New Bias Calibration for Robust Estimation in Small Areas

TL;DR

This paper introduces two new bias calibration methods for robust small area estimation that improve efficiency and reveal different insights in real data applications, especially in the presence of outliers.

Contribution

The paper proposes two novel bias calibration approaches for robust small area estimation, extending existing estimators to handle outliers more effectively.

Findings

Methods outperform existing bias calibration techniques in simulations.

Application to Italian labour market data shows different results from traditional methods.

New approaches improve estimation accuracy in small areas with outliers.

Abstract

Using sample surveys as a cost effective tool to provide estimates for characteristics of interest at population and sub-populations (area/domain) level has a long tradition in "small area estimation". However, the existence of outliers in the sample data can significantly affect the estimation for areas in which they occur, especially where the domain-sample size is small. Based on existing robust estimators for small area estimation we propose two novel approaches for bias calibration. A series of simulations shows that our methods lead to more efficient estimators in comparison with other existing bias-calibration methods. As a real data example we apply our estimators to obtain \textit{Gini} coefficients in labour market areas of the Tuscany region of Italy, where our sources of information are the EU-SILC survey and the Italian census. This analysis shows that the new methods reveal a different picture than existing methods. We extend our ideas to predictions for non-sampled areas.