International Stock Markets’ Reactions to EU Climate Policy Shocks
While policies to combat climate change are designed to address a global problem, they are generally implemented at the national level. Nevertheless, the impact of domestic climate policies may spill over internationally given countries’ economic and financial interdependence. For example, a carbon tax charged to domestic firms for their use of fossil fuels may lead the firms to charge higher prices to their domestic and foreign customers; given the importance of global value chains in modern economies, the impact of that carbon tax may propagate across multiple layers of cross-border production linkages. In this post, we quantify the spillover effects of climate policies on forward-looking asset prices globally by estimating the impact of carbon price shocks in the European Union’s Emissions Trading System (EU ETS) on stock prices across a broad set of country-industry pairs. In other words, we measure how asset markets evaluate the impact of changes to the carbon price on growth and profitability prospects of the firms.
What Do Climate Risk Indices Measure?
As interest in understanding the economic impacts of climate change grows, the climate economics and finance literature has developed a number of indices to quantify climate risks. Various approaches have been employed, utilizing firm-level emissions data, financial market data (from equity and derivatives markets), or textual data. Focusing on the latter approach, we conduct descriptive analyses of six text-based climate risk indices from published or well-cited papers. In this blog post, we highlight the differences and commonalities across these indices.
Flood Risk Outside Flood Zones — A Look at Mortgage Lending in Risky Areas
In support of the National Flood Insurance Program (NFIP), the Federal Emergency Management Agency (FEMA) creates flood maps that indicate areas with high flood risk, where mortgage applicants must buy flood insurance. The effects of flood insurance mandates were discussed in detail in a prior blog series. In 2021 alone, more than $200 billion worth of mortgages were originated in areas covered by a flood map. However, these maps are discrete, whereas the underlying flood risk may be continuous, and they are sometimes outdated. As a result, official flood maps may not fully capture the true flood risk an area faces. In this post, we make use of unique property-level mortgage data and find that in 2021, mortgages worth over $600 billion were originated in areas with high flood risk but no flood map. We examine what types of lenders are aware of this “unmapped” flood risk and how they adjust their lending practices. We find that—on average—lenders are more reluctant to lend in these unmapped yet risky regions. Those that do, such as nonbanks, are more aggressive at securitizing and selling off risky loans.
Banks versus Hurricanes
The impacts of hurricanes analyzed in the previous post in this series may be far-reaching in the Second District. In a new Staff Report, we study how banks in Puerto Rico fared after Hurricane Maria struck the island on September 17, 2017. Maria makes a worst case in some respects because the economy and banks there were vulnerable beforehand, and because Maria struck just two weeks after Hurricane Irma flooded the island. Despite the immense destruction and disruption Maria caused, we find that the island’s economy and banks recovered surprisingly quickly. We discuss the various protections—including homeowners’ insurance, federal aid, and mortgage guarantees—that helped buttress the island’s economy and banks.
Flood‑Prone Basement Housing in New York City and the Impact on Low‑ and Moderate‑Income Renters
Hurricane Ida, which struck New York in early September 2021, exposed the region’s vulnerability to extreme rainfall and inland flooding. The storm created massive damage to the housing stock, particularly low-lying units. This post measures the storm’s impact on basement housing stock and, following the focus on more-at-risk populations from the two previous entries in this series, analyzes the attendant impact on low-income and immigrant populations. We find that basements in select census tracts are at high risk of flooding, affecting an estimated 10 percent of low-income and immigrant New Yorkers.
Small Business Recovery after Natural Disasters in the Fed’s Second District
A previous Liberty Street Economics post found that minority-owned small businesses in the Federal Reserve’s Second District have been particularly vulnerable to natural disasters. Here we focus on the aftermath of disasters (such as hurricanes, floods, wildfires, droughts, and winter storms) and examine disparities in the ability of these firms to reopen their businesses and access disaster relief. Our results indicate that while white- and minority-owned firms remain closed for similar durations, the latter are more reliant on external funding from government and private sources to cope with disaster losses.
How Do Natural Disasters Affect Small Business Owners in the Fed’s Second District?
In this post, we follow up on the previous Liberty Street Economics post in this series by studying other impacts of extreme weather on the real sector. Data from the Federal Reserve’s Small Business Credit Survey (SBCS) shed light on how small businesses in the Second District are impacted by natural disasters (such as hurricanes, floods, wildfires, droughts, and winter storms). Among our findings are that increasing shares of small business firms in the region sustain losses from natural disasters, with minority-owned firms suffering losses at a disproportionately higher rate than white-owned firms. For many minority-owned firms, these losses make up a larger portion of their total revenues. In a companion post, we will explore the post-disaster recovery of small firms in the Second District: how long do they remain closed and what are their sources of disaster relief?
Flood Risk and Firm Location Decisions in the Fed’s Second District
The intensity, duration, and frequency of flooding have increased over the past few decades. According to the Federal Emergency Management Agency (FEMA), 99 percent of U.S. counties have been impacted by a flooding event since 1999. As the frequency of flood events continues to increase, the number of people, buildings, and agriculture exposed to flood risk is only likely to grow. As a previous post points out, measuring the geographical accuracy of such risk is important and may impact bank lending. In this post, we focus on the distribution of flood risk within the Federal Reserve’s Second District and examine its effect on establishment location decisions over the last two decades.
How Do Banks Lend in Inaccurate Flood Zones in the Fed’s Second District?
In our previous post, we identified the degree to which flood maps in the Federal Reserve’s Second District are inaccurate. In this post, we use our data on the accuracy of flood maps to examine how banks lend in “inaccurately mapped” areas, again focusing on the Second District in particular. We find that banks are seemingly aware of poor-quality flood maps and are generally less likely to lend in such regions, thereby demonstrating a degree of flood risk management or risk aversion. This propensity to avoid lending in inaccurately mapped areas can be seen in jumbo as well as non-jumbo loans, once we account for a series of confounding effects. The results for the Second District largely mirror those for the rest of the nation, with inaccuracies leading to similar reductions in lending, especially among non-jumbo loans.
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.