Research Article: Graph configuration model based evaluation of the education-occupation match

Date Published: March 6, 2018

Publisher: Public Library of Science

Author(s): Laszlo Gadar, Janos Abonyi, Pin-Yu Chen.


To study education—occupation matchings we developed a bipartite network model of education to work transition and a graph configuration model based metric. We studied the career paths of 15 thousand Hungarian students based on the integrated database of the National Tax Administration, the National Health Insurance Fund, and the higher education information system of the Hungarian Government. A brief analysis of gender pay gap and the spatial distribution of over-education is presented to demonstrate the background of the research and the resulted open dataset. We highlighted the hierarchical and clustered structure of the career paths based on the multi-resolution analysis of the graph modularity. The results of the cluster analysis can support policymakers to fine-tune the fragmented program structure of higher education.

Partial Text

Policymakers need solid information on how labour market evaluates higher education graduates. Institutions also should collect and analyse relevant information about their graduates for the management of their programs [1]. Since the salary and the chance of finding a job are important decision factors at the college attendance [2], university and program level public information about the career paths are also important to candidates of higher education [3].

Administrative data based career path analysis can of support governmental policy making and program development of higher education institutes. To support the extraction of useful information from these databases we developed a graph-based data structure to represent the career path of higher education graduates. Education—occupation mismatch can be analysed based on the bipartite graph of bachelor programs and occupations encoded by International Standard Classification of Occupations (ISCO) code system. We modified the Newman modularity measure to evaluate the matching of the programs and the professions. Based on this measure the hidden structure of career paths can also be clustered and visualised.




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