The Opioid Epidemic and Consumer Credit Supply:

Evidence from Credit Cards

Sumit Agarwal 1 Wenli Li2 Raluca A. Roman2 Nonna Sorokina3

1National University of Singapore

2Federal Reserve Bank of Philadelphia

3The Pennsylvania State University

Presentation at the FDIC Consumer Research Symposium, Washington DC

March 15, 2024

The views expressed are those of the authors and do not represent the views of the Federal Reserve Bank

of Philadelphia or the Federal Reserve System.
Introduction

▶ Longest deadly ongoing national health crisis in the U.S. Stats Over Time By Demos

▶ Growing evidence opioid abuse ↓ labor force participation and ↑ unemployment.

▶ Opioids afect worker health and reduce longevity. (e.g., Case and Deaton, 2015).

▶ Drug misusers more likely absent from work. (e.g., Van Hasselt, Keyes, Bray and Miller, 2015).

▶ Depressed labor force participation intertwined the opioid crisis (e.g., Krueger, 2017).

▶ Strong ↓ on employment-to-population, hours worked, earnings; ↑ in unemployment and

disability applications and benefciaries (e.g., Case and Deaton, 2015; Park and Powell, 2020).

▶ Strong negative labor efects on both prime age men and women (e.g., Case and Deaton, 2015).

▶ Opioids ↓ subsequent individual employment (e.g., Ouimet, Simintzi and Ye, 2020).

▶ Crisis afects consumers and communities fnancial health and poses evolving and elusive

risks to lenders supplying credit to consumers due to information asymmetry.

▶ Evidence on municipal bond issuance, subprime auto performance, house values, bank

deposits (Cornaggia, Hund, Nguyen and Ye, 2021; Custodio, Cvijanovic and Wiedeman, 2021;

Jansen, 2021; Li and Ye, 2022)).
Big picture on this paper and what we fnd

▶ Contribution: Add to the literature by examining the spillover efects of the opioid crisis

on consumer credit supply and the efectiveness of recent anti-opioid laws/regulations.

▶ Use the consumer credit card market as a laboratory.

▶ To disentangle credit supply, use unsolicited credit card ofer mailings by banks to consumers

▶ To identify causal efects, we employ instrumental variables, PSM, and contiguous counties,

and a battery of supply and demand factors and fxed efects.

▶ Main Results:

▶ We fnd that banks ↓ credit supply to consumers signifcantly in counties highly exposed to

opioid abuse by ofering higher interest rates, lower credit card limits, less rewards, and

reduce overall credit ofers.

▶ The adverse efects are stronger for riskier consumers, minorities, younger people, and people with

lower income.

▶ Supply-oriented laws are somewhat helpful, but demand-oriented laws are not.

▶ Opioid-induced credit supply contraction led to ↓ in local consumer spending, suggesting

important social welfare implications.
Literature

▶ Large literature on the origins of the opioid crisis.

▶ See Currie and Schwandt (2021) and Maclean, Mallatt, Ruhm and Simon (2020) for a review.

▶ Neither contemporaneous nor long-term economic conditions can explain the epidemic.

▶ Instead, physicians’ beliefs in pain medication, aggressive marketing by pharmaceutical

companies, little past public oversight are the key driving forces.

▶ Literature on the economic impact of the opioid crisis.

▶ Labor market (discussed above)

▶ Firm formation, survival and growth (e.g., Ouimet, Simintzi and Ye (2020), Rietveld and

Patel (2021), Sumell (2020), Langford (2021)).

▶ Small but growing literature on the efects of the opioid crisis on fnance.

▶ Municipal bond, local house value, subprime auto loan performance, bank deposits
Theoretical Arguments

▶ Individuals residing in areas with higher opioid exposure may be more at-risk for opioid

abuse or addiction and hence fnancially vulnerable.

▶ For highly dependent substance abusers, cost of drugs alone can throw of fnances, they are

also less employable.

▶ The addiction can further lead to other unsound fnancial decisions due to a “reinforcer

pathology” (e.g., Bickel, Athamneh, Snider, Craft, DeHart, Kaplan and Basso (2020)).

▶ Lenders may curtail credit supply via harsher terms and less credit ofered if they try to

reduce credit risk exposure from opioid abuse if consumers default more in highly

afected areas, which pushes upward their loan portfolio risk.

▶ Banks care about long-term viable consumers, opioid-afected not viable in long term.

▶ Banks may also incur increased costs for screening and ongoing monitoring.

▶ Given difculty to separate long-term viable from nonviable customers due to information

asymmetry and social stigma, banks reduce credit in the afected markets along both the

intensive margin (higher spread, lower limit) and the extensive margin (reduced ofer, (Stiglitz

and Weiss 1981)).
Data and Empirical Challenges
Our Approach: Data

▶ Opioid Mortality Rates: (Confdential) CDC All-County Mortality Micro Data.

▶ Credit Supply: unsolicited credit card ofer mailings by banks to consumers from the

Anonymized Mintel Direct Mail Monitor Data & TransUnion LLC Match File.

▶ Proprietary survey of nationally representative US consumers merged with TransUnion credit

bureau consumer data. Credit ofers are a direct informative measure of consumer credit

supply, helping circumvent challenges of disentangling supply from demand forces that plague

other studies (e.g., Han, Keys and Li (2018)).

▶ Supplemental Data:

▶ Opioid Marketing: Centers for Medicare and Medicaid Services Open Payment database.

▶ Consumer Credit Performance and Local Consumption: FR Y-14M supervisory regulatory

credit card data reported to the Federal Reserve System by large U.S. BHCs for stress testing

purposes.

▶ Bank-level Consumer Portfolio Data: Call Report.

▶ Other County-level Economic Data: BLS, Census ACS, FDIC SOD, and other sources.
Measurement of Opioid Abuse

▶ Do not observe individual opioid usage nor health status. We follow the literature and

construct county level opioid crisis exposure measures based on opioid death rates

▶ Based on confdential opioid-related death rates collected from the CDC/National Vital

Statistics System (NCHS).

▶ The measures serve as proxies for the opioid epidemic severity and help tackle the challenge of

not observing consumers’ opioid use or abuse or their general health.

▶ Consumers’ drug abuse is then measured via the severity of the opioid crisis based on opioid

deaths in the consumer’s county of residence.

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

▶ That is, in the absence of perfect information on the afected 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.

▶ We then ask: Are banks less likely to supply credit or apply more stringent terms to

individuals living in more opioid-afected areas?
Opioid Death Rates across US Counties (2019)
Empirical Challenges: Endogeneity

▶ Common conditions/shocks drive both the opioid crisis intensity and credit outcomes.

▶ Conduct two-stage least square (2SLS) regression analyses that use instrumental variables for

the opioid crisis intensity.

▶ Introduce extensive sets of control variables and fxed efects that capture heterogeneity in

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

▶ Other confounding consumer events or crises could afect our results.

▶ Focus on the years 2010-2019 so that our results are not contaminated by the implementation

of the Credit CARD Act of 2009, the Global Financial Crisis over 2007-2009, or the

COVID-19 outbreak from 2020 onwards.

▶ These ten years mark the second and the third waves of the opioid epidemic that recorded perhaps

the most dangerous abuse using both prescription and more illicit opioids.

▶ Focus on opioid death rates as the primary measure of crisis intensity and use prescriptions as

robustness. In addition to being comprehensive and comparable across counties, it may better

capture the development in the opioid epidemic since 2010, the period of our analyses, that is,

the rise in illicit opioid drug use.
Instrument: “MKT Doctors/1000Pop”

▶ MKT Doctors/1000Pop, captures the scale of pharmaceutical industry’s opioid

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

marketing visits and payments related to opioids per 1000 population in a county.

▶ Follows Hadland, Rivera-Aguirre, Marshall and Cerda (2019).

▶ The data for this instrument was acquired as part of disclosure requirements mandated

by the Physician Payments Sunshine Act and is collected from Centers for Medicare and

Medicaid Services Open Payments database.

▶ Hadland, Krieger and Marshall (2017) show that pharmaceutical companies invest tens of

millions of dollars annually in direct-to-physician marketing of opioids.

▶ 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 to be correlated with consumer or bank credit

behavior other than through the increased risks imposed by the opioid abuse itself.
Validating Instrument: Relevancy .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
Validating Instrument: More Checks

▶ Several studies have shown 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.

(Aliprantis, Lee and Schweitzer (2020); Currie and Schwandt (2021); Alpert, Evans, Lieber, Powell (2022)).

▶ Table shows little correlation between the instrument and various economic and other county characteristics.
Methodology
IV Empirical Models: First Stage

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

instrument and the same set of controls as those included in the second stage for the corresponding

analysis:

OpioidExpc,t−1=

β0 + β1IVc,t−1 + β2CountyControlsc,t−1 + +β3OtherConsumer/BankControlsi,c,t−1 + OtherFE + εi,t

▶ where:

▶ where i indexes individual or bank, c indexes county, and t indexes time periods (months or quarters).

▶ OpioidExp = 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.

▶ IV= The main instrumental variable is MKTDoctors/1000Pop, the number of doctors receiving opioid

marketing payments from pharmaceutical companies per 1,000 population per year which is time

variant, covering 2013 onwards.
IV Empirical Models: Second Stage for Consumer Credit Supply

▶ In IV second stage, we examine the relation between predicted opioid crisis exposure and consumer

credit supply using the equation:

Yi,c,t = γ0 + γ1OpioidExp \ c,t−1 + +γ3ConsumerControlsi,t−1 + 4CountyControlsc,t−1 + F E + μi,c,t

▶ where:

▶ i indexes consumer, c indexes county, and t indexes time periods (months).

▶ Yi,c,t = credit card ofer terms such as the RateSpread, diference between the ofered credit card

APR and one-month Treasury-bill, or Ln(Limit), the natural log of the ofered credit card limit.

▶ ConsumerControlsi,t−1= consumer controls (as of 2-3 months prior to ofer): credit score, consumer

income, recent and past delinquency (90+ days), other derogatory information such as foreclosures,

past bankruptcy flings, previous other credit cards, previous high credit utilization (80% or higher),

natural log of the number of recent credit inquiries (proxying for credit demand), age, homeowner,

married, no children, education level, and indicator for non-minority/white consumers.

▶ CountyControlsc,t−1 = 11 county controls (lagged one period): median income, unemployment rate,

bank local market concentration, population density, percent of male, race concentration, percent of

people in various age ranges, percent high education, and inequality (gini coefcient).

▶ FEs: state by year-month, lender by year-month, lender by state, lender, state, year-month.

▶ Errors are double-clustered by marketing campaign and year-month.
Empirical Results
Main Results: Opioid Epidemic and Consumer Credit Supply

— IV First Stage
Main Results: Opioid Epidemic and Consumer Credit Supply

— IV Second Stage
Prescribed vs. Illicit Opioid Deaths

— IV Second Stage
Alternative Exposure Measure: Prescription Rates

— IV Second Stage
Alternative IV: MKTPayments/1000Pop

— IV Second Stage
Alternative IV: High Purdue MKT

— IV Second Stage
Heterogenous Efects: High Credit Risk Consumers (Score < 620)

— IV Second Stage
Heterogenous Efects: Minority Consumers — IV Second Stage
Extensive Margin: Rewards/Promotions

— IV Second Stage
Extensive Margin: Likelihood of Credit Card Ofer

— IV Second Stage
Understanding the Mechanisms: Y-14M Consumer Behavior/Quality

— IV Second Stage
Understanding the Mechanisms: Call Reports Bank Card Portfolio Quality

— IV Second Stage
Reconfrming main efects with diferent dataset: Y-14M Card Terms

— IV Second Stage
Opioid Abuse and Local Consumption

— IV Second Stage
Opioid Supply-Oriented Laws
Opioid Demand/User-Oriented Laws
Opioid State-Level Supply and Demand Laws — IV Second Stage
Opioid State-Level Supply and Demand Laws (cont.) — IV Second Stage
Opioid State-Level Supply and Demand Laws (cont.)

— IV Second Stage
Opioid State-Level Supply and Demand Laws (cont.)

— IV Second Stage
Additional Identifcation and Other Robustness Checks

▶ Conduct tests to address identifcation and/or rule out alternative explanations:

▶ Employ PSM analyses 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 nine 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 main results also using FR Y14-M credit card supervisory data.

▶ Conduct additional cross-sectional tests by consumer characteristics.

▶ All of our approaches consistently show statistically as well as economically signifcant

adverse efects on consumer credit risk and credit supply caused by opioid abuse.

▶ Additionally, although the crisis afected the overall population, the negative efects are larger

for riskier, minorities (particularly Blacks), low income, and younger consumers.
Key Takeaways

▶ This paper investigates the efects of the opioid crisis on consumer credit markets.

▶ We have several key fndings, all economically sizable:

▶ Credit supply to consumers: Banks have become reluctant to lend in areas with signifcant

exposure to opioids: i) they are less likely to send their ofers of credit in the exposed regions;

ii) when they do solicit consumers for credit in those areas, the ofers have ↑ higher interest

rates and ↓ lower credit limits.

▶ Local consumer consumption: local consumer spending signifcantly ↓ in the highly

opioid-crisis afected areas.

▶ The current wave of laws and regulations passed to reduce the devastating efects of the

opioid crisis on communities → mixed efects.

▶ The supply-oriented laws (Opioid Prescription Limiting Law, the mandatory PDMPs, and the

Triplicate Prescription Law) appear to help mitigate some of the negative infuence of the

opioid epidemic, including both on some of opioid deaths as well as revert some of the

negative efects on consumer credit supply.

▶ The demand-oriented laws are less benefcial as per fndings in our analysis.
Policy Implications

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

by the relatively higher credit risk in the highly opioid-afected areas.

▶ The ↓ consumer credit supply, nevertheless, could create a negative feedback loop depriving

the opioid-afected regions of the much-needed liquidity for recovery.

▶ The ↓ consumer consumption in hardly-hit local markets may suggest important negative

externalities → social welfare and macro-policy implications — given that consumer spending

accounts for the vast majority of US gross domestic product and economic growth.

▶ 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

eforts, enhance welfare, and restore growth and resilience in the opioid-afected

consumer markets.
Thank you!
Big picture on the Opioid Epidemic

▶ The opioid epidemic has led to a staggering number of overdose deaths, opioid disorders,

family breakups, and community problems.

▶ > 1 million American people died from 1999 to present.

▶ > 2 millions Americans are struggling with opioid disorders.

▶ More and more deadly over time, afecting a larger spectra of the population.

▶ > 30% of patients prescribed opioids for chronic pain end up misusing them.

▶ Rise of fentanyl critical concern: involved in over half of opioid-related overdose deaths.

▶ Behind every statistic is a real person, a real family, and a real community sufering. This

epidemic is not just a number; it’s a human tragedy that demands attention and action.

▶ "Addiction is a family disease; one person uses, but whole family sufers." - Sherry Woodard

▶ "This epidemic doesn’t discriminate; it afects people of all backgrounds." - Chris Christie

▶ "Addiction is a health issue, not a moral failing." - Michael Botticelli

▶ "The opioid epidemic is a national emergency." - Alex Azar, Donald Trump

▶ "This is a battle we cannot aford to lose." - Joe Biden

Return
Additional Figures: Prescription Rates across US Counties (2019)
Additional Figures: Instrument MKTDoctors/1000Pop across US Counties

(2019)
Additional Figures: Opioid Death Rates Over Time

Return
Additional Figures: Opioid Death Rates By Demographics

Return
Additional Figures: Prescription Rates across US Counties (2019)
Additional Figures: Instrument MKTDoctors/1000Pop across US Counties

(2019)