From the Ground Up: The Causal Effect of Micro-Geography on New Business Formation
Bibliographic record
Abstract
Research on entrepreneurship emphasizes the importance of geographic locations. However, much of this literature focuses on regions and cities, overlooking the potentially important impact of neighborhood configurations. We refer to these configurations as micro-geographies and theorize that proximity to local pedestrian flows has a causal effect on individual rates of entrepreneurship. To test this notion, we use data from a multi-story, public housing complex in Colombia where residents are randomly assigned to live close to or distant from ground-floor pedestrian flows. We find that individuals assigned to the ground floor are more likely to become entrepreneurs and that their entrepreneurial ventures earn significantly more than those on upper floors. For the entrepreneurs in our context, operating a small business on the ground floor versus an upper floor means the difference between living above or below the poverty line. This study reveals that minor differences in spatial location have a powerful, causal effect on new business emergence and performance. More broadly, it extends the literature on entrepreneurial location choice to highlight the important effect of local, neighborhood configurations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".