Chris Auld wrote:
>
> Bryan Caplan wrote:
>
> > Most of these wind up being dependent variables at some point in their
> > book.
>
> I'm not sure why that helps their case at all -- it's as if they've
> produced a whole bunch of reduced form equations, treating age,
> and, probably inappropriately, SES and AFQT as the only exogenous
> regressors. Take any one of the outcomes they consider and the
> relevant related discussion. Cast it as a study on that particular
> outcome. Would it be accepted by any economics journal? I don't
> think so -- so why should I think that if we put a couple of dozen
> of these together, we arrive at something compelling?
I think so. Anyone can get one marginally convincing result. But
getting hundreds shows something. There is even a formal test using
this intuition - the p-lambda test. Eight results (out of 10)
significant the 20% level, for example, could almost never arise from
pure chance.
> > variable. I think there are good practical reasons not to do this, and
> > there is a wealth of research that uses simplified indices in place of
> > the kitchen sink (e.g. "Democracy" indices, "Rule of Law" indices, "Bank
> > Failure" indices, etc.)
>
> Sure, but here there was no reason not to simply put the four variables
> in each regression, rather then the ad hoc index.
There's as much or as little reason to use an index in TBC as anywhere.
Should they have put in each test sub-scale separately, too?
>
> > My memory is not too good here - I read a few pieces by Heckman on this,
> > but nothing that I remember reaching results that were "dramatically"
> > different.
>
> You don't take it as problematic for M/H's conclusions that Heckman
> found there was no way to seperate the effects of intelligence from
> education?
I don't think it's possible for them to show there's "no way" to do it.
They could certainly point out data limitations, and offer their
judgment that these are insuperable. But I wouldn't call that judgment
a "finding."
> That he and coathors showed the returns to intelligence
> vary markedly across subpopulations?
If there's "no way" to do it for the whole population, how did they
manage? :-) Seriously, I'd expect that you could re-do almost anyone's
results this way. In each case, you would learn more, but unless the
whole-sample results drastically reversed I don't see why it's so
interesting.
> > What you call an "endogeneity problem" doesn't fit the usual textbook
> > description. Normally correlation among independent variables isn't a
> > problem, though it complicates the interpretation if changing one
> > variable almost always changes the other.
>
> I am referring to correlation between the error term and regressors as
> "endogeneity," which is of course the usual textbook defintion.
OK.
> Correlation amongst the independent variables is actually a problem (it
> reduces precision of the estimates), it's just not a problem that causes
> inconsistency.
OK.
> > > That said, I'm not sure education, at least, is particularly
> > > well measured in most datasets, as we generally ignore quality measures.
> >
> > Well-measured compared to what?
>
> Lots of things (to take some obvious examples: gender, age, nationality,
> region of residence).
OK, if that's your benchmark of quality. It doesn't leave much.
> > > And recall M/H "solve" the colinearity problem they have between education
> > > and IQ by *dropping* education. Some solution!
> >
> > Though it may appall you, many textbooks recommend it.
>
> Many textbooks recommend dropping a regressor, then interpreting the
> coefficients on the remaining regressors _as if_ the dropped regressor
> was still in the equation? That's clearly not true. What are these
> "many textbooks," out of curiosity (I can't recall ever reading anyone
> recommending that one "solve" colinearity problems by just tossing
> out regressors)? And surely they note that such an exclusion affects how
> one should interpret the coefficients on the remaining regressors, no?
I'm sure they put it more circumspectly, but that's what it amounts to.
If you did a regression with both "what you said your education is" and
"what your brother said your education is" and the SEs on both exploded,
what would any econometrics prof tell you? That "there's no way to
decide" which matters, and we have to be agnostic? That's the purist
answer, but I think most practioners would tell you to drop the second
measure and call the results from the new specification the "return to
education."
> > In spite of all
> > of his reservations about TBC, Bill Dickens felt pretty comfortable with
> > their omission.
>
> Perhaps Bill can explain why?
Alas, we are bereft of his wisdom here because of his other commitments.
--
Prof. Bryan Caplan [EMAIL PROTECTED]
http://www.gmu.edu/departments/economics/bcaplan
"[W]hen we attempt to prove by direct argument, what is really
self-evident, the reasoning will always be inconclusive; for it
will either take for granted the thing to be proved, or something
not more evident; and so, instead of giving strength to the
conclusion, will rather tempt those to doubt of it, who never
did so before."
-- Thomas Reid, _Essays on the Active Powers of the Human Mind_
