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Thursday 5 February 2015

Beyond the Dismal Science

The Passionate Science

Back in the nineteenth century Thomas Carlyle described economics as “the dismal science”, a term that’s been wheeled out ever since whenever some hackneyed journalist or febrile blogger feels the need to criticise something to do with money. It’s a snappy little phrase, and is all too often justified.

Well, now a couple of behavioral economists have written a book that attempts to refute that label. In the words of John List and Uri Gneezy in The Why Axis: Hidden Motives and the Undiscovered Economics of Everyday Life economics is a passionate science – one that is:
“Fully engaged with the entire spectrum of the human emotions … and with the capacity to produce results that can change society for the better”.
So if you want to know how to price wine correctly, or to improve the performance of students or get people to donate more money to charity or level the playing field for women at work then this is the place to start. And if you want a tool kit to improve your performance at, say, investing, then you’re in the right place.


But first a word on that cute little soundbite of Thomas Carlyle’s, so often used to denigrate economics.  The legend is that Carlyle coined it after reading Thomas Malthus, who predicted that human population growth would inevitably be checked by limits to our ability to feed ourselves: a form of survival of the fittest. Reading Malthus led to one of Charles Darwin’s key insights into the formulation of evolutionary theory. Malthus was wrong, and Darwin was right, but such is the nature of the advancement of human knowledge.

However, as Robert Dixon has demonstrated it’s likely that Carlyle’s phrase originated in a rather less benign environment, in his support for slavery in the West Indies. Economic theory of the time was heavily oriented to a laissez faire approach, emphasising the free market economy. Slavery finds no support in such a model of political economy and it was this that Carlyle found dismal. It’s interesting that List and Gneezy bring this full circle, to show how modern racism is as often grounded in economics as it is in simple hatred.

Field Experiments

At the heart of the book is an idea I've written about several times before – the field experiment. Traditionally psychology experiments take place in the laboratory where different variables can be controlled, such that only the condition under consideration is actually being examined: the classic scientific experiment.

Unfortunately laboratory experiments on humans suffer from disadvantages that those on inanimate matter don’t. Lumps of plutonium don’t generally try to figure out why scientists are experimenting on them, and adjust their behavior accordingly. Nor do they communicate amongst themselves in order to obtain the optimal rewards from their experimenters.

Humans, on the other hand, do all of these things. Researchers have to go to all sorts of trouble to obscure what they’re really doing.  Which means that results from the laboratory don’t always hold up in the real world; which can be a bit of a problem when you’re trying to design interventions or economic theory, or simply trying to predict the next financial crash.

In field experiments, however, the researchers look for naturally occurring situations that they can manipulate to extract economic insights. The participants don’t know they’re in an experiment so they don’t adjust their behavior, and as such the results have real validity. As List and Gneezy demonstrate this type of experiment can be used to change peoples’ lives for the better, and often at little cost.

Breaking Behavioral Economics

In a previous article, The Death of Home economicus, I discussed one of John List’s earlier experiments in which he examined whether the predictions of behavioral economics held under all conditions. What he discovered outside of the laboratory, by trading sporting memorabilia, is that they do – for inexperienced traders. The novice is loss adverse, and liable to suffer from the disposition effect. But more experienced traders have learned to manage these biases, even if they can't completely eradicate them.

Rewind, and think about the laboratory situation: here everyone is inexperienced, and no one has much of a clue as to what is going on. Everyone’s a novice and behaves like one. In the real world, in the stockmarket, a lot of us have been doing this stuff for a long time. We’re not perfect, but nor are we as imperfect as the core models of behavioral economics would have us believe: but you need to get out of the laboratory to prove this.

What Works and What Doesn’t

At the core of the book is the idea of using real-world situations to figure out what works and what doesn’t. Some of this seems incredibly basic, yet the results aren’t intuitive at all. Consider, for instance, their project to improve students’ attainment levels: the US ranks fifth in the world in terms of per pupil spending but high school education rates are around those of Turkey and Mexico: economically a lot of money is being wasted, and a lot of lives too.

They started with a simple idea – bribe the kids. Incentives work, if targeted properly. So they ran a range of experiments. In one experiment they offered the kids $20 for getting a better result on a standardized test than they did the last time they took the test. The average student score improved by 5 to 10 percentile points, to put low attainment children on a similar rating to that of wealthier middle class students.

Not surprising you might think. However, the first the students knew about this incentive was after they’d sat down to take the test: they did no additional preparation in order to obtain the reward. When you can see no reason to excel you won’t bother, but give someone a reason to care and you can get surprising results. Incentives matter, and a lot of the book looks at how to best target incentives to get the maximum effect.

Working in the Real World

List and Gneezy range over a whole host of issues, demonstrating how economics can have real impacts on people if you understand how money interacts with motivation. This is not theory, but real-world practice. For instance, a disabled driver is quoted 30% more for the same car repair than an able bodied person. Yet if the disabled driver simply says to the mechanic “I am getting three price quotes today”, the price differential disappears. The intervention is trivial, once you understand the problem.

This is an example of “economic discrimination”.  It’s not that that there’s any animus towards the victim, it’s purely that the perpetrator is making the economic calculation that they can take advantage of the situation. A bigot will always behave like a bigot – but someone who discriminates purely for economic advantage isn’t bigoted, they’re just badly incentivised. And a bit immoral.

Nudge Plus

The book also introduces an interesting twist on nudge theory. It’s possible to ”nudge” people towards better lifestyle decisions by making those options default choices – you have to opt out to not be an organ donor, or to not pay into your pension, and so on. The problem with this is: who decides what’s best for you? Using psychology to get people to do what is right for them may be the tip of a very nasty iceberg.

But List and Gneezy have managed to demonstrate that you can get even better results by making opt-in processes very simple. It’s not that people are fundamentally unwilling to do what’s right for them, it’s just that it’s all too often too complicated. As they put it:
“To increase peoples’ savings rates, many argue that the default trick can work well. Our results suggest that simply lowering nuisances and explaining savings rules, clearly and simply, might do a similar trick.”
In the Field

Well, we can hope. There’s an entire industry out there that’s built on obfuscation and confusion. It’s unlikely to change its behavior without regulation. And it spends a lot of our money ensuring that regulation isn’t going to happen.

The Why Axis isn’t a great book, but it is an important one. Amongst other things it discusses the difference between great companies and poor ones, and how the former use field experiments and the latter don’t. (Roughly, buy Intuit, sell Netflix). But most importantly, it’s a primer for us all. If you want to figure out what really works then consciously set out to find out by experimenting. As investors we can all do this, all the time: and then we are really are practising a passionate science.

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