You can’t borrow from the future!

“We are borrowing from the future” is a common phrase you might hear from economists musing about the state of the economy; about the behaviour of individuals, businesses and especially of government.

These statements arise in discussions about ageing, stimulus, social security, public investment, public debts, health, education and almost every other public policy topics in which economists self-declare some degree of expertise. To really drive home the entrenched nature of such thinking in economics, here’s Satyajit Das saying “Debt allows society to borrow from the future” and here’s something purporting to be an economics text saying the same thing.

Oh, and it’s a favourite line the double-speak repertoire of Tony Abbott and Joe Hockey.

All of this is truly odd. It’s nonsense really. Perhaps expected from politicians, but not from a profession that usually ‘looks through’ the veil of money to the utilisation of real resources in the economy.

The confusion rests on a conflation of money with resources; if money equals a claim on resources then borrowed money, or debts in general, therefore equates to resources borrowed from the future. Will Ricardian Equivalence ever die?

All debts are transfers of purchasing power for current resources, despite new bank-issued debts not requiring current funding from a third party (as in the loanable funds model). In a direct credit transaction (peer to peer lending or credit channels including loanable funds) one party gives up their current purchasing power to another, with repayments and interest being a reversing of the transaction over time. No borrowing from the future there.

When new money is created through lending from the banking system, the same thing occurs, except that the society as a whole transfers resources to the entity spending the new money through inflation via their newly available purchasing power. This is usually known as by the concept of seniorage, though rarely is new lending discussed in these terms.

The whole point is that future resources don’t exist yet, so they can’t be consumed in the present! There is no transfer of resources - no hover boards are removed from the future and brought into the present via lending.

Which brings us back to often hotly debated idea of counter-cyclical fiscal policy, which is fundamentally used to increase demand for current production outputs, increase labour demand and employment and inflation, and invest in capital goods to be used in future period to produce those as yet uncertain future products.

Luckily there are some common sense economists out there. At least there was back in 1961 when Abba Lerner wrote this note about the impossibility of shifting burdens onto the future for society as a whole in response to a rather confusing article attempting to say the opposite in the American Economics Association’s most prestigious journal in 1960. Some of the ‘new generation’ are feeling the need to repeat this mantra in blog form.

If all of this isn’t enough, here’s the clincher - if today’s debt is borrowing from future generations, can’t we simply use tomorrow’s debt to borrow from later future generations indefinitely for the infinite future? Yes, yes we can.

Money and debt are mere tools of social goals. They are not the real resources of the economy but records of transaction and ownership claims. We can change the rules at any point to suit our social desires - debts can be forgiven, defaulted on, inflated away, or they can be used to justify war.

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Everything I was afraid to ask about Bitcoin but did

The econ-blogosphere has been Bitcoin crazy for a while now. I haven’t quite understood what all the fuss is about, and knowing the personalities involved in much of the hype, I was afraid to ask too many detailed questions.

But I did anyway.

I finally put together my views following Rabee Tourky’s post at Core Economics, and a recent note by CommSec’s Craig James earlier in the week.

So what are the big questions about Bitcoin that need answering? There are two: What is its purpose? And, how will it maintain value and avoid volatility?

To answer the first question it is worth starting with Bitcoin founder Satoshi Nakamoto’s paper about a peer-to-peer electronic cash system. He repeatedly remarks that the benefit of electronic cash is being able to avoid intermediary financial institutions, thus cutting down transaction costs, and that the reversibility of such facilitated transitions is an inherent weakness. I quote from the paper at length.

While the system works well enough for most transactions, it still suffers from the inherent weaknesses of the trust based model. Completely non-reversible transactions are not really possible, since financial institutions cannot avoid mediating disputes. The cost of mediation increases transaction costs, limiting the minimum practical transaction size and cutting off the possibility for small casual transactions, and there is a broader cost in the loss of ability to make non-reversible payments for non- reversible services. With the possibility of reversal, the need for trust spreads. Merchants must be wary of their customers, hassling them for more information than they would otherwise need. A certain percentage of fraud is accepted as unavoidable. These costs and payment uncertainties can be avoided in person by using physical currency, but no mechanism exists to make payments over a communications channel without a trusted party. 

What is needed is an electronic payment system based on cryptographic proof instead of trust, allowing any two willing parties to transact directly with each other without the need for a trusted third party. Transactions that are computationally impractical to reverse would protect sellers from fraud, and routine escrow mechanisms could easily be implemented to protect buyers. In this paper, we propose a solution to the double-spending problem using a peer-to-peer distributed timestamp server to generate computational proof of the chronological order of transactions. The system is secure as long as honest nodes collectively control more CPU power than any cooperating group of attacker nodes.

Here’s where the circularity of arguments about trust comes in, and where my first question about the purpose of Bitcoin becomes rather confusing. What sort of transaction would buyers be willing to undertake without a trusted intermediary? Twitter was not much help either…

@Rumplestatskin Donations to political organizations (e.g. Wikileaks). Possibly some international transactions. And of course ideology.
— Nicolai Hähnle (@nhaehnle) November 20, 2013

@Rumplestatskin it's not reversable so the transaction can be trusted - once it is done it's done
— marklanderson (@marklanderson) November 20, 2013

But even in the case of ‘dodgy anonymous transaction’ as one of my mates suggested on Facebook, the whole point of Bitcoin is a record of transactions or ‘money as memory’. A court could order Bitcoin's miners, online waller suppliers or others involved in the network to disclose knowledge of transaction details and wallet identities in any case. Not only that, US officials have shut down digital currency operations in the past.

Authorities have also been looking into the criminal aspects of virtual currencies. Wolf Richter’s exposition of Bitcoin examines some of their discussions.

Officials from the Secret Service, the Treasury’s Financial Crimes Enforcement Network, and the Justice Department bragged to the committee about successful investigations of crimes where bitcoin or other virtual currencies were used, including the busts of Silk Road, eGold, and Liberty Reserve. They were confident that they knew how to tamp down on criminal use of virtual currencies. No one expressed outright alarm about the new world of bit coin.

Since every transaction of every bitcoin is forever recorded and part of the system, Mythili Raman, acting assistant attorney general at the Justice Department’s criminal division, pointed out that “cash is still probably the best medium for laundering money.” And she admitted that “many virtual currency systems offer legitimate financial services and have the potential to promote more efficient global commerce.” 

At the word legitimate, bitcoin soared. And I mean, SOARED.

My line of thinking about potential benefits of Bitcoins is to consider what sort of transaction I would like to be unable to reverse. Would I ever purchase items on eBay with irreversible electronic cash, assuming that eBay itself did not provide any other intermediary role apart from advertising? Nakamoto seems to suggest that the cost of financial intermediaries excludes very small transactions, yet facilities like Flatter seems to overcome this problem through batching transactions.

The success of Paypal as an online payment system is partly due to the insurance it buys for both buyer and seller for the transaction. Anyone who refused payment from Paypal would be signalling their untrustworthiness or unwillingness to meet conditions of any mediated dispute. The point being, rather than creating a payment system that doesn’t rely on trust, using Bitcoin over other payment methods will itself signal a lack of trust. All transactions require some trust. There is no escaping that. Online that is even more important. For example, you pay me with Bitcoins, then I don't post your goods, what recourse do you have?

So far there is no reasonable answer to my first question about the purpose of Bitcoins. 

My second question unfortunately reveals similar unsatisfactory answers. If Bitcoins really are limited by constraints on ‘mining’, then that will mean that in a situation where they are in demand as a medium of exchange, they will also be increasing in value and be a means of investment. As more people prefer to hold Bitcoins as investments rather than exchange them, this will push their value higher still. If you can’t see it coming, the end result is a massive bubble followed by a crash when the herd realises that their investment value was purely based on herd mentality, without any fundamental resources backing it, and that the system is no longer being used as a medium of exchange. This view has been put forward previously by Eric Posner.

It’s not like alternative payment methods have not been tried many times before. Bartercard springs to mind as one system that survives in its business-to-business niche. 

So let’s summarise. Bitcoins have been severely hyped online yet almost no one can suggest scenarios for both buyers and sellers in which they are actually a more useful medium of exchange than current costly reversible transactions. Furthermore, the ability for Bitcoins to hold there value is severely hampered by the nature of their technically limited supply. To top it off the only people I know of who have owned Bitcoins were speculating and never used them to transact. I can only conclude that this episode will go down in history as a lesson about the nature of money and trust in facilitating trade.

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What limits housing supply… one more time

My long term view, based on experience in the property development industry and in planning regulations for water and infrastructure, is that zoning is not a binding constraint on the rate of supply of new housing.

There is no doubt that zoning and other planning measures limit the type and scale of use on any particular site, as they are intended, but in aggregate they do not constrain the rate of new housing. For zoning to truly hamper housing supply there must be no undeveloped lots available within a zoned area. Indeed, all land must currently be at its highest value use, meaning these would cease to be a development industry altogether, and land banking would be the stuff of imagination.

One reason for the confusion around housing supply is the simplification inherent in almost all economic models of markets whereby the free entry condition means that any positive NPV project is instantly produced. There is no time in the model, and therefore no ability to delay investment.

To deal with the realities of the irreversibility of investments and the ability to delay, Black, Scholes and Merton in the 1970s developed methods for valuing the option to invest in irreversible capital at some point in the future. Merton and Scholes were even awarded an Economics Nobel for their trouble in 1997.

Following these methods a large body of work has emerged that addresses firm choices with real options - that is, when the firm faces genuine options to delay irreversible investments. Firms then face compound investment decisions; what to invest in, and when to invest in it. 

Why is this important for housing supply? Well, only in a world where real options exist can land remain undeveloped or in low value uses when higher value uses exist. Thus any analysis of land markets that is able to account for the large volume of undeveloped land must be based on the real options of land owners.

I have written about research into the nature of durable goods markets in the past, particularly the debate over the Coase Conjecture of how a monopoly land owner would drip feed supply to maximise the value of their land, since by building more homes now they will compete with the home they build in the future.

Yet real options is far more general, embedding these ideas into a much more robust theory. So what happens to land models when you account for real options?

Strangely enough in 1985 Sheridan Titman asked this exact question and published his results in a little journal called the American Economic Review, in an article entitled Land Prices under Uncertainty.

Titman constructs a model based on the idea of options to reveal the types of fundamental characteristics the drive the choice by a land owner to develop, which include the expectations of future changes to the optimal density of development, as well as future rental price paths. 

Under his model of real options in land (and indeed any model derived from this proposition) the land owners response to external conditions is quite different that in the basic model of perfect markets. He writes

It is shown that the initiation of height restrictions, perhaps for the purpose of limiting growth in an area, may lead to an increase in building activity in the area because of the consequent decrease in uncertainty regarding the optimal height of the buildings, and thus has the immediate affect of increase in the number of building units in an area.

This is something I’ve said before, and it is worth repeating. Increasing zoning in an area provides an incentive for land owners to delay development and hold out for further changes in zoning. The reverse is also true, and I’m sure everyone would agree that if you announced a reduced maximum density in an area that there would be a rush of development prior to the loss of the ‘option’ to develop greater densities.

A similar situation will happen with changes to developer costs. Infrastructure charges are often blamed for the high cost of development, but in any theoretical picture involving real options, reductions in infrastructure charges will delay rather than accelerate development. Similar problems arise with stamp duty. Calls to reduce stamp duty arise due to equity concerns, yet they have been shown not to increase housing prices, and in other markets such transaction taxes, or Tobin taxes, are being proposed to reduce volatility.

Sure I’m all for land taxes, but replacing a rather good tax in the form of stamp duty, rather than highly distortionary taxes such as payroll and income tax, provides a much smaller social gain. 

Given that rental prices in most Australian capital cities are falling relative to incomes, land markets must be functioning as roughly intended. My earlier ideas on rental controls may also reduce the rate of growth of housing prices, leading to increases in housing investment as the payoff from withholding undeveloped land decreases.

For all the talk of ‘elastifying supply’, there if very little in the way of considered logical thought about exactly what factors generate the current rate of new housing supply. Only by acknowledging the real options for future development held by land owners can we begin to understand the true fundamentals driving housing supply patterns.

Economics makes you selfish

I was motivated to write this post by fellow Australian young economist Gabriela D’Souza

"After Taking Economics, Students Become More Selfish and Expect Worse of Others" Well that's just common sense.
— Gabriela D'Souza (@gabster0191) October 24, 2013

I disagree. Selfishness is not common sense. It all seems to have started with this article, part of the periodic publicity the sprouts up around new studies into the selfishness of economists and economics students.

There is now quite a deal of evidence that economists are ‘more selfish’ than other groups. Here is some research showing lower rates of donations by economics students. Here is research showing economics students lie more, and here is a good summary of other research. The evidence is overwhelming that economists act in ways which most people find unacceptably selfish.

To me this body of evidence reveals the massive disconnect between mainstream theory in economics, that rests on the fundamental notion that greed or selfishness is the driver of coordination in a market economy, and the reality that social cooperation rests fundamentally on trust.

I would certainly agree with Francis Amasa Walker’s 1879 interpretation of the apparent social “odor” of economists arising from their disregard of “…the customs and beliefs that tie individuals to their occupations and locations and lead them to act in ways contrary to the predictions of economic theory.”

As Frans de Waal explains “Economists are being indoctrinated into a cardboard version of human nature, which they hold true to such a degree that their own behavior has begun to resemble it… Exposure in class after class to the capitalist self-interest model apparently kills off whatever prosocial tendencies these students have to begin with. They give up trusting others, and conversely others give up trusting them. Hence the bad odor.”

Without justifying this behaviour, let me just make it clear that economic indoctrination teaches that this apparently selfish behaviour is both what everyone actually does (despite ample evidence to the contrary), and that through self interest we prosper. They have swallowed this iconic Adam Smith quote hook line and sinker.

It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest

I want to use this post to provide an example of how such a view, an economic way of thinking, can lead you astray in everyday life. I draw on ideas from my good friend Uwe Dulleck, whose expertise is credence goods.

Credence goods are those whose value or utility can never be known by the buyer due to information asymmetries. The classic examples are doctors, who can prescribe medication for a diagnosed illness which you will never know is what you are truly suffering from. Or car mechanics, who diagnose mechanical failures and sell repairs, without the customer being able to know whether such repairs were either needed or carried out.

As usual Uwe’s research centres on some important questions

Under which conditions do experts have an incentive to exploit the informational problems associated with markets for diagnosis and treatment? What types of fraud exist? What are the methods and institutions for dealing with these informational problems? Under which conditions does the market provide incentives to deter fraudulent behaviour? And what happens if all or some of those conditions are violated?

Uwe introduces a simple example of a behaviour that, by economic reasoning, is expected to reduce fraud in credence goods markets

For some of us a feasible solution might be… to ask the mechanic to put the replaced part in the back of the car and to inspect the defect of this part. 

Uwe is cautious about whether this advice is sound. As am I. But I reckon that most economists would be more than happy to take this advice based on the ‘economic intuition’.

But does the common sense of unselfish non-economists also support this behaviour? Or is this an example of how the economic model of self-interest can lead us astray? I suggest the latter. And as a peek at my conclusion, the behaviour I might advise is to buy the mechanic a six-pack of beer.

Imagine you are a mechanic. Occasionally you realise that a customer is a bit of a sucker with too much money, so you charge them a little extra for some repairs you didn’t do. Most of the time you are pretty straightforward and honest.

One day a new customer comes in. They don’t seem particularly knowledge about cars, and since this is their first visit there is nothing to suggest they will become a regular customer. You diagnose the problem with their car, which is a very typical problem in that model, and explain that the repair could involve replacing certain parts, but you won’t know till you start taking things apart. This new customer agrees to go ahead with the repair, but asks you to put the old parts in the boot when you are done. It’s an odd request.

You realise that by making this request the customer has revealed that they are less knowledgable about cars than you thought, have no trust in you, and are solely relying on seeing a bunch of parts in the boot to judge your service.

What do you do? I’ll tell you what I would do. I would grab a bunch of parts from around the workshop and stick them in the boot, then charge for parts and repairs I didn’t do.

By following the behaviour suggested by a model of selfish individuals you have inadvertently signalled you complete ignorance about cars and a complete lack of trust.

Now imagine you are the mechanic who dealt with this customer and they didn’t ask for you to put the old parts in the boot. Maybe you still fleeced them a little and replaced a couple of parts that really didn’t need replacing. When the customer comes to collect the car they bring you six-pack of beer and thank you for your good work as they are so dependent on having a reliable car.

Would you fleece them again next time?

My point is that society deals with credence goods through the establishment of trust, either through non-market signals, like memberships of reputable societies, or ongoing social relationships. That mainstream economic theory ignores the fundamental role of trust and the cooperative behaviours that results from it, leaves their advice typically unsuited for many circumstances. As experimentalists know, in repeated games many forms of cooperation can become entrenched, yet most economic theory relies on the selfish response to a one-shot game.

Until economics courses around the world move beyond indoctrinating students into “cardboard version of human nature” we will continue to have selfish economists.

Economic models are plausible stories

‘Economists do it with models’ is one of the favourite insider jokes of the econ tribe. I recently tweeted that it would be nicer if economists did it with evidence. One of Australia’s most switched-on young economists responded and I elaborated my original point.

@ZacGross Models can't do that. Caus/corr problem needs knowledge of data generation. Model=assumed knowledge. Value is in predictive power
— Cameron Murray (@Rumplestatskin) October 12, 2013

It is a very common attitude in economics. Models, their solutions and any data correlations consistent with those solutions, are believed to constitute evidence that the assumptions embedded in the model accurately capture causal relations of some real life phenomena.

But of course that’s not the case. The key value of a scientific model is in its ability to predict outcomes in new situations, but also to generate new questions and directions for research. The model is not the answer, its a tool for discovery.

I have been reading Australian sociologist Duncan Watts’ book Everything is Obvious, which reminded me of the importance of evidence and the limitations of the model-building and correlation approach that almost defines economics.

Watts, a physicist turned sociologist whose work on networks is revolutionising the discipline, is completely frank about the near impossibility of determining causality in the one-shot experiment that is real life. In the section ‘Whoever tells the best story wins’, he concludes that 

Part of the problem is also that social scientists, like everyone else, participate in social life and so feel as if they can understand why people do what they do simply by thinking about it. It is not surprising, therefore, that many social scientific explanations suffer from the same weaknesses—ex post facto assertions of rationality, representative individuals, special people, and correlation substituting for causation—that pervade our commonsense explanations as well.

No matter how much your model appeals to your intuitive reasoning, or how well it fits the data, it cannot be shown to be of scientific value unless it offers useful predictions. For the economists out there just consider that models of constrained optimisation are simply a bunch of simultaneous equations, which read equally well in reverse (as do correlations). Moreover, micro-models of this persuasion almost always overlook methods of aggregation, leaving us to guess what sort of aggregate patterns should occur in the data. 

A discussion on the use of economic models would be incomplete without referring to Milton Friedman’s views that the reality of assumptions are unrelated to the usefulness of a model.

Consider the problem of predicting the shots made by an expert billiard player. It seems not at all unreasonable that excellent predictions would be yielded by the hypothesis that the billiard player made his shots as if he knew the complicated mathematical formulas that would give the optimum directions of travel, could estimate accurately by eye the angles, etc., describing the location of the balls, could make lightning calculations from the formulas, and could then make the balls travel in the direction indicated by the formulas. Our confidence in this hypothesis is not based on the belief that billiard players, even expert ones, can or do go through the process described; it derives rather from the belief that, unless in some way or other they were capable of reaching essentially the same result, they would not in fact be expert billiard players. 

My reading of this passage is that models should be judged on their predictive powers rather than their assumptions. Yet it also implies that if more plausible assumptions are possible that yield similar predictions, perhaps these generate more plausible models. 

If I were to propose a model of expert billiard play I wouldn’t start with the laws of physics but rather with a model of learning by trial and error. This simple model not only has more plausible assumptions, but predicts ‘expertness’ in billiards correlates with practice. It is also a general model applicable to such games as lawn bowls, where Friedman’s calculating-man model would require significant modifications to account for the weighted bowls. Friedman’s model is merely an assumption about the data-generating process. It translates to “if I know the data-generation process from the point when a ball is struck, I can use that knowledge to make a useful model that includes a prior point in time”. 

To reiterate, data can’t verify, support or prove (or even contradict) the causal assumptions in a model unless we have controlled part, or all, of the data generation process (either through experiment, natural, field or otherwise). 

Meanwhile, we have a whole field of econometrics that attempts to match models to data - refining the art of assumption-hiding and promoting the illusion of causality testing. For example, Angrist and Pischke’s book Mostly Harmless Econometrics: An Empiricist’s Companion is very loose with notions of causality. They say

Two things distinguish the discipline of econometrics from the older sister field of statistics. One is the lack of shyness about causality. Causal inference has always been the name of the game in applied econometrics. Statistician Paul Holland (1986) cautions that there can be “no causation without manipulation,” a maxim that would seem to rule out causal inference from nonexperimental data. Less thoughtful observers fall back on the truism that “correlation is not causality.” Like most people who work with data for a living, we believe that correlation can sometimes provide pretty good evidence of a causal relation, even when the variable of interest is not being manipulated by the researcher of experimenter.

They go on in the quoted chapter to discuss the use of instrumental variables methods address part of the causality problem. But recall the requirements of a useful instrument 

a variable (the instrument, which we’ll call Zi), that is correlated with the causal variable of interest Si, but uncorrelated with any other determinants of the dependent variable.

If you are thinking a little here you would realise we have simply introduced a second layer of model assumptions about the true data-generation process. You may believe there is a valid reason to do this, but again, the model can’t say whether this reason is sound or not. You are simply deferring one assumption about the nature of the world to an alternative, and perhaps more plausible assumption. 

What is more interesting is that founders of the instrumental variables method where challenged in the 1920s by the problem of causal inference in a model of markets with supply and demand curves. Since price is the simultaneous solution to supply and demand in the model there was no way to differentiate relative movements of the curves. Such problems persist to this day when applying demand/supply models to market analysis. 

Models aren't quite the scientific tools economics often believe them to be. At best they offer plausible stories about a particular phenomena and provide some predictive power. The religious attachment of the economics discipline to its core models is at times quite astounding.

It is genuinely challenging for social scientists to make gains in knowledge under the uncontrollable conditions of real life, and I can only hope that the future of research involves far more experimentation, either in the lab or in the field. In the mean time I hope the profession can be far more honest about the limits to knowledge, more humble in its policy recommendations, and more open to competing views of the world whose claims often stand on equal scientific footing.

Quote of the day

I've started Truman F. Bewley's book Why Wages Don't Fall During a Recession.  The book reports Bewley's research program that involved interviewing "more 300 businesspeople, labor leaders, counselors of the unemployed and business consultants in the Northeast of the United States during the recession of the early 1990s".

Here's the quote.

From the interviews, I conclude that wage rigidity stems from a desire to encourage loyalty, a motive that superficially seems incompatible with layoffs. My findings support none of the existing economic theories of wage rigidity, except those emphasizing the impact of pay cuts on morale. Other theories fail in part because they are based on the unrealistic psychological assumptions that people's abilities do not depend on their state of mind and that they are rational in the simplistic sense that they maximize a utility that depends only on their own consumption and working conditions, not the welfare of others.

To me this type of research is an avenue too rarely employed in economic research (a notable exception being Alan Blinder's work). It suggests that the an economic toolbox that contains only rationality, and is unable to incorporate other crucial elements of human behaviour such as loyalty, is destined to poorly explain real patterns of social organisation.

Top young economists

Believing that the next generation of economists will be for more rigorous and honest about their research keeps me happy. Today I’m not so happy. 

Here’s a list I came across last year, which recently came across my screen again. It compiles the views of 8 top young economists on where economics is going. And it’s bloody frustrating.

It’s frustrating because the more I read of economic research these days, the more it seems to neatly fall into either ‘the optimal control’ game, the ‘label the residual game, or the “correlation game”. And these young guns are simply rehashing these same games.

The ‘optimal control game’ is about mathematising an idea so that it fits into the structure of the calculus of optimal control. Often it doesn’t matter how you squeeze your idea into this mould; simply pluck a term out of thin air, call it optimism, certainty, talent, or some such unmeasurable or knowable thing and stick it in an equation. You then you solve the equations, finding the optimal point, which you can compare to the solutions to the equations when you ‘shock the model’ by changing a parameter - maybe a magical bout of increased optimism.

This method tells us very little about anything, especially when the terms are often vague unmeasurable concepts, and when there is not clue about how the optimal point can be obtained in reality if you don’t actually start there. 

‘Label the residual’ is the game of writing a model that has a term which captures all the unmeasured variation from the other terms. Noah Smith described it here. 

The ‘correlation game’ is about mining data for relationships that suit your audience. 

There is no prediction game, despite this being the true test of a theory. 

There is also no reward for exploring new methods of analysis, and much criticism when it is tried. And to my genuine concern, outcomes of the unscientific games being played are taken seriously not only within the profession, but often in the political arena.

With such self-inflicted constraints the discipline struggles to adequately answer even the most basic questions it is tasked with - why are some people and countries wealthier than others, why is wealth distributed the way it is, what is money, why is there a business cycle, and more. 

So it is of interest that we examine what the insiders, the next leaders of the ‘tribes of econ’, see as the direction the field is heading. Are they ready to break free from these silly games, testing new models of cooperation and interaction, bringing dynamic mathematical tools to the economics toolbox, and gaining qualitative input from the real decision-makers in the sectors they analyse? Will they incorporate political elements, experimental regularities, and the mechanics of real institutions into their research? 

I wish I could be so optimistic. 

* I disclose that I don’t personally know any of these economists and haven’t read their work for this article (I lied, I have poked around their work a little). 

Nicholas Bloom 
Why are developing countries poor? It’s a good question and a fundamental one to economics for a long time. But poor Bloom seems to be playing a new version of the ‘label the residual game’. He says

I think the answer is complex and linked to a combination of factors around history, geography, luck, etc. I am personally working on management practices: people in developing countries are poor because wages are low, and wages are low because firms are very unproductive, and firms seem to be unproductive in large part because of bad management.

The one thing I give him credit for is that he is involved in randomised experiments, avoiding falling into the “correlation game”. But do we believe that poor countries are poor mainly because there are bit disorganised? Doesn’t that beg the question of why they are disorganised? Or is it actually about uncertainty fairy?

Ray Chetty

How can we increase the rate of economic growth and overall well-being, and how can we reduce the rate of poverty? Good questions. Not new at all, but Chetty seems not overly certain about where to look, naming just about every policy there is as a potential mechanism for growth. This seems like a very broad program of labelling the residual.

Gauti Eggertsson 

How to use monetary and fiscal policy to eliminate unemployment and control inflation? These fundamental questions of economics are still on the table after centuries of research and policy experimentation. Why is that?

We seem to know that credit creation is the real inflation driver, and we seem pretty sure it is closely related to capital investment and employment. But if questioning banking and money remains taboo in economics, I can’t see change happening fast, especially when the top young guns seem to think introducing financial frictions into models is the way forward. Back to the 'optimal control game' then.

Xavier Gabaix

How to model realistic economic agents? Now there’s a better question. But Gabaix seems very certain that some form of bounded rationality is the answer here, which is of course to say, that no one wants to drop their foundation methods, prefer to play a more sophisticated version of the ‘optimal control game'.

 
Gita Gopinath

How does one accomplish sustainable growth without large boom-bust cycles? Again, another classic problem of economics still waiting for a decent answer. Gopinath at least raises some important issues routinely ignored in economic research - global imbalances, currency wars, capital controls etc. But again, we here about “understanding the propagation of shocks across economies”, which implies we are playing the “optimal control game” once again and don’t really want to think about the mechanics of economic interactions, investment and so forth. 

Peter Leeson

What is the status of the rationality postulate?

If we view economics as an “engine” for understanding the world, the rationality postulate was that engine in nearly all of economics until quite recently. The rise of behavioral economics has challenged the usefulness and, in a more subtle but radical way, the legitimacy of the rationality engine.

A very vague and non committed response by Leeson. He suggests that beliefs about rationality drive economists views and their interpretations of events. But still, what sort of solution to rationality’s status would Leeson be happy with? 

Glen Weyl

Glen seems to think that the rise of digital information, and the outsourcing of decisions we are making to automated computer systems, is threatening to overwhelm Hayek’s argument that “free markets were necessary in order to allow the incorporation of information held by dispersed individuals into social decisions”.

Which is strange, because every economic model of markets requires that all the information is available to every decision maker before any trade occurs. AS I have said before “if prices can convey all information, then there must also exist an alternative non-price method to convey that information which governments could use for an alternative allocation system.”

Justin Wolfers 

What will economists do with more data? Well, Justin thinks economists will continue expanding their social science empire in the spirit of Gary Becker. Of course, any statistician will tell you that uncontrolled data alone can’t generate any useful scientific findings.  “Economic theory will become the tool we use to structure our investigation of the data”. I hope not. Since economic theory doesn’t usually tell us what to expect in the data at large, given the general failure to consider problems of aggregation.

This is really a sad story. The econ tribes wield so much political influence, yet have so little to offer.  These same questions could have been asked 30 years ago and would have seemed cutting edge.  So much has happened, in the world, and very little it seems in economics [1].

If I was to answer I would say the big question for economics is finding new tools that are able to incorporate dynamic elements and evolving strategies of cooperation, addressing all the aggregation problems that arise from the 'optimal control game'.  I would say that on the policy side the big questions are about the mechanics of real institutions, for example the banking system and credit creation, and of the political games that arise when new institutions are formed.  For me the future of economics includes power, rents, and conflict. 

But today I doubt we will see the future I imagine. 

[1] Many researchers will deny that little has changed.  We do know that the economics community is more empirical now than ever. But without theory, uncontrolled data can’t reveal much of scientific value.  And theory seems stuck in a time warp. 

House price signal flashes green

Back in May 2013 I called the end of Australia's slow melt in home prices.  I based this call on a number of market measures, but the crucial one was my own indicator of when to buy and sell housing.  I developed this indicator two years back as a way to capture the potential success of the investment strategy of 'buy on yields, sell on capital gains'. To reflect this strategy I use the ratio of mortgage interest to rental yields to capture the relative returns from yield versus the expected returns from capital gains.

The buy signal arrived at the beginning of 2013.  Since then prices have been rising again in most capital cities, but especially so in Sydney, which dominates the capital city housing prices indexes.  I have updated my chart to reflect this year's data, and extrapolated with predictions for December 2013.

So why is this relevant now?

Because many people who correctly called the overvaluation of Australian housing prior to the financial crisis have ignored the massive transition that occurred during the past five years. The slow melt really did let out the air from the bubble.

Now we have a situation where just about every Australian economic commentator is picking sides in discussion about whether this year's price gains are a new bubble. A former bubble denier has even switched sides.

So why if the data is so plainly showing that we are very far from a bubble, and in fact prices are relatively low right now, why isn't it obvious to everyone? I can think of just a few reasons for confusion.

1. Australian house prices are still high when compared internationally. This argument doesn't really have much merit. Our wages are also higher, and this is reflected in higher rents, which determine the returns on housing assets. 
2. Individual homes in established suburbs seem to keep increasing in price. But this ignores that the relative position of these homes increases as the city grows. Thus what was last decade's cheap fringe suburb in next decades desirable urban location. 
3. Anchoring bias.  $1,000,000 is still seen as some kind of benchmarks of obscene wealth. Yet, the repayments on this loan amount are about 20% lower now than during 2005-2008, while household incomes are up significantly. 

Today's news is that Sydney and Melbourne home prices are up over 5% for the September quarter. Yes, this is a new cycle, but we are only at the beginning and I believe it will be milder in nominal terms that previous cycles.

Thing I wish economists never said

One thing I find surprising about economics is the misuse of models as tools for reasoning.

Think about the basic micro-models. What do they say? They say all markets are in equilibrium. Okay, if that’s the approach you want to take. So how did they get there? Tatonnement? A Walrasian auction. We get there via a central planner who facilitates the equilibrium price before trade is allowed to take place! 

This lack of substance in the fundamental model of markets should beg the question of why markets are preferred over alternative forms of planning? You actually need some other theory to support the assertion that markets are the best resource allocation tools - but such a theory does not exist. 

One problem you face trying to get economists to think deeply about the real life applications of their beloved models is that you have to pretend to take these nonsense models seriously. Don’t be surprised if you get arguments to the effect that real life commercial behaviours must be wrong because the model is right. 

With that short rant over, I want to share a list of phrases and terms that I wish economists never said. These terms illustrate that vacuous nature of economic reasoning, as as you will see, they test the patience of any thoughtful observer. 

Fundamentals

Probably top of the list of things you say when you have no idea what is happening in a market - “we need to look at the fundamentals”. Okay, WTF are they? Just your pet theory? You actually need a deep understanding of the institutional history, legal and regulatory structures of markets, and a decent technical understanding to even know what the fundamentals might be. Plucking an uninformed notion of supply and demand out of thin air is not understanding fundamentals.

Second best

Most people who use this term actually don’t understand its meaning. In the equilibrium model of markets if one market is not in equilibrium, then no other market can be in equilibrium - it’s all or none. The idea of second best is that if in reality there are some markets that aren’t in equilibrium, improving overall outcomes might mean implementing policies that make other markets diverge further from their theoretical equilibrium.

Basically it means that trying to make an outcome perfect in one market might lead to counteracting effects in other market that negate any beneficial effects.

For me this term implies that the equilibrium model of markets is a poor tool for analysis of the economy. However the term implies that market model is first best, and real life is second best - how truly odd to say that.

Bad equilibrium

A assume the economy is in equilibrium. When shit goes bad, assume that the economy is in a ‘bad equilibrium’. WTF does that even mean?

Free market

Really, the ‘free market’ is just a system of highly rigid social institutions that define rights and constrains activities. One frustrating alternative to this phrase is “let the market decide”, which doesn't actually means anything of interest, because markets are merely a product of alternative regulatory constraints. The term creates a diversion from important social issues because it reinforces the assumption that market allocations automatically fulfil social goals, and any interruption of their operation will have costly social repercussions.

The NBN is a great example of this. The social aim is to provide equitable access at a consistent high standard to fibre internet through cross-subsidies between city and country users. The market alternative is that few high value city location will get access to an almost identical service, possibly at lower prices. We decide whether markets are the appropriate tool to fulfil social aims - markets don't decide anything. 

Remember, we can create institutions to produce whatever social goals we have in mind. Market outcomes are great in many circumstances, but they too have social outcome that might justify alternative forms of allocation. 

Shock

As if the regular business cycle, the subject of now centuries of economic research, somehow arises from somewhere external to the commercial activities of society.

Malinvestment

This Austrian economics terminology is both subjective and confusing at the same time. A school of thought that has its own ideas of creative destruction (implying an unpredictable future and therefore lots of poorly allocated capital) somehow expects all capital to be perfectly allocated or this system will collapse? Unfortunately this term provides an excuse to points the finger for economic woes at whatever your pet hate is at the time (usually government involvement in setting nominal interest rates).

Reform

I will let Zac Gross have a go at this one.

‘Reform’ is very sexy word. It is often deployed to cloak policy in feel good vibes and to create an aura of leadership and vision. So everyone in the policy-sphere wants to think of themselves as reformers and many a complete bastard has appropriated this lovely, but overused, title.

Land and housing are durable goods (or why demand/supply framework fails)

The ‘standard’ urban economic model is based on Alonso, Muth and Mills combined work in the 1960s to fashion a mono-centric urban space into an economic equilibrium model. For some reason this model has gained traction in the literature despite it gross misrepresentation of housing markets.

I will let the reader enjoy the comments from David Pines’ 1987 review of the urban economics literature

The static approach in the Alonso-Mills-Muth model is useless in explaining many stylized facts regarding the urban structure and its evolution through time. In the static analysis... land is continuously utilized within the city boundaries and the city boundaries are continuously extended with income and population size.
...
The reason for the failure of the static model in explaining these ‘irregularities’ is that the housing stock is assumed to be perfectly malleable, which, of course, is highly unrealistic. 

What this means is that every time there is a marginal change in any of the parameters of the model - a new person moves to the city, the rental price of the second best land use increases, or the efficiency of construction methods change - the whole city is wiped clean of housing, and the land owners and people sit down around the camp fire and decide a new optimal allocation of housing under the new conditions, then the whole stock of housing is rebuilt in an instant to that new specification. There is never a vacant site or development opportunity. 

Yes, this bizarre model was used by RBA used just a few years ago to help them understand regulatory impacts on housing supply. 

However we have known since 1989, when Bagnoli, Salant and Swierzbinski explored the durable goods monopoly problem posed earlier by Ronald Coase, that the optimal revenue raising strategy of land owners is withhold new supply of housing to future periods to maintain price levels. 

Thus, the constraint on new supply is the number of buyers willing to pay current prices or above in a given time period. If the number of buyers dries up, the optimal solution for the land owner is not to develop until such demand arises. Thus speculative booms are likely to coincide with construction booms, as investors rush into housing markets allowing developers to sell larger volumes of new stock at current prices. 

This point probably needs an example to really drive it home. Imagine you are subdividing a lot into three smaller housing lots. Your market research suggest that $300,000 per lot should be an achievable price. You put them on the market for that price. It takes 4 months to get the price you are after for one block. 

You get offered $280,000 for the second block after another 4 months. But you know that if you accept this price that you will most likely have to accept that price for the third block as well (especially given that sales prices are public records). 

The big question - the one that determines the rate of housing supply - is how long to wait for sales to maintain prices. Do you make a better return if you accept $280,000 and increase the supply now, or is it better to wait until you can get a price of $300,000 and defer new housing supply? 

The answer that Bagnoli found was that if the sellers are more patient than buyers on average (the have lower discount rates), than it is optimal to wait. It is better to sell one per year at $300,000, than 3 per year at $280,000. 

In the mainstream world of AMM’s economic model you shouldn’t have bothered waiting at all. You should have dropped the price immediately until you sold all three blocks on the first day. 

You might want introduce ‘competitive’ land developers at this point. Say my neighbour also subdivides their block into 3 smaller lots. With this less constrained supply surely now the rate of new housing construction will be higher? 

Actually it is not. 

The return maximising strategy is for each land owner to wait, and still sell just one of the now 6 potential lots each year. The land owners compete with each other for a sale, which encourages innovation in design to better appeal to buyers (to get the sale instead of their competitor), but it doesn’t bring forward supply.

Perhaps a little story from my time with a major residential property developer might help.  Remember the days when people would queue at sales offices for new subdivisions.  By sheer luck we were faced with this sort of crazy demand when we released sales of a new building at the Sunshine Coast.  Early that morning it seemed we would be able to sell the whole building within a day or two. So did we?

Of course not. We crossed all the prices out and wrote new ones 20% higher.  That certainly slowed the sales right down, and it took a couple of years to sell the whole building after that.  We simply did the profit maximising thing of withholding new supply. 

By understanding these fundamental processes at the heart of the housing supply debate it leads to very different conclusions about the types of policies that might trigger increasing housing investment - policies that focus on the discount rate of the land owners. 

This could include rent controls (decreasing future expected returns), incrementally ratcheting up land taxes (decreasing returns from not developing), announcing tighter building height restrictions in future periods (to encourage land owners to develop before the new restrictions are implemented), removing land tax exemptions for approved but undeveloped lots, and more. 

That last one is interesting.  In Queensland developers get a 40% discount on land taxes if they have subdivided a lot, but not sold it.  All this does is provide incentives to withhold land for longer, and the cost of doing so is reduced. Of course, developers will simply change the timing of the subdivision to be closer to the sales (getting approval, pre-sales, and then subdividing). But on average it must be a good move to remove this discount. 

The logic of durable goods means the extensive land banking by developers, which in Australia is currently around 19 years supply at current rates of sales, is actually a rent-seeking strategy - an attempt to buy land at a low price with one zoning, only to have it rezoned for more intensive uses before being developed.  It is not about anticipating supply needs and navigating regulations.

It means that developers stage developments in order to bring forward some construction and make the location more desirable for buyer of future stages.  Staging is about delaying development of new homes. 

Lastly, it means that reasoning of shortages or supply constraints due to land price differentials near zoning boundaries, such as by Grimes and Liang, is faulty at best. Of course differently zoned land at the same location is worth a different amount, because the value of that land is the capitalised income from its highest and best use minus the construction costs of that use. It has nothing to do with expectations of future rents or any such thing. 

I highly recommend reading my previous post in interpreting housing market indicators to fully grasp the potential misinterpretation of housing indicators from using the wrong model.