Targeting social assistance in Azerbaijan: what can we learn from micro‐data?
Bibliographic record
Abstract
This article investigates the targeting performance of social assistance programmes in Azerbaijan, a low‐income country in transition from a centrally‐planned economy to a market economy. Micro‐data from a nationally‐representative household survey are used, through the employment of several empirical approaches, to assess the extent to which social assistance identified the neediest households. The set of empirical evidence presented in this article shows a low targeting effectiveness of the social assistance programmes. It indicates that a significant proportion of the poor did not benefit from social assistance, whilst a substantial share of social benefits was leaked to the non‐poor. The receipt of benefits was also weakly associated with a variety of indicators of living standards including consumption, education, number of children, ownership of dwelling, household durables, motor vehicles, agricultural assets, and access to sanitation and utility supply as well as social capital. It is argued that the current social assistance programmes should be reformed to increase the success in reaching the poor.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".