Challenges in vulnerability mapping Guro Aandahl, CICERO, and Dr Robin Leichenko, Rutgers University...
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Transcript of Challenges in vulnerability mapping Guro Aandahl, CICERO, and Dr Robin Leichenko, Rutgers University...
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
• 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
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?
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)
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
Poverty and vulnerability
• Are poor less able to cope?
Yes.– Less resources
– Sell off productive resources
– Fall down the poverty ratchet
Dimensions of vulnerability (our operationalization)
• Social development
• Technological development
• Biophysical conditions
Index for each of these factors.
V.-Dimension Wanted variablesEmpowerment Child sex rate (”missing girls” or excess girl mortality)
Female literacy level
Literacy level
Fertility level
Share of landholdings by farm size
% Landless agricultural labourers
Technology Irrigation rate
Infrastructure Development Index (CMIE)
Source of irrigation
Access to safe drinking water
Fertilizer consumption
Poverty People below poverty line
Infant Mortality Rate
Housing status
Dependency on agriculture
Employment in agriculture
V.-Dimension Available dataEmpowerment Child sex rate (”missing girls” or excess girl mortality)
Female literacy level
Literacy level
Fertility level
Share of landholdings by farm size
% Landless agricultural labourers
Technology Irrigation rate
Infrastructure Development Index (CMIE)
Source of irrigation
Access to safe drinking water
Fertilizer consumption
Poverty People below poverty line
Infant Mortality Rate
Housing status
Dependency on agriculture
Employment in agriculture
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)
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
Normalization
• HDI method (UNDP): Normalization to the range
• But to which range?
)()(
100minmax
min
xxxxi
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)
Goalposts: actual range or predefined?
Indicator Occuring range [1991,2001]
Independent min and max
Agricultural dependency
6,6% - 94,7% 0% - 100%
Agricultural labourers 0,06% - 88,25% 0% – 100%
Literacy 13,7% - 95,7% 10%– 100%
Female literacy 4,2% - 93,97% 0% – 100%
”Missing girls” 43,2% - 48,5%* 40,0% - 48,5%*
How to lie with maps: Classification
• Exaggerate non-significant differences
• Hide significant differences
Anantapur, Andhra Pradesh – four years of drought
• Been running at loss for four years
• Taken son out of private school
• Sold his car
• Incurring debt
Large landowner
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