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Record W4285495184 · doi:10.1002/ise3.10

Subsidy and product diversity in the presence of buyer power

2022· article· en· W4285495184 on OpenAlexaff
Zhiqi Chen, Hong Ding

Bibliographic record

VenueInternational Studies of Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsSubsidyMonopolistic competitionMonopolyProduct (mathematics)MicroeconomicsDiversity (politics)EconomicsCompetition (biology)Social WelfareWelfareEconomic surplusProduct differentiationBusinessIndustrial organizationMarket economyEcology

Abstract

fetched live from OpenAlex

Abstract This paper analyzes the effectiveness of government subsidies in promoting product diversity when a downstream firm has buyer power. Using an extension of the Dixit‐Stiglitz model of monopolistic competition, we compare the effects of subsidies on the equilibrium number of differentiated products and social welfare in the case where products are sold directly to consumers versus the case where they are distributed through a monopoly retailer with buyer power. We find that a production subsidy promotes product diversity in both cases, but the mechanisms through which a subsidy raises the number of products are different. Compared with the case where products are distributed directly to consumers, retailer buyer power reduces product diversity and social welfare. Furthermore, it weakens the effectiveness of the subsidy in promoting product diversity. At any given subsidy rate the equilibrium number of products is smaller, and a rise in the subsidy rate leads to a smaller increase in the number of products.

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)0.002
metaresearch head score (Gemma)0.010
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.070
GPT teacher head0.251
Teacher spread0.181 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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