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Record W2097067739

A Canadian Business Sector Data Base and New Estimates of Canadian TFP Growth

2012· preprint· en· W2097067739 on OpenAlexaboutno aff
W. Erwin Diewert, Emily Yu

Bibliographic record

VenueRePEc: Research Papers in Economics · 2012
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsTotal factor productivityEconomicsProductivityFalling (accident)Labour economicsCapital (architecture)Business sectorBusiness cycleAgricultural economicsEconomyMacroeconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

Using new data from Statistics Canada, the paper shows that the productivity performance of the business sector of the Canadian economy has been reasonably satisfactory over the past 51 years. In particular, traditional gross income Total Factor Productivity (TFP) growth averaged 1.026 percentage points per year over the period 1961-2011. The focus of the study is on the real income generated by the business sector of the Canadian economy. The growth of quality adjusted labour input growth was the main driver of growth in real income followed by TFP growth, followed by growth in capital input and then by falling real import prices. However, in recent years, the contribution of falling real import prices turned out to be more than twice as important as capital deepening. The study encountered many data problems which should be addressed in future work on Canadian business sector productivity performance.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.951
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0180.044
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.004

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 head0.114
GPT teacher head0.278
Teacher spread0.164 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations4
Published2012
Admission routes1
Has abstractyes

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