What does concern me about my discipline, however, is that its current core — by which I mainly mean the so-called dynamic stochastic general equilibrium approach — has become so mesmerized with its own internal logic that it has begun to confuse the precision it has achieved about its own world with the precision that it has about the real one …
The dynamic stochastic general equilibrium strategy is so attractive, and even plain addictive, because it allows one to generate impulse responses that can be fully described in terms of seemingly scientific statements. The model is an irresistible snake-charmer … So we are left with the tension between a type of answer to which we aspire but that has limited connection with reality (the core) and more sensible but incomplete answers (the periphery) …
My point is that by some strange herding process the core of macroeconomics seems to transform things that may have been useful modeling short-cuts into a part of a new and artificial “reality,” and now suddenly everyone uses the same language, which in the next iteration gets confused with, and eventually replaces, reality.
Caballero’s article emphasises that the future is inherently uncertain, particularly when it comes to forecasting and implementing economic policies. No amount of statistical analysis, econometrics, decision theory or game theory can fully overcome this fundamental reality.
Donald Trump keeps telling us that in his make-believe world the United States has the world’s hottest economy. But that’s not what the data from the real world say.
China’s economy was already one-third larger than the US economy when Trump took office. However, the gap has increased substantially over the last year and three quarters. China’s economy is growing at roughly a 4.5 percent annual rate, while the US economy has grown at roughly a 2.3 percent annual rate. (These data are taken from the I.M.F. World Economic Outlook Data Portal and include projections for the second half of 2026.)
As a result, since Trump took office, China’s economy (including Hong Kong) has grown by more than $3.43 trillion, while the US economy has grown by $1.24 trillion.
The fact that China has a bigger economy and the gap is growing need not be a problem if we have a stable international system. However, if the United States imagines that it is not bound by international law or norms, not having the biggest economy in the world could be a problem. The rest of us have to live in the real world, not Donald Trump’s imaginary one.
This year’s ‘Nobel Prize’ in economics goes to two American economists for developing micro-founded ‘representative agent’ DSGE models. The Prize Committee writes:
This year’s laureates in the economic sciences, Robert E. Hall and Michael Woodford, have developed new macroeconomic models that have significantly improved our ability to implement effective stabilisation policy. On the basis of these models, well-timed fiscal and monetary policy measures can reduce the economy’s peaks and troughs …
Rather than basing the models on statistical links between macroeconomic variables, such as inflation, production and consumption, they chose to build them on a microeconomic foundation. This encompasses an understanding for how the total mass of individual households and businesses affect the economy through their actions.
What a joke!
Those models have definitely not improved our ability to increase economic stability. And, when used, they certainly have not reduced the volatility of our economies.
The unsellability of DSGE models — private-sector firms do not pay lots of money to use DSGE models — is one strong argument against DSGE.
But it is not the most damning critique of it.
To me, the most damning critiques that can be levelled against DSGE models are the following two: Read more…
The debate on a billionaire tax has intensified in recent years as inequality has grown both here at home and around the world. A billionaire tax could generate significant tax revenues that could help finance welfare, schools and major public investments in infrastructure, energy and climate. From a fairness perspective, it is also deeply offensive that billionaires, thanks to sophisticated tax planning, proportionally pay far less than an ordinary wage earner. Beyond that, it is a question of democracy. Extreme wealth concentration poses a threat to democracy. When a small group of super-rich people pay lower tax than ‘ordinary’ people, trust in the whole fabric of society is undermined.
The counterarguments one often hears when demands are made to introduce higher taxes on the super-rich are that it would simply lead to billionaires moving abroad. But the risk of capital flight is, as Zucman and his co-authors point out, exaggerated, as experience from Norway and Sweden show: Read more…
Having followed economic policy debate closely for four decades, I’ve learned that common sense often has no place. It is a standard practice for two completely opposite concerns to be raised, with the leading participants seemingly having no idea they are contradictory.
The best current example is that the view that AI is about to lead to astonishing breakthroughs in productivity, leading to unimagined wealth, while at the same time leading pundits fret about how the government debt is bankrupting the country. In some cases, such as Elon Musk, the same person makes both arguments very strongly, apparently unaware that they are contradictory positions.
Anyhow, one of my favorites in the category of silly ideas from supposedly smart people is that AI will take all the jobs. Bill Gates gave us this story in an interview with Ezra Klein a few days ago.
As I and others have pointed out, there is little evidence to date that AI has had any noticeable impact on employment outside of a few narrow sectors. The economists who have tried to do more careful sectoral analyses have found limited effects. At most, they project that we will see productivity gains comparable to what we saw in the tech boom of the 1990s, still less than what we had during the 1947-73 Golden Age.
But suppose Gates is right that we actually do so a massive surge in productivity much larger than these economists predict. This still does not imply massive job loss, even assuming no large-scale government spending, for the simple reason that we can shorten work weeks and work years.
The language and mathematics of probability is a compelling way of analysing games of chance. And similar models have proved useful in some branches of physics …
But most of the problems we face in politics, business (including finance) and society are not like that. We do not have, and never will have, the kind of understanding of human behaviour which emulates the understanding of physical behaviour which yields equations of planetary motion. Worse, human behaviour changes over time in a way that the equations of planetary motion do not …
Discourse about uncertainty has fallen victim to a pseudo-science. When no meaningful quantification is possible, algebra can provide only spurious precision, while at the same time the language becomes casual and sloppy.
To understand real-world ‘non-routine’ decisions and unforeseeable changes in behaviour, ergodic probability distributions are of no avail. In a world full of genuine uncertainty — where real historical time rules the roost — the probabilities that ruled the past are not those that will rule the future.
Time is what prevents everything from happening at once. To simply assume that economic processes are ergodic and concentrate on ensemble averages — and, a fortiori, in any relevant sense timeless — is not a sensible way of dealing with the kind of genuine uncertainty that permeates open systems such as economies.
What is important about the fact that real social and economic processes are non-ergodic is that uncertainty — not risk — rules the roost. Thinking about uncertainty in terms of ‘rational expectations’ and ‘ensemble averages’ has had seriously bad repercussions for the financial system. Read more…
All the political experts are telling us that the Republicans face a shellacking in November. The Democrats seem virtually certain to win the House and are increasingly likely to take the Senate. If that happens, it will put an effective end to the Trump administration.
Donald Trump clearly hates losing and has no respect for democracy. He tried to steal the last election he lost in 2020. For that reason, we have good reason to expect he will try to steal this one.
However, a major restraining factor is the likelihood that the stock market will crash. Trump’s billionaire buddies care far more about their money than about him. If letting Trump continue to play king is going to cost them a large share of their fortunes, they will use their considerable power to insist that the election results be respected.
To be clear, I’m not under any illusion that these people respect democracy. Many of them, like Elon Musk, openly express their contempt for it. But overturning a democratic election is the sort of lawlessness that is likely to send investors fleeing and markets tumbling.
We all know that Trump has already taken huge strides to move the country away from a law-governed system. He regularly sells pardons to people convicted of everything from fraud and tax evasion to drug dealing and child sex trafficking. A contribution to Trump’s campaign funds can win tariff relief or favorable regulatory rulings. And he has turned the White House into a sales office for Trump crypto and all sorts of other Trump products.
These practices have already undermined the country’s status as a safe place to do business. In Donald Trump’s America, a business can be hit by a shakedown attempt at almost any time for any reason. This is undoubtedly part of the story in the sharp rise in interest rates since Trump took office.
But stealing an election is a qualitative leap into Zimbabwe-land. If an election can be stolen in broad daylight, Trump can do anything, and no investment in the United States is safe.
And to be clear, there is zero doubt that we would be talking about stealing an election. All of Trump’s claims of fraud are batshit crazy, as everyone who has tried to examine them has determined, and every court that heard the cases has ruled.
In fact, it is surprising how little fraud along Trumpian lines exists. The Department of Homeland Security flagged 16,000 cases in Nevada of potential non-citizens who were registered to vote. It whittled this down to a list of 185 people that it turned over to the state’s election officials. On closer inspection, the state found that every last one of these people was in fact a citizen.
Virtually all the immigrants who come here do so to work, and hope to eventually get citizenship. They are not going to jeopardize this opportunity by illegally voting and committing a felony. Still, we might think that a few would mistakenly register, but at least in Nevada, a state with over 2.0 million registered voters, this does not appear to be the case.
So, everyone who is not in the Trump cult will know that the claims of fraud are nonsense and that Trump has stolen an election in broad daylight. This is in effect saying there is no more law in the United States with Trump in the White House.
This is likely to translate into stock and bond market meltdowns very quickly. Foreigners hold close to $20 trillion in U.S. stock and another $16 trillion in government and private debt. Much of this money could leave quickly.
It’s not that these foreign countries and wealth holders are necessarily huge lovers of democracy, but they put their money into the United States under the expectation that it was a country that respected the rule of law. Trump stealing an election is saying as clearly as possible that the law means nothing in the United States. Investors demand a much higher return on investment when they put money in a country where dictators can do whatever they want.
Take the case of Kazakhstan, a good example of a dictatorship with limited respect for the rule of law. It pays almost 14% interest on its 10-year government bonds. The interest rate on 10-year bonds in the United States stands at roughly 5.3%, about 0.6 percentage points higher than when Trump took office. But even if interest rates never get near Kazakhstan levels, bondholders would take massive losses if rates just rose 1-2 percentage points. They all understand this and likely would look to get their money out before the crash.
The same logic applies to stocks. Foreign investors can shift much of their money to countries that do respect the rule of law. And it’s not just foreign investors. Most brokerage houses offer options with 401(k)s to invest in foreign funds as an alternative to U.S. stock and bond funds. Many people already have a substantial share of their assets in these funds. That percentage is sure to rise considerably if Trump throws our election results in the garbage.
The effect of people who invested in the United States, shifting trillions into countries that actually are safe havens, will be an unprecedented crash in the stock and bond markets. The recognition of this fact may be sufficient to motivate Donald Trump’s billionaire buddies to tell him that he has to respect the results of the election. That may be our best hope, because clearly Trump has contempt for voters and democracy, and his cabinet and other top aides all feel the same way.
In the 1930s, Lionel Robbins laid down the basic commandments of the discipline when he said that the premises on which economics was founded followed from ‘deduction from simple assumptions reflecting very elementary facts of general experience’, and as such were ‘as universal as the laws of mathematics or mechanics, and as little capable of “suspension”.
Ah yes, general experience. What did Albert Einstein allegedly say about common sense? A funny thing happened on its way to becoming a science: economics seldom tested its premises empirically. Only in recent years has there been serious investigation of its core assumptions and, all too often, they’ve been found wanting.
Unlike in physics, there are no universal and immutable laws of economics. You can’t will gravity out of existence. But as the recurrence of speculative bubbles shows, you can unleash ‘animal spirits’ so that human behaviour and prices themselves defy economic gravity …
Given this willful blindness, the current reaction against economists is understandable. In response, a ‘data revolution’ has prompted many economists to do more grunt work with their data, while engaging in public debates about the practicality of their work. Less science, more social. That is a recipe for an economics that might yet redeem the experts.
Physics envy still lingers among many mainstream economists. It is also one of the main reasons for the pseudo-scientific character of modern mainstream economics. The use of mathematics in mainstream economics — in contrast to physics — has not given us much by way of accurate explanations and reliable predictions of real-world phenomena.
Turning economics into a ‘pseudo-natural science’ is — as Keynes made very clear in a letter to Roy Harrod in 1938 — something that must be firmly ‘repelled’.
To imitate the form of research in the natural sciences is no solution to our methodological problems. All too often, an analysis that is paraded as ‘rigorous’ and ‘stringent’ proves, on critical examination, to be not only empty but grossly flawed, and to lack both logical coherence and correspondence with reality. The appearance of exactness is no substitute for genuine scientific substance. What is required is a method that is truly adequate to the subject-matter. Formal elegance may impress, but it cannot compensate for a failure to engage with the messy, contingent and uncertain character of the world we actually inhabit. What matters is not whether an argument looks like physics, but whether it helps us to understand and explain real economic and social phenomena.
Over the past weeks, yours truly has been re-reading some books that strongly influenced my thinking in my youth. One of these books is Hannah Arendt’s The Human Condition.
What struck me on this re-reading is that there is quite a strong affinity between one of Arendt’s central concerns — the ‘Archimedean point’ — and the criticism that I myself (and other critical realists such as Roy Bhaskar and Tony Lawson) have levelled at economic science in several of my books.
Hannah Arendt’s notion of the Archimedean point is a sharp critique of the idea that we can occupy a position outside the world we are trying to understand. The metaphor goes back to Archimedes’ famous claim that, given a fixed point outside the Earth, he could move the world. For Arendt, the Archimedean point becomes an image of modern science’s striving for a perspective that stands outside human beings’ concrete and historically situated experience.
Modern science has, Arendt argues, increasingly sought to view the world from ‘outside’. From Galileo onwards, mathematization, abstraction and, later, the perspective offered by the space age have made it possible to view the Earth and humanity in ways no longer grounded in our immediate experience of the world. The problem is not scientific knowledge as such, but the belief that we can thereby attain a completely neutral and god-like perspective. We can never entirely leave the world of which we ourselves are part.
Here there is an interesting point of contact with critical realism. As a critical realist, I have long been sceptical of precisely this idea of knowledge that can somehow place itself outside the reality it seeks to understand. Our theories, concepts and methods are historically and socially situated. But this does not mean that reality itself is relative to our perspectives. On the contrary, the fundamental premise of critical realism is that there is a reality that exists independently of our beliefs about it.
This is a crucial distinction between critical realism and relativism. The fact that our knowledge is situated does not mean that what we seek to understand is a product of our perspective. We must therefore distinguish between ontology — what exists — and epistemology — how we can acquire knowledge of it. Reality is independent of our theories, while our knowledge of reality is always provisional, historically mediated and open to correction. Read more…
Among the many recent controversies around AI, there is the claim that to solve a longstanding mathematical problem, OpenAI stole the work of the New York University mathematician Tristan Buckmaster and a colleague, Levent Alpöge, who also is a researcher for Anthropic. I have no ability to assess the validity of the claim, but as I understand it, the argument is that OpenAI had access to work done by Buckmaster and Alpöge. It then built on this work to quickly rush ahead and find a solution, which also had a $1 million prize attached to it.
Insofar as that accurately describes what OpenAI did, this seems like it would be equivalent to having a professor doing a seminar for colleagues and then having one of the attendees do a few additions and claim the work as their own. This would not be viewed as very collegial behavior in a normal academic department.
I’m not sure what claim a professor would have against a colleague under such circumstances. I suppose if their work was properly cited, there would not be a claim of plagiarism. (OpenAI’s work did not directly cite the work of Buckmaster and Alpöge, from my understanding.) I suppose there could be a basis for disputing rights to the million-dollar prize. But the problem seems much greater.
If AI can effectively steal the work of others, and claim it as its own, then it undermines the practice of openness in science that has allowed for enormous progress for centuries. This practice depended on norms, whereby scientists would properly credit the work of their peers.
Mainstream economic theory today is still in the storytelling business, whereby economic theorists create mathematical make-believe analogue models of the target system — usually conceived as the real economic system. This mathematical modelling activity is considered useful and essential. To understand and explain relations between different entities in the real economy, the predominant strategy is to build mathematical models and make things happen in these ‘analogue-economy models’, rather than engineering events in actual economies.
To rely on escapist mathematical models serves to confuse and hinder, rather than help us to solve the difficult and complex challenges that societies face. Using abstract mathematics and trying to emulate the natural sciences is not a fruitful way to develop methods for understanding and explaining our economies and societies.
Without strong evidence, all kinds of absurd claims and nonsense may pretend to be science. Let us not forget what Romer said in his masterful attack on ‘post-real’ economics a couple of years ago:
“Maths cannot establish the truth value of a fact. Never has. Never will.”
We must demand a stronger justification than the rather watered-down versions of ‘anything goes’ when it comes to the main postulates on which mainstream economics is founded. If one proposes ‘efficient markets’ or ‘rational expectations’, one also has to support their underlying assumptions. As a rule, none are given, which makes it rather puzzling how things like ‘efficient markets’ and ‘rational expectations’ have become the standard modelling assumptions made in much of modern macroeconomics. The reason for this sad state of ‘modern’ economics is that economists often mistake mathematical beauty for truth. It would be far better if they instead made sure they kept their hands clean!
Regular readers know that I am skeptical that AI will lead to a massive uptick in productivity growth, and therefore large-scale job loss (as is the Congressional Budget Office), but it is important to realize we have been here before. Specifically, we have had long periods where productivity grew rapidly. It did not lead to mass unemployment or growing inequality. It led to broadly shared prosperity. That was because we had institutions and legal structures in place that promoted equality, instead of inequality.
This is important to realize, since most of the policy discussions by the AI believers seem based on the idea that we have never before seen technologies that lead to rapid gains in productivity, which is what AI-caused job loss means. From 1947 to 1973, productivity grew at an annual rate of roughly 3.0 percent. Instead of causing mass unemployment, this resulted in rapid gains in wages and living standards for the vast majority of the population.
It would be very impressive if AI leads to 3.0 percent average annual productivity growth over the next decade, but even if it somehow led to even faster growth, with the right institutional structure we would have no problem ensuring the gains were broadly shared. In the post-World War II golden age, many countries in Europe enjoyed even more rapid gains in productivity. These countries also had widely shared prosperity. If AI does produce an unprecedented boom, we do know how to structure the economy so that the benefits are widely shared. We did it before.
Unions: The Key Institution
The most important of the institutions ensuring productivity gains were widely shared was unions. Roughly a third of private sector workers were in unions in the 1950s and 1960s. Unions acted to ensure that workers shared the benefits of higher productivity in higher pay and benefits. Today, just over 6 percent of the private sector workforce is in unions. As a result, relatively few workers have the bargaining power to secure wage gains in line with productivity growth.
The plunge in unionization rates did not happen because workers suddenly decided they no longer felt they needed representation in dealing with their employers. It happened because the laws were deliberately structured to make it far more difficult for unions to organize and bargain effectively.
Probably the most important change was the Taft-Hartley Act, passed in 1947. This law severely restricted freedom of contract so that companies and unions could no longer sign agreements requiring that everyone represented by a union pay a fee for their representation. This scheme, misleadingly labeled “right-to-work,” eventually allowed workers in most states to freeload on those that chose to pay to support the union. That made it far more difficult to organize and sustain a union.
In addition, the mechanism for enforcing worker rights, the National Labor Relations Board, became a virtual joke in dealing with employer law-breaking. In other areas of law, say a company fails to carry out a contract, there are clear and serious legal penalties. However, even in cases where companies egregiously violate the law, say by firing workers for leading an organizing effort, the penalties are trivial. In a context where there is no serious consequence, companies have become far more aggressive in fighting unionization even when it means violating the law.
It is easy to see the extent to which the decline in unionization is driven by laws and institutions, not technology and workers’ tastes. Canada, which has a very similar culture and economy, has not seen a remotely similar decline in unionization rates over the last half century.
If we want to raise unionization rates in the United States, we can adopt similar policies to Canada — most importantly, requiring first contract arbitration. Many unions never get a first contract because companies just stall rather than negotiate seriously. Having recourse to arbitration will give companies real incentive to negotiate in good faith and ends in a contract if they don’t.
Higher Minimum Wages
From 1937 to 1968 the minimum wage increased roughly in step with productivity growth. This had a huge impact not just on workers getting the minimum wage, but also for workers considerably higher up the wage ladder. This did not lead to mass unemployment. The unemployment rate in 1968 was under 4.0 percent.
If the minimum wage had continued to rise in step with productivity, it would be close to $28 an hour today. That would put it above the current median wage. This means that more than half of the workforce would be directly getting higher pay. Another 20-30 percent could expect to see wages increased through a spillover effect. This step alone would go far towards addressing the rise in inequality over the last half-century.
Shorter Workweeks and Work Years
Perhaps the most painful aspect of our great minds’ pontifications on the AI-driven job loss is that they seem unable to comprehend that the length of the workweek/work year is not fixed. To put this as simply as possible: 150 million jobs at 40 hours a week, 50 weeks a year, involve equivalent work to 300 million jobs at 20 hours a week, 50 weeks a year. That is probably too simple for people in high-level debates to understand, but most ordinary people can get it.
We made the 40-hour workweek the standard with the 1937 Fair Labor Standards Act. That was almost 90 years ago. It is reasonable that if we really see a massive AI productivity boom, to shorten the workweek further, say to 36 hours, 32 hours, or even less. We can also mandate vacation time, as every other wealthy nation has done. Fewer workdays, like shorter workweeks, spread work among more workers.
AI Productivity Growth Is Not a Problem
It’s entertaining to see the country’s great minds struggling with how to deal with the fallout from an AI productivity boom that may never occur, but the reality is that we have known for many decades how to ensure that the gains from productivity growth are widely shared. We need to teach our elites a little history, not reinvent the wheel.
We also should stop pursuing policies designed to redistribute income upward, like longer and stronger patent and copyright laws, private equity-friendly bankruptcy laws, and special protections for huge social media platforms like Facebook and X. The key point is that the policies needed to reverse inequality and ensure broadly shared prosperity are easily identified. We shouldn’t let the great minds appear to struggle with the problem, as they wring their hands and pretend otherwise.
“Adam Smith said a bunch of stuff that was mostly right. Marx said a bunch of stuff that was wrong. Keynes fixed some things up, messed some others. Then Lucas broke everything again. Now we’ve basically got it all worked out, except for money, growth, recessions, banks, bubbles, inflation, unemployment, exchange rates, and what human beings are about.”
That is the potted history of economics according to a post on X [aka Twitter] by Professor Luis Garicano of LSE.
Pretty much sums it up. You can quibble with the detail — as no doubt you all will — but it seems about right.
Perhaps a different entry point would be to use Gunnar Myrdal as our guide as simply say that economics is “complicated opinion”, meaning that is opinion dressed up to look good in formal attire.
Or, perhaps, we should take the wisdom of Herbert Simon seriously when he suggested that someone arriving from outer space to study the economy here on earth would see that most economic activity takes place within odd things called “firms” and involves production, whereas earthly economists confine their analysis to the far less important arena of exchange. You could say the same thing, I assume, about how people engage with economic activity — they work wherever there is work to be done for many more hours than they engage in exchange. So which is the more important to understand?
I leave it to you. I am still enjoying my summer off.
There are many kinds of useless economics held in high regard within the mainstream economics establishment today. Few — if any — are less deserved than the macroeconomic theory and method, mostly associated with Nobel laureates Finn Kydland, Robert Lucas, Edward Prescott and Thomas Sargent, known as calibration.
In physics, it may not be straining credulity too much to model processes as ergodic — where time and history do not really matter — but in the social and historical sciences it is plainly absurd. If societies and economies were ergodic worlds, why would econometricians fervently discuss things such as structural breaks and regime shifts? The fact that they do is an indication of the unrealistic nature of treating open systems as if they were analysable with ergodic concepts.
The future is not reducible to a known set of prospects. It is not like sitting at the roulette table and calculating what the future outcomes of spinning the wheel will be. Reading Sargent and other calibrationists, one is reminded of Robert Clower’s apt remark that
much economics is so far removed from anything that remotely resembles the real world that it’s often difficult for economists to take their own subject seriously.
Instead of just assuming calibration and rational expectations to be correct, one ought to confront the hypothesis with the available evidence. It is not enough to construct models. Anyone can do that. To be seriously interesting, models must have an aim. They must have an intended use. If the intention of calibration and rational expectations is to help us explain real economies, they must be evaluated from that perspective. A model or hypothesis without specific applicability is not really worthy of our interest.
To say, as Edward Prescott does, that
one can only test if some theory, whether it incorporates rational expectations or, for that matter, irrational expectations, is or is not consistent with observations,
is not enough. Without strong evidence, all kinds of absurd claims and nonsense may masquerade as science. We must demand more justification than this rather watered-down version of ‘anything goes’ when it comes to rationality postulates. If one proposes rational expectations, one must also justify its underlying assumptions. None is given, which makes it rather puzzling how rational expectations has become the standard modelling assumption in much of modern macroeconomics. Perhaps the reason is, as Paul Krugman has it, that economists often mistake
beauty, clad in impressive-looking mathematics, for truth.
But I think Prescott’s view also explains why calibration economists are not particularly interested in empirical examination of how real choices and decisions are made in real economies. In the hands of Lucas, Prescott and Sargent, rational expectations has been transformed from an — in principle — testable hypothesis into an irrefutable proposition. Irrefutable propositions may be comfortable — like religious convictions or ideological dogmas — but they are not science.
For more than a century, investors, both foreign and domestic, felt comfortable having their money in the United States because they knew it was governed by the rule of law. This meant, for example, if there was a contract dispute, they could reasonably expect it would be decided in a court based on the merits, not based on political connections. The same is true for regulatory rulings, such as the Securities and Exchange Commission’s (SEC) ruling on corporate filings or the Food and Drug Administration’s (FDA) approval of drugs.
Under Trump, this is no longer true. If a company lied about its profits on its financial statements, investors could present evidence to the SEC and expect that it would impose sanctions if there was a solid case, but not under Trump. If the company is a big Trump contributor, it’s likely he will instruct the SEC to ignore the complaint or rule in favor of the perp.
The same applies with all the bureaucracies, as the Supreme Court has said Trump can tell them to do whatever he likes. The FDA can refuse to approve the drug of a company whose CEO has criticized him. The Federal Aviation Administration can refuse to recognize the safety of an airplane manufactured by a company that Trump dislikes.
The story goes on. Trump has a green light to use all parts of the government to punish companies and individuals that anger him and to favor those who hand him money.
This is an infinitely greater threat to the credit of the US government and the strength of the dollar than the debt. That fact should be obvious to anyone who is not an elite pundit.
How would you react if a renowned physicist, say, Richard Feynman, were telling you that sometimes force is proportional to acceleration and at other times it is proportional to acceleration squared? I guess you would be unimpressed. But actually, what most mainstream economists do amounts to the same strange thing when it comes to theory development and model modification.
In mainstream economic theory, preferences are standardly expressed in the form of a utility function. But although expected utility theory has been known for a long time to be both theoretically and descriptively inadequate, mainstream economists all over the world gladly continue to use it, as though its deficiencies were unknown or unheard of.
What most mainstream economists try to do in the face of the obvious theoretical and behavioural inadequacies of expected utility theory is to mend it marginally. But that cannot be the right attitude when facing scientific anomalies. When models are plainly wrong, you’d better replace them! Instead of mending the broken pieces, it would be much better to concentrate on developing descriptively accurate models of choice under uncertainty.
Expected utility theory is seriously flawed, since it does not take into consideration the basic fact that people’s choices are influenced by changes in their wealth. Whereas standard microeconomic theory assumes that preferences are stable over time, behavioural economists have forcefully, again and again, shown that preferences are not fixed but vary with different reference points. How can a theory that does not allow for people having different reference points from which they consider their options have a (typically unquestioned) axiomatic status within economic theory?
Much of what experimental and behavioural economics comes up with is really bad news for mainstream economic theory. It unequivocally shows that expected utility theory is nothing but transmogrifying truth.
But mainstream economists do not see this, since they have the weird idea that economics is nothing but a smorgasbord of ‘thought-experimental’ models. For every purpose you may have, there is always an appropriate model to pick.
But, really, there have to be some limits to the flexibility of a theory!
If you can freely substitute any part of the core and auxiliary sets of assumptions and still consider that you are dealing with the same theory, well, then it is not a theory, but a chameleon.
The big problem with the mainstream cherry-picking view of models is, of course, that the theories and models presented get totally immunised against all critique. A sure way to get rid of all kinds of ‘anomalies’, yes, but at a far too high price. So people do not behave optimising? No problem, we have models that assume satisficing! So people do not maximise expected utility? No problem, we have models that assume … etc., etc…
A theory that accommodates any observed phenomena whatsoever by creating a new special model for the occasion, and a fortiori has no chance of being tested severely and found wanting, is of little real value.
Could you persuade an ordinary decent person that murder might sometimes be morally admirable? Deep moral convictions are not easily shaken. A person may be confused, tempted, frightened, or pressured into wrongdoing, but to calmly reconsider the moral status of murder itself — that seems almost impossible. Yet in one of Harvard’s most famous lecture series, Professor Michael Sandel (Justice: The Moral Side of Murder) performs precisely this feat with remarkable ease and elegance. Let us learn the art of rhetoric from a master of the craft by watching how he unsettles the deep-seated moral convictions of unsuspecting and innocent freshmen at Harvard.
The setting itself is part of the performance. Sandel stands in a vast Harvard lecture hall before nearly a thousand freshmen, many of them newly arrived from schools and towns where they had long been regarded as exceptional. They are intelligent, ambitious, eager to prove they belong among America’s ruling elites. But they are also suddenly anonymous — one face among hundreds of equally brilliant peers. Above them stands a world-famous Harvard professor: confident, articulate, relaxed, and in complete command of the room. Every question is public. Every answer is a small performance before classmates equally hungry to display sophistication and nuance. Before the first trolley has even begun to move, the stage has already been carefully prepared.
Sandel begins innocently enough. A trolley is racing down a track toward five workers who will certainly be killed unless someone pulls a switch, diverting the trolley onto another track where only one man stands. Should you pull the switch? READ MORE: The Trolley Trick: Marginalizing Morality in Elite Education – WEA Pedagogy Blog
The concept of a ‘growth imperative’ — the idea that capitalist economies must continually expand — can be fruitfully examined through the lens of what we now call endogenous growth theory, largely developed by ‘Nobel laureate’ Paul Romer. In this framework, the growth imperative is effectively internalised within the economic system, echoing, in some respects, the insights of Marx.
In the traditional Solow model of neoclassical growth theory, growth was viewed as an exogenous process, primarily driven by technological progress. Under the assumption of diminishing returns, capital accumulation would eventually slow down, pushing the economy toward a steady-state growth path. Growth, in this view, was not imperative. A stable equilibrium with zero growth was conceivable.
Romer’s endogenous theory fundamentally alters this picture. Here, the driver of growth is technological innovation and the accumulation of knowledge, conceptualised as an internal outcome of market-driven investment.
In his seminal article Endogenous Technological Change (1990), Romer identifies knowledge as the most important driver of growth. He makes a crucial distinction between ordinary objects (cars, refrigerators, computers) and knowledge (formulas, recipes, patents). Knowledge, for Romer, functions like a set of instructions that tells us how to use resources more effectively. Unlike material objects, knowledge is non-rivalrous: my use of a formula does not diminish your ability to use it. While material resources and individual labour are subject to diminishing returns, knowledge generates increasing returns. An apple can only be eaten by one person, but the recipe for an apple pie can be used by everyone simultaneously.
Romer convincingly demonstrates the centrality of knowledge production for long-term prosperity. If ideas and knowledge are decisive for growth and welfare, then debates about secular stagnation should shift focus away from interest rates and tax rates — macroeconomists’ usual preoccupations — toward educational policy, research investment, and teacher salaries. The challenge for politics is to build institutions that promote knowledge creation while ensuring its benefits are widely shared.
Investments in research and development, education (human capital), and public infrastructure generate positive spillovers that can sustain increasing returns at the aggregate level. Growth thus becomes a product of intentional, profit-motivated activity. This endogenisation of innovation is what links the theory directly to the growth imperative. Firms and states alike are locked in a relentless race to innovate, capturing temporary monopoly profits. To stop innovating is to risk extinction. The dynamic of “standing on the shoulders of giants” reinforces this bias: past discoveries provide the foundation for new ones, making growth self-reinforcing.
The financial system, in particular, embodies this imperative. It channels capital based on expectations of rising future earnings. In a zero-growth economy, however, labour-saving innovations would not be absorbed by expanding industries, but would instead create structural unemployment and falling wages.
By locating the source of growth in market processes of investment and competition, endogenous growth theory suggests that capitalism cannot easily shift into a steady-state mode. Addressing the challenges posed by the growth imperative — especially its environmental consequences — requires more than modest adjustments. It demands a fundamental re-engineering of the economic structures that make perpetual expansion a structural necessity.
MMT is fundamentally a reaction to the way money is described in mainstream economic theory, where money is seen as something that people save by depositing it in banks, and which banks in turn can lend out by creating credit. Thus, money creation by banks presupposes that private individuals save. But that idea is completely wrong; that is not how money works. What MMT highlights is that money is created ex nihilo (out of nothing). The bank presses a key on a keyboard and creates a loan, thereby also creating new money.
Few issues in politics and economics are discussed more today, and understood less, than the national debt. There is much clamour for a reduction of the debt, but few explain why, or how such a reduction would lead to a better economy or a fairer society. And there seem to be no limits to the — especially macroeconomic — catastrophes and problems that a large national debt is claimed to cause: unemployment, inflation, higher interest rates, lower productivity growth, increased burdens for future generations, and so on.
But the truth is that the national debt should not necessarily be feared, especially if it is financed domestically (though foreign loans can also benefit the economy if invested properly). Some members of society hold bonds and earn interest on them, while others must pay the taxes that ultimately service the interest on the debt. The debt is not a net loss for society as a whole because the debt essentially balances itself between the two groups. If the state issues bonds at low interest rates, unemployment can decrease without necessarily leading to strong inflationary pressure. Nor is the intergenerational burden a real one, because the debt — if spent wisely — through its effects on investments and employment, actually leaves future generations as net winners. There can, of course, be unwanted negative distributional effects for future generations, but this is often a minor problem, since when our children and grandchildren pay back the national debt, those payments will be made to our children and grandchildren.
For both John Maynard Keynes and Abba Lerner, it was clear that the state can promote full employment and a stable price level — and that it should use its power to do so. If that means the state must incur debt and (more or less temporarily) run an imbalance in its budget — then so be it! In itself, national debt is neither good nor bad. It is a tool for achieving two overarching macroeconomic goals: full employment and price stability. The ultimate objective is not to achieve a balanced budget or to reduce the national debt, regardless of the impact they have on the macroeconomic goals. If ‘sound finances’, austerity, and balanced budgets lead to increased unemployment and destabilising prices, they must be abandoned.
"We cannot solve our problems with the same thinking we used when we created them." Albert Einstein
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Nicola Acocella (Italy, University of Rome) Robert Costanza (USA, Portland State University) Wolfgang Drechsler ( Estonia, Tallinn University of Technology) Kevin Gallagher (USA, Boston University) Jo Marie Griesgraber (USA, New Rules for Global Finance Coalition) Bernard Guerrien (France, Université Paris 1 Panthéon-Sorbonne) Michael Hudson (USA, University of Missouri at Kansas City) Frederic S. Lee (USA, University of Missouri at Kansas City) Anne Mayhew (USA, University of Tennessee) Gustavo Marqués (Argentina, Universidad de Buenos Aires) Julie A. Nelson (USA, University of Massachusetts, Boston) Paul Ormerod (UK, Volterra Consulting) Richard Parker (USA, Harvard University) Ann Pettifor (UK, Policy Research in Macroeconomics) Alicia Puyana (Mexico, Latin American School of Social Sciences) Jacques Sapir (France, École des hautes études en sciences socials) Peter Söderbaum (Sweden, School of Sustainable Development of Society and Technology) Peter Radford (USA, The Radford Free Press) David Ruccio (USA, Notre Dame University) Immanuel Wallerstein (USA, Yale University)
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