What the index measures
This project describes food-system vulnerability through trade dependencies, projected exposure and response capacity. It organizes seven indicators into three dimensions: sensitivity (five food-system indicators), exposure (the projection-based climate indicator, called “hazard” in the dataset), and coping capacity (ND-GAIN readiness).
This is the project’s composite framework. In IPCC AR6 terminology, hazard is a separate component of risk, rather than a part of vulnerability itself. The grouping should not be read as an official IPCC definition or an endorsed index.
The data describe 2014–2023. The index does not predict when a disruption will occur or how many people would go hungry.
The seven indicators
Food import dependency
Sensitivity · % · Higher values mean more vulnerability
Food-calorie-weighted import dependency profile, bounded for scoring. Not a traced share of imported calories eaten.
Feed import dependency
Sensitivity · % · Higher values mean more vulnerability
Import dependency of the feed basket, weighted by reported feed tonnes.
Food import burden
Sensitivity · % of exports · Higher values mean more vulnerability
Food imports as a percentage of merchandise exports. Source observations are three-year averages; 100% is the scoring saturation threshold.
Supplier concentration
Sensitivity · HHI · Higher values mean more vulnerability
Concentration of import suppliers, aggregated across commodity groups. 10,000 means a single supplier within a group.
Production concentration
Sensitivity · HHI · Higher values mean more vulnerability
Concentration of domestic production by tonnes. Heavy products can dominate this provisional measure.
Climate exposure
Exposure · 0–1 · Higher values mean more vulnerability
Normalized cereal-yield and population projections. This projection-based component is largely constant over the study window, not annual weather.
Adaptive capacity
Coping capacity · 0–1 · Higher raw values mean more capacity
ND-GAIN readiness. Higher is better: stronger capacity to respond to a shock. Darker shading indicates lower readiness.
Why dependency can exceed 100%
Gross dependency divides imports by domestic supply: production plus imports minus exports. Processing, re-exports and stock flows can produce values above 100%. Item ratios are bounded for the scoring calculation; uncapped diagnostic values remain available in country profiles.
The food basket weights item-level dependency by reported food calories. The feed basket uses reported feed tonnes. These are weighted dependency profiles, not an allocation of imported units to final food or feed use.
From indicators to scores
Indicators are normalized onto a common 0–1 vulnerability scale with fixed bounds and a floor of 0.01 for geometric aggregation. Readiness is direction-adjusted: stronger adaptive capacity means a smaller vulnerability value. Climate components have separately normalized directions before combination.
Weights sum to one. A geometric mean reduces compensability compared with an arithmetic average, but it is not a worst-case rule: low vulnerability on one indicator can still offset higher vulnerability on another.
The published headline is the equal-weight score averaged across 20 imputations. The hierarchical method is an alternative, not the calculation behind that headline.
Equal weights · 20-imputation mean
Headline snapshot: all seven indicators weighted equally, averaged across 20 filled-in datasets.
Equal weights · reference
All seven indicators weighted equally on the reference imputation. Compare with the other reference methods to isolate weighting.
Hierarchical · reference
Equal weight across sensitivity, exposure and coping after averaging within each dimension; reference imputation.
PCA · reference
Weights derived from the principal components of the reference data. A sensitivity check, not a uniquely correct weighting.
Exposure only · reference
Six indicators with adaptive capacity excluded, on the reference imputation.
Compare the four reference methods to isolate weighting choices. Comparing the headline mean against a reference method also changes the imputation summary, so that difference is not purely a weighting effect.
Missing data and uncertainty
The pipeline fills missing indicator observations using multiple imputation. The exported headline summarizes 20 completed datasets. Minimum and maximum scores form an imputation range, not a confidence interval and not a complete account of methodological uncertainty.
The site displays this range only with the mean equal-weight method. Alternative reference methods have no corresponding imputation bands in this snapshot. Missing exported values appear as unavailable, never as zero. Raw and normalized fields may reflect different pipeline stages and rounding, so the normalized score should not be reconstructed from a rounded raw display.
Checking the cereal calculation
FAO publishes a cereal import-dependency series. The standalone model recomputes that cereal measure and compares it against FAO’s published series. This checks a specific dependency calculation; it does not validate the full composite or its ability to predict food insecurity.
The 95th-percentile absolute error is 12.56 percentage points. Tail discrepancies remain despite the strong ranking agreement.
Research limitations
The repository’s September 2026 methods review calls for major revision. The website presents the existing exported results so they can be inspected, while keeping these limitations visible:
- Outcome validation needs revision. The review identifies dropped censored undernourishment observations and use of the same outcome during model development. It cannot be treated as a clean held-out validation.
- Exposure is projection-based. Cereal-yield projections include extreme values; their normalization can move rankings substantially. Population change is demographic pressure, not climate hazard.
- Indicators are correlated. Exposure and coping contain related development information. Nominal weights do not necessarily describe their effective influence.
- Production concentration is provisional. Its tonnage weighting makes heavy products dominant and is not a direct measure of ecological crop diversity.
- Supplier aggregation matters. Diet-calorie weighting can emphasize concentration even where imported shares are small.
- Time resolution varies. Food import burden comes from three-year averages; projection-based climate exposure contributes little annual change. An annual row should not imply every indicator was independently observed that year.
- Uncertainty is incomplete. Supplied ranges cover imputation variation for one method. They do not cover alternative normalization, weighting, source revisions or structural assumptions.
These limitations are reasons to examine components and compare methods, rather than interpret a single rank as a precise forecast. Research updates will require a new versioned data export.
Sources and provenance
Exported 2026-08-28 from pipeline revision 860875e. Coverage: 178 countries and 1,780 country-years.
- CEPII BACI: bilateral trade, HS12, V202601.
- FAOSTAT Food Balance Sheets: supply, production, food and feed baskets.
- FAOSTAT Food Security: import burden and cereal replication reference.
- ND-GAIN Country Index: exposure projections and readiness.
The source repository is currently private. The published data snapshot and field guide are available on the downloads page.