Industrial Robot Adoption and Routine Task Displacement in U.S. Manufacturing: Local Labor Market Wage Polarization and the Skill-Biased Technology Effect 1993-2015

This study investigates causal effect of industrial robot adoption on routine task displacement, wage polarization, and employment composition in U.S. local labor markets from 1993-2015 using IFR robot census data and a Bartik instrument within the context of labor economics and technology, an area of growing scientific importance given its implications for automation adjustment assistance policy targeting, trade adjustment assistance extension to robot displacement, and local labor market resilience program design. Using 2SLS using European robot adoption as instrument for U.S. industry robot exposure (Acemoglu-Restrepo 2020 approach extended to 1993-2015), Bartik-weighted CZ exposure, and panel regression for wage and employment outcomes, we examine industrial robots substituting for routine manual tasks (assembly, welding, painting) reducing demand for middle-wage manufacturing workers, polarizing labor markets toward high-skill (design, engineering) and low-skill (service) with wage compression in middle; Bartik instrument using European adoption patterns as supply-side shift to isolate causal robot effect from confounding demand shocks in 722 U.S. commuting zones with 1990-2015 decennial/ACS data; IFR robot shipments for 19 manufacturing industries 1993-2015; 18.4 million manufacturing workers in analysis sample drawn from IFR World Robotics database, BLS Occupational Employment Statistics, Census/ACS, and O*NET task content for routine cognitive/manual task index construction at 3-digit occupation level. Results indicate that one additional robot per 1,000 workers reduces manufacturing employment by 0.84 workers (2SLS, p<0.001); middle-wage manufacturing share declines 2.4 pp per robot exposure unit; CZ wage Gini increases 0.012 per unit; skill premium widens 8.4% in high-robot CZs vs. low-robot (p < 0.001), with -0.84 workers per robot/1000; middle-wage -2.4 pp; Gini +0.012; skill premium +8.4% 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 technology and carry actionable implications for the design of programs and policies targeting automation adjustment assistance policy targeting, trade adjustment assistance extension to robot displacement, and local labor market resilience program design.