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.
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.
At the end of March, we launched the Weekly Economic Index (WEI) as a tool to monitor changes in real activity during the pandemic. The rapid deterioration in economic conditions made it important to assess developments as soon as possible, rather than waiting for monthly and quarterly data to be released. In this post, we describe how the WEI has measured the effects of COVID-19. So far in 2020, the WEI has synthesized daily and weekly data to measure GDP growth remarkably well. We document this performance, and we offer some guidance on evaluating the WEI’s forecasting abilities based on 2020 data and interpreting WEI updates and revisions.
Uyanga Byambaa, Beverly Hirtle, Anna Kovner, and Matthew Plosser
Supervision and regulation are critical tools for the promotion of stability and soundness in the financial sector. In a prior post, we discussed findings from our recent research paper which examines the impact of supervision on bank performance (see earlier post How Does Supervision Affect Banks?). As described in that post, we exploit new supervisory data and develop a novel strategy to estimate the impact of supervision on bank risk taking, earnings, and growth. We find that bank holding companies (BHCs or “banks”) that receive more supervisory attention have less risky loan portfolios, but do not have lower growth or profitability. In this post, we examine the benefits of supervision over time, and especially during banking industry downturns.
The COVID-19 outbreak has sparked urgent questions about the impact of pandemics, and associated countermeasures, on the real economy. Policymakers are in uncharted territory, with little guidance on what the expected economic fallout will be and how the crisis should be managed. In this blog post, we use insights from a recent research paper to discuss two sets of questions. First, what are the real economic effects of a pandemic—and are these effects temporary or persistent? Second, how does the local public health response affect the economic severity of the pandemic? In particular, do non-pharmaceutical interventions (NPIs) such as social distancing have economic costs, or do policies that slow the spread of the pandemic also reduce its economic severity?
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” were 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.
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.
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.
Rajashri Chakrabarti, Michelle Jiang, and William Nober
In an earlier post, we studied how educational attainment affects labor market outcomes and earnings inequality. In this post, we investigate whether these labor market effects were preserved across the last business cycle: Did students with certain types of educational attainment weather the recession better?
Andrew F. Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw
The New York Fed’s Center for Microeconomic Data today released our Quarterly Report on Household Debt and Credit for the fourth quarter of 2017. Along with this report, we have posted an update of state-level data on balances and delinquencies for 2017. Overall aggregate debt balances increased again, with growth in all types of balances except for home equity lines of credit. In our post on the first quarter of 2017 we reported that overall balances had surpassed their peak set in the third quarter of 2008—the result of a slow but steady climb from several years of sharp deleveraging during the Great Recession.
This series examines the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (FRBNY DSGE) model—a structural model used by Bank researchers to understand the workings of the U.S. economy and provide economic forecasts.
The severe recession experienced by the U.S. economy between December 2007 and June 2009 has given way to a disappointing recovery. It took three and a half years for GDP to return to its pre-recession peak, and by most accounts this broad measure of economic activity remains below trend today. What precipitated the U.S. economy into the worst recession since the Great Depression? And what headwinds are holding back the recovery? Are these headwinds permanent, calling for a revision of our assessment of the economy’s speed limit? Or are they transitory, although very long-lasting, as the historical record on the persistent damages inflicted by financial crisis seems to suggest? In this post, we address these questions through the lens of the FRBNY DSGE model.
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.
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