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The New York Fed engages with individuals, households and businesses in the Second District and maintains an active dialogue in the region. The Bank gathers and shares regional economic intelligence to inform our community and policy makers, and promotes sound financial and economic decisions through community development and education programs.
James Conklin, W. Scott Frame, Kristopher Gerardi, and Haoyang Liu
Editor’s note: When this post was first published, the chart labels for “Non-Boom Counties” was incorrect; the labels have been corrected. (February 26, 12:00 pm)
The role of subprime mortgage lending in the U.S. housing boom of the 2000s is hotly debated in academic literature. One prevailing
narrative ascribes the unprecedented home price growth during the mid-2000s to an expansion in mortgage lending to subprime borrowers. This post, based on our recent working paper, “Villains or Scapegoats? The Role of Subprime Borrowers in Driving the U.S. Housing Boom,” presents evidence that is inconsistent with conventional wisdom. In particular, we show that the housing boom and the subprime boom occurred in different places.
In recent years there has been a lot of interest in the effect of income inequality (heterogeneity) on the economy, from both academics and policymakers. Researchers have developed Heterogeneous Agent New Keynesian (HANK) models that incorporate heterogeneity and uninsurable idiosyncratic risk into the New Keynesian models that have become a cornerstone of monetary policy analysis. This research has argued that heterogeneity and idiosyncratic risk change many features of New Keynesian models – the
transmission of conventional monetary policy, the forward guidance puzzle, fiscal multipliers, the efficacy of targeted transfers and automatic stabilizers, among others. However, the source of the difference between HANK and representative agent New Keynesian (RANK) models remains unclear. This is because HANK models are typically not analytically tractable, leaving it unclear what exactly is driving the results. To shed light on the macroeconomic consequences of heterogeneity, we develop a stylized HANK model that contains key features present in more complicated HANK models.
Large firms play an integral role in aggregate economic activity owing to their size and production linkages. Events specific to these large firms can thus have significant effects on the macroeconomy. Quantifying these effects is tricky, however, given the complexity of the production process and the difficulty in identifying firm-level events. The recent pause in Boeing’s 737 MAX production is a striking example of such an event or “shock” to a large firm. This post applies a basic framework that is grounded in economic theory to provide a back-of-the envelope calculation of how the “737 MAX shock” could impact U.S. GDP growth in the first quarter of 2020.
In our previous post, we presented evidence suggesting that labor market indicators provide the most reliable information for dating the U.S. business cycle. In this post, we further develop the case. In fact, the unemployment rate has provided an almost perfect record of distinguishing the beginning of recessions in the post-war U.S. economy. We also show that using more granular labor market data, such as by region or industry, also provides valuable information about the state of the business cycle.
Andrew Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw
The New York Fed’s Center for Microeconomic Data today released the Quarterly Report on Household Debt and Credit for the fourth quarter of 2019. Total household debt balances grew by $193 billion in the fourth quarter, marking a $601 billion increase in household debt balances in 2019, the largest annual gain since 2007. The main driver was a $433 billion annual upswing in mortgage balances, also the largest since 2007. Auto loan and credit card balances both increased by a brisk $57 billion last year, while student loan balances climbed by a more muted $51 billion, well below the $114 billion increase recorded in 2013—the fastest pace of growth for the series. The source for the Quarterly Report is the New York Fed’s Consumer Credit Panel—a panel data set that now spans twenty-one years, 1999-2019. The unique panel design allows us to identify new entrants to the credit market: as young people age into having credit reports and using credit products, they are “born” into the panel, enabling us to observe the credit behavior of young borrowers.
The study of the business cycle—fluctuations in aggregate economic activity between times of widespread expansion and contraction—is one of the foremost pursuits in macroeconomics. But even distinguishing periods of expansion and recession can be challenging. In this post, we discuss different conceptual approaches to dating the business cycle, study their past performance for the U.S. economy, and highlight the informativeness of labor market indicators.
Getting health insurance in America is intimately connected to choosing whether and where to work. Therefore, it should not be surprising that the U.S. health insurance market may influence, and be influenced by, the market for higher education—which itself is closely tied to the labor market. In this post, and the staff report it is based on, we investigate the effects of the largest overhaul of health insurance in the United States in recent decades—the Patient Protection and Affordable Care Act of 2010 (ACA) -- on college enrollment choices.
After the global financial crisis, regulatory changes were implemented to support financial stability, with some changes directly addressing capital and liquidity in bank holding companies (BHCs) and others targeting BHC size and complexity. Although the overall size of the largest U.S. BHCs has not decreased since the crisis, the organizational complexity of these same organizations has declined, with less notable changes being observed in their range of businesses and geographic scope (Goldberg and Meehl, forthcoming). In this post, we explore how different types of BHC risks—risks that can influence the probability that a BHC is stressed, as well as the chance of systemic implications—have changed over time. The results are mixed: Levels of most BHC risks tend to be higher than in the years immediately preceding the crisis, but are markedly lower than the levels seen during and immediately following the crisis.
Liberty Street Economics features insight and analysis from New York Fed economists working at the intersection of research and policy. Launched in 2011, the blog takes its name from the Bank’s headquarters at 33 Liberty Street in Manhattan’s Financial District.
The editors are Michael Fleming, Andrew Haughwout, Thomas Klitgaard, and Asani Sarkar, all economists in the Bank’s Research Group.
The views expressed are those of the authors, and do not necessarily reflect the position of the New York Fed or the Federal Reserve System.
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