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Poverty and Inequality Mapping Based on a Unit-Level Log-Normal Mixture Model
[Zeitschriftenartikel]
Abstract Estimating poverty and inequality parameters for small sub-populations with adequate precision is often beyond the reach of ordinary survey-weighted methods because of small sample sizes. In small area estimation, survey data and auxiliary information are combined, in most cases using a model. In th... mehr
Estimating poverty and inequality parameters for small sub-populations with adequate precision is often beyond the reach of ordinary survey-weighted methods because of small sample sizes. In small area estimation, survey data and auxiliary information are combined, in most cases using a model. In this paper, motivated by the analysis of EU-SILC data for Italy, we target the estimation of a selection of poverty and inequality indicators, that is mean, headcount ratio and quintile share ratio, adopting a Bayesian approach. We consider unit-level models specified on the log transformation of a skewed variable (equivalized income). We show how a finite mixture of log-normals provides a substantial improvement in the quality of fit with respect to a single log-normal model. Unfortunately, working with these distributions leads, for some estimands, to the non-existence of posterior moments whenever priors for the variance components are not carefully chosen, as our theoretical results show. To allow the use of moments in posterior summaries, we recommend generalized inverse Gaussian distributions as priors for variance components, guiding the choice of hyperparameters.... weniger
Thesaurusschlagwörter
Armut; Ungleichheit; Modell; Italien
Klassifikation
Erhebungstechniken und Analysetechniken der Sozialwissenschaften
Freie Schlagwörter
generalized inverse Gaussian; hierarchical Bayes; nested error model; prior sensitivity; EU-SILC 2012
Sprache Dokument
Englisch
Publikationsjahr
2022
Seitenangabe
S. 2073-2096
Zeitschriftentitel
Journal of the Royal Statistical Society, Series A (Statistics in Society), 185 (2022) 4
DOI
https://doi.org/10.1111/rssa.12872
ISSN
1467-985X
Status
Veröffentlichungsversion; begutachtet (peer reviewed)