Michael Ganslmeier works at LSE as a Fellow in the Methodology Department. He has a PhD from the University of Oxford, a master's degree from LSE, and a bachelor's degree from Zeppelin University. In his doctoral thesis, he used causal inference and machine learning methods to study how social policies affect the way people vote. Michael has also worked as a researcher and consultant for many organizations, including the International Monetary Fund, World Bank, University of Oxford, LSE, University College London, and King's College London.

Research Interests

In his research, he delves into economics, political economy, and political science. His work can be summarized in two main areas: In the field of political economy, he explores the impact of social policies on the voting behaviour of individuals. For example, in his job market paper, he made a significant contribution to the understanding of economic voting by investigating how promises of expanding social benefits influence alignment with the party making these pledges. His research revealed a substantial and temporary persuasive effect of benefit expansions on political and voting behaviour, earning him the Barnett Prize at the University of Oxford.

In the field of environmental economics, Michael research various aspects of climate change, particularly how firms, individuals and governments change their behaviour in times of global warming. He collaborates with the World Bank and the International Monetary Fund, harnessing detailed satellite, sensor and text data to examine how individuals and businesses adapt their behaviours in response to environmental policies and different climate change phenomena.

Michael primarily utilizes causal inference methods to assess the effects of policies and interventions. He leans towards quasi-experimental research designs, such as regression discontinuity and difference-in-differences, to answer questions where physical randomization controlled by the researcher is not feasible. Additionally, in conjunction with causal methods, Michael also employs machine learning techniques, including natural language processing approaches, to create innovative measurements of various social indicators that capture the behaviour of individuals, businesses, and policymakers.

For more information can be found on his website or his Google Scholar.