I don't know how the number is estimated, but in your two examples the
number is being used in two different ways, and that's an important
difference. In the case of the Nall report (which I have not read), the
item in question is a trend or cluster (or lack thereof). The necessary
key assumption about GA hours is that there be consistancy across the
data sample being used. Systematic error in the number is not anywhere
near as important, since the trends would still show (or not).
Systematic error would tend to cancel out, while random error would not.
(Indeed, random error is what causes the illusion of clusters).

In the case of subsidies, the =actual= number is important. We are not
looking at trends, but rather, whether a certain value is (or is not)
greater than zero. To that end, random error would tend to cancel out,
and systematic error would propagate.

True that sampling errors would tend to cancel out in the trend analysis
that Nall uses if the estimating method is statistically valid (truly
random sample, sufficient sample size, etc) and that the BTS number is a
point in time estimate of the subsidy. Meaning that there is standard
deviation around the subsidy per hour flown -- the BTS study might have
provided a range for the subsidy.

But I find it interesting that a key assumption underlying both studies is
questioned in one case, but accepted in the other.