Slowly, economists are revealing the weaknesses in their theories

Economics is changing. It’s relying less on theorising about how the economy works, and more on testing to see whether there’s hard empirical (observable) evidence to support those theories.

Advances in digitisation and the information revolution have made much more statistical information about aspects of economic activity available, and made it easier to analyse these new “data sets” using improved statistical tests of, for instance, whether the correlation between A and B is causal – whether A is causing B, or B is causing A, or whether they’re both being caused by C.

But another development in recent decades is economists losing their reluctance to test the validity of their theories by performing experiments. Let me tell you about two new examples of empirical research by Australian academic economists, one involving data analysis and the other a laboratory experiment.

We see a lot of calls for reform that take the form: change taxes or labour laws in a way that just happens to benefit me directly, and this will make “jobs and growth” so much better for everyone.

These reformers always convey the impression that the changes they want are backed by long established, self-evident economic principles. And they can usually find professional economists willing to say “yes, that’s right”.

But what gets me is that, when the self-declared reformers get their “reform”, it’s rare for anyone to bother going back to check whether it really did do wonders for jobs and growth. Wouldn’t there be something to learn if it was a great success, or if it wasn’t?

Do you remember back in 2017, when employers were campaigning for a reduction in weekend penalty rates? The retailers and the hospitality industry told the Fair Work Commission that making them pay much higher wage rates on Saturday and Sunday was discouraging some businesses from opening on weekends, to the detriment of the public’s convenience.

If only penalty rates were lower, more businesses would open on weekends, or stay open for longer, meaning consumers would spend more, and more workers would be employed for more hours, leaving everyone better off.

The employers got strong support from the Productivity Commission and some economist expert witnesses. So the commission decided to reduce the Sunday and public holiday penalty rates in the relevant awards by 25 to 50 percentage points, phased in over three years.

Associate Professor Martin O’Brien, of the University of Wollongong’s Sydney Business School, commissioned a longitudinal survey (looking at the same people over time) of about 1830 employees and about 240 owner-managers or employers, dividing the workers between those on awards and a control group of those on enterprise agreements (and so not directly affected).

The economists’ standard, “neo-classical” model of the way demand and supply interact to determine the market price, with movements in the price feeding back to influence the quantity that buyers demand and the quantity sellers want to supply, does predict that a fall in the price of Sunday labour will lead employers to demand more of it.

So what did the survey find? It could find no effect on employment in the retail and hospitality sectors. This is consistent with a growing body of mainly American empirical evidence that, contrary to neo-classical theory, increases in minimum wages have little effect on employment.

But here’s an interesting twist: a majority of employers reported not making the reduction in penalty rates and a majority of employees reported not receiving any reduction.

One explanation for this is that employers didn’t pass on the cuts because they valued staff loyalty and commitment. If so, this fits with the judgment of many labour economists that the relationship between a firm and its workers is far more nuanced than can be captured by the neo-classical assumption that price is the only motivator.

An alternative explanation, however, is that those employers didn’t cut the Sunday penalty rate because they weren’t paying it in the first place.

Turning to the laboratory experiment, it tests the much more theoretical assumption that the behaviour of people engaged in economic activities is guided by their “rational expectations” about what will happen in the future.

Economists have come to care about what people expect to happen because this affects the way people behave, and so affects the future we get. In recent decades, many mathematical models of the macro economy have used the assumption that people form their beliefs about the future in a “rational” way to make the maths more rigorous.

By “rational” they mean that people respond to new information by immediately and fully adjusting their expectations – beliefs – about what will happen to prices, the economy’s growth or whatever. Which is a lovely idea, but how realistic is it?

Dr Timo Henckel, of the Research School of Economics at the Australian National University, Dr Gordon Menzies, of the University of Technology Sydney, and Professor Daniel Zizzo, of the University of Queensland, analysed the results of an experiment conducted by Professor Peter Moffatt, of the University of East Anglia, involving 245 students answering questions.

On receiving each piece of new information, the subjects had first to decide whether to adjust their beliefs and then, if so, by how much. The experimenters found that the subjects reacted very differently.

They found that, in general, people don’t update their beliefs with each new piece of information. And when they do, they tend not to adjust their beliefs by as much as they probably should. In other words, people display a kind of belief conservatism, holding on to a belief for longer than they should.

They found that this conservatism is explained to some extent by people’s inattention – they were distracted by other issues – and to some extent by the complexity of the issue: it was “cognitively taxing”.

It turns out that very few people – just 3 per cent of the subjects – display the rational expectations economists assume in their model-building. Most people’s behaviour, the authors say, is better described as “inferential expectations”.

Now, you may not be wildly surprised by these findings. But, in the academic world, common sense doesn’t get you far. You must be able to demonstrate things the academic way.

Even so, Henckel says that the responses of the experiment’s subjects extend to many parts of life, from the behaviour of investors in the share and other financial markets – this is how bubbles develop – to people’s political convictions, where they hold on to beliefs for far too long, ignoring much contrary evidence.

Indeed, inferential expectations apply even to scientists, who form a view of the world which they will revise or overturn only if there is overwhelming evidence to the contrary. So don’t expect economic modellers to abandon their convenient assumption of rational expectations any time soon.

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How economics could get better at solving real world problems

The study of economics has lost its way because economists have laboured for decades to make their social science more mathematical and thus more like a physical science. They’ve failed to see that what they should have been doing is deepening their understanding of how the behaviour of “economic agents” (aka humans) is driven by them being social animals.

In short, to be of more use to humanity, economics should have become more of a social science, not less.

This is the conclusion I draw from the sweeping criticism of modern economics made by two leading British economics professors, John Kay and Mervyn King, in their book, Radical Uncertainty: Decision-making for an unknowable future.

But don’t hold your breath waiting for economists to see the error of their ways. There are two kinds of economist: academic economists and practising economists, who work for banks, businesses and particularly governments or, these days, are self-employed as “economic consultants”.

Whenever I criticise “economists” – which I see as part of the service I provide to readers – the academics always assume I’m talking about them. It rarely occurs to them that I’m usually talking about their former students, economic practitioners – the ones who matter more to readers because they have far more direct influence over the policies governments and businesses pursue.

You see from this just how inward-looking, self-referential and self-sustaining academic economics has become. The discipline’s almost impervious to criticism. Criticism from outside the profession (including “the popular press”) can usually be dismissed as coming from fools who know no economics. If you’re not an economist, how could anything you say have merit?

But Kay and King are insiders. As governor of the Bank of England, King was highly regarded internationally. Kay has had a long career as an academic, author, management consultant, Financial Times columnist and head of government inquiries.

So their criticism will just be ignored, as has been most of the informed criticism that came before them. Their arguments will be misrepresented – such as that they seem opposed to all use of maths and statistics in economics. They’re not. But there’ll be little face-to-face debate. Too discomforting.

Trouble is, the push to increase the “mathiness” of economics has gone for so long that all the people at the top of the world’s economics faculties got there by being better mathematicians than their rivals.

They don’t want to be told their greatest area of expertise was a wrong turn. Similarly, all the people at the bottom of the academic tree know promotion will come mainly by demonstrating how good they are at maths.

Kay and King complain that economics has become more about technique – how you do it – than about the importance of the problems it is (or isn’t) helping people grapple with in the real world. (This may help explain why, in many universities, economics is losing out to business faculties.)

In support of their case for economics needing to be more of a social science, Kay and King note there are three styles of reasoning: deductive, inductive and “abductive”. Deductive reasoning reaches logical conclusions from stated premises.

Inductive reasoning seeks to generalise from observations, and may be supported or refuted by later experience. Abductive reasoning seeks to provide the best explanation for a particular event. We do this all the time. When we say, for instance, “I think the bus is late because of congestion in Collins Street”.

Kay and King say all three forms of reasoning have a role to play in our efforts to understand the world. Physical scientists (and mathy economists) prefer to stick to deductive reasoning. But this is possible only when we study the “small world” where all the facts and probabilities are known – the world of the laws of physics and games of chance.

In the “large world”, where we must make decisions with far from complete knowledge, we have to rely more on inductive and abductive reasoning. “When events are essentially one-of-a-kind, which is often the case in the world of radical uncertainty, abductive reasoning is indispensable,” they say.

And, so far from thinking “as if” we were human calculating machines, “humans are social animals and communication plays an important role in decision-making. We frame our thinking in terms of narratives.”

Able leaders – whether in business, politics or everyday life – make decisions, both personal and collective, by talking with others and being open to challenge from them.

The Nobel prize-winning economist Professor Robert Schiller, of Yale, has cottoned on to the importance of narratives in explaining the behaviour of financial markets, but few others have seen it. Most academic economists just want to be left alone to play the mathematical games they find so fascinating.

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Why economists get so many of their predictions wrong

Sometimes the study of economics – which has gone on for at least 250 years – can take a wrong turn. Many economists would like to believe their disciple is more advanced than ever, but in the most important economics book of 2020 two leading British economists argue that, in its efforts to become more “rigorous”, it’s gone seriously astray.

The book is Radical Uncertainty: Decision-making for an unknowable future, by Professor John Kay of Oxford University and Professor Mervyn King, a former governor of the Bank of England.

The great push in economics since World War II has been to make the subject more rigorous and scientific by expressing its arguments and reasoning in mathematical equations rather than words and diagrams.

The physical sciences have long been highly mathematical. Economists are sometimes accused of trying to distinguish their discipline from the other social sciences by making it more like physics.

Economics is now so dominated by maths it’s almost become a branch of applied mathematics. Sometimes I think that newly minted economics lecturers know more about maths than they do about the economy.

Kay and King don’t object to the greater use of maths (and I think economists have done well in using advanced statistical techniques to go beyond finding mere correlations to identifying causal relationships).

But the authors do argue that, in their efforts to make conventional economic theory more amenable to mathematical reasoning, economists have added some further simplifying assumptions about the way people and businesses and economic policymakers are assumed to behave which take economic theory even further away from reality.

They note that when, in 2004, the scientists at NASA launched a rocket to orbit around Mercury, they calculated that it would travel 4.9 billion miles and enter the orbit in March 2011. They got it exactly right.

Why? Because the equations of planetary motion have been well understood since the 17th century. Because those equations describing the way the planets move are “stationary” – meaning they haven’t changed in millions of years. And because nothing that humans do or believe has any effect on the way the planets move.

Then there’s probability theory. You know that, in games of chance, the probability of throwing five heads in a row with an unbiased coin, or the probability that the next card you’re dealt is the ace of spades can be exactly calculated.

In 1921, Professor Frank Knight of Chicago University famously argued that a distinction should be drawn between “risk” and “uncertainty”. Risk applied to cases where the probability of something happening could be calculated with precision. Uncertainty applied to the far more common cases where no one could say with any certainty what would happen.

Kay and King argue that economics took a wrong turn when Knight’s successor at Chicago, a chap called Milton Friedman, announced this was a false distinction. As far as he was concerned, it could safely be assumed that you could attach a probability to each possible outcome and then multiply these together to get the “expected outcome”.

So economists were able to get on with reducing everything to equations and using them to make their predictions about what would happen in the economy.

The authors charge that, rather than facing up to all the uncertainty surrounding the economic decisions humans make, economics has fallen into the trap of using a couple of convenient but unwarranted assumptions to make economics more like a physical science and like a game of chance where the probability of things happening can be calculated accurately.

There’s a big element of self-delusion in this. If you accuse an economist of thinking they know what the future holds, they’ll vehemently deny it. No one could be so silly. But the truth is they go on analysing economic behaviour and making predictions in ways that implicitly assume it is possible to know the future.

Kay and King make three points in their book. First, the world of economics, business and finance is “non-stationary” – it’s not governed by unchanging scientific laws. “Different individuals and groups will make different assessments and arrive at different decisions, and often there will be no objectively right answer, either before or after the event,” they say.

Why not? Because we so often have to make decisions while not knowing all there is to know about the choices and consequences we face in the world right now, let alone what will happen in the future.

Second, the uncertainty that surrounds us means people cannot and do not “optimise”. Economics assumes that individuals seek to maximise their satisfaction or “utility”, businesses maximise shareholder value and public policymakers maximise social welfare – each within the various “constraints” they face.

But, in reality, no one makes decisions the way economic textbooks say they do. Economists know this, but have convinced themselves they can still make accurate predictions by assuming people behave “as if” they were following the textbook. That is, people do it unconsciously and so behave “rationally”.

Kay and King argue that people don’t behave rationally in the narrow way economists use that word to mean, but neither do they behave irrationally. Rather, people behave rationally in the common meaning of the word: they do the best they can with the limited information available.

Third, the authors say humans are social animals, which means communication with other people plays an important role in the way people make decisions. We develop our thinking by forming stories (“narratives”) which we use to convince others and to debate which way we should jump. We’ve built a market economy of extraordinary complexity by developing networks of trust, cooperation and coordination.

We live in a world that abounds in “radical” uncertainty – having to make decisions without all the information we need. Rather than imagining they can understand and predict how people behave by doing mathematical calculations, economists need to understand how humans press on with life and business despite the uncertainty - and usually don’t do too badly.

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