To be considered for the Young Economist Award: Advertising Restrictions and Competition in the Children’s Breakfast Cereal Industry
Bibliographic record
Abstract
This paper takes advantage of the ban on advertising directed at children in the Canadian province of Quebec to examine the nature of advertising in the market for a particular children’s product, and to determine whether the advertising restriction differentially impacts certain varieties. Using a unique data set from the Print Mea-surement Bureau of Canada which surveys the purchasing behavior of some fifteen thousand Canadian households annually, as well as advertising and price data from AC Nielsen, I examine the children’s breakfast cereal market in Canada. If advertising in this market is informative, it should help to overcome perceived product differenti-ation, and so should lead to price competition and lower prices. If it is persuasive, it should generate the perception that there are fewer substitutes for promoted brands, and so should increase perceived differentiation and prices. I show that prices are higher in Quebec than in other Canadian regions, suggesting that the role of advertis-ing in this market is to inform consumers about existence. If advertising is informative, a ban on advertising should increase the market shares of older, better-known brands and decrease the market shares newer and/or less well-known brands. This predic-tion is confirmed in the data: established brands have higher market share in Quebec than in Canadian regions where advertising is permitted, and the opposite is true for non-established brands.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.007 |
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".