Tracking Inattention

This study proposes a real-time estimate of inattention, based on micro-level data. I show that a simple specification that estimates the persistence of a forecaster's deviation from the mean provides a direct estimate of parameters of information frictions according to prominent models of expectations. The new estimate can also be interpreted as a hybrid measure of both information frictions and behavioral frictions. Using the new specification, I revise several key findings documented in the previous literature. I find higher levels of inattention and document new forms of variations over time and across variables, horizons, individuals, and types of agents. I also report new results from long-run forecasts and document an unprecedented response to COVID-19.

Worker Heterogeneity, Selection, and Unemployment Dynamics in a Pandemic

We employ a new Keynesian model with random search in the labor market and endogenous selection among heterogeneous workers to investigate the impact of a pandemic-induced recession on the distribution of unemployment across workers. In such a recession, workers whose unemployment spells in normal times are inefficiently frequent and long are disproportionately affected. This remains true even when the pandemic initially causes mass layoffs that affect workers broadly or if many separations represent temporary layoffs. Monetary policy that responds to labor market variables affects unemployment for all workers but does relatively little for the distribution of unemployment across workers types.