Inequality platform member Fane Naja Groes receives grant by Independent Research Fund Denmark
Description of the project:
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 considered key for understanding why different workers are paid different wages, why productivity differs across firms, and how government policies affect worker reallocation and aggregate economic performance. Using economic theory, we propose a method that allows us to identify latent firm and worker characteristics. Building on cutting edge advances in computer science, we develop an original machine learning algorithm that allows us to implement this identification strategy in large matched employer-employee datasets. Our method 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 method we develop to empirically study the Danish economy using register data.