Future of Work
16/06/2023
Overview
Technologies are intertwined in our daily lives, in what we do at work, and the problems we face as a society. Hence, in many ways, technological change shapes our long-term prospects, essentially what is possible for anyone in society. The role of Artificial Intelligence (AI) in forging our future is particularly marked because of its massively scalable applications. Due to such scalability, AI-induced technical change can both create large-scale opportunities and risks, making it difficult to predict its economic consequences. Similar to other automating technologies, AI has improved productivity and created jobs at the cost of obsolescing many workers’ skills. However, unlike gradual technical change, the speed of the AI revolution prevents affected workers from acquiring new skills or transitioning to similar occupations, creating a significant skill mismatch and concentration of opportunities. The following projects attempt to understand and explain how human skills and technologies produce value in the workplace. The outcome, I hope for, is guiding labor intervention to shape a better future of work.
Deconstructing Human Capital to Construct Hierarchical Nestedness
Moh Hosseinioun, Frank Neffke, LT Zhang, and Hyejin Youn. Link to preprint: https://arxiv.org/abs/2303.15629 Abstract: Modern economies generate immensely diverse complex goods and services by coordinating efforts and know-how of people in vast networks that span across the globe. This increasing complexity puts us under the pressure of acquiring an ever-increasing specialized and yet diverse skill portfolio in order to stay effective members of a complex economy. Here, we analyze the skill portfolios of workers in an effort to understand the latent structure and evolution of these portfolios. Analyzing the U.S. survey data (2003-2019) and 20 million resumes, we uncover a tree structure of vertical skill dependencies such that skills that only a few jobs need (specialized) are located at the leaves under the broadly demanded (general skills). The resulting structure exhibits an unbalanced tree shape. The unbalanced shape allows the further categorization of specialized skills: nested branching out of a deeply rooted sturdy trunk reflecting a dense web of common prerequisites, and un-nested lacking such support. Our longitudinal analyses show individuals indeed become more specialized, going down the nested paths as moving up the career ladder to enjoy higher wage premiums. The specialization, however, is most likely accompanied by demands for a higher level of general skills, and furthermore, specialization without the strengthening of general skills is deprived of wage premiums. We examine the geographic and demographic distribution of skills to explain disparities in wealth. Finally, historical changes in occupation skill requirements show these branches have become more fragmented over the decade, suggesting the increasing labor gap. The figure shows the Skill Hierarchy. Node sizes are proportional to education (less than high school to post-doctorate) and colored according to our skill clusters formed by their prevalence across the economy, from the most commonly used general skills in red; intermediate skills in green; to specific skills in blue that are niche and used by particular occupations. The vertical position of skills depends on their generality, while the horizontal position depends on education.
Technological Complementarity: the Division of Know-how and the Future of Work
Joint work with Jae Hyuk Park, Frank Neffke, and Hyejin Youn. Abstract: By applying bundles of technologies, workers put the embedded knowledge of a diversity of human experts to use. Here, we examine the relationship between such bundles of technologies used by human labor. To do so, we measure complementarity between technologies which can explain part of wages unexplained by other factors. The figure shows a Two-mode network between occupations and tools/technologies utilized by occupations in 2019. The size of the node represents the degree of nodes: the number of unique occupations using a target tool or technology, for tool and technology nodes, and the number of unique tools and technology that a target occupation uses for occupation nodes. Visualization prepared by Jae Hyuk Park.
