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Record W2041376856 · doi:10.5539/ijef.v3n4p54

Is the Lottery Product an Inferior Good in Higher Income Countries?

2011· article· en· W2041376856 on OpenAlexvenueno aff
Maria João Kaizeler, Horácio C. Faustino

Bibliographic record

VenueInternational Journal of Economics and Finance · 2011
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsLotteryPer capitaEconomicsPer capita incomeGross domestic productEconometric modelDemographic economicsProduct (mathematics)Value (mathematics)Monetary economicsLabour economicsEconometricsPopulationMacroeconomicsMicroeconomicsDemographyMathematicsStatistics

Abstract

fetched live from OpenAlex

Do the populations of low per-capita income countries participate with a stronger desire to win and spend relatively more money on lottery products? Is such a desire to buy lottery products constant, or does it decrease when the country reaches a higher per-capita income class? To answer these questions, this paper uses econometric models with significant explanatory variables. The results confirm the hypothesis that the lower income-class countries spend more than the higher income-class countries. However, the results do not confirm the hypothesis that lottery products may be considered an inferior good in countries belonging to the higher per-capita income class. The results also show that for all countries, there is an inverted U relationship between per-capita sales and per-capita GDP and up to a specific value, the per-capita lottery sales decrease as per-capita GDP increases, becoming an inferior good as a result.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.102
GPT teacher head0.341
Teacher spread0.240 · 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 designObservational
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
Published2011
Admission routes1
Has abstractyes

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