What is neoclassical economics – a rejoinder to Noahpinion

14 June, 2013 at 11:07 | Posted in Economics | 16 Comments

neoclassical-economics-with-related-tags-and-termsNoah Smith – on his blog Noahpinion – writes that when yours truly and other heterodox economists criticize neoclassical economics

the term “neoclassical” gets slung around quite a lot, usually as a perjorative (sic!) … The idea is that “neoclassical” econ is the dominant paradigm, and that the “heterodox” schools are competing paradigms that lost out, and were, to use Kuhn’s terminology, “simply read out of the profession…and subsequently ignored.”

He then goes on to define neoclassical economics with the help of Wikipedia:

“Neoclassical economics is a term variously used for approaches to economics focusing on the determination of prices, outputs, and income distributions in markets through supply and demand, often mediated through a hypothesized maximization of utility by income-constrained individuals and of profits by cost-constrained firms employing available information and factors of production, in accordance with rational choice theory.”

OK, makes sense. Assumption of individual rationality, utility maximization, and supply/demand. One or more of things terms probably describes most of mainstream economics theory.

The basic problem with this definition of neoclassical economics – basically arguing that  the differentia specifica of neoclassical economics is its use of demand and supply, utility maximization and rational choice – is that it doesn’t get things quite right. As we all know, there is an endless list of mainstream models that more or less distance themselves from one or the other of these characteristics. So the heart of neoclassical economic theory lies elsewhere.

The essence of neoclassical economic theory is its exclusive use of a deductivist Euclidean methodology. A methodology  that is more or less imposed as constituting economics, and, usually, without a smack of argument.

The theories and models that neoclassical economists construct describe imaginary worlds using a combination of formal sign systems such as mathematics and ordinary language. The descriptions made are extremely thin and to a large degree disconnected to the specific contexts of the targeted system than one (usually) wants to (partially) represent. This is not by chance. These closed formalistic-mathematical theories and models are constructed for the purpose of being able to deliver purportedly rigorous deductions that may somehow by be exportable to the target system. By analyzing a few causal factors in their “laboratories” they hope they can perform “thought experiments” and observe how these factors operate on their own and without impediments or confounders.

Unfortunately, this is not so. The reason for this is that economic causes never act in a socio-economic vacuum. Causes have to be set in a contextual structure to be able to operate. This structure has to take some form or other, but instead of incorporating structures that are true to the target system, the settings made in economic models are rather based on formalistic mathematical tractability. In the models they appear as unrealistic assumptions, usually playing a decisive role in getting the deductive machinery deliver “precise” and “rigorous” results. This, of course, makes exporting to real world target systems problematic, since these models – as part of a deductivist covering-law tradition in economics – are thought to deliver general and far-reaching conclusions that are externally valid. But how can we be sure the lessons learned in these theories and models have external validity, when based on highly specific unrealistic assumptions? As a rule, the more specific and concrete the structures, the less generalizable the results. Admitting that we in principle can move from (partial) falsehoods in theories and models to truth in real world target systems does not take us very far, unless a thorough explication of the relation between theory, model and the real world target system is made. If models assume representative actors, rational expectations, market clearing and equilibrium, and we know that real people and markets cannot be expected to obey these assumptions, the warrants for supposing that conclusions or hypothesis of causally relevant mechanisms or regularities can be bridged, are obviously non-justifiable. To have a deductive warrant for things happening in a closed model is no guarantee for them being preserved when applied to an open real world target system.

Henry Louis Mencken once wrote that “there is always an easy solution to every human problem – neat, plausible and wrong.” And neoclassical economics has indeed been wrong. Its main result, so far, has been to demonstrate the futility of trying to build a satisfactory bridge between formalistic-axiomatic deductivist models and real world target systems. Assuming, for example, perfect knowledge, instant market clearing and approximating aggregate behaviour with unrealistically heroic assumptions of representative actors, just will not do. The assumptions made, surreptitiously eliminate the very phenomena we want to study: uncertainty, disequilibrium, structural instability and problems of aggregation and coordination between different individuals and groups.

The punch line is that most of the problems that neoclassical economics is wrestling with, issues from its attempts at formalistic modeling per se of social phenomena. Reducing microeconomics to refinements of hyper-rational Bayesian deductivist models is not a viable way forward. It will only sentence to irrelevance the most interesting real world economic problems. And as someone has so wisely remarked, murder is unfortunately the only way to reduce biology to chemistry – reducing macroeconomics to Walrasian general equilibrium microeconomics basically means committing the same crime.

If scientific progress in economics – as Robert Lucas and other latter days neoclassical economists seem to think – lies in our ability to tell “better and better stories” without considering the realm of imagination and ideas a retreat from real world target systems reality, one would of course think our economics journal being filled with articles supporting the stories with empirical evidence. However, contrary to Noah Smith, I would argue that the journals still show a striking and embarrassing paucity of empirical studies that (try to) substantiate these theoretical claims. Equally amazing is how little one has to say about the relationship between the model and real world target systems. It is as though thinking explicit discussion, argumentation and justification on the subject not required. Neoclassical economic theory is obviously navigating in dire straits.

The latest financial-economic crisis has definitely shown the shortcomings of neoclassical economic theory. What went wrong, according to Paul Krugman, was basically that economists “mistook beauty, clad in impressive-looking mathematics, for truth.” This is certainly true as far as it goes. But it doesn’t go deep enough. Mathematics is just a means towards the goal – modeling the economy as a closed deductivist system.

If the ultimate criteria of success of a deductivist system is to what extent it predicts and coheres with (parts of) reality, modern neoclassical economics seems to be a hopeless misallocation of scientific resources. To focus scientific endeavours on proving things in models, is a gross misapprehension of what an economic theory ought to be about. Deductivist models and methods disconnected from reality are not relevant to predict, explain or understand real world economic target systems. These systems do not conform to the restricted closed-system structure the neoclassical modeling strategy presupposes.

Neoclassical economic theory still today consists mainly in investigating economic models. It has since long given up on the real world and contents itself with proving things about thought up worlds. Empirical evidence only plays a minor role in neoclassical economic theory, where models largely function as substitutes for empirical evidence.

What is wrong with neoclassical economics is not that it employs models per se, but that it employs poor models. They are poor because they do not bridge to the real world target system in which we live. But “facts kick”, and hopefully humbled by the manifest failure of its theoretical pretences, the one-sided, almost religious, insistence on mathematical deductivist modeling as the only scientific activity worthy of pursuing in economics will give way to methodological pluralism based on ontological considerations rather than formalistic tractability.

What stops economists exploring new ideas

13 June, 2013 at 17:10 | Posted in Economics | 1 Comment

There are plenty of economists who will happily admit the limits of their discipline, and be nominally open to the idea of other theories. However, I find that when pushed on this, they reveal that they simply cannot think any other way than roughly along the lines of neoclassical economics. My hypothesis is that this is because economist’s approach has a ‘neat and tidy’ feel to it: people are ‘well-behaved’; markets tend to clear, people are, on average, right about things, and so forth. Therefore, economist’s immediate reaction to criticisms is “if not our approach, then what? It would be modelling anarchy!” …
 
msg-new-way-of-thinking
 
The economist’s mentality extends up to the highest echelons of economics modelling, and culminates in the ‘DSGE or die’ approach, described well on Noah Smith’s blog by Roger Farmer:

“If one takes the more normal use of disequilibrium to mean agents trading at non-Walrasian prices, … I do not think we should revisit that agenda. Just as in classical and new-Keynesian models where there is a unique equilibrium, the concept of disequilibrium in multiple equilibrium models is an irrelevant distraction.”

When questioned about his approach, Farmer would probably suggest that if we do not assume markets tend to clear, and that agents are, on average, correct, then what exactly do we assume? A harsh evaluation would be to suggest this is really an argument from personal incredulity. There is simply no need to assume markets tend to clear to build a theory – John Maynard Keynes showed us as much in The General Theory, a book economists seem to have a hard time understanding precisely because it doesn’t fit their approach. Furthermore, the physical sciences have shown us that systems can be chaotic but model-able, and even follow recognisable paths …

Ultimately, the only thing stopping economists exploring new ideas is economists. There is a wide breadth of non-equilibrium, non-market clearing and non-rational modelling going on. Economists have a stock of reasons that these are wrong: the Lucas Critique, Milton Friedman’s methodology, the ‘as if‘ argument and so forth. Yet they often fail to listen to the counterarguments to these points and simply use them to defer to their preferred approach. If economists really want to broaden the scope of the discipline rather than merely tweaking it around the edges, they must be prepared to understand how alternative approaches work, and why they can be valid. Otherwise they will continue to give the impression – right or wrong – of ivory tower intellectuals, completely out of touch with reality and closed off from new ideas.

Unlearning Economics

Chebyshev’s Inequality Theorem (student stuff)

12 June, 2013 at 16:21 | Posted in Statistics & Econometrics | Leave a comment

Chebyshev’s Inequality Theorem – named after Russian mathematician Pafnuty Chebyshev – states that for a population (or sample) at most 1/kof the distribution’s values can be more than k standard deviations away from the mean. The beauty of the theorem is that although we may not know the exact distribution of the data – e.g. if it’s normally distributed  - we may still say with certitude (since the theorem holds universally)  that there are bounds on probabilities!

Markov’s Inequality

12 June, 2013 at 12:04 | Posted in Statistics & Econometrics | Leave a comment

One of the most beautiful results of probability theory is Markov’s inequality (after the Russian mathematician Andrei Markov (1856-1922)):

If X is a non-negative stochastic variable (X ≥ 0) with a finite expectation value E(X), then for every a > 0

P{X ≥ a} ≤ E(X)/a

If, e.g., the production of cars in a factory during a week is assumed to be a stochastic variable with an expectation value (mean) of 50 units, we can – based on nothing else but the inequality – conclude that the probability that the production for a week would be greater than 100 units can not exceed 50% [P(X≥100)≤(50/100)=0.5 = 50%]

I still feel a humble awe at this immensely powerful result. Without knowing anything else but an expected value (mean) of a probability distribution we can deduce upper limits for probabilities. The result hits me as equally suprising today as thirty years ago when I first run into it as a student of mathematical statistics.

[For a derivation of the inequality, see e.g. Sheldon Ross, Introduction to Probability and Statistics for Engineers and Scientists, Academic Press, 2009, p. 129]

A quick refresher on Cumulative Distribution Functions (student stuff)

11 June, 2013 at 09:44 | Posted in Statistics & Econometrics | Leave a comment

 

Unemployment benefits and speed limits

10 June, 2013 at 19:53 | Posted in Economics | Leave a comment

One way to think about this is to say that unemployment benefits may, perhaps, reduce the economy’s speed limit, if we think of speed as inversely related to unemployment. And this suggests an analogy.speed_limitImagine that you’re driving along a stretch of highway where the legal speed limit is 55 miles an hour. Unfortunately, however, you’re caught in a traffic jam, making an average of just 15 miles an hour. And the guy next to you says, “I blame those bureaucrats at the highway authority — if only they would raise the speed limit to 65, we’d be going 10 miles an hour faster.”

Dumb, right? Well, so is the claim that unemployment benefits are causing today’s high unemployment.

Paul Krugman

Fun with statistics

10 June, 2013 at 15:25 | Posted in Statistics & Econometrics | 1 Comment

Yours truly gave a PhD course in statistics for students in education and sports this semester. And between teaching them all about Chebyshev’s Theorem, Beta Distributions, Moment-Generating Functions and the Neyman-Pearson Lemma, I tried to remind them that statistics can actually also be fun …
 

Award the 2013 Nobel Peace Prize to whistleblower Bradley Manning

9 June, 2013 at 19:13 | Posted in Politics & Society | Leave a comment

If you witnessed war crimes, if you saw incredible things, awful things, things that belonged in the public domain and not in some server stored in a dark room in Washington, what would you do?
 

Austerity policies – total horseshit

9 June, 2013 at 18:36 | Posted in Economics | Leave a comment

 

Microfoundations – neither laws, nor true

8 June, 2013 at 17:27 | Posted in Economics | 2 Comments

Oxford professor Simon Wren-Lewis doesn’t agree with Paul Krugman’s statement that

the whole microfoundations crusade is based on one predictive success some 35 years ago; there have been no significant payoffs since.

Why does Wren-Lewis disagree? He writes:

I think the two most important microfoundation led innovations in macro have been intertemporal consumption and rational expectations. I have already talked about the former in an earlier post … [s]o let me focus on rational expectations …  [T]he adoption of rational expectations was not the result of some previous empirical failure. Instead it represented, as Lucas said, a consistency axiom …

I think macroeconomics today is much better than it was 40 years ago as a result of the microfoundations approach. I also argued in my previous post that a microfoundations purist position – that this is the only valid way to do macro – is a mistake. The interesting questions are in between. Can the microfoundations approach embrace all kinds of heterogeneity, or will such models lose their attractiveness in their complexity? Does sticking with simple, representative agent macro impart some kind of bias? Does a microfoundations approach discourage investigation of the more ‘difficult’ but more important issues? Might both these questions suggest a link between too simple a micro based view and a failure to understand what was going on before the financial crash? Are alternatives to microfoundations modelling methodologically coherent? Is empirical evidence ever going to be strong and clear enough to trump internal consistency? These are difficult and often quite subtle questions that any simplistic for and against microfoundations debate will just obscure.

On this argumentation I would like to add the following comments:

(1) The fact that Lucas introduced rational expectatuions as a consistency axiom is not really an argument to why we should accept it as an acceptable assumption in a theory or model purporting to explain real macroeconomic processes (see e. g. Robert Lucas, rational expectations, and the understanding of business cycles).

(2) “Now virtually any empirical claim in macro is contestable” Wren-Lewis writes. Yes, but so is virtually also any claim in micro (see e. g. When the model is the message – modern neoclassical economics).

(3) To the two questions “Can the microfoundations approach embrace all kinds of heterogeneity, or will such models lose their attractiveness in their complexity?” and “Does sticking with simple, representative agent macro impart some kind of bias?” I would unequivocally answer yes (I have given the reasons why e. g. in David Levine is totally wrong on the rational expectations hypothesis , so I will not repeat the argumentation here).

(4) “Are alternatives to microfoundations modelling methodologically coherent?” Well, I don’t know. But one thing I do  know, is that the kind of miocrofoundationalist macroeconomics that New Classical economists in the vein of Lucas and Sargent and the so called New Keynesian economists in the vein of Mankiw et consortes are pursuing, are not methodologically coherent (as I have argued e. g. in What is (wrong with) economic theory?) And that ought to be rather embarrassing for those ilks of macroeconomists to whom axiomatics and deductivity is the hallmark of science tout court.

So in the Wren-Lewis – Krugman discussion on microfoundations I think Krugman is closer to truth with his remark that

what we call “microfoundations” are not like physical laws. Heck, they’re not even true.

On the significance of significance tests – Andrew Gelman vs. Deborah Mayo

8 June, 2013 at 10:12 | Posted in Statistics & Econometrics | Leave a comment

Mayo says: 
June 4, 2013 at 9:46 pm 
Andrew: You seem to have undergone a gestalt switch from the Gelman of a short time ago–the one who embraced significance tests. 
http://www.rmm-journal.de/downloads/Article_Gelman.pdf

Andrew says: 
June 4, 2013 at 10:08 pm 
Mayo:
I believed, and still believe, in checking the fit of a model by comparing data to hypothetical replications. This is not the same as significance testing in which a p-value is used to decide whether to reject a model or whether to believe that a finding is true.

Mayo says: 
June 4, 2013 at 10:38 pm 
Gelman: I don’t know that significance tests are used to decide that a finding is true, and I’m surprised to see you endorsing/spreading the hackneyed and much lampooned view of significance tests, p-values, etc. despite so many of us trying to correct the record. And statistical hypothesis testing denies uncertainty? Where in the world do you get this? (I know it’s not because they don’t use posterior probabilities…)
But never mind, let me ask: when you check the fit of a model using p-value assessments, are you not inferring the adequacy/inadequacy of the model? Tell me what you are doing if not. I don’t particularly like calling it a decision, neither do many people, and I like viewing the output as “whether to believe” even less. But I don’t know what your output is supposed to be.

Andrew says: 
June 4, 2013 at 10:53 pm 
Mayo: 
1. I don’t think hypothesis testing inherently denies uncertainty. But I do think that it is used by many researchers as a way of avoiding uncertainty: it’s all too common for “significant” to be interpreted as “true” and “non-significant” to be interpreted as “zero.” Consider, for example, all the trash science we’ve been discussing on this blog recently, studies that may have some scientific content but which get ruined by their authors’ deterministic interpretations.
2. When I check the fit of a model, I’m assessing its adequacy for some purpose. This is not the same as looking for p< .05 or p<.01 in order to go around saying that some theory is now true.

Mayo says: 
June 4, 2013 at 11:04 pm 
Andrew: I fail to see how a deterministic interpretation could go hand in hand with error probabilities; and I never hear even the worst test abusers declare a theory is not true, give me A break…
So when you assess adequacy for a purpose, what does this mean? Adequate vs inadequate for a purpose is pretty dichotomous. Do you assess how adequate? I’m unclear as to where the uncertainty enters for you, because as I understand it is not in terms of a posterior probability.

Andrew says: 
June 4, 2013 at 11:18 pm 
Mayo:
Here’s a quote from a researcher, I posted it on the blog a few days ago: “Our results demonstrate that physically weak males are more reluctant than physically strong males to assert their self-interest…”
Here’s another quote: “Ovulation led single women to become more liberal, less religious, and more likely to vote for Barack Obama. In contrast, ovulation led married women to become more conservative, more religious, and more likely to vote for Mitt Romney.”
These are deterministic statements based on nothing more than p-values that happen to be statistically significant. Researchers make these sorts of statements all the time. It’s not your fault, I’m not saying you would do this, but it’s a serious problem.
Along similar lines, we’ll see claims that a treatment has an effect on men and not on women, when really what is happening is that p< .05 for the men in the study and p>.05 for the women.
In addition to brushing away uncertainty, people also seem to want to brush away uncertainty, thus talking about “the effect” as if it is a constant across all groups and all people. A recent example featured on this blog was a study primarily of male college students which was referred repeatedly (by its authors, not just by reporters and public relations people) as a study of “men” with no qualifications.
P.S. Bayesians do this too, indeed there’s a whole industry (which I hate) of Bayesian methods for getting the posterior probability that a null hypothesis is true. Bayesians use different methods but often have the misguided goal of other statisticians, to deny uncertainty and variation.

Mayo says: 
June 5, 2013 at 6:47 pm 
These moves from observed associations, and even correlations, to causal claims are poorly warranted, but these are classic fallacies that go beyond tests to reading all manner of “explanations” into the data. I find it very odd to view this as a denial of uncertainty by significance tests. Even if they got their statistics right, the link from stat to substantive causal claim would exist. I just find it odd to regard the statistical vs substantive and correlation vs cause fallacies, which every child knows, some kind of shortcoming with significance tests. Any method or no method can commit these fallacies, especially from observational studies. But when you berate the tests as somehow responsible, you misleadingly suggest that other methods are better, rather than worse. At least error statistical methods can identify the flaws at 3 levels (data, statistical inference, stat-> substantive causal claim) in a systematic way. We can spot the flaws a mile off…
I still don’t know where you want the uncertainty to show up; I’ve indicated how I do.

Andrew says: 
June 5, 2013 at 8:34 pm 
Mayo:
You write, “I still don’t know where you want the uncertainty to show up;” I want the uncertainty to show up in a posterior distribution for continuous parameters, as described in my books.

Mayo says: 
June 6, 2013 at 9:59 am 
Andrew (couldn’t post under your comment). You write, “I want the uncertainty to show up in a posterior distribution for continuous parameters”. Let’s see if I have this right. You would report the posterior probabilities that a model was adequate for a goal. Yes? Now you have also said you are a falsificationist. So is your falsification rule to move from a low enough posterior probability in the adequacy of a model, to the falsity of a claim that the model of is adequate (for the goal). And would high enough posterior in the adequacy of a model translate into something like, not being able to falsify its adequacy or perhaps, accepting it as adequate (the latter would not be falsificationist, but might be more sensible than the former). Or are you no longer falsificationist-leaning.

Andrew says: 
June 6, 2013 at 10:56 am 
Mayo: 
No, I would not “report the posterior probabilities that a model was adequate for a goal.” That makes no sense to me. I would report the posterior distribution of parameters and make probabilistic predictions within a model.

Mayo says: 
June 6, 2013 at 5:14 pm 
Andrew: Well if you’re going to falsify as a result, you need a rule from these posteriors to infer the predictions are met satisfactorily or not. Else there is no warrant for rejecting/improving the model. That’s the kind of thing significance tests can do. But specifically, with respect to the misleading interpretations of data that you were just listing, it isn’t obvious how they are avoided by you. The data may fit these hypotheses swimmingly. 
Anyhow, this is not the place to discuss this further. In signing off, I just want to record my objection to (mis)portraying statistical tests and other error statistical methods as flawed because of some blatant, age-old misuses or misleading language, like “demonstrate” (flaws that are at least detectable and self-correctable by these same methods, whereas they might remain hidden by other methods now in use). [Those examples should not even be regarded as seeking evidence but at best colorful and often pseudoscientific interpretations.] When the Higgs particle physicists found their 2 and 3 standard deviation effects were disappearing with new data—just to mention a recent example from my blog—they did not say the flaw was with the p-values! They tightened up their analyses and made them more demanding. They didn’t report posterior distributions for the properties of the Higgs, but they were able to make inferences about their values, and identify gaps for further analysis.
http://errorstatistics.com/2013/03/17/update-on-higgs-data-analysis-statistical-flukes-1/

Statistical Modeling, Causal Inference, and Social Science

For my own take on significance tests see here, here, here, and here.

A quick refresher on Probability Density Functions (student stuff)

8 June, 2013 at 09:31 | Posted in Statistics & Econometrics | Leave a comment

 

Elegy of the uprooting

7 June, 2013 at 20:00 | Posted in Varia | Leave a comment

 

Krugman – more Wicksell than Keynes

6 June, 2013 at 20:15 | Posted in Economics | 4 Comments

In a recent blogpost Paul Krugman comes back to his idea that it would be great if the Fed stimulated inflationary expectations so that investments would increase. I don’t have any problem with this idea per se, but I don’t think it’s of the stature that Krugman seems to think. But although I have written extensively on Knut Wicksell and consider him the greatest Swedish economist ever, I definitely - since Krugman portrays himself as “sorta-kinda Keynesian” - have to question his invocation of Knut Wicksell for his ideas on the “natural” rate of interest. Krugman writes (emphasis added):

Start with the very simplest view of how Fed policy affects the economy: the Fed sets short-term interest rates, and other things equal a lower rate leads to higher output; the “natural rate” of interest … is the rate at which output equals potential, that is, at which there are neither inflationary nor deflationary pressures …

What does this tell us? First of all, that there is nothing “artificial” or “unnatural” about low interest rates; they’re low because demand is low, and the Fed is responding appropriately. If anything, the “unnatural” situation is that rates are too high, because they’re constrained by the zero lower bound (rates can’t go below zero, except for some minor technical bobbles, because people can always just hold cash).

wicksell3Second, the Fed’s inability to get rates as low as they should be justifies a search for policies that can fill this policy gap. Fiscal stimulus is one such policy; unconventional monetary policies of various kinds are another. Actually, the natural policy — natural in a Wicksellian sense, and also the one that in terms of standard economics should produce the least distortion — would be a credible commitment to higher inflation.

Now consider what Keynes himself wrote in General Theory:

In my Treatise on Money I defined what purported to be a unique rate of interest, which I called the natural rate of interest¾namely, the rate of interest which, in the terminology of my Treatise, preserved equality between the rate of saving (as there defined) and the rate of investment. I believed this to be a development and clarification of Wicksell’s ‘natural rate of interest’, which was, according to him, the rate which would preserve the stability of some, not quite clearly specified, price-level.

I had, however, overlooked the fact that in any given society there is, on this definition, a different natural rate of interest for each hypothetical level of employment. And, similarly, for every rate of interest there is a level of employment for which that rate is the ‘natural’ rate, in the sense that the system will be in equilibrium with that rate of interest and that level of employment. Thus it was a mistake to speak of the natural rate of interest or to suggest that the above definition would yield a unique value for the rate of interest irrespective of the level of employment. I had not then understood that, in certain conditions, the system could be in equilibrium with less than full employment.

I am now no longer of the opinion that the [Wicksellian] concept of a ‘natural’ rate of interest, which previously seemed to me a most promising idea, has anything very useful or significant to contribute to our analysis. It is merely the rate of interest which will preserve the status quo; and, in general, we have no predominant interest in the status quo as such.

History repeats itself, first as tragedy, second as farce

6 June, 2013 at 18:51 | Posted in Economics, Politics & Society | 3 Comments

These are the days: I stopped reading ‘The economic consequences of the peace’ to read the IMF report on Greece. Did anything change (emphasis added)?

The IMF on Greece, 2013:

One way to make the debt outlook more sustainable would have been to attempt to restructure the debt from the beginning. However, PSI was not part of the original program. This was in contrast with the Fund program in Uruguay in 2002 and Jamaica in 2011 where PSI was announced upfront … Yet in Greece, on the eve of the program, the authorities dismissed debt restructuring as a “red herring” that was off the table for the Greek government and had not been proposed by the Fund … In fact, debt restructuring had been considered by the parties to the negotiations but had been ruled out by the euro area … Some Eurozone partners emphasized moral hazard arguments against restructuring. A rescue package for Greece that incorporated debt restructuring would likely have difficulty being approved, as would be necessary, by all the euro area parliaments … Nonetheless, many commentators considered debt restructuring to be inevitable. With debt restructuring off the table, Greece faced two alternatives: default immediately, or move ahead as if debt restructuring could be avoided. The latter strategy was adopted, but in the event, this only served to delay debt restructuring and allowed many private creditors to escape.

Keynes, ‘The economic consequences of the peace’, about the negotiations in 1919:

As soon as it was admitted that it was in fact impossible to make Germany pay the expenses of both sides, and that the unloading of their liabilities upon the enemy was not practicable, the position of the Ministers of Finance of France and Italy became untenable. Thus a scientific consideration of Germany’s capacity to pay was from the outset out of court. The expectations which the exigencies of politics had made it necessary to raise were so very remote from the truth that a slight distortion of figures was no use, and it was necessary to ignore the facts entirely. The resulting unveracity was fundamental. On a basis of so much falsehood it became impossible to erect any constructive financial policy which was workable. For this reason amongst others, a magnanimous financial policy was essential.

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