On the Distributional Consequences of Responding Aggressively to Inflation
This post discusses the distributional consequences of an aggressive policy response to inflation using a Heterogeneous Agent New Keynesian (HANK) model. We find that, when facing demand shocks, stabilizing inflation and real activity go hand in hand, with very large benefits for households at the bottom of the wealth distribution. The converse is true however when facing supply shocks: stabilizing inflation makes real outcomes more volatile, especially for poorer households. We conclude that distributional considerations make it much more important for policy to take into account the tradeoffs between stabilizing inflation and economic activity. This is because the optimal policy response depends very strongly on whether these tradeoffs are present (that is, when the economy is facing supply shocks) or absent (when the economy is facing demand shocks).
Wealth Inequality by Age in the Post‑Pandemic Era
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.
The Effect of Inequality on the Transmission of Monetary and Fiscal Policy
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.
The Role of Educational Attainment in Household Debt and Delinquency Disparities
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.
Unequal Distribution of Delinquencies by Gender, Race, and Education
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.
Credit, Income, and Inequality
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.
Black and White Differences in the Labor Market Recovery from COVID‑19
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.