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.

Fabrizi, E., Ferrante, M., Pacei, S., Trivisano, C., Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains, <<COMPUTATIONAL STATISTICS & DATA ANALYSIS>>, 2011; 55 (4): 1736-1747. [doi:10.1016/j.csda.2010.11.001] [http://hdl.handle.net/10807/19681]

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

Fabrizi, Enrico;
2011

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.
2011
Inglese
Fabrizi, E., Ferrante, M., Pacei, S., Trivisano, C., Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains, <<COMPUTATIONAL STATISTICS & DATA ANALYSIS>>, 2011; 55 (4): 1736-1747. [doi:10.1016/j.csda.2010.11.001] [http://hdl.handle.net/10807/19681]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/19681
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