Extended beta models for poverty mapping. An application integrating survey and remote sensing data in Bangladesh

De Nicolò, Silvia ; Fabrizi, Enrico ; Gardini, Aldo (2022) Extended beta models for poverty mapping. An application integrating survey and remote sensing data in Bangladesh. Bologna: Dipartimento di Scienze Statistiche "Paolo Fortunati", Alma Mater Studiorum Università di Bologna, p. 25. DOI 10.6092/unibo/amsacta/7074. In: Quaderni di Dipartimento. Serie Ricerche (3). ISSN 1973-9346.
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Abstract

The paper targets the estimation of the poverty rate at the Upazila level in Bangladesh through the use of Demographic and Health Survey (DHS) data. Upazilas are administrative regions equivalent to counties or boroughs whose sample sizes are not large enough to provide reliable estimates or are even absent. We tackle this issue by proposing a small area estimation model complementing survey data with remote sensing information at the area level. We specify an Extended Beta mixed regression model within the Bayesian framework, allowing it to accommodate the peculiarities of sample data and to predict out-of-sample rates. In particular, it enables to include estimates equal to either 0 or 1 and to model the strong intra-cluster correlation. We aim at proposing a method that can be implemented by statistical offices as a routine. In this spirit, we consider a regularizing prior for coefficients rather than a model selection approach, to deal with a large number of auxiliary variables. We compare our methods with existing alternatives using a design-based simulation exercise and illustrate its potential with the motivating application.

Abstract
Tipologia del documento
Monografia (Working paper)
Autori
AutoreAffiliazioneORCID
De Nicolò, SilviaDipartimento di Scienze Statistiche “P.Fortunati”, Alma Mater Studiorum Università di Bologna0000-0001-5052-6527
Fabrizi, EnricoDISES, Università Cattolica del S.Cuore, Milano0000-0003-2504-7043
Gardini, AldoDipartimento di Scienze Statistiche “P.Fortunati”, Alma Mater Studiorum Università di Bologna0000-0002-2164-5815
Parole chiave
ITA: Demographic Health Survey, Modelli gerarchici Bayesiani, Shrinkage prior, Stima per piccole aree ENG: Demographic Health Survey, Hierarchical Bayes, Shrinkage priors, Small area estimation
Settori scientifico-disciplinari
ISSN
1973-9346
DOI
Data di deposito
07 Nov 2022 07:47
Ultima modifica
07 Nov 2022 07:47
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