Estimation of the prevalence of moderate and severe food insecurity in Chilean municipalities using small area estimation methodsFood & Agriculture Org. [Author] [Author], 2 lug 2024 - 52 pagine This report presents a comprehensive overview of the methodology and findings stemming from the application of small area estimation (SAE) techniques to the 2020 National Socioeconomic Characterization Survey (CASEN) in Chile. Specifically, it focuses on deriving comuna-level estimates for SDG indicator 2.1.2, which measures the Prevalence of Moderate and Severe Food Insecurity based on the Food Insecurity Experience Scale (FIES). The document describes outlines the systematic approach employed in fitting the Fay-Herriot area-level SAE model. The results underscore the significant variation in the prevalence rates of moderate and severe food insecurity across different comunas in Chile. These findings not only underscore the necessity but also the feasibility of utilizing SAE techniques to yield more granular estimates. Such detailed insights are crucial for informed decision-making processes aimed at addressing food insecurity at the local level. |
Parole e frasi comuni
Akaike information criterion approach with non-informative arc-sin transformation area estimation methods area-level auxiliary variables Bayesian approach Bootstrap CASEN Chile Chilean municipalities Chillán Cook’s distance Direct Direct_ FH_ direct estimator Direct FH FH_CV Direct_ FH_ FH disaggregation domain District Domain Direct Domain Direct FH ECLAC elaboration estimates of SDG FH District Domain FH estimator FH FH_CV MSE FH model FH_ FH District FH_CV MSE CV Food Insecurity Experience frequentist homoscedasticity implemented Insecurity Experience Scale insecurity in Chilean intra-class correlation item response theory lack of money mean square error measures moderate and severe moderately or severely MSDF municipalities using small municipality level non-informative priors obtained parameter of interest population posterior distributions prevalence of moderate probability quality criteria random effects Rasch model raw score residuals SAE estimates SAE model sampling design SDG indicator 2.1.2 severe food insecurity small area estimation Sustainable Development Goals trimmed sampling weights variance