GCRI · Economic Development
Food energy water economics
Food, energy and water are inputs to every sector and large shares of household budgets in poorer countries. Their prices, markets, subsidies and investment shape growth, inflation, poverty and fiscal balances, and they are linked to each other. This page sets out how these resource markets affect development, how price and access are measured, and what evidence serves the ministries and regulators involved.
Food energy water economics on the record.
Food energy water economics: how the risk develops.
Critical cost and livelihood dependencies. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Food prices dominate poor budgets
Poor households spend a large share of income on food, so food price spikes cut real incomes sharply and can push people into poverty.
Energy prices pass through the economy
Fuel and electricity costs feed into transport, production and food prices, spreading energy shocks across sectors.
Subsidies are costly and regressive
Universal fuel and electricity subsidies cost budgets heavily and often benefit wealthier households most. Targeted support costs less per poor household.
Water is underpriced and underinvested
Low tariffs and weak utilities leave water infrastructure underfunded, raising costs for those served by vendors and reducing reliability.
Water now in Africa.
Night lights, population density, flood water and true-colour imagery over Africa, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 4
Water now, for this page
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.
Water now, framed on Africa.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 HLS S30Sources 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
- 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
- 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 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
- SMAP Level 4 soil moisture and snow mass, NASA Global Modeling and Assimilation Office; distributed by the NASA National Snow and Ice Data Center DAAC; 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
- 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 HLS S30 (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).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
- Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
- 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 (14 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
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026
The economics of the nexus show where costs fall.
Findings are designed for the Country Systems Risk and Resilience series. Environmental-economic accounts (SEEA) record water and energy use by industry.
- Water and energy accounts.
- Subsidy reform evaluations.
Shock propagation model.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Shock propagation model
Food energy water economics · National economy · Scenario: import price shock
Output impact by channel
Most affected sectors
| Food processing | ▼ 8 | 3062 | Backward |
| Transport | ▼ 6 | 5411 | Forward |
| Construction | ▼ 4 | 4826 | Backward |
| Retail | ▼ 3 | 7308 | Demand |
Fiscal room
local units, billions, 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 |
|---|---|---|---|---|
| Food inflation | percent change in food consumer prices | monthly | Consumer price indices | Published statistics |
| Share of food in household spending | percent | per survey | Household budget surveys | ESTIMATED |
| Energy subsidies | percent of GDP | annual | IMF and IEA estimates | ESTIMATED |
| Electricity access | percent of population | annual | Tracking SDG 7 | ESTIMATED |
| Water tariff cost recovery | ratio of revenue to operating cost | annual | Utility data | REPORTED |
| Cost of a healthy diet | currency per person per day | annual | FAO and World Bank estimates | ESTIMATED |
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. Teaching model with stated illustrative values, not a prediction. The map on this page shows Africa; the model itself is not tied to a place.
Controls
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 11%
- Assets still working
- 31%
- Amplification of the initial damage
- 6.2 times
- Steps to settle
- 4
Numbers, methods and sources
| Step | Water working (%) | Power working (%) | Telecoms working (%) |
|---|---|---|---|
| 0 | 94 | 100 | 72 |
| 1 | 72 | 100 | 44 |
| 2 | 39 | 83 | 28 |
| 3 | 33 | 56 | 11 |
| 4 | 33 | 50 | 11 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Economic Development in Foundry
- Tested at Economic Development in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Economic Development in Reports
- Taught in Faculty of Finance and Economics in Academy
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 |
|---|---|---|
| FAOSTAT food price and food security data | Prices and cost of a healthy diet | Open, CC BY 4.0 |
| IMF fossil fuel subsidy estimates | Explicit and implicit energy subsidies | Public, IMF terms |
| Tracking SDG 7 report | Energy access by country | Open, CC BY 3.0 IGO |
| IBNET utility data | Water tariffs and cost recovery | Public, World Bank terms (to check) |
| National consumer price indices | Food and energy inflation | Public records, licence per statistics office |
Model food energy water economics as a system. Run the cascade.
Assembled from the Nexus Standards area profile Financial and economic 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
- Sea-level rise
- Dam failure
- Coastal flood (set off downstream)
- Storm surge (set off downstream)
- High-tide flooding (set off downstream)
- Coastal erosion (set off downstream)
- Saltwater intrusion (set off downstream)
- Estuary flood (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Coastal habitat squeeze (set off downstream)
- Wetland loss (set off downstream)
- Mangrove loss (set off downstream)
- Groundwater flood (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Climate-driven migration and relocation (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)NRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-H118Hazard family: Organised crime and illicit economies (NHS-1 soc.crime)NRS-H114Hazard family: Macroeconomic and fiscal shocks (NHS-1 soc.macro-fiscal)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-U27Universe platform profile: Sovereign Capital Nexus
Observation
- IMF data services (WEO, IFS, FSI via SDMX) (listed for reference)
- BIS Data Portal (credit, debt service, property prices, banking statistics) (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- OECD Data Explorer (SDMX API) (listed for reference)
- Macroeconomic Dashboard
- Debt service as a proportion of exports of goods and services
- Financial Soundness Indicators
Example geography
- Western Europe: 152 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
Who decides, and what the evidence serves.
Economic 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 finance and energy | How to reform subsidies while protecting the poor | Subsidy distribution reviews; incidence analyses |
| Ministries of agriculture and social protection | How to respond to food price spikes | Price shock incidence evidence |
| Water and energy regulators | How to set tariffs that fund services and remain affordable | Cost recovery and affordability evidence |
Outputs this topic produces
- Price shock incidence analyses by household group
- Subsidy cost and distribution reviews
- Resource access and affordability profiles
- Evidence notes on reform options
The sector signature of food energy water economics.
The sectors food energy water economics touches and how strongly they depend on each other: what a study has to observe and model together.
Signature
Water, Energy and Food
Water gives energy cooling water and hydropower (0.6); water gives food irrigation (0.8): the strongest relations between the sectors this subject joins.
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Energy → Infrastructure · power for transport and telecoms0.8
- Water → Energy · cooling water and hydropower0.6
- Water → Food · irrigation0.8
- Infrastructure → Water · pipes, dams and treatment works0.6
What experts ask about essential costs.
- How much of household spending goes to food, energy and water, and how does that vary by income?
- How do price changes in one of these goods affect the others?
- How do subsidies and tariffs affect fiscal budgets and incentives?
- Which livelihoods depend directly on agriculture, water and energy?
- How do climate shocks affect prices and incomes in these sectors?
How food energy water economics 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.
Sensitivity analysis
Which inputs move the result for food energy water economics most, one at a time across their stated ranges.
How essential costs shape development.
Poorer households spend a larger share of their income on food and energy, so price increases hit them hardest and can push people into poverty. Rising food prices can reduce nutrition, with long-term effects on health and learning.
The three goods are interlinked. Energy is used to pump and treat water and to produce fertiliser, process and transport food; water is used in agriculture and in many forms of power generation. A shock in one can raise costs in the others.
Many governments subsidise food, fuel or water to protect households. Subsidies can be costly, poorly targeted and hard to reform, and they can encourage overuse. Reform without adequate compensation can cause hardship and unrest.
Methods and their limits.
- 01
Household budget surveys
Data on spending patterns by income group. Surveys are infrequent.
- 02
Price transmission analysis
Studying how prices pass through markets using econometrics. Transmission changes with policy.
- 03
Microsimulation
Estimating effects of price or subsidy changes on households. Behavioural responses are simplified.
- 04
Coupled sector models
Linking food, energy and water models with coupled systems modelling. Coupling assumptions matter.
Price data in many markets are incomplete, and informal markets are poorly captured.
How it is studied, and what the methods cannot say.
- 01Economics and development economicsGrowth diagnostics · Structural transformation analysis · Distributional incidenceApplied here
- 02EconometricsShock transmission models · Panel analysis across countries · Nowcasting with alternative dataApplied here
- 03Causal inference and impact evaluationRandomised evaluations · Evaluating place-based policies · Regression discontinuityApplied here
- 04Network science and contagionProduct space analysis · Input-output shock propagation · Financial interconnectednessApplied 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.
Questions and answers.
Why do food prices matter for poverty?
Who benefits from fuel subsidies?
What is cost recovery in water services?
Why study these three together?
Does the platform take a view on subsidies?
Are livelihoods included?
How do climate shocks affect these costs?
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