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Displaced workers have been shown to endure persistent losses years beyond their initial job separation events. These losses are especially amplified during recessions. (1) One explanation for greater persistence in downturns relative to booms, is that firms and industries on the margin of structural change permanently shift the types of tasks and occupations demanded after a large negative shock (Aghion et al. (2005)), but these new occupations do not match the stock of human capital held by those currently displaced. In response to COVID-19, firms with products and services that complement social-distancing (like Amazon distribution centers) may continue hiring during and beyond the recovery, while workers displaced from higher risk industries with more stagnant demand (for example, airport personnel, local retail clerks) are left to adjust to less familiar job opportunities. As some industries reopen gradually while others remain stunted, what role might workforce development programs have in bridging the skill gap such that displaced workers are best prepared for this new reality of work?
News headlines highlighting the loss of at least 30 million jobs (so far) underscore the massive shock that has hit the U.S. economy and the dislocation, hardship, and stress it has caused for so many American workers. But how accurately does this number actually capture the number of net job losses? In this post, we look at some of the statistical anomalies and quirks in the weekly claims series and offer a guide to interpreting these numbers. What we find is that the relationship between jobless claims and payroll employment for the month can vary substantially, depending on the nature, timing, and persistence of the disaster.
Olivier Armantier, Gizem Koşar, Rachel Pomerantz, Daphne Skandalis, Kyle Smith, Giorgio Topa, and Wilbert van der Klaauw
In a recent blog post, we showed that consumer expectations worsened sharply through March, as the COVID-19 epidemic spread and affected a growing part of the U.S. population. In this post, we document how much of this deterioration can be directly attributed to the coronavirus outbreak. We then explore how the effect of the outbreak has varied over time and across demographic groups.
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.
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.
Jaison R. Abel, Jason Bram, Richard Deitz, and Jonathan Hastings
At today’s regional economic press briefing, we highlighted some recent softening in the tri-state regional economy (New York, Northern New Jersey, and Fairfield County, Connecticut)—a noteworthy contrast from our briefing a year ago, when economic growth and job creation were fairly brisk. We also showed that Puerto Rico and the U.S. Virgin Islands, which are part of the New York Fed’s district, both continue to face major challenges but have made significant economic progress following the catastrophic hurricanes of 2017.
Economic analysis is often geared toward understanding the average effects of a given policy or program. Likewise, economic policies frequently target the average person or firm. While averages are undoubtedly useful reference points for researchers and policymakers, they don’t tell the whole story: it is vital to understand how the effects of economic trends and government policies vary across geographic, demographic, and socioeconomic boundaries. It is also important to assess the underlying causes of the various inequalities we observe around us, whether they are related to income, health, or any other set of indicators. Starting today, we are running a series of six blog posts (apart from this introductory post), each of which focuses on an interesting case of heterogeneity in the United States.
Two years after hurricanes Irma and Maria wreaked havoc on Puerto Rico and the U.S. Virgin Islands, the two territories’ economies have moved in very different directions. When the hurricanes struck, both were already in long economic slumps and had significant fiscal problems. As of the summer of 2019, however, Puerto Rico’s economy was showing considerable signs of improvement since the hurricanes, while the Virgin Islands’ economy remained mired in a deep slump through the end of 2018, though signs of a nascent recovery have emerged in 2019. In this post, we assess the contrasting trends of these two economies since the hurricanes and attempt to explain the forces driving these trends.
The federal minimum wage, currently set at $7.25 per hour, has remained unchanged for the longest stretch of time since its 1938 inception under the Fair Labor Standards Act. With the real purchasing power of the federal minimum wage eroded by inflation, many states and municipalities have raised their local minimum wages. As of July 2019, fourteen states plus the District of Columbia—home to 35 percent of Americans—have minimum wages above $10 per hour, as do numerous localities scattered across other states. New York is among a handful of states—along with California, Connecticut, Illinois, Maryland, Massachusetts, and New Jersey—that has passed legislation to eventually increase minimum wages to $15 per hour. While New York began raising its minimum wage from $7.25 per hour in 2014, neighboring Pennsylvania has left its minimum wage unchanged at the federal floor. Minimum-wage variation between contiguous states has allowed researchers to evaluate the respective impacts on employment and average earnings. In this post, we gauge the effect of New York’s recent minimum-wage hikes by comparing low-wage sectors in counties along the New York-Pennsylvania border.
The Federal Reserve Bank of New York’s July 2019 SCE Labor Market Survey shows a year-over-year rise in employer-to-employer transitions as well as an increase in transitions into unemployment. Satisfaction with promotion opportunities and wage compensation was largely unchanged, while satisfaction with non-wage benefits retreated. Regarding expectations, the average expected wage offer (conditional on receiving one) and the average reservation wage—the lowest wage at which respondents would be willing to accept a new job—both increased. Expectations regarding job transitions were largely stable.
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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