Mike has a deep understanding of bond markets, macro policy, and money. It was a good chance to ask him about Modern Monetary Theory (MMT), which is a school of thought in economics that focuses on monetary operations and both sides of the monetary balance, which includes the debt (the liability to the government) and the asset (the Treasury bond held by the public or other organisation).

Is the MMT view really that different from the standard macro view? And where does a lot of the fear about public debt come from? Who really bears the cost of interest on public debt?

We dig into these questions. Enjoy the conversation.

Follow Mike’s terrific Housing Hell Substack here.

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Post-budget, there has been a lot of fuss in Australia’s media about taxes.

Today, I want to experiment with simply sharing a few concepts that help me think coherently about taxation as an economist, and where the confusion and blind spots are in the taxation discourse.

The main question I ask to help make sense of it all is, “Where do I send the guy with the gun?”

Tax bases versus people taxed

The important thing is that a tax base is not a person, except in the case of a head or poll tax (which is what we call a per-person tax on simply being alive for a period).

People have claims to things after tax rules have been applied to tax bases.

The value you own is the value after the rules of society are applied, not before. Your home is your home after the rules of society about property rights apply, not before. Your income is your income after the rules of society about taxation apply.

We all know this. People are incentivised by their after-tax incomes, not their “before tax” personal income, or what I will call the tax base for personal income taxes.

One of the biggest conceptual confusions is that taxes take your income. No. Your income is determined after the rules have been applied and all the price and output adjustments have taken place.

Tax bases are not people—they are places we can send a guy with a gun.

This has a few implications.

First, it means that taxing big, observable things that cannot be hidden are the best tax bases. That’s why historically taxes were raised from trade—on imports and exports that went on ships, and on land and its trade.

The tax base could be the tonnage of the ship, and you could charge a fixed tax per commodity, or it could be the value of the cargo, and you charge a percentage on that (called a tariff). On land, the tax base could be the area of land, on which a per-area tax would be applied, or the value, which would be a percentage land value tax, or the value of all fixed improvements, a property tax, or it can also include mobile property like motor vehicles that are big and easy to track.

As the economy became more sophisticated, we began taxing tax bases that were previously unobservable and therefore hard to enforce, such as personal income. In a world with minimal record-keeping and few large-scale businesses, this is hard to do. So we created systems like withholding, whereby businesses that pay employees don’t even give the employees the before-tax income (the tax base), but give them the after-tax income and apply the tax to the tax base on behalf of the tax office.

We can easily send the guy with a gun to big organisations that own major facilities.

This also means that the complexity of the tax base is a killer. The Petroleum Resources Rent Tax (PRRT) applies to a tax base that is a special convoluted accounting construct that leaves many opportunities to account differently to minimise it.

Taxing unrealised capital gains (change in value of assets owned), for example, fails not because of any economic theory about the nature of this gain versus an income payment, but because there is no tax base there to be taxed. Without the sale, there is no value to tax. It fails because we don’t know where to send the guy with the gun.

In fact, taxing income from capital (dividends and capital gains from owning businesses, interest payments on loans, etc) is tricky in general because ownership and lending can occur across national borders for not much more cost within them. Why not relocate ownership to a lower-taxing jurisdiction? Ireland, for example, has one of the lower corporate taxes in Europe, so attracts a lot of ownership into the jurisdiction, most famously aircraft leasing services.

People who gain from minimising the tax base will always do it.

So make sure tax bases are an easy place to send a guy with a gun and won’t simply disappear or be relocated because of a tax.

Want to find out more about how property rights create predictable incentives and price patterns?

Come to my two-day intensive Land and Housing Economics Workshop in Brisbane tomorrow.

Will the workshop help you invest? Probably. Will it help you understand policy? Definitely. Will it help you cut through the media nonsense about housing? Certainly.

There are a few spots left. Last chance to book here.

Tax incidence

When we say tax incidence, what we mean is the net effect on the after-tax wealth and incomes of different people after new tax rules have been applied.

Adam Smith notably described how difficult it is to really work out which people are worse off or better off after a new tax is applied because behaviour and prices adjust (see the Wealth of Nations, page 690 of the version here).

Economists call the net effect on the income and wealth of different people due to a tax the tax incidence.

For example, stamp duty is a tax that applies to the tax base of the transaction price of a home. It is the responsibility of the buyer in the transaction to pay this tax to the state, not the seller. But the tax incidence is not on the buyer, but the seller. We know that the price of homes adjusts upwards by a dollar for every dollar a stamp duty is reduced, and vice versa. Imposing a stamp duty reduces the seller’s wealth and income on net, not the buyer’s, even though the buyer sends the money to the revenue office.

If you conflated the administrative payment of a tax with the “people it taxes”, you would be wrong. It can often be the case that this is true, but usually the tax incidence is less than 100% of the person or company with the responsibility of tax payments on a tax base.

Any given tax setting is just a system of rules operating, and there are no “before-rules” outcomes, except insofar as there are measurable things called tax bases.

For example, the stamp duty described above decreases the value of property trades, and in doing so, decreases its own tax base. The property value is already different because of the tax, so that value is not the “before tax” outcome.

Tax equivalences

Often, analysis of taxation will revert to what I call ad hoc equivalences. This means that people impose a model of how they think a fair world works and compare a tax to that imagined world.

A popular example of this is to assume that taxes on the tax base of personal income are actually meant to be taxes on personal consumption. Here’s an example of such an equivalence claim.

This is important - we have a background principle for an income tax of equal sacrifice where we are thinking about people in similar situations sacrificing similar amounts of consumption in order to fund government services. In all of this discussion this is a central principle.

Why should income taxes try to reflect an underlying principle involving consumption? The Goods and Services Tax (GST) is already a tax on the tax base of the value of consumer goods.

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Where does this central principle come from?

Consider supporting FET to read on to find out what’s wrong with this claim.

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Since then, I have kept track of his views on evolving macro-economic and demographic trends.

This conversation was a chance for me to see how his independent analysis had led him to some similar views to mine on housing markets, but also test where we differ on the likely social, economic and political effects of long-term demographic trends.

It was a surprise to hear that we have both arrived at a similar view on the application of monetary policy being too broad for many of the industry-specific inflation concerns we have today.

Enjoy this conversation and find Moses on Twitter here and at his Random Walk Substack below.

Random Walk

"A multidisciplinary cast of mind . . . with a yen for synthesis." - Charlie Munger (about himself, not Random Walk)

By Moses Sternstein

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Prosper Australia released a report last week explaining how and why to price development rights.

Download a copy here.

The basic idea is that property rights are created and defined by the state. So when high-value rights are added to the bundle of an existing owner, they should be priced rather than given for free.

As the Prosper report explains:

The legal right to use land in specified ways is how society regulates the use of a common resource for the public good. There are no natural rights to landownership, and no rights inherent in land title – rather, the bundle of rights that makes up landownership is always and everywhere a creation of the state, and relies on active enforcement by the state.

…

The right to develop or intensify land use is not inherently part of this private bundle. In modern planning systems, development rights are retained by the state to be granted through planning approvals.

The limited nature of property rights makes total sense when we talk about other rights. The property rights to undersea mining, radio spectrum, or intellectual property are all clearly invented by the state and limited, and changes to those rights are a negotiation with the community that creates and enforces them.

The same is true with property rights to spaces (i.e. land).

Before I share an extract of the report with more explanation, I want to push back on a common argument that I expect in response.

That argument is that if you upzone small areas, the value of the extra property right is high, but if you upzone a whole city, the extra value is low, and hence there won’t be much value to capture from pricing the extra rights.

As one recent article noted:

Development rights are only valuable because our planning systems have choked the supply of new housing in the most desirable parts of our cities, driving up prices for everyone.

This is wrong.

Development rights are valuable because asset markets price them such that there is no arbitrage. That’s it. Just like all property rights.

For example, say you owned the intellectual property rights to Medicine A, where the rights bundle expires in ten years. Your competitors own the intellectual property rights to Medicine B, but their rights bundle lasts in perpetuity.

Even if the same profits could be generated from each medicine each year, one property would be worth more than the other. As it must. The market price today of any property rights, intellectual or real, accounts for all future flows of profits and their risk.

If we changed the property rights for Medicine A through “intellectual property upzoning” so that they now last in perpetuity rather than ten years, would that mean there is more competition for longer-lasting intellectual property, and less competition for shorter-lasting intellectual property, so that the price of Medicine B and its intellectual property value will fall?

No.

The pricing of property in any form, whether real or intellectual, always reflects the most profitable way to exploit that bundle of rights.

We, as a society that creates, defines and enforces property, could sell the longer-term intellectual property rights for Medicine A. Only a ten-year period of rights has been granted. So from year eleven onwards, the state holds in reserve the intellectual property rights.

These longer-term rights could be auctioned off, whether in the form of a bundle that lasts another ten years or a bundle that lasts from the end of the current ten years in perpetuity.

To give them away to the owner of Medicine A’s ten-year intellectual property rights bundle would be a massive unpriced transfer. Yet that is how we do zoning in the cities.

Zoning rules describe what new property rights the state is willing to grant in different locations, and the planning system, through the approval process, hands over those valuable new property rights for free to those who apply.

Scroll down for a report extract and PDF.

Want to find out more about how property rights create predictable incentives and price patterns? Come to my two-day intensive Land and Housing Economics Workshop in Brisbane.

Will the workshop help you invest? Probably. Will it help you understand policy? Definitely. Will it help you cut through the media nonsense about housing? Certainly.

Book here.

Pricing development rights

There is a deep unfairness at the heart of Australian housing policy. State governments, by way of their planning systems, are routinely giving away public assets worth billions of dollars each year.

Legal permission to develop land in new and more profitable ways is a valuable property right. Development rights are created by and belong to the public. When transferred to private landowners, they should be priced at their private value. Right now, most states are handing them over for free.

The result is a system of enormous windfall gains. By way of rezoning and planning permission, development rights worth an estimated $11 billion are granted to private beneficiaries each year. This silent, legalised giveaway delivers vast sums to some of Australia’s wealthiest landowners, with the public receiving nothing in return. This is plainly unjust.

Development rights are public property, held in reserve for public purposes. To transfer these to private landowners is a healthy and necessary part of economic growth and change. But the value of these rights should accrue to the public, not the private owners who happen to hold title.

Such a giveaway would be plainly unacceptable if rights of the same value were transferred in the form of a separate, horizontally-defined land title. When public land is given away, or sold at ‘mates rates’, the injustice is obvious to all. And when private landowners sell land with a planning permit attached, they expect the price received to reflect the value of the permit. By contrast, the equivalent giveaway of public airspace remains the status quo within Australian planning systems. This is clearly unacceptable, and as the next section shows, the value being given away is enormous

The estimated $8 billion raised each year could pay for the abolition of stamp duty for all first home buyers, benefiting 90,000 buyers each year, while also funding an additional 195,000 social housing dwellings, a 43% increase on the current stock.

Pricing Development Rights 2026

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Are they the fairest tax that helps everyone start life on a more even footing? Or are they a nightmare of rules that require intrusive monitoring of gifts and that destroy family businesses?

We take some initial steps towards understanding how much tax revenue is possible, the problems involved, and the intergenerational issues at play.

An article of mine from 2014 on inheritance taxes is here.

And a link to The Australia Institute report we mentioned on inheritance taxes and gift duties is here.

Don’t forget that I am hosting a Land and Housing Economics workshop in Brisbane on 9-10th June 2026. Find out more here and come along to get into the weeds on property economics.

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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Here is a housing chart that looks like a banana.

In this post, I will show you some more banana charts and explain the economic reasons why such patterns are common but often misinterpreted.

Here’s another banana chart that was recently popular in housing circles. It was interpreted to mean that faster housing permit rates cause lower monthly rents.

Here’s one plotting the gross yield and purchase price of rental properties. This was interpreted to be the result of a trade-off between risk and return, notably of the lower long-term returns due to the poor condition of the housing amongst low-price, high-yield homes.

Here’s one with a rotated banana plotting lot land size (x-axis) and per square metre price (y-axis) in a major new subdivision. This banana shape has been interpreted by the popular analytical method of Ed Glaeser to be the result of planning regulations. He thinks that in the absence of zoning regulations, we would see a straight horizontal line.

Here’s another. The author interpreted it this way: “There’s no reason we can’t have wealthy and affordable cities. We just have to increase the denominator of aggregate income per household.” Regulations are implied to be the cause of the lack of data in the top-right corner of the chart.

Finally, here’s a banana chart that emerges when you plot financial returns and volatility (risk). It’s not housing, but it shows a similar pattern, with a rotated banana. The interpretation of this relationship is simple—markets price in risk.

Each banana chart usually has an ad hoc interpretation. You will notice many more charts and many more ad hoc interpretations as you dig deeper into housing or any other market.

Don’t forget about the upcoming Land & Housing Economics Workshop I am hosting in Brisbane in June. Tickets are here, and the event flyer with full details is below.

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Also, check out this recent conversation I had with Tim Williams on the Gimme Shelter podcast.

Is there an economic reason for these charts?

The reason for these banana-shaped relationships is simple but subtle.

Markets price in all trade-offs.

What does this mean?

Read on and subscribe to find out (you can also unlock this article with a free trial)

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This article was first published in 2017, but is relevant again in the context of the recent Artemis II mission and Blue Origin’s successful rocket landings.

Don’t forget about the upcoming Land & Housing Economics Workshop I am hosting in Brisbane in June. Tickets are here, and the event flyer with full details is below.

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Elon Musk has lost billions of dollars for nearly a decade trying to make electric vehicles at Tesla in a way that has never been done before.

In the new tech-billionaire space-race, where Musk has also been active with his SpaceX company, his competitor Blue Origin, run by Amazon CEO Jeff Bezos, has been running at an enormous financial loss, requiring Bezos to sell over a billion dollars of Amazon stock a year to fund his space venture.

In both of these sectors, electric vehicles and space transport, there is no guarantee of any long-term profit - there are threats from multiple new entrants as well as incumbents in both sectors. Yet billions of dollars are being poured into these experimental investments.

Facebook burned through $80 billion on its Metaverse, which is as real a loss for the economy as if the government wasted $80 billion on a failed technology misadventure.

But there is nothing “market-like” about long-term, long-shot gambles like these. This behaviour falls outside any core economic theory about the efficiency of markets in allocating resources. After all, most billionaires don’t spend their fortunes on privately-funded industrial policy with only a fleeting chance of any future payoff.

If a country wishes to expand its production capabilities, it seems foolish to rely on the type of luck that the United States finds itself with. Australia’s billionaires, for example, are busy fighting to keep their generous tax loopholes and government-protected monopolies, innovating more in their lobbying and corruption than in production.

Australia’s rich list is peppered with billionaires who made their fortune on the back of favourable government deals that handed them economic windfalls. There is barely a single innovator amongst them.

It should be no surprise then that Australia’s multi-decade de-industrialisation has finally become a mainstream political topic. According to research from Harvard, in the 20 years prior to 2015, Australia dropped from 49 to 77 in the country rankings of the complexity of their production capacity - that is, how advanced and diverse the country’s manufacturing capabilities are.

And our lazy billionaires were nowhere in sight.

Despite the benefit of billionaire industrial policy and a stable position in the country's ranking of economic complexity, Donald Trump made it to the White House, selling the message that manufacturing still matters in the United States.

So what does effective industrial policy look like when it doesn’t rely on lucky billionaires whose ambitions may not always match those of the rest of us?

The answers are simple.

If society accepts that losses from some experimental investments to make the impossible possible are worth the risk, it doesn’t matter who takes the losses—the public via government, or billionaires. After all, the macroeconomic implications are the same; real resources, the land, materials, human labour and its brainpower, are all diverted from productive uses to high-risk high-stakes experiments, be it in space, or the electrification of transport.

Typically, governments have been one of the few large organisations able and willing to make large long-term losses for a small chance of a broad long-term gain. There have often also been military motivations lurking in the background of such investments, but this need not always be the case.

NASA’s spending on the original space race, for example, was itself an extensive piece of industrial policy. NASA’s spending peaked at $5.9 billion in 1966, which was 4.4% of the federal budget, with limited direct commercial return. The graph below shows just how abnormally large the spending program was in the 1960s, and a back-of-the-envelope calculation shows it to be about 100 times larger than losses being made in the current billionaire space race.

In effect, the 1960s spending on the space race was a decade-long loss-making venture undertaken by the US government that radically expanded the production capabilities of the country. Indeed, if not for this earlier period of enormously costly industrial policy, the current private space race would have little reason to exist.

What happens with these types of experimental investments is that any future benefits often emerge indirectly. The materials and computer technology developed in the 1960s space race have dispersed through the economy, creating broad and indirect benefits that are very hard to see, but that nevertheless have expanded global production capabilities.

The other thing that competent governments have traditionally been good at is overcoming coordination problems that would stifle innovation happening in any one particular area. What good is a boom in private flight, for example, without air traffic control and a system to coordinate it? What good is developing the capability to build railroads without the ability to acquire the land to put them on?

Here again, we see billionaire industrial policy playing this coordinating role. Tesla has been investing in both new electric vehicles and the large infrastructure network of high-speed charging stations necessary for them to become a fully useful replacement for the petroleum-fuelled vehicle fleet. And again, like all vehicle manufacturers, they are relying on earlier public investments in the coordinating system that is the road network itself.

The trick to understanding the logic of industrial policy is this. Markets won’t automatically experiment with high-risk new investments with a fleeting chance of any future direct return. Somewhere, someone must make losses for a long time while experimenting with new types of production, knowing that there may never be a direct future return. Somewhere, someone must coordinate to avoid external conflicts that can arise and stifle new production.

With renewed interest in manufacturing, renewable energy, and space, governments can take on these traditional industrial policy roles while also supporting billionaire industrial policy if it emerges as well. There is no conflict. But if we want to build things that have never been built before, someone has to take on the short-term economic losses to get there.

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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Don’t forget about the upcoming Land & Housing Economics Workshop I am hosting in Brisbane in June.

There is ONLY ONE WEEK LEFT to get your discounted early bird prices.

Tickets are here, and the event flyer with full details is below.

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Wages are not incomes

I have previously explained that the wage price index published by the Australian Bureau of Statistics (ABS) should not be used as a proxy for personal or household incomes.

But it keeps happening.

I’m going to explain why this is inappropriate once again. I hope you all share this article when you see this error being made in future.

Households earn incomes from employment (salaries and wages), running a business (called mixed income), owning assets (shares, property, etc.) and from welfare payments.

If you want to know how much households are earning, you need to add up all of these sources of income.

The wage price index is not a good proxy for that total. It’s not even a good proxy for the wage share of that total.

That’s because the wage price index is not a measure of wage or salary income, but of the change in hourly wage for the same set of jobs in the economy. It is a price index, like the Consumer Price Index (CPI), but instead of measuring the relative price of goods and services over time, it measures relative hourly wages and salaries over time.

When the composition of jobs done in the economy changes so that there are fewer low-wage jobs and more high-wage ones, then incomes from salaries and wages will rise, even if the wage price index doesn’t.

Since people buy homes from their incomes, not from the wages for an unchanging average job, making comparisons between this index and the cost of housing is a huge conceptual error.

Below, I plot total per capita incomes in nominal terms from all sources (blue) against median incomes from salary and wage employment (red) and the wage price index (yellow).

Notice that per capita incomes have risen by 1.5x the wage price index over the past five years. This means the actual money households have to spend on housing and other goods has grown 50% faster than the hourly wage for the same set of job. Using the latter to assess the affordability of housing (or other goods) is wrong.

Australia’s housing turnover

One thing I find strange is that while the price of homes is set by all participants trading both new and existing homes in the market, there is a singular obsession pushing down prices by orchestrating a greater willingness to sell new homes. Little attention is paid to the willingness to sell homes from the existing stock of over 11 million homes.

Look at the chart below.

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Have you heard that Norway taxes their gas at 78% of profits, but Australia’s offshore gas pays next to nothing in terms of royalties or resource-specific payments? Yes, Australia has a Petroleum Resources Rent Tax (PRRT), which is meant to do the job of Norway’s oil and gas taxes. But it never seems to raise much in revenue, despite recent global gas price booms.

My mates at The Australia Institute (TAI) and Konrad at Punters’ Politics have been banging the drum about this for years. Now, Senator David Pocock is calling for a 25% levy on gas exports.

I highly recommend a paid subscription—it also gets you a big discount on my upcoming in-person workshop in Brisbane on the 9th and 10th of June.

There are TWO WEEKS LEFT of discounted early bird prices.

Tickets here, and a PDF event flyer with full details is downloadable below.

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Charging a market price for the right to extract minerals is not some socialist fantasy. Australia’s states own the property rights to onshore minerals and energy resources, while the federal government owns the property rights to offshore deposits.

It is the purest capitalist logic that the owner of a resource should act in a commercial manner and negotiate a market price for the right to that resource from those who extract it.

The tough question is how to price these resource rights.

Three common ways to price resources

State ownership

The first way for a government to capture resource rents is to own (partially or fully) both the resources and the company that leases those resources for extraction, refinement and sale.

Globally, this is one of the most common ways to ensure that excess profits during a resource price boom are shared with the state resource owner. It avoids the need for an extra layer of pricing and is akin to a business owning the building it operates from rather than renting it. However, it also entails the state taking financial risks on the major capital investments necessary to extract oil and gas.

The table below is from my 2024 report with Tim Helm, Rent-controlled resources: Why are we under-charging Australia’s mining tenants? It illustrates how common this approach is. Indeed, so successful is this approach in places with low-cost oil and gas reserves that resource profits replace a massive amount of taxation. Many Gulf nations, such as Saudi Arabia, have no taxes and fully fund their public sector from oil profits.

Special taxes on resource profits (super-profit taxes)

The PRRT is a type of super-profits tax that applies to offshore oil and gas in the federal government’s jurisdiction.

The PRRT essentially first allows the resource lessees a certain rate of profits, which are caught in the regular tax system. But after a certain amount of profit, a special extra tax applies to those super-profits above that.

Back in 2017, there was a review into the PRRT, which was raising very little revenue despite the expansion of offshore oil and gas.

Why not? If profits were high, the tax should raise revenue. Maybe profits weren’t high?

Right there is the catch with trying to tax super-profits. Profits are an accounting construct. There are many margins to play with to reduce top-line revenue, increase costs, and reduce bottom-line profits for the purposes of tax.

The PRRT built in features like allowing generous escalations of carried-forward losses, which do not apply in the regular tax system, and have been the main mechanism for reducing profits in the special accounts subject to the PRRT.

Imagine how this type of mechanism for paying rent to a resource owner would work for a business tenant paying rent to a shopping centre landlord.

The landlord would ask the business tenant, “Did you make any excess profits last year?” The tenant would produce accounts they have created to show that, no, in fact, they didn’t, and the two might then argue about it in an unending game of cat-and-mouse.

To avoid this nonsense, most landlords charge a market rent regardless of the income of the particular tenant.

Norway is able to administer such taxes more effectively as the state there is also a part owner of all facilities tapping their gas resources.

My submission to the 2017 PRRT review is attached below.

2017 PRRT Review Prosper Australia Submission

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Royalties

Instead of taxing the bottom line profit, after the accountants have gone to work subtracting everything they can from the top line, a resource owner can simply charge for the resource based on top line revenue from resource sales, using a royalty.

This is the standard way Australian states charge for their onshore mineral and energy resources. Royalties are also common in other areas of property, like intellectual property, where owners of rights get a percentage share of top-line revenues from books, music and so on.

This is exactly what David Pocock and TAI are proposing for offshore oil and gas—a top-line tax of 25% of revenue.

Since a royalty applies to the top line, the rates are typically much lower. Coal royalties in New South Wales, for example, are about 10%. Iron ore royalties are about 7.5% in Western Australia.

These are simple to enforce, as we can just send the tax man to the port or rail line to check production volumes and prices, and companies generally have an incentive to be honest with investors about high top-line revenues and business growth.

The problem with fixed royalty rates is that price variation is high over time and across resources

A 10% top line royalty on gas revenues seems better than the PRRT for offshore oil and gas. In fact, that is what I proposed to the PRRT review back in 2017 as a replacement for the PRRT.

But, as you might imagine, the simplicity of a fixed royalty rate has a trade-off.

Resource markets are characterised by:

1. large variations in costs of extraction across deposits, and

2. large variations in the market price of the tradeable resource over time.

Property markets will often vary prices by location and time. For example, the price to rent a holiday home varies not only with its location, but also over the year, being much higher during peak periods like school holidays.

The illustration below shows how the variation in costs of extraction across different resource locations means a fixed rate royalty cannot share in the economic rents of lower-cost resource deposits and will discourage higher-cost deposits from being worked.

On the left, we see resource deposits ordered from Deposit A, with the lowest extraction cost, to Deposit F, with the highest cost. A fixed rate royalty will reduce the net-of-royalty price, shown by the decline in the orange price line. The shaded grey area is the royalty revenue.

Notice that the lessee of Deposit A gets a huge excess profit, or super-profit, but Deposit F becomes uneconomical because the net-of-royalty price is not high enough for the lessee to earn a return on the capital they need to invest to begin working it.

On the right, we see a situation in which property owners price optimally to leave only a small super-profit for each lessee. Each gets a normal return on capital invested, but the resource property owner collects more from Deposit A, a little less for Deposit B, and so on, while collecting nothing for Deposit F.

This would maximise the return to the resource owner while ensuring maximum output of the resource. This is the intended way the PRRT and other super-profit tax regimes operate.

In Australia, we do make some adjustments to royalty rates to reflect the differing costs of extracting different resources. For example, in New South Wales, coal royalties are 10.8% for open-cut mines and 8.8% for deep underground mines

But the other pricing issue is the variation in price over time, which can be much more important than variation in extraction cost across deposits of the same resource in the same jurisdiction.

The next illustration shows what happens when the price of the tradeable extracted resource doubles.

On the left, with a fixed royalty, the resource owner gets the same proportion of the higher revenue, so revenue doubles when there is a doubling in price. But the actual amount of excess profits may have tripled, or more, as shown on the right.

So fixed rate royalties, when prices are low, make marginal deposits uneconomical, and when prices rise, collect only a small proportion of the excess profits available.

How to make royalties work better

After this little economic lesson, what should we make of a 25% royalty on gas exports?

I think it is probably good politics but bad policy.

Yes, a 25% royalty rate when the gas price is abnormally high would be good. But gas prices rise and fall quickly.

Let’s have a quick look at the market.

Asian gas prices are high relative to the last two years at 19 USD per MMBTU. But not excessively so. Prices were half of that just months ago.

A 25% tax at, say, $20/MMBTU equals $4, leaving a net-of-royalty $16/MMBTU for the gas lessees. But at a $10 price, the same 25% leaves lesses only $8 net of royalties. At a $5 market price, they receive just $4 net of royalties.

When the global price is $20, states should probably charge more than 25%. But if the price is $5, they should charge a lot less.

Other than shifting to gameable resource super-profits taxes, what can we do?

The solution is to apply a variable royalty that operates much like progressive income taxes, with higher marginal rates at higher resource market prices.

Queensland has applied this approach to gas and coal in recent years, raising billions in extra revenue during the post-COVID resource price spikes.

Below is the Queensland royalty schedule for liquid petroleum. The royalty rate is 3% of revenue below a price of $50/bbl, with a marginal rate of 11.5% applies for prices from $50/bbl to $100/bbl and a marginal rate of 12.5% applying above $100/bbl.

For coal, the approach is shown below, with marginal rates rising from 7% to 40%. Notice that the royalty price rises more than in proportion to the market price. This is exactly what it should do to capture super-profits.

This is the type of aggressive variable royalty schedule I would apply to offshore gas. Yes, states should charge a high marginal and average royalty when the price is abnormally high, to get a share of the boom. But they should also charge a much lower rate when the price is low. Variable royalties do just that.

Variable royalties are also a feature of privately negotiated prices. Often, intellectual property royalties are like this—the more books you sell, the higher the royalty rate you get. Applying variable royalties to resources seems te me to be a more commercial approach for government resource owners than fixed royalties.

A final royalty design tweak

You might notice that defining market price thresholds and percentage rates in a royalty schedule opens up the same problems as leaving income tax brackets fixed over time. What about inflation? What about bracket creep?

We have indeed had substantial inflation since Queensland’s variable coal and gas royalty schedules were introduced in 2022. A $100 coal price doesn’t leave mining companies and their shareholders with as much profit as it used to, especially when their input costs have risen by nearly 50% over the past few years. That is certain to lead to ongoing lobbying and endless efforts at the back end of state government to work out what kind of adjustments are necessary.

Tim Helm and I proposed that instead of fixing nominal brackets and dealing with these problems, states could instead scale the royalty rate based on the ratio of the current resource price to a long-term median price metric. This would allow for a set-and-forget approach with only one political bargain over the base rate of royalty.

The formula would take the ratio of the current benchmark price, or the current price received by a firm with longer-term contracts, and the median market price over a long period, such as 10 years. Then it would apply this scaling factor to a base royalty rate.

So if the base rate is 7% of revenue and the current market price of the extracted resource is the same as the 10-year median, the miner pays a 7% royalty.

But if the miners are receiving double the 10-year median during a boom period, they would pay a 14% royalty. This results in a royalty price that scales with the square of the current price relative to the long-term benchmark, which is exactly the kind of more-than-proportional growth in royalty revenue the schedule of marginal tax brackets is trying to achieve.

The beauty of this model is that resource prices can boom by many multiples, but cannot fall below zero. So there is a massive asymmetry of price variation to the upside relative to the median 10-year price.

The attached documents below explain this in more detail and show various simulations. They show that adopting this type of scaled variable royalty would have raised about $15 billion per year in extra revenue if applied to coal, iron ore and gas over the decade from 2013 to 2023.

Scaled Royalty Working Paper

7.7MB ∙ PDF file

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Prosperrentcontrolledroyaltiesreportnov2024

1.73MB ∙ PDF file

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Prosper Rent Controlled Royalties

1.73MB ∙ PDF file

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So what?

The debate about taxing gas needs to have some sensible economic logic behind it. A 25% flat royalty on the top line might be fine if gas prices never fall substantially. But we know that resource markets are subject to booms and busts.

A scaled variable royalty can achieve the same outcome in a boom, and an even higher average royalty during very large price booms, while scaling back automatically when resource prices inevitably fall again.

Combine this approach with different base rates for deposits with quite different extraction costs and states could end up with nearly the full effect of an economically ideal super-profits tax without any of the accounting trickery.

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So how does an economist think about war, and emergent cultural and political forces that create conflicts amongst groups?

Paul Frijters runs Academia Libera Mentis, a new education institution for minds that dare. He documents this grand education experiment here at his Subtack.

Paul and I go way back. Paul was my PhD supervisor, and we co-wrote Rigged, a book that dives deep into the way political favouritism operates in Australia. He has studied the way human societies function, particularly how the underlying emotional drivers of greed and love generate loyal groups as well as fierce competition.

He even has a book with Gigi Foster about that called An Economic Theory of Greed, Love, Groups, and Networks.

This underlying understanding of humanity should be extremely helpful for assessing potential scenarios in major global events, like the war in Iran. Although I am very much on board with the economic thinking about greed, love and groups, I am very much in the dark about the history and current tensions driving this new conflict in the Middle East.

Paul has recently lived in Saudi Arabia and has been studying the region in detail. So enjoy this chat and keep in mind how things have unfolded between when we recorded, on 16th March, and what is happening when you listen to it.

Stories of Academia Libera Mentis

The unfolding story of a new academy in a castle in the Ardennes where young minds discover how things actually work, how to form productive proud communities, and how to reform our broken societies.

You can find Paul’s academy website with details of upcoming open days and events here.

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A persistent idea in planning and housing policy debates is this: Cities need enough sites that are zoned for housing and financially feasible today to build many new homes for decades into the future.

How many is enough? Who knows.

In New Zealand, councils are required to estimate feasible zoned housing capacity. This has led to arguments about whether the current zoned capacity for triple or quadruple the number of homes is enough. In Canada and many parts of the United States, similar debates are playing out.

In Victoria, Australia, councils are required to “ensure sufficient realisable capacity” exists in their planning schemes, while in New South Wales, councils “need to identify the theoretical development capacity in their existing land use controls” and take into account “the feasibility of development and likely take up rates of the capacity identified.”

The Grattan Institute released a report last year arguing that:

The federal government should ask the Productivity Commission to regularly assess the performance of state and territory land-use planning systems, including through regular assessments of commercially feasible capacity for new homes.

The idea that feasible zoned housing capacity is both extremely important and easy to identify is embedded in many administrative processes around the world.

But it is not. This whole exercise is seriously flawed.

Why?

Because it ignores basic economics and hence fails the efficient property markets hypothesis test. Since all the gains to future development are priced in today, there should be no excess returns to development. Finding feasible housing capacity is equivalent to finding excess returns in the share markets—essentially impossible on average.

Let’s dig in to see why in more detail.

I highly recommend a paid subscription—it also gets you a big discount on my upcoming in-person workshop in Brisbane on the 9th and 10th of June.

Tickets here, and a PDF event flyer with full details is downloadable below.

Ground Rules Workshop Flyer

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The efficient property markets hypothesis test

Feasible housing capacity is usually taken to mean that the returns on today’s costs from development today exceed the cost and risk of development today.

In practice, this is measured by examining sites across a city or region and estimating the potential revenue from developing each site to the highest density allowed under zoning rules.

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One thing Steve taught me is to really think about the hidden assumptions in our economic models, which has been extremely valuable to me during my economic journey.

Our conversation starts with Steve’s concerns about the Vietnam War draft, and moves on to his epiphanies about what was missing from economics education. We discuss his attempts to model the key insights of Hyman Minsky about the economic behaviour that generates macroeconomic cycles, and the state of the economic debate.

Oh, and we talk about the very controversial issue of money creation by banks and the public Treasury!

Find Steve’s YouTube channel here.

Steve’s Debunking Economics podcast is here.

And of course, Steve writes on Substack at Building a New Economics, which is where you can find his latest writings and conversations.

Building a New Economics

The systems-based alternative to mainstream economics

By Steve Keen

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It surprises me that many people confidently know how many homes there should be.

They loudly claim that the current quantity is the wrong one. “There’s a housing shortage!” they scream.

Even more than knowing what the quantity of homes should be, they also know what type of homes should be built and at which locations. Craziest of all, they know what the price of homes should be!

I wish I had such powers to solve the economic calculation problem.

We wouldn’t need markets nor private property. We could simply build the right homes in the right places and allocate them to the right people at the right price.

Even funnier is that many who loudly proclaim a shortage don’t know how many homes or people there are, or how these two numbers have changed over time. A twitter poll I ran a couple of weeks back showed that the majority of people thought that the number of dwellings had grown much slower than the population for the past 30 years.

But the data shows that in fact the housing stock grew 60% while the population grew 50% (though remember that demographics plays a big role too).

Another surprise is the rewriting of housing history constantly taking place. When I search the archives for that mysterious abundant housing era, I can never find it. Every place or time I’m told is a great example of cheap and abundant housing seems much worse than recent years.

I’ve even heard that 19th-century European cities had abundant housing. Yes, they changed and grew a lot. For example, Paris tripled its population in the second half of the 19th century, from 1m to 2.8m. But this is exactly the same population and growth experienced by my city of Brisbane in the past 50 years. And home sizes nearly doubled in Brisbane, too. And yet we still complain of a housing shortage.

Which era has abundant housing and which does not?

Don’t forget about the upcoming Land & Housing Economics Workshop I am hosting in Brisbane in June. Tickets are here and the event flyer with full details is below.

Ground Rules Workshop Flyer

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Today, I want to take a tour across time and space to see how the apparent housing shortage always seems to be present and always based on complete nonsense analysis, which I unpick for you. The ever-present housing shortage is perhaps an even more deceptive idea than that of the ever-present skills shortage.

Oh, and remember, if there isn’t a current housing shortage, then there is an imminent one. Must not rest on our laurels!

History of shortages

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But Jim is more than a businessman.

He has written multiple books, has a PhD, and will run for election in Northcote, Victoria with the Libertarian party in November 2026.

I wanted to find out what motivated Jim to run for politics, ask him about where he sees government waste and interference reducing our quality of life, and of course broach the topic of fertility, which he has a unique view on.

It turns out that zoning and planning regulations are a big deal for Jim. Although we weren’t able to agree on much about this topic, we were able to break apart why our views differed.

Find out more about Jim, his books, and try chatting with “AI Jim” at his website, jimpenman.com.au. Find him on X/Twitter here, and pre-order his new book Birth Rate Crisis here.

Don’t forget about my upcoming Land and Housing Economics workshop on 9th and 10th June in Brisbane. Tickets here. PDF flyer attached below

Ground Rules Workshop Flyer

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The global housing debate is dominated by the idea that planning regulations affect the way a city’s housing market responds to changes in demand, leading to the wrong number of homes being built.

Sydney. Vancouver. Los Angeles. You name it.

Want to dive deep into the economics of land and housing?

On the 9th and 10th of June 2026, I will present a two-day workshop in Brisbane—Ground Rules: Land & Housing Economics for Professionals.

Get your Early Bird tickets and peruse the workshop details at the Eventbrite booking page. Make that professional development budget work for you!

Discounts are available for annual paid FET subscribers and groups of three or more.

All enquiries to cameron@fresheconomicthinking.com.

Those regulations might include zoning that limits density in high value areas, development charges on new builds, NIMBYism and its legal challenges, or delays in permitting or approvals. Whatever they are, they are widely thought to cause a slower rate of housing production and higher rents and prices.

In the economic jargon, these regulations are thought to make housing supply less elastic, meaning that for any change in demand leading to a certain price increase, fewer new homes are built.

As has been pointed out many times here at Fresh Economic Thinking, this particular story is full of common misunderstandings about how both how town planning operates and the fundamental economic forces in property markets.

The point I want to make clear today is that if you try and measure supply elasticity at different times and over different periods, you can get very different estimates under the same regulatory conditions.

Instead, what you are measuring are predominantly the trends within a longer-term housing market cycle.

These figures are therefore not very informative and potentially highly misleading.

Using short time windows to measure supply elasticity is a common error. This is why the more detailed academic studies uses periods as long as possible for their analysis, with many papers using all the comparable data from the first reliable collections in the 1970s and the subsequent four or five decades, with some even separating our boom and bust cycles because of their large effects on measurement.

To get some clarity before we dive in, a confusing reality is that a variety of different things are called supply elasticity. Two main ones are flow elasticity, and stock elasticity.

Flow elasticity is the rate of change in the quantity per period of new homes built in a region divided by the rate of change of price. So if the rate of new homes built per year rises from 1,000 to 1,500 when the prices increase from $100,000 to $120,000 then the flow elasticity (using the midpoint formula) is (500/1250)/(20,000/110,000) = 2.2.

Stock elasticity is the rate of chance in the stock of dwellings divided by the rate of change of price. So if the number of homes in an area rises from 1,000,000 to 1,100,000 over a ten year period when the price has increased from $100,000 to $120,000, then the stock elasticity is (100,000/1,050,000)/(20,000/110,000) = 0.52.

These are not comparable with each other.

Indeed, it is also not clear why the stock of homes should be related in the long run to the asset value of housing, and not the rental price, as asset prices are heavily affected by interest rates and tax settings.

When measuring flow elasticity, lags and time period choices matter a great deal, as does the choice of controls (such as for population or income).

Another problem is that the flow of new housing might be low in one city, but the variation of that flow in response to price might be very high, leading to a large measured flow elasticity in a city with hardly any new homes being built and hence a low stock elasticity.

Consider two cities. City A builds 1% of the stock per year on average, but 2% in a boom a none in a bust. City B builds 2% of the stock per year on average, but with hardly any variation in this flow rate (say, 2.2% in a boom and 1.8% in a bust). If they both have identical price patterns, City A will have a high flow elasticity and low stock elasticity, and vice-versa for City B.

The point is that the elasticity measurement game can reveal your assumptions as much as any real underlying economic relationship.

Here I demonstrate the effect of the market cycle on supply elasticity measurement. To do this I, calculate the stock elasticity based on the price of homes being the inflation-adjusted asset price over two different recent time periods at different points in the housing cycle in cities in Australia, Canada, and the United States.

Using stock elasticity should be less sensitive to short term variations and issues about lags and so forth, but is more open to issues about endpoint selection (which is very much the point of this exercise!).

This exercise demonstrates how problematic it can be to use short term estimates (or changes in them) to guide policy advice.

Australia

The chart below shows our simple stock elasticity of supply estimate for the three biggest Australian cities over the 2011-18 period in blue and the 2018-25 in yellow.

From the perspective of 2018, looking back over seven years, you would see that Brisbane had the highest supply elasticity. You might advise other states to copy Brisbane’s zoning regulations to get higher supply elasticity.

But then, from the perspective of 2025, looking back over seven years, you would see that Brisbane now has the lowest supply elasticity. You might at this point advise Brisbane to copy the zoning regulations of Sydney or Melbourne to get a higher supply elasticity.

Canada

It might not surprise you but similar cyclical variations arise in Canada too.

From 2010-16, our results are similar to an influential CMHC report that looked at a long expansion period from 1992-2016. Vancouver and Toronto appear to be supply laggards, with a low measured stock elasticity of supply.

But what about in the years since? These years differ in that they have see two periods of substantial real price declines (2016-20 and 2022-25).

We see complete reversals in measured flow elasticity of supply.

Toronto was inelastic and should have learnt from Montreal in the first period, but then if you trust these measurements, they are now supply leaders that Montreal should learn from.

The negative result for Vancouver in the second period is due to the fact that real prices fell 6% while the stock of housing grew 17%. Wow!

Of course, economic theory has trouble dealing with this unless we invoke a supply shock, which moves out the entire supply curve. So I don’t think it is wise to look for an interpretation beyond the data capturing cyclical changes and asset prices being a poor metric for the price of housing services.1

A study from New Zealand found an unexpected negative relationship between population growth and dwelling stock growth using data from 2012-23, then simply threw the data out because it was inconvenient to the economic story they were trying to tell. Read more about that here:

United States

Lastly, a quick look at the United States.

We see a large cyclical gain in measured stock elasticity of supply in Los Angeles and most other cities in the second period, and we see the famous Texas “supply leaders” with higher overall elasticity.

But again, we need to recognise the cycle.

Prices have fallen dramatically in Austin since they peaked at the end of 2022. Had we only looked at the 2018-22 period, then its measured supply elasticity would be back at 0.6, and not far out of line with many other cities.

I know many readers might want to bail out these measure now by thinking “Yeah, but Houston and Austin seem always to be higher than the other cities, so the measure must be telling us something!”

And I would caution that you are seeing only the patterns that confirm your beliefs, as the data also requires you to believe that in 2016 Vancouver suddenly became even better at building homes than Austin.

So what?

Taking measures of supply elasticity as a proxy for regulation without recognising the cyclical nature of property markets is a bad idea. Cycles are long, price corrections can be swift, and different cities are often in different phases of their cycle at the same time. Interpreting these numbers as key indicators of the effect of planning regulations is a big leap.

Your choice of time period, start and end dates, as well as data sources, all matter. You might use asset prices, of which there are a variety of indexes, market averages, all of which don’t quite match up. Usually you would adjust them for inflation. Or you can take rents as the price of home, and deal with the variety of different rental price metrics in each city (I explain the difference between advertised rents, paid rents, etc in the below downloadable Explainer).

Explainer Rental Price And Vacancy Metrics

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Each data source will also have a different slightly different cyclical timing.

What we know is that when we zoom out over time and space and control of demand variation, all cities follow the same basic economics of housing production, including the emergence of price cycles.

A recent paper by Federal Reserve Bank of San Francisco researchers documents this comprehensively, finding that there is essentially no difference in the rate of new housing production in response to similar changes in demand over a 40-year period across major United States metro areas.

Here’s a detailed explanation of that paper and its methods.

Ad hoc approaches measuring supply elasticity over short periods within a longer cycle (that can last two decades) have been used to justify a raft of planning policy changes—some good, some bad, and some involving billions in value created and given away to connected political mates.

We should argue about planning regulations on their own local merits. But when we do, we need avoid jumping straight to elasticity-type reasoning based on arbitrary measures.

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For example, the rise in autism is almost solely due to expanded diagnosis and the incentives to get a diagnosis. This is especially relevant for policies like the National Disability Insurance Scheme (NDIS) in Australia, which pays out on the basis of a diagnosis.

Worse, because this idea of a rise in autism has become a global meme, people and politicians are already searching for answers and interventions to prevent it from getting worse! Before asking the question “Is the data correct and showing what we think?”, we jump to the question “What should we do about it?”

Indeed, many of the troubling social trends that seem to capture our collective minds are pure data measurement artefacts. They aren’t real. We can stop worrying.

Find Jay’s terrific Substack here.

Cremieux Recueil

I discuss papers that ought to be discussed.

And for his excellent regular posting on Twitter/X find him here.

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Henry George was a man of the 1800s.

Born in 1839, he was preoccupied with the coexistence of progress and poverty during a volatile period of economic change, marked by severe boom-and-bust cycles. His solution was to tax land—put a price on excluding others from the gifts of nature. He also opined on the economic benefits of free trade and the trouble with tariffs and protectionism. His lifetime of insights led to a global social and political Georgism movement. He died in 1897 as the 1890s depression was ending.

Ronald Coase was a man of the 1900s.

Born in 1910, he was preoccupied with transaction costs, which create a trade-off between using market and non-market means of coordination, and provide the incentive for the existence of firms. For our exposition, it was, however, his related ideas on the nature of property rights as low-cost tools for economic coordination that provide incentives for efficient allocations and investments. His lifetime of insights was quietly appreciated by policymakers and economists, informing the development of new institutions like radio spectrum property rights, but led to no public global movements. He died in 2013 as the financial crisis was ending.

The economic concerns of the 2020s are similar to those from the times these great thinkers lived, including access to property and housing, the role of tariffs and trade on social and economic change, and the boom-and-bust cycle.

Today, I want to imagine what a meeting between these two great thinkers would be like. How would their intellectual and moral arguments unfold?

What would Coase make of the idea of taxing land as a remedy for either boom and bust cycles or accumulating inequality and access to housing?

What would George make of the arguments for efficiency of property rights in general, which apply as much to land as any form of property we create (intellectual property, mineral rights, airspace rights, radio spectrum, etc.)?

They seem like obvious allies, but the record doesn’t show it.

The more I grapple with the economics of property, and particularly the housing concerns that come and go with the cycle, the more I feel it is high time for the Georgist movement to modernise and incorporate the economic lessons of the past century, especially the insights of Ronald Coase.1

Two important ideas about property rights

Coase’s idea

Ronald Coase reckons property rights are useful.

One of his insights was that the exclusionary nature of property rights, combined with low-cost trading opportunities, incentivises an allocation of those rights to individuals and firms that value them the most. Property rights generate economic efficiency if they can be traded at low cost.

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But much of the commentary misunderstands how town planning rules operate.

I chat with Sydney town planner Tim Sneesby about how the system works, from a strategic level of creating zones and desired outcomes at a broad level, including earmarking infrastructure locations, to the day-to-day operations of assessing applications against those broader plans.

I also push a little on some of the potential perverse incentives—wouldn’t town planners prefer to create cumbersome rules to keep themselves in a job and make their roles valuable even if there is no (or negative!) social benefit from those rules?

For example, this article notes a rise in town planners per new home developed and the economic cost of regulating so many aspects of building designs.

Let me know what you think?

All comments are personal views and not those of Tim’s employer or associated entities.

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Every day, we hear details of fraud and scams—whether it is gaming the National Disability Insurance Scheme (NDIS), the now infamous daycare subsidy frauds in the United States, or “jobs for mates” situations where government boards and agencies are stacked with connected people as a way to siphon money to them.

These are not sideline issues. Dealing with them is a central problem for sustaining a productive and cohesive society. To sustain this society, we need to differentiate between negative-sum transactions and positive-sum ones. Then, we must ensure a system of oversight and enforcement prevents as many costly negative-sum transactions as possible. This is a fundamental requirement for economic growth.

Trading favours is a natural human tendency. We have evolved cooperative brains that can generate an amazing amount of productive human cooperation. But these same brains help us to cooperate via informal favour trades amongst a few people to impose enormous costs on the rest of society.

I spent my PhD years from 2012 to 2016 studying the economics of political favouritism. The back-scratching process I uncovered was dubbed the Game of Mates, which is the title of my 2017 self-published book with Paul Frijters, documenting political favouritism and its costs over the past couple of decades in Australia.

In 2022, that book was revised and updated and released under the new title Rigged.

The central problem of the Game of Mates is the conflict between allocating the resources of society in a way that maximises total value to society, or total value to a subset of society who might repay you in the future for that decision.

It is a game of back-scratching played on social credit. This is why the revolving door between regulators and the regulated is so common.

Studying back-scratching in the wild faces a major problem.

Favours are almost impossible to objectively observe. Not only is there a powerful incentive to conceal favours, but determining the ‘no favour’ counterfactual is almost impossible. Was the government contract given to the most efficient firm? Or was it a favour to the winner because an alternative bidder could have delivered a better outcome for the price? More often than not, we just don’t know.

One way I studied this process was by creating a computer-based experiment and recruiting hundreds of student participants to play it.

Studying back-scratching in a controlled experiment, while sacrificing realism, allows a close examination of the fundamental cooperative processes at play. In fact, I have played this game in the classroom many times, and it helps teach students about how difficult and unnatural it truly is to make decisions in the interests of society as a whole.

There is a long history of cooperation games in social psychology and economics. The ‘big new things’ I could do included seeing the emergence of alliances (often multiple ones), measuring the costs of back-scratching against an efficient counterfactual, and testing some institutional designs that might discourage or curtail back-scratching.

That experiment is published here. You can download a copy below.

The back-scratching game

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Today I will explain the game and describe the lessons I learned.

In doing so, I will show that political favouritism is not a selfish game played by evil people, but a pro-social cooperative game that often feels good. This is why it is so hard to create and sustain institutions that harness our human nature for positive-sum cooperation while avoiding negative-sum cooperation.

The back-scratching game

The basic experiment has four players in a group (the minimal number for an in-group and out-group to form), with one player able to choose which of the others to receive a prize of $25 in a round (in experimental currency). The player who receives the prize allocates a fresh $25 the next round to one of the other three.

If this were the whole experimental design, the best thing to do would be to form a back-scratching alliance with one other player and trade the $25 favour back and forth. Over 25 rounds, an alliance pair would make $625, while the other two would make nothing.

What makes back-scratching costly in my experiment is that each of the potential recipients of the prize is given a randomly shuffled ‘productivity number’ each round, either 1, 2 or 3. This number determines an extra payoff for every player for that round, in addition to the $25 directly allocated.

Give the prize to the player who has a productivity number of 1 in that round, and the group gets $1 each. Give the prize to the player with 3, and the group gets $3 each. Think of this productivity number as reflecting the efficiency of firms competing for a government contract. It represents a conflict between choosing what is best for the total society, and what is better for a single selected other person.

The best thing to do for the whole group is to allocate to the player with a productivity number of 3 each round. That provides a total group payoff of $37 for each round ($25 prize plus $3 for each of the four players), and an average payoff to each player over 25 rounds of the experiment of $231.25.

With the extra payoff from the productivity of the prize-receiving player each round, this creates a cost to taking the back-scratching approach and trying to reciprocate each round.

To sustain a back-scratching alliance means not choosing the most productive player for the prize in two-thirds of the rounds. Over 25 rounds, back-scratching earns the alliance $725 between them, or $362 each, a 57% higher payoff than the overall best strategy for the whole group of four players.

The outside group of two players not in the alliance earns $100, or $50 each.

But the total group of four players only makes $825 instead of $925. There is a $100 efficieny loss. The economy is 11% smaller than it otherwise would be due to the back-scratching, meaning that back-scratching is a negative-sum exchange relative to the alternative.

I have run this experiment in a variety of forms with hundreds of groups, and also run it in the classroom with the productivity number given on a sheet of paper, with a group of four standing up at the front of the class, and the payoffs recorded on the whiteboard.

When I first designed the experiment, I was worried that the honest young university students who participated would rarely see the opportunity to form a back-scratching alliance, let alone act on it.

But in fact, most groups did see alliances form, even if just for a few rounds.

I paid the participants in real money, as is the norm for economics experiments (rather than experimental currency). Typically, alliance pairs reduced the benefit to others of AUD 30 to increase their alliance earnings by AUD 20 during two experiments totalling 50 rounds. That’s a social loss to the whole society of AUD 10.

Surprisingly, alliance players were happy about their actions.

They thought they had been cooperative and helpful by doing their mate a favour, and didn’t feel guilty about the costs they imposed on others. They also rationalised their behaviour, saying that forming an alliance is a justifiable strategy, while also concealing it by lying when explicitly asked in a later survey if they had formed an alliance.

I tested two institutional changes in the experiment.

First, a staff rotation, a common anti-collusion policy. This was enacted by reducing the probability of the player who received the prize being able to allocate in the next round. This was somewhat effective when the experiment began with this policy, but not so effective when it was implemented after alliances had formed. In this scenario, the alliance pair and the outsiders simply took turns at favouring the other person who had previously emerged as their own partner.

Second was a low-rent policy, mimicking bureaucratic procedures to limit the size of prize able to be allocated with discretion. This was enacted by reducing the prize so that instead of $25 per round, it was $3. Again, this was effective only when part of the initial experiment, not after alliances had formed. Despite there being no financial advantage from continuing an alliance with this small prize, because there was a risk of losing power to the other two players, the inefficient alliance continued.

The fact that neither was particularly effective at stopping back-scratching, and at best only at preventing it from emerging, indicates how difficult the challenge of eliminating negative-sum exchanges in our political and governance systems really is.

Some observations from these experiments are:

• Loyalty is strong. Rotation policies are good, unless those people being rotated in have existing loyalties. This means there is a trade-off for regulators; staff with more industry experience are also likely to come with stronger prior alliances and hence be more prone to back-scratching. In politics, it means voting in a different political party brings with it the alliances of that party.

• Bureaucracy can work. The array of internal procedures emerging in our large private organisations could come from seeking internal fairness and firm-level optimisation over favouritism within the firm.

• Social norms are strong. In organisations or groups where some people are observed doing the right thing for the group, this quickly becomes the norm. Whereas, where favouritism is observed, the group descends into counterproductive back-scratching unless this is quickly punished. In some groups, I could observe them first cooperating, then forming an alliance, then if they got control, the others punishing that alliance for a few rounds with an alliance of their own, and then returning to the group cooperation.

• Be loyal, but not too loyal. If your alliance partner fails to come through with favours when expected, it pays to look for someone else whose back needs scratching.

None of this is really rocket science.

If this sounds obvious to you, then you’ve probably learnt a lot about human behaviour through life experience. It also means the experiment is capturing important elements of real phenomena.

So what?

After spending three years researching this topic, of which this experiment is a small part, I have concluded that group formation through favouritism is a core determinant of political outcomes. The trick for business, government, and politics is to set up rules and enforcement mechanisms that catch this negative-sum cooperation and favour-trading and set the example of norms that maximise benefits for all.

To return to the deep insight into human nature, we are evolved cooperators, but usually only with a small, identifiable group, not with an abstract notion of society or the country at large. Joshua Green explains the evolutionary basis for this.

Morality evolved to enable cooperation, but this conclusion comes with an important caveat. Biologically speaking, humans were designed for cooperation, but only with some people. Our moral brains evolved for cooperation within groups, and perhaps only within the context of personal relationships. Our moral brains did not evolve for cooperation between groups (at least not all groups). How do we know this? Because universal cooperation is inconsistent with the principles of natural selection. I wish it were otherwise, but there’s no escaping this conclusion

It is not about bad people. Corruption is about good, pro-social people, who feel good about doing their mates a favour—after all, surely what’s good for my mates is good for society, right?

This is why minimising corruption is a perennial economic, legal, and political battle.

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That’s the argument made by David Maywald in his new book, The Relentless War on Masculinity.

He doesn’t argue that women have done this. But even men in power are still fighting the battles of many decades ago. David looks at education and health outcomes, noting that women became the majority of university students four decades ago.

There are four ways in which the cultural and political bias is expressed, called the Four Horsewomen. Once you understand them, they are hard to miss.

• Misandry — the hatred of men, as well as the systemic contempt for men.

• Gamma bias — the psychological tendency to interpret male and female behaviour through different lenses.
  A man who asserts himself is labelled aggressive, while a woman doing the same is praised as confident. A mother who works long hours is celebrated for her ambition, while a father who does so is criticised for neglecting his family.

• Gynocentrism — societies that focus on women, are primarily concerned with female perspectives and interests, and take a feminine point of view.

• Gaslighting — convincing men that their concerns are imaginary. Speak up about family law bias, and you’re told you must hate women. Question the “gender pay gap” narrative, and you’re accused of being sexist.

David explains how to perceive these biases by whether the same judgment would be made regardless of the gender of the person being observed.

As a father with two sons reaching adulthood, the most interesting data point was that young men (aged 15-24) only sit behind men over 65 in the strength of their views about traditional gender roles. Perhaps the pendulum is swinging back.

Here’s a chart showing these trends, courtesy of analysis by e61 Institute.

Finally, an excerpt from the book is in the article below at David’s Substack.

Celebrating Masculinity

The Four Horsewomen of Modern Feminism

For much of the past half-century, Western societies (particularly Anglo countries) have employed a combination of four powerful forces that tilt public opinion, change policies, and shift cultural norms heavily in favour of females—often to the detriment of males…

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5 months ago · 15 likes · 5 comments · Celebrating Masculinity

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