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Record W3033002662 · doi:10.1002/smj.3195

Corporate social responsibility of U.S.‐listed firms headquartered in tax havens

2020· article· en· W3033002662 on OpenAlexafffund
Dongyoung Lee

Bibliographic record

VenueStrategic Management Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsMcGill University
FundersMcGill University
KeywordsTax havenCorporate social responsibilityBusinessCorporate taxHavenAccountingTax avoidanceSample (material)Monetary economicsDemographic economicsEconomicsDouble taxationFinancePolitical sciencePublic relations

Abstract

fetched live from OpenAlex

Abstract Research Summary Using 138 firm‐year observations for 46 U.S.‐listed firms headquartered in tax havens from 2004 to 2013, this study employs a matched‐sample design and documents that the level of corporate social responsibility (CSR) engagement is relatively lower for firms with tax haven headquarters (HQ) than for those with U.S. HQ. This result is robust to the use of firm philanthropy as a measure of CSR engagement and holds true in an environment with high CSR expectations from U.S. communities. In an alternative setting of HQ relocations within the United States, we use a difference‐in‐differences methodology and find that when firms move their HQ to states with lower corporate income taxes, they decrease the level of CSR engagement. Overall findings are consistent with corporate culture theory. Managerial Summary This article examines CSR engagement of U.S.‐listed firms headquartered in tax havens. Using data from 2004 to 2013, we find that firms with tax haven HQ exhibit a relatively lower level of CSR engagement than otherwise similar firms headquartered in the United States. In the same vein, tax‐haven‐headquartered firms tend to give less to charity, even when they face high CSR expectations from U.S. communities. In an alternative setting of HQ relocations within the United States, we document that the level of corporate social engagement is more likely to drop for firms that move their HQ to lower‐tax regions. We interpret our findings as evidence of corporate culture affecting both the tax avoidance and CSR activities of firms headquartered in tax havens.

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 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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.097
GPT teacher head0.259
Teacher spread0.162 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations38
Published2020
Admission routes2
Has abstractyes

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