BusinessDay 24 Aug 2018

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BUSINESS DAY Friday 24 August 2018
Harvard
Business
Review
ManagementDigest
A closer look at how the opioid epidemic affects employment
Janet Currie
The toll that the opioid
epidemic has
taken on the United
States is undeniable.
On average,
115 Americans die every day
from a drug overdose involving
an opioid, and even more
suffer the debilitating effects
of addiction. Despite state and
federal efforts to curb the crisis,
there is no sign that the epidemic
is letting up. Whichever
way the data is sliced, things
look bad and are getting worse.
Inevitably, the effects of this
crisis touch multiple aspects
of people’s lives: their families,
their communities and, of
course, their workplaces. One
narrative suggests that addiction
leads to job loss and lower
labor force participation. In
fact, the Organization for Economic
Cooperation and Development
recently stated that the
opioid epidemic is responsible
for recent declines in labor
force participation in the U.S.
The data, however, points
to a different reality. As the
epidemic continues to rage,
unemployment is at its lowest
level in decades. Furthermore,
the numbers suggest
that many people taking opioids
are actually employed:
Comprehensive prescription
data reveals that nearly 85% of
opioids prescribed for working
age people are paid for by private
health insurance, which
is overwhelmingly employer
provided. While not everyone
who uses opioids gets them directly
from a physician, the fact
is that many people who take
opioids either begin by using
or continue to use legally prescribed
medications that are
paid for by employer-provided
health insurance.
So what’s the actual connection
between prescription opioids
and the labor market?
To answer this question,
we analyzed data on all opioid
prescriptions filled at pharmacies
across the U.S. from 2006
to 2014. We aimed to identify
the causal effect of opioid prescriptions
on employment,
which is a difficult task for at
least two reasons.
First, the areas that have
been hardest hit by the opioid
epidemic are different from
areas that have seen less dramatic
rises in opioid abuse
for many reasons other than
employment opportunities.
For example, West Virginia
has higher rates of both opioid
abuse and unemployment
than California. While opioid
abuse and unemployment will
therefore be correlated when
comparing West Virginia to
California, this does not mean
that opioid use causes unemployment
or vice versa. The
two states are different for a
variety of reasons, such as demographic
composition and
educational attainment. Any of
these factors, or a combination
of them, could be to blame for
both high substance abuse and
poor labor market conditions.
Since areas are different, we
examined how employment
within a given area changes
as prescription rates fluctuate.
That is, instead of comparing
West Virginia to California
at a given point in time, we
compare West Virginia to West
Virginia and California to California
over time. This withinlocation
analysis shows that
changes in opioid prescriptions
per capita are not associated
with changes in employment.
Second, while this kind of
analysis controls for any timeinvariant
differences across locations,
another complication
remains. 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 correlation.
To identify what’s really
going on, we need to find
something that affects opioid
prescribing but has no independent
effect on employment.
To understand how this
might work, imagine a helicopter
dropping a bunch of opioid
prescriptions on a town. This
would increase opioid consumption,
but it wouldn’t have
any effect on employment except
through this channel. In
our analysis, we treat opioid
prescriptions to adults 65 and
older as this helicopter drop.
Doctors who have a high
propensity to prescribe opioids
to the elderly also on average
have a high propensity to
prescribe opioids to working
age people — and opioid prescriptions
to the elderly should
have no direct effect on the
employment of working age
people. Even though some elderly
people work, and opioids
may have some impact on their
employment, it is unlikely that
competition from the elderly
is a major factor affecting employment
of prime age adults.
We can therefore use fluctuations
in prescriptions to the
elderly to isolate changes in
opioid consumption that are
driven by fluctuations in local
prescribing practices rather
than by changes in local economic
conditions.
Our analysis demonstrates
that there is no simple causal
relationship between opioids
and employment. While
there is a positive, but small,
relationship between changes
in opioid prescribing and
changes in employment for females
in areas with low levels
of education, this relationship
disappears among women in
counties with higher levels of
education. Furthermore, regardless
of local education,
there is no systematic relationship
between changes in opioid
prescribing and changes in
employment rates for men.
Many observers have noted
that regions that experienced
the largest increases in opioid
use over the past decade, like
Appalachia, have had persistently
low employment. However,
it is important to keep in
mind that these areas had low
employment for decades before
the opioid epidemic. Our
results indicate that the correlation
between opioid use
and low employment in these
areas is largely a coincidence
2017 Harvard Business School Publishing Corp. Distributed by The New York Times Syndicate
and could be due to other factors,
such as the prescribing
practice styles of physicians in
those areas.
Similarly, some studies have
found that a high fraction of
people who are out of the labor
force take pain medication.
However, this does not prove
that taking pain medication
causes people to drop out of
the labor force. For example,
someone with chronic back
pain might drop out of the labor
force because of his condition
and then be prescribed
opioids. In this case, it would
be the patient’s back pain, not
his opioid use, that caused him
to leave the labor force.
In short, while the opioid
epidemic has caused widereaching
devastation, aggregate
employment appears not
to be one of its victims. Furthermore,
evidence suggests
that poor economic conditions
cannot be blamed for the crisis
itself. What this means is that
we must look at the opioid epidemic
for what it is: a self-inflicted
perfect storm that arose
from a combination of newly
available opioids, new attitudes
about the importance of
pain management, loose prescribing
practices and a lack
of professional accountability.
The solution to the problem
lies in addressing these root
causes.
Janet Currie is the Henry Putnam
professor of economics and
public affairs at Princeton University,
the co-director of Princeton’s
Center for Health and
Wellbeing and the co-director
of the Program on Families and
Children at the National Bureau
of Economic Research. Molly
Schnell is currently a postdoctoral
research fellow at the Stanford
Institute for Economic Policy
Research and will be joining
Northwestern University as an
assistant professor of economics
in July 2019.
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