Micro-level dynamics of social assistance receipt. Evidence from 4 European countries
Bibliographic record
Abstract
This paper presents a study of the monthly dynamics of social assistance benefit receipt - in particular the distribution of spell lengths and the incidence of repeat receipt - in four European countries: Luxembourg, the Netherlands, Norway and Sweden. The analysis is based on four separate administrative panel data sets with long observation periods. Benefit dynamics vary considerably across countries over the eight-year period from January 2001 to December 2008: In the two Nordic countries, short-term benefit receipt is the norm with only around 6% and 11% of spells in Norway and Sweden lasting longer than 12 months. Most recipients however have multiple spells, and the majority of benefit leavers return to benefits within three months of leaving. In Luxembourg and the Netherlands, long-term benefit receipt is frequent, with median spell lengths of 14 and 9 months, respectively, and one-third and one-quarter of all spells lasting 24 months or longer. Benefit leavers in these countries are by contrast much less likely to return to benefit receipt after exit. The total duration of benefit receipt per individual across spells is two to three times as high in the Netherlands and Luxembourg than in Norway and Sweden over the eight-year period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".