MétaCan
Menu
Back to cohort
Record W3167780675 · doi:10.11575/prism/38883

When the truth hurts: ordinary selling price regulation in a monopoly

2021· dissertation· en· W3167780675 on OpenAlexfundno aff
Babak Sahragardjooneghani

Bibliographic record

VenueOpen MIND · 2021
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsnot available
FundersStrongSharif University of TechnologyUniversity of Calgary
KeywordsMonopolyEconomicsBusinessLaw and economicsMicroeconomics

Abstract

fetched live from OpenAlex

How does restricting firms to communicate a truthful ordinary selling price affect pricing and profits when some consumers are uncertain about product quality? In this thesis we analyze a two-period monopoly model to study the welfare effect of ordinary selling price (OSP) regulation. In the model, quality is observed by informed consumers who buy in the first period. However, consumers who arrive in market in the second period are not able to discern quality, but must infer it indirectly through prices. We first characterize the necessary conditions for OSP regulation to make the first-period price informative for second-period consumers. We show that OSP regulation has no effect when the proportion of uninformed consumers is high. This means regulation is ineffective when it would be most useful. We then compare the equilibrium outcome when OSP is effective to the equilibrium outcome in an unregulated environment. A simple welfare measure indicates that restricting firms to communicate a truthful first-period price has no effect on the uninformed consumers' expected surplus, but does create a deadweight loss from deceptive pricing in the first period. This deceptive pricing occurs because OSP regulations provide incentives for a low-quality firm to charge a high initial price when doing so enables it to earn excess profits in the second period.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0250.001

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.

Opus teacher head0.044
GPT teacher head0.266
Teacher spread0.223 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOpen MINDSame topicMerger and Competition AnalysisFrench-language works237,207