Future Shock? The Impact of Automation on Canada’s Labour Market
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,017 | 0,005 |
| Communication savante | 0,009 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».