麦肯齐·斯科特的捐赠:以质量调整寿命年(QALYs)衡量
MacKenzie Scott's giving, in quality-adjusted life years

原始链接: https://maxghenis.com/mackenzie-scott-qaly/

该模型通过将各类资助转化为质量调整寿命年(QALYs),评估了麦肯齐·斯科特(MacKenzie Scott)价值 300 亿美元(经通胀调整后)慈善投资组合的健康影响。该模型采用 GiveWell 式的方法论,将捐赠归纳为 13 种干预原型(如医疗补助扩张、社区卫生中心和住房),并通过蒙特卡洛模拟来处理不确定性。 该模型的一个关键组成部分是“证据立场”滑动条,它根据因果证据的强弱来调整每项干预措施的权重。结果差异显著,从怀疑态度(对因果关系设定更高标准)到轻信态度(完全接受所宣称的效果)不等。 尽管该模型为评估健康结果提供了一个严谨的框架,但它也承认了一个重大局限性:它仅关注 QALYs。由于斯科特的捐赠中有很大一部分针对的是经济流动性、教育和社会公平——这些领域与健康的因果联系难以衡量——因此该模型很可能低估了其慈善事业的整体社会影响。最终,分析表明,虽然斯科特的投资组合极具影响力,但直接的全球健康干预措施(如 GiveWell 所资助的项目)目前在每美元的健康收益上具有更高的边际回报。

这篇 Hacker News 的讨论文章批评了近期对麦肯齐·斯科特(MacKenzie Scott)慈善事业的分析,重点评估了其干预措施在“质量调整寿命年”(QALYs)方面的成本效益。 原发帖者认为,该分析中关于每 QALY 的成本估算过于悲观。一位评论者指出,斯科特的投资(如卫生设施和免疫接种)具有巨大的“乘数效应”。他们认为,通过降低儿童死亡率和支持妇女健康,这些干预措施能够通过未来几十年的生产力创造巨大的长期经济价值。从这个角度来看,作者认为该分析低估了其影响力,并暗示其真实的投资回报率远高于报告中的数字。 另一位用户强调了这种经济框架中的伦理张力,指出了此类评估中冷酷的算计——特别提到,关于避免怀孕所带来的经济利益的论点,隐含地将那些本可能出生者的生命视为“零经济价值”。
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原文

The most important control is evidence stance: from skeptical (~70,000 QALYs — each effect weighted by how well its study identifies causation) to credulous (~200,000 — every cited effect at face value). That gap, not the dollar figure, is the real uncertainty.

About this model

MacKenzie Scott's Yield Giving network has made over $26 billion in 2,700+ gifts since 2019 — $26.3 billion through 2025 by CNBC's year-end accounting. This page asks what that buys in quality-adjusted life-years, the unit health economists use to compare a death averted against years lived in better health.

It is a GiveWell-style model: 13 intervention archetypes, each cost-per-QALY drawn where possible from a published causal estimate (Medicaid mortality, community health centers, supportive housing, collaborative-care depression), each effect shrunk toward zero in proportion to how well its study identifies causation, and the whole thing rerun through thousands of Monte Carlo draws each time you move a slider.

I built the model with Claude; every estimate here is a model output, not a measured fact. The Python package, tests, and sources are on GitHub; this page runs a checked TypeScript implementation in the browser, reading the exported parameter file.

How it works

Each Monte Carlo draw takes the giving — each year's gifts inflated to 2026 dollars ($26.39 billion nominal ≈ $30.3 billion, so the dollars and the cost-effectiveness evidence share one price level) — allocates it across the archetypes (a Dirichlet whose centers come from Scott's own gift database — dollar amounts are disclosed for about two-thirds of the money, and each organization's dollars are split across its reported focus areas and mapped to the 13 archetypes; the undisclosed remainder is imputed from her announced year totals, scaled by each recipient's pre-gift IRS 990 revenue), assigns each a cost-per-QALY, and multiplies by two independent discounts:

The gift-size data yields one measured regularity along the way: across the 1,313 disclosed gift–revenue pairs, gift size scales with the recipient's pre-gift revenue to the power 0.41 (R² 0.37) — a 10× larger organization receives about 2.5× more money, not 10× more. That fitted elasticity, not proportionality, weights the imputation of the undisclosed gifts.

  • Causal credibility — how well the effect is identified, drawn from the study's design tier: a lottery RCT (income → mortality) is trusted; an associational SNAP correlation is shrunk hard; an assumption-only bucket (arts, civic) goes to near zero. This is the evidence stance slider.
  • Realization — the fraction of the studied effect a marginal unrestricted grant actually delivers.

The model also prices the same dollars at the global-health frontier. GiveWell's current impact estimates put the 2022–2024 program averages at ~$4,000 per life saved (Malaria Consortium) to ~$5,500 (AMF nets). A child death averted at ~age 1 is ~25 discounted QALYs under this model's own conventions (~65 remaining years, 3% discount, utility ~0.87), so those endpoints — inflated to 2026 dollars — become roughly $175–$241 per QALY-equivalent; I model the benchmark as loguniform $150–$260, handicapped with the same realization and credibility as Scott's portfolio (and rescaled at other discount rates) so the comparison is like-for-like. At the skeptical defaults, the frontier delivers roughly 1,500× more health per marginal dollar.

That multiple is a marginal comparison — the next dollar, not the whole portfolio. Frontier-priced opportunities are scarce: GiveWell directed $397 million in all of 2024 and moves its cost-effectiveness bar with the money it expects to raise — funding down to ~6× cash when flush, back up to 10× when projections fell. Malaria control, the deepest frontier bucket, absorbed $3.9 billion in 2024 against a $9.3 billion target, while ~610,000 people died. And the implied frontier counterfactual — ~105 million QALYs at the default settings — would mean averting ~4.2 million child deaths, most of a full year of the world's under-5 deaths; no amount of money buys that at bed-net prices. Redeploying the full $30 billion would ride up the marginal-cost curve — to a few hundred times rather than ~1,500×, at my guess — softer in magnitude, same in direction. The floor is direct cash, the one option with effectively unbounded capacity, which GiveWell now scores at 3–4× its own historic benchmark: in health-only terms, somewhere under 50–100×.

What this doesn't capture

A QALY is a health metric. Most of Scott's giving targets economic mobility, education, and equity, whose value is largely non-health — income, opportunity, rights, wellbeing. The model therefore understates her total social impact; it answers one specific question. The largest dollar buckets (equity & justice at ~22%, education at ~18%) contribute little health precisely because no credible study ties those grants to QALYs, not because the giving lacks value.

Key sources

Full annotated bibliography, parameter file, and the tested Python package: github.com/MaxGhenis/mackenzie-scott-qaly.

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