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Occupational mobility and automation: a data-driven network model.

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Automation significantly impacts the labor market by affecting worker transitions. The occupational mobility network structure is crucial, influencing unemployment levels, especially for low-wage workers facing automation.

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Area of Science:

  • Economics
  • Labor Economics
  • Sociology

Background:

  • Automation's impact on the labor market is a growing concern.
  • Previous studies focused on occupation-specific automation risks, neglecting worker reallocation.
  • A holistic view of labor reallocation and employment prospects during job transitions is needed.

Purpose of the Study:

  • To develop a data-driven model analyzing worker movement through occupational networks.
  • To assess the impact of automation scenarios on labor reallocation and employment.
  • To provide occupation-specific estimates of unemployment changes due to automation shocks.

Main Methods:

  • Developed a data-driven model to simulate worker transitions in an occupational mobility network.
  • Analyzed macro-level labor market dynamics, reproducing the Beveridge curve.
  • Estimated micro-level, occupation-specific changes in short and long-term unemployment.

Main Results:

  • The occupational mobility network structure significantly influences unemployment levels.
  • Certain network positions offer limited job transition opportunities.
  • Automation scenarios disproportionately increase long-term unemployment for low-wage occupations.

Conclusions:

  • Labor reallocation dynamics are complex and heavily influenced by network structures.
  • Automation poses a greater long-term unemployment risk to workers in less connected occupations.
  • Policy interventions should consider network effects to mitigate automation's adverse labor market impacts.