What is a Labor Market? Classifying Workers and Jobs Using Network Theory

TL;DR

This paper introduces a network theory-based data-driven method to classify workers and jobs, revealing heterogeneity and improving wage prediction accuracy over traditional occupation-based classifications.

Contribution

It develops a novel network theory approach to classify worker and job heterogeneity, enhancing understanding and predictive power in labor market analysis.

Findings

Classifications reveal important heterogeneity missed by traditional methods.

Model predictions of wage changes are more accurate with the new classifications.

Labor market shock effects are significantly larger when using the new classifications.

Abstract

This paper develops a new data-driven approach to characterizing latent worker skill and job task heterogeneity by applying an empirical tool from network theory to large-scale Brazilian administrative data on worker--job matching. We microfound this tool using a standard equilibrium model of workers matching with jobs according to comparative advantage. Our classifications identify important dimensions of worker and job heterogeneity that standard classifications based on occupations and sectors miss. The equilibrium model based on our classifications more accurately predicts wage changes in response to the 2016 Olympics than a model based on occupations and sectors. Additionally, for a large simulated shock to demand for workers, we show that reduced form estimates of the effects of labor market shock exposure on workers' earnings are nearly 4 times larger when workers and jobs are classified using our classifications as opposed to occupations and sectors.