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Record W4403978442 · doi:10.2308/tar-2021-0604

Bank Entry Barriers and Firms’ Risk-Taking

2024· article· en· W4403978442 on OpenAlexfundno aff
Sudipta Basu, Mahsa Kaviani, Hosein Maleki

Bibliographic record

VenueThe Accounting Review · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
FundersTemple UniversityQueen's UniversityDrexel University
KeywordsBusinessFinancial systemFinanceAccounting

Abstract

fetched live from OpenAlex

ABSTRACT We study how nonfinancial firms’ operating risks change after bank competition increases. By exploiting the 1990s staggered regulatory reforms across U.S. states that allowed interstate banking and branching, we show that out-of-state bank entry was associated with lower borrower risk-taking on average. Large, profitable, safe, and geographically diversified firms signed up as new clients of large entrant banks, which offered larger and cheaper loans that reflected their higher efficiency and risk reduction through geographical diversification. We argue that these large banks could substitute for local relationship lending with more data collection from branches in multiple states. Firms that began borrowing from entrant banks increased capital expenditures and project-specific financing and kept R&D expenses stable but reduced R&D risk. Firms that continued borrowing from incumbent banks paid higher interest rates and increased their risk, suggesting that their credit access fell. States that opened up more had bigger changes in these outcomes. Data availability: Data are available from the public sources cited in the text. JEL Classifications: G21; G28; G32.

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)0.002
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: none
Teacher disagreement score0.655
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.248
Teacher spread0.229 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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