Editor’s note: Since this post was first published, percentages cited in the first paragraph have been corrected. (February 7, 1pm)
Following our post on racial and ethnic wealth gaps, here we turn to the distribution of wealth across age groups, focusing on how the picture has changed since the beginning of the pandemic. As of 2019, individuals under 40 years old held just 4.9 percent of total U.S. wealth despite comprising 37 percent of the adult population. Conversely, individuals over age 54 made up a similar share of the population and held 71.6 percent of total wealth. Since 2019, we find a slight narrowing of these wealth disparities across age groups, likely driven by expanded ownership of financial assets among younger Americans.
Wealth is unevenly distributed across racial and ethnic groups in the United States. In this first post in a two-part series on wealth inequality, we use the Distributional Financial Accounts (DFA) to document these disparities between Black, Hispanic, and white households from the first quarter of 2019 to the third quarter of 2023 for wealth and a variety of asset and liability categories. We find that these disparities have been exacerbated since the pandemic, likely due to rapid growth in the financial assets more often held by white individuals.
The New York Fed recently released its latest set of Equitable Growth Indicators (EGIs). Updated quarterly, the EGIs continue to report demographic and geographic differences in inflation, earnings (real and nominal), employment, and consumer spending (real and nominal) at the national level. This release also launches a set of national wealth EGIs (which will be examined more closely on Liberty Street Economics early next year). Going forward, EGI releases will also include a set of regional EGIs, which will present disparities in inflation, earnings (real and nominal), employment, and consumer spending (real and nominal) in our region. Drawing on the just released EGIs, in this post, we present recent gender gaps in the labor market at the national and regional levels. We provide a picture of how gender wage and employment disparities have evolved since the pandemic, examining and contrasting gaps at the national and regional level. We find that the gaps between the employment rates and earnings of men and women have declined steadily following the pandemic, but have declined perceptibly more so in our region than in the nation.
As inflation has risen to forty-year highs, inflation inequality—disparities in the rates of inflation experienced by different demographic and economic groups– has become an increasingly important concern. In this three-part blog series, we revisit our main finding from June—that inflation inequality has increased across racial and ethnic groups—and provide estimates of differential inflation rates across groups based on income, education, age, and geographic location. We also use an updated methodology for computing inflation disparities by focusing on more disaggregated categories of spending, which corroborates our earlier findings and substantiates our conclusion that inflation inequality is a pronounced feature of the current inflationary episode.
Monetary policy can have a meaningful impact on inequality, as recent theoretical and empirical studies suggest. In light of this, how should policy be conducted? And how does inequality affect the transmission of monetary policy? These are the topics covered in the second part of the recent symposium on “Heterogeneity in Macroeconomics: Implications for Policy,” hosted by the new Applied Macroeconomics and Econometrics Center (AMEC) of the New York Fed on November 12.
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.
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.
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.
This is the second post in a series that aims to understand the gap in COVID-19 intensity by race and income. In our post yesterday, 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 the use of public transportation and increased 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 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).