A discussion on: “Extreme Wildfires, Distant 

Air Pollution, and Household Financial 

Health” and “The Opioid Epidemic and 

Consumer Credit Supply: Evidence from 

Credit Cards” 

By: Alejandro del Valle 

Georgia State University 

adelvalle@gsu.edu 
 

 

 

 

 

Overview of the papers 

• Two interesting papers documenting the credit market effects of 

understudied health shocks (fires and the opioid epidemic) 

• Methodologically, these papers showcase the value (and difficulty) of 

bringing together information on these health shocks with multiple 

administrative records on the financial behavior of households 

• The papers provide a very detailed description of the behavior of 

households in the credit market in the aftermath of these shocks 
 

 

 

 

 

 

 

 

Extreme Wildfires, Distant Air Pollution, and 

Household Financial Health 

• Very nice paper! 

• Impressive integration of many data sources 

• Highlights both the direct effect of extreme fires on the credit market 

and their indirect effect through worsening air quality 

• The indirect effect is important because, while smaller than the direct 

effect, it affects far more people 

• Extreme Fire affecting more than 1,000 structures or (3% of all fires) 

• Most of the evidence comes from the 2018 Camp Fire in California 
 

 

 

 

 

 

 

Direct effects of extreme fires (continued) 

• From the CCP and CoreLogic: Increased out-migration, lower house prices 

• From the CCP: Increases in delinquencies: mortgage, credit cards, personal loan 

• From the Y14: credit card repayment in excess of spending 

• These “results appear puzzling at face value,” but the authors argue that

increased delinquencies are concentrated among renters, while homeowners

experience a decline in balances perhaps driven by insurance payouts 

• My previous work on the effects of Hurricane Harvey (del Valle et al. JFQA, 2024) 

finds a very similar pattern of results on the Y14 

• Could these results be alternatively explained by worst-off households observed

in the CCP falling into delinquency while better-off households observed in Y14

successfully taking advantage of low-cost credit offers, insurance payouts, and

SBA loans available to them? 
 

 

 

 

 

 

Under the hood 

• Identification reminds me of 

Gallagher et al. JAERE 2022 

• What is driving these boundaries 

• Meteorological conditions 

• Geography – infrastructure 

• Self-protection investments 

• Fire suppression (CALFIRE) 

• The paper could be even more persuasive by convincing the reader that 

these groups are comparable before the fire 

• This a good setting for a spatial RD 
 

 

 

 

 

 

 

 

Under the hood (continued) 

• SUTVA violations are also a concern. Controls may also be directly 

affected by the fire (e.g., the work in the treated area) 

• The results and methods conflict at some level with each other. 

Migration is shown to be important, but the financial health results 

come from a sample of those who remain 

• Could the result be driven by sample selection? 

• Fortunately, there is no need for bounding arguments. The Y14 and CCP track 

those that move 
 

 

 

 

 

 

 

 

 

Indirect effects of extreme fires 

• Smoke from fires leads to excess air pollution 

• Effects of excess air pollution attributable to fires 

• Worst financial outcomes. Increased spending, reduced repayment, 

increased balances, increased delinquency 

• Increase health expenditure channel 

• Evidence from Google trends on health concerns 

• Evidence from emergency rooms 

• Income channel 

• No evidence presented (seems worth exploring) 

• Back of the envelope suggests large cost for New Yorkers from Canadian fires 
 

 

 

 

Under the hood (continued) 

• The results come from comparisons between households exposed to air

pollution levels in the top quartile and those in the bottom quartile before

and after the Camp Fire

• Why throw away all this information? I would be even more convinced by a

clear dose-response finding. Perfect application for the methods of Callaway 

et al. (NBER 2024) 

• Why limit the analysis to between 5 to 30 miles from the fire? The motivation 

in the intro was that New York could be affected by smoke from Canadian fires

2,000 miles away 

• I'm concerned with bad controls (current credit score and limit) 

• The back of the envelope needs some clarification. Reduced exposure should 

depend not only on the duration of exposure but also on the distance from

the fire and the meteorological conditions. I'm not ready to buy that Canadian 

fires led to billions in additional credit card debt 
 

 

 

 

 

 

 

 

Under the hood (continued) 

• Spatial dependence is a problem in this setting. Conley errors may 

provide a way forward 

• Do we know how fires are affecting credit conditions? Effect on 

prices, terms, etc. The Y14 contains all this information 

• Do we know how fires are affecting the supply of credit? The Mintel 

data could be useful (as I have learned from the next paper) 

• Readability could be improved 

• Order of tables and description in manuscript 

• Enough decimals on tables to read the results 

• First stage IV results missing 
 

 

 

 

 

 

 

 

The Opioid Epidemic and Consumer Credit 

Supply: Evidence from Credit Cards 

A very nice and comprehensive paper on an understudied phenomenon 

• This paper is many papers in one 

• The effect of the opioid epidemic on: 

• credit supply extensive and intensive margin (as measured from credit card mail offers) 

• credit supply for subgroups (minorities, low-income, younger consumers) 

• consumer credit performance 

• banks’ consumer loan portfolio 

• consumer spending 

• The Effects of 6 opioid policies on prescriptions, deaths, and credit supply 

• This paper uses several empirical strategies (IV, Fixed effects, PSM)

and information from many sources (Mintel, Y14, regulatory call

reports data...). Again, impressive integration of data sources 
 

 

 

Overview 

• Carefully done, clever instrument (s), many robustness checks 

• The key idea is that the opioid epidemic leads to losses in income and

income volatility, so we expect banks to curtail credit in response. Since 

banks have a hard time teasing out opioid abusers, they use market-level 

measures of the opioid crisis in their credit models. This response from 

banks could create negative social externalities 

• I don’t think the theory section on opioid abuse and loan repayment

choices is needed to motivate the analysis. 

• I would like to know if the regulatory framework allows banks to use this

information for risk rating. Do banks use this type of information in 

practice? 

• Confidential CDC data on death rates seems difficult to use for risk rating.

Would excess mortality not be a simpler and more comprehensive marketlevel measure of the crisis? 
 

 

 

 

 

 

 

 

 

 

Some questions and clarifications 

• In any case, I’m almost ready to buy the paper's main results, that is: 

1. banks contracted credit supply to consumers in counties heavily exposed to 

opioid abuse. 

2. This contraction is concentrated among minorities, low-income consumers, 

and younger individuals. 

• But..., is credit supply really being measured? Are the credit mail 

offers binding? It would be nice to see what is happening to 

originations. This can be done with the Y14 

• How do we distinguish banks doing risk management from low-risk 

types migrating away from counties heavily affected by the opioid 

epidemic? This can also be done with the Y14 
 

 

 

 

 

 

 

 

 

 

Some questions and clarifications (continued) 

• Expanding on the previous point, there are 3,143 counties in the US, 

but only 4,000 households were surveyed in the credit mailing 

dataset (Mintel). It is challenging to work with such a dataset. In 

particular 

• I'm concerned about sample selection driving the results. Is the response rate 

to Mintel related to the opioid epidemic? 

• It would be helpful for readers, especially in the sub-group analysis, to know 

the number of observations available for estimation 

• At the county level, I would expect this dataset to produce a very unbalanced 

panel. It would be worth discussing how this feature of the data affects the 

analysis of the effectiveness of opioid policies 
Some questions and clarifications (continued) 

• Very clever IV. I buy the results, but It would be helpful to be explicit about

the assumption required to recover the LATE and to characterize the

compliers 

• The IV is convincing. Why include controls at all? the controls vary between

datasets and specifications, making it hard for readers to keep track of

what is in the model 

• Many of the controls could be considered outcomes of opioid abuse. I’m 

concerned about bad controls 

• The results on higher rates of non-performing loans (section on bank

consumer loan portfolio performance) are important, but it is not clear to

me why the proportion of bank branches in the county is a good measure

of exposure. Would we not expect banks without a physical presence in a

county to compete for customers through other means, such as through

mailed offers? 
 

 

 

 

 

 

 

 

 

 

 

 

Some questions and clarifications (continued) 

• To interpret the county consumption results, it would be helpful to 

know, on average, what fraction of overall consumption corresponds 

to spending on credit cards 

• To put everything together, it would be nice to have a back-of-theenvelope estimate of the overall contraction in credit supply 

• Other more technical stuff 

• PSM increases imbalance. If you want to reduce modeled dependence, I 

would encourage the authors to use coarsened matching instead 

• I'm also concerned with inference. The Standard errors are described as 

multiway by marketing-camping level and year-month, but are these 

dimensions not nested? Why not cluster at the county level? 

• There is great readability improvement between the November and 

February versions