What We Know for Sure that Just Ain't So

“What gets us into trouble is not what we don't know. It's what we know for sure that just ain't so.” Mark Twain

David Smith has a piece in the Times yesterday that, as always, is worth reading. But David is a little too certain for my taste. I prefer to follow the dictum that prediction is difficult; especially about the future.

David discusses the Buzzfeed leak of the assessment of Brexit prospects by 'experts' at the behest of the Conservative government. Here is David with a defense against the Brexiteers:

"It was a great scoop for BuzzFeed, though no surprise to economists. Overwhelmingly, credible analysis shows a similar picture. It also produced the usual nonsense, of the kind that says that if you cannot forecast next year how can you forecast for 15 years? This, of course, is not standard economic forecasting but conditional analysis, which looks at the relative difference compared with the baseline when you introduce frictions into trade with your largest trading partner, reduce your attractiveness for foreign direct investment and cut the supply of EU migrant workers. It sets that against modest gains, only over a very long period, from non-EU trade deals."

Brexit may indeed be bad for the economy. But it doesn’t help to overstate the case. However you look at it, the statement that remaining in the EU would lead to an 8% larger economy, 15 years from now,  than leaving the EU with a 'Hard Brexit', is a forecast. And that forecast has standard errors attached. Those standard errors are very large indeed. 

It is also worth remembering that all forecasts assume that we can plan for the future using known statistical probabilities: Like rolling a die that comes up heads with a known probability. That is not the real world. The Bank of England famously produces fan charts that show not only the median forecast but the probability distribution of likely outcomes. For several forecasts in a row following the 2008 crisis, all the realized values of projected inflation were outside of the range of statistical projections. They were Black-Swan events.

Some of us will probably be worse off under Brexit in the near future. Perhaps all of us will be. I have no idea what the consequences will be relative to staying in the EU in 15 years time and nor does anyone else. "What gets us into trouble is not what we don't know. It's what we know for sure that just ain't so." Fifteen years from now, every outcome is a Black-Swan event.

The Brexit arguments are political. They are not Economic. The Economics is clear. Trade between countries increases the ability to produce goods and services. But when the relative prices faced by a country change, some people will gain and others will lose.

The gainers from globalization were those who have skills that are valued internationally and those who own capital that can be combined with cheaper labor abroad. The losers were those who are now competing with cheaper labor in distant lands. 

It is not enough to repeat the trite phrase that the gainers can compensate the losers unless we come up with a credible plan of how that compensation will be achieved. That is not a trivial matter.   David Autor and co-authors have made a credible case that trade with China caused a loss of US manufacturing jobs and recent research at NIESR by Francesca Foliano and Rebecca Riley finds similar results in the UK. 

When the job of the car worker in Northumberland moves to Eastern Europe because his factory was physically dismantled and shipped overseas, that person can be forgiven for blaming EU membership. And that person understands uncertainty better than 90% of experts who, we are told, agree that Brexit was the wrong decision. When you have been let down by politicians on both sides of the aisle, the unknown seems a lot less scary.

Freedom of the Press and Internet Filters

Here are a few thoughts that were inspired by Richard Baldwin's tweet, Random Sunday Findings...

“Freedom of the Press is guaranteed only to those who own one”. A.J. Liebling. It was naïve to think that the internet would change the balance in favor of a more balanced flow of ideas when social media filters the content of our feed

Internet filters feed us ideas that reinforce our own existing biases. If you are on the left, try creating a new internet persona on the right and follow only right leaning feed. If you are on the right, try the opposite.

Justin Lahart points me to this page on the WSJ that lets you run your own experiment on facebook.

The problem of self-confirming biases existed before the advent of social media. In the UK some people read the Daily Mail, some read the Guardian. And it did not only apply to print media. Our perception of social reality was heavily influenced by a small number of TV stations.  In the UK in the 1960s there were two stations; the BBC and the ITV. In the US there were three Network News stations. 

For better or worse, before the internet, most of us shared our window on the external world. Internet filters are polarizing our views in a way that is destructive to social cohesion by feeding us very different self-reinforcing views of the external world.

How much debt do we need? My answer: 70% of GDP

In a post in 2015 I pointed out that government debt is not a bad thing. Here, I elaborate on that idea and I ask, and answer, a simple question: how much debt do we need? My answer: 70% of GDP is a good guess.

In a recent post, Simon Wren-Lewis asks and answers some of the same questions I discuss here. My focus is narrower than Simon’s. I will focus in on the question: what is the right amount of debt?  I will also abstract from one reason why debt should not be zero. That reason, discussed here by Martin Wolf, and here by Isabella Kaminska, is that the public sector does not only accrue debt; it also owns public assets. I will claim that, even if we did not need to build roads and bridges, it would still be a good idea for the public sector to accumulate debt. My argument is based on a remarkable implication of basic economic theory that was first discussed by Paul Samuelson. If we borrow from our children and our grandchildren, everybody, including all future generations, will be better off.

If a household borrows money to pay for a new car, that debt might be paid back over a period of five years or more. Debt that is accrued to help pay for an investment good, like a car, is widely understood to be a good thing. By borrowing to pay for a car, we arrange for the series of benefits we receive by driving to work or to school every day to be matched with the series of payments we make as we pay back the loan used to purchase the car. Debt accrued by a household to facilitate an investment is widely perceived to be privately beneficial.

Suppose instead, a person borrows to pay for an extravagant lifestyle. Instead of taking out a loan and buying a car, that person maxes out their credit cards to throw expensive parties. To pay back that debt, he or she will need to plan for a period of austere living in future years.  Debt accrued by a household to finance an extravagant lifestyle is widely perceived to be deviant behaviour that is discouraged by social norms. But should we apply those same norms to government behaviour?

If government borrows money to pay for a new road or rail network, the new transportation infrastructure will generate benefits to future generations. It is only fair that those generations should help pay for the investments they enjoy and, for that reason, debt accrued to pay for social investment is widely recognized to be socially beneficial. The principle that all government debt should be used to finance infrastructure investments is sometimes called the golden rule of public finance. It is a commonly held belief that government debt should only finance government investment; but it is a belief that does not survive more careful scrutiny.

Governments are not like households. If a household borrows from a bank it will eventually need to repay the money it borrowed. If a government borrows money from the public, it may never repay that money. It is a myth that government debt is repaid by running public surpluses. In reality, the ratio of outstanding debt to GDP shrinks as the economy grows faster than the interest rate at which the government is borrowing.

In the title to this post I raised the question: How much debt do we need? Economic theory provides an answer to that question and it is never zero. In a series of papers that I am writing with Pawel Zabczyk of the Bank of England, soon to be circulated, we show that a fairly standard model of trade between generations can lead to some very non-standard conclusions. We use Samuelson’s  overlapping generations model, which has been widely used to analyse questions of trade between people of different generations. For a calibrated version that we use as an example, the right answer to my opening question; How much debt do we need? is 70% of GDP.

The main theme of my work with Pawel is that governments are not like households. That point has been made many times by many people. Paul Krugman, for example, makes the case here in a NY Times piece. Although the reason often given is that government expenditure can raise employment through a fiscal multiplier, there is a more fundamental reason why we should not eliminate government debt. And this reason applies even if the economy is always operating at full employment. Debt facilitates trade between current and future generations.  

The figure of 70% that I give in this blog is based on some back of the envelope calculations that Pawel and I use to calibrate our theoretical paper and my subjective confidence bands around that figure are large. The optimal size of public sector debt in the UK might be 5% and it might be 140%. But of one thing I am certain. The right answer to my question; how much debt do we need? is never zero!

Is Unemployment Too Low?

GDP increases over time for two reasons. First, the economy produces more output because we use more labour and more capital. Second, the economy produces more output because we use better techniques over time. Traveling from London to Glasgow on a high-speed train is much faster than travelling there in a horse-drawn carriage. An increase in GDP for this second reason is called productivity growth.

Productivity has been growing at a rate of roughly 1.7% per year since the beginning of the industrial revolution. In the absence of productivity growth, we would have the same standard of living as our grandparents. When productivity grows at 1.7%, our standard of living doubles every forty years or so.  I have graphed US productivity in Box 1.

But although productivity grows on average; it does not grow the same amount every month. Some months are periods when GDP per person grows faster than normal. Other months, are periods when GDP per person grows slower than normal.  In the 1980s, macroeconomists of the real business cycle (RBC) school convinced the profession that these random fluctuations in productivity growth, above or below trend growth, are the main cause of recessions.

In the period from 1953 to 1980, productivity and employment moved in the same direction in the US data. When productivity was high, so was employment. That fact supported the RBC theory. But since 1980, this stylized fact has reversed. In more recent data, high productivity goes hand in hand with low employment. How can we explain this reversal?

In Box 2 I plot a graph of US labour productivity (in blue) and the unemployment rate (in red). Both series are expressed as deviations from a flexible trend and the unemployment rate is lagged by four quarters. This figure shows that, when unemployment is low, productivity will be below trend one year later. I will refer to this as the productivity-unemployment puzzle because macroeconomists have operated on the assumption that productivity and employment move in the same direction. That should imply that productivity and unemployment move in opposite directions. The figure shows that, on the contrary, productivity and unemployment move in the same direction once we account for time-lags. The correlation between unemployment in year t, and productivity in year t+1, is strongly positive and has remained stable in the entire post-war period. How can we explain that fact?

In my book Prosperity for All, I provide an explanation for the positive correlation between productivity and unemployment.  When demand is low, firms employ fewer workers and there are more unemployed people searching for jobs. Firms find it easier to fill vacant positions and their overhead costs for recruitment fall. That fall in recruiting costs shows up as higher productivity. I am open to other possible explanations and I invite you to think about how this correlation might be credibly explained.

The unemployment-productivity puzzle is not confined to the US. Last week, Amit Kara and Ana Rincon-Aznar two of my colleagues at NIESR, published a blog on the connection between total factor productivity (TFP) and employment in the UK.* That blog featured the graph in Box 3 that plots total factor productivity on the y-axis against employment on the x-axis. Here is what Amit and Ana said about this graph in their post.

“We find … a long-standing trade-off between employment and productivity such that periods of high productivity are associated with low employment and vice versa. … TFP is thought to capture technological change and efficiency gains and is generally considered to be independent of an increase in the quantity and quality of labour and capital inputs.”

The productivity-unemployment puzzle presents a dilemma for policy makers. If the theory I describe in Prosperity for All is the right explanation for these data, then very low unemployment is as bad for the economy as very high unemployment. This graph suggests an intriguing question: Is unemployment too low? Very low unemployment is achieved by diverting resources from the activity of producing goods, to the activity of filling vacant jobs. Perhaps the UK has moved too far in the pursuit of full employment.

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NOTE: *Productivity is measured in two ways. Labour productivity is the ratio of GDP to employment. Total Factor Productivity or TFP, is a more sophisticated measure that takes account of the input not only of labour, but also of capital. For the most part, these measures move closely together.

Making Sense of Chaos with the Windy-Boat

Last week my Rebuild Macro colleague Doyne Farmer, asked “Are Business Cycles Chaotic?”  Doyne’s answer is that economies are complex chaotic systems. He draws an analogy with meteorology and he compares the mathematics of business cycles to the science of the weather. I agree with Doyne on this point. But why do we care?

I wrote a piece on this topic, Not Keen on more Chaos in the Future of Macroeconomics, on my personal blog, Roger Farmer’s Economic Window. Although I agree that economies are chaotic systems, I do not agree with the way that Doyne proposes to address that issue.

Doyne uses the rocking horse metaphor that I discussed in depth in my book How the Economy Works. According to this metaphor, which dates back to Wicksell in the late nineteenth century, the economy is like a rocking horse shocked repeatedly and randomly by a child with a club.  The behaviour of the rocking horse is nothing like the dynamics of the shocks; which are random blows with no intertemporal pattern. Nor is it like the behaviour of the rocker, which displays a smooth cyclical return to its rest point after any single shock. Instead, the rocking horse moves randomly through time in predictable ways. This is the way most conventional economists see the world.

Why is Doyne unhappy with the way most conventional economists see the world?  In his own work, Doyne models complex systems with millions of interacting agents. He sees a parallel between his economic models and meteorological models of the weather. Chaotic systems never return to a single point: they keep moving in ways that, although deterministic, are nevertheless unpredictable. 

In How the Economy Works, I argued that the rocking horse metaphor is a very bad approximation to a chaotic system because it makes a strong prediction that is contradicted by data. The rocking horse, if struck just once, always returns to the same point. The data do not. The US unemployment rate has wandered randomly between 3% and 25% but it does not return to a single point. Central Bank economists construct rocking horse models in which the unemployment rate fluctuates around a unique rate that they call the natural rate of unemployment. The natural rate of unemployment is a myth that does not, and has never, existed in the data.

The rocking horse is the wrong way to approximate a complex dynamical system. Is there a better one?  I believe so. In my book How the Economy Works I provide an alternative narrative that I call the windy-boat metaphor. In this narrative, the economy is a sailboat on the ocean with a broken rudder. The wind blows the boat here and there and after a strong gust it never returns to the same point. The windy-boat metaphor leads to approximations to complex systems that, although simple, do not predict that the system is self-stabilizing. Instead it leads to models that display what mathematicians call hysteresis. Perhaps we will eventually have good models of the non-linear dynamics of real world economies. In the meantime, our simple models should provide good approximations to those dynamics that are not obviously contradicted by the facts.  

Conventional macroeconomists approximate the world with the rocking horse model. That is one way of cutting the Gordian knot of complexity theory. But, as Doyne points out, it leads to some pretty silly conclusions. In my work, I replace the rocking horse model with a simple alternative. The windy-boat metaphor makes sense of chaos. It predicts many of the simple correlations we see in data and it provides a viable model of real-world economies that fits the facts.