Analysing relationships among socio- economic, environmental, governance, and water supply and sanitation variables in developing countries Summary Sept Celine DONDEYNAZ Supervisors: Prof Chen, Dr C Carmona-Moreno, Dr X Zhang

Background 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 United Nations Millennium Goals for Development International initiative to reduce poverty by 2015 To reach this objective intermediate goals were established 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 variables ¹ = > 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 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...) 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 value Methods 2. Expectation – Maximization algorithm combined with bootstraps (EMB) 1 1.Manual Hot deck imputation for series having few missing data ¹ Amelia II software is provided by Honaker James, King Gary, Blackwell Matthew,

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 Africa Step 2 : Checking Variable Relationships Coherence (PCA Analysis) Group 1 Group 2 Group 4 Group 3 Figure 1: the first two PCA factors of variables, (accumulated variability equal to 43,02%) Adjusted R2 = > 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 coverage 53% variability explained For water supply coverage 70% variability explained Test phase on Africa Step 3 : Getting first key variables (Linear Regression) SourceValueSEPr > |t|Lower bound (95%) Upper bound (95%) Mortal_u < UrbanPop.pop_ WGI.PS.AV WGI.GE GrowthUrbanPop_ RatioGirls.to.boys Tot.Irrigation....Agr_are a CPI PovertyRates %diarrhea in urban slums Environmental.gov Gross.enrolement Health.expenditure Source ValueSEPr > |t|Lower bound (95%) Upper bound (95%) FertilRates Mortal_u UrbanPop.pop_ WGI.PS.AV WGI.RofL RatioGirls.to.boys Tot.Irrigation CPI BOD.emission Environmental.gov Key elements 1. Mortality of children under 5 2. The environmental management capacity but non only 3. Living conditions 4. The urbanisation process 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 Confirm and expand analyses on Africa 1.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 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 ?