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Record W3195317711 · doi:10.1111/1911-3846.12726

Managerial Career Concerns and Corporate Tax Avoidance: Evidence from the Inevitable Disclosure Doctrine*

2021· article· en· W3195317711 on OpenAlexvenueno aff
Ningzhong Li, Terry Shevlin, Weining Zhang

Bibliographic record

VenueContemporary Accounting Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveDoctrineOrder (exchange)BusinessTax avoidanceService (business)Public economicsLabour economicsMarketingEconomicsFinanceDouble taxationMicroeconomicsLawPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT While managers' career concerns have been shown to be influential in shaping their decisions, there is little evidence of the impact such concerns may have on managers' tax avoidance incentives. This study examines the causal effect of managers' career concerns on tax avoidance using the staggered recognition by state courts of the inevitable disclosure doctrine (IDD), a trade secret protection doctrine that places greater restrictions on managers from joining or forming a rival company. We argue that the IDD recognition increases the cost of job loss for managers whose current jobs may be in jeopardy, thereby increasing their incentive to avoid taxes in order to positively change their current employer's evaluation of their ability. The IDD recognition also reduces outside opportunities for high‐ability managers, and thereby reduces their incentive to avoid taxes in order to positively change external employers' evaluation of their ability. Using a difference‐in‐differences design, we provide evidence consistent with these predictions. We further show these effects are stronger for CEOs in their early years of service in the focal firms when the market is more uncertain about their ability. Our findings suggest that managers take into account the impact of tax avoidance on their career outcomes when making tax avoidance decisions.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0000.001
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 head0.169
GPT teacher head0.319
Teacher spread0.150 · 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.

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

Citations65
Published2021
Admission routes1
Has abstractyes

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