The Federal Reserve Bank of New York works to promote sound and well-functioning financial systems and markets through its provision of industry and payment services, advancement of infrastructure reform in key markets and training and educational support to international institutions.
David Dam, Davide Melcangi, Laura Pilossoph, and Will Schirmer
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.
Ruchi Avtar, Rajashri Chakrabarti, Maxim Pinkovskiy, and Giorgio Topa
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.
Olivier Armantier, Leo Goldman, Gizem Koşar, and Wilbert van der Klaauw
In October, we reported evidence on how households used their first economic impact payments, which they started to receive in mid-April 2020 as part of the CARES Act, and how they expected to use a second stimulus payment. In this post, we exploit new survey data to examine how households used the second round of stimulus checks, issued starting at the end of December 2020 as part of the Coronavirus Response and Relief Supplemental Appropriations (CRRSA) Act, and we investigate how they plan to use the third round authorized in March under the American Rescue Plan Act. We find remarkable stability in how stimulus checks are used over the three rounds, with a slight decline in the share dedicated to consumption and a proportional increase in the share saved. The average share of stimulus payments that households set aside for consumption—what economists call the marginal propensity to consume (MPC)—declined from 29 percent in the first round to 26 percent in the second and to 25 percent in the third.
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.
As the COVID-19 pandemic took hold in the United States, in just two months—between February and April 2020—the nation saw well over 20 million workers lose their jobs, an unprecedented 15 percent decline. Since then, substantial progress has been made, but employment still remains 5 percent below its pre-pandemic level. However, not all workers have been affected equally. This post is the first in a three-part series exploring disparities in labor market outcomes during the pandemic—and represents an extension of ongoing research into heterogeneities and inequalities in people’s experience across large segments of the economy including access to credit, health, housing, and education. Here we find that some workers were much more likely to lose their jobs than others, particularly lower-wage workers and those without a college degree, as well as women, minorities, and younger workers. However, as jobs have returned during the recovery, many of these differences have narrowed considerably, though some gaps are widening again as the labor market has weakened due to a renewed surge in the coronavirus. The next post in the series examines differences in patterns of commuting during the pandemic, and finds that workers in low-income and Black- and Hispanic-majority communities were more likely to commute for work. The final post in the series analyzes unemployment dynamics during the pandemic, and finds that Black workers experienced a lower job-finding rate and a higher separation rate into unemployment than white workers during the recovery, though this trend has reversed to some extent recently.
Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy
This is the second post in a series that aims to understand the gap in COVID-19 intensity by race and income. In our first post, we looked at how comorbidities, uninsurance rates, and health resources may help to explain the race and income gap observed in COVID-19 intensity. We found that a quarter of the income gap and more than a third of the racial gap in case rates are explained by health status and system factors. In this post, we look at two factors related to indoor density—namely public transportation use and home crowding. Here, we will aim to understand whether these two factors affect overall COVID-19 intensity, whether the income and racial gaps of COVID-19 can be further explained when we additionally include these factors, and whether and to what extent these factors independently account for income and racial gaps in COVID-19 intensity (without controlling for the factors considered in the other posts in this series).
Ruchi Avtar, Rajashri Chakrabarti, and Maxim Pinkovskiy
Our previous work documents that low-income and majority-minority areas were considerably more affected by COVID-19, as captured by markedly higher case and death rates. In a four-part series starting with this post, we seek to understand the reasons behind these income and racial disparities. Do disparities in health status translate into disparities in COVID-19 intensity? Does the health system play a role through health insurance and hospital capacity? Can disparities in COVID-19 intensity be explained by high-density, crowded environments? Does social distancing, pollution, or the age composition of the county matter? Does the prevalence of essential service jobs make a difference? This post will focus on the first two questions. The next three posts in this series will focus on the remaining questions. The posts will follow a similar structure. In each post, we will aim to understand whether the factors considered in that post affect overall COVID-19 intensity, whether the racial and income gaps can be further explained when we additionally include the factors in consideration in that post, and whether and to what extent the factors under consideration in that post independently affect racial and income gaps in COVID-19 intensity (without controlling for the factors considered in the other posts in this series).
Rajashri Chakrabarti, Maxim Pinkovskiy, Will Nober, and Lindsay Meyerson
Does health insurance improve health? This question, while apparently a tautology, has been the subject of considerable economic debate. In light of the COVID-19 pandemic, it has acquired a greater urgency as the lack of universal health insurance has been cited as a cause of the profound racial gap in coronavirus cases, and as a cause of U.S. difficulties in managing the pandemic more generally. However, estimating the effect of health insurance is difficult because it is (generally) not assigned at random. In this post, we approach this question in a novel way by exploiting a natural experiment—the adoption of the Affordable Care Act (ACA) Medicaid expansion by some states but not others—to tease out the causal effect of a type of health insurance on COVID-19 intensity.
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