Machine learning for labour market matching

This paper develops a large-scale application to improve the labour market matching process with model- and algorithm-based statistical methods. We use comprehensive administrative data on employment biographies covering individual and job-related information of workers in Germany. We estimate the probability that a job seeker gets employed in a certain occupational field. For this purpose, we make predictions with common statistical methods (OLS, Logit) and machine learning (ML) methods. The findings suggest that ML performs best regarding the out-of-sample classification error. In terms of the unemployment rate hypothetically, the advantage of ML compared to the common statistical methods would stand for a diference of 0.3 - 1.0 percentage points.