Job-to-job Mobility, Outside Options, and Bargaining: A Machine Learning Approach

Outside options play a central role in models of wage determination, yet the employment opportunities available to individual workers are not directly observable. I develop an individual-level measure of the value of alternative employment, rooted in a discrete choice model of labor supply estimated from observed job-to-job transitions using neural networks to account flexibly for differences in workers' preferences and their compatibility with potential employers. I validate the measure in linked employeremployee data from the Veneto region of Italy, showing that workers with higher measured outside options recover more quickly from mass layoffs. Improvements in outside options lead to greater job-to-job mobility and higher wages within ongoing employment spells: a one standard deviation increase raises wages by approximately 1.4%. An instrumental variable design based on large-firm openings confirms the causal reading of these results. The findings replicate in Portuguese linked employer-employee data, where increases in employers' value added also pass through more strongly to the wages of workers with better outside options. Taken together, these findings support wage bargaining as an empirically relevant mechanism of wage determination.