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 1. Can we measure vulnerability? operationalization 2. Can we find data – and trust it? data availability and reliability 3. How do we define different levels of vulnerability? normalization and classification 4. 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 defenceless­ ness, 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. V.-DimensionWanted variables EmpowermentChild sex rate (”missing girls” or excess girl mortality) Female literacy level Literacy level Fertility level Share of landholdings by farm size % Landless agricultural labourers TechnologyIrrigation rate Infrastructure Development Index (CMIE) Source of irrigation Access to safe drinking water Fertilizer consumption PovertyPeople below poverty line Infant Mortality Rate Housing status Dependency on agriculture Employment in agriculture

11. V.-DimensionAvailable data EmpowermentChild sex rate (”missing girls” or excess girl mortality) Female literacy level Literacy level Fertility level Share of landholdings by farm size % Landless agricultural labourers TechnologyIrrigation rate Infrastructure Development Index (CMIE) Source of irrigation Access to safe drinking water Fertilizer consumption PovertyPeople below poverty line Infant Mortality Rate Housing status Dependency on agriculture Employment in agriculture

12. 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)

13. 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

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

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

16. 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)

17. Alternative goalposts 1. Actually occuring range or 2. Predefined maximum and minimum values

18. Goalposts: actual range or predefined? IndicatorOccuring range [1991,2001] Independent min and max Agricultural dependency 6,6% - 94,7%0% - 100% Agricultural labourers0,06% - 88,25%0% – 100% Literacy13,7% - 95,7%10%– 100% Female literacy4,2% - 93,97%0% – 100% ”Missing girls”43,2% - 48,5%*40,0% - 48,5%*

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

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

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

22. Data distribution for social index, 1991

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

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

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

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

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

28. Been running at loss for four years Taken son out of private school Sold his car Incurring debt Large landowner

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

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