The Opioid Epidemic and Consumer Credit Supply: 

Evidence from Credit Cards* 

Sumit Agarwal† Wenli Li‡ Raluca Roman§ Nonna Sorokina¶ 

February 28, 2024 

Abstract 

Using a unique dataset of unsolicited credit card offer mailings by banks to consumers, 

we investigate how opioid abuse affects consumer credit supply. To identify causal effects, we employ instrumental variables, propensity score matching, and contiguous 

counties techniques, and control for a battery of demand and supply factors and fxed 

effects. We fnd that banks contract credit supply to consumers in counties highly exposed to opioid abuse by offering higher interest rates, lower credit card limits, and 

fewer rewards and reducing credit offers overall. Further analyses using the supervisory Federal Reserve Y-14M credit card dataset confrm these effects. What is more, the 

credit contraction disproportionately impacts riskier consumers, minorities (particularly black people), low-income consumers, and younger individuals. Our examination of various state-level anti-opioid abuse legislation shows that opioid supply-oriented 

laws are somewhat helpful in curbing opioid overdoses or mitigating the credit supply contraction, but demand-oriented laws are not. Finally, we uncover that the opioid 

abuse-induced credit contraction has important social welfare implications: Local consumer spending signifcantly declines in the highly-affected areas. 

JEL Codes: G01, G28, D10, D12, E58 

Keywords: Opioid Epidemic, Household Finance, Credit Supply, Spending, Risk 

*The views expressed herein are solely those of the authors and do not represent the views of the Federal 

Reserve Bank of Philadelphia or the Federal Reserve System. We thank Anuj Shahani, Molly Graebner from 

Mintel and Geng Li with kind help with data questions about the Mintel/TranUnion Match File dataset. 

We also thank Scott Hadland and his co-authors for sharing their data on non-research transfer marketing 

for opioids from pharmaceutical companies to physicians. Finally, we thank Senay Agca, Nicole Boyson, 

Kim Cornaggia, Thomas Flanagan, John Hund, Zihan Yue, and seminar/conference participants at the Penn 

State University, the Federal Reserve Bank of Philadelphia, the Interagency Risk Quantifcation Forum at the 

Federal Deposit Insurance Corporation, the Federal Reserve Bank of Dallas, the AFA Annual Meetings, the 

AFFECT Early Ideas Workshop, the New Zealand Finance Association Meetings, the Australian Banking 

and Finance Conference, the North America Econometric Society Summer Meetings, the Society of Economic Dynamics Annual Meetings, the Second Fischer-Shain Center Research Conference, and the Fixed 

Income and Financial Intermediation Research Conference for their comments. The authors are grateful for 

the ”Runner-Up Paper Award” from the New Zealand Finance Association. 

†National University of Singapore,ushakri@yahoo.com. ‡Federal Reserve Bank of Philadelphia, wenli.li@phil.frb.org. §Federal Reserve Bank of Philadelphia, raluca.roman@phil.frb.org. ¶The Pennsylvania State University, sorokina@psu.edu. 
1 Introduction 

For the past two and a half decades, the U.S. has suffered tremendously from one of the 

largest and deadliest public health crises — the opioid epidemic, with no solution yet in sight.1 

Over a million people died from overdoses involving opioids from 1999 to present (Figure 1),2 another two million are currently struggling with opioid-related disorders.3 Behind every statistic is 

a real person, a real family, and a real community suffering. It is a crisis that demands attention and 

action. The crisis has worsened over time, affecting an increasingly large demographic strata of the 

population, particularly minorities, working-age and young men, and the less educated (Figure 2). 

Not surprisingly, there is now growing evidence linking opioid abuse to reduced productive economic activities such as reduced labor force participation and increased unemployment.4 

The reduction in productive economic activities associated with opioid abuse necessarily 

leads to consumer income losses and income volatility, two important determining factors of loan 

repayment and credit access. Opioid abusers who use credit to sustain their addiction face additional default risk due to increases in expenditures related to their addiction and unsound decisions due to ”reinforcer pathology.” Lenders, however, cannot directly detect individuals vulnerable to opioid addiction and/or those who would use the fnancing to sustain their addiction. The 

social stigma associated with opioid addictions exacerbates information asymmetry as individuals are afraid to seek help for their addiction for fear of revealing their addiction history. Lenders 

may also incur increased costs for screening and ongoing monitoring in affected areas. As a result, 

lenders may abstain from and/or curtail credit in harder-hit opioid areas to reduce exposure. 

This paper investigates the spillover effects of the opioid epidemic on consumer credit supply using the credit card market as a laboratory. The credit card market is more likely used by the 

opioid-impacted population as it does not require collateral. The credit card market has over 175 

1The other major health crisis is the recent global COVID-19 outbreak with also over one million deaths, but 

its effects were largely contained by the quick vaccine development and implementation. 

2see, among others, Quinones (2015), and the Centers for Disease Control and Prevention (CDC) 2021, 

https://www.cdc.gov/nchs/pressroom/nchs press releases 2021 20211117.htm. 

3Https://www.cdc.gov/opioids/basics/epidemic.html. 4See Case and Deaton (2015), Van Hasselt, Keyes, Bray and Miller (2015), Krueger (2017), Harris, Kesslery, 

Murray and Glenn (2019), Park and Powell (2021), Aliprantis, Lee and Schweitzer (2020), and Ouimet, 

Simintzi and Ye (2020). 

1 
million users in the U.S. and spans over 80% of the consumers.5 Credit cards are also signifcant 

determinants of bank risk, partly due to their unsecured nature, inducing signifcant loss given 

default. Sudden and large rises in consumer defaults can deteriorate banks’ portfolio quality and 

can contribute to widespread fnancial distress and fnancial crises. 

We construct our individual credit supply variables using bank credit card mail offers data 

from the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC 

Match File (Mintel/TransUnion Match File). Such credit offers are a direct informative measure 

of consumer credit supply by the banks, helping circumvent challenges of disentangling supply 

from demand forces that plague other studies (e.g., Han, Keys and Li (2018); Dettling and Hsu 

(2021)). We focus on the years between 2010 and 2019 so that our results are not contaminated 

by the implementation of the Credit Card Accountability Responsibility and Disclosure (CARD) 

Act of 2009, the Great Recession over 2007-2009, or the COVID-19 pandemic from 2020 onward. 

The years covered in our analyses mark the last two waves of the opioid epidemic, that recorded 

perhaps the most dangerous abuse using both prescription and illicit opioids.6 

To measure the severity of the opioid crisis, we follow the literature reviewed in the next 

section and construct, at the county level, exposure measures based on confdential opioid-related 

death rates collected from the CDC/National Center for Health Statistics (NCHS).7 Consumers’ 

drug abuse is then measured via the severity of the opioid crisis in their county of residence. This 

measurement choice likely replicates the fnancial institutions’ credit risk management models. 

That is, in the absence of perfect information on the affected individuals, fnancial institutions’ 

credit models resort to instead capturing average opioid risk treatment based on the crisis intensity 

in the individuals’ local market of residence.8 Such policies can create negative social externalities. 

5See https://www.federalreserve.gov/publications/fles/2018-report-economic-well-being-us-households201905.pdf or https://fles.consumerfnance.gov/f/documents/cfpb consumer-credit-card-marketreport 2021.pdf. 

6The frst opioid wave involves prescription opioid deaths from the 1990s to 2009; second wave marks the 

rise in heroin deaths from 2010-2012; and the third wave marks the rise in the synthetic opioid deaths, 

particularly from illicitly manufactured fentanyl. 

7National Center for Health Statistics, 2020. All-County Mortality Micro Data, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. 

8Other studies on local treatments and/or bank policies in the presence of borrower information asymmetry 

include Suf (2007); Karlan and Zinman (2009); Berger, Espinosa-Vega, Frame and Miller (2011); DeFusco, 

Tang and Yannelis (2022); and Mian, Suf and Khoshkhou (2023). 

2 
Our main fndings are as follows. Lenders reduce credit supply signifcantly in areas with 

higher exposure to the opioid crisis by charging higher interest rates (1.2 percentage points higher) 

and offering much smaller credit limits (17% decrease), particularly to consumers with higher perceived credit risk (based on credit score, past delinquency, and derogatory flings, etc.), minorities, 

low income, and younger consumers in those areas. Furthermore, banks offer less credit card rewards (4% decrease) and are also much less likely (10% decrease) to solicit consumers for credit 

cards in areas highly exposed to the opioid crisis. Consistent with these credit supply contractions, 

our analyses of loan performance suggest that lenders decrease credit supply because of increased 

credit risk in these areas. Specifcally, consumers in counties with higher exposure to the opioid 

crisis experience more days past due, higher probability of default on credit cards, make reduced 

payments, or have lower credit scores. Additional studies of bank balance sheet indicate that 

single-branch banks with presence in the more exposed areas experience higher non-performing 

loans across credit cards and the unsecured consumer sector. We further conduct a welfare analysis 

by investigating effects of the opioid crisis on consumer consumption as in other household fnance 

literature (e.g., Mian and Suf (2012); Mian, Rao and Suf (2013); Mian, Suf and Khoshkhou (2023); 

Mian, Suf and Verner (2020)). We demonstrate that the credit policy adopted by banks vis-a-vis `

the opioid abuse crisis has had severe externalities: the reduced credit supply has led to signifcant 

declines in consumer local consumption, i.e., 4.3% as captured by total purchases per county population. Finally, our analyses of the various state-level anti-opioid regulations targeting both the 

supply and the demand for opioids reveal that only the supply-oriented laws show some positive 

effects on curbing the opioid prescription and death rates or reversing the credit contraction in 

affected areas. The demand-oriented laws often have no positive or even reverse effects. 

The identifcation challenge here and a common concern in the literature is that these negative credit consequences and the opioid exposure may both arise from negative economic conditions that are not observed or controlled for, i.e., the so-called ”deaths of despair” (Ruhm (2019)). 

To mitigate this concern and isolate the relations studied, we frst saturate our models with numerous demand and supply factors and fxed effects by taking advantage of the richness of our 

datasets. Then, to more formally alleviate the endogeneity concerns and identify causal effects 

of the opioid crisis, we employ an instrumental variable (IV) methodology by exploiting supply 

3 
shocks in opioid marketing and distribution. Our approach relies on the observation that prescription opioids are involved in at least 40% of all opioid overdoses in the U.S. (e.g., Hadland, Krieger 

and Marshall (2017)) and the majority of illegitimate drug users start taking opioids prescribed 

by their physicians, even if many later progress to illicit opioids (e.g., Kaestner and Engy (2019); 

Coffn, Rowe, Oman, Sinchek, Santos, Faul, Bagnulo, Mohamed and Vittinghoff (2020)). It is also 

motivated by the fndings in recent economic literature that emphasize the lack of strong correlation between economic activities and opioid abuse (e.g., Ruhm (2019); Currie, Jin and Schnell 

(2019); Currie and Schwandt (2021); McGranahan, Parker et al. (2021)). 

Our main instrument captures the scale of the pharmaceutical industry’s opioid marketing 

to physicians, particularly the number of physicians who receive non-research marketing visits 

and payments per 1,000 population in a county. This variable is available annually starting in 

2013, when the Physician Payments Sunshine Act came into effect. Hadland, Krieger and Marshall 

(2017) show that pharmaceutical companies invest tens of millions of dollars annually in direct-tophysician marketing of opioids, while Hadland, Rivera-Aguirre, Marshall and Cerda (2019) show 

that opioid prescriptions and mortality from opioid overdoses went up with the increase in the 

number of physicians receiving marketing compensation for opioids. This opioid marketing to 

physicians is unlikely correlated with the consumer or bank credit behavior other than through 

the increased risks brought on by the opioid abuse itself. 

Our results are robust to using alternative instruments based on marketing payments made 

by the pharmaceutical companies to physicians or using the aggressive pre-sample marketing of 

OxyContin by Purdue Pharma between 1997 and 2002, after its market introduction in 1996. Regarding the latter, Purdue increased its marketing and promotion budget by almost 800% over 

1997-2002, marketing the drug aggressively to physicians and pharmacies under the slogan “The 

One to Start With and the One to Stay With,” and turning OxyContin into the most abused prescription opioid by 2004 (e.g., Van Zee (2009); Cornaggia, Hund, Nguyen and Ye (2021)). The 

growth rates in the locally received OxyContin pills in these early periods were shown to directly 

impact the rate of opioid prescription by doctors as well as elevated mortality in the later periods, 

but has little direct correlation with either the fnancial situation of people or bank lending choices 

in the affected areas (e.g., Aliprantis, Lee and Schweitzer (2020), Alpert, Evans, Lieber and Powell 

4 
(2022); Currie and Schwandt (2021)). 

We also conduct numerous other robustness analyses to address identifcation and/or rule 

out alternative explanations: use alternative defnitions for the opioid crisis intensity such as opioid prescription and illicit deaths rates or use actual opioid prescription rates; employ univariate 

and regression analyses using propensity score matching where we match the high-quartile opioid deaths counties to other non-treated counties by year and county characteristics using several matching techniques; use contiguous counties to high opioid death counties only; control for 

even more local market factors; use multiple death causes instead of underlying causes; exclude 

Florida, which was an epicenter for the opioid crisis distribution; exclude zero-death counties; reconfrm results also using a completely different dataset based on credit card supervisory data; and 

conduct different cross-sectional tests by consumer characteristics. All of our approaches, despite 

sometimes covering somewhat different sample periods due to data availability, consistently show 

statistically as well as economically signifcant adverse effects on consumer credit risk and credit 

supply caused by opioid abuse. Additionally, we also uncover evidence that although the opioid 

crisis had affected the overall population, the negative credit supply effects are larger for riskier 

consumers, minorities particularly African American, low income, and younger consumers. 

Finally, we analyze the effectiveness of recent laws and regulations about opioid abuse. 

These laws have only been studied one at a time even though they often overlap. By contrast, we 

run a horse race and test six different opioid-related laws at the state level in cross-sectional tests 

or sample splits: those laws that target opioid supply including the ”Opioid Prescription Limiting 

Law”, the mandatory ”Prescription Drug Monitoring Program (PDMP) Law”, and the ”Triplicate 

Prescription Law;” and those that affect opioid demand or users including the ”Naloxone Law,” the 

”Good Samaritan Law,” and the ”Medical Marijuana Permitting Law.” We fnd strong and positive 

effects from the opioid supply-oriented laws in reducing opioid prescriptions and opioid prescription 

death rates, but limited effects in reducing illicit opioid death rates. Not surprisingly, as a result, 

we fnd positive effects from the laws that target opioid supply in mitigating credit supply reduction by banks to consumers. In contrast, the opioid demand-oriented laws have little benefcial or 

even unfavorable effects on both opioid deaths and consumer credit supply. 

Understanding the opioid crisis effects on consumer markets and the effectiveness of recent 

5 
2 

laws and regulations may be helpful for policymakers and fnancial institutions to devise effective 

strategies to combat the crisis and allocate resources where they are needed most. Results in this 

study may help inspire targeted interventions to minimize the crisis fnancial and social impact 

while safeguarding the well-being of consumers and their communities. 

The rest of the paper is organized as follows. We discuss the related literature in Section 2. 

Section 3 presents two simple toy models to illustrate how opioid abuse affects an individual’s 

decision to make loan payments and a lender’s decision on loan terms, respectively. The datasets 

used for our analyses are described in Section 4. Our empirical strategy is described in Section 5. 

Section 6 presents our results. Section 7 concludes. 

Literature Review 

This paper relates to several strands of literature. First and foremost, there is a large literature 

in the medical as well as economics felds that studies the determinants of opioid abuse. See Currie 

and Schwandt (2021) and Maclean, Mallatt, Ruhm and Simon (2020) for a review of this literature. 

The studies generally conclude that neither contemporaneous nor long-term economic conditions 

can explain a large part of the opioid epidemic. Instead, the opioids spread in the country results 

from three key factors: a change in beliefs among physicians that pain was not treated adequately; 

aggressive marketing by pharmaceuticals who made the claim that the new generation of opioids 

may have been effective at treating pain with little risk of addiction; and fnally, until recently, there 

was little public oversight of opioid prescriptions by doctors. This literature inspires our choice of 

instruments as we alluded to in the Introduction. 

There also exists a relatively large literature studying the economic impact of the opioid epidemic. For example, several papers fnd a detrimental impact of opioid abuse on employee productivity and labor market participation (e.g., Van Hasselt, Keyes, Bray and Miller (2015), Krueger 

(2017); Aliprantis, Lee and Schweitzer (2020); Harris, Kesslery, Murray and Glenn (2019); and Park 

and Powell (2021)). Focusing on frm outcomes, Ouimet, Simintzi and Ye (2020) fnd that frm 

growth is negatively affected by the exposure to opioid-affected areas as the eroding labor market 

conditions force frms to invest more in technology and substitute capital for the relatively scarcer 

labor. Rietveld and Patel (2021) and Sumell (2020) fnd negative impacts on new small frm forma6 
tion and survival. Finally, Langford (2021) fnds that opioid use reduces net frm entry and results 

in a shift in industrial composition due to labor supply issues in the affected areas, driving longterm stagnation and fscal diffculties. This literature serves as evidence of the channels through 

which the opioid crisis affects the consumer markets we study here. 

By comparison, the literature on the effects of the opioid epidemic on fnance is small. Cornaggia, Hund, Nguyen and Ye (2021) fnd negative impacts of the local opioid abuse on municipal 

bonds, which impede municipalities’ ability to provide the necessary public services and infrastructure. Custodio, Cvijanovic and Wiedemann (2021) fnd lower housing values in areas more 

affected by the opioid epidemic, which are mitigated by the passage of state laws aimed at curbing 

opioid abuse. D’Lima and Thibodeau (2022) fnd that house price changes around opioid dispensaries are negatively associated with the quantity of opioids dispensed. Jansen (2019) uses data on 

subprime automotive loans acquired from a U.S. lender and documents an increase in consumer 

defaults in subprime auto loans as a result of local market opioid abuse problems. Lastly, Li and 

Yue (2022) study the spillover effects of the opioid epidemic through the banking network and 

fnd a negative link between local opioid supply and deposit growth at both the county and bank 

level. We add to this literature by providing the frst study of the credit supply and consumption 

consequences of the local opioid abuse using the credit card market as a laboratory. 

3 Simple Models of Opioid Abuse and Consumer Finance 

We present two simple models to illustrate how opioid abuse affects an individual’s decision 

to make loan payments and a lender’s decision on loan terms, respectively. 

3.1 Opioid Abuse and Consumer Loan Repayment Decision 

Consider a static model where an individual, after receiving his income and facing necessary 

consumption such as basic food and rents denoted by c, decides whether to make a loan payment 

(1 + r) ∗ b. The term r represents the interest on the loan b. His income is a product of his employment probability e and the wage w he is able to command. If the individual is risk neutral, then 

7 
the decision is simply captured by his net gain from payment, 

e ∗ w − c − (1 + r) ∗ b. (1) 

The individual will only make the payment if the term in equation (1) is nonegative. Let φ denote 

the repayment decision, then we have φ = 1, if e ∗ w − c ≥ (1 + r)b, and φ = 0 otherwise.9 

For a highly dependent opioid user, the drug cost increases his necessary consumption c. 

Moreover, according to Bickel, Athamneh, Snider, Craft, DeHart, Kaplan and Basso (2020), the 

addiction itself can lead to other unsound decisions due to a “reinforcer pathology” that increases 

the individuals’ overvaluation of short-term tangible rewards and undervaluation of long-term 

negative consequences, in addition to impulsivity, nonconformity to rules, and cognitive issues. 

All these make him less employable and reduce the wages he can command (see the literature 

review), i.e., both e and w are likely smaller. Last, as we discuss next in lenders’ decisions, the 

person may also face higher interest rate r. If the person is not addicted to opioids but lives in 

an area heavily exposed to the epidemic, drug cost is no longer an issue, but he may still receive 

a lower income and be charged a higher interest rate because of the spillover effect due to the 

information problem employers and lenders face (see our discussion in the next subsection). 

All of these factors suggest that a person in an area heavily exposed to opioids is more at 

risk of defaulting on his loan obligations and thus a potentially ”riskier” credit borrower. The one 

countering force in our simple model is if the person also borrows less voluntarily or due to credit 

rationing, that is, b is smaller.10 

When we aggregate individual behavior to, for example, the county level, the discussion 

above suggests that the areas with high-opioid exposure will likely have more consumers default 

on their loan obligations. An immediate implication is that banks with higher operational exposure 

to these areas will have riskier consumer loan portfolios. 

9For simplicity here, we rule out partial loan payment cases. 

10In dynamic models where consumers may need to borrow in many periods and lenders can impose punishment on those who default, drug addicts, having large discount factor, will also be less affected by the 

punishment. 

8 
3.2 Opioid Abuse and Consumer Credit Lending Decision 

A lender decides the loan amount b and the interest rate r, and his payoff is as follows, 

φ ∗ (1 + r) ∗ b − (1 + rd) ∗ b, (2) 

assuming that the per-unit cost of funding is rd and the loan is noncollateralized. If the lender 

observes the repayment probabilities φ, then, in a competitive environment/under a zero proft 

condition, he sets the interest rate r = (1 + rd)/φ − 1, which decreases with φ. 

The challenge posed by the opioid abuse to a lender is information asymmetry. The lender 

will have to make inferences based on public data such as aggregate opioid-related drug overdoses. Consider two individuals living in areas with different exposures to the opioid abuse crisis, 

which, in our setup, can be captured by their repayment probability φ1 and φ2, and φ1 < φ2. Everything else the same and absent of other signals, the lender will approximate each individual’s 

repayment probability with the average payment probability of the area that he resides in. It then 

follows that individual 1 will be charged a higher interest rate than individual 2 despite that the 

two look similar in all other aspects.11 

The discussion so far illustrates why lenders would charge individuals in high opioid exposure areas higher interest rates for a given loan amount. Turning to the lenders’ loan making decision, according to Stiglitz and Weiss (1981), credit rationing can arise under certain conditions with 

information asymmetry. For example, Consider an environment where individuals have different 

probability distributions of income y, and different addiction or exposure to opioids captured by 

θ, F(y, θ), and they need to borrow a fxed amount b. Additionally, there is a fxed cost d associated with each defaulted loan for the lender. This problem maps into that in Stiglitz and Weiss 

(1981) (see Alternative Suffcient Conditions for Credit Rationing, pp. 399), where the expected revenue for lenders as a function of the interest rate charged will be hump shaped due to information 

asymmetry provided that a small change/rise in interest rate induces a large change/worsening in 

11Even in perfect information environment, opioid addiction differs from other consumption by lowering 

abusers’ productivity, life time earnings, health conditions, and early deaths, and thus reduce debt borrowing. We thank Thomas Flanagan for the point. 

9 
applicant pool. As a result, lenders will not lend if the perceived opioid exposure exceeds certain 

threshold. In other words, credit rationing arises in those cases. 

To summarize, our discussions indicate that individuals in the high-opioid crisis exposure 

areas are at higher risk of default, that banks operating in those harder-hit areas have riskier consumer loan portfolios, and that lenders are likely to lend less to individuals in those areas if at all 

and/or charge them higher interest rates to reduce credit risk. These are the hypotheses that we 

will test in the next sections. 

4 Data Sources and Data Collection 

We use three types of data: information on opioid crisis intensity and marketing practices; 

information on consumer credit supply, and local economic and demographic information. Data 

measuring opioid crisis intensity and marketing practices are at the county by year level. Data 

measuring credit offers are at the individual/offer by year-month level. In additional analyses 

testing potential underlying channels for our main results, we use data on consumer loan performance at bank by county by year-month (or county by year-month) level, and bank loan portfolio 

risk at the bank by year-quarter level. 

4.1 Opioid Mortality and Marketing Practices 

4.1.1 Opioid Mortality Rates 

We obtain restricted-use mortality data from the CDC (the All-County Mortality Micro Data; 

NCHS, 2020). These data provide the precise cause of every death in every county and hence allow us to accurately identify all opioid-related deaths by location. From this data, we construct the 

number of opioid-related deaths scaled by the county’s population (in 10K) in each year. In some 

additional analyses, we also differentiate between prescription- and illicit-drugs-related deaths. 

Prescription-deaths capture the illegal diversion of legally manufactured prescription opioids for 

non-medical use and unfortunate externalities of medical use of the prescription opioids, while 

illicit deaths are related to the use of “street drugs,” such as heroin or illicitly manufactured fen10 
tanyl.12 A high opioid mortality rate is indicative of a high addiction rate, and public offcials also 

rely on such mortality rates as one of the best metrics to monitor the opioid crisis across regions.13 

We focus on opioid mortality as our primary measure of opioid abuse. In addition to being 

comprehensive and comparable across counties, this measurement, in comparison to opioid prescription rates often used in the literature, better captures the progression in the opioid epidemic 

since 2010, the period of our analyses, that is, the rise in illicit opioid drug abuse. 

We supplement the mortality opioid data with opioid prescriptions in some additional analyses. We use the opioid prescribing rates per capita, per county each year derived from the CDC 

public data.14 The CDC’s prescribing data originates in the IQVIA Transactional Data Warehouse 

(TDW), which is based on a sample of approximately 59,000 non-hospital retail pharmacies. These 

pharmacies dispense about 90% of all retail prescriptions in the country. Several prior studies fnd 

that opioid prescriptions are a good proxy for opioid addiction and abuse and/or fnd a positive 

correlation between rates of prescriptions and subsequent abuse in an area (e.g., Schnell (2019); 

Ouimet, Simintzi and Ye (2020)). 

4.1.2 Opioid Distribution and Marketing 

We construct the main opioid marketing instrument based on the non-research transfer marketing information from the pharmaceutical industry to physicians following Hadland, RiveraAguirre, Marshall and Cerda (2019). Specifcally, we collect data on the number of physicians 

being marketed opioids by their practice county and by year from 2013 onward from the Centers 

for Medicare and Medicaid Services Open Payments database.15 

12To construct opioid-related deaths, we follow Cornaggia, Hund, Nguyen and Ye (2021) (Appendix A.1) by 

identifying drug-related deaths frst, i.e., those with underlying ICD-10 cause codes X40-X44 (accidental poisoning), X60-X64 (intentional poisoning), X85 (homicide), and Y10-Y14 (undetermined intent). We then narrow to causes related to opioids, i.e., those with a contributing cause code of T40.0 (opium), T40.1 (heroin), 

T40.2-T40.3 (prescription), and T40.4 (synthetic opioids, primarily fentanyl). Finally, we use the multiple 

cause portion of the death certifcate and assign to Illicit category all deaths that have opium (T40.0), heroin 

(T40.1), and synthetic opioids (T40.4) causes and assign the rest (T40.2–T40.3) to the prescription category. 

13The death data used here are superior to the public CDC data on opioid deaths as the public data omit 

counties with fewer than 10 drug-poisoning deaths, thus leaving out nearly half the population. This lefttail censoring also creates time series problems as some counties were reported in some years but not others. 

14See https://www.cdc.gov/drugoverdose/maps/rxrate-maps.html. 

15Centers for Medicare & Medicaid Services. Open Payments dataset, 

https://www.cms.gov/openpayments/explore-the-data/dataset-downloads.html, accessed March 12 

2022. The database is mandated by the Physician Payments Sunshine Act. 

11 
For robustness test, we construct another instrument based on non-research transfer marketing payments from the pharmaceutical industry to physicians again following Hadland, RiveraAguirre, Marshall and Cerda (2019). We also construct an opioid marketing instrument based on 

the aggressiveness of Purdue Pharma’s marketing of OxyContin in the pre-crisis era. We hand 

collect data on all Oxycodone pills distributed to each zip code each year from archived Drug 

Enforcement Administration (DEA) reports. We then aggregate the data to the county level and 

compute the county growth rate of Oxycodone pills distributed between 1997 (the year after OxyContin was introduced) and 2002. 

4.2 Consumer Credit Supply and Other Consumer Finance Information 

4.2.1 Consumer Credit Supply 

For credit supply, we use the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC Match File (Mintel/TransUnion Match File) proprietary survey of 

U.S. consumers merged with TransUnion consumer credit bureau characteristics over 2010 to 2019, 

which was also de-personalized after the merging process. Each month, Mintel selects about 4,000 

consumers from a pool of one million consumers that Mintel acquired from a large survey service provider. Mintel gives each consumer a set of envelopes and asks the consumer to put mail 

from an array of sectors, including credit offers, into the envelopes and send them back to Mintel 

weekly during the participating month. Once receiving the envelopes, Mintel records almost all 

information from the credit offers, whether a consumer receives an offer, and credit terms of the 

contracts offered, such as interest rates and credit limits. 

The Mintel credit offers monthly data were merged with credit bureau information on the 

consumers from TransUnion and subsequently anonymized to protect the confdentiality of the 

survey participants. The combined data are the Mintel/TransUnion Match fle that we use in our 

analysis.16 We focus on credit card offers, which have the best data coverage, and ”banks” that 

are fltered using lender names containing keywords such as “bank,” “bancorp,” “banco,” etc. We 

keep in our analysis only those credit offers that have non-missing APR purchase rates and limits 

16The merge is conducted by the vendor for the anonymized fle, and we only work with the anonymized fle. 

12 
for the offers, as well as non-missing consumer characteristics. The consumer credit score and 

score ranges used in this analysis are from the Mintel/TransUnion Match fle. 

4.2.2 Other Supplementary Data 

Consumer Credit Performance For consumer credit quality/performance, we use the Federal Reserve FR Y-14M regulatory report, collected by the Board of Governors of the Federal Reserve System in pursuance of the annual comprehensive capital analysis and review (CCAR) of 

large U.S. bank holding companies, as required by the 2010 Dodd-Frank Wall Street Reform and 

Consumer Protection Act. The monthly report for each account originated and managed by the 

reporting banks, contains detailed information on borrower characteristics, credit card days past 

due, loan probability of default (PD), payments, terms, and also purchases. This credit card dataset 

is very large, having more than 500 million observations per month. We employ a 0.1% random 

loan-level sample for existing credit card accounts (having being in existence for at least 12 months) 

that are nationally representative. We work with existing accounts so that we observe their credit 

behavior and quality as well as their spending patterns. 

The banks in the FR Y-14M report dataset are dominant players in the credit cards market, 

holding a combined market share of over 75% as of December 2019, so the accounts are likely representative of the market as a whole.17 To remove reporting errors, we exclude from our sample 

loans that are subject to SOP 03-03 accounting (i.e., it is purchased credit-impaired loan or a purchased loan with evidence of deteriorating credit quality since origination); loans with erroneous 

credit scores, credit scores are missing or outside the range of 300 to 900; loans with missing or 

credit limit or APR; and accounts that are deactivated and/or inactive. 

Bank-Level Consumer Portfolio Data The quarterly regulatory Consolidated Reports of 

Condition and Income (Call Reports) help extend our study to bank level. Call Reports are provided by the Federal Financial Institutions Examination Council (FFIEC) Central Data Repository’s 

Public Data Distribution. Every national bank, state member bank, and insured nonmember bank 

is required by the FFIEC to fle a Call Report as of the close of business on the last day of each cal17This is based on market share assessments of these banks’ balances in the FR Y-14M compared to the credit 

card balances in the Federal Reserve Bank of New York Quarterly Report on Household Debt and Credit as 

of 2019:Q4 available at https://www.newyorkfed.org/medialibrary/interactives/householdcredit/data. 

13 
5 

endar quarter, i.e., the report date. Call Reports provide information on the institution’s balance 

sheet, income statement, and a narrative explaining elements of the fnancial statements. We focus 

on nonperforming loans ratios for credit cards and the unsecured consumer segment. 

County-Level Expenditure and Other Economic Data We proxy county level consumption 

by aggregating domestic credit card purchases provided by the FR Y-14M data discussed above to 

the bank level by county and by year-month. We obtain similar results using data aggregated at 

the county by year-month level (shown in Appendix Table A6). 

We obtain average income from the Bureau of Economic Analysis (BEA), unemployment 

rate from Bureau of Labor Statistics (BLS), and bank competition in the county measured by the 

Herfndahl-Hirschman Index (HHI) of deposits based on the FDIC Summary of Deposits data. We 

obtain additional county demographic information such as population by race, gender, age, educational attainment, and inequality from the U.S. Census Bureau American Community Surveys. 

Estimation Strategy 

We do not observe directly consumers’ opioid usage and health status and, therefore, cannot 

directly test the impact of the opioid usage on credit supply. Instead, we test whether banks are 

less likely to supply credit or apply more stringent terms to individuals in more opioid-affected 

areas. However, as mentioned in the Introduction, this approach likely replicates the banks’ credit 

models which also resort to measures of crisis intensity in the individuals’ local market of residence 

to capture risk from the opioid crisis given they face information asymmetry and are unable to 

(legally) observe and/or target affected individuals. We measure a county’s exposure to the opioid 

crisis by its opioid death rates. For each credit supply variable, we test whether the opioid exposure 

has any explanatory power in addition to the control variables. The exposure measures are lagged 

by one year. 

Estimating the effects of the opioid crisis on consumers and banks raises endogeneity concerns as common conditions or shocks may drive both the opioid crisis intensity and the credit 

outcomes. To attenuate these concerns and ensure we identify the causal relationship between 

opioid epidemic exposure and various consumer credit consequences, we conduct two-stage least 

14 
square (2SLS) regression analyses that use instrumental variables for the opioid crisis intensity. 

Additionally, we introduce an extensive set of control variables that capture heterogeneity in 

county, consumer, and bank characteristics as relevant in different parts of our analyses. All our 

controls in all analyses are lagged one period (one year, one quarter, or several months, based on 

data availability). At the county level, we saturate the model with 11 different controls, comprising 

indicators of local economic conditions, including median income, income inequality (gini), and 

unemployment rate, as well as a variety of demographic characteristics such as population density, 

race, gender, age, and educational attainment composition. We also control for bank’s local market 

concentration (HHI of deposits), to account for potential uneven access to banking services and 

credit terms. Finally, we include combinations of state, bank, and time fxed effects, pertinent to 

each dataset and analysis, to account for additional time-varying and unobserved characteristics. 

5.1 Instrumental Variable First-Stage Specifcation 

In the frst stage across all our analyses, we regress the opioid crisis exposure variable, opioid 

death rates or opioid prescription rates measured either continuously or as a dummy indicating 

whether the county is in the nation’s top 50 percentile, on the instrument and the same set of 

controls as those included in the second stage for the corresponding analysis. The general frststage specifcation is as follows: 

OpioidExpc,t−1 = γ0 + γ1 IVc,t−1 + γ2CountyControlss,t−1 + γ3OtherFE 

(3) 

+ γ4OtherConsumer/BankControlsi,c,t−1 + μc,t−1, 

where i indicates individual or bank, c county, and t time. 

As discussed in Section 4.1.2, the main instrumental variable (IV) we use is MKTDoctors/1000Pop, 

the number of doctors receiving opioid marketing payments from pharmaceutical companies per 

1,000 population per year in the main analyses, which is time variant, covering 2013 onward. In 

robustness tests, we also use as additional IV, MKTPayments/1000Pop, the number of non-research 

marketing payments made to doctors by pharmaceutical companies per 1,000 population per year, 

also time variant. Finally, we use as an alternative IV, Purdue MKT (OxyContin Growth ’97-’02), 

the growth rate in each county in the distribution of OxyContin pills between 1997 and 2002 for 

15 
robustness test, which is time invariant. 

5.2 Second-Stage Specifcations 

We next discuss the econometric models for the IV second stage credit outcome analyses. We 

use OpioidExp \c,t−1 to denote the predicted value of OpioidExpc,t−1 obtained from the frst stage. 

5.2.1 Consumer Credit Supply 

The credit supply Mintel/TransUnion Match fle data are at the credit offer by year-month 

level. Our outcome variables are the bank’s willingness to lend to different categories of consumers 

refected in the likelihood of unsolicited credit card offers, as well as the credit terms applied to 

those offers captured by Yi,c,t for consumer i in local market (county) c at time (year-month) t: 

Yi,c,t = δ0 + δ1OpioidExp \c,t−1 + δ2ConsumerControlsi,t−1 + δ3CountyControlsc,t−1 + FE + ξi,c,t, 

(4) 

where Yi,c,t refers to one of the main credit card offer terms such as the RateSpread, the difference 

between the offered credit card APR and one-month Treasury bill, or Ln(Limit), the natural log of 

the offered credit card limit. In additional analyses, we also analyze Reward/Promotion, a binary indicating whether a credit card offer includes rewards and/or promotions, and Card Offer, a binary 

indicating a consumer is receiving a credit card offer in a particular month or not. 

Consumer-level controls (measured as of 2-3 months prior to the credit offer) include a very 

rich set of fnancial and demographic characteristics. These include credit scores range dummies 

(<580 (left out category), [580,660), [660,720), [720,800), and ≥800), the natural log of consumer 

income, binaries for recent as well as other past delinquency (90 days or more past due) on any 

of the credits held, other derogatory information such as foreclosures, past bankruptcy flings, 

previous other credit cards, previous high credit card utilization (80% or higher), as well as the 

natural log of the number of recent credit inquiries (proxying for consumer credit demand). We 

also include age range binaries (<25 (left out category), [25,44), [45,64), and ≥65) to account for 

potential nonlinearity in credit supply, indicators for homeowner, married, no children, education 

level (less than college (left out category), having completed some college, college, or higher than 

16 
college education), and indicators for non-minority or white consumers. Finally, we include all 

county-level controls (lagged one period) discussed above. 

We also include a battery of fxed effects including lender by year-month, state by yearmonth, lender by state, as well as lender, state, and year-month fxed effects, whenever possible, 

to capture lender health and business models and practices over time, local market changes over 

time, bank strategies across states, as well as unobserved factors at the lender, state, or year-month 

levels. Standard errors are double-clustered at the marketing campaign and year-month level. 18,19 

5.2.2 Other Consumer Finance Outcomes 

Consumer Credit Performance For consumer credit performance, we use the FR Y-14M data, 

where the unit of observation is bank by county by year-month or county by year-month. The 

outcome variables for consumer credit performance are: average days past due, probability of 

default (PD), average payment, and average consumer credit score. 

Our estimation specifcation of consumer credit performance for local market (county) c at 

time t is as follows: 

Yc,t = β0 + β1OpioidExp \c,t−1 + β2CountyControlsc,t−1 + FE + εc,t, (5) 

where Yc,t is one of the outcome variables. We include the same county by year information (also 

lagged one period) and fxed effects as those in the credit supply analyses. Standard errors are 

clustered at the county level. 

Bank-Level Consumer Portfolio Risk For bank-level consumer credit risk, we use the regulatory Call Reports data, where the unit of observation is bank by year-quarter. The opioid crisis 

variables and the instruments here are weighted averages of a bank’s exposure to the opioid death 

rates or opioid marketing practices, across all counties in which the bank operates, using the pro18Note that we are able to include lender by year-month fxed effects for all our credit card terms analyses as 

all credit offers are associated with a lender, but not for the regressions looking at the likelihood of getting a 

credit card offer as not all consumers get an offer from a lender. 

19A unique strength of the Mintel/TranUnion Match data is that it reports all consumers and their characteristics regardless of whether they received a credit card offer in a particular month, allowing us to study 

the credit supply at the extensive margin in addition to the intensive margin based on credit card terms for 

those who did receive an offer. 

17 
portion of bank branches in the county as weight.20 The frst stage is modeled as per equation (3) 

above. The outcome variables here are the bank’s non-performing loans for credit card debt or 

other unsecured consumer loans relative to bank total assets. Specifcally, our estimation specifcation of bank consumer loan portfolio performance for a bank i at time (year-quarter) t follows: 

Yi,t = ψ0 + ψ1OpioidExp \i,t−1 + ψ2BankControlsi,t−1 + ψ3CountyControlsc,t−1ψ4FE + ζi,t, (6) 

where Yi,t refers to proxies of bank portfolio performance. Controls for bank characteristics (lagged 

one period) include tier 1 capital ratio, liquidity ratio, bank proftability, the natural log of bank 

total assets, and bank age. We also include bank weighted exposures to various economic and 

demographic county conditions other than the opioid crisis as those used in the credit supply 

analyses but aggregated at the bank level, based on the shares of bank branches in each county of 

operation. Standard errors are clustered at the bank level. 

5.2.3 Consumer Consumption 

For local consumer consumption, we aggregate the Y-14M domestic credit card purchases by 

bank by county by year-month or county by year-month. Let Yc,t denote the consumer consumption for county c at time t, the estimation equation is as follows, 

Yc,t = θ0 + θ1OpioidExp \c,t−1 + θ2CountyControlsc,t−1 + FE + ηc,t, (7) 

where the county level controls and fxed effects are the same as those used in the credit supply 

specifcation. Standard errors are clustered at the county level. 

6 Empirical Results 

6.1 Opioid Abuse Intensity over Time and Space 

We measure opioid abuse intensity at the county level by opioid-related death rates per 10k 

county population. Figure 1 presents the evolution of opioid-related overall deaths and when 

20Branch deposit data are sourced from the FDIC Summary of Deposits. 

18 
split by prescription and illicit drugs over time. The fgure captures the two important waves in 

the crisis: the heroin (mostly illicit) overdose wave from 2010 to 2012; and the synthetic (illicitly 

manufactured) opioid overdose wave from 2013 onward. 

As Figure 1 demonstrates, the overall opioid death rates have been moving up steadily over 

our sample period, driven by rises in the illicit death rates. By comparison, the prescription death 

rates remain stable at relatively low levels.21 As noted by prior research, many of the initial users 

of prescribed opioids progressed to illicit or illegal opioid use. Later, the availability of relatively 

cheap and easy to produce street drugs such as fentanyl further fueled the surge in illicit opioid 

use. As a result, the overall opioid deaths accelerated rapidly from 2013 onward, just as illicit 

opioid deaths started to register high growth. 

Figure 2 illustrates changes in consumer demographics in opioid-related deaths over time. 

Overall, the opioid crisis appears to be widespread among all races, age groups, genders, and 

people of various education levels. However, we note a few shifts in these demographics over time. 

First, while we continue to see a rise in white opioid death rates, the rises in death rates are more 

signifcant among minorities, particularly Blacks.22 Second, while all age groups are affected, there 

is clearly a higher proportion of working age people, and this proportion is consistently increasing 

over time. Third, both men and women die from overdoses, but men are disproportionately more 

affected, and the gap between genders increases more in the last illicit wave. Lastly, among people 

of various educational attainment who die from opioids, we observe a higher percent of deaths 

among people with lower levels of education (high school or less) and this gap widens signifcantly 

in the last illicit wave. We will exploit these heterogeneities in some of our later credit supply 

analyses to understand whether certain demographic groups are treated differently than others. 

Figures 3 provides the geographical distribution of opioid-related death rates using the confdential CDC mortality data across counties in 2019. The darker red indicates areas with higher 

deaths or prescription rates. We observe stark regional variation in crisis intensity: areas in the 

21This is likely due to the decline in opioid prescription rates starting in 2012 resulting from policies aimed at 

reducing opioid abuse.The Prescription Drug Monitoring Programs (PDMPs) are examples of such policies 

operated by states and established to collect opioid prescription data and facilitate the sharing of this data 

between providers and authorities, in an attempt to reduce opioid abuses (e.g., Buchmueller and Carey 

(2018)). We investigate the effects of the opioid-related laws in later sections. 

22Note that as a share of the total population, there are more opioid deaths among whites than among blacks, 

though the rise in the latter has been faster. 

19 
middle and north of the country are less affected than areas in the West and the South. 

6.2 Opioid Crisis and Marketing/Medical Practices: The Instrument 

The construction of our instruments refects the argument that the geographic differences in 

opioid abuse are closely related to the differing medical practice of doctors, as well as the differing 

marketing practices of pharmaceutical companies. Deteriorating economic conditions, by contrast, 

are not a signifcant driver for these differences.23 

Formally, in order for our instrument of local opioid marketing/medical practices to be valid, 

it must be correlated with opioid abuse intensity. Figure 4 plots the average MKT Doctors/1000Pop, 

the number of doctors in the county who received marketing visits and payments (from pharmaceutical companies) for opioids per 1,000 county population, over 2013-2019. Figure 5 presents 

binned scatter plots of our opioid intensity measure, Opioid Death Rate, against the instrument after 

controlling for year and state fxed effects. 

The opioid intensity measure show a positive correlation with our instrument, as evidenced 

by both the geographical distribution as well as the scatter plot, which is striking but not surprising. According to Hadland, Krieger and Marshall (2017) and Hadland, Cerda, Li, Krieger and  ́

Marshall (2018), between 2013 and 2015, approximately 1 in 12 U.S. physicians received opioidrelated marketing visits and payments; this proportion was even higher for family physicians, 

among whom 1 in 5 received opioid-related marketing support. Marketing strategies of the pharmaceutical companies include visits and direct payments to the doctors for promotion of opioids 

as well as more intense early distribution. 

Furthermore, Table 2 Panel A for credit supply below more formally discusses the frst-stage 

estimation results for credit supply using Mintel/TransUnion Match File analyses. Those analyses 

demonstrate a signifcant positive association between our measures of opioid abuse intensity and 

the instrument, after controlling for a wide range of consumer and county characteristics as well 

as location and time fxed effects. Moreover, the weak identifcation and underidentifcation tests 

suggest that the instrument is relevant and valid. 

23See Maclean, Mallatt, Ruhm and Simon (2020), Ouimet, Simintzi and Ye (2020), Currie and Schwandt (2021), 

and papers cited therein for detailed discussion. 

20 
Having established that our instrument satisfes the relevancy requirement, we now turn to 

discussing whether it also satisfes the exclusion requirement. There are reasons to believe that 

marketing of opioids should not have a direct causal effect on consumer fnancial outcomes other 

than through its infuence on the opioid prescriptions and deaths. Neither consumers nor banks 

have any control over the opioid marketing in their area, nor is it reasonable to assume that they 

would relocate just to be in an area with more aggressive opioid marketing. Further, more marketing of opioids alone, if it does not lead to any changes in opioid prescriptions and deaths, it 

is unlikely to affect in any way consumer credit outcomes. Finally, as mentioned in the Introduction, several studies in prior literature show that demand-side factors alone, such as physical pain, 

depression despair, and social isolation due to poor economies can only explain a small fraction 

of the increase in opioid use and deaths. Moreover, despite the fact that some economic changes 

over the past few decades may be related in some cases to opioid overdose deaths, such an impact on the rise in overall opioid use remains modest.24 We confrm in Table 1 Panel B that there 

exists little correlation between our instrument, MKT Doctors/1000Pop, and various key economic 

and other county characteristics, including income, unemployment rates, labor force participation 

rates, house price indices, average consumer credit score, and poverty rates. 

6.3 Main Results 

6.3.1 Consumer Credit Supply 

Our theory in Section 3 suggests that banks reduce their credit card supply to consumers 

in counties with high opioid crisis intensity. We test this hypothesis by examining both bank 

credit card offers terms, credit supply at intensive margin, and the likelihood of a consumer receiving credit card offers, credit supply at extensive margin. We use the Mintel/TransUnion Match 

File, which includes direct measures of bank credit supply as banks send unsolicited offers to the 

prospective credit card consumers. 

Table 1 Panel A presents summary statistics for the key variables used in this part of the 

analyses. We note that consumers in the study have relatively sound fnancial profles, with a 

24See, among many others, Cutler and Glaeser (2021), Alpert, Evans, Lieber and Powell (2022), and papers 

reviewed in Maclean, Mallatt, Ruhm and Simon (2020). 

21 
mean credit score of 703, and an average income of $57, 411. In other details, we fnd that 21% 

of the consumers have had at least one 90+ days past due delinquency on any credit product, 

7% have fled for bankruptcy in the past, and 2% have had credit card utilization rate at 80% or 

higher in the past. Demographically, the average consumer is 50 years old, 75% of consumers are 

homeowners, 31% are married, and 41% have no children. During the period of our study, county 

overall opioid death averaged 1.2 per 10,000 population while illicit opioid deaths averaged 0.86 

per 10,000 population. The opioid prescription rates average 0.72 per capita. 

Tables 2 report the IV 2SLS regression estimates for the effects of the opioid crisis on consumer credit card terms, where Panel A shows the frst-stage IV results, and Panel B shows the 

second-stage IV estimates, when using MKTDoctors/1000Pop instrument. As above, for brevity, 

we only include the coeffcients of interest. The key dependent variables are either Rate Spread, the 

APR credit card spread, or Limit expressed as either (Ln(Limit)), the natural log of the offered credit 

card limit or (Limit($)), the actual limit in dollar value. The main independent variables are the two 

opioid intensity measures both lagged one year, corresponding to continuous opioid deaths rates 

or indicators for high opioid abuse marked at the top 50th percentile in different specifcations. As 

discussed in Section 5, we control for consumer credit quality in many ways, including credit score 

ranges, income, past delinquency, past derogatory flings, past bankruptcy flings, past high credit 

utilization, as well as for credit demand based on consumer credit inquiries and other personal 

characteristics as of 2-3 months prior to the credit offer. We also control for a rich set of economic 

and demographic county characteristics, plus numerous fxed effects to isolate as well as possible 

the effects studied. Thus, we include: lender-year-month, state-year-month, lender-state, lender, 

state, and year-month fxed effects, to absorb variation in lender and state conditions over time, 

or lender over state as well as to account for other unobserved factors at the lender, state, or time 

levels. 

In all cases, the IV frst-stage estimates indicate that our instruments are signifcantly positively associated with higher opioid crisis intensity, while the IV frst-stage statistics show that 

instruments are relevant and valid.25 The IV second-stage estimates further show that consumers 

25We check the frst stage statistics in all our IV 2SLS analyses that we use in this paper, and all are in line with 

expectations. For brevity, we do not report these in all tables, but they are available from the authors upon 

request. 

22 
residing in counties more affected by opioid abuse experience signifcantly lower credit supply at 

the intensive margin.26 For instance, individuals living in counties with opioid death rates in the 

nation’s top 50th percentile receive, on average, a credit card interest rate that is 1.2 percentage 

points higher, and a credit limit that is $194 lower. These numbers are economically signifcant, as 

they amount to a 7 percent (= 1.2/17) increase in interest rate and a 17 percent reduction in credit 

limit for an average borrower. 

6.3.1.1 Using Alternative Opioid Death Measures 

Given the changes over time in drugs responsible for opioid deaths, with illicit drugs becoming more prominent in recent years than prescription drugs, Table 3 reiterates our main results 

for credit supply terms for consumers when looking separately at rates of prescription and illicit 

opioid deaths. Panel A reports the frst-stage results where we show that the instrument continues 

to work well for both measures. Panel B reports IV second-stage results when using MKTDoctors/1000Pop as instrument for opioid abuse intensity. We fnd signifcant increases in credit card 

spreads and lower credit card limits from both types of death rates, however, magnitudes and signifcance are much larger for the illicit opioid deaths when measured as whether the county is in 

the nation’s top 50th percentile or not. 

6.3.1.2 Using Opioid Prescription Rate 

An alternative measure of opioid exposure that has been used in the literature is opioid 

prescription rate, which played an important role prior to 2014, i.e., during the frst and the second 

waves of the opioid crisis. In Table 4, we repeat our analysis using county opioid prescription 

rates, either continuous or as an indicator of whether it exceeds the nation’s median rate. Panel A 

confrms that our instrument is positively signifcantly associated with the opioid prescription rate 

as well. Then, as indicated in Panel B, we see statistically signifcant and economically important 

negative effect on credit supply, though the effects are somewhat smaller than our benchmark 

estimates. 

26Appendix Table A3 Panel C reaches similar conclusions using OLS estimations. 

23 
6.3.1.3 Using Different Instruments 

We next repeat our benchmark analyses using two alternative instruments: the marketing 

payments per 100 county population and the growth rates in each county in the distribution of 

OxyContin pills bewteen 1997 and 2002. 

We report the frst and second stage results in Table 5 Panel A and Panel B, respectively. 

Again, we continue to see opioid abuses having a large and signifcant effect on local credit supply 

and the effects are particularly large in magnitude when we use the second instrument, the growth 

rates of the distribution of OxyContin pills from 1997 and 2002. 

6.3.1.4 Alternative Identifcation Strategies 

A potential concern with our benchmark analyses is that our results could be prone to selfselection bias if consumers are not randomly assigned across counties, and the opioid crisis determinants at the county level may affect credit terms. To help dispel the competing explanation that 

our results may spuriously refect differences in the characteristics of high- and low-opioid crisis 

counties rather than the opioid crisis intensity per se, we conduct several additional analyses. 

First, we conduct a univariate analysis based on several propensity score matching (PSM) 

techniques in Table A2 Panel A. We match counties in the 25th percentile of the distribution each 

year in terms of opioid intensity with other counties similar in terms of economic and demographic 

characteristics as used in our main analysis based on predicted propensity scores. We use several 

matching techniques, including one-to-one matching without replacement, matching each treated 

county (high opioid group) to the nearest untreated (control, low opioid group) county each year. 

This technique ensures we do not have multiple control counties assigned to the same treated 

one, which can lead to a smaller control group than the treated group. We also use one-to-one 

matching with replacement, which differs in that each treated county is matched to the nearest 

control county even if the latter is used more than once. Additionally, we use nearest-neighbor 

matching with n=2, n=3, and n=5 with replacement, which matches each high opioid county with 

the two, three, or fve low opioid counties with the closest propensity scores, respectively. We 

calculate the opioid crisis effect on credit card terms as the mean difference between high-opioid 

24 
counties’ terms and those of their matched low-opioid peers. All differences show signifcantly 

harsher credit card terms in high-opioid counties relative to the control group. 

Second, we use IV 2SLS regression analysis based on constrained samples comprising counties in the top 25th percentile of the distribution each year in terms of opioid intensity with other 

low opioid death counties similar in characteristics using one-to-one matching without and with 

replacement and report results in Table A2 Panels B and C. 

Finally, in another approach as reported in Table A2 Panel D, we match high opioid counties 

in the top 25th percentile of the distribution with their neighboring counties that are in the low 

opioid remaining group and again run IV 2SLS regressions analysis using this constrained sample. 

Neighboring counties are assumed to have very similar economic and other conditions, making 

the two groups more comparable. Despite the signifcant loss in the number of observations, in 

all these additional regression analyses, we continue to fnd signifcantly harsher credit card terms 

(higher rate spread and lower limits) for consumers in highly affected opioid counties. 

6.3.1.5 Other Robustness Tests 

We conduct additional robustness tests and report the results in the Appendix Table A3. 

First, we add nine more county-level controls including county labor force participation rate, average credit score, air pollution, house price growth rate, percent of school dropouts, the percentage 

of a county’s population claiming affliation with an organized religion, and the relative strength 

of the Democratic/Republican party as captured by county presidential election/voting outcomes, 

poverty rate, as well as percent of population in poor health (Panel A). Next, we use alternative 

opioid death rates based on multiple death causes instead of single death causes as in the benchmark (Panel B). We also conduct simple OLS regressions (Panel C); exclude counties with zero 

opioid-related deaths (Panel D); and, fnally, we exclude Florida from the analysis as Florida was 

an epicenter for the opioid drug distribution. In all of these analyses, we continue to fnd significantly adverse effects on consumer credit supply from opioid epidemic exposure both in credit 

card interest rates as well as the credit card limits offered by banks. 

25 
6.4 Consumer Heterogeneity Tests 

Higher-risk borrowers can be more easily affected by external shocks, and we conjecture that 

banks may exercise caution toward the more vulnerable categories of consumers in highly opioidaffected areas. Moreover, our earlier Figure 2 about the evolution of the crisis by demographics 

showed stark and disproportionately higher opioid death rates in the recent illicit opioid waves 

for low education people (important to note because education tends to be highly correlated with 

consumer income and credit score), minorities particularly Blacks, males, and younger and/or 

working-age people. The richness of our credit supply data allows us to test whether our main 

fndings may differ across these characteristics. Specifcally, we analyze interactions between the 

opioid crisis intensity and consumer high credit risk indicators, while continuing to use MKTDoctors/1000Pop as an instrument for opioid abuse intensity. Results from the IV 2SLS second stage 

on high risk consumers (credit score below 620) and minorities are reported in Table 6; and results 

on other consumer characteristics such as past deep delinquency (90 days past due), low income 

(<30K), and younger consumers (< 25 years old) are in Tables A4 and A5 in the Appendix. 

We consistently observe that banks apply additionally harsher credit card terms for riskier 

consumers, as proxied by their credit score and past delinquency history, in highly opioid-affected 

counties. Also importantly, within a county, minorities, particularly Blacks, receive worse credit 

terms than others (about 1.6 percentage points higher interest rate and about $350 decline in credit 

card limit offered). Low-income individuals, those with income less than $30k are also treated 

much more harshly by lenders. Young people are also charger higher rates in high exposure areas, 

however, the effects on their credit limits are negative but not statistically signifcant. 

6.5 Credit Card Rewards and Likelihood of Credit Card Offers 

The Mintel dataset allows us to measure another element of credit pricing, that is, offers 

of rewards/promotions, in addition to credit supply on the extensive margin, credit card offer 

likelihood. We repeat our analyses using credit card rewards and credit card offer likelihood as 

our dependent variables and report the IV 2SLS second stage results in Table 7. Note that for credit 

card rewards, we use the same offer-level sample as above, while for likelihood of credit card offer, 

26 
we use an extended larger sample that includes consumers with and without offers in each month. 

Our analyses reveal that individuals in higher exposure counties are less likely to receive credit 

card rewards and promotions by 4 percentage points. Importantly, credit card offer likelihood also 

declines signifcantly (by 10 percentage points) in counties with higher opioid abuse. 

6.6 Effectiveness of Recent Opioid Policies 

Given the severity of the opioid crisis and its adverse economic impact, a number of opioidrelated laws and regulations were enacted in recent years in an effort to combat the opioid epidemic. The existing studies that attempt to estimate the implications of those regulations either 

yield mixed results or only consider one such law at a time, making it diffcult to draw policy 

conclusions. For example, Kaestner and Engy (2019) fnd that the Prescription Drug Monitoring 

Programs (PDMPs) reduce prescription rates, but do not help reduce opioid deaths or improve 

socioeconomic outcomes. In contrast, Cornaggia, Hund, Nguyen and Ye (2021) fnd that adoption 

of PDMPs reduces opioid deaths and also partially reverses some negative effects on municipal 

fnance. Doleac and Mukherjee (2019) fnd increased opioid abuse after increased access to Naloxone (which reverses opioid overdose), likely due to increasing risk taken by opioid addicts given 

they know there is an antidote in place to save their lives. 

We add to this debate by investigating the effects of six prominent opioid-related laws on 

consumers and consumer fnance outcomes, out of which three are opioid supply-oriented laws and 

the other three are demand-oriented opioid laws. Table A7 in the Appendix shows descriptions of all 

these laws. We focus on the impact on consumer credit supply, as this is the margin that has the 

most implications on local economic recovery. 

Of the supply-related laws, the state ”Opioid Limiting Law” targets opioids prescriptions. For 

instance, certain states limit prescriptions for frst-time users or for acute or postoperatory pain or 

other uses or set other limits on the number of prescriptions or overall quantity of opioids that can 

be prescribed by physicians to a patient. The ”Opioid PDMP Law” collect and track opioid prescriptions and connect prescribers, dispensers, law enforcement, and Medicare authorities. Some 

states mandate the use of PDMPs by prescribers while others make it voluntary. We focus on 

the mandatory PDMPs given prior research fnds these to be more likely to affect behavior. The 

27 
”Triplicate Prescription Law” requires that three copies of an opioid prescription be issued: The 

prescriber keeps one copy, another is kept by the pharmacist, while the third is sent to a state 

agency by the pharmacist. Alpert, Evans, Lieber and Powell (2022) show how strict monitoring of 

opioid prescriptions via special prescription documentation in triplicate requirement substantially 

reduces opioid use and related deaths in those states once epidemic unfolds. 

Among the demand-oriented laws, the ”Naloxone Law” increases access to and allows the prescribing and dispensing of Naloxone (an opioid receptor antagonist that reverses opiate overdose) 

by various third parties to users with documented risk factors for overdose (Davis and Carr (2015)). 

The ”Good Samaritan Law,” provides immunity to drug users for certain drug crimes when they 

call for help for a person experiencing a drug overdose. Lastly, the ”Medical Marijuana Permitting Law.” Initial studies showed a decline in overdoses in medical marijuana permitting states, 

but later studies documented a reversal increasing rather than decreasing opioid overdose deaths 

(e.g., Shover, Davis, Gordon and Humphreys (2019)).27 

We take advantage of the staggered implementation of the four state-level opioid laws, ”Opioid Limiting Law,” ”Opioid PDMP Law,” ”Nalaxone Law,” and ”Good Samaritan Law,” by running a difference-in-difference (DID) regression specifcation to evaluate the effectiveness of the 

laws and their infuence on consumers and consumer fnance. For the ”Triplicate Prescription 

Law” and ”Medical Marijuana Permitting Law,” we use fxed effects and/or sample splits. 

We frst examine the effects of opioid laws on prescription and opioid mortality rates, including total, prescription mortality, and illicit mortality rates, and report results in Table 8 Panel 

A using county-level regressions over 2010-2019, while including all county controls from our main 

specifcations and additional fxed effects. The fxed effects include county, state, and year for the 

effects of opioid-time-varying laws, and only year fxed effects for the state time-invariant ones. 

Conditional on a strong set of controls for local markets and time, we uncover very different 

impacts among the supply-oriented and demand-oriented opioid laws. Specifcally, all supply-related 

laws have some benefcial effects in reducing opioid prescriptions and prescription opioid death 

rates, with the opposite impact on the opioid illegal and, hence, total deaths. These results sug27The ”Good Samaritan Law” and ”Medical Marijuana Permitting Law” data are from the Opioid Environment Policy Scan (OEPS) from University of Chicago. 

28 
gest that the laws passed do not help dissuade illegal drug activities. An exception is the ”Triplicate Prescription Law,” which attenuates opioid deaths from both prescription and illegal sources. 

Turning to the three demand-oriented laws, only the ”Medical Marijuana Permitting Law” was able 

to reduce both the opioid prescription rates and the opioid prescription related death rates. These 

results establish that not all laws are the same, consistent with the mixed fndings on deaths in 

prior research.28 

Table 8 Panel B conducts a horse race among the effects of different state laws on consumer 

credit supply. We show the effects of time-varying state laws in Panel B1, and sample splits for 

the time-invariant laws in Panels B2 and B3. Our key dependent variables are interest rate spreads 

and credit card limits, while we also include our main opioid intensity measures, all consumer and 

county controls, and fxed effects as in our main analyses. Same as above, we instrument opioid 

intensity with MKTDoctors/1000Pop, and report IV 2LS second stage estimates in all cases. 

Table 8 Panel B1 shows that the supply-related laws — the ”Opioid Prescription Limiting Law” 

and the mandatory ”Opioid PDMP Law” — yield positive effects on consumer credit supply reversing partially the negative consequences of the opioid crisis, while the demand-related laws — 

the ”Naloxone Law” and the ”Good Samaritan Law” — have either no effects or even negative effects on credit supply for consumers. Panels B2 and B3 indicate no negative effect on rates though 

negative effects on credit limits on consumers in states that implemented the supply-related law 

”Triplicate Prescription Law.” By comparison, in states that did not implement the law, the negative effects show up in both dimensions and are much larger. We also fnd that only states that 

implemented the demand-related ”Medical Marijuana Permitting Law” yield negative credit supply 

effects. 

To summarize, the supply-related laws (the ”Opioid Prescription Limiting Law,” the mandatory ”Opioid PDMP Law,” and the ”Triplicate Prescription Law”) all tend to have positive reversal 

effects on consumer market credit supply, while the demand-related laws (”Naloxone Law,” ”Good 

Samaritan Law,” ”Medical Marijuana Permitting Law”) appear to help less or even induce some 

detrimental effects on consumer credit, and potentially intensify the opioid crisis.29 Importantly, 

28Results are similar in a sample that starts earlier in 2007 instead of 2010. 

29The different effects are likely due to the different nature and intent of the laws, and are somewhat consistent 

with prior research. 

29 
we found that the supply-related laws that do have benefcial effects on reducing opioid prescriptions and deaths also tend to exhibit mitigating effects in consumer credit supply. 

6.6.1 Possible Underlying Mechanisms for Credit Supply 

To understand our credit supply results, we next investigate consumer credit performance 

as well as bank portfolio risk and how they vary with their exposure to the opioid epidemic. 

6.6.1.1 Consumer Credit Performance 

For consumer credit performance, we use information from FR Y14-M on credit cards accounts’ days past due, bank-estimated loan probability of default (PD), the monthly payments 

made by consumers, as well as their refreshed credit scores. We aggregate the information to the 

bank-county-year-month level to arrive at averages for the bank-county for each given year-month. 

The results are reported in Table 9 Panel A. We observe that borrowers in high opioid exposure counties tend to have longer days of past due, higher bank-assessed loan probability of 

default (PD), lower monthly payments, and lower updated credit scores. For example, the average loan PD increases by 1.2 percentage points and the average consumer refreshed credit score 

decreases by 16 points (or a 2% decrease relative to the mean of 745) in high- versus low-opioid 

affected areas. These results suggest higher credit risk associated with consumers living in areas 

with high opioid exposure. Those people are either more likely to abuse opioids if they live in the 

high-exposure counties or may be more fnancially vulnerable to opioid abuse in those counties. 

As we discussed in the Introduction and the Literature Review, opioid abuse reduces individuals’ 

employment and earnings potential as well as frms’ hiring. This labor channel alone would lead 

to enhanced credit risk, according to the model presented earlier in Section 3. Most importantly, 

the evidence here suggests that credit card borrowers in highly exposed areas pose signifcantly 

higher credit risk for the lenders, which may explain some of their cautious credit supply behavior. 

In the Appendix Table A6 Panel A we repeat our analyses using a county-year-month sample, and 

our results continue to hold. 

30 
6.6.1.2 Bank Consumer Loan Portfolio Performance 

Given that consumers in areas hard-hit by opioids are more likely to default on their fnancial obligations, we next test whether banks more exposed to the opioid crisis via their local branch 

network or operations suffer more from nonperforming loans across their consumer loan portfolios. Specifcally, we test whether exposed banks that operate in only one county and likely to have 

a harder time diversifying their risk exposure from the opioid crisis, may suffer from credit risk in 

their portfolios. Our estimation results are reported in Table 9 Panel B where we examine credit 

card nonperforming loans ratio as well as noperforming loans ratios for unsecured consumer credit 

using IV 2SLS analysis and the same instrument we use above. Our second stage IV estimates 

show that banks confned to more severely affected counties report higher non-performing loans 

in credit card products as well as total unsecured consumer loans. This evidence further helps 

corroborate our story that banks experience more materialized credit risk in their loan portfolios, 

hence the decline in credit supply to opioid-affected areas. 

6.6.2 The Welfare Implications of the Opioid Crisis — Consumer Spending 

Before we conclude, we reconfrm our main credit supply effects using the supervisory FR 

Y-14M credit card dataset and also explore the likely possible macro real effects of the opioid crisis, 

in both cases using the same IV 2SLS analysis employed throughout our study. In these analyses 

we use a bank-county-year-month sample. We construct two aggregate measures of consumption: 

total purchases per county population and total purchases relative to credit limit. 

Our estimation results are reported in Table 10. Panel A reports effects of the opioid crisis intensity on elements of credit supply, including average cycle APR, natural log of average 

credit card limit, and percent of accounts with rewards and promotions. Panel B reports effects on 

consumer spending proxied by the credit card purchases made by consumers as provided by FR 

Y-14M. Our second stage IV estimates in Panel A reconfrm that credit supply declines in counties 

more affected by the opioid crisis as evidenced by higher cycle APRs, lower credit card limits, and 

less accounts with rewards. Effects are also economically meaningful. 

Our analyses reveal that individuals in higher (top 50th percentile) versus lower opioid 

31 
7 

abuse exposure (lower 50th percentile) counties pay about 2.1 percentage points higher APR, have 

lower average credit limits by about 9.7 percentage points, and are less likely to receive credit card 

rewards and promotions by 11 percentage points. Importantly, credit card offer likelihood also 

declines signifcantly (by 10 percentage points) in counties with higher opioid abuse. 

Table 10 Panel B fnds that counties with higher opioid exposure incur signifcant declines in 

consumer credit card spending. For example, the total purchases per population decline by about 

4.3 percentage points in higher versus lower opioid death exposure counties. These effects are also 

confrmed using other proxies for consumer spending, including the total purchases per limit and 

the average consumer purchase. 

These effects are even larger when re-estimating the effects using an aggregated countyyear-month sample but without the lender year-month fxed effects as shown in Appendix Table 

A6 Panel C. These results indicate that the credit policy adopted by banks - targeting high opioidexposure locations - to deal with the repercussion of the opioid pandemic has important welfare 

implications. 30 

Conclusions 

The opioid epidemic in the U.S. has left far-reaching and lingering consequences on the 

health and social conditions of U.S. local communities for over two-and-a-half decades. In this 

paper, we discover unfavorable credit supply consequences of this crisis on consumers: banks 

are reluctant to lend in areas with signifcant exposure to opioid abuse. They are less likely to 

send credit offers in the highly exposed areas; however, when they do still solicit consumers for 

credit in those areas, the offers have much higher interest rates, lower credit limits, and less rewards/promotions. The credit supply constriction seems to harm harder the riskier consumers, 

the minorities (particularly Blacks), low-income, and younger consumers. 

The wave of laws and regulations passed to reduce the devastating effects of the opioid crisis 

on communities raises a question whether the legislative effort helped mitigate some of the nega30For opioid abusers or potential opioid abusers, reduced credit may imply reduced purchase of opioids, 

which would then have a positive effect despite the overall reduction of consumption. We have no data 

to test this hypothesis. However, the medical literature on addiction suggests that opioid addicts are more 

likely to cut their other consumption frst before they cut their drug use. 

32 
tive effects uncovered in the study. Our analysis of six different opioid-related laws (three supplyrelated and three demand-related laws) suggests different effects across supply- and demand-oriented 

laws in mitigating both the crisis and credit supply effects on consumers. The opioid supply-oriented 

laws (”Opioid Prescription Limiting Law,” the mandatory ”Opioid PDMP Law,” and the ”Triplicate Prescription Law”) all appear to mitigate some of the negative impacts of the opioid epidemic 

on consumers and their credit supply, while the demand-related laws are less benefcial or can even 

aggravate the opioid crisis. 

From a policy standpoint, the cautious behavior of banks appears to be partially justifed by 

the relatively higher credit risk in the highly opioid-affected areas. The reduced consumer credit 

supply, nevertheless, could create a negative feedback loop depriving the opioid-affected regions 

of the much-needed liquidity for recovery. Indeed, we fnd that the opioid-crisis induced credit 

supply contraction has important welfare consequences: consumer spending sharply decreases in 

hardly-hit local markets. This latter may suggest important macro-policy implications given that 

consumer spending accounts for the vast majority of U.S. gross domestic product and economic 

growth. It also remained unanswered whether the reduced credit access may help reduce opioid 

addiction as a result of reduced buying power. Thus, it is natural to ask: where should we go 

from here i.e., ”quo vadis.” These fndings here may prove useful for policymakers to better understand the impact of the opioid crisis and formulate adequate policies concerning consumers 

to help recovery efforts, enhance welfare, and restore growth and resilience in the opioid-affected 

consumer markets. One possible policy may involve programs that jointly target both opioid addiction treatment and credit repair and assistance via fnancial education and counseling without 

stigma, which may help for consumers and communities affected by the opioid addiction regain 

fnancial stability, resilience, and well being. 

33 
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36 
Figure 1 : Opioid-Related Death Rates Over Time 

This line chart depicts the time trend of total opioid-related death rates, illicit opioid-related death rates, and 

prescription opioid-related death rates per 10k population. Data sources: CDC/NCHS, National Center for 

Health Statistics, Mortality, restrictive version for 2010-2019, and the public version for 2020. 

37 
Figure 2 : Opioid Death Rates by Consumer Demographics 

This fgure plots overall opioid-related death rates per 10K population by consumer demographics (age 

groups, gender, race groups, and education groups) over time. Rates are constructed relative to their respective population. Data sources: CDC/NCHS, National Center for Health Statistics, Mortality, restrictive 

version for 2010-2019, and the public version for 2020. The public version doesn’t contain information by 

education. 

Panel A. Opioid Death Rates by Consumer Race Panel B. Opioid Death Rates by Consumer Age 

Panel C. Opioid Death Rates by Consumer Gender Panel D. Opioid Death Rates by Consumer Education 

38 
Figure 3 : Opioid-Related Death Rates across U.S. Counties 

This fgure presents the geographical distribution of opioid-related death rates (per 10K population) across 

U.S. counties for year 2019. Darker red colors represent higher death rates. Data sources: CDC/NCHS, 

National Center for Health Statistics, Mortality. 

Figure 4 : Instrument ”MKT Doctors/1000Pop” across U.S. Counties 

This fgure presents the geographical distribution of physicians receiving pharmaceutical industry marketing for opioids across U.S. counties over 2013-2019. The fgure presents 10 categories that were obtained 

based on an equal deciles’ methodology, with darker colors representing higher marketing rates; 1 indicates 

that the counties’ marketing rates ranked in the bottom decile of the country, while 10 indicates that the 

counties’ marketing rates ranked in the top decile of the nation. Thus, darker colors show higher opioid 

marketing intensity. Data sources: Open Payments Database and Hadland, Rivera-Aguirre, Marshall and 

Cerda (2019). 

39 
Figure 5 : Validating the Instrument: Relevancy 

This fgure provides binned scatter plot of opioid-related deaths per 10K population versus pharmaceutical industry opioid drug marketing (doctors receiving marketing payments per 1,000 people, MKT Doctors/1000Pop) after taking out the state and year fxed effect. Data sources: CDC/NCHS, National Center for 

Health Statistics, Mortality, CDC/IQVIA Xponent, Hadland, Rivera-Aguirre, Marshall and Cerda (2019), 

Open Payments Database, U.S. Drug Enforcement Administration (DEA) and Cornaggia, Hund, Nguyen 

and Ye (2021). .4 .6 .8 1 1.2 1.4 Opioid Deaths per 10K Pop

0 .1 .2 .3 .4 .5

Doctors receiving payments per 1000 Pop

 Total Prescription Illicit

40 
Table 1: Summary Statistics 

This table reports in Panel A summary statistics (mean, p50, p25, p75, and number of observations) for the 

key variables in our analyses. Variable defnitions and data sources are in Appendix Table A1. The sample 

is based on the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC 

Match File for analyzing credit card supply to consumers. The data are focused on institutions identifed 

as ”banks” in the Mintel/TransUnion Match File. All demographic attributes are from the Mintel. Panel 

B shows correlations of our instrumental variable (MKT Doctors/1000Pop with county economic and other 

characteristics. 

Panel A: Mintel/TransUnion Match File Variables 

mean p50 std p25 p75 N 

Dependent Variables 

Rate Spread 

Ln(Limit) 

Limit ($) 

Rewards/Promo 

Credit Card Offer 

17.305 

6.447 

941.145 

0.900 

0.564 

15.760 

6.217 

500.000 

1.000 

1.000 

5.076 

0.776 

1170.751 

0.300 

0.496 

12.970 

6.217 

500.000 

1.000 

0.000 

22.770 

6.909 

1000.000 

1.000 

1.000 

197,371 

197,371 

197,371 

197,371 

392,101 

Key Independent Variables 

Opioid Death Rate 

High Opioid Death Rate 

Prescription Opioid Death Rate 

Illicit Opioid Death Rate 

Opioid Prescription Rate 

1.212 

0.513 

0.500 

0.864 

0.721 

0.916 

1.000 

0.416 

0.542 

0.684 

1.025 

0.500 

0.393 

0.943 

0.295 

0.526 

0.000 

0.229 

0.258 

0.505 

1.573 

1.000 

0.650 

1.126 

0.869 

197,371 

197,371 

197,371 

197,371 

197,350 

Instrumental Variables 

MKT Doctors/1000Pop 

MKTPayments/1000Pop 

Purdue MKT (Oxycontin Growth ’97-’02) 

0.140 

0.542 

6.020 

0.120 

0.417 

5.211 

0.093 

0.459 

3.510 

0.072 

0.201 

3.760 

0.188 

0.752 

7.315 

197,371 

197,371 

369,169 

Consumer Controls 

Consumer Credit Score 

Credit Score 580 660 

Credit Score 660 720 

Credit Score 720 800 

Credit Score 800plus 

Deep Delinq 

Recent Delinq 

Other Derogatory 

Bankruptcy Filer 

High Util (≥80%) 

Ln(1+ No Credit Inquiries) 

Has Prior Cards 

Consumer Age 

Age 25to44 

Age 45to64 

Age 65plus 

Married 

No Kids 

White 

Miss Race 

Educ: Some College 

Educ: College 

Educ: Post College 

Miss Educ 

Homeowner 

Ln(Consumer Income) 

702.980 

0.267 

0.209 

0.243 

0.194 

0.213 

0.085 

0.235 

0.067 

0.024 

0.336 

0.940 

49.779 

0.355 

0.418 

0.186 

0.310 

0.406 

0.410 

0.501 

0.105 

0.122 

0.058 

0.317 

0.753 

10.958 

699.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

1.000 

50.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

1.000 

0.000 

0.000 

0.000 

0.000 

1.000 

11.082 

92.653 

0.442 

0.406 

0.429 

0.395 

0.410 

0.278 

0.424 

0.251 

0.155 

0.517 

0.237 

15.706 

0.479 

0.493 

0.389 

0.462 

0.491 

0.492 

0.500 

0.307 

0.328 

0.234 

0.465 

0.431 

0.821 

633.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

1.000 

37.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

1.000 

10.532 

782.000 

1.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.000 

0.693 

1.000 

61.000 

1.000 

1.000 

0.000 

1.000 

1.000 

1.000 

1.000 

0.000 

0.000 

0.000 

1.000 

1.000 

11.379 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

197,371 

41 
Table 1: Summary Statistics (cont.) 

This table reports in Panel A summary statistics (mean, p50, p25, p75, and number of observations) for the 

key variables in our analyses. Variable defnitions and data sources are in Appendix Table A1. The sample 

is based on the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC 

Match File for analyzing credit card supply to consumers. The data are focused on institutions identifed 

as ”banks” in the Mintel/TransUnion Match File. All demographic attributes are from the Mintel. Panel 

B shows correlations of our instrumental variable (MKT Doctors/1000Pop with county economic and other 

characteristics. 

Panel A: Mintel/TransUnion Match File Variables (cont.) 

mean p50 std p25 p75 N 

County Controls 

Ln(County Income) 16.922 17.040 1.493 15.871 17.979 197,371 

County Unemployment Rate 4.900 4.633 1.580 3.800 5.700 197,371 

County Bank HHI 0.174 0.144 0.107 0.114 0.189 197,371 

County Population Density 1882.788 688.603 5495.129 255.714 1671.863 197,371 

County Race HHI 0.679 0.668 0.197 0.540 0.795 197,371 

County % Male 0.491 0.490 0.010 0.485 0.495 197,371 

County % Age 25 44 0.263 0.262 0.032 0.242 0.284 197,371 

County % Age 45 64 0.265 0.265 0.024 0.249 0.281 197,371 

County % Age 65plus 0.144 0.139 0.037 0.121 0.160 197,371 

County % High Education (≥ College) 0.601 0.606 0.089 0.543 0.662 197,371 

County Inequality: Gini Coeffcient 0.457 0.457 0.034 0.434 0.479 197,371 

Panel B: Correlations of Instrument with County-Level Conditions 

MKT Doctors/1000Pop Correlation Coeffcient 

County Personal Income -0.018 

County per Capita Income -0.001 

County HPI Growth -0.038 

County Labor Participation Rate -0.023 

County Unemployment Rate -0.068 

County Average FICO Score 0.025 

County Poverty Rate 0.019 

County Crime Rate -0.008 

County Population Density 0.008 

County Population -0.028 

County Race HHI -0.023 

County % Male -0.122 

County Average Age 0.117 

County % High Education (≥ College) 0.033 

County Inequality: Gini Coeffcient 0.122 

42 
Table 2: Effects of the Opioid Crisis on Credit Card Supply to Consumers 

This table reports regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument 

for explaining the relationship between opioid crisis intensity (Opioid Death Rate and High Opioid Death Rate), based on data from CDC) 

and bank credit card terms: rate spread and credit card limit. Panel A reports the frst-stage IV and Panel B reports second-stage IV 

estimates from offer-level regressions. All variables are constructed using the anonymized Mintel Comperemedia Inc. Direct Mail 

Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are focused on lenders identifed as 

”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer controls include: credit score 

ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past 

bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator 

for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment 

rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, 

percent of people with higher education, and inequality. All regressions include State × Year-Month, Lender × Year-Month, Lender × 

State, Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard errors are double-clustered 

by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is 

indicated by *, **, and ***, respectively. 

Panel A: IV First Stage 

Dependent Variables: Opioid Death Rate High Opioid Death Rate 

Model: (1) (2) 

Mkt Doctors/1000Popc,t−1 1.0349∗∗∗

(21.65) 

0.4511∗∗∗

(12.85) 

Fit statistics 

Observations 197,371 197,371 

Adj. R2 0.559 0.421 

Fixed effects 

State × Year-Month ✓ ✓

Lender × Year-Month ✓ ✓

Lender × State ✓ ✓

Lender, State, Year-Month ✓ ✓

Consumer & County controls ✓ ✓

Panel B: IV Second Stage 

Dependent Variables: 

Model: 

Rate Spread 

(1) 

Ln(Limit) 

(2) 

Limit ($) 

(3) 

Rate Spread 

(4) 

Ln(Limit) 

(5) 

Limit ($) 

(6) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

0.5191∗∗∗

(4.95) 

-0.0720∗∗∗

(-3.58) 

-84.4863∗∗∗

(-2.77) 

1.1909∗∗∗

(4.92) 

-0.1652∗∗∗

(3.58) 

-193.8267∗∗∗

(-2.77) 

Fit statistics 

Observations 

Adj. R2 

197,371 

0.317 

197,371 

0.157 

197,371 

0.083 

197,371 

0.311 

197,371 

0.154 

197,371 

0.081 

IV frst-stage statistics 

KP rk Wald F-stat (Weak-ID) 

KP rk LM-stat (Under-ID) 

1787∗∗∗

1782∗∗∗

1787∗∗∗

1782∗∗∗

1787∗∗∗

1082∗∗∗

1786∗∗∗

1087∗∗∗

1786∗∗∗

1087∗∗∗

1786∗∗∗

1087∗∗∗

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

43 
Table 3: Using Prescription and Illicit Opioid Deaths 

This table reports regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument 

for explaining the relationship between opioid crisis intensity (Prescription Opioid Death Rate, High Prescription Death Rate and Illicit 

Death Rate, High Illicit Opioid Death Rate), based on data from CDC) and bank credit card terms: rate spread and credit card limit. Panel 

A reports the frst-stage IV and Panel B reports second-stage IV estimates from offer-level regressions. All variables are constructed 

using the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card 

mail offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are 

from Mintel. Consumer controls include: credit score ranges, indicators for past deep delinquency, recent delinquency, past derogatory 

flings such as foreclosure and collections, past bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past 

credit cards, consumer age ranges, married, indicator for no kids, White, education indicators, homeowner, and consumer income. 

County controls include: county income, unemployment rate, bank market concentration, population density, percent of males, race 

concentration, percent of people in various age ranges, percent of people with higher education, and inequality. All regressions include 

State × Year-Month, Lender × Year-Month, Lender × State, Lender, State, and Year-Month fxed effects. Variables are defned in Appendix 

Table A1. Standard errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. 

Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel A: IV First Stage 

Dependent Variables: Prescription Opioid High Prescription Opioid Illicit Opioid High Illicit Opioid 

Death Rate Death Rate Death Rate Death Rate 

Model: (1) (2) (3) (4) 

Mkt Doctors/1000Popc,t−1 0.6190*** 

(25.96) 

0.8977*** 

(27.88) 

0.6316*** 

(14.37) 

0.2549*** 

(8.46) 

Fit statistics 

Observations 197,371 197,371 197,371 197,371 

Adj. R2 0.429 0.329 0.615 0.491 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓

Panel B: IV Second Stage 

Dependent Variables: 

Model: 

Rate Spread 

(1) 

Ln(Limit) 

(2) 

Rate Spread 

(3) 

Ln(Limit) 

(4) 

Rate Spread 

(5) 

Ln(Limit) 

(6) 

Rate Spread 

(7) 

Ln(Limit) 

(8) 

Prescription Opioid Death Ratec,t−1 

High Prescription Opioid Death Ratec,t−1 

Illicit Opioid Death Ratec,t−1 

High Illicit Opioid Death Ratec,t−1 

0.8679*** 

(4.96) 

-0.1204*** 

(-3.59) 

0.5984*** 

(4.96) 

-0.0830*** 

(-3.59) 

0.8505*** 

(4.91) 

-0.1180*** 

(-3.57) 

2.1072*** 

(4.83) 

-0.2922*** 

(-3.55) 

Fit statistics 

Observations 

Adj. R2 

197,371 

0.321 

197,371 

0.159 

197,371 

0.321 

197,371 

0.160 

197,371 

0.308 

197,371 

0.152 

197,371 

0.285 

197,371 

0.139 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

44 
Table 4: Using Opioid Prescription Rate 

This table reports regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument 

for explaining the relationship between opioid crisis intensity (Opioid Prescription Rate and High Opioid Prescription Rate), based on 

data from CDC) and bank credit card terms: rate spread and credit card limit. Panel A reports the frst-stage IV and Panel B reports 

second-stage IV estimates from offer-level regressions. All variables are constructed using the anonymized Mintel Comperemedia 

Inc. Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are focused on lenders 

identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer controls include: credit 

score ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past 

bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator 

for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment 

rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, 

percent of people with higher education, and inequality. All regressions include State × Year-Month, Lender × Year-Month, Lender × 

State, Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard errors are double-clustered 

by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is 

indicated by *, **, and ***, respectively. 

Panel A: IV First Stage 

Dependent Variables: Opioid Prescription Rate High Opioid Prescription Rate 

Model: (1) (2) 

Mkt Doctors/1000Popc,t−1 0.9671*** 

(55.93) 

1.3144*** 

(44.64) 

Fit statistics 

Observations 197,367 197,367 

Adj. R2 0.739 0.538 

Fixed effects 

State × Year-Month ✓ ✓

Lender × Year-Month ✓ ✓

Lender × State ✓ ✓

Lender, State, Year-Month ✓ ✓

Consumer & County controls ✓ ✓

Panel B: IV Second Stage 

Dependent Variables: Rate Spread Ln(Limit) Rate Spread Ln(Limit) 

Model: (1) (2) (3) (4) 

Opioid Prescription Ratec,t−1 0.5578*** -0.0769*** 

(4.99) (-3.59) 

High Opioid Prescription Ratec,t−1 0.4104*** -0.0565*** 

(4.99) (-3.58) 

Fit statistics 

Observations 197,367 197,367 197,367 197,367 

Adj. R2 0.325 0.162 0.325 0.162 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓

45 
Table 5: Using Different Instrumental Variables (IVs) 

This table reports regression estimates from IV 2SLS regressions (equations (3) and (4)) using two alternative instrumental variables 

(IVs), ”Mkt Payments/1000Pop” and ”High Purdue Mkt” for explaining the relationship between opioid crisis intensity (Opioid Death 

Rate and High Opioid Death Rate), based on data from CDC) and bank credit card terms: rate spread and credit card limit. Panel A 

reports the IV estimates using ”Mkt Payments/1000Pop” as instrument and Panel B reports IV estimates using ”High Purdue Mkt” 

as instrument from offer-level regressions. All variables are constructed using the anonymized Mintel Comperemedia Inc. Direct 

Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are focused on lenders identifed 

as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer controls include: credit score 

ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past 

bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator 

for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment 

rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, 

percent of people with higher education, and inequality. All regressions include State × Year-Month, Lender × Year-Month, Lender × 

State, Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard errors are double-clustered 

by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is 

indicated by *, **, and ***, respectively. 

Panel A: Using ”Mkt Payments/100Pop” as IV 

Dependent Variables: 

Model: 

Opioid 

Death Rate 

(1) 

High Opioid 

Death Rate 

(2) 

Rate 

Spread 

(3) 

Ln 

(Limit) 

(4) 

Rate 

Spread 

(5) 

Ln 

(Limit) 

(6) 

Mkt Doctors/1000Popc,t−1 0.3004*** 

(24.67) 

0.1095*** 

(19.73) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

0.2814*** 

(4.00) 

-0.0311** 

(-2.31) 

0.7723*** 

(3.99) 

-0.0854** 

(-2.31) 

Fit statistics 

Observations 

Adj. R2 

197,371 

0.564 

197,371 

0.422 

197,371 

0.323 

197,371 

0.162 

197,371 

0.319 

197,371 

0.161 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

Panel B: Using ”High Purdue Mkt” as IV 

Dependent Variables: 

Model: 

Opioid 

Death Rate 

(1) 

High Opioid 

Death Rate 

(2) 

Rate 

Spread 

(3) 

Ln 

(Limit) 

(4) 

Rate 

Spread 

(5) 

Ln 

(Limit) 

(6) 

High Purdue Mktc 0.0512*** 

(14.81) 

0.0079*** 

(3.46) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

0.7834*** 

(2.89) 

-0.1224* 

(-1.95) 

5.0599** 

(2.37) 

-0.7904* 

(-1.77) 

Fit statistics 

Observations 

Adj. R2 

369,162 

0.544 

369,162 

0.343 

369,162 

0.250 

369,162 

0.097 

369,162 

-0.115 

369,162 

-0.101 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State 

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

46 
Table 6: Heterogeneous Effects for High Credit Risk and Minority Consumers 

This table examines how the effects of opioid crisis intensity on bank credit card terms differ by consumer credit risk and race using 

interactions of consumer ”High Credit Risk” (Credit Score <620) with opioid intensity in Panel A and interactions of minority groups 

(Black, Hispanic, Other) and opioid intensity in Panel B. We report estimates from IV 2SLS regressions (equations (3) and (4)) using the 

”Mkt Doctors/1000Pop” as an instrument for opioid crisis intensity (Opioid Death Rate and High Opioid Death Rate), based on data from 

CDC). All variables are constructed using the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC 

Match File for analyzing credit card mail offers. Consumer controls include: credit score ranges, indicators for past deep delinquency, 

recent delinquency, past derogatory flings such as foreclosure and collections, past bankruptcy flings, past high utilization (≥ 80%), 

number of credit inquiries, past credit cards, consumer age ranges, married, indicator for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment rate, bank market concentration, population 

density, percent of males, race concentration, percent of people in various age ranges, percent of people with higher education, and 

inequality. Variables are defned in Appendix Table A1. Standard errors are clustered by Marketing Campaign and Year-Month and 

t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel A: High Credit Risk (Credit Score <620) 

Dependent Variables: Rate Spread Ln(Limit) Limit ($) Rate Spread Ln(Limit) Limit ($) 

Model: (1) (2) (3) (4) (5) (6) 

Opioid Death Ratec,t−1 x High Credit Riski,c,t−1 0.1320*** -0.0150*** -9.2690** 

(9.38) (-5.87) (-2.42) 

High Opioid Death Ratec,t−1 x High Credit Riski,c,t−1 2.9878*** -0.3405*** -212.6968** 

(9.45) (-5.96) (-2.49) 

Opioid Death Rate 0.2567** -0.0374* -64.5647* c,t−1 

(2.11) (-1.69) (-1.94) 

High Opioid Death Rate 0.6788** -0.0958* -153.6938** c,t−1 

(2.46) (-1.92) (-2.06) 

High Credit Riski,c,t−1 0.3853** -0.0463 -121.2126** 0.4633*** -0.0550* -125.8480*** 

(2.22) (-1.46) (-2.56) (2.80) (-1.84) (-2.81) 

Fit statistics 

Observations 197,371 197,371 197,371 197,371 197,371 197,371 

Adj. R2 0.208 0.118 0.063 0.196 0.115 0.108 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

Panel B: Black, Hispanic, Other Minority Consumers 

Dependent Variables: Rate Spread Ln(Limit) Limit ($) Rate Spread Ln(Limit) Limit ($) 

Model: (1) (2) (3) (4) (5) (6) 

Opioid Death Ratec,t−1 x Blacki,c,t 0.0657*** -0.0126*** -14.7536** 

(2.81) (-2.81) (-2.17) 

Opioid Death Ratec,t−1 x Hispanici,c,t -0.0259 -0.0053 -6.9868 

(-1.32) (-1.40) (-1.22) 

Opioid Death Ratec,t−1 x Otheri,c,t -0.0124 0.0074 13.9453 

(-0.36) (1.11) (1.38) 

High Opioid Death Ratec,t−1 x Blacki,c,t 1.6060*** -0.2992*** -350.4262** 

(3.08) (-3.01) (-2.32) 

High Opioid Death Ratec,t−1 x Hispanici,c,t -0.5629 -0.0961 -127.482 

(-1.45) (-1.29) (-1.13) 

High Opioid Death Ratec,t−1 x Otheri,c,t -0.1696 0.1553 302.4677 

(-0.21) (1.01) (1.3) 

Opioid Death Rate 0.4823*** -0.0616*** -73.6077** c,t−1 

(4.30) (-2.87) (-2.26) 

High Opioid Death Rate 1.0878*** -0.1411*** -169.3513** c,t−1 

(4.25) (-2.89) (-2.29) 

Blacki,c,t -0.5181* 0.1201** 145.4275* -0.5337** 0.1189** 143.5116* 

(-1.81) (2.19) (1.75) (-2.01) (2.35) (1.87) 

Hispanici,c,t 0.4932*** -0.0009 5.3688 0.4526*** -0.0109 -7.9967 

(2.75) (-0.03) (0.10) (3.07) (-0.39) (-0.19) 

Otheri,c,t 0.288 -0.0702 -112.593 0.2283 -0.056 -90.9313 

(0.87) (-1.11) (-1.18) (0.77) (-0.99) (-1.06) 

Fit statistics 

Observations 197,371 197,371 197,371 197,371 197,371 197,371 

Adj. R2 0.316 0.156 0.082 0.311 0.153 0.079 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Consumer & County controls ✓ 47 ✓ ✓ ✓ ✓ ✓
Table 7: Additional Analyses: Credit Card Rewards and Likelihood of Credit Card Offer 

This table reports regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument 

for explaining the relationship between opioid crisis intensity (Opioid Death Rate and High Opioid Death Rate), based on data from CDC) 

and two additional bank credit card elements: rewards/promotions and likelihood of a credit card offer. Panel A reports secondstage IV estimates for credit card rewards/promotions from offer-level data, while Panel B reports estimates for the likelihood credit 

card offer using an extended sample covering all mailings of consumers with and without credit card offers in each month. All 

variables are constructed using the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC Match 

File for analyzing credit card mail offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. 

Demographic attributes are from Mintel. Consumer controls include: credit score ranges, indicators for past deep delinquency, recent 

delinquency, past derogatory flings such as foreclosure and collections, past bankruptcy flings, past high utilization (≥ 80%), number 

of credit inquiries, past credit cards, consumer age ranges, married, indicator for no kids, White, education indicators, homeowner, 

and consumer income. County controls include: county income, unemployment rate, bank market concentration, population density, 

percent of males, race concentration, percent of people in various age ranges, percent of people with higher education, and inequality. 

All regressions in Panel A include State × Year-Month, Lender × Year-Month, Lender × State, Lender, State, and Year-Month fxed effects. 

Panel B Panel A includes State × Year-Month, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard 

errors are double-clustered by Marketing Campaign (State) and Year-Month in Panels A(B) and t-statistics are reported in parentheses. 

Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel A: IV 2LS for Credit Card Rewards/Promotions 

Dependent Variables: 

Model: 

Opioid 

Death Rate 

(1) 

High Opioid 

Death Rate 

(2) 

Rewards/ 

Promotions 

(3) 

Rewards/ 

Promotions 

(4) 

Mkt Doctors/1000Popc,t−1 1.0349*** 

(21.65) 

0.4511*** 

(12.85) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

-0.0173** 

(-2.38) 

-0.0396** 

(-2.37) 

Fit statistics 

Observations 

Adj. R2 

197,371 

0.559 

197,371 

0.421 

197,371 

0.057 

197,371 

0.055 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

Consumer & County controls ✓ ✓ ✓ ✓

Panel B: IV 2SLS for Likelihood of Credit Card Offer 

Dependent Variables: Opioid High Opioid Credit Credit 

Death Rate Death Rate Card Offer Card Offer 

Model: (1) (2) (3) (4) 

Mkt Doctors/1000Popc,t−1 11.1551*** 

(3.01) 

0.5140** 

(2.42) 

Opioid Death Ratec,t−1 -0.0046*** 

(4.70) 

High Opioid Death Ratec,t−1 -0.1005*** 

(-4.70) 

Fit statistics 

Observations 392,101 392,101 392,101 392,101 

Adj. R2 0.547 0.403 0.115 0.112 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓

State, Year-Month ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓

48 
Table 8: Opioid Supply and Opioid Demand Laws 

This table examines the impact of state opioid laws on opioid prescription and deaths in Panel A (using a county-year level sample), 

and on consumer credit supply in Panel B (using our main offer-level sample). We cover 3 opioid-supply oriented laws (Opioid Limiting Law, PDMP Law, Triplicate Prescription Law) and 3 demand/user oriented laws(Naloxone Law, Good Samaritan Law, Medical 

Marijuana Permitting Law). All laws are time variant during our sample period except for ”Triplicate Prescription Law” and ”Medical 

Marijuana Permitting Law.” Panel B reports estimates from IV 2SLS regressions (equations (3) and (4)) using ”Mkt Doctors/1000Pop” 

as an instrument for opioid crisis intensity (Opioid Death Rate and High Opioid Death Rate)). Variables used in Panel B are constructed 

using the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card 

mail offers. The data are restricted to lenders identifed as ”banks.” Consumer controls include credit score ranges, indicators for past 

deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past bankruptcy flings, past high 

utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator for no kids, White, education 

indicators, homeowner, and consumer income. County controls include county income, unemployment rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, percent of people with higher 

education, and inequality. Standard errors are clustered by Marketing Campaign and Year-Month; and t-statistics are in parentheses. 

Variables are defned in Appendix Table A1. Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel A: Effects of ”Opioid Supply and Opioid Demand Laws” on Opioid Prescriptions and Deaths 

Dependent Variables: 

Model: 

Opioid 

Prescription 

Rate 

(1) 

Opioid 

Death 

Rate 

(2) 

Opioid 

Prescription 

Death Rate 

(3) 

Opioid 

Illicit 

Death Rate 

(4) 

Opioid 

Prescription 

Rate 

(5) 

Opioid 

Death 

Rate 

(6) 

Opioid 

Prescription 

Death Rate 

(7) 

Opioid 

Illicit 

Death Rate 

(8) 

Opioid Supply Laws: 

Opioid Limiting Law x Posts,t s 

Opioid PDMP Law x Posts,t s 

Triplicate Prescription Laws 

Opioid Demand Laws: 

Nalaxone Laws x Posts,t 

Samaritean Laws x Posts,t 

Medical Marijuana Permitting Laws 

-0.0297*** 

[-5.10] 

-0.0757*** 

[-17.04] 

0.001 

[0.27] 

-0.0128*** 

[-3.64] 

0.2317*** 

[10.78] 

0.1754*** 

[7.73] 

0.017 

[0.95] 

0.0360** 

[2.12] 

-0.0400*** 

[-2.84] 

-0.0785*** 

[-4.54] 

0.0213 

[1.59] 

0.0026 

[0.21] 

0.2941*** 

[16.39] 

0.3011*** 

[18.49] 

[0.007] 

[-0.56] 

0.0334*** 

[2.66] 

-0.1215*** 

[-19.85] 

-0.0701*** 

[-13.81] 

-0.3287*** 

[-25.37] 

0.0554*** 

[4.23] 

-0.2054*** 

[-23.46] 

-0.0450*** 

[-5.21] 

-0.1699*** 

[-17.62] 

0.1106*** 

[11.16] 

Fit statistics 

Observations 

Adj. R2 

27,955 

0.866 

30,563 

0.488 

30,563 

0.394 

30,563 

0.474 

28,052 

0.295 

30,565 

0.136 

30,565 

0.063 

30,565 

0.193 

Fixed effects 

County, State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

County controls ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Panel B: Effects of Opioid Laws on Credit Card Terms 

Panel B1: Time-Variant ”Opioid Supply and Opioid User Laws” 

Dependent Variables: 

Model: 

Rate Spread 

(1) 

Ln(Limit) 

(2) 

Rate Spread 

(3) 

Ln(Limit) 

(4) 

Opioid Supply Laws: 

Opioid Limiting Law x Posts,t s 

Opioid PDMP Law x Posts,t s 

Opioid Demand Laws: 

Nalaxone Laws x Posts,t 

Samaritean Laws x Posts,t 

-0.2280*** 

(-4.16) 

-0.2263*** 

(-3.90) 

0.0772** 

(2.47) 

0.0538* 

(1.67) 

0.0198* 

(1.89) 

0.0379*** 

(3.42) 

0.0084 

(1.40) 

-0.0108* 

(-1.74) 

-0.1073*** 

(-3.28) 

-0.1661*** 

(-3.42) 

-0.0192 

(-0.49) 

0.0938*** 

(2.66) 

0.0046 

(0.74) 

0.0304*** 

(3.29) 

0.0204*** 

(2.76) 

-0.0158** 

(-2.35) 

Opioid Crisis Variables: 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

0.4783*** 

(3.99) 

-0.0599*** 

(-2.62) 

1.0462*** 

(3.98) 

-0.1310*** 

(-2.62) 

Fit statistics 

Observations 

Adj. R2 

197,448 

0.322 

197,448 

0.161 

197,448 

0.318 

197,448 

0.160 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

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Consumer & County controls ✓ ✓ ✓ ✓

49 
Table 8: Opioid Supply and Opioid User Laws (cont.) 

This table conducts a horse race to examine the impact of 6 different opioid state laws in the US on opioid prescription and deaths 

in Panel A (using a county-year level sample), and on consumer credit supply in Panel B (using our main offer-level sample). We 

cover 3 opioid-supply oriented laws (Opioid Limiting Law, PDMP Law, Triplicate Prescription Law) and 3 demand/user oriented 

laws(Naloxone Law, Good Samaritan Law, Medical Marijuana Permitting Law). All laws are time variant, except for ”Triplicate Prescription Law” and ”Medical Marijuana Permitting Law”, which are time-invariant over our sample period. Panel B reports regression 

estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” as an instrument for opioid crisis intensity (Opioid Death Rate and High Opioid Death Rate), based on data from CDC). All variables used in Panel B are constructed using 

the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail 

offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from 

Mintel. Consumer controls include: credit score ranges, indicators for past deep delinquency, recent delinquency, past derogatory 

flings such as foreclosure and collections, past bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past 

credit cards, consumer age ranges, married, indicator for no kids, White, education indicators, homeowner, and consumer income. 

County controls include: county income, unemployment rate, bank market concentration, population density, percent of males, race 

concentration, percent of people in various age ranges, percent of people with higher education, and inequality. In Panel A using a 

county-year sample, regressions include County, State, and Year fxed effects in columns 1-4 and Year fxed effects in columns 5-8. In 

Panel B, using our offer-level sample, all regressions include State × Year-Month, Lender × Year-Month, Lender × State, Lender, State, and 

Year-Month fxed effects. Standard errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are reported 

in parentheses. Variables are defned in Appendix Table A1. Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, 

respectively. 

Panel B2: Opioid Supply Law: ”Triplicate Prescription Law” (Time-Invariant) 

Dependent Variables: 

Model: 

Yes 

Triplicate Prescription Law? 

No Yes No 

Rate Spread 

(1) 

Ln(Limit) 

(2) 

Rate Spread Ln(Limit) Rate Spread 

(3) (4) (5) 

Ln(Limit) 

(6) 

Rate Spread 

(7) 

Ln(Limit) 

(8) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

0.2384 

(1.56) 

-0.0611** 

(-2.05) 

0.6990*** 

(4.48) 

-0.0814*** 

(-2.76) 

0.4216 

(1.56) 

-0.1080** 

(-2.05) 

1.9144*** 

(4.41) 

-0.2229*** 

(-2.74) 

Fit statistics 

Observations 

Adj. R2 

58,762 

0.321 

58,762 

0.161 

138,352 

0.308 

138,352 

0.155 

58,762 

0.320 

58,762 

0.160 

138,352 

0.286 

138,352 

0.146 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

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Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Panel B3: Opioid Demand Law: ”Medical Marijuana Permitting Law” (Time-Invariant) 

Dependent Variables: 

Model: 

Yes 

Medical Marijuana Permitting Law? 

No Yes No 

Rate Spread 

(1) 

Ln(Limit) 

(2) 

Rate Spread Ln(Limit) Rate Spread Ln(Limit) 

(3) (4) (5) (6) 

Rate Spread 

(7) 

Ln(Limit) 

(8) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

0.4240*** 

(4.77) 

-0.0707*** 

(-4.14) 

0.3554 

(0.76) 

-0.0242 

(-0.27) 

1.1621*** 

(4.74) 

-0.1937*** 

(-4.13) 

0.5663 

(0.76) 

-0.0385 

(-0.27) 

Fit statistics 

Observations 

Adj. R2 

133,304 

0.311 

133,304 

0.153 

63,829 

0.347 

63,829 

0.176 

133,304 

0.302 

133,304 

0.147 

63,829 

0.347 

63,829 

0.176 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

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50 
Table 9: Possible Underlying Mechanisms using Additional Datasets 

This table reports regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument 

for explaining the relationship between opioid crisis intensity and consumer credit card behavior and/or quality in Panel A and bank 

credit card and unsecured consumer portfolio quality in Panel B. Opioid crisis intensity is measured as Opioid Death Rate and High 

Opioid Death Rate, based on data from CDC. Consumer credit card behavior and/or quality is measured several ways as: Ln(Avg 

Days Past Due), Avg Loan Probability of Default (PD), Ln(Avg Payment), and Avg Updated Consumer Credit Score). Bank credit 

card portfolio quality is measured as the nonperforming loans ratios of NPL Credit Cards and NPL Unsecured Consumer Credit. The 

analysis in Panel A uses aggregated bank-county-year-month data from the supervisory FR Y-14M credit card dataset based on a 0.1% 

random sample for existing consumer accounts (loan age ≥ 12 months). Analysis in Panel B uses public bank-quarter data from the 

FFIEC Call Reports. County controls include: county income, unemployment rate, bank market concentration, population density, 

percent of males, race concentration, percent of people in various age ranges, percent of people with higher education, and inequality. 

All regressions include State × Year-Month, Lender × Year-Month, Lender × State, Lender, State, and Year-Month fxed effects. Variables 

are defned in Appendix Table A1. Standard errors are double-clustered by County and Year-Month and t-statistics are reported in 

parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel A: IV 2SLS Effects: Effects on Credit Card Consumer Credit Behavior and/or Quality 

Dependent Variables: 

Model: 

Opioid 

Death Rate 

(1) 

High Opioid 

Death Rate 

(2) 

Ln(Avg Days 

Past Due) 

(3) 

Avg Prob 

Default (PD) 

(4) 

Ln(Avg 

Payment) 

(5) 

Avg Credit 

Score 

(6) 

Ln(Avg Days 

Past Due) 

(7) 

Avg Prob 

Default (PD) 

(8) 

Ln(Avg 

Payment) 

(9) 

Avg Credit 

Score 

(10) 

Mkt Doctors/1000Popc,t−1 0.5578*** 

(61.99) 

0.0904*** 

(22.65) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

0.0860*** 

(8.10) 

0.0020*** 

(2.61) 

-0.1391*** 

(-9.46) 

-2.6294*** 

(-4.03) 

0.5305*** 

(7.69) 

0.0124*** 

(2.59) 

-0.8584*** 

(-8.82) 

-16.2190*** 

(-3.97) 

Fit statistics 

Observations 

Adj. R2 

1,009,322 

0.050 

1,009,322 

0.050 

1,009,313 

0.088 

694,562 

0.002 

1,009,138 

0.090 

1,009,322 

0.017 

1,009,313 

0.002 

694,562 

0.001 

1,009,138 

0.072 

1,009,322 

0.009 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

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Panel B: IV 2SLS Effects on Bank Credit Card Portfolio Quality 

Dependent Variables: NPL NPL Unsecured NPL NPL Unsecured 

Credit Cards Consumer Credit Credit Cards Consumer Credit 

Model: (1) (2) (3) (4) 

Opioid Death Rateb,t−1 1.3449** 1.5780** 

(2.20) (2.35) 

High Opioid Death Rateb,t−1 1.2325*** 1.7757*** 

(3.71) (4.07) 

Fit statistics 

Observations 16,866 16,866 16,866 16,866 

Adj. R2 0.750 0.750 0.708 0.709 

Fixed effects 

Lender, Year-Quarter ✓ ✓ ✓ ✓

Lender & County controls ✓ ✓ ✓ ✓

51 
Table 10: Possible Macro Real Effects of The Opioid Crisis - Consumer Spending 

This table reports regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument 

for explaining the relationship between opioid crisis intensity and consumer credit card terms to consumers in Panel A, and consumer 

credit card spending in Panel B. Opioid crisis intensity is measured as Opioid Death Rate and High Opioid Death Rate, based on data 

from CDC. Credit terms are measured several ways as: Avg Cycle APR, Ln(Avg Limit), and Pct Rewards (percent of accounts with 

rewards). Consumer spending is measured as Total Purchase/Pop, Total Purchase/Limit, or Ln(Avg Purchase). All analyses in this 

table use aggregated bank-county-year-month data from the supervisory FR Y-14M credit card dataset based on a 0.1% random sample 

for existing consumer accounts (loan age ≥ 12 months). Analysis in Panel B uses public bank-quarter data from the FFIEC Call Reports. 

County controls include: county income, unemployment rate, bank market concentration, population density, percent of males, race 

concentration, percent of people in various age ranges, percent of people with higher education, and inequality. All regressions include 

State × Year-Month, Lender × Year-Month, Lender × State, Lender, State, and Year-Month fxed effects. Variables are defned in Appendix 

Table A1. Standard errors are double-clustered by County and Year-Month in Panel A and clustered by Lender in Panel B, and tstatistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel A: IV 2SLS Effects: Reconfrm Results for Credit Card Terms 

Dependent Variables: Avg Cycle Ln(Avg Pct Cards w/ Avg Cycle Ln(Avg Pct Cards w/ 

APR Limit) Rewards APR Limit) Rewards 

Model: (1) (2) (3) (4) (5) (6) 

Opioid Death Rate 0.3433*** -0.0158* -0.0178*** c,t−1 

(5.30) (-1.89) (-4.12) 

High Opioid Death Rate 2.1138*** -0.0973* -0.1099*** c,t−1 

(5.17) (-1.88) (-4.06) 

Fit statistics 

Observations 1,008,285 1,009,322 1,009,322 1,008,285 1,009,322 1,009,322 

Adj. R2 0.001 0.055 0.002 0.001 0.048 0.001 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓

County controls ✓ ✓ ✓ ✓ ✓ ✓

Panel B: IV 2SLS Effects on Consumer Spending 

Dependent Variables: 

Model: 

Total 

Purchase 

/Pop 

(1) 

Total 

Purchase 

/Limit 

(2) 

Ln 

(Avg 

Purchase) 

(3) 

Total 

Purchase 

/Pop 

(4) 

Total 

Purchase 

/Limit 

(5) 

Ln 

(Avg 

Purchase) 

(6) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

-0.0070*** 

(-6.90) 

-0.0149*** 

(-6.14) 

-0.0920** 

(-2.40) 

-0.0431*** 

(-6.65) 

-0.0922*** 

(-5.95) 

-0.5705** 

(-2.39) 

Fit statistics 

Observations 

Adj. R2 

1,008,631 

0.021 

1,008,631 

0.001 

1,004,460 

0.151 

1,008,631 

0.010 

1,008,631 

0.001 

1,004,460 

0.142 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

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County controls ✓ ✓ ✓ ✓ ✓ ✓

52 
Internet Appendix: Supplementary Materials and Analyses 

Table A1: Variable Defnitions and Sources 

This table provides defnitions and data sources for the variables used in the analysis. Panel A shows variables used in all analyses, 

including opioid intensity measures from the Centers for Disease Control and Prevention (briefy noted in tables and below as CDC), 

instrumental variables from several sources, and county characteristics from several sources noted below. Panel B shows additional 

variables from the anonymized FBRNY Consumer Credit Panel/Equifax dataset (FRBNY CCP). Panel C shows additional variables 

from the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC Match File (briefy noted in tables 

and below as Mintel/TransUnion Match File). Consumer demographic attributes are from the Mintel/TransUnion Match File. Panel 

D shows additional variables from the public bank FFIEC Call Reports data and FDIC Summary of Deposits (SoD). Panel E provides 

summary statistics for the Call Reports analysis. 

Variable Defnition Source 

Key Independent Variables 

Opioid Death Rate Opioid deaths per 10K SEER population in the county, lagged one CDC/NCHS, National Center for Health Statistics 

year. Bank-level analysis uses a weighted measure using the 

fraction of bank branches in the county as a weight. 

High Opioid Death Rate Indicator for high total opioid death rate in the county in the top CDC/NCHS, National Center for Health Statistics 

50th percentile lagged 1 year. Bank-level analysis uses a weighted 

measure using the fraction of bank branches in the county as a 

weight. 

Prescription Opioid Death Rate Opioid deaths due to prescription opioids per 10K SEER popula- CDC/NCHS, National Center for Health Statistics 

tion in the county, lagged 1 year. 

Illicit Opioid Death Rate Opioid deaths due to illicit opioids per 10K SEER population in the CDC/NCHS, National Center for Health Statistics 

county, lagged 1 year. 

Opioid Prescription Rate Opioid prescriptions per capita in the county, lagged one year. CDC/IQVIA Xponent 

Bank-level analysis uses a weighted measure using the fraction of 

bank branches in the county as a weight. 

High Opioid Prescription Rate Indicator for high prescription opioid death rate in the county in CDC/IQVIA Xponent 

the top 50th percentile lagged 1 year. Bank-level analysis uses 

a weighted measure using the fraction of bank branches in the 

county as a weight. 

Instrumental Variables 

MKT Doctors/1000Pop Number of doctors in the county who received marketing pay- Hadland et al. (2019), Open Payments Database 

ments from pharmaceutical companies to prescribe opioids per 

1,000 county population each year. Bank-level analysis uses a 

weighted measure using the fraction of bank branches in the 

county as a weight. 

High Purdue MKT (OxyContinGrowth ’97-’02) Indicator for counties in the upper 50th percentile of the distri- DEA, Cornaggia et al. (2021) 

bution of the percentage change in the quantity of OxyContin 

distributed by Purdue Pharma between 1997 and 2002. Banklevel analysis uses a weighted measure using the fraction of bank 

branches in the county as a weight. 

Purdue MKT (OxyContin Growth ’97-’02) Percentage change in the quantity of OxyContin distributed by DEA, Cornaggia et al. (2021) 

Purdue Pharma in the county between 1997 and 2002. Banklevel analysis uses a weighted measure using the fraction of bank 

branches in the county as a weight. 

County Characteristics 

Ln(County Income) Natural log of county income, lagged 1 year. Bureau of Economic Analysis 

County Unemployment Rate County unemployment rate lagged 1 quarter. Haver Analytics/BLS 

County Bank HHI Bank HHI of deposits at the county level. FDIC Summary of Deposits (SoD) 

County Population Density County population density. U.S. Census Bureau 

County Race HHI County HHI for population races. U.S. Census American Community Surveys 

County % Male County percent of male population. U.S. Census American Community Surveys 

County % Age 25 44 County percent population ages 25-44. U.S. Census American Community Surveys 

County % Age 45 64 County percent population ages 45-64. U.S. Census American Community Surveys 

County % Age 65plus County percent population ages 65 and above. U.S. Census American Community Surveys 

County % High Education (≥ College) County percent of population with higher education. U.S. Census American Community Surveys 

County Inequality: Gini Coeffcient County inequality proxied by the Gini Coeffcient. U.S. Census American Community Surveys 

i 
Table A1: Variable Defnitions and Sources (cont.) 

Variable Defnition Source 

Key Dependent Variables 

Rate Spread The APR Spread over the one-month Treasury bonds. Mintel/TransUnion Match File 

Ln(Limit) Natural log of credit card limit in the offer. Mintel/TransUnion Match File 

Limit ($) Credit card limit in the offer in dollars. Mintel/TransUnion Match File 

Card Offer Dummy for a credit card offer, and zero otherwise. Mintel/TransUnion Match File 

Consumer Characteristics 

Consumer Credit Score Credit score, as of 2-3 months prior to the offer. Mintel/TransUnion Match File 

Credit Score Less580 Credit score range: less than 580 or 300-580, as of 2-3 months prior Mintel/TransUnion Match File 

to the offer. 

Credit Score 580 660 Credit score range: 580-660, as of 2-3 months prior to the offer. Mintel/TransUnion Match File 

Credit Score 660 720 Credit score range: 660-720, as of 2-3 months prior to the offer. Mintel/TransUnion Match File 

Credit Score 720 800 Credit score range: 720-800, as of 2-3 months prior to the offer. Mintel/TransUnion Match File 

Credit Score 800plus Credit score range: greater or equal to 800. Mintel/TransUnion Match File 

Deep Delinq Indicator for consumers with past deep delinquency 90 days past Mintel/TransUnion Match File 

due or more on their loans, as of 2-3 months prior to the offer. 

Recent Delinq Indicator for consumers with recent delinquency 90 days past due Mintel/TransUnion Match File 

or more on their loans, as of 2-3 months prior to the offer. 

Other Derogatory Indicator for consumers with past derogatory flings such as fore- Mintel/TransUnion Match File 

closure, collections etc., as of 2-3 months prior to the offer. 

Bankruptcy Filer Indicator for consumers with past bankruptcy flings, as of 2-3 Mintel/TransUnion Match File 

months prior to the offer. 

High Util (≥80%) Indicator for consumers with high credit card utilization in the past Mintel/TransUnion Match File 

(80% or more), as of 2-3 months prior to the offer. 

Ln(1+ No Credit Inquiries) Natural log of one plus number of credit inquiries by the consumer, Mintel/TransUnion Match File 

as of 2-3 months prior to the offer. 

Has Prior Cards Indicator for consumers who have prior credit cards, as of 2-3 Mintel/TransUnion Match File 

months prior to the offer. 

Consumer Age Consumer age. Mintel/TransUnion Match File 

Age Less25 Consume age below 25. Mintel/TransUnion Match File 

Age 25to44 Consumer age range 25 to 44. Mintel/TransUnion Match File 

Age 45to64 Consumer age range 45 to 64. Mintel/TransUnion Match File 

Age 65plus Consumer age 65 and above. Mintel/TransUnion Match File 

Married Indicator for married consumers, as of 2-3 months prior to the of- Mintel/TransUnion Match File 

fer. 

No Kids Indicator if the consumer has no kids, as of 2-3 months prior to the Mintel/TransUnion Match File 

offer. 

White Indicator for White or non-minority consumers. Mintel/TransUnion Match File 

Miss Race Indicator for missing/unreported race. Mintel/TransUnion Match File 

Educ: Some College Indicator for education: some college. Mintel/TransUnion Match File 

Educ: College Indicator for education: college. Mintel/TransUnion Match File 

Educ: Post College Indicator for education: post-college. Mintel/TransUnion Match File 

Miss Educ Indicator for missing/unreported education. Mintel/TransUnion Match File 

Homeowner Indicator for homeowners, as of 2-3 months prior to the offer. Mintel/TransUnion Match File 

Ln(Consumer Income) Natural log of consumer annual income, as of 2-3 months prior to Mintel/TransUnion Match File 

the offer. 

ii 
Table A2: More Identifcation: Propensity Score Matching (PSM) & Contiguous Counties 

This table reports estimates from both univariate results and IV 2SLS regression results (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument for explaining the relationship between opioid crisis intensity (Opioid Death Rate and High Opioid Death 

Rate), based on data from CDC) and bank credit card terms: rate spread and credit card limit. Panel A reports univariate evidence 

and Panels B and C report second-stage IV regression estimates from PSM analyses, where counties with a high opipid death rate (top 

25%) are matched using several techniques (1:1 matching without replacement, 1:1 matching with replacement, nearest neighbor (n=2), 

nearest neighbor (n=3), and nearest neighbor (n=5)) to counties with a low opioid death rate, based on similar characteristics, including the instrument ”Mkt Doctors/1000Pop”. Finally, Panel D reports IV regression estimates when using contiguous counties only to 

the counties with a high opipid death rate (top 25%). All variables are constructed using the anonymized Mintel Comperemedia Inc. 

Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer controls include: credit 

score ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past 

bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator 

for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment 

rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, 

percent of people with higher education, and inequality. All regressions include State × Year-Month, Lender × Year-Month, Lender × State, 

Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by 

*, **, and ***, respectively. 

Panel A: Univariate Evidence using Different PSM Methods 

(1) (2) (3) (4) (5) (6) (7) (8) 

Dependent Variable Rate Spread Ln(Limit) 

PSM Estimation (common support) Treated Control Difference t-stat Treated Control Difference t-stat 

1:1 Matching without replacement 17.46 17.24 0.22 7.11*** 6.425 6.44 -0.015 -3.18*** 

1:1 Matching with replacement 17.46 16.98 0.48 4.16*** 6.425 6.53 -0.105 -5.85*** 

Nearest neighbor (n=2) 17.46 17.2 0.26 3.01*** 6.425 6.48 -0.055 -4.18*** 

Nearest neighbor (n=3) 17.46 17.25 0.21 2.88*** 6.425 6.469 -0.044 -3.88*** 

Nearest neighbor (n=5) 17.46 17.23 0.23 3.76*** 6.425 6.459 -0.034 -3.56*** 

Panel B: IV 2SLS with PSM Sample (1:1 Matching without replacement) 

Dependent Variables: 

Model: 

Opioid 

Death Rate 

(1) 

High Opioid 

Death Rate 

(2) 

Rate 

Spread 

(3) 

Ln 

(Limit) 

(4) 

Rate 

Spread 

(5) 

Ln 

(Limit) 

(6) 

Mkt Doctors/1000Popc,t−1 0.5168*** 

(6.83) 

0.2672*** 

(6.93) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

1.0998*** 

(3.84) 

-0.2700*** 

(-4.80) 

2.1276*** 

(3.86) 

-0.5222*** 

(-4.84) 

Fit statistics 

Observations 

Adj. R2 

100,576 

0.471 

100,576 

0.298 

100,576 

0.263 

100,576 

0.027 

100,576 

0.271 

100,576 

0.046 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

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Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

iii 
Table A2: Propensity Score Matching (PSM) & Contiguous Counties (cont.) 

This table reports estimates from both univariate results and IV 2SLS regression results (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument for explaining the relationship between opioid crisis intensity (Opioid Death Rate and High Opioid Death 

Rate), based on data from CDC) and bank credit card terms: rate spread and credit card limit. Panel A reports univariate evidence 

and Panels B and C report second-stage IV regression estimates from PSM analyses, where counties with a high opipid death rate (top 

25%) are matched using several techniques (1:1 matching without replacement, 1:1 matching with replacement, nearest neighbor (n=2), 

nearest neighbor (n=3), and nearest neighbor (n=5)) to counties with a low opioid death rate, based on similar characteristics, including the instrument ”Mkt Doctors/1000Pop”. Finally, Panel D reports IV regression estimates when using contiguous counties only to 

the counties with a high opipid death rate (top 25%). All variables are constructed using the anonymized Mintel Comperemedia Inc. 

Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer controls include: credit 

score ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past 

bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator 

for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment 

rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, 

percent of people with higher education, and inequality. All regressions include State × Year-Month, Lender × Year-Month, Lender × State, 

Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by 

*, **, and ***, respectively. 

Panel C: IV 2SLS with PSM Sample (1:1 Matching with replacement) 

Dependent Variables: 

Model: 

Opioid 

Death Rate 

(1) 

High Opioid 

Death Rate 

(2) 

Rate 

Spread 

(3) 

Ln 

(Limit) 

(4) 

Rate 

Spread 

(5) 

Ln 

(Limit) 

(6) 

Mkt Doctors/1000Popc,t−1 0.4591*** 

(3.19) 

0.1398** 

(2.31) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

2.5353*** 

(7.48) 

-0.1241** 

(-2.36) 

8.3256*** 

(6.16) 

-0.4074** 

(-2.31) 

Fit statistics 

Observations 

Adj. R2 

101,145 

0.531 

101,145 

0.449 

101,145 

0.001 

101,145 

0.170 

101,145 

0.474 

101,145 

0.136 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

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Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

Panel D: IV 2SLS using Contiguous Counties Only 

Dependent Variables: 

Model: 

Opioid 

Death Rate 

(1) 

High Opioid 

Death Rate 

(2) 

Rate 

Spread 

(3) 

Ln 

(Limit) 

(4) 

Rate 

Spread 

(5) 

Ln 

(Limit) 

(6) 

Mkt Doctors/1000Popc,t−1 0.9774*** 

(7.44) 

0.3058*** 

(7.42) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

1.0145*** 

(4.76) 

-0.1402*** 

(-3.48) 

3.2420*** 

(4.78) 

-0.4481*** 

(-3.49) 

Fit statistics 

Observations 

Adj. R2 

64,276 

0.601 

64,276 

0.366 

64,276 

0.278 

64,276 

0.131 

64,276 

0.284 

64,276 

0.139 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

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Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

iv 
Table A3: Additional Tests to Support the Main Findings 

This table reports robustness checks for explaining the relationship between opioid crisis intensity (Opioid Death Rate and High Opioid 

Death Rate), based on data from CDC) and bank credit card terms: rate spread and credit card limit. Panel A reports results when additionally including more county-level controls; Panel B reports results when using multiple death causes rather than underlying death 

cause for construction of our opioid intensity death measures; Panel C reports results using OLS estimates instead of IV estimates; 

Panel D reports results when excluding counties with ”zero deaths”; and Panel D reports results when excluding the states of Florida. 

We report in all cases other than Panel C regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” as an instrument for opioid intensity. All variables are constructed using the anonymized Mintel Comperemedia Inc. 

Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer controls include: credit 

score ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past 

bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator 

for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment 

rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, 

percent of people with higher education, and inequality. All regressions include State × Year-Month, Lender × Year-Month, Lender × State, 

Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by 

*, **, and ***, respectively. 

Panel A: IV 2SLS with Even More County-Level Controls 

Dependent Variables: Rate Spread Ln(Limit) Rate Spread Ln(Limit) 

Model: (1) (2) (3) (4) 

Opioid Death Rate 0.4728*** -0.0648*** c,t−1 

(4.27) (-3.06) 

High Opioid Death Rate 1.1738*** -0.1610*** c,t−1 

(4.25) (-3.05) 

Additional Controls 

County Labor Participation Rate 0.5144 -0.4368*** 0.4620 -0.4296*** c,t−1 

(1.15) (-5.10) (1.04) (-5.04) 

County Avg Credit Score -0.0001 0.0038** -0.0001 c,t−1 

(2.28) (-0.44) (2.29) (-0.46) 

County Air Pollution -0.0510*** 0.0041* -0.0555*** 0.0047* c,t−1 

(-4.23) (1.76) (-4.31) (1.90) 

County ∆ HPI -0.0078** 0.0021*** -0.0076** 0.0020*** c,t−1 

(-2.14) (2.97) (-2.08) (2.92) 

County % School Dropouts -1.7398*** -0.0433 -0.9908* -0.1460 c,t−1 

(-3.60) (-0.47) (-1.81) (-1.40) 

County % Religious Pop -0.0011 0.0365* -0.1161 0.0523*** c,t−1 

(-0.01) (1.70) (-1.18) (2.79) 

County Politics 0.0039 -0.0004 0.0011 0.0000 c,t−1 

(0.29) (-0.15) (0.08) (0.00) 

County Poverty Rate 0.5590 -0.0576 1.5609*** -0.1950* c,t−1 

(0.88) (-0.47) (2.65) (-1.73) 

County % Poor Health Pop -0.0087* 0.0018** -0.0151*** 0.0026*** c,t−1 

(-1.95) (2.06) (-2.98) (2.73) 

Fit statistics 

Observations 195,004 195,004 195,004 195,004 

Adj. R2 0.319 0.158 0.312 0.154 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓

v 
Table A3: Additional Tests to Support the Main Findings (cont.) 

This table reports robustness checks for explaining the relationship between opioid crisis intensity (Opioid Death Rate and High Opioid 

Death Rate), based on data from CDC) and bank credit card terms: rate spread and credit card limit. Panel A reports results when additionally including more county-level controls; Panel B reports results when using multiple death causes rather than underlying death 

cause for construction of our opioid intensity death measures; Panel C reports results using OLS estimates instead of IV estimates; 

Panel D reports results when excluding counties with ”zero deaths”; and Panel D reports results when excluding the states of Florida. 

We report in all cases other than Panel C regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” as an instrument for opioid intensity. All variables are constructed using the anonymized Mintel Comperemedia Inc. 

Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer controls include: credit 

score ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past 

bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator 

for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment 

rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, 

percent of people with higher education, and inequality. All regressions include State × Year-Month, Lender × Year-Month, Lender × State, 

Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by 

*, **, and ***, respectively. 

Panel B: IV 2SLS - Alternative Opioid Death Rate based on Multiple Death Causes 

Dependent Variables: Rate Spread Ln(Limit) Rate Spread Ln(Limit) 

Model: (1) (2) (3) (4) 

Opioid Death Ratec,t−1 0.5069*** -0.0703*** 

(4.94) (-3.58) 

High Opioid Death Ratec,t−1 1.2413*** -0.1722*** 

(4.91) (-3.57) 

Fit statistics 

Observations 197,398 197,398 197,398 197,398 

Adj. R2 0.317 0.157 0.310 0.154 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓

Panel C: Results using OLS Method 

Dependent Variables: 

Model: 

Rate Spread 

(1) 

Rate Spread 

(2) 

Rate Spread 

(3) 

Rate Spread 

(4) 

Ln(Limit) 

(5) 

Ln(Limit) 

(6) 

Ln(Limit) 

(7) 

Ln(Limit) 

(8) 

Opioid Death Ratec,t−1 

High Opioid Death Ratec,t−1 

Opioid Illicit Death Ratec,t−1 

High Opioid Illicit Death Ratec,t−1 

0.0216** 

(2.27) 

0.0184 

(1.28) 

0.0263** 

(2.34) 

0.0298* 

(1.84) 

-0.0026 

(-1.24) 

-0.0076** 

(-2.28) 

-0.0045* 

(-1.88) 

-0.0088** 

(-2.28) 

Fit statistics 

Observations 

Adj. R2 

370802 

0.662 

370802 

0.662 

370802 

0.662 

370802 

0.662 

370802 

0.428 

370802 

0.428 

370802 

0.428 

370802 

0.428 

Fixed effects 

State × Year-Month 

Lender × Year-Month 

Lender × State 

Lender, State, Year-Month 

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Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

vi 
Table A3: Additional Tests to Support the Main Findings (cont.) 

This table reports robustness checks for explaining the relationship between opioid crisis intensity (Opioid Death Rate and High Opioid 

Death Rate), based on data from CDC) and bank credit card terms: rate spread and credit card limit. Panel A reports results when additionally including more county-level controls; Panel B reports results when using multiple death causes rather than underlying death 

cause for construction of our opioid intensity death measures; Panel C reports results using OLS estimates instead of IV estimates; 

Panel D reports results when excluding counties with ”zero deaths”; and Panel D reports results when excluding the states of Florida. 

We report in all cases other than Panel C regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” as an instrument for opioid intensity. All variables are constructed using the anonymized Mintel Comperemedia Inc. 

Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer controls include: credit 

score ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past 

bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator 

for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment 

rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, 

percent of people with higher education, and inequality. All regressions include State × Year-Month, Lender × Year-Month, Lender × State, 

Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by 

*, **, and ***, respectively. 

Panel C: Results using OLS Method (cont.) 

Dependent Variables: Rate Spread Ln(Limit) Rate Spread Ln(Limit) 

Model: (1) (2) (3) (4) 

Opioid Prescription Rate 0.1934*** -0.0227*** c,t−1 

(5.53) (-3.36) 

High Opioid Prescription Rate 0.1142*** -0.0130*** c,t−1 

(6.71) (-3.45) 

Fit statistics 

Observations 369,263 369,263 369,263 369,263 

Adj. R2 0.662 0.428 0.662 0.428 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓

Panel D: IV 2SLS Excluding Counties with ”Zero Deaths” 

Dependent Variables: Rate Spread Ln(Limit) Rate Spread Ln(Limit) 

Model: (1) (2) (3) (4) 

Opioid Death Ratec,t−1 0.4682*** -0.0718*** 

(4.42) (-3.54) 

High Opioid Death Ratec,t−1 1.0678*** -0.1639*** 

(4.40) (-3.54) 

Fit statistics 

Observations 194,293 194,293 194,293 194,293 

Adj. R2 0.317 0.157 0.312 0.154 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓

vii 
Table A3: Additional Tests to Support the Main Findings (cont.) 

This table reports robustness checks for explaining the relationship between opioid crisis intensity (Opioid Death Rate and High Opioid 

Death Rate), based on data from CDC) and bank credit card terms: rate spread and credit card limit. Panel A reports results when additionally including more county-level controls; Panel B reports results when using multiple death causes rather than underlying death 

cause for construction of our opioid intensity death measures; Panel C reports results using OLS estimates instead of IV estimates; 

Panel D reports results when excluding counties with ”zero deaths”; and Panel D reports results when excluding the states of Florida. 

We report in all cases other than Panel C regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” as an instrument for opioid intensity. All variables are constructed using the anonymized Mintel Comperemedia Inc. 

Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer controls include: credit 

score ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past 

bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer age ranges, married, indicator 

for no kids, White, education indicators, homeowner, and consumer income. County controls include: county income, unemployment 

rate, bank market concentration, population density, percent of males, race concentration, percent of people in various age ranges, 

percent of people with higher education, and inequality. All regressions include State × Year-Month, Lender × Year-Month, Lender × State, 

Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by 

*, **, and ***, respectively. 

Panel E: IV 2SLS Excluding Florida 

Dependent Variables: Rate Spread Ln(Limit) Rate Spread Ln(Limit) 

Model: (1) (2) (3) (4) 

Opioid Death Ratec,t−1 0.7523*** -0.1003*** 

(5.79) (-4.04) 

High Opioid Death Ratec,t−1 1.8456*** -0.2461*** 

(5.72) (-4.02) 

Fit statistics 

Observations 182,900 182,900 182,900 182,900 

Adj. R2 0.308 0.153 0.293 0.146 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓

viii 
Table A4: Additional Heterogeneous Effects for High Risk and Minority Consumers 

This table examines how the effects of opioid crisis intensity on bank credit card terms (rate spread and credit card limit) differ by 

consumer credit risk using interactions of consumer ”High Credit Risk” and opioid intensity. We defne ”High Credit Risk” as either 

”Subprime” (Credit Score <620) in Panel A or ”Deep Delinquency” past 90+ days past due (DPD)) in Panel B. We report regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” as an instrument for opioid crisis intensity 

(Opioid Death Rate and High Opioid Death Rate), based on data from CDC). All variables are constructed using the anonymized Mintel 

Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC Match File for analyzing credit card mail offers. The data are 

focused on lenders identifed as ”banks” in the Mintel/TransUnion Match File. Demographic attributes are from Mintel. Consumer 

controls include: credit score ranges, indicators for past deep delinquency, recent delinquency, past derogatory flings such as foreclosure and collections, past bankruptcy flings, past high utilization (≥ 80%), number of credit inquiries, past credit cards, consumer 

age ranges, married, indicator for no kids, White, education indicators, homeowner, and consumer income. County controls include: 

county income, unemployment rate, bank market concentration, population density, percent of males, race concentration, percent of 

people in various age ranges, percent of people with higher education, and inequality. All regressions include State × Year-Month, 

Lender × Year-Month, Lender × State, Lender, State, and Year-Month fxed effects. Variables are defned in Appendix Table A1. Standard 

errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are reported in parentheses. Signifcance at the 

10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel A: Deep Delinquency (90+ DPD) 

Dependent Variables: Rate Spread Ln(Limit) Limit ($) Rate Spread Ln(Limit) Limit ($) 

Model: (1) (2) (3) (4) (5) (6) 

Opioid Death Ratec,t−1 x High Credit Riski,c,t−1 0.1509*** -0.0136*** -4.8549 

(11.08) (-5.31) (-1.26) 

High Opioid Death Ratec,t−1 x High Credit Riski,c,t−1 3.1775*** -0.2894*** -110.594 

(11.25) (-5.48) (-1.39) 

Opioid Death Rate 0.1864 -0.0440** -79.9785** c,t−1 

(1.62) (-2.03) (-2.46) 

High Opioid Death Rate 0.4822* -0.1050** -183.0233** c,t−1 

(1.85) (-2.16) (-2.50) 

High Credit Riski,c,t−1 -0.2803* -0.0379 -153.6166*** -0.0931 -0.0529** -154.4175*** 

(-1.72) (-1.24) (-3.34) (-0.65) (-1.98) (-3.83) 

Fit statistics 

Observations 197,371 197,371 197,371 197,371 197,371 197,371 

Adj. R2 0.278 0.141 0.08 0.268 0.138 0.077 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

Panel B: Minority Consumers 

Dependent Variables: Rate Spread Ln(Limit) Limit ($) Rate Spread Ln(Limit) Limit ($) 

Model: (1) (2) (3) (4) (5) (6) 

Opioid Death Ratec,t−1 x Minorityi,c,t 0.0176 -0.0058** -6.3736* 

(1.32 (-2.26) (-1.65) 

High Opioid Death Ratec,t−1 x Minorityi,c,t 0.4314 -0.1316** -146.0764* 

(1.5) (-2.40) (-1.76) 

Opioid Death Rate 0.4902*** -0.0625*** -74.0231** c,t−1 

(4.43) (-2.95) (-2.30) 

High Opioid Death Rate 1.1179*** -0.1429*** -169.0837** c,t−1 

(4.42) (-2.96) (-2.31) 

Minorityi,c,t 0.068 0.0287 35.6529 0.0599 0.0258 32.7751 

(0.48) (1.06) (0.87) (0.48) (1.08) (0.90) 

Fit statistics 

Observations 197,371 197,371 197,371 197,371 197,371 197,371 

Adj. R2 0.317 0.157 0.083 0.312 0.154 0.081 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

ix 
Table A5: Additional Heterogeneous Effects for Low Income and Young Consumers 

This table examines how the effects of opioid crisis intensity on bank credit card terms differ by consumer income and age using interactions of consumer low income (≤$30k) with opioid intensity in Panel A and young (≤25 yrs) with opioid intensity in Panel B. In all 

cases, we report IV 2SLS regression estimates (equations (3) and (4)) using ”Mkt Doctors/1000Pop” as an instrument for opioid crisis intensity. All variables are constructed using the anonymized Mintel Comperemedia Inc. Direct Mail Monitor Data and TransUnion LLC 

Match File for analyzing credit card mail offers. The data are focused on lenders identifed as ”banks” in the Mintel/TransUnion Match 

File. Demographic attributes are from Mintel. Consumer controls include: credit score ranges, indicators for past deep delinquency, 

recent delinquency, past derogatory flings such as foreclosure and collections, past bankruptcy, past high utilization (≥ 80%), number 

of credit inquiries, past credit cards, consumer age ranges, married, indicator for no kids, White, education indicators, homeowner, 

and consumer income. County controls include: county income, unemployment rate, bank market concentration, population density, 

percent of males, race concentration, percent of people in various age ranges, percent of people with higher education, and inequality. 

All regressions include State × Year-Month, Lender × Year-Month, Lender × State, Lender, State, and Year-Month fxed effects. Variables 

are defned in Appendix Table A1. Standard errors are double-clustered by Marketing Campaign and Year-Month and t-statistics are 

reported in parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel A: Low Income (≤$30k) Consumers 

Dependent Variables: Rate Spread Ln(Limit) Limit ($) Rate Spread Ln(Limit) Limit ($) 

Model: (1) (2) (3) (4) (5) (6) 

Opioid Death Ratec,t−1 x Low Incomei,c,t−1 0.0445*** -0.0062** -4.2787 

(2.78) (-2.03) (-0.92) 

High Opioid Death Ratec,t−1 x Low Incomei,c,t−1 1.2101*** -0.1701** -130.927 

(3.30) (-2.43) (-1.24) 

Opioid Death Rate 0.4627*** -0.0673*** -83.8340*** c,t−1 

(4.26) (-3.24) (-2.66) 

High Opioid Death Rate 1.0367*** -0.1507*** -187.9986*** c,t−1 

(4.22) (-3.22) (-2.65) 

Low Incomei,c,t−1 -0.2939 0.0424 9.2884 -0.3751* 0.0542 24.324 

(-1.48) (1.11) (0.16) (-1.93) (1.46) (0.43) 

Fit statistics 

Observations 197,371 197,371 197,371 197,371 197,371 197,371 

Adj. R2 0.314 0.153 0.081 0.305 0.148 0.078 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

Panel B: Young (≤25 yrs) Consumers 

Dependent Variables: Rate Spread Ln(Limit) Limit ($) Rate Spread Ln(Limit) Limit ($) 

Model: (1) (2) (3) (4) (5) (6) 

Opioid Death Ratec,t−1 x Youngi,c,t 0.0627* -0.0072 -7.7118 

(1.72) (-1.03) (-0.73) 

High Opioid Death Ratec,t−1 x Youngi,c,t 1.8895** -0.2259 -247.88 

(2.18) (-1.37) (-0.99) 

Opioid Death Rate 0.5106*** -0.0705*** -82.6125*** c,t−1 

(4.85) (-3.50) (-2.71) 

High Opioid Death Rate 1.1798*** -0.1623*** -189.9956*** c,t−1 

(4.93) (-3.56) (-2.75) 

Youngi,c,t 0.5136 -0.0845 -80.6218 0.2715 -0.0521 -43.159 

(1.21) (-1.04) (-0.65) (0.60) (-0.60) (-0.33) 

Fit statistics 

Observations 197,371 197,371 197,371 197,371 197,371 197,371 

Adj. R2 0.315 0.157 0.083 0.306 0.152 0.08 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Lender × State ✓ ✓ ✓ ✓ ✓ ✓

Lender, State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓

Consumer & County controls ✓ ✓ ✓ ✓ ✓ ✓

x 
Table A6: Extra Results for Mechanisms, Credit, & Spending (County-Year-Month) 

This table reports regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument 

for explaining the relationship between opioid crisis intensity and consumer credit card behavior and/or quality in Panel A, bank 

credit card terms to consumers in Panel B, and consumer credit card spending in Panel C. Opioid crisis intensity is measured as 

Opioid Death Rate and High Opioid Death Rate, based on data from CDC. Consumer credit card behavior and/or quality is measured 

several ways as: Ln(Avg Days Past Due), Avg Loan Probability of Default (PD), Ln(Avg Payment), and Avg Updated Consumer Credit 

Score). Credit terms are measured several ways as: Avg Cycle APR, Limit/Pop, and Pct Rewards (percent of accounts with rewards). 

Consumer spending is measured as Total Purchase/Pop or Total Purchase/Limit. All these analyses use aggregated bank-countyyear-month data from the supervisory FR Y-14M credit card dataset based on a 0.1% random sample for existing consumer accounts 

(loan age ≥ 12 months). County controls include: county income, unemployment rate, bank market concentration, population density, 

percent of males, race concentration, percent of people in various age ranges, percent of people with higher education, and inequality. 

All regressions include State × Year-Month, Lender × Year-Month, Lender × State, Lender, State, and Year-Month fxed effects. Variables 

are defned in Appendix Table A1. Standard errors are double-clustered by County and Year-Month and t-statistics are reported in 

parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel A: IV 2SLS Effects: Effects on Credit Card Consumer Credit Behavior and/or Quality 

Dependent Variables: Opioid High Opioid Ln(Avg Days Avg Prob Ln(Avg Avg Credit Ln(Avg Days Avg Prob Ln(Avg Avg Credit 

Death Rate Death Rate Past Due) Default (PD) Payment) Score Past Due) Default (PD) Payment) Score 

Model: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 

Mkt Doctors/1000Popc,t−1 0.4388*** 

(17.92) 

0.0669*** 

(6.04) 

Opioid Death Ratec,t−1 0.2367*** 0.0034*** -0.6148*** -7.1076*** 

(5.84) (2.40) (-11.01) (-5.33) 

High Opioid Death Ratec,t−1 1.5526*** 0.0233*** -3.6161*** -46.6208*** 

(4.32) (2.23) (-6.11) (-4.12) 

Fit statistics 

Observations 119,482 119,096 119,482 119,482 119,096 119,482 118,823 118,823 119,482 119,482 

Adj. R2 0.048 0.052 0.019 0.006 0.060 0.080 0.004 0.001 0.010 0.020 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

County controls ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Panel B: IV 2SLS Effects: Reconfrm Results for Credit Card Terms 

Dependent Variables: Avg Cycle Ln(Avg Pct Cards w/ Avg Cycle Ln(Avg Pct Cards 

APR Limit) Rewards APR Limit) w/Rewards 

Model: (1) (2) (3) (4) (5) (6) 

Opioid Death Rate 0.4674*** -0.0832*** -0.0913*** c,t−1 

(4.81) (-6.09) (-9.76) 

High Opioid Death Rate 3.0659*** -0.5456*** -0.5992*** c,t−1 

(3.85) (-4.42) (-5.37) 

Fit statistics 

Observations 119,482 119,482 119,482 119,482 119,482 119,482 

Adj. R2 0.004 0.086 0.009 0.001 0.003 0.001 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓

County controls ✓ ✓ ✓ ✓ ✓ ✓

xi 
Table A6: Extra Results on Mechanisms, Credit, & Spending (County-Year-Month) (cont.) 

This table reports regression estimates from IV 2SLS regressions (equations (3) and (4)) using the ”Mkt Doctors/1000Pop” instrument 

for explaining the relationship between opioid crisis intensity and consumer credit card behavior and/or quality in Panel A, bank credit 

card terms to consumers in Panel B, and consumer credit card spending in Panel C. Opioid crisis intensity is measured as Opioid Death 

Rate and High Opioid Death Rate, based on data from CDC. Consumer credit card behavior and/or quality is measured several ways as: 

Ln(Avg Days Past Due), Avg Loan Probability of Default (PD), Ln(Avg Payment), and Avg Updated Consumer Credit Score). Credit 

terms are measured several ways as: Avg Cycle APR, Ln(Avg Limit), and Pct Rewards (percent of accounts with rewards). Consumer 

spending is measured as Total Purchase/Pop, Total Purchase/Limit, or Ln(Avg Purchase). All these analyses use aggregated countyyear-month data from the supervisory FR Y-14M credit card dataset based on a 0.1% random sample for existing consumer accounts 

(loan age ≥ 12 months). County controls include: county income, unemployment rate, bank market concentration, population density, 

percent of males, race concentration, percent of people in various age ranges, percent of people with higher education, and inequality. 

All regressions include State × Year-Month, Lender × Year-Month, Lender × State, Lender, State, and Year-Month fxed effects. Variables 

are defned in Appendix Table A1. Standard errors are double-clustered by County and Year-Month and t-statistics are reported in 

parentheses. Signifcance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively. 

Panel C: IV 2SLS Effects on Consumer Spending 

Dependent Variables: Total Total Ln Total Total Ln 

Purchase Purchase (Avg Purchase Purchase (Avg 

/Pop /Limit Purchase) /Pop /Limit Purchase) 

Model: (1) (2) (3) (4) (5) (6) 

Opioid Death Rate -0.1244*** -0.0145*** -0.3960*** c,t−1 

(-10.93) (-6.57) (-9.02) 

High Opioid Death Rate -0.8162*** -0.0950*** -2.1450*** c,t−1 

(-5.56) (-4.60) (-6.09) 

Fit statistics 

Observations 119,482 119,482 117,142 119,482 119,482 117,142 

Adj. R2 0.113 0.012 0.112 0.030 0.003 0.032 

Fixed effects 

State × Year-Month ✓ ✓ ✓ ✓ ✓ ✓

State, Year-Month ✓ ✓ ✓ ✓ ✓ ✓

County controls ✓ ✓ ✓ ✓ ✓ ✓

xii 
Table A7: Summary of Opioid-Related State Laws 

This table summarizes six state policies and laws aimed at reducing opioid abuse and opioid-related harm. We separate them into 

supply-related and demand-related laws. 

Name Description Implementing States & First Source 

Year Implemented 

Supply-Related Laws 

”Opioid Limiting Law” Limits prescriptions to a 4-, 5-, or 7-

day supply for frst time users or for 

acute or postoperatory pain or other 

uses or set other limits on the number of prescriptions or overall quantity of opioids that can be prescribed 

by physicians to a patient. 

2016: AZ, CT, ME, MA, NE, NH, NY, 

NC, PA, RI; 2017: AK, CO, DE, HI, 

ID, KY, LA, MI, MN, MO, NV, NJ, 

OH, UT, VT, VA, WA; 2018: FL, OK, 

SC, TN, WV. 

The Ballotpedia, Opioid Prescription Polcies 

by States, the National 

Conference of State Legislators (NCSL), Individual State Websites 

& Custodio, Cvijanovic 

and Wiedemann (2021) 

Mandatory ”Opioid 

PDMP Law” 

The Prescription Drug Monitoring 

Program (PDMP) collects and tracks 

opioid prescriptions and connect 

prescribers, dispensers, law enforcement, and Medicare authorities. The 

2012: KY, NM, WV; 2013: NY, TN, 

VT; 2014: GA, IN, MA; 2015: CT, NJ, 

NV, OH, OK, VA; 2016: NH, RI. 

The Opioid Environment Policy Scan 

(OEPS), University of 

Chicago 

mandatory law requires that prescribers must access the PDMP system before prescribing an opioid as 

interpreted by the Prescription Drug 

Abuse Policy System (PDAPS). 

”Triplicate Prescription 

Law” 

Requires three copies of an opioid 

prescription issued and kept by, respectively, the prescriber, the pharmacist, and a state agency that maintains a database from these forms to 

States with active triplicate programs at the time of OxyContin’s 

launch in 1996: CA, ID, IL, NY, and 

TX. 

Alpert, Evans, Lieber 

and Powell (2022) 

monitor and investigate prescribing 

irregularities and diversion. 

Demand-Related Laws 

”Naloxone Law” Increases access to and allows 

the prescribing and dispensing 

of Naloxone (an opioid receptor 

antagonist that reverses opiate 

overdose) by various third parties to 

users with documented risk factors 

Passed law before 2010: CA, CT, 

NM, NY; in 2010: IL, WA; 2012: MA, 

RI; 2013: CO, DC, KY, MD, NJ, NC, 

OK, OR, VT, VA; 2014: DE, GA, ME, 

MI, MN, OH, PA, TN, UT, WI; 2015: 

AL, AR, FL, ID, IN, LA, MS, NE, NV, 

The Opioid Environment Policy Scan 

(OEPS), University of 

Chicago 

for overdose. NH, ND, SC, TX, WV; 2016: AK, AZ, 

HI, IA, MO, SD; 2017: KS, MT, WY. 

”Good Samaritan Law” Provides immunity to drug users for 

certain drug crimes when they call 

for help for a person experiencing 

a drug overdose, again potentially 

helping reduce deaths. 

Any Samaritan Law started before 

2010: AK, KS, ME, MD, NM, OK, 

TX, WY; in 2010: WA; 2011: CT; NY; 

2012: CO, FL, IL, MA, RI; 2013: CA, 

DE, DC, NJ, NC, VT; 2014: GA, IN, 

LA, MN, PA, UT, WI; 2015: AL, AR, 

The Opioid Environment Policy Scan 

(OEPS), University of 

Chicago 

HI, KY, MS, NV, NH, ND, TN, VA, 

WV; 2016: OH, OR; 2017: MI, MO, 

MT, NE, SC, SD; 2018: AZ, ID, IA. 

”Medical Marijuana 

Permitting Law” 

Accepts and legalizes marijuana for 

medical purposes. 

Law in effect during our sample period (2010-2019): AK, AZ, AR, CA, 

CO, CT, DE, DC, FL, HI, IL, ME, MD, 

MA, MI, MN, MT, NV, NH, NJ, NM, 

NY, OH, OR, PA, RI, VT, WA. 

The Opioid Environment Policy Scan 

(OEPS), University of 

Chicago 

xiii