Machine Learning for Labour Market Matching

Abstract

"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 and machine learning (ML) methods. The findings suggest that ML performs better than the other methods regarding the out-of-sample classification error. In terms of the unemployment rate, the advantage of ML would stand for a difference of 2.9 - 3.6 percentage points." (Author's abstract, IAB-Doku) ((en))

Cite article

Mühlbauer, S. & Weber, E. (2022): Machine Learning for Labour Market Matching. (IAB-Discussion Paper 03/2022), Nürnberg, 37 p. DOI:10.48720/IAB.DP.2203