SHAPING ECONOMIC OPPORTUNITY: UNDERSTANDING THE INFLUENCE OF INDUSTRIAL AUTOMATION ON INTERGENERATIONAL MOBILITY IN THE US

Abstract

The United States labor market has undergone significant transformations over the past decades, resulting in job polarization and the disappearance of well-paid middle-class positions. While the causes of this phenomenon are debated among scholars, evidence suggests that automation plays a significant role. As automation technologies continue to advance, many experts view this as the onset of a "fourth industrial revolution," comparable to historical shifts brought about by the rise of factories, assembly lines, and computer technologies. Despite considerable research on the impact of automation on incumbent workers, there remains a knowledge gap regarding its effects on the economic prospects of children growing up in deindustrializing communities.This paper aims to address this gap by examining the relationship between automation and intergenerational income mobility in the United States. The analysis links local income mobility to variations in exposure to robot adoption across 722 commuting zones. Focusing specifically on industrial robots, which have significantly affected the manufacturing industry, the study investigates their impact on the economic mobility of individuals. By combining historical data on industrial specialization with information on robot adoption from the International Federation of Robotics, the study measures the level of automation exposure for each commuting zone. Income attainment data for individuals born in the early 1980s is obtained from Chetty et al. (2014), allowing for an analysis of relative mobility and upward mobility out of the bottom income bracket. The findings reveal that community-level exposure to automation hampers upward mobility, reinforcing the transmission of economic status across generations. The study identifies two mechanisms underlying these effects: diminished job prospects for individuals entering the labor market and early life-course consequences of job losses in the community. Importantly, the study demonstrates that the negative impact of automation is evident in the educational attainment of children and becomes more pronounced with increased exposure to automation during childhood. These results dismiss the possibility that the labor market alone drives this relationship. Additionally, the study highlights gender disparities, with the effects of automation primarily affecting sons rather than daughters. Complex patterns emerge concerning racial differences, as black individuals appear to be less disadvantaged by automation, partly attributable to their lower initial mobility prospects.

In conclusion, this research offers fresh insights into how recent technological disruptions have shaped intergenerational opportunity patterns in the United States. By examining the impact of automation on income mobility, the study provides valuable evidence for policymakers and researchers interested in understanding the consequences of technological advancements on society.