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Record W2096346433 · doi:10.1080/13504851.2010.543065

Tax-induced multiple equilibria

2011· article· en· W2096346433 on OpenAlexafffundabout
John Whalley, Shunming Zhang

Bibliographic record

VenueApplied Economics Letters · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsWestern University
FundersNational Social Science Fund of ChinaNational Natural Science Foundation of ChinaInternational Development Research Centre
KeywordsEconomicsConstant elasticity of substitutionComparative staticsNuméraireUniquenessConverseDistortion (music)WelfareConsumption (sociology)Mathematical economicsGeneral equilibrium theoryConsumption taxMicroeconomicsElasticity of substitutionEconomyTax reformIndirect taxProduction (economics)Public economicsMathematicsComputer science

Abstract

fetched live from OpenAlex

In this article, we present examples of tax-distorted general equilibrium economies in which equilibria are unique in the absence of taxes, but taxes generate multiplicity when introduced over a range of tax rates. We also provide converse examples of economies with multiple no-tax equilibria, but where taxes when introduced induce uniqueness. Both Foster and Sonnenschein (1970 Foster, E. and Sonnenschein, H. 1970. Price distortion and economic welfare. Econometrica, 38: 281–97. [Google Scholar]) and Kehoe (1985 Kehoe, T. J. 1985. The comparative statics properties of tax models. Canadian Journal of Economics, 18: 314–34. [Google Scholar]) discussed the possibility of tax-induced multiplicity. Here, we show how in 2-individual 2-good pure exchange economies with Constant Elasticity of Substitution/Linear Expenditure System (CES/LES) preferences such cases can occur. We also provide ranges of consumption tax rates over which these cases can occur for alternative parameterizations.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.058
GPT teacher head0.176
Teacher spread0.118 · 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; both teacher heads agree on what is shown here.

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
Published2011
Admission routes3
Has abstractyes

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