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Record W2217280606

The devil is in the detail: The distributional consequences of personal income tax sharing in the Australian federation

2015· article· en· W2217280606 on OpenAlexaboutno aff
Richard Eccleston, Neil Warren

Bibliographic record

VenueeCite Digital Repository (University of Tasmania) · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsState income taxPublic economicsTax reformAd valorem taxTax avoidanceValue-added taxDirect taxIndirect taxEconomicsGross incomeIncome taxBusinessEconomic policy
DOInot available

Abstract

fetched live from OpenAlex

The March 2014 Report of the National Commission of Audit and the Commonwealths tax reform and federalism discussion papers published in the first half of 2015 provided some early insights into the Coalition Governments agenda for reforming the national tax system and fiscal federalism. One proposal with potential to facilitate state tax reform and potential to partly address the vertical fiscal imbalance in the Australian federation is to encourage states and territories to introduce personal income tax levies or surcharges on the same tax base as the federal income tax.Granting states access to the personal income tax base could address a number of widely recognised policy problems. A modest state personal income levy could be used to replace inefficient state level transaction taxes, improving efficiency and equity within the national tax system. A more ambitious option would be for the Commonwealth to cut the federal income tax, creating the tax space for the states to use the federal income tax base to raise a significant portion of revenue. However, if such a levy is based on taxpayers state of residence (which is required to create inter-jurisdictional competition), then, based on the experience of other federations such as the United States and Canada, per capita revenue will varyconsiderably from state to state. This article outlines these policy design issues beforeusing ATO income tax data to present an analysis of how revenues from different stateincome tax levies and surcharges would be distributed across the Australian federationand what issues this raises for the introduction of a state income tax in Australia.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.057
GPT teacher head0.218
Teacher spread0.161 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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