The Opioid Crisis, Employee Health Capital, and Corporate Information Production

AbstractThis study examines the effect of employee health on corporate information production. Utilizing exposure to opioid abuse as the proxy for employee health, we find that firms headquartered in counties of high opioid prescription rate produce significantly less accurate management earnings forecasts. This result is robust to controlling for other dimensions of human capital and to utilizing the effective anti-opioid legislation across states as a plausibly exogenous variation that limits the prescriptions of opioid. The negative effect of opioid abuse is stronger for firms facing higher forecast difficulty, and is mitigated for firms with easier access to opioid treatment, for firms with superior employee welfare policies, and for firms with a corporate social responsibility (CSR) committee. We also show that managers delay earnings announcements and reduce forecast precision amidst high local opioid activity. Finally, we show that investors react less strongly to news in forecasts issued by firms located in high opioid areas, consistent with their recognition of the potential adverse effect of opioid abuse on information production within the firm.Keywords: Opioid crisisManagement forecastsHealth capitalJEL codes: G02J01J30M51 AcknowledgementsWe thank Chen Chen, Neil Fargher, Jeroen Koenraadt (discussant), Gary Monroe, Rencheng Wang, Yangxin Yu and participants in EAA 2022 Annual Conference for their useful comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data Availability StatementThe data used in this study are available from the public sources identified in the paper.Supplemental Data and Research MaterialsSupplemental data for this article can be accessed on the Taylor & Francis website, doi:10.1080/09638180.2023.2272622.Appendix OA: Discussion of alternate explanations for our main findings.Table A1: Tests of Alternative ExplanationsTable A2: Tests of the Market Reaction to Management ForecastsNotes1 See: https://www2.deloitte.com/us/en/insights/topics/leadership/ceo-role-employee-health-wellness.html.2 The widespread opioid abuse has reached crisis levels in the US, with more than 10 million Americans estimated to misuse opioids annually, resulting in $50 billion in healthcare and criminal justice costs, as well as $32 billion in productivity losses (Florence et al., Citation2021).3 For example, American Addiction Centers (2022) suggests that opioids (e.g., OxyContin, Vicodin, Fentanyl, Xanax and sleeping pills like Ambien) have made significant marks to white-collar professionals as these employees often resort to these pills for stress relief, relaxation, or to get some sleep at night. See: https://americanaddictioncenters.org/workforce-addiction/white-collar.4 In untabulated results, we further explore whether the attributes of the management team and/or workforce attenuate or exacerbate the negative effect of opioid abuse on managerial forecast accuracy. We find that the effect of opioid abuse is more pronounced when the workforce is more ethnically diverse and younger. This finding aligns with prior literature, which suggests that pharmaceutical companies tend to target counties with younger and more diverse populations in their marketing of opioids (Hadland et al., Citation2019).5 See: https://www.cdc.gov/opioids/basics/epidemic.html/.6 In 2019, Johnson & Johnson was ordered to pay $572 million for its deceptive conducts that contributes to opioid crisis. Purdue Pharma offered $10 billion to $12 billion to settle more than 2,000 opioid lawsuits against the company for starting and sustaining the opioid crisis. See detailed discussion via https://time.com/5662827/johnson-opioid-crisis-lawsuits/ and https://www.nbcnews.com/news/us-news/purdue-pharma-offers-10-12-billion-settle-opioid-claims-n1046526.7 Deborah Hersman, the president and CEO of the National Safety Council, said ‘Employers must understand that the most dangerously misused drug today may be sitting in employees’ medicine cabinets. Even when they are taken as prescribed, prescription drugs and opioids can impair workers’ performance and create hazards on the job’. Consistent with this, Greg Zoeller, the Indiana Attorney General, said ‘prescription opioid abuse can cause impairment, injury and may lead employees to bad choices, such as theft or embezzlement from their employers’. See detailed discussion via https://www.nsc.org/in-the-newsroom/prescription-drug-abuse-impacts-80-of-indiana-workplaces-says-national-safety-council-poll.8 CDC data on opioid prescriptions is available from 2006 onwards.9 10K header data was downloaded from https://sraf.nd.edu/data/augmented-10-x-header-data.10 We include firm fixed effects to mitigate the concern that our results can be attributed to unobservable time-invariant factors (e.g., firms’ long-run disclosure policies and inherent forecast difficulty). By doing so, we exploit the variation within firms over time. Our results are robust to controlling the following fixed effects: 1) firm and year-quarter fixed effects, 2) state, firm and year-quarter fixed effects, 3) county and year-quarter fixed effects, and 4) firm and industry × year-quarter fixed effects.11 To calculate this effect on accuracy, we apply the following equation: (25.484 [standard deviation of Opioid in Table 1] × (−0.010) [coeff. on Opioid in column 2 of Table 2] ÷ 3.099 [standard deviation of Accuracy in Table 1].12 Our results may suffer from sample selection bias if local rates of opioid abuse can affect the likelihood of a firm issuing management earnings forecasts. However, we find in Section 8.1 that opioid abuse does not affect the likelihood of management forecast issuance. Our results are also robust to using a Heckman selection model or using only bundled annual forecasts.13 The timing of state-level PDMPs is acquired from http://pdaps.org/.14 The location of opioid treatment facilities is obtained from: https://dpt2.samhsa.gov/treatment/directory.aspx.15 The database is accessible at https://www.slcg.com.16 We follow prior literature to standardize opioid strength using the morphine milligram equivalent (MME) value for each pill (e.g., oxycodone is 50% stronger than hydrocodone, so it has an MME multiplier of 1.5).17 We first regress Accuracy on Opioid, absent of controls and fixed effects, and obtain the R2 of baseline effect. We next regress Accuracy on Opioid, control variables and fixed effects as Eq. (1) and obtain the R2 of controlled effect. Computing δ* requires setting a value for R2max. Following prior studies (Oster, Citation2019), we set R2max as 1.3×R2 of controlled effect.