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Many students are reconsidering their decision to go to college in the fall due to the coronavirus pandemic. Indeed, college enrollment is expected to be down sharply as a growing number of would-be college students consider taking a gap year. In part, this pullback reflects concerns about health and safety if colleges resume in-person classes, or missing out on the “college experience” if classes are held online. In addition, poor labor market prospects due to staggeringly high unemployment may be leading some to conclude that college is no longer worth it in this economic environment. In this post, we provide an economic perspective on going to college during the pandemic. Perhaps surprisingly, we find that the return to college actually increases, largely because the opportunity cost of attending school has declined. Furthermore, we show there are sizeable hidden costs to delaying college that erode the value of a college degree, even in the current economic environment. In fact, we estimate that taking a gap year reduces the return to college by a quarter and can cost tens of thousands of dollars in lost lifetime earnings.
Jaison R. Abel, Jason Bram, Richard Deitz, and Benjamin G. Hyman
The Federal Reserve Bank of New York’s June business surveys show some signs of improvement in the regional economy. Following two months of unprecedented decline due to the coronavirus pandemic, indicators of business activity point to a slower pace of contraction in the service sector and signs of a rebound in the manufacturing sector. Even more encouraging, as the regional economy has begun to reopen, many businesses have started to recall workers who were laid off or put on furlough since the start of the pandemic. Some have even hired new workers. Moreover, businesses expect to recall even more workers over the next month. Looking ahead, firms have become increasingly optimistic that conditions will improve in the coming months.
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?
Gizem Koşar, Kyle Smith, and Wilbert van der Klaauw
The New York Fed’s Center for Microeconomic Data released results today from its April 2020 SCE Public Policy Survey, which provides information on consumers' expectations regarding future changes to a wide range of fiscal and social insurance policies and the potential impact of these changes on their households. These data have been collected every four months since October 2015 as part of our Survey of Consumer Expectations (SCE). Given the ongoing COVID-19 pandemic, households face significant uncertainty about their personal situations and the general economic environment when forming plans and making decisions. Tracking individuals’ subjective beliefs about future government policy changes is important for understanding and predicting their behavior in terms of spending and labor supply, which will be crucial in forecasting the economic recovery in the months ahead.
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.
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.
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.
In our previous post, we presented evidence suggesting that labor market indicators provide the most reliable information for dating the U.S. business cycle. In this post, we further develop the case. In fact, the unemployment rate has provided an almost perfect record of distinguishing the beginning of recessions in the post-war U.S. economy. We also show that using more granular labor market data, such as by region or industry, also provides valuable information about the state of the business cycle.
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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