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Record W2739304706

Dynamic Comparative Advantage in International Shipbuilding: The Transition from Wood to Steel

2017· preprint· en· W2739304706 on OpenAlexaboutno aff
W. Walker Hanlon

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Zones and Regional Development
Canadian institutionsnot available
Fundersnot available
KeywordsShipbuildingCompetition (biology)Production (economics)TariffDemiseContext (archaeology)EconomyGovernment (linguistics)BusinessInternational tradeIndustrial organizationEngineeringEconomicsGeographyArchaeologyEcologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Can temporary initial input cost advantages have a long-run impact on the spatial distribution of production and trade? I study this question in the context of the international shipbuilding industry during the transition from wood to metal ship production (1850-1912). Input price advantages gave Britain an early lead in metal shipbuilding, while the U.S. and Canada specialized in wood ship production. However, after 1890, Britain's initial price advantages disappeared. By comparing production patterns on the Atlantic Coast of North America, which faced British competition, to the Great Lakes, which were isolated from competition, I show that British competition substantially reduced the ability of North American producers to transition to metal ship production. I also exploit additional sources of variation to show how government protection and support moderated these effects for some Atlantic Coast producers, allowing them to survive the demise of wood shipbuilding. Finally, I provide evidence that the mechanism driving the persistence of Britain's lead was the development of large pools of skilled craft workers. These results shed light on the role of past conditions in influencing current production and trade patterns and with implications for the use of industrial policy and tariff protection.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.073
GPT teacher head0.334
Teacher spread0.261 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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