Bibliographic record
Abstract
By reducing transport costs infrastructure can impact wages, the distribution of population and welfare among other important variables. In this paper I exploit a natural experiment provided by the opening of the Panama Canal and intercoastal cargo routes connecting the west and east coasts of Canada through the canal to examine the causal impact of a reduction of domestic trade costs. The particular characteristics of this setting allow me to estimate the causal impact without recurring to instrumental variable strategies. The estimates are also not confounded with the Keynesian effect of building new infrastructure since no infrastructure was actually setup in Canada. Using least cost path routes along the Canadian transport grid I determine treated municipalities. The paper documents the positive impact of the reduction of transport costs on population and the value of real property but a negative impact on nominal wages. I then use a simplified version of an economic geography model with perfect mobility of workers to compute domestic trade shares between Canadian municipalities and productivities at the municipal level. I use these empirical results and the model, to quantify general equilibrium changes in wages, population and trade shares triggered by the reduction in domestic transport costs. Finally, I show that the opening of intercoastal shipping routes had a large positive welfare effect across Canadian municipalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".