EAGER: Analysis of Workforce Supply Chains for Emerging Technology Areas

Award ID: 2435250

U.S. policy is spurring a range of ambitious public and private investments that seek to improve national industrial capacity, economic security, and competitiveness large-scale capacity investments will create thousands of new jobs, often in occupations without a significant base of current employment in the regions where investments are made. Meeting this new demand for skills will require policymakers, employers and trainers to identify other occupations that partially meet new job requirements, and to quantify what skills may be needed for workers to transition into new opportunities. This project will develop and improve methods to help evaluate the potential readiness of regional workforces to meet the skill demand created by large-scale industry transitions. This project will also help produce capabilities that can provide insight into possible transition opportunities for workers whose employment may be disrupted by technological and economic transformation. Firms, trainers, government, labor groups and other key decision-makers lack consistent, data-driven 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 (such as identifying skill gaps to be closed through training programs). This project will leverage an approach to estimating the similarity of requirements between different occupations, as well as other potential indicators of the feasibility of worker transitions from one occupation into another. These indicators will be tested against longitudinal evidence of realized occupational transitions and used to specify models that quantify the number of workers who may satisfy a minimum level of readiness for demand in any given occupation, and boundary estimates on the rate at which such workers might transition into the in-demand occupation. The outputs of this project will include an empirically validated user-tool that helps estimate potential stock and mobility of workers in every US metropolitan statistical area to meet demand for any occupation within the BLS SOC taxonomy, and the potential gaps between "candidate" occupations and the in-demand occupation. Underlying analytical models of the tool and its performance against historic mobility trends will be published. This tool will be applied to support a regional case analysis of workforce readiness for capacity-building in microelectronics in Florida and energy storage in New York. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Sarah Goforth