William Easterly · 2006
Why the West's Efforts to Aid the Rest Have Done So Much Ill and So Little Good
Rough AI truth score
67/100
Easterly's attack on unaccountable grand plans and weak beneficiary feedback remains persuasive. His planner-searcher contrast is a useful design test rather than a law: many effective programs combine centralized capacity, local adaptation, experimentation, and accountable delivery.
Based on three central claims · medium confidence
Development history contains repeated target-driven projects with weak maintenance, poor adaptation, and donor-facing metrics. Some large programs do create feedback, learn, and deliver at scale, so size and planning are less predictive than accountability, capability, and iteration.
Multiple donors, earmarks, input targets, and limited beneficiary voice can diffuse responsibility, and aid evaluations often struggle with attribution. Independent impact evaluation, transparent procurement, country ownership, and outcome-linked financing address parts of this problem but are unevenly used.
Experimentation and direct feedback improve discovery, and local agents often detect needs outsiders miss. Searchers still need finance, law, infrastructure, coordination, and scale; successful Asian development and public-health campaigns commonly blended decentralized learning with capable planning.
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