Riccardo Di Francesco
University of Southern Denmark
I am an applied econometrician specialising in causal inference and causal machine learning, developing tools for heterogeneous treatment effects and ordered outcomes with applications to health and labour economics. My job market paper applies language models to Danish corporate disclosures to measure firms' orientation toward employee well-being, and uses within-worker variation from job mobility to estimate its causal effect on worker health using population-wide administrative registers. More broadly, my applied agenda examines how economic and social environments shape worker health, team production, and social preferences, drawing on data from administrative registers, professional sports leagues, and online platforms.
Job market paper: Well-being salience in corporate disclosures and worker health Details →
