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Author:García, Daniel I. 

Discussion Paper
How Have Racial and Ethnic Earnings Gaps Changed after COVID-19?

Racial and ethnic earnings disparities have been salient features of the U.S. economy for decades. Between the pandemic-driven recession in 2020 and the rising inflation since 2021, workers’ real and nominal earnings have seen rapid change. To get a sense of how recent economic conditions have affected earnings disparities, we examine real and nominal weekly earnings trends for Asian, Black, Hispanic, and white workers. We find that average real weekly earnings have been declining in the past year, but less so for Black and Hispanic workers than for white and Asian workers. Black and ...
Liberty Street Economics , Paper 20221020a

Discussion Paper
Nonresidential construction spending is likely not as weak as it seems

Unlike any other major component of GDP, private investment in nonresidential structures excluding drilling and mining (henceforth "NRS") has steadily declined since the start of 2020. Figure 1 shows the evolution of GDP as well as the main components of private domestic final demand since 2019.
FEDS Notes , Paper 2023-03-24-1

Discussion Paper
An Early Evaluation of the Effects of the Pandemic on Living Arrangements and Household Formation

As with many other aspects of life—including the record-setting decline in employment—the COVID-19 pandemic has profoundly affected the living arrangements of millions of Americans. In this note, we document a fact that has as yet received little attention:
FEDS Notes , Paper 2020-08-07

Discussion Paper
The Unusual Composition of Demand during the Pandemic

In most recessions, household spending on goods—particularly durables—and housing tends to fall sharply and remain weak for many quarters. In contrast, services spending has generally responded little to business cycles. This time, however, the opposite has occurred, as shown in Figure 1.
FEDS Notes , Paper 2021-01-14

Working Paper
Estimating Aggregate Data Center Investment with Project-level Data

Data center investment in the U.S. has increased rapidly in the post-pandemic era, and plans for future investment have surged further. Forecasting investment at such a turning point is an important but potentially fraught exercise, especially given lags in aggregate data availability. We develop a straightforward method to forecast aggregate investment using project-level microdata and a small number of parameters: specifically, abandonment rates, time from planning to start, and time from start to completion. As a key validation of our approach, we generate estimates that match the recent ...
Finance and Economics Discussion Series , Paper 2025-109

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