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Does Auctioning of Entry Licences Induce Collusion? An Experimental Study

2006· article· en· W2130728920 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Review of Economic Studies · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsnot available
FundersUniversität WienKoninklijke Nederlandse Akademie van WetenschappenUniversity College LondonHarvard UniversityYork UniversityPurdue University
KeywordsCollusionOligopolySunk costsMonopolyEconomicsMicroeconomicsProfit (economics)Argument (complex analysis)Industrial organizationCournot competition

Abstract

fetched live from OpenAlex

We use experiments to examine whether the auctioning of entry rights affects the behaviour of market entrants. Standard economic arguments suggest that the licence fee paid at the auction will not affect pricing since it constitutes a sunk cost. This argument is not uncontested though, and this paper puts it to an experimental test. Our results indicate that an auction of entry licences has a significant positive effect on average prices in oligopoly but not in monopoly. These results are consistent with the conjecture that entry fees induce players to take more risk in pursuit of higher expected profits. In oligopoly, entry fees increase the probability that the market entrants coordinate on a collusive price path. In monopoly, taking more risk does not make sense since average prices are already close to the profit-maximizing price.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.115
GPT teacher head0.444
Teacher spread0.329 · 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