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Record W2516792222 · doi:10.1177/1091142116666671

Tax Incidence and Demand Convexity in Cournot, Bertrand, and Cournot–Bertrand Models

2016· article· en· W2516792222 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePublic Finance Review · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsMcMaster University
FundersOregon State UniversityMichigan State UniversityWashington State University
KeywordsCournot competitionBertrand competitionBertrand paradox (economics)EconomicsMicroeconomicsMathematical economicsOligopoly

Abstract

fetched live from OpenAlex

We investigate the price effect of an excise tax in a duopoly setting. Previous studies have considered the Cournot and Bertrand models but ignore the Cournot–Bertrand model in which one firm competes in output and the other firm competes in price. This omission is important because Cournot–Bertrand behavior is observed in the real world, and the Cournot–Bertrand model provides dramatically different results. Unlike in the Cournot and Bertrand models, we find that firms in the same industry have different pass-through rates in the Cournot–Bertrand model even when they face identical demand and cost conditions. This provides another reason why tax incidence policy is so complex.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.822
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.039
GPT teacher head0.235
Teacher spread0.195 · 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