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Record W4409693721 · doi:10.1111/beer.12804

Redefining Purpose: The Effect of the 2019 Business Roundtable Statement on Corporate Tax Strategies

2025· article· en· W4409693721 on OpenAlexaff
Sadok El Ghoul, Omrane Guedhami, Rana Jamshed

Bibliographic record

VenueBusiness Ethics the Environment & Responsibility · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStatement (logic)AccountingBusinessCorporate taxEconomicsLaw and economicsPolitical scienceDouble taxationTax avoidanceFinanceLaw

Abstract

fetched live from OpenAlex

ABSTRACT Business Roundtable (BRT) firms have faced intense scrutiny from investors, media, and the public following their 2019 “Statement on the Purpose of a Corporation,” which marked a shift from shareholder‐centric governance to a stakeholder‐focused approach. This shift has sparked debate over whether BRT firms are genuinely committed to social responsibility or merely using it as a branding strategy without implementing meaningful changes. This paper contributes to the debate by empirically examining a key dimension of social responsibility—corporate tax behavior. Using a difference‐in‐difference analysis covering 2004–2022, we find that BRT firms engage in higher levels of tax avoidance than other publicly listed U.S. firms. More importantly, our results indicate that BRT firms have not significantly adjusted their tax behavior since the 2019 Statement, suggesting a disconnect between their stated commitment and actual practices. Our findings provide new insights into the social responsibility of BRT firms and contribute to the broader literature on the relationship between corporate tax avoidance and CSR.

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)0.004
metaresearch head score (Gemma)0.029
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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.268
Teacher spread0.228 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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