Back to the lesson·Lesson 4 of 8·Coping is a sequence, not a score
76.3% flagged by one, 3.4% by all four
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What this lesson covers
- Four answers to one question
- The overlap
- Why they disagree, in order of importance
- What not to do about it
- What to do instead
- The rule this course is built on
- What comes next
Speaker notes
Four instruments on the same 1,955 households give prevalences of 7.4%, 42.9%, 51.0% and 48.2%. The union is 76.3% and the intersection is 3.4%. Every argument in the rest of this course starts from that gap.Four answers to one question — In Python
import pandas as pd survey = pd.read_csv("food-security-survey-2024.v1.csv") coping = pd.read_csv("livelihood-coping-2024.v1.csv") data = survey.merge(coping, on="household_id", how="inner", suffixes=("", "_lcs")) flags = pd.DataFrame({ "poor_consumption": fcs <= 28, # FCS on the 28/42 set "hunger": hhs >= 2, # HHS moderate or severe "high_coping": rcsi >= 19, # rCSI at or above the median "crisis_strategies": severity.isin(["crisis", "emergency"]), }) flags = flags.dropna() print(f"analysable on all four: {len(flags)} of {len(survey)}") print((flags.mean() * 100).round(1))Speaker notes
Every instrument in the last two lessons claims to identify food-insecure households. Put them on the same households and count.Four answers to one question — In R
library(dplyr) flags |> summarise(across(everything(), ~ mean(.x)), n = n())Four answers to one question
Instrument Households flagged Share Poor food consumption 144 7.4% Moderate or severe hunger 839 42.9% High coping index 997 51.0% Crisis or emergency strategies 943 48.2% Four answers to one question
- A sevenfold range on the same 1,955 households — These are not four estimates of one quantity with sampling error…
Speaker notes
A sevenfold range on the same 1,955 households. These are not four estimates of one quantity with sampling error between them. They are four different quantities, each correctly measured.The overlap — In Python
count = flags.sum(axis=1) print(count.value_counts().sort_index()) print(f"any: {(count >= 1).mean():.1%} all four: {(count == 4).mean():.1%}")The overlap — In R
flags |> mutate(n_flags = rowSums(across(everything()))) |> count(n_flags)The overlap
Flagged by Households Share None of the four 464 23.7% One 591 30.2% Two 434 22.2% Three 400 20.5% All four 66 3.4% The overlap
- 76.3% of households are flagged by at least one instrument and 3.4% by all four — Both numbers are defensible answers…
Speaker notes
76.3% of households are flagged by at least one instrument and 3.4% by all four. Both numbers are defensible answers to "how many households are food insecure", and they are twenty-two times apart. This is the single most important table in the course. Everything that follows — the evidence table, the convergence rule, the working group — exists because this table looks the way it does.Why they disagree, in order of importance
- They measure different moments in the same process — Coping runs ahead of consumption
- They have different sensitivities — The FCS at 7.4% is the least sensitive instrument here by a wide margin, because…
- Some ask about behaviour and some about experience — rCSI and LCS ask what a household did; HHS asks what it *went…
Speaker notes
They measure different moments in the same process. Coping runs ahead of consumption. A household sells its goats in June and is still eating in July, so the LCS flags it and the FCS does not. Reading only consumption sees the crisis about four months late. They have different sensitivities. The FCS at 7.4% is the least sensitive instrument here by a wide margin, because its threshold was written to identify severe dietary deprivation rather than stress. That is not a defect — it is what makes a poor FCS a strong signal when it appears. Some ask about behaviour and some about experience. rCSI and LCS ask what a household did; HHS asks what it went through. A household with resources copes without hunger; a household without resources goes hungry without coping, because there is nothing left to sell.Why they disagree, in order of importance — In Python
nothing_left = flags["hunger"] & ~flags["crisis_strategies"] print(f"hungry, no crisis strategies: {nothing_left.sum()} households")Why they disagree, in order of importance — In R
flags |> filter(hunger, !crisis_strategies) |> nrow()Why they disagree, in order of importance
- 282 households are hungry and using no crisis or emergency strategy — That is the group a coping-only analysis loses,…
Speaker notes
282 households are hungry and using no crisis or emergency strategy. That is the group a coping-only analysis loses, and it is the group furthest along.What not to do about it
- Average them into a composite score — There is no defensible weighting, the instruments are on different scales, and a…
- Pick the one that gives the number you expected — This happens more often than the previous one and is harder to see,…
- Take the intersection to be safe — 3.4% is the most conservative estimate and it excludes the 400 households flagged by…
Speaker notes
Three repairs that all look principled. Average them into a composite score. There is no defensible weighting, the instruments are on different scales, and a household at 7.4% on one and 51% on another is not "at 29%". Composite food security indices exist and every one of them buries the disagreement rather than resolving it. Pick the one that gives the number you expected. This happens more often than the previous one and is harder to see, because each individual choice is defensible in isolation. Choose the instrument before you compute it, on the question being asked, and write the choice down. Take the intersection to be safe. 3.4% is the most conservative estimate and it excludes the 400 households flagged by three instruments out of four. Being conservative about a caseload is not caution; it is a decision to serve fewer people, and it should be made as one.What to do instead
- Report all four, with what each is for
Speaker notes
Report all four, with what each is for.What to do instead — Example
Food security indicators, lean season 2024, 1,955 households Poor food consumption (FCS ≤28) 7.4% severe dietary deprivation Moderate or severe hunger (HHS ≥2) 42.9% experienced deprivation High coping (rCSI ≥19) 51.0% consumption-based coping Crisis or emergency strategies (LCS) 48.2% asset depletion Flagged by at least one 76.3% Flagged by all four 3.4% These are four different quantities, not four estimates of one. The spread between them is the finding: coping and asset depletion are widespread while severe dietary deprivation is not yet, which is the profile of a population early in a deterioration rather than late in one.What to do instead
- The last sentence is the analysis — The pattern across the four — high coping, high asset depletion, low severe…
Speaker notes
The last sentence is the analysis. The pattern across the four — high coping, high asset depletion, low severe consumption deficit — is a diagnosis, and it is only available because the four disagree. A single composite would have produced one number and no diagnosis at all.The rule this course is built on
- Convergence of evidence means agreement across independent instruments raises confidence, and disagreement is information rather than error
Speaker notes
Convergence of evidence means agreement across independent instruments raises confidence, and disagreement is information rather than error. Where three instruments agree and one does not, ask what the fourth measures that the others do not, before deciding it is wrong. In this population the odd one out is the FCS, and what it measures that the others do not is severity — so the disagreement says the deterioration has not yet reached diets, which is a finding about timing and an argument for acting now.What comes next
- Everything so far is what households reported about themselves.
Speaker notes
Everything so far is what households reported about themselves. The next unit is the market they buy from — an independent line of evidence that does not depend on anybody's recall, and that turns out to explain the timing of all four indicators.