PERCEIVE project - Deliverable D2.4 "Report on the probabilistic model of estimation of citizens’ identification with the EU project and ranking of the case study regions"

This report aims to develop a probabilistic model to help identifying clusters of citizens and regions according to their level of identification with the EU project, accounting for the influence of regional drivers as well as for the level of implementation of the Cohesion Policy . We obtain a classification of individuals and regions through the estimation of probabilities of class membership at both levels and the identification of individual and regional characteristics affecting these probabilities. The results will be used also to rank the regions according to the level of citizens’ identification with the EU project. The model builds on Latent Class analysis and multilevel modelling. From a set of observed variables representing the components of the concept of individual identification, we identify latent classes for identification with EU and the classification of individuals. Unobserved (latent) regional effects are modelled as a discrete latent variable and allow to group the regions into a small number of clusters. We use data from the survey implemented within the PERCEIVE project and focusing on i) respondents’ awareness of EU Regional Policy, ii) their identification with Europe, country, region, and European values, iii) their Political attitudes and values, iv) support for the Cohesion Policy, as well as demographic and socio-economic characteristics.