-quality data using standardized concepts and classifications is recognized internationally. Statistics Canada also has the ability, with the required confidentiality protections in place, to combine and link these data with data from other sources to produce the statistical information needed to address national data gaps.

Statistics Canada must build on the new avenues of collaboration created as governments and experts came together in response to the COVID-19 pandemic. This involves working with governments at all levels, other organizations, and new public and private sector partners to produce nationally comparable data that are representative of all Canadians. It also involves developing a close relationship with Canadians to better understand how to maintain their trust as an independent national statistical office and being transparent with regard to the privacy and confidentiality of Canadians' personal information.

Statistical data requirements and funding should be explicitly included in the planning for all federal government programs. There is presently no standard or coordinated way to assess priority data requirements within the federal government. Statistics Canada works closely with most federal departments and organizations in reviewing their data needs. However, these discussions tend to involve only one or two departments at a time, reducing the scope and richness of the information collected. Statistics Canada is also often not actively consulted in the planning of new federal programs, limiting the statistical measures that should be produced.

The federal government should enable Statistics Canada to work collectively with all departments to establish, maintain and act upon a national data strategy that recognizes the interactions between economic, social, health, environmental and energy issues. Data and statistical information should be formally integrated in federal planning processes to more aptly measure, monitor and evaluate federal program outcomes.

2.1 Critical data gaps

It is essential that the country's decision makers have high-quality data and statistical information that represent all regions of Canada and the full range of experiences of individual Canadians. Statistics Canada's current statistical output is vast. Users can access statistical tables, data files and analyses on just about any topic of interest.

At the same time, these data do not always tell the whole story. Information that spans the social, economic and geographic spectrum is often not available. The rapid rise of the digital economy and the impacts of climate change on the environment are examples of areas where new types of data are required to measure impacts on Canadian society and on the Canadian economy. Understanding the barriers faced by racialized groups and Indigenous Peoples also requires more detailed and disaggregated data on employment, income, health and justice.

This year's report focuses on two areas where critical data gaps have long existed. These have become especially evident recently, with the COVID-19 pandemic and increased global awareness of racial inequities.

Data gaps on health and health care

Experts have been saying for years that national health data in Canada are seriously deficient, resulting in inadequate measures of the population's health status and the functioning of the health care sector. Rectifying this situation must be a top national statistics priority. Federally, health data are collected primarily by Statistics Canada (health status and health care) and the Canadian Institute for Health Information (health system performance).

A substantial amount of health data presently exists within provincial and territorial jurisdictions, and it is increasing as hospitals and community clinics adopt new technologies to collect and use health information. This information has tremendous potential for national research on health care and population health. Yet Canada-wide health data are largely fragmented, often unavailable and inconsistent.

This became quickly apparent during the COVID-19 pandemic, when key health data were seriously lacking and inadequate for providing decision makers with the statistical indicators they needed. For example, basic information on COVID-19 confirmed cases and deaths, as well as more detailed information such as that found in hospital records, suffered from delays, incomplete and missing data, and inconsistent definitions across jurisdictions.

These data gaps and inconsistencies have led to serious shortcomings in the timeliness, completeness and quality of Canadian health care and health outcome data. In turn, this has greatly impaired the ability of governments at all levels to monitor and assess the evolution of the pandemic, let alone address serious health issues in Canada.

Barriers to national health data

Provincial, territorial and regional health authorities collect institution-specific health data primarily to administer health care services within their own jurisdictions. Consistency across regions in concepts, definitions, specific data elements collected and completeness of records is often not a priority. It takes months and sometimes longer for information as basic as that from death certificates to become part of the nationwide data that are needed to track deaths related to the pandemic. The methods used to collect medical records from hospitals and community clinics also range widely, from faxed documents to electronic records transferred directly to centralized health care databases. As well, the various software systems designed to collect and retain information such as medical records are often incompatible, limiting the information public health agencies have on important areas.

Some health authorities have invoked provincial data protection laws as barriers to sharing certain information outside their borders. However, the sharing of identifiable data with Statistics Canada is permitted under their data protection laws, in accordance with the Statistics Act. There is also a strong reticence on the part of many provincial and territorial health organizations and communities to share data across health care systems within Canada. Some health officials do not feel that their programs should be subject to scrutiny outside their jurisdiction.

A national health data infrastructure is essential both for supporting health policies and the health care Canadians receive and, more specifically, for managing emergencies such as the current pandemic. The federal government transfers billions of dollars annually to the provinces and territories to help fund health care services, with increases likely in the future for long-term care and possibly pharmacare. The funding of these services must include a provision for nationally comparable health data to measure the state of health and health care in Canada, and the functioning of the health care sector.

Data gaps on racialized groups and Indigenous peoples

The ability to address barriers faced by racialized groups and Indigenous peoples in Canada is seriously hampered by the lack of timely, consistent and disaggregated data.

While the data gaps are not new, recent events in Canada and the United States have brought them to the forefront. For example, the data needed to properly examine the impact of the COVID-19 pandemic on the health and well-being of racialized groups, particularly Black Canadians, and Indigenous communities have not been available. Public outcry has increased following the deaths of Black people at the hands of police officers in the United States and Canada. Supporters of movements such as Black Lives Matter and Indigenous Lives Matter are demanding reforms to address systemic discrimination in areas such as health, employment, housing and justice.

Canada is among the world's most ethnically diverse countries. More than one-fifth of Canadians identify as belonging to a visible minority group. This proportion is projected to increase, as they represent a large majority of new immigrants to Canada, particularly in large cities.

Despite their growing numbers, there have been relatively few national studies of how these groups are faring in Canada. With the census as the main source of information, reports tend to be descriptive profiles of immigrants, visible minorities and Indigenous groups, including general analyses of changes in housing, employment and income. Much of the information available to decision makers is highly aggregated, partial and anecdotal.

Canada needs much more comprehensive data to inform the current debates on the barriers many Canadians face to fully engage in all aspects of society and the economy. It is essential to look beyond the census for high-quality statistical information disaggregated by racialized and Indigenous groups that integrate elements such as family, housing, education, employment, income and well-being.

Surveys generally do not have a large enough sample size to produce detailed disaggregated data, though the Canadian Community Health Survey, the Indigenous Peoples Survey, and more recently, the Labour Force Survey do provide general trends for visible minorities and for Indigenous people living off reserve.

To make inroads in developing a national infrastructure for data by race and by Indigenous group, the focus must include governments' administrative data in areas such as labour, education, health, housing and justice. While a large number of federal, provincial and territorial government departments and organizations already share their administrative data with Statistics Canada, few of these sources include data by racialized and Indigenous groups.

There is pressure from many Canadians and decision makers for government departments to begin incorporating information on race and on Indigenous peoples into their datasets for statistical purposes. Some Canadians may hesitate to share this information with government authorities, but, at the same time, many within these groups have long called for authorities such as police forces to collect this information.

There have been encouraging initiatives. Statistics Canada is presently in discussions with the Public Health Agency of Canada and the Canadian Institute for Health Information on how nationally standardized concepts and definitions must be applied to their planned collection of race-based health data. Also, Statistics Canada and the country's police chiefs have agreed to collect this information when compiling information on victims and accused people to address data gaps for Indigenous peoples and other sociodemographic groups. Statistics Canada has also created the Advisory Committee on Ethnocultural and Immigration Statistics and the Working Group on Black Communities in Canada to counsel the agency.

Statistics Canada is engaging with national Indigenous organizations to provide statistical capacity building that is grounded in the needs of Indigenous peoples. Efforts are being made to identify where data gaps exist and how Statistics Canada data sources and expertise can help improve data quality and access, and support decision making. Statistics Canada's "Statistics on Indigenous peoples" web portal enables users to access data on Indigenous communities on topics such as children and families, health and well-being, education, and work.

Nevertheless, critical data gaps remain, and more needs to be done to address them.

2.2 Serious imbalances in funding statistical programs

It is important that public and private sector decision makers have high-quality data and statistical information that represent all regions of Canada and the full range of circumstances of individual Canadians. Stable core funding for Statistics Canada's programs is essential to having this information. Statistics Canada is given the resources to produce economic indicators for about 20 key areas of economic activity, such as the gross domestic product (GDP), consumer prices and employment. During the COVID-19 pandemic, the agency has been able to continue producing these data, which are critical for assessing the economic impact of the crisis. However, there is an increasing focus on social and environmental data, which measure other important contributors to well-being, beyond the traditional economic measures. This is reflected in a growing international consensus on the need to go beyond the GDP, recognizing that social, health, economic and environmental factors all affect people's well-being. How these various factors interact and affect each other also has significant impacts on individuals, as well as on national and regional economies.

Many of Statistics Canada's key social statistics programs, and certain economic, environmental and energy programs, are largely dependent on ad hoc funding and cost-recovery transfers from federal departments. Support for these programs is often based on the siloed needs of one or two departments. These programs' vulnerability to cuts can significantly affect important and more comprehensive data needs and areas of research. Stable core funding for these programs is essential to support a national data strategy that includes all factors affecting society and the economy.

3. Privacy and data sharing

Statistics Canada has the authority under the Statistics Act to collect personal data to produce the social and economic statistical information that forms the foundation for data-driven decision making for the well-being of all Canadians. For over 100 years, Canadians have provided this information to Statistics Canada, which has maintained the confidentiality of these data and produced statistics without revealing identifiable information about individuals, in accordance with the Statistics Act.

It is essential that citizens understand the importance of evidence-based decision making for Canada to succeed in the new data economy. Governments also need to recognize that traditional ways of collecting information are no longer sufficient. They must support Statistics Canada in its work to provide the key statistical information needed by governments and Canadians to address the increasingly complex and dynamic challenges they face.

There should be a more extensive conversation with Canadians about the alignment between privacy and the need for data for effective decision making. This discussion would facilitate mutual understanding by Canadians and governments of the issues at hand and provide a forum for the exchanges that need to occur for Canada to truly benefit from an independent and trusted source of official statistics. The country needs a solid foundation for government accountability and evidence-based decision making by both the public and the private sectors.

Statistics Canada has been working with expert groups in Canada and with national statistical offices from around the world to explore the possibility of producing high-quality statistics from digital administrative data—both data from governments (e.g., tax files, health care encounters, property tax assessments, driver's licences) and big data from the private sector (e.g., retail transactions, credit card transactions, mortgages, other debt). Being able to use these new, primarily electronic, sources of data will enable Statistics Canada to address the critical needs for new and more disaggregated data in Canada—data that are integrated across the social, health, economic, environmental and energy domains.

Many countries are reviewing their data protection laws, given both the dramatic increase in the prevalence and use of personal information from administrative data, and growing concerns about the data holdings of multinational social media companies. In doing so, they recognize the importance of collecting personal information for specific legitimate purposes, when done under the country's legal authority and in a transparent manner. For example, the European Union's General Data Protection Regulation recognizes the need for national statistical offices to access personal information, permitting the flow of the information for statistical research for the public good, without requiring consent.

In Canada, Statistics Canada's project to collect detailed data on banking and credit card transactions has drawn particular attention. These new sources of information are key to addressing emerging critical data gaps in Canada's economic and financial measures as a result of important changes to consumer patterns and debt. In response to concerns raised by some Canadians, Statistics Canada suspended its work to address them before proceeding with the project. The agency is also collaborating with the Office of the Privacy Commissioner to address concerns as a result of complaints it received about this project. After investigating these complaints, the Privacy Commissioner of Canada concluded that Statistics Canada was not in contravention of the Privacy Act.

The need for transparency on matters of privacy and confidentiality is essential to maintaining public trust. Statistics Canada must clearly inform Canadians why the information it collects is needed and explain the measures it takes to protect the confidentiality of Canadians' personal information. The need for transparency is especially heightened in this project, given the sensitivity of personal banking information and the volume and detail of information that may be collected.

Moving forward, the agency needs to engage with a focus on guiding principles to meet the rapidly changing data context. Statistics Canada is working in consultation with the Privacy Commissioner of Canada to develop a new methodology framework based on the principles of necessity and proportionality. This methodology framework, which the agency is sharing with the global statistical community, is a significant and thoughtful initiative.

The framework recognizes Statistics Canada's legal authority under the Statistics Act to collect personal information for statistical purposes, and Statistics Canada's legal obligation under the same act to ensure the confidentiality of this information. The framework also recognizes the country's data protection laws. These include the federal Privacy Act, which sets out how personal information held by the federal government and federal public sector institutions is used, stored and shared, and the Personal Information Protection and Electronic Documents Act, which sets out how organizations engaged in commercial activities must handle personal information.

3.1 Provincial and territorial data sharing

The provinces and territories have a long history of sharing administrative data with Statistics Canada in areas such as vital statistics, education and justice. Statistics Canada has the authority to collect these data under the Statistics Act, and most provinces and territories have provisions in their data protection laws to enable them to share data for statistical purposes for the public good.

For many years, some provincial and territorial health authorities have invoked provincial data protection laws as barriers to sharing certain health data. This has contributed in part to the poor state of national health data, as seen during the COVID-19 pandemic. In response to questions about privacy issues during the pandemic, the Privacy Commissioner of Canada stated that, "During a public health crisis, privacy laws still apply, but they are not a barrier to appropriate information sharing."

Discussions about data sharing must be broadened to include priority needs for national data. To make measurable progress, Statistics Canada must have federal government support to play a leadership role and build on the new avenues of government collaboration created in response to the pandemic.

Many provinces and territories are reviewing their data protection laws to take into account new technologies for collecting and sharing personal information. Statistics Canada must work with them to ensure that revisions to data protection laws recognize the importance of official national statistics and that there are no legislative ambiguities with regard to Statistics Canada's legal authority to collect data from their jurisdictions.

4. Microdata access

The need for a modern infrastructure to access Statistics Canada's microdata, including secure remote access, has never been greater, as duly authorized researchers look to inform governments and Canadians on issues such as the social and economic impacts of the COVID-19 pandemic.

To meet the specific information needs of Canadians, the agency has introduced web portals to transform complex data into easy-to-understand visuals. The "COVID-19: A data perspective" portal is a good example. Created in response to the pandemic, it provides governments and Canadians access in one place to a wide array of relevant health, social and economic statistical information with tables, infographics, interactive maps, data visualizations and statistical analyses.

For many years, Statistics Canada has provided students and researchers with a range of ways to access data, with strict security restrictions for access to confidential microdata. Non-confidential Public Use Microdata Files are used extensively by postsecondary students through the Data Liberation Initiative. Students and duly authorized researchers can also use the online Real Time Remote Access system available for most social surveys. The output is largely descriptive and useful for general findings and preliminary research activities. It presently requires knowledge of SAS programming, which limits access to the data for some researchers.

Confidential microdata can be accessed through Statistics Canada's Research Data Centres (RDCs). These are secure facilities located on university campuses that offer access to Statistics Canada's more detailed—and therefore most analytically powerful—data holdings. They include detailed microdata from Statistics Canada's household surveys, census data and an increasing number of administrative datasets such as the cancer registry. Since the opening of the first RDC in 2000, the Canadian Research Data Centre Network has expanded and now includes over 30 secure data laboratories in which over 2,000 duly authorized researchers across Canada conduct advanced quantitative social science and health research.

Secure access to anonymized business and economic microdata is provided to government researchers through the Canadian Centre for Data Development and Economic Research Program at Statistics Canada headquarters in Ottawa.

4.1 Modernizing the Microdata Access Program

The RDCs, with their physical data laboratories, have become outdated and are no longer able to adequately support Canada's research and analysis needs. The COVID-19 pandemic has made clear the need for Statistics Canada to transition from the RDCs' physical infrastructure to new distributed modes of access. Once a world leader, Statistics Canada has fallen behind. The agency is currently modernizing its microdata access infrastructure with more sophisticated datasets, secure remote access technologies and expansion of secure access to anonymized business data in the RDCs. This is a long-awaited initiative that will greatly improve the quality and depth of research and analysis in Canada across all sectors. However, the timeline of well into 2022 for full implementation of secure remote microdata access is too long and should be accelerated.

With the wealth of statistical information, data expertise and technical savvy found in public, academic and private institutions across the country, there are tremendous opportunities to transform how data are developed and used in Canada. Researchers in Canada currently have secure access to a vast amount of data from a wide range of sources, including from government administrative data sources, universities and the private sector. The explosion of big data and data analytics is also generating a growing pool of talented data scientists. A modernized research data access program will greatly facilitate and support the statistical research required to address the increasingly complex and multifaceted issues faced by Canadians.

Statistics Canada must also modernize and streamline its administrative processes, such as the authentication of researchers. Statistics Canada should look to international models such as that used in the Netherlands, where the authentication process includes a class of "duly authorized researchers" who may be required to take training on privacy and security and must be affiliated with a government department, university or institute for scientific research. As in Canada, research must be for statistical purposes as opposed to private commercial research.

There is also interest from national and regional Indigenous organizations in developing and implementing information and research data centres in their communities. This would provide Indigenous peoples with better access to Statistics Canada microdata and other Indigenous microdata on the health, social and economic well-being of Indigenous communities. Analytic capacity would also be expanded to include the use of new data, analytic techniques and technology to support research, planning and development and to build statistical capacity to assist vulnerable Indigenous communities.

Endnotes

1. "Factors associated with COVID-19-related death using OpenSAFELY," Nature, July 2020.
2. The Employment Equity Act defines members of visible minorities as "persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour." Many data users use visible minority as a proxy for race.
3. "Commissioner publishes framework to assessprivacy-impactful initiatives inresponse to COVID-19."
4. Statistics Netherlands, "Microdata: Conducting your own research."

Under the authority of the Statistics Act, Statistics Canada is hereby requesting the following information, which will be used solely for statistical and research purposes and will be protected in accordance with the provisions of the Statistics Act and any other applicable law. This is a mandatory request for data.

• Employment and remuneration
  • Gender of federally appointed judges
  • List of employees and work email addresses for Government employees working in Nunavut
• Revenue and expenditures
  • Data on support for businesses through the Business Innovation and Growth Support (BIGS) programs
• Other content related to Government
  • Data on capital projects and asset management

Employment and remuneration

Gender of federally appointed judges

What information is being requested?

Statistics Canada is requesting a list of federally appointed judges, including the following variables: first and last name (or a unique identifier), gender (or sex), type of court, location (of court), and appointment start date and appointment end date (or removal).

What personal information is included in this request?

This request contains personal information such as the first and last name (or unique personal identifier) and gender (or sex) of federally appointed judges. The personal identifiers are required to perform data processing activities only, such as removing duplicates from files, imputing missing values (if necessary) and tracking records over time. Only aggregate statistics will be disseminated.

What years of data will be requested?

Annual data as of 2018 is being requested.

From whom will the information be requested?

This information is being requested from the Office of the Commissioner for Federal Judicial Affairs Canada

Why is this information being requested?

Introduced in Budget 2018, the Gender Results Framework (GRF) represents the Government of Canada's vision for gender equality. The GRF contains 43 indicators designed to track how Canada is currently performing; define what is needed to achieve greater equality and determine how progress will be measured going forward.

Greater gender balance and diversity in the judicial system will enable the system to be more responsive to the differing needs and situations of all Canadians.

Statistics Canada is requesting this information to create and publish statistics on the gender distribution of federally appointed judges. These statistics will be used by policy makers and researchers to track the gender distribution of federally appointed judges over time.

Statistics Canada may also use the information for other statistical and research purposes.

Why were these organizations selected as data providers?

The Office of the Commissioner for Federal Judicial Affairs Canada (FJA) is responsible for collecting demographic information on judicial applicants and appointees based on voluntary disclosure by candidates through self-identification.

When will this information be requested?

December 2020 and onward (yearly)

What Statistics Canada programs will primarily use these data?

Gender Statistics Program

When was this request published?

December 3, 2020

List of employees and work email addresses for Government employees working in Nunavut

What information is being requested?

Statistics Canada is requesting employee information for government employees working in Nunavut to create the Nunavut Government Employee Survey (NGES) survey frame. The requested information includes employee lists containing their Personal Record Identifier (PRI), full name, sex, date of birth, department name, and work email addresses.

What personal information is included in this request?

This request contains personal information such as the employee's PRI, first name, last name, sex, date of birth, work email address, and department name.

Personal identifiers, including PRI, first name, last name, sex, date of birth and department name, are necessary to update the survey frame and facilitate data linkages for statistical purposes only. The data will be linked to federal employee payroll files, which are currently used by Statistics Canada, to create the NGES survey frame. Once the data are linked, all direct identifiers - such as names, addresses, telephone numbers, or any other identifying information - will be removed and the data will be anonymized.

What years of data will be requested?

The NGES is conducted every five years.

Data will be collected starting with the 2021 reference year.

From whom will the information be requested?

This information will be requested from government departments with employees located in Nunavut.

Why is this information being requested?

Article 23 of the Nunavut Agreement aims to increase Inuit participation in government employment to a representative level through an ongoing Nunavut Inuit Labour Force Analysis (NILFA). By strengthening Inuit representation in public institutions in Nunavut, the NILFA is supporting Inuit self-determination. As a signatory to the Nunavut Agreement, the NILFA is a federal government obligation.

Statistics Canada is requesting this data to verify and update employee payroll data (already held by Statistics Canada's Centre for Labour Market Information Division) which is used to create the Government of Canada component of the NGES frame. Work email address will also be used to send the electronic questionnaire link to Government employees in Nunavut.

As part of the NILFA, the NGES was designed to collect data on Inuit enrolled under the Nunavut Agreement regarding their availability, interest and level of preparedness for government employment. This data informs the development of Inuit Employment Plans and Pre-Employment Training Programs within both the Government of Nunavut, as well as the Government of Canada within Nunavut.

Nunavut Tunngavik Incorporated, which represents the interests of Inuit enrolled under the Nunavut Agreement, and a member of the NILFA Technical Working Group, supports this data acquisition. Without the acquisition of these data, Statistics Canada would not be able to ensure all individuals in the NGES target population are identified on the survey frame. Furthermore, the agency would not be able to send out the electronic questionnaire link to the survey sample of Government employees in Nunavut, ultimately excluding them from the survey.

Statistics Canada may also use the information for other statistical and research purposes.

Why were these organizations selected as data providers?

These organizations meet the definitions in Article 23 of the Nunavut Agreement because they are Federal departments that have offices in Nunavut, and Treasury Board is their employer.

When will this information be requested?

Starting in September 2020

What Statistics Canada programs will primarily use these data?

The Nunavut Government Employee Survey (NGES)

When was this request published?

February 10, 2025

Summary of Changes

Language has been updated to make it clear that this is an ongoing request as the NGES occurs every five years.

Revenue and expenditures

Data on support for businesses through the Business Innovation and Growth Support (BIGS) programs

What information is being requested?

Information on the support granted through the governmental BIGS programs. Such information includes the value of support, the transaction dates, the types of support, the names of the beneficiary enterprises, their business number, their contact information, the project numbers, the effective dates of the agreements, and the amounts of the agreements.

What personal information is included in this request?

This request does not contain any personal information.

What years of data will be requested?

Open-ended, data beginning with the 2007 reference year.

From whom will the information be requested?

From all federal departments or agencies that have Business Innovation and Growth Support (BIGS) programs.

Why is this information being requested?

Statistics Canada requires this information to produce and publish statistics on government programs that support business growth and innovation. These statistics will help Statistics Canada, together with the Treasury Board Secretariat, identify the impacts of business innovation and growth support programs. The data will also be used by policy makers, researchers and stakeholders to assess and measure the performance of these programs.

Statistics Canada may also use the information for other statistical and research purposes.

Why were these organizations selected as data providers?

These organizations collect information on the business innovation and growth programs they administer and keep that information up to date.

When will this information be requested?

Starting in April 2021

What Statistics Canada programs will primarily use these data?

Business Innovation and Growth Support (BIGS)

When was this request published?

March 26, 2021

Other content related to Government

Data on capital projects and asset management

What information is being requested?

Statistics Canada is requesting additional (new) data tables from the Integrated Capital Management System (ICMS) which is a web-based application that manages key ISC-funded First Nations community assets and all First Nation infrastructure investments projects. The ICMS includes organizational, funding, land, and financial data integrated for capital planning and oversight.

In addition to the information already held, Statistics Canada is formally requesting additional data tables on grants and contributions—including agreements, payments, and reports with Indigenous partners.

What personal information is included in this request?

No personal information is included in this request.

What years of data will be requested?

Annual data as of 2018, ongoing.

From whom will the information be requested?

Indigenous Services Canada

Why is this information being requested?

The information will be used by Statistics Canada solely for the purposes of the Act, that is, for statistical and research purposes only and not for administrative or regulatory purposes.

The Integrated Capital Management System (ICMS) data is being requested to support research projects aimed at validating findings from Statistics Canada's infrastructure surveys; supplementing survey results in reports; developing projections of on-reserve housing; testing methodologies—such as comparing infrastructure availability on and off reserve; contributing to the development of administrative data related to buildings; and identifying infrastructure gaps affecting Indigenous peoples.

Why were these organizations selected as data providers?

Indigenous Services Canada is the only organization that holds this data.

When will this information be requested?

August 2025

What Statistics Canada programs will primarily use these data?

Programs that have used the ICMS data include Demography, for projecting on-reserve housing; Canada's Core Infrastructure Survey, for validating findings; the Centre for Special Business Projects, for testing methodologies to compare infrastructure availability on and off reserve; and the Statistical Business Register, for developing an administrative dataset on buildings.

When was this request published?

September 17, 2025

Summaries of disseminated products

Dissemination Plan

CHMS Content summary for cycles 1 to 8

• The content summary document is divided into separate tables which list all of the content topics in the survey by age group of respondent. There are tables on the household questionnaire and specimen collection, mobile examination centre (MEC) physical measures and specimen collection, MEC questionnaire, laboratory biospecimen, laboratory indoor air sample tests and laboratory tap water sample tests. The laboratory tables also provide information on analytical ranges and conversion factors.

Data User Guide – Cycles 1 to 6

• The user guide includes information on survey content, sample design, data collection, data processing, weighting, data quality, file usage, as well as guidelines for tabulation, analysis and release.
• Cycle 6 release to start October 2020

Derived Variables (DVs) documentation – Cycles 1 to 6

• There are separate DV documents for the following types of DVs: household and mobile examination centre (MEC), medication, activity monitor, non-environmental laboratory measures, fluoride and volatile organic compounds, and other environmental laboratory measures.
• Cycle 6 release to start October 2020

Data Dictionaries – Cycles 1 to 6

• There are separate data dictionaries for the following data files: household full sample, mobile examination centre full sample, medication full sample, hearing full sample, activity monitor full sample, activity monitor subsample, indoor air subsample – household level, indoor air subsample – person level, fasting blood subsample, red blood cell fatty acids subsample, fluoride household level subsample – in tap water, VOC household level subsample – in tap water, fluoride person level subsample – in urine and tap water, VOC person level subsample – in blood and tap water, non-environmental lab data full sample, environment lab blood and urine full sample, acrylamide (environmental blood subsample), methyl mercury (environmental blood subsample), NNAL and glucoronides (environmental urine subsample), and environment urine main subsample. Note: not all subsamples are available in every cycle
• Cycle 6 release to start October 2020

Supporting documentation for the climate and air quality file – Cycle 3
Sampling Documentation – Cycle 1 to 5
Presentations on using CHMS data – Cycles 1 to 5

• CHMS Data User Workshop

Instructions for Combining Multiple Cycles of Canadian Health Measures Survey (CHMS) Data
Postal Code File – justification needed

• Contains postal code information for every respondent in the survey.

Relationship File – justification needed

• Identifies the relationship between 2 respondents in the same household (adult/child)

Relationship File – Feasibility Report

• Using the relationship files and paired respondent data in the CHMS: Feasibility study – an update

CHMS Errata – Cycles 1 to 6

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For more information or to obtain copies of the documents in the list above, please contact Statistics Canada's Statistical Information Service (toll-free 1-800-263-1136; 514-283-8300; infostats@statcan.gc.ca).

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 fifth survey of the CPSS is CPSS5 – Technology Use and Cyber Security during the Pandemic. It was administered from September 14, 2020 until September 20, 2020.

Any questions 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 for 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.

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.

CPSS5 – Technology Use and Cyber Security during the Pandemic

All participants of 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 CPSS5 and a Secure Access Code to complete the survey online. Collection for the survey began on September 14th, 2020. Reminder emails were sent on September 15, September 17 and September 19. On September 17 in the afternoon, SMS reminders were sent (where a phone number was available) to sampled people aged 18 to 24 to encourage them to respond. The application remained open until September 20, 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 CPSS5 – Technology Use and Cyber Security during the Pandemic. 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 CPSS5 – Technology Use and Cyber Security during the Pandemic.

Table 4.1.1a Participation in 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 for the Panel for CPSS
n	31,896	31,628	7,242	22.9%

Table 4.1.1b Response Rates to the CPSS5 – Technology Use and Cyber Security during the Pandemic

	Stages of the Sample for the CPSS5 – Technology Use and Cyber Security during the Pandemic
	Panelists for the CPSS (with valid email addresses)	Respondents of CPSS5 – Technology Use and Cyber Security during the Pandemic	Collection Response Rate for CPSS5 – Technology Use and Cyber Security during the Pandemic	Cumulative Response Rate
n	7,242	3,961	54.7%	12.5%

As shown in Table 4.1.1b, the collection response rate for the CPSS5 – Technology Use and Cyber Security during the Pandemic is 54.7%. However, when nonparticipation in the panel is factored in, the cumulative response rate to the survey is 12.5%. 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 CPSS5 – Technology Use and Cyber Security during the Pandemic 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 types 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 CPSS5 – Technology Use and Cyber Security during the Pandemic was conducted from September 14th-20th, 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 (sometimes 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 errors are the errors 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 errors are defined as the errors that result 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 CPSS5 – Technology Use and Cyber Security during the Pandemic.

The weighting of the sample for the CPSS5 – Technology Use and Cyber Security during the Pandemic 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 CPSS5 – Technology Use and Cyber Security during the Pandemic.

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 CPSS5 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 CPSS5 – Technology Use and Cyber Security during the Pandemic. 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. 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 CPSS5, 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 CPSS5 – Technology Use and Cyber Security during the Pandemic were by age group and sex by geographic region, where the youngest age group for males in the Atlantic region, collapsed with the second youngest age group. 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 CPSS5 – Technology Use and Cyber Security during the Pandemic 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 for 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 CPSS5 – Technology Use and Cyber Security during the Pandemic. 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 no 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	CanadaFootnote 1	3,961	0%
	Newfoundland and Labrador	118	-7.3%
	Prince Edward Island	82	8.7%
	Nova Scotia	234	3.0%
	New Brunswick	174	-0.6%
	Quebec	664	0%
	Ontario	1,145	0%
	Manitoba	314	-3.7%
	Saskatchewan	273	7.4%
	Alberta	423	-0.8%
	British Columbia	534	0%
Footnote 1 Based on the 10 provinces; the territories are excluded Return to footnote 1 referrer

Table 6.2 Slippage rates by age group

Area	Domain	n	Slippage Rate
Age group	15-24	195	3.2%
	25-34	446	-2.7%
	35-44	643	0%
	45-54	624	0%
	55-64	925	0%
	65+	1,128	0%

After the collection of CPSS5 – Technology Use and Cyber Security during the Pandemic, 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 CPSS5 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 W5 of CPSS (n=3,961)	% Point Difference
Education	Less than High SchoolTable 6.3 Footnote 11	15.5%	12.5%	2.9%
	High School no higher certification	25.9%	25.4%	0.5%
	Post-secondary certificationTable 6.3 Footnote 11	58.6%	62.0%	-3.4%
Labour Force Status	Employed	61.2%	62.7%	-1.5%
	Unemployed	3.4%	3.3%	0.1%
	Not in Labour Force	35.3%	34.0%	1.3%
Country of Birth	CanadaTable 6.3 Footnote 11	71.7%	76.2%	-4.5%
Marital Status	Married/Common-law	60.4%	61.2%	-0.8%
	Divorced, separated, widowed	12.8%	11.4%	1.3%
	Single, never married	26.9%	27.4%	-0.5%
Kids	Presence of childrenTable 6.3 Footnote 11	31.7%	34.6%	-2.9%
Household Size	Single person	14.4%	14.6%	-0.2%
	Two person HH	34.8%	36.4%	-1.6%
	Three or more people	18.4%	18.1%	0.2%
Eligible people for panel	One eligible person aged 15+	15.9%	16.1%	-0.2%
	Two eligible peopleTable 6.3 Footnote 11	49.3%	52.3%	-3.0%
	Three or more eligible peopleTable 6.3 Footnote 11	34.8%	31.7%	3.2%
Dwelling	Apartment	12.1%	12.0%	0.1%
	Rented	24.8%	24.9%	-0.1%
Occupational Code	Management occupations (NOC0)	6.0%	6.3%	-0.2%
	Business Finance and Administration (NOC1)	10.7%	11.2%	-0.5%
	Natural and Applied Sciences and related occupations (NOC2)Table 6.3 Footnote 11	5.2%	6.5%	-1.3%
	Health Occupations (NOC3)	4.7%	4.4%	0.4%
	Occupations in education, law and social, community and government services (NOC4)	7.6%	8.0%	-0.4%
	Occupations in art, culture, recreation and sports (NOC5)	2.5%	3.1%	-0.6%
	Sales and service occupations (NOC6)	16.6%	17.5%	-0.9%
	Trades, transport and equipment operators and related occupations (NOC7)	9.6%	9.3%	0.3%
	Natural resources, agriculture and related production occupations (NOC8)	1.6%	1.3%	0.4%
	Occupations in manufacturing and utilities (NOC9)	2.9%	2.3%	0.6%
Footnote 1 Estimates that are significantly different at α= 5%. Return to tablenote 1 referrer

While many estimates do not show significant change, the significant differences show that some bias remains in the CPSS5 – Technology Use and Cyber Security during the Pandemic. There is an underrepresentation of those where there were three or more eligible participants for the panel, and of people with less than a high school diploma. And there is an overrepresentation of those with a post-secondary certification, of people born in Canada, of people working in NOC2, of households where there were two eligible participants for the panel, and of households with children. These small differences should be kept in mind when using the CPSS5 – Technology Use and Cyber Security during the Pandemic 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.

Release date: November 5, 2020 Updated: September 21, 2021

The purpose of this notice is to advise all stakeholders and users of the National Occupational Classification (NOC) that the new 2021 classification's numbering system will be significantly modified as part of a major structural revision. The NOC 2021 is scheduled to be released in early 2021.

• Background
• Revising the NOC
• Impact on users
• Schedule A – NOC 2016
• Schedule B – NOC 2021

Background

Every ten years, the National Occupational Classification (NOC) undergoes a major structural revision whereby the existing occupational groups are reviewed alongside input collected from many relevant stakeholders through a consultation process. The NOC has been developed and is maintained as part of a collaborative partnership between Statistics Canada (STC) and Employment and Social Development Canada (ESDC). The release of the NOC 2021 will be the product of this 10-year cycle and will reflect changes in the economy and the nature of work. Input from the public, and particularly stakeholders has been a key part of the revision process.

The current NOC structure (NOC 2016) is categorized based on two major attributes of jobs, the "Broad Occupational Category" and the "Skill Level", as classification criteria. The former is defined as the type of work performed, with respect to the educational discipline or field of study for entry into an occupation and the industry of employment (e.g. health occupations or sales and service occupations). The "Skill Level" categorization is defined first by the amount and type of education and training usually required to enter and perform the duties of an occupation, but also considers experience, complexity and responsibilities. See Schedule A for details.

Revising the NOC

During consultation, it was suggested to add a new "Skill Level" to the current categorization, to clarify the distinction in formal training or education actually required among unit groups, especially in the current "Skill Level B", which has a wide range of formal training or educational requirements. The NOC 2016 "Skill Level B" includes all occupations usually requiring two to three years of post-secondary education at community college, institute of technology or CÉGEP or two to five years of apprenticeship training. In the NOC 2016, 211 occupations (42%) were classified under "Skill Level B", creating a disproportionately large group and thereby limiting the ability to analyze distinctions amongst a large percentage of occupations.

Another observation during the revision process was the use of the "Skill Level" categorization in the NOC as possibly being misleading because training and education, which are the main building blocks of the NOC's "Skill Level" categorization, are not considered as "skills" in the labour market. With regards to skills, many countries and organizations are currently developing their own skills taxonomy (which include concepts such as numeracy and literacy). Therefore, it was deemed appropriate for the NOC to move away from the "Skill Level" categorization.

The NOC 2021 revision will overhaul the "Skill Level" structure by introducing a new categorization representing the degree of Training, Education, Experience and Responsibilities (TEER) required for an occupation.

The new "TEER" categorization redefines the requirements of the occupation by reconsidering the type of education, training and experience required for entry, as well as the complexities and responsibilities typical of an occupation. In general, the greater the range and complexity of occupational tasks, the greater the amount of formal education and training, previous experience, on-the-job training, and in some instances responsibility, required to competently perform the set of tasks for that occupation.

Legislative and senior management occupations are classified in "TEER" 0 and defined as Management as they generally require and have a significant level of experience, knowledge and responsibilities related to resource planning and directing. Occupations classified under "TEER" 1 usually require university education or previous experience and expertise in subject matter knowledge from a related occupation found within TEER 2. Occupations usually requiring post-secondary education of two to three years, or apprenticeship training of at least two years, or occupations with supervisory or significant safety responsibilities are classified in "TEER" 2, and "TEER" 3 for those occupations requiring less than two years of post-secondary education or on-the-job training, training courses or specific work experience of more than six months. Occupations usually requiring a high-school diploma or no formal education are classified in "TEER" 4 or "TEER" 5. See Schedule B for the complete NOC 2021 restructure.

These changes significantly improve how the NOC classification takes into account the distinctions in formal training and educational requirements and better reflects skill and knowledge development occurring through on-the-job experience. At the same time, it increases the homogeneity of the distribution of unit groups within the classification, and addresses concerns about the "Skill Level" categorization and the distribution of unit groups among them.

The redesign of the NOC for 2021 moves away from the current NOC four "Skill Level" categories to an innovative six-grouping "TEER" categorization. This change is necessary for several reasons. First, the "Skill Level" terminology is often misleading for many stakeholders. This change will reduce confusion. Second, some NOC users artificially create or infer a low- and high-skill categorization. This redesign moves away from high/low skill categorization as the TEER more accurately captures differences in occupational requirements, which in turn will aid in the analysis of occupations.

The transition from the "Skill Level" to the "TEER" categorization makes the distribution of occupations across the "TEER categories" more balanced. The change in the distribution of unit groups is summarized in the tables below.

Distribution of NOC Unit Groups by Skill Level

Distribution of NOC Unit Groups by Skill Level

NOC 2016
Skill Level A	28%
Skill Level B	42%
Skill Level C	24%
Skill Level D	6%

Distribution of NOC Unit Groups by TEER

Distribution of NOC Unit Groups by TEER

NOC 2021
TEER Category 0	9%
TEER Category 1	19%
TEER Category 2	31%
TEER Category 3	14%
TEER Category 4	18%
TEER Category 5	9%

Note: The NOC 2021 final distribution may change when structure is finalized.

Impact on users

The structure and format of the current National Occupational Classification 2016 version are based on the four-tiered hierarchical arrangement of occupational groups with successive levels of disaggregation. It contains broad occupational categories, major, minor and unit groups.

The format of NOC 2021 will use a five-tiered hierarchical arrangement of occupational groups with successive levels of disaggregation and will contain broad, major, sub-major, minor and unit groupings. The structure of the National Occupational Classification 2021 is based on two key occupational categorizations: Occupational categories and TEER categories, which are identified in the first two digits of the NOC 2021 5-digit code. The 5-digit code will be structured as follows: XX.XXX. See Schedule B for details of the two important groupings.

It is important to note that the redesign of the NOC will have significant implications for several Statistics Canada (STC) Surveys, such as the Labour Force Survey (LFS), and ESDC programs such as the Temporary Foreign Worker Program and Employment Insurance program. This change may have significant impact on various programs throughout other federal departments, as well as provincial, territorial and municipal governments and many users of the NOC.

The NOC 2021 will be published in early 2021 and will become the departmental standard for data collection and dissemination for occupations at Statistics Canada. Implementation dates for the new classification version will vary based on when programs, entities, organizations or individuals decide to use it. For example, Immigration, Refugee and Citizenship Canada (IRCC), in conjunction with ESDC, is aiming to adopt the revised NOC structure in spring 2022 for the management of temporary and permanent resident programs. These dates will be confirmed on IRCC websites closer to the date of implementation.

As a normal practice, in advance of a full classification revision release, Statistics Canada will provide a spreadsheet of the actual structure of the classification, including the unit group numbers and corresponding titles. We will also provide a correspondence table between the NOC 2016 and the NOC 2021 unit groups and their corresponding titles. These products will be posted on our website by December 2020. This notice is being sent out now to inform all NOC users of the upcoming change which is currently being finalized. In early 2021, the full classification will be released, including the Leading Statements, Main Duties, Employment Requirements, Example Titles, Inclusions, Exclusions and Additional Information.

For additional questions, please contact the Statistics Canada NOC team at:
statcan.opmicquestionsmailbox-bgpvpcicourieldequestions.statcan@statcan.gc.ca

Schedule A – NOC 2016

NOC 2016

NOC 2016

The skill type category is…	when the first digit is…
Management occupations	0
Business, finance and administration occupations	1
Natural and applied sciences and related occupations	2
Health occupations	3
Occupations in education, law and social, community and government services	4
Occupations in art, culture, recreation and sport	5
Sales and service occupations	6
Trades, transport and equipment operators and related occupations	7
Natural resources, agriculture and related production occupations	8
Occupations in manufacturing and utilities	9

NOC 2016 skill level criteria - education/training and other criteria

NOC 2016 skill level criteria - education/training and other criteria

The Skill Level category is…	when the second digit is…
Skill Level A	0 or 1
Skill Level B	2 or 3
Skill Level C	4 or 5
Skill Level D	6 or 7

Skill Level A

• University degree (bachelor's, master's or doctorate)

Skill Level B

• Two to three years of post-secondary education at community college, institute of technology or CÉGEP
  or
• Two to five years of apprenticeship training
  or
• Three to four years of secondary school and more than two years of on-the-job training, occupation-specific training courses or specific work experience
• Occupations with supervisory responsibilities are also assigned to skill level B.
• Occupations with significant health and safety responsibilities (e.g., fire fighters, police officers and licensed practical nurses) are assigned to skill level B.

Skill Level C

• Completion of secondary school and some short-duration courses or training specific to the occupation
  or
• Some secondary school education, with up to two years of on-the-job training, training courses or specific work experience

Skill Level D

• Short work demonstration or on-the-job training
  or
• No formal educational requirements

Skill level is referenced in the code for all occupations with the exception of management occupations. For all non-management occupations, the second digit of the numerical code corresponds to skill level. Skill levels are identified as follows: level A – 0 or 1; level B – 2 or 3; level C – 4 or 5; and level D – 6 or 7.

Schedule B – NOC 2021

Schedule B – NOC 2021

Title of Hierarchy	Format	Digit	Represents:
Broad Category	X	First Digit – X	Occupational categorization
Major Group	XX	Second Digit xX	TEER categorization
Sub-major Group	XX.X	xx.X	Top level of the Sub-Major Group
Minor Group	XX.XX	xx.XX	Hierarchy within the Sub-Major Group
Unit Group	XX.XXX	xx.XXX	Hierarchy within the Minor Group

Note: The first digit identifies the Occupation, the second digit identifies the TEER. Therefore, the first 2 digits put together are identified as the Major Group. The next 3 digits identify their hierarchy within the groups.

Schedule B – NOC 2021

Broad Category - Occupation	when the first digit is…
Legislative and senior management occupations	0
Business, finance and administration occupations	1
Natural and applied sciences and related occupations	2
Health occupations	3
Occupations in education, law and social, community and government services	4
Occupations in art, culture, recreation and sport	5
Sales and service occupations	6
Trades, transport and equipment operators and related occupations	7
Natural resources, agriculture and related production occupations	8
Occupations in manufacturing and utilities	9

Schedule B – NOC 2021

The Training, Education, Experience and Responsibility (TEER)	when the second digit is…
Management - TEER	0
Completion of a university degree (bachelor's, master's or doctorate); or Previous experience and expertise in subject matter knowledge from a related occupation found in TEER 2 (when applicable).	1
Completion of a post-secondary education program of two to three years at community college, institute of technology or CÉGEP; or Completion of an apprenticeship training program of two to five years; or Occupations with supervisory or significant safety (e.g. police officers and firefighters) responsibilities; or Several years of experience in a related occupation from TEER 3 (when applicable).	2
Completion of a post-secondary education program of less than two years at community college, institute of technology or CÉGEP; or Completion of an apprenticeship training program of less than two years; or More than six months of on-the-job training, training courses or specific work experience with some secondary school education; or Several years of experience in a related occupation from TEER 4 (when applicable).	3
Completion of secondary school; or Several weeks of on-the-job training with some secondary school education; or Experience in a related occupation from TEER 5 (when applicable).	4
Short work demonstration and no formal educational requirements.	5