My Interview with Cloud Yip: Part 1:


A couple of months ago, I had the pleasure of speaking with Cloud Yip. Cloud is running a series of interviews under the title of “Where is the General Theory of the 21st Century” and I was privileged to be included in that series. The interview was published in its entirety a couple of weeks ago but, because it is quite long, I will be serialising it on my blog over the next few weeks.

In his series, Cloud asks prominent macroeconomists: “Why haven’t economists come up with a new General Theory after the Great Recession?” Those of you who have been following my blog will not be surprised by my answer. The theory of macroeconomics, described in my book Prosperity for All, makes fundamental changes to the dominant paradigm. And it leads to fundamentally different policy conclusions from either classical or New Keynesian alternatives. 

Q: Do you think that there have been "revolutionary" changes in macroeconomics since the Great Recession?

F: Yes and no. In my own work, I have made some major changes to macroeconomics. I will leave it to others to decide if they are revolutionary. But in my view, most macroeconomists are carrying on with business as usual. And that is discouraging because macroeconomics needs to change.
The dominant paradigm before the Great Recession was New-Keynesian economics. That paradigm is widely perceived to have failed in two key dimensions. It didn’t include a financial sector and it had no role for unemployment. New Keynesian economists have tried to fix the NK model by adding in these features and there have been some notable contributions. But for the most part, attempts to fix the NK model are akin to rearranging the deckchairs on the Titanic.
Economics is not an experimental science. As a consequence, frequently, people pursue avenues of research that are simply wrong or mistaken.
In my view, economics took a wrong path in the 1950s. Back in 1928, there was a book published by Pigou called "Industrial Fluctuations." It is a very rich verbal theory about the causes of business cycles. According to Pigou, there are six different causes of business cycles. Those include what we would now call productivity shocks, monetary disturbances, sunspot shocks, that is, shocks to business confidence; agricultural disturbances, changes in tastes and news shocks.
Then in 1929, there was the stock market crash, and in 1936, Keynes wrote the General Theory. The General Theory was a revolutionary change in the way we think about the world. It was revolutionary because, instead of thinking of the economic system in a capitalist economy as self-stabilizing, Keynes's vision was of a dysfunctional world in which high unemployment can persist for a very long time.
A few years ago, I wrote a book called "How the Economy Works". In it I described two metaphors. The first was that of Pigou's book in which the economy is like a rocking horse hit repeatedly and randomly by a kid with a club. The movement of the rocking horse is partly caused by the shocks of the club and partly caused by the internal dynamics of the rocker. We've modelled this system for decades using linear stochastic difference equations.
In my book, I provide a different metaphor to capture Keynes' insight that the economy can get stuck in an equilibrium with high unemployment. I call that metaphor the "windy-boat model". The economy is not like a rocking horse; it is like a sailboat on the ocean with a broken rudder. When the wind blows the boat, instead of always returning to the same point, the boat can become stranded a long way from a safe harbour. 
In the language of equilibrium theory, Frisch's analogy leads to a model with a unique steady-state equilibrium: the rocking horse always comes to rest at the same point. In the windy-boat model, which is, I think, the essence of the General Theory, the economy can get stuck with high unemployment for an extended period.
In the immediate aftermath of the Great Depression in the 1940s and 1950s, the economic model we were using was based on ideas from the General Theory. Then in 1955, Samuelson wrote the third edition of his introductory textbook, in which he introduced the concept of the neo-classical synthesis.
In Samuelson's view, a view that has dominated the discipline since 1955, the economy is classical in the long run but Keynesian in the short run. Samuelson defined the short-run as the period over which prices don't adjust. He defined the long-run as the period over which the economy has had enough time to return to a classical full-employment equilibrium. According to the neo-classical synthesis, the economy is temporarily away from the "social planning optimum", but only temporarily.
In 1982, with the birth of Real Business Cycle Theory (RBC), economists gave up on Keynesian economics and we returned to the ideas of Pigou. Real Business Cycle theory formalized Pigou's model of the economy, but instead of the rich verbal theory of Industrial Fluctuations, RBC theorists constructed complicated mathematical models. And because the mathematics was complicated, the models were very simple and, initially, driven by a single productivity shock. In the period from 1982 up through 2008, most macroeconomists were engaged in a research program that was, essentially, adding the shocks back to Pigou's vision of the rocking horse model.
What happened in 2008 and in the aftermath of the Great Recession has, or should, cause us to rethink the entire enterprise of macroeconomics. In my work, I have formalized the main ideas in Keynes' General Theory. These ideas are vastly different to those that preceded Keynes and they are very different from the ideas that have guided macroeconomics since the 1980s. Keynes argued that there are multiple steady-state equilibria and that the economy can get stuck in an equilibrium with high persistent involuntary unemployment. In my work, I have formalized that idea.

Q: Why, in your view, did economists, in the 1980s, give up on Keynesian economics?

F: The General Theory was incomplete. It was incomplete because it eliminated the idea of the labour supply curve but didn’t replace it with any convincing alternative.
Keynes argued that the economy is on the labour demand curve, but he threw away the labour supply curve and replaced it with the idea of involuntary unemployment. That was always somewhat unsatisfactory theoretically.  Involuntary unemployment is open to a number of criticisms. For example, why don’t firms offer to employ unemployed workers for lower wages when those workers would willingly accept a lower wage if they are involuntarily unemployed? This is a theoretical problem that was left hanging in the General Theory.


Then the other issue in the General Theory is that there was never a theory of what determines the price level. Hicks and Hansen, who interpreted the General Theory, considered it to be a short-term theory in which prices are temporarily fixed. Around the time that Samuelson was writing the third edition of his textbook, a New Zealander, William Phillips, published the article "The Relation between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861-1957". This empirical article demonstrated that there had been a stable relationship between wage inflation and unemployment in nearly a century of UK data. This has been known ever since as the Phillips curve.
Samuelson used the Phillips curve to bring together the short run and the long run. He saw it as a wage adjustment equation which explained how excess demand pressure would cause wages to rise. As wages and prices changed, the economy would return to its long-run steady state. The problem with that explanation is that as soon as Phillips had written the article, the Phillips Curve disappeared. There hasn't been a stable Phillips Curve in data anywhere in any advanced economy that I know of since the mid 1960s.

Giovanni Nicolò and I wrote a paper recently (Farmer and Nicolò 2017) that replaces the Phillips Curve with an alternative equation, the Belief Function,  that I introduced in my 1993 book, The Macroeconomics of Self-fulfilling Prophecies. Giovanni and I showed in our paper that a three-equation model closed with the Belief Function instead of the Philips Curve provides a much better fit to US data. We find that a Bayesian economist who placed equal weight on both theories before confronting them with data would find overwhelming evidence that the Belief Function was the better approach.

Next week I will continue this serialisation of my interview with Cloud, and among other things, I will discuss my views on rational expectations.

And the 2017 Economics Nobel Prize goes to ...


Today’s announcement of a Nobel Prize for Richard Thaler is richly deserved and I congratulate the Nobel committee for recognising the importance of the growing influence of behavioural economics that Richard helped to create. This is a significant ‘nudge’ towards recognising the importance of beliefs as fundamental, an idea that I use in my own work in a macroeconomic context. In July, I co-organized a conference at the Bank of England on the connection between behavioural economics and macroeconomics so I am pleased that the connection of psychology to economics will be more widely perceived as significant with the award of this year’s Nobel Prize.

Richard Thaler’s work is widely cited as recognising that human beings are not rational and in a very narrow sense, that is true. On hearing that he had won the Nobel prize, Richard is quoted as saying the most important impact of his work is the recognitions that “economic agents are humans” and “money decisions are not made strictly rationally”.  

Rationality means many things to many people and there are both broad and narrow definitions of what exactly it means. Under the broad definition, one that I have always liked, it is an organising principle that categorises human action. Rationality means that we always choose our preferred action. What is our preferred action? It is the one we choose. This idea is captured in Samuelson’s discussion of revealed preference in Foundations of Economic Analysis. Although rationality by this definition is a tautology, it is a useful tautology that plays the same role in economics as the Newtonian concept of “action at a distance”.

There is another, much narrower definition of rationality, that is formalized in a set of axioms that was introduced by John Von Neumann and Oscar Morgenstern in their magisterial tome, the Theory of Games and Economic Behavior”. Those axioms make a great deal of sense when applied to choice over monetary outcomes. They make much less sense when applied to complex decisions that involve sequential choices and payoffs of different commodities at different point in time.  It is this second definition of rationality that has shown to be violated in experimental situations and that is the take-off point for Thaler’s work on how best to present choices to people that help them make ‘good’ decisions.

If you want to know more about Richard’s work, I highly recommend the book Nudge, where you will learn about them in Richard’s own words along with that of his co-author Cass Sunstein. That work has already found its way into public policy decisions and, in the U.K., led to the creation of the ‘Nudge’ unit, an arm of the U.K. government that uses Thaler’s work to influence public decisions.

Keynesian Economics Without the Phillips Curve

Policy makers at central banks have been puzzled by the fact that inflation is weak even though the unemployment rate is low and the economy is operating at or close to capacity. Their puzzlement arises from the fact that they are looking at data through the lens of the New Keynesian (NK) model in which the connection between the unemployment rate and the inflation rate is driven by the Phillips curve.


In a recent paper joint with Giovanni Nicolò, we compared two models of the interest rate, the unemployment rate and the inflation rate.  One theory, the NK model, consists of a demand equation, a policy rule and a Phillips curve. The other, the Farmer Monetary (FM) model, replaces the Phillips curve with a new equation: the belief function. We show that the FM model outperforms the NK model by a large margin when used to explain United States data. 

To make this case, we ran a horse race in which we assigned equal prior probability to two models. One was a conventional New Keynesian model that consists of a demand equation, a policy rule and a Phillips curve. The other was the FM model. The FM model shares the demand curve and the policy rule in common with the NK model but replaces the Phillips curve with a new equation; the belief function.

The belief function captures the idea that psychology, aka animal spirits, drive aggregate demand. It is a fundamental equation with the same methodological status as preferences and technology.  To operationalise the belief function, we assumed that people make forecasts of future nominal income growth based on observations of current nominal income growth. If x is the percentage growth rate of nominal GDP this year and E[x’] is the expected rate of growth of nominal GDP growth next year we assumed that x = E[x’].

We estimated both models using Bayesian statistics and we compared their posterior probabilities. Our findings are summarised in Table 2, reproduced from our paper.  The table reports what statisticians call the posterior odds ratio. As is common in this literature, we compared the models over two separate sub-samples; one for the period from 1954 to 1979 and the other from 1983 to 2007. Our findings show that an agnostic economist who placed equal prior weight on both theories would conclude that the FM model knocks the NK model out of the ball park. The data overwhelmingly favours the FM model.


We explain our findings in the paper by appealing to a property that mathematicians call hysteresis.

Conventional dynamical systems have a stable steady state that acts as an attractor. The economy will converge to that steady state, no matter where it starts. The FM model does not share that property. Although the economy follows a unique path from any initial condition, the FM model has a continuum of possible steady states and which one the economy ends up at depends on initial conditions.

The FM model explains the data better than the NK model because the unemployment rate in US data does not return to any single point. In some decades, the average unemployment rate is 6%: in others, it is 3%. And in the Great Depression it did not fall below 15% for a decade. The unemployment rate, the inflation rate and the interest rate are so persistent in US data that they are better explained as co-integrated random walks than as mean-reverting processes.  The FM model captures that fact. The NK model does not.

What does it mean for two series to be co-integrated? I have explained that idea elsewhere by offering the metaphor of two drunks walking down the street, tied together with a rope. The drunks can end up anywhere, but they will never end up too far apart. The same is true of the inflation rate, the unemployment rate and the interest rate in the US data.

As I have argued on many occasions, the NK model is wrong and there has been no stable Phillips curve in the data of any country I am aware ever since Phillips wrote his eponymous article in 1958. My paper with Giovanni provides further empirical evidence for the Farmer Monetary Model, an alternative paradigm that I have written about in a series of books and papers. Most recently, in Prosperity for All, I make the case for active central bank intervention in the asset markets as a complimentary approach to interest rate control.

In a separate paper, Animal Spirits in a Monetary Model, Konstantin Platonov and I have explored the theory that underlies the empirical work in my joint work with Giovanni. The research programme we are engaged in should be of interest to policy makers in central banks and treasuries throughout the world who are increasingly realising that the Phillips curve is broken. In Keynesian Economics Without the Phillips Curve, we have shown how to replace the Phillips curve with the belief function, an alternative theory of the connection between unemployment and inflation that better explains the facts.  

On Refereeing: Do we have Confidence in our Economic Institutions?


Like most academics, I spend much of my time asking for money from research councils. So, it is a welcome change for me to sit on the other side of the table in my role on the management team of Rebuilding Macroeconomics. This is an initiative located at the National Institute of Economic and Social Research in the UK and funded by the Economic and Social Research Council. Our remit is to act as gatekeepers to distribute approximately £2.4 million over the next four years to projects that have the potential to transform macroeconomics back to a truly policy relevant social science. We are seeking risky projects that combine insights from different disciplines that would not normally be funded and we expect that not all of them will succeed. It is our hope that one or more of the projects we fund will lead to academic advances and new solutions to the pressing policy issues of our time. 

In addition to my role on the management team for Rebuilding Macroeconomics, I am Research Director at NIESR. I have already learnt a great deal from discussions with the other members of the team. Our Principal Investigator, Angus Armstrong, has experience in government and the private sector and until recently he was Director of Macroeconomics at NIESR. Laura Bear, a Professor of Anthropology at the London School of Economics has worked extensively on the anthropology of the urban economy and she brings a refreshing perspective to the sometimes-insular world of economics. Doyne Farmer, no relation, is a complexity theorist based at Oxford University who runs the INET Complexity Economics Centre at the Oxford Martin School. Doyne was trained as a physicist and he has a long association with the Santa Fe Institute in New Mexico. And last, but by no means least, David Tuckett is a psychologist at University College London where he directs the UCL Centre for the Study of Decision Making Under Uncertainty. As you might imagine, conversations among this diverse group have been eye-opening for all of us.

We have chosen to allocate funds by identifying a number of ‘hubs’ that are loosely based around a set of pressing public issues. So far, we have identified three: 1) Can globalisation benefit all? 2)  Why are economies unstable? and 3) Do we have confidence in economic institutions? In this post, I want to focus on the third of these questions which evolved from conversations between those of us on the management team and that Laura and I have spent quite a bit of time refining. 

We can break institutions into two broad groups: Academic institutions that shape the culture of economists. And government and policy institutions that transmit this culture to the wider public sphere. Research on academic institutions involves the organisation of economic education in universities, the journal structure, the rules for promotion and tenure in academic departments and the socialisation and seminar culture of the tribe of the Econ. Research on policy making institutions like the Bank of England, the Treasury and the IMF involves the way that insular thinking, learned in graduate schools, is transmitted to society at large. 

Insular thinking is reflected, for example, in economic journal publishing, a process that is highly centralised around five leading journals. These are the American Economic Review, the Quarterly Journal of Economics, the Review of Economic Studies, Econometrica and the Journal of Political Economy. For a young newly appointed lecturer, publishing a paper in one of these top five journals is a pre-requisite for promotion in a leading economic department in the United States, the United Kingdom and many of the top Continental European departments. This process is more often than not depressingly slow. Even for a well-established leading economist, publication in a top five journal is never guaranteed. And when a paper is finally published, it is after rejection from three or more other journals and the collective efforts of a coterie of referees. This experience, as I learned from Doyne, is not characteristic of the natural sciences.

                         From Redpen/Blackpen twitter feed

                         From Redpen/Blackpen twitter feed

In economics, the expected time from writing a working paper to publication in a journal is around four years. That assumes that the researcher is shooting for a top journal and is prepared to accept several rejections along the way. When a paper is finally accepted it must, more often than not, be extensively rewritten to meet the proclivities of the referees. In my experience, not all of the referee reports lead to improvements. Sometimes, the input of dedicated referees can improve the final product. At other times, referee comments lead to monstrous additions as the editor incorporates the inconsistent approaches of referees with conflicting views of what the paper is about. 

It is not like that in other disciplines. I will paraphrase from my memory of a conversation with Doyne, so if you are a physicist or a biologist with new information, please feel free to let me know in the comment section of this blog. In physics, a researcher is rightfully upset if she does not receive feedback within a month. And that feedback involves short comments and an up or down decision. In physics, there is far less of a hierarchy of journals. Publications are swift and many journals have equal weight in promotion and tenure decisions.

I do not know why economics and physics are so different but I suspect that it is related to the fact that economics is not an experimental science. In macroeconomics, in particular, there are often many competing explanations for the same limited facts and it would be destructive to progress if every newly minted graduate student were to propose their own new theory to explain those facts. Instead, internal discipline is maintained by a priestly caste who monitor what can and cannot be published. 

The internal discipline of macroeconomics enables most of us to engage in what Thomas Kuhn calls ‘normal science’. But occasionally there are large events like the Great Depression of the 1930’s, the Great Stagflation of the 1970’s or the Great Recession of 2008, that cause us to re-evaluate our preconceived ideas. A journal culture that works well in normal times can, in periods of revolution, become deeply suppressive and destructive of creative thought. Now would be a very good time to re-evaluate our culture and perhaps, just perhaps, we can learn something from physics.

Where's the Inflation? Where's the Beef?

In a 1984 advertising campaign, Wendy’s Hamburgers featured the character actress Clara Peller.  Clara peers disappointedly at a burger from a rival chain that, while well stocked with bread, has remarkably little meat. Her rallying cry: Where’s the beef? was taken up as a political slogan by Vice Presidential Candidate Walter Mondale and it captured the imagination of a generation. 


Today, as we stare at a Fed balance sheet of $4.5 trillion and rates of price change at or below 2% one can envisage a millennial Clara Peller metaphorically peering at a bloated Fed balance sheet and pleading; Where’s the inflation?

In a 2009 review of Akerlof and Shiller’s book, ‘Animal Spirits’, Greg Hill pointed out that I made the following claim: “History has taught that a massive expansion of liquidity will lead to inflation”. My review was designed to be critical of slavish applications of 1950s Keynesian remedies to twenty-first century problems.  I stand by that critique. There is a reason we rejected Keynesian economics in the 1970s. It didn’t work the way it was supposed to. In particular, Keynesian economics had nothing to say about the most important economic issue of the 1960s and 1970s: the simultaneous appearance of inflation and unemployment for which the British politician, Ian Macleod coined the term ‘stagflation’.