Does the System Matter? Surplus Directed to Society in Monopolistic and License-Based Gambling Provision.
Bibliographic record
Abstract
The comparative advantages of license-based and monopolistic gambling regimes have been discussed in previous literature from the perspective of their capacity to prevent harms, but less is known about the ability of different regimes to produce public revenue. Gambling is nevertheless an important source of revenue for public service provision. The current paper compares figures from the financial statements of two monopolistic gambling providers in Finland (Veikkaus) and Norway (Norsk Tipping), to four license-based companies operating in the Italian market (Snaitech, Sisal, Gamenet and HBG gaming) to analyze how much surplus they contribute to their host societies and what kind of factors these amounts depend on. The results show that overall, the Nordic monopolistic operations appear more effective in terms of producing gambling surplus to society than the Italian license-based companies. This difference is analyzed in terms of game product portfolios, operating costs, and levels of normal profit. The role of operating costs appears to be the most important factor explaining the lower surplus generated by Italian companies. However, the bulk of these operating costs are directed to the redistribution network which creates employment. If these employment effects are considered, both licensing and monopolistic regimes appear similarly effective. We conclude by problematizing the use of financial effectiveness as a measure for good gambling policy. High surplus collected for societies is also related to high overall gambling volumes that go against public health objectives of reducing harms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".