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Record W4293510688 · doi:10.3386/w30402

Free-Riding Yankees: Canada and the Panama Canal

2022· report· en· W4293510688 on OpenAlexaboutno aff
Sebastián Galiani, Luis Fernando Jaramillo, Mateo Uribe-Castro

Bibliographic record

VenueNational Bureau of Economic Research · 2022
Typereport
Languageen
FieldSocial Sciences
TopicCuban History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsPanama canalPanamaFree ridingGeographyBusinessComputer scienceEconomicsComputer securityInternational trade

Abstract

fetched live from OpenAlex

We study the impact of the Panama Canal on the development of Canada's manufacturing sector in the years from 1900 to 1939. Using newly digitized county-level data from the Census of Manufactures and a market-access approach, we exploit the plausibly exogenous nature of this historical episode to study how changes in transportation costs influence the location of economic activity and productivity dynamics. Our reduced-form estimates show that lowered shipping costs led to greater market integration of marginally productive Canadian counties with key markets both inside and outside of Canada. This development permitted the reallocation of production activity to places whose production levels had been inefficiently low before the Canal opened. A shift from the 25th to the 75th percentile in terms of gains in market access brought about by the opening of the Canal led to a 9% increase in manufacturing revenues and input expenditures. Productivity rose by 13%. These effects persist when general equilibrium effects are considered: the closure of the Canal in 1939 would have resulted in economic losses equivalent to 1.86% of GDP, chiefly as a result of the restriction of the country's access to international markets. Altogether, these results suggest that the Canal substantially altered the economic geography of the Western Hemisphere in the first half of the twentieth century.

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)0.016
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.354
GPT teacher head0.501
Teacher spread0.148 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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