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Making It Count

from Lars Syll

Modern econometrics is fundamentally based on assuming — usually without any explicit justification — that we can gain causal knowledge by considering independent variables that may have an impact on the variation of a dependent variable. This is however, far from self-evident. Often the fundamental causes are constant forces that are not amenable to the kind of analysis econometrics supplies us with. As Stanley Lieberson has it in his modern classic Making It Count:

LiebersonOne can always say whether, in a given empirical context, a given variable or theory accounts for more variation than another. But it is almost certain that the variation observed is not universal over time and place. Hence the use of such a criterion first requires a conclusion about the variation over time and place in the dependent variable. If such an analysis is not forthcoming, the theoretical conclusion is undermined by the absence of information …

Moreover, it is questionable whether one can draw much of a conclusion about causal forces from simple analysis of the observed variation … To wit, it is vital that one have an understanding, or at least a working hypothesis, about what is causing the event per se; variation in the magnitude of the event will not provide the answer to that question.

Trygve Haavelmo was making a somewhat similar point back in 1941, when criticizing the treatmeant of the interest variable in Tinbergen’s regression analyses. The regression coefficient of the interest rate variable being zero was according to Haavelmo not sufficient for inferring that “variations in the rate of interest play only a minor role, or no role at all, in the changes in investment activity.” Interest rates may very well play a decisive indirect role by influencing other causally effective variables. And:

the rate of interest may not have varied much during the statistical testing period, and for this reason the rate of interest would not “explain” very much of the variation in net profit (and thereby the variation in investment) which has actually taken place during this period. But one cannot conclude that the rate of influence would be inefficient as an autonomous regulator, which is, after all, the important point.

Causality in economics — and other social sciences — can never solely be a question of statistical inference. Causality entails more than predictability, and to really in depth explain social phenomena requires theory. Analysis of variation — the foundation of all econometrics — can never in itself reveal how these variations are brought about. First when we are able to tie actions, processes or structures to the statistical relations detected, can we say that we are getting at relevant explanations of causation. Too much in love with axiomatic-deductive modeling, neoclassical economists especially tend to forget that accounting for causation — how causes bring about their effects — demands deep subject-matter knowledge and acquaintance with the intricate fabrics and contexts. As already Keynes argued in his A Treatise on Probability, statistics and econometrics should not primarily be seen as means of inferring causality from observational data, but rather as description of patterns of associations and correlations that we may use as suggestions of possible causal relations.

  1. patrick newman
    November 18, 2016 at 10:56 am

    Why oh why wont businessmen behave like economists!

  2. November 18, 2016 at 12:56 pm

    The advantage of using word logic over statistical abstraction is like using the windshield instead of the rear view mirror while driving forward.

    • November 18, 2016 at 5:38 pm

      I like it! But not putting so many guide-word stickers on the windscreen that one’s eyes can’t see past them. Better a head-up display with its computer logic projecting just the few guide-lines relevant at any given time? That is, using both sides of one’s brain?

      How refreshing for Lars to take us back to years when there were still people around who did that.

  3. November 18, 2016 at 2:46 pm

    “The advantage of using word logic over statistical abstraction is…” mis-direction, or confusion.Quote SODDY…” ” Heads I win, tails you lose “…”…(U)sually by some such lying phrase as ” Every
    loan makes a deposit “….Genuine and Fictitious Loans.
    For a loan, if it is a genuine loan, does not make a deposit, because
    what the borrower gets the lender gives up, and
    there is no increase in the quantity of money, but
    only an alteration in the identity of the individual
    owners of it. But if the lender gives up nothing
    at all what the borrower receives is a new issue
    of money and the quantity is proportionately
    increased.
    So elaborately has the real nature of
    this ridiculous proceeding been surrounded with
    confusion by some of the cleverest and most
    skilful advocates the world has ever known, that
    it still is something of a mystery to ordinary
    people, who hold their heads and confess they
    are ” unable to understand finance “.
    It is not
    intended
    that they should.
    But if, instead of
    trying to puzzle it out along the lines of ” what
    you get for money “, these people will reverse
    the procedure, as in this book, and do so on the
    of ” what you give up for it “, the trick is clear
    enough. ” (“The Role Of Money” FREE DOWNLOAD-https://archive.org/details/roleofmoney032861mbp )

  4. antireifier
    November 19, 2016 at 2:13 pm

    https://www.ineteconomics.org/events/finance-society-secular-stagnation. Comments about economics claiming to be a science and an interesting definition of money — namely “…perpetual non-interest bearing debt.”

  5. November 20, 2016 at 6:06 am

    “Causality entails more than predictability, and to really in depth explain social phenomena requires theory.”

    If we look a bit more closely, however, this statement isn’t just incomplete and wrong, it is foolish. Causality connects a cause with an effect in the most common, person of the street understanding. The problem is connecting all three: cause, effect, theory. When we look around how do we figure out what’s a cause. Smoking causes cancer, they say. But many other things might cause cancer. And many other theories might apply to explaining cancer. Next, what is an effect? Cancer the effect of smoking. But smoking has other possible effects and it’s difficult to observe smoking acting as cause. Lastly, there is theory. Theory supposedly connects all the pieces, cause with effect and effect with cause. Conclusion: it is not possible to connect any set of observations of data called cause and data called effect through only one theory. There are an unlimited number of possible theories to explain such observations and their connections. The scientist must use her/his judgement to choose one of these. And since it not possible to say with certainty that all the observations are equivalent, again we are left with scientist’s judgement to make choices about equivalence. Seems the clear-cut connections among theory, cause, and effect are not clear cut at all.

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