Transcript Challenges in vulnerability mapping Guro Aandahl

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?
Can we measure
vulnerability?
operationalization
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)
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.
Can we find data – and trust
it?
data availability and reliability
V.-Dimension
Wanted variables
Empowerment
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 data
Empowerment
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
How do we define different
levels of vulnerability?
Normalization and classification
Normalization
• HDI method (UNDP): Normalization to the range
( xi  xmin )
100 
( xmax  xmin )
• But to which range?
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)
Alternative goalposts
1. Actually occuring range
or
2. Predefined maximum and minimum values
Goalposts: actual range or predefined?
Indicator
Occuring range
[1991,2001]
Independent min and
max
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%*
Agricultural
dependency
Normalization: range (2) vs predefined max and min (3)
Normalization: range (2) vs predefined max and min (3)
- impact on ranks
How to lie with maps: Classification
• Exaggerate non-significant differences
• Hide significant differences
Data distribution for social index, 1991
SV91_2
80.00
70.00
Vulnerability score
60.00
50.00
40.00
30.00
20.00
10.00
0.00
1
13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277 289 301 313 325 337 349 361 373 385 397 409 421 433
Districts
Data distribution for social index, 1991 –
natural breaks (minimized variance within groups)
Data distribution, norm to range
80.00
70.00
Vulnerability score
60.00
50.00
40.00
30.00
20.00
10.00
0.00
1
13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277 289 301 313 325 337 349 361 373 385 397 409 421 433
Districts
Data distribution for social index, 1991 –
quantiles (groups are equal size, 20% of pop)
Data distribution, norm to range
80.00
70.00
Vulnerability score
60.00
50.00
40.00
30.00
20.00
10.00
0.00
1
13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277 289 301 313 325 337 349 361 373 385 397 409 421 433
Districts
Classification: natural breaks (nb) vs quantiles (qnt)
Ground truth and causal
analysis
The need for field work and case
studies
Anantapur, Andhra Pradesh – four years of
drought
Anantapur villagers
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
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
To conclude
”All maps state an argument about the world” (Harley)
• Know your concepts
• Know your data
• Know your people