We discuss recent publications in economic history and persistence studies that try to explain global income inequalities. The course starts with the Neolithic Revolutions, before we discuss different theories for the emergence of cultural norms and institutions. The final parts of the lecture look into different explanations for the fertility transition and escape from the Malthusian trap, before discussing recent findings on the Industrial Revolutions and Globalization. The course combines the introduction of historical explanations for the roots of economic development with in-depth discussions of empirical methods and hands-on replication exercises.

This block course introduces students to the main methods of causal inference. After a general introduction into R and regression analysis, the course commences with a deep discussion of causal inference. We derive the main assumptions for causal identification and use simulations to investigate potential estimation biases. Afterwards, the course introduces the main causal identification methods: Randomized Controlled Trials, Instrumental Variables, (Staggered) Difference-in-Differences, and (Spatial) Regression Discontinuity Designs. By combining theoretical background with intuition from simulations and hands-on experience from exercises and replications, the students acquire the skills to conduct and discuss causal empirical analyses independently.

We discuss recent empirical publications that trace the historical roots of development differences. The course covers the Neolithic Revolutions, the emergence of cultural norms and institutions, as well as topics in industrialization and globalization. The course combines the introduction of historical explanations for the roots of economic development with in-depth discussions of empirical methods and hands-on replication exercises.

This course gives students a general introduction into statistical and econometric methods, and teaches them how to apply these methods in R. We start with a general introduction into statistical programming in R and commence with in-depth discussion of data visualization and regression analysis. The final part of the course focuses on causal inference and teaches students how to think about endogeneity-induced biases in DAGs and the Potential Outcomes Framework, and how to apply Instrumental Variables, Difference-in-Differences, and Regression Discontinuity Designs to tackle these biases.

This first-semester course introduces students of the development economics track in Heidelberg into the macroeconomic literature on (long-run) economic development. We discuss macroeconomic growth theories together with recent empirical publications. The course introduces students to recent findings in economic history, international trade, institutions, conflict studies, structural transformation, development aid, and international organizations. We put special emphasis on teaching students the skill set to critically evaluate empirical findings that attempt to test hypothesis from economic growth theory.

Our institute offers this course to prepare students for the mandatory Macroeconomics Exam in the Bachelor studies. In nine lectures, we repeat the main contents of the main lecture. Among other things, we repeat the analysis of business cycles with the IS-LM modell, discuss the Philipps Curve and its implications for monetary policy, and discuss the main assumptions of the Solow growth model.