Staff StudiesFederal Deposit Insurance Corporation 

Staff Studies 

Report No. 2022-03 

Severe Weather Events and Local 

Economic and Banking Conditions 

www.fdic.gov/cfr • @FDICgov • #FDICCFR • #FDICResearch 

June 2022 

Severe Weather Events and Local Economic and Banking Conditions 

John Anderlik; Alan Bush; Richard Cofer, Jr.; Kathy Kalser; and John McGee* 

Abstract 

This paper summarizes a limited study conducted by the Federal Deposit Insurance Corporation (FDIC) in 2021 to analyze the 

net effect that six of the most severe weather events over the past two decades had on local economic conditions, community 

bank performance, and the structure of the local banking landscape. The study analyzes economic and banking performance 

before and after each event, including the relative performance of low- and moderate-income (LMI) areas and community 

banks headquartered in the local area. The study concludes that the severe weather events affected local communities and 

banks differently. Studied areas with more vibrant economies ahead of a weather event generally recovered within a twoyear period, while areas with struggling economies ahead of a weather event had more difficulty recovering. Government 

assistance, insurance proceeds, and other aid were instrumental in reducing the financial consequences of the severe 

weather events on community banks. No banks headquartered in any of the event areas failed during the study period. This 

paper concludes with suggestions for further research. 

*The views expressed are those of the authors and do not necessarily reflect the official positions of the Federal Deposit Insurance Corporation or the

United States. 

1 
In 2021 the FDIC performed a study and related research to assess the net effects of specific, severe weather events on 

local economic conditions, community bank performance, and the structure of the local banking landscape.1 The study 

analyzes six of the most severe weather events in U.S history: three of the costliest hurricanes in history (Katrina, Harvey, and 

Irma); the strongest drought in 50 years, which significantly affected several Midwest states; and the two costliest wildfires 

in history (Camp and Tubbs). The study assesses quarterly economic and banking performance for the two years following 

each weather event and compares them to results for the two years before each event. The study also compares the relative 

economic performance of low- and moderate-income (LMI) areas and community banks headquartered in these areas before 

and after each weather event, when possible.2 

The study has several limitations. First, its scope is limited. In any given year, numerous severe weather events in the 

United States cause damages in excess of $1 billion. This study focuses on six such events over the past two decades. 

Although small in number, the events were extraordinarily damaging and costly. Second, the study is historical; if weather 

events worsen in the coming years as many climate experts predict, the ability to draw conclusions from past weather events 

may become less reliable. Third, the study focuses only on discrete severe weather events, which represent a subset of 

physical risks associated with climate change, and the study does not assess potential transition risks. 

Physical risks generally refer to the financial losses resulting from harm to people and property arising from acute, 

climate-related events, such as hurricanes, wildfires, floods, and heatwaves, and chronic shifts in climate, including higher 

average temperatures, changes in precipitation patterns, sea level rise, and ocean acidification. Transition risks refer to 

stresses to certain financial institutions or sectors arising from the shifts in policy, consumer and business sentiment, or 

technologies associated with the process of adjusting toward a low-carbon economy. This discussion of climate-related 

financial risks focuses only on a subset of physical risks to communities and banks arising from certain historical weatherrelated events. Because transition risks are longer-term, prospective risks, they are beyond the scope of this retrospective 

review. 

Hurricanes 

Climate-change literature frequently focuses on hurricanes because of their destructive power. In this analysis, we study 

the effects of three of the top five costliest hurricanes in U.S. history—Katrina, Harvey, and Irma—that struck hurricane-prone 

areas along the Gulf Coast.3 

I. Katrina (August 2005)

Katrina made landfall on August 29, 2005, in Louisiana and Mississippi as a Category 3 hurricane after achieving Category 

5 status in the Gulf of Mexico.4 Katrina was notable for its storm surge, which reached as high as 28 feet. Floodwalls and 

levees were breached in New Orleans, causing 80 percent of the city to be flooded by as much as 20 feet of water within a 

1

 This study was originally conducted in mid-2021. The event window of the analysis included two years after the event, so the most recent economic and 

banking data used are from 2020. The authors revisited the analysis and the data in May 2022 and determined that the paper’s conclusions are still valid. 

Community banks are used in this paper because, by definition, they have a limited geographic scope and would be more likely to be affected by a local 

disaster than banks with a wider scope. The criteria for defining “community banks” was originally developed in the 2012 FDIC Community Banking Study: 

https://www.fdic.gov/resources/community-banking/report/2012/2012-cbi-study-full.pdf. 

2

 The Community Reinvestment Act defines “low- and moderate-income (LMI) area” as an assessment area in which, relative to the metropolitan statistical, 

division, or statewide nonmetropolitan areas, the median family income is below 80 percent. For a deeper description of this term, see Ben Horowitz, 

“Defining ‘Low- and Moderate-Income’ and ‘Assessment Area’,” Federal Reserve Bank of Minneapolis, March 8, 2018, https://www.minneapolisfed.org/ 

article/2018/defining-low--and-moderate-income-and-assessment-areas#:~:text=LMI%20census%20tracts%20are%20identifiable,nonmetropolitan%20

area%20median%20family%20income. The two wildfires examined in this study did not affect any LMI counties as defined in this paper. See Appendix 1 for 

the study’s methodology and definitions, including the definition of an “LMI county.” Appendix 2 provides details on the areas associated with each weatherrelated event. 

3

 Since mid-2021, Hurricane Ida surpassed Irma as the fifth most costly hurricane. For details on how event areas were selected for this study, see Appendix 1.

4

 Unless otherwise noted, all data in this paragraph are from Richard D. Knabb, Jamie R. Rhome, and Daniel P. Brown, “Tropical Cyclone Report, Hurricane 

Katrina, 23-30 August 2005,” December 20, 2005, National Oceanic and Atmospheric Administration (NOAA) National Hurricane Center.

2 
day of the hurricane’s landfall. Some areas remained flooded for six weeks. The flood destroyed entire neighborhoods in 

New Orleans and severely damaged coastline communities in Mississippi. Katrina resulted in the loss of approximately 1,800 

lives. More than one million homes were damaged or destroyed and three million residents registered for Federal Emergency 

Management Agency (FEMA) assistance.5 At the time, Katrina was the third-deadliest hurricane to make landfall in the United 

States since 1900. At an inflation-adjusted $176 billion, Katrina is the most costly hurricane in U.S. history.6 

Although Katrina affected five states in all, our analysis focuses on the five counties in Louisiana and Mississippi where the 

hurricane made landfall. Map 1 shows the five counties that comprise the event area in this analysis: one LMI county (Orleans 

Parish in the New Orleans, Louisiana, metro area) and four non-LMI counties (one in Louisiana and three in Mississippi). 

Map 1 

Hurricane Katrina Severely Affected Several Counties in Louisiana and Mississippi

County Designations 

Non-LMI

LMI

Sources: FEMA and FFIEC. 

Note: LMI is a low- and moderate-income county. LMI counties are designated based on the populations of LMI census tracts within the 

county. 

Orleans Parish in Louisiana was suffering from a weak economy before the hurricane. In the four quarters that preceded 

Katrina, employment in the county declined 11 percent (or nearly 28,000 jobs). In contrast, employment in the non-LMI 

counties was relatively flat over those four quarters. Job losses in Orleans Parish were widespread, with six of eleven sectors 

losing at least 11 percent of their job base in the four quarters that preceded Katrina.7 

5

 U.S. Department of Housing and Urban Development, “A Look Back at Hurricane Katrina,” September 21, 2021, https://www.huduser.gov/portal/pdredge/ 

pdr-edge-frm-asst-sec-092121.html. 

6

 NOAA National Centers for Environmental Information, “Costliest U.S. Tropical Cyclones,” https://ncdc.noaa.gov/billions/dcmi.pdf. All hurricane cost data 

are based on 2021 Consumer Price Index adjusted cost. 

7

 The two sectors that lost the most jobs were education and health services (8,190 jobs) and leisure and hospitality (5,695 jobs). 

3 
-

Before the hurricane, the economy in the New Orleans metro area was lagging well behind national trends, and job 

opportunities were limited for many residents.8 At approximately $35,000, estimated median household income in the New 

Orleans metro area was well below the average of approximately $45,000 for the nation. Low-paying jobs in sectors such as 

leisure and hospitality were prevalent, and the metro had a much smaller share of higher-paying manufacturing jobs than 

the nation.9 

The combination of its weak economy, the high number of low-paying jobs, and the severity and duration of flooding 

distinguish Orleans Parish from the four non-LMI counties. Employment in Orleans Parish plunged 36 percent five quarters 

after the hurricane and had recovered only modestly eight quarters after the hurricane (Chart 1). In contrast, the four nonLMI counties lost just 3.6 percent of their jobs four quarters after the hurricane, and eight quarters after the hurricane these 

counties had added 4.3 percent to their pre-event employment level. Population also plummeted during this period. Eight 

quarters after Katrina, population in Orleans Parish was 45.6 percent below its pre-event level. In the non-LMI counties, 

population was down just 2.9 percent by the same point. The damage to Orleans Parish lasted well beyond the study period; 

as of first quarter 2021, total employment and population in the county still had not returned to pre-Katrina levels.10 

Chart 1 

LMI Areas Lost One Third of Jobs After Katrina

Employment 

Indexed, 2005Q2=100 

120 

LMI Area 

110 Non-LMI Area 

100 

90 

80 

70 

60 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

2003Q2 2005Q2 2007Q2 

Source: Bureau of Labor Statistics. 

Note: Dotted line represents the quarter end before the weather event took place. LMI is a low- and moderate-income county. Data are 

quarterly. 

Katrina adversely affected community banks in the event area, especially the LMI area of Orleans Parish. One quarter after 

the event, the 12 community banks headquartered in Orleans Parish reported a spike in past-due and nonaccrual (PDNA) loan 

ratios from 1.8 percent to 10.1 percent (Chart 2). In contrast, the average PDNA loan ratio among the ten community banks in 

non-LMI counties rose from 1.7 percent to 4.2 percent in the quarter after the event. The increase in PDNA loan ratios among 

8

 Michael L. Dolfman, Solidelle Fortier Wasser, and Bruce Bergman, “The Effects of Hurricane Katrina on the New Orleans Economy,” Monthly Labor Review, 

June 2007, p. 3, https://www.bls.gov/opub/mlr/2007/06/art1full.pdf. 

9

 Based on Bureau of Labor Statistics data, Moody’s Analytics creates “Wage Tier” employment categories. According to Moody’s, the number of high-wage 

earners in the New Orleans metro area peaked in 1982. Almost 30 percent of those jobs were gone by the time Katrina made landfall, and almost half were 

gone a decade after the hurricane.

10 As of first quarter 2021, employment in Orleans Parish was 18.5 percent below the pre-Katrina level and population was 27.8 percent below the pre-Katrina level.

4 
Orleans Parish banks was widespread, with all but two banks reporting higher PDNA ratios in third quarter 2005 compared 

with second quarter 2005. Commercial real estate portfolios largely drove the increases. 

Through a combination of charge-offs and improving loan quality, banks in Orleans Parish reported declining PDNA ratios 

from fourth quarter 2005 through the end of the study period in second quarter 2007. Orleans Parish banks reported PDNA 

ratios of 3.4 percent in second quarter 2006, a year after the event, down nearly two-thirds from the peak. By first quarter 2007, 

seven quarters after the event, the PDNA ratios of Orleans Parish banks and banks in non-LMI counties converged once again. 

Chart 2 

LMI Area Community Banks Reported High Levels of Delinquent Loans Immediately After Katrina

Past-Due and Nonaccrual Loan Ratio 

Average, weighted by loans, percent 

12 

10 

LMI Area 

8 Non-LMI Area 

6 

4 

2 

0 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 

2003Q2 2005Q2 2007Q2 

Source: FDIC. 

Note: Dotted line represents the quarter end before the weather event took place. LMI is a low- and moderate-income county. Data are 

annualized quarterly. 

Orleans Parish bank earnings and loan growth metrics also diverged substantially from banks in non-LMI counties. The 

median return on assets (ROA) ratio at Orleans Parish banks fell from 0.94 percent in second quarter 2005 to negative 0.78 

percent in fourth quarter 2005. Banks in non-LMI counties reported a smaller decline in median ROAs in the two quarters 

following the event. Orleans Parish bank ROAs recovered in first quarter 2006 to reach the level of banks in non-LMI counties. 

In terms of total loans, on an indexed basis (with second quarter 2005, the quarter prior to Katrina, set to 100), Orleans Parish 

banks lost loan volume beginning three quarters after the event and recovered to an indexed level of 110.4 by the end of the 

study period in second quarter 2007. In contrast, loan volumes at banks in non-LMI counties rose consistently after the event, 

reaching 139.8 on an indexed basis by second quarter 2007. 

Branch offices in LMI and non-LMI areas diverged after the hurricane. Branch offices in Orleans Parish declined 7 percent 

between June 30, 2005 and June 30, 2007, while branches in non-LMI counties in the event area increased 18 percent over the 

same period. No banks failed in the parish or the non-LMI counties during the study period. 

Our study does not examine the extent to which federal assistance, insurance proceeds, and other forms of financial assistance 

contributed to the decline in PDNA ratios or the increase in median ROA ratios for banks in the areas affected by Katrina. 

5 
II. Harvey (August 2017)

Harvey made landfall on August 25, 2017, as a Category 4 hurricane near Rockport, Texas.11 Harvey was the most significant 

tropical cyclone rainfall event in U.S. history, both in scope and peak rainfall amounts. The highest storm total rainfall report 

from Harvey was more than 60 inches. The storm quickly weakened to a tropical storm but remained over land—primarily the 

Houston metro area—for two days, dropping large amounts of rain on the metro area. The meteorological impacts from Harvey 

lasted a record 117 hours. At least 68 lives were lost from the direct impact of Harvey and as many as 35 lives were lost due to 

indirect results of the storm. Almost 220,000 structures were damaged or destroyed by Harvey, and more than 895,000 residents 

registered for FEMA assistance.12 Harvey was the second-costliest hurricane in U.S. history at an inflation-adjusted $136 billion.13 

Harvey’s event area encompassed 37 counties, all in Texas, of which 3 were LMI counties (Map 2). Harris County 

dominates the LMI counties. Harris County is part of the Houston metro area and has a population of more than four million, 

making it the largest county by population in Texas. 

Map 2 

Hurricane Harvey Hit the Houston Metro Area and Much of the Texas Coastline 

County Designations 

Non-LMI

LMI

Sources: FEMA and FFIEC. 

Note: LMI is a low- and moderate-income county. LMI counties are designated based on the populations of LMI census tracts within the 

county. 

11 Unless otherwise noted, all data in this paragraph are from NOAA’s National Hurricane Center, “Tropical Cyclone Report, Hurricane Harvey,” May 9, 2018.

12 The Governor’s Commission to Rebuild Texas, “Eye of the Storm,” November 2018, https://www.rebuildtexas.today/wp-content/uploads/

sites/52/2018/12/12-11-18-EYE-OF-THE-STORM-digital.pdf. 

13 NOAA National Centers for Environmental Information, “Costliest U.S. Tropical Cyclones.” All hurricane cost data are based on 2021 Consumer Price Index 

adjusted cost. 

6 
Because Harris County and Orleans Parish are significant areas in terms of population, it is sensible to compare the LMI 

areas in the Katrina and Harvey events. Unlike Orleans Parish, which was performing poorly well ahead of Katrina, Harvey’s 

LMI counties were improving before the hurricane. The Houston metro area was recovering well from the oil price crash of 

2014–16, and Harris County showed modest job gains in the year preceding the event. 

Harvey’s impact on its LMI counties was far less substantial than Katrina’s. Job growth in Harvey’s LMI counties lagged 

that of non-LMI counties in the first year following the hurricane, but the pace of job growth in LMI counties improved 

significantly in the second year following Harvey (Chart 3). Harvey’s LMI counties added jobs at a 0.4 percent rate in the four 

quarters before Harvey; that rate increased to 1.0 percent in the four quarters following the event and 3.6 percent in the next 

four quarters. That trend contrasts sharply with the large job losses in Orleans Parish following Katrina. The output of Harvey’s 

LMI counties showed the same trend—modest growth for a year followed by more robust growth— again, in contrast to the 

experience of Orleans Parish. 

Non-LMI counties in the Harvey event area experienced solid job growth before Harvey that accelerated immediately 

following the event. In the four quarters before Harvey, non-LMI counties experienced job gains of 1.4 percent. In the four quarters 

following Harvey, employment in these counties grew 2.5 percent and then another 4.0 percent in the next four quarters. 

Chart 3 

Employment in LMI Counties Lagged Non-LMI County Growth for a Year After Harvey but Then Strengthened

Employment 

Indexed, 2017Q2=100 

120 

110 

100 

90 LMI Area

Non-LMI Area

80 

70 

60 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 

2015Q2 2017Q2 2019Q2 

Source: Bureau of Labor Statistics. 

Note: Dotted line represents the quarter end before the weather event took place. LMI is a low- and moderate-income county. Data are 

quarterly. 

Community banks in Harvey’s event area also fared much better than those in Katrina’s event area. Neither banks in LMI 

counties nor those in non-LMI counties reported a surge in PDNA loan ratios after Harvey (Chart 4). Loan delinquencies in LMI 

counties were lower than in non-LMI areas before Harvey but increased modestly a year after the event. Other financial ratios 

were relatively unaffected by Harvey. For instance, the ROA at banks in LMI areas increased in the first year after the hurricane 

but remained low. At banks in non-LMI counties, the median ROA was largely flat for two years following the event. However, 

our study does not examine the extent to which federal assistance insurance proceeds and other forms of financial assistance 

contributed to the vitality of banks in the areas affected by Harvey. 

7 
Chart 4 

Community Banks Did Not Report a Significant Increase in Delinquent Loans After Harvey

Past-Due and Nonaccrual Loan Ratio 

Average, weighted by loans, percent 

12 

10 

8 LMI Area 

6 Non-LMI Area 

4 

2 

0 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

2015Q2 2017Q2 2019Q2 

Source: FDIC. 

Note: Dotted line represents the quarter end before the weather event took place. LMI is a low- and moderate-income county. Data are 

annualized quarterly. 

III. Irma (September 2017)

Irma made landfall in the Florida Keys on September 10, 2017, as a Category 4 hurricane after making landfall in Cuba 

as a Category 5 storm.14 Irma tracked from the Florida Keys through the center of Florida before dissipating in Alabama. 

With maximum winds of 185 miles per hour, Irma became the strongest storm on record in the Atlantic Ocean outside of 

the Caribbean and Gulf of Mexico. In the United States, at least 10 lives were lost from the direct impact of Irma and 82 lives 

were lost due to indirect results of the storm. While structural damage was widespread throughout the state of Florida, 

approximately 25 percent of homes in the Florida Keys were destroyed and another 65 percent of homes suffered significant 

damage.15 At an inflation-adjusted $55 billion, Irma was the fifth-costliest hurricane in U.S. history.16 

Irma’s event area encompassed 49 counties in Florida, of which 11 were LMI counties (Map 3). 

Despite the large amount of damage that Irma caused across Florida, the hurricane had little effect on the state’s 

economy. For example, both LMI and non-LMI counties in the event area had similarly strong job markets before the 

hurricane, and the strong trends continued in the two years following the event (Chart 5). The same trends can be seen in 

gross domestic product (GDP) and unemployment rates, which continued to improve in both LMI and non-LMI counties after 

Irma. Total population continued to rise after the event but at a slower pace in LMI counties. 

Bank performance also seemed relatively unchanged by the hurricane. PDNA ratios at banks headquartered in LMI 

and non-LMI counties were similar before the event, and banks in LMI counties reported lower ratios during the two years 

following the hurricane (Chart 6). Median ROA ratios for the two sets of banks were similar for much of the study period. 

14 Unless otherwise noted, all data in this paragraph are from NOAA’s National Hurricane Center, “Tropical Cyclone Report, Hurricane Irma,” June 30, 2018.

15 FEMA, “Hurricane Irma in Florida,” December 2018, https://www.fema.gov/sites/default/files/2020-07/mat-report_hurricane-irma_florida.pdf. 

16 NOAA National Centers for Environmental Information, “Costliest U.S. Tropical Cyclones.” All hurricane cost data are based on 2021 Consumer Price Index 

adjusted cost. 

8 
Map 3 

Hurricane Irma Affected Most of Florida

County Designations 

Non-LMI

LMI

Sources: FEMA and FFIEC. 

Note: LMI is a low- and moderate-income county. LMI counties are designated based on the populations of LMI census tracts within the 

county. 

Chart 5

 Irma Did Not Substantially Affect Employment Levels in LMI or Non-LMI Counties

Employment 

Indexed, 2017Q2=100 

120 

110 

100 

90 LMI Area 

Non-LMI Area 80 

70 

60 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 

2015Q2 2017Q2 2019Q2 

Source: Bureau of Labor Statistics. 

Note: Dotted line represents the quarter end before the weather event took place. LMI is a low- and moderate-income county. Data are 

quarterly. 

9 
However, our study does not examine the extent to which federal assistance, insurance proceeds, and other forms of financial 

assistance contributed to the vitality of banks in the areas affected by Irma. 

Chart 6 

Community Bank Loan Portfolios in LMI and Non-LMI Counties Continued to Improve After Irma

Past-Due and Nonaccrual Loan Ratio 

Average, weighted by loans, percent 

12 

10 

8 

LMI Area 

6 Non-LMI Area 

4 

2 

0 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

2015Q2 2017Q2 2019Q2 

Source: FDIC. 

Note: Dotted line represents the quarter end before the weather event took place. LMI is a low- and moderate-income county. Data are 

annualized quarterly. 

Midwest Drought 

The 2012–2013 Midwest drought formed when several regional droughts combined into a nationwide drought.17 At its 

peak in September 2012, the drought encompassed two-thirds of the continental United States, making it the worst drought 

in more than 50 years.18 In the Central Great Plains, precipitation shortfalls during the summer of 2012 were the worst on 

record dating to 1895.19 

Drought has outsized effects on agricultural producers, and agricultural losses from this drought event were significant. 

This analysis focused on states in the Midwest, where banks have substantial exposures to agriculture. To be included in 

the event area, counties had to be in a “severe” drought or worse condition for much of 2012 and 2013.20 The event area 

encompassed 84 counties, of which 12 were LMI counties (Map 4). 

Economic conditions in the event area’s 12 LMI counties were weakening before the start of the drought, and the 

weakness persisted throughout the drought. At year-end 2011, total employment in the LMI counties was 26,200, down 

from 26,500 at year-end 2010. Total employment fell even further to 25,900 by the time the drought concluded at year-end 

2013. In contrast, employment levels in the event area’s 72 non-LMI counties continued to increase through the drought 

17 Richard R. Heim Jr., “A Comparison of the Early Twenty-First Century Drought in the United States to the 1930s and 1950s Drought Episodes,” Bulletin of the 

American Meteorological Society 98, no. 12 (2017), https://journals.ametsoc.org/view/journals/bams/98/12/bams-d-16-0080.1.xml. 

18 Heim (2017). 

19 M. Hoerling, J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. Schubert, and R. Seager, “Causes and Predictability of the 2012 Great Plains Drought,” 

Bulletin of the American Meteorological Society 95, no. 2 (2014). 

20 See Appendix 1 for more details on the selection criteria. 

10 
Map 4 

The 2012–2013 Drought Affected Many Rural Counties in Nebraska, Kansas, and Colorado

County Designations 

Non-LMI

LMI

Sources: U.S. Drought Monitor and FFIEC. 

Note: LMI is a low- and moderate-income county. LMI counties are designated based on the populations of LMI census tracts within the 

county. 

event (Chart 7). Two years following the drought, however, employment levels in LMI counties had improved substantially, 

nearly converging with employment levels in non-LMI counties. Trends in GDP and unemployment rates in LMI and non-LMI 

areas followed a pattern similar to employment, with LMI areas lagging non-LMI areas during the drought but improving 

substantially after the event. 

Population growth in LMI counties lagged relative to non-LMI counties before and during the drought event. Total 

population in LMI counties declined annually from 78,600 at year-end 2010 to 76,500 at year-end 2013, at which point population 

stabilized. Meanwhile, total population among non-LMI counties declined far more slowly throughout the study period. 

The drought had little adverse effect on community banks headquartered in the event area. PDNA ratios in both LMI and 

non-LMI counties were lower after the drought (2014) than before the drought (2011). Banks in LMI counties reported a slight 

decline in their PDNA ratio during the drought, while banks in non-LMI counties continued to report a trend of declining PDNA 

ratios that existed before the event (Chart 8). Government assistance likely played a key role in maintaining farmers’ financial 

health during the drought. 

11 
Chart 7 

LMI Areas Recovered Quickly After the Drought Ended

Employment 

Indexed, 2011=100 

120 

110 

100 

90 

80 

70 

60 

2010 2011 2012 2013 

Drought Event 

2012 Through 2013 

Source: Bureau of Labor Statistics. 

Note: LMI is a low- and moderate-income county. Data are annual. 

LMI Area 

Non-LMI Area 

2014 2015 

Chart 8 

Community Bank Loan Portfolios Continued to Improve After the Drought

Past-Due and Nonaccrual Loan Ratio 

Average, weighted by loans, percent 

12 

10 

8 

LMI Area 

6 Non-LMI Area 

4 

2 

0 

2010 2011 2012 2013 2014 2015 

Drought Event 

2012 Through 2013 

Source: FDIC. 

Note: LMI is a low- and moderate-income county. Data are annual. 

12 
Wildfires

The Camp and Tubbs wildfires, both of which affected northern California, are the two costliest wildfires in the nation 

in terms of estimated insured losses.21 California has the largest number of properties at risk of wildfire.22 With minimal 

precipitation during summer, vegetation and grass typically become very dry. Also, strong wind gusts that normally occur in 

northern California in the fall can accelerate or even trigger fires by causing falling debris. Finally, California’s large population 

and increased development near wildlands contribute to the rising prevalence and cost of wildfires in the state.23 In 2020, 

California accounted for 40 percent of the burned acreage in the nation, a disproportionately large share relative to its 

4 percent share of the nation’s land area.24 

I. Camp Wildfire (November 2018)

The Camp Wildfire occurred in Butte County, California, 170 miles northeast of San Francisco. The wildfire started in the 

northern part of the county and quickly spread south, causing significant damage in the town of Paradise. The Camp Wildfire 

lasted 17 days before it was fully contained. It ranks as the nation’s costliest wildfire with estimated insured losses of an 

inflation-adjusted $10.8 billion.25 The wildfire burned more than 153,000 acres, destroyed 18,800 structures, and resulted in 85 

fatalities.26 

An investigation by California’s Department of Forestry and Fire Protection (Cal Fire) concluded that the Camp Wildfire 

was caused by a broken electrical transmission line. While the wildfire was triggered by the faulty power line, the lack of 

rainfall, strong winds, and low humidity accelerated the spread of the fire. 27 Low precipitation contributed to dead shrubs 

around the bottom of trees that created a path for the wildfire to climb from the ground to the treetops, which intensified the 

fire. The area typically has five inches of rain by early autumn, but it had less than one-half inch of rain in the same period 

before the Camp Wildfire.28 

The Camp Wildfire affected Butte County. Since Butte County is not an LMI county, the analysis compared the county’s 

employment levels and banking conditions with those in two adjacent counties, Sutter and Yuba, which were not affected by 

the wildfire (Map 5).29 

21 Note that the wildfires are discussed in their order of severity, not chronologically.

22 Insurance Information Institute, https://www.iii.org/fact-statistic/facts-statistics-wildfires#Wildland%20fires. 

23 For human-caused wildfires, see National Park Service, “Wildfire Causes and Evaluations,” https://www.nps.gov/articles/wildfire-causes-and-evaluation.

htm. For dry California winds aiding wildfire spread, see Brandon McClung and Clifford F. Mass, “The Strong, Dry Winds of Central and Northern California: 

Climatology and Synoptic Evolution,” October 1, 2020, Weather and Forecasting 35, no. 5 (October 2020), https://journals.ametsoc.org/view/journals/ 

wefo/35/5/wafD190221.xml. For California’s seasonal climate, see Tom Di Liberto, “Hot, Dry Summer and Slow Start to Wet Season Primed California for 

November 2018 Fires,” November 15, 2018, NOAA, https://www.climate.gov/news-features/event-tracker/hot-dry-summer-and-slow-start-wet-seasonprimed-california-november. For movement to wildland and costs of wildfires, see American Academy of Actuaries Extreme Events and Property Lines 

Committee, “Wildfire – An Issue Paper: Lessons Learned from the 2017 to 2021 Events,” January 2022, https://www.actuary.org/sites/default/files/2022-02/

Wildfire.2022_.pdf. 

24 Nearly 18 percent of the nation’s wildfires and 40 percent of burned acreage in 2020 were in California. See National Interagency Fire Center, “National 

Report of Wildland Fires and Acres Burned by State,” https://www.predictiveservices.nifc.gov/intelligence/2020_statssumm/fires_acres20.pdf. For 

state area measurements, see the U.S. Census Bureau, “State Area Measurements and Internal Point Coordinates,” https://www.census.gov/geographies/ 

reference-files/2010/geo/state-area.html. 

25 Insurance Information Institute, “Facts + Statistics: Wildfires,” https://www.iii.org/fact-statistic/facts-statistics-wildfires#Wildland%20fires. Estimated 

insurance losses are adjusted to 2021 dollars. 

26 Cal Fire, https://www.fire.ca.gov/incidents/2018/11/8/camp-fire/. 

27 Cal Fire, “CAL FIRE Investigators Determine Cause of the Camp Fire,” press release, May 15, 2019, https://www.fire.ca.gov/media/5121/campfire_cause.

pdf. For weather conditions leading to fire, see National Weather Service, “Service Assessment: November 2018 Camp Fire,” January 2020, https://www. 

weather.gov/media/publications/assessments/sa1162SignedReport.pdf. 

28 NOAA National Centers for Environmental Information, https://www.ncdc.noaa.gov/cdo-web/. 

29 We found no LMI counties in the Camp Wildfire event area or the Tubbs Wildfire event area using the same methodology for determining LMI counties that 

was used for the analysis of hurricanes and drought. In lieu of LMI counties, the wildfire analysis evaluated economic and banking conditions in neighboring 

counties. See Appendix 1 for further details. 

13 
Map 5 

The Camp Wildfire Occurred in Butte County, in Northern 

California 

County Designations 

Wildfire Counties 

Comparison Counties 

Source: National Interagency Fire Center. 

Before the wildfire, economic activity in Butte 

County and the comparison counties followed a 

similar path of steady employment growth (Chart 

9).30 In the two years before the event, employment 

increased 3.4 percent in Butte County, slightly 

below the growth rate for the comparison 

counties. Immediately following the wildfire, 

employment in Butte County was stable, as 

cleanup efforts and construction activity to repair 

structural damage to the area’s infrastructure 

supported the county’s employment level. But 

after initial rebuilding activity, Butte County’s 

employment declined steadily.31 Two years after 

the wildfire, employment in Butte County was 

12.3 percent below its level before the wildfire. In 

contrast, employment in the comparison counties 

was up two years after the wildfire. Employment 

was up 4.8 percent in the year following the event 

in the comparison counties before declining at the 

start of the pandemic in 2020. 

Chart 9 

Employment in Butte County Weakened After the Initial Rebuilding Activity

Employment 

Indexed, 2018Q3=100 Pandemic 

120 

110 

100 

90 

80 Camp Wildfire Area 

Comparison Area 70 

60 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

2016Q3 2018Q3 2020Q3 

Source: Bureau of Labor Statistics. 

Note: Dotted line represents the quarter end before the wildfire event took place. Data are quarterly. 

30 Bureau of Economic Analysis/Moody’s Data Buffet. As of third quarter 2018, Butte County’s economic base, defined as the combination of GDP and 

employment base, was almost double that of comparison counties. Butte County’s GDP was $11.6 billion and its employment base was 83,000. The GDP of 

comparison counties was $6.6 billion and their employment base was 44,000. The population for Butte County was 230,000, nearly 25 percent larger before 

the wildfire than the comparison counties’ combined population of 175,000. 

31 Economic and Planning Systems, Inc. and Industrial Economics, Inc., “Final Report: Camp Fire Regional Economic Impact Analysis,” January 2021, 

https://3coreedc.org/wp-content/uploads/2021/03/Camp-Fire-Regional-Economic-Impact-Analysis-January-2021.pdf. 

14 
Trends in economic activity and population between Butte County and the comparison counties before and after the 

Camp Wildfire mirror employment trends, marked by deterioration in Butte County. In the two years before the wildfire, Butte 

County’s GDP rose 5.4 percent before contracting 4.5 percent during the two years after the Camp Wildfire. By comparison, 

economic activity in the comparison counties continued to grow after the wildfire but at a slower rate than before the event.32 

Similarly, population declined in Butte County following the wildfire but increased slightly in the comparison counties.33 

The effect of the Camp Wildfire on the town of Paradise was more severe. More than 85 percent of the town’s homes 

were destroyed and only a small fraction of homes have been rebuilt.34 Most of the town’s residents left and have not 

returned. The population in Paradise is less than 25 percent of its pre-fire level, down from almost 27,000 residents to about 

6,000 residents.35 While some of the town’s residents received insurance and other aid after the fire, many had difficulty 

renewing homeowners insurance and moved elsewhere. Some people who left Paradise relocated within Butte County, thus 

cushioning the effect of the Camp Wildfire on the county’s population and employment base.36 

Despite the damage from the Camp Wildfire, the two community banks headquartered in Butte County did not 

experience significant asset quality deterioration. Instead, these institutions performed similarly to the two community banks 

headquartered in the comparison counties (Chart 10).37 The PDNA rate for one community bank in Butte County peaked at 

0.8 percent in second quarter 2019, less than one year after the event. The PDNA rate at the other community bank in Butte 

Chart 10 

Asset Quality Ratios Among Community Banks Headquartered in Butte County and the Comparison Counties 

Remained Low Following the Camp Wildfire

Past-Due and Nonaccrual Loan Ratio 

Average, weighted by loans, percent Pandemic

12 

10 

8 

Camp Wildfire Area 6 

Comparison Area 4 

2 

0 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

2016Q3 2018Q3 2020Q3 

Source: FDIC. 

Note: Dotted line represents the quarter end before the wildfire event took place. Data are annualized quarterly. 

32 Bureau of Economic Analysis/Moody’s Data Buffet. GDP increased 6.8 percent in the comparison counties during the two years before the Camp Wildfire 

and slowed to 3.1 percent growth during two years after the wildfire.

33 Bureau of Economic Analysis/Moody’s Data Buffet. Two years after the wildfire, population in Butte County was 7.9 percent less than before the wildfire, 

while population in the comparison counties had increased by 1.9 percent. 

34 California Department of Finance, data as of January 1, 2021, https://www.dof.ca.gov/Forecasting/Demographics/Estimates/e-5/. 

35 California Department of Finance, data as of January 1, 2021. 

36 Peter Hansen, “Mapping a Displaced Population,” November 7, 2019, Chico State Today, https://today.csuchico.edu/mapping-a-displaced-population/. 

37 FDIC, Consolidated Reports of Condition and Income. This analysis includes two community banks with combined total assets of $476 million that were 

headquartered in Butte County as of third quarter 2018. Another bank was also headquartered in Butte County but is a noncommunity bank and was not included in 

this analysis. Two community banks were headquartered in the comparison counties as of third quarter 2018, with combined total assets of $481 million. 

15 
County remained well below 0.2 percent for the two years following the wildfire. Charge-off rates were very low or zero for the 

community banks headquartered in Butte County and the comparison counties through the entire study period. There was 

no discernable disruption to ROA for the community banks in Butte County or the comparison counties.38 

With the influx of insurance proceeds and aid to local residents and businesses, deposits held in bank branches in Butte 

County increased strongly after the Camp Wildfire. After rising 5.2 percent in the year before the wildfire, deposits increased 

36.8 percent in the year after the wildfire. Deposits held in branches in the comparison counties increased 2.3 percent in the 

year after the wildfire.39 

II. Tubbs Wildfire (October 2017)

The Tubbs Wildfire occurred about a year before the Camp Wildfire and was the nation’s costliest fire at that time. The 

Tubbs Wildfire started near Calistoga in the northern part of Napa County. Fueled by strong southerly winds, the fire quickly 

spread south to Sonoma County during the night. 

An investigation by Cal Fire determined that the Tubbs Wildfire was caused by a private electrical system next to a 

residence.40 Other studies suggest that strong winds either directly damaged power lines or caused trees or debris to fall on 

electrical systems, triggering the sparks that started the wildfire. The speed of the wildfire during the night, propelled by warm, 

strong winds typical to the area in the fall, contributed to fatalities and the spread of the wildfire into populated areas.41 Further, 

the summer of 2017 was at the time California’s warmest summer on record, resulting in dry vegetation that fueled the wildfire.42 

The city of Santa Rosa, in Sonoma County, incurred the most economic damage. Upon containment nearly three weeks 

after its start, the Tubbs Wildfire caused more than $9 billion in estimated insured losses, damaged nearly 5,600 structures, 

burned nearly 37,000 acres, and resulted in 22 fatalities.43 

The number of acres burned by the Tubbs Wildfire was one-quarter the acres destroyed in the Camp Wildfire, yet the 

estimated dollar cost was close to that of the Camp Wildfire. Property values and density of structures and homes destroyed 

in the Tubbs Wildfire were higher than the Camp Wildfire.44 

Like the Camp Wildfire, no LMI counties were affected by the Tubbs Wildfire, so the analysis used two adjacent 

comparison counties: Contra Costa and Solano (Map 6).45 

Employment in the event counties of Napa and Sonoma increased steadily after the Tubbs Wildfire and followed a 

similar path as the comparison counties (Chart 11). Combined, employment increased 1.9 percent in the year following the 

wildfire in Napa and Sonoma counties compared with 1.3 percent in the comparison counties. An increase in construction 

38 The ROA for the two Butte County community banks following the wildfire was steady and similar to the comparison group, and no bank reported an ROA 

lower than 1.36 percent following the wildfire. 

39 FDIC, Summary of Deposits. The sum of the deposits includes all branches in Butte County and in the comparison counties for the periods between June 

30, 2017 and June 30, 2018, and between June 30, 2018 and June 30, 2019. 

40 Cal Fire, “CAL FIRE Investigators Determine the Cause of the Tubbs Fire,” press release, January 24, 2019, https://www.fire.ca.gov/media/5124/tubbscause1v.pdf. 

41 Clifford F. Mass and David Ovens, “The Northern California Wildfires of 8–9 October 2017: The Role of a Major Downslope Wind Event,” Bulletin of the 

American Meteorological Society 100, no. 2 (February 1, 2019): 235-256, https://journals.ametsoc.org/view/journals/bams/100/2/bams-d-18-0037.1.xml. 

42 Debra Kahn and Anne C. Mulkern, “Scientists See Climate Change in California’s Wildfires,” Scientific American, October 12, 2017, https://www. 

scientificamerican.com/article/scientists-see-climate-change-in-californias-wildfires/; and NOAA National Centers for Environmental Information, https:// 

www.ncdc.noaa.gov/sotc/national/201708. 

43 Cal Fire, https://www.fire.ca.gov/incidents/2017/10/8/tubbs-fire-central-lnu-complex/; and Insurance Information Institute, “Facts + Statistics: Wildfires,” 

https://www.iii.org/fact-statistic/facts-statistics-wildfires. 

44 Insurance Information Institute, “Facts + Statistics: Wildfires;” and U.S. Forest Service, “Most California Fires Occur in Area of Wildland-Urban Interface with 

Less Fuel and More People,” press release, September 24, 2019, https://www.nrs.fs.fed.us/news/release/wui-interface-intermix. 

45 Bureau of Economic Analysis/Moody’s Data Buffet. The counties in the comparison area had combined population and employment levels that were twice 

the size of Napa and Sonoma counties. As of third quarter 2017, employment in the comparison counties was 513,300 jobs and population was 1.6 million 

people, compared with 278,500 jobs and a population of 641,500 people in Napa and Sonoma counties. 

16 
Map 6 

The Tubbs Wildfire Caused Damage in Napa and Sonoma 

Counties 

County Designations 

Wildfire Counties 

Comparison Counties 

Source: National Interagency Fire Center. 

jobs temporarily boosted employment in the affected 

counties as the area rebuilt.46 Economic activity in 

Napa and Sonoma and the comparison counties 

continued to grow following the wildfire. However, 

population declined slightly in Napa and Sonoma 

in the two years after the event while increasing 

modestly in the comparison counties.47 

While economic conditions in Butte County 

weakened following the Camp Wildfire, the Napa and 

Sonoma employment levels were not meaningfully 

affected by the Tubbs Wildfire. Several factors explain 

the difference in economic performance in the 

counties affected by the two wildfires. The Tubbs 

Wildfire area had a larger employment, population, 

and economic base than the Camp Wildfire area. 

Combined, the employment, population, and 

economic base in Napa and Sonoma counties prewildfire was nearly three times that of Butte County.48 Napa and Sonoma also had a greater share of higher-wage jobs than 

Chart 11 

Employment in Areas Affected by the Tubbs Wildfire Was Unchanged Relative to Comparison Counties

Employment 

Indexed, 2017Q3=100 

120 

110 

100 

90 Tubbs Wildfire Area 

80 Comparison Area 

70 

60 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 

2015Q3 2017Q3 2019Q3 

Source: Bureau of Labor Statistics. 

Note: Dotted line represents the quarter end before the wildfire event took place. Data are quarterly. 

46 Bureau of Labor Statistics. In the year after the Tubbs Wildfire, construction jobs in the event counties increased 18.9 percent. The share of construction jobs 

in the event counties increased from 6.4 percent in third quarter 2017 to 7.3 percent in third quarter 2018. 

47 Bureau of Economic Analysis/Moody’s Data Buffet. In the two years after the event, as of third quarter 2019, the combined GDP for Napa and Sonoma 

counties grew 2.6 percent. In the same period, GDP in the comparison counties increased 5.7 percent. From third quarter 2017 through third quarter 2019, the 

population for Napa and Sonoma counties fell 1.4 percent, while population in the comparison counties increased 0.8 percent. 

48 Bureau of Economic Analysis/Moody’s Data Buffet. As of third quarter 2017, the population of Napa and Sonoma counties was 641,500. Butte County’s 

population at the time of its event, third quarter 2018, was 230,400. The combined GDP for Napa and Sonoma counties was $40 billion in third quarter 2017 

compared with $11.6 billion for Butte County in third quarter 2018. As of third quarter 2017, Napa and Sonoma counties combined had 278,500 jobs; as of 

third quarter 2018, Butte County had 82,700 jobs. 

17 
the Camp Wildfire area, including professional services jobs.49 Napa and Sonoma counties were also more affluent than Butte 

County. As of 2017, household income in Napa and Sonoma was 1.6 times higher than Butte County, and the median home 

price was more than twice as high.50 The larger economic landscape helped insulate the economies of Napa and Sonoma 

from long-term repercussions, as many residents elected to rebuild their homes after the fire instead of leaving the area. 

Similar to banks in the Camp Wildfire area, community banks headquartered in the Tubbs Wildfire area did not 

report asset quality weakness following the wildfire (Chart 12).51 PDNA ratios remained low for the four community 

banks headquartered in Napa and Sonoma counties before and after the wildfire event, and the ratios were similar to 

the four community banks headquartered in the comparison counties. The net charge-off rate among community banks 

headquartered in the event area and comparison counties also remained low following the wildfire. 

Chart 12 

Asset Quality Ratios Among Community Banks in Napa, Sonoma, and the Comparison Counties Remained 

Favorable After the Tubbs Wildfire

Past-Due and Nonaccrual Loan Ratio 

Average, weighted by loans, percent

12 

10 

8 

6 Tubbs Wildfire Area 

Comparison Area 4 

2 

0 

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 

2015Q3 2017Q3 2019Q3 

Source: FDIC. 

Note: Dotted line represents the quarter end before the wildfire event took place. Data are annualized quarterly. 

Deposits held in branches in Sonoma County, which incurred the most dollar and structural damage from the Tubbs 

Wildfire, increased 12.6 percent in the year after the event as insurance proceeds and other aid flowed into the community. This 

deposit growth rate exceeded the 7.0 percent growth rate in the year preceding the wildfire.52 Deposits also rose in branches in 

the comparison counties following the wildfire but at a rate lower than deposits held in branches in Sonoma County. 

49 Bureau of Economic Analysis/Haver Analytics. As of 2019, the share of professional, scientific, and technical services jobs in Butte County was 4.6 percent, 

compared with 7.5 percent in Sonoma County. 

50 Bureau of the Census and Redfin/Haver Analytics. In 2017, the median household income was $49,000 in Butte County, $79,000 in Sonoma County, and 

$84,000 in Napa County. As of third quarter 2017, the median home price in the Chico metro area, contiguous with Butte County, was $293,000. The median 

home price in the Santa Rosa metro area, contiguous with Sonoma County, was $583,000. 

51 FDIC, Consolidated Reports of Condition and Income. As of third quarter 2017, one community bank was headquartered in Napa County and three 

community banks were headquartered in Sonoma County. Assets of the four banks totaled $3.5 billion. Four community banks were headquartered 

in the comparison counties with total assets of nearly $3.6 billion. Two noncommunity banks were headquartered in the affected counties and three 

noncommunity banks were headquartered in the comparison counties. 

52 FDIC, Summary of Deposits. The sum of the deposits includes all branches in Sonoma County and in the comparison counties for the periods between 

June 30, 2016 and June 30, 2017, and between June 30, 2017 and June 30, 2018. Deposits held in branches in Napa County rose 6.0 percent in the year after 

the wildfire similar to the deposit growth rate in the year preceding the wildfire. 

18 
Government and Insurance Support Provides Insulation 

Government assistance, federal disaster loan payments, insurance proceeds, and other sources of financial and 

economic support, risk mitigation strategies, and forbearance programs to borrowers in affected communities have helped 

insulate community banks from the negative effects of individual severe weather events.53 

Hurricanes: Federal support for communities, companies, and individuals affected by hurricanes tends to be 

substantial. A June 2016 Congressional Budget Office report stated that federal spending as a percentage of economic data 

for hurricanes from August 2005 to the present averaged 60 percent of the total economic damage.54 The same report said 

that total government spending for Katrina was nearly 75 percent of total economic damage.55 

Drought: Federal assistance likely mitigated much of the adverse economic effects of the drought across the 2012– 

2013 event area. In 2012, drought disaster assistance and drought-related crop insurance indemnity payments totaled 

approximately 6 percent of total U.S. crop cash receipts, the second-highest level in records back to 1970.56 Governmentsubsidized crop insurance policies paid out $720.6 million to drought-event counties in 2012 and $913.3 million to droughtevent counties in 2013.57 In addition, direct government payments to farmers in drought-event counties totaled $503.9 million 

in 2012 and $692.8 million in 2013.58 

Wildfires: Insurance companies paid $18.7 billion to victims of the two wildfires in this study, while FEMA paid out at 

least $102 million in direct assistance to victims and $744 million to state and local governments. In addition, a major utility 

company was found liable for starting several fires and has set aside a fund of $13.5 billion to resolve claims by individual 

victims related to these fires, including the two in this study. Our study does not examine the extent to which this financial 

assistance contributed to the resiliency of banks in the areas affected by these two wildfires. 

Conclusions 

The study’s results suggest that severe weather events can be economically damaging to a local area, particularly if the 

affected area’s economy is struggling before the event or has a smaller, less diverse economic base. Conversely, underlying 

economic strength before a negative weather event appears to contribute to the strength and speed of the recovery after the 

event. Specifically: 

• Differences in the economic performance of LMI counties and non-LMI counties were evident following 

Hurricane Katrina. 

• Hurricanes Harvey and Irma affected their LMI counties less than Hurricane Katrina, in part because of 

modest-to-strong economic growth before the hurricanes. 

• LMI areas fared worse economically than non-LMI areas during the two-year Midwest drought, but the trends

were in line with those preceding the event. The differences dissolved within a year after the drought ended. 

• The Camp Wildfire area had a less vibrant economy before the fire, and the fire affected the event area more 

than the Tubbs Wildfire affected its area. 

53 Our research into this area is not comprehensive. See our suggestions for further research into assistance that helps to insulate communities and banks 

from the effects of severe weather events.

54 Congressional Budget Office, “Potential Increases in Hurricane Damage in the United States: Implications for the Federal Budget,” June 2016, p. 24, Table 4, 

https://www.cbo.gov/sites/default/files/114th-congress-2015-2016/reports/51518-hurricane-damage.pdf. 

55 Ibid, p. 17, Table 3. 

56 Steven Wallander, Elizabeth Marshall, and Marcel Aillery, “Farmers Employ Strategies to Reduce Risk of Drought Damages,” Amber Waves, June 5, 2017, 

https://www.ers.usda.gov/amber-waves/2017/june/farmers-employ-strategies-to-reduce-risk-of-drought-damages/. 

57 USDA Risk Management Agency, Cause of Loss Historical Data Files, https://www.rma.usda.gov/SummaryOfBusiness/CauseOfLoss. Other federal 

government actions to assist farmers during extreme weather events include permitting emergency grazing of Conservation Reserve Program land, increased 

eligibility for low-cost emergency loans from the Farm Service Agency, and deferment of Farm Services Agency loan payments. See USDA Disaster Assistance 

Programs At a Glance, https://www.farmers.gov/sites/default/files/2021-09/fsa-disasterassistance-at-a-glance-sept.-2021.pdf. 

58 USDA/Moody’s Data Buffet.

19 
 

Except for Katrina, the events studied had only a modest effect on community bank performance and asset quality. 

This modest effect was likely due to the amount of government aid, insurance proceeds, and other sources of financial 

and nonfinancial assistance that helped insulate community banks in the affected areas from deterioration in financial 

performance measures, including profitability and asset quality. Three branch offices in the LMI area affected by Katrina 

closed in the two years following the hurricane; the other event areas did not experience this effect. No banks headquartered 

in the event areas failed during our study period. 

Suggested Future Research

This analysis studied the effects of significant severe weather events on community banks and LMI communities. Climatechange models predict increased frequency and severity of similar events. Consequently, the authors suggest future research 

in the areas below to illuminate the areas in the financial system that may absorb or transmit climate-related risks, the actions 

that banks or other financial agents take to mitigate or exacerbate risk, other sources of climate-related financial risk beyond 

the scope of this analysis, and bank responses to repeated climate events that may better reflect climate change predictions. 

• The role of insurance in climate-related financial risks at banks – Given that insurance firms (or other privatesector companies) absorb weather-related losses, future research might evaluate how climate change 

could affect the insurance industry. For example, research could explore the potential for rapidly increasing 

premiums, the decreasing insurability of its clients, or the potential fragility of firms’ ability to pay claims when 

faced with more costly disasters. 59 Future research might also examine how such changes may ultimately 

transmit to the banking sector. 

• The role of government programs and climate-related financial risks at banks – Direct government aid, loan

programs, and government insurance programs influence the extent to which banks realize losses associated 

with severe weather events. Future research may help quantify the extent to which government programs 

shield banks from climate-related financial risk or affect risk-taking incentives by banks and borrowers. 

• Bank responses to climate-related financial risks – Bank actions can mitigate or exacerbate their exposures to 

climate-related financial risks. Underwriting requirements, resale of loans on secondary markets, and other 

bank decisions can affect the climate-related financial risk exposures to banks, the transmission or insulation 

of those risks to elsewhere in the financial sector, and credit availability to LMI communities. Future research 

may help to quantify the extent to which bank actions affect risk outcomes. 

• Transition risk – This study focuses on certain physical risks resulting from severe weather events, but 

transition risks are another source of climate-related financial risks. The authors suggest that climate-related 

transition risks be explored further. Future research could determine how transition risks could be transmitted 

to communities and banks and which communities are most vulnerable to transition risk. 

• Cumulative risk – Though climate change is characterized by increases in the frequency and severity of 

weather-related events, this study focuses on areas harmed by individual severe weather events. The FDIC 

has not studied the effect of sequential severe weather events or the cumulative impact that climate-related 

financial risks could have on bank performance, asset quality, and the ongoing effectiveness of risk mitigation 

strategies. One potential area to study is the communities on the Louisiana coast that incurred damage from 

three major hurricanes—Laura, Ida, and Nicholas—between August 2020 and September 2021. 

59 For example, the insurance claims associated with Hurricane Andrew in 1992 resulted in the subsequent failure of seven U.S. property and casualty 

insurers. See Lynne McChristian, “Hurricane Andrew and Insurance: The Enduring Impact of an Historic Storm,” Insurance Information Institute, August 2012, 

p. 4, https://www.iii.org/sites/default/files/paper_HurricaneAndrew_final.pdf. 

20 
Appendix 1 – Methodology 

Event-area selection criteria: Specific criteria for each type of weather event studied determined the definition of the 

affected area. 

• Hurricanes: We selected three of the top-five costliest hurricanes in U.S. history (as of June 2021) in inflationadjusted dollars. 

◊ For Harvey and Irma, we used the Federal Emergency Management Agency’s (FEMA) Large Incident 

Claims Database to determine damage at the census-tract level. To ensure that we did not include 

areas with nominal damage, we eliminated the bottom one-third of the tracts in the database by the 

amount of damage. To aggregate the census-tract level data to the county level, we included any 

county that had one or more census tracts with the required level of damage. 

◊ We used a different methodology for Katrina because FEMA’s Large Incident Claims Database begins in 

2010, after Katrina occurred. Therefore, we used FEMA’s Disaster Declarations Database, which identified 

census tracts in which at least 80 percent of homes sustained damage. As with Irma and Harvey, if a 

county had one or more census tracts that met the criteria, it was included in the event area. 

• Drought: We chose the 2012–2013 drought because of its severity and length. Because drought has outsized 

impacts on agriculture, and because agriculturally exposed banks tend to be in the center of the country, 

we specifically studied states in the Midwest and the Great Plains. The National Oceanic and Atmospheric 

Administration (NOAA) produces a weekly U.S. Drought Monitor map that displays areas by their drought 

status: D1 (Moderate Drought), D2 (Severe Drought), D3 (Extreme Drought), and D4 (Exceptional Drought). To 

be included in the drought event area, a county could not be located in a metropolitan area and had to be 

in a Severe Drought or worse condition for both: 75 percent of the total weeks in the 2012 and 2013 summer 

growing seasons, and 75 percent of the 104 total weeks in 2012 and 2013.60 

• Wildfires: We selected the Camp and Tubbs wildfires because they were the most destructive in U.S. history 

in terms of both structures burned and insured losses. Data on structures damaged or destroyed are from the 

California Department of Forestry and Fire Protection (Cal Fire), and data on insured losses are from Aon via 

the Insurance Information Institute. 

◊ For selection into the “comparison area” for each fire, a county had to be adjacent to the wildfire area, 

had to not have been affected by the wildfire, and had to have similar population and banking activity 

to the affected counties. 

Banks: We studied community banks, as defined by the FDIC’s Community Bank Study, headquartered in counties in 

each event area, and community banks headquartered outside of the event area but which had at least half of their offices 

and deposits in the area.61 

County-level analysis versus census-tract level: Our initial goal was to perform this analysis at the census-tract level. 

However, we determined that the economic data available at the census tract level were either unavailable or unreliable on 

a quarterly basis. Much of the event data were available and reliable at the census-tract level, so we aggregated census-tract 

data to the county level to perform the economic analysis. All results in this study are at the county level. 

County LMI designations: Census tract income designations are from the Federal Financial Institutions Examination 

Council (FFIEC) Census Flat File based on the year of the event. For example, we based income designations for Hurricane 

Katrina on the 2005 FFIEC Income Data. We aggregated census tract populations to the county level. We summed the number 

of people living in LMI designated census tracts and expressed the total as a percentage of the county’s total population. 

We designated a county as an LMI county if it exceeded its respective state’s mean share of LMI population by at least one 

standard deviation. 

60 Because our focus was on the agricultural impacts of drought, it was reasonable to exclude the two counties in metropolitan areas that otherwise met 

these criteria. 

61 Only one community bank met the criteria for being included in this study because it had branches in the event areas, not its headquarters. 

21 
Study periods: The study periods for hurricanes and wildfires cover the eight quarters before the event and the eight 

quarters following the event. We define “time zero,” the time of the event, as the calendar quarter that immediately preceded 

the event. For example, third quarter data would be defined as time zero for an event that occurred in October, November, or 

December. Given its longer duration, we studied the drought event over a six-year period from 2010 through 2015, examined 

at year-ends. Year-ends 2010 and 2011 represent the two years before the event, year-ends 2012 and 2013 capture the event 

period, and year-ends 2014 and 2015 span the two-year period following the drought event. 

Appendix 2 

Event Areas Details 

Event 

Number of Counties Number of Banks 

Event Area LMI 

Non-LMI or 

Comparison 

Area Event Area LMI Counties 

Non-LMI or 

Comparison 

Area 

Hurricane Harvey 37 3 34 77 22 55 

Hurricane Irma 49 11 38 101 40 61 

Hurricane Katrina 5 1 4 22 12 10 

Midwest Drought 84 12 72 122 17 105 

Camp Wildfire 3 1 2 4 2 2 

Tubbs Wildfire 4 2 2 8 4 4 

Source: FDIC. 

Note: LMI is a low- and moderate-income county. Wildfire events did not contain LMI areas. Instead, adjacent non-LMI counties were 

selected as comparison counties. Number of banks is based on number of banks headquartered in designated area at the quarter end 

immediately preceding the quarter in which the event began. 

22