Title: Small area estimation of latent economic wellbeing
Authors: Angelo Moretti - Manchester Metropolitan University (United Kingdom) [presenting]
Natalie Shlomo - University of Manchester (United Kingdom)
Joseph Sakshaug - German Institute for Employment Research (Germany)
Abstract: Factor analysis (FA) models are used in data dimensionality reduction problems where the variability among observed variables can be described through a smaller number of unobserved latent variables. This approach is often used to estimate the multidimensionality of wellbeing. We employ FA models and use multivariate EBLUP (MEBLUP) to predict a vector of means of factor scores representing economic wellbeing for small areas. We compare this approach to the standard approach whereby we use SAE (univariate and multivariate) to estimate a dashboard of EBLUPs on original variables and then averaged. Our simulation study shows that the use of factor scores provides estimates with lower variability than weighted and simple averages of standardised MEBLUPs and univariate EBLUPs. Moreover, we find that when the correlation in the observed data is taken into account before small area estimates are computed multivariate modelling does not provide large improvements in the precision of the estimates over the univariate modelling. We close with an application using the EU survey on income and living conditions data with a particular focus on economic wellbeing.