MAPPING PROFILES OF SERVICE PROVISION IN POVERTY ANALYSIS… · 2009-03-25 · MAPPING PROFILES OF...

MAPPING PROFILES OF SERVICE PROVISION IN POVERTY ANALYSIS:

A case study of water delivery in Kalyan-Dombivli, India

Jocelyne Annie M.-C. Houndebasso Ahoga February, 2009

MAPPING PROFILES OF SERVICE PROVISION IN POVERTY ANALYSIS:

A case study of water delivery in Kalyan-Dombivli, India

by

Jocelyne Annie M.-C. Houndebasso Ahoga

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Urban Planning and Management

Thesis Assessment Board

Prof. Dr. Ir. M. F. A. M. van Maarseveen: Chairman Dr. Karin Pfeffer.……………………… : External Examiner Dr. Javier A. Martinez ………………… : First Supervisor Ir. Mark J. G. Brussel …………………. : Second Supervisor

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer

This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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Abstract

Poverty mapping techniques applied to public service delivery can inform targeting policies. However, there is a need for a well tried-out tool to reporting the demand. As such, indices have proven excellent policy tools. For the case of water provision, the concept of water poverty index (WPI) has developed recently as a policy and management, and lobbying tool for various scales. At ward level in the city of Kalyan-Dombivli (India), this study based on a people-centred concept in poverty mapping, took the concept of access beyond the statistics of the provision to the true level of associated deprivations. The aim was to embed the WPI in local conditions of access to water and delivery situation for mapping city profile in water provision, and examine the usefulness of the index at policy level. The index was constructed with the composite approach applied to the components and the index with a focus on the access component, derived from ten water deprivation indicators collected from a household survey. The mapping was achieved by ranking the wards for each component and the index. Regression and correlation analysis at household and ward levels reveal sensitivity of the index to the access component. Correlation analysis with the new Index of Multiple Deprivations designated access to water as an indicator of overall poverty in the wards surveyed. Chi-square showed significant association with households’ satisfaction attesting the suitability of the designed WPI to mapping water provision. Further, a significant association found between access and response time to complaints and with households’ grievance demonstrated the usefulness of the WPI at policy level. In sum, introducing households’ water-related deprivations in the WPI’s structure through the access component reinforces its efficacy to map city profiles in water delivery in a people-centred approach. The WPI reveals also a relevant input to water provision planning and management.

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Acknowledgements

It is more a pleasure than a duty to take advantage of this space to acknowledge the authors of the various inputs to this achievement.

First of all, I express my gratitude to Nuffic and ITC for giving me the opportunity of this study which indebted me to better contribution to the development of my nation.

My utmost gratitude is for my supervisors for all support and guidance throughout the process of this work. Mr. Javier Martinez, you encouraged me from the very beginning; you trusted me and strengthened my motivation and self-confidence. Many many thanks. Mr. Mark Brussel, it was always a victory, being able to answer straightforwardly your questions. Thanks for their value-added to my work.

Special thanks to the program director, Ms. Monika Kuffer for constant encouragement and support during hardship. I take this opportunity to thank also all UPM staff members. I can’t help naming Mr. Frans van den Bosch along with Ms. Monika Kuffer. From both of you I’m taking the most amazing home! Of course I cannot forget ‘TAZ’ or ‘expert on tap’ model in the instrumental view of policy-research interface or … With my best regard to you all, it has been a privilege, being your student.

I would like to say my regard and gratitude to Prof. Dr. Ir. Alfred Stein for his kind help with statistics. I also give thanks to the project research team, Prof. Dr. I. S. A. Baud, Dr. N. Sridharan, Dr. K. Pfeffer, and Mrs. Tara van Dijk for advice and support during the field work.

I’m grateful to UPM colleagues for the mutual support for getting started and facing new challenges. Thank you, Shubham Mishra for your support in India during field work. Outside UPM, Priscilla, Herve, and the Friday group, it was great getting to know you and having mutual support and friendship.

Many thanks to Mr Raphael Edou, my employer who always trusted me, encouraged and supported me in my quest for higher education.

I’m thankful to my brilliant family and my fantastic friends for they precious support to me as well as the family in my absence. My gratitude goes especially to my wonderful mother, mama Salamatou, for her unfailing support. And I’m indebted to my lovely daughter, Thehilatel, who said: “mama, just go; I will pray for you every day” … Thank you darling for letting me go. I owe you 18 months of your precious life. And last, but the most, to my beloved husband, Mr Ahoga D. Augustin. This work counts back to your amazing love and endless support … Thanks is a very weak word.

Jocelyne Annie Marie-Claire Houndebasso Ahoga Enschede, February 2009.

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Table of contents

Abstract ………………………………………………………………………………………………...i Acknowledgement …………………………………………………………………………………….iiList of figures …………………………………………………………………………………………vi List of tables ………………………………………………………………………………………….vii List of acronyms …………………………………………………………………………………….viii

1. Introduction ………………………………………………………………………………………1 1.1. Introduction to poverty mapping and service delivery ……………………………………1 1.2. Setting of the research …………………………………………………………………….2 1.3. Definition of the major concepts used in the research ……………………………............3 1.3.1. Sustainable livelihoods framework ………………………………………………3 1.3.2. Index of Multiple Deprivations …………………………………………………..4 1.3.3. Water Poverty Index ………………………………………………………...........4 1.3.4. The variable Access in the Water Poverty Index …………………………...........5 1.4. Research problem definition ………………………………………………………...........6 1.5. Research objectives ……………………………………………………………………….6 1.5.1 Main Objective ……………………………………………………………...........6 1.5.2 Sub-objectives ……………………………………………………………………7 1.6. Research Questions ……………………………………………………………………….7 1.7. Conceptual framework …………...……………………………………………………….8 1.8. The WPI framework in the study area …………………………………………………...10 1.8.1. The model ………………………………………………………………….........10 1.8.2. The variables of the WPI in the study area ……………………………………..10 1.8.3. The specific framework of the WPI for the study area …………………………13 1.9. Research design ………………………………………………………………………….14 1.10. Outline of the thesis ………………………………………………………………...........152. Urban service delivery and water poverty ……………………………………………….........16 2.1. Service delivery …………………………………………………………………….........16 2.1.1 The concept of service delivery …………………………………………………16 2.1.2 Basics of an analytical framework in service delivery …………………….........17 2.2. Urban poverty mapping …………………………………………………………….........19 2.2.1 Poverty measuring and mapping ………………………………………...............20 2.2.2 Indicators, Geographical Information System, Remote sensing and poverty

mapping …………………………………………………………………………22 2.2.3 The case of water poverty ………………………………………………….........24 2.3. Critical review of the water poverty index ………………………………………………26 2.3.1 Limitations of the water poverty index …………………………………….........26 2.3.2 Concern about the water poverty index at high resolution ……………………...28 2.4. Conclusion: mapping profiles of water provision with the water poverty index ………..293. Study area and data capture …...………………………………………………………………31 3.1. Study area in the background of the research design …………………………………....31

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3.1.1 Area units in Kalyan-Dombivli and study area delineation …………………….31 3.1.2 Water supply in Kalyan-Dombivli Municipal Corporation ……………………..33 3.2. Data collection …………………………………………………………………………...34 3.2.1 The sampling design ……………………………………………………….........34 3.2.2 Determination of the sample size ………………………………………….........35 3.2.3 Characteristics of the wards surveyed …………………………………………..36 3.2.4 Field work difficulties …………………………………………………………..36 3.3. Results of the household survey …………………………………………………………37 3.3.1 The survey ……………………………………………………………………....37 3.3.2 Exploratory data analysis .………………………………………………….........38 3.4 Summary: study area and data captured …………………………………………............454. Design of the water poverty index ……………………………………………………………..46 4.1. Design of the index ………………………………………………………………………46 4.1.1 The variable Resources …………………………………………………….........46 4.1.2 The variable Access: the access index …………………………………………..47 4.1.3 The variable Capacity: the capacity index ……………………………………...50 4.1.4 The variable use …………………………………………………………………51 4.1.5 The variable environment ………………………………………………….........51 4.1.6 Weighting of the variables ………………………………………………………53 4.2. Inference of the water poverty index to the population …………………………….........54 4.2.1 Story line and model specification ……………………………………………...54 4.2.2 Assumptions ……………………………………………………………….........55 4.2.3 Hypothesis setting ………………………………………………………….........56 4.2.4 Procedure ………………………………………………………………………..56 4.2.5 Extrapolation of the WPI to the entire study area ………………………………57 4.3. Investigation of the water poverty index ………………………………………………...57 4.3.1 Sensitivity of the index to the components …………………………...................57 4.3.2 Variability conditions of the index ……………………………………………...58 4.3.3 Assessment of the WPI …………………………………………………….........58 4.4 Further analysis of the water poverty index ……………………………………………..58 4.4.1 Relation of the index to overall poverty ………………………….......................59 4.4.2 Appropriateness of the index to report the city’s water delivery situation ……..59 4.4.3 Evaluation of the water poverty index at policy level …………………………..59 4.5 Visualisation of the water poverty index ………………………………………………...60 4.6 Summary: methodology and analysis of the WPI in a flowchart ………………….…….605. The water poverty index: computation, investigation and analysis …………...…………….62 5.1 Data processing …………………………………………………………………………..62 5.1.1 WPI at household level …………………………………………………….........62 5.1.2 Descriptives of the WPI and components at household level …………………..66 5.1.3 Modelling of the WPI ……..……………………………………………….........67 5.1.4 Modelling of the WPI aggregated at ward level ………………………………...69 5.1.5 Spatial distribution of water poverty in the study area …………………….........73 5.1.6 Exploratory analysis for the WPI extrapolated at ward level …………………...76 5.2 Analysis 1: Investigation of the water poverty index in Kalyan-Dombivli ………………77

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5.2.1. Sensitivity (to the components) analysis ………………………………………..77 5.2.2 Variability conditions analysis ………………………….....................................80 5.3 Analysis 2: The water poverty index and the poverty landscape of K-D …………...........82 5.3.1 The new index of multiple deprivations in Kalyan-Dombivli …………………..82 5.3.2 Correlation analysis for IMD and WPI …………………………………….........84 5.4 Analysis 3: The water poverty index at policy level ……………………………………..87 5.4.1 Appropriateness of the WPI for mapping the water delivery situation …………87 5.4.2 Policy response to the water poverty index …………………..............................88 5.5 Summary of the computation and investigation of the water poverty index ……………..916. Discussion of the findings ………………………………………………………………………92 6.1 The index …………………………………………………………………………………92 6.1.1 Assessment of the WPI components ……………………………………….........92 6.1.2 Assessment of the resulting index ………………………………………………94 6.2 The usefulness of the index ………………………………………………………………96 6.2.1 Discussion of the results at policy level ………………………………………...96 6.2.2 Visual interface for policy makers ……………………………………………...99 6.2.3 Water poverty index as input to planning ……………………………………...1027. Conclusions and recommendations …………………………………………………………104 7.1 Summary of the research ………………………………………………………………..104 7.1.1 The research frame ……………………………………………………………..104 7.1.2 The case study ………………………………………………………………….105 7.1.3 The research results …………………………………………………………….106 7.2 Conclusion ………………………………………………………………………………107 7.3 Recommendations ………………………………………………………………………108References …………………………………………………………………………………………..109Annexure ……………………………………………………………………………………………113 O The raw data …………………………………………………………………………….114 A Access index …………………………………………………………………………….118 B Capacity index …………………………………………………………………………..120 C The variable Use …………………………………………………………………….......122 D The water poverty index …………………………………………………………….......123 E Modelling of the water poverty index …………………………………………………..125 F Sensitivity analysis ………………………………………………………………….......136 G Water poverty index at policy level …………………………………………………….141

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List of figures

1.1 The sustainable Livelihoods framework ………………………………………………………..31.2 Conceptual framework ………………………………………………………………………….81.3 The water poverty index framework for the study area ………………………………….........141.4 The research flow ……………………………………………………………………………..15

3.1 Study area in level of unsatisfied public services demand map ………………………………313.2 Study area in level of service map ……………………………………………………….........333.3 Ward 81 as surveyed …………………………………………………………………………..363.4 Graphical summary of the collected data ……………………………………………………..403.5 Characteristics of the supply ………………………………………………….........................413.6 Source of water used by households ……………………………………………………..........423.7 Time spent in water collection ………………………………………………………………..423.8 Household size and water use …………………………………………………………………433.9 Prevention strategies …………………………………………………………………………..433.10 Socio-economic characteristics ………………………………………………………….........44

4.1 Visualisation of the water poverty level in the study area ………………………………........604.2 WPI: summary of design and analysis in a flowchart ………………………………………...61

5.1 WPI at household level in surveyed wards ……………………………………………………635.2 WPI and components at household level in boxplots …………………………………………675.3 Graph of WPI at ward level …………………………………………………………………...695.4 Trends in the correlation of the WPI to variables at ward level for the wards surveyed ……..725.5 Water poverty index in study area as extrapolated from the census 2001 ……………………755.6 Boxplot of the WPI extrapolated ……………………………………………………………...765.7 Correlation of the WPI to access power ………………………………………………………795.8 The new Index of Multiple Deprivations in the study area …………………………………...835.9 WPI and households’ satisfaction with the water service ……………………………….........885.10 Access and response time to complaint ………………………………………………….........895.11 Access and main problem in accessing the water service …………………………………….90

6.1 Water poverty level in Kalyan-Dombivli ……………………………………………………1006.2 The water poverty index and components in the surveyed wards ……………………….......101

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List of tables

1.1 Summary of WPI components ………………………………………………………………...10

2.1 Decision-making components and the role of indicators ……………………………………..23

3.1 Wards composing the study area ……………………………………………………………...323.2 Standard deviation for sample size purpose …………………………………………………..353.3 Characteristics of the wards surveyed ………………………………………………………...363.4 Not usable questions of the household questionnaire ………………………………………...373.5 Descriptive statistics of the collected parameters ……………………………………………..39

4.1 Concept of the variable Access in this study …………………………………………….........474.2 Weights of deprivation parameters ……………………………………………………………484.3 Coding of deprivation parameters ……………………………………………………….........494.4 Weights of income parameters ………………………………………………………………..504.5 Values of the variable environment …………………………………………………………...514.6 Water source monitoring parameters and benchmarks ………………………………………..524.7 Hypothetical weights to be added to WPI structure …………………………………………..53

5.1 Descriptive statistics of the WPI and components at household level ………………………..665.2 WPI at ward level ……………………………………………………………………………..695.3 Percentage of main workers in ward …………………………………………………….........745.4 Descriptive statistics of the WPI extrapolated at ward level ………………………………….765.5 Correlation between variables in the six surveyed wards …………………………………….785.6 The new IMD and components in Kalyan-Dombivli …………………………………………845.7 Variables in the correlation IMD-WPI analysis ………………………………………………855.8 Correlation IMD and Capitals ………………………………………………………………...855.9 Correlation IMD and WPI …………………………………………………………………….86

6.1 Not-standardized ward mean of the WPI and components in the wards surveyed …………100

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List of acronyms

CDP City Development Plan CPHEEO Central Public Health Engineering and Environment Organisation GIS Geographical Information System GLM General Linear Model HDI Human Development Index IMD Index of Multiple Deprivations ITC International Institute for Geo-Information Science and Earth Observation KDMC Kalyan-Dombivli Municipal Corporation -2LL -2 Log Likelihoods LMM Linear Mixed Model LPCD Litres per capita per day MLD Million Litres per Day RC Random Coefficient (model) SURD Sustainable Urban-Regional Dynamics UK United Kingdom UNDP United Nations Development Program UNICEF United Nations International Children’s Emergency Fund WHO World Health Organisation WPI Water Poverty Index WSAM Water Situation Assessment Model WWI Water Wealth Indicator

MAPPING PROFILES OF SERVICE PROVISION IN POVERTY ANALYSIS: A CASE STUDY OF WATER DELIVERY IN K-D, INDIA

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1. Introduction

This introductory chapter sets the frame of the research starting by the background and justification. The definition of major concepts enlightens the research problem formulation leading to research objectives and research questions. A conceptual framework is designed first for the research, followed by a second one for the water poverty index to be embedded specifically in the study area. The research design is equally reported to give an overview of the whole work.

1.1. Introduction to poverty mapping and service delivery

The aim of poverty mapping is to estimate geographically the determinants of lack of well-being at area unit level with disaggregated socioeconomic or other type of data. Its output is an estimate of poverty pockets and inequality distribution across the city which can be visualized by maps to enhance communication of the results. “ Poverty mapping is essentially a tool; its functionality must therefore be seen and evaluated in light of the objectives for which it is put to use – the research and policy questions and hypotheses upon which it can shed light”(Davis, 2003, p. 4). As such, poverty map can evidence multiple deprivations structured in spatial boundaries and inform targeting policy. This could also improve public resource allocation and enhance social equity in service delivery. Public service is delivered for citizens’ satisfaction and well-being. In developing countries, service provision is mainly characterized by underinvestment in operation and maintenance and unequal pattern of the provision. There are also other determinants such as service price and quality option which need to be consistent with the ability to pay for the service. In assessing the state of service delivery, access data should therefore, be adjusted to these aspects. Instead, “access data usually include individuals with all-day access as well as individuals with access for just a few hours a day”(Briceno-Garmendia et al., 2004, p. 13). This highlights some aspects of the access such as variability in the supply and less obvious issues like entitlement to or capacity to afford the service. In India for instance, an assessment of infrastructure delivery shows that an important part of the population is living in slum-like conditions and there is major lacuna in urban infrastructure and services provision despite considerable efforts made (Mehta and Pathak, 1998). Conventionally delivered services are for the poor luxuries or energy and time consuming. In the case of water supply, expectations are low and “households are quite prepared to continue accessing water through emergency hand pumps, even in urban areas with high levels of on-plot sanitation, potentially a significant health risk”(Gessler et al., 2008, p. 55). Poverty adds to the acuity of the state of non proper access to services. Recently, Mumbai has been (along with two other Indian mega cities) the scene for the development of the index of multiple deprivations to capture and map the several facets of urban poverty. The city of Kalyan-Dombivli, site of the present research, is an important satellite township of Mumbai with more than one million inhabitants in 2001. Due to various population factors in the municipal corporation, there is shortage of all public services. More than 44% of the population was living in slums and slum-like structures in 2001. Urban poverty is addressed by means of slum upgrading (CDP, 2007, pp. 25-32). The index of multiple deprivations is currently being designed for the city


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and the issue of poverty looked at in several dimensions including service provision by a group of researchers. Mapping the profile of the city for water provision will give the opportunity to analyse some basic characteristics at chosen levels in the dynamic of the delivery. The variations in access to water along with some other livelihoods issues such as the availability of water resources are caught in the concept of water poverty and presented in an index, the “Water Poverty Index” which can quantify this particular type of deprivation at various scales, such as community, water basin or national level.

1.2. Setting of the research

The motive of mapping deprivations is to show their typology in space and maybe in time for various purposes, mainly for addressing poverty. Deprivations facing urban poor households have multiple sources which could be structural or behavioural or both. As a compound deficit of important livelihood assets or lack of the required level of resources to achieve a given minimum standard of subsistence, deprivations sustain vulnerability to poverty. Various authors have shown the important role of asset ownership in building well-being and the contribution of access to public good in that undertaking1. The level of access a household has to public goods influences the total resources it can exhibit as livelihood assets portfolio. Meanwhile, in any conditions, households develop various compensatory strategies in the face of privation. This is one key rationale for mapping households coping strategies along with their multiple deprivations. Moreover, it gives opportunity to target the real need of people and improve effectiveness in building better living conditions for the poor. In this line, the ongoing research program involving ITC and focusing on how urban governance networks can tackle urban inequalities and households’ deprivations in Indian large cities has targeted Kalyan-Dombivli for duplicating the experience of designing the new Index of Multiple Deprivations for Delhi and some other cities. The assumption of slum-dwellers being the urban poor and the housing conditions being the only need considered worthy to be taken care of by the local government raise the importance of the research under consideration for the city of Kalyan-Dombivli. Outputs from Delhi revealed that urban poor are not concentrated in slums as always assumed. In case this applies to Kalyan-Dombivli, it may have some implications for public services delivery and area-based policies. Even though area-based policies can not avoid ecological fallacy, mapping deprivations could help to better target not only the areas but also the type and scope of the services to prioritise. The study in Delhi uses the livelihoods assets framework and results in a better insight into the multiple natures of poverty hotspots and their non-concentration in slums. Policy makers are said to gain better targeting potential with the new index (Baud et al., 2008). The present study, in the perspective of similar results for Kalyan-Dombivli, aims at highlighting households’ water-related deprivations in a water poverty index to mapping the city situation in water provision and exploring the usefulness of the index in targeting policies along with the new Index of Multiple Deprivations designed for the city.

1 This notion is included in the concept of livelihoods framework. Some authors hereafter named applied it in urban context: I. S. A. Baud, Carole Rakodi, Caroline O. N. Moser, etc.


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1.3. Definition of major concepts used in the research

Four of the major concepts dealt with in this research are defined hereafter. The rest are addressed in chapter 2. The concepts defined here are related to the background of poverty mapping in a people-centred approach and the measuring of water poverty as it is meant to be in the research. After the sustainable livelihoods framework, and the new Index of Multiple Deprivations, the variable Access is highlighted in presenting the water poverty index. The variable Access is the most representative of people’s share in public goods among the components of the water poverty index.

1.3.1. Sustainable livelihoods framework

The sustainable livelihoods framework conceives development from a people-centred perspective. It is defined on the basis of five types of asset or capitals namely, social, human, financial, physical and natural with the following meanings (Moriarty and Butterworth, 2003, pp. 30-31).

Figure 1.1: The Sustainable Livelihoods framework

Source: adopted from DFID Sustainable Livelihoods Presentation, http://www.livelihoods.org/info/Tools/SL-Proj1b.ppt

Human capital relates to skills, knowledge, capacity to work, and health of individuals available within the household (labour).

Natural capital is the resource stock (e.g. trees, land, water, clean air) upon which people rely and benefit, both directly and indirectly.

Vulnerability context

Shocks Trends Seasons

Policies & Institutions

(Transforming Structures & Processes)

Structures Government Private sector

Processes Lows Policies Culture

Livelihood Strategies

Livelihood Outcomes

* Sustainable use of N R base * Income * Well-Being * Vulnerability * Food security

Livelihood Capital Assets

Human

Natural

Financial Physical

Social


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Financial capital includes savings (cash, bank deposits or liquid assets such as livestock and jewellery), access to credit, and regular inflows of money including earned income, pensions, and remittances.

Social capital is … the social resources upon which people draw in pursuit of their livelihood objectives.

Physical capital comprises basic infrastructure like water supply and sanitation (of adequate quantity and quality), energy (that is both clean and affordable), affordable transport systems, good communications and access to information, shelter (of adequate quality and durability) and physical goods like bicycles, sewing machines, agricultural equipment, and household goods.

Other elements in figure 1.1 stemming from the concept are defined as follows:

Livelihood activities include all the activities that people engage as part of making their living. Livelihood strategies are the full portfolio of livelihood activities, but linked to an understanding of

the choices and decisions underlying them. Livelihood outcomes are the achievements – the results – of livelihood strategies. Influencing factors are made of policy, institutions and processes. The vulnerability context is composed of chocks, trends and seasonality.

The approach frames well how different households’ livelihood activities, built on the range of assets available, are transformed as part of wider livelihood strategies into livelihood outcomes with influencing factors and the vulnerability context playing a crucial role. In this regard, service delivery shows as part of the process of households’ asset portfolio building via the physical capital.

1.3.2. The new Index of Multiple deprivations (IMD)

The concept of multiple deprivations counts back to the view of poverty as multidimensional. Accordingly, the new Index of Multiple Deprivations tries to capture the several households’ deprivations in one index. It is a composite index using the sustainable livelihoods framework as base to define its variables and parameters. For example, in the case of Delhi, an Indian mega city, the index models the different capitals according to the livelihood vulnerability framework and the study area’s characteristics as follows: social discrimination for social capital, education and employment for human capital, monetary situation and households assets for financial capital, and source of drinking water, sanitation, electricity supply and over crowding for the physical capital. In a design somewhat different of former methods and approaches in poverty measurement, the new IMD makes use of indicators derived from census data disaggregated at electoral ward level and coupled with households coping strategies. Spatial patterns of deprivations and inequalities are better highlighted by this poverty mapping method which shows satisfaction at policy level (Baud et al., 2008).

1.3.3. Water Poverty Index (WPI)

The concept of water poverty index is to assemble water availability and related issues with households’ personal capability to access it (Sullivan, 2005). The WPI is a weighted additive quantity,


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standardized on a scale of 0 to 100, which is considered the best situation. It is composed of five variables defined as below. Each variable gains its importance from political decision and thus limits the researcher’s entitlement to assumptions for the weighting without sufficient local knowledge of the study area. Indeed, the weighting scheme makes the index flexible and gives the possibility to adjust it to the importance given to each variable (Sullivan et al., 2006a, p. 415).

� Resources – how much water is available, taking into account seasonal and inter-annual variability and water quality

� Access – how well provisioned the population is, including domestic use and irrigation � Capacity to manage water resources, based on education, health and access to financing � Use – captures the use of the water and its contribution to the wider economy � Environment – attempts to capture the environmental impact of water management to ensure

long-term ecological integrity

The conceptual relation of these variables to the livelihoods framework is expressed in Access relating to social and financial capitals, Use to physical and financial capitals, Capacity to human, social and financial capitals, Resources to natural, physical and financial capitals, and Environment to natural capital (Sullivan et al., 2003, p. 193). The version of the livelihoods framework to address urban poverty (Rakodi, 2002) makes it easier to apply the water poverty index in urban context. In substance, these WPI variables highlight the importance of local infrastructure and the physical and socioeconomic environment factors for capturing the availability of water resource and the households’ ability to access it. Despite the missing link of institutional dimension, these variables together are likely to reproduce the profile of a city for water delivery.

1.3.4. The variable Access in the Water Poverty Index

With the livelihoods framework which is a people-centred approach, Access can be seen as an important feature as it depicts the part of public services households can have in building their water-related livelihoods asset portfolio. In the WPI, the variable Access conceptually results from the following data, at community level (Sullivan et al., 2006a, p. 416).

� Access to clean water as percent households having piped water supply � Reports of conflict over water use � Access to sanitation as percent population � Percent water carried by women � Time spent in water collection, including waiting � Access to irrigation coverage adjusted by climate and cultural characteristics

In the urban context, the physical and social aspects sought in the variable may be reduced to two or three of these points. Commonly, “the proportion of the population with access to safe drinking water is an indicator expressed as the percentage of people using improved drinking water sources or delivery points … Improved drinking water technologies are more likely to provide safe drinking water than those characterized as unimproved”(WHO and UNICEF, 2006, p. 4). This definition is mute about the level of access. For example, the percentage of people using improved water sources doesn’t say anything


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about the daily duration of their access, the moment of the access, the quantity of water that those people are entitled to, etc. “There is no much point having a water standpipe within 100 metres if there is no water in the pipe most of the time, or if the queues are so long that households are unable to collect the water they need”(Satterthwaite, 2003, p. 186). These aspects are all part of the access and should be captured in an index such as the WPI to reflect most of the poverty facets at a high resolution (household level).

1.4. Research problem definition

From the previous section, it appears that the notion of access to urban services can be very limitative in its common implementation and narrowed by the deciles of the provision. The hidden part counting for effectiveness, quality, quantity, and continuity in the supply, is better addressed by looking at the outcomes from the users’ point of view (Zérah, 2000). Households facing these deprivations develop a set of strategies to anticipate or fight back. This behavioural response to the failure of services delivery – in some aspects – may be a good proxy for assessing the magnitude of this last. Based on above, the concept of access should be widened and extended to the notion of reliability of the water supply as put by Zérah (2000). She sets the reliable service as the one delivered not only in wanted quantity and required quality but also on time and during the service life of the facility delivered. Several related issues are highlighted by this view of the concept of access such as equity, duration of the delivery, the variability in the quantity delivered, the variability of the quality, and the aging effect during the life time of the infrastructure or service. Consequently, these parameters cannot be ignored in mapping urban water delivery if the real state of the access is to be shown. In the water poverty index, some of the parameters mentioned above are rather identified for the variable Resources with different meaning (Sullivan et al., 2006a, p. 416). In early experiences, the difficulty of capturing them was due to failure in water management data availability. In the case of Eastern cape where the WPI was mapped at municipal level, the researchers come to the conclusion that “this component should be representative of the total available yield from the catchment as this would take into account both the total volume of the resource as well as the influence that infrastructure such as dams would have on the reliability of this resource”(Cullis, 2005, p. 22). Consequently, the identified parameters are unlikely to suffer any overlap in the WPI if they are captured in the variable Access when Resources counts for the water yield for households’ use. Responding to the hidden aspects identified as usually left out in the expression of access to water, supported by the fact that the figures and statistics are necessary but not sufficient for evaluating the level of service provision and the intrinsic level of households’ access, this study intends to enhance the variable Access with households’ deprivation parameters in the water poverty index for mapping the profile of the city in water service delivery.

1.5. Research objectives

This investigation is carried out in the direction of the main objective narrowed in the five specific objectives listed hereafter.

1.5.1. Main objective

� To determine the spatial distribution of the study area’s main characteristics in water delivery.


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1.5.2. Sub-objectives

� To design the water poverty index (WPI) at a suitable scale. � To study the sensitivity of the water poverty index. � To study the appropriateness of the water poverty index to report the current spatial

distribution of water delivery. � To study the linkages between city profile in water delivery and poverty. � To sketch the policy response to the designed water poverty index in the study area.

1.6. Research questions

In the attempt to respond to the research problem, the sub-objectives listed above are further elaborated into research questions to guide the investigation.

Sub-objective 1: To design the water poverty index (WPI) at a suitable scale � What is the suitable mapping unit for the study area? � What are the variables, parameters and indicators used to produce the WPI? � What are the effects of the physical and socioeconomic context of the study area on those

variables, parameters and indicators? � Is accessibility an issue in the study area to justify the design of a WPI? � Does the accessibility fully cover the need of water?

o Is the municipal water delivery effective and reliable? o What is the dependency of households on various sources of water supply? o What are the households coping strategies? o Can they be caught in the access indicator for the WPI?

� What is the WPI per mapping unit in the study area?

Sub-objective 2: To study the sensitivity of the water poverty index: � What is the influence of each variable on the overall index?� Are there other parameters influencing the index in the study area?� What is revealed about the index?

Sub-objective 3: To study the appropriateness of the water poverty index to report the current spatial distribution of water delivery:

� Does the WPI adequately represent the level of access to water and delivery performance in the mapping units?

� In case it does, what is revealed about the service performance? o To what extent is the demand satisfied? o What is found about efficiency? o What is discovered about effectiveness? o What is established about equity?

� Does the index present or suffer from any feature specific to the study area? � Is the index robust against ecological fallacy? � What are the most water deprived areas or mapping units?


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Conceptual framework for mapping city profiles in water delivery as a specific type of poverty

Resources

Access Environment

Capacity Use

Water Poverty Index

Level of access to water in related deprivations � Management-specific � Revealed by households’

behaviour as compensatory strategies

Sustainable Livelihoods Framework

Poverty mapping Ranking the mapping units

Service delivery As a concept influencing: � Management and monitoring

of water provision � Planning of water provision � Decision-making in water

provision City profile mapping in water delivery

Concept of Multiple Deprivations

Sub-objective 4: To study the linkages between city profile in water delivery and poverty: � Does the distribution pattern of water delivery in the city hold a relation to overall poverty

level? � If it does, what is the sensitivity of the correlation to the determinants of the WPI? � What is the status of the study area regarding water delivery in a poverty landscape?

Sub-objective 5: To sketch the policy response to the designed water poverty index in the study area � What is the policy value of the Water poverty Index? � What is the policy context of the study area in water provision? � In which ways could the WPI be useful for the study area in addressing water poverty?

1.7. Conceptual framework

The conceptual framework is shown in figure 1.2. It is composed of several entities, each of which is presented hereafter through the rationale of it being in the conceptual framework or the role in the research.

Figure 1.2: Conceptual framework


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� Concept of multiple deprivations This concept was defined in section 1.3.2. In the framework, it influences the water poverty index. Consequently in this study, households are assumed having several sources of deprivations in trying to access water service.

� Sustainable livelihoods framework In section 1.3.1, this concept was widely presented. It is the most important background concept in this study. It shapes the study approach to people-centred. Accordingly, the water poverty index is intended to be designed at household level. The livelihood concept supports the concept of multiple deprivations and also the water poverty index as shown in section 1.3.3. The relation of the water poverty index’s each variable to the livelihoods capitals makes it a potential analytical tool in addressing poverty. The sustainable livelihood concept influences directly the level of access to water when this last is taken as part of households’ asset base.

� The water poverty index This concept was described in section 1.3.3. As shown in the figure, the water poverty index is the major input to the research objective which is to map city profile in water delivery. It is meant to be enhanced in the variable Access to give effective account of deprivations in accessing water service.

� Level of access to water The concept shaping this variable is described in section 1.3.4. The variable Access is specifically conceived from households’ deprivations to keep consistency with the concept of multiple deprivations. This is further supported by Zérah’s work (1998, 2000) from which it could be derived that the essence of households’ water-related deprivations is a relevant input to the process of finding the true level of access they have to water service.

� Poverty mapping and city profiles mapping The concept of poverty mapping is developed in section 2.2. The water poverty index outputs will be translated into water delivery profiles map with poverty mapping techniques.

� Service delivery The concept of service delivery is developed in section 2.1. This concept will help defining the usefulness of the designed water poverty index at planning and management as well as decision-making levels.

The conceptual framework reads as the water poverty index being designed to serve as input to mapping city profile in water provision in one hand, and the water poverty index being enhanced through the variable Access with some deprivation parameters, every other variables remaining

more or less the same as defined in the concept in the other hand.

Subsequent to the development above, the main objective of the research reads as to design the variable Access within the water poverty index from local households’ water-related deprivations and map the city profile with the index. Consequently, a second framework focusing on the index will complete this first. Details on the design of the index are given in the next section.


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1.8. The WPI framework in the study area2

The framework of the water poverty index for the study area is designed in this section. The current study is using essentially survey data because of non completeness of census data3 or data from other sources. With the purpose to keep intrinsically the original concept in the WPI to be designed, the rationale of each variable is taken from a reference model.

1.8.1. The model

The model chosen to backup this study is one of the pilot WPI designs which took place in Eastern Cape (Cullis, 2005, pp. 21-30). That experiment was conducted at municipal level. It is the lowest scale experience in the literature accessed by the researcher of the current work. It is also the closest to this research which is designing the index at household level4. Table 1.1 is a summary of the parameters used for the five variables of the water poverty index in the model study.

Table 1.1: Summary of WPI components

WPI Components Variables Used to Represent the WPI Components Data Source

Resource Per capita surface and ground water yield at 98% assurance [m3/c] WSAM*

Access Percentage of household with access to a protected water source (%) Census 2001

Capacity Households able to afford a basic suite of service (%) Population with above Grade 4 level of education (%) Census 2001

Use Average per capita domestic water requirements (l/c/d) WSAM

Environment Average Present Ecological Class (rank) WSAM

* WSAM: Water Situation Assessment Model Source: Cullis, 2005

In the light of table 1.1 and the conceptual definition of each component of the water poverty index, a set of parameters is identified for the design of the index in the study area.

1.8.2. The variables of the WPI in the study area

For each variable, the rationale is first given from the general concept and the model and next, the proposal of this study for the variable is specified.

2 The study area is introduced in chapter 3. 3 The census data 2001 got is partial and do not give all information needed for the calculation of the WPI. Some of the said characteristics are available for health wards which are at a too highly aggregated level for the purpose of this research. 4 The choice of household level responds to the approach of the research and comes from literature (see section 2.3.1) where it is understood as the scale giving the highest accuracy for the water poverty index.


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The variable Resources

� Rationale of the variable from the concept ‘Resources’ refers to the water naturally available and the existing infrastructure for making it available for use. Three aspects of the availability – the amount, the variability or reliability, and the quality – are captured in the variable. Variability relates to seasonality and quality in the case of tap water has to meet international standards for the water to be considered available5. In the model, it is said that this variable should give account of “the total available yield from the catchment as this would take into account both the total volume of the resource as well as the influence that infrastructure such as dams would have on the reliability of this resource”(Cullis, 2005, p. 22).

� Local parameters In the context of this research, the water poverty index is household-centred, based on the livelihoods approach introduced in section 1.3.1. Consequently, the resource for the household is the water ready for use through the supply system. That is the average per capita delivered per day like in the model.In this study, the quality of the resource – that is the quality of the water coming from households’ tap – can not be considered because of data shortage. However, the issue of quality is taken into account in households’ deprivations parameters.

The variable Access

� Rationale of the variable With the purpose to meet the conceptual aim of this variable, which is to report people’s access to water for daily use, some water supply reliability characteristics will be captured in it along with households’ related deprivations. The model uses a single percentage from census. This illustrates the core of the present work which questions the concept of access as a simple percentage and is attempting to incorporate the translation of related deprivations into the overall value of the access. The water supply network covers most of the study area and water supply is physically in the reach of all (CDP, 2007). Nonetheless, as put by Satterthwaite (2003, pp. 184-186), attention should be paid to the water quality, ease of access, regularity of supply, etc. to avoid confounding proximity of public facilities with access to them.

� Local parameters The ten parameters for characterising the households’ access to water and related deprivations are identified and listed hereafter. The identification is based on Zérah (1998, 2000) and Satterthwaite (2003). The parameters will be combined into the access index.

- Moment of reception of the municipal water The water supplied during night time for example imposes the constraint of using the time meant for rest to manage and save the water for day time and no water time use.

5 “These three values can then be reduced to a single indicator for primary resource availability, and one for actual resource availability. An indicator on a scale of 0 to 10 is developed which gives a combined assessment of the three factors: amount, variability/reliability, and quality of the water. While this single indicator gives an overall result for availability of the resource, the information relating to the three separate aspects is still valuable, and should be retained so that the impact of the various components can be seen in the final result” (Mlote et al., 2002, p. 6).


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- Duration of the reception First, this parameter indicates that the supply is not round the clock. More, it is a discriminating factor in a context of varying level of service. Both aspects depict deprivations within the access.

- Quantity received The quantity supplied will give account of the unsatisfied demand of households.

- Source of drinking water and source of water for other use These parameters orient to the freedom of the household in the use of water, see if they have to shift source and develop a strategy for getting safe water. It is an expression of internal deprivation.

- Compensatory source at break of the municipal supply This will indicate the capacity of facing hardship in the face of unavailable municipal supply; but also and most, it will indicate the choices available and in the reach of the household for replacement water. It is also a discriminating factor.

- Daily collection time This deprivation is explained by the financial and economical value of time.

- Collection time from compensatory source This parameter is separated because it is an extra deprivation and it doesn’t have a daily pattern like the previous one.

- Prevention strategy I (for the quantity) Households develop prevention strategies against no water time. To benefit from the quantity of water given in a limited time, they are obliged to save it. This involves one time and continuous cost of money, time, etc., but also may be a threat to safety and health with regard to the maintenance and condition of the saving means.

- Prevention strategy II (treatment level for quality) The level of treatment of the drinking water participates in the prevention of the household’s vulnerability to water related diseases. The act of treating the water and the mean of the treatment are discriminating factors among households.

The variable Capacity

� Rationale of the variable In the study model, households are declared capable when they are able to pay for the municipal water service and sufficiently educated to manage efficiently their water supply (Cullis, 2005). The model combined affordability of basic services suite and level of education. This study takes into account level of income and level of education since the limited field work time cannot permit to measure successfully the households’ affordability of basic services suite.

� Local parameters The five parameters hereafter listed are identified and combined to give a proxy value of the level of income. These parameters are the same as those used in the work of Baud et al. (2008).

- Ownership of vehicle - Type of house - Tenure type - Type of school for children - Ownership of telephone


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The level of education is directly considered from the households’ survey.

The variable Use

� Rationale of the variable From the community level experience, this variable gives account of the urban consumption of water which is mainly composed of domestic consumption rate and industrial use (Sullivan et al., 2006a, p. 416). In this study, it is assimilated to the domestic use and taken as the household per capita daily consumption of water.

� Local parameter The household daily consumption of water and the household size are taken from the survey.

The variable Environment

� Rationale of the variable The environmental dimension in the index is to capture the expected balance needed between the anthropocentric and the eco-centric perspective6 in the use of water and sustainability needs. In the current development of the index, the representation of ecosystem water demand is still limited (Mlote et al., 2002). Nevertheless, this variable is taken as “an evaluation of the environmental integrity of the water resource used to provide domestic water supply. This is particularly relevant to communities that obtain their water directly from the resource but is also significant in the case where water is supplied via an infrastructure scheme as it would give an indication of the level of treatment required with associated cost implications”(Cullis, 2005, p. 27).

� Local parameters This last statement by the model and the example in table 1.1 guide the design of the variable. In the face of data shortage to evaluate the ecological state of the source, a proxy value is derived from the water source quality monitoring data.

1.8.3. The specific framework of the WPI for the study area

As a summary of the development above putting each variable in the context of the study area and the aim of the current study, the framework of the water poverty index in the study area is shown in figure 1.3.

6 This expresses the human need and the ecological need and functions. The importance of the ecological function is highlighted by the original authors of the index who identified it as missing aspects to work on in future developments and revision of the water poverty index (Sullivan et al., 2006, p. 424ss).


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Figure 1.3: The water poverty index framework for the study area

1.9. Research design

The research design is flowcharted in figure 1.4, which reads as follows. � The design of the present research is rooted in literature review. � The theoretical background as shown in the conceptual framework sheds light on the research

problem and guides to the methodological approach by specifying the required input data as well as their sources.

� The methodological approach results from the theoretical context derived from literature. It comprises:

o The capture of the input data. o The data processing and the analysis with the support of technical tools for getting

relevant outputs and conclusions. � The tools identified for the analysis are essentially statistical methods, and GIS techniques

along with analytical approaches and benchmarks derived from the theoretical context. � The research will conclude and lead to recommendations set with the theoretical concept of

the research in the background.

Water poverty index framework for the study area

Environment Proxy value from monitoring data

Vehicle ownership

Telephone ownership

House type

Tenure type

Type of school for children

Income level index

Level of education

Capacity index

Use Daily per capita quantity used

Resources Daily per capita quantity delivered

Access index Source of drinking water

Source for other use of water

Replacement source

Moment of supply

Duration of supply

Quantity of supply

Drinking water treatment

Daily collection time

Replacement collection time

Prevention strategies


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Figure 1.4: The research flow

1.10. Outline of the thesis

The present work is organised in seven chapters hereafter listed. � The current chapter one reports on the general and specific background of the study and the

research design. � Chapter two highlights the salient points and state-of-art in service delivery, poverty mapping

and water poverty. � Chapter three, the first of the two methodology chapters, after a quick introduction of the

study area reports the most important features of the data collection and presents the raw data. � Chapter four describes the methodology for the design and the analysis of the water poverty

index. � The application of the methodology to data processing for the designed water poverty index is

performed, and the results are analysed in chapter five. � In chapter six, the findings are discussed with focus on the usefulness of the designed index

from management and policy point of view. � Chapter seven concludes the work and gives some recommendations.

Literature Review

Research Questions

Research Objectives

Research Problem

Theoretical context * Service delivery * Poverty mapping * Water poverty * Livelihoods framework * Urban HH multiple deprivations

Methodological approach

Data capture

Source of input data *Census data *HH survey *Interview of local authorities *Municipal water management data *GIS data

Theoretical concept

Technical support *GIS Techniques *Statistics Techniques *Theoretical benchmarks

Conclusions and recommendations

Data processing and analysis

Research findings


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2. Urban service delivery and water poverty

The research program heading the current study is aiming at “mapping the profiles of multiple household deprivations and also ‘hotspots’ of urban deprivations and household coping strategies”7. In this line, the present chapter two is reporting the literature review in three sub-chapters namely, service delivery, poverty mapping and water poverty index. The concept of service delivery is recalled in order to detect a structural framework that could be useful later in the study to evaluate the profile of the city according to the current research’s topic. More importantly, the act of poverty mapping isaddressed with focus on the case of water poverty. The water poverty index, the up-to-date measure of water poverty, is then introduced and examined in the light of the critics. The controversial discourse about the water poverty index is of interest because it is a relatively new concept delivered as a work in progress.

2.1. Service delivery

Mapping the profile of a city in a public service delivery requires first to understand the concept of service delivery itself and to capture its structural agenda for picturing the status of the city regarding the said service delivery. In the current sub-chapter, the concept of service delivery is examined first, followed by the identification of some features characterising public goods provision.

2.1.1. The concept of service delivery

Urban service is defined as “… one which serves the public interest by accomplishing one or more of the following purposes: preserving life, liberty and property; and promoting public enlightenment, happiness, domestic tranquillity and the general welfare. It is provided by one or more of the sectors in the economy through government regulation, co-production, or direct provision”(Baer, 1985, p. 886). This definition presents more than one category of provider, states the goals of urban service delivery and sets its importance for citizens. It highlights that urban service delivery involves a purpose, a provider, and a mean or method of its provision. The dimension of citizens’ needs satisfaction for well-being introduces the recipient’s involvement in the mechanism as a fourth characteristic. For several decades the concept of urban service provision has not evolved, but its implementation – to some extent. Some dimensions such as public participation and business-like management have caught international attention lately against the traditional view of service delivery being the exclusive duty of local or central government or both. In any case, “urban services require substantial resources, and a concern with the effectiveness of their performance has resulted in … management strategies to

7 See ITC, research code SURD 22, 2008.


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oversee operation and maintenance more effectively, financing strategies to provide resources for rehabilitation efforts[,] … introduction of new technologies to improve the infrastructure base … and changes to institutional design. However, there has been a realisation that such supply-side techniques alone will not solve infrastructure problems and, increasingly, service users are encouraged to get involved in service delivery”(Cavill and Sohail, 2004, pp. 155-156). The notion of service user involvement merely put in the definition of service delivery gains increasing attention because of the increasing importance given to people in development paradigms. To date, and from the definition to the importance of involving the service user in the process of delivery along with infrastructures and management for effectiveness in the delivery, urban service provision can be summarised as follows.

Service delivery is a dynamic in which the provider and the receiver are supposed to play each an important role regulated by government for the satisfaction of all. As such, it is resource consuming

and requires investment and commitment from all.

2.1.2. Basics for an analytical framework in service delivery

From the concept, it appears that the dynamic represented by the fulfilment of the purpose of service delivery is controlled by the provider and the receiver at the two ends of the service being delivered. These two ends of the dynamic set the analysis of service delivery into a bi dimensional framework in terms of reversibility (the possibility of changing the output of a service delivery to adjust an unsuitable outcome for the consumer’s satisfaction) or non-reversibility as put by Baer (1985). According to this author, this bi dimensional framework is not enough to apprehend the complexity of service delivery. He “stresses the need to look at not only the primary effects (outcomes) from the distribution of services, but also the secondary and tertiary ones.8 We might find that the distribution pattern of certain services was more a function of these secondary outcomes (the externalities, say, stemming from a proposed noxious facility’s location) which swamped the primary effects (the purpose of the service) in determining the pattern of service delivery”(1985, p. 896). From this point of view, an urban planner has to consider service delivery in an integrative perspective if the overall satisfaction of the citizen is to be fulfilled. As a consequence, a complete analytical framework will have to look also to interactions between different types of service delivered. Nevertheless, the analytical framework set by Baer pictures the dynamic of a typical delivery. In short,

The framework for highlighting the characteristics of a particular type of service delivery is set first by the provider and the receiver, then and mainly both by the output and outcome(s) of the service, seeking in priority citizen’s satisfaction. One consequence is that service delivery should always be

put in a wider context of the overall well-being of citizens.

The framework introduced above from Baer is embedded in the traditional research question (on services) of “who gets what, when, how?” which covers the prototype of service delivery. More than the deliverer and the receiver, the two other characteristics identified above – the purpose and the

8 Baer reports the idea of Jones, B.D. 1982, ‘Assessing the Products of Government’, in Analyzing Urban-Service Distributions, ed. R.C. Rich, Lexington: D.C. Heath.


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mean or method – are caught in the “what, when and how”. These hold the issue of access in several dimensions. For the location-related accessibility for instance, urban systems could be “seen as pools of scarce and unevenly-spread resources and facilities … from which residents benefit to varying degrees according to their willingness and ability to overcome the physical barriers of distance as well as financial barriers to resources in the market economy and the social, psychological and educational barriers to resources in the public domain”(Knox, 1980, p. 368). This example underlines access-related issues such as inequality, deprivation, lack of social or financial ability and capacity to access public goods, which transcend the physical accessibility, expressed here in the friction of the distance, and equally affect all other dimensions. In addition, the example supports the general answer to the traditional research question (mentioned above) stating “… that bureaucratic decision rules largely account for service levels and distribution patterns in urban areas”(Baer, 1985, p. 889)9. Given that service allocation is strongly influenced by governance or bureaucratic rules and socioeconomic status or social power, the powerless and socioeconomic poor experience deprivation. However, Nivola argues that “either way the model of ‘who gets what’ is systematic: in the first instance a purposive scheme is being perpetrated. In the second, there is a ‘pluralist bias’ which is also predictable: middle and upper-middle class districts, because of superior organizational resources, get favourable treatment; the ‘powerless’ urban poor are invariably short-changed; and the lower-middle class or working class sectors tend to drop somewhere in between”(1978, p. 59). Nevertheless, despite that the model of ‘who gets what’ is unsystematic, and without judging the reasons explaining it, the responsibility of the government in the effectiveness of the delivery is to be stressed as in the definition. In any case, it can be said that

Urban service is well patterned by the question ‘who gets what, when, how?’ which is strongly shaped by socioeconomic characteristics and political power of individuals or clusters of citizens.

In addition to the influence of socioeconomic clusters as predictable source for the differentiation of citizens regarding the level of access they have to public goods, the “how and when” may lead to more features depicting the outcomes of the service being delivered. Eventually, the driving forces of the ‘who gets what, when, how?’ add a policy dimension to the framework. Thus, the intrinsic level of access to urban service is a tangible result of the local governance policy and is likely to be a realistic basis for delivery performance evaluation in a people-centred approach focusing on outcome from the user’s point of view. While assessing the quality dimension of water service in Delhi, Zérah comes to highlight “the importance of the concept of service which is differentiated according to users and usage … and raises the question of the social cost of deficient infrastructure for the community”(1998, p. 290). Here, the burden put on the population is the result of inadequate infrastructure and inefficient management depicting the local government’s performance. It can also be derived from the experience referred to in this example (Zérah, 1998, 2000) that the policy dimension shapes the answer to the above mentioned research question. Like for the investigation

9 The author reports a peer-agreed idea. However, he argues that “the reality is more complex” and searches for the complexity in the “basic structural determinants of service disbursement” (p. 889) to distinguish between types of service (capital or labour intensive). These refinements are not reported because in this study the interest is on people-focused effects of service delivery and related deprivations.


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from the receiver’s perspective, the performance evaluation could be taken from the provider’s side. “For this purpose, one has to establish benchmarks based on the history of an enterprise and the experience of others”(Esfahani, 2005, p. 206). The use of benchmark to evaluate service provision, from this author’s view, is limited by the necessity to discriminate between the effects of exogenous factors and the consequences of the act of service provision. It needs therefore the subjective judgement of experts and the use of performance indicators. “There is a lot of folk wisdom about what works and what does not. But these ideas are not generalized and cannot be systematically transferred from one case to another”(Esfahani, 2005, p. 208). Despite the reported difficulty to generalise this approach, it can be said that it will involve the policy dimension because of the use of indicators. To sum up, in either approach,

The policy dimension is always part of the framework for assessing .public services. As such, the assessment can be performed for example through the outcomes of the service delivered, approached

from the user’s perspective.

A recent development in service delivery is the involvement of private sector in the delivery process in several ways. “Alternatives range from complete public provision to a complete private provision to a mix of public and private provision, including public-private partnerships”(Kitchen, 2005, p. 145). This inclusion of private sector in public services delivery is in tune with Baer’s definition and keeps the right for government “to set standards and specify conditions and … generally retain overall responsibility through the use of contractual arrangements. The private sector’s role is to deliver services according to the specifications and conditions laid out by government (ibid.) In the particular case of developing countries, service delivery is done mainly by public sector and is characterised by poor performance. Access to services is hypothetical and unsatisfactory. Service cost recovery is only partial and pricing is often more responsive to political considerations than cost of provision (Deichmann and Lall, 2003).

In any case, the pattern of service delivery remains with the six determinants, the provider, the receiver, the purpose and the method, playing a crucial role in the outputs and outcomes.

2.2. Urban poverty mapping

Urban poverty mapping has been focusing scholarly and political attention for a while. Poverty measurement is traditionally done according to the concept framing the analysis; that is poverty conceived as absolute or relative. The concept of absolute poverty considers it in econometric terms and will apply a normative poverty line to measure it. However, this concept is evolving as the multidimensionality of poverty is increasingly being recognized by a wider audience of researchers. The concept of relative poverty recognizes it as a multidimensional phenomenon composed of various types of deprivations that hinder households’ efforts in well-being pursuing. It considers all together several aspects of the said lack of well being in poverty mapping. Hereafter, in this section are presented some implements in measuring and mapping poverty.


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2.2.1. Poverty measuring and mapping

Since the pioneer work of Booth (in urban socio spatial differentiation) who attempts to capture and communicate people’s social conditions with mapping techniques (Pacione, 2005), poverty mapping mostly aims at the question “where are the poor?” and provides ground for area-based policy to better target the needs of the poor. “Poverty mapping is essentially a tool; its functionality must therefore be seen and evaluated in light of the objectives for which it is put to use – the research and policy questions and hypotheses upon which it can shed light”(Davis, 2003, p. 4). The major poverty mapping methods applied in various contexts reviewed by this author are listed hereafter.

� The small-area estimation refined into Household-level method and Community-level data method uses census and survey data at disaggregated level. These statistical techniques inference the findings in the sample to the population under investigation assuming that the relationships found in the sample hold true for the population.

� The multivariate basic-needs index which could be weighted or not. It is also applied at disaggregated level. When it is weighted, multivariate statistical techniques namely, principal components, factor analysis and ordinary least squares are applied to the index.

� The combination of qualitative information and secondary data using the concept of livelihood strategies to map poverty. This method can be primarily qualitative or primarily secondary or a statistical analysis of qualitative information combined with secondary data.

� Extrapolation of participatory approaches. According to this method, “participatory assessments measure poverty in terms of local perceptions of poverty, which are identified and then extrapolated and quantified in order to construct regional poverty measures”(p. 19).

� Direct measurement of household-survey data permits to have statistics-based poverty maps. The challenge here is the sampling strategy and the level of aggregation which have to fit the requirements for a disaggregated poverty map.

� Direct measurement of census data. The census data can be mapped for various types of information collected from population.

In addition to the concept, the purpose of poverty mapping has shaped a variety of methods with different outputs for the ordinal ranking of mapping units.

The outcome of these methods is the ranking of the mapping units, often on the basis of a single value, mostly, an index combining several parameters to represent the poverty level in a given frame. They are based on survey data, census data or a combination of both or a combination with secondary data. Even though behind the method is playing the concept of poverty shaping the analysis, it is hard to distinguish in the list above the one-dimensional measure with only income data. More and more, “there is considerable agreement that poverty is a multidimensional problem involving a number of monetary and non-monetary handicaps. … The multidimensional approach is thus more than ever required to better understand the performance of a given country in the battle against poverty in all its aspects”(Bibi, 2005, pp. 33-34). This author reviewed various approaches in measuring poverty as multidimensional in various dimensions and come to the conclusion that the knowledge of the theoretical framework and the limitations of each approach is necessary for the right choice of measurement method regarding the specific circumstances of one’s research since a change of method can change drastically the ranking of mapping units. In the same view, Davis (2003) stresses the level of disaggregation as a limitation since it can lead to different area targeting scheme. Bibi (2005)


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distinguishes axiomatic and non-axiomatic approaches in measuring poverty. Non-axiomatic approaches use several aggregated welfare indicators examined simultaneously or combined at individual level. An axiomatic approach “emphasizes the desirable properties (axioms) that a poverty index must respect”(p.15). Most methods reviewed by Bibi are expenditure-data-based and asset-based methods came along as alternatives found in the face of expenditure data shortage.

The choice of method does matter in poverty measuring and mapping, and should be done in an informed way. To date, the poverty measurement methods seeking to achieve ordinal comparison of

welfare distribution are either expenditure-data-based or asset-data-based.

From the report above, two main approaches have served to measure and map poverty to date: the income / consumption concept and the composite index that compounds several characteristics of well-being or the asset base. Between both approaches, health (life expectancy), education (literacy) and real Gross Domestic Product per capita are combined in the approach developed by the United Nations Development Program (UNDP) for the calculation of the HDI or Human Development Index10 (UNDP, 1990). This example shows evolvement in the view of a normative poverty line for measuring well-being as an only function of income or consumption. The index approach attempts to go beyond the econometric concept and sees poverty as resulting from the compound effect of various and multiple deprivations.

Some illustrations of poverty mapping

In the UK, several indexes of multiple deprivations have been designed by small-area estimation using households’ survey and census data for poverty mapping. In this experience, ten domains are identified and represented by an indicator or index and the domain indicators are further combined into one index of multiple deprivations. “The domains each represent a type of deprivation that is measured as directly as possible, rather than comprising a set of ‘vulnerable groups’ (that is, groups of people at risk of deprivation). Each domain contains a number of indicators”(Noble et al., 2006, p. 176). In a setting different of that of this “deprivation domain framework”, Moser (1998) experimented poverty measurement with an asset vulnerability framework. She first discriminated between poverty and vulnerability, next distinguished between vulnerability and capacities and further related vulnerability to asset ownership11 before applying the asset vulnerability framework in an urban study using consumption characteristics as starting point. The approach measures how households manage their asset portfolio in a context of urban economic crisis based on the following: labour, human capital (social and economic infrastructure), productive asset (housing), household relations and

10 As a result of the double shift in the perception of development, first from economic to socioeconomic and then to human development, the HDI still uses the concept of poverty line but measures “development not as the expansion of commodities and wealth but as the widening of human choices” (p. 105). 11 Vulnerability is explained by the fact that poverty is dynamic and people can be vulnerable and not yet poor. Human capabilities are made of the many resources that make them less vulnerable to economic shock. Asset ownership is like a guarantee against vulnerability. A growing asset base decreases the vulnerability and an eroding one augment insecurity.


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social capital. The study evidences “… the limitations of income-poverty measurements to capture complex external factors affecting the poor as well as their responses to economic difficulty. The asset framework goes beyond a “static” measuring of the poor, towards classifying the capabilities of poor population to use their resources to reduce their vulnerability”(Moser, 1998, p. 14). Another approach centring the concern on the poor themselves and building again on their asset portfolio is modelled in the sustainable livelihoods framework. The multidimensionality of poverty is caught in the livelihoods framework composed of human capital, social and political capital, physical capital, financial capital and natural capital in addition to the asset base on which households build their livelihoods. The concept of livelihoods set in previous works for addressing rural poverty is appropriated by Rakodi (2002) for urban poverty. “The livelihoods framework suggests that there is a close link between the overall asset status of an individual, household or group, the resources on which it can draw in the face of hardship and its level of security. Moreover, the assets available influence the scope for it to improve its well-being, both directly by increasing its security and indirectly by increasing people’s ability to influence the policies and organizations which govern access to assets and define livelihood options”(Rakodi, 2002, p. 12). This framework has successfully been used to map urban poverty by means of a new Index of Multiple Deprivations in Delhi, India (Baud et al., 2008). The new index of multiple deprivations is described in section 1.3.2 above.

These examples map poverty as multiple deprivations and the concept of sustainable livelihoods permits to capture urban poverty along with household’s compensatory strategies.

2.2.2. Indicators, Geographical Information System, Remote sensing and poverty mapping

Along with the methods reviewed, the purpose of poverty mapping which is to answer the question ‘where are the poor?’ and inform targeting policies makes it necessary to make use of Geographical Information System as a tool which can be analytical or communicative or both. GIS can take several roles in poverty mapping as shown in the two sub-sections below.

GIS, Remote Sensing, and poverty mapping

In achieving the process of mapping, communication of the results (ranking of the mapping units) is better done through geo-visualisation. In addition, “the spatial location of poor people facilitates integration of data from sources such as satellites, censuses, household surveys, sectoral surveys, models and simulations for the analysis of the determinants and impacts of poverty”(Davis, 2003, p. 4). For example, satellite data could be helpful in using the method of combination of qualitative information and secondary data mentioned above combining local expert knowledge, high resolution image and local records to generate participatory poverty profiles (Turkstra and Raithelhuber, 2004). These authors, applying another mapping method in Nairobi but using a LandSat satellite image as a backdrop image to a Census Enumeration Area map to check areas classified as slum, found noticeable heterogeneity problems related to slum locations and boundaries. This is the proof that remotely sensed image can be very supportive as a complementary source of information in poverty mapping but also an even important source in data poor environment. After mapping the slum incidence in Census Enumeration Areas in Nairobi and the access to improved water at sub-city and Kebele level to identify urban inequities in Addis Ababa, these authors come to an interesting conclusion. “GIS communicates this message [the situation being shown in the map from the study] in


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a convincing way, which is easy to understand for an audience of mostly non-technical decision-makers at the local level. Especially in combination with high resolution satellite images as an objective medium (what the censor sees is what you get), GIS maps as a communication tool are very convincing”(Turkstra and Raithelhuber, 2004, p. 12). The usefulness of remotely sensed data is further demonstrated by another group of researchers who introduce a high resolution image in the analysis in the particular case of the experience in Delhi reported above. After application of the new IMD framework, the spatial heterogeneity of poverty at ward level (level of disaggregation of the new IMD) has been evidenced by the works of Sliuzas et al. (2008) and Sliuzas and Kuffer (2008). From visual inspection of a very high resolution image, they established variations within areas labelled deprived as a result of the new IMD computation. However, they found a strong correlation between the proportion of physically deprived areas and the index of deprivations. This experience evidences also the drawback of data aggregation which can hide spatial heterogeneity of poverty.

In poverty mapping, the use of GIS takes the interest beyond the limits of the qualitative approach and allows underlining geographic differentiations in trends, and strategically combining information

from various sources. Considering the purpose of poverty mapping, the use of high resolution image to enhance geo-visualisation can better inform policy-makers.

GIS, indicators, and poverty mapping

In the context of the current study aiming at constructing an index for mapping a specific aspect of poverty in a city, a particular application of GIS is of interest. It is its use in the monitoring of urban inequalities. Building on the work of Webster (1993) relating to the usefulness of GIS in urban planning and analysis, Martinez-Martin et al. (2005) explain the role of GIS in the construction of indicators and come to the conclusion that “with the use of GIS, indicators can be used to monitor the different aspects of intra-urban inequality”(p. 43). In this role, GIS appears as a tool in decision-making process as pictured in table 2.1 taken from Martinez-Martin et al.

Table 2.1: Decision-making components and the role of indicators

Decision-making component Scientific inputs Indicators

Problem identification Description and prediction Descriptive and baseline

indicators + GIS visualisation

Goal setting; Plan Generation;

Evaluation of alternatives; Choice of solution

Prescription

Descriptive indicators; Normative and target indicators + GIS analysis (communication role in participatory planning)

Implementation Description, prediction and

prescription Normative indicators

+ GIS analysis

Monitoring (the implementation of the plan

and the problems) Description and prediction

Idem problem identification + performance indicators

Source: adopted from Martinez-Martin et al., 2005, p. 38


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The informative input of this table 2.1 to the current literature review is beyond the sole poverty mapping which is put here in a wider context of planning analysis. The table presents different phases in decision-making along with their scientific inputs as well as the required GIS role and indicators. The starting point of the process is ‘problem identification’ followed by ‘plan generation’ for ‘implementation’ in the third step and ‘monitoring’ as the last step. The purpose of problem identification is to assess the demand for public goods as lack of provision or negative externalities to be corrected. The usefulness of GIS in the process is due to its ability to handle data organisation, spatial analysis and visualisation (Martinez-Martin et al., 2005; Webster, 1993) purposely at each step of the process with the choice of specific indicators. At implementation stage for example, normative indicators and GIS analysis are appropriate. Referring back to the purpose of poverty mapping put by Davis (2003) as to be a tool for research or policy making in one hand, and the purpose of urban service delivery which is seeking citizens’ overall well-being and satisfaction in another hand, the necessity to monitor urban inequalities and the importance of indicators are ascertained. All mapping methods make use of indicators. “Indicators-based studies represent a way of measuring the quality of a place that can be used for policy-making purposes… Three major types of indicators have developed over time: social, urban, and neighbourhood indicators”(Ghose and Huxhold, 2002, p. 6). These authors show how these types of indicator share the original concept but differ in the geographical scale of the data used in their design. Martinez-Martin et al. (2005) explain that “the advantage of using urban indicators to measure inequalities is that they can communicate in a simple way and can detect and quantify inequalities and monitor tendencies towards (non-)equalisation. They can also be used to prioritise areas of action and policy intervention”(p. 26). Considering the heterogeneous and multidimensional nature of spatial inequalities, they stated that according to its function in a specific phase of policy cycle, an indicator can be classified as descriptive or baseline indicator, normative or target indicator, or performance or outcome indicator. The participation of these three types of urban indicators in the dynamic of decision-making process is shown in table 2.1 along with the relevant GIS role.

The policy informing ability of poverty mapping rests to some instance on the choice of the right set of indicators and the appropriate use of GIS in developing and implementing them. Urban indicators

are particularly relevant in measuring inequalities.

2.2.3. The case of water poverty

There is a strong link between poverty and water as put by Sullivan: “water is essential for life, and an adequate water supply is a prerequisite for human and economic development”(2002, p. 1195). In this view, mapping water related poverty is part of mapping poverty and is addressing at least the physical capital in the pentagram of households’ asset base (see figure 1.1). In the sustainable livelihoods framework, the physical capital includes individual as well as public resources in terms of equipment used in daily work for a living, and public infrastructure and services (Rakodi, 2002). In this frame,the concept of water poverty index (WPI) which is to assemble water availability related issues and households’ personal capability to access it (Sullivan, 2005) appears an integrated measure of water poverty.


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The Water Poverty Index

The Water Poverty Index was introduced in sections 1.3.3 and 1.3.4; thus the current section is complementing what is already presented. However, only the major steps taken forward in the life of the index are reported here.

� The water poverty index is conceived to be a comprehensive tool in water management at a variety of levels. As such, it needs right stakeholders involvement, suitable design, and appropriate methodology for the calculation of the index. Having set the frame for these, “the most important challenge is to develop the appropriate degree of political will and institutional acceptance which will allow the index to be used as an objective criterion addressing water poverty”(Sullivan, 2002, p. 1207). The author came to this conclusion after examining the development of the index including water availability and quality issues, ecological water demand along with socioeconomic drivers of poverty in various calculation approaches.

� The theoretical background of the index is explained in Mlote et al. (2002) stressing the sustainable livelihoods framework in the dynamic of development and the link to the water poverty index, the role of indicators as policy tools, the political and institutional issues and the hydrological and environmental aspects affecting the index. The raison d’être of the index is “… to develop an evaluation tool for assessing poverty in relation to water resource availability”(p. 2). With this regard, poverty is defined as ‘capability deprivation’ (Desai, 1995)12.

� Applying the concept and methodology at national level, the WPI has been calculated for some 148 countries “… to focus attention at international level on improving water management performance across the world …”(Lawrence et al., 2002, p. 10). This permits to rank countries regarding their performance in the water sector. “It does seem to give some sensible results but it does not pretend to be definitive nor offer a totally accurate measure of the situation.” (ibid.)

� A focus on the community level through the application of the methodology in three countries permits to detail the composite approach (based on the approach used in the Human Development Index) adopted in the calculation of the WPI. On the basis of survey data and national statistics, each variable of the WPI is obtained by aggregating a set of sub components using again the composite approach. The variables are then standardised on a scale of 0 to 100 before they aggregation by the method of weighted average, giving a low score of the WPI to indicate a high level of water poverty (Sullivan et al., 2003; Sullivan, 2005).

12 Mlote et al. are citing Desai (1995). The five basic capabilities reported as to maintain livelihood choices are: � Capability to stay alive/enjoy prolonged life � Capability to ensure biological reproduction � Capability to healthy living � Capability for social interaction � Capability to have knowledge and freedom of expression and taught

They conclude that having access to adequate water supply for domestic and productive use can clearly be linked to most of these capabilities (p. 2).


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� “While it is considered that the current formulation of the WPI would have considerable benefits if implemented more widely as it stands, there are a number of issues which it would be advantageous to investigate in greater depth in order to take the ideas of the WPI further and enhance its usefulness”(Sullivan and Meigh, 2003, p. 520). Consequently, the index is reviewed by these authors regarding scale and distinction between rural and urban indices issues along with the design of each variable. Practicability of the index and usefulness at policy level are also addressed.

� The impact of the scale is further examined by considering the index at catchment scale, district level and national level, each scale being relevant for water management. An important conclusion is “that values on any measure are really applicable only at the scale represented by the data used to generate them”(Sullivan et al., 2006a, p. 424).

� Despite that at the various scales, the authors present the design of the index as a work in progress, this relatively new concept is being experimented and has recently given birth to the concept of water wealth indicator (WWI) to highlight the link between water, poverty and food security at water basin level (Sullivan et al., 2006b).

The water poverty index in its current state is a work in progress and should be taught out carefully before any application. Based on the conceptual link with the sustainable livelihoods shown in the theoretical background, the WPI taken at household level (using survey data) is likely to map water poverty in an asset-based like approach with relevant results. In analysing water poverty and any other aspect of poverty, in the light of the experience of Delhi, the livelihoods framework appears an excellent mean to capture within the capitals, the multiple deprivations facing households and to centre the analysis on them.

2.3. Critical review of the water poverty index

As shown in the previous section, water poverty is nowadays caught in an index, the “Water Poverty Index” which can quantify this particular type of deprivation at various scales, from household to national or water basin level. The notion of water poverty index is widely accepted but some criticisms have arisen about the method of calculation. Several authors have addressed in various ways missing or inadequately managed issues in the water poverty index. Some of these are reported hereafter.

2.3.1. Limitations of the water poverty index

The authors state that “the objective of developing a Water Poverty Index is to produce a holistic policy tool, drawing on both the physical and social sciences, and having application throughout the world”(Sullivan, 2002, p. 1196). This objective is yet to be fulfilled by further structural developments as the water poverty index, in its current design, misses at least “… issues such as physical water availability, water quality and ecological water demand … [.] It is essential to recognize the importance of institutional issues as they impact on water access, and to ensure that some measure of this is included in the structure of the WPI”(Sullivan, 2002, p. 1205). This missing links list is further populated by the notice that “… vapour flows, rainfall and important ecological functions of resource areas (e.g. forests, grasslands, wetlands etc.) are missing from the WPI and will be incorporated in future revisions”(Sullivan et al., 2006a, p. 424).


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These comments and some others from the authors of the water poverty index attest that the structure of the index is an opened end even though the concept seems to be sealed. The missing links are strong in themselves but their importance may vary with the scale of measurement. However, the institutional aspects can be qualified as “scale less” in the view that their importance do not depend on the scale. Specifically, the relation of the institutional side to access makes it highly important in a context of people-centred analysis approach.

Despite the achievement represented by the index, a wide range of review has responded to this new approach in water poverty assessment. For example, while commenting on the outcomes of the water poverty index designed for international comparison, F. Molle and P. Mollinga (2003) notice incongruity in the ranking of countries scoring similarly with very different background in their water resources. These authors come to the conclusion that “… it is crucial to distinguish between indicators that focus on a particular aspect of water scarcity (e.g. percentage of population with access to tap water) and those who address more systemic dimensions, where management or socio-political aspects, for example, are paramount”(p. 536). These authors call to avoid the combination of different types of indicators reporting different realities. This is also to not loose in the aggregation the complexity of water scarcity’s problem as experienced with the international water poverty index. Likewise, some other aspects are reviewed by other authors. Gleick (2002) emphasize the absence of water supply fluctuations and water allocation issues among users13. Feitelson and Chenoweth (2002) underline the failure of the WPI to assess the capacity (social resources within the society) to address water issues and the unreliable procedure of the weighting14. Shah and van Koppen (2006) state, with statistical proof, that the variable access is the real indicator of water poverty15. Moreover, the concept of water poverty is questioned by Komnenic et al. (2008) arguing that “the term water poverty itself creates confusion, being too general to address the complex issues that exist behind these two words. In addition …, the water – poverty nexus continues to be discussed in both scientific and popular publications”(2008, p. 2). Some of the issues they reviewed as defining water poverty are resource availability, social causes of unavailability, affordability of the cost of supply for the country, affordability of water to households, inadequate and insufficient supply, income poverty, lack of capability to obtain or lack of entitlement to water, difficulties in securing adequate and reliable access to water, and water – poverty nexus.

The highlighted limitations of the concept and the methodology for water poverty evaluation suggest how much the driving forces of the water poverty issues may be considered insufficiently caught and

not very well managed by the approach of the WPI.

13 The analysis of this author is commented by Komnenic et al. 2008, p. 2 from Gleick, P.H., 2002. The World’s Water 2002-2003: The Biennial Report on Freshwater Resources. Island Press, Washington DC. 14 Ibid. The source is: Feitelson, E., Chenoweth, J., 2002. Water poverty: towards a meaningful indicator. Water Policy 4 (3), 263– 281. 15 Ibid. The source is: Shah, T., van Koppen, B., 2006. Is India ripe for integrated resources management? fitting water policy to national development context. Economic and Political Weekly.


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2.3.2. Concern about the water poverty index at high resolution16

The interest of this study is to address water related deprivations in the wider context of the research on urban households’ deprivations. In this view, the water poverty index chosen as benchmark is to be applied at high resolution in order to capture as accurately as possible the households’ deprivations. Accordingly, the current section expresses concern about the index at high resolution in three aspects, namely, impact of institutional aspects, impact of the livelihood framework in the background and impact of the question of weighting on the resolution.

� Despite the number and scope of the shortcomings identified for the WPI from literature, Komnenic et al. conclude that “the accuracy of water poverty mapping increases with increased resolution and the same can be said about the WPI itself”(2008, p. 3). Indeed, three of the five components, namely, Access, Capacity and Use, concern people and the two others are related to nature. Therefore, increasing resolution brings the index closer to the smallest unit of measurement of the user – that is household level – thus increases the accuracy of the index. Yet, as stated previously, the relation of the institutional aspects to Access will influence the WPI despite the scale. Consequently, it is important to account for them in the WPI design when seeking accuracy. Then arise some questions (stemming also from the fact that the index is a work in progress) such as:

How to capture institutional aspects in the water poverty index at high resolution? What could be the magnitude of their impact? Does the impact increases or decreases with the resolution?

� The accuracy established for the water poverty index at high resolution concerns the “people related” part of the index. The variables Resources and Environment can not be specific at household level. They are forcibly shared at least at community level. Thus, the livelihoods framework which uses the asset profile of households is a suitable background for the water poverty index in the urban context as it justifies the putting together of the five variables telling – a large part of – the story of households’ asset portfolio regarding water. As such and despite the shortcomings identified by the critics, water poverty could be modelled at high resolution as part of the asset profile of households, mainly in the physical capital with the different aspects of related deprivations and the households coping strategies. In other words, applying the WPI at the lowest possible scale is likely to produce reliable results as put by Komnenic et al. (2008). At such a scale, the conceptual link between the livelihoods framework and the water poverty index (Mlote et al., 2002)17 is highlighted. However, at least one question remains:

16 High resolution refers to household level as scale of measurement of the parameters and development of the water poverty index in this study. The term is taken from Komnenic et al. who use it to mean the change of scale of measurement. 17 These authors relate one or two livelihood capitals to each variable of the water poverty index. See section 1.3.3.


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Given that values of the WPI on any measure are meaningfully applicable only at the scale represented by the data used to generate them (Sullivan et al., 2006a, p. 424), is the suitability of the

livelihood framework as background for the water poverty index sensitive to the resolution?

� The weighting of the variables is conceptually dependant on stakeholders and have been criticised as such. For the dissemination of the index, the authors of the water poverty index suggest to keep the separate variables as well as their combination into the index for decision making purposes in the light of the political value given to each variable. Even though these are standardised in the index, the act of weighting may distort their story telling in the overall value as pointed out by Molle and Mollinga. With interest in the resolution, the question is to see if there is any trade-off between the scale and the weighting. For assessing what could be the impact of the scale on the inconvenience brought by the weighting, a comparison can be made with the drawback of aggregation. Knowing that some aspects captured in the variables could be hidden by the problem of aggregation, a high resolution functions as a sort of shock-absorber for the index. Nevertheless, the ratio imposed to a variable in the index by the weighting remains. Consequently,

The question of weighting seems not sensitive to the scale.

2.4. Conclusion: mapping profiles of water provision with the WPI

From the literature review, achieving the research objective will ask to combine and integrate several frameworks from service delivery, poverty mapping and mapping water poverty. Beyond the ordinal ranking of the mapping units, the six determinants of service delivery which are the provider, the receiver, the purpose, the method, the outputs, and the outcomes will be put in a water governance scheme to assess the profile of the study area in water provision. “The term water governance encompasses the political, economic and social processes and institutions by which governments, civil society, and the private sector make decisions about how best to use, develop and manage water resources”(UNDP, 2004, p. 10). As such, water governance is partly addressed by the WPI since it is meant to be a comprehensive tool in water management at various scales. Through its composition, the index represents several aspects of water management. The policy level potential of the index is addressed in Sullivan and Meigh (2003, pp. 515-516) mentioning the following.

� The value of the WPI lies in its holistic approach, bringing together a wide range of issues relevant to water resources and people’s livelihoods.

� Due to its systematic approach which is open and transparent to all, it provides a powerful tool for the prioritisation of needs.

� It can empower decision-makers to act impartially by allowing them to justify their choices, based on a rational and transparent framework.

� It gives local communities an opportunity to express their needs in a systematic way, helping them to lobby for action.


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� A single number can be used to represent the situation at a particular location, so it is likely to appeal to policy-makers. At the same time the underlying complexities need not be lost – the component values can be retained and can easily be visualised18.

With such a potential, the WPI, in addition to mapping the city profile and enable targeting policies,could be a useful monitoring tool if updated over time at reasonable time scale.

18 The authors always perform the visualisation of the WPI in a pentagram representation of the components of the index.


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3. Study area and data capture

This chapter deals with the study area delineation and the data collection. The study area is first presented with regard to the interest of the research by the wards involved in the study and the characteristics of water supply management in the municipal corporation. Next, is reported the data collection starting by the sampling strategies for the household survey which is the main data source. The last sub-chapter reports the field work results and the exploratory data analysis.

3.1. Study area in the background of the research design

The study area is presented here in terms of wards concerned by the study and characteristics of water provision in the municipal corporation.

3.1.1. Area units in Kalyan-Dombivli and study area delineation

Kalyan-Dombivli, an Indian city, is located in the District of Thane within the State of Maharashtra in Mumbai Metropolitan Region. The municipal corporation of Kalyan-Dombivli is composed of 7 administrative wards, 7 sectors, and 107 elected wards in 2008. The Census Enumerator Area in 2001 is composed of 96 wards whose boundaries mismatch those of the 107 elected wards.

Since the census 2001 data is more informative than the attributes attached to the 107 wards, the 96 wards boundaries are chosen as the basis for the present research which is seeking the most disaggregated level. The research area is delineated considering the following.

� Due to time pressure on field work, the 96 wards in the 7 sectors can not be addressed by the study. Consequently, sectors 1 and 2 are identified.

Figure 3.1: Study area in level of unsatisfied public services demand map Source: Adapted from Baid, 2008 This map presents only five sectors including the study area. The two missing sectors present a low gap in service demand satisfaction.


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� They are chosen because they represent the two cities of Kalyan and Dombivli (joined as the heart of the municipal corporation) and are said to present the same gap demand-supply regarding urban services (Baid, 2008)19.

� This seeming global homogeneity of characteristics in unsatisfied public service demand can permit to sample some census 2001 wards in the two cities and inference the findings to the rest of the study area.

The two sectors cover 50 out of the 96 Census 2001 Enumerator Areas as detailed in table 3.1 below. The total number of households in the municipal corporation is 275,932 in 2001. From 1,047,297 inhabitants in 2001, the population is estimated at 1,263,000 in 2006 with a compound annual growth rate of 3.82% (CDP, 2007, p. 27). The rapid population growth is due to several factors such as the position of the city vis-à-vis Mumbai on which depend an increasing number of people for livelihoods activities. The study area is mainly covered by high and medium levels of the municipal water supply according to the service level map (figure 3.2). Three levels of water service are offered in the municipal corporation, namely, high level with 135 to 160 litres per capita per day; medium level with 100 to 135 litres per capita per day; and low level with 60 to 100 litres per capita per day. The boundaries of the water service level areas do not match the 96 electoral wards boundaries.

Table 3.1: Wards composing the study area

Number of wards and level of water service City

High level Medium level Total in

ward

Kalyan

9, 10, 11, 12, 15, 16, 26, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 Total = 24

13, 14, 17, 24, 25, 27, 28 Total = 7

31

Dombivli 80, 81, 83, 84, 85, 95, 96 Total = 7

70, 71, 74, 86, 87, 88, 89, 90, 91, 92, 93, 94 Total = 12

19

Total by level of service

31 19 50

19 In this work, the author underlines the impact of sudden population growth on the water supply services as a gap of 100 million litres per day and an increase in water borne diseases especially in sectors 4 and 5 hosting the highest proportion of slum population in the municipal corporation (this is interesting for the current research because the sectors 4 and 5 are located between the two sectors forming the study area which might be affected by the phenomenon of spill-over). As a result of demand-trend analysis, this researcher found the highest gaps in sectors 1, 2, and 4.


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Figure 3.2: Study area in level of service map

Source: Adapted from water service map, Kalyan-Dombivli Municipal Corporation

3.1.2. Water supply in Kalyan-Dombivli Municipal Corporation

Some characteristics of the water supplied by the KDMC (CDP, 2007, pp. 69-87) are reported hereafter.

� Resources � Source: Ulhas and Kalu Rivers. � Currently exploited: 238,000 m3 for a population of nearly 1,263,000 in 2006. � Quantity yield: 188 litres per capita per day. � Quality: conventional treatment composed of flocculation, chlorination, flash mixing,

clarification, rapid sand filtration and post filter disinfections. � Infrastructure used: two water treatment plants. � Storage capacity: (80,000 m3) is below the threshold of 33% of the demand as set by

the CPHEEO in India. � Restrictions and threats:

o The KDMC purifies approximately 140,000 m3 from the sources and 98,000 m3 are purchased from other agencies.

� Source for future demand: the two rivers flow within the limits of the municipal corporation and could cover the city water demand as forecasted at least up to 2025 with an improved treatment and storage capacities.


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� Management � Coverage of the distribution system: about 100% of the populated area of the 107

actual electoral wards with mains and lateral supply lines. � Total length of the distribution network: 342 km for main and lateral supply lines. � Extent of the current demand: ground water is used by citizens as supplement to the

unsatisfied water demand. � Restriction and threats:

o Changeability of the specified minimum residual pressure throughout the system.

o Pipelines in the network are old in some part of the old city. � Institutional scheme

� The Municipal Corporation is responsible for design, construction, installation and maintenance of city water supply.

� The maintenance of weirs at the river sources is done by Government of Maharashtra department of irrigation.

� The Municipal Corporation water supply department is composed of seven sub-divisions responsible for daily maintenance and repair of various installations among others within their jurisdiction.

3.2. Data collection

Due to field work constraints, the primary data collected is mainly populated by the household survey carried out in a couple of wards sampled in the study area. The sampling is done in two stages.

3.2.1. The sampling design

� First, a stratified random sampling is applied for the choice of wards within the two sectors. � Sampling frame:

The sampling frame is all 50 wards in the two cities. � Strata:

The two strata considered here are water service level and city or sector. � Sampling units in appropriate strata:

Details of wards in city and service level area are given in table 3.1. � Sample size:

o Three in Kalyan high supply level. o One in Kalyan medium level of supply. o One in Dombivli high service level. o One in Dombivli medium service level.

� Selection of sampling units: The fishbowl draw method is applied to choose the six required wards.

The total number of ward to survey is limited to 6 (as 12% of the study ward population) due to field work time pressure. Consequently, the number of ward per stratum is not calculated, but decided upon. Four wards are chosen in Kalyan because it is the old city and is likely to bring out several aspects of deprivations. Two wards are chosen in medium service level because the area covered on the map (figure 3.2) is less than the one of high level.


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� At the second stage, a systematic sampling is applied. � Sampling frame:

Buildings are considered as sampling units. Therefore, the building map is taken as the sampling frame or list of all elements in the study population.

� Sample size The sample size is fixed at a minimum of 40 in ward. See section 3.2.2.

� Width of sampling interval A grid is overlaid and fitted to each ward according to its shape. The interval is the cell size due to the method applied for the simple random sampling.

� Appliance of a simple random sampling One building is chosen per cell around the middle of the cell. If the household addressed refuses to respond, the next one is chosen. In case of non success in the spotted building, the nearest one is gone to for a new attempt.

3.2.2. Determination of the sample size

The sample size in ward is determined by the following formula.

z*2 x �2 n = ----------- m2

where, n is the sample size z* = 1.96 for 95% confidence interval � is the standard deviation m is the margin of error

To determine the standard deviation �, a pilot survey is done in the six wards identified and the daily time spent in water collection is considered. The result is shown in table 3.2 below.

Table 3.2: Standard deviation for sample size purpose

Descriptive Statistics N Minimum Maximum Mean Std. Deviation Time drinking w. collection 81 15 240 54.01 39.705Time non drink. w. collection 80 15 180 76.00 35.296Water collection total time 81 30 360 129.81 61.316Valid N (listwise) 80

� The margin of error is fixed at a maximum of 20 minutes � From the formula, the sample size increases when the standard deviation increases. Thus

the value taken from the SPSS output above is 61.316. Then, n = 36.11. � With a non response rate of 5%, n = 37.92

From these results, the number of household to survey in each ward is fixed to a minimum of 40.


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3.2.3. Characteristics of the wards surveyed

In table 3.3 displaying the characteristics of the surveyed wards, the values between brackets are the minimum percentages of households population to be interviewed per identified ward on the basis of the number 40 calculated above.

Table 3.3: Characteristics of the wards surveyed

Ward and water service level

High level Medium level City

Ward number

Number HH 2001

Population 2001

Ward number

Number HH 2001

Population 2001

16 2160 (1.85%) 10338 14 2697

(1.48%) 12680

43 1750 (2.29%) 8568 - - - Kalyan

47 1571 (2.55%) 8471 - - -

Total Kalyan - 5481 27377 - 2697 12680

Dombivli 81 3187 (1.26%) 12933 94 2273

(1.76%) 9243

Total Dombivli - 3187 12933 - 2273 9243

T. study area - 8668 40310 - 4970 21923

3.2.4. Field work difficulties

Different types of difficulties occurred before and during the data collection and hindered the research. Some are reported hereafter. � Field work preparation suffered non

availability of utility data. Also in the field, getting them was not straightforward. This has impacted the organisation of the work.

� The interview of authorities and key informants results only in information that is already found in the City Development Plan 2007.

� The secondary data got is the strict minimum for the current study. They either are at a highly aggregated level or insufficiently cover the study area. For example, that is the case for the water supply network map which is partially available for the city of Dombivli.

� For the household survey, the difficulties are related to the maps overlaid for use as field guide; the building map or the road map or both do not always match the field truth. It was very time

Figure 3.3 : Ward 81 as surveyed


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consuming to adjust with them. In some neighbourhoods, the two maps are completely out of date. In figure 3.3 related to ward 81, the cells in green represent such neighbourhood.

� Another type of difficulty for the household survey was the language barrier. Administration, education and trade are done in the local language and few people can speak English. As a consequence, the survey had to be done by the medium of interpreter.

� In the face of time shortage and language barrier, it was necessary (but not easy) to find some local people to help with the survey. Even though they where trained and supervised by the researcher, the results may miss some unexpressed realities.

To sum up, due to the field work circumstances, the secondary data collection was very time consuming with little result while the household survey was handicapped by time pressure and communication problems resulting in the reduction of the number of ward to explore.

3.3. Results of the household survey

The rationale of the survey is to understand households’ coping strategies in the face of unsatisfactory water delivery and to assess the real level of access they have to that service. In this section, the results of the survey are presented starting from the shortcomings of the questions addressed to households up to the general configuration of the acquired dataset.

3.3.1. The survey

The questionnaire in annexure O1 is composed of five major parts. It is presented as prepared for the survey with little knowledge of the study area. In the field, some of the questions prove irrelevant or not discriminating for the households surveyed. They are reported in table 3.4 hereafter.

Table 3.4: Not usable questions of the household questionnaire

Question number Field result

3. Perception of the living area

Was non discriminating since answers given by households in the same sector were very similar

6. Organisation provider of water to household

Was irrelevant since every respondent named the KDMC

5. Provision of water to the house

Question restricted by the fact that every respondent use KDMC water daily. The daily transportation issue was dismissed by households.

13. Appending to questions 7, 8, 9

Was irrelevant since public taps have been removed from the study area and replaced by group connections.

12. Impact of water quality

Didn’t give any tangible result maybe because most of the households treat their drinking water.

14.a. Proxy for water pressure

This experimental question didn’t work because it was intended to go to all households with the same bucket and fill it up during conversation;

but the researcher was told that it is culturally rude in the context.

The data is collected from six wards located in areas labelled with the two levels of water service applied in the city. Hereafter is given the number of households surveyed per ward.


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� Sample size o Ward 14 – Kalyan, medium service level, 40 households. o Ward 16 – Kalyan, high service level, 48 households. o Ward 43 – Kalyan, high service level, 53 households. o Ward 47 – Kalyan, high service level, 67 households. o Ward 81 – Dombivli, high service level, 50 households. o Ward 94 – Dombivli, medium service level, 62 households. The total number of households surveyed is 320.

� The main of the dataset is composed of three groups of variables related to socioeconomic characteristics, access to water and satisfaction with the service. About forty parameters are collected per households. Details of the parameters measured are in the questionnaire used, annexure O1. The design of the questionnaire intended to collect a lot of information on urban households’ condition and socioeconomic behaviour regarding water. Consequently, not all of the parameters collected are going to participate automatically in the design of the water poverty index but the core of the study will be based on the access data. The most important of the collected data is addressed in the next section.

3.3.2. Exploratory data analysis

The exploratory data analysis is done in two steps. First, the distribution of the data is checked with numerical and graphical summary (boxplots); further, the data is visualised in its raw format knowing that the distribution of categorical variables is displayed by bar graphs and pie charts which use the counts or percentages of each category. Next, a chi-square test is performed to detect any existing relationships in the raw data.

Numerical and graphical summary

The numerical summary of the data is presented in table 3.5 and the graphical summary in figure 3.4. From the table, the values of the skewness and the kurtosis are used to see the normality status of the distribution. While a normal distribution will hold a value of 0 for these two, a pointy distribution will represent a positive kurtosis value and a flat distribution will have a negative value. Positive values indicate a right skewness. On this basis, table 3.5 leads to the conclusion that some of the distributions are close to normal. This is confirmed by the boxplots in figure 3.4, where the distribution of the parameters shows skewness and outliers. Before using any of these directly in further analysis, the normality status of the distribution will be finalised by checking statistical significance of the previous findings. The outcome will be the basis for the choice of the appropriate test statistic for the analysis of the data20.

20 To test for normality, the normal Q-Q plot and the Kolmogorov Smirnov test will be performed in SPSS. The Q-Q chart plots the expected normal values against the actual values of the data as individual points (Field, 2005). In case a transformation fails to normalise the data, the test statistic to be chosen have to be resistant to non normality of the data.


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Table 3.5: Descriptive statistics of the collected parameters

Descriptive Statistics

N Rang

e Min. Max Mean

Std. Dev.

Variance

Skewness Kurtosis

Stat. Stat. Stat. Stat. Stat. Std.

Error Stat. Stat. Stat.

Std. Error

Stat. Std.

Error

HH size 320 7 1 8 3,54 ,058 1,044 1,089 ,374 ,136 1,216 ,272

House Type 320 3 0 3 ,90 ,051 ,906 ,821 ,269 ,136 -1,557 ,272

Tenure Type 320 1 0 1 ,75 ,024 ,434 ,188 -1,160 ,136 -,658 ,272

Vehicle Ownership

320 7 0 7 1,37 ,103 1,849 3,419 ,884 ,136 -,805 ,272

Telephone Ownership

320 3 0 3 1,23 ,052 ,932 ,868 ,952 ,136 -,016 ,272

Type of School Chi.

320 8 0 8 1,82 ,157 2,801 7,844 1,425 ,136 ,501 ,272

Service Satisfaction

320 2 0 2 ,74 ,048 ,852 ,726 ,524 ,136 -1,423 ,272

Response Time

320 6 0 6 3,21 ,078 1,400 1,961 -,556 ,136 ,066 ,272

Main Problem

320 4 0 4 2,03 ,084 1,510 2,281 -,284 ,136 -1,396 ,272

Source Dr. Water

320 4 0 4 ,61 ,036 ,639 ,408 1,799 ,136 8,186 ,272

Source Other Use

320 4 0 4 ,96 ,061 1,098 1,205 1,425 ,136 1,348 ,272

Replacem. Source

320 2 0 2 1,05 ,042 ,752 ,565 -,077 ,136 -1,223 ,272

Moment Supply

320 1 0 1 ,42 ,028 ,494 ,244 ,331 ,136 -1,902 ,272

Duration Supply

320 3 0 3 2,11 ,029 ,513 ,263 ,028 ,136 1,262 ,272

Quantity Supply

320 3 0 3 1,50 ,034 ,603 ,364 ,074 ,136 -,373 ,272

Daily Col. Time

320 5 0 5 1,36 ,051 ,920 ,846 1,601 ,136 3,505 ,272

Replacem. Col. Time

320 4 0 4 1,14 ,053 ,952 ,907 ,519 ,136 -,427 ,272

Storing Strategy

320 1 0 1 ,91 ,016 ,292 ,085 -2,801 ,136 5,880 ,272

Type Water Treatment

320 4 0 4 1,80 ,059 1,063 1,130 ,353 ,136 ,268 ,272

Valid N (listwise)

320


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Figure 3.4: Graphical summary of the collected data

See annexure O2 for the category values represented by the coding numbers for each of the nine first variables and the two collection times. The values of the codes are the same as in table 4.3 for the last eight parameters.


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A further exploration is taken with the parameters participating into the design of the water poverty index related to access to water and socioeconomic characteristics respectively. These parameters are:

� Moment of reception of the municipal water, duration of the reception, quantity received, source of drinking water, source of water for other use, replacement source at break of the municipal supply, daily collection time, collection time for replacement source, daily quantity of water used, prevention strategy I (for quantity), prevention strategy II (for quality and health).

� Household size, ownership of vehicle, type of house, tenure type, type of school for children, ownership of telephone.

Each of these parameters is presented hereafter and a chi-square test is performed for them to detect any relationship in the raw data.

Presentation of the row data

The data is presented using graphs. Discrete values are represented in pie and class values are represented in bar chart.

Figure 3.5: Characteristics of the supply

58% of the surveyed population receive the water during daytime at the opposite of 42% receiving it during a time they are supposed to be resting. The water is given twice in the time interval. The duration of the supply displayed here is the sum of the two partial durations. None of the households have the water round the clock. Only one household declared having it for 7 hours while 235 out of 320 receive it for three hours. 153 of these households receive between 500 and 1000 litres against 149 for 100 to 500 litres of water per day.


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Figure 3.6: Source of water used by households

The survey shows high dependency on the municipal supply for drinking water with just 1.25% of households using tube well and 0.31, hand pump. This dependency decreases for other uses. Tube well and hand pump which are then used mostly as complementary sources increase in they frequency. The use of these two sources is more extensive to face situations of break of the municipal source which is then replaced by water tankers. In such case, the most used source is hand pump by 43% of the surveyed population.

Figure 3.7: Time spent in water collection


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The majority of the surveyed households spend 1 to 3 hours in daily water collection. This time frame drops below one hour for the same proportion of the household population at break of the municipal source.

Figure 3.8: Household size and water use

The majority of surveyed households is composed of 3 (37%) or 4 (35%) people. Family size above 5 or below 2 is infrequent. The use of water is mostly at 500 to 1000 litres per household per day. None of the households present a daily use level below 100 litres.

Figure 3.9: Prevention strategies

Storing the water is the prevention strategy adopted by all households for no water time. The use of tank is adopted by 9% of the population against 91% using buckets among the surveyed households.


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The necessity of treating the drinking water is widely spread in the surveyed population and boiling is the most used method (56% of households) followed by filters (32%). Nevertheless, 12% (the same proportion as electronic filter users) of the population do not treat their drinking water.

Figure 3.10: Socioeconomic characteristics

The data collected on households’ socioeconomic characteristics is looking at ownership of vehicle, ownership of telephone, house type, tenure type and type of school for children. The results reveal that 57% of the households do not own any vehicle. This may be due to the availability of railway facilities to Mumbai city and metropolitan region on which depend number of people for livelihood activities. Meanwhile, 29% of households possess a scooter which is a type of vehicle used by local people as taxi. The use of mobile phone is large, covering 65% of the surveyed households, and another 19% of them combine its use with the one of land lines. Most of the households own they house and 65% of the houses visited are one-room houses. For the type of school for children, the survey results in 58% of non applicable cases occurring when children in the family are grown up. 24% of children attend ‘Penchayat Samitu’ (sort of low cost private schools within community) and 13% go to private school against 5% for public school.


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Hypothesis setting for chi-square tests The aim is to check for any existing relationship among pairs of variables in the dataset. Thus hypothesises are as follows for each pair of variables. The null hypothesis (H0) is that there is no association between the two variables in the pair. Accordingly, the alternative hypothesis (Ha) states that there is a relationship between the two variables.

The level of confidence is 95% and the level of significance is α = 0.05.

Reporting the Chi-Square Tests

SPSS outputs for the chi-square tests are in annexure O2. The choice of chi-square is motivated by the nature of the variables which are categorical and the willingness to keep them in the raw format. According to the results, we have to reject the null hypothesis and conclude that there is an association between the two variables of each pair. However, the effect size indicated by Cramer’s statistic which is small or medium is indefinable for the association between daily collection time and source of other uses of water. The stronger association is found between quantity received and quantity used; it is reported hereafter.

There was a significant association between the quantity of water received and the quantity used χ2 (6) = 1.193E2, p < 0.05. 4 cells of the contingency table have expected counts less than 5. Cramer’s statistic is 0.432, p < 0.05. It is significant, suggesting a medium effect in the relationship.

Although the effect in the relationship is medium in half of the cases, the violation of the chi-square’s assumption on expected frequencies in almost all cases might have brought a loss of statistical power to the test which then fails to detect a genuine effect in the relationships (Field, 2005). Nevertheless, the conclusion on existing relationships in the collected data holds.

3.4. Summary: Study area and data captured

Three main points are relevant to sum up this third chapter. � The study area in the municipal corporation of Kalyan-Dombivli is delineated in the high

unsatisfied public service demand area namely, sectors 1 and 2. These two sectors comprise 50 of the 96 enumerator areas of census 2001 which is meant to be used in this study.

� The coverage of the water distribution system is said to be about 100% of the populated area of the municipal corporation with mains and lateral supply lines (CDP, 2007). The municipal water provision is done by graduate service level. On the water supply area map, the study area intersects with high and medium service levels areas.

� The core of the data is captured from household survey in 6 out of the 50 electoral wards resulting in 320 data units. Three sets of parameters are collected on each household namely, socioeconomic characteristics, access to water service and service satisfaction. The data identified for the calculation of the WPI are not completely normally distributed, but present existing relationships, though with small or medium effect size.


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4. Design and analysis of the water poverty index

This chapter four describes the methodology for the design and the analysis of the water poverty index. It is addressed in five sequences namely, design of the index, inference of the index to population, investigation of the index, analysis of the index at policy level and visualisation of the index. Given that the WPI is designed on the basis of a household survey in six wards sampled out of fifty identified in the study area, inference is performed to achieve the ordinal ranking of the wards in the city. Afterward, the designed index is analysed in four aspects: sensitivity of the index to the components (since they are individually important to water management), assessment of the index (as specifically designed), relation of the index to poverty (for the overall fit of the study to the research project), and evaluation of the index at policy level (since it is the conceptual objective of the index). The question of visualisation is the last addressed – at household and ward levels. The methodology framing the design of the index is not straightforward. Each sub-sequence responds to a different method.

4.1. Design of the index

The conceptual framework of the WPI is described in section 1.8 and pictured in figure 1.3. The methodology adopted for its calculation is the one applied by the authors of the index (Cullis and O’Regan, 2004; Sullivan et al., 2006a; Sullivan, 2005) implementing it at community level; that is the composite index approach. Using survey data and national statistics, each variable of the WPI is got by aggregating a set of sub-components using again the composite index approach. The variables are then standardised on a scale of 0 to 100 before they aggregation by weighted average, giving a low score of the WPI to indicate a high level of water poverty (Sullivan, 2005). From the concept, the variables entering the composition of the WPI are: Resources, Access, Capacity, Use and Environment. Hereafter is described the design of each variable in this study. For the weighting of the parameters – and also for the WPI – the ideal would have been to have it locally defined by involving stakeholders in order for the weights to reflect their main interests and the importance given to each variable as put by Sullivan et al. (2006a). Unfortunately, the time pressure on field work didn’t allow it. In addition, the only written source found on the study area in the interest of this work is the city development plan which is limited regarding this type of information. Consequently, the weights given to parameters are worked out by the researcher.

4.1.1. The variable Resources

As described in section 1.8.2, for the value of the variable Resources, the planned quantities to be supplied by the municipal corporation in the areas concerned are chosen. That is (hereafter in bold) the lowest of the two values shown on the service level map (in figure 3.2). Ward 14: 100 (100 to 135) litres per capita per day


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Ward 16: 135 (135 to 160 litres per capita per day Ward 43: 135 (135 to 160) litres per capita per day Ward 47: 135 (135 to 160) litres per capita per day Ward 81: 135 (135 to 160) litres per capita per day Ward 94: 100 (100 to 135) litres per capita per day

4.1.2. The variable Access: the access index

� Design of the variable The ten parameters composing the variable Access from the concept are described in section 1.8.2. The conception of the variable as defined in this study is schematized in table 4.1 next.

Table 4.1: Concept of the variable Access in this study

Characteristics of the access Identification factors Value Remark

Municipal access to water

Municipal water within or close to the premises

1 (absolute)

This is already the access from the supply point of view

Related deprivations Ten parameters weighted hereafter

Calculated value

This is specific to each household depending on its characteristics

Effective access to water

Difference between the two types of indicators

Difference in values

The real state of the access is represented in this value

Building on this table 4.1, a water deprivation index is calculated by standardising the ten parameters on a scale of 0 to 1, weighting them by researcher’s judgement and aggregating them by weighted average. The value is subtracted from 1 (the value of the absolute access) to get the overall access value for each household.

� Design of the weights To assign weight to the parameters, four clusters of varying importance are identified in the dataset (see table 4.2). They are associated to the municipal supply (No. 1) and households’ related behaviour (Nos. 2, 3 and 4). The importance of each cluster varies with households depending on their condition. For example, a household with limited socioeconomic characteristics receiving the water at a time where all members are out for daily business could rank high cluster No. 1 because of the necessity to save the water for non-supply time. Meanwhile, a house wife may complain more about collection time. This would have been best sorted out by stakeholders’ judgement to reflect the study area’s truth. Nevertheless in the impossibility to have this best situation, weights are given the values displayed in table 4.2. Their design puts mainly an internal trade-off between time spent in water collection and prevention strategies considering that a good strategy reduces the collection time. Details of each cluster are as follows.

o Within cluster 1, moment of supply, duration and quantity receive increasing rates because a household can develop a strategy against unsatisfactory moment of supply and duration to make the most of the quantity received. Thus, the quantity of water supplied appears the most important in the cluster.

o In the second cluster, the source of drinking water is weighted higher because of its importance for health. The source of coping at break of the municipal source is weighted


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very low because it is an event out of the daily pattern of water management by households.

o The same condition applies to the time spent on it while the daily collection time is highly weighted as a very important deprivation parameter with regard to the economic and financial value of time.

o For the prevention strategies, households are settled in the habit of storing the water and generally treating it for drinking. Prevention strategy I is weighted the same as collection time to keep the internal trade-off.

Outputs of deprivation index are in annexure A1.

Table 4.2: Weights of deprivation parameters

Weight (wi)

Label Value Cluster Cluster value

wmsweight of Moment of reception of the supply 0.01

wds weight of Duration of the reception 0.04

wqs weight of Quantity received 0.10

1. Characteristics of municipal

supply 0.15

wsd weight of Source of drinking water 0.10

wso weight of Source of other use 0.04

wcbweight of Source of coping at break of municipal supply 0.01

2. Different sources used by

households 0.15

wct weight of Daily collection time 0.30

wtbweight of Collection time at break of municipal supply 0.05

3. Time spent in water collection 0.35

wps weight of Prevention strategy I (quantity) 0.30

wtlweight of Prevention strategy II (treatment level for quality) 0.05

4. Prevention strategies 0.35

Total 1 - 1

For data entry, the ten parameters are coded in a way that the highest value depicts the worst situation as shown in table 4.3 hereafter. Consequently, a high deprivation value characterizes a poorer household. The story telling of this table 4.3 is that the household without deprivation in the study area21 (thus with absolute access) receives daily over 1000 litres of water for more than 7 hours at day

21 In the study area, as revealed by the household survey, no one gets water for seven hours and the storage and treatment of the drinking water are like normal practices in water management.


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time through in-house connection which is its only source. The water stored in thank is drank through electronic filter. The collection time is almost nil except for break of the municipal supply whereas the water is then collected from a water tank supplier.

Table 4.3: Coding of deprivation parameters

Parameter Values Code Night (after midnight) 2 Day and night (between 7 am and 11pm and after midnight) 1 Moment of reception of the supply

Day (between 7 am and 11 pm) 0 < 1 hour 3 1 - 3 hours 2 3 - 7 hours 1

Duration of the reception

> 7 hours 0 < 100 litres 3 100 – 500 litres 2 500 – 1000 litres 1

Quantity received

> 1000 litres 0 Tube well 4 Hand pump 3 Water tanker supply 2 Group connection 1

Source of drinking water

Individual HH connection 0 Tube well 4 Hand pump 3 Water tanker supply 2 Group connection 1

Source for other use

Individual HH connection 0 Tube well 2 Hand pump 1

Source of coping at break of municipal supply

Water tanker supply 0 Daily collection time Recorded collection time -

Collection time at break of municipal supply Recorded collection time -

Storing in buckets 1 Prevention strategy I (quantity) Storing in tank 0

No treatment 4 Alum tablets 3 Boiling 2 Ordinary filter 1

Prevention strategy II (treatment level for quality)

Electronic filter 0

See Outputs of the variable Access in annexure A2.


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4.1.3. The variable Capacity: the capacity index

The capacity index, as described in section 1.8.2, is a function of the level of education and the income level. It is computed here at household level by first fitting a model to the proxy parameters to determine the income level and further introducing the level of education to get the capacity value.

Income level

For the level of income, the five parameters listed in table 4.4 below are compounded. Their values are first standardized and then aggregated by weighted average to get the income level’s value for households. The value of the weight given by researcher’s judgement for each parameter is displayed in the following table.

Table 4.4: Weights of income parameters

Weight Label Value 1 wov weight of Ownership of vehicle 0.40 wth weight of Type of house 0.30 Wtt weight of Tenure type 0.15

wtsweight of Type of school for children 0.10

wot weight of Ownership of telephone 0.05 Total 1

According to the context of the city, ownership of telephone (mobile) does not seem to be a function of income; consequently, it is weighted low on the list. There might be a tie between the type of school for children and the tenure type. The same could happen with the type of house and ownership of vehicle which could be considered the most obvious signs of income level. However, the habitual pressure on housing market and security of tenure in a context of fast growing population22 guide to the choice of higher weights for ownership of vehicle over type of house and tenure type over type of school for children.

Education level

While income level is represented by an index, education level is the response got as level of education of the head of the household during the survey. It is set by Grade 4 as threshold23.

22 That is the case for the city of Kalyan-Dombivli that witnesses a substantial population growth due to its position to Mumbai of which it is an important satellite city hosting a strategic railways junction. 23 The threshold is taken from Cullis (2005) who states that “it is usually assumed that at a certain educational stage some information about basic health practices, especially regarding water use, is disseminated” (p. 24). She indicated Grade 4 as “the threshold level of education at which people are sufficiently educated to manage they water supply efficiently” (p. 24) in the context of her research. This is adopted in the current research in the face of data shortage.


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Capacity index

For the capacity index, the income level and the level of education are standardised and aggregated with the respective weights of 0.8 and 0.2. The weight of the level of education is kept low because from the survey, it is revealed that the requirement of treating the drinking water is well spread in the population and the households coping strategies against different types of deprivations are well known and appear like general knowledge in the city. Even though some people don’t treat the drinking water, they recognize its necessity and evoke lack of affordability for not applying the knowledge. Consult outputs in annexure B1 and B2.

4.1.4. The variable Use

As defined in section 1.8.2, the variable is taken as the per capita daily water consumption in household measured in litres per capita per day. For the computation, the daily quantity of water used by household is divided by the household size. See outputs in annexure C1.

4.1.5. The variable Environment

The variable as described in section 1.8.2, gives account of the need for environmental integrity and sustainability. The water source (Ulhas and Kalu Rivers for the entire municipal corporation) is perennial as the two rivers flow within the limits of the municipal corporation and that is taken as a guarantee for sustainability. The environmental integrity is assimilated to the actual ecological state of the source. In the face of data shortage, the water source monitoring data are used. Table 4.6 below displays the water monitoring parameters, the parameters required by WHO and the benchmark for drinking water source in India. From that table, it is viewed that the three sets of requirements don’t match and only three out of the nineteen parameters benchmarked by WHO are measured for monitoring. Due to that configuration of the dataset, the environment value is measured on the Indian scale by assessing the gap to the benchmark for the parameters identified (the highest value since January 2006 is considered for each parameter for better highlighting any possible abnormality). The gap values are standardised and aggregated by equal weight summation into one value representing the environmental state of each source with regard to the other.

Table 4.5: Values of the variable environment

Source Parameters Value Date of measurement

Gap to benchmark

Standardised value

Value for environment

pH 8.91 23/05/06 + 0.09 + 0.45 DO 7.3 21/02/08 + 3.30 + 1 BOD 32.0 31/01/06 - 29 - 1

Kalu river

source T. coliforms 350.0 09/01/07 + 4650 -

0.15

pH 8.80 23/05/07 + 0.20 + 1 DO 6.6 12/09/06 + 2.60 + 0.79 BOD 26.0 23/05/07 - 23.0 - 0.79

Ulhas river

source T. coliforms No

value - N/A N/A

1/3


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The environment value is 1/3 for the study area which is serviced from the Ulhas river source.

Table 4.6: Water source monitoring parameters and benchmarks

Monitoring parameters in the Kalyan-Dombivli Municipal Corporation24

Parameters and standards required by WHO for

drinking water in 2006 (WHO, 2006)25

Indian benchmark – drinking water source

after conventional treatment and disinfection

(Goldar and Banerjee, 2004)

Parameter Ulhas river Kalu river Parameter Value Parameter Value

pH 7.48 7.49 No guideline - pH 6 < pH < 9 Turbidity NTO 4.54 2.71 Not mentioned - - - Conductivity 11264.17 4464.68 Not in table - - - DO 4.13 mg/l 5.19 mg/l No guideline - Dissolved Oxygen >=4 mg/l BOD 13.28 mg/l 8.51 mg/l Not in table - B O D 5 days 20oC <=3 mg/l COD 74.67 46.98 Not in table - - - SS 48.40 36.00 Not in table - - - FDS 11167.73 4495.00 Not in table - - - TDS 9630.67 3364.95 No guideline - - - % Free Ammoniac 0.14 0.02 No guideline - - - TAN 1.38 1.21 Not in table - - - Nitrite Nitrogen 6.16 mg/l 4.48 mg/l Nitrite 3 mg/l - - Nitrate Nitrogen 0.95 mg/l 1.26 mg/l Nitrate 50 mg/l - - TKN 3.77 2.99 Not in table - - - Chlorides 4305.47 1332.61 No guideline - - - Ca Hardness 743.33 260.95 Not in table - - - Mg Hardness 899.20 351.05 Not in table - - - Total Hardness 1706.27 612.00 Not in table - - - Total Alkalinity 107.60 77.58 Not in table - - - Boron 1.51 1.34 Boron 0.5 mg/l - - Sulphate 458.84 176.09 No guideline - - - Phosphate 0.37 0.56 Not in table - - - Sodium 1374.50 620.44 No guideline - - - ? ? ? Aluminium 0.2 mg/l - - ? ? ? Antimony 0.02 mg/l - - ? ? ? Arsenic 0.01 mg/l - - ? ? ? Barium 0.7 mg/l - - ? ? ? Cadmium 0.003 mg/l - - ? ? ? Chromium 0.05 mg/l - - ? ? ? Copper 2 mg/l - - ? ? ? Cyanide 0.07 mg/l - - ? ? ? Fluoride 1.5 mg/l - - ? ? ? Lead 0.01 mg/l - - ? ? ? Manganese 0.4 mg/l - - ? ? ? Mercury 0.006 mg/l - - ? ? ? Molybdenum 0.07 mg/l - - ? ? ? Nickel 0.07 mg/l - - ? ? ? selenium 0.01 mg/l - - ? ? ? Uranium 0.015 mg/l - -

Total coliform ? 196.67 - - Total coliforms organism MPN/100ml

Shall be 5000 or less

Faecal coliform ? 50.00 - - - -

24 Values are average of all observations for each parameter during the year 2006 – the most completed and recent year in the data set. 25 The parameters are reported as in the original table. Empty cells are reported here as “Not in table”.


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4.1.6. Weighting of the variables

The WPI is a composite index made of the combination of the five variables described above. For each variable, an index is calculated from the values of the indicators or parameters identified. In any case of aggregation, the weighted average is the method used. The weights are chosen to sum to 1 so that a trade-off is created between the criteria. The standardization is done on a scale of 0 to 100 with a low score of WPI indicating a high level of water deprivation (Sullivan et al., 2006a). The water poverty index is calculated from the following formula.

wrR + waA + wcC + wuU + weE WPI = -------------------------------------------

wr + wa + wc + wu + we Where,

wi is the weight applied to variable i R = value of the variable ResourcesA = value of index of AccessC = value of index of CapacityU = value of the variable UseE = value of the variable EnvironmentIn the concept of the WPI, it is recommended that the values of wi be set by stakeholders relevant to the considered level of water poverty through a multi-criteria decision analysis process. Otherwise, the following table gives some recommended values (Cullis, 2005, p. 29)26.

Table 4.7: Hypothetical weights to be added to WPI structure

Local conditions descriptors Variable weights Hydrological

condition Economic condition

National priorities Resource Access Capacity Use Enviro

nment

Very good Unknown Agriculture Industry and Social

1 2 2 3 1

Average Average Social 1 2 2 1 1

Very Good Good Environment and Social 1 2 2 1 2

Unknown Unknown Industry and Agriculture 1 2 2 2 1

Source: Sullivan et al. (2002), cited by Cullis, 2005, p. 29.

For the study area which is a city, the most fitting combination is “average average social” for the local conditions descriptors. Consequently, the weights’ values are, wr = 1; wa = 2; wc = 2; wu = 1; and we = 1. Outputs of the WPI computed at household level are available in annexure D1.

26 This author was citing Sullivan, C. A. and Meigh, J. R. and Fediw, T. S. 2002, Derivation and Testing of the Water Poverty Index Phase I: Final Report, DFID.


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4.2. Inference of the water poverty index to population

The water poverty index is calculated from random samples of households within a random sample of 6 wards out of 50. As put in the introduction to this chapter, inference is the logical continuation of the process of designing the WPI to map water management situation in the city since it will enable to deduct the water poverty situation in the non surveyed wards from the preceding outputs. To achieve the inference, a statistical approach is taken and the Linear Mixed Model (LMM) procedure is the method identified in response to the sampling design which was a multi-stage sampling since LMM handle well “situations in which experimental units are nested in a hierarchy”(SPSS, 2005, p. 1).

4.2.1. Story line and model specifications

The purpose here is to fit a Random Coefficient (RC) model27 which is a specific Hierarchical Linear Model to the water poverty data. From the sampling strategy, households are nested within electoral wards which are nested within service level areas. However, this RC model deals with the two first levels because of the study area which intersects with only two types of service area (see chapter 3). The level 1 of the model concerns the households and the level 2 deals with the wards. The inference is done by a two-level model of level one dependant with a level two covariate responding to the following formulas (SPSS, 2005, pp. 4; 7).

• For the fixed effects model,

Yi = Xi� + �i [1]

• For the mixed model,

Yi = Xi�+ Zi�i + �i [2]

Were X� models the fixed effect structure given by the water poverty index Z�models the random effects brought by wards Y is the vector of responses X is the fixed effects design matrix Z is the design matrix of random effects � is the vector of fixed effects parameters � is the vector of random effects parameters � is the vector of residual error

27 “A random coefficients model is a type of hierarchical linear model, but one in which each different group at the higher level … is assumed to have its own different slope and intercept with respect to predicting the dependant variable” (Garson, 2008, p. 9). In this study, it is assumed then that each ward’s slope and intercept are at random from a population of all possible slopes and intercepts for the WPI and its predictor variable.


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The predictor variable considered in this study is the access power (see section 5.2.1). The model specifications are as follows, based on Garson (2008, p. 9).

• WPI is the level 1 dependant variable • Access power is the level 1 covariate • Access power is also the level 1 fixed effect.

o It is the fixed factor to be modelled. o These fixed effects will estimate the average relation of access power to WPI.

• Access power besides, is the level 1 random effect. o The random effects will estimate the variability of slopes, intercepts and slope-

intercepts interactions across the grouping variable which is ward. o Each ward has its own estimated slope and intercept. o The slope and intercept of access power will be modelled as random effects to meet

the Random Coefficients Model’s requirements. • Ward is the subjects grouping variable

o Ward is at level 2 o Ward is assumed as random effect

• Ward is also the subject at level 2. o Households are independent observations within a ward

• In the face of no information to specify the covariance structure, it will be set at Unstructured when running the analysis in SPSS.

The above specifications model the access power as both fixed and random effects and ward is assumed as random effect.

4.2.2. Assumptions

Assumptions are taken from Garson, (2008, pp. 13-14). • Normal distribution: the Random Coefficients models assume a normal distribution because

of empirical Maximum Likelihood estimation. o The normality will be checked for the data and be taken into account in interpreting

outputs. • Random grouping: the level 2 variable is assumed to be a random sample of the population.

o The wards participating in the study where chosen randomly by the fishbowl draw method.

• Independent observations: independency to grouping variable is not assumed for individual observations.

o The grouping variable in the current case is the ward characterised mainly by the level of supply, the duration of the supply and the quantity supplied. During the survey, it was not possible to capture all parameters characterising a ward and likely may influence the level of water poverty28.

28 For example, proportion and position of slum and slum-like areas within wards.


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o The intra class correlation will be performed between the WPI and the access power to check independency and accept or reject the choice of multi-level modelling rather than GLM.

• Independent groups: groups are assumed to be independent and to have the same covariance structure.

o There is not dependency relation between the wards in this study. o The covariance is “unstructured” as there is no information to properly specify it.

• Level 2 sample size: the group sample size should be at least 100 to avoid that the standard errors of the second level variances are estimated too small.

o The total ward population in the study area is 50 and only 6 have been sampled. o It is suggested that with small samples, bootstrapping be used to estimate standard

errors. • Level 1 sample size: unbalanced samples sizes within groups will lead to inflated type I error

for tests of parameter estimates and overall fit. o The number of households surveyed per ward varies from 40 to 67. One main

advantage is that the FIML estimators will be more robust as they do for unbalanced designs.

o For the type I error, a post hoc test will be performed. The Games-Howell procedure is chosen because it is powerful and accurate when sample sizes are unequal (Field, 2005, pp. 340-341).

4.2.3. Hypothesis setting

To achieve the inference of the WPI to the population, the modelling will compare its variation over household and over ward. The hypotheses are as follows. The null model predicts the WPI in all wards with an intercept term. Its function is to give the baseline

-2Log Likelihoods used for testing significance of improvement with fixed or random model.

The fixed model considers all wards having the same intercept and assumes that there is no difference between WPI means per ward.

The mixed model states that each ward has a different group mean and a different intercept. It tests the variability of the WPI within wards and between wards.


4.2.4. Procedure

The procedure for the RC model is based on Garson (2008). 1. Independency check for observations with intra class correlation. 2. Normality check for the data, the water poverty index and the access power. 3. Data processing in SPSS.

3.1. Baseline or null model which include only intercept. 3.2. Fixed model with the base level independent variables included and the variance components of the slopes constrained to zero.


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3.3. Chi-square difference test to check whether the fixed model has a more significant fit than the baseline model. If it does, then go to 3.4. If it doesn’t, then shift to 3.6.

3.4. RC model. 3.5. Validation of the RC model. 3.6. Simpler model / General Linear Model. 3.7. Validation of the simpler model.

4. Interpretation of the results. 5. Conclusion.

4.2.5. Extrapolation of the WPI to the entire study area

The model designed for the WPI is applied for each ward of the study area. Given that the survey was done only in six wards out of fifty, the purpose is to find an anchor point between the survey data and the census dataset that enables to extrapolate the findings to all the identified wards. The procedure is as follows.

� Aggregate the WPI to ward level for the wards surveyed. � Identify the best predictor of the WPI among its variables. � Find in the census dataset the parameters that are similar in their definition to the best

predicting variable of the WPI. � Proceed by iteration to find which of the identified parameters predicts best the index for

the surveyed wards. � Build the mathematical model for the extrapolation of the index to the study area. � Select the values of the identified best parameter for the wards located in the study area

and compute an extrapolated value for the WPI accordingly using the mathematical model.

4.3. Investigation of the designed water poverty index

The methodology for the investigation of the index is not straightforward. In the accessed literature – in the case of international comparison (Lawrence et al., 2002) and in the experience in South Africa (Cullis, 2005) – the index is analysed looking at the intra correlation of the variables and between the WPI and the HDI. Assessment of the index after design is found only in the case of Benin (Heidecke, 2005). Building on those authors, the index is investigated in the present study in three aspects namely, sensitivity of the index to the components, sensitivity to other variables and global assessment of the designed index.

4.3.1. Sensitivity of the index to the components

The investigation of the WPI in its sensitivity to its variables is justified by their individual importance to water management. The statistical method of correlation analysis performed in pilot studies such as for the international comparison or Eastern Cape in South Africa will be conducted to evaluate this type of sensitivity.


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4.3.2. Variability conditions of the index

Further to the correlation analysis, the variability conditions of the index within and between wards are examined through its components. Since the index is calculated for samples of households within a sample of wards, the variability condition might be more informative on the influence of some other households and ward characteristics. The statistical method of one-way ANOVA will be used to achieve this intention.

4.3.3. Assessment of the WPI

The framework for the evaluation of the index is not found directly in the accessed literature. At early stages of the development of the index, this problem was solved by relying on the methodology adopted. “In order to evaluate something, we need to have some kind of standard to measure it by, and this is difficult in this case as there is at present no real alternative measure to assess the links between water and poverty with which we can compare our work. We have however produced a preliminary assessment of six different approaches to calculating a Water Poverty Index, which in themselves can be compared”(Mlote et al., 2002, p. 15). These authors affirm that the group of researchers at the consultative conceptualisation workshop adopted the composite approach as the best.

Assessment of the variables

In these conditions, the assessment of the index in the current study is performed by examining each variable in reference to the most recent paper found in accessed literature from the authors of the index dealing with the concept and conception of the variables; that is Sullivan and Meigh (2003). The difficulty to access papers from the critics of the index prevents also from using a contradictory benchmark for the evaluation of the index. Finally, the operationalisation, the outcome and the limitations are examined for each variable in reference to the authors’ benchmark and the current research context.

Assessment of the resulting index

After the variables, the index itself as specifically designed is assessed. The framework adopted is taken from the case of Benin. It is the only case in the accessed literature where the index is evaluated after design. The evaluation is “… in terms of accuracy, replicability, versatility and usability”(Heidecke, 2005, p. 27). This author justifies the need for evaluating the index by the fact that despite the success of the design for that country, “… there is scope for further development of the WPI” (ibid.) This applies for the current research and more, as a tool developed to inform policy makers, the WPI should be well-tried.

4.4. Further analysis of the water poverty index

So far, two research objectives have been covered namely, to design the WPI at a suitable scale and to study its sensitivity. The three remaining objectives concern the appropriateness of the index to report the city’s water delivery situation, the index’s behaviour in a poverty landscape and its usefulness at policy level. These last three research objectives are addressed in the current sub-chapter. Each aspect of the analysis responds to a specific rationale and is conducted differently.


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4.4.1. Relation of the index to overall poverty

� Rationale The relation of the index to poverty situation in the city is to be found out because of the heading of the current research which is to look at the multiple deprivations facing urban households in Indian mega cities. As a single value holding the responsibility to give account of the deprivation status of households in the city, the new Index of Multiple Deprivations is meant to inform policy making29. The specific aspect of water treated separately in this study is going to be overlaid with the multiple deprivations’ results to enhance policy informing for targeting interventions.

� Method The methodology in this sub-section is in three steps. First, the new IMD is calculated at the level of data availability using the framework developed by Baud et al. (2008, p. 1395). Then the value of the new IMD in each mapping unit is given to all surveyed ward located in it. Finally, the statistical method of correlation analysis is applied to the IMD and the WPI in the surveyed wards to detect any existing relationship.

4.4.2. Appropriateness of the WPI to report the city’s water delivery situation

� Rationale Taking advantage of the position of Esfahani (2005), Zérah (1998, 2000), and Baer (1985) on an analytical framework in service delivery developed in section 2.1.2, from which it was concluded that public services delivery could be assessed through the outcomes of the service delivered seen from the user’s point of view, the appropriateness of the index to reflect the city truth in water delivery is looked at from the households’ appreciation of water delivery outcome in their city.

� Method The statistical method of Pearson’s chi-square test of independence of two categorical variables is performed between the index and the satisfaction of households with the water service. The assumption here is that an association between the two variables will prove the appropriateness of the index for the purpose.

4.4.3. Evaluation of the water poverty index at policy level

In section 2.2.2, the links between indicators and decision-making was shown. Building on the frameworks from Baer (1985), Webster (1993) and Martinez-Martin et al. (2005), the policy response to the water poverty index in the context of the study area is derived through the following steps30.

� WPI is considered as a baseline and descriptive indicator of the water delivery situation in the city

29 The new IMD is presented along with the different capitals’ index to enhance information for targeting programmes. 30 Information from table 2.1 is used here, mainly the first and second rows, for problem identification and goal setting. Implementation and monitoring (the last two rows in the table) are not addressed here since the research objective related to this development is to sketch a policy response to the designed index in the study area.


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� By the use of GIS for visualisation, the elected areas for targeting intervention are identified

� For goal setting, o The traditional research question in service delivery is applied to identify the possible

domains of intervention o A chi-square test is performed to understand the substance of that intervention from

the dataset available.

4.5. Visualisation of the water poverty index

At household level

The visualisation of the water poverty index at household level in the surveyed wards is done in ArcMap showing the index’s value as classes of absolute quantitative data. The output is in figure 5.1.

At ward level

The process of the visualisation of the index at ward level in ArcMap is shown in figure 4.1. The output is in figure 5.5.

Figure 4.1: Visualisation of the water poverty level in the study area

4.6. Summary: Methodology of the design and analysis of the WPI in a flowchart

The different methods described in this chapter for the different parts of the analysis are summarised in the flowchart hereafter.

Export in ArcGIS the wards of the study area

Export from Excel to ArcGIS the table of

extrapolated WPI in wards of the study area

Join them on the attribute “ward number”

Define classes of the WPI, apply cosmetics and display

the distribution of water poverty level in the city


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Figure 4.2: WPI: summary of design and analysis in a flowchart

The flowchart concluding this chapter four outlines the process of management of the data to get the research outputs. Following the theoretical background, the methodology integrates a number of methods responding to each aspect of the conceptual framework. The working scheme reads as follows.

� The aim is to calculate the WPI applying the composite index approach found in literature. � Design the variable access highlighting household water-related deprivations instead of taking

the percentage of population with access to safe water as found in literature about the conception of the WPI. Consequently, the composite index is also applied to have this variable from the several parameters collected from households.

� The designed index has to be investigated and inferred. Due to data collection strategy and from literature, statistics are used as a tool to achieve the goal.

� The data analysis oriented by the research objectives also makes use of statistics as a tool. In sum, the methodology applied in this study is the composite index derived from literature for the calculation of the WPI, backed up by statistical techniques for analysis.

HH survey by two-stage sampling

Linear mixed model Linear regression Trend fitting Iteration Extrapolation

Composite index approach

Numerical summary Graphical summary (Boxplots) Distribution (bar charts, pi charts) Chi-square

Correlation analysis ANOVA Theoretical benchmark & framework

Framework Baud et al. (2008) Correlation analysis

Correlation analysis

Chi square

Chi square Theoretical framework

Method applied

Data capture

Inference WPI

Investigation WPI

WPI design and calculation

Exploratory data analysis

Design of the new IMD

Relationship WPI - IMD

WPI as water delivery mirror?

WPI for decision-making ?

Step in the process

Dat

a pr

oces

sing

D

ata

anal

ysis


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5. The water poverty index: computation, investigation and Analysis

The objective of this chapter is to present the results of the collected data processed according to the various methods described in chapter four, to investigate the designed water poverty index and to analyse it in the light of the research objectives. In the first sub-chapter, the data is processed at household level by direct computation from the survey data and at ward level by the modelling and extrapolation procedures. In the second sub chapter, the first stage analysis of the index is performed according to the scheme set in the methodology chapter. The third sub-chapter studies the index in a poverty landscape and the forth sub-chapter explores the usefulness of the designed water poverty index at management and policy levels.

5.1. Data processing

This sub-chapter is committed to the processing of the collected data for producing the water poverty index at household and ward levels in the study area.

5.1.1. WPI at household level

Computation

The computation of the water poverty index is done at household level for the six wards surveyed according to the methodology described in section 4.1. See annexure for computation outputs in tables indicated as follows. A1: deprivation index A2: access index B1: income index B2: capacity index C1: use values D1: The water poverty index at household level.

Visualisation

The water poverty index in the surveyed wards is visualised as absolute quantitative data. The distribution of the index within each ward is displayed in three classes set according to the group mean (all surveyed wards considered together) and the standard deviation. The relative position of each ward to the group of surveyed wards is therefore highlighted. Each ward is mapped separately and shown along with its position in the study area.

The WPI at household level is visualised in figure 5.1.


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Figure 5.1: WPI at household level in surveyed wards

The WPI values are displayed here in three classes. The first class is below the mean minus two times the standard deviation (49.50). The second class is above the first class and below the mean minus one standard deviation (59.17). The third class is above the mean minus one standard deviation. From the map, the third class is the most populated. However, all values being below the group mean in the ward, the conclusion is that water poverty is high in ward 14.

For this ward, the first class is below the mean (68.84), the second is between mean and mean plus one standard deviation (78.51), the third is above mean plus one standard deviation. The third class is the least populated showing a high water poverty level in ward 47.


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Like in the previous case, the first class is below the mean, the second between mean and mean plus one standard deviation, the third is above mean plus one standard deviation. The third class is the most populated showing a low water poverty level in ward 43.

Again, the first class is below the mean, the second is between mean and mean plus one standard deviation, the third is above mean plus one standard deviation. The third class is the most populated showing a low water poverty level in ward 81.


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For ward 16 also, the first class is below the mean, the second is between mean and mean plus one standard deviation, and the third is above mean plus one standard deviation. The first class is the least populated showing a low water poverty level in ward 16.

Similar to the case of the four previous wards, the first class is below the mean, the second is between mean and mean plus one standard deviation, the third is above mean plus one standard deviation. The third class is the least populated showing a high water poverty level in ward 94.


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5.1.2. Descriptives of the WPI and components at household level

The designed water poverty index and components are explored here to detect the behaviour of the data and decide about the level of the analysis. The index and components are presented with their descriptive statistics in table 5.1 and boxplots in figure 5.2. Due to the concept and the design, the variable Environment holds the same value throughout the study area and the variable Resources is the same for the same water service level wards (see section 4.1). Consequently, the three remaining variables are participating into the exploratory analysis of the index.

Table 5.1: Descriptive statistics of the WPI and components at household level

Descriptive Statistics

N Range Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic StatisticStd.

Error Statistic

Std. Error

Access 320 56,85 25,96 82,81 47,9878 10,72531 1,479 ,136 2,536 ,272

Capacity 320 98,52 1,48 100,00 46,6014 21,90305 ,153 ,136 -,789 ,272

Use 320 152,38 14,29 166,67 1,3509E2 42,79873 -1,413 ,136 1,222 ,272

WPI 320 46,60 44,65 91,25 68,8410 9,66515 ,133 ,136 -,599 ,272

Valid N (listwise)

320

From this table 5.1 and the figure 5.2, the characteristics of the water poverty index are as follows.� WPI shows less variation than Access and Capacity. Its range is also the lowest. It doesn’t

present any outlier, but is slightly left skewed. Most households present a WPI well above 60. � Access is the second best regarding the standard deviation. The range is the medium one in the

set of indexes. This variable presents several outliers and is left skewed. The majority of households present an access value between 42 and 55.

� Capacity shows the highest standard deviation, the highest mean, the highest range and the highest skewness of the three indexes. It doesn’t present any outlier, but is right skewed. Capacity scores lie between 35 and 62 for most households.

� The variable Use presents a minimum of 14 litres and a maximum of 167 litres per capita per day. There are several outliers and the variable is strongly skewed to the left. The daily use of water is well above 125 litres per capita for most households.

The presence of skewness in the distribution of the four variables shows that some households experience the reality carried by those variables differently from the majority. Consequently, any analysis should be performed at household level since these extreme cases would be lost in any aggregation.


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Figure 5.2: WPI and components at household level in boxplots

5.1.3. Modelling of the WPI

The modelling of the WPI calculated at household level will enable inference to the population. The procedure for modelling the WPI is described in section 4.2.4. It is applied here stepwise. Access capacity or access power is chosen for the linear mixed modelling as explained in section 5.2.1.

1. Intra class correlation The intra class correlation (see section 5.2.1) is high with the R2 value being of 0.47 to 0.94. There was a significant relationship between the variable access power and the water poverty index in all wards, r = 0.686 for ward 14, 0.970 for ward 16, 0.966 for ward 43, 0.799 for ward 47, 0.945 for ward 81, and 0.871 for ward 94, p (one tailed) < 0.05. The linear mixed model is probably an adequate choice for the inference of the WPI.


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2. Normality check for the data Box plots of the data show skewness and outliers attesting the non normality. The normality status is checked with a Q-Q plot and a Kolmogorov Smirnov test statistics. This test fails to give significance to the non-normality of the data in some wards. Normality is assumed for all wards for running the analysis and it will be considered again in the interpretation of the outputs. The attempt to transform the data fails to solve the normality problems in all wards. See annexure E1 for details on normality check.

3. Model design Details and SPSS outputs concerning the model design are shown in annexure E2.

a. Null model � The -2LL indicates the best fit of the model to the data with a value of – 679.652. � The intercept is the only fixed effect. There is 95% confidence that its value is

significantly different from 0, F (1, 4.988) = 773.475, p < 0.05. � The standard error on residual is 0.000517. The random effect factor, ward, is not

significant because the p value of 0.127 for the Wald Z (= 1.526) is higher than 0.05. This leads to the conclusion that WPI do not vary significantly by ward. Consequently, an analysis with just fixed factors might be enough. See annexure E2.1.

b. Fixed model � The -2LL indicates the best fit of the model to the data. Its value is – 667.638. � Ward is significant as fixed effect, F (5, 314) = 29.540, p = 0.000 < 0.05. � Intercept (interpreted as the overall mean of the dependant variable) shows a slope

significantly different from 0 and indicates that ward has an effect on the mean value of WPI significantly different from 0, F (1, 314) = 2.289E4, p = 0.000 < 0.05.

� For the fixed effects estimates, there is 95% confidence that coefficients for wards are significantly different from 0 and ward is significantly related to WPI.

� For the estimate of covariance parameters, as there is neither random effect nor interaction effect, we have only the conditional estimates for the residual, that is the unexplained variance in WPI after controlling for access power and sampling of the random factor, ward. The value of the estimates is significant, Wald Z = 12.530, p < 0.05. The standard error on residual is 0.000517. See annexure E2.2.

c. Mixed model � The Chi-square difference test statistic is not significant (χ2 (1) = -12.113, p > 0.05).

Therefore, there is no need to add higher modifier and perform a Linear Mixed Modelling. A simpler model will suffice. See annexure E2.3.

d. The simpler model: the linear regression The choice of the simpler model is guided by the results already on shore. From both the null and fixed models, it is revealed that ward is significant as fixed effect and the WPI mean roughly do not vary significantly from ward to ward. Considering the probable difference in the input of each variable, despite the similarity of the resulting index, the linear regression considering the WPI at


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ward level (the ward mean) is chosen for the modelling which is performed in the next section. The choice for ward level is constrained by the format of the census data which is going to be used for the inference. The census gives the data at ward level. Thus, the requirement of studying the index at household level established in section 5.1.2 cannot be fulfilled here.

5.1.4. Modelling of the WPI aggregated at ward level

The water poverty index aggregated at ward level (by computing the mean value) is presented in table 5.2 hereafter. All variables’ values are standardised on a scale of 0 to 10031.

Table 5.2: WPI at ward level

Variables of the water poverty index Ward Reso

urce Access Capacity Use

Environment

WPI Access*

fit residuals

Capacity* linear fit residuals

Ward 14 74.07 57.71 33.20 77.22 100 61.88 0.2273 -0.9148

Ward 16 100 48.96 60.29 91.54 100 73.15 -0.2696 -1.8163

Ward 43 100 63.19 53.26 91.36 100 74.89 0.1447 3.0823

Ward 47 100 53.61 30.72 70.63 100 62.76 0.5963 1.0795

Ward 81 100 63.05 59.19 81.22 100 75.10 0.8854 0.6279

Ward 94 74.07 61.15 45.96 76.16 100 66.47 -1.5591 -2.0578

* This column is addressed after equation [4].

In the table, the lowest values of the index appear in the medium service level wards except for ward 47 whose value is close to these even though it is located in a high level service area. It is the same for the variables Use and Capacity. For the variable Access, ward 16 presents the lowest value. The relative situation of each ward regarding others is better visualised in the spider graph in figure 5.3. Another feature revealed by this table is that the values of the index are highest in Dombivli for the two service levels. This finding match the field truth because Dombivli is more developed than Kalyan and probably influences on ward features and households’ characteristics.

Figure 5.3: Graph of WPI at ward level

31 The standardisation is done for the values at household level before the aggregation to ward level.

WPI at ward level

0

50

100Resource

Access

CapacityUse

Environment

ward 14 ward 16 ward 43ward 47 ward 81 ward 94


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This graph in figure 5.3 shows the water poverty index for the six wards surveyed as a set of its five components. The six wards show roughly the same trend for all variables.

The linear regression identified in the previous section as the model to fit to the WPI data is reported hereafter. Since the purpose of the modelling is to use the census data for inference to the study area (see section 4.2.5), the regression is performed for each variable of the index to ease the choice of the adjusting variable later. Due to the concept and the specific design of the index in the current research, the variable Resources is constant throughout a ward and across wards of the same service level and the variable Environment is the same for all wards in the study area (see section 4.1). This leaves the variation of the WPI in the study area to depend basically on the three remaining variables, the Access, the Capacity and the Use.

Hypothesis setting for the linear regression model The linear regression is looking at the three variables Access, Capacity and Use as predicting the water poverty index. The null hypothesis (H0) is that the variable (Access or Capacity or Use) doesn’t make any important contribution to predicting the water poverty index. Accordingly, the alternative hypothesis (Ha) states that the variable contributes to predicting the index. The level of confidence is 95% and the level of significance is α = 0.05.

Reporting the linear regression model Outputs of the regression analysis are in annexure E3. For the variables Capacity and Use, and according to the results, we have to reject the null hypothesis and conclude that the variable makes a significant contribution to predicting the WPI. For the variable Access, the test fails to give significance to its contribution to the WPI. The regression model is reported hereafter.

� Access From the analysis, only 6.8% of the variation in the water poverty index can be explained by the variation in the variable Access, supporting the conclusion that this model predicts badly the WPI. R2 = 0.068, F = 0.292 (p = 0.618), t constant = 1.772 (p = 0.151), t Access = 0.540 (p = 0.618).

� Capacity The analysis reveals that 89.5% of the variation in the WPI can be explained by the variation in the variable Capacity leading to the conclusion that the variable Capacity makes a significant contribution to predicting the WPI. R2 = 0.895, F = 34.115 (p = 0.004), t constant = 12.824 (p = 0.000), t Capacity = 5.841 (p = 0.004).

� Use From the SPSS output, 65.5 % of the variation in the water poverty index can be explained by the variation in the variable Use meaning that this regression model predicts the WPI at the threshold of significance. R2 = 0.655, F = 7.611 (p = 0.051), t constant = 1.284 (p = 0.268), t Use = 2.759 (p = 0.051).

The regression analysis shows the variable capacity as the best significant predictor of the WPI with the equation: Y = 0.449X + 47.878 [3]

Where Y is the water poverty index and X, the variable Capacity.


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Exploring a better fit for the model with the variable Access

Considering the main interest of this study which is on the urban households multiple deprivations, the data is checked for another type of model showing a better relation between the index and the variable Access. With a quadratic polynomial regression, the variable Access shows a large effect with an R2 value of 0.980 against 0.895 for capacity and 0.685 for use. The predicting equation is:

Y = 0.2624X2 – 29.335X + 880.67 [4]

Where Y is the water poverty index and X is the Access.

The column before last in table 5.2 displays the residuals on the WPI using equation [4]. The model fits disregarding the level of service and the residuals are higher in Dombivli. For the quadratic trend detected in the two other variables, the case of capacity is interesting because the goodness-of-fit is exactly the same as for the linear regression. Residuals on the WPI using equation [3] are shown in the last column of table 5.2. These residuals are higher than with the previous model (equation [4]) as expected with the lower goodness-of-fit.

Concluding the modelling of the WPI

Despite the better mathematical model found for the variable Access, it cannot be taught of statistically as “the change in the outcome associated with a unit change in the predictor”(Field, 2005, p. 155). In this view, the correlation of the index with the variable Capacity is more direct. In addition, with a Pearson’s correlation coefficient of 0.95, capacity shows a large effect in the model. Consequently, the model the most appropriate for inference of the WPI is equation [3].

Figure 5.4 displays the trends fitted to the WPI in the regression analysis.

Limitations

� A major drawback of this model fitting is the sample size. Six is a very limited number to conduct a good analysis. Nevertheless, with the very good fit of the models, the inference can be considered fail-safe at 95% confidence.

� One analytical interest of these findings is the possibility to relate mathematically (and in the frame of the values of the variable Access), at ward level, water poverty to level of access (thus, households’ deprivations); yet this is not statistically useful.

� The model concluded for the water poverty index is only useful with availability of Capacitydata. In the study area, only the six wards surveyed avail those data. As a consequence, a further step is required to determine the spatial distribution of the designed index in the study area.


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Figure 5.4: Trends in the correlation of the WPI to people-centred variables at ward level for the wards surveyed


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5.1.5. Spatial distribution of water poverty in the study area

To determine the spatial distribution of water poverty in the study area, the census data is used as basis for the generalisation of the findings in the six surveyed wards to the remaining forty-four in the study area (see section 4.2.5). As stated previously, instead of the polynomial model for ward means relating the index to the variable Access, the linear model using the Capacity is chosen for its statistical simplicity. More, the matching possibility with the available portion of the census data is higher with Capacity than it is with the variable Access. Indeed, the census data got during field work is very limited. Considering the design of the capacity index (composed of income level and education level), the similar elements in the census data are the employment and literacy features. These are ‘ward population’, ‘number of workers in ward’, ‘number of main workers in wards’, and ‘number of literate people’. With the last three parameters, the spatial distribution of the index is determined as follows, referring to section 4.2.5.

� Plot the WPI against each of the three identified parameters for the six wards surveyed � Fit trend lines to the plotted data32. � Choose the parameter with the highest R2 value. � Correct33 and use its equation to extrapolate a value for the WPI in the wards of the study

area. o Build again repeatedly the mathematical model with the same parameters on the

basis of five wards and validate the model with the sixth, assuring for each of the six wards to serve for the validation once.

o Chose from the six mathematical models obtained, the equation producing the least standard error.

o Apply the chosen model to extrapolate the WPI for the study area. � Visualize in ArcMap.

After plotting the three identified parameters as percentage in ward, the linear trend gives as R2

values, 0.071 for ‘main workers’, 0.142 for ‘workers’, and 0.269 for ‘literate people’. With a quadratic polynomial trend the highest value of 0.6013 is found for the parameter ‘main worker’ which is therefore chosen for the approximation of the index in all wards of the study area.

Iterations for building the model for the extrapolation of the WPI

Six iterations are performed with the six wards. For each case, the model summary, the mathematical model, the predicted values with the error terms, and the plot are reported in one table (see annexure E4). For all mathematical models, Y represents the ward water poverty index and X represents the percentage of main workers in ward. During the iteration process, the validation with the sixth ward permits to quantity the uncertainties in the model built on the basis of the other five wards.

32 This will lead to the best mathematical equation (highest R2) generating low residuals. The interest in this undertaking is to get values for water poverty level, not to establish a statistically meaningful relationship between the index and the identified census variable. 33 The model needs to be validated before being applied to the study area. Due to the limitation of the dataset, five wards are used to build the model which is validated by the sixth. After sketching this for the six wards, the better situation will be taken as reference.


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Choosing the model for extrapolation of the WPI

With the difficulty to prove statistical significance of the residuals on validation predicted values, the model presenting the lowest residual is considered. That is the model excluding ward 81. Unfortunately, like for four other cases this model does not show significance. Meanwhile, from the model summaries tables in annexure E4, the better fit and lowest standard error of the estimates are shown when ward 47 is left out for the model design. In that case, residuals are very low, except for ward 47 where it is the highest of the series. Since ward 47 is over-predicted by all models, it means that it somehow powers down their goodness-of-fit. Therefore, the best is to have it out of the chosen model. Thus, the final choice is on the model in equation [5] excluding ward 47.

Y = –0.475X2 + 30.218X – 403.458 [5]

Limitations

Although this choice of predicting model has the advantage of keeping a good accuracy for all other wards, the major consequence is that all wards resembling ward 47 will have a largely over-estimated WPI value. Any attempt to correct the model for ward 47 will fail to apply to the study area because of the difficulty to ‘recognise’ wards similar to the 47. As shown in table 5.3 hereafter, the only variable in use, ‘percentage of main worker in ward’, doesn’t offer any discrimination in the figures.

Table 5.3: Percentage of main workers in ward

Ward WPI Percentage of main worker in ward

14 61,88 26,36

16 73,15 28,19

43 74,89 29,94

47 62,76 28,56

81 75,10 33,40

94 66,47 36,64

Ward 47, as will reveal the analysis is probably a slum-like ward. Recalling that from census 2001, 44% of the population lived in slum and slum-like conditions (CDP, 2007)34, the risk here is to underestimate the water poverty condition of an important part of the population since high values of WPI indicates better conditions. To point out another limitation, it is worthy to highlight that the fitting model is only an approximation from an eight years old dataset in the face of data shortage. Recalling the growth (mainly of the population and thus in informal development) conditions of the city which is said to be fast because of the position to Mumbai (CDP, 2007), the figures used to build the model are likely to have greatly changed. This is made more understandable by the City Development Plan where is

34 There is no data on the population living in slum-like conditions in the acquired geodatabase from field work.


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reported the shortage of all urban services as a consequence of ‘various population factors’ (pp. 27-30). This view is further supported by the reference that the study area is among the most demanding sectors of public services in the municipal corporation (see section 3.1.1). A third limitation is that the water poverty level is represented by only one value in the wards not surveyed instead of six comprising the components. The usefulness of these extrapolated values is therefore restricted to the global ranking of wards in the study area; they will have very little part in the analysis which requires the components along with the water poverty index.

The extrapolated values of the WPI in the study area are in annexure D2 and displayed in figure 5.5.

Figure 5.5: Water Poverty Index in study area as extrapolated from census 2001

The map is displayed in two classes below city mean (73.16) and one above. The first class is below the mean minus two standard deviations (65.12); the second is above the first class and below the city mean. Referring to the values of the six surveyed wards in tale 5.2, ward 47 is in the same range as 14 and 94 and ward 16 is close to 43 and 81. Thus, the map visualises how the model over-predicted ward 47 and under-predicted ward 16. Due to the same effect of the model on similar wards, some of the wards are probably in the wrong poverty level class, for example in the medium poverty level


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class (65.13-73.16) instead of the high poverty level class (58.00-65.12). With data shortage, it is difficult to detect such cases.

5.1.6. Exploratory analysis for the WPI extrapolated at ward level

For the city, only the index extrapolated at ward level is available, since the components could not be extrapolated due to data shortage. The figures and graph for exploring the index are presented in table 5.4 and figure 5.6. Table 5.4: Descriptive statistics of the WPI extrapolated at ward level

N Range Minim

um Maxim

um Mean Std.

Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Std. Error Statistic StatisticStd. Error Statistic

Std. Error

WPI 50 19,09 58,05 77,14 73,1580 ,56838 4,01907 -1,580 ,337 3,201 ,662

Valid N (listwise)

50

When comparing these figures in table 5.4 to the descriptive statistics of the index for surveyed wards (table 5.1), the gaps are important. The trends are as follows.

� The mean is higher here than previously.

� The standard deviation is lower in this case.

� The minimum is higher and the maximum lower, thus the range is lower here.

� The skewness is higher with a higher standard error in the current case.

� From figure 5.6, wards 6 and 17 appear as outliers and the distribution is left skewed suggesting that there are some wards behaving very differently from others with regard to the WPI. The majority of households present a WPI between 71 and 76.

Figure 5.6: Boxplot of WPI extrapolated The comparison of the two statistics reveals the double effect of aggregation and extrapolation on the index. Once more, the data proves that the lowest level of aggregation is the best for analysis.


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5.2. Analysis 1: Investigation of the water poverty index in Kalyan-Dombivli

Referring to section 5.1.2 stating the usefulness of it, all analysis in this sub-chapter is conducted at household level. The investigation of the index is done by the sensitivity analysis achieved through the correlation analysis (performed by the authors of the index during pilot experiences) and refined by looking at the variability conditions of the index and components.

5.2.1. Sensitivity (to the components) analysis

The correlation analysis is conducted between the WPI at household level and the variables for the surveyed wards. Due to the constancy of the variables Environment and Resources as explained in section 5.1.2, the correlation analysis concerns the three other variables.

Hypothesis setting for the correlation analysis The aim being to look at the correlation among the variables and with the WPI, the null hypothesis (H0) for each pair of variables is that there is no correlation between those variables. Accordingly, the alternative hypothesis (Ha) states that there exists a correlation between the two variables under consideration.


Reporting the correlation analysis

Correlation matrixes are compiled in table 5.5. According to the results in that table, we have to reject the null hypothesis wherever Sig. < 0.05 and conclude that there is a significant correlation between the two variables considered. In the cases where Sig. > 0.05, the conclusion is that the test fails to detect a correlation between the two variables.

The table reveals for example that there was a significant relationship between Access and WPI in ward 94, r = 0.561, p (one tailed) < 0.05 (the significance is ascertained even at 0.01 level).

The general observations are: � Access is negatively related to all variables in ward 14 with significance only on Capacity.

The Pearson correlation coefficient is very low for that relation in every other ward with no significance.

� The relation of Access to Use shows no significance in any ward except in the 47 where it is at the threshold value of 0.05 with the highest Pearson correlation coefficient of 0.203.

� With the WPI, Access shows no significance only in ward 14 with the highest correlation coefficient in ward 81 (0.663).

� Capacity shows significant correlation with Use in wards 16, 43 and 81. � The relationship between Capacity and WPI is highly significant in all wards. � Use and WPI are also significantly correlated in all wards with the highest correlation

coefficients in wards 14 and 47. � All variables show significant correlation to the WPI, except in ward 14 with Access.


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Table 5.5: Correlation between variables in the six surveyed wards

Correlations

WARD 14 Access Capacity Use Pearson Correlation -,477**Capacity

Sig. (1-tailed) ,001Pearson Correlation -,114 ,194Use

Sig. (1-tailed) ,242 ,115Pearson Correlation -,047 ,624** ,828**WPI

Sig. (1-tailed) ,386 ,000 ,000N = 40

**. Correlation is significant at the 0.01 level (1-tailed).

WARD 47 Access Capacity Use Pearson Correlation -,058Capacity

Sig. (1-tailed) ,320Pearson Correlation ,203* ,149Use

Sig. (1-tailed) ,050 ,114Pearson Correlation ,353** ,708** ,755**WPI

Sig. (1-tailed) ,002 ,000 ,000N = 67

*. Correlation is significant at the 0.05 level (1-tailed). **. Correlation is significant at the 0.01 level (1-tailed).

WARD 16 Access Capacity Use Pearson Correlation ,164Capacity

Sig. (1-tailed) ,132Pearson Correlation ,048 ,246*Use


Sig. (1-tailed) ,000 ,000 ,001N = 48

**. Correlation is significant at the 0.01 level (1-tailed). *. Correlation is significant at the 0.05 level (1-tailed).


Sig. (1-tailed) ,070Pearson Correlation ,060 ,308*Use


Sig. (1-tailed) ,000 ,000 ,000N = 50

**. Correlation is significant at the 0.01 level (1-tailed). *. Correlation is significant at the 0.05 level (1-tailed).


Sig. (1-tailed) ,273Pearson Correlation ,048 ,358**Use


Sig. (1-tailed) ,000 ,000 ,000N = 53



Sig. (1-tailed) ,087Pearson Correlation ,011 ,059Use


Sig. (1-tailed) ,000 ,000 ,000N = 62


In conclusion to the correlation analysis, the WPI is directly related to Capacity and Use which are also correlated in high service level wards except the 47. This suggests that water availability and/or capacity increase the use in one hand and that there are some characteristics that decrease the service


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level in ward 47 in the other. Those characteristics may be related to the ward or to the households (illegal connexion for example). The WPI is also correlated to Access in all wards except the 14 with a Pearson correlation coefficient always lower than for Capacity.

Exploring a meaningful predictor for the WPI

Considering the purpose of modelling, Access would have been a more meaningful predictor for the index than Capacity or Use in the current research focusing on household deprivations. In this view, a combination of Access and Capacity (the best correlated variable to the index) is examined. This combined variable is obtained by taking the average of the two values and called ‘the access power’. The rationale of this combined variable is to account in the same time for the capacity to manage water and the intrinsic actual access to it. The access power is used in the attempt of linear mixed modelling of the WPI. The correlation is reported and shown in figure 5.7 hereafter.

Figure 5.7: Correlation of the WPI to access power There was a significant relationship between the variable access power and the water poverty index in all wards, r = 0.686 for ward 14, 0.970 for ward 16, 0.966 for ward 43, 0.799 for ward 47, 0.945 for ward 81, and 0.871 for ward 94, p (one tailed) < 0.05. The Pearson correlation coefficients are higher here than with the sole Capacity. And the amount of variability in the WPI explained by the access power (R2) is higher for high service level wards, except for the 47. The correlation is positive for all wards with a high access power leading to a high WPI. Recalling that the higher the WPI, the better the situation (section 1.3.3), it means that households with high capacity have higher access to water and thus low water poverty.

Comparing the sensitivity at household and ward levels

The regression analysis in section 5.1.4 (ward level) shows the index more correlated to Capacity than Use and not to Access while the correlation analysis (household level) in the current section presents a sound relationship between the index and Access. Judging by the Pearson correlation coefficients, this relationship is the weakest in low service level wards but is highly significant in all wards except 14. In the face of such differences at different scales, it can be retained that:

� Given that the results at household level are more accurate than those at ward level (because of aggregation), the water poverty index is correlated to Access but the relationship is weaker than in the case of Capacity.

� The index looses much of its dependence on Access when aggregated at ward level. � The sensitivity of the index is mostly on Access.


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5.2.2. Variability conditions analysis

In the previous section, table 5.5 shows that the amount of variability in the index explained by a given variable varies from a ward to another. This section takes the sensitivity analysis a bit further and looks at the variability pattern of the index and its components. One way ANOVA is performed for the purpose.

Hypothesis setting for ANOVA The aim is to compare the wards regarding the variability of the WPI. The null hypothesis (H0) is that there is no difference between the means of the six wards. Accordingly, the alternative hypothesis (Ha) states that at least one mean is different and therefore, the ward related presents some differences regarding others.

The level of confidence is 95% and the level of significance is α = 0.05. The same hypothesises hold for the testing of the variables of the WPI.

Variability of the water poverty index

The between and within ward variability of the index are checked upon with one way ANOVA as structured in Field (2005). The SPSS outputs of the one way ANOVA are displayed in annexure F1 and reported hereafter.

According to the results, we have to reject the null hypothesis and conclude that the WPI mean is significantly different in the wards, F(5, 143.289) = 35.931, p < 0.05, w = 0.56.

o The within wards test reports high unsystematic variation of the WPI. o The between wards found significant differences for ward means which are

discriminated further with post hoc tests. The model explains third of the variations as the model sum of squares is 0.956 and the residual sum of squares is 2.033.

The Hochberg’s GT2 and the Games-Howell are the post hoc tests performed with the ANOVA. They agree to give non significance to the difference between ward means for ward 14 & 47, and 14 & 94, and 16 & 43, and 16 & 81, and 43 & 81 and 47 & 94. The Homogeneous subset showing statistically similar ward means confirms the two first tests with the conclusion that wards 14, 47 and 94 are statistically the same regarding their group mean, and wards 16, 43 and 81 have statistically the same mean. These findings match the study field truth pattern in the following way:

� Wards 14 and 94 are from medium level service area.� Wards 16, 43, and 81 are from high level service area. � Ward 47 is located in high level service area but is a slum like ward.

In partial conclusion, the water poverty index is significantly service level and ward dependant. The ward dependency may be explained by unknown ward characteristics influencing households’ behaviour and reception of water provision. These influencing factors could also result from low service level islands within level of service areas or other factors such as socioeconomic characteristics. Another finding in this section is that the within wards variations are unsystematic and there are statistical similarity clusters for the WPI ward means. These findings are checked in the next sub-section for the variables dealt with in the analysis.


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Variability of the WPI components

In this sub-section, ANOVA is performed with the same specifications as for the WPI in the previous sub-section. With the results, we have to reject the null hypothesis for all variables and conclude that the mean is significantly different in the wards. The ANOVA is reported hereafter along with the interpretation of outputs for heterogeneous detection. See these outputs in annexure F2.

� Variable Access� The mean of the variable Access is statistically different in the wards, F(5, 140.397) =

12.040, p < 0.05, w = 0.38. The model explains little (a bit more than the sixth) of the variations as the model sum of squares (0.586) is very low regarding the residual sum of squares (3.084).

� From post hoc test as reported here, the homogeneous subsets table presents three clusters of wards with statistically similar means: 16 & 47, and 47 & 14, and 14 & 94 & 81 & 43. See annexure F1.

� Variable Capacity� The mean of the variable Capacity is statistically different in the wards, F(5, 142.890)

= 30.774, p < 0.05, w = 0.52. The model explains nearly third of the variations as the model sum of squares is 4.337 and the residual sum of squares is 10.967.

� The homogeneous subsets in post hoc test report three clusters of statistically similar means: 47 & 14, and 94 & 43, and 43 & 81 & 16.

� Variable Use� The variable Use presents a mean significantly different in the wards, F(5, 142.072) =

9.554, p < 0.05, w = 0.30. The model explains very little (nearly the ninth) of the variations as the model sum of squares (62387.829) is very low regarding the residual sum of squares (521934.439).

� From post hoc test as reported hereafter, the three clusters of ward similar means are: 47 & 94 & 14 & 81, and 14 & 81 & 43, and 81 & 43 & 16.

� Access capacity � For the variable access power, the mean is statistically different in the wards, F(5,

141.877) = 29.501, p < 0.05, w = 0.50. The model explains nearly third of the variations as the model sum of squares is 1.387 and the residual sum of squares is 3.815.

� Three clusters of statistical means are revealed in the homogeneous subsets’ table: wards 47 & 14, and 94 & 16 & 43, and 16 & 43 & 81.

All variables present statistically different ward means with clusters of similarity and unsystematic variability within wards as did the water poverty index in the previous sub-section. None of the similarity clusters match completely the water poverty index’s or the service level. However, wards 14 and 47 appear always in the same cluster. More, they are isolated for the variables Capacity and access capacity as for the WPI. This leads to two deductions:

o Access and Capacity together influence the WPI more than other variables. As a matter of fact, the portion of the variation explained by the model (1/3) is the same for the WPI, the


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variable Capacity and the combined variable access capacity. Half of this portion (1/6) is explained by Access which thus has little effect on Capacity in the combination of both variables.

o Influencing factors for the variables are of the same type in all wards and their combination isolate wards 14 and 47. Ward 14 being a slum and ward 47 being probably a slum-like one enable the assumption that the influencing factors are unmeasured households’ socioeconomic characteristics and unidentified ward characteristics.

So far, the examination of the designed index and its components maintained at household level reveals non patterned variability within wards and statistically similar ward mean in wards with the same service level and similar influencing factors. These influencing factors are non captured ward characteristics and unmeasured socioeconomic characteristics of households and probably other anthropocentric and ecological dynamics. The index shows also service level dependency and the combination of the variables Access and Capacity strongly influences the WPI ward mean pattern.

5.3. Analysis 2: The water poverty index and the poverty landscape of Kalyan-Dombivli

The work on urban households’ multiple deprivations in Kalyan-Dombivli is still in progress. Consequently, there is little material to support the analysis in this sub-chapter. Nevertheless, the new IMD is calculated and compared to the WPI as described in section 4.4.1.

5.3.1. The new Index of Multiple Deprivations in Kalyan-Dombivli

The new IMD is calculated at Administrative ward level, which represents seven area differentiations for the whole municipal corporation. The census 2001 data is used for this purpose as indicated in section 4.4.1. The new IMD as developed by Baud et al. (2008) is composed of four of the five capitals35 of the livelihoods framework as shown below. The indicators within the capitals and the capitals within the index are combined by weighted sum and considering them having equal weight.

� Percentage of households in scheduled caste is taken for social capital. � Percentage of literate people, percentage of main workers and household dependency rate36

are chosen for human capital. � Percentage of households using banking services and percentage of households with a scooter

represent the financial capital. � Percentage of households using a hand pump, percentage of households having no latrine,

percentage of households having no electricity and percentage of households having little

35 The natural capital is not taken into account in the framework developed by Baud et al. (2008) because it concerns livelihood aspects not captured in the census data (see note 23, p. 1407). 36 The meaning of that indicator gives the way of its calculation. “What the earned income means for the household depends on the number of ‘consumers’ within the household it has to maintain. For this reason, we have included the ratio of people dependant on the working people within the household, quantified by the average number of dependants per household at ward level”(Baud et al., 2008, p. 1394).


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space37 are taken for the physical capital. See annexure G4 for details of the calculation. The results of the new IMD are summarized in table 5.6 and visualized in figure 5.8 hereafter.

Figure 5.8: The new Index of Multiple Deprivations in the study area

These table and figure show the lowest poverty level in Dombivli hosting administrative wards G and H. The highest values of the IMD appear in wards A and D. The medium values are in wards B, C, and F. The last column of the table indicates the location on the six surveyed wards within the municipal corporation. Thus, surveyed ward 43 for example is located in administrative ward B and has an IMD value of 0.71 and a social capital index of 0.65. Referring to previous results, a gap is observed between the IMD and the WPI. The case of wards 43 and 47 behaving very differently for the WPI but having the same value range for the IMD is instructive. This can be explained by the draw back of aggregation on the IMD (meaning that both wards do have different IMD values) or by the fact that the IMD reflecting cumulative poverty could

37 The number of dwelling rooms occupied by the households is presented in the census in seven categories as no exclusive room, one room, two rooms, three rooms, four rooms, five rooms, and six rooms or more. The households having little space are considered as having no exclusive room or having more than two people for one room.


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be similar for two locations as a result of the combination of diverse types of deprivation scoring differently.

Table 5.6: The new IMD and components38 in Kalyan-Dombivli

Adm. Ward39

Social Capital

Human Capital

Financial Capital

Physical Capital IMD Surveyed

wards 1 A 0,95 0,74 0,60 1,00 0,82 - 2 B 0,65 0,75 1,00 0,43 0,71 43 3 C 0,46 0,90 0,95 0,61 0,73 47 4 D 1,00 0,80 0,60 0,82 0,80 14 & 16 6 F 0,52 1,00 0,93 0,42 0,72 - 7 G 0,27 1,00 0,91 0,35 0,63 94 8 H 0,38 0,91 0,72 0,41 0,60 81

5.3.2. Correlation analysis for IMD and WPI

The correlation analysis for the IMD and the WPI is performed between the two indexes as well as their variables and considering the WPI aggregated at electoral ward level. It is done first for the IMD and the capitals to ease the analysis of the WPI behaviour regarding the IMD40. Table 5.7 displays the variables participating into the correlation analysis.

Hypothesis setting � Capitals and IMD

The objective is to find out whether there is a correlation among the Capitals and with the IMD. The null hypothesis (H0) is that there is no correlation between each pair of variables. Accordingly, the alternative hypothesis (Ha) states that there exists a correlation between the considered pair of variables.

� Components of WPI, WPI, Capitals and IMD For the three components, the WPI, the Capitals and the IMD, the null hypothesis is that there is no correlation between pairs of variables and the alternative one is that a relation exists.


38 The values reported for the components of the IMD in this table are the standardized values interring the computation of the IMD. 39 In the census, the wards are enumerated 1, 2, 3, 4, 5, 6, 7, 8 while in the geodatabase they are named A, B, C, D, E, F G, and H. In 2002 ward E (or 5) has been removed from the municipal corporation (CDP, 2007, p. 51). That is why it is missing in the list of wards and the outputs. 40 The normal procedure would be to study thoroughly the IMD, to see for example the level of participation of each capital to the overall level of poverty. This task is being taken care of by the team of researchers leading the project heading the current study. Thus, the index is introduced here just for analysis purpose.


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Table 5.7: Variables in the correlation IMD-WPI analysis

Variables of the WPI Capitals of the IMD Ward

Access Capacity Use WPI

Soc. C. Hum. C. Fin. C. Phys. C. IMD

14* 0.58 0.33 0.77 0,62 1,00 0,80 0,60 0,82 0,80

16* 0.49 0.60 0.92 0,73 1,00 0,80 0,60 0,82 0,80

43 0.63 0.53 0.91 0,75 0,65 0,75 1,00 0,43 0,71

47 0.54 0.31 0.71 0,63 0,46 0,90 0,95 0,61 0,73

81 0.63 0.59 0.81 0,75 0,38 0,91 0,72 0,41 0,60

94 0.61 0.46 0.76 0,66 0,27 1,00 0,91 0,35 0,63

* As stated in the previous section, wards 14 and 16 located in the same administrative ward hold exactly the same values for the IMD and components as seen in the table. There is not enough information to disaggregate the IMD for each electoral ward. According to the results, we have to reject the null hypothesis for Social and Human Capitals, Socialand Physical Capitals, Social Capital and IMD, Physical Capital and IMD, Physical Capital and Access, and Access and IMD, and conclude that there is significant correlation between each pair of variables. In others cases, the test fails to detect a relationship in pairs of variables. Correlation matrixes are displayed in tables 5.10 and 5.11 hereafter.

Table 5.8: Correlation IMD and Capitals

Correlations Social Capital

Human Capital

Financial Capital

PhysicalCapital

Pearson Correlation -,795*Human Capital Sig. (1-tailed) ,029

Pearson Correlation -,669 ,228Financial Capital Sig. (1-tailed) ,073 ,332

Pearson Correlation ,890** -,517 -,702Physical Capital Sig. (1-tailed) ,009 ,147 ,060

Pearson Correlation ,910** -,667 -,461 ,934**IMD

Sig. (1-tailed) ,006 ,074 ,179 ,003N = 6

*. Correlation is significant at the 0.05 level (1-tailed).


From table 5.8, � The analysis shows the highest significance for the correlation of Physical Capital to IMD,

followed by Social Capital to IMD, and Social Capital to Physical Capital. � The relationship between Social Capital and Human Capital is negative. � Financial Capital doesn’t present any significant correlation with any other component nor

with the index. The high correlation of the index to Physical Capital in the study area is interesting for this study addressing a specific aspect of that capital.


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Table 5.9: Correlation IMD and WPI

Correlations Social Capital

Human Capital

Financial Capital

Physical Capital New IMD

Pearson Correlation -,556 ,178 ,424 -,793* -,736*Access

Sig. (1-tailed) ,126 ,368 ,201 ,030 ,048Pearson Correlation ,001 -,149 -,192 -,258 -,305Capacity

Sig. (1-tailed) ,500 ,389 ,358 ,311 ,279Pearson Correlation ,460 -,676 -,183 ,090 ,201Use

Sig. (1-tailed) ,179 ,070 ,364 ,433 ,351Pearson Correlation -,056 -,277 ,007 -,357 -,347WPI

Sig. (1-tailed) ,458 ,297 ,495 ,244 ,250 N = 6 *. Correlation is significant at the 0.05 level (1-tailed).

From table 5.9, � The correlation shown between Access, Physical Capital and the IMD match the findings

from the previous table. The negative relationship reads as a high access to water resulting in ‘less’ lack of physical capital and low level of poverty.

� The variable Access of the WPI represents one sub-component of the IMD in the physical capital. Their correlation reveals this variable as a tie point between the two indexes.

� The non correlation of the WPI and the IMD or the physical capital is in the continuation of the previous findings in section 5.1.4 showing no direct relationship between Access and WPI aggregated at ward level.

To sum up the findings, access to water reveals as an indicator of overall poverty in the wards surveyed.

Limitations

Despite the consistency of the findings from the correlation analysis, there are some limitations. � Like in the case of the water poverty index and its components, the correlation analysis may

have been hindered by the number of cases which is six. � The level of analysis doesn’t suit the dataset according to the conclusion in section 5.1.2

which suggests it to be the household one. This might have impacted on the results as demonstrated in concluding section 5.2.1.

� The IMD is designed at a very highly aggregated level (Administrative wards) while the analysis is performed at a lower level of aggregation (electoral wards). The case of Wards 14 and 16 is informative (see table 5.7). Since they are both located in administrative ward D, the same IMD and capitals values have to be given to them while they hold very different WPI and components calculated values. Consequently, this situation might have affected the accuracy of the results.


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� This analysis misses the slum issues which are from common poverty factors and measures in India (Baud et al., 2008, p. 1401), due to the scale of design of the IMD which fails the index to address them in a rough analysis as done in this study41.

5.4. Analysis 3: The water poverty index at policy level

The policy potential of the WPI is examined in this sub-chapter first by appraising the ability of the index to map the city profile in water delivery and second by exploring the policy response to the index.

5.4.1. Appropriateness of the WPI for mapping the water delivery situation in the city of Kalyan-Dombivli

With the purpose to study the appropriateness of the WPI for mapping water poverty in the study area, Pearson’s chi-square test of independence of two categorical variables is performed between the index and the satisfaction of households with the water service as explained in section 4.4.2. The assumption here is that an association between the two variables will prove the appropriateness of the index to reflect the outcome of water delivery. This variable ‘satisfaction of households with the service’ is chosen because it wasn’t used in the design of the index. It is therefore playing a validating role for the dataset used in addition to being a medium for testing the accuracy of the designed index.

Hypothesis setting

The null hypothesis (H0) is that there is no association between the water poverty index and the satisfaction of households with the water service. Accordingly, the alternative hypothesis (Ha) states that there exists an association between the two variables.


Reporting the test

With the analysis’ results, we have to reject the null hypothesis and conclude that the chi-square detects the existence of a significant association between the water poverty index and the households’ satisfaction with the service. χ2 (4) = 69.72, p < 0.01. Cramer’s statistic is 0.33, p < 0.01. It is significant, suggesting a medium effect in the relationship.

The findings from the contingency table are graphed in figure 5.9.

The graph reveals a high percentage of households scoring below the group mean for the water poverty index (53%). The middle-class comprises 27% of households and the better-off households

41 This investigation, which is not the objective of the current study, is probably being taken care of by the research team. In any case, it wouldn’t have been convenient for the analysis in this case due to disaggregation needed from administrative ward to electoral ward level.


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are 20% of the total. In sum, satisfaction with the service increases with WPI, thus with decreasing water poverty level. The most unsatisfied households are those experiencing high water deprivations.

Figure 5.9: WPI and households’ satisfaction with the water service

For the chi-square, the WPI is defined in three classes. � The first class is

below the group mean (68.84).

� The second class is above the group mean and within one standard deviation (78.51).

� The third class is defined above group mean plus one standard deviation.

In conclusion to the chi-square analysis, the assumption made about the suitability of the index to reflect the outcome of water delivery in the city is met. And the WPI correlates with expressed need of the population in terms of access to safe water. See section 6.2.1 for conclusion on the appropriateness of the index to picture city condition in water delivery.

5.4.2. Policy response to water poverty index

As explained in section 4.4.3, a chi-square test is performed with two new variables taken from the household survey namely, response time to complaints and main problem faced in accessing the water service, to explore the possible policy response to the designed index.

Hypothesis setting

The null hypothesis (H0) is that there is no association between Access and ‘response time to complaints’ (or Access and ‘main problem with the water service’). Accordingly, the alternative hypothesis (Ha) states that there exists an association between the two variables.


Contingency tables are in annexure G2 and G3.


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Reporting the test for response time

From analysis outputs, we have to reject the null hypothesis and conclude that the chi-square reveals the existence of a significant association between Access and the response time to households’ complaints. χ2 (4) = 28.86, p < 0.01. Cramer’s statistic is 0.212, p < 0.01. It is significant, suggesting a small effect in the relationship. The results show that response time to complaints is exclusively within a week for high access households and the dominant for the two other classes too, where the more than a week response time decreases with increasing level of access. A high proportion of surveyed households present a low level of access and a very few of them do not express their complaints about the water service.


Figure 5.10: Access and response time to complaint

For the chi-square test, the access’ value is defined in three classes. � The first class is

below the group mean (47.99)

� The second class is above the group mean and within one standard deviation (58.72).

� The third class is defined above group mean plus one standard deviation.

Like in the case of the WPI, the graph of level of access to water reveals a high percentage of households scoring below the group mean (61%). The proportion is 30% for the middle class and the better-off group is the smallest with 9% of the total number of households. In total, the chi-square analysis shows high percentage of within a week reaction to complaints (77.5%), but doesn’t give the quality of the performance (for example, say if the response is satisfactory for the consumer). Consequently, actions to be taken may address the improvement of response time as well as the quality of the response after more information. In the next section, the analysis is taken further to the main problems facing households in accessing water service.


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Reporting the test for main problems

From the results of the analysis, we have to reject the null hypothesis and conclude that the chi-square test highlights a significant association between Access and the main problems faced households with the water service. χ2 (6) = 74.127, p < 0.01. Cramer’s statistic is 0.34, p < 0.01. It is significant, suggesting a medium effect in the relationship.


Figure 5.11: Access and main problem in accessing the water service

For the chi-square test, the classes are kept as defined previously for the variable Access.

The graph reads as the major problems are presented by households as respectively, poor response to complaint, no maintenance and low frequency or pressure in the lower access group. In the middle class, poor response to complaint is the most recurrent followed by low frequency / pressure. The high access class complains most about low frequency / pressure. The proportion of households not reporting problems with the water service increases with increasing level of access. This test complements the previous one and brings light and more focus on households’ frustrations. See section 6.2.1 for conclusion on the usefulness of the water poverty index at policy level.


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5.5. Summary of the computation and investigation of the water poverty index

Applying the methodology described in chapter four, the water poverty index has been computed in this chapter five at household level according to the level of data collection. It has been aggregated at ward level and extrapolated to cover the study area. After several iterations, a mathematical model has been derived to perform the extrapolation based on census data. As specific feature, the index has shown sensitivity to the variable Access in the sense that a change of scale from household to ward level implied a loss in the strength of correlation. It shows also dependency to level of water service and to not captured households’ conditions and ward dynamics. The assessment of the spatial behaviour of the index at both household and ward scales showed unstructured variability inside wards and statistically significant similarities between same service levels ward means. A two-phase analysis addressed the index at management and policy levels. First, the index was put to correlation analysis with the new IMD calculated with the framework developed in Baud et al. (2008). A significant correlation was found only between the variable Access, the Physical Capital and the IMD, leading to the conclusion that in considering the overall poverty, access to water is enough to indicate water poverty in the context of the study area. Nevertheless, putting forth the sensitivity of the index to change of scale, the good correlation found previously between the water poverty index and the variable Access at household level suggests an existing (but hidden by aggregation) correlation between the WPI and the IMD. In that case, water poverty would be a sign of poverty and vice versa. Building on theoretical frameworks, a chi-square test with a significant association found between the WPI and the households’ satisfaction with water service, the appropriateness of the index to picture the water delivery situation in the study area has been ascertained. In a step further and applying again theoretical frameworks, the substance of a possible policy response to the designed index has been derived from significant associations found between the WPI and the report of main problem facing households in accessing water service and with the response time to households’ complaints. The results of these three last chi-square tests are to be more elaborated in the next chapter. A major limitation to the statistical findings identified throughout this chapter is the number of wards involved in the analysis of the index. At household level, the sample size, 320, is adequate for good results, while at ward level the sample size, 6, is a very small number for accuracy of results. Nevertheless, the statistical results are set at 95% confidence level.


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6. Discussion of the findings

The goal of this study is to design the water poverty index and use it to map the city profiles in water delivery. It was meant to use primary and secondary data to address the provider as well as the receiver of the service along with infrastructures and policies. Unfortunately, the secondary data got is very limited – the water network for example is incomplete. Consequently, the design and analysis of the index rest on the household survey which concerns six wards out of fifty. With these limitations of the data input to the research, the analysis misses some interesting aspects such as spatial analysis. Nevertheless, every research objectives have been targeted and some output got. The discussion in this chapter is aiming at examining the findings with respect to the research objectives. For the purpose, the designed index is assessed first and its usefulness at policy and planning levels is examined in the second sub-chapter.

6.1. The index

The designed index is investigated in this sub-chapter through the variables as well as the resulting index as described in section 4.3.3. Doing this, the designed index and the variables are put in the wider research context related to the ongoing process of the water poverty index development as a concept and as a policy tool.

6.1.1. Assessment of the WPI components

For each variable of the index, the operationalisation and the resulting outcome and eventually the limitations are examined.

The variable Resources

At household level, the variable Resources indicating water availability is taken as the minimum per capita quantity indicated by the service level map to be delivered daily in the targeted area. “The Resources component differs from others in the index in that it can not be determined from household survey data but requires hydrological and hydro-geological information gathered from other sources”(Sullivan and Meigh, 2003, p. 522). In this study carried out in the context of a city, the value of Resources (counting for quantity, quality and variability of the water source) is taken as being already processed. The quantity is guaranteed by the availability of the river source flowing within municipal corporation limits, quality is met (rather supposed to be met) by the treatment given to the river water at the municipal water plant and variability is powered down by the use of infrastructure


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(see section 3.1.2). As such, the daily average per capita yield (188 litres42) could have been considered for all studied wards as they all depend on the Ulhas river source. However, for more accuracy, the quantity assigned per service level area is considered to also highlight the discrimination in water allocation within the city and try to capture it in the index. One limitation here is the accuracy of service level map (figure 3.2) used in this study which is unknown. For example, the variation of the residual pressure throughout the system mentioned in the City Development Plan cannot be located.

The variable Access

While assessing the index from pilot experience, Sullivan and Meigh (2003) suggest to capture the effect of infrastructure in the variable Access instead of Resources because “consideration of whether or not people have access to a sufficient quantity of water, an adequate quality of water, and whether it is available with an acceptable degree of reliability can all been thought of as aspects of access to water and they are all crucially dependant on infrastructure”(p. 523). In the current study context, this variable has focused on several deprivation aspects, specifically infrastructure and management related ones along with households’ coping strategies. As such, the variable in its current format fulfils the authors’ recommendations. However, there is a seeming overlap with the variable Resources on infrastructure. This is unavoidable for two reasons. First, Access as designed is people-focused and has to give account of their multiple deprivations and secondly, in the city, Resourcesreaches households via infrastructure. In the situation to inform on households’ deprivations, aspects of infrastructure captured in the two variables complement each other more than they overlap.

The variable Capacity

The variable Capacity combining the level of income and the level of education is made of six parameters collected from households. Its design follows the original concept which is already people-centred. The main characteristic of the outcome in this study is the higher correlation shown by the variable to the WPI regarding the Access. The story telling of this correlation is that a household financially able to face the challenge of paying for access to water and sufficiently educated to manage well the acquired water is less water poor in the study area. It highlights that a minimum (in quantity or quality) of water is always there, somewhere around people and the problem is the cost of access to it otherwise no one will survive (Satterthwaite, 2003). Thus, the role of this variable in the WPI appears as to go beyond the access and qualify households for water poverty status. As such, it may have some overlap with the first people-centred variable since the level of access of a household to water depends on its capacity and ability to successfully overcome deprivations. Yet, the variable Access cannot be withdrawn from the index (to avoid the overlap) without adulterating its meaning because this variable carries among others the complaints of households regarding the management

42 The household per capita daily water received could have also been used in the design of the index, but it might not have made a great difference since according to households, the quantity received is not exactly the same every days.


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and the infrastructures. Nevertheless, the overlap is in terms of Capacity influencing Access and thus both variables can participate into the water poverty status of a household.

The variable Use

This variable is taken as the domestic use by households to centre the index on them. In an urban setting and in the frame of the current research, this is the most obvious design. It fits well the index at high resolution as does the variable Resources which is also drawn close to the households. Both are expressed in litres per capita per day. Taken together, they could picture quickly the trends in demand satisfaction for policy purpose. The use may also be limited by the supply depending on the ease with which the complementary source is manageable. In this study, the lowest values for Use are found in lower supply level areas and in ward 47 (shifted from high to medium level by the sensitivity analysis) and the maximum of Use value is similar in all wards but with different frequencies. The variable as designed in this study is limited by the difficulty to know the daily quantity of water exactly used by households who gave a class value as answer to the survey question.

The variable Environment

This variable is still under examination for several reasons such as its dual meaning in the urban or rural context (Sullivan and Meigh, 2003; Sullivan et al., 2006a). The escape in this study produces an output which is a relative value for each river source with regard to the other. As a consequence, the study area depending on one river source, the variable holds the same value for all surveyed wards. Finally, it could have been skipped in the index. Nevertheless, the flat value held by it in this study doesn’t distort the structure and permits to keep the accuracy of the index regarding the resolution. Dealing with data shortage, the choice was made to find a value for the variable without compromising the integrity of the designed index. Despite that the process permits to allot a value to the variable Environment, it doesn’t match the concept which demands to foresee the ecological integrity in a sustainability perspective. The value got here is related to the quality of the raw water and tells about the impact of the environment on the water but not the impact of the management of the water on the environment. As mentioned by the original authors of the index, the variable needs to be worked out thoroughly. As a result of this situation, the striking question is about the usefulness of the variable in the index if it cannot be exactly captured (as in the case of Eastern Cape taken as the model in this study, see section 1.8.2). And specifically in the frame of this research, the question was about how to draw the variable close to households to get the high resolution. Considering that in the case of Resources, this was done by means of infrastructures, the variable could have represented for example the city environment and the environmental impact of infrastructures on water supply in the city for sustainable management. As such, the variable would have taken into account the environment of the households in the city which would then not have held the same value throughout the study area. This approach could not be taken in the study because of data shortage. The water network for example was only partly available.

6.1.2. Assessment of the resulting index

As conclusion to the assessment of the components, the index is completely designed at high resolution (household level). It is lacking the environmental dimension as predicted for a city context


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by the authors of the index from the origin. In the reference to the livelihood framework, the relation of Resources to the physical capital (because of infrastructures) is highlighted. So would have been Environment if designed as just discussed in the preceding sub-section43. The resulting index is evaluated hereafter with the framework presented in section 4.3.3.

� Accuracy Three out of the four variables composing the index in the study area are taken from household survey. The accuracy of the fourth one is discussed in the sub-section above on the variable Resources. With these, the index is accurate at household level which is the scale of measurement of the parameters during the survey. At ward level, the relation between the index and its components is similar to the one at household level44 leading to the assumption of accuracy. However, this accuracy might be put to question by the number of ward surveyed even though the number of household having participated into the correlation analysis is high enough for sound conclusions. A survey of a higher number of wards better spread in the study area might have reported different results at ward level. Unfortunately, for this study, the time pressure during field work didn’t allow to extend the number of wards to be surveyed.

� Replicability The design of the WPI in this study “would be replicable if the same data choices are made for other years, scales or countries”(Heidecke, 2005, p. 28) with expectations of sound results. The data choices in this study are guided by the approach which is people-focused and the socio-cultural context of the study area. With this regard, the index is replicable at household level since the design wouldn’t fit any other scale. However, in another setting, the choice and management of parameters will have to be adjusted to the socio-cultural context.

� Versatility The interdisciplinary output sought by the authors from the origin of the index is preserved in this study45. Despite the missing dimension of environment and even if some driving forces may be hidden by aggregation, the WPI as designed can be useful for several aspects of water management since accuracy is found both at household and ward levels. Each variable kept separately is a potential tool for stakeholders. With the Access component for example, consumer associations or local groups can lobby for action. Likewise, Capacity as a measure of socioeconomic characteristics can be a useful input to water provision planning or decision-making in discriminating between two locations presenting the same overall water poverty level.

43 In Sullivan et al. 2003, Environment relates only to natural capital. 44 The relationship pattern shown by the regression analysis which models the index aggregated at ward level is for a large part the same as the one found in the correlation analysis performed for the index at household level: Capacity predicts well the index, followed by Use while Access shows almost no correlation at ward level and an existing and significant – though weaker than expected – relationship at household level. The comparison made in section 5.2.1 retained the results at household level to characterise the index. 45 “The Water Poverty Index has been developed by an interdisciplinary team of environmental and social scientists, engineers and water resource professionals”(Mlote et al., 2002, p. 18).


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� Usability and utility One major feature of this study is that the WPI is designed exclusively in urban context avoiding the rural-urban duality in the measurement of the parameters. As such, the index is meant for direct use by the municipal corporation which is in charge of water management for the city as well as relevant stakeholders. The usefulness is enhanced by the versatility of the index.

Examining water indicators, Molle and Mollinga, (2003) came to the conclusion that “… they even out spatial and temporal heterogeneities that are paramount in the occurrence and understanding of water scarcity”(p. 536). This study tries to avoid that by designing the WPI at household level. Unfortunately, the variability within ward could not been modelled since it is not patterned. However, the accuracy of the index is kept when aggregated at ward level. Even though it is designed at household level, the resolution will be lowered by aggregation at ward level to inform area-based policies and the spatial heterogeneity will be lost as put by Molle and Mollinga. The temporal aspect evoked by these authors in their concern about the drawback of water indicators may not be crucial in urban context where infrastructure and management can power down to some extent seasonality problems. Nevertheless, another type of temporal aspect can be captured in the index if it is treated as a time-series data based on the fluctuating nature of the livelihood capitals backing up the concept46. The interest of such undertaking would be for example for monitoring purposes. These last aspects along with some others, planning and management-related, are examined next as the usefulness of the designed index.

6.2. The usefulness of the index

The findings on the usefulness of the designed index are examined in this sub-chapter. First, the discussion addresses the index at management and policy levels. After the policy level, a step further is taken to examine the index in professional planning practice.

6.2.1. Discussion of the results at policy level

In this section, the appropriateness of the index to map city profile, and the frame as well as the substance of policy response to the index are addressed. Both aspects call on all research results and are built on theoretical frameworks. As explained in sections 4.4.2 and 4.4.3, the rationale of the choice of variables used in the chi-square tests at policy level is that a public service delivery can be assessed by the outcomes considered from the user’s perspective. The three variables used are taken from the household survey.

46 While establishing the links between the livelihoods framework and the variables of the water poverty index, Sullivan et al. (2003) state that “as development occurs over time, there will be inevitable changes in the extent and availability of … livelihood capitals … To address any kind of poverty, access to these capital types must be redistributed …”(p. 193).


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Appropriateness of the WPI for mapping city profile in water delivery

The suitability of the index for mapping city profiles is derived from all outputs in addition to the tested association with households’ satisfaction with the water service. So far, several findings have arisen about the index as designed in this study. They read as follows.

� It is accurate, replicable and versatile. � It is water service level dependant, thus a sort of feed back to the management. � Similar values can be discriminated by their specific combination of variables. Consequently,

within water poor areas (cluster of similar values), further targeting is made possible. � The index is sensitive to households’ socioeconomic characteristics. Along with its variables,

it could target more than direct water issues. � It is strongly correlated to the component Capacity at household as well as ward level. This

variable can play as a short cut to the water poverty level. � It is correlated to a less extent to Access at household level. Households’ deprivations can

only be seen in the variable Access, thus the possibility to present each variable enhance the usefulness of the water poverty index for picturing the current situation.

� When extrapolated at city level, the WPI shows clustering of wards with similar water poverty level, defining water poor areas.

� The index doesn’t show correlation with the new IMD but its variable Access is highly correlated with the Physical Capital of the new IMD meaning that access to water is a sign of low poverty level in the study area.

� It presents highly significant association with households’ satisfaction with the water service. This is an expression of the consistency of the index with households’ perception of the service delivered, making the index a measure of the outcome of water delivery.

These findings are each the conclusion from the analysis of a specific aspect of the index. They characterise the index and make it usable in the dynamic of water delivery. Since the function of mapping is to picture the situation, these findings together can achieve it. To give an example from section 5.2.2, while the ward level water poverty index presents significant similarities shared by same service level wards, it hides a certain amount of heterogeneity and diverse combinations of the different components as shown by table 5.2. As such, wards 43 and 81 scoring respectively 74.89 and 75.10 for the index present respectively 63.19 and 63.05 for the variable Access, 53.26 and 59.19 for the variable Capacity and 91.36 and 81.22 for the variable Use. This diversity in the combination of the variables makes the index useful for mapping the profiles of the city in water delivery because it can show several aspects of the dynamic. With the versatility of the index and beyond a single value representing the overall situation, the availability of each component has the potential to enhance intervention policies and lobbying. In addition, considering the structural agenda of service delivery (provider, receiver, purpose, mean, output and outcome) described in chapter two and the ability of the water poverty index to address water management mentioned in section 2.4 and supported by Sullivan and Meigh (2003, pp. 515-516), the water poverty index as designed in this study is suitable for mapping the city profile in water delivery. As such, it can be useful for the provider, the receiver, the management (purpose and mean), measuring the output and evaluating the outcome since each of these aspects is addressed by either the overall index, either the components.


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Frame of the policy response to the water poverty index

The policy response is relevant to the usefulness of the index and refers to its conception. After discussing several approaches to set up a water poverty index, Sullivan concludes that “in reality, the most important challenge is to develop the appropriate degree of political will and institutional acceptance which will allow the index to be used as an objective criterion addressing water poverty”(2002, p. 1207). The political will and institutional acceptance could be informed by the traditional research question in service delivery, ‘who gets what, when, how?’ (Baer, 1985) examined in section 2.1.2. This question could be answered by the WPI and components. The possible policy response – aiming at supporting households in their fight against poverty – to the designed index, will work out this answer and take action consequently. Here are some salient points stemming from the analysis.

� Referring to the correlation analysis, the low correlation of the index with the Access in all wards is indicating that the value of Access needs to be enhanced (see section 5.2.1). However, it doesn’t say how or in which domain or aspects. Considering the composition of that variable which is mainly of households’ deprivations, it is the quality of the access that is revealed therefore as to be enhanced at least in aspects addressed in de design of the variable.

� The strong correlation found between the new IMD, the Physical Capital and the Access in section 5.3.2 sustains the view that access should be a priority area for policy intervention. The high correlation indicates that the Physical Capital is considerably causative to the overall level of deprivation and access to water being part of that capital in the new IMD, intervening on access will be a double response from policy makers.

� About infrastructure and management (particularly associated with the ‘when, how?’ of the research question), during the assessment of the index, it was shown that they are partly caught in the variables Resources and Access. Improvement of infrastructure and management may participate to improve the quality of access. Unfortunately, infrastructure and managements are insufficiently caught in the index at this stage of development and in the current study as shown previously. Despite the difficulty to evaluate the shortcomings in management and infrastructure, yet some problems are known such as oldness of the pipes in some parts of the city or water losses (CDP, 2007), which need to be corrected to improve the quality of water service (thus, the access).

In sum, a possible policy response to the water poverty index as designed in this study rests on the improvement of households’ access.

Exploring the substance of the policy response to the water poverty index

The framework ‘who gets what, when, how’ points out the variable Access in the frame of this study. To find out the substance of the possible policy response to the WPI designed in this study through the quality of access, two chi-square tests are performed between Access and response time to complaints and Access and the main problem in accessing the water service. These variables are chosen because they didn’t enter the composition of the water poverty index and the assumption is that an association found will indicate the aspects of deprivations to be tackled by policy intervention. The assumption is met, leading to the following, for policy intervention in targeting the most deprived areas.

� Improve the response time to complaints. � Improve the quality of response.


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� Improve maintenance of infrastructure. � Take action on the water network and improve the minimum residual pressure throughout the

supply system. This list is far from all-comprehensive when one considers the design of the variable access which was based on households’ deprivations such as daily duration of the water supply, daily quantity supplied, water quality, etc.

In conclusion, at policy level the index appears suitable for mapping the city profiles in water delivery and the analysis reveals the variable access as orienting the possible policy response. Several aspects are identified as substance of the response. In the next section, a visual interface is presented to aid policy comprehension of and reaction to the designed index.

6.2.2. Visual interface for policy makers

Appending to the previous section, this section gives some visual aids for policy-makers. The conclusion about the index as a policy tool is made with two maps regarding the priority areas and the needs to be addressed. � Figure 6.1 displays the water poverty situation in the city of Kalyan-Dombivli in three levels47:

o Low: WPI scoring from 73.17 to 77.14. (Above city mean) o Medium: WPI scoring from 65.13 to 73.16 (city mean). o High: WPI scoring from 58.00 to 65.12 (city mean minus two standard deviations).

From the map, wards that should be targeted in priority for intervention programmes – the high water poverty level wards – are viewed in red. Most of the low water poverty level wards are located in high water service level areas. Along with the map of the WPI should be shown the map of each component which would point out priority areas for households in the most deprived wards. Unfortunately, such maps couldn’t be produced in this study due to data shortage. However, the process is completed by producing the maps for the six wards surveyed. � The six wards surveyed are presented on the visual medium in figure 6.2. It displays the

components as well as the index in those wards. The map in figure 6.2 is more accurate than the first one since it is based on measured and not extrapolated values. For each variable, the classes are set using the group mean and the standard deviation considered from the household level computation as cut-off, since a group mean or standard deviation for six aggregated ward values will not have much meaning. Classes are defined hereafter. The variables’ values are in table 6.1.

The water poverty index o Class 1: 66.85 – 75.10: Above group mean. o Class 2: 64.01 – 66.84: Above class 3 and below group mean. o Class 3: 61.88 – 64.00: Below group mean minus half the standard deviation.

The variable Accesso Class 1: 48.00 – 52.33: Above group mean. o Class 2: 42.63 – 47.99: Above class 3 and below group mean. o Class 3: 40.54 – 42.62: Below mean minus half standard deviation.

47 This map is the same as in figure 5.5.


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The variable Capacityo Class 1:46.61 – 60.29: Above group mean. o Class 2:35.66 – 46.60: Above class 3 and below group mean. o Class 3:30.72 – 35.65: Below mean minus half standard deviation.

The variable Useo Class 1:135.10 – 155.91: Above group mean. o Class 2:124.40 – 135.09: Above class 3 and below group mean. o Class 3:117.73 – 124.39: Below mean minus a quarter of the standard deviation.

Table 6.1: Not standardized ward means of the WPI and components in the wards surveyed

Ward Access index Capacity index Use (lpcd) WPI 14 47.79 33.20 128.71 61.88 16 40.54 60.29 155.91 73.15 43 52.33 53.26 152.27 74.89 47 44.39 30.72 117.73 62.76 81 52.22 59.19 135.37 75.10 94 50.64 45.96 126.94 66.47

Figure 6.1: Water poverty level in Kalyan-Dombivli


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Figure 6.2: The WPI and components in the surveyed wards

Look up table Class boundaries

Variable Class Lower Upper

61.88–64.00 Minimum value Mean minus ½ SD 64.01–66.84 Mean minus ½ SD Mean

Water poverty index 66.85–75.10 Mean Maximum value

40.54–42.62 Minimum value Mean minus ½ SD 42.63–47.99 Mean minus ½ SD Mean Access 48.00–52.33 Mean Maximum value 30.72–35.65 Minimum value Mean minus ½ SD 35.66–46.60 Mean minus ½ SD Mean Capacity 46.61–60.29 Mean Maximum value 117.73– 24.39 Minimum value Mean minus ¼ SD 124.40– 35.09 Mean minus ¼ SD Mean Use 135.10– 55.91 Mean Maximum value

SD = Standard Deviation


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From figure 6.2, wards 14 and 47 are the most water deprived areas. They score below the group mean for all variables. However, water poverty hotspots don’t show the same level of need regarding all components. Consequently, different types of interventions are required for different types of components.

� In wards 14 and 47, water-related deprivations are worsen by socioeconomic characteristics. Consequently, priorities for households in those wards are socioeconomic needs such as housing conditions in addition to the water-related needs listed in the previous section. Although scoring well on Resources, ward 47 is the worst-off and should get high priority.

� Ward 94 is at risk despite the good score in access to water. The service level along with socioeconomic characteristics reveal as the priority issues.

� Despite the overall good state of ward 16 for the WPI, it is at risk regarding access to water. � Wards 43 and 81 appear as the best-off.

The policy response to the designed index in this study can not be given a comprehensive list of actions. This work produces a ‘user interface’ to inform decision-makers and constitute a database for action and monitoring since the diversity of deprivations resulting in the same level of water poverty can be specifically addressed. The index designed can therefore be useful as planning as well as management tool. This possibility is briefly examined for closing the discussion on the research findings.

6.2.3. Water poverty index as input to planning

This section concludes the discussion in the present chapter by the possible role of the water poverty index in urban water planning and management. The suitability of the index as input to planning is examined in the theoretical framework from Webster (1993). Put in the GIS agenda of planning practitioners, the water poverty index can be considered as a comprehensive planning tool. “The WPI provides a transparent framework on which decisions in water planning and management can be based”(Sullivan et al., 2003, p. 197). This statement rests on the versatility of the index allowing it to address several aspects of water management. To highlight the planning potential of the designed index, we first recall the rationale for planning according to Webster (1993). Planning is about the provision of two kinds of public goods: goods related to the built-up environment and goods related to policies in substantive planning theory. In this framework, the water poverty index is examined as descriptive indicator corresponding to the decision-making component of problem identification and as scientific input to planning48 (see table 2.1 in section 2.2.2). This problem identification is performed by measuring the demand for public goods. Webster distinguishes the derived demand (using demand indicators) and the expressed demand (using expression of demand by members of the public). The data requirements for the two types of public goods are as follows according to Webster (1993, pp. 711-714).

� Data on the demand for public investment in built up environment

48 The water poverty index is assumed here to be more useful as baseline and or performance indicator. Therefore, from Webster, the scientific inputs are more or less the same.


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o Monitoring supply of infrastructure from public and private operators responding to the question “where is the existing infrastructure?”

o Monitoring the location of consumers with the question “where are the demand points?”

o Monitoring the strength of consumers’ demand for infrastructure. � Data on the demand for government regulation

o Description of the supply of government regulation with the question “where are the existing policies?”

o Description of the demand for new policy responding to the question “where are the spill-over effects?”

More than the “where are the poor?” of poverty mapping, these ‘where’ questions can shape together the enterprise of citizen’s well-being building sought in public service delivery. The possible usefulness of the water poverty index in such way is depicted as follows.

� The variables Resources and EnvironmentThese variables, if designed adequately can help answering the first question in data requirement. Taken as a relative value, they could go beyond and permit to rank area units in quality assessment for prioritising purposes by extending the question to “what is the quality of the existing water supply infrastructure?” or “what is the interplay between these infrastructures and other types?”49 The missing link of institutional aspects in the original conception of the index limits the possibility to provide data on the demand for government regulation. Nevertheless, Resources and Environmentcould be useful if overlaid on existing policy maps; one could then derive location wise, failures of existing policy in water delivery. The third question could become then, “where are the shortcomings of the existing policies?”

� The variables Access, Capacity and UseThese variables can answer the second question in data requirement, each in its way. The variable Access, as designed in this study is the counterpart of households’ water-related deprivations. As a composite index, it is giving not only the overall demand, but also reporting several aspects of that demand with each component. The Capacity entirely shaped in the socioeconomic characteristics and the Use taken as the per capita consumption all carry different location-related consumer information. The extent of deprivations could be an indicator of the demand for new policy. More, spill-over effects could be derived from these data processed on a good time scale.

Starting as a baseline indicator, the WPI could be replicated over time at regular intervals to monitor the intra-urban inequalities in water delivery.

To conclude from all above, the water poverty index as designed reveals a useful tool as input to water provision planning and management.

49 This refers to the deduction in section 2.1.2 that public goods allocation should always be put in the wider context of the overall satisfaction and well-being of the citizens.


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7. Conclusions and recommendations

The objective of this last chapter is to summarise the study and present the achievements, the shortcomings and the open ends of the research. This set of information is shaped into three sub-chapters: the summary of the research, the conclusions and the recommendations.

7.1. Summary of the research

The first sub-chapter is recapitulating the research and appending to the entire work to scrutinise the results and report them within and possibly, beyond the study context. It is organised in three parts addressing the research frame, the case study, and the results.

7.1.1. The research frame

The research frame presents a summary of the theoretical background and the methodology applied in the study.

Theoretical background in brief

The theoretical background of this study aiming at mapping the study area’s main characteristics in water delivery with a locally designed water poverty index, well tried-out at management and policy levels, is two-folded. First, the water poverty index as a concept was identified and considered alongwith poverty mapping and public service delivery as a mean for mapping the water service provision in the study area. The water poverty index is conceptually composed of five variables: Resources, Access, Capacity, Use and Environment. In an attempt to respond to the research project – which is interested in the multiple deprivations facing urban households – heading this study, the current work focused on the variable Access in the water poverty index’s structure. Consequently, in the second fold of the theoretical background, the level of access to public goods as part of households’ asset base considered with the livelihood concept in the background was chosen to shape the variable Access for a better fit to the above mentioned project in addressing a specific type of urban households’ deprivations. It was considered that “if the criteria by which to judge who has provision for water and sanitation are set too low, the problem disappears. In a sense, 100 per cent of the population, both rural and urban, in all nations has “access to water” since, without that access, they would die”(Satterthwaite, 2003, p. 186). With this statement stressing the cost of access to water against the deciles of the provision, the variable Access was shaped as people-centred. This particular design of the Access has been incorporated to the original structure of the water poverty index to achieve the goal of the research. Accordingly, households’ water related deprivations were highlighted by taking the concept of access beyond the statistics of the provision to the level of households’ needs satisfaction. Therefore, based on Zérah (1998, 2000) and Satterthwaite (2003), ten indicators have been identified to compute the access index, namely, moment of reception of the municipal water, duration of the reception, quantity received, source of drinking water and source of water for other use, replacement source at break of the municipal supply, daily collection time,


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collection time from replacement source, prevention strategy I (for the quantity), prevention strategy II (treatment level for quality). Another important feature of the theoretical background is the scale of measurement for the water poverty index. In literature, it is established that the efficacy and applicability of the water poverty index are scale-of-design bounded and the accuracy of the results is scale dependant in the same direction giving better accuracy at high resolution. Therefore, the household level was identified for the design of the water poverty index in the study area.

Methodology in a nutshell

The methodology applied in this study was much shaped by the original structure of the water poverty index, its specific conception for the study area and the context of the study area, covering from data capture to outputs analysis. Responding to the scale suitability requirement, a household survey based on a two stage-sampling strategy was applied for the collection of the data. Building on Sullivan et al. (2006a), the composite index approach has been applied for the computation of the variables as well as the resulting water poverty index. Statistic techniques of chi-square, correlation, regression, and ANOVA have served for inference and analysis of the results. The structural agenda of service delivery and the role of GIS-based indicators in poverty mapping derived from literature were also called upon to enhance the analysis.

7.1.2. The case study

The research under consideration has used a case study as a mean to implement the research frame. This section is examining how the interplay between the research questions, the methods, the data, the study area, and the results have shaped the case study. In applying the methods and theories summarised above to the case study, the research was guided by five research sub-objectives disaggregated into twenty research questions. The suitability of the research questions was revealed by their usefulness in designing the analysis for achieving the sub-objectives. Even though some questions were inter related (calling for exploring several aspects of the same fact and sometimes going beyond the scope of this study; that is the case of question 2, sub-objective 3), most of them were sufficiently specific to be addressed. Further, the suitability of the questions to hit the target is superimposed on the suitability of the methodology by the fact that all objectives were achieved (as shown in the next section) since the methods applied give satisfactory results. The satisfaction stemmed from the data collected which is sufficient at household level for sound statistical analysis. At ward level, it has proved insufficient to give strength to statistical results. However, this impacted very little on the research since the identified level of analysis for accuracy of the results is the household level which was sufficiently provided with data. The opportunity of having the data at household level permits to answer the research questions in the frame of the theoretical agenda. In addition, the delineation of the two pieces of the study area in the same level of unsatisfied public service demand and different locations in the municipal corporation made the study area appropriate to answer the research questions. This was meant to offer substance for detecting influencing factors in assessing the sensitivity of the designed index. Unfortunately, this benefit could not be taken from the analysis, due to the small number of wards involved in the study and also the huge limitation of the secondary data which prevent from having any spatial analysis involving infrastructures. Nevertheless, the results met expectations.


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7.1.3. The research results

The results of the research are recapitulated here sub-objective wise.

To design the water poverty index at a suitable scale

The choice of scale and the design of the methodology introduced the research to the first sub-objective. The embedment of the index’s concept in the socioeconomic context, the households’ behavioural strategies and the water management characteristics in the study area permit to design a local water poverty index at household level. The questions related to this objective have shaped the questionnaire for household survey which captured the desired characteristics.

To study the sensitivity of the water poverty index

Addressing the second sub-objective, the index was found sensitive to the variable Access, and to non captured anthropological and ecological dynamics. It was also found service level dependant and presenting unsystematic variations within wards and little variations between same water service level wards. The two restricting conditions found in literature and mentioned in section 7.1.1 above were presented by the index: its accuracy decreased with decreasing resolution; thus when aggregated at ward level, the relationship with the three people-centred variables changed slightly. The consequence is that any application at that lower resolution may suffer of that decrease of the accuracy.

To study the appropriateness of the water poverty index to report the current spatial distribution of water delivery

This third sub-objective has built on the two previous results in addition to theoretical frameworks. Beyond the first sign of appropriateness that is the water service level dependency, the index showed significant correlation with the variable Access and significant association with households’ satisfaction with the water service attesting that the index does represent to some extent the level of access to water and delivery performance in the study area. The design and analysis of the index at household level makes it robust against ecological fallacy which “refers to the problem of inferring characteristics of individuals from aggregate data referring to a population”(Pacione, 2005, p. 668).

To study the correlation between city profile in water delivery and poverty

This fourth sub-objective has suffered from scale of data availability. With the correlation analysis,the water poverty index didn’t show any relation to figures of poverty at electoral ward level. However, a significant correlation was found between the variable Access, the Physical Capital and the new Index of Multiple Deprivations at that level. Based on the existence of correlation between Access and WPI at household level, it was established that a correlation might exist between the WPI, the Physical Capital and the IMD, but be hidden by the high level of aggregation. Consequently, in the study area, even though the water poverty index proved most visible in the poverty landscape through the variable Access the necessity of adjoining the WPI to the IMD for addressing water-specific aspects of poverty was established.

To sketch the policy response to the designed water poverty index in the study area

Even though the policy value of the water poverty index was found in literature (Sullivan and Meigh, 2003, pp. 515-516), it couldn’t be directly applied to address this fifth sub-objective. Instead, the basis of the appropriateness of the index to reflect the study area in water delivery was considered. Since it


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concentrates on the Access, this variable was taken into chi-square tests with response time to complaints and main problems faced in accessing water service to establish a possible policy response scheme to the index. Beyond this one time use, the usefulness of the water poverty index is probably the best if it serves as monitoring tool to update the policy response design. Finally, the index was found usable as input to water planning and management. To sum up this section, all research questions were answered and the analysis has provided further information and put the designed index in the wider context of the water poverty index development ongoing process with the exclusive design of the Access at household level.

7.2. Conclusions

To conclude the research, the theoretical and practical values of the research are summarised.

Theoretical value derived from other results got in the analysis

� Agreement with previous research When assessing the index as designed in this study, each variable as well as the overall index was found in agreement with benchmarks set from concept and pilots experiences. For example, the versatility (in the sense of getting an interdisciplinary output able to address several water management facets) of the index as wanted from the concept was demonstrated.

� Replicability of the results Another characteristic of the index revealed by the assessment is that it is replicable at household level which is the level of design. Since the approach in this study was people-focused, the same methods could be applied to other cases with assumption of similar results in the frame of the socio-cultural context.

� Originality The design of the variable Access is particularly interesting for its complete embedment in households’ water-related deprivations.

Finally, the theoretical value of the research is carried by the design of the methodology.

Practical value and limitations of the study

While the theoretical value of this study is visible in the methodology, its practical value is hindered by the unavoidable limitations related to the data. Nevertheless, beyond these limitations, the practical value of the research is at management and decision-making levels (at least for the surveyed wards). Here are the limitations:

� A major missing link in the study is the lack of the interplay of the index and water supply infrastructures. The high shortage of the secondary data reduced the study to the use of the sole survey data.

� The number of wards involved in the research is too low. The limited number of wards may have restricted the possibility to detect spatial influences on the designed index.

� The census data used to extrapolate the water poverty index to the city is outdated (2001). This limitation is important regarding the population growth condition of the city which is fast (CDP, 2007).

� The specific case of slums and slum-like areas in the city since they represent 44% of the population in 2001 (CDP, 2007). The analysis couldn’t address them because of data shortage


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on actual population density in ward as well as in slums and on slum-like areas’ boundaries50.

In sum, the theoretical value of this study rests on the specific design of the index at household level with tangible results for practical application (in the surveyed wards) in policy making. Mapping urban households’ multiple deprivations calls to consider several aspects of their asset portfolio and address them in the capitals of the livelihoods framework. This is a people-centred and policy-oriented enterprise. These two characteristics have shaped the present research, to map city profiles in service provision. Since mapping profiles of water service provision addresses a specific type of households’ deprivations, the water poverty index was identified in this study to picture the water delivery situation in the city. It has proven relevant at management and planning as well as policy levels.

7.3. Recommendations

The research as summarised in the first sub-chapter brought out the theoretical and the practical values of the study. The way forward is presented in this section in four aspects.

� About the study approach In an attempt to overcome the limitations about slum and slum-like areas in this study, it is suggested that in further research, the data be made available and the approach integrate a specific treat for those areas in the data collection as well as the analysis to address thoroughly intra-urban issues such as equity in water service delivery.

� About the data collection Working against the shortcomings related to limited ward number, it is suggested that sufficient time be given to field work to target more wards (well spread in the study area) in future studies.

� About the design In further development of this research, it would be interesting to consider the following:

o Resources as per capita water quantity actually received by household. o Environment as of the infrastructure’s comprising pipe material, quality, age, etc. and

their interplay with the city environment. In such case, these two variables would be mostly dependant on the physical capital and carry more meaning in urban context. They could become a monitoring tool for the supply of water infrastructure.

� For further research o The use of high resolution remotely sensed image and water supply scheme with the

support of water poverty index could serve to derive characteristics of water poor areas. o The same data could serve to determine areas at risk of more water poverty due to

changes in the built up environment or informal growth. This type of study would be useful for policy intervention for anticipating coming obstacles or monitoring.

o The index can be set as time series data, focusing on informal population growth, and analysed to forecast changes.

50 At least, the percentage of slum area in ward could have been taken to analyse the impact of slums on the index but it was not possible to have updated information on population density to sustain such analysis. For example, high slum population density might have implied a lot of illegal connections, and shifted the water service level.


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Annexure

Annexure O: The raw data ……………………………………………………………….......114 Annexure O1: Households’ questionnaire ………………………………………………114 Annexure O2: Exploratory data analysis ………………………………………………..116Annexure A: Access index ……………………………………………………………………118 Annexure A1: Outputs of the deprivation index ………………………………………..118 Annexure A2: Outputs of the access index ……………………………………………..119Annexure B: Capacity index ………………………………………………………………….120 Annexure B1: Outputs of the level of income …………………………………………120 Annexure B2: Outputs of capacity index ………………………………………………121Annexure C: The variable use ………………………………………………………………..122 Annexure C1: Outputs of the variable use ………………………………………….......122Annexure D: The water poverty index ………………………………………………………123 Annexure D1: Output of the water poverty index at household level …………………..123 Annexure D2: The water poverty index in the study area ………………………………124Annexure E: Modelling of the WPI ………………………………………………………….125 Annexure E1: Normality check …………………………………………………………125 Annexure E2: Linear Mixed Modelling …………………………………………….......126 Annexure E3: Regression Model ……………………………………………………….128 Annexure E4: Trend fitting for extrapolation …………………………………………..130Annexure F: Sensitivity analysis ………………………..........................................................136 Annexure F1: One way ANOVA WPI ………………………………………………….136 Annexure F2: One way ANOVA with the variables ……………………………….......139Annexure G: Water poverty index at policy level …………………………………………..141 Annexure G1: Chi-square WPI – service satisfaction …………………………………..141 Annexure G2: Chi-square Access – response time to complaint ……………………….141 Annexure G3: Chi-square Access – main problem with water service …………………142 Annexure G4: Calculation of the new Index of Multiple Deprivations ………………...143


114

Annexure O1: Household questionnaire Household questionnaire on access to water services in K.-D.

Preliminaries Name of person carrying out the questionnaire: ………………………………………………………………………....... Date: ……………………………………………………………………………………………………………………….. Neighbourhood characteristicsAdministrative ward: 1 2

Electoral Ward No.: 14 16 43 47 81 94

Neighbourhood type: HIG MIG LIG Slum Section 1: Household data

1. Respondent details Position in the family: Husband Wife Other Specify ………………………2. Household composition: Household Members

Relationship Age Sex Occupation / main economic activities

Level of education 3. Household’s perception of the living area:

1 a. What is good about living in this area? 2 3 4

………………………………………………………………………………………………………………………

5 b. What is the inconvenience/living here? 6 7 8

……………………………………….……………………………………….……………………………………….

4. Household income (proxy indicators for the income level): please tick: a) Ownership of vehicles b) Telephone c) House status

of occupatione) House type f) Type of school where

children are studyingg) E.B. Connection

Public 1 2 >2

None Tenant Thatch / hut / katcha / shawl / etc. Panchayat Samitu

EB connection

Cycle Mobile Owner One room pucca house Private Schools Y NMotor cycle / scooter

Land line d) Position to main road

Apartment /house 2 bedrooms Both public and private school

Car Both < = 100 m Apart./ house >=3 bedroomsOn your observation ascertain whether > 100 m Independent house

Public & Panchayat Samitu

the E.B Connection is: Private & P. Samitu

Else …....... ………...…………...……………...

Legal Illegal Unable to ascertain Evening school

Section 2: Water provision and access 5. Provision of water to the house a. Who is responsible for getting water to the house? ……………………………………………………………………b. How is it transported? …………………………………………………………………………………..………………c. Any related problem? …………………………………...………………………………………………………………6. Who / which organization provide water to your household? a. For drinking: KDMC NGO ……….……….. Private sector ………….…... Other ...………... b. For other purposes: KDMC NGO ….………… Private sector ………….…... Other ...............

7. drinking

8. other purposes 11. Municipal water supply and

water usage details: 10. HH C. S. for W. quality. Do you purify your drinking water? How? Daily No Alternate day

Source of water for …

Number of hours spent per day

Number of hours spent per day

9. How much do you spend on water per month? (Rupees)

Yes Once / 3 days

Tube well Straining

a. Supply schedule (days per week)

Once / week Hand pump Alum tablets Night

Ordinary filter Day Individual HH connect.

Electronic filter

b. Moment of availability Both

Boiling < 1 hour Group con. (apartments)

1 - 3 hours

Public tap Other ……………………. ………………………........ 3 - 7 hours

c. Duration of the supply per day (hours)

> 7 hours < 100 lts

Corporation public stand post

100 - 500 500 - 1000 W. tanker

supply

12. How many times did children under 5 years suffer the following in the last 3 months?

d. Amount supplied per day

> 1000


115

Diarrhoea < 100 lts W. installed tanks

Cholera 100 - 500

Private tank Ascariasis 500 - 1000 Schitosomiasis

e. How much water do you use per day?

> 1000 Other …….. ……………

Typhoid

Total hours Other ………………... Other ………………………………………………………………….

14. Households’ coping strategies in the face of unsatisfactory service (some aspects) 13. If the respondent is a

public tap user, fill in the following:

< 0.5 km

a. How long does it take to fill in a 15-1 bucket? (time taken by the filling process)

……………………………………... ………………………………………

0.5-1 km a. Distance of Household from public tap >1 km

b. Where do you get the water from in case of break in the municipal supply (source)?

……………………………………... ……………………………………...

<25 25- 50 c. How long does it take (to get it from other sources)? ………………………………………

………………………………………50-75 75-100 d. What is your prevention strategy? ………………………………………

………………………………………

b. Number of households per public tap

> 100 1

e. How long does it take (e.g. to store the water in prevention to the shortage)?

………………………………………………………………………………

2 c. Number of public taps in the locality > 2 f. What is your storage capacity? ………………………………………

………………………………………. 16. a. Compared to 5 years ago, is your water service …? better worse the same b. How would you explain that? ...………………………………………………………………………………………. 15. Complaints & Grievance redresal (please tick) a) Who do you complain to in case of bad service?

Local or Ward councillor / civic body representative

c) What are the main problems faced in accessing the water service?

d) Are you satisfied with the service?

e) If not satisfied, what is the solution? who else could do a better job?

Neighbourhood Committee or Resident Community Volunteer

b) How long does it take to correct the problem (average time for initial response and its solution)?

Low frequency / pressure Satisfied Resident welfare assoc.

Local political leader Within 1 day No maintenance Reasonably Local association Local community leader Within 2 days Poor response to complaints Not satisfied Privatization Local corporation /ward office

3 to 4 days NGOs

Resident welfare Associations

Within 1week

Provider > 1 week Other ……………………….. > 2 weeks

Other …………………………... …………………………………. …………………………………. ………………………………….

Most important expectation ……. …………………. ………………….

Other …………...... ……………………. …………………….

Section 3: A glance at some related issues

Public toilet Solid waste management Sewerage system17. How many toilets do you have in the house?

18. Do you make use of a public toilet?

19. Who / which organization provide Solid waste management service to your household?

20. How is the solid waste management service provided to your house / locality?

21. Indicate the solid waste collection schedule in your locality

22. Which organisation provides Sewerage system to your household?

23. Indicate how the sewerage service is provided?

Yes KDMC Door-to-door collection

Daily KDMC Underground sewerage system connection

No NGO Segregation at source

Alternate days NGO Open drains outside house (concrete)

Exnora Neighbourhood cleaning

Once in 3 days Private sector Open drains in street, not improved

Private sector Dustbin Once / week Nothing NGO

Other ……. …………... …………... …………... …………... …………...

Other ……………….. ………………………………………………

Other ………... ……………….

Other ……... ……………... ……………...

Other ……………………... ……………... ………………

Other …………………. ……………………....... ………………………...

Section 4: General observation of the interviewer • Any other remarks you want to make on the household like remarkable facts or important things you observed in

the house or while interviewing. …………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………........


116

Annexure O2: exploratory data analysis

O.2.1: Coding of the parameters

O2.2: Chi square tests

The SPSS outputs are displayed for chi-square Quantity received – Quantity used as an example. For the rest, the chi-square results are reported.

Chi-Square Tests

Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Point Probability

Pearson Chi-Square 1,193E2a 6 ,000 ,000

Likelihood Ratio 118,989 6 ,000 ,000

Fisher's Exact Test 110,872 ,000

Linear-by-Linear Association 84,839b 1 ,000 ,000 ,000 ,000

N of Valid Cases 320

a. 4 cells (33,3%) have expected count less than 5. The minimum expected count is ,83.

b. The standardized statistic is -9,211.

Symmetric Measures

Value Approx. Sig. Exact Sig.

Phi ,611 ,000 ,000

Cramer's V ,432 ,000 ,000

Nominal by Nominal

Contingency Coefficient ,521 ,000 ,000

N of Valid Cases 320

Variable Code Category value Variable Code Category value 0 One room house 0 No complaint 1 Apartment/2-bedroom house 1 Within a week House type 2 Apartment/3-bedroom house

Response time to complaint

2 More than a week 0 Tenant 0 Satisfied

Tenure type 1 Owner 1 Reasonably satisfied 0 None

Service satisfaction

2 Not satisfied 1 Bicycle 0 No major problem 2 Scooter 1 No maintenance

Vehicle ownership

3 Car 2 Poor response to complaint 0 None

Main problem with water

service 3 Low frequency / pressure

1 Mobile 0 60 2 Land line 1 61 – 120

Telephone ownership

3 Both 2 121 – 180 0 Not applicable 3 181 – 240 1 Community school 4 241 – 300 2 Public school

Daily collection time

(in minutes)

5 301 – 390

Type of school for children

3 Private school 0 0 – 30 1 31 – 60

Replacement collection time

(in minutes) 2 61 – 120


117

Quantity received * Daily quantity used Crosstabulation

Daily quantity used (in litres)

> 1000 500-1000 100-500 Total

Count 0 5 3 8

Expected Count ,8 6,0 1,2 8,0

% within Quantity received ,0% 62,5% 37,5% 100,0%

% within Daily quantity used ,0% 2,1% 6,1% 2,5%

> 1000

% of Total ,0% 1,6% ,9% 2,5%

Count 2 105 46 153

Expected Count 15,8 113,8 23,4 153,0

% within Quantity received 1,3% 68,6% 30,1% 100,0%

% within Daily quantity used 6,1% 44,1% 93,9% 47,8%

500-1000

% of Total ,6% 32,8% 14,4% 47,8%

Count 23 126 0 149

Expected Count 15,4 110,8 22,8 149,0

% within Quantity received 15,4% 84,6% ,0% 100,0%

% within Daily quantity used 69,7% 52,9% ,0% 46,6%

100-500

% of Total 7,2% 39,4% ,0% 46,6%

Count 8 2 0 10

Expected Count 1,0 7,4 1,5 10,0

% within Quantity received 80,0% 20,0% ,0% 100,0%

% within Daily quantity used 24,2% ,8% ,0% 3,1%

Quantity received

< 100

% of Total 2,5% ,6% ,0% 3,1%

Count 33 238 49 320

Expected Count 33,0 238,0 49,0 320,0

% within Quantity received 10,3% 74,4% 15,3% 100,0%

% within Daily quantity used 100,0% 100,0% 100,0% 100,0%

Total

% of Total 10,3% 74,4% 15,3% 100,0%

Report of other cases � Moment of supply – Duration of the supply: χ2 (3) = 26.518, p < 0.05. Expected counts are greater than 5 except

for supply duration greater than 7 hours during day or night. Cramer’s statistic is 0.29, p < 0.05. It is significant, suggesting a medium effect in the relationship.

� Moment of supply – Quantity Received: χ2 (3) = 29.215, p < 0.05. Expected counts are greater than 5 except for quantity supplied greater than 1000 litres during the day or the night and quantity supplied less than 100 litres during the night.

Cramer’s statistic is 0.30, p < 0.05. It is significant, suggesting a medium effect in the relationship. � Moment of supply – Storing Strategy: χ2 (1) = 8.359, p < 0.05. All expected counts > 5.

Cramer’s statistic is 0.162, p < 0.05. It is significant, suggesting a small effect in the relationship. � Moment of supply – Daily collection time: χ2 (5) = 28.570, p < 0.05. 4 expected counts < 5.

Cramer’s statistic is 0.299, p < 0.05. It is significant, suggesting a medium effect in the relationship. � Duration of supply – Quantity received: χ2 (9) = 92.198, p < 0.05. 8 expected counts < 5.

Cramer’s statistic is 0.31, p < 0.05. It is significant, suggesting a medium effect in the relationship. � Duration of the supply – Quantity used: χ2 (6) = 35.339, p < 0.05. 5 expected counts < 5.

Cramer’s statistic is 0.235, p < 0.05. It is significant suggesting a small effect in the relationship.� Duration of the supply – Daily collection time: χ2 (15) = 57.966, p < 0.05. 15 expected counts < 5.

Cramer’s statistic is 0.246, p < 0.05. It is significant, suggesting a small effect in the relationship. � Quantity received – Daily collection time: χ2 (15) = 1.288E2, p < 0.05. 15 expected counts < 5.


118

Cramer’s statistic is 0.366, p < 0.05. It is significant, suggesting a medium effect in the relationship. � Quantity used – Daily collection time: χ2 (10) = 70.908, p < 0.05. 9 expected counts < 5.

Cramer’s statistic is 0.33, p < 0.05. It is significant, suggesting a medium effect in the relationship. � Quantity used – Storing strategy: χ2 (2) = 11.405, p < 0.05. 2 expected counts < 5.

Cramer’s statistic is 0.19, p < 0.05. It is significant, suggesting a small effect in the relationship.� Quantity used – Household size: χ2 (8) = 17.461, p < 0.05. 4 expected counts < 5.

Cramer’s statistic is 0.165, p < 0.05. It is significant, suggesting a small effect in the relationship. � Daily collection time – Source of other uses: χ2 (15) = 19.906, p < 0.05. 13 expected counts < 5.

Cramer’s statistic is 0.14, p > 0.05. It is not significant at 0.05, suggesting that the effect is not genuine in the relationship. � Daily collection time Storing strategy: χ2 (5) = 12.125, p < 0.05. 5 expected counts < 5.

Cramer’s statistic is 0.195, p < 0.05. It is significant, suggesting a small effect in the relationship.

Annexure A: Access index

Annexure A1: Outputs of the deprivation index

Ward 14 Ward 16 Ward 43 Ward 47 Ward 81 Ward 94 HH Depriv. HH Depriv. HH Depriv. HH Depriv. HH Depriv. HH Depriv.

1 0.58 1 0.64 1 0.74 1 0.51 1 0.21 1 0.57 2 0.57 2 0.64 2 0.53 2 0.58 2 0.51 2 0.52 3 0.60 3 0.66 3 0.49 3 0.57 3 0.24 3 0.59 4 0.60 4 0.70 4 0.48 4 0.55 4 0.21 4 0.58 5 0.60 5 0.55 5 0.17 5 0.53 5 0.20 5 0.66 6 0.58 6 0.35 6 0.45 6 0.53 6 0.54 6 0.63 7 0.54 7 0.73 7 0.43 7 0.51 7 0.50 7 0.57 8 0.54 8 0.26 8 0.54 8 0.72 8 0.21 8 0.52 9 0.58 9 0.35 9 0.50 9 0.52 9 0.50 9 0.56

10 0.58 10 0.35 10 0.50 10 0.49 10 0.22 10 0.54 11 0.55 11 0.64 11 0.44 11 0.50 11 0.21 11 0.54 12 0.49 12 0.59 12 0.49 12 0.52 12 0.49 12 0.46 13 0.50 13 0.59 13 0.49 13 0.56 13 0.68 13 0.56 14 0.49 14 0.59 14 0.49 14 0.50 14 0.19 14 0.61 15 0.55 15 0.56 15 0.53 15 0.52 15 0.45 15 0.56 16 0.50 16 0.59 16 0.32 16 0.61 16 0.47 16 0.21 17 0.51 17 0.58 17 0.24 17 0.48 17 0.44 17 0.20 18 0.49 18 0.67 18 0.56 18 0.53 18 0.45 18 0.48 19 0.47 19 0.60 19 0.26 19 0.60 19 0.45 19 0.20 20 0.57 20 0.65 20 0.23 20 0.58 20 0.48 20 0.21 21 0.47 21 0.70 21 0.23 21 0.52 21 0.48 21 0.50 22 0.51 22 0.62 22 0.23 22 0.56 22 0.54 22 0.55 23 0.49 23 0.66 23 0.56 23 0.53 23 0.43 23 0.52 24 0.43 24 0.55 24 0.22 24 0.49 24 0.47 24 0.55 25 0.47 25 0.62 25 0.52 25 0.62 25 0.42 25 0.22 26 0.49 26 0.63 26 0.57 26 0.60 26 0.45 26 0.22 27 0.48 27 0.55 27 0.52 27 0.60 27 0.52 27 0.50 28 0.46 28 0.59 28 0.52 28 0.58 28 0.51 28 0.49 29 0.48 29 0.60 29 0.50 29 0.67 29 0.54 29 0.52 30 0.47 30 0.55 30 0.57 30 0.54 30 0.56 30 0.48 31 0.52 31 0.63 31 0.57 31 0.64 31 0.57 31 0.53 32 0.50 32 0.58 32 0.54 32 0.60 32 0.53 32 0.50 33 0.50 33 0.59 33 0.51 33 0.63 33 0.56 33 0.52 34 0.54 34 0.62 34 0.59 34 0.67 34 0.57 34 0.50 35 0.49 35 0.65 35 0.55 35 0.52 35 0.53 35 0.53 36 0.57 36 0.64 36 0.50 36 0.65 36 0.52 36 0.54 37 0.53 37 0.60 37 0.52 37 0.55 37 0.54 37 0.55 38 0.55 38 0.61 38 0.51 38 0.66 38 0.57 38 0.52 39 0.52 39 0.61 39 0.52 39 0.51 39 0.53 39 0.54 40 0.52 40 0.58 40 0.56 40 0.56 40 0.50 40 0.53

41 0.58 41 0.52 41 0.47 41 0.56 41 0.53 42 0.57 42 0.24 42 0.56 42 0.56 42 0.52 43 0.70 43 0.24 43 0.53 43 0.64 43 0.52 44 0.66 44 0.55 44 0.55 44 0.58 44 0.54 45 0.66 45 0.64 45 0.50 45 0.56 45 0.51 46 0.57 46 0.67 46 0.53 46 0.60 46 0.53


119

47 0.63 47 0.58 47 0.56 47 0.61 47 0.49 48 0.66 48 0.60 48 0.51 48 0.55 48 0.52 49 0.53 49 0.54 49 0.65 49 0.52 50 0.57 50 0.58 50 0.56 50 0.50 51 0.27 51 0.54 51 0.52 52 0.56 52 0.57 52 0.56 53 0.58 53 0.56 53 0.49 54 0.57 54 0.52 55 0.57 55 0.46 56 0.56 56 0.45 57 0.58 57 0.50 58 0.57 58 0.48 59 0.54 59 0.46 60 0.56 60 0.48 61 0.48 61 0.48 62 0.63 62 0.43 63 0.58 64 0.60 65 0.54 66 0.28 67 0.58

Annexure A2: Outputs of the access index

Ward 14 Ward 16 Ward 43 Ward 47 Ward 81 Ward 94 HH Access HH Access HH Access HH Access HH Access HH Access

1 42,44 1 36,04 1 25,96 1 49,01 1 78,73 1 42,57 2 42,74 2 36,04 2 47,03 2 41,97 2 49,01 2 48,44 3 40,13 3 33,71 3 51,46 3 42,69 3 76,40 3 40,82 4 40,13 4 30,26 4 51,56 4 45,44 4 79,01 4 42,44 5 40,13 5 45,27 5 82,81 5 47,04 5 79,89 5 34,46 6 42,44 6 64,79 6 54,84 6 46,93 6 45,94 6 36,74 7 45,99 7 26,81 7 56,93 7 49,20 7 49,78 7 42,94 8 45,77 8 74,02 8 46,06 8 27,65 8 78,63 8 48,44 9 42,44 9 64,79 9 50,33 9 48,04 9 49,73 9 43,54

10 42,44 10 64,79 10 50,33 10 51,43 10 77,50 10 45,74 11 45,48 11 36,04 11 56,26 11 50,16 11 79,40 11 45,82 12 51,11 12 40,79 12 51,39 12 48,21 12 50,76 12 53,82 13 49,75 13 41,41 13 50,64 13 43,99 13 32,03 13 44,32 14 51,26 14 40,96 14 50,56 14 49,78 14 80,50 14 39,07 15 44,52 15 44,12 15 46,83 15 47,52 15 55,30 15 43,82 16 49,85 16 40,62 16 67,60 16 38,54 16 53,00 16 79,29 17 49,42 17 42,06 17 76,12 17 51,51 17 55,74 17 80,09 18 51,03 18 32,56 18 44,33 18 47,44 18 54,53 18 51,73 19 52,64 19 40,13 19 74,02 19 39,57 19 54,64 19 79,50 20 43,19 20 34,97 20 76,83 20 42,44 20 52,44 20 78,78 21 52,66 21 30,31 21 76,83 21 47,60 21 52,23 21 50,03 22 48,80 22 37,63 22 76,83 22 43,69 22 46,14 22 45,04 23 51,03 23 33,85 23 43,81 23 47,22 23 56,64 23 48,07 24 56,98 24 45,49 24 77,83 24 50,72 24 53,00 24 45,22 25 52,85 25 37,79 25 47,67 25 38,24 25 58,14 25 78,45 26 51,41 26 36,54 26 43,50 26 39,57 26 55,18 26 78,17 27 52,46 27 44,52 27 47,77 27 40,16 27 48,33 27 50,00 28 53,60 28 40,69 28 47,77 28 42,38 28 48,85 28 50,95 29 51,59 29 40,40 29 50,06 29 32,96 29 45,77 29 48,15 30 52,76 30 44,52 30 43,19 30 45,96 30 44,14 30 51,95 31 47,69 31 36,52 31 43,19 31 36,49 31 42,85 31 47,31 32 49,52 32 42,02 32 46,33 32 40,04 32 46,52 32 49,99 33 50,33 33 41,24 33 49,02 33 37,24 33 44,21 33 47,84 34 45,62 34 37,57 34 41,28 34 33,46 34 43,19 34 50,19 35 50,69 35 35,10 35 45,06 35 47,69 35 46,52 35 47,19 36 43,00 36 36,29 36 50,02 36 34,94 36 47,52 36 45,69 37 46,69 37 40,15 37 47,77 37 45,20 37 45,52 37 44,69 38 45,19 38 38,54 38 49,02 38 33,94 38 43,44 38 47,69 39 47,69 39 38,54 39 47,52 39 48,94 39 47,15 39 45,69 40 48,31 40 41,65 40 43,82 40 43,94 40 49,69 40 46,69

41 41,90 41 48,47 41 53,18 41 43,82 41 47,31


120

42 43,15 42 75,52 42 43,94 42 44,15 42 47,69 43 29,92 43 75,83 43 46,93 43 35,77 43 47,84 44 33,92 44 44,85 44 44,62 44 42,44 44 45,69 45 33,92 45 35,99 45 49,56 45 44,13 45 48,84 46 42,90 46 33,49 46 47,22 46 39,63 46 46,69 47 36,87 47 41,69 47 44,25 47 39,07 47 51,20 48 34,01 48 40,24 48 49,46 48 44,71 48 48,23 49 46,54 49 45,75 49 34,65 49 48,31 50 42,54 50 42,29 50 44,40 50 50,46 51 72,83 51 46,45 51 48,31 52 43,71 52 42,91 52 44,23 53 41,57 53 43,58 53 50,92 54 42,81 54 48,31 55 42,53 55 53,64 56 44,06 56 55,30 57 42,43 57 49,82 58 42,81 58 51,69 59 46,06 59 53,89 60 43,68 60 51,79 61 52,21 61 51,79 62 37,33 62 56,52 63 41,63 64 39,72 65 46,06 66 71,93 67 41,94

Annexure B: Capacity index

Annexure B1: Output of the level of income

Ward 14 Ward 16 Ward 43 Ward 47 Ward 81 Ward 94 HH Lev inc HH Lev inc HH Lev inc HH Lev inc HH Lev inc HH Lev inc

1 0.25 1 0.60 1 0.18 1 0.15 1 0.37 1 0.70 2 0.32 2 0.63 2 0.02 2 0.15 2 0.47 2 0.12 3 0.35 3 0.06 3 0.02 3 0.60 3 0.37 3 0.17 4 0.08 4 0.63 4 0.17 4 0.40 4 0.70 4 0.14 5 0.18 5 0.60 5 0.20 5 0.03 5 0.60 5 0.12 6 0.18 6 0.63 6 0.27 6 0.18 6 0.60 6 0.12 7 0.18 7 0.60 7 0.05 7 0.23 7 0.42 7 0.30 8 0.33 8 0.63 8 0.63 8 0.00 8 0.37 8 0.25 9 0.18 9 0.63 9 0.40 9 0.03 9 0.47 9 0.44

10 0.25 10 0.70 10 0.40 10 0.19 10 0.70 10 0.00 11 0.15 11 0.17 11 0.32 11 0.18 11 0.60 11 0.32 12 0.15 12 0.28 12 0.40 12 0.15 12 0.37 12 0.03 13 0.03 13 0.03 13 0.60 13 0.19 13 0.17 13 0.50 14 0.22 14 0.70 14 0.43 14 0.04 14 0.52 14 0.12 15 0.18 15 0.03 15 0.43 15 0.08 15 0.40 15 0.35 16 0.15 16 0.47 16 0.40 16 0.00 16 0.73 16 0.60 17 0.22 17 0.45 17 0.50 17 0.03 17 0.17 17 0.42 18 0.03 18 0.73 18 0.73 18 0.27 18 0.47 18 0.58 19 0.22 19 0.43 19 0.50 19 0.32 19 0.47 19 0.23 20 0.25 20 0.19 20 0.50 20 0.17 20 0.47 20 0.37 21 0.03 21 0.08 21 0.40 21 0.15 21 0.40 21 0.60 22 0.03 22 0.20 22 0.50 22 0.60 22 0.19 22 0.25 23 0.03 23 0.15 23 0.00 23 0.17 23 0.19 23 0.45 24 0.15 24 0.30 24 0.35 24 0.00 24 0.19 24 0.63 25 0.00 25 0.40 25 0.37 25 0.50 25 0.19 25 0.42 26 0.18 26 0.70 26 0.73 26 0.27 26 0.63 26 0.63 27 0.15 27 0.50 27 0.63 27 0.12 27 0.73 27 0.25 28 0.03 28 0.37 28 0.73 28 0.12 28 0.45 28 0.42 29 0.18 29 0.87 29 0.63 29 0.27 29 0.63 29 0.40 30 0.03 30 0.12 30 0.90 30 0.27 30 0.73 30 0.22 31 0.18 31 0.12 31 0.11 31 0.27 31 0.32 31 0.17 32 0.18 32 0.50 32 0.80 32 0.27 32 0.73 32 0.22 33 0.22 33 0.58 33 0.73 33 0.19 33 0.73 33 0.22 34 0.25 34 0.37 34 0.26 34 0.40 34 0.70 34 0.17


121

35 0.15 35 0.12 35 0.63 35 0.19 35 0.63 35 0.17 36 0.15 36 0.70 36 0.63 36 0.29 36 0.70 36 0.73 37 0.15 37 0.70 37 0.46 37 0.00 37 0.63 37 0.60 38 0.18 38 0.50 38 0.63 38 0.42 38 0.17 38 0.37 39 0.15 39 0.53 39 0.73 39 0.42 39 0.29 39 0.19 40 0.15 40 0.07 40 0.34 40 0.17 40 0.27 40 0.19

41 0.63 41 0.34 41 0.22 41 0.35 41 0.18 42 0.70 42 0.40 42 0.17 42 0.19 42 0.60 43 0.70 43 0.40 43 0.17 43 0.52 43 0.70 44 0.63 44 0.29 44 0.19 44 0.14 44 0.45 45 0.63 45 0.29 45 0.17 45 0.29 45 0.42 46 0.70 46 0.19 46 0.15 46 0.32 46 0.17 47 0.52 47 0.14 47 0.40 47 0.20 47 0.19 48 0.63 48 0.52 48 0.40 48 0.35 48 0.17 49 0.17 49 0.17 49 0.12 49 0.17 50 0.12 50 0.19 50 0.70 50 0.17 51 0.40 51 0.18 51 0.37 52 0.12 52 0.00 52 0.42 53 0.27 53 0.00 53 0.40 54 0.15 54 0.17 55 0.27 55 0.03 56 0.00 56 0.03 57 0.00 57 0.20 58 0.00 58 0.00 59 0.00 59 0.19 60 0.00 60 0.03 61 0.02 61 0.10 62 0.04 62 0.42 63 0.02 64 0.37 65 0.04 66 0.60 67 0.52

Annexure B2: Output of the capacity index

Ward 14 Ward 16 Ward 43 Ward 47 Ward 81 Ward 94 HH Capacity HH Capacity HH Capacity HH Capacity HH Capacity HH Capacity

1 42,22 1 72,91 1 35,56 1 33,33 1 52,59 1 81,80 2 48,78 2 75,87 2 11,48 2 13,33 2 61,48 2 30,37 3 51,01 3 25,08 3 1,48 3 52,91 3 52,59 3 35,45 4 26,67 4 75,87 4 34,81 4 35,13 4 81,80 4 32,59 5 35,56 5 72,91 5 37,78 5 22,22 5 72,91 5 30,37 6 35,56 6 75,87 6 23,70 6 35,56 6 72,91 6 30,37 7 35,56 7 72,91 7 4,44 7 20,00 7 57,67 7 46,56 8 48,89 8 75,87 8 75,87 8 20,00 8 52,59 8 42,12 9 35,56 9 75,87 9 55,13 9 22,22 9 61,48 9 59,26

10 42,22 10 81,80 10 55,13 10 37,04 10 81,80 10 20,00 11 33,33 11 34,81 11 48,15 11 35,56 11 72,91 11 48,78 12 33,33 12 44,76 12 55,13 12 13,33 12 52,59 12 12,96 13 22,22 13 22,22 13 72,91 13 37,04 13 34,81 13 64,02 14 39,89 14 81,80 14 58,10 14 3,70 14 66,56 14 30,37 15 35,56 15 22,22 15 48,10 15 6,67 15 55,56 15 51,01 16 33,33 16 61,48 16 55,56 16 20,00 16 84,76 16 72,91 17 39,89 17 60,32 17 64,44 17 2,22 17 34,81 17 57,67 18 22,22 18 84,76 18 84,76 18 43,70 18 61,48 18 71,43 19 39,89 19 58,10 19 64,44 19 48,78 19 61,48 19 40,32 20 42,12 20 37,04 20 64,44 20 34,81 20 61,48 20 52,59 21 12,22 21 27,30 21 55,56 21 13,33 21 55,56 21 72,91 22 12,22 22 37,78 22 64,44 22 72,91 22 37,04 22 42,12 23 12,22 23 23,33 23 20,00 23 24,81 23 37,04 23 59,58 24 33,33 24 46,67 24 51,11 24 10,00 24 37,04 24 75,87 25 10,00 25 55,13 25 52,59 25 64,02 25 37,04 25 57,67 26 35,56 26 81,80 26 84,76 26 23,70 26 75,87 26 75,87 27 33,33 27 64,02 27 75,87 27 10,37 27 84,76 27 42,12 28 22,22 28 52,59 28 84,76 28 20,37 28 59,58 28 57,67 29 35,56 29 97,04 29 75,87 29 43,70 29 75,87 29 55,13


122

30 22,22 30 10,37 30 100,00 30 43,70 30 84,76 30 39,89 31 35,56 31 30,37 31 29,52 31 43,70 31 48,15 31 24,81 32 35,56 32 64,02 32 91,11 32 43,70 32 84,76 32 39,89 33 39,89 33 71,43 33 84,76 33 37,04 33 84,76 33 29,89 34 42,12 34 52,59 34 42,86 34 55,45 34 81,80 34 24,81 35 33,33 35 30,37 35 75,87 35 37,04 35 75,87 35 24,81 36 33,33 36 81,80 36 75,87 36 45,93 36 81,80 36 84,76 37 33,33 37 81,80 37 60,63 37 10,00 37 75,87 37 72,91 38 35,56 38 64,02 38 75,87 38 57,35 38 35,45 38 52,59 39 33,33 39 66,98 39 84,76 39 57,35 39 45,93 39 37,04 40 33,33 40 26,56 40 50,37 40 34,81 40 43,70 40 37,04

41 75,87 41 50,37 41 39,89 41 51,01 41 35,56 42 81,80 42 55,56 42 34,81 42 37,04 42 72,91 43 81,80 43 55,56 43 34,81 43 66,56 43 81,80 44 75,87 44 45,93 44 37,04 44 32,59 44 59,58 45 75,87 45 35,93 45 34,81 45 45,93 45 57,35 46 81,80 46 37,04 46 23,33 46 48,78 46 34,81 47 66,56 47 32,59 47 55,13 47 37,67 47 37,04 48 75,87 48 66,24 48 55,13 48 51,01 48 34,81 49 35,45 49 34,81 49 30,37 49 34,81 50 30,37 50 37,04 50 81,80 50 34,81 51 55,56 51 35,56 51 52,59 52 10,37 52 10,00 52 57,67 53 23,70 53 10,00 53 55,13 54 23,33 54 34,81 55 33,70 55 22,22 56 10,00 56 22,22 57 20,00 57 37,78 58 10,00 58 20,00 59 10,00 59 37,04 60 10,00 60 22,22 61 11,48 61 28,78 62 3,70 62 57,35 63 11,48 64 52,59 65 13,70 66 62,91 67 56,24

Annexure C: Outputs of the variable Use (in litres per capita per day)

Ward 14 Ward 16 Ward 43 Ward 47 Ward 81 Ward 94 HH Use_14 HH Use_16 HH Use_43 HH Use_47 HH Use_81 HH Use_94

1 125 1 166.67 1 20 1 166.67 1 166.67 1 125 2 50 2 125 2 83.33 2 25 2 125.00 2 166.67 3 166.67 3 166.67 3 100 3 166.67 3 125.00 3 166.67 4 166.67 4 166.67 4 100 4 125 4 125.00 4 125 5 166.67 5 166.67 5 125 5 25 5 166.67 5 166.67 6 125 6 166.67 6 100 6 20 6 166.67 6 125 7 166.67 7 125 7 166.67 7 16.67 7 166.67 7 125 8 125 8 125 8 166.67 8 25 8 100.00 8 166.67 9 125 9 125 9 125 9 25 9 166.67 9 83.33

10 166.67 10 166.67 10 166.67 10 14.29 10 125.00 10 125 11 166.67 11 166.67 11 83.33 11 125 11 166.67 11 5012 166.67 12 166.67 12 166.67 12 25 12 166.67 12 166.67 13 166.67 13 166.67 13 125 13 33.33 13 125.00 13 166.67 14 125 14 166.67 14 166.67 14 20 14 125.00 14 166.67 15 20 15 166.67 15 125 15 125 15 125.00 15 166.67 16 166.67 16 166.67 16 166.67 16 33.33 16 125.00 16 100 17 125 17 166.67 17 166.67 17 166.67 17 125.00 17 166.67 18 100 18 125 18 166.67 18 166.67 18 125.00 18 166.67 19 125 19 166.67 19 166.67 19 100 19 125.00 19 125 20 125 20 125 20 166.67 20 125 20 125.00 20 166.67 21 125 21 125 21 166.67 21 166.67 21 166.67 21 166.67 22 20 22 166.67 22 166.67 22 125 22 14.29 22 125 23 125 23 100 23 166.67 23 125 23 100.00 23 125


123

24 166.67 24 166.67 24 125 24 166.67 24 125.00 24 100 25 100 25 166.67 25 166.67 25 125 25 166.67 25 125 26 125 26 166.67 26 166.67 26 125 26 166.67 26 166.67 27 125 27 166.67 27 166.67 27 166.67 27 166.67 27 125 28 100 28 166.67 28 166.67 28 100 28 125.00 28 166.67 29 100 29 166.67 29 166.67 29 25 29 166.67 29 166.67 30 125 30 166.67 30 166.67 30 166.67 30 125.00 30 166.67 31 125 31 50 31 166.67 31 166.67 31 62.50 31 166.6732 100 32 166.67 32 166.67 32 166.67 32 125.00 32 125 33 125 33 166.67 33 166.67 33 125 33 166.67 33 125 34 166.67 34 166.67 34 166.67 34 25 34 166.67 34 166.67 35 166.67 35 125 35 166.67 35 166.67 35 166.67 35 166.67 36 166.67 36 166.67 36 166.67 36 166.67 36 100.00 36 100 37 125 37 166.67 37 166.67 37 166.67 37 166.67 37 100 38 125 38 166.67 38 166.67 38 100 38 166.67 38 166.67 39 166.67 39 166.67 39 166.67 39 125 39 166.67 39 166.67 40 100 40 166.67 40 166.67 40 83.33 40 50.00 40 166.67

41 166.67 41 166.67 41 166.67 41 166.67 41 100 42 166.67 42 125 42 125 42 125.00 42 100 43 166.67 43 166.67 43 83.33 43 100.00 43 166.667 44 166.67 44 166.67 44 166.67 44 125.00 44 166.67 45 166.67 45 166.67 45 166.67 45 100.00 45 166.67 46 166.67 46 166.67 46 166.67 46 125.00 46 166.67 47 166.67 47 166.67 47 166.67 47 166.67 47 20 48 166.67 48 166.67 48 166.67 48 166.67 48 33.33 49 166.67 49 125 49 100.00 49 25 50 166.67 50 100 50 166.67 50 125 51 166.67 51 166.67 51 125 52 166.67 52 166.67 52 125 53 166.67 53 250 53 25 54 166.67 54 125 55 100 55 100 56 125 56 166.67 57 166.67 57 125 58 166.67 58 125 59 125 59 33.33 60 166.67 60 25 61 166.67 61 25 62 25 62 100 63 100 64 166.67 65 33.33 66 166.67 67 166.67

Annexure D: The Water Poverty Index

Annexure D1: The computed WPI at household level

Ward 14 Ward 16 Ward 43 Ward 47 Ward 81 Ward 94 HH WPI HH WPI HH WPI HH WPI HH WPI HH WPI

1 62.29 1 76.12 1 49.40 1 69.29 1 85.05 1 73.64 2 57.84 2 73.40 2 55.22 2 49.01 2 73.76 2 64.54 3 67.57 3 61.65 3 55.32 3 72.70 3 80.67 3 63.37 4 60.62 4 74.97 4 64.88 4 65.00 4 89.92 4 59.54 5 63.16 5 79.31 5 78.65 5 53.29 5 91.25 5 59.72 6 60.38 6 86.89 6 62.84 6 56.64 6 79.54 6 56.93 7 65.18 7 69.37 7 63.77 7 52.69 7 76.51 7 63.70 8 65.34 8 86.50 8 80.43 8 45.97 8 79.30 8 67.90 9 60.38 9 83.32 9 72.40 9 53.64 9 77.58 9 63.97

10 65.86 10 88.58 10 75.97 10 58.12 10 89.40 10 57.08 11 64.37 11 65.24 11 68.88 11 66.75 11 91.08 11 58.90 12 66.31 12 69.72 12 76.34 12 51.16 12 75.40 12 61.43 13 62.67 13 63.49 13 77.59 13 57.19 13 60.28 13 72.74 14 64.67 14 80.36 14 76.90 14 48.52 14 86.08 14 61.31


124

15 52.10 15 64.43 15 69.18 15 57.59 15 74.24 15 68.85 16 65.88 16 74.44 16 82.05 16 50.44 16 81.79 16 81.63 17 64.03 17 74.60 17 87.53 17 61.27 17 68.46 17 83.26 18 57.40 18 74.74 18 82.37 18 71.71 18 75.67 18 77.41 19 65.14 19 73.30 19 86.81 19 64.73 19 75.70 19 74.53 20 62.52 20 61.93 20 87.78 20 63.87 20 74.95 20 81.36 21 57.24 21 57.54 21 85.24 21 63.09 21 76.75 21 77.25 22 46.91 22 66.63 22 87.78 22 75.19 22 56.30 22 63.15 23 56.68 23 55.49 23 63.69 23 62.67 23 67.27 23 69.19 24 68.34 24 71.89 24 80.74 24 63.21 24 68.15 24 70.72 25 54.53 25 71.65 25 74.33 25 70.77 25 73.50 25 79.13 26 63.48 26 78.83 26 82.08 26 59.71 26 83.57 26 87.80 27 63.20 27 76.51 27 81.02 27 59.68 27 83.75 27 64.87 28 58.28 28 71.92 28 83.56 28 57.59 28 73.16 28 73.21 29 61.40 29 84.52 29 81.81 29 54.57 29 80.33 29 71.52 30 60.14 30 61.18 30 86.33 30 71.20 30 78.73 30 68.47 31 62.19 31 54.13 31 66.19 31 67.93 31 62.47 31 62.57 32 60.68 32 75.65 32 84.87 32 69.16 32 79.55 32 64.23 33 64.35 33 77.49 33 83.99 33 62.72 33 82.33 33 60.63 34 66.93 34 70.85 34 69.34 34 58.10 34 81.13 34 63.56 35 66.17 35 60.07 35 80.08 35 69.89 35 80.58 35 62.52 36 63.51 36 78.75 36 81.79 36 68.03 36 76.91 36 73.42 37 61.21 37 80.08 37 76.66 37 61.31 37 80.24 37 69.69 38 61.33 38 74.45 38 81.45 38 65.24 38 67.97 38 70.63 39 65.13 39 75.29 39 83.47 39 72.56 39 72.25 39 65.50 40 59.63 40 64.82 40 72.37 40 60.82 40 62.49 40 65.84

41 78.99 41 73.97 41 72.60 41 72.55 41 59.92 42 81.12 42 81.21 42 64.39 42 65.10 42 70.72 43 76.55 43 84.89 43 61.85 43 68.50 43 79.03 44 76.24 44 71.45 44 68.83 44 63.24 44 71.94 45 76.24 45 65.54 45 69.90 45 65.49 45 72.39 46 81.03 46 64.99 46 65.81 46 66.90 46 65.21 47 74.60 47 66.55 47 73.88 47 67.10 47 54.83 48 76.27 48 75.67 48 75.67 48 72.86 48 54.31 49 69.04 49 65.02 49 57.78 49 53.63 50 66.21 50 62.31 50 81.55 50 62.94 51 83.86 51 69.04 51 67.28 52 60.90 52 60.52 52 67.32 53 63.97 53 60.75 53 60.33 54 64.30 54 62.20 55 61.44 55 58.29 56 57.35 56 64.58 57 63.21 57 63.56 58 60.49 58 59.13 59 58.04 59 56.90 60 60.78 60 51.23 61 64.15 61 53.10 62 44.65 62 69.33 63 54.79 64 71.59 65 51.24 66 85.65 67 73.40

Annexure D2: The water poverty index in the study area

Ward WPI Ward WPI Ward WPI Ward WPI Ward WPI 9 72,18 25 72,94 40 71,16 50 74,16 87 73,43

10 76,00 26 71,27 41 76,57 70 76,10 88 71,49 11 77,04 27 72,95 42 76,39 71 75,90 89 71,58 12 77,11 28 72,59 43 75,47 74 76,26 90 75,90 13 66,20 33 77,14 44 77,07 80 68,11 91 76,51 14 63,02 35 68,95 45 76,61 81 75,94 92 69,27 15 75,76 36 58,05 46 75,14 83 77,08 93 70,98 16 70,91 37 76,48 47 72,11 84 76,22 94 66,03 17 74,95 38 76,61 48 73,26 85 68,11 95 71,81 24 71,33 39 76,36 49 76,37 86 73,39 96 71,64


125

Annexure E: Modelling of the water poverty index

Annexure E1: Normality check 1.1. Normality check for water poverty index

A side by side box plot is used to check the normality status of the data. The SPSS output shows some skewness and outliers. Thus, the data is not normally distributed. The significance of the non-normality of the data checked

with a Q-Q plot and a Kolmogorov Smirnov test statistics gives the results in the table above. The test fails to give significance to the non normality of the data in wards other than 16 and 43. Normality is assumed for those two wards to run the analysis and will be considered again in the interpretation of the outputs. The attempt to transform the data fails to solve the normality problem for all wards.

1.2 Normality check for access power The data is plotted again in side by side box plot for the normality check. Once again, the SPSS output shows some skewness and outliers for all wards. Thus, the data is not normally distributed. The significance of the non-normality of the data checked with a Q-Q plot and a Kolmogorov Smirnov test statistics gives the results in the table below. As previously, the test fails to give significance to the non normality of the data in wards 43, 94 and ward 16 is just

above the threshold (p = 0.062). Normality is assumed for the three wards with non normal data to run the analysis and will be considered again in the interpretation of the outputs for the same reasons as previously.

Tests of Normality WPI Kolmogorov-Smirnova Shapiro-Wilk Ward

Statistic df Sig. Statistic df Sig. 14 ,119 40 ,156 ,913 40 ,00516 ,146 48 ,012 ,962 48 ,11843 ,142 53 ,010 ,937 53 ,00847 ,059 67 ,200* ,989 67 ,80781 ,079 50 ,200* ,981 50 ,58794 ,096 62 ,200* ,976 62 ,279

a. Lilliefors Significance Correction *. This is a lower bound of the true significance.

Tests of Normality Access power Kolmogorov-Smirnova Shapiro-Wilk

Ward Statistic df Sig. Statistic df Sig.

14 ,105 40 ,200* ,940 40 ,03516 ,124 48 ,062 ,953 48 ,05343 ,178 53 ,000 ,914 53 ,00147 ,093 67 ,200* ,960 67 ,03181 ,105 50 ,200* ,978 50 ,46494 ,128 62 ,013 ,941 62 ,005

a. Lilliefors Significance Correction *. This is a lower bound of the true significance.


126

Annexure E2: Linear Mixed Modelling Annexure E2.1: Baseline model

• Goodness of fit in Information Criteria table

Information Criteriaa

-2 Restricted Log Likelihood -679,652

Akaike's Information Criterion (AIC) -675,652

Hurvich and Tsai's Criterion (AICC) -675,614

Bozdogan's Criterion (CAIC) -666,121

Schwarz's Bayesian Criterion (BIC) -668,121

The information criteria are displayed in smaller-is-better forms.

a. Dependent Variable: WPI.

From the output, the -2LL indicate a better fit of the model to the data.

• Tests of Fixed Effects

Type III Tests of Fixed Effectsa

Source Numerator df Denominator df F Sig.

Intercept 1 4,988 773,475 ,000


This table and the following one show the intercept as the only fixed effect. There is 95% confidence that the ‘intersect’ is different from 0 as the p < 0.05.

• Estimates of Fixed effects

Estimates of Fixed Effectsa

95% Confidence Interval Parameter Estimate Std. Error df t Sig.

Lower Bound Upper Bound

Intercept ,690396 ,024824 4,988 27,811 ,000 ,626535 ,754256


• Estimates of Covariance parameters

Estimates of Covariance Parametersa

95% Confidence Interval Parameter Estimate Std. Error Wald Z Sig.


Residual ,006475 ,000517 12,530 ,000 ,005537 ,007571

Intercept [subject = Wardcode] Variance ,003573 ,002341 1,526 ,127 ,000989 ,012907


From this last table, the random effect factor ward is not significant because the p value of 0.127 for the Wald Z (= 1.526) is higher than 0.05. This leads to the conclusion that the water poverty index do not vary by ward. Consequently, an analysis with just fixed factors might be enough.


127

Annexure E2.2: Fixed effects model• Goodness of fit in Information Criteria table

Information Criteriaa

-2 Restricted Log Likelihood -667,638

Akaike's Information Criterion (AIC) -663,638

Hurvich and Tsai's Criterion (AICC) -663,600

Bozdogan's Criterion (CAIC) -654,140

Schwarz's Bayesian Criterion (BIC) -656,140

The information criteria are displayed in smaller-is-better forms.


Like previously, the output shows that the -2LL indicate a better fit of the model to the data. • Tests of Fixed Effects

Type III Tests of Fixed Effectsa

Source Numerator df Denominator df F Sig.

Intercept 1 314 2,289E4 ,000

Wardcode 5 314 29,540 ,000


This table evidence significance for ward as fixed effect since the p value is far less than 0.05. The intercept interpreted as the overall mean of the dependant variable has a slope significantly different from 0, meaning with 95% confidence that ward has an effect on water poverty index significantly different from 0.

• Estimates of Fixed effects

Estimates of Fixed Effectsb

95% Confidence Interval Parameter Estimate Std. Error df t Sig.


Intercept ,664674 ,010219 314 65,041 ,000 ,644567 ,684781

[Ward=14] -,045920 ,016319 314 -2,814 ,005 -,078029 -,013812

[Ward=16] ,066815 ,015470 314 4,319 ,000 ,036376 ,097253

[Ward=43] ,084265 ,015053 314 5,598 ,000 ,054647 ,113883

[Ward=47] -,037114 ,014180 314 -2,617 ,009 -,065014 -,009215

[Ward=81] ,086346 ,015295 314 5,645 ,000 ,056253 ,116440

[Ward=94] 0a 0 . . . . .

a. This parameter is set to zero because it is redundant.

b. Dependent Variable: WPI.

The values of the significance level in this table show 95% confidence that coefficients for wards are significant. The overall intersect also is significantly different from 0, judging by the p value. Ward 94 is the reference in this analysis. About the last table next, as there is neither random effect nor interaction effect, we have only the conditional estimates for the residual, that is the unexplained variance in water poverty index after controlling for access power and sampling of the random factor, ward. The value of the estimates is significant, Wald Z = 12.530, p < 0.05. The standard error is 0.000517.

• Estimates of Covariance parameters Estimates of Covariance Parametersa

95% Confidence Interval Parameter Estimate Std. Error Wald Z Sig.


Residual ,006475 ,000517 12,530 ,000 ,005537 ,007571



128

Annexure E2.3: Chi-square difference test • The likelihoods ratio test statistic for assessing the null model against the fixed model is (– 679.652) – (– 667.638)

= – 12.01451

• This statistic has a chi-square distribution. • The degree of freedom is 1. • The p value for this statistic is given by (1 / (– 12.014)) + 0.00051752 = – 0.083 • p > 0.05

This test statistic is not significant. Therefore, there is no need to add higher modifier and perform a Linear Mixed Modelling. A simpler model will suffice.

Annexure E3: The Regression Model

Annexure E3.1: With the variable Access

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 ,261a ,068 -,165 6,56803

a. Predictors: (Constant), Access

From the table, only 6.8% of the variation in the water poverty index can be explained by the variation in the variable access. We can conclude that the influence of other variables on the WPI is important.

ANOVAb

Model Sum of Squares df Mean Square F Sig.

Regression 12,591 1 12,591 ,292 ,618a

Residual 172,556 4 43,139

1

Total 185,147 5

a. Predictors: (Constant), Access

b. Dependent Variable: WPI

This table reporting the analysis of variance shows a large residual sum of squares against a small model sum of squares. In addition, the F-ratio is not significant (p = 0.618). This leads to the conclusion that this regression model does not predict the WPI well.

Coefficientsa

Unstandardized Coefficients Standardized Coefficients Model

B Std. Error Beta t Sig.

(Constant) 52,958 29,892 1,772 ,1511

Access ,278 ,514 ,261 ,540 ,618

a. Dependent Variable: WPI

None of the two coefficients is significantly different from 0 as their t values have a p value greater than 0.05. The contribution of the variable Access to predicting the WPI is not significant.

51 These are –2LL values taken from the information criteria table for the two models. 52 This is the value of the standard error on residual


129

Annexure E3.2: With the variable Capacity Model Summary


1 ,946a ,895 ,869 2,20400

a. Predictors: (Constant), Capacity

ANOVAb


Regression 165,717 1 165,717 34,115 ,004a

Residual 19,430 4 4,858

1

Total 185,147 5

a. Predictors: (Constant), Capacity


Coefficientsa



(Constant) 47,878 3,733 12,824 ,0001

Capacity ,449 ,077 ,946 5,841 ,004


Annexure E3.3: With the variable UseModel Summary


1 ,810a ,655 ,569 3,99324

a. Predictors: (Constant), Use

ANOVAb


Regression 121,363 1 121,363 7,611 ,051a

Residual 63,784 4 15,946

1

Total 185,147 5

a. Predictors: (Constant), Use


Coefficientsa



(Constant) 21,999 17,130 1,284 ,2681

Use ,578 ,210 ,810 2,759 ,051



130

Annexure E4: trend fitting for extrapolation Table 1: Predicting model without ward 14

Model Summary

R R Square Adjusted R Square Std. Error of the Estimate

,642 ,413 -,175 6,025

The independent variable is Percentage of main worker in ward.

ANOVA

Sum of Squares df Mean Square F Sig.

Regression 50,995 2 25,497 ,702 ,587

Residual 72,605 2 36,302

Total 123,600 4


The fitting equation is: Y = –0.468X2 + 30.153X – 410.07

Ward Predicted value Error

14 59.55 2.33

16 68.09 5.06

43 73.26 1.63

47 69.42 -6.66

81 75.05 0.05

94 66.54 -0.07 .

• The model without ward 14 presents a standard error of the estimate of 6.025 and an R2 value of 0.413

• From the SPSS outputs, none of the coefficients in the fitting equation show significance.

• Ward 14 in the model holds a predicted value of 59.55 and an error of 2.33. This error is in the same range as for the wards participating in the construction of the model

• The error term is the highest for ward 47 which is over-predicted by the model.

The regression model is not significant F(2) = 0.702, p > 0.05.


131

Table 2: Predicting model without ward 16

Model Summary


,844 ,713 ,425 4,868


ANOVA


Regression 117,491 2 58,746 2,479 ,287

Residual 47,402 2 23,701

Total 164,893 4




14 59.79 2.09

16 67.20 5.95

43 71.86 3.03

47 68.41 -5.65

81 73.88 1.22

94 67.15 -0.68

• The model excluding ward 16 presents a standard error of the estimate of 4.868 and an R2

value of 0.713. • From the SPSS outputs, none of

the coefficients in the fitting equation show significance.

• Ward 16 in the model holds a predicted value of 67.20 and an error of 5.95. This error is out of the range of errors presented by the wards participating in the building of the model.

• The error term is the highest for ward 16 which is under-predicted by the model.

The regression model is not significant, F(2) = 2.479, p > 0.05.


132


Model Summary


,722 ,522 ,043 5,871


ANOVA


Regression 75,177 2 37,589 1,091 ,478

Residual 68,926 2 34,463

Total 144,103 4




14 61.51 0.37

16 68.09 5.05

43 72.45 2.44

47 69.13 -6.37

81 73.57 1.53

94 67.06 -0.59

• The model living out ward 43 presents a standard error of the estimate of 5.871 and an R2

value of 0.522. • From the SPSS outputs, none of

the coefficients in the fitting equation show significance.

• Ward 43 in the model holds a predicted value of 72.45 and an error of 2.44. This error is in the same range as for the wards participating in the model.


The regression model is not significant, F(2) = 1.091, p > 0.05.


133


Model Summary


,972 ,945 ,890 1,944


ANOVA


Regression 130,238 2 65,119 17,232 ,055

Residual 7,558 2 3,779

Total 137,796 4




14 63.04 -1.16

16 70.93 2.22

43 75.50 -0.61

47 72.11 -9.35

81 75.97 -0.87

94 66.06 0.41

• The model excluding ward 47 presents a standard error of the estimate of 1.944 and an R2 value of 0.945.

• From the SPSS outputs, all coefficients in the fitting equation show significance.

• Ward 47 in the model holds a predicted value of 72.11 and an error of -9.35. This error term is well above the one of the wards participating in the model.

• Ward 47 is highly over-predicted by the model.

The regression model is just significant, F(2) = 17.232, p = 0.055.


134


Model Summary


,694 ,482 -,036 6,046


ANOVA


Regression 67,990 2 33,995 ,930 ,518

Residual 73,113 2 36,556

Total 141,103 4




14 61.63 0.25

16 68.57 4.58

43 72.74 2.15

47 69.65 -6.89

81 74.18 0.92

94 66.56 -0.09

• The model without ward 81 presents a standard error of the estimate of 6.046 and an R2 value of 0.482.


• Ward 81 in the model holds a predicted value of 74.18 and an error of 0.92. This error is in the same range as for the wards participating in the model.


he regression model is not significant, F(2) = 0.930, p > 0.05


135


Model Summary


,769 ,591 ,182 6,020


ANOVA


Regression 104,728 2 52,364 1,445 ,409

Residual 72,483 2 36,241

Total 177,211 4




14 61.80 0.08

16 68.43 4.72

43 72.72 2.17

47 69.50 -6.74

81 75.32 -0.22

94 70.54 -4.07

• The model living out ward 94 presents a standard error of the estimate of 6.020 and an R2 value of 0.591.


• Ward 94 in the model holds a predicted value of 70.54 and an error of -4.07. This error is in the same range as for the wards participating in the construction of the model.


The regression model is not significant, F(2) = 1.445, p > 0.05


136

Annexure F: Sensitivity analysis Annexure F1: One way ANOVA WPI From the LMM, it is known that the WPI roughly do not vary with ward. Analysis of variance is performed in this section to analyse the variability of the data. Two of the LMM assumptions may question the use of ANOVA. That is the non normality of the data in all wards and the independence of observations. In one hand, the correlation established then was within groups and not across groups. In another, ANOVA is a robust procedure. The SPSS outputs are displayed hereafter.

Test of Homogeneity of Variances WPI

Levene Statistic df1 df2 Sig.

4,968 5 314 ,000

This table tests the null hypothesis that the variances of the groups are the same. From the p value which is less than 0.05, the Levene’s test is significant. Consequently, the variances are significantly different. One assumption of ANOVA is violated. We will report the Welch F later to sort it out.

ANOVA WPI


Between Groups ,956 5 ,191 29,540 ,000

Within Groups 2,033 314 ,006

Total 2,989 319

• The overall effect of the water poverty index is given by the Between Groups. It is proved significant for the hypothesis to test whether the group means are the same as the p value for the F-ratio is less than 0.05. However we will cross check the F ratio’s significance with the Welch test (next table) because of the violation of the assumption of homogeneity of variance. The Sum of Squares (variations that can not be explained by the model) is 0.956 and the degree of freedom is 5.

• The Within Groups reports unsystematic variation in the data (may be the influence of household’s differences in socio economic characteristics or approach in drinking water treatment for example, etc.) The residual sum of squares is 2.033 and the degree of freedom is 314. The model explains very little of the variation as 0.956 < 2.033.

Robust Tests of Equality of Means WPI

Statistica df1 df2 Sig.

Welch 35,931 5 143,289 ,000

Brown-Forsythe 30,502 5 283,024 ,000

a. Asymptotically F distributed.

This table is important because the Levene’s test was significant. The F-ratio is higher for the Welch test than previously, but it is still significant with the same degree of freedom. Consequently, the conclusion that the group means are statistically different remains.

Post Hoc tests

Multiple Comparisons

Dependent Variable: WPI

95% Confidence Interval (I) Ward code

(J) Ward code

Mean Difference (I-J)

Std. Error Sig. Lower Bound Upper Bound

2 -,11274* ,01723 ,000 -,1635 -,0619

3 -,13019* ,01685 ,000 -,1799 -,0805

4 -,00881 ,01608 1,000 -,0562 ,0386

5 -,13227* ,01707 ,000 -,1826 -,0819

1

6 -,04592 ,01632 ,075 -,0940 ,0022

Hochberg

2 1 ,11274* ,01723 ,000 ,0619 ,1635


137

3 -,01745 ,01603 ,992 -,0647 ,0298

4 ,10393* ,01522 ,000 ,0591 ,1488

5 -,01953 ,01626 ,979 -,0675 ,0284

6 ,06681* ,01547 ,000 ,0212 ,1124

1 ,13019* ,01685 ,000 ,0805 ,1799

2 ,01745 ,01603 ,992 -,0298 ,0647

4 ,12138* ,01479 ,000 ,0778 ,1650

5 -,00208 ,01586 1,000 -,0489 ,0447

3

6 ,08427* ,01505 ,000 ,0399 ,1287

1 ,00881 ,01608 1,000 -,0386 ,0562

2 -,10393* ,01522 ,000 -,1488 -,0591

3 -,12138* ,01479 ,000 -,1650 -,0778

5 -,12346* ,01504 ,000 -,1678 -,0791

4

6 -,03711 ,01418 ,130 -,0789 ,0047

1 ,13227* ,01707 ,000 ,0819 ,1826

2 ,01953 ,01626 ,979 -,0284 ,0675

3 ,00208 ,01586 1,000 -,0447 ,0489

4 ,12346* ,01504 ,000 ,0791 ,1678

5

6 ,08635* ,01529 ,000 ,0412 ,1315

1 ,04592 ,01632 ,075 -,0022 ,0940

2 -,06681* ,01547 ,000 -,1124 -,0212

3 -,08427* ,01505 ,000 -,1287 -,0399

4 ,03711 ,01418 ,130 -,0047 ,0789

6

5 -,08635* ,01529 ,000 -,1315 -,0412

2 -,11274* ,01370 ,000 -,1528 -,0726

3 -,13019* ,01473 ,000 -,1732 -,0871

4 -,00881 ,01186 ,976 -,0432 ,0256

5 -,13227* ,01395 ,000 -,1731 -,0915

1

6 -,04592* ,01237 ,005 -,0819 -,0100

1 ,11274* ,01370 ,000 ,0726 ,1528

3 -,01745 ,01762 ,920 -,0687 ,0337

4 ,10393* ,01530 ,000 ,0595 ,1484

5 -,01953 ,01697 ,859 -,0689 ,0298

2

6 ,06681* ,01570 ,001 ,0212 ,1124

1 ,13019* ,01473 ,000 ,0871 ,1732

2 ,01745 ,01762 ,920 -,0337 ,0687

4 ,12138* ,01622 ,000 ,0743 ,1685

5 -,00208 ,01781 1,000 -,0538 ,0497

3

6 ,08427* ,01660 ,000 ,0361 ,1325

1 ,00881 ,01186 ,976 -,0256 ,0432

2 -,10393* ,01530 ,000 -,1484 -,0595

3 -,12138* ,01622 ,000 -,1685 -,0743

5 -,12346* ,01552 ,000 -,1686 -,0784

4

6 -,03711 ,01411 ,098 -,0780 ,0037

1 ,13227* ,01395 ,000 ,0915 ,1731

2 ,01953 ,01697 ,859 -,0298 ,0689

3 ,00208 ,01781 1,000 -,0497 ,0538

4 ,12346* ,01552 ,000 ,0784 ,1686

5

6 ,08635* ,01592 ,000 ,0401 ,1326

1 ,04592* ,01237 ,005 ,0100 ,0819

2 -,06681* ,01570 ,001 -,1124 -,0212

Games-Howell

6

3 -,08427* ,01660 ,000 -,1325 -,0361


138

4 ,03711 ,01411 ,098 -,0037 ,0780

5 -,08635* ,01592 ,000 -,1326 -,0401

*. The mean difference is significant at the 0.05 level.

• The Hochberg’s GT2 test compares each ward to the remaining wards. For each pair of wards, the difference between means is shown, along with the standard error of that difference and the significance level at the 95% confidence interval. Some of the differences prove to be significant. Non significance is shown between wards 14 & 47, 14 & 94, 16 & 43, 16 & 81, 43 & 81 and 47 & 94.

• The Games-Howell results show exactly the same pattern as the Hochberg test which is that the pairs of wards in the comparison reveal the same level of confidence for the mean difference. These results validate the Hochberg’s test and permit to keep the profile of the results.

Homogeneous Subsets

WPI

Subset for alpha = 0.05 Ward code N

1 2

1 40 ,6188

4 67 ,6276

6 62 ,6647

2 48 ,7315

3 53 ,7489

5 50 ,7510

Ryan-Einot-Gabriel-Welsch Rangea

Sig. ,057 ,709

1 40 ,6188

4 67 ,6276

6 62 ,6647

2 48 ,7315

3 53 ,7489

5 50 ,7510

Hochbergb

Sig. ,057 ,973

Means for groups in homogeneous subsets are displayed.

a. Critical values are not monotonic for these data. Substitutions have been made to ensure monotonicity. Type I error is therefore smaller.

b. Uses Harmonic Mean Sample Size = 51,833.

This table shows subset groups with statistically similar means (proved by the significance value of each subset). Two subsets appear with three wards each. Wards 14, 43 & 94 are similar in cluster 1 and wards 16, 43 & 81 are similar in cluster 2. This pattern fits the study field truth as wards 14 & 94 are located in medium service level area, 16 & 43 & 81 are in high service level area and ward 47 is also in high service level area, but is a slum-like. This leads to the conclusion that the water poverty index is service level dependant. The case of ward 47 requires further analysis to discover other influencing factors.

The effect size r2 = 0.956 / 2.989 = 0.32 r = 0.57 It is a nearly large effect w2 = (0.956 – 5*0.006) / (2.989 + 0.006) = 0.31 w = 0.56


139

Annexure F2: One way ANOVA with the variables

Annexure F2.1: ANOVA SPSS outputs for the variable Access

Test of Homogeneity of Variances Access


7,649 5 314 ,000

ANOVA Access


Between Groups ,586 5 ,117 11,926 ,000


Total 3,670 319

Robust Tests of Equality of Means Access


Welch 12,040 5 140,397 ,000

Brown-Forsythe 11,968 5 225,396 ,000


Homogeneous subset table Three clusters of wards with statistically similar means are revealed: 16 & 47, 47 & 14, and 14 & 94 & 81 & 43. Effect size w2 = (0.586 – 5*0.010) / (3.670 + 0.010) = 0.15 w = 0.38 It is a medium effect.

Annexure F2.2: ANOVA SPSS outputs for the variable CapacityTest of Homogeneity of Variances Capacity


7,522 5 314 ,000

ANOVA Capacity


Between Groups 4,337 5 ,867 24,837 ,000


Total 15,304 319

Robust Tests of Equality of Means Capacity


Welch 30,774 5 142,890 ,000

Brown-Forsythe 25,379 5 264,319 ,000


Homogeneous subset table Three homogeneous subsets of wards are revealed: 47 & 14, 94 & 43, and 43 & 81 & 16. Effect size w2 = (4.337 – 5*0.035) / (15.304 + 0.035) = 0.27 w = 0.52 It is a large effect.


140

Annexure F2.3: ANOVA SPSS outputs for the variable Use

Test of Homogeneity of Variances Use


10,456 5 314 ,000

ANOVA Use


Between Groups 62387,829 5 12477,566 7,507 ,000

Within Groups 521934,439 314 1662,212

Total 584322,268 319

Robust Tests of Equality of Means Use


Welch 9,554 5 142,072 ,000

Brown-Forsythe 8,156 5 271,521 ,000


Homogeneous subset table Three clusters of ward similar means are revealed: 47 & 94 & 14 & 81, 14 & 81 & 43, and 81 & 43 & 16.

Effect size w2 = (62387.829 – 5*1662.212) / (584322.268 + 1662.212) = 0.09 w = 0.30 It is a medium effect.

Annexure F2.4: ANOVA SPSS outputs for the variable access power

Test of Homogeneity of Variances Access power


10,767 5 314 ,000

ANOVA Access power


Between Groups 1,387 5 ,277 22,836 ,000


Total 5,202 319

Robust Tests of Equality of Means Access power


Welch 29,501 5 141,877 ,000

Brown-Forsythe 23,253 5 252,771 ,000


Homogeneous subset table Three clusters of statistical means are revealed: 47 & 14, 94 & 16 & 43, and 16 & 43 & 81. Effect size w2 = (1.387 – 5*0.012) / (5.202 + 0.012) = 0.25 w = 0.50 It is a large effect.


141

Annexure G: Analysis at policy level Annexure G1: Cross tabulation WPI – Satisfaction with the service

WPI * Satisfaction with the service Crosstabulation

Satisfaction with the service

Satisfied Reasonably

satisfied Not satisfied

Total

Count 55 55 60 170

Expected Count 89,2 35,6 45,2 170,0

% within WPI 32,4% 32,4% 35,3% 100,0%

% within Satisfaction with the service 32,7% 82,1% 70,6% 53,1%

44.65 – 68.84

% of Total 17,2% 17,2% 18,8% 53,1%

Count 57 9 21 87

Expected Count 45,7 18,2 23,1 87,0

% within WPI 65,5% 10,3% 24,1% 100,0%


68.85 – 78.51

% of Total 17,8% 2,8% 6,6% 27,2%

Count 56 3 4 63

Expected Count 33,1 13,2 16,7 63,0

% within WPI 88,9% 4,8% 6,3% 100,0%


WPI

78.52 – 91.26

% of Total 17,5% ,9% 1,2% 19,7%

Count 168 67 85 320

Expected Count 168,0 67,0 85,0 320,0

% within WPI 52,5% 20,9% 26,6% 100,0%


Total

% of Total 52,5% 20,9% 26,6% 100,0%

Annexure G2: Cross tabulation Access – Response time to complaint

Access * Response time Crosstabulation

Response time

No complaint Within a

week More than a

week

Total

Count 5 161 28 194

Expected Count 13,3 150,4 30,3 194,0

% within Access 2,6% 83,0% 14,4% 100,0%

% within Response time 22,7% 64,9% 56,0% 60,6%

25.96 – 47.99

% of Total 1,6% 50,3% 8,8% 60,6%

Count 17 61 19 97

Expected Count 6,7 75,2 15,2 97,0

% within Access 17,5% 62,9% 19,6% 100,0%


48.00 – 58.72

% of Total 5,3% 19,1% 5,9% 30,3%

Access


142

Count 0 26 3 29

Expected Count 2,0 22,5 4,5 29,0

% within Access ,0% 89,7% 10,3% 100,0%

% within Response time ,0% 10,5% 6,0% 9,1%

58.73 – 82.82

% of Total ,0% 8,1% ,9% 9,1%

Count 22 248 50 320

Expected Count 22,0 248,0 50,0 320,0

% within Access 6,9% 77,5% 15,6% 100,0%


Total

% of Total 6,9% 77,5% 15,6% 100,0%

Annexure G3: Cross tabulation Access – Main problem in accessing water service

Access * Main problem with water service Crosstabulation

Main problem with water service

No major problem

No maintenance

Poor response to complaint

Low frequency or pressure

Total

Count 32 61 65 36 194

Expected Count 60,6 40,6 55,8 37,0 194,0

% within Access 16,5% 31,4% 33,5% 18,6% 100,0%

% within Main problem with water service

32,0% 91,0% 70,7% 59,0% 60,6%

25.96 – 47.99

% of Total 10,0% 19,1% 20,3% 11,2% 60,6%

Count 46 6 26 18 96

Expected Count 30,0 20,1 27,6 18,3 96,0

% within Access 47,9% 6,2% 27,1% 18,8% 100,0%


46,0% 9,0% 28,3% 29,5% 30,0%

48.00 – 58.72

% of Total 14,4% 1,9% 8,1% 5,6% 30,0%

Count 22 0 1 7 30

Expected Count 9,4 6,3 8,6 5,7 30,0

% within Access 73,3% ,0% 3,3% 23,3% 100,0%


22,0% ,0% 1,1% 11,5% 9,4%

Access

58.73 – 82.82

% of Total 6,9% ,0% ,3% 2,2% 9,4%

Count 100 67 92 61 320

Expected Count 100,0 67,0 92,0 61,0 320,0

% within Access 31,2% 20,9% 28,8% 19,1% 100,0%


100,0% 100,0% 100,0% 100,0% 100,0%

Total

% of Total 31,2% 20,9% 28,8% 19,1% 100,0%


143

Annexure G4: Calculation of the IMD

The data The input data to the IMD is taken from a census 2001 population table, a census 2001 asset base table and the administrative wards boundaries with the following characteristics and constraints:

� In the geodatabase, the administrative wards (which seems to be the health wards) are numbered A, B, C, D, E, F and G. In the census asset base table, they are 1, 2, 3, 4, 5, 6, and 7.

� In 2002, ward E (or 5) was removed from the municipal corporation (CDP, 2007, p. 51). Consequently, it is removed here also during data preparation.

� The census asset base table is at administrative ward level and the population table is at electoral ward level. � The numbers of households summed up in the two census tables do not match. � The table matching the ward boundaries in the geodatabase is chosen as reference and every indicators calculated

from the population table are extrapolated to adjust the value to the total number of households in the asset base table for accuracy and consistency purpose.

The indicators Indicators as calculated are shown in the following table.

Calculation and data source IMD input Census Population table Census Asset Base table (reference)

Percentage of households in scheduled caste

Nber HH_SC / Total population Adjusted to the total number of households in this table

Percentage of literate people Nber literate / Total population Adjusted to the total number of households in this table

Percentage of main workers Nber Main_W / Total population Adjusted to the total number of households in this table

household dependency rate Nber Main_W / (Marg_W + non Worker)

Adjusted to the total number of households in this table

Percentage of households using banking services

- Nber_HH_BS / T_nber_HH

Percentage of households with a scooter

- Nber_HH_Sc / T_nber_HH

Percentage of households using a hand pump

- Nber_HH_HP / T_nber_HH

Percentage of households having no latrine

- Nber_HH_noL / T_nber_HH

Percentage of households having no electricity

- Nber_HH_noE / T_nber_HH

Percentage of households having little space

- Number of HH with more than 2 persons per room / T_nber_HH

The capitals The indicators within the capitals are combined by weighted sum and considering them having equal weight. The methodology is as follows:

� Standardise each of the ten indicators. � Sum them up per capital. � Divide the sum by the number of indicator in the capital to get the capital index.

The IMD Like previously, the capitals are combined by weighted sum with equal weight into the IMD as follows:

� Standardise each index � Summed them up � Divide them by 4 (the number of capitals) to have the IMD value.

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