Potential Flood Map Inaccuracies in the Fed’s Second District
The National Flood Insurance Program (NFIP) flood maps, which designate areas at risk of flooding, are updated periodically through the Federal Emergency Management Agency (FEMA) and community efforts. Even so, many maps are several years old. As the previous two posts in the Extreme Weather series show, climate-related risks vary geographically. It is therefore important to produce accurate maps of such risks, like flooding. In this post we use detailed data on the flood risk faced by individual dwellings as well as digitized FEMA flood maps to tease out the degree to which flood maps in the Second District are inaccurate. Since inaccurate maps may leave households or banks exposed to the risk of uninsured flood damage, understanding map inaccuracies is key. We show that, when aggregated to the census tract level, a large number of maps do not fully capture flood risk. However, we are also able to show that updates do in fact improve map quality.
Transition Risks in the Fed’s Second District and the Nation
Climate change may pose two types of risk to the economy—from policies and consumer preferences as the energy system transitions to a lower dependence on carbon (in other words, transition risks) or from damages stemming from the direct impacts of climate change (physical risks). In this post, we follow up on our previous post that studied the exposure of the Federal Reserve’s Second District to physical risks by considering how transition risks affect different parts of the District and how they differentially affect the District relative to the nation. We find that, relative to other regions of the U.S., the economy of the Second District has considerably less exposure to fossil fuels. However, the cost of reducing even this relatively low economic dependence on carbon is still likely to be considerable.
Comparing Physical Risk: The Fed’s Second District versus the Nation
In this post, we discuss the climate-related risks faced by the Federal Reserve’s Second District and compare these with risks faced by the nation as a whole. The comparison helps contextualize the risks while framing them in the broader context of a changing climate at the national level. We show that the continental Second District—an area consisting of New York State, the twelve northern-most counties of New Jersey, and Fairfield County in Connecticut—faces fewer and less severe climate-related physical risks than the nation as a whole. However, the areas that comprise the Second District still rank somewhat high in key risks that include “heat stress.” This holds true especially for New York City.
Blog Series on the Economic and Financial Impacts of Extreme Weather Events in the Fed’s Second District
The frequency and ferocity of extreme weather events, such as flooding, storms, and deadly heat waves, have been on the rise in recent years. These climate events, along with human adaption to cope with them, may have large effects on the economy and financial markets. It is therefore paramount to provide research about the economy’s vulnerability to climate events for policymakers, households, financial institutions, and other players in the world economy to make informed decisions. In the coming days, we are going to present a series of nine posts that attempt to take a step in this direction while focusing on the Federal Reserve System’s Second District (NY, northern NJ, southwest CT, Puerto Rico, and the U.S. Virgin Islands). The twelve Federal Reserve Districts are depicted in this map.
How Has Treasury Market Liquidity Evolved in 2023?
In a 2022 post, we showed how liquidity conditions in the U.S. Treasury securities market had worsened as supply disruptions, high inflation, and geopolitical conflict increased uncertainty about the expected path of interest rates. In this post, we revisit some commonly used metrics to assess how market liquidity has evolved since. We find that liquidity worsened abruptly in March 2023 after the failures of Silicon Valley Bank and Signature Bank, but then quickly improved to levels close to those of the preceding year. As in 2022, liquidity in 2023 continues to closely track the level that would be expected by the path of interest rate volatility.
Do Large Firms Generate Positive Productivity Spillovers?
Numerous studies have documented the rising dominance of large firms over the last few decades in many industrialized countries. Many research papers have focused on the potential negative effects of this increased market concentration, raising concerns about market power in both labor and product markets. In a new study, we investigate whether large firms also generate positive effects. Our research shows that large firms generate significant positive total factor productivity (TFP) spillovers to their domestic suppliers. To date, these types of spillovers have only been identified for multinational enterprises located in developing countries. Using firm-to-firm transaction data for an industrialized country, Belgium, we find that large domestic firms, as well as multinationals, generate positive TFP spillovers.
Spending Down Pandemic Savings Is an “Only‑in‑the‑U.S.” Phenomenon
Household saving soared in the United States and other high-income economies during the pandemic, as consumers cut back on spending while government policies supported incomes. More recently, saving behavior has diverged, with the U.S. saving rate dropping below its pre-pandemic average while saving rates elsewhere have remained above their pre-pandemic averages. As a result, U.S. consumers have been spending down the “excess savings” built up during the pandemic while the excess savings abroad remain untapped. This divergent behavior helps explain why U.S. GDP has returned to its pre-pandemic trend path even as GDP levels in other high-income economies continue to run well below trend.
Does Income Inequality Affect Small Firms?
The share of income going to high-income households has increased significantly in the United States in recent decades. In 1980, the average income share of earners in the top 10 percent was around 30 percent. However, by 2015, it had surpassed 45 percent. The employment share of small firms has also declined, with a decrease of approximately 5 percentage points over the same period. In this post, we use variation across states to show a correlation between these two developments, with states having the greatest increase in the upper income share also tending to be those with the biggest job creation declines in small firms compared to large firms. One explanation for this correlation is that the increase in the income share of the highest income earners reduced deposits in small and medium-size banks from what they otherwise would have been. In doing so, this shift in income reduced the available credit for small firms, putting them at a disadvantage relative to large firms.
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