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Record W2058686587 · doi:10.1177/109114210002800103

Revenue Structures, The Perceived Price of Government Output, and Public Expenditures

2000· article· en· W2058686587 on OpenAlexaffabout
Vaughan Dickson, Weiqiu Yu

Bibliographic record

VenuePublic Finance Review · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRevenueEconomicsGovernment revenueTax revenuePublic economicsMonetary economicsIllusionPublic financeDebtMacroeconomicsFinance

Abstract

fetched live from OpenAlex

This article examines how revenue structures, through fiscal illusion effects, influence government spending. It does so with a regression model in which public spending depends on the perceived price of public services and other demand variables. The authors construct a perceived price term that provides a more general framework for testing together several hypotheses of fiscal illusion. The perceived price depends on public revenues, decomposed into nine revenue sources, and is a weighted average of how fully taxpayers recognize the cost of each revenue source. For a sample of the 10 Canadian provinces from 1961 to 1992, the authors find that revenue structures influence spending, that tax revenues are perceived more acutely than other major revenue sources (borrowing, grants), and that some taxes are recognized more than others. The results can be viewed as consistent with debt illusion, flypaper, and income-elastic versions of fiscal illusion while casting doubt on using the Herfindahl index to represent fiscal illusion. The authors also find learning by taxpayers (declining fiscal illusion) during the period.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.224
Teacher spread0.182 · 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

Citations10
Published2000
Admission routes2
Has abstractyes

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