What is a Labor Market? Classifying Workers and Jobs Using Network Theory
Creators: name: Fogel, Jamie
Publisher: name: My University
Year: 2022
Subjects: subject: Labor economics
subject: Economics of networks
subject: Local labor markets
subject: Machine learning
subject: Economics of gender
subject: Economics
subject: Business and Economics
subject: Social Sciences

description: This dissertation combines economic theory and network theory to develop a new methodology for identifying latent worker and job heterogeneity from the network of worker-job matches in linked employer-employee data sets. Chapter 1 develops most of the theory and describes the methodology in detail before applying it to estimating the effects of labor demand shocks on workers. Chapter 2 extends the methodology developed in Chapter 1 and applies it to gender wage gap decompositions. Finally, Chapter 3 uses the methodology from Chapter 1 to impute occupation on the U.S. Census Bureau's Longitudinal Employer Household Dynamics (LEHD) data set. Chapter 1, which is co-authored with Bernardo Modenesi, 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. It microfounds this tool using a standard equilibrium model of workers matching with jobs according to comparative advantage. The classifications identify important dimensions of worker and job heterogeneity that standard classifications based on occupations and sectors miss. The equilibrium model based on these 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, the chapter shows 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 these new classifications as opposed to occupations and sectors. Chapter 2, which is co-authored with Bernardo Modenesi, measures gender discrimination by decomposing male-female differences in average wages into a component explained by male and female workers having different productivity distributions and a component explained by equally productive male and female workers being paid differently. This requires researchers to build reliable counterfactuals by identifying all relevant controls such that male workers are compared to female workers who are identical in all aspects relevant to pay other than their gender, conditional on controls. To do this, this chapter (i) develops a new economically principled network-based approach to control for unobserved worker skill and job task heterogeneity using the information revealed by detailed data on worker-job matching patterns, (ii) non-parametrically estimates counterfactual wage functions for male and female workers, (iii) introduces a correction for the possibility that the male and female productivity distributions do not overlap, and (iv) applies these new methods by revisiting gender wage gap decompositions using improved counterfactuals based on (i), (ii) and (iii). The chapter decomposes the gender wage gap in Rio de Janeiro, Brazil and finds that the gender wage gap is almost entirely explained by male and female workers who possess similar skills and perform similar tasks being paid different wages. Chapter 3 attempts to impute occupation on the LEHD by exploiting the information contained in the LEHD's rich set of worker-job matches using the method developed in Chapter 1. It finds that while the information contained in these matches is informative about economic outcomes like earnings, it is minimally informative about occupation. In particular, the information gleaned from worker-job matches has minimal predictive power for occupation when other variables like industry are included as predictors.