The Workforce Supply Chains Initiative
The Workforce Supply Chain Initiative at The Block Center for Technology and Society is developing an end-to-end portfolio of data-driven tools, assessments, and policy guidance to help workers, employers, and policy makers navigate challenges and opportunities related to meeting the United State’s workforce needs.
U.S. policy is spurring a range of ambitious public and private investments that seek to improve national industrial capacity, economic security, and competitiveness, including in R&D and production of critical technologies. These investments serve national goals, but they also create regional skill demand shocks to both construct and staff new capacity (e.g. semiconductor fabs), sometimes for industries without historic regional presence. In novel industrial or technological cases, successful workforce development will require local workers to make occupational transitions. Skill supply-demand gaps impeding such transitions can constrain the success of large investments. Parallel workforce demands can draw on and exhaust common pools of talent, with opportunities for closing gaps shaped in part by local training resources and in part by the technology and process design choices of firms. Closing these gaps can also offer improved economic outcomes for workers who gain skills or transition to new roles.
Firms, trainers, government, labor groups and other key decision-makers lack consistent, datadriven methods for evaluating workforce feasibility. Rather than a one-time study for a specific project or technology, a flexible and repeatable capability is needed for decision-support across a range of industrial scenarios, to identify for any given investment proposal the conditions under which that proposal may be feasible from a workforce standpoint, and to support the development of a data-driven strategy for meeting workforce needs.
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Our work
Nikhil George, Christophe Combemale, Ramayya Krishnan, Rahul Telang
How can we utilize the wealth of near-real-time information in skills (from job postings) and worker movement (from resumes) available today to guide workforce development programs? We develop a new measure of inter-occupation skill similarity by leveraging skill information in vacancy postings and the language capabilities of a pre-trained language model.
Technological anxiety is at least as old as the industrial revolution. It is not surprising then that the fast development of generative artificial intelligence (genAI) products has spurred research and analysis on the impact that this technology will have on labor markets. In this chapter we contribute to this discussion by making three points: we look at how the structure of tasks can facilitate or impede the adoption of genAI; we look at how workers of different types can choose to use genAI; finally, we look at where workers are likely to seek employment if they are displaced from their work due to genAI. The common connection between the three points made in this chapter is the focus on economic decision making.
Expansionary industrial policies, such as the CHIPS and Science Act, are followed with notable surges in labor demand within the industries they target. In the case of the CHIPS and Science Act, industries such as semiconductor manufacturing experienced significant influxes in financial investment, with $231 billion being committed to Semiconductors & Electronics thus far. Recognizing the imperative to address such labor demand shocks, we propose a novel operational methodology that assesses potential supply-demand skill discrepancies, and incorporates factors such as the intertemporal occupational rates of transition and regional wage distributions.
Our team
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The Workforce Supply Chains Initiative is a collaborative research portfolio housed in 一本道无码's Block Center for Technology and Society. The Block Center is grateful to the Advanced Robotics for Manufacturing Institute, the Richard King Mellon Foundation, and the National Science Foundation for their support of this work.