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. 

Reteaching economics in practice

Reforming economics teaching has been a heated topic of debate since the financial crisis. My personal views on this align closely with The University of Manchester’s Post Crash Economics Society (PCES), who have been calling for a pluralist approach that would rid economics teaching of its neoclassical core, and replace it with a critical study of economic questions and the diverse methods and approaches being taken to understand and answer those questions. The group Reteaching Economics has similar aims. Certainly a focus on empirical methods would be elevated to a core element, as the question of how can we know things is a primary concern to critical analysis. In all such an approach is not an excuse to avoid mathematics, but to embrace a the wide range of mathematical tools in use.

Change like this is a difficult task, and will ask a lot of students and teachers alike. But in my view it is also a difficult task to teach and learn the bland, abstract and often irrelevant economics that fills the undergraduate textbooks today. Even very complex concepts can be reduced into small components with memorable analogies that make them digestible. And if done right, a new approach should trigger more of a desire in students to learn and apply these new concepts and tools.

I have tried to shift my own teaching towards this approach. The purpose of this post is to share an example of taking an existing course, and the various constraints that came with it, and shifting it towards a more critical and pluralist teaching approach.

Last year I taught Managerial Economics and The University of Queensland. The student body were not all economics majors, but were drawn from across the university’s disciplines.

One constraint was the prescribed textbook (which I could not change by the time I began teaching). It contained repeated lessons in applying optimal control problems willy-nilly to possible problems that might face a firm manager. Like how should I profit-maximise when I have two goods that are substitutes?

Solving an equation that relies on knowing the cross-price elasticity between your products is actually not very useful. The important questions are much deeper and more critical, like “How do I know the own-price and cross-price elasticities in real life?” I mean, are firms really just changing prices all the time to check on their elasticities? Or “What is the current price and why was it set at this level?”

So what did I change in the course?

I made sure that before they embarked on the “textbook” concepts that they were given a broader outline of the big ideas that are implicit in the textbook view, yet completely hidden. Like “What do prices do?” And, “If prices allocate resources, why do firms exist?” You can download my first week’s notes here (I make lecture notes a seperate reference document and use lecture slides as a tool to build and explore example problems during class). To see my emphasis on providing a pluralist view, this is one of the exam questions I snuck in.

Which is the most accurate comment on the following statement - “Firms maximise profits”?
A. Always, because by definition they are profit maximisers.
B. Never, as profits do not always accurately reflect long run payoffs.
C. Often, and always in preference to potentially conflicting objectives.
D. Sometimes, but they may achieve this through intermediate objectives.

I also cut short many weeks of textbook regurgitation and replaced it with concepts and tools that don’t fit in the paradigm, but are nevertheless quite useful in practice. For example, I introduced a simple analysis of real options in investment decisions, which captures many of the realities of irreversible firm decisions (here). I also included a week on networks and evolution that introduced some simple models to capture some of the more interesting firm and market dynamics (here), and allowed us to have informed discussions on questions like performance pay for individuals or teams, and revisit the reasons firms exist at all. When I cover information problems, I discuss adverse selection, but also beneficial selection, and look at how the data shows the opposite of what the textbooks predict in Australian health insurance markets.

I also changed the assessment items from almost purely multiple choice and short answer exams, to a mix of some multiple choice, short and long answer questions that allow concepts to be applied to real life situations and data, and an assignment.

The assignment was about a real company facing real changing market conditions. Students were expected to determine the relevant data they needed to collect to complete the parts of the assignment, then find it, then use it (run appropriate regressions), then interpret it, and present it all in a professional way. Some of them told me it was the best assignment they had done in their whole course in terms of actually learning something interesting, and producing something they can be proud of. This was a relief. I had spent a lot of time preparing it.

Some of the economics majors said it was the only assignment they had ever been asked to do! This was quite worrying, but completely in consistent with economics courses globally. The PCES survey showed that a fifth of economics subject have multiple choice questions make up more than 90% of the course grade, and in half of subjects they make up over 50% of the grade. As a general rule, economists are trained to solve equations and answer multiple choice questions.

So what is stopping these changes from happening across the board?

First, few academics maintain the connection with the wider business and public policy community to have a ready supply of topical recent examples where economics can be applied. They are so narrow in their research focus, that when teaching outside this area, they simply conform to the standard textbook.

Second, many academics are trained neoclassical economists only. When my exam draft was reviewed by another academic for errors, they noted that they had never heard of real options, nor the evolutionary and network models in my course. This becomes an even more serious problem in courses where completely wrong models still fill the textbooks, such as the case with the money multiplier. I have had to stand up in class and say “Ignore chapter 19 of the textbook, it is simply wrong.” Some economists are too loyal and “profession proud” to say something like that.

Third, it is a lot of work for staff. For my teaching last year I spent about 18 hours preparing each lecture. This meant that I had new examples to use, new data, and updated readings that applied economic ideas to recent events. For the assignment I also had to research and write my own version of the assignment to make sure I was asking something that was possible to do, and that the data would reveal something that wasn't apparent on the surface (in this case that a firm which is often thought of as being "high end" actually sold inferior goods). When you want to have students research relevant recent real-life examples, this is what you have to do.

Fourth, some students prefer to just get the grade and don't like the ambiguity that a more pluralist approach entails. They have learnt how to study for multiple choice exams, how to solve equations, and so forth. Having to actually think, while being uncertain about how your thinking will go in the assessment, becomes a challenge.

I’d be interested to hear how others have gone about improving their economics teaching to be more critical and pluralist, and what constraints they faced.

Land tax becomes respectable part of tax debate

After decades of political pressure that systematically clawed back state and local government’s ability to tax land, the debate has now swung back to this most efficient of taxes.

Land taxes are apparently a hot topic for debate at the years NSW Labor annual conference.

New sweet-talking Prime Minister Malcolm Turnbull even said the formerly unspeakable words on national television over the weekend (at the 6.30minute minute mark). Though he says while it is a great policy economically, it is politically 11 out of 10 in terms of difficulty. No $hit. When your voter based is dominated by homeowners, and your party made up of the country’s wealthiest landowners, you ain’t got a chance.

Let’s do the hypothetical anyway. How much could be raised from state land taxes?

In NSW just the exemptions to the current 1.6% (2% over 2.5million in value) land tax amount to $700million per year.

In Queensland the exemptions to the current land tax regime cost the state $1.3billion in 2014-15, with the components of the costs of the exemptions summarised in the below image.

Yes, you will see a $23million per year land developers concession - at tax with the exact opposite incentives to efficient tax, reducing the cost of not developing land.

In Victoria land tax concessions are forecast to cost $2.9biliion in the current financial year, amongst a bunch of other concessions that amounted to a total of $4.9billion. See the summary from the budget papers below.

So just in these three east coast states we have about $5billion per year just in land tax exemptions, plus many billions in other exemptions, including on gambling. If we remove the land tax concessions and double the land tax rate to around 4%, these three states could raise another $13billion every year.

But that is just the start of the tax concessions for the nation’s wealthiest.

What about another tax loophole? The capital gains tax exemptions. Treasury estimates it costs the country $56billion per year.

Or discounted taxation on super contributions? There’s another $27billion per year.

There are simply billions lying on the table in obvious tax loopholes for the rich, with land tax exemptions just one of many.

We have seen one big change - saying the words land tax has become acceptable for a politician. Now let’s hope this change starts snowballing, and that states can fight the propaganda of vested interests to use their tax powers more wisely and efficiently.

Die solar roads. Just die.

Humans are quite smart for hairless apes. But sometimes I wonder whether we really do have the edge over our distant cousins.

Here’s an example. Solar roads. For some reason, the most idiotic idea ever still seems to gain popular attention and funding. France is now committing to investment in solar roads.

But, you are thinking, that actually does sound quite interesting and potentially wonderful. Oh, how innovative.

I know right? The urge to jump on board this idea seems irresistible. But that’s our monkey brain doing the thinking. Because when you switch on your rational mind the whole this looks like a big joke.

Our instincts are not good at thinking about a new idea in relation to a particular alternative. It takes conscious rational thinking to realise that for this to be a good idea, we need to think of alternatives to compare it to. Our default is to compare solar roads to no solar investment, hence the urge to think it sounds great.

So here’s an alternative. Solar panels anywhere but roads!

The solar roads concept also implies it is solving a problem that doesn’t exist. In this case, a problem of insufficient space for solar panels. But that is absolutely not a problem. Estimates suggest there are 400sqkms of just residential roofs space in Australia that could accommodate solar panels. More than enough to our total electricity needs, and that ignores the large industrial and commercial spaces available.

Here’s my very brief list of why the idea is stupid.

1. Roads have things on them that block the sun, like cars, people, trees and buildings shading them and so forth. 
2. Roads cannot be angled to efficiently capture sunlight. 
3. Roads get dirty. 
4. Roads need a superstructure above the solar panel that will reduce efficiency. 
5. Building solar roads means expensive excavations and repairs that will block traffic flow. 
6. The technology to do it is rubbish. 

You may have heard that in Amsterdam there is a trial of a solar bike path. I think the title of this article sums up their result “That Fancy New Solar Bike Path In Amsterdam Is Utter Bullshit"

That article says it all.

What about a better alternative? How about building a roof over bikeways covered in solar? This alternative has a few things going for it

1. No excavation 
2. Cheaper 
3. Keeps cyclists dry 
4. Keeps snow off the bike path in cold climates
5. Can be angled to the sun 
6. Is proven technology 
7. Provides shade in hot climates 

Plus more.

And, it has been done before quite successfully.

So please, turn off your monkey brain. And when your friends on social media get excited about solar roads, send them here. Or send them this.