This paper explores the determinants of deviations of ex-post budget outcomes from first-release outcomes published towards the end of the year of budget implementation. The predictive content of the first-release outcomes is important, because these figures are an input for the next budget and...

The econometrics literature proposed several new causal machine learning methods (CML) in the past few years. These methods harness the strength of machine learning methods to flexibly model the relationship between the treatment, outcome and confounders, while providing valid inferential...

A novel debate within competition policy and regulation circles is whether autonomous machine learning algorithms may learn to collude on prices. We show that when fims face short-run price commitments, independent Q-learning (a simple but well-established self-learning algorithm) learns to...

This study examines the applicability of modern Data Science techniques in the domain of Strategy. We apply novel techniques from the field of machine learning and text analysis. We proceed in two steps. First, we compare different machine learning techniques to traditional regression methods in...

When merging firms across large databases in the absence of common identifiers, text algorithms can help. I propose a high-performance fuzzy firm name matching algorithm that uses existing computational methods and works even under hardware restrictions. The algorithm consists of four steps,...

To stimulate companies to take corporate social responsibility collectively, for example for fair trade or the environment, their agreements may be exempted from cartel law. To qualify, the public benefits must compensate consumers for higher prices of the private good. We study the balancing...