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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.
Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw
Across the United States, the cost of all types of higher education has been rising faster than overall inflation for more than two decades. Despite rising costs, aggregate undergraduate enrollment rose steadily between 2000 and 2010 before leveling off and dipping slightly to its current level. Rising college costs have steadily increased dependence on student debt for college financing, with many students and parents turning to federal and private loans to pay for higher education. An earlier post in this series reported that borrowers in majority Black areas have higher student loan balances and rates of default than those in both majority white and majority Hispanic areas. In this post, we study how differences in college attendance rates and in the types of colleges attended generate heterogeneity in loan experiences. Specifically, using nationwide data, we analyze heterogeneities in college-going and heterogeneities in student debt and default experiences by college type across individuals living in majority Black, majority Hispanic, and majority white zip codes.
Simon Mongey, Laura Pilossoph, and Alexander Weinberg
In the wake of the coronavirus outbreak, nearly all U.S. states imposed social distancing policies to combat the spread of illness. To the extent that work can be done from home, some workers moved their offices to their abodes. Others, however, are unable to continue working as their usual tasks require a specific location or environment, or involve close proximity to others. Which types of jobs cannot be done from home and which types of jobs require close personal proximity to others? What share of overall U.S. employment falls in these categories? And, given that these jobs will be the most adversely affected, what are the characteristics of workers employed in these jobs? The final question is of particular importance as the government designs and implements policies aimed at helping the workers hardest hit by the pandemic.
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.
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.
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