Title: The impact of job stability on monetary poverty in Italy: causal small area estimation
Abstract: Job stability refers to the security and predictability of employment, including factors such as long-term contracts, adequate wages, social security benefits, and access to training and career development opportunities. Stable employment can play a crucial role in reducing poverty, as it provides individuals and households with a stable income as well as improves their overall and subjective economic well-being. In this work, we leverage the EU-SILC survey and census data to assess the causal effect of job stability on monetary poverty across provinces in Italy. To this end, we propose a causal small area estimation (CSAE) framework for heterogeneous treatment effect estimation in which only a negligible fraction of outcomes is actually observed at the provincial level. Our estimators are more stable than the classical causal inference tools as they borrow strength from the other sources of data at the expense of additional modelling assumptions. On top of that, our new methodology proves to be successful in recovering provincial heterogeneity of the effect of job stability across six regions in Italy.
Bio: I am a Lecturer (Assistant Professor) in the School of Mathematics at the University of Bristol, affiliated with the Institute for Statistical Science. Prior to joining the University of Bristol, I did postdocs at the University of California, Berkeley, the University of Toronto, and the University of Cambridge, where I worked with Mark van der Laan, Dehan Kong, and Qingyuan Zhao, respectively. I completed my PhD in the University of Geneva, where I was advised by Stefan Sperlich and co-advised by María José Lombardía. My research interest lies at the intersection of survey methodology, causal inference and machine learning. During my PhD I worked on theoretical aspects of simultaneous, post-selection and computational inference with some applications in economics and social sciences. Afterwards, I broadened my research agenda by trying to solve some open problems in causal inference and merging machine learning with survey sampling methodology.