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Record W4409601111 · doi:10.1111/acfi.70030

CEOs' Political Ideology, Social Capital and Corporate Tax Avoidance

2025· article· en· W4409601111 on OpenAlexaff
Tien‐Shih Hsieh, Jeong‐Bon Kim, Zhihong Wang

Bibliographic record

VenueAccounting and Finance · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTax avoidancePoliticsCorporate taxIdeologySocial capitalEconomicsPolitical economyBusinessMonetary economicsAccountingDouble taxationPolitical sciencePublic economicsLaw

Abstract

fetched live from OpenAlex

ABSTRACT Building on institutional theory, we explore the interaction between social capital and CEO political ideology on corporate tax avoidance. Using CEOs' political donations to identify their political ideology, we find that firms led by Republican CEOs tend to have lower (higher) effective tax rates when located in communities with lower (higher) social capital, indicating a higher (lower) propensity for tax avoidance, compared to firms led by non‐Republican CEOs. Our findings support the U.S. Internal Revenue Service's (IRS) view that tax compliance behaviour is inherently a sociopolitical construct. Our results suggest that tax policymakers and other regulators should pay more attention to social capital at the community level to foster ethical corporate behaviour in relation to tax compliance. Overall, we provide the tax authorities with useful insights into corporate tax compliance, which are applicable to both U.S. and international settings, particularly in regions with distinct political and social norms.

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)0.000
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 score0.661
Threshold uncertainty score0.758

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 communication0.0000.001
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 head0.220
Teacher spread0.206 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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