ORIGINAL ARTICLE

Application of fuzzy models to assess susceptibility to droughtsfrom a socio-economic perspective

Lilibeth A. Acosta-Michlik Æ K. S. Kavi Kumar ÆRichard J. T. Klein Æ Sabine Campe

Received: 27 July 2008 / Accepted: 27 July 2008 / Published online: 17 September 2008

� Springer-Verlag 2008

Abstract By combining the concepts of environmental

stress, state susceptibility and environmental crisis,

‘‘Security Diagram’’ (SD) provides a quantitative approach

to assessing environmental change and human security.

The SD is a tool that clearly presents in a diagram the

security situation of a population or region affected by a

particular environmental crisis. Its underlying concept

emphasises that the higher the level of environmental stress

and socio-economic susceptibility, the higher the proba-

bility of the occurrence of crisis. Focusing on drought, this

study analyses the susceptibility of case study regions in

India, Portugal, and Russia from a socio-economic per-

spective. A conceptual framework of socio-economic

susceptibility is developed based on the economic devel-

opment theories of modernisation and dependency. Fuzzy

set theory is used to generate susceptibility indices from a

range of national and sub-national indicators, including

financial resources, agricultural dependency and infra-

structure development (for economic susceptibility), and

health condition, educational attainment and gender

inequality (for social susceptibility). Results indicate that

socio-economic susceptibility over the period 1980–1995

was highest in India, followed by Russia and (since 1989)

lowest in Portugal. Globalisation is likely to contribute to

changes in the level of socio-economic susceptibility over

time. Moreover, specific social and economic structures

unique in each country (e.g., the role of women in society

in India, the socialist legacy in Russia) may explain dif-

ferences in susceptibility between the case study regions.

Keywords Adaptive capacity � Economic development �Environmental stress � Fuzzy logic � Globalisation �Susceptibility � Vulnerability

Introduction

From the traditional issues on military threats, territorial

integrity and political independence, research on human

security has shifted its focus to non-conventional threats

including environmental stress brought about by global

warming. ‘‘It is now accepted that environmental stress,

often the result of global environmental change, coupled

with increasingly vulnerable societies, may contribute to

insecurity and even conflict’’ (Lonergan et al. 2000).

L. A. Acosta-Michlik (&)

School of Environmental Science and Management,

University of the Philippines, College,

Los Banos, Laguna, Philippines

e-mail: lilibeth.acosta@uclouvain.be

L. A. Acosta-Michlik

Centre for the study of Environmental Change

and Sustainability, University of Edinburgh, Crew Building,

King’s Buildings, Edinburgh EH9 3JN, UK

L. A. Acosta-Michlik

Unite d’economie rurale, Universite Catholique de Louvain,

Croix du Sud 2/15, 1348 Louvain-la-Neuve, Belgium

K. S. Kavi Kumar

Madras School of Economics,

Gandhi Mandapam Road, 600 025 Chennai, India

e-mail: kavi@mse.ac.in

R. J. T. Klein

Stockholm Environment Institute, Lilla Nygatan 1,

11128 Stockholm, Sweden

e-mail: richard.klein@sei.se

S. Campe

Sonderforschungsbereich 700, Freie Universitat Berlin,

Binger Straße 40, 14197 Berlin, Germany

e-mail: scampe@zedat.fu-berlin.de

123

Reg Environ Change (2008) 8:151–160

DOI 10.1007/s10113-008-0058-4

Alcamo and Endejan (2001) have developed Security

Diagrams to provide quantitative meaning to earlier studies

linking environmental change and human security (Homer-

Dixon 1994; Lietzmann and Vest 1999), which were

mostly qualitative. Security Diagrams assess the likelihood

and degree of an environment-related crisis to identify the

locality of the crisis and affected population. They can also

help to develop a broader view of how climate change may

affect national, regional and global security. Security

Diagrams have three components: environmental stress,

state susceptibility and environmental crisis. These com-

ponents are defined as follows (Alcamo and Endejan 2001):

• Environmental stress is the intensity of an environ-

mental change that involves an undesirable departure

from long-term or ‘‘normal’’ conditions; is normally of

short duration; and is directly or indirectly influenced

by society.

• State susceptibility is the inability of a state to resist and

recover from crisis brought on by environmental stress.

• Environmental crisis is an unstable or crucial time of

undesirable outcome that is brought on by environmen-

tal stress, and requires extraordinary and emergency

measures to counteract.

Environmental stress and state susceptibility make up

the y- and x-axes of the two-dimensional Security

Diagrams (Fig. 1). The points on the diagram show the

security situation at a sub-national, national, regional or

global scale over time, hence the term ‘‘Security

Diagrams’’. The higher the level of environmental stress

and state susceptibility, the higher the probability of inci-

dence of an environmental crisis. In Fig. 1, the crisis

probability curve (CPC) represents the probability of a

crisis occurring. CPC1 stands for low probability of crisis

and CPC2 for high probability of crisis. Environmental

crises, which are represented by the grey points in the

Security Diagrams, are likely to occur when the points are

near the CPC2 curve. At these points, environmental stress

and state susceptibility need not necessarily both be high.

Environmental crisis can take place when environmental

stress is low but state susceptibility is high, and conversely.

The Security Diagrams as presented in Fig. 1 can also

be a useful tool for assessing vulnerability to climate

change as environmental stress and state susceptibility can

be seen as defining the degree of vulnerability. The Inter-

governmental Panel on Climate Change (IPCC) defines

vulnerability as ‘‘the degree to which a system is suscep-

tible to, or unable to cope with, adverse effects of climate

change, including climate variability and extremes. It is a

function of the character, magnitude and rate of climate

variation to which a system is exposed, its sensitivity and

its adaptive capacity’’ (IPCC 2001). Thus, Security Dia-

grams can contribute to vulnerability research as the

estimation of environmental stress involves assessments of

exposure and sensitivity, and susceptibility is roughly the

inverse of adaptive capacity.

This paper focuses on the approach to measure suscep-

tibility from a socio-economic perspective. The empirical

application of CPC is discussed in Acosta-Michlik et al.

(2006). The next section of the paper redefines the three

components of Security Diagrams to focus the analysis on

the socio-economic dimension of susceptibility. The third

section explains the conceptual framework for socio-eco-

nomic susceptibility, the formulation of which is supported

by two theories of economic development. Presented and

discussed in the fourth section are the results of the fuzzy

models for the case study regions in India (Andhra Pra-

desh), Portugal (Algarve and Alentejo) and Russia

(Volgograd and Saratov) for the period 1980–1995.

Redefining the security diagrams from a socio-economic

perspective

To focus the analysis on the socio-economic perspective

requires a redefinition of the three components of the

Security Diagrams. Various events related to climate

change, such as droughts, floods, and cyclones, can cause

environmental stress. The manner and degree by which

these events affect society are diverse. In this paper, we

focus on water scarcity as the source of environmental

stress. The effects of water scarcity or drought vary

according to the intensity or extent of water stress. For

example, the decrease in the amount of precipitation affects

the intensity of water stress, and the increase in the area of

cultivation affects the extent of water stress. As for the

economic impacts of water stress, the timing of the drought

is another critical factor for the affected population, par-

ticularly for those active in agriculture. If drought occurs

WS

CPC 2

CPC1

0StateState SusceptibilitySusceptibility

WS

Env

iron

men

tal S

tres

s

0StateState SusceptibilitySusceptibility

Environmental Crisis

WS

0StateState SusceptibilitySusceptibility

WS

Env

iron

men

tal S

tres

s

0 SSStateState SusceptibilitySusceptibility

Environmental Crisis

Fig. 1 The components of the security diagrams

152 L. A. Acosta-Michlik et al.

123

before a planting season, farmers could delay their pro-

duction activities to avoid economic damages. However, if

the event happens in the middle of the planting season,

farmers would not only receive lower income but also lose

their capital and human investments. The duration of water

stress is also important in assessing the economic impacts

of drought on agriculture. For instance, some crops can

endure longer periods of water stress, others cannot. In

summary, the intensity, extent, timing and duration of a

change in normal water availability are important deter-

minants of water stress.

The ability of individuals or communities to resist and

recover from water stress influences susceptibility. Con-

temporary vulnerability literature defines this human

ability as adaptive capacity, which is determined among

others by social and economic factors. As mentioned by

Downing et al. (2001), this view on vulnerability builds

upon earlier work of Nobel laureate Sen (1981):

‘‘[Sen’s] framework of food entitlements refers to the

ability to command food through legal and customary

means. It includes production [using human and

capital resources], exchanges [in the markets] and

transfers [through trade].’’

Thus, the economics of market forces and the manage-

ment of scarce resources inform the conceptualisation of

adaptive capacity. However, markets are imperfect and can

fail to provide the expected services. In this case, an

established body needs to regulate the ‘‘invisible hand’’ in

the markets as suggested in other fields of economics.

‘‘The humanistic economics viewpoint would not

seek to abolish the market mechanism but would seek

to restrain and supplement it to a significant degree.

In order to facilitate greater social system stability

over the long term, increased government interven-

tion would be required in order both to decentralize

economic activity and to promote a more deliberately

egalitarian distribution of income (Pearce and Turner

1990).’’

This suggests that, from a socio-economic perspective, a

combination of factors addressing both market and gov-

ernment infrastructures are useful in understanding

adaptive capacity. These factors include (1) a system that

offers stable market and affordable resources, (2) an eco-

nomic infrastructure that is built to make efficient use of

available resources, and (3) a social infrastructure that

ensures equal distribution of market revenues. In this paper

we use these factors to develop a framework for socio-

economic susceptibility. Considering these factors, a soci-

ety becomes susceptible to drought when the government is

unable to protect and support it from adverse water stress,

particularly when market forces fail to provide the

necessary resources to cope with the crisis. It follows that

environmental crisis implies the need for external support

to help the state to cope with the problem. The components

of Security Diagrams from a socio-economic perspective

are thus defined as follows:

• Water stress is the intensity, extent, timing and duration

of a change in normal water resource availability that

disrupts economic and human activities.

• Socio-economic susceptibility is the inability of the

state and society to protect and support communities

from adverse water stress if market mechanisms fail to

provide the necessary resources for coping with the

stress.

• Environmental crisis is an unstable or critical economic

and human state of affairs caused by the susceptibility

of a society to water stress, which has serious adverse

consequences on economic development and requires

national or international emergency support.

Socio-economic framework for susceptibility

The framework developed here is based on the notion that

susceptibility is related to the inability of the market and

state to provide sufficient economic and social resources to

society for coping with water stress. With globalisation

increasing the mobility of goods, services and resources

(e.g., human, capital), local markets on which society lar-

gely depends for its livelihood and income are becoming

increasingly affected by conditions in the global market.

Moreover, foreign exchange earnings on which a state

partly depends to support society are affected by the trade

agreements in the global market. O’Brien and Leichenko

(2000) are among the few scholars who explicitly consider

globalisation in studying environmental risks. Their con-

cept of ‘‘double exposure’’ suggests that communities

characterised by economic marginalisation and high-risk

environments are potential ‘‘double losers’’. Whether a

community is likely to win or lose depends much on the

impacts of globalisation on that community and how it can

react on these impacts.

There are several economic theories explaining the

effects of globalisation, particularly of free trade on

economies and societies. The economic development the-

ories of modernisation and trade dependency are

particularly relevant here hence these were used in this

paper to guide the development of framework for socio-

economic susceptibility. These theories propose two

opposing ideas on economic growth and human well-being

across countries as a result of free trade.

Modernisation (or free trade) theory supports neoclas-

sical economics’ assertions that free trade is an engine of

Application of fuzzy models to assess susceptibility to droughts from a socio-economic perspective 153

123

growth, that agriculture serves as the backbone of indus-

trialisation and that technology (such as irrigation and

hydropower) increases productivity. This is consistent with

the development path of industrialised and newly indus-

trialising countries, where foreign capital and trade, as well

as agricultural development and industrialisation, are cen-

tral to achieving better standards of living, health and

education. Modernisation theory recognises the important

role of globalisation in raising the economic and social

well-being of society. According to the World Bank

(2002), 24 developing countries have increased their inte-

gration into the world economy over the two decades

ending in the late 1990s. Consequently, they have achieved

higher growth in incomes, higher life expectancy and better

education. These indicators are used to measure human

well-being, and increasingly to assess adaptive capacity.

Globalisation has thus helped some developing countries to

increase adaptive capacity or, in other words, to decrease

susceptibility. However, the same World Bank study

revealed that in the same period many other developing

countries experienced a fall in trade to GDP ratio. These

countries have experienced a contraction in economic

output and an increase in poverty.1

The trade dependency theory explains why global trade

and industrialisation, which are central to globalisation, fail

to bring about economic growth to these countries. Indus-

trialisation falls short of developing an urban sector capable

of absorbing surplus agricultural labourers, thus depressing

wages in agriculture. Also, the theory argues that trade

dependency has worsened the gap between rich and poor

countries because of unequal exchange and prices for raw

materials and processed products (Shen and Williamson

2001). Countries with low trade revenues were forced to

borrow foreign exchange to finance their imports. As a

result, they divert financial resources from development and

welfare programmes to service debts, with negative socio-

economic effects. Lack of financial resources can result in

poor irrigation infrastructure, hindering agricultural devel-

opment, and in weak social services, affecting social well-

being. The former is further aggravated by hydropower, an

important input into industrialisation in many countries,

competing with irrigation for water supply (Michalland

et al. 1997; Howitt and Sexton 1998). As for social services,

Gupta et al. (2002) show that increased public spending on

education and health care results to improvements in both

access to and attainment in schools, and reduces mortality

rates for infants and children. However, low-income

countries invest less in improving social well-being of

society. Mundle (1998) points out that it is empirically

observed that the ratio of public expenditure to GDP tends

to rise with rising per capita income. Finally, the depen-

dency theory argues that women’s lives have grown worse

with modernisation because their work receives less finan-

cial reward and thus less social value (Bonvillain 2001).

However, women have an important role in improving the

health condition of society. As Caldwell (1993) argues,

maternal literacy is a more important determinant of child

survival than income.

Contrary to modernisation theory, trade dependency

theory argues that globalisation does not necessarily

increase human well-being. It can even cause well-being to

deteriorate, thus increasing susceptibility. Although mod-

ernisation and dependency lead to opposing theories, they

both recognise the interlinkage between markets and the

ability of the state in improving the socio-economic well-

being of society. These theories also suggest relevant

socio-economic determinants of adaptive capacity, which

are useful in analysing susceptibility within the context of

globalisation. The determinants of economic development

include financial resources, agricultural dependency and

infrastructure development, and the determinants of social

well-being are health condition, educational attainment and

gender inequality. Susceptibility is low in countries with

high financial resources and educational attainment, with

low agricultural dependency and gender inequality, and

with good infrastructure development and health condition.

Figure 2 shows the conceptual framework adopted for

developing the socio-economic susceptibility using these

determinants. Although several indicators exist for each of

these determinants, only those that have good regional

time-series data for all the case study regions were included

in the susceptibility framework to allow for cross-county

comparisons.

Fig. 2 Conceptual framework for socio-economic susceptibility

1 Research on natural hazards and food security shows that poverty

reduces the capacity of a society to adapt to environmental stress

(Blaikie et al. 1994; Bohle et al. 1994).

154 L. A. Acosta-Michlik et al.

123

Developing susceptibility indices using fuzzy models

Methods

This paper measures the degree of state susceptibility using

indices ranging from 0 to 1, where susceptibility is negli-

gible at 0 and very high at 1. The indices were generated

through a stepwise aggregation of the indicators based on

the susceptibility framework (Fig. 2). The stepwise

aggregation yielded three sets of indices:

1. First level of aggregation: indices of the determinants

of susceptibility

2. Second level of aggregation: indices of economic and

social susceptibility

3. Third level of aggregation: indices of socio-economic

susceptibility

The method applied to produce indices is fuzzy logic,

based on fuzzy set theory. Unlike classical set theory, which

produces two-logic values (i.e., either 0 or 1), fuzzy set

theory is based on a multi-valued logic producing indices

between 0 and 1. The use of a two-logic approach would be

limited to determining only whether susceptibility exists or

not, whereas a multi-logic approach can be used to assess

the degree of susceptibility. With the latter it is also possible

to attach linguistic values such as low, moderate and high to

certain index value ranges. Linguistic values are useful for

fuzzy concepts to ‘‘quantify the vagueness and imprecision

of interpretations’’ (Mays et al. 1997). Thus, they are useful

for evaluating state susceptibility, which does not have an

objective yardstick to assess its relative magnitude.

Eierdanz et al. (2008) discuss the application of the

fuzzy logic to the concept of susceptibility and the

description of the components of a fuzzy logic model.

Figure 3 summarizes the three components of a fuzzy

model: fuzzification, fuzzy inference and defuzzification.

Fuzzification translates data into linguistic values and

defines the degree of a membership lA. A membership

function is a curve that defines how each point in the input

space is mapped into a membership value (or degree of

membership) between 0 and 1. The fuzzy inference

involves an implication and aggregation process (Corne-

lissen et al. 2001). The implication process evaluates

inference rules, which are ‘‘if-then’’ statements describing

the possible relationships between the input variables. The

aggregation process combines the fuzzy conclusions (i.e.,

the area under the truncated membership functions) in each

inference rule. Defuzzification then converts the aggre-

gated fuzzy conclusions into a numerical assessment,

which ranges from 0 to 1. The numerical assessment is the

index value of the output variable.

For the first level of aggregation, six fuzzy models were

applied to the susceptibility framework. The input variables

were the indicators and the output variables were the var-

ious determinants of susceptibility. For the second level of

aggregation, the indices of the six determinants were used

as input variables for computing the indices of economic

and social susceptibility. Two fuzzy models were applied

in the second level of aggregation. Finally, the indices of

economic and social susceptibility were used to develop

indices of state susceptibility from one fuzzy model. The

next section discusses the results from these fuzzy models.

Fig. 3 The components of a

fuzzy logic model

Application of fuzzy models to assess susceptibility to droughts from a socio-economic perspective 155

123

The case study areas

Three case study regions with different economic, social

and institutional settings were selected—Andhra Pradesh,

India; Volgograd and Saratov, Russia; and Algarve and

Alentejo, Portugal. These regions experience frequent

droughts and have varying level of socio-economic sus-

ceptibility—Andhra Pradesh is a lower income developing

region highly dependent on agriculture; Volgograd/Saratov

is a low to middle income region with a mixed industrial

and agricultural economy; and Algarve/Alentjo is a middle

to high income region supported largely by tourism. With

an area of about 275,000 km2, Andhra Pradesh is the fifth

largest state in India and the fourth most populous. Nearly

three quarters of the state’s 75 million population live in

rural areas. Although agriculture’s share to GDP has sig-

nificantly declined, the sector remains the lifeblood of the

state’s economy, contributing over a third of the state’s

GDP as well as providing a livelihood for over 70% of the

population and employment to over 80% of the labour

force. Landlessness is high with 10% of the population

owning 44% of the land. More than half of the population

in Andhra Pradesh are agricultural labourers. The state’s

agriculture is constrained by low productivity, lack of

assured supply of inputs, lack of technology and cropping

systems suited to dryland conditions, poor resources and

inadequate extension and support services. Almost 60% of

the gross cropped area lack stable irrigation and are cate-

gorized as ‘‘rainfed drylands’’. Since 1960 many districts in

Andhra Pradesh have been frequently affected by droughts,

which have often caused shortage of drinking water, loss of

agricultural livelihoods, migration of families, cumulative

indebtedness, and in recent years, even suicides of farmers.

The regions of Algarve and Alentejo in Portugal have

also frequently suffered from droughts in recent years

because of their relatively warm and dry climate. The

temperature in summer can reach 35�C causing water

shortage, which affects the economy’s tourism and agri-

culture. Portugal’s economy is very diverse so wealth

remains rather unevenly distributed. Whilst the areas

around the capital Lisbon have per capita income that is

close to European average, those in Alentejo remain very

poor. With an area of 26,000 km2, Alentejo region covers a

quarter of the Portugal’s land area and is home to only 5%

of the country’s 10 million people. The region is pre-

dominantly agriculture with very low contribution to the

national GDP because many areas are classified as less

favoured areas or marginal areas. Out migration is one of

the most important economic problems in the region. The

Algarve region in Portugal has a land area of 5,411 km2,

which extends along the coastal lines. As compared to

Alentejo, many people here capitalize on tourism. Never-

theless, both regions are among the poorest in the country

and thus receive structural support from the European

Commission. In 2000 unemployment rate in Alentejo was

5.5% and in Algarve 5.4%, which are above the European

average.

The case study regions in Russia, Volgograd and Sara-

tov, are located in the south of Volga reservoir. Volgograd

has a land area of 113,900 km2 with almost 3 million

population and Saratov 380 km2 with less than one million

population. The Volgograd region has 190 rivers and two

big dams that support the water supply for both industry

and agriculture. Nevertheless, droughts affect agriculture

very much not only because the temperature can become

very high in summer (34–45�C), but also because two-

thirds of the annual rain falls in summer when evaporation

is very high. Sandstorms are often experienced in the

region due to droughts and strong winds. Saratov is in the

heart of the economic centre of Volga. The continental

climate in Saratov is milder than in Volgograd, with a

maximum temperature of 37�C in summer. The region is

very suitable for agriculture, but it is also rich in mineral

resources. Consequently, machine and electric industries,

petroleum and chemical industries, and food processing

companies are important in Saratov region. With its 180

small rivers and milder summer temperature, the region has

better water supply than Volgograd. The decentralization

of power in 1991 resulted in restructuring of the Russian

economy, which led to agriculture privatization and indi-

vidual ownership. However, the privatized agriculture

system was not very successful so the farmers group

themselves again in some form of collective farming. The

political crisis in 1998 almost ruined the existence of many

small industries as well as middle- and low-income fami-

lies in these regions.

Discussion of the results of the fuzzy models

First level of aggregation

The results of the fuzzy models for the first level of

aggregation are presented in web diagrams, where each co-

ordinate refers to the six determinants of state susceptibility

(Fig. 4). The level of susceptibility for these determinants

increases from the centre to the edge of the web. This

implies that the larger the area in the web, the higher the

susceptibility. Thus, the region of Andhra Pradesh in India

has the highest overall socio-economic susceptibility in the

first half of the 1990s. Andhra Pradesh’s susceptibility is

highest in terms of financial resources. In addition, com-

pared with other case study regions, Andhra Pradesh has

the lowest educational attainment and the poorest health

condition. The weak social services combined with the

lack of financial resources in this region agree with the

156 L. A. Acosta-Michlik et al.

123

dependency theory’s assertion that low-income countries

invest less in improving social well-being of society. In

contrast, high financial resources in the Algarve and A-

lentejo regions in Portugal are combined with high

educational attainment and good health condition. Since

Portugal joined the European Community in 1986, the

country has experienced not only growth in income, but

also improvement in educational attainment. Portugal is

thus among those countries that has gained from globali-

sation, which is consistent with the development path of

the modernisation theory.

The regions of Volgograd and Saratov in Russia have

only slightly higher financial susceptibility compared with

the case study regions in Portugal. The regions in Russia

have also comparable educational attainment and health

condition, although not quite as high as those in Portugal. In

Russia the link between integration into the world economy

and social well-being is not straightforward because of the

legacy of the former Soviet Union. However, the socialist

regime promoted equal access of women not only to social

services but also to employment, which has contributed to

low gender inequality in Volgograd and Saratov. In con-

trast, the gender inequality in Andhra Pradesh is high

because of the influence of India’s social structure and

cultural values that result in women’s marginal role in

society. This in turn is further aggravated by the impacts of

globalisation on Indian economy and society. According to

Dunlop and Velkoff (1999), although cultural restrictions

were the main impediments to female employment in the

past, now shortage of jobs throughout India (caused by

global economic crises) also contributes to low female

employment. Moreover, technological progress promoted

through globalisation has a negative effect on women’s

employment opportunities in India. ‘‘When a new tech-

nology is introduced to automate specific manual labour,

women may lose their jobs because they are often respon-

sible for the manual duties’’ (Dunlop and Velkoff 1999).

The susceptibility in terms of infrastructure develop-

ment was lowest in Andhra Pradesh, India. The extensive

irrigation programme of the Indian government in the past

as a measure to promote agricultural development has

contributed to a good agricultural infrastructure. The share

of the agriculture sector to the region’s GDP has decreased.

Despite this, agriculture in Andhra Pradesh remains sus-

ceptible because of the large number of labour force

engaged in agriculture. Unlike in Portugal and Russia,

where large farms characterise the agricultural sector, a

large number of small farmers continue to dominate the

Indian agriculture. About 78% of farm holdings in India are

small (less than 2 hectares) and in 1991 they commanded

only 33% of the total net cropped area (Sulaiman and Holt

2002). The majority of the chronically poor in India are

either near-landless (i.e., small farm holders) or landless

(i.e., farm labourers). Dev (1988) as cited by Mehta and

Shah (2001) ‘‘attributes the higher incidence of poverty

among agricultural labour households to their earnings

from wage employment being too low to enable them to

reach the poverty line’’. As explained by the dependency

theory, the failure of the industrial sector to absorb surplus

agricultural labourers depresses the wages in agriculture.

Second level of aggregation

As shown in Fig. 5, Andhra Pradesh in India has the

highest level of economic susceptibility in the first half of

the 1980s, but it decreased significantly in the following

decade. Economic susceptibility in this region is as low as

in the regions of Volgograd and Saratov in Russia in the

second half of the 1980s. The lowest economic suscepti-

bility is experienced in the regions of Algarve and Alentejo

in Portugal. Compared with economic susceptibility, no

significant decline in social susceptibility is observed for

all the case study regions from 1980 to 1995 (Fig. 5). The

level of social susceptibility in Andhra Pradesh has

remained very high throughout this period. This level is

three times higher than those estimated for Algarve and

Alentejo in Portugal and for Volgograd and Saratov in

Russia. Whilst social susceptibility has remained relatively

stagnant in the regions of Portugal, it has increased slightly

in the regions of Russia from the middle of the 1980s to the

Fig. 4 Cross-country

comparison of the determinants

of socio-economic

susceptibility, 1991–1995

Application of fuzzy models to assess susceptibility to droughts from a socio-economic perspective 157

123

first half of the 1990s. The increase is due to declining

health and education condition in Russia after the break-

down of the Soviet Union. Government spending on

education dropped by 63% from 1991 to 1992 and life

expectancy fell from 69 to 64 years from 1991 to 1994.

Gavrilova et al. (2000) mentioned that the ‘‘shock therapy’’

introduced by the Russian government in 1992 caused

adverse changes resulting in an unprecedented upsurge in

mortality. The therapy that included abolishing price con-

trol in a formerly monopolised economy resulted in soaring

consumer prices, a rapid decline in real wages, a nearly

complete loss of personal savings, and a tremendous

increase in poverty.

Third level of aggregation

The development in the overall socio-economic suscepti-

bility in the case study regions in India, Portugal and

Russia from 1980 to 1995 is presented in Fig. 6. Socio-

economic susceptibility in the region of Andhra Pradesh in

India was high almost throughout the 1980s. It has declined

continuously at a rate faster than socio-economic suscep-

tibility in the regions of Portugal and Russia from 1980 to

1995. Nonetheless, with susceptibility index levelling off

during the first half of the 1990s, Andhra Pradesh remains

the most susceptible region to drought. The fall in GDP per

capita in Andhra Pradesh in 1991 interrupted the decreas-

ing trend in socio-economic susceptibility. The change in

GDP per capita was accompanied by a slight increase in

India’s foreign debt. India was among many countries

badly hit by the global economic crisis in 1991, which,

among others, was caused by (1) the 1990 Gulf War

resulting to oil price shocks; (2) the collapse of the Soviet

Union, which was India’s major trading partner and source

of foreign aid; and (3) the sharp depletion of foreign

exchange reserves due to continuing budget deficits.

Socio-economic susceptibility in the Volgograd and

Saratov regions in Russia was at a low level, particularly in

the late 1980s and early 1990s. In the middle of the 1990s

it went up from a low to moderate level because of

the increase not only in social susceptibility but also

in economic susceptibility. This increase in economic

susceptibility is not obvious in Fig. 5 because high sus-

ceptibility indices from 1993 to 1995 are compensated by

the significant fall in the indices from 1990 to 1992. In

particular the significant growth in foreign debt in 1993

caused a sharp increase in economic susceptibility in Rus-

sia. The combined effects of a global economic crisis and

domestic structural adjustment in the early 1990s have

contributed to the increase in socio-economic susceptibility

in the regions of Volgograd and Saratov in Russia. In the

regions of Algarve and Alentejo in Portugal, socio-eco-

nomic susceptibility has remained relatively stable at a

moderate level from 1980 to 1988. Since 1989 these regions

have shown significant decline in socio-economic suscep-

tibility, achieving their lowest index in 1992. From 1989 to

1995, the regions in Portugal have continuously experi-

enced lower socio-economic susceptibility than the regions

in Russia. The economic structural change after joining the

European Union in 1986 has helped to reduce socio-eco-

nomic susceptibility in the case study regions in Portugal.

Similar to Russia, there was an increase in socio-economic

susceptibility in Portugal due to an increase in foreign debt.

0.00

0.20

0.40

0.60

0.80

Andhra Pradesh, India

Algarve & Alentejo,Portugal

Volgograd & Saratov,Russia

Economic Susceptibility

0.00

0.20

0.40

0.60

0.80

Andhra Pradesh, India

Algarve & Alentejo,Portugal

Volgograd & Saratov,Russia

Social Susceptibility

1980-1985 1986-1990 1991-1995

Fig. 5 Cross-country comparison of economic and social suscepti-

bility, 1980–1995

Fig. 6 Cross-country

comparison of socio-economic

susceptibility indices, 1980–

1995

158 L. A. Acosta-Michlik et al.

123

Compared with Russia, however, Portugal’s foreign debt

was low. Therefore, socio-economic susceptibility in Por-

tugal remained low from 1993 to 1995.

Conclusions

This paper provides a top-down framework for assessing

state susceptibility from a socio-economic perspective

based on the economic development theories of moderni-

sation and dependency. The socio-economic framework of

susceptibility developed in this paper is hoped to contribute

not only to the improvement of the Security Diagrams

concept but also, by understanding adaptive capacity, to

vulnerability research in the context of global change. The

empirical relevance of the framework is shown by applying

fuzzy models to generate susceptibility indices for selected

regions in India, Portugal and Russia, which have experi-

enced drought-related crises. The results of the models

indicate that socio-economic susceptibility over the period

1980–1995 was highest in India, followed by Russia and

(since 1989) lowest in Portugal. Globalisation contributes

to the changes in the level of socio-economic susceptibility

over time. Moreover, specific social and economic struc-

tures unique in each country (e.g., the role of women in

society in India, the socialist legacy in Russia) explain

differences in the level of susceptibility among the case

study regions. The results of the study showed that the top-

down approach using the Security Diagrams concept pro-

vides a useful tool for cross-country or inter-regional

comparison of socio-economic susceptibility over time.

Study such as this can thus provide general policy direction

for identifying not only the most susceptible countries to a

given environmental stress, but also sectors or sub-sectors

that require improvement to reduce susceptibility in a

particular country. If applied to a larger number of coun-

tries, it could provide guidance for negotiating adaptation

support for countries with the highest level of vulnerability.

However, to further improve the relevance of the results

for national policy guidance and support, the application of

the socio-economic framework should among others con-

sider adaptation programmes and/or measures most relevant

to the environmental stress, include indicators that can

capture the feedback effects of these measures on the

capacity of communities to adapt to future stress, and carry

out analysis at a lower administrative level to capture the

differences in susceptibility of communities with divergent

values and culture. For example, one important issue iden-

tified in this study is the need to complement top-down

methods with a bottom-up investigations to better under-

stand local and sub-national issues affecting susceptibility to

present-day climate extremes, and to identify strategies for

reducing susceptibility that are suitable a society’s socio-

economic structure. In the Security Diagrams Project, a

bottom-up framework using a psychology-based perspective

was applied to assess the agents (in this case farm house-

holds’) capacity to cope with the threat of droughts. Such a

bottom-up framework can complement the top-down

framework to understand the suitability of particular adap-

tation measure to the local needs and values of communities.

This is particularly important because the adoption rate of

adaptation measures is influenced not only by the agents’

accessibility to these measures, but also by their perception

and understanding of the threat of a given environmental

stress. The latter is emphasised in the framework of ‘‘inter-

vulnerability’’ (Acosta-Michlik 2005; Acosta-Michlik and

Rounsevell 2008), which suggests that vulnerability to

global changes is a function not only of exposure, sensitivity

and adaptive capacity, but also cognition. Cognition is an

important determinant of vulnerability because it allows the

agents to receive and exchange information, to perceive and

evaluate risks, to identify and weigh options, to make

decisions and perform actions, and to modify and update his

profile according to the outcome of these actions.

Acknowledgments The authors would like to acknowledge the

contribution of their colleagues in the Security Diagrams Project,

which was part of their work at the Potsdam Institute for Climate

Impact Research (PIK) in Germany. The DEKLIM Program of the

German Ministry of Education and Research provided funding for this

project.

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