Socioeconomic correlates of the lottery rollover effect in Toronto, Canada
Bibliographic record
Abstract
Background: In a lottery with a progressive jackpot, the rollover effect refers to an increase in revenue or engagement with an accumulating jackpot size. Using an ecological dataset of lottery ticket sales aggregated by postcode, we test two corollaries of the rollover effect. First, how does the rollover effect change in neighborhoods with higher or lower socioeconomic status? Second, how do fluctuations on a progressive-prize lottery affect the consumption of fixed-prize lottery tickets in the same neighborhoods, in line with economic notions of ‘substitution’ versus ‘complementarity’? Methods: We used time-series data on ticket sales from 2012-2015 from 3 progressive-prize lotteries (Lotto 649, Lotto Max, and Lottario) in Toronto, Canada, aggregated for 95 forward-sortation area (FSA) postcodes. Regression models controlled for other cyclical fluctuations including day of week, month of year, and common paydays. Results: Jackpot size positively predicted lottery ticket sales in all models, with a large effect size. There was a significant interaction between jackpot size and neighborhood SES, such that lottery sales in higher SES neighborhoods were more sensitive to jackpot size, although the effect sizes were negligible. Sales of fixed-prize lotteries were positively related to sales of progressive-prize lotteries, supporting complementarity. We observed a significant interaction between SES and progressive-prize sales, in which fixed-prize sales were more affected by progressive-prize sales in higher SES neighborhoods. Conclusion: Both the effect of larger jackpots on ticket sales, and the effects of progressive-prize sales on a second lottery type, are attenuated within more disadvantaged (i.e. lower SES) neighborhoods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".