Although most of those infected with COVID-19 have recovered relatively quickly, a substantial share has not, and remains symptomatic months or even years later, in what is commonly referred to as long COVID. Data on the incidence of long COVID is scarce, but recent Census Bureau data suggest that sixteen million working age Americans suffer from it. The economic costs of long COVID is estimated to be in the trillions. While many with long COVID have dropped out of the labor force because they can no longer work, many others appear to be working despite having disabilities related to the disease. Indeed, there has been an increase of around 1.7 million disabled persons in the U.S. since the pandemic began, and there are close to one million newly disabled workers. These disabled workers can benefit from workplace accommodations to help them remain productive and stay on the job, particularly as the majority deal with fatigue and brain fog, the hallmarks of long COVID.
The recovery since the onset of the pandemic has been characterized by a tight labor market and rising nominal wage growth. In this post, we look at labor market conditions from a more granular, sectoral point of view focusing on data covering the nine major industries. This breakdown is motivated by the exceptionality of the pandemic episode, the way it has asymmetrically affected sectors of the economy, and by the possibility of exploiting sectoral heterogeneities to understand the drivers of recent labor market dynamics. We document that wage pressures are highest in the sectors with the largest employment shortfall relative to their pre-pandemic trend path, but that other factors explain most of the wage growth differentials. We suggest that one key factor is the extent of physical contact that has had to be compensated for by offering higher wages. One implication of our analysis is that, as COVID-related factors recede, sectoral imbalances could be restored from the supply side as employment recovers back toward the pre-pandemic trend.
One of the two monetary policy goals of the Federal Reserve System— one-half of our dual mandate—is to aim for “maximum employment.” However, labor market outcomes are not monolithic, and different demographic and economic groups experience different labor market outcomes. In this post, we analyze heterogeneity in employment rates by race and ethnicity, focusing on the COVID-19 recession of March-April 2020 and its aftermath. We find that the demographic employment gaps temporarily increased during the onset of the pandemic but narrowed back by spring 2022 to close to where they were in 2019. In the second post of this series, we will focus on heterogeneity in inflation rates, the second part of our dual mandate.
Women’s labor force participation grew precipitously in the latter half of the 20th century, but by around the year 2000, that progress had stalled. In fact, the labor force participation rate for prime-age women (those aged 25 to 54) fell four percentage points between 2000 and 2015, breaking a decades-long trend. However, as the labor market gained traction in the aftermath of the Great Recession, more women were drawn into the labor force. In less than five years, between 2015 and early 2020, women’s labor force participation had recovered nearly all of the ground lost over the prior fifteen years. Then the pandemic hit, erasing these gains. In recent months, as the economy has begun to heal, women’s labor force participation has increased again, but there is much ground to be made up, especially for Black and Hispanic women. A strong labor market with rising wages, as was the case in the years leading up to the pandemic, will be instrumental in bringing more women back into the labor force.
The services sector was hit hard during the COVID-19 pandemic. Small businesses were particularly affected, and many of them were forced to close. We examine the state of these firms using micro data from Homebase (HB), a scheduling and time tracking tool that is used by around 100,000 businesses, mostly small firms, in the leisure and hospitality and retail industries. The data reveal that 35 percent of businesses that were active prior to the pandemic are still closed and that most have been inactive for twenty weeks or longer. We estimate that each additional week of being closed reduces the probability that a business reopens by 2 percentage points. Moreover, an additional week of business closure lowers the share of workers that are rehired at reopening. Our estimates imply that only about 4 percent of the workers that are still laid off from the currently closed businesses will eventually be rehired.
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.
The New York-Northern New Jersey region experienced an unprecedented downturn earlier this year, one more severe than that of the nation, and the region is still struggling to make up the ground that was lost. That is the key takeaway at an economic press briefing held today by the New York Fed examining economic conditions during the pandemic in the Federal Reserve’s Second District. Despite the substantial recovery so far, business activity, consumer spending, and employment are all still well below pre-pandemic levels in much of the region, and fiscal pressures are mounting for state and local governments. Importantly, job losses among lower-income workers and people of color have been particularly consequential. The pace of recovery was already slowing in the region before the most recent surge in coronavirus cases, and we are now seeing signs of renewed weakening as we enter the winter.
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.
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, be they 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 today.