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.

We analyze how skill transferability and the local industry mix affect the adjustment costs of workers hit by a trade shock. Using German administrative data and novel measures of economic distance we construct an index of labor market absorptiveness that captures the degree to which workers from a particular industry are able to reallocate into other jobs. Among manufacturing workers, we find that the earnings loss associated with increased import exposure is much higher for those who live in the least absorptive regions. We conclude that the local industry composition plays an important role in the adjustment processes of workers.

Die Aussichten für die internationale Konjunktur bleiben leicht eingetrübt. In Europa setzt sich die zaghafte Erholung fort. In Deutschland vermehren sich die Anzeichen für eine konjunkturelle Besserung. Alles in allem wird die Produktion im Sommerhalbjahr wohl nur verhalten ausgeweitet, doch ab Herbst dürfte die Belebung Fahrt aufnehmen. Das Bruttoinlandsprodukt dürfte im Jahr 2024 um 0,3% expandieren, für 2025 prognostiziert das IWH einen Zuwachs um 1,5%.

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.