Download full text
(2.169Mb)
Citation Suggestion
Please use the following Persistent Identifier (PID) to cite this document:
https://nbn-resolving.org/urn:nbn:de:0168-ssoar-92841-2
Exports for your reference manager
Poverty and Inequality Mapping Based on a Unit-Level Log-Normal Mixture Model
[journal article]
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... view more
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.... view less
Keywords
poverty; inequality; model; Italy
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
generalized inverse Gaussian; hierarchical Bayes; nested error model; prior sensitivity; EU-SILC 2012
Document language
English
Publication Year
2022
Page/Pages
p. 2073-2096
Journal
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
Published Version; peer reviewed