Statistics plays a crucial role in providing a deep insight into economical and social phenomena, by supplying quantitative methods and reliable data. In particular, National Institutes in charge of the production of official statistics have nowadays to face the growing need of timely, high quality and relevant estimates of parameters of interest via sample surveys. However, these requirements have to meet also the need to moderate costs, reduce the response burden on units, and fully exploit the opportunities provided by developments in technology. The objective of the project stems from open research issues concerning these general topics and will focus on the statistical estimation of indicators of social cohesion and sustainability, with a special attention to youth unemployment and economic wellbeing, and will be achieved by means of two Work Packages of research.
The first Work Package (WP1) aims at the estimation of labour market indicators by fully exploiting the information gathered by the Italian Labor Force Survey (LFS) to provide tools to monitor their evolution over time and at local geographical levels. LFS is designed to provide reliable estimates of such indicators at the Province level (LAU1). Therefore, computing estimates of the unemployment rate for young people (aged 15-24) at LAU1 level, or at finer geographical definitions such as local labour market areas or metropolitan cities, requires ad-hoc statistical tools for such unplanned domains (or small areas). Several aspects concerning estimation for such small areas will be analyzed thoroughly, with a particular emphasis on issues related to the categorical nature of most of the variables involved in the computation of the indicators from the LFS, the spatial and temporal structure of the data and the coherence between aggregated model-based small area estimates and direct (design-based) estimates for larger or planned areas (benchmarking property).
The second objective (WP2) focuses on tools to map and monitor households' wealth in Italy. Computation of indicators of economic wellbeing is mainly based on data coming from the European Survey on Income and Living Conditions (EUSILC) run in Italy by the National Institute of Statistics (ISTAT) and the Survey of Households Income and Wealth (SHIW) run by the Bank of Italy. The former provides reliable estimates at regional level, while the latter does not. Therefore, as a final objective of WP2, a more detailed geographical level analysis will be approached in order to identify the critical areas to direct specific policies and for which novel ad-hoc small area estimation techniques are required. A peculiarity of EUSILC and SHIW is that of surveying sensitive items (income, wealth and living conditions) that may introduce non-sampling errors: a main objective is to handle the bias introduced by non-ignorable nonresponse and to introduce novel techniques to reduce nonresponse rates and increase data quality.
