How Does Advertising Depend on Competition? Evidence from U.S. Brewing
Bibliographic record
Abstract
The relationship between market structure and advertising has been extensively studied, but has generated sharply opposing theoretical predictions, as well as inconclusive empirical findings, likely because of severe endogeneity concerns. We exploit the 2008 merger of Miller and Coors in the U.S. brewing industry to examine how changes in local concentration affect firms’ advertising behavior. Well-established regional preferences over beer brands, and the sharp increase in concentration from the merger, make this an excellent setting to analyze this question. We find a significant positive effect of local market concentration on advertising expenditures: a 100-point increase in the Herfindahl–Hirschmann Index measure of concentration increases advertising per capita by about 5%. Our findings shed light on how and when firms choose to deploy advertising. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2889. This paper was accepted by Eric Anderson, marketing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".