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Record W3125430313 · doi:10.34989/swp-2005-12

Do Exchange Rates Affect the Capital-Labour Ratio? Panel Evidence from Canadian Manufacturing Industries

2021· preprint· en· W3125430313 on OpenAlexaffabout
Danny Leung, Terence Yuen

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsBank of Canada
Fundersnot available
KeywordsDepreciation (economics)EconomicsLabour economicsRelative priceProductivityCapital (architecture)Liberian dollarExchange ratePanel dataCost of capitalMonetary economicsEconometricsHuman capitalCapital formationMacroeconomicsMicroeconomicsFinancial capitalMarket economy

Abstract

fetched live from OpenAlex

Using industry-level data for Canadian manufacturing industries from 1981 to 1997, the authors find empirical evidence of a negative relationship between the capital-labour ratio and the user cost of capital relative to the price of labour. A 10 per cent increase in the user cost of the machinery and equipment (M&E) relative to the price of labour results in a 3.3 per cent decrease in the M&E-labour ratio in the long run. Assuming complete exchange rate pass-through into imported M&E prices, the maximum effect of a permanent 10 per cent depreciation in the exchange rate is a 5.2 per cent increase in the user cost of M&E, and a 1.7 per cent decline in the M&E-labour ratio. This result implies that the cumulative growth of the M&E-labour ratio during the 1991–97 period would have been 2.3 percentage points higher had the dollar not depreciated. This may appear to be significant, but, considering that M&E as a share of total capital and capital's share of nominal output are both approximately one-third, in terms of a simple growth accounting framework, the effect on labour productivity is small.

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)0.007
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 score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.109
GPT teacher head0.301
Teacher spread0.191 · 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".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicFirm Innovation and GrowthFrench-language works237,207