Abstract
The Gini coefficient is a popular concentration measure often used in the analysis of
economic inequality. Estimates of this index for small regions may be useful to properly
represent inequalities within local communities. However, the small area estimation for
the Gini coefficient has not been thoroughly investigated. A method based on area level
models, thereby avoiding the assumption of the availability of Census data at the micro
level, is proposed. A modified design based estimator for the coefficient with reduced small
sample bias is suggested as input for the small area model, while a hierarchical Beta mixed
regression model is introduced to combine survey data and auxiliary information. The
methodology is illustrated by means of an example based on Italian data from the European
Union Survey on Income and Living Conditions.
Lingua originale | English |
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pagine (da-a) | 223-234 |
Numero di pagine | 12 |
Rivista | COMPUTATIONAL STATISTICS & DATA ANALYSIS |
DOI | |
Stato di pubblicazione | Pubblicato - 2016 |
Keywords
- Hierarchical Bayes, Beta regression, Income inequality, random effects, Markov Chain Monte Carlo, Variance components