Canadian Health Measures Survey - Cycle 6 (2018-2019) Data accuracy: TOB subsample

Proportion of people with measures of Total NNAL above the limit of detection
Proportion of people with measures of Total NNAL above the limit of detection
Age group Sex proportion (%) c.v.(%)
ages 6 to 11 Males 18.0 26.4
ages 6 to 11 Females 14.1 29.0
ages 12 to 19 Males 26.8 17.2
ages 12 to 19 Females 20.2 17.2
ages 20 to 39 Males 34.2 20.3
ages 20 to 39 Females 29.4 13.9
ages 40 to 59 Males 33.8 18.7
ages 40 to 59 Females 31.2 18.4
ages 60 to 79 Males 23.5 21.4
ages 60 to 79 Females 13.7 14.6

Supplement to Statistics Canada's Generic Privacy Impact Assessment related to the Survey of Employees under Federal Jurisdiction (SEFJ)

Date: November 2021

Program manager: Director, Centre for Social Data Integration and Development
Director General, Census Subject Matter, Social Insights, Integration and Innovation

Reference to Personal Information Bank (PIB):

Personal information collected through the Survey of Employees under Federal Jurisdiction is described in Statistics Canada's "Special Surveys" Personal Information Bank. The Personal Information Bank refers to information collected through Statistics Canada's ad hoc surveys, which are not part of the regular survey taking activities of the Agency. They cover a variety of socio-economic topics including health, housing, labour market, education and literacy, as well as demographic data.

The "Special Surveys" Personal Information Bank (Bank number: StatCan PPU 026) is published on the Statistics Canada website under the latest Information about programs and Information Holdings chapter.

Description of statistical activity:

Statistics Canada, under the authority of the Statistics ActFootnote 1, will conduct the Survey of Employees under Federal Jurisdiction on behalf of the Department of Employment and Social Development Canada with the possibility of conducting another iteration in the future. This voluntary targeted survey will collect information on the quality of employees' work conditions, access to benefits and flexible work arrangements, labour relations, work-related well-being and workplace health and safety including harassment and discrimination. The information from this survey will guide research and analysis to update the Canada Labour Code. Statistics Canada will publish aggregate results in the Daily (the Agency's official release bulletin) summarizing the survey findings along with data tables. These data will be fully anonymized and non-confidential, without any direct personal identifiers, which prevents the possibility of identifying individuals. The Department of Employment and Social Development Canada will access the data file, with all personal identifiers removed, in the Research Data CentresFootnote 2 and will only be permitted to release aggregate results, which are fully anonymized and non-confidential. They will use these data to:

  • Identify the prevalence of work conditions that do not follow the Labour Code by industry
  • Ensure fair treatment and compensation for employees
  • Improve administration of labour standards
  • Determine areas within the Labour Code that should be updated
  • Identify if procedures and support to employees are in place that respond to incidents of harassment and violence if they do occur
  • Identify if changes are required to the regulations dealing with harassment and violence in the workplace

This survey will collect information from employees living in Canada who worked for a business under federal jurisdiction. Contact information was obtained either from employees' tax recordsFootnote 3 or from their employerFootnote 4. In addition to questions about working conditions, this survey will also collect information such as age, sex at birth, gender, sexual orientation, marital status, salary or wage earned, Indigenous identity, country of birth, immigration status and related information, citizenship, population group, level of education, and if they have a disability. Responses will be aggregated to ensure that no individual can be identified in published results.

Reason for supplement:

While the Generic Privacy Impact Assessment (PIA) addresses most of the privacy and security risks related to statistical activities conducted by Statistics Canada, this supplement describes additional measures being implemented due to the sensitivity of the information being collected. As is the case with all PIAs, Statistics Canada's privacy framework ensures that elements of privacy protection and privacy controls are documented and applied.

The Survey of Employees under Federal Jurisdiction will collect information on working conditions such as workplace health and safety including harassment and discrimination. Questions on harassment and discrimination could be considered sensitive and may cause distress for some respondents. This supplement also presents an analysis of the necessity and proportionality of collecting this sensitive information.

Necessity and Proportionality

The collection of personal information for the Survey of Employees under Federal Jurisdiction can be justified against Statistics Canada's Necessity and Proportionality Framework:

  1. Necessity:

    Data from the Survey of Employees under Federal Jurisdiction seeks to improve our understanding of employee working conditions within federally regulated workplaces to support the Government of Canada's strategy to update the Canada Labour Code. This includes workplaces that are free from harassment, violence and discrimination.

    The content and scope of the survey were developed by Statistics Canada and the Department of Employment and Social Development Canada and tested by Statistics Canada's Questionnaire Design Resource Centre. The content of the survey was deemed necessary for understanding and creating specific policies to address and prevent sexual harassment, non-sexual harassment and discrimination based on race, sex, sexual orientation, or gender identity. These data will be analysed alongside industry categories to determine if there is an association between working conditions and an increased risk of sexual harassment, non-sexual harassment and discrimination in the workplace.

    The Department of Employment and Social Development Canada will analyze the survey results, in aggregate form and without personal identifiers, to assess the working conditions, including the health and safety of these employees. The information will also be used to inform and update the Canada Labour Code. Only employees who worked for a business under federal jurisdiction during the reference period of 2020 are eligible to participate in the Survey of Employees under Federal Jurisdiction. As such, respondents will first be asked to confirm if they worked for a specific employer in order to establish whether they can take part in the survey. The demographic data including: age, sex at birth, gender, sexual orientation, marital status, Indigenous identity, country of birth, immigration status and related information, citizenship, population group, level of education and if they have a disability will be collected and used for analysis of sub-populations. These data serve to better understand if certain characteristics, such as age, gender, education or race are associated with an increased risk of sexual harassment, non-sexual harassment and discrimination in the workplace.
    The survey data file, with all personal identifiers removed, will be made available to researchers in the Research Data Centres (RDC)Footnote 5 upon approval of requests to access the data for statistical researchFootnote 6. Statistics Canada's directives and policies ensure the confidentiality of any data released from the RDC. Only aggregate results, which are fully anonymized and non-confidential, without direct personal identifiers, which precludes the possibility of re-identifying individuals, can be released from the RDC. Individual responses will be grouped with those of others when reporting results and results for very small groups will not be published or shared with government departments or agencies. This will also reduce any potential impact on vulnerable populations or subsets of populations, as the grouping of results will protect the confidentiality of individual responses.

    Although there are currently no plans for record linkage, direct personal identifiers such as name will be retained on a separate file in a secure location for potential linkage opportunities in the future.

    Statistics Canada's microdata linkage and related statistical activities were assessed in Statistics Canada's Generic Privacy Impact Assessment.Footnote 7 All data linkage activities are subject to established governanceFootnote 8, and are assessed against the privacy principles of necessity and proportionalityFootnote 9. All approved linkages are published on Statistics Canada's websiteFootnote 10.

  2. Effectiveness - Working assumptions:

    The survey will be conducted using a self-reported electronic questionnaire. The sample frame for this survey will be developed by Statistics Canada methodologists using administrative files within the Agency, which contain personal information such as name and contact information. The sample frame is created to ensure the population of employees under federal jurisdiction is represented. This information will be used to contact individuals selected for the survey. Prior to collection, selected individuals will be sent an invitation letter with details about the survey such as the survey purpose and the protection of confidentiality, as well as a brochure with information for workplace safety and mental health resources. The letter will include a unique secure access code for the respondent to access the electronic questionnaire on Statistics Canada's secure survey infrastructure.

    Statistics Canada interviewers will conduct follow-up phone calls with people who have not responded after six weeks. This will also provide respondents with an opportunity to complete the survey over the telephone with a trained Statistics Canada interviewer.

    The collection period will be approximately three months. Statistics Canada will follow all directives and policies for the development, collection, and dissemination of the survey. Survey responses will not be directly attached to respondents' personal identifiers, such as their name, address or phone numbers. Other personal information collected during the survey such as age, sex at birth, gender, sexual orientation, marital status, salary or wage earned, Indigenous identity, country of birth, immigration status and related information, citizenship, population group, level of education and if they have a disability, will be grouped to create statistics for publication of survey results and used for analysis of sub-populations.

  3. Proportionality:

    A sample size of 37,500 has been assessed as necessary by methodologists to produce statistics of sufficient quality by industry level of federally regulated workplaces. Employment and Social Development Canada has also identified the need for estimates at the industry level, as a smaller sample size would not be expected to yield estimates of sufficient quality. If a future iteration of SEFJ occurs, its sample design will be reviewed and updated based on information gathered from this iteration.

    Experts at Statistics Canada and at the Employment and Social Development Canada have been consulted on the scope and methodology of the SEFJ. Questions from other Statistics Canada surveys have been included in the SEFJ. This content has undergone three rounds of qualitative testing and no sensitivity issues were identified during qualitative testing. As well, three questions on the COVID-19 pandemic were added to assess the impact of the pandemic on unemployment and the health and safety encountered by the respondents. These questions underwent informal qualitative testing and were reviewed by the Questionnaire Design Resource Centre, who have been involved in testing a series of questionnaires on the COVID-19 pandemic.

    Each question and response category was carefully considered to ensure that it would measure the research questions and help inform future decisions related to the quality of employees' work conditions including harassment or discrimination.

    Proportionality has also been considered based on data sensitivity and ethics:

    • Data sensitivity: The data collected for the SEFJ could be of a sensitive nature due to some of the elements being measured. To reduce the risk of sensitive information being disclosed, these data will be processed according to Statistics Canada best practices. In particular, personal identifier variables (e.g. address, etc.) are stored in a file separate from the survey data and accessible to only a limited number of employees on a need-to-know basis, and are never disclosed. They are retained for no more than two years after collection is completed.
    • Ethics: Experts at Statistics Canada and ESDC have been consulted to ensure that the collection of data for the SEFJ will be done ethically. Respondents will be informed in the questionnaire that their participation is voluntary and will be provided with the survey topics before being asked any questions. A national resource on mental health and workplace safety will be listed on the brochure (see appendix A) and mailed to the respondents.

    The benefits of the findings, which are expected to support decision making for the federal government and are aimed at measuring working conditions to improve Canada Labour Code standards, are believed to be proportional to the potential risks to privacy.

  4. Alternatives:

    Currently, there are no other data sources that gather information on the working conditions of employees under federal jurisdiction. Many survey methodologies were explored. However, based on discussions between subject matter and methodology experts at Statistics Canada and the Department of Employment and Social Development Canada, it was determined that a survey with at least 37,500 units was necessary to produce reliable and accurate results by industry. Releasing data at these aggregated levels will reduce the potential to identify impacts on vulnerable populations, subsets of populations, and groups, while providing meaningful results.

Mitigation factors:

Some questions on the Survey of Employees under Federal Jurisdiction are considered sensitive as they relate to sexual harassment, non-sexual harassment and discrimination in the workplace encountered by individuals and the outcomes those problems have had on their lives. The overall risk of harm to the survey respondents has been deemed manageable with existing Statistics Canada safeguards that are described in Statistics Canada's Generic Privacy Impact Assessment, as well as with the following measures:

Mental Health and Workplace Safety Resources

A brochure will be mailed to respondents that provides details about the survey, such as the survey purpose, how the survey results will be used, the protection of confidentiality, as well as a national resource for mental health and workplace safety.

Transparency

As with all Statistic Canada surveys, prior to the survey, respondents will be informed of the survey purpose, allowing them to decide if they want to participate. This information will be provided through invitation and reminder letters, and reiterated at the beginning of the online questionnaire. Respondents will also be informed that their participation is voluntary before being asked any questions. Information about the survey, a brochure, and the survey questionnaire will be made available on Statistics Canada's website on the day collection starts.

Confidentiality

Individual responses will be grouped with those of others when reporting results. Individual responses and results for very small groups will never be published or shared with government departments or agencies. Careful analysis of the data and consideration will be given prior to the release of aggregate data to ensure that vulnerable individuals are not disproportionally impacted.

Conclusion:

This assessment concludes that, with the existing Statistics Canada safeguards and mitigation factors listed above, any remaining risks are such that Statistics Canada is prepared to accept and manage the risk.

Formal approval:

This Supplementary Privacy Impact Assessment has been reviewed and recommended for approval by Statistics Canada's Chief Privacy Officer, Director General for Modern Statistical Methods and Data Science, and Assistant Chief Statistician for Social, Health and Labour Statistics.

The Chief Statistician of Canada has the authority for section 10 of the Privacy Act for Statistics Canada, and is responsible for the Agency's operations, including the program area mentioned in this Supplementary Privacy Impact Assessment.

This Privacy Impact Assessment has been approved by the Chief Statistician of Canada.

For Information only. This is an electronic survey example for information purposes only. This is not a working questionnaire.

Getting started

Why are we conducting this survey?

This survey is a census of plants that crush oilseeds into oil and meal. Data collected are part of supply-disposition statistics of major grains and allow the calculation of the domestic disappearance component. They are also required to verify grain production and farm stocks.

The data are used by the provincial governments, the Food and Agriculture Organization of the United Nations and related industries for market analysis, particularly of supply-disposition of grain.

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

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

Other important information

Authorization to collect this information

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

Confidentiality

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

Record linkages

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

Data-sharing agreements

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

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

For this survey, there are Section 11 agreements with the provincial statistical agencies of Newfoundland and Labrador, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia. The shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province.

Business or organization and contact information

Business or organization and contact information - Question identifier: 1
Verify or provide the business or organization's legal and operating name and correct where needed.

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

  • Legal name
  • Operating name (if applicable)

Business or organization and contact information - Question identifier: 2
Verify or provide the contact information of the designated business or organization contact person for this questionnaire, and correct information if needed.

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

  • First name
  • Last name
  • Title
  • Preferred language of communication
    • English
    • French
  • Mailing address (number and street)
  • City
  • Province, territory or state
  • Postal code or ZIP code
    • Example: A9A 9A9 or 12345-1234
  • Country
  • Email address
  • Telephone number (including area code)
    • Example: 123-123-1234
  • Extension number (if applicable)
  • Fax number (including area code)
    • Example: 123-123-1234

Business or organization and contact information - Question identifier: 3
Verify or provide the current operational status of the business or organization identified by the legal and operating name above.

  1. Operational - Go to question 4
  2. Not currently operational
    e.g., temporarily or permanently closed, change of ownership

    Why is this business or organization not currently operational?
    • Seasonal operations - Go to question 3a.
    • Ceased operations - Go to question 3b.
    • Sold operations - Go to question 3c.
    • Amalgamated with other businesses or organizations - Go to question 3d.
    • Temporarily inactive but will re-open - Go to question 3e.
    • No longer operating because of other reasons - Go to question 3f.

Business or organization and contact information - Question identifier: 3a
Seasonal operations

  • When did this business or organization close for the season?
    Date - YYYY/MM/DD
  • When does this business or organization expect to resume operations?
    Date - YYYY/MM/DD

Go to question 4

Business or organization and contact information - Question identifier: 3b
Ceased operations

  • When did this business or organization cease operations?
    Date - YYYY/MM/DD
  • Why did this business or organization cease operations?
    • Bankruptcy
    • Liquidation
    • Dissolution
    • Other - Specify the other reasons why the operations ceased

Go to question 4

Business or organization and contact information - Question identifier: 3c
Sold operations

  • When was this business or organization sold?
    Date - YYYY/MM/DD
  • What is the legal name of the buyer?

Go to question 4

Business or organization and contact information - Question identifier: 3d
Amalgamated with other businesses or organizations

  • When did this business or organization amalgamate?
    Date - YYYY/MM/DD
  • What is the legal name of the resulting or continuing business or organization?
  • What are the legal names of the other amalgamated businesses or organizations?

Go to question 4

Business or organization and contact information - Question identifier: 3e
Temporarily inactive but will re-open

  • When did this business or organization become temporarily inactive?
    Date - YYYY/MM/DD
  • When does this business or organization expect to resume operations?
    Date - YYYY/MM/DD
  • Why is this business or organization temporarily inactive?

Go to question 4

Business or organization and contact information - Question identifier: 3f
No longer operating due to other reasons

  • When did this business or organization cease operations?
    Date - YYYY/MM/DD
  • Why did this business or organization cease operations?

Business or organization and contact information - Question identifier: 4
Verify or provide the current main activity of the business or organization identified by the legal and operating name.

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

1 - This is the current main activity. - Go to next section

2 - This is not the current main activity.
Please provide a brief but precise description of this business or organization's main activity.

  • e.g., breakfast cereal manufacturing, shoe store, software development

Business or organization and contact information - Question identifier: 5
Was this business or organization's main activity ever classified as “Oil seed processing”?

  • Yes
  • No – Go to next section

Business or organization and contact information - Question identifier: 6
When did the main activity change?

  • Date - YYYY/MM/DD

Oilseeds — raw material crushed and month-end stocks

Oilseeds — raw material crushed and month-end stocks - Question identifier: 1
For the reference month, what were the quantities of raw material crushed on a cleaned basis (clean net) and the month-end stocks for the following oilseeds?

Include:

  • raw materials crushed at this operation only
  • stocks of oilseed and oilseed products held on Canadian soil only.

Oilseeds

  • Canola
    Quantity of raw material (metric tonnes)
    Stocks of oilseeds (metric tonnes)
  • Soybeans
    Quantity of raw material (metric tonnes)
    Stocks of oilseeds (metric tonnes)
  • All other oilseeds — specify
    Quantity of raw material (metric tonnes)
    Stocks of oilseeds (metric tonnes)

Oilseed products and month-end stocks

Oilseed products and month-end stocks - Question identifier: 2
For the reference month, what were the quantities of oil and meal produced and the month-end stocks for the following oilseed products?

Include: stocks of oilseed and oilseed products held on Canadian soil only.

Oilseed products

  • Canola oil - Quantity produced (metric tonnes)
  • Canola meal - Quantity produced (metric tonnes)
  • Soybean oil - Quantity produced (metric tonnes)
  • Soybean meal - Quantity produced (metric tonnes)
  • All other oilseeds reported at question 1c — oil - Quantity produced (metric tonnes)
  • All other oilseeds reported at question 1c — meal - Quantity produced (metric tonnes)
  • Canola oil - Stocks of oilseed products (metric tonnes)
  • Canola meal - Stocks of oilseed products (metric tonnes)
  • Soybean oil - Stocks of oilseed products (metric tonnes)
  • Soybean meal - Stocks of oilseed products (metric tonnes)
  • All other oilseeds reported at question 1c — oil - Stocks of oilseed products (metric tonnes)
  • All other oilseeds reported at question 1c — meal - Stocks of oilseed products (metric tonnes)

Canola received from Canadian producers

Canola received from Canadian producers - Question identifier: 3
What were the quantities of canola received from Canadian producers for the reference month and for the crop year to date?

Include: canola received directly from Canadian producers from all collection points.

Exclude:

  • canola imported from other countries
  • grain received from Canadian Grain Commission licensed elevators.

Provincial producers

  • Manitoba producers - Quantity received in reference month (metric tonnes)
  • Saskatchewan producers - Quantity received in reference month (metric tonnes)
  • Alberta producers - Quantity received in reference month (metric tonnes)
  • British Columbia producers - Quantity received in reference month (metric tonnes)
  • Total - Quantity received in reference month (metric tonnes)
  • Manitoba producers - Quantity received Crop year to date (metric tonnes)
  • Saskatchewan producers - Quantity received Crop year to date (metric tonnes)
  • Alberta producers - Quantity received Crop year to date (metric tonnes)
  • British Columbia producers - Quantity received Crop year to date (metric tonnes)
  • Total - Quantity received Crop year to date (metric tonnes)

Changes or events

Changes or events - Question identifier: 5
Indicate any changes or events that affected the reported values for this business or organization compared with the last reporting period.

Select all that apply.

  • Strike or lock-out
  • Exchange rate impact
  • Price changes in goods or services sold
  • Contracting out
  • Organizational change
  • Price changes in labour or raw materials
  • Natural disaster
  • Recession
  • Change in product line
  • Sold business or business units
  • Expansion
  • New or lost contract
  • Plant closures
  • Acquisition of business or business units
  • Other changes or events — specify:

OR

  • No changes or events

Contact person

Contact person - Question identifier: 6
Statistics Canada may need to contact the person who completed this questionnaire for further information.

If the contact person is the same as on cover page, Go to the "Feedback" section

Otherwise, who is the best person to contact about this questionnaire?

  • First name
  • Last name
  • Title
  • Email address
  • Telephone number (including area code)
    • Example: 123-123-1234
  • Extension number (if applicable)
  • Fax number (including area code)
    • Example: 123-123-1234

Feedback

Feedback - Question identifier: 7
How long did it take to complete this questionnaire?

Include: the time spent gathering the necessary information.

Hours:
Minutes:

Feedback - Question identifier: 8
Do you have any comments about this questionnaire?

Before submitting the questionnaire

If you do not need to review your information, press the Next button to continue.

Please note that you will not be able to change any information you reported once you have submitted the questionnaire.

You can print this questionnaire once you have completed and submitted it.

Canadian Statistics Advisory Council 2021 Annual Report: General Summary - Strengthening the foundation of our National Statistical System

Release date: December 16, 2021

PDF version (2.15 MB)

Information and data are among Canada's most valuable resources. Data derived from a wide range of sectors and areas of interest are essential to informed decision making for pandemic recovery initiatives, for reconciliation and a renewed relationship with Indigenous peoples, for tackling the climate crisis, and for addressing key issues the country will face for decades to come.

Leading-edge Canadian public and private sector organizations are driving the use of digital information to better understand the issues we face. There is a wealth of public and private data in this country that is not part of the national statistical system. When they are built upon common concepts and definitions and shared standards, these data can be key to meeting the needs of Canadians. To do this effectively, it means building strong relationships that promote the value of data and connection of information.

First Nations, Inuit and Métis communities are each developing a distinctions-based approach to asserting their unique jurisdiction, ownership and control over their data that relates to their identity, their people, language, history, culture and communities. Information, data and capacity development investments are important at the community, regional and national levels to support these efforts.

Adapting governance and data stewardship to a digital society

It is recommended that the Minister of Innovation, Science and Industry and the Chief Statistician:

  • 1.1 Take leadership in supporting a national data system that:
    1. delivers collaborative frameworks that include clearly defined roles and accountabilities for Statistics Canada and partners from public, Indigenous, private, and other sectors.
    2. establishes Statistics Canada's role as the national data steward, based on a whole of government approach to defining and prioritizing data needs as an integral part of federal program planning; and
    3. applies legislation and policies to support and incentivise active administration of national data standards and real-time nationwide data flows involving all government jurisdictions.
  • 1.2 Through clearly defined and distinctions-based governance partnerships with Indigenous organizations and communities, support the advancement of First Nations, Inuit and Métis led governance capacities, data priorities and active participation in and contribution to the national data system.

To build truly nation-wide data means that legislation and policy must not only be in place, but also clear on issues of data stewardship and need for data. Working within the national statistical system, Statistics Canada is often constrained in its ability to deliver what Canadians expect and need to create prosperity and well-being in a world marked by a digital future. Even with digital modernization efforts, the statistical system is hampered by fragmentation, unused data and unmet data needs in critical sectors. These are largely a consequence of the inheritance of an outdated governance structure between Statistics Canada and federal departments, provinces and territories. The federal government must be a leader and support new governance models that bring broader perspectives and partnerships.

Adapting statistical legislation to reflect the needs of a modern digital national statistical system

It is recommended that the Minister of Innovation, Science and Industry and the Chief Statistician:

  • 2.1 update the Statistics Act to reflect a modern digital society.
  • 2.2 clarify and strengthen in the Statistics Act, Statistics Canada's data stewardship role.
  • 2.3 introduce a new category of accredited users from government, academic and private research institutions, and Indigenous organizations and communities who would be granted access to more disaggregated microdata without having to be deemed employees of Statistics Canada.
  • 2.4 update the Statistics Act to support participation of Statistics Canada and federal departments in reconciliation efforts and a renewed relationship with First Nations, Inuit and Métis with respect to coordination and governance of data and information, to support planning, building capacity and decision making by all stakeholders to address critical data needs.

For Canada to succeed in an increasingly dynamic digital world, Statistics Canada's role is key. The agency is an independent and trusted source of official statistics and provides a solid foundation for government accountability and evidence-based decision making by both the public and the private sectors, which benefits all Canadians.

Strong, clear and unambiguous statistical legislation is important to support the national data system including its national data strategies. Modern digital technology for collecting, transferring and sharing statistical information is not well reflected in the Statistics Act, which affects how the statistical legislation is interpreted. Statistics Canada's role as data steward in the country's statistical systems needs to be clarified and strengthened in the Statistics Act.

Leveraging opportunities for addressing critical data needs

It is recommended that the Minister of Innovation, Science and Industry and the Chief Statistician:

  • 3.1 develop and communicate principles for working with multidisciplinary connected datasets as an important component of national data strategies.
  • 3.2 broaden and deepen reciprocal relationships with federal departments and agencies, other levels of government, Indigenous jurisdictions and the private sector to enable sharing of data to build truly national and nationwide data infrastructures
    1. invest in and provide incentives for the effective implementation of advanced real-time software and communications technologies to enable data sharing and connecting of data across jurisdictions and organizations.
    2. invest in innovative data collection and measures that move beyond econocentric environment models to include elements of quality of life and sustainability.

Issues such as the global COVID-19 pandemic, racial injustice, the tragedy of Indigenous residential schools and the climate crisis have heightened a growing recognition among Canadians and decision makers of how important reliable and timely detailed information is to understand many of the issues facing Canadians.

There is a need for nimble, flexible data systems, as pressing problems and critical events are often unexpected. In general, the pace of change today occurs much more quickly than the change captured in quinquennial census information or data collected from annual surveys. There are new untapped sources of information that can provide more real-time data and accurate portraits of Canadians and their communities.

Data sources on their own generally do not provide the breadth, depth or interconnections required to examine more complex issues such as socio-economic inequalities and environmental impacts from and on businesses. To support these analyses, datasets need to be constructed from multiple sources under clearly specified confidentiality and security protocols. Increasingly, researchers need to be able to link and connect relevant variables on demand.

Intermediate Session on Considerations for Displaying Data - A Case Study (19220008)

Data visualizations are a powerful tool to explore and present ideas. In response to feedback from information session participants, this session uses a case study approach to illustrate how to explore your data and decide which visualizations help tell your audience a data story. Designed for a beginner to intermediate audience, the session focuses on one of the hardest parts of designing graphs and charts: knowing where to start.

English Information Sessions

French Information Session

Monthly Survey of Manufacturing: National Weighted Rates by Source and Characteristic - October 2021

National Weighted Rates by Source and Characteristic, October 2021
Table summary
The information is grouped by Sales of goods manufactured, Raw materials and components, Goods / work in process, Finished goods manufactured, Unfilled Orders, Capacity utilization rates (appearing as row headers), and Data source as the first row of column headers, then Response or edited, and Imputed as the second row of column headers, calculated by percentage.
  Data source
Response or edited Imputed
%
Sales of goods manufactured 85.9 14.1
Raw materials and components 76.2 23.8
Goods / work in process 76.7 23.3
Finished goods manufactured 78.6 21.4
Unfilled Orders 84.8 15.2
Capacity utilization rates 72 28.0

Monthly Survey of Manufacturing: National Level CVs by Characteristic – October 2021

National Level CVs by Characteristic
Table summary
This table displays the results of Monthly Survey of Manufacturing: National Level CVs by Characteristic. The information is grouped by Month from September 2020 to September 2021 (appearing as row headers), and Sales of goods manufactured, Raw materials and components inventories, Goods / work in process inventories, Finished goods manufactured inventories and Unfilled Orders, calculated in percentage (appearing as column headers).
Month Sales of goods manufactured Raw materials and components inventories Goods / work in process inventories Finished goods manufactured inventories Unfilled Orders
%
October 2020 0.68 0.99 1.31 1.56 1.11
November 2020 0.68 1.05 1.21 1.48 1.16
December 2020 0.69 1.02 1.20 1.46 1.30
January 2021 0.80 1.00 1.24 1.59 1.42
February 2021 0.75 0.99 1.50 1.67 1.30
March 2021 0.71 1.01 1.45 1.69 1.35
April 2021 0.78 1.04 1.56 1.74 1.53
May 2021 0.79 1.04 1.48 1.58 1.45
June 2021 0.73 1.02 1.44 1.69 1.36
July 2021 0.78 1.05 1.48 1.67 1.37
August 2021 0.73 1.06 1.53 1.84 1.46
September 2021 0.79 1.07 1.56 1.86 1.36
October 2021 0.76 1.04 1.56 1.71 1.40

Implementing MLOps with Azure

By: Jules Kuehn, Shared Services Canada

Machine Learning Operations is a variation of DevOps that addresses concerns specific to Machine learning (ML). Like DevOps, MLOps enables the continuous integration and deployment (CI/CD) of Machine learning (ML) models, but it also automates re-training on new data and tracks the results of different training runs (or experiments).

A common issue with ML models is declining performance over time. This is known as a "drift" (visit The Ultimate Guide to Model Retraining for more information on drift). Imagine an ML model that predicts whether a house in Ottawa will sell above its asking price, when given information about the house and the listing price. When the model was deployed five years ago, it was able to make this prediction with 95% accuracy. However, if the model was never re-trained with updated data, its predictions would not reflect Ottawa's current housing market and would therefore be less accurate. To resolve this issue, an MLOps system can automatically re-train and re-deploy models to incorporate more recent data and track the model's performance over time.

Shared Services Canada's (SSC) Data Science and Artificial Intelligence team has developed several ML models as proof-of-concept solutions for SSC business problems. As a starting point in the MLOps journey, the team collaborated with Microsoft to develop a functional MLOps solution entirely within the Azure ecosystem.

The MLOps system spans several components such as source control, experiment tracking, model registries, CI/CD pipelines, Azure ML APIs, Docker and Kubernetes. Using this system enables the team to continuously deliver REST APIs for the best-performing ML models and make them available on the newly developed Government of Canada API Store.

Model development

To speed up implementation, the team used Azure Software as a Service (SaaS) offerings to accomplish the majority of tasks. This included data loading with Azure Data Factory, model development in Azure Databricks notebooks, experiment tracking and model deployment with Azure ML, and source control and CI/CD with Azure DevOps.

Tracking experiments and models

The Databricks notebooks log run metrics and register models in an Azure ML workspace when a model training run is complete (visit Log & view metrics and log files and Model Class for more information). This is useful when runs are initiated manually during model development and when they're executed as a job within CI/CD pipelines. During model development, it is possible to track improvements to metrics, such as accuracy, while adjusting hyper parameters. When run as a pipeline job, you can monitor changes to metrics as new data is used in re-training.

Source control and continuous integration

The source control repository for this model is made up of three folders:

  1. Notebooks—the Databricks notebooks code
  2. Pipelines—two pipelines to train and deploy models
  3. API—the code to wrap the trained model in a REST API.
Figure 1 – General source control repository structure

Figure 1 – General source control repository structure

Description - Figure 1

Tree diagram of DevOps repository with 3 top level folders. The first folder is Notebooks, which is connected via Databricks Git Sync to model_train.py. The second folder is pipelines, which contains two subfolders, each containing Pipeline YAML and Python scripts. These subfolders are named "ci / train" and "deploy". The third top level folder is "API", which contains score.py and a tests subfolder, which contains PyTest scripts.

Notebook pull request pipeline

Although literate programming with notebooks (e.g. Jupyter) is common practice for data science, cloud notebook environments do not always integrate effectively with source control. If multiple team members are working on a project, notebooks can become disorganized. The team developed a workflow that incorporates best practices for source control management, such as feature branches and integration tests in pull requests.

Figure 2 – Data science notebooks

Figure 2 – Data science notebooks

Description - Figure 2

A disorderly desk with papers labelled "Data Science Notebooks" scattered all over its surface, on the floor and stuffed into a nearby garbage bin.

Within Databricks, all notebooks in a fixed-location main folder are synchronized to follow the main branch in an Azure DevOps git repository. Before changing the model code, a team member creates a copy of this folder in Databricks and a corresponding new branch in DevOps, then sets up the git sync between them. When they're satisfied with the changes, the team member commits the notebooks in Databricks, then creates a pull request (PR) in DevOps.

Any PR that includes changes to notebook code triggers a CI pipeline that ensures changes to the notebooks will not break. It begins by copying the feature branch notebooks to a fixed-location integration test folder referenced by a Databricks job, which is then triggered by the pipeline through the Databricks API.

To speed up the execution of this test, a parameter is passed to the notebook indicating that this is  a test and not a full training job. The model is trained on a 5% sample for one epoch and the resultant model is not registered.

Figure 3 – CI and Train Pipelines with Databricks

Figure 3 – CI and Train Pipelines with Databricks

Description - Figure 3

Diagram of CI and Train Pipelines. Step 1: Copy notebook source to Databricks. Step 2: Start Databricks job to train model. Step 3: Wait for Databricks "success".

The pipeline continues to poll Databricks until the job is complete. If the notebook executes successfully, merging to the main branch may continue.

Model deployment

Since the SSC team plans to deliver most of their models on the GC API Store, they want to move from notebooks to REST API applications as quickly and reliably as possible.

Containerizing the model

For simple applications, the Azure ML API can deploy a registered model as a containerized application using a few lines of code at the end of a notebook. However, this option does not address many operational requirements such as scaling. More importantly, it doesn't allow much flexibility to pre- and post-process model inputs and outputs. Instead, we use the Model.package() from the Azure ML Software Development Kit function from the Azure ML Software Development Kit (SDK) to create a Docker image. Then, the image is deployed to a previously configured Kubernetes cluster and the endpoint is registered with the GC API Store.

By default, the function pulls the latest registered version of the model, but can also use the experiment logs to dynamically select a model based on any metric that was logged from the notebook (e.g. to minimize loss).

Deployment pipeline

Figure 4 – Deployment Pipeline

Figure 4 – Deployment Pipeline

Description - Figure 4

Diagram of deployment pipeline with 3 root stages: Test, Build, and Deploy. The Test stage runs PyTest API tests and local_deploy_test.py, which involves Docker retrieving a model from the Azure ML registry. The Build stage runs build_push_image.py, which also involves Docker retrieving a model from the Azure ML registry, but also pushes the Docker container to the Azure Container Registry. The Deploy stage runs the command line application kubectl, which connects to Azure Kubernetes and retrieves the container from the Azure Container Registry.

As its name implies, Azure DevOps offers more than just source control – it can also define pipelines to automate CI and CD tasks. The pipelines are defined by YAML files and leverage Bash and Python scripts.

Unlike the notebook PR pipeline, the deployment pipeline is triggered by any commit to the main branch. It's comprised of three stages:

  • Testing the code: Using PyTest, unit test the API with correct and incorrect inputs. As an integration test, Model.deploy() the web service locally on the agent pool VM and run similar tests, but over HTTP.
  • Building and registering the Docker container: Build a Docker image with Model.package(), passing in custom API code. Register the container to an Azure Container Registry.
  • Deploying to Kubernetes: Using kubectl apply, connect to the previously-configured Azure Kubernetes Service, connect to the previously-configured Azure Kubernetes Service. Pass a manifest file pointing to the new image in the container registry.

This process retains the same API endpoints through redeployments and does not disrupt delivering the application through the GC API Store.

Model re-training pipeline

The model re-training pipeline is similar to the pull request pipeline but it runs a different Databricks job that points to the main branch notebook. The notebook logs the run metrics and registers the new model in Azure ML, then triggers the deployment pipeline.

Model training can be resource intensive. Running the notebook as a job on Databricks gives us the option of selecting a high performance compute cluster (including GPU). The clusters are automatically deallocated when the training run is complete.

Rather than being triggered by a particular event, pipeline runs can also be scheduled (visit Configure schedules for pipelines for more information). Many of the models rely on data from SSC's Enterprise Data Repository (EDR), so the team can schedule the model re-training pipeline to follow the EDR's refresh cycle. This ensures that the deployed model is always based on the most current data.

Conclusion

To provide a repeatable workflow for deploying ML models to the GC API Store, SSC integrated several Azure SaaS offerings to make a functional MLOps solution

  • Azure DevOps: Source code repository; CI/CD and re-training pipelines
  • Azure Databricks: ML model development in notebooks; synchronized to DevOps git repository
  • Azure ML: Experiment tracking and model registration; building Docker images
  • Azure Kubernetes Service: Container serving; pointed to by the GC API Store.

Finally, it's worth noting that this approach is just one of many possible solutions. The Azure ML APIs that the SDK relies on are under active development and change frequently. The team is continuing to explore open-source and self-hosted options. The MLOps journey is far from over, but it's well under way!

Please email the SSC Data Science and Artificial Intelligence team: ssc.dsai-sdia.spc@canada.ca, if you have any questions about this implementation or if you just want to chat about machine learning.

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Registered Apprenticeship Information System (RAIS) Guide, 2020

Concepts used by the Registered Apprenticeship Information System (RAIS)

Designated trades

Apprenticeship training and trade qualifications in Canada are governed by the provincial and territorial jurisdictions. These jurisdictions determine the trades, for which, apprenticeship training is made available as well as the trades, for which, certificates are granted. These are referred to as designated trades. The jurisdictions also determine which of the designated trades require certification in order to work unsupervised in the trade. The list of designated trades varies considerably between the jurisdictions. Data from the Registered Apprenticeship Information System (RAIS) include those trades that are designated in at least one province or territory.

Registered apprentices are people who are in a supervised work training program in a designated trade within their provincial or territorial jurisdiction. The apprentice must be registered with the appropriate governing body (usually a Ministry of Education or Labour or a trade specific industry's governing body) in order to complete the training.

Trade Qualifiers or Trade Challengers are people who have worked in a specific trade for an extended period of time, without necessarily having ever been an apprentice, and who have received certification from a jurisdiction, usually done via a skills assessment examination in the trade.

Registrations

The total registrations in apprenticeship programs is the count of any registrations that occurred during the reporting period (from January to December of the calendar year) within one of the 13 jurisdictions (province or territories).

Total registrations = Already registered + New registrations + Reinstatements

  • Already registered - the number of registrations carried forward from the previous calendar year
  • New registrations - new entrants to any apprenticeship program that occurred during the 12 months reporting period
  • Reinstatements - registrations by people who had left an apprenticeship program in a specific trade in a previous year and had returned to the same apprenticeship program during the reporting period

Red Seal and non-Red Seal Programs

The Red Seal Program sets common standards assessing the skills of tradespersons across Canada in specific trades, referred to as the "Red Seal" trades. Tradespersons who meet the Red Seal standards, through examination, receive a Red Seal endorsement on their provincial/territorial trade certificates. The Red Seal endorsement provides recognition that your certificate meets an interprovincial standard that is recognized in each province and territory.

Non-Red Seal trades do not have interprovincial standards. Many of these trades do not have an examination requirement in order to work in the trade.

Certification

The requirements for granting a certificate varies by jurisdiction in Canada. In most instances, an apprentice is issued a certificate if he or she completes requirements such as supervised on-the-job training, technical training, as well as passing one or more examinations. Most trade qualifiers (Challengers), meanwhile, become certified once they pass an examination.

Certification terminology

There are jurisdictional differences in the names of certificates awarded.

They may include:

  • Certificate of Apprenticeship
  • Diploma of Qualification
  • Certificate of Qualification
  • Journeyperson's Certificate
  • Certificat d'aptitude
  • Certificat de compagnon
  • Certificat de compétence
  • Diplôme d'apprentissage

Federal, provincial and territorial changes pertinent to the interpretation of RAIS data

1. Revisions have been made to the Quebec 1991 to 2005 data, which also changed the previous Canada totals.

2. Prior to 1999, Nunavut was part of the Northwest Territories.

3. Starting in 2003, a change occurred in the reporting of Newfoundland and Labrador's information concerning newly registered apprentices and cancellations/suspensions.

4. The British Columbia data have been revised in 2005. This changed the previous Canada totals for 2005.

5. Starting with the 2005 reporting year, Prince Edward Island changed their information system and this may have affected historical comparisons. At the end of 2006, Prince Edward Island made some adjustments and revisions to their database which accounted for the change in the carry-over of registered apprentices for the beginning of 2007. In 2007, an increase in new registrations is, to some extent, related to a demand for skilled workers outside of the province. In 2008, due to technical difficulties during the redesign of their Registered Apprenticeship Information System, Prince Edward Island was not able to report a number of apprentices.

6. In 2006, minor trade code revisions were made to Manitoba.

7. In 2006 and 2007, differences may occur in Ontario related to the carry-over totals of active apprentices between both years. This is a result of the conversion of client data into Ontario's new database system. As a result, a clean-up of inactive clients occurred and this adjusted the active total of registered apprentices and their carry-over into 2007.

8. As of 2008, the portion of total Quebec trade information coming from Emploi-Quebec (EQ) is no longer being provided in aggregated form. The data from the province includes all trades with the exception of the automotive sector.

9. In 2008, Alberta incorrectly included the Industrial warehousing trade with the Partsperson and Partsperson (material) trades and also excluded the Construction Craft Worker trade.

10. In 2008, a distinct feature of the Rig Technician trade is that although individuals may be registered as apprentices in the trade in Ontario, their certificates are granted as trade qualifiers (challengers).

11. In 2008, Alberta reported a large number of discontinued apprentices, which was a result of them implementing a series of cancellations and suspensions of inactive apprentices.

12. In 2008 and 2009, new Quebec legislation affecting the Emploi-Quebec (EQ) sector trade was introduced. This resulted in some changes in the reporting of registered apprenticeship registrations.

13. An adjustment has been made to the Joiner trade in British Columbia, to include the trade in the Interior finishing major trade group, rather than in the previous Carpenter's major trade group.

14. In 2010, the Emploi-Quebec (EQ) data included revised trade programs where some of the trades have been segmented into several levels. This segmentation created possible multiple registrations and completions by a single individual apprentice, where previously only one registration and completion existed for this individual.

15. In 2011, the Electronics technician (Consumer Products) trade was no longer designated as a Red Seal trade.

16. In 2012, the Gasfitter - Class A and Gasfitter - Class B trades were designated as Red Seal trades.

17. In 2013, changes in provincial regulations governing drinking water related trades reported by Emploi-Quebec (EQ), have resulted in program changes, as well as the transferring of responsibility of some of these trades to the Conseil de la Construction du Québec (CCQ).

18. Begining in 2013, Ontario's data is received from two organizations. The registration data continues to be reported by the Ministry of Advanced Education Skills Development (MASED). They are also responsible for issuing Certificates of Apprenticeships upon the completion of technical training and on-the-job hours. The Ontario College of Trades (OCOT) is responsible for reporting data on Certificates of Qualifications, which are issued to apprentices upon the completion of a certification exam. This administrative practice has affected the RAIS data in a number of different ways.

  1. On April 8, 2013, MASED awarded a Certificate of Apprenticeship to approximately 6,000 apprentices who had completed their technical training and on-the-job hours, and had not yet received a Certificate of Qualification.
  2. There are discrepancies in the number of apprentices in Ontario due to differences in how MASED and OCOT define an apprentice. OCOT considers apprentices to be their members, for whom they have received membership applications with payment of annual membership fees. MASED considers apprentices to be individuals for whom they have received signed training agreements. In the MASED registration data, apprentices can have active and inactive statuses, which can also contribute to discrepancies. Inactive apprentices are apprentices with whom MASED have not received information about their progression in their apprenticeship program for more than a certain period of time. Active and inactive apprentices are included in the RAIS data. As such, the RAIS data may include previously registered apprentices, who have since discontinued their apprenticeship program, but have not yet informed MASED that they have discontinued their program.
  3. Beginning in 2013, apprentices who discontinued from apprenticeship programs in the past, but who remained on the database as already registered apprentices began to be removed from MASED records. These removals appear in the RAIS data files in the following years. The clean-up occurred during odd years (2013, 2015, and 2017). After discussion with the Ontario data partners in 2019, it was indicated that the last of these batch discontinuations were completed in 2017. As a result, there will be less of a spike in discontinuations, and more of a normalized trend from here starting in 2018 and onwards. Normal discontinuation figures for the province will be about 5,000 to 7,000 per year.
  4. In 2014 and 2015, apprentices who did not receive their Certificate of Qualification or Certificate of Apprenticeship in the same year were classified as trade qualifiers (Challengers) rather than apprentices. To align the RAIS data with the standard definition of trade qualifier (Challengers), these records were reclassified as apprentices with the release of the 2016 RAIS data. This revision led to a decrease of about 2,600 trade qualifiers (Challengers) in Ontario in both 2014 and 2015 compared to the previously released data.

19. In 2013, a regulatory change came into effect which affects both Ornamental ironworkers and Structural steel erectors under the jurisdiction of the Conseil de la Construction du Québec (CCQ). Workers in these two trades are now considered Ironworkers. Both the 2014 and 2015 reference years were also impacted by these regulatory changes.

20. In 2013, changes were made to the Automotive Service Technician trades in British Columbia. Apprentices no longer have to complete mandatory work-based training hours at each program level before progressing to the next level of technical training. The 2014 reference year was also impacted by these changes.

21. Certificates in the Steamfitter/Pipefitter trade under the Conseil de la Construction du Québec (CCQ), also include Plumbers.

22. Starting in 2013, Building/Construction Metalworker are coded to Metal Workers (other) instead of being included in the 'Other' category.

23. In 2014, the Heavy Equipment Operator (Dozer), Heavy Equipment Operator (Excavator) and Heavy Equipment Operator (Tractor-Loader-Backhoe) trades were designated as Red Seal trades.

24. Trade qualifiers (Challengers) in trades governed by Emploi-Quebec (EQ) represents certificates granted to individuals who received recognition for previously completed training. Emploi-Quebec (EQ) may, for example, recognize training in the case where an individual has a certificate in other provinces, territories, countries, or if the individual received a Diploma of Vocational Studies (DVS) in Quebec. These trade qualifiers (Challengers) also represent certificates granted as part of the regular re-certification process required in certain trades.

25. In March of 2014, there were changes made to the eligibility for the Apprenticeship Training Tax Credit (ATTC) in Ontario. This may have affected registration counts in some trades including those for information technology.

26. Prior to 2014, three welder programs (level A, level B, and level C) were offered in British Columbia. Starting in 2014, these three programs began to be phased out and replaced by a single apprenticeship program for welders. This change will impact registrations and certifications in this trade for the years following 2014.

27. Starting in 2017, changes are being made to the Automotive Service Technician program in British Columbia. The program is being restructured to align with other Canadian jurisdictions Automotive Service Technician Red Seal programs. These changes impacted reinstatement totals for 2017 and will potentially influence registrations counts for years following 2017.

28. In July 2018, Manitoba announced that it will perform a data clean-up every two years, starting with the 2019 reporting year. This clean-up resulted in lower numbers for both registrations and certifications for the 2019 reporting year.

29. In 2013, the structural steel erector trade and locksmith trade merged to become the ironworker worker trade. Transitional measures were put in place for journeypersons in these trades, which ended in July 2018.

30. British Columbia has some broad categories of trades where it is possible to receive a certificate after each level is completed, while other jurisdictions only certify apprentices after completing the final level.

  1. In 2019, the Industry Training Authority (ITA) made a decision to group some of their trades under one general trade. For example, Automotive Service Technician 1, Automotive Service Technician 2, and Automotive Service Technician 3 were combined into Automotive Service Technician.
  2. All the trades under Welder were not consolidated, but a general version of the Welder trade was created in 2019.
  3. Also, some apprenticeships were deactivated for certain trades and replaced by Challenge Pathway only, which is for trade qualifiers. Rig Technician, Petroleum Equipment Service Technician, and Water Well Driller are examples of these trades.

31. Starting December 1st, 2019, British Columbia will no longer offer technical training for the Rig Technician apprenticeship program. The apprentices continuing in this trade were taking their technical training in Alberta; however, Alberta no longer offers technical training for this trade and is in the process of de-designating this apprenticeship. Individuals can still receive a designation in trade by challenging the exam in British Columbia.

32. During the reference year of 2020, as a result of the pandemic some provinces cancelled or postponed in-class training, exams and apprenticeships throughout 2020. Counts for various indicators might be considered historical lows due to the pandemic in 2020. This created a larger deviation in the data for RAIS 2020 registrations, certifications and discontinuations.