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Predicting Job Match Quality: A Machine Learning Approach

Employment agencies and jobseekers can be provided with job alternatives more easily.

This paper develops a large-scale algorithm-based application to improve the match quality in the labor market. We use comprehensive administrative data on employment biographies in Germany to predict job match quality in terms of job stability and wages. Compared to the latter approach, we find that XGBoost performs better for pattern recognition, it analyzes large amounts of data in an efficient way and minimizes the prediction error in the application.

IAB-Discussion Paper 9/2024: Predicting Job Match Quality: A Machine Learning Approach