Quantifying the impact of water and sanitation service with a climate resilience perspective: a case study in Metro Manila, Philippines

8/13/2019 Quantifying the impact of water and sanitation service with a climate resilience perspective: a case study in Metro

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Quantifying the Impact of Water and Sanitation Service with a Climate

Resilience Perspective

A Case Study in Metro Manila, Philippines

Julian Doczi

A dissertation submitted to the School of International Development of the University of East

Anglia in partial fulfillment of the requirements for the degree of Master of Science inClimate Change and International Development

4 September 2012


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Word Count: 11,902

2012

Julian Doczi

All Rights Reserved


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Table of Contents

LIST OF ACRONYMS ........................................................................................................... 5

LIST OF FIGURES ................................................................................................................. 6

LIST OF TABLES ................................................................................................................... 7

SUMMARY .............................................................................................................................. 9

KEYWORDS ............................................................................................................................ 9

ACKNOWLEDGEMENTS .................................................................................................. 10

CHAPTER 1: INTRODUCTION, LITERATURE REVIEW, & CONCEPTUAL

FRAMEWORK ...................................................................................................................... 11

INTRODUCTION &LITERATURE REVIEW............................................................................... 11

AIMS &OBJECTIVES ............................................................................................................. 13

THE CASE STUDY:METRO MANILA ...................................................................................... 15

Overview ........................................................................................................................... 15

WatSan Situation .............................................................................................................. 15

Climate Impacts, Vulnerabilities, & Adaptations ............................................................. 17

CONCEPTUAL FRAMEWORK................................................................................................... 19

Hypotheses of the IE ......................................................................................................... 19

Source of Data .................................................................................................................. 20

Key Methodology .............................................................................................................. 21

CHAPTER 2: METHODOLOGY........................................................................................ 23

DATA COLLECTION ............................................................................................................... 23

OUTCOME VARIABLE SELECTION .......................................................................................... 24

EXPLANATORY VARIABLE SELECTION .................................................................................. 25

CONTROLLING FOR MOVEMENT ACROSS ZONES ................................................................... 28

CONTROL VARIABLE SELECTION.......................................................................................... 30

DATA ANALYSIS ................................................................................................................... 31

METHODOLOGICAL LIMITATIONS.......................................................................................... 32

RESEARCH ETHICS ................................................................................................................ 32

FUNDING ............................................................................................................................... 33

CHAPTER 3: RESULTS, ANALYSIS, AND DISCUSSION ............................................ 34

SUMMARY OF IERESULTS..................................................................................................... 34

Summary Tests of Means & Distributions ........................................................................ 34

DID Regressions ............................................................................................................... 37

DISCUSSION OF IERESULTS .................................................................................................. 39

Trends and Findings ......................................................................................................... 39

Sources of Error ............................................................................................................... 41

Overall Findings in Metro Manilas Climate Resilience Context.................................... 43

CHAPTER 4: CONCLUSIONS ........................................................................................... 45


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RESULTS TABLES............................................................................................................... 46

SECTION1:SUMMARYSTATISTICSFORCHILDHEALTH ...................................... 46

SECTION2:SUMMARYSTATISTICSFORWOMENSWEALTH&EDUCATION .. 49

SECTION3:OTHERSUMMARYSTATISTICS,USINGTHEWOMENSRECODE ... 54

SECTION4:DIDREGRESSIONTABLESONCHILDHEALTH ................................... 61SECTION5:DIDREGRESSIONTABLESONWOMENSWEALTH&EDUCATION 64

CHAPTER 5: REFERENCES .............................................................................................. 70

APPENDIX 1COMPARISON OF MWCI AND MWSI SERVICE INDICATORS ... 78

APPENDIX 2VARIABLE DEFINITIONS & CHARACTERISTICS ......................... 81

APPENDIX 3STATA FILES ............................................................................................ 85

APPENDIX 4ETHICAL APPROVAL FORM ............................................................... 86

APPENDIX 5COPY OF THE DATA USE AUTHORISATION EMAIL FROMMEASURE DHS .................................................................................................................... 87

APPENDIX 6COPY OF THE DATA USE AGREEMENT WITH THE PHILIPPINE

NSO ......................................................................................................................................... 89


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List of Acronyms

APIS Annual Poverty Indicators Survey

AR4 Fourth Assessment Report of the IPCC

DID Difference-in-difference regressionDFID UK Department for International Development

FIES Family Income and Expenditure Survey

GHG Greenhouse gases

IE Impact evaluation

IPCC Intergovernmental Panel on Climate Change

IWRM Integrated Water Resources Management

JMP Joint Monitoring Programme for Water Supply and SanitationMDG Millennium Development Goals

MICS Multiple Indicator Cluster Survey

MWCI Manila Water Company, Inc.

MWSI Maynilad Water Services, Inc.

MWSS Metropolitan Waterworks and Sewerage Service

DHS Demographic and Health Survey

NSO National Statistics Office of the Philippines

OECD Organisation for Economic Cooperation and Development

PAGASA Philippine Atmospheric, Geophysical, and Astronomical Services

Administration

PRECIS Providing Regional Climates for Impact Studies

PSU Primary sampling unit

RCT Randomised controlled trial

UEA University of East Anglia

UNICEF United Nations Childrens Fund

USAID United States Agency for International Development

WatSan Water and sanitation

WEAP Water Evaluation and Planning System

WHO World Health Organisation


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List of Figures

FIGURE 1.MAP OF METRO MANILA ......................................................................................... 15

FIGURE 2.AMAP OF THE POST-1997MWSSSERVICE AREA,WITH MWSIMANAGING THE

WEST ZONE AND MWCIMANAGING THE EAST ZONE....................................................... 16

FIGURE 3.CLIMATE CHANGE VULNERABILITY MAP OF SOUTHEAST ASIA. ............................... 19

FIGURE 4.ALINE FOR A PUBLIC TAP IN THE EARLY DAYS OF THE MWSISERVICE AREA.......... 28

FIGURE 5.COMPARISON OF WATER PRODUCTION FOR MWSI&MWCI .................................. 78

FIGURE 6.COMPARISON OF SERVICE RELIABILITY (HOURS/DAY OF SERVICE)FOR MWSI&

MWCI ............................................................................................................................... 78

FIGURE 7.COMPARISON OF WATER CONSUMPTION FOR MWSI&MWCI ................................ 79

FIGURE 8.COMPARISON OF NEW CUSTOMER CONNECTION FEES FOR MWCI&MWSI ............ 79

FIGURE 9.COMPARISON OF SERVICE CONNECTIONS AND NON-REVENUE WATER FOR MWCI&

MWSI ............................................................................................................................... 79

FIGURE 10.COMPARISON OF THE SANITATION AND SEWERAGE TARGETS FOR MWCI/MWSIINTHE MWSSAGREEMENT VERSUS THEIR ACTUAL ACHIEVEMENTS. .................................... 80
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List of Tables

TABLE 1.SIMPLIFIED,COLOUR-CODED SUMMARY TABLE OF THE RESULTS FROM TABLES 415

.......................................................................................................................................... 36

TABLE 2.SIMPLIFIED,COLOUR-CODED SUMMARY TABLE OF THE RESULTS FROM TABLES 16

22 ...................................................................................................................................... 37

TABLE 3.SIMPLIFIED,COLOUR-CODED SUMMARY TABLE OF THE DIDREGRESSION RESULTS

FROM TABLES 2330 ........................................................................................................ 38

TABLE 4.SUMMARY TEST FOR DIFFERENCES IN THE MEAN AND DISTRIBUTION OF THE THREE

SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF CHILD DIARRHEA IN THE

EAST AND WEST ZONE IN 1998AND 2008 ......................................................................... 47


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF CHILD DIARRHEA FOR

PIPED WATER (WITH)SERVICE IN 1998AND 2008 ............................................................ 47

TABLE 6.SUMMARY TEST FOR DIFFERENCES IN THE MEAN AND DISTRIBUTION OF THE THREESUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF CHILD DIARRHEA FOR

PIPED WATER (WITHOUT)SERVICE IN 1998AND 2008 ..................................................... 48


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF CHILD DIARRHEA FOR

BOTH 1998AND 2008 ........................................................................................................ 48


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF WOMENS WEALTH IN THE

EAST AND WEST ZONE IN 1998AND 2008 ......................................................................... 50


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF WOMENS EDUCATION IN

THE EAST AND WEST ZONE IN 1998AND 2008 .................................................................. 50


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF WOMENS WEALTH FOR

PIPED WATER (WITH)SERVICE IN 1998AND 2008 ............................................................ 51



PIPED WATER (WITHOUT)SERVICE IN 1998AND 2008 ..................................................... 51


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF WOMENS EDUCATION

FOR PIPED WATER (WITH)SERVICE IN 1998AND 2008 ..................................................... 52


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF WOMENS EDUCATION

FOR PIPED WATER (WITHOUT)SERVICE IN 1998AND 2008 .............................................. 52



BOTH 1998AND 2008 ........................................................................................................ 53


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF WOMENS EDUCATIONFOR BOTH 1998AND 2008 ................................................................................................. 53


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SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,FOR WATER TREATMENT IN

THE EAST AND WEST ZONE FOR BOTH 1998AND 2008 ...................................................... 55


SUB

-POPULATIONS

,AND

ENTIRE

,U

NSAMPLEDP

OPULATION,FOR

ON

-PREMISES

WATER IN

THE EAST AND WEST ZONE FOR BOTH 1998AND 2008 ...................................................... 55


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,FOR PRESENCE OF A FLUSH

TOILET IN THE EAST AND WEST ZONE FOR BOTH 1998AND 2008 ..................................... 56


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,FOR PIPED WATER (WITH

AND WITHOUT)SERVICE IN THE EAST AND WEST ZONE FOR BOTH 1998AND 2008 .......... 57


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF WATER TREATMENT FOR

PIPED WATER (WITH AND WITHOUT)SERVICE FOR BOTH 1998AND 2008........................ 58


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF ON-PREMISES WATER FOR

PIPED WATER (WITH AND WITHOUT)SERVICE FOR BOTH 1998AND 2008........................ 59


SUB-POPULATIONS,AND ENTIRE,UNSAMPLED POPULATION,OF PRESENCE OF A FLUSH

TOILET FOR PIPED WATER (WITH AND WITHOUT)SERVICE FOR BOTH 1998AND 2008 .... 60

TABLE 23.DIDIMPACT REGRESSIONS OF EAST/WEST ZONE ASSIGNMENT ON THE THREE SUB-

POPULATIONS OF CHILD DIARRHEA FROM 1998TO 2008 .................................................. 62

TABLE 24.DIDIMPACT REGRESSIONS OF PIPED WATER (WITH AND WITHOUT)ASSIGNMENTON THE THREE SUB-POPULATIONS OF CHILD DIARRHEA FROM 1998TO 2008 .................. 62

TABLE 25.DIDIMPACT REGRESSIONS OF PIPED WATER (WITH AND WITHOUT)ASSIGNMENT

ON THE THREE SUB-POPULATIONS OF CHILD DIARRHEA FROM 1998TO 2008,WITH

CONTROL VARIABLES........................................................................................................ 63

TABLE 26.DIDIMPACT REGRESSIONS OF EAST/WEST ZONE ASSIGNMENT ON THE THREE SUB-

POPULATIONS OF WOMEN'S WEALTH &EDUCATION FROM 1998TO 2008 ........................ 65


ON THE THREE SUB-POPULATIONS OF WOMENS WEALTH FROM 1998TO 2008 ............... 66


ON THE THREE SUB-POPULATIONS OF WOMENS WEALTH FROM 1998TO 2008,WITH



ON THE THREE SUB-POPULATIONS OF WOMENS EDUCATION FROM 1998TO 2008 .......... 68


ON THE THREE SUB-POPULATIONS OF WOMENS EDUCATION FROM 1998TO 2008,WITH

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Summary

Water and sanitation (WatSan) provision is an important development topic, with

numerous studies quantifying its positive impacts on local populations and its potential

vulnerabilities to forthcoming climate change impacts. However, characterisations of howexisting WatSan interventions have already impacted on the climate resilience of their

populations are still lacking. This was thus attempted here, to perform a quantitative impact

evaluation of a WatSan intervention for a relevant case study, as has been done before, but to

do so here with a climate resilience perspective. This was undertaken for Metro Manila,

Philippines, whose well-documented 1997 WatSan privatisation served as a quasi-experiment

with which to quantify the impact of a proactive WatSan utility versus one with worse service,

as the metropolis was split in half for this activity and each half was awarded to a differentcompany. From 19972007, one company performed well, while the other performed more

poorly. Thus, the technique of difference-in-difference regression was applied to

Demographic and Health Survey (DHS) data from 1998 and 2008, to attempt to determine

whether the better services of the proactive WatSan utility actually had a discernible impact

on its population. In taking a climate resilience perspective, the outcomes used to measure

this impact were chosen to be the health, wealth, and education of the population, as these

both reflect the WatSan service and determine an individuals resilience to climate impacts.

Although the analysis suffered from methodological/technical issues related to the DHS

dataset, its results nevertheless showed evidence of a positive impact on these outcomes by

the proactive utility versus the other utility. This thus illustrates the important role that

existing WatSan interventions have played in improving climate resilience and, therefore, the

importance of ensuring that these systems are adequately climate-proofed, to prevent

backsliding on these resilience gains made to date.

Keywords

Water and Sanitation; Impact Evaluation; Climate Change; Philippines


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Acknowledgements

The author wishes to thank the following people who assisted with this dissertation: Dr

Ben DExelle of UEA (for providing invaluable advice and comments as supervisor), Prof

Declan Conway & Prof Bruce Lankford of UEA (for providing advice), the UEA School ofInternational Development Dissertation Support Fund (for providing financial support), the

staff at the National Statistics Office of the Philippines (for providing survey data), the

administrator of the MeasureDHS website (for approving my application to access the sites

survey data), the staff at the Manila Water Company, Inc., Maynilad Water Services, Inc.,

and the Metropolitan Waterworks and Sewerage Systems (for providing data and advice), Mr

Steve Samuels of Statalist and Mr Sunil Kumar of UEA (for providing invaluable assistance

with Stata analysis), Ms Lisa Lumbao of AECOM International Development (for providinga relevant case study), and all those friends, family, and other colleagues who contributed to

the success of this endeavour. All errors and omissions are my own.


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Chapter 1: Introduction, Literature Review, & Conceptual Framework

Introduction & Literature Review

The international development topics of water and sanitation provision (WatSan) and

climate change action are incorporated within the UN Millennium Development Goals

(MDGs), but, as the 2015 deadline approaches, progress toward meeting these goals remains

limited (UN, 2012). Although the MDG target for providing improved drinking water has

been met, over 780 million people worldwide still lack access to this essential service, while

over 2.5 billion people still lack access to the similarly essential service of improved

sanitation, the MDG target for which will not be met by 2015 (WHO, 2010a; JMP, 2012).

The importance of seeking improvement on this situation is undeniable, with a wide variety

of authors reporting on the economic benefits that can be gained from investing in WatSan,

including Hutton, Haller, & Bartram (2004, 2006, 2007a, 2007b, 2008), who performed

global cost-benefit analyses on WatSan interventions and reported that every US$1 invested

in them yielded economic returns of between US$5$46, depending on the intervention.

Likewise, the OECD (2011) reported that WatSan-related diseases accounted for 10 per cent

of global disease burden and that benefit-to-cost ratios for basic WatSan investments in

developing countries were as high as seven to one.

Similarly for climate change, the steady rise of global greenhouse gas (GHG) emissions

as reported by the Intergovernmental Panel on Climate Change (IPCC) in its Fourth

Assessment Report (AR4) has been relentless, and with an international agreement on

climate change mitigation still elusive, this trend seems likely to continue (IPCC, 2007, UN,

2012). The importance, therefore, of preparing to adapt to the predicted near-term impacts of

climate changeby investing in projects to decrease vulnerability and increase resilience is

similarly undeniable. This is especially true for developing countries, whose currently low

levels of adaptive capacity make them more vulnerable and less resilient to the climate

impacts that will affect them (Adger et al., 2003, Sperling, 2003, Stern, 2007, Ch. 3 & 4).

Several studies already exist that link these two topics and discuss how WatSan services

including physical infrastructure, natural components, and soft practices like hygiene

may be vulnerable to forthcoming climate impacts. The key study on these linkages was the

Vision 2030 report by the WHO and the UK Department for International Development

(DFID) (2009), which highlighted this point by assessing the vulnerabilities of current

WatSan technologies under three different rainfall scenarios and making recommendations on


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ways to climate-proof them in the future, so as to avoid backsliding on the WatSan MDG

targets. Likewise, Satterthwaite et al. (2007) and Wilbanks et al. (2007) highlighted how

primary climate impacts, like increased droughts or floods, can not only directly damage

WatSan infrastructure, but can also change local water demand and thereby reduce water

supply availability or decrease water/wastewater treatment efficiency. Nilsen et al. (2011)

and Ahmad and Rodgers (2012) similarly detail how climate change will specifically impact

WatSan systems and recommend infrastructural adaptation solutions, though Batchelor et al.

(2009) caution that infrastructural climate-proofing will, alone, cease being effective as

climate impacts worsen. They recommend instead a stronger focus on promoting better

WatSan governance and tools like integrated water resources management (IWRM) to

decrease the overall vulnerability of WatSan systems. Finally, Confalonieri et al. (2007) note

the broader threats that climate impacts on water (e.g. rainfall alterations, water quality,

surface water availability) pose to increasing the global burden of water-related diseases,

especially for those without WatSan services.

However, there is a lack of studies to date that characterise how existing WatSan systems,

even if not climate-proofed, have (or have not) already reduced climate vulnerability and

built climate resilience in their populations. If an assessment of this nature is desired, then

insight can be drawn from the growing field of quantitative impact evaluation. As proponentslike Baker (2000), Ravallion (2001, 2008), and Khandker et al. (2010) explain, an impact

evaluation (IE) attempts to study whether changes in an outcome of interest in a population

can be specifically attributed to the programme/technology/practice of interest, or whether

some other factor is responsible for these changes. It does this, in theory, by comparing the

population receiving the intervention (i.e. the treatment group) with that same population if it

had not received the intervention, known as the counterfactual. However, since having both

these realities occur at once is impossible, the counterfactual is usually approximated through

use of control groups with similar characteristics to those of the treated population. This

works best by undertaking IEs as an integrated part of a development project, enforcing

rigorous experimental methodologies on the project like randomised controlled trials (RCTs)

(e.g. Burtless (1995) and Duflo et al. (2007)). However, it can also work well by undertaking

IEs ex poston projects with quasi-experimental attributes, and then by using more complex


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statistical techniques on data collected throughout the intervention period, in order to account

for the lack of random assignment.1

Although IE is still a burgeoning field, several such IEs have already been undertaken on

specific WatSan technologies/practices, though these studies did not consider their impacts

from a climate resilience perspective. A key outcome of interest in these existing studies has

been child health, with quantitative methods used to determine the impact of various

interventions on, inter alia, child diarrhea, mortality, and growth stunting outcomes. These

included: Jalan and Ravallion (2003), on the impact of piped water in rural India; Nanan et al.

(2003), on the impact of a WatSan community education programme in Pakistan; Galdo and

Briceno (2005), on the impact of WatSan infrastructure expansion in Quito, Ecuador; Galiani

et al. (2005), on the impact of water privatisation in Argentina; Barreto et al. (2007), on the

impact of increased sewer coverage in northeast Brazil; and Capuno and Tan (2012), on the

impact of improved water and sanitation in the Philippines.

Aims & Objectives

With these previous WatSan IEs in mind, the aim of this particular research was to

attempt another such IE on a new case study, but to do so with a climate resilience

perspective. The idea was to provide a characterisation of how the improvement of a local

WatSan system, even if not yet climate-proofed, has or has not reduced climate vulnerability

and built resilience in its population, via the measurable impact it had on this population. This

research thus took the following objectives:

1. Select a relevant case study in a developing country and examine its general WatSansituation and current climate change situation (impacts/vulnerabilities/adaptations);

2. Perform a quantitative IE for a WatSan intervention in this case study, to quantify theimpact of the improvement of this intervention on its population; and

3. Discuss how this WatSan impact translates into an increase or decrease of climateresilience for its population.

This study adds value to the academic community because, in addition to generating

another WatSan IE of an interesting local case study that has not been previously studied, this

study is also one of the first to consider how this quantitative WatSan impact translates into

an increase or decrease of climate resilience. This then gives an indication of the importance

1Note also that this data need not necessarily be collected by the project itself, thus its quasi-experimentalnature. As will be illustrated here, relevant survey data from other sources is often adequate for the investigation.


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of ensuring that the WatSan system is appropriately climate-proofed, to avoid backsliding on

this acquired resilience if climate impacts are to affect the system. These findings will thus be

policy-relevant and serve as additional evidence for the importance of investment in both

WatSan and climate adaptation.

In choosing the case study here, a couple key criteria needed to be met. Since neither

funding nor time were available to perform an actual RCT-based experiment, the case study

needed to have quasi-experimental characteristics for its WatSan intervention(s) and also

needed available survey data from the same time period to use for analysis. After basic

investigations, Metro Manila, the Philippines, from 1997-2007, met these criteria well.

Although its WatSan story has already been documented extensively, it has not yet been

subject to a quantitative IE, so this study still creates new knowledge on the topic through the

use of this technique.

The remainder of this chapter will take the following layout:

First, I will describe the selected case study in more detail, discussing both MetroManilas WatSan intervention as well as the regions predicted climate impacts,

vulnerabilities, and ongoing adaptation actions;

Second, I will lay out a conceptual framework for the IE, by first outlining my keyarguments and hypotheses used in the analysis to link impact to climate resilience,

then discussing the available survey data for the study, and then detailing the IE

method chosen to study itdifference-in-difference regression.

This will then be followed by Chapter 2: Methodology, where I detail first the process of

survey data collection, then discuss the processing and analytical procedures undertaken on it.

This is then followed by Chapter 3: Results, Analysis, and Discussion, where I present the IE

results, then discuss their trends, sources of error, and implications for climate resilience in

their populations. A brief concluding chapter follows.


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The Case Study: Metro Manila

Overview

Metro Manila, with an estimated population of over 16 million (WHO, 2010b), is a

conglomeration of 17 cities/municipalities that together create the Philippine capital. At

nearly 19,000 people per square kilometre, it is one of the

most densely populated urban areas in the world (NSCB,

2008), and the metropolis/country also suffers from the

highest inequality rate in Southeast Asia (The Philippine Star,

2011). These, and its high proportions of slums and largely

unplanned land use patterns, make it highly vulnerable to

natural disasters (Luz and Paladio-Melosantos, 2012). Figure