Could the impact of a public policy help us evaluate the changes that have been implemented? An analysis of non-take-up of Spanish minimum income benefits
Bibliographic record
Abstract
This paper provides new evidence on why people who are eligible to receive a benefit do not apply for it, an occurrence most commonly referred to as “non-take-up”. It examines the relationship between the characteristics of the Guaranteed Minimum Income (GMI) and the non-take-up rate achieved by these benefits. This study looks into five main causal conditions in the design of a GMI: the amount of the benefit, the duration of the benefit, the administration's resolution times, the documentation requirements and an aggregation of supply side factors. The sample used corresponds to the 19 existing regional GMI programmes in Spain. The existence of relationships between causal conditions is tested using the Fuzzy-set Qualitative Comparative Analysis (FsQCA) methodology. The results show that there are three different combinations of conditions that result in less than 45% coverage of a GMI. With these results it is possible to evaluate ex ante whether the Spanish Minimum Vital Income (MVI) can avoid the non-take-up problem that other GMIs have in Spain. We find that the new MVI does not follow any of the combined conditions that lead to the failings of the GMI’s coverage rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".