Mainstream monetary theory — neat, plausible, and utterly wrong

22 June, 2017 at 16:21 | Posted in Economics | 2 Comments

In modern times legal currencies are totally based on fiat. Currencies no longer have intrinsic value (as gold and silver). What gives them value is basically the legal status given to them by government and the simple fact that you have to pay your taxes with them. That also enables governments to run a kind of monopoly business where it never can run out of money. Hence spending becomes the prime mover and taxing and borrowing is degraded to following acts. If we have a depression, the solution, then, is not austerity. It is spending. Budget deficits are not the major problem, since fiat money means that governments can always make more of them.

Financing quantitative easing, fiscal expansion, and other similar operations, is made possible by simply crediting a bank account and thereby – by a single keystroke – actually creating money. One of the most important reasons why so many countries are still stuck in depression-like economic quagmires is that people in general – including most mainstream economists – simply don’t understand the workings of modern monetary systems. The result is totally and utterly wrong-headed austerity policies, emanating out of a groundless fear of creating inflation via central banks printing money, in a situation where we rather should fear deflation and inadequate effective demand.

The mainstream neoclassical textbook concept of money multiplier assumes that banks automatically expand the credit money supply to a multiple of their aggregate reserves.  If the required currency-deposit reserve ratio is 5%, the money supply should be about twenty times larger than the aggregate reserves of banks.  In this way the money multiplier concept assumes that the central bank controls the money supply by setting the required reserve ratio.

In his Macroeconomics – just to take an example – Greg Mankiw writes:

We can now see that the money supply is proportional to the monetary base. The factor of proportionality … is called the money multiplier … Each dollar of the monetary base produces m dollars of money. Because the monetary base has a multiplied effect on the money supply, the monetary base is called high-powered money.

The money multiplier concept is – as can be seen from the quote above – nothing but one big fallacy. This is not the way credit is created in a monetary economy. It’s nothing but a monetary myth that the monetary base can play such a decisive role in a modern credit-run economy with fiat money.

In the real world banks first extend credits and then look for reserves. So the money multiplier basically also gets the causation wrong. At a deep fundamental level the supply of money is endogenous.

garbageOne may rightly wonder why on earth this pet mainstream neoclassical fairy tale is still in the textbooks and taught to economics undergraduates. Giving the impression that banks exist simply to passively transfer savings into investment, it is such a gross misrepresentation of what goes on in the real world, that there is only one place for it — and that is in the …

Den blomstertid

22 June, 2017 at 16:03 | Posted in Varia | Leave a comment


The American carnage

22 June, 2017 at 13:38 | Posted in Economics | Leave a comment

President Trump, in his inaugural address and elsewhere, rightly says that over the decades since 1980 American household distributions of income and wealth became strikingly unequal. But if recent budget and legislative proposals from Trump and the House of Representatives come into effect, today’s distributional mess would become visibly worse.

I will sketch how the mess happened, then I will propose some ideas about how it might be cleaned up. I will show that even with lucky institutional changes and good policy, it would take several more decades to undo the “American carnage” that the president described …

trickleTrump and the Congress’s budget and legislative proposals could only work for his “struggling families” and “forgotten people” if they would generate strong trickle-down growth. Structural constraints on income distribution and wealth dynamics won’t let trickle-down happen. His slogan about “America First” is for the top one percent of income distribution – effectively a “capitalist” class – not for “workers” in the middle of the income distribution or the struggling, forgotten households further down.

I have outlined a feasible progressive alternative, which would generate broad-based progress. Progressive changes may not take hold. If not, and if Trump-style interventions materialize, the distributional mess and “American carnage” will only get worse.

Lance Taylor

Simpson’s paradox

21 June, 2017 at 08:29 | Posted in Statistics & Econometrics | Leave a comment

From a more theoretical perspective, Simpson’s paradox importantly shows that causality can never be reduced to a question of statistics or probabilities, unless you are — miraculously — able to keep constant all other factors that influence the probability of the outcome studied.

To understand causality we always have to relate it to a specific causal structure. Statistical correlations are never enough. No structure, no causality.

Simpson’s paradox is an interesting paradox in itself, but it can also highlight a deficiency in the traditional econometric approach towards causality. Say you have 1000 observations on men and an equal amount of  observations on women applying for admission to university studies, and that 70% of men are admitted, but only 30% of women. Running a logistic regression to find out the odds ratios (and probabilities) for men and women on admission, females seem to be in a less favourable position (‘discriminated’ against) compared to males (male odds are 2.33, female odds are 0.43, giving an odds ratio of 5.44). But once we find out that males and females apply to different departments we may well get a Simpson’s paradox result where males turn out to be ‘discriminated’ against (say 800 male apply for economics studies (680 admitted) and 200 for physics studies (20 admitted), and 100 female apply for economics studies (90 admitted) and 900 for physics studies (210 admitted) — giving odds ratios of 0.62 and 0.37).

Econometric patterns should never be seen as anything else than possible clues to follow. From a critical realist perspective it is obvious that behind observable data there are real structures and mechanisms operating, things that are  — if we really want to understand, explain and (possibly) predict things in the real world — more important to get hold of than to simply correlate and regress observable variables.

Math cannot establish the truth value of a fact. Never has. Never will.

Paul Romer

Logistic regression (student stuff)

21 June, 2017 at 08:25 | Posted in Statistics & Econometrics | Leave a comment


And in the video below (in Swedish) yours truly shows how to perform a logit regression using Gretl:

Ekonomi och ojämlikhet

20 June, 2017 at 14:22 | Posted in Economics | Leave a comment

chartFörra hösten arrangerade Malmö högskola ett samtal om ekonomi och ojämlikhet i dagens Sverige. Under Cecilia Nebels kompetenta ledning samtalade serietecknaren Sara Granér, professor Tapio Salonen och yours truly om vad de växande inkomst- och förmögenhetsklyftorna gör med vårt samhälle.

Ni som inte hade möjlighet vara där, kan följa samtalet här.

Do you want to get a Nobel prize? Eat chocolate and move to Chicago!

20 June, 2017 at 12:53 | Posted in Varia | 2 Comments


As we’ve noticed, again and again, correlation is not the same as causation …

If you want to get the prize in economics — and want to be on the sure side — yours truly would suggest you complement  your intake of chocolate with a move to Chicago.

Out of the 78 laureates that have been awarded “The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel,” 28 have been affiliated to The University of Chicago — that is 36%. The world is really a small place when it comes to economics …

Causality matters!

20 June, 2017 at 10:29 | Posted in Statistics & Econometrics | 1 Comment


Causality in social sciences — and economics — can never solely be a question of statistical inference. Causality entails more than predictability, and to really in depth explain social phenomena require 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.

5cd674ec7348d0620e102a79a71f0063Most facts have many different, possible, alternative explanations, but we want to find the best of all contrastive (since all real explanation takes place relative to a set of alternatives) explanations. So which is the best explanation? Many scientists, influenced by statistical reasoning, think that the likeliest explanation is the best explanation. But the likelihood of x is not in itself a strong argument for thinking it explains y. I would rather argue that what makes one explanation better than another are things like aiming for and finding powerful, deep, causal, features and mechanisms that we have warranted and justified reasons to believe in. Statistical — especially the variety based on a Bayesian epistemology — reasoning generally has no room for these kinds of explanatory considerations. The only thing that matters is the probabilistic relation between evidence and hypothesis. That is also one of the main reasons I find abduction — inference to the best explanation — a better description and account of what constitute actual scientific reasoning and inferences.

For more on these issues — see the chapter “Capturing causality in economics and the limits of statistical inference” in my On the use and misuse of theories and models in economics.

In the social sciences … regression is used to discover relationships or to disentangle cause and effect. However, investigators have only vague ideas as to the relevant variables and their causal order; functional forms are chosen on the basis of convenience or familiarity; serious problems of measurement are often encountered.

Regression may offer useful ways of summarizing the data and making predictions. Investigators may be able to use summaries and predictions to draw substantive conclusions. However, I see no cases in which regression equations, let alone the more complex methods, have succeeded as engines for discovering causal relationships.

David Freedman

Some statisticians and data scientists think that algorithmic formalisms somehow give them access to causality. That is, however, simply not true. Assuming ‘convenient’ things like faithfulness or stability is not to give proofs. It’s to assume what has to be proven. Deductive-axiomatic methods used in statistics do no produce evidence for causal inferences. The real casuality we are searching for is the one existing in the real-world around us. If their is no warranted connection between axiomatically derived theorems and the real-world, well, then we haven’t really obtained the causation we are looking for.

Hauptstadt Hamburg (personal)

19 June, 2017 at 15:31 | Posted in Varia | 1 Comment

Yours truly is heading (again) for Hamburg this summer, so of course I just had to buy this …


Leontief on the dismal state of economics

18 June, 2017 at 19:25 | Posted in Economics | 1 Comment

Much of current academic teaching and research has been criticized for its lack of relevance, that is, of immediate practical impact … I submit that the consistently indifferent performance in practical applications is in fact a symptom of a fundamental imbalance in the present state of our discipline. The weak and all too slowly growing empirical foundation clearly cannot support the proliferating superstructure of pure, or should I say, speculative economic theory …

leontif_nobel_fullUncritical enthusiasm for mathematical formulation tends often to conceal the ephemeral substantive content of the argument behind the formidable front of algebraic signs … In the presentation of a new model, attention nowadays is usually centered on a step-by-step derivation of its formal properties. But if the author — or at least the referee who recommended the manuscript for publication — is technically competent, such mathematical manipulations, however long and intricate, can even without further checking be accepted as correct. Nevertheless, they are usually spelled out at great length. By the time it comes to interpretation of the substantive conclusions, the assumptions on which the model has been based are easily forgotten. But it is precisely the empirical validity of these assumptions on which the usefulness of the entire exercise depends.

What is really needed, in most cases, is a very difficult and seldom very neat assessment and verification of these assumptions in terms of observed facts. Here mathematics cannot help and because of this, the interest and enthusiasm of the model builder suddenly begins to flag: “If you do not like my set of assumptions, give me another and I will gladly make you another model; have your pick.” …

But shouldn’t this harsh judgment be suspended in the face of the impressive volume of econometric work? The answer is decidedly no. This work can be in general characterized as an attempt to compensate for the glaring weakness of the data base available to us by the widest possible use of more and more sophisticated statistical techniques. Alongside the mounting pile of elaborate theoretical models we see a fast-growing stock of equally intricate statistical tools. These are intended to stretch to the limit the meager supply of facts … Like the economic models they are supposed to implement, the validity of these statistical tools depends itself on the acceptance of certain convenient assumptions pertaining to stochastic properties of the phenomena which the particular models are intended to explain; assumptions that can be seldom verified.

Wassily Leontief

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