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Record W3009127691

Do Constituency Statutes Deter Tax Avoidance

2019· article· en· W3009127691 on OpenAlexfundno aff
Kaishu Wu, Hua Ye

Bibliographic record

VenueScholarSpace (University of Hawaii at Manoa) · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsStatutePolitical scienceBusinessLawLaw and economicsEconomics
DOInot available

Abstract

fetched live from OpenAlex

The constituency statutes, passed mainly in the U.S. in the last century, allow firm directors to consider the interests of stakeholders other than shareholders (i.e., non-financial stakeholders) when making business decisions. One type of critical decisions managers make pertains to corporate tax planning, which creates value for the shareholders at the expense of the public interest or social welfare. In this paper, we investigate whether this law change with a permissive nature affects directors, and hence, managers' attitude towards corporate tax avoidance. Employing a staggered difference-in-difference method, we find that firms incorporated in the states that have adopted constituency statutes exhibit significantly higher ETRs based on current tax expense, but not total tax expense or cash tax paid. This causal relationship suggests that managers, with the permission to consider the social impact of tax avoidance, become less aggressive in tax planning. We further find that the effect of adoption is stronger for financially unconstrained firms and firms in retail businesses, where the demand (cost) for tax avoidance is lower (higher). Finally, we show that our main results are driven by firms located in states with a high sense of social responsibility and the firms with high levels of tax avoidance prior to the adoption. Overall, the findings in this paper suggest a positive social impact brought by the passage of constituency legislations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.997

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 communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.005

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 head0.010
GPT teacher head0.177
Teacher spread0.167 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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