Bibliographic record
Abstract
Firms located in dense urban areas present higher productivity due to the flow of ideas and innovation in these areas. Through this productivity channel, the urban density characteristics of the areas where firms are located affect the stock returns. We use high-resolution satellite images from Google Earth to develop an exogenous measure of potential density increase (PDI) for the 95 most populated metropolitan statistical areas (MSAs) in the US. This measure represents the proportion of area in the total area within a 1 hour drive from the center of the MSA that could rapidly increase its density. We find that firms located in areas with a high potential density increase present lower stock returns: on average a 10% higher PDI of an MSA results in a 0.29% lower excess stock return of firms located in this MSA. The research leading to these results has received financial support from the Public-Private Sector Research Center at IESE, the Ministry of Economy and Competitiveness (Ref. ECO2015-63711-P), and AGAUR (Ref: 2014-SGR-1496). The paper has been presented at the Paris Financial Management Conference (PFMC); North American Regional Science (NARSC) meetings in Vancouver; EFMD Solvay Job Fair in Brussels; and the IESE Research Workshop.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".