Philipp Dörrenberg

Prof. Dr. Philipp Dörrenberg serves as Professor for Business Administration and Taxation at the University of Mannheim's Business School within the Department of Accounting and Taxation. His academic responsibilities span teaching across Bachelor, Master, and PhD programs, with courses including Tax Planning (TAX 660), Causal Data Science for Business Decision Making (TAX 620), and Reading Courses in Taxation Research (TAX 922/TAX 923). Based at Schloss, Ostflügel – Room O 260 in Mannheim, Germany, he maintains an active research agenda and supervises doctoral students.

Dörrenberg's research focuses on taxation (both corporate and individual) and applied behavioral economics, utilizing diverse empirical methodologies including financial market data analysis, administrative tax return datasets, causal inference techniques, survey research, and laboratory/field experiments. His work examines how taxation influences business decisions, explores taxpayer behavior, and investigates the intersection of economic policy with human decision-making. He has developed frameworks for understanding tax planning opportunities that remain applicable despite changing tax legislation, emphasizing consideration of 'All Parties,' 'All Taxes,' and 'All Costs' in business decision contexts.

His publication portfolio demonstrates significant scholarly impact, with recent work under review at top journals including American Economic Journal: Economic Policy, Journal of the European Economic Association, and Economic Journal. Several papers have been accepted for publication in prestigious outlets such as Journal of Political Economy Microeconomics, Management Science, and Journal of Public Economics. His research often involves collaborations with other leading scholars in the field and addresses timely topics including tax compliance, behavioral responses to taxation, and the economic implications of digital transformation.

Dörrenberg leads the German Business Panel, a valuable firm-level dataset for accounting and taxation research, demonstrating his commitment to building research infrastructure. His teaching philosophy emphasizes practical application of empirical methods, as evidenced by courses that train students in statistical software (particularly R) and analysis of large business databases like Amadeus and Compustat. He has also contributed to understanding how causal inference methods can address business questions regarding cause-and-effect relationships beyond mere correlation.