The relative (un)certainty of subnational population decline

Over the three years up to March of this year, I was involved in a Marsden Fund project led by Natalie Jackson, looking at subnational depopulation in New Zealand. That is, among other things we were trying to explain why some areas of New Zealand have been declining in population over time, and continue to do so. The outputs of that project have been summarised in a recent issue of the journal Policy Quarterly.

My contribution to that issue of Policy Quarterly (from pages 55-60) is entitled "The relative (un)certainty of subnational population decline", and looks at how certain (or uncertain) population decline is for different territorial authorities in New Zealand. However, the article has a broader purpose, and is worth reading because it outlines some of the key points that decision-makers need to understand about population projections, especially in terms of their uncertainty. If you know nothing about population projections, other than that they are forecasts of the future that can be useful for decision-making, then you should read the article.

The main results categories New Zealand's territorial authorities (TAs) by the probability that they will experience a decline in population over the decades 2023-2033 and 2043-2053. I won't spoil the results by naming particular TAs, but here's a summary:

...the number of TAs appearing in each category increases between the two periods. More TAs are facing population decline in the 2043–2053 decade than in the 2023–2033 decade. This corroborates recent work that has shown similar results... In the 2023–2033 decade 20 TAs face a 90 percent or greater probability of population decline, compared with 26 TAs in the 2043–2053 decade. Granted, these TAs have relatively small population, representing 12.2 percent of the national population in 2023 (for the 2023–2033 group based on median population size) and 17.2 percent of the national population in 2043 (for the 2043–2053 group).

Unsurprisingly, rural and peripheral areas face the highest probability of future population decline. Other papers in that issue of Policy Quarterly posit some reasons why we observe population decline in particular areas. My paper is descriptive and future-focused, and doesn't explore the institutional or other non-demographic factors that might explain why some rural areas, rather than others, are projected to experience population decline. That's something for future research.

Read more:

• The Maxim Institute on dealing with population decline
• Newsflash! Population growth will be highest on the fringes of fast-growing urban areas
• Winners and losers in population growth
• Forget 'zombie towns', there's entire 'zombie districts' coming to a rural area near you

This is what happens when you have shortages of rental property

On Tuesday, Hawkes Bay Today reported:

A Napier woman was shocked to see the price of a rental home increase by $10 in just one day.

Mandy Clayton of Tamatea, who has rented for three decades, fears people are bidding on rental properties out of sheer desperation.

"I saw it advertised in the morning at a price and then later that night when I decided to apply for it, it had gone up - and that's honestly not the first time I'd seen it [a price increase]," she said.

Clayton, suspecting applicants had offered to pay more, asked the landlord about the price increase but said they told her the first price was incorrect.

A Tenancy Services spokesperson said there was nothing in the Residential Tenancies Act 1986 that precluded a tenant indicating what they would be willing to pay for a rental property.

"The process of rental bidding reverses the offer/acceptance role that usually occurs. It is the potential tenants who make the offer - it is then the decision of the landlord which offer they accept.

"This process removes the landlord from the rent-setting equation and allows the prospective tenant to be an active participant in the setting of 'market rent'."

To be clear, this doesn't remove the landlord from the rent-setting equation. However, this is exactly what we would expect to happen if there is a shortage of rental housing, based on the simple model of supply and demand - when there is a shortage of a good, the price should rise.

As shown in the diagram below, if the current rent (R1) is below the equilibrium rent (R0), the quantity of rental properties demanded (QD1) exceeds the quantity of rental properties supplied (QS1). There is a shortage. In other words, at least some of the tenants who want to rent at the current market rent (R1) miss out on a property. So, what do they do? If they are willing and able to pay a higher rent, they could find themselves a willing landlord, and offer to pay slightly more than R1, to ensure they don't miss out. So, tenants will bid the rent up, until eventually the market reaches equilibrium at R0, where the quantity demanded and quantity supplied are both equal to Q0.

Other than tenants bidding up rents, what else might we see? Tenants might be willing to pay 'letting fees' or 'key payments' to secure their rental property. They might pay the landlord to have their references checked, or they may pay more weeks of rent in advance, or pay a higher rent that has a 'discount' after six months. Tenants may agree to cover costs like lawn mowing or simple maintenance that often the landlord would pay for. There are lots of ways that additional payments to secure a rental can be 'hidden', all of which makes Labour's plans to ban letting fees somewhat of a joke. There are so many alternatives, and it isn't only the landlords to blame - they wouldn't charge those fees if tenants were not willing to pay them to secure a rental property. And tenants wouldn't feel the need to pay those fees if there wasn't a shortage of rental housing.

Read more:

• Students, rental shortages, and renting over the summer
• January tradition continued... Rent increases again
• A January tradition - complaints about rising rents
• Out-of-control rent increases in Auckland - why?

Free agents, the draft, and the winner's curse

The NBA season is just six weeks away, and the NFL starts this Thursday (or Friday for those of us in New Zealand). So, thinking about this recent article by Ben Falk is timely, and gives an excellent explanation of the winner's curse:

Three real estate investors are bidding on a building of unclear value. The first doesn’t think it’s a great property and enters a low bid. The second decides it has a lot of untapped potential and enters a very high bid. The third can see both pros and cons and offers something in between. The winner, of course, will be the second investor, the one who values it the most.

But here’s the problem: that investor is also the most likely to have overvalued the property. If this second investor is wrong about their guess that the building has untapped potential they’ll be stuck paying too much. This is a phenomenon known as the Winner’s Curse, where the winner of the bid will be the most likely to have priced it incorrectly. It applies to many types of bidding situations, and NBA free agency is no exception.

The team paying the most for a free agent is the team who values that free agent the most — who likely overvalues the player the most. It’s possible that all of the other teams with money undervalue the player and so the winner gets good value. But more likely than not the winning bidder will be the one that overpays the most.

The winner's curse doesn't just apply to free agents, but also to the draft. Teams don't have perfect information about each player available in the draft. Some teams will over-estimate the value of a player (and want to draft that player earlier), and other teams will under-estimate the value of a player (and want to draft that player later). Holding other things constant (like each team's needs for players in particular positions), the team that holds the most positive views of a player (that is, the team that most over-estimates their value) will draft that player. This suggests that many players are drafted too high - the winner's curse. As evidence of this, the NFL in particular has a long list of high draft picks who turned out to be busts (try this list, or this one).

Falk's article is also interesting in that it talks about endowment effects. Quasi-rational decision makers are loss averse - we value losses much more than otherwise-equivalent gains. That makes us are unwilling to give up something that we already have, or in other words we require more in compensation to give it up than what we would have been willing to pay to obtain it in the first place. Falk notes that:

Some overpay to keep their own free agents, falling victim to the endowment effect and thinking more highly of something they already have.

I'm less sold that endowment effects are an issue in pro sports. An endowment effect would require the teams to be trading players. With free agency that doesn't happen - they deal direct with the players. However, where teams do trade players (in exchange for draft picks or other players), they may hold out for more than the player is actually worth. Which probably reinforces the winner's curse for the team that receives the traded player.

[HT: Marginal Revolution, back in July]

The economics of the money-back guarantee

Back in July in The Conversation, Yalcin Akcay (Melbourne Business School) and Tamer Boyaci (ESMT Berlin) wrote an interesting piece on the economics of money-back guarantees:

...retailers are increasingly offering extra services such as warranty plans, free shipping and guarantees to reassure them. Selling with the “money-back guarantee” is a prime example of this.

This is because the economics of the money-back guarantee can work for retailers. These businesses allow customers to return products that do not meet their expectations — as a result of poor quality or a mismatch in taste — for a full or partial refund. Essentially offering their customers an insurance against the perceived risk of the product.

And research shows these retailers make a profit with this type of guarantee, given specific conditions.

For an article on "the economics of the money-back guarantee" though, I think it missed the most important economic aspects of this practice. Many goods are what economists call experience goods - goods where some of the characteristics (such as the quality of the good) are known to the seller, but are unknown to the buyer until after they have completed their purchase and received the good. The quality of the good is private information, and this creates an adverse selection problem which is especially problematic in the case of online sales.

In the absence of any other information (see below), since buyers don't know the quality of the goods they are purchasing online, they will assume that any goods on offer online are low quality. This is a pooling equilibrium - in the buyers' eyes, all goods are the same (low) quality. This lowers the amount that buyers are willing to pay for online purchases. Since the buyers aren't willing to pay much for goods of unknown quality, this drives sellers of high-quality goods out of the marketplace, because they can't receive a high enough price to justify selling their high-quality goods. The market for high-quality goods online collapses - the market fails.

Markets (including online markets) have developed ways to deal with this adverse selection problem, and generate a separating equilibrium (where high-quality and low-quality goods can be separated). One way is through signalling. With signalling, the informed party (the seller in this case) finds a way to credibly reveal the private information (the quality of the good that is being sold) to the uninformed party (the buyer). There are two important conditions for a signal to be effective: (1) it needs to be costly; and (2) it needs to be costly in a way that makes it unattractive for those selling low-quality goods to attempt (such as being more costly to those selling low-quality goods). These conditions are important, because if they are not fulfilled, then those with low quality could signal themselves as having high quality.

Money-back guarantees are a good example of an effective signal of high quality. They are costly - as Akcay and Boyaci note:

Customer returns cost retailers more than US$260 billion (equivalent to 8% of total retail sales) annually in the United States alone.

Those goods that are returned must then be sold at a discount, which is costly to the retailer. Money-back guarantees are also more costly if you are selling low-quality goods, since low-quality goods will be more likely to be returned. This makes it unattractive for sellers of low-quality goods to offer money-back guarantees. So buyers can be more confident that they are buying goods that are high-quality, when a money-back guarantee is offered. They can use this information to separate goods that are more likely to be high quality (those with money-back guarantees) from those that are more likely to be low quality (a separating equilibrium).

It is this signalling aspect that is the most important, when it comes to the economics of the money-back guarantee.