>>> Part 2 of 4...
Two-and-a-half years later the Navy had discharged forty-three percent
of those who had tested positive and only nineteen percent of those
who had tested negative during the same recruitment period. Sounds
like former potheads make lousy soldiers, right? Walsh even suggested
to me that the lingering effects of pre-service marijuana use may have
been responsible.  "We do know that it stays in the system for a long
time," he noted.
But hold on. Look at the difference between the positives and the
negatives. The positives were about twice as likely to be black and to
lack a high school diploma. Why not blame poor education or cultural
factors correlated with race for their high discharge rate? (In fact,
civil rights groups have complained that the Navy uses testing to keep
blacks out of the service.)
Actually, there is an even simpler explanation. One of the conditions
for being admitted to the Navy after testing positive for marijuana was
agreeing to submit to rigorous "surveillance" thereafter, including
unusually frequent drug tests (all naval personnel undergo three such
tests a year). A third of the positives were discharged because they
failed another drug test.
There are two problems here. One is that these sailors had no
documented loss of productivity and exhibited no misbehavior except
for drug use. So the natural conclusion isn't that people who have
used drugs are more likely to be deficient workers (the only
conclusion that would justify workplace testing), but rather that
people who have used drugs are more likely than others to use drugs
in the future. But even this conclusion is undermined by the second
problem with the study: these sailors, facing more frequent testing
than sailors in the control group, stood a better chance of getting
caught using drugs.
If one discounts the sailors discharged for failing subsequent drug
tests the difference in the discharge rates between the positives and
the negatives shrinks from twenty-four to ten percent. Other forms of
extra surveillance could easily account for that difference. Obviously,
the more closely you watch someone, the more likely you are to see him
misbehaving, especially if you think he is a troublemaker to begin
with.
Another study cited by Walsh, conducted by the Utah Power and Light
Company, makes the Navy experiment look like a paragon of scientific
rigor.  The Utah study compared the work history of employees who
tested positive for drugs with a control group of employees whose ages
and jobs were similar.  The data showed a "significant difference
between drug users and non-users in terms of being involved in
accidents, being absent from work, and overutilization of health
benefits," Walsh told me.
When one reads the study two flaws quickly stand out: there were only
twelve positives in all (eleven for marijuana and one for cocaine), an
absurdly small sample, and the control group was never tested for
drugs.  The study's conclusion could be rejected on these grounds
alone.
But there is an even bigger problem. Eight of the twelve "drug abusers"
(to use the Utah researchers' term) were tested because they were in
accidents, and some were injured and needed time off to recuperate. Of
the four remaining positives, two were tested for other
performance-related problems and two because they had enrolled in a
substance-abuse program. High absenteeism almost invariably precedes--
and precipitates--both performance-related testing and submission to a
substance-abuse program. Moreover, all employees who undergo a test are
suspended until the results come back, which usually takes three or
four days.
Incredibly, the Utah researchers included in their calculations the
accidents and absenteeism directly associated with the testing of the
twelve subjects. By this logic, you could link any trait to accidents
and absenteeism. Round up employees who have been in an accident or
have been absent a lot, test them for, say, type 0 blood, and send them
home for a few more days of absenteeism. Then compare the accident and
absenteeism rates within the type 0 group with those for a "control"
group with no particular history of accidents or absenteeism -- and
whose blood type, in keeping with the Utah methodology, wouldn't even
be tested; it would just be assumed not to be type 0. Surprise,
surprise: type Os had more accidents and missed work more often than
people whose blood wasn't tested. Better get rid of everyone with type
0 blood.
And what about the "overutilization of health benefits" that Walsh had 
mentioned? He apparently misspoke. In the introduction to the NIDA 
monograph he calls the health benefits data "inconclusive." In fact, the 
positives consumed almost fifty percent less in health benefits than the 
control group. If the positives had used fifty percent more, would Walsh 
have found that "inconclusive"?
The other utility study cited by Walsh, which was done at the Georgia 
Power, Company, also focused on employees tested "for cause." But the 
Georgia researchers used a different control group: the 116 people who 
came up positive were compared with 713 who passed the test. This 
comparison, ostensibly fairer than that in the Utah study, found that the 
positives missed about five more days of work per year than the negatives.
But even the methodology that yielded this modest finding is flawed. The 
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