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Record W4392657605 · doi:10.1007/s11151-024-09949-x

Screening Through Investment: Evidence from the Chinese Automobile Industry

2024· article· en· W4392657605 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

VenueReview of Industrial Organization · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversity of Toronto
FundersLingnan University
KeywordsInvestment (military)Quality (philosophy)BusinessCompetition (biology)Product (mathematics)Industrial organizationCapital (architecture)CommerceMonetary economicsFinanceEconomics

Abstract

fetched live from OpenAlex

Abstract This paper proposes a competition theory to explain the role of automobile dealers’ investment in a vertical contract with manufacturers. Dealer contracts specify manufacturer-suggested retail prices and elements of dealer quality. Dealer quality investments require minimum financial capital where manufacturers impose these limits on dealers. The required dealer investment screens for qualified dealers and incentivizes the desired dealer quality. The prediction is that promotional services, prices, and gross returns are greater for high-quality brands than that for standard-quality brands. To test the theory, we collected data on auto dealers in China in June 2015 for an empirical analysis. Our findings support these predictions: Dealer investment (registered capital) is positively correlated with brand average product prices. In addition, the registered capital is higher when the aggregate demand is greater since high demand increases returns, which induces dealers to increase their investment.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.284
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