Study programme

This course applies microeconomic theory to analyse pressing contemporary challenges, offering both theoretical rigour and in-depth engagement with real-world issues. It addresses two key questions: first, how do current developments – such as technological progress, internationalisation and climate change – affect markets? For example, how does technological progress increase firms’ market power, and what are the implications for income inequality? Second, how can markets be used to address these challenges? For instance, how can emissions trading systems help reduce pollution, and how can matching markets improve outcomes in sectors like elderly care? By combining analytical depth with practical relevance, this course equips students to critically evaluate and apply microeconomic insights to today’s most urgent problems.

This course offers an in-depth and hands-on extension of the skills and techniques introduced in the Causal Inference for Policy Evaluation course. It focuses on advanced and modern quasi-experimental methods and research skills that are directly applicable to thesis work. Building on the foundational concepts from the first course, this course deepens students’ understanding of core designs such as randomised experiments, selection of observables and instrumental variables, while introducing new approaches like causal machine learning and synthetic control. Special emphasis is placed on the practical implementation of these methods, with dedicated programming labs in R. The course is designed to be taken alongside Causal Inference for Policy Evaluation, with the content and timing carefully coordinated to reinforce and expand students’ methodological toolkit.

This course applies the quasi-experimental methods covered in the Data Science and Analysis for Economists course to current economics and societal challenges, aiming to promote the implementation of broad prosperity. The topics covered include technology and globalisation, institutions such as minimum wage and labour unions, and sustainability and climate change. Each topic will be covered using empirical data either stemming from contemporaneous scientific papers or real-world data sources. By the end of the course, students will have acquired skills to perform their own data analyses using recent empirical analysis software and apply identification strategies for quasi-experiments. Students will also have the opportunity to calibrate models using software such as R, Matlab or Mathematica.