Predicting Unemployment with Machine Learning Based on Registry Data

Many statistical models have been developed to understand the causes of unemployment, but predicting unemployment has received less attention. In this study, we develop a model to predict the labour market state of a person based on machine learning trained with a large administrative unemployment registry. The model specifies individuals as Markov chains with person specific transition rates. We evaluate the model on three tasks, where the goal is to predict who has the highest risk of escaping unemployment, becoming unemployed, and being unemployed at any given time. We obtain good performance (AUC: 0.80) for the machine learning model of lifetime unemployment, and very good performance (AUC: 0.90+) to the near future when we know the recent labour market state of a person. We find that person information affects the predictions in an intuitive way, but there still are significant differences that can be learned by utilizing labour market histories.