> Chris, could you summarize the alleged deficiencies of the Bell Curve?
> -fabio
Many others have critiqued their methods, their interpretation of
the psychometric literature, and their analysis of their own
original results. You can find lengthy criticism in:
Kincheloe et al (1996) Measured Lies: the Bell Curve Examined.
Devlin et al (1997) Intelligence and Success: Is it All in the Genes?
Scientists Respond to the Bell Curve.
Here's my take on some aspects: almost all of the original results are
based on logit models that look like
y* = constant + b(SES) + c(AFQT) + d(age) + extreme value noise.
There are rarely other covariates, where there they aren't exactly
exhaustive. SES is simply a weighted sum of father's and mother's income,
family income (of the respondent's parents), and an index of
parents' occupational status. The weights are more or less ad hoc, so
including SES is the same as including all these covariates, but then
adding three ad hoc linear restrictions.
Now, the outcomes y* include: unemployment, levels of educational
attainment, poverty, marital status, illegitimate births, welfare
dependency, low birth weight children, criminal activities, and so on.
Consider any one of these outcomes, say, "the subject was in the top
decile on an index of self-reported crime." Suppose anyone on this list
took NLS-Y data, constructed an indicator for that condition, and typed:
logit criminal age mothersed fathersed faminc occind afqt
into an econometric package (remembering income and occupation refer to
the family where the respondent grew up, not to the respondent). We then
arbitrarily add three restrictions to get
logit criminal age ses afqt.
We then interpret the coefficient on AFQT as the causal effect of higher
cognitive ability on propensity to be a criminal, all else equal, write up
our result, and send it off to a journal.
Of course, it barely stops moving across the editor's desk before being
popped back in the mail, rejected. Where to start documenting the
problems with this interpretation of the regression above? The
respondent's own income, gender, occupation, marital status, health, and
so on have been excluded. Since all of these outcomes are related to
the distibution of intelligence, the coefficient on AFQT reflects all
these effects, not the marginal impact of intelligence. It is highly
doubtful SES is controlling for background adequately, and we haven't
even controlled for education! Education! It is true that the authors
stratify by coarse educational groupings, but that's nowhere near good
enough. And remember that, as my little Monte Carlo showed, even a little
endogeneity causes big problems in this context, even if they'd gone to
the trouble of properly trying to hold all else equal.
So, take a whole bunch of more or less uninterpretable logit regressions,
make some lousy conclusions from them, and write a book: that's the bulk
of the Bell Curve. the other part concerns how race factors into all this,
and I'm not even going to go there.
Individually, none of Herrnstein and Murray's results would pass muster
as an undergraduate term paper in economics, much less a study in a
refereed journal. And if you sum junk, you just get aggregate junk.
Chris Auld (403)220-4098
Economics, University of Calgary <mailto:[EMAIL PROTECTED]>
Calgary, Alberta, Canada <URL:http://jerry.ss.ucalgary.ca/>
