Cognitive and Non-Cognitive Skills for the Peruvian Labor Market: Addressing Measurement Error through Latent Skills Estimations

Abstract

Evidence from developed country data suggests that cognitive and non-cognitive skills contribute to improved labor market outcomes. This paper tests this hypothesis in a developing country by using an individual-level data set from Peru that incorporates modules to measure cognitive and non-cognitive skills. The paper estimates a structural latent model with unobserved heterogeneity to capture full ability rather than just measured skill. It also applies standard ordinary least squares techniques for comparison. The analysis confirms that cognitive and non-cognitive skills are positively correlated with a range of labor market outcomes in Peru. In particular, cognitive skills positively correlate with wages and the probability of being a wage worker, white-collar, and formal worker, with verbal fluency and numeric ability playing particularly strong roles. The results are robust to methodology. The patterns are less uniform for non-cognitive skills. For instance, perseverance of effort (grit) emerges strongly for most outcomes regardless of methodology. However, plasticity—an aggregation of openness to experience and emotional stability—is only correlated with employment, and only when using the structural latent model. The ordinary least squares method also finds that the disaggregated non-cognitive skills of kindness, cooperation, emotional stability, and openness to experience emerge significantly, mostly for the wage estimates. The different results derived from the ordinary least squares and the structural model with latent skills suggest strong measurement bias in most non-cognitive skills measurement. These findings, although only correlational because of the use of a single cross-section, suggest that recent efforts by the Peruvian government to incorporate non-cognitive skill development into the school curriculum are justified.