The Impact of Unionization on Industry Adoption of Labor Saving Technologies: A Machine Learning Approach

Abstract

Innovation and the adoption of new technologies create value, bring about greater efficiency, and are key drivers of growth and productivity for the industries of the American economy. This paper seeks to examine the impact industry unionization has on the adoption of new labor saving technologies, specifically the transfer machine technology which was a key labor saving innovation in the 1st wave of automation since the mid 19th century. I develop a novel machine learning approach to classify United States patents which cite the original transfer machine patents by their respective industries of application. I then construct measures of both the extent and the speed of diffusion of these patents into a given industry. Following, I utilize both duration and time series analysis to conclude that greater industry unionization is significantly negatively associated with the adoption of the labor saving transfer machine technology.