Surviving Competition: Neighbourhood Shops versus Convenience Chains
Bibliographic record
Abstract
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 examines the impacts of the opening of chain-run convenience stores on one of the world’s most ubiquitous microenterprises: owner-operated shops. To address endogeneity in time and location of chains’ opening, I pair two-way fixed effects with a novel instrument that shifts the profitability of chains but not of shops at the neighbourhood level. Expanding the number of chain outlets from zero to the neighbourhood average of 6.7 stores reduces the number of shops by 15%, a decline driven not by increased shop exits but by decreased shop entries. Shops retain their sales of fresh products and keep 96% of their customers, but customers visit shops less frequently and spend less on packaged goods. Surviving shops leverage competitive advantages stemming from being owner operated, such as lower agency costs, cultivating relationships with neighbours, and offering customers informal credit. The welfare gains of convenience chains replacing shops increase with household income; the poorest households experience a welfare loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".