From: thompson@atlas.socsci.umn.edu (T. Scott Thompson)
Subject: Re: (Re: Becker won the Nobel Prize?)
Reply-To: thompson@atlas.socsci.umn.edu
Organization: Economics Department, University of Minnesota
Date: Fri, 16 Oct 1992 16:46:12 GMT

cave@cis.rand.org (Jonathan Cave) writes:

>In article <thompson.719167540@daphne.socsci.umn.edu> thompson@atlas.socsci.umn.edu writes:
>|
>|The only problem with this is that human capital is difficult to
>|define in an empirically meaningful way.  Sure you can measure
>|schooling, test scores, training, etc., but translating this into
>|meaningful measures of "skill" or "knowledge" is a fuzzy science at
>|best.

>Measures of physical capital aren't all that good, either.  The same
>semi-bald (OK, fuzzy) shortcuts work for both: efficiency units.

What do you mean by "work" here?  Efficiency units only "solve" the
measurement problem if you are willing to specify some kind of
restrictions on how observable inputs to human capital formation
(education, training, etc.) translate into actual changes in
productivity.  It is precisely the fact that we don't know what these
restrictions should be that motivated my original comment.

>|Human capital theory has great explanatory power only because it
>|introduces lots of important, but unobservable, variables into
>|economic theory.  That is, it gives models lots of new degrees of
>|freedom for fitting the data by allowing any lack of fit to be made up
>|by unobserved "human capital endowments."
>|
>Explanatory power is not (should not be?) simply a matter of R squared or
>Mallow's CP, etc.  An explanation is something that makes sense by itself.
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This is a very subjective criteria.  What "makes sense" to one person
may look like nonsense to someone else.  On the other hand, I agree
that simple "fit" statistics like R-squared are not to be considered
very authoritative.

>|Human capital theory therefore _reduces_ the predictive content of
>|economic models, while at the same time allowing better retrospective
>|fits to the data.

>Only for models that remain agnostic as to changes in unobserveables, and
>limited to quantitative predictions.  Even good econometric models have to
>invoke some kind of unobserveable.  How about Bayesian priors?  How about
>the expanding field of model uncertainty?

Exactly my point.  If you introduce unobservables (like human capital
endowments) then in order to make any kinds of predictions at all you
must impose some kinds of unverifiable restrictions on the model
(stochastic structure for unobservables, Bayesian prior (i.e.
subjective) restrictions on the model's parameters, etc.

Don't get me wrong.  _Any_ predictive exercise involves a high degree
of subjective faith about certain aspects of the model.  I am simply
claiming that when there are more unobservables we must take more
things on faith in order to generate a prediction.  This does not
necessarily lead to worse predictions than we get from models that
simply ignore the unobserved variables by assuming that they aren't
there or aren't important.

I did not mean to imply that models without human capital variables
outperform models with them.  I did mean to imply that the phenomena
of human capital tends to make models more flexible while imposing few
(if any) testable restrictions directly.
--
T. Scott Thompson              email:  thompson@atlas.socsci.umn.edu
Department of Economics        phone:  (612) 625-0119
University of Minnesota        fax:    (612) 624-0209
Newsgroups: sci.econ