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Record W2109423004 · doi:10.1177/152397210900900302

Consumption versus Income Taxation: Three Moments in the Political Economy of Fiscal Choice

2009· article· en· W2109423004 on OpenAlexaff
Stanley L. Winer, George Warskett, Walter Hettich

Bibliographic record

VenuePublic Finance and Management · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsCarleton University
Fundersnot available
KeywordsEconomicsConsumption (sociology)VotingContext (archaeology)Distribution (mathematics)NormativeVariance (accounting)Public goodSkewnessMicroeconomicsPoliticsPublic economicsIncome distributionInequalityEconometrics

Abstract

fetched live from OpenAlex

Most modern economies tax both consumption and labour income. While there is an extensive normative literature on the optimal mix of the two taxes, there is little examination of what determines the actual mix in a well specified political economy context. We use a multi-dimensional spatial voting framework to simulate endogenous political tax equilibria. the model accommodates complex interactions between many of the first three moments (mean, variance and skewness) of three distributions identified in the literature as crucial: the distribution of income, of preferences for public goods and the distribution of political influence. to simplify, we focus on a balanced and an asymmetric society and analyze how different combinations of distributional moments interact in the determination of tax equilibria. Interesting links emerge between the nature of the distribution of preferences for public expenditure, income inequality and the relative importance of consumption taxation. the analysis suggests that studies of single taxes have limited relevance for the explanation of the observed tax mix.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.450

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 science0.0000.000
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 head0.056
GPT teacher head0.252
Teacher spread0.195 · 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 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

Citations2
Published2009
Admission routes1
Has abstractyes

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