Mapping the impact of robotic investment on the U.S. manufacturing industry: a multi-level longitudinal analysis of employment, industry firm composition, and occupational change

Abstract

The purpose of this study was to investigate how capital expenditures (CAPEX) on robotic automation are reshaping employment patterns, occupational structures, and firm composition within the U.S. manufacturing sector. The study focuses on industry sectors categorized under the North American Industry Classification System (NAICS) codes 31–33 from 2018 to 2021. The study delves into the growing discussion about the long-term labor market effects of automation, specifically extending beyond traditional manual labor roles. The existing literature primarily focuses on the displacement of blue-collar jobs. This study centers on white-collar supervisory occupations, including industrial production managers, engineers, technicians, and front-line supervisors. This research is designed as a descriptive study, utilizing publicly accessible national and state-level statistics from the U.S. Census Bureau and the Bureau of Labor Statistics. Using simple statistical methods, correlation analysis, and industry quartile groupings to find patterns in CAPEX spending and how it relates to the number of firms, total manufacturing jobs, and job types at different levels of analysis. Lagged relationships are also explored to capture delayed labor responses to investment activity. The key findings indicate a steady rise in investment in robotic automation during the research period, including the years affected by the COVID-19 pandemic. Conversely, there has been a notable decline in manufacturing employment accompanied by a trend toward firm consolidation. At the occupational level, white-collar employment outcomes varied: managerial roles showed consistent positive alignment with both year-matched and lagged CAPEX levels, while engineering and technical occupations displayed more volatility. At the state level, high-CAPEX states tended to have higher concentrations of white-collar employment but did not consistently gain firms, revealing regional disparities in the diffusion and benefits of automation. By focusing on white-collar supervisory labor and employing a descriptive-analytic approach, this study provides new empirical evidence on how automation is shaping workforce structures and organizational dynamics within the U.S. manufacturing sector. The results are relevant for workforce development initiatives, organizational planning, and state and federal policy efforts. They indicate that responses to industrial automation should be flexible, occupation-specific, and regionally tailored to reflect the uneven impacts of robotic investment in the industry. A key finding is that while capital expenditures in automation have grown, their effect on white-collar employment varies significantly across regions and industries, suggesting a non-linear, context-dependent relationship between automation and organizational change.