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Grabbing Hands and Helping Hands: The Role of Concentrated Ownership during Crisis

2012· article· en· W2901328335 on OpenAlexaff
Prashant Shukla, Éric Gedajlovic, Marc van Essen

Bibliographic record

VenueAcademy of Management Proceedings · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDiscretionBusinessFinancial crisisHelping handMarket economyEconomicsLawPolitical science

Abstract

fetched live from OpenAlex

Using meta-analytic techniques on data from 70 primary studies, we investigate whether concentrated owners help or try to grab benefits from their firms during times of crisis. We find evidence suggesting that concentrated owners generally extend a helping hand, and thus boost firm performance, during crisis. However, certain qualifications apply: Institutional development, which varies considerably across the 4 continents represented in our primary studies, has a varied impact on the focal relationship. We find that the helping tendencies of concentrated owners during crisis are positively moderated by the development of financial markets and the size of a nation’s informal economy, but are negatively moderated by the presence of legal institutions designed to curb the discretion of concentrated owners. Thus, institutions can facilitate or hinder the helping hands of concentrated owners during crisis. Also, the performance effect varies considerably depending upon identity of owners. We find that inside owners and stable owners extend a helping hand to firms during crisis, but market owners do not. Furthermore, only inside owners continue to have the same (positive) effect on performance during non-crisis periods, during which stable and market owners have just the opposite effect.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.219
Teacher spread0.193 · 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 teacher head, 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

Citations1
Published2012
Admission routes1
Has abstractyes

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