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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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 teacher head, 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".