Bibliographic record
Abstract
This paper estimates the effect of air transport on local production in the manufacturing and service sectors. The analysis is conducted using data from the United States. These effects are important for the design of policies that aim to develop an airport or to otherwise attract airlines to operate to and from the airport. This type of policy is commonly employed by national and local governments, indeed most large commercial airports in the United States and Canada are publicly owned. Though it is common for local authorities to construct new runways, terminals, or other facilities, the effects of such policies are not currently well understood. The estimation of the effect of airport size on local production is subject to an obvious endogeneity problem, as airlines are likely to expand operations in response to increased demand. This problem is addressed by using the 1944 National Airport Plan of the Civil Aeronautics Administration to instrument for the distribution of airports. The National Airport Plan passes the statistical tests for relevance and exogeneity, which is not surprising as the federal funding connected to it was important for the rapid development of the air network after the Second World War and the criteria used to select sites was unrelated even to the contemporary distribution of sectors. Better air connections are found to have a positive effect on the size of the local service sector, with an elasticity of approximately 0.2. A larger airport – hosting more frequent flights to a wider range of destinations – is associated with a greater share of the population in the metropolitan area being employed in the service sector. This is interpreted as indicating that services are being produced in the metropolitan areas with larger airports for export elsewhere. There appears to be no effect on non-tradable services, such as beauty salons and auto repair, precisely as we would expect. The effect on manufacturing is negative, though this appears to reflect substitution to the expanded service sector rather than a direct effect, as the analysis of manufacturing shipments between pairs of cities does not appear to be related to air traffic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".