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Appendix B: Methodology used for the design and analysis of household surveys and data.

Vulnerability: A Complex Term

There is no one single definition for the term "vulnerability" and no one single way of measuring it. Different disciplines define it and measure it differently, but the one common trend among all of them is the idea that the concept is related to levels and types of risks to which people/communities are exposed. Table B.1 summarizes some of the most commonly used definitions.

The differences between the approaches can be reduced to the tendency of each discipline "to focus on different components of risk, household responses to risk and welfare outcomes". All approaches have their strengths and weaknesses: some are considered strong in their conceptual framework but weak in their empirical approach (i.e., how it is measured) and vice versa. The definition used in this study is eclectic: it borrows from all of these disciplines.

Selection of Study Areas--Vulnerability Mapping

Vulnerability indices are commonly used in the field as a way to measure vulnerability by different researchers and institutions. Two such indices are the "Food Insecurity and Vulnerability Information and Mapping System" (FIVIMS), developed by the Food and Agriculture Organization of the United Nations (FAO); and the "Vulnerability Analysis and Mapping (VAM)", produced by the World Food Program in cooperation with FIVIMS. (58)

In this study, a vulnerability index was also developed to guide district selection. Case study sites were identified based on a vulnerability analysis using a "Principles, Criteria and Indicators" (PC&I) framework, together with a Geographic Information System (GIS). Rather than assigning weights and scores on an ad hoc basis, Principal Component Analysis (PCA)59 was employed to provide a statistical basis for determining the effect of each variable on the target variable, i.e. agricultural vulnerability. (60)

The drought- and flood-prone areas were demarcated and then overlaid with other maps containing information on other biophysical, social, and economic parameters. The basin was used as the geographical unit in the development of the maps. By superimposing maps with the different parameters and showing their fluctuation from one year to another over a reasonable period of time, a map depicting different degrees of variation is produced which serves as the basis for selecting specific sub-areas for analysis.

In this study, the secondary data on biophysical, social, and economic indicators such as gross cropped area, cropping patterns, groundwater availability, and an Infrastructure Development Index (LDI), among others, was compiled over different years spanning a 10-year period, for comparison purposes. The data was collected from various sources including the Survey of India (SOI), the Census of India (COI), the Central Ground Water Control Board (CGWB), the Central Water Commission (CWC), the National Bureau of Soil Survey and Land-Use Planning (NBSS & LUP), the National Atlas & Thematic Mapping Organization (NATMO), the Center for Monitoring Indian Economy (CMIE), the Indian Agricultural Statistics, Volumes I & II, the Agricultural Census, and the Maharashtra and National Information Center (NIC).

Using PCA, a vulnerability index was created which allocates degrees of vulnerability to districts: low, moderate, high, very high, and extremely high. Districts were classified according to the index and maps were then developed for the states of Andhra Pradesh, Orissa, and Maharashtra. An overlay of different profiles for these states thus forms the basis for the selection of the districts in each state, except for Orissa where official data was not available to allow for a comparison of vulnerability over time. Consequently, the district selection in Orissa was guided by a combination of (a) analysis of secondary data and (b) the extent of the geographical area which is considered to be liable to floods. Based on this analysis, some districts were deemed to face greater threats than others due to a combination of high biophysical and social vulnerability and limited infrastructure development.

The final selection of districts in the selected river basins was made to purposely capture a range of vulnerability patterns given their different socio-economic, technological, and biophysical conditions.

Field Surveys

Despite the fact that more than two districts in each of the three states were selected for climate projection (five in Maharashtra and four Andhra Pradesh), further prioritization of districts was necessary in conducting field surveys due to limits in time and budget. Thus, field surveys were carried out in two districts in each state. In the end, the districts of Anantapur and Chittoor in Andhra Pradesh, Jagatsinghpur and Puri in Orissa, and Ahmednagar and Nashik in Maharashtra were chosen for the study. The objectives of the surveys conducted were the following:

* to assess the coping capacities and vulnerabilities of communities in dealing effectively with droughts and floods; and

* to determine the factors that influence the effective implementation of coping measures at field level.

Institutional surveys were carried out to collect information on the central and state government plans and programs being implemented in the state and to ascertain their efficacy in enhancing the capacities of communities in dealing effectively with climate variability and conditions of extreme weather, including drought and floods. The field surveys sought to collect information on the communities' perceptions on (a) the intensity of droughts/floods, (b) the crops grown in the region, (c) the change in cropping patterns, irrigation, livelihood options and migration, (d) infrastructure, (e) the availability of financial services and schemes, and (f) the importance of insurance. Through these surveys, an attempt is made to undertake a critical review of policy and community-oriented interventions that enhance the capacities of communities to cope during extreme climate situations. In all, 1,640 households were surveyed: 570 households in Andhra Pradesh, 650 households in Orissa, and 420 households in Maharashtra.

Development of Tools for Institutional and Field Surveys

Questionnaires designed for implementation in drought and flood circumstances as well as other Participatory Rural Appraisal (PRA) tools were used. Secondary data including sketch maps, transect walk, collation of time-line information and trend-lines, seasonal cropping calendar mapping, institutional mapping, problem tree analysis, and problem and opportunity ranking was collected. In addition, group discussions, interviews, focus group discussions, and institutional surveys were carried out.

The questionnaires were pre-tested in pilot surveys in Rajasthan (a drought-prone area). It provided insights about the available quantitative information and its usefulness for the purpose of the survey, and it was improved and modified accordingly.

The lack of proper recorded information at the village level posed a major constraint to quantitative/statistical analysis.

Selection of Villages in Identified Districts Based on Analyses of Secondary Data

The selection of villages in each district was based on the screening of village-level secondary data collected from the census office. This data was collected for parameters including village land area, land use, cultivated and irrigated land, and availability of infrastructure including education, bank/credit, society, communication, power facility, and services like health care. The data was used for the preliminary selection of villages within each district. These were later confirmed by discussions with officials in government departments at the district level as well as other localized non-governmental organizations and communities at the village level.

Village Classification Based on Irrigation

All villages lying within a district were classified into one of three levels based on their irrigated area as a percentage of their total agricultural area: low (0-33%), moderate (33-66%), or high (66-100%).

Village Classification Based on Infrastructure Development

An infrastructure index was developed by considering the existence or level of certain facilities and services at the village level including the availability of drinking water, education facilities, medical facilities, electricity, banks, agricultural society, and communication linkages.

The villages were assigned to one of four categories according to their irrigation- and infrastructure-based classification. The purpose of this categorization was to select villages that were representative of different contexts which may further the understanding of the factors underlining the different levels of vulnerabilities. These broad criteria on irrigation and infrastructure are used to classify the villages in a matrix as the one shown here below.


The sample size, n, for each target population is computed using the formula by Murthy, (1977):

n = N * n/N + n -1

Here, n = [c.sup.2]/[e.sup.2] where c is the population coefficient of variation and e is the allowed percentage of error. ./Vis the target population size (480).

To obtain a representative sample, proportionate sampling based on landholdings were conducted. Records indicating household land category were collected from the Tehsildar (61). The survey was conducted based on the various landholding categories: >4 acres (large farmers); 1-4 acres (medium farmers); < 1 acre (small farmers) (62); and landless. After conducting the survey, all the data was coded and entered, and were used for quantitative analysis. A reference manual was also developed to facilitate viewing and referencing.

Measure of Income Volatility

The Coefficient of Variation (CV) is used to understand the extent to which household incomes are volatile to the impact of drought/floods events (63). The CV is simply a measure of the deviation of 'impact year' income from 'normal year' income.

CV is defined as the ratio of the Standard Deviation to the Mean ([mu]) and is defined by the following formula:

[c.sub.v] = [sigma]/[mu]

In this formula, [sigma] is standard deviation and [mu] is average income (64). It is often represented as a percentage by multiplying the above by 100.

An advantage of the CV is that it is free from the units of the variables, and it thus permits comparisons with respect to their variability. The CV is commonly used since it is a quantity without physical units. Although the CV indicates the magnitude of variations, it fails to capture the directional shifts in income. As a substantial majority of the surveyed households experienced drops in income in an impact year, few 'outlier' households that showed an increase in income during an impact year were segregated out.
Table B.1: Definitions of Vulnerability

Literature Definition of Vulnerability

Economics It is an outcome of a process of
(57) household responses to risks,
 given a set of underlying
 conditions. Often times, the
 outcome is poverty.

Sustainable It is the probability that
Livelihoods "livelihood stress" will occur--
 with more stress or a higher
 probability implying increased
 vulnerability. Also, "the balance
 between the sensitivity and
 resilience of a livelihood

Food It is the risk of irreversible
Security physical or mental impairment
 due to insufficient intake of
 macro or micronutrients.

Disaster It is the characteristics of a
Management person or group in terms of their
 capacity to anticipate, cope with,
 and recover from the impact of a
 natural disaster. It is an
 underlying condition separate
 from that of the risky events that
 may trigger the outcome. It
 refers to risks as "hazards".

Discipline/ What is Measured How
Literature it is Measured

Economics The fall of income beyond
(57) the poverty line or
 changes in consumption
 are measured.

Sustainable The loss of livelihood,
Livelihoods continued vulnerability to
 subsequent shocks and
 vulnerability changes over
 time are the subjects of
 interest. The assessments
 are specific to population
 or society. It uses a case
 study approach.

Food Vulnerability mapping
Security and indexes. A number of
 analytical techniques are
 used to examine the
 degree of correspondence
 between the concept of
 food security and the
 indicators chosen to
 measure it.

Disaster Vulnerability =
Management Hazard--Coping
 Household characteristics
 are key determinants in
 that they affect either side
 of the equation.
 The use of vulnerability
 mapping is also

Literature Criticism

Economics There is an underlying
(57) presumption that all
 losses can be measured in
 monetary terms.

Sustainable It tends to use terms and
Livelihoods concepts that are unclear
 or not widely accepted. It
 is not clear how changes
 in vulnerability would be
 evaluated over time when
 some indicators show a
 positive change while
 others a negative one.

Food It usually lacks a
Security benchmark to which
 indicators can be
 compared. It recognizes
 that vulnerability is made
 up of different
 components, but it ignores
 the specific process by
 which the components
 interact to determine
 overall vulnerability.

Disaster There is a lack of
Management precision in the language
 used, which leads to
 confusion. At times, it
 fails to be specific about
 what constitutes loss or
 damage, or whether it
 matters who endures

Figure B.1 Example of the Village Classification in the
Infrastructure-Irrigation Matrix

 High Irrigation Low Irrigation

High For instance: For instance:
Infrastructure Korhate in Manesaudram in
 Maharashtra Andhra Pradesh

Low For instance: For instance:
Infrastructure Neramatla in Andhra Brahmanapalle in
 Pradesh Andhra Pradesh
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Title Annotation:Climate Change Impacts in Drought and Flood Affected Areas: Case Studies in India
Publication:Climate Change Impacts In Drought and Flood Affected Areas: Case Studies In India
Date:Jun 1, 2008
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