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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 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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2008
Admission routes2
Has abstractyes

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