Locational rent vs. monopoly rent on the side-businesses of transport infrastructure
Bibliographic record
Abstract
This paper analyzes the relationship between locational and monopoly rents, developing a model consisting of an airport, shops at the airport, and shops at another (non-airport) location. When the unweighted sum of the utility of the representative consumer and the airport profit is maximized, no monopoly rent exists and thus, monopoly rent is independent of locational rent, but a higher locational rent results in a lower social welfare in equilibrium. On the other hand, when the airport profit is maximized, monopoly rent is an increasing function of locational rent. An increase in locational rent reduces welfare in equilibrium if the effect of the locational rent on the demand of the good sold at the airport is small. As a special case, we demonstrate that the monopoly rent can be separated from locational rent if the demand function of the good sold at an airport is multiplicatively separable regarding locational rent. Another extreme result occurs when the airport and non-airport goods are perfect substitutes: we derive one-to-one relationship between monopoly and locational rents, i.e., an increase in locational rent by one unit immediately implies an increase in monopoly rent by the same amount. Imperfect competition for shops at an airport and another location partly modifies the results, which suggests that market structure in the goods market affects the relationship between monopoly and locational rents. Our analysis demonstrates that in general, locational rent affects monopoly rent and social welfare and the dichotomy between locational and monopoly rents does not hold.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".