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Record W4212910398 · doi:10.3386/w10978

Theft and Taxes

2004· report· en· W4212910398 on OpenAlexaff
Mihir Desai, Alexander Dyck, Luigi Zingales

Bibliographic record

VenueNational Bureau of Economic Research · 2004
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBusinessComputer securityComputer science

Abstract

fetched live from OpenAlex

This paper analyzes the interaction between corporate taxes and corporate governance.We show that the characteristics of a taxation system affect the extraction of private benefits by company insiders.A higher tax rate increases the amount of income insiders divert and thus worsens governance outcomes.In contrast, stronger tax enforcement reduces diversion and, in so doing, can raise the stock market value of a company in spite of the increase in the tax burden.We also show that the corporate governance system affects the level of tax revenues and the sensitivity of tax revenues to tax changes.When the corporate governance system is ineffective (i.e., when it is easy to divert income), an increase in the tax rate can reduce tax revenues.We test this prediction in a panel of countries.Consistent with the model, we find that corporate tax rate increases have smaller (in fact, negative) effects on revenues when corporate governance is weaker.Finally, this approach provides a novel justification for the existence of a separate corporate tax based on profits.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.360
GPT teacher head0.446
Teacher spread0.086 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2004
Admission routes1
Has abstractyes

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