Opioid Use and Employment: A Complicated Relationship

Opioid use can be traced back at least as far as the end of the 3rd millennium B.C., with notable crises worldwide in both the 19th and 20th centuries. However, today’s epidemic is “the worst drug addiction epidemic in [U.S.] history,” accounting for the deaths of 72,000 Americans in 2017 (an unprecedented rate, even higher than deaths due to AIDS at the peak of that epidemic in 1995). This represents an increase of more than 650 percent from 1990 to 2016, such that deaths from drug overdoses now account for more deaths than car accidents and gun homicides combined. Policymakers, understandably, have been attempting to find solutions, with numerous investigations, reports, and even a bipartisan agreement that is currently working its way through Congress.

Given this context, Janet Currie, Jonas Yin and Molly Schnell’s recent NBER paper sought to answer the question in its title: “U.S. Employment and Opioids: Is There a Connection?”

This study showed that 50 percent of prime working age white men who were out of the labor force reported “chronic pain and daily use of opioid pain medications.” Prompted by this finding, the authors asked two key questions: 1) Does a lack of employment opportunities cause people to “turn to opioids?” and 2) Does increased prescription of opioids cause job loss by creating addiction in workers? To answer these questions, quarterly, county-level data were analyzed to measure prescriptions of opioids, linked employment data, and population.

The researchers’ unique estimate models allowed them to address both directions of causality between employment and opioids. To answer their first question, they created a model using opioid prescriptions per capita as the dependent variable and lagged employment-to-population ratios as the independent variable. To answer their second question, they created a model with employment-to-population ratios as the dependent variable and lagged opioid prescriptions as the independent variable. The first model used the composition of county employment by industry to account for national industry-level employment fluctuations, and the second used opioid prescriptions for the elderly (non-working age) to account for local variation in prescribing behavior.

The results of the first model were “ambiguous,” showing evidence of a large positive effect of employment on opioid prescriptions, which remained large and positive only for older women in counties with education below the median. At the same time, evidence emerged that higher employment-to-population ratios reduce opioid prescription per capita among young workers in counties with education above the median. The researchers suggested an explanation for these findings, proposing that older women in counties with higher employment were better able to access medical care as a result of employer-provided health insurance. Meanwhile, younger workers with higher education levels may be less likely to turn to opioids in “act[s] of despair” when they have more employment opportunities. Alternatively, this subset of workers may be more able to be selective about employment opportunities, finding jobs that allow them to avoid pain and injury.

The results of the second model surprised researchers by showing evidence that higher opioid prescription rates significantly increased the employment-to-population ratios, though after instrumentation the relationship remained significant only among women. Again, the researchers proposed an explanation for this surprise: opioid prescriptions may help some female workers stay in the labor force. The authors concluded that “the relationship between opioid prescribing and employment is considerably murkier than simple narratives would suggest” and argued that effective policy proposals in response should account for the employment of persons addicted to opioids.

There are several limitations to this study. For practical purposes, the researchers combined counties with under 100,000 people, despite the rural-urban divide displayed in opioid use patterns, and did not analyze the data by race, despite opioid use patterns disproportionately affecting white people. These shortcomings may hamper the study’s ability to draw meaningful and accurate conclusions. Additionally, as noted by the authors, it was assumed that people filled prescriptions within the same county they lived and were presumed to work, and that the majority of people with opioid use problems had prescriptions for the opioids they took. Of those who misused pain medication within the past year, current data suggest that only 37.5 percent received the medication from their own health care provider. Most damningly, while opioid deaths continue to rise, the number of opioid prescriptions has been in decline since 2011.

This study is informative regarding the 37.5 percent of people for whom the most recent source of misused opioids was their health care provider. But given that synthetic opioids such as fentanyl recently surpassed prescription opioids as the opioids most responsible for death, it is hard to argue that this 37.5 percent should remain the sole focus for researchers and policymakers moving forward.

Article Source: Currie, Janet, Jonas Y. Jin, and Molly Schnell. 2018. “U.S. Employment and Opioids: Is There a Connection?” National Bureau of Economic Research. Working Paper No. 24440.

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