Author(s): 

Britta Gehrke, Ernst Maug, Stefan Obernberger, Christoph Schneider, Peter Severin

Cooperation Partners:

Institut für Arbeit und Beruf (IAB) 

Description:

We study the reallocation of labor in mergers and acquisitions in Germany. Overall restructuring is large. Targets lose more than half of their labor force within two years after the acquisition relative to a matched sample. The increase in employee turnover is also substantial. Acquirers have a better-educated, better-paid, and more qualified workforce than targets. Restructuring of targets makes the composition and compensation of their workforce more similar to that of acquirers. Mergers create internal labor markets, but these are quantitatively much less significant than the increase in hiring from and departures to the external labor market. We argue that merged firms build “knowledge-based hierarchies,” which better economize on the scarce skills of managers and highly-skilled employees and have been associated with improved productivity. We find little to support alternative theories based on the notion that acquisitions address skill shortages or create internal labor

Author(s): 

Rajiv Sabherwal, Hartmut Höhle, Tobias Nisius, Florian Pethig, Fareed Zandkarimi

Cooperation Partners: 

Aalberts Material Technology GmbH

Description:

In this project we are checking the effect of reclamations on several behaviors of production staff. Using process mining allows us to measure production-level behavior of workers. These measures are collected over a three years period of time to be analyzed via a Difference-in-Difference experiment. The expected results should broaden our understanding of employee responses to error.

We are looking into core-production data of a very large company. Applying statistical methods on production-level data is one major contribution of our work. Applying process mining as a method for investigating such phenomena is presented in this study. Also, the expected outcome(s) of studying the research problem, i.e., employee response to errors, provide practitioners with new insights on making effective decisions before or after an error is reported.

During the data collection phase, we collected 1.4 million production cases via access to company’s ERP. In order to apply process mining 500+ lines of SQL code developed to transform the data into process mining Eventlog. Also, a java artifact was exclusively designed to collect data for the Dif-in-Dif analysis. The aggregated dataset was then analyzed by Stata.

Author(s): 

Marcel Olbert, Christoph Spengel

Cooperation Partners: 

Chair of Business Administration and Taxation II

Description:

By tracking the development of organizational structure over time, the insights from this project help to understand how multinational firms structure their value chains and businesses models globally. The findings can be used to evaluate policy measures and to provide recommendations to legislators.

This project uses data on financial characteristics, legal status, and shareholder structures of more than 250 million firms around the globe to investigate the determinants of organizational structures and firms’ complexity. To this end, a sophisticated algorithm is used to iteratively link information on individual firms to create a group of affiliated firms that ultimately belong to the same global owner. This study attempts to explain why multinational firms employ complex organizational structures and ambiguous legal strategies such as founding subsidiaries in tax havens.