Daniele Caratelli

I am a research economist at the U.S. Treasury Office of Financial Research. My research focuses on macroeconomics, specifically on monetary economics and labor macroeconomics.
Views expressed here are mine and do not necessarily reflect those of the U.S. Treasury or Office of Financial Research.

For more details here is my CV.


Working Papers:

The Long-term Decline of the U.S. Job Ladder (Apr `24)
with Aniket Baksy and Niklas Engbom

We develop a methodology to consistently estimate employer-to-employer (EE) mobility toward higher paying jobs based on publicly available microdata from the Current Population Survey, and use it to document three trends over the past half century. First, such EE mobility fell by half between 1979 and 2023. Second, its decline reduced annual wage growth by over one percentage point. Third, the decline was particularly pronounced for women, those without a college degree, and new cohorts. We find little support for the notion that the decline resulted from workers being better matched with their current jobs or the labor market being worse at matching workers and firms. Instead, based on long-run variation across U.S. states, we present evidence consistent with the view that greater labor market concentration reduced workers' opportunities to transition toward higher paying employers.

The More You Learn, the Fewer Places You'll Go: The Rise in Education and the Decline in Worker Mobility (Oct `23)
with Aniket Baksy

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.

Optimal Monetary Policy under Menu Costs (Nov `23)
with Basil Halperin

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, following a sectoral shock, nominal wage targeting reduces the welfare loss arising from menu costs by 81% compared to inflation targeting.
Supported by the Washington Center for Equitable Growth
Media: Marginal Revolution

Labor Market Recoveries Across the Wealth Distribution (Feb `24)

I study how wealth impacts workers' job-switching behavior and their 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 accommodating risk-aversion, wealth accumulation, and on-the-job search. Precautionary job-keeping accounts for half the earnings gap between low- and high-wealth workers after the Great Recession. The Pandemic stimulus weakened this motive leading to the strong job-switching recovery the US has recently experienced.
Winner of the 2022 Best Job Market Paper Award, EEA and UniCredit Foundation


Macroeconomic Nowcasting and Forecasting with Big Data
with Brandyn Bok, Domenico Giannone, Argia Sbordone,
Andrea Tambalotti
(Annual Review of Economics, 2018)

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.

Blog posts:

Opening the Toolbox: The Nowcasting Code on GitHub
with Patrick Adams, Brandyn Bok, Domenico Giannone,
Eric Qian, Argia Sbordone, Camilla Schneier, Andrea Tambalotti
(Liberty Street Economics, 2018)

Just Released: Introducing the New York Fed Staff Nowcast
with Grant Aarons, Matt Cocci, Domenico Giannone,
Argia Sbordone, Andrea Tambalotti
(Liberty Street Economics, 2016)