MétaCan
Menu
Back to cohort

The Effects of Selling Complements and Substitutes on Consumer Willingness to Pay: Evidence from a Laboratory Experiment

2008· article· fr· W2103192080 on OpenAlexvenueno aff
Matthew C. Rousu, Robert Beach, Jay R. Corrigan

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2008
Typearticle
Languagefr
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsnot available
FundersIowa State University
KeywordsEconomicsWelfare economicsMicroeconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.249
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomieSame topicAuction Theory and ApplicationsFrench-language works237,207