{"id":56789,"date":"2025-08-10T10:07:16","date_gmt":"2025-08-10T10:07:16","guid":{"rendered":"https:\/\/www.newsbeep.com\/uk\/56789\/"},"modified":"2025-08-10T10:07:16","modified_gmt":"2025-08-10T10:07:16","slug":"the-changing-nature-of-work-in-canada-1987-to-2024","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/uk\/56789\/","title":{"rendered":"The changing nature of work in Canada: 1987 to 2024"},"content":{"rendered":"

Technology is constantly evolving, and this can change the tasks that human employees perform in the workplace. For much of history, technological advances have remained within the realm of routine, manual tasks. Accordingly, workers performing these tasks have been the ones facing the risk of job transformation or perhaps even job displacement. For example, Frank et al. (2021) found that from 1987 to 2018, the share of Canadian workers employed in jobs associated with routine, manual tasks declined gradually, while the share employed in jobs associated with non-routine, cognitive tasks increased gradually. Frenette (2023) demonstrated that these trends accelerated somewhat over the course of the COVID-19 pandemic (2019 to 2022), possibly because firms may have increased their reliance on automation technology to make the production and delivery of their goods and services more resilient to potential future shutdowns.<\/p>\n

Since 2022, artificial intelligence (AI) has become widely available through the release of large language models, such as ChatGPT and others. Unlike automation and earlier forms of technology, AI has the potential to alter the jobs held by highly skilled workers. In fact, Mehdi and Frenette (2024) report that, \u201cIn 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.\u201d In general, the occupations associated with high potential exposure to AI are those requiring higher levels of education. Those that are highly complementary with AI, and thus may benefit from AI, include professions such as doctors, nurses, teachers and electrical engineers. In contrast, employees in business, finance, and information and communications technologies have less potential complementarity with AI and, as a result, may end up competing with AI. Of course, possible scenarios can only occur in the future if workplaces adopt AI on a large scale. To date, AI implementation has been fairly low\u2014Bryan et al. (2024) report that 6.1% of Canadian businesses had used AI in producing goods and delivering services over the last 12 months (as of the second quarter of 2024). Nevertheless, AI has the potential to expand in use, especially if a critical mass point is reached, and firms are compelled to adopt the technology to remain competitive.<\/p>\n

Another factor that could affect competitiveness and the need to adopt new technology is globalization. Indeed, new trade agreements could increase the need to invest in new technology as firms compete on a larger international scale.<\/p>\n

As technology and globalization evolve, it is important to continue tracking how the nature of work is evolving in the Canadian workplace. The pace at which changes are happening is particularly important since there are fears that technology may significantly alter or even completely replace certain jobs. More gradual changes may provide workers and entrants to the Canadian labour market more time to adapt. The goal of this short article is to update the trends documented by Frank et al. (2021) and Frenette (2023) with Labour Force Survey (LFS) data covering the 1987-to-2024 period.Note\u00a0<\/a> Workers are classified into four work task groups based on their occupationNote\u00a0<\/a> and grounded by the approach pioneered by Autor et al. (2003):Note\u00a0<\/a><\/p>\n


\n managerial, professional and technical occupations (non-routine, cognitive tasks)Note\u00a0<\/a>
\n service occupations (non-routine, manual tasks)
\n sales, clerical and administrative support occupations (routine, cognitive tasks)
\n production, craft, repair and operative occupations (routine, manual tasks). <\/p>\n

The shares of workers in each of the four groups over the 1987-to-2024 period are shown in Chart 1 (men) and in Chart 2 (women). As reported by Frank et al. (2021) and Frenette (2023), the nature of work changed gradually from 1987 to the late 2010s (before the COVID-19 pandemic). More specifically, the workforce slowly but consistently moved towards occupations associated with non-routine, cognitive tasks, and away from occupations associated with routine, manual tasks. The share of men employed in managerial, professional and technical occupations increased from 23.5% in 1987 to 29.6% in 2019 (+6.1 percentage points), while the share employed in production, craft, repair and operative occupations declined from 47.1% in 1987 to 37.9% in 2019 (-9.1 percentage points). The trend was even more pronounced for women in managerial, professional and technical occupations (from 23.7% to 33.3% over the same period, or +9.7 percentage points), but less pronounced in absolute terms in production, craft, repair and operative occupations, likely owing to the fact that a smaller share of women were employed in such jobs (from 10.0% to 6.3% over the same period, or -3.7 percentage points). However, women did register a substantial decline in the share of sales, clerical and administrative support occupations (from 41.3% in 1987 to 33.8% in 2019, or -7.5 percentage points).<\/p>\n

\"Chart<\/p>\n


\n Data table for Chart 1 <\/p>\n


\n
\n
\n\t\tData table for Chart 1

\n\t\tTable summary
The information is grouped by Year (appearing as row headers), Managerial, professional and technical occupations (non-routine, cognitive tasks), Service occupations (non-routine, manual tasks), Sales, clerical and administrative support occupations (routine, cognitive tasks) and Production, craft, repair and operative occupations (routine, manual tasks), calculated using percent units of measure (appearing as column headers).
\n
\n
\n
\n Year
\n Managerial, professional and technical occupations (non-routine, cognitive tasks)
\n Service occupations (non-routine, manual tasks)
\n Sales, clerical and administrative support occupations (routine, cognitive tasks)
\n Production, craft, repair and operative occupations (routine, manual tasks)
\n
\n
\n percent
\n
\n
\n
\n
\n \t\t\tNote: The vertical blue lines demarcate key periods analyzed in the article: 1987 to 2019, 2019 to 2022, and 2022 to 2024.
\n\t\t\t

\n\t\t\tSource: Statistics Canada, Labour Force Survey.
\n
\n
\n
\n
\n
\n 1987
\n 23.5
\n 13.7
\n 15.7
\n 47.1
\n
\n
\n 1988
\n 24.4
\n 13.5
\n 15.8
\n 46.3
\n
\n
\n 1989
\n 23.9
\n 13.7
\n 15.3
\n 47.1
\n
\n
\n 1990
\n 24.0
\n 13.8
\n 15.9
\n 46.3
\n
\n
\n 1991
\n 25.4
\n 13.8
\n 15.6
\n 45.2
\n
\n
\n 1992
\n 25.2
\n 14.2
\n 16.1
\n 44.6
\n
\n
\n 1993
\n 25.5
\n 14.5
\n 15.8
\n 44.1
\n
\n
\n 1994
\n 26.3
\n 14.3
\n 15.4
\n 44.0
\n
\n
\n 1995
\n 26.8
\n 14.2
\n 14.7
\n 44.3
\n
\n
\n 1996
\n 25.9
\n 14.4
\n 15.0
\n 44.7
\n
\n
\n 1997
\n 26.8
\n 13.9
\n 14.5
\n 44.8
\n
\n
\n 1998
\n 26.7
\n 13.9
\n 15.3
\n 44.0
\n
\n
\n 1999
\n 26.9
\n 13.6
\n 15.6
\n 43.9
\n
\n
\n 2000
\n 26.9
\n 13.6
\n 16.0
\n 43.5
\n
\n
\n 2001
\n 26.0
\n 13.6
\n 17.0
\n 43.4
\n
\n
\n 2002
\n 26.1
\n 13.9
\n 16.6
\n 43.5
\n
\n
\n 2003
\n 25.4
\n 14.1
\n 16.7
\n 43.8
\n
\n
\n 2004
\n 25.5
\n 14.0
\n 17.0
\n 43.5
\n
\n
\n 2005
\n 26.7
\n 14.1
\n 16.6
\n 42.7
\n
\n
\n 2006
\n 27.3
\n 14.0
\n 16.4
\n 42.3
\n
\n
\n 2007
\n 27.1
\n 14.7
\n 16.4
\n 41.9
\n
\n
\n 2008
\n 27.8
\n 14.5
\n 16.5
\n 41.3
\n
\n
\n 2009
\n 27.7
\n 14.8
\n 16.6
\n 41.0
\n
\n
\n 2010
\n 28.3
\n 15.6
\n 16.2
\n 39.9
\n
\n
\n 2011
\n 27.7
\n 15.1
\n 17.0
\n 40.3
\n
\n
\n 2012
\n 27.5
\n 15.0
\n 16.3
\n 41.2
\n
\n
\n 2013
\n 27.8
\n 15.0
\n 16.7
\n 40.5
\n
\n
\n 2014
\n 27.9
\n 15.1
\n 16.7
\n 40.3
\n
\n
\n 2015
\n 28.6
\n 15.2
\n 16.6
\n 39.6
\n
\n
\n 2016
\n 28.9
\n 15.2
\n 16.9
\n 39.1
\n
\n
\n 2017
\n 29.1
\n 15.3
\n 17.1
\n 38.5
\n
\n
\n 2018
\n 28.6
\n 15.1
\n 17.4
\n 38.8
\n
\n
\n 2019
\n 29.6
\n 15.1
\n 17.4
\n 37.9
\n
\n
\n 2020
\n 30.7
\n 14.9
\n 17.6
\n 36.8
\n
\n
\n 2021
\n 31.6
\n 13.7
\n 18.3
\n 36.5
\n
\n
\n 2022
\n 32.9
\n 13.5
\n 17.2
\n 36.5
\n
\n
\n 2023
\n 33.3
\n 14.3
\n 16.8
\n 35.6
\n
\n
\n 2024
\n 34.2
\n 13.9
\n 16.7
\n 35.1
\n
\n <\/p>\n

<\/p>\n

\"Chart<\/p>\n


\n Data table for Chart 2 <\/p>\n


\n
\n
\n\t\tData table for Chart 2

\n\t\tTable summary
The information is grouped by Year (appearing as row headers), Managerial, professional and technical occupations (non-routine, cognitive tasks), Service occupations (non-routine, manual tasks), Sales, clerical and administrative support occupations (routine, cognitive tasks) and Production, craft, repair and operative occupations (routine, manual tasks), calculated using percent units of measure (appearing as column headers).
\n
\n
\n
\n Year
\n Managerial, professional and technical occupations (non-routine, cognitive tasks)
\n Service occupations (non-routine, manual tasks)
\n Sales, clerical and administrative support occupations (routine, cognitive tasks)
\n Production, craft, repair and operative occupations (routine, manual tasks)
\n
\n
\n percent
\n
\n
\n
\n
\n \t\t\tNote: The vertical blue lines demarcate key periods analyzed in the article: 1987 to 2019, 2019 to 2022, and 2022 to 2024.
\n\t\t\t

\n\t\t\tSource: Statistics Canada, Labour Force Survey.
\n
\n
\n
\n
\n
\n 1987
\n 23.7
\n 25.0
\n 41.3
\n 10.0
\n
\n
\n 1988
\n 24.2
\n 24.0
\n 41.9
\n 10.0
\n
\n
\n 1989
\n 24.1
\n 24.2
\n 41.8
\n 9.9
\n
\n
\n 1990
\n 25.8
\n 23.9
\n 40.7
\n 9.6
\n
\n
\n 1991
\n 26.4
\n 23.7
\n 40.8
\n 9.1
\n
\n
\n 1992
\n 27.1
\n 23.9
\n 40.4
\n 8.6
\n
\n
\n 1993
\n 27.7
\n 24.0
\n 39.9
\n 8.4
\n
\n
\n 1994
\n 28.5
\n 23.9
\n 39.2
\n 8.4
\n
\n
\n 1995
\n 29.1
\n 23.5
\n 38.9
\n 8.5
\n
\n
\n 1996
\n 28.8
\n 24.6
\n 37.9
\n 8.7
\n
\n
\n 1997
\n 29.0
\n 24.2
\n 37.8
\n 9.0
\n
\n
\n 1998
\n 29.5
\n 23.7
\n 37.4
\n 9.4
\n
\n
\n 1999
\n 28.6
\n 24.4
\n 37.6
\n 9.4
\n
\n
\n 2000
\n 28.2
\n 24.9
\n 37.5
\n 9.4
\n
\n
\n 2001
\n 28.0
\n 24.9
\n 38.0
\n 9.1
\n
\n
\n 2002
\n 27.8
\n 25.4
\n 37.6
\n 9.2
\n
\n
\n 2003
\n 27.9
\n 25.5
\n 37.5
\n 9.1
\n
\n
\n 2004
\n 27.9
\n 25.7
\n 37.1
\n 9.3
\n
\n
\n 2005
\n 29.0
\n 25.1
\n 37.4
\n 8.6
\n
\n
\n 2006
\n 29.4
\n 25.9
\n 36.6
\n 8.2
\n
\n
\n 2007
\n 29.5
\n 26.2
\n 36.7
\n 7.6
\n
\n
\n 2008
\n 30.1
\n 26.1
\n 36.5
\n 7.3
\n
\n
\n 2009
\n 30.7
\n 26.3
\n 36.2
\n 6.7
\n
\n
\n 2010
\n 31.1
\n 26.6
\n 36.2
\n 6.1
\n
\n
\n 2011
\n 30.8
\n 26.9
\n 35.8
\n 6.6
\n
\n
\n 2012
\n 31.7
\n 26.9
\n 35.0
\n 6.4
\n
\n
\n 2013
\n 31.7
\n 26.7
\n 35.3
\n 6.3
\n
\n
\n 2014
\n 31.5
\n 27.1
\n 35.3
\n 6.2
\n
\n
\n 2015
\n 32.2
\n 26.8
\n 34.9
\n 6.1
\n
\n
\n 2016
\n 32.8
\n 26.6
\n 34.2
\n 6.4
\n
\n
\n 2017
\n 32.6
\n 26.5
\n 34.8
\n 6.2
\n
\n
\n 2018
\n 32.8
\n 26.4
\n 34.5
\n 6.3
\n
\n
\n 2019
\n 33.3
\n 26.6
\n 33.8
\n 6.3
\n
\n
\n 2020
\n 35.0
\n 25.6
\n 33.6
\n 5.9
\n
\n
\n 2021
\n 36.3
\n 24.2
\n 33.5
\n 6.0
\n
\n
\n 2022
\n 36.7
\n 23.8
\n 33.3
\n 6.2
\n
\n
\n 2023
\n 37.6
\n 23.7
\n 32.7
\n 6.0
\n
\n
\n 2024
\n 39.3
\n 23.7
\n 31.2
\n 5.8
\n
\n <\/p>\n

<\/p>\n

The movement towards managerial, professional and technical occupations accelerated during the relatively short 2019-to-2022 period for both men (+3.3 percentage points) and women (+3.4 percentage points). The downward trend in production, craft, repair and operative occupations also accelerated during this time for men (-1.5 percentage points), but not for women (-0.1 percentage points). In addition, a new trend emerged over the pandemic, as the share employed in service occupations declined for men (-1.6 percentage points) and women (-2.8 percentage points). This marked a reversal from the moderate growth registered from 1987 to 2019 (+1.4 percentage points for men and +1.6 percentage points for women).Note\u00a0<\/a><\/p>\n

From 2022 to 2024, further significant increases in the share of managerial, professional and technical jobs were registered by men (+1.4 percentage points) and women (+2.6 percentage points), as were further declines in the share of production, craft, repair and operative jobs (-1.3 percentage points for men and -0.4 percentage points for women). Women also continued their downward trend of employment in sales, clerical and administrative support occupations (-2.1 percentage points). However, the downward trend in service occupations observed during the pandemic ceased after 2022, likely because of the reopening of the economy and, in particular, the services sector.<\/p>\n

For the entirety of the period (1987 to 2024), managerial, professional and technical occupations became more prevalent for men (+10.7 percentage points in absolute terms, or +45.5% in relative terms) and for women (+15.6 percentage points, or +65.8%). In contrast, production, craft, repair and operative occupations became less prevalent for men (-11.9 percentage points, or -25.3%) and women (-4.2 percentage points, or -41.8%). Women also registered a substantial decline in the share of jobs in sales, clerical and administrative support occupations (-10.1 percentage points, or -24.3%). These changes were fairly gradual for the most part.<\/p>\n

The trends over the 1987-to-2024 period varied to some extent across the 10 provinces.Note\u00a0<\/a> The share of jobs classified as managerial, professional and technical occupations rose in all 10 provinces for men (increases ranging from 59.9% in Ontario and 54.4% in British Columbia to 8.0% in both Prince Edward Island and Saskatchewan) and for women (increases ranging from 79.6% in Ontario, 65.1% in British Columbia and 64.4% in Quebec to 39.1% in Manitoba).<\/p>\n

Production, craft, repair and operative occupations became less prevalent for men over this period (decreases ranging from 32.9% in Ontario to 8.9% in Prince Edward Island). For women, Alberta registered a slight increase of 1.8%, while all other provinces registered declines (with the largest being 64.4% in Newfoundland and Labrador).<\/p>\n

Finally, all 10 provinces posted decreases in the share of women employed in sales, clerical and administrative support occupations over the 1987-to-2024 period (declines ranging from 29.6% in Ontario to 4.0% in Prince Edward Island).<\/p>\n

In summary, recent trends in the nature of work have continued the long-term trend away from routine, manual work and towards non-routine, cognitive work. Although AI may be expected to curb employment growth in certain non-routine, cognitive occupations (Mehdi and Frenette 2024), there is no evidence to date of a slowdown in the growth of this broad class of occupations, possibly because of the relatively low adoption of AI by Canadian businesses. <\/p>\n

Authors<\/p>\n

Marc Frenette is with the Social Analysis and Modelling Division, Analytical Studies and Modelling Branch, at Statistics Canada.<\/p>\n

References<\/p>\n

Autor, D.G., Levy, F., and Murnane, R.J. 2003. The skill content of recent technological change: an empirical exploration<\/a>. The Quarterly Journal of Economics, 118 (4), pp. 1279-1333. <\/p>\n

Bryan, V., Sood, S, and Johnston, C. 2024. Analysis on expected use of artificial intelligence by businesses in Canada, third quarter of 2024<\/a>. Analysis in Brief.<\/p>\n

Frank, K., Yang, Z., and Frenette, M. 2021. The changing nature of work in Canada amid recent advances in automation technology<\/a>. Economic and Social Reports, 1 (1). <\/p>\n

Frenette, M. 2023. The changing nature of work since the onset of the COVID-19 pandemic<\/a>. Economic and Social Reports, 3 (7). <\/p>\n

Frenette, M., and Frank, K. 2020. Automation and Job Transformation in Canada: Who\u2019s at Risk?<\/a> Analytical Studies Branch Research Paper Series, no. 448.<\/p>\n

Mehdi, T., and Frenette, M. 2024. Exposure to artificial intelligence in Canadian jobs: Experimental estimates<\/a>. Economic and Social Reports, 4 (9).<\/p>\n","protected":false},"excerpt":{"rendered":"Technology is constantly evolving, and this can change the tasks that human employees perform in the workplace. For…\n","protected":false},"author":2,"featured_media":56790,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[554,84,30955,1371,56,54,55,1908],"class_list":{"0":"post-56789","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-jobs","8":"tag-ai","9":"tag-business","10":"tag-computerization","11":"tag-jobs","12":"tag-uk","13":"tag-united-kingdom","14":"tag-unitedkingdom","15":"tag-work"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/56789","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/comments?post=56789"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/56789\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media\/56790"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media?parent=56789"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/categories?post=56789"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/tags?post=56789"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}