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Record W1582497594 · doi:10.34989/sdp-2010-5

Relative Price Movements and Labour Productivity in Canada: A VAR Analysis

2021· preprint· en· W1582497594 on OpenAlexaffabout
Michael Dolega, David Dupuis, Lise Pichette

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsBank of Canada
Fundersnot available
KeywordsProductivityRelative priceEconomicsExchange rateEnergy (signal processing)International economicsLabour economicsMonetary economicsMacroeconomics

Abstract

fetched live from OpenAlex

In recent years, the Canadian economy has been affected by strong movements in relative prices brought about by the surging costs of energy and non-energy commodities, with significant implications for the terms of trade, the exchange rate, and the allocation of resources across Canadian sectors and regions. While the energy and mining industries have benefited from these movements, the pressure on the manufacturing sector has intensified, since many firms in this sector were already dealing with growing competition from low-cost economies such as China. The adjustments undertaken within the Canadian economy are readily noticeable through investment decisions, as well as through production and employment reallocation. Using vector autoregressive techniques, the authors examine how an appreciation in commodity prices and the subsequent reallocation of resources across sectors will affect hours worked and output growth and, ultimately, aggregate and sectoral labour productivity growth in Canada. Results suggest that the impact of a positive relative price shock will – in the adjustment process – lower productivity growth in the primary and the non-tradable sectors, and increase it somewhat in the manufacturing sector. The overall impact appears to be slightly negative on aggregate labour productivity growth, but this effect is only temporary.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.210
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 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

Citations2
Published2021
Admission routes2
Has abstractyes

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