Extreme Wildfres, Distant Air Pollution, and Household 

Financial Health * 

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

December 7, 2023 

ABSTRACT 

We link detailed wildfre burn, satellite smoke plume, and ground-level pollution data to estimate the efects of extreme 

wildfre and related smoke and air pollution events on housing and consumer fnancial outcomes. Findings provide 

novel evidence of elevated spending, indebtedness, and loan delinquencies among households distant from the burn 

perimeter but exposed to high levels of wildfre-attributed air pollution. Results also show higher levels of fnancial 

distress among renters in the burn zone, particularly those with lower credit scores. Financial distress among homeowners within the fre perimeter is less prevalent, likely owing to insurance payout. Findings also show out-migration 

and declines in house values in wildfre burn areas. The adverse smoke and pollution efects are salient to a substantial 

geographically dispersed population and add appreciably to the household fnancial impacts of extreme wildfres. 

JEL Classifcation: R23, Q53, Q54, D12. 

Keywords: Wildfres, Air Pollution, Consumer Credit, Financial Distress, Spending. 

*We thank Andy Anderson, Ding Du, Bruno Gerard, Umit Gurun, Sam Hughes, Ivan Ivanov, Amine Ouazad, 

Jo Weill, Eric Zou, and seminar and conference participants at AEA, AFFECT New Ideas Session, AREUEA National conference, AREUEA Virtual Seminars, Baruch College, BI Norwegian Business School, Community Banking 

Research Conference, CSWEP CeMENT Workshop, Federal Reserve Banks of Dallas, New York and Philadelphia, 

Federal Reserve System Applied Micro, Federal Reserve System Climate Meeting, FHFA Econ Summit, Freddie 

Mac, Hebrew University of Jerusalem, Lone Star Finance Conference, LSE/Imperial/King’s Workshop in Environmental Economics, OSWEET, and SMU for helpful comments and discussion. We thank Jonah Danziger and Myles 

Winkley for excellent research assistance and Maura Fernbacher for editorial help. The information presented herein 

(including any applicable table, chart, graph, or the like) is based on data provided by CoreLogic, Y-14M regulatory 

reports, FRBNY Consumer Credit Panel/Equifax Data (CCP), McDash, and the Federal Reserve. Those data are used 

as a source, but all calculations, fndings, and assertions are those of the authors. The views expressed in this paper 

are solely those of the authors and do not necessarily refect the views of the Federal Reserve Banks of Philadelphia 

and Dallas or the Federal Reserve System. 

†Federal Reserve Bank of Philadelphia (e-mail: xudong.an@phil.frb.org). 

‡UCLA Anderson School of Management (e-mail: sgabriel@anderson.ucla.edu). 

§Federal Reserve Bank of Dallas (e-mail: Nitzan.TzurIlan@dal.frb.org). 
I. Introduction 

Recent decades have witnessed more frequent and more extreme wildfre events. U.S. wildfres on average were 

four times in size, triple in frequency, and more widespread during the 2000s than in prior decades (Iglesias et al., 

2022). The National Oceanic and Atmospheric Administration (NOAA) since 2000 has recorded 15 wildfre events 

incurring damage in excess of 1 billion dollars.1 Adverse environmental impacts of wildfres often extend well beyond 

the burn perimeter: In 2020, smoke from wildfres on average fully covered U.S. and California counties for 20 and 

64 days, respectively. More recently, in the wake of 500 active wildfre events in eastern Canada in June 2023, heavy 

smoke and particulate emissions blanketed 122 million people across major parts of the Northeast and North Central 

United States, resulting in some of the most polluted days on record.2 According to the Stanford ECHO Lab, smoke 

exposure associated with Canadian wildfres through mid-2023 was substantially worse than total cumulative exposure 

in every year since 2006.3 While an emerging literature has sought to document direct economic efects of climate 

shocks, including those pertinent to housing and fnancial markets (see, for example, Bernstein et al. (2019), Keys and 

Mulder (2020), and Bakkensen and Barrage (2017)), there has been only limited attention to the efects of extreme 

wildfres and related smoke events on household fnancial outcomes. Adverse economic efects of those smoke and 

pollution events likely are salient to large populations beyond the fre zone. 

As broadly appreciated, air pollution is adverse both to health (Deryugina et al., 2019)

4 and non-health outcomes 

(see Aguilar-Gomez et al. (2022) for a review). Approximately one-third of U.S. households include someone with 

an existing respiratory health condition at risk of serious medical complications in the wake of prolonged exposure 

to the fne particulate matter (PM2.5) found in smoke (McCafrey and Olsen (2012). Wildfre smoke and related 

spikes in particulate emissions may result in increased demand for both goods and services that mitigate deleterious 

1According to the NOAA, the United States has routinely spent more than $1 billion per year in recent decades to 

fght wildfres. 

2On June 27, 2023, the Michigan Pollution Control Agency issued its 23rd air quality alert of the year as compared to the issuance of two or three alerts in a usual year. See New York Times and Fox Weather, June 28, 2023. 

Wildfre smoke, like other forms of air pollution, contains particulate matter that enters the lungs and can pass into the 

bloodstream. Smoke also carries other pollutants, such as ozone, carbon monoxide, and a range of VOCs. 

3Cumulative smoke exposure was measured as PM2.5 per day summed across afected days. See Tweet by Marshall 

B. Burke at Stanford ECHO Lab: https://twitter.com/marshallbburke/status/1677227498487029760?s=

51&t=-2di0znAHCwH V0x5vSv0w

4Reid et al. (2016), Cascio (2018), and Xu et al. (2020) showed that an increase in air pollution can lead to 

signifcant adverse health outcomes. Other studies of the health efects of wildfre smoke have linked exposure to 

increases in adult mortality (Miller et al., 2021), increases in infant mortality (Jayachandran, 2009), elevated risk of 

low birth weight (McCoy and Walsh, 2018), and reductions in lung capacity (Pakhtigian, 2022). 

1 
air pollution efects (notably including increased medical and medical equipment spending). Smoke events also have 

been shown to result in work interruption and reduced earnings (Borgschulte et al. (2022)), increased trafc accidents 

(Matthews (2018)), and reduced business activity in tourism and outdoor recreation (Stotts et al. (2018)). Together, 

these outcomes suggest income loss and deterioration in household fnancial status in the immediate aftermath of the 

smoke event.5 In this paper, we assess household fnancial distress associated both with wildfre burn area destruction 

and with fre-attributed smoke and particulate emissions that extend to large geographies and populations beyond the 

fre zone. 

Our analysis is based on the combination of highly-articulated datasets on wildfres, wildfre-induced smoke 

plumes, attributable and localized air pollution, and consumer economic and fnancial outcomes. We use the U.S. 

National Incident Command System Incident Status Summary Forms to identify wildfres that caused signifcant 

structural damage (St Denis et al., 2020).6 We then apply high-resolution satellite remote sensing data to identify 

the locations and temporal incidence of related wildfre smoke plumes (Miller et al., 2021). We also employ daily 

ground monitor readings for Environmental Protection Agency (EPA) “criteria pollutants,” including a metric of particulate matter (PM2.5), to measure ground level pollution as well as to estimate the increment therein attributable 

to wildfre-related smoke. For wildfre afected populations, we compile information on housing market outcomes 

and migration. Further, we employ highly articulated consumer-level and loan-level datasets, including the FRBNY 

Consumer Credit Panel/Equifax Data (CCP); the Equifax Credit Risk Servicing McDash (CRISM)7; and the Federal 

Reserve Y-14M Capital Assessments and Stress Testing Data to measure consumer outcomes. The granularity of the 

data provides unique opportunities to identify the causal impacts both of wildfres and related attributable smoke and 

pollution. Despite the growing incidence of extreme wildfre and related particulate emission events, there is limited 

evidence of their efects on household fnancial well-being.8 

Our research design enables us to separate fre efects from those of fre-attributable smoke and air pollution. 

To assess fre treatment efects for households in wildfre burn zones, we use a diference-in-diferences approach in 

panel regression settings to compare migration patterns, house prices, credit usage, and credit performance within the 

5Further, long-run longitudinal studies have shown that exposure to adverse environmental conditions in early 

childhood can result in lower levels of educational attainment and earnings later in life (Isen et al., 2017). 

6Table A.1 shows the wildfre distribution in our sample. The data include 135 wildfres between 2016 and 2020; 

69 of them were in California, 14 in Oregon, and nine in Florida. 

7CRISM is a match between anonymous credit fles from the Equifax consumer credit database and loan level 

mortgage data from Black Knight McDash. 

8There has been some progress addressing these issues in recent years (Sharygin (2021), Winkler and Rouleau 

(2021), Issler et al. (2020), and McConnell et al. (2021)). 

2 
fre perimeter (the treatment group) to those same outcomes in 1- and 5-mile rings beyond the fre zone (the control 

group).9 Figure 1 shows the geographic location of the fve extreme wildfres in California between 2016 and 2020, and 

the 1-, 5-, and 10-mile peripheral rings. In Figure 2 we zoom in on the Camp Fire, to better explore the fre footprint 

and peripheral rings at the census block level. Our sample design allows us to diference away smoke incidence and 

thus identify fre efects (see additional discussion below). Results of the fre analyses show a signifcant increase 

in net migration from tracts that experienced the most destructive wildfres as well as a marked decrease in house 

prices in the quarters immediately following the fre event. We also fnd a near-term increase in mortgage, credit card, 

and personal loan delinquency among consumers in the fre zone, with a more pronounced efect for the much larger 

Camp Fire than for the three other extreme wildfres. Adverse household fre zone treatment efects typically persisted 

multiple quarters after the fre. 

To better understand the delinquency results, we use individual account-level data from the Y-14M to study credit 

card spending, repayment, and monthly balance. Interestingly, we fnd that post-fre, treated households in the fre 

zone on average increased spending but paid down credit card debt even more, resulting in a decline in monthly 

balance. While the combination of reduced credit card indebtedness (repayment in excess of spending) and increased 

delinquency seem puzzling, further analysis showed that the reduction of credit card balance occurred largely among 

homeowners, whereas increased credit card delinquencies were evidenced among the renter population, especially 

those with lower credit scores. 

Fire damage typically is covered by homeowner’s insurance. In recent years, in areas of increased wildfre-related 

insurance payout and risk, related coverage in California often has been excluded from the standard homeowner’s 

insurance policy. In response, the State of California has made limited fre coverage available via the California FAIR 

Plan (Biswas et al., 2023).10 Among homeowners, our estimated attenuation in adverse household fnancial impacts 

(including paydown in credit card balances post-wildfre) likely refects use of funds from insurance claim payout to 

reduce debt, consistent with fndings from the food disaster literature (Gallagher and Hartley (2017), Billings et al. 

(2022)). In contrast, renters typically receive limited fre insurance payout and may experience fnancial distress owing 

9For more information, see Figure 2. Our results are also robust to defnition of control groups that are 1-2 miles 

from the fre zone and 1-10 miles from the fre zone. 

10The California FAIR Plan provides basic insurance to satisfy the lender requirement that the home be insured 

against the risk of fre. While the FAIR Plan policy covers damage from fre, smoke, lightning, and windstorms, 

it does not cover other common elements of homeowners property insurance including theft, food, earthquake, or 

personal liability. The California FAIR Plan coverage is typically more expensive than private policies owing to the 

high-concentration of high risk borrowers. 

3 
to use of their own limited resources to cope with adverse fre efect, including work disruption as well as event-related 

health expenses. 

We then explore the household fnancial efects of wildfre-attributable smoke and particulate emissions as are 

difused broadly beyond the fre zone. We frst show that extreme wildfres cause marked increases in air pollution. 

Similar to Miller et al. (2021), we employ daily satellite-based measures of wildfre smoke plumes in a diference-indiferences framework to estimate related ground-level air pollution efects as measured by PM2.5. After establishing 

the causal relationship between smoke and air pollution, we proceed to estimate the impacts of fre-induced air pollution on credit outcomes. Again we apply a diference-in-diferences framework in a panel regression setting. In an 

efort to assure that variations in ground level air pollution derive from fre-related smoke, we adopt two approaches. 

First, we create a measure of fre-attributable air pollution by taking the diference between fre month ground pollution PM2.5 levels and same month PM2.5 levels in the prior year. Second, we estimate the efects of particulate 

emissions using an instrumental variable approach. Due to the granularity of our data, we include consumer- or credit 

account-level fxed efects, so as to largely alleviate concerns of omitted variable bias. 

Our results provide new evidence of adverse causal impacts of distant wildfre-induced air pollution on credit 

outcomes. We fnd signifcant increases in credit card spending as well as marked declines in credit card payments in 

the wake of the smoke event. Those fndings are largely evidenced among zip codes well beyond the fre zone that 

experienced large spikes in wildfre-induced pollution in the quarters immediately following the wildfre event. Results 

also show sizable increases in child emergency visits and asthma emergency department visits well beyond the fre zone 

and in the wake of a wildfre-induced smoke event. In the fve quarters following the Camp Fire, the combination of 

added credit card spending and reduced credit card repayment among consumers experiencing high levels of wildfreinduced particulate pollution resulted in an additional $500 per annum in credit card balance. Further analysis indicates 

that the reduction in credit card repayment is evidenced primarily among lower credit score borrowers, consistent with 

the idea that those borrowers, in the absence of adequate government assistance, typically have fewer resources to 

cope with natural disasters. In contrast, the increase in credit card spending is found largely among prime borrowers. 

Those borrowers likely have the capacity to spend more on preventive measures to combat air pollution induced by 

the wildfre. 

As anticipated, the estimated far-fung wildfre-attributable pollution treatment efects are smaller in magnitude 

than those estimated for burn zone households. For example, the Camp Fire resulted in an average 45 percent increase 

in the likelihood of credit card past due among burn zone households, whereas distant wildfre-attributable emissions 

4 
and particulate pollution are associated with a 20 percent increase in credit card past due. However, the pollution 

results are highly salient, given the substantially larger geographies and populations treated by far-fung wildfrerelated emissions. As detailed below, if we conservatively impute estimated pollution efects of the Camp Fire to 

the 19 million people in the New York Metro Area exposed to heavy smoke and pollution in the wake of the 2023 

Canadian wildfres, a back-of-the-envelope calculation suggests that afected households incurred an incremental $3 

billion in credit card spending and an added $4 billion in credit card debt. 

Three recent papers, including Issler et al. (2020), McConnell et al. (2021), and Biswas et al. (2023), examine 

the efects of wildfres on burn zone housing and consumer outcomes.11 We augment those studies to estimate the 

efects of extreme wildfres and wildfre-attributed air pollution on a broad array of household fnancial outcomes. 

For example, both burn zone and broadly dispersed wildfre-induced air pollution result in increased delinquencies 

in personal and retail store debt as well as higher levels of mortgage and credit card debt. We also fnd interesting 

heterogeneity in treatment efects, whereby estimated fre efects on credit card debt and repayment difer among 

homeowners and renters, likely owing in part to the provision of damage-related insurance payouts to homeowners 

with damaged properties in the fre zone. The incidence of far-fung smoke and air pollution events has become 

signifcantly more pronounced in the wake of major wildfre events in North America and Europe during the summer 

of 2023. Failure to account for broadly difused and growing consequential fre emissions efects yields only a partial 

and incomplete picture of these extreme climatic events. 

The remainder of the paper is organized as follows: Section II describes the data and sample construction. Section 

III discusses the framework and empirical methodology used in the paper, whereas Section IV presents the empirical 

results. Section V concludes. 

11There are a few other papers that evaluate the efect of air pollution on housing and credit outcomes. Amini et al. 

(2022) analyze the causal efect of air pollution on Iran’s housing market by exploiting increases in air pollution due to 

sanctions that targeted gasoline imports and fnd that a 10% increase in the outdoor concentration of nitrogen dioxide 

leads to a decrease in housing prices of around 0.6%–0.8%. Zheng et al. (2014) use data from China and fnd that 

a 10% decrease in neighborhood pollution is associated with a 0.76% increase in local home prices, and Chay and 

Greenstone (2003) estimate an elasticity in the range of 0.20 to 0.35. Lopez and Tzur-Ilan (2023) analyze the efect 

of air pollution exposure on rent prices, using quasi-experimental exposures to wildfre smoke shocks, and fnd that an 

increase in one unit of PM2.5 reduces the average rent by 0.7%. 

5 
II. Data 

A. Data on Wildfres 

We employ information on wildfre damage compiled by the U.S. Department of Homeland Security National 

Incident Management System/Incident Command System (ICS). While these data have been publicly available for 

many years, they have only recently been processed by St Denis et al. (2020) into an accessible format available for 

broad utilization. A major beneft of the ICS data set is that it reports direct measures of hazard impact (e.g., counts 

of structures destroyed or damaged), rather than the dollar value of damaged property. The latter approach, utilized 

by the Spatial Hazards Events and Losses Database for the United States (SHELDUS) and the National Oceanic and 

Atmospheric Administration (NOAA) National Centers for Environmental Information, fails to distinguish between 

widespread fre-related structural damage and that to a small number of high-value properties. The ICS data provide 

insights important to the assessment of household fnancial impacts of wildfre disasters (for more information, see 

McConnell et al. (2021)). For purposes of this study, we linked the ICS data to the U.S. Forest Service Monitoring 

Trends in Burn Severity (MTBS) database, which documents the spatial footprint of wildfre burn perimeters (Eidenshink et al. (2007)). For sampled wildfre events, we identify the census blocks/tracts/zip codes included in the fre 

burn perimeter and beyond. We focus on extreme wildfres that damaged or destroyed more than 1,000 structures (for 

a list of extreme wildfres, see Table 1). Those fres account for roughly 3 percent of all wildfres. 

B. Wildfre Smoke Data 

Miller et al. (2021) developed measures of daily smoke exposure using information on wildfre smoke from 

the NOAA’s Hazard Mapping System (HMS).12 The HMS uses observations from the Geostationary Operational 

Environmental Satellite, which produces imagery at a 1-km resolution for visual bands and a 2-km resolution for 

infrared bands, to identify fre and smoke emissions over the contiguous United States (Ruminski et al., 2006). Smoke 

analysts process the satellite data to draw geo-referenced polygons that represent the spatial difusion of wildfre smoke 

plumes detected each day. Plumes are typically drawn twice per day, once shortly before sunrise and once shortly after 

sunset. We similarly employ the HMS smoke plume data from 2016 to 2020 to construct an indicator of smoke 

exposure at the tract level for each day of the sample period. Our primary measure of smoke exposure is an indicator 

12These data come from an operational group of NOAA experts who rely on satellite imageries to identify the 

location and the movements of every wildfre smoke plume in the U.S.. 

6 
of whether a tract is fully covered by a smoke plume on a given day. 

C. Pollution Data 

We obtain ambient air pollution data from the EPA’s Air Quality System. We use daily ground monitor readings 

for EPA “criteria pollutants,” including a measure of particulate matter (PM2.5). To measure air pollution for a tract, 

we take the distance-weighted average of two or three valid readings for each pollutant from monitors closest to a 

tract’s centroid. We spatially intersect these data with census tract boundary fles and link them to individual-level 

administrative records. 

Figure 5 and Appendix Figures A.1 and A.2 show changes in wildfre smoke and pollution levels for the 2018 

Camp Fire, Carr Fire, and Thomas Fire in California in the months prior to and following the fre. Wildfre smoke 

plumes are an important source of air pollution and travel hundreds of miles downwind, allowing us to identify the 

distant efects of smoke exposure separately from wildfre damage within the burn perimeter. We also use data from 

Childs et al. (2022), who develop a machine learning model of daily wildfre-driven PM2.5 concentrations using a 

combination of ground, satellite, and reanalysis data sources. The authors generate daily estimates of smoke PM2.5 

over a 10 km-by-10 km grid across the contiguous U.S. from 2006 to 2020.13 

D. Credit, Housing, and Migration Data 

We measure household credit outcomes using the Federal Reserve Bank of New York Consumer Credit Panel/Equifax 

data (CCP). The CCP is a nationally representative 5% random sample of individuals with a credit report.14 The panel 

provides detailed credit-report data for (anonymous) individuals and households in quarterly increments beginning 

in 1999. The data cover all major categories of household debt, including mortgages and credit cards, inclusive of 

number of accounts, balances, and credit delinquencies. For more information, see Lee and van der Klaauw (2010). 

The CCP also can be used to measure migration as we can trace individual consumers moving from one location (e.g., 

census tract or census block) to another using the consumer’s mailing address. 

13Childs et al. (2022) fnd that the number of people in locations with at least one day of smoke PM2.5 above 100 

μg/m3 per year has increased 27-fold over the last decade, including nearly 25 million people in 2020 alone. We use 

this estimation to calculate the salient efect of wildfre smoke. 

14The database also contains information on all persons with credit fles residing in the same household as the 

primary sampled individual. Household members are added to the sample based on the mailing address in the existing 

credit fles. 

7 
In order to contrast outcomes across housing tenure status (homeowners vs renters), we leverage another dataset, 

the Credit Risk Insight Servicing McDash (CRISM) data. CRISM is an anonymous credit fle match from Equifax’s 

full population of consumer credit reports to the Black Knight McDash loan level mortgage dataset (as compared 

to the 5 percent random sample of the CCP).15 Hence, all borrowers in CRISM are mortgage borrowers and thus 

homeowners. CRISM covers about 60 percent of the U.S. mortgage market during our sample period. Another 

advantage of the CRISM data is that it is updated monthly, rather than quarterly as in the CCP. 

Our primary source for housing market outcomes is the CoreLogic Home Price Index (HPI) database. The CoreLogic HPI is quarterly in frequency and available at the census region, state, Core Based Statistical Area (CBSA), 

county, and zip code levels. We use the zip code level HPI and convert it to a census tract level HPI using the zip codecensus tract crosswalk. The CoreLogic HPI is constructed using the weighted repeat sales methodology. In addition 

to the price indices, the database also includes information on the number of repeated sales used to build the index for 

the date period specifed, and information on the median home price for repeat sales observations for the geography 

and period specifed. We also used information from the U.S. Department of Housing and Urban Development (HUD) 

together with the United States Postal Service (USPS) on addresses identifed by the USPS as having been ”Vacant” 

or ”No-Stat” in the previous quarter to measure the changes in the share of vacant residential properties over time. 

E. Consumer Spending Data 

To supplement the CCP data, we obtain account-level information on consumer credit card activity from the 

Federal Reserve Y-14M regulatory reports. In addition to its higher frequency, the monthly Y-14M data have an 

important advantage, in that they contain detailed credit card spending, payment and balance information, tracking 

the same accounts monthly. The Y-14M data also contain anonymized up-to-date information on the consumer and 

the account. Such information includes borrower contemporaneous credit score, current spending limit of the credit 

card account, age of the account, contemporaneous interest rate, and borrower geographic location at the 9-digit zip 

code.16 The data also contain credit performance information including an account past due indicator. See Agarwal 

et al. (2020) for more information. The Y14M credit card data are available from June 2013. For purposes of our 

study, we use data from January 2016 to December 2019, centering around the month of each wildfre in our analysis. 

15CRISM is constructed with a proprietary and confdential matching process. In the matching process, Equifax 

uses anonymous felds such as original and current mortgage balance, origination date, zip Code, and payment history 

to match each loan in the McDash dataset to a particular consumer’s tradeline in Equifax. 

16Some accounts only have the 5-digit zip code. 

8 
F. Summary Statistics 

Table A.2 reports summary credit information on individuals living in the wildfre zones, compared to those living 

(1-5 miles) outside the fre zones, during the six quarters before and after the fre event. Summary statistics are reported 

for average outcomes for the set of fve sampled extreme wildfres (Camp, Carr, Thomas, Central LNU Complex, and 

LNU Lightning Complex fres). The table shows that individuals residing in the fre zones were older and had a higher 

Equifax Risk Score and lower mortgage balance. In terms of the number of credit accounts, individuals residing in the 

fre zones had fewer credit card and personal loan accounts, but a higher number of frst mortgage accounts. Further, 

individuals residing in the fre zones were less likely to be delinquent on their personal loans, on average, but more 

likely to be delinquent on their mortgage loans. Overall, individuals residing in the fre zones had lower bank card 

balances but higher personal loan balances, on average. 

III. Research Strategy 

A. Efects of Wildfres on Migration, House Prices, and Household Financial Outcomes 

To assess the efects of extreme wildfres on migration, house price, and household fnancial outcomes, we estimate 

panel data models in a diference-in-diferences framework at the census tract and individual consumer/account-level. 

Consistent with Figures 3, A.4, A.5, and A.6, we assume that trends in outcomes are similar for the treated and control 

groups absent the wildfres. 

A.1. Census Tract-Level Diference-in-Diferences Estimates of Extreme Wildfre Impacts on Migration and House Prices 

We compare net-migration (out-migration minus in-migration) and house price changes in wildfre “treated” tracts 

(i.e., tracts within the burn area) relative to “control” tracts (e.g., tracts 1-5 miles from the fre perimeter) for the 

composite sample of all fve extreme wildfres. We take a “donut approach” by carving out the areas that are 0-1 

miles from the fre perimeter to obviate the need to assess spillover efects of the wildfres on immediate surrounding 

areas. We also present results for the Camp Fire, the largest wildfre to date in terms of structure loss (for more details, 

see Table 1). We compare pre-event quarters with post-event quarters. In the case of the November 2018 Camp 

Fire, we limit the analysis to eight pre-event quarters and six post-event quarters so as to avoid possible COVID-19 

9 
contamination commencing from the frst quarter of 2020 and to allow clean assessment of fre efects on housing and 

credit outcomes rather than those associated with COVID-19. 

All census tract migration and house price models employ a diference-in-diferences specifcation to estimate the 

efects of wildfre structure loss on net migration and on house prices. The models take the general form: 

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

where Yc,t is a measure of net-migration in census tract c in quarter t, defned as the total number of out-migrants minus 

in-migrants divided by the total population at the start of a period within a tract. Firec,t represents a fre loss indicator 

(1 or 0), Postc,t represents a post-fre indicator (1 or 0), and εc,t represents the error term. The interaction between 

these variables is the primary term of interest, with a signifcant coefcient indicating that net migration or house price 

changes associated with fre-afected units are signifcantly diferent in the post-fre period relative to outcomes in 

neighboring control tracts. We also include two-way fxed efects, quarter fxed efects and census tract fxed efects, 

to account for unobserved time-varying factors and for time-invariant characteristics of each spatial unit. As discussed 

below, we undertake similar analyses of house prices. All models report heteroskedasticity consistent robust standard 

errors clustered by census tract. 

A.2. Consumer- and Account-Level Diference-in-Diferences Estimates of Extreme Wildfre 

Impacts on Household Financial Outcomes 

We next employ similar models to assess the efects of extreme fre events on households’ fnancial outcomes. We 

use consumer-level panel data from CCP and CRISM for pre- and post-event quarters to estimate the following model: 

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

where Yi,t is the outcome measure for individual i in time t (quarterly for CCP and monthly for CRISM). The Firei,t 

term is a dummy variable that takes on the value of one if the individual resides in a census block in the fre zone 

and zero if the census block is outside the fre zone (1 mile and up to 5 miles). The categorical term Posti,t takes on 

the value of 1 after the fre event and 0 prior to the event. τt and ζi are time- and consumer/account-fxed efects. In 

this specifcation, we interpret the interaction term as the efect of living in a treated census block in quarter/month t 

10 
relative to the fre quarter. 

We also use the Federal Reserve’s Y-14M data to estimate a similar panel data model in a diference-in-diferences 

framework. As discussed above, the Y-14M data are monthly in frequency at the individual credit card account-level. 

More importantly, they include detailed spending and payment information, in addition to the balance and delinquency 

information available in the CCP and CRISM. 

B. Efects of Wildfre Smoke and Pollution on Household Financial Outcomes 

B.1. The Efect of Smoke on Air Pollution 

We next turn to assessment of smoke and pollution efects. As discussed below, we use variation in wildfre 

smoke and related air pollution exposure to identify the causal efects of wildfre-induced shocks to air pollution on 

credit outcomes. Wildfre smoke plumes are a natural source of air pollution and travel far from the wildfre event, 

allowing us to identify the efects of far fung smoke and pollution exposure as distinct from the burn zone fre efects. 

The pollution emissions exposure analysis is undertaken for households living up to 30 miles from the fre perimeter. 

We frst present evidence of the average efect of wildfre smoke on local air quality using the following event study 

specifcation: 

X

20

PM2.5c,d = βτ ∗ S mokeDayc,d+τ + αc,day−o f −year + αc,month−year + εc,d. (3) 

τ=−20 

Figure A.3 shows results of an event study of the efects of smoke on air pollution among census tracts that 

experienced smoke and those that did not for the 20 days before and after the Camp Fire. In the aftermath of the Camp 

3 Fire, there was a sharp increase in pollution levels in the census tracts that experienced smoke to roughly 60 μg/m , 

equivalent to pollution levels measured in Beijing that same day.17 

Next, we aggregate the daily smoke exposure data to the monthly level to construct our focal independent variable 

and observe its efect on PM2.5 for all zip codes that are located 30 miles from the fre event. The timeframe extends 

to 12 months after the fre. Using observations for each zip code z and month-year t: 

PM2.5z,t = β ∗ S mokeDaysMonthz,t + τt + ζz + εz,t, (4) 

17According to the CDC, exposure to PM2.5 above 12 is considered risky and has negative health consequences. 

11 
where S mokeDaysMonthz,t is defned as the number of smoke days in month t in zip code z. The regression equation 

includes zip code and month-year fxed efects. In some specifcations, we use annual fxed efects (instead of monthyear fxed efects). We also examine the efect of changes in smoke on changes in pollution, using delta smoke and 

pollution terms, which are calculated as the changes in smoke days and pollution levels compared with the same month 

in 2015. 

B.2. Efects of Wildfre-Induced Air Pollution on Household Financial Outcomes 

To estimate the efects of wildfre-induced air pollution on household fnancial outcomes, we again employ panel 

data models in a diference-in-diferences framework. To isolate the efect of broadly-difused smoke and air pollution 

from that of the wildfre itself, we focus on zip codes outside the wildfre burn area but within 30 miles from the fre 

perimeter. We rank order zip codes surrounding each fre based on the level of pollution in the four weeks immediately 

following the onset of the fre and then divide those zip codes into three groups: treated zip codes defned by those 

in the top quartile of particulate pollution; control zip codes defned by those in the bottom quartile of particulate 

pollution; and remaining zip codes which are excluded from the analysis. On the time dimension, we defne the 

sample to include fve to eight quarters, depending on data availability, before and after each fre. We estimate the 

following model: 

Yi,t = γ ∗ Pollutionz ∗ A f ter f irez,t + Xi,tB⃗ + τt + ζi + εi,t, (5) 

where Yi,t is the outcome measure for individual/account i at time t (quarterly frequency for the CCP and monthly 

frequency for the CRISM and Y-14M). Pollutionz is a dummy variable that takes on the value of one if the individual 

resides in zip code z that experienced pollution levels in excess of the 75th percentile within four weeks of the fre, and 

zero if not. The categorical term A f ter f ire takes on the value of one after the fre event and zero prior to the event. 

Xi,t are time-varying borrower characteristics such as updated borrower Equifax Risk Score. τt and ζi are time- and 

consumer/account-fxed efects. 

To assure that variations in ground level air pollution derive from fre-related smoke, we adopt two approaches. 

Firstly, we create a measure of fre-attributable air pollution by taking the diference between fre month ground 

pollution PM2.5 levels and baseline PM2.5 levels, defned as the same month PM2.5 levels in the prior three years 

(excluding any days when there was wildfre-related smoke). Hence, the regression becomes: 

Yi,t = γ ∗ ∆PM2.5z ∗ A f ter f irez,t + Xi,tB⃗ + τt + ζi + εi,t. (6) 

12 
We also estimate the efect of fre-induced air pollution on household fnancial outcomes using an instrumental 

variable approach. One instrument is the number of smoke days experienced by a zip code in a specifc month. The 

frst stage of this instrumental variable approach is similar to that specifed in equation 4. However, here we leverage 

the work of Childs et al. (2022), which provides a more sophisticated approach to frst-stage estimation. Childs et al. 

(2022) use a machine learning model to estimate smoke-driven pollutants for the contiguous U.S. from 2006 to 2020. 

We use their estimates of smoke PM2.5 and run the second stage of our IV regression as: 

Y \ ⃗ i,t = γ ∗ PM2.5z ∗ A f ter f irez,t + Xi,tB + τt + ζi + εi,t. (7) 

Here the PM\2.5z are the zip code-level daily estimates obtained from the Stanford University Environmental 

Change and Human Outcomes (ECHO) Lab 18 aggregated to monthly frequency. In order to evaluate how wildfreinduced air pollution dissipates over time, we also run event-study type of regressions in a similar diference-indiferences setting. 

It is noteworthy that when we estimate the fre efects, we focus on treated areas within the fre boundary and 

control areas up to 5 miles from the fre perimeter. As discussed below, the entirety of the spatial footprint of the 

fre study (both treatment and control areas) were treated by fre-related smoke. Hence, our diference-in-diferences 

approach by design diferences out the smoke/pollution efects so as to allow us to identify burn zone fre efects. In 

our subsequent analysis of smoke and particulate air pollution, we carve out the fre perimeter and immediate adjacent 

areas. Here our sample design allows us to focus on distant smoke and pollution efects 5 to 30 miles beyond the fre 

zone. 

IV. Results 

A. Efects of Extreme Wildfres on Net-Migration and House Prices 

Table 2 presents fndings of estimation of the efects of extreme wildfres on household net-migration. We compare 

wildfre treated tracts (e.g., tracts within the burn footprint) to control tracts for the Camp Fire. The Camp Fire occurred 

in November 2018 in Butte County, California and destroyed more than 18,000 buildings in the town of Paradise 

and surrounding unincorporated areas of Magalia, Concow, and Yankee Hill. To date, that fre is the most extreme 

18https://www.stanfordecholab.com/wildfire smoke. 

13 
of U.S. wildfre events, destroying more than twice as many structures as any other sampled extreme wildfre (See 

Table 1). The frst three columns in Table 2 compare the fre zone to those 1-5 miles from the Camp Fire. Overall, 

fndings indicate that the Camp Fire is associated with sizable and signifcant net out-migration among residents of 

surrounding control zones. The estimated migration efects are larger among control census tracts more proximate 

to the fre treatment area and decline monotonically with distance from the fre zone. Columns 4 and 5 of Table 2 

compare fre zone tracts to those that are 1-10 miles from the fre (column 4) and to tracts that are 1-20 miles from the 

fre (column 5). Overall, while the Camp Fire did not appear to signifcantly afect in-migration, fndings do indicate a 

signifcant increment to out-migration. Census tracts up to 5 miles from the perimeter of the Camp Fire experienced an 

additional 18 net exits per 1,000 residents compared with fre zone tracts. The estimated efect is stable to tracts that are 

10 and 20 miles from the Camp Fire. We further explore the time dynamic of estimated fre-related migration efects. 

Figure A.4 shows that adverse efects of Camp Fire on out- and net-migration are substantial in the year following the 

fre; subsequently, household migratory fows largely revert to pre-fre levels. The results are consistent with previous 

research on the efect of the Camp Fire (Issler et al. (2020), McConnell et al. (2021)).19 Our fndings also are consistent 

with prior research on a smaller subset of FEMA disaster-declared wildfres (Winkler and Rouleau, 2021), and with 

dynamic spatial models of the U.S. economy of adaptation to climate change (Cruz and Rossi-Hansberg (2021), Bilal 

and Rossi-Hansberg (2023)). 

Table 3 reports on census tract level diference-in-diferences estimates of the efect of the 2018 Camp Fire on 

house prices. Findings indicate that the Camp Fire caused a 17.5 percent decline in house prices in the fre zone 

compared with control census tracts some six quarters subsequent to the fre event. In dollar terms, Table 3 shows 

that the Camp Fire caused a decline of $34,553 (compared with a median repeat sales value of $280,007 in the treated 

Camp Fire area). Table 3 also shows that the repeat sales median house price in the area treated by the Camp Fire 

remained damped by roughly $10,000 throughout the post-fre study period. Column 4 in Table 3 shows a signifcant 

increase in residential vacancy rates. 

Our fndings on migration response to extreme wildfre events are consistent with other papers showing similar 

net exit of population in the wake of other climate-related natural disasters. Indeed, a growing literature identifes 

migration as among the most consequential outcomes of and adaptive mechanisms to climate change (Black et al., 

2011). Among papers focused on the U.S., Mullins and Bharadwaj (2021) apply IRS county place-to-place data for 

19McConnell et al. (2021) similarly found that among the top 5 percent most destructive wildfres, wildfre damage 

resulted in out-migration of residents. 

14 
the period 1983-2017 and fnd that an additional day of mean temperature between 80 and90◦F results in increased 

annual out-migration of households by 0.43% relative to a day with a mean temperature between 60 and 70◦F, while 

a single additional day with greater than 90◦F increases yearly outgoing migration by 0.96%. Boustan et al. (2012) 

estimate the long-run U.S. migration response to natural disasters and found signifcant reductions to in-migration to 

counties struck by foods and hurricanes. Gallagher and Hartley (2017) estimate an elevated out-migration response 

and only partial subsequent return among New Orleans residents that experienced higher levels of fooding in the 

wake of Hurricane Katrina. Bleemer and van der Klaauw (2019) and Deryugina et al. (2018) similarly fnd large and 

persistent efects of Hurricane Katrina on movement of New Orleans residents from the city. 

B. Efects of Extreme Wildfres on Household Financial Distress 

The impact of wildfre events on household fnance is unclear a priori. On the one hand, wildfres may result in 

destruction of physical property and disruption of work so as to result in household fnancial distress. However, compensation of household economic loss via wildfre or standard homeowners insurance may mitigate fnancial distress. 

Further, governmental and philanthropic emergency assistance may help to dampen adverse fnancial efects. Existing 

empirical evidence is mixed. For example, for mortgage performance, Issler et al. (2020) fnd little impact of wildfre 

on household fnance. Biswas et al. (2023) fnd some evidence of elevated mortgage delinquencies only among damaged properties in fre burn areas. McConnell et al. (2021) provide evidence that consumer credit distress, including 

loan delinquency, personal bankruptcy, and home foreclosure, improve rather than deteriorate in the aftermath of a 

wildfre, but that the changes are not statistically signifcant. 

To explore the efect of extreme wildfres on household fnancial distress, we frstly employ the FRBNY Consumer Credit Panel/Equifax Data (CCP). Recall that the CCP is a 5% random sample of all U.S. individuals with a 

credit fle and includes consumer-level quarterly panel data containing detailed information on consumer liabilities, 

delinquencies, and other characteristics. Table 4 shows the efect of the Camp Fire on consumer fnancial distress, 

estimated in a diference-in-diferences framework following equation 2. Note that our treated group is comprised of 

consumers living in census blocks within the Camp Fire burn footprint, whereas the control group includes consumers 

living 1-5 miles from the fre perimeter. Dependent variables in columns 1-4 include mortgage delinquency, credit 

card delinquency, personal loan delinquency, and retail/store card delinquency, respectively. Columns 1-3 indicate 

that the Camp Fire resulted in statistically signifcant increases in mortgage, consumer credit card, and personal loan 

delinquencies. For example, Column 2 shows that consumers living in Camp Fire burn zone experienced an additional 

15 
2 percentage point (pp) rise in credit card delinquency following the Camp Fire, compared to consumers not directly 

afected by the fre (those living 1-5 miles from the fre perimeter). This efect is economically signifcant given an average credit card delinquency rate of about 4 percent in our sample. All regressions include year-quarter and consumer 

fxed efects. 

Table 4 provides estimates of average treatment efects over the eight quarters following the Camp Fire. In 

Figure 3, we plot the quarter-by-quarter treatment efects of the fre, estimated using a similar diference-in-diferences 

approach. Panels A-D show estimated efects on consumer total delinquency (delinquencies across all credit accounts), 

mortgage delinquency, credit card delinquency, and personal loan delinquency, respectively. Findings indicate that the 

estimated wildfre efects on credit card delinquency persisted over the full two year post-fre period. 

To better understand the efects of wildfre on credit delinquencies, we turn next to the Federal Reserve Y-14M 

credit card data. An advantage of the Y14M is that we observe not only delinquencies but also credit card spending, 

repayment, and balance at the monthly account level. We follow the same diference-in-diferences approach in estimating wildfre efects using the account-month panel. The granularity of our data further allow us to include two-way 

fxed efects. In addition, we control for time-varying account attributes such as updated borrower credit score and 

current credit limit of the account. 

In Table 5, we report our estimates of the efects of the Camp Fire on credit card spending, payment, balance, 

and past due in columns 1-4, respectively. To account for possible seasonality, we use year-over-year changes in our 

dependent variables. Changes in credit card spending, repayment, and balance, as shown in the frst three columns, 

are annualized dollar amounts. As shown in the table, borrowers residing in the wildfre burn area engaged in roughly 

$1,100 per annum additional spending in the 14 months following the fre, relative to borrowers residing 1-5 miles 

outside the burn area (column 1). Interestingly, estimates also show that fre zone residents engaged in about $1,500 

per annum more in repayment, relative to those outside of the fre burn zone (column 2). As a result, households living 

within the wildfre burn perimeter accumulated an estimated $1,900 per annum less in credit card debt (column 3). 

Column 4 of Table 5 shows an elevated account past due among borrowers residing in the wildfre burn area, consistent 

with the increased credit card delinquency result we see from the CCP analysis discussed above. 

In Figure 4, we plot our estimated efects of the Camp Fire on credit card spending and repayment quarter-overquarter. Results show a clear increase in credit card spending and repayment immediately following the wildfre among 

borrowers residing in the fre zone. The increases in spending and repayment peaked in the second quarter post-fre 

and then tapered in quarters 3-5. 

16 
The combination of reduced credit card indebtedness (repayment in excess of spending) and increased delinquency/past due shown in Tables 4 and 5 appear puzzling on face value. In that regard, we have two hypotheses: 

Firstly, we hypothesize that homeowners whose property was damaged by the fre used payouts from insurance claims 

to reduce debt inclusive of credit card balance. Secondly, households who did not receive insurance payout were likely 

to become delinquent due to increased credit card spending to cope with the wildfre. 

Unfortunately, the Y-14 data does not contain information on borrower tenure status. We thus return to the CCP 

data and segment our sample into homeowners and renters.20 We further separate high Equifax Risk Score borrowers 

from low Equifax Risk Score borrowers. We then repeat our diference-in-diferences analysis using the segmented 

CCP sample. Table 6 reports our results on credit card balance (Panel A) and credit card delinquency (Panel B). From 

these results, we see that homeowners residing in the fre zone (and thus likely to have experienced property damage) 

did pay down their credit card balance more than those in the control group (Panel A column 2). For those consumers, 

we do not fnd any increase in credit card delinquency (Panel B columns 1 and 2). In contrast, the elevated credit card 

delinquencies emanate from lower Equifax Risk Score renters (Panel B column 3). 

These results and the results in the previous two tables paint a picture of interplay between fre damage and 

insurance payout in shaping consumer fnancial outcomes. Specifcally, the Camp Fire caused property damage and 

work disruptions; in order to cope with the adverse wildfre efects, consumers spent more using their credit cards. 

However, homeowners who received insurance claims payout had greater fnancial capacity to pay down their debt 

inclusive of credit card balance; in contrast, renters lacking insurance payout had fewer fnancial resources to pay 

down their elevated credit card debt and were more likely to fall into delinquency. 

The insurance claims payout story is consistent with Gallagher and Hartley (2017) fndings of mortgage borrowers 

using food insurance payout to pay down their mortgages. Further, it is also supported by additional analysis displayed 

in Appendix Figures A.5 and A.6. In Appendix Figure A.5, we see that for borrowers who remained in the fre zone 

subsequent to the Camp Fire, both number of credit card accounts and credit card balance were reduced signifcantly 

after the fre.21 In Appendix Figure A.6, fndings from CRISM data show the decline in credit card accounts and the 

balance was signifcantly larger for mortgage borrowers residing in the fre zone.22 

20In CCP, we defne consumers with a positive mortgage balance as homeowners. By doing so, we include cash 

buyers/owners in the renter category, which can cause some aggregation bias in the renter analysis. Therefore, we 

excluded renters living at the same address for more than three years to avoid counting consumers as renters if they 

are actually homeowners with a zero mortgage balance. 

21The number of personal loan accounts and retail credit card accounts was reduced signifcantly after the fre. 

22Borrowers in the CRISM sample are all mortgage borrowers as CRISM is a match between McDash mortgage 

17 
C. The Efect of Smoke on Air Pollution 

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. in recent years, and as much as 50% of PM2.5 in 

some Western regions. Further, spatial patterns in ambient smoke exposure do not coincide with typical socioeconomic pollution exposure gradients. Borgschulte et al. (2022) show how smoke events map to daily ground-level air 

quality, using an event study that regresses PM2.5 on a series of indicators for smoke exposure. We employ a similar 

approach. In Figure A.3 we show the efect of wildfre-related smoke on air pollution, using an event study of the 

20 days prior to and after the Camp Fire and among census tracts that were and were not exposed to the smoke. As 

evident, in the aftermath of the Camp Fire, in census tracts treated by wildfre-related smoke, pollution levels increased 

sharply, to 60 μg/m3, roughly equivalent to pollution levels measured in Beijing on that same day. Table A.5 presents 

summary information on fre-related smoke and particulate air pollution (both in levels and in changes in those terms 

compared with the same month in 2015) for each of the California extreme wildfres. On average, in the aftermath 

of the Camp Fire, for example, fndings indicate a monthly average of fve smoke days with pollution levels of 12.4. 

According to the CDC, exposure to PM2.5 above 12 is considered risky and associated with negative consequences.23 

Concerns regarding adverse efects of wildfre-attributable smoke and particulate pollution intensifed in the North 

Central and Northeast U.S. in June 2023 in the wake of widespread Canadian wildfres, which adversely impacted 

large geographies and millions of households. 

Table A.6 shows the efect of smoke days (and changes therein) on air particulate pollution levels, controlling for 

zip code and year /(or month-year) fxed efects. We assess those efects for the 12 months subsequent to the wildfres 

(and separately for Camp and Thomas fres). Results of the analysis indicate a positive and signifcant efect in most 

specifcations. Column 1 in Table A.6 shows that a one standard deviation increase in the number of smoke days 

(11.3) is associated with an increase in pollution of 4.3 (compared to a mean of pollution levels after the fres of 9.7).24 

Column 2 shows that on average, for all fve fres, a one standard deviation increase in delta smoke (the change in 

smoke days in the same months relative to 2015) is associated with an increase in pollution of 2.8 (compared with 

a mean of pollution levels after the fres of 1.3). We fnd similar efects for the Camp and Thomas fres. Column 

servicing reports and consumer credit reports. 

23Burke et al. (2022) fnd that since 2016, wildfre smoke has reversed previous improvements in average annual 

PM2.5 concentrations in two-thirds of U.S. states, eroding 23% of previous gains on average in those states and over 

50% in multiple western states. 

24 3 Borgschulte et al. (2022) fnd that an average smoke day increases PM2.5 by 2.2 μg/m on the day of exposure, 

about one-third of a standard deviation in the distribution of daily particulate matter. 

18 
4 in Table A.6 shows that a one standard deviation increase in smoke days in the two years after the Camp Fire is 

associated with an increase in pollution of 6.1 (compared to a mean of pollution after the Camp Fire of 12.4). Also, 

column 8 in Table A.6 shows that a one standard deviation increase in smoke days in the two years after the Thomas 

Fire is associated with a pollution increase of 0.5 (compared with a mean of pollution after the Thomas fre of 6.8).25 

As is evident, the increment in particulate pollution attributable to extreme wildfres is sizable. 

D. Efects of Air Pollution on Borrower Credit Outcomes 

In this section, we explore the efect of wildfre smoke-attributable air pollution on household credit outcomes. 

As discussed above, expansive geographies and large populations beyond the actual burn perimeter may be treated 

by wildfre-attributable smoke and air pollution. Indeed, heavy smoke and pollution emissions from the roughly 500 

active Canadian wildfres in June 2023 resulted in dangerous and unhealthy air for tens of millions of households in 

the North Central and Northeast United States. 

The existing literature points to some potential channels through which wildfre smoke could afect household 

fnancial health. First, the impact could be through the health-related spending channel. Among the most widely 

documented adverse efects of ambient air pollution are those associated with health, inclusive of increases in hospitalization rates and premature mortality among children and the elderly (Chay and Greenstone (2003), Jayachandran 

(2009), Chen et al. (2013), Deryugina et al. (2019), Anderson (2020)). Approximately one-third of U.S. households 

include someone with an existing respiratory health condition at risk of serious medical complications in the wake of 

prolonged exposure to the fne particulate matter (PM2.5) found in smoke (McCafrey and Olsen (2012). Therefore, 

households may face elevated indebtedness due to medical or related preventative measure spending as necessary to 

cope with the smoke. 

Second, the wildfre-related smoke may afect household fnancial health via an income channel. Smoke events 

may lead to work interruption (Borgschulte et al. (2022)), increased trafc accidents (Matthews (2018)), reduced 

tourism and outdoor recreation (Stotts et al. (2018)),26 and more generally to a reduction in business sales (Addoum 

et al. (2023)), resulting in income loss and deterioration in household fnancial status in the immediate aftermath of the 

event. Further, existing research shows that exposure to air pollution reduces earnings. For example, Borgschulte et al. 

(2022) fnd that a day of county wildfre smoke exposure reduces quarterly per capita earnings by $5.20, representing 

25One possible explanation for the low levels of smoke and pollution after the Thomas Fire is the relatively open 

topography and proximity to the ocean of the burn perimeter. 

26See, also, “Up in Smoke: Canada’s Outdoor Summer Season,” New York Times, July 25, 2023. 

19 
a roughly 0.10 percent reduction in quarterly mean earnings of $5,359.70.27 Borgschulte et al. (2022) also report that 

each day of wildfre smoke reduces quarterly county employment by about 80 per million people aged 16 and older, a 

0.013 percent decline relative to the sample average employment rate of 62.6 percent. Therefore, households exposed 

to wildfre smoke could face fnancial distress due to reduction in income. 

Table 7 shows the efects of air pollution emanating from the Camp Fire on credit delinquencies among distant 

households, defned as those located 5-30 miles from the Camp Fire boundary. We compare consumers residing in zip 

codes that were exposed to pollution levels in excess of the 75th percentile of the air pollution distribution (treatment 

group) to those residing in zip codes with pollution levels in the bottom quartile (control group), before and after 

the Camp Fire. The time frame of the analysis is two years prior to and 18 months after the Camp Fire (until the 

start of the COVID-19 pandemic). Column 1 in Panel A estimates an additional one percentage point increment in 

the likelihood of mortgage delinquency for the treatment group in the wake of exposure to relatively higher levels of 

wildfre-attributed air pollution. As a reference, the average mortgage delinquency rate in our sample is 1 percent. 

Columns 2 through 4 show that exposure to high levels of pollution also result in increases in credit card, personal 

loan, and retail/store card delinquencies. All these results are economically signifcant. 

In Panel B of Table 7, we present results of IV estimation, as specifed in equation 7. Estimates are qualitatively 

consistent with those shown in Panel A. Again, relatively high wildfre-related air pollution results in statistically and 

economically elevated credit and retail/store card delinquencies. However, in some cases, the IV approach does not 

yield precise estimates of the efects of wildfre-attributed air pollution on credit outcomes. 

Table 8 shows the efects of Camp Fire-induced air pollution on credit card usage and credit performance, using the 

Federal Reserve Y-14M data. Panel A of the table shows estimates of pollution efects using year-over-year changes in 

PM2.5 as specifed in equation 6. The outcome variables include monthly measures of credit card spending, repayment, 

account balance, and the likelihood of account past due. To account for possible seasonality, we use year-over-year 

changes in our dependent variables. Changes in credit card spending, repayment, and balance are computed as the 

annualized dollar amount. Results indicate that borrowers exposed to higher levels of wildfre-related air pollution 

on average increase their spending by $389 on an annual basis, relative to those exposed to lower levels of wildfreinduced air pollution. Also, repayment of credit card debt among treated households was $173 less on an annual basis. 

As a result, treated households accumulated roughly $500 more in annualized credit card debt. Findings are consistent 

27They also fnd that a 1 μg/m3 (approximately 10 percent) increase in quarterly PM2.5 concentration generates 

losses in per capita earnings amounting to $103, or about 1.8 percent of quarterly earnings. 

20 
with prior results suggesting that households exposed to severe wildfre-induced air pollution spend more (e.g., likely 

owing in part to smoke-induced health issues) and earn less, resulting in a reduced ability to repay their debt. 

In terms of credit performance, those exposed to relatively severe wildfre-attributed air pollution showed a 2.2% 

elevated likelihood of having an account past due. This fnding is economically signifcant, given an average rate of 

account past due of 11.6%. While the estimated efect of wildfre-related pollution on household credit performance 

is not as large as that computed for wildfre structural damage (above), bear in mind that the smoke-treated group is 

largely dispersed and comprises a substantially larger population. 

In Table 8 Panel B, we present the results of our IV estimation, as specifed in equation 7. Estimates are consistent 

with those shown in Panel A. Again, heavy wildfre-induced air pollution resulted in additional credit card spending 

and reduced repayment among treated borrowers. Borrowers exposed to heavy wildfre-induced air pollution accumulated more credit card debt and were more likely to have a credit account past due. As discussed previously, the 

regressions include substantial controls such as highly granular fxed efects and time-varying borrower attributes such 

as refreshed borrower credit score and account current credit limit. 

Above we cite the literature and discuss potential health-related and income channels through which wildfre 

smoke may afect household spending, debt repayment, and fnancial distress. In addition, we provide further corroborating evidence of the efects of the Camp Fire on internet searches for pollution mitigants and emergency department 

visits. Appendix Figure A.7 shows marked increases in Google Trends internet search for air purifers as well as 

elevated concerns regarding smoke inhalation and health deterioration in the immediate aftermath of the Camp Fire. 

Further, in Table Appendix A.8 , we estimate the efects of the Camp Fire-induced pollution on number of emergency 

department visits for children and number of emergency department visits for individuals with asthma. In the wake 

of the Camp Fire, we see an increase in those adverse health indicators among counties that experienced high levels 

of pollution compared to counties in the bottom quartile of pollution exposure. Our fndings are consistent with the 

Agency for Healthcare Research and Quality analysis of state inpatient and emergency department databases in California documenting an increase in smoke inhalation and emergency room visits after the Camp Fire.28 We believe 

that the above documented Google search and adverse health efects of wildfre-related smoke and pollution, along 

with fndings in the existing literature, provide a reasonable explanation for the evidenced efects of such events on 

household spending, indebtedness, and fnancial distress. 

Table 9 reports on heterogeneity in smoke efects among population stratifed by credit score. Results here con28https://hcup-us.ahrq.gov/reports/ataglance/HCUPAnalysisCA2018Wildfres.pdf. 

21 
form qualitatively to above analyses of wildfre burn efects. For example, estimates indicate that the reduction in 

credit card repayment is evidenced primarily among lower credit score borrowers, consistent with the idea that those 

borrowers in the absence of adequate government assistance typically have fewer resources to cope with natural disasters. In contrast, the increase in credit card spending is found largely among prime borrowers. Those borrowers likely 

have the capacity to spend more on preventive measures to combat air pollution induced by the wildfre. In Appendix 

Table A.7 , we compare our IV wildfre-induced pollution estimates across diferent extreme wildfres. While results 

vary across wildfres, they are qualitatively consistent in indicating that consumers exposed to relatively heavy air 

pollution spend more and repay less, compared to those exposed to low or no pollution. 

In Figure 6, we plot the time-varying efects of wildfre-induced air pollution on credit card usage. In the initial 

quarter following a wildfre, we see a marked increment in credit card spending. The estimated efect remains elevated 

in the next few quarters but tends to dissipate over time. We also see reduced credit card repayment in the quarters 

after the wildfre for those who were exposed to relatively heavy air pollution. 

Finally, we undertake back-of-the-envelope calculations to infer the smoke and pollution efects of the extreme 

2023 Canadian wildfres on the fnancial status of exposed U.S. households. We frst compute downwind spending 

and debt efects of the 2018 Camp Fire smoke events for San Francisco Bay Area households and then compare to 

estimates of 2023 Canadian wildfre smoke events for New York Metro Area households. Our metric of particulate 

pollution, PM2.5, was substantially elevated and in the unhealthy range of 100 in both areas as a result of the extreme 

Wildfres. Smoke plumes from the Canadian wildfres resulted in heavy pollution for the 19 million residents of the 

New York Metro Area for a duration of roughly 4 days. In contrast, the 8 million residents of the San Francisco Bay 

Area residents were exposed to heavy Camp Fire related smoke and pollution for roughly 9-1/2 days. If we discount 

the Camp Fire point estimate by roughly 60 percent to account for the reduced duration of the Canadian wildfre 

pollution efects, results suggest that afected Americans in the New York area incurred an incremental $3.1 billion in 

annualized credit card spending and $4 billion in annualized credit card debt as a direct result of the 2023 Canadian 

smoke and pollution events, roughly on par with estimates for the Bay Area.29 Future research should undertake 

detailed analyses to assess the external validity of our smoke and pollution estimates to more recent extreme wildfre 

events. 

29Note that an incremental $4 billion in annualized consumer credit card debt is roughly equivalent to .4 percent of 

the $1 trillion in credit card debt outstanding. 

22 
V. Conclusions 

Despite the growing incidence, severity, and geography of extreme wildfre events, there exists limited evidence of 

localized burn as well as dispersed smoke and pollution efects on household fnancial well-being. Adverse household 

fnancial efects extend well beyond fre perimeters and owe to substantial, far fung, and lingering wildfre-related 

smoke and particulate emissions. In this paper, we provide estimates of wildfre and attributable smoke and particulate 

pollution efects across a broad array of household economic and fnancial indicators. The analysis derives from the 

combination of highly-articulated datasets on wildfres, wildfre-induced smoke, air pollution, and consumer economic 

and fnancial outcomes. Using a diference-in-diferences approach, we compare migration patterns, house prices, and 

credit usage in fre zones to outcomes in 1- and 5-mile rings beyond the fre perimeter, before and after wildfre events. 

We fnd a signifcant increase in net migration from tracts that experienced the most destructive wildfres as well as 

marked declines in house prices in the quarters immediately following the fre event. Among burn zone households, 

we also fnd an increase in fnancial distress, measured by mortgage, credit card, and personal loan delinquencies. 

For example, in the case of the Camp Fire, households living within the fre perimeter recorded an increase in bank 

credit card delinquency rates of 2 percentage points compared to an average level of 4 percent. Further analyses reveal 

that elevated credit card delinquencies largely were associated with lower credit score renter households. In contrast, 

homeowners in the fre zone were able to pay of their credit card balances faster than usual, perhaps owing to payout 

of insurance claims. 

We then explore the household fnancial efects of wildfre-attributed but broadly-difused smoke and air pollution 

events. We provide new estimates of the causal concentration-response relationship between air pollution and fnancial 

outcomes using quasi-experimental exposures to wildfre smoke. As evidenced in the 2023 Canadian wildfres, those 

events can emit large amounts of smoke that contain harmful pollutants and drift for hundreds of miles, often afecting 

substantial population far from the fre zones. Our analysis frst assesses variation in air quality induced by wildfre 

smoke. Using satellite-based measures of daily smoke plumes for the entire U.S., we estimate the efects of the 

wildfre-related smoke on changes in ground-level PM2.5. We then estimate the relationship between smoke-induced 

air pollution and credit outcomes using a panel data model with two-way fxed efects. Exposure to heavy pollution 

resulted in an increase in mortgage, credit card, and retail/store card delinquencies. Households living in zip codes 

with high levels of pollution from the Camp Fire, for example, also demonstrated added credit card spending and 

lower credit card repayment. The estimated efects of wildfre-induced pollution on household credit usage and credit 

23 
performance are smaller in magnitude than those associated with wildfre-treated households within the burn zone. 

For example, the Camp Fire resulted in an average 45 percent increase in the likelihood of credit card past due among 

burn zone households, whereas distant wildfre-attributable emissions and particulate pollution are associated with 

a 20 percent increase in credit card past due. That said, the estimated wildfre-induced particulate pollution efects 

are salient to substantial population dispersed across expansive geographies beyond the burn perimeter. For example, 

a conservative imputation of our estimates to New York consumers adversely afected by the downwind smoke and 

pollution associated with the 2023 extreme Canadian wildfres suggests increments of $3 billion in household credit 

card spending and $4 billion in household credit card debt. 

Overall, our fndings indicate that adverse efects of wildfres can go far beyond the fre perimeter. Failure to 

account for broadly difused and consequential smoke and pollution events yields a partial and incomplete rendering 

of household fnancial efects of extreme wildfres. Finally, future research should assess the external validity of 

fndings to severe wildfre and related smoke and pollution events increasingly evidenced in such places as eastern 

Canada and southern Europe. 

24 
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28 
Figure 1. Extreme Wildfres in California between 2016 and 2020 and the 1-, 5-, and 10-Mile 

Peripheral Rings 

Notes: This fgure shows the geographic location of the extreme wildfres (with more than 1,000 destroyed structures) 

in California between 2016 and 2020. The red area is the fre footprint; the brown, orange, and yellow areas are the 

1-mile, 5-mile, and 10-mile peripheral rings, respectively. Source: U.S. Forest Service Monitoring Trends in Burn 

Severity (MTBS) database. 

29 
Figure 2. Treatment and Control Areas in the Camp Fire Analyses 

Notes: This fgure shows the treatment and control areas in the Camp Fire analyses. The red area is the fre footprint, 

which is the treatment area; the brown area is a 1-mile peripheral ring, which we carve out in our analysis; the orange 

area is a 1- to 5-mile peripheral ring, which is the control area; and the yellow area is a 5- to 10-mile peripheral ring, 

which is an alternative control area. The border lines are census blocks in California. Source: U.S. Forest Service 

Monitoring Trends in Burn Severity (MTBS) database. 

30 
Panel A Panel B 

Panel C Panel D 

Figure 3. The Efect of the Camp Fire on Household Financial Distress 

Notes: This fgure shows the temporal pattern of the estimated Camp Fire efect on consumer credit delinquency rates 

in a diference-in-diferences framework. We compare consumers living in wildfre-burned areas (the treatment group) 

with those that are 1-5 miles away from the fre perimeter (the control group), before and after the Camp Fire. Control 

variables include quarter-year and consumer fxed efects. Sources: Federal Reserve Bank of New York Consumer 

Credit Panel/Equifax data (CCP). 

31 
Panel A Credit card spending 

Panel B Credit card repayment 

Figure 4. Efects of the Camp Fire on Credit Card Spending and Repayment 

Notes: This fgure shows the temporal pattern of the estimated Camp Fire efect on credit card spending and repayment 

in a diference-in-diferences framework. We compare borrowers living in wildfre burn areas (the treatment group) 

with those that are 1-5 miles away from the fre perimeter (the control group), before and after the Camp Fire. We 

include account fxed efects and year-month fxed efects. Additional time-varying control variables include refreshed 

borrower credit score and current credit limit of the credit card account. Sources: Air pollution data are from the EPA’s 

Air Quality System and credit card data are from the Federal Reserve Y-14M. 

32 
Two Months Before the Fire: One Month Before the Fire: Smoke delta levels: 

Month of the Fire: One Month After the Fire: Pollution Delta levels 

Two Months After the Fire: 

Figure 5. Delta Smoke and Pollution – Camp Fire 

Notes: This fgure shows the variation in changes in smoke and pollution (relative to the same months in 2015) two 

months before and after the Camp Fire. The red area is the fre footprint. The black circle is a radius of 30 miles 

from the fre. The border lines are zip codes. Each zip code is colored in gray or blue according to the change in the 

number of smoke days in the current month relative to the same month in 2015 (the base year). The dots represent the 

pollution monitors, where shades of green represent a decline in pollution levels compared with the same month in 

2015 (the base year). The orange-red color of the pollution monitors means an increase in pollution levels compared 

with the same month in 2015. Sources: NOAA’s Hazard Mapping System (HMS) and EPA’s Air Quality System. 

33 
Panel A Credit card spending 

Panel B Credit card repayment 

Figure 6. Efects of Camp Fire-Induced Air Pollution on Credit Card Spending and Repayment 

Notes: This fgure shows the temporal pattern of the estimated efect of Camp Fire-induced air pollution on credit card 

spending and repayment in a diference-in-diferences framework. We focus on areas that are 5-30 miles away from 

the Camp Fire perimeter (to isolate smoke efect from direct fre efect) and compare borrowers that were exposed 

to heavy pollution (pollution level above the 75th percentile) to those exposed to light pollution (pollution level in 

the bottom quartile), before and after the Camp Fire. We include account fxed efects and year-month fxed efects. 

Additional time-varying control variables include refreshed borrower credit score and current credit limit of the credit 

card account. Sources: Air pollution data are from the EPA’s Air Quality System, and credit card data are from the 

Federal Reserve Y-14M. 

34 
Table 1. List of Extreme Wildfres in the U.S. Between 2016 and 2020 

Fire Name Destroyed Structures Date State 

Camp 

Central LNU Complex 

Glendale 

North Complex 

Chimney Tops 

Carr 

LNU Lightning Complex 

CZU AUG Lightning 

Beachie Creek 

Glass 

Thomas 

17,764 

6,862 

3,000 

2,288 

2,018 

1,610 

1,469 

1,329 

1,292 

1,198 

1,053 

11/8/2018 CA 

10/9/2017 CA 

1/29/2016 OK 

8/17/2020 CA 

11/23/2016 TN 

7/23/2018 CA 

8/17/2020 CA 

8/16/2020 CA 

8/16/2020 OR 

9/27/2020 CA 

12/4/2017 CA 

Notes: This table lists all the extreme wildfres (destroyed over 1,000 structures) in the United States in 2016-2020. The table also includes the 

number of destroyed structures, the date, and each fre’s location (state). Sources: Data on the location and destruction of the fres has been processed 

by St Denis et al. (2020), using information from the U.S. National Incident Management System/Incident Command System (ICS). 

35 
Table 2. Efects of Camp Fire on Net 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 

Notes: This table shows the results of the estimation of the efect of the Camp Fire on migration. We compare wildfre-treated tracts (e.g., tracts 

within the burn footprint) to control tracts, before and after the event. The time frame is two years before and after each fre. All columns include 

time and location fxes efect. The frst three columns show the results of the estimation of the efect of the Camp Fire on in-migration, out-migration, 

and net-migration. Net migration is defned as the out-migration minus in migration as a percentage of the population in the census tract. Column 4 

compares net-migration between the fre zone to tracts that are from 1-10 miles from the fre, and column 5 compares tracts in the fre zone to those 

1-20 miles from the fre. Standard errors clustered by census tract in parentheses: ***p < 0.01, **p < 0.05, and *p < 0.1. Sources: Federal Reserve 

Bank of New York Consumer Credit Panel/Equifax data (CCP). 

36 
Table 3. Efects of the Camp Fire on House Prices 

1 2 3 4 

House Price Number of Repeated Sales Residential 

Index Transactions Median Price Vacancy 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 mean 244.4 20.6 280,007 0.03 

Notes: This table shows the results of diference-in-diferences estimation of the efect of the 2018 Camp Fire on house prices and vacancy rate for 

census tracts in the fre zone vs. census tracts 1 to 5 miles farther from the Camp Fire perimeter. Column 1 reports the efect of Camp Fire on the 

home price index, column 2 reports the efect on number of sales transactions, column 3 on repeat median sales price, and column 4 on residential 

vacancy rate. All estimations include time and location fxed efect. The time frame is two years before and after each fre. Standard errors clustered 

by census blocks in parentheses: ***p < 0.01, **p < 0.05, and *p < 0.1. Sources: CoreLogic Home Price Index (HPI) and U.S. Department of 

Housing and Urban Development. 

37 
Table 4. Efects of the Camp Fire on Consumer Financial Distress 

1 2 3 4 

Mortgage Credit Card Personal Loan Retail/Store 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 mean 0.01 0.04 0.08 0.12 

Notes: This table shows the results of the estimation of the efect of the Camp fre on delinquency rates. We compare consumers residing in 

wildfre-treated census blocks (e.g., blocks within the burn footprint) to those residing in control blocks up to 1-5 miles from the fre, before and 

after the fre. All specifcations include consumer and time-fxed efects. The analysis includes eight quarters prior to and six quarters after the fre 

event. We focused on the fnancial decisions of only those households who were present in the sample throughout to avoid comparison of diferent 

sampled populations before and after the fre. Standard errors clustered by census tract in parentheses: ***p < 0.01, **p < 0.05, and *p < 0.1. 

Sources: Federal Reserve Bank of New York Consumer Credit Panel/Equifax data (CCP). 

38 
Table 5. Efects of the Camp Fire on Credit Card Spending and Repayment 

1 2 3 4 

∆ Spending ∆ Payment ∆ Balance ∆ Past Due 

Treated × Post 1112.313*** 1557.085*** -1889.632*** 0.043*** 

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

Time-varying 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 mean -67.483 349.869 464.118 0.095 

Notes: This table shows the diference-in-diferences estimates of the efect of wildfre on credit card spending, repayment, balance, and past due. 

We compare borrowers residing in wildfre burn areas to those residing between 1 to 5 miles from the fre perimeter, before and after the Camp 

Fire. The time frame is 14 months before and after the Camp Fire. Year-over-year change (∆) in spending, payment, and balance are annualized 

dollar amounts. Time-varying borrower attributes include updated borrower credit score and current credit limit of the credit card account. Robust 

standard errors in parentheses (error terms clustered at the zip code-level): ***p < 0.01, **p < 0.05, and *p < 0.1. Sources: U.S. Department of 

Homeland Security National Incident Management System/Incident Command System (ICS) and U.S. Forest Service Monitoring Trends in Burn 

Severity (MTBS) databases; Federal Reserve Y-14M for credit card data. 

39 
Table 6. Heterogeneous Efects of Camp Fire on Credit Card Balance and Delinquency 

Homeowners Renters 

1 2 3 4 

Panel A: Credit Balance Equifax Risk Equifax Risk Equifax Risk Equifax Risk 

Scores ≤ 720 Scores > 720 Scores ≤ 720 Scores > 720 

Treated × Post -1,115.22 -1,401.26*** 

(845.81) (474.45) 

Time-varying bor- ✓ ✓

rower attributes 

Consumer FE + +

Year-qtr FE + +

-522.10 -195.61 

(319.48) (640.36) 

✓ ✓

+ +

+ +

Observations 637 4,358 3,009 1,528 

R-squared 0.98 0.90 0.97 0.92 

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

Panel B: Delinquency Equifax Risk Equifax Risk Equifax Risk Equifax Risk 

Scores ≤ 720 Scores > 720 Scores ≤ 720 Scores > 720 

Treated × Post 0.01 0.00 

(0.00) (0.00) 

Time-varying bor- ✓ ✓

rower attributes 

Consumer FE + +

Year-qtr FE + +

Observations 3,213 16,434 

R-squared 0.80 0.60 

0.09*** 0.00 

(0.02) (0.00) 

✓ ✓

+ +

+ +

8,692 3,102 

0.73 0.08 

Dependent variable mean 0.01 0.00 0.06 0.00 

Notes: This table shows the efect of the Camp Fire on credit card balance and delinquency, in Panel A and Panel B respectively, based on subsamples 

for diferent credit score segments. We compare consumers residing in wildfre-treated census blocks (e.g., blocks within the burn footprint) to those 

residing in control blocks up to 1-5 miles from the fre, before and after the fres. The time frame is two years before and after the Camp Fire. We 

focused on the fnancial decisions of only those households who were present in the sample throughout to avoid comparison of diferent sampled 

populations before and after the fre. Homeowners were defned as those with a positive mortgage balance. We excluded renters living in the same 

address for more than three years to avoid counting consumers as renters if they are actually homeowners with a zero mortgage balance. Standard 

errors clustered by census tract in parentheses: ***p < 0.01, **p < 0.05, and *p < 0.1. Sources: Federal Reserve Bank of New York Consumer 

Credit Panel/Equifax data (CCP). 

40 
Table 7. Efects of Camp Fire-Induced Pollution on Credit Outcomes 

1 2 3 4 

Panel A Mortgage Credit Card Personal Loan Retail/Store Card 

Delinquency Delinquency Delinquency Delinquency 

Treated × Post 0.01*** 0.02*** 0.05** 0.02*** 

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

Time-varying 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/Store Card 

Delinquency Delinquency Delinquency Delinquency 

Treated × Post 0.01 0.02* 0.01 0.02* 

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

Time-varying 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 

Notes: This table shows the OLS and IV estimates, in Panel A and Panel B respectively, of the efect of wildfre-related air pollution on credit 

delinquencies. We compare wildfre-treated zip codes that were exposed to pollution levels above the 75th percentile, to those with lower pollution 

levels, below the 25 percentile, before and after the Camp Fire. We focus on zip codes located 5 to 30 miles from the Camp Fire. The time frame is 

two years before the Camp Fire and 18 months after. Robust standard errors in parentheses (error terms clustered at zip code-level): ***p < 0.01, 

**p < 0.05, and *p < 0.1. Sources: Air pollution data were obtained from the EPA’s Air Quality System, and Federal Reserve Bank of New York 

Consumer Credit Panel/Equifax data (CCP). 

41 
Table 8. Efects of Camp Fire-Induced Pollution on Credit Card Spending and Repayment 

1 2 3 4 

Panel A ∆ Spending ∆ Payment ∆ Balance ∆ Past Due 

Treated × Post 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 

Treated × Post 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 

Notes: This table shows the OLS and IV estimates, in Panel A and Panel B respectively, of the efect of wildfre-related air pollution on credit card 

spending, payment, balance, and past due in a diference-in-diferences framework. We focus on areas that are 5-30 miles away from the Camp Fire 

perimeter (to isolate smoke efect from direct fre efect) and compare borrowers that were in zip codes exposed to heavy pollution (pollution level 

above the 75 percentile) to those in zip codes exposed to light pollution (pollution level in the bottom quartile), before and after the Camp Fire. The 

time frame is 14 months before and after the Camp Fire. Year-over-year change (∆) in spending and payment are annualized dollar amounts. We 

include account fxed efects and year-month fxed efects. Additional time-varying control variables include refreshed borrower credit score and 

current credit limit of the credit card account. Robust standard errors in parentheses (error terms clustered at the zip code-level): ***p < 0.01, **p 

< 0.05, and *p < 0.1. Sources: EPA’s Air Quality System for air pollution data; Federal Reserve Y-14M for credit card data. 

42 
Table 9. 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 

Treated × Post 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 

Treated × Post -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 

Notes: This table shows the IV estimates of the efect of wildfre-related air pollution on credit card spending and payment, in Panel A and Panel B 

respectively, based on subsamples for diferent credit score segments. We compare borrowers in wildfre-treated zip codes that were exposed to high 

pollution levels (those in the upper quartile), to those in zip codes exposed to lower pollution levels (those in the bottom quartile), before and after 

the Camp Fire. The time frame is two years before and after the Camp wildfre. Year-over-year change (∆) in spending and payment are annualized 

dollar amounts. Time-varying borrower attributes include current credit score (40-point) bins and current credit limit bins. Robust standard errors 

in parentheses (error terms clustered at the zip code-level): ***p < 0.01, **p < 0.05, and *p < 0.1. Sources: EPA’s Air Quality System for air 

pollution data; Federal Reserve Y-14M for credit card data. 

43 
Two Months Before the Fire: One Month Before the Fire: Smoke delta levels: 

Month of the Fire: One Month After the Fire: Pollution Delta levels 

Two Months After the Fire: 

Figure A.1. Delta Smoke and Pollution - Carr Fire 

Notes: This fgure shows the variation in changes in smoke and pollution (relative to the same months in 2015) two 

months before and after the Carr Fire. The red area is the Carr Fire footprint. The black circle is a radius of 30 miles 

from the fre. The border lines are zip codes. Each zip code is colored in gray or blue according to the change in the 

number of smoke days in the current month relative to the same month in 2015 (the base year). The dots represent the 

pollution monitors, with green shades representing a decline in pollution levels relative to the same month in 2015 (the 

base year). The orange-red color of the pollution monitors means an increase in pollution levels relative to the same 

month in 2015. Sources: NOAA’s Hazard Mapping System (HMS) and EPA’s Air Quality System. 

44 
Two Months Before the Fire: One Month Before the Fire: Smoke delta levels: 

Month of the Fire: One Month After the Fire: Pollution Delta levels 

Two Months After the Fire: 

Figure A.2. Delta Smoke and Pollution - Thomas Fire 

Notes: This fgure shows the variation in changes in smoke and pollution (relative to the same months in 2015) two 

months before and after the Thomas Fire. The red area is the Thomas Fire footprint. The black circle is a radius of 30 

miles from the fre. The border lines are zip codes. Each zip code is colored in gray or blue according to the change in 

the number of smoke days in the current month relative to the same month in 2015 (the base year). The dots represent 

the pollution monitors, where the meaning of green colors represents a decline in pollution levels relative to the same 

month in 2015 (the base year). The orange-red color of the pollution monitors means an increase in pollution levels 

compared with the same month in 2015. Sources: NOAA’s Hazard Mapping System (HMS) and EPA’s Air Quality 

System. 

45 
Figure A.3. Wildfre Smoke Elevated PM2.5 After the Camp Fire 

Notes: This fgure shows the efect of wildfre smoke on pollution levels for all the zip codes up to 30 miles from the 

fre perimeter, using an event study 20 days before and after the Camp Fire, between census tracts that experienced 

smoke, and census tracts without smoke, as showed in equation 3. The vertical gray line represents the start date of 

the Camp Fire. Sources: NOAA’s Hazard Mapping System (HMS) and EPA’s Air Quality System. 

46 
Figure A.4. The Efect of 2018 Camp Fire on Out-Migration and Net-Migration 

Notes: This fgure shows the time dynamic of estimated Camp Fire-related out-migration and net-migration efects, 

between households living in the fre zone, to households living in census tracts 1 to 5 miles from the Camp Fire 

zone. The fgure shows the patterns a few quarters prior and subsequent to the Camp Fire event, occurred in California 

during November 2018. Sources: Federal Reserve Bank of New York Consumer Credit Panel/Equifax data (CCP). 

47 
Figure A.5. The Efect of 2018 Camp Fire on the Number of Accounts and Credit Balance - from 

the FRBNY Consumer Credit Panel/Equifax Data 

Notes: This fgure shows the time dynamic of estimated Camp Fire-related number of mortgage account, number of 

bank credit card accounts, mortgage balance, and credit card balance between households living in the fre zone, to 

households living in census tracts that are 1 to 5 miles from the Camp Fire zone. The changes in balance restricted to 

households that experienced no change in the number of accounts. The fgure shows the time dynamic patterns a few 

quarters prior and subsequent to the Camp Fire event, occurred in California during November 2018. Sources: Federal 

Reserve Bank of New York Consumer Credit Panel/Equifax data (CCP). 

48 
Figure A.6. The Efect of 2018 Camp Fire on Credit Balance and Number of Accounts - From the 

CRISM 

Notes: This fgure shows the time dynamic of estimated Camp Fire-related credit balance and number of credit accounts, between households living in the fre zone, to households living in census tracts that are 1 to 5 miles from 

the Camp Fire zone. The changes in balance restricted to households that experienced no change in the number of 

accounts. The fgure shows the time dynamic patterns from the CRISM, 24 months prior and subsequent to the Camp 

Fire event, occurred in California during November 2018. Sources: Credit Risk Insight Servicing McDash (CRISM). 

49 
Figure A.7. Efects of Camp Fire-Induced Air Pollution on Health Symptoms and Adaptation 

Notes: This fgure shows the temporal pattern of the estimated efect of Camp Fire-induced air pollution on real-time 

search indicators of spending as a response to pollution exposure and possible health efects. Using Google Trends 

data, we consider search keywords for adaptation, such as “Air purifer,” “Air flter,” and “Air pollution,” and health 

symptoms, such as “Smoke inhalation.” The vertical line represents the start date of the Camp Fire. Sources: search 

query data from Google Trends. 

50 
Table A.1 . List of Wildfres Across States Between 2016 and 2020 

State Freq Percent Cum. 

AK 1 0.7 0.7 

AZ 4 3.0 3.7 

CA 69 51.1 54.8 

CO 7 5.2 60.0 

FL 9 6.7 66.7 

ID 2 1.5 68.2 

KS 1 0.7 68.9 

MT 6 4.4 73.3 

NV 2 1.5 74.8 

OK 5 3.7 78.5 

OR 14 10.4 88.9 

TX 1 0.7 89.6 

UT 3 2.2 91.8 

WA 8 5.9 97.8 

WY 3 2.2 100.0 

Total 135 100 

Notes: This table shows the wildfres distribution in our sample. Data include 135 wildfres between 2016 and 2020, 69 of them are in California, 

14 in Oregon, and 9 in Florida. Source: U.S. National Incident Command System. 

51 
Table A.2 . Descriptive Statistics 

Variable Fire Zone Outside Fire Zone 

Obs Mean Std. Dev. Obs Mean Std. Dev. 

Total Bank Card Balance 19,726 5,169 10,088 135,350 5,273 9,895 

Personal Loan Balance 4,197 6,611 19,648 23,599 5,437 21,021 

First Mortgage Balance 5,911 299,602 381,336 27,596 331,056 306,070 

Credit Card Delinquency Rate 15,249 0.04 0.17 84,248 0.04 0.17 

Personal Loan Delinquency Rate 2,511 0.07 0.25 14,459 0.08 0.26 

First Mortgage Delinquency Rate 5,911 0.02 0.13 27,596 0.01 0.12 

Number Credit Card Accounts 18,890 2.02 2.06 101,697 2.06 2.14 

Number Personal Loan Accounts 18,890 0.32 0.70 101,697 0.33 0.71 

Number First Mortgage Accounts 18,890 0.39 0.72 101,697 0.32 0.63 

Equifax Risk Score 18,747 732.55 96.21 101,019 718.09 96.81 

Age 21,916 66.36 20.88 116,092 58.95 20.82 

Notes: This table provides summary statistics for the samples of households living in the fre zone and those that live outside the fre zone (and 

up to fve miles). The time frame is two years before and after each of the fve wildfres. The table shows the average among the fve diferent 

fres (Camp, Carr, Thomas, Central LNU Complex, and LNU Lightning Complex). Sources: Federal Reserve Bank of New York Consumer Credit 

Panel/Equifax data (CCP). 

52 
Table A.3 . Heterogeneous Efects of Extreme Wildfres on Financial Distress: Diferent Fires 

1 2 3 4 5 6 

Carr Thomas Central LNU Complex 

Bank card Mortgage Bank card 

Delinquency Delinquency Delinquency 

Mortgage 

Delinquency 

Bank card 

Delinquency 

Mortgage 

Delinquency 

-0.00 -0.04* -0.00 -0.01 0.01 0.03** 

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

0.00 0.02 -0.01 0.02* -0.01 0.01 

(0.01) (0.01) (0.00) (0.01) (0.01) (0.01) 

✓ ✓ ✓ ✓ ✓ ✓

+ + + + + +

+ + + + + +

57,087 19,398 126,403 7,257 89,411 1,297 

0.14 0.16 0.14 0.23 0.14 0.19 

Treated 

Treated × Post 

Time-varying borrower attributes 

Census tract FE 

Year-qtr FE 

Observations 

R-squared 

Dependent variable 0.04 0.02 0.03 0.02 0.03 0.02 

Notes: This table shows the results of the estimation of the efect of Carr, Thomas, and LNU Fires on delinquency rates. We compare consumers 

residing in wildfre-treated census blocks (e.g., blocks within the burn footprint) to those residing in control blocks up to 1-5 miles from the fre, 

before and after the fres. All specifcations include borrowers’ characteristics (age, based on year of birth, and Equifax risk score), location, and 

time-fxed efects. The analysis includes eight quarters prior to and eight quarters after the fre event. We focused on the fnancial decisions of only 

those households who were present in the sample throughout to avoid comparison of diferent sampled populations before and after the fre. We 

further controlled for the characteristics of sampled households, including their age and Equifax risk score. Standard errors clustered by census tract 

in parentheses: ***p < 0.01, **p < 0.05, and *p < 0.1. Sources: Federal Reserve Bank of New York Consumer Credit Panel/Equifax data (CCP). 

53 
Table A.4 . Efects of Extreme Wildfres on the Number of Credit Accounts 

1 2 3 4 5 6 7 8 

Camp Fire Thomas Fire Carr Fire Central LNU Complex 

Bank First Bank First Bank First Bank First MortCard Mortgage Card Mortgage Card Mortgage Card gage NumNumber Number Number Number Number Number Number ber 

Accounts Accounts Accounts Accounts 

Treated 0.25 0.033 -0.04 -0.03 0.34 0.19** 0.07 0.13* 

(0.27) (0.06) (0.19) (0.07) (0.23) (0.09) (0.27) (0.08) 

Treated × Post -0.37*** -0.12*** -0.09* -0.00 -0.29*** -0.02 -0.15* -0.01 

(0.07) (0.02) (0.05) (0.02) (0.11) (0.03) (0.08) (0.02) 

Borrowers Char- ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

acteristics 

censustract fe + + + + + + + +

Year-Month fe + + + + + + + +

Observations 87,367 87,367 149,839 149,839 68,183 68,183 122,107 122,107 

R-squared 0.04 0.12 0.03 0.11 0.03 0.10 0.03 0.16 

Dependent variable 1.85 0.28 2.12 0.33 1.94 0.32 2.27 0.37 

Notes: This table shows the results of estimation of the efect of extreme fres on the number of accounts. We compare consumers residing in 

wildfre-treated census blocks (e.g., blocks within the burn footprint) to those residing in control blocks up to 1-5 miles from the fre, before and 

after the fres. We focused on the fnancial decisions of only those households who were present in the sample throughout to avoid comparison of 

diferent sampled populations before and after the fre. All specifcations include borrowers’ characteristics (age and Equifax Risk Score), location, 

and time-fxed efects. The analysis includes months prior and subsequent to the fre. Standard errors clustered by census tract in parentheses: ***p 

< 0.01, **p < 0.05, and *p < 0.1. Sources: Federal Reserve Bank of New York Consumer Credit Panel/Equifax data (CCP). 

54 
Table A.5 . Summary Statistics for Smoke and Pollution 

1 2 3 4 5 6 7 8 9 10 11 12 

After the event Camp Fire Carr Fire Thomas Fire Central LNU Complex 

Obs Mean S.d. Obs Mean S.d. Obs Mean S.d. Obs Mean S.d. 

smoke days 151,229 5.3 8.2 106,214 3.8 6.0 353,926 0.2 0.4 183,419 4.3 5.3 

smoke delta 151,229 1.3 4.7 106,214 1.1 3.4 353,926 -3.0 3.2 183,419 -0.3 7.0 

pm25 151,229 12.4 13.7 106,214 6.1 3.0 353,926 6.8 2.6 183,419 7.7 3.4 

pm25 delta 151,229 3.7 13.1 106,214 0.9 3.1 353,926 -2.6 1.8 183,419 0.0 2.8 

Notes: This table provides summary statistics for smoke days, pollution levels (pm2.5), and the change in smoke days 

and pollution levels compared with the same month in 2015, for each of the fve wildfres in our paper. The time 

frame is eight quarters after each fre. We explore all zip codes 30 miles from each fre. Sources: Air pollution data 

were obtained from the EPA’s Air Quality System, and measures of daily smoke exposure were developed by Miller 

et al. (2021) using analysis of wildfre smoke produced by the National Oceanic and Atmospheric Administration 

(NOAA)’s Hazard Mapping System (HMS). 

55 
Table A.6 . Efects of Wildfre Smoke on Air Pollution 

1 2 3 4 5 6 7 8 9 10 

All Fires Camp Fire Thomas Fire 

pm25 pm25 delta pm25 pm25 pm25 delta pm25 delta pm25 pm25 pm25 delta pm25 delta 

smoke days 0.38*** 

(0.12) 

smoke delta 0.54*** 

(0.14) 

zipcode fe + +

Year-Month fe 

Observations 2,097,259 2,097,259 

R-squared 0.38 0.39 

0.05 1.15*** 

(0.05) (0.04) 

0.07 1.85*** 

(0.16) (0.15) 

+ + + +

+ +

231,078 231,078 231,078 231,078 

0.96 0.63 0.95 0.61 

2.44*** 1.25*** 

(0.33) (0.13) 

2.09*** 0.91*** 

(0.39) (0.12) 

+ + + +

+ +

703,494 703,494 703,494 703,494 

0.79 0.55 0.77 0.49 

Notes: This table shows the results of the estimation of the efect of smoke (and the change in smoke days) on pollution levels, controlling for zip 

code and year (or month-year) fxed efects. The time frame is twelve months after the fres (and separately for Camp and Thomas Fires). We 

explore all zip codes that are 30 miles from each fre. Standard errors clustered by census tract in parentheses: ***p < 0.01, **p < 0.05, and *p <

0.1. Sources: Measures of daily smoke exposure were developed by Miller et al. (2021) using analysis of wildfre smoke produced by the National 

Oceanic and Atmospheric Administration’s Hazard Mapping System (HMS). Air pollution data were obtained from the EPA’s Air Quality System. 

56 
Table A.7 . Heterogeneous Efects of Wildfre-Induced Pollution on Credit Card Spending and 

Repayment: Diferent Fires 

1 2 3 4 

Panel A: ∆ Spending Camp Fire Thomas Fire Carr Fire Central LNU 

Treated × Post 383.133*** 398.239 863.962*** 141.364 

(61.975) (576.381) (396.155) (294.493) 

Time-varying borrower attributes ✓ ✓ ✓ ✓

Account FE + + + +

Month-year FE + + + +

Observations 701,778 160,516 92,115 381,834 

R-squared 0.078 0.100 0.053 0.045 

Dependent variable mean -398.244 -347.286 -245.364 -296.653 

Panel B: ∆ Payment Camp Fire Thomas Fire Carr Fire Central LNU 

Treated × Post -167.930*** -280.467 -1636.310*** -258.522 

(38.180) (406.342) (118.780) (292.491) 

Time-varying borrower attributes ✓ ✓ ✓ ✓

Account FE + + + +

Year-Month FE + + + +

Observations 701,778 160,516 92,115 381,834 

R-squared 0.050 0.051 0.022 0.027 

Dependent variable mean 428.649 72.140 124.304 132.852 

Notes: This table shows the IV estimates of the heterogeneous efects of air pollution attributed to diferent wildfres on credit card spending and 

payment, in Panel A and Panel B respectively. We focus on areas that are 5-30 miles away from the wildfre perimeter (to isolate smoke efect from 

direct fre efect) and compare borrowers in zip codes that were exposed to heavy pollution (pollution level above the 75 percentile) to those in zip 

codes exposed to light pollution (pollution level in the bottom quartile), before and after the wildfre. The time frame is one to two years before and 

after each wildfre, depending on specifc wildfre. Year-over-year change (∆) in spending and payment are annualized dollar amounts. We include 

account fxed efects and year-month fxed efects. Additional time-varying control variables include refreshed borrower credit score and current 

credit limit of the credit card account. Robust standard errors in parentheses (error terms clustered at the zip code-level): ***p < 0.01, **p < 0.05, 

and *p < 0.1. Sources: EPA’s Air Quality System for air pollution data; Federal Reserve Y-14M for credit card data. 

57 
Table A.8 . Efects of Camp Fire-Induced Pollution on Health Outcomes 

1 2 3 4 

Emergency Number of Emergency Number of 

Visits - Kids Asthma ED Visits - Kids Asthma ED 

Visits 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 

Notes: This table shows the efect of wildfre-related air pollution on health outcomes in a diference-in-diferences framework. We focus on areas 

that are 5-30 miles away from the Camp Fire perimeter (to isolate smoke efect from direct fre efect) and compare counties exposed to heavy 

pollution (pollution level above the 75 percentile), and are marked as Treated to those exposed to light pollution (pollution level in the bottom 

quartile), before and after the Camp Fire. The variable DeltaTreated is defned using the change in pollution using the pollution level in the same 

county in the prior three years before the Camp Fire as the baseline (here, again, treated counties are those that experienced an increase in pollution 

in the upper quartile). The time frame is 14 months before and after the Camp Fire. Robust standard errors in parentheses (error terms clustered at 

the county level). ***p < 0.01, **p < 0.05, and *p < 0.1. Sources: EPA’s Air Quality System for air pollution data; California Health and Human 

Services Open Data Portal and Kids Data Portal for health data. 

58