Endogenous Regional Economic Growth through Transportation Investment
Bibliographic record
Abstract
This paper demonstrates how road improvements can be interpreted as productivity improvements in the transportation services industry. Such economic gains are then translated into a general equilibrium context in the case of the Peace Bridge between Buffalo, New York, and Fort Erie, Ontario, Canada. Variables exogenous to the model are (a) the measured travel time enhancements to be incurred on the improved link and (b) the forecast value of shipments by each industry to be moved across it. This information is used to estimate industries’ responses to these travel time reductions in terms of direct transportation costs, inventory carrying costs, and the value of failed shipments. Extensive interviews with carriers and producers support the notion that improved travel times encourage producers to extend their market areas and thereby increase their production in the short run. To measure production change, a regional input–output model was recalibrated for each shipping industry's productivity improvements and their trade coefficients were commensurately adjusted. The economic contributions of the shipments to the economic area around Buffalo before and after the improvement were then compared. It is recognized that long-run transportation cost reductions are as likely to occur as reduced costs to producers (rather than as production increases). Nonetheless, it is asserted that by treating producer benefits strictly as production changes, producers’ surplus is more readily measured. Thus, this approach converts the measurable change directly attributable to a transportation investment—travel time savings—into broader economywide changes. Moreover, these changes in jobs, household incomes, and taxes can be reported with industry and geographic specificity, which is often a requirement for valid project-specific evaluations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".