Analysing relationships among socioeconomic, environmental, governance,
and water supply and sanitation variables
in developing countries
Summary Sept 2009-2010
Celine DONDEYNAZ
Supervisors: Prof Chen, Dr C Carmona-Moreno, Dr X Zhang
Background
United Nations Millennium Goals for Development
International initiative to reduce poverty by 2015
To reach this objective intermediate goals were established
GOAL 7 : Environmental sustainability
Target 3
Halve, 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
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 QUESTIONS
1. 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 described
132 indicators examined
shortlist of 53 indicators
Logical framework of data
Environmental Cluster
• Water resources availability
(Water poverty index, Water
stress, water bodies ...)
Human pressure Cluster
• Activities pressure ( water demand,
irrigation level, industrial pollution,
production indexes..)
• Demographic pressure ( growth,
• Land cover indicators (dryland
repartition Urban-rural
coverage, forest cover..)
Accessibility to WSS Cluster
• Population access to Sanitation
• Population access to Water Supply
Governance cluster
Country Well being Cluster
Stability and level of
violence, government
effectiveness, rule of
law, regulatory quality ,
control of corruption
Official Development
aid flow : global and
WSS ODA
• Health indicators (water-born
disease, mortality, life expectancy..)
• Poverty indicators ( HDI, National
poverty index, education level...)
•Education indicators
Missing data treatment
3.0
2.5
2.0
1.5
0.0
1.Manual Hot deck imputation for series having
few missing data
0.5
1.0
Relative Density
Objective : Qualitative approach
–> find order of range rather
than exact value
Methods
Observed and Imputed values of NBI
0.0
0.2
0.4
0.6
0.8
1.0
1.2
NBI -- Percent Missing: 0.16
2. Expectation – Maximization
algorithm combined with bootstraps
(EMB)1
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
Figure 1: the first two PCA factors of variables, (accumulated variability equal to 43,02%)
Test phase on Africa
Step 2 : Checking Variable Relationships Coherence
(PCA Analysis)
Adjusted R2 = 50.386
-> Coherence of the
relationships observed with
expectations :
0.8
PRECIPIT
TIWRR.
WITH.Dom.
WITH.Ind
0.6
ESI.
Malaria.2004
Group 4
WaterBodies
0.4
WaterPoverty.
Literacyrate.youth
NBI
Environmental.gov
BOD.emissions
F2
On F1 axis group 1-2
representing the society
development – poverty
0.2
HDI.2005
GDP.PPP.
-1
GI Afr
TOT.AIS.2004
-0.6
-0.8
water_.hous_connect.
CPI.
FertilRates
-0.4
WGI.RQ
Tot.Irrigation
WGI.GE
WGI.RofL
-0.2
0.2
0
DAM.Capacity.Pond.Surf
Agri.Area.
-0.2
ODA.WSS.TOT
LifeExpectBirth
GrowthUrban
0.8
0.6
PovertyRates
Children with diarrhea
HPI.1.
0.4
1
GrowthRural
AgriProdIndex.
Particip to IEAg
Group 2
%diarrhea in urban slums
-0.4
X.DryLands
Tot.WITH.
-0.6
WaterUseInt.Agri
-0.8
Coherency of the dataset
on Africa
Mortal_u5
Femal.economic.activity
0
Group 1
On F2 group 3-4 represents
the balance between water
demand and resources
Official.Dev.Aid
RatioGirls.to.boys
UrbanPop
TOT..AIWS.
School Enrolment
WGI.W.A.2004
Health.expenditurel
WGI.PS.AV.2004
F1
Test phase on Africa
Step 3 : Getting first key variables (Linear
Regression)
For water supply coverage
70% variability explained
Value
SE
Pr > |t|
Source
FertilRates2000.2005
Mortal_u5.2005
UrbanPop.pop_2005
WGI.PS.AV.2004
WGI.RofL.2004
RatioGirls.to.boys.98.01
Tot.Irrigation2003
CPI.2004
BOD.emission 98
Environmental.gov
-0.206
0.221
0.365
-0.396
0.640
0.245
0.133
-0.346
0.131
0.234
0.246
0.213
0.127
0.171
0.291
0.124
0.123
0.216
0.123
0.129
0.408
0.305
0.006
0.026
0.033
0.055
0.285
0.118
0.293
0.078
For water supply coverage
53% variability explained
Lower
bound
(95%)
-0.702
-0.208
0.108
-0.741
0.053
-0.005
-0.115
-0.783
-0.117
-0.027
Upper
bound
(95%)
0.291
0.651
0.622
-0.050
1.227
0.496
0.381
0.091
0.379
0.495
Key elements
1. Mortality of children under 5
2. The environmental management capacity but
non only
3. Living conditions
4. The urbanisation process
Source
Value
SE
Pr > |t|
Mortal_u5.2005
UrbanPop.pop_2005
WGI.PS.AV.2004
-0.584
0.176
-0.226
0.130
0.100
0.115
< 0.0001
0.086
0.056
Lower
bound
(95%)
-0.847
-0.026
-0.459
Upper
bound
(95%)
-0.320
0.379
0.006
WGI.GE2004
GrowthUrbanPop_2000.
2005
RatioGirls.to.boys.98.01
Tot.Irrigation....Agr_are
a.2003
CPI.2004
PovertyRates.1987.2006
.
%diarrhea in urban
slums
Environmental.gov
Gross.enrolement
Health.expenditure
-0.357
0.184
0.182
0.096
0.057
0.064
-0.726
-0.011
0.012
0.378
0.115
0.137
0.129
0.094
0.380
0.154
-0.147
-0.054
0.376
0.328
0.343
0.244
0.189
0.102
0.078
0.021
-0.040
0.038
0.725
0.451
-0.224
0.124
0.078
-0.475
0.026
0.388
-0.276
0.239
0.110
0.149
0.155
0.001
0.073
0.132
0.165
-0.578
-0.075
0.611
0.027
0.552
Key elements
1.The governance aspects (general +environmental)
2. The urbanisation process
3.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
Octobre 10
Confirm and expand analyses on Africa
1.
2.
Decembre 10
Complementary analyses
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
May 2011
5. Analyze different country behaviors to build country
profiles
Publication 2009-2010
• Article on dataset building, data collection,
imputation and verification of coherence
 almost ready ( Conference 2011)
• Article on preliminary results on Africa
To be submitted in December 2010
• JRC Report to be published by mid-October
Conclusion
Thanks you for attention
Questions ?