The Integrated Food Security Phase Classification (IPC) system, created in 2004 by a consortium of partner organisations, guides where and when humanitarian aid goes. IPC analyses cover about 30 countries and are used to allocate more than $6 billion in aid each year. A study published in Nature Food finds that IPC assessments systematically underestimate hunger.
Researchers led by Hope Michelson and Erin Lentz, with Kathy Baylis and Chungmann Kim, were approached by IPC in 2021 to evaluate the process. They began with about 20 interviews and then analysed nearly 10,000 food security assessments covering 917 million individuals across 33 countries from 2017 to 2023, producing 2.8 billion person observations.
The analysis showed clear “bunching” just below the 20% phase 3 crisis threshold. The team estimated 293.1 million people in phase 3 or higher, while IPC assessments reported 226.9 million — a difference of 66.2 million people (about one in five). The authors say conservative committee decisions when indicators conflict partly explain the undercount, and they recommend better data, improved decision-making, and careful use of machine learning to support experts.
Difficult words
- classification — a system for grouping items by type
- consortium — a group of organizations working together
- allocate — to give resources or money for a purpose
- assessment — a detailed examination of a situation or needassessments
- underestimate — to think something is smaller or less serious
- bunching — a group of values close together
- threshold — a limit or level that changes a decision
- indicator — a sign or measure showing a conditionindicators
- conservative — careful and avoiding risk or big changes
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Discussion questions
- Do you think using machine learning could help improve food security assessments? Why or why not?
- How could better data change decisions about where humanitarian aid goes?
- What information from local communities would be most useful for these assessments and why?
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