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

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 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.

Can we find data – and trust it?

data availability and reliability

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

How do we define different levels of vulnerability?

Normalization and classification

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)

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

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%*

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

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

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

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

Ground truth and causal analysis

The need for field work and case studies

Anantapur villagers

Anantapur, Andhra Pradesh – four years of drought

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

To conclude

”All maps state an argument about the world” (Harley)

• Know your concepts

• Know your data

• Know your people