 Neufassung der Ausbildungsmarkt-TypisierungAnette Haas,
Adrian Lerche,
Michael Moritz,
Van Phan thi Hong,
Alice Wilkens and
Wolfgang Dauth
No 202608, IAB-Forschungsbericht from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany] Abstract: "As part of its controlling activities, the Federal Employment Agency (BA) uses regional typologies developed by the IAB based on a special two-stage classification approach. By accounting for heterogeneity of regional labor markets, in the BA’s controlling system these typologies serve as a meaningful basis for comparing and assessing the performance of the 144 regional employment agencies of the BA. Agencies that face a similar set of challenges due to their regional conditions are grouped together as “comparison types” (Vergleichstypen). The IAB has recently developed a new version of the vocational training market typology for the Federal Employment Agency (BA) accounting for changes in regional labor market conditions, and changes in the groups of people interested in vocational training. In the new classification system, the target variable for the training market is the entry-rate of all individuals interested in vocational training. The number of individuals interested in vocational training who entered the program (cumulative entries over several months as of September 30, 2025) is considered in relation to the young resident population (ages 15 to 20). The intake of individuals interested in training refers to a newly defined group of people, as a subset of all individuals receiving counseling (clients under the SGB III and SGB II legal frameworks). This group is in the focus of career counseling. The IAB typology is based on a two-stage classification approach. In the first stage, framework conditions relevant to the target variables are identified and their relative importance is determined. In the second stage, agency districts with similar framework conditions are assigned to comparison types. To identify differences of importance between regional labor markets, a large set of variables (116) is used as a starting point. A selection of the relevant economic and other conditions is made using the LASSO method, a machine learning algorithm. The method optimizes the predictive quality of the target variables and selects the smallest possible number of variables (parsimony-based modeling). The variables selected in the first stage are subject to a cluster analysis in the second stage to generate the different comparison groups. Prior to this, the variables are standardized and weighted. This ensures that framework conditions with greater explanatory power are given greater weight. The cluster analysis yielded 11 comparative types. The individual types differ significantly from one another in terms of the proportion of applicants interested in vocational training, the youth ratio, the seasonal span, the proportion of unplaced applicants, and the geography. The 11 types can be grouped into four major groups which differ in terms of housing costs, urbanization, and the proportion of young people interested in vocational training. Group I consists of employment agencies in predominantly rural areas, often in East Germany or northern and southeastern Bavaria. Housing costs are below average, and these agency districts typically have average to above-average proportions of individuals interested in vocational training. Group II, on the other hand, focuses on districts with average housing costs and higher population densities. Group III, characterized by predominantly urban districts, is further distinguished by above-average housing costs. Group IV consists of two subtypes: a) large cities and b) districts surrounding metropolitan areas, typically in western Germany. The proportion of individuals interested in vocational training in this group is significantly below average." (Author's abstract, IAB-Doku) ((en)) Pages: 26 pages Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:iab:iabfob:202608 DOI: 10.48720/IAB.FB.2608 Access Statistics for this paper More papers in IAB-Forschungsbericht from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany] Contact information at EDIRC. |
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