1. Measuring Vulnerability

1. 1. Measuring Vulnerability START Advanced Institute on Vulnerability May 11, 2004 Karen O’Brien CICERO, University of Oslo Email: karen.obrien@cicero.uio.no

2. Measuring Vulnerability: • Theoretical issues • Conceptualizations of vulnerability • Practical issues • The use of scenarios

3. Definitions of Vulnerability • ”an aggregate measure of human welfare that integrates environmental, social, economic and political exposure to a range of harmful perturbations” (Bohle et al. 1994) • “…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” (Chambers 1989) • ”Vulnerability: the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes.(IPCC 2001)

4. Why measure vulnerability? • Identify magnitude of threats, such as climate change; • Guide decision-making on international aid and investment; • Prioritize aid for climate change adaptation; • Identify measures to reduce vulnerability.

5. Can vulnerability be measured? • Vulnerability is a characteristic, trait, or condition; not readily measured or observable, thus we need proxy measures and indicators; • Vulnerability is relative, not absolute; • Everyone is vulnerable, but some are more vulnerable than others; • Vulnerability relates to consequences or outcomes, and not to the agent itself; • Defining levels of vulnerability that prompt actions or interventions is a social and political process.

6. What is the opposite of vulnerability? • Is there an opposite? • Is it resilience, adaptability, or human security?

7. Conceptualizing vulnerability • Vulnerability can be conceptualized in different ways. • Any conceptualization of vulnerability can be interpreted in different ways. • Conceptualizations and interpretation of vulnerability have implications for what is measured and how it is measured. • Vulnerability measures can have political and economic consequences; transparency (in both concepts and methods) is necessary.

8. Biophysical vulnerability • Focuses on ecological processes, exposure to processes of physical change; • Indicators include length of growing season; frost days, intense precipitation, etc.

9. Social vulnerability • Focus on social, political, economic and cultural determinants of vulnerability. • Indicators include education, income, and other proxy data (social capital, entitlements, livelihood diversification).

10. Climate change vulnerability IPCC vulnerability framework: V = f(E, S, AC) E = Exposure S = Sensitivity AC = Adaptive Capacity

11. Exposure • The degree of climate stress upon a particular unit of analysis • Climate stress: • long-term climate conditions • climate variability • magnitude and frequency of extreme events

12. Sensitivity • The degree to which a system will respond, either positively or negatively, to a change in climate.

13. Adaptive Capacity • The capacity of a system to adjust in response to actual or expected climate stimuli, their effects, or impacts. The degree to which adjustments in practices, processes, or structures can moderate or offset the potential for damage or take advantage of opportunities created by a given change in climate.

14. Interpretation 1: • Vulnerability analysis as a means of defining the extent of the climate problem • Vulnerability = Impacts – Adaptations • Adaptability defines vulnerability • Vulnerability is the end-point of the analysis

15. Interpretation 2: • Vulnerability analysis as a means of identifying what to do about climate change. • Vulnerability is shaped by adaptive capacity. • Vulnerability determines adaptability • Vulnerability is the starting point of the analysis. • Under this interpretation, we need measures of the social processes that contribute to vulnerability.

16. Implications • End point: We need better GCM scenarios, better process models, and better quantifications of adaptation; • Starting point: We need better understanding of coping capacity, adaptive capacity, outcomes of social processes, and measures of well-being.

17. Measuring vulnerability:Practical challenges • How should indicators be chosen? • Are adequate data available? • How should composite indicators be developed? • How can measures of vulnerability be validated?

18. Choosing indicators: Deductive approach • Theory driven: Start from theory or hypothesis; find indicators that might support or reject the hypothesis. • Example: Adger and Kelly (2000) hypothesize that the architecture of entitlements is a key determinant of vulnerability in Vietnam; thus they identify income levels, income inequality and diversity of livelihood as key indicators.

19. Choosing indicators: Inductive approach • Data driven: Examine lots of data, look for patterns and examine correlations or statistical relationships. Generalizations can be used to develop conceptual models and theories. • Example: Ramachandran and Eastman (1997) analyzed 92 variables to explain the need for food assistance in West Africa. Using statistical methods, they identified the contributions of different variables to vulnerability.

20. Reality: • Eriksen and Kelly (submitted) point out that in most national level assessments of vulnerability, the selection of indicators is based on a ”rudimentary theoretical appreciation of vulnerability (which is often, it is only fair to say, all that is available)”. Few ”inductive” indicator studies explicitly discuss implications of findings for vulnerability theory. • Most studies that measure vulnerability are ”not easily distinguishable as either deductive or inductive…”

21. Data • Need for reliable, readily available, and representative data for desired indicators of vulnerability. • Compiling national data is difficult. National level vulnerability assessments often rely on existing global data sets (FAO, World Bank, UNDP, WRI, etc.) • More detailed data usually available for sub-national assessments (e.g., census data)

22. 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, p. 58)

23. Social relations exemplified in different sources of irrigation statistics for India • Irrigation Department • Irrigation data as basis for repayment of water fee to maintain irrigation facilities • Revenue office • Irrigation data as 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

26. Does the choice of indicators and index matter? ”In one sense, this is an empirical question. The analyst should test different formulations—choices of indicators, transformations, modes of aggregation, variations in data quality, etc. If the overall rankings do not differ much, then one could argue for the simplest formulation. Compiling an index is not however an end in itself. The form of the index may itself be part of the process of getting support for the index and its policy implications.” Source: Downing et al. 2001

27. Dynamics of vulnerability • Vulnerability is dynamic; indicators are often static. • Snapshots of vulnerability do not tell us who is becoming more vulnerable (or less vulnerable) as time goes on.

28. Creating composite indices • Vulnerability is multi-dimensional; there is no one indicator that adequately represents vulnerability. • Composite indices can provide a more complex measure of vulnerability. • Many potential methods exist for aggregating indicators (e.g., indiscriminate aggregation, weighted indicators, targeted indicators, contingent indicators, dynamic indicators, heirarchical vulnerability indices, vulnerability profiles)

29. Creating composite indices • ”Unless a verifiable outcome variable is available, there is no clear reason to choose a particular approach. A guiding principle may be to keep the analysis transparent and accessible to end users.” (Downing et al. 2001)

30. Verifying measures of vulnerability • ”Verification conveys authority and credibility, but also contributes to improving the understanding of vulnerability and hence the representation of processes in indicator studies” (Eriksen and Kelly, submitted)

31. Verifying measures of vulnerability • In the case of deductive approaches, verification involves assessment of goodness of fit between theoretical predictions and empirical evidence. • In the case of inductive approaches, the statistical analysis must incorporate verification of any results through testing on independent data. • Unfortunately, such verification has been limited in existing studies of vulnerability indicators. Source: Eriksen and Kelly, article submitted to MASGC

32. Verifying measures of vulnerability • Is the outcome acceptable? • Does the ranking match what people expect based on their experience? • Can anomalies be explained? • Who should be the judge? • How can dissenting views be represented? Source: Downing et al. 2001

33. Measuring vulnerability: Scenarios • When we are concerned about future conditions (e.g., under climate change), and we want to project vulnerability into the future, we need scenarios. • Focusing on present-day vulnerability to future climate change can provide a starting point for actions or interventions to reduce vulnerability; less useful for assessing the extent of the climate change problem.

34. Different types of scenarios: • Climate change scenarios: Generated by general circulation models (GCMs) or synthetic scenarios (+/- 10% precipitation, 30 cm sea level rise, etc.); • The output of GCMs depend on assumptions about greenhouse gas emissions, feedbacks, etc. SRES scenarios represent emissions according to different development trajectories; • Vulnerability will depend on social and economic trends (economic development, population growth); • However, globalization is creating structural social, economic and political changes, thus extrapolation of trends into the future may not be sufficient to describe the future.

35. Scenarios • How can we incorporate future scenarios into measures of vulnerability? • What types of uncertainty are added to vulnerability measures? • How can measures of vulnerability based on scenarios be validated?

36. 2. Mapping Vulnerability

37. Why map vulnerability? • Vulnerability can be both socially and spatially referenced (it is associated with social and environmental phenomena, which often have locational components); • Measures of vulnerability can be visualized through mapping, and patterns can be identified and analyzed through spatial analysis (tomorrow’s lecture!).

38. How to map vulnerability? • Mental mapping • Remote sensing (NDVI) • Geographic Information Systems and Science (GIS)

39. Examples of vulnerability maps:

42. The issue of scale • National scale assessments of vulnerability (to produce a global map) • Regional vulnerability assessments (e.g., West Africa) • Sub-national vulnerability assessments (e.g., Norway, India)

43. National level vulnerability maps • Need indicators common to all countries (comparable time periods, units) • Present coarse generalizations; hide sub-national variations and ”pockets of vulnerability.” • Can be useful for broad comparisons, correlation with other national statistics (GHG emissions)

44. Regional-level vulnerability maps • Represents differential vulnerability across regions; • Context-specific indicators can be chosen; • Potentially greater availability of data (from regional institutions, or compiled from national statistics); • Useful for identification of regional ”hot spots” and policy analysis.

45. Sub-national vulnerability maps • Represents variations in vulnerability within one country, state, county, district, or village; • Potentially larger amount of data available (but large data gaps can still exist); • Can be used to develop national adaptation strategies, aid distribution, development plans, etc.

46. Challenges • Integrating raster and vector or biophysical and social data; • Normalization and weighting of indicators; • Classification

47. Example of Mapping Approach • Vulnerability of Agriculture to Climate Change in Norway