Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains

Enrico Fabrizi, Maria Rosaria Ferrante, Silvia Pacei, Carlo Trivisano

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

A model-based small area method for calculating estimates of poverty rates based on different thresholds for subsets of the Italian population is proposed. The subsets are obtained by cross-classifying by household type and administrative region. The suggested estimators satisfy the following coherence properties: (i) within a given area, rates associated with increasing thresholds are monotonically increasing; (ii) interval estimators have lower and upper bounds within the interval (0, 1); (iii) when a large domain-specific sample is available the small area estimate is close to the one obtained using standard design-based methods; (iv) estimates of poverty rates should also be produced for domains for which there is no sample or when no poor households are included in the sample. A hierarchical Bayesian approach to estimation is adopted. Posterior distributions are approximated by means of MCMC computation methods. Empirical analysis is based on data from the 2005 wave of the EU-SILC survey.
Original languageEnglish
Pages (from-to)1736-1747
Number of pages12
JournalCOMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume55
Publication statusPublished - 2011

Keywords

  • Beta distribution
  • Fay Herriot model
  • Hierarchical Bayes modeling

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