Safety Nets and Food Programs in Asia: A Comparative Perspective
Bibliographic record
Abstract
Many countries adopted safety net programs to deal with the food crisis of 2008. However, such programs are often beset with targeting errors, inefficiencies, and fraud. Despite this, there is no systematic comparative analysis of safety nets. The objective of this paper is to identify generic issues germane to safety net design and their role in determining success. We examine the performance of safety net programs in Bangladesh, India, Indonesia, and the Philippines in terms of people covered, food distributed, and income support provided. These countries spend 1%-3% of their gross domestic product on safety nets — small in relation to developing and industrial economies. We find an across-the-board failure of targeting in the four countries. The reasons range from elite capture, incorrect identification of the poor, their lack of access, barriers to participation, and regional allocation biases. Even if perfect targeting could cover the entire target group and eliminate leakage to non-target groups, the target groups may not receive the full subsidy due to illegal diversions, operational inefficiencies, and excess costs of public agencies. The success of the safety nets will depend on increasing the participation of the poor and minimizing program waste. Computerization of supply chains to track grain supplies can reduce diversion, and switching from in-kind to cash transfers can cut administrative and other costs of physical handling. The mix of tools would depend upon the economic, political, cultural, and social backgrounds of the country, and its administrative and fiscal capabilities to provide safety net programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".