Graph configuration model based evaluation of the education-occupation match.

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

Number of clusters in case of different <i>α</i>.

<p>Number of clusters in case of different <i>α</i>.</p

Gender pay gap grouped by education program areas.

<p>Gender pay gap grouped by education program areas.</p

Top 10 weakest connection.

<p>Top 10 weakest connection.</p

Distribution of the weighted degrees of the occupations.

<p>Distribution of the weighted degrees of the occupations.</p

The modules obtained by the Louvain algorithm of purified program/occupation bipartite graph.

<p>The modules obtained by the Louvain algorithm of purified program/occupation bipartite graph.</p

Variables of the dataset.

<p>Variables of the dataset.</p

Distribution of graduates working in occupation category that requires higher education degree (HEd).

<p>Distribution of graduates working in occupation category that requires higher education degree (HEd).</p

The Settlement Structure Is Reflected in Personal Investments: Distance-Dependent Network Modularity-Based Measurement of Regional Attractiveness

How are ownership relationships distributed in the geographical space? Is physical proximity a significant factor in investment decisions? What is the impact of the capital city? How can the structure of investment patterns characterize the attractiveness and development of economic regions? To explore these issues, we analyze the network of company ownership in Hungary and determine how are connections are distributed in geographical space. Based on the calculation of the internal and external linking probabilities, we propose several measures to evaluate the attractiveness of towns and geographic regions. Community detection based on several null models indicates that modules of the network coincide with administrative regions, in which Budapest is the absolute centre, and where county centres function as hubs. Gravity model-based modularity analysis highlights that, besides the strong attraction of Budapest, geographical distance has a significant influence over the frequency of connections and the target nodes play the most significant role in link formation, which confirms that the analysis of the directed company-ownership network gives a good indication of regional attractiveness

Distribution of graduates that work on occupation which requiring higher education degree by counties in Hungary.

<p>Distribution of graduates that work on occupation which requiring higher education degree by counties in Hungary.</p