The Unexpected Consequences of Job Search Monitoring: Disability Instead of Employment?
Bibliographic record
Abstract
This paper investigates how the implementation of Job Search Monitoring (JSM) programs over the last two decades could have impacted the rise of disability rates in OECD countries. To do so, we use an RDD design to study how a JSM program that was implemented in 2006 in Belgium could have played a role not only in the transition to employment and inactivity but also in the transition to disability. The RDD exploits the fact that the program was only targeted at long‐term unemployed workers below the age of 50. Our results show that the JSM program has had a large impact on the transition rate from unemployment to disability and no impact on the transition rate to employment or inactivity. More precisely, individuals just below the age of 50 (the treatment group) are 1.43 percentage points (115%) more likely than individuals just above the age cut‐off (the control group) to enter into disability during the next quarter. Looking at heterogeneous effects, we find that the effect is above all important for women and more particularly for single‐women households. Overall, our study shows that JSM programs can have spillover effects on other social security branches, such as work disability. This is an important concern since it implies that JSM programs can push some individuals even further away from the labour market. Finally, our results show that the implementation of JSM could, constitute a viable explanation for the rise of the disability rate amongst unemployed workers.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".