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Record W4403914109 · doi:10.55016/ojs/sppp.v17i1.80080

Productivity Growth in Canada: What is Going On?

2024· article· en· W4403914109 on OpenAlexaboutno aff
Tim Sargent

Bibliographic record

VenueThe School of Public Policy Publications · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityAgricultural economicsEconomic geographyBusinessRegional scienceEconomicsGeographyEconomic growth

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.944

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.018
Science and technology studies0.0100.005
Scholarly communication0.0140.006
Open science0.0040.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0140.002

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.

Opus teacher head0.047
GPT teacher head0.248
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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