DP17451 Robot Adoption, Worker-Firm Sorting and Wage Inequality: Evidence from Administrative Panel Data
Leveraging the geographic dimension of a large administrative panel on employer- employee contracts, we study the impact of robot adoption on wage inequality through changes in worker-firm assortativity. Using recently developed methods to correctly and robustly estimate worker and firm unobserved characteristics, we find that robot adoption increases wage inequality by fostering both horizontal and vertical task specialization across firms. In local economies where robot penetration has been more pronounced, workers performing similar tasks have disproportionately clustered in the same firms (‘segregation’). Moreover, such clustering has been characterized by the concentration of higher earners performing more complex tasks in firms paying higher wages (‘sorting’). These firms are more productive and poach more aggressively. We rationalize these findings through a simple extension of a well-established class of models with two-sided heterogeneity, on-the-job search, rent sharing and employee Bertrand poaching. We conclude that our empirical findings reveal the presence of both ‘routine-biased technological change’ (RBTC), whereby new technology decreases the relative demand for workers in traditional routine tasks, and ‘core-biased technological change’ (CBTC), whereby new technology requires workers with specialized knowledge independently of their tasks being more or less routine intensive.