indus floods research project, result from the field · basin outside the sphere of irrigation...

Appendix 2

INDUS FLOODS RESEARCH PROJECT, RESULT FROM THE FIELD

JUNE 5, 2013

Submitted by:

Fawad Khan and Sharmeen Malik

ISET-PK

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TABLE OF CONTENTS

INTRODUCTION AND BACKGROUND ...................................................................... 4

CONCEPTUAL FRAMEWORK ................................................................................... 5

Methodology ......................................................................................................... 7 Site Selection ......................................................................................................................................................... 8 Qualitative .............................................................................................................................................................. 8 Quantitative ........................................................................................................................................................... 8 Questionnaires ...................................................................................................................................................... 9 Recovery Index ................................................................................................................................................... 10 Hypothesis Testing ........................................................................................................................................... 11 Further Purposive Sampling ....................................................................................................................... 11

Results ................................................................................................................. 12 Villages Selected ............................................................................................................................................... 12 Recovery and Services .................................................................................................................................... 13

Charsadda ........................................................................................................................................................... 16 Study Villages ..................................................................................................................................................... 17 Recovery and Services .................................................................................................................................... 17

Dadu ...................................................................................................................................................................... 22 Study Villages ..................................................................................................................................................... 22 Recovery and Services: ................................................................................................................................... 23

Tharparkar ......................................................................................................................................................... 27 Study Villages ..................................................................................................................................................... 27 Recovery and Services: ................................................................................................................................... 29

Role of Services in Resilience ................................................................................ 31

Conclusions and Implications ............................................................................... 33

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TABLES AND FIGURES

Table 1: DATA COLLECTION TOOLS .................................................................................. 9 Table 2: USE AND DURATION OF USE OF SERVICES IN CHITRAL ............................. 13 Table 3: HOUSEHOLD CHARACTERIZATION-CHARSADDA ........................................ 18 Table 4: CHARSADDA USE AND DURATION OF USE OF SERVICES ............................ 19 Table 5: SIGNIFICANT SERVICES IN BOTH VILLAGES IN CHARSADDA .................... 21 Table 6: HOUSEHOLD CHARACTERIZATION-DADU .................................................... 23 Table 8: SERVICES BETWEEN VILLAGES IN DADU ...................................................... 26 Table 9: HOUSEHOLD CHARACTERIZATION-THARPARKAR ..................................... 28 Table 10: USE AND DURATION OF USE OF SERVICES ................................................. 29 Figure 3: SHISHKOH'S DURATION OF LATRINE/TOILET USAGE ............................... 15 Figure 4: COMMUNITY MEETING IN CHARSADDA ....................................................... 17 Figure 5: MAKESHIFT HOUSING IN CHARSADDA ......................................................... 18 Figure 6: AGRA'S TIMELINE OF SANITATION USE ........................................................ 21 Figure 7: LUQMAN KHAN SHAHANI ............................................................................... 22 Figure 8: SHAHANI'S TIMELINE OF HAND-PUMP USE ............................................... 25 Figure 9: PORTABLE TOILET IN SEELARO ..................................................................... 27 Figure 10: COMMUNITY MEMBERS IN BHAKUO .......................................................... 28

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INTRODUCTION AND BACKGROUND

The impacts of the 2010 Indus floods in Pakistan represent a fundamental challenge that crosses all aspects of life in the country. From livelihoods of rural populations to food supply to urban areas, the core gateway transport, communication, energy, health, water control and institutional systems on which populations depend failed during the floods. The flood had immediate consequences for people across all levels of society in Pakistan but the impact on the poor and marginal populations was direct and severe. While the government struggled to meet the basic survival needs of the affected population, other organizations some of them militant, stepped in to fill the void. The impacts of flood, if unaddressed could further undermine not just Pakistan’s economic future but also it’s stability as a nation. Ultimately the consequences will reverberate across South Asia. The Indus basin in Pakistan is home to the largest contiguous surface irrigation system in the world and probably has the most highly regulated hydrology globally. The extent of human modification of the river system renders Indus’ hydrology more cultural and social rather than natural (Wescoat and Leichenko 1994). Prior research on hazards in Pakistan has demonstrated that vulnerability to hazards is constructed along class and gender inequalities and is embedded in everyday geographies of access to resources, state policies and social power (Halvorson 2002 & 2003). Until 1973, the national flood policy in Pakistan was that of risk acceptance. After the public outcry in the aftermath of the 1973 floods, the government reworked the policy from risk acceptance to risk control, primarily through engineered flood protection infrastructure (Mustafa and Wescoat 1997). But the engineered infrastructure and relief oriented flood policy has no understanding or sensitivity to issues of social vulnerability. The policy is further complicated by the impending uncertainty that climate change will bring. The dominant paradigm in the past few decades has been driven by the doctrine of “taming the mighty Indus” which the water resource engineers thought that they had successfully accomplished and emerging uncertainties finds no salience in the conventional paradigm. Therefore, the need for informed research based policy reformulation on flood hazards and vulnerability in Pakistan has become more urgent than ever. Such research must focus on social aspects and political economy of floods in the Indus basin outside the sphere of irrigation management per se, since there is a massive knowledge gap on performance of other systems that help build resilience of marginalised populations. Within this larger reality lie questions of access to services and resilience of the systems themselves to floods or droughts. Attention needs to be given to how marginalised groups in the population are threatened by major flood events and how floods affect their access to gateway services such as land, water, communication, energy etc. The 2010 floods in the Indus recorded as the largest in history, forces us to re-examine on how we manage and live with the river. While addressing the United Nation’s General Assembly in connection to the floods the Secretary General said, "Almost 20 million people need shelter, food and emergency care. That is more than the entire population hit by the Indian Ocean tsunami, the Kashmir earthquake, Cyclone Nargis, and the earthquake in Haiti—combined." Although widely devastating, such events also provide an opportunity to study and learn how extreme climate events affect people, and to

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generate knowledge on how one could start learning to adapt to such events. A large part of this question is also to understand the factors that make people vulnerable to such events and then provide answers to how one could plan to rebuild in a way that those vulnerabilities are minimized. Knowledge generated on certain aspects of the cause and nature of differential impact on different population groups can be beneficial to the one of the largest recovery effort in Pakistan’s history. According to the joint damage needs assessment undertaken by the Asian Development Bank and the World Bank for Government of Pakistan the recovery would be in order of US$ 8.74 billion to 10.85 billion. This research project is impelled by ISETs innovative work on disaster risk reduction in the country for the past four years and more broadly on climate change and adaptation in South Asia. There is an immediate need to re-examine how we study such events and given the scale of the social impact it is an opportunity to move away from the traditional hydrological/ engineering treatment of the subject to look more directly into the social aspects of the problems. This can provide valuable knowledge for dealing with similar climate based events in Pakistan and elsewhere.

CONCEPTUAL FRAMEWORK

In both the flood and drought cases, we hypothesize that resilience (including food security) is negatively affected where core gateway systems1 are fragile and subject to failure and where populations are socially or economically marginalized. Social and economic marginalization is hypothesized as the primary factor limiting both access to and the ability to use and obtain benefits from gateway systems. As a result, food insecurity during flood or drought events is likely to be highest where fragile systems and marginal populations overlap. This intersection makes those affected more vulnerable. Resilience, the reverse of vulnerability, will be highest where gateway systems are flood and drought adapted and where access to benefits that population get from system is not constrained by social marginality.2 This can be illustrated in Figure 1 below.

1 See ISET 2008 where the adaptation framework defines core (such as ecosystem:

water, air, land) and gateway systems (such as communication, financial, social services etc.), the role of institutions, organization and networks, governance and that of social protection.

2 This is very similar to the argument Sen made in relation to floods and food security in Bangladesh.

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Figure 1

Using systems as a basis for analysis allows us to identify causes of vulnerability where it is treated as a symptom rather than trying to define it as a state in itself, which becomes very problematic because of complexity and subjectivity of defining the social aspects of it. Also without an analysis of the core and gateway systems it is very hard to find resilient pathways for reconstruction or development if systems and their access are not considered as an integral cause of vulnerability or resilience

System

Climate Change Vulnerability

Capacity Agency

1. How does the system function? 2. Impact of Climate Change

3. Who is least able to respond to shocks and stresses?

Poverty reduction

Climate impacts on systems (Risk)

Climate impacts on agents (DRR)

Climate Impacts on vulnerable systems and vulnerable agents

Adapted from ISET (2008)

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METHODOLOGY

Following is the process followed in undertaking the field study:

1. Hypothesis: Availability, access and the reduced fragility of systems and services builds resilience (defined as the ability to recover following disruptive events such as the 2010 and 2011 floods) in communities. Specific hypothesis:

a) households with reliable access to basic systems and services will recover more quickly from the 2010 floods; and

b) the duration for which they were used before the floods will increase their adaptive capacity and hence aid a relatively faster recovery.

2. Identification of under-serviced areas/populations in flood-affected area using

census.

3. In post disaster situation, identification of vulnerable and resilient through recovery status using SLD methods for qualitative information along with initial identification of recovery levels among households and surveys for both quantitative and qualitative data (SLD/survey).

4. Document service availability differentials between resilient and vulnerable (quantity, quality, time).

5. Analyze quantitative data to identify services that are statistically associated with rapid recovery and therefore can be interpreted as building resilience.

6. Analyze qualitative data to evaluate associations (or the lack there of) between service access and recovery. Evaluate the combination of quantitative and qualitative data to interpret potential relationships between services and resilience at the household and community level.

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SITE SELECTION To incorporate a large geographical variation along the Indus transact, the study sample includes the following areas (Figure below):

o High mountains (Chitral district);

o Indus-Kabul confluence and piedmont (Charsada district);

o Plains (Dadu district);

o Desert /coastal (Tharparkar district).

These areas represent the major physical features of the Indus River basin. They include the upper reaches where glacial melt feeds the river, the piedmont, the plains, and the dessert. In addition, they also cover a wide social and political spectrum in Pakistan and were severely affected by the 2010 floods. In each selected district, at least two ‘most affected’ villages were chosen and within each, a minimum of 30 households were surveyed. As a preliminary exercise, an index was created that used census data to identify villages that had relatively lesser access and use of basic services. This was done so that we targeted relatively less developed areas and also to make sure that service differentials existed in the study areas. The field teams were asked to choose areas that were severely affected by floods and had similar exposure to make comparisons possible.

QUALITATIVE The qualitative part of the study was conducted through a shared learning process that employed participatory tools. The process and tools were jointly developed with field teams and Table 1 below shows the type of data collected and the tools that were used. This exercise allowed us to document the local context in terms of geography and the livelihoods systems. It also highlights the climate related hazards that are expected to impact livelihoods adversely.

QUANTITATIVE In the shared learning process the teams asked the community to jointly identify households that were recovering well and those who were relatively slow in recovery. In each village (location) at least 15 households were identified in each category. In some places it was not always possible to have the requisite number of households so

Figure 2: GEOGRAPHICAL LOCATION OF SELECTED DISTRICTS

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additional/neighboring villages were included. A total of 235 households were surveyed in all four districts using purposive sampling criteria. Table 1: DATA COLLECTION TOOLS

Pre-Steps (Revenue village level) Comparison between most vulnerable and resilient groups (gender segregated)

Hazard Livelihood Availability and Access to Services

Social Networks

o Identification, Selection of UCs & Villages

o Initial Contact– (Community Activists)

o Transect Walk

o System/Services

o Vulnerability of services

o Basic Information

o Mapping

o Natural Resources/ boundaries

o Hazard Mapping

o Timelines (event/impacts)

Selection of communities that are resilient and vulnerable (with similar exposure to hazard)

Ranking;

Responses;

Proposed Solutions/ Ranking;

Institutional options.

Diagrams: (livelihood Sources);

Calendars (Seasonal, agricultural etc);

Daily Gender Workload Charts;

Decision Making Matrix.

Listing of services/ timeline;

Decision Making Matrix;

Mobility matrix (internal and external).

Narrative;

Matrix of Institutional Mapping (organization, community, network, linkages etc);

Political affiliation;

QUESTIONNAIRES A village level questionnaire was developed to record the basic facts about the villages and more specifically to record the availability of services in that village. More than 17 basic services including eco-system (such as land, forest, pastures), basic gateway services (such as drinking water, education, health and communication) were recorded (see questionnaire). At the same time the access and use of each service was recorded from each of the selected household (questionnaire attached). All this data was collected through recall for the period before the floods. Several services were made available to these communities in the recovery phase but those have not been considered for our analysis.

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RECOVERY INDEX This index was created to measure the material recovery of the surveyed households (see questionnaire). We used the damage data of housing as a measure for recovery in terms of how much of the housing assets were rebuilt using the following formula;

Recovery rate (RR) = !"#$%"&' !" !"#$%&'% !" !""#$" !"#$% !ℎ! !"##$!"#"$% !"# !" !"##$%

RR (%) = (!"!!")

(!"!!")× 100%

where Aa= Assets after the flood Ab= Assets before the flood Ac= Current Assets

Among assets owned by families, housing structure is by far the most expensive and accounts for majority of the material assets. Housing damage data is also readily available and easily verifiable, and therefore the housing structure was used as the proxy for material recovery. Moreover, since the research took place in a data scarce environment, recall data had to be used, in which case housing structure damage and recovery was most likely to be remembered accurately. In Mithi District, where houses are made of mud, a locally available natural material, most houses had been reconstructed by the time the villages were surveyed. In that case the recovery rate was measured by the date of reconstruction3. The recovery rate was used to triangulate the information given in qualitative shared learning dialogues. The index was applied to the well-recovered and lesser-recovered groups of households. There were a number of households that had to be eliminated from the sample because they did not meet the housing recovery criteria. Households with minimal damages to the housing structures were also eliminated to keep rigor in the sampling criteria. Statistical testing was also conducted to ensure that the extent of damage to the housing structure was not affecting the recovery rates and we found no significant correlation between the extent of damages and recovery rate in all locations (10) except one, namely Kharkai in Charsadda. The lack of this relation is due to the fact that only families with significant damage to the household were used in the sample. As the recovery index measures recovery in relation to the original asset base, it is not biased by the absolute value of the assets. Therefore, the recovery index does not differentiate on the basis of absolute wealth. Poverty measurement and reduction is a topic that is of much importance and needs research on its own merit.

3 The data showed that houses were rebuilt after harvests so they could be easily grouped by harvests after which they were built.

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HYPOTHESIS TESTING In each case study area statistical testing was performed to see if there was any significant difference in use of services (and duration since they were accessed) between those who recovered faster and those who did not within each site. This test was performed for each service and its subsets. For example it was used for communication but also as a subset, which included telephone, cell phone, radios, televisions etc. The populations were compared within villages and also among villages in the same district, where there was homogeneity in the geography, exposure and level of development. In Chitral district, due to geography, it was not possible to find differences within each of the villages due to their small sizes and homogeneity. Also the location of household made a big difference in the exposure to flood (flash floods in this case). Therefore two villages within the same valley were compared to ensure that the exposure was similar to the flooding events. As we had purposively sampled and then triangulated the households through the material recovery index, the sample sizes were small (between 5-25 for each group), and this in turn led to the use of non-parametric tests to verify correlations. Chi-Square and Fischer exact test were employed, with the caveat that it does not reveal the direction of correlation. Along with access to services we also tested to see how the cumulative effect of having used a service over a period of time influenced the differentiation between the slow and fast recovered. Man-Whittney U test was used to test this relationship and in this case we also had additional information on the direction of the correlation.

FURTHER PURPOSIVE SAMPLING Where there was a fewer number of sampled households for services used, we considered including more households, which had the desired characteristics in terms of access to services and damages to housing structures and exposure in order to further improve the data base for the study. However, in some cases it was found that these households were simply not available. For example, in Dadu there were only three households in our selected village that had title to their land and that was not a large enough sample to conduct statistical testing but was a primary cause for early recovery. For other systems/services the number of variables (twenty plus) and the number of households required was too large to fulfill the requirement of an increased sample size based on the selection criteria and would have further delayed the collection of data and increased the cost significantly. Hence it was decided to not increase the sample and draw conclusions from the available data set.

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RESULTS

Following is a summary of results. In addition to the qualitative exploration on status of recovery and services through the shared learning process of community dialogues and interviews on a household level of those who recovered well and those who are still struggling, we also conducted statistical testing on the differentials of services provided. The results are broken down by study areas and into services where there was a statistically significant difference in access and use (over time). Some of the differences may not have been statistically captured here because of the small sample size. However, there is a clear statistically significant indication that when a few of the critical gateway services are missing, the communities’ ability to recover from unexpected shocks is negatively impacted. See accompanied appendices for descriptions of each study site.

VILLAGES SELECTED In our study we chose two villages in the same valley based on the recovery of critical infrastructure that was destroyed by 2010 floods. Unlike other sites, we were not able to choose households from the same settlement that recovered slower or faster. The reason for this was that the exposure to flash floods varies greatly depending on the specific location of the homestead and in the small settlements that characterize these villages, it was hard to find a large enough sample of households with the same level of exposure. Also there is homogeneity in services available at the settlement level and hence a useful comparison cannot be made. Therefore, it was deemed better to compare two settlements with varying recovery rates and service availability to see if any correlation could be established. In Shishikoh valley, Gawoch was recovering very slowly whereas the upstream village of Madaklasht made a speedy recovery. Figure 3: CHITRAL

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RECOVERY AND SERVICES Table 2 below indicates where there is a statistically significant correlation between access to services (and the duration of access) and recovery rates (See Appendix A for a description of the study site). Services shown with a check mark alone are positively correlated with the speed of recovery at the household level; those with a [()] symbol are negatively correlated. Our original hypotheses were: (1) that access to key services prior to the flood would show a positive correlation with recovery rates; and (2) that the duration of service access would also show the same as over time the benefits of services such as health, education and social mobilization accrue and increase the adaptive capacity of the people who use them. As can be seen below, when the time dimension was included, the number of services having a positive correlation with recovery rates increased. When we compared the use of basic services we saw many differences. There was a difference in use of education facilities and also in health. People in Gawoch used government run Basic Health Unit and religious leader for their health and had a slower recovery compared to those in Madaklasht, who used a relatively higher quality NGO clinic, private hospitals and veterinary clinic. The access and use of electricity was also significantly higher among the population that recovered faster in Madaklasht. They also had higher usage of TV and post office. Furthermore, they had road links and, as would be expected given the higher access to remittance income from family members working in Karachi, better access to credit. Although it may seem that Shishikoh is a richer community, there was no significant difference in the value of housing structures between the sample households that were surveyed in this study. The people on Madaklasht probably had more possessions inside the household like televisions and mobile phones. On the other hand the population from Gouch had more vehicles for transport. Table 2: USE AND DURATION OF USE OF SERVICES IN CHITRAL

Statistically Significant Difference in Use of Services SERVICE USED BEFORE THE FLOOD DURATION OF USE

Pearson’s chi square

Significance (2 sided)

Fisher’s exact test (2 sided)

Probability of a chance occurance

Mean rank recovered

Mean rank not recovered

Mann-Whitney U


Health

BHU/RHC 27.000 0.000(★★★) 0.000 0% 6.500 20.000 0.000 0.000 (★★★)

Hospital 4.636 0.031★★ 0.054 5%

Lady health worker/health

0.000★★★ 0.000 0%

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Statistically Significant Difference in Use of Services

SERVICE USED BEFORE THE FLOOD DURATION OF USE





Mean rank recovered


Mann-Whitney U


worker/ visitor 19.853

Medical store 6.238 0.013★★ 0.021 2%

NGO clinic 27.000 0.000★★★ 0.000 0% 21.500 8.000 0.000 0.000★★★

Religious leaders 27.000 0.000(★★★) 0.000 0% 6.500 20.000 0.000 0.000 (★★★)

Veterinary clinic 5.870 0.015★★ 0.028 3% 16.500 12.000 60.000 0.018★★

Info/Communication

Post Office 16.875 0.000★★★ 0.000 0% 19.630 9.500 22.500 0.000★★★

TV 5.870 0.015★★ 0.028 3% 16.500 12.000 60.000 0.018★★

Wireless loop 10.500 16.800 48.000 0.024 (★★)

Fuel/Energy Electricity 10.800 0.001★★★ 0.001 0% 19.670 9.470 22.000 0.001★★★ Sanitation Latrine/toilet 19.630 9.500 22.500 0.000★★★ Water supply Piped to house 6.500 20.000 0.000 0.000 (★★★) Water course 27.000 0.000(★★★) 0.000 0% 6.500 20.000 0.000 0.000 (★★★) Mobility Availability of link road

27.000 0.000★★★ 0.000 0%

Access of main road

21.500 8.000 0.000 0.000★★★

Distance to main road

6.500 20.000 0.000 0.000★★★

Transportation Car 27.000 0.000(★★★) 0.000 0% 6.500 20.000 0.000 0.000 (★★★) 4X4 6.500 20.000 0.000 0.000 (★★★) Public transportation

6.500 20.000 0.000 0.000 (★★★)

Credit services Banks 4.219 0.040★★ 0.075 8% 15.880 12.500 67.500 0.044★★ Community/village organization

5.870 0.015★★ 0.028 3% 16.500 12.000 60.000 0.018★★

Shopkeepers 18.920 10.670 40.000 0.004★★★ Land

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Statistically Significant Difference in Use of Services

SERVICE USED BEFORE THE FLOOD DURATION OF USE





Mean rank recovered


Mann-Whitney U


Years owned 9.880 17.300 40.500 0.013 (★★) Social CBO membership 6.075 0.014★★ 0.037 4%

Note: () represents a negative correlation; ★★★ represents a 99% level of significance; ★★ represents a 95% level of significance Interpretation of the above results is important in order to understand the situation in Chitral. In Shishikoh, the installation of electricity was preceded and supported by social organization. The duration of availability of electricity seems to have had a ripple effect on social development outcomes such as health, education and communication. Places with electricity tend to have a higher probability of having resident teachers, health workers and other public services. Using timelines we saw further statistically significant differences in the usage of latrines and credit facilities. In Gouch (slow recovery), nearly all households had latrines but they were installed in 1999, whereas in Madaklasht (fast recovery) half the households had latrines almost a decade before then. This result is illustrated in the figure below. Figure 4: SHISHKOH'S DURATION OF LATRINE/TOILET USE

There was also a statistically significant difference in the utilization of credit between the two villages. Availability and use of credit can help build resilience in livelihoods. It can be used for consumption smoothing and income diversification. In Chitral, one of the major uses of micro credit is to finance travel to work in other parts of the country and floods in the valley do not affect income from such work. Whereas those who depend on local natural resources are severely impacted (see field study) by damages of the floods.

0

20

40

60

80

100

120

9 7 4 2 1

% of households

Number of years service available

Shishikoh's duration of latrine/toilet usage

Recovered

Not recovered

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In Shishikoh there was an inverse relation observed between the ownership of vehicles and recovery. This may be explained by the fact that majority of the male population in Madaklasht is employed in Karachi (women do not usually own cars etc.) and there are a number of transporters living in Gawoch. As a result, instead of being primarily an indicator of wealth and access to services, in Chitral vehicle ownership tends to indicate dependence on local natural resource based livelihoods and the supporting road and transport systems for income. Both of these were disrupted during the floods. Duration of land ownership was also significantly correlated with recovery rates. The newly owned land usually belongs to the nomads who have recently settled. These nomads and new comers have marginal land in the peripheries. They have relatively recently shifted from a transhumant livelihood to a more settled one as grazers and farmers but remain highly vulnerable to changes in threatened natural resources. Use of piped water was less common among those who recovered faster. This result seemed spurious at first and needed further investigation. Chitral has drinking quality surface water in the form of numerous springs and snowmelt fed streams; so piped water cannot improve the quality of this service and so due to the universal availability of good quality water we do not expect a major effect on the population’s health and adaptive capacity. Forest plot of meta analysis by Fewtrell and Colford (2004) shows that water supply projects and reduction in diarrhea do not always have positive relation and can be problematic especially if water is stored at home and/or there are poor hygiene practices. This result shows that gateway services that lead to better resilience are context specific and provision of just any basic service does not necessarily get better outcomes. We have already shown that the people in Madaklasht had adopted improved hygiene practices much earlier than people in Gowch. As a summary, we can say that the people of Madaklasht were more adapted and recovered faster by reducing their dependence on the threatened natural resources. In Gowch, the dependence is still high and the households and village services recovered much slower compared to the Madaklasht. Historically, Madaklasht has had very high social cohesion because of a unique cultural background and, collectively, were able to attract services from NGOs among which electricity was the earliest and most transformative service leading to the cascading effect of other services being improved in terms of health, education and communication, which further allowed the community to diversify into non-forest dependent livelihoods. In Gowch people are still stuck in a maladaptive cycle where every flood brings devastation and more forest is sold to the forest department, which make the slopes more unstable and future flash floods more severe. Electricity is now available for over nine years and services are also improving. Despite land disputes and ethnic differences a certain level of social organization has developed to acquire these services and the awareness of their importance.

Charsadda

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STUDY VILLAGES Figure 5: COMMUNITY MEETING IN CHARSADDA

RECOVERY AND SERVICES Only 2 percent of the surveyed households had fully recovered in terms of housing structure. Almost half (49%) were more than 75% recovered and the rest were without rooms (27%) or with less than 50% recovery (22%).

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Figure 6: MAKESHIFT HOUSING IN CHARSADDA

Table 3: HOUSEHOLD CHARACTERIZATION-CHARSADDA

‘Fast recovered’ Households

‘Slow recovered’ Households

Recovered or relatively recovered from the flood disaster.

Not recovered from flood disaster.

Have regular source of income/employment.

Have low-income employment; daily labour etc.

Ability to avail services outside the village or district.

Access to services limited to village or union council.

Have say in community/village matters. Have limited say in matters regarding village/community.

Source: Shared Learning Dialogues in Charsadda

Testing for service availability, Agra showed a similar pattern to Chitral but it was not so pronounced. In the remaining three districts, the contrast in the well-recovered and less recovered groups was not so stark because households were chosen from the same village.

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Due to the nature of the (sheet/inundation) flooding, it was possible to find households with same exposure in the same settlement, which make the differences much more subtle and revealing at the same time. Table 4: CHARSADDA USE AND DURATION OF USE OF SERVICES

Statistically Significant Difference in Service Use Among groups with different recovery rates

USED BEFORE THE FLOOD

DURATION OF USE

Agra Kharkai

Agra Kharkai

SERVICE Pearson’s chi square



Probability chance of occurance

Mean rank recovered


Mann-Whitnery U


Mean rank recovered


Mann-Whitnery U


Health

Lady health worker/health worker/visitor

11.360

18.400 54.000 0.020 (★★)

Housing

Housing area

12.500 5.830 14.000 0.020★★

Education

7.241 0.007★★★

0.012 0%

Info/Communication

TV 8.061

0.005★★★

0.013

1%

12.330 6.000

14.000

0.008★★★

Fuel/Energy

Electricity

3.958 0.047★★

0.106 11%

LPG

12.040 6.5000

17.500

0.017★★

Sanitation

41.990 32.520

504.500

0.043★★

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Statistically Significant Difference in Service Use Among groups with different recovery rates

USED BEFORE THE FLOOD

DURATION OF USE

Agra Kharkai

Agra Kharkai

SERVICE Pearson’s chi square



Probability chance of occurance

Mean rank recovered


Mann-Whitnery U


Mean rank recovered


Mann-Whitnery U


Mobility

Access to main rd 3.832 0.050

★★ 0.123 12%

12.000 6.570

18.000

0.042★★

Transportation

Animal

3.958 0.047★★

0.106 11%

Bicycle

3.958 0.047★★

0.106 11%

Credit services

Shopkeeper

6.465 0.011★★★

0.017 2%

12.040 6.500

17.500

0.017★★

Farm Land

Land ownership 3.958 0.047

★★ 0.106 11%

Note: () represents a negative correlation; ★★★ represents a 99% level of significance; ★★ represents a 95% level of significance In Agra there were some significant differences. The well recovered had better access to main roads and used credit facilities from shopkeepers. They had also been using sanitation facilities for longer. (See graph below)

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Figure 7: AGRA'S TIMELINE OF SANITATION USE

The composite indicator for sanitation facilities includes latrines, drainage/pavement in the street and solid waste. Drainage was particularly bad in Agra as reported from the qualitative study and, as the graph above clearly documents, households that had drains tended to have much better recovery rates. The previously rural settlements are growing denser with increased population. Disposal of wastewater and sanitation hence is becoming a major issue in the area. Both men and women among problems in Charsadda have rated health issues very high. Whereas, they report lack of health facilities as a major issue, lack of sanitation is not ranked as high in their priority list (see district report). Lack of drinking water is however rated high on priorities for the villages. Sanitation and drainage in denser settlements can bring tremendous changes in the life quality and socio-economic development of communities. The world renowned Orangi Pilot Project in Karachi was based on one single intervention and sanitation changed the life of the poor in that area. Orangi’s story is well documented and the subject of many books and research papers. This may be due to the fact that Kharkai had low availability of many basic services coupled with homogeneity. For example, all households that had latrines recovered faster and there were none with latrines in the group with slow recovery, however, the low coverage (25% of recovered) did not yield statistical significance. . Table 5: SIGNIFICANT SERVICES IN BOTH VILLAGES IN CHARSADDA

Comparison of service with recovery in both villages in Charsadda combined Services Usage before floods Duration of use Pearson

’s chi square




Mean rank recovered


Mann-Whitney U


Education 7.364 0.007 0.012 0% Electricity 4.297 0.038 0.055 6% Transportati 10.978 0.001 0.001 0% 152.090 136.130 9197.000 0.001

0 4 8 13

15 21 25

29 36 40

44 44 48

4 4 4 4 4 4 7 7 7 14 18

22 29

0 10 20 30 40 50 60

60 55 52 38 34 30 22 14 12 11 5 4 2

Percentage using

Number of years using service

Agra's timeline of sanitation use Recovered Not recovered

22

Comparison of service with recovery in both villages in Charsadda combined Services Usage before floods Duration of use Pearson

’s chi square




Mean rank recovered


Mann-Whitney U


on Land ownership

4.297 0.038 0.055 6%

People who recovered well also had better education access and used electricity. They also had better access to roads and utilized credit services from shopkeepers. The population that recovered well also had title to their land. Use of transportation was significantly different and in the qualitative analysis, the villagers had identified the ability of using services outside the villages as the key difference in the ability to recover, corroborated by this analysis. For further analysis and a description of the study site, refer to Appendix B.

Dadu

STUDY VILLAGES Figure 8: SAEED KHAN SHAHANI

23

RECOVERY AND SERVICES: In Dadu we noticed that the recovery/reconstruction in housing stock assets happened in spurts that were closely related with harvests. Therefore, we can assume that the surplus from agriculture was the main source of income that helped recovery. When we asked the communities what were the main characteristics of those who recovered faster or slower they agreed on the following description. Table 6: HOUSEHOLD CHARACTERIZATION-DADU

CHARACTERISTICS OF ‘FAST RECOVERED’ AND ‘SLOW RECOVERED’ GROUPS IN DADU ‘Slow recovered’ Group ‘Fast recovered’ Group

1. Still living in temporary shelters or in personal damaged households

2. Having no or less agricultural land

3. Having no formal employment & unskilled labor

4. Family having no or fewer wage earners

5. Without any savings

6. Limited livelihood options

1. Living in their own repaired houses

2. Having agricultural land

3. Having formal Employment (Government or any public sector) and skilled labor

4. Family having more wage earners

5. Have some savings

6. Dependent on multiple sources of income

As with Charsadda, the number of services in Dadu that are closely correlated with rates of recovery is relatively limited. This may be due to the extensive damage to all livelihood support systems and extended period of inundation (three to four months). The table below shows key services related to water supply and access to credit were both statistically significantly associated with recovery rates. In addition, land title was a significant factor. For further analysis and a description of the study site, refer to Appendix C. Table 7; DADU'S USE AND DURATION OF USE OF SERVICES

DISTRICT

USED BEFORE THE FLOOD DURATION OF USE

Shahani Seelaro

Shahani Seelaro

24

SERVICES

Pearson’s chi

square


Fisher’s exact test

(2 sided)

Probability

of chanc

e occura

nce

Mean rank recovered


Mann-Whitney U


Mean rank recovered


Mann-Whitney U


Housing

Area

19.500

11.290

24.500

0.013★★

# of pacca rooms

6.250 2.750 1.000

0.034★★

Water supply

Hand pump

4.257 0.039★★

0.062

6%

19.860

11.160

22.000

0.007★★★

Credit services

Banks

16.210

12.500

47.500

0.017★★

Food Storage

3.914 0.048★★

0.081

8%

Agricultural Land ownership

Farmland with title (area)

19.360

11.340

25.500

0.016★★

Note: () represents a negative correlation; ★★★ represents a 99% level of significance; ★★ represents a 95% level of significance; In Shahani, the few households who held actual title to their land recovered much faster because they were able to sow and reap a bumper crop following the floods. Where people held title to their land, the quality of housing construction was also visibly superior. Other groups, who lacked land title recovered more slowly primarily because they worked on share cropping arrangements and, as a result, weren’t able to generate as much income from the post flood crops. In addition, land title is a major factor influencing

25

access to credit and relief supplies. Hari’s4 were particularly disadvantaged as they were unable to get the fertilizer and seed distributed by relief organizations as they did not possess any land titles, nor could they afford to buy it after the floods. As a result they earned only 25% share of the next crop as opposed to the usual 50% when they provide the inputs. This time the input was provided by larger landlords who accessed the flood relief on basis of their land ownership. Due to small number of these households the statistical testing could not be used. People who used water from hand pumps before the floods recovered faster as compared to those who did not. They also tended to have better food availability. Moreover, although not statistically significant due to a small sample (coverage), all the households that had laterines (14%) were in the fast recovering group and the slower recovering group had none. Figure 9: SHAHANI'S TIMELINE OF HAND-PUMP USAGE

Duration of use of drinking water from hand-pumps was positively correlated to the recovery when compared to people who used surface water for drinking. Therefore, the hypothesis that duration of use of a service (better drinking water in this case) has a cumulative effect on the ability to recover after the flood. Water quality in Dadu is very poor as ground water is brackish and not potable. Most people resort to drinking from irrigation channels and that has a negative effect in their health and growth in the long term. There are however small pockets where the seepage from canal or the river water allows the use of hand-pumps to extract somewhat filtered sweet water from the ground. Relatively the quality of this water is much better than the surface water. Drinking this water relieves people of the high gastro-intestinal disease burden that affects. Where the diseases are chronic the effects are similar to malnutrition. Additionally, the well-recovered households had access to credit. Credit was cited as means for buying seeds and other inputs for the next seasons harvest. As the floods were unexpected and grain was stored at the ground level most of it was washed away. This

4 Hari is the term used for sharecroppers and they tend to be landless

29 43 43 43

100 100 100

0 0 5.3 10.6

47.4 52.7 58

0

20

40

60

80

100

35 30 20 12 10 5 4

Percent using

Number of years using service

Shahani's timeline of hand-‐pump use Recovered Not recovered

26

made seed availability very poor and its price shot up very high. Those who were able to access credit were able to buy seeds and other inputs to be able to sow in the next season. There was very little difference in the well and less recovered in Seelaro. Most of the basic services available had arrived only after the flood as a part of the reconstruction effort. When we compared the recovery between Shahani villages and Seelaro, we found that the housing stock recovery was significantly better in Shahani villages. Since both had similar levels of sheet flooding we tested the difference in services between the two villages (as we had done in Chitral). When we compared the difference in access or use of the service and duration of use, there were significant differences.

Table 8: SERVICES BETWEEN VILLAGES IN DADU

Statistically Significant Different Use of Services Between Villages in Dadu SERVICE Used Before The Flood Duration of Use Pearson

’s chi square




Mean rank recovered


Mann-Whitney U


Education 19.448 0.000★★★

0.000 0%

Type of

housing

12.088 0.001★★★

0.001 0%

Electricity 12.774 0.000★★★

0.000 0% 15.070 24.160 85.500 0.009★★★

Sanitation 6.054 0.014★★

0.019 2% 85.850 92.330 3468.000 0.014★★

Mobility 18.427 0.000★★★

0.000 0% 14.500 34.060 26.000 0.000★★★

Transportati

on

4.782 0.029★★

0.032 3% 128.720 145.520 7682.500 0.003★★★

Forest &

Rangeland

5.696 0.017★★

0.024 2%

CBO

Membership

32.446 0.000★★★

0.000 0%

Note: () represents a negative correlation; ★★★ represents a 99% level of significance; ★★ represents a 95% level of significance; The largest difference was in the availability of education and use of electricity among the villages. Use of electricity also allows use of communication and information such as cell phones and television. This raises awareness and may be the reason for earlier adoption of improved sanitation, which once again reduces disease burden and unhealthy growth. As we had seen in Charsadda, people with better mobility are able to access better basic services outside their place of residence. With virtually no basic services in both villages, those who had the ability to access services from outside the villages were at a distinct advantage in diversifying their lives.

27

Much like Chitral, the people of Shahani villages were members of community based organizations and were able to access more services from NGOs before the floods and also relief aid after the floods. In Shahani villages, the area of katcha houses was significantly larger among those who recovered faster but once again the overall stock of houses had no correlation with recovery. The ownership title to land was found significant as explained above. In Seelaro village, among residents of pucca houses, those who recovered faster tended to have more rooms. For overall housing (including the majority of katcha) this result does not hold true and hence one cannot say that total quantity of assets in terms of housing stock played a role in rate of recovery of the housing stock. It is noteworthy that Seelaro received a lot of aid after the floods, where toilets were given to each household etc., but it did not seem to have helped the recovery in the short term. If used and maintained for some time, one would expect that it might help in developing adaptive capacity through better health and productivity. Also hygiene education and drainage would be necessary to take advantage of the infrastructure provided. Figure 10: PORTABLE TOILET IN SEELARO

Tharparkar

STUDY VILLAGES .

28

Figure 11: COMMUNITY MEMBERS IN BHAKUO

Two village named Bhaku and Haryar were selected for the study. Since almost all the houses “Chounras” (traditional huts) are made of grasses that are readily available, the houses were constructed much faster than in other study sites. Although floods in Tharpakar came in 2011, most of the houses were reconstructed by the time we surveyed the villages. In this case we did not use the recovery index but rather the dates of completing as a measure of those who recovered faster and those who did not. When we asked the people of the characteristics of people who recovered faster compared to others they responded with the following description. Table 9: HOUSEHOLD CHARACTERIZATION-THARPARKAR

CHARACTERISTICS OF ‘FAST RECOVERED’ AND ‘SLOW RECOVERED’ GROUPS - DADU

‘Slow recovered’ ‘Fast recovered’

Still living in temporary shelters or in damaged households

Having no source of income or no formal employment

Possession of less than six livestock

Having more than 6 dependent members in the family

Unable to cultivate on schedule

Living in their own repaired houses

Having formal employment (Government or any public sector position)

Having enough livestock

Families comprising of 6 members

Able to cultivate land on schedule

Able to benefit from the sale of milk-multiple sources of income.

29

RECOVERY AND SERVICES: Due to homogeneity and lack of many basic services there were few statistically different usage patterns in Tharparkar. We could not find populations that used a hospital or NGO clinic versus those who did not, because none are available. In Bhakuo, those who recovered faster used better health facilities (Basic Health Unit/Rural Hospital) than those who did not and relied on Lady Health Workers. A significantly larger portion of well recovered also accessed aid from NGOs. In Haryar, the less recovered had higher political participation and had higher membership in NGOs, which is counter-intuitive. The reasons for this could not be understood. It is possible that membership in NGOs, however, was very recent and may not have shown its impact. It may also indicate that the more needy had joined the organization and hence the NGOs have targeted the vulnerable population well. Also voting by itself does not indicate the level of activism or involvement in political processes. Haryar in Tharparkar, once again showed no significant difference in duration of use in services, except higher level of recovery support from NGOs was negatively correlated with recovery. This implies that the NGOs were able to target the deserving households that needed more support in recovery. In Bhakou, once again those who used Basic Health Units were in higher numbers among those who recovered faster as compared to those who depended on the Lady Health Workers. Duration of use of electricity correlated with rapid recovery. Also those who had received financial support from relatives and neighbors were in higher proportion among the slow recovery group. Table 10: USE AND DURATION OF USE OF SERVICES

Statistically Different Use of Services Among Villages of Tharparkar

USED BEFORE THE FLOODS DURATION OF USE

SERVICE

Bhakuo Haryar Bhakuo Haryar


Significance (2

sided)

Fisher’

s exact

test (2 sided)

Probability of

chance

occuranc

e




Probability of chance occurance

Mean rank recovered


Mann-Whitney U


Mean rank recovered


Mann-Whitney U


Health

BHU/RHC

12.

190 7.360

23.50

0

0.021★★

30

Statistically Different Use of Services Among Villages of Tharparkar

USED BEFORE THE FLOODS DURATION OF USE

SERVICE

Bhakuo Haryar Bhakuo Haryar


Significance (2

sided)

Fisher’

s exact

test (2 sided)

Probability of

chance

occuranc

e




Probability of chance occurance

Mean rank recovered


Mann-Whitney U


Mean rank recovered


Mann-Whitney U


Lady health workers/health worker/visitor

8.690

13.860

22.00

0

0.038(★★)

Fuel/Energy

Electricity

9.130

3.500

4.500

0.022★★

Social Protection

NGOs

4.127

0.042(★★★

)

0.111

11% 6.9 11.

900

10.50

0

0.042 (★★)

Relatives/Neighbors

6.550

12.000

6.000

0.027 (★★)

Political Party

Affiliation

5.209

0.025(★★)

0.083

8%

Note: () represents a negative correlation; ★★★ represents a 99% level of significance; ★★ represents a 95% level of significance; Since there was not a significant difference between recoveries of the two villages we combined the faster recovered groups and the slower recovered groups from both villages

31

to see if we could find statistical significance by increasing the sample. This time only use of electricity came out to be significant which was already apparent in Haryar. For further analysis and a description of the study site, refer to Appendix D.

ROLE OF SERVICES IN RESILIENCE

From the above analysis we can see that there is a clear correlation between both access and duration of usage and the rate of recovery measures through housing assets. Critical gateway services for each area are dependent on geographic and social conditions and the level of development in the area. Some general conclusions can however be drawn. The areas chosen for this study are mostly rural communities, majority of which derive there livelihoods from natural resources and have access to a very low level of social sector services and hence, a few basic services tend to be showing the difference between those who managed to recover faster than those who did not. In some cases theses services are universally helpful in building resilience and in others they are dependent on the local conditions. The summary results for different services are outlined below.

ELECTRICITY Where there is no universal coverage, access to electricity was a major difference between those who recovered faster and those who did not. Electricity opens doors to many other services especially communications etc., elongates the workday and can have indirect effects like improving girls enrollment in schools. Public sector professionals are also more likely to serve in areas with electricity and do not like to be posted in areas where electricity is not available. Therefore, availability of electricity increases the availability of services that in the long run helps communities diversify livelihoods through skill enhancement, knowledge and the ability to communicate as we saw in Chitral, where people used seasonal migration as a strategy for reducing reliance on the forest. Supply of electricity had a similar effect in Tharparkar, which is a desert. Dadu and Charsadda district also had a higher number of well-recovered households having access to electricity.

WATER Improved drinking water was a critical service in Dadu where people depend on poor quality surface water. This water is usually taken from irrigation watercourses and is not healthy. Households with access to hand-pumps and “sweet” ground water tended to recover faster. Such interventions could be useful for most of the Southern Punjab and Sindh where the poorest people in Pakistan live and where floods contaminate surface water supplies. In Chitral, however, where the surface water is of high quality, piped water scheme did not seem to make a difference.

SANITATION Sanitation coverage is abysmally low in Pakistan. Where these services were used (in Charsadda and Chitral) there was a clear difference in recovery rates. In the plains, having drainage seemed to be making the difference. Although sometimes the coverage was too small for latrine usage to make statistically significant conclusions, its use was

32

exclusive to those households that recovered well in Dadu, Charsadda and Chitral. Therefore, sanitation seems to be the most common thread among those who recovered better from floods in all geographical areas. Proper drainage along the streets was also important and where this did not exist people recovered slower and feces were seen in the streets. Where there were drains and/or streets were paved, people were less likely to defecate. Sanitation is, of course, likely to be of most important in densely populated areas where inundation flooding occurs and results in contamination of surface water sources. It is relatively less important in areas such as Chitral or desert locations where population densities are low and the potential for contamination of drinking water sources is also low.

HEALTH Health was found to be important in all places and that corroborates the water and sanitation results. People who had access to better health facilities tended to recover faster. Lady health workers seemed to be the health care providers of last resort and tended to those who could not access any other health facilities (hence a negative relationship). Given the health care coverage that visiting health workers provide, more emphasis on training and support of LHWs may help improve the provision of health services. The NGOs clinic seemed to be providing better services than LHW and government Basic/Rural Health Units in Chitral. This may be an alternative to the formal system for improving healthcare in Pakistan.

CREDIT Credit services played a key role in Charsadda, Chitral and Dadu. In Chitral it was used to finance the trip down country for seasonal migration and consumption smoothing over winter months. Similarly, people with access to credit had better recovery in both Dadu and Charsadda. In Charsadda, it was mainly credit from shopkeeper, whereas in Dadu it was formal credit from banks. In Tharparkar, which has a very small cash based economy and works more through barter, people reported that those who could access credit bought the scarce and expensive seed after the 2011 floods. Therefore they took advantage of the soil moisture and recovered faster. Others had to wait till the next season, when seeds were more widely available. Although in Tharparkar, we could not statistically prove this relationship due to a small sample size, but seed availability had been a well know factor in faster recovery. Di Falco (2011) through a very complex experimental design came to the conclusion that farmers in Ethopia with access to credit were much more likely to adapt and benefit from climate change.

MOBILITY AND TRANSPORT In areas with a good road network, communities are able to access basic services if they have accessable mobility and transport. This may be restricted by cost as we saw in Charsadda, where only those who could afford were using it to access better services, or by social norms where female children are not allowed to attend schools that are outside the village. Both in Seelaro, Dadu and Shishikoh, Chitral communities were restricted from mobility because of the vulnerability of the roads to floods and landslides, respectively. In such cases only services that were locally available could be used. Due to the presence of NGOs and social cohesion in Madaklasht people were able to get these services locally but communities in Gowch and Seelaro were either not well organized or did not have alternative institutional options (Dadu) for the provision of these services.

33

Due to the lack of access and electricity, doctors and teachers could not staff even the facilities given by the government.

CONCLUSIONS AND IMPLICATIONS

It would be logical to assume that these services allow people to develop adaptive capacity in terms of income diversification and changing strategies as a calamity hits them. A corollary to this hypothesis would be that those who do not have these basic gateway services before the calamity hits them have a harder time recovering. For adaptation and development planning, this has significant implications. Since most climate related hazards are unpredictable most of the adaptation and recovery is expected to be autonomous for which the vulnerable and exposed communities need to have basic adaptive capacity to recover. Despite the record UN appeal for recovery we see that it had a very small role in the actual recovery of most households that we surveyed and it was up to their own resilience on how fast they recovered and adapted to the new realities after the floods. Therefore, unless they have these critical basic services available and accessible, any adaptation intervention would not be very effective. Hence investment in these critical services as identified through our analysis for each site is essential part of adaptation planning and implementation. Also this study shows that there is a new dimension to the tension that is found between adaptation and development activities where they go hand in hand and strengthen the outcomes in both development and ability to adapt to climate change. It also provides credible avenues for investment into adaptation that has co-benefits in terms of development. In a rural agro-pastoral system, as a word of caution one would say that not all development interventions necessarily build resilience and one has to be careful in identifying the critical interventions as many can also be maladaptive and exacerbate the pressures that populations and the climate are putting on our ecosystems. For example, in droughts, digging deeper and pumping out water may work in recovery in the short term but may actually increase vulnerability to future events. All good adaptation strategies should reduce pressure on natural resources and possibly conserve the threatened resource. The methods used in this study can be useful in assessing the critical gateway services for any disaster struck area and offer a useful way of utilizing damage and recovery assessments for future development and resilience planning. Since the method depends on lessons from disaster recovery its use will be limited to those areas that have recently been hit with disaster, however, we are also aware that disasters are catalysts for bringing investment into areas that are neglected in the usual development processes and hence this methodology can be very useful in spending the large amounts of disaster related funding in ways that support the much needed development and also provide a basis for building resilience in the uncertain and risk laden future.

34

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