Urban sprawl and productivity: Evidence from US metropolitan areas
Bibliographic record
Abstract
This paper draws on urban agglomeration theories to empirically investigate the relationship between the economic performance of US metropolitan areas and their respective amounts of sprawl. To measure urban sprawl, we construct a distinctive measure that captures the distribution of population density and land-use within metropolitan areas. Using both ordinary least squares (OLS) and instrumental variables (IVs) approaches, we find that higher levels of urban sprawl are negatively associated with average labour productivity. This pattern holds even within given industries or within given occupational classifications. Resumen Este artículo se basa en las teorías de aglomeración urbana para investigar empíricamente la relación entre el desempeño económico de las áreas metropolitanas de los EE.UU. y la dispersión de cada una. Para medir la dispersión urbana, creamos una medida distintiva que incorpora la distribución de la densidad de población y el uso del suelo dentro de áreas metropolitanas. Por medio del uso de métodos de mínimos cuadrados ordinarios (MCO) y de variables instrumentales (VI), descubrimos que los niveles elevados de dispersión urbana están asociados con una productividad laboral media. Esta patrón se mantiene incluso para sectores o clasificaciones laborales específicas.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".