Research
What is a Labor Market? Classifying Workers and Jobs Using Network Theory (with Bernardo Modenesi, Job Market Paper)
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.
Detailed Wage Gap Decompositions: Controlling for Unobserved Worker Heterogeneity using Network Theory (with Bernardo Modenesi)
Recent advances in the literature of decomposition methods in economics have allowed for the identification and estimation of detailed wage gap decompositions. Differences in wages are decomposed into a component explained by skills and a residual component that may reflect factors such as discrimination. In the context of such detailed decompositions, building reliable counterfactuals requires using tighter controls to ensure that similar workers are correctly identified by making sure that important unobserved variables such as skills are controlled for, as well as comparing only workers with similar observable characteristics. This paper contributes to the wage decomposition literature in two main ways: (i) developing an economic principled network based approach to control for unobserved worker skills and job task heterogeneity; and (ii) extending existing generic decomposition tools to accommodate for potential lack of overlapping supports in covariates between groups being compared, which is likely to be the norm in more detailed decompositions. We illustrate the methodology by decomposing the gender wage gap in Brazil. We find that better controlling for unobserved worker and job heterogeneity reduces the portion of the gender wage gap that cannot be explained by covariates and thus plausibly reflects discrimination. However, even with detailed controls, male workers still outearn female workers by 14%.
Valuing American Cities: A Revealed Preference Approach
This paper estimates the indirect utility, or value, of living in each city in the United States using a revealed preference argument. The paper uses a tool from network theory to compute the central tendency of city-to-city flows and integrates it with a discrete choice model of city choice in order to translate flows into a value with economic meaning. The measure of value is persistent and correlated with a number of city characteristics. I then use a Bartik-style instrument to estimate the effects of local labor demand shocks on city value and find no effect.
Defining Labor Markets and Estimating Market Power Using Network Theory (with Bernardo Modenesi, in progress)
This paper develops a new approach to defining the scope of skill and task-based labor markets and uses it to compute labor market power. Building upon tools from network theory, we classify workers into latent types and jobs into ``markets'' by exploiting the network structure of worker-job links and worker movement between jobs, inherent in linked employer-employee data. Intuitively, two workers belong to the same latent type if they have similar probabilities of working in the same market, and two jobs belong to the same market if they have similar probabilities of hiring the same workers. We use discrete choice methods to infer the productivity of each worker type when matched with each market using the logic that worker-job matches that pay more and occur more frequently in equilibrium reveal themselves to be more productive. Using this high-dimensional productivity matrix, we compute a measure of market concentration, similar to a HHI, that accounts for the fact that workers may match with multiple markets and that some markets are ``closer'' to each other, in the sense of hiring more similar workers. Using the market concentration measure, we compute labor supply elasticities and markdowns for individual firms.
Imputing occupation in the Longitudinal Employer-Household Dynamics (LEHD) (with Bernardo Modenesi and Dylan Nelson, in progress)
Op-Ed
Opinion: Restrictive zoning laws perpetuate neighborhood segregation (with Zachary Ackerman)