Credit Market Imperfections, Urban Land Rents and the Henry George Theorem
Bibliographic record
Abstract
Cet article étudie l’impact du marché du crédit sur les prix du foncier et ses implications en termes de politiques fiscales. Nous introduisons un coût à l’emprunt et une contrainte d’apport personnel dans le modèle standard d’économie urbaine. Ces imperfections s’avèrent réduire les prix de la terre dans les localisations les plus attractives. Cette baisse est d’autant plus forte que les terres sont rares et les villes peuplées et dotées d’infrastructures de transport inefficaces. La contrainte d’apport personnel peut générer des écarts d’utilités d’équilibre entre des ménages initialement homogènes. Le théorème d’Henry George, selon lequel une taxe confisquant les rentes foncières suffit à financer les biens publics, doit être amendé en présence de contraintes de crédit. Pour chaque type d’imperfections du marché du crédit, nous proposons un système de taxation optimale .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".