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Record W6928173653 · doi:10.34989/tr-33

An Econometric Model of the Steel Trade

2021· article· en· W6928173653 on OpenAlexaboutno aff

Bibliographic record

VenueBank of Canada Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDepreciation (economics)Balance of tradeRelative priceProductivityConsumption (sociology)Econometric modelExchange rateWork (physics)Terms of trade

Abstract

fetched live from OpenAlex

In this report, the author uses steel as a case study for an analysis at the industrial level of forces at work in the international economy that have had an important impact on recent Canadian economic performance. Prominent among those forces are cost competitiveness and aggregate demand in Canada and abroad. The author presents a model of the steel industry featuring relative price effects on trade and consumption volumes as well as price, volume, productivity and wage responses to demand pressure. Simulations over the period 1977Q1-80Q1 suggest that the industry's output and profits have benefited from the depreciation of the Canadian dollar. They also reveal that an increase in the price of foreign exchange causes the relative price of steel to rise, discouraging steel consumption. In addition, imports are further reduced by substitution from foreign to domestic sources. As regards the trade balance in steel, this favourable substitution effect is counteracted after a while by the impetus that depreciation gives to domestic aggregate demand. The model is thus able to shed light on some otherwise puzzling developments. For example, the surge of steel imports and slowdown of exports in 1979, a period in which the Canadian industry's cost competitiveness was strong, can be explained in terms of the depreciation-aided acceleration of domestic economic activity, which resulted in tight constraints on the ability of the industry to satisfy additional customers' orders.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.106
GPT teacher head0.370
Teacher spread0.264 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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