An Empirical Model of Industry Dynamics with Common Uncertainty and\nLearning from the Actions of Competitors
Bibliographic record
Abstract
This paper advances our collective knowledge about the role of learning\nin retail agglomeration. Uncertainty about new markets provides an\nopportunity for sequential learning, where one rm s past entry\ndecisions signal to others the potential pro tability of risky markets.\nThe setting is Canada s hamburger fast food industry from its early days\nin 1970 to 2005, for which simple analysis of my unique data reveals\nempirical patterns pointing towards retail agglomeration. The notion\nthat uninformed potential entrants have an incentive to learn, but not\ninformed incumbents, motivates an intuitive double-di¤erence\napproach that separately identi es learning by exploiting\ndi¤erences in the way potential entrants and incumbents react to\nspillovers. This identi cation strategy con rms that information\nexternalities are key drivers of agglomeration. Esti- mates from a\ndynamic oligopoly model of entry with information externalities provide\nfurther evidence of learning, as I show that common uncertainty matters.\nCounterfac- tual analysis reveals that an industry with uncertainty is\ninitially less competitive than an industry with certainty, but catches\nup over time. Furthermore, there are many instances in which chains\nenter markets they would have avoided had they not faced uncertainty.\nFinally, consistent with the interpretation of uncertainty as an entry\nbarrier, I nd that chains place signi cant premiums on certainty at\nproportions beyond 2% of their total value from being monopolists.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".