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Property Taxes in the Real World

2022· article· en· W4321513707 on OpenAlexvenueno aff
Enid Slack

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsProperty taxEquity (law)Public economicsEconomicsTax reformPoliticsCorporate governanceIndirect taxRevenueIdeal (ethics)Ad valorem taxLaw and economicsValue-added taxFinancePolitical scienceLaw

Abstract

fetched live from OpenAlex

Property taxes are regarded by economists as good taxes for local governments. Yet property tax revenues rarely exceed 3 percent of gross domestic product in any country and usually amount to much less than that. Moreover, jurisdictions that levy property taxes rarely do so in a way that adheres to economic principles. Political pressure to maintain the tax burden at or near its current level for the sake of stability or to favour one group of taxpayers over another often overrides the economics principles of efficiency and equity. The author asserts that no matter how good the property tax is in theory, we need to consider the real-world constraints on the tax. The author sets out what an ideal property tax would look like, contrasting that ideal with what exists around the world, then describes the unique characteristics of the tax that help to explain the gap between theory and practice. Some promising strategies for reform are proposed, along with cautions regarding problematic approaches. Policy makers are well advised to link property tax reform to broader reforms in public sector management aimed at improving public services and governance. Public support for the reform is also very important. Successful implementation requires sustained political will, technical capacity, systems and procedures, financial resources, and time, but it is well worth the effort.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.230
Teacher spread0.208 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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