Onshore and offshore gambling among indebted individuals in bank transaction data
Bibliographic record
Abstract
Background Offshore gambling is connected to elevated levels of gambling consumption, problems, and harm. This study investigates whether and how offshore gambling is associated with indebtedness.Method We investigate the association between indebtedness and offshore gambling using banking data (N = 23,231) collected between 2018 and 2021 by a Finnish debt consolidation service. The analysis focuses on gambling consumption and unsecured loans amongst those who gamble onshore only, those who gamble offshore only, and those who gamble both onshore and offshore. We categorized all transactions to the Finnish gambling monopoly as onshore and all transactions to other providers or using payment intermediaries as offshore. We employed descriptive statistics, testing significance using Mann–Whitney U test and Kruskal–Wallis test and applied quantile regression with tests of equality of distinct slopes.Results Offshore gambling made up 96% of deposits in euros. Sixty-nine percent of all money deposited to offshore providers used payment intermediaries. When the gambled product could be identified, 77% of deposits were made to online casino websites. Offshore gambling was associated with higher levels of gambling consumption and gambling losses than onshore gambling. However, those who gambled both onshore and offshore had even higher levels of gambling consumption and unsecured debt than those gambling offshore only. The association between unsecured loans and gambling deposits is confirmed in quantile regressions, particularly amongst those with the highest consumption.Conclusions Online gambling in all forms is connected to financial hardship. Tighter regulatory controls are needed on online gambling markets and payday loan industries to protect public health.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".