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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.
David Dam, Meghana Gaur, Fatih Karahan, Laura Pilossoph, and Will Schirmer
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, the 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.
Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy
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.
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.
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?
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.
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.
Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw
The rising cost of a college education has become an important topic of discussion among both policymakers and practitioners. At least eleven states have recently introduced programs to make public two-year education tuition free, including New York, which is rolling out its Excelsior Scholarship to provide tuition-free four-year college education to low-income students across the SUNY and CUNY systems. Prior to these new initiatives, New York, had already instituted merit scholarship programs that subsidize the cost of college conditional on academic performance and in-state attendance. Given the rising cost of college and the increased prevalence of tuition-subsidy programs, it’s important for us to understand the effects of such programs on students, and whether these effects vary by income and race. While a rich body of work has studied the effects of merit scholarship programs on educational attainment, the same is not true for the effects on financial outcomes of students, such as debt and repayment. This blog post reports preliminary findings from ongoing work, which is one of the first research initiatives to understand such effects.
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