Marie Labussière: Workers Confronting Technological Change

Marie Labussière has recently joined the CRIS as a researcher. In addition to her institutional profile, she shares here some insights into her background, research interests, and first projects.  

• Professional background 
  During my studies at the National School of Statistics and Economic Administration (ENSAE Paris), several internships in sociology labs allowed me to explore how data and statistical tools can deepen our understanding of social inequalities. After earning my degree in statistics and economics, I chose to pursue the Master’s in “Interdisciplinary Studies” (EHESS/ENS-PSL) to broaden my theoretical and methodological approaches. I then completed my PhD at Maastricht University (Netherlands), before working for four years as a postdoctoral researcher at the University of Amsterdam. I joined CRIS in January 2026.
• Research interests
  My work spans several areas of research, but they all share a common focus: understanding how inequalities between individuals are shaped over the life course, in interaction with institutional and socio-economic contexts. For example, my PhD examined how children of immigrants navigate the Dutch education system depending on whether—and when—their parents acquired Dutch citizenship, a process itself shaped by changes in naturalization laws. In a similar vein, I study how career trajectories are shaped in a context of rapid labor market transformations.
• Research agenda: first projects at CRIS
  My current research agenda focuses on the relationship between technological change and inequalities among workers. As AI rapidly reshapes the world of work, we still lack robust tools to measure these transformations and to understand how they affect career paths depending on workers’ education and skills. I therefore explore how new methods—such as text analysis and predictive techniques—as well as new data sources like job postings, can help us better capture these dynamics.  Convinced of the value of interdisciplinary approaches, I have also developed a course at the intersection of sociology, economics, and anthropology on these issues.
• Methods and commitments  
  I strongly believe in the value of statistical methods, while remaining aware of potential errors, biases, and misuse in data and results. In a context of growing distrust in numbers, it is essential to strengthen research practices and ensure transparency and reproducibility. This involves sharing code—and data when possible—as well as all the elements needed to reproduce an analysis. Because writing reproducible code requires time and specific skills, I have developed workshops to promote these practices and actively contribute to networks dedicated to reproducible research.

Full list of publications (Google Scholar)