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Social Vulnerability Indices

Social Vulnerability Indices. Theoretical exercises or invaluable assessment tools?. Melanie Gall Hazards Research Lab University of South Carolina 2006 Summer Academy for Social Vulnerability, Munich UNU-EHS/MunichRe Foundation. Social Vulnerability and Risk Indices.

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Social Vulnerability Indices

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  1. Social Vulnerability Indices Theoretical exercises or invaluable assessment tools? Melanie GallHazards Research LabUniversity of South Carolina 2006 Summer Academy for Social Vulnerability, MunichUNU-EHS/MunichRe Foundation

  2. Social Vulnerability and Risk Indices • Global indexing programmes • Natural Disaster Hotspots (World Bank/Columbia Univ., 2005) • Disaster Risk Index (DRI) (UNEP/GRID-Geneva, UNDP, 2004) • The Americas programme (IDEA/IDB, 2004) • Social Vulnerability Indices • Human Wellbeing Index (Prescott-Allen, 2001) • Human Vulnerability Component of Environmental Sustainability Index (Esty et al., 2002 & 2005) • Predictive Indicator of Vulnerability (Adger et al., 2004) • Social Vulnerability Index (Cutter et al, 2003)

  3. Research Questions • Is the DRI a qualitatively sound measure? • Is the DRI a quantitatively sound measure • Does the DRI capture social vulnerability? • Does the DRI meet its purpose? Model of 2015Gridded Populationof the World (CIESIN)

  4. Background ‘Risk’ = Hazard * Population * Vulnerability Fatalities = Exposure * Indicators { • Estimation of risk of dying associated with • Earthquakes • Floods • Hurricanes • Droughts • 249 countries and territories

  5. Background (cont.) • Input Data: • World Bank, UNDP, FAO, WHO, UNICEF, UNDESA • EM-DAT • Internal calculations • Methodology: • Time period (1980-2000) • Delineation of 21-year average physical exposure • 21-year averages for socio-economic variables • Step-wise linear regression (weighting) • Explained variance (R2)

  6. Background (cont.) • Earthquakes • Hurricanes • Floods • Droughts

  7. 1. Is the DRI a qualitatively sound measure? Acknowledged shortcomings: • Bias towards medium to large-scale events • Bias towards developing countries • Bias towards large countries • Temporal bias • Bias towards certain hazard types • Bias towards physical exposure  No ranking (high sensitivity)

  8. 1. Is the DRI a qualitatively sound measure? (cont.) Hidden shortcomings: • Causality vs. correlation • Hazard-specific vs. all-hazards vulnerability • Retrospective vs. trend identification • Frequency vs. normalization • Ecological fallacy

  9. 100% 80% 60% Hurricane - Agriculture Hurricane - HDI Hurricane - Phys. Exp. 40% Earthquake - Urban growth Earthquake - Phys. Exp. Flood - GDP per capita Flood - Pop. Density 20% Flood - Phys Exp. Drought - Water access Drought - Phys. Exp. 0% 2. Is the DRI a quantitatively sound measure? Total OrderSensitivity Statistics Hurricane Flood Earthquake Drought DRI DRIHEF

  10. 2. Is the DRI a quantitatively sound measure? (cont.) TOTAL DRI based on Hurricane, Flood, Earthquake & Drought Top 5: India, Bangladesh, China, Philippines, Indonesia

  11. 2. Is the DRI a quantitatively sound measure? (cont.) DRI as a combination of any of the four risks (hurricane, flood, earthquake and/or drought) Top 5: Afghanistan, Bulgaria, Ethiopia, India, Bangladesh

  12. 100% 90% 80% 70% 60% 50% 40% Agriculture 30% HDI Urban growth 20% GDP per capita (ppp) Pop. Density 10% Water Access 0% Hurricane Flood Earthquake Drought DRI DRIVHEF 3. Does the DRI capture social vulnerability? Total OrderSensitivity Statistics

  13. 3. Does the DRI capture social vulnerability? (cont.) Total Vulnerability as a combination of eight indicators Top 5: Bangladesh, Burundi, Rwanda, India, Comoros

  14. 3. Does the DRI meet its purpose? • Target audience: policy makers, intl. organizations • Purpose: • indexing risk and vulnerability  comparability • policy advice and evaluation  parsimony, uncertainty • benchmarking, trends  sensitivity, responsiveness

  15. Next Steps • Common assessment methodology • Common set of vulnerability factors • Comparative evaluation • Spatial variability of indices • Change scales and time • Needs: • Improved documentation of hazard-related losses • Integration of local vulnerability studies • Availability and access to vulnerability data • Incorporation of post-normal science tools

  16. Thank you very much for your attention! Comments are welcome at melanie.gall@sc.edu

  17. Conclusions • Approach: deductive vs. inductive • Data: incomplete • Technique: output-driven vs. input-driven • Representativeness: physical vs. social • Output: parsimony vs. validity  DRI stands out in regard to • Integration • Representation

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