Canada’s Got Tech Talent

What is this project about

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Canada’s Got Tech Talent report series provides a comprehensive overview of the size of the tech workforce in Canada, where tech work is growing and shrinking geographically, the demographics of who Canadian tech workers are, and their pay and participation rates. 

Using the most comprehensive data source on workers in Canada, the 2021 long form Canadian census, we examine whether there has been any growth from 2016 to 2021—a period that saw tremendous changes in the Canadian economy—while taking stock on progresses (or lack thereof) made to advance diversity in the tech workforce. 

From being billed as a solution to Canada’s “productivity crisis”, to villains in a story of careless tech leaders creating net societal ills, the tech sector, and its leaders, have never been under a finer microscope as it has been in 2024. 

Tech workers have been working in the economy for years, not just by creating new technologies, but by helping people and organizations use and maintain existing technologies.

For Canada to create the right policy that helps attract the best tech talent to contribute to solving the country’s productivity crisis, and ensure responsible governance of how new technology is developed, it is crucial to understand the landscape of those who currently work in the tech sector. 

Canada’s Got Tech Talent  an update of the 2019 Who Are Canada’s Tech Workers report to assess what has changed since 2016 using Census 2021 data.

Reports

Canada’s tech workers and their compensation

BOLD IDEA: In an economy increasingly influenced by the rapid rise of technology, understanding the make-up of Canada’s tech workforce is crucial to identifying opportunities to improve the nation’s competitive edge on the world stage.

Diversity of Canada’s tech workers

BOLD IDEA: There are big diversity challenges within Canada’s tech workforce. Addressing the barriers that exist for women, Indigenous Peoples, and other marginalized tech workers is a critical step in moving the sector forward.

Canada’s Nonprofit Tech Workforce

BOLD IDEA: The non-profit sector is the cornerstone of community services in Canada. We need targeted policy responses to understand, and then unlock, the nonprofit digital workforce to ensure critical resources continue to serve all Canadians who need them.

This project was conceived by the Canadian Centre for Nonprofit Digital Resilience in collaboration with, Blueprint, Digital Governance Council and Imagine Canada.

The Demand for Digital Skills in Canadaʼs Nonprofit Sector

BOLD IDEA: The nonprofit sector employs over 2 million Canadians, yet struggles to keep pace with increased digital skills demand. Investment and support in this workforce sector will ensure their services continue to meet Canadians where they are.

This project was conceived by the Canadian Centre for Nonprofit Digital Resilience in collaboration with, Blueprint, Digital Governance Council and Imagine Canada.

How we define tech occupations

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In this appendix, we detail the full methodology we employed. Following the approach of Who Are Canada’s Tech Workers, we identify tech occupations based on the skills that make up a tech occupation.

To identify occupations that require digital skills in Canada (tech occupations), we rely on the United States Department of Labour’s Occupational Information Network (O*NET) database. This database contains information on 923 distinct occupations on different aspects of the jobs. This work relies on information relating to occupational skills, knowledges, and work activities (SKWs). This data is collected from either job incumbents or occupational experts and is updated periodically to remain accurate.

To select the SKWs relevant to digital skills, we apply two principles (drawn directly from Who Are Canada’s Tech Workers):

1. The skills, knowledge, and work activity must relate directly to technology use or technology creation. 
2. If an occupation has a strong requirement for any of the previously identified tech skills, it will qualify as a tech occupation. 

The first principle relates directly to the decision of which SKWs to include in the analysis, while the second provides direction on how to aggregate the included SKWs. This second principle is discussed in more detail below.

Using these principles, we identified six SKWs that are tracked within O*NET that constitute tech skills: 

• Interacting with Computers: Using computers and computer systems (including hardware and software) to program, write software, setup functions, enter data, or process information. 
• Computers and Electronics: Knowledge of circuit boards, processors, chips, electronic equipment, and computer hardware and software, including applications and programming. 
• Programming: Writing computer programs for various purposes. 
• Technology Design: Generating or adapting equipment and technology to serve user needs. 
• Engineering and Technology: Knowledge of the practical application of engineering science and technology. This includes applying principles, techniques, procedures, and equipment to the design and production of various goods and services. 
• Telecommunications: Knowledge of transmission, broadcasting, switching, control, and operation of telecommunications systems. 

For each of these SKWs, O*NET tracks two distinct measures: level and importance. Level refers to the level at which one is required to know the SKW – higher levels will require more complex knowledge of the SKW. Conversely, importance refers to how vital a given SKW is to an occupation – while an occupation can be required to perform a task at a high level of complexity, that knowledge may not be used frequently in the occupation. 

Both level and importance are measured using ordinal scales (for level, the scale ranges from 1 to 7, while for importance the scale ranges from 1 to 5). In both cases, a higher score indicates either a higher level of importance or level of complexity. However, as the scales are ordinal rather than linear, we cannot compare changes directly. For example, an increase of importance from 1 to 2 does not necessarily reflect a doubling of importance, and it is also not necessarily the same as an increase from 2 to 3 or 3 to 4. 

Additionally, each SKW is scored using a unique set of levels – specific anchors are given for different values and these anchors are not comparable across SKWs. For example, a 3 for level for “Mathematics” is not necessarily the same as a 3 for “Programming” and so these are not directly comparable.

To solve for this, we focus on the ordinal scale within each SKW (whether one number is larger than another) rather than the cardinal scale (by how much one number is larger than another). 

As a result, we rank all occupations within each of the SKWs listed above, then aggregate the resulting six rankings into one composite measure. This is done by multiplying importance and level together and taking the harmonic mean of the results.

Combining the two measures for each SKW is O*NET’s recommended way of using them, as it incorporates both the complexity and the importance of a particular SKW to an occupation. While O*NET also recommends normalizing the two scales before combining them, as the two measures have different ranges, we do not do that here as we are not interested in cardinal measures. Instead, after multiplying the raw scores, we use them to rank each occupation for each of the six tech skills we selected. 

We then use the harmonic mean to aggregate all six SKW scores together. A harmonic mean is defined as the reciprocal to the arithmetic mean of the reciprocals of the inputs:

This has distinct benefits over traditional (arithmetic) means or geometric means. Harmonic means reward highly scoring occupations on any SKWs, while not penalize an occupation for ranking lowly on a different SKW. This follows the second principle outlined above, stating that as long as an occupation relies heavily on any one tech SKW it should be a tech occupation.

To convert this American data and match it to Canadian data, we manually connect O*NET occupations to Canadian National Occupational Categories (NOCs) using the 5 digit level for NOCs. There are 510 NOCs at this level of specificity. While previous work has compared O*NET occupations to 4 digit NOC occupations, recent updates to the NOC methodology mean that current 5 digit NOCs are most comparable to O*NET occupations. 

Tech Occupations Identified

Using the methodology for ranking tech work above, we identified the most digitally intensive occupations in Canada. Consistent with the methodology from Who are Canada’s Tech Workers, occupations in the 95th percentile and above are classified as tech occupations. Those occupations are listed below:

Aerospace engineers Broadcast technicians Chemical engineers Computer and information systems managers Computer engineers (except software engineers and designers) Computer network and web technicians Computer systems developers and programmers Cybersecurity specialists Data scientists Database analysts and data administrators Electrical and electronics engineers Financial and investment analysts Geological engineers Industrial designers Industrial engineering and manufacturing technologists and technicians

Information systems specialists Information systems testing technicians Mechanical engineers Metallurgical and materials engineers Mining engineers Other professional engineers Other professional occupations in physical sciences Physicists and astronomers Software developers and programmers Software engineers and designers Telecommunication carriers managers Telecommunications equipment installation and cable television service technicians Telecommunications line and cable installers and repairers Web designers Web developers and programmers