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Business Group Performance in China: Ownership and Temporal Considerations

2009· article· en· W2103503714 on OpenAlexaff
Michael Carney, Daniel Shapiro, Yao Tang

Bibliographic record

VenueManagement and Organization Review · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityConcordia University
Fundersnot available
KeywordsState ownershipChinaCorporate groupBusinessValue (mathematics)Quality (philosophy)State (computer science)Market economyEmerging marketsEconomicsMonetary economicsFinanceCorporate governancePolitical science

Abstract

fetched live from OpenAlex

We address the institutional voids hypothesis, which suggests affiliation with a business group will improve a firm's performance in circumstances of poor-quality institutions and extensive market failures. We hypothesize that initial positive effects of group affiliation should decline as the quality of market institutions improves. Further, we hypothesize that differences in state and private ownership will influence the value and persistence of firm affiliation. Using data on 476 publicly listed firms in 1999 and 467 matched firms in 2004, we find support for a temporal hypothesis that affiliation with a business group improves performance, but the value of group affiliation declines over time. We also find support for a state ‘helping hand’ hypothesis that suggests firms with high levels of state ownership initially experienced an amplified value effect from their group affiliation, which disappeared by 2004. The results suggest that China's policy makers are beginning to establish an institutional and market infrastructure that is conducive to entry by unaffiliated, freestanding firms.

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)0.004
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 score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.196
Teacher spread0.183 · 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

Citations75
Published2009
Admission routes1
Has abstractyes

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