Privatisation in practice : A study from an insurance perspective into the effects of privatisation of public sickness- and disability programs in The Netherlands, Germany and Canada
Bibliographic record
Abstract
This thesis aims to identify the effects of privatisation of public disability programs in The Netherlands, Germany and Canada. It does so from an insurance perspective (defined as: seeking a balance between the extent of cover and its cost). Subsequently, some conclusions are drawn as regards an optimal allocation of roles between ‘public’ and ‘private’ in disability insurance. Both public and private disability insurance are being dominated by an ‘inconvenient truth’, the presence of behavioural effects. Consequently, the same applies to the effects of privatisation. Other findings are – inter alia – that market failure (often quoted as the rationale for state intervention) is not an issue, but that demand anomalies are. The thesis concludes that whilst public insurance is probably the best way to address demand anomalies, private insurers are better equipped to deal with behavioural effects. It therefore suggests that in an optimal allocation of roles between’ public’ and ‘private’ public disability insurance should be restricted to ‘basic’ cover (that is less exposed to behavioural effects) and leave it to private insurers to provide additional cover. In this way a balance between ‘public’ and ‘private’ will contribute to the required balance between the extent of cover and cost. Casper de Jong graduated in Dutch Law from Leyden University (Netherlands) in 1969. He worked some thirty years in the insurance industry and held management and supervising positions in several countries. He wrote this thesis after he retired.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".