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Challenges in vulnerability mapping

Challenges in vulnerability mapping. Guro Aandahl , CICERO, and Dr Robin Leichenko, Rutgers University Presented at the GECHS Open Meeting in Montreal 16.-18. October 2003. Vulnerability to Climate Change and Economic Changes in Indian Agriculture.

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Challenges in vulnerability mapping

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  1. Challenges in vulnerability mapping Guro Aandahl, CICERO,and Dr Robin Leichenko, Rutgers University Presented at the GECHS Open Meeting in Montreal 16.-18. October 2003

  2. Vulnerability to Climate Change and Economic Changes in Indian Agriculture • Aim: Assess vulnerability of Indian agriculture to climate change in the context of economic changes. Identify highly vulnerable areas and groups, and provide policy makers with advise on how to reduce the vulnerability of farmers • TERI (India), CICERO (Norway), IISD (Canada) • Funded by CIDA, the Canadian International Development Authority, and the Norwegian Ministry of Foreign Affairs

  3. Methods – challenges and choices • Can we measure vulnerability? • operationalization • Can we find data – and trust it? • data availability and reliability • How do we define different levels of vulnerability? • normalization and classification • Is mapping enough?

  4. Can we measure vulnerability? operationalization

  5. Vulnerability - definition “…the exposure to contingencies and stress, and difficulty in coping with them. Vulnerability thus has two sides: an external side of risks, shocks and stress to which an individual or household is subject; and an internal side which is defenceless­ness, meaning a lack of means to cope without damaging loss” (p.1, Chambers 1989)

  6. Poverty and vulnerability • Are poor more likely to be exposed? • To computer viruses: clearly not • To earthquakes: Gujarat 2001, middle class people died • To climate change, droughts, floods etc: yes, to a certain extent • The poorest often live on and from marginal lands and floodplains • However, drought (or erratic rainfall) hits everybody

  7. Poverty and vulnerability • Are poor less able to cope? • Yes. • Less resources • Sell off productive resources • Fall down the poverty ratchet

  8. Dimensions of vulnerability (our operationalization) • Social development • Technological development • Biophysical conditions  Index for each of these factors.

  9. Can we find data – and trust it? data availability and reliability

  10. Reliability: The social nature of data “Data are usually treated unproblematically except for technical concerns about errors. But data are much more than technical compilations. Every data set represents a myriad of social relations.” (Taylor and Johnston 1995, p58)

  11. Data and social relations:Example: Sources of Irrigation statistics • Irrigation Department • Basis for repayment of water fee to maintain irrigation facilities • Revenue office • Basis for land taxes which are higher for irrigated lands • Agriculture Department • Supposed to survey all land in the district  No consistency between these sources

  12. How do we define different levels of vulnerability? Normalization and classification

  13. Normalization • HDI method (UNDP): Normalization to the range • But to which range?

  14. Fixing of ”goalposts” • Comparison in space • Who should we measure against? • …and time • Retrospective: What has happened in earlier periods? • Prospective: What are projections for the future? (reference: Anand and Sen 1994)

  15. Alternative goalposts • Actually occuring range or • Predefined maximum and minimum values

  16. Goalposts: actual range or predefined?

  17. Normalization: range (2) vs predefined max and min (3)

  18. Normalization: range (2) vs predefined max and min (3)- impact on ranks

  19. How to lie with maps: Classification • Exaggerate non-significant differences • Hide significant differences

  20. Data distribution for social index, 1991

  21. Data distribution for social index, 1991 – natural breaks (minimized variance within groups)

  22. Data distribution for social index, 1991 –quantiles (groups are equal size, 20% of pop)

  23. Classification: natural breaks (nb) vs quantiles (qnt)

  24. Ground truth and causal analysis The need for field work and case studies

  25. Anantapur, Andhra Pradesh – four years of drought Anantapur villagers

  26. Anantapur, Andhra Pradesh – four years of drought Large landowner • Been running at loss for four years • Taken son out of private school • Sold his car • Incurring debt

  27. Anantapur, Andhra Pradesh – four years of drought Poor peasants, labourers • Has to migrate for work • Last in line for village well • Incurring debt • Gets work through food for work programme

  28. To conclude ”All maps state an argument about the world” (Harley) • Know your concepts • Know your data • Know your people

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