Skills-approximate occupations: using networks to guide jobs retraining

Area of Science:

• Economics
• Labor Market Analysis
• Network Science

Background:

• Economic development agencies face challenges in minimizing unemployment caused by disruptions like technological change, trade wars, and recessions.
• Re-employing displaced workers and filling labor shortages are key policy concerns.

Purpose of the Study:

• To quantify the proximity of occupations based on inherent skills.
• To develop a model for identifying worker retraining pathways and addressing labor market needs.

Main Methods:

• Modeled US labor as a weighted network of interdependent skills, using skill co-occurrence patterns.
• Derived link values from geographical skill co-occurrence data.
• Quantified occupation proximity and identified skill gaps hindering worker transitions.

Main Results:

• Developed a skills network to measure occupational proximity.
• Identified missing skills that impede worker transitions between occupations.
• Applied the method to assess retraining pathways in Washington, DC, amidst pandemic and economic shifts.

Conclusions:

• The skills network provides a tool for policymakers to identify proximate occupations for worker retraining.
• The methodology can inform strategies for mitigating unemployment during economic shocks.
• The study offers insights into labor market dynamics and skill-based transitions.