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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
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.
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.
We define substance abuse as the use of any drugs or alcohol to excess.
We focus on the year of the underlying violation, not the year in which the doctor received the sanction/punishment.
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).
For an excellent literature review of the workplace implications of employee alcohol and drug use, see Harris and Heft (1992).
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.
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.
Daily cocaine users, by comparison, spend an average of $1737 per month on their habit (Kilmer et al., 2014).
According to the U.S. Bureau of Labor Statistics, the 2020 median before-tax pay for physicians and surgeons is about $208,000.
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).
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.
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.
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).
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.
Due to data limitations, we do not have variables directly measuring personality traits. We thus control for variables that may correlate with personality traits.
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.
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.
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.
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.
Adamson, T. E., Baldwin, D. C., Sheehan, T. J., & Oppenberg, A. A. (1997). Characteristics of surgeons with high and low malpractice claims rates. Western Journal of Medicine, 166(1), 37–44.
Ahmed, A. S., & Duellman, S. (2013). Managerial overconfidence and accounting conservatism. Journal of Accounting Research, 51(1), 1–30.
American Institute of Certified Public Accountants (AICPA). (2002). Consideration of fraud in a financial statement audit. Statement on Auditing Standards No. 99. New York, AICPA
Arizona Department of Health Services. (2017). 50 State review on opioid related policy. Phoenix
Armstrong, C. S., Larcker, D. F., Ormazabal, G., & Taylor, D. J. (2013). The relation between equity incentives and misreporting: The role of risk-taking incentives. Journal of Financial Economics, 109(2), 327–350.
Association of American Medical Colleges (AAMC). (2012). U.S. Medical School Applicants and Students 1982–1983 to 2011–2012. American Association of Medical Colleges
Association of Certified Fraud Examiners (ACFE). 2008. Report to the Nation on Occupational Fraud and Abuse. Austin, Association of Certified Fraud Examiners
Association of Certified Fraud Examiners (ACFE). 2016. Report to the Nations on Occupational Fraud and Abuse. Austin, Association of Certified Fraud Examiners
Attridge, M. (2015). Employee assistance programs. Wiley encyclopedia of management (pp. 1–3). Wiley.
Baldisseri, M. R. (2007). Impaired healthcare professional. Critical Care Medicine, 35(2 Suppl), S106-116.
Banerjee, S., M. Humphery-Jenner, V. K. Nanda, & T. M. Tham. (2015). Executive overconfidence and securities class actions. Working paper
Beasley, M. S. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review, 71(4), 443–465.
Becker, G. S., & Murphy, K. M. (1988). A theory of rational addiction. Journal of Political Economy, 96(4), 675–700.
Benmelech, E., & Frydman, C. (2014). Military CEOs. Journal of Financial Economics, 117(1), 43–59.
Bickel, W. K., & Marsch, L. A. (2001). Toward a behavioral economic understanding of drug dependence: Delay discounting processes. Addiction, 96(1), 73–86.
Bickel, W. K., Odum, A. L., & Madden, G. J. (1999). Impulsivity and cigarette smoking: Delay discounting in current, never, and ex-smokers. Psychopharmacology (berl), 146(4), 447–454.
Biegelman, M. T., & Bartow, J. T. (2012). Executive roadmap to fraud prevention and internal control: Creating a culture of compliance. Wiley.
Blickle, G., Schlegel, A., Fassbender, P., & Klein, U. (2006). Some personality correlates of business white-collar crime. Applied Psychology, 55(2), 220–233.
Bondurant, S. R., Lindo, J. M., & Swensen, I. D. (2018). Substance abuse treatment centers and local crime. Journal of Urban Economics, 104, 124–133.
Bush, D., & R. Lipari. (2015). Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings. Center for Behavioral Health Statistics and Quality Short Report. Rockville, MD: Substance Abuse and Mental Health Services Administration
Call, A. C., Kedia, S., & Rajgopal, S. (2016). Rank and file employees and the discovery of misreporting: The role of stock options. Journal of Accounting and Economics, 62(2), 277–300.
Callen, J. L., & Fang, X. (2015). Religion and stock price crash risk. Journal of Financial and Quantitative Analysis, 50(1–2), 169–195.
Christian, C. (1994). Voluntary Compliance with the individual income tax: Results from the 1988 TCMP Study. In The IRS Research Bulletin. Publication 1500. Washington, DC: U.S. Dept. of the Treasury, Internal Revenue Service
Coffey, S. F., Gudleski, G. D., Saladin, M. E., & Brady, K. T. (2003). Impulsivity and rapid discounting of delayed hypothetical rewards in cocaine-dependent individuals. Experimental and Clinical Psychopharmacology, 11(1), 18–25.
Comer, D. R. (1994). Crossroads—A case against workplace drug testing. Organization Science, 5(2), 259–267.
Cummings, S. M., Merlo, L., & Cottler, L. (2011). Mechanisms of prescription drug diversion among impaired physicians. Journal of Addictive Diseases, 30(3), 195–202.
Dechow, P. M., Ge, W., Larson, C., & Sloan, R. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17–82.
Denis, D. J., Hanouna, P., & Sarin, A. (2006). Is there a dark side to incentive compensation? Journal of Corporate Finance, 12(3), 467–488.
Dickhaut, J., Basu, S., McCabe, K., & Waymire, G. (2010). Neuroaccounting: Consilience between the biologically evolved brain and culturally evolved accounting principles. Accounting Horizons, 24(2), 221–255.
Dimmock, S. G., & Gerken, W. C. (2012). Predicting fraud by investment managers. Journal of Financial Economics, 105(1), 153–173.
Drakopoulou Dodd, S., & Gotsis, G. (2007). The interrelationships between entrepreneurship and religion. The International Journal of Entrepreneurship and Innovation, 8(2), 93–104.
Farber, D. B. (2005). Restoring trust after fraud: Does corporate governance matter? The Accounting Review, 80(2), 539–561.
Faupel, C. E. (1988). Heroin use, crime and employment status. Journal of Drug Issues, 18(3), 467–479.
Feng, M., Ge, W., Luo, S., & Shevlin, T. (2011). Why do CFOs become involved in material accounting manipulations? Journal of Accounting and Economics, 51(1–2), 21–36.
Festinger, L. A. (1957). A theory of cognitive dissonance. Peterson.
Festinger, L. A. (1962). An introduction to the theory of dissonance. In L. A. Festinger (Ed.), A theory of cognitive dissonance. Stanford University Press.
Fox, K., Merrill, J. C., Chang, H. H., & Califano, J. A., Jr. (1995). Estimating the costs of substance abuse to the Medicaid hospital care program. American Journal of Public Health, 85(1), 48–54.
French, M. T., Roebuck, M. C., & Alexandre, P. K. (2001). Illicit drug use, employment, and labor force participation. Southern Economic Journal, 68(2), 349–368.
French, M. T., Roebuck, M. C., & Alexandre, P. K. (2004). To test or not to test: Do workplace drug testing programs discourage employee drug use? Social Science Research, 33(1), 45–63.
Geuijen, P., de Rond, M., Kuppens, J., Atsma, F., Schene, A., de Haan, H., de Jong, C., & Schellekens, A. (2020). Physicians’ norms and attitudes towards substance use in colleague physicians: A cross-sectional survey in the Netherlands. PLoS ONE, 15(4), e0231084.
Green, L., Fry, A. F., & Myerson, J. (1994). Discounting of delayed rewards: A life-span comparison. Psychological Science, 5(1), 33–36.
Guglielmo, W. (2011). Medscape Physician Compensation Report: 2011. Medscape
Guo, J., Huang, P., Zhang, Y., & Zhou, N. (2015). The effect of employee treatment policies on internal control weaknesses and financial restatements. The Accounting Review, 91(4), 1167–1194.
Hamilton, K. R., & Potenza, M. N. (2012). Relations among delay discounting, addictions, and money mismanagement: implications and future directions. The American Journal of Drug and Alcohol Abuse, 38(1), 30–42.
Harris, J., & Bromiley, P. (2007). Incentives to cheat: The influence of executive compensation and firm performance on financial misrepresentation. Organization Science, 18(3), 350–367.
Harris, M. M., & Heft, L. L. (1992). Alcohol and drug use in the workplace: Issues, controversies, and directions for future research. Journal of Management, 18(2), 239–266.
Hartwell, T. D., Steele, P., French, M. T., Potter, F. J., Rodman, N. F., & Zarkin, G. A. (1996). Aiding troubled employees: The prevalence, cost, and characteristics of employee assistance programs in the United States. American Journal of Public Health, 86(6), 804–808.
Hedden, L., Lavergne, M. R., McGrail, K. M., Law, M. R., Cheng, L., Ahuja, M. A., & Barer, M. L. (2017). Patterns of physician retirement and pre-retirement activity: A population-based cohort study. Canadian Medical Association Journal, 189(49), E1517–E1523.
Heil, S. H., Johnson, M. W., Higgins, S. T., & Bickel, W. K. (2006). Delay discounting in currently using and currently abstinent cocaine-dependent outpatients and non-drug-using matched controls. Addictive Behaviors, 31(7), 1290–1294.
Helland, E., Klick, J., & Tabarrok, A. (2005). Data watch: Tort-uring the data. Journal of Economic Perspectives, 19(2), 207–220.
Helland, E., & Lee, G. (2010). Bargaining in the shadow of the website: Disclosure’s impact on medical malpractice litigation. American Law and Economics Review, 12(2), 462–508.
Hobson, J. L., Mayew, W. J., & Venkatachalam, M. (2012). Analyzing speech to detect financial misreporting. Journal of Accounting Research, 50(2), 349–392.
Hoffman, W. F., Schwartz, D. L., Huckans, M. S., McFarland, B. H., Meiri, G., Stevens, A. A., & Mitchell, S. H. (2008). Cortical activation during delay discounting in abstinent methamphetamine dependent individuals. Psychopharmacology (berl), 201(2), 183–193.
Hogan, C. E., Rezaee, Z., Riley, R. A., Jr., & Velury, U. K. (2008). Financial statement fraud: Insights from the academic literature. Auditing: A Journal of Practice & Theory, 27(2), 231–252.
Hughes, P. H., Brandenburg, N., Baldwin, D. C., Jr., Storr, C. L., Williams, K. M., Anthony, J. C., & Sheehan, D. V. (1992). Prevalence of substance use among US physicians. JAMA, 267(17), 2333–2339.
Inciardi, J. A. (1981). The Impact of Drug Use on Street Crime. Working paper
Jena, A. B., Seabury, S., Lakdawalla, D., & Chandra, A. (2011). Malpractice risk according to physician specialty. The New England Journal of Medicine, 365(7), 629–636.
Jentsch, J. D., & Taylor, J. R. (1999). Impulsivity resulting from frontostriatal dysfunction in drug abuse: Implications for the control of behavior by reward-related stimuli. Psychopharmacology (berl), 146(4), 373–390.
Jia, Y., Van Lent, L., & Zeng, Y. (2014). Masculinity, testosterone, and financial misreporting. Journal of Accounting Research, 52(5), 1195–1246.
Karpoff, J. M., Lee, D. S., & Martin, G. S. (2008). The cost to firms of cooking the books. Journal of Financial and Quantitative Analysis, 43(03), 581–611.
Kilmer, B., S. S. Everingham, J. P. Caulkins, G. Midgette, R. L. Pacula, P. H. Reuter, R. M. Burns, B. Han, and R. Lundberg. (2014). What America’s users spend on illegal drugs. Prepared for the Office of National Drug Control Policy. RAND Corporation
Kim, J.-B., Li, Y., & Zhang, L. (2011). CFOs versus CEOs: Equity incentives and crashes. Journal of Financial Economics, 101(3), 713–730.
King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9(2), 137–163.
Kinlock, T. W., O’Grady, K. E., & Hanlon, T. E. (2003). Prediction of the criminal activity of incarcerated drug-abusing offenders. Journal of Drug Issues, 33(4), 897–920.
Kirby, K. N., Petry, N. M., & Bickel, W. K. (1999). Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. Journal of Experimental Psychology: General, 128(1), 78–87.
Klein, A. (2002). Audit committee, board of director characteristics, and earnings management. Journal of Accounting and Economics, 33(3), 375–400.
Kocher, M. S., Dichtel, L., Kasser, J. R., Gebhardt, M. C., & Katz, J. N. (2008). Orthopedic board certification and physician performance: An analysis of medical malpractice, hospital disciplinary action, and state medical board disciplinary action rates. American Journal of Orthopedics, 37(2), 73–75.
Koerber, C. P., & Neck, C. P. (2006). Religion in the workplace: Implications for financial fraud and organizational decision making. Journal of Management, Spirituality & Religion, 3(4), 305–318.
Koob, G., & Bloom, F. (1988). Cellular and molecular mechanisms of drug dependence. Science, 242(4879), 715–723.
Koob, G. F., & Volkow, N. D. (2016). Neurobiology of addiction: A neurocircuitry analysis. The Lancet Psychiatry, 3(8), 760–773.
Larcker, D. F., & Zakolyukina, A. A. (2012). Detecting deceptive discussions in conference calls. Journal of Accounting Research, 50(2), 495–540.
Lehman, W. E., & Simpson, D. D. (1992). Employee substance use and on-the-job behaviors. Journal of Applied Psychology, 77(3), 309–321.
Lennox, C., & Pittman, J. A. (2010). Big five audits and accounting fraud. Contemporary Accounting Research, 27(1), 209–247.
Li, C. R., Huang, C., Yan, P., Bhagwagar, Z., Milivojevic, V., & Sinha, R. (2007). Neural correlates of impulse control during stop signal inhibition in cocaine-dependent men. Neuropsychopharmacology, 33(8), 1798–1806.
Lowenstein, M., Hossain, E., Yang, W., Grande, D., Perrone, J., Neuman, M. D., Ashburn, M., & Delgado, M. K. (2020). Impact of a state opioid prescribing limit and electronic medical record alert on opioid prescriptions: A difference-in-differences analysis. Journal of General Internal Medicine, 35(3), 662–671.
Madden, G. J., Petry, N. M., Badger, G. J., & Bickel, W. K. (1997). Impulsive and self-control choices in opioid-dependent patients and non-drug-using control participants: Drug and monetary rewards. Experimental and Clinical Psychopharmacology, 5(3), 256–262.
Majors, T. M. (2016). The interaction of communicating measurement uncertainty and the dark triad on managers’ reporting decisions. The Accounting Review, 91(3), 973–992.
Mangione, T. W., Howland, J., Amick, B., Cote, J., Lee, M., Bell, N., & Levine, S. (1999). Employee drinking practices and work performance. Journal of Studies on Alcohol, 60(2), 261–270.
McAuliffe, P. F., Gold, M. S., Bajpai, L., Merves, M. L., Frost-Pineda, K., Pomm, R. M., Goldberger, B. A., Melker, R. J., & Cendán, J. C. (2006). Second-hand exposure to aerosolized intravenous anesthetics propofol and fentanyl may cause sensitization and subsequent opiate addiction among anesthesiologists and surgeons. Medical Hypotheses, 66(5), 874–882.
McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306(5695), 503–507.
McDaniel, M. A. (1988). Does pre-employment drug use predict on-the-job suitability? Personnel Psychology, 41(4), 717–729.
McDonald, J., Schleifer, L., Richards, J. B., & de Wit, H. (2003). Effects of THC on behavioral measures of impulsivity in humans. Neuropsychopharmacology, 28(7), 1356–1365.
Mendez, I. A., Simon, N. W., Hart, N., Mitchell, M. R., Nation, J. R., Wellman, P. J., & Setlow, B. (2010). Self-administered cocaine causes long-lasting increases in impulsive choice in a delay discounting task. Behavioral Neuroscience, 124(4), 470–477.
Merlo, L. J., Singhakant, S., Cummings, S. M., & Cottler, L. B. (2013). Reasons for misuse of prescription medication among physicians undergoing monitoring by a physician health program. Journal of Addiction Medicine, 7(5), 349–353.
Moeller, F., & Dougherty, D. M. (2002). Impulsivity and substance abuse: What is the connection? Addictive Disorders & Their Treatment, 1(1), 3–10.
Murphy, P. R., & Dacin, M. T. (2011). Psychological pathways to fraud: Understanding and preventing fraud in organizations. Journal of Business Ethics, 101(4), 601–618.
National Institute for Occupational Safety and Health (NIOSH). (2020). NIOSH Total Worker Health® Program. Retrieved from https://www.cdc.gov/niosh/twh/default.html
Nordstrom, B. R., & Dackis, C. A. (2011). Drugs and crime. Journal of Psychiatry & Law, 39(4), 663–687.
Normand, J., Salyards, S. D., & Mahoney, J. J. (1990). An evaluation of preemployment drug testing. Journal of Applied Psychology, 75(6), 629–639.
Oreskovich, M. R., Shanafelt, T., Dyrbye, L. N., Tan, L., Sotile, W., Satele, D., West, C. P., Sloan, J., & Boone, S. (2015). The prevalence of substance use disorders in American physicians. The American Journal on Addictions, 24(1), 30–38.
Orviska, M., & Hudson, J. (2003). Tax evasion, civic duty and the law abiding citizen. European Journal of Political Economy, 19(1), 83–102.
Parish, D. C. (1989). Relation of the pre-employment drug testing result to employment status. Journal of General Internal Medicine, 4(1), 44–47.
Petry, N. M. (2001). Delay discounting of money and alcohol in actively using alcoholics, currently abstinent alcoholics, and controls. Psychopharmacology (berl), 154(3), 243–250.
Porcano, T. M. (1988). Correlates of tax evasion. Journal of Economic Psychology, 9(1), 47–67.
Potnuru, P. P., Patel, S. D., Birnbach, D. J., Epstein, R. H., & Dudaryk, R. (2021). Effects of state law limiting postoperative opioid prescription in patients after cesarean delivery. Anesthesia & Analgesia, 132(3), 752–760.
Rajgopal, S., & White, R. M. (2019). Cheating when in the hole: The case of New York city Taxis. Accounting, Organizations and Society, 79, 101070.
Reneman, L., Lavalaye, J., Schmand, B., de Wolff, F. A., van den Brink, W., den Heeten, G. J., & Booij, J. (2001). Cortical Serotonin transporter density and verbal memory in individuals who stopped using 3,4-methylenedioxymethamphetamine (MDMA or “Ecstasy”): Preliminary findings. Archives of General Psychiatry, 58(10), 901–906.
Rijsenbilt, A., & Commandeur, H. (2013). Narcissus enters the courtroom: CEO narcissism and fraud. Journal of Business Ethics, 117(2), 413–429.
Ruedy, N. E., Moore, C., Gino, F., & Schweitzer, M. E. (2013). The cheater’s high: The unexpected affective benefits of unethical behavior. Journal of Personality and Social Psychology, 105(4), 531–548.
Substance Abuse and Mental Health Services Administration (SAMHSA). (2020). Key substance use and mental health indicators in the United States: Results from the 2019 National Survey on Drug Use and Health (HHS Publication No. PEP20-07-01-001, NSDUH Series H-55). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. Retrieved from https://www.samhsa.gov/data/
Scanlon, W. F. (1991). Alcoholism and drug abuse in the workplace: Managing care and costs through employee assistance programs. ABC-CLIO
Schrand, C. M., & Zechman, S. L. C. (2012). Executive overconfidence and the slippery slope to financial misreporting. Journal of Accounting and Economics, 53(1–2), 311–329.
Schuerger, J. M., Tait, E., & Tavernelli, M. (1982). Temporal stability of personality by questionnaire. Journal of Personality and Social Psychology, 43(1), 176–182.
Seidman, J. S. (1939). Catching up with employee frauds. The Accounting Review, 14(4), 415–424.
Shu, L. L., & Gino, F. (2012). Sweeping dishonesty under the rug: How unethical actions lead to forgetting of moral rules. Journal of Personality and Social Psychology, 102(6), 1164–1177.
Silver, M., Hamilton, A., Biswas, A., & Warrick, N. (2016). A systematic review of physician retirement planning. Human Resources for Health, 14(1), 67.
Skipper, G., Fletcher, C., Rocha-Judd, R., & Brase, D. (2004). Tramadol abuse and dependence among physicians. JAMA, 292(15), 1815–1819.
Slemrod, J., Blumenthal, M., & Christian, C. (2001). Taxpayer response to an increased probability of audit: Evidence from a controlled experiment in Minnesota. Journal of Public Economics, 79(3), 455–483.
Sloan, F. A., Mergenhagen, P. M., Burfield, W. B., Bovbjerg, R. R., & Hassan, M. (1989). Medical malpractice experience of physicians: Predictable or haphazard? JAMA, 262(23), 3291–3297.
Smith, A. (1759). The Theory of Moral Sentiments. Millar, Kincaid, and Bell
Stock, J., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In D. W. K. Andrews (Ed.), Identification and inference for econometric models (pp. 80–108). Cambridge University Press.
Teninbaum, G. (2013). Reforming the national practitioner data bank to promote fair med-mal outcomes. William & Mary Policy Review, 5(1), 83–120.
Tomz, M., King, G., & Zeng, L. (2003). ReLogit: rare events logistic regression. Journal of Statistical Software, 8(2), 1–27.
Tuttle, B. (2013). Older financial professionals seek solace from stress—Survey. eFinancialCareers.
Warner, D. O., Berge, K., Sun, H., Harman, A., & Wang, T. (2020). Substance use disorder in physicians after completion of training in anesthesiology in the United States from 1977 to 2013. Anesthesiology, 133(2), 342–349.
Watson, G., Selfridge, N., & Wright, B. (2020). The opioid-impaired provider: A call for national guidance to maximize rehabilitation while protecting patient safety. Health Science Reports, 3(4), e193.
Waymire, G. B. (2014). Neuroscience and ultimate causation in accounting research. The Accounting Review, 89(6), 2011–2019.
Yenerall, J., & McPheeters, M. (2020). The effect of an opioid prescription days’ supply limit on patients receiving long-term opioid treatment. International Journal of Drug Policy, 77, 102662.
Zahra, S. A., Priem, R. L., & Rasheed, A. A. (2005). The antecedents and consequences of top management fraud. Journal of Management, 31(6), 803–828.
Zhang, H., Tallavajhala, S., Kapadia, S. N., Jeng, P. J., Shi, Y., Wen, H., & Bao, Y. (2020). State opioid limits and volume of opioid prescriptions received by medicaid patients. Medical Care, 58(12), 1111–1115.
Zwerling, C., Ryan, J., & Orav, E. J. (1990). The efficacy of preemployment drug screening for marijuana and cocaine in predicting employment outcome. JAMA, 264(20), 2639–2643.
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.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
Basis for action (NPDB Code)
NPDB code definition
Insurance fraud (Medicare and other Federal Gov. program)
Insurance fraud (Medicaid or other State Gov. program)
Insurance fraud (Non-government or private insurance)
Fraud in obtaining license or credentials
Misappropriation of patient property or other property
Failure to repay overpayment
Violation of federal or state tax code
Improper or abusive billing practices
Submitting false claims
Fraud, kickbacks and other prohibited activities
Felony conviction related to health care fraud
Conviction Re: fraud
Misrepresentation of credentials
Exploiting a patient for financial gain
Insurance fraud (medicare, medicaid or other insurance)
Filing false reports or falsifying records
Fraud, deceit or material omission in obtaining license or credentials
Alcohol and/or other substance abuse
Unable to practice safely by reason of alcohol or other substance abuse
Drug screening violation
Felony conviction Re: controlled substance violation
Conviction Re: controlled substances
Violation of drug-free workplace act
Narcotics violation or other violation of drug statutes
Non-sexual dual relationship or boundary violation
Conviction Re: patient abuse or neglect
Conviction Re: obstruction of an investigation
Criminal conviction, not classified
Violation of by-laws, protocols or guidelines
Malpractice, negligence, and medical mistakes
Inadequate or improper infection control practices
Practicing without a license
Practicing beyond scope of practice
Allowing unlicensed person to practice
Lack of appropriately qualified professionals
Incompetence, malpractice, negligence (legacy format reports)
Failure to provide Med Resnble or Nec. items/services
Furnishing unnecessary or substandard items/services
Incompetence, malpractice, negligence (legacy format reports)
Failure to provide Med Resnble or Nec. items/services
Furnishing unnecessary or substandard items/services
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.
Rights and permissions
About this article
Cite this article
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
- Substance abuse
- Delay discounting