Notice bibliographique
Résumé
Canada is seriously lagging in productivity growth, which is the only means countries have to raise their citizens’ standard of living. Overall, Canadian business productivity fell by 0.6 per cent over the past five years. This is in sharp contrast to the United States, which enjoyed a 10.1 per cent increase over the same period. This trend of faster U.S. growth has held true since the mid-1990s, with Canadian productivity rising by about half as much as the American rate. In fact, Canada trails not only the U.S. but all advanced countries in Northern and Western Europe, as well as Australia. Going by sector, Canada’s recent productivity declines have been concentrated in holding companies, transportation and warehousing, construction and manufacturing. The latter three categories are responsible for two-thirds of the decline in productivity. For transportation and warehousing, the effects of the COVID-19 pandemic on travel are a major contributor. For construction, the decline comes from residential and non-residential work, as opposed to engineering construction. For manufacturing, a significant source is transportation equipment manufacturing, particularly in the automotive sector. Provincially, Alberta, Saskatchewan and Newfoundland and Labrador have the highest productivity, thanks to the oil and gas industry. B.C., Ontario, Quebec and Manitoba are slightly below the national average while the Maritime provinces have productivity levels 25 to 31 per cent lower than the national average. However, Ontario and Alberta are responsible for the lion’s share of Canada’s slumping productivity growth, due to their weight in the national economy. Ontario is behind 48 per cent of the decline, while Alberta’s share is 22 per cent. From 2020–23, Canada’s capital intensity grew only slightly — not much faster than hours worked. This means that investment has not been high enough to boost productivity. Canadian investment fell from 2.1 per cent annually from 1998–2019 to just 0.5 per cent annually between 2020 and 2023. The main culprits are non-residential buildings (such as offices and factories), along with machinery and equipment. Investment in these capital goods decreased by 5.9 per cent annually in the non-residential sector and by 3.1 per cent in machinery and equipment. The fall in non-residential investment is likely the result of more people working from home. The fall in machinery and equipment is more puzzling. With tight labour markets, companies should want to invest in automation to save on labour costs, but this doesn’t seem to be happening. Canada has seen essentially no productivity growth in recent years, and much of the decrease is in a few core sectors. The picture is not complete, but since the output of those industries is easy to measure, it suggests that the slowdown is real. To increase productivity, governments should look at income tax rates, excessive red tape, regulatory harmonization, a lack of competition and barriers to foreign entry into the economy. Governments also need to look at improving their own productivity to avoid crowding out the private sector and to free up resources. There isn’t a one-size-fits-all solution. A broad range of policy options are necessary to solve Canada’s productivity problem.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,005 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,007 | 0,018 |
| Études des sciences et des technologies | 0,010 | 0,005 |
| Communication savante | 0,014 | 0,006 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
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 ».