Gambling during economic downturns: a comparative Nordic study of gambling revenue data
Bibliographic record
Abstract
Background Gambling expenditure is influenced by a range of individual-level and societal factors. Changes in these may also impact gambling consumption. Yet, little research has investigated how macroeconomic changes, such as changes in disposable income and economic downturns, translate to gambling consumption. Some forms of gambling may be impacted by economic downturns, but no comparative evidence is available.Methods The current study uses longitudinal gambling company revenue data (2019–2022) and household disposable income data to chart how economic fluctuations and downturns during the early 2020s have impacted gambling consumption at a product category level in three Nordic countries (Sweden, Finland, and Denmark).Results Results show that overall, Nordic gambling markets were not affected by changes in household disposable income during this period. However, at product category level, we found a significant negative association between disposable income and lotteries and EGMs in Sweden, as well as a significant positive association between disposable income and online casinos and betting in Finland.Conclusion Gambling is not ordinary leisure consumption, and the consumption of gambling can even increase during financial uncertainty. However, these processes can vary across contexts based on differences in gambling provision and baseline gambling consumption.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".