Graph configuration model based evaluation of the education-occupation match

Area of Science:

• Socioeconomics
• Network Science
• Educational Research

Background:

• Analyzing education-occupation matchings is crucial for understanding labor market dynamics.
• Existing research often lacks comprehensive, longitudinal data on career trajectories.
• The gender pay gap and over-education present significant societal challenges.

Purpose of the Study:

• To develop and apply a novel bipartite network model for analyzing education-to-work transitions.
• To investigate the career paths of a large cohort of Hungarian students.
• To provide an open dataset and insights into career structures and related socioeconomic factors.

Main Methods:

• Developed a bipartite network model and a graph configuration model metric.
• Utilized an integrated database combining tax, health insurance, and higher education data for 15,000 Hungarian students.
• Applied multi-resolution analysis of graph modularity for hierarchical and clustered structure identification.

Main Results:

• Identified a hierarchical and clustered structure in student career paths.
• Presented an analysis of the gender pay gap and spatial distribution of over-education.
• Generated an open dataset detailing education-occupation transitions.

Conclusions:

• The identified career path structures can inform higher education policy.
• Fine-tuning fragmented higher education program structures is supported by these findings.
• The study provides a robust methodological framework for future labor market research.