Course · Intermediate · Sector Analysis
Food Security Analysis and IPC
Compute FCS, rCSI, HHS and LCS correctly, then use them the way an IPC analysis workshop does — as converging evidence, not as a single score.
What you will be able to do
- Compute the Food Consumption Score from its raw components, and state which threshold set you applied and why
- Score the Household Hunger Scale and reduced Coping Strategy Index with the exclusion rules each one requires
- Classify a household on the Livelihood Coping Strategies module by its most severe strategy rather than by a count
- Read a price series through its seasonality, and detect a market panel that changed composition
- Compute terms of trade and explain what they show that neither price shows alone
- Assemble an evidence table and say what a Phase 3 classification does and does not assert
Standards and methodologies
Food security is the sector with the most standardised indicators on this platform and the widest gap between computing one and understanding it. Every instrument here has a published definition, a scoring rule and a threshold, and almost every one of them is routinely applied with one step missing.
The course runs on three files. The household food security survey carries the raw components of the Food Consumption Score, the Household Hunger Scale and the reduced Coping Strategy Index, so all three have to be built rather than read off. It gains a Livelihood Coping Strategies module here, keyed on household, because the survey shipped without it and a module is a second file rather than a rewritten one. The market price series is new: twenty-four months, twelve markets, seven commodities and a wage.
Three results shape the course, all computed from those files.
Four indicators on the same 1,955 households give four different answers. Poor food consumption catches 7.4% of them; moderate or severe hunger 42.9%; a high coping index 51.0%; crisis or emergency livelihood strategies 48.2%. 76.3% are flagged by at least one and 3.4% by all four. That gap is the reason the IPC convenes a working group rather than running a formula, and it is the argument this course is built around.
Seasonality is bigger than most programme effects. Median maize runs 75 gourdes per kilogram at the December harvest and 130 at the July lean-season peak. A comparison of two survey rounds taken at different points in that cycle measures the calendar.
A goat’s price fell 18% and its purchasing power fell 62%. In 2023 a goat bought 69.4 kg of maize; at the 2024 lean-season peak it bought 26.7. Neither the goat series nor the maize series shows that on its own.
Both Python and R throughout. You should have done module 3 — this course assumes you can defend a denominator, put an interval on a proportion, and write an indicator reference sheet before you write the analysis.
Practice
Reading the lessons is not the same as having done the work. Each of these applies the course to a dataset it did not teach on.
- Lab · 180 minFour answers to one questionBuild all four food security indicators from raw components, put them on the same households, and find they disagree by a factor of seven. Then assemble the evidence table that reports the disagreement instead of resolving it.
- Exercise · 75 minThe baseline was in DecemberA donor wants to know why maize is up 113% since the baseline. The baseline was taken at the harvest and the follow-up at the lean peak. Separate the calendar from the deterioration, and write the reply.
Progress
Take it offline
The whole course as a typeset PDF — every lesson, every code example, the data dictionary and the indicator definitions. Generated from the same source as this page.
The LaTeX source ships alongside each PDF, so an organisation can rebrand the handout or fold a lesson into its own training pack.