A comment on Keen’s “Credit plus GDP” measure

Background

More than anyone, Steve Keen has raised awareness of the role of banking and money creation in driving economic cycles. Not only this, he has published just about every presentation and paper of his freely online, and participated in a variety of forums online and in the media.

Not only is he out there putting forward new approaches and ideas, he is doing so in a way that allows every man and his dog to nit pick at his work. That takes guts.

Because of the public nature of his work for the past decade, his analysis and communication of these big ideas has evolved to become extremely powerful and hard to ignore. Just about everyone I talk to in the economics crowd these days has been influenced by Keen’s work in some way. For me personally, the latest lesson from Keen as been his reconciliation of accounting identities with the dynamic effect of additional demand from credit creation - a change in understanding that I think could offer a clear path forward for dynamic monetary methods. 

The issue
In the spirit of this public debate I want to take issue with one of Keen’s recent ideas; adding change in credit (debt) to GDP. But I want to do so constructively so that as a profession we can incrementally improve our economic analysis and understanding of macroeconomic phenomena. 

In this video, from 16mins onwards, Keen explains the idea and attaches a particular interpretation to the summation of GDP and the flow of new credit (change in the credit balance), as “aggregate expenditure” (AE). The slide below summarises.

Where this measure of AE differs from the standard view is that it can include expenditure on transfers, such as assets sales, rather than just newly produced goods. It therefore includes expenditure on real estate (not just new housing construction), and leveraged expenditure in equities markets. We could probably better call it “aggregate transactional expenditure”. 

What he misses, however, is that there is no one-to-one relationship between credit creation and its use in asset markets. Quite a bit of new credit is used to fund investment in new buildings, and other “capital” goods, the value of which is already included in GDP calculation. 

Let’s take an example. I build a new home. The land costs $100,000, and the building I have designed will cost $100,000 to build on that land. I buy the land using my saved wages, and borrow $100,000 to construct the building. In this case, this $100,000 of new credit created by my bank for me to pay my builder is counted as new capital (or what the Australian statistical agency calls “gross fixed capital formation”). It is in nominal GDP. Adding the $100,000 in new credit to the $100,000 of new housing in GDP makes no sense. Similar situations abound in large investment projects, where bank funding for pipelines, rail lines, energy facilities and so forth, is already part of measured GDP. 

Even in cases where new credit can appear to be pure speculation on asset markets, such as the purchase of an existing home, that credit can then be used to purchase consumer goods, which are counted in GDP. For example, the seller of the home I buy with my new mortgage can turn around and buy a car, a boat, or go on a holiday, with the new money created when I took out a loan to buy their house. The net effect is as if they had used new credit (money) to directly buy those consumer goods.

If I haven’t been clear, I don’t think the simplification of calling GDP the expenditure in a year financed by existing money is appropriate. And I am not sure what it adds to Keen's analysis, since credit dynamics alone make similar predictions of the macroeconomic cycle without adding them to GDP. If it is merely a case of "here's a metric that correlates well with the historical data", I have no issue. Whatever works is fine. But if we attach meaning to it above and beyond this, than we need to understand what the metrics really are. 

Economic domains approach
I think of this issue in terms of economic domains. GDP is not a measure of the use of money - it neither measures transactions, nor balances of accounts or any such thing. It is our best measurement of real goods and services produced by the market economy and the government sector. It only records purchases by households and governments of new goods and services, ignoring all asset market transactions, second hand goods transactions, intermediate production transactions, and so forth. In fact, in order to add up the value added of goods and services, it includes only a select few transactions to ensure no double counting, and a reasonable approximation of new production only. 

In terms of my economic domains approach to economic analysis, we can see that despite both being measured in $/period (which is simply the common measure used to aggregate different types of goods), GDP is a measure of real production, while new credit is a measure of the change in the balances of our monetary accounting system. This means that without knowing all the monetary balances and transactions in the whole economy, we can’t precisely know the link between new credit and the production of real goods and services. There are also price effects to consider, both in the real goods markets, as well as asset markets, which changes incentives for real capital investment. The question mark in the image below shows where this type of monetary analysis must focus - understanding the mechanisms by which new money creation affects GDP.

In terms of the simple examples I used earlier, the diagram below shows how some share of new credit is already counted in GDP when it funds new capital investment. But even here, it is not clear what the relationship between new credit and GDP should be like. 

Credit and GDP differently

Perhaps it is more useful to look at the effectiveness of new credit in creating goods and services instead of asset price inflation (or inflation generally). Call it “growth efficiency of credit”. And why not? That’s what the World Bank calls it.

There are two extremes I have in mind in this analysis. 

First, if all new credit is directed to new capital investments, we would expect a very close match between credit creation and increases in nominal GDP. Second, if all credit creation is to fund asset purchases, we might expect a much lower relationship between new credit and GDP growth. 

This idea fits nicely with the story that we should use the banking system to support new capital investment instead of funding asset purchases, which simply leads to asset price growth and speculative cycles. In this story it matters what new credit (money) is used for, not just the levels of new credit. 

To examine the idea of credit efficiency, I take the change in nominal GDP (increase in real market goods and services in current dollar terms) over a period, and the change in total credit (i.e. the new credit) to see how effectively the new credit is being directed towards real production instead of asset markets. The result is in the chart below. 

What a surprise! Nothing at all like I expected. I had in mind a very cyclical, hard-to-decipher, pattern. 

Instead we see two main periods of stability. Prior to 1990 the ratio was rather stable, with a mean value of 3.42, meaning that for every dollar of new credit, GDP grew by $0.29. In the second stable period, between 1993 and 2008, the ratio was 7.33, meaning that GDP grew by $0.14 for every new dollar of credit on average. 

I suspect that these stable period levels in some way reflect the prudential controls on lending that prevailed at each time period, such as loan-to-value ratio limits, savings requirements, and other such things in the case of mortgage lending. But also, I suspect there is a feedback loop at play - from more credit creation, leading to higher home prices, which leads to lower interest rates, supporting more credit creation and higher prices, and on it goes. It is these types of feedbacks that Keen's dynamic methods can capture. 

But what of the unstable periods?

There was one in 1990, which we know preceded a recession in the early 1990s. And there is a lull in credit while GDP growth remained strong in 2009-10, perhaps due to government stimulus. But now we appear to be in a period of very inefficient credit use, where a new dollar of credit has bought just $0.05 of GDP over the past three years. I suspect this has a lot to do with the winding down of mining investment (I’m unsure of the degree of domestic bank lending for these expansions), at the same time as a massive ramp up in investor mortgages in Sydney and Melbourne, which do not feed directly into consumption and investment.

Overall we could say that the patterns seen here supports Minky's idea that "stability is destabilising".

So what is the meaning of this?

It is a little worrying to see a “pre-recession” pattern forming at the moment. But without looking more widely at the predictive power of such patterns, I'm hesitant to make strong predictions, though I don't expect any surprise economic boom in the coming few years.

In terms of economic theory, at the very least I now have a story about how the money domain leads to changes in the real resources (GDP) domain through directing new funds towards new capital investment, or towards asset purchases. This is one mechanism that provides a relationship between these domains. I suspect there is a second important mechanism, and this is the effect of new credit on asset values, and the subsequent effect on brining forward or delaying investment for asset owners. In terms of my economic domains approach, this mechanisms relies on a monetary effect on the value of property rights (assets), and an subsequent effect on new capital investment because of these value changes. I have attempt to look at the second part of this mechanism before. 

There is also a literature in this area, which also looks at the “credit-to-GDP gap” (see here, here and here for examples). I see plenty of scope to reconcile Keen’s ideas (focusing on growth rather than levels) with the work of others. There is even an international database on total credit available to researchers following a lot of interest in this idea from the Bank of International Settlements. 

So what

I hope that by highlighting problematic conceptual issue with adding GPD to change in credit, this post improves future debates about the link between the monetary system and economic activity. My impression is that we currently have a group of economists looking at monetary models of the economy, and a bunch of them looking at models of only real goods and services, and they are talking past each other because the are using terms to mean different things. Using my economic domains approach makes this conflict in terminology obvious, and I hope points towards ways to reconcile approaches in a meaningful way.

Lessons from Brexit

I didn’t predict this outcome. Few did. I thought it was too soon. But I wasn’t naive about the politics of the situation. One of my main concerns was that the Remain campaigners seemed overly attached to unrealistic models of economic doom, while simultaneously accusing the other side of spreading lies.

Almost nobody I asked could give me an economic reason to be in the EU. I read nothing that made any sense from this outsider’s perspective. No one could point to a particular policy change and clearly say exactly how the economic ramifications would play out. They couldn’t really. Because nobody knew, or even knows now, what the word of UK trade and immigration policy will look like post-EU.

The whole question is political.

Let me briefly note some of the main lessons I see from this experience. This post is as much a record of my thinking as it is a commentary on politics.

1. Facts don’t matter and politics rewards lies
Anyone able to make an objective assessment of the day-to-day behaviour of successful politicians in any country knows this. Lying is the main ingredient in political success. Yet the Remain campaign seemed to somehow think that stating facts could change people’s minds. For apparently scientifically-minded technocrats, that is absurdly naive.

2. Economic effects will be serious
This is the big lie that the Remain campaign couldn’t bring itself to admit was a lie. If they could admit this, they would have seen the campaign period as a battle of lies, and get over their foolish attachment to their own truth.

That the pound dropped a touch in a short period after the referendum result is economically meaningless. All it says is that currency traders were surprised. We also live in a world embroiled in a currency war, each country looking to deflate to stimulate its export sector. Yet somehow the weak pound is a bad thing for UK.

When other countries observe how economical benign it was for the UK to leave, others will follow, and this lie will become all too obvious even to those who believe it now. As James Galbraith said “such warnings will be even less credible when heard the next time.”

3. Technocrats underestimated peoples willingness to blame outsiders
War is the nature of civilisation. People are tribal animals. Yet somehow the mental model of Remain-side technocrats was too full of ideology over observation. People always blame outsiders for their problems. Always have. Always will. There doesn’t have to be truth in it and telling them ‘facts’ can actually strengthen their beliefs.

4. Naive support for the EU rent-seekers
Many people don't actually benefit from the free movement of labour across the EU. Highly educated professionals do. But your average labourer doesn’t. For most people they see only costs to political integration with Europe. And indeed, any benefits come at the cost of an enormous layer of bureaucracy and rent-seeking.

In many minds, the question is whether you want your political rent-seekers locally raised, or part of the outsider group you are inclined to blame for your troubles. The answer here is obvious.

5. High immigration is disruptive
Take a look at Germany. The refugee crisis really gave them no choice but to accept a huge influx of new immigrants. To maintain internal cohesion will require a massive propaganda effort, coupled with a massive intervention effort to teach the language and culture to the new immigrants.

It’s something that the left doesn’t like to speak about, but the evidence is pretty clear. High rates of immigration are disruptive to social institutions that share a group’s wealth. This is a fact of human nature.

Be honest now. I’m sure you can think of some person, or some group, that you perceive as an outsider and genuinely don’t want to lend a hand to, perhaps you even want to punish them. Absolute humanism, utilitarianism, or whatever you call it, where all lives are equally important, is pure fantasy. We are tribal, and the veil of equality is always a within-group phenomenon.

Last word
In all, the political ramifications of Brexit are far less interesting than the volumes of words spilt about it suggest. Some leaders will come and go as the internal transition is navigated. It’s no big deal. One will stick eventually.

And I think the one who sticks in the UK will have a surprisingly social agenda. A pro-UK agenda. If history is a guide, this is what people want once you've choked off immigration.

Other rich countries in the EU will see how “surprisingly” successful the transition has been and also leave. The EU in its current form is over. Without direct democratic input and fiscal unification it lasted longer than could be expected. We can only hope that what comes out of the EU rubble are the peaceful nation states that it helped create, and decades on we can say that the EU served its purpose of bringing widespread peace across Europe.

Or am I too naive?

In all honesty, if I was voting I would have voted Remain. But not for any rational reason. I just would have conformed to the expectations of my social group. And because of the social reinforcement, I probably would have become very passionate about my position. As a remote observer with no particular interests, it is much easier to see the underlying logic of the situation.

Robinson Crusoe’s real economic choices

The Robinson Crusoe economy is widely used as a teaching aid in economics to explain concepts such as comparative advantage and equilibrium in an exchange economy.

In my view, the use of the Crusoe economy as a teaching aide often trains students to focus only on a few very narrow ideas, and ignore many of the fundamentally important elements involved in economic production and coordination in Crusoe's world.

This will be obvious to you when you watch the latest “stranded on an island” reality television show. The lessons of economics simply don’t correspond to the type of production and coordination I see in them.

In this post I want to show how Crusoe’s story can be an effective teaching aide, and demonstrate a structured, yet pluralist, approach to economic inquiry, in the way I have previously proposed. This structure breaks down an economic problem into various domains of interest, along with a number of guiding questions about aggregation timing, and prediction, in order to assess the types of concepts, models and analysis being applied in each domain.

Rather than use Crusoe's story to justify a concept - “This is specialisation, remember it by the story of Crusoe”, the reverse approach could be taken. “Here is the story of Crusoe, how would we understand that economics of the story?” Having a structured method of economic inquiry then allows students to ‘discover’ the economic concepts you really want to teach them.

Where would such a method of inquiry lead us in Crusoe’s story?

First, here is the standard use of Crusoe, which typically focuses on specialisation when Crusoe is joined by Friday. For some reason he has an absolute advantage in producing fish and coconuts, as each day he can produce more both than his companion Friday. But his relative advantage is in coconuts. The graph such as the one below is then used to demonstrate the gains to specialisation. The dashed light blue line is their combined output without specialisation, and the green line is with each producing the product in which they have a relative advantage, and the gap between these lines near the kink of the green line is the gain from specialisation.

This lesson is simple, and true enough on its face. But it is very incomplete.

So what about my alternative use of the Crusoe economy for teaching economics?

Social and political environment
We first inquire about how Crusoe and Friday came to understand what their social arrangements should look like, noting that any subsequent economic activity will be embedded within these social relationships. In the actual story Crusoe rescues Friday and ‘employs’ him as a servant, teaches him Christianity, and generally creates a hierarchal social relationship. Under this condition the idea that Crusoe and Friday will equally and jointly make decisions about their production activities and trade is a little strange. What will happen is that Crusoe will use the social arrangement to dictate their joint activities in order to fulfil his wishes, which are themselves a product of his previous social environment.

What other social structures could there be in a two-person economy? One case might be that the two are parent and child. In this case the parent will likely have power to dictate the activities of the child, but will also have an incentive to invest in the relationship in order for the child to reciprocate in the future.

The story can be used to open lines of enquiry about social and political structures and their analysis, revisiting this simple case to enforce the basic concepts later introduced.

Money and legal rights
Moving on from social and political environment, we can then interrogate the related economic domains of money and property rights. We can ask questions about who owns what, and how accounts will be kept. Unless we understand the rights of our two gents, we aren’t going to make much progress in understanding the situation.

For example, if Crusoe is a more productive fisherman because he excludes Friday from the most fertile fishing areas by claiming property rights over these areas, this opens up a new puzzle. Would the productivity of Crusoe and Friday be different if they had a different system of property rights? Maybe if Friday could access those parts of the island Crusoe set aside for himself, total output could be much higher. Hence this story can be used to show how understanding the distribution of property rights can help answer questions about whether there are more efficient alternative allocative institutions. For example, maybe cooperative production by fishing together one day, and then collecting coconuts together the next, is even better than any outcome from specialisation.

In the money domain we can ask how Crusoe and Friday plan to keep accounts if they specialise, as per the standard economic account, yet some days the fishing is better than others, and some seasons the coconut palms are not fruiting so prolifically. If their daily output of fish and coconuts fluctuates, we create an inter-temporal problem of smoothing production and consumption via accounting.

What would these accounts look like? Would Crusoe credit Friday some fish when coconuts are slim pickings, and vice versa? At what price could a debt in fish be repaid with coconuts. These questions about how accounts are kept and how they evolve through time really matter for how Crusoe and Friday coordinate their production.

We can use the story as a stepping stone to another story, of the Capitol Hill babysitting coop, and its monetary system, before leaping off to study large-scale monetary systems and central banking.

Real resources and welfare
In the domain of real resources we can ask questions about whether Crusoe has always been more productive, and look at how he came to be; a question of timing. Perhaps he has a fishing net that Friday cannot access. If this is that case were again need to ask the question of why it is Crusoe’s net in the first place it it washed up on the beach.

Putting this to the side, if Crusoe did specialise in coconuts while possessing a fishing net, there will be a loss in potential joint production because of the idle capital of the net. We know that the existence of fixed capital can break down the logic specialisation, undermining the clear-cut beneficial outcome that is presented in textbook economics. And even if Crusoe is somehow innately better skilled at the two activities, it merely begs the question of how he learnt to be better and what is stopping Friday from learning the skills and even surpassing Crusoe’s productivity. These lines of inquiry can lead into the study of trade, and arguments about managerial economics and learning, infant industries, trade protection, and so forth.

We can even build on the standard specialisation story. In the earlier graph I have also plotted a dotted orange line showing a 50:50 split of the output from Crusoe and Friday specialising. Notice that this line sits fully below Crusoe’s own production frontier. What this means is that while there is a gain from trade, it is not clear how it should be split. Obviously, given the existing legal situation and capital stock, Crusoe is more productive and will be able to extract a greater than half share of the resulting larger combined economic pie. But how much?

If we deal just with the kink in the combined production frontier for a moment, if Friday receives a 37.5% share of combined output he is just as well off as going it alone and not trading. At that same kink point, Crusoe is just as well off if he receives 53.9% of the combined production of fish and coconuts.

Here we have a problem. This trade produces a surplus that needs to be shared. Somewhere between 37.5 and 46.1% of combined output to Friday and the rest to Crusoe will make them both better off in pure output terms. How this surplus gets divided is a defining issue of economic distribution and welfare which is fundamentally ignored by most economists. It is a question of who gets the rights to surpluses generated by trade. If Crusoe captures all the surplus, inequality on the island will start increasing, but if Friday can capture most of it, their wealth will become more equal.

Because Crusoe and Friday now face the problem of how to allocate their economic surplus, the story allows us to introduce ideas of utilitarianism, including how welfare can be assessed or improved.

The story also provides scope to lead into questions about whether the standard story of specialisation makes useful predictions applicable to the modern world. On this account, some of the basic predictions of the standard story of specialisation and trade fail, as Hill and Myatt explain

…since the industrialized nations are so similar – similar economic structures, resources and technology – they likely have similar opportunity costs in production. Thus, they would not be expected to trade much with one another. But in fact industrialized countries trade extensively with one another. He says: ‘Over 70 per cent of the exports of industrialized countries go to other industrialized countries … These facts appear to be inconsistent with comparative advantage theory’

We should be asking why that it, and looking at what else may be happening. 

Indeed, we can take the next step after discussing specialisation to ask how the scale and diversity of Crusoe and Friday’s economic output can be increased once they have learnt to optimally collect coconuts and go fishing. We then talk about capital investment, innovation, and so forth.

So what?
The story of Crusoe is usually seen as a memorable simplification for teaching a couple of basic economic concepts. But I argue that it can instead be used to teach a structured and coherent pluralist approach to economic inquiry. In doing so, this approach makes clear the many hidden assumptions necessary to concentrate on the economic analysis of specific domains of the Crusoe economy, and ensure students understand from the very beginning that this is the norm in economic analysis, and to remain critical.

Time to revisit how we calculate expectations?

The below presentation by Dr Ole Peters opened my mind. If there was one thing I believed was a reasonable implicit assumption of economics, it was determining the expectation value upon which agents base their decisions as the “ensemble mean” of a large number of draws from a distribution. Surely there is nothing about this simple method that could undermine the main conclusions about rational expectations, whether humans act that way or not? Surely this is a logical benchmark, regardless of whether actual human behaviour deviates from it.

But now I’m not so sure. Below is a video of Dr Peters making the case that non-ergodicity (according to the physics interpretation of the word) of many economic processes means that taking the ensemble mean as an expectation for an individual is probably not a good, or rational, expectation upon which to base your decisions.

I encourage you to watch it all.


Let me first be very clear about the terminology he is using. He uses the term ergodic to describe a process where the average across the time dimension is the same as the average across another dimension.

Rolling a dice is a good example. The expected distribution of outcomes from rolling a single dice in a 10,000 roll sequence is the same as the expected distribution of rolling 10,000 dice once each. That process is ergodic [1].

But many processes are not like this. You cannot just keep playing over time and expect to converge to the mean.

Peter’s example is this. You start with a $100 balance. You flip a coin. Heads means you win 50% of your current balance. Tails means you lose 40%. Then repeat.

Taking the ensemble mean entails reasoning by way of imagining a large number coin flips at each time period and taking the mean of these fictitious flips. That means the expectation value based on the ensemble mean of the first coin toss is (0.5x$50 + 0.5*$-40) = $5, or a 5% gain. Using this reasoning, the expectation for the second sequential coin toss is (0.5*52.5 + 0.5 * $-42) = $5.25 (another 5% gain).

The ensemble expectation is that this process will generate a 5% compound growth rate over time.

But if I start this process and keep playing long enough over time, I will never converge to that 5% expectation. The process is non-ergodic.

In the left graph above I show in blue the ensemble mean at each period of a simulation of 20,000 runs of this process for 100 time periods (on a log scale). It looks just like our 5% compound growth rate (as it should).

The dashed orange lines are a sample of runs of the simulation. Notably the distribution of those runs is heavily biased towards final balances of around $1 (remembering the starting balance was $100).

In fact, out of the 20,000 runs in my simulation, 17,000 lost money over the 100 time periods, having a final balance less than their $100 starting balance. Even more starkly, more than half the runs had less than $1 after 100 time periods. The right hand graph shows the final round balances of the 20,000 simulations on a log scale. You can read more about the mathematics here.

So if almost everybody losses from this process, how can the ensemble mean of 5% compound growth be a reasonable expectation value? It cannot. For someone who is only going to experience a single path through a non-ergodic process, basing your behaviour on an expectation using the ensemble mean probably won’t be an effective way to navigate economic variations.

I see two areas of economics where we may have been mislead by thinking of the ensemble mean as reasonable expectation.

First is a very micro level concern: behavioural biases. The whole idea of endowment effects and loses aversion make sense in a world dominated by non-ergodic processes. We hate losing what we have because it very often decreases our ability to make future gains. And we should certainly avoid being on one of the losing trajectories of a non-ergodic process.

The second is a macro level concern: insurance and retirement. Insurance pools resources at a given point in time across individuals in the insurance scheme in order that those who are lucky enough to be winners at that point in time, make a transfer to those who are losers. By doing this, risk is shared amongst the pool of insurance scheme participants [2].

Retirement and disability support schemes are social insurance schemes. They pool the resources of those lucky enough to be able to earn money at each point in time, and transfer it to those that are unable to.

But there has been a big trend towards self-insurance for retirement. In the US they are 401k plans, and in Australia superannuation schemes. Here the idea is that rather than pooling with others at each point in time (as in a public pensions systems), why not pool with your past and future self to smooth out your income?

You can immediately see the problem here. If the process of earning and saving non-ergodic and similar in character to the example here, such a system won’t be able to replace public pensions, as many individuals earning and saving paths will never recover during their working life to support their retirement. Unless you want the poor elderly living on the street, some public retirement insurance will be necessary.

Undoubtedly there are many more areas of economics where this subtle shift in thinning can help improve out understanding of the world (I’m thinking especially about Gigerenzer’s ideas of heuristics approach as being ways humans have evolved to navigate non-ergodic processes).

I will leave the last word to Robert Solow, who has had similar misgivings (for over 30 years!) about our assumptions of ergodicity (a stationary stochastic process) which undermine rational expectations.

I ask myself what I could legitimately assume a person to have rational expectations about, the technical answer would be, I think, about the realization of a stationary stochastic process, such as the outcome of the toss of a coin or anything that can be modeled as the outcome of a random process that is stationary. If I don’t think that the economic implications of the outbreak of World war II were regarded by most people as the realization of a stationary stochastic process. In that case, the concept of rational expectations does not make any sense. Similarly, the major innovations cannot be thought of as the outcome of a random process. In that case the probability calculus does not apply.

fn[1]. He does not use the term as it is often used in economics as describing what often falls under the term Lucas critique, or in sociology is called performativity. Basically, it is the idea that the introducing a model of the world creates a reaction to that modal. Take a sports example. As a basketball coach I look at the past data and see that three point shots should be take more because they aren’t defended well. I then create plays (models) that capitalise on this. But because my opponents respond to the model, the success of the model is fleeting.

fn[2]. Peters himself has a paper on The Insurance Puzzle. The puzzle is that if it is profitable to offer insurance, it is not profitable to get insurance. The typical solution invokes non-linear utility to solve it. Peters offers an alternative. My take is on the economic implications of this - if people can smooth through time for retirement than there is not logic to social insurance.

The great Australian town planning give-away

It is the gift that keeps on giving for the Australian property developer lobby. Planning gains. Betterment. Whatever you call it, it is a multi-billion dollar give-away to the politically connected happening every year.

It works like this. Property developers buy land with the accompanying right to use it for a certain purpose, which is typically prescribed in the local council planning documents. They then lobby their mates in power to change the prescribed uses in the plan, in the process giving them a new property right which they did not pay the previous owner for. Nor did they pay the government for that new right. It was a gift.

But in Australia’s beloved capital city this game of giving planning gifts to your mates doesn’t work. There is no gift. In the Australian Capital Territory, if you want more property rights, you pay the government for them.

The ACT government achieves this in two ways. First, it has a public body that plays the role of land developer, the Land Development Agency, which converts land into urban uses, invests in infrastructure, and sells the new plots of land at market prices. When it sells this land it comes with the requirement to build on that land within two years in accordance with the purpose clause of the land title. By acting as the developer, 100% of the windfall planning gains goes to the government in manner that is economically efficient.

Second, if you have land that can be developed to higher uses within relevant zoning rules of the town plan, you must pay the government a Lease Variation Charge (formerly a Change of Use Charge) of 75% of the value gains to the land from allowing the higher value use.

These two schemes earned the ACT government $164 million and $19 million in 2014-15 respectively. That’s $183 million in revenue that would be given away to land developers in other states.

So how big is the great betterment give-away occurring in other states? We can scale up the ACT data to get a good estimate of the size of this give-away happening in the rest of the country.

There are two main adjustments necessary to do this. First is to adjust for the dwelling price differences across states. While the two schemes apply to all types of land, including residential and commercial, the residential values dominate. I therefore adjust the figure by the ratio of state median dwelling prices to ACT median prices to get the price ratio. I then adjust for the number of new dwellings in other states completed in that year to get the dwelling ratio. I then calculate the total scaling factor as price ratio times the dwelling ratio. Then I multiply this by the ACT betterment revenue and sum across states.

The result is summarised below. And the answer is $11 billion.

	Median price (May 2015) CoreLogic	Trend new private dwellings (ABS, year to June 2015, State)	Price ratio	Dwelling ratio	Scaling factor	Scaled revenue ($m)
Sydney	$ 691,000	51,368	1.39	14.13	19.57	3,582
Melbourne	$ 502,000	64,529	1.01	17.75	17.86	3,267
Brisbane	$ 424,000	42,055	0.85	11.57	9.83	1,799
Adelaide	$ 383,000	10,079	0.77	2.77	2.13	389
Perth	$ 528,000	30,343	1.06	8.35	8.83	1,616
Hobart	$ 299,000	2,734	0.60	0.75	0.45	83
Darwin	$ 510,000	1,648	1.02	0.45	0.46	85
Canberra	$ 499,000	3,636	1.00	1.00	1.00	183
Total						10,821

That sounds right to me. $11 billion is what the Australian states gave away to landowners and property developers in 2014-15, that they could have recouped had they had the system of betterment taxes that the ACT has had since 1971.

As a final point, you might think that the degree to which the ACT government controls land uses might have some effect on slowing new investment in dwellings. This is not the case. The ACT has the largest homes in the country, and has the same bedrooms/person ratio (a measure of dwelling stock per capita) as Queensland, slightly behind Tasmania, but in front of NSW and Victoria. While I remain cautious about the ability for such systems to be taken advantage of, I see the current system of private landowners taking planning gains and determining the new supply even more prone to political corruption and favouritism. Rezoning gifts don't even come with obligations on developers in other states to actually build what they promise. They can sell the land with the new rights the following day and cash in their gains. 

Throw out the standard urban economics model

The workhorse model of urban economics is the Alonso-Muth-Mills (AMM) model of the mono-centric city (the modern treatment is attributable to Jan Bruckner). This model is basically the representative agent optimal-control model of neoclassical economics. It is modified with additional functions that account for the cost of commuting to a city centre from different distances and allows capital, K, to be optimally geographically dispersed as well.

Sweet right?

The only problem is this. When you convert the model to English you realise it has little basis in reality. The only real pattern that is consistent with the model is that higher buildings are near the city centre. But I could come up with a million other models that are consistent with that pattern.

One of the main flaws in the AMM model is that there is no possibility for development of sites within the city into new buildings. Every site is already used at its optimal level. There are no vacant sites or sites with old buildings ready for knock-down and reuse. There is no development industry. There are no landowners.

Also because of the comparative-static nature of how the model is used, 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 homes and buildings. The single social planner who controls everything in the city then dictates that the whole city will be rebuilt with a new optimal allocation of housing and commercial buildings under new conditions, and this whole new stock of buildings rebuilt in an instant to that new specification.

Don’t believe me? Here are comments from eminent urban economist David Pines from back 1987 making the same point.

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.

Perfectly malleable. That’s the crux here. Behinds this term hides the complete nonsense I just described about the constant rebuilding of the entire city.

This is a massive problem for anyone wishing to apply economics to urban planning. Because in the AMM model any constraint on land use — be it a natural feature such as a lake, river, or mountain, or a regulatory constraint in the form of height limits, floor area restrictions (FAR), or greenbelts — increases prices by forcing the malleable capital stock of homes and buildings to spread further from the city centre.

But this simply cannot be true outside of the model. There are so many contradictions between the model and reality that its conclusions cannot be taken seriously. For example, the existence of a development industry that takes sites that are vacant or in low-value uses and invests in new buildings isn’t captured in the model. There is no such mechanism because there is no vacant or under-utilised land. Every piece of space already has a building at the perfect economically-optimal height for that location.

I created the below image to show the common real-life elements of real cities that can’t exist in the standard AMM model. Let me explain.

The horizontal axis represents the distance from the centre of the town. Imagine taking a slice of the city along the roadside as you drive outwards from the city centre. You will see the density of buildings fall, which are represented here in dark grey. What you see in the real world is just the dark grey. 

The world of the AMM model is represented by the blue line, showing the optimal development density at each point along the road at a given time. In the city centre, where rents are highest, it is optimal to build higher buildings. Higher rents justify the investment in taller buildings. But then as you go further from the city centre, the rents at each location can only justify a smaller building on each site. I call this the “site economic frontier” because for each individual site at a given point in time, it is the economic limit of development.

In the AMM model, the whole city is full to the blue line. Always and everywhere. So you can begin to see the problem. There are substantial gaps between this model outcome and the reality of the grey buildings.

Moving along, the dashed orange line represents town planning constraints. Near the city centre I have shown how a height limit will create a gap between the site economic frontier and the “planned frontier”, or planning limit. I have also shown how such gaps are created by site-specific controls such as heritage protection (meaning you can’t demolish the building and then build to the site’s economic frontier). And I have shown how city boundaries like greenbelts or urban footprints create a similar gap.

The blue shading is therefore the economic-planned frontier gap. In the AMM model this is a problem, because before introducing such a gap the city is full to the brim, with buildings always built in every location to the economic frontier, so it results in a net loss of dwellings and buildings, even after accounting for feedback into higher prices and a higher economic frontier in other areas.

Yet in the real-world view, introducing such a gap changes nothing. Buildings are not demolished and rebuilt in different locations. Landowners in certain locations are simply limited by a “regulatory geography” rather than the “economic geography”, neither of which the city as a whole is anywhere near.

The existence of the light orange shading — the gap between the currently built city and the planned frontier — also cannot exist in the AMM model. There are no development opportunities. Even worse, there are no vacant land sites. This is an even bigger problem for the model.

I highlight this particular point by shading the gap between vacant sites and the planned frontier in darker orange because in the real world these are the most likely sites to be next developed. On the left of the diagram I also have a little curve that is supposed to show the probability of a site being developed as a function of its currently developed density or height. The smaller the current development, the more likely that site will be developed next, as there are lower costs in doing so in terms of demolition.

Overall then, we have a diagram that shows the major problems for the standard AMM model of urban economics. There can be no development industry in the AMM model because there is no planned-frontier gap. But even worse, the fact that reality doesn’t fit well to the model means that there must be some other mechanism determining the rate of investment in new housing and development. Something entirely ignored in this model. And even worse, entirely ignored in the current popular textbooks on urban economics.

I have been through this before. Vacant land is a perpetual real option to invest. The optimal timing of when to invest in a building — to exercise the development option — is when you expect that doing so maximises its value (read up on the Bellman equation if it takes your fancy). Otherwise, you wait. Because while today it might be optimal to build a five-storey building, in a couple of years it might be optimal to build a 12-storey building, providing even greater incomes. And you can’t do both.

This turns the standard model on its head. It means that because planning controls, such as height limits, take away this future option to build higher buildings, the value of waiting to build is lower, and the typical landowner will bring forward their investment, increasing the rate of new dwelling supply.

To me, the fact that the standard AMM model doesn't fit the data, and because we know land is best characterised as a real option, it must surely be time to throw out this model and update the textbooks.

Update: Read more about the option to delay development here.

The mysterious real interest rate of economic theory

The mysterious real interest rate – the one typically denoted as r in economic theory – does not have a real-life counterpart. This is a problem for economic theory. And it is a major problem for policymakers relying on monetary policy to boost economic activity.

While we think of the nominal interest rate minus inflation as getting close to the theoretical concept of real interest rates, changing this value in practice through central bank operations does not actually change the real return on capital and stimulate investment through that channel.

Why?

Because the price of capital is determined by the interest rate! We have known this for a long time. Joan Robinson wrote about the circularity of reasoning when we measure the quantity of capital by its price. She was ignored. As I expect to be.

For those who want to understand a little deeper, here are some more details. First, we take the standard economic view. In this view there is a thing called capital, K, that has a fixed cost (because it is a machine or building etc.), and each unit of K has an income-earning potential, net of depreciation, each period, which I call I. To buy each K people borrow money at the rate, r, meaning that as long as the ratio I/K > r it is profitable to invest in more capital, K.

So if my business can generate $100,000 in extra profit each from an extra machine, the business might see the value in spending $1,000,000 on that machine if they can borrow to pay for it at a 9% interest rate (costing $90,000 per year in interest), rather than an 11% interest rate (an annual interest cost of $110,000).

However here’s the circularity problem. The gains from a lower cost of new investment are made whether the investment is undertaken or not because they become capitalised in the value of the business immediately. That is because the value of the option to expand is always captured in the market value of the assets of the business.

What is this option I speak of? Where did it come from all of a sudden?

The way I snuck this into my definition of capital is part of the fundamental problem that permeates all the economic debates about capital. One group talks about capital as machines — independent robots, vehicles, machines and tools, who get to keep the returns from their existence. Yes, my bulldozer gets income from its efforts in this view, not the owner of the bulldozer. Because once you have an ownership structure overlaid, you have a system of property rights which contain real options for investment, and they have a value.

Think about land. Land is often referred to as capital, but it is nothing but a piece of paper offering a particular set of rights to a three-dimensional chunk of the universe. Land is an ownership right, not a physical object. See my mud map of economic concepts to help see what I mean here.

Once we have shifted to a view of capital of a system of property rights, some of which have physical machines attached to them — like a building attached to land rights, or a truck attached to various rights held by a trucking company — we can begin to see the circularity problem more clearly.

We now have a world were investors maximise the return on their property rights, not one where machines decide how to maximise the return on themselves.

This means that anyone making a decision to invest in new machines must take into account the current value of their property rights as part of the cost of capital. Because the full opportunity cost of the investment in a machine is the next best alternative, which is to sell the property rights at market value. In the diagram below I try to capture the idea that all physical capital — buildings, machines and so forth — are attached to property rights, and that only if we look at the value of the whole can we get the true cost of new capital investment from the perspective of owners of property rights.

Let us now see the effect of decreasing interest rates in a world of property rights, and where the value of these rights is part of the cost of capital. We will take the simplest case of a piece of vacant land, where the full value of the property right is from the option to build a $1 million building on that land to earn a future income of $100,000 per year. Here only the building is part of physical capital in standard economic theory.

We will then see what effect a reduction in interest rates has on the cost of “property plus capital”, and therefore the incentive to invest for owners of property rights. The table below summarises.

	Before	After	Further
Interest rate	11%	9%	7%
Income from investment	$100,000	$100,000	$100,000
Capitalised value of income	$909,091	$1,111,111	$1,428,571
Cost of standard K	$1,000,000	$1,000,000	$1,000,000
Return on standard K	-9%	11%	43%
Value of property right	-$90,909.09	$111,111.11	$428,571.43
Cost property rights K	$909,091	$1,111,111	$1,428,571
Rate of return on property rights K	0%	0%	0%

Let me walk you through this. The interest rate is the real interest rate. Take it as the nominal interest rate in a zero inflation environment for simplicity. The income from investment is the annual income after the building is built. The value of that income is capitalised at the new interest rate to show the static value. Then we see that when the interest rate is reduced, the $1 million building gets a positive rate of return, and hence the change to the interest rate will provide the incentive to invest.

As a side note, the alternative way to see this is to simply assume that the cost of the building is borrowed at the interest rate, as I did earlier when discussing the standard view. In this case, the cost of capital is $110,000 per year before the interest rate fall, and $90,000 per year after the interest rate drop, shifting the investment from an unviable to viable way earn the $100,000 per year.

But, if we consider the value of the property right as well, we have a different picture. Here, the value of the property right is the residual after taking the investment return (capitalised value of income) and subtracting the physical investment cost (cost of investment). With interest rates of 9% in the 'Before' case, the value is negative, and there is clearly no return on capital (i.e. for property valuers out there, this building is not the highest and best use of the land). But even after the interest rate is dropped to 9%, the return on the combined “property plus capital” is zero, because the cost of capital now includes the opportunity cost of selling the property right at a positive price.

Even if we decrease interest rates further, say to 7%, the rate of return on “property plus capital” is still zero, as I show in the last column. Owners of property rights simply gain at the expense of those in society who do not own substantial property rights and will be future buyers of those rights.

Under this view, the investment effect of lower interest rates disappears. The reason is that the capital of economic theory, and hence the real interest rate of economic theory, cannot be detached from the reality of a system of property ownership rights.

I’m not the only one to say this either. Once you are in a world of property rights and real options, the key determinant of investment is not the real interest rate of standard theory. Here’s Raj Chetty showing that increasing interest rates from low levels can bring forward investment — the exact opposite of the standard view. In a world of property rights an real options, the key factor is not what to invest, but when to invest in order to maximise the rate of growth in the value of your property rights. Hence there is a huge role for speculation on the price of property rights, and a clear logic behind following the herd during asset cycles. Under these conditions, it is also the case the reducing interest rates reduces the cost of delaying investment, and may, in fact, slow rates of investment and economic activity!

Let me summarise. First, standard theory has machines earning incomes and ignores the system of property rights it attempts to model. Second, once you incorporate a system of property rights these right have values, and the value of these rights must be added to the cost of machines to calculate the economic (opportunity cost) of capital. Third, once you have done this, changing the nominal interest rate (or even nominal rate minus inflation) changes no investment incentives, as all property rights holders immediate gain the value, which becomes a cost of investment. Finally, other factors that affect the cost of delaying investment by owners of property rights probably have a larger effect on investment, and in fact, decreasing interest rates decreases the cost of delaying investment.

This is not to say that there may be some effect of monetary policy through other channels, such as decreasing interest costs of borrowers, allowing them to increase spending. But if this is the dominant effect, without an investment incentive, then loose monetary policy may primarily inflate asset prices and not economic activity. This prediction gels with the reality of the past decade.

Structuring the unstructured: A pluralist economics mud map

In a previous article (“Reforming Economics: The Challenge”), I made the point that organising the jumbled schools of economic thought into a coherent pluralist curriculum faces both a social and a technical challenge. These two challenges go hand in hand to some degree, since the teaching within any discipline largely reflects the sociology of its practitioners. Social conventions are reflected in teaching, and teaching reinforces those social conventions.

In economics, and the undergraduate courses where the majority of students get their complete economics training, this means uniform domination by the neoclassical approach – mirroring its dominance in mainstream journals and research activities. Even the now decades-old behavioural and experimental school is all but ignored in core economic textbooks, reflecting a contested relationship with the mainstream and an inability to escape the fringes of the discipline.

But I hold an optimistic view. There is a feedback loop that can be broken by offering an attractive teaching alternative that presents an array of approaches and allows for conflicting ideas and methodology to be examined side by side. Such an alternative needs a very broad conceptual map on which each school of thought can be placed, and it needs tools to comprehensively understand and assess the validity of each. Such a map can allow a logical positioning of competing methods across domains of economic interest and can illuminate where and when there might be overlaps or conflicts. Such a map could facilitate a broader language of economics by enabling ideas to be accurately communicated between different schools of thought.

Absent a map, teaching a pluralist curriculum faces huge consistency problems. Others have attempted the relatively straightforward task of incorporating behavioural approaches into core economics courses. They find that conflict between ideas is a teaching obstacle and that there is a tendency to pick winners and losers in such circumstances.

…integrating such insights into microeconomics is not easy. One has to tread a fine line between integrating new and important results without making the standard theory look completely useless to students. This is often advanced as an argument not to integrate newer results at all. I tend to disagree with this view. It supposes that students are unable to hold two opposing thoughts in their head, which I find more than a little patronizing.

Readers may agree that standard microeconomic theory is completely useless, but teaching a pluralist approach does require giving credit to the mainstream where it may be relevant. Finding that relevance needs a map.

To illustrate, I’ve drawn a mud-map of the economic terrain in a very broad sense. The boxes on the map below represent islands of interest that are related to each other, but are conceptually different domains that cannot be conflated. Money for example, is not a real resource, or a traded widget, as it is almost universally assumed to be in neoclassical models. It is a set of accounts. It has its own domain. Real resources in the economy are also not the set of legal rights over who owns what. These rights are entirely separate. To be clear, money can be thought of as a type of ownership right, so the money and legal rights domains share a common red ‘ownership’, or `power’, environment in my map.

Welfare is another domain: distinct but related to real resources, the system of legal rights and the monetary environment. It is this domain that provides a moral foundation for the economy. This domain shares its yellow terrain with the real resources, since can both be considered the area in which mainstream economic theory operates, currently being bridged by the rather inadequate welfare theorems.

Outside these economic islands is the sea of the social and political environment. Any economic domain is enclosed within a social environment, and any analysis of economic problems depends crucially on conditions within this environment. Think of this domain as the evolving norms and institutional structures of society.

Floating in this sea are three tools, or ways to interrogate ideas, that can be carried to each economic island to explore and assess different approaches and to guide learning and discussion.

The first tool is timing. We know the world is dynamic, so asking the question of how timing is dealt with in any economic approach is crucial. History is irrelevant in most mainstream models, while the future is certain (though sometimes risky). Equilibrium arises instantly without adjustment. Asking the question of timing provides clarity about what conditions would need to be met in order for an approach to be valid, and why it might break down. It would expose that a number of diverse theoretical approaches have a common ignorance of time, while others (e.g., evolutionary economics) hold it as their core insight.

The next tool is aggregation. At what level of aggregation is analysis taking place? Is it important that individual and aggregate behaviour may differ? This tool is used to avoid the macro-micro distinction and concentrate on relevant economic questions. After all, a single market is already a macro-economy of buyers and sellers. Firms too are aggregates of individuals, and their existence is worthy of discussion in the legal rights domain. Questions about the how and why of aggregation are some of the least-asked but most important in any domain. How can we aggregate welfare or capital? How are rights divided between and within entities? What level of analysis matters in the monetary system?

Lastly, there is the tool of prediction. This represents the ‘So what?’ question that needs to be asked of any method of analysis on every economic island. Even if an analytical approach seems to adequately address questions of timing and aggregation, it can’t pass scientific muster unless it provides useful predictions. If different schools of thought generate different predictions, it should be possible to trace backwards through the questions of timing and aggregation and (being clear about which domains are being examined and how they are linked) to find the causes of those different predictions.

It might help here to provide an example of how this map can aid the teaching of different ideas in economics and facilitate communication between schools of thought. The map could be used in two ways; either as a way to structure courses, by exploring domains and tools and incorporating different approaches where relevant, or as a way of structuring inquiry into different schools of thought as they arise in the curriculum.

If one were using this map for structured learning about Godley and Lavoie’s monetary circuit models, students would first learn about the institutional setup that produces the monetary domain, including what exactly money is, and how certain relationships to the legal rights and real resources domains are implied. They would then note the way timing is treated and how and why firms, government, banks and other sectors of the economy are aggregated. Finally, they would explore the types of predictions and how they are linked between the money, legal rights, resources and welfare domains.

Later, when learning about mainstream growth theory, it would be clear that this approach resides in the real resources domain where time is condensed to a single point, while its representative agent (or ‘social planner’) deals with aggregation. Predictions of such growth models vary, but most generate only static equilibrium outcomes unless some external shock hits the system. Both the monetary and legal domains are assumed to allow for the outcomes of the models. In practice, the capital terminology used in these models is often confused for capital in the legal rights domain.

We could dig down further into the neoclassical view to explore the still controversial question of what is capital. Here we would see that in standard growth models capital is a collection of physical objects. Yet because we cannot aggregate quantitative measures of the diverse physical objects, but natural and produces, we end up accounting for them by monetary measure of value that are instead comparative measures of value of property rights, which are constructed by our institutions.



By situating these two approaches on the map, and seeing the differences as to aggregation, timing and predictions, we can more clearly see under what conditions they correspond or conflict. For example, investing in more physical capital generates higher growth in both, but only in a monetary circuit model can we potentially say anything about the adjustment processes in the economy. Further, both take the social and political environment as given and only indirectly relate to the welfare domain through an implicit assumptions about the relationship between aggregate resources and aggregate welfare.

It may take a little work, but this type of structured thinking is helpful.

I am not alone in trying to put some structure around the jumble of economic schools. Ha-Joon Chang has mapped schools of economic thought based on particular domains of interest. In his latest book Economics: The User’s Guide there are many of the same general themes of timing (Economies change through…, The world is…) and aggregation (The economy is made up of…), and also the suggestion that different economic approaches focus on certain areas of the economy, such as production or exchange. 

If we want a viable pluralist economics curriculum, a way to structure ideas from the diverse schools of thought is absolutely crucial, and I hope this mud-map of economic domains can provide a starting point. New pluralist textbooks and teaching materials based on a structured inquiry can demonstrate how diverse ideas can be brought together, without creating conflict and without transforming the exercise into merely a process of selecting a school of thought that aligns with the student’s existing ideologies. I hope this outcome can emerge from the combined efforts of IDEA Economics, Evonomics, Rethinking Economics and other reformers.

First published at IDEA Economics

Reforming economics: The Challenge

And so the debate rages. Economics needs to change. Always does.

The challenge of reforming economics cannot by overstated. Modern mainstream economics has remained dominant in our universities and governments despite overwhelming evidence against most of its core principles, and despite decades of attempted revolutions. The concept of a static equilibrium and the ‘representative agent’ method of aggregation are just two notions that have been repeatedly shown to be internally inconsistent; not just by outsiders, but by many of the leaders in the mainstream. Yet they continue to dominate the discipline.

The core remains unchanged.

Outdated and economically-irrelevant concepts still fill the pages of introductory textbooks. From there they fill the minds of each new generation of students, who pass on these ideas to the next generation of students, and across society more broadly. Breaking the feedback loops in this system is what is required to transform the discipline.

The call for pluralism is an admirable end goal, upon which most reformers agree. But my view is that previous attempts at reforming economics have failed because they avoided, or inadequately understood, the two main barriers to change. While they may at first seem insurmountable, without leveraging change at these points the mainstream will stay locked-in as the dominant approach to economic analysis.

The first main barrier is social. Economics as a discipline typically rewards tribalism over reconciliation. If you’ve been following economics blogs in recent years you will have a pretty clear understanding of this. But the same dynamic happens in all of academia. Journals are often aligned with particular views on what are acceptable methods and concepts and act as the gatekeepers to the tribe, requiring all comers to offer sacrifices to tribal elders. Moreover, the mainstream represents over 80% of the discipline, so any change promoted by minority groups will be frowned on. This is the social reality.

In essence, the social challenge is to bring the tribes together in a way that makes them all feel like insiders in a new larger group. This means not starting fights with powerful tribes, especially not the current mainstream. It means highlighting any common ground where tribes agree and giving credit to how they contribute to an enhanced view of economics.

I will talk more about social barriers to change in economics at length in the future. For now though, this is enough context to discuss the second main barrier, which is a technical one.

The technical problem is - how do you teach a pluralist program when there is no recognised structure for presenting content from many schools of thought, which can often be contradictory, and when very few academics are themselves sufficiently trained to to so?

Teaching a pluralist curriculum shouldn’t be about presenting the economics discipline as one of feuding tribes. I share Simon Wren-Lewis's fear that a pluralist curriculum could become a one-stop shop for students, who get to browse the tribes before joining the one that most aligns with their existing political ideology.

Instead, I sincerely hope that we can train a generation of economists to be aware of the legacy of each school of thought, but acknowledge the common ground between them.

There is an old saying that if you ask five economists and you'll get five different answers - six if one went to Harvard. Can we teach a pluralist curriculum which would bring economists onto the same page so that when you ask five economists a question, you get one good answer?

Approaching this problem needs a systematic solution. For example, we need to think about how to structure teaching around topics and concepts that allow students to study problems and evaluate potential approaches, and their evidence. We need an alternative textbook, or set of them, that can satisfyingly demonstrate the approach being called for, and ultimately offer a replacement foundation upon which to build a pluralist curriculum.

Presently, even the best mainstream textbooks merely tack on a few comments on alternative approaches. For example, the currently popular experimental or behavioural economics schools, despite being widely being regarded as a revolution in economic thinking and economic science, are given very little credence in the most popular textbooks. After pouring through the text of 25 popular undergraduate microeconomic textbooks, Lombardini-Riipinien and Autio find that

… ten of the 25 textbooks examined make no reference at all to behavioral economics; six dedicate less than 1% of total pages to it, six between 1% and 2.6%, and three between 6% and 11%. When behavioral economics is discussed, the focus tends to be on bounded rationality rather than on bounded self-interest or bounded willpower.

Experimental economics is not discussed at all in ten textbooks, twelve textbooks dedicate less than 0.6% of total pages to it, while three dedicate between 2% and 10% of total pages.

Joan Robinson tried to comprehensively rewrite the core introductory economics textbook with John Eatwell in 1973. While the book does a superb job of putting economic analysis in a philosophical and historical context, it offers no coherent backbone upon which to build an understanding of economics. For example, after reading the book I learnt very little to aid me in answering practical day-to-day questions about the economy. Where does money come from? How do we measure unemployment? How could we assess alternative options for addressing negative externalities? Is the very concept of eternality useful, since it implies the existence of a no-externality world?

What is needed is a way to structure the exploration of economic analysis by arranging around economic problems around some core domains. Approaches from various schools of thought can be brought into the analysis where appropriate, with the common ground and links between them highlighted.

Unless the community of economic reformers can make the effort to reconstruct the way economic is taught, and make the tough decisions about how to structure new core texts, including what to leave in and what to leave out, then change will remain elusive. We can’t call for change in the challenging tribal social environment of economics without offering an attractive alternative - one that embraces the best from each of the schools of thought and finds common ground without creating a new set of outsiders.

Originally published at IDEA Economics

Queensland housing supply

The planning process is often held to be partly to blame for housing prices in Australia. Unfortunately, there is very little data about the planning system in general, and information on the stock of approvals and the number of new approvals is typically even worse.

In Queensland, the data has become better over the past few years as Council's have begun reporting to the State their approvals activity, and their estimates for the capacity of their planning scheme to provide new dwellings. Councils report that they currently have land zoned for around 660,000 new detached houses. That's about 40 years worth of new detached dwellings able to be built without any changes to zoning laws or any approvals outside the code. To put that in perspective, the total population growth in Queensland is about 70,000 people per year, who require one home per 2.6 people, or 26,000 new homes per year of both apartments (about 10,000) and detached houses (about 16,000).

There are also current approvals for 107,000 new homes, or around 4 year's worth, and 24,000 new approvals being granted each year.

For more fine-grain analysis of the housing supply pipeline, I have created the visualisation below using data from the Queensland Government Statisticians Office. It allows you to see the historical trends in planning approvals for residential dwellings, as well as the stock of current approvals, the lapses of approvals, and so forth. Click on the map to get the historical time series of these indicators for that region. 

Nowhere can I see that councils have hit an approvals limit that might have constrained the rate of supply of new dwellings in an area.