Sanctioned to Starve? The Impact of Economic Sanctions on Food Security in Targeted States
Bibliographic record
Abstract
Economic sanctions are imposed by senders to create the maximum economic damage in order to coerce the target to change its objectionable policy, however its impact may have varying and unintended consequences for targets. One of such possible consequences could be sanctions exacerbating the state of food insecurity in targeted states. In this chapter, we analyze whether the imposition of sanctions adversely affects food security in targeted states by looking at its impact on different measures of food security from both Food and Agriculture Organization (FAO) and International Food Policy Research Institute (IFPRI). Our results show the imposition of sanctions has deleterious effect on food security. Sanctions significantly increases the composite index of global hunger index (GHI), and also adversely affect the availability and stability dimensions of food security. In addition, we find that employing financial and trade sanctions simultaneously have a more pronounced adverse impact on food security compared to when they are used separately. Our results are robust to different empirical approaches (fixed effects and entropy balancing) as well as to different data sources (FAO and IFPRI).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".