## Quandary of the ZimStats, and the abracadabra economics which is colloquially referred to as “blending”

**By Colls Ndlovu**

Some of the arguments presented in this article are to be found in various chapters in my book entitled, “The Age of Keynesian Economics” (pictured).

The past few days saw the Zimstats doing something unprecedented, that is, restating the July 2020 inflation rate by revising it from about 840% down to 485%, doing so after some abracadabra economics which it colloquially referred to as “blending”.

Needless to say this so-called blending is nothing less than voodoo economics, the whole exercise lacked transparency and therefore open to misperceptions of abuse by the authorities.

Instead of doing the “blending”, the mechanics of what ZimStats should have done is very simple because it could have easily calculated the US$ denominated inflation and the Zim$ denominated inflation separately without comingling the two via this so-called blending.

But that is a debate for another day.

What is at issue today is the tendency by government agencies to abuse the mathematicisation of economics.

From the very emergence of mathematics within the field of economics, there have always been vigorous contestations around this issue. The period up to the 1970s, was characterised by reliance on what was called the Philip’s curve.

The Philip’s curve presumably depicted the supposed trade-off between inflation and unemployment, that is to say, when inflation increased, unemployment was shown to be decreasing, and vice-versa.

This was the prevailing orthodoxy for many years up to the early 1970s, when something strange began to happen: there emerged a simultaneous occurrence of inflation together with unemployment at elevated levels contrary to the long held conviction that the two variables had an inverse relationship.

Encroachment of mathematics into economics

As stated above, the conviction was that when inflation increases, unemployment falls and vice-versa. But over the years, the hitherto unquestionable picture shown by the Philip’s curve was reduced into disrepute.

All the mathematical elegance that had come with it went up in the proverbial smoke. The world learnt the hard way that inflation does not create employment and therefore could not be expected to lower unemployment.

If anything, it was learnt that inflation destroyed jobs and ultimately the country’s economy.

The hyperinflation of countries like Zimbabwe and the accompanying joblessness cemented the ill-fated Philip’s curve.

The encroachment of mathematics into economics led to an offshoot of economics called econometrics.

The legendary British economist, Lord John Maynard Keynes, formerly of Cambridge University, criticised econometrics arguing that, in the wrong hands, could ruin the economics profession.

Econometricians themselves dismissed Keynes criticism of econometrics as being misconceived. They pointed out, for example, that Keynes own work in “The General Theory of Employment, Interest and Money” benefitted a lot from econometrics.

Moreover, they argued that the views of Keynes and monetary economist, Milton Friedman, formerly of the University of Chicago, sometimes unwittingly converged on econometrics.

Notwithstanding the fact that econometricians have made impressive contributions to the advancement of economics through exceptional statistical models, some doubts still remain as to the overall value add to the economics profession. It would seem that the erroneous belief within the econometrics profession in the 1960s that inflation could reduce unemployment caused later day econometricians to question the whole edifice upon which the econometric profession was resting.

Technical knowledge

Legendary Harvard University’s Prof Joseph Schumpeter also criticised econometrics. Historians of economic thought have also weighed in with the verdict of history on econometrics. Their verdict is that it was lamentable for Keynes to contemptuously dismiss econometrics the way he did. Some have even argued that Keynes only did so due to ill-health in the last decade of his life prior to his death in 1946.

Others attributed Keynes’ criticism to his “technical rustiness and tactical predilections” with Prof Samuelson formerly of the Massachusetts Institute of Technology, arguing that Keynes “did not really have the necessary technical knowledge to understand what he was criticising”.

Some critics of Keynes have suggested that he was sympathetic to the econometrics profession and its contributions to the broader economics profession citing his use of the word “alchemy” as implying that it was a gesture of encouragement.

Supporters of Keynes still argued that had he lived longer, Keynes might have become a computer-based econometrics modeller. Don Patinkin, argued that Keynes criticism was still applicable to modern contemporary econometrics.

Contrary to popular but often misguided belief, Keynes appreciated the qualities of “a real trained statistician”.

Statistics is the backbone of econometrics

Statistics is the backbone of econometrics. Keynes’ well known academic disputes was with Karl Pearson “over the appropriate statistical methods of studying the effects of parental alcoholism on offspring”. This dispute according to Harrod, illustrated “the pitfalls of statistical inference”.

It has been argued that one of the key themes of Keynes’ career was studying, “the logical basis of statistical modes of argument” and the search for “the principles of sound induction,” which might constitute “a good scientific argument.”

In his formative years, Keynes wanted to specialize in logic and statistical theory. His book, A Treatise on Probability, bears testimony to his effort at covering, “the whole field of empirical thinking… It would be difficult to find a parallel for a comprehensive attack of this kind since the days of Aristotle”, remarked Harrod in 1951.

At Cambridge University, the title of his final section of his fellowship dissertation read “The Foundations of Statistical Inference,” which concluded with an “Outline of a Constructive Theory.”

In his “Treatise on Money”, Keynes remarked that statistics are of fundamental importance to suggest theories, to test them and make them convincing and to eliminate impressionism.

In “The General Theory”, he called a statistical examination of the relationship between changes in money wages and changes in real wages.

Mathematical machinery

At some stage Keynes remarked that “all his best ideas came from messing around with figures and seeing what they must mean.”

Notwithstanding his empathy for mathematics and statistics, Keynes generally opposed the use of mathematical methods in both statistics and economics. In order to draw any inference, he preferred experimental to statistical methods, arguing that certain methods of statistical analysis led to invalid conclusions.

He also viewed with suspicion the investigations of samples rather than complete populations in order to reach a particular conclusion.

According to Leeson, Keynes was suspicious of all numbers derived by formulae from non-experimental data, “especially when the original data had been suppressed”.

On this, Keynes argued robustly, to “enable the reader to formulate some sort of independent judgment”. The real character of the evidence must be displayed, not just the products derived from applying “mathematical machinery”. In his opinion, “graphs were highly suitable for publicity or propaganda purposes”.

Keynes’ contempt for technical graphs was unequivocal, warning for example of the “horrid examples of the evils of the graphical method unsupported by tables of figures, both for accurate understanding and particularly to facilitate the use of the same material by other people it is essential that graphs should not be published by themselves but only when supported by the tables which will lead up to them.

It would be an exceedingly good rule to forbid in any scientific periodical the publication of graphs unsupported by tables”.

The suspicion of quackery

According to Keynes biographer, Skidelsky, in 1923, Keynes helped launch the London and Cambridge Economic Series barometric survey of business conditions, and he repeatedly campaigned for improved economic statistics, “not to be used for regression analysis, but to offer intuitive insights into reality”.

Keynes argued that statistics, whose sole aim was “to satisfy the troublesome and often trifling curiosity of the academic statistician” could not be relied upon, adding that, “the suspicion of quackery has not yet disappeared from statistics. there is still about it for scientists a smack of astrology, of alchemy”.

In the 1930s, Keynes vigorously opposed non-quantitative economics.

According to Robinson: “It was Keynes’ natural inclination to approach any problem from the angle of measurement of phenomena. . . . but just as he was suspicious of ideas that could not be verified by measurement, so he was skeptical also of the adventures of the statisticians into the world of correlations built on insufficient logical foundations”.

Keynes contended that “elaborate calculations tend to confuse, even though they might also impress, all readers outside a very restricted class.”

Undeterred, Keynes argued vigorously, “it is difficult to know how properly to characterise the work of a statistician who uses in controversy a table of this description with complete dogmatic assurance and without making plain to the reader the principles of its construction”.

Furthermore, he identified the problem of what he termed the “multicollinearity” between variables which exposes econometricians to “the extraordinarily difficult and deceptive complications of spurious correlations.”

To Keynes, statistical coefficients, “becomes like those puzzles for children where you write down your age, multiply, add this and that, subtract something else, and eventually end up with the number of the Beast in Revelation”.

The question of whether correlation coefficients were stable across a subseries needed to be investigated given the emphasis on the mathematical complications.

Futility and fruitlessness of reducing human conduct

Retorted Keynes, “how are these coefficients arrived at? . . . one gets the impression that it is a process of fitting a linear equation through trial and error”.

It would seem that Keynes’ vehement opposition to econometrics was for the same reason that he opposed classical economists and their methods: “Progress in economics consists almost entirely in a progressive improvement in the choice of models.

The grave fault of the later classical school, has been to overwork a too simple or out-of-date model.

But it is the essence of a model that one does not fill in real values for the variable functions. To do so would make it useless as a model”.

Economics according to Keynes was not precise: “It was too easy to distort it and create for it the impression of exactitude that it really lacked, and by subjecting it to mathematical manipulation also to wind up with a seriously distorted picture of the economy” .

Keynes argued that economics is not about mathematical proof or a legal document. Economics is all about “thought, and therefore equations are not a substitute for it, but at most a guide or embodiment”.

He complained of the futility and fruitlessness of reducing human conduct to a set of equations, and of using “the collection of facts for the prediction of future frequencies and associations” which could by no means correlate or parallel.

What seemed to concern Keynes most was that the “statisticians’ occupational disease should not become the economists’ occupation”. Moreover, he argued that “to convert a model into a quantitative formula is to destroy its usefulness as an instrument of thought.

Multicolinearity

By filling in figures, which one can be quite sure will not apply next time, so far from increasing the value of his instrument, he has destroyed it”.

To Keynes, most of the claims derived from statistical inference were inadmissible from the perspective of logic and were evidence of “mathematical charlatanry”.

As has already been highlighted under multicolinearity, multiple correlation analysis was deemed by Keynes to be “too elaborate and adds little or nothing”, adding that this type of analysis requires that a complete set of relevant variables is included and is accurately measurable.

Keynes contended that there would be a “serious misrepresentation of the causal process, if in fact some significant factors have been omitted”. The material to which economic models are applied is, “in too many respects, not homogenous through time”.

Accordingly, one can conclude that econometrics is inappropriate in cases where “political, social and physiological factors, including such things as government policy, the progress of invention, and the state of expectations may be significant. In particular, it is inapplicable to the problems of the Business Cycle”.

The recent revision and restatement of Zimbabwe’s inflation rate must be seen against the backdrop of the foregoing debate about the intrusion of mathematics into the field of economics and its distorting consequences.

*Colls Ndlovu, a currency expert, is an award-winning economist and central banker, and is the inventor of the NCX Currency Index. He can be contacted on collsndlovu@gmail.com*

History made as the Ndlovu Currency Confidence Index (NCX) on the Zim$ debuts at 38%

History made as the Ndlovu Currency Confidence Index (NCX) on the Zim$ debuts at 38%

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