Paul Collier · 2007
Why the Poorest Countries Are Failing and What Can Be Done About It
Cover via Open LibraryRough AI truth score
67/100
Collier usefully redirects attention to countries diverging from the wider developing world and to interacting risks of conflict, resources, geography, and governance. The traps are probabilistic rather than a fixed club, and subsequent poverty geography and causal work weaken a single billion-person category.
Based on three central claims · medium confidence
Cross-country data around publication did reveal chronically poor, conflict-affected, and institutionally weak states missed by broad emerging-market optimism. The membership and location of extreme poverty change, and many poor people live in middle-income countries, so the category is not durable.
Each factor has substantial empirical support as a development risk, and interaction is plausible, especially where institutions are weak. None is destiny: peaceful resource-rich states, successful landlocked economies, institutional reform, and regional integration show large conditional variation.
The package sensibly matches different instruments to different constraints, and targeted interventions can help. Identification is difficult, external security and standards can backfire, aid effects are heterogeneous, and implementation depends on local political legitimacy that a cross-country prescription cannot guarantee.
What this number means. It is an AI-generated first-pass judgment of three central factual or causal claims—not a rating, exhaustive fact-check, or human peer review. Claim credits are 100% for supported, 75% for mostly supported, 50% for mixed, and 25% for weak, then averaged and rounded. Lower confidence means the score should move more as better evidence arrives.
Method three-central-claims/0.1.0 · checked 2026-09-01 · 3/3 selected claims assessed · method and source audit