Fresh Economic Thinking

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.

How to get a decent public return from Australia's gas resources—A 25% export tax? Or something else?

Cameron Murray — Sun, 19 Apr 2026 22:00:34 GMT

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.

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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

1.04MB ∙ PDF file

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:

large variations in costs of extraction across deposits, and
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

Prosperrentcontrolledroyaltiesreportnov2024

1.73MB ∙ PDF file

Prosper Rent Controlled Royalties

1.73MB ∙ PDF file

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.

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

FET #74: How to think about scenarios in the Iran as an economist and scientist, with Paul Frijters

Cameron Murray — Sun, 12 Apr 2026 22:01:14 GMT

Economics can be thought of as the study of human conflict. Prices are one way to settle conflicts over who gets what.

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.

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

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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The Efficient Property Markets Hypothesis: Why Feasible Zoned Housing Capacity Is an Illusion

Cameron Murray — Mon, 06 Apr 2026 22:00:36 GMT

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

2MB ∙ PDF file

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.

FET #73: Steve Keen on why economists are often blind to the macro cycle (plus housing and more)

Cameron Murray — Sun, 29 Mar 2026 22:01:28 GMT

One of the most requested guests on FET has been Professor Steve Keen. Steve has been a student of economics since the 1970s, and a critic of the oversimplifications of the neoclassical economic school. He was an academic at the University of Western Sydney for many years, and Head of School at Kingston University in the mid-2010s.

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.

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

Theme: Happy Swing by Serge Quadrado Music—Creative Commons Licence CC BY-NC 4.0

So, about that "housing shortage"...

Cameron Murray — Sun, 22 Mar 2026 22:01:03 GMT

The so-called housing shortage, which plays such a great role in the press nowadays, does not consist in the fact that the working class generally lives in bad, overcrowded and unhealthy dwellings. This shortage is not something peculiar to the present; it is not even one of the sufferings peculiar to the modern proletariat in contradistinction to all earlier oppressed classes. On the contrary, all oppressed classes in all periods suffered more or less uniformly from it.

— Frederick Engels, The Housing Question (translated pamphlet series), 1872.

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

2MB ∙ PDF file

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

FET #72: Jim Penman on zoning, politics and fertility

Cameron Murray — Sun, 15 Mar 2026 22:01:07 GMT

Most Aussie’s know Jim Penman’s face from the side of green trailers dragged through the suburbs as his franchisees mow lawns.

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

2MB ∙ PDF file

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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Interested in learning more? Fresh Economic Thinking runs in-person and online workshops to help your organisation dig into the economic issues you face and learn powerful insights.

Why Your Supply Elasticity Number Is Probably Wrong

Cameron Murray — Sun, 08 Mar 2026 22:01:15 GMT

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

426KB ∙ PDF file

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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The asset price of homes MUST vary due to tax settings, interest rates and other factors. Check out this FET article for more.

FET #71: Crémieux on how bad science and fraud lead to bad policy

Cameron Murray — Sun, 01 Mar 2026 22:01:32 GMT

Jay Lasker, aka Crémieux on Twitter/X, has spent the past few years digging into bad science and its bad data, especially in health science.

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.

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

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What if Ronald Coase met Henry George? Coase says create property rights. George says tax them.

Cameron Murray — Sun, 22 Feb 2026 22:00:17 GMT

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.

FET #70: Tim Sneesby is a real life town planner. Why is he making housing so unaffordable?

Cameron Murray — Sun, 15 Feb 2026 22:00:19 GMT

You may have heard that our town planning system of rules and institutions that govern land uses in cities and often across the country is stifling new housing production, causing high prices.

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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What an economics experiment taught me about corruption and human nature

Cameron Murray — Sun, 08 Feb 2026 23:24:25 GMT

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.

The original self-published book, Game of Mates, from 2017 (no longer available)

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

The updated and expanded version was released as Rigged in 2022

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

803KB ∙ PDF file

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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The Four Horsewomen of Modern Feminism

FET #69: David Maywald explains how the war on masculinity affects health, education, and families

Cameron Murray — Sun, 01 Feb 2026 22:01:08 GMT

Has the cultural and political pendulum swung too far in favour of women?

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

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…

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

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Is 2026 the peak of Australia's housing cycle? Or is there not really one cycle at all?

Cameron Murray — Sun, 25 Jan 2026 22:00:50 GMT

Happy Australia Day 2026!

There is nothing more patriotic I could write about today than home prices, so enjoy this deep dive.

What makes 2026 an interesting year is how it fits with a commonly held view about the existence of regular 18-year cycles in property markets. It is now 18 years since the 2008 crisis, so 2026 should see the next big one (market price correction).

Right?

Well, I’m not so sure.

To provide some context for what follows, here are some facts about the housing market in Australia and globally over the past decade.

Much of the global increase in rents and prices since COVID merely reflects overall inflation. Inflation has also increased household incomes (unevenly). For example, Australian inflation from 2021 to 2025 was about 20%. PropTrack’s all dwellings national index is up about 23% over the same period.
Australia had a unique post-financial-crisis surge in apartment prices and development during the 2010s, predominantly in Sydney (which saw apartment prices rise 60% nominally from 2010 to 2016). Yet Brisbane apartment prices were up only 5% nominally in the same period.
Since then, in inflation-adjusted (real) terms, Sydney apartment prices have fallen 10%. Looking abroad, places like Tokyo, which are widely thought to have town planning systems that prevent rising dwelling prices, have seen apartment prices rise 60% in real terms since 2016.
Australian cities have seen asynchronous boom and bust cycles, but this is also the case when comparing Australia to global peers like Canada and New Zealand. Canada as a whole (though also with different patterns in each city) saw prices relatively flat from before the 2008 financial crisis, right up until 2015. Then, a price cycle emerged, which had a pause for a couple of years before COVID, only to resurge again spectacularly.
This is a similar story to New Zealand.
Now, both of these countries have seen national price declines for multiple years, particularly in real terms, despite falling interest rates. Will they see a 2026 crash even after two or three years of falling prices?

This context is important.

Too often, we get caught up in the idea of One Big Property Cycle and when the next big crash will hit the market. But in reality, every city and national market is constantly adjusting to overall macroeconomic and financial conditions at varying rates.

Had you delayed buying an apartment in Sydney in 2010 because the One Big Property Cycle theory said you had another fourteen years to make your money on the upswing, you would have missed out on all of the 60% nominal capital gains in the upswing, which happened before 2016. Had you bought in 2016 to capitalise on the second part of the cycle, you would have seen no capital gains.

But had you avoided buying an apartment in Brisbane in 2010, it would have saved you the costly carrying costs during nearly a decade with no capital gains. Only in 2021 did apartment prices in Brisbane exceed their 2010 prices.

Rather than property being at the heart of the cycle, causing regular 18-year synchronised booms and busts, it might be better to imagine that there are slow-moving overall macroeconomic and financial cycles, and that property markets respond to these conditions in different cities and countries at different rates, sometimes overshooting in both directions.

Within this view, different cities and countries should never be able to independently deviate too far in terms of their relative property pricing for too long—the repricing process keeps relative prices between cities and towns, houses and units, and between macroeconomically comparable countries, within a moderate range.

When cities in Canada and New Zealand experience significant price corrections, we should be mindful that Australian housing prices are becoming relatively more expensive.

Today, I dig deeper into the relative pricing of housing assets across Australia at the start of 2026 along three dimensions.

The price of apartments compared to detached homes.
The price of major cities relative to each other.
The rental returns on housing relative to the returns on other investments.

These price relativities can help us understand which direction the housing market in different cities might take, providing a much more nuanced view than simply counting 18-years and applying this logic to all property types in all cities.

While some people like to look at intermediate market measures like vacancy rates or months of inventory, it is actually relative pricing that really matters. These intermediate market activity metrics only tell us that prices or rents are adjusting in a certain direction—but these adjustments only happen because of relative pricing disparities.

Let’s proceed.

Apartments versus detached houses

One unique characteristic of the housing market has been the change in preferences for detached housing since COVID. The chart below shows the change in the ratio of house prices to apartment prices nationally since 2010. While home prices increased by 10% points more than apartments in the decade prior to 2020, they increased another 20% points more in the few years after.

FET #68: Mark Changizi on the Hidden Logic Behind Emotion, Language, and Social Order

Cameron Murray — Sun, 18 Jan 2026 22:15:23 GMT

During the COVID panic one of the sane voices was congitive scientist Mark Changizi. He saw the resulting panic as an emergent social and cultural phenomenon, and went as far as to sue the Department of Health and Human Services (DHHS) for directing social media companies to censor what they deemed as misinformation.

In this conversation, Mark talks of his intellectual journey, from mathematician and physicist to congitive scientist. He describes the overarching view of human perception and coordination as evolved tools and how leads to social patterns that would be replicated in any civilised large scale cooperative creatures. Would aliens also be debating free markets and communism?

Here’s one example of evolved traits.

Why do humans perceive visual illusions rather than have an accurate visual receptor? Because our visual perception evolved to anticipate outcomes, not accurately represent the light hitting the eyeballs.

This was a fascinating discussion, and stay tuned for Mark’s comments on academia and his life since leaving academia 16 years ago.

His latest book is called Motorcycle Mind, and I would recommend his previous book Expressly Human.

Please find Mark at the following.

Youtube: https://www.youtube.com/@markchangizi
X: https://x.com/MarkChangizi
Website: https://www.changizi.com

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How evolution opens the black box of market competition

Cameron Murray — Sun, 11 Jan 2026 22:30:17 GMT

Competition policy, also known as anti-trust policy, is having a renaissance. Even the housing sector is a target, with the Productivity Commission noting that barriers to entry could impede competition in its recent report on homebuilding.

Economic regulators typically rely on simple economic theories of competition, with optimising firms whose incentives are driven by the number of competitors in a relevant market. This is known to be a simplification, so on top of this core theory, other ideas are added in an ad hoc way, such as business failures (exit) and emergent new businesses (entry), and the selection effect of consumer choice.

I argue that this approach gets it backwards.

An explicitly evolutionary approach, which starts with entry and exit within a constraining competitive environment, will generate what looks like optimising behaviour in the aggregate. It also highlights how a lack of competition between firms can lead to competition within firms that reduces efficiency.

Does a different theory result in different policy insights? I’m not quite sure. But I think it offers a more coherent view. Let me know your opinions in the comments.

Here’s a summary of the story I want to tell about the how an evolutionary model of competition works.

Market competition is a selection mechanisms where relatively higher profitability replicates, even if firms are not trying to maximise profitability.
However, the level at which competition occurs matters.
Firms need to be cooperative internally, but competitive externally (like sports teams).
If firms become internally competitive, the fact that some people in the firm gain rank by sabotaging others becomes a problem.
Nevertheless, firm-level selection can rid the market of inefficient and uncooperative firms.

Core theoretical differences

Consider two alternative processes that lead to what looks like optimising in the aggregate.

Process A: Rational firms choose optimally and profitably
Process B: Irrational firms choose randomly, and markets select for profitability

In Process A, all firms are rational. No one goes bust. The future is well known in advance. But in Process B, firms start and fail all the time, and it doesn’t matter whether we can know the future because agents who survive in the market will be those who have so far successfully predicted the future (well enough to survive) in terms of their internal operations and consumer preferences.

When we observe the outcome of both processes, it will appear that the surviving businesses we observe are optimising to maximise profitability and meet consumer preferences, as higher profitability means a greater chance of survival.

That agents—firms, customers, etc—who make somewhat random decisions under constraints can still generate emergent market-wide outcomes that exhibit what looks like optimising behaviour was noted by Gary Becker decades ago.

I explain that logic in this article:

Here, I dig further into the idea that the best models of competitive markets are evolutionary ones that embed the idea that cooperation and competition operate at different levels, rather than models of perfectly informed optimising firms.

This approach synthesises many insights about monopoly power versus competition, and the existence and persistence of firms themselves as low-cost coordinating devices (a la Coase).

An evolutionary approach

The main starting point in an evolutionary model of competitive markets is not to assume that people or firms know the best way to organise or what to produce. They only learn by doing, and it is the interaction of that doing (experimentation) in markets of other experimenting firms and individuals, that selects for what looks for profitability and what looks like optimised behaviour.

The core ingredients of any evolutionary process are

variation,
selection, and
replication (inheritance).

In the natural world, genes vary a little over each generation of an organism through the recombination of parental genes. The environment selects for which genetic traits produce more offspring, which replicates those genes at higher rates in the next generation.

Firms are organisms in a market environment. They can adapt over time, but sometimes not enough for a changing environment and go extinct. New firms can emerge by recombining other resources, and existing firms can evolve in response to selection pressures.

What makes a firm, or business, a unit of evolutionary selection and not the individuals within it? We usually think of traits of individual animals, not of the traits of a herd of elephants or a pride of lions.

This is where the insights of David Sloan Wilson come in. He is one of the scientists who showed that evolutionary processes act simultaneously at multiple levels—the cell, the organism, and the tribe of organisms. The theory is called multilevel selection.

This is much clearer when we think of animal species like bees, where the hive acts in such a cooperative way internally with multiple set roles for different creatures, that it doesn’t make a lot of sense for individual bee traits to be selected for in the environment, but that traits of the whole hive and its behaviour are what are selected for by the environment.

In my approach, firms are like a beehive, where the internal cooperation is what leads to success in an environment of competition with other firms (other hives). Firms cooperate internally to compete in an external evolutionary environment.

Let me explain.

Cooperation and competition at different levels

Imagine that within a firm, every interaction amongst employees can be either cooperative, which results in improved production efficiency, or competitive, which helps one of the individual employees (conditional on the other being cooperative), but reduces the overall efficiency of the firm.

It’s like facing a prisoner’s dilemma repeatedly for every internal company decision.

Uncooperative behaviour might be as simple as employees wasting resources, blaming others for failures rather than working together to get an efficient outcome, or it could be as competitive and nasty as sabotaging the work of others in the firm to make yourself look good, which might be good for the individual, but bad for the company.

Cooperative behaviour might involve groups quickly agreeing on incremental innovation in processes to remove duplication and waste, or implementing new products or designing new production methods, and so forth.

There is really no obvious recipe for cooperative success that can be enacted in advance. Trial and error within and between firms is necessary.

An example from Amazon can help get your mind around the idea that it really is hard to foster internal cooperation and avoid conflict within firms:

At Amazon, workers are encouraged to tear apart one another’s ideas in meetings, toil long and late (emails arrive past midnight, followed by text messages asking why they were not answered), and held to standards that the company boasts are “unreasonably high.” The internal phone directory instructs colleagues on how to send secret feedback to one another’s bosses. Employees say it is frequently used to sabotage others. (Source)

A process designed to increase cooperation and get groups to an efficient outcome can easily be sabotaged and result in costly conflict. But who could know that in advance without testing the system by implementing it?

The table below shows the stylised conflict between individual choices to cooperate or compete within a firm. For two people (A and B) who randomly meet within a firm, they can both cooperate and earn an individual payoff of 10 each (top left cell with A, B individual payoffs listed), giving the firm an overall payoff of 20. Or, one person can defect while the other cooperates, giving that person a payoff of 15, but only a payoff of 0 for the cooperator, and an overall firm payoff of 15, which is lower than if people were cooperating. And the bottom right cell shows the payoffs if both people are competitive (the defect from cooperation), giving each a lower payoff of 5, and the firm a payoff of 10 (the sum of both people’s payoffs).

Clearly, the best thing within a firm is for all interactions to be cooperative to get the highest total firm payoff, but there remains an incentive for each individual within the firm to occasionally defect and get a higher personal payoff.

Now, let’s think about market competition operating at a firm level. With more competition, would we expect the evolution of the market to result in the success of more competitive individuals or more cooperative individuals?

The diagram below shows a series of three selection stages down the rows from time one to time three. Each colour represents a single firm. So in the top row, there are four firms (blue, green, yellow and orange).

Each small table shows in column N the number of cooperating or defecting individuals within the firm. So in the top row blue table, there are 20 cooperators and no defectors in that firm. The next column, P, shows the average payoff to each person from random interactions amongst other firm staff. In the top row, the blue table has an average personal payoff is 10 because all 20 staff are cooperators and every interaction with another cooperator in the firm gives a payoff of 10. The total firm (or group) payoff is in column G and is 200 in this instance (20 people getting a payoff of 10 each).

The next firm in the top row, in green, has within it 15 cooperating individuals and 5 defectors. The average personal payoff for the cooperators in that firm is 7.5 because they have a 1 in 4 chance of dealing with a defector, and a 3 in 4 chance of dealing with another cooperator. The defectors have a higher personal payoff of 12.5 for the same reason.

Moving across the top row, the yellow firm has 10 cooperators and 10 defectors. This firm is a nasty place to be, and half the time, the firm is busy with staff blaming each other and not producing efficiently. The payoff (or total efficiency) for the firm is much lower, at a total of 150.

The last orange firm is mostly defectors, perhaps an extreme version of our Amazon example. The total payoff for this firm is just 125.

Outside these tables on the right side is a column N, which is the sum total of the number of people who are cooperators or defectors in each time period. In time one (the top row), there are 50 cooperators amongst the firms (20 in blue, 15 in green, 10 in yellow, and 5 in orange), and 30 defectors.

Moving from time one to time two, or going down a row, is a selection stage in the competitive evolutionary game of market competition amongst firms. That is, only the most efficient firms survive, and the least efficient die off from lack of customers from their poor value products made inefficiently. In fact, in this example, the most efficient firm expands to take up the market niche left by the firm that dies off.

So when we move to the second row in time two, the least efficient orange firm has died off, and the most efficient blue firm has expanded to satisfy that market niche.

But notice this. When we add up the total cooperators and defectors working in all the firms in the market at time two, there are now 65 cooperators (15 extra), and 15 defectors (15 less), compared to time one.

Competition at the firm level has led to the selection of the most internally cooperative and hence efficient firms, not the most internally competitive.

Going down one more row shows the new, relatively least efficient yellow firm also dies off. What worked at one point in time for them, when the environment was full of firms less cooperative than them, does not work at all points in time. Success in this evolutionary process is always relative to other firms in the market environment.

What is the result of this process?

We end up with firms that are highly cooperative and internally efficient, resulting in profitability. It looks like they are profit maximising. But they could have many alternative internal objectives, such as market share, cost minimisation, or quality maximisation. Whatever those firm objectives are, if they result in more internal cooperation, leading to efficiency and profitability, then they survive.

Another insight is that competition provides benefits when it acts as a selection mechanism that favours cooperative and efficient groups (or firms) that enable total production to expand. Variations that improve efficiency and cooperation within firms will, over time, be selected for by consumer choices in the market.

But what if competition happens within firms? What if individuals were truly competitive all the time?

Within-firm competition and monopoly

When there is a lack of competition between firms, the selection pressure arises within firms. This is common within government departments, which are monopolies and do not face selection pressures externally.

To illustrate the problem with competition within firms, let us think about the scenario of a large firm with multiple departments making multiple products for a variety of different customers. We can also think of large bureaucracies in general, such as government departments.

Perhaps the above example has led you to think that competition within company departments might be a good way to select the best ones.

Unfortunately, this approach has a huge incentive problem. The relative success of one department might be due to passing off costs to, or sabotaging, another. Within-firm competition that results in an evolutionary selection process is very risky, and it is well known that ‘silos’ in firms can result in conflict between what is best for each silo and what is best for the firm.

Unfortunately on most occasions, silos encourage behaviours that are beneficial to the occupants of the silo, but are often not in the best interest of the overall business or its customers. It also plays into the hands of corporate politics, since silos help to keep things private. And we all know that in office politics information is power. A recent survey from the American Management Association showed that 83% of executives said that silos existed in their companies and that 97% think they have a negative effect. (Source)

I capture the idea of relative competition within firms, which can include sabotage or passing on external costs to other departments, in the table below.

Here, the company has two departments (each small table labelled Dep. 1 and Dep. 2), and within each department, there is a choice to cooperate on either project A, which provides that department with a payoff of 20, or project B, which provides a payoff to that department of 10. However, project A comes with an external cost to the other department of 15.

For each department, it is better to cooperate on A, giving them 20 each, but also inflicting an external cost of 15 each. The overall company payoff is just 10 in this situation. However, if the departments each cooperate internally on B, the overall firm payoff is double, at 20, as there are no other externalised costs.

Thus, for large organisations, the emergence of silos that are blind to the situation of other parts of the company may end up with a choice of projects and investments that are not overall optimal and efficient.

Companies that find ways to ensure they maintain this inter-departmental efficiency as they grow are those that the market will select for.

Notice that this problem is a much more serious one in governments where there is no government-level selection pressure. At best, there is an occasional change of government in a democracy, but rarely does this provide strong incentives to change operational processes all that much.

Indeed, the incentive to sabotage other groups and inflict costs on them also arises with market competition in general, which is why competition laws and market regulations to minimise such negative external costs are so important to harness competition for the broader public benefit.

What about the chickens?

A model of a competitive selection process for cooperative firms provides more clarity about what economists mean when they speak of the benefits of competition.

Rather than simple models of competition being about the number of firms producing similar products, it shows that the underlying process of eliminating underperforming firms so that better-performing ones flourish. This process is open to competition by any means, whether internal production methods or by satisfying consumer demands with new products and services.

I don’t think there is a major contradiction between the mainstream economic view and the evolutionary view about the role and benefits of competition. It is just that competition is a black-box in simple standard models, with a few ideas added on in an ad hoc way, as is the existence of firms that themselves use internal low-cost cooperation methods.

Consider how a simple economic approach, that all competition is good, would inform the chicken breeding experiments of William Muir.

Muir bred chickens and either selected for a) the most productive individual egg-laying chicken, or b) the most productive cage of egg-laying chickens (in each cage were nine chickens).

The first method favored the nastiest hens who achieved their productivity by suppressing the productivity of other hens. After six generations, Muir had produced a nation of psychopaths, who plucked and murdered each other in their incessant attacks. No wonder egg productivity plummeted!
In the second approach, he selected the most productive groups and because they were already a group that worked well together, they included peaceful and cooperative hens. (Source)

Egg production by the cooperative cages increased 160% over just a few generations (more detail here). His results demonstrate that group selection is a process that increases the number of cooperators and total efficiency, whereas individual competition undermines the benefits of cooperation.

Could standard economic models of competition predict this? I don’t think so. A multilevel evolutionary approach sheds much more light on the process of competition.

Please share your thoughts below.

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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Supply starts when speculation ends: Searching for a theory of housing production

Cameron Murray — Sun, 04 Jan 2026 22:15:16 GMT

Wouldn’t it be great if we had a coherent way of understanding the economics of housing supply?

You might be surprised, given the confidence of the prognostications of public commentators, that most don’t have a broadly accepted economic theory of why homes are built.

It’s all ad hoc and often contradictory.

Here, Tim Helm walks through the main empirical patterns that need to be explained by our economic theory of housing supply, and steps through why most current approaches are a poor fit.

A good written explanation is here.

Thanks for listening.

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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Interested in learning more? Fresh Economic Thinking runs in-person and online workshops to help your organisation dig into the economic issues you face and learn powerful insights.

Reflections on 2025 at FET

Cameron Murray — Mon, 22 Dec 2025 00:15:23 GMT

Thank you all for your continued support.

A year ago, I sent this email to my then 5,800 subscribers with a Christmas update.

Looking back now, I realise what a busy year 2024 was—not only in terms of professional output, but in my personal life. It was a year of multiple deaths in the family and a few amongst friends.

This year has been different. More reflective. More focus on the personal and family. Adding to the sense of a life turning point, my eldest son finished school this year.

The whole midlife crisis thing is beginning to make a lot of sense to me. What’s the point of spending time and energy articulating coherent economic analysis when demand for it is so niche?

Expect more reflection in 2026.

How is FET doing? Paid subscribers are now hovering at 220 — a special thanks to you all. Total subscribers are around 6,900 and growing steadily. Up 1,100 in the year.

As I noted last year, I planned to give myself two years of writing to see what audience I could attract. I am two years in now. Things are slower than I had hoped, but the quality of the audience here is very high, and this motivates me. My plans to host economics workshops and in-person events were essentially put on hold this year, but I am gearing up for the first half of 2026. Email me if that interests you or your organisation (but expect a slow response as I am travelling until 6th Jan).

Any help to grow FET by sharing articles on your preferred social media or by emailing your mates is very much appreciated. You can even give a gift subscription.

What have I contributed in 2025?

12 deep dive articles for paid FET supporters
16 free FET articles (thank you to all guest writers)
19 FET podcasts
Dozens of media appearances (including SBS Insights)
Presented on resource royalty pricing at the Australian Conference of Economists
Hosted a Big Dialogue on the “cost of living crisis”
Posted plenty of content on the FET YouTube channel
Wrote a report for Shelter NSW illustrating some unappreciated inclusionary zoning economics

Density Deals

1.38MB ∙ PDF file

A report on the economics of inclusionary zoning in NSW

I’ve also been on numerous trips to Saudi Arabia to teach in the executive education program at MBSC. Doing this fly-in-fly-out work over the past few years has allowed me to experience the rapid and positive economic and social change taking place there. This work started as a massive burden—the travel, the preparation, and the high demands of large classes with all-day experiential teaching—but has now become a highlight of my year. If any readers are in Riyadh, please get in touch to meet up sometime.

Before I share the top ten most-read FET articles of 2025 and wrap up the year, I wanted to share an update on the Speculative Index, which measures the degree of cyclical over- and under-valuation in the housing market. It is the ratio of the mortgage interest rate to the gross yield of housing. A higher ratio means that the rent is covering less of the return given up by buying housing (e.g. the mortgage interest), and therefore the purchases in these conditions must be justified by expectations of rising prices or rents in the future. When the index is low, it means that rent covers more of the return given up, and so little growth is needed to justify prices at their current levels. Hence the name Speculative Index.

We see very clearly the cycle in property markets and the overvalued periods in the late 1980s, the late 2010s, and today. This is quite normal cyclical behaviour. Undesirable? Probably, like the business cycle in general.

But we are already seeing that rising rents and lower mortgage rates are adjusting us back to less over-valued in the past year or so. If we trust the cyclical patterns here, we can expect a continuation of 2025 trends with some combination of falling prices, falling interest rates, or rising rents for the next few years.

See you next year, and I appreciate all your support.

Don’t forget to check the FET podcast.

Top ten of 2025

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FET #67: The story of building a new academy for free thinkers, in a castle, X-Men style

Cameron Murray — Mon, 15 Dec 2025 00:15:17 GMT

Previously, I have described an educational experiment happening in a castle in Belgium, called Academia Libera Mentis (ALM). It is run by my friend and co-author, Paul Frijters, with his wife Erika Turkstra, and alongside Gigi Foster and many others.

In this episode, we discuss the story of this endeavour, as told in the upcoming book Minds that Dare. You can read about the trials and tribulations of this educational endeavour at the ALM substack.

Check out the ALM website and the courses available in 2026.

With a bit of luck, you might see me at ALM at some point in 2026.

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

Theme: Happy Swing by Serge Quadrado Music—Creative Commons Licence CC BY-NC 4.0

Interested in learning more? Fresh Economic Thinking runs in-person and online workshops to help your organisation dig into the economic issues you face and learn powerful insights.

FET #66: Energy, economics, and policy blindspots with Ben Beattie

Cameron Murray — Sun, 07 Dec 2025 22:15:39 GMT

A long chat with power engineer Ben Beattie about Australia’s energy conversation, some of the muddled economics and arguments in favour of privatisation, public provision, and more.

Check out Ben’s Baseload Podcast here.

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

Theme: Happy Swing by Serge Quadrado Music—Creative Commons Licence CC BY-NC 4.0

Interested in learning more? Fresh Economic Thinking runs in-person and online workshops to help your organisation dig into the economic issues you face and learn powerful insights.