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4/27: Following strong reader interest, we began sharing the Weekly Economic Index on the New York Fed’s public website. Look for it twice weekly on Tuesday and Thursday at 11:30 a.m.
Economists are well-practiced at assessing real activity based on familiar aggregate time series, like the unemployment rate, industrial production, or GDP growth. However, these series represent monthly or quarterly averages of economic conditions, and are only available at a considerable lag, after the month or quarter ends. When the economy hits sudden headwinds, like the COVID-19 pandemic, conditions can evolve rapidly. How can we monitor the high-frequency evolution of the economy in “real time”?
As a result of the coronavirus outbreak, New York State, New Jersey, and Connecticut have closed nonessential businesses and schools and asked residents to stay home in an effort to slow the spread of the virus. These actions are unprecedented, and the economic impacts are likely to be temporary but severe, and difficult to track and measure. With conditions changing so rapidly, timely data on the economic impacts of the outbreak and resultant policies on businesses and people are both scarce and important. In this post, we provide some very recent information on the economic effects of the coronavirus outbreak in the tri-state region based on responses to a special survey we fielded between March 20 and March 24. The results are striking, though perhaps not surprising: roughly half of the service firms surveyed and well over a third of manufacturers said they have already implemented at least a partial temporary shutdown, and more firms plan to do so in the near future. Further, 40 percent of service firms and 30 percent of manufacturers are reporting staff reductions, and many firms are noting difficulty accessing credit and are concerned about their solvency.
The COVID-19 outbreak has sparked urgent questions about the impact of pandemics, and associated countermeasures, on the real economy. Policymakers are in uncharted territory, with little guidance on what the expected economic fallout will be and how the crisis should be managed. In this blog post, we use insights from a recent research paper to discuss two sets of questions. First, what are the real economic effects of a pandemic—and are these effects temporary or persistent? Second, how does the local public health response affect the economic severity of the pandemic? In particular, do non-pharmaceutical interventions (NPIs) such as social distancing have economic costs, or do policies that slow the spread of the pandemic also reduce its economic severity?
How does access to consumer credit affect the job finding behavior of displaced workers? Are these workers looking for jobs at larger and more productive firms? What is the impact of consumer credit on the amount of time it takes to find a job? In recent work with Ethan Cohen-Cole we explore these questions by building a new data set of individual credit reports (from TransUnion) merged with administrative earnings data. We describe our approach and our results in this post.
Job-to-job transitions—those job moves that occur without an intervening spell of unemployment—have been discussed in the literature as a driver of wage growth. Economists typically describe the labor market as a “job ladder” that workers climb by moving to jobs with higher pay, stronger wage growth, and better benefits. It is important, however, that these transitions not be interspersed with periods of unemployment, both because such downtime could lead to a loss in accumulated human capital and because “on-the-job search” is more effective than searching while unemployed. Yet little is known about what leads workers to search for jobs while employed. This post aims to shed light on one such possible mechanism—namely, how current job satisfaction is related to job search behavior.
René Chalom, Fatih Karahan, Brendan Moore, and Giorgio Topa
The growth rate of hourly earnings is a widely used indicator to assess the economic progress of U.S. workers, as well as the health of the labor market. It is also a measure of wage pressures that could potentially spill over into inflationary pressures in a tightening labor market. Hourly earnings growth, on average, has gradually risen over the course of the current expansion, under way since the end of the Great Recession. But how have different groups of workers fared in this regard? Have hourly earnings risen uniformly at all points of the wage distribution, or have some segments of the workforce been left behind? In this post, we take a close look at earnings growth over the past two decades at different points of the wage distribution and for various demographic groups. Our goal is to examine whether there are any significant patterns in the evolution of the distribution of earnings, as opposed to just looking at the behavior of aggregate earnings growth. We focus primarily on hourly earnings growth, although our findings apply to total earnings as well.
Technological change and globalization have caused a massive transformation in the U.S. economy. While creating new opportunities for many workers, these forces have eliminated millions of good-paying jobs, particularly routine jobs in the manufacturing sector. Indeed, a great deal of attention
has focused on the consequences
of the loss of blue-collar production jobs
for prime‑age men. What is often overlooked, however, is that
women have also been hit hard
by the loss of routine jobs, particularly administrative support jobs—a type of routine work that has historically been largely performed by women. In this post, we show that the combined loss of production and administrative support jobs since 2000 is actually more than three times as large for prime-age women than prime-age men.
While average outcomes serve as important yardsticks for how the economy is doing, understanding heterogeneity—how outcomes vary across a population—is key to understanding both the whole picture and the implications of any given policy. Following our six-part look at heterogeneity in October 2019, we now turn our focus to heterogeneity in the labor market—the subject of four posts set for release tomorrow morning. Average labor market statistics mask a lot of underlying variability—disparities that factor into labor market dynamics. While we have written about labor market heterogeneity before, this series is an attempt to pull together in a cohesive way new insights on the labor market and highlight details that are not immediately obvious when we study aggregate labor market statistics.
Liberty Street Economics features insight and analysis from New York Fed economists working at the intersection of research and policy. Launched in 2011, the blog takes its name from the Bank’s headquarters at 33 Liberty Street in Manhattan’s Financial District.
The editors are Michael Fleming, Andrew Haughwout, Thomas Klitgaard, and Asani Sarkar, all economists in the Bank’s Research Group.
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The views expressed are those of the authors, and do not necessarily reflect the position of the New York Fed or the Federal Reserve System.
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