Skill networks and measures of complex human capital

Area of Science:

• Labor Economics
• Network Analysis
• Human Capital Theory

Background:

• Traditional measures of human capital may not fully capture skill value.
• Understanding skill interdependencies is crucial for wage determination.
• Online freelance platforms offer rich data for skill analysis.

Purpose of the Study:

• To propose and validate a network-based method for measuring worker skills.
• To analyze how skill relationships influence wages in the freelance market.
• To differentiate the impact of skill diversity on earnings.

Main Methods:

• Applied network analysis to skill data from an online freelance website.
• Categorized skills based on their market relationships.
• Correlated network-based skill measures with worker wages.

Main Results:

• Network analysis identified endogenous skill categories with varying wage potentials.
• Network-based human capital measures provided insights beyond traditional metrics.
• Workers with diverse skills earned higher wages than specialized workers.
• Distinguished between 'jacks-of-all-trades' and 'synergistic' workers.
• Synergistic workers, whose skills combine effectively, earned more than jacks-of-all-trades.

Conclusions:

• A network-based approach offers a novel way to measure and understand worker skills.
• Skill diversity, particularly synergistic skill combinations, is a significant driver of higher wages.
• The findings have implications for skill development, career planning, and labor market analysis.