We argue that structural changes to the U.S. labor market over the past 40 years have lowered real wage growth by 3.3 percentage points by reducing job-to-job mobility toward higher paying jobs. A textbook job ladder model---where workers occasionally receive outside job offers and experience job separations---predicts that the gap between the distribution of wages among hires from unemployment and the overall wage distribution reflects the intensity of competition for employed workers. Using Current Population Survey data from 1982 to 2022, we document that this gap has narrowed substantially since the mid-1980s, suggesting a decline in net mobility toward higher paying jobs. Estimating an extended quantitative version of the textbook model, we find that increased job-to-job mobility into lower-paying (but potentially higher-value) jobs has reduced real wage growth by 0.6 percentage points, while a decline in upward mobility toward higher-paying jobs has contributed a 2.5 percentage point decline. Of this, lower aggregate matching efficiency accounts for a 1.3 percentage point reduction, decreased search efficiency or intensity among employed workers explains 0.9 percentage points, and rising employer concentration contributes 0.6 percentage points.

Why has worker mobility in the United States declined so much over the past decades? While previous work attributes this decline to reduced labor market dynamism, this paper reveals that one third of this decline is due to increased educational attainment among workers. Higher education affects labor mobility in two ways. First, having a larger share of young workers in school rather than in the labor market precludes these very workers, who are typically the most mobile, from switching jobs and occupations. Second, education provides workers an alternative to learning about their ''type'' making educated workers less reliant on experimenting with new jobs.

We analytically characterize optimal monetary policy in a multisector economy with menu costs and show that inflation and output should move inversely following sectoral shocks. That is, after negative productivity shocks, inflation should be allowed to rise, and vice versa. In a baseline parameterization, optimal policy stabilizes nominal wages. This nominal wage targeting contrasts with inflation targeting, the optimal policy prescribed by the textbook New Keynesian model in which firms are permitted to adjust their prices only randomly and exogenously. The key intuition is that stabilizing inflation causes shocks to spill over across sectors, needlessly increasing the number of firms that must pay the fixed menu cost of price adjustment compared to optimal policy. Finally, we show in a quantitative model that moving from inflation to nominal wage targeting improves welfare by 0.32% in consumption equivalent terms.

I study how wealth impacts workers' job-switching behavior and earnings through a precautionary job-keeping motive. All else equal, low-wealth workers are less willing to switch jobs because such moves increase their short-term risk of job loss. I quantify this channel using a search and matching model where wages are determined by a generalized alternating offer bargaining protocol that accommodates risk aversion, wealth accumulation, and on-the-job search. Precautionary job-keeping accounts for 43% of the earnings gap between low- and high-wealth workers after the Great Recession. The pandemic stimulus weakened this motive, fueling the strong recovery in job-switching in the United States.

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.