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Substance Abuse and Workplace Fraud: Evidence from Physicians

Journal of Business Ethics volume 183, pages 585–602 (2023)Cite this article

Abstract

We examine the relation between worker substance abuse and workplace fraud in a sample of medical doctors. Relative to their peers, we observe that doctors engaging in substance abuse are between 50 and 100 times more likely to commit fraud in a given year. This result is consistent with research suggesting that substance abuse both creates financial pressures and impairs the functioning of cognitive self-regulatory mechanisms. Our results are robust in within-subject tests and between-subject tests, as well as in tests using instrumental variables that exploit exogenous variation in the state-level availability of opioids, a commonly abused substance.

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Data Availability

The National Practitioner Data Bank public research file used in this paper is freely available for public download at https://www.npdb.hrsa.gov/resources/publicData.jsp.

Notes

  1. We define workplace fraud as misappropriation of assets, corruption, and financial statement fraud. We further explain these types of workplace fraud in Sect. Background and Hypothesis Development.

  2. We define substance abuse as the use of any drugs or alcohol to excess.

  3. We focus on the year of the underlying violation, not the year in which the doctor received the sanction/punishment.

  4. Employee Assistance Programs are work-life and wellness services that an employer provides to employees that often include programs focused on providing counseling and treatment for employees troubled with substance abuse and other personal problems (see Attridge, 2015; Hartwell et al., 1996; Scanlon, 1991). Over 80% of medium and large employers in the United States provide these programs (Attridge, 2015).

  5. For additional, nuanced discussions of the impact of religion on workplace fraud, see Drakopoulou Dodd and Gotsis (2007) and Koerber and Neck (2006).

  6. For an excellent literature review of the workplace implications of employee alcohol and drug use, see Harris and Heft (1992).

  7. According to the 2019 National Survey on Drug Use and Health (SAMHSA, 2020), about 75.5% of the respondents who perceived that they had problems with their use of alcohol and recreational drugs considered themselves to be in recovery or to have recovered from their alcohol or drug use problems.

  8. The 2019 National Survey on Drug Use and Health (SAMHSA, 2020) suggests that 70% of the respondents with an alcohol or recreational drug use disorder are employed. Current policy initiatives focusing on this population include the Centers for Disease Control and Prevention (CDC) led Total Worker Health Program (NIOSH, 2020), which aims to engage employers and employees to prevent work-related hazards and risks related to substance abuse.

  9. Daily cocaine users, by comparison, spend an average of $1737 per month on their habit (Kilmer et al., 2014).

  10. According to the U.S. Bureau of Labor Statistics, the 2020 median before-tax pay for physicians and surgeons is about $208,000.

  11. For thorough discussions of rationalization, see Murphy and Dacin (2011) and Festinger (1957, 1962).

  12. Studies have found similar results in substance users dependent on cocaine (Coffey et al., 2003; Heil et al., 2006; Mendez et al., 2010), marijuana (McDonald et al., 2003), amphetamines (Hoffman et al., 2008), alcohol, and tobacco (Bickel et al., 1999; Petry, 2001).

  13. Neurologically, this impaired inhibitory control stemming from substance addiction is thought to originate from the damaging effect of substance abuse on the frontal cortex, which is the part of the brain responsible for estimating consequences and distinguishing good actions from bad (Jentsch & Taylor, 1999; Li et al., 2007).

  14. In our setting, substance abusers (our treatment sample) refer to doctors who got caught abusing substances in a given period. If a doctor was in remission or had recovered from substance abuse in that certain period, then this doctor is part of our control sample. We draw our inference from between period comparisons for each doctor.

  15. Like any data source, the NPDB is imperfect and is the subject of fair criticism involving data coverage and loopholes (Helland & Lee, 2010; Teninbaum, 2013). Much of this criticism focuses on physicians being able to avoid being individually named in malpractice lawsuits if their employer assumes liability and excludes the physician from the settlement. However, given that our identification exploits differences in physicians already named in malpractice lawsuits, selection issues pertaining to entering the sample are less problematic. Helland et al. (2005) argue that the NPDB data is appropriate for econometric analysis of physicians and physician labor markets, and accordingly we view the associated imperfections in the data as unfortunate but acceptable.

  16. About 30% of medical malpractice lawsuits involve a payout at conclusion, and many suits are dismissed. Annually, about 8% of physicians in high-risk specialties are involved in malpractice suits with a payout, and about 2% of physicians in low-risk specialties are successfully sued for malpractice (Jena et al., 2011).

  17. American medical schools produced about 16,000 graduates per year during the 1980s and 1990s (AAMC, 2012), suggesting that about 25% of the potential underlying population of practicing physicians enters our sample.

  18. Due to data limitations, we do not have variables directly measuring personality traits. We thus control for variables that may correlate with personality traits.

  19. As fraud in year t is our dependent variable, for this prior fraud control, the subscript i,<t indicates that we do not include current year fraud in the measurement (i.e., we only include years < t.). We treat the license suspension control variable similarly, as license suspensions are also highly collinear with our dependent variable.

  20. We are not able to explicitly test the exclusion restriction given that we only have one instrument, but conceptually it is difficult to envision a channel other than drug use by which opioid prescription limits affect doctors’ propensity to commit fraud in the workplace.

  21. Two of the dummy variables indicating future treatment actually load with a negative and statistically significant coefficient, suggesting that a doctor who will get sanctioned for substance abuse in the future is less like to engage in workplace fraud in the current year.

  22. Conventional logit models may be biased in cases where the dependent variable is a rare event (i.e., dependent variable differs from the mode in less than 5% of cases; see King & Zeng, 2001). To correct for this potential bias, Tomz et al. (2003) develop a logit model specifically for rare events. We use this rare event specification in model 3 of Table 8, as our dependent variable Fraudi,t only differs from the mode of zero in 0.03% of cases.

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Acknowledgements

For helpful feedback that greatly improved this paper, we thank Jivas Chakravarthy, Roy Chandler, Scott Emett, Carie Ford, Emily Griffith, Gaurav Gupta, Artur Hugon, Kathryn Kadous, Steve Kaplan, Jason Kuang, Jordan Lowe, Jason MacGregor, Michal Matejka, Mason Snow, Derrald Stice, E. Kay Stice, J. Han Stice, and workshop participants at Arizona State University, Emory University, the 2017 AAA Southeast Region Meeting, the UEBS 2018 Interdisciplinary Perspectives on Accounting Conference, and the 2018 AAA Annual Meeting. We are also grateful to Min Kim for providing very capable research assistance.

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Authors and Affiliations

  1. Accounting and Business Law Department, Hankamer School of Business, Baylor University, Waco, USA

    Melanie Millar

  2. School of Accountancy, W.P. Carey School of Business, Arizona State University, Tempe, USA

    Roger M. White

  3. Accounting and Information System Division, Sauder School of Business, University of British Columbia, Vancouver, Canada

    Xin Zheng

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  1. Melanie Millar
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  2. Roger M. White
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Correspondence to Roger M. White.

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Appendices

Appendix A

NPDB Sanctions Mapped to Covariates and Variables of Interest

This appendix lists the types of sanctions that we collect in our data set, and it maps how we code those sanction types into our test and control variables. Note that this data set originates from the National Practitioner Data Bank, which the Department of Health and Human Services maintains; this databank tracks sanctions that state-specific boards of medicine issue to licensed physicians.

Sanction category

Basis for action (NPDB Code)

NPDB code definition

Fraud

5

Fraud (Unspecified)

6

Insurance fraud (Medicare and other Federal Gov. program)

7

Insurance fraud (Medicaid or other State Gov. program)

8

Insurance fraud (Non-government or private insurance)

9

Fraud in obtaining license or credentials

16

Misappropriation of patient property or other property

21

Failure to repay overpayment

36

Violation of federal or state tax code

55

Improper or abusive billing practices

56

Submitting false claims

57

Fraud, kickbacks and other prohibited activities

60

Felony conviction related to health care fraud

64

Conviction Re: fraud

81

Misrepresentation of credentials

D3

Exploiting a patient for financial gain

E1

Insurance fraud (medicare, medicaid or other insurance)

E3

Filing false reports or falsifying records

E4

Fraud, deceit or material omission in obtaining license or credentials

Substance abuse

1

Alcohol and/or other substance abuse

F2

Unable to practice safely by reason of alcohol or other substance abuse

3

Narcotics violation

35

Drug screening violation

61

Felony conviction Re: controlled substance violation

66

Conviction Re: controlled substances

75

Violation of drug-free workplace act

H1

Narcotics violation or other violation of drug statutes

Sex offense

D1

Sexual misconduct

D2

Non-sexual dual relationship or boundary violation

Criminal

19

Criminal conviction

62

Program-related conviction

63

Conviction Re: patient abuse or neglect

65

Conviction Re: obstruction of an investigation

69

Criminal conviction, not classified

70

Violation of by-laws, protocols or guidelines

Unprofessionalism

10

Unprofessional conduct

Malpractice, negligence, and medical mistakes

12

Malpractice

13

Negligence

14

Patient abuse

15

Patient neglect

17

Inadequate or improper infection control practices

25

Practicing without a license

29

Practicing beyond scope of practice

30

Allowing unlicensed person to practice

32

Lack of appropriately qualified professionals

52

Incompetence, malpractice, negligence (legacy format reports)

53

Failure to provide Med Resnble or Nec. items/services

54

Furnishing unnecessary or substandard items/services

52

Incompetence, malpractice, negligence (legacy format reports)

53

Failure to provide Med Resnble or Nec. items/services

54

Furnishing unnecessary or substandard items/services

Appendix B

Variable Definitions

Fraudi,t: The doctor (i) violated fraud statutes in the current year (t), even if the punishment/sanction is imposed in a different year. See Appendix A for a list of fraud violations and Table 1 for the frequencies of these violations in our data.

Substance Abuse Violationi,t: The doctor (i) violated substance abuse statutes in the current year t, even if the punishment/sanction is imposed in a different year. See Appendix A for a list of the different types of substance abuse violations in the NPDB data.

Fraud Violationi,<t: The doctor (i) has violated fraud statutes in any past year (< t). See Appendix A for a list of these sanctions in the NPDB data.

Sex Offense Violationi,≤t: The doctor (i) has violated sexual offense statutes in years ≤ t. See Appendix A for a list of these sanctions in the NPDB data.

Unprofessionalism Violationi,≤t: The doctor (i) has violated professionalism statutes in years ≤ t. See Appendix A for a list of these sanctions in the NPDB data.

Criminal Violationi,≤t: The doctor (i) has violated criminal statutes in years ≤ t. See Appendix A for a list of these sanctions in the NPDB data.

Malpractice Violationi,≤t: The doctor (i) has violated malpractice statutes in years ≤ t. See Appendix A for a list of these sanctions in the NPDB data.

# Malpractice Lawsuits Settledi,≤t: The count of medical malpractice lawsuits the doctor (i) has settled in years ≤ t. This includes malpractice lawsuits that an insurance company has settled on behalf of the doctor.

Cumulative Malpractice Settlement $i,≤t: The cumulative dollar value of medical malpractice lawsuits the doctor (i) has settled in years ≤ t. This includes malpractice lawsuits that an insurance company has settled on behalf of the doctor.

License Suspensioni,<t: The doctor (i) has had a medical license suspended (permanently or temporarily) by a state medical board or other regulator in a prior year (< t).

License Reinstatementi,≤t: The doctor (i) has had a medical license suspended (permanently or temporarily) and then reinstated by a state medical board or other regulator in years ≤ t.

Tenure in yearsi,t: Number of years that have elapsed between year t and the earliest year in the decade of the doctor i’s medical school graduation decade. (The NPDB data does not provide more granular data on graduation date.)

1990s Graduation Cohorti,t: We use only the 1980s and 1990s cohorts of medical school graduates in this study. In our between-subjects models, we include a dummy variable for 1990s cohort, with the 1980s cohort serving as the excluded category.

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Millar, M., White, R.M. & Zheng, X. Substance Abuse and Workplace Fraud: Evidence from Physicians. J Bus Ethics 183, 585–602 (2023). https://doi.org/10.1007/s10551-022-05065-6

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Keywords

  • Substance abuse
  • Fraud
  • Impulsivity
  • Delay discounting

JEL Classification

  • K42
  • G40
  • M12
  • M41
  • M51