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Record W3121784952

The Economics of Casino Taxation

2006· article· en· W3121784952 on OpenAlexaff
Hasret Balcıoğlu, Glenn P. Jenkins

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsWelfareTax revenueEconomicsInvestment (military)RevenueDouble taxationInternational taxationIndirect taxAd valorem taxTax reformPublic economicsInternational economicsMonetary economicsFinanceMarket economy
DOInot available

Abstract

fetched live from OpenAlex

In this paper, a model of the costs of a casino is developed that focuses on the implications for economic welfare of different taxation schemes for casinos. The situation being considered is in a country where casinos cater exclusively to foreign tourists. The goal of the country is to determine the maximum amount of taxes that can be extracted from the activities of this sector under different systems of taxation. When the price of gambling is set by regulation above its competitive level, the economic losses created by excessive investment in the sector can be reduced by taxation. A turnover tax on the amount gambled can maximize both tax revenue and the economic welfare of the country. Due administrative constraints, a number of countries rely on the taxation of the casinos' fixed assets or a combination of a turnover tax and a tax on fixed costs. The model is applied to the situation in North Cyprus. The annual economic efficiency loss from its poorly designed tax policies on casino gambling is estimated to be about 0.5 percent of GDP.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.017
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.313
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2006
Admission routes1
Has abstractyes

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