Housing subsidy and UBI confusion

When the Australia government introduced a cash grant for first home buyers, the aggregate effect was to increase home prices by roughly the amount of the grant, quickly negating its effect on affordability.

This observation has led many people to mistakenly believe that giving cash grants in any form will pass through one-to-one into higher home prices (or rents). In discussions of all types of welfare—from UBI, to traditional welfare payments—this error comes up.

The error comes about because people fail to see that when given a choice, people spread their extra buying power across all the different types of goods they consume. An income subsidy is not the same as a subsidy for a particular type of expenditure.

Economists have been studying the way spending patterns vary with income for over 150 years. Ernst Engel noticed in 1857 that as incomes rise, households spend a lower proportion of their income on necessities like food. This observation became known as Engel’s Law, and the income-spending relationships for different goods became known as Engel Curves.

Housing, like food, is a necessity. As such, the share of income spent on housing usually falls as incomes grow. The Australian data shows that even for private renters—where one would expect competition from higher-income renters to bid up housing rents—the share of income spent on rent falls from nearly 50% of gross income for the lowest income quintile households to just 13% for the highest-income households.

This data might seem to imply that it is possible for up to 50% of a cash welfare payment to “pass through” to landlords for low-income households. But remember, this is not the marginal amount that would come out of extra income. Because the share of spending on housing falls as income rises, the spending on housing out of the extra income must be far lower than the average. In fact, across income quintiles in Australia, the marginal additional spending on housing per dollar of additional income sits tightly in the 5-7c range. It may be possible that long-run adjustments mean that more than this marginal amount is spent on housing out of extra income, but it will always be less than the average amount.

The story is rather different, however, if welfare payments are tied to a particular type of spending. This even more important in the case of housing, where the total stock changes extremely slowly and where landowners have monopolistic incentives to prefer price gains over investing in additional supply.

An example is if everyone received a fixed $1000 per month that could only be spent on housing. Because this money cannot be spread across the consumption basket, people would soon learn that they are best off using it to bid up the rent to access their preferred housing location. The macroeconomic reality is that this additional buying power will chase roughly the same number of dwellings, increasing their price.

The difference between a “general income subsidy” and a “housing expenditure subsidy” can be shown using Engel curves. The chart below shows three Engel curves for a household, with the orange representing housing. Blue represents other normal goods, where expenditure rises with income, but a bit faster than for necessities (as per Engel’s Law). The green curve is an inferior good. Household spend less on these goods after their income reaches a certain level.

A “general income subsidy” shifts the household up to a higher income level, and they spend more on all the types of goods in their consumption basket. The effect on housing expenditure is relatively small, as expected by our previous 5-7% assessment of marginal housing expenditure.

The next chart shows the effect of a “housing expenditure subsidy”. The total income of the household is unchanged. They are only able to direct the subsidy towards their housing expenditure. Here, the effect will be to boost buyer competition for scarce housing locations and increase home rents (or prices). This was the case with the first home buyers grant.

Though it is tempting to see them as quite similar, subsidising household incomes and subsidising a particular type of expenditure have rather different economic effects. 

Microeconomic success, macroeconomic failure

When I teach macroeconomics, I use a dog and bone analogy to demonstrate that the macro-economy is not equivalent to just “adding up” the micro.

Let’s see the analogy in action.

In the dog and bone economy, ten dogs repeatedly try to find nine bones buried in the yard. Each round, at least one dog misses out. We think that this outcome is undesirable— we can’t have an economy with over 10% dog “bone poverty” and perpetual “dog unemployment”!

Some astute dog economists notice that dogs that miss out on a bone are usually a little slower, or have some other traits that make them relatively poor performers. They reason that there is a “skills mismatch” that, if corrected, could solve the macro-economic problems in the dog economy.

These economists go the extra mile and conduct some randomised controlled trials on interventions that seem promising.

1. Give the dogs that miss out a head start
2. Provide the dogs that miss out advice about where to find the bones
3. Train the dogs that miss out to sniff out bones better

After trialling each of these interventions, the results come in. They are astounding!

In each policy experiment, dogs that missed out on finding a bone 75% of the time in the control group only missed out 5% of the time in the treatment group.

The researchers responded to media enquiries about their results. “This is the largest effect I’ve ever seen in a social science intervention,” they said.

If it can be replicated at scale, the experimenters may have hit on a powerful new tool for dismantling bone poverty in the dog economy. Policymakers are now looking to invest in expanding these programs in dog parks across the country.

I don’t know about you, but it always helps me to understand what is really going on when we talk in the abstract. In the dog economy, it is clear that regardless of the microeconomic success of these interventions, there is still going to be “dog poverty” and “dog unemployment” because of the macroeconomic conditions. There are always nine bones and ten dogs. At least one dog still misses out and experiences “dog poverty”.

Helping someone jump the queue for access to scarce resources is obviously going to help that individual. But it can’t help everyone in the queue.

And yet, these microeconomic “queue-jumping” policies are politically attractive. Job training is widely thought to be an important tool for solving unemployment. But if the unemployed are competing over scarce jobs, then job training can only change the preferred ordering of candidates.

A recently popular policy in this vein has been “intensive housing counselling”. This involves lobbying landlords on behalf of housing voucher tenants and advising these tenants to move to “high opportunity areas”. Not surprisingly, these tenants took up the professional advice and assistance given to them.

As one tenant noted, after deciding where they would like to move, the housing counsellors “pretty much took care of the rest. I gave them my information, they gave my information to the leasing office, they applied for me, and they helped with the first month’s rent and the renter’s insurance for a year.”

Making renting and finding a home easier is great. I’m not going to argue against that.

But what puzzles me is this. Like the nine dogs and ten bones, not everyone in a “low opportunity area” can move to a “high opportunity area”. And in fact, as people start to move out of these “low opportunity areas” those areas will have even fewer economic opportunities for residents that ultimately move into them! The policy can’t “add up” to the macro, despite its success at the micro-level.

So what sort of policies do work at a macro level?

In the dog economy, the thing that works is to compress the “bone distribution”—take the nine bones, cut off one-tenth of each bone, and let the ten dogs access 9/10ths of a bone each. Alternatively, have a handler keep some bones in reserve to share amongst the dogs that miss out. Macroeconomic success requires a mechanism that changes the nature of the game itself, rather than the individual behaviour within it.

Two problems with opportunity cost

If there is one idea that defines economics, it is opportunity cost. Unfortunately, muddled thinking about this idea means that across the economics discipline it is applied rather inconsistently. Economists often use the word to mean whatever they want it to mean. 

In its most basic form, opportunity cost just means your next best alternative use of resources. What opportunity did you forgo to undertake this action instead of an alternative? But it gets much more difficult to translate this idea consistently into more detailed economic theories.

I want to highlight two big inconsistencies with the use of opportunity cost in economics. To do that I want to start with a question that triggered a mini-controversy in the discipline a few years back when it was revealed that economists did worse than chance in answering a multiple-choice textbook question about opportunity cost.

The question was:

You won a free ticket to see an Eric Clapton concert (which has no resale value). Bob Dylan is performing on the same night and is your next‐best alternative activity. Tickets to see Dylan cost $40. On any given day, you would be willing to pay up to $50 to see Dylan. There are no other costs of seeing either performer.
What is the opportunity cost of seeing Eric Clapton? A. 0, B. 10, C. 40, D. 50.

According to the textbooks, the answer is B.

There are two mistakes here.

Comparison with different costs

First, the two given alternatives have different resource costs. If you see Dylan you have $40 less to spend. Therefore, a clean comparison of opportunity costs requires us to compare these alternatives

A. See Clapton for free.
B. See Dylan that night and have $40 less to spend.

If we don’t account for the full costs of each alternative, we end up with ridiculous scenarios, like comparing the profit from investing $1m with the profit from investing $1k. It makes no sense when we abstract into raw financial terms, and it makes no sense here either.

Strictly speaking, the correct answer is $10 minus the net benefit from my next best use of $40. But then again, maybe I don't know what opportunity cost is either!

Alternative options are not discrete

Another problem with opportunity cost is that, in reality, there is a continuum of alternatives to any action. The next best option to Alternative A is usually doing Alternative A but cutting some corners slightly. In this case, if seeing the Clapton concert is Alternative A, then seeing Clapton and going to the bathroom when your favourite song is played might be a “next best” alternative.

We could, if we want, break out any of the discrete alternative actions into an infinite array of alternatives. Each of those could be broken out again until we have a continuum.

If we zoom in on this continuum, then the opportunity cost is always equal to best Alternative, and even the opportunity cost of the best Alternative is itself.

This point is important. If, for example, we think that supply curves include opportunity costs of resources, then economic profits are always zero or below by definition.

In a topic I study, property markets, this is also important. Many people think that the second-best alternative use of land sets the price. For example, in regard to the price of land for housing:

…in the absence of any restrictions on supply, the price of raw land on the fringes should be tied reasonably closely to its value in alternative uses, such as agriculture.

Why is agriculture the next best alternative to housing? Surely there are multiple residential subdivision options that are alternatives, and some will be better than others.

Property valuers (appraisers) are clear that the value of property rights comes from its highest and best use, but for some reason many economists think they know better. Valuers test out the various legal options for land use to determine which one provides the highest value to land, and it is this use that determines its value.

The opportunity cost logic, in this case, becomes more absurd when we think about the case where there are three possible legal uses of land—say agriculture, industrial, and residential (in order of value). If the second-best alternative sets the price, then you can make the land cheaper by regulating against the second-best use of industrial development, making agricultural use the second-best alternative and decreasing land prices for housing.

And I haven't even considered the case when there is only one allowable use of land. Doesn't this make the second-best use to do nothing, therefore bringing the land price to zero?

Like many seemingly insightful economic ideas opportunity cost is less powerful than it appears and often confuses more than it clarifies. 

NOTE: Here are some recent articles that follow up on the original survey question that show just how varied the interpretations of opportunity cost can be.

The bathtub analogy of housing supply

Many people hold the view that rezoning land to allow higher density residential uses on each plot will accelerate the rate of city housing development.

I think this is wrong.

The main reason I think this is because there are a finite number of new buyers per period, and residential developers are not in the business of competing with themselves on price. No sane developer floods the market with new housing just because the regulations are changed to allow them to build 100, rather than 50, homes on their lot. In fact, they might just build at the same rate on that lot for twice as long before moving to the next location.

A key confusion in housing supply and zoning discussions is that density limits per lot are interpreted incorrectly as a constraint the rate of new housing supply per period. New homes per lot is not the variable of interest in city housing supply. New homes per year across all lots in a city is the critical variable.

Zoning constrains the location of different densities of housing, but not the total rate of supply across all lots in a city. [1]

In the past, I have tried to dispel some of the key problems with the standard static economic models that conflate the allowable density per lot with the rate of supply. This is what I said then about these models 

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

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

Also because of the comparative-static nature of how the model is used, every time there is a marginal change in any of the parameters of the model — a new person moves to the city, the rental price of the second best land use increases, or the efficiency of construction methods change — the whole city is wiped clean of homes and buildings. The single social planner who controls everything in the city then dictates that the whole city will be rebuilt with a new optimal allocation of housing and commercial buildings under new conditions, and this whole new stock of buildings rebuilt in an instant to that new specification. 

In that blogpost I introduced some new ideas about how to conceptualise regulatory constraints using this diagram.

I want to now offer a simple “bathtub” analogy that demonstrates why our thinking about housing supply and zoning is often misguided. 

Imagine a city region is like a bathtub. The limit on total development, if every location was used to its highest-value use, is the depth of the tub. This is affected by geographic, regulatory, and economic constraints. The water level is the current total stock of housing across the city. Lastly, the dripping water from the tap is how fast new development is occurring across the total city to increase the total stock of housing. 


The question is, what part of this bathtub situation would you address with policy changes to increase the depth of the water? The depth of the bathtub, or the rate of water flow from the tap?

Changing the depth of the tub is a bit like rezoning the whole city for higher density. It seems intuitively like a good idea, but if the city is nowhere near its bathtub capacity, what mechanism is there for this to affect the rate at which the tub gets filled?

The more effective approach is the look at the tap, and the rate at which new housing is developed. This can involve a few things, like making it more costly for landowners to delay converting land into higher-value residential uses. Or, it can mean redirecting credit flows into new, rather than existing housing, to encourage new supply. Regardless, when you start to look at the tap you see that the key variable that needs to be tweaked by policy are the dynamic incentives of landowners—delaying, or slowing, development needs to be made relatively more costly.

However, when you start to focus on the rate of supply you realise that the challenge of tackling price booms with supply is far from as simple as they seem. To even maintain the current drip feed rate of new housing requires a substantial portion of the workforce, and it doesn’t change the total stock very much (just a couple of per cent per year).

In Australia, for example, our housing tap drips at a rate that is around 2% of the total stock, and it requires something like 5-7% of the workforce to build at this rate (and more in some cities with high rates of housing construction).

To have a meaningful effect on the total stock housing, and therefore the price, requires an economically significant long-term construction boom. For example, increasing the rate of new supply by 50% for a decade—employing more than 7.5% of the total workforce instead of about 5%—will increase the total stock by just 9.8%. By any metric, this will have a price effect in the range of 5-15%. The point being, the large changes in the rate of supply have small effects on the total stock and these require a large share of economic resources shifted away from current uses and towards housing construction over a long period, particularly in boom cities.

Now, I am totally supportive of a sustained effort to build more housing to provide more options for households. But I am against pretending that rezoning means that developers voluntarily, and dramatically, increase the rate that they supply new housing to such a degree that they subsume a substantial portion of the workforce while at the same time reducing the price of the asset that earns them a living.

To change the rate of supply requires changing the dynamic incentives of landowners by making it relatively more costly to delay new housing development. This cost to delay means that bringing forward development, even if a lower price must be accepted, becomes viable. These types of changes will be labelled as punitive by landowners, but that’s how you know they are effective—it forces them to build housing when they prefer not to.

Finally, we can always create non-market housing institutions that build new housing regardless of market conditions, allowing this organisation to actually build at a rate that will depress prices, or offer housing to residents at below market prices.

With a bit of luck I hope that in future conversations about housing supply and zoning that the rate of new housing supply per period across all lots is no longer conflated with the allowable density of housing per lot.

fn. [1] I do note that some cities may very well have planning regulations that are so poorly designed that they do in fact constrain the rate of supply.

Update: Total employment in housing construction reduced to remove the engineering construction workforce.