Informal Rungs on the Job Ladder: Theory and Evidence from Brazil

This dissertation studies a labor market where heterogeneous workers climb a job ladder with informal and formal rungs. In this environment, the incidence of informal jobs in a worker's job ladder is a function of her skill level and the economy's history of aggregate states. I estimate the model in Brazilian labor-force survey data, and show it successfully reproduces the observed heterogeneity and dynamics around informality. In equilibrium, informal jobs are less productive and are subject to higher layoff risk than their formal counterparts. However, workers rely on informal contracts not only to smooth transitions between employment and non-employment, but also to advance their careers through moves within and between jobs. According to the model, stronger enforcement of penalties against informal matches (i) increases unemployment and self-employment, (ii) dampens job-to-job transitions, (iii) reduces total output, and (iv) disproportionately hurts the low skilled

Firm-Level Risk Exposures and Stock Returns in the Wake of Covid-19

Firm-level stock returns differ enormously in reaction to COVID-19 news. We characterize these reactions using the Risk Factors discussions in pre-pandemic 10-K filings and two text-analytic approaches: expert-curated dictionaries and supervised machine learning (ML). Bad COVID-19 news lowers returns for firms with high exposures to travel, traditional retail, aircraft production and energy supply — directly and via downstream demand linkages — and raises them for firms with high exposures to healthcare policy, e-commerce, web services, drug trials and materials that feed into supply chains for semiconductors, cloud computing and telecommunications. Monetary and fiscal policy responses to the pandemic strongly impact firm-level returns as well, but differently than pandemic news. Despite methodological differences, dictionary and ML approaches yield remarkably congruent return predictions. Importantly though, ML operates on a vastly larger feature space, yielding richer characterizations of risk exposures and outperforming the dictionary approach in goodness-of-fit. By integrating elements of both approaches, we uncover new risk factors and sharpen our explanations for firm-level returns. To illustrate the broader utility of our methods, we also apply them to explain firm-level returns in reaction to the March 2020 Super Tuesday election results