Preliminary Evidence of Surviving Competition: Neighborhood Shops vs. Convenience Chains
Bibliographic record
Abstract
Hundreds of millions of microenterprises in emerging economies face increased competition from the entry and expansion of large firms that offer similar products. This paper studies how one of the worlds most prevalent microenterprises, neighborhood shops, confront competition from convenience chains (e.g., 7-Eleven) in Mexico. To address the endogeneity in time and location of chains store openings, I pair two-way fixed effects with a novel instrument that, at the neighborhood level, shifts the profitability of chains but not of shops. An expansion from zero to the average number of chain stores in a neighborhood reduces the number of shops by 16%. Consistent with the theoretical framework, this reduction is not driven by an increase in shop exit but by a decrease in shop entry. Shops retain their sales of fresh products and 96% of their customers, but customers visit shops less often and spend less on non-fresh and packed goods. I present evidence consistent with shops surviving by exploiting comparative advantages stemming from being small and owner-operated, such as lower agency costs, building relationships with the community, and offering informal credit.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".