Trickle-Down Technology and Screening of a Durable Goods Monopolist
Bibliographic record
Abstract
We show that when it takes time for a durable goods monopolist to make its high-end new technology accessible to low-end market (the trickle-down technology constraint), the monopolist's high-end product might have a higher-than-optimum quality. This result differs from conventional screening models, in which the qualities of non-durable goods supplied by a monopolist never exceed the optimum, and only consumers with the highest valuation consume the efficient quality. In another literature discussing a durable goods monopolist who delays the introduction of low-end product as a marketing strategy, but not due to the trickle-down constraint, the qualities will not exceed the optimum either. Our results show that the trickle-down constraint will make the monopolist chooses a higher-than-optimum quality when the difference of the valuations of high demand and low demand consumers are in certain ranges. The intuition follows Spence (1975): the efficient quality is determined by the marginal cost and the average of all consumers' marginal valuations, while the monopolist chooses quality such that the marginal cost equals the marginal consumer's marginal valuation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".