On average, 115 Americans die every day from a drug overdose involving an opioid. This includes drugs such as prescription pain relievers, synthetics like fentanyl and illegal drugs such as heroin. About 80 percent of people who use heroin first misused prescription opioids. The abuse of these drugs has been a serious national crisis for decades. It not only affects the individuals’ physical health and the nation’s social health, but it also affects the economic welfare of the country, particularly on employment.
There is disagreement among experts as to what is the effect on the economy of the opioid crisis. Some believe that a major effect of the drug epidemic is job loss and lower labor-force participation. A cursory glance of the data suggests this. In fact, among prime-age, white men who are out of the labor force, 50 percent are taking prescription opioids daily.
Others believe that the effect on the job market due to the opioid crisis is not so big; the unemployment rate is at an all-time low, and yet the epidemic is more vicious than ever. According to statistics, a good number of people taking opioids are actually employed and functioning. And, according to the Harvard Business Review, “nearly 85 percent of opioids prescribed for working-age people are paid for by private health insurance, which is overwhelmingly employer provided.”
Difficulties in studying the issue
The Harvard Business Review (HBR) has analyzed data on prescriptions filled across the U.S. from 2006 to 2014. The study includes gender, age group, residential ZIP code and payer (public or private) for the prescription. The main goal of HBR’s study was to go beyond correlation and find “the causal effect of opioid prescriptions on employment.” To do so is difficult for two reasons.
First, those areas hit hardest by the opioid crisis are different from those areas that have been hit less hard for reasons other than job opportunities. For instance, in a comparison between West Virginia and California, West Virginia has much higher rates of unemployment and opioid addiction. This might lead to correlations between the two when compared to the same factors in California. But there are many other differences between the two states — for instance, educational attainment and demographic composition.
These factors also could be blamed for high unemployment or high substance abuse (or both). One way to study the issue is to look at prescription rate fluctuations within areas as opposed to between areas. Comparing West Virginia to West Virginia instead of West Virginia to California, for instance, shows that prescriptions per capita “are not associated with changes in employment.” That is, increases in opioid prescriptions do not necessarily reduce employment.
Second, although HBR’s study controls for differences across locations by studying within areas and not between areas, explaining the relationship between opioid addiction and employment is more complicated.
“Let’s say, for example, that Charleston, West Virginia, unveils a new public transportation system that safely and affordably connects the greater metropolitan area. This public transportation system allows those who were previously isolated to connect with employment opportunities, thereby increasing employment. It also reduces traffic accidents since fewer people opt to drive, thereby reducing opioids prescribed for post-accident pain. In this case, we would find that opioid use and employment are correlated within West Virginia over time, although this relationship is still not causal: there’s really a third factor — the opening of the new public transportation system — that is behind the two.”
What’s really going on?
To figure out what is going on, a factor needs to be identified with zero direct effect on employment, but with an effect on opioid prescribing. For instance, HBR offers this analogy:
“…imagine a helicopter drop of opioid prescriptions on a town. This drop will increase opioid consumption, but it will not have any effect on employment except through this channel. Measuring how employment changed as a result of this helicopter drop would, therefore, tell us how increasing opioid consumption causally affects employment.”
HBR decided to treat opioid prescriptions to the elderly as this helicopter drop. This is because they found that those doctors who treated people 65 and older with opioids also tended to do the same with people of working age. “And opioid prescriptions to the elderly should have no direct impact on the employment of working age people.” HBR determined that by identifying prescriptions to the elderly, they could isolate changes in opioid consumption that “are driven by fluctuations in local prescribing practices rather than by changes in local economic conditions.” This “helicopter drop” methodology is known as identifying “instrumental variables.”
What did HBR find?
According to HBR, there is no “simple, causal” relationship between opioids and employment. Regardless of local education, “There is no systematic relationship between changes in opioid prescribing and changes in employment rates.”
Although many have noted that places with high levels of opioid abuse, like Appalachia, have had consistently low levels of employment, it is important to keep in mind that “those areas had low employment for decades prior to the opioid epidemic.” The study shows that the correlation between unemployment and opioid addiction and abuse is primarily a coincidence.
While the opioid epidemic has caused a widespread catastrophe throughout the United States, employment has not been one of its victims: “Evidence suggests that poor economic conditions cannot be blamed for the crisis itself.” The issue is mostly a self-contained storm, arising from the creation and accessibility of a wide variety of opioids, new attitudes about the importance of pain management, loose prescribing practices and a lack of professional accountability. The solution lies in addressing these causes and not necessarily scapegoating economic factors.
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