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Record W3124458664 · doi:10.1177/0148558x17724890

Bowling Alone, Bowling Together: Is Social Capital Priced in Bank Loans?

2017· article· en· W3124458664 on OpenAlexafffund
Agnes Cheng, Jing Wang, Ning Zhang, Sha Zhao

Bibliographic record

VenueJournal of Accounting Auditing & Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsQueen's University
FundersUniversity of Texas at DallasFeng Chia UniversityHong Kong Polytechnic UniversityQueen's UniversityNational Chengchi University
KeywordsLoanSocial capitalBusinessCapital (architecture)Cost of capitalFinanceEconomicsDemographic economicsLabour economicsMarket economyPolitical science

Abstract

fetched live from OpenAlex

We investigate whether the societal-level social capital enjoyed by firms affects the cost of their bank loans. Employing a measure of societal-level social capital for U.S. counties, we find that firms with higher societal-level social capital are associated with lower loan spreads. To further identify causality, we explore two events: Using a sample of firms that relocate their headquarters for tax reasons, we find that firms that move to lower (higher) social capital counties experience a higher (lower) cost of bank loans following relocations. The second event was the terrorist attack on September 11, 2001. After the disaster, social capital in affected counties—mainly in the State of New York, the State of Virginia, and adjacent counties—increased through social capital building efforts. We show that firms headquartered in the affected counties experience significantly lower loan spreads than other firms after the attack. Our findings contribute to the understanding of how societal-level social capital promotes economic development through its impact on financing costs.

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)0.003
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0020.005
Open science0.0020.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.236
Teacher spread0.225 · 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.

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

Citations39
Published2017
Admission routes2
Has abstractyes

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