case study: exploring demographic …...maliks(2012)! ! !6!!! introduction!...

Appendix 4

CASE STUDY: EXPLORING DEMOGRAPHIC DIMENSIONS OF

FLOOD VULNERABILITY IN RURAL CHARSADDA, PAKISTAN

AUTHOR: SHARMEEN MALIK

MSC. APPLIED ENVIRONMENTAL ECONOMICS SEPTEMBER 2012

Malik S (2012) 1

ABSTRACT

This paper explores the demographic characteristics that affect

flood vulnerability in rural Charsadda, Pakistan. It develops a

Demographic Vulnerability Ranking (DVR) method and applies

it to vulnerable households in Charsadda, in order to

distinguish between the more vulnerable and less vulnerable

households. The ranking assesses household vulnerability

using the demographic composition of the individuals that

make the household. The method is validated against the

Fawad Khan Damage and Recovery Index, developed by the

Institute of Social and Environmental Transition Pakistan

(ISET-‐PK)(Khan 2012), which differentiates between

vulnerable and resilient households to floods, using damage

and recovery data. Although, specific to Charsadda, the DVR

method can be used in other locations, provided that they are

geographically and culturally similar to Charsadda. The DVR

examines household size, gender, adult illiteracy, age

dependency and income dependency of the individuals within

the household in order to assess vulnerability to floods. This

study identifies gender as the most vital demographic

characteristic, in households recovering from floods in

Malik S (2012) 2

Charsadda. It then follows that early relief and recovery

operations for floods could focus on targeting households with

a higher gender ratio in order to be most effective.

Keywords: Floods; 2010 Indus Floods; Pakistan; Charsadda;

Vulnerability; Climate Change; Demographics; Gender.

Malik S (2012) 3

TABLE OF CONTENTS ACKNOWLEDGMENT ............................................................. ERROR! BOOKMARK NOT DEFINED.

ABSTRACT ................................................................................................................................................ 1

FIGURES AND TABLES ........................................................................................................................... 5

INTRODUCTION ...................................................................................................................................... 6 BACKGROUND .............................................................................................................................................................. 7 Pakistan ...................................................................................................................................................................... 7 Floods ........................................................................................................................................................................ 10

CASE STUDY .............................................................................................................................................................. 14 Research questions .............................................................................................................................................. 15 Charsadda ............................................................................................................................................................... 15

LITERATURE REVIEW ........................................................................................................................ 17 FLOODS ...................................................................................................................................................................... 17 VULNERABILITY ....................................................................................................................................................... 23 DEMOGRAPHICS ....................................................................................................................................................... 25 THEORETICAL FRAMEWORK, KEY CONCEPTS AND THEORIES AND EXPECTED CONTRIBUTION ................. 29

METHODOLOGY ................................................................................................................................... 33 COMPUTATION OF RATIOS ..................................................................................................................................... 37 COMPUTATION OF SCORES ..................................................................................................................................... 40

RESULTS AND ANALYSIS ................................................................................................................... 43 DESCRIPTIVES .......................................................................................................................................................... 44 Household Size Ratio .......................................................................................................................................... 46 Gender Ratio ........................................................................................................................................................... 47 Adult Illiteracy Ratio .......................................................................................................................................... 47 Age Dependency Ratio ....................................................................................................................................... 49 Cumulative Income Dependency Ratio ....................................................................................................... 50 Demographic Vulnerability Ranking ........................................................................................................... 51

STATISTICAL SIGNIFICANCE ................................................................................................................................... 53 FINDINGS ................................................................................................................................................................... 55

CONCLUSIONS ....................................................................................................................................... 57

REFERENCES ......................................................................................................................................... 59

APPENDICES .......................................................................................................................................... 64 ANNEXURE I: HOUSEHOLD QUESTIONNAIRE ..................................................................................................... 64

Malik S (2012) 4

ANNEXURE II: DEMOGRAPHIC RATIOS CALCULATION ..................................................................................... 66 ANNEXURE III: DVR CALCULATIONS FOR ALL DEMOGRAPHICS & AGGREGATE RANKING FOR ALL 56 HOUSEHOLDS ............................................................................................................................................................ 68

Malik S (2012) 5

FIGURES AND TABLES

Figure 1: Flood Monitoring Map-‐July 2010-‐Charsadda & Nowshera Districts ............ 16

Table 1: DVR Computation ................................................................................................................. 43

Figure 2: Demographic and DVR Mapping ................................................................................... 45

Table 2: Average ranking for demographic ratios and DVR ................................................. 45

Figure 3: Cumulative individuals in 11 households in each category .............................. 46

Figure 4: Gender Profile ....................................................................................................................... 47

Figure 5: Illiteracy distribution as % of individuals ................................................................ 48

Figure 6: Level of Adult Literacy Distribution as % of adults .............................................. 48

Figure 7: Age distribution as % of individuals ........................................................................... 49

Figure 8: Income dependency as % of individuals ................................................................... 50

Figure 9: Primary activity as % of individuals ........................................................................... 51

Figure 10: DVR as compared to the FK Index-‐% of individuals .......................................... 52

Figure 11: Flood Vulnerability Ranking and Demographic Indicator Trends .............. 52

Table 3: Chi-‐squared test for DVR against the FK Index ........................................................ 53

Table 4: Chi-‐squeared test for demographic attributes against the FK Index .............. 54

Malik S (2012) 6

INTRODUCTION

This dissertation explores the flood prone geographical

location of Charsadda, Pakistan, which was severely affected by

the Indus Floods of 2010. The purpose of this paper is two

fold: (1) to understand the effects that demographic

dimensions have on vulnerability of flood-‐affected rural

households; (2) to develop an easy-‐to-‐use vulnerability

ranking method, which will allow development agencies to

focus program design on the most destitute households in

vulnerable communities. To this end, this paper examines the

potential affects that household size, gender, literacy, age and

employment have on flood vulnerability.

This paper is organized into five sections. The first section

provides an overview through background information on

Pakistan and floods. It also presents the case study by

identifying the research questions and introducing Charsadda

as the location of analysis. The second section is comprised of

a literature review with a focus on floods, vulnerability and

demographics. The theoretical framework for this research is

also laid out in this section. The third section explains the

methodology behind the Demographic Vulnerability Ranking

Malik S (2012) 7

method (DVR). The fourth section presents the results and

offers analysis of the individual demographics involved in the

DVR method. A conclusion finishes the report in the fifth

section.

BACKGROUND PAKISTAN As a developing country, Pakistan has no shortage of

challenges that need to be addressed if it is to advance on a

path of sustainable development. The Gross Domestic Product

(GDP) per capita was recorded at 668.6 USD and 672.1 USD for

November and December 2011 respectively; the inflation rate

for the same period was recorded at 11 and 10.2 (Trading

Economics 2012a, 2012b). This only begins to scratch the

surface when trying to comprehend the situation. A lack of

resources that are essential for a prospering economy, such as

water, electricity, skilled labor and employment, only add to

this trend. Add to this a vibrant informal employment sector

(including child labor), a dwindling tourism industry and an

increasingly disappearing working middle class population and

the future looks far from optimistic.

Furthermore, the cultural and ideological practices are such

that they can prevent minority groups from breaking out of

poverty. Women are especially marginalized and in some case

Malik S (2012) 8

are prevented from realizing basic liberties and prevented

from achieving independence. Despite being a Muslim country,

Pakistan has minority segments, which are often neglected, as

well as sects within mainstream Islam, which are often in

conflict with each other. In addition, the on-‐going threat of

fundamentalist organizations has the affect of further

preventing both rural and urban, but especially rural

communities from developing and has the effect of creating an

overwhelming number of internally displaced persons.

The political and judiciary scene of the country is the icing on

the cake! Often the object of ridicule and comic relief, there is

great frustration within the population with the inadequacy,

complacency and inequitable polices of those who have been

delegated the responsibility of governance.

Lastly, in addition to the political and economic instability, the

past ten years has seen its share of natural disasters. Whether

due to climate change or a result of natural cycles, the

consequences have been severe. The 2005 earthquake,

droughts leading to the threat of salt water intrusion, a

decreasing water table, glacier ice melts, increasing intensity of

heat waves and cold spells and floods, all have the effect of

setting Pakistan further back in it’s goals of development and

Malik S (2012) 9

pushing already vulnerable communities currently living in

inadequate conditions further in despair.

Pakistan is a predominantly agricultural society, which heavily

relies on its water resource for social economic stability. The

main source of water for most of the country is the Indus

Water Basin, which includes the Indus River and its tributaries:

Jhelum, Chenab, Ravi and Sutlej. This basin is fed from

Himalayan glacial melt and an annual monsoon season. The

country experiences variable temperatures and precipitation,

with rainfall occurring in both summers and winters (Sultana,

Ali, Iqbal and Khan 2009). Temperature rise associated with

climate change can have dire consequences for the natural

landscape and for the economic and sustainable development

of the country and can potentially immensely affect its need for

food and water security in the coming years.

Adaptive capacity, through the adoption of measures that

reduce climatic vulnerabilities in communities and increases

their capabilities to absorb such shocks enables them to

recover in a cost effective and timely manner (Parry, Canziani,

Palutikof, van der Linden and Hanson 2007). These measures,

should be an integral part of any decision making process

Malik S (2012) 10

especially when projects, plans and programs are being

deliberated for implementation and approval.

FLOODS

CLIMATE CHANGE

“Climate change in IPCC1 usage refers to any change in climate over time,

whether due to natural variability or as a result of human activity. This usage

differs from that in the Framework Convention on Climate Change, where

climate change refers to a change of climate that is attributed directly or

indirectly to human activity that alters the composition of the global

atmosphere and that is in addition to natural climate variability observed over

comparable time periods.” (Parry et al 2007 p. 21)

As illustrated by the definition itself, climate change has been

at the center of much debate as to its causes and effects and

what should be included when trying to measure climate

change. However, there is enough research and evidence to

support that climate change is in fact a harsh reality.

According to a 2010 report prepared by the National Oceanic

and Atmospheric Administration (NOAA), a rise has been

observed in temperature over land, temperature over oceans,

sea surface temperature, sea level, ocean heat content,

humidity and air temperature near surface, for the past sixty

1 Intergovernmental Panel on Climate Change

Malik S (2012) 11

years if not more. The same report indicates a decrease in

snow cover, glaciers and sea ice (Arndt, Baringer and Johnson

2010). All these trends indicate the reality that the earth is

warming which has implications for extreme climatic events.

Another dilemma which deals with equity issues is that most of

the resultant effects will impact countries and communities

which contributed little to the anthropogenic causes of climate

change nor did they benefit from the industrialization that led

to increased emissions which have contributed substantially to

it (Flavin and Engelman 2009). Due to these reasons, amongst

others, despite some momentum, the potential of climate

change mitigation is yet to be realized. This makes measures

that increase preparedness and minimize vulnerabilities all the

more essential when developing coping and development

strategies.

Malik S (2012) 12

2010 FLOODS

The water cycle is perhaps the most effected by climate change.

Its effects are observed through changes in precipitation,

evapotranspiration, water table levels, sea-‐level rise and the

melting of glaciers to name a few. In addition, changes in the

ocean oscillation cycle and the warming of the troposphere, all

combined with the trends listed above, result in increased

probabilities of extreme events, including the frequency and

intensity of floods (Houghton 2009).

The 2010 Indus Floods are one such example. The forces at

work in the Indus River are extremely hard to predict due to

variability in glacier melt and precipitation during the

monsoon months (Hassan and Ansari 2010). Climate change

will only exacerbate such uncertainty.

As a developing country Pakistan is greatly lacking in its

capacity to predict climate change induced extreme events and

changes in weather patterns. This makes it all the more

difficult for Pakistan to develop disaster mitigation strategies

and responses (Haque I U, Anum and Ruhmma 2009).

According the Maple Croft Climate Change Vulnerability Index,

Pakistan has become more vulnerable to climatic hazards, with

a ranking of 29 prior to the 2010 floods, downgraded to 16

after the floods (Maple Croft 2011).

Malik S (2012) 13

Even though extreme floods occurred in 1973, 1992, 2006 and

2010 (Ahmad, Kazmi and Pervez 2011), the magnitude of the

damages of the 2010 floods, in terms of losses to the number of

people affected and infrastructure destroyed was

unprecedented. Livelihoods and food supplies were further

threatened as a result of 23% of the year’s harvest being

washed away (Anon 2010).

In addition to the obvious material losses and the

humanitarian crisis that ensued, waterborne diseases added to

the complexities of the aftermath of the floods (Synnott 2010).

Even more devastating was the realization that communities

could potentially have been warned of the oncoming

devastation and damages could have been minimized.

Adequately maintained flood-‐protection infrastructures

(Gaurav, Sinha and Panda 2011), and dissemination of

information regarding coping strategies to floods, combined

with a more efficiently coordinated local response could have

minimized the negative impacts of the floods.

All these factors then contributed to the vulnerabilities of the

population affected. With loss or damage to housing and

infrastructure, livelihoods, assets and lives in addition to

increased morbidity, masses of people were prevented from

Malik S (2012) 14

rebuilding their lives. As a consequence, inhabitants of the

community were unable to return home and in the event that

they were, the conditions that they faced led to negative

psychological effects (Warraich, Zaidi and Patel 2011).

CASE STUDY This paper aims to explore the demographic characteristics of

individuals that affect the adaptive capacity to floods of two

villages in Charsadda, Pakistan. In addition, it develops a

household vulnerability ranking method based on

demographics, in order to assist in the vulnerability mapping

of households within communities. The demographic variables

explored are household size, gender, literacy, age and

employment. The analysis of these characteristics can in turn

help target the allocation of funds and resources to where they

are needed most in order to aid in future development in

Charsadda. Additionally, the index can also be extremely

useful when trying to assess the effectiveness of development

or flood mitigation projects, which are community-‐based and

to help target actions towards intra-‐village equity issues that

may arise during implementation. It has already been

established by Ahmad et al 2011, that “linkages between

disaster management at national and local socio-‐economic

development processes are most often ignored, resulting in re-‐

Malik S (2012) 15

creation of risks in already flood prone communities”. This

implies that an integrated approach is essential for not only

future sustainable development and economic growth, but also

for the development of coping mechanisms to natural disasters.

RESEARCH QUEST IONS 1-‐What demographic characteristics make households more or less

vulnerable to floods in rural Charsadda, Pakistan?

2-‐ Can a demographic vulnerability ranking method be used to identify

households with a higher vulnerability to floods in flood prone

Charsadda?

CHARSADDA Figure 1 on the following page shows a map of the areas

affected by the floods of 2010 in the Charsadda district of

Khyber Pakhtunkhwa, Pakistan. Located 17km from Peshawar,

Charsadda is a rural district divided into 46 Union Councils and

is endowed with three rivers: River Jindi, Kabul River and the

Swat River (SMEDA NWFP 2009).

The yellow arrow indicates the Agra Union Council, whereas

the orange arrow indicates the Kharkai Union Council. As can

be seen Charsadda was one of the districts most severely

affected by the 2010 floods. Initial reports indicated that 5,000

homes were under water and that a total of 20 villages were

Malik S (2012) 16

affected with vital road linkages to the nearest city also being

destroyed in the process (Global Peace Pioneers 2010).

FIGURE 1: FLOOD MONITORING MAP-‐JULY 2010-‐CHARSADDA & NOWSHERA DISTRICTS

SOURCE: GOVERNMENT OF PAKISTAN AND UN HABITAT (RELIEF WEB 2010)

Malik S (2012) 17

LITERATURE REVIEW

The literature review was carried out using Academic

Complete, GreenFile, E-‐Journals and resources from the

Institute for Social and Environmental Transition Pakistan

(ISET-‐PK), as well as from Rural Support Programmes Network

(RSPN), Islamabad. In addition, Google was also used in order

to search for background information and news articles. The

exercise was challenging, as there is limited research with

regards to floods, demographics and flood vulnerability in the

geographic area of Charsadda, and in Pakistan in general.

Suffice to say; an integrated approach that includes looking at

all the individual demographic characteristics collectively,

particularly in the context of climate change, is missing all

together.

FLOODS That climate change is occurring is by now a well-‐established

fact. According to Adger, Huq, Brown, Conway and Hulme

(2003), the impacts of this phenomenon will be felt “through

changes in water, natural resources, food systems, marine

ecosystems and through the need to cope with a changing

regime of weather extremes” (p. 185). Measures taken

Malik S (2012) 18

towards adaptation will play an essential role in paving a way

towards strong sustainability.

Nicol and Kaur (2009) distinguish two ways in which climate

change will affect the hydrological cycle. The ‘knowns and

unknowns’:

“While there is greater certainty on the impact on the hydrological system…

how this leads to changes in runoff and river flows, flooding and drought

patterns is less certain. This is because of the many human and physical

feedback loops that determine resource behavior at the more local scale.” (p. 1)

Changes in the hydrological cycle due to climate change will

have severe impacts with increasing frequency and intensity.

Unharnessed global warming is projected to amount to a

doubling in the number of days heavy rainfalls are observed,

which will inevitably have dire consequences on the patterns

of floods (Houghton 2009).

In addition, there is enough evidence to deduce that glaciers

are retreating and at an increasingly rapid rate (Dessler, A and

Parson, Edward A 2010). The Himalayan glacier feeds the

Indus River. Glacial Lake Outburst Flooding (GLOF) and

increased rate of melting will have severe impacts on

communities living near the glacier and downstream. More

broadly, impacts will be seen in terms of water availability,

Malik S (2012) 19

increased siltation and changes in precipitation patterns (Xu J,

Grumbine R E, Shrestha A, Eriksson M, Yang X, Wang Y and

Wilkes A 2009). It is vital then that adaptation strategies

should recognize these issues and plan to mange the resultant

increased run off in order to minimize the increased risk of

flooding.

In trying to assess future trends and scenarios, climate

modeling has come a long way through improvements in

computer capabilities. Although progress in prediction and

simulations has been observed, climate modeling still remains

a highly debated topic. This is in part due to the “large amount

of natural variability in both the observations and the

simulations” (Houghton 2009 p.125). The nature and

interactions between natural and anthropogenic forcings

further lead to difficulties in predictions, as does the scale of

analysis. Although dictated by the technology and resources

available, the scale heavily affects modeling as models at the

global level of analysis can downplay the effects of climatic

events at the regional level, while regional models may predict

events that at the global level are not as significant (Houghton

2009).

Malik S (2012) 20

Furthermore, the basic issue of forecasting climatic events

requires a range of knowledge, technology and expertise.

Where traditionally linear analysis is carried out in the study of

seasonality, Hassan et al (2010) present a nonlinear model for

analysis. This is a welcome approach to analysis and

prediction given the frequency and intensity of climate change

related events. However, such projections are beyond

Pakistan’s current capabilities.

Although not specific to Charsadda, the Indus Basin is already a

closed basin, in that much of the water has been allocated to

specific uses. Man made diversions and poorly maintained

water reservoirs are also a characteristic of this basin (Gaurav,

Sinha and Panda 2011). Much of these decisions for allocation

and development have been made without consideration to

natural ecosystem flows. As a result, the basin is suffering

from a decrease in the number of wetlands, destruction of

mangroves, and a decline in biodiversity and aquatic life in

addition to decreased replenishment of aquifers (Hassan et al

2010).

Furthermore, poor practices in water resource management,

monitoring and enforcement, and a lack of transparency and

knowledge in institutions have led to problems of siltation and

Malik S (2012) 21

salination. This combination of practices has also led to

informal communities being developed close to the riverbed,

which in times of extreme floods as the kind experienced in

2010, overflowed and wreaked havoc (Mustafa 2005).

Another pressing issue was that of deforestation. Forests are

known to act as natural barriers in times of flood. Certain

species are known to have the capacity to provide vital soil

moisture in times of drought, in addition to the well

documented role forests play as carbon sinks. Pakistan has

been experiencing a deforestation rate of 4% per year; this

coupled with increased soil erosion has led to a further

reduction in the storage capacity of reservoirs (Naser 2004). It

may be interesting to note here that the deforestation rate may

increase substantially in the coming years as the energy crisis

in Pakistan is increasingly pushing industries towards

acquiring wood in order to provide energy to continue

production, the consequences of which can be detrimental in

more ways than one (Owner of pharmaceutical factory 2012).

As a country that is very much at the center stage in the ‘war

on terror’, concerns about the harboring of extremist

organizations and their role in channeling funds and aiding in

humanitarian efforts is very much a harsh reality when talking

Malik S (2012) 22

about relief, reconstruction and rehabilitation efforts. This

issue can have adverse effects on already vulnerable

communities by preventing humanitarian aid and aid workers

from providing potentially life-‐saving assistance at times of

crisis and on the long and winding road towards rehabilitation

and recovery. In addition the very real concept of donor

fatigue can also explain why, as seen in the 2010 floods, there

was a significant lag in the occurrence of the disaster and the

pledging and dispersal of funds by donor governments and

agencies (Burki 2010). For this reason, it is essential that the

resources that are dispensed for the betterment of the

communities are allocated efficiently and targeted accurately

so that those who are most vulnerable are catered to.

Moreover, another gap that remains in literature is how local

knowledge can be used to contribute to better mitigation and

adaptation strategies at the national level. As Mustafa (2002)

points out:

“It is quite remarkable that the political ecologists, who have been concerned

with highlighting ordinary people’s knowledge and their struggles for equity

and justice, have not drawn upon the perception studies tradition’s strength in

systematically highlighting local knowledge” (p. 103).

Malik S (2012) 23

VULNERABIL ITY When searching for literature pertaining to the 2010 floods,

Charsadda and the issue of vulnerability in rural areas, it was

found that articles dealing with adaptation studies, climate

change, policies and approaches to disaster management and

reconstruction and rehabilitation in the context of the 2010

floods, were lacking in an integrated approach.

Vulnerability has been identified in most literature as a key

characteristic of those affected. It has been defined by various

agencies and individuals, however how that vulnerability can

be identified, targeted and minimized or how it can be a

hindrance to broader measures of adaptation still remains

unexamined. The United Nations Environment Programme

(UNEP) under its ‘Climate Agenda’ has promoted the study of

“Climate Impact Assessments and Response Strategies” as

being vital in efforts to reduce vulnerability (Usher 2000 p. 46).

While, such recommendations have failed to receive any

momentum in the decision-‐making machinery of Pakistan,

hope can be found as Adger et al (2003 p. 186) identify an

‘emerging research agenda focused on identifying generic

determinants of resilience’.

Broadly speaking, the evolution of vulnerability assessments

can be traced to three approaches; the risk-‐hazard approach,

Malik S (2012) 24

BOX 1: APPROACHES TO VULNERABILITY ASSESSMENTS

the political economy/ political ecology approach and the

ecological resilience approach. Box 1 below taken from Eakin

and Leurs (2006 p. 369-‐71) presents an overview of the three

approaches.

APPROACHES TO VULNERABILITY ASSESSMENTS

“Risk-‐hazard approaches tend to consider negative outcomes as functions of

both biophysical risk factors and the “potential for loss” (Cutter 1996 and

Brooks 2003) of a specific exposed population”.

“Political-‐economy perspectives on vulnerability emphasize the sociopolitical,

cultural, and economic factors that together explain differential exposure to

hazards, differential impacts, and, most importantly, different capacities to

recuperate from past impacts and/or to cope and adapt to future threats”.

“Ecological resilience refers to the “ability to absorb change and disturbance

and still maintain the same relationships” (Holling 1973 p. 14) that control a

system’s behavior”.

Malik S (2012) 25

Climate change and floods have the ability to affect all

component of human life. They effect the social, economic,

political and environmental conditions governing an entity.

Resilience or the absorbance of such shocks is dictated by the

historical experience to them and the adaptation measures that

are in place whether through formal networks or through the

implementation of local knowledge (Balica, Douben and Wright

2009).

DEMOGRAPHICS Much research has been done on gender, children, illiteracy,

employment and population size when talking about climate

change. Bailey (2011) recognizes that analyses of

demographic attributes have dictated the ability of social and

cultural systems to affect vulnerability. Although often

inherent in nature, development organizations have in the past

implemented numerous programs and projects in order to

optimize these attributes. Where this article does discuss the

effect climate change has on demographics it does not address

how these attributes affect vulnerability to resultant shocks

and disasters.

Population size has many implications when talking about

flood vulnerability. Populous areas incur more damages in

times of natural disasters. Weak institutions and governance

Malik S (2012) 26

further aggravate already dismal conditions especially in the

context of developing countries. It has been estimated that

Pakistan’s population will “reach 400 million or more in the

year 2035” (Malik 2000 p98). This places great concern over

both food and water security of the country and if history and

experience indicate anything, it is that in all likelihood such

decisions will not only be made without incorporating

sustainability, ecosystem flows and the environment into

consideration, but also that inter and intra village equity will

not be addressed sufficiently. More research is needed that

moves away from considering population at the regional,

national or district level and towards the analysis of the

household and individual level. Such work could give us

valuable insight on the decision-‐making mechanisms

prevailing in rural households and would therefore help us

understand what coping measures are taken and how aid

agencies could influence them.

The female population is the most affected when talking about

flood vulnerability. They are the ones who are often

responsible for preparing for floods and then managing the

recovery process after the disaster has passed. This, in turn,

has the effect of overburdening them further. In addition to

fulfilling the role of caretaker to the young, elderly and

Malik S (2012) 27

diseased, they have to rebuild houses and manage farms.

Moreover, women are responsible for their daily chores

including managing the house, cleaning, collection and

preparation of food and water (Neefjes, Minh, Nelson, Tran and

Dankelman I 2009). At times of flood the activity of water

collection can be more time consuming. Since local water

points become polluted, women and children have to travel

further to acquire water. Although an area of much research,

more work could be done in assessing local knowledge that has

been accumulated by women in their roles as caretakers and

rehabilitators. Supporting this indigenous knowledge would

be more beneficial than trying to implement standardized

development projects (Chowdhury 2001). Awareness

campaigns by development organizations could benefit greatly

and be more effective if they took advantage of perception

studies of women’s perspectives of their roles within their

homes and their communities.

Much work has been done on education, and it is by now well

established that education and more broadly, access to

information, is key when trying to improve a family’s, village’s,

or country’s ability to cope with natural disasters (Jones, Ludi

and Levine 2010). Education not only enables individuals to be

better aware of resources available to them but it also

Malik S (2012) 28

empowers them through increased income so that they can

contribute positively to their immediate families, and in turn to

society, by both disseminating knowledge and diversifying

livelihoods. Furthermore, educated mothers tend to raise

educated children (Baker and Stevenson 1986). In terms of

disasters, educated people are also better able to distinguish

between good and bad practices regarding disaster mitigation

and are more likely to be proactive in disaster preparedness.

Following up on the educational and information aspect,

income plays an important role when trying to achieve an

improved standard of living. In rural villages of developing

countries where people live within extended patriarchal

families, the wage earner is responsible for the individual

dependents. This can greatly affect a household’s ability to

recover from the kind of shocks that are associated with floods

and/or climate change. An important challenge remains in

quantifying non-‐paid labor, be it women’s contribution to

household responsibilities or on-‐farm labor, or other family

members who often work the family assets without being paid

and accounted for in formal surveys and statistics. This is the

cause of a huge gap as this unaccounted segment contributes

significantly to the economic conditions of the household,

Malik S (2012) 29

through allowing their male counterparts to explore more

income generating activities elsewhere (Neefjes et al. 2009).

Another dependency which is characteristic of rural

households or households living within an extend family

system is age dependency. This recognizes that the elderly or

the young are dependent on those members of the household

who are in their ‘productive’ years. This is probably a better

measure as it takes into account a woman’s contribution,

which is often recognized but as yet is hard to assess when

talking about income, since traditionally women are often

engaged in unpaid work (Neefjes et al. 2009).

All in all, despite the huge amount of literature on

demographics (especially gender), researchers have not

explored the links between impacts associated with climate

change in an integrated manner with demographic attributes

of a household’s ability to recover or absorb these shocks.

THEORETICAL FRAMEWORK, KEY CONCEPTS AND THEORIES AND EXPECTED CONTRIBUTION The human ecology approach, developed by Gilbert White,

emphasizes “the role of scientific knowledge, the need for

understanding perceptions and behavior with regard to

resources and hazards, and the ultimate expansion of the range

of choice open to individuals, communities, and societies in

Malik S (2012) 30

their resource management decisions” (Mustafa 2002 p. 95). It

is with this element in mind that this paper intends to form an

understanding of the concept of vulnerability with regards to

floods, by attempting to understand how demographic

characteristics in Charsadda, affect an individuals ability to

recover from natural disasters, thereby affecting the

households ability to recover as well.

Adger et al (2003 p. 181) describe vulnerability as:

“a socially constructed phenomenon influenced by institution and economic

dynamics. The vulnerability of a system to climate change is determined by its

exposure, by its physical setting and sensitivity, and by its ability and

opportunity to adapt to change”.

Mustafa (2005 p. 566) cites Dow (1992) and Cutter (1996)

when defining vulnerability:

“as the susceptibility of hazard victims to suffer damage from extreme events

and their relative inability to recover from those events on account of their

social positionality”.

The development and use of indicators and indices to rank

vulnerability is continuously evolving. Pakistan has seen

development in this area mostly aimed at the issue of poverty.

Where this kind of research has been carried out it has not

gone beyond the district level. In the context of climate change

Malik S (2012) 31

and floods, Khan and Salman (2012) develop a Human

Vulnerability Index for Pakistan using primary data in addition

to Pakistan’s 1998 census data in order to classify vulnerable

and resilient districts in the context of climate change hazards.

This index uses five indicators: population, knowledge, housing,

standard of living and livestock and agriculture. However this

approach does not go beyond the district level to the

household or individual level. In addition, Khan (2012)

employs the use of household damage and recovery data, and

timelines of services in order to assess the difference between

vulnerable and resilient households in the Indus Basin at the

time of the 2010 floods.

Keeping such definitions and theories in mind, this paper

intends to approach the research questions inductively, with

an anticipation of the theory evolving through findings, a

critical realist epistemology and a constructionist view of the

social entity. Such an approach is taken because an

understanding of the forces at play that determine enabling

conditions for the affected population is vital in understanding

the actions that are taken by them. Furthermore, as suggested

by the constructionist school of thought, the conditions and

dynamics that govern such communities are constantly being

evolved in order to accommodate new challenges as they

Malik S (2012) 32

emerge. In recognizing that poverty and marginalization do

play vital roles in influencing vulnerability, Adger et al (2003 p.

182) point out that “vulnerability to future climate change is

likely to have distinct characteristics and create new

vulnerabilities”; understanding such process would then give

valuable insight into the workings of local communities and the

mechanisms by which they evolve towards adaptation to

climate related events.

Malik S (2012) 33

METHODOLOGY

For the purpose of this paper, primary data that was collected

for an ongoing study titled ‘Scoping long-‐term research agenda

for climate change adaptation in the Indus basin through local

embedded capacities’ (Indus Floods Research Project IFRP),

was used. Funded by the International Development Research

Centre (IDRC) and Department for International Development

(DFID), the Institute of Social and Environmental Transition

(ISET-‐PK) is carrying out this study in collaboration with Rural

Support Programmes Network (RSPN). The purpose of the

study is to examine the affects that gateway services have on

the recovery of flood affected households. To this end, it

examines not only the availability but also the temporal aspect

by analyzing how time duration of availability also affects

recovery.

The IFRP has collected data from 235 households in 4 districts

throughout the Indus Basin. In addition to gateway services,

the survey also gathered demographic data, which has been

utilized for the purpose of this dissertation. Out of the four

Malik S (2012) 34

districts, Charsadda was selected as it was severely affected by

floods also for to its proximity to Islamabad.

Districts were selected by the IFRP in relation to their location

on the Indus Basin. By developing a social vulnerability index

that employed the 1998 Government of Pakistan Population

and Housing Census and using population, women <15 or >50,

infrastructure type and safe drinking water as indicators, the

IFRP then selected two Union Councils from each site. Agra

and Kharkai were selected as a result of scoring amongst the

top five in this index.

The next step was to select the households. With it already

established that Agra and Kharkai were the most vulnerable

Union Councils within the Charsadda District; hazard mapping

and other participatory rural appraisal techniques were used

in order to identify 12-‐15 vulnerable and 12-‐15 less vulnerable

households in each village. In order to achieve this,

researchers from RSPN already working in these areas were

employed as they had the familiarity and language fluency

needed to carry out the investigation. Training workshops

were held for the teams in order to familiarize and relay to

them the ethics, formats and procedures of different

quantitative and qualitative research techniques.

Malik S (2012) 35

Furthermore, the IFRP team conducted stakeholder meetings

with researchers, community leaders and mobilizers, residents

and key persons in the local, provincial and national

government. Participatory tools including hazard maps of the

village, transect walks, problem ranking, gender workload

charts and seasonal and daily calendars were also used to help

identify prospective households. The team then used all this

valuable information to select the households and develop the

household questionnaire. A number of focus group discussions

were also held but these followed a conversational format in

order to assess the issues important to the participants. Due to

the cultural setting of Charsadda, these were segregated

groups and conducted by the same gendered researchers.

Finally households selected met the following criteria:

RESIL IENT HOUSEHOLDS

• Recovered or relatively recovered from the flood disasters

• Have a regular source of income/employment

• Access to services outside the village; to district or neighboring districts

• Have a say in community/village matters

Malik S (2012) 36

VULNERABLE HOUSEHOLDS

• Not recovered from flood disasters

• Have low-‐income employment/daily labor

• Access to services limited to village or union council

• Have a limited say in matters regarding village/community

In the end, 25 resilient households and 31 vulnerable

households collectively participated in the survey. The survey,

although extensive, helps to paint a complete picture of the

households by quantifying aspects of their socio-‐economic

conditions and of services available to them. This survey, as

mentioned above, also collected demographic data, which has

been used in this dissertation. The relevant section is included

as Annexure I.

The Demographic Vulnerability Ranking (DVR) developed in

this paper attempts to assess vulnerability at the household

level by taking into account household size, gender, age

dependency, illiteracy, and income dependency of individuals

constituting the respective households.

The criteria for the indicators borrows from Khan et al

(2012)’s methodology in that they are objectively verifiable

and measureable but in addition, also (1) they should be

considered essential when talking of demographic

characteristics of individuals constituting a household, in the

Malik S (2012) 37

context of a rural setting (2) and they are those attributes that

can influence recovery of households in the context of floods.

COMPUTATION OF RATIOS Raw data was used from the IFRP Household Survey in order

to calculate the following ratios. The method used for

calculation has been developed based on methods used by the

Carolina Population Center (Carolina Population Center 2012):

HOUSEHOLD SIZE (HS) – This measure is not a ratio as it is measured as the

total number of individuals living as part of one household. Across the board, in

the majority of the literature on climate change and disaster risk reduction,

population density is considered to be one of the determining factors in an

area’s vulnerability to natural disasters. Adopting this element to the

household level, it is used in our calculation to understand the affect household

size may have on the recovery process.

GENDER RATIO (GR) – Calculated as the number of females to men within a

household.

𝐺𝑅 = # 𝑜𝑓 𝑓𝑒𝑚𝑎𝑙𝑒𝑠 # 𝑜𝑓 𝑚𝑎𝑙𝑒𝑠

This ratio is read as number of females per males. The higher the ratio the

more females to males there are; the lower the ratio the less females to males

there are; a result of 1 implies unity (a perfect balance).

Malik S (2012) 38

ADULT ILL ITERACY RATIO (AILR) – Adult representing individuals >15 years of

age, this indicator is measured as the total number of illiterate adults divided

by the total number of literate adults in a household.

𝐴𝐼𝐿𝑅 = 𝐼𝑙𝑙𝑖𝑡𝑒𝑟𝑎𝑡𝑒 𝑎𝑑𝑢𝑙𝑡𝑠 !!"𝐿𝑖𝑡𝑒𝑟𝑎𝑡𝑒 𝑎𝑑𝑢𝑙𝑡𝑠 !!"

This ratio is read as number of illiterate adults per literate adult. A higher

value indicates more illiterate to literate persons; a low value indicates more

literate to illiterate; and a value of 1 indicates unity.

AGE DEPENDENCY RATIO (ADR) – This ratio indicates the number of

‘unproductive’ members within a household that are dependent on the

‘productive’ members.

𝐴𝐷𝑅 = 𝐴𝑔𝑒!!!" + 𝐴𝑔𝑒!"!

𝐴𝑔𝑒!"!!"

This ratio represents the number of ‘unproductive’ per ‘productive’ individuals.

Again, a higher value indicates more ‘unproductive’ to ‘productive’; a low value

indicates more ‘productive’ to ‘unproductive’; and 1 represents unity.

CUMULATIVE INCOME DEPENDENCY RATIO (CIDR) – This ratio represents the

number of people dependent on the wage earner(s) of the house. In order to

tabulate this ratio, the daily labor ratio and monthly labor ratio were first

calculated and then combined in order to acquire the CIDR. For the following

ratios, a high value indicates a higher number of the ‘numerator’ per

Malik S (2012) 39

‘denominator’, while a lower number indicates a lower number of ‘numerators’

per ‘denominator’; 1 implies unity.

DAILY LABOR RATIO (DLR):

This categorization includes farm laborers, off-‐farm skilled

laborers and off-‐farm unskilled laborers and is calculated as:

𝐷𝐿𝑅 = # 𝑜𝑓𝑑𝑎𝑖𝑙𝑦 𝑙𝑎𝑏𝑜𝑟# 𝑜𝑓 𝑎𝑙𝑙 𝑜𝑡ℎ𝑒𝑟𝑠

MONTHLY LABOR RATIO (MLR):

This includes individuals with ownership of their own farms or

who are currently employed in government jobs, private jobs

or in their own businesses. This is calculated as:

𝑀𝐿𝑅 = # 𝑜𝑓 𝑚𝑜𝑛𝑡𝑙𝑦 𝑙𝑎𝑏𝑜𝑟# 𝑜𝑓 𝑎𝑙𝑙 𝑜𝑡ℎ𝑒𝑟𝑠

These two were then added to give us the CIDR2:

𝐶𝐼𝐷𝑅 = 𝐷𝐿𝑅 +𝑀𝐿𝑅

Annexure II gives the table of results for the calculation of the

ratios for all 56 households. The household ratios are also then

assigned to the individuals with in the household. The table

2 Note: For all calculations above, a value of zero for the denominator gave a result of approaching infinite. Therefore to illustrate the extreme vulnerability, they were assigned the ‘maximum value + 1’ for that demographic.

Malik S (2012) 40

also includes the FK Index, as developed for the IFRP based on

damage and recovery data (Khan 2012), for comparison.

COMPUTATION OF SCORES In order to rank the households based on the above ratios, the

scoring methodology of the Economic Freedom in Pakistan –

Sub National Index 2009 was employed. The main reason

being that this is a unit free, standard normalization technique

(Salman and Arbi 2009):

“Thus even when the raw data is not available in percentage form, this formula

brings out a score, which is not sensitive to the unit of raw data.” (Salman et al

p. 11).

In addition, by providing comparative scores, it awards a

maximum value of 10 and a minimum value of 0. The formula

for computation has been modified so that it may be used to

rank vulnerability. The formula is first applied to each ratio

and then the number of variables divides the sum of these in

order to give a numerical rank. This rank is also assigned to

the individuals within the household.

Malik S (2012) 41

The computation is as follows:

𝑹𝒂𝒏𝒌𝒊𝒏𝒈𝒅 =𝑽𝒎𝒂𝒙 − 𝑽𝒊𝑽𝒎𝒂𝒙 − 𝑽𝒎𝒊𝒏

×𝟏𝟎

where:

𝑑 = The demographic attribute being ranked.

𝑉!"# = Value of the indicator with the highest ratio or

percentage in that category.

𝑉!"# = Value of the indicator with the lowest ratio or

percentage in that category.

𝑉! = Value of ratio or percentage of individual households.

Once all the demographic characteristics have been ranked, the

Demographic Vulnerability Ranking can be computed as

follows:

𝐷𝑉𝑅 = 𝑅𝑎𝑛𝑘𝑖𝑛𝑔!! +⋯+ 𝑅𝑎𝑛𝑘𝑖𝑛𝑔!!

𝑛

where:

𝐷𝑉𝑅 = Demographic Vulnerability Ranking

𝑑 = The demographic attribute being ranked.

𝑛 = The number of demographic attributes being assessed.

The resultant scores, as mentioned above, are between 0 and

10, with 0 being the least vulnerable and 10 being the most

Malik S (2012) 42

vulnerable. The lower the score the less vulnerable a

household is, whereas the higher the score the more

vulnerable the household is.

After the ranking is carried out, the households are ranked in

an ascending order and divided into five groups. As there are

56 households in total, each group has a total of 11 households

while the median group has 12.

The 11 highest and 11 lowest ranking households are then

isolated from the rest of the data in order to analyze the

difference between the two groups. The results of the

individual ranking within these are then compared with the FK

Index and statistically tested using the Chi-‐squared test in

order to assess whether or not there is any statistical

significance. This in turn validates the ranking method as it

illustrates how effective it is when trying to rank vulnerability

at the household level by looking at its smallest unit of analysis,

i.e. the individual.

Malik S (2012) 43

RESULTS AND ANALYSIS

The final ranking using the DVR for all 56 households is

presented as Annexure III. Table 1 below illustrates the scores

for the first and last groups.

TABLE 1: DVR COMPUTATION

N HSr GRr AILRr ADRr CIDRr DVR FK Index 24 3.077 0.250 0.833 0.278 1.481 1.184 1

1 0.000 1.250 1.667 0.000 5.556 1.694 1

38 3.077 0.625 1.667 0.000 4.444 1.963 0 21 5.385 1.000 0.208 0.000 3.704 2.059 1

35 1.538 1.250 0.000 1.667 6.667 2.224 1 14 1.538 0.417 5.000 0.000 5.185 2.428 1

56 1.538 0.417 5.000 0.000 5.185 2.428 1 22 3.077 0.625 0.833 1.667 6.111 2.463 1

11 0.769 2.500 3.333 0.000 6.111 2.543 1

4 0.000 1.250 1.667 0.000 10.000 2.583 0 28 2.308 1.875 2.500 0.417 5.833 2.587 0

25 6.923 1.042 6.667 2.000 6.667 4.660 1 52 3.077 2.500 10.000 1.667 6.111 4.671 0

31 1.538 3.750 10.000 1.667 6.667 4.724 0

33 1.538 3.750 10.000 1.667 6.667 4.724 1 9 2.308 1.875 10.000 2.500 7.222 4.781 0

45 2.308 1.875 10.000 2.500 7.222 4.781 0 46 4.615 8.750 0.000 0.556 10.000 4.784 0

48 4.615 0.417 10.000 2.778 6.667 4.895 1 41 3.846 0.500 10.000 10.000 1.389 5.147 0

32 4.615 3.750 10.000 1.000 6.667 5.206 1

49 3.846 0.500 10.000 4.167 8.333 5.369 0 HSr: Household Size Ratio rank GRr: Gender Ratio rank AILR: Adult Illiteracy Ratio rank ADRr: Age Dependency Ratio rank CIDRr: Cumulative Income Dependency rank DVR: Demographic Vulnerability Ranking FK Index: Fawad Khan Damage & Recovery Index; 1-‐Recovered, 2-‐Not recovered (source: Khan 2012)

Malik S (2012) 44

In the above table, the DVR is given in ascending order as those

on the lower end are less vulnerable and those households that

ranked higher are more vulnerable. The FK Index denotes the

categorization done in the Indus Floods Research Project. This

index is based on the infrastructure damage and recovery data

and categorizes households as ‘recovered’ or ‘not recovered’

on the basis of how much physical damage they have been able

to absorb and rebuild. At first glance, we can see that there are

a few discrepancies between the two indices. This does not

imply one or the other is wrong, only that three of the houses

ranked as ‘less vulnerable’ under the DVR method have not

managed to recover, while four of the ‘more vulnerable’

households under the DVR have ‘recovered’.

DESCRIPTIVES Using the DVR data, vulnerability mapping can be carried out

and is presented in Figure 2. Here, average ranks are taken for

each of the indicators and the DVR index and then mapped on a

radar chart, in order to allow comparison. ‘Low ranking’

indicates ‘less vulnerable’ while ‘high ranking’ indicates ‘more

vulnerable’.

Malik S (2012) 45

FIGURE 2: DEMOGRAPHIC AND DVR MAPPING

HSr: Household Size Ratio rank GRr: Gender Ratio rank AILR: Adult Illiteracy Ratio rank ADRr: Age Dependency Ratio rank CIDRr: Cumulative Income Dependency rank DVR: Demographic Vulnerability Ranking

Table 2 below shows the average values for the demographic

ratio ranks and the Demographic Vulnerability Ranking.

TABLE 2: AVERAGE RANKING FOR DEMOGRAPHIC RATIOS AND DVR

HSr GRr AILRr ADRr CIDRr DVR Low-‐ranking 2.028 1.042 2.064 0.366 5.480 2.196 High-‐ranking 3.566 2.610 8.788 2.773 6.692 4.886 HSr: Household Size Ratio rank GRr: Gender Ratio rank AILR: Adult Illiteracy Ratio rank ADRr: Age Dependency Ratio rank CIDRr: Cumulative Income Dependency rank DVR: Demographic Vulnerability Ranking

0.000 2.000 4.000 6.000 8.000 10.000

HSr

GRr

AILRr

ADRr

CIDRr

DVR

Vulnerability Mapping

Low ranking

High ranking

Malik S (2012) 46

Figure 2 and Table 2 both illustrate the extent to which

households that ranked low on the DVR differ from the

households that ranked high. On first glance, it can be seen

that the ‘low ranking’ (less vulnerable) scored lower, on

average, on all the demographics than the ‘high ranking’ (more

vulnerable) households.

HOUSEHOLD SIZE RATIO Household size, on average, is larger for the ‘high ranking’ than the ‘low ranking’ by

approximately one and a half points at 2.03 and 3.57 respectively. Figure 3 below

shows the cumulative number of individuals living in 11 households within the two

categories. The average household size for the ‘low ranking’ is 4.6 while the average

size for the ‘high ranking’ is 6.6.

FIGURE 3: CUMULATIVE INDIVIDUALS IN 11 HOUSEHOLDS IN EACH CATEGORY

51 73

Low ranking High ranking

Cumulative Individuals

Cumulative Individuals

Malik S (2012) 47

GENDER RATIO On the vulnerability map (Figure 2), Gender Ratio ranking shows the same trend with

‘high ranking’ households, on average having approximately double the gender ratio

than the ‘low ranking’ households. The average rank for the ‘low ranking’ households

is 1.04 while for the ‘high ranking’ households the average rank is 2.6. Figure 4 below

shows the gender profile as a percentage of the total number of people in that

category. Clearly, there are a larger number of males than females in the ‘low ranking’

category and a larger number of females than males in the ‘high ranking’ category.

FIGURE 4: GENDER PROFILE

ADULT ILL ITERACY RATIO Adult Illiteracy Ratio ranking in Figure 2 shows a staggering difference between the

two groups. The ‘high ranking’ group ranked approximately three and a half more

points than the ‘low ranking’ group. The average rank for the former is 8.79, while the

latter ranked 2.06 on average. Figure 5 on the next page shows the distribution of

adult illiteracy as a percentage of individuals in each category. 58% of the ‘less

vulnerable’ are literate, while 79% of the ‘more vulnerable’ people are illiterate.

61% 45%

39%

55%


Gender ProXile as % of individuals

Male Female

Malik S (2012) 48

FIGURE 5: ILLITERACY DISTRIBUTION AS % OF INDIVIDUALS

Figure 6 below graphically represents the level of Adult Literacy achieved as a

percentage of individuals in the two categories. The literate ‘more vulnerable’

individuals have not been educated beyond the 8th grade while for the literate, ‘less

vulnerable’, 29% are educated beyond the 10th grade.

FIGURE 6: LEVEL OF ADULT LITERACY DISTRIBUTION AS % OF ADULTS

58% 21%

42%

79%


Illiteracy distribution as % of individuals

Not illiterate Illiterate

42%

9%

4%

7%

9%

29%

79%

18%

3%

Not Literate

Literate

Primary (1-‐5)

Middle (6-‐8)

Matriculte (9-‐10)

Above

Column Labels

Level of Adult Literacy Distribution as % of adults

More vulnerable Less vulnerable

Malik S (2012) 49

AGE DEPENDENCY RATIO Age Dependency Ratio ranking in Figure 2 shows an extremely low rank on average

for the ‘low ranking’ households. This indicates that for the ‘less vulnerable’

households, there is a lesser proportion of the ‘unproductive’ age group in relation to

the ‘productive’ age group than for the ‘more vulnerable’ households. The average Age

Dependency Ratio ranking for the ‘low ranking’ and ‘high ranking’ is .37 and 2.77

respectively. Figure 7 below shows the age distribution as a percentage of the

individuals within the categories. 55% of the ‘low ranking’ individuals are in the ‘non

productive’ age group, while 88% of the ‘high ranking’ individuals fall into the

‘productive’ age group.

FIGURE 7: AGE DISTRIBUTION AS % OF INDIVIDUALS

10%

57%

31%

2%

55%

29%

16%

0-‐14

15-‐35

36-‐64

65-‐75

Age Distribution as % of individuals


Malik S (2012) 50

CUMULATIVE INCOME DEPENDENCY RATIO Cumulative Income Dependency Ratio ranking in the Vulnerability map of Figure 2

shows that there is only an approximately one-‐point difference between the ‘less

vulnerable’ and the ‘more vulnerable’. The average ranking for this demographic are

5.48 and 6.69 for the ‘low’ and ‘high ranking’ respectively. There is less of a difference

in this demographic than the others previously discussed. Figure 8 below shows the

percentage of ‘earners’ and the percentage of ‘dependents’ for each category. There

are more ‘dependents’ than ‘earners’ in the ‘high ranking’ category, while the ‘low

ranking’ difference is quite small.

FIGURE 8: INCOME DEPENDENCY AS % OF INDIVIDUALS

Figure 9 on the next page shows a classification of the primary activity that individuals

are involved in as a percentage of the number of individuals in their respective

categories. The ‘less vulnerable’ are distributed across all the categories while the

‘more vulnerable’ are concentrated in the non-‐income generating activities or

activities that provide daily wages.

45% 26%

55% 74%


Income Dependency as % of individuals

Earners Dependents

Malik S (2012) 51

FIGURE 9: PRIMARY ACTIVITY AS % OF INDIVIDUALS

DEMOGRAPHIC VULNERABIL ITY RANKING Demographic Vulnerability Ranking (DVR) gives us the aggregate rank for the ‘low

ranking’ and ‘high ranking’ households. The average rank for the former is 2.20, while

for the later it is 4.89. Figure 10 below illustrates the percentage of individuals that

have’ recovered’ from the ‘low ranking’ category -‐75% as classified by the FK Index;

and the percentage of those who have ‘not recovered’ in the ‘high ranking’ category-‐

58%.

2%

4%

24%

2%

8%

4%

2%

6%

33%

12%

4%

1%

1%

1%

19%

1%

1%

45%

12%

16%

Own Farming

Farm Laborer

Off-‐Farm Skill. Lab.

Off-‐Farm Unskill. Lab.

Gov't Job

Private Job

Business

Other

Unemployed

HH Work

Student

Child/Infant (<5)

Primary Activity as % of individuals


Malik S (2012) 52

FIGURE 10: DVR AS COMPARED TO THE FK INDEX-‐% OF INDIVIDUALS

Lastly, in order to illustrate the trend of these demographics in

relation the DVR, Figure 11 below represents the ratio

rankings and DVR for all 56 households, arranged in an

ascending order according to the DVR.

FIGURE 11: FLOOD VULNERABILITY RANKING AND DEMOGRAPHIC INDICATOR TRENDS

75% 42%

25% 58%


DVR as compared to the FK Index-‐% of individuals

Recovered Not recovered

0.000

10.000

20.000

30.000

40.000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55

Flood Vulnerability Ranking & Demographic Indicators

HSr AILr GRr ADRr CIDRr r

Malik S (2012) 53

STATISTICAL SIGNIFICANCE In order to test for statistical significance the Chi-‐squared test

was used at the 95% confidence level. This was done because

the distribution of the frequency is not normal and the testing

was carried out using two independent categorical variables:

‘Recovered/Not recovered’ and ‘Low ranking/High ranking’.

The first test was carried out to assess the statistical

significance of the DVR against the FK Index. To this end the

following hypothesis was tested:

𝐻!: There is no relationship between the DVR and the FK Index.

𝐻!: There is a relationship.

Table 3 below represents the results that were derived using

the SPSS software.

TABLE 3: CHI-‐SQUARED TEST FOR DVR AGAINST THE FK INDEX

Pearson's chi square

Significance (2 Sided)

Fisher's exact test (2 Sided)

Reject Null Hypothesis

Probability of chance occurrence

RECOVERY (FK INDEX) 12.491 0.000 0.000 Yes 0%

The test resulted in a value of 12.491 for the Pearson’s chi-‐

square and two-‐sided significance of 0. Since this value is less

than .05, we can be confident in rejected the null hypothesis, i.e.

there is no relationship between the DVR and FK Index, at the

Malik S (2012) 54

95% confidence level. Also the Fisher’s exact two-‐sided test

gives us a value of 0, indicating that there is a 0% probability

that this was a chance occurrence.

The next step is to test whether the individual demographics

have a statistically significant relationship with the FK Index.

Again testing at the 95% confidence level, 𝑑 denotes the

demographic attribute being tested. The hypothesis is as

follows:

𝐻!: There is no relationship between 𝑑 and the FK Index.

𝐻!: There is a relationship.

The resultant table is presented below as Table 4.

TABLE 4: CHI-‐SQUEARED TEST FOR DEMOGRAPHIC ATTRIBUTES AGAINST THE FK INDEX

Pearson’s chi square

Significance (2 Sided)

Fisher's exact test (2 Sided)

Reject Null Hypothesis

Probability of chance occurrence

GENDER RATIO 17.632 0.000 0.000 Yes 0% AGE DEPENDENCY 2.412 0.120 0.148 No 15% HH SIZE 0.820 0.365 0.446 No 45% AIL 1.526 0.217 0.275 No 28% INCOME DEPENDENCY 0.391 0.532 0.589 No 59%

Here, we can see that for ‘Gender’, we get a Pearson’s chi

square value of 17.632 with a two-‐sided significance of 0,

therefore we can reject the null hypothesis, i.e. there is no

relationship between the Gender Ratio ranking and the FK

Malik S (2012) 55

Index at the 95% confidence level. Again, the Fisher’s exact

two-‐sided test gives a value of 0, implying a 0% probability of a

chance occurrence.

FINDINGS Household vulnerabilities cannot be assessed at the household

level without recognizing the individuals within that unit. This

is because demographic characteristics define individuals and

their relationships within a household, and therefore

individual vulnerabilities based on demographic

characteristics accumulated, represent the vulnerability of the

household.

More than any other demographic attribute, ‘Gender’ is the

single most influential factor in a household’s ability to recover

from floods. While other demographics showed no statistically

significant relationship to damage and recovery, ‘Gender’ was

statistically significant, as illustrated by a higher proportion of

females to males in the ‘not recovered’ category of the FK Index.

While the statistical test does not indicate the direction of the

relationship, this characteristic is statistically significant at the

95% confidence level or even at the 99% confidence level.

Finally, it is found that household work, as a primary activity, is

prevalent in both ‘low ranking’ and ‘high ranking’ households.

Malik S (2012) 56

It is essential therefore to recognize and to develop valuation

tools that can easily and efficiently quantify this activity. In

order to give it the appropriate weightage, calculation of

household income should incorporate the opportunity cost of

household work. Not recognizing women’s household work as

a contributor to rural economy greatly undervalues women’s

roles in society.

Malik S (2012) 57

CONCLUSIONS

In answering the research question, ‘What demographic

characteristics make households more or less vulnerable to

floods in rural Charsadda, Pakistan?’, it was found that ‘Gender’

was the most significant demographic characteristic affecting

vulnerability to floods in rural Charsadda, Pakistan.

At the outset of the research it was anticipated that

demographic attributes such as household size, gender, adult

illiteracy, age dependency and income dependency, would all

contribute to a household’s vulnerability to floods. Taking

these into account would then allow flood mitigation, early

relief, recovery and adaption efforts to be more effective.

However, the findings are much simpler, in that, statistical

testing showed no significance for any of the demographic

attributes apart from ‘Gender’. It can be inferred from this that,

when designing policies, plans and programmes for flood

mitigation and adaptation, recognizing the importance of

targeting the female population can yield significant impacts.

The second research question, ‘Can a demographic vulnerability

ranking method be used to identify households with a higher

Malik S (2012) 58

vulnerability to floods in flood prone Charsadda?’ was answered

in the affirmative. A demographic vulnerability ranking

method, can in fact be used to determine vulnerability at the

household level to floods in flood prone Charsadda, with the

caveat that the method is applied to the individuals who

constitute the household.

Future scope for further research includes focusing on ‘Gender’

and floods. Developing valuation methods that incorporate

‘household work’ into the primary activities of a household

would be a good starting point. In addition, in order take the

method developed in this paper further, it would be useful to

analyze the various dimensions to ‘Gender’ that may influence

flood vulnerability, particularly in rural Charsadda.

Malik S (2012) 59

REFERENCES

Adger W N, Huq S, Brown K, Conway D and Hulme M (2003) Adaptation to climate change in the developing world. Progress in Development Studies 3(179)

Ahmad F, Kazmi S F and Pervez T (2011) Human response to hydro-‐meteorological disasters: A case study of the 2010 flash floods in Pakistan. Journal of Geography and Regional Planning 4(9) 518-‐524

Anon (2010) Washed Up. Economist 396(8697) 34-‐35

Arndt DS, Baringer MO, Johnson MR (eds) (2010) State of the Climate in 2009: Highlights. National Oceanic and Atmospheric Administration (NOAA) Report, pp. 2-‐7.

Bailey AJ (2011) Population geographies and climate change. Progress in Human Geography 35(5) 686-‐695

Baker D P and Stevenson D L (1986) Mother’s strategies for children’s school achievement: Managing the transition to high school. Sociology of Education 59(July) 156-‐166

Balica S F, Douben N and Wright N G (2009) Flood vulnerability indices at varying spatial scales. Water Science & Technology 60(10) 2571-‐2580

Brooks N (2003) Vulnerability, risk and adaptation: a conceptual framework. Working paper 38 Tyndall Cent. Glob. Environ. Change, University of East Anglia, Norwich, UK. http://www.tyndall.ac.uk/publications/working_papers/working_papers.shtml

Burki T (2010) Aid slows to a trickle as Pakistan crisis enters a new phase. Lancet 376(9746) 1041-‐1042

Carolina Population Center (2012) Lesson 3: Creating a Demographic Profile.

Malik S (2012) 60

http://www.cpc.unc.edu/measure/training/online-‐courses/non-‐certficate-‐courses/pap/lesson-‐3

Carrol L (1987) Alice’s adventures in wonderland and Through the looking-‐glass p 221. Chancellor Press, London, 304 pp.

Chowdhury M (2001) Women’s Technological Innovations and Adaptations for Disaster Mitigation: A Case Study of Charlands in Bangladesh. UN DAW Expert Broup Meeting on “Environmental management and the mitigation of natural disasters: a gender perspective”, Ankara, Turkey.

Cutter S (1996) Vulnerability to environmental hazards. Progress in Human Geography 20 (4) 529–39.

Dessler A and Parson E A (2009) The Science and Politics of Global Climate Change: A guide to the debate pp 66-‐67. Cambridge University Press, United Kingdom, 211 pp.

Dow K (1992) Exploring differences in our common future(s): the meaning of vulnerability to global environmental change. Geoforum 23 (3) 417–36.

Eakin H and Luers L (2006) Assessing the vulnerability of social-‐environmental systems. Annual Review of Environmental Resources 31 365-‐394

Flavin C and Engelman R (2009) The perfect storm. In: 2009 State of the World: Confronting Climate Change. Worldwatch Institute, Earthscan, London UK, pp. 9-‐11

Gaurav K, Sinha R and Panda P K (2011) The Indus flood of 2010 in Pakistan: a perspective analysis using remote sensing data. Nat Hazards 59 1815-‐1826

Global Peace Pioneers (2010) Global Peace Pioneers: Situation update Monsoon 2010. http://www.globalpeace.net.pk/reports/624Charsadda_Flood_Assessment_report_Aug_01_2010_2.pdf

Hassan S A and Ansari M R K (2010) Nonlinear analysis of seasonality and stochasiticity of the Indus River. Hydrological

Malik S (2012) 61

Sciences Journal/Journal des Sciences Hydrologiques 55(2) 250-‐265

Haque I U, Anum and Ruhmma (2009) Effects of climate change on planning and managing water services in the Potohar region of Pakistan. Electronic Journal of Environmental, Agricultural & Food Chemistry 8(8) 647-‐656

Houghton J (2009) Global Warming The Complete Briefing pp 155-‐161. Cambridge University Press, United Kingdom, 438 pp.

Holling CS (1973) Resilience and stability of ecological systems. Annual Review of Ecological Systems 4 1-‐23

Jones L, Ludi E, Levine S (2010) Towards a Characterisation of Adaptive Capacity: A Framework for Analysing Adaptive Capacity at the Local Level. ODI background note. Overseas development Institute, London.

Khan F (2012) Scoping long-‐term research agenda for climate change adaptation in the Indus basin through local embedded capacities. Working Paper: Indus Floods Research Project, Institute of Social and Environmental Transition, Pakistan

Khan F A and Salman A (2012) A simple human vulnerability index to climate change hazards for Pakistan. Working Paper: SZABIST Islamabad, Pakistan

Malik A H (2000) Integrated Urban Lei River (North Pakistan) Water Resources Management. International Journal of Water Resources Development 16(1) 97-‐117

Maple Croft, “maplecroft.com/about/news/ccvi.gtml”, Maple Coft, 2011. http://maplecroft.com/about/news/ccvi.html accessed 14 April 2011

Mustafa D (2002) Linking Access and Vulnerability: Perceptions of Irrigation and Flood Management in Pakistan. Professional Geographer 54(1) 94

Mustafa D (2005) The production of an Urban Hazardscape in Pakistan: Modernity, Vulnerability, and the Range of Choice.

Malik S (2012) 62

Annals of the Association of American Geographers 95(3) 566-‐586

Naser I F (2004) Responding to the Water Crisis in Pakistan. Water Resources Development 20(2) 177-‐192

Neefjes K, Minh Hai V, Nelson V, Tran VA, Dankelman I (2009) Responding to Climate Change in Viet Nam: Opportunities for Improving Gender Equality. A Policy Discussion Paper, Oxfam and UNDP Vietnam, pp. 27-‐39 and 61-‐62

Nicol A and Kaur N (2009) Adapting to Climate Change in the Water Sector. ODI Background Note, Overseas Development Institute, London

Owner of pharmaceutical factory (2012) Private conversation. Lahore, Pakistan

Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) (2007) Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, pp. 7-‐22.

Relief Web (2010) Pakistan: Flood Monitoring Map -‐ July 2010 -‐ Charsadda & Nowshera Districts. http://reliefweb.int/map/pakistan/pakistan-‐flood-‐monitoring-‐map-‐july-‐2010-‐charsadda-‐nowshera-‐districts accessed 15 May 2012

Salman A and Arbi KA (2009) Economic Freedom in Pakistan-‐ Sub National Index 2009. Development Pool, Islamabad, 52 pp

SMEDA NWFP (2009) Small & Medium Enterprises Development Authority, District Profile Charsadda, Ministry of Industries and Production, Government of Pakistan http://www.smeda.org/downloads/Districts_Profile_Charsadda.pdf access Aug 25, 2012

Sultana H, Ali N, Iqbal M and Khan A (2009) Vulerability and adaptability of wheat production in different climatic zones of Pakistan under climate change scenarios. Climate Change 94(1-‐2) 123-‐142

Malik S (2012) 63

Synnott H (2010) After the Flood. Survival (00396338)

Trading Economics (2012a) Pakistan GDP Per Capita. http://www.tradingeconomics.com/pakistan/gdp-per-capita accessed September 7, 2012.

Trading Economics (2012b) Pakistan Inflation Rate http://www.tradingeconomics.com/pakistan/inflation-cpi accessed September 7, 2012.

Usher P (2000) Integrating Impacts into Adaptation Measures. Environmental Monitoring and Assessment 61(1) 37

Warraich H, Zaidi A KM and Patel K (2011) Floods in Pakistan: a public health crisis. Bull World Health Organ (89) 236-‐237

Xu J, Grumbine R E, Shrestha A, Eriksson M, Yang X, Wang Y and Wilkes A (2009), The Melting Himalayas: Cascading Effects of Climate Change on Water, Biodiversity, and Livelihoods. Conservation Biology (23) 520–530

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APPENDICES ANNEXURE I: HOUSEHOLD QUESTIONNAIRE

Malik S (2012) 65

Malik S (2012) 66

ANNEXURE II: DEMOGRAPHIC RATIOS CALCULATION

HS: HOUSEHOLD SIZE GR: GENDER RATIO AILR: ADULT ILLITERACY RATIO ADR: AGE DEPENDENCY RATIO CIDR: CUMULATIVE INCOME DEPENDENCY RATIO FK INDEX: FAWAD KHAN DAMAGE AND RECOVERY INDEX; 1-‐RECOVERED, 0-‐NOT RECOVERED (SOURCE: KHAN 2012) N HS GR AILR ADR CIDR FK Index 1 2.00 1.00 1.00 0.00 10.00 1 2 6.00 1.00 1.00 2.00 14.00 0 3 6.00 0.50 2.00 1.00 11.00 0 4 2.00 1.00 1.00 0.00 18.00 0 5 9.00 0.80 0.50 2.00 17.00 1 6 4.00 3.00 3.00 0.00 12.00 0 7 7.00 0.40 2.00 1.33 11.50 0 8 4.00 0.33 6.00 0.00 9.33 0 9 5.00 1.50 6.00 1.50 13.00 0 10 6.00 1.00 2.00 1.00 10.00 0 11 3.00 2.00 2.00 0.00 11.00 1 12 9.00 0.80 1.00 0.50 11.00 1 13 9.00 2.00 0.17 0.50 9.00 1 14 4.00 0.33 3.00 0.00 9.33 1 15 7.00 2.50 1.00 2.50 15.00 1 16 5.00 0.67 6.00 1.50 13.00 1 17 8.00 3.00 1.33 0.33 12.00 1 18 11.00 1.20 0.33 0.38 18.00 1 19 15.00 1.14 0.20 2.00 13.00 1 20 4.00 3.00 0.50 1.00 12.00 1 21 9.00 0.80 0.13 0.00 6.67 1 22 6.00 0.50 0.50 1.00 11.00 1 23 12.00 0.71 0.33 0.50 12.00 1 24 6.00 0.20 0.50 0.17 2.67 1 25 11.00 0.83 4.00 1.20 12.00 1 26 6.00 1.00 0.00 2.00 18.00 0 27 9.00 0.50 6.00 0.50 11.00 1 28 5.00 1.50 1.50 0.25 10.50 0 29 4.00 1.00 6.00 1.00 12.00 0 30 6.00 0.20 6.00 0.50 14.00 0 31 4.00 3.00 6.00 1.00 12.00 0 32 8.00 3.00 6.00 0.60 12.00 1 33 4.00 3.00 6.00 1.00 12.00 1 34 5.00 1.50 6.00 0.67 3.00 0 35 4.00 1.00 0.00 1.00 12.00 1 36 2.00 8.00 6.00 0.00 0.00 1 37 10.00 1.00 4.00 1.00 8.00 0 38 6.00 0.50 1.00 0.00 8.00 0

Malik S (2012) 67

39 8.00 0.33 5.00 1.67 9.60 1 40 6.00 1.00 1.00 2.00 14.00 1 41 7.00 0.40 6.00 6.00 2.50 0 42 3.00 0.50 6.00 0.50 11.00 1 43 6.00 0.50 6.00 0.50 11.00 1 44 3.00 0.50 6.00 0.00 9.50 1 45 5.00 1.50 6.00 1.50 13.00 0 46 8.00 7.00 0.00 0.33 18.00 0 47 2.00 1.00 6.00 0.00 10.00 0 48 8.00 0.33 6.00 1.67 12.00 1 49 7.00 0.40 6.00 2.50 15.00 0 50 5.00 0.25 6.00 0.67 1.33 1 51 4.00 0.00 6.00 1.00 10.00 0 52 6.00 2.00 6.00 1.00 11.00 0 53 5.00 0.25 2.00 1.50 10.50 0 54 5.00 1.50 1.00 1.50 18.00 0 55 9.00 1.25 0.17 0.29 17.00 1 56 4.00 0.33 3.00 0.00 9.33 1

Malik S (2012) 68

ANNEXURE III: DVR CALCULATIONS FOR ALL DEMOGRAPHICS & AGGREGATE RANKING FOR ALL 56 HOUSEHOLDS

HSr: Household Size rank GRr: Gender Ratio rank AILRr: Adult Illiteracy Ratio rank ADRr: Age Dependency Ratio rank CIDRr: Cumulative Income Dependency Ratio rank DVR: Demographic Vulnerability Ranking FK Index: Fawad Khan Damage and Recovery index; 1-‐Recovered, 0-‐Not recovered (source: Khan 2012) N HSr GRr AILRr ADRr CIDRr DVR FK Index 24 3.077 0.250 0.833 0.278 1.481 1.184 1 1 0.000 1.250 1.667 0.000 5.556 1.694 1 38 3.077 0.625 1.667 0.000 4.444 1.963 0 21 5.385 1.000 0.208 0.000 3.704 2.059 1 35 1.538 1.250 0.000 1.667 6.667 2.224 1 14 1.538 0.417 5.000 0.000 5.185 2.428 1 56 1.538 0.417 5.000 0.000 5.185 2.428 1 22 3.077 0.625 0.833 1.667 6.111 2.463 1 11 0.769 2.500 3.333 0.000 6.111 2.543 1 4 0.000 1.250 1.667 0.000 10.000 2.583 0 28 2.308 1.875 2.500 0.417 5.833 2.587 0 13 5.385 2.500 0.278 0.833 5.000 2.799 1 53 2.308 0.313 3.333 2.500 5.833 2.857 0 20 1.538 3.750 0.833 1.667 6.667 2.891 1 50 2.308 0.313 10.000 1.111 0.741 2.894 1 3 3.077 0.625 3.333 1.667 6.111 2.963 0 10 3.077 1.250 3.333 1.667 5.556 2.976 0 12 5.385 1.000 1.667 0.833 6.111 2.999 1 7 3.846 0.500 3.333 2.222 6.389 3.258 0 23 7.692 0.893 0.556 0.833 6.667 3.328 1 44 0.769 0.625 10.000 0.000 5.278 3.334 1 47 0.000 1.250 10.000 0.000 5.556 3.361 0 6 1.538 3.750 5.000 0.000 6.667 3.391 0 34 2.308 1.875 10.000 1.111 1.667 3.392 0 2 3.077 1.250 1.667 3.333 7.778 3.421 0 40 3.077 1.250 1.667 3.333 7.778 3.421 1 8 1.538 0.417 10.000 0.000 5.185 3.428 0 55 5.385 1.563 0.278 0.476 9.444 3.429 1 26 3.077 1.250 0.000 3.333 10.000 3.532 0 17 4.615 3.750 2.222 0.556 6.667 3.562 1 42 0.769 0.625 10.000 0.833 6.111 3.668 1 54 2.308 1.875 1.667 2.500 10.000 3.670 0 51 1.538 0.000 10.000 1.667 5.556 3.752 0 18 6.923 1.500 0.556 0.625 10.000 3.921 1

Malik S (2012) 69

5 5.385 1.000 0.833 3.333 9.444 3.999 1 36 0.000 10.000 10.000 0.000 0.000 4.000 1 37 6.154 1.250 6.667 1.667 4.444 4.036 0 43 3.077 0.625 10.000 0.833 6.111 4.129 1 29 1.538 1.250 10.000 1.667 6.667 4.224 0 15 3.846 3.125 1.667 4.167 8.333 4.228 1 39 4.615 0.417 8.333 2.778 5.333 4.295 1 30 3.077 0.250 10.000 0.833 7.778 4.388 0 19 10.000 1.429 0.333 3.333 7.222 4.463 1 16 2.308 0.833 10.000 2.500 7.222 4.573 1 27 5.385 0.625 10.000 0.833 6.111 4.591 1 25 6.923 1.042 6.667 2.000 6.667 4.660 1 52 3.077 2.500 10.000 1.667 6.111 4.671 0 31 1.538 3.750 10.000 1.667 6.667 4.724 0 33 1.538 3.750 10.000 1.667 6.667 4.724 1 9 2.308 1.875 10.000 2.500 7.222 4.781 0 45 2.308 1.875 10.000 2.500 7.222 4.781 0 46 4.615 8.750 0.000 0.556 10.000 4.784 0 48 4.615 0.417 10.000 2.778 6.667 4.895 1 41 3.846 0.500 10.000 10.000 1.389 5.147 0 32 4.615 3.750 10.000 1.000 6.667 5.206 1 49 3.846 0.500 10.000 4.167 8.333 5.369 0

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