Future Shock? The Impact of Automation on Canada’s Labour Market
Bibliographic record
Abstract
Throughout history, technological change has helped lift people out of squalor and poverty, raised standards of living and improved well-being. Technological change, however, can also be disruptive – rendering specific occupations and skills obsolete, unsettling economic structures and contributing to unemployment and economic uncertainty. Innovation is a driving factor of productivity and economic growth, but increasing productivity means that fewer people are needed to produce the same amount of goods. The increasing pace of technological change has led some to speculate that, in the digital era, technology might destroy old jobs faster than new ones are created. Job losses can occur, however, only if innovation outstrips growth in demand for new products and services. As well, the potential for automation does not necessarily translate into actual automation: the decision to automate depends on factors such as firm size, competitive pressure and the cost of a machine versus the cost of human labour. This Commentary assesses the impact of technological change on Canada’s labour market over the past 30 years and highlights its implications for the near future. If the past is any guide, a continuation of gradual changes can be expected in the demand for skills in the labour force. This is a natural market reaction to technological change. There is unlikely to be a drastic shift in employment due to automation in the near future, although some industries and types of occupations will be more disrupted than others. Here, public policy could both encourage automation and prepare the workforce for the transition. Key findings are as follows: • It is very unlikely that employment in occupations highly susceptible to automation (35 percent of Canada’s employment) will be completely replaced by smart machines over the next few years. • Canadian employment is concentrated in industries that have a low risk of automation, with industries where less than a quarter of the jobs are susceptible to automation accounting for 27.5 percent of total employment (4.9 million jobs). Industries where more than three-quarters of the jobs are at high risk of automation account for only 1.7 percent of employment (310, 000 jobs). This implies that Canada’s diversified economy and labour force are well positioned to adapt to rapid technological change. • Occupations high in abstract, complex-decision-making skills with a strong focus on creativity, critical thinking and interpersonal social skills have a relatively low risk of being automated. An increase in demand for these skills is likely over the near and medium term. • As the rate of technological progress increases and digitization permeates different occupations and industries, technical job-specific skills might become obsolete relatively quickly. This indicates a need to increase opportunities for continuous education and lifelong learning. Educational institutions such as colleges, technical schools and apprenticeship programs likely will have an expanded role over the lifecycle of employment as people learn to adapt to changing conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".