Urban pull: The roles of amenities and employment
Bibliographic record
Abstract
This paper leverages new measurement of neighborhood consumption amenities to demonstrate that housing prices and rents in U.S. cities are likely determined nearly as much by access to amenities as by access to employment. We extend the Alonso–Muth–Mills model, allowing residents to derive utility from within-city trips to amenities. The model delivers standard estimable log-linear pricing equations as well as new measures of local amenities—based on a destination’s popularity during leisure hours—and of access to consumption amenities city wide. We find our amenity measures add substantial explanatory power, have large effects in magnitude, and reduce naive estimates of commute costs by 30%. Elasticities of rents with respect to employment access are 20%–50% larger than those with respect to amenity access. The findings hold using a variety of alternative measures and are neither driven by density nor fully explained by the locations of business establishments. These results suggest the potential resilience of cities to changes in employment locations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".