Water, governance and human development variables in developing countries: multivariate inter-relationships analysis and statistical modelling using Bayesian networks

In the last decades, we have assisted to an important expansion of the number of indicators for measuring the development of a country− from the GDP per capita, households’ consumption indicators, demographic and medical indicators, schooling rates to governance indexes. This has produced in a first time the development of composite indicators to explain and synthesise the spatial and temporal changes of these different indicators− the Human Development Index (HDI) and its adjusted versions, Multidimensional Poverty Index (MPI), or the Water Poverty Index (WPI), to provide policy makers simple figures to help them in their decisions. The main difficulty faced by the researchers was to explain complex behaviours through single indicators. This research develops a framework to explain and contribute to the better understanding of the relationships between the existing single and complex indicators in the domain of Water Supply and Sanitation (WSS) in Developing Countries. This framework is based on the Bayesian Networks modelling method (Castelletti & Soncini-Sessa, 2007a), (Giné Garriga et al., 2009), (Dondeynaz et al., 2013). In addition to building this analytical framework, this research also aims at measuring and analysing the distribution and the influence of Official Development Assistance (ODA) in recipient countries. The approach chosen is global, targeting cross-countries analysis and comparison to capture the principal key variables of water supply and sanitation coverage expansion and its benefits for the country development. Therefore, this research proposes a methodological framework using Bayesian models for analysing water supply and sanitation access levels together with governance, human development (education, health, and income), water resources, the uses of these resources and the ODA. The research outputs could support national decision making and/or donors’ strategies, in particular the European Union. Variables and data are collected at national country scale for 101 developing countries observations in a new database (WatSan4dev) for year 2004. Five country profiles are identified and ranged around five main thematic axes using multivariate and clustering analyses. The countries from profiles 4 and 5 were the least favoured in terms of development and access to WSS, therefore should benefit from ODA support. However, countries from profile 5 received rather low ODA inputs in 2004, possibly as shown from the models because of their relative instability and poor governance. The modelling approach is led by the principles of robustness and replicability and took into account data availability and nature using Bayesian Networks. It is found that WSS access is strongly associated to country development (+35 % probability change) that is first sensitive, as expected, to the income level. The urbanisation level is the second strong factor associated to development with the limit of slums development. Health care and advanced governance complete these key factors. Lastly, WSS is sensitive to ODA CI where high-level ODA is estimate to benefit first to poor (45%) and middle (34%) development countries at 79% probability. This modelling allowed, in addition, running probabilistic scenarios to test hypotheses and measure the probable changes on WSS and the development. The methodological process, the outputs of multivariate analysis, the five countries profiles, the Bayesian modelling as well as examples of scenarios are described and analysed. The reference date is first 2004. The analytical and modelling process is then applied to the 2000-2008 period