Extreme Wildfres, Distant Air Pollution, and

Household Financial Health

Xudong An, Stuart A. Gabriel and Nitzan Tzur-Ilan

March 2024

2024 FDIC Consumer Research Symposium

Disclaimer: The views expressed here are solely those of the authors and do not represent

those of the Federal Reserve Bank of Philadelphia, Federal Reserve Bank of Dallas, or the

Federal Reserve System. 1
Wildfre destruction has increased over time

▶ Since 2000 the U.S. has witnessed 15 wildfres causing more than a $1

billion in damage

▶ The U.S. has spent more than $1 billion per year to fght wildfres,

including $1.6 billion in 2019 (NOAA, 2021)

▶ Despite their growing severity, there remains limited literature on the

economic efects of wildfres

2
Wildfres result in widely difused smoke and pollution

▶ The efects of wildfre smoke are typically ignored in estimates of

overall damage from wildfres

▶ Wildfre smoke is the primary source of elevated air pollution

▶ Air pollution has negative efects on human health, the labor market,

educational attainment and earnings later in life

▶ The health and income efects may impact household fnancial health

▶ The adverse smoke and pollution estimates are salient to a substantial

geographically dispersed population

3
This Paper

▶ Explores the efects of extreme wildfre, smoke, and air

pollution events on housing and credit outcomes

▶ We fnd an increase in out-migration from fre zones, house

price declines, and an increase in household credit distress

▶ We also fnd higher levels of fnancial distress among burn

area renters with lower credit scores

▶ We provide evidence of elevated spending, indebtedness,

and loan delinquencies among households distant from the

fre but exposed to high smoke and pollution levels

▶ Households living outside of fre zones are not insured

against the risks associated with smoke and pollution

4
Data

▶ Extreme Wildfres: data on property damage from the US

National Incident Command System (St Denis et al., 2020)

▶ wildfres destroyed 1000s of structures

▶ linked to Monitoring Trends in Burn Severity datafootprints of wildfre burn perimeters (Eidenshink et al., 2007)

▶ Wildfre Smoke Data: produced by NOAA using

Geostationary Operational Environmental Satellite (Miller et al.,

2021, Ruminski et al., 2006)

▶ Pollution Data: ground monitor readings for U.S. EPA and

data from Stanford ECHO Lab

▶ Economic and Household Financial Data: CoreLogic,

Equifax Consumer Credit Panel, CRISM, Y-14 datasets

5
List of Extreme Wildfres in the U.S. Between 2016-2020

Fire Name Destroyed Structures Date State

CAMP 17,764 11/8/2018 CA

Central LNU Complex 6,862 10/9/2017 CA

Glendale 3,000 1/29/2016 OK

North Complex 2,288 8/17/2020 CA

Chimney Tops 2,018 11/23/2016 TN

CARR 1,610 7/23/2018 CA

LNU Lightning Complex 1,469 8/17/2020 CA

CZU AUG Lightning 1,329 8/16/2020 CA

Beachie Creek 1,292 8/16/2020 OR

Glass 1,198 9/27/2020 CA

Thomas 1,053 12/4/2017 CA

Notes: This table lists the extreme wildfres (which destroyed over 1,000

structures) in the United States in 2016-2020. Source: St Denis et al. (2020).

6
Extreme Wildfres in CA and the Diferent Peripheral Rings

▶ The geographic location of the extreme wildfres (with more than 1,000

destroyed structures) in CA between 2016 and 2020

▶ The red area is the fre footprint, the brown area is the 1-mile peripheral

ring, the orange is 5 miles, and the yellow area is the 10-mile ring. 7
Treatment and Control Areas in the Camp Fire

Analyses

▶ The red area is the fre footprint, the brown area is the 1-mile peripheral

ring, the orange is 5 miles, and the yellow is the 10-mile ring.

▶ The border lines are census blocks in California.

8
Identifcation Strategy - the Efect of Wildfres

We employ a diference-in-diferences identifcation strategy:

Yc,t = β ∗ Firec,t ∗ Postc,t + τt + ζc + εc,t (1)

Where:

▶ Yc,t - migration patterns or house prices in census tract c in quarter t

▶ Firec,t represents a fre loss indicator (1 or 0)

▶ Postc,t represents a post-fre indicator (1 or 0)

▶ τt and ζc are quarter fxed efects and tract fxed efects

Consumer-level panel data from CCP and CRISM:

Yi,t = β ∗ Firei,t ∗ Posti,t + τt + ζi + εit, (2)

▶ Yi,t - outcome measure for individual i in time t (quarterly for CCP

and monthly for CRISM).

9
The Efect of the Camp Fire on In and Out Migration

1 2 3 4 5

Move-in Move-out Net migration

Treated × Post 0 vs 5 miles 1.97 19.2*** 17.7***

(2.87) (1.92) (5.27)

Treated×Post 0 vs 10 miles 9.28***

(2.23)

Treated×Post 0 vs 20 miles 3.2***

(1.29)

Census tract FE + + + + +

Year-qtr FE + + + + +

Observations 470 470 470 674 1,023

R-squared 0.49 0.47 0.15 0.11 0.10

Dependent variable mean 36.02 33.18 6.96 2.31 2.67

▶ Out migration for extreme wildfres (9 per 1K residents) campfre (19 per

1K residents)

▶ Most of the efect comes from out-migration

▶ Results inline with McConnell et al. (2021)

10
The Efect of 2018 Camp Fire on Migration

▶ Household migratory fows largely revert to pre-fre levels

▶ Our fndings are consistent with other papers showing similar the net

exit of the population in the wake of other climate-related natural

disasters (Black et al.,2011; Boustan et al., 2012; Gallagher and Hartley,

2017; Bleemer and van der Klaauw, 2019 and Deryugina et al., 2018)

11
The Efect of 2018 Camp Fire on House Prices

1 2 3 4

House Price Number of Repeated Residential

Index Transac- Sales Median Vacancy

tions Price Rate

Treated×Post -17.54*** -4.22*** -34,553.88*** 0.08***

(0.93) (1.84) (4,937.23) (0.01)

Census tract FE + + + +

Year-qtr FE + + + +

Observations 475 475 475 353

R-squared 0.84 0.80 0.75 0.56

Dependent variable 244.4 20.6 280,007 0.03

▶ Camp Fire caused a 17.5% decline in house prices in the fre zone

▶ In dollar terms, the Camp Fire caused a decline of $34.5k

▶ The efect decreases over time

▶ Larger efect for the Camp Fire

12
The Efect of Camp Fire on Financial Outcomes

1 2 3 4

Mortgage Credit Card Personal Loan Retail Card

Delinquency Delinquency Delinquency Delinquency

Treated×Post 0.02* 0.02*** 0.05* 0.02

(0.01) (0.01) (0.03) (0.02)

Consumer FE + + + +

Year-qtr FE + + + +

Observations 20,686 71,964 11,544 17,282

R-squared 0.54 0.77 0.74 0.73

Dependent variable 0.01 0.04 0.08 0.12

▶ Individual-level panel data from the CCP for eight pre-event quarters and

eight post-event quarters

13
Time dynamic: Camp Fire-related credit default rates

Panel A Panel B

Panel C Panel D

14
Possible Causes for the Financial Distress

1 2 3 4

∆ Spending ∆ Payment ∆ Balance ∆ Past Due

Treated × Post 1,112.313*** 1,557.085*** -1,889.632*** 0.043***

(96.845) (99.488) (194.097) (0.006)

Borrower attributes ✓ ✓ ✓ ✓

Account FE + + + +

Year-month FE + + + +

Observations 1,084,138 1,084,138 1,084,138 1,084,138

R-squared 0.064 0.039 0.261 0.255

Dependent variable -67.483 349.869 464.118 0.095

▶ Households reporting damage from an extreme wildfre event typically

receive an insurance payout

▶ The fre insurance payof might not be adequate to cover the expenses

related to the fre event.

15
Time dynamic: Camp Fire Efects on Spending and

Payment

Spending Over Time: Payment Over Time:

▶ The results also show a decline in the number of credit card

accounts, using both the CCP and CRISM datasets, for all the

fres.

Number of Accounts

16
Insurance Market

▶ Historically and during the time frame of this study, fre damage

was covered by homeowner’s insurance

▶ Homeowners pay down credit debt post-wildfre likely refects use

of insurance claims payout (consistent with food disaster

literature)

▶ Renters typically receive signifcantly less payout from fre

insurance and may experience work disruption and event-related

health expenses

▶ Specifcally, the Camp Fire caused property damage and work

disruptions; to cope with the adverse wildfre efects, consumers

spent more using their credit cards

17
Heterogeneous Efects of Camp Fire on Credit Card

Balance and Delinquency

Homeowners Renters

1 2 3 4

Credit Balance CS ≤ 720 CS > 720 CS ≤ 720 CS > 720

T reated × P ost

borrower attributes

-1,115.22

(845.81)

✓

-1,401.26***

(474.45)

✓

-522.10

(319.48)

✓

-195.61

(640.36)

✓

Consumer FE + + + +

Year-qtr FE + + + +

Observations

R-squared

637

0.98

4,358

0.90

3,009

0.97

1,528

0.92

Dependent variable 7,743.2 3,784.3 2,949.9 2,021.5

Delinquency CS ≤ 720 CS > 720 CS ≤ 720 CS > 720

T reated × P ost 0.01 0.00 0.09*** 0.00

(0.00) (0.00) (0.02) (0.00)

borrower attributes ✓ ✓ ✓ ✓

Consumer FE + + + +

Year-qtr FE + + + +

Observations 3,213 16,434 8,692 3,102

R-squared 0.80 0.60 0.73 0.08

Dependent variable 0.01 0.00 0.06 0.00

18
Daily Wildfre Smoke PM2.5 Across US, 2020

Source: Stanford Echo Lab

19
Wildfre Smoke Elevated PM2.5 After the Camp Fire

X20

PM2.5cd = βτ ∗ SmokeDayc,d+τ + αcdayofyear + αstateyear + εct, (3)

τ=−20

▶ Wildfres are widely recognized as major contributors to air pollution

▶ Burke et al. (2021) estimate that wildfres have been the source of up to 25%

of PM2.5 recorded in the U.S., and 50% of PM2.5 in some Western regions 20
Efects of Air Pollution on Borrower Credit Outcomes

▶ Among the most widely documented adverse efects of ambient

air pollution are those associated with health (Chay and Greenstone

(2003), Jayachandran (2009), Chen et al. (2013), Deryugina et al. (2019),

Anderson (2020))

▶ Smoke events may also lead to work interruption (Borgschulte et

al., 2022), increased trafc accidents (Matthews, 2018), and reduced

tourism and outdoor recreation (Stotts et al., 2018), resulting in

income loss and deterioration in household fnancial status

▶ Borgschulte et al. (2022) fnd that a day of county wildfre smoke

exposure reduces the quarterly mean earnings of $5,359

▶ Each day of wildfre smoke reduces quarterly county employment

by about 80 per million people aged 16 and older

Health and Spending

21
Efects of Wildfre-Induced Air Pollution on Household

Financial Outcomes

We employ a panel data model with fxed efects in a DiD

framework:

Yi,t = γ ∗ Pollutionz ∗ Af terf irez,t + Xi,tB⃗ + τt + ζi + εi,t, (4)

Where:

▶ Yit - outcome measure for individual/account i at time t

▶ Pollutionz - dummy variable that takes on the value of one if the

individual resides in zip code z that experienced pollution levels above

the 75th percentiles within four weeks of the fre

▶ Af terf ire - takes on the value of one after the fre event and zero

before the event.

▶ τt and ζi are time- and consumer/account-fxed efects.

▶ Outside of the fre zone, 5-30 miles, after the fre

22
IV Estimation

To ensure that variations in PM2.5 derive from fre-related

smoke, we adopt two approaches:

▶ Create a measure of fre-attributable air pollution by taking the

diference between fre month PM2.5 levels and baseline levels:

Y ⃗ i,t = γ ∗ ∆PM2.5z ∗ Af terf irez,t + Xi,tB + τt + ζi + εi,t (5)

▶ IV: we leverage the work of Childs et al. (2022), who use a

machine learning model to estimate smoke-driven pollutants

▶ We use their estimates of smoke PM2.5 and run the second stage of

our IV regression as:

Y \ ⃗ i,t = γ ∗ PM2.5z ∗ Af terf irez,t + Xi,tB + τt + ζi + εi,t. (6)

▶ PM\2.5z - daily estimates obtained from the Stanford ECHO Lab

aggregated to a monthly frequency.

23
Efects of Pollution on Delinquency

Panel A Mortgage Credit Card Personal Loan Retail Card

Delinquency Delinquency Delinquency Delinquency

T reated × P ost 0.01*** 0.02*** 0.05** 0.02***

(0.00) (0.00) (0.00) (0.00)

borrower attributes ✓ ✓ ✓ ✓

Consumer FE + + + +

Year-qtr FE + + + +

Observations 5,846 20,730 3,023 5,007

R-squared 0.31 0.78 0.76 0.79

Dependent variable 0.01 0.04 0.13 0.10

Panel B Mortgage Credit Card Personal Loan Retail Card

Delinquency Delinquency Delinquency Delinquency

T reated × P ost 0.01 0.02* 0.01 0.02*

(0.01) (0.01) (0.01) (0.01)

borrower attributes ✓ ✓ ✓ ✓

Borrower FE + + + +

Q-year FE + + + +

Observations 3,892 5,893 6,035 5,861

R-squared 0.59 0.71 0.72 0.72

Dependent variable 0.02 0.04 0.11 0.11

24
Efects of Camp Fire-Induced Pollution on Credit Card Spending

and Payment

1 2 3 4

Panel A ∆ Spending ∆ Payment ∆ Balance ∆ Past Due

T reated × P ost 389.056*** -173.050*** 502.849*** 0.022***

(62.530) (40.885) (103.710) (0.001)

Time-varying borrower attributes ✓ ✓ ✓ ✓

Account FE + + + +

Month-year FE + + + +

Observations 712,567 712,567 712,567 712,567

R-squared 0.079 0.052 0.257 0.092

Dependent variable mean -391.821 435.421 1,160.981 0.116

Panel B: IV ∆ Spending ∆ Payment ∆ Balance ∆ Past Due

T reated × P ost 383.133*** -167.930*** 525.183*** 0.021***

(61.975) (38.180) (118.780) (0.001)

Time-varying borrower attributes ✓ ✓ ✓ ✓

Account FE + + + +

Year-Month + + + +

Observations 701,778 701,778 701,778 701,778

R-squared 0.078 0.050 0.258 0.093

Dependent variable mean -398.244 428.649 1188.219 0.117

▶ Households exposed to severe air pollution might have to spend more to cope with

smoke-induced health issues and might have experienced reduced income and thus less

ability to repay their debt

25
Time-Varying Efects of Pollution on Credit Card

Spending

Spending Over Time: Payment Over Time:

26
Heterogeneous Efects of Wildfre-Induced Pollution on Credit

Card Spending and Payment: Diferent Credit Score Segments

1 2

Panel A: ∆ Spending Credit Score ≤ 720 Credit Score > 720

T reated × P ost 140.061 535.442***

(107.843) (88.154)

Time-varying borrower attributes ✓ ✓

Account FE + +

Year-month FE + +

Observations 249,317 449,846

R-squared 0.131 0.076

Dependent variable mean -1,048.704 -36.189

Panel B: ∆ Payment Credit Score ≤ 720 Credit Score > 720

T reated × P ost -445.491*** -26.773

(89.364) (70.242)

Time-varying borrower attributes ✓ ✓

Account FE + +

Year-Month FE + +

Observations 249,317 449,846

R-squared 0.093 0.052

Dependent variable mean 489.834 394.592

27
Conclusion

▶ We examine the housing and credit response to wildfres and

wildfre-related smoke and pollution

▶ We fnd a strong efect of extreme wildfres on net migration,

house prices, and consumer fnancial distress

▶ The fre insurance payof is not adequate to cover the expenses

related to the fre event

▶ We fnd signifcant efects of wildfre-related smoke and pollution

on credit card spending, balance, and delinquency

▶ Fire insurance is typically included in the homeowners’ policy,

but this paper shed light on damage from the indirect efects of

the fres

▶ The adverse efects of wildfres can go far beyond the fre

perimeter, and failure to account for smoke and pollution efects

yields incomplete fnancial efects of extreme wildfres

28
The Efect of 2018 Camp Fire on the Number of

Accounts and Credit Balance - From the CCP

29
The Efect of 2018 Camp Fire on Credit Balance and

Number of Accounts - From the CRISM

Back

30
The Demand for Air Purifers has Increased with Rising

Air Pollution

31
Emergency Department Visits Around November 2018

The Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and

Utilization Project (HCUP)

32
Indications for Health Efects of the Camp Fire-induced

Air Pollution

1 2 3 4

Emergency Number Emergency Number

visits - kids of Asthma visits - kids of Asthma

ED Visits ED Visits

Treated × Post 3,149* 1,153*

(1,068) (689.1)

DeltaTreated × Post 1,298* 1,153*

(733.2) (689.1)

County FE + + + +

Year-month FE + + + +

Observations 264 212 228 220

R-squared 0.753 0.15 0.536 0.432

Dependent variable mean 122,222 148 122,222 148

Back

33
1816: The Year without a Summer

“The fnancial and economic difculties...were exacerbated by extremely

cold, dark weather across northern Europe and the northeastern United

States in 1816.”

“The poor weather was caused by the eruption in the Dutch East Indies

(Indonesia) of Mount Tambora, which spewed smoke and ash into the

atmosphere, obscuring the sun.”

“The cold and dark caused widespread crop failures and severe famine

across the Northern Hemisphere... People were observed eating “bread” of

sawdust and straw.”

– Jim Narron and Donald P. Morgan,

“The Crisis of 1816, the Year without a Summer,

and Sunspot Equilibria,” Liberty Street Economics.

34