The impact of financial constraints on banks’ cash tax avoidance
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate whether and how banks’ financial constraints affect their cash tax avoidance. The authors hypothesize that banks engage in more tax planning to generate additional cash to mitigate their financial constraints. Design/methodology/approach The authors use a sample of US banks to conduct the panel regression analysis. The authors measure the bank tax avoidance using the cash effective tax rate and measure the bank financial constraints using the Z-score and annual payout ratio. The authors further use the implementation of the Dodd–Frank Act as a quasi-natural experiment to conduct the difference-in-difference analysis. Findings The authors document that financially constrained banks exhibit lower cash effective tax rates. The authors further show that banks facing greater financial constraints are less likely to pursue tax-saving activities following the Dodd–Frank Act. Moreover, the authors find that non-performing loans increase the influence of financial constraints on tax avoidance, while a financial crisis amplifies the impact of financial constraints on bank cash tax savings. Originality/value By extending previous research on financial constraints and tax planning, this paper is the first study to recognize financial constraints, along with the Dodd–Frank Act, as determinants of banks’ tax avoidance. This study informs policymakers about the regulation of tax avoidance in the banking industry and sheds light on possible future research on banks’ tax-planning strategies.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".