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Individual humanitarian index datasets

individual_indices.Rd

Seventeen consistently structured datasets, one for each published index in the collection. Each uses the same 195-country master geography so datasets can be compared or collated by iso3; rows outside an index's published coverage contain missing source and score fields.

Format

Each object is a data frame with 195 rows and 19 variables:

country

Country name from the master geography.

iso3

Three-letter ISO 3166-1 country code.

region

United Nations M49 region.

subregion

United Nations M49 subregion.

index_id

Stable short identifier for the index.

index_name

Human-readable name of the index.

source_country

Country label in the publisher's source; missing when the country was not present in that source.

score

Publisher score on its original scale.

score_label

Publisher display label when meaningful, including censored values, score ranges, percentages, or classifications.

reference_year

Year or period represented by the observation.

edition

Publisher's edition or release label.

score_direction

Whether a higher or lower publisher score denotes greater vulnerability: higher_worse or lower_worse.

rankable

Whether the index is eligible for ranking.

eligible_for_counts

Whether an available rank contributes to country-level summary counts and proportions.

n_scored

Number of countries with an exact numeric score for the index.

rank

Rank among scored countries, with 1 denoting most vulnerable; ties receive the minimum rank.

decile

Vulnerability decile derived from rank, from 1 (most vulnerable) to 10 (least vulnerable).

top_10

Whether the country has vulnerability rank 10 or better.

top_20

Whether the country has vulnerability rank 20 or better.

Source

Publisher details, URLs, and source-specific retrieval dates are in humanitarian_index_sources. CliF-VI uses the June 2025 methodology.

Details

Publisher scores retain their original scales. Rankings are calculated separately within each index's exact numeric coverage; missing and censored scores are not ranked. Deciles are min(10, floor(10 * (rank - 1) / n_scored) + 1). Ties use minimum rank, so more than 10 or 20 countries can have top_10 or top_20 set to TRUE.

The included datasets are:

  • inform_risk: INFORM Risk, 2026 v0.7.2. Measures humanitarian crisis and disaster risk; higher scores indicate greater vulnerability.

  • inform_severity: INFORM Severity, June 2026. Measures current crisis severity; higher scores indicate greater vulnerability. Coverage is the publisher's country sheet.

  • underfunded_crisis: Underfunded Crisis Index, 2025. Reports the cumulative share of requirements funded for 2021–2025; lower percentages indicate greater vulnerability. Regional plans are excluded.

  • oecd_fragility: OECD Multidimensional Fragility, States of Fragility 2025. Measures exposure to risk and insufficient resilience; lower scores indicate greater vulnerability. The public artifact covers 61 high- or extreme-fragility contexts.

  • worldrisk: WorldRiskIndex, 2025. Measures disaster risk from exposure and societal vulnerability; higher scores indicate greater vulnerability.

  • nd_gain: ND-GAIN Country Index, 2026 release using 2024 scores. Measures climate vulnerability and adaptation readiness; lower overall scores indicate greater vulnerability.

  • hdi: Human Development Index, Human Development Report 2025 using 2023 scores. Measures health, education, and living standards; lower scores indicate greater vulnerability.

  • mpi: Global Multidimensional Poverty Index, 2025. Measures overlapping household deprivations; higher scores indicate greater vulnerability. Reference years vary by country survey.

  • ghi: Global Hunger Index, 2025. Measures hunger from undernourishment and child-health indicators; higher scores indicate greater vulnerability. Only exact values are ranked; censored values and ranges remain labels.

  • ghs: Global Health Security Index, 2021. Measures epidemic and pandemic preparedness; lower scores indicate greater vulnerability.

  • wps: Women, Peace and Security Index, 2025/26. Measures women's inclusion, justice, and security; lower scores indicate greater vulnerability.

  • un_mvi: United Nations Multidimensional Vulnerability Index, High-Level Panel results using 2023 data. Measures structural vulnerability and lack of resilience; higher scores indicate greater vulnerability.

  • debt_distress: IMF debt-distress classification, 31 March 2026. The published class is paired with a derived ordinal code from 1 (low) to 4 (in debt distress); it is intentionally not ranked or placed in deciles.

  • searo: Sexual Exploitation and Abuse Risk Overview, 2026 v1.2 using December 2025 data. Measures contextual safeguarding risk; higher scores indicate greater vulnerability.

  • disaster_displacement: IDMC Disaster Displacement Risk Model, GDRM 2.0. Intended to report current-climate annual average displacement, with higher values indicating greater vulnerability. Values are missing in this snapshot because no stable country table was available.

  • internal_displacement: IDMC Internal Displacement Index, 2022 values published in 2023. Measures policy, capacity, drivers, and impacts; lower scores indicate greater vulnerability. Numeric coverage is 44 countries.

  • clif_vi: Climate Finance Vulnerability Index (CliF-VI), 2025 prototype, 2050 pessimistic projection. Combines climate risk with financial vulnerability; higher scores indicate greater vulnerability. Scores cover 188 countries and retain publisher precision on the 0–100 scale. Ranks are recalculated with 1 denoting most vulnerable, reversing the publisher's ranking direction. Ranks contribute to top-10 and top-20 summaries. Climate risk incorporates INFORM Risk 2025 and INFORM Climate Change 2022 projections; this is not an independent measure of current risk. Supplementary governance scores are not part of the overall index.

See also

collate_indices(), humanitarian_index_sources

Examples

data(inform_risk)

head(inform_risk[!is.na(inform_risk$score),
                 c("country", "score", "rank", "decile", "top_10")])
#>               country score rank decile top_10
#> 1         Afghanistan   7.8    6      1   TRUE
#> 2             Albania   3.1  118      7  FALSE
#> 3             Algeria   4.3   75      4  FALSE
#> 5              Angola   6.0   27      2  FALSE
#> 6 Antigua and Barbuda   2.1  165      9  FALSE
#> 7           Argentina   3.7   94      5  FALSE