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B0333
Title: Poverty mapping in small areas under a two-fold nested error regression model Authors:  Domingo Morales - University Miguel Hernandez of Elche (Spain) [presenting]
Isabel Molina - Universidad Carlos III de Madrid (Spain)
Yolanda Marhuenda - Universidad Miguel Hernandez de Elche (Spain)
Jon Rao - Carleton University (Canada)
Abstract: Small area estimation procedures typically provide more reliable poverty estimates than direct methods. These models include area effects to account for the unexplained between-area heterogeneity. When poverty figures are sought at two different aggregation levels, domains and subdomains, it is reasonable to assume a two-fold nested error model including random effects explaining the heterogeneity at the two levels of aggregation. The empirical best (EB) method is introduced for estimating of additive parameters in small areas, under a two-fold model. Under this model, analytical expressions for the EB estimators of poverty incidences and gaps in domains or subdomains are given. The obtained EB estimates of the totals for all the subdomains in a given domain add up to the EB estimate of the domain total. We develop a bootstrap estimator of the mean squared error (MSE) of EB estimators and study the effect on the MSE of a misspecification of the area effects. In simulations, we compare the estimators obtained under the two-fold model with those obtained under models with only domain effects or only subdomain effects, when all subdomains are sampled or when there are unsampled subdomains. The methodology is applied to poverty mapping in counties of the Spanish region of Valencia by gender. Results show great variation in the poverty incidence and gap across the counties from this region, with more counties affected by extreme poverty when restricting ourselves to women.