Caterina Ruggeri Laderchi Catching Two Birds with the Same Stone? The Effectiveness of Food Transfers on Nutrition and Monetary PovertyKilling two birds with the same stone? The effectiveness of food transfers on nutrition and monetary poverty.
Bibliographic record
Abstract
Food transfers, reaching about 40 % of Peruvian households and providing about a quarter of household income to their beneficiaries, represent a central element in Peru’s strategy to fight extreme poverty. In this paper we analyse the effectiveness of these transfers in alleviating both non-monetary (i.e. nutritional) and monetary dimensions of poverty. After reviewing the main theoretical arguments discussed in the literature on the effectiveness of food transfers, as well as the salient characteristics of the Peruvian system, we proceed to our empirical analysis. When investigating the performance of transfers in reaching the most deprived in terms of income and nutrition, we find that the overall impact of the transfers is progressive in both cases, though not much of the benefit is captured by the poor. The circumstance that income is not a significant determinant of the amount received, and that less benefits are received in rural areas, are put forward as explanations for this finding. When considering the impact of food transfers on nutrition, we find that they increase household access to food, but that their impact on child malnutrition is not statistically different from the effect of other income sources. When considering the impact of the transfers on monetary poverty, we find that their direct impact is enhanced by the incentives they provide to increased work effort. Overall our results suggest that the pursuit of different poverty reduction objectives can result in mutually reinforcing outcomes, though much could be done to enhance the nutritional impact of food transfers in Peru.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".