Research
Research
Credit Access in the United States (with Trevor J. Bakker, Stefanie DeLuca, Eric English, Nathaniel Hendren, and Daniel Herbst
We construct new population-level linked administrative data to study households’ access to credit in the United States. By age 25, Black adults, those who grew up in low-income families, and those raised in the Southeast or Appalachia already have significantly lower credit scores than other groups, and these differences persist throughout adulthood. These gaps translate into smaller credit balances, more credit inquiries, higher credit utilization, and greater reliance on high-cost alternative financial services. Evaluating two definitions of algorithmic bias yields opposing results. Scores are miscalibrated against
traditionally advantaged groups: conditional on a score, Black and low-parental-income individuals fall delinquent more often. Yet scores are unbalanced against traditionally disadvantaged groups: among borrowers with no future delinquency, Black and low-parental-income individuals receive lower scores. Eliminating both biases and reducing gaps in credit access requires reducing systematic differences in delinquencies, which emerge in one’s twenties through missed payments on credit cards, student loans, and other bills. Comprehensive measures of individuals’ income profiles and observed wealth explain only a small portion of these repayment gaps. In contrast, most geographic variation in repayment reflects the causal effect of childhood exposure to place. Counties that promote upward mobility also promote repayment and expand credit access, suggesting that common place-level factors may drive behaviors in both credit and labor markets. We discuss suggestive evidence for several mechanisms of our results, including the role of social and cultural capital. We conclude that gaps in credit access by race, class, and hometown have roots in childhood environments.
Market Definition Bias in Studies of (Labor) Market Power (with Bernardo Modenesi and Ben Scuderi, in progress)
This paper demonstrates two distinct and quantitatively important biases introduced by using an ``incorrect'' definition of market boundaries when attempting to make inferences about labor market powerThe first source of bias, long recognized in the antitrust literature, stems from mismeasurement of relative firm size: the same firm will appear artificially dominant when markets are drawn too narrowly and artificially competitive when they are drawn too broadly. We derive a novel second source of bias, which we term \emph{elasticity bias}, that generates statistical attenuation of estimates of key parameters that govern model-based conclusions about the size and distribution of markdowns across employers and markets. In simulations calibrated to Brazilian administrative data, we show that the second channel is an order of magnitude more important than the first. Further, we show that market definition bias can be large in empirically-relevant cases where the relative rate of misclassification may be modest, as with \emph{administrative} labor--market boundaries such as industry/occupation--region cells adopted by virtually all existing studies. We propose an alternative \emph{network-based} procedure for defining labor market boundaries that extends the algorithm of Fogel and Modenesi (2022). Drawing upon the empirical strategy of \cite{Felix}, we show that relative to using administrative market definitions, using network-based market definitions yields estimates with 40\% larger markdown dispersion and overturns several qualitative conclusions about which workers are harmed by monopsony power. Finally, we propose a simple diagnostic that allows practitioners to pick among off-the-shelf classifications when using a data-driven one is infeasible.
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.
Imputing occupation in the Longitudinal Employer-Household Dynamics (LEHD) (with Bernardo Modenesi and Dylan Nelson, in progress)
Opinion: Restrictive zoning laws perpetuate neighborhood segregation (with Zachary Ackerman)