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Record W4402424648 · doi:10.1111/jbfa.12830

Information acquisition and tax avoidance: Evidence from a natural experiment

2024· article· en· W4402424648 on OpenAlexaff
Caiyue Ouyang, Jeffrey Pittman, Jiacai Xiong, Jun Yao

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNatural (archaeology)Natural experimentTax avoidanceBusinessComputer scienceCognitive psychologyEconomicsPublic economicsPsychologyDouble taxationMedicineHistory

Abstract

fetched live from OpenAlex

Abstract Analyzing the launch of high‐speed rail (HSR) services in China as a natural experiment, we identify a positive externality stemming from lower information acquisition costs: the reduction in firms’ overinvestment in tax avoidance. Specifically, we find that outsiders undertake more corporate site visits and firms engage in less tax avoidance after the opening of HSR lines in the cities where these firms are located, leading to enhanced firm value. In another result consistent with expectations, we document that the impact of the introduction of HSR lines on tax avoidance is concentrated in firms in which insiders exhibit a high propensity to extract rents through aggressive tax strategies. Our results imply that more efficient transportation facilitates site visits and the acquisition of firm‐specific information, particularly soft information. This improvement strengthens external monitoring, thereby limiting the ability of insiders to accumulate private benefits under the guise of tax avoidance that benefits all shareholders as the residual claimants.

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 categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.999

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 communication0.0020.015
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 head0.016
GPT teacher head0.236
Teacher spread0.220 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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