Noncognitive Skills and Labor Market Outcomes: A Machine Learning Approach

We study the importance of noncognitive skills in explaining differences in the labor market performance of individuals by means of machine learning techniques. Unlike previous em- pirical approaches centering around the within-sample explanatory power of noncognitive skills our approach focuses on the out-of-sample forecasting and classification qualities of noncognitive skills. Moreover, we show that machine learning techniques can cope with the challenge of selecting the most relevant covariates from big data with a whopping number of covariates on personality traits. This enables us to construct new personality indices with larger predictive power. In our empirical application we study the role of noncognitive skills for individual earnings and unemployment based on the British Cohort Study (BCS). The longitudinal character of the BCS enables us to analyze predictive power of early childhood environment and early cognitive and noncognitive skills on adult labor market outcomes. The results of the analysis show that there is a potential of a long run in uence of early childhood variables on the earnings and unemployment.