TCU Neeley Research Highlight – Professor of Operations Management, Tyson Browning, alongside other experts share insights with industry and academic leaders.
August 31, 2023
TCU Neeley's’ Tyson Browning, professor of operations management, partnered with Dawei Zhang, Gang Peng, and Yuliang Yao, to examine information technology (IT)-labor relationships. As technology advances it tends to replace low-education jobs while complementing high-education roles, though its impact varies based on job routine and AI exposure. College-educated workers face potential displacement in routine tasks, while graduate-educated workers generally benefit from technological advancements. (Information Systems Research, 2023).
Abstract
Although information technology (IT) is increasingly replacing human labor, the IT-labor relationship is more nuanced than it appears. We examine the IT-labor relationship in terms of various levels of education, intensities of routine tasks, and exposure to artificial intelligence (AI). Making use of an industry-level data set covering 60 U.S. industries from 1998 to 2013, we adopt an innovative measure of elasticity of substitution that enables us to capture the asymmetric price impact between IT and labor. Our findings indicate that IT generally complements high-education labor (master’s degree or above), while substituting for low-education labor (high school degree or below). For middle-education labor (bachelor’s or associate’s degree), however, the IT-labor relationship is more nuanced: They are complements in non-routine-intensive industries, but substitutes in routine-intensive industries. We also find that IT is a complement (substitute) with high-education labor in industries with lower (higher) AI exposure and remains a net substitute for low- and middle-education labor, regardless of their AI exposure. Our findings suggest that even college-educated labor has now become susceptible to IT displacement, whereas labor with graduate education largely remains a strong complement to IT (with an exception in high-AI-exposure industries). Theoretical and policy implications are discussed.
