Evidence-based policy and (big) data in macroeconomics

This course provides an introduction to the concept of causality coined by Judea Pearl in popular science ("Book of why") and its connection to applied economic and econometric empirical economic research. We first show the importance of using a causal model to think about economic policy and business management issues. Based on causal graphs, we next discuss how to empirically identify and quantify causal effects. We will then apply causal analyses to current economic policy and business management issues/questions and consider what variation in the data allows us to make causal statements. From the field of industrial organization, for example, we will ask how the effects of a price change of a good on the quantity demanded can be estimated from market data and what problems can arise in the process. In the field of marketing, we will ask why different customers pay different prices for the same good and what empirical problems arise in the estimation. In the field of personnel economics, we will ask what problems there are in identifying the gender pay gap. From the field of labor economics, we will ask what effects does the minimum wage have? Other topics from the field of macroeconomics (How important were demand/supply shocks for inflation after Corona?), growth or microeconomics (Empirical design of auctions) can also be discussed. The effects of automation, housing market policy or spatial mobility can also be discussed. It is essential that students learn how economic knowledge can be generated and which scientific instruments are available for this purpose