State Fragility: Towards a Conceptual Framework
IMF Working Papers, October 3, 2025
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Bibliographic details
- Authors: Aliona Cebotari, Enrique Chueca-Montuenga, Yoro Diallo, Yunsheng Ma, Rima A Turk, Weining Xin, Harold Zavarce
- Published: October 3, 2025
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798229028578.001
Overview
- Purpose: Proposes a general conceptual framework for state fragility that disentangles the identification of fragility from its underlying drivers.
- Core reduction: Identification of state fragility is reduced to the responsiveness of economic or political systems to shocks.
- Publication: IMF Working Papers, Working Paper No. 2025/205; Pages: 55; Volume: 2025; Issue: 205; DOI: https://doi.org/10.5089/9798229028578.001; ISBN: 9798229028578; ISSN: 1018-5941.
- Authors: Aliona Cebotari, Enrique Chueca-Montuenga, Yoro Diallo, Yunsheng Ma, Rima A Turk, Weining Xin, Harold Zavarce.
- Date: October 3, 2025.
Conceptual framework and definitions
- Fragility definition (extension of Taleb’s work):
- Fragility is a disproportionate (supra-linear) response to negative shocks.
- Fragility includes an underwhelming (sub-linear) response to positive shocks or to time.
- Two distinct forms of fragility:
- Fragility to stress: breakdowns in political or economic systems in response to negative shocks.
- Chronic fragility: inability of economies to generate growth over time.
Manifested fragility vs latent fragility
- Manifested fragility:
- Identification where the produced list of fragile states closely aligns with classifications by international organizations.
- Latent fragility:
- Identification where underlying drivers offer a probabilistic proximity to a fragile response.
- Latent fragility can be used to infer risk even when fragility is not yet manifested.
Methodology illustrated: coups d’état as a probe of political fragility
- Approach:
- Focus on coups d’état as reflective of political fragility.
- Use machine learning to examine coup drivers and their nonlinear interactions.
- Derive implied coup probabilities across all countries.
- Emphasis:
- Nonlinear interactions among drivers are central to identifying latent fragility.
- Machine learning provides probabilistic assessments of proximity to fragile responses.
Key findings and analytical insights
- The responsiveness framework separates identification (system response) from drivers (structural fundamentals and stressors).
- Fragility characterization:
- Supra-linear responses to negative shocks identify systems prone to breakdown.
- Sub-linear responses to positive shocks or to time identify systems with chronic growth incapacity.
- Application results:
- Manifested fragility lists produced by the framework closely align with international organization classifications.
- Latent fragility estimates (via coup probability modeling) provide cross-country implied probabilities of political fragility.
Policy implications and recommendations
- Strategic priorities for engagement with fragile states:
- Prioritize elimination of sources of fragility through strengthened structural fundamentals.
- Mitigate stressors through stronger policies.
- Expected returns:
- Both strengthening fundamentals and mitigating stressors have higher returns in chronically fragile environments.
- Operational lesson:
- Distinguish interventions aimed at reducing susceptibility to negative shocks from those aimed at fostering sustained positive responses (growth over time).
IMF Working Paper No. 2025/205. By Aliona Cebotari, Enrique Chueca-Montuenga, Yoro Diallo, Yunsheng Ma, Rima A Turk, Weining Xin, and Harold Zavarce. October 3, 2025.
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