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Record W4399025300 · doi:10.1086/727103

Vertical Integration and Market Foreclosure in Media Markets: Evidence from the Chinese Motion Picture Industry

2024· article· en· W4399025300 on OpenAlexaff
Ricard Gil, Chun‐Yu Ho, Li Xu, Yaying Zhou

Bibliographic record

VenueThe Journal of Law and Economics · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsForeclosureBusinessVertical integrationEconomicsIndustrial organizationFinance

Abstract

fetched live from OpenAlex

This paper investigates the impact of vertical integration and market foreclosure in media markets. Using theater-movie-day-level data from China, we show that integrated theaters charge lower prices, enjoy higher attendance, allocate more screenings, and run their own movies longer than movies of other distributors. Despite these differences, there is no evidence consistent with anticompetitive input and customer foreclosure in integrated theaters. On the one hand, integrated and independent theaters screen the same share of integrated and independent movies. On the other hand, revenue differences between continued theater-owned movies and discontinued independent movies are inconsistent with customer-market-foreclosure motives given existing differences in distribution incentives between integrated and nonintegrated structures. Finally, we estimate a random-coefficient discrete-choice model of movie demand and show that integrated theaters deliver a higher level of utility with integrated movies than with independent movies through the direct effects of lower prices and more screenings.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.226
Teacher spread0.204 · 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 designObservational
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

Citations5
Published2024
Admission routes1
Has abstractyes

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