Relationships between socio‐economic status and lottery gambling across lottery types: neighborhood‐level evidence from a large city
Bibliographic record
Abstract
BACKGROUND AND AIMS: Lottery gambling participation tends to be higher among lower socio-economic status (SES) individuals, but it is unclear how this relationship differs as a function of lottery type. We estimated how the relationship between SES and lottery gambling rates varies across different types of lottery gambling: fixed-prize, progressive-prize (jackpot) and instant-win (scratch card) lottery tickets in a large Canadian city. DESIGN: Neighborhood-level lottery purchase data obtained from the Ontario Lottery and Gaming Commission were analysed in conjunction with demographic data. Mixed-effects regression was used to assess simultaneously how neighborhood-level SES predicts per-person lottery gambling rates across fixed-prize, progressive-prize lottery and instant-win lotteries. SETTING AND PARTICIPANTS: Neighborhoods in Toronto, Ontario, Canada in the years 2012-15. MEASUREMENTS: Per-capita sales in dollars (CAD) of fixed-prize lottery, progressive-prize lottery and instant-win tickets in Toronto postal codes. SES was estimated as a composite of income, years of education and white-collar employment. FINDINGS: Lower-SES neighborhoods engaged in higher rates of lottery gambling overall [β = -0.084, standard error (SE) = 0.24, P = 0.0007]. The predictive effect of SES varied significantly by lottery type (fixed-prize: β = -0.105, SE = 0.004, P < 0.0001, instant-win: β = -0.054, SE = 0.004, P < 0.0001; relative to progressive-prize). The predictive effect of SES was strongest for fixed-prize lotteries and weakest for progressive-prize lotteries, such that we did not observe a significant predictive effect of SES for progressive-prize lotteries (β = -0.031, SE = 0.024, P = 0.198). CONCLUSIONS: People in lower socio-economic status neighborhoods in Toronto, Canada appear to engage in more lottery gambling than those in higher socio-economic status neighborhoods, with the difference being largest for fixed prize lotteries followed by instant win lotteries, and no clear difference for progressive prize lotteries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".