Abstract:

In our fast changing global village, the wealth of nations and the wealth of individuals are to some extent determined by a production factor which is called human capital. Nations are seen to be more competitive and therefore create a higher level of wealth if they have a better educated workforce. On an individual basis human capital, which is seen as ones skills and competencies, also define the success on the labor market. This success on the labor market generates individual wealth. The probability of an individual receiving a proper job is assumed to be higher, if the skills, competencies and signals of the employee reflect the skills, competencies and signals required at the job market. The paper wants to show how to explore the required skills, competencies and signals by screening job announcements and analyze them via text mining techniques.
The paper wants to explain how to pre-process harvested data (it is possible to harvest data for 51 countries) with text mining methods and analyze them with a wide range of multivariate methods. The most important methods used were namely: hierarchical clustering, multi-dimensional scaling and k-means clustering.
Additionally, spatial statistic was used to analyze regional aspects of demanded skills and job announcement distribution. All statistical procedures were executed with the open source statistical software R and familiar packages.
Managerial implications of the work are defined by the support of curricular decisions by job market monitoring.

Keywords:

Job announcement, text mining, spatial statistic, job market monitoring, curriculum planning.