TY - JOUR
T1 - Small area estimation of the Gini concentration coefficient
AU - Fabrizi, Enrico
AU - Trivisano, Carlo
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Hierarchical Bayes, Beta regression, Income inequality, random effects, Markov Chain Monte Carlo, Variance components
KW - Hierarchical Bayes, Beta regression, Income inequality, random effects, Markov Chain Monte Carlo, Variance components
UR - http://hdl.handle.net/10807/76043
U2 - 10.1016/j.csda.2016.01.010
DO - 10.1016/j.csda.2016.01.010
M3 - Article
SN - 0167-9473
SP - 223
EP - 234
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -