Cost of ex post versus ex ante finance
The cost of borrowing, reallocating and delaying after a disaster is compared with the cost of pre-arranged instruments over many simulated years.
GRA · Disaster Risk Finance
Spatial financeWhere capital meets hazard, exposure and transition
Economics and development economics for disaster risk finance
Economics studies how disasters affect output, budgets and households, and how financing choices change outcomes. In disaster risk finance it supplies cost-benefit analysis of instruments and investments, fiscal impact estimates and evaluation of household effects.
01Register
Economics and development economics for disaster risk finance on the record.
Method recordSTEM method
- MethodEconomics and development economics
- StewardEconomic Development
- Applied inDisaster Risk Finance
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What economics and development economics does here.
Economics and development economics study how societies produce, distribute and consume resources and how living standards change. They estimate the costs of shocks and the trade-offs among policy responses. In disaster and crisis, sovereign and public finance, development finance and insurance and catastrophe, economic analysis quantifies indirect losses as shocks spread through supply chains and labour markets. It supports prioritising investment in prevention and recovery. The canonical description is on the STEM Commons.
Household welfare effects
Survey and administrative data are used to estimate how early cash affects consumption, assets and recovery, with comparison groups stated.
Fiscal impact of disasters
Revenue, spending and debt changes after past events are estimated to inform contingent liability assessments.
03Monitor
Econometric model.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Econometric model
Disaster risk finance · Economics and development · Run R-2436
Estimates stableGRA · Disaster Risk Finance
Estimates
robust standard errors
| Contingent liability, 1-in-50 | 0.41 | 0.18 | [−0.4, 0.6] | Yes |
| Pre-arranged finance | −0.09 | 0.13 | [0.0, 0.8] | Yes |
| Time to disburse | 0.10 | 0.05 | [−0.2, 0.4] | Yes |
| Basis risk | 0.74 | 0.20 | [0.0, 0.3] | Yes |
Method card
- Method
- Economics and development economics
- Sample
- 8053 obs
- Design
- panel, fixed effects
- Label
- MODELLED
Residuals by group
04Data
What the method consumes.
Datasets and records used by economics and development economics on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| National accounts and fiscal data | Macro and budget effects | Published statistics |
| Household surveys | Welfare effects | OBSERVED |
| Programme evaluations | Instrument effects | ESTIMATED |
05Loss model
Loss exceedance, average annual loss and probable maximum loss.
The loss a portfolio in the footprint can expect per year and at the 1 in 100 and 1 in 250 levels that insurers and lenders price, from the same three blocks every catastrophe model uses. Teaching model with stated illustrative values, not a prediction.
Controls
- Typical annual hazard intensity (location)
- 1.00 intensity index
- Spread of extremes (scale)
- 0.80 intensity index
- Intensity at which half the value is lost
- 4.00 intensity index
- Dispersion of vulnerability
- 0.55
- Share of value in the hazard footprint
- 40%
- Protection standard (return period)
- 50 years
Readouts
- Average annual loss
- 0.51% of value
- Loss at 1 in 100 years
- 24.5% of value
- Loss at 1 in 250 years
- 28.4% of value
- Intensity at 1 in 100 years
- 4.68 intensity index
Numbers, methods and sources
| Return period (years) | Intensity (intensity index) | Loss (% of value) |
|---|---|---|
| 1 | 0.09 | 0.00 |
| 2 | 1.27 | 0.00 |
| 4 | 1.91 | 0.00 |
| 7 | 2.47 | 0.00 |
| 13 | 3.00 | 0.00 |
| 24 | 3.51 | 0.00 |
| 44 | 4.02 | 0.00 |
| 82 | 4.53 | 23.55 |
| 154 | 5.03 | 26.44 |
| 287 | 5.53 | 28.87 |
| 536 | 6.03 | 30.88 |
| 1,000 | 6.53 | 32.53 |
Methods
- Gumbel extreme-value hazard Gumbel (1958), Statistics of Extremes, Columbia University Press
- Lognormal fragility (vulnerability) FEMA (2003), HAZUS-MH Technical Manual
- Exceedance-probability curve and average annual loss Grossi and Kunreuther (2005), Catastrophe Modeling, Springer
- Levee effect Di Baldassarre and others (2013), Socio-hydrology, HESS 17
- 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:
- Rivers Natural Earth
- Urban extents Natural Earth
- Cities Natural Earth
- Earthquakes M4.5+, past week (USGS) U.S. Geological Survey · register usgs.earthquake.summary-feeds
- Rain rate, last half hour (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
- Built-up and settlement extent, 2010 (HBASE, Landsat) CIESIN/Columbia, NASA SEDAC HBASE · register sedac.context-urban-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Disaster Risk Finance in Foundry
- Tested at Disaster Risk Finance in Universe
- Codified in Hazards and Disaster Risk Reduction in Standards
- Published in Disaster Risk Finance in Reports
- Taught in Faculty of Hazards, Disasters and Resilience in Academy
06Validation
How results are checked.
Models are assessed against historical episodes and out-of-sample data. Parameter estimates are compared across studies and identification strategies.
| Check | How it is done |
|---|---|
| Counterfactual stated | Each estimate states what it compares against |
| Discount rate sensitivity | Results are shown across discount rates |
| External validity | Context limits are stated with results |
Equilibrium models may understate disruption and nonlinear cascades. Informal economies and non-market losses are poorly measured.
07Exposure, loss and capital
What it means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Financing instruments by layer
How retention and transfer instruments could share losses from economics and development economics for disaster risk finance, from frequent to extreme.
| Layer | Who holds the risk | Speed of funds | Main limit |
|---|---|---|---|
| Retention and reserves | Budget, contingency funds | Immediate | Size of the budget |
| Contingent credit | Development banks, ministries of finance | Days to weeks | Debt capacity |
| Parametric cover and risk pools | Regional pools, insurers | Days, on a trigger | Basis risk |
| Reinsurance and catastrophe bonds | Reinsurers, capital markets investors | Weeks to months | Cost of capital |
| Residual loss | Households, firms, humanitarian funding | Uncertain | Unfunded |
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
09Stewardship
Who maintains the method.
Economic Development stewards economics and development economics for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of economics and development economics in this field?
Equilibrium models may understate disruption and nonlinear cascades. Informal economies and non-market losses are poorly measured.
How are results checked?
Models are assessed against historical episodes and out-of-sample data. Parameter estimates are compared across studies and identification strategies.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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