Abstract: This research sheds new light on the public-private pay gap by gender in France by focusing on lifetime earnings and part-time work. Utilizing a sequential Expectation-Maximization algorithm to model unobserved heterogeneity, the analysis reveals that (small) hourly wage gaps between sectors amount to significant lifetime value disparities, especially for women. The study posits this as a potential "gender cost" of privatization, showing that women earn a higher public premium than men. By including part-time work, predominantly done by women, and calculating lifetime values, this research provides a more holistic view of the public pay gap and its intersection with gender.

Abstract: This paper explores the extent to which job-to-job transitions correlate with monetary incentives in the French labor market from 2011-2019. We employ k-means clustering to categorize firms and Expectation-Maximization (EM) algorithms to classify workers, incorporating occupational data to refine worker classification. Building on the AKM (Abowd, Kramarz, and Margolis) literature, we re-evaluate the extent to which previously identified worker effects can be attributed to occupational choices instead. 

Abstract: This study examines the impact of France Telecom's privatization on female full-time employment, with implications for UN SDG 5 on gender equality. Using forecasted treatment effects and bootstrapping, data from 1997-2007 are analyzed to estimate the Average Treatment Effect on female full-time employment. Deterministic trends forecast counterfactual outcomes, while bootstrapping estimates the ATE's confidence intervals. Findings reveal a significant decline in female full-time employment post-privatization. This case study highlights the importance of considering gender costs in privatization policies and underscores the need for robust methods in policy analysis. Further research should explore long-term implications and mechanisms affecting gender equality.