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Book (series)Methodological note on new estimates of the prevalence of undernourishment in China 2020
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No results found.This paper presents new estimates of the extent of food consumption inequality in mainland China and discusses their implications for the estimated prevalence of undernourishment (PoU). The new food consumption inequality estimates are based on the joint analysis of food consumption and food expenditure data obtained from two separate household surveys, covering the period from 2011 to 2017. The results reveal much less inequality in dietary energy consumption than previously assumed and imply a substantial downward revision of the estimated series of the PoU for China, which becomes more in line with other assessments of food insecurity and with other development indicators. -
Book (series)Refinements to the FAO methodology for estimating the prevalence of undernourishment indicator 2014
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No results found.The FAO prevalence of undernourishment (PoU) indicator monitors progress towards Millennium Development Goal target 1C of halving, between 1990 and 2015, the proportion of people suffering from hunger. Estimates of the number of undernourished (NoU) - calculated by multiplying the PoU by the size of the reference population - are used to monitor progress towards the World Food Summit goal of reducing by half the number of people suffering from undernourishment. The PoU indicator is defined a s the probability that a randomly selected individual from the reference population is found to consume less than his/her calorie requirement for an active and healthy life. This paper reports on refinements to the methodology for estimating the Prevalence of Undernourishment that were adopted during the preparation of the State of Food Insecurity in the World Report 2014. The paper reviews the method adopted for selecting the functional form of the probability density function for the calculati on of the PoU, which uses a data-driven criterion. It proposes revised methods for estimating the variability (CV) and asymmetry (SK) parameters from available household survey, based on a leave-out-one cross validation approach. This approach is shown to be more conservative and stable across different country datasets than alternative methods. Following, the paper describes a regression approach for controlling for excess variability due to differences between food acquisition and consumptio n in surveys, which allows for a seasonality adjustment. Finally, the paper introduces an updated regression for computing variability measures in the absence of reliable household surveys, which incorporates the effect of food prices along with those of per capita income levels and inequality. -
DocumentConcept note and ToRs of the National Training Workshop on Food Security SDG Indicators 2.1.1: Prevalence of Undernourishment (PoU) & 2.1.2(FIES) in Myanmar
26-30 August 2019, Nay Phi Taw, Myanmar
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