Mechanisms of poverty alleviation: anti-poverty effects of non-means-tested and means-tested benefits in five welfare states
Bibliographic record
Abstract
Substantial cross-national differences in poverty alleviation are well documented. But the extent to which different parts of the social transfer system account for this variation is still relatively unexamined. This paper analyses the redistributive effects of specific social policy institutions in a comparative perspective. The main question is to what extent non-means-tested entitlements and means-tested benefits reduce relative economic poverty in different institutional settings. It is shown that the structure of non-means-tested benefits is more important than that of meanstested benefits in explaining differences in poverty alleviation across countries. The paper also presents a new method for estimating the anti-poverty effects of separate parts of the social transfer system. This method decomposes the anti-poverty effects of a set of social transfers into independent and combined effects, which produces more valid results than prevalent methods used to assess the impact of a particular transfer on poverty. The countries included in this study are Canada, Germany, Sweden, the United Kingdom and the United States. The empirical analyses are based on data from the Social Citizenship Indicators Programme (SCIP) and Luxembourg Income Study (LIS) describing the situation in the mid-1990s.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".