Use of spatial information in small area models for unemployment rate estimation at sub-provincial areas in Italy

The goal of this paper is to analyze the possibility to improve the performance of the estimation at sub-regional level from ISTAT Labour Force Survey. In particular, we refer to estimation of unemployment rates for small domains cutting across survey strata, i.e. Local Labour Market Areas, defined as aggregation of municipalities. Currently, such quantities are estimated by means of an EBLUP based on a linear mixed model with spatially correlated area effects and covariates given by the area level unemployment rate at previous census and sex by age classes. In this work we explore the use of alternative models to incorporate spatial information at different levels. In particular, we investigate the use of different distance measures in the correlation structure among small areas. In addition, additive models are employed to include spatial information at the municipality level using low-rank thin plate splines. Finally, small area estimators based on logistic (mixed) models are explored to account more properly for the binary nature of the response variable. Spatial information is included in this type of models too. The performance of the aforementioned methods is studied via simulation experiments on 2001 Census data.