Mitchell Valdes-Bobes
PhD Candidate
Welcome to my academic website! I’m Mitchell Valdes-Bobes, a PhD candidate in Economics at the University of Wisconsin–Madison. My work sits at the intersection of labor economics and macroeconomics, where I use structural search models to study how policy and firm organization shape labor-market outcomes.
Research Interests:
- Labor-market search, mobility, and compensation.
- Macroeconomic shocks and their effects on labor markets.
- Causal inference & applied econometrics
- Labor and workforce data science
- Machine learning for labor-market analysis.
Current Focus:
- Analyzing the effects of remote work on labor market outcomes.
- Exploring the implications of generative AI on labor markets.
Please don’t hesitate to contact me at valdsbobes@wisc.edu if you have questions about my work or are interested in potential collaborations.
Job Market Paper
- Why Remote Work Stuck: A Structural Decomposition of the Post-Pandemic Equilibrium2025Abstract: The post-pandemic labor market featured a persistent increase in remote work arrangements. We ask whether this re-valuation of work is mostly due to a shift in worker preferences for flexibility or advances in technology that make remote work more feasible. We develop and estimate a general equilibrium search model featuring heterogeneity in worker skill and idiosyncratic worker tastes. A key innovation is our novel, continuous measure of occupational teleworkability, which allows the model to capture rich heterogeneity in remote-work potential across firms and occupations. We estimate the model’s deep parameters via the Simulated Method of Moments (SMM), disciplined by rich microdata from two distinct periods: 2019 (pre-pandemic) and 2024 (post-pandemic). Our analysis reveals that the shift is overwhelmingly driven by a profound revaluation of in-office time by workers. A structural decomposition shows that this preference shock accounts for 56.8% of the total increase in the average share of remote work, while concurrent shocks to remote work technology account for 33.0% of the shift.