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Record W3135611994 · doi:10.1287/mnsc.2020.3906

The Influence of Corporate Income Taxes on Investment Location: Evidence from Corporate Headquarters Relocations

2021· article· en· W3135611994 on OpenAlexaff
Travis Chow, Sterling Huang, Kenneth J. Klassen, Jeffrey Ng

Bibliographic record

VenueManagement Science · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCorporate taxApportionmentState (computer science)Point (geometry)Investment (military)Income taxBusinessEconomicsAccountingPublic economicsMonetary economicsTax reformTax avoidancePolitics

Abstract

fetched live from OpenAlex

This study examines the effects of jurisdictions’ corporate taxes and other policies on firms’ headquarters (HQ) location decisions. Using changes in state corporate income tax rates across time and states as the setting, we find that a one-percentage-point increase in the HQ state corporate income tax rate increases the likelihood of firms relocating their HQ out of the state by 16.8%, and an equivalent decrease in the HQ state rate decreases the likelihood of HQ relocations by 9.1%. Exploiting the unique tax policy features within the state apportionment system lends strong support to the interpretation that taxation drives this effect. Our analyses also demonstrate that state income tax features affect the destination of the HQ move. We contribute to the literature on corporate decision making by showing how state income taxation affects a real corporate decision that has significant economic consequences for the company and the state. This paper was accepted by Brian Bushee, accounting.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.048
GPT teacher head0.245
Teacher spread0.197 · 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 designObservational
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

Citations37
Published2021
Admission routes1
Has abstractyes

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