| Geography | Month |
|---|---|
| 202008 | |
| % | |
| Canada | 0.7 |
| Newfoundland and Labrador | 1.1 |
| Prince Edward Island | 1.1 |
| Nova Scotia | 2.9 |
| New Brunswick | 1.3 |
| Quebec | 1.6 |
| Ontario | 1.4 |
| Manitoba | 1.8 |
| Saskatchewan | 2.3 |
| Alberta | 1.5 |
| British Columbia | 1.6 |
| Yukon Territory | 1.6 |
| Northwest Territories | 0.7 |
| Nunavut | 1.5 |
Statistics Canada produces objective, high-quality statistical information for the whole of Canada. The statistical information produced relates to the commercial, industrial, financial, social, economic, environmental and general activities and conditions of the people of Canada.
Statistics Canada has always fostered a culture of innovation. Change is constant, and the agency's modernization journey will continue to change the way it does business to meet the needs of Canadians.
The modernization journey revolves around five key pillars, which were developed in collaboration with stakeholders and data users to better understand their information needs.
The agency helped Canadians understand the story behind the data through many statistical capacity, data literacy and communications initiatives. Assisted by new methods and numerous collaborative partnerships, the agency expanded its data holdings and access to data. At the same time, the agency properly managed risks, prepared for the 2021 Census and delivered on key priorities, such as providing more disaggregated data on topics such as gender, region and ethnicity.
In 2019–20, Statistics Canada was able to adapt quickly to the new reality caused by the COVID-19 pandemic thanks to the advances it had already made toward a modern and flexible workplace.
As part of its modernization initiative, Statistics Canada changed the way it works, manages its teams and meets organizational goals, including risk mitigation. This transformation brought about a cultural shift, in line with Blueprint 2020, by focusing on building a modern, capable and high-performing workforce.
A corporate culture change was initiated to make Statistics Canada a more agile, flexible and responsive organization. The agency's vision of a modern and flexible workplace is a workplace that fosters a culture of innovation and connectivity, and that improves how it and its mobile employees leverage digital technology and space.
The agency introduced four new culture values: curious and always learning, purposeful, trustworthy, and caring and inclusive. These values were designed to help guide employees throughout the modernization journey. A wide range of activities took place during 2019–20 to help promote these values, including a learning fair, panel discussions and the development of a Culture Passport.
The agency's ability to react to the COVID-19 pandemic demonstrated the progress made in this cultural change. Statistics Canada established a COVID-19 Task Force on January 27, 2020, before the pandemic hit North America, to monitor the latest information globally and within Canada. When the Ontario government announced that citizens must take exceptional public health measures to protect themselves from the virus, the agency transitioned to an entirely remote workforce on March 13, 2020. This included successfully transitioning approximately 7,500 employees to remote working overnight, and invoking only 12% of the agency's business continuity plans. All 22 mission-critical operations were kept fully operational within the existing bandwidth and remote access capabilities. Communications to all staff and senior management continued daily throughout March 2020 to keep employees informed of changing business operations and health and safety measures.
Statistics Canada has also activated new programs that provide critical specialized data and statistics that are needed to accurately model and track important topics, such as personal protective equipment, contact tracing, and targeted economic and social statistics. These new programs inform decision makers on the current situation in Canada and how to adjust their policies to best suit the needs of Canadians on a daily basis.
To meet Canadians' current and emerging data needs in a timely, responsive and agile manner, Statistics Canada continuously monitors its internal and external environment to develop risk mitigation strategies. The agency has a flexible integrated risk management framework to systematically identify, understand, manage, monitor and communicate risk. With the new corporate culture in mind, the agency identified several key potential risks for 2019–20:
To address these risks, the agency continued to adapt and to evolve its governance oversight by implementing a principled performance model and by creating a Governance, Risk and Compliance (GRC) Division that will offer a well-coordinated and integrated approach to reliably achieve objectives while addressing uncertainty and acting with integrity. More specifically, during 2019–20, the GRC Division implemented key principles for risk-based governance to increase the effectiveness of the internal governance structure. This included ensuring that governance is informed by risk, and assigning clear accountabilities to each committee to drive strategy to reach identified strategic outcomes. Namely, the agency
To complement internal governance systems, the agency also relied on the advice and recommendations of external governance bodies, such as the Departmental Audit Committee (DAC) and the Canadian Statistics Advisory Council (CSAC). The DAC provides assurances on the adequacy and effectiveness of Statistics Canada's management systems, and the CSAC provides information to the Chief Statistician and the Minister on the overall health of the national statistical system.
With their inaugural meeting in July 2019, the Advisory Council on Ethics and Modernization of Microdata Access (ACEMMA) provided guidance to Statistics Canada on data access, privacy and data governance to maintain and support the data needs of Canadians. The ACEMMA has a wealth of knowledge and experience in ethics and will help support the agency's overall risk mitigation strategy when considering new data sources.
The agency sought to further strengthen the trust of Canadians by showing how it protects their privacy every day. In 2019–20, through the Trust Centre, and social media platforms, the agency released an infographic called Administrative data: Why it matters to you, and five infobytes based on Joe Anonymous, a video that makes these important concepts accessible to all Canadians in a visual format. The Trust Centre, is managed in a responsive manner, with new material added regularly.
As a further step, the agency made investments—both technological and methodological—to ensure the reliability, timeliness, scalability and security of its data.
Statistics Canada's focus on user-centric service delivery is about ensuring that users have the information they want when they want it and how they want it. Through engagement and outreach activities, Statistics Canada learned that Canadians wanted more visuals, which make the data easier to understand. However, many partners with higher data-literacy skills still wanted data tables.
To meet Canadians' diverse range of needs, the agency provided information on its website in various formats, including data tables, infographics, interactive maps and other data visualizations.
In 2019–20, Canadians could access 37,254 data products on the Statistics Canada website and 7,368 data tables through the open government portal. In 2019–20, there were just under 20.3 million visits to the agency's website—making it one of the most visited federal government websites. For users who preferred to obtain data through application program interfaces (APIs), the agency received over an average of 250,000 API calls per month.
The agency has created a number of portals to improve user experience. These portals are gateways or hubs for accessing all Statistics Canada's information on a particular subject. In 2019–20, 21 new portals were created. In 2018–19, there were just two. These new portals provide improved access to data on the motor vehicle sector, seniors and aging, Indigenous peoples, poverty, health, housing, price indexes, education, courts, correctional services, economic accounts, agriculture and food, business and consumer services, travel and tourism, and culture statistics.
Data visualizations, including online interactive tools and static infographics, provide data to Canadians in an easy-to-use and visually appealing format. In 2019–20 alone, the agency added 89 infographics, bringing the agency total to 251 infographics.
Similarly, to meet the needs of Canadians, the number of interactive data visualization tools continued to climb. Out of the agency's total of 82 data visualization tools, 39 were created in 2019–20. This fiscal year, visualization tools were created for a broad range of themes, including transportation, immigration, income, crime, education, imports and exports, price indexes, retail commodities, Internet use, gross domestic product, trade, and housing statistics.
Videos were used more often and more strategically, with 30 weekly videos released on gross domestic product, the Consumer Price Index and the Labour Force Survey. More than 30 other videos were created for tailored subjects, such as data in a changing world, modernizing to serve Canadians, the 50th anniversary of the Official Languages Act, and the faces of Statistics Canada.
Statistics Canada also provided Canadians with data insights through 1,229 articles in The Daily, the agency's official release bulletin and the first line of communication with the media and the public. The agency also published 48 issues of The Weekly Review, a summary of the week's top statistical stories, released on the last working day of each week. This product further increases the accessibility of data released through The Daily.
Many Canadians look to obtain statistics through the news media. The media relations team responded to over 2,200 media inquiries and recorded more than 56,000 mentions of Statistics Canada in the national media.
To improve user experience, the My StatCan feature allows subscribers to customize their view of the agency's website, select specific publications and data products, and receive customizable data through email. Over 59,000 data users subscribed to My StatCan in 2019–20, a 7.2% increase in subscribers and 5.3% above the target for the year.
In 2019–20, Statistics Canada improved its ability to share its work through an increased digital presence and increased outreach. The agency now has a presence on Reddit and Instagram, in addition to Twitter, Facebook, LinkedIn and YouTube. Similarly, the agency shared over 2,500 social media publications in 2019–20, and its posts reached over 12.2 million online users. These users engaged with the agency's social media posts over 350,000 times.
In 2019–20 Statistics Canada translated over 6 million words—this ensured Canadians could access to all the agency's information in the official language of their choice. In addition, the agency promoted the use of both official languages through regular messages and activities, including the celebration of the 50th anniversary of the Official Languages Act.
The census is the largest program at Statistics Canada and has focused on a user-centric delivery model for many cycles. During 2019–20, preparations for the 2021 Census included numerous consultations and partnership activities to ensure that users have the data they need.
Building on the previous year's work of consultations and qualitative testing, in 2019–20, the census questionnaires were tested quantitatively during the 2019 Census Test. The 2019 Census Test evaluated changes to the wording and flow of some questions, as well as the potential addition of new questions. The test also incorporated the evaluation of new communications material and variations to further improve collection methods. The content was tested by a sample of nearly 135,000 households and was reviewed by nine analysis panels.
Agency officials also met with representatives of 14 federal departments and other interested organizations. To better understand the needs of Indigenous organizations and communities, more than 60 in-person discussions were held in 30 Indigenous and non-Indigenous communities across Canada, with more than 400 contributors. Statistics Canada also met with individuals from organizations representing official language minority communities, organizations representing or providing services to Canadians with disabilities, immigrant and ethnic communities and organizations, LGBTQ2 communities, academia, businesses, and non-profit organizations.
Based on the findings from consultations and discussions, Statistics Canada proposed changes to census content to respond to key priorities identified by participants. This included new questions on sex at birth, gender, veterans, minority language rights-holders, Indigenous identity, multiple modes of transportation, and labour market activities. These questions were added so that everyone can see themselves in the census—a key desire heard from Canadians. The 2021 Census will also include new labour and commuting questions. Understanding the changing nature of the labour market and the skills people bring to it is critical for Canada to remain competitive in a global market economy.
“For many decades, we have worked very closely with Statistics Canada. Our countries are very similar in our census: vast geographies and our need to work closely with First Nations people in our work. The StatCan collection innovations that we have mirrored have brought us better outcomes for our populations who have benefitted from greater choice in the ways we interact.”
This rigorous testing in 2019–20 set the stage for a 2021 Census that will provide accurate data needed to support Canadian communities as they evolve, adapt and continue to recover from the COVID-19 pandemic. Already, data from the 2016 Census have been key to the emergency response. In the early days of the pandemic, crucial demographic information on vulnerable populations was provided to public health authorities and emergency services officials. This information was instrumental in informing policy making in the context of the pandemic, and helped guide decisions on where government support was most needed.
Census data users are asking for more information, delivered at an increasingly granular level. Given the work completed in 2019–20, Statistics Canada is prepared to meet these needs while continuing to listen to and engage broadly with organizations and individuals representing various government departments, Indigenous leadership, the general public, communities, the private sector and academics to ensure that the agency remains in touch with the interests and needs of Canadians.
As Statistics Canada progressed with its preparations for the 2021 Census, the agency supported the positive outcomes of the live 2019 Census Behaviour Test to improve statistical and operational processes and the risk posture for the 2021 Census. This was a critical step in ensuring that the 2021 Census collects and disseminates relevant data of the highest quality to support evidence-based decision-making by public and non-public sector users within Canadian society. Statistics Canada worked in close collaboration with Shared Services Canada to take steps to ensure key infrastructure is in place to deliver the 2021 Census.
A key pillar of the agency's modernization framework is collaboration and engagement with partners, including the sharing of expertise and expanded access to data. During 2019–20, Statistics Canada continued to increase collaboration and engagement.
In the summer of 2019, the agency expanded its focus on collaboration and engagement with partners through organizational changes that included creating a corporate Strategic Engagement Field, led by a new assistant chief statistician and guided by a new innovative strategy.
To foster increased collaboration with other external organizations, many speeches and special outreach and engagement events occurred. Specific external audiences included international statistical officials visiting the agency, and organizations such as the Empire Club, the Canadian Chamber of Commerce and affiliate members, the Federation of Canadian Municipalities, the Canadian Research Data Centre Network, and the Canadian Association of Chiefs of Police.
Statistics Canada took a leadership role on the international stage. Its collaboration with various partners resulted in significant successes and milestones over the past fiscal year. For example, for the Conference of European Statisticians (CES), Statistics Canada led an in-depth review on measuring well-being in digital society, which covered data collection practices in 40 countries. The Chief Statistician and other executives assumed the role of chairperson for multiple international groups. In 2019–20, Statistics Canada assumed the roles of chair, co-chair or member for 14 out of the 18 United Nations Economic Commission for Europe CES working groups.
Statistics Canada increased the number of collaborative projects and the sharing of new information during 2019–20. These projects ranged in size, length and formality. During 2019–20, Statistics Canada entered into 1,700 formal agreements with cost-recovery clients and partners to provide custom requests, workshops, statistical surveys and related services.
The agency has greatly increased collaborative activities related to housing statistics. In 2019, in partnership with the Canada Mortgage and Housing Corporation, Statistics Canada shared the results of the new Canadian Housing Survey to provide data on various topics, including social and affordable housing, wait-list times for housing, perceptions of well-being, and social inclusion.
In support of the National Housing Strategy, Statistics Canada also provided data on at-risk veterans. In partnership with Veterans Affairs Canada, the agency used 2016 Census records linked with a cohort of Canadian veterans to produce data on housing affordability, sustainability and accessibility.
Results from these two projects provide information on whether Canadians have housing that meets their needs and that they can afford. This helps policy makers to ensure that more Canadians have access to an affordable home.
In addition to housing statistics, the agency engaged with many partners to gather energy information. In June 2019, Statistics Canada launched the Canadian Energy Information Portal, a first step in creating a hub for energy information to address long-standing concerns regarding dispersed datasets and gaps in energy data that impact researchers, analysts, decision makers, etc. Feedback from portal users and user consultation and focus group testing informed the design of the Canadian Centre for Energy Information (CCEI), with the purpose of providing a convenient hub for information on Canada's energy future. In parallel with this design, the CCEI engaged with a broad base of stakeholders on their data needs and priorities, including participants of the Energy Modelling Initiative, federal partners from Natural Resources Canada, Environment and Climate Change Canada, the Canada Energy Regulator, provincial and territorial energy departments, and industry. The CCEI also established itself on social media with the hashtag #energynews. Foundational work done to document concepts and variables related to energy will form the base of a data standard with which to connect dispersed information.
Statistics Canada participated in other collaborative projects that resulted in increased access to data for partners and allowed them to better meet their organizational goals and public policy objectives that benefit society. The agency
Statistics Canada also expanded collaborative activities with partners to help better understand the economic conditions within Canada. The agency
Statistics Canada also participated in unique collaborative efforts with global implications related to the environment. For instance, in collaboration with Employment and Social Development Canada, the agency conducted extensive consultations with other federal departments and organizations to identify Canadian indicators for the Sustainable Development Goals.
Through the agency's leadership role in the development of, and commitment to, global indicators supporting the 2030 Agenda, Statistics Canada published additional indicators, more granular data and new visualizations on the Sustainable Development Goals Hub. The agency also worked with other departments, organizations and subject-matter experts to identify the best data sources for the 60 indicators in the Canadian Indicator Framework and to develop disaggregated data for vulnerable populations.
As part of the agency's mission to continue to provide trusted insight to Canadians, Statistics Canada strives to find ways to increase access to new and untapped data. The agency is also increasing its use of administrative data, modelling and new leading-edge methodologies to increase data capacity and reduce the response burden on Canadians. This includes the development of the agency's Necessity and Proportionality Framework, which aims to balance society's data needs with the protection of Canadians' privacy.
Data science enables the integration and efficient use of big and unstructured data sources to create new, high-quality, relevant and easily accessible products. New approaches that integrate data (e.g., from text, satellites, large digital data sources and big data) have been used by Statistics Canada to address evolving expectations in a constantly adapting manner.
For example, electronic scanner databases with sales and product information are now available from major retailers. Statistics Canada uses machine learning to classify all the product descriptions in the scanner data, and then obtains aggregate sales data for each area. This approach has resulted in a high degree of automation and accurate, detailed retail data. It also reduces the response burden for major retailers. The data and machine learning models for the first retailer are now being used in the Retail Commodity Survey and the Monthly Retail Trade Survey, with the other retailers to follow. This approach will also be used for other surveys.
During 2019–20, in response to a clear data need and with the help of collaborative activities and partnerships, Statistics Canada developed innovative research efforts on opioids.
In June 2019, the City of Surrey Opioid Summit: From Data to Action hosted 60 experts, and Statistics Canada data insights fuelled the discussion. The opioids work undertaken by Statistics Canada highlights the agency's ongoing commitment in support of all levels of government to address the most significant challenges currently facing Canadian communities. Statistics Canada securely and privately gathered data from across various social domains to provide an unprecedented lens for delivering meaningful insights to Canadians on the opioid crisis to inform responses by program and policy makers. Health Canada also provided Statistics Canada with additional funding to expand the Surrey project to the whole of British Columbia, and selected jurisdictions in Alberta and Ontario.
The agency launched a project with Ryerson University to determine the possibility of using machine learning to identify specific causes of death (e.g., opioid-related deaths, cycling-related deaths) recorded in the Canadian Coroner and Medical Examiner Database (CCMED). The CCMED is a unique source of information on preventable deaths, including opioid and other drug overdoses. In addition to the demographic information on the decedent, the CCMED contains information on the circumstances surrounding the death, and detailed narratives on each death investigated by coroners and medical examiners. These narratives provide a rich source of contextual information on the circumstances of a death.
The agency also analyzed the impact of the opioid crisis on life expectancy rates in Canada. Canada has experienced and continues to experience a serious opioid overdose crisis. The impact of the crisis on the Canadian population can be measured in different ways. Life expectancy is one of the most general measures of overall population health, reflecting the number of years a person would be expected to live based on the rates of death in a population in a given year. From 2016 to 2017, for the first time in over four decades, life expectancy at birth did not increase for either males or females. This trend was largely attributable to the opioid crisis.
The innovative linkages combining information on opioid use and socioeconomic data were used to create analytical products, including a profile of opioid users and trajectory analysis to identify events leading to an opioid event. A feasibility study was conducted to identify data sources available to study the impacts of other harmful drugs (e.g., methamphetamine).
Globally, there is limited intelligence surrounding the value of the digital economy and data. However, its economic importance cannot be denied. To address this data gap, in May 2019, Statistics Canada released "Measuring digital economic activities in Canada: Initial estimates," a digital economic account with estimates of the value of digital economic activities in Canada from 2010 to 2017. These activities included digitalization enablers, such as IT infrastructure, e-commerce transactions and digital delivery to consumers.
In June 2019, Statistics Canada released a new experimental framework to measure the value of data, databases and data science in Canada. Experimental estimates based on this framework were released a month later. Statistics Canada is working with international counterparts to set global standards to measure data's economic impact.
The agency also conducted research to better understand how and where the digital economy fits into the Canadian System of Macroeconomic Accounts and how it can be measured. For example, research is underway on how the widening productivity gap between the most productive firms and all other firms affects the distribution of individual employment earnings.
As announced in Budget 2018, the Treasury Board of Canada Secretariat and Statistics Canada are working together to improve performance and impact assessments for innovation-related programs.
Statistics Canada is using a data-driven approach to create consistent and comparable performance and impact measures for government innovation programs. For example, to measure business innovation, the Entrepreneurship and Linkable File Environment (ELFE) section used administrative data from 18 federal departments to produce a research database to help various government departments evaluate the effectiveness of their programs. The ELFE section also has a visual tool to help analysts track business indicators using a range of variables.
The agency's modernization culture has inspired innovative projects across the agency. In 2019–20, Statistics Canada developed many new leading-edge approaches to reduce survey response burden on Canadians and provide new, improved or timelier data and services to Canadians.
Among the successes regarding economic statistics, the agency
Among the accomplishments achieved in social statistics, the agency
Advances were also made in statistical methodologies. For instance, the agency tested privacy-preserving techniques, including homomorphic encryption, which provides data protection for highly sensitive data while enabling data processing. The agency also addressed a broad range of privacy concerns through the development of the Necessity and Proportionality Framework.
In December 2019, the Privacy Commissioner of Canada announced the findings from his year-long investigation into two projects undertaken by Statistics Canada that were designed to access Canadians' financial microdata through banks and credit rating agencies.
The investigation found that Statistics Canada complied with the spirit and intent of both the Statistics Act and the Privacy Act, but recommended that the future versions of the two projects take into account the necessity (i.e., need and justification) of the data being collected and the proportionality (i.e., appropriate magnitude of the effort as it relates to the need) of the sample being designed.
After strong collaboration with the Office of the Privacy Commissioner, Statistics Canada has become a global leader in its implementation of a ground-breaking Necessity and Proportionality Framework. This framework ensures and demonstrates more transparency for Canadians about the agency's processes to protect the privacy and confidentiality of information.
The Necessity and Proportionality Framework was shared at the United Nations Statistical Commission by the Chief Statistician and the Privacy Commissioner of Canada. Statistics Canada continues to lead work in this area while sharing progress with Canadians through the Trust Centre on its website.
This framework allows for the evaluation of data proportionality and necessity, while simultaneously ensuring that statistical values—such as quality of information, the protection of personal information, and confidentiality—remain intact. The framework is now fully imbedded into the data acquisition process and fully integrated in the privacy impact assessment process.
Canadians, businesses and associations are now assured that the information gathered by Statistics Canada has been obtained in a fully transparent and ethical manner. Also, by enabling the appropriate acquisition of data from a variety of data sources, the framework provides stakeholders with access to more precise and timely information for use in public policy-making and business decision-making that benefits all Canadians.
In 2019–20, the agency led a series of external engagement and co-creation initiatives with public and private sector partners to build data literacy and promote data-driven decision-making. This involved welcoming partners from more than 25 federal departments and agencies, provincial and municipal governments, academia, the private sector, and civil society to participate in innovation hackathons on high-profile topics including the Sustainable Development Goals, early learning and child care, urban transit, empowering citizen science, food security, and workplace mental health measurement. The agency, through its regional services areas, provided training courses that helped users understand the use of data.
More specifically, Statistics Canada has made important contributions in building statistical capacity and fostering data in specific subject matter areas, including Indigenous statistics, and gender, diversity and inclusion data. The agency also showed leadership in the development of both the federal data strategy and its own data strategy.
As part of the Indigenous Statistical Capacity Development Initiative, the Centre for Indigenous Statistics and Partnerships engaged with over 127 Indigenous communities, organizations and governments and successfully completed three pilot projects, which included training Apatisiiwin Skills Development (formerly the Cree Human Resources Development Office) to design, collect, process and analyze a survey in 10 communities. This led to a three-year plan to develop and deliver 15 courses to help Indigenous communities and organizations build their own data and research capacities.
The agency released three publications as part of a comprehensive release strategy that encompassed three booklets, three infographics and one interactive map. The analytical publications focused on employment among First Nations people living off reserve, and Métis and Inuit participation in the wage and land-based economies in Inuit Nunangat.
In 2019–20, the agency developed indexes, indicators and portals to address the statistical needs of Indigenous communities, organizations and leadership.
Key products included
To address data gaps, the agency calculated school participation rates for children aged 4 to 6 living on reserve by using administrative data from Indigenous Services Canada.
Statistics Canada is not only on the leading edge of new technologies, but also at the vanguard of efforts to address data gaps to help Canadians get the necessary information to make important decisions. The Centre for Gender, Diversity and Inclusion Statistics (CGDIS) is an example of a Statistics Canada initiative that focuses on providing new information to Canadians and building statistical capacity.
For instance, in 2019–20, the CGDIS released 19 tables on the Gender, Diversity and Inclusion Statistics Hub in support of the Gender Results Framework. In addition, a new publication to publish gender-based analysis plus (GBA+) articles was created. The CGDIS also published a conceptual and methodological overview of the gender pay gap and published a number of articles and infographics to address key policy needs and raise awareness of issues related to gender, diversity and inclusion.
Positive feedback from data users was received for four products that specifically highlighted new disaggregated data on Black communities in Canada, with a focus for policy departments to identify the socioeconomic issues facing Black communities in Canada. Statistics Canada released a socioeconomic portrait of Canada's Black population in February 2020 to support the Government of Canada's priority to address socioeconomic issues faced by Black Canadians.
In 2019–20, the CGDIS continued to generate new information to improve statistical standards for GBA+. Statistics Canada developed the capacity to acquire data from other departments that can be "housed" at Statistics Canada to help measure progress on the Gender Results Framework indicators. For example, data from the Band Governance Management System were acquired with Crown–Indigenous Relations and Northern Affairs Canada to develop indicators on the gender composition of First Nations band councils and the proportion of Chiefs in First Nations communities who are women. In addition, work is being undertaken in collecting and disseminating ethnocultural statistics. With support from Canadian Heritage, the new cycle of the General Social Survey on Social Identity will allow for the disaggregation of some specific ethnocultural groups to help address issues related to anti-racism, such as discrimination.
Finally, 2019–20 saw the continued building of statistical capacity for the CGDIS. It worked closely with the Canada School of Public Service to develop training materials and to deliver a GBA+ premium course. The CGDIS reviews course content annually, sits on discussion panels, and co-presents the module Geeking out About Data, which guides participants through selecting a data source, disaggregating data and interpreting results using a GBA+ perspective. The CGDIS also released the results from the Survey of Safety in Public and Private Spaces. These results helped to expand knowledge on gender-based violence among the general population in a new way by including a wider range of behaviours that are on the continuum of gender-based violence, but may not necessarily meet the criminal threshold.
In 2019–20, work was undertaken to deliver three high-priority initiatives in support of the government-wide implementation of the Data Strategy Roadmap for the Federal Public Service.
Statistics Canada also collaborated with the Canada School of Public Service (CSPS) to pilot an approach to measure data literacy, and engaged with other departments such as Innovation, Science and Economic Development Canada, Employment and Social Development Canada, the Privy Council Office, the Canadian Northern Economic Development Agency and Global Affairs Canada to discuss scaling beyond the agency. Support was provided on a number of other CSPS initiatives: establishing data literacy competencies, guiding the development of the "Discover Data" training course, and contributing to the CSPS data literacy training project to produce online training resources in core data literacy areas (e.g., quality, stewardship, analysis).
The Statistics Canada Data Strategy (SCDS) was released internally to the federal public service in late September 2019 and publicly in late April 2020. The SCDS provides a roadmap for how Statistics Canada will continue to govern and manage its valuable data assets as part of its modernization agenda and in alignment with and response to other federal government strategies and initiatives, including the Data Strategy Roadmap for the Federal Public Service, Canada's 2018–2020 National Action Plan on Open Government, and the Treasury Board Secretariat Digital Operations Strategic Plan: 2018–2022.
In a related project, Statistics Canada is leading the implementation of a proof-of-concept external data stewardship engagement office. With the end goal of building capacity and fostering data literacy, this office will directly engage with other federal departments and agencies to optimize the use of data to facilitate sharing and integration, reduce duplication, and increase trust and transparency.
Across the agency, employees are working to improve results and to ensure targets are both relevant and ambitious. The agency made significant progress and reached 7 out of 11 performance indicator targets for 2019–20, and has improved its results relative to previous years. As the Departmental Results Framework matures, the agency is integrating performance indicator results into its decision-making processes to ensure value for Canadians and alignment of resources with government priorities.
| Departmental results | Performance indicators | Target | Date to achieve target | 2017–18 Actual results | 2018–19 Actual results | 2019–20 Actual results |
|---|---|---|---|---|---|---|
| Statistical information is of high quality | Number of post-release corrections due to accuracy | 0 | March 31, 2020 | 3 | 2 | 1 |
| Number of international forums of which Statistics Canada is a member | 170 to 190 | March 31, 2020 | 168 | 184 | 190 | |
| Percentage of international standards with which Statistics Canada conforms | 90%Table note 1 | March 31, 2020 | 89% | 88% | 88%Table note 2 | |
| Statistical information is available and accessed | Number of visits to Statistics Canada website | 17,000,000Table note 3 | March 31, 2020 | 26,461,926 | 19,752,776Table note 4 | 20,285,269Table note 5 |
| Percentage of website visitors that found what they were looking for | 77% | March 31, 2020 | 76% | 79% | 78% | |
| Number of interactions on social media | 400,000Table note 6 | March 31, 2020 | 559,709 | 358,673Table note 7 | 521,441Table note 8,Table note 9 | |
| Number of statistical products available on the website | 37,300 | March 31, 2020 | 33,642 | 35,920 | 37,254 | |
| Number of Statistics Canada data tables available on the Open Data Portal | 7,150 | March 31, 2020 | 7,162 | 6,944Table note 10 | 7,386 | |
| Statistical information is relevant | Number of media citations on Statistics Canada data | 70,000 | March 31, 2020 | 67,539 | 74,657Table note 11 | 56,921Table note 12 |
| Number of journal citations | 23,000 | March 31, 2020 | 23,903 | 22,716 | 26,505Table note 13 | |
| Percentage of users satisfied with statistical information | 80% | March 31, 2020 | 79% | 80% | 80% | |
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| 2019–20 Main Estimates | 2019–20 Planned spending | 2019–20 Total authorities available for use | 2019–20 Actual spending (authorities used) | 2019–20 Difference (Actual spending minus Planned spending) | |
|---|---|---|---|---|---|
| Gross Expenditures | 551,104,432 | 551,104,432 | 600,534,042 | 584,770,894 | 33,666,462 |
| Respendable Revenue | -120,000,000 | -120,000,000 | -120,038,495 | -120,038,495 | -38,495 |
| Net Expenditures | 431,104,432 | 431,104,432 | 480,495,547 | 464,732,399 | 33,627,967 |
| 2019–20 Planned full-time equivalents | 2019–20 Actual full-time equivalents | 2019–20 Difference (Actual full-time equivalents minus Planned full-time equivalents) | |
|---|---|---|---|
| Gross expenditures | 5,501 | 5,595 | 94 |
| Respendable Revenue | -1,321 | -1,366 | -44 |
| Net Expenditures | 4,180 | 4,229 | 50 |
The difference between planned spending and actual spending is the result of an increase in resources for new initiatives from Budget 2018 & 2019. These initiatives include the New Anti-racism strategy, implementing a "Data Analytics as a Service" platform, co-developing an Indigenous Statistical Capacity Development Initiative, and enhancing GBA+ analysis through the creation of a Centre for Gender, Diversity and Inclusion Statistics.
The difference is also attributable to retroactive pay from the ratification of new collective agreements and budget carried forward from 2018–19 to 2019–20, allowing the agency to meet the needs of its cyclical programs and to invest in its integrated strategic planning process.
Furthermore, full-time equivalents vary slightly as a result of differences between the actual salary rates paid and the estimated average salary rates used to forecast planned spending.
Financial, human resources and performance information for Statistics Canada’s Program Inventory is available in GC InfoBase.
Internal Services are those groups of related activities and resources that the federal government considers to be services in support of programs and/or required to meet corporate obligations of an organization. Internal Services refers to the activities and resources of the 10 distinct service categories that support program delivery in the organization, regardless of the Internal Services delivery model in a department. The 10 service categories are
All Internal Services have been engaged with the agency's modernization agenda and have become more efficient and user-centric. Internal Services strengthened and modernized the agency's governance, performance management and risk management frameworks to support compliance and ensure the agency is efficiently aligning its resources.
Efficiencies and improvements have been made by leveraging technology, monitoring business processes and measuring performance. The agency increased data analytics within its Internal Services to provide quick and direct insight into the health of the organization.
The modernization agenda is a significant transformation. Statistics Canada continues its proactive approach to monitor the agency's health through change, to identify trends in areas such as turnover, sick leave, job satisfaction and morale, and to take action to improve these trends.
The agency envisions a diverse, inclusive, respectful and healthy workplace that is agile and resilient to change. To support this vision, the agency has focused on strategies to measure and support organizational health, is developing an organizational health framework/index and indicators, and is collecting important information through pulse surveys and focus groups.
In line with these efforts, Statistics Canada continued to integrate performance measurement into program management and corporate planning to support and guide the agency's modernization journey. Specifically, performance measurement workshops were conducted with all program managers to develop a comprehensive agency logic model and measurement framework that aligns activities and outputs to outcomes. The resulting suite of indicators will allow the agency to better measure whether it is achieving its modernization agenda.
To meet Canadians' need for timely and meaningful data, Statistics Canada's modernization agenda needed to ensure proper controls were in place to mitigate risk internally. In 2019–20, Internal Audit and Evaluation used results achieved and lessons learned to assure management that innovative and ongoing program delivery mitigated risks. These functions provided management with trusted and neutral information to inform decision making within the agency.
Throughout 2019–20, the Employee Equity, Diversity and Inclusion team conducted many activities and awareness campaigns to increase the visibility and effectiveness of resources available to all Statistics Canada employees. For example, tools and initiatives—including the Integrity and Respect hotline, access to appointed Integrity and Respect Awareness Officers, and promotion of the Employee Assistance Program and Informal Conflict Management Services—have been implemented within the agency. In addition, a multitude of training opportunities and discussion forums have been promoted to help employees broaden their perspectives and create a more inclusive environment.
Statistics Canada launched two action plans to support a culture of diversity and inclusion. The first was the 2019–20 Employment Equity and Diversity Action Plan, which identified three key achievements: a data-driven approach to diversity and inclusion, the elimination and prevention of representation gaps, and a work environment where all employees feel included. Key action items included mandatory staff training, tracking and identifying statistical trends in representation and progression of employment equity groups, monitoring Statistics Canada's diversity compared with Canadian society, and reviewing assessment tools by the Employment Equity and Diversity Section. The second action plan released was the Integrity and Respect Action Plan. Both of these plans were published as communication tools available to employees and illustrated organizational commitments and accountabilities.
Internal Services enable partners across the agency to make strategic management decisions—supported by data—that are related to human resources, financial management, procurement, accommodations, informatics services and more. A number of new trailblazing projects demonstrate the expansion of data analytics with a user-centric approach within the agency.
The agency continues to move toward a more modern and flexible work environment through the development of open workspace concepts, additional network bandwidth, and remote work procedures, including mandatory employee training.
In addition, in partnership with Public Services and Procurement Canada, the agency launched the GCcoworking pilot. This pilot allows employees to use conveniently located workspaces outside the agency's main offices. This approach reduces the agency's carbon footprint, improves work–life balance and encourages collaboration.
In support of modern comptrollership, procurement activities continue to be made more efficient and modern. This included reducing manual processes (electronic submission and acceptance) and decentralizing the 2021 Census procurement process to a more efficient and regionally benefitting approach. Social and green procurement approaches included purchasing paper with at least 30% recycled content and using Indigenous vendors for IT equipment and furniture. Similarly, furniture was purchased from CORCAN, which provides employment and employability skills training to incarcerated offenders.
The agency established a renewed information management vision, with foundational principles and alignment to internationally recognized frameworks, to drive key business outcomes. This project is moving forward to deliver key information and data capabilities to meet the needs of the modernization agenda and major program delivery.
To provide full transparency into the agency's efficiency and modernization efforts, the agency launched its first IT Plan of Record. This report provides greater visibility to the IT-enabled investments within the agency. It transparently details all IT-enabled investments across projects, products and services. In addition, to streamline help desk costs, two separate walk-up services for employee support (the Genius bar and the Accounts Desk) were consolidated to create a single station that now provides a broader set of services and expanded service hours, while reducing costs.
Further focusing on IT operations and lifecycle management, Statistics Canada advanced its technological modernization agenda through a number of complex, large-scale informatics projects:
| 2019–20 Main Estimates | 2019–20 Planned spending | 2019–20 Total authorities available for use | 2019–20 Actual spending (authorities used) | 2019–20 Difference (Actual spending minus Planned spending) |
|---|---|---|---|---|
| 64,345,374 | 64,345,374 | 82,048,294 | 82,217,225 | 17,871,851 |
| 2019–20 Planned full-time equivalents | 2019–20 Actual full-time equivalents | 2019–20 Difference (Actual full-time equivalents minus Planned full-time equivalents) |
|---|---|---|
| 566 | 626 | 60 |
The difference between planned spending and actual spending is mainly related to an increase in resources for a new initiative, approved in 2018–19, to migrate the infrastructure to the cloud, as well as additional spending related to internal IT support and pressures related to the Government of Canada pay system.
Although additional expenditures were prioritized under Internal Services, the agency's overall spending did not exceed its total authorities.
Financial, human resources and performance information for Statistics Canada's Program Inventory is available in the GC InfoBase.
| Total actual spending for 2019–20 | Total actual full-time equivalents for 2019–20 | |
|---|---|---|
| Total gross expenditures | 666,988,119 | 6,221 |
| Respendable revenues | -120,038,495 | -1,366 |
| Total net expenditures | 546,949,624 | 4,856 |
During 2019–20, Statistics Canada made significant progress toward its modernization goals, including its goals to engage with Canadians, share best practices and expertise, build capacity, and generate new, innovative solutions to help create a data-driven society and economy. The initiatives and projects featured in this report demonstrate the impact that the agency has had on the lives of Canadians.
In response to the COVID-19 pandemic, the agency transitioned to a remote workplace overnight—a feat only possible because of in-flight modernization activities.
The agency is client-focused; it wants to ensure that users have the information they want when they want it and how they want it.
This past year, Statistics Canada pursued an unprecedented number of collaborative activities and initiatives across all levels of government; internationally; and with the private sector, non-governmental organizations and others.
The agency's modernization journey paved the way for work on leading-edge methods. Canadians and businesses want detailed, high-quality, real-time statistical information, and the agency is committed to using innovative methods to meet these increasing data needs.
For a data-driven society and economy, society must understand and use statistics. To help build statistical capacity with partners and foster data literacy among Canadians so they can effectively use the agency's data, Statistics Canada provided leadership nationally and internationally.
As the impacts of COVID-19 continue to be felt, Statistics Canada continues to capture an accurate social and economic portrait of the nation and provide Canadians with the information they need to make important decisions during these extraordinary times. The agency also accelerated the collection and release of COVID-19-related information to create new insights that are urgently needed to respond to and address the COVID-19 pandemic.
For more information on Statistics Canada's plans, priorities and results achieved, see the "Results: what we achieved" section of this report.
It is a pleasure to outline Statistics Canada's accomplishments over the 2019–20 fiscal year in this Departmental Results Report. In 2019–20, the agency experienced unprecedented change and further opportunity to serve Canadians with credible, trusted and quality data and insights.
COVID-19 fuelled an extraordinary demand for a better understanding of Canada's society and economy in more granular detail, near real-time and in an integrated manner. Leveraging the agency's modernization efforts that were already underway, Statistics Canada's dedicated and committed experts securely transitioned to remote work practically overnight, and transformed many of the agency's programs to bring greater value to decision makers.
In addition to providing key statistics that drive Canada's economy and society, Statistics Canada is using good data management and stewardship practices to track the impacts of COVID-19 and to help Canada to better manage critical resources, such as personal protective equipment inventories. The agency has introduced new collection mechanisms and new methodologies by working in partnership with other departments to bring disaggregated data and new insights to Canadians in a timely manner. I thank Canadians for their remarkable support in helping the agency in its efforts. Their support showed how much Canadians value the agency's high-quality evidence and data, which help to influence decisions that impact all of Canada.
Statistics Canada is grateful for the advice and guidance of the members of the newly formed Canadian Statistics Advisory Council, as well as the Advisory Council on Ethics and Modernization of Microdata Access, provincial and territorial statistical focal points, numerous subject matter and technical advisory committees, and international colleagues.
Although more work is needed, the agency is making important strides in supporting key government priorities such as gender, diversity and inclusion, physical and mental health, energy statistics, the environment, and sustainable growth. Statistics Canada is also working to meet the increasing dependence on data to fuel Canada's economy and jobs, and is working to support building statistical capacity in Indigenous organizations.
The agency is also grateful to organizations and Canadians who participated in this past year's content consultations for the 2021 Census of Population and Census of Agriculture. The input received will help to ensure next year's censuses provide the solid evidence base needed for years to come.
Statistics Canada has operated in a transparent manner for over a century, earning the trust of Canadians. The agency is sharing even more information about what it does and how it goes about providing high-quality statistics. I invite Canadians to visit Statistics Canada's Trust Centre to learn more about how the agency works for them. I also invite Canadians to get to know the agency's dedicated employees in our video series, Faces of StatCan.
Canadians look to Statistics Canada to provide independent, credible and trusted information at a time when it can be challenging to differentiate information quality. I invite all Canadians, businesses and organizations to explore the agency's resources, which include dashboards, hubs and portals, releases in The Daily, data repositories, and hundreds of analytical papers. All these resources are available for free on the Statistics Canada website.
Statistics Canada is grateful for your support and trust, and the agency looks forward to continuing to serve you by delivering even greater insights through data for a better Canada.
Anil Arora
Chief Statistician of Canada
Dissemination Plan
CHMS Content summary for cycles 1 to 8
Data User Guide – Cycles 1 to 6
Derived Variables (DVs) documentation – Cycles 1 to 6
Data Dictionaries – Cycles 1 to 6
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
Instructions for Combining Multiple Cycles of Canadian Health Measures Survey (CHMS) Data
Postal Code File – justification needed
Relationship File – justification needed
Relationship File – Feasibility Report
CHMS Errata – Cycles 1 to 6
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).
| Geography | Month | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201908 | 201909 | 201910 | 201911 | 201912 | 202001 | 202002 | 202003 | 202004 | 202005 | 202006 | 202007 | 202008 | |
| percentage | |||||||||||||
| Canada | 0.6 | 0.6 | 0.6 | 0.6 | 0.8 | 0.7 | 0.7 | 0.6 | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 |
| Newfoundland and Labrador | 0.4 | 0.4 | 0.4 | 0.3 | 0.2 | 0.7 | 0.3 | 1.2 | 0.7 | 0.5 | 0.1 | 0.2 | 0.4 |
| Prince Edward Island | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Nova Scotia | 2.4 | 2.7 | 2.1 | 2.2 | 6.8 | 2.6 | 2.0 | 2.8 | 3.3 | 4.0 | 2.3 | 1.5 | 1.8 |
| New Brunswick | 1.9 | 1.1 | 1.4 | 3.8 | 1.7 | 2.6 | 1.2 | 1.3 | 2.1 | 3.3 | 1.9 | 2.1 | 4.8 |
| Quebec | 1.7 | 1.7 | 1.7 | 1.7 | 2.2 | 1.4 | 2.1 | 1.6 | 2.4 | 2.0 | 1.9 | 1.8 | 2.1 |
| Ontario | 0.9 | 1.0 | 1.0 | 0.8 | 1.2 | 1.2 | 0.9 | 1.0 | 1.2 | 1.1 | 1.1 | 1.1 | 0.9 |
| Manitoba | 1.0 | 1.1 | 1.7 | 0.9 | 2.6 | 1.3 | 0.8 | 1.0 | 2.9 | 2.8 | 1.2 | 1.2 | 2.1 |
| Saskatchewan | 1.1 | 0.9 | 0.7 | 1.0 | 0.7 | 0.5 | 0.6 | 0.5 | 1.2 | 0.7 | 0.7 | 1.1 | 1.6 |
| Alberta | 0.9 | 1.9 | 1.3 | 1.4 | 1.1 | 1.0 | 0.9 | 1.2 | 2.9 | 2.9 | 2.3 | 2.3 | 1.9 |
| British Columbia | 1.4 | 1.4 | 1.1 | 1.5 | 1.4 | 1.3 | 1.6 | 1.5 | 1.3 | 1.7 | 1.6 | 1.3 | 1.9 |
| Yukon Territory | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Northwest Territories | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Nunavut | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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
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 |
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.
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.
Survey errors come from a variety of different sources. They can be classified into two main categories: non-sampling errors and 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.
| 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% |
| 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).
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:
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.
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.
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:
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.
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:
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.
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.
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.
| 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% | |
|
|||
| 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.
| 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% | |
|
||||
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.
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.
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
| 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
| 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.
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
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
| 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
Skill Level B
Skill Level C
Skill Level D
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.
| 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.
| 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 |
| 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 |
Biobanking helps advance the health of current and future generations through scientific discovery. Summaries of approved projects are posted in the Projects section of the CHMS biobank page. This informs participants on how their samples are being used. Occasionally, a small number of samples will be used for quality control purposes.
Researchers from recognized institutions can submit research project proposals to access these biospecimens. After a research project application is received at Statistics Canada:
If you wish to withdraw your biospecimens from a specific research project or from all future research, you have to send us a written request by email to statcan.ccahs-ecsac.statcan@statcan.gc.ca. Please provide your full name, the approximate date and home address at the time of your survey completion and the date of birth. This information will be solely used to ensure that the correct samples are removed and destroyed.
Send us your feedback. If you have ideas, or suggestions about the project, or further questions about the use of biospecimens for health research, or about the protection of your privacy, please reach us by:
The Canadian COVID-19 Antibody and Health Survey (CCAHS) is a survey designed to help evaluate the extent of the health status associated with the COVID-19 pandemic such as active COVID-19 infections and the prevalence of COVID-19 antibodies among a representative sample of Canadians. The survey also provides a platform to explore emerging public health issues, including the impact of COVID-19 on health and social well-being.
Learn more about the Canadian COVID-19 Antibody and Health Survey
The CCAHS stores dried blood spot and saliva samples from consenting Canadians aged 18 and older. Additional biospecimens from the Canadian Health Measures Survey (CHMS) are available on the CHMS Biobank page.
The CCAHS is enhanced by the national, provincial and territorial representability of its cohort and the possibility of merging the biospecimens’ data with the CCAHS questionnaire content, which includes topics covering respondents’ COVID-19 symptoms and status, risk for acquisition, risk factors, health behavior changes related to COVID-19, health assessments, and more.
For more information for researchers, please consult the Researchers page for the Canadian Health Measures Survey (CHMS)
By consenting to storage of their dried blood and saliva samples for use in future health studies, participants contribute to advances in health care and research. We ensure scientific excellence while protecting the privacy and confidentiality of our respondents.
For more information on your antibody test results, please visit the following website.
Results – Frequently asked questions
If you are feeling stressed because of the CCAHS or the COVID-19 pandemic in general, please visit the following for a list of tips and resources to help with your mental and physical health during these stressful times.
For all inquiries regarding the CCAHS, e-mail: statcan.ccahs-ecsac.statcan@statcan.gc.ca
For all inquiries regarding the Biobank, e-mail: statcan.chms-biobank-ecms-biobanque.statcan@statcan.gc.ca
