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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Network-driven differences in mobility and optimal transitions among automatable jobs.

Jordan D Dworkin1

  • 1Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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|August 17, 2019
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Job automation impacts workers differently based on skills. This study uses network science to map job connections, revealing how shared skills can ease transitions and mitigate automation risks for a more adaptable workforce.

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Area of Science:

  • Social Sciences
  • Network Science
  • Labor Economics

Background:

  • Widespread job automation necessitates workforce adaptation through reskilling or job transitions.
  • Existing worker skills significantly influence the ease or difficulty of adapting to automation.
  • Assessing automation's impact requires understanding the interconnectedness of jobs based on skill requirements.

Purpose of the Study:

  • To investigate the links between jobs using network science and statistics based on required skills, knowledge, and abilities.
  • To develop a model for quantifying the benefits of specific job transitions.
  • To quantify the potential benefits of enhancing individual skills for career mobility and adaptation.

Main Methods:

  • Network science and statistical analysis to map job interdependencies.
  • Development and optimization of a model for job transition benefit quantification.
  • Analysis of skill sharing and its role in facilitating career pathways.

Main Results:

  • The network structure of jobs reveals varied impacts of automation across sectors, increasing the burden in some and decreasing it in others.
  • Considering shared skills in job transition models provides superior recommendations compared to relying solely on automatability and job growth.
  • Quantified benefits of skill enhancement for improving job transition success and within-occupation skill adaptation.

Conclusions:

  • Job interdependencies, mapped through skills, are crucial for understanding automation's complex effects.
  • A network-based approach offers a more nuanced understanding of workforce vulnerability and resilience to automation.
  • Optimizing skill development and transition pathways is key to navigating the future of work.