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Negative interest rates and corporate tax behavior in banks

2025· article· en· W4412552952 on OpenAlex
Alexander Edwards, Michael L. Marin, Yuchen Wu

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

VenueJournal of Accounting and Public Policy · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of Toronto
FundersAustrian Science FundDeutsche ForschungsgemeinschaftUniversity of Oklahoma
KeywordsCorporate taxAccountingBusinessFinancial systemInterest rateMonetary economicsEconomicsDouble taxationTax avoidanceFinance

Abstract

fetched live from OpenAlex

Using a sample of OECD domestic banks and a difference-in-difference research design, we examine the impact of Negative Interest Rate (NIR) regimes on corporate tax behavior. We document that the introduction of NIRs is associated with a 2.3 to 2.6 percentage point decrease in effective tax rates. The effect of NIRs is more pronounced in banks with lower distance to default, and in countries with lower tax enforcement or lower trust in the government. Collectively, our results suggest that the increased costs associated with NIRs are borne by commercial banks which lead to an increase in their respective tax planning.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.066
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.040
GPT teacher head0.282
Teacher spread0.242 · 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