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Record W2171185429

Productivity growth and prices in Canada: What can we learn from the U. S. Experience?

2001· preprint· en· W2171185429 on OpenAlexaboutno aff
Tiff Macklem, James Yetman

Bibliographic record

VenueRePEc: Research Papers in Economics · 2001
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityInflation (cosmology)EconomicsGoods and servicesMonetary policyEconomyMonetary economicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

In recent years, there has been increasing discussion about the possible emergence of a new economy. In this paper we review recent developments in productivity growth and prices of final goods and services in the United States in an effort to identify early indicators of whether the Canadian economy is on a path to follow the United States to higher productivity growth. We put particular emphasis on the behaviour of prices, since monetary policy in Canada is directed towards maintaining low and stable inflation. Although there is little evidence to date of a U.S.-style acceleration in productivity growth in Canada, we suggest that there are several reasons to be cautiously optimistic that Canada will follow the U.S. experience to some degree. We formalize one aspect of this hypothesis using estimated, expectations-augmented Phillips curves. We present evidence for the United States of changes in the relationship between prices and output that would be consistent with the emergence of the new economy, the effects of which have been largely concentrated in the provision of final goods. We then provide evidence of a similar break for Canada in 2000. However, with only two quarters of data for 2000, considerable uncertainty remains as to the timing, size, and the duration of any acceleration in productivity growth in Canada.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.043
GPT teacher head0.255
Teacher spread0.212 · 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.

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

Citations6
Published2001
Admission routes1
Has abstractyes

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