Sendhil Mullainathan and Eldar Shafir · 2013

Scarcity

Why Having Too Little Means So Much

Rough AI truth score

50/100

Scarcity can capture attention, tax working memory, and encourage short-horizon tradeoffs, but the evidence is more context-sensitive than the book's unified bandwidth account suggests. Financial strain and time pressure matter; so do selection, stress, institutions, and material constraints. Replications and reanalyses leave some prominent demonstrations uncertain.

Based on three central claims · medium confidence

The three claims

01Mostly supported

Immediate scarcity captures attention and can impair performance on other cognitive tasks.

Lab and field studies find attentional tunneling and cognitive load under acute financial or time pressure. Effect sizes vary, and mechanisms may include stress, distraction, sleep, and environmental instability rather than one general bandwidth tax.

Scarcity and Cognitive Function Scarcity Effects on Decision Making

02Mixed

A common scarcity mindset explains short-term borrowing, neglect of future costs, and cycles of poverty.

Tunneling can rationally prioritize urgent needs and create future costs, but borrowing and poverty persistence also reflect income volatility, high prices, weak insurance, discrimination, and restricted choices. A psychological mechanism alone cannot establish the book's broad causal story.

Scarcity and Economic Decision-Making Poverty and Cognitive Function: Reassessment

03Weak

The bandwidth mechanism is robust and largely comparable across money, time, calories, and social resources.

There is evidence for cross-domain attentional narrowing, but operationalizations and boundary conditions differ substantially. Reanalyses and replication work weaken claims of a single large, portable cognitive effect.

An Opportunity for Self-Replication Empirical Audit of Scarcity Research

Other claims worth checking

Slack can buffer shocks and prevent cascading tradeoffs.
Urgent deadlines may boost focus while causing neglect elsewhere.

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