Cities and product variety: evidence from restaurants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article measures restaurant variety in US cities and argues that city structure directly increases product variety by spatially aggregating demand. I discuss a model of entry thresholds in which market size is a function of both population and geographic space and evaluate implications of this model with a new data set of 127, 000 restaurants across 726 cities. I find that geographic concentration of a population leads to a greater number of cuisines and the likelihood of having a specific cuisine is increasing in population and population density, with the rarest cuisines found only in the biggest, densest cities. Further, there is a strong hierarchical pattern to the distribution of variety across cities in which the specific cuisines available can be predicted by the total count. These findings parallel empirical work on Central Place Theory and provide evidence that demand aggregation has a significant impact on consumer product variety.
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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 | 0.002 |
| 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 it