Assessing water related poverty using the sustainable livelihoods framework

The research described in this working paper started within the Mekong Basin Focal Project (BFP) of the CGIAR Challenge Program on Water and Food.Local circumstances and adaptations affect water-related interventions and livelihood outcomes. This local variation is crucial for developing resilient l...

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Main Author E. Kemp-Benedict, S. Bharwani, E. dela Rosa, C. Krittasudthacheewa, N. Matin
Format Publication
LanguageEnglish
Published 01.01.2009
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Summary:The research described in this working paper started within the Mekong Basin Focal Project (BFP) of the CGIAR Challenge Program on Water and Food.Local circumstances and adaptations affect water-related interventions and livelihood outcomes. This local variation is crucial for developing resilient livelihood strategies, but also creates significant analytical challenges to assessing the likely impacts of water-related interventions. This report presents an approach using a probabilistic, ‘fuzzy’ model of the links between water and livelihoods that takes these fundamental uncertainties into account. The model is grounded in the Sustainable Livelihoods framework, and is implemented as a Bayesian network. The approach is applied to data from a previous study in Northeast Thailand, and the research was supplemented by field visits and key informant interviews at farms, communities, and universities in Northeast Thailand. This report presents a conceptual framework for analysing water-related interventions on poverty, an elicitation approach, and an example application. Also, it presents three innovations that resulted from the project: a novel way to represent institutions within the Sustainable Livelihood framework, a Bayesian approach to representing indicators as indirect evidence of a quantity of interest, and an elicitation technique for the conditional probability tables within a Bayesian network model.
Bibliography:http://www.dfid.gov.uk/r4d/PDF/Outputs/WaterfoodCP/BayesLH_WP_100114.pdf