The aim of the project is to provide official statistics producers a set of useful and usable tools to disseminate routinely estimates of relevant socio-economic indicators for small geographical regions or other subsets of the national population. The relevant indicators will be chosen within the set considered by ISTAT and CNEL for the measurement of Equitable and Sustainable wellbeing (BES). The availability of reliable estimates of relevant indicators for small sub-populations will provide the public (policy makers, press, researchers, interested citizens) information about detailed geographical, social patterns and time evolution of (un)employment, poverty and social cohesion in Italy. In turn, this may represent the basis for the analysis of regional disparities, the identification of disadvantaged social groups and the apportioning of public funds according to efficient criteria. The research topics addressed in this project are in line with some of the central themes of Horizon 2020, which aims at promoting innovative and inclusive European societies. To reach the broadest possible audience, possibly beyond the country's borders, newly proposed procedures will be made available to the public via the project web site and R software packages. The project has the potential to provide a progress beyond the state of the art from a scientific point of view because:
(1) Labor market indicators obtained from Italian LFS are statistically reliable at national, regional and provincial level (LAU1). The novelty of the project is to provide appropriate methodology to provide statistically sound estimates for unplanned domains, i.e. smaller sub-populations obtained cross-classifying the indicators by groups (gender, age) and geography or smaller geographical units such as local labour market areas. The focus will be especially on the new indicators introduced by the BES project to complement traditional employment and unemployment rates. The project starts from the foreground obtained by the European projects SAMPLE, AMELI, ESSnet-SAE and goes beyond their methodological results. In particular, extensions of existing methodologies are required to handle variables that are categorical in nature and present clear spatio-temporal patterns, to produce model based estimates that take properly into account the complex sampling design used in the Italian LFS and to achieve benchmarking with estimates released at higher level planned domains and grant coherence.
(2) The most popular indicators of poverty and inequality are based on monetary variables such as income or consumption. The project will also take into consideration those indicators of social cohesion based non-monetary aspects of the phenomenon, such as social exclusion, vulnerability, fragility and material deprivation. It aims at developing procedures for reliably estimating these indicators for both large domains (NUTS2) and small areas (LAU1). Different measures describe different aspects of households living conditions and individual wellbeing that may be assumed to be related to underlying latent variables. Moreover, measuring the adverse effect of deprivation with respect to several dimensions at the same time requires multidimensional poverty indicators. A final goal of the project for this objective is the definition of ad-hoc small area estimation methods based on latent variable models to account for the multidimensional and latent nature of the variables of interest. Although based on a multidimensional approach, the calculation of average indicators overlooks their distribution in the population of concern. This project aims at the calculation of all poverty indicators for different quantiles of the population. Indicators specifically aimed at measuring inequality in the level of economic wellbeing will also be considered.
(3) EUSILC and SHIW surveys are likely to be affected by non-sampling errors as they deal with sensitive items (income, wealth, living and health conditions). Ignoring the impact of non-sampling errors may bias severely the final estimates frustrating the improvements realized by the application of small area methodologies. The novelty of the project is in the definition of new methodologies that (a) evaluate and treat unit nonresponse when the probability of response can either take extreme values (exactly zero, i.e. we are dealing with elusive populations) or can depend on the variables of interest (non-ignorable nonresponse) using very different perspectives from those employed so far in the literature to deal with it and (b) improve response rates and measurement quality using improvements of randomized response methodologies.
