I am an economist by training who enjoys working on research questions at the intersection of economics, sociology and political science. In particular, labour and migration research offers questions on social and economic phenomena and enhances thinking interdisciplinary. Thus, pondering economic incentives, socially driven behaviour and politically motivated attitude changes are part of my daily business.
As an empiricist, my work builds primarily on two pillars: data and methods. Through my job, I am always interested in new causal inference methods for observational and experimental data. Since machine learning methods have also found their way into evaluation research, it never gets boring for a researcher. But I am also very interested in dimension reduction methods, especially in the context of Big Data.
For the analyses of my research projects, Stata is my workhorse. It provides the most common econometric methods, and newer techniques are often offered freely by user-written commands from the Stata community. You can check out my packages on my GitHub Page. My expertise is in writing Stata packages for parametric non-linear estimation methods. During my time at the alma mater, I gave lectures on the introduction to Stata for Bachelor’s, Master’s, and PhD students in the department of economics. You can find the first two lecture sessions under the tab Introduction to Stata. More to come in the future.
Most of my work – like any empiricist – is spent collecting, cleaning, and analysing data. Usually, I employ individual survey and administrative data in my research projects. I like merging hard and soft indicators because it can provide a more holistic picture of personal choices, behaviours, and trends. Within the project to evaluate the German Participation Opportunities Act, I had the chance to develop and test new soft indicators to measure social participation and employability for a survey.
In recent years, I have gained a particular interest in geospatial data. Linking geographic information and individual data will help us better understand how the social environment and environmental conditions may influence personal decisions and behaviours. Currently, I am working on building a database of topographic information at the NUTS level 3 in European countries. Since Stata’s capabilities in this area are limited, I am working mainly in R and Python for this project.
In my spare time, I love to hike. If I have a long weekend off, I pack my tent, sleeping bag, and portable stove. Then, off I go. For my weekend hikes, I usually choose routes in Germany. But in the holidays, I like to try less known hiking trails. Without my Garmin, I would be lost one or two times. It’s an incredible feeling of freedom when you’re sitting on a cliff overlooking the sea, and there is no sign of any civilisation within kilometres.