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Record W3022324983

A Sectoral Analysis of Ontario's Weak Productivity Growth

2013· preprint· en· W3022324983 on OpenAlexaboutno aff
Peter S. Spiro

Bibliographic record

VenueRePEc: Research Papers in Economics · 2013
Typepreprint
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityEconomicsMultifactor productivityTotal factor productivityLabour economicsEconomyMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Ontario's private sector has had zero productivity growth in the latest six year period. Ontario performed much worse than the rest of Canada or the United States. This is obviously a cause for concern. Productivity is an important measure of progress in the economy, as it is associated with rising standards of living in the long run.\n\nThe question is whether this observation about productivity is significant in its own right, or if it is more a symptom of the overall state of the economy. The Ontario economy has been hit by major external shocks, resulting in plunging exports and a declining private sector employment rate. Is productivity an independent causal factor, or merely the residual outcome of weak demand?\n\nThis paper examines the issue through detailed sectoral data. It examines the diversity of productivity performance in about 50 industrial sectors. The picture that emerges is that the overall productivity growth rate is not really representative. It is the random outcome of a wide range of underlying variation. There are some important sectors (e.g., retail trade and finance) that have maintained decent productivity growth. There are some sectors, especially in manufacturing, where the level of productivity is currently far below its previous level. This is not merely weak growth, but decline. In some industries (e.g., steel), some of the largest players have shut down, essentially changing the character of that sector even though the name remains the same.\n\nOverall, the implication is that the weakness of productivity is caused by weak aggregate demand. Historically, productivity growth has been pro-cyclical, being positively correlated with demand growth. Strong demand creates economies of scale and distributes overhead costs over a larger base. The weakness of demand in recent years has also been associated with compositional shifts in the economy. Employment and output have plunged in manufacturing (whose level of productivity is above the economy-wide average), while it has grown in some service sectors with below average productivity. Such compositional shifts would reduce the average productivity of the economy even if there was no change in the productivity of any individual sectors.\n\nOntario had positive productivity growth in the service sector, but underperformed the strong growth found in the rest of Canada. Here, too, the explanation is likely found in diseconomies of scale due to weaker demand. For example, the higher productivity growth in retail and wholesale trade in the rest of Canada was associated with growth in sales that was two-thirds higher than in Ontario over the past six years.\n\nThe weakness of demand in Ontario is largely due to falling exports, caused by the high Canadian dollar and the weak US economy. This can be considered in a positive light. While a strong rebound in exports does not appear to be around the corner, the worst is probably behind us. There should be a continuing gradual improvement in exports in the coming years, leading to some increase in productivity growth. The Ontario government should focus its policy levers, which are admittedly constrained, on helping to further the upward trend in exports.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.058
GPT teacher head0.354
Teacher spread0.296 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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