Economic and Social Reports
Exposure to artificial intelligence in Canadian jobs: Experimental estimates
DOI: https://doi.org/10.25318/36280001202400900004-eng
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Recent developments in artificial intelligence (AI) have raised questions about the future of work. Debates centre primarily around the possibility of AI displacing some human workers. Predicting the effects of technological transformation on the labour market is challenging. This is especially true for AI given the uncertainty surrounding the breadth of its potential; the pace of its development and implementation; and how workers, businesses and governments might react and adapt.
Past studies on technological transformation examined the potential impact of automation on the Canadian labour market (Frenette and Frank, 2020). Automation is generally understood to be the use of machines to perform simple, routine and non-cognitive tasks. AI, on the other hand, can perform complex, non-routine and cognitive tasks. AI’s capabilities are growing, and it is unclear how powerful it may be in the future.
While it is difficult to predict the net impact of AI on jobs in Canada, Mehdi and Morissette (2024)—integrating data from the 2016 and 2021 censuses of population with data from the Occupational Information Network—offer some experimental estimates of occupational exposure to AI using the methodology developed by Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). The measure used is the complementarity-adjusted AI occupational exposure index, which can classify jobs into three AI groups using the median AI occupational exposure index and complementarity scores: (1) high exposure and low complementarity, (2) high exposure and high complementarity, and (3) low exposure (regardless of the degree of complementarity). The first two groups consist of jobs that may be highly exposed to AI, but the first group may have relatively more tasks that could be replaced by AI in the future, while the second group may have relatively more tasks that are highly complementary with AI. The third group of jobs are those that may be less exposed to AI than the first two groups, regardless of the degree of complementarity.
Employers may not immediately replace human labour with AI, even if it is technologically feasible to do so, because of financial, legal and institutional constraints. Consequently, exposure to AI does not necessarily imply a risk of job loss. At the very least, it could imply a certain degree of job transformation. Given the uncertainty surrounding AI, the experimental estimates presented here should be interpreted with caution. Only time will tell how the impact of AI will unfold.
The majority of workers in Canada are in jobs that may be highly exposed to job transformation related to artificial intelligence, but about half of them could benefit from it
In May 2021, 31% of employees aged 18 to 64 in Canada were in jobs that may be highly exposed to AI and relatively less complementary with it, 29% were in jobs that may be highly exposed to and highly complementary with AI, and 40% were in jobs that may not be highly exposed to AI. These findings were largely unchanged compared with those of May 2016 and are consistent with international evidence from other advanced economies, such as the United States (Cazzaniga et al., 2024)
Figure 1 illustrates the potential for AI to transform a broad set of occupations, regardless of skill level. Jobs that could be highly exposed to AI-related job transformation are generally those that require higher education. Despite this, professions such as doctors, nurses, teachers and electrical engineers may be highly complementary with AI technologies.Note In contrast, professions such as those in business, finance, and information and communications technologies—which may also require higher education—have less potential complementarity with AI. However, this does not necessarily mean that these jobs will be in less demand in the future because of AI, as many of them are critical to the economy. Instead, AI could play a transformational role, leading to the creation of new tasks within these jobs, or to new jobs entirely.
Description for figure
Potential artificial intelligence occupational exposure (AIOE) and complementarity in Canada
This chart shows a scatter plot with the AI occupational exposure index ranging from 5 to 7 on the horizontal axis and the complementarity index ranging from 0.4 to 0.8 on the vertical axis. There are 490 data points. Each data point represents an occupation as per the 4-digit National Occupation Classification version 2016 and are colour-coded with three different colours. The colours are used to distinguish the occupations according to their minimum educational requirement. Occupations requiring a bachelor's degree or higher are represented by blue, occupations requiring some postsecondary education below bachelor's degree are represented by green, and occupations requiring high school or less education are represented by red. The chart shows the relationship between AI occupational exposure and the extent to which AI can play a complementary role in a given occupation. A higher AI occupational exposure index is associated with greater potential occupational exposure to AI. A higher complementarity index is associated with greater potential complementarity with AI. The median AI occupational exposure index score of 6 and the median complementarity index score of 0.6 are used to group the various occupations into four quadrants. The top-left quadrant contain data points representing occupations which might be relatively less exposed to AI and highly complementary with AI. The majority of occupations in that quadrant require some postsecondary education below bachelor's degree but there are also a few which require high school or less education. Some examples include firefighters, plumbers, and carpenters. The bottom-left quadrant contain data points representing occupations which might also be relatively less exposed to AI but also less complementary with AI. The majority of occupations in that quadrant require high school or less education but there are also a few which require some postsecondary education below bachelor's degree. Some examples include food and beverage servers, labourers in processing, manufacturing and utilities, and welders and related machine operators. The top-right quadrant contain data points representing occupations which might be highly exposed to AI and highly complementary with AI. The majority of occupations in that quadrant require a bachelor's degree or higher education but there are a few which require some postsecondary education below bachelor's degree. Some examples include general practitioners and family physicians, secondary school teachers, and electrical engineers. The bottom-right quadrant contain data points representing occupations which might be highly exposed to AI but less complementary with AI. This quadrant has fewer data points than the other quadrants and the occupations represented by the data points have a mixture of educational requirements. Some examples include data entry clerks, economists, computer network technicians, and computer programmers and interactive media developers.
Notes: The AIOE index and potential complementarity are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). An occupation is considered high-exposure if its AIOE index exceeds the median AIOE across all occupations (6.0) and considered low-exposure otherwise. Similarly, an occupation is considered high-complementarity if its complementarity parameter exceeds the median complementarity across all occupations (0.6) and considered low-complementarity otherwise. Occupations in this chart are based on the 4-digit National Occupational Classification (NOC) 2016 version 1.3 converted from the United States Standard Occupational Classification (SOC) 2018. Of the 500 NOC occupations, 10 occupations which represented less than 1% of Canadian employment, were excluded due to a lack of Occupational Information Network (O*NET) data for computing the AIOE or complementarity parameter.
Source: Occupational Information Network (O*NET) version 28.2.
Certain groups of workers, such as those living in urban areas, women, higher earners and highly educated individuals, are more likely to be employed in jobs that could be highly exposed to AI-related job transformation than other groups. However, they are also more likely to be employed in jobs that could be highly complementary with AI. The AI exposure differences among these groups of workers are largely driven by differences in the mix of occupations they hold. The ability to adapt to technological changes could also vary across individuals.
Highly educated workers are more likely than their less educated counterparts to be in jobs that are highly exposed to job transformation related to artificial intelligence
While previous waves of technological transformation primarily affected less educated workers, AI may be more likely to affect more highly educated workers, because these workers are more likely to be in jobs that tend to have cognitive-oriented tasks. Chart 1 shows that 83% to 90% of workers with a bachelor’s degree or higher were in jobs that could be highly exposed to AI-related job transformation. However, more than half of these highly educated workers are in jobs that may be highly complementary with AI. Workers whose highest level of education was an apprenticeship or a trades certificate were the least likely to be in jobs that may be highly exposed to AI-related job transformation (27%). These workers are more likely to be employed in the skilled trades, which may be relatively less exposed to AI-related job transformation.
Data table for Chart 1
| Highest level of education | High exposure, low complementarity | High exposure, high complementarity | Low exposure |
|---|---|---|---|
| percentage of employees | |||
| High school or less | 25 | 13 | 62 |
| Apprenticeship or trades certificate or diploma | 15 | 12 | 73 |
| College, CEGEP or other certificate or diploma below bachelor's degree | 36 | 26 | 38 |
| Bachelor's degree | 37 | 46 | 17 |
| Graduate degree | 32 | 58 | 10 |
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Notes: The sample consists of employees aged 18 to 64 living off reserve in private dwellings, excluding full-time members of the Canadian Armed Forces. The artificial intelligence occupational exposure index and potential complementarity are computed using Occupational Information Network data and are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). Sources: Statistics Canada, Census of Population, 2021; and Occupational Information Network version 28.2. |
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Conclusion
Recent advances in AI have fuelled excitement, as well as concerns, regarding its implications for the economy and beyond. While previous waves of technological transformation raised concerns regarding the future of jobs involving routine and manual tasks, a broader segment of the labour force could be affected in an era when AI is increasingly becoming capable of performing non-routine and cognitive tasks typically done by highly skilled workers.
It is unclear what the net impact of AI will be on jobs in Canada. Experimental estimates of occupational exposure to AI suggest that higher-skilled jobs may be more exposed to AI-related job transformation than lower-skilled jobs. But half of the workers in jobs highly exposed to AI-related transformation may also benefit from it, as long as AI complements the work they do and, when required, they have the necessary skills to leverage it.
The experimental estimates presented here are meant to be forward-looking based on the current state of AI. They do not account for economic dynamics such as the long-term adaptability of workers, businesses and governments. Moreover, the estimates are based on a subset of current AI applications and capture only a narrow view of AI (i.e., generative AI, or AI that responds passively to requests). They exclude more general forms of AI that may be integrated with robotics hardware and have advanced capabilities to think and act autonomously. As AI’s capabilities grow over time, the applicability of the method used in this study could decrease.
The results from this study can inform labour market policies related to reskilling and career planning, but it should be noted that wide-scale implementation of new technologies can take time. There is also uncertainty surrounding the scale of AI adoption by businesses. Even if AI were to have no net impact on jobs, it may still have implications for other facets of the economy, such as labour productivity and income inequality. How workers, businesses and governments respond and adapt to the potentially evolving developments and implementation of AI remains to be seen.
Authors
Tahsin Mehdi and Marc Frenette are with the Social Analysis and Modelling Division, Analytical Studies and Modelling Branch, Statistics Canada.
References
Cazzaniga, M., Jaumotte, F., Li, L., Melina, G., Panton, A. J., Pizzinelli, C., Rockall, E. J. and M. M. Tavares. 2024. Gen-AI: Artificial intelligence and the future of work. International Monetary Fund, Working Paper no. 1.
Felten, E., Raj, M. and R. Seamans. 2021. Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and its Potential Uses. Strategic Management Journal 42(12): 2195-2217.
Frenette, M. and K. Frank. 2020. Automation and Job Transformation in Canada: Who’s at Risk? Analytical Studies Branch Research Paper Series. Statistics Canada Catalogue no. 11F0019M. Ottawa: Statistics Canada.
Mehdi, T. and R. Morissette. 2024. Experimental Estimates of Potential AI Occupational Exposure in Canada. Analytical Studies Branch Research Paper Series. Statistics Canada Catalogue no. 11F0019M. Ottawa: Statistics Canada.
Pizzinelli, C., Panton, A. J., Tavares, M. M., Cazzaniga, M. and L. Li. 2023. Labour market exposure to AI: Cross-country differences and distributional implications. International Monetary Fund, Staff Discussion Notes no. 216.
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