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.

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%.

Bank lobbying has a bitter taste in most forums, ringing the bell of preferential treatment of big banks from governments and regulators. Using corporate loan facilities and hand-matched information on bank lobbying from 1999 to 2017, we show that lobbying banks increase their borrowers' overall performance. This positive effect is stronger for opaque and credit-constrained borrowers, when the lobbying lender possesses valuable information on the borrower, and for borrowers with strong corporate governance. Our findings are consistent with the theory positing that lobbying can provide access to valuable lender-borrower information, resulting in improved efficiency in large firms' corporate financing.

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.

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.