Industrial Robot Adoption, Routine Task Displacement, and Wage Polarization in U.S. Manufacturing: Commuting Zone Evidence From the IFR Robot Census 2000-2022

This study investigates causal effects of industrial robot adoption on routine task employment displacement, wage polarization, and manufacturing wage bill composition across U.S. commuting zones from 2000-2022 within the context of labor economics and economics of technological change, an area of growing scientific importance given its implications for automation-affected worker retraining program targeting, trade adjustment assistance reform, and manufacturing wage inequality monitoring framework. Using commuting zone panel with shift-share IV instrument (Acemoglu-Restrepo IFR European robot penetration as instrument for U.S. exposure), 2SLS estimation of robot effects on employment and wages by task content quintile, we examine industrial robots substituting for routine manual task workers (assembly, material handling) in affected CZs, reducing employment and real wages in middle-skill routine manufacturing occupations while complementing non-routine cognitive workers and creating some demand for robot maintenance technical roles in 722 U.S. commuting zones (2000, 2010, 2015, 2022 panels) linked to IFR robot density by industry-CZ cell and CPS/ACS employment, hours, and wage outcomes for 8.4 million CPS respondents drawn from IFR World Robotics Report (robot density per 1000 manufacturing workers), linked by 4-digit SIC industry to CBP employment, matched to CZ geography with Census and CPS wage/employment data. Results indicate that one additional robot per 1000 manufacturing workers reduces CZ employment 0.24% and reduces real wages 0.48% in routine task quintiles 2-3 (2SLS), with wage polarization index increasing 0.18 SD per robot quartile increase; non-routine cognitive wage premium expands 12.4% in high-robot CZs (p < 0.001), with -0.24% employment per robot/1000 workers; -0.48% routine wages; non-routine premium +12.4% as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to labor economics and economics of technological change and carry actionable implications for the design of programs and policies targeting automation-affected worker retraining program targeting, trade adjustment assistance reform, and manufacturing wage inequality monitoring framework.