Firm Expansion, Size Spillovers, and Market Dominance in Retail Chain Dynamics
Bibliographic record
Abstract
We develop and estimate a dynamic game of strategic firm expansion and contraction decisions to study the role of firm size in future profitability and market dominance. Modeling firm size is important because retail chain dynamics are more richly driven by expansion and contraction than de novo entry or permanent exit. Additionally, anticipated size spillovers may influence the strategies of forward-looking firms, making it difficult to analyze the effects of size without explicitly accounting for these in the expectations and, hence, decisions of firms. Expansion may also be profitable for some firms while detrimental for others. Thus, we explicitly model and allow for heterogeneity in the dynamic link between firm size and profits as well as potential for persistent brand effects through firm-specific unobservable factors. As a methodological contribution, we surmount the hurdle of estimating the model by extending a two-step procedure that circumvents solving the game. The first stage combines semiparametric conditional choice probability estimation with a particle filter to eliminate the serially correlated unobservable components. The second stage uses a forward simulation approach to estimate the payoff parameters. Data on Canadian hamburger chains from their inception in 1970 to 2005 provide evidence of firm-specific heterogeneity in brand effects, size spillovers, and persistence in profitability. This heterogeneous dynamic linkage shows how McDonald’s becomes dominant and other chains falter as they evolve, thus affecting market structure and industry concentration. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2814 . This paper was accepted by J. Miguel Villas-Boas, marketing.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".