This report describes the novel key between establishment and enterprise identifiers. This key enables the combination of administrative employment data of the Institute for Employment Research (IAB) with external enterprise data that has a Creditreform or Moodys (former Bureau van Dijk) identifier. To establish this combination, we performed a record linkage between the address data of the IAB and the address data of the Mannheim Enterprise Panel (MUP) hosted at the Leibniz Centre for European Economic Research (ZEW). We briefly describe the record linkage process, present quality and representativeness checks and also discuss limitations of the data. The key will be the base of standard data products that will be available via the Research Data Centre of the IAB (FDZ). Moreover, the presented key allows to generate customized data sets.

The Mannheim Enterprise Panel linked to the Establishment History Panel (MUP-BHP) is made up of cross-sectional data sets from 2010 onwards. Each cross-section includes all limited liability companies (GmbH) from the Mannheim Company Panel (MUP) and their permanent establishments throughout Germany that are recorded in the Establishment History Panel (BHP) (BeH) as of 31.12. This linking of establishments and businesses is based on a record linkage of the address data of the MUP held by the Leibniz Centre for European Economic Research and the business address data at the IAB. The individual cross-sectional data sets contain information on the establishments and companies as well as an additional file on shareholders. The data sets can be linked to form a panel. This data report describes the Mannheim Enterprise Panel linked to the Establishment History Panel (MUP-BHP) 2010-2020.

The recent advances in automation technology, robotics in particular, have sparked a heated debate over the future of labor and human society at large. The ongoing process of robotization may engender profound impacts on various segments of the labor market. Given the far-reaching implications of robots, it is thus very important to understand the scale and scope of robot use and characteristics of robot users. However, the main challenge is the limited availability of robot data at the microeconomic level (Raj and Seamans, 2018). Due to the data constraint, the bulk of the existing literature relies on cross-country industry-level data from the International Federation of Robotics (IFR). The lack of micro-level robot data makes it difficult to paint a comprehensive picture of robotization in industrial settings, and perhaps more importantly, to assess how within-industry firm level heterogeneity manifests itself in robot use and adoption.