Bibliographic record
Abstract
We apply the Berry, Levinsohn and Pakes (1995) market equilibrium model (BLP) to data from 30 brands of beers sold in 12 U.S. cities over 20 quarters (1988-92) to estimate the consumers' taste for beer characteristics (price, alcohol content, and calories) as well as for the cultural region of origin (USA, Anglo-European, Germanic, and countries bordering the U.S.). Consumer heterogeneity is allowed with respect to age, income and gender. Overall we end up with 7,200 beer brand observations (30x12x20) and 13,920 (58 random draws x 12 x 20) consumer observations. Empirical results indicate that indeed there is home bias with respect to European beers and somewhat less so with respect to beers from bordering countries (Mexico and Canada). Home bias is more accentuated among older males who are more affluent. Furthermore, the own-price elasticities and the cross price elasticities of demand are higher for foreign beers, indicating a higher degree of loyalty and differentiation for domestic beers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".