Analysing relationships among socio- economic, environmental, governance, and water supply and sanitation variables in developing countries Summary Sept 2009-2010

Celine DONDEYNAZSupervisors: Prof Chen, Dr C Carmona-Moreno, Dr X Zhang

Background

GOAL 7 : Environmental sustainability Target 3Halve, by 2015, the proportion of the population without sustainable access to safe drinking water and basic sanitation

Indicators on Water Supply and Sanitation (WSS)-Proportion of the population having access to improved water source -Proportion of the population having access to improved sanitation

United Nations Millennium Goals for Development

International initiative to reduce poverty by 2015

To reach this objective intermediate goals were established

http://www.un.org/millenniumgoals/

Pit latrine in Lalibela , Ethiopia, C.Dondeynaz

Subject and questions

The efficiency of the WSS management in a specific developing country = a combination of a wide range of variables¹= > a complex and a cross cutting issue

OBJECTIVE :Better understand the keys elements involved in an improved WSS management.

Main QUESTIONS1. Are the different variables and data coherent enough to establish spatial-temporal behaviors?

2. Can be established measurable protocols and can behavior patterns be extrapolated in time and at other spatial scales?

3. Can data and patterns be integrated into a tool for better understanding these mechanisms ?

¹ Integrated water resources management Principles laid down at the International Conference on Water and the Environment held in Dublin in January 1992

DATA COLLECTION

Scope of the data collection International data providers : UNEP – FAO – JRC – WB … Scale : National country level over the world Time series : consistency issue requires a strict

examination of data coherence and methodologies. 2004 year of reference

Variables selection criteria Relevance : potential role regarding water supply and

sanitation Data availability : enough observations Reliability : produced by trustfully providers and

described132 indicators examined shortlist of 53 indicators

Logical framework of dataEnvironmental Cluster

• Water resources availability

(Water poverty index, Water stress, water bodies ...)

• Land cover indicators (dryland coverage, forest cover..)

Human pressure Cluster

• Activities pressure ( water demand, irrigation level, industrial pollution, production indexes..)

• Demographic pressure ( growth, repartition Urban-rural

Accessibility to WSS Cluster

• Population access to Sanitation

• Population access to Water Supply

Country Well being Cluster

• Health indicators (water-born disease, mortality, life expectancy..)

• Poverty indicators ( HDI, National poverty index, education level...)

•Education indicators

Official Development aid flow : global and WSS ODA

Governance cluster

Stability and level of violence, government effectiveness, rule of law, regulatory quality , control of corruption

Missing data treatment

Objective : Qualitative approach –> find order of range rather than

exact valueMethods

0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Observed and Imputed values of NBI

NBI -- Percent Missing: 0.16

Re

lativ

e D

en

sity

2. Expectation – Maximization algorithm combined with bootstraps (EMB)1

1.Manual Hot deck imputation for series having few missing data

¹Amelia II software is provided by Honaker James, King Gary, Blackwell Matthew, http://gking.harvard.edu/amelia/

Verification of dataset coherence

Initial verification process 1. Variable normalization

2. Principal Component Analysis (PCA) performance to see correlations

3. Linear regression to find out key elements explaining the WSS level paying attention to coherence

Step 1. Checking the Normal Distribution of the variables

• Standard normalization not possible on the worldwide dataset because of too diverse behaviour among countries

• So Restriction on African data to smaller dataset as a preliminary phase

Test phase on AfricaStep 2 : Checking Variable Relationships

Coherence (PCA Analysis)

Agri.Area.

WaterBodies

Particip to IEAg

WGI.RofL

WGI.RQ

NBI

WGI.W.A.2004

RatioGirls.to.boys

GI Afr

WGI.GE

PovertyRates

Malaria.2004

CPI.

Official.Dev.Aid

Environmental.gov

ODA.WSS.TOT

WGI.PS.AV.2004

X.DryLands

Femal.economic.activity

DAM.Capacity.Pond.Surf

WaterUseInt.Agri

TOT..AIWS.

GrowthUrban

School EnrolmentHealth.expenditurel

Tot.Irrigation

GrowthRural

TOT.AIS.2004

ESI.

Literacyrate.youth

PRECIPIT

water_.hous_connect.

HDI.2005

HPI.1.

FertilRates

LifeExpectBirth

Tot.WITH.

%diarrhea in urban slums

WaterPoverty.

Mortal_u5

BOD.emissions

GDP.PPP.

WITH.IndTIWRR.WITH.Dom.

AgriProdIndex.

Children with diarrhea

UrbanPop

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

F1

F2

Group 1 Group 2

Group 4

Group 3

Figure 1: the first two PCA factors of variables, (accumulated variability equal to 43,02%)Figure 1: the first two PCA factors of variables, (accumulated variability equal to 43,02%)

Adjusted R2 = 50.386-> Coherence of the relationships observed with expectations :

On F1 axis group 1-2 representing the society development – poverty

On F2 group 3-4 represents the balance between water demand and resources

Coherency of the dataset on Africa

For water supply coverage53% variability explained

For water supply coverage70% variability explained

Test phase on AfricaStep 3 : Getting first key variables (Linear Regression)

Source Value SE Pr > |t| Lower bound (95%)

Upper bound (95%)

Mortal_u5.2005 -0.584 0.130 < 0.0001 -0.847 -0.320UrbanPop.pop_2005 0.176 0.100 0.086 -0.026 0.379

WGI.PS.AV.2004 -0.226 0.115 0.056 -0.459 0.006

WGI.GE2004 -0.357 0.182 0.057 -0.726 0.012GrowthUrbanPop_2000.

20050.184 0.096 0.064 -0.011 0.378

RatioGirls.to.boys.98.01 0.115 0.129 0.380 -0.147 0.376Tot.Irrigation....Agr_are

a.20030.137 0.094 0.154 -0.054 0.328

CPI.2004 0.343 0.189 0.078 -0.040 0.725PovertyRates.1987.2006

.0.244 0.102 0.021 0.038 0.451

%diarrhea in urban slums

-0.224 0.124 0.078 -0.475 0.026

Environmental.gov 0.388 0.110 0.001 0.165 0.611Gross.enrolement -0.276 0.149 0.073 -0.578 0.027

Health.expenditure 0.239 0.155 0.132 -0.075 0.552

Source

Value SE Pr > |t| Lower bound (95%)

Upper bound (95%)

FertilRates2000.2005 -0.206 0.246 0.408 -0.702 0.291

Mortal_u5.2005 0.221 0.213 0.305 -0.208 0.651

UrbanPop.pop_2005 0.365 0.127 0.006 0.108 0.622

WGI.PS.AV.2004 -0.396 0.171 0.026 -0.741 -0.050

WGI.RofL.2004 0.640 0.291 0.033 0.053 1.227

RatioGirls.to.boys.98.01 0.245 0.124 0.055 -0.005 0.496

Tot.Irrigation2003 0.133 0.123 0.285 -0.115 0.381

CPI.2004 -0.346 0.216 0.118 -0.783 0.091

BOD.emission 98 0.131 0.123 0.293 -0.117 0.379

Environmental.gov 0.234 0.129 0.078 -0.027 0.495

Key elements 1. Mortality of children under 52. The environmental management capacity but non only3. Living conditions4. The urbanisation process

Key elements1.The governance aspects (general +environmental)2. The urbanisation process3.The irrigation capacity and BOD as expressing

technical progress level.4. An unexpected point is the education of girl at primary

level.

Next activities and planning

Confirm and expand analyses on Africa1. Complementary analyses

2. Find complementary variables to increase the level of variability explained (sanitation)

3.Paper Submission for publication

4. Regroup variables to end up with few key indicators explaining the WSS level

5. Analyze different country behaviors to build country profiles

Octobre 10

Decembre 10

May 2011

Publication 2009-2010

• Article on dataset building, data collection, imputation and verification of coherence

almost ready ( Conference 2011)

• Article on preliminary results on AfricaTo be submitted in December 2010

• JRC Report to be published by mid-October

Conclusion

Thanks you for attention Questions ?