Exploring the impact of secession on food prices: a case study of Sudan
Bibliographic record
Abstract
Abstract The fragmentation of an economy through secession leads to a significant economic contraction. Specifically, secession is expected to severely impact food prices. This study examines the Sudanese economy, which faced profound consequences after South Sudan’s secession in July 2011. The economic effects of South Sudan’s secession remain underexplored, and this study aims to fill this gap by assessing its impact on food security, nine years post-secession. Using a Difference-in-Differences (DiD) with fixed effects, we quantify the effects of the secession. Moreover, to understand mechanisms driving the observed food price increases, mediation analysis has been conducted using the vegetable index and the number of violent events as mediator variables. Sudanese districts are considered the treatment group, while districts in neighboring countries—Kenya, Chad, and Ethiopia—serve as the control group. Synthetic DiD and Synthetic Control Method are employed as robustness checks. The consistent findings indicate that South Sudan’s secession led to around 72.00% increase in food prices in Sudan, threatening the country’s food security. This study provides an estimate of the economic consequences of secession on the food price index in a fragmented economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".