Challenging the disability benefit trap across the OECD
Bibliographic record
Abstract
Introduction Increasingly, disability benefits have become a trap for potential recipients who, once on benefit, typically stay there until retirement age. They are equally a trap for policy makers, who face – and, by and large, have failed to address – a choice between spending both political and financial capital in reforming what in nearly every country are patently seriously flawed policies, or ‘letting sleeping dogs lie’. Unfortunately, there appear to be few votes to be gained by reforming disability policies. Only when policy begins to collapse under the weight of its own contradictions do governments summon up the courage to introduce change. And these contradictions are legion: a policy designed for permanent disability having to cope with medical conditions which may be temporary; a benefit policy designed for those who cannot work yet in practice many or most recipients wish to work, and so on. This chapter briefly describes the magnitude of the dilemma across the Organisation for Economic Co-operation and Development (OECD), arguing that current policies are both expensive and yet fail to achieve satisfactory outcomes for people with disabilities themselves. It then discusses the primary causes driving current outcomes. Subsequently, it looks at disability policy trends in OECD countries since around the mid-1980s before turning to some very general policy conclusions. The chapter heavily relies on a 20-country comparative analysis published in early 2003 (OECD, 2003). The chapter concludes that no other area of social policy has been as ineffective in meeting the new challenges and in achieving its stated objectives as disability policy. The first problem: growing levels of benefit receipt At the turn of the 21st century, incapacity-related public cash spending across the OECD was as high as 2.3% of GDP, 2.6 times higher than unemployment-related spending (Figure 9.1). Only in Denmark was the latter higher than the former, and in Belgium, France and Canada cash spending on the two programmes was at the same level. In several countries, on the contrary, including the Czech Republic, Hungary, Iceland, Norway, Switzerland and the UK, incapacity-related cash spending was six to 12 times higher than unemployment-related cash spending. In the light of this, the strong focus of social policy and research on unemployment rather than disability issues seems unjustified.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".