Stuck in a Fragility Trap: The Case of the Central African Republic Civil War
Bibliographic record
Abstract
This study utilizes the synthetic control method to assess the economic consequences of the ongoing civil war in the Central African Republic since December 2012. Drawing on a donor pool of low-income and lower-middle-income countries, it constructs a synthetic counterfactual to depict the economic trajectory in the absence of conflict. The analysis reveals a significant decline in national gross domestic product (GDP) per capita, estimated between 45.3 percent and 47.8 percent over a decade of conflict, resulting in a cumulative GDP loss of US$29.7 billion to US$32.4 billion (purchasing power parity, PPP, adjusted). Two model specifications are employed, one using pre-treatment outcomes and the other integrating external covariates. Robustness checks support the findings, indicating a minimum 10-year decline of 35.3 percent in GDP per capita. Even considering the 2003 coup, this civil war has the most detrimental economic impact. The analysis remains robust when incorporating GDP data from remote sensing sources. These effects align with the fragility trap concept, portraying one of the highest economic impacts of civil conflict in terms of relative GDP per capita decline.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".