Economic inequalities referring to specific regions are crucial in deepening spatial heterogeneity. Income surveys are generally planned to produce reliable estimates at countries or macroregion levels, thus we implement a small area model for a set of inequality measures (Gini, Relative Theil and Atkinson indexes) to obtain microregion estimates. Considering that inequality estimators are unit-interval defined with skewed and heavy-tailed distributions, we propose a Bayesian hierarchical model at area level involving a Beta mixture. An application on EU-SILC data is carried out and a design-based simulation is performed. Our model outperforms in terms of bias, coverage and error the standard Beta regression model. Moreover, we extend the analysis of inequality estimators by deriving their approximate variance functions.

Silvia De Nicolò, M.R.F. (2022). Small area estimation of inequality measures using mixtures of Beta. Bologna : Dipartimento di Scienze Statistiche "P. Fortunati" [10.6092/unibo/amsacta/7073].

Small area estimation of inequality measures using mixtures of Beta

Silvia De Nicolò;Maria Rosaria Ferrante;Silvia Pacei
2022

Abstract

Economic inequalities referring to specific regions are crucial in deepening spatial heterogeneity. Income surveys are generally planned to produce reliable estimates at countries or macroregion levels, thus we implement a small area model for a set of inequality measures (Gini, Relative Theil and Atkinson indexes) to obtain microregion estimates. Considering that inequality estimators are unit-interval defined with skewed and heavy-tailed distributions, we propose a Bayesian hierarchical model at area level involving a Beta mixture. An application on EU-SILC data is carried out and a design-based simulation is performed. Our model outperforms in terms of bias, coverage and error the standard Beta regression model. Moreover, we extend the analysis of inequality estimators by deriving their approximate variance functions.
2022
27
Silvia De Nicolò, M.R.F. (2022). Small area estimation of inequality measures using mixtures of Beta. Bologna : Dipartimento di Scienze Statistiche "P. Fortunati" [10.6092/unibo/amsacta/7073].
Silvia De Nicolò, Maria Rosaria Ferrante, Silvia Pacei
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/909638
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