Conference Agenda

Over recent decades, increasing longevity, declining fertility, and the spread of new family forms have transformed family structures, even by reinforcing the role of extended kinship. How cohesive are kinship networks in Italy and how much do relatives feel obliged to support each other in times of need? Through a factorial survey experiment, this paper addresses social norms of supportive obligations within both nuclear and extended kinship networks. Data are gathered from an ad-hoc online survey, planned in the framework of the Prin Pnrr KinHealth project, exploring various aspects of kinship ties and providing a comprehensive view of family relationships of middle-aged adults. It includes a vignette-based design, presenting respondents with a fictional scenario about a 50-year-old adult whose relative cannot walk for six months due to a car accident. We manipulate three dimensions (kin type, the protagonist’s gender, and the relative’s gender) and assign their combinations randomly to respondents to assess their causal effects on respondents’ views about the extent and type of support the protagonist should provide to the relative. This research will yield new insights into kinship as a social environment of shared responsibility, implying consequences for public health services and the demand for institutional care.

The international literature has often depicted Italy as an old-fashioned and traditional society, at least demographically, but this vision stands to fall. The conundrum of rapid changes in family-related behavior and the enduring low fertility and sky-rocking age at first birth make Italy especially interesting to study prevailing social norms and attitudes towards Assisted Reproductive Technology (ART). The existing legal framework seems rooted in societal norms; however, it remains uncertain whether these regulations align with contemporary attitudes toward ART. This paper addresses these questions by taking a novel perspective to assess the role of social norms and attitudes towards ART in Italy through a factorial survey experiment. In our experimental setting, we expose the respondents to several scenarios (vignettes) characterized by different couple's characteristics and ask them what this couple should and will do with respect to the eventual use of ART to achieve their fertility goals.

In the social sciences, there is a growing need to identify clusters within high-dimensional datasets, as in topic modeling applications or large survey data. For example, recent surveys have investigated how urban areas accommodate older adults—a pressing concern as populations age in Italy and across Europe. Identifying clusters of neighborhoods based on age-friendliness could help city planners and policymakers evaluate urban environments at various levels of detail,

supporting targeted interventions to enhance well-being for older adults. To address this, we apply a recent Bayesian model-based clustering approach that enables multi-resolution analysis of city areas, revealing age-friendliness patterns on both broad and fine scales. This method utilizes an infinite mixture model with a hierarchical structure, offering flexibility in interpreting clusters at multiple resolutions. By incorporating covariates, the approach adapts dynamically to potential hierarchies among clusters, enhancing its interpretive power. The effectiveness of this model is shown through clustering age-friendly neighborhoods, with insights available at varying scales.

Additionally, the methodology generalizes to other clustering challenges, offering a versatile tool for complex social data analysis.

This study examines the European Union's Cohesion Policy in Italy, focusing on the development of education and job inclusion capabilities. By analyzing the short summaries of the implemented projects using word embeddings based topic modeling techniques, we identify the extent to which these initiatives prioritize education and training, and then we identify in the text the reference to migrant populations using a Large Language Model, Mistral-7B. The results show that Italy's developmental perspectives emphasize education as a key capability, with a focus on vocational training, education infrastructure, and digital literacy. However, we also find that the inclusion of migrant workers in these initiatives is limited.

This study investigates the association between climate change news coverage and fertility outcomes. While climate change media coverage has been shown to be related to environmental attitudes, its connection to fertility outcome remains unexplored. Using word embedding techniques, we develop a continuous emotionality index of climate change news, offering a more nuanced analysis of media sentiment compared to traditional dictionary-based text mining approaches. Linking this emotionality index to individual-level fertility data from the UK "Understanding Society" panel survey, we provide a longitudinal framework to examine how exposure to emotionally charged news about climate change correlates with fertility outcomes for first and second parity. We also assess how this relationship is moderated by environmental attitudes and political orientation. By incorporating data from digital news outlets with varying political leanings, this study offers new insights into how climate change narratives are connected to fertility, contributing to broader discussions on media's role in shaping demographic trends.