In 2021, Assistant Professor Andreas Hagemann developed a new econometric methodology that addresses the complexities of clustered data to enhance the accuracy and reliability of empirical work in economics and related fields. Typical examples of clusters are firms, cities, or states. The central challenge is that units within clusters may influence one another or may be influenced by similar environmental factors in ways that cannot be observed. Empirical researchers know that neglecting to account for clusters can yield results where non-existent effects erroneously appear as highly significant. Hagemann's research agenda developed new tools to address this issue in challenging and empirically relevant scenarios. His work has had a substantial impact on econometric theory and empirical practice. For instance, the methodology he developed is now the standard option for clustering in the canonical implementation of quantile regression in the statistical programming language R.