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Graduate Research Assistant

The SGA will work under the supervision of Dr. Modestino to investigate the accelerated adoption of new technologies in response to the COVID-19 pandemic and how this affects the demand for skills. The data collected through this research will be used by the National Science Foundation (NSF) to inform design requirements for next generation systems in manufacturing and other related industries. The SGA will assist with administering a survey of roughly 100 manufacturing firms, conducting 10-15 follow-up interviews, and developing three case studies to measure changes in production, technology adoption, and skill requirements as well as pre-existing firm, market and workforce characteristics. In addition, the SGA will conduct a landscape analysis using a proprietary database of 300 million job postings from Lightcast (formerly Burning Glass). Using both the qualitative and quantitative data, the SGA will assess what the impact of the pandemic on firm technology adoption, employer skill requirements, the manufacturing industry, and the broader economy. The SGA will present their findings at monthly zoom meetings with co-authors, assist with writing sections of the final report for NSF, and also co-author a peer-reviewed journal article if appropriate.

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  • Project Title

    Accelerating the Future of Work? Understanding Future Shifts in Technology Adoption in Response to the COVID-19 Pandemic

  • Faculty / Project Lead

    Alicia Sasser Modestino

  • Project Description

    This NSF funded project under the Future of Work at the Human-Technology Frontier (FW-HTF) program collects ephemeral data to investigate the accelerated adoption of new technologies in response to the COVID-19 pandemic and how this affects the demand for skills. In particular, as manufacturing in the U.S. has continued to shift from assembly of basic products to more high-precision production there is a greater need for quality and inspection that has changed the types of skills demanded of production workers needed to fill critical advanced manufacturing positions. In previous work, Dr. Modestino used the variation in labor market conditions to show upskilling and downskilling responds to the greater availability of workers (e.g. places with higher/lower unemployment rates). However, there is the potential for endogeneity of which firms are hiring and which jobs get posted during recession (e.g., shifting composition) could account for changes in skills required. The pandemic provides a natural experiment. Although both supply and demand were shifting during the pandemic, it was largely driven by the virus (supply) and/or mandated (demand) so we can get clearer predictions to test up/down skilling. Please visit Professor Modestino’s web site ( for background reading on publications that have already emerged from this research.

  • Qualifications Necessary

    The ideal candidate will possess all of the required qualifications listed below plus one or two of the additional qualifications. Required Qualifications: • Experience programming in STATA to do descriptive and regression analysis • Experience working with large datasets • Basic statistical knowledge including regression analysis • Experience conducting social science research in economics, sociology, or a related field • Deadline oriented to achieve external milestones required by funder or policy partner Additional Qualifications: • Knowledge of survey methodology • Understanding of workforce development programs • Ability to create professional presentations for meetings with external partners • Able to write policy reports for a lay audience