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Resilience analysis of Karamoja, Uganda 2016

FAO resilience analysis No. 10












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    This report is part of a series of country level analyses prepared by the FAO Resilience Analysis and Policies (RAP) Team. The series aims at providing programming and policy guidance to policy makers, practitioners, UN agencies, NGO and other stakeholders by identifying the key factors that contribute to the resilience of households in food insecure countries and regions. The analysis is largely based on the use of the FAO Resilience Index Measurement and Analysis (RIMA) tool. Findings are inte grated with other more traditional measures of poverty and food insecurity.
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    Karamoja (Uganda) 2016: Baseline analysis 2017
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    The region of Karamoja, located in the northeast of Uganda, is an area of particular interest to many humanitarian organizations for several reasons: food insecurity is a major challenge in the region; conflict both between communities (also known as clans) in Karamoja, and between communities in Karamoja and those in bordering countries are rife; a high level of climate variability undermines the capacity to utilize the region’s natural resources, as they are affected by droughts, floods and dr y spells. UNICEF), FAO and WFP have been working in Karamoja for more than twenty years and together have developed a Joint Resilience Strategy (JRS) for the region. The overall goal of this JRS is to improve food security and nutrition status in the region during the period from 2016 to 2020.
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    A dynamic analysis of resilience in Uganda 2016
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    Resilience is, nowadays, one of the keywords in the policy debate on development. Measuring resilience and how it varies over time is dramatically important for policy makers and people living in risk-prone environments. This paper applies econometric techniques for estimating household resilience using the so-called Resilience Index Measurement and Analysis (RIMA) recently proposed by FAO (2013). It then adopts transition matrices to estimate how resilience changes over time. Finally, multinomi al logit and bivariate probit models are estimated to identify the main drivers of change.

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