GCRI · Society and Human Development
Poverty and inequality
Poverty is a lack of income, assets and basic capabilities; inequality is how unevenly resources and opportunities are spread between people and groups. Shocks such as disasters, price spikes and pandemics push people into poverty and widen gaps, while recovery is often slower for the poor. This page sets out how poverty and inequality are measured and how they respond to shocks, and what evidence serves the governments and agencies working to reduce them.
Poverty and inequality on the record.
Poverty and inequality: how the risk develops.
Exposure, distribution and social protection. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Shocks push people below the line
Many households live just above a poverty line. Illness, job loss, a failed harvest or a price spike can push them below it, and recovery can take years.
Deprivations overlap
Poor households often lack several things at once: income, education, health, water and housing. Measuring only income misses these overlapping deprivations.
Disadvantage persists across generations
Poor nutrition, missed schooling and lack of assets in childhood reduce earnings later, passing disadvantage on.
Gaps follow group lines
Inequality often follows gender, ethnicity, disability, location and migration status, which averages hide unless data are disaggregated.
People and exposure in Latin America and the Caribbean.
Population density, night lights and daytime surface heat over Latin America and the Caribbean, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
People and exposure, for this page
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- OMI Aura (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite retrieval; clouds leave gaps
- Newest at build
- 2026-10-10
- Resolution
- about 2 km grid
- Uncertainty
- Satellite column of nitrogen dioxide in the lower atmosphere on the day shown, mostly from combustion; not a ground-level concentration or a health warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
People and exposure, framed on Latin America and the Caribbean.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- OMI nitrogen dioxide (Aura) and OMPS aerosol index (Suomi NPP), NASA; distributed by the NASA GES DISC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (9 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Nitrogen dioxide, lower atmosphere column: OMI Aura, NASA GES DISC · 10 Oct 2026
Poverty concentrates exposure to every domain’s failures.
Findings are designed for the Country Systems Risk and Resilience series. The Global Multidimensional Poverty Index and the Human Development Report 2025 are the main references; no country is ranked.
- Disaggregated survey data.
- Programme evaluations.
Humanitarian needs overview.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Population below the international poverty line | percent of population | per survey | Household surveys compiled in the Poverty and Inequality Platform | ESTIMATED |
| Multidimensional poverty headcount | percent of population | per survey | Global MPI | ESTIMATED |
| Gini index of income or consumption | index | per survey | Household surveys | ESTIMATED |
| Income share of the bottom forty percent | percent of total income | per survey | Household surveys | ESTIMATED |
| Social protection coverage | percent of population covered by at least one benefit | per assessment | ILO World Social Protection data | Published statistics |
| Poverty rates disaggregated by sex, age and disability | percent by group | per survey | Household surveys | ESTIMATED |
Model poverty and inequality as a system. Run the cascade.
Assembled from the Nexus Standards area profile Societal and information risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Civil unrest
- Supply-chain disruption (set off downstream)
- Currency crisis (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H117Hazard family: Social cohesion and atrocity risk (NHS-1 soc.cohesion)NRS-X7Information integrity and public trustNRS-D2Society, inclusion and human development riskNRS-H52Hazard family: Information-integrity attacks (NHS-1 cyber.integrity)NRS-H79Hazard family: Mental health crises (NHS-1 health.mental)NRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)
Observation
- WorldPop gridded population (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- Global SDG Indicators Database (listed for reference)
- Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
- Proportion of people living below 50 per cent of median income
- Number of countries that adopt and implement guarantees for public access to information
Example geography
- The Horn of Africa: 253 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| World Bank Poverty and Inequality Platform | Poverty and inequality estimates from household surveys | Open, CC BY 4.0 |
| UNDP and OPHI Global Multidimensional Poverty Index | Overlapping deprivations by country and region | Public, terms per publisher |
| ILO World Social Protection data | Coverage of social protection | Open, CC BY 4.0 |
| World Inequality Database | Income and wealth distribution series | Open, CC BY 4.0 |
| DHS and MICS household surveys | Deprivations in health, education and living conditions | Free with registration, survey terms |
How poverty and inequality is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in poverty and inequality: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Who decides, and what the evidence serves.
Society and Human Development prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Ministries of social development and finance | Where and how to target poverty reduction | Profiles; small-area maps |
| Social protection agencies | Who should be covered and how support should scale in shocks | Vulnerability analyses; coverage data |
| Statistics offices | How to improve poverty measurement and disaggregation | Data gap evidence |
Outputs this topic produces
- Poverty and inequality profiles disaggregated by group
- Shock vulnerability analyses for households near the line
- Small-area poverty maps with uncertainty
- Evidence notes for social protection design
The questions experts ask.
- Who is poor, by which measure, and how stable is their situation?
- How are exposure and losses distributed across income, gender, age and location?
- Do shocks push households into poverty traps from which they cannot recover?
- Which social protection instruments reach people in time, and which leave gaps?
- How does inequality affect a society’s aggregate resilience?
Exposure, coping and traps.
Poverty is multidimensional: income, but also health, education, housing, water, sanitation and security. Poor households tend to be more exposed because land and housing in safe locations cost more, and more vulnerable because a given loss represents a larger share of what they own. When a shock hits, households cope by drawing on savings, borrowing, selling assets, migrating for work or reducing consumption. Some strategies are reversible; others, such as selling productive assets or withdrawing children from school, reduce future income.
Research on poverty dynamics identifies thresholds below which households cannot rebuild, creating traps. Inequality matters beyond individual hardship: unequal societies tend to invest less in shared protection, and the same aggregate shock produces larger human losses. Social protection, including cash transfers, public works, pensions and social insurance, reduces these effects, particularly when systems can scale up quickly after a shock.
Gender and life stage cut across income. Women, children and older people often face distinct risks within the same household, from unequal control of resources to care burdens that rise in crises, which is why analysis increasingly looks inside households rather than treating them as single units.
Measurement and evaluation.
Household data
Income, consumption and asset surveys measure poverty; they are infrequent and miss mobile and informal populations.
Small-area estimation
Combining surveys, census and satellite data to map poverty; models can blur local differences.
Impact evaluation
Experimental and quasi-experimental methods test what social protection achieves; results may not transfer across contexts.
Limits: monetary poverty lines capture only part of deprivation, intra-household inequality is hard to observe, and data on the poorest are often the weakest.
How it is studied, and what the methods cannot say.
- 01Social and behavioural scienceSurvey design for hard-to-reach groups · Qualitative and participatory researchApplied here
- 02EconometricsPoverty dynamics with panel data · Price shock incidence · Inequality decompositionApplied here
- 03Spatial statistics and geostatisticsSmall-area poverty estimation · Vulnerability and hazard overlayApplied here
- 04Causal inference and impact evaluationEvaluating cash transfers · Effects of disasters on households · Education programme evaluationApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Where this page leads across the Consortium.
Questions and answers.
What is the international poverty line?
What is multidimensional poverty?
Why disaggregate poverty data?
Does the platform decide who is eligible for support?
Why is inequality a risk issue?
Are poverty maps published?
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