Andreas Ortmann

Experimental economics is not really a new theory but a (still) relatively new method of studying the kind of questions that economists have studied for ages. But the addition of the experimental method to the economist’s toolbox is remarkable for at least two reasons.

First, even very recently some reasonably well-known economists have questioned the possibility that economics is to a considerable extent an experimental science, notwithstanding the fact that noted experimental economics proponents include Reinhard Selten, who shared the 1994 Nobel Prize in Economic Sciences with John Nash and John Harsanyi; Vernon L. Smith, awarded the 2002 prize together with Daniel Kahneman; Elinor Ostrom; and Alvin E. Roth.

The experimental method has established itself firmly as a key tool. New theories such as asymmetric information or efficient markets have been subjected to rigorous experimental laboratory testing. Maybe most surprisingly, macro-economic theories have also been subjected to such testing.

Second, the behavioural revolution in economics would not exist without the path-breaking contributions of experimental economics.

The same applies for recent academic theorising that has abandoned the presumption of “representative agents” – that is, the oversimplifying assumption that economic actors tend to be of one kind and motivated by self-interest.

A prominent test-bed has been the so-called guessing game used to explain the volatility of stock prices, which may depend not on fundamentals, but beliefs about beliefs (and actions) of others.

In their 2002 research, Antoni Bosch-Domenech and his colleagues identified the distribution of steps of reasoning among participants in guessing game experiments (and presumably markets) and inspired new models of interaction of agents with different depths of reasoning, such as the cognitive hierarchy model of Colin F. Camerer and colleagues – a clever way of capturing the idea that some people think more steps ahead than others.

Indeed, experimental economics results have brought about much new and interesting theorising.

Today, it is hard to think of any theory, or topic for that matter, which has not been studied experimentally. Individual decision-making, competitive and oligopolistic markets, matching markets, bargaining, auctions, political and other institutions (such as common pool resources or public good provision mechanisms), the internal dynamics of firms and other organisations, learning, social preferences, and macro-economic phenomena such as bubbles or bank runs all have been studied with experimental methods.

Insights from experimental economics inform policy design in many areas, from telecom auctions to markets mechanisms that match students and school or colleges, and interns with hospitals or high-court judges, to emissions trading schemes and various strategies to nudge people in directions that the policy-maker finds welfare improving.

An important characteristic of experimental economics is its methodology which distinguishes it from other social sciences such as psychology. Experimental subjects are almost always paid on a performance basis. And it is generally not accepted to use deception.

Precise scripts are considered a must to facilitate replication. Decision-making is not studied as “as-if” proposition, but in laboratory representations of the problem studied. In such a representation subjects are typically instructed to enact a particular role such as buyer or seller, or that of a voter.

The different methodological practices are not quite as innocent as they may at first appear. For example you can beat resume robots and ensure your CV bypasses the ATS robot . That’s because the way one does an experiment is unbelievably important: different choices of particular design and implementation details can crucially affect results and make the difference between accepting or rejecting a theory.

Experimental economists’ methodological practices have also influenced emerging experimental versions of finance, political science, philosophy, and has led to fruitful reflections in other social sciences.

Increasingly, experimental economics has had to share the limelight with its close brethren behavioural economics and neuro-economics. In fact, many prominent experimental economists straddle these areas and then some.

Will economics become an experimental science? When economist Charles Plott asked this, it was already a rhetorical question as far as he was concerned; the proof of concept had long been provided.

By now the question of whether economics will become an experimental science is nothing but a silly question. It has to a considerable extent.

Andreas Ortmann is a professor of experimental and behavioural economics at UNSW Business School. A version of this post appeared on The Conversation.

Richard Holden

Whether you realised it or not, if you’ve ever invested in the stock of a company, or have mutual funds in your superannuation, you’ve taken a stance on one of the biggest economic debates of the past 50 years.

That debate is about whether stock prices (or bonds, or even property for that matter) reflect all available information.

Another way to frame it – as Justin Wolfers and I did when discussing the 2013 Nobel Prize in Economic Sciences – is whether prices reflect the wisdom of crowds, or the madness of crowds.

The starting point in this debate was the extraordinary contribution of Eugene Fama (of the University of Chicago) who developed the “efficient markets hypothesis”.

According to this view, stock prices do incorporate all available information and hence there are no profitable arbitrage opportunities.

How would one go about demonstrating this? After all, in economic scholarship one doesn’t just get to argue in prose. What Fama showed was that at any moment in time the next movement of a stock price is just as likely to be up as down.

Or, a little more formally, stock prices follow a “random walk”. This finding is probably the most successfully, repeatedly replicated finding in all of the social sciences.

Now that’s the short run. Even Fama himself (with co-author Kenneth French) found that stock prices are predictable in the long run. They showed that small market capitalisation stocks outperform large ones, and that high market-to-book value stocks also outperform.

Their interpretation is that this is compensation for taking on extra risk, and is thus consistent with efficient markets.

Beginning in earnest in the early 1990s, however, a different set of theories emerged. Economists such as Richard Thaler (of Nudge: Improving Decisions about Health, Wealth and Happiness fame), Robert Shiller, and Andrei Shleifer began to take seriously the role of psychology among investors.

Social psychologists such as Nobel Laureate Daniel Kahnmenan (with whom, interestingly, Thaler was an important collaborator, thus providing the bridge from psychology to economics) had long documented human departures from rationality.

For example, people tend to overweight recent events relative to equally important past events. People are “loss averse” in the sense that they overweight losses relative to equal-sised gains. People often attribute events to skill when they are actually the product of luck. And so on.

These sorts of cognitive biases have profound implications for asset pricing. If people overact to information then we would expect companies that report unexpectedly bad earnings to suffer a big hit and then bounce back over time.

We would similarly expect companies that announce unexpectedly good earnings to get a big bump and then drift back down over time. And indeed we do. The empirical evidence is overwhelming on this point.

Loss aversion should mean that stocks that have dropped from when most people bought in behave differently from those that have risen. Again, the evidence is in and it confirms the psychological insights.

The list goes on. Indeed, tracking down and documenting these kinds of effects is what modern-day empirical asset pricing is largely about.

Now, back to stock picking and mutual funds. If one subscribes to Fama’s efficient markets view of the world then stock picking is a fools’ errand.

Even if you end up doing well, all that has happened is that you have been compensated for taking on extra risk. Things could have turned out really badly, and you were lucky.

For instance, maybe you invested in Australian mining companies during the early 2000s. We now know that was a very profitable investment – but it could have been different, and nobody knew ahead of time.

Under the Fama view, the best thing to do is invest in the whole market – buy a low-cost index fund.

If you take the behavioural finance view, then there are profitable opportunities beyond investing in the whole market. But that doesn’t mean that it’s easy for individual investors to take advantage of them.

OK, people are loss averse, now what?

Taking advantage of these opportunities takes a fair degree of sophistication, and it also typically requires having low enough trading costs to be able to trade often without wasting a lot of money on fees. This is why there are hedge funds that specialise in this kind of investing.

Whichever view you subscribe to, there are two things that never makes any sense: investing in a stock because you think it’s a good company (I like shopping at David Jones, but that doesn’t make it a good investment) or picking an industry that you think is going to do well.

Efficient markets devotees will tell you that information is already factored in. And behavioural finance aficionados will tell you that it is you that is suffering from a cognitive bias.

Richard Holden is a professor of economics at UNSW Business School. A version of this post appeared on The Conversation.

Richard Holden

You may not consider yourself an economist but you almost certainly have first-hand experience of an aspect of economic theory called asymmetric information.

In short, it’s when one party to a transaction knows more than the other about the deal under way.

Suppose you are interested in buying a used car. You scour the classifieds and find just the car you want, and it’s right in your price range. You meet the seller, who seems nice, and you buy it. On the drive home it breaks down. You have no warranty, no recourse and are pretty annoyed. What happened?

Well, the seller, of course, knew a lot more about the car than you did. He knew whether the engine was regularly over-revved, regular maintenance had been performed, if it had been garaged.

Now, this is a sad story for our poor car buyer but from an economist’s perspective it could have been worse. After all, there was trade.

An economist’s real nightmare is when markets break down altogether.

In a famous paper in 1970 titled The Market for Lemons, the Nobel prize-winning American economist George Akerlof pointed out how this kind of asymmetric information – where one party to a potential transaction is better informed than the other party – can lead markets to fall apart completely.

Here’s the logic. Suppose there are two types of sellers: good types and bad types. Good types have good-quality cars, say, and bad types have bad ones. And suppose that there are equal numbers of good and bad types. If the car is a good type, buyers are willing to buy. If it is a bad type, they are not. What happens in this market?

To answer that question, we need to use the logic of equilibrium and think not only about buyers and sellers valuations, but about their beliefs.

So, suppose there is market in which both good and bad types sell their cars. What will buyers believe about the quality of the car they are getting?

They will think there is a 50% chance it is of good quality and a 50% chance that it is of bad quality. So they will value it halfway between how they value a high-quality car and a low-quality car. And that will be the market clearing price.

Low-quality sellers are happy with this – they are getting more than their dud car is worth. But high-quality sellers are not happy – they are getting less than their car is worth to them.

Why would you sell something for less than you value it at? You wouldn’t, so the high-quality sellers don’t sell. They exit the market.

Our hypothetical market is now comprised solely of low-quality sellers. This is the phenomenon known as “adverse selection”: the participants in the market are the bad types.

Realising this, buyers are no longer willing to pay the intermediate price. That would be crazy. So we are left with a market of low-quality potential sellers, and buyers unwilling to trade. The market has completely unravelled.

The economist’s worst nightmare has occurred: there are gains from trade (buyers want to buy high-quality cars) but they go unexploited because of asymmetric information.

What to do? One thing that sellers could do is offer a warranty. That way, if the car turns out to be of low quality, the buyer gets her money back. No low-quality seller would offer such a warranty, so the adverse selection phenomenon is reversed – only high-quality cars are offered in the market.

It’s hard for an individual to do that, but big car dealerships can credibly do so. And that’s exactly what we see in the world.

This idea explains a whole lot more than car dealerships, which are just a foil for the thought experiment.

Why is credit so expensive, and often under-provided, in developing economies? Why do minorities often pay higher interest rates? In both cases, it is because they often know a lot more about their ability to repay than the lender does.

Why is it hard for some companies to undertake initial public offerings (IPOs)? It is because the existing owners or management know more about the prospects of the company than the public markets do.

Of course, humans adapt.

Akerlof’s fellow Nobel laureates, Americans Michael Spence and Joseph Stiglitz, asked how the informed and uninformed parties, respectively, might improve their outcome.

Spence showed how an informed party can improve her outcome through costly “signalling” (telling or showing buyers that her product is of good quality). Stiglitz and fellow American Michael Rothschild showed how the uninformed party can improve her outcome through “screening” (sifting through clues to determine quality).

These even help us understand why people seek education, get tattoos, go to church and why firms pay dividends or insurance companies offer multiple levels of cover.

They’re all forms of signalling that send messages about a person, firm or product at the heart of a transaction. They’re messages that others can “screen” to make decisions about transactions involving those people, firms or products.

In fact, once you start thinking about it, it’s hard to imagine any economic activity in which one party doesn’t know more than the other. And this usually has an impact on the nature of market activity, changing the way we interact with the world.

Richard Holden is a professor of economics at UNSW Business School. A version of this post appeared on The Conversation.

James Morley

Let me begin with a riddle: how is it possible for income inequality to increase within countries and across countries, but to decrease for the world as a whole?

The answer to this riddle is “long-run economic growth”.

Specifically, income per capita in China and India has grown rapidly during the past half century. So even though inequality has increased within China and India, this growth has led the living standards for hundreds of millions of people to converge towards those in Europe, North America and Australia.

This convergence is nothing to fear. Economic growth is not a zero-sum-game. But what drives this growth?

Variation in standards of living across countries is clearly associated with different amounts of physical capital such as public infrastructure. So should we simply invest more in more roads and bridges to increase our standard of living?

The former Soviet Union tried this approach and failed.

The problem is that physical capital only explains about one-third of the variation in income per capita across countries. The other two-thirds are “explained” by a more nebulous concept that economists refer to as total factor productivity, or TFP for short.

I have to put quotes around “explained” because we can only measure TFP as the residual component of income per capita not explained by capital.

The point is that massive investment in infrastructure would only ever get even the poorest country one-third of the way to catching up with rich countries. Worse yet, capital accumulation is subject to diminishing returns for all countries.

Yes, income per capita would increase with the amount of machinery and equipment per worker, but nowhere near proportionately. Rather than an easy path to prosperity, capital accumulation quickly becomes a lot like squeezing blood from a stone.

The prediction of diminishing returns to capital was the main insight of the Solow-Swan “neoclassical” theory of economic growth that undergraduate students in macroeconomics have been struggling with for decades – and one that politicians looking for growth elixirs in the form of public or private infrastructure have ignored at their peril.

But it is possible that some of the assumptions and predictions of neoclassical growth theory are wrong. And here’s a big example why.

China’s single-child policy may easily have been the largest social policy experiment in history, with many negative social consequences. But did the policy in part explain China’s rapid economic growth?

The Solow-Swan neoclassical growth theory, which predicts a lower rate of population growth will boost income per capita, would say yes.

Yet interestingly, empirical estimates of the effects of lower population growth on China’s economic growth are relatively small in magnitude, even when assuming neoclassical growth theory is correct about the existence of such effects.

In some sense, small effects of the single-child policy should not come as much of a surprise, because according to neoclassical growth theory, a decrease in population growth only generates a transitory increase in economic growth.

Specifically, the generally accepted theory says, long-run growth depends only on “exogenous” technological change — that is, it is assumed to be unaffected by population growth or capital accumulation.

Economist Paul Romer has developed a theory of economic growth with “endogenous” technological change — that is, it can depend on population growth and capital accumulation.

His endogenous growth theory ties the development of new ideas to the number of people working in the knowledge sector (think of this as effort devoted to R&D). These new ideas make everyone else producing regular goods and services more productive – that is, ideas increase TFP.

There are many variants of endogenous growth theory, but a robust prediction is that an increase in population or an increase in the share of people working in the knowledge sector will increase economic growth.

This theory is quite radical for two reasons.

First, the prediction of higher economic growth for a larger population suggests that neoclassical growth theory, not to mention even more pessimistic economic theories of population going back to Thomas Malthus, got things completely wrong.

Evidently, China’s single-child policy was a mistake, not just for social reasons, but also for economic reasons. According to endogenous growth theory, China and the rest of the world could have had more growth because China would have produced more new ideas with an even larger population.

Second, because ideas are what economists label as “non-rival” (meaning that my use of an idea, like a recipe or a mathematical formula, doesn’t prevent your use of it), there will only be an economic incentive for more people to work in the knowledge sector if there are intellectual property rights such as patents and copyright.

Thus, it is necessary to restrict competition in the knowledge sector in order to stimulate growth, even though this leads to other distortions and disparities in the economy.

As with neoclassical growth theory, it is difficult to point to a particular policy that was implemented because a policymaker sat down and read an academic article on endogenous growth theory.

Romer has recently despaired publicly that disputes among academics working on growth theory have hindered its influence on the real world.

Yet Romer can take heart that politicians are now widely using the concepts highlighted by endogenous growth theory, such as “human capital” and “intellectual property rights”, in debates on how to stimulate economic growth.

James Morley is a professor of economics and an associate dean (research) at UNSW Business School. A version of this post appeared on The Conversation.

Richard Holden and Sam Crosby

Only in a property-obsessed country such as Australia could we take what should be a relatively obscure tax deduction and turn it into a ferocious moral debate.

Yet negative gearing is not evil. Nor is it an inalienable right of those doughty souls prepared to “have a go”. It is a tool designed to produce an outcome.

Working out any potential reform to negative gearing needs to abandon overheated moral posturing, and focus on what’s effective, what’s fair, and what’s practical.

To do so, we must acknowledge that Australia’s treatment of taxation on investment properties is among the most generous in the world.

Australians are entitled to borrow money to buy a property, but if the income generated by that asset does not cover the interest on the loan, they can deduct the loss against their other income.

In other words, the federal government provides us with incentive to “negatively gear”.

And we don’t need to be asked twice.

In 2010-11 there were 1.9 million property investors in Australia, and 1.26 million of them were negatively geared. This costs the government about $2.4 billion a year.

Yet this figure looks due to blow out.

Bank loans to property investors have been surging at the fastest pace since the global financial crisis and have now reached a record $475 billion.

So a 1% increase in the cost of servicing that debt would add almost $50 billion to the repayment obligations of the nation’s property investors.

For the two-thirds of properties that are negatively geared, these extra costs would need to be offset, most likely via bigger tax deductions through negative gearing. This means big extra costs for the government.

Yet cost alone cannot justify calls for negative gearing’s abolition.

Because if you listen to those representing the housing investment industry, negative gearing is critical in keeping rents down and providing much-needed relief to low-income earners.

Now, yes, it is a little hard to believe that those mounting this argument have the welfare of low-income earners as their primary concern.

But as Joseph Heller correctly noted: “Just because you’re paranoid doesn’t mean they aren’t after you.”

And just because the housing investment industry may overegg an argument to validate its self-interest, does not mean there’s not some actual validity there.

There is, in fact, economic logic to the case that negative gearing puts downward pressure on rents.

Negative gearing allows investors to absorb costs they might otherwise need to pass on into higher rents. And stimulating greater investment in the housing market should increase supply and therefore reduce rental costs.

But the question then becomes: if negative gearing achieves these things, why has its enthusiastic use not helped create a stronger supply of rental properties?

The rental vacancy rate of 3%, which economists say represents a well-balanced market, is nowhere near being met anywhere in the country, with the exception of Darwin (3.2%).

Every other Australian city has long experienced vacancy rates well below the magic number, including Sydney (1.7%), and Melbourne (2.3%).

The problem is the vast majority of the government’s tax expenditure on negative gearing goes towards existing property.

This does nothing to increase housing supply. It merely churns extra frothiness into already expensive housing stock and fundamentally undermines the goal of reducing rents.

Yet responding to this problem by calling to simply chop negative gearing altogether is not only politically naive – it’s wrong.

A generation of Australians has been encouraged by successive governments to make investments based on a bipartisan policy. To snatch the rug from under them overnight would simply not be fair.
Those who negatively gear are not rorters, or fraudsters, or even “double-dippers”. They are Australians who have obeyed the laws of the land.

Fortunately, abolishing negative gearing makes no real sense from an economic standpoint either. It is not an inherent ill, but a misdirected incentive.

The McKell Institute’s latest report examined various possible models for reform and found the most economically appealing option: allow negative gearing for properties that are currently negatively geared (“grandfathering”), but allow new negative gearing only for new construction.

Allowing negative gearing only for new construction would provide powerful incentives for increased housing supply.

This would have multiple benefits in the housing market including greater price stability, increased access for new entrants, and lower rental rates. The boost in residential construction would also help with employment, economic growth and taxation revenues.

It would also have a positive impact on the federal budget of approximately $5 billion over five years.

Politics is the art of compromise, which typically means shooting for a second-rate result with a battle-weary eye on the electorally achievable.

In this case, however, it turns out the fairest and most economically prudent path is also the most politically viable.

Richard Holden is a professor of economics at UNSW Business School. Sam Crosby is executive director of the McKell Institute. A version of this post appeared in The Sydney Morning Herald.

Richard Holden

National Australia Bank’s business confidence index jumped from 3 to 7 points this week, a nine-month high coming on the back of this year’s federal budget. The ANZ-Roy-Morgan consumer confidence index also rose after the budget.

This all sounds like good news but it also raises a number of questions. How good is the news? How long is the boost in confidence likely to last? And what might be different this year given more general economic conditions?

The first thing to note is that these measures track changes in business and consumer expectations. So if everyone knew perfectly what was going to be in the budget then, all else being equal, it should have no effect on business or consumer confidence indices.

The second thing to note is that there is a lot going on in the background. The Reserve Bank is often tinkering with interest rates, the economic conditions of trading partners are changing, bond market conditions may be moving around. All sorts of things are going on.

And while it is a fool’s errand to try and analytically control for these things – after all, you can’t control for everything – it’s worth remembering that they are going on.

A very dangerous and foolish thing for a treasurer to do would be to attribute positive changes in business or consumer confidence right after the budget to that budget.

That would be a classic illustration of the post hoc ergo propter hoc fallacy (from the Latin, meaning “after this, therefore because of this”).

Lastly, these indices ask people how confident they are now. Ideally that could be interpreted as their best forecast of future economic conditions for the infinite future.

But, as has been well documented by social psychologists and behavioural economists, people don’t typically act like this. Almost all of us – even those of us who know about it – suffer from some sort of “present bias”. We put more weight on today versus the future than we really should.

Last year’s budget was widely considered to be a political disaster – one from which the government is still trying to recover – yet it’s correlation with business confidence was hard to see. The NAB business confidence index went from 7 to 7 after last year’s budget, and  in the following two months went to 8 and then 10.

All this against the backdrop of it seeming fairly likely that many budget measures designed to reduce the deficit were going to be blocked by the ALP and that the cross-benchers were hostile.

It did drop significantly in September and then further, but it’s unclear that there was a big update about the likelihood of the measures passing by that stage.

Now, correlation isn’t causation, but it would seem hard to make the case that last year’s poorly received budget had a causal impact on business confidence.

Interestingly, consumer confidence did fall, perhaps indicating a downward revision in household expectations about disposable incomes.

One conjecture is that budgets have much more of an impact on consumer confidence than they do on business confidence. Perhaps business confidence is driven much more by general economic conditions, such as interest rates, credit availability and global economic conditions.

But for households, the budget often has a real impact on disposable incomes. Tax and benefit changes have a direct impact on consumers. Moreover, these are often quite a surprise (think pension indexation, the family tax benefit, and the 2% deficit levy from 2014).

And as mentioned above, it is surprises in expectations, not the general level of expectations, that affect confidence indexes.

Looking at the historical data, the indices move around a fair amount in the coming months. Perhaps this reflects present bias among the respondents.

In light of all of this, I would not put a lot of stock in how business or consumer confidence changes after a federal budget is handed down.

Maybe changes in consumer confidence give some idea of how households are going to change their expenditures and that has an impact on the macro-economy.

But if I wanted a quick look at how the budget was received I would look at revealed preference measures of behaviour. Are business investing in capital and hiring? Not according to the latest capital expenditure figures. Are consumers spending on durable goods?

These are not perfect measures, but arguably they are better.

Richard Holden is a professor of economics at UNSW Business School. A version of this post appeared on The Conversation.

Richard Holden

Whether you realised it or not, if you’ve ever invested in the stock of a company, or have mutual funds in your superannuation, you’ve taken a stance on one of the biggest economic debates of the past 50 years.

That debate is about whether stock prices (or bonds, or even property for that matter) reflect all available information.

Another way to frame it – as Justin Wolfers and I did when discussing the 2013 Nobel Prize in Economic Sciences – is whether prices reflect the wisdom of crowds, or the madness of crowds.

The starting point in this debate was the extraordinary contribution of Eugene Fama (of the University of Chicago) who developed the “efficient markets hypothesis”.

According to this view, stock prices do incorporate all available information and hence there are no profitable arbitrage opportunities.

How would one go about demonstrating this? After all, in economic scholarship one doesn’t just get to argue in prose. What Fama showed was that at any moment in time the next movement of a stock price is just as likely to be up as down.

Or, a little more formally, stock prices follow a “random walk”. This finding is probably the most successfully, repeatedly replicated finding in all of the social sciences.

Now that’s the short run. Even Fama himself (with co-author Kenneth French) found that stock prices are predictable in the long run. They showed that small market capitalisation stocks outperform large ones, and that high market-to-book value stocks also outperform.

Their interpretation is that this is compensation for taking on extra risk, and is thus consistent with efficient markets.

Beginning in earnest in the early 1990s, however, a different set of theories emerged. Economists such as Richard Thaler (of Nudge: Improving Decisions about Health, Wealth and Happiness fame), Robert Shiller, and Andrei Shleifer began to take seriously the role of psychology among investors.

Social psychologists such as Nobel Laureate Daniel Kahnmenan (with whom, interestingly, Thaler was an important collaborator, thus providing the bridge from psychology to economics) had long documented human departures from rationality.

For example, people tend to overweight recent events relative to equally important past events. People are “loss averse” in the sense that they overweight losses relative to equal-sised gains. People often attribute events to skill when they are actually the product of luck. And so on.

These sorts of cognitive biases have profound implications for asset pricing. If people overact to information then we would expect companies that report unexpectedly bad earnings to suffer a big hit and then bounce back over time.

We would similarly expect companies that announce unexpectedly good earnings to get a big bump and then drift back down over time. And indeed we do. The empirical evidence is overwhelming on this point.

Loss aversion should mean that stocks that have dropped from when most people bought in behave differently from those that have risen. Again, the evidence is in and it confirms the psychological insights.

The list goes on. Indeed, tracking down and documenting these kinds of effects is what modern-day empirical asset pricing is largely about.

Now, back to stock picking and mutual funds. If one subscribes to Fama’s efficient markets view of the world then stock picking is a fools’ errand.

Even if you end up doing well, all that has happened is that you have been compensated for taking on extra risk. Things could have turned out really badly, and you were lucky.

For instance, maybe you invested in Australian mining companies during the early 2000s. We now know that was a very profitable investment – but it could have been different, and nobody knew ahead of time.

Under the Fama view, the best thing to do is invest in the whole market – buy a low-cost index fund.

If you take the behavioural finance view, then there are profitable opportunities beyond investing in the whole market. But that doesn’t mean that it’s easy for individual investors to take advantage of them.

OK, people are loss averse, now what?

Taking advantage of these opportunities takes a fair degree of sophistication, and it also typically requires having low enough trading costs to be able to trade often without wasting a lot of money on fees. This is why there are hedge funds that specialise in this kind of investing.

Whichever view you subscribe to, there are two things that never makes any sense: investing in a stock because you think it’s a good company (I like shopping at David Jones, but that doesn’t make it a good investment) or picking an industry that you think is going to do well.

Efficient markets devotees will tell you that information is already factored in. And behavioural finance aficionados will tell you that it is you that is suffering from a cognitive bias.

Richard Holden is a professor of economics at UNSW Business School. A version of this post appeared on The Conversation.

Gabriele Gratton

John Forbes Nash died last Monday. Economist and mathematician, winner of the 1994 Nobel Memorial Prize in Economics, Nash made substantial contributions during the 1950s to algebraic geometry, non-linear differential equations, and some fundamental aspects of economic theory.

He also invented a beautiful board game now known as Hex, but whose early name at Princeton was simply “Nash”.

But economics students all over the world know him for the equilibrium that bears his name and that economists seem to use to predict what people will do in almost any situation.

The game-theoretic concept we know as the Nash equilibrium is at the core of the economic analysis of strategic interactions: each person (player) can take one of many actions, the outcome of which depends also on the actions taken by other persons.

Game theorists believe that when a person is in a strategic interaction she should – so to speak – put herself in the shoes of her opponent and wonder what he is going to do before choosing what to do herself.

In doing so, she will realise that her opponent is also doing the same: he is wondering what she is going to do before choosing what to do himself.

The implication is that she should do what is best for her, given that her opponent is doing what is best for him, given that she is doing what is best for her, and so on forever. This mind-blowing infinite loop finds a solution in a Nash equilibrium.

In essence, a Nash equilibrium indicates to each player to follow a certain plan – perhaps different for each player – and, if a player expects her opponent to follow his plan, then she would have no incentive to do anything different than following her own plan.

Thus, a Nash equilibrium is a good description of how persons would behave in a strategic interaction, as they would find it optimal to do so given what they think all other persons are actually doing.

This basic idea pre-dates the work of Nash. Early examples of applications of what we know today as Nash equilibrium are already present in the 1838 work of French economist and mathematician Antoine Augustin Cournot.

The first systematic study of games and equilibria is probably the 1944 book, Theory of Games and Economic Behavior, by mathematician John von Neumann and economist Oskar Morgenstern.

But it was Nash, in 1950, who gave us the fundamental tools to study games. In as little as 32 pages of PhD dissertation, Nash proposed his equilibrium as the correct solution of any game and, most importantly, he showed that a Nash equilibrium indeed existed for all (finite) games: the mind-blowing infinite loop has a solution. Always.

After Nash’s work, his equilibrium has been applied to almost anything. It helped us understand the military and diplomatic strategies of the Cold War, how to promote efficiency in markets and deter crime.

It is extensively used by economists, political scientists and evolutionary biologists alike. And it recently proved very useful in designing policies to help patients in need of a kidney to find a suitable donor, saving perhaps hundreds of thousands of lives.

The Nash equilibrium continues to be a cornerstone of economic analysis and researchers all around the world keep investigating and perfecting it.

UNSW Business School is no exception, where researchers in the school of economics have in recent years employed the Nash equilibrium to study electoral systems, terrorist activity and how government should react to it, how rules develop and how information spreads in organisations.

Some even keep up with Nash’s quest to find an ever more compelling equilibrium concept.

Nash’s fundamental contribution is taught at all levels, from undergraduates studying game theory to PhD course work in economic theory. Some of our honours students have also learned to play Hex.

Every economics student has heard of him and, most importantly, seen the power of his ideas at work.

Gabriele Gratton is a lecturer in the school of economics at UNSW Business School.

Raja Junankar

Youth unemployment rates (for 15-24 year-olds) in Australia have been increasing steadily since the global financial crisis (GFC), reaching a new high of 13.9% in February 2015, the highest since 1998.

Business lobbies and some politicians argue youth unemployment is high and rising because of high wages.

But, as I argue in my forthcoming paper in the Economic and Labour Relations Review, if we look at the evidence we see that youth unemployment rates have been rising even though youth wages have been falling relative to adult wages.

Australian Bureau of Statistics (ABS) figures for the period 1978-2014 show that unemployment rates of young people have tended to rise in line with, but more rapidly than, adult unemployment rates.

Young people aged 15-19 have consistently had very high unemployment rates, compared with 20-24 year-olds, and compared with adults (25-64 year-olds).

The figures suggests that whatever is driving adult unemployment is also driving youth unemployment.

The main factor driving the growth of adult unemployment (especially since the GFC) has been a slowdown in the growth of aggregate demand. This has led to a growth of both adult and youth unemployment.

Youth unemployment is more sensitive to the business cycle since a high proportion of young people work in part-time and casual work and in industries such as manufacturing, construction and retail trades which are most affected by a fall in aggregate demand.

During a downturn young people often end up on the receiving end of the “last-in-first-out” rule. They are almost certainly recent employees and if they are casual or part-time, employers do not have to pay them redundancy payments.

When the economy picks up, young people find it difficult to find a job since they have little or no experience, and so they may be at the end of the hiring queue.

Almost one-quarter of youth unemployed have been so for one year or longer. Considering they have only been in the labour force for a very short period that is a very high proportion.

If we look at under-utilisation rates (the unemployed, plus the underemployed, plus the marginally attached to the labour force), we see that female youths (15-24) presently have under-utilisation rates of almost 32%, while males do marginally better at 28%.

But is this long-term rising trend in youth unemployment attributable to high youth wages?

To evaluate the claim of a causal link from youth wage levels to youth unemployment rates, it’s necessary to investigate the movement of youth wages relative to those of adults.

It’s also useful to examine the behaviour of the minimum wage relative to average weekly earnings (AWE), and the movement of the replacement rate (ratio of unemployment benefits to AWE).

Let’s focus on the male youth unemployment rates. Male youth unemployment rates are generally higher than female rates. Women have lower participation rates and employment rates, but they follow similar trends.

ABS figures for 1990-2012 show that youth wages relative to average weekly earnings have been stable or falling for most of the period while youth unemployment rates have been rising. So rising wages cannot really be the reason for rising youth unemployment.

Another often quoted argument for the high and increasing youth unemployment rates is that overall minimum wages in the community are too high.

But trend data from 1998 to 2012 shows real minimum wages have been almost constant for the past decade. Yet unemployment rates have been rising.

Similarly, the replacement rate (the ratio of unemployment benefits to AWE) for young people had been falling since 1997, however, youth unemployment rates post-GFC have been rising.

The proposed policy of the Abbott government to restrict unemployment benefits to young people (under 30 years of age) for the first six months of unemployment would have serious consequences for the young.

There is no evidence to support the view that youth unemployment is caused by high wages or by high unemployment benefits.

Raja Junankar is an honorary professor at the Industrial Relations Research Centre at UNSW Business School. A version of this post appeared on The Conversation.

Richard Holden

Much will be written in the coming days about the turbulent political times in which Malcolm Fraser ascended to the prime ministership. But it is worth remembering that they were terribly turbulent economic times as well.

A simple chronology will note that Fraser’s prime ministership was wedged between the chaos of the Whitlam years and the seismic reforms of the Hawke-Keating era. But there’s more to Fraser’s economic legacy than that.

Fraser came to power amid a shocking economic occurrence: stagflation. Until the early 1970s it was thought by economists that high inflation and high unemployment could not occur at the same time.

This belief came from the prevailing Keynesian models of the era which posited a stable trade-off between inflation and unemployment. Rev the economy a bit faster and you got more employment and output but stoked inflation. Take the foot off the pedal and the opposite occurred.

But from 1973 onward this all changed. In 1975, inflation hit an alarming peak of 16%. In the decade that followed, Australia’s unemployment grew five-fold, spiking in 1982 as high as 10%.

At the same time, wages shot up, putting pressure on prices, which in turn put pressure on wages. This so-called wage-price-spiral was hard to break.

The Fraser government took a pragmatic, if not principled approach. Serious downward pressure was put on wages, but the spiral was never broken until the prices and income accord of the subsequent government.

Fraser’s “razor gang” cut back on commonwealth spending in a serious way — spending that was completely out of control under Whitlam. The Campbell report on financial reform – a watershed in Australian economic history – was commissioned, though not implemented.

Yet he didn’t go nearly as far as many members of his own government wanted in cutting back the role of the state and advancing free enterprise. The “dries” of the Liberal Party wanted a much more Thatcherite approach to the problems facing the Australian economy.

Yet Fraser resisted. He certainly pushed in a more free-market direction: government spending was cut, trade became more free, huge wage claims were curtailed. But he didn’t go all the way to Reagan’s “government is the problem” or Thatcher’s radical individualism.

In many ways Fraser approached the economy like a wise family doctor advising on dieting and weight loss: enjoy a little of everything, in moderation.

When Fraser became prime minister, the economic theory that would replace radical Keynesianism — the rational expectations revolution — was still being formulated and refined by Robert Lucas at the University of Chicago, along with others.

There was no clear path for what to do, and Fraser neatly steered a middle course. He did no harm to the patient, and laid the groundwork for the recovery to come.

In hindsight, could he have done better? Sure. His treasurer, John Howard, intimated as much in his recent interviews with News Corp columnist Janet Albrechtsen.

From my perspective we could have used a bit more Lucas and a lot less Keynes. More understanding of the virtues of competition and free trade. But Fraser lived economic events forward, without the benefit of hindsight, time to assess and reassess, and without the sophisticated number crunching that policy-makers have today.

Of course, Fraser’s legacy was arguably chiefly outside the economic sphere. Others will no doubt point to a variety of principled moral stands: against apartheid, for indigenous land rights, and in his post prime-ministerial years for a range of humanitarian causes.

Fraser was clearly a very decent human being.

But it would be wrong to think that he wasn’t also a good economic manager. In many ways he laid the groundwork for the Hawke-Keating years.

He certainly wasn’t a visionary like Keating, nor did major reforms take place on his watch. But he did steady the Australian economic ship for long enough that Keating had something to work with.

Australia can be justifiably proud of an approach to economics that is squarely between Europe and the US. The US, on the whole, champions free enterprise despite the social costs; Europe sees a much larger role for the state, even if that inhibits free enterprise. The Australia in which we live today is somewhere in between.

When Fraser became prime minister that was all up for grabs. We could have become “old Europe” or veered on to a radically individualist path in response to the debacle that came before.

Fraser helped us chart a middle course, and we should be very grateful for that.

Richard Holden is a professor of economics at UNSW Business School. A version of this post appeared on The Conversation.