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Record W4406593657 · doi:10.1111/ecpo.12333

Does Tax‐Aggressive Behavior Motivate More CSR Engagement?

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

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.014
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