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Record W4392948396 · doi:10.1016/j.econlet.2024.111649

Administrative procedures as tax enforcement tools

2024· article· en· W4392948396 on OpenAlexafffund
Wei Cui, Jeffrey Hicks, Michael Wiebe

Bibliographic record

VenueEconomics Letters · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsUniversity of TorontoUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEnforcementBusinessPublic economicsEconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

We study how common administrative procedures affect firm tax evasion. We begin with the counter-intuitive observation that many firms bunch above, rather than below, large notches in China’s corporate income tax. Cross-sectional patterns suggest that administrative procedures in the prepayment and refund system served as de facto enforcement tools that prevented some firms from accessing the reduced tax rates below the notches. Following a regulatory reform that eliminated these procedures, bunching below the notches increased dramatically. The results imply a trade-off between reducing administrative barriers and allowing much taxpayer non-compliance in low-compliance environments.

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)0.005
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.063
GPT teacher head0.266
Teacher spread0.203 · 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 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

Citations5
Published2024
Admission routes2
Has abstractyes

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