Exercises

Complete these exercises in your Week 2 workbook. Bring the completed workbook to the session. Budget around 60 minutes.

Before you start: open the workbook here. Google Docs will prompt you to make a copy — click “Make a copy” so you have your own version to fill in. You’ll need to be signed into a Google account (personal or work) for this to work.

The goal this week is to take you from reading about cost-effectiveness analysis to actually doing one — roughly. The full instructions are in the workbook. It consists of five parts:

Part 1 — Trace the model (~20 min). Open GiveWell’s Malaria Consortium cost-effectiveness model and work through the Burkina Faso column. You’ll record the key inputs, follow how they combine to produce “cost per death averted,” and pick apart one of GiveWell’s adjustments.

Part 2 — Stress-test an input (~10 min). Pick one input you’re least confident in. See what happens to the bottom line if it’s 50% higher or 50% lower. This builds intuition for which assumptions actually move the answer.

Part 3 — Spot the weakest link (~5 min). In two or three sentences, name what you’d most want to check independently before trusting GiveWell’s recommendation — and what would have to be false for it to be wrong.

Part 4 — Build a simple CEA yourself (~20 min). This is the heart of the workbook. You’ll use a four-number formula —

Cost per death averted  =  cost per person reached  ÷  (baseline mortality rate  ×  efficacy)

— first to follow a worked example for insecticide-treated nets (ITNs), then to produce your own back-of-envelope estimate for vitamin A supplementation using inputs provided in the workbook. You’ll compare your number to GiveWell’s adjusted figure and account for the gap.

Part 5 — Reflect (~5 min). A short prompt to consolidate what a cost-effectiveness estimate does — and doesn’t — tell you.

By the end, you won’t have built a GiveWell-quality model. But you’ll have read one critically, stress-tested it, and produced a simple one of your own — which puts you ahead of most people who use cost-effectiveness numbers.