Quarterly Financial Report for the quarter ended June 30, 2023

Statement outlining results, risks and significant changes in operations, personnel and program

A) Introduction

Statistics Canada's mandate

Statistics Canada ("the agency") is a member of the Innovation, Science and Industry portfolio.

Statistics Canada's role is to ensure that Canadians have access to a trusted source of statistics on Canada that meets their highest priority needs.

The agency's mandate derives primarily from the Statistics Act. The Act requires that the agency collects, compiles, analyzes and publishes statistical information on the economic, social, and general conditions of the country and its people. It also requires that Statistics Canada conduct the census of population and the census of agriculture every fifth year and protects the confidentiality of the information with which it is entrusted.

Statistics Canada also has a mandate to co-ordinate and lead the national statistical system. The agency is considered a leader, among statistical agencies around the world, in co–ordinating statistical activities to reduce duplication and reporting burden.

More information on Statistics Canada's mandate, roles, responsibilities and programs can be found in the 2023-2024 Main Estimates and in the Statistics Canada 2023-2024 Departmental Plan.

The Quarterly Financial Report:

  • should be read in conjunction with the 2023-2024 Main Estimates;
  • has been prepared by management, as required by Section 65.1 of the Financial Administration Act, and in the form and manner prescribed by Treasury Board of Canada Secretariat;
  • has not been subject to an external audit or review.

Statistics Canada has the authority to collect and spend revenue from other federal government departments and agencies, as well as from external clients, for statistical services and products.

Basis of presentation

This quarterly report has been prepared by management using an expenditure basis of accounting. The accompanying Statement of Authorities includes the agency's spending authorities granted by Parliament and those used by the agency consistent with the Main Estimates for the 2023-2024 fiscal year. This quarterly report has been prepared using a special purpose financial reporting framework designed to meet financial information needs with respect to the use of spending authorities.

The authority of Parliament is required before moneys can be spent by the Government. Approvals are given in the form of annually approved limits through appropriation acts or through legislation in the form of statutory spending authority for specific purposes.

The agency uses the full accrual method of accounting to prepare and present its annual departmental financial statements that are part of the departmental results reporting process. However, the spending authorities voted by Parliament remain on an expenditure basis.

B) Highlights of fiscal quarter and fiscal year-to-date results

This section highlights the significant items that contributed to the net increase in resources available for the year, as well as actual expenditures for the quarter ended June 30.

Comparison of gross budgetary authorities and expenditures as of June 30, 2022, and June 30, 2023, in thousands of dollars
Description for Chart 1: Comparison of gross budgetary authorities and expenditures as of June 30, 2022, and June 30, 2023, in thousands of dollars

This bar graph shows Statistics Canada's budgetary authorities and expenditures, in thousands of dollars, as of June 30, 2022 and 2023:

  • As at June 30, 2022
    • Net budgetary authorities: $576,698
    • Vote netting authority: $120,000
    • Total authority: $696,698
    • Net expenditures for the period ending June 30: $185,286
    • Year-to-date revenues spent from vote netting authority for the period ending June 30: $11,675
    • Total expenditures: $196,961
  • As at June 30, 2023
    • Net budgetary authorities: $619,835
    • Vote netting authority: $120,000
    • Total authority: $739,835
    • Net expenditures for the period ending June 30: $184,915
    • Year-to-date revenues spent from vote netting authority for the period ending June 30: $3,990
    • Total expenditures: $188,905

Chart 1 outlines the gross budgetary authorities, which represent the resources available for use for the year as of June 30.

Significant changes to authorities

Total authorities available for 2023-24 have increased by $43.1 million, or 6.2%, from the previous year, from $696.7 million to $739.8 million (Chart 1). The net increase is mostly the result of the following:

  • An increase of $87.2 million for funding received to cover the initial planning phase and development activities related to the 2026 Census of Population and 2026 Census of Agriculture programs;
  • A decrease of $48 million for the 2021 Census of Population and 2021 Census of Agriculture programs due to cyclical nature of funding winding down;
  • A decrease of $1.8 million for the Disaggregated Data Action Plan;
  • An increase of $1.3 million for salary increases related to latest rounds of collective bargaining;
  • An increase of $6.7 million for various initiatives including Statistical Survey Operations Modernization, Canada Dental Benefit, Federal Action Plan to Strengthen Internal Trade, Higher Education Intellectual Property Commercialization and Advancing a Circular Plastics Economy for Canada.

In addition to the appropriations allocated to the agency through the Main Estimates, Statistics Canada also has vote net authority within Vote 1, which entitles the agency to spend revenues collected from other federal government departments, agencies, and external clients to provide statistical services. The vote netting authority is stable at $120 million when comparing the first quarter of fiscal years 2022-2023 and 2023-2024.

Significant changes to expenditures

Year-to-date net expenditures recorded to the end of the first quarter decreased by $0.4 million, or 0.2% from the previous year, from $185.3 million to $184.9 million (see Table A: Variation in Departmental Expenditures by Standard Object).

Statistics Canada spent approximately 29.8% of its authorities by the end of the first quarter, compared with 32.1% in the same quarter of 2022-2023.

Table A: Variation in Departmental Expenditures by Standard Object (unaudited)
This table displays the variance of departmental expenditures by standard object between fiscal 2021-2022 and 2022-2023. The variance is calculated for year to date expenditures as at the end of the first quarter. The row headers provide information by standard object. The column headers provide information in thousands of dollars and percentage variance for the year to date variation.
Departmental Expenditures Variation by Standard Object: Q1 year-to-date variation between fiscal year 2022-2023 and 2023-2024
$'000 %
(01) Personnel -6,633 -3.9
(02) Transportation and communications 393 11.0
(03) Information 1 0.1
(04) Professional and special services 1,834 22.1
(05) Rentals -1,641 -16.2
(06) Repair and maintenance -69 -44.8
(07) Utilities, materials and supplies -140 -65.1
(08) Acquisition of land, buildings and works - N/A
(09) Acquisition of machinery and equipment -1,141 -72.5
(10) Transfer payments - N/A
(12) Other subsidies and payments -660 -79.8
Total gross budgetary expenditures -8,056 -4.1
Less revenues netted against expenditures:
Revenues -7,685 -65.8
Total net budgetary expenditures -371 -0.2
Note: Explanations are provided for variances of more than $1 million.

Personnel: The decrease is mainly due to spendings for seasonal, casual, and students' salaries, offset by a slight increase related to cost-recovery work following the dissemination of the 2021 Census of Population.

Professional and special services: The increase is mainly due to expenses with IT consultants and timing difference in invoicing compared to the first quarter of 2022-2023.

Rentals: The decrease is mainly due to a one-time invoice for a software licence paid in the first quarter of 2022-2023.

Acquisition of machinery and equipment: The decrease is mainly due to the purchase of computers in the first quarter of 2022-2023.

Revenues: The decrease is mainly due to a timing difference in invoicing compared to last year.

C) Significant changes to operations, personnel and programs

In 2023-2024, the following changes in operations and program activities are underway:

  • The Census program is ramping down operations for the 2021 cycle and is in the planning phase for the 2026 Censuses of Population and Agriculture programs.
  • Budget 2023 announced funding for new initiatives such as the Canadian Dental Care program and the Official Languages Action Plan.
  • Budget 2023 announced a commitment to refocus government spending:
    • Budget 2023 proposes to reduce spending on consulting, other professional services, and travel by roughly 15 per cent starting in 2023-2024. The government will focus on targeting these reductions on professional services, particularly management consulting.
    • Budget 2023 proposes to phase in a roughly 3 per cent reduction of eligible spending by departments and agencies by 2026-2027.
  • Statistics Canada is committed to effective management of its programs and services. In anticipation of the announcement of pending reductions, Statistics Canada launched a review in 2022 to identify efficiencies and reductions to programs or services.

D) Risks and uncertainties

Statistics Canada will address the issues and corresponding uncertainties raised in this Quarterly Financial Report by implementing corresponding risk mitigation measures captured in the 2023-2024 Corporate Risk Profile and at the program level.

Statistics Canada continues to pursue and invest in modernizing business processes and tools to maintain its relevance and maximize the value it provides to Canadians. To address uncertainties, the agency is implementing the Census of Environment, the Quality of Life Framework for Canada and the Disaggregated Data Action Plan initiatives to meet the evolving needs of users and remain relevant as an agency. The agency is also remaining vigilant to cyber threats while supporting the use of modern methods with a functional digital infrastructure.

Statistics Canada requires a skilled workforce to achieve its objectives; however, it is difficult to compete with other organizations in the data ecosystem and the current labour market situation. To address uncertainties, Statistics Canada will create partnerships with other government departments, international organizations, and IT Industry partners to find innovative ways to collaborate on bridging gaps in digital skills and IT human resource shortfalls. The agency will continue promoting a strong workplace culture, a healthy work-life balance and advance on the Equity, Diversity and Inclusion Action Plan. In addition, it will focus on existing employees and continue its effort to achieve greater diversity and inclusion across its workforce and promote and support accessibility.

Statistics Canada continues its collaboration with federal partners to access IT services and support to realise its modernization objectives and to implement the Cloud Optimization Activities. To address uncertainties, the agency is working closely with its federal partners, while adhering to the agency's notable financial planning management practices, integrated strategic planning framework as well as strengthening its financial stewardship.

Approval by senior officials

Approved by:

Anil Arora, Chief Statistician
Ottawa, Ontario
Signed on: August 23rd, 2023

Kathleen Mitchell, Chief Financial Officer
Ottawa, Ontario
Signed on: August 15th, 2023

Appendix

Statement of Authorities (unaudited)
This table displays the departmental authorities for fiscal years 2022-2023 and 2023-2024. The row headers provide information by type of authority, Vote 105 – Net operating expenditures, Statutory authority and Total Budgetary authorities. The column headers provide information in thousands of dollars for Total available for use for the year ending March 31; used during the quarter ended June 30; and year to date used at quarter-end of both fiscal years.
  Fiscal year 2023-2024 Fiscal year 2022–2023
Total available for use for the year ending March 31, 2024Table note * Used during the quarter ended June 30, 2023 Year-to-date used at quarter-end Total available for use for the year ending March 31, 2023Table note * Used during the quarter ended June 30, 2022 Year-to-date used at quarter-end
in thousands of dollars
Vote 1 — Net operating expenditures 530,377 166,191 166,191 496,731 165,294 165,294
Statutory authority — Contribution to employee benefit plans 89,458 18,724 18,724 79,967 19,992 19,992
Total budgetary authorities 619,835 184,915 184,915 576,698 185,286 185,286
Table note *

Includes only Authorities available for use and granted by Parliament at quarter-end.

Return to the first table note * referrer
Departmental budgetary expenditures by Standard Object (unaudited)
This table displays the departmental expenditures by standard object for fiscal years 2022-2023 and 2023-2024. The row headers provide information by standard object for expenditures and revenues. The column headers provide information in thousands of dollars for planned expenditures for the year ending March 31; expended during the quarter ended June 30; and year to date used at quarter-end of both fiscal years.
  Fiscal year 2023-2024 Fiscal year 2022–2023
Planned expenditures for the year ending March 31, 2024 Expended during the quarter ended June 30, 2023 Year-to-date used at quarter-end Planned expenditures for the year ending March 31, 2023 Expended during the quarter ended June 30, 2022 Year-to-date used at quarter-end
in thousands of dollars
Expenditures:
(01) Personnel 636,127 164,220 164,220 613,079 170,853 170,853
(02) Transportation and communications 11,992 3,979 3,979 11,745 3,586 3,586
(03) Information 8,682 1,340 1,340 9,041 1,339 1,339
(04) Professional and special services 48,413 10,120 10,120 35,898 8,286 8,286
(05) Rentals 21,089 8,487 8,487 17,160 10,128 10,128
(06) Repair and maintenance 972 85 85 475 154 154
(07) Utilities, materials and supplies 1,642 75 75 1,736 215 215
(08) Acquisition of land, buildings and works 557 - - 555 - -
(09) Acquisition of machinery and equipment 10,304 432 432 6,962 1,573 1,573
(10) Transfer payments - - - - - -
(12) Other subsidies and payments 57 167 167 47 827 827
Total gross budgetary expenditures 739,835 188,905 188,905 696,698 196,961 196,961
Less revenues netted against expenditures:
Revenues 120,000 3,990 3,990 120,000 11,675 11,675
Total revenues netted against expenditures 120,000 3,990 3,990 120,000 11,675 11,675
Total net budgetary expenditures 619,835 184,915 184,915 576,698 185,286 185,286

Production level code in Data Science

By David Chiumera, Statistics Canada

In recent years, the field of data science has experienced explosive growth, with businesses across many sectors investing heavily in data-driven solutions to optimize decision-making processes. However, the success of any data science project relies heavily on the quality of the code that underpins it. Writing production-level code is crucial to ensure that data science models and applications can be deployed and maintained effectively, enabling businesses to realize the full value of their investment in data science.

Production-level code refers to code that is designed to meet the needs of the end user, with a focus on scalability, robustness, and maintainability. This contrasts with code that is written purely for experimentation and exploratory purposes, which may not be optimized for production use. Writing production-level code is essential for data science projects as it allows for the efficient deployment of solutions into production environments, where they can be integrated with other systems and used to inform decision-making.

Production-level code has several key benefits for data science projects. Firstly, it ensures that data science solutions can be easily deployed and maintained. Secondly, it reduces the risk of errors, vulnerabilities, and downtime. Lastly, it facilitates collaboration between data scientists and software developers, enabling them to work together more effectively to deliver high-quality solutions. Finally, it promotes code reuse and transparency, allowing data scientists to share their work with others and build on existing code to improve future projects.

Overall, production-level code is an essential component of any successful data science project. By prioritizing the development of high-quality, scalable, and maintainable code, businesses can ensure that their investment in data science delivers maximum value, enabling them to make more informed decisions and gain a competitive edge in today's data-driven economy.

Scope of Data Science and its various applications

The scope of data science is vast, encompassing a broad range of techniques and tools used to extract insights from data. At its core, data science involves the collection, cleaning, and analysis of data to identify patterns and make predictions. Its applications are numerous, ranging from business intelligence and marketing analytics to healthcare and scientific research. Data science is used to solve a wide range of problems, such as predicting consumer behavior, detecting fraud, optimizing operations, and improving healthcare outcomes. As the amount of data generated continues to grow, the scope of data science is expected to expand further, with increasing emphasis on the use of advanced techniques such as machine learning and artificial intelligence.

Proper programming and software engineering practices for Data Scientists

Proper programming and software engineering practices are essential for building robust data science applications that can be deployed and maintained effectively. Robust applications are those that are reliable, scalable, and efficient, with a focus on meeting the needs of the end user. There are several types of programming and software engineering practices that are particularly important in the context of data science, such as version control, automated testing, documentation, security, code optimization, and proper use of design patterns to name a few.

By following proper practices, data scientists can build robust applications that are reliable, scalable, and efficient, with a focus on meeting the needs of the end user. This is critical for ensuring that data science solutions deliver maximum value to businesses and other organizations.

Administrative Data Pre-processing Project (ADP) project and its purpose - an example.

The ADP project is a Field 7 application that required involvement from the Data Science Division to refactor a citizen developed component due to a variety of issues that were negatively impacting its production readiness. Specifically, the codebase used to integrate workflows external to the system was found to be lacking in adherence to established programming practices, leading to a cumbersome and difficult user experience. Moreover, there was a notable absence of meaningful feedback from the program upon failure, making it difficult to diagnose and address issues.

Further exacerbating the problem, the codebase was also found to be lacking in documentation, error logging, and meaningful error messages for users. The codebase was overly coupled, making it difficult to modify or extend the functionality of the program as needed, and there were no unit tests in place to ensure reliability or accuracy. Additionally, the code was overfitted to a single example, which made it challenging to generalize to other use cases, there were also several desired features that were not present to meet the needs of the client.

Given these issues, the ability for the ADP project to pre-process semi-structured data was seriously compromised. The lack of feedback and documentation made it exceedingly difficult for the client to use the integrated workflows effectively, if at all, leading to frustration and inefficiencies. The program outputs were often inconsistent with expectations, and the absence of unit tests meant that reliability and accuracy were not assured. In summary, the ADP project's need for a refactor of the integrated workflows (a.k.a. clean-up or redesign) was multifaceted and involved addressing a range of programming and engineering challenges to ensure a more robust and production-ready application. To accomplish this, we used a Red Green refactoring approach to improve the quality of the product.

Red Green vs Green Red approach to refactoring

Refactoring is the process of restructuring existing code in order to improve its quality, readability, maintainability, and performance. This can involve a variety of activities, including cleaning up code formatting, eliminating code duplication, improving naming conventions, and introducing new abstractions and design patterns.

There are several reasons why refactoring is beneficial. Firstly, it can improve the overall quality of the codebase, making it easier to understand and maintain. This can save time and effort over the long term, especially as codebases become larger and more complex. Additionally, refactoring can improve performance and reduce the risk of bugs or errors, leading to a more reliable and robust application.

One popular approach to refactoring is the "Red Green" approach, as part of the test-driven development process. In the Red Green approach, a failing test case is written before any code is written or refactored. This failing test is then followed by writing the minimum amount of code required to make the test pass, before proceeding to refactor the code to a better state if necessary. In contrast, the Green Red approach is the reverse of this, where the code is written before the test cases are written and run.

The benefits of the Red Green approach include the ability to catch errors early in the development process, leading to fewer bugs and more efficient development cycles. The approach also emphasizes test-driven development, which can lead to more reliable and accurate code. Additionally, it encourages developers to consider the user experience from the outset, ensuring that the codebase is designed with the end user in mind.

Figure 1: Red Green Refactor
Figure 1: Red Green Refactor

The first step, the Red component, refers to writing a test that fails. From here the code is modified to make the test pass, which refers to the Green component. Lastly, any refactoring that needs to be done to further improve the codebase is done, another test is created and run to test which fails, this is the red component again. The cycle continues indefinitely until the desired state is reached terminating the feedback loop.

In the case of the ADP project, the Red Green approach was applied during the refactoring process. This led to a smooth deployment process, with the application being more reliable, robust, and easier to use. By applying this approach, we were able to address the various programming and engineering challenges facing the project, resulting in a more efficient, effective, stable, and production-ready application.

Standard Practices Often Missing in Data Science Work

While data science has become a critical field in many industries, it is not without its challenges. One of the biggest issues is the lack of standard practices that are often missing in data science work. While there are many standard practices that can improve the quality, maintainability, and reproducibility of data science code, many data scientists overlook them in favor of quick solutions.

This section will cover some of the most important standard practices that are often missing in data science work. These include:

  • version control
  • testing code (unit, integration, system, acceptance)
  • documentation
  • code reviews
  • ensuring reproducibility
  • adhering to style guidelines (i.e. PEP standards)
  • using type hints
  • writing clear docstrings
  • logging errors
  • validating data
  • writing low-overhead code
  • implementing continuous integration and continuous deployment (CI/CD) processes

By following these standard practices, data scientists can improve the quality and reliability of their code, reduce errors and bugs, and make their work more accessible to others.

Documenting Code

Documenting code is crucial for making code understandable and usable by other developers. In data science, this can include documenting data cleaning, feature engineering, model training, and evaluation steps. Without proper documentation, it can be difficult for others to understand what the code does, what assumptions were made, and what trade-offs were considered. It can also make it difficult to reproduce results, which is a fundamental aspect of scientific research as well as building robust and reliable applications.

Writing Clear Docstrings

Docstrings are strings that provide documentation for functions, classes, and modules. They are typically written in a special format that can be easily parsed by tools like Sphinx to generate documentation. Writing clear docstrings can help other developers understand what a function or module does, what arguments it takes, and what it returns. It can also provide examples of how to use the code, which can make it easier for other developers to integrate the code into their own projects.

def complex (real=0.0, imag=0.0):
"""Form a complex number.
Keyword arguments:
real -- the real part (default 0.0)
imag -- the imaginary part (default 0.0)
"""
if imag == 0.0 and real == 0.0:
return compelx_zero
...

Multi-Line Docstring Example

Adhering to Style Guidelines

Style guidelines in code play a crucial role in ensuring readability, maintainability, and consistency across a project. By adhering to these guidelines, developers can enhance collaboration and reduce the risk of errors. Consistent indentation, clear variable naming, concise commenting, and following established conventions are some key elements of effective style guidelines that contribute to producing high-quality, well-organized code. An example of this are PEP (Python Enhancement Proposal) standards, which provide guidelines and best practices for writing Python code. It ensures that code can be understood by other Python developers, which is important in collaborative projects but also for general maintainability. Some PEP standards address naming conventions, code formatting, and how to handle errors and exceptions.

Using Type Hints

Type hints are annotations that indicate the type of a variable or function argument. They are not strictly necessary for Python code to run, but they can improve code readability, maintainability, and reliability. Type hints can help detect errors earlier in the development process and make code easier to understand by other developers. They also provide better interactive development environment (IDE) support and can improve performance by allowing for more efficient memory allocation.

Version Control

Version control is the process of managing changes to code and other files over time. It allows developers to track and revert changes, collaborate on code, and ensure that everyone is working with the same version of the code. In data science, version control is particularly important because experiments can generate large amounts of data and code. By using version control, data scientists can ensure that they can reproduce and compare results across different versions of their code and data. It also provides a way to track and document changes, which can be important for compliance and auditing purposes.

Figure 2: Version Control Illustration
Figure 2: Version Control Illustration

A master branch (V1) is created as the main project. A new branch off shooting V1 is created in order to develop and test until the modifications are ready to be merged with V1, creating V2 of the master branch. V2 is then released.

Testing Code

Testing code is the formal (and sometimes automated) verification of the completeness, quality, and accuracy of code against expected results. Testing code is essential for ensuring that the codebase  works as expected and can be relied upon. In data science, testing can include unit tests for functions and classes, integration tests for models and pipelines, and validation tests for datasets. By testing code, data scientists can catch errors and bugs earlier in the development process and ensure that changes to the code do not introduce new problems. This can save time and resources in the long run by reducing the likelihood of unexpected errors and improving the overall quality of the code.

Code Reviews

Code reviews are a process in which other developers review new code and code changes to ensure that they meet quality and style standards, are maintainable, and meet the project requirements. In data science, code reviews can be particularly important because experiments can generate complex code and data, and because data scientists often work independently or in small teams. Code reviews can catch errors, ensure that code adheres to best practices and project requirements, and promote knowledge sharing and collaboration among team members.

Ensuring Reproducibility

Reproducibility is a critical aspect of scientific research and data science. Reproducible results are necessary for verifying and building on previous research, and for ensuring that results are consistent, valid and reliable. In data science, ensuring reproducibility can include documenting code and data, using version control, rigorous testing, and providing detailed instructions for running experiments. By ensuring reproducibility, data scientists can make their results more trustworthy and credible and can increase confidence in their findings.

Logging

Logging refers to the act of keeping a register of events that occur in a computer system. This is important for troubleshooting, information gathering, security, providing audit info, among other reasons. It generally refers to writing messages to a log file. Logging is a crucial part of developing robust and reliable software, including data science applications. Logging errors can help identify issues with the application, which in turn helps to debug and improve it. By logging errors, developers can gain visibility into what went wrong in the application, which can help them diagnose the problem and take corrective action.

Logging also enables developers to track the performance of the application over time, allowing them to identify potential bottlenecks and areas for improvement. This can be particularly important for data science applications that may be dealing with large datasets or complex algorithms.

Overall, logging is an essential practice for developing and maintaining high-quality data science applications.

Writing Low-Overhead Code

When it comes to data science applications, performance is often a key consideration. To ensure that the application is fast and responsive, it's important to write code that is optimized for speed and efficiency.

One way to achieve this is by writing low-overhead code. Low-overhead code is code that uses minimal resources and has a low computational cost. This can help to improve the performance of the application, particularly when dealing with large datasets or complex algorithms.

Writing low-overhead code requires careful consideration of the algorithms and data structures used in the application, as well as attention to detail when it comes to memory usage and processing efficiency. Thought should be given to the system needs and overall architecture and design of a system up front to avoid major design changes down the road.

Additionally, low-overhead code is easily maintained requiring infrequent reviews and updates. This is important as it reduces the cost to maintain systems and allows for more focused development on improvements or new solutions.

Overall, writing low-overhead code is an important practice for data scientists looking to develop fast and responsive applications that can handle large datasets and complex analyses while keeping maintenance costs low.

Data Validation

Data validation is the process of checking that the input data meets certain requirements or standards. Data validation is another important practice in data science as it can help to identify errors or inconsistencies in the data before they impact the analysis or modeling process.

Data validation can take many forms, from checking that the data is in the correct format to verifying that it falls within expected ranges or values. Different types of data validation checks exist, such as type, format, correctness, consistency, and uniqueness. By validating data, data scientists can ensure that their analyses are based on accurate and reliable data, which can improve the accuracy and credibility of their results.

Continuous Integration and Continuous Deployment (CI/CD)

Continuous Integration and Continuous Deployment (CI/CD) is a set of best practices for automating the process of building, testing, and deploying software. CI/CD can help to improve the quality and reliability of data science applications by ensuring that changes are tested thoroughly and deployed quickly and reliably.

CI/CD involves automating the process of building, testing, and deploying software, often using tools and platforms such as Jenkins, GitLab, or GitHub Actions. By automating these processes, developers can ensure that the application is built and tested consistently, and that any errors or issues that block deployment of problematic code are identified and addressed quickly.

CI/CD can also help to improve collaboration among team members, by ensuring that changes are integrated and tested as soon as they are made, rather than waiting for a periodic release cycle.

Figure 3: CI/CD
Figure 3: CI/CD

The image illustrates a repeating process represented by the infinity symbol sectioned into 8 unequal parts. Starting from the middle and moving counterclockwise the first of these parts are: plan, code, build, and continuous testing. Then continuing from the last piece, which was in the center, moving clockwise the parts are: release, deploy, operate, and then monitor, before moving back to the original state of plan.

Overall, CI/CD is an important practice for data scientists looking to develop and deploy high-quality data science applications quickly and reliably.

Conclusion

In summary, production-level code is critical for data science projects and applications. Proper programming practices and software engineering principles such as adhering to PEP standards, using type hints, writing clear docstrings, version control, testing code, logging errors, validating data, writing low-overhead code, implementing continuous integration and continuous deployment (CI/CD), and ensuring reproducibility are essential for creating robust, maintainable, and scalable applications.

Not following these practices can result in difficulties such as a lack of documentation, no error logging, no meaningful error messages for users, highly coupled code, overfitted code to a single example, lacking features desired by clients, and failure to provide feedback upon failure. These issues can severely impact production readiness and frustrate users. If a user is frustrated, then productivity will be impacted and result in negative downstream impacts on businesses’ ability to effectively deliver their mandate.

The most practical tip for implementing production-level code is to work together, assign clear responsibilities and deadlines, and understand the importance of each of these concepts. By doing so, it becomes easy to implement these practices in projects and create maintainable and scalable applications.

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Introduction to Cryptographic Techniques: Trusted Execution Environment

Hardware-based protection of data in use that can be applied anywhere

By: Betty Ann Bryanton, Canada Revenue Agency

Introduction

The increasing popularity of connected devices and the prevalence of technologies, such as cloud, mobile computing, and the Internet of Things (IoT), has strained existing security capabilities and exposed "gaps in data security" (Lowans, 2020). Organizations that handle Personally Identifiable Information (PII) must "mitigate threats that target the confidentiality and integrity of either the application, or the data in system memory" (The Confidential Computing Consortium, 2021).

As a result, Gartner predicts, "by 2025, 50% of large organizations will adopt privacy-enhancing computation (PEC)Footnote1 for processing data in untrusted environmentsFootnote2 and multiparty data analytics use cases" (Gartner, 2020). Of the several PEC techniques, Trusted Execution Environment is the only one that relies on hardware to accomplish its privacy-enhancing goal.

What is a Trusted Execution Environment?

A Trusted Execution Environment (TEE), or Secure Enclave as they are sometimes known, is an environment built with special hardware modules that allows for a secure area inside the device. This isolated environment runs in parallel with the operating system (OS). Input is passed into the TEE and computation is performed within the TEE ('secure world'), thereby protected from the rest of the untrusted system ('normal world'). These secure and isolated environments protect content confidentiality and integrity, preventing unauthorizedFootnote3 access to, or modification of, applications and data while in use.

The term 'confidential computing' is often used synonymously with TEE; they are related, but distinct. As per the Confidential Computing Consortium (CCC),Footnote4 confidential computing is enabled by the TEE; further, confidential computing provided by the hardware-based TEE is independent of topographical location (no mention of cloud, a user's device, etc.), processors (a regular processor or a separate one), or isolation techniques (e.g., whether encryption is used).

Why is hardware necessary?

"Security is only as strong as the layers below it, since security in any layer of the compute stack could potentially be circumvented by a breach at an underlying layer" (The Confidential Computing Consortium, 2021). By moving security to its lowest silicon level, this reduces potential compromise since it minimizes dependencies higher in the stack (e.g., from the OS, peripherals, and their administrators and vendors).

Why is it important?

Using a TEE allows a massive range of functionality to be provided to the user, while still meeting the requirements of privacy and confidentiality, without risking data when it is decrypted during processing. This allows users to secure intellectual property and ensure that PII is inaccessible. This protects against insider threats, attackers running malicious code, or unknown cloud providers. As such, TEEs represent a crucial layer in a layered security approach (aka defense-in-depth) and "have the potential to significantly boost the security of systems" (Lindell, 2020).

Uses

A TEE "can be applied anywhere including public cloud servers, on-premises servers, gateways, IoT devices, EdgeFootnote5 deployments, user devices, etc." (The Confidential Computing Consortium, 2021).

As per Confidential Computing: Hardware-Based Trusted Execution for Applications and Data, below is a summary of possible use cases for a TEE.

  • Keys, secrets, credentials, tokens: These high-value assets are the 'keys to the kingdom.' Historically, the storage and processing of these assets required an on-premises hardware security module (HSM), but within TEEs, applications to manage these assets can provide security comparable to a traditional HSM.
  • Multi-party computing: TEEs allow organizations, such as those offering financial services or healthcare, to take advantage of shared data (e.g., federated analytics) without compromising the data sources.
  • Mobile, personal computing, and IoT devices: Device manufacturers or application developers include TEEs to provide assurances that personal data is not observable during sharing or processing.
  • Point of sale devices / payment processing: To protect user-entered information, such as a PIN, the input from the number pad is only readable by code within the device's hardware-based TEE, thereby ensuring it cannot be read or attacked by malicious software that may exist on the device.

Benefits

  • Controlled environment: Since the TEE runs on specialized hardware, it is controlled, and it prevents eavesdropping while encrypted data is decrypted.
  • Privacy: It is possible to encrypt PII in a database; however, to process the data, it must be decrypted, at which point it is vulnerable to any attacker and to insider threats. If the data is only ever decrypted and processed inside the TEE, it is isolated from unauthorized users, thereby safeguarding data privacy.
  • Speed: Since the TEE is a secure enclave already, code or data may exist in unencrypted form in the TEE. If so, "this allows execution within the TEE to be much faster than execution tied to complex cryptography" (Choi & Butler, 2019).
  • Trust: Since the data in the TEE is not obfuscated (as in some of the other PEC techniques), this provides a comfort level that the computation and its results are correct, i.e., not having errors introduced by the obfuscation techniques.
  • Separation of concerns: As there are two distinct environments, there is a separation of workload and data administered and owned by the 'normal world' versus workload and data isolated in the 'secure world.' This protects against insider threats and potentially corrupt workloads running on the same device.
  • Decryption: If the data is encrypted in the TEE, it must be decrypted for processing; however, that decryption benefits by being contained within a tightly controlled space.

Challenges

  • Implementation: Implementation is challenging and requires customized knowledge and expertise, whether building the entire secure OS from scratch, employing a trusted OS from a commercial vendor, or implementing emerging components such as Software Development Kits (SDKs), libraries, or utilities.
  • Lack of standardization: Not all TEEs offer the same security guarantees or the same requirements for integration with existing and new code.
  • Design specification: It is the TEE developer's responsibility to ensure secure TEE design. Mere existence of a TEE is not enough.
  • Lock-in: There is potential for lock-in and dependencies with hardware vendors, TEE developers, or proprietary processing (due to lack of standardization).
  • Not bullet proof: There is the possibility for side-channel attacksFootnote6, vulnerable application code, or hardware-based security vulnerabilities, e.g., in the hardware chip, which can make the whole security model collapse.
  • Performance and cost: In comparison to setup and processing in a 'normal world', using a TEE ('secure world') negatively impacts performance and will cost more.

What's possible now?

TEEs are provided by solutions such as Intel's Software Guard eXtensions (SGX) or ARM's TrustZone; via hardware vendor Software Development Kits (SDKs); or with abstraction layers (e.g., Google's Asylo) that eliminate the requirement to code explicitly for a TEE.

Many cloud vendors (e.g., Alibaba, Microsoft, IBM, and Oracle) are now providing TEE capabilities as a dedicated low-level service aligned with their computation offerings. However, due to lack of standardization, the specifications offered by cloud vendors should be closely examined to ensure they meet the organization's desired privacy and security requirements (Fritsch, Bartley, & Ni, 2020).

What's next?

While protecting sensitive data poses significant architecture, governance, and technology challenges, using a TEE may provide a starting point for an alternative means of enhancing security from the lowest level.

However, a TEE is not plug-and-play; it is a technically challenging mechanism that "should be reserved for the highest-risk use cases" (Lowans, 2020). Nonetheless, "it is certainly harder to steal secrets from inside [a secured TEE than from the unsecured 'normal world']. It makes the attacker's job harder, and that is always a good thing" (Lindell, 2020).

Related Topics

Homomorphic Encryption, Secure Multiparty Computation, differential privacy, data anonymization, Trusted Platform Module.

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References

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Why are we conducting this survey?

This survey collects data on capital expenditures in Canada. The information is used by federal and provincial government departments and agencies, trade associations, universities and international organizations for policy development and as a measure of regional economic activity.

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

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

Other important information

Authorization to collect this information

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

Confidentiality

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

Record linkages

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

Data-sharing agreements

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

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

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

Section 12 of the Statistics Act provides for the sharing of information with federal, provincial or territorial government organizations. Under Section 12, you may refuse to share your information with any of these organizations by writing a letter of objection to the Chief Statistician, specifying the organizations with which you do not want Statistics Canada to share your data and mailing it to the following address:

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

You may also contact us by email at statcan.esd-helpdesk-dse-bureaudedepannage.statcan@canada.ca- this link will open in a new window or by fax at 613-951-6583.

For this survey, there are Section 12 agreements with the statistical agencies of Prince Edward Island, the Northwest Territories and Nunavut as well as Environment and Climate Change Canada, Infrastructure Canada, the Canada Energy Regulator, Natural Resources Canada and Sustainability Development Technology Canada.

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

Business or organization and contact information

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

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

Legal Name

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

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

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

Operating Name

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

Legal name

Operating name (if applicable)

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

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

First name

Last name

Title

Preferred language of communication

  • English
  • French

Mailing address (number and street)

City

Province, territory or state

Postal code or ZIP (Zone Improvement Plan) code
Example: A9A 9A9 or 12345-1234

Country
  • Afghanistan
  • Åland Islands
  • Albania
  • Algeria
  • American Samoa
  • Andorra
  • Angola
  • Anguilla
  • Antarctica
  • Antigua and Barbuda
  • Argentina
  • Armenia
  • Aruba
  • Australia
  • Austria
  • Azerbaijan
  • Bahamas
  • Bahrain
  • Bangladesh
  • Barbados
  • Belarus
  • Belgium
  • Belize
  • Benin
  • Bermuda
  • Bhutan
  • Bolivia
  • Bonaire, Sint Eustatius and Saba
  • Bosnia and Herzegovina
  • Botswana
  • Bouvet Island
  • Brazil
  • British Indian Ocean Territory
  • Brunei Darussalam
  • Bulgaria
  • Burkina Faso
  • Burma (Myanmar)
  • Burundi
  • Cambodia
  • Cameroon
  • Canada
  • Cape Verde
  • Cayman Islands
  • Central African Republic
  • Chad
  • Chile
  • China
  • Christmas Island
  • Cocos (Keeling) Islands
  • Colombia
  • Comoros
  • Congo, Republic of the
  • Congo, The Democratic Republic of the
  • Cook Islands
  • Costa Rica
  • Côte d'Ivoire
  • Croatia
  • Cuba
  • Curaçao
  • Cyprus
  • Czech Republic
  • Denmark
  • Djibouti
  • Dominica
  • Dominican Republic
  • Ecuador
  • Egypt
  • El Salvador
  • Equatorial Guinea
  • Eritrea
  • Estonia
  • Ethiopia
  • Falkland Islands (Malvinas)
  • Faroe Islands
  • Fiji
  • Finland
  • France
  • French Guiana
  • French Polynesia
  • French Southern Territories
  • Gabon
  • Gambia
  • Georgia
  • Germany
  • Ghana
  • Gibraltar
  • Greece
  • Greenland
  • Grenada
  • Guadeloupe
  • Guam
  • Guatemala
  • Guernsey
  • Guinea
  • Guinea-Bissau
  • Guyana
  • Haiti
  • Heard Island and McDonald Islands
  • Holy See (Vatican City State)
  • Honduras
  • Hong Kong Special Administrative Region
  • Hungary
  • Iceland
  • India
  • Indonesia
  • Iran
  • Iraq
  • Ireland, Republic of
  • Isle of Man
  • Israel
  • Italy
  • Jamaica
  • Japan
  • Jersey
  • Jordan
  • Kazakhstan
  • Kenya
  • Kiribati
  • Korea, North
  • Korea, South
  • Kosovo
  • Kuwait
  • Kyrgyzstan
  • Laos
  • Latvia
  • Lebanon
  • Lesotho
  • Liberia
  • Libya
  • Liechtenstein
  • Lithuania
  • Luxembourg
  • Macao Special Administrative Region
  • Macedonia, Republic of
  • Madagascar
  • Malawi
  • Malaysia
  • Maldives
  • Mali
  • Malta
  • Marshall Islands
  • Martinique
  • Mauritania
  • Mauritius
  • Mayotte
  • Mexico
  • Micronesia, Federated States of
  • Moldova
  • Monaco
  • Mongolia
  • Montenegro
  • Montserrat
  • Morocco
  • Mozambique
  • Namibia
  • Nauru
  • Nepal
  • Netherlands
  • New Caledonia
  • New Zealand
  • Nicaragua
  • Niger
  • Nigeria
  • Niue
  • Norfolk Island
  • Northern Mariana Islands
  • Norway
  • Oman
  • Pakistan
  • Palau
  • Panama
  • Papua New Guinea
  • Paraguay
  • Peru
  • Philippines
  • Pitcairn
  • Poland
  • Portugal
  • Puerto Rico
  • Qatar
  • Réunion
  • Romania
  • Russian Federation
  • Rwanda
  • Saint Barthélemy
  • Saint Helena
  • Saint Kitts and Nevis
  • Saint Lucia
  • Saint Martin (French part)
  • Saint Pierre and Miquelon
  • Saint Vincent and the Grenadines
  • Samoa
  • San Marino
  • Sao Tome and Principe
  • Sark
  • Saudi Arabia
  • Senegal
  • Serbia
  • Seychelles
  • Sierra Leone
  • Singapore
  • Sint Maarten (Dutch part)
  • Slovakia
  • Slovenia
  • Solomon Islands
  • Somalia
  • South Africa, Republic of
  • South Georgia and the South Sandwich Islands
  • South Sudan
  • Spain
  • Sri Lanka
  • Sudan
  • Suriname
  • Svalbard and Jan Mayen
  • Swaziland
  • Sweden
  • Switzerland
  • Syria
  • Taiwan
  • Tajikistan
  • Tanzania
  • Thailand
  • Timor-Leste
  • Togo
  • Tokelau
  • Tonga
  • Trinidad and Tobago
  • Tunisia
  • Turkey
  • Turkmenistan
  • Turks and Caicos Islands
  • Tuvalu
  • Uganda
  • Ukraine
  • United Arab Emirates
  • United Kingdom
  • United States
  • United States Minor Outlying Islands
  • Uruguay
  • Uzbekistan
  • Vanuatu
  • Venezuela
  • Viet Nam
  • Virgin Islands, British
  • Virgin Islands, United States
  • Wallis and Futuna
  • West Bank and Gaza Strip (Palestine)
  • Western Sahara
  • Yemen
  • Zambia
  • Zimbabwe

Email address
Example: user@example.gov.ca

Telephone number (including area code)
Example: 123-123-1234

Extension number (if applicable)

Fax number (including area code)
Example: 123-123-1234

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

  • Operational
  • Not currently operational
  • e.g., (for example) temporarily or permanently closed, change of ownership

Why is this business or organization not currently operational?

  • Seasonal operations
  • Ceased operations
  • Sold operations
  • Amalgamated with other businesses or organizations
  • Temporarily inactive but will re-open
  • No longer operating due to other reasons

When did this business or organization close for the season?

  • Date
    Example: YYYY-MM-DD

When does this business or organization expect to resume operations?

  • Date
    Example: YYYY-MM-DD

When did this business or organization cease operations?

  • Date
    Example: YYYY-MM-DD

Why did this business or organization cease operations?

  • Bankruptcy
  • Liquidation
  • Dissolution
  • Other

Specify the other reasons why the operations ceased.

When was this business or organization sold?

  • Date
    Example: YYYY-MM-DD

What is the legal name of the buyer?

When did this business or organization amalgamate?

  • Date
    Example: 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?

When did this business or organization become temporarily inactive?

  • Date
    Example: YYYY-MM-DD

When does this business or organization expect to resume operations?

  • Date
    Example: YYYY-MM-DD

Why is this business or organization temporarily inactive?

When did this business or organization cease operations?

  • Date
    Example: YYYY-MM-DD

Why did this business or organization cease operations?

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

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

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

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

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

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

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

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

Description and examples

  • This is the current main activity
  • This is not the current main activity

Provide a brief but precise description of this business or organization's main activity.

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

Main activity

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

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

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

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

How to search:

  • if desired, you can filter the search results by first selecting this business or organization's activity sector
  • enter keywords or a brief description that best describes this business or organization main activity
  • press the Search button to search the database for an activity that best matches the keywords or description you provided
  • then select an activity from the list.

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

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

Enter keywords or a brief description, then press the Search button

Reporting period information

1. What are the start and end dates of this organization's 2023 fiscal year?

Note: For this survey, the end date should fall between April 1, 2023 and March 31, 2024.

Here are twelve common fiscal periods that fall within the targeted dates:

  • May 1, 2022 to April 30, 2023
  • June 1, 2022 to May 31, 2023
  • July 1, 2022 to June 30, 2023
  • August 1, 2022 to July 31, 2023
  • September 1, 2022 to August 31, 2023
  • October 1, 2022 to September 30, 2023
  • November 1, 2022 to October 31, 2023
  • December 1, 2022 to November 30, 2023
  • January 1, 2023 to December 31, 2023
  • February 1, 2023 to January 31, 2024
  • March 1, 2023 to February 28, 2024
  • April 1, 2023 to March 31, 2024.

Here are other examples of fiscal periods that fall within the required dates:

  • September 18, 2022 to September 15, 2023 (e.g., floating year-end)
  • June 1, 2023 to December 31, 2023 (e.g., a newly opened business).

Fiscal Year Start
Example: YYYY-MM-DD

  • Date

Fiscal Year-End
Example: YYYY-MM-DD

  • Date

2. What is the reason the reporting period does not cover a full year?

Select all that apply.

Seasonal operations

New business

Change of ownership

Temporarily inactive

Change of fiscal year

Ceased operations

Other reason - specify:

Additional reporting instructions

3. Throughout this questionnaire, please report financial information in thousands of Canadian dollars.

For example, an amount of $763,880.25 should be reported as:

CAN$ '000

I will report in the format above.

Capital Expenditures - Preliminary Estimate 2023

4. For the fiscal year 2023, what are this organization's preliminary estimates for capital expenditures?

Include: all capitalized overhead and capitalized interest.

  • When there are partnerships and joint venture activities or projects, report the expenditures reflecting this corporation's net interest in such projects or ventures.
  • Report all dollar amounts in thousands of Canadian dollars ('000).
  • Exclude sales tax.
  • When precise figures are not available, please provide your best estimates.

If there are no capital expenditures, please enter '0'.

A. Oil and gas rights acquisition and retention costs (exclude inter-company sales or transfers):

Include acquisition costs and fees for oil and gas rights (include bonuses, legal fees and filing fees), and oil and gas retention costs.

B. Exploration and evaluation, capitalized or expensed ( e.g. , leases and licences, seismic, exploration drilling):

These expenditures include mineral rights fees and retention costs, geological, geophysical and seismic expenses, exploration drilling, and other costs incurred during the reporting period in order to determine whether oil or gas reserves exist and can be exploited commercially. Report gross expenditures, before deducting any incentive grants, incurred for oil and gas activities on a contracted basis and/or by your own employees. Exclude the cost of land acquired from other oil and gas companies.

C. Building construction ( e.g. , process building, office building, camp, storage building, and maintenance garage):

Include capital expenditures on buildings such as office buildings, camps, warehouses, maintenance garages, workshops, and laboratories. Fixtures, facilities and equipment that are integral parts of the building are included.

D. Other construction assets ( e.g. , development drilling and completions, processing facilities, natural gas plants, upgraders):

Include all infrastructure, other than buildings, such as the cost of well pads, extraction and processing infrastructure and plants, upgrading units, transportation infrastructure, water and sewage infrastructure, tailings, pipelines and wellhead production facilities (pumpjacks, separators, etc. ). Include all preconstruction planning and design costs such as development drilling, regulatory approvals, environmental assessments, engineering and consulting fees and any materials supplied to construction contractors for installation, as well as site clearance and preparation. Equipment which is installed as an integral or built-in feature of a fixed structure ( e.g. , casings, tanks, steam generators, pumps, electrical apparatus, separators, flow lines, etc. ) should be reported with the construction asset; however, when the equipment is replaced within an existing structure, the replacement cost should be reported in machinery and equipment (sustaining capital).

E. Machinery and equipment purchases ( e.g. , trucks, shovels, computers, etc. ):

Include transportation equipment for people and materials, computers, software, communication equipment, and processing equipment not included in the above categories.

Preliminary Estimate

  2023 Preliminary Estimate ( CAN$ '000 )
Oil and gas rights acquisitions and retention costs  
Exploration and evaluation  
Non-residential building construction  
Development and other construction  
Machinery and equipment  
Total  

Total

Research and Development

5. For the fiscal year 2023, did this organization perform scientific research and development in Canada of at least $10,000 or outsource (contract-out) to another organization scientific research and development activities of at least $10,000?

Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge. For an activity to be an R&D activity, it must satisfy five core criteria:

  1. To be aimed at new findings (novel);
  2. To be based on original, not obvious, concepts and hypothesis (creative);
  3. To be uncertain about the final outcome (uncertainty);
  4. To be planned and budgeted (systematic);
  5. To lead to results to could be possibly reproduced (transferable/ or reproducible).

The term R&D covers three types of activity: basic research, applied research and experimental development. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.

  • Yes
  • No

Capital Expenditures - Intentions 2024

6. For the fiscal year 2024, what are this organization's intentions for capital expenditures?

Include: all capitalized overhead and capitalized interest.

  • When there are partnerships and joint venture activities or projects, report the expenditures reflecting this corporation's net interest in such projects or ventures.
  • Report all dollar amounts in thousands of Canadian dollars ('000).
  • Do not include sales tax.
  • Percentages should be rounded to whole numbers.
  • When precise figures are not available, please provide your best estimates.

If there are no capital expenditures, please enter '0'.

A. Oil and gas rights acquisition and retention costs (exclude inter-company sales or transfers):

Include acquisition costs and fees for oil and gas rights (include bonuses, legal fees and filing fees), and oil and gas retention costs

B. Exploration and evaluation, capitalized or expensed ( e.g. , leases and licences, seismic, exploration drilling):

These expenditures include mineral rights fees and retention costs, geological, geophysical and seismic expenses, exploration drilling, and other costs incurred during the reporting period in order to determine whether oil or gas reserves exist and can be exploited commercially. Report gross expenditures, before deducting any incentive grants, incurred for oil and gas activities on a contracted basis and/or by your own employees. Exclude the cost of land acquired from other oil and gas companies.

C. Building construction ( e.g. , process building, office building, camp, storage building, and maintenance garage):

Include capital expenditures on buildings such as office buildings, camps, warehouses, maintenance garages, workshops, and laboratories. Fixtures, facilities and equipment that are integral parts of the building are included.

D. Other construction assets ( e.g. , development drilling and completions, processing facilities, natural gas plants, upgraders):

Include all infrastructure, other than buildings, such as the cost of well pads, extraction and processing infrastructure and plants, upgrading units, transportation infrastructure, water and sewage infrastructure, tailings, pipelines and wellhead production facilities (pumpjacks, separators, etc. ). Include all preconstruction planning and design costs such as development drilling, regulatory approvals, environmental assessments, engineering and consulting fees and any materials supplied to construction contractors for installation, as well as site clearance and preparation. Equipment which is installed as an integral or built-in feature of a fixed structure ( e.g. , casings, tanks, steam generators, pumps, electrical apparatus, separators, flow lines, etc. ) should be reported with the construction asset; however, when the equipment is replaced within an existing structure, the replacement cost should be reported in machinery and equipment (sustaining capital).

E. Machinery and equipment purchases ( e.g. , trucks, shovels, computers, etc. ):

Include transportation equipment for people and materials, computers, software, communication equipment, and processing equipment not included in the above categories.

Intentions 2023

  Intentions 2023 ( CAN$ '000 )
Oil and gas rights acquisitions and retention costs  
Exploration and evaluation  
Non-residential building construction  
Development and other construction  
Machinery and equipment  
Total  

Total

In order to reduce future follow-up, please select one of the following options.

You could also make corrections to the current cycle by pressing the Previous button.

You have not reported anything for 2024, but have entered data for 2023. Is this correct? If you do not intend on having any capital expenditures in the 2024 fiscal year, please return to the previous page and enter `0`s. If this information is not yet available, please press the Next button.

  • I confirm that all values are correct.
  • I am unable to confirm that all values are correct.

7.  You have not reported any capital expenditure intentions for 2024.

Please indicate the reason.

  • Zero capital expenditure intentions for 2024
  • Figures not available but plans are for no change in capital expenditures for 2024
  • Figures not available but plans are for an increase in capital expenditures for 2024
  • Figures not available but plans are for a decrease in capital expenditures for 2024

Research and Development

8. For the 2024 fiscal year, does this organization plan on performing scientific research and development in Canada of at least $10,000 or outsourcing (contracting-out) to another organization scientific research and development activities of at least $10,000?

Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge. For an activity to be an R&D activity, it must satisfy five core criteria:

  1. To be aimed at new findings (novel);
  2. To be based on original, not obvious, concepts and hypothesis (creative);
  3. To be uncertain about the final outcome (uncertainty);
  4. To be planned and budgeted (systematic);
  5. To lead to results to could be possibly reproduced (transferable/ or reproducible).

The term R&D covers three types of activity: basic research, applied research and experimental development. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.

  • Yes
  • No

Notification of intent to extract web data

9. Does this business have a website?

Statistics Canada engages in web-data extraction, also known as web scraping, which is a process by which information is gathered and copied from the Web using automated scripts or robots, for retrieval and analysis. As a result, we may visit the website for this organization to search for and compile additional information. The use of web scraping is part of a broader effort to reduce the response burden on organizations, as well as produce additional statistical indicators to ensure that our data remain accurate and relevant.

We will strive to ensure that the data collection does not interfere with the functionality of the website. Any data collected will be used by Statistics Canada for statistical and research purposes only, in accordance with the agency’s privacy and confidentiality mandate.

More information regarding Statistics Canada’s web scraping initiative- this link will open in a new window.

Learn more about Statistics Canada’s transparency and accountability- this link will open in a new window.

If you have any questions or concerns, please contact Statistics Canada Client Services, toll-free at 1-877-949-9492 [Teletypewriter or Telecommunication device for the deaf/teletype machine (TTY): 1-800-363-7629] or by email at infostats@statcan.gc.ca - this link will open in a new window. Additional information about this survey can be found by selecting the following link: Annual Capital Expenditures Survey: Preliminary Estimate for 2023 and Intentions for 2024.

Changes or events

10. 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.

  • Labour shortages or employee absences
  • Disruptions in supply chains
  • Deferred plans to future or projects on hold
  • 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
    Specify the other changes or events:
  • No changes or events

Contact person

11. Statistics Canada may need to contact the person who completed this questionnaire for further information.

Is [Provided Given Names], [Provided Family Name] the best person to contact?

  • Yes
  • No

Who is the best person to contact about this questionnaire?

First name:

Last name:

Title:

Email address:
Example: user@example.gov.ca

Telephone number (including area code):
Example: 123-123-1234

Extension number (if applicable):
The maximum number of characters is 5.

Fax number (including area code):
Example: 123-123-1234

Feedback

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

Include the time spent gathering the necessary information.

  • Hours:
  • Minutes:

13. Do you have any comments about this questionnaire?

Enter your comments
200 characters available

Monthly Survey of Manufacturing: National Level CVs by Characteristic - June 2023

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 (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
%
June 2022 0.68 1.16 1.52 1.76 1.44
July 2022 0.69 1.11 1.76 1.52 1.36
August 2022 0.68 1.14 1.76 1.58 1.36
September 2022 0.66 1.07 1.83 1.58 1.48
October 2022 0.66 1.10 1.82 1.55 1.48
November 2022 0.65 1.10 1.68 1.58 1.46
December 2022 0.61 1.08 1.89 1.57 1.47
January 2023 0.65 1.14 1.81 1.40 1.47
February 2023 0.68 1.15 1.87 1.39 1.53
March 2023 0.66 1.11 1.73 1.39 1.44
April 2023 0.69 1.09 1.61 1.35 1.42
May 2023 0.68 1.11 1.71 1.44 1.43
June 2023 0.70 1.11 1.77 1.49 1.45

2021 Public Consultation on Gender and Sexual Diversity Statistical Metadata Standards - What We Heard Report

PDF version (PDF, 254.2 KB)

Introduction

For this consultation, members of the Canadian public and international partners were invited to review and provide feedback on Statistics Canada's gender and sexual diversity statistical metadata standardsFootnote 1. Specifically, Statistics Canada was seeking feedback on proposed updates to the standard for gender of person and new standards for sexual orientation and LGBTQ2+Footnote 2 status. Statistical standards for gender and sexual diversity (such as the definition of each concept and the classification which establishes its categories) allow for the reporting of statistically diverse groups of the population in a consistent manner. This report summarizes the feedback received from the consultation. For more information on statistical standards as well as the additional engagement activities that took place to inform standards on gender of person, sexual orientation of person, and LGBTQ2+ status of person see Consulting Canadians landing page and StatCan Plus article.

Gender

Statistics Canada initially released new sex at birth and gender variables and classifications on April 13, 2018. Prior to the 2021 Census – in which the gender question was asked for the first time, and the 'at birth' precision was added to 'sex'– Statistics Canada reviewed the gender standard to ensure its relevance. Other engagement activities including a targeted expert consultation supplemented this public consultation in order to update the gender standard.

The updated sex at birth and gender standards were released on October 1, 2021. Among other changes, the definition of gender, the usage sections and the comparison to relevant internationally recognized standards were expanded. In addition, some category names and definitions in the classifications were updated.

Sexual orientation and LGBTQ2+ population

Statistics Canada has been collecting information about sexual orientation since 2003. The variable 'sexual orientation of person' used in the consultation included proposed classifications for the main components of sexual orientation - sexual identity, sexual attraction, and sexual behaviour - which could be measured separately.

The new sex at birth and gender standards have allowed for a more nuanced understanding of sexual orientation and the ability to collect data on the full LGBTQ2+ population. The creation of standards on sexual orientation and LGBTQ2+ status of person will establish a framework to address information gaps on sexual and gender diversity in Canada.

Consultation overview

The purpose of the consultation was to ask data producers and users; representatives of civil society organizations; government bodies at the federal, provincial and local levels; academics and researchers; and all other interested parties, including the general public, to submit feedback regarding the proposed updates to the standard for gender and the new standards for sexual orientation and LGBTQ2+ status.

The consultation was conducted electronically and publicized through public announcements that described the proposed updates to the standard for gender of person, and proposed new standards for sexual orientation of person and LGBTQ2+ status of person. The announcements also listed the types of inputs sought, provided a timeline for the consultation and gave contact information for interested parties to make submissions and contact Statistics Canada with questions and comments.

Announcements were disseminated through the Statistics Canada's website and social media. In addition, stakeholders and partners, including civil society organizations, as well as a number of researchers in the field of gender and sexual diversity and gender studies, were invited by email to participate and encouraged to share the consultation invitation with others within their network.

Interested parties were invited to submit written proposals to Statistics Canada. The official consultation period started on February 2, 2021 and closed on March 12, 2021. In addition to the public consultation, virtual meetings were organized with key stakeholders and researchers to gather their feedback.

Summary of submissions

Statistics Canada received 205 responses by email in both official languages from a range of individuals and organizations:

  • 19 responses from academics or research groups;
  • 31 responses from organizations, such as civil society organizations and government departments or agencies at the federal, provincial or territorial level in Canada and overseas;
  • 155 responses from the general public.

The consultation also included a number of follow up discussions with academics and subject matter experts.

Statistics Canada is committed to respecting the privacy of consultation participants. All personal information created, held or collected by the Agency is protected by the Privacy Act. As such, the identity of organizations, individuals and academics who participated in the consultation process are kept confidential.

Summary of feedback on the updated gender standard

Definition – Gender

The consultation materials presented an updated definition of gender. In this update, gender was defined as "a person's social or personal identity as a man, woman or non-binary person (a person who is not exclusively male or female)." This definition included the following concepts:

  • gender identity (felt gender), which is the gender that a person feels internally, and;
  • gender expression (lived gender), which is the gender a person expresses publicly in their daily life, including at work, at home or in the broader community.

The proposed definition stated that a person's current gender may differ from the sex they were assigned at birth (male or female) and that a person's gender may change over time.

Some of the most consistent feedback received regarding the English version of the gender definition was related to the incongruent use of the biological terms 'male' and 'female'. Respondents also commented that the gender standard conflated sex and gender. To this end, a number of suggestions were received regarding terminology. These included suggestions for the use of 'male', 'female' (or 'intersex') when referring to the biological characteristics of sex, and 'men/boy', 'women/girl', 'transgender', 'cisgender' and 'non-binary' when referring to gender identities. Suggestions were received for the use of gender terminology in the non-binary definition, replacing 'male' or 'female' with 'man' or 'woman'.

Input was received providing suggested modifications to the definitions of gender identity and expression. Feedback was also received on the sex at birth variable which reflected differing perspectives. Some respondents suggested that more emphasis be put on sex assigned at birth, while others suggested that sex is not assigned at birth, but rather observed and reported, and recommended using other terminology.

Usage – Gender

The proposed usage section included the following explanation, among other content:

The variable 'Gender of person' and the 'Classification of gender' are expected to be used by default in most social statistics programs at Statistics Canada. The variable 'Sex of person' and the 'Classification of sex' are to be used in conjunction with the variable 'Gender of person' and the 'Classification of gender', where information on sex at birth is needed.

While comments specifically referencing the gender usage section were limited, some overarching feedback was received that expressed disagreement with the overall concept of gender identity and communicated concerns about self-identification into protected groups. Some respondents were not supportive of the introduction of the gender variable by default at Statistics Canada and argued that collecting data on gender, rather than sex, could disrupt the historical comparability of the data and result in a loss of informationFootnote 3.

Classification – gender

The proposed Classification of gender contained three categories: male gender; female gender; and non-binary gender. The 'non-binary' gender category of the classification is intended to capture relevant write-in responses to the gender question where a respondent indicates being neither exclusively 'man' nor 'woman'.

A few respondents suggested that the classification contain additional categories, such as a Two-Spirit category, with the recommendation that the response option only be available to Indigenous respondents when asked on surveys.

Feedback received regarding the English version of the classifications of gender and transgender status was similar to the comments mentioned above regarding the use of the biological terms 'male' and 'female' in the proposed definitions, with 'man' and 'woman' as suggested replacements.

Comments regarding the reference to 'current gender' (e.g., "This category includes persons whose current gender was reported as male") were received, which suggested the removal of the term 'current'. Similar comments were made regarding the use of the word 'current' in the Classification of transgender status.

Classification – transgender status

Consultations sought input on a classification consisting of the following two broader categories with their respective subcategories (along with their definitions, not presented here):

  • 1. Cisgender person
    • 1.1 Cisgender man
    • 1.2 Cisgender woman
  • 2. Transgender person
    • 2.1 Transgender man
    • 2.2 Transgender woman
    • 2.3 Transgender non-binary person

Respondents suggested the creation of a third, standalone category, 'Non-binary person', rather than being included as a sub-category of 'Transgender person'.

Respondents also provided feedback regarding the terminology. A few commented that the terms 'trans' and 'transgender' are not necessarily interchangeable, while others suggested replacing the term 'cisgender' with 'non-transgender'. A few respondents suggested using the term 'gender modality' as the name of the classification; for example, the Classification of transgender status could be called the Classification of gender modality.

Summary of feedback on sexual orientation

Definition – sexual orientation

In the proposed standard, sexual orientation was presented as a multidimensional concept defined as an umbrella term that includes a person's sexual identity, sexual attraction and sexual behaviour. Sexual identity refers to how a person perceives their sexuality (e.g., lesbian, straight, bisexual), sexual attraction refers to whom a person finds sexually appealing, and sexual behaviour refers to with whom a person engages in sexual activity. A person's sexual orientation may change over time.

Input was supportive of sexual orientation being a multidimensional concept. Some minor changes were suggested for how to define sexual orientation as well as its different components. Feedback was received in favour of using the term 'sexual orientation' rather than 'sexual identity'. It was also suggested that the definition of sexual orientation should include the concept of emotional attraction.

Usage – sexual orientation

Feedback regarding usage mainly consisted of the need for transparency around the rationale for collecting data on sexual orientation, ensuring data are only collected as needed. While the consultation did not specifically focus on the minimum age for responding to sexual orientation question, a few organizations and academics provided input on the proposed minimum age of 15. They noted the value of having data on youth who are LGB+ (lesbian, gay, bisexual or of a sexual orientation other than heterosexual), and felt that a rationale should be provided for requiring a minimum age for asking questions about sexual orientation. Similarly, it was suggested that the minimum age to collect data on sexual orientation should be lower than 15 or that a minimum age may not be needed at all.

Classification - sexual identity

Consultations sought input on the Classification of sexual identity which included proposed categories along with their definitions. The classification included: 'heterosexual or straight'; 'gay or lesbian'; 'bisexual'; 'pansexual'; 'asexual'; 'queer'; and 'Two-Spirit'. Some respondents suggested including more categories, while others thought the classification should include fewer categories.

It was also pointed out that some of the proposed categories were not mutually exclusive and that this should be addressed (e.g., a person could be both Two-Spirit and bisexual, or asexual and gay, or queer and lesbian). In addition to not being a mutually exclusive category, 'queer' saw some support, but some respondents also suggested to avoid this term loaded with political history and potential derogatory interpretation. Input was also received that the 'Two-Spirit' category was a distinct concept requiring a separate measure only made available to Indigenous respondents.

In addition, feedback was received suggesting that the proposed definitions of different sexual orientations conflated sex and gender by referring to attraction based on sex and/or gender. Some respondents felt that the definition of sexual orientation should solely be based on sex. Other input suggested that sexual orientation definitions include being attracted to a person's gender expression, along with their sex and gender.

Feedback from different types of respondents (i.e., individuals, academics, and organizations) recommended combining the 'bisexual' and 'pansexual' categories, as these terms may overlap and be used interchangeably, making the two categories not mutually exclusive. It was pointed out in some comments that responses may be influenced by whether a person conceptualizes sex/gender as binary or not.

The proposed Classification of sexual identity also included higher levels of aggregation, including category groupings 'heterosexual or straight' and 'minority sexual identity'. Some of the most consistent feedback was that the 'minority sexual identity' category carried a negative connotation and that it was inappropriate. Other feedback suggested that different sexual identities should not be aggregated together.

Summary of feedback on sexual attraction

Classification - sexual attraction

The proposed Classification of sexual attraction was presented in two versions, each including a number of categories for respondents to identify their sexual attraction. One version measured attraction in reference to the respondent's own gender, without specifying the gender or genders of persons that they are attracted to (e.g., 'person only attracted to person of a different gender'). The other version specified the gender or genders of persons to which the respondent is attracted to (e.g., 'person only attracted to persons of male gender'). Each version also included categories for people who are 'equally attracted' to more than one gender, as well as for people who do not experience sexual attraction or who are unsure of their sexual attraction.

While feedback on which version was preferable was very limited, one of the key issues identified in responses was that both versions included too much detail or that they were too complicated. Others argued that sexual attraction should be defined on the basis of sex rather than gender. While both versions included a category for people who do not experience sexual attraction, some comments suggested that the classification should be more inclusive of people with little or no sexual attraction (i.e., people on the asexual spectrum). It was also suggested to re-name the 'unsure' category to 'questioning'.

Summary of feedback on sexual behaviour

Classification - sexual behaviour

Overall, this classification elicited stronger reactions than the other classifications. Some feedback indicated understanding the need to refer to the concept of sex rather than gender in the context of sexual behaviour. However, many comments expressed surprise or confusion that sex rather than gender terminology was used in the proposed sexual behaviour classification, which differed from the proposed sexual identity and sexual attraction classifications which used gender terminology. Some suggested that the purpose of the Classification of sexual behaviour was unclear, and proposed that the classification provide some base standard definitions of 'sexual activity'. Other input suggested to shift the focus away from the sex of the sexual partners towards specific acts.

A significant amount of feedback from different sources (i.e., organizations, individuals and academics) noted that intersex people were only included as partners in the Classification of sexual behaviour and that there was not a specific category for intersex respondents. Some input also indicated that no definition of intersex was provided.

Some respondents suggested that the number of sexual partners should be included within the sexual behaviour dimension. Finally, it was recommended not to refer to 'men who have sex with men' in the classification as the term may have a negative connotation to some people.

Summary of feedback on LGBTQ2+ status

Definition – LGBTQ2+

Statistics Canada is committed to supporting disaggregated data analysis in order to highlight the experiences of specific segments of the population. Recognizing that sample size may be an issue for small populations, the consultation proposed an aggregate LGBTQ2+ standard to establish a consistent approach to combining data on gender identity and sexual orientation. Input was sought on the proposed definition of LGBTQ2+ status as well as the choice of acronym. The proposed definition was that "LGBTQ2+ status refers to whether or not a person is lesbian, gay, bisexual, transgender, queer, Two-Spirit, or another non-binary gender or minority sexual identity." Feedback received focused on the acronym rather than the proposed definition. The majority of feedback proposed to move the '2' referring to Two-Spirit people at the beginning of the acronym to acknowledge Indigenous people in the context of reconciliation.

Classification – LGBTQ2+

Feedback was sought on the proposed Classification of LGBTQ2+  status as two distinct categories (i.e., 'LGBTQ2+ person' and 'non-LGBTQ2+ person' ('heterosexual and cisgender person') as well as their definitions. Some feedback argued against aggregating diverse populations under one umbrella category as these groups have different experiences and are not homogenous in their characteristics. However, others indicated that this approach was a useful way to analyze complex issues experienced by the LGBTQ2+ population as a whole.

Next steps

Statistics Canada has completed the review process for the updated gender standard and the new sexual orientation standard. The updated gender standard was released on October 1, 2021. All of the comments received during this consultation and other engagements activities were taken into account, and many are reflected in this updated standard.

The new sexual orientation standard was released on August 16, 2023. The public consultation summarized in this What We Heard report was one of four phases that informed the development of the sexual orientation standard. In addition to the public consultation, Statistics Canada undertook a targeted expert consultation, focus groups, and a testing phase which consisted of one-on-one interviews.

The focus groups and testing were conducted in English and French and engaged diverse participants from urban and rural communities in different regions across the country. Participants included LGBTQ2+ and non-LGBTQ2+ individuals from a range of ages, genders and socio-economic status groups. Focus groups and testing also engaged Indigenous Two-Spirit participants, as well as immigrant and racialized participants.

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Date modified:

In August 2023, the following questions measuring the Labour Market and Socioeconomic Indicators were added to the Labour Force Survey as a supplement.

The purpose of this survey is to identify changing dynamics within the Canadian labour market, and measure important socioeconomic indicators by gathering data on topics such as type of employment, quality of employment, support payments and unmet health care needs.

Questionnaire flow within the collection application is controlled dynamically based on responses provided throughout the survey. Therefore, some respondents will not receive all questions, and there is a small chance that some households will not receive any questions at all. This is based on their answers to certain LFS questions.

Labour Market and Socio-economic Indicators

ENTRY_Q01 / EQ 1 – From the following list, please select the household member that will be completing this questionnaire on behalf of the entire household.

LMI_Q01 / EQ 2 – What forms of payment [do/does] [you/respondent name/this person] receive in [your/his/her/their] main job or business?

LMI_Q02 / EQ 3 – What is the main form of payment in [your/his/her/their] main job or business?

LMI_Q03 / EQ 4 – You previously mentioned that [you/respondent name/this person] [are/is] self-employed in [your/his/her/their] main job.
Over the last 12 months, was at least 50% of [your/respondent name's/this person's] main business activity reliant on:

LMI_Q04 / EQ 5 – Which of these relationships is most important for [your/respondent name's/this person's] main business?

LMI_Q05 / EQ 6 – Does this [client/supplier/website or app/other company or person/agency, broker or other type of intermediary]:

LMI_Q06 / EQ 7 – What would happen if [your/respondent name's/this person's] relationship with this [client/supplier/website or app/other company or person/agency, broker or other type of intermediary] ended?

LMI_Q07 / EQ 8 – When did [you/respondent name/ this person] start working with this [client/supplier/website or app/other company or person/agency, broker or other type of intermediary]?

LMI_Q08 / EQ 9 – As part of [your/his/her/their] main business, could [you/respondent name/ this person] hire paid help if [you/he/she/this person] wanted to delegate some tasks?

LMI_Q09 / EQ 10 – How many clients did [you/respondent name/ this person] have over the last 12 months in [your/his/her/their] main business?

LMI_Q10 / EQ 11 – Does [your/respondent name's/this person's] main business operate…?

LMI_Q11 / EQ 12 – In [your/his/her/their] main job, [do/does] [you/respondent name/ this person] have a written agreement or an oral agreement with [your/his/her/their] employer?

LMI_Q12 / EQ 13 – In [your/respondent name's/this person's] main job, does [your/his/her/their] employer contribute to Employment Insurance [EI] on [your/respondent name's/this person's] behalf?

LMI_Q13 / EQ 14 – Is [your/respondent name's/this person's] main job permanent?

LMI_Q14 / EQ 15 – In what way is [your/respondent name's/this person's] main job not permanent?

LMI_Q15 / EQ 16 – In [your/his/her/their] main job [are/is] [you/he/she/they] paid by a private employment or placement agency that is different from the company [you/he/she/this person] work[s] for?

LMI_Q16 / EQ 17 – What is the total duration of [your/respondent name's/this person's] contract or agreement in [your/his/her/their] main job?

LMI_Q17 / EQ 18 – In [your/respondent name's/this person's] main job, [are/is] [you/he/she/respondent's name] guaranteed a minimum number of work hours per pay period?

LMI_Q18 / EQ 19 – [Do/Does] [you/respondent name/ this person] want a permanent job at this time?

LMI_Q19 / EQ 20 – What is the main reason why [you/respondent name/ this person] [do/does] not want a permanent job?

SCC1_Q05 / EQ 21 – In the last 12 months, did [you/respondent's name] receive support payments from a former spouse or partner?

SCC1_Q10 / EQ 22 – What is your best estimate of the amount of support payments [you/he/she/this person] received in the last 12 months?

SCC2_Q05 / EQ 23 – In the last 12 months, did [you/respondent's name] make support payments to a former spouse or partner?

SCC2_Q10 / EQ 24 – What is your best estimate of the total amount [you/he/she/this person] paid in support payments in the last 12 months?

SCC3_Q05 / EQ 25 – In the last 12 months, did [you/respondent's name] pay for child care, so that [you/he/she/they] could work at a paid job?

SCC3_Q10 / EQ 26 – What is your best estimate, of the total amount [you/he/she/this person] paid for child care in the last 12 months?

DSQ_Q01 / EQ 27 – [Do/Does] [you/respondent's name] have any difficulty seeing?

DSQ_Q02 / EQ 28 – [Do/Does] [you/he/she/this person] wear glasses or contact lenses to improve [your/respondent name's/this person's] vision?

DSQ_Q03 / EQ 29 – [Which/With [your/respondent name's/this person's] glasses or contact lenses, which] of the following best describes [your/respondent's name] ability to see?

DSQ_Q04 / EQ 30 – How often does this [difficulty seeing/seeing condition] limit [your/his/her/their] daily activities?

DSQ_Q05 / EQ 31 – [Do/Does] [you/respondent's name] have any difficulty hearing?

DSQ_Q06 / EQ 32 – [Do/Does] [you/he/she/this person] use a hearing aid or cochlear implant?

DSQ_Q07 / EQ 33 – With [your/respondent name's/this person's] hearing aid or cochlear implant which] of the following best describes [your/respondent's name] ability to hear?

DSQ_Q08 / EQ 34 – How often does this [difficulty hearing/hearing condition] limit [your/his/her/their] daily activities?

DSQ_Q09 / EQ 35 – [Do/Does] [you/respondent's name] have any difficulty walking, using stairs, using [your/his/her/their] hands or fingers or doing other physical activities?

DSQ_Q10 / EQ 36 – How much difficulty [do/does] [you/he/she/this person] have walking on a flat surface for 15 minutes without resting?

DSQ_Q11 / EQ 37 – How much difficulty [do/does] [you/he/she/this person] have walking up or down a flight of stairs, about 12 steps without resting?

DSQ_Q12 / EQ 38 – How often [does this difficulty walking/does this difficulty using stairs/do these difficulties] limit [your/his/her/their] daily activities?

DSQ_Q13 / EQ 39 – How much difficulty [do/does] [you/respondent's name] have bending down and picking up an object from the floor?

DSQ_Q14 / EQ 40 – How much difficulty [do/does] [you/he/she/this person] have reaching in any direction, for example, above [your/his/her/their] head?

DSQ_Q15 / EQ 41 – How often [does this difficulty bending down and picking up an object/does this difficulty reaching/do these difficulties] limit [your/his/her/their] daily activities?

DSQ_Q16 / EQ 42 – How much difficulty [do/does] [you/respondent's name] have using [your/his/her/their] fingers to grasp small objects like a pencil or scissors?

DSQ_Q17 / EQ 43 – How often does this difficulty using [your/his/her/their] fingers limit [your/his/her/their] daily activities?

DSQ_Q18 / EQ 44 – [Do/Does] [you/respondent's name] have pain that is always present?

DSQ_Q19 / EQ 45 – [Do/Does] [you/he/she/this person] [also] have periods of pain that reoccur from time to time?

DSQ_Q20 / EQ 46 – How often does this pain limit [your/his/her/their] daily activities?

DSQ_Q21 / EQ 47 – When [you/respondent's name] [are/is] experiencing this pain, how much difficulty [do/does] [you/he/she/they] have with [your/his/her/their] daily activities?

DSQ_Q22 / EQ 48 – [Do/Does] [you/respondent's name] have any difficulty learning, remembering or concentrating?

DSQ_Q23 / EQ 49 – Do you think [you/respondent's name] [have/has] a condition that makes it difficult in general for [you/him/her/them] to learn? This may include learning disabilities such as dyslexia, hyperactivity, attention problems, etc.

DSQ_Q24 / EQ 50 – Has a teacher, doctor or other health care professional ever said that [you/respondent's name] had a learning disability?

DSQ_Q25 / EQ 51 – How often are [your/his/her/their] daily activities limited by this condition?

DSQ_Q26 / EQ 52 – How much difficulty [do/does] [you/respondent's name] have with [your/his/her/their] daily activities because of this condition?

DSQ_Q27 / EQ 53 – Has a doctor, psychologist or other health care professional ever said that [you/respondent's name] had a developmental disability or disorder? This may include Down syndrome, autism, Asperger syndrome, mental impairment due to lack of oxygen at birth, etc.

DSQ_Q28 / EQ 54 – How often are [your/respondent's name] daily activities limited by this condition?

DSQ_Q29 / EQ 55 – How much difficulty [do/does] [you/respondent's name] have with [your/his/her/their] daily activities because of this condition?

DSQ_Q30 / EQ 56 – [Do/Does] [you/he/she/this person] have any ongoing memory problems or periods of confusion?

DSQ_Q31 / EQ 57 – How often are [your/his/her/their] daily activities limited by this problem?

DSQ_Q32 / EQ 58 – How much difficulty [do/does] [you/respondent's name] have with [your/his/her/their] daily activities because of this problem?

DSQ_Q33 / EQ 59 – [Do/Does] [you/respondent's name] have any emotional, psychological or mental health conditions?

DSQ_Q34 / EQ 60 – How often are [your/his/her/their] daily activities limited by this condition?

DSQ_Q35 / EQ 61 – When [you/respondent's name] [are/is] experiencing this condition, how much difficulty [do/does] [you/he/she/they] have with [your/his/her/their] daily activities?

DSQ_Q36 / EQ 62 – [Do/Does] [you/respondent's name] have any other health problem or long-term condition that has lasted or is expected to last for six months or more?

DSQ_Q37 / EQ 63 – How often does this health problem or long-term condition limit [your/his/her/their] daily activities?

DSQ_Q38 / EQ 64 – [Do/Does] [you/respondent's name] have pain that is always present?

DSQ_Q39 / EQ 65 – [Do/Does] [you/he/she/this person] [also] have periods of pain that reoccur from time to time?

DSQ_Q40 / EQ 66 – How often does this pain limit [your/his/her/their] daily activities?

DSQ_Q41 / EQ 67 – When [you/respondent's name] [are/is] experiencing this pain, how much difficulty [do/does] [you/he/she/they] have with [your/his/her/their] daily activities?

UNC_Q005 / EQ 68 – During the past 12 months, was there ever a time when [you/respondent's name] felt that [you/he/she/they] needed health care, other than homecare services, but [you/he/she/they] did not receive it?

UNC_Q010 / EQ 69 – Thinking of the most recent time [you/respondent's name] felt this way, why didn't [you/he/she/they] get care?

UNC_Q015 / EQ 70 – Again, thinking of the most recent time, what was the type of care that was needed?

UNC_Q020 / EQ 71 – Did [you/he/she/this person] actively try to obtain the health care that was needed?

UNC_Q025 / EQ 72 – Where did [you/he/she/this person] try to get the service [you/he/she/they] [were/was] seeking?

Participate in the consultation for the update of the Canadian Research and Development Classification (CRDC) 2020 V1.0

Opened: August 2023
Closed: October 2023 Results posted: March 2024

Introduction

The Social Sciences and Humanities Research Council of Canada (SSHRC), the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Foundation for Innovation (CFI), the Canadian Institutes of Health Research (CIHR), and Statistics Canada have collaboratively developed and released a new Canadian Research and Development Classification (CRDC) 2020 Version 1.0 in October 2020. This shared standard classification is available for use by the federal research granting agencies, Statistics Canada and any other organization or individual that find it useful to implement. The CRDC is aligned with international research and development classification standards.

Statistics Canada, as custodian of the CRDC, and its close partner research funding agencies, have agreed to undertake minor revisions of the classification every year or two, and major revision every five years. In fact, all parties already agreed that the first CRDC 2020 version 1.0 will be revised within 2 years of its first release date, and on a five-year cycle after that, with the possibility of 'evergreening' for minor changes once a year to reflect the changes in the research fields. We now have the opportunity to revise the CRDC 2020 V1.0 after being released for more than 2 years.

This consultation was only targeted toward Field of research (FOR) of the CRDC 2020 V1.0.

Consultative engagement objectives

This consultation aimed to gather feedback from users who have already implemented the classification, as well as other interested parties who might want to suggest updates or changes, but not significant conceptual or structural ones (which are reserved for the 5-year revision cycle).

Federal research funding agencies, Statistics Canada's statistical programs related to R&D data, members of the research community and their partners, and Canadians who feel the need for the CRDC 2020 V1.0 to be revised at this time, are invited to provide feedback for the revision of the Field of research (FOR) of the CRDC Version 1.0.

The feedback will be analyzed, and recommendations for changes or revisions to the CRDC will be made, following 2 key steps:

  1. Collection of feedback and data to assess classification revision needs and gaps
    1. Launch of a consultation process that will capture the needs and gaps of the CRDC 2020 V1.0 - FOR as perceived mainly by the federal research granting agencies, Statistics Canada and the research community
    2. Analysis of data collected at the research funding agencies to identify any missing fields of research
  2. Review of CRDC 2020 V1.0 - FOR and validation of proposed changes
    1. Review of feedback and analysis to inform any possible revisions
    2. Validation of proposed revisions with field expertise

Closing date

This consultation is closed.

Results of the consultative engagement

Statistics Canada received feedback from a variety of people such as members of the research community and organizations, and we want to thank participants for their contributions to this consultative engagement initiative. Their feedback have helped guide the revision to the CRDC 2020 V1.0.

We invite you to read the report on the Revision of the Canadian Research and Development Classification (CRDC) 2020 Version 1.0.

How to provide feedback during the consultation?

Proposals for the revision of the Field of research (FOR) of the CRDC 2020 V1.0 revisions must contain the contact information of those submitting the change request:

  • Full Name
  • Organization (when an individual is proposing changes on behalf of an organization)
  • Mailing address
  • Email address
  • Phone number

Should additional information or clarification to the proposal be required, participants might be contacted.

Proposals must be submitted by email to statcan.crdc-ccrd.statcan@statcan.gc.ca

Consultation guidelines

Individuals or organizations are encouraged to follow the guidelines below when developing their proposals.

Proposals should:

  • be based on the CRDC 2020 V1.0 - FOR, therefore reading it is important before submitting changes;
  • clearly identify the proposed addition or change to the Field of research (FOR) of the CRDC 2020 Version 1.0; this can include the creation of entirely new classification items related to the classes and subclasses or modifications to existing classification items within the classes and subclasses. This consultation will not result in the modification of higher-level classifications items (divisions and groups);
  • outline the rationale and include supporting information for the proposed change;
  • when possible, describe the empirical significance (i.e., expenses, value-added or GDP, number of researchers, etc.) of proposed changes, and especially real structural changes (resulting in a change in the scope of a current classification item);
  • be consistent with classification principles (e.g., mutual exclusivity, exhaustiveness and homogeneity within categories);
  • be relevant, that is, proposals should:
    • describe the present analytical interest;
    • define how the change would enhance the usefulness of data;
    • be based on appropriate statistical research or subject matter expertise.

Please consider the questions below when preparing your input for the consultation on the revision of CRDC 2020 V1.0-FOR:

  • Are there research and development (R&D) services or activities for which you cannot find a satisfactory CRDC code?
  • Are there R&D activities or services that you find difficult to place in CRDC 2020 V1.0?
  • Are any R&D activities or services missing?
  • Are there R&D or combinations of R&D that have significant economic value and analytical interest that you would like to see with a specific or separate CRDC classification item (classes and subclasses)?
  • Are there classification items you find difficult to use because their descriptions are vague or unclear?
  • Are there pairs of classification items you find difficult to distinguish from each other? Are there boundaries that could be clarified?
  • Are there R&D activities or services that you are able to locate in CRDC 2020 V.10, but you would like to have them located in a different classification item or level of R&D activities? Please clearly indicate why;
  • Is the language or terminology used in CRDC 2020 V1.0 in need of updating to be consistent with current usage in the research field?

Note that submissions do not need to cover every topic; you can submit your comments or proposals on your specific area(s) of concern only.

The following criteria can be used to review the proposals received:

  • consistency with classification principles such as mutual exclusivity, exhaustiveness, and homogeneity of R&D activities or services within categories, with no overlapping to avoid double counting;
  • have empirical significance as an R&D activity or service, expenditures (government and private sectors), number of researchers involved, etc.;
  • are related to collectable and publishable data;
  • be relevant, that is, it must be of analytical interest, result in data useful to users, and be based on appropriate statistical research, subject-matter expertise, and administrative relevance.
  • be consistent with the Canadian System of National Accounts to some extent (for statistical purposes);
  • special attention could be given to specific R&D activities or services, including:
    • new or emerging R&D activities or services;
    • R&D related to new or advanced technologies;
    • any field of research that may be missing from the current version of the classification.

Treatment of proposals

Statistics Canada will review all proposals received in collaboration with research funding agencies. They reserve the right to use independent parties or other government employees, if deemed necessary, to assess proposals.

The federal research granting agencies and Statistics Canada will consider feedback received from this consultation to finalize the revision of Canadian Research and Development Classification (CRDC) 2020 V1.0 - FOR, which will be published in early 2024, with a new version which could be either CRDC 2020 V1.1 or CRDC 2020 Version 2.0 depending on the extend of the approved changes.

If deemed appropriate, a representative of Statistics Canada or the research funding agencies will contact respondents (including virtual or physical meetings) to ask additional questions or seek clarification on a particular aspect of their proposal.

A report summarizing the findings of this consultation will be published on the Statistics Canada website later in 2024.

Please note each proposal will not necessarily result in a change to the CRDC 2020 V1.0.

Official languages

Proposals may be written in either of Canada's official languages - English or French.

Confidentiality

Statistics Canada is committed to respecting the privacy of consultation participants. All personal information created, held or collected by the Agency is protected by the Privacy Act. For more information on Statistics Canada's privacy policies, please consult the Privacy notice.

Note of appreciation

We thank all respondents in advance for their interest and participation in this consultation on the revision of the Canadian Research and Development Classification (CRDC) 2020 Version 1.0 - FOR. Your contributions are valuable to us.

In August 2023, questions measuring the Labour Market Indicators were added to the Labour Force Survey as a supplement.

Questionnaire flow within the collection application is controlled dynamically based on responses provided throughout the survey. Therefore, some respondents will not receive all questions, and there is a small chance that some households will not receive any questions at all. This is based on their answers to certain LFS questions.

Labour Market Indicators

ENTRY_Q01 / EQ 1 - From the following list, please select the household member that will be completing this questionnaire on behalf of the entire household.

WFH_Q01 / EQ 2 – At the present time, in which of the following locations [do/does] [you/respondent name/this person] usually work as part of [your/his/her/their] main job or business?

WRK_Q01 / EQ 3 - On which of the following days [do/does] [you/respondent name/this person] usually go to [your/his/her/their] worksite in [your/his/her/their] main job or business?

WRK_Q02 / EQ 4 - In [your/his/her/their] main job or business, [do/does] [you/respondent name/this person] have the possibility to work at home or from another location of [your/his/her/their] choice?

WFH_Q04 / EQ 5 - Ideally, what proportion of [your/his/her/their] work hours would [you/respondent name/this person] prefer to work at home as part of [your/his/her/their] main job or business?

WFH_Q02 / EQ 6 - Last week, what proportion of [your/his/her/their] work hours did [you/respondent name/this person] work at home as part of [your/his/her/their] main job or business?

RMJ_Q01 / EQ 7 - What is the main reason [you/respondent name/this person] worked at more than one job or business?

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