The U.S. labor force participation rate (LFPR) currently stands at 62.5 percent, 0.8 percentage point below its level in February 2020. This “participation gap” translates into 2.1 million workers out of the labor force. In this post, we evaluate three potential drivers of the gap: First, population aging from the baby boomers reaching retirement age puts downward pressure on participation. Second, the share of individuals of retirement age that are actually retired has risen since the onset of the COVID-19 pandemic. Finally, long COVID and disability more generally may induce more people to leave the labor force. We find that nearly all of the participation gap can be explained by population aging, which caused a significant rise in the number of retirements. Higher retirement rates compared to pre-COVID have had only a modest effect, while disability has virtually no effect.
The sharp slowdown in China’s property sector has reignited debate over the country’s future role as a net provider of savings to the global economy. The debate revolves around whether a sustained decline in property investment will spur a long-term increase in China’s current account surplus, given the country’s high savings rate. However, China’s rapidly aging population presents opposing forces that complicate this story. The shift of a large share of its population from working life to retirement will reduce savings supply even as a shrinking labor force will reduce investment demand. In this post, we focus on the demographic part of the story and find that this force will exert considerable downward pressure on China’s current account surplus in coming years.
On August 24, 2022, the White House released a plan to cancel federal student loans for most borrowers. In April, we wrote about the costs and who most benefits from a few hypothetical loan forgiveness proposals using our Consumer Credit Panel, based on Equifax credit report data. In this post, we update our framework to consider the White House plan now that parameters are known, with estimates for the total amount of forgiven loans and the distribution of who holds federal student loans before and after the proposed debt jubilee.
This post concludes a three-part series exploring the gender, racial, and educational disparities of debt outcomes of college students. In the previous two posts, we examined how debt holding and delinquency behaviors vary among students of different race and gender, breaking up our analyses by level of degree pursued by the student. We found that Black and Hispanic students were less likely than white students to take on credit card debt, auto loans, and mortgage debt, but experienced higher rates of delinquency in each of these debt areas by the age of 30. In contrast, Black students were more likely to take out student debt and both Black and Hispanic students experienced higher rates of student debt delinquency. We found that Asian students broadly followed reverse patterns from Black and Hispanic students by age 30. They were more likely than white students to acquire mortgages and less likely to hold student debt, but their delinquency patterns were in general similar to those of white students. Women were less likely to hold an auto loan or mortgage and more likely to hold student debt by age 30, and in most cases their delinquency outcomes were indistinguishable from males. In this post, we seek to understand mechanisms behind these racial and gender disparities and examine the role of educational attainment in explaining these patterns.
This post is the second in a three-part series exploring racial, gender, and educational differences in household debt outcomes. In the first post, we examined how the propensity to take out household debt and loan amounts varied among students by race, gender, and education level, finding notable differences across all of these dimensions. Were these disparities in debt behavior by gender, race, and education level associated with differences in financial stress, as captured by delinquencies? This post focuses on this question.
Household debt has risen markedly since 2013 and amounts to more than $15 trillion dollars. While the aggregate volume of household debt has been well-documented, literature on the gender, racial and education distribution of debt is lacking, largely because of an absence of adequate data that combine debt, demographic, and education information. In a three-part series beginning with this post, we seek to bridge this gap. In this first post, we focus on differences in debt holding behavior across race and gender. Specifically, we explore gender and racial disparities in different types of household debt and delinquencies—for auto, mortgage, credit card, and student loans—while distinguishing between students pursuing associate’s (AA) and bachelor’s (BA) degrees. In the second post in this series, we investigate gender and racial disparities in delinquencies across these various kinds of consumer debt. We close with a third post where we try to understand some of the mechanisms behind differences in debt and delinquencies across gender and race.
Access to credit plays a central role in shaping economic opportunities of households and businesses. Access to credit also plays a crucial role in helping an economy successfully exit from the pandemic doldrums. The ability to get a loan may allow individuals to purchase a home, invest in education and training, or start and then expand a business. Hence access to credit has important implications for upward mobility and potentially also for inequality. Adverse selection and moral hazard problems due to asymmetric information between lenders and borrowers affect credit availability. Because of these information issues, lenders may limit credit or post higher lending rates and often require borrowers to pledge collateral. Consequently, relatively poor individuals with limited capital endowment may experience credit denial, irrespective of the quality of their investment ideas. As a result, their exclusion from credit access can hinder economic mobility and entrench income inequality. In this post, we describe the results of our recent paper which contributes to the understanding of this mechanism.
Spending on goods and services that were constrained during the pandemic is expected to grow at a fast pace as the economy reopens. In this post, we look at detailed spending data to track which consumption categories were the most constrained by the pandemic due to social distancing. We find that, in 2019, high-income households typically spent relatively more on these pandemic-constrained goods and services. Our findings suggest that these consumers may have strongly reduced consumption during the pandemic and will likely play a crucial role in unleashing pent-up demand when pandemic restrictions ease.
This post is the first in a two-part series that seeks to understand whether consumer spending patterns during the COVID-19 pandemic evolved differentially across counties by race and income. As the pandemic hit and social distancing restrictions were put into place in March 2020, consumer spending plummeted. Subsequently, as social distancing restrictions began to be relaxed later in spring 2020, consumer spending started to rebound. We find that higher-income counties had a considerably steeper decline and a shallower recovery than low-income counties did. The differences by race were also sizeable as the pandemic struck but became considerably more muted after summer of 2020. The decline and the recovery until the end of summer were sharper for majority-minority (MM) than majority nonminority (MNM) counties, while both sets of counties showed similar growth in spending after that. The second post in this series highlights the goods and services that were most adversely affected (or “constrained”) by the pandemic. Then, differentiating households by income, that post explores which households were more exposed to these pandemic-constrained expenditure categories.
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.