This paper studies why, even after accounting for standard characteristics, low-wealth workers experience larger falls and slower recoveries in earnings than high-wealth workers do after the onset of recessions. I show that differences in job-switching and job-losing rates are important in explaining these dynamics. To do so, I build a quantitative search and matching model with incomplete markets, on-the-job search, and in which workers who switch jobs experience an increase in the risk of subsequent job-loss, a fact I document empirically. Wages are determined by a generalized alternating offer bargaining protocol that accommodates risk-aversion and wealth accumulation. Using this model, I conclude that cyclical differences in job-switching and job-losing by wealth, which the model can endogenously reproduce, explain 40 percent of the gap in the earnings recovery between low- and high-wealth workers following the Great Recession. I apply the model to study the post-Pandemic behavior of job-switching and show that fiscal stimulus alleviated its fall and sustained its recovery.
We analytically characterize optimal monetary policy in a multisector economy with menu costs and contrast it with the textbook New Keynesian model based on Calvo pricing. Following a sectoral productivity shock, the textbook model prescribes zero inflation, providing a formal justification for inflation targeting. In contrast, under menu costs, policy should ``look through'' sectoral shocks and allow inflation to move inversely with output. We provide sharp intuition for this result: stabilizing inflation causes shocks to spill over across sectors, forcing firms to adjust unnecessarily. Finally, in a quantitative model, moving from inflation targeting towards optimal policy improves welfare by 0.32%.
Data, data, data… Economists know their importance well, especially when it comes to monitoring macroeconomic conditions - the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before so-called big data became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate best practices of forecasters on trading desks, at central banks, and in other market-monitoring roles. We present in detail the methodology underlying the New York Fed Staff Nowcast, which employs these innovative techniques to produce early estimates of GDP growth, synthesizing a wide range of macroeconomic data as they become available.
