The Effects of Selling Complements and Substitutes on Consumer Willingness to Pay: Evidence from a Laboratory Experiment
Bibliographic record
Abstract
Basic economic theory predicts that a consumer's willingness to pay for a good is affected by the availability of complements and substitutes. In an auction setting, this theory implies that the presence of complements would increase bid prices for a good, while the presence of substitutes would decrease bid prices for a good. We designed an experiment that allows the calculation of inverse elasticities, the inverse‐demand equivalent of conventional price elasticities. Our results show that the availability of complements and substitutes affects bids in the expected directions. This finding has important implications for researchers who design experimental auctions. Selon la théorie économique de base, la volonté de payer d'un consommateur pour obtenir un bien est influencée par la disponibilité de compléments et de substituts. Dans un scénario de vente aux enchères, cette théorie implique que la présence de compléments ferait augmenter le prix offert pour un bien, tandis que la présence de substituts ferait diminuer le prix offert. Nous avons conçu une expérience qui a permis de calculer les élasticités inverses, l'équivalent de la demande inverse des élasticités‐prix classiques. Nos résultats ont montré que la disponibilité de compléments et de substituts influence les offres d'achat dans les directions prévues. Cette observation a d'importantes répercussions pour les chercheurs qui conçoivent des ventes aux enchères expérimentales.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".