Bibliographic record
Abstract
This dissertation consists of three articles on social assistance benefit receipt dynamics in European countries. The first article presents an analysis of state dependence in benefit receipt in Germany based on annual survey data from the German Socio-Economic Panel. The observation period extends from 1995 to 2011, thus covering the 2005 'Hartz reforms'. I estimate a series of dynamic random-effects probit models to control for observed and unobserved heterogeneity and the endogeneity of initial conditions. The high observed state dependence has a substantial structural component, with benefit receipt one year ago being associated with an increase in the likelihood of receipt today by 13 percentage points. There is only little evidence for time-variation in state dependence. The second article presents evidence on spell durations and the frequency of repeat spells using monthly administrative data from Luxembourg, the Netherlands, Norway and Sweden. 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. In Luxembourg and the Netherlands, long-term benefit receipt is frequent, with median spell durations of 14 and 9 months, respectively, and one-third and one-quarter of all spells lasting 24 months or longer. The total duration of benefit receipt across spells is much higher in the Netherlands and Luxembourg than in Norway and Sweden. The third article tests the validity of one of the central assumptions of dynamic discrete-choice models of benefit dynamics, the conditional Markov property. Using monthly administrative data from Norway, the article shows that the Markov property is violated as estimated state dependence is affected by the chosen time unit of analysis. The standard model can be improved by permitting for different entry and persistence equations and duration and occurrence dependence in benefit receipt.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".