MétaCan
Menu
Back to cohort
Record W1558162056

Recent Productivity Development in the United States and Canada: Implications for the Canada-U.S. Productivity and Income Gap

2002· article· en· W1558162056 on OpenAlexvenueaboutno aff
Andrew Sharpe

Bibliographic record

VenueInternational productivity monitor · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityEconomicsPaceTotal factor productivityRecessionSlowdownDemographic economicsAgricultural economicsDevelopment economicsLabour economicsEconomic growthMacroeconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

The economic slowdown of 2001 reduced productivity growth in both the United States and Canada. This development has raised the question of the sustainability or permanency of the pace of productivity growth experienced during the 1995-2000 period in the United States and the likelihood of robust U.S. productivity growth spreading to Canada. In this article, Andrew Sharpe from the Centre for the Study of Living Standards compares productivity trends in the United States and Canada in 2001 to those during earlier postwar downturns and recessions. He finds that aggregate labour productivity growth held up better in 2001 in both countries than it did on average in the past, a development which may suggest an upward shift in trend productivity growth. Productivity growth in 2001 in the United States was faster than in Canada, as it was during the second half of the 1990s. This resulted in a further widening of the Canada-U.S. productivity gap and, since productivity is the key driver of income trends, in the income gap as well. If U.S. productivity growth continues at the pace experienced during the second half of the 1990s, as appears likely, Canada will need a major acceleration in productivity growth to prevent further deterioration in our relative productivity and income positions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.331
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

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

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 head0.048
GPT teacher head0.230
Teacher spread0.182 · 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 teacher head, 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

Citations0
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueInternational productivity monitorSame topicEconomic Growth and ProductivityFrench-language works237,207