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Record W1602822857

ON EQUILIBRIUM IN MONOPOLISTIC COMPETITION

2006· article· en· W1602822857 on OpenAlexaff
Richard L. Carson

Bibliographic record

VenueEastern Economic Journal · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsCarleton University
Fundersnot available
KeywordsMonopolistic competitionEconomicsMicroeconomicsProduct differentiationCompetition (biology)UniquenessRevenueProduct (mathematics)Production (economics)MonopolyCournot competitionMathematics
DOInot available

Abstract

fetched live from OpenAlex

The price, output, and quality of a monopolistic competitor are determined by maximizing the difference between its revenue and its cost, where cost is measured exclusive of the rent on its product-specialized inputs. It can be argued that such a firm must have unique inputs that are specialized to its unique product—since product differentiation is otherwise compatible with perfect competition—and the uniqueness of these inputs allows them to earn positive rent, even in long-run equilibrium. The inclusion of rent in cost gives rise to the traditional Chamberlinian solution, in which (rent-inclusive) average cost is tangent to demand and therefore downward-sloping. But if rent is excluded, average cost may be constant or even upward-sloping in equilibrium, and in this sense, monopolistic competition need not give rise to excess capacity or to production facilities that are too small. The basic conclusion is that monopolistic competition improves welfare—that is to say, it creates consumer and producer surplus—by creating variety without necessarily reducing output.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0280.003

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 head0.018
GPT teacher head0.204
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2006
Admission routes1
Has abstractyes

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