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Record W1543954364 · doi:10.3386/w14020

Measuring the Output and Prices of the Lottery Sector: An Application of Implicit Expected Utility Theory

2008· preprint· en· W1543954364 on OpenAlexaffabout
Kam Yu

Bibliographic record

VenueNational Bureau of Economic Research · 2008
Typepreprint
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsLakehead University
Fundersnot available
KeywordsLotteryEconometricsEconomicsMathematical economicsComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

Using implicit expected utility theory, a money metric of utility derived from playing a lottery game is developed.Output of the lottery sector can be defined as the difference in utility with and without the game.Using a kinked parametric functional form, outputs of the Canadian Lotto 6/49 are estimated.Results show that this direct economic approach yield an average output which is almost three times of the official GDP, which takes total factor costs as output.A by-product of the estimation is an implicit price index for lottery, which can serve as a cost-of-living index for the CPI.The estimated price elasticity of demand -0.67 closely resembles results for the U.K. and Israel in previous studies.

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)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.610
GPT teacher head0.538
Teacher spread0.071 · 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 designSimulation or modeling
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

Citations9
Published2008
Admission routes2
Has abstractyes

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