Fane Naja Groes Receives Grant from Danmarks Frie Forskningsfond
"Compensation for workers' productive attributes, not captured by the characteristics that can be directly observed in the data, and sorting between workers and firms based on these attributes, are widely considered key for understanding why different workers are paid different wages, why productivity differs across firms, and how government policies affect worker reallocation and the aggregate economic performance. Using economic theory, we propose a method that allows to identify latent firm and worker characteristics in the data. Building on cutting edge advances in computer science, we develop an original machine learning algorithm that allows to implement this identification strategy in large matched employer-employee datasets. This allows us to measure the consequences for wages, output and productivity from moving any individual worker to any individual firm in the economy. We apply the methods we develop to empirically study the Danish economy using register data."