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Record W4406593657 · doi:10.1111/ecpo.12333

Does Tax‐Aggressive Behavior Motivate More CSR Engagement?

2025· article· en· W4406593657 on OpenAlexaboutno aff
Xin Wang, Kam Fong Chan, Millicent Chang, Yuan George Shan, Joey Yang

Bibliographic record

VenueEconomics and Politics · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
FundersUniversity of Adelaide
KeywordsEconomicsCorporate social responsibilityPublic economicsPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

ABSTRACT Using an international sample of listed companies from 36 countries, we investigate whether firms' engagement in tax‐aggressive behavior drives them to increase their corporate social responsibility (CSR) and environmental, social, and governance (ESG) activities. Consistent with the reputation risk mitigation theory, our results show that U.S. and Canadian firms ramp up their ESG activities 4 years after engaging in tax‐aggressive practices, aligning with the typical duration for the IRS (Internal Revenue Service) investigations. In contrast, firms in other countries act sooner, within 2–3 years. Further analysis shows that firms in countries with stringent law enforcement, and those adopting International Financial Reporting Standards, are less likely to enhance CSR/ESG activities following aggressive tax policies. These findings highlight the significant influence of regulatory and disclosure environments in shaping corporate behavior in tax policies.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.233
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2025
Admission routes1
Has abstractyes

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