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.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".