The COVID-19 pandemic has dramatically changed the way Americans spend their time. One of the most enduring shifts has occurred in the workplace, with millions of employees making the switch to work from home. Even as the pandemic has waned, more than 15 percent of full-time employees remain fully remote and an additional 30 percent work in hybrid arrangements (Barrero, Bloom, and Davis). These changes have substantially reduced time spent commuting to work; in the aggregate, Americans now spend 60 million fewer hours traveling to work each day. In this post, we investigate how people spend this saved time on other activities. Using detailed data from the American Time Use Survey (ATUS), we find that employed individuals allocate their saved commute time toward leisure activities and sleeping, while reducing overall work hours.
When the pandemic hit in early 2020, many businesses quickly and significantly expanded opportunities for their employees to work from home, resulting in a large increase in the share of work being done remotely. Now, more than two years later, how much work is being done from home? In this post, we update our analysis from last year on the extent of remote work in the region. As has been found by others, we find that some of the increase in remote work that began early in the pandemic is sticking. According to firms responding to our August regional business surveys, about 20 percent of all service work and 7 percent of manufacturing work is now being conducted remotely, well above shares before the pandemic, and firms expect little change in these shares a year from now. While responses were mixed, slightly more firms indicated that remote working had reduced rather than increased productivity. Interestingly, however, the rise in remote work has not led to widespread reductions in the amount of workspace being utilized by businesses in the region.
The initial phase of the pandemic saw the euro area and U.S unemployment rates behave quite differently, with the rate for the United States rising much more dramatically than the euro area rate. Two years on, the rates for both regions are back near pre-pandemic levels. A key difference, though, is that U.S. employment levels were down by 3.0 million jobs in 2021:Q4 relative to pre-pandemic levels, while the number of euro area jobs was up 600,000. A look at employment by industry shows that both regions had large shortfalls in the accommodation and food services industries, as expected. A key difference is the government sector, with the number of those jobs in the euro area up by 1.5 million, while the government sector in the United States shed 600,000.
Household saving has soared in the United States and other high-income countries during the COVID-19 pandemic, despite widespread declines in wages and other private income streams. This post highlights the role of fiscal policy in driving the saving boom, through stepped-up social benefits and other income support measures. Indeed, in the United States, Japan, and Canada, government assistance has pushed household income above its pre-pandemic trajectory. We argue that the larger scale of government assistance in these countries helps explain why saving in these countries has risen more strongly than in the euro area. Going forward, how freely households spend out of their newly accumulated savings will be a key factor determining the strength of economic recoveries.
The ongoing COVID-19 pandemic and the various measures put in place to contain it caused a rapid deterioration in labor market conditions for many workers and plunged the nation into recession. The unemployment rate increased dramatically during the COVID recession, rising from 3.5 percent in February to 14.8 percent in April, accompanied by an almost three percentage point decline in labor force participation. While the subsequent labor market recovery in the aggregate has exceeded even some of the most optimistic scenarios put forth soon after this dramatic rise, this recovery has been markedly weaker for the Black population. In this post, we document several striking differences in labor market outcomes by race and use Current Population Survey (CPS) data to better understand them.
The introduction of numerous social distancing policies across the United States, combined with voluntary pullbacks in activity as responses to the COVID-19 outbreak, resulted in differences emerging in the types of work that were done from home and those that were not. Workers at businesses more likely to require in-person work—for example, some, but not all, workers in healthcare, retail, agriculture and construction—continued to come in on a regular basis. In contrast, workers in many other businesses, such as IT and finance, were generally better able to switch to working from home rather than commuting daily to work. In this post, we aim to understand whether following the onset of the pandemic there was a wedge in the incidence of commuting for work across income and race. And how did this difference, if any, change as the economy slowly recovered? We take advantage of a unique data source, SafeGraph cell phone data, to identify workers who continued to commute to work in low income versus higher income and majority-minority (MM) versus other counties.
In addition to its terrible human toll, the COVID-19 pandemic has also caused massive disruption in labor markets. In the United States alone, more than 25 million people lost their jobs during the first wave of the pandemic. While many have returned to work since then, a large number have remained unemployed for a prolonged period of time. The number of long-term unemployed (defined as those jobless for twenty-seven weeks or longer) has surged from 1.1 million to almost 4 million. An important concern is that the long-term unemployed face worse employment prospects, but prior work has provided no consensus on what drives this decline in employment prospects. This post discusses new findings using data on elicited beliefs of unemployed job seekers to uncover the forces driving long-term unemployment.
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.
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.