Endogenous sunk costs and the geographic differences in the market structures of CPG categories
Bibliographic record
Abstract
We describe the industrial market structure of CPG categories. The analysis uses a unique database spanning 31 consumer package goods (CPG) categories, 39 months, and the 50 largest US metropolitan markets. We organize our description of market structure around the notion that firms can improve brand perceptions through advertising investments, as in Sutton’s endogenous sunk cost theory. The richness of our data allow us to go beyond Sutton’s bounds test and to study the underlying forces bounding concentration away from zero. Observed advertising levels escalate in larger US markets. At the same time, the number of advertised brands in an industry appears to be invariant to market size. Therefore, the size-distribution of brands across markets is characterized by bigger (i.e. more heavily advertised) as opposed to more brands in larger markets. Correspondingly, observed concentration levels in advertising-intensive industries are bounded away from zero irrespective of market size.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".