The increased use of artificial intelligence (AI) technologies brings changes to the composition of workers within a firm and to the organization of a firm. However, two key challenges hinder analysis of this relationship: i) the challenge of measuring firm-level AI investments and ii) the lack of data on the labor composition and labor organization within a firm.
Babina et al. (2022) overcome this challenge by creating a measure for workforce composition that assesses the relationship between a firm’s AI investment and the workforce composition. This measure captures the “stock of current employees” and “demand for new employees” among U.S. firms by utilizing resume data from Cognism and job posting data from Burning Glass. The resume data enables the researchers to analyze the “stock of” current employees while the job posting data enables the analysis of firms’ demand for new workers. The measurement is then utilized to answer the question, “are AI investments associated with changes in labor composition and workforce organization?”.
Utilizing the methodology employed in Babina et al. (2021), the researchers find that firms that initially have a more educated workforce are more likely to invest in AI. Additionally, increased investments in AI are associated with an increasing flattening of the hierarchical structure, a higher share of workers with college or advanced degrees and a higher share of workers with a degree in STEM.