In case my question was unclear, here are some more specifics.
I am trying to estimate something like the following:
qreg lnwage pwl labcon pwllabcon
where pwl=1 when prevailing wage law is in effect and 0 when it is not
labcon=1 if worker is a construction laborer, 0 if not
pwllabcon = pwl*labcon, so that it equals 1 if pwl is in effect and workers
is construction laborer, 0 otherwise
sample is restricted to laborers (across industries)
because I am looking at one state, I have only 30 observations on workers
for whom pwlabcon=1. I assume that this sample size is too small to estimate
anything but the median regression; but what guidelines are there for the
necessary cell size if you want to estimate qregs for the 10th or 90th
quantile?
Thanks again,
Jeannette
----- Original Message -----
From: "Jeannette Wicks-Lim" <[email protected]>
To: <[email protected]>
Sent: Monday, February 07, 2005 9:56 AM
Subject: Sample size and QREG
I have a very basic statistical question about the use of quantiles to
tease out policy impacts. I am attempting to use quantile regressions
(QREG) to detect the impact of state-level prevailing wage law repeals (the
elimination of wage floors for construction workers in publicly funded
projects) on very specific groups of workers at different points in their
wage distribution -- defined by state and
occupation. I have just over 30 observations who I code as "treated"
(treatment is not directly observed -- I am using the CPS, and in fact, I
can only assume that a subset of these 30 observations are actually
treated).
Is there any rule of thumb about when your sample is too small to reliably
estimate percentiles? Are there any guidelines similar to the general rule
that 30 observations is (more or less)
sufficient to estimate a mean? Any articles that anyone can point me
toward?
Thanks so much,
Jeannette Wicks-Lim
Department of Economics
University of Massachusetts, Amherst
(413) 577-0820
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