Organizational Labor Flow Networks and Career Forecasting

Area of Science:

• Econophysics
• Network Science
• Organizational Behavior

Background:

• Employee movement is crucial in economics and management but understudied in econophysics.
• Existing models often lack the resolution to capture internal organizational dynamics.

Purpose of the Study:

• To develop and test high-resolution internal labor market networks.
• To apply network analysis to understand employee mobility within a large organization.

Main Methods:

• Constructed empirically calibrated networks of internal labor markets.
• Defined nodes and links based on job positions (operating units, occupational codes).
• Utilized Markov processes (with and without limited memory) for analysis.

Main Results:

• Network descriptions of internal labor markets demonstrated strong predictive power.
• Organizational labor flow networks based on operational units exhibited a power law feature.
• This power law mirrors firm size distributions in economies, indicating broad applicability.

Conclusions:

• Internal labor market networks offer a novel approach to studying careers.
• The findings suggest a pervasive regularity in economic entities, connecting organizational and economic structures.
• This work bridges disciplines studying career paths.