Economic Data Science
Faculty of Business and Economics
Emma Bacci is a PhD student in Economics at the University of Fribourg and a research and teaching assistant at UniDistance Suisse, co-supervised by Professors Martin Huber and Anthony Strittmatter.
Emma Bacci holds a Master's degree in Economics and Econometrics (LMEC) from the University of Bologna (2023), where she focused on econometric methods for policy evaluation, particularly machine learning for causal inference. She was fascinated by these tools because they can uncover causal relationships in complex settings. They also offer a way past what she has long seen as a key limitation of economic analysis: reducing human behavior to a small set of variables that interact linearly.
Her master's thesis was a result of a six-month internship at the Italian National Institute of Statistics (ISTAT). Using causal trees and causal forests, she studied heterogeneity in the "child penalty", meaning the effect of having a child on women's earnings. She profiled households in which gender inequality between partners (so within family units) widens most after the birth of a child.
She also spent a year as a trainee in the Directorate General Statistics at the European Central Bank. There she saw firsthand how data are collected, and how the way a dataset is constructed shapes the results of any economic analysis built on it.
Emma Bacci has always been passionate about economic research focused on Causal Inference and Policy Evaluation, as she believes it to be one of the fields of economics that has the most impact on the world and direct consequences on people's lives. In her PhD, she aims to bridge the gap between advanced econometric and causal machine learning methods and practical public policy questions, particularly in labor and local economic policy. She works on projects in collaboration with federal and local institutions across Switzerland.
Faculty of Business and Economics