Open Conference Systems, ITACOSM 2019 - Survey and Data Science

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Small area estimation of entropy inequality measures
Maria Rosaria Ferrante, Silvia Pacei

Building: Learning Center Morgagni
Room: Aula 210
Date: 2019-06-06 09:00 AM – 10:30 AM
Last modified: 2019-05-06

Abstract


Small area statistics on economic inequality are becoming important for better planning public regional policies. We focus on the estimation of entropy inequality measures in Italian provinces by using data taken from the EU-SILC sample survey for Italy. In EU-SILC survey the number of units sampled at provincial level is generally too small to obtain reliable estimates, and the use of small area estimation models is advisable. We consider small area models specified at area level that include the “direct†survey weighted estimators. In these models “direct†estimators are usually assumed to be unbiased and normally distributed. Nevertheless, in the case of inequality measures, design-based estimators are known to be biased for small sample sizes. To solve this problem, we search for a correction that can produce approximately unbiased direct estimators. Moreover, due to the range of values that these estimators can assume and to the possible asymmetry of their distribution, the normality assumption could be inadequate in small area estimation models. More flexible distributions are compared and explored as alternative to the normal one.