The Living Conditions (LC) department aims to produce research of exceptional scientific calibre and societal significance on the topics of public policies, welfare, and socio-economic inequalities. With the goal of enhancing understanding of the drivers of poverty, inequality, and social cohesion, the department conducts research on policy-relevant questions, including the efficiency, sustainability, and role of social policies and the welfare state.

The department is by nature interdisciplinary and multidisciplinary and studies the various dimensions of individuals' and families' lives that influence and are influenced by inequality, from early childhood to retirement. Our research draws on a variety of perspectives and statistical and econometrics methodologies, and rich datasets including survey and administrative data, to analyze income, wealth, inequality, poverty, and social inclusion. The department's work also includes the evaluation of the impact of various fiscal, family, educational, health, pension, and other social policies.

In addition to its own research initiatives, LC also contributes to the interdisciplinary research programs of the Institute, collaborating with the Labour Market and Urban Development and Mobility Departments on critical issues such as labour market policies, the housing crisis, environmental challenges, and their effect on people's behaviour.

The participation of LC in the Institute's "Health & Health Systems" transversal research program covers topics ranging from the impact of ageing on health and the economy to health inequalities. Together with the "Crossing Borders" transversal program, LC investigates the effects of migration on social cohesion, voters’ preferences, and the education system.

LC is committed to advancing the new institutional Competence Centres on Data Science and Simulation, as well as Experimental and Participatory Research. Our contributions to these areas range from studying innovative educational methodologies, such as digital learning platforms, to developing advanced tools, including behavioural and dynamic microsimulation and cutting-edge machine learning methods. We also contribute through the development of qualitative and mixed methods.